LITERATURE REVIEW
Technology Adoption Author: Stephen Denham Lecturer: Dr. Frank Bannister Prepared for: ST4500 Strategic Information Systems Submitted: 23st January 2012
Literature Review | Technology Adoption
Denham S.
Abstract Technology advances can improve our businesses, societies and lives. However, any new technology can be disruptive to the status quo and face resistance. Understanding the dynamics that drive technology use can help our world progress. This piece analyses academic literature on technology adoption. Its key finding is that the most valuable contributions to this subject are theories adapted from other social sciences.
1
Literature Review | Technology Adoption
Denham S.
Motivation The Radio took 38 years to reach 50 million users. Television – 13 years. The Internet – 4 years. The iPod did it in 3. Facebook reached 200 million users in less than a year (Qualman, 2009). These statistics show a clear acceleration in how technology is becoming increasingly adopted by the masses. Technology has the ability to increase the efficiency and effectiveness of the world in which we live. No matter how advanced the technology, the most complex component will always be the people who use it. T he study technology adoption is relevant to a variety of people, from developers to government policy makers.
Diffusion In 1962, Everett M. Rogers published the Diffusion of Innovations. The book defined the process of diffusion as the process through which innovations are adopted. The five stages of the decision innovation process are knowledge, persuasion, decision, implementation and confirmation. As well as this individual level process, Rogers defines five adopter categories for populations through the bell/S curve, shown in Figure 1. It is important to note that Rogers was not the first to highlight this trend. The ‘S Curve of Diffusion’ was studied as early as 1903 by the French sociological pioneer, Gabriel Tarde (Rogers, 1962). Rogers’ addition was little more than labelled sections of the curve, however his work has become the default citation. Although this model is easily validated empirically, technology adoption literature has not utilised it as one might expect. It is often referenced in introductory statements, but not given further in-depth study like other models described in this review. The theory of diffusion has come into mainstream popularity in the last decade. Seth Godin (2003), often described as the world’s top marketing guru, used it in his TEDTalk. He said: “this stuff applies to everybody, regardless of what we do… what we are living in, is a century of idea diffusion”. The Diffusion Curve was also a key element in the best selling book Tipping Point (Gladwell, 2000). Although these are not academic articles, as this review set out to analyse, they highlight the importance of this topic.
2
Literature Review | Technology Adoption
Denham S.
Figure 1. Diffusion of Innovations
Technology Acceptance Model The most discussed contribution to this topic is the Technology Acceptance Model (TAM), which was Fred F. Davis’s 1986 doctorial dissertation (shown in Figure 2). TAM is an adaptation of the Theory of Reasoned Action (TRA), which was ‘designed to explain virtually any human behaviour’. This was published 3 years on (Davis et al., 1989). The TAM model highlights perceived ease of use (PEU) and perceived usefulness (PU) as being fundamental drivers of technology adoption, which are influenced by external variables.
Figure 2: Technology Acceptance Model Just a month later, Davis had another paper published in the MIS Quarterly entitled Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology (1989). This paper is also often cited as a core reading on the topic. In it, Davis outlines his theoretical foundations which draw from a variety of topics including marketing, behavioural science and organisational information systems.
Extensions and Implementations of TAM
3
Literature Review | Technology Adoption
Denham S.
The most prolific writer on TAM since its original conception is Viswanath Vankesha. Vankesha, Fred D. Davis and two others, produced the most comprehensive review on the subject and developed their model - Unified Theory of Acceptance and Use of Technology (UTAUT) shown in Figure 3.
Figure 3: Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003) Recent articles have often been attempts to apply technology adoption theory to specific products or demographics. In doing so, these studies have pushed the boundaries of existing models by highlighting problems. Renaud and Biljon (2008) concentrate on the adoption of mobile phones by the elderly – ‘the grey market’. This study is unique in that it highlights how the purchasing stage of the diffusion process may be irrelevant. Many of those studied had to them from sons or daughters as a gift yet still chose not to adopt the technology into their lives. In this case, Roger’s five-step diffusion process loses much of its applicability. The study offers the Senior Technology Acceptance & Adoption Model (STAM) shown in Figure 4.
4
Literature Review | Technology Adoption
Denham S.
Figure 4: Senior Technology Acceptance & Adoption Model (STAM) Amoako-Gyampah and Salam (2004) provide yet another analysis of TAM, applied to Enterprise Resource Planning (ERP) system implementation. These are fully integrated organisation information systems. In their model, the basic TAM framework is preceded by communication, training and their interactions with shared belief in the benefits of the ERP system. This is clearly shown in their research model shown in Figure 5.
Figure 5: TAM in an ERP Implementation Environment This piece begins with a short literature review on TAM and ERP implementation research. It lists the following critical success factors of ERP implementation projects: top management support, strong business justification, training of employees, project communication, properly defined roles for all employees and user involvement. Their
5
Literature Review | Technology Adoption
Denham S.
study looks at the impact of two of these factors on perceived usefulness and perceived ease of use. It has also removed the external variables module without explanation. Where this piece falls short, is in ignoring of the other critical success factors’ impact on adoption. In the article’s abstract it states the ‘study evaluated the impact of… two widely recognized technology implementation success factors (training and communication) on the perceived usefulness and perceived ease of use during technology implementation’. It is not clear why other success factors were not also studied. By the definitions given, business justification should have a strong impact on perceived usefulness.
Conflicting Views UTAUT and STAM differ from TAM as they omit the attitude module. UTAUT places more attention to the external factors. STAM ‘replaced the multi-faceted attitude module with modules depicting the progression from first ownership towards actual acceptance.’ Renaud and Biljon (2008) argue that UTAUT ignores ‘facilitation conditions’ such as infrastructure or nominal cost. Brown et al. (2002) argue that by definition, TAM is not strictly applicable to scenarios where technology is mandated. In previous studies, users could reduce their use of a technology or work around it. In circumstances where a technology is essential for an employee’s role, the TAM theory is not longer valid. An employee may still fully intent to use a system (BI) even if they expect it to be difficult (PEU) and decrease their job performance (PU), because using this system is necessary for them to keep their job. This is supported in the factor analysis loadings. One could argue that job retention is perceived usefulness however Davis et al. (1989) explicitly defined perceived usefulness ‘as the prospective user’s subjective probability that using a specific application system will increase his or her job performance within an organizational context’. This suggests that TAM must widen its definition to a broader term such as perceived value. UTAUT accounts for this with the voluntariness of use component. It is clear the TAM model has been the subject of much discussion, more than any other theory in technology adoption literature. It is possible that it has narrowed the focus for many writers who could be discussing technology adoption on a macro level instead. Although TAM is imperfect, it does highlight the key dynamics in a
6
Literature Review | Technology Adoption
Denham S.
simple way. Figures 2-5 show that as TAM has been modified, it has gained complexity, and as with all models, greater complexity limits accessibility.
Social Media The explosion of social media has made large changes to the business landscape. It has allowed people to find and share information with more ease and speed. Peng and Mu (2011) set out to test how online networks reflected real world social networks. They used data from online teams of open-source software developers. Their research methods are questionable in a number of ways. One example of this can be seen in their third hypothesis – ‘project leaders have a stronger influence on the adoption of a new technology as compared with other members’. This is a poor hypothesis as many definitions of a leader necessitate having influence. Unfortunately, although this paper uses complex mathematical techniques, its findings are laboriously explained and rather obvious. Online social networks have been around for several years now, but this paper treats them as a completely unknown quantity. As discussed in the Continued Use section, the biggest effect of the social media revolution is online advocacy and discussion, defined by UTAUT as social influence.
Continued Use Today it is easier that ever to try or switch between products and services. This allows customers to be more fickle. For example, through the Facebook platform, websites allow users to sign up to their product through their Facebook account with a single click. No new username or password is required. One of the most recent of the articles reviewed (Venkatesh and Goyal, 2010), discusses the expectation-disconfirmation theory (EDT), which also has routes in marketing and customer behaviour research. EDT models how users’ (or customers’) actual experiences differ to their pre-exposure expectations. It is based on the idea that ‘satisfaction is a function of the size and direction of disconfirmation’. This means if the user’s experience is more positive than he or she expected, they will be proportionally positively satisfied, and vice versa. EDT filled a gap left by the TAM model. TAM states that high expectations should be set, because high positive expectations lead to high intension to use. It does places 7
Literature Review | Technology Adoption
Denham S.
little emphasis on the importance of expectations being realistic, and so, TAM is a short-term strategy. In theory, EDT is more sustainable, however, it is modelled in such a complex way that it is not accessible to practitioners (see Methodology). A similar message is simply expressed in Edelman’s HBR paper on the customer loyalty loop, which is an extension of Roger’s earlier work on the five-step diffusion process. As seen in Figure 6, Edelman sees adoption as a continuous process. Ideas like this, which focus more on advocacy, are increasingly popular as users are more connected through online social networks. Marketing literature such as this has been quicker to understand the importance of online discussion in influencing consumer behaviour.
Figure 6: The Loyalty Loop (Edelman, 2010)
Practical Guidance A common theme in much of the literature on this subject is the attempt to provide practical guidance to policy makers and practitioners. Butler and Sellbom (2002) provide a clear accessible study of technology adoption barriers in educational institutions. Apart from a brief explanation of the Diffusion Curve, they refrain from any other formal model to guide their study and opt for a simple survey from which they produce simple guidelines for teaching. These include creating reliability, showing institutional support and analysing the technology’s benefit. Venkatesh and Goyal (2010) warn managers of the dangers of overselling information systems, however they also caution away from an ‘under-promise and over-deliver’ approach.
Methodology
8
Literature Review | Technology Adoption
Denham S.
Research on the topic has largely been done through surveys, focus groups and interviews. In most instances, the surveys were valid representations of the populations under study, but not necessarily representative of all scenarios of technology adoption. Where models such as TAM were being tested, researchers applied factor analysis methods. This allowed intangible quantities, such as user attitudes, to be quantified and correlated. Venkatesh and Goyal (2010) argue strongly that studies have been too linear and simplistic. They revert to polynomial models, as shown in Figure 7 to capture multidimensional relationships. Unfortunately, as with all complex models, it suffers from being inaccessible to practitioners and so the value added is rather marginal.
Figure 7: Polynomial Response Surface for Perceived Usefulness Predicting Behavioural Intention When studying social networks effect, Euclidian distance was used to measure the closeness of clusters of individuals in the network (Peng and Mu, 2011). The speed of adoption was selected as the outcome variable. This limited the study in that it only measures projects which were eventually adopted, ignoring those that were cancelled.
Further Research Contrasting views exist on our understanding of technology adoption. In
2003
(Venkatesh et al.) said that ‘UTAUT explains as much as 70 per cent of the variance 9
Literature Review | Technology Adoption
Denham S.
in intention, it is possible that we may be approaching the practical limits of our ability to explain individual acceptance and usage decisions in organizations’. This statement is self-serving, after all, Venkatesh did publish two more articles on the topic (Venkatesh and Goyal, 2010, Venkatesh and Bala, 2008). Compared to similar areas, there is relatively little research into how peoples’ attitude to technology has changed in relation to adoption. Thus far, efforts to understand and model adoption have been good, however largely repetitive. Finally, as already suggested, a great deal more could be done to apply the work of marketers to technology adoption. The interdisciplinary nature of the topic could be better embraced, rather than a side note. Novel additions to the field are likely to come from those who apply and adapt theories from other social sciences. Purely internal analysis is restrictive. It is likely that any future novel ideas will come from outside this specific domain.
Conclusion Technology adoption, on an individual level, has chiefly been focused an a few models which have been discussed at length. As with any model, complexity is costly as it is less accessible for practitioners. Given this topic’s relevance to such a variety of people, simplicity is more appropriate. The Technology Acceptance Model has been the most prevalent, and although it is not universally applicable, it does highlight the fundamental cost-benefit dynamic. On a population level, technology adoption has not been studied in detail, other than the diffusion curve, which has fallen off the raider for most writers and is now seen primarily as a marketing topic. In some ways, technology adoption literature has become consumed by discussions of the TAM model. The most recent trend has been a greater focus on social factors, which is of greater importance due to the rise of online social media. The most important point raised here is that this is an interdisciplinary topic. The most important contributions to the subject have come when researchers looked to other disciplines for novel approaches. If further progress is to be made in this field, it is likely to come from further adaptations of similar areas of research.
10
Literature Review | Technology Adoption
Denham S.
References AMOAKO-GYAMPAH, K. & SALAM, A. F. 2004. An Extension of the Technology Acceptance Model in an ERP Implementation Environment. Information & Management, 41, 731-745. BROWN, S., MASSEY, A., MONTOYA-WEISS, M. & BURKMAN, J. 2002. Do I Really Have To? User Acceptance of Mandated Technology. European Journal of Information System, 11, 283-295. BUTLER, D. L. & SELLBOM, M. 2002. Barriers to Adopting Technology for Teaching and Learning. Educause Quarterly, 22-28. DAVIS, F. D. 1989. Perceived Usefullness, Perceived Ease of Use, and Use Acceotance of Information Technology. MIS Quarterly, 35, 982-1003. DAVIS, F. D., BAGOZZI, R. P. & WARSHAW, P. R. 1989. User Acceptance of Computer
Technology:
A
Comparison
of
Two
Theoretical
Models.
Management Science, 35, 982-1003. EDELMAN, D. C. 2010. Branding in The Digital Age: You’re Spending Your Money In All the Wrong Places. Harvard Business Review, 88, 62-69. GLADWELL, M. 2000. The Tipping Point: How Little Things Can Make a Big Difference, Boston, Little, Brown and Company. GODIN, S. 2003. Sliced Bread and Other Marketing Delights. TED Conferences. Monterey, California. PENG, G. & MU, J. 2011. Technology Adoption in Online Social Networks. Product Development & Management Association, 28, 133-145. QUALMAN, E. 2009. Socialnomics, New Jersey, John Wiley & Sons. RENAUD, K. & BILJON, J. V. 2008. Predicting Technology Acceptance and Adoption by the Elderly: A Qualitative study. SAICSIT, South Africa, 210-219. ROGERS, E. M. 1962. Diffusion of Innovations, New York, The Free Press. VENKATESH, V. & BALA, H. 2008. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39, 273-315. VENKATESH, V. & GOYAL, S. 2010. Expectation Disconfirmation and Technology Adoption: Polynomial Modeling and Response Surface Analysis. MIS Quarterly, 34, 281-303. VENKATESH, V., MORRIS, M. G., DAVIS, G. B. & DAVIS, F. D. 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27, 425-478.
11