Overview of Established Theories on User Acceptance (UTAUT, TAM, PIIT and ISSM)

Megat Shariffudin B Zulkifli, DrUniversiti Putra Malaysia

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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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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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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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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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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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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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Documents

Overview of Established Theories on User Acceptance (UTAUT, TAM, PIIT and ISSM)