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(Bandura, 1986). This paper provides a critical review on the technology acceptance literature.
consistently failed to predict as they purported to in many empirical studies.
this behavior (Fishbein and Ajzen, 1975).
We can clearly identify the core positions of the two belief constructs: perceived usefulness and perceived ease of use. 1994. such as personality traits and demographic characteristics (Gefen and Straub. and sum the entire set of resulting weights to determine their behavioral intention. 1997. He suggests that perceived usefulness and perceived ease of use are the two most important individual beliefs about using an information technology. Perceived Usefulness Perceived Ease of Use Attitude toward Usage Intention to Use Actual Usage Figure 2. Davis finds that perceived usefulness is the strongest predictor of an individual’s intention to use an information technology. For instance. perceived behavioral control. such as trialability. visibility.. 3 .. Taylor and Todd. The definition of perceived usefulness is based on the expectancy-value model underlying the Theory of Reasoned Action. 2001). Researchers have also extended TAM (Wixom and Todd. 2000. Karahanna et al. perceived usefulness and perceived ease of use. Therefore.320). Some researchers introduce external variables or moderating factors to the two major belief constructs (perceived usefulness and perceived ease of use). 1999. Perceived usefulness is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (p.320). then lead to individual behavior intention and actual behavior. researchers have widely used the Technology Acceptance Model to study the adoption of various technologies and TAM has arguably become the most influential theory in the IS field. we can say that the structure and main assumptions of these models remain the same as those of the Technology Acceptance Model (TAM). 1989) In the Information Systems field. Figure 3 shows the various TAM extensions. Mathieson et al. The Technology Acceptance Model (TAM) (Davis. or result demonstrability (Agarwal and Prasad. 1997. 2001.multiply the normative belief strength by the motivation to comply with that referent. some researchers introduce many other factors to the model. Venkatesh and Morris. such as subjective norm.. Perceived ease of use is defined as “the degree to which a person believes that using a particular system would be free of effort” (p. Venkatesh. Other researchers introduce additional belief factors from the diffusion of innovation literature. Technology Acceptance Model and Its Extensions Based on the Theory of Reasoned Action. 2000). 1995). 2005). These two behavioral beliefs. and self-efficacy (Hartwick and Barki. Plouffe et al. Davis (1989) develops the Technology Acceptance Model to find out what factors cause people to accept or reject an information technology (Figure 2).
1959. the social influence of compliance effect will occur (French and Raven. social influences can influence intention indirectly through the mechanism of internalization and identification. 1958. Internalization refers to the process when an individual incorporates the important referent’s belief into his or her own belief structure (Kelman. demographics. 1980). If an individual perceives that an important social actor has the ability to punish nonbehavior or reward behavior. and perceived ease of use) to predict the adoption of an information technology (Figure 4). result demonstrability. External Variables (personality traits.g. 2005) Next. Venkatesh and Davis (2000) use the construct of subjective norm to capture social influences. Kelman.g.) Perceived Usefulness Attitude toward Usage Perceived Ease of Use Intention to Use Actual Usage 1. a widely known extended TA model called TAM2 will be discussed. 1964. TAM2 suggests that in mandatory contexts. perceived behavioral control 2. 4 . Factors from related models (e. The Extensions to TAM (adapted from Wixom and Todd.302). Pfeffer. trial ability. system characteristics. output quality. Venkatesh and Davis (2000) develop TAM2 by adding social influences (subjective norm. Kelman. Their definition of subjective norm is consistent with that in TRA (Fishbein and Ajzen 1975. voluntariness. 1969. 1982). 1958. Warshaw. subjective norm. compatibility Figure 3. Warshaw. and image) and cognitive instrumental processes (job relevance. 1958.3. In voluntary contexts. Additional belief factors (e. Identification means that an individual can gain a membership in a social group or achieve a higher status within the group by performing a behavior (Blau. p. 1980). subjective norm has a direct effect on intention through the mechanism of compliance. Kiesler and Kiesler.
a potential user’s judgment of job relevance goes through a compatibility test (Venkatesh and Davis.. TAM2 posits that result demonstrability has a positive effect on perceived usefulness.e. TAM2 retains perceived ease of use from TAM as a direct determinant of perceived usefulness. TAM2 posits that output quality has a positive effect on perceived usefulness. Extrinsic motivation is defined as the perception that users want to perform an activity “because it is perceived to be instrumental in achieving valued outcomes that 5 .203). perceived usefulness)”(p. Job relevance is defined as “an individual’s perception regarding the degree to which the target system is applicable to his or her job” (p. result demonstrability. It is defined as the “tangibility of the results of using the innovation” (Moore and Benbasat. Based on the theories on the mental matching process. 2000).191). output quality. 2000) TAM2 theorizes that there are four cognitive instrumental determinants of perceived usefulness: job relevance.Experience Voluntariness Subjective Norm Image Job Relevance Perceived Usefulness Intention to Use Usage Behavior Perceived Ease of Use Output Quality Result Demonstrability Figure 4. Venkatesh and Davis (2000) suggest that judgments of output quality take the form of a profitability test. 1991. (1992) apply the motivational theory to study information technology adoption and use. The Motivation Model suggests that individuals’ behavior is based on extrinsic and intrinsic motivations. given a choice set containing multiple relevant systems. Result demonstrability is the third determinant of perceived usefulness. The Motivation Model Davis et al. “in which. one would be inclined to choose a system that delivers the highest output quality” (p. p. TAM2 posits that job relevance has a positive effect on perceived usefulness. The Extended Technology Acceptance Model (TAM2) (Venkatesh and Davis. Output quality is another determinant of perceived usefulness. Output quality refers to an individual’s perception about how well the system performs the tasks. TAM 2 theorizes that “people use a mental representation for assessing the match between important work goals and the consequences of performing the act of using a system as a basis for forming judgments about the use-performance contingency (i. and perceived ease of use.192).191).
149). 1991) Combined TAM and TPB Taylor and Todd (1995) develop a hybrid model by combining the predictors of TPB with the constructs of perceived usefulness and ease of use from TAM (Figure 6). The Theory of Planned Behavior (Ajzen. 188). p. p. perceived behavioral control is defined as “perceptions of internal and external constraints on behavior” (Taylor and Todd 1995. This model is also called the Decomposed Theory of Planned behavior because the belief structure is decomposed in the model. Belief and Evaluations Normative beliefs and motivation to comply Control Beliefs And Perceived Facilitation Attitude toward Behavior Subjective Norm Behavior Intention Actual Behavior Perceived Behavior Control Figure 5. p. perceived ease of use. 1112). 1997.. Mathieson 1991. Individuals assess perceived behavior control using a method similar to the expectancy-value model. 1997). Ajzen (1991) develops the Theory of Planned Behavior (TPB) to extend TRA to consider the mandatory situation (figure 5). The normative belief structure includes peer 6 .. 1992. such as improved job performance. pay.. Perceived behavioral control is defined as “the perceived ease or difficulty of performing the behavior” (Ajzen 1991. Intrinsic motivation relates to perceptions of pleasure and satisfaction from performing the behavior (Vallerand. Venkatesh. Perceived usefulness. Computer playfulness and enjoyment are examples of intrinsic motivation (Davis et al. TPB has also been widely applied to understand the individual acceptance and use of different technologies (Harrison et al. p. The attitude is decomposed to include perceived usefulness. and subjective norm are examples of extrinsic motivation. perceived ease of use and compatibility. In the context of IS research. He adds a new construct of perceived behavioral control in TPB. The Theory of Planned Behavior (TPB) is similar to TRA in that TPB also assumes that individuals are rational decision makers. individuals multiply the belief’s strength by the perceived power of the control factor. Taylor and Todd 1995b).. 1112). 2000).are distinct from the activity itself. 1992. not in a mandatory context. or promotions” (Davis et al. 1992. The Theory of Planned Behavior The Theory of Reasoned Action (TRA) is used to predict an individual’s behavior only in a real voluntary situation. Users want to perform an activity “for no apparent reinforcement other than the process of performing the activity per se” (Davis et al. For each in a set of control beliefs.
(1991) refine Triandis’ model to predict PC utilization behavior (Figure 7). disgust. 127). The control belief structure includes self-efficacy. • Long-term consequences: “Outcomes that have a pay-off in the future” (p. • Social Factors: “individual’s internalization of the reference group's subjective culture. 129). Beliefs belong to the cognitive component of attitudes.influence and superior influence. Triandis (1980) makes a distinction between cognitive and affective components of attitudes. what they have usually done (habits). displeasure. 128). “Behavior is determined by what people would like to do (attitudes).126). • Complexity: “the degree to which an innovation is perceived as relatively difficult to understand and use” (p. 1995) The Model of PC Utilization Triandis’ (1977) theory of attitudes and behavior is a competing perspective to TRA and TPB.. 126). • Affect Towards Use: “feelings of joy. in specific social situations” (p. 129). Combined TAM and TPB (Taylor and Todd. or hate associated by an individual with a particular act” (p. p. 1991. 7 . Thompson et al. 129). The major constructs in the model and their definitions include: • Job-fit: “the extent to which an individual believes that using [a technology] can enhance the performance of his or her job” (p. resource facilitating conditions and technology facilitating conditions. and by the expected consequences of their behavior” (Thompson et al. or pleasure. • Facilitating Conditions: “provision of support for users of PCs may be one type of facilitating condition that can influence system utilization” (p. what they think they should do (social norms). and specific interpersonal agreements that the individual has made with others. or depression. elation. Perceived Usefulness Ease of Use Attitude toward Behavior Subjective Norm Behavior Intention Actual Behavior Compatibility Peer Influence Superior’s Influence Self-Efficacy Perceived Behavior Control Resource Facilitating Conditions Technology Facilitating Conditions Figure 6.
In the Information Systems field. 1991) The Innovation Diffusion Theory The Innovation Diffusion Theory (Rogers. 8 .. • Image: “The degree to which use of an innovation is perceived to enhance one's image or status in one's social system” (p. or of free will” (p. 1999. complexity.Job-fit Social Factors Complexity Long-term Consequences PC Utilization Affect Towards Use Facilitating Conditions Figure 7. 195). • Visibility: The degree to which one can see others using the system in the organization. 203). The set includes: • Relative Advantage: “the degree to which an innovation is perceived as being better than its precursor” (p. • Compatibility: “the degree to which an innovation is perceived as being consistent with the existing values. Plouffe et al. • Results Demonstrability: “the tangibility of the results of using the innovation. needs.195). Karahanna et al.. including their observability and communicability” (p. 1998. Rogers identifies five attributes of an innovation that influence the adoption and acceptance behavior: relative advantage. and past experiences of potential adopters” (p. and observability. 1997. 195). • Ease of use: “the degree to which an innovation is perceived as being difficult to use” (p. 2001). trialability. 195). Moore and Benbasat (1991) expand this attributes set to study information technology acceptance. 1995) has been used to study a variety of innovations. Innovation diffusion research regards individuals’ perceptions about these characteristics of an information technology as important factors in influencing an individual’s acceptance behavior (Agarwal and Prasad. compatibility.. • Voluntariness of Use: “the degree to which use of the innovation is perceived as being voluntary. The Model of PC Utilization (Thompson et al. 195).
including personal and performance-related ones. extrinsic motivation (from the Motivational Model).. The Unified Theory of Acceptance and Use of Technology (UTAUT) Based on the most significant constructs from the above eight theories and models. 450). personal factors (in the form of cognitive factors.. In contrast. and social influence. Affect and anxiety are the two affective factors. The Social Cognitive Theory (SCT) gives prominence to the concept of self-efficacy (Compeau et al. (2003) also find that the influence of facilitating conditions on usage is moderated by age and experience of the individual. 447). • Social influence: “the degree to which an individual perceives that important others believe that he or she should use the new system” (p. 9 . These three constructs are defined as follows: • Performance expectancy is defined as “the degree to which the user expects that using the system will help him or her attain gains in job performance” (p. Personal-related outcome expectations are concerned with individuals’ esteem and sense of accomplishment. Venkatesh et al. 453). This new construct has five root constructs: perceived usefulness (from TAM/TAM2. All of them are comprised of the most influential constructs of the eight models or theories discussed above. relative advantage (from the Innovation Diffusion Theory). The three constructs are performance expectancy. and the Innovation Diffusion Theory assume that there are only unidirectional causal relationships among the major variables in their models. 1999). and behaviors are determined reciprocally. 1995). 1986) suggests that environmental factors. Self-efficacy is defined as the judgment of one’s ability to use a technology to accomplish a particular job or task (Compeau and Higgins.g. (2003) formulate a new model called the Unified Theory of Acceptance and Use of Technology (UTAUT). Performancerelated outcome expectations are concerned with job-related outcomes. and the successful interactions with the technology also influence the cognitive perceptions (Compeau et al. effort expectancy. UTAUT shares other TA models’ major assumptions. An individual’s cognitive competences influence the behavior of using a technology. Outcome expectations. They define facilitating conditions as “the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” (p. affective factors etc. using a computer). Venkatesh et al.). • Effort expectancy: “the degree of ease associated with the use of the system” (p. As a survey instrument incorporating the most influential constructs from the eight technology acceptance theories and models. Affect refers to an individual's liking for a particular behavior (e.The Social Cognitive Theory The Theory of Planned Behavior (TPB). SCT posits that selfefficacy influences both personal and performance-related outcome expectations (Compeau and Higgins 1995). Anxiety refers to an individual’s anxious or emotional reaction in performing a behavior (e. 1999). and outcome expectations (from the Social Cognitive Theory).g.. the Technology Acceptance Model (TAM). the Social Cognitive Theory (Bandura. 451). Combined TAM and TPB). are major cognitive factors in influencing users’ behavior (Compeau and Higgins 1995). UTAUT suggests that three constructs are the main determinants of intention to use an information technology. computer use)..
The Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. (5) PEOU-BI. we will discuss the evidence that is not consistent with what the extant TA models and theories predict. (2003) perform a qualitative meta-analysis on TAM. (2003): (1) PEOU-PU. Attitude (AT). (8) BI-U. They find that TAM fails to predict in many studies (shown in Table1). Behavior Intention (BI) and Actual Use (U). Table 1 Where TAM Failed to Predict (Adapted from Legris et al.. (4) PU-BI. TAM has the following components: Perceived Usefulness (PU). Perceived Ease of Use (PEOU). Based on these five components. (3) PEOU-AT. (6) AT-BI. (7) AT-U. 10 relations are examined by Legris et al. (2) PU-AT. In its original version. 2003) Evidence Inconsistent with Extant TA models Next.Performance Expectancy Behavioral Intention Use Behavior Effort Expectancy Social Influence Facilitating Conditions Age Gender Experience Voluntariness of Use Figure 8. and (10) PU-U. Legris et al. 2000) Author Davis et al.(1989) PEO UPU PU-AT PEOU PU PE -AT OUBI BI No Post Training 10 AT AT BI-BI -U U PE OU -U PU -U . (9) PEOU-U..
social influences. perceived ease of use.Author PEO UPU PU-AT PEOU PU PE -AT OUBI BI AT AT BI-BI -U U PE OU -U PU -U Subramanian(19 94) Voice mail Customer dial-up No No Taylor and Todd(1995a) Keil et al. The following subsection discusses in detail the inconsistent evidence with regard to the major constructs in the extant TA models and theories. Jackson et al. However. attitude. 221). We can see from the table 1 that there are inconsistent or even contradictory empirical results for all major relations in TAM. (1997) Bajaj and Nidumolu (1998) Gefen and Keil(1998) Lucas and Spitler (1999) Hu et al. Venkatesh and Davis. Perceived Usefulness The TA models posit that perceived usefulness is the strongest predictor of an individual’s intention to use an information technology (Davis.(1999a) Szajna (1996) No No No Reverse No No No No No No No No No No No No No No No No Pre-implementation Pos-implementation No indicates that the relation is found to be non-significant and reverse indicates that the relation is found to be significant but negative. Bajaj and Nidumoulu (1998) even find evidence to the contrary. 2000. These major constructs include perceived usefulness..“[u]sefulness will negatively affect the attitude towards using the IS” (emphasized by the original authors) (p. 2003). (1997) found no relation between perceived usefulness and attitude. (1995) No No Taylor and Todd (1995b) With experience Without experience Jackson et al. and facilitating factors. 1989. 11 . Venkatesh et al.
the regression results suggest that ease of use may be an antecedent of usefulness.g. For instance. Jackson et al. Hu et al. Chau (1996) reports “…there is no significant. 1991). Lucas and Spitler (1999). (1995a). behavior attitude. 1989. 2001. Gefen and Keil (1998). and actual use (Davis. Keil et al. Davis et al. Mathieson. direct relationship between perceived ease of use of the technology and intention to use. many other researchers find no empirical evidence to support the relation between perceived ease of use and perceived usefulness (Chau and Hu. 450). 712). Subramanian. Subramanian (1994) find no relation between perceived ease of use and behavior intention. Venkatesh et al.Jackson et al. Mathieson. (1999).. Hu et al.89).303).g. Effort expectancy is defined as “the degree of ease associated with the use of the system” (p. Lucas and Spitler (1999). Szajna (1996). 1994. rather than a parallel. which has been observed to be a significant predictor of acceptance in a wider variety of research (Davis.. Jackson et al. Chau (1996).572). (2003) use the construct of effort expectancy to capture the concepts of perceived ease of use (TAM/TAM2). Lu et al. In other words. 334). complexity. 1998. (1997) report: “[T]he finding of a nonsignificant relationship between perceived usefulness and behavioral intention is surprising” (p. and Szajna (1996) find no empirical evidence to support the relation between perceived ease of use and actual use. Moore and Benbasat. 379). (1999) find no evidence to support the relation between PEOU and attitude either. direct determinant of usage” (p. 1997. For instance. and Bajaj and Nidumoulu (1998) find no empirical support for the relation between perceived usefulness and actual use. This finding concurs with that of the original TAM but contradicts the results obtained in many previous studies (e. However. On the relation between perceived ease of use and perceived usefulness.. 1999. Chau and Hu (2001) report that “contrary to the assertion of TAM and the findings reported by some prior research (e. and ease of use. whether or not the technology is easy to use influences the user’s intention to use only indirectly via the perception of near-term usefulness. (1997) and Lucas and Spitler (1999) find no empirical evidence to support the relation between perceived usefulness and behavior intention. perceived ease of use was not found to have any significant effects on perceived usefulness or attitude” (p. 197). 1989. In UTAUT. Keil et al. 1994). intention. where ease of use was a significant determinant of intention to use a computer technology” (p. Agarwal and Prasad (1997). Agarwal and Prasad (1997) report that “ease of use. 12 . Davis (1989) suggests that: “from a causal perspective. Venkatesh. Hu et al. 1991. 1991). (1995a) conclude that “no amount of ease of use (EOU) will compensate for low usefulness” (p. Lucas and Spitler (1999). 1999). Moore and Benbasat. Lucas and Spitler (1999) report: “The variables that researchers most frequently study in this model are not significant in our research” (p.. Bajaj and Nidumolu. did not appear as a significant determinant” (p. Perceived Ease of Use The TA models suggest that perceived ease of use has a significant influence on perceived usefulness.. 1991). (1989) find no evidence to support the relation between perceived ease of use and attitude in the post training period.
and SCT. and affect (SCT). knowing these contingencies cannot help much in explaining the equivocal results reported in the literature.. many other studies report evidence to prove the construct of subjective norms to be non-significant (Chau and Hu. 1999. Venkatesh et al.. in other models. Thompson et al. b). In a meta-analysis of the technology acceptance model. 2003). Lewis et al. Chau and Hu (2002) find that attitude “appeared to be the second most important determinant of a physician’s intention for accepting telemedicine technology” (p..Attitude toward Using Technology Attitude toward using technology refers to an individual’s overall affective reaction to using a system (Venkatesh et al. However. Mathieson.. 469). TPB/DTPB. For instance. and MM. Dishaw and Strong. 1999. attitude has no significant effect on users’ behavioral intention. the attitude construct is among the strongest predictors of behavior intention (Davis et al. 1989. 1995). 1975. For instance. 1994. researchers find that social influences have inconsistent roles in the empirical studies. For instance. affect toward use (MPCU). (2003) find no empirical support for the expected relation either. (2005).. 1995). However. TAM2 suggests that in mandatory contexts. The study by Lu et al. Social Influences Venkatesh and Davis (2000) use the construct of subjective norm to capture social influences in their model of TAM2. Lucas and Spitler (1999) report that “organizational variables such as social norms and the nature of the job are more important in predicting the use of technology than are users’ perceptions of the technology” (p. This construct is closely related with four constructs in the existing models: attitude toward behavior (TRA. (1997) report that “the lack of support for P5. C-TAM-TPB). Venkatesh et al. the effect of attitude on behavioral intention. (1991). 1991). 13 . In some models. intrinsic motivation (MM).379). and even then during early stages of experience/adoption” (p. TPB/DTPB. 1991. However. and Jackson et al. Schepers & Wetzels (2007) also find that social norms are important in influencing users’ attitude towards use. Fishbein and Ajzen. Lu et al. (2003) suggest that social influences are “more likely to be salient to older workers. Taylor and Todd. Venkatesh et al. MPCU. We need to understand why social influences only work for some people but not for others. Some researchers have reported empirical evidence to suggest that this construct is significant (Hartwick and Barki. (2005) finds that social influences from social networks and the sense of image play important roles in shaping individual’s perceptions of usefulness and ease of use. Venkatesh et al. such as TRA. Lucas and Spitler (1999). Dishaw and Strong (1999) report: “subjective norms are not important in understanding individual choices to use IT” (p. 2001. 307). Facilitating Conditions Many studies have suggested that facilitating conditions affect use. Taylor and Todd (1995a. However. Karahanna and Straub. Davis et al. 1989. (2003) suggest that since the effect of attitude has been captured by the performance and effort expectancies and therefore. either directly or through behavioral intention (Thompson et al. 2003). such as C-TAM-TPB. Taylor and Todd. 15). 304). social influences have a direct effect on intention to use. Jackson et al. is somewhat puzzling…” (p. (1997) find no empirical support for the relationship between attitude and behavior intention. the construct of attitude is not significant. particularly women.
. These are the inconsistencies these theories and models cannot easily explain. (Table 2 presents additional evidence to show that the extant technology acceptance models have consistently failed to predict as they purported to). More importantly. (2005) find no evidence to support the relation between facilitating conditions (such as training) and technology usage. facilitating conditions such as the availability of training and support for the use of information technology had no impact on perceptions of ease of use or usefulness of E-mail”(p.(2003) define facilitating conditions as “the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” (p. Mawhinney and Lederer. Table 2. many other empirical studies find the facilitating conditions to be nonsignificant (Gallivan et al. More research needed to be conducted to find out why these inconsistencies exist. 1999. We made explicit these assumptions underlying these TA models and theories. (1991) also find a non-significant relation between usage and the facilitating conditions such as training and technical support. However. (2003) suggest that the influence of facilitating conditions on usage is moderated by age and experience of the individual. Venkatesh et al. (1997) and Thompson et al. 1990). (1999) Venkatesh et al. Additional Evidence Showing Where the TA models Fail to Predict Author Chau and Hu (2001) Hu (2001) Agarwal and Prasad (1997) Subramanian(1994) Chau (1996) Hu et al. 1999). For instance. (2005) Lucas and Spitler (1999) Taylor and Todd (1995) Schepers & Wetzels PEOUPU No No PEOU PEOU -U BI AT BI Social Facilitating Influences Conditions No No No No No Yes Yes Yes Yes Yes Yes 14 . 2005. we identified a significant body of literature that reports inconsistent results with these models. 453). Conclusion This paper provided a critical review of the technology acceptance literature. However knowing these contingencies cannot help much in providing any meaningful guidance to implementation practices. Karahanna and Straub (1999) report that “surprisingly. Karahanna and Straub. (2003) Hartwick and Barki (1994) Karahanna and Straub (1999) Lu et al. Igbaria et al. Gallivan et al.
(1991) Taylor and Todd (1995) AT BI Social Facilitating Influences Conditions No No No No No No No No No Yes No indicates that the relation was found to be non-significant. Yes indicates that the relation was found to be significant.(1989) Dishaw and Strong (1999) Mathieson (1991) Gallivan et al.Author PEOUPU PEOU PEOU -U BI (2007) Chau and Hu (2001) Davis et al. 15 . (1997) Mawhinney and Lederer (1990) Karahanna and Straub (1999) Thompson et al. (2005) Igbaria et al.
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