PhD: Knowledge of the Web by Kelly Page

W ORLD W IDE W EB P ERCEPTION AND U SE : I NVESTIGATING THE R OLE OF W EB K NOWLEDGE

BY KELLY LOUISE PAGE

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

UNIVERSITY OF NEW SOUTH WALES M A Y 2003

A BSTRACT The focus of this thesis is the impact of user knowledge on web usage. A framework is proposed that brings together research from the fields of consumer research, cognitive science and information systems. This framework sees knowledge of the web as an influence on perceptions of the web, which in turn influences current web session usage. These perceptions relate to perceived usefulness of the web and perceived ease of use.

There are three main research questions:

What is the relationship between a user’s perceptions of the web and a person’s current web session usage?

What is the relationship between a user’s knowledge content of the web and a person’s perceived usefulness of the web?

What is the relationship between a user’s knowledge content of the web and a person’s perceived ease of web use?

To test a variety of hypotheses suggested by the framework, a large-scale on-line survey was developed. In analysing the relationships between the constructs, respondents were grouped into users with and without web-site design and maintenance experience.

Results show that what users think they know about how to use the web is a strong predictor of both how easy and how useful they think the web is. This highlights the importance of user perceptions, especially when considering how users use the web. What users actually know about the web, especially what certain features and attributes are, also has an influence on how easy and how useful they think the web is. Significantly, these results apply for users with and without web-site design and maintenance experience.

These findings help us to understand the relationship between a user’s confidence with technology and how easy and useful that person finds the technology. This is © Kelly Louise Page

particularly important in the context of the adoption and use of user-directed technologies (e.g., PCs, notebooks, mobile phones, touch-screen e-kiosks, ATMs, email and the web). Potentially, this understanding can be a source of new product ideas, of innovative designs, and of new uses. It also might suggest communication themes for product promotion and product demonstration.

Keywords: web usage, user knowledge, user-directed systems

A CKNOWLEDGEMENTS Two roads diverged in a wood, and I I took the one less travelled by, And that has made all the difference. -Robert Frost -

As taking the road less travelled makes all the difference, so too does the support, guidance and dedication of so many individuals that provided the necessary foundation for this thesis. I take great pleasure in acknowledging those who knowingly and unwittingly contributed to this task; a task that I set before myself in January 1998.

First mention must go to my supervisor, Professor Mark Uncles, whose words of wisdom and encouragement, and whose dedication and support provided the pillars upon which this document is based. Mark, your knowledge, your thoughts and your presence enabled me not only to start my PhD but to also bring it all the way to completion. I thank you for your patience, your friendship and the insights and direction you have afforded me, I will remain always your loyal colleague. I would also like thank Dr Cynthia Webster, a wonderful colleague and a cherished friend, Cynthia thank you for your support, guidance and the mentor you became. I would also like to thank Professor Paul Patterson, Dr Elizabeth Cowley, Dr Pam Morrison, Dr Chris Dubelaar, Dr Jack Cadeaux, Ms Debra Caldow and Dr Timothy Bock for their helpful comments and guidance over the past 5 years.

A special thanks must also go to the heart, the soul and the essence of the School of Marketing at UNSW - Ms Nadia Withers, Ms Margot Decelis and Ms Paul Aldwell. ‘Girls’, thank you for your friendship, your good humour and your cherished ear – you will remain with me always.

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Special thanks must also go to the commercial supporters of this study, The Campaign Palace, DoubleClick Australia, ZDNet, Australian NetGuide and The Australian IT, for their financial support and commercial assistance.

On a more personal note, I would like to thank my parents, Doug and Donata Page, my sisters Tara and Nicci Page, and my friends Tracey Kluck and Shelly Coughran, for their continued support and encouragement. Last but not least, to David, for his forbearance while I worked long hours and in isolation and for his continued love and support throughout everything.

April 20th, 2003

D EDICATION I dedicate this work to: - My Parents Doug and Donata Page, whose continual encouragement and belief in me throughout my life afforded me the motivation to commence and follow this life path. Mum and Dad, you stayed with me throughout it all and, as when I crossed the road so many times as a child, along this adult path you also held my hand.

- My FiancĂŠ David Stanley Thomas, the Welshman whose integrity, spirit and soul provided the inspiration, the love and the support that enabled me to achieve this milestone in my life. David, you afforded me not only your love and rational presence throughout this undertaking but also an unforgiving sense of humour that reminded me continually about who I am and why I decided to set upon this path.

TABLE OF C ONTENTS Abstract .............................................................................................................................................i Acknowledgements....................................................................................................................iii Dedication.......................................................................................................................................v Table of Contents........................................................................................................................ vi List of Figures............................................................................................................................... ix List of Tables ..................................................................................................................................x Chapter 1: Introduction ...............................................................................................................1 1.1 Introduction.......................................................................................................................1 1.2 Research Overview ..........................................................................................................2 1.3 Research Questions..........................................................................................................7 1.4 Summary Findings...........................................................................................................8 1.5 Research Motivation and Contribution.......................................................................9 1.6 Research Limitations and Recommendations..........................................................12 1.7 Introduction Summary .................................................................................................13 Chapter 2: Research Background â&#x20AC;&#x201C; Electronic Technology and the User ....................15 2.1 Introduction.....................................................................................................................15 2.2 Electronic Technology...................................................................................................15 2.3 Electronic Technology and The User .........................................................................20 2.4 E-Technology and The User: Summary ....................................................................26 Chapter 3: Predicting Current Web Session Usage...........................................................28 3.1 Introduction.....................................................................................................................28 3.2 Consumer Usage: Research Perspectives .................................................................29 3.3 Current Usage and Past Usage Experience ..............................................................31 3.4 Types of Current Usage Experience...........................................................................33 3.5 Determinants of Current Web Session Usage..........................................................43 3.6 Current Web Session Usage: Summary ....................................................................45 Chapter 4: Perceived Ease of Web Use and Web Usefulness..........................................46 4.1 Introduction.....................................................................................................................46 4.2 Perception ........................................................................................................................47 4.3 Technology Acceptance Model (TAM) .....................................................................48 4.4 Web perceptions and Current Web Session Usage ................................................55 4.5 Determinants of Web Perception................................................................................60 4.6 Web Perception: Summary ..........................................................................................61 Chapter 5: User Knowledge Content of the Web ...............................................................62 5.1 Introduction.....................................................................................................................62 5.2 Knowledge and Behaviour ..........................................................................................63 5.3 Consumer Knowledge ..................................................................................................64 5.4 User Knowledge Content .............................................................................................65 5.5 User Knowledge Scope .................................................................................................73 5.6 Measurement of Knowledge Content .......................................................................74

5.7 User Knowledge Content and web Perceptions .....................................................77 5.8 User Web Knowledge Content: Summary ...............................................................83 Chapter 6: Research Questions and Hypotheses................................................................84 6.1 Introduction.....................................................................................................................84 6.2 RQ1: Web Perception & Usage....................................................................................85 6.3 RQ2: Web KNowledge & Usefulness ........................................................................85 6.4 RQ3: Web KNowledge & Ease of Use .......................................................................86 Chapter 7: Construct Operationalisation..............................................................................88 7.1 Introduction.....................................................................................................................88 7.2 Scale Generation & Testing: Methodology...............................................................88 7.3 Study One: Web Usage and User Web Perceptions ...............................................94 7.4 Study Two: Actual and Perceived Web Knowledge Content ............................107 7.5 Scale Development: Summary ..................................................................................118 Chapter 8: Research Methodology .......................................................................................120 8.1 Introduction...................................................................................................................120 8.2 Hypothesis Testing Research Design.......................................................................120 8.3 Sampling Design ..........................................................................................................125 8.4 Analytical Design .........................................................................................................129 8.5 Pilot Study Administration and Schedule..............................................................134 8.6 Main Study Administration and Schedule.............................................................136 8.7 Research Methodology: Summary ...........................................................................136 Chapter 9: Descriptive Research Results ............................................................................137 9.1 Introduction...................................................................................................................137 9.2 Response Analysis .......................................................................................................137 9.3 Sample Description......................................................................................................140 9.4 Measurement Assessment and Treatment .............................................................143 9.5 Sample and Variable Description .............................................................................154 9.6 PEWU and PWU: Replication and Validation.......................................................156 9.7 Descriptive Research Results: Summary ................................................................157 Chapter 10: Empirical Results Multivariate Analyses ...................................................159 10.1 Introduction.................................................................................................................159 10.2 Data Exploration ........................................................................................................159 10.3 Research Question One.............................................................................................160 10.4 Research Question Two ............................................................................................170 10.5 Research Question Three..........................................................................................175 10.6 Multivariate Analysis: Summary............................................................................181 Chapter 11: Empirical Discussion.........................................................................................182 11.1 Introduction.................................................................................................................182 11.2 Discussion: Web Perception & Usage....................................................................182 11.3 RQ2&3 Discussion: Web Knowledge & Perpcetions .........................................191 11.4 Empirical Discussion: Summary.............................................................................195 Chapter 12: Implications, Contributions, Limitations and Extensions ......................196 12.1 Introduction.................................................................................................................196

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12.2 Research Contributions.............................................................................................196 12.3 Research Limitations .................................................................................................202 12.4 Extensions and Future research..............................................................................208 Reference List.............................................................................................................................212 Appendices .................................................................................................................................224 Appendix A: Previous TAM Research ..........................................................................226 Appendix B: Primary Exploratory Research ................................................................228 Appendix C: Variable Conceptualisation & Operationalisation (Pre/Post Test)..243 Appendix D: Scale Development (Student Sample One: n=128) .............................245 Appendix E: Scale Development (Student Sample Two: n=153) .............................246 Appendix F: Web Site & Web Survey Design..............................................................247 Appendix G: Web Survey Advertising & Publicity....................................................260 Appendix H: Web Site Performance Statisticzs...........................................................263 Appendix I: DoubleClick™ Banner Ad Campaign Report/s....................................264 Appendix J: Scale Validation (Web Sample: n=2077) .................................................265 Appendix K: Scale Performance Comparison (Student & Web Sample)...............267 Appendix L: Variable Distribution.................................................................................268 Appendix M: Multiple Regression – Residual Plots...................................................271 Appendix N: Normality P-P Plots ..................................................................................274 Appendix O: Multiple Regression Assumption Check .............................................277 Appendix P: Sample & Variable Description...............................................................278 Appendix Q: Bivariate Analysis – Convergent Validation .......................................283 Appendix R: Nonparametric Correlation Coefficients: Spearman Rho.................335 Appendix S: ANOVA Reported Mean Scores .............................................................337

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L IST OF F IGURES Figure 1: Graphical Representation of the Dissertation (RQ1-RQ3) ............................................................ 3 Figure 2: The Web Browser: A GUI for the World Wide Web.................................................................... 22 Figure 3: Current Web Session Usage (RQ1) .................................................................................................. 29 Figure 4: Web Perceptions (RQ1)....................................................................................................................... 46 Figure 5: The Process of ‘Perception’ (Solomon 1994) .................................................................................. 47 Figure 6: Hypothesized Technology Acceptance Model (TAM) (Davis 1986) ........................................ 49 Figure 7: TAM Results – Survey Methodology (Davis 1986) ...................................................................... 50 Figure 8: TAM Results – Experimental Methodology (Davis 1986) .......................................................... 51 Figure 9: Perception of the Web and Current Web Session Usage (RQ1) ................................................ 55 Figure 10: Web Knowledge Content (RQ2 and RQ3) ................................................................................... 62 Figure 11: A Simple Model of the Consumer Decision Making Process .................................................. 64 Figure 12: Graphical Representation of the Dissertation (RQ1-RQ3)........................................................ 84 Figure 13: Survey Creation Date - Main Web Study (Oct 2000 to Jan 2001) .......................................... 139 Figure 14: Source of Respondent Study Awareness.................................................................................... 140 Figure 15: Main Web Sample - Age Category Distribution....................................................................... 141

L IST OF TABLES Table 1: Categories of Usage – Media/Vehicles Examples........................................................................... 36 Table 2: Motivations/Concerns of Web Use .................................................................................................... 40 Table 3: 2x2 Typology of Knowledge Content ............................................................................................... 73 Table 4: Consumer Knowledge Scope .............................................................................................................. 74 Table 5: Construct Conceptualisation............................................................................................................... 89 Table 6: Sample: Independent Student Groups.............................................................................................. 95 Table 7: Variance Explained of Breadth of Session Use.............................................................................. 102 Table 8: Variance Explained of Depth of Session Use................................................................................. 103 Table 9: Variance Explained of Perceived Ease of Web Use...................................................................... 105 Table 10: Variance Explained of Perceived Usefulness of the Web ......................................................... 106 Table 11: Sample Description - Independent Student Groups.................................................................. 108 Table 12: Variance Explained of Common Procedural Web Knowledge............................................... 112 Table 13: Variance Explained of Common Declarative Web Knowledge .............................................. 113 Table 14: Variance Explained of Specialised Procedural Web Knowledge............................................ 114 Table 15: Variance Explained of Specialised Declarative Web Knowledge ........................................... 114 Table 16: Perceived Overall Knowledge Content: Spearman’s Rho Correlation Coefficient............. 117 Table 17: Variance Explained of Perceived Procedural Web Knowledge .............................................. 117 Table 18: Variance Explained of Perceived Declarative Web Knowledge Content ............................. 118 Table 19: Comparative Attributes of Differing Modes of Survey Administration............................... 123 Table 20: Advantages and Disadvantages of Internet Surveys ................................................................ 124 Table 21: Pilot Sample Gender Distribution: Comparison......................................................................... 135 Table 22: Timing of Survey Receipt (Oct 2000 to Jan 2001) - Main Web Sample .................................. 138 Table 23: Main Web Sample Gender Distribution: Comparison .............................................................. 141 Table 24: Situational Variety: Spearman’s Rho Correlation Coefficient ................................................ 144 Table 25: Variance Explained of Current Web Session Usage Extent - Breadth ................................... 145 Table 26: Variance Explained of Current Web Session Usage Extent - Depth ...................................... 146 Table 27: Variance Explained of Perceived Ease of Web Use.................................................................... 147 Table 28: Variance Explained of Perceived Web Usefulness..................................................................... 148 Table 29: Variance Explained of Actual Common Procedural Web Knowledge Content.................. 149 Table 30: Variance Explained of Actual Specialised Procedural Web Knowledge Content .............. 150 Table 31: Variance Explained of Actual Common Declarative Web Knowledge Content................. 150 Table 32: Variance Explained of Actual Specialised Declarative Web Knowledge Content............. 151 Table 33: Perceived Overall Knowledge Content: Spearman’s Rho Correlation Coefficient............. 152 Table 34: Variance Explained of Perceived Declarative Web Knowledge Content ............................. 153 Table 35: Variance Explained of Perceived Procedural Web Knowledge Content .............................. 153 Table 36: PEWU & PWU – Spearman’s Rho Correlation Coefficient...................................................... 157 Table 37: Web User Group A – No WSD/M Experience: RQ1 .................................................................. 161 Table 38: Web User Group B – With WSD/M Experience: RQ1 ............................................................... 161 Table 39: Multiple Regression Results MRA1: WSUF = F (PWU & PEWU) .......................................... 162 Table 40: Multiple Regression Results MRA2: WSUVS = F (PWU & PEWU) ...................................... 163 Table 41: Multiple Regression Results MRA3: WSUVMNO1 = F (PEWU & PWU)............................ 164 Table 42: Multiple Regression Results MRA4: WSUEB = F (PWU & PEWU) ...................................... 165 Table 43: Multiple Regression Results MRA5: WSUED = F (PWU & PEWU)...................................... 167 Table 44: Multiple Regression Results MRA6: WSUEDUR = F (PWU & PEWU)................................ 168 Table 45: Web User Group A - No WSD/M Experience: RQ1................................................................... 169

Table 46: Web User Group B - WSD/M Experience: RQ1 .......................................................................... 170 Table 47: Web User Group A – No WSD/M Experience: RQ2 .................................................................. 170 Table 48: Web User Group B – With WSD/M Experience: RQ2 ............................................................... 171 Table 49: ANOVA: Effect of Actual and Perceived Knowledge on PWU.............................................. 172 Table 50: Multiple Regression Results MRA7: PWU = F (Actual & Perceived Knowledge)............. 173 Table 51: Web User Group A - No WSD/M Experience: RQ2................................................................... 175 Table 52: Web User Group B – With WSD/M Experience: RQ2 ............................................................... 175 Table 53: Web User Group A – No WSD/M Experience: RQ3 .................................................................. 176 Table 54: Web User Group B – With WSD/M Experience: RQ3 ............................................................... 176 Table 55: ANOVA: Effect of Actual and Perceived Knowledge on PEWU ........................................... 177 Table 56: Multiple Regression Results MRA8: PEWU = F (Actual & Perceived Knowledge) .......... 179 Table 57: Web User Group A – No WSD/M Experience: RQ3 .................................................................. 180 Table 58: Web User Group B – With WSD/M Experience: RQ3 ............................................................... 180

C HAPTER 1: I NTRODUCTION ‘The beginning is the most important part of the work’ - Plato (427 BC - 347 BC) The Republic

1.1 INTRODUCTION It was argued by Norman (1990) that computers of the future will be invisible in the sense that users will be unaware that they are even using a computer. This argument is already apparent in today’s electronic environment, as some computing devices are naked to the human eye within products such as the automobile, the telephone handset, the microwave oven, the cassette and CD player, electronic calculators, vending machines and even household whitegoods (i.e., refrigerator, dryer and washing machine).

However for many established and developing electronic information and communication technologies, the presence of a complex electronic system or userdirected system interface is highly apparent to those currently using the system and those who will use it in the future (Barwise 2001). This is evident from the personal computer (PC) and notebook, to the mobile phone and personal digital assistant (PDA) and from touch-screen e-kiosks to ATM’s to the World Wide Web, electronic mail and other internet-based resources. Thus the use and perception of these complex and userdirected electronic technologies is highly dependent on an understanding and knowledge of the system and its uses. For example, to use a PC, one needs to know how to install programs, open and save documents; to use a mobile phone one needs to know how to create an address book entry and retrieve messages; to use the World Wide Web

on the internet, one needs to know how to use search engines and search directories, how to browse web sites and how to bookmark web sites.

In an era characterised by rapid electronic development, our society is becoming increasingly dependent on complex electronic information and communication technologies (ICT), as evident above. Consequently the effective use and understanding of electronic technologies, and user interaction with theses systems, has become an essential user requirement â&#x20AC;&#x201C; but little research has been undertaken to investigate consumer knowledge and consequent understanding of these systems, especially in light of developments in computer-based technologies like the web.

Those who utilise user-directed hypermedia computer-based technologies can exercise unprecedented control over the use and management of the system and the contents of the system with which they interact (Rust and Oliver 1994). Drawing on studies in consumer research on consumer knowledge, and in information technology on user perceptions, this study investigates user knowledge and perceptions of the World Wide Web (simply described as the web)1. It also proposes a framework for investigating the effect user knowledge and perceptions of this highly complex and technologically driven system may have on system usage.

1.2 RESEARCH OVERVIEW This dissertation investigates the relationships between the constructs depicted in Figure 1. Its aim is to better understand and determine the influence certain user characteristics (i.e., knowledge content and perception) have on current web session usage.

Although the core focus of this research is the web, the context and variables under investigation have a much wider application to current and future forms of user-directed electronic technologies and thus should not be limited by the technology discussed.

Figure 1: Graphical Representation of the Dissertation (RQ1-RQ3)

The relationships proposed in this dissertation are further compared across two web user groups; namely, those users with, and those users without, web site design and maintenance experience. This segmentation is motivated by the changing profile and experience of users currently adopting the web and that are predicted to use the web in the future. The early penetration of the web within society was dominated by a population of users with direct work-related experience and knowledge of the system that they were using (e.g., web site developers and information technology architects). However as the population of web users grows, the profile is changing and we are beginning to see more users not defined by the same work-related and system development parameters as early web adopters. Therefore, users with and without web site design and maintenance experience are compared for each relationship proposed in this dissertation to aid the determination of current web session usage.

1.2.1 ELECTRONIC TECHNOLOGY USAGE Understanding why people use certain products, and engage in certain behaviours, has proven to be one of the most challenging issues in the study of human behaviour. This is particularly apparent when studying the changing environment of information and communication technologies. To aid successful design and implementation of

hypermedia computer-based systems, like the web, research into the usage of these systems is required.

Current research investigating an individual’s usage of the web specifically goes very little beyond descriptive profiles (e.g., demographics) and thus offers limited accounts of explanatory variables of usage behaviour. However, Korgaonkar and Wolin (1999) state that understanding why and how consumers use the web may be the key to unlocking the web’s capacity. Furthermore, many researchers do not take into account the temporal aspect of ‘usage’ when measuring web usage. Ram and Jung (1990) contend that in contrast to the discrete event of purchase, usage is a continuous event which may change over the length of time of exposure or ownership to the stimuli in question. Hence, researchers should measure both past and current (present) usage experience. In this research current usage experience is of central interest and is defined as the act of using the web for some purpose at the time the measurements were made (i.e., recalled over the last week or month). Past usage experience refers to the act of using the web for some purpose prior to the time current measurements were made (Delbridge and Bernard 1998). This theme is considered in Chapters 2 and 3, giving rise to a number of hypotheses about current web session usage behaviour. These are tested in the empirical sections of this thesis (Chapters 10 and 11).

1.2.2 PERCEPTIONS OF ELECTRONIC TECHNOLOGY From academic and industry studies, it is evident that the adoption and use of electronic technologies is influenced by factors integral to the consumer. For example, internal factors such as demographics (Communications and Interactive 1999b; Gefen and Straub 1997), perceptions (Agarwal and Prasad 1998; Colley, Henry, Holmes, and James 1996; Morris and Dillion 1997), computer experience (Handzic and Low 1999; Knapp, Miller, and Levine 1987; Novak and Hoffman 1997; Schumacher and Morahan-Martin 2001; Taylor and Todd 1995), and attitudes (Communications and Interactive 1999b; Diaz, Hammond, and McWilliam 1997; Hubona and Geitz 1997; Kay 1993) have been examined. External factors such as computer system and technology characteristics

(Rumpradit and Donnell 1999; Swanson 1988) and organisational constraints have also been taken into account in past studies. Of specific interest to this dissertation is the body of research examining user perceptions of computer-based systems, as developed by Davis (1986) in his Technology Acceptance Model (hereafter TAM), and how these perceptions influence system usage.

The main focus for a large percentage of TAM studies has been the investigation of the relationship between user perceptions of the system and system use in an organisational setting, as dictated by work-related usage motivations (e.g., word processing or organisational communication). Less attention has been paid to non-organisational settings. With new developments in information and communication technologies and the changing profile of users (i.e., with both advanced and limited computing experience), motivations for usage maybe be changing. So too might the environment within which the use of technology occurs. This is considered in Chapter 4.

In this dissertation it is argued that a distinct relationship will exist between users’ perceptions of the web and current web session usage because of:

the changing roles and profiles of users and their motivations for system usage;

the complexity of the user interface of the web compared to other electronic devices (e.g., graphical browsers versus the TV and VCR)

the user-directed machine and person interactivity of the web,

It is also examined if this relationship differs across users who have experience with web site design and maintenance (e.g., webmaster/IT specialist) and users without this experience (e.g., end-users of the system).

1.2.3 KNOWLEDGE CONTENT OF ELECTRONIC TECHNOLOGY Numerous studies of TAM have examined user perceptions of information technologies and their relationship with system adoption and use. However only a few studies have extended beyond looking at characteristics of the system and basic usage experience (i.e., computing experience) as determinants of these system perceptions. In this dissertation,

it is argued that characteristics of the user play a large role in forming a userâ&#x20AC;&#x2122;s perception of the system. As developments in information and communication technologies are influencing the role performed by users and the level of interdependence between a user and the web interface this study particularly examines the relationship between user knowledge content of the web and the usersâ&#x20AC;&#x2122; perceived ease of web use and perceived web usefulness.

From an examination of the literature it is apparent that consumers can have different types of knowledge content: procedural (i.e., knowledge of how to use features to do a task), and declarative (i.e., knowledge of what features and terms are). Furthermore, it is shown in this dissertation that the scope of knowledge content among users may differ: from common (i.e., knowledge of basic features and terms) to specialised (i.e., knowledge of more advanced features and terms). The very nature and complexity of user-directed systems like the web provide a basis for investigation of both the scope and type of user knowledge content and how these may influence user perceptions of the system in question. This is considered in Chapter 5.

It is further apparent that discrepancies arise in the measurement of knowledge content. One of the most common methods for measuring consumer knowledge of electronic technologies has been the use of proxies to infer knowledge (i.e., usage experience and purchase behaviour). But, the use of proxies to infer knowledge stored in memory assumes that people learn from experience at the same rate when presented with different products. By contrast, Hoch and Deighton (1989) and Brucks (1985) contend that the more complex the product, the wider the gap between experience and true expertise is likely to become. In other words less may be learnt from experience with a complex product than from experience of a simple product. Thus, to aid the examination of the relationship between user knowledge content of the web and user web perceptions, measures of user knowledge content (i.e., type and scope) are developed further.

Here it is argued that a relationship will exist between user knowledge content of the web and user web perceptions for the same reasons as given before, namely:

the changing roles and profiles of users and their motivations for system usage;

the complexity of the user interface of the web compared to other electronic devices (e.g., graphical browsers versus the TV and VCR)

the user-directed machine and person interactivity of the web,

As before, it is also examined if this relationship differs across users who have experience with web site design and maintenance (e.g., webmaster/IT specialist) and users without this experience (e.g., end-user of the system).

1.3 THE RESEARCH QUESTIONS In summary, the core objective of this dissertation is to investigate the relationship between the constructs depicted in Figure 1, with a view to answering the question:

What is the relationship between a user’s perceptions and knowledge of the web, and a person’s current web session usage?

This general question is divided into three research questions, each with specific underlying hypotheses:

What is the relationship between a user’s perceptions of the web (i.e., both ease of use and usefulness) and a person’s current web session usage? (RQ1).

What is the relationship between a user’s knowledge content of the web and a person’s perceived usefulness of the web? (RQ2).

What is the relationship between a user’s knowledge content of the web and a person’s perceived ease of web use? (RQ3).

An elaboration of these questions into the hypotheses to be tested is presented in Chapters 3-5 and summarised in Chapter 6. The methods used to examine the specific constructs and test the detailed hypotheses are described in Chapters 7 and 8.

1.4 SUMMARY FINDINGS Descriptive and empirical findings from this study are presented in full in Chapters 9 to 10. The purpose here is simply to highlight a few of the main results to be kept in mind when reading earlier sections of the dissertation. The summary results of each research question will now be discussed in turn.

1.4.1 RESEARCH QUESTION 1 (H1-H6/MRA1) 2 For users with no web-site design and maintenance (WSD/M) experience:

Perceived ease of web use had a positive effect on web session usage frequency and web session usage duration;

Perceived web usefulness had a positive effect on web session situational variety;

Perceived web usefulness firstly, and perceived ease of web use secondly, had a positive effect on web session motivational variety;

Perceived ease of web use and perceived web usefulness had a positive effect on depth of web session use;

For users with web-site design and maintenance (WSD/M) experience:

Perceived web usefulness had a positive effect on web session usage frequency; web session motivational variety; and breadth of web session use;

Perceived ease of web use had a positive effect on duration of web session use;

Perceived ease of web use firstly, and perceived web usefulness secondly, had a positive effect on depth of web session use.

1.4.2 RESEARCH QUESTION 2 (H7-H13 & MRA2)3 For users with no WSD/M experience;

Perceived procedural and perceived declarative web knowledge had a positive effect on perceived web usefulness;

H1-6 = Hypothesis 1 to 6; MRA1 = Multiple Regression Analysis 1

H7-13 = Hypothesis 7 to 13; MRA2 = Multiple Regression Analysis 2

Actual common procedural and common declarative web knowledge had a positive effect on perceived web usefulness;

Perceived procedural web knowledge content had the strongest positive impact on perceived web usefulness

For users with WSD/M experience;

Perceived procedural and perceived overall web knowledge content had a positive effect on perceived web usefulness;

Perceived procedural web knowledge content had the strongest positive impact on perceived web usefulness

1.4.3 RESEARCH QUESTION 3 (H14-H20 & MRA3)4 For users with no WSD/M experience;

Perceived procedural and perceived declarative web knowledge had a positive effect on perceived ease of web use;

Actual common procedural web knowledge had a positive effect on perceived ease of web use;

Perceived procedural web knowledge content had the strongest positive impact on perceived ease of web use;

For users with WSD/M experience;

Perceived procedural and perceived declarative web knowledge had a positive effect on perceived ease of web use;

Actual common declarative web knowledge had a positive effect on perceived ease of web use;

Perceived procedural web knowledge content had the strongest positive impact on perceived ease of web use.

1.5 RESEARCH MOTIVATION AND CONTRIBUTION

H14-20 = Hypothesis 14 to 20; MRA3 = Multiple Regression Analysis 3

1.5.1 MANAGERIAL MOTIVATIONS The number of consumers using the web across differing consumer segments is changing. Users with more advanced knowledge and experience with computing technology are becoming more stable and users with less computing experience are increasing. Hence, there is a need to investigate these differing user groups. Furthermore, industry reports of dissatisfied site experiences, low levels of transactional activity on-line, and the poor performance of the dot coms provide evidence that a better understanding of the user and the user-web interaction is required. However little is known about how and why users are using this electronic technology and what are the determinants of consumer web usage.

In summary this research is motivated by the need to:

profile developing web user populations and distinguish different groups of web users (e.g., experienced versus inexperienced).

increase management understanding of web usage and web perceptions,

develop a profile of user knowledge content and understanding of the web,

base user segmentation on differing perceptions and knowledge of technology so as to help determine how long, or how often it is used,

assist in the development of help-files and user-based information services by which to increase user knowledge of the web and thus achieve wider penetration,

provide a possible model for understanding user perceptions, knowledge and activities with other complex user-directed electronic technologies (e.g., WAP, PDA’s, electronic vending machines, etc.).

1.5.2 ACADEMIC MOTIVATIONS Academic research has concentrated on understanding the mechanics of the internet, describing the evolution of the number of hosts, number of users, and general characteristics of key players with this electronic technology. As stated by Berthon, Pitt, and Watson (1996b), back in 1996, ‘most of the work done so far has been of a descriptive nature – what the medium is’. The lack of understanding of the consumer and of

consumer behaviour on the internet is motivation enough to further investigate users of the web.

In sum, this research was motivated by the need for:

valid and reliable standardised measures of current web session usage,

further validation of TAM in a non-organisational context,

further development of objective measures of user knowledge content of the web, in order to shift away from the use of proxies,

an examination of user-based determinants of user system perceptions, as opposed to a focus on system-based determinants,

empirical research investigating the responses of users to interactive media and userdirected (externally paced) technologies (e.g., Web), in contrast to the bias of most research on user responses to passive non-user-directed (internally paced) technologies (e.g., TV, Radio).

1.5.3 RESEARCH CONTRIBUTION A number of key contributions are proposed as a result of this research, with increased elaboration of these presented in Chapter 12. In brief, these contributions include (but are not limited to):

The development of more refined, tested and validated self-report measures of ‘postpurchase’ system usage and, in particular, extending the literature on ‘usage measurement’ to include three specific areas – the measurement of frequency, variety and the extent of system use,

Empirical support for the argument put forward but not tested by Moore (1991) and Adams et al. (1992) that usage context influences the effect of user perceptions of a system on usage,

Further refinement and validation of measures for the study of perceived ease of web use and perceived web usefulness. Measures are developed to assess a user’s perceived ease of use and perceived usefulness of certain web functions (e.g., shopping, communication, information search, etc.),

This study further extends work conducted on the Technology Acceptance Model (TAM) toward predicting system perceptions and usage. Limited research had been conducted that explored how and why system perceptions were formed. This dissertation contributes to TAM by investigating the influence of a specific personal factor, knowledge content, on a user’s perception of the stimuli of interest, i.e., the web, extending the literature on basic system characteristics as predictors of system perceptions,

Within the knowledge literature, terms have been used and misused, giving rise to semantic confusion. In this dissertation a consistent and simplified definition of knowledge is set down,

One of the most common methods for measuring consumer knowledge, especially in the technology area, has been the use of proxies. This study goes beyond the use of proxies to infer knowledge of the electronic system in question. Developing, validating and testing objective and subjective measures of user knowledge content of the web across two very different user groups – users with and without web site design and maintenance experience,

1.6 RESEARCH LIMITATIONS AND RECOMMENDATIONS 1.6.1 RESEARCH LIMITATIONS No study is without its limitations, and a number of these are described in Chapter 12. In brief, these include:

The Australian sample might limit international generalisability of results.

Given the rapidity with which technologies and the profile of those using the technologies change, the relevance of the results of this study might vary over time.

Specific limitations are identified with respect to how some of the measures were operationalised in this study.

Limitations further exist as a result of the recruitment method used (i.e., banner ad campaign); the approach to sample self-selection; the use of self-reporting measures;

an inability to screen completely for multiple-survey responses; and also the impact of survey length.

1.6.2 RECOMMENDATIONS FOR FUTURE RESEARCH A number of areas for future research arise from this study. These include, but are not limited to:

The measurement of actual procedural knowledge using an experiment or taskbased research approach.

Further development and refinement of the actual knowledge scales for validation of the difference between common and specialised knowledge (scope), as well as the procedural and declarative (type) of knowledge content.

Replication of the study between differing samples to increase the generalisability of the research findings. For example, a comparative analysis between users and nonusers of web-technology; between users in differing geographical regions, such as the US, UK, Australia and Asia, etc.

Further investigation of the determinants of user knowledge content of the web. How, and from where, do users acquire their knowledge about how to use the web; is it from experience and use of the technology, from media sources, from personal communication, or perhaps from formal training?

1.7 INTRODUCTION SUMMARY As introduced and briefly summarized in this Chapter, the goal of this dissertation is to explore the relationships between user knowledge content, perceived web usefulness, perceived ease of web use and current web session usage. The relationships proposed are an extension of the usability framework that is encapsulated in the Technology Acceptance Model (TAM). In the next chapter, these themes are discussed in a general way. The attributes, features and uses of electronic technologies are considered. This provides background information and offers a rationale for why further research and investigation is important.

Following on from Chapter 2, Chapter 3 presents past research on consumer usage behaviour, Chapter 4 discusses past research on user perceptions of electronic technology, and Chapter 5 examines user knowledge content of the web. In sum, the discussion presented from Chapter 2 through to Chapter 5 sets the foundation for the relationships proposed and tested in this dissertation.

C HAPTER 2: R ESEARCH B ACKGROUND – E LECTRONIC T ECHNOLOGY AND THE U SER THE PARADOX OF TECHNOLOGY ’The same technology that simplifies life by providing more functions in each device also complicates life by making the device harder to learn and harder to use’ - Norman (1990)

2.1 INTRODUCTION In this chapter, a review of the management and development of user-driven technology is presented. Conceptual ideas and empirical findings are considered, drawing on the disciplines of information technology, communications marketing and consumer research. The electronic technology investigated in this study is the hypermedia computer-based technology of the World Wide Web (hereafter web) on the internet. This study examines the web as a developing electronic technology, drawing from established communications and information systems theory to investigate the impact of user characteristics on the user-web interaction.

An overview of electronic technologies, and the electronic technology of interest in this dissertation, is presented in section 2.2. A discussion of the relationship between electronic technology and the user follows in section 2.3.

2.2 ELECTRONIC TECHNOLOGY Electronic information and communication technologies range from basic telephone services to personal computing, and networks to broadcast devices. Due to the range of

devices available, and their system complexity, it is difficult to categorise them to form mutually exclusive and exhaustive groups. Although numerous electronic information and communication devices exist, one technology has especially captivated the attention of the public and business. This is the internet and the web. Without question, interest in this has increased more rapidly and widely than for any other electronic device in recent decades. Its penetration has not surpassed that of broadcast devices like TV and Radio or the telephone, however the impact of the internet and the web on not just business but individuals is without question.

To understand the uniqueness of this electronic technology it is important to review its development and key characteristics.

2.2.1 ELECTRONIC TECHNOLOGY: THE WORLD WIDE WEB In everyday use, the terms internet and web are used interchangeably and often thought of as referring to the same entity. However, these terms actually make reference to two distinct electronic technologies, although they are very closely inter-linked. The distinction is explained briefly.

2.2.1.1 The Internet The word internet is short for internetwork, a decentralised ‘international’ network of interconnected computer networks based on a standard systems protocol called Internet Protocol (hereafter IP) (Ainscough and Luckett 1996; Hoffman and Novak 1996; Pallab 1996). The development of IP and software that understands this protocol has become the means of transmission for the internet. In scope, this network of connected computers over telephone networks spans regions, borders and countries.

Since the early 1970’s, the connections and networks have been run by universities, school systems, libraries, federal and state governments and the military. Broader business use of the internet only became possible in the early 1990’s when it was

identified that the commercialisation was essential for further investment and development (Hofacker 1999). This was realised by the development of a front-end search environment, called the web that added a hypermedia5 capability to the internet. As the development of the web was the key to the commercialisation and rapid growth of the internet, the web is the communication technology further examined in this study.

2.2.1.2 The World Wide Web The commercial potential of the Internet was realized with the development of the web, a distributed hypermedia system. Prior to this development, the Internet was text-based, command driven and user unfriendly (Lawrence, Corbitt, Fisher, Lawrence, and Tidwell 2000). However, following its inception, the web - supported at the client level by a graphical user interface (GUI) – had a profound influence on the usability and commercialisation of the Internet.

Technically, the web rests on three enabling protocols: hypertext mark-up language (HTML), hypertext transfer protocol (HTTP), and Uniform Resource Locators (URL’s). HTML specifies a simple mark-up language for describing and displaying information; HTTP specifies the form and nature of the request/retrieval process that occurs between a web client (e.g., a web browser housed on a PC) and a web server connected to the internet. URL’s are used to specify the location of documents housed on web servers on the internet (Lawrence et al. 2000; Hofacker 1999).

Hypertext is thus the key technological development underlying the communications structure and process of the web. Hypertext and hypermedia development and research extend back to the early 1960’s. They basically enable the structuring of information as an associative network of nodes and links, free from the linear sequential structure that dominates most printed documents (Bieber, Vitali, Ashman, Balasubramanian, and Oinas-Kukkonen 1997).

Hypermedia is the combination of the node and link access of hypertext and the synchronisation facility of multimedia, as discussed in section 2.2.1.2 (Bornman and Von Solms 1993).

Hypertext systems are databases in which chunks of information are linked together in a non-sequential way. This provides a vehicle for intuitive non-linear access to information (Bornman and Von Solms 1993; Smith and Wilson 1993). In a typical hypertext system, a window on the computer screen would correspond to a node in the database. Users navigate through the system by selecting buttons or hotspots within the window that activates links to other nodes. Smith and Wilson (1993) specify three methods of exploring a hypertext system, by:

text string, key word or attribute value;

following links and opening successive nodes; or

using a browser, a graphical representation of the network.

Multimedia content is the computer-based combination of static text, image and graphics, and dynamic audio, video and animation content (Bornman and Von Solms 1993). These are time based. Multimedia, like video and audio, typically have synchronisation requirements where components are presented in some author-defined temporal order (Rada 1995). For example, time dependencies exist in the sequence of images on a video as synchronised with sound.

Hypermedia is the combination of the node and link access of hypertext and the synchronisation facility of multimedia (Bornman and Von Solms 1993). It is from the premise of hypermedia being the key technological development underlying the communications structure and process of the web that provided the impetus for Hoffman and Novakʹs (1996) reference to the web, and supported interfaces, as the most current form of a hypermedia computer-mediated environment (hereafter this will be referred to as HCME).

HCME’s relate to a range of electronic technologies (i.e., web, personal digital assistant (PDA), touch-screen e-Kiosks, etc.) and these electronic technologies differ to non-HCME based electronic technologies (i.e., TV, radio, etc.) in terms of vividness, interactivity, media pacing (i.e., external/internal) and the flow of information and communication

transfer. Given the amount of research already conducted on non-HCME based electronic technologies it is important to further profile HCME’s in terms of the aforementioned characteristics.

2.2.2 WEB CHARACTERISTICS The web is seen as a network-wide graphical interface based on hypermedia technology which enables users to provide and interactively access hypermedia content (i.e., machine interactivity), and to communicate through the medium (i.e. person interactivity) (Hoffman and Novak 1996; p53). The key distinguishing features of the web are:

It uses the network structure of the internet (Ainscough and Luckett 1996; Hofacker 1999; Lawrence et al. 2000) ;

Based on hypermedia (Rada 1995; Bieber et al. 1997; Lawrence et al. 2000);

Information transfer is externally and internally paced6 (van Raaij 1998; Stangelove 1996; McWilliam, Hammond, and Diaz 1997; Blattberg and Deighton 1991; Berthon, Pitt, and Watson 1996a; Ariely 2000);

Both person and machine interactivity are facilitated (Steuer 1992; Hoffman and Novak 1996);

It is supported by a graphical user interface (GUI) and digital computer technology (Rumpradit and Donnell 1999; Encarnacaeo, Loseries, and Sifaqui 1999; Church 1999);

It is seen as an environment that is directly experienced by users (Hoffman 2000; Hoffman and Novak 1996);

It facilitates a many-to-many computer-mediated flow of communication between users (Hoffman and Novak 1996);

Pacing refers to who controls the speed and sequence of information transfer. With some electronic technologies the speed and sequence of information transfer is controlled by the sender (i.e., it is externally paced) or the receiver of the information (i.e., it is internally paced). For example, broadcast television and radio are two externally paced electronic technologies, where as the web has the capability to enable both internal and external pacing.

It is an electronic technology that tends to be media rich, information rich and fairly vivid (Valacich, Paranka, George, and Nunamaker 1993; Glaser 1997).

These characteristics enable the web to be classified as a highly complex, information rich, user-direct electronic information and communication technology.

2.3 ELECTRONIC TECHNOLOGY AND THE USER With the development and rapid diffusion of electronic technologies consumers are presented with more and more electronic information and communication resources. This turns the spot light on users, the focus of this dissertation. The background to this research is thus the investigation of human-computer interaction (HCI).

2.3.1 HUMAN-COMPUTER INTERACTION (HCI) Regarded as the study of the interaction between humans and computers, HCI is a field of research and development with the objective of designing, constructing and evaluating computer-based interactive systems and interfaces (Booth 1989; Hartson 1998). Human-computer interaction is viewed as a form of communication between two parties that have quite different sending and receiving capabilities. The user interface is regarded as a communication channel – a place between a human and a computer at which the two make contact, interact and communicate (Church 1999).

Moran (1981) defines the ‘interface’ as consisting of everything the user comes in contact with while using the system – physically, perceptually, and conceptually. Marchionini (1995) builds on this and his discussion of an interface is most useful to illustrate the association between HCI and the communication process. He defines an interface as a channel of communication and adds that the ‘interface serves as an intermediary between the user and the database [of knowledge being searched]’ and that this database can reside in ‘people, books, libraries, and maps as well as in a variety of electronic information systems’. In summary, interaction with an interface, electronic or not, is a

form of exchange with both the interface and user encoding and decoding a stream of symbols flowing to the users, from the user and to-and-from the user to accomplish communication and instil meaning.

The core motivation for the investigation of HCI is the end goal of system usability system ‘ease of use’ and ‘usefulness’. Hartson (1998) specifies that usability is seated within the user’s perspective of the interaction with a computer system and the interface, and not just about the components of the interface itself. The underlying principle of the investigation of HCI is grounded in the process of communication transfer and a dialogue that involves few spoken words, but the exchange of meaningful symbols. This communication exchange requires at least partially overlapping fields of experience to facilitate the acquisition of meaning from the symbols by all participants (Church 1999; Heinich, Molenda, and Russell 1989). Otherwise no communication occurs. A shared meaning can thus be seen as the key mediating factor in the success of communication transfer in human-computer interactions.

An example where communication fails, and meaning is not acquired during a HCI, is the interaction between a user and the electronic interface of a home video recorder (VCR). Many users of a VCR do not understand how to use the pre-record and date/time settings of the device. This interaction is confounded by the design of the VCR and its functions and also the design of the user manual used to aid VCR users. In summary, knowledge of the VCR and its functions is the primary ingredient that would facilitate increased ease of use and usefulness of this device, thus enabling system usability.

The specific theme of this study examines the user interface component of the web on the internet. The most common user interface that individuals use to access the web on the internet is a ‘web browser’ - more technically known as a ‘web client’. A web browser is a graphical user interface (GUI) that allows the user to access information in the form of sound, text, graphics, and video clips on the internet providing visual representations of actual objects or operations. Browsers such as Netscape® Navigator or Communicator and Microsoft® Internet Explorer (combined with supporting software) provide an

interface to the web on the Internet via HTTP. Figure 2 provides a screen shot of two common web GUI’s.

Figure 2: The Web Browser: A GUI for the World Wide Web

The web browser is a hypermedia-based graphical interface that facilitates user navigation and interactivity in the distributed hypermedia system of the Web.

2.3.2 THE WEB AND USER NAVIGATION Web navigation, the process of self-directed movement through the web (Hoffman and Novak 1996), involves the use of a web browser Navigation of this system is determined by the characteristics of the system being used. The nature and structure of the configuration of the web, supports two navigational activities: ‘system browsing’ and ‘information search and retrieval’.

The ability to explore a hypertext system by link traversal is referred to as system browsing (Smith and Wilson 1993), whereas retrieval is the direct search for queried associations (Rada 1995). Brown (1988) differentiates between browsing and searching mechanisms by defining ‘browsing’ as knowing where you are in a database, and wanting to know what information is there; as opposed to ‘searching’ which is knowing what information is required and wishing to find it in a database.

Rada (1995) further states that a characteristic of browsing is going from node to node in the course of discovering what is in the information space and whether a vaguely articulated information need might be satisfied by something in that information space. Information retrieval systems focus on keyword-based automated searching in conjunction with Boolean operators (e.g., AND, OR, etc.), statistical word weighting and document relevance rankings for query modification (Brown 1988). In searching, people express a query and the information system returns a set of node contents which match or are related to the query. Knuth and Brush (1990) suggest that the ability to browse the system is the primary characteristic distinguishing a hypertext system from a database system (where information retrieval is the main navigational activity).

Both browsing and information retrieval navigational activities on the web are nonlinear in nature and provide essentially unlimited freedom of choice and greater control for the user about information alternatives. This is in contrast to the restrictive navigational options available in most non-hypermedia based communications technologies (i.e., television and offline newspapers). Hoffman and Novak (1996) comment that network navigation in HCMEâ&#x20AC;&#x2122;s permit greater freedom of choice than the centrally controlled interactive multimedia systems such as video-on-demand and home shopping networks.

2.3.3 THE WEB AND USER INTERACTIVITY In addition to the specific navigational options addressed above, the hypermedia computer-mediated system of the web enables users to engage in different forms of interactivity, both person and machine interactivity, which comprise web interactivity.

The hypermedia computer based communication technology of the web, and its graphical user interface, provides users with unprecedented control over the management and use of the delivery system with which they interact. However, despite the fact that the interactive delivery system offers the user a wider range of choices and

simultaneously greater individualization than non-interactive delivery systems (Rust and Oliver 1994), many aspects of graphical user interface use are not well understood. In particular, the influence of the physical constraints of the user (e.g., memory capabilities, information transfer limits, and computational ability) on the success of the human-computer interaction need to be further examined (Rumpradit and Donnell 1999). Smith and Wilson (1993) further state that from a behavioural viewpoint, even though hypertext systems have much to contribute in regards to the effectiveness and usability of network systems, they may also create a new set of usability problems, particularly in terms of network navigation and the use of graphical user interfaces.

2.3.4 THE WEB AND USABILITY The investigation of user interaction with the web is important. This is because of the complex and information-rich nature of the communication technology of the web, the changing role of the user in the communication transmission, and the heightened role of interdependence that has evolved between the communication delivery system and the user. Specific problems in these areas may inhibit system and behaviour adoption and continued use.

For example, significant problems exist in using graphical browsers. Conklin (1987) indicates that graphical browsers rely on the highly developed visual spatial processing of the human visual system. As nodes and links are placed in two or three dimensional space users have to orientate themselves by visual cues just as when they are walking through a familiar city. Conklin (1987) also points out that there is no natural typography for an information space like the web. So, until a person is familiar with any given layout of a hypertext document, that person is by definition, disorientated. Users may be disorientated by a large number of nodes and links, frequent changes in the network, slow or awkward response to inputs, and limited visual orientation (Conklin 1987).

Foss (1989) further categorises three specific problems of participating in hypertext environments:

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Lack of Closure: the problems that arise from unfamiliarity with the structure or conceptual organization of the network. For example, not knowing the extent of a network, or what proportion of relevant items remains to be seen.

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Cognitive Overhead: the problems that stem from the cognitive demands placed on the user of a hypertext system. The user must decide which path to take through the network but may find interesting sidetracks that distract attention from the main task.

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Learning by Browsing: the problems caused by general inexperience with the hypertext system, which gives rise to difficulties either remembering, consolidating and/or understanding the semantic content of nodes. This results in a lack of detailed memory of any particular item and an inability to summarise what has been learned.

These problems of using hypermedia computer-mediated systems give rise to the need for an understanding as to what inhibits user adoption and use of the web. Current research examines certain psychological characteristics of web users and user segments. For example research investigating the determinants of web use have looked at certain user navigational behaviours (Hoffman and Novak 1996), the principle of flow (Hoffman, Novak, and Yung 1998; Novak and Hoffman 1997), and individual predispositions, such as involvement and experience (Swoboda 1998). Diaz, Hammond, and McWilliam (1997) further conceptually reinforced Petty, Cacioppo, and Schuman (1983) and Petty and Priester (1994) supposition that an elective, interactive and novel medium naturally brings greater consumer involvement. Research also has examined different user segments, such as novice and more experienced web users (Diaz et al. 1997), teenagers, young adults (Napoli and Ewing 1998), people in different geographic markets (Teo, Lim, and Lai 1997), and different user perceptions (Briggs and Hollis 1997; Ducoffe 1996; Eighmey 1997; Maddox and Mehta 1997; Teo et al. 1997).

In 1984 Rice (1984) flagged that little effort had been allocated to the analysis of the interaction between the characteristics of media technology and the characteristics of the users themselves, this position still stands today with hypermedia computer-mediated communication systems. Although the aforementioned research is highly pertinent and

valid, a theoretical framework of media use and the implications of the interplay of media-user characteristics and their effect on system adoption and usability is altogether lacking in the disciplines of marketing and media communications. Hypertext system interface and design issues (i.e., button style or window placement) are often the focus of research papers in this area (Rada 1995). Most attention in the study of communication delivery systems has concentrated on the components of the message (i.e., source, content, etc.) and the effects of such message characteristics on the audience (i.e., recall, intention, etc.) (Weaver 1988). With respect to the web, research has examined user responses to web sites (Eighmey 1997; Eighmey and McCord 1998), web site features and design (Napoli and Ewing 1998), and specific usage trends such as newsgroup usage (Sivadas, Grewal, and Kellaris 1998).

Hodkinson and Kiel (1997) also identify a lack of scholarly investigations of user-directed technologies, although there has been some. For instance, researchers have examined the web in the context of general marketing communications tools (Hoffman and Novak 1996), for offline and online web advertising (Briggs and Hollis, 1997; Ducoffe 1996; Maddox and Mehta 1997) and for examining the impact of new digital media on the use of other communication technologies (Coffey and Stipp 1997; Napoli and Ewing 1998). It is evident that progress has been made by some researchers, but much remains to be studied.

2.4 E-TECHNOLOGY AND THE USER: SUMMARY No longer is it enough to profile usage behaviour of certain electronic technologies or to understand how the content of the technology influences behaviour. Due to the increase in highly complex, user-directed, communication technologies (e.g., touch-screen ekiosks, electronic organizers such as PDAâ&#x20AC;&#x2122;s and wireless system technologies such as WAP and iMode), understanding and determining consumer usage of the technology itself is now paramount.

As outlined in this chapter, users of user-directed hypermedia computer-based communication technologies can exercise unprecedented control over the use and management of the system and the contents of the system with which they interact (Rust and Oliver 1994). Drawing from the cognitive science literature, and studies in consumer research on consumer decision-making and information search/acquisition, this study investigates user knowledge and perceptions of an electronic technology – the web. It also proposes a framework for investigating the effect user knowledge and perceptions of a highly complex and technologically driven system may have on system usage. This gives rise to the general research question:

What is the relationship between a user’s perceptions and knowledge of the web, and a person’s current web session usage?

Next, in Chapter 3, ‘current web session usage’ is discussed. It provides an impetus as to why predicting and determining current usage is of importance in today’s electronic information and communication environment. Following this, Chapter 4 discusses the two core components theorized in this dissertation as the determinants of current web session usage - a user’s perceptions and knowledge of the web.

C HAPTER 3: P REDICTING C URRENT W EB S ESSION U SAGE

‘Riches do not consist in the possession of treasures, but in the use made of them.’ - Napoleon Bonaparte (1769-1821)

3.1 INTRODUCTION Understanding why people use certain products and engage in certain behaviours is a challenge, particularly when the environment changes as rapidly as in the area of electronic technology. To aid successful design and implementation of hypermedia computer-based systems, like the web, research into the usage of these systems is required. Current research is limited and in many instances it goes little beyond profiling web users. However, Korgaonkar and Wolin (1999) state that understanding why and how consumers use the web may be the key to unlocking the web’s capacity. Therefore, this chapter presents an overview of research into current usage experience of the web and provides a foundation for a model of ‘current web session usage’. As presented in Figure 3, the construct ‘current web session usage’ is one of the key dependent variables of the model tested here.

Specifically this chapter:

Draws a distinction between current usage experience and past usage experience and explains why this is important (section 3.3).

Conceptualises three categories of current web session usage (section 3.4).

Lays the foundation for discussion in subsequent chapters of user perceptions of the web as an antecedent of current web session usage (section 3.5).

Figure 3: Current Web Session Usage (RQ1)

Before, addressing these themes in some detail, three leading perspectives adopted in the literature for the investigation of consumer usage behaviour are discussed (section 3.2).

3.2 CONSUMER USAGE: RESEARCH PERSPECTIVES Within the marketing literature, the examination of consumer product use has been investigated from three research perspectives - the social interaction perspective, the experiential consumption perspective and the functional utilisation perspective. The social interaction perspective examines the symbolic aspects of usage. It examines social meanings attached to the consumption of intangible product attributes in the case of socially conspicuous products such as a car or house (Belk, Bahn and Mayer 1982; Solomon 1983). The experiential consumption perspective investigates post-purchase usage, especially consumer experiences such as ‘fantasies, feelings and fun’ - the hedonic consumption of products (Holbrook and Hirschman 1982). Thirdly, the functional utilisation perspective examines the functional use of products and their attributes in

different situations (McAlister and Pessemier 1982; Srivastava, Shocker and Day 1978). An extension of the last approach is the ‘users and gratification’ perspective.

The functional utilization perspective is particularly relevant here because of the way the web is used. The web is an electronic technology that offers multiple features and functions enabling users to select combinations of features/functions and also to create different situations for each application. Furthermore, Ram and Jung (1990) note that the usage of durables, such as personal computers and VCR’s, are the focal point of the functionalist perspective of product usage research.

3.2.1 USES & GRATIFICATIONS PERSPECTIVE OF MEDIA RESEARCH The uses and gratification perspective of media research focuses on audience motivation and use. This approach arose out of the functionalist perspective of mass media first articulated during the 1940s in research concerning the effects of radio programs on members of the listening audience. A psychologist and mass media researcher, Herzog described the functionalist perspective as focusing on the question of the satisfaction people say they derive from using a particular mass medium (Herzog 1944). These selfreported perceptions and motivations gave researchers insight into the factors that attracted, and continued to attract, audiences to specific mass media.

Subsequently, mass communication researchers have used the functionalist or gratification perspective in research concerning the use of various mass media, particularly television (Rubin 1994). For example, certain types of television programmes have been shown to be related to various human needs, including information acquisition, escape, emotional release, companionship, reality exploration, and value reinforcement (Rubin 1994).

This perspective has been applied to understanding user motivations and behaviour in the context of cable television (Donohew, Palmgreen and Rayburn 1987); message content (Swanson 1987); TV remote control devices (Walker and Bellamy 1991);

computer aided instructional settings (Kuehn 1994); television commercials (Schlinger 1979) and the opportunities for relaxation offered by use of digital media (Barwise and Hammond 1998). Furthermore, McGuire (1974) points out that this perspective appears to be particularly useful in explaining the continuing use of a medium. While initial use of a medium may result from accidental exposure, curiosity about new things or participation in a fad, continuing use of a medium is likely to dissipate in the absence of audience rewards for continued reading, listening and/or viewing.

Furthermore, the uses and gratifications perspective rests on the basic assumption that the audience is actively involved in media usage as opposed to being a passive recipient of media content. As previously discussed, hypermedia based technologies such as the web offer a rich, vivid, highly interactive computer-mediated environment in which audience members can actively search for information, can view information in a wide range of content formats, and can interact with numerous information sources. The uses and gratifications perspective is thus very much in line with web use, and this perspective is adopted here. However, before discussing the antecedents of web usage experience, an overview of the literature on product and media usage is presented. What do we exactly mean by the construct ‘usage experience’?

3.3 CURRENT USAGE AND PAST USAGE EXPERIENCE Current usage and past usage experience - an agreed definition of these constructs is hard to find because so many different interpretations and measures have been proposed. Within the literature, current usage experience and past usage experience are often treated as one and the same (e.g., Bettman and Park 1980; Dishaw and Strong 1999). However, this might hide important effects, particularly for internet applications like the web (Kraut, Mukhopadhyay, Szczypula, Kiesler, and Scherlis 1998). This may be due to the web’s more adaptable and more complex characteristics in comparison to many household devices currently in use. For example, many consumer products and media alternatives, such as the VCR, the television, the radio, have a narrow range of uses. By contrast, the web offers a variety of applications. Hence a user’s early

experiences are likely to have an impact on later usage, but the two should not be treated as one and the same – the behaviours of people who experiment with the web in the first few months are not going to be the same as those with five years experience.

Ram and Jung (1990) contend that in contrast to the discrete event of purchase, usage is a continuous event which may change over the length of time of exposure or ownership to the stimuli in question. Hence, researchers are advised to measure both past and present usage experience. In this research current usage experience is defined as the act of using the web for some purpose at the time the measurement were made. Past usage experience refers to the act of using the web for some purpose prior to the time the measurements were made (Delbridge and Bernard 1998). The former construct – current usage experience – is of prime interest here.

3.3.1 THE IMPLICATIONS OF INVESTIGATING USAGE EXPERIENCE Observing consumers as they use products can be an important source of new product ideas and can lead to ideas for new product uses or product design and development. Furthermore, new markets for existing products can be indicated, as well as appropriate communication themes for product promotion. Considering the importance of the economic success and also the high rate of new product failure of new or existing products it becomes crucial to identify factors fostering and inhibiting consumer adoption and use. Understanding how products, or in this case, how electronic technologies are used, and what determines their usage, is thus an important part of researching and understanding consumer behaviour.

Usage also has important implications for the communication of product information to the consumer. Ram and Jung (1990), for example, showed that only a small number of respondents reported the use of certain features of durable goods, with some respondents not even aware of these features. This result is extremely apparent in studies of technology-related products (Higgins and Shanklin 1992); for example, among American VCR owners one-third do not record programs while absent from home –

despite this being a key feature of a VCR (Rosen and Weil 1995). This suggests that manufactures need to facilitate consumer usage of these features by designing userfriendly manuals or improving other communications.

Current usage could also be used as the basis for segmenting product markets. For example, Potter et al. (1988) attempted to identify the profiles of five usage segments for VCRs. Studies of computer usage in the workplace have had a wide range of uses too. They have been used to determine training needs, to determine the effectiveness of system implementation, to establish time costs associated with certain work tasks, and/or to monitor work output. Research related to the implementation of information systems has provided ample illustration of how usage estimates facilitate the evaluation of system success. For example, user receptivity towards computers (Sarris, Sawyer and Quigley 1993; Saltz, Saltz and Rabkin 1985) and the effect of computer implementation and use (Knapp, Miller and Levine 1987). Having shown the importance of studying usage, attention turns to the different types of current usage experience.

3.4 TYPES OF CURRENT USAGE EXPERIENCE Ram and Jung (1990) proposed two categories of product usage experience - usage frequency and usage variety. Zaichkowsky (1985b) also discusses two categories of product usage - depth of consumption and breadth of consumption - and relates these to involvement and expertise. Zaichkowskyʹs (1985b) depth of consumption category is equivalent to Ram and Jungʹs (1990) category of usage frequency. Seeley and Targett (1997) further showed that computer use comprised three categories - frequency, depth and breadth of use. Therefore, it is possible to derive the following four categories of product usage:

Usage Frequency refers to how often a product is used within a certain time frame. For example, how often the web is accessed in a week.

Usage Variety refers to the different use motivations and different situations in which a product is used. For example, using the web for information search and

shopping (motivational variety) and accessing the web at home and at work (situational variety).

Breadth of Use refers to the number of different types or brands of a product owned or used in a category within a given current time frame. For example, the number of different types of web sites (e.g., search engine, shopping, email, etc.) used within the last week.

Depth of Use refers to the overall number of items within a category used within a certain current time frame. For example, the total number of web sites accessed in the last week.

Ram and Jung (1990) differentiate frequency and variety of consumption on several characteristics. The authors propose that usage frequency can be driven mainly by task requirements of the consumer, while usage variety depends both on the variety of features offered by the product and the variety of usage situations (Ram and Jung 1990). Furthermore, there are likely to be temporal variations in the two categories. Usage frequency may be high immediately after purchase, whereas usage variety may be dependent on a user’s skill and knowledge and thus high after continued use (Ram and Jung 1990). Both also could be considered manifestations of different consumer needs: frequency - routine needs, and usage variety - variety seeking needs. In addition, an increase in usage variety is likely to have a positive impact on the market development of the product. For instance, the higher the technical sophistication, the greater number of potential uses, and thus the greater the number of potential users.

Much of the research on usage experience has been product-specific. Such as studies of VCR’s (Harvey and Rothe 1986; Levy 1980; Levy 1981; Potter et al. 1988) and personal computers (Dutton, Kovaric and Steinfield 1985; Mentzer, Schuster and Roberts 1987). Typically, product usage has been studied in the context of pre-purchase decisionmaking (Belk 1979; Bettman and Park 1980; Johnson and Russo 1984; McAlister and Pessemier 1982; Srivastava et al. 1978) and seldom post-purchase consumption. The focus in this dissertation is somewhat different – the focus is on current usage of media

technology, specifically the web on the internet, during post-purchase consumption. This media focus is now explained.

3.4.1 USAGE AND COMMUNICATION MEDIA Media usage has been studied, in detail, for a long time. In part, this is because of demands by media owners for high quality usage data. For example, from the 1950’s TV viewing information was collected through organizations such as Arbitron in the US and AGB Ltd in the UK. Similar data have been collected for newspapers and magazine readership, radio listening, cable viewing, etc. Systematic studies of media usage included Goodhardt, Ehrenberg and Collins (1975) and Barwise and Ehrenberg (1988) on TV viewing patterns and audience appreciation of programmes. In more recent years web usage data has been collected by firms such as Forrester Research in the US and UK and ACNielsen in Australasia.

When looking at the relationship of ʹproduct usageʹ to ‘media usageʹ one can begin to see similar implications from this line of investigation. Consumer segmentation is one possible example. Bawa and Shoemaker (1987) discussed an extensive body of literature on segmenting consumers based on their use of broadcast and direct response media. The logic of segmenting on the basis of frequency of readership, viewership or patronage of media vehicles can be found in much of the media research. For example, Urban (1976) suggested that heavy and light magazine readers might respond differently to ads with different creative appeals.

As further discussed by Chatterjee, Hoffman and Novak (1998), segmenting users on the basis of their media usage frequency yields insights on whether the medium attracts and retains readers/listener/viewers that are more or less responsive to an advertiser’s communication. This information is important when evaluating the efficiency and effectiveness of media. Furthermore, Chatterjee et al. (1998) show that differences in frequency may lead to differences in response to repeated passive ad exposures, competing ads of other sponsors and prior ad exposure.

Frequency of media usage has been the most popular measure of media usage experience. However, Olney, Holbrook and Batra (1991) also identified viewing time as an important dependent variable in a model of advertising effects. This is consistent with Holbrook and Gardnerʹs (1993) argument that duration time is a critical outcome measure of consumption experiences and may be a useful behavioural indicator of experiential versus goal-directed orientations. Dreze and Zufryden (1997d) identified depth of web-site usage as important, defined as the number of pages accessed. The following categories of web use are further discussed and investigated: frequency, variety, breadth, depth and duration of use. In this study the latter three are proposed to comprise three dimensions of ‘usage extent’. Therefore, ‘usage frequency’, ‘usage variety’ and ‘usage extent’ are further conceptualised and discussed in relation to current web usage experience. These categories are presented in Table 1, together with examples of broad media types (e.g., web versus magazines) and specific media vehicles (e.g., web site such as yahoo.com.au versus a magazine title such as Time).

Table 1: Categories of Usage – Media/Vehicles Examples Current Usage Experience Construct Usage Frequency

Media

Dimension

Mag Title (Time)

Web

Magazines

Domain

How often a week do you visit/use Yahoo.com.au?

How often a week do you read/buy Time Magazine?

How often a week do you access the web?

How often a week do you buy/read magazines?

Situational

In a (time frame) from how many locations do you use/visit Yahoo.com.au?

In a (time frame) from how many locations do you buy/read Time Magazine?

In a (time frame) from how many locations do you access the web?

In a (time frame) from how many locations do you buy/read magazines?

Motivational

In a (time frame) what are the main reasons you use/visit Yahoo.com.au?

In a (time frame) what are the main reasons you buy/read Time Magazine?

In a (time frame) what are the main reasons you access the web?

In a (time frame) what are the main reasons you buy/read magazines?

Duration (Time)

In a (time frame), how many hours/minutes would you spend visiting/using Yahoo.com.au?

In a (time frame) how many hours/minutes would you spend reading Time Magazine?

In a (time frame) how many hours/minutes would you access the web?

In a (time frame) how many hours/minutes would you spend buying/reading magazines ?

Usage Variety

Usage Extent

Media Vehicle Web Site (Yahoo.com.au)

Breadth (Familiarity)

Number of new/different site features used in a visit

Number of new/different sections read in an issue

Number of new/different web sites used in a session

Number of new/different magazines read/bought in a month

Depth (Amount)

Overall number of pages visited at the site in a visit

Overall number of pages read in an issue

Overall number of sites visited in a session

Overall number of magazines read/bought in a month

3.4.2 CURRENT WEB USAGE: WEB SESSION VERSUS WEB SITE VISIT Use of a hypermedia system like the web is termed network navigation. Hoffman and Novak (1996) define network navigation as the process of self-directed movement through an HCME. This non-linear search and retrieval process provides essentially unlimited freedom of choice and greater control for the user about information alternatives. This may be contrasted with the restrictive navigation options available in traditional media such as television and print media or even the centrally controlled interactive multi-media systems such as video-on-demand and home shopping.

By engaging in user-oriented network navigation the user participates in a ‘web session’. During a web session the user might visit a series of web sites (site visitation) to acquire information, advertising or promotional content about products, services or users, or to communicate, or to complete electronic transactions. Therefore a ʹsessionʹ is used to refer to the macro perspective of ʹweb usageʹ, and ʹvisitationʹ the micro perspective of ʹweb site usageʹ. This study discusses the implications of current (present) usage experience of the web from the macro perspective of web usage (i.e., column three of the examples in Table 1). This is now explained in greater detail.

3.4.2.1 Current Web Session Usage Frequency Usage frequency is defined as how often a product is used within a certain time frame, either within a product context (Zaichkowsky 1985b; Ram and Jung 1990; Seeley and Targett 1997) or with respect to media (Urban 1976; Bawa and Shoemaker 1987; Chatterjee et al. 1998). In this dissertation usage frequency is defined with respect to current web session use: ‘how often a session on the web is undertaken within a certain

time frame, such as a day, a week, or a month. This definition is consistent with existing descriptive measures of web usage by academics. For example, Novak et al. (1998) examined web usage frequency and Sivadas et al. (1998) asked respondents about their usage frequency of the web. In addition, industry market research analysts, www.consult.com (1999), examine frequency of web use by measuring ʹuse of browser over a day, a week and a monthʹ, and Jupiter (1999) also use similar definitions of current web session frequency.

A unique position taken by Novak et al. (1998) was that in addition to measuring present frequency of use of the web, they should also measure ‘anticipated or future usage frequency of the medium over the next year’. However, Nunes (2000) found that individuals are unable to predict their own future usage or, at the very least, they find this difficult to do. For the purpose of this study, current session usage frequency of the web is only examined in terms of present usage frequency, not future usage.

Potential segmentation of consumers on the basis of their frequency of visits yields many insights. Chatterjee et al. (1998) segmented their sample into four groups according to site visit frequency and they noted that as frequency of usage increases, consumers tend to stay longer at sites and are exposed to more passive ads, but they click on few passive ads or browse through active ads during visits. Napoli and Ewing (1998) also segmented users based on their frequency of use, classifying them into heavy, moderate or light users, and identifying distinct behaviours.

The influence of user perceptions of the web on the frequency of current web usage experience will be discussed in section 3.5 and chapter 4.

3.4.2.2 Current Web Session Usage Variety The term variety refers to the extent to which items in a set are different or distinct (Desai and Hoyer 2000). Thus, this second category refers to the different motivations for which, and situations in which, a product is used (Ram and Jung 1990; Zaichkowsky 1985b). In

the context of this dissertation, the focus is on current session usage variety of the web. This comprises the variety of situations and variety of motivations in which the web is used within a current time frame.

Usage consumption can occur in a variety of different situations and thus the number and type of situations in which usage takes place are worth investigating (Desai and Hoyer 2000). With respect to media, we are exposed to, and may use, various media in the home, at work, at school, while in transit, and in a number of other situations. Certain external factors will influence the situations available for media use, for example accessibility, infrastructure, socio-economic status and so forth. In this dissertation, it is proposed that internal user characteristics also play a role in influencing the type and number of situations where certain media are used.

Due to the user-driven and complex nature of the web, individual characteristics are believed to heavily influence a user’s choice of usage situation and thus the variety of situations in which the web is accessed. Usage variety is also determined by the number and type of motivations for web use. Motives are regarded as general predispositions that influence an individual’s action to fulfil a need or want – such as the need for information or the need to communicate (Schiffman, Bednall, Watson, and Kanuk 1997). Rubin (1993) contends that motives are key components of audience activity. In addition, different categories of motivations may appeal to differing types of people, may engage people differently, and may offer different satisfactions and rewards.

The uses and gratifications perspective has been applied successfully to a range of new media and related technologies to explain user motivations for media use. As previously discussed (section 3.2), the web is more adaptable and has more complex characteristics than many household devices currently in use. Typically, television has a fairly narrow range of uses and an individual’s usage motivation is heavily influenced by the capacity of this medium to entertain. By contrast, due to the complexity of features and attributes of the web, web usage may be driven by a greater variety of motives.

For example, Rafaeli (1986) examined audience member reports regarding the use of electronic bulletin boards. These users report recreation, entertainment and diversion as the primary motivations for use, followed by learning what others think and controversial content and communication. This study revealed a wide range of uses and gratifications with computer-mediated communication and its potential for personal communication.

Korgaonkar and Wolin (1999) further identified seven motivations and concerns (Table 2). These suggest that consumers use the web for many more reasons than to retrieve information or to communicate. In a similar way, Papacharissi and Rubin (2000) yielded five primary motivations for using the internet: interpersonal utility, passing time, information seeking, convenience and entertainment. The most salient motivation identified was information seeking.

Table 2: Motivations/Concerns of Web Use Motivation Social escapism Transactional security and privacy Information Interactive control Socialization

Description

Non-transactional privacy

Economic activity

Characterise the web as a pleasurable, fun, and enjoyable activity that allows a person to escape. Characterises concerns about giving personal and transactionalbased information and thus relates to privacy and security concerns. Describes how consumer use the web for their self-education and information needs. Describes the interaction and control that users have with the web. Represents the role of the web as a facilitator of interpersonal communication and activities. Concerns about privacy in general, rather than the security and privacy issues related to web transactions. Characterises the collection of information for learning and information purposes as well as for shopping and buying motivations.

Adapted from Korgaonkar and Wolin 1999

From a review of the literature it is evident that a number of descriptive profiles of web usage motivation are evident. Users are driven to satisfy needs of hedonism, informational utility, communication and transactional needs. However, what is lacking

in the literature are the predictors of web usage motivations. For example, an individual might be driven to satisfy a need for information and thus searches the web; but, what factors directly influence this individual’s use of the web to satisfy the felt need? Characteristics of the medium certainly influence its ability to fulfil this need, but characteristics of the user may also influence the choice to use the web above other alternatives.

The influence of user perceptions of the web on the variety of current web usage behaviour will be discussed in section 3.5 and chapter 4.

3.4.2.3 Current Web Session Use Extent The final category to be discussed is the current extent of web session use. This is defined as the degree of session use of the web, as opposed to variety and frequency of current use. This construct comprises the dimensions of: duration of use (time), breadth of use (range) and depth of use (amount) of the web (See Table 1).

Visit duration has been defined as the time between consumer entry to and exit from a web site (Chatterjee et al. 1998; Dreze and Zufryden 1997d). From the macro perspective adopted in this study, duration of web use in a session is thus defined as the time between logging-on and logging-off the web. Segmenting consumers on the basis of their session duration yields many insights. Holbrook and Gardner (1993) argued that duration time is a critical outcome measure of consumption experiences and may be a useful behaviour indicator of experiential versus goal-directed orientations. Olney, Holbrook and Batra (1991) further identified viewing time as a dependent variable in a model of advertising effects.

Breadth of use, when applied to product usage, refers to the range of different and/or new types/brands of products owned or used in a product class within a given time frame (e.g., number of different and/or new brands – Sony™, TEAC™, etc.). Thus, breadth of web session use is defined as the range of different sites or tools that are used.

For example, this would correlate with the number of new, unfamiliar or different web sites or search engines accessed.

Session depth is defined as the total number of web sites or search tools used within a given time frame. For example the total number of VCR’s owned, irrespective of brand. Depth of web usage has been measured from a micro perspective, thus depth of site use during a site visitation involves the measurement of the number of pages accessed. From a macro perspective this would correspond to the total number of web sites accessed during a web session (regardless of whether they are the same type of web site such as search engines, e-commerce sites, etc.).

Dreze and Zufryden (1997a) developed two effectiveness measures that were particularly relevant for the study of music web sites. The model of web site effectiveness is based on the number of pages accessed (visit depth), and time spent during site visitation (visit duration), with these measures explained by site attributes (e.g., pp 53). Dreze and Zufrydenʹs (1997d) model provides a potentially useful approach for evaluating and designing web site contents and configurations (i.e., background, image size, sound file display, and celebrity endorsement, use of java and frames, as well as operating system).

Descriptive profiles of the extent of web usage are reviewed in the literature (e.g., Diaz et al. 1997; Eighmey 1997; Novak and Hoffman 1997; Seeley and Targett 1997; Spink, Bateman, and Jansen 1999). However, as with other categories of web usage, there has been little investigation of the predictors of the extent of web usage, at either the macro and micro levels. It is evident that system characteristics such as access, modem, and computer configuration would influence the extent of usage of the web. However, due to the user-directed nature of the medium, it is proposed here that user characteristics may also be an influence. Therefore, the influence of user perceptions of the web on current web session usage experience will be discussed in section 3.5 and chapter 4.

In summary, web usage behaviour is defined in terms of current web session usage frequency, usage variety, and extent of use. In the next section the proposed antecedents of web usage are introduced, leading to the main theme of this dissertation, i.e., the influence of consumer perceptions of the web on current web session usage (i.e., frequency, variety and extent).

3.5 DETERMINANTS OF CURRENT WEB SESSION USAGE Ram and JungĘšs (1990) conceptualisation of product usage not only describes usage patterns, but also gives insight into the dynamics of usage shifts over time â&#x20AC;&#x201C; including changing patterns of consumer needs, variations in consumer skill levels, and likely product market developments. Determining usage may also influence forecasts of user adoption and product diffusion in the marketplace. Hence the importance of investigating the determinants of web usage experience. So, what influences the frequency, variety and extent of current web session use?

Many studies profile and provide a descriptive account of usage, however very few actually propose explanations for web usage. Nevertheless, a number of theories can be drawn upon from the areas of consumer behaviour, communications and management information systems. These theories stem from characteristics of the medium itself (i.e., from the graphical user interface and system design) (Conklin 1987; Foss 1989; Davis 1986); characteristics of the individual (i.e., from web experience) (Diaz, Hammond and McWilliam 1997), and web involvement (McWilliam, Hammond and Diaz 1997); and from features of the usage situation (i.e., from accessibility) (Cheung, Chang, and Lai 2000; Srivastava et al. 1978).

The primary concern of a number of studies in management information systems and communications has been the investigation of the influence of system characteristics on system use. In contrast, in this dissertation, the influence of individual characteristics are believed to determine the use of media in general, the motives for using media, and the conditions for contact with media. For example, gender, age, income and occupation are

three heavily used variables to explain web use by industry and academic researchers’ alike. In addition, Swoboda (1998) concluded that the conditions under which consumers use interactive media are primarily determined by their involvement, and their experience in using such electronic systems. As summarised by Swoboda (1998), selective media consumption and direct media contact are primarily determined by information needs, expectations regarding purchase relevant information, situational involvement and previous consumption experience.

However, in an area such as web usage and network navigation of hypertext systems, user perceptions of the medium may play a significant role in influencing current web session usage frequency, usage variety and the usage extent. Drawing from the uses and gratifications theory of media research, a number of researchers have looked at user perceptions from the micro perspective of usage – i.e., web site use. Eighmey (1997) investigated the impact of perceptions of site design and site satisfaction on web use and found that users are assisted by information being placed in an enjoyable context and the site being ‘easy to use’. In a further study, Eighmey and McCord (1998) identified that site factors associated with entertainment value, personal relevance and information involvement accounted for the largest proportion of total variance in web site satisfaction. The responses of the research participants in this study confirmed the earlier study, that an enjoyable context is important. Napoli and Ewing (1998) further identified a number of attributes of web sites that are deemed most important by users and found these attributes to be correlated with the dimensions of information content, entertainment value, personal relevance and efficiency.

These studies show the value of investigating user perceptions of the web. However, the focus has been on the micro perspective investigating user perceptions of the structure and content of specific web sites. As outlined in section 3.4, this study investigates the web itself, not specific web sites. Therefore, the first of three main research questions for this thesis is:

RQ1: What is the relationship between a user’s perception of the web and a person’s current web session usage?

This study draws from a model developed in information technology and management information systems to aid investigation of the determinants of electronic system usage. In chapter 4 the model - the Technology Acceptance Model (TAM) – is discussed, together with a number of hypotheses.

3.6 CURRENT WEB SESSION USAGE: SUMMARY Understanding why people use certain products and engage in certain behaviours is a challenging issue. This is particularly so where the environment changes, as is the case with the increasing use of electronic technologies. Therefore, to aid successful design and implementation of hypermedia computer-based systems, like the web, research into usage of these systems is required. This was discussed in this chapter. Specifically:

The distinction between current usage and past usage was drawn.

Three categories of current web session usage were conceptualised.

The foundations were laid for a discussion of user perceptions of the web as an antecedent of current web session usage (the theme of Chapter 4).

The next chapter discusses user perceptions of the web, and specifically reviews the Technology Acceptance Model (TAM), as determinants of current web session usage.

C HAPTER 4: PERCEIVED EASE OF WEB USE AND WEB USEFULNESS

‘The world changes according to the way people see it, and if you alter even by a millimetre the way people look at reality, than you can change it’ - James Arthur Baldwin (1924-87)

4.1 INTRODUCTION Computer adoption, acceptance and usage by consumers depends on internal factors (i.e., demographics, perceptions, computer experience, computer attitudes, innovativeness), and external ones (i.e., computer characteristics and organisational context). The acceptability, adoption and use of the web – as against computer adoption in general – presents its own challenges. For instance, in Chapter 2, problems using graphical browsers were discussed (Conklin 1987; Foss 1989). Such problems may influence a user’s perception of the web and inhibit use. Therefore, in this chapter, the impact on usage of the web of two specific perceptions is examined - perceived ease of use and perceived usefulness (Figure 4).

Figure 4: Web Perceptions (RQ1)

The process of perception is investigated, drawing on principles from consumer psychology. These principles have been applied in the fields of Information Technology (IT) and Management Information Systems (MIS) to gauge the impact of consumer perceptions on information system adoption and use. Particularly relevant in this context is the Technology Acceptance Model (TAM). This discussion provides a platform for addressing RQ1 – the relationship between user perceptions of the web and current web session usage.

4.2 PERCEPTION Perception is formally defined as the ‘process by which an individual selects, organises and interprets stimuli into a meaningful and coherent picture of the world’ (Schiffman, Bednall, Watson and Kanuk 1997). The process is depicted in Figure 5.

Figure 5: The Process of ‘Perception’ (Solomon 1994)

The study of perception, according to Schiffman et al., (1997) is largely the study of what we subconsciously add to, or subtract from, raw sensory inputs to produce our own private picture of the world. Our perception of the world is thus formed by our individual needs, drives, past experiences, motives, personality and learning. Therefore, different individuals derive different meanings from the same sensory information, i.e.

‘The Cognitive map of the individual is not a photographic representation of the physical world; it is rather a partial, personal construction in which certain objects, selected out by the individual for a major role, are perceived in an individual manner. Every perceiver is, as it were, to some degree a non-representational artist, painting a picture of the world that expresses his [her] individual view of reality’ (Krech, Cruchfiled and Ballachey 1962)

In the context of purchase and information search behaviour, the buying decision is characterised as involving the assignment of a specific value to each product which might be purchased. This value is determined subjectively and is dependent on the individual’s perception that the item is capable of fulfilling particular needs (Kassarjian and Robertson 1968).

In the more specific context of a user’s perception and adoption of technology, the Technology Acceptance Model (hereafter TAM) has been the basis of most research. The TAM model is drawn from the management information systems discipline. It was developed to explain user acceptance and adoption of computer-based information technology. The approach is consistent with the diffusion theory proposed by Rogers (1995). He proposed that adoption/rejection is based on five key perceptions about an innovation – relative advantage, compatibility, complexity, trial-ability and observability. Moore and Benbasat (1991), however, found that only three innovation characteristics – compatibility, relative advantage and complexity – have consistently related to adoption. Relative advantage is akin to ‘perceived usefulness’ and complexity is likened to ‘perceived ease of use’ (Davis 1986).

As the web is an electronic technology grounded in hypermedia computer-based information technology (see Chapter 2), TAM is examined in this dissertation to assess the influence of perceptions of the web on current web session usage (RQ1).

4.3 TECHNOLOGY ACCEPTANCE MODEL (TAM) 4.3.1 DEVELOPMENT OF TAM Understanding why people accept or reject electronic technology has proven to be one of the most challenging issues in information systems research (Swanson 1988). A longstanding objective of MIS research has been to improve our understanding of the factors that influence successful development and implementation of computer-based systems

in organizations (Keen 1980). Studies from these areas have investigated the impact of users’ internal beliefs and attitudes of computer-based systems on consequent usage behaviour (Srinivasan 1985; Swanson 1987). Furthermore, they have examined how these internal beliefs and attitudes are influenced by various external factors (e.g., system technical design) (Benbasat and Dexter 1986) and user characteristics (e.g., cognitive style) (Huber 1983). However, research findings have been mixed and inconclusive about perception as a determinant of user adoption, acceptance and use of the system.

From this premise, Davis (1986) developed and tested an adapted form of Fishbein and Ajzenʹs (1975) theory of reasoned action (TRA), called the technology acceptance model (TAM) (Figure 6). This was meant to explain computer usage behaviour and specifically looked at the development and testing of the effect of system characteristics on user acceptance of computer-based information systems.

Figure 6: Hypothesized Technology Acceptance Model (TAM) (Davis 1986)

A potential user’s overall attitude toward using a given system is hypothesized to be a major determinant of whether or not he/she actually uses it. This attitude toward using is in turn a function of two major beliefs: perceived usefulness and perceived ease of use. Perceived usefulness is defined as ‘the degree to which an individual believes that using a particular system would enhance his or her job performance’ and perceived ease of use is defined as ‘the degree to which an individual believes that using a particular system would be free of physical and mental effort’ (Davis 1986). Perceived ease of use further

has a causal effect on perceived usefulness and design features directly influence perceived usefulness and perceived ease of use.

Tests of the model confirmed several of the relationships hypothesized and refuted others. Both a survey and an experiment were conducted. In the survey, Davis (1986) found that:

System design features had a significant effect on perceived ease of use;

Perceived ease of use had a significant effect on both usefulness and attitude;

Perceived usefulness had a significant effect on attitude;

Attitude had a direct effect on usage behaviour.

Contrary to the hypotheses it was found that:

System design features exert a direct effect on attitudes;

System design features do not have a significant effect on perceived usefulness;

Perceived usefulness has a significant direct effect on usage behaviour.

Therefore, the TAM motivational variables – attitude toward using, perceived usefulness and perceived ease of use – taken together, fully mediate between system design features and self-reported usage behaviour. The survey results are shown in Figure 7. Behaviour in this case relates to use of PROFs e-mail and XEDIT™ file editor.

Figure 7: TAM Results – Survey Methodology (Davis 1986)

Experimental data further supported the theoretical causal structure of TAM. Perceived usefulness had a significant effect on attitude toward using the system. It also had a significant direct effect on self-predicted usage behaviour. Perceived ease of use, by contrast, had a limited effect on attitude toward using and no direct effect on behaviour. Perceived usefulness was thus found to be more important than perceived ease of use at determining self-predicted system usage. The experimental results are shown in Figure 8. Behaviour in this instance related to the use of Chartmaster™ and Pendraw™.

Figure 8: TAM Results – Experimental Methodology (Davis 1986)

4.3.2 EMPIRICAL TESTING OF TAM Further investigation of TAM has found that (refer to Appendix A):

Perceived usefulness is a primary determinant and perceived ease of use is a secondary determinant of intentions to use computer-based systems (for WriteOne™, Davis, Bagozzi and Warshaw 1989a; Bagozzi, Davis and Warshaw 1992).

Perceived usefulness and ease of use are significantly correlated with self-reported indicators of system use (for the PROFS e-mail system and the XEDIT™ file editor, Davis 1989b; and for micro-computers, Igbaria, Guimaraes and Davis 1995).

Perceived usefulness has a significantly stronger relationship with system usage than perceived ease of use (for the PROFS™ e-mail system and the XEDIT™ file editor, Davis 1989b; for e-mail and voice mail, Adams, Nelson and Todd 1992; for word processing, Bronson 1999; and for CONFIG™, Gefen and Keil 1998).

Perceived ease of use is an antecedent of perceived usefulness (for the PROFS™ email system and the XEDIT™ file editor, Davis 1989b; for word processing, Bronson

1999; for Microsoft™ Word and Excel, Chau 1996; for micro-computers Igbaria et al. 1995; and for CONFIG™, Gefen and Keil 1998).

Perceived ease of use has a significant positive effect on the attitude toward using an information system (for debugger DBG™ program, Bajaj and Nidumolu 1998).

The effect of perceived usefulness on intention is only partially mediated by attitude toward using the system (for the PROFS™ e-mail system and the XEDIT™ file editor, Davis 1986; Davis 1989b).

Perceived developer response has a significant effect on both perceived ease of use and perceived usefulness (for CONFIG™, Gefen and Keil 1998).

However, in contrast to the above findings, further research has also reported that:

Perceived ease of use does not have a significant relationship with perceived usefulness (for debugger DBG™ program, Bajaj and Nidumolu 1998).

Perceived ease of use does not have a significant relationship with behavioural intention (for Microsoft™ Word and Excel, Chau 1996).

The importance and explanatory power of perceived ease of use and usefulness on usage behaviour is inconsistent across different computer-based systems (for Wordperfect™, Lotus™ 1-2-3, and Harvard Graphics™, Adams et al. 1992).

A respecified eight indicator three-factor model incorporating ‘perceived effectiveness’ is deemed better suited to the underlying pattern of correlations (for email, Segars and Grover 1993).

A modification of TAM, the breaking of perceived usefulness into near-term and long-term usefulness, was statistically valid with perceived near-term usefulness having a stronger influence on behavioural intention than perceived long-term usefulness (for Microsoft™ Word and Excel, Chau 1996).

In summary, Davis (1986) conceived that TAM’s belief-attitude-intention-behaviour relationship predicts user acceptance of information technology. Models and theories such as self-efficacy theory, cost-benefit research, expectancy theory, innovation research, and channel disposition have also supported TAM. As further outlined above, researchers have further considered modifications of TAM and considered additional

relationships because of inconsistent findings. One of the major modifications considered has been the removal of the constructs of intention to use and/or attitude and instead the investigation of the direct effect of perceived ease of use and perceived usefulness have directly on usage.

4.3.3 TAM - SYSTEM CONTEXT From the above discussion, it is evident that researchers have validated TAM by investigating several different applications including, e-mail, voice mail, word processing, and spreadsheet information systems. In fact most information technology adoption studies focus on stand-alone systems (i.e., word processing packages, spreadsheets, etc.) in an institutional or industrial setting. The aforementioned studies have predominately tested TAM in the context of an organisational and/or educational setting, for the use of information processing and communication systems, and with reference to improving performance and outcomes. Adams et al. (1992) suggests that problems might arise in testing TAM in these contexts as system usage may be considered mandatory or, if not mandated, the system has become a defacto standard. For example, the low explanatory power of TAM for Wordperfectâ&#x201E;˘, found by Adams et al. (1992), was possibly influenced by the view of what others thought users should be doing â&#x20AC;&#x201C; thus, the subjective norm is having an influence. Moore and Benbasat (1991) report that mandatory use of information technology has a positive impact on usage and that, in situations of mandated use, other factors tend to have less ability to explain adoption and use.

TAM has also been applied to a number of technological system developments such as the web. With specific reference to the web it has been found that (Appendix A):

Â&#x192;

Perceived usefulness and perceived ease of use predict usage but that usefulness has a stronger effect (Fenech 1997; Teo, Lim and Lai 1999; Lederer, Maupin, Sena and Zhaung 2000).

Perceived ease of use predicts usage (for Netscape™, Morris and Dillion 1997); perceived usefulness and attitudes (Moon and Kim 2001) (for Netscape™, Morris and Dillion 1997).

Behavioural intention has a significant influence on usage (for Netscape™, Bagozzi et al. 1992).

Perceived ease of use has a significant effect on perceived enjoyment (Teo et al. 1999).

Perceived usefulness has a significant effect on and behavioural intention (Moore and Benbasat 1991); and on attitudes (Moon and Kim 2001) (for Netscape™, Morris and Dillion 1997).

Perceived playfulness has a significant effect on attitudes toward using the Web and behavioural intention (Moon and Kim 2001).

Attitudes have a significant effect on behavioural intention and this has had a significant impact on usage (Moon and Kim 2001) (for Netscape ™, Morris and Dillion 1997).

However, it should be noted that these applications of TAM to the web and browser software are investigated within organisational and/or educational settings. As commented by Fenech (1997), ‘the application of TAM, as proposed by Davis (1986) and further modified by Davis et al. (1989a) and Davis (1989b), to different technological acceptance situations and systems, should add credibility to TAM.’ Furthermore, by testing TAM on an information system that is not characterised by mandatory use, the factors highlighted by Moore and Benbasat (1991) and the problems suggested by Adams et al. (1992) are likely to be minimised.

Dishaw and Strong (1999) further contend that one of the major weaknesses of TAM for understanding IT usage is its lack of task focus. They indicate that IT is a tool for which users accomplish certain organisational goals and thus inclusion of task characteristics ought to provide a better model of IT usage. In this dissertation, therefore, TAM is tested outside the organisational and/or education setting, and on a system characterised by a number of use motivations, namely the web.

4.4 WEB PERCEPTIONS AND CURRENT WEB SESSION USAGE The first research question of this dissertation was stated in Chapter 3:

RQ1: What is the relationship between a userâ&#x20AC;&#x2122;s perceptions of the web and a personâ&#x20AC;&#x2122;s current web session usage?

Current web session usage is defined as the act or fact of using the web for some purpose within the current time period (i.e., within the period in which the measurement occurred) (Delbridge and Bernard 1998) (Chapter 3). It was further noted that current web session usage comprises three categories: usage frequency, usage variety and extent of use. Prior research has focused on the impact of user perceptions on usage frequency and, to a lesser extent, usage volume and duration. Here all three categories are examined (Figure 9). Figure 9: Perception of the Web and Current Web Session Usage (RQ1)

4.4.1 TAM & CURRENT WEB SESSION USAGE A number of measures have been used in the TAM literature for the testing of current usage experience of the information systems under investigation. Current usage frequency of the information system is the most heavily used measure of system usage. For non-web based systems, overall usage frequency (Davis 1986; Bajaj and Nidumolu 1998; Bagozzi et al. 1992; Adams et al. 1992; Davis 1989b) and frequency during a certain time-frame (e.g., a week) (Bajaj and Nidumolu 1998), have been consistently used to measure system usage. In addition, duration of usage (Davis 1986; Adams et al. 1992),

amount of use (Adams et al. 1992), and potential usage (Bajaj and Nidumolu 1998) have been used.

For web-based systems, overall usage frequency (Moon and Kim 2001; Teo et al. 1999; Lederer et al. 2000; Fenech 1997), frequency during a certain time-frame (e.g., daily, weekly) (Teo et al. 1999) and volume of usage (Moon and Kim 2001; Fenech 1997), have been used as indicators of web-based system usage. Teo et al. (1999) also defined internet usage in terms of the diversity of usage – this is consistent with the fact that there are many tasks/motivations for using the web (i.e., getting information, getting product support, communication with people, getting free resources, purchasing/shopping, applying for a job, carrying out swapping/selling transactions, etc.). These indicators of frequency, duration, volume and diversity of use are consistent with the three categories of usage proposed in Chapter 3. Each is examined in order to derive the hypotheses underlying RQ1.

4.4.2 PREDICTING CURRENT WEB SESSION USAGE FREQUENCY As defined earlier, current web session usage frequency is defined as ‘how often a session on the web is undertaken within a certain time frame’ (i.e., how often the web is accessed). Adams et al. (1992) in study 1, Davis (1989b) and Igbaria et al. (1995) found a positive relationship between perceived ease of use and usage frequency for non-web based systems. With respect to the internet and web-based systems, there is some support for a relationship between perceived ease of web use and current web session usage frequency (Teo et al. 1999; Karahanna and Straub 1999; Lederer et al. 2000; Fenech 1997).

However, inconsistency in findings is evident in the literature with studies also reporting a minimal or no relationship between perceived ease of use and usage frequency (Davis 1986; Adams et al. 1992 in study 2, Bagozzi et al. 1992; Taylor and Todd 1995). One explanation for these mixed results is the nature of the relationship. A curvilinear

relationship may exist which, if assessed by linear analytical techniques, might be classed as ‘no relationship’.

For example, frequency of use may be very low among those with few skills or those who see themselves as technophobes (Sinkovics, Stottinger, Schlegelmilch and Woodruffe-Burton 1999). But frequency of use might rise among those who are developing skills – they respond to the novelty and challenge of the system. Frequency of use may fall again among those who see the system as easy to use – this might be because of boredom or greater efficiency of use. It is hypothesized that:

H1A: Perceived ease of use of the web will have a curvilinear relationship with current web session usage frequency.

It is further evident in the literature that perceived usefulness has a stronger relationship than perceived ease of use with usage frequency for non-web based (Davis 1986; Adams et al. 1992; Davis 1989b), and web-based systems (Teo et al. 1999; Karahanna and Straub 1999, Gefen and Straub 1997; Lederer et al. 2000; Fenech 1997). Thus the more useful the web is perceived to be the higher the frequency of use. Thus:

H2A: Perceived usefulness of the web will have a positive relationship with current web session usage frequency.

4.4.3 PREDICTING CURRENT WEB SESSION USAGE VARIETY In Chapter 3, current web session usage variety was defined as ‘the number of different situations and different motivations for web usage, independent of how frequently it is used’. Teo et al. (1999) measured diversity of internet usage by looking at tasks and motivations. They found that perceived ease of use had a positive impact on diversity of use and further argued that users are driven to adopt and use a system primarily because of the function it is capable of performing. For example, if a system is very useful, the user is more than willing to cope with an element of usage difficulty. Igbaria

et al., (1995) further found support for the relationship between perceived ease of use and usage variety. Thus:

H3A: Perceived ease of use of the web will have a curvilinear relationship with current web session usage variety (Situational). H3B: Perceived ease of use of the web will have a curvilinear relationship with current web session usage variety (Motivational).

H4A: Perceived usefulness of the web will have a positive relationship with current web session usage variety (Situational). H4B: Perceived usefulness of the web will have a positive relationship with current web session usage variety (Motivational).

4.4.4 PREDICTING CURRENT WEB SESSION USAGE EXTENT In Chapter 3, current web session usage extent was defined as ‘the degree of web session usage and comprises the degree of duration (time), breadth (range) and depth (amount) of session use’.

When perceived ease of use is low, use of the web will also be low. Users will not find the web easy to use at first. They will not bookmark a lot of sites, they are unlikely to visit a lot of sites and/or different new sites, and they will spend relatively less time surfing the web. However, as perceived ease of use rises to a medium level, current usage will increase – users will explore the web and thus the time spent and the number and breadth of applications accessed will rise. As ease of use becomes high, efficiency in use will occur. There might be a fall in the number of web sites that are accessed (e.g., because of bookmarking) (depth of web use), web sessions might take less time (duration of use), and fewer new sites might be accessed (breadth). Firstly, then, it is hypothesized that:

H5A: Perceived ease of use of the web will have a curvilinear relationship with current web session usage extent (Breadth). H5B: Perceived ease of use of the web will have a curvilinear relationship with current web session usage extent (Depth). H5C: Perceived ease of use of the web will have a curvilinear relationship with current web session usage extent (Duration).

Overall the central proposition is that the less useful you perceive the web to be to use, the fewer sites will be accessed and bookmarks recorded. At a medium level of usefulness, the number of sites accessed and bookmarked will increase, as will the time spent online. Number of sites accessed and so forth will further increase as you come to perceive a high degree of usefulness.

Secondly it is hypothesised that: H6A: Perceived usefulness of the web will have a positive relationship with current web session usage extent (Breadth). H6B: Perceived usefulness of the web will have a positive relationship with current web session usage extent (Depth). H6C: Perceived usefulness of the web will have a positive relationship with current web session usage extent (Duration).

It is acknowledged that external factors will have an impact too, such as where the medium is accessed and the time available to the user.

4.4.5 SUMMARY: USER WEB PERCPETIONS AND WEB USAGE In summary, perceived usefulness of the web will have a positive influence on current session usage and perceived ease of use will have a curvilinear relationship with current session usage. Overall this indicates that perceived usefulness is the primary influence, and perceived ease of use the secondary influence, on web usage behaviour.

4.5 DETERMINANTS OF WEB PERCEPTION Due to the hypothesised influence of user perceptions on behaviour, it is worth examining what may predict and/or determines a user’s perception of the web. Examining, profiling and further understanding these determinants of perception can aid marketers and decision makers. They might, for instance, influence users to engage in increased usage behaviour, explore the web for new and different sites and tools, etc.

Two determinants are noted: characteristics of the system, and characteristics of users. This builds on the ideas of Krech et al. (1962) who suggested human perception is influenced by two distinct factors: stimulus factors (e.g., browser) and personal factors (e.g., experience), and further specified that perception is a result of both. As outlined in Kassarjian and Robertson (1968), the perceptual organization of stimuli in the nervous system is related directly to the nature of the physical object and, furthermore, is in part determined by the motivations and need value-systems of the observer (Kassarjian and Robertson 1968). Hence an interaction occurs between the stimuli and the observer in the formation of human perception.

Much of the TAM literature focuses on characteristics of the system (Davis, 1986). By contrast, in this dissertation emphasis is given to the influence of a specific personal factor, knowledge content, on a user’s perception of the web. This relates to issues concerning the impact of experience and learning, training and education, exposure to communications and the process of information gathering.

The perception and consequent acceptance of computer technology is thus influenced by the technology itself and the level of use, comfort, skill and/or expertise of the individual using the technology (Davis and Bostrum 1993; Nelson 1990). This gives rise to the research questions to be further outlined in Chapter 5:

RQ2: What is the relationship between a user’s knowledge content of the web and a person’s perceived usefulness of the web?

RQ3: What is the relationship between a user’s knowledge content of the web and a person’s perceived ease of web use?

4.6 WEB PERCEPTION: SUMMARY Use of the web presents challenges for users. For instance, there may be problems in using graphical browsers and these problems might inhibit web use – or influence a user’s perception of the system. Here, the influence of two specific perceptions are considered in relation to three categories of current web session usage. Several relationships are hypothesized. In doing so, the work draws upon the Technology Acceptance Model (TAM). In addition, this chapter lays the foundation for the investigation of the determinants of user perceptions of the web. In particular, it introduces the influence that user knowledge content of the web may have on a user’s perceived ease of use of the web (RQ2) and perceived usefulness (RQ3) of the web.

C HAPTER 5: U SER K NOWLEDGE C ONTENT OF THE

W EB

‘True knowledge exists in knowing that you know nothing. And in knowing that you know nothing, that makes you the smartest of all’ - Socrates (470-399 BC)

5.1 INTRODUCTION Drawing from the cognitive science literature, and studies in consumer research on consumer decision-making, information search and acquisition, this chapter presents a review of the literature relating to consumer knowledge of media technology, and specifically the web. In the previous chapter, user perceptions were reported to influence the adoption and acceptance of web-based technology. This chapter examines human knowledge content of the web as a determinant of user perceptions of media technology (Figure 10). Studied are the relationships between:

User knowledge content of the web and perceived usefulness of the web (RQ2);

User knowledge content of the web and perceived ease of web use (RQ3);

Figure 10: Web Knowledge Content (RQ2 and RQ3)

A discussion follows on the conceptualisation of consumer knowledge content.

5.2 KNOWLEDGE AND BEHAVIOUR There are many models of the buyer decision-making process, such as those proposed by Nicosia (1966), Howard and Sheth (1969), Engel, Kollat and Blackwell (1968). In a revision to their models of the buyer’s decision-making process, Engel et al. (1990) included exposure, attention, comprehension/perception, yielding/acceptance, retention, memory, external search and internal search. Furthermore, Bettman (1979) included attention, information acquisition and evaluation, memory search, external search and decision processes. Both of these models address the concept of gathering and using information, despite not directly referring to the acquisition or role of consumer ʹknowledge.ʹ These models, however, have come under criticism and debate regarding their testability and plausibility as realistic views of consumer behaviour (Fletcher 1988; Foxall 1980; Robertson 1974).

Notwithstanding the above criticisms, support for the stages of the decision making process, from problem recognition to post-purchase behaviour, is evident in the literature and as such the decision making process has been reported in a number of generic marketing texts (i.e., Assael 1995; Barry 1986; Cravens and Woodruff 1986; Hawkins et al. 1997; Kindra, Laroche and Muller 1994; Kotler and Armstrong 1994; Schiffman et al. 1997; Solomon 1994). The model in Figure 11 shows a five step sequence that a consumer goes through during the decision making process. These steps include:

the recognition of a need or problem;

a search for information of the options available prior to purchase;

an evaluation of the available alternatives;

the actual purchase of a product; and

post purchase behaviour, which includes an evaluation of the product post-purchase or post-consumption.

Figure 11: A Simple Model of the Consumer Decision Making Process (Schiffman et al 1997; Hawkins et al 1997)

Research has been directed at understanding the effects of knowledge on consumer behaviour, such as information search and decision making, for example Feick et al. (1992). However, the role of knowledge is not explicit in the simple model presented in Figure 11; rather, it is implied that the acquisition of knowledge takes place within the second stage (i.e. information search) and is used in the third stage (i.e., evaluation of alternatives). Researchers, however, have recognised both the importance of and need for research on consumer knowledge stating that ‘despite the recognised importance of knowledge-related variables, consumer knowledge has only recently become an independent area of research and theorising’ (Alba and Hutchinson 1987). Alba and Marmorstein (1987) further stressed the need for research of consumer knowledge stating ‘a growing body of literature attests to the importance of consumer knowledge as an area of investigation in consumer research’.

5.3 CONSUMER KNOWLEDGE Drawing on literature from the cognitive sciences and marketing, this research makes use of a simplified definition of consumer knowledge. It is very simply defined as the body of facts and principles (information or understanding) accumulated by mankind (stored in memory) about a domain (Delbridge and Bernard, 1998). This information is structured or organized in memory in certain formats (knowledge structures), differs in its type of contents (knowledge content) and may be measured in different ways (knowledge measurement). In addition, in the knowledge literature use of the terms ‘expertise’, ‘novice’, and ‘familiarity’ give rise to an additional characteristic of consumer knowledge - knowledge scope. Following Brucks and Mitchell (1981) and Kanwar et al.

(1981), consumer knowledge is characterized by the content, structure and measurement of the information about a ‘domain’ stored in memory.

Presented in this study are two well-accepted types of consumer knowledge content (declarative and procedural knowledge) and two additional types (common and specialized knowledge content). The proposal of these two additional types of knowledge content has been drawn from recent empirical studies and discussions of familiarity and expertise. This results in the conceptualisation in this chapter, and later operationalisation in chapter 7, of a 2x2 typology of knowledge content.

The way information (i.e., knowledge) is encoded, structured or organized in human memory is important, however the concept of schemata, and similar concepts, such as frames and scripts, provide the background for the current area of investigation. This research dissertation examines the type, scope and measurement of consumer knowledge content that is structured in a consumer’s memory, but not the nature or diagrammatic profile of its organization and thus knowledge structure is not directly examined. This perspective is adopted as past empirical and conceptual research proposed that the type and measurement of knowledge content stored in a consumers memory guides, controls and influences human behaviour. In addition, structure is not examined, in keeping with the view that appropriate measures of content need to be devised before the structure or organization of the content can be assessed (Mitchell 1981; Brucks and Mitchell 1981).

5.4 USER KNOWLEDGE CONTENT Knowledge content refers to the type of information stored in a consumer memory of a particular domain. For example, information pertaining to the procedures, terminology or facts required for ‘driving a car’. The process of how humans learn information can assist our understanding of the different types of knowledge content stored in memory. For example, as will be discussed later in this chapter, learning is viewed as a three stage process (Anderson 1990; Fitt 1964) involving firstly a cognitive stage in which factual

knowledge of the domain is learnt (i.e., learning what a car is and what gears are), then an associative stage, in which a method for forming productions and procedural skills is developed (i.e., learning how to drive a car and how to change gears) and a third stage, an autonomous stage, in which use of the productions becomes faster and more automatic (i.e., habitual). From this process of learning, two distinctive types of knowledge content consistently emerge, namely declarative knowledge (i.e., what) and procedural knowledge (i.e., how) (Anderson 1976; Anderson 1983; Best 1989; Dodd and White 1980).

Baddeley (1991) further states that learning is used for the acquisition of domain specific knowledge and the mastering of new skills. The discussion by Baddeley (1991) lends itself to the proposal by Alba and Hutchinson (1987) of two components of knowledge, familiarity and expertise. The treatment of familiarity and expertise as synonymous with knowledge (Dacin and Mitchell 1984; Johnson and Russo 1984; Spence and Brucks 1997; Chi, Glaser and Rees 1982; Hastie 1982) provides impetus in this study for the classification of two additional types of knowledge content, common and specialized knowledge content. This classification is guided by the assumption that consumers not only have differing knowledge of ‘what’ gears are or ‘how’ to use them, but can also have stored in their memory, more common knowledge about how to change gears and/or specialized information about ‘how’ a gearbox works.

These components of the 2x2 typology are discussed further in the following subsections.

5.4.1 PROCEDURAL AND DECLARATIVE KNOWLEDGE CONTENT Two distinctive types of knowledge content consistently emerge from the literature in cognitive psychology, namely declarative and procedural knowledge (Anderson 1976; Anderson 1983; Best 1989; Dodd and White 1980). These two types of knowledge content are critical concepts for this research, and differ both in their nature and in the functions

which they allow humans to perform. Declarative and Procedural knowledge content are therefore further defined and compared in the following sections.

5.4.1.1 Declarative Knowledge Content Referred to as knowledge about concepts, objects or events (Brucks and Mitchell 1981; Brucks 1986), declarative knowledge is defined as ʹfactual information that is somewhat static in nature which is usually describableʹ (Best 1989, p7). This factual information is stored in memory as an organised structure, and is usually related to other structures and is organised as schema to aid comprehension. Best (1989) further added that ʹdeclarative knowledge is flexible and can often be organised to suit our purposesʹ (p7). Anderson (1983) indicated that declarative memory has strength, and that this strength reflects the frequency of use and importance to the person of the item of information, partly explaining why some facts are remembered and others forgotten. Brucks, (1986) further developed a typology of knowledge content proposing that declarative knowledge comprised information about the attributes, terminology, evaluative criteria, facts and usage situations of the domain of interest. For example, ‘this car is fast’ is a factual statement about the car, whereas ‘colour’ and ‘model’ may be considered ʹattributesʹ of a car.

5.4.1.2 Procedural Knowledge Content In comparison, procedural knowledge content refers to the dynamic information underlying skilful actions (Best 1989, p7); thus, the knowledge of rules for taking action that is believed to be stored and organized into production systems (Brucks 1986). Brucks and Mitchell (1981) specified that the basic elements of production systems are condition-action statements with further refinement by Brucks (1986) that procedural knowledge comprised information about the process and procedures for domain usage and decision-making. For example, the statement, ‘to stop a fast car, the brake pedal is pushed towards the car floor’ is an example of a condition-action statement; i.e., condition (stop car) - action (push brake pedal to floor). Best (1989) added that the

organization of procedural knowledge content is not well understood, and that procedural knowledge is not very describable.

5.4.1.3 Comparative Discussion The basic distinction, then, is between ʹknowing what ʹ (declarative) and ʹknowing howʹ (procedural). While both declarative and procedural knowledge can guide behaviour, the latter is considered of greater influence on actual behaviour. For example, in the process of driving a car, a set of instructions can be followed, providing an example of declarative knowledge guiding behaviour. However, instructions in many situations are either not available or not so easy to interpret and thus require procedural knowledge (Anderson 1983). Therefore, to drive a car, knowing ʹhow to drive a carʹ (procedural) will have more influence on the end behaviour, (i.e., driving) than knowing ʹwhat driving a car isʹ (declarative).

While these two terms appear to be concise and definitive, it has been noted that the difference between declarative and procedural knowledge may not be as distinct as the definitions imply (Anderson 1983; Best 1989; Baddeley 1991). There is evidence to indicate that some knowledge is initially encoded in a declarative format, and if used sufficiently often, it is either transformed or becomes encoded as procedural knowledge, either in whole or in part (Best 1989). Anderson (1983) referred to the acquisition of ʹproductionsʹ as indicating that they are not ʹdirectly acquiredʹ but evolve within a personʹs mind, stating: ʹThe acquisition of productions is unlike the acquisition of facts or cognitive units in the declarative component. It is not possible to simply add a production in the way it is possible to simple encode a cognitive unit. Rather, procedural learning occurs only in executing a skill, thus one learns by doing. This is one of the reasons why procedural learning is a much more gradual process than declarative learningʹ (Anderson 1983, p215)

It therefore appears that in general, declarative knowledge is acquired for a given domain. Some of the declarative knowledge is then gradually transformed into a

procedurally encoded format which allows people to do things, for example, to learn a skill or to comprehend domain information. Therefore, the acquisition of a new skill commences with an interpretive stage using declarative representations and, with time, skill specific productions are compiled (Anderson 1983). However, during this transformation process there is a phase when the difference between declarative and procedural encoding formats is unclear and difficult to differentiate. As the true nature of this interaction is not well understood (Anderson 1983), no clear determination of the exact boundary between declarative and procedural knowledge is evident.

A number of researchers have studied buyer ʹproductʹ knowledge content with mixed findings and mixed support for the measurable difference between declarative and procedural knowledge content. For example, Brucks and Mitchell (1981) identified the difference between declarative and procedural knowledge, but did not test them separately; Mitchell (1981) reviewed a number of methods for measuring declarative and procedural knowledge; and Dacin and Mitchell (1984) measured existing declarative knowledge and identified that some aspects of declarative product knowledge could be measured.

To summarise, two types of knowledge content – declarative and procedural – have been proposed as representing very different forms of knowledge, and further as performing different roles in memory. It is also apparent from the above discussion that in some circumstances the clarity between these two types of knowledge can become muddied and unclear. However, the ability of procedural knowledge content to control behaviour and declarative knowledge content to guide behaviour highlights the importance of further research into these types of knowledge content. In addition, from the discussions on familiarity and expertise, two additional types of knowledge content are conceptualised in this research - common and specialized knowledge content.

5.4.2 COMMON AND SPECIALISED KNOWLEDGE CONTENT The terms ‘expertise’, ‘novice’, and ‘familiarity’ give rise to discussion about the classification of these elements within the knowledge framework. Do they pertain to the amount of knowledge acquired by a consumer, how information is structured in consumer memory, how information is used by the consumer, or the type of knowledge content stored in memory? For example, expertise and familiarity has been defined in the literature with reference to the source (Alba and Hutchinson 1987; Spence and Brucks 1997), the outcome (Alba and Hutchinson 1987; Enis 1995), and the amount (Chi et al. 1982) of knowledge that a consumer has acquired.

Alba and Hutchinson (1987) use the term expertise ‘very broadly that includes both the cognitive structures and cognitive processes for taking action.’ Therefore, discussing expertise in the context of how information is stored and used by a consumer. In addition, their definition of product familiarity (i.e., the number of product related experiences that have been accumulated by the consumer) represents neither the quality nor kind of experience accumulated, just the quantity. In many of these studies, familiarity and expertise are treated as synonymous with knowledge (Johnson and Russo 1984; Bettman and Park 1980). These considerations give rise to the view that the expert/novice or familiar/unfamiliar distinctions in the literature are reflective of different types of knowledge content. From this, specialized and common knowledge content are conceptualised, as is now discussed.

5.4.2.1 Specialised Knowledge Content Conceptual and empirical studies of expertise have made reference to the quantity and type of knowledge content that a consumer has acquired as key characteristics that differentiate experts from novices. Defined by Spence and Brucks (1997), an expert or someone with expertise has acquired domain specific knowledge through experience and training. Chi et al. (1982) further identify that expertise is the possession of a large body of experience, knowledge and procedural skill. In the marketing literature expertise is further regarded as the high level of relevant skill and knowledge an individual has to

perform product related tasks successfully (Alba and Hutchinson 1987; Homer and Kahle 1990; Belch and Belch 1995).

The Macquarie Concise English Dictionary (1998) defines an expert as a person who has special skill or knowledge in some particular field, a specialist (Delbridge and Bernard 1998). For example a ‘mechanic’ has a level of specialised skill with the mechanics of a car engine. It is further outlined that having expertise is the possession of special skill or knowledge as trained by practice and thus is to be skilful or skilled. The foundation of these definitions stems from the terms special and specialized, thus distinguished or different from what is ordinary or usual. From the above discussion, this study defines specialised knowledge as ʹskilled and/or extraordinary information about a domain of interest required to perform skilled domain related tasks successfully.ʹ An expert would be classified as having a high level of specialized knowledge and a novice a low level of specialised knowledge.

de Bont and Schoormans (1995) find evidence to support the view that the problems inherent to the early stages of the product-development process poses less of a concern for consumers with much product expertise (i.e., those having specialized knowledge), than those with little product expertise. This is because of an expert’s detailed cognitive structure with respect to products in the category, the availability of more product related information in their cognitive structure, and their ability to discriminate between relevant and irrelevant information, in addition to an ability to infer benefits from product attributes.

5.4.2.2 Common Knowledge Content In the marketing literature, familiarity is conceptualised as knowledge of the product class (Johnson and Russo 1984) and the number of ‘product’ related experiences accumulated by an individual (Alba and Hutchinson 1987). Product related experiences involve advertising exposure, product purchase, or product usage. This term, familiarity, has been used in association with expertise (Johnson and Russo 1984), however Hastieʹs

(1982) commentary on generic knowledge actually provides more depth as to the type of knowledge content that an individual classed as ‘familiar’ might have.

Hastie (1982) specifies that generic knowledge includes general information about classes of products, and instances exemplifying those products. The Macquarie Concise English Dictionary (1998) defines familiar as commonly or generally known or seen (Delbridge and Bernard, 1998)). For example, a familiar and common element of a car would be the gears and a familiar or common procedure would be using them. The foundation of this definition stems from the term ‘common’ – i.e., widespread, ordinarily, generally or publicly known (Delbridge and Bernard 1998). Based on these definitions, common knowledge is described here as ‘general and/or publicly known information of the domain of interest required to perform general and common domain related tasks successfully.ʹ It is therefore proposed in this study that a consumer’s familiarity is based on common knowledge, stored in a consumer’s memory.

5.4.3 KNOWLEDGE CONTENT: SUMMARY A 2x2 typology of knowledge content is derived (Table 3). These four types of knowledge content are defined as:

Common Declarative

‘General and/or publicly known static information of facts, terms, attributes (what) of X, required to perform general and common domain related tasks successfully’.

Specialised Declarative

ʹSkilled and/or extraordinary static information of facts, terms, attributes (what) of X, required to perform skilled domain related tasks successfully.ʹ

Common Procedural

‘General and/or publicly known dynamic information underlying skilful actions (how) of using X, required to perform general and common domain related tasks successfully’

Specialised Procedural

ʹSkilled and/or extraordinary dynamic information underlying skilful actions (how) of using X, required to perform skilled domain related tasks successfully.ʹ

Table 3: 2x2 Typology of Knowledge Content

Specialised Common

Declarative

Procedural

Specialised Declarative Common Declarative

Specialised Procedural Common Procedural

5.5 USER KNOWLEDGE SCOPE The segmentation of consumers based on their knowledge is very strongly attuned to segmentation based on the possession of high, moderate or low levels of information stored in memory. For example, Johnson and Russo (1984) classify respondents into three categories of familiarity, low, moderate and high and refer to them as ‘snapshots’ in the development of an ‘expert’ consumer. Thus, an increase in familiarity is purported to result in an increase in an individual’s level of expertise (Alba and Hutchinson 1987). As previously discussed, expertise has also been defined as the possession of a large body of experience, knowledge and procedural skill (Chi et al. 1982). The informationuse hypothesis proposed by a large number of researchers that underlies the aforementioned position proposes that the amount of information used should be greater for those with expertise, than those without.

However, Shanteau (1992) contends that the amount of information used or stored in memory does not necessarily reflect a degree of expertise, but that the type of information used or stored does. Shanteau (1992) classifies the type of information as either relevant/irrelevant. This study develops his argument, but adopts a different approach to classifying expertise with reference to the type of knowledge content stored in memory. Specifically, a further characteristic of consumer knowledge is conceptualised – knowledge scope. Knowledge scope is defined as the extent or range of the type of knowledge content stored in a consumer memory. This then can be used as a

basis for classifying consumers: the scope of specialized knowledge content that a consumer acquires will indicate their classification as an expert or novice, and the scope of common knowledge acquired will indicate familiarity or unfamiliarity with the domain of interest. This enables consumers to be classified as both having common and specialized knowledge of the domain of interest.

For example, a car mechanic with 15 years vocational experience might be categorized as highly familiar and as having specialised knowledge of cars. However, a school teacher with 15 years vocational experience might be categorized as highly familiar but with low specialized knowledge of cars. See Table 4 for a depiction. Although in this example, vocation could be used as a proxy for the type of knowledge content, depending on the domain of interest, this might not always be the case. For example, what about the school teacher whose hobby is car remodelling or the mechanic who specialises in motorbike or powerboat engines?

Table 4: Consumer Knowledge Scope Declarative

Specialised

Common

Procedural

High

Low

High

Low

Specialised Declarative (Expert) Common Declarative (Familiar)

Specialised Declarative (Novice) Common Declarative (Unfamiliar)

Specialised Procedural (Expert) Common Procedural (Familiar)

Specialised Procedural (Novice) Common Procedural (Unfamiliar)

5.6 MEASUREMENT OF KNOWLEDGE CONTENT 5.6.1 OBJECTIVE AND SUBJECTIVE KNOWLEDGE MEASUREMENT It is important at this stage to discuss the measurement of consumer knowledge, particularly the use of proxies and the use of objective and subjective methods for measuring consumer knowledge.

One of the most common methods for measuring consumer knowledge has been the use of proxies to infer consumer knowledge. For example, heavy use has been made of domain usage and purchase experience to measure consumer knowledge (Bettman and

Park 1980; Johnson and Russo 1984; Cole, Gaeth and Singh 1986; Woodside, Trappey and MacDonald 1997; Park, Mothersbaugh and Feick 1994). This might be a convenient approach, but it is not ideal.

In addition, objective and subjective methods for measuring consumer knowledge have been well documented (Brucks 1985; Dacin and Mitchell 1984; Rao and Olson 1990). Objective measurement is defined as an unbiased method, free from personal feelings or prejudice, used to assess the information about a domain (i.e., that belong to a domain) that is actually stored in a consumer’s memory. Therefore, objective measurement will assess ‘actual’ knowledge of the domain of interest through the undertaking of tasks, questions or by means of observation. Subjective measurement, on the other hand, is defined as a method used to assess individual perceptions and introspective thoughts about the information of a domain (i.e., that belong to the individual) that is believed by the consumer to be stored in their memory.

In practice, these methods are sometimes confused with ‘types of knowledge content’. A number of studies treat objective and subjective methods of knowledge measurement like ‘types of knowledge content’ and in addition provide definitions with respect to the domain of interest and the type of knowledge content investigated. For example, Alba and Hutchinson (1987) and Homer and Kahle (1990) define objective knowledge as ʹinformation that is actually stored in a consumer memory, comprising familiarity and expertise with a product class.ʹ Subjective knowledge is defined as ʹhow familiar or expert a user perceives that they are about a productʹ (Alba and Marmorstein 1987; Brucks 1985). Thus, methods of knowledge measurement are defined with reference to both the scope and content of the information stored in memory.

The definitions adopted in this study do not see the treatment of the method as a ‘type of knowledge content’, but do distinguish between the measurement of ʹwhat an individual thinks they knowʹ (perception) and ʹwhat an individual actually knowsʹ (stored in memory). In particular, subjective measurement is used to assess ‘perceived’ or ‘selfassessed’ knowledge of the domain, investigated through self-report measures.

5.6.2 COMPARATIVE DISCUSSION Proxies are heavily used as a way to infer a consumer’s actual knowledge. These methods assume that people learn from experience and at the same rate. In addition, the argument has been presented by Kanwar et al. (1981), Marks and Olson (1981), and Alba and Hutchinson (1987), that objective measures of knowledge were operationally and conceptually distinct from subjective measures.

However, there is some doubt about the correlation between what people think they know, what their experience is, and what is actually stored in a consumers memory (DeNisi and Shaw 1977; Fischoff, Slovic and Lichtenstein 1977; Lichtenstein and Fischoff 1977; Nelson, Leonesio, Shimamura and Landwehr 1982; Park 1982; Schacter 1983). For example, Brucks (1985) concluded that experience measures and subjective measures, while useful in other contexts, do not represent measures of the actual knowledge stored in a consumer’s memory. Feick et al., (1992a) further found only modest correlations (0.4 to 0.6) between subjective and objective measures of knowledge. Park et al. (1994) suggested this weak correlation could be explained by differing accessibility of memory cues that underlie information retrieved by self-assessed and objective measures. Both these results support the findings of Brucks (1985). However, Selnes and Gronhaug (1986) reported a significant relationship between subjective and objective measures and Cole et al. (1986) found significant correlations between objective and subjective measures in the case of a single product (convergent validity), and low correlations in the case of different products (divergent validity). Clearly, results are mixed.

Selnes and Gronhaug (1986) and Mitchell (1981) contend that selection of objective or subjective measures of knowledge is dependent on the purpose of the research. Objective measures are preferable when the research is related to the consumer’s ability to encode new information or to discriminate and choose between alternatives. Subjective evaluation of knowledge should have a significant impact on the motivation to conduct various behaviours. Support for the measurement of what is actually stored in a consumer’s memory is identified by Brucks and Mitchell (1981) and Engel, Blackwell and Miniard (1990), concluding that objective measures of knowledge were better than

experience and self-report measures. Engel et al. (1990), however, also cautioned that objective measurement ʹis by no means an easy task, given the vast array of relevant knowledge that a consumer may possess about a domainʹ.

5.7 USER KNOWLEDGE CONTENT AND WEB PERCEPTIONS This study investigates the objective and subjective measurement of consumer knowledge content of the web. This is important for a variety of reasons. As presented in Chapter 2, arguments presented by Mandelli (1997) and Hoffman and Novak (1996) support the view that developments in electronic technology are changing the roles performed by users of the web. The level of interdependence between electronic technologies and the user is also changing. Because of the active and changing role played by the user, he or she makes a contribution towards the success of the communication process. However, as previously discussed, graphical browsers used to interact with the web rely on the highly developed visual spatial processing of the human visual system and as there is no natural typography for the web, until one is familiar with any given layout of a hypertext document, one is by definition, disorientated (Conklin 1987).

Three specific problems of participating in a hypermedia system – ʹlack of closureʹ ʹcognitive overhead’ and ʹlearning by browsingʹ (Foss 1989) – were also discussed in Chapter 2. These problems may be influenced by a user’s type of knowledge content of the hypermedia system and the user-interface. Reed and Oughton (1997), for example, found that hypermedia knowledge in general had a large impact on user productivity. It was found that learning style and hypermedia knowledge play an important role in how users learn with and about hypermedia.

Furthermore, Novak and Hoffmanʹs (1997) subjective measurement of procedural knowledge content presented empirical evidence that perceived skill and challenge predict online consumer search and pre-purchase behaviour. Preliminary results by Diaz, Hammond and McWilliam (1997), identified that experience with the web (i.e., as a

proxy for knowledge) might be an important moderator of: attitude toward the medium, the reported success of the activity undertaken, and the placement of value on webbased information.

Their results further suggested that experience may not influence behaviour and attitudes in a linear fashion, but that the most experienced users might be enthusiasts for the medium, while moderate users may be technically competent, but enjoy the web less than the enthusiasts (and less than novices). Experienced (i.e., heavy) users, according to Diaz et al., (1997), further found the web more legible, more stimulating and possessed higher company-brand recall than novices. They found no specific difference between novices and those who are moderate users of the web for any of the question asking about information on the web. However, major differences existed between the most experienced users and the moderate and novice users, with the former placing a higher value on web-based information. Heavy users were more than likely to feel that the web experience matched their expectations and they were more likely to agree that the information gathered was interesting.

These applications and findings are very suggestive, however there are significant problems with these studies. For this reason, further investigation and measurement of consumer knowledge of the web is needed. For example, the study by Diaz et al. (1997) differentiated novice and expert users of the web is a very loose way, based on hours spent on-line. By contrast, using the definition discussed in section 5.4, a web expert would be defined as having a high level of specialized web knowledge, thus ‘skilled and/or extraordinary information (procedural and declarative) about the web required to perform skilled web related tasks successfully.’ Thus, Diaz et al. (1997) only measures a user’s experience and not actual knowledge content.

In fact this use of proxy measures of a consumer’s knowledge of the web is widespread in the academic literature and commercially, with most investigations of web users moving very little beyond measures of direct experience (i.e., hours, months and years medium used) and ʹperceived knowledgeʹ of the web (i.e., ʹI am extremely skilled at

using the web, compared to most usersʹ (Novak, Hoffman and Yung 1998). However, as previously discussed, a poor correlation exists between what people think they know, what their experience is and what is actually stored in a consumer’s memory.

This argues for further research to examine consumer knowledge content of hypermedia computer-based technologies like the web. It also argues for the development of reliable measures of consumer knowledge of the web. By extension, this means looking at the role consumer knowledge of the medium plays in influencing medium use and perception. As commented by Diaz et al. (1997) consumers have varying amounts of knowledge about both the products they are interested in and about the environments in which they access these products. This dissertation’s main objective is thus to investigate the relationship between a user’s knowledge content of the web and a user’s perception of the web.

5.7.1 WEB KNOWLEDGE CONTENT AND PERCEIVED WEB USEFULNESS This research question specifically looks at the relationship between user knowledge content and perceived usefulness of the web. It asks:

RQ2: What is the relationship between a user’s knowledge content of the web and a person’s perceived usefulness of the web?

In this study is hypothesised that a strong positive relationship exists between common and specialised declarative knowledge content and perceived usefulness, and that an inverted-u-shaped relationship exists between common procedural knowledge content and perceived usefulness, and a weak relationship will occur with specialised procedural knowledge content.

With increasing declarative knowledge, users should acquire a perception of how the web and its features ʹshouldʹ work. Thus a strong positive relationship is initially hypothesised. However, as users gain common and specialised procedural knowledge content about the web, it is theorised they will start to realise the negative aspects of

usage, and thus become aware of system based problems that inhibit use. Thus high common and specialised declarative knowledge makes them more aware of the ʹperceived uselessness of the webʹ due to inefficiencies in navigation and information acquisition (i.e., clutter).

Handzic and Low (1999) found that more experienced users of processing programs (Microsoft Word, etc.) had more favourable perceptions of the usefulness of the technology. They felt that as users become more experienced with using processing programs, they become more aware of certain program features and also more efficient in the use of its attributes. However, the present study is concentrating on the web, not processing systems – it is the difference between investigation systems and creation systems – it is quite plausible to hypothesise a different relationship.

Therefore it is hypothesised that: H7A: Actual common procedural knowledge of the web will have a curvilinear relationship with perceived web usefulness H8A: Actual common declarative knowledge of the web will have a positive relationship with perceived web usefulness H9A: Actual specialized procedural knowledge of the web will have a positive relationship with perceived web usefulness H10A: Actual specialised declarative knowledge of the web will have a positive relationship with perceived web usefulness

H11A: Perceived procedural knowledge of the web will have a positive relationship with perceived web usefulness H12A: Perceived declarative knowledge of the web will have a positive relationship with perceived web usefulness H13A: Perceived general knowledge of the web will have a positive relationship with perceived web usefulness

5.7.2 WEB KNOWLEDGE CONTENT AND PERCEIVED EASE OF WEB USE It is asked:

RQ3: What is the relationship between a user’s knowledge content of the web and a person’s perceived ease of web use?

Individuals are constantly making decisions about accepting, adopting and using computer and information technologies. In the diffusion theory proposed by Rogers (1995) innovation adoption is viewed as a process of uncertainty reduction and information gathering; i.e., information gathering about the existence of the innovation as well as its characteristics and features through a social system within which adopters are situated. Nelson (1990) states that the acceptance of computer technology depends on the technology itself and the level of use, comfort, skill or expertise of the individual using the technology. Of relevance to this dissertation, research has reported perceived ease of use and perceived usefulness as determinants of innovation adoption acceptance and use. An understanding of this means practitioners and researchers might be better able to design training programs to effectively manipulate perceptions to foster increased adoption and acceptance (Venkatesh and Davis 1996).

However, little attention has been paid to what leads to the development of certain perceptions about an innovation (Agarwal and Prasad 1998; Venkatesh and Davis 1996). Handzic and Low (2000) found that subjects with moderate-high experience tended to perceive information technology as substantially more useful than those with low experience. They also found that a relatively modest level of experience may be sufficient for individuals to gain most of the relevant knowledge about various aspects of a given technology. With respect to new media, King and Xia (1997) note that an individual’s perceptions of media will vary widely according to that person’s skill, comfort and use of the media.

Karahanna and Straub (1999) examined the psychological origins of perceived ease of use and perceived usefulness. They found that usefulness of email technology was

determined by social influences, perceived ease of use, and social presence. A considerable body of evidence discusses the impact experience and learning has on user perceptions of information systems. Hubona and Geitz (1997) note that standardised interfaces promote ease of use, but training and education are also important.

One of the most crucial aspects of these findings is that with experience and exposure to communications comes the acquisition of knowledge. Thus, past experience and exposure to communications influences a user’s perceptions of the web through the acquisition of domain specific information. This is further emphasized by Agarwal and Prasad (1998) who state that the relationship between how information is obtained and the development of perceptions about the innovation has not been extensively studied.

The propositions presented here are consistent with existing theory in this area – as discussed above. However, this study goes one step further and hypothesises that ease of web use is actually affected by web knowledge content. Specifically, it is proposed that due to the fact that the web is experience driven, a strong relationship will exist between perceived ease of web use and the acquisition of common and specialised procedural knowledge, and a weak relationship will exist with common declarative knowledge, and no relationship with specialised declarative knowledge. As users become more experienced, they gain more procedural knowledge, which makes the web seem easier to use.

It is further theorised that a stronger relationship will exist between perceived knowledge content and a user’s perception of ease of web use than with actual knowledge content of the web stored in memory. This proposition is motivated by the finding that experience is more accessible in memory than product-related information that is stored in memory (Park et al. 1994).

Overall, it is proposed that: H14A: Actual common procedural knowledge of the web will have a positive relationship with perceived ease of web use

H15A: Actual common declarative knowledge of the web will have a positive relationship with perceived ease of web use H16A: Actual specialized procedural knowledge of the web will have a positive relationship with perceived ease of web use H17A: Actual specialised declarative knowledge of the web will have a positive relationship with perceived ease of web use

H18A: Perceived procedural knowledge of the web will have a positive relationship with perceived ease of web use H19A: Perceived declarative knowledge of the web will have a positive relationship with perceived ease of web use H20A: Perceived general knowledge of the web will have a positive relationship with perceived ease of web use

5.8 USER WEB KNOWLEDGE CONTENT: SUMMARY As introduced in Chapter 2 and further detailed in this chapter, one objective of this dissertation is the investigation of actual and perceived knowledge content of the web. This chapter discussed the conceptualisation of consumer knowledge content, proposing a 2x2 typology of knowledge content. Further, this chapter theorised the influence of actual and perceived knowledge content of the web on a users perceived ease of use and usefulness of the web. Therefore asking:

RQ2: What is the relationship between a user’s knowledge content of the web and a person’s perceived usefulness of the web?’

RQ3: What is the relationship between a user’s knowledge content of the web and a person’s perceived ease of web use?

C HAPTER 6: R ESEARCH Q UESTIONS AND H YPOTHESES

6.1 INTRODUCTION Outlined in this chapter are the main research questions and hypotheses that were discussed in the first section of the dissertation and that will be tested in the final sections of this dissertation. Overall, this dissertation investigates the relationships between the constructs depicted in Figure 12 to better understand and determine the influence of certain user characteristics, on current web session usage experience.

“What is the relationship between a user’s knowledge and perception of the web and a person’s current web session usage?”

Figure 12: Graphical Representation of the Dissertation (RQ1-RQ3)

Specifically, it is proposed that:

Perceived usefulness of the web is a primary determinant of current usage experience, and perceived ease of use is a secondary determinant (RQ1);

Actual common/specialised declarative knowledge content is a primary determinant of perceived usefulness of the web, and actual common/specialised procedural and perceived knowledge content are secondary determinants (RQ2);

Actual common/specialised procedural and perceived knowledge content of the web are primary determinants of perceived ease of web use, and actual common/specialised declarative knowledge content is a secondary determinant (RQ3).

6.2 RQ1: WEB PERCEPTION & USAGE RQ1: What is the relationship between a user’s perception of the web and a person’s current web session usage?

H1A: Perceived ease of use of the web will have a curvilinear relationship with current web session usage frequency H2A: Perceived usefulness of the web will have a strong positive relationship with current web session usage frequency H3A-B: Perceived ease of use of the web will have a curvilinear relationship with current web session usage variety H4A-B: Perceived usefulness of the web will have a strong positive relationship with current web session usage variety H5A-C: Perceived ease of use of the web will have a curvilinear relationship with current web session usage extent H6A-C: Perceived usefulness of the web will have a strong positive relationship with current web session usage extent

6.3 RQ2: WEB KNOWLEDGE & USEFULNESS RQ2: What is the relationship between a user’s knowledge content of the web and a person’s perceived usefulness of the web?

H7A: Actual common procedural knowledge of the web will have a curvilinear relationship with perceived web usefulness H8A: Actual common declarative knowledge of the web will have a positive relationship with perceived web usefulness H9A: Actual specialized procedural knowledge of the web will have a positive relationship with perceived web usefulness H10A: Actual specialised declarative knowledge of the web will have a positive with perceived web usefulness H11A: Perceived procedural knowledge of the web will have a positive relationship with perceived web usefulness H12A: Perceived declarative knowledge of the web will have a positive relationship with perceived web usefulness H13A: Perceived general knowledge of the web will have a positive relationship with perceived web usefulness

6.4 RQ3: WEB KNOWLEDGE & EASE OF USE RQ3: What is the relationship between a userâ&#x20AC;&#x2122;s knowledge content of the web and a personâ&#x20AC;&#x2122;s perceived ease of web use?

H14A: Actual common procedural knowledge of the web will have a positive relationship with perceived ease of web use H15A: Actual common declarative knowledge of the web will have a positive relationship with perceived ease of web use H16A: Actual specialized procedural knowledge of the web will have a positive relationship with perceived ease of web use H17A: Actual specialised declarative knowledge of the web will have a positive relationship with perceived ease of web use H18A: Perceived procedural knowledge of the web will have a strong relationship with perceived ease of web use

H19A: Perceived declarative knowledge of the web will have a strong positive relationship with perceived ease of web use H20A: Perceived general knowledge of the web will have a strong positive relationship with perceived ease of web use.

The next section of this dissertation discusses the methodological approach that was adopted for the development of items and the testing of hypotheses.

C HAPTER 7: C ONSTRUCT O PERATIONALISATION ʹWhat does it mean if a finding is significant or that the ultimate in statistical analytical techniques have been applied, if the data collection instrument generated invalid data at the outsetʹ (Jacoby, 1978)

7.1 INTRODUCTION The above statement supports the argument presented by Churchill (1979) that the critical element in the evolution of a body of knowledge is the development of better measures of the variables investigated. For this reason it is important to develop reliable and valid instruments to measure the constructs that are of interest in this dissertation. This is especially relevant given the lack of standardised measures in some of the more popularist studies of the use of electronic technologies. The overview of measurement development presented by Kaplan (1964), the framework discussed by Churchill (1979), and further discussion of item/scale development by Gerbing and Anderson (1988) and Rossiter and Kayande (1999), assisted in the development, adjustment and purification of items in this dissertation.

7.2 SCALE GENERATION & TESTING: METHODOLOGY Items were developed for the measurement of current web session usage, perceived ease of web use, perceived web usefulness and actual and perceived web knowledge content.

7.2.1 ITEM GENERATION To develop a pool of items to measure each of the constructs, primary and secondary exploratory research was undertaken, followed by descriptive research during the scale/item testing stage of item generation. First, in order to establish initial content validity, the item generation process involved a number of primary exploratory studies –

an expert survey, a novice observational study, web site and help file content analyses and a small number of in-depth interviews. A brief overview of the findings of these preliminary exploratory studies is presented in Appendix B. Second, secondary data analysis of existing scales and items (i.e., academic and industry measures) was conducted. The conceptualisation of the items derived and to be tested is presented in the Table 5 below. Table 5: Construct Conceptualisation ID WSUF WSUVS WSUVMNO1 WSUEB WSUED WSUEDUR PEWU PWU

ACPWK

ACDWK

ASPWK

ASDWK

SWPK

SWDK SWOK

Scale Usage Frequency Usage Variety Situational Usage Variety Motive

Conceptualisation Current Web Session Usage How often the web is accessed within a certain time frame (last month) Number and type of locations from which the web is accessed

Number of motivations for which the web is accessed Number of new and/or different web sites and/or search tools Usage Extent Breadth accessed Usage Extent Depth Total number of web sites and/or search tools accessed Usage Extent Duration The time for which a session on the web lasts Web Perceptions Perceived Ease of Web Degree to which the user believes that using the World Wide Web Use would be free from effort Perceived Web Degree to which the user believes that using the World Wide Web Usefulness would enhance his/her usage performance Actual Web Knowledge Content Actual Common General and/or publicly known dynamic information underlying Procedural Web skilful actions (how) of using X, required to perform general and Knowledge common domain related tasks successfully Actual Common General and/or publicly known static information of facts, terms, Declarative Web attributes (what) of X, required to perform general and common Knowledge domain related tasks successfully Actual Specialised Skilled and/or extraordinary dynamic information underlying Procedural Web skilful actions (how) of using X, required to perform skilled knowledge domain related tasks successfully Actual Specialised Skilled and/or extraordinary static information of facts, terms, Declarative Web attributes (what) of X, required to perform skilled domain related Knowledge tasks successfully Perceived Web Knowledge Content An individual’s personal judgement of the level of knowledge Perceived Procedural stored in their memory about how to use certain features and/or Web Knowledge terms of the web An individual’s personal judgement of the level of knowledge Perceived Web stored in their memory about what certain features and/or terms of Knowledge the web are. Perceived Overall Web An individual’s personal judgement of the overall level of Knowledge knowledge content about the web stored in their memory

7.2.2 ITEM TESTING AND PURIFICATION Two cross-sectional research designs using two paper-based questionnaires to separate samples was implemented as the method for data collection for scale/item testing and

validation. One questionnaire was administered to two student samples across two disciplines. This tested the properties of the scale items measuring perceived ease of web use, perceived web usefulness and current web session usage experience. The second questionnaire was administered to three student samples across three disciplines. This tested the properties of the scales measuring actual and perceived web knowledge content. Samples were selected based on web-usage experience and web-course content integration.

The total sample size for each study consisted of at least 100 respondents in accordance with the sample size requirements for conducting exploratory factor analysis (Hair, Anderson, Tatham, and Black, 1995). The samples for the two studies were convenience samples administered in a semi-controlled environment. In comparison to the administration of questionnaires in uncontrolled environments, where response rates can be alarmingly low (Yu and Cooper, 1983), the questionnaire was administered in a controlled environment to increase response rates and also to allow for differences in knowledge levels of respondents.

Prior to scale testing and analysis an assessment was made of the appropriateness of aggregating the samples (that is, the combination of two samples for one study, and the combination of three samples for the other study). Then a principal components exploratory factor analysis was conducted on the each scale developed to assess scale dimensionality. Scale/item reliability was further assessed.

7.2.3 ANALYTICAL DESIGN Internal consistency reliability analysis and an exploratory principal component factor analysis was conducted on all multi-item measures. This was done to determine the stability and dimensional structure of the scales and to examine the reported consistency in variable measurement of all instruments.

7.2.3.1 Internal Consistency Reliability Analysis Reliability refers to the extent to which a scale produces consistent results if repeated measurements are made (Perreault and Leigh, 1989). Malhotra et al. (1996) suggests that reliability can be further defined as the extent to which measures are free from random error, XR. If XR = 0, the measure is perfectly reliable. Reliability is assessed by determining the proportion of systematic variation in the scale. This is done by determining the association between scores obtained from different administrations of the scale. If the association is high, the scale yields consistent results and is therefore reliable. Hair, Anderson, Tatham and Black (1995) state that reliability is a measure of the internal consistency of the construct indicators, depicting the degree to which they indicate the common latent construct (Hair et al., 1995: p641). More reliable measures provide the researcher with greater confidence that the individual indicators are all consistent in their measurements. Approaches for assessing reliability include the testretest, alternative forms, and internal consistency methods (Malhotra et al., 1996).

Internal consistency reliability is used to assess the reliability of a scale where several items are summed to form a total score (Malhotra et al., 1996). The items should be consistent in what they indicate about the characteristic. As the item generation process developed scales that would require the summation of respondentsâ&#x20AC;&#x2122; results, internal consistency methods were used to assess the overall reliability of these scales.

A measurement of internal consistency reliability is the coefficient or Cronbachâ&#x20AC;&#x2122;s alpha. The coefficient alpha is the average of all possible split-half coefficients resulting from different ways of splitting the scale items (Cronbach, 1951 in Malhotra et al., 1996). This coefficient varies from 0 to 1. Malhotra et al. (1996) and Tull and Hawkins (1993) indicate that a value of 0.6 or less generally indicates unsatisfactory internal consistency reliability. Hair et al. (1995) and Nunnally (1978) stipulate a commonly used threshold value for acceptable reliability is a Cronbach Alpha of 0.7 or higher. However, they suggest that values below 0.7 have been deemed acceptable if the research is exploratory in nature. This is supported by Easterby-Smith, Thorpe and Lowe (1991).

From this review, measures with coefficient alpha of 0.6 or higher were regarded as reliable in both the item testing phase of this study and also during the process of scale validation (discussed in later chapters).

7.2.3.2 Exploratory Factor Analysis Factor analysis is a multivariate statistical technique that can be ‘utilised to examine the underlying patterns or relationships for a large number of variables and to determine whether or not the information can be condensed or summarised into a smaller set of factors or components’ (Hair et al., 1995: p365). Defined as an ‘interdependence technique’ in which factors are formed to maximise their explanation of the entire set of variables, the two primary reasons for conducting a factor analysis are summarisation and data reduction (Hair et al., 1995). These objectives can be achieved from either an exploratory or confirmatory viewpoint. An exploratory factor analysis is used here to summarise the variables and explore the structure of the measurement instruments.

A principal component factor model was deemed appropriate, as the primary concern is prediction of the minimum number of factors needed to account for the maximum proportion of the variance in the original set of variables.

Measure of Sampling Adequacy A measure of sampling adequacy (MSA) was applied. This provides a measure of the extent to which variables belong together and are thus appropriate for factor analysis. Kaiser and Rice (1974) as stated in Hair et al (1995) suggest excluding individual variables that have MSA levels below 0.50. Deriving Dimensions and Assessing Overall Fit The next step involves the selection of the number of components to be retained for further analysis. Principal components analysis was used for this step. The following four criteria were used to derive the dimensions:

Latent Root Criterion - Eigenvalues > 1

Percent of Variance Criterion - Using the cumulative percentages of the variance extracted by successive factors as the criterion.

Scree Test Criterion - Visually examining the ʹplot of latent rootʹ (eigenvalues) against the number of factors in order of extraction and assessing the optimum number of factors that can be extracted before the amount of unique variance begins to dominate the common variance structure.

A Priori Criterion - Extract according to scale development and design and prior studies.

The question then arises of whether the constructs are uni- or multi-dimensional. As with any factor analysis, there is no exact quantitative basis for deciding the number of dimensions to extract. A standard latent root criterion is used, where only those dimensions with initial eigenvalues of at least 1 are retained. In a number of cases here this results in uni-dimensional constructs. Given prior theory, this is not unexpected. Moreover, it has the advantage of greatly simplifying subsequent analyses. However, the latent root criterion tends to be quite conservative and therefore, for completeness, results are also presented based on the percentage of variance criterion (Hair et al. 1995, pp377-379). It is suggested that in some subsequent analyses it may be informative to work with the greater number of dimensions that this criterion generates. Interpreting the Factors Important to this statistical application are factor loadings. Factor loadings are the correlations of each variable within the factor under analysis and they indicate the degree of correspondence between the variable and the factor, with higher loadings indicating that the variable is representative of the factor (Hair et al., 1995). The criteria for determining the significance of factor loadings within this research were based on the guidelines reported by Hair et al. (1995) in relation to sample size. These authors suggest that in a sample of 120 respondents, factor loadings of 0.50 or higher are significant and for a sample of 150 respondents factor loadings of 0.45 are considered significant. Therefore, scale items that report a factor loading lower than 0.50 for study one (i.e., web

usage and user perceptions) and scale items that report a factor loading lower than 0.45 for study two (i.e., web knowledge content) will be discarded.

7.2.4 ITEM GENERATION: SUMMARY In summary, the aforementioned item generation, item testing and purification process facilitated the development and refinement of instruments to measure the variables of interest. In the next two sections specific details are given of the item generation and item testing and purification process for the scales measuring web usage and user web perceptions (i.e., study one) and actual and perceived knowledge content of the web (i.e., study two).

7.3 STUDY ONE: WEB USAGE AND USER WEB PERCEPTIONS This study used existing scales, theory and primary exploratory research to develop measures of the three areas of current web usage experience and user web perceptions. The results identified items that reliably measure current web usage frequency, usage variety (situational), usage variety (motivational), usage extent (breadth), usage extent (depth) and usage extent (duration). Existing scales from research in Management Information Systems (MIS) were also used to develop measures of perceived ease of web use and perceived usefulness of the web. The results identified scale items that reliably measured perceived ease of the web use and perceived usefulness of the web.

Firstly the research and sampling design for this study will be discussed followed by the item generation process for each scale and the presentation of the scale testing results for the items measuring current web session usage and user web perceptions.

7.3.1 RESEARCH AND SAMPLING DESIGN 7.3.1.1 Sampling Design To assess the properties of the scale items developed to test perception and current usage experience of the web, the sample was drawn from postgraduate (61%) and

undergraduate (39%) university courses (refer to Table 6). Convenience and domain experience were the primary motivations for sample selection.

Table 6: Sample: Independent Student Groups Program

Discipline

Postgraduate (Masters)

Marketing (Marketing in Asia)

Undergraduate (First Yr)

Education (Educational Psychology)

Sample 88

Response

Useable

Date Admin

Admin

Tues 4th April 2000

In-class Paper Survey

50 (2 surveys removed due to incompletion)

Fri 14th April 2000

In-class Paper Survey

The postgraduate marketing class was sampled because these were expected to hold variable perceptions. As the course being undertaken was not administered on the web, it is expected that a variance in perceptions of the web may exist. Some would be aware of the web as a marketing tool, others not. This group also matched Australian web-users in general, these being described as predominantly ʹhigher educatedʹ and ʹmore affluentʹ individuals that hold predominantly ʹprofessionalʹ vocations (www.consult.com, 1999).

A lower level of web use and experience was expected among the first-year undergraduates. These would have been exposed to only a limited amount of online course material. Exposure to the web would thus be either in association with personal usage or usage for other educational programs (this data was not acquired from responses).

7.3.1.2 Survey Administration The survey instrument to measure user web perceptions (i.e., PEWU and PWU) and current web usage (frequency, variety and extent) was administered in paper-format. This was intended to increase the variance in domain experience. If conducted online, experience and perceptual bias might be introduced.

Surveys were distributed in a class environment. Participation was voluntary, however course convenors emphasized the importance of the students participation. Despite high

expected response rates due to the method of administration, it was expected that the quality of the responses would be variable because of the limited intrinsic motivation to participate. 152 surveys were administered with 128 useable responses.

7.3.1.3 Sample Comparison To determine the appropriateness of data aggregation of the two groups, an independent-sample means comparison analysis was conducted. It was found that the means for the 20 scale items measuring perceived ease of web use, and 23 scale items measuring perceived web usefulness, were comparable. The samples were also comparable on the descriptive items measured.

It was found that the samples performed as expected on the descriptive items measured. For example, both samples had a high incidence of full-time enrolments (P=87%, U=98%) and a comparable gender distribution (Female: P=63%, U=78%). The age distribution for the samples was reasonably comparable, although the undergraduate sample was slightly younger (P= 73% - 27 and younger, U=80% - 19 and younger). In addition, the samples were not comparable in terms of past web usage experience, with 75% of the postgraduate sample having 3-6 years web experience and 68% of the undergraduate sample having less than 2 years web experience. This result, however was expected and the primary motivation for sample selection. Based on the above analyses, aggregation of the independent samples was conducted to facilitate scale analysis and development.

7.3.1.4 Scale Development and Analysis For the scales developed measuring current web session usage and user web perceptions, a principal components factor model with a varimax rotation was used. The number of dimensions to be extracted for further analysis was selected based on the following four criteria: the latent root criterion, cumulative variance percentages, the scree plot, and prior knowledge. In addition, factor loadings of +/- 0.50 and above were considered significant and thus for interpretation purposes, only items with factor loadings above this threshold were considered. Reliability analyses were also performed

on all four scales to check for internal consistency, with items deleted if the corrected item-total correlation fell below 0.6. In the following sections, each scale is discussed in turn.

7.3.2 DESCRIPTIVE RESULTS The aggregate sample had a skewed gender distribution with 88 female respondents (69%) and 40 male respondents (31%). The age distribution was also skewed, with 78% of the sample aged less than 25 years of age. Given the sampling design described above, a significant bias towards the young age group is not surprising. However, even though this sample is not representative of the demographic profile of the Australian web population, this should not affect the results as the purpose here is scale development.

With regard to respondents direct web usage experience, the following results were identified: 84% of the sample had 3 or more yearâ&#x20AC;&#x2122;s computer experience, 40% had less than 2 years and 43% had 3 to 4 years web experience; 56% accessed the web on a daily basis and 42% on a weekly basis; 43% use the web on average for less than 1 hour per occasion and 46% for 1-3 hours. Furthermore, 42% of the sample access the web from 2 to 3 different locations and 56% of the sample had between 2-3 email accounts. From this description it is inferred that the sample has a medium to high level of direct web usage experience.

7.3.3 ITEM GENERATION & TESTING: CURRENT WEB SESSION USAGE 7.3.3.1 Item Generation From the discussion presented in chapter 3 and summarised in Appendix C, three categories of constructs of current usage experience, frequency of use, variety of use and extent of use were conceptualised. Based on this conceptual understanding and the endeavour to develop reliable and valid measures of current web session usage, an item pool was generated. The items generated were designed to build on existing measures,

and extend them into the context of web usage. Therefore existing measures of usage experience were reviewed and primary exploratory research undertaken.

7.3.3.2 Measurement Method Existing research on consumer usage experience was reviewed in the item generation process to assess existing item structure and design. These items were modified for this dissertation to correspond with the study context, current web session use. Examined from a purchase context, self-reports were found to suffer from biases such as forgetting, ambiguous questioning, reporting errors, deliberate falsification and interviewer bias (Wind and Lerner, 1979). These biases are likely to exist in the usage context as well. In addition, unlike purchase which is a discrete event, usage is a continuous event which may change over the period of ownership or access. Despite these problems with selfreports, Ram and Jung (1990) suggest that systematically designed self-reports can provide reliable and valid measures of usage, and also can save a considerable amount of time and effort compared to the use of purchase diaries. Therefore, self-report measures of usage behaviour are employed.

7.3.3.3 Scale: Current Web Session Usage Frequency Item Generation Ram and Jung (1990) indicated the use of a number of items, with multiple-choice response formats, for the self-report measurement of product usage frequency. This approach is consistent with research studies that have measured the frequency of use of personal computers and computer-based software (Swoboda, 1998; Bagozzi et al., 1992; Davis, 1989b; Davis et al., 1989a); the internet and internet related services (Bronson, 1999; GVU, 1998; Napoli and Ewing, 1998; Sivadas et al., 1998; Teo et al., 1999); and other technologically based systems such as ATMâ&#x20AC;&#x2122;s, VCRâ&#x20AC;&#x2122;s, and cameraâ&#x20AC;&#x2122;s (Ram and Jung, 1990; Sinkovics et al., 1999; Zaichkowsky, 1985b). In addition, this item structure is consistent with not only academic research but also industry and government research on internet and web usage frequency (ABS, 1998; GVU, 1998; www.consult.com, 1999). Thus, one

item with an 8-category multiple-choice question will be used to measure current web session usage frequency.

Item Testing From the 8-categories ranging from 1=once a month to 8=5 or more times a day, the median was 6=once a day.

7.3.3.4 Scale/s: Current Web Session Usage Variety - Situational and Motivational Item Generation As identified in chapter 3 and summarised in Appendix C, current web session usage variety comprises situational and motivational variety.

Motivational Variety In industry and government research the reason for internet and web usage has been measured using one check-list question, where respondents are asked to check the reasons for product usage (ABS, 1998; eMarketer, 1999; GVU, 1998; Jupiter, 1999a; www.consult.com, 1998; www.consult.com, 1999). Motivational variety thus will be measured using a single-item 12-category checklist. This item will be coded according to the number of items checked, indicating the number of motivations for web session use, and thus will comprise a final measure of the number of motivations for which the web is used.

Situational Variety The locations from which the internet, the web, electronic kiosks and/or computers have been used was measured using a single item, with either a check-list question or a multiple choice question (ABS, 1998; www.consult.com, 1998; GVU, 1998; Napoli and Ewing, 1998). In this study, situation variety was measured using one item: a 6-category

measure, with a multiple choice question, with the respondent indicating the number of locations from which the web is accessed.

Item Testing Motivational Variety A 12-category check-list is used to measure the motivations for current web session use. This is coded according to the number of items checked and comprises a 1-item scale. The summation of the number of motives checked ranged from 0 = 0 motivations to 12 = 12 motivations, with a median of 4 = 4 motivations.

Situational Variety A single-item 6-category measure, with a multiple-choice response format, is used to measure the number of locations from which the web is accessed. From the 6-categories ranging from 1 = 1 location to 6 = 6 or more locations, the median was 2 = 2 locations.

7.3.3.5 Scale/s: Current Web Session Usage Extent â&#x20AC;&#x201C; Breadth, Depth and Duration Item Generation As identified in chapter 3 and summarised in Appendix C, current web session usage extent comprises breadth, depth and duration of web session use. Dreze and Zufryden (1997d) developed models for two effectiveness measures that they found most relevant to their study of web sites: number of pages accessed (depth) and time spent during a site visit (duration). These authors used mechanical observation of log-file data for their study. However, as this study is concerned with the macro perspective of web use, as opposed the micro perspective of web site visitation and use, mechanical observation is not an appropriate means of measurement. A review of the literature revealed a number of item formats that would be appropriate for the measurement of web session usage extent â&#x20AC;&#x201C; breadth, depth and duration.

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Session Breadth With respect to breadth of web session use (i.e., the number of new and/or different web sites and/or search tools accessed), a review of the literature revealed only a few suitable measures. For example, Teo et al., (1999) uses a 7-item Likert scale to measure diversity of internet usage. Hoffman et al., (1998) used an 8-item Likert scale to measure if respondents visit the same or different web sites (Îą = 0.79) Therefore to measure user perceptions of the number of new and/or different web sites they currently access, four Likert scales (with 7-point strongly agree-strongly disagree responses) were developed.

Session Depth With respect to the depth of web session use (i.e., the total number of web sites and/or search tools accessed), a review of the literature revealed a variety of possible measures. Depth of product usage has been measured with, for example, open end-ended questions asking the respondent to specify how many messages have been sent and received (Davis, 1989b; Karahanna and Straub, 1999), and multiple choice responses have been used to measure the number of ATM cards owned (Sinkovics et al., 1999) and the number of purchases made (ABS, 1998; Midgley, 1983). A check-list response format has also been used to measure the number of brands trialed (Schaefer, 1997). With respect to the web, it was felt a better measure would be to ask respondents to indicate their level of agreement or disagreement with certain statements with respect to the number of web sites they might visit, search tools they might use in a given session on the web, and the number of bookmarks in their favourites folder. Thus four Likert scales were developed to measure current web session usage depth.

Session Duration Duration of web session use also seemed an interesting scale item to review. In the literature, one scaled multiple choice question is used to measure the time spent with media technologies such as the television (Napoli and Ewing, 1998), computers and software (Davis, 1986; Davis, 1989b; Dishaw and Strong, 1999; GVU, 1998; Hubona and Geitz, 1997), and the internet and internet-related services such as the web (GVU, 1998;

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Jupiter, 1999a; Moon and Kim, 2001; Napoli and Ewing, 1998; Novak et al., 1998; Teo et al., 1999; www.consult.com, 1999). Duration of web session use is measured here with one 8-category multiple choice question.

Item Testing Session Breadth Initial data screening and analysis of correlation patterns identified that individually and collectively all 4-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.710, Bartlett聞s Test of Sphericity: Approx. Chi-Square = 72.016, df = 6, Sig. = 0.000). Thus a principal components analysis with a varimax rotation was conducted. This analysis extracted 2 dimensions that explained 70% of the variance of current web session usage extent - breadth. Further examination resulted in 1-item being deleted as the corrected item-total correlation fell below the threshold value of 0.6. The final scale thus comprises 3-items with a total scale reliability of 0.7. These 3items measure 2 dimensions that explain a corrected 82% of the variance of the construct. See Table 7 for scale and dimension variance and Appendix D for final item factor loadings.

Table 7: Variance Explained of Breadth of Session Use Initial Eigenvalues

Component

Total

1 2 3

1.8 .7 .5

% of Cumulative Variance % 60 60 22 82 18 100

Extraction Sums of Squared Loadings Total 1.8 .7

% of Cumulative Variance % 60 60 22 82

Rotation Sums of Squared Loadings Total 1.4 1.0

% of Cumulative Variance % 46 46 36 82

Extraction Method: Principal Component Analysis with a Varimax Rotation

Session Depth of Use Initial data screening and analysis of the correlation patterns identified that individually and collectively all 4-items meet the necessary threshold for sampling adequacy (KMO Measure of Sampling Adequacy = 0.701, Bartlett聞s Test of Sphericity: Approx. Chi-Square

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= 193.032, df = 6, Sig. = 0.000). Thus a principal components analysis with a varimax rotation was conducted. This analysis extracted 3 dimensions that explained 94% of the variance of current web session usage extent - depth. Results showed dimension 1 accounting for 43% of the variance, dimension 2 for 26% of variance and dimension 3 25% of the variance. The final scale thus comprises 4-items with a total scale reliability of 0.81. See Table 8 for scale and dimension variance and Appendix D for final item factor loadings. Table 8: Variance Explained of Depth of Session Use Initial Eigenvalues

Component

Total

1 2 3

3.0 .8 .5

% of Cumulative Variance % 63 63 19 83 11 94

Extraction Sums of Squared Loadings Total 3 .8 .5

% of Cumulative Variance % 63 63 19 83 11 94

Rotation Sums of Squared Loadings Total 1.7 1.0 1.0

% of Cumulative Variance % 43 43 26 69 25 94

Extraction Method: Principal Component Analysis with a Varimax Rotation

Session Duration of Use From the 8-categories ranging from 1 = Less than 15 minutes to 8 = 13 or more hours, the median was 4 = 1-3 hours.

7.3.3.6 Item Generation and Testing Summary: Current Web Session Usage In summary, the structure of the items developed to measure the categories of current web session usage frequency, usage variety and usage extent were developed in accordance with existing academic, industry and government based scales. The specific content of the scale items also was improved by conducting a number of preliminary qualitative studies. See Appendix B.

From the item generation process this study operationalised constructs measuring frequency of session use (1-item), variety of session use - situational (1-item), variety of session use – motivational, extent of session use - breadth (4-items), extent of session use – depth (4-items), and extent of session use – duration (1-item). The reliability and dimensionality of all multi-item scales was tested using exploratory factor analysis and

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reliability analysis. A summary is presented in Appendix C and the final item factor loadings are shown in Appendix D.

7.3.4 SCALE GENERATION AND TESTING: USER WEB PERCEPTIONS Based on this conceptual understanding and the endeavour to develop a generally applicable scale for measuring usage perceptions of web users, an item pool was generated. The items generated were designed to build on existing measures, and extend them into the context of user perceptions of the web. Therefore, both content analysis of existing scales and a number of primary exploratory studies (as presented in Appendix B) were conducted. Earlier research on the perceived ease of use and usefulness (Davis, 1986; Davis, 1989a; Adams, 1992; Segars, 1993; Davis, 1989b) was reviewed to assess existing item structure and design. The results of this review are detailed below.

7.3.4.1 Scale: Perceived Ease of Web Use Item Generation It is evident from a review of the literature that the main way to measure perceived ease of use is with multi-item scales, using Likert questions. This variable is usually seen as a uni-dimensional construct measuring the perceived ease of use of computers and information technology, computer-based software, database systems and support tools, and more recently the internet and internet-based systems such as the web, email and individual web sites. However, a task focus needs to be introduced here too. From content analysis of the existing scales, and from qualitative studies outlined later in this chapter, a number of scale items were developed to assess the predicted dimensions of perceived ease of web use. Specifically, 20-scaled items with a Likert question response format were developed to measure a userâ&#x20AC;&#x2122;s perceived ease of web use for certain tasks.

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Item Testing Initial data screening and analysis of correlation patterns identified that individually and collectively all 20-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.925, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 1852.041, df = 190, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 16-items with factor loadings equal to or above 0.6 that explained 70% of the variance of perceived ease of web use. Following further analysis to check the internal consistency, a further 2-items were removed – these fell below the corrected item-total correlation threshold of 0.6. After the removal of items due to low factor loadings and low item-total reliability, a final factor analysis was conducted.

The final perceived ease of web use scale thus comprises 14-items with a total scale reliability of 0.94. These 14-items measure 3 dimensions that explain a corrected 72% of the variance of the construct. See Table 9 for scale and dimension variance and Appendix D for final item factor loadings.

Table 9: Variance Explained of Perceived Ease of Web Use Initial Eigenvalues

Dimension

Total

1 2 3 4

8.0 1.4 .9 .7

% of Cumulative Variance % 56 56 10 66 7 73 5 78

Extraction Sums of Squared Loadings Total 7.9 1.4 .9

% of Cumulative Variance % 56 56 10 66 7 73

Rotation Sums of Squared Loadings Total 4.1 4.1 2.0

% of Cumulative Variance % 30 30 29 59 14 73

Extraction Method: Principal Component Analysis with a Varimax Rotation

7.3.4.2 Scale: Perceived Web Usefulness Item Generation A user’s perceived usefulness of the web for certain tasks is measured using the same principles and procedures as for perceived ease of web use. This resulted in the development of 23- scaled items with a Likert question response format.

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Item Testing Initial data screening and analysis of correlation patterns identified that individually and collectively all 23-items meet the necessary requirements for exploratory factor analysis (KMO Measure of Sampling Adequacy = 0.874, Bartlettʹs Test of Sphericity: Approx. ChiSquare = 1717.465, df = 253, Sig. = 0.000). Thus a principal components analysis with a varimax rotation was conducted. This analysis extracted 20-items that explained 6 dimensions and 70% of the variance of perceived usefulness of the web. Following further analysis to check for the internal consistency of the scale, a further 6-items were removed – these fell below the corrected item-total correlation threshold of 0.6. After the removal of items due to low factor loadings and low item-total reliability a final factor analysis was conducted.

The final perceived web usefulness scale comprised 14-items with a total scale reliability of 0.9. These 14-items measure 4 dimensions that explain a corrected 75% of the variance of the construct. See Table 10 for scale and dimension variance and Appendix D for final item factor loadings.

Table 10: Variance Explained of Perceived Usefulness of the Web Initial Eigenvalues

Component

Total

1 2 3 4 5

7 2 1 .9 .7

% of Cumulative Variance % 47 47 13 61 8 69 6 75 5 80

Extraction Sums of Squared Loadings Total 7 2 1 .9

% of Cumulative Variance % 47 47 13 61 8 69 6 75

Rotation Sums of Squared Loadings Total 3.2 3.1 2.7 1.5

% of Cumulative Variance % 23 23 22 46 19 65 10 75

Extraction Method: Principal Component Analysis with a Varimax Rotation

7.3.4.3 Item Generation and Testing Summary: User Perceptions of the Web In summary, from the item generation process 20-scaled items were developed to measure a user’s perceived ease of web use and 23-scaled items were developed to measure a user’s perceived usefulness of the web.

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Following item generation, all items were tested using an exploratory factor analysis and reliability analysis. The results identify 14-items that reliably measure 3 dimensions of perceived ease of web use and 14-items that reliably measure 4 dimensions of perceived web usefulness. A summary is presented in Appendix C and the final factor loadings are shown in Appendix D.

7.4 STUDY TWO: ACTUAL AND PERCEIVED WEB KNOWLEDGE CONTENT To assist in the measurement of independent variables in this dissertation, objective and subjective measures of web knowledge content were developed. These provide an assessment of the type and scope of knowledge content a consumer actually has of the system they are using. Results identify scale items that reliably measure actual ‘Common Declarative’, ‘Common Procedural’, ‘Specialized Declarative’ and ‘Specialized Procedural’ knowledge content of the web and measures that reliably measure perceived ‘Procedural’, ‘Declarative’ and ‘Overall’ knowledge content of the web.

The research and sampling design for this study is presented first. This is followed by a discussion of the item generation process for each scale and the scale testing results.

7.4.1 RESEARCH AND SAMPLING DESIGN 7.4.1.1 Sampling Design To assess the properties of the scales, four independent samples were recruited. The samples were drawn from one postgraduate and three undergraduate university student groups (see Table 11). Convenience and domain experience were the primary motivations for selection. Across the samples respondents were expected to have varying levels of knowledge of the web.

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Table 11: Sample Description - Independent Student Groups No.

Program

Discipline (Subject)

PG (Masters) UG (First Yr) UG (Third Yr)

Marketing (Elements of Marketing) Science (Conservation, Biology and Biodensity) Communications (New Technology A)

UG (First Yr)

Education (Edu. Psychology)

2 3 4

EKL

Sample

Response

Useable

Admin

L/M/H

L/M

M/H

100

L/M

PG = Postgraduate; UG = Undergraduate; EKL = Expected Knowledge Level (L=Low, M=Moderate, H=High) TH = Take Home; IC = In-class

7.4.1.2 Survey Administration The survey instrument was administered in paper-format. This was intended to increase the variance in domain experience and reduce the chances of perceptual bias (a potential problem if the study had been conducted on the web). Some 297 surveys were administered with 153 useable responses (a 52% response rate).

7.4.1.3 Sample Comparison To assist in the process of data aggregation and scale item analysis, the original 5category multiple-choice responses and 3-category True/False/Donâ&#x20AC;&#x2122;t Know responses were reduced to a 2-category format (1=correct/knowledge, 0=incorrect/no knowledge). This followed Park et al. (1994) and the recommendations of Malhotra, Hall, Shaw and Crisp (1996). Data were cross-tabulated to see how the four samples compared. For both the multiple-choice and true/false items measuring actual web knowledge there was variance in the data, with all four samples behaving as expected (i.e. the Communications sample performed med-high, Marketing was low-med-high and Education and Science were low-med). In addition the three samples performed as expected across the three scales measuring perceived knowledge content of the web. The individual samples were also comparable on the descriptive items measured (based on a visual comparison of descriptive variables). Therefore, it was deemed appropriate to aggregate the samples.

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7.4.1.4 Scale Development and Analysis For the four scales developed measuring actual knowledge content of the web and the three scales measuring perceived knowledge content of the web, a principal components factor model with a varimax rotation was used. The number of dimensions to be extracted for further analysis was selected based on the following four criteria: the latent root criterion, cumulative variance percentages, the scree plot, and prior knowledge. In addition, factor loadings of +/- 0.45 and above were considered significant and thus for interpretation purposes, only items with factor loadings above this threshold were considered. Reliability analyses were also performed on all four scales to check for internal consistency, with items deleted if the corrected item-total correlation fell below 0.6. In the following sections, each scale is discussed in turn.

7.4.2 DESCRIPTIVE RESULTS The aggregate sample had a skewed gender distribution with 106 female respondents (69%) and 47 male respondents (31%). The age distribution was also skewed with 75% of the sample less than 24 years of age. With regard to respondents’ direct web usage experience, the following results were found: 13% of the sample had less than 2 years computer experience, 18% had 3-4 years experience, 29% had 5-6 years experience and 40% had 7 or more years experience. With respect to web experience, 22% had been using the web for between 6-11 months, 30% between 1-2 years and 39% between 3-4 years.

7.4.3 SCALE GENERATION AND TESTING: ACTUAL WEB KNOWLEDGE CONTENT To assist in the successful design and adoption of hypermedia computer-based systems, like the web, objective measures are developed to assess the type of knowledge content a consumer actually has of the system. Results identify reliable scale items that objectively measure ‘Common Declarative’, ‘Common Procedural’, ‘Specialized Declarative’ and ‘Specialized Procedural’ knowledge content of the web.

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The focus of the study is actual consumer knowledge content of the web. However, as cautioned by Engel et al. (1990), objective measurement of knowledge is ‘by no means an easy task, given the vast array of relevant knowledge that a consumer may possess’. Thus, before developing measures of the variables defined in chapter five of this dissertation and summarised in Appendix C, previous research on consumer knowledge content and objective measurement was considered. In addition, primary exploratory research was also undertaken. 7.4.3.1 Item Generation: Actual Knowledge Content Item Structure Generation Previous research shows that actual consumer knowledge has been assessed using brand/attribute recall and elicitation methods (Brucks 1985; Selnes and Gronhaug 1986; Brucks 1986; Mitchell 1981; Park, Feick and Mothersbaugh 1992), in-depth interviews (Dacin and Mitchell 1984), task-allocation methods (Mitchell 1981; Russo and Johnson 1980), responses to multiple choice questions (Zaichkowsky 1985b; Park et al. 1994), open-ended lists (Brucks 1985; Selnes and Gronhaug 1986), and true/false questions (Rao and Sieben 1992; Park et al. 1994; Cole et al. 1986).

Multiple-choice and true/false item structures were adopted here. A response option of ‘don’t know’ was added to both item structures to increase the chances of measuring actual knowledge as opposed to capturing the respondent’s ability to guess correctly. All items were recoded as correct/incorrect prior to analysis with ‘don’t know’ recoded as ‘incorrect’ (in line with respondents’ own admission of ‘no knowledge’). Item Content Generation Brucks and Mitchell (1981) proposed a typology for classifying the knowledge that a consumer has about a particular product category or purchase decision. ‘Product class knowledge’ was seen as comprising: terminology, specific facts, relationships, criteria for evaluation, and procedural information (p754). Specific facts represent attribute (declarative) knowledge, procedural information relates to benefit (procedural) knowledge, while relationships and criteria for evaluation relate to more abstract ideas.

110

Brucks (1986) further developed this typology, arguing that “consumer knowledge can be classified and measured by its content and that the typology proposed was comprehensive, reliable and able to classify knowledge into empirically distinguishable categories” (p58). The Brucks (1986) typology comprises eight items giving rise to correlations of procedural and declarative knowledge as discussed earlier. In summary, Brucksʹ (1986) research has two important implications for research into consumer product knowledge. Firstly, measures of consumer knowledge should cover the full range of product knowledge, and secondly that the different types of knowledge content (i.e., procedural and/or declarative) will have varying effects on the decision making process. The first of these conclusions has direct implications for the development of standardised measures for this dissertation.

In addition, in order to establish initial content validity, the item-generation process involved a number of qualitative steps. Firstly, a set of free response questions were constructed on the basis of the typology developed by Brucks and Mitchell (1981) and later defined by Brucks (1985) and Brucks (1986). These free response question were used as the foundation of an expert survey conducted among a panel of web designers and web-marketing experts – they were able to comment on those aspects of the web that are required or used for web navigation from a design perspective (i.e., terminology, attributes, facts, evaluative criteria, usage situations). Further information was obtained from an observational study of novice users, from web help files and web site content analyses, and from in-depth interviews with web users. The results of these studies are detailed in Appendix B.

Item Generation Summary With this procedure, a pool of 110 items was developed to measure consumer knowledge of web attributes, terminology, facts, evaluative criteria, usage procedures, benefits, and condition-action statements for web navigation. The panel of experts rated all 110 items - with first round ratings grouping items as measuring either declarative or procedural knowledge and second round ratings grouping items as measuring either

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specialised or common knowledge of the web. Nine items were deleted as categorisation was not consistent across the panel. A final set of items was derived, consisting of 13items measuring common procedural knowledge, 31-items measuring common declarative knowledge, 19-items measuring specialised procedural knowledge, and 28items measuring specialised declarative knowledge of the web. These measures were independent of subjective views, open-ended questions and experience and usage measures, and thus are considered to be objective measures. The conceptualisation and operationalisation of these variables, pre-item testing and post item-testing, and purification is summarised in Appendix C.

7.4.3.2 Item Testing: Actual Knowledge Content Actual Common Procedural Web Knowledge Content Initial data screening and analysis of correlation patterns identified that individually and collectively all 13 items met the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.723, Bartlett聞s Test of Sphericity: Approx. Chi-Square = 410.173, df = 78, Sig. = 0.000). Thus a principal components analysis with a varimax rotation was conducted. Only those items with factor loadings above +/-0.45 were considered. This reduced the scale from 13 items to 9 items, with a further 3 items removed due to low item-total correlations. The final factor analysis identified 6-items that extracted 3 dimensions and explained 75% of the variance. Total scale reliability was 0.8. See Table 12 for scale and dimension variance explained and Appendix E for the final item factor loadings.

Table 12: Explained Variance of Common Procedural Web Knowledge Initial Eigenvalues

Component

Total

1 2 3

3.0 .8 .7

% of Cumulative Variance % 50 50 14 63 12 75

Extraction Sums of Squared Loadings Total 3.0 .8 .7

% of Cumulative Variance % 50 50 14 63 12 75

Extraction Method: Principal Components Analysis with a Varimax Rotation

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Rotation Sums of Squared Loadings Total 1.9 1.6 1.1

% of Cumulative Variance % 31 31 26 58 18 75

Actual Common Declarative Knowledge Content Initial data screening and analysis of the correlation pattern showed that 28 of the 31 items, individually and collectively, met the necessary threshold of sampling adequacy for factor analytical investigations (KMO Measure of Sampling Adequacy = 0.840, Bartlett聞s Test of Sphericity: Approx. Chi-Square = 1381.676, df = 378, Sig. = 0.000). Thus a principal components analysis with a varimax rotation was conducted. Only those items with factor loadings above +/-0.45 and item-total correlations at 0.6 or above were considered. This reduced the items from 28 items to 10 items. The final factor analysis identified 10-items that formed 2-dimensions and explained 57% of the variance, with a total scale reliability of 0.9. See Table 13 for scale and dimension variance and Appendix E for final item factor loadings.

Table 13: Variance Explained of Common Declarative Web Knowledge Initial Eigenvalues

Component

Total

1 2

4.8 .9

% of Cumulative Variance % 48 48 9 57

Extraction Sums of Squared Loadings Total 4.8 .9

% of Cumulative Variance % 48 48 9 57

Rotation Sums of Squared Loadings Total 3.0 2.7

% of Cumulative Variance % 30 30 27 57

Extraction Method: Principal Components Analysis with a Varimax Rotation

Actual Specialised Procedural Knowledge Content Initial data screening and analysis of correlation patterns identified 1 of the 19 items measuring specialized procedural knowledge had a MSA level lower than .50. This item was removed resulting in the remaining set of 18 variables, collectively and individually, meeting the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.832, Bartlett聞s Test of Sphericity: Approx. Chi-Square = 814.871, df = 153, Sig. = 0.000). The principal components analysis with a varimax rotation was conducted. Only those items with factor loadings above +/-0.45 and item-total correlations at 0.6 or above were considered. This reduced the scale from 19 items to 11 items. These 11-items extracted 3 dimensions and explained 59% of the variance, with a total scale reliability of 0.8. See Table 14 for scale and dimension variance and Appendix E for final item factor loadings.

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Table 14: Variance Explained of Specialised Procedural Web Knowledge Initial Eigenvalues

Component

Total

1 2 3

4.2 1.3 .9

% of Cumulative Variance % 38 38 12 50 9 59

Extraction Sums of Squared Loadings Total 4.1 1.3 .9

% of Cumulative Variance % 38 38 12 50 9 59

Rotation Sums of Squared Loadings Total 2.3 2.2 2.0

% of Cumulative Variance % 21 21 20 40 18 59

Extraction Method: Principal Components Analysis with a Varimax Rotation

Actual Specialised Declarative Knowledge Content For the items measuring specialised declarative knowledge of the web, 4 of the 29 items had MSA levels of less than .50. These items were omitted resulting in the reduced set of 25 items, collectively and individually, meeting the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.819, Bartlett聞s Test of Sphericity: Approx. Chi-Square = 536.658, df = 55, Sig. = 0.000). Thus a principal components exploratory factor analysis with a varimax rotation was conducted. Only those items with factor loadings above +/-0.45 and item-total correlations at 0.6 or above were considered. This reduced the scale from 25 items to 11 items that extracted 3 dimensions and explained 59% of the variance. Total scale reliability of 0.9. See Table 15 for scale and dimension variance and Appendix E for final item factor loadings.

Table 15: Variance Explained of Specialised Declarative Web Knowledge Initial Eigenvalues

Component

Total

1 2 3

4.4 1.0 .9

% of Cumulative Variance % 41 41 9 50 8 59

Extraction Sums of Squared Loadings Total 4.5 1.0 .9

% of Cumulative Variance % 41 41 9 50 8 59

Extraction Method: Principal Components Analysis with a Varimax Rotation

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Rotation Sums of Squared Loadings Total 2.6 2.1 1.8

% of Cumulative Variance % 24 24 19 42 16 59

7.4.3.3 Item Generation and Testing Summary: Actual Web Knowledge Content In summary, the above analysis identified scale items that reliably measured actual common declarative (10-items), actual common procedural (6-items), actual specialised declarative (11-items) and actual specialised procedural (11-items) knowledge content of the web.

7.4.4 SCALE GENERATION AND TESTING: PERCEIVED WEB KNOWLEDGE CONTENT Subjective measurement is defined as a method used to assess individual perceptions and introspective thoughts about the information of a domain believed by a consumer to be stored in their memory. This is measured through self-report or self-assessment measures. For the purpose of this dissertation, subjective measures of a userâ&#x20AC;&#x2122;s web knowledge content are developed to assess the knowledge content a consumer thinks they have of the web. Results identify scale items that reliably measure perceived overall, perceived procedural, and perceived declarative knowledge content of the web.

7.4.4.1 Item Generation: Perceived Knowledge Content Item Structure Generation A number of existing scales were analysed to determine the measurement level and question response format used to measure perceived knowledge content of the web. Of specific interest to this study has been the prior use of subjective measures to measure perceived knowledge content of a computer-based or technological product. Measures, with multiple-choice questions, have been used to measure the perceived knowledge content of personal computers (Selnes and Gronhaug, 1986). Measures, with semantic differential questions, have been used to assess perceived knowledge of sewing machines (Brucks, 1985). In a number of studies Likert scales have been used to measure perceived knowledge of CD players (Park et al., 1992; Park et al., 1994) and cars (Johnson and Russo, 1984), and the perceived level of skill in relation to the web (Novak and Hoffman, 1997). A review of other existing scales of perceived knowledge content of

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other products and/or areas identified that a majority measure perceived knowledge content with Likert questions (Cole et al., 1986; Park et al., 1992; Johnson and Russo, 1984; Rao and Sieben, 1992; Yale and Gilly, 1995; Hulland and Kleinmuntz, 1994). Therefore, Likert questions are used here.

Item Content Generation The exploratory studies were also used to help establish the content of the scale items to be generated. The results of these studies are detailed in Appendix B.

Item Generation: Summary From this process, a pool of 20 items was developed that subjectively measured consumer knowledge content of web attributes, terminology, facts, evaluative criteria, usage procedures, benefits, and condition-action statements for web navigation. A panel of experts rated all 20 items - with first round ratings grouping items as measuring either declarative, procedural or overall knowledge content. As the items developed were generic (i.e., about attributes in general and not specifically questioning a user’s knowledge of ‘cookies’ for example), it was difficult to differentiate the scope and thus the ‘common’ and ‘specialised’ nature of the items. Therefore, the scope of knowledge content was not measured subjectively. A final set of items was derived, consisting of 3items measuring perceived overall knowledge content, 9-items measuring perceived declarative knowledge content, and 8-items measuring perceived procedural knowledge content.

7.4.4.2 Item Testing: Perceived Web Knowledge Content Perceived Overall Knowledge Content Initial data screening and item-total reliability analysis on the 3-items measuring perceived overall web knowledge content identified that 1-item be removed as it reported low item-total reliability, thus resulting in 2-items measuring perceived overall

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knowledge content of the web. These two items were further investigated using Spearman’s Rho correlation coefficient. Table 16 shows a significant positive relationship between the two items (rs = .757, p<.01). Thus the items are deemed appropriate for summation to measure perceived overall web knowledge content.

Table 16: Perceived Overall Knowledge Content: SWOK1 & SWOK2 Spearman’s Rho Correlation Coefficient SWOK2 Spearman’s Rho

Correlation Coefficient

.750

Sig. (2-tailed)

.000

153

SWOK1

** Correlation significant at the .01 level (1-tailed)

Perceived Procedural Knowledge Content Initial data screening and analysis of correlation patterns identified that individually and collectively all 8 items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.835, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 632.463, df = 21, Sig. = 0.000). Thus a principal components analysis was conducted. Further assessment of item-total correlations found that 4 of the 8-items have low scores, i.e., below the 0.6 threshold. These items were omitted. Final results showed 1 dimension accounting for 77% of the variance with a reliability of 0.9. See Table 17 for scale variance and Appendix E for final item factor loadings.

Table 17: Variance Explained of Perceived Procedural Web Knowledge Initial Eigenvalues Component 1

Total 3.1

% of Variance 77

Extraction Sums of Squared Loadings Cumulative % Total % of Variance 77 3.1 77

Cumulative % 77

Extraction Method: Principal Components Analysis with a Varimax Rotation

Perceived Declarative Knowledge Content For the items measuring perceived declarative knowledge content of the web, 2 of the 9 items had MSA levels of less than .50. These items were omitted. The reduced set of 7-

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items collectively and individually met the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.947, Bartlettʹs Test of Sphericity: Approx. ChiSquare = 1327.274, df = 36, Sig. = 0.000). The principal components analysis extracted 1 dimension that explained 75% of the variance. Total scale reliability was 0.9. See Table 18 for dimension variance and Appendix E for final item factor loadings.

Table 18: Variance Explained of Perceived Declarative Web Knowledge Content Initial Eigenvalues

Component 1

Total 5.3

% of Variance Cumulative % 75 75

Extraction Sums of Squared Loadings Total 5.3

% of Variance 75

Cumulative % 75

Extraction Method: Principal Components Analysis with a Varimax Rotation

7.4.4.3 Item Testing and Generation Summary: Perceived Web Knowledge Content In summary, the results identify 2-items that reliably and subjectively measure perceived overall web knowledge content; 7-items that reliably and subjectively measure perceived declarative knowledge content and; 4-items that reliably and subjectively measure perceived procedural knowledge content.

7.5 SCALE DEVELOPMENT: SUMMARY The proposed operationalisation of the independent and dependent variables builds on existing theoretical frameworks and contributes to an understanding of how to measure user behaviour on the web. The procedure results in the development of reliable measures of current web session usage frequency (1-item); current web session usage variety – situational (1-item), current web session usage variety – motivational (1-item), current web session usage extent – breadth (3-items), current web session usage extent – depth (4-items), perceived ease of web use (14-items), perceived web usefulness (14items), actual common procedural web knowledge content (6-items), actual common declarative web knowledge content (10-items), actual specialised procedural web knowledge content (11-items), actual specialised declarative web knowledge content (11items), perceived procedural web knowledge content (5-items), perceived declarative

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web knowledge content (7-items), and perceived overall web knowledge content (2items). See Appendix C.

Following scale and instrument development, the relationships discussed in this study are tested using a web-based survey. The research design for hypothesis testing is discussed in the next chapter.

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C HAPTER 8: R ESEARCH M ETHODOLOGY ‘What we plan we build.’ Conte Vittorio Alfieri (1749-1803) Italian playwright

8.1 INTRODUCTION The methodology is described. A cross-sectional web-based survey instrument was used to test the questions posed in this dissertation. This consisted of six sections with single and multi-item measurement scales. The entire survey consisted of 180 formally structured pre-coded questions, the response to which was entered into Filemaker Pro™ for further storage, organization and preliminary analysis. A non-probability selfselected sampling design was used with the sample recruited using monetary incentives, online advertising and offline publicity.

8.2 HYPOTHESIS TESTING RESEARCH DESIGN 8.2.1 QUANTITATIVE DATA COLLECTION METHOD Qualitative data were collected during the scale development phase of this research to help generate measurement items. Once developed, these items then were used quantitatively to measure and test the hypotheses.

8.2.2 CONCLUSIVE DESCRIPTIVE RESEARCH DESIGN A research design is the detailed blueprint used to guide a research study towards its objectives. There are three basic research designs: exploratory, conclusive descriptive and

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conclusive causal. For the purpose of testing the hypotheses proposed in this dissertation, a descriptive conclusive research design was adopted. This design is used to verify and test descriptive relationships and propositions developed by exploratory research. The primary objective of descriptive research is the description of something (e.g., behaviour) and it may be conducted cross-sectionally or longitudinally with either survey or observational data collection methods.

8.2.2.1 Cross-sectional versus Longitudinal Research Design Rao (1980) reports that theoretical relationships in consumer behaviour are tested and

validated with the assistance of data collected through descriptive research, either crosssectional or longitudinal. Cross-sectional research records the variable of interest at one point in time, giving a snapshot view of the variable of interest. In comparison, longitudinal research involves the repeated measurement of the variable of interest from a fixed sample over time (Malhotra et al. 1996). As it was desirable to have a snapshot of web users, this dissertation uses a single cross-sectional research design and measures the constructs discussed using a web-based survey data collection instrument.

8.2.2.2 Survey versus Observational Data Collection Methods The two main methods of data collection for descriptive research are either survey or observational methods. As identified in Appendix B, this dissertation used observational methods to explore the constructs and generate scale items. However, the main data collection method used here is survey based. It relies on questioning respondents through a structured formalised technique (Malhotra et al. 1996). With the advantage of minimising interviewer bias, which may exist in focus groups or interviews, survey administration is an appropriate method for collecting data on consumerâ&#x20AC;&#x2122;s attitudes, knowledge and behaviours (Tull and Hawkins (1993). Questionnaires also have the advantage of ease of coding, time efficiency and cost effectiveness (Tull and Hawkins 1993). A survey design is therefore easy to administer, the data obtained are reliable, and variability in question interpretation can be reduced.

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8.2.2.3 Computer Assisted Web-based Mode of Survey Administration Survey methods are classified by mode of administration, such as telephone, personal interviews, or mail interviews. A self-administered computer-assisted web-based survey instrument was used to collect information from a sample of experts (see Appendix B) and a self-administered paper-based survey instrument was used to collect the data to test the scale items generated from the preliminary exploratory studies (see Chapter 7). For the main study, a self-administered computer-assisted web-based survey instrument was hosted on the Internet.

The effectiveness and feasibility of survey instruments have been profoundly influenced by electronic technologies such as the telephone, facsimiles, personal computers and more recently Internet-related technologies such as email and the web. Electronic technologies have improved the efficiency of personal interviews (face-to-face) and mail surveys and also provided completely new modes of survey administration such as diskby-mail (DBM), computer-assisted telephone interviewing (CATI), computer-assisted personal interviewing (CAPI), and Internet-based electronic survey modes such as email and web-based survey instruments. For the purpose of this study, a web-based Internet survey was used to test the hypotheses proposed.

These surveys have many advantages over traditional surveying methods. Table 19 compares the attributes of Internet (web), personal, telephone, and mail surveys and shows that web-based Internet surveys have relative low cost, fast turnaround and survey completion, high geographic coverage and high response rates. However, while a large proportion of the general population still does not have access to the Internet online, researchers continue to face the problem of biased samples. Apart from surveys specifically for the Internet population, such as in this study, Internet-based surveys often need to be supplemented with traditional forms of surveying. This is particularly true for countries outside the United States where there are significantly smaller percentages of the population with Internet access.

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Table 19: Comparative Attributes of Differing Modes of Survey Administration Attributes

Internet (web)

Personal

Telephone

Costs

Very Low

Very high

Medium

Mail Low

Speed of turnaround

Fast

Instant

Slow

Response rate

High

Very high

Medium

Low

Population segments accessible

Few

Possible access to all segments

Less than mail but more than Internet

Many

Feasible geographic reach of survey

Very High

Very Low

Medium

High

Accessibility of medium to respondents

Low

Varies

Medium

Very high

Fast Long Time taken Source: Pope, Tam, Forrest and Henderson (1997), pg 22.

Medium

Long

Despite some research studies reporting virtually identical internal reliability across differing modes of survey administration (Booth-Kewly, Edwards, and Rosenfeld, 1992) and also limited influence of survey mode on the data quality received (Yun and Trumbo, 2000), concern has been expressed as to the quality of Internet survey responses. Nevertheless, Joinson (1999) found that respondents actually reported lower social anxiety and less social desirability bias when using the Internet-based survey compared to paper and pen modes of survey administration.

There are several factors that should be considered when employing the Internet as a data-gathering tool. Watt (1999) reports a detailed comparative breakdown of the financial difference between developing and administering web-based, email and surface mail surveys, with web-based surveys showing the lowest cost (as stated in Yun and Trumbo, 2000). However, Watt (1999) indicated that for some web-based surveys the cost might out weigh that of mail-based surveys if the population size is held constant (this is because of the costs incurred in survey programming and network monitoring and administration â&#x20AC;&#x201C; however labour costs are hard to calculate). An overview of some of the advantages and disadvantages of Internet surveys is depicted in Table 20.

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Table 20: Advantages and Disadvantages of Internet Surveys Internet Surveys Advantages

Disadvantages

High response rates Greater response accuracy Have the potential to be more enjoyable and aesthetically pleasing Less expensive Faster turnaround Customised surveys Easier data transfer User convenience Geographic coverage No interviewer bias Flexible graphical representation

Sample self-selection Un-representativeness of the general population Multicultural considerations Shorter attention span of respondents Lack of interpersonal nuances Untruthfulness of respondents Multiple entries User experience and system knowledge effects on survey completion Privacy and security concerns Respondent time and financial access costs Browser incompatibility

Sources: Forrest (1999), Oppermann (1995), Smith (1997)

8.2.2.4 Web-based Survey Instrument In summary, a web-based survey instrument was used as the primary form of data collection. This consisted of six sections of a survey delivered electronically to a Filemaker Pro™ database configured to record and store the completed survey entries. The survey consisted of 180 formally structured pre-coded questions with response categories provided for 176 questions. Only 85 of these questions are used in this dissertation to assess the hypotheses. The generation of these items was discussed in Chapter 7 and factor analysis results are presented in Appendices D and E. The web site and web survey design are depicted in Appendix F.

Prior to pilot study administration, testing of both the survey instrument and web site was conducted. Field-based usability pre-testing was conducted on a small convenience sample of end-users who accessed the survey from a number of differing computing platforms and geographic locations. Feedback on the survey and processing accuracy system was conducted, giving rise to a number of changes to the format of the survey (i.e., colours and layout) and the removal of interaction errors between the database and the web form.

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8.2.3 RESEARCH DESIGN: SUMMARY In summary, a single cross-sectional descriptive research design, using a computerassisted web-based mode of survey administration to quantitatively collect and treat the data necessary to test the hypotheses, was implemented.

8.3 SAMPLING DESIGN 8.3.1 SAMPLING FRAME A sample is selected to make inferences about the broader population. Past studies of usage behaviour have used a number of parameters for sample selection: product usage experience (Kouchakadjian and Fietkiewicz 2000; Nunes 2000; Ram and Jung 1990); education (Sinkovics et al. 1999); computer and media use (Coffey and Stipp 1997; Lin 1992); geographic region (Van den Bulck 1999; Jeffres and Atkin 1996), and employment classification (Seeley and Targett 1999; van Braak 2001). Research into usage behaviour on the web has predominately used samples defined by web access and use (Korgaonkar and Wolin, 1999; Kraut et al., 1998; Chatterjee et al., 1998); senior education (Eighmey and McCord, 1998; Diaz et al., 1997; Schumacher and Morahan-Martin, 2001; Papacharissi and Rubin, 2000); newsgroup use (Sivadas et al., 1998); and employment classification (Stevens, Williams, and Smith, 2000; Henry and Stone, 1999).

As the core of this dissertation is the profiling of current web usage behaviour, the main parameter used for sample delineation is web usage experience. This is consistent with past studies of computer, media and internet use.

8.3.2 SAMPLING TECHNIQUE Sampling techniques can be classified into two groups: non-probability and probability sampling (Huck, Cormier and Bounds 1974). Random or probabilistic sampling creates a sample based on probability theory in which each individual from the population has an equal chance of being selected (Tull and Hawkins 1993). Such sampling methods include

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simple random sampling, cluster sampling, stratified random sampling and multi-stage random sampling (Sudman 1976). Non-probabilistic sampling assumes that certain individuals have a greater chance of being selected from the sample population (Huck et al. 1974). Neuman (1997) describes convenience, quota, snowball and purposive sampling as non-probabilistic sampling techniques.

The superiority of random probability sampling over non-probability techniques, as argued by Sproull (1995), is associated with sampling error. As non-probability sampling relies on the judgement of the researcher and is limited in its ability to provide an accurate statement of representativeness of the population, sampling error is increased (Weiers 1988; Neuman 1997). However, the advantages of non-probability sampling are: 1) the speed with which respondents are identified, 2) the ease of access to a sample group, especially with respect to the internet population, and 3) the low costs involved in obtaining a sizeable sample (Malhotra et al. 1996).

The internet, by its very nature, poses a number of unique problems in guaranteeing a random sample. Unlike telephone and mail surveys in which samples can be produced through census lists and electoral rolls, the internet has no central registry of users (Kaye and Johnson 1999; Taylor 2000). A large number of people also still do not access and use the internet and thus attempting to reach users of internet-related technologies like the web through traditional methods such as the telephone or mail may result in high levels of non-response. In addition, by selecting a random sample of the general population there would be a large number of nonusers, who are not the focus of this study. Therefore, to reach an audience of web users, a user survey was posted on the web (as previously discussed, see also Appendix F), and various mechanisms were used to drive respondents to the measurement instrument (described below). In summary, nonprobability sampling using solicitation techniques to recruit a sample of self-selected web users for survey participation was undertaken.

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8.3.3 SAMPLE RECRUITMENT In an attempt to reduce possible non-sampling and sampling error, participation incentives and advertising and publicity were used.

8.3.3.1 Competition and Incentives To encourage participation of web users, a neutral incentive was employed. This offered participants the chance to win one of four cash rewards ranging from between $A49.30 and $A642.15. Monetary incentives were selected over the offering of tangible goods and/or intangible holiday or service experiences as commonly used in a number of industry funded surveys (e.g., www.consult.com 1999). This was done to keep down the costs of prizes and to ensure the incentives would appeal to all types of respondents.

8.3.3.2 Advertising and Publicity Online Banner Advertising Banner advertisements - simple advertisements inviting visitors to click to be exposed to a target web page – are commonly used to attract users to web sites and web-based surveys. User responses to this form of advertising are measured by counting ‘clickthroughs’. ‘Click-throughs’ are necessary to move respondents from the site and into the survey itself – they can be considered similar to a cover letter in a mail survey (Tuten, Bosnjak, and Bandilla 2000).

Thus, web banner advertisements were used in this study to increase awareness of the survey and to entice users to visit the web site and complete the survey. Although a quite inexpensive means to attract survey respondents, the risks with this method are two fold. Firstly, the probability of seeing any banner advertisement or banner campaign is directly proportional to the user’s total amount of internet usage. For example, if the frequency of web use is 20 times a month the user is 20 times more likely to see the banner campaign than a user who accesses the web just once a month. Thus, using banner recruitment may bias the sample toward heavy internet users. Secondly, the

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saturation of banner ads on one type of web site will result in a heavy sample bias toward site type and topic area, and, if this is not the intention, the problem needs to be addressed. Therefore, for this dissertation, banner ads were spread across a large number of differing sites.

Three banner ads were developed, corresponding to the first three prizes of the competition. The banner ad campaign was developed by The Campaign Palace (http://www.thecampaignpalace.com.au) in response to a campaign brief provided by the researcher (see Appendix G for the banner advertisements). The Australian DoubleClick banner advertising network (http://www.doubleclick.net) was the main provider used for banner ad placement throughout October and December 2000, however a smaller amount of inventory was also supplied by individual site and portal vendors to further assist in obtaining a representative sample of Australian web users. These included ADNet AU (http://www.zdnet.com.au), targeting an affluent Australian audience of 100,000 technology users, and the Rural Press Australian network (http://www.ruralpress.com), which is targeted at the four out of every 10 people who live outside capital cities.

Additional Online Advertising Additional online advertising took the form of web site URL links from various vendors (http://ww.zdnet.com.au; http://www.alexonline.com; http://australianit.news.com.au; http://www.netguide.com.au; http://www.freeaccess.com.au). Web site search engine

registration and email recruitment was also undertaken to drive online traffic.

Offline Publicity To minimise sampling error a number of methods of offline publicity were also used to drive traffic to the web survey. These included a media release targeting local, regional and state newspapers throughout Australia; a newspaper article in The Australian (IT section) (Appendix G); radio interviews on 2SM (http://www.2sm.com.au); ABC 936 Hobart (http://www.abc.net.au/hobart) and Rhythm 87.6 FM (http://www.rhythfm.com), and

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a review of the study in the January 2001 issue of the Australian NetGuide, targeted at average web users. The Australian NetGuide has an audit of 40,814 copies per issue, making it the number 1 selling internet magazine in the country (this ranks it in the top 100 magazines in any category in Australia). The magazine has national distribution through newsagents and supermarkets throughout Australia (http://www.netguide.com.au) (Appendix G). In addition, a small amount of word-ormouth was expected to help promote the study.

8.3.4 SAMPLING DESIGN: SUMMARY In summary, non-probability sampling using solicitation techniques to recruit a sample of self-selected web users for survey participation was undertaken. Participation incentives, and both offline publicity and online advertising, were used to drive respondents to the measurement instrument.

8.4 ANALYTICAL DESIGN A number of analytical techniques were used to describe the sample, assess the items generated to measure the constructs, discuss sample performance on the variables measured and to test the hypotheses proposed. These techniques are discussed in the following sections of this chapter.

8.4.1 RESPONSE ANALYSIS AND SAMPLE DESCRIPTION The form of non-probability sampling design used in this study makes it difficult to determine the response rate. The exact number of web users exposed to the online banner ad campaign and the offline publicity is not known. However, three sources of â&#x20AC;&#x2DC;audience behaviour dataâ&#x20AC;&#x2122; from the web site, the banner ads, and the database, were used to make response rate and timing estimates.

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Specifically, the web site log-file was used to assess the total number of unique visitors to the web site compared with the total number of completed surveys received. Furthermore, the banner ad campaign reports presenting data of the total number of unique users exposed to the banner ads was compared with the total number of completed surveys recruited from the banner ad campaign. Finally, as the study was conducted over a period of three and a half months, it was possible to examine the ‘date of creation’ in the database to assess any patterns in the timing of usable responses. In addition, this latter information was also used to test for any significant differences between those who were recruited early on and those who were recruited at a late stage. Descriptive statistics, such as frequency distributions and cross-tabulations, and graphical analysis of the sample characteristics were used to describe the sample.

8.4.2 ITEM AND SCALE MEASUREMENT ASESSMENT To determine the stability and dimensional structure of the scales, an internal consistency reliability analysis and an exploratory principal component factor analysis was conducted on all multi-item measures. For single-item measures, means and medians were assessed. This was conducted to see whether the scales remained consistent from the development stage (described in Chapter 7) to the final stage (presented in Chapter 9). See chapter 7 for a discussion of the techniques and their application in this study.

8.4.3 EMPIRICAL ANALYSIS: HYPOTHESIS TESTING 8.4.3.1 Sample Treatment: ‘Web Site Maintenance and Design Experience’ In this study comparison is made of the observed differences between two user groups: those users with and without web site design and maintenance experience. To compare these two groups, the sample was split based on the variable ’Web Site Design and Maintenance Experience’ (WSDMEXP2), a variable with 0 = No experience and

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1=Experience. In subsequent chapters, these two user groups are referred to as ‘with no WSD/M experience’ and ‘with WSD/M experience’.

8.4.3.2 Graphical Analysis (Scatterplots) To display graphically the relationships between the independent and dependent variables examined in this study, scatterplots were produced. These help to show visually: 1) the nature of the hypothesised relationships (i.e., linear or non-linear), 2) the direction of the relationships hypothesised (positive/negative or inverted/u-shaped), and 3) the strength of the relationships (none, weak, moderate or strong). Simple scatterplots were prepared with the dependent variable plotted on the y-axis and the independent variable plotted on the x-axis. As this study also examined the observed differences between users with and without web site design and maintenance experience, two scatterplots were assessed for each hypothesis. The results of these visual comparisons are discussed in subsequent chapters.

8.4.3.3 Bivariate Analysis: Hypothesis Testing Of the 26 hypotheses proposed in this dissertation, eighteen are ‘linear’, seven are ‘curvilinear’ and one is ‘no relationship’. Thus a variety of inferential statistical techniques had to be used to test these hypotheses.

Linear Relationships For metric data Pearson’s correlation coefficient can be used to assess linear relationships. However, the eighteen linear relationships that are hypothesised in this dissertation make use of a mix of data types. Certain data violate the assumptions of parametric statistics. Moreover, from a visual assessment of the distribution of sample scores it was found that seven out of the eight violated the assumption of normality, which is required for the conduct of parametric statistics (see Appendix L). Therefore, the rank-order correlation coefficient (Spearman’s rho) was used to test eleven of the relationships.

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Furthermore, from a visual and statistical assessment of the distribution of sample scores for the measures it was found that all seven violated the assumption of normality (See Appendix L). Therefore, the rank-order correlation coefficient (Spearman’s rho) was also used to test the eight relationships hypothesised to exist between the independent and dependent variables.

Curvilinear (Non-linear) Relationships Seven non-linear relationships are hypothesised between differing types of data. De Vaus (2002) indicates that to determine if a non-linear relationship is present, a coefficient that is sensitive to non-linear relationships is required. For example, the Spearman rho correlation coefficient, although suitable for measures and when parametric assumptions have been violated, is not the most suitable coefficient for testing for a curvilinear relationship. De Vaus (2002) indicates that coefficients designed for nominal variables (e.g., Cramerʹs V and Goodman and Kruskal’s tau) will detect nonlinear relationships.

Therefore, the variables between which curvilinear relationships are hypothesised will be reduced to 3-category level measures and the association tested using Cramer’s V and Goodman and Kruskal’s tau. These chi-squared coefficients are selected, as against Phi and Yule’s Q, as the variables examined have more than two categories each (i.e., are larger than a 2x2). It is expected that some information may be lost in this process, however this is the price to pay for testing whether a curvilinear relationship exists.

Following an assessment of the presence of a in/significant relationship using the chisquared coefficients, De Vaus (2002) advises comparing both a linear measure of association (e.g., Gamma) and a non-linear measure (e.g., Goodman and Kruskalʹs Tau). As a general rule, if Goodman and Kruskalʹs tau is higher than Gamma, it indicates a non-linear relationship may exist between the two variables examined. At which point De Vaus, (2002) indicates that a closer examination of the 3x3 cross-tabulation should be conducted. This procedure is used in Appendix Q.

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Significance Testing For large samples, statistical testing should be based on the stringent significance level of 0.01. As this dissertation has an entire sample of n=2077, which has been split into two smaller groups (with experience = 1177 and no experience = 900), 0.01 is the primary and 0.05 the secondary significance levels used to test the hypotheses.

8.4.3.4 Multivariate Analysis: Hypothesis Testing Hypotheses were further analysed using a multivariate statistical technique. The 26 hypotheses were reduced to 8 specific relationships (MRA1-8) and were tested using a split-wise multiple regression method. The level of significance for these tests was held constant with that used in the bivariate analyses.

To conduct the step-wise multiple regressions, all 8 relationships were assessed against the assumptions set down for the conduct of regression analysis (summarised in Appendix O). Namely:

Metric Dependent Variable. Reference can be made to Appendix C with respect to the measurement level of the dependent variables used.

Linearity. The bivariate analysis conducted earlier examined the presence or absence of linear relationships between the independent and dependent variables. The assumption of linearity was further assessed by visually examining the residual plots of each multiple regression across the two user groups.

Normality. Univariate analysis of linearity was assessed using graphical analysis (i.e., histograms) for non-metric data and statistical analysis (i.e., normality test) for metric data. In addition, further assessment of the normal probability plot for users with and without WSD/M experience was conducted.

Homoscedasticity. To assess the presence of homo/heteroscedasticity an examination of the residuals was conducted for users with and without WSD/M experience.

Independent Errors. According to Field (2000) and Hair et al. (1995), an assessment of the Durbin-Watson (D) test statistic is conducted to assess the assumption of

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independent errors. According to Field (2000), this test statistic can vary from 0 to 4 with a value of 2 meaning that the residuals are uncorrelated. However, the DurbanWatson test statistic is dependent on the number of independent variables in the model and the number of observations and thus should be assessed accordingly (Field, 2000). The number of independent variables used in MRA1-6 is two and for MRA7 and MRA8 the number is seven. With 100 observations or above, and two independent variables, if the Durbin-Watson test statistic is below 1.50 (p<0.01) or 1.63 (p<0.05) residual correlation is present. With 100 observations or above, and five or more independent variables, if the Durbin-Watson test statistic is below 1.44 (p<0.01) or 1.57 (p<0.05) residual correlation is present.

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Multicollinearity. As cited in Field (2000) and reiterated in Hair et al. (1995) if the largest variance inflation factor (VIF) is greater than 10 or the average VIF is substantially greater than 1, then the regression may be biased due to multicollinearity. In addition, if the tolerance statistic is below 0.1 a serious problem with multicollinearity may exist, and if below 0.2 a potential problem may exist.

From an assessment of the above, limitations were noted in conducting the step-wise multiple regression if a number of the assumptions were violated, or one assumption shows great area of concern. These are noted in Chapter 10 and discussed further in Chapter 12. Data were not transformed to â&#x20AC;&#x2DC;remedyâ&#x20AC;&#x2122; these violations, partly because transformations may change relationships between variables in ways that are hard to interpret. However, this subject is discussed further in Chapter 12.

8.5 PILOT STUDY ADMINISTRATION AND SCHEDULE A pilot study was conducted in early October 2000 to assess the performance of the databases, to check the structure of the web-site, to check for clarity and errors, and to see the sample response rate generated from the banner advertising. In contrast to earlier samples used, the pilot study was drawn from a sample of web users similar to those expected for the final sample. It was scheduled to run until a quota of over 150

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respondents were recruited and/or the aforementioned issues were ironed out. A number of structural and database changes were made as a result.

8.5.1 RESPONSE ANALYSIS From the web site log-file analysis presented in Appendix H, of those who visited the web site during the pilot study (unique visitors: n=1232) a total of 20% completed the survey. 140 responses were recruited in the first few days with a total of 253 responses recruited over a 3-day period. 6 responses were removed (1=duplicate, 5=incomplete), resulting in 245 usable responses.

The timing of responses showed that 41% of the usable responses were received on the first day of the pilot, 54% on the second day and 5% on the third day. In addition, 80% of the usable respondents became aware of the study from the banner ad campaign, 13% from a web site hyperlink, 2.4% from a search engine query and 1.6% from word-ofmouth.

8.5.2 PILOT SAMPLE DESCRIPTION As an indicator of the representativeness of the pilot sample, the sample recruited had an uneven gender distribution with 36% female and 64 % male respondents. From a comparison of the gender distribution of the pilot sample with industry and government statistics, this result indicated that the pilot study represented Australian web users in 1999, not in 2000 (Table 21). This was important as the sample to be recruited for the final study needed to be as representative of current web users as possible.

Table 21: Pilot Sample Gender Distribution: Comparison www.consult

NetRatings (2000)

ABS (2000)

NOIE (2000)

(April 1999)

(July)

(May)

This Pilot Study

Male

Female

Gender

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Some 57% of the pilot sample were aged between 25-44 years of age. This age distribution is comparable to that reported by www.consult.com (1999) and slightly larger than the 46% reported by NetRatings (2000) (for the age range 25-49 years). 47% of the pilot sample were employed full-time, and 47% and 22% resided in NSW and Victoria respectively.

In summary, it was evident from these results that additional advertising and/or promotion of the web survey offline would be required to ensure the recruitment of a more representative sample of Australian web users and to avoid some non-response biases.

8.6 MAIN STUDY ADMINISTRATION AND SCHEDULE The main ‘Web Audience Study’ was launched on the 16th October 2000 and continued for a period of 3 ½ months until January 31st 2001, with offline publicity and online banner advertising scheduled throughout this period. The pre-Christmas period was selected for a number of reasons. Firstly, this period followed the summer Olympics in Sydney 2000, thus excluding any sampling bias and response error that might have occurred if administered during the Olympic period. Secondly, it was an opportune time to maximise responses because of the reported increase in web usage due to preChristmas shopping and browsing online (Jupiter, 1999a; Jupiter and Interactive, 1999b).

8.7 RESEARCH METHODOLOGY: SUMMARY In summary, a single cross-sectional research design using a web-based internet survey was used. Non-probability sampling was employed, with sample recruitment aided by the use of monetary incentives, online advertising and offline publicity.

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C HAPTER 9: D ESCRIPTIVE R ESEARCH R ESULTS â&#x20AC;&#x2DC;The voice of the people hath some divineness in it, else how should so many men agree to be of one mindâ&#x20AC;&#x2122; - Francis Bacon (1561-1626)

9.1 INTRODUCTION This chapter provides a descriptive account of the web sample, and an assessment of the reliability and dimensional structure of the scale items used to measure the variables in this study. An analysis of responses is presented first (section 9.2). This is followed by a review of the sources used to recruit the web sample, and a descriptive profile of those who responded (section 9.3). The final section of this chapter (section 9.4) then compares and contrasts the reliability and dimensionality of the scale items used in this web sample, to their earlier performance with the student sample.

9.2 RESPONSE ANALYSIS The main web sample consists of 2246 respondents, 169 responses were removed (7=duplicates, 162 incomplete) thus leaving 2077 usable responses (2022=residents, 52=non-residents). Hair et al., (1995) recommend that for multivariate and bivariate analysis the sample size should be at least 5 times the number of variables (i.e., parameters) in the model. Since the proposed model has 15 variables, the minimum response necessary would be 75 observations. The sample size of 2077 is thus far in excess of this recommendation.

Comley (2000) summarises the response rates of a number of virtual surveys in 1999 (i.e., email and web-based), and most were in the range 15% to 29%. Ray, Griggs, & Tabor (2001) summarised the response rates in their survey as follows: 41% (academic sector),

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31% (general web sector), and 19% (business sector). From the web site log file analysis presented in Appendix H, of those who visited the web site during the main study (unique visitors: n=5104) a total of 41% submitted a usable survey. This is comparable to response rates identified by Ray et al. (2001). However, this response rate has been hard to determine given the methods used to recruit respondents (i.e., banner advertising), and therefore the rate of 41% might be regarded as quite generous. The fact that well over half those who visited the site did not submit a usable survey indicates that perhaps the length and format of the survey was off-putting.

As indicated in the next section of this chapter and reported in Appendix I, according to the banner advertising reports a total of 867, 617 unique users were exposed to the banner ads placed in October and December. Of this number, a total of 893 usable responses were obtained. This provides a response of 0.1% of web users exposed to the banner advertising, actually submitted a completed survey. Clearly, different ways to calculate the figures give rise to very different response rates.

When assessing the pattern of responses over the study period, the last two weeks in October 2000 and the first two weeks in December 2000 yielded the greatest portion of usable responses (see Table 22 and Figure 13).

Table 22: Timing of Survey Receipt (Oct 2000 to Jan 2001) - Main Web Sample Date of Survey Receipt (Usable Surveys Only) Month October November Decembera January 18-31st 1-14th 15-30th 1-14th 15-27th 6-14th 15-31st Date 41 10 10 26 4 3 43 % 851 219 216 540 90 65 96 n a Due to downtime of the hosting server on the 27th of December and a resulting conflict between the server the database, no entries were received between the 27th December to the 6th of January.

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Figure 13: Survey Creation Date - Main Web Study (Oct 2000 to Jan 2001)

With他 indicating the main phases of banner advertising, from Table 22 and Figure 13, it is evident that survey responses are coincident with the banner advertising campaigns. This will be discussed in greater detail in the next section.

A test was conducted to compare the means of the early versus late respondents. The sample (n=2077) was split into equal groups (n=207) to compare means for each variable measured. The variable means for the first 207 (Group 1) and the last 206 (Group 10) respondents were compared. The tests revealed no significant differences in the means for 13 of the 17 comparisons. However, significant differences in the means were reported for WSUEB (p=0.036), WSUED (p=0.000), WSUEDUR (p=0.000), WSUF (p=0.042) and Age (p=0.026). In general, there appear to be some differences between early and late respondents, but not on a scale to give rise to major concern.

9.2.1 STUDY AWARENESS Study awareness is reported in Figure 14. It is evident that 43% (n=893) of the sample were recruited from the banner ad campaigns, a further 27% (n=551) from a link on a web site, and 13% (n=270) from an Email or Email list. Promotion off the web (2%), advertising off the web (2%), and WOM (2%) only accounted for a very limited number of usable responses.

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Figure 14: Source of Respondent Study Awareness From the Actual Researcher 0.53% Promotion off the Web (e.g., Poster, Flyer) 2.02% Advertising off the Web (e.g., TV, Radio) 2.27% Word of Mouth (eg., friend, co-worker) 1.98%

Other 8.58%

An Email or Email List 13.01%

A Search Engine Query 2.02%

A Link on a Web Site

26.55% A Banner Ad 43.04%

Study Awareness An Email or Email List A Banner Ad

Advertising off the Web (e.g., TV, Radio) Promotion off the Web (e.g., Poster, Flyer)

A Link on a Web Site A Search Engine Query Word of Mouth (eg., friend, co-worker)

From the Actual Researcher Other

Pies show counts

The banner ad performance statistics are reported in Appendix I.

9.3 SAMPLE DESCRIPTION 9.3.1 RESIDENCY STATUS To ensure an Australian sample, a screening question was included at the beginning of the survey – i.e., “Are you an Australian resident?” – if not, an open ended form field was incorporated that allowed respondents to indicate their place of residence. It was found that 98% of completed survey responses were from Australian residents. The additional 2% were residents in Canada, China, East Timor, Hong Kong, Indonesia, Netherlands, NZ, Norway, Samoa, Switzerland, Taiwan, USA, and the UK. Prior to data collection those survey responses derived from non-Australian residents were going to be removed from the final sample of respondents. However one discrepancy was overlooked in that although resident in another country, respondents might have been located in Australia for a number of years on temporary work or student visas and thus exposed to Australian Internet standards and/or environment. Thus, these respondents were included in the final sample. However for future reference - in addition to residence status as defined by the Australian Immigration Department, further

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information should be obtained as to their current place of residence and period at this location.

9.3.2 DEMOGRAPHICS The sample recruited has a fairly even gender distribution with 56% of respondents male and 44% female. As evident in Table 23 the gender distribution of respondents is comparable to the gender distribution reported in industry and government reports for 2000 and 2001. Furthermore, as opposed to the sample description obtained from the pilot (see section 8.5.2 and Table 21), the main study sample is a far better representation of the gender distribution of Australian web users.

Table 23: Main Web Sample Gender Distribution: Comparison www.consult

ABS, (2000) (May)

NetRatings, (2000) (July)

NOIE, (2001)

(April 1999)

(June)

Main PhD Study

Male

Female

Gender

With respect to age, 52% of respondents are aged 30 years and younger and 48% of respondents are aged 31 and over (See Figure 15).

Figure 15: Main Web Sample - Age Category Distribution

55 years and over 46 - 54 years

40 - 45 years

4.77%

Younger than 18 years

8.43%

11.17%

18 - 24 years 22.92%

10.30%

Age Category Younger than 18 years 18 - 24 years 25 - 30 years 31 - 39 years 40 - 45 years 46 - 54 years 55 years and over

31 - 39 years 21.67% 25 - 30 years 20.75%

Pies show counts

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In addition:

45% of the sample were full-time wage earners. 5.5% did not indicate their labour force status and 5.5% indicated “other”. This might comprise a number of categories not specified in the survey (e.g., homemaker, etc.).

19% of the sample worked in Profession-IT; 8% Professional - Business; 18% did not indicate their current occupation and the remainder of the sample was distributed amongst a number of occupation ranging from electrician to customer service/sales and clerical/admin.

25% did not indicate the industry worked in, and the remainder of the sample were distributed amongst a large number of industries with the largest comprising Computing and IT (15%); Education (8%); Government Administration and Defence (6%); Retail Trade (6%) and Banking, Finance and Insurance Sector (5%).

19% of the sample were students – 13% undergraduate and 6% postgraduate.

Over 50% had achieved an undergraduate university degree or higher, indicating a highly educated sample. This is consistent with the Australian web user being highly educated (www.consult.com 1999; ABS 2000 and NOIE 2001). In summary, 5% left school before 15; 7% achieved their junior school certificate; 17% their senior certificate; 8% vocational training; 11% an associate diploma or certificate course; 29% an undergraduate degree and the remaining 18% had postgraduate qualifications. A final 3% had achieved a level of professional education.

44% of the sample resided in NSW; 19% in Queensland; 18% in Victoria; 7% in Western Australia; 6% in South Australia; 3% in ACT; 2% in Tasmania and 1% in the North Territory.

9.3.3 WEB SITE DESIGN & MAINTENANCE (WSD/M) EXPERIENCE In addition to hypothesis testing, the observed differences between two user groups is also examined. Within the sample recruited for this study (n=2077), 57% (n=1177) indicated they had web-site design and maintenance (WSD/M) experience and 43% (n=900) indicated they did not have this experience. For all analyses presented and discussed in Chapters 10, 11 and 12 the sample is split into these two user groups.

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9.4 MEASUREMENT ASSESSMENT AND TREATMENT To assess the reliability and dimensionality of the scales, reliability and factor analyses were conducted on all multi-item measures. Descriptive statistics (median’s) were also used to compare the performance of single-item measures with earlier item development. This was conducted in order to assess the stability and dimensional structure of the scales and the reported consistency with scale properties reported during scale development. The method for scale development was based on analysis of two students samples (n = 128 & n =152) (documented in Chapter 7), whereas results here for scale validation are for the main web sample (n = 2077). The method for scale validation is consistent with the method conducted scale development. See Chapter 7, section 7.2 for a discussion as to the method that was also used for scale validation. In all cases items are coded in ascending order (e.g., 1 = “lowest”, 8 = “highest”).

9.4.1 SCALE/ITEM VALIDATION: CURRENT WEB SESSION USAGE 9.4.1.1 Current Web Session Usage Frequency ‘Current web session usage frequency’ was operationalised using a single-item scale (see Chapter 7). The item responses were coded from lowest frequency (1 = “once a month”) to highest frequency (8 = “5 or more times a day”), with a median for the web sample of 7 = “2-4 times a day”. By comparison, the median usage frequency category for the student sample was 6 = “once a day”. Thus, although comparable in many respects, the web sample used the web more frequently than the student sample.

9.4.1.2 Current Web Session Usage Variety – Situational ‘Current web session usage variety – situational’ was measured using 2 measures. The first item measuring the number of situations from which the web is accessed was a scale item asking respondents to indicate the number of locations from which they accessed the web in a typical week. The second item measured the types of locations from which respondents accessed the web with a 10-category check-list. This second item was

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recoded 0 = “not used” and 1 = “used”, with responses summated to form a 1-item scale that reported the number of types of locations from which the web is accessed with a range 1 to 10 locations.

To measure current web session usage variety – situational, the two items were further investigated using Spearman’s Rho correlation coefficient. Table 24 shows a significant positive relationship between the two items (rs = .598, p<.01). Thus the items will be summated to measure current web session usage variety - situational

Table 24: Situational Variety: Location No. & Location Type No. Spearman’s Rho Correlation Coefficient Situational Variety Location Type No. Spearman’s Rho

Situational Variety Location No.

Correlation Coefficient

.598

Sig. (2-tailed)

.000

2077

** Correlation significant at the .01 level (1-tailed)

9.4.1.3 Current Web Session Usage Variety – Motivational ‘Current web session usage variety – motivational’ was operationalised using a singleitem with a 12-category checklist (Chapter 7). Following data collection this item was coded 0 (“no motive”) and 1 (“motive”) for each motive listed. These codes were then summated to form a 1-item measure of the number of motives for current web session usage – motivation, with a range of 0 = “no motives” to 12 = “12 motives”. From the resultant 13-category response format, the median for the web sample was 6 (6 motives for usage). In comparison, the median for the student sample was 4 (4 motivations for usage). Thus the web sample, on average, used the web for a larger number of reasons.

9.4.1.4 Current Web Session Usage Extent – Breadth Exploratory factor analysis was performed on the initial 3-item Likert scales measuring ‘Current Web Session Usage Extent – Breadth’ to summarise the data in terms of a set of underlying dimensions that make up the multi-item scale. Initial data screening and

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analysis of correlation patterns identified that individually and collectively all 3 items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.634, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 888.149, df = 3, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 2 dimensions which explain 83% of the variance of ‘Current Web Session Usage Extent – Breadth’ with a total scale reliability of 0.7. See Table 25 for scale and dimension variance and Appendix J for item factor loadings.

Table 25: Variance Explained of Current Web Session Usage Extent - Breadth

Component

Initial Eigenvalues Total

1 2

1.7 .7

% of Variance 59 24

Cumulative % 59 83

Extraction Sums of Squared Loadings Total 1.5 1.0

% of Variance 49 34

Cumulative % 49 83

Extraction Method: Principal Components Analysis with a Varimax Rotation

These results are consistent with those reported for the student sample (Chapter 7 and Appendix K). In fact, the 3-items developed and first tested on the small student sample explained 82% of the variance, which compares with 83% for the web sample. Dimensionality and total scale reliability are consistent too.

9.4.1.5 Current Web Session Usage Extent – Depth Exploratory factor analysis was performed on the initial 4-item scale with a Likert question response format measuring ‘Current Web Session Usage Extent – Depth’. Initial data screening and analysis of correlation patterns identified that individually and collectively all 4 items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.585, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 1142.059, df = 6, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 3 dimensions that explained 89% of the variance of current web session usage extent – depth, with a total scale reliability of 0.6. See Table 26 for scale and dimension variance and Appendix J for item factor loadings.

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Table 26: Variance Explained of Current Web Session Usage Extent - Depth

Component

Initial Eigenvalues Total

1 2 3

1.9 1.0 .7

% of Variance 47 24 18

Cumulative % 47 71 89

Extraction Sums of Squared Loadings Total 1.5 1.0 1.0

% of Variance 38 26 25

Cumulative % 38 64 89

Extraction Method: Principal Components Analysis with a Varimax Rotation

These results are consistent with those reported for the student sample (see Chapter 7 and Appendix K). The 4-items developed and first tested on the small student sample explained 94% of the variance. In the web sample, these 4-items explained slightly less variance at 89% of the variance, with consistent results in scale dimensionality. However, the total scale reliability is less in the web sample (alpha = 0.6) as compared to a higher reported level in the student sample (0.8).

9.4.1.6 Current Web Session Usage Extent - Duration ‘Current web session usage extent – duration’ was operationalised using a single-item measure with an 8-category multiple-choice question. Following data collection this item was coded 1 to 8 (1 = “less than 15 minutes duration” to 8 = “13 or more hours duration”). The median for the web sample was category 4 = “1-3 hours”. This result is consistent with the median for the student sample, which is also category was also category 4 = “1-3 hours”.

9.4.2 SCALE/ITEM VALIDATION: USER PERCEPTIONS OF THE WEB 9.4.2.1 Perceived Ease of Web Use Exploratory factor analysis was performed on the 14-item scale with Likert questions measuring ‘Perceived Ease of Web Use’. Initial data screening and analysis of correlation patterns identified that individually and collectively all 14-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.913, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 11372.124, df = 91, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 4

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dimensions that explained 64% of the variance. Three items were removed from the analysis as they had factor loadings below +/- 0.6, resulting in a total of 11-items with a total scale reliability of 0.9. See Table 27 for scale and dimension variance and Appendix J for item factor loadings.

Table 27: Variance Explained of Perceived Ease of Web Use

Component

Initial Eigenvalues Total

1 2 3 4

5.9 1.2 1.0 0.8

% of Variance 42 9 7 6

Cumulative % 42 51 58 64

Extraction Sums of Squared Loadings Total 3.4 2.0 1.7 1.7

% of Variance 24 15 13 12

Cumulative % 24 39 52 64

Extraction Method: Principal Components Analysis with a Varimax Rotation

These results are slightly inconsistent with those reported from the student sample (Chapter 7 and Appendix K). The 14-items developed and first tested on the small student sample explained 72% of the variance. However, In the web sample, these 14items were reduced to 11-items and explained slightly less variance (64%). In addition, the dimensionality of the scale changed from 3 dimensions during scale testing to 4 dimensions. Despite these changes in the dimensionality of the scale, and also a reduction in items, the reduced 11-item scale reported consistent total scale reliability of 0.9.

9.4.2.2 Perceived Web Usefulness Exploratory factor analysis was performed on the initial 14-item scale with Likert questions measuring ‘Perceived Web Usefulness’. Initial data screening and analysis of correlation patterns identified that individually and collectively all 14-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.881, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 12614.496, df = 91, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 4 dimensions that explained 70% of the variance, with a total scale reliability of

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0.9. See Table 28 for scale and dimension variance and Appendix J for item factor loadings.

Table 28: Variance Explained of Perceived Web Usefulness

Component

Initial Eigenvalues Total

1 2 3 4

5.4 1.8 1.6 0.8

% of Variance 39 13 12 6

Cumulative % 39 52 64 70

Extraction Sums of Squared Loadings Total 3.2 2.8 2.4 1.2

% of Variance 23 20 18 9

Cumulative % 23 43 61 70

Extraction Method: Principal Components Analysis with a Varimax Rotation

These results are extremely consistent with those reported from the student sample (see Chapter 7 and Appendix K). In fact, the 14-items developed and first tested on the small student sample explained the same amount of variance (70%), extracted the same number of dimensions (4) and reported the same degree of total scale reliability at 0.9.

9.4.3 SCALE VALIDATION: ACTUAL WEB KNOWLEDGE CONTENT For all true/false and multiple choice items measuring actual knowledge content of the web a correct item was coded as 1 (1 = “knowledge”) and an incorrect answer or a answer of ‘Don’t Know’ was coded as 0 (0 = “no knowledge”). Following the exploratory factor analysis outlined below, these items were then summated to from an overall measure of the four scales measuring actual common procedural, actual common declarative, actual specialised procedural and actual specialised declarative knowledge content of the web.

9.4.3.1 Actual Common Procedural Web Knowledge Content Exploratory factor analysis was performed on the initial 6-item scale measuring ‘Actual Common Procedural Knowledge Content of the Web’. Initial data screening and analysis of correlation patterns identified that individually and collectively all 6-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.792, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 2781.046, df = 15, Sig. = 0.000).

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Thus a principal components exploratory factor analysis was conducted. This analysis extracted 3 dimensions that explained 73% of the variance of ‘Actual Common Procedural Web Knowledge Content’ with a total scale reliability of 0.7. See Table 29 for scale and dimension variance and Appendix J for item factor loadings.

Table 29: Variance Explained of Actual Common Procedural Web Knowledge Content

Component

Initial Eigenvalues Total

1 2 3

2.7 0.9 0.8

% of Variance 45 15 13

Cumulative % 45 60 73

Extraction Sums of Squared Loadings Total 2.1 1.2 1.0

% of Variance 36 21 16

Cumulative % 36 57 73

Extraction Method: Principal Components Analysis with a Varimax Rotation

These results are consistent with those from the student sample (see Chapter 7 and Appendix K). The 6-items developed and first tested on the small student sample explained 75% of the variance. In the web sample, these 6-items explained a slightly lower, yet comparable 73% of the variance, with consistent results in the dimensionality and total scale reliability.

9.4.3.2 Actual Specialised Procedural Web Knowledge Content Exploratory factor analysis was performed on the initial 11-item scale measuring ‘Actual Specialised Procedural Web Knowledge Content’. Initial data screening and analysis of correlation patterns identified that individually and collectively all 11-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.870, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 4486.578, df = 55, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 4 dimensions that explained 61% of the variance, with a total scale reliability of 0.8. See Table 30 for dimension and scale variance and Appendix J for item factor loadings.

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Table 30: Variance Explained of Actual Specialised Procedural Web Knowledge Content

Component

Initial Eigenvalues Total

1 2 3 4

3.7 1.1 1.0 0.9

% of Variance 33 10 9 9

Cumulative % 33 43 52 61

Extraction Sums of Squared Loadings Total 2.2 1.8 1.6 1.1

% of Variance 20 17 14 10

Cumulative % 20 37 51 61

Extraction Method: Principal Components Analysis with a Varimax Rotation

These results are consistent with those from the student sample (see Chapter 7 and Appendix K). The 11-items developed and first tested on the small student sample explained 58% of the variance. In the web sample, these 11-items explained a slightly higher 61% of the variance, extracted an additional dimension and reported consistent total scale reliability.

9.4.3.3 Actual Common Declarative Web Knowledge Content Exploratory factor analysis was performed on the initial 10-item scale measuring ‘Actual Common Declarative Web Knowledge Content’. Initial data screening and analysis of correlation patterns identified that individually and collectively all 10-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.876, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 5297.685, df = 45, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 3 dimensions that explain 59% of the variance, with a total scale reliability of 0.8. See Table 31 for dimension and scale variance and Appendix J for item factor loadings.

Table 31: Variance Explained of Actual Common Declarative Web Knowledge Content

Component

Initial Eigenvalues Total

1 2 3

3.9 1.1 0.8

% of Variance 39 11 8

Cumulative % 39 50 58

Extraction Sums of Squared Loadings Total 2.4 2.0 1.4

Extraction Method: Principal Components Analysis with a Varimax Rotation

150

% of Variance 24 20 15

Cumulative % 24 44 59

These results are consistent with those from the student sample (see Chapter 7 and Appendix K). The 10-items developed and first tested on the small student sample explained 57% of the variance. In the web sample, these 10-items explained a slightly higher 59% of the variance, extracted an additional dimension and reported consistent total scale reliability.

9.4.3.4 Actual Specialised Declarative Web Knowledge Content Exploratory factor analysis was performed on the initial 10-item scale measuring ‘Actual Specialised Declarative Web Knowledge Content’. Initial data screening and analysis of correlation patterns identified that individually and collectively all 10-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.879, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 6080.714, df = 45, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 5 dimensions that explain 76% of the variance, with a total scale reliability of 0.8. See Table 32 for scale and dimension variance and Appendix J for item factor loadings.

Table 32: Variance Explained of ‘Actual Specialised Declarative Web Knowledge Content

Component

Initial Eigenvalues Total

1 2 3 4 5

4.1 0.9 0.8 0.7 0.7

% of Variance 41 10 9 8 7

Cumulative % 41 51 60 68 75

Extraction Sums of Squared Loadings Total 1.8 1.7 1.4 1.3 1.1

% of Variance 19 18 15 13 11

Cumulative % 19 37 52 65 76

Extraction Method: Principal Components Analysis with a Varimax Rotation

These results are inconsistent with those from the student sample (see Chapter 7 and Appendix K). The 10-items developed and first tested on the small student sample explained 59% of the variance, whereas in the web sample these 10-items explained a higher 75% of the variance, extracted two additional dimensions and reported consistent total scale reliability.

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9.4.4 SCALE VALIDATION: PERCEIVED WEB KNOWLEDGE CONTENT 9.4.4.1 Perceived Overall Web Knowledge Content To measure current overall web knowledge content, two items were used. These two items were further investigated using Spearman’s Rho correlation coefficient. Table 33 shows a significant positive relationship between the two items (rs = .750, p<.01). Thus the items will be summated to measure perceived overall web knowledge content.

Table 33: Perceived Overall Knowledge Content: SWOK1 & SWOK2 Spearman’s Rho Correlation Coefficient SWOK2 Spearman’s Rho

SWOK1

Correlation Coefficient

.750

Sig. (2-tailed)

.000

2077

** Correlation significant at the .01 level (1-tailed)

These results are consistent with those reported from the student sample (see Chapter 7). In fact, the 2-items developed and first tested on the small student sample reported a correlation coefficient of r=.757, (p<0.01), very similar to that reported by the web sample.

9.4.4.2 Perceived Declarative Web Knowledge Content Exploratory factor analysis was performed on the initial 7-item scale measuring ‘Perceived Declarative Web Knowledge Content’. Initial data screening and analysis of correlation patterns identified that individually and collectively all 7-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.939, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 11559.443, df = 21, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 1 dimension that explained 73% of the variance, with a total scale reliability of 0.9. See Table 34 for scale and dimension variance and Appendix J for item factor loadings.

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Table 34: Variance Explained of Perceived Declarative Web Knowledge Content

Component

Initial Eigenvalues Total

5.1

% of Variance 73

Cumulative % 73

Extraction Sums of Squared Loadings Total 5.1

% of Variance 73

Cumulative % 73

Extraction Method: Principal Components Analysis with a Varimax Rotation

These results are consistent with those from the student sample (see Chapter 7 and Appendix K). The 7-items developed and first tested on the small student sample explained 75% of the variance, and in the web sample these 7-items explained 73% of the variance, and reported consistent uni-dimensionality and total scale reliability.

9.4.4.3 Perceived Procedural Web Knowledge Content Exploratory factor analysis was performed on the initial 4-item scale measuring ‘Perceived Procedural Web Knowledge Content’. Initial data screening and analysis of correlation patterns identified that individually and collectively all 4-items meet the necessary threshold of sampling adequacy (KMO Measure of Sampling Adequacy = 0.847, Bartlettʹs Test of Sphericity: Approx. Chi-Square = 5661.848, df = 6, Sig. = 0.000). Thus a principal components exploratory factor analysis was conducted. This analysis extracted 1 dimension that explained 79% of the variance, with a total scale reliability of 0.9. See Table 35 for sale and dimension variance and Appendix J for item factor loadings.

Table 35: Variance Explained of Perceived Procedural Web Knowledge Content

Component

Initial Eigenvalues Total

3.1

% of Variance 79

Cumulative % 79

Extraction Sums of Squared Loadings Total 3.1

% of Variance 79

Cumulative % 79

Extraction Method: Principal Components Analysis with a Varimax Rotation

These results are consistent with those from the student sample (see Chapter 7 and Appendix K). The 4-items developed and first tested on the small student sample explained 77% of the variance, and in the web sample these 4-items explained 79% of the variance, and reported consistent uni-dimensionality and total scale reliability.

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9.4.5 SCALE ASSESSMENT: SUMMARY In summary, all the scales were broadly consistent across the web sample and the student sample, in terms of their dimensionality, the percentage of construct variance that they explained, and total scale reliability. Where inconsistency was identified, in the case of the scale items measuring perceived ease of web use and actual specialised declarative web knowledge, the resulting scale structure and reliability was an improved result on the scale development studies. Overall, the scales developed here appear to be valid.

For the remainder of this dissertation, analyses will only refer to the ‘overall construct’ or ‘variable’ and not its underlying dimensions (factors) that were extracted. This is to firstly explore the relationships between the constructs with further analysis recommended in Chapter 12 to investigate more specifically the relationship between the underlying dimensions (factors) of each.

9.5 SAMPLE & VARIABLE DESCRIPTION 9.5.1 FREQUENCY ANALYSIS The reported frequencies for the entire and split sample across the variables: actual web knowledge, perceived web knowledge, web perceptions, and current web session usage, are reported in Appendix P (Table P1 and Figures P1-P15). The frequency percentages reported are partitioned into ‘low’, ‘medium’ and ‘high’ sample scores for the entire sample (n=2077), and the two sample groups, users with WSD/M experience (n=1177) and without WSD/M experience (n=900).

A review of the frequency scores reveals that:

Users with WSD/M experience have consistently high actual web knowledge scores, whereas users with no WSD/M experience report medium to high scores, and lowmedium scores for actual specialised declarative knowledge content of the web.

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Users with WSD/M experience have consistently high perceptions of how much they know about the web, whereas users with no WSD/M experience report medium to high scores of perceived web knowledge.

Users with WSD/M experience and with no WSD/M experience report consistently medium to high levels of perceived ease of web use and perceived web usefulness.

Users with WSD/M experience report consistently higher level of usage frequency, situational variety, motivational variety, usage depth and usage duration than users with no WSD/M experience. However both user groups report similar levels of usage breadth.

Thus, overall, the web sample recruited performs consistently med-to-high across all the variables surveyed, and both users with and without WSD/M experience differ across the variables examined.

9.5.2 SAMPLE MEAN COMPARATIVE ANALYSIS In this section the mean scores for users with and without web site design and/or maintenance (WSD/M) experience are compared to identify if there is any statistically significant difference between the two users groups for their performance across the variables investigated in this dissertation. The mean scores for both web user groups (Table P2) and the t-test results (Table P3) are presented in Appendix P. The results are summarized here.

As reported in Appendix P (Table P2) and consistent with the aforementioned frequencies, the results identify that users with no WSD/M experience on average have lower mean scores for actual web knowledge, perceived web knowledge, perceived ease of web use and perceived web usefulness, and current web session usage – except for breadth of usage extent. On this item the mean scores between the two groups are similar, with no significant difference.

As reported in Appendix P (Table P3), users with no WSD/M experience on average have significantly less actual knowledge of the web, less perceived knowledge of the

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web, perceive the web as less easier and less useful to use, and use the web less frequently, with less number of motivations, at a lower number and type of locations, for a lower duration of time, accessing fewer web sites and search engines than users with WSD/M experience. However no statistically significant difference between users with and without WSD/M experience was found, for the number of different and/or similar web sites or search engines each group accessed (i.e., usage breadth).

9.5.3 SAMPLE & VARIABLE DESCRIPTION: SUMMARY In summary, there is a statistical significant difference between users with web site design and/or maintenance (WSD/M) experience and those users without this experience on their level of actual knowledge, perceived knowledge, how easy and useful they think the web is, whereas their usage of the web, and usage breadth of the web is similar across the two users groups.

9.6 PEWU & PWU: REPLICATION AND VALIDATION To further validate the measure of perceived ease of web use and perceived web usefulness, a relationship proposed by Davis (1986) and further validated by other TAM researchers was replicated here. Davis (1986) hypothesised that perceived ease of use will have a significant direct effect on perceived usefulness stating that, all else being equal, a system which is easier to use will result in greater usefulness for the user. He reported a relatively strong relationship between perceived ease of use and perceived usefulness (r=.64).

For non-web-based electronic systems, this hypothesis has been further supported within the literature (Davis et al. 1989a; Davis 1989b; Adams et al. 1992; Taylor and Todd 1995; Igbaria et al. 1995; Chau 1996; Gefen and Keil 1998; Bronson 1999; Karahanna and Straub 1999). In addition, with respect to web-based systems, and as tested from the perspective of organisational-based motivations, a positive relationship has also been supported (Morris and Dillion 1997; Teo et al. 1999; Moon and Kim 2001). Given that the

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variables were not normally distributed in the present sample, a non-parametric correlation was conducted (Spearman’s Rho) (see Chapter 8 for a discussion). Results are presented in Table 36.

Table 36: Perceived Ease of Web Use & Perceived Web Usefulness – Spearman’s Rho Correlation Coefficient Web Site Design and Maintenance Experience (0/1)

No Experience

Experience

Perceived Web Usefulness (Sum) Spearman’s Rho

Spearman’s Rho

Perceived Ease of Web Use (Sum)

Correlation Coefficient

.755

Sig. (1-tailed)

.000

900

Correlation Coefficient

.731

Sig. (1-tailed)

.000

1177

**. Correlation significant at the .01 level (1-tailed)

Table 36 shows a significant positive relationship between PEWU and PWU for both web users with no web site design and maintenance experience (rs = .755, p<.01) and those with this experience (rs = .731, p<.01). Therefore, the easier the web is to use, the more useful users find the web. The statistically significant positive relationship found in this study, is consistent with the findings of Davis (1986) and other researchers.

9.7 DESCRIPTIVE RESEARCH RESULTS: SUMMARY In summary, 2077 usable responses were obtained for the web survey. This amounted to an impressive 41% response rate from those who visited the web site, but a response of only 0.1% of those who were exposed to the banner ad campaign. The scales used in the web survey were broadly consistent in their dimensionality and reliability with those developed from the student samples and the measures of perceived ease of web use and perceived web usefulness were further validated. Furthermore, significant differences in mean scores across the constructs measured were identified to exist between users with and without WSD/M experience. This descriptive finding highlights the importance of segmenting the sample according to their level of web site design and/or maintenance experience in the further analyses to be conducted.

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The next chapter, Chapter 10, presents the results of the relationships hypothesized in Chapters 3-to-5 and summarized in Chapter 6. In brief, Chapter 10 presents the results of the multivariate analyses with further validation of these findings presented in Appendix Q.

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C HAPTER 10: E MPIRICAL R ESULTS M ULTIVARIATE A NALYSES â&#x20AC;&#x2DC;Truth in science can be defined as the working hypothesis best suited to open the way to the next better oneâ&#x20AC;&#x2122; Konrad Lorenz (1903-1989) Austrian Ethologist

10.1 INTRODUCTION The hypotheses that were introduced in this dissertation are examined here using analysis of variance (ANOVA) and stepwise multiple regression analysis (MRA). The motivation for using these techniques, and the assumptions that were assessed to confirm the suitability of this procedure, were described in Chapter 8. The results of assumption testing are to be found in Appendix O. It is apparent that in most instances at least one of the assumptions has been violated (e.g., it is hard to avoid the problem of multicollinearity when so many of the measures refer to closely related constructs). This is not uncommon and, as is often the case with multivariate techniques, results need to be interpreted cautiously. Where several of the assumptions are violated, conclusions must be treated as provisional. This is an important caveat to keep in mind. Bivariate analyses have also been used to further validate these findings and are presented in Appendix Q.

10.2 DATA EXPLORATION Before investigating the specific research questions and hypotheses proposed in this dissertation, an exploration of the specific variables measured was conducted. To explore the variables and the relationships between them, a correlation analysis was

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conducted, the results of which are discussed here with the correlation matrix presented in Appendix R. The total sample is presented in Table R1 and the split sample results for users with and without WSD/M experience are presented in Table R2 in Appendix R. In totality, it was identified that 80% of the correlations for the total sample (n=2077), 70% for the users with no WSD/M experience, and 60% for users with WSD/M experience, were statistically significant. More specifically, some observed trends identified in these tables are:

Actual knowledge has a significant positive correlation with perceived knowledge of the web;

Perceived knowledge of the web has a significant positive correlation with current web session usage (except breadth of web use), perceived ease of web use and perceived web usefulness;

Perceived ease of web use and perceived web usefulness has significant positive correlation with the number of motives for web use and the depth of web use;

The number of motives for web session use has a significant positive correlation with the number and types of situations and frequency of web session use.

When comparing the two web users groups (see Table R2 in Appendix R), it is evident that the significant correlations reported for the users with no WSD/M experience are on average stronger than those reported for users with WSD/M experience. Furthermore, slight variation in some of the correlations is also reported identifying key differences between the two groups.

The specific research questions and hypotheses put forward in this dissertation will each now be looked at in turn.

10.3 RESEARCH QUESTION ONE Research question 1 asks: what is the relationship between a user’s perceptions of the web and a person’s current web session usage?

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From the bivariate analyses presented in Appendix Q, 6 out of 12 hypotheses tested were supported for users with no web site design and maintenance experience (n = 900). Some 5 out of 12 hypotheses were supported for users with experience (n = 1177). These hypotheses form the basis of the first 6 multiple regression analyses conducted in this chapter (see MRA1-6, Table 37 and Table 38).

Table 37: Web User Group A – No WSD/M Experience: RQ1 Bivariate Label

Independent

Relationship

Dependent

Bivariate Result (Appendix Q)

H1A

PEWU

Curvilinear

WSUF

Reject (Accept Null)

H2A

PWU

Positive

WSUF

Accept (Reject Null)

H3A

PEWU

Curvilinear

WSUVS

Reject (Accept Null)

H4A

PWU

Positive

WSUVS

Accept (Reject Null)

H3B

PEWU

Curvilinear

WSUVMNO1

Reject (Accept Null)

H4B

PWU

Positive

WSUVMNO1

Accept (Reject Null)

H5A

PEWU

Curvilinear

WSUEB

Accept (Reject Null)

H6A

PWU

Positive

WSUEB

Reject (Accept Null)

H5B

PEWU

Curvilinear

WSUED

Reject (Accept Null)

H6B

PWU

Positive

WSUED

Accept (Reject Null)

H5C

PEWU

Curvilinear

WSUEDUR

Reject (Accept Null)

H6C

PWU

Positive

WSUEDUR

Accept (Reject Null)

Multivariate Label MRA1 MRA2 MRA3 MRA4 MRA5 MRA6

Table 38: Web User Group B – With WSD/M Experience: RQ1 Bivariate Label

Independent

Relationship

Dependent

Bivariate Result (Appendix Q)

H1A

PEWU

Curvilinear

WSUF

Reject (Accept Null)

H2A

PWU

Positive

WSUF

Accept (Reject Null)

H3A

PEWU

Curvilinear

WSUVS

Reject (Accept Null)

H4A

PWU

Positive

WSUVS

Accept (Reject Null)

H3B

PEWU

Curvilinear

WSUVMNO1

Reject (Accept Null)

H4B

PWU

Positive

WSUVMNO1

Accept (Reject Null)

H5A

PEWU

Curvilinear

WSUEB

Reject (Accept Null)

H6A

PWU

Positive

WSUEB

Reject (Accept Null)

H5B

PEWU

Curvilinear

WSUED

Reject (Accept Null)

H6B

PWU

Positive

WSUED

Accept (Reject Null)

H5C

PEWU

Curvilinear

WSUEDUR

Reject (Accept Null)

H6C

PWU

Positive

WSUEDUR

Accept (Reject Null)

Multivariate Label MRA1 MRA2 MRA3 MRA4 MRA5 MRA6

10.3.1 MRA1: WSUF = F (PWU & PEWU) To test H1a and H2a, multiple regression analysis one (MRA1) examines the influence that the independent variables perceived web usefulness (PWU) and perceived ease of

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web use (PEWU) have on the dependent variable, current web session usage frequency (WSUF).

10.3.1.1 Statistical Assessment Table 39 contains the results of the stepwise multiple regression. Of the 6 assumptions, 3 have been violated (Appendix O).

For users with no WSD/M experience, PEWU has a significant positive effect on WSUF (t = 3.92, p<.01), however it only explains 1.6% of the variance in WSUF (Adj. R2 = .016). Under the stepwise method, PWU was excluded from this model.

By contrast, for users with WSD//M experience, PWU has a significant positive effect (t = 4.64, p<.01), however it only explains 1.7% of the variance in WSUF (Adj. R2 = .017). PEWU was excluded from the model.

Table 39: Multiple Regression Results MRA1: Web Session Usage Frequency = F (Perceived Web Usefulness & Perceived Ease of Web Use) WSD/M Exp.

Dep. Var.

Adj. R2

No Exp.a

Web Sesison Usage Frequency

.017

.016

With Exp.b

Web Sesison Usage Frequency

.018

.017

Independent Variable

S.E. (b)

Constant

5.650

.226

Perceived Ease of Web Use

0.016

.004

Constant

6.148

.201

Perceived Web Usefulness

0.013

.003

.130

t. stat.

Sig. Level

25.016

15.342

.000

3.917 30.620

.134

.000 21.568

4.644

.000 .000

a No WSD/M experience – excluded variables: Perceived Web Usefulness (PWU) b With WSD/M experience – excluded variables: Perceived Ease of Web Use (PEWU)

10.3.1.2 Summary Perceived ease of web use (PEWU) has a weak positive relationship with current web session usage frequency for users with no WSD/M experience, and perceived web usefulness (PWU) has a positive relationship for users with WSD/M experience.

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10.3.2 MRA2: WSUVS = F (PWU & PEWU) To test H3a and H4a, multiple regression analysis two (MRA2) examines the influence that the independent variables perceived web usefulness (PWU) and perceived ease of web use (PEWU) have on the dependent variable, current web session usage variety – situational (WSUVS).

10.3.2.1 Statistical Assessment Table 40 contains the results of the stepwise multiple regression. Of the 6 assumptions, only 2 have not been violated (Appendix O).

For users with no WSD/M experience, PWU was found to be the best predictor of WSUVS having a positive effect (t = 6.91, p <.01), however it only explained 0.06% of the variance in WSUVS (Adj. R2 = .006). PEWU was removed from the model. For users with WSD/M experience, both PEWU and PWU were removed from the model – i.e., neither were significant predictors of WSUVS.

Table 40: Multiple Regression Results MRA2: Web Session Usage Variety - Situational = F (Perceived Web Usefulness & Perceived Ease of Web Use) WSD/M Exp.

Dep. Var.

No Exp.a

Web Session Usage Variety - Situational

Adj. R2

.007

.006

Independent Variable

S.E. (b)

Constant

2.637

.382

Perceived Web Usefulness

0.013

.005

.082

t. stat.

Sig. Level

6.909

6.122

.000

2.474

.000

a No WSD/M experience – excluded variables: Perceived Ease of Web Use (PEWU) b With WSD/M experience – excluded variables: Perceived Ease of Web Use (PEWU); Perceived Web Usefulness (PWU)

10.3.2.2 Summary Perceived web usefulness (PWU) was the best determinant of the variety of situations from which the web is accessed for users with no WSD/M experience. However for users with this experience, neither perceived ease of use, nor perceived web usefulness, was a significant predictor.

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10.3.3 MRA3: WSUVMNO1 = F (PWU & PEWU) To test H3b and H4b, multiple regression analysis three (MRA3) examines the influence that the independent variables perceived web usefulness (PWU) and perceived ease of web use (PEWU) have on the dependent variable, current web session usage variety – motive number (WSUVMNO1).

10.3.3.1 Statistical Assessment Table 41 contains the results of the stepwise multiple regression. Of the 6 assumptions, 4 have not been violated (Appendix O).

For users with no WSD/M experience, both PEWU and PWU had a significant positive effect on WSUVMNO1 (t = 5.50, p <.01, and t = 2.30, p <.01, respectively), with this model explaining 13.6% of the variance in WSUVMNO1. PWU had a greater impact than PEWU.

For users with WSD/M experience, PEWU was removed from the model, with only PWU having a significant positive effect on WSUVMNO1 (t = 7.47, p <.01). This explained only 4.4% of the variance in WSUVMNO1 (Adj. R2 = .044).

Table 41: Multiple Regression Results MRA3: Web Session Usage Variety - Motive = F (Perceived Ease of Web Use & Perceived Web Usefulness) WSD/M Exp.

No Exp.

With Expa

Dep. Var.

Adj. R2

Web Session Usage Variety Motive

.138

.136

Web Session Usage Variety Motive

Independent Variable

S.E. (b)

Constant

-.385

.447

0.055

.010

.274

5.496

.000

0.027

.012

.115

2.304

.000

Constant

3.101

.436

Perceived Web Usefulness

0.045

.006

Perceived Web Usefulness Perceived Ease of Web Use .045

.044

t. stat.

Sig. Level

-.861

71.679

.390

7.112 .213

7.468

a With WSD/M experience – excluded variables: Perceived Ease of Web Use (PEWU)

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55.769

.000 .000

10.3.3.2 Summary Perceived ease of use and perceived web usefulness both had a significant positive effect on the number of motivations for which the web is used by users with no WSD/M experience. Perceived web usefulness was the stronger predictor. In comparison, for users with WSD/M experience, only perceived usefulness of the web had a significant positive impact on the number of motivations for web use.

10.3.4 MRA4: WSUEB = F (PWU & PEWU) To test H5a and H6a, multiple regression analysis four (MRA4) examines the influence that the independent variables perceived web usefulness (PWU) and perceived ease of web use (PEWU) have on the dependent variable, current web session usage extent breadth (WSUEB).

10.3.4.1 Statistical Assessment Table 42 contains the results of the stepwise multiple regression. Of the 6 assumptions, 4 have not been violated (Appendix O).

For users with no WSD/M experience, both PEWU and PWU were excluded from the model. For users with WSD/M experience, PWU had a significant negative effect on WSUEB (t = -2.05, p <.05), but it only explained 0.3% of the variance in WSUEB (Adj. R2 = .003) for this user group.

Table 42: Multiple Regression Results MRA4: Web Session Usage Extent - Breadth = F (Perceived Web Usefulness & Perceived Ease of Web Use) WSD/M Exp.

Dep. Var.

Adj. R2

With Expa

Web Session Usage Extent - Breadth

.004

.003

Independent Variable

S.E. (b)

Constant

7.389

.522

Perceived Web Usefulness

-0.015

.007

-.060

t. stat.

Sig. Level

14.143

4.216

.000

-2.053

a No WSD/M experience – excluded variables: Perceived Ease of Web Use (PEWU) b With WSD/M experience – excluded variables: Perceived Ease of Web Use (PEWU); Perceived Web Usefulness (PWU)

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.040

10.3.4.2 Summary Neither PEWU nor PWU is a good predictor of WSUEB for users with no WSD/M experience. Only perceived web usefulness is a predictor of WSUEB for users with WSD/M experience, and the magnitude of this negative relationship is quite small.

10.3.5 MRA5: WSUED = F (PWU & PEWU) To test H5b and H6b, multiple regression analysis five (MRA5) examines the influence that the independent variables perceived web usefulness (PWU) and perceived ease of web use (PEWU) have on the dependent variable, current web session usage extent depth (WSUED).

10.3.5.1 Statistical Assessment Table 43 contains the results of the stepwise multiple regression. Of the 6 assumptions, 5 have not been violated (Appendix O).

For users with no WSD/M experience, both PEWU and PWU had a significant positive effect on WSUED (t = 5.38, p <.01, and t = 3.73, p < .01, respectively), and they explained a total 17.5% of the variance in WSUED (Adj. R2 = .175). It is evident from these results that PEWU was the primary determinant of WSUED, with PWU in a secondary role.

For users with WSD/M experience, both PEWU and PWU had a significant positive effect on WSUED (t = 3.18, p <.01, and t = 2.36, p < .01, respectively), and they explained a total 4.8% of the variance in WSUED (Adj. R2 = .048). It is evident that PEWU was the primary, and PWU the secondary, determinant of WSUED.

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Table 43: Multiple Regression Results MRA5: Web Session Usage Extent - Depth = F (Perceived Web Usefulness & Perceived Ease of Web Use) WSD/M Exp.

No Exp

With Exp

Independent Variable

S.E. (b)

.175

Constant

7.157

.791

.111

.021

.262

5.377

.000

0.066

.018

.182

3.731

.000

.048

Perceived Ease of Web Use Perceived Web Usefulness Constant

13.712

.770

0.058

.018

.136

3.180

.000

0.035

.015

.101

2.364

.000

Dep. Var.

Adj. R2

Web Session Usage Extent â&#x20AC;&#x201C; Depth

.177

Web Session Usage Extent Depth

.049

Perceived Ease of Web Use Perceived Web Usefulness

Î&#x2019;

t. stat.

9.052

96.170

17.819

30.478

Sig. Level .000

.000

10.3.5.2 Summary Perceived ease of web use was the primary determinant, and perceived web usefulness the secondary determinant, of the depth of web use for both web users with and without WSD/M experience. These positive relationships were somewhat stronger for users with no WSD/M experience than those with this experience.

10.3.6 MRA6: WSUEDUR = F (PWU & PEWU) To test H5c and H6c, multiple regression analysis six (MRA6) examines the influence that the independent variables perceived web usefulness (PWU) and perceived ease of web use (PEWU) have on the dependent variable, current web session usage extent duration (WSUEDUR).

10.3.6.1 Statistical Assessment Table 44 contains the results of the stepwise multiple regression. Of the 6 assumptions, 3 are violated and another is questionable (Appendix O).

For users with no WSD/M experience, PWU was excluded from the model and PEWU was the primary determinant of WSUEDUR (t = 7.06, p <.01), but this positive relationship only explained 5.2% of the variance in WSUEDUR (Adj. R2 = .052).

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For users with WSD/M experience, PWU was again excluded, and PEWU had a significant positive effect on WSUEDUR (t = 3.68, p <.01), although it explained only 1.1% of the variance in WSUEDUR (Adj. R2 = .011).

Table 44: Multiple Regression Results MRA6: Web Session Usage Extent - Duration = F (Perceived Web Usefulness & Perceived Ease of Web Use) WSD/M Exp.

No Expa

With Expb

Dep. Var.

Adj. R2

Web Session Usage Extent – Duration

.053

.052

Web Session Usage Extent Duration

.011

Independent Variable

S.E. (b)

Constant

1.667

.237

Perceived Ease of Web Use

0.030

.004

Constant

2.788

.280

Perceived Ease of Web Use

0.018

.005

.229

t. stat.

Sig. Level

7.045

49.874

.000

7.062 9.953

.107

.000 13.567

3.683

.000 .000

a No WSD/M experience – excluded variables: Perceived Web Usefulness (PWU) b With WSD/M experience – excluded variables: Perceived Web Usefulness (PWU)

10.3.6.2 Summary Perceived ease of web use was the primary determinant of how long the web is used, for both web user groups. However, this positive relationship was stronger for users with no WSD/M experience.

10.3.7 RESEARCH QUESTION ONE: SUMMARY From the above analysis it was found that:

PEWU was the primary determinant of how frequently the web is used for users with no WSD/M experience – having a positive relationship - and PWU is the primary determinant for users with WSD/M experience, also having a positive effect. (MRA1)

PWU was the best predictor of the variety of situations from where the web is accessed for users with no WSD/M experience, having a positive effect. Neither ease of use, nor usefulness, were determinants for users with WSD/M experience (MRA2)

For web users with no WSD/M experience, PWU was the primary and PEWU the secondary determinant of the number of motivations for web use, both having a positive effect. By comparison, only PWU had a positive effect on the number of motivations for web use for users with WSD/M experience (MRA3).

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Neither, PEWU or PWU were determinants for breadth of web session use for user with no WSD/M experience. However PWU was the main determinant for users with WSD/M experience, having a negative effect. PEWU was removed for this user group (MRA4).

PEWU was the primary, and PWU the secondary, determinant of the depth of web session use for both web users groups, having a positive effect. However, the model explained more variance for users with no WSD/M experience, than those with this experience (MRA5).

Perceived ease of use was the primary determinant of how long the web is used, for both web user groups, and was stronger for users with no WSD/M experience. PWU was removed from the model. All relationships found were positive (MRA6).

These results show that a significant difference is present between users with and without WSD/M experience. This use of the web is influenced by ease of web use and web usefulness, but the influence is somewhat different across the two user groups. It is also evident that a difference exists between the type of usage behaviour being determined (i.e., frequency, duration, variety etc) for each user group. For example, both perceived ease of web use and perceived web use are important for predicting usage behaviour for users with no WSD/M experience, however for users with this experience, perceived web usefulness is of core importance. See summary Table 45 and Table 46 below for comparative results from the multivariate analyses conducted in this chapter and the bivariate analyses presented in Appendix Q.

Table 45: Web User Group A - No WSD/M Experience: RQ1 Bivariate Label

Independent

Relationship

Dependent

Bivariate Result (Appendix Q)

H1A

PEWU

Curvilinear

WSUF

Reject (Accept Null)

H2A

PWU

Positive

WSUF

Accept (Reject Null)

H3A

PEWU

Curvilinear

WSUVS

Reject (Accept Null)

H4A

PWU

Positive

WSUVS

Accept (Reject Null)

H3B

PEWU

Curvilinear

WSUVMNO1

Reject (Accept Null)

H4B

PWU

Positive

WSUVMNO1

Accept (Reject Null)

H5A

PEWU

Curvilinear

WSUEB

Accept (Reject Null)

H6A

PWU

Positive

WSUEB

Reject (Accept Null)

H5B

PEWU

Curvilinear

WSUED

Reject (Accept Null)

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Multivariate Result (Chapter 10) PEWU (+) PWU (+) PEWU (+) & PWU (+) PEWU (+) & PWU (+)

H6B

PWU

Positive

WSUED

Accept (Reject Null)

H5C

PEWU

Curvilinear

WSUEDUR

Reject (Accept Null)

H6C

PWU

Positive

WSUEDUR

Accept (Reject Null)

PEWU (+)

Table 46: Web User Group B - WSD/M Experience: RQ1 Dependent

Bivariate Result (Appendix Q)

Curvilinear

WSUF

Reject (Accept Null)

Positive

WSUF

Accept (Reject Null)

PEWU

Curvilinear

WSUVS

Reject (Accept Null)

H4A

PWU

Positive

WSUVS

Accept (Reject Null)

H3B

PEWU

Curvilinear

WSUVMNO1

Reject (Accept Null)

H4B

PWU

Positive

WSUVMNO1

Accept (Reject Null)

H5A

PEWU

Curvilinear

WSUEB

Reject (Accept Null)

Bivariate Label

Independent

Relationship

H1A

PEWU

H2A

PWU

H3A

H6A

PWU

Positive

WSUEB

Reject (Accept Null)

H5B

PEWU

Curvilinear

WSUED

Reject (Accept Null)

H6B

PWU

Positive

WSUED

Accept (Reject Null)

H5C

PEWU

Curvilinear

WSUEDUR

Reject (Accept Null)

H6C

PWU

Positive

WSUEDUR

Accept (Reject Null)

Multivariate Result (Chapter 10) PWU (+) PWU (+) PWU (-) PEWU (+) & PWU (+) PEWU (+)

10.4 RESEARCH QUESTION TWO Research question 2 asks: what is the relationship between a user’s knowledge content of the web and a person’s perceived usefulness of the web? As summarized in Table 47 below, for web users with no web site design and maintenance experience (n = 900), 6 out of 7 hypotheses were supported in the bivariate analyses. As summarized in Table 48 below, for web users with experience, 5 out of 7 hypotheses were supported. For more detailed review, see Appendix Q.

Table 47: Web User Group A – No WSD/M Experience: RQ2 Independent

Relationship

Dependent

Bivariate Result (Appendix Q)

H7A

ACPWK

Curvilinear

PWU

Reject (Accept Null)

H8A

ACDWK

Positive

PWU

Accept (Reject Null)

H9A

ASPWK

Positive

PWU

Accept (Reject Null)

H10A

ASDWK

Positive

PWU

Accept (Reject Null)

H11A

SWPK

Positive

PWU

Accept (Reject Null)

H12A

SWDK

Positive

PWU

Accept (Reject Null)

H13A

SWOK

Positive

PWU

Accept (Reject Null)

Bivariate Label

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Multivariate Label

MRA7

Table 48: Web User Group B â&#x20AC;&#x201C; With WSD/M Experience: RQ2 Bivariate Label

Dependent

Bivariate Result (Appendix Q)

Independent

Relationship

H7A

ACPWK

Curvilinear

PWU

Reject (Accept Null)

H8A

ACDWK

Positive

PWU

Accept (Reject Null)

H9A

ASPWK

Positive

PWU

Accept (Reject Null)

H10A

ASDWK

Positive

PWU

Reject (Accept Null)

H11A

SWPK

Positive

PWU

Accept (Reject Null)

H12A

SWDK

Positive

PWU

Accept (Reject Null)

H13A

SWOK

Positive

PWU

Accept (Reject Null)

Multivariate Label

MRA7

Two multivariate techniques will now be used to further test these hypotheses. ANOVA will firstly be used to explore these 7 hypotheses and determine the mean effect that actual and perceived knowledge content of the web has on a userâ&#x20AC;&#x2122;s level of perceived web usefulness. Following this analysis, stepwise multiple regression analysis will be used to more specifically identify which types of actual and perceived web knowledge content are statistically significant determinants of perceived usefulness of the web for each web user group (MRA7).

10.4.1 ANOVA: PWU = F (ACTUAL & PERCEIVED WEB KNOWLEDGE) ANOVA is used to analyse if the level (i.e., low, medium and high) of actual and perceived web knowledge content a user has influences his/her perceptions of how useful the web is to use. There are four measures of actual knowledge content and three measures of perceived knowledge content og the web.

10.4.1.1 Statistical Assessment As presented in Table 49, the result of the ANOVAs reveals that the level of actual and perceived web knowledge content (i.e., low, medium and high) that a web user has, has a significant effect on their level of perceived usefulness of the web. A closer look at the mean scores (see Appendix S) shows that the level of actual common procedural, actual common declarative, actual specialised procedural, actual specialised declarative, perceived overall, perceived procedural, and perceived declarative has a significant positive impact on the level of perceived web usefulness. Thus, the more users know and

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think they know about the web, the more useful they will find the web to use. This result is consistent for both web users with and without WSD/M experience. See Table 49.

Table 49: ANOVA: Effect of Level of Actual [4] and Perceived [3] Web Knowledge Content (L/M/H) on Perceived Web Usefulness Knowledge Content

Actual Common Procedural

Actual Common Declarative

Actual Specialised Procedural

Actual Specialised Declarative

Perceived Overall

Perceived Procedural

Perceived Declarative

Groups Between Within Total Between Within Total Between Within Total Between Within Total Between Within Total Between Within Total Between Within Total

No WSD/M Experience df 2 897 899 2 897 899 2 897 899 2 897 899 2 897 899 2 897 899 2 897 899

F. Stat.

Sig. Level

71.207

.000*

75.007

.000*

25.927

.000*

11.214

.000*

96.596

.000*

135.57

.000*

115.109

.000*

With WSD/M Experience df 2 1174 1176 2 1174 1176 2 1174 1176 2 1174 1176 2 1174 1176 2 1174 1176 2 1174 1176

F. Stat.

Sig. Level

13.872

.000*

9.841

.000*

11.044

.000*

4.923

.000*

65.219

.000*

67.264

.000*

66.462

.000*

* p<0.01

10.4.1.2 Summary In summary, from the above ANOVA, it is evident that for both web users with and without WD/M experience that actual and perceived web knowledge content has a significant positive effect on perceived web usefulness.

10.4.2 MRA7: PWU = F (ACTUAL & PERCEIVED WEB KNOWLEDGE) To further test H7a to H13a, multiple regression analysis seven (MRA7) examines the influence on perceived web usefulness (PWU) of the following independent variables: actual common procedural web knowledge (ACPWK), actual common declarative web knowledge (ACDWK), actual specialised procedural web knowledge (ASPWK), actual specialised declarative web knowledge (ASDWK), perceived procedural web knowledge

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(SWPK), perceived declarative web knowledge (SWDK) and perceived overall web knowledge (SWOK).

10.4.2.1 Statistical Assessment Table 50 contains the stepwise multiple regression results. Of the 6 assumptions, 2 are violated and another (lack of multicollinearity) is possibly violated (Appendix O).

Table 50: Multiple Regression Results MRA7: Perceived Web Usefulness = F (Actual [4] & Perceived [3] Knowledge Content) WSD/M Exp.

Dep. Var.

Adj. R2

No Expa

Perceived Web Usefulness

.322

.317

With Expb

Perceived Web Usefulness

Independent Variable

S.E. (b)

Constant

39.671

1.540

1.021

.146

.440

6.974

.000

1.313

.352

.154

3.726

.000

-.761

.159

.184

-4.786

.000

.883

.235

.179

3.755

.000

.288

.089

.201

3.243

.001

-.851

.297

.188

-2.863

.004

41.429

2.116

1.192

.155

.363

7.693

.000

-.710

.150

.142

-4.731

.000

.667

.263

.119

2.530

.001

Perceived Procedural Web Knowledge Actual Common Procedural Web Knowledge Actual Specialised Declarative Web Knowledge Actual Common Declarative Web Knowledge Perceived Declarative Web Knowledge Perceived Overall Web Knowledge .176

.174

Constant Perceived Procedrual Web Knowledge Actual Specialised Declarative Web Knowledge Perceived Overall Web Knowledge

t. stat.

Sig. Level

25.754

70.668

.000

19.582

83.480

.000

a No WSD/M experience – excluded variables: Actual Specialised Procedural Web Knowledge (ASPWK) b With WSD/M experience – excluded variables: Actual Common Procedural Web Knowledge (ACPWK); Actual Common Declarative Web Knowledge (ACDWK); Actual Specialised Procedural Web Knowledge (ASPWK); Perceived Declarative Web Knowledge (SWDK)

For users with no WSD/M experience, SWPK (t = 6.97, p <.01), ACPWK (t = 3.73, p <.01), ACDWK (t = 3.76, p <.01) and SWDK (t = 3.24, p <.01) had a significant positive effect, and ASDWK (t = -4.79, p <.01) and SWOK (t = -2.86, p <.01) had a significant negative effect, on PWU. Collectively, these variables explained 31.7% of the variance in perceived

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web usefulness. ASPWK was excluded from the model. SWPK was the strongest determinant of PWU.

For users with WSD/M experience, SWPK (t = 7.69, p <.01) and SWOK (t = 2.53, p <.01) had a positive effect, and ASDWK (t = -4.73, p <.01) a negative effect, on PWU, with the 4 other variables excluded. The 3-variable model explained 17.4% of the variance in perceived web usefulness.

10.4.2.2. Summary Perceived procedural web knowledge content was the strongest positive predictor of perceived web usefulness for users with no web site design and maintenance experience. This was followed closely by actual common declarative and actual common procedural web knowledge content. Actual specialised declarative web knowledge and perceived overall web knowledge had a negative effect.

In comparison, for users with WSD/M experience, perceived procedural web knowledge and perceived overall web knowledge are the key positive determinants. Actual specialised declarative web knowledge is a negative determinant of perceived web usefulness.

10.4.3 RESEARCH QUESTION TWO: SUMMARY For web users with no WSD/M experience, what they think they know about how to use the web and what certain web features are, and what they actually know about common procedures and attributes of the web, increases how useful they perceive the web to be. Whereas the more they actually know about specialised features and attributes of the web and what they think they know overall, decreases how useful they think the web is.

In comparison, for users with WSD/M experience, what they think they know about how to use the web and what they think they know overall, increases how useful they think

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the web is. Whereas what they actually know about specialised attributes and features of the web decreases how useful they perceive the web to be. See summary Table 51 and Table 52 below for comparative results from the bivariate (Appendix Q) and multivariate analyses presented in this chapter.

Table 51: Web User Group A - No WSD/M Experience: RQ2 Bivariate Label

Dependent

Bivariate Result (Appendix Q)

Curvilinear

PWU

Reject (Accept Null)

Positive

PWU

Accept (Reject Null)

Independent

Relationship

H7A

ACPWK

H8A

ACDWK

H9A

ASPWK

Positive

PWU

Accept (Reject Null)

H10A

ASDWK

Positive

PWU

Accept (Reject Null)

H11A

SWPK

Positive

PWU

Accept (Reject Null)

H12A

SWDK

Positive

PWU

Accept (Reject Null)

H13A

SWOK

Positive

PWU

Accept (Reject Null)

Multivariate Result (Chapter 10) SWPK (+) ACPWK (+) ASDWK (-) ACDWK (+) SWDK (+) SWOK (-)

Table 52: Web User Group B – With WSD/M Experience: RQ2 Bivariate Label

Bivariate Result (Appendix Q)

Independent

Relationship

Dependent

H7A

ACPWK

Curvilinear

PWU

Reject (Accept Null)

H8A

ACDWK

Positive

PWU

Accept (Reject Null)

H9A

ASPWK

Positive

PWU

Accept (Reject Null)

H10A

ASDWK

Positive

PWU

Reject (Accept Null)

H11A

SWPK

Positive

PWU

Accept (Reject Null)

H12A

SWDK

Positive

PWU

Accept (Reject Null)

H13A

SWOK

Positive

PWU

Accept (Reject Null)

Multivariate Result (Chapter 10)

SWPK (+) ASDWK (-) SWOK (+)

10.5 RESEARCH QUESTION THREE Research question 3 asks: what is the relationship between a user’s knowledge content of the web and a person’s perceived ease of web use? As summarized in Table 53 and Table 54 below, for web users with no web site design and maintenance experience, 7 out of 7 hypotheses were supported in the bivariate analyses and for web users with experience, 6 out of 7 hypotheses were supported. See Appendix Q.

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Table 53: Web User Group A – No WSD/M Experience: RQ3 Independent

Relationship

Dependent

Bivariate Result (Appendix Q)

H14A

ACPWK

Positive

PEWU

Accept (Reject Null)

H15A

ACDWK

Positive

PEWU

Accept (Reject Null)

H16A

ASPWK

Positive

PEWU

Accept (Reject Null)

H17A

ASDWK

Positive

PEWU

Accept (Reject Null)

H18A

SWPK

Positive

PEWU

Accept (Reject Null)

H19A

SWDK

Positive

PEWU

Accept (Reject Null)

H20A

SWOK

Positive

PEWU

Accept (Reject Null)

Bivariate Label

Multivariate Label

MRA8

Table 54: Web User Group B – With WSD/M Experience: RQ3 Independent

Relationship

Dependent

Bivariate Result (Appendix Q)

H14A

ACPWK

Positive

PEWU

Accept (Reject Null)

H15A

ACDWK

Positive

PEWU

Accept (Reject Null)

H16A

ASPWK

Positive

PEWU

Accept (Reject Null)

Bivariate Label

H17A

ASDWK

Positive

PEWU

Reject (Accept Null)

H18A

SWPK

Positive

PEWU

Accept (Reject Null)

H19A

SWDK

Positive

PEWU

Accept (Reject Null)

H20A

SWOK

Positive

PEWU

Accept (Reject Null)

Multivariate Label

MRA8

Two multivariate techniques will now be used to further test these hypotheses. ANOVA will firstly be used to explore these 7 hypotheses and determine the mean effect that actual and perceived knowledge content of the web has on a user’s level of perceived ease of web use. Following this analysis, stepwise multiple regression analysis will be used to more specifically identify which types of actual and perceived knowledge content are statistically significant determinants of perceived ease of web use for each user group (MRA8).

10.5.1 ANOVA: PEWU = F (ACTUAL & PERCEIVED WEB KNOWLEDGE) ANOVA is used to analyse if the level (i.e., low, medium and high) of actual and perceived web knowledge content a users has effects their perceptions of how easy the web is to use. There are four measures of actual knowledge and three measures of perceived knowledge content of the web.

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10.5.1.1 Statistical Assessment As presented in Table Table 55, the results of the ANOVAs reveal that the level of perceived and actual web knowledge content (i.e., low, medium and high) has a significant effect on a userâ&#x20AC;&#x2122;s perceived ease of use of the web. A closer look at the mean scores (see Appendix S) shows that the level of actual common procedural, actual common declarative, actual specialised procedural, actual specialised declarative, perceived overall, perceived procedural, and perceived declarative has a significant positive impact on the level of perceived ease of web use. Thus, the more users know and think they know about the web, the easier users will find the web to use. This result is consistent for web users with and without WSD/M experience. See Table 55.

Table 55: ANOVA: Effect of Level of Actual [4] and Perceived [3] Web Knowledge Content (L/M/H) on Perceived Ease of Web Use Knowledge Content

Actual Common Procedural

Actual Common Declarative

Actual Specialised Procedural

Actual Specialised Declarative

Perceived Overall

Perceived Procedural

Perceived Declarative

Groups Between Within Total Between Within Total Between Within Total Between Within Total Between Within Total Between Within Total Between Within Total

No WSD/M Experience df 2 897 899 2 897 899 2 897 899 2 897 899 2 897 899 2 897 899 2 897 899

F. Stat.

Sig. Level

57.215

.000*

49.728

.000*

29.077

.000*

13.139

.000*

198.167

.000*

328.571

.000*

263.789

.000*

With WSD/M Experience df 2 1174 1176 2 1174 1176 2 1174 1176 2 1174 1176 2 1174 1176 2 1174 1176 2 1174 1176

F. Stat.

Sig. Level

11.518

.000*

8.981

.000*

14.269

.000*

7.257

.001*

83.681

.000*

115.911

.000*

110.421

.000*

* p<0.01

10.5.1.2 Summary In summary, from the above ANOVAs, it is evident that for both web users with and without WD/M experience that actual [4] and perceived [3] web knowledge content has a significant positive effect on perceived ease of web use.

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10.5.2 MRA8: PEWU = F (ACTUAL & PERCEIVED WEB KNOWLEDGE) To further test H14a to H20a, multiple regression analysis eight (MRA8) examines the influence on perceived ease of web use (PEWU) of the following independent variables: actual common procedural web knowledge (ACPWK), actual common declarative web knowledge (ACDWK), actual specialised procedural web knowledge (ASPWK), actual specialised declarative web knowledge (ASDWK), perceived procedural web knowledge (SWPK), perceived declarative web knowledge (SWDK) and perceived overall web knowledge (SWOK).

10.5.2.1 Statistical Assessment Table 56 contains results of the stepwise multiple regression. Of the 6 assumptions, 1 is violated and another (lack of multicollinearity) is possibly violated (Appendix O).

For users with no WSD/M experience, SWPK (t = 12.93, p <.01), ACPWK (t = 6.01, p <.01) and SWDK (t = 6.57, p <.01) had a significant positive effect, and ASDWK (t = -5.90, p <.01) and SWOK (t = -5.06, p <.01) had a significant negative effect, on PEWU. These 5 variables explained a total 54.4% of the variance in perceived ease of web use, with perceived procedural web knowledge being the strongest determinant. Actual common declarative web knowledge (ACDWK) and actual specialised procedural web knowledge (ASPWK) were removed from the model for this user group.

For users with WSD/M experience, SWPK (t = 9.34, p <.05), SWDK (t = 2.72, p <.01) and ACDWK (t = 2.45, p <.01) all had a significant positive effect, and ASDWK (t = -7.11, p <.01) had a significant negative effect, on perceived ease of web use. These 4 variables explained a total 27.9% of the variance in perceived ease of web use. Actual common procedural web knowledge (ACPWK), actual specialised procedural web knowledge (ASPWK), and perceived overall web knowledge (SWOK) were all removed from the model.

178

Table 56: Multiple Regression Results MRA8: Perceived Ease of Web Use = F (Actual [4] & Perceived [3] Knowledge Content) WSD/M Exp.

Dep. Var.

No Expa

Perceived Ease of Web Use

With Expb

Perceived Ease of Web Use

Adj. R2

.546

.544

Independent Variable

S.E. (b)

Constant

22.964

1.058

1.308

.101

.662

12.934

.000

-.584

.099

-.166

-5.899

.000

1.184

.197

.163

6.005

.000

.405

.062

.333

6.566

.000

-1.046

.207

-.272

-5.063

.000

24.374

1.813

1.219

.130

.457

9.340

.000

-.970

.136

-.239

-7.114

.000

.191

.070

.135

2.721

.001

.464

.189

.078

2.452

.003

Perceived Procedural Web Knowledge Actual Specialised Declarative Web Knowledge Actual Common Prodcedural Web Knowledge Perceived Declarative Web Knowledge Perceived Overall Web Knowledge .282

.279

Constant Perceived Procedural Web Knowledge Actual Specialised Declarative Web Knowledge Perceived Declarative Web Knowledge Actual Common Declarative Web Knowledge

t. stat. 21.700

13.441

Sig. Level

215.441

.000

114.890

.000

a No WSD/M experience – excluded variables: Actual Common Declarative Web Knowledge (ACDWK); Actual Specialised Procedural Web Knowledge (ASPWK) b With WSD/M experience – excluded variables: Actual Common Procedural Web Knowledge (ACPWK); Actual Specialised Procedural Web Knowledge (ASPWK); Perceived Overall Web Knowledge (SWOK)

10.5.2.2 Summary Perceived procedural web knowledge was the strongest positive determinant of perceived ease of web use for users with no WSD/M experience, with perceived declarative web knowledge and actual common procedural web knowledge being additional positive determinants. Actual specialised declarative, and perceived overall web knowledge, had a negative effect on PEWU.

For users with no WSD/M experience, perceived procedural web knowledge was the strongest positive determinants of perceived ease of web use. Actual specialised declarative web knowledge was the strongest negative determinant.

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10.5.3 RESEARCH QUESTION THREE: SUMMARY For web users with no WSD/M experience, what they think they know about how to use the web, their actual common knowledge about web procedures, and what they think they know about common attributes and features of the web, increases how easy they think the web is. However, what they actually know about specialised features and attributes of the web, and what they think they know overall, has a negative effect on how easy they think the web is to use.

For users with WSD/M experience, what they think they know about how to use the web, and what certain attributes and features are, and what they actually know about common attributes and features, increases how easy they think the web is to use. What they actually know about specialised features and attributes of the web decreases how easy they think the web is to use. These results are broadly consistent across both user groups. See Table 57 and Table 58 below for comparative results from the bivariate and multivariate analyses.

Table 57: Web User Group A – No WSD/M Experience: RQ3 Independent

Relationship

Dependent

Bivariate Result (Appendix Q)

H14A

ACPWK

Positive

PEWU

Accept (Reject Null)

H15A

ACDWK

Positive

PEWU

Accept (Reject Null)

Bivariate Label

H16A

ASPWK

Positive

PEWU

Accept (Reject Null)

H17A

ASDWK

Positive

PEWU

Accept (Reject Null)

H18A

SWPK

Positive

PEWU

Accept (Reject Null)

H19A

SWDK

Positive

PEWU

Accept (Reject Null)

H20A

SWOK

Positive

PEWU

Accept (Reject Null)

Multivariate Result (Chapter 10) SWPK (+) ASDWK (-) ACPWK (+) SWDK(+) SWOK (-)

Table 58: Web User Group B – With WSD/M Experience: RQ3 Bivariate Label

Independent

Relationship

Dependent

Bivariate Result (Appendix Q)

H14A

ACPWK

Positive

PEWU

Accept (Reject Null)

H15A

ACDWK

Positive

PEWU

Accept (Reject Null)

H16A

ASPWK

Positive

PEWU

Accept (Reject Null)

H17A

ASDWK

Positive

PEWU

Reject (Accept Null)

H18A

SWPK

Positive

PEWU

Accept (Reject Null)

H19A

SWDK

Positive

PEWU

Accept (Reject Null)

H20A

SWOK

Positive

PEWU

Accept (Reject Null)

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Multivariate Result (Chapter 10)

SWPK (+) ASDWK (-) SWDK (+) ACDWK (+)

10.6 MULTIVARIATE ANALYSIS: SUMMARY In summary, for users with no WSD/M experience, how easy the web is to use is a primary determinant of their overall web usage. Perceived usefulness is a secondary determinant. Furthermore, what this user group thinks they know about the web, and what they actually know, are key determinants of how easy and useful they see the web.

For users with WSD/M experience, how useful the web is to use is a primary determinant of their web usage. Perceived ease of use is a secondary determinant. Furthermore, what this web user group thinks they know about how to use the web is a primary determinant of how easy and how useful they perceive the web to be.

The results reported in this chapter are further validated with bivariate analyses that are presented in Appendix Q. Next, in Chapter 11, the results reported here are discussed, and related back to earlier discussions in Chapters 3-5.

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C HAPTER 11: E MPIRICAL D ISCUSSION ‘Free and fair discussion will ever be found the firmest friend to truth’ - G. Campbell -

11.1 INTRODUCTION This chapter discusses the main results of the study, and compares and contrasts them with findings from previous studies of web system use and usability. Results for research question 1 are discussed first. This question investigated the relationship between web user perceptions and usage. Secondly, the implications drawn from the relationship between a user’s knowledge of the web and their perceived usefulness of the web are discussed. Thirdly, the implications of the findings derived from the investigation of user knowledge of the web and their perceived ease of web use are discussed. All results are compared across two web user groups, those with and without web site design and maintenance (WSD/M) experience.

11.2 DISCUSSION: WEB PERCEPTION & USAGE Research question 1 asks what is the relationship between a user’s perceptions of the web and that person’s current web session usage? This was investigated by looking at the relationship between perceived ease of web use and perceived web usefulness with 1) web session usage frequency; 2) situational variety; 3) motivational variety; 4) breadth of use; 5) depth of use and 6) duration of use. These are discussed in turn.

11.2.1 PREDICTING WEB SESSION USAGE FREQUENCY (WSUF) Perceived web usefulness was found to have a very weak positive relationship with web session usage frequency for both web user groups. No curvilinear relationship existed between perceived ease of use and web session usage frequency for either user group.

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Further investigation using multiple regression revealed that how easy a user with no WSD/M experience perceives the web is in fact the primary positive determinant of how frequently they will use the web. Perceived usefulness was not found to be a good predictor of web session usage frequency for this user group. Thus, the easier this group perceive the web to be, the more frequently they will actually use it.

In contrast, how useful users with WSD/M experience perceived the web was a primary positive determinant of how frequently they use it. Thus, for this group, the more useful they perceive the web to be the more frequently they will use it.

11.2.1.1 Discussion The findings reported for users with WSD/M experience are consistent with the results obtained in the original TAM study on PROFs™ email, XEDIT™, Chartmaster™ and Pendraw™ by Davis (1986) where perceived usefulness of the system had a profound effect on self-predicted usage behaviour and no statistically significant effect was found for perceived ease of use. This is also consistent with subsequent studies of other processing and communication programs such as WriteOne™ (Davis et al., 1989a; Bagozzi et al., 1992); PROFs™ email and EXIDT™ (Davis, 1989b); of micro-computers (Igbaria et al., 1995); of electronic and voice mail (Adams et al., 1992); of word processing programs (Bronson, 1999); and of CONFIG™ (Gefen and Keil, 1998).

Also, the relationship found for users with no WSD/M experience between perceived ease of use and usage frequency is consistent with research conducted by Morris and Dillion (1997) for the browser program Netscape™ and for web studies by Fenech (1997), Teo et al. (1999) and Lederer et al. (2000). These researchers found that perceived ease of use was a determinant of usage.

These findings provide further support for the suggestion put forward by Moore and Benbasat (1991) and Adams et al. (1992) that the mandatory use of the system in an organisational setting might have an impact on perceptions and usage of a system. These

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authors also emphasise the influence of subjective norms – i.e., perceptions influenced by the view of what others think users should be doing. In the current study, use of the system was segmented according to those that ‘had mandatory use experience’ and those that ‘did not’, as defined by WSD/M experience. Differences were found between these two groups. If your use of the web was not defined by a work-related role (i.e., no experience designing and maintaining web sites) how easy you perceive the web is the primary determinant of how frequently you use it. In contrast, if your use is defined by a work-related role (i.e., experience with designing and maintaining web sites) then how useful you perceive the web is the primary determinant of your usage frequency.

This finding has implications not only for web site design, but also for marketing communications directed at certain user groups. In these cases, the aim might be to promote an online presence or use of the web through advertising, publicity, PR, or even word-of-mouth. For example, for communications aimed at those users with less experience, emphasis of the ease of use of the system is paramount. In contrast, for users with more specialised experience, communication of the more useful benefits of the system is most important.

11.2.2 PREDICTING WEB SESSION USAGE VARIETY: SITUATIONAL (WSUVS) Perceived web usefulness was found to have a positive relationship for both users with and without WSD/M experience. No curvilinear relationship was found between perceived ease of use and web session usage variety–situational for either group.

Investigation using multiple regression revealed that how useful a user with no WSD/M experience perceives the web was the primary positive determinant of the number of situations from which they will access the web, thus increasing situational variety. Perceived ease of use was not a good predictor for this user group.

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In contrast, for users with WSD/M experience, neither how easy, nor how useful the web was perceived to be, had any impact on the number of situations from which the web was accessed.

11.2.2.1 Discussion These findings show that no direct significant relationship exists between perceived ease of web use and the number of situations from which the web is accessed for either web users with or without WSD/M experience. Limited research is available with which to compare these results; however, if compared to the investigation by Igbaria et al. (1995) that identified that perceived ease of use of a system had a positive impact on usage variety and the Teo et al. (1999) study of the diversity of system use, the current results are inconsistent.

By contrast, it was found that perceived web usefulness was a good determinant of the number of situations/locations from which the web would be accessed for users with no WSD/M experience. Thus, the more useful the web is seen to be, so it will be accessed from more situations and/or locations. This result is consistent with the findings reported by Igbaria et al. (1995) and Teo et al. (1999) who argued that the more useful a system the more variety and diversity of use will occur. The implication is that to increase the number of situations and/or locations from which those with no WSD/M experience access the web, their perception of the webâ&#x20AC;&#x2122;s usefulness needs to be increased. This, in turn, has implications for the design of web sites and the marketing communications used to communicate to this user group about web sites and services â&#x20AC;&#x201C; e.g., advertising to direct users to retail and brand sites for online shopping or promotions to encourage use of services such as internet cafĂŠs.

Perceived usefulness was not a good predictor for users with WSD/M experience. Again, this result appears to be at odds with earlier studies. However, it must be kept in mind that other external factors (e.g., physical access, cost, location, etc.) might be very

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important determinants of the number of situations/locations from where the web is accessed.

11.2.3 PREDICTING WEB SESSION USAGE VARIETY: MOTIVATIONAL (WSUVMNO1) Perceived web usefulness was found to positively relate to the number of motivations for web use. No curvilinear relationship was found between perceived ease of web use and the number of motivations for web use for either user group.

Investigation using multiple regression revealed that for users with no WSD/M experience how useful they perceived the web to be, and then how easy they perceived it to be, had a positive effect on the number of motivations they had for using the web. In contrast, for users with WSD/M experience how useful they thought the web was, was the primary positive determinant of the number of motivations they had for using it. How easy they thought the web was had no additional effect.

11.2.3.1 Discussion How useful the web is perceived to be was the primary determinant of the number of motivations for its use by both user groups. Thus, the more useful the web is perceived, the greater the number of motivations users have for using it. This result is consistent with that reported by Teo et al. (1999) in that users come to adopt and use a system primarily because of the functions it is capable of performing. It is also consistent with the argument put forward by Ram and Jung (1990) that product usage variety is dependent on the variety of features offered by the product. As indicated in Chapters 2 and 3, the web is a very complex system with numerous functions and uses â&#x20AC;&#x201C; users who realise this have more motivations for use.

In this sample the relationship is more moderate in strength for users with no WSD/M experience than it is for users with WSD/M experience. Perhaps for the latter group other

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elements such as work related factors (i.e., job description) also influence motivations for web use and thus both perceived usefulness and work task expectations (i.e., subjective norms) will influence the number of motivations for use. This latter distinction is consistent with the argument put forward by Moore and Benbasat (1991) and Adams et al. (1992) that the mandatory use of the system in an organisational setting, and the influence of the subjective norm, impact end usage â&#x20AC;&#x201C; in this case they influence the number of motivations for system usage.

The results for users with no WSD/M experience are consistent with the findings reported by Igbaria et al. (1995) and Teo et al. (1999) that perceived ease of system use will have a positive impact on usage variety and diversity. This begs the question as to the importance of usefulness over ease of use with respect to providing increased motivation for use. It seems that no matter how easy a system is to use, how useful it is for various tasks provides the main influence on the number of motivations for system usage. The implication is that web sites must be useful, then users will have more reasons to use and return to the site than if it was merely easy to use. If the site is not that easy to use, users will still have a number of reasons to use the site if it is deemed useful. By contrast, for those users who do have experience with WSD/M, perceptions of ease of use do not influence the number of motivations for usage. Usefulness alone is the significant determinant of the number of motivations for web use.

11.2.4 PREDICTING WEB SESSION USAGE EXTENT: BREADTH (WSUEB) A curvilinear relationship, as hypothesised, was found between perceived ease of web use and breadth of web session use for users with no WSD/M experience. This was not found for users with this experience. Furthermore, no relationship was found between perceived web usefulness and breadth of web session use for either user group.

Investigation using multiple regression revealed that neither how useful nor how easy the web was thought to be were determinants of breadth of web session use for users with no WSD/M experience. In comparison, how useful the web was perceived to be was

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a primary negative determinant of web session usage breadth for users with WSD/M experience. These experienced users access fewer different and/or new web sites, and also access fewer search tools.

11.2.4.1 Discussion As specified above for users with no WSD/M experience, perceived usefulness in the bivariate and multivariate analysis was not found to influence breadth of web session use. Thus, no matter how useful or not they perceive the web, this will not influence the breadth of different types of web sites or search tools used. A u-shaped relationship was identified between perceived ease of use and breadth of session use in the bivariate analysis - possibly explaining why no linear relationship was found for this user group in the multivariate analyses (i.e., it was curvilinear not linear). The relationship implies that at first, when the web is seen as not very easy to use, the number of new or different sites/search engines used is quite low (i.e., exploration may be low). As the web becomes moderately easier to use, and as users explore a little, the number of new/different sites or search engines accessed grows. Then, as the web is seen as very easy to use, the number of new/different web sites or search engines decreases â&#x20AC;&#x201C; perhaps as use becomes more routine and as favourite sites and/or search tools are bookmarked and repeatedly used.

For users with WSD/M experience, as the perceived usefulness of the web increases, the number of new and/or differing sites visited or search tools used slightly decreases. The implication is that the more useful the web becomes, so the less exploration across new and/or differing sites occurs, perhaps as users develop a â&#x20AC;&#x2DC;choice setâ&#x20AC;&#x2122; (i.e., a list of regular bookmarked sites). This suggests experience gives rise to online loyalty toward web sites and/or search tools. And, perhaps, implies a need to see how quickly users develop their list of favourite sites and bookmarks.

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11.2.5 PREDICTING WEB SESSION USAGE EXTENT: DEPTH (WSUED) Perceived web usefulness was found to have a positive relationship with depth of web session use for both user groups. No curvilinear relationship was found between perceived ease of web use and depth of web session use.

Further investigation using multiple regression revealed that for both user groups, how easy they thought the web was, followed by how useful they thought it was, will have a positive effect on the total number of web sites and search tools used.

11.2.5.1 Discussion The main difference between the two groups is that perceptions of ease of use and usefulness have a larger impact on the depth of current web session usage for users without WSD/M experience than for users with this experience. Dreze and Zufryden (1997a) found that site visit depth was explained by web site attributes; that is, the more site attributes the greater the site depth, the more effective the site attributes the more site depth, and so forth. Therefore, perhaps perceived ease of use mediates the relationship between system features and usage depth. For example, the better designed the attributes the easier the web is to use, and the more useful it is seen, and thus the more sites and/or search tools that will be used.

This finding also might relate to the â&#x20AC;&#x2DC;use effectivenessâ&#x20AC;&#x2122; variable explored by Segars and Grover (1993) as an additional factor of TAM that influences usage. This states that usage increases as the perceived effectiveness of a system increases. Thus, the more effective a system, the greater the total number of sites and/or search tools accessed.

In sum, this finding opens up the question of the role of user perceptions of a system in relation to system attributes and drivers of systems use. This has implications for the development and testing of system usability.

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11.2.6 PREDICTING WEB SESSION USAGE EXTENT: DURATION (WSUEDUR) Perceived web usefulness was found to have a positive relationship with duration of web session use for both user groups. No curvilinear relationship was found between perceived ease of web use and duration of web session use.

Further investigation using multiple regression revealed that the easier both user groups found the web, the longer they used it, irrespective of its perceived usefulness (i.e., perceived usefulness was removed from the model).

11.2.6.1 Discussion Dreze and Zufryden (1997a) report that the duration of visits was explained by web site attributes. Therefore, attributes of the web site and the web system have a direct impact on session usage duration, not an indirect effect through perceived ease of use. The findings in this dissertation are in contrast, with perceived ease of use possibly being a moderator, not a mediator, between system features and session duration. Further support for this comes from a study conducted by Hoffman and Novak (1996) who argue that perceived ease of use of a system promotes seamless navigation and when consumers are ‘seamlessly’ navigating though a web site, they are in a state of ‘flow’, resulting in more time spent at the site.

Holbrook and Gardner (1993) also argue that duration time is a critical outcome measure of consumption experiences and may be a useful behaviour indicator of experiential versus goal-directed orientations. Perhaps, then, the easier a system is to use, the longer it will be used for, and the more experiential or exploratory information seeking behaviour will be performed.

These findings further open up the discussion as to what other factors may influence how long the web is used, in addition to perceived ease of web use. For example, intrinsic factors such as task/motivation for which the web is used (i.e., checking email,

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navigating, shopping, etc.) might play a role. So too might extrinsic factors, such as cost, system capability (i.e., download speed), and web site/system features (i.e., browser used, use of frames, java, flash, operating system, ISP, etc.).

11.2.7 RQ1 SUMMARY: WEB PERCEPTION AND WEB USAGE The findings suggest users with no WSD/M experience should think the web is:

easy to use and they will use it more often and for longer;

useful and they will access it from a variety of situations/locations;

firstly useful, and then easy to use, and they will have more motivations for using it;

firstly easy to use, followed by useful, and they will access and/or use a larger number of sites and/or search tools.

Users with WSD/M experience should think the web is:

useful and they will use it more frequently, have more motivations for using it, and will access an increased number of different and/or new sites and search tools;

easy to use and they will use it for longer;

firstly easy to use, and then useful, and they will access overall a larger number of sites and/or search tools.

11.3 RQ2&3 DISCUSSION: WEB KNOWLEDGE & PERPCETIONS Very few studies investigating TAM have explored the determinants of a user’s perceptual beliefs about the systems in question. Past research has concentrated on explaining how the beliefs in the model lead to system use, not what leads to these perceptions. In contrast, this study investigated user knowledge content of the system as a determinant of their perceptions. This is discussed further in relation to research questions 2 and 3.

Difficulty arises when attempting to discuss and compare the results with earlier work because that work has been so limited. Nevertheless, a number of studies have used

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domain-related experience as a proxy measure for actual knowledge content and subjective measures to infer ‘actual knowledge’, and this provides a basis for comparison.

11.3.1 RQ2: PREDICTING PERCEIVED WEB USEFULNESS For users with no WSD/M experience, perceived usefulness increases when they think they know how to use the web, when they think they know about certain common features and attributes of the web, when they actually know about common procedures for using the web, and when they actually know about certain common features and attributes. In contrast, what they actually know about specialised features and attributes and what they think they know overall, will decrease how useful they regard the web.

For users with WSD/M experience, perceived usefulness increases when they think they know how to use the web and when they think they know about the web overall. Consistent with users with no WSD/M experience, what they actually know about specialised features and attributes will decrease how useful they regard the web. 11.3.1.1 Discussion From the above it is evident that to make users with WSD/M experience see the web as more useful, they need to think they know a lot overall about the web and more specifically how to use it. An interesting thing to note here is that these users rely more on what they think they know about the web as an indicator of how useful it is, than what they actually know – despite having more technical experience. This result is consistent with results found by Handzic and Low (1999) if ‘experience’ is used as a proxy for knowledge. They found that more experienced users of processing programs had more favourable perceptions of the usefulness of the technology. Perhaps, as users become more experienced with using processing programs, they become more aware of certain program features and also more efficient in the use of its attributes. This notion is supported by Reed and Ouchton (1997) who found that hypermedia knowledge in general had a large impact on user productivity and Thompson et al. (1994) who found that experience had a strong direct effect on usage.

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For users with no WSD/M experience, to see the web as more useful they need to not only think they know a lot about how to use the web, and what common features and attributes are, but also have a good actual common knowledge of how to use the web and what certain web features are. An additional factor here may be the effect of overall educational level. Igbaria (1993), in another technology-based study, found that educational level has a significant negative effect on computer anxiety and a significant positive effect on perceived usefulness. Educational level as a proxy for knowledge is supported by Brancheau and Wetherbe (1990) who found that early adopters of spreadsheet software were likely to be more highly educated than late adopters. In some cases, for users with no WSD/M experience, knowledge acts as a means by which to increase confidence with the system (i.e., reducing anxiety). It may also increase the perceived usefulness of the system, if there is a degree of common and perceived knowledge â&#x20AC;&#x201C; as opposed to actual specialised knowledge.

In sum, confidence in a system, and perceptions of system usefulness, are influenced by perceived and actual knowledge of the electronic system. This is true for both user groups, although there are some differences in the type of perceived and actual knowledge and the degree of effect. Diaz, et al (1997) further identified that experience with the web, as a proxy for knowledge, was an important moderator of attitude toward the medium and that experienced users found the web more legible and more stimulating.

11.3.2 RQ3: PREDICTING PERCEIVED EASE OF WEB USE For users with and without WSD/M experience, what they think they know about how to use the web, what they think they know about web features and attributes, and their common knowledge about how to use the web, all have a positive influence on how easy they think the web is to use. In contrast, what these groups actually know about specialised attributes and features has a negative effect on how easy they think the web is to use.

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11.3.2.1 Discussion It was theorised in this study that a stronger relationship would exist between perceived knowledge content and a user’s perception of ease of use than with actual knowledge content of the web. This proposition was motivated by the finding that experience is more accessible in memory than stored knowledge (Park et al. 1994). Perceptions – to the extent that they are based on experience – might also be more accessible in memory. This appears to be the case for both user groups.

Handzic and Low (1999) also reported that ease of use is related to information technology experience; that is, the more experience you have, the easier you will find a system to use. In this dissertation it was found that for both user groups, what they thought they knew about how to use a system had the strongest positive influence on how easy they thought the system was to use. This relationship was somewhat stronger for those with no WSD/M experience, than those with this experience. In these circumstances, training is of the upmost importance – to give users experience and to build confidence. This, in turn, is likely to result in them regarding the system as easier to use.

Venkatesh and Davis (1996), in an expansion of TAM that focused on the antecedent variables of perceived ease of use, theorized that direct experience with software moderates the relationship between objective usability and perceived ease of use. Objective usability of a system is a measure of how easy it is to use, derived from comparing what it would take for an expert to complete a task using the system to what it would take for a novice to complete the same task using the same system. Venkatesh and Davis (1996) predicted that objective usability would be a predictor of perceived ease of use only after an individual had direct experience with the software. They found support for their predictions and the results reported here appear to be consistent with these conclusions.

11.3.3 RQ2 AND RQ3 SUMMARY: PREDICTING WEB PERCEPTIONS In summary, for users with no WSD/M experience:

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Make them think they know a lot about how to use the web, and also about what certain features and attributes are, and they will think the web is more useful and easier to use.

Make sure they actually have a good common knowledge of how to use the web, and what certain features and attributes are, and they will see the web as more useful.

Make sure they actually have a good common knowledge of how to use the web and they will see the web as easier to use.

For users with WSD/M experience:

Make them think they know a lot about how to use the web, and that overall they know a lot about the web, and they will think the web is more useful.

Make them think they know a lot about how to use the web, and what certain features and attributes are, and they will think the web is easy to use.

Make sure they actually have a good common knowledge about what certain features and attributes are and they will think the web is easy to use.

11.4 EMPIRICAL DISCUSSION: SUMMARY The findings reported here contribute to the debate about the relationship between a user’s confidence with technology and how easy and useful they find the technology. Although actual common knowledge of the system has a positive effect on user perceptions, what a user thinks they know about how to use a system (i.e., perceived procedural knowledge) is evidently the strongest predictor of both how easy and how useful they think the system is. This is the case for users with and without WSD/M experience, although there are some differential effects across these two groups.

Discussed in the next chapter, Chapter 12, are the academic and managerial contributions of this dissertation, the areas for future research, and also a number of limitations.

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C HAPTER 12: I MPLICATIONS , C ONTRIBUTIONS , L IMITATIONS AND E XTENSIONS â&#x20AC;&#x2DC;Whereas in art nothing worth doing can be done without genius, in science even a very moderate capacity can contribute to a supreme achievementâ&#x20AC;&#x2122; - Bertrand Russell (1872 - 1970)

12.1 INTRODUCTION This study drew on studies from consumer research and information technology to investigate user knowledge and user perceptions of the web. A framework was developed to depict the effect user knowledge and perceptions of this highly complex and technologically driven system might have on system usage. A number of hypotheses were proposed, tested and analysed. In this final chapter, Chapter 12, we discuss the academic and managerial contributions of this study, the areas for future research, and also a number of limitations and extensions.

12.2 RESEARCH CONTRIBUTIONS As indicated at the beginning of this dissertation, observing consumers as they use products can be an important source of new product ideas and can lead to ideas for new product uses or product design and development. Furthermore, new markets for existing products can be indicated, as well as appropriate communication themes for product promotion. Considering the economic importance of new products and their high rate of failure, it becomes crucial to identify factors fostering and inhibiting consumer adoption and use. Understanding how products and electronic technologies

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are used, and what determines their usage, is thus an important part of researching and understanding consumer behaviour.

Usage has important implications for the communication of product information to the consumer. Ram and Jung (1990), for example, showed that only a small number of respondents reported the use of certain features of durable goods, with some respondents not even aware of these features. This result is extremely apparent in the research on technology-related products (Higgins and Shanklin 1992). Current usage could also be used as the basis for segmenting product markets. For example Potter et al. (1988) attempted to identify the profiles of five usage segments for VCRs. Studies of computer usage in the workplace have had a wide range of uses too. They have been used to determine training needs, to determine the effectiveness of system implementation, to establish time costs associated with certain work tasks, and to monitor work output. Research related to the implementation of information systems has provided ample examples of how usage estimates facilitate the evaluation of system success. For example, user receptivity towards computers (Sarris, Sawyer and Quigley 1993; Saltz, Saltz and Rabkin 1985) and the effect of computer implementation and use (Knapp, Miller and Levine 1987).

In a general sense, therefore, it is valuable to profile system usage and understand predictors of use. This study, in particular, contributes to our understanding in three core areas: ‘system usage’, ‘system perceptions’ and ‘system knowledge content’.

12.2.1 SYSTEM USAGE This study contributes three core elements to the analysis of system usage, namely: 1) the measurement of system usage; 2) the role of usage context on system use; and 3) determinants of system use. These three are discussed in turn.

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12.2.1.1 Measurement of System Usage In this study, the development of more refined, tested and validated self-report measures of ‘post-purchase’ system usage extends the literature on ‘usage measures’ to include three specific areas – the measurement of frequency, variety and extent of system use. Furthermore, the study differentiates measures of ‘current web session usage’ from measures of ‘past web session usage’ – something that is not done in much of the academic and commercial research where web usage behaviour is investigated.

12.2.1.2 System Usage Context Empirical support was presented in this dissertation for the argument put forward but not tested by Moore (1991) and Adams et al (1992) that the usage context influences the effect of user perceptions of a system on usage. It was found that for users with no WSD/M experience their usage was primarily influenced by how easy to use they thought the web was, and then secondly by how useful. Whereas for users with WSD/M experience, their usage was primarily influenced by how useful they regarded it. This result has important implications not only for the Technology Acceptance Model (TAM) but also for web site design and marketing communications.

12.2.1.3 Predicting System Usage This study further extends work conducted on TAM toward predicting system usage. Firstly, most of the earlier TAM studies only test the effect that perceived ease of use and perceived usefulness have on usage frequency. In this study, a wider number of measures of usage was included to obtain a better overall picture of the impact of user perceptions on system usage. Thus, the measures included not only usage frequency, but situational and motivational variety, and breadth, depth and duration of system use. These are important measures of usage and thus should be included in TAM to obtain a more accurate and complete picture of the effect of user perceptions.

Secondly, this dissertation identified that PEWU and PWU had differential effects on usage frequency, depending on which user group a person belongs to. In essence, if you

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had experience with designing and maintaining web sites, than how useful you found the web was the primary predictor of how frequently you used it. In comparison, for users with no experience designing and maintaining web sites, then how easy you thought the web was to use became the main predictor of how frequently you used it. This result helps to explain conflicting results in the literature, relative to TAM, of the relationship between PEWU and PU and usage frequency.

Thirdly, usefulness was found to be the primary predictor of the number of motivations for web session use. Thus, the more useful the system is, the increased number of motivations users will have for using it. This result is interesting as both commercially and academically there is a strong emphasis on first improving how easy a system is to use by looking at how it is designed before addressing the functions for which it is used and thus why a user might use it. In this study, how easy the system was to use had only a secondary influence on the number of motives for use. Whereas perceived usefulness was found to be the key to driving an increased number of usage motives.

Fourthly, how easy the web is to use has a positive effect on the total number of web sites and/or search tools a user will use. Thus, the easier the system is to use, the increased number of functions and/or features will be used. This might be related to another core finding â&#x20AC;&#x201C; that the easier the system is to use, the longer it will be used â&#x20AC;&#x201C; usage duration. Thus, in this case, because the system is perceived as easy to use, more features and functions are used and, in turn, it is used for longer.

In summary, depending on the user group that is being targeted, it is important to understand the core difference between the effect of how useful they perceive the system to be and how easy they perceive it to be. This has a large impact on usage frequency of the system, the number of locations from which it is accessed, the number of motives they have for using it, the different functions/attributes of the system they use, the total number of functions they use and how long they will use it.

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12.2.2 USER PERCEPTIONS This study contributes two core elements to system perceptions, namely 1) the measurement of system perceptions and 2) determinants of system perceptions. These two are discussed in turn.

12.2.2.1 Measurement of Web User Perceptions The study further refines and validates measures of perceived ease of web use (PEWU) and perceived web usefulness (PWU). Most measures of PEU and PU are global measures of user perceptions (i.e., overall evaluation) and they do not take into consideration the functions for which the system might be used. This has mainly been as a result of their testing on systems dictated by only one or two core functions information processing (e.g., MS Word) and communication (e.g., email software).

Therefore, an extra effort was made in this study to develop measures that actually measure perceived ease of use and usefulness for certain system functions (e.g., shopping, communication, information search, etc.), as well as providing an overall evaluation. This increases not only the usefulness of the scales developed, but also the ability to further understand and better tailor systems to user needs.

12.2.2.2 Predicting User Perceptions This study further extends work conducted on TAM toward predicting system perceptions. TAM predicts the acceptance of end-user applications by specifying causal relationships among perceptual beliefs, attitudes and the adoption and acceptance of system technologies. This aspect of the model has received great attention. However, very few studies explore the determinants of a userâ&#x20AC;&#x2122;s perceptual beliefs about the systems in question. Although highly valuable, in the development and further testing of TAM, past research has concentrated on explaining how the beliefs in the model lead to system use. By contrast, little has been done to explore how and why these beliefs were formed. As stated by Karahanna and Straub (1999), what explains how a user comes to

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believe that a system is useful in his or her job? What would be the antecedents for the belief that a system is simple or difficult to use?

The original model posited by Davis (1986), and later modified by a number of researchers, put considerable emphasis on the characteristics of the system (i.e., design, attributes etc) as antecedents of perceived ease of use and perceived usefulness of the system in question. But authors such as Krech et al. (1962) suggest this is only half the story. They categorise the human perception process as influenced by two distinct factors: stimulus factors (e.g., browser) and personal factors (e.g., experience), further specifying that perception is a result of both. Due to the heavy focus in the literature on the characteristics of the system as an influence on consumer behaviour, this dissertation has investigated the influence of a specific personal factor, knowledge content, on a userâ&#x20AC;&#x2122;s perception of the web.

12.2.3 USER KNOWLEDGE CONTENT The study contributes two core elements to system knowledge, namely 1) knowledge conceptualisation and 2) knowledge operationalisation. These are discussed in turn.

12.2.3.1 Knowledge Conceptualisation Within the knowledge literature, use and misuse of knowledge terminology, has led to a certain amount of semantic confusion. For example, familiarity, expertise, procedural, declarative and subjective knowledge have all been defined as types of knowledge. Thus, it was necessary to draw from the cognitive sciences and marketing to establish a consistent and simplified definition of knowledge.

Knowledge was defined as the body of facts and principles (information or understanding) accumulated by mankind (stored in memory) about a domain (Delbridge and Bernard, 1998). This information is structured or organized in memory in certain formats (knowledge structures), differs in its type of content (procedural and

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declarative) and it scope (novice and expert) and may be measured in different ways (objective and subjective).

12.2.3.2 Knowledge Operationalisation Heavy emphasis was also placed on both the objective and subjective measurement of consumer knowledge content. From the existing literature a number of issues arise such as the use of proxies to measure knowledge content and the treatment of knowledge measurement as types of knowledge content. One of the most common methods for measuring consumer knowledge, especially in the technology area, has been the use of proxies to infer consumer knowledge. For example, domain usage and purchase experience have been heavily used. This study went beyond the use of proxies.

In addition, objective and subjective methods for measuring consumer knowledge have been well documented (Brucks 1985; Dacin and Mitchell 1984; Rao and Olson 1990). Nevertheless, heavy use of subjective measures in the technology sector has necessitated the development of measures of actual knowledge of a system. Support for the measurement of what is actually stored in a consumerâ&#x20AC;&#x2122;s memory was identified by Brucks and Mitchell (1981) and Engel, Blackwell and Miniard (1990), concluding that objective measures of knowledge were better than experience and self-report measures. Thus, in this study, objective and subjective measures of user knowledge content of the web were developed, validated and tested across two very differing user groups â&#x20AC;&#x201C; user with and without web site design and maintenance experience.

12.3 RESEARCH LIMITATIONS No study is without its limitations, and this study is no different. A number of limitations have been identified and are outlined in the following sub-sections.

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12.3.1 GENERALISABILITY LIMITATIONS Even though the internet and web are global electronic technologies, this study was limited to an Australian-based sample of respondents. Although a limitation when generalising to the â&#x20AC;&#x2DC;international web audienceâ&#x20AC;&#x2122;, it has the advantage of providing a more homogeneous sample with respect to basic cultural differences in the assessment of user perceptions, knowledge and use of the web. Care must be taken when generalising the results of this study across geographical boundaries to web users in other countries, who may be exposed to differing technological environments and subject to different societal values and cultural influences.

12.3.2 RESEARCH CURRENCY LIMITATIONS Given the rapidity with which technologies change and the changing profile of those using the technologies, the results are likely to be somewhat time dependent. For example, the browser software and web sites upon which the analyses were based will change and thus the measures developed and tested in this study will need to be continually updated. The measurement scales developed for this study will require continual re-evaluation and updating in tune with the changing technology.

12.3.3 VARIABLE MEASUREMENT LIMITATIONS Very consistent results are reported between the scale testing phase (Chapter 7) and the final study (Chapter 9). This gives confidence in the procedure and the results. But, a few specific limitations were identified with respect to how some of the measures were operationalised in the study.

12.3.3.1 Actual Knowledge Content Further refinement of the items developed to measure actual knowledge content of the web would be beneficial. This is because of the possibility of response error due to survey length and respondent inaccuracies when answering questions.

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In addition, the items used to measure actual common declarative, actual specialised declarative, and actual specialised procedural knowledge, only explain 57%, 59% and 50% of the variance respectively. Further scale development and refinement might increase the percentage of the construct explained by including ‘other’ features, terms and/or areas of information required when using the web.

Furthermore, actual procedural web knowledge content (common and specialised) was measured here using objective tests (i.e., true/false, etc.) which are in fact more suitable for the measurement of declarative knowledge. To assess procedural knowledge more appropriately, practical task-based objective tests such as an online experiment need to be carried out. This might further increase the differentiation between procedural and declarative knowledge content.

12.3.3.2 Perceived Scope of User Knowledge Content As evident during the scale development stage, a limitation exists with the difficulty of measuring the perceived scope of knowledge content that a user has. Further scale development and testing is required to ascertain subjective measures of the scope of web knowledge content. A further review of existing scales revels that in some cases respondents are asked to rate their perceived knowledge content as compared to ‘an expert’ or ‘a novice’. This might be one means by which to measure perceived common and specialised knowledge content.

12.3.3.3 Current Web Session Usage Variety - Situational It was felt that two items would be better to measure current web usage variety – situational. This is because situations for web use can differ in variety by the type and number of locations from which the web is access. This was a post hoc adjustment, made after item testing and purification.

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12.3.4 SAMPLE RECRUITMENT & INSTRUMENT LIMITATIONS A contribution is made in terms of creating, developing and maintaining the web-based survey. This resulted in over 2,000 responses, a figure that compares extremely favourably with most off-line surveys (see Appendix F for details). Nevertheless, some improvements should be kept in mind for future studies.

12.3.4.1 Recruitment Method The banner advertising campaign was not run for the entire duration of the study due to both cost considerations and the availability of banner ad inventory. Thus, although the level of ad exposure was highly effective for the study at hand (with n = 2077), this approach might have had an impact on the type and number of respondents that were recruited. It certainly had an impact on when respondents were recruited.

12.3.4.2 Self-selection and Self-reporting The survey method allowed participants to select themselves for inclusion after initial awareness of the study from both online and offline advertising and publicity. Thus, there is likely to be some self-selection bias associated with this. Although a test for response bias was performed between early, mid and late respondents, it is still entirely possible that those web users with more experience and increased knowledge content of the web formed the majority of those responding â&#x20AC;&#x201C; this has been documented in other web-based studies. To try to minimise this problem efforts were made to increase awareness of the study among those with less experience and/or knowledge content of the web; for example, through the choice of media vehicles that novices might use.

In addition, usage and demographic statistics were self-reported, rather than observed, and thus care should be taken in interpreting and generalising from these results.

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12.3.4.3 Multi-Response Every attempt was made to track users by collecting personal details. This was to ensure respondents only answered the survey once. However, this process was ad-hoc and thus it cannot be said for sure that there were not any case of multiple-response. The use of voluntary â&#x20AC;&#x2DC;opt-inâ&#x20AC;&#x2122; mail lists, a web-based panel, or the use of advanced technical features to lock-out multiple responses from the one client location (i.e., to prevent multiple responses from one computer) could have been used to reduce the occurrence of multiple-responses. However, due to the unavailability and additional expense of these options, they were pursued.

12.3.4.4 Survey Instrument Design The design and length of the survey might have influenced the quality of the information that some respondents provided. While pilot study results showed that people were willing to complete the survey, it undeniably was seen as lengthy. This could have had an impact on who chose to complete the whole survey. Also, it is likely that time pressure and fatigue would have had an impact on the accuracy of the responses from some of the respondents, especially as many uses pay for their internet access and thus completing the survey would mean not only a time cost, but a financial cost too.

12.3.5 ANALYTICAL LIMITATIONS The study makes a contribution in terms of scale development. This involves the use of both qualitative pre-analysis and quantitative testing (see Appendix B). Again, however, improvements could be made in future studies.

12.3.5.1 The Relationships Further investigation of the curvilinear relationships might be useful. One approach is to reduce the measures with more than 3 categories to 3-categories (i.e., low, medium and high); this facilitates chi-square analyses (see Appendix Q), but it also results in the loss of information. Such data reduction may impact the results of the bivariate analyses. An

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alternative approach is to transform the data. However, the number of possible transformations is large and this then runs the danger of introducing new difficulties (Hair, et al. 1995).

Further investigation of the relationships between the underlying dimensions (factors) of each contruct might prove for more detailed results. As noted in this dissertation, only the relationships between the overriding constructs were examined and not the underlying dimensions of each construct. Thus, in subsequent analyses it may be informative to examine the greater number of dimensions that some of the exploratory factor analyses suggest.

12.3.5.2 Regression Analyses A number of the assumptions required to undertake the multiple regressions in Chapter 10 were violated (see Appendix O). One particular concern is the possibility of multicollinearity. However, given the exploratory nature of these analyses stepwise multiple regression analysis was deemed to be adequate and appropriate. It is noted that many studies in this field of research are likely to suffer similar limitations, although surprisingly few of the published studies report whether the assumptions were violated or not. In general, the results of these analyses should be looked at with an element of caution before further validation of the results is conducted.

A problem in many regression analyses is the impact of influential observations (Hair et al., 1995). Robust regression techniques are needed to deal with this. However, with a sample of over 2,000 this is very unlikely to be a problem here.

There are alternatives ways to carry out the regression analysis. The dependent variables could have been converted into dichotomous variables, or a small number of categorical variables, and then logistic regressions might have been run. There is also scope for structural equation modelling, to identify the set of causal relationships between the constructs of interest. This is something that should be pursued in future studies.

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12.4 EXTENSIONS AND FUTURE RESEARCH At least four areas for further research are noted: replication of this study with a similar sample and across geographic samples, a comparison of both users and non-users of the web, extensions to the model, and the testing of the framework across differing electronic technologies and other media. These are discussed in turn.

12.4.1 REPLICATIONS TO CONFIRM THE RESULTS Two types of replication could be conducted to further validate the findings. Firstly, exact replication of the study on an Australian web sample under similar conditions and with similar methods of recruitment. Secondly, differentiated replication over space and time. For example, the study could be replicated using web samples from the United Kingdom, Europe and, most importantly given the longevity of use and the origins of the web, the United States of America. This form of replication would aid both the validation of framework proposed and the measures used, and would also assist in increasing the generalisability of the results. Given changes in the development and use of the web, time-based replications might be worthwhile too.

Another way to confirm the results is to use multi-mode sampling (i.e., online and offline). This is recommended by Yun and Trumbo (2000).

12.4.2 COMPARISONS OF USERS AND NON-USERS OF THE WEB Multi-mode sampling methods (i.e., online and offline) might assist in the recruitment of an increased number of novice users and secure responses from some non-users, thereby increasing the variance in the sample. This would further aid the comparison of different types of user, and allow for some comparisons to be drawn with non-users (‘usage intent’ might be measured among non-users).

This is important because by no means all demographic groups have participated in the information revolution – those who are poorer, less educated, from rural areas, and

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females consistently have been slower to use both computers and the internet (Bikson and Panis, 1997; Tapscott, 1998). Although the gender differential is changing, the gap between those with and without computer and/or internet access has serious consequences. In some cases this usage differential is not just based on access to technology, but is also on a willingness to have access. According to Schumacher and Morahan-Martin (2001) this is creating a society of digital haves, and have-nots. As stated by Tapscott (1998) â&#x20AC;&#x2DC;the issue is not just access to â&#x20AC;Ś new (technology), but rather whether differences in availability of services, technology fluency, motivation and opportunities to learn may lead to a two tiered world of knowers and know-nots, doers and do-notsâ&#x20AC;&#x2122; (p256).

Furthermore, computers and internet expertise is reported by Schumacher and MorahanMartin (2001) as having important educational and economic benefits. The stratification between those with and those without internet access is creating a digital divide. The rich are going to be getting even richer in terms of information. The information poor will become even more impoverished as government bodies, community organizations and corporations shift resources from their ordinary channels of communication onto the internet (Schumacher and Morahan-Martin, 2001).

The framework presented in this dissertation provides a means to further explore differences between users and non-users of the web. The results could help those who are attempting to increase the intentions-to-use of non-users through communications and education programs. The results also highlight to both commercial and government groups the implications of abandoning traditional channels of information flow (i.e., non-web based communications).

In addition to the inclusion of non-users, other bases of comparison can be envisaged. For example, comparisons of different demographic and psychographic samples (e.g., by age, by an urban-rural split, etc.), and different user samples (e.g., by profession, by online shopping experience, by level of involvement in the web, etc.). Given the capacity

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of on-line surveys to result in large sample sizes there would seem to be greater scope here for dividing the data into sub-groups and drawing comparisons between these.

12.4.3 EXTENSIONS TO THE MODEL In order to make progress, and to focus on the main hypotheses, only a partial model has been investigated here. As a consequence, many potentially important factors have been set to one side â&#x20AC;&#x201C; for example, usage intentions and situational factors. The importance of these has been emphasised in other consumer behaviour studies (Foxall 1980). Clearly, the model needs to be extended to make proper allowance for these. Extensions of this nature might also entail the use of slightly different methods, such as the creation of an on-line user panel to monitor peopleâ&#x20AC;&#x2122;s attitudes, intentions and usage over time.

Other dependent variables can be envisaged. Usage, for example, is measurable in terms of hits, page views, visits, visitors, etc. A certain amount of this information was collected during the course of the on-line survey (see Appendix H), and this holds out the promise of further analysis.

12.4.4 OTHER ELECTRONIC TECHNOLOGIES AND OTHER MEDIA It is also important to further validate and extend the results by applying the framework to existing, developing and new electronic tools, technologies and media. The digital revolution has the potential to bring about many changes (Barwise and Hammond 1998). Within the context of HCME-based electronic systems, like the web, the framework developed here could be applied to a range of electronic technologies (e.g., the web itself, personal digital assistants (PDAs), touch-screen e-kiosks, etc.). These electronic technologies differ from non-HCME based electronic technologies (i.e., TV, radio, etc.) in terms of vividness, interactivity, media pacing (i.e., external/internal) and the flow of information and communication transfer. The way consumers see and use all these

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technologies is of significance for media owners, systems operators, government users, commercial advertisers and commercial users.

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A PPENDICES

In the pages that follow are a series of technical appendices that provide background support and/or further technical information of the steps undertaken for the conduct of this research. In brief,

Appendices A and B provide a summary of past research into the Technology Acceptance Model (TAM);

Appendices B, C, D, and E present information about the conceptualization, operationalisation and testing of the constructs measured in this study;

Appendices F, G, H, and I, present the survey instrument, details of the web site and advertising and publicity tactics used to create survey awareness, drive traffic and capture web user responses;

Appendices J and K provide information about the validation of the scales/items used to measure the constructs discussed in this study;

Appendices L, P and R provides a descriptive profile of the constructs measured, the relationship between each construct (i.e., correlation matrix) and the performance of the sample across these constructs; and

Appendices M, N, O, Q, & S provide technical information about the conduct of the bivariate and multivariate analyses used to test the hypotheses set down in this study.

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A PPENDICES - TABLE OF C ONTENTS Appendix A: Previous TAM Research ....................................................................................................... 226 Appendix B: Primary Exploratory Research............................................................................................. 228 Appendix C: Variable Conceptualisation and Operationalisation (Pre/Post Test) .......................... 243 Appendix D: Scale Development (Student Sample One: n=128).......................................................... 245 Appendix E: Scale Development (Student Sample Two: n=153).......................................................... 246 Appendix F: Web Site and Web Survey Design....................................................................................... 247 Appendix G: Web Survey Advertising and Publicity............................................................................. 260 Appendix H: Web Site Performance Statistics.......................................................................................... 263 Appendix I: DoubleClick™ Banner Ad Campaign Report/s ................................................................ 264 Appendix J: Scale Validation (Web Sample: n=2077).............................................................................. 265 Appendix K: Scale Performance Comparison (Student Samples and Web Sample)....................... 267 Appendix L: Variable Distribution ............................................................................................................. 268 Appendix M: Multiple Regression – Residual Plots ............................................................................... 271 Appendix N: Multiple Regression – Normality P-P Plot ....................................................................... 274 Appendix O: Multiple Regression Assumption Check .......................................................................... 277 Appendix P: Sample & Variable Description ........................................................................................... 278 Appendix Q: Bivariate Analysis - Convergent Validation .................................................................... 283 Appendix R: Nonparametric Correlation Coefficients: Spearman Rho.............................................. 335 Appendix S: ANOVA Reported Mean Scores .......................................................................................... 337

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APPENDIX A: PREVIOUS TAM RESEARCH Authors

Constructs

System Context

Methodology Context

Findings (Indep. > Dep.)

Davis (1986)

EV(S), U, EOU, A, BI, Usage

PROFs™, XEDIT™, Chartmaster™, Pendraw™

Survey (Organisational) Experiment (Academic)

EV(S)>EOU, EV(S)>A, EOU>A, U>A, A>Usage, U>Usage

Davis, Bagozzi, & Warshaw (1989a)

U, EOU, A, BI, Usage

WriteOne™

Experiment (Academic)

EOU>U, U>A, EOU>A, A>BI, U>BI, BI>Usage

Davis (1989b)

U, EOU, Usage

PROFs™, XEDIT™, Chartmaster™, Pendraw™

Survey (Organisational) Experiment (Organisational)

U>Usage EOU>Usage

Bagozzi, Davis, & Warshaw (1992)

EOU, U, BI (Two Time Intervals), Usage

WriteOne™

Experiment (Academic)

U>BI, EOU>BI, BI>Usage

Adams, Nelson, & Todd, (1992)

U, EOU, Usage

E-mail, V-mail, Wordperfect™, Lotus Notes™ 123, Harvard Graphics™

Survey (Organisational)

EOU>Usage U>Usage, EOU↔U

Segars & Grover (1993)

EOU, U

Re-examination of Adams et al. (1992) (Organisational)

Three Factor Model: U, E, EOU

Taylor & Todd (1995)

U, EOU, A, Subjective Norm, Perceived Behavioural Control, BI, Behaviour

Computing Resource Centre

Survey (Academic)

EOU>U, U>A, EOU>A, A>BI, SN>BI, PBC>BI, BI>B, PBC>B

Igbaria, Guimaraes, & Davis (1995)

EV, EOU, U, Usage

Micro-computer

Survey (Organisational/Academic)

EV>EOU, EV>U, EOU>U, EOU>Usage, U>Usage

Chau (1996)

EOU, Near-term U, Long-term U, BI

Microsoft™ Word and Excel

Survey (Organisational)

EOU>Near-term U, EOU>BI, Near-term U>Longterm U, Near-term U>BI, Long-term U>BI

Morris & Dillion (1997)

EOU, U, A, BI, Usage

Netscape™

Survey

EOU>U, U>A, EOU>A, U>BI, A>BI, BI>Usage

Gefen & Straub (1997)

Gender, U, EOU, Usage, SPIR

E-Mail

Survey (Organisational)

Gender>SPIR, Gender>U, Gender>EOU, SPIR>U, U>Usage

Fenech (1997)

CS, U, EOU, A, UV, UF

Word Wide Web

Survey (Academic - For Work)

U>Usage, EOU>Usage,

Bajaj & Nidumolu (1998)

EV, U, EOU, A, Usage

Debugger™ (DBG)

Survey (Organisational)

Past Usage>EOU A>Usage EOU>A

Gefen & Keil (1998)

PDR; U, EOU, U

CONFIG™

Survey (Organisational)

EOU>U, U>Usage, PDR>EOU, PDR>U

U, EOU, SP Usage

Web based Inspection system (WIPS)

Experiment (Work)

EOU > U, U > SPUsage, EOU > SP Usage

Laitenberger & Dreyer (1998)

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Dishaw & Strong (1999)

EOU, U, A, BI, Usage, TE, TTF, TF, TC

COBOL™

Survey (Organisational)

TF > EOU, TE > EOU, TE > U, TTF > EOU,

Bronson (1999)

U, CA, S, EOU, Usage

Word Processing

Survey (Academic – Job Performance)

CA>U, CA>EOU, S>U, EOU>U, PU>Usage,

Teo, Lim, & Lai (1999)

U, EOU, Usage, PE

Internet

Web Survey –

EOU>U, EOU>Usage, EOU>PE, U>Usage, PE>Usage

Karahanna & Straub (1999)

U, EOU, SP, SI, ACC, SUPP, Usage

E-mail

Survey (Organisational)

SI>U, EOU>U, SP>U, ACC>EOU EOU>Usage, U>Usage

Dishaw and Strong (1999)

EOU, TE, TTF, U, A, BI, TU, TC, TF

MVS COBOL.CICS

Survey (Organisational)

TF>EOU, TE>EOU, TE>U, TTF>EOU

Handzic (2000)

EV(PE), U, EOU

MetaEdit™, Microsoft™ Word

Survey (Academic)

EV(PE)>EOU, EV(PE)>U

Lederer, Maupin, Sena, & Zhaung (2000)

UA, EOUA, Usage, U, EOU

Web Site (Respondent Specified)

E-mail Survey (Organisational)

EOU>Usage, U>Usage EOUA-U>-EOU EOUA-F>EOU UA-IQ>U

Moon & Kim (2001)

PP, EOU, U, A, BI, Usage

World Wide Web

Survey (Organisational)

EOU>PP, EOU>U, PP>A, EOU>A, U>A, A>BI, U>BI, PP>BI, BI-Usage

A BI CA CS E EOU EOUA-U EOU-F EV(S) PBC PDR PE

a Legend Attitude Behavioural Intention Computer Anxiety Computer Self-efficacy Effectiveness Ease of Use EOU Antecedent-Ease of Understanding EOU Antecedent-Ease of Finding External Variable (System) Perceived Behavioural Control Perceived Developer Support Perceived Enjoyment

PP SPIR SP Usage TC TE TF TTF U UA-IQ UF UV

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Perceived Playfulness Perceived Social Presence + Information Richness (System characteristics); Self-predicted Usage Task Characteristics. Tool Experience Tool Functionality Task-Technology Fit Usefulness U Antecedent-Usefulness Information Quality Usage Frequency Usage Volume

APPENDIX B: PRIMARY EXPLORATORY RESEARCH B.1 INTRODUCTION As outlined in the dissertation itself, to develop a pool of items to measure each of the variables, a review of earlier research and an analysis of industry documentation was conducted to ascertain the structure and content of the scale items. In addition, in order to establish the content validity of the scale items, the item generation process also involved a number of preliminary exploratory studies.

Due to the exploratory nature of these studies, and the core focus of this dissertation being hypothesis testing not scale development, only a brief review of these studies will be outlined in the following sections of this appendix. The overall results of all studies are presented in Table B3, B4, and B5.

B.2 EXPLORATORY RESEARCH Exploratory research is conducted to formulate and define an area of research (Malhotra et al. 1996). It is used to gain insights into the general nature of the problem, the possible decision alternatives, and/or the relevant variables that may need to be considered. Exploratory research may consist of secondary data analysis (e.g., academic literature) and primary data analysis (e.g., qualitative studies). Aaker et al., (1995) contends that the research methods adopted are highly flexible, unstructured and typically qualitative.

The preliminary exploratory studies conducted in this dissertation to aid item generation are now discussed in turn. These studies consist of:

An expert survey (1999),

A novice observational study (1999),

Browser help files content analysis (1999),

Web site content analysis (1999 and 2000),

In-depth Interviews (2000).

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B.3 EXPERT SURVEY To generate a concise and representative list of items a panel of web and web site design experts were approached to discuss the concept of web knowledge. The purpose was to develop an inventory of what constitutes web knowledge, categorised by terminology, available attributes, and evaluative criteria of attributes and usage situations. From this discussion and further exploratory research, a list of terms, attributes and usage situations was derived to help further develop items measuring actual web knowledge. This approach is adapted from Brucks (1985) and his attempt to measure sewing machine knowledge.

B.3.1 Sampling Design To assess the appropriateness of the language and terminology used and the readability and wording of the items generated to measure the construct of web knowledge, a convenience sample was selected as derived from a â&#x20AC;&#x2DC;mediaâ&#x20AC;&#x2122; publications list. The total list was screened with respect to occupations and a final list of 122 senior web site designers and consultants from leading Australian firms were included in the final sample. The 122 respondents were contacted by email inviting them to participate in the expert open-ended survey. 25 emails were returned with permanent fatal errors indicating non-active email addresses or with respondents unwilling to participate. Thus a total of 97 successful emails were sent out. Of the sample contacted, only 17 survey entries were received, however only 12 of these entries were usable as 5 of the surveys were not-completed (Response Rate = 13%)

B.3.2 Survey Contents The survey contents were adapted from Brucks (1985) measurement of sewing machine knowledge and consisted of four core questions addressing knowledge of web terminology, web attributes and features, knowledge of evaluative criteria used with respect to the web and web usage situations. See Table B1 for a description.

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Table B1: Expert Survey Free Response Question Description Knowledge Context Web Terminology

Web Attributes

Criteria for Evaluating the Web & Web Sites Web Usage Situations

Question Description A free response question ask subjects to list and define terms (i.e., those commonly found in web guides and book glossaries) used to describe features of the web and web sites. A free response question asks subjects to list all possible features of the web, including the items not perceived as being important, that a user might encounter when navigating a the web. To reduce measurement error caused by the variance in subjects listing of standard and obvious attributes, Brucks (1985) introduced another question asking the subjects to list features that are common to sewing machines. In this study, subjects are asked to tick the most commonly found features of the web. The free response question directs respondents to write down everything (i.e., attributes and features) that users deem important with the web and the criteria by which they are important (i.e., why). The free response question asks subjects to List the situational characteristics that might influence web site navigation, to explain how each situation might influence navigation and to rank how each situational characteristic according to its level of influence on navigation

Table B3, B4, and B5 present the final summation scores in descending order of most frequent to least frequent frequency of term, feature and procedure use in the experts sampled. These results present the frequency of occurrence in the surveys returned.

B.4 NOVICE OBSERVATIONAL STUDY Observation can be used as both a quantitative or qualitative research methodology for marketing research. Here a relatively unstructured observational study is undertaken of novice web users learning what the web is and how to use it. The study was intended to be exploratory (Bootee and Mathews, 1999). This approach to data collection was undertaken to acquire first-hand knowledge and observation of novice web users, their queries and the type of information they might first learn about with respect to what the web is and how to use the web.

B.4.1 Sampling Design This study is based on observation of two 2-hour short courses about navigating the web: ʹSurf the net - Level 1ʹ and ʹSurf the net - Level 2ʹ. These courses were offered by a community college and aimed to provide participants with base level information abut ‘how to navigate the World Wide Web and find information’. In both classes it could be said that participants were members of the general community with no/limited knowledge of the web and how to use it. Observation occurred in a natural setting and was disguised to both the instructor and the class participants to minimize the risk of

230

interference. In this instance the observer acted as a full participant In the course. The observer did not at any point ask questions or influence the class direction, but undertook the instructors activities and took notes as if a novice. The course instructor was debriefed at the completion of the two short cou5rses and permission was sought for the use of the material collected. Each class consisted of 12 adults. B.4.2 Unit of Analysis The unit of analysis consisted of both content material discussed and taught regarding â&#x20AC;&#x2DC;how to surf the webâ&#x20AC;&#x2122; and content of student questions. The notes taken were later imported into Nudistâ&#x201E;˘ and the script coded according to the subject matter discussed. The unit of analysis included web terminology, features of the web and web sites discussed, the discussion of various web behaviours, and information about other Internet-supported systems. B.4.3 Results Table B3, B4 and B5 present the final summation scores in descending order from most frequent to least frequent frequency of term, feature and procedure used. These results present the frequency of text unit occurrence in the class scripts.

B.5 CONTENT ANALYSES Content analysis is used to analyse written material, using carefully applied rules (Kolbe and Burnett, 1991). It is an appropriate method when the phenomenon to be observed is communication. Malhotra et al. (1996) and Neuman (1997) indicate that the unit of analysis may be words (different words or types of words in a message), characters (individuals and/or objects), themes (propositions), space and time measures (length or duration of the message), or topics (subject of the message). Web site design and promotional content has also been the focus of a number of research studies that incorporate content analysis methodology. Dreze and Zufryden (1997a) for example looked at the impact of background, image size, sound file display, celebrity endorsement, use of java, frames and operating system on the number of pages accessed and time spent at a web site. Li (1998) conducted a content analysis of three US newspapers and found that Internet newspapers gave more priority to providing textual 231

information than graphical. Esrock and Leichty (1999) revealed through content analysis that web pages are not used to their fullest potential by corporate entities to communicate to a multiplicity of audiences. DʹAngelo and Little (1998) further provide an overview of different guidelines for effective web site design that have been published, despite minimal research supporting the guidelines proposed. For example, guidelines for the use of navigational tools (e.g., links to home page and help page), practical considerations for images (e.g., how many per page and size), colour (e.g., number of colours per screen and type), audio and video (e.g., file size), content (e.g., text only option, text position, search capability), and general visual characteristics such as layout (e.g., page segmentation, use of white space). DʹAngelo and Little (1998), from this review, compiled a list of ten characteristics specific to the visual and practical considerations for web page design and assessed the use of these characteristics on twenty web sites. However, they only looked at the visual and practical considerations for web site design, not the navigational options. B.5.1 Help File Content Analysis To aid the development of a number of scale items required for the conduct of this dissertation, content analyses were conducted of three help files and over 80 web sites (413 web pages).

Three help files were content analysed. The help files for both Netscape™ (204KB) and Microsoft Explorer™ (237KB), and a leading ISP’s navigation help file for its members (Ozemail™ - 63KB). These files were chosen as they act as a consistent user-based resource to assist in web and web browser use. To identify consistent web sites that offer help tools and/or information about the web proves very difficult due to the large number of web sites that are available. Therefore, by sampling the help files associated with the most ‘used’ and/or ‘preferred’ browser software, a degree of consistency is upheld.

Only sections of the Help/FAQ files that relate to actual usage (i.e., navigation, shopping, etc.) of the web/browser were analysed. Sections corresponding to web site design and

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development were not analysed as the purpose of the analysis was to ascertain user exposure to content related to use of the web and/or browser.

B.5.1.1 Sampling Design In a report issued by www.consult.com (1999), the main browsers preferred by Australian web users were Microsoft Internet Explorer 4 (42%) and Netscape 4/Netscape Communicator (32%). In addition, the help file of one of the largest ISPs in Australia, Ozemail, was considered.

B.5.1.2 Unit of Analysis and Coding The contextual unit of analysis is the â&#x20AC;&#x2DC;Help/FAQâ&#x20AC;&#x2122; files of both browsers and the ISP. Manifest coding was used to count the frequency with which certain terms and features were mentioned in the files, as identified from the literature analysis and expert survey. The help files in text (.txt) format were imported into Microsoftâ&#x201E;˘ Word and Nudist to conduct the analysis. The frequency of features was assessed by counting the number of times these features were mentioned in the help files. In addition, latent coding was used to assign certain meanings to sections of the help files in accordance with past research on user knowledge, perception, and use of the web. B.5.1.3 Results Tables B3, B4 and B5 present the final summation scores in descending order from mostto-least frequent use of terms, features and procedures in the help-files sampled. These results present the frequency of text-unit occurrence in the help files.

B.5.2 Web Site Content Analyses The contextual units of analysis are web pages however, it is first necessary to explain how the web sites containing these pages were sampled.

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B.5.2.1 Sampling Design Data for this study comes from a number of individual web pages at a number of sampled web sites. As the purpose of this study is the development of scale items that validly measure a user’s knowledge, perception and use of the web, the most trafficked sites by users is deemed to be an appropriate criterion to aid sample selection (following Weare and Lin 2000). The top 20 web sites accessed by Australians in 1999 and 2000 (ranked monthly by traffic) were used to create a sampling frame. The source, Microplex – ISP [http://www.mpx.com.au]†, based its rankings on an Australia-wide sample of web requests by Australians. These rankings were calculated using raw traffic, with some sites aggregated to a common URL. This gave a minimum of 480 web sites (20 web sites x 24 months = 480).

A web site, however, can range from 1 to thousands of web pages. Thus a certain number of pages were selected to increase the manageability of the task at hand. Thus an average of 5 pages per web site, 2450 web pages (html files), could be analysed. To identify a workable sample size, a two-month window was selected for analysis (January and February; 20 web sites x 2 months in each of 2 years = 80 web sites). Only 37 unique web sites were sampled as analysis of the top 20 web sites took place on four different occasions, and a number of sites were analysed more than once. For example, http://www.ninemsn.com.au was in the top 20 web sites on all four occasions, whereas http://www.careerone.com.au only made it into the top 20 on one out of the 4 occasions.

Due to the dynamic nature of the web, web sites are continually changing and being updated. Hence, web sites that were content analysed before were not excluded, but included in the process as it was assumed a degree of change would occur in the coding. The web sites analysed are presented in Table B2. A total of 37 unique web sites and a total of 413 web pages (i.e., a web page sampling ratio of 16.8%), averaging 5 pages per site, were analysed.

† Note Microplex was acquired in late 2000 by Optusnet [http://www.optusnet.com.au]

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Five pages were considered for each site. To ensure consistency, three of these were standardized: the home page, a FAQ or Help page, and a Privacy and or Security Policy page. The two other pages ranged from shopping pages to news and information pages.

Table B2: Web Sites Analysed Week Ending: WS/CA Code

Web Site URL

Content

29-01-99

26-02-99

28-01-00

25-02-00

WS/CA-0001

www.ausopen.org

Event

WS/CA-0002

www.ninemsn.com.au

Media

WS/CA-0003

www.abc.net.au

Media

WS/CA-0004

www.whitepages.com.au

Directory

WS/CA-0024

www.battleofthesexes.com

Media

WS/CA-0025

www.cricket.org

Sport

WS/CA-0026

www.realestate.com.au

Real Estate

WS/CA-0027

www.sanford.com.au

Finance

WS/CA-0028

www.afr.com.au

Newspaper

WS/CA-0029

www.start.com.au

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WS/CA-0030

www.property.com.au

Real Estate

WS/CA-0031

www.jobsearch.gov.au

Government

WS/CA-0032

www.national.com.au

Finance

WS/CA-0033

www.seek.com.au

Employment

WS/CA-0034

www.commbank.com.au

Finance

WS/CA-0035

www.hotcopper.com.au

Finance

WS/CA-0036

www.careerone.com.au

Employment

WS/CA-0037

www.citysearch.com.au

Guide

WS/CA â&#x20AC;&#x201C; Web Site Content Analysis â&#x20AC;&#x201C; Numerical ID - not in top 20 for week ending X. * includes all Fairfax Online Ad traffic ** includes the Australian.com.au

B.5.2.2 Unit of Analysis and Coding Manifest coding (MC) was the main form of coding used, however some latent coding (LC) was used to increase the propensity of the item counted as being a true representative of the item analysed. The list of terms and features was identified through the past literature analysis and exploratory studies.

Despite the fact that two or more raters are often used in content analyses (Kolbe and Burnett 1991), only one coder was involved in the data collection process. To increase objectivity and decrease error in the data collection process, a coding sheet and set of rules and procedures was used. Data were entered into a database for manageability and then exported to a SPSS format suitable for data analysis. B.5.2.3 Descriptive Results Tables B3 and B4 present the final summation scores in descending order from most-toleast frequent use of terms and features. The top five terms were: links, home, cookie/s, browser and download (Table B3). The top five features were: textual links, page sections, graphical links, still graphics, and other logos (Table B4).

B.6 INDEPTH INTERVIEWS 5 semi-structured in-depth interviews were conducted to further ascertain from users how and what they use to navigate the web. Respondents were asked to talk the

236

interviewer through their navigation process when given two tasks to complete using the web. This method of exploratory data collection was used to ascertain the type of procedural and declarative based knowledge required to undertake web-based information search behaviour.

B.6.1 Sampling Design A small convenience sample consisting of five final year undergraduate commerce students was recruited using email recruitment. The sample consisted of four male and one female interviewees that were between the ages of 22 and 28 years of age. All participants self-reported: low to medium use of internet and/or web-based chat services; some purchasing experience; medium to high level of experience with computers, the web overall, and using the web for information search; and a high level of email use experience. Furthermore, the sample self-reported a medium to high level of perceived expertise with the web. Respondents were asked to indicate their preferred browser and browser default page so as to align the surfing observed during the interview closely with their usual navigational behaviour.

B.6.2 Task Description After completing a consent form, and survey on background questions, the interviewee was given a task description and asked to complete the task. No specified time period was allocated for task completion and it was not required that the student complete the task. A time period of 20 minutes was allocated to each task. The tasks were used as a foundation to acquire information about navigational behaviour. While undertaking the tasks students were asked to verbalise what they were doing and were asked a number of pre-set questions by the interviewer to aid their verbalisation of the behaviour being conducted. The first task consisted of finding information about suitable accommodation in a specified region for an up and coming â&#x20AC;&#x2DC;weekend awayâ&#x20AC;&#x2122; and the second task required interviewees to locate information about a certain book title they were interested in purchasing.

237

The interviewer played a very passive role listening to the interviewees and only where needed asked probing questions as to the intervieweeâ&#x20AC;&#x2122;s behaviour and understanding of various terms, features and web use. At the conclusion of both tasks the interviewees were debriefed as to the purpose of the study and what in fact the interviewer was observing (most interviewees thought the purpose was to elicit task specified information).

B.6.3 Results The frequency of certain terms, features, and procedures or behaviours mentioned or undertaken is presented in Tables B3, B4 and B5.

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Table B3: Frequency of Web and Web Site Terminology Expert Survey (n=12) Most –to Least Frequent

Observational Study (n = 15) Frequency of Text Units

Help Files CA (n = 3 files) Frequency of Text Units

Web Sites CA (n = 413pgs) Most –to Least Frequent

In-depth Interviews (n = 5) Frequency of Text Units

Java Script HTML Hyper/links Plug-in CGI Scripts E-commerce Banner Ads Bandwidth Meta-tags Frames Search Engine JPEG Flash Internet Email Browser URL Cookies Bookmarks Homepage WWW Web Ring FTP GIF Download HTTP

Boolean (19) Internet (16) Domain (11) Browser (10) Web (9) URL (5) Home Page (4) Http (3) Web Page (3) Link (3) ISP (3) Hyperlink (1) Surfing (1) Favourites/Bookmarks (1) Email (1) Flamming (1) Spamming (1)

URL (605) Http (396) Browser (278) Link (203) Internet (178) Web (121) Home Page (47) java (46) Jpeg (45) Gif (34) Email (31) Encryption (20) Bandwidth (18) Search Directory (16) Virus (13) Cookies (9) Domain (8) Hypertext (6) Search Engine (4) Hypermedia (3) ftp (1)

Link (625) Home (584) Cookie (409) Browser (403) Download (318) FAQ (235) Directory (190) Real Audio (148) Cache (144) Server (132) Upload (82) Bookmark (59) Java (56) URL (56) Domain (50) Frame (46) Flash (44) Streaming (38) Search Engine (37) Metaword (36) SSL (27) Encryption (27) Banner Ad (27) Bandwidth (25) Plugin (12) Boolean (12) Shockwave (12) Crawler (9) Hit (8)

URL (27) Home Page (24) Bookmark (16) Link (14) Frame (13) Search Engine (12) Browser (11) Download (11) Directory (10) Server (8) FAQ (5) Plugin (4) Boolean (3) Portal (3) World Wide Web (2) Webmaster (2) Shockwave (2) Streaming (2) Java (2) Encryption (1) SSL (1) Domain (1) Banner Ad (1) Crawler (0) Hit (0) Bandwidth (0) Metaword (0) Flash (0) Upload 0) Cache (0) Cookie (0)

Table B4: Frequency of Web and Web Site Features 239

Expert Survey (n=12) Most –to Least Frequent

Observational Study (n = 15) Frequency of Text Units

Help Files CA (n = 3 files) Frequency of Text Units

Web Sites CA (n = 413pgs) Most –to Least Frequent

In-depth Interviews (n = 5) Frequency of Text Units

Text HTML Banner Ad Nav/Menu Bars Frames Graphics Java/script FAQ Feedback What’s New Audio Animation Search Engines Video Cascading Style Sheets (CSS) Buttons Site Maps Sound Forms Payment Facilities Hyper/links

Search Directory (11) Page Title (8) Hyperlink (7) Search Engine (6) Bookmark (5) Email (5) Default Page (3) Button (3) Browser Icon (3) Graphics (3) Menu Bar (2) Chat (2) Text (1) Web Site (1) Tool Bar (1) Address Bar (1) Scroll Bar (1) Drop-down Menu (1) Site Design (0) Web Phone (0)

Html (508) Text (111) Bookmarks (110) Toolbar Buttons (103) Buttons (39) Frames (36) Plug-ins (31) FAQ URL Components (21) Graphics (12) Security Information (11) Video (10) Sound (10) Security Indicator (7) Internal / External Images (7) Whatʹs New (7) Dynamic Html (6) Bandwidth Settings (6) Mailto Links (4) Search Engine (4) Links (3) https (3) Location Field (1) History Items (1) Search Tool (1) Find Tool (1) Scroll Bar (1) Animation (0)

Textual Links (21176) Page Sections (2189) Graphical Links (2150) Still Graphics (1725) Other Logo (933) Drop Down Menu (785) Vendor Logo (684) Feedback Link (550) Navigation Bar Top (522) Animated Graphics (446) Action Button (443) Help Link (430) Navigation Bar Left (307) Privacy & Security Policy (297) Ad Button Right (289) Navigation bar Bottom (209) FAQ (209) Search Site (187) Banner Ad Top (182) Ad Button Top (173) Ad Button Bottom (140) Whatʹs New (134) Ad Button Left (127) Email Entry (90) Text Only Option (89) Site Map Option (88) Audio (82) Frames Top (77) Video (63) Frames Bottom (56) Search Web (54) Banner Ad Bottom (39) Navigation bar Right (30) Frames Left (23) No Frames Option (13) Error Message (11) Https (4) Popup Menu (3)

Link/s (46) Button (Back, Stop etc) (28) Frames (17) Tabs (12) Shopping Cart (11) Search Engine (11) Drop Down Menu (10) Hyperlink (9) Pop-up Ads (7) Video (4) URL (6) FAQ (2) Graphics (2) Http (2) Banner Ad (1) Text Only (1) Site Map (1) What’s New (1) Audio (1) Other Logo (1) Advertising (0) Web Ring (0) Vendor Logo (0) Page Sections (0) Search Web (0) Email Entry (0) Search Site (0) Ad Button (0) Privacy & Security Policy (0) Help Link (0) Animation (0) Navigation Bar (0) Feedback Link (0) Https (0)

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Table B5: Frequency of Web and Web Site Procedures (Behaviours) Expert Survey (n=12) Most –to Least Frequent

Observational Study (n = 15) Frequency of Text Units

Help Files CA (n = 3 files) Frequency of Text Units

Situational Characteristics

Behaviour (s)

Connection Speed Response Times Computer Literacy Cost of Access Who Pays User Aim/Motive Computer System Graphics/Size Place of access Location

Search (48) Using Search Terms (20) Adding Favourites (19) Using Boolean Operators (11) Drag & Drop (9) Clicking (6) Deleting Favourites (6) Composing Email (6) Internet Use (5) Surfing (4) Category Search (4) Video Conferencing (3) Encrypt/Decrypt (3) Domain Purchase (2) Close Browser (2) Editing Address Bar (1) Link Visitation (1) Back/Forward (1) Browser icon Movement (1) Go To (1)

Using Bookmarks (143) Using Links (103) Editing Bookmarks (64) Stopping Transfer (57) Using a Search Engine (57) Saving Web Pages (45) Download (43) Using Cache (39) Setting Bookmark Preferences (35) Using History Menu (34) Using the Location Field (33) Forward & Back (29) Using Home Button (26) Using Reload Button (25) Accessing the Internet (25) Using Frames (23) Using Tool Bar and Menu Links (23) Printing Web Pages (20) Turning Images Off (16) Using a URL (13) Open Browser Window (12) Searching History List (12) Viewing Menu List (11) Navigating a Page (11) Filling in Forms (10) Selecting Default Home Page (9) Searching for information (8) Open a Web Page (8) Auto Scroll (8) Status Message Area (7) Using Multiple Browser Windows (7)

Scrolling (33) Editing URL (21) Clicking (19) Retracing Steps (Back) (18) Search Engine Use – Boolean (17) Navigation (14) Browser Use (13) Shopping (12) Adding Favourites (11) Search Engine Use – Search Terms (9) Page Navigation (8) Link Visitation (8) Spamming (4) Find in Page (4) Smart Searching (1)

241

Web Sites CA (n = 413pgs) Most –to Least Frequent

In-depth Interviews (n = 5) Frequency of Text Units

Additional System Characteristics

Speed (71) Email (32) Cursor Change (28) URL Contents (21) Browser Icon (18) System Transfer (10) Error – Page Not Found (6) Status Bar (6) Error – Connection (4)

Viewing the Component Bar (7) Displaying Previously Viewed Pages (7) Displaying Pop-up Menu (7) Change Page Background (7) Finding a Bookmark (7) Accepting Cookie (7) Reporting Error Message (6) Clicking (5) Find in Page (5) Identifying Used Links (4) Browser Icon Animation (3) Progress bar (3) Using the Component Bar (3) Learn about Browser (3) Using Navigation Tool Bar (3) Viewing Bookmarks and History (3) Scrolling (2) Automatic Update Pages (2) Using the Search Button (2) Using Guide Button (2) Using Images Button (2) Using Security Button (2) Changing Mouse Cursor (1) Find and Return to Pages (1)

242

APPENDIX C: VARIABLE CONCEPTUALISATION AND OPERATIONALISATION (PRE/POST TEST) ID

Scale

WSUF

Current Web Session Usage Frequency

WSUVS

Current Web Session Usage Variety (Situational)

Operationalisation (Pre-item Testing)

Operationalisation (Post-item Testing)

How often the web is accessed within a certain time frame

1-item, 8-category Measure

Number and type of locations from which the web is accessed

1-item, 6-category Measure

1-item, 8-category Measure 1-item 6-category Measure and a 1-item, 10-category Measure

Conceptualisation

Questionnaire (Section/Item)

Current Web Session Usage

WSUVMNO1 WSUEB WSUED WSUEDUR

Current Web Session Usage Variety (Motivational) Current Web Session Usage Extent (Breadth) Current Web Session Usage Extent (Depth) Current Web Session Usage Extent (Duration)

Number of motivations for which the web is accessed Number of new and/or different web sites and search tools accessed Total number of web sites and search tools accessed The time with which a session on the web lasts

D15

D16 & D17

1-item, 12-category Measure 4-item, 7-point Likert Scale (1=SA, 7=SD) 4-item, 7-point Likert Scale (1=SA, 7=SD)

1-item, 12-category Measure 3-item, 7-point Likert Scale (1=SA, 7=SD) 4-item, 7-point Likert Scale (1=SA, 7=SD)

1-item, 8-category measure

D18 D1, D3, D8 D4, D6, D9, D11 D20

Web Perceptions A1, A3, A4, A6, A16, A24, A28, A31, A42, A45, A57 A2, A7, A15, A18, A20, A29, A34, A40, A44, A47, A49, A51, A53, A55

PEWU

Perceived Ease of Web Use

Degree to which the user believes that using the World Wide Web would be free from effort

20 item, 7-point Likert Scale (1=SA, 7=SD)

11 item, 7-point Likert scale (1=SA, 7=SD)

PWU

Perceived Web Usefulness

Degree to which a user believes that using the World wide Web would enhance his or her usage performance

23 item, 7-point Likert Scale (1=SA, 7=SD)

14 item, 7-point Likert scale (1=SA, 7=SD)

13 -scale items: 11 x 3-category (T/F/DK) and 2 x 5- category (MC)

6-item scale 3-category measure (T/F/DK)

B2, B6, B10, B13, B26, B34

11-item scale 3-category measure (T/F/DK)

B4, B8, B12, B14, B18, B20, B22, B24, B30, B32

10 -scale items: 5 x 3-category (T/F/DK) and 2 x 5- category (MC)

B11, B15, B17, B19, B21, B23 B27, B28, B37, B38

Actual Web Knowledge

ACPWK

Actual Common Procedural Web Knowledge Content

ASPWK

Actual Specialised Procedural Web Knowledge Content

ACDWK

Actual Common Declarative Web Knowledge Content

General and/or publicly known dynamic information underlying skilful actions (how) of using X, required to perform general and common domain related tasks successfully Skilled and/or extraordinary dynamic information underlying skilful actions (how) of using X, required to perform skilled domain related tasks successfully General and/or publicly known static information of facts, terms, attributes (what) of X, required to perform general and common domain related tasks successfully

243

19 -scale items: 17 x 3-category (T/F/DK) and 2 x 5- category (MC) 31 -scale items: 27 x 3-category (T/F/DK) and 4 x 5- category (MC)

ASDWK

Actual Specialised Declarative Web Knowledge Content

SWPK

Perceived Procedural Web Knowledge Content

SWDK

Perceived Declarative Web Knowledge Content

SWOK

Perceived Overall Web Knowledge Content

Skilled and/or extraordinary static information of facts, terms, attributes (what) of X, required to perform skilled domain related tasks successfully

28 -scale items: 24 x 3-category (T/F/DK) and 4 x 5- category (MC)

10 -scale items: 10 x 3-category (T/F/DK) and 1 x 5- category (MC)

B1, B3, B5, B7, B25, B29, B31, B33, B35, B36

8-items, 7-point Likert Scale (1=SA, 7=SD)

4-items, 7-point Likert Scale (1=SA, 7=SD)

A5, A8, A21, A43

9-items, 7-point Likert Scale (1=SA, 7=SD)

7-items, 7-point Likert Scale (1=SA, 7=SD)

A12, A14, A19, A25, A32, A38, A52

3-items, 7-point Likert Scale (1=SA, 7=SD)

2-items, 7-point Likert Scale (1=SA, 7=SD)

A23, A48

Perceived Web Knowledge An individual’s personal judgement of the level of knowledge stored in their memory about how to use certain features and/or terms of the web An individual’s personal judgement of the level of knowledge stored in their memory about what certain features and/or terms of the web are An individual’s personal judgement of the overall level of knowledge content about the web stored in their memory

a: After item testing and purification an additional item was added to measure WSUVS. Thus WSUVS after item-testing and purification would include 2-items.

244

APPENDIX D: SCALE DEVELOPMENT - ITEM FACTOR LOADINGS (STUDENT SAMPLE ONE: N=128) Web Session Usage Extent – Breadth SS1: Q40 .910 SS1: Q43 .713 SS1: Q71 .960 Dimension Site Search Engine Dimension Variance 46% 36% Dimension Reliability/Correlation r = 0.4 NA Scale Variance 82% Scale Reliability 0.7

Web Session Usage Extent – Depth SS1: Q27 .900 SS1: Q45 .892 SS1: Q41 .941 SS1: Q70 Dimension Link Search Engine Dimension Variance 43% 26% Dimension Reliability/Correlation r = 0.8 NA Scale Variance 94% Scale Reliability 0.8

.928 Site 25% NA

Perceived Web Usefulness Perceived Ease of Web Use SS1: Q48 SS1: Q47 SS1: Q67 SS1: Q39 SS1: Q38 SS1: Q21 SS1: Q37 SS1: Q49 SS1: Q35 SS1: Q51 SS1: Q19 SS1: Q34 SS1: Q52 SS1: Q50 Dimension Dimension Variance Dimension Reliability/Correlation Scale Variance Scale Reliability

.878 .853 .756 .673 .650 .785 .780 .763 .688 .721 .720 .659

Comm. 23% 0.9

Purchase 22% 0.9

Information 19% 0.8

.658 .630 Quality 10% r = 0.6

75% 0.9

245

SS1: Q53 SS1: Q55 SS1: Q54 SS1: Q60 SS1: Q59 SS1: Q61 SS1: Q62 SS1: Q29 SS1: Q31 SS1: Q30 SS1: Q58 SS1: Q32 SS1: Q18 SS1: Q33 Dimension Dimension Variance Dimension Reliability/Correlation Scale Variance Scale Reliability

.789 .756 .725 .699 .671 .657 .605 .784 .776 .764 .694 .647

Behavioural 29% 0.9

Informational 29% 0.9 73% 0.9

.820 .692 Transactional 13% r = 0.7

APPENDIX E: SCALE DEVELOPMENT - ITEM FACTOR LOADINGS (STUDENT SAMPLE TWO: N=153) Actual Common Procedural SS2: Q20 .819 SS2: Q75 .754 SS2: Q77 .666 SS2: Q76 .888 SS2: Q78 .686 SS2: Q37 Dimension Speed of Use Web features Dimension Variance 31% 26% Dimension Reliability 0.7 r = 0.5 Scale Variance 75% Scale Reliability 0.8

.968 Updates 18% NA

Actual Specialised Procedural SS2: Q21 SS2: Q18 SS2: Q73 SS2: Q39 SS2: Q74 SS2: Q36 SS2: Q79 SS2: Q57 SS2: Q51 SS2: Q23 SS2: Q19 Dimension Dimension Variance Dimension Reliability Scale Variance Scale Reliability

.790 .734 .652 .564

.834 .706 Cookies 16% r =0.6

Actual Common Declarative SS2: Q71 .742 SS2: Q26 .734 SS2: Q25 .714 SS2: Q44 .713 SS2: Q42 .534 SS2: Q33 .793 SS2: Q52 .720 SS2: Q60 .708 SS2: Q61 .547 SS2: Q34 .540 Dimension Standards Tools & Terms Dimension Variance 30% 27% Dimension Reliability 0.8 0.8 Scale Variance 57% Scale Reliability 0.9 Perceived Procedural Web Knowledge

Perceived Declarative Web Knowledge .809 .777 .714

Web Features 21% 0.7

Actual Specialised Declarative SS2: Q63 .791 SS2: Q65 .701 SS2: Q70 .629 SS2: Q40 .609 SS2: Q22 .603 SS2: Q28 .686 SS2: Q49 .587 SS2: Q26 .581 SS2: Q59 .509 SS2: Q46 SS2: Q17 Dimension Tools & Terms Standards Dimension Variance 24% 19% Dimension Reliability 0.8 0.6 Scale Variance 59% Scale Reliability 0.9

Speed of Use 20% 0.7 59% 0.8

.853 .680 .525 .486 Quality 18% 0.7

SS2: Q13 SS2: Q4 SS2: Q3 SS2: Q18 SS2: Q11 SS2: Q10 SS2: Q14 Dimension Scale Variance Scale Reliability

.915 .912 .891 .867 .860 .818 .805 1 75% 0.9

246

SS2: Q5 SS2: Q2 SS2: Q8 SS2: Q7 Dimension Scale Variance Scale Reliability

.937 .879 .876 .815 1 77% 0.9

Perceived Overall Web Knowledge SS2: Q1 r =.0.8 SS2: Q12

APPENDIX F: WEB SITE AND WEB SURVEY DESIGN F.1 WEB SITE DESIGN There were a number of considerations in designing a web-site to serve as the host for this survey. The objective of the design was to present a credible and non-commercial image of the research project to prospective respondents and to do so in a way that made it easy for them to complete the survey. Thus, a very simple site structure was devised, with limited external links to encourage survey participation and decrease respondent web-site exit behaviour (Figure F1).

Figure F1: Phd Web Audience Study - Simple Web Site Structure

The web site was hosted on the main web server (http://www.unsw.edu.au) within the UNSW University Wide Network (UWN). A domain name for the web site was registered (http://www.webaudience.unsw.edu.au) for period of 12 months to increase ease of respondent access to the site through URL recall and to increase the perceived credibility of the study (Nielsen, 1999). Given these hosting decisions and the use of official university symbols on the web site, the UNSW â&#x20AC;&#x2DC;Electronic Identity Standards Policyâ&#x20AC;&#x2122; (as at May 2000) was adhered to and the web site design was also approved by the UNSW web co-ordination unit.

247

F.2 WEB SURVEY DESIGN The foundations for designing the interactive web-based survey comprised: the standard Internet protocol (hypertext mark-up language, HTML), FileMaker Pro™ , and Claris Dynamic Markup Language (CDML) and additional common gateway interface (CGI) scripting for handling interactions with FileMaker Pro™.

The web survey was designed for ease of completion. The large number of questions asked (Q=180) was broken up into 6 separate sections, including an introduction with screening questions. For example, as the study was targeting Australian residents, respondents were first asked to indicate their residency status, this was followed with a question asking the respondents for the source of study awareness. Following the completion of these questions respondents then navigated the following 5 survey categories that included questions covering ‘Web Perceptions’, ‘Web Knowledge’, ‘Web Communications’, ‘Respondent Web Usage’ and concluding with descriptive information about the respondent (i.e., demographics, geodemographics, etc.).

Respondents navigated these sections using navigational tools provided in the survey design (i.e., hot-linked buttons) and upon survey completion used a ‘Submit’ button to execute web-survey and database interaction. The items used in the survey (as discussed in Chapter 7) used differing question response formats and thus differing field types were used to correspond to each question response format. For example, intervalmeasured Likert scales were measured using ‘radio buttons’; ordinal-measured multiplechoice questions were measured with ‘drop-down menus’; and nominal-measured check-list responses were measured with ‘check-boxes’.

A number of empirical studies have demonstrated that the psychometric properties of measurement scales can be affected by the ordering of items within questionnaires. To minimise this potential problem, questions were intermixed and the ordering was varied within each section.

248

The design of the web-site, and the survey instrument, are shown in Figures F2, F3 and F4.

Figure F2: Web Site - Home Page (Index.html)

Title & Tool bar

Introduction & Hot-link Button to Survey

Method of Cash Prize Generation

Link to Terms & Hot-link button Conditions to Survey

Data use & treatment and researcher contact details

249

Figure F3: Web Site - Competition Terms and Conditions (terms.html)

Title & Tool bar

Competition terms & conditions as approved by state and territory gaming regulations

Gaming License

Hotlink button to survey

250

Figure F4: Web Site - Survey (Questionnaire.html)

Survey introduction and screening questions

Section A: Perceptions of the World Wide Web

251

Section A: Continued

252

Section B: Actual Knowledge Content of the World Wide Web

253

Section B: Continued

Section C: Exposure to and Provision of Communication about the Web

254

Section C: Continued

255

Section D: Current and Past Web Session Usage Experience

256

Section D: Continued

257

Section E: Respondent Descriptive Information

258

Section E: Continued

259

APPENDIX G: WEB SURVEY ADVERTISING AND PUBLICITY G.1 ONLINE BANNER ADVERTISING G.1.1 Banner Ad – No.3 (Crashed Car)

G.1.2 Banner Ad – No.2 (Computer)

G.1.3 Banner Ad – No. 1 (Surfboard)

260

G.2 OFFLINE PUBLICITY Official UNSW Media Release

Newspaper Article (The Australian IT (Tuesday 14/11/00; p3)

261

Magazine Review: Australian NetGuide (January, 2001, p16).

262

APPENDIX H: WEB SITE PERFORMANCE STATISTICS Web Site Performance Statistics: Pilot Study (10-12th Oct) and Main Study (18th Oct 2000 to 31st Jan 20001)

Hits

Page Views

Visits

Visitors

October (Pilot)

October

November

December

January

10-12B1

18-31B1

01-15B3

16-30B3

1-15B2 & B3

16-31B3

1-15

16-31

Main Study Total

Entire Site (Successful)

22,275

59317

14881

9817

41664

4020

5867

4922

140,488

Average Per Day

7,425

4236

992

654

2777

251

391

307

1,325

Home Page

1,167

3608

692

426

2214

191

276

248

7,650

Page Views (Impressions)

1,927

5618

1265

792

3460

361

491

420

12,407

Average Per Day

642

401

230

117

Document Views

1,927

5618

1265

792

3460

361

491

420

12,407

Visits

1,642

4244

883

561

3083

292

383

328

9,774

Average Per Day

547

303

205

Average Visit Length

00:03:38

00:03:21

00:02:48

00:03:17

00:02:36

00:03:15

00:02:28

00:02:34

00:03:00

Median Visit Length

00:01:28

00:01:22

00:01:27

00:01:23

00:01:40

00:01:20

00:01:33

00:01:26

International Visits

24.23%

23.68%

22.42%

16.57%

25.49%

26.02%

23.75%

21.34%

23.72%

Visits of Unknown Origin

21.43%

21.22%

24.23%

19.25%

20.27%

17.8%

17.49%

17.07%

20.69%

Visits from Australia

54.32%

55.08%

53.34%

64.17%

54.23%

56.16%

58.74%

61.58%

55.57%

Unique Visitors

1,232

2643

601

385

1929

210

251

232

5,104

Who Visited Once

1,053

2177

492

309

1593

169

197

195

4,170

Who Visited More Than Once

179

466

109

336

934

B1: Banner Ad Campaign 1 (DoubleClick Network Placement) – 22nd to 29th October 2000; B2: Banner Ad Campaign 2 (DoubleClick Network Placement) – 05th to 12th December 2000; B3: Banner Ad Campaign 3 (Small Network Placement) – 30th October to 31st December 2000

263

APPENDIX I: DOUBLECLICKâ&#x201E;˘ BANNER AD CAMPAIGN REPORT/S Banner Ad Placement (1) (Oct 2000)

Banner Ad Placement (2) (Dec 2000)

Total number of unique users who saw ads

478 095

389 522

Average number of exposures per user

3.42

3.37

Click-rate per user

0.85%

0.73%

Impressions

Clicks

Click Rate

Impressions

Clicks

Click Rate

Total

1634006

4079

0.25%

1 313 988

2 833

0.22%

Ad 3 (Crashed Car)

546101

1483

0.27%

437 801

1 031

0.24%

Ad 1 (Computer)

543512

1299

0.24%

437 800

917

0.21%

Ad 2 (Surfboard)

544393

1297

0.24%

438 387

885

0.20%

seek.com.au

266 632

750

0.28%

seek.com.au

235 351

655

0.28%

goeureka.com.au

446 665

593

0.13%

goeureka.com.au

314 593

401

0.13%

2.10%

homepage.av.netwrk.au

40 547

193

0.48%

109 679

154

0.14%

43 262

151

0.35%

12 629

132

1.05%

39 685

125

0.31%

86 674

101

0.12%

Top 10 Placement Sites (Descending by Clicks)

disney.au

18 651

391

resultpage.av.network.au

167 434

243

0.15%

quicken.com.au

118 004

230

0.19%

au.mirror.nasdaq.com

35 833

215

0.60%

melb.tradingpost

101 915

202

0.20%

property.com.au

82 629

144

0.17%

264

resultpage.av.network.au investorweb.com.au disney.au cdnow.au melb.tradingpost

APPENDIX J: SCALE VALIDATION - ITEM FACTOR LOADINGS (WEB SAMPLE: N=2077) Web Session Usage Extent - Breadth WS1-D8: WSUEB1M .863 WS1-D1: WSUEB2M .824 WS1-D13: WSUEB3M .979 Dimension Site Search Engine Dimension Variance 49% 34% Dimension Reliability/Correlation r = 0.5 NA Scale Variance 83% Scale Reliability 0.7

Web Session Usage Extent - Depth WS1-D6: WSUED1M .885 WS1-D11: WSUED2M .843 WS1-D9: WSUED3M .967 WS1-D4: WSUED4M Dimension Link Search Engine Dimension Variance 38% 26% Dimension Reliability/Correlation r = 0.5 NA Scale Variance 89% Scale Reliability 0.6

Perceived Ease of Web Use WS1-A24: PEWU014 WS1-A16: PEWU 013 WS1-A28: PEWU 003 WS1-A3: PEWU 007 WS1-A1: PEWU 006 WS1-A31: PEWU 005 WS1-A4: PEWU 002 WS1-A42: PEWU 001 WS1-A57: PEWU 012 WS1-A6: PEWU 004 WS1-A45: PEWU 009 Dimension Dimension Variance Dimension Reliability/Correlation Scale Variance Scale Reliability

.857 .750 .825 .784

Search 15% r=7

.977 Site 25% NA

Perceived Web Usefulness

.808 .788 .770 .678

Learn 24% 0.9

Web Session Usage Variety - Situational WS1-D16: WSUVS1 r = 0.6 WS1-D17: WSUVS2

Transaction 13% r = 0.5 64% 0.9

.747 .648 .617 Comm. 12% 0.6

WS1-A34: PWU011 WS1-A7: PWU 012 WS1-A51: PWU 013 WS1-A55: PWU 014 WS1-A44: PWU 010 WS1-A29: PWU 004 WS1-A2: PWU 001 WS1-A18: PWU 002 WS1-A53: PWU 003 WS1-A40: PWU 009 WS1-A49: PWU 008 WS1-A15: PWU 007 WS1-A20: PWU 006 WS1-A47: PWU 005 Dimension Dimension Variance Dimension Reliability/Correlation Scale Variance Scale Reliability

265

.844 .820 .787 .762 .662 .827 .821 .786 .722 .830 .791 .747

Comm. 23% 0.9

Purchase 20% 0.8

Information 18% 0.8 70% 0.9

.780 .641 Quality 9% r = 0.5

Actual Specialised Procedural Web Knowledge WS1-B30: OWPK10M WS1-B16: OWPK6M WS1-B18: OWPK9M WS1-B14: OWPK7M WS1-B12: OWPK2M WS1-B22: OWPK11M WS1-B20: OWPK4M WS1-B4: OWPK15M WS1-B24: OWPK16M WS1-B8: OWPK8M WS1-B32: OWPK14M Dimension Dimension Variance Dimension Reliability/Correlation

Actual Common Procedural Web Knowledge

.692 .604 .566 .544

WS1-B13: OWPK13M WS1-B6: OWPK17M WS1-B34: OWPK 3M WS1-B26: OWPK 12M WS1-B10: OWPK 5M WS1-B2: OWPK1M Dimension Dimension Variance Dimension Reliability Scale Variance Scale Reliability

.813 .592 .572 .806 .800 .632 Browser 20% 0.8

Site Access 17% 0.7

Scale Variance Scale Reliability

Boolean 14% 0.6

.944 Speed 10% NA

Actual Specialised Declarative Web Knowledge .797 .551

Dimension

Peripheral

Dimension Variance Dimension Reliability/Correlation Scale Variance Scale Reliability

19% 0.6

.758 .707 .678 .871 .688 .936 .560 .919 Tools & Terms 18% 0.7

.847 .842 .697

WS1-A48: SWOK2 WS1-A23: SWOK1

Privacy

Performance

Security

15% r = 0.5 76% 0.8

13% r = 0.5

11% NA

WS1-B23: OWDK12M WS1-B28: OWDK 6M WS1-B21: OWDK 11M WS1-B15: OWDK 14M WS1-B27: OWDK 10M WS1-B38: OWDK 21M WS1-B37: OWDK 20M WS1-B19: OWDK 9M WS1-B17: OWDK 8M WS1-B11: OWDK 7M Dimension Dimension Variance Dimension Reliability Scale Variance Scale Reliability

266

r = 0.8

.861 .649 Speed 36% 0.8

Browser 21% 0.4 73% 0.7

.923 Whatâ&#x20AC;&#x2122;s New 16% NA

Actual Common Declarative Web Knowledge

61% 0.8

WS1-B29: OWDK15M WS1-B36: OWDK19M WS1-B33: OWDK17M WS1-B7: OWDK4M WS1-B31: OWDK16M WS1-B1: OWDK1M WS1-B35: OWDK18M WS1-B25: OWDK3M WS1-B5: OWDK13M WS1-B3: OWDK2M

Perceived Overall Web Knowledge

.813 .792 .614 .580 .479

Standards 20% 0.7 59% 0.8

WS1-A52: SWDK1 WS1-A38: SWDK3 WS1-A14: SWDK5 WS1-A25: SWDK6 WS1-A32: SWDK7 WS1-A19: SWDK4

.896 .885 .875 .870 .840 .835

WS1-A12: SWDK2 Dimension Scale Variance Scale Reliability

.780 1 73% 0.9

Perceived Procedural Web Knowledge

.757 .754 .626 .470 Tools & Terms 24% 0.7

Perceived Declarative Web Knowledge

.846 Security 15% NA

WS1-A21: SWPK1 WS1-A8: SWPK4 WS1-A43: SWPK3 WS1-A5: SWPk2 Dimension Scale Variance Scale Reliability

.921 .900 .869 .859 1 79% 0.9

APPENDIX K: SCALE PERFORMANCE COMPARISON (STUDENT SAMPLES AND WEB SAMPLE) Scale

Scale Development (Student Samples)

Scales

Dimensions

VAR%

Scale Validation (Web Sample) α

Dimensions

VAR%

Current Web Session Usage Experience

WSUF

Web Session Usage Frequency (1-item 8-category Scale)

WSUVNO1

WSUV – No of motivations (1-item 13-category Scale)

WSUVS

Web Session Usage Variety – Situation (2-item Scale)

WSUEB

Web Session Usage Extent – Breadth (3-item Scale)

WSUED

Web Session Usage Extent – Depth (4-item Scale )

WSUEDUR

Web Session Usage Extent – Duration (1-item Scale)

User Web Perceptions

PEWU

Perceived Ease of Web Use (11-item Scale)

PWU

Perceived Web Usefulness (14-item Scale)

Actual Web Knowledge Content

ACPWK

Actual Common Procedural Web Know. Content (6-item Scale)

ACDWK

Actual Common Declarative Web Know. Content (10-item Scale)

ASPWK

Actual Specialised Procedural Web Know. Content (11-items Scale)

ASDWK

Actual Specialised Declarative Web Know. Content (10-item Scale)

Perceived Web Knowledge Content

SWOK

Perceived Overall Web Knowledge Content (2-item Scale)

SWDK

Perceived Declarative Web Knowledge Content (7-item Scale)

SWPK

Perceived Procedural Web Knowledge Content (4-item Scale)

267

APPENDIX L: VARIABLE DISTRIBUTION L.1 HISTORGRAMS Figure L1: WSUF Normality Test (-) 800

Figure L2: WSUEDUR Normality Test (+)

Figure L3: WSUVMNO1 Normality Test 700

1000

600 800

600

500 600

400

400 300

400

200

200 200 Std. Dev = 1.48

Std. Dev = 1.21 N = 2077.00 1.0

2.0

3.0

4.0

5.0

6.0

7.0

N = 2077.00

0 1.0

8.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Duration of Session Use

Web Session Use Freqency

Figure L4: WSUVS Normality Test (+)

Mean = 5.7 N = 2077.00

0 0.0

2.0

4.0

6.0

8.0

10.0

12.0

No. of Use Motivations (Sum)

Figure L5: ACPWK Normality Test (-) 1200

1000

Std. Dev = 2.50

100

Mean = 3.6

Mean = 6.8 0

Figure L6: ASPWK Normality Test (-) 1000

1000 800

800 800

600

400

400 Std. Dev = 1.22

200

Mean = 5.2

Std. Dev = 2.14 Mean = 4.1 N = 2077.00

0 2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

N = 2077.00

0 0.0

1.0

2.0

3.0

4.0

5.0

200

Std. Dev = 2.43

6.0

Actual Common Procedural Web Knowledge (Sum)

Mean = 8.1 N = 2077.00

0 0.0

Situational Variety - (Sum)

2.0

4.0

6.0

8.0

10.0

12.0

Actual Specialised Procedural Web Knowledge (Sum)

268

Figure L7: ACDWK Normality Test (-)

Figure L8: ASDWK Normality Test (-)

1400

700

1200

600

1000

500

800

400

600

300

400

200 Std. Dev = 2.15

200

Mean = 8.4 N = 2077.00

0 0.0

2.0

4.0

6.0

8.0

10.0

Actual Common Declarative Web Knowledge (Sum)

Std. Dev = 2.98

100

Mean = 6.4 N = 2077.00

0 0.0

2.0

4.0

6.0

8.0

10.0

Actual Specialised Declarative Web Knowledge (Sum)

L.2 NORMALITY TEST It is assumed that the variables listed in Table L1 are normally distributed in the population from which the sample (n=2077) is drawn. Thus it is hypothesized that each variable will have a normal distribution to be representation of the population. The results presented in Table L1. It is worth noting, however, that due to the large sample size (n=2077) the test of significance is very sensitive. Thus both graphical plots and statistical tests were used to assess normality, however for brevity in this report only the statistics are reported here.

269

Table L1: Variable/s â&#x20AC;&#x201C;Test of Normality Label

No.

Shape

Normality Test

Skew

Kurtosis

Statistic

Sig.

Description

Current Web Session Usage Extent WSEB

2077

.656

.216

5.258

0.000

Since the significance is 0.000 the null hypothesis is rejected The distribution of WSUEB is different from the assumed distribution (a normal distribution) and there is virtually no chance that this difference is due to sampling error. It is thus highly probable that WSUEB is not normally distributed. After further examination WSUEB has a positive skewed relatively peaked distribution (PS / LD). Since the significance is 0.000 the null hypothesis is rejected The distribution of WSUED is different from the assumed distribution (a normal distribution) and there is virtually no chance that this difference is due to sampling error. It is thus highly probable that WSUED is not normally distributed. After further examination WSUED has a negative skewed relatively flat distribution. (NS / PD)

WSUED

2077

-.350

-.025

3.826

0.000

PEWU

2077

-.712

.513

3.562

0.000

Since the significance is 0.000 the null hypothesis is rejected The distribution of PEWU is different from the assumed distribution (a normal distribution) and there is virtually no chance that this difference is due to sampling error. It is thus highly probable that PEWU is not normally distributed. After further examination PEWU has a negative skewed relatively peaked distribution (NS / LD)

PWU

2077

-.490

.263

2.319

0.000

Since the significance is 0.000 the null hypothesis is rejected The distribution of PWU is different from the assumed distribution (a normal distribution) and there is virtually no chance that this difference is due to sampling error. It is thus highly probable that PWU is not normally distributed. After further examination PWU has a negative skewed relatively peaked distribution (NS / LD)

SWPK

2077

-1.055

.870

6.022

0.000

SWDK

2077

-.650

-.065

3.792

0.000

SWOK

2077

-.693

.021

5.718

0.000

Since the significance is 0.000 the null hypothesis is rejected The distribution of SWPK is different from the assumed distribution (a normal distribution) and there is virtually no chance that this difference is due to sampling error. It is thus highly probable that SWPK is not normally distributed. After further examination SWPK has a negative skewed relatively peaked distribution (NS / LD) Since the significance is 0.000 the null hypothesis is rejected The distribution of SWDK is different from the assumed distribution (a normal distribution) and there is virtually no chance that this difference is due to sampling error. It is thus highly probable that SWDK is not normally distributed. After further examination SWDK has a negative skewed relatively flat distribution (NS / PD) Since the significance is 0.000 the null hypothesis is rejected The distribution of SWOK is different from the assumed distribution (a normal distribution) and there is virtually no chance that this difference is due to sampling error. It is thus highly probable that SWOK is not normally distributed. After further examination SWOK has a negative skewed relatively peaked distribution (NS / LD)

Perceived Ease of Web Use and Web Usefulness

Perceived Web Knowledge

b NS = Negative Skew; PS = Positive Skew; ND = Normal Distribution; LD = Leptokurtic (Peaked) Distribution; PD = Platykurtic (Flat) Distribution

270

APPENDIX M: MULTIPLE REGRESSION â&#x20AC;&#x201C; RESIDUAL PLOTS Figure M-1a: MRA1 Residual Plot WSDMEXP2:

Figure M-2a: MRA2 Residual Plot

Figure M-3a: MRA3 Residual Plot

0 No Experience

2 5

0 No Experience WSDMEXP2:

0 No Experience

-2

-3

-4 -5 -4

-3

-2

-1

Regression Standardized Predicted Value

Regression Standardized Residual

-1

Regression Standardized Residual

WSDMEXP2: 1

-1 -2

-1

-2

-3 -4 -4

-4

-3

-2

-1

-3

-2

-1

Regression Standardized Predicted Value

Figure M-1b: MRA1 Residual Plot WSDMEXP2:

1 Experience

Figure M-2b: MRA2 Residual Plot

Figure M-3b: MRA3 Residual Plot WSDMEXP2:

WSDMEXP2:

1 Experience 2

Regression Standardized Residual

0 3

-2

-4

-6 -4

-3

-2

-1

Regression Standardized Predicted Value

Regression Standardized Residual

1 Experience

-1

-2

-3 -5

-4

-3

-2

-1

-2 -5

-4

-3

-2

-1

Regression Standardized Predicted Value

271

Regression Standardized Predicted Value

Figure M-4a: MRA4 Residual Plot

WSDMEXP2: WSDMEXP2:

0 No Experience

4 3

Regression Standardized Residual

-1

-2

Regression Standardized Residual

2 1 0 -1 -2

-2

-1

-3

WSDMEXP2:

Regression Standardized Residual

-1

-2 1

-2

Regression Standardized Predicted Value

-1

-2

-1

Figure M-5b: MRA5 Residual Plot

-3

Regression Standardized Predicted Value

1 Experience

-1

-3

-4

-2

Figure M-4b: MRA4 Residual Plot

-3

-1

Regression Standardized Predicted Value

WSDMEXP2:

-4

-4 -3

-3

Figure M-6b: MRA6 Residual Plot

1 Experience

WSDMEXP2:

Regression Standardized Residual

WSDMEXP2:

0 No Experience

Regression Standardized Residual

Figure M-6a: MRA6 Residual Plot

Figure M-5a: MRA5 Residual Plot

-1

-2

-3 -4 -4

-3

-2

-1

Regression Standardized Predicted Value

272

1 Experience

-1

-2 -3 -4

-3

-2

-1

Regression Standardized Predicted Value

Figure M-7a: MRA7 Residual Plot WSDMEXP2:

Figure M-8a: MRA8 Residual Plot

0 No Experience

WSDMEXP2:

0 No Experience

Regression Standardized Residual

0 -1 -2 -3 -4 -5 -4

-3

-2

-1

-2

-4

-6 -3

Regression Standardized Predicted Value

Figure M-7b: MRA7 Residual Plot WSDMEXP2:

-1

Figure M-8b: MRA8 Residual Plot

1 Experience

WSDMEXP2:

1 Experience

-2

-4

-6 -5

-4

-3

-2

-1

Regression Standardized Predicted Value

Regression Standardized Residual

-2

Regression Standardized Predicted Value

-2

-4

-6 -5

-4

-3

-2

-1

Regression Standardized Predicted Value

273

APPENDIX N: MULTIPLE REGRESSION â&#x20AC;&#x201C; NORMALITY P-P PLOT Figure N-1a: MRA1 Normality P-P Plot

Figure N-2a: MRA2 Normality P-P Plot

Normal P-P Plot of Regression Standardized Residual

WSDMEXP2:

Normal P-P Plot of Regression Standardized Residu

0 No Experience

WSDMEXP2:

1.00

Figure N-3a: MRA3 Normality P-P Plot Normal P-P Plot of Regression Standardized Residual

0 No Experience

WSDMEXP2:

1.00

0 No Experience

1.00 .75

.75

0.00 0.00

.25

.50

.75

1.00

.50

.25

0.00 0.00

Observed Cum Prob

.25

.50

.75

1.00

Expected Cum Prob

.25

Expected Cum Prob

.75 .50

.50

.25

0.00 0.00

Observed Cum Prob

Figure N-1b: MRA1 Normality P-P Plot

.25

.50

.75

1.00

Observed Cum Prob

Figure N-2b: MRA2 Normality P-P Plot

Figure N-3b: MRA3 Normality P-P Plot

Normal P-P Plot of Regression Standardized Residua WSDMEXP2:

Normal P-P Plot of Regression Standardized Residual WSDMEXP2:

1 Experience

Normal P-P Plot of Regression Standardized Residual

1.00

1 Experience

WSDMEXP2:

1 Experience

1.00

.75 .75

.25

0.00 0.00

.25

.50

Observed Cum Prob

.75

1.00

.50

Expected Cum Prob

.50

Expected Cum Prob

.75

.25

.50

.25

0.00 0.00

.25

.50

Observed Cum Prob

274

.75

1.00

.25

.50

Observed Cum Prob

.75

1.00

Figure N-4a: MRA4 Normality P-P Plot

Figure N-5a: MRA5 Normality P-P Plot

Normal P-P Plot of Regression Standardized Residual WSDMEXP2:

Figure N-6a: MRA6 Normality P-P Plot

Normal P-P Plot of Regression Standardized Residual

0 No Experience

WSDMEXP2:

1.00

.75

WSDMEXP2:

0 No Experience

1.00

0 No Experience

.50

.25

0.00 0.00

.25

.50

.75

1.00

.50

Expected Cum Prob

.50

Expected Cum Prob

.75

.25

0.00 0.00

Observed Cum Prob

.25

.50

.75

.25

0.00 0.00

1.00

Observed Cum Prob

Figure N-4b: MRA4 Normality P-P Plot

.25

.50

.75

1.00

Observed Cum Prob

Figure N-5b: MRA5 Normality P-P Plot

Figure N-6b: MRA6 Normality P-P Plot

Normal P-P Plot of Regression Standardized Residual WSDMEXP2:

1 Experience

Normal P-P Plot of Regression Standardized Residua

1.00

WSDMEXP2:

1 Experience

1.00

1 Experience

1.00

.75

.50

0.00 0.00

.25

.50

Observed Cum Prob

.75

1.00

.50

Expected Cum Prob

.25

Expected Cum Prob

.75

.25

0.00 0.00

.25

.50

Observed Cum Prob

275

.75

1.00

.25

0.00 0.00

.25

Observed Cum Prob

.50

.75

1.00

Figure N-7a: MRA7 Normality P-P Plot 0 No Experience

WSDMEXP2:

1.00

.75

.50

Expected Cum Prob

WSDMEXP2:

Figure N-8a: MRA8 Normality P-P Plot

.25

0.00 0.00

.25

.50

.75

.25

0.00

1.00

0.00

Observed Cum Prob

.50

.75

1.00

Figure N-8b: MRA8 Normality P-P Plot

1 Experience

WSDMEXP2:

1.00

.75

.50

1 Experience

.50

.25

0.00 0.00

.25

.50

.75

1.00

Expected Cum Prob

.25

Observed Cum Prob

Figure N-7b: MRA7 Normality P-P Plot WSDMEXP2:

0 No Experience

.25

0.00 0.00

.25

Observed Cum Prob

276

.50

.75

1.00

APPENDIX O: MULTIPLE REGRESSION ASSUMPTION CHECK Table Legend:

2 = assumption violated; 3 = assumption met; ? = possible violation Assumption Description

Multiple Regressions MRA1

MRA2

MRA3

MRA4

MRA5

MRA6

MRA7

MRA8

Metric Dependent Variable

Linearity

Normality (See Appendix N for the normal probability plots)

Homoscedasticity (See Appendix M for residual examination)

Independent Errors (From an assessment of Durbin-Watson Statistic)

Multicollinearity (From an assessment of VIF and tolerance statistic)1

Some collinearity may be present between the independent variables given the close nature of the area of context investigated (i.e., perception & knowledge)

277

APPENDIX P: SAMPLE & VARIABLE DESCRIPTION P.1 FREQUENCY ANALYSIS Table P1: Frequency Distribution (%L/%M/%H) Overall Sample (n=2077) %Low

%Med

%High

Without WSD/M Experience (n=900) %Low

%Med

With WSD/M Experience (n=1177)

%High

%Low

%Med

%High

Actual knowledge of the Web

Common Procedural1 Common Declarative2 Specialised Procedural3 Specialised Declarative2

Perceived Knowledge of the Web

Overall

Procedural

Declarative

Ease of Use

Usefulness

Web Perceptions

Current Web Session Usage

Frequency Situational Variety5 Motivational Variety6 Extent Breadth7 4

Extent Depth8 Extent Duration9

1: Low = 0-2 correct out of 6; Medium = 3-4 correct out of 6; High = 5-6 correct out of 6. 2: Low = 0-3 correct out of 10; Medium = 4-6 correct out of 10; High = 7-10 correct out of 10. 3: Low = 0-3 correct out of 11; Medium = 4-7 correct out of 11; High = 8-11 correct out of 11. 4: Low = Once a month to Once every two weeks; Medium = Once a week to 4-6 times a week; High = Once a day to 5 or more times a day. 5: Low = 1-2 locations & 0-3 location types; Medium = 3-4 locations & 4-7 locations types; High = 5 or more locations & 8-11 location types 6: Low = 0-3 use motivations; Medium = 4-8 use motivations; High = 9-12 use motivations. 7: Low = Use the same search engines, web sites and type of sites in a web session; High = Use the different search engines, web sites and types of sites in a web session 8: Low = Low number of bookmarks/favourites saved, use only a couple of search engines, visit few web sites; High = High number of bookmarks/favourites saved, use a large number of search engines, visit a large number of search engines 9: Low = Less than 1 hour in a web session; 1-6 hours during a web session; 7-13 hours during a web session

278

Figure P1: Actual Comm Dec. Knowledge

Figure P2: Actual Comm Proc. Knowledge

Figure P3: Actual Spec. Dec. Knowledge

Figure P4: Actual Spec. Proc. Knowledge

279

Figure P5: Perceived Overall Knowledge

Figure P6: Perceived Proc. Knowledge

Figure P7: Perceived Dec. Knowledge

Figure P8: Perceived Ease of Web Use

Figure P9: Perceived Web Usefulness

Figure P10: Web Use Frequency

280

Figure P11: Web Use: Sit. Variety

Figure P12: Web Use: Motive Variety

Figure P13: Web Use Extent: Breadth

Figure P14: Web Use Extent: Depth

Figure P15: Web Use Extent: Duration

281

P.2 SAMPLE MEAN COMPARATIVE ANALYSIS Table P2: Mean Scores and Standard Deviations (SD) Web Site Design &/or Maintenance (WSD/M) Experience Without WSD/M (n=900) With WSD/M (n = 1177) Mean SD Mean SD Actual Knowledge

Common Procedural Common Declarative Specialised Procedural Specialised Declarative

4.77

1.433

5.51

.895

7.52

2.470

9.08

1.562

6.95

2.675

9.03

1.762

4.72

2.956

7.69

2.275

Perceived Knowledge

Overall Procedural Declarative

8.74

2.700

11.64

2.037

19.21

5.254

24.01

3.470

8.524

39.62

6.524

30.25

Web Perceptions

Ease of Use Usefulness

54.11

10.380

57.98

9.248

69.87

12.194

72.33

11.404

Frequency Situational Variety Motivational Variety Extent Breadth Extent Depth Extent Duration

6.52

1.286

7.07

1.082

3.57

1.973

4.49

2.107

4.89

2.430

6.32

2.382

9.00

3.373

8.89

3.548

17.76

4.400

19.64

3.964

3.31

1.373

3.81

1.521

Current Web Usage

Table P2: t-test Results T-test Actual Knowledge

Common Procedural Common Declarative Specialised Procedural Specialised Declarative

Sig.

-14.385

.000

-17.620

.000

-21.314

.000

-25.905

.000

Perceived Knowledge

Overall Procedural Declarative

-27.908

.000

-24.979

.000

-28.383

.000

Web Perceptions

Ease of Use Usefulness

-8.958

.000

-4.729

.000

Current Web Usage

Frequency Situational Variety Motivational Variety Extent Breadth Extent Depth Extent Duration

-10.350

.000

-10.147

.000

-13.416

.000

.719

.472

-10.233

.000

-7.714

.000

282

APPENDIX Q: BIVARIATE ANALYSIS - CONVERGENT VALIDATION Q.1 INTRODUCTION In this appendix the results of bivariate analyses are reported. Nonparametric bivariate statistics and contingency table analysis (i.e., cross tabulations) are used to further validate the findings reported in Chapter 10. Further discussion as to the treatment of variables and the motivation for the use of nonparametric bivariate methods can be found in Chapter 8 (section 8.4.3.3).

For each hypothesis tested, the results are presented and discussed stating the observed differences between those users without, and those with, web site design and maintenance experience (WSD/M Experience). These two groups are labelled: ‘Web User Group A (No WSD/M Experience)’ and ‘Web User Group B (With WSD/M Experience)’. The descriptive profile of each user group was presented in Chapter 9 (section 9.3.3), and in Appendix P.

Q.2 RESEARCH QUESTION 1: WEB PERCEPTION & USAGE Question 1 asks: What is the relationship between a user’s perceptions of the web and a person’s current web session usage? To examine this question more specifically, 12 hypotheses were introduced in Chapter 4, proposing the relationship between a user’s perceived usefulness and ease of use of the web and that person’s current web session usage frequency, variety and extent. Results are presented below.

Q.2.1 H1A: PEWU & WSUF H1A proposes a curvilinear (L/H/M) relationship between perceived ease of web use (PEWU) and current web session usage frequency (WSUF).

283

H1A: Perceived ease of web use (PEWU) will have a curvilinear (L/H/M) relationship with current web session usage frequency (WSUF). H0A: Perceived ease of web use (PEWU) will have no relationship with current web session usage frequency (WSUF).

As indicated in H1A, a curvilinear relationship is proposed to occur between PEWU and WSUF. Therefore, as explained in Chapter 8, the significance, nature (i.e., linear or curvilinear/non-linear), direction (i.e., positive/negative, inverted ushape/u-shape) and strength (i.e., weak, moderate, high) of the relationship proposed will be assessed firstly by comparing the Chi-squared based correlation coefficients (i.e., linear with non-linear) and secondly by examining a 3x3 crosstabulation of the two variables. Q.2.1.1 Correlation Analysis The chi-squared based correlation coefficients for H1A are reported in Table Q1.

Table Q1: Chi-square Correlation Coefficients (Symmetric and Directional Measures) H1A: Perceived Ease of Web Use & Web Session Usage Frequency Web Site Design and Maintenance Experience

Nominal by Nominal (non-linear) Ordinal by Ordinal (linear) No. of Valid Cases Nominal by Nominal Experience (non-linear) Ordinal by Ordinal (linear) No. of Valid Cases a. Not assuming the null hypothesis b. Based on chi-squared approximation

No Experience

Statistic

Value

Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma

.002 .052 .007 .015 900 .000 .043 -.017 -.040 1177

Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma

Asymp. Std. Errora

.002 .032 .065 .001 .028 .067

Approx. Sig.

.605b .297 .818 .818 .953b .370 .553 .553

Web User Group A: No WSD/M Experience From looking at the non-linear correlation coefficients in Table Q1, it is evident that a small positive association exists between PEWU and WSUF for users with no WSD/M experience. However, this association is not statistically significant. The

284

linear correlation coefficients reported in Table Q1 tell the same story. To assess the possibility of a curvilinear association, Goodman and Kruskal’s tau is compared with Gamma. As Goodman and Kruskal’s tau is less than Gamma, we can infer that there is no curvilinear relationship for web users without WSD/M experience.

Web User Group B: With WSD/M Experience When examining the non-linear correlation coefficients for users with WSD/M experience in Table Q1, it is evident that a very weak positive association exists between PEWU and WSUF. But, again, this result is not statistically significant. This very weak positive result is inconsistent with the negative linear correlation coefficients reported in Table Q1, however these also are not statistically significant. To assess the possibility of a curvilinear relationship, Goodman and Kruskal’s tau and Gamma are compared. For users with WSD/M experience Goodman and Kruskal’s tau is in fact greater than Gamma. Thus, for this user group a curvilinear relationship may exist between PEWU and WSUF.

Q.2.1.2 Cross-tabulation Although the findings for both web user groups are not statistically significant, further examination of the 3x3 cross-tabulation is warranted to assess the nature of the relationship between PEWU and WSUF for users with WSD/M experience – given that a curvilinear relationship may be present.

Web User Group A: No WSD/M Experience Table Q2 shows that no clear significant pattern (linear or curvilinear) exists between PEWU and WSUF for users who have no WSD/M experience. Thus H1A is rejected and H0A accepted for this user group.

285

Table Q2: 3x3 Cross-tabulation H1A: Perceived Ease of Web Use * Web Session Usage Frequency Web Session Use Freqency (L/M/H) * Perceived Ease of Web Use (L/M/H) Crosstabulation Web Site Design & Maintenance Experience (0/1) No Experience

Perceived Ease of Web Use (L/M/H) Low Web Session Use Freqency (L/M/H)

Low

Expected Count

8.7

13.7

23.0

.0%

1.5%

3.4%

2.6%

Count % within Perceived Ease of Web Use (L/M/H) Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Total

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Experience

Web Session Use Freqency (L/M/H)

Low

194

340

202.5

340.0

34.8%

40.5%

36.2%

37.8%

198

324

537

13.7

203.5

319.8

537.0

65.2%

58.1%

60.4%

59.7%

341

536

900

23.0

341.0

536.0

900.0 100.0%

100.0%

Expected Count

3.7

5.9

10.0

.0%

.2%

1.3%

.8%

Count % within Perceived Ease of Web Use (L/M/H) Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Total

138 128.8

100.0%

Expected Count

High

8 8.7

Count % within Perceived Ease of Web Use (L/M/H)

Medium

Total

Expected Count

High

% within Perceived Ease of Web Use (L/M/H) Medium

Medium

Count

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

150

256

10.0

95.9

150.1

256.0

19.6%

22.0%

21.7%

21.8%

343

531

911

35.6

341.3

534.1

911.0

80.4%

77.8%

77.0%

77.4%

441

690

1177

46.0

441.0

690.0

1177.0

100.0%

Web User Group B: With WSD/M Experience Consistent with the linear correlation coefficients reported in Table Q1, for users who have experience with WSD/M, it seems that an extremely weak negative (linear) relationship might exist between PEWU and WSUF. However as these findings are not statistically significant, and the relationship detected is linear, not curvilinear as hypothesised, H1A is rejected and H0A accepted.

Q.2.1.3 H1A: Summary No statistically significant curvilinear relationship was found between PEWU and WSUF. This was the case for web users with and web users without WSD/M experience. Thus H1A is rejected and H0A is accepted for both user groups.

286

Q.2.2 H2A: PWU & WSUF H2A proposes a positive (L/M/H) relationship between perceived web usefulness (PWU) and current web session usage frequency (WSUF).

H2A: Perceived web usefulness (PWU) will have a positive relationship with current web session usage frequency (WSUF). H0A: Perceived web usefulness (PWU) will have no relationship with current web session usage frequency (WSUF).

Q.2.2.1 Correlation Analysis As the variables examined in H2A are measured using different types of data, and the distribution of variable scores for both PWU and WSUF are negative, Spearmanʹs rho (rs) is the most suitable statistic to use (see Chapter 8). H2A is a directional hypothesis and a 1-tailed test is conducted. This is reported for H2A in Table Q3.

Table Q3: Nonparametric Correlation Coefficient: Spearman Rho (rs) H2A: Perceived Web Usefulness & Web Session Usage Frequency Web Site Design and Maintenance Experience (0/1)

Web Session Usage Frequency

Perceived Web Usefulness (Sum) Spearman’s Perceived Web Experience Rho Usefulness (Sum) *. Correlation significant at the .05 level (1-tailed) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.058 .040 900 .113 .000 1177

Web User Group A: No WSD/M Experience. There is a weak positive relationship between PWU and WSUF for users with no WSD/M experience, and this is significant. Thus, H2A is accepted and H0A rejected for this user group. Web User Group B: With WSD/M Experience 287

There is a positive relationship between PWU and WSUF for users with WSD/M experience, and this is significant. Thus, H2A is accepted and H0A rejected for this user group.

Q.2.2.2 H2A: Summary H2A is supported for both web user groups, indicating a positive relationship exists between PWU and WSUF. For both groups this relationship is fairly weak, however it is weakest for users without web site design and maintenance experience.

Q.2.3 H3A: PEWU & WSUVS H3A proposes a curvilinear (L/H/M) relationship between perceived ease of web use (PEWU) and current web session usage variety - situational (WSUVS).

H3A: Perceived ease of web use (PEWU) will have a curvilinear (L/H/M) relationship with current web session usage variety - situational (WSUVS). H0A: Perceived ease of web use (PEWU) will have no relationship with current web session usage variety - situational (WSUVS).

These relationships are assessed by comparing the Chi-squared based correlation coefficients and by examining a 3x3 cross-tabulation of the two variables.

Q.2.3.1 Correlation Analysis The Chi-squared correlation coefficients for H3A are reported in Table Q4.

288

Table Q4: Chi-square Correlation Coefficients (Symmetric & Directional) H3A: Perceived Ease of Web Use & Web Session Usage Variety - Situational Web Site Design and Maintenance Experience

Nominal by Nominal (non-linear) Ordinal by Ordinal (Linear) No. of Valid Cases Nominal by Nominal Experience (non-linear) Ordinal by Ordinal (Linear) No. of Valid Cases a. Not assuming the null hypothesis b. Based on chi-squared approximation

No Experience

Statistic

Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma

Value

.000 .016 -.005 -.017 900 .002 .035 .011 .028 1177

Asymp. Std. Errora

.001 .033 .101 .003 .029 .077

Approx. Sig.

.959b .979 .870 .870 .393b .589 .714 .714

Web User Group A: No WSD/M Experience It is evident from the non-linear correlation coefficients in Table Q4, that no statistically significant association exists between PEWU and WSUVS for users without WSD/M experience. This result is also consistent with the linear correlation coefficients reported in the Table Q4 for this user group. To assess the possible presence of a curvilinear association, Goodman and Kruskal’s tau is compared with Gamma. As Goodman and Kruskal’s tau is slightly greater than Gamma, we can surmise that a very weak curvilinear relationship may be present.

Web User Group B: With WSD/M Experience When examining the non-linear correlation coefficients for users with WSD/M experience in Table Q4, it is evident that a small association that is not statistically significant exists between PEWU and WSUVS. This result is consistent with the linear correlation coefficients reported in Table Q4. To assess the possible presence of a curvilinear relationship, Goodman and Kruskalʹs tau and Gamma are compared. For users with WSD/M experience, Goodman and Kruskalʹs tau was less than Gamma. Thus, for this user group a curvilinear relationship may not exist between PEWU and WSUVS.

289

Q.2.3.2 Cross-tabulation Although the above findings reported for both web user groups are not statistically significant, an examination of the 3x3 cross-tabulation is further warranted to confirm the nature of the relationship between PEWU and WSVS for users with no WSD/M experience. Web User Group A: No WSD/M Experience With respect to the nature of the relationship proposed in H3A, it is evident in Table Q5 that a very weak u-shaped curvilinear relationship exists between PEWU and WSUVS for users with no WSD/M experience. Despite a curvilinear relationship being found, this result is not statistically significant and thus, H3A is rejected and H0A accepted for this user group. Web User Group B: With WSD/M Experience With respect to the nature of the relationship proposed in H3A, it is evident in Table Q5 that a very weak inverted u-shape curvilinear relationship exists between PEWU and WSUVS for users who have WSD/M experience. Despite a curvilinear relationship being found, this result is not statistically significant and thus, H3A is rejected and H0A accepted for this user group.

290

Table Q5: 3x3 Cross-tabulation - H3A: Perceived Ease of Web Use & Web Session Usage Variety â&#x20AC;&#x201C; Situational Situational Variety - (L/M/H) * Perceived Ease of Web Use (L/M/H) Crosstabulation Web Site Design & Maintenance Experience (0/1) No Experience

Perceived Ease of Web Use (L/M/H) Low Situational Low Variety - (L/M/H)

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Medium

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

High

794.0

91.3%

87.7%

88.4%

88.2%

103

2.6

39.0

61.3

103.0

8.7%

12.0%

11.2%

11.4%

1.8

3.0

.0%

.3%

.4%

.3%

341

536

900

23.0

341.0

536.0

900.0

100.0%

Count Expected Count

376

575

987

38.6

369.8

578.6

987.0 83.9%

78.3%

85.3%

83.3%

Count

112

184

Expected Count

7.2

68.9

107.9

184.0

21.7%

14.1%

16.2%

15.6%

% within Perceived Ease of Web Use (L/M/H) Count

Expected Count

2.2

3.5

6.0

.0%

.7%

.4%

.5%

441

690

1177

46.0

441.0

690.0

1177.0

100.0%

% within Perceived Ease of Web Use (L/M/H) Total

472.9

1.1

% within Perceived Ease of Web Use (L/M/H)

High

300.8

% within Perceived Ease of Web Use (L/M/H)

Medium

794

20.3

Count

Situational Low Variety - (L/M/H)

Total

474

Expected Count

Experience

High

299

Count % within Perceived Ease of Web Use (L/M/H)

Total

Medium 21

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Q.2.3.3 H3A: Summary H3A proposed that a curvilinear relationship would exist between PEWU and WSUVS. After an examination of the association between PEWU and WSUVS, it is evident that in fact a very weak curvilinear relationship might exist for both web users groups. For web users with no WSD/M experience, a u-shaped relationship may be present, and for web users with WSD/M experience an inverted u-shaped relationship may be present. However, these relationships are not statistically significant for either user group, and the strength of the curvilinear relationships is extremely weak. Thus, H3A is rejected and H0A is accepted for both groups.

Q.2.4 H3B: PEWU & WSUVMNO1 H3B proposes a curvilinear (L/H/M) relationship between perceived ease of web use (PEWU) and current web session usage variety - motivational (WSUVMNO1). 291

H3B: Perceived ease of web use (PEWU) will have a curvilinear (L/H/M) relationship with current web session usage variety – motivational (WSUVMNO1). H0: Perceived ease of web use (PEWU) will have no relationship with current web session usage variety – motivational (WSUVMNO1).

These relationships are assessed by comparing the Chi-squared based correlation coefficients and by examining a 3x3 cross-tabulation of the two variables.

10.2.4.1 Correlation Analysis The Chi-squared correlation coefficients for H3B are reported in Table Q6.

Table Q6: Chi-squared Correlation Coefficients (Symmetric and Directional) H3B: Perceived Ease of Web Use & Web Session Usage Variety - Motive Web Site Design and Maintenance Experience

Nominal by Nominal (non-linear) Ordinal by Ordinal (Linear) No. of Valid Cases Experience Nominal by Nominal (non-linear) Ordinal by Ordinal (Linear) No. of Valid Cases a. Not assuming the null hypothesis b. Based on chi-squared approximation

No Experience

Statistic

Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma

Value

.005 .064 .036 .067 900 .000 .018 .015 .027 1177

Asymp. Std. Errora

.004 .032 .060 .001 .027 .050

Approx. Sig.

.078b .119 .261 .261 .954b .948 .589 .589

Web User Group A: No WSD/M Experience By examining the non-linear correlation coefficients in Table Q6, a small association is seen between PEWU and WSUVMNO1 for users with no WSD/M experience. It is further evident that this relationship is not statistically significant. This result is consistent with the linear correlation coefficients reported in Table Q6. To assess the possible presence of a curvilinear association, Goodman and

292

Kruskal’s tau is compared with Gamma. As Goodman and Kruskal’s tau is less than Gamma, we can surmise that a curvilinear relationship is not present. Web User Group B: With WSD/M Experience From examining the non-linear correlation coefficients for users with WSD/M experience, it is evident that no association exists between PEWU and WSUVMNO1. Furthermore, this result is not statistically significant. This result is consistent with the linear correlation coefficients reported in Table Q6. To assess the possible presence of a curvilinear association, Goodman and Kruskalʹs tau and Gamma are compared. For users with WSD/M experience it was identified that Goodman and Kruskalʹs tau was less than Gamma and thus a curvilinear relationship is not present.

Q.2.4.2 Cross-tabulation Although these findings are in fact not statistically significant, an examination of the 3x3 cross-tabulation is warranted to further assess the nature of the relationship between PEWU and WSUVMNO1. Web User Group A: No WSD/M Experience Consistent with the Chi-squared correlation coefficients reported in Table Q6, a moderate positive pattern was found to exist between PEWU and WSUVMNO1 in Table Q7 for web users without WSD/M experience. Therefore, H3B is rejected and H0A is accepted for this user group. Web User Group B: With WSD/M Experience Consistent with the Chi-squared correlation coefficients reported in Table Q6, a weak positive pattern was found to exist between PEWU and WSUVMNO1 in Table Q7 for web users with WSD/M experience. Therefore, H3B is rejected and H0A is accepted for this user group.

293

TableQ7: 3x3 Cross tabulation - H3B: Perceived Ease of Web Use & Web Session Usage Variety â&#x20AC;&#x201C; Motive No of Use Motivation (L/M/H) * Perceived Ease of Web Use (L/M/H) Crosstabulation Web Site Design & Maintenance Experience (0/1) No Experience

Perceived Ease of Web Use (L/M/H) Low No of Use Motivation (L/M/H)

Low

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Medium

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

High

Total

Count

240.0

403.0

69.6%

44.3%

44.0%

44.8%

167

255

429

11.0

162.5

255.5

429.0

30.4%

49.0%

47.6%

47.7%

40.5

68.0

% within Perceived Ease of Web Use (L/M/H)

.0%

6.7%

8.4%

7.6%

Count

Count Expected Count % within Perceived Ease of Web Use (L/M/H) Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Total

152.7

% within Perceived Ease of Web Use (L/M/H)

High

10.3

25.8

Expected Count

Medium

Total 403

Count

Low

High 236

1.7

% within Perceived Ease of Web Use (L/M/H) No of Use Motivation (L/M/H)

Medium 151

Expected Count

Experience

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

341

536

900

23.0

341.0

536.0

900.0

100.0%

109

168

289

11.3

108.3

169.4

289.0

26.1%

24.7%

24.3%

24.6%

254

387

666

26.0

249.5

390.4

666.0

54.3%

57.6%

56.1%

56.6%

135

222

8.7

83.2

130.1

222.0

19.6%

17.7%

19.6%

18.9%

441

690

1177

46.0

441.0

690.0

1177.0

100.0%

Q.2.4.3 H3B: Summary A curvilinear relationship was hypothesised to exist between PEWU and WSUVMNO1 in H3B. However, this was not found for either user group. In fact, both groups exhibit signs of a positive relationship between PEWU and WSUVMNO1, not a curvilinear one. In summary, H3B is rejected and H0A is accepted for users both with and without web site design and maintenance experience.

Q.2.5 H4A: PWU & WSUVS H4A proposes a positive relationship between perceived web usefulness (PWU) and current web session usage variety â&#x20AC;&#x201C; situational (WSUVS).

294

H4A: Perceived web usefulness (PWU) will have a positive relationship with current web session usage variety – situational (WSUVS). H0: Perceived web usefulness (PWU) will have no relationship with current web session usage variety – situational (WSUVS).

Q.2.5.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H4A in Table Q8. Web User Group A: No WSD/M Experience There is a statistically significant and very weak positive relationship between PWU and WSUVS for users with no WSD/M experience. Therefore, H4A is accepted and H0A is rejected for this user group. Web User Group B: With WSD/M Experience There is a statistically significant and extremely weak positive relationship between PWU and WSUVS for users with WSD/M experience. Thus H4A is accepted and H0A is rejected for this user group.

Table Q8: Nonparametric Correlation Coefficient: Spearman Rho (rs) H4A: Perceived Web Usefulness & Web Session Usage Variety - Situational Web Site Design and Maintenance Experience (0/1)

Situational Variety (Sum)

Perceived Web Usefulness (Sum) Spearman’s Perceived Web Experience Rho Usefulness (Sum) *. Correlation significant at the .05 level (1-tailed)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.061 .035 900 .054 .033 1177

Q.2.5.2 H4A: Summary H4A is supported for both web user groups, indicating a statistically significant weak positive relationship between PWU and WSUVS. 295

Q.2.6 H4B: PWU & WSUVMNO1 H4B proposes a positive relationship between perceived web usefulness (PWU) and current web session usage variety – motivation (WSUVMNO1).

H4B: Perceived web usefulness (PWU) will have a positive relationship with current web session usage variety – motivational (WSUVMNO1). H0B: Perceived web usefulness (PWU) will have no relationship with current web session usage variety – Motivation (WSUVMNO1).

Q.2.6.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H4B in Table Q9.

Table Q9: Nonparametric Correlation Coefficient: Spearman Rho (rs) H4B: Perceived Web Usefulness & Web Session Usage Variety - Motive Web Site Design and Maintenance Experience (0/1)

No. of Use Motivations

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.322 .000 900 .211 .000 1177

Web User Group A: No WSD/M Experience As shown in Table Q9, according to the Spearman rho correlation coefficient, there is a statistically significant and moderately positive relationship between PWU and WSUVMNO1 for users with no WSD/M experience. Thus H4B is accepted and H0B is rejected for this user group.

296

Web User Group B: With WSD/M Experience As shown in Table Q9, according to the Spearman rho correlation coefficient there is a statistically significant and low positive relationship between PWU and WSUVMNO1. Thus H4B is accepted and H0B is rejected for this user group.

Q.2.6.2 H4B: Summary H4B is supported for both web user groups indicating that a positive relationship exists between PWU and WSUVMNO1. In addition, it is evident that this relationship is weaker for users with web site design and maintenance experience than those without this experience. Thus, as perceived usefulness of the web increases users without web site design and maintenance experience have a stronger tendency to use the web for a larger number of reasons than those with web site design and maintenance experience.

Q.2.7 H5A: PEWU & WSUEB H5A proposes a curvilinear (L/H/M) relationship between perceived ease of web use (PEWU) and current web session usage extent - breadth (WSUEB).

H5A: Perceived ease of web use (PEWU) will have a curvilinear (L/H/M) relationship with current web session usage extent â&#x20AC;&#x201C; breadth (WSUEB). H0: Perceived ease of web use (PEWU) will have no relationship with current web session usage extent â&#x20AC;&#x201C; breadth (WSUEB).

These relationships are assessed by comparing the Chi-squared based correlation coefficients and by examining a 3x3 cross-tabulation of the two variables.

Q.2.7.1 Correlation Analysis The Chi-squared correlation coefficients for H5A are reported in Q10.

297

Table Q10: Chi-squared Correlation Coefficients (Symmetric and Directional) H5A: Perceived Ease of Web Use & Web Session Usage Extent - Breadth Web Site Design and Maintenance Experience

No Experience

Statistic

Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma

Value

0.007 0.079 -0.085 -0.167 900 0.000 0.037 0.015 0.030 1177

Asymp. Std. Errora

0.005 0.033 0.062 0.001 0.028 0.055

Approx. Sig.

0.019b 0.023 0.009 0.009 0.883b 0.512 0.580 0.580

Web User Group A: No WSD/M Experience A very weak positive association exists between PEWU and WSUEB for users with no experience with WSD/M. It is further evident that this relationship is statistically significant. In contrast the linear correlation coefficients report a very weak negative statistical significant relationship between PEWU and WSUEB. Thus to assess the possible presence of a curvilinear association between PEWU and WSUEB, Goodman and Kruskal’s tau is compared with Gamma. As Goodman and Kruskal’s tau is slightly greater than Gamma, a very weak curvilinear relationship may actually be present. Web User Group B: With WSD/M Experience A negligible association exists between PEWU and WSUEB and it is further evident that this relationship is not statistically significant. This result is consistent with the linear correlation coefficients reported in the Table Q10. To assess the possibility of a curvilinear relationship, Goodman and Kruskalʹs tau and Gamma are compared. It was identified that Goodman and Kruskalʹs tau was less than Gamma and thus for a curvilinear relationship does not exist between PEWU and WSUEB for this user group.

298

Q.2.7.2 Cross-tabulation An examination of the 3x3 cross-tabulation is warranted to further assess the nature of the relationship between PEWU and WSUEB, especially for users with no WSD/M experience as a weak curvilinear relationship was identified for this group in the correlation coefficients.

Table Q11: 3x3 Cross tabulation - H5A: PEWU & WSUEB Web Session Use Extent - Breadth (L/M/H) * Perceived Ease of Web Use (L/M/H) Crosstabulation Web Site Design & Maintenance Experience (0/1) No Experience

Perceived Ease of Web Use (L/M/H) Low Web Session Use Extent - Breadth (L/M/H)

Low

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Medium

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

High

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Total

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Experience

Web Session Use Extent - Breadth (L/M/H)

Low

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Medium

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

High

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Total

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Medium

High

Total

193

350

557

14.2

211.0

331.7

557.0

60.9%

56.6%

65.3%

61.9%

130

167

303

7.7

114.8

180.5

303.0

26.1%

38.1%

31.2%

33.7%

1.0

15.2

23.8

40.0

13.0%

5.3%

3.5%

4.4%

341

536

900

23.0

341.0

536.0

900.0

100.0%

279

428

736

28.9

276.1

431.1

736.0

63.0%

63.4%

62.3%

62.7%

140

220

377

14.8

141.4

220.8

377.0

37.0%

31.8%

32.0%

32.1%

2.4

22.5

35.1

60.0

.0%

4.8%

5.7%

5.1%

440

687

1173

46.0

440.0

687.0

1173.0

100.0%

Web User Group A: No WSD/M Experience With respect to the nature of the relationship proposed in H5A, it is evident in Table Q11 that a very weak u-shaped relationship might exist between PEWU and WSUEB for web users with no WSD/M experience. Thus, as a statistically significant curvilinear relationship was found, H5A is accepted and H0A is rejected for this user group.

299

Web User Group B: With WSD/M Experience With respect to the nature of the relationship proposed in H5A, as evident in Table Q11, no clear pattern exists between PEWU and WSUEB for web users with WSD/M experience. Thus, H5A is rejected and H0A is accepted for this user group. Q.2.7.3 H5A: Summary A u-shaped relationship between PEWU and WSUEB is statistically significant for users with no web site design and/or maintenance experience. Thus, H5A is accepted and H0A is rejected for this user group. However, no clear relationship was identified between PEWU and WSUEB for users with WSD/M experience. Thus, for this user group H5A is rejected and H0A accepted.

Q.2.8 H5B: PEWU & WSUED H5B proposes a curvilinear (L/H/M) relationship between perceived ease of web use (PEWU) and current web session usage extent - depth (WSUED).

H5B: Perceived ease of web use (PEWU) will have a curvilinear (L/H/M) relationship with current web session usage extent â&#x20AC;&#x201C; depth (WSUED). H0B: Perceived ease of web use (PEWU) will have no relationship with current web session usage extent â&#x20AC;&#x201C; depth (WSUED).

These relationships are assessed by comparing the Chi-squared based correlation coefficients and by examining a 3x3 cross-tabulation of the two variables.

Q.2.8.1 Correlation Analysis The Chi-squared correlation coefficients for H5B are reported in Table Q12.

300

Table Q12: Chi-squared Correlation Coefficients (Symmetric and Directional) H5B: Perceived Ease of Web Use & Web Session Usage Extent - Depth Web Site Design and Maintenance Experience

No Experience

Statistic

Value

Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma

.010 .082 -.065 -.123 900 .001 .036 .021 .039 1177

Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma

Asymp. Std. Errora

.006 .033 .062 .002 .028 .053

Approx. Sig.

.001b .016 .048 .048 .492b .558 .461 .461

Web User Group A: No WSD/M Experience By examining the non-linear correlation coefficients in Table Q12, a statistically significant very weak positive association exists between PEWU and WSUED for users with no WSD/M experience. This result is not consistent with the linear correlation coefficients reported in Table Q12 as these show a statistically significant and weak negative relationship between PEWU and WSUED. Therefore, to assess the possible presence of a curvilinear association Goodman and Kruskal’s tau is compared with Gamma. As Goodman and Kruskal’s tau is slightly greater than Gamma , a very weak curvilinear relationship may be present between PEWU and WSUED for users with no WSD/M experience.

Web User Group B: With WSD/M Experience By examining the non-linear correlation coefficients in Table Q12 for web users with WSD/M experience, it is evident that a statistically insignificant negligible association exists between PEWU and WSUED. This result is consistent with the linear correlation coefficients reported in Table Q12. To assess if a relationship may be curvilinear, Goodman and Kruskalʹs tau and Gamma are compared and it was identified that Goodman and Kruskalʹs tau was less than Gamma. Thus for this user group a curvilinear relationship does not exist between PEWU and WSUED. 301

Q.2.8.2 Cross-tabulation An examination of the 3x3 cross-tabulation is thus warranted to further assess the nature of the relationship between PEWU and WSED for users with no WSD/M experience as a curvilinear relationship may be exist.

Table Q13: 3x3 Cross tabulation - H5B: Perceived Ease of Web Use & Web Session Usage Extent - Depth Web Session Use Extent - Depth (L/M/H) * Perceived Ease of Web Use (L/M/H) Crosstabulation Web Site Design & Maintenance Experience (0/1) No Experience

Perceived Ease of Web Use (L/M/H) Low Web Session Use Extent - Depth (L/M/H)

Low

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Medium

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

High

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Total

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Experience

Web Session Use Extent - Depth (L/M/H)

Low

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Medium

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

High

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Total

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Medium 35

2.1 13.0%

High

Total 45

31.4

49.5

83.0

10.3%

8.4%

9.2%

190

359

561

14.4

212.2

334.5

561.0

52.2%

55.9%

67.0%

62.4%

115

132

255

6.5

96.4

152.0

255.0

34.8%

33.8%

24.6%

28.4%

340

536

899

23.0

340.0

536.0

899.0

100.0%

2.0

18.8

29.3

50.0

4.3%

3.4%

4.8%

4.3%

239

341

604

23.6

226.7

353.7

604.0

52.2%

54.2%

49.6%

51.4%

187

314

521

20.4

195.5

305.1

521.0

43.5%

42.4%

45.6%

44.3%

441

688

1175

46.0

441.0

688.0

1175.0

100.0%

Web User Group A: No WSD/M Experience With respect to the nature of the relationship proposed in H5B, it is evident in Table Q13 that a weak positive relationship exists between PEWU and WSUED for this user group. As this finding is not consistent with H5B, that a curvilinear relationship will exist between PEWU and WSUED, H5B is rejected and H0B accepted for this user group.

302

Web User Group: Experience Consistent with the results reported in Table Q12, it is evident in Table Q13 that no clear pattern exists between PEWU and WSUED for users with WSD/M experience and thus H5B is rejected and H0B is accepted for this user group.

Q.2.8.3 H5B: Summary A statistically significant weak positive relationship was found to occur between PEWU and WSUED for users with no WSD/M experience as opposed to the hypothesised curvilinear relationship. As shown in Table Q14, further examination of Spearman’s Rho (rs) correlation coefficient for this web user group further supported this result showing a statistically significant moderate positive relationship between PEWU and WSUED. As this result does not support the relationship hypothesised, H5B was rejected and H0B was accepted for this user group.

Table Q14: Nonparametric Correlation Coefficient: Spearman Rho (rs) H5B: Perceived Ease of Web Use & Web Session Usage Extent - Depth Web Site Design and Maintenance Experience (0/1)

Web Session Usage Extent – Depth (Sum)

Perceived Ease of Web Use (Sum) Spearman’s Perceived Ease of Experience Rho Web Use (Sum) *. Correlation significant at the .05 level (1-tailed) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.372 .000 900 .211 .000 1177

In comparison, for users with web site design and/or maintenance experience, no statistically significant or clear relationship was found to exist between PEWU and WSUED for this user group. Thus, H5B was rejected and H0B was accepted for this user group.

303

Q.2.9 H5C: PEWU & WSUEDUR H5C proposes a curvilinear (L/H/M) relationship between perceived ease of web use (PEWU) and current web session usage extent - duration (WSUEDUR).

H5C: Perceived ease of web use (PEWU) will have a curvilinear (L/H/M) relationship with current web session usage extent – duration (WSUEDUR). H0C: Perceived ease of web use (PEWU) will have no relationship with current web session usage extent – duration (WSUEDUR).

These relationships are assessed by comparing the Chi-squared based correlation coefficients and by examining a 3x3 cross-tabulation of the two variables.

Q.2.9.1 Correlation Analysis The Chi-square correlation coefficients for H5C are reported in Table Q15.

Table Q15: Chi-squared Correlation Coefficients (Symmetric & Directional) H5C: Perceived Ease of Web Use & Web Session Usage Extent - Duration Web Site Design and Maintenance Experience

No Experience

Statistic

Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma

Value

.001 .034 -.005 -.011 900 .001 .026 -.020 -.038 1177

Asymp. Std. Errora

.001 .032 .063 .002 .028 .051

Approx. Sig.

.897b .726 .866 .866 .700 .815 .459 .459

Web User Group A: No WSD/M Experience No clear association exists between PEWU and WSUEDUR for users without WSD/M experience. It is further evident that this result is not statistically significant. This result is consistent with the linear correlation coefficients reported 304

in Table Q15. To assess the possibility of a curvilinear association Goodman and Kruskal’s tau is compared with Gamma. As Goodman and Kruskal’s tau is slightly greater than Gamma a very weak curvilinear relationship may be present.

Web User Group B: With WSD/M Experience A small positive association exists between PEWU and WSUEDUR, but this association is not statistically significant. This result is not consistent with the linear correlation coefficients reported in Table Q15 that report statistically insignificant negative association. Thus, to assess the possible presence of curvilinear association, Goodman and Kruskalʹs tau and Gamma are compared identifying that Goodman and Kruskalʹs tau was slightly greater than Gamma. Thus for this user group a very weak curvilinear relationship may exist between PEWU and WSUEDUR.

Q.2.9.2 Cross-tabulation An examination of the 3x3 cross-tabulation is warranted to further assess the nature of the relationship between PEWU and WSUEDUR, for both user groups as the possible presence of curvilinear relationships was identified.

Web User Group A: No WSD/M Experience With respect to the nature of the relationship proposed in H5C, as evident in Table Q16, no clear pattern is exists between PEWU and WSUEDUR. Thus H5C is rejected and H0C is accepted for this user group.

Web User Group B: With WSD/M Experience With respect to the nature of the relationship proposed in H5C, as evident in Table Q16, an extremely weak u-shaped relationship exists between PEWU and WSUEDUR for web users with WSD/M experience. However, this is not statistically significant - H5C is rejected and H0 is accepted for this user group.

305

Table Q16: 3x3 Cross-tabulation - H5C: Perceived Ease of Web Use & Web Session Usage Extent - Duration Duration of Session Use (L/M/H) * Perceived Ease of Web Use (L/M/H) Crosstabulation Web Site Design & Maintenance Experience (0/1) No Experience

Perceived Ease of Web Use (L/M/H) Low Duration of Session Use (L/M/H)

Low

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Medium

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

High

420.0

47.2%

46.7%

43.5%

46.0%

175

260

447

11.4

169.4

266.2

447.0 49.7%

Expected Count

12.5

19.7

33.0

4.3%

2.6%

4.3%

3.7%

341

536

900

23.0

341.0

536.0

900.0

100.0%

152

261

431

16.8

161.5

252.7

431.0

39.1%

34.5%

37.8%

36.6%

243

358

625

24.4

234.2

366.4

625.0

52.2%

55.1%

51.9%

53.1%

121

4.7

45.3

70.9

121.0

8.7%

10.4%

10.3%

441

690

1177

46.0

441.0

690.0

1177.0

100.0%

Count

Count Expected Count % within Perceived Ease of Web Use (L/M/H) Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Total

250.1

48.5%

% within Perceived Ease of Web Use (L/M/H)

High

159.1

Expected Count

Medium

10.7

51.3%

Count

Low

Total 420

% within Perceived Ease of Web Use (L/M/H) Duration of Session Use (L/M/H)

High 253

52.2%

Expected Count

Experience

Medium 157

Count % within Perceived Ease of Web Use (L/M/H)

Total

Count Expected Count % within Perceived Ease of Web Use (L/M/H)

Q.2.9.3 H5C: Summary For web users with no web site design experience, no statistically significant pattern was found to occur between PEWU and WSUEDUR. For those with web site design and maintenance experience, a very weak u-shaped relationship was found to exist between PEWU and WSUEDUR, however this association was not statistically significant. Thus, H5C was rejected and H0C accepted for both user groups.

Q.2.10 H6A: PWU & WSUEB H6A proposes a positive relationship between perceived web usefulness (PWU) and current web session usage extent - breadth (WSUEB).

306

H6A: Perceived web usefulness (PWU) will have a positive relationship with current web session usage extent – breadth (WSUEB). H0A: Perceived web usefulness (PWU) will have no relationship with current web session usage extent – breadth (WSUEB).

Q2.10.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H6A in Table Q17.

Table Q17: Nonparametric Correlation Coefficient: Spearman Rho (rs) H6A: Perceived Web Usefulness & Web Session Usage Extent – Breadth Web Site Design and Maintenance Experience (0/1)

Web Session Usage Extent – Breadth (Sum)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

-.086 .005 900 -.056 .028 1177

Web User Group A: No WSD/M Experience. There is a statistically significant and extremely weak negative relationship between PWU and WSUEB for users with no WSD/M experience. Thus H6A is rejected and H0A accepted for this user group. Web User Group B: With WSD/M Experience There is a statistically significant and extremely small negative relationship between PWU and WSUEB for users with WSD/M experience. Thus H6A is rejected and H0A accepted for this user group. Q.2.10.2 H6A: Summary In summary, H6A is not supported for either web user group as the results indicate that as opposed to the hypothesised positive association between PWU

307

and WSUEB, a statistically significant and extremely weak negative relationship exists. Thus H6A is rejected and H0A accepted for both web user groups.

Q.2.11 H6B: PWU & WSUED H6B proposes a positive relationship between perceived web usefulness (PWU) and current web session usage extent - depth (WSUED).

H6B: Perceived web usefulness (PWU) will have a positive relationship with current web session usage extent – depth (WSUED). H0B: Perceived web usefulness (PWU) will have no relationship with current web session usage extent – depth (WSUED).

Q.2.11.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H6B in Table Q18.

Web User Group A: No WSD/M Experience. There is a statistically significant and moderately positive relationship between PWU and WSUED for users with no WSD/M experience. Thus H6B is accepted and H0B is rejected for this user group.

Web User Group B: With WSD/M Experience There is a statistically significant low positive relationship between PWU and WSUED for users with WSD/M experience. Thus H6B is accepted and H0B is rejected for this user group.

308

Table Q18: Nonparametric Correlation Coefficient: Spearman Rho (rs) H6B: Perceived Web Usefulness & Web Session Usage Extent Depth Web Site Design and Maintenance Experience (0/1)

Web Session Usage Extent – Depth (Sum)

Perceived Web Usefulness (Sum) Spearman’s Perceived Web Experience Rho Usefulness (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.360 .000 900 .190 .000 1177

Q.2.11.2 H6B: Summary H6B is supported for both web user groups as the results indicate a statistically significant positive relationship exists between PWU and WSUED for both web user groups. However this relationship is stronger for web users without WSD/M experience than it is for those users with this experience. Thus H6B is accepted and H0B is rejected for both user groups.

Q.2.12 H6C: PWU & WSUEDUR H6C proposes a positive relationship between perceived web usefulness (PWU) and current web session usage extent – duration (WSUEDUR).

H6C: Perceived web usefulness (PWU) will have a positive relationship with current web session usage extent – duration (WSUEDUR). H0C: Perceived web usefulness (PWU) will have no relationship with current web session usage extent – duration (WSUEDUR).

Q.2.12.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H6C in Table Q19.

309

Table Q19: Nonparametric Correlation Coefficient: Spearman Rho (rs) H6C: Perceived Web Usefulness & Web Session Usage Extent - Duration Web Site Design and Maintenance Experience (0/1)

Duration of Web Session Use (Sum)

Perceived Web Usefulness (Sum) Spearmanâ&#x20AC;&#x2122;s Perceived Web Experience Rho Usefulness (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearmanâ&#x20AC;&#x2122;s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.176 .000 900 .112 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant low positive relationship between PWU and WSUEDUR for users with no WSD/M experience. Thus H6C is accepted and H0C is rejected for this user group. Web User Group: Experience There is a statistically significant low positive relationship between PWU and WSUEDUR for users with WSD/M experience. Thus H6C is accepted and H0C is rejected for this user group.

Q.2.12.2 H6C: Summary H6C is supported for both web user groups. The results indicate a statistically significant positive relationship exists between PWU and WSUEDUR for both user groups. However, the strength of the positive relationships reported is extremely low for both groups.

Q.2.13 RESEARCH QUESTION ONE: SUMMARY Q.2.13.1 Web User Group A: No WSD/M Experience In summary, as shown below in Table Q20, 6 out of 12 hypotheses were supported for users with no web site design and maintenance experience.

310

Table Q20: Web User Group A: RQ1 Hypothesis Result Summary Bivariate Label

Indep.

Hypothesised Relationship

Dep.

Hypothesised Result

H1A

PEWU

Curvilinear

WSUF

Reject (Accept Null)

No clear pattern (N.S)

H2A

PWU

Positive

WSUF

Accept (Reject Null)

Very weak positive (S)

H3A

PEWU

Curvilinear

WSUVS

Reject (Accept Null)

Weak u-shape (N.S)

H3B

PEWU

Curvilinear

WSUVMNO1

Reject (Accept Null)

Very weak positive (N.S)

H4A

PWU

Positive

WSUVS

Accept (Reject Null)

Very weak positive (S)

H4B

PWU

Positive

WSUVMNO1

Accept (Reject Null)

Moderate positive (S)

H5A

PEWU

Curvilinear

WSUEB

Accept (Reject Null)

Weak u-shaped (S)

H5B

PEWU

Curvilinear

WSUED

Reject (Accept Null)

Weak positive (S)

H5C

PEWU

Curvilinear

WSUEDUR

Reject (Accept Null)

No clear pattern (N.S) Very weak negative (S)

Association Founda

H6A

PWU

Positive

WSUEB

Reject (Accept Null)

H6B

PWU

Positive

WSUED

Accept (Reject Null)

Moderate positive (S)

H6C

PWU

Positive

WSUEDUR

Accept (Reject Null)

Very weak positive (S)

a N.S = Not Statistically Significant; S=Statistically Significant.

Q.2.13.2 Web User Group B: With WSD/M Experience In summary, as shown below in Table Q21, 5 out of 12 hypotheses were supported for users with web site design and maintenance experience.

Table Q21: Web User Group B: RQ1 Hypothesis Result Summary Bivariate Label

Indep.

Hypothesised Relationship

Dep.

Hypothesised Result

Association Founda

H1A

PEWU

H2A

PWU

Curvilinear

WSUF

Reject (Accept Null)

Very weak negative (N.S)

Positive

WSUF

Accept (Reject Null)

H3A

Weak positive (S)

PEWU

Curvilinear

WSUVS

Reject (Accept Null)

Inverted u-shape (N.S)

H3B

PEWU

Curvilinear

WSUVMNO1

Reject (Accept Null)

Very weak positive (S)

H4A

PWU

Positive

WSUVS

Accept (Reject Null)

Very weak positive (S)

H4B

PWU

Positive

WSUVMNO1

Accept (Reject Null)

Weak Positive (S)

H5A

PEWU

Curvilinear

WSUEB

Reject (Accept Null)

No clear Pattern (N.S)

H5B

PEWU

Curvilinear

WSUED

Reject (Accept Null)

No Clear Pattern (N.S)

H5C

PEWU

Curvilinear

WSUEDUR

Reject (Accept Null)

Very weak u-shaped (N.S)

H6A

PWU

Positive

WSUEB

Reject (Accept Null)

Very weak Negative (S)

H6B

PWU

Positive

WSUED

Accept (Reject Null)

Weak Positive (S)

H6C

PWU

Positive

WSUEDUR

Accept (Reject Null)

Weak Positive (S)

a N.S = Not Statistically Significant; S = Statistically Significant.

Q.3 RESEARCH QUESTION TWO Question 2, asks: What is the relationship between a user’s knowledge content of the web and a person’s perceived usefulness of the web? To examine this question more specifically, 7 hypotheses were introduced in Chapter 5 with results reported here in the following sections of this appendix.

311

Q.3.1 H7A: ACPWK & PWU H7A proposes a curvilinear relationship between actual common procedural web knowledge (ACPWK) and perceived web usefulness (PWU).

H7A: Actual common procedural web knowledge (ACPWK) will have a curvilinear relationship with perceived web usefulness (PWU). H0A: Actual common procedural web knowledge (ACPWK) will have no relationship with perceived web usefulness (PWU).

These relationships are assessed by comparing the Chi-squared based correlation coefficients and by examining a 3x3 cross-tabulation of the two variables. Q.3.1.1 Correlation Analysis Chi-square based correlation coefficients for H7A are reported in Table Q22.

Table Q22: Chi-squared Correlation Coefficients (Symmetric and Directional) H7A: Actual Common Procedural Web Knowledge & Perceived Web Usefulness Web Site Design and Maintenance Experience

No Experience

Statistic

Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma Goodman and Kruskal’s tau Cramer’s V Kendall’s tau-b Gamma

Value

.004 .046 -.013 -.027 900 .003 .042 -.030 -.105 1177

Asymp. Std. Errora

.004 .032 .069 .003 .028 .100

Approx. Sig.

.177b .421 .692 .692 .142b .378 .291 .291

Web User Group A: No WSD/M Experience A negligible association exists between ACPWK and PWU for users with no WSD/M experience. This relationship is not statistically significant. This result is not consistent with the linear correlation coefficients reported in Table Q22 as they report the presence of statistically insignificant negative association. To assess the possible presence of a curvilinear association Goodman and Kruskalʹs tau is 312

compared with Gamma and as Goodman and Kruskal聞s tau is slightly greater than Gamma, we can surmise that a curvilinear relationship may be present.

Web User Group B: With WSD/M Experience A statistically insignificant and negligible positive association exists between ACPWK and PWU. This result is not consistent with the linear correlation coefficients reported in Table Q22 that report a statistically insignificant negative relationship. To assess the possible presence of a curvilinear relationship, Goodman and Kruskal聞s tau and Gamma are compared and it was identified that Goodman and Kruskal聞s tau was greater than Gamma. Thus, for this user group a curvilinear relationship may exist between ACPWK and PWU.

Q.3.1.2 Cross-tabulation Although these findings are in fact not statistically significant an examination of the 3x3 cross-tabulation is warranted to further assess the nature of the relationship between ACPWK and PWU.

Web User Group A: No WSD/M Experience With respect to H7A, it is evident from Table Q23 that a very weak u-shaped relationship exists between ACPWK and PWU. As this result is not statistically significant, H7A is rejected and H0A is accepted for this user group.

Web User Group B: With WSD/M Experience With respect to H7A, it is evident from Table Q23 that a very weak u-shaped relationship might exist between ACPWK and PWU for this user group. As this relationship is not statistically significant, H7A is accepted and H0A is rejected for this user group.

313

Table Q23: 3x3 Cross-tabulation - H7A: Actual Common Procedural Web Knowledge & Perceived Web Usefulness Perceived Web Usefulness (L/M/H) * Actual Common Procedural Web Knowledge (L/M/H) Crosstabulation Actual Common Procedural Web Knowledge (L/M/H)

Web Site Design & Maintenance Experience (0/1) No Experience

Low Perceived Web Usefulness (L/M/H)

Low

Count Expected Count % within Actual Common Procedural Web Knowledge (L/M/H)

Medium

Count Expected Count % within Actual Common Procedural Web Knowledge (L/M/H)

High

Count Expected Count % within Actual Common Procedural Web Knowledge (L/M/H)

Total

Count Expected Count % within Actual Common Procedural Web Knowledge (L/M/H)

Experience

Perceived Web Usefulness (L/M/H)

Low

1.1

3.2

10.7

15.0

1.6%

1.0%

1.9%

1.7%

267

370

25.9

79.8

264.3

370.0

49.2%

37.1%

41.5%

41.1%

120

364

515

36.1

111.0

367.9

515.0

49.2%

61.9%

56.6%

57.2%

194

643

900

63.0

194.0

643.0

900.0

100.0%

2.0

26.5

29.0

.0%

2.4%

2.5%

Count

465

504

Expected Count

8.6

35.1

460.3

504.0

55.0%

34.1%

43.3%

42.8%

Count Expected Count % within Actual Common Procedural Web Knowledge (L/M/H)

Total

Total 12

Expected Count

% within Actual Common Procedural Web Knowledge (L/M/H) High

High 2

Count % within Actual Common Procedural Web Knowledge (L/M/H)

Medium

Medium 1

Count Expected Count % within Actual Common Procedural Web Knowledge (L/M/H)

583

644

10.9

44.9

588.2

644.0

45.0%

63.4%

54.2%

54.7%

1075

1177

20.0

82.0

1075.0

1177.0

100.0%

Q.3.1.3 H7A: Summary It is evident that for both user groups a statistically insignificant but very weak ushaped relationship was found between ACPWK and PWU. Thus, H7A was not supported for either user group. (Reject H7A and Accept H0).

Q.3.2 H8A: ACDWK & PWU H8A proposes a positive relationship between actual common declarative web knowledge (ACDWK) and perceived web usefulness (PWU).

314

H8A: Actual common declarative web knowledge (ACDWK) will have a positive relationship with perceived web usefulness (PWU). H0A: Actual common declarative web knowledge (ACDWK) will have no relationship with perceived web usefulness (PWU).

Q.3.2.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H8A in Table Q23.

Table Q24: Nonparametric Correlation Coefficient: Spearman Rho (rs) H8A: Actual Common Declarative Web Knowledge & Perceived Web Usefulness Web Site Design and Maintenance Experience (0/1)

Perceived Web Usefulness (Sum)

Actual Common Declarative Web Knowledge (Sum) Spearman’s Actual Common Experience Rho Declarative Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.221 .000 900 .092 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and small positive relationship between ACDWK and PWU for users with no WSD/M experience. Thus, H8A is supported for this user group and H0A is rejected.

Web User Group B: With WSD/M Experience There is a statistically significant and very weak positive relationship between ACDWK and PWU for users with WSD/M experience. Thus, H8A is supported for this user group and H0A is rejected.

315

Q.3.2.2 H8A: Summary The results indicate that a statistically significant positive relationship exists between ACDWK and PWU for both user groups. It is also evident that this relationship is stronger for those without web site design and maintenance experience than those with this experience. Thus, H8A is accepted and H0A is rejected for both groups.

Q.3.3 H9A: ASPWK & PWU H9A proposes a positive relationship between actual specialised procedural web knowledge (ASPWK) and perceived web usefulness (PWU).

H9A: Actual specialised procedural web knowledge (ASPWK) will have a positive relationship with perceived web usefulness (PWU). H0A: Actual specialised procedural web knowledge (ASPWK) will have no relationship with perceived web usefulness (PWU).

Q.3.3.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H9A in Table Q25.

Table Q25: Nonparametric Correlation Coefficient: Spearman Rho (rs) H9A: Actual Specialised Procedural Web Knowledge & Perceived Web Usefulness Web Site Design and Maintenance Experience (0/1)

Perceived Web Usefulness (Sum)

Actual Specialised Procedural Web Knowledge (Sum) Spearman’s Actual Specialised Experience Rho Procedural Web Knowledge (Sum) *. Correlation significant at the .05 level (1-tailed) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

316

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.182 .000 900 .049 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and weak positive relationship between ASPWK and PWU for users with no WSD/M experience. Thus, H9A is supported for this user group and H0A is rejected.

Web User Group B: With WSD/M Experience There is a statistically significant and very weak positive relationship between ASPWK and PWU for users with WSD/M experience. Thus, H9A is supported and H0A is rejected for this user group.

Q.3.3.2 H9A: Summary A statistically significant positive relationship exists between ASPWK and PWU for both groups. It is also evident that this relationship is stronger for those without web site design and maintenance experience than those with this experience. Thus, H9A is accepted and H0A is rejected for both groups.

Q.3.4 H10A: ASDWK & PWU H10A proposes a positive relationship between actual specialised declarative web knowledge (ASDWK) and perceived web usefulness (PWU).

H10A: Actual specialised declarative web knowledge (ASDWK) will have a positive relationship with perceived web usefulness (PWU). H0A: Actual specialised declarative web knowledge (ASDWK) will have no relationship with perceived web usefulness (PWU).

Q.3.4.1 Correlation Analysis Spearman聞s rho (rs) is the most suitable statistic to use here. This is reported for H10A in Table Q26.

317

Table Q26: Nonparametric Correlation Coefficient: Spearman Rho (rs) H10A: Actual Specialised Declarative Web Knowledge & Perceived Web Usefulness Web Site Design and Maintenance Experience (0/1)

Perceived Web Usefulness (Sum)

Actual Specialised Declarative Web Knowledge (Sum) Spearmanâ&#x20AC;&#x2122;s Actual Specialised Experience Rho Declarative Web Knowledge (Sum) *. Correlation significant at the .05 level (1-tailed) **. Correlation significant at the .01 level (1-tailed) N.S = Not Statistically Significant.

No Experience

Spearmanâ&#x20AC;&#x2122;s Rho

Correlation Coefficient Sig. (1-tailed)

.156 .000

900

Correlation Coefficient Sig. (1-tailed)

.038 .096

1177

N.S

Web User Group A: No WSD/M Experience There is a statistically significant and small positive relationship between ASDWK and PWU for users with no WSD/M experience. Thus, H10A is supported and H0A rejected for this user group. Web User Group B: With WSD/M Experience There is a statistically insignificant and extremely weak positive relationship between ASDWK and PWU for users with WSD/M experience. Thus, H10A is rejected and H0A accepted for this user group.

Q.3.4.2 H10A: Summary There is a very weak positive statistically significant relationship between ASDWK and PWU for users without web site design and maintenance experience. In contrast, an insignificant and negligible positive relationship exists between ASDWK and PWU for users with experience.

Q.3.5 H11A: SWPK & PWU H11A proposes a positive relationship between perceived procedural web knowledge (SWPK) and perceived web usefulness (PWU). 318

H11A: Perceived procedural web knowledge (SWPK) will have a positive (+) relationship with perceived web usefulness (PWU). H0A: Perceived Procedural web knowledge (SWPK) will have no relationship with perceived web usefulness (PWU).

Q.3.5.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H11A in Table Q27.

Table Q27: Nonparametric Correlation Coefficient: Spearman Rho (rs) H11A: Perceived Procedural Web Knowledge & Perceived Web Usefulness Web Site Design and Maintenance Experience (0/1)

Perceived Web Usefulness (Sum)

Perceived Procedural Web Knowledge (Sum) Spearman’s Perceived Experience Rho Procedural Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.478 .000 900 .371 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and moderately positive relationship between SWPK and PWU for users with no WSD/M experience. Thus, H11A is supported and H0A rejected for this user group.

Web User Group B: With WSD/M Experience There is a statistically significant and moderately positive relationship between SWPK and PWU for users with WSD/M experience. Thus, H11A is supported and H0A rejected for this user group.

319

Q.3.5.2 H11A: Summary H11A is supported for both web user groups indicating that a statistically significant moderate positive relationship exists between SWPK and PWU. This relationship is slightly stronger for those users without WSD/M experience, than those with experience.

Q.3.6 H12A: SWDK & PWU H12A proposes a positive relationship between perceived declarative web knowledge (SWDK) and perceived web usefulness (PWU).

H12A: Perceived declarative web knowledge (SWDK) will have a positive relationship with perceived web usefulness (PWU). H0: Perceived declarative web knowledge (SWDK) will have no relationship with perceived web usefulness (PWU).

Q.3.6.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H12A in Table Q28.

Table Q28: Nonparametric Correlation Coefficient: Spearman Rho (rs) H12A: Perceived Declarative Web Knowledge & Perceived Web Usefulness Web Site Design and Maintenance Experience (0/1)

Perceived Web Usefulness (Sum)

Perceived Declarative Web Knowledge (Sum) Spearman’s Perceived Experience Rho Declarative Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

320

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.447 .000 900 .337 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and moderately positive relationship between SWDK and PWU for users with no WSD/M experience. Thus, H12A is supported for this user group and H0A rejected.

Web User Group B: With WSD/M Experience There is a statistically significant and moderately positive relationship between SWDK and PWU for users with WSD/M experience. Thus, H12A is supported and H0A rejected for this user group.

Q.3.6.2 H12A: Summary H12A is supported for both user groups indicating that a statistically significant moderate positive relationship exists between SWDK and PWU. In addition, this relationship is stronger for those without WSD/M experience, than those with experience.

Q.3.7 H13A: SWOK & PWU H13A proposes a positive relationship between perceived overall web knowledge (SWOK) and perceived web usefulness (PWU).

H13A: Perceived overall web knowledge (SWOK) will have a positive (+) relationship with perceived web usefulness (PWU). H0: Perceived overall web knowledge (SWOK) will have no relationship with perceived web usefulness (PWU).

Q.3.7.1 Correlation Analysis Spearman聞s rho (rs) is the most suitable statistic to use here. This is reported for H13A in Table Q29.

321

Table Q29: Nonparametric Correlation Coefficient: Spearman Rho (rs) H13A: Perceived Overall Web Knowledge & Perceived Web Usefulness Web Site Design and Maintenance Experience (0/1)

Perceived Web Usefulness (Sum)

Perceived Overall Web Knowledge (Sum) Spearmanâ&#x20AC;&#x2122;s Perceived Overall Experience Rho Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearmanâ&#x20AC;&#x2122;s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.394 .000 900 .329 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and moderately positive relationship between SWOK and PWU for users with no WSD/M experience. Thus, H13A is supported for this user group and H0A is rejected. Web User Group B: With WSD/M Experience There is a statistically significant and moderately positive relationship between SWOK and PWU for users with WSD/M experience. Thus, H13A is supported and H0A rejected for this user group. Q.3.7.2 H13A: Summary H13A is supported for both groups indicating that a statistically significant positive relationship exists between SWOK and PWU. In addition, this relationship is stronger for those without WSD/M experience, than those with this experience.

Q.3.8 RESEARCH QUESTION TWO: SUMMARY Q.3.8.1 Web User Group A: No WSD/M Experience In summary, as shown below in Table Q30, 6 out of 7 hypotheses are supported for web users with no web site design and maintenance experience.

322

Table Q30: Web User Group A: RQ2 Hypothesis Result Summary Hypothesised Result

Association Founda

Indep.

Hypothesised Relationship

H7A

ACPWK

Curvilinear

PWU

Reject (Accept Null)

u-shaped (N.S)

H8A

ACDWK

Positive

PWU

Accept (Reject Null)

Weak positive (S) Weak positive (S)

Bivariate Label

Dep.

H9A

ASPWK

Positive

PWU

Accept (Reject Null)

H10A

ASDWK

Positive

PWU

Accept (Reject Null)

Weak positive (S)

H11A

SWPK

Positive

PWU

Accept (Reject Null)

Moderate positive (S)

H12A

SWDK

Positive

PWU

Accept (Reject Null)

Moderate positive (S)

H13A

SWOK

Positive

PWU

Accept (Reject Null)

Moderate positive (S)

a N.S = Not Statistically Significant; S = Statistically Significant.

10.3.8.2 Web User Group B: With WSD/M Experience In summary, as shown below in Table Q31, 5 out of 7 hypotheses is supported for web users with web site design and maintenance experience.

Table Q31: Web User Group B: RQ2 Hypothesis Result Summary Hypothesised Result

Association Founda

Indep.

Hypothesised Relationship

Dep.

H7A

ACPWK

Curvilinear

PWU

Reject (Accept Null)

u-shaped (N.S)

H8A

ACDWK

Positive

PWU

Accept (Reject Null)

Very weak positive (S)

Bivariate Label

H9A

ASPWK

Positive

PWU

Accept (Reject Null)

Very weak positive (S)

H10A

ASDWK

Positive

PWU

Reject (Accept Null)

Very weak positive (N.S.)

H11A

SWPK

Positive

PWU

Accept (Reject Null)

Moderate positive (S)

H12A

SWDK

Positive

PWU

Accept (Reject Null)

Moderate positive (S)

H13A

SWOK

Positive

PWU

Accept (Reject Null)

Moderate positive (S)

a N.S = Not Statistically Significant; S = Statistically Significant.

Q.4 RESEARCH QUESTION THREE Question 3, asks: What is the relationship between a userâ&#x20AC;&#x2122;s knowledge content of the web and a personâ&#x20AC;&#x2122;s perceived ease of web use? To examine this question more specifically, 7 hypotheses were introduced in Chapter 5, proposing the relationship between a users knowledge content of the web and their perceived ease of web use. Results are presented below.

Q.4.1 H14A: ACPWK & PEWU H14A proposes a positive relationship between actual common procedural web knowledge (ACPWK) and perceived ease of web use (PEWU). 323

H14A: Actual common procedural web knowledge of the web will have a positive relationship with perceived ease of web use (PEWU). H0: Actual common procedural web knowledge of the web will have no relationship with perceived ease of web use (PEWU).

Q.4.1.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H14A in Table Q32.

Table Q32: Nonparametric Correlation Coefficient: Spearman Rho (rs) H14A: Actual Common Procedural Web Knowledge & Perceived Web Usefulness Web Site Design and Maintenance Experience (0/1)

Perceived Ease of Web Use (Sum)

Actual Common Procedural Web Knowledge (Sum) Spearman’s Actual Common Experience Rho Procedural Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.233 .000 900 .150 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and weak positive relationship between ACPWK and PEWU for users with no WSD/M experience. Thus, H14A is accepted and H0A rejected for this user group. Web User Group B: With WSD/M Experience There is a statistically significant and weak positive relationship between ACPWK and PEWU for users with WSD/M experience. Thus, H14A is accepted and H0A is rejected for this user group.

324

Q.4.1.2 H14A: Summary H14A is supported for both user groups, indicating that a statistically significant weak positive relationship exists between ACPWK and PEWU. This relationship is stronger for users without web site design and maintenance experience than those with this experience.

Q.4.2 H15A: ACPWK & PEWU H15A proposes a positive relationship between actual common declarative web knowledge (ACDWK) and perceived ease of web use (PEWU).

H15A: Actual common declarative web knowledge (ACDWK) of the web will have a positive relationship with perceived ease of web use (PEWU). H0A: Actual common declarative web knowledge (ACDWK) will have no relationship with perceived ease of web use (PEWU).

Q.4.2.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H15A in Table Q33.

Table Q33: Nonparametric Correlation Coefficient: Spearman Rho (rs) H15A: Actual Common Declarative Web Knowledge & Perceived Ease of Web Use Web Site Design and Maintenance Experience (0/1)

Perceived Ease of Web Use (Sum)

Actual Common Declarative Web Knowledge (Sum) Spearman’s Actual Common Experience Rho Declarative Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

325

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.242 .000 900 .145 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and small positive relationship between ACDWK and PEWU for users with no WSD/M experience. Thus, H15A is supported and H0A rejected for this user group.

Web User Group B: With WSD/M Experience There is a statistically significant and small positive relationship between ACDWK and PEWU for users with WSD/M experience. Thus, H15A is supported and H0A rejected for this user group.

Q.4.2.2 H15A: Summary H15A is supported for both user groups, indicating that a statistically significant weak positive relationship exists between ACDWK and PEWU. This relationship is stronger for users without web site design and maintenance experience than those with this experience.

Q.4.3 H16A: ASPWK & PEWU H16A proposes a positive relationship between actual specialised procedural web knowledge (ASPWK) and perceived ease of web use (PEWU).

H16A: Actual specialised procedural web knowledge (ASPWK) will have a positive relationship with perceived ease of web use (PEWU). H0: Actual specialised procedural web knowledge (ASPWK) will have no relationship with perceived ease of web use (PEWU).

Q.4.3.1 Correlation Analysis Spearman聞s rho (rs) is the most suitable statistic to use here. This is reported for H16A in Table Q34.

326

Web User Group A: No WSD/M Experience There is a statistically significant and weak positive relationship between ASPWK and PEWU for users with no WSD/M experience. Thus, H16A is supported and H0A rejected for this user group.

Web User Group B: With WSD/M Experience There is a statistically significant and very weak positive relationship between ASPWK and PEWU for users with web WSD/M experience. Thus, H16A is supported and H0A is rejected for this user group.

Table Q34: Nonparametric Correlation Coefficient: Spearman Rho (rs) H16A: Actual Specialised Procedural Web Knowledge & Perceived Ease of Web Use Web Site Design and Maintenance Experience (0/1)

Perceived Ease of Web Use (Sum)

Actual Specialised Procedural Web Knowledge (Sum) Spearmanâ&#x20AC;&#x2122;s Actual Specialised Experience Rho Procedural Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearmanâ&#x20AC;&#x2122;s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.208 .000 900 .080 .003 1177

Q.4.3.2 H16A: Summary H16A is supported for both user groups, indicating that a statistically significant positive relationship exists between ASPWK and PEWU. This relationship is stronger for users without web site design and maintenance experience than those with this experience.

Q.4.4 H17A: ASDWK & PEWU H17A proposes a positive relationship between actual specialised declarative web knowledge (ASDWK) and perceived ease of web use (PEWU).

327

H17A: Actual specialised declarative web knowledge (ASDWK) will have a positive relationship with perceived ease of web use (PEWU). H0: Actual specialised declarative web knowledge (ASDWK) will have no relationship with perceived ease of web use (PEWU).

Q.4.4.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H17A in Table Q35.

Table Q35: Nonparametric Correlation Coefficient: Spearman Rho (rs) H17A: Actual Specialised Declarative Web Knowledge & Perceived Ease of Web Use Web Site Design and Maintenance Experience (0/1)

Perceived Ease of Web Use (Sum)

Actual Specialised Declarative Web Knowledge (Sum) Spearman’s Actual Specialised Experience Rho Declarative Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.165 .000 900 .040 .086 1177

N.S

Web User Group A: No WSD/M Experience There is a statistically significant and very weak positive relationship between ASDWK and PEWU for users with no WSD/M experience. Thus, H17A is supported and H0A is rejected for this user group.

Web User Group B: With WSD/M Experience There is extremely weak and statistically insignificant relationship between ASDWK and PEWU for users with WSD/M experience. Thus, H17A is rejected and H0A is accepted for this user group.

328

Q.4.4.2 H17A: Summary H17A is supported only for web users without web site design and maintenance experience, indicating that a statistically significant but very weak positive relationship exists between ASDWK and PEWU for this group. No statistically significant relationship was found to exist between ASDWK and PEWU for users with experience. Thus for web users without experience H17A is accepted and H0A rejected, and for web users with experience H17A is rejected and H0A is accepted.

Q.4.5 H18A: SWPK & PEWU H18A proposes a positive relationship between perceived procedural web knowledge (SWPK) and perceived ease of web use (PEWU).

H18A: Perceived procedural web knowledge (SWPK) will have a positive relationship with perceived ease of web use (PEWU). H0A: Perceived procedural web knowledge (SWPK) will have no relationship with perceived ease of web use (PEWU).

Q.4.5.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H18A in Table Q36.

Table Q36: Nonparametric Correlation Coefficient: Spearman Rho (rs) H18A: Perceived Procedural Web Knowledge & Perceived Ease of Web Use Web Site Design and Maintenance Experience (0/1)

Perceived Ease of Web Use (Sum)

Perceived Procedural Web Knowledge (Sum) Spearman’s Perceived Procedural Experience Rho Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

329

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.658 .000 900 .463 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and strong positive relationship between SWPK and PEWU for users with no WSD/M experience. Thus, H18A is supported and H0A is rejected for this user group.

Web User Group B: With WSD/M Experience There is a statistically significant and moderately positive relationship between SWPK and PEWU for users with WSD/M experience. Thus, H18A is supported and H0A is rejected for this user group.

Q.4.5.2 H18A: Summary H18A is supported for both groups, indicating that a statistically significant positive relationship exists between SWPK and PEWU for both groups. This relationship is stronger for users without web site design and maintenance experience than for those with experience.

Q.4.6 H19A: SWDK & PEWU H19A proposes a positive relationship between perceived declarative web knowledge (SWDK) and perceived ease of web use (PEWU).

H19A: Perceived declarative web knowledge (SWDK) will have a positive relationship with perceived ease of web use (PEWU). H0A: Perceived declarative web knowledge (SWDK) will have no relationship with perceived ease of web use (PEWU).

Q.4.6.1 Correlation Analysis Spearman聞s rho (rs) is the most suitable statistic to use here. This is reported for H19A in Table Q37.

330

Table Q37: Nonparametric Correlation Coefficient: Spearman Rho (rs) H19A: Perceived Declarative Web Knowledge & Perceived Ease of Web Use Web Site Design and Maintenance Experience (0/1)

Perceived Ease of Web Use (Sum)

Perceived Declarative Web Knowledge (Sum) Spearmanâ&#x20AC;&#x2122;s Perceived Declarative Experience Rho Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearmanâ&#x20AC;&#x2122;s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.629 .000 900 .418 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and strong positive relationship between SWDK and PEWU for users with no WSD/M experience. Thus, H19A is supported and H0A is rejected for this user group. Web User Group B: With WSD/M Experience There is a statistically significant and moderately positive relationship between SWDK and PEWU for users with WSD/M experience. Thus, H19A is supported and H0A rejected for this user group.

Q.4.6.2 H19A: Summary H19A is supported for both groups, indicating that a statistically significant positive relationship exists between SWDK and PEWU for both groups. This relationship is stronger for users without web site design and maintenance experience than those with this experience.

Q.4.7 H20A: SWOK & PEWU H20A proposes a positive relationship between perceived overall web knowledge (SWOK) and perceived ease of web use (PEWU).

331

H20A: Perceived overall web knowledge (SWOK) will have a positive relationship with perceived ease of web use (PEWU). H0A: Perceived overall web knowledge (SWOK) will have no relationship with perceived ease of web use (PEWU).

Q.4.7.1 Correlation Analysis Spearmanʹs rho (rs) is the most suitable statistic to use here. This is reported for H20A in Table Q38.

Table Q38: Nonparametric Correlation Coefficient: Spearman Rho (rs) H20A: Perceived Overall Web Knowledge & Perceived Ease of Web Use Web Site Design and Maintenance Experience (0/1)

Perceived Ease of Web Use (Sum)

Perceived Overall Web Knowledge (Sum) Spearman’s Perceived Overall Experience Rho Web Knowledge (Sum) **. Correlation significant at the .01 level (1-tailed)

No Experience

Spearman’s Rho

Correlation Coefficient Sig. (1-tailed) N Correlation Coefficient Sig. (1-tailed) N

.535 .000 900 .366 .000 1177

Web User Group A: No WSD/M Experience There is a statistically significant and strong positive relationship between SWOK and PEWU for users with no WSD/M experience. Thus, H20A is supported and H0A is rejected for this user group.

Web User Group B: With WSD/M Experience There is a statistically significant and moderately positive relationship between SWOK and PEWU for users with WSD/M experience. Thus, H20A is supported and H0A is rejected for this user group.

332

Q.4.7.2 H20A: Summary H20A is supported for both groups, indicating that a statistically significant positive relationship exists between SWOK and PEWU for both groups. This relationship is stronger for users without web site design and maintenance experience than for those with experience.

Q.4.8 RESEARCH QUESTION THREE: SUMMARY Q.4.8.1 Web User Group A: No WSD/M Experience In summary, 7 out of 7 hypotheses are supported for users with no web site design and maintenance experience (Table Q39).

Table Q39: Web User Group A: RQ3 Hypothesis Result Summary Indep.

Hypothesised Relationship

Dep.

Hypothesis Testing Result

Association Founda

H14A

ACPWK

Positive

PEWU

Accept (Reject Null)

Weak positive (S)

H15A

ACDWK

Positive

PEWU

Accept (Reject Null)

Weak positive (S)

H16A

ASPWK

Positive

PEWU

Accept (Reject Null)

Weak positive (S)

H17A

ASDWK

Positive

PEWU

Accept (Reject Null)

Weak positive (S)

H18A

SWPK

Positive

PEWU

Accept (Reject Null)

Strong positive (S)

H19A

SWDK

Positive

PEWU

Accept (Reject Null)

Strong positive (S)

H20A

SWOK

Positive

PEWU

Accept (Reject Null)

Strong positive (S)

Bivariate Label

a N.S = Not Statistically Significant; S = Statistically Significant.

Web User Group B: With WSD/M Experience In summary, 6 out of 7 hypotheses are supported for users with experience in web site design and maintenance experience (Table Q40).

Table Q40: Web User Group B: RQ3 Hypothesis Result Summary Indep.

Hypothesised Relationship

Dep.

Hypothesis Testing Result

Association Founda

H14A

ACPWK

Positive

PEWU

Accept (Reject Null)

Weak positive (S)

H15A

ACDWK

Positive

PEWU

Accept (Reject Null)

Weak positive (S)

H16A

ASPWK

Positive

PEWU

Accept (Reject Null)

Very weak positive (S))

H17A

ASDWK

Positive

PEWU

Reject (Accept Null)

Very weak positive (N.S.)

H18A

SWPK

Positive

PEWU

Accept (Reject Null)

Moderate positive (S)

H19A

SWDK

Positive

PEWU

Accept (Reject Null)

Moderate positive (S)

H20A

SWOK

Positive

PEWU

Accept (Reject Null)

Moderate positive (S)

Bivariate Label

a N.S = Not Statistically Significant; S = Statistically Significant.

333

Q.5 EMPIRICAL BIVARIATE RESULT SUMMARY For the first research question investigating the relationship between a user’s perceptions of the web and current web session usage it was found that of the 6 hypothesised curvilinear relationships only one was supported for users with no WSD/M experience (PEWU & WSUEB). Furthermore, all 6 were rejected for users with WSD/M experience. In essence, the relationships were either linear or not statistically significant. It was further found that 5 of the 6 hypothesised positive linear relationships were supported for both web users with and without WSD/M experience. This gives strong support for the hypothesized positive relationship between PWU and current web session usage.

For the second research question investigating the relationship between a user’s knowledge of the web and levels of perceived usefulness of the web, 6 out of the 7 hypotheses proposed were supported for users with no WSD/M experience. This shows strong evidence of a positive linear relationship between knowledge and perceived web usefulness. For users with this experience, 5 of the 7 hypotheses were supported.

For the third and final research question that investigates the relationship between a user’s knowledge of the web and levels of perceived ease of web use, 6 out of the 7 hypotheses were supported for both web users with and without WSD/M experience, showing strong evidence of a positive relationship between knowledge and perceived ease of web use for both user groups.

Given the number of statistically significant bivariate relationships, the results presented here in this appendix provide further validation for the results of the stepwise multiple regression analyses presented in Chapter 10.

334

APPENDIX R: NONPARAMETRIC CORRELATION COEFFICIENTS: SPEARMAN RHO

Table R1: Nonparametric Correlation Coefficient: Spearman Rho Total Sample (n=2077) – All Variables WSUF WSUVS WSUVMNO1 WSUEB WSUED WSUEDUR PEWU PWU ACPWK ACDWK ASPWK ASDWK SWOK SWPK SWDK

WSUF

WSUVS

WSUVMNO1

WSUEB

WSUED

WSUEDUR

PEWU

PWU

ACPWK

ACDWK

ASPWK

ASDWK

SWOK

SWPK

SWDK

1.00

.18** 1.00

.24** .25** 1.00

.02 -.03 .08** 1.00

.13** .12** .30** .06** 1.00

.04** .06** .24** .03 .24** 1.00

.14** .09** .27** -.06** .31** .19** 1.00

.11** .07** .27** -.07** .28** .15** .74** 1.00

.12** .16** .27** .02 .21** .16** .22** .15** 1.00

.19** .18** .30** .01 .17** .11** .24** .17** .45** 1.00

.22** .20** .32** .01 .19** .13** .20** .14** .47** .59** 1.00

.28** .20** .34** .01 .21** .15** .17** .12** .48** .66** .69** 1.00

.34** .22** .37** -.07** .33** .22** .47** .36** .40** .50** .52** .57** 1.00

.34** .20** .37** -.06** .36** .24** .56** .41** .41** .50** .51** .54** .89** 1.00

.34** .22** .38** -.06** .35** .23** .52** .38** .42** .50** .52** .58** .89** .88** 1.00

* Correlation significant at the .05 level (2-tailed) ** Correlation significant at the .01 level (2-tailed)

335

Table R2: Nonparametric Correlation Coefficients: Spearman Rho - Users with (n=1177) and Users without (n=900) WSD/M Experience

With No WSD/M Experience

With WSD/M Experience

WSUF WSUVS WSUVMNO1 WSUEB WSUED WSUEDUR PEWU PWU ACPWK ACDWK ASPWK ASDWK SWOK SWPK SWDK WSUF WSUVS WSUVMNO1 WSUEB WSUED WSUEDUR PEWU PWU ACPWK ACDWK ASPWK ASDWK SWOK SWPK SWDK

WSUF

WSUVS

WSUVMNO1

WSUEB

WSUED

WSUEDUR

PEWU

PWU

ACPWK

ACDWK

ASPWK

ASDWK

SWOK

SWPK

SWDK

1.0

-.01 1.00

.07* .24** 1.00

-.01 -.02 .10** 1.00

.12** .13** .36** .07 1.00

-.07* .10** .27** .03 .28** 1.00

.09** .06 .30** -.06 .37** .21** 1.00

.06 .06 .32** -.09* .36** .18** .76** 1.00

-.04 .17** .28** .03 .24** .20** .23** .19** 1.00

.06 .22** .28** .03 .19** .14** .24** .22** .51** 1.00

.06 .21** .32** .04 .24** .14** .21** .18** .51** .64** 1.00

.14** .19** .29** .06 .22** .12** .17** .16** .5** .69** .70** 1.00

.20** .13** .25** -.10** .35** .15** .54** .40** .34** .42** .45** .45** 1.00

.23** .10** .28** -.10** .38** .18** .66** .48** .34** .43** .42** .41** .85** 1.00

1.00

.23** 1.00

.26** .18** 1.00

.04 -.03 .08** 1.00

.08 .03 .19** .08** 1.00

.07* -.03 .19** .07 .17** 1.00

.10** .03 .18** -.05 .21** .14** 1.00

.11** .05 .21** -.06 .19** .11** .73** 1.00

.11** .06 .16** .03 .09** .08** .15** .10** 1.00

.13** .01 .19** .03 .04 .02 .15** .10** .24** 1.00

.18** .04 .17** .03 .02 .04 .08** .05 .29** .37** 1.00

.22** .06 .22** .01 .06** .09** .04 .04 .28** .46** .51** 1.00

.28** .12** .30** -.04 .20** .19** .37** .33** .26** .32** .33** .39** 1.00

.27** .11** .29** -.03 .25** .21** .46** .37** .31** .31** .33** .38** .80** 1.00

.20** .09** .24** -.12** .37** .16** .63** .45** .33** .40** .40** .40** .85** .85** 1.00 .28** .13** .31** -.04 .21** .20** .42** .34** .30** .31** .34** .43** .85** .84** 1.00

* Correlation significant at the .05 level (2-tailed); ** Correlation significant at the .01 level (2-tailed)

336

APPENDIX S: ANOVA REPORTED MEAN SCORES Table S1: Mean Scores: Effect of Level of Actual [4] and Perceived [3] Web Knowledge Content (L/M/H) on Perceived Web Usefulness No WSD/M Experience

Knowledge Content

With WSD/M Experience

Mean

Actual Common Procedural

Low Med High

53.92 69.00 71.69

63 194 900

15.728 11.298 10.937

59.19 72.13 72.59

20 82 1075

12.538 11.730 11.223

Actual Common Declarative

Low Med High

53.07 69.57 71.88

60 295 545

14.715 11.571 10.709

61.87 71.74 72.61

22 101 1054

13.887 11.130 11.280

Actual Specialised Procedural

Low Med High

65.15 71.75 71.36

240 378 282

14.109 10.674 11.208

65.05 72.24 72.78

50 268 859

12.409 12.030 11.006

Actual Specialised Declarative

Low Med High

67.95 72.15 71.02

436 269 195

12.917 11.102 11.308

69.27 72.49 72.75

121 281 775

12.200 11.852 11.049

Perceived Overall

Low Med High

60.80 70.74 75.53

192 473 235

13.560 9.938 10.990

58.59 67.42 74.40

24 294 859

14.010 10.660 10.787

Perceived Procedural

Low Med High

57.66 67.73 75.01

121 347 432

13.802 9.856 10.351

48.15 65.25 73.74

9 169 999

11.652 10.983 10.775

Perceived Declarative

Low Med High

59.91 69.82 76.32

166 474 260

14.017 9.640 10.764

56.43 66.94 74.28

17 271 889

14.795 10.726 10.761

Table S2: Mean Scores: Effect of Level of Actual [4] and Perceived [3] Web Knowledge Content (L/M/H) on Perceived Ease of Web Use No WSD/M Experience

Knowledge Content

With WSD/M Experience

Mean

Actual Common Procedural

Low Med High

43.02 51.69 55.93

63 194 643

13.349 10.754 9.041

48.64 56.80 58.24

20 82 1075

10.699 9.652 9.099

Actual Common Declarative

Low Med High

42.77 53.17 55.87

60 295 545

12.617 10.650 9.040

50.68 56.41 58.28

22 101 1054

10.745 9.020 9.167

Actual Specialised Procedural

Low Med High

49.88 55.42 55.95

240 378 282

11.977 9.103 9.515

51.41 57.65 58.47

50 268 859

10.867 9.250 9.003

Actual Specialised Declarative

Low Med High

52.31 55.85 55.74

436 269 195

11.114 9.450 9.206

54.97 58.44 58.28

121 281 775

10.043 9.240 9.046

Perceived Overall

Low Med High

43.96 55.08 60.46

192 473 235

10.793 8.004 7.924

44.33 53.77 59.80

24 294 859

10.534 8.973 8.492

Perceived Procedural

Low Med High

39.75 51.93 59.88

121 347 432

9.814 7.881 7.280

34.63 50.48 59.46

9 169 999

7.215 8.573 8.448

Perceived Declarative

Low Med High

42.58 54.15 61.40

166 474 260

10.295 7.919 7.310

40.98 52.67 59.92

17 271 889

9.833 8.431 8.483

337