Technology Adoption
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Transcript of 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.
Literature Review | Technology Adoption Denham S.
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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.
Literature Review | Technology Adoption Denham S.
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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
Literature Review | Technology Adoption Denham S.
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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
Literature Review | Technology Adoption Denham S.
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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
Literature Review | Technology Adoption Denham S.
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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
Literature Review | Technology Adoption Denham S.
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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
Literature Review | Technology Adoption Denham S.
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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.
Literature Review | Technology Adoption Denham S.
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References
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