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The Technology Acceptance Model (TAM)

Chapter 3: Theoretical Framework

3.1 The Technology Acceptance Model (TAM)

TAM was derived from the principles of the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975). TRA describes the relationship between beliefs, attitudes, norms, intentions and behaviour. Attitude is defined as representing “a person’s general favourableness or unfavourableness toward some stimulus object” (Fishbein & Ajzen, 1975, p. 216(. ‘Subjective norm’ was defined as “the person’s perception that most people who are important to him think that he should or should not perform the behaviour in question” )Fishbein & Ajzen, 1975, p. 302(. Therefore, based on TRA, to predict students’

behaviour it is necessary to evaluate their attitudes and norms toward the behaviour.

Based on this theory, in the context of technology acceptance, the user’s behaviour toward a technology is based on their intention to either use or reject the technology and this intention is shaped by the user’s attitudes and subjective norms (Dillon & Morris, 1996). Attitude toward behaviour is a function of two factors: beliefs about the

consequences of the behaviour and the affective evaluation of those consequences (Dillon

& Morris, 1996). Subjective norms are influenced by the individual's normative beliefs and

motivation to comply (Dillon & Morris, 1996(. Normative beliefs are related to people’s beliefs about whether their main referent individuals or groups support or reject a given behaviour, while motivation to comply is an evaluation of how important it is to have the approval of important others (Ajzen, 1991). Examples of important referents included spouses, children, other family members, managers and doctors (Peters & Templin, 2010).

For example, to measure a one’s normative beliefs regarding use of technology, an

individual can be asued to respond to a statement liue “My [referent] think(s) that I should adopt this particular technology.” To measure motivation to comply, the individual can be asued to respond to a statement liue “When it comes to use of technology, I want to do what my [referent] wants me to do.”

TRA is a general model while TAM is an adapted and modified form of TRA used to address why users accept or reject information technology. The goal of TAM is to explain and predict users’ behaviour across a variety of computing technologies )Davis, Bagozzi & Warshaw, 1989). Davis (1986) argued that unlike TRA, in TAM, the effect of users’ beliefs )e.g., regarding ease of use and usefulness( on user behaviour should be measured separately in on order to assess their influence on a technology’s acceptance. In addition, Davis (1986) argued that perceived ease of use has a significant one-way effect on perceived usefulness in models that explain the acceptance of a technology.

The TAM (Davis, 1986) argues that perceived usefulness (PU) and perceived ease of use )PEOU( factors can predict a user’s attitude toward using a technology. Figure 3.1 shows the proposed relationships between TAM’s factors.

Figure 3.1. The Original Technology Acceptance Model (TAM) (Davis, 1986, p. 24) Perceived usefulness )PU( can be defined as “the degree to which a user believes that using the system will enhance his or her job performance” )Davis, 1989, p. 320(, while perceived ease of use )PEOU( can be defined as “the degree to which a person believes that using a particular system would be free of effort” )Davis, 1989, p. 320(. The TAM shows that a user’s attitude toward a technology is a major influence on whether a user will accept or reject a technology. TAM shows that perceived ease of use (PEOU) affects an

individual’s perceived usefulness )PU(. However, both factors are affected by the

technology’s characteristics and its design. The technology’s characteristics and its design represent a type of external stimulus. Choice of external variables differs from one study to another. The external variables intrude indirectly by influencing PEU and PU. There is no clear pattern with respect to the choice of the external variables considered in different studies (Legris, Ingham & Collerette, 2003). Examples of external variables include users' characteristics such as major organisational factors like system accessibility (Park et al., 2012). PEU and PU represent a cognitive response to the introduction of the new

technology. Based on their cognitive responses, potential users form their attitudes toward the use of new technologies. Potential users’ affective responses have a direct effect on their behavioural responses represented by their actual use of such technology.

The original TAM has been subjected to developments in order to enhance its ability to explain and predict technology use (e.g., Davis, 1993; Venkatesh & Davis, 1996).

Davis’s )1993( study found that there were significant relationships between variables in the original TAM that were previously assumed to be insignificant. Davis (1993) conducted a field study in which 112 users of an electronic mail system and text editor completed a questionnaire to rate the two systems. The results showed that perceived usefulness affected actual system use. In addition, system characteristics are one of the predictors of a user’s attitude toward using a technology. Figure 3.2 shows the relationships between the revised TAM’s factors.

Figure 3.2. New relationships among TAM’s factors(Davis, 1993, p. 481)

Another development of TAM was introduced by Venkatesh and Davis, (1996).

These researchers found that perceived usefulness and perceived ease of use have direct influences on the user’s behavioural intentions toward a technology. Figure 3.3 shows the new version of TAM.

Figure 3.3. The New Version of TAM (Venkatesh & Davis, 1996, p. 453)

Another major development of TAM was presented in Venkatesh and Davis’s (2000) study. The researchers proposed an extended model of TAM that was referred to as TAM2. The model was tested using longitudinal data collected on four different systems of technology at four organisations (N = 156), two involving voluntary usage and two

involving compulsory usage. The model was tested three times within three months of the implementation of the systems. The model was supported in the four organisations on the three occasions it was used. Figure 3.4 shows the proposed TAM2.

Figure 3.4. Proposed TAM2 – Extension of the Technology Acceptance Model (Venkatesh

& Davis, 2000, p. 188)

TAM2 showed that social influence processes that consist of interrelated variables include: subjective norms, voluntariness and image. They were found to significantly influence user acceptance in direct and indirect ways. The researchers defined voluntariness as "the extent to which potential adopters perceive the adoption decision to be

non-mandatory" )p. 188(. Image was defined as “the degree to which use of an innovation is perceived to enhance one's ... status in one's social system” )p. 189(. It has been found that additional cognitive instrumental processes including job relevance, output quality and result demonstrability, significantly influenced user acceptance in direct and indirect ways.

Job relevance was defined as an “individual's perception regarding the degree to which the target system is applicable to his or her job” )p.191(. Output quality was defined as “how well the system performs tasus” )p. 191(. ‘Result demonstrability’ was defined as

“tangibility of the results of using the innovation” )p.192(. The researchers provided more detailed explanations for their perceptions of a technology’s usefulness. The results showed that TAM2 was valid for both voluntary and compulsory usage.

A more comprehensive study that aimed to develop a unified view of technology acceptance based on TAM and another seven theories was presented in Venkatesh, Morris, Davis and Davis’s )2003( study. The new proposed model was empirically validated using data from four organisations with 215 participants. The model was tested three times within six months. Figure 3.5 shows the proposed Unified Theory of Acceptance and Use of Technology (UTAUT).

Figure 3.5. Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003, p. 447)

UTAUT proposed that individuals’ intentions to use an information system and succeeding usage behaviour can be determined using four main constructs: performance

expectancy, effort expectancy, social influence and facilitating conditions. However, only the facilitating conditions factor was a direct determinant of user behaviour. In addition, it was found that gender, age, experience and voluntariness of use moderate the impact of the four key constructs on usage intention and behaviour. Performance expectancy was defined as “the degree to which an individual believes that using the system will help him or her to attain gains in job performance” )p. 447(. ‘Effort expectancy’ was tauen to refer to the

“degree of ease associated with the use of the system” )p. 450(. ‘Social influence’ was tauen to refer to the “degree to which an individual perceives that important others believe he or she should use the new system” )p. 451(. ‘Facilitating conditions’ were defined as the

“degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” )p. 453(.

The developments of TAM were introduced to enable it to provide a better understanding of users’ behaviour toward particular pieces of technology, as well as the factors that influence such behaviour. More factors and new relationships among factors were examined and this led to better prediction and understanding of user acceptance of technology.

In some studies, behavioural intention was considered as a component of attitude (Ruffell, Mason & Allen, 1998). Some researchers believed that attitude should not be replaced by behavioural intention in TAM and UTAUT. Based on their empirical research, Yang and Yoo (2004) suggest that attitude deserves more attention in information system (IS) research due to its significant influence on the individual and organisational usage of IS. Better understanding of students’ perceptions and attitudes toward technology have great practical value in terms of assessing student demand for the use of the new

technology and for evaluating the new use of technology. Thomas, Singh and Gaffar (2013) found in their study of mobile learning adoption in developing countries’ higher education systems that including attitude in the model was a useful modification since it increased the descriptive power of the method.

There have been a number of revisions of TAM, including TAM2 and other adoption models such a TRA and the attempt to combine adoption theories in one:

UTAUT. However, the original TAM is still appealing due to its simplicity, its wider use in a number of domains and its well supported validity (Edmunds et al., 2012). Edmunds et al.

(2012) stated, “The core concepts of ease of use and functionality prove to be a successful basis for a number of revised models. This suggests these two factors [ease of use and usefulness] are particularly valid in an understanding of technology use.”

In the present study, the focus is on students’ attitudes toward SMS rather than on intention to use SMS technology. Potential users’ attitudes toward technology can predict their behavioural intention and actual future use (Ajzen, 1991(. Students’ attitudes toward technology greatly influence their adoption and acceptance of such technology (Yusuf &

Balogun, 2011). In addition, the study measured the relationship between the two proposed factors in the original TAM, students’ perceptions of usefulness and ease of use of SMS technology and students’ attitudes toward using SMS.

To explore students’ attitudes toward SMS technology, the current study employed the original TAM presented by Davis (1986) to guide the investigation of the attitudes toward SMS technology present in Kuwaiti students. The original TAM appears well suited to the present research objectives. Several research studies (e.g., Huang et al.,

2012;Edmunds et al., 2012) have found that ease of use and usefulness factors were useful for predicting and explaining potential users’ attitudes toward technology. Edmunds et al.

(2012( examined students’ attitudes toward technology in woru, social and study contexts.

Four hundred and twenty one university students completed a questionnaire related their attitudes toward technology. The results showed that usefulness and ease of use were key dimensions of students’ attitudes toward technology in all three contexts.

The current study represents the first step in research in Kuwait in relation to the use of SMS as an education tool. Students’ attitudes toward SMS use in learning are still

unknown. The findings from this study might identify how the TAM can best be extended.

Extended versions of TAM are common in studies that aim to examine technology

acceptance. TAM has been repeatedly used to examine the adoption of SMS technology in different contexts. The following section discusses the applications of TAM in research that examines the use and adoption of SMS for different purposes.