technology adoption

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.

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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.


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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.


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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


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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.


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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


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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


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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


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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


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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


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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.


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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.

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