overview of established theories on user acceptance (utaut, tam, piit and issm)
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Overview on user behaviour adoption models from UTAUT, ISSM, PIIT and TAM.TRANSCRIPT
Megat Shariffudin B Zulkifli, DrUniversiti Putra Malaysia
Overview of Established Theories on User Acceptance
Major theories of technology adoption mostly originated from studies on computer
adoption within an organizational context, where the purpose is to improve employ-
ees' acceptance of IT in work place and increase their performance (Venkatesh et.
al., 2000). E-government technology adoption research has been mostly concerned
with the adoption of online government services using web technologies (Murali et.
al., 2009). However, in order to understand factors affecting government user’s adop-
tion of new technologies used in e-government context and increase the use of eBid-
ding, it is important to extend the theorizing beyond and Internet technology. It is also
necessary to take into consideration individuals' sourcing officials perceptions and
satisfaction about the system and how these factors may also influence how the offi -
cials perceive the usefulness and ease of use of the eBidding in the working environ-
ment.
Research on Information System (IS) users’ adoption and use has been done
extensively and several models have been developed to explain users’ acceptance
and use. The models originated from different theoretical disciplines such as
psychology, sociology and information systems (Oliveira, 2005). To assess the
adoption scenario of Information Systems (IS) applications in the market, such as e-
procurement and internet banking, a lot of previous studies and research have been
carried out and various frameworks were proposed to identify the factors influencing
the acceptance of technology in the consumer context (Rosen, 2004). Information
systems literature is also filled with studies that have examined technology
acceptance within an organization. The Technology Acceptance Model (TAM) (Davis
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Megat Shariffudin B Zulkifli, DrUniversiti Putra Malaysia
et. al., 1989), Diffusion of Innovation Theory (IDT) (Rogers, 2003), and the Unified
Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et. al., 2003)
have all identified factors that affect an individual’s intention to use or the actual use
of information technology. In the following paragraphs, a review of literature on
individual adoption of new technology in a working environment will be outlined and
discussed.
Technology Acceptance Model (TAM)
One of the most common models used by researchers in the study of individual's
adoption of technology is Technology Acceptance Model (TAM) (Davis, 1989). TAM
focuses on individual perceptions about technology use. TAM argues that individuals'
beliefs and perceptions about technology use form their attitudes toward the
technology, and those attitudes in turn determine their intention to adopt or not to
adopt an innovation like eBidding. TAM has received praises from earlier researchers
on its contribution towards our understanding into consumer behavior.
Although TAM was first introduced in 1989, it is still being widely used. According to
Jeyaraj et. al., (2006), TAM is the most widely used model for identifying factors that
contribute towards individual acceptance of a technology. Han (2003) argued that
since its introduction, the Technology Adoption Model (TAM) was tested and adopted
across a wide range of IS applications, communication technologies, database
systems and Internet applications (e.g., information services, online services, virtual
workplace systems and digital libraries).
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TAM suggests that when users are presented with a new piece of technology, a
number of factors influence their decision about how and when they will use the
technology (Venkatesh, 2003). To explain this, two perceived attributes or measures
are used: perceived usefulness (PU) and perceived ease of use (PEOU).
a. perceived usefulness (PU) is defined as whether the technology will enhance
the user’s job performance ; and
b. perceived ease of use (PEOU) is defined as whether the technology when
used by the user will be free from effort (Davis, 1989).
Figure 3. Technology Acceptance Model
Source : (Davis et. al, 1989).
The integrity of the original TAM is demonstrated through empirical research, which
extends the model to different settings, providing consistency and good re-test
reliability, confirming the validity of the original Davis model. It may be argued that
TAM provides a useful framework for exploring the motivational issues affecting the
adoption of a technology in a working environment (Oliveira, 2005).
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Davis refined and retested the TAM model to test the effect of social influence on
behaviour intention (Davis, 2003). The significant difference of Davis’ new model,
Technology Adoption Model 2, was that it extended TAM to measure several new
social influence dimensions to include additional key determinants of TAM’s
“perceived ease of use” constructs, incorporating social influences and cognitive
instrumental processes. Additional elements to the TAM within the social influence
category include “subjective norm”, “voluntariness”, “image” and “experience”.
The attribute “Subjective norm” acknowledges the influence of peers on whether they
should perform the behaviour in question. While “voluntariness” accounts the effect of
mandatory and non-mandatory usage on usage intentions. “Image” is the degree to
which a technology may affect the status of an individual while “experience” suggests
that the direct effect of a subjective norm may subside over time with increased
system experience. “Job relevance” determines what tasks can be performed with a
given system; “output quality” posits that individuals will always assess how well a
system performs tasks, and “result demonstrability” relates to how tangible the results
are as a result of using a technology (Venkatesh et al., 2000).
All of these elements help to explain the PU construct of the original TAM, and may
enable the design of organisational interventions that promote usage of new
systems. However, the TAM2 is limited in that it only explores the basis of the PU
component and ignores the PEOU construct, providing a less-holistic view of factors
that can be addressed to maximise usage.
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The TAM 2 model was further extended to Technology Acceptance Model 3 (TAM3)
by Venkatesh and Bala, (2008) that combines the TAM2 and the model of the
determinants of perceived ease of use (Venkatesh et al., 2000) to explain PEOU in
addition to the PU determinants, as per the TAM2. The additional factors to the TAM3
are “computer self-efficacy”, “perception of external control”, “computer anxiety” and
“computer playfulness” as depicted in Figure 3.
The attribute “Computer self-efficacy” relates to the level of belief of an individual has
the ability to perform a task. While “perception of external control” determines
whether an individual believes the organisational and technical support is suitable.
“Computer anxiety” encompasses the level of fear associated with using a new
system, and “computer playfulness” represents the intrinsic motivation for using a
novel technology. “Perceived enjoyment” is defined as the extent to which the activity
of using a system is perceived to be satisfying (Venkatesh et al., 2000) and is
expected to increase with experience whilst computer playfulness will decrease over
time. “Objective usability” involves an individual making a comparison of the actual
level of effort required to complete specific tasks (Venkatesh et al., 2000).
Although the TAM3 is more comprehensive in that it provides interventions to boost
PEOU as well as PU, it is argued that these are focused on the individual and not in
the wider implementation context. In this sense, wider organisational issues such as
the influence of supervision and level of involvement in the decision-making process
may also play a part in determining the acceptance of a new technology.
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However, many research state that Technology Acceptance Model (TAM) itself is
insufficient to explain users' decisions to adopt technologies, therefore they use
Technology Acceptance Model (TAM) as a base model and extended the model by
adding additional variables to the model depending on the types of technologies they
studied.
Kamarulzaman (2007) on his study of internet shopping adoption drew upon TAM
and included personal and cognitive influence. Other researchers have also tried to
combine Technology Acceptance Model (TAM) with other technology adoption
models.
Hernandez and Mazzon (2007) applied Technology Acceptance Model (TAM) with
other technology adoption models such as Innovation Diffusion Model and
Technology Acceptance Model 2 (TAM2), which is an extension of Technology
Acceptance Model (TAM) in their study on online banking implementation in Brazil.
In summary, TAM has been successfully employed in various studies in explaining
individual user acceptance and usage behavior in a working environment but need to
extend by adding additional variables to the model depending on the types of
technologies they studied.
Diffusion of Innovations (DOI)
Another theory which has received similar attention by scholars in explaining
consumer behavior towards new technology is the Rogers’ Innovation Diffusion
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Theory (Rogers, 1995). DOI theory focuses on the individual characteristics that
relate to technology adoption behavior. The DOI is a widely used model in behavioral
sciences in investigating individual adoption of innovations (Oliveira, 2005). The
purpose of the DOI theory is to understand how and why users either embrace or
reject innovations (Rogers, 2003). Rogers (1995) studied characteristics of
individuals in terms of openness to innovations, and he developed DOI, which
proposes that individual’s react differently to change based on a stable
predisposition.
DOI focuses on diffusion of innovation, which refers to the process, by which an
innovation is communicated through certain channels over time among the members
of social systems (Rogers, 2003). While Fichman (2000) defines innovation diffusion
as the process by which a technology spreads and adopted across a population.
Therefore diffusion of innovations is the study of how ideas or practices come to be
adopted by the individuals within an environment.
According to DOI and the perceived attributes theory, an innovation will diffuse at an
increased rate of diffusion if an innovation could be tried on a limited basis before
adoption (trialability) ; an innovation offers observable results (observability) ; the new
technology has an advantage relative to other innovations or the status quo (relative
advantage); or the technology is not overly complex (complexity) ; and whether the
innovation is compatible with existing practices and values (compatibility).
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Figure 4. Rogers’ Perceived Attributes influencing Individual Adoption of Innovation
Perceived Attributes of Innovations
Source: Rogers, 2003)
These attributes are interrelated empirically but each is conceptually distinct, and the
selection of these attributes is based on past research as well as a desire for
maximum generality and parsimonious as the following:
a). “Relative advantage” is the extent to an innovation is perceived as superior to
the innovation it supersedes. The degree of Relative Advantage is often
expressed as economic profitability, as conveying social prestige, or in other
ways (Rogers 2003). Kendall, Tung, Chua, Hong, Ng, and Tan (2001) refined
relative advantage as the benefit in terms of lower business costs, wider
market coverage, preference to upgrade other business ventures than to
adopt electronic commerce, and importance of doing business on the internet
in the future.
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Megat Shariffudin B Zulkifli, DrUniversiti Putra Malaysia
b). “Compatibility” refers to the degree to which an innovation agrees with the
values of the culture that will adopt it. Compatibility also refers to level of
congruence of the proposed innovations with one’s existing set of values,
needs, and past experiences.
c). “Complexity” is the ease with which innovations can be learned potential
users, including the degree of effort required to adopt the innovations.
Complexity has also been referred to as the perceptions of how difficult an
innovation is to understand and use, and how it can exert a negative force on
the Rate of Adoption (Murphy 2003). In all of the studies reviewed, if an
innovation has higher complexity, it is hypothesized to have a negative
impact upon the individual’s attitude toward use (Kendall et al, 2001).
d). “Trialability” is the degree to which innovations can be piloted on a small
scale to test their relative efficacy before adoption (Rogers, 2003).
Innovations available for trial for a period of time are generally more
acceptable to individuals than those simply thrust upon them. According to
Murphy (2003), trialability refers to the degree to which an innovation can be
tried out or experimented with on a limited basis before commitment is
required in order to determine how well it works under specific conditions.
Innovations that can be tried before adoption are adopted more rapidly than
those that cannot, especially among those who adopt earlier relative to the
majority of potential adopters.
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e). “Observability” refers to positive outcomes one sees from implementing the
innovations. If the benefits of an innovation are visible to intended adopters, it
will be adopted more easily (Denis et. al., 2002 and Ovretveit et. al., 2002).
Initiatives to make more visible the benefits of an innovation i.e through
demonstrations will increase the likelihood of their assimilation. Similar to
relative advantage, compatibility, and trialability, observability also is
positively correlated with the rate of adoption of an innovation.
f). “Rate of Adoption” describe the expected behavioural outcome and the
extent to which an individual in the organization has a positive attitude toward
using a new technology.
DOI has been applied to over thousands of empirical studies since 1962 (Rogers,
2003) including studies of business-level organizational innovation technology
adoption (Frambach and Schillewaert, 2002). Researchers argued that DOI (Rogers,
1995 ; 2003) serves as a fundamental theoretical base of innovation adoption
research in many disciplines, including sociology, communications, marketing,
education, etc. (Oliveira, 2005).
In summary, the DOI is a well-established and widely used in information technology
(IT) diffusion-related research (Braak, 2001 and Oliveira, 2005), and provides an
excellent fit with the goal of understanding individual or firm’s initial attitudes toward
adopting technology.
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Personal Innovativeness in Information Technology (PIIT)
According to Individual Innovativeness Theory (Rogers 1995), innovators are the
people who are risk takers interested in taking the initiative and time to try something
new. Agarwal and Prasad (1998) conceptualized and operationalized the construct
PIIT, which they define as the willingness of an individual to try out any new
information technology. (PIIT) is defined as the willingness of an individual to try out
new information technology. Individuals with higher levels of PIIT are expected to
develop more positive perceptions about the innovation in terms of advantage, ease
of use, compatibility, etc. and have more positive intentions toward use of a new IT
(Agarwal & Prasad, 1998).
The role of personal innovativeness in individuals’ adoption of innovations has also
been acknowledged in innovation diffusion studies (Rogers, 1995) wherein early
users of an innovation are considered “innovative”. PIIT was found affect the
relationship between compatibility and usage intentions of the World Wide Web
(Agarwal & Prasad, 1998b), as well as Ease of Use and Usefulness of other Internet
technologies (Lewis, Agarwal, & Sambamurthy, 2003). The authors argued that
personal innovativeness in information technology (PIIT) is hypothesized to exhibit
moderating effects on individuals’ perceptions about a new technology. Although the
authors had theorized PIIT would exhibit a moderating influence on the relationships
between the three salient perceptions and usage intentions, their study results for
their sample and technology tests showed significant moderation was observed only
for compatibility. Despite the outcomes of the study, the authors supported the notion
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that PIIT potentially represents a construct that might be salient for examining
innovation behaviour with respect to computing technology.
Figure 5. Moderator model of Personal Innovativeness in Information
Technology
(PIIT)
Source : (Agarwal and Prasad, 1998)
PIIT constructs are as the following:
a. “Usefulness” refers to the degree of ease associated with the use of the
system similar to performance expectancy;
b. “Ease of use” refers to the degree to which an individual believes that using
the system helps him or her improve job performance similar to effort
expectancy;
c. “Compatibility” is the degree to which an innovation is perceived as consistent
with existing values, past experiences, and needs of potential adopters;
d. “Behavioural intention” refers to the willingness of individuals to work hard and
exert effort in order to achieve the given behaviour
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PIIT would act as a key moderator for the antecedents and as well as the
consequences of perceptions (i.e. relative advantage, ease of use and compatibility),
more specifically, for compatibility (Agarwal and Prasad, 1998).
Unified Theory of Acceptance and Use of Technology (UTAUT)
The Unified Theory of Acceptance and Use of Technology (UTAUT) model draws
upon and integrates eight previously developed models and/or theories that relate to
technology acceptance and use particularly, Theory of Reasoned Action, the TAM,
the Motivational Model, the Theory of Planned Behavior, a model combining the TAM
and the Theory of Planned Behavior, the Model of Personal Computer Utilization, the
Innovation Diffusion Theory, and the Social Cognitive Theory (Venkatesh et. al.,
(2003). The authors concluded that Unified Theory of Acceptance and Use of
Technology (UTAUT) is a definitive model that synthesizes what is known and
provides a foundation for research in individual acceptance of technology in a
working environment. Anderson and Schwager (2004) found that the UTAUT model
was extremely useful in identifying the rate of adoption of IT, especially within large
sample populations. Anderson et. al., (2004) validated UTAUT constructs with
performance expectancy as the most important driver for PC tablet adoption in their
study to find the drivers of user acceptance of tablet PCs in higher education. Li and
Kishore (2006) validated UTAUT construct scales (performance expectancy and
effort expectancy) in the context of acceptance of an online community web log
system. Other studies which have applied the UTAUT are in mobile payment
adoption (Mallat, 2007) and mobile communication (Wu et al., 2007).
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Figure 6. Unified Theory of Acceptance and Use of Technology (UTAUT)
Source : (Venkatesh et. al, 2003)
UTAUT Model as Figure 7 was formulated with four direct determinants on intention
and usage behavior: performance expectancy, effort expectancy, social influence and
facilitating conditions, as explained in the following:
a. “Performance expectancy” is defined as the degree to which an individual
believes that using the system will help him or her to attain gains in job
performance. Venkatesh et. al., (2003) contended performance expectancy is
the strongest predictor of intention.
b. “Effort expectancy” is defined as the degree of ease associated with the use of
the system;
c. “Social influence” is defined as the degree to which an individual perceives
that the important others believe he or she should use the new system
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d. “Facilitating conditions” are the variables asserted to have a direct impact on
system usage. They define as the degree to which an individual believes that
an organizational and technical infrastructure exists to support use of the
system. Prior literature also revealed that facilitating conditions do have
influence on intention to use (Wu et. al., 2007).
e. “User experience” is defined as the time elapsed since the initial use of the IT
application (Venkatesh, et. al., 2003). Prior research suggests that increase
inexperience would decrease the influence of effort expectancy and social
influence on behavioral intention to use the information system.
f. “Voluntariness” as in a voluntary use environment, users believe that they
have a choice in the technology adoption or use decision (Brown, et. al.,
2003). This is in contrast to a mandatory use environment, users believe that it
is compulsory to use the technology (Venkatesh, et. al., 2003; Venkatesh and
Davis, 2000).
g. “Gender” is defined as biological sex. Gender differences have been
demonstrated in various ways. Studies found that women experience higher
levels of computer anxiety and lower levels of computer self-efficacy than men
(Venkatesh et. al., 2003);
h. “Age” - Morris and Venkatesh (2000) found that in the short term, subjective
norm, attitude toward using technology and perceived behavioural control
have a significant impact on older workers while only attitude toward using the
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technology has an impact on younger workers. Attitude towards using the
technology is more important to younger workers than older workers.
The four determinants are independent factors, which will affect the dependent factor
the Behavioral Intention and Usage Behavior. Behavioral Intention is defined as a
measure of the strength of one’s intention to perform a specified behavior (Venkatesh
et. al., 2003). Attitude toward use is defined as an individual’s overall affective
reaction to using a system (Venkatesh, et. al., 2003).
Since its inception in 2003, researchers have been employing UTAUT in various field
of studies. UTAUT was consistently able to account for 70% of the variance (adjuster
R2) in usage intentions, which is an improvement over each of the original eight
models and their extensions, which only explained between 17 and 42 percent of the
variance. (Venkatesh et. al., 2003). Anderson and Schwager (2004) transformed the
UTAUT into an analysis process in which gender, age, experience, and the
voluntariness of use were used as the defining population variables. This helped the
researchers identify patterns of use in twelve specific areas based upon the four
constructs and the qualities of the sample population.
In summary, the UTAUT is an effective means of assessing and presenting data on
user acceptance, especially when user demographic information is taken into
account. The UTAUT model has contributed a significant understanding towards user
acceptance in providing a unified framework for overlapping user acceptance
theories.
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Model of Information System Success
The DeLone and McLean (2003) Model of Information Systems (IS) Success is one
of the most cited and commonly-used models in the IS literature. The authors
contended that the model has been utilized to measure success of the e-commerce
adoption. The IS Success Model created by DeLone and McLean (1992)
incorporates the six different dimensions of IS success that the authors identified in
their extensive review of the literature. This model, although published in 1992, was
based on theoretical and empirical IS research conducted by numerous researchers
and the model provides comprehensive review of different IS success measures
(Delone and McLean, 1992).
According to DeLone and McLean (1995), system quality and information quality both
affect use and user satisfaction, both being antecedents of individual impact, and this
individual impact should ultimately affect the organizational impact. A system can be
evaluated in terms of information, system, and service quality and these
characteristics affect subsequent use or intention to use and user satisfaction. As a
result of using the system, certain benefits will be achieved which net benefits will
(positively or negatively) influence user satisfaction and further information system
use and adoption. The Model constructs comprise of system quality, information
quality, system use, user satisfaction, individual impact and organizational impact.
In the Updated IS Success Model, DeLone and McLean (2003) proposed an updated
model which includes two important modifications and a clarification. First, the
updated model includes the service quality dimension to the model, and second, the
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authors group both impact measures (individual impact and organizational impact)
into a single measure called net benefits. The authors also clarify that, in a process
sense, use should happen before user satisfaction, and a positive experience with
the use of the system will increase the satisfaction of the user. Furthermore, an
increased user satisfaction will increase the intention to use which ultimately will
increase use (DeLone and McLean 2003).
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Figure 7. Updated IS Success Model
Source : (Delone & McLean, 2003)
In the 10-Year Update of the Model, DeLone and McLean provide a more detailed
description of each one of the shades or dimension of IS success included in the
model. With this update, the authors propose that the model leads itself to be used
not only in already existing information systems, but also in new and developing
systems such as e-commerce, e-government, knowledge management systems and
web-based technologies. The updated model comprise of the following constructs:-
a. “System quality” refers to those characteristics that are needed or desired in
an IS. Some of the measurement examples that the authors provide are ease
of use, system flexibility, system reliability, ease of learning, intuitiveness,
sophistication, and response times. System quality also refers to the quality of
the performance of the system. System quality consists of five major
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dimensions which include flexibility, reliability, response time, accessibility, and
integration (Nelson, et. al., 2005).
b. “Information quality” represents the output of the system in terms of how
relevant, understandable, accurate, concise, complete, timely, and useable is
the output produced. Information quality refers to the quality of the output of
the information system. Nelson, et. al., . (2005) suggested that in addition to
the quality of the output, information quality should consider who uses the
information, the application being used and the task being completed. Nelson,
et. al., (2005) also posit that information quality consists of four dimensions:
accuracy, completeness, currency, and format.
c. “Service quality” is referred to the support that the users of the system receive
from their IT area personnel (i.e. responsiveness and knowledge) (DeLone
and McLean, 2003). Service quality should also cover the support that the
service provider offers to the customer regardless of what business unit
provides it before, during, and after the e-commerce exchange. Nelson, et. al.,
(2005) posit that service quality consists of five dimensions: tangibles,
reliability, responsiveness, assurance and empathy.
d. “System use” is defined as the quantity and manner of utilization of the
system. In terms of operationalization, system use is measured as the amount,
frequency, nature, extent, and purpose of the use. Usage also refers to any
type of interaction that customers, visitors, or browsers have with the e-
commerce/e-procurement site.
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e. “User satisfaction” captures how the user feels about the whole experience
with the system starting from the system itself, moving to the output as an
outcome of the system, and finally including the support services that are
provided by the system. User satisfaction measures the customers‘ opinions of
the e-commerce/e-government system during the complete service cycle.
f. “Net benefits” covers how much the IS adds to the success of the individual,
group, organization, industry, or even nations (Petter et. al., 2009). Net
benefits attempts to measure the impact of the system on customers,
suppliers, employees, organizations, markets, industries, economics, and
even our societies (DeLone and McLean 2003). Net benefits include the
various impacts such as societal impact, individual impact, and organizational
impact (Venkatesh, et. al., 2003).
Based on the updated DeLone & McLean IS success model, the quality antecedents
to user beliefs comprises of three types of quality factors, information quality, service
quality, and system quality, can be regarded as the key factors of success in an IS
(DeLone and McLean, 2003, 2004). Essentially, successful IS adoption can rely on
user acceptance of IS (DeLone and McLean, 2003). The model has been employed
by researchers in various information systems.
In summary, the IS/Technology user acceptance literature provides a useful
theoretical lens to study e-procurement auction implementation, particularly eBidding
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adoption. It allows us to evaluate the importance of different factors impacting the
decision by government procuring officials to adopt the eBidding.
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Overview of User Adoption Models (continue)
Many MIS researchers have studied acceptance of new technologies over the past
two decades. From the previous analysis of the theories used in the individual
adoption of technology indicates that they mainly revolve around the Roger’s (1995)
Diffusion of Innovation Theory (DOI), Davies’ (1989) Technology Acceptance Model
(TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT)
(Venkatesh et. al., 2003) have been used for the last two decades to explain possible
consumer behavior with respect to adoption and acceptance patterns of new
technologies and innovations. The most applied, tested and refined model is the TAM
followed by UTAUT and DOI (Tobbin, 2011).
Technology Acceptance Model (TAM) (Davis et. al., 1989) which focuses on
technology acceptance provides perceived usefulness and ease of use as
antecedents to behavioral intentions. These two beliefs create a favorable disposition
or intention toward using the IT that consequently affects its use. Perceived
Usefulness (PU) is said to be the degree to which a person thinks that using a
particular system will enhance his or her performance. Perceived Ease of Use
(PEOU) is defined as the degree to which a person believes that using a particular
system will be free of effort (Davis, 1989).
Rogers (1995) through IDT has examined adoption behaviors and has identified
several attributes of an innovation that influence acceptance behavior. The attributes
are relative advantage, complexity, trialability, compatibility, and observability.
Relative Advantage: the degree to which the innovation is perceived as being better
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than the practice it supersedes; Compatibility: the extent to which adopting the
innovation is compatible with what people do; Complexity: the degree to which an
innovation is perceived as relatively difficult to understand and use; Trialability: the
degree to which an innovation may be experimented with on a limited basis before
making an adoption (or rejection) decision; and Observability: the degree to which
the results of an innovation are visible to others (Rogers, 1995).
Venkatesh et. al., (2003) have proposed a unified model (UTAUT) to explain user
acceptance. They have integrated eight different models to develop UTAUT. The
basic notion underlying UTAUT is that three antecedents will predict behavioral
intentions: Performance Expectancy (formerly Perceived Usefulness), Effort
Expectancy (formerly Perceived Ease-of-Use), and Social Influence (not in the
original TAM model). A direct antecedent of actual behavior is Facilitating Conditions.
The model suggests that its four constructs (i.e., performance expectancy, effort
expectancy, social influence, and facilitating conditions) contribute a crucial role in
making the adoption decision. The control variables moderate the relationships of the
four antecedents of intentions gender, age, experience, and voluntariness of use.
Agarwal and Prasad (1999)’ Personal Innovativeness in Information Technology
(PIIT) model is based on the premise that people having a high degree of personal
innovativeness. They tend to form positive attitudes about the perceived usefulness
and ease of use of the technology, and are willing to try out any new information
technology. The key constructs of PIIT (similar to PU and PEOU in TAM) are
Usefulness refers to the degree of ease associated with the use of the system similar
to Performance Expectancy ; Ease of use refers to the degree to which an individual
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believes that using the system helps him or her improve job performance similar to
Effort Expectancy; Compatibility is the degree to which an innovation is perceived as
consistent with existing values, past experiences, and needs of potential adopters
and Behavioural intention refers to the willingness of individuals to work hard and
exert effort in order to achieve the given behaviour. PIIT is posited to have a
moderating effect because it measures a user's perceptions about their comfort level
with information technology (Agarwal and Prasad, 1998).
Another model related to behavioural intention-based theory from technology factor
perspective is the Information System Success Model by Delone and McLean (1995).
The authors proposed an updated model of IS success to handle electronic
commerce (e-commerce) applications. They have extended their earlier model
(DeLone and McLean, 2003) and have added an additional Service Quality, System
Quality and Information Quality dimensions. They have suggested to use Intention to
Use and Usage Behavior as measures of IS success. The Model was also updated
by inclusion of Net Benefits derived from intention to use and user satisfaction
(DeLone and McLean, 2003).
Models Comparisons
TAM and DOI are considered to be similar in some constructs and supplement one
another. Some similarities can be drawn between Relative Advantage and Perceived
Usefulness; Complexity and Perceived Ease of Use (PEOU) to the extent that some
researchers identifies the TAM constructs as a subset of the Innovation Diffusion
Theory (Wu and Wang, 2005).
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Megat Shariffudin B Zulkifli, DrUniversiti Putra Malaysia
The weakness of TAM is although TAM includes user behavior intention and
information systems theory, the model ignores social factors and the model is often
criticized for focusing on the intention to use as opposed to actual usage. In addition,
the shortcomings of TAM are the low explanatory power of the model (40 per cent on
average) and the inconsistent relationship among constructs (Zhang and Sun, 2006).
While the weakness of DOI is that the model does not provide evidence on how
attitude evolves into accept/reject decisions (Chen et. al., 2002). DOI is also not
suitable in the context of the eBidding because the eBidding is a mandated system,
where the suppliers who wish to participate in the bidding for tenders or contracts
need to use the system (George, 2007). Since, DOI implies the voluntariness of use,
the theory is not applicable to this study. As such TAM and DOI are considered
unsuitable to be employed in the study.
IS Success Model limitation is that it cannot provide explanation as to why the same
application system can be adopted in different ways, with different effects in various
settings (Tsiknakis and Kouroubali, 2008). UTAUT limitation is that did not take into
account the interactions between individuals and organizations simultaneous with
interaction between organization and its environment (Ghobakhloo et. al., 2010).
IS Success Model and the UTAUT model are both by itself is a comprehensive
model. Each is internally sound and based on well-tested behaviour intention models.
Each is internally sound and based directly on well-tested attitude/ behaviour models.
Both models define almost similar dependant constructs, where in IS Success Model
it is called Intention to Use or Use, whereas in the UTAUT model it is called
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Megat Shariffudin B Zulkifli, DrUniversiti Putra Malaysia
Behavioural Intention. However, for this dependant construct, each model defines
different independent constructs. In the UTAUT model, Behavioural Intentions are
determined by Performance Expectancy, Effort Expectancy and Social Influence
while in the Updated IS Success Model, Intention to Use or Usage is determined by
Information Quality, System Quality and Service Quality.
The UTAUT model incorporates moderating variables, gender, age, experience and
voluntariness of use, which may or may not have an influence on user acceptance of
technology. PIIT is posited to have a moderating effect on user's behavioral intention
while the IS Success Model, DOI and TAM has none of the moderating variables.
The models comparisons are as Table 5
Table 5. Models’ comparisons
No Authors IndependentVariables
Moderating Variables
Dependent Variables
1. Technology Acceptance Model(Davis et. al., , 1989)
- Perceived usefulness- Perceived ease of use
Behavioral intention
2. Diffusion of Innovation (DOI)(Rogers, 2003)
- Trialability- Observability- Relative advantage- Complexity- Compatibility
Rate of adoption
3. Personal innovativeness in information technology (PIIT)(Agarwal and Prasad, 1999)
- Perceived usefulness- Perceived ease of use- Compatibility
- Personal innovativeness in information technology (PIIT)
Behavioral intention
4. Unified Theory of Acceptance and Use of Technology (UTAUT)Venkatesh et. al., (2003)
-Performance expectancy- Effort expectancy- Social influence- Facilitating conditions
- Gender- Age- Experience- Voluntariness of use
Usage behavior
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Megat Shariffudin B Zulkifli, DrUniversiti Putra Malaysia
No Authors IndependentVariables
Moderating Variables
Dependent Variables
5. Updated IS success Model(DeLone and McLean, 2003)
- System quality- Information quality- Service quality
Net benefits
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