Technology acceptance model: Wikis


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From Wikipedia, the free encyclopedia

The Technology Acceptance Model (TAM) is an information systems theory that models how users come to accept and use a technology. The model suggests that when users are presented with a new technology, a number of factors influence their decision about how and when they will use it, notably:

  • Perceived usefulness (PU) - This was defined by Fred Davis as "the degree to which a person believes that using a particular system would enhance his or her job performance".
  • Perceived ease-of-use (PEOU) - Davis defined this as "the degree to which a person believes that using a particular system would be free from effort" (Davis, 1989).



TAM is one of the most influential extensions of Ajzen and Fishbein’s theory of reasoned action (TRA) in the literature. It was developed by Fred Davis and Richard Bagozzi (Bagozzi et al., 1992; Davis et al., 1989). TAM replaces many of TRA’s attitude measures with the two technology acceptance measures— ease of use, and usefulness. TRA and TAM, both of which have strong behavioural elements, assume that when someone forms an intention to act, that they will be free to act without limitation. In the real world there will be many constraints, such as limit the freedom to act (Bagozzi et al., 1992).

Bagozzi, Davis and Warshaw say:

Because new technologies such as personal computers are complex and an element of uncertainty exists in the minds of decision makers with respect to the successful adoption of them, people form attitudes and intentions toward trying to learn to use the new technology prior to initiating efforts directed at using. Attitudes towards usage and intentions to use may be ill-formed or lacking in conviction or else may occur only after preliminary strivings to learn to use the technology evolve. Thus, actual usage may not be a direct or immediate consequence of such attitudes and intentions. (Bagozzi et al., 1992)

Earlier research on the diffusion of innovations also suggested a prominent role for perceived ease of use. Tornatzky and Klein (1982) analysed the adoption, finding that compatibility, relative advantage, and complexity had the most significant relationships with adoption across a broad range of innovation types. Eason studied perceived usefulness in terms of a fit between systems, tasks and job profiles, using the terms "task fit" to describe the metric (quoted in Stewart, 1986)


Several researchers have replicated Davis’s original study (Davis, 1989) to provide empirical evidence on the relationships that exist between usefulness, ease of use and system use (Adams, Nelson & Todd, 1992; Davis et al., 1989; Hendrickson, Massey & Cronan, 1993; Segars & Grover, 1993; Subramanian, 1994; Szajna, 1994). Much attention has focused on testing the robustness and validity of the questionnaire instrument used by Davis. Adams et al. (1992) replicated the work of Davis (1989) to demonstrate the validity and reliability of his instrument and his measurement scales. They also extended it to different settings and, using two different samples, they demonstrated the internal consistency and replication reliability of the two scales. Hendrickson et al. (1993) found high reliability and good test-retest reliability. Szajna (1994) found that the instrument had predictive validity for intent to use, self-reported usage and attitude toward use. The sum of this research has confirmed the validity of the Davis instrument, and to support its use with different populations of users and different software choices.

Segars and Grover (1993) re-examined Adams et al.’s (1992) replication of the Davis work. They were critical of the measurement model used, and postulated a different model based on three constructs: usefulness, effectiveness, and ease-of-use. These findings do not yet seem to have been replicated.

Mark Keil and his colleagues have developed (or, perhaps rendered more popularisable) Davis’s model into what they call the Usefulness/EOU Grid, which is a 2×2 grid where each quadrant represents a different combination of the two attributes. In the context of software use, this provides a mechanism for discussing the current mix of usefulness and EOU for particular software packages, and for plotting a different course if a different mix is desired, such as the introduction of even more powerful software (Keil, Beranek & Konsynski, 1995).

Criticisms of TAM as a "theory" include its lack of falsifiability, questionable heuristic value, limited explanatory and predictive power, triviality, and lack of any practical value. (Chuttur, 2009)

Venkatesh and Davis extended the original TAM model to explain perceived usefulness and usage intentions in terms of social influence and cognitive instrumental processes. The extended model, referred to as TAM2, was tested in both voluntary and mandatory settings. The results strongly supported TAM2 (Venkatesh and Davis, 2000).

In an attempt to integrate the main competing user acceptance models, Venkatesh et al. formulated the Unified Theory of Acceptance and Use of Technology (UTAUT). This model was found to outperform each of the individual models (Adjusted R square of 69 percent) (Venkatesh et al., 2003).

For a recent analysis and critique of TAM see Bagozzi (2007).

Independent of TAM, Scherer developed the Matching Person & Technology Model in 1986 as part of her National Science Foundation-funded dissertation research. The MPT Model is fully described in her 1993 text, "Living in the State of Stuck," now in its 4th edition. The MPT Model has accompanying assessment measures used in technology selection and decision-making, as well as outcomes research on differences among technology users, non-users, avoiders, and partical/reluctant users.

See also


Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16, 227-247.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.

Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244-254.

Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human Relations, 45(7), 660-686.

Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 15, 215-227.

Bass, F. M. (1986). The adoption of a marketing model: Comments and observations. In V. Mahajan & Y. Wind (Eds.), Innovation Diffusion Models of New Product Acceptance. Cambridge, Mass.: Ballinger.

Budd, R. J. (1987). Response bias and the theory of reasoned action. Social Cognition, 5, 95-107.

Chuttur M.Y. (2009). "Overview of the Technology Acceptance Model: Origins, Developments and Future Directions," Indiana University, USA . Sprouts: Working Papers on Information Systems, 9(37). [1]

Czaja, S. J., Hammond, K., Blascovich, J. J., & Swede, H. (1986). Learning to use a word processing system as a function of training strategy. Behaviour and Information Technology, 5, 203-216.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003.

Hendrickson, A. R., Massey, P. D., & Cronan, T. P. (1993). On the test-retest reliability of perceived usefulness and perceived ease of use scales. MIS Quarterly, 17, 227-230.

Keil, M., Beranek, P. M., & Konsynski, B. R. (1995). Usefulness and ease of use: field study evidence regarding task considerations. Decision Support Systems, 13(1), 75-91.

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.

Scherer, M. J. (2005). Living in the State of Stuck, Fourth Edition. Cambridge, MA: Brookline Books.

Scherer, M.J. (2004). Connecting to Learn: Educational and Assistive Technology for People with Disabilities. Washington, DC: American Psychological Association (APA) Books.

Scherer, M.J. (Ed.). (2002). Assistive Technology: Matching Device and Consumer for Successful Rehabilitation. Washington, DC: APA Books.

Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17, 517-525.

Stewart, T. (1986). Task fit, ease-of-use and computer facilities. In N. Bjørn-Andersen, K. Eason, & D. Robey (Eds.), Managing computer impact: An international study of management and organizations (pp. 63–76). Norwood, NJ: Ablex.

Subramanian, G. H. (1994). A replication of perceived usefulness and perceived ease of use measurement. Decision Sciences, 25(5/6), 863-873.

Szajna, B. (1994). Software evaluation and choice: predictive evaluation of the Technology Acceptance Instrument. MIS Quarterly, 18(3), 319-324.

Tornatzky, L. G., & Klein, R. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29, 28-45.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, (46:2), 186-204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, (27:3), 425-478.

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273-315.

Wildemuth, B. M. (1992). An empirically grounded model of the adoption of intellectual technologies. Journal of the American Society for Information Science, 43(3), 210-224.


Study guide

Up to date as of January 14, 2010

From Wikiversity


Technology Acceptance Models

1. Literature Review

The term ‘acceptance’ is used from authors with different background and approaches. In fact, in the literature, acceptance hasn’t a unique definition. TAM (Davis, 1989) describes acceptance as ‘users decision about how and when the will use technology’. Martinez (Martinez-Torres et al., 2003) notice that initial use (acceptance) is the first critical step toward e-learning, while sustainable success depends on its continued use (continuance). There is large variety of studies focus on ICT acceptance (Ngai, Poon & Chan, 2005; Abdul-Gader, 1996; Adams, Nelson &Todd, 1992; Igbaria, Guimaraes & Davis, 1995). As mentioned before, a plethora of models have been developed to explain the technology acceptance in general and Information and Communication Technology (ICT) in particular. Nowadays in this area, the E-learning and the WWW as a basis for learning activities has expanded dramatically. In this study we present some of the most common models that used in this topic.

1.1 TRA

The Theory of Reasoned Action (TRA) proposed by Fishbein and Ajzen (1975) to explain and predict the people’s behaviour in a specific situation. TRA is a well-known model in the social psychology domain. According to TRA a person’s actual behaviour is driven by the intension to perform the behaviour. Individual’s attitude toward the behaviour and subjective norms are the ‘loading factors’ toward behavioural intention. Attitude is a person’s positive or negative feeling, and tendency towards an idea, behaviour. Subjective norm is defined as an individual's perception of whether people important to the individual think the behaviour should be performed. The Figure1 and the associate Table1 below give us a more wide view.

Figure1. Theory of Reasoned Action TRA (Fishbein & Ajzen, 1975).

Table1. The structure of TRA.

Attitude Toward

Behavior Behavioral Beliefs → “an individual’s feelings about performing the target behavior” (Fishbein and Ajzen (1975, p. 216) Behavioral Intention Actual Behavior Subjective Norm Normative Beliefs → “the person’s perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein and Ajzen (1975, p. 302

1.2 TPB

The Ajzen’s Theory of Planned Behavior (TPB) is another well-known model. TPB is a well known theory (grounded on sociology) that has been used to explain social behavior and information technology use (Ajzen, 1985, 1991; Conner & Armitage, 1998; Dillon & Morris, 1996; Sutton, 1998; Kwon & Onwuegbuzie, 2005). More specifically, according to Ajzen (Ajzen, 1985, 1991), intention is an immediate predictor of behavior. This intention is loaded by Subjective Norm –SN- (i.e. perceived social pressure), PBC (the beliefs about the ability to control the behavior) and one’s attitude towards a behavior. Further more, a behavioral belief (a specific behavior lead to a specific outcome), weighted by the evaluated desirability of this outcome forms an attitude (Kwon & Onwuegbuzie, 2005). Ajzen (Ajzen 1991, p. 188), defines PBC as “the perceived easy or difficulty of performing the behavior”. TPB views the control that people have over their behavior as lying on a continuum from behaviors that are easily performed to those requiring considerable effort, resources, etc. The Figure2 and the associate Table2 below give us a more global view.

Figure2. The Theory of Planned Behaviour –TPB- (Ajzen, 1985, 1991)

Table2. Structure of TPB.

Behavioral Beliefs (BE) The same as TRA Attitude → Intension Behavior Normative Beliefs (NM) The same as TRA Subjective Norm (SN) → Control Beliefs (CP) “the perceived ease or difficulty of performing the behavior” (Ajzen 1991, p. 188) Perceived Behavioral Control (PBC) →

1.3 TTF

Task technology fit model (TTF). Dishaw and Strong (Dishaw & Strong, 1999) claims that the only reason for IT use is if the available to the end user functions fit the user needs and activities. The basic version of TTF that has been tested (Goodhue & Thompson, 1995). Actually, the TTF match the demands of a task and the capabilities of the chosen technology. The very early version does not include the ‘Actual Tool Use’ as an outcome variable, because they didn’t focus on behavior. As Goodhue (1995) notice, individual abilities, such as computer literacy and experience become common additions in later versions of TTF. Dishaw et al (2002) provide us with another modification of the TTF including the factor of computer self-efficacy.

Figure3. A basic task-technology fit (TTF) model, adapted from Dishaw & Strong, (1999, p. 11)

1.4 IDT

Innovation diffusion theory (IDT) (Rogers, 1983), is another model also grounded in social psychology. Since 1940’s the social scientists coin the terms diffusion and diffusion theory (Rogers, 1983). This theory provides a framework with which we can make predictions for the time period that is necessary for a technology to be accepted. Constructs are the characteristics of the new technology, the communication networks and the characteristics of the adopters. We can see innovation diffusion as a set of four basic elements: the innovation, the time, the communication process and the social system. Here, the concept of a new idea is passed from one member of a social system to another. Moore and Benbasat (1991) redefined a number of constructs for use to examine individual technology acceptance such as relative advantage, easy of use, image, compatibility and results demonstrability.

1.5 EDT

Expectation-disconfirmation model (EDT) according to Premkumar & Bhattacherjee (2006) is based on expectation-disconfirmation-satisfaction paradigm. Oliver (1980) introduced EDT to explain the critical factors of consumer satisfaction/dissatisfaction, in the marketing area. Here product information and marketing formed a pre-usage initial expectation. After that the customers use the product and form a perception of product performance. The comparison of initial expectation vs. perceived performance drives to the disconfirmation for the product. After that the customer forms his/her satisfaction level.. The EDT is validated in IT by Bhattacherjee (2001) in a study for online banking services. Further more Bhattacherjee and Premkumar (2004) used EDT in order to explain changes in beliefs and attitudes toward IT usage.

Figure4. EDT structure.

1.6 TAM

Technology acceptance model (Davis, 1989; Davis, Bagozzi & Warshaw, 1989). TAM was adapted from the Theory of Reasoned Action –TRA-. Maybe the most well-known and widely accepted and cited model is the technology acceptance model (TAM). Davis (1985; 1989) developed the TAM to explain the computer usage and acceptance of information technology. As Money & Turner (2004) notice, the Institute for Scientific Information Social Science Citation indexed more than 300 journal citations of the initial TAM paper published by Davis et al. (1989).

22:20, 12 March 2008 (UTC)Snadek Figure5. Technology Acceptance Model (Davis, 1989).

According to Davis (1993, p.1) ‘user acceptance is often the pivotal factor determine the success or failure of an information system’. The term external variables include all the system design features. These features have a direct influence on perceived usefulness (PU) and perceived easy of use (PEOU), while attitude toward using has an indirect influence effect to the actual system use. Davis (1993, p. 477) defines PEOU as “the degree to which an individual believes that using a particular system would be free of physical and mental effort”, and PU as “the degree to which an individual believes that using a particular system would be enhance his/her job performance. As Davis et al (1989) states, the goal is to provide us with an explanation of the determinants of information systems acceptance. Similar to TRA user beliefs determine the attitude toward using the information system. This attitude drives to intention behavior to use which lead to actual system use. Dishaw and Strong (1999, pp. 9-21) pointed out a weak point of TAM about task focus. According to them TAM differs from TRA “in two keys”. The first is that define PEOU and PU as external variables that determine the intention to use not the actual use. The second key is that TAM does not include subjective norms. Yi (Yi et al., 2005), claims that TAM and IDT have similarities, More specific PEOU and PU are conceptual similar to relative advantage and complexity (the opposite of easy of use). As Taylor and Todd (1995) claims, TAM performs slightly better compared with the Theory of Planned Behavior (TPB). Table3 (appendix) summarizes the implementation of TAM in wide range of areas and give us a significant number of TAM applications and extensions.

1.7 TAM2

Venkatesh and Davis (2000), proposed an extension of TAM, the TAM2. TAM2 include social influence process such subjective norm, and cognitive instrumental process such as job relevance, output quality and result demonstrability. The Figure6 below, describes the revised TAM, the TAM2

Figure6. TAM2 (Venkatesh & Davis, 2000 p.188).


Venkatesh et al. (2003), proposed the Unified Theory of Acceptance and Use as a composition of eight prominent models (TRA, TAM, Motivational Model, TPB, Combined TAM-TPB, PC Utilization, IDT and Social Cognitive Theory). The UTAUT model aims to explain user behavioural intentions to use an IS and subsequent usage behaviour. According to this theory 4 critical constructs are direct determinants of usage intention and behaviour (Venkatesh et. al., 2003). The core constructs are: • performance expectancy • effort expectancy • social influence, and • facilitating conditions)

Gender, age, experience, and voluntariness of use are posited to mediate the impact of the four key constructs on usage intention and behaviour (Venkatesh et. al., 2003). Subsequent validation of UTAUT in a longitudinal study found it to account for 70% of the variance in usage intention (Venkatesh et. al., 2003). The Figure7 below describes the UTAUM model.

Figure7. UTAUM (Venkatesh et al. , 2003).

2. Conclusions

However every attempt of building an e-learning system, apart from the theoretical knowledge and the technical documentation, also requires the adoption and the active support of those that it addresses, the students. E-learning becomes more and more important. In order to reduce cost / benefit ratio, we must examine the gap between system design and system acceptance. So the study of the technology acceptance models becomes more and more important and critical.


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