Consumer Knowledge of the Web

CASE Insights: Exploring Marketing’s Evolution Through Technology

ARTICLE INSIGHT Consumer Knowledge of the World Wide Web: Conceptualization and Measurement FULL ARTICLE Page, K. L. & Uncles, M. D. (2004) Consumer knowledge of the World Wide Web: Conceptualisation and measurement, Psychology & Marketing, Vol. 21 (8, August), 575-593.

ISSN: 0742-6046. Full-text: http://dx.doi.org/10.1002/mar.20023

WHY WRITE THIS ARTICLE? While researching consumer knowledge of technologies, what consumers know and what they think they know, I found that many academic and industry research studies about technology use experience, or how long you have been using a technology as a proxy for how knowledgeable or ‘expert’ you are.

I found this methodology really flawed as it assumes people learn in the same way, from the same information and at the same rate. This is like saying that a mechanic and a non-mechanic who have both been driving a car for 20 years, have the same knowledge about how to drive a car and it’s inner workings. In essence it ignores the context from which knowledge about a specific domain might be acquired.

Further research revealed that: 1. very few researchers could agree on what knowledge is and how it differed from experience, expertise, learning; 2. there was limited agreement on the differing types, scope, levels of knowledge and how it could be measurement; 3. there were very few established measurement instruments developed that looked at how we can measure a users knowledge of a technology.

So, in order to research user knowledge of the World Wide Web and how it influences user perceptions and web usage for a larger study, I needed to develop a series of questions that at the time of development could be used to get a snapshot of a users type of knowledge, scope of knowledge and level of knowledge of the Web.

WHY IS KNOWLEDGE OF THE WEB IMPORTANT To Improve Web Design & How We Communicate Consumers are surrounded by an array of electronic technologies. Many of these technologies are user-directed; notably the Web, touch-based ATMs and e-kiosks, wireless system such as WAP and iMODE, and electronic organizers like palm pilots. From a marketing viewpoint, it is important to understand the characteristics of the users of these

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technologies (e.g., what do they know about the features of these technologies, and how to use them?).

Armed with insights to these questions, it should be easier to refine and augment existing technologies, and design and develop new technologies that are of real value to consumers.

Also, communications to inform consumers of the benefits and uses of these technologies might be more effective, resulting in an increased likelihood of adoption and use.

A rtic le In s ig h t •

Understanding what consumers know about technologies can help to facilitate usable and relevant design of technologies and how we communicate to users about them. i.e., how we design and how we talk about technologies will differ according to a users knowledge.

The Web is a User-directed Complex System For users, there are at least three problems with a user-directed technology like the Web: “lack of closure”, “cognitive overhead” and “learning by browsing” (Foss, 1989). Lack of closure arises when the structure and organization of the network is unknown, and thus users do not know the extent of the network, or the proportion of relevant items that remains to be seen. Secondly, problems stem from the cognitive demands placed on the user of a hypertext document, hence “cognitive overhead”. Thirdly, “learning by browsing” may pose difficulties for the inexperienced, giving rise to difficulties remembering, consolidating and understanding the content of a hypertext system.

All this requires a more active and changing role for the user compared to the use of simpler electronic technologies like broadcast radio or television. Given these complexities of both the system and user characteristics and consequently the many challenges that arise during Web usage, it is argued that the ability of users to manage these challenges crucially depends on their knowledge and experience of the Web. A rtic le In s ig h t •

Knowledge is even more important in a web context. The web is changing the role of user in accessing information and the web itself is more complex in its design and functionality.

As such three core problems arise with user-directed technologies like the web: lack of closure, cognitive overhead, and learning by browser.

CLASSIFYING WEB KNOWLEDGE CONTENT A first step in this study was to classify what is “knowledge content” – i.e., the types of information stored in consumer memory of a particular domain. The research was not looking at how this information was stored or structured in memory, just what types they were and the scope of this knowledge. Two classifications of consumer knowledge were defined in this article, drawing on literature from the cognitive sciences and marketing.

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Type of knowledge content: o Declarative (knowledge of what) o Procedural (knowledge of how)

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Scope of knowledge content: o Specialized (specific & technical knowledge) o Common (general & public knowledge)

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Examples of knowledge content of the web include, information about the procedures, terminology, or facts required for using the Web. Thus, consumers might not only differ in their knowledge of what search engines are and how to use them, but also by the scope of this knowledge, is it more common or technical knowledge of what search engines are and how to use them. This is depicted in the paper as a 2 x 2 typology (Table 1).

Table 1. A Typology of Consumer Knowledge Declarative Specialised Common

High Specialised Declarative (Expert) Common Declarative (Familiar)

Procedural

Low Specialised Declarative (Novice) Common Declarative (Unfamiliar)

High Specialised Procedural (Expert) Common Procedural (Familiar)

Low Specialised Procedural (Novice) Common Procedural (Unfamiliar)

Source: Page & Uncles (2004).

Article Insight •

Users can demonstrate differing types of knowledge (knowledge of how & what); and differing scope of knowledge (common and more specialised knowledge) about the web.

HOW WAS THE STUDY DONE? The study used multiple methods to gain insight into how to measure knowledge of the web.

Firstly was the task of developing the scale items. Based on qualitative and quantitative analyses, four scales were developed to measure common declarative, common procedural, specialized declarative, and specialized procedural knowledge content of the Web. Six (6) different studies were undertaken. These included: 1.

2.

3.

4.

5.

Survey of 12 industry and academic experts working in the area (e.g., web design) Observation of 30 participants in a novice web training course Content analysis of Top 20 ‘most accessed’ websites by Australians in January 1999 Content analysis of Top 20 ‘most accessed’ websites by Australians in January 2000 Content analysis of Web browser help files for Netscape and Internet Explorer in 2000.

Secondly was to test the robustness and validity of the scale items developed, four independent samples were recruited. To ensure difference in knowledge content in both scope and type, these were drawn from one postgraduate and three undergraduate university student groups whom were carefully selected to ensure there would be variance in respondents’ knowledge and experience of the Web. Some 297 surveys were administered with 153 useable responses (52% response rate).

Note: The purpose of this study was to develop the scale items. Another larger study was undertaken testing the scales on a consumer sample of 2055 web users to further validate the scales.

WHAT DID THE STUDY FIND? Developed robust measures The measurement scales developed reliable and valid measures of consumer knowledge

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content of the web, providing a more objective means of classifying and segmenting users based on the knowledge of the web they have stored in their memories.

Moves away from use of proxies to infer knowledge This study contrasts with some of the proxies used in previous academic and commercial research. Many of these proxies are purely experienced-based, and often a poor correlation has been found between consumer experiences and what is stored in consumer memories. In this study, fairly low positive correlations between the four different types of consumer knowledge content and both the length of computer use and length of Web use.

Despite the simplicity of the proxy measures, the results here support previous work namely, that while experience and usage have some relationship to knowledge stored in an individual’s memory, they are not adequate proxy measures on their own.

SO WHAT? • • • • •

Consider profiling users beyond adoption and usage measures Additional variable for user segmentation based on consumer knowledge Provide guidance for system development and network design to improve consumer use and experience of web-based systems Profile how knowledge is structured (not just scope and type stored in memory) Further refinement of scales is required in every context applied as time-specific given the rapid changes of both technologies and users of these technologies.

Article Insight •

There is poor relationship between usage experience (how long used computers and how long used the web); and a users knowledge stored in memory.

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Don’t rely on proxies like usage experience to infer knowledge (i.e., if someone is experience or an expert); consider moving beyond just behavioural measures of website users to profile and segment web users.

SOURCES • Page, K. L. & Uncles, M. D. (2004) Consumer knowledge of the World Wide Web: Conceptualisation and measurement, Psychology & Marketing, Vol. 21 (8, August), 575-593 • Foss, C. L. (1989). Tools for reading and browsing hypertext. Information Processing Management, 25, 407-418.

NOTES A full-text copy of the article can be obtained from: http://dx.doi.org/10.1002/mar.20023

ACKNOWLEDGEMENTS CASE Insights would like to acknowledge the support of the following groups and individuals in the development of this work: Campaign Palace (Sydney); DoubleClick Australia; University of New South Wales (UNSW); Objective Results (Sydney).

AUTHORS Dr. Kelly Page | @drkellypage

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ABOUT CASE INSIGHTS http://www.caseinsights.com CASE Insights is a research initiative grounded on the use of case methodologies and a social-system worldview. The core aim of the initiative is to explore, document and share insights about Marketing’s evolution through technology.

Grounded on the principle of generating and sharing knowledge, and the importance of relevance in research, CASE Insights are created using multiple methodologies and in collaboration with industry, academic and government partners.

This work is licensed under a Creative Commons Attribution Non-commercial Share Alike 3.0 UK License.

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