Technology acceptance model
Technology Acceptance Models[edit | edit source]
1. Literature Review[edit | edit source]
The term ‘acceptance’ is used from authors with different background and approaches. In fact, in the literature, acceptance does not have a unique definition. TAM (Davis, 1989) describes acceptance as ‘users decision about how and when they will use technology’. Martinez (Martinez-Torres et al., 2008) 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[edit | edit source]
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 intention 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.
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[edit | edit source]
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.
Figure 2. 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[edit | edit source]
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[edit | edit source]
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[edit | edit source]
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. Recently, Islam and Mäntymäki (2011) performed a meta-analysis on the EDT relationships accross studies published in IT usage research area. They found EDT relationships were significant across studies.
Figure4. EDT structure.
1.6 TAM[edit | edit source]
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[edit | edit source]
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).
1.8 UTAUT[edit | edit source]
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. UTAUT (Venkatesh et al. , 2003).
2. Conclusions[edit | edit source]
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.