Technology Acceptance Models as a tool for a successful E-learning implementation

Nikolaos Kourakos, City University of London, PhD Candidate, e-mail: snadek@gmail.com

Margarita Antoniou, Ministry Of Education, MA Modern Literature, e-mail: marantoniou@yahoo.com

Abstract

It is argued that the rapid evolution of Information and Communications Technology (ICT) and specifically of multimedia and Internet, has given the motive to introduce them to the education system. The online delivery of education starts in 1990s with the parallel explosion of the Internet usage.

After four decades of e-learning initiatives, the crucial point for today’s e-learning implementation is to pass to a sustainable phase. As many authors notice, the sustainable implementation of e-learning especially from Universities is a current hot item (Krupaa, Mandl & Jense, 2002). There are lots of factors that need to be considered while implementing an e-learning solution. There is a need to identify the factors that support and boost sustainability of e-learning. One of the most critical factors is the acceptance of the solution from the participants. Performing a literature review, we found a noticeable number of researches in this area.

This paper makes an exploratory study in the area of e-learning and the models that exams the technology acceptance of this solution, especially by learners. It describes various models that seek to explain learner’s behavioral and actual intention to use a technology system.

The study on technology (e-learning) acceptance models is useful for both academic and practitioners of e-learning, especially under the sustainability issues.

1. LITERATURE REVIEW

There is large variety of studies focus on ICT acceptance (Ngai, Poon & Chan, 2005; Abdul-Gader, 1996Adams, 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.

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).

Table 1. The structure of TRA.

Attitude Toward

Behavior

Behavioral Beliefs

→

“an individual’s feelings about performing the target behavior” (Fishbein and Ajzen (1975, p. 216)

Beh

avio

ral

Inte

nti

on

Act

ual

Beh

avio

r

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), intension is an immediate predictor of behavior. This intension 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 →

Inte

nsi

on

Beh

avio

rNormative 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, 1988) 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) (figure3 appendix). 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 (1998; 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, (p. 11)

1.4 IDT

Innovation diffusion theory (IDT) (Rogers, 1993), 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). (The Davis’s model is shown in figure5, appendix).

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 intension 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.

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 (appendix) describes the revised TAM

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

1.8 UTAUT

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 describes the UTAUM model.

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

2. SUMMARY

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 that is 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.

Appendix

Table3 Tam extensions / implementations.

Researcher Year Field – TAM extensions

F DAVIS 1991 Original TAM

D. Straub et al. 1997 TAM across cultures

M. Igbaria & M. Tan 1997 Technology Acceptance

R Agarwal & E. Karahanna 1998 TAM and Compatibility beliefs

M. Dishaw & D. Strong 1999 Extending TAM with task-technology fit constructs

T. Teo et al. 1999 Intrinsic and extrinsic motivation

Y. Malhorta & D. Galleta 1999 Extending TAM and Social Inluence

A. Lederer et al. 2000 World Wide Web

H. van der Heijden 2000 TAM and Website Usage

H. van der Heijden 2000 E-TAM

J. C-C Lin & H. Lu 2000 Behavioural intention and web site use

J. CC Lin H. Lu 2000 Towards an understanding of the behavioural intention

V. Venkatesh & F. Davis 2000 TAM2

V.Venkatesh & F. Davis 2000 Theoretical extension of TAM

A. Bhattacherjee 2001 E-commerce

J.W Moon & Y.G. Kim 2001 TAM and WWW context

Lei-da Chen 2001 Online consumers

R. Horton et al. 2001 Explaining intanet use with TAM

J. Lee et al. 2002 TAM and Virtual learning environment

J. Thong et al. 2002 TAM and digital libraries

S.-S. Liaw 2002 WWW Environment

W. Chismar S. Wiley-Patton 2002 TAM and Physicians

W. Chismar S. Wiley-Patton 2002 TAM and Internet in Pediatrics

H. Selim 2003 TAM course websites

J-S. Lee et al. 2003 TAM, Social Networking, Distance Learning

L. Stoel & K.H. Lee 2003 Web-based courseware

M.K.O. Lee et al. 2003 Internet based learning

P. Legris et al. 2003 Critical review of TAM

P.Jen-Hua et al. 2003 Law officers

V.Venkatesh et al. 2003 TAM toward a unified view

Y.P. J-H. Hu et al. 2003 School teachers

Yong Jin Kin et al. 2003 The role of attitude

Y-S Wang 2003 TAM Asynchronous learning systems

C. Gardner & D. Alonso 2004 TAM and Internet Technology

C.S. Ong et al. 2004 TAM, engineer's e-learning system

Chorhg-Shyong Ong & Jung-Yu Lai 2004 Gender differences

H. Sun & P. Zhang 2004 Methodological analysis of TAM

H. Sun & P. Zhang 2004 Methodological analysis of TAM

Hee-dong Yang & Youngjin 2004 Revisiting TAM

J-H Wu & S-C Wag 2004 M-commerce

K. Amoako-Gyampah & A.F. Salam 2004 ERP environment

K. Pituch, Y. Lee 2004 TAM and e-learning use

Lei-DA Chen & J. Tan 2004 Virtual stores acceptance

T. Pikkarainen et al. 2004 On-line banking

W. Money, A. Turner 2004 TAM and knowledge management system

C.Colin & A. Goh 2005 Validation of TAM

E. Carayannis & E. Turner 2005 Public Key Information Technology

E.W.T. Ngai et al. 2005 TAM and WebCT

Hung-Pin Shih 2005 Utilization behavior

J.Y. Imsook et al. 2005 TAM and t-commerce

J-H Wu 2005 TAM and mobile commerce

Jieun Yu et al. 2005 t-commerce

L. Dadayan & E. Ferro 2005 E-gov

L.Carter & F. Belanger 2005 E-gov

M. K. O Lee et al. 2005 Internet-based learning

M. Lee et al. 2005 TAM, Internet-based learning, intrinsic and extrinsic motivation

Mun Y. Yi et al. 2005 TAM and individual professionals

Mun Y. Yi et al. 2005 TAM and Individual professionals

R. Saade & B. Bahli 2005 On-line learning and an extension of TAM

S.H. Liu et al. 2005 TAM, e-learning acceptance

V. Lai & H.Li 2005 Internet Banking

W. Cheung & W. Huang 2005 Internet usage in university education

W.W. Ma & et al. 2005 TAM, computer technology, students and teachers

X. Deng et al. 2005 Office suite applications

A. Burton-Jones & G. Hubona 2006 External Variables

Angela Lin 2006 B2B systems

B Landry et al. 2006 TAM and Blackboard e-learning system

C.E. Porter & N.Donthu 2006 Internet usage

Chin-Shan Lu et al. 2006 Intension to use internet services

G. Premkumar & A. Bhattacherjee 2006 TAM and competing models

H. Sun & P. Zhang 2006 TAM and moderating factors

H. Sun & P. Zhang 2006 Moderating factors

H.Sun & P. Zhang 2006 The role of moderating factors

H.-Y. Lee et al. 2006 Recommender systems

J. C. Roca et al. 2006 Extension of TAM

M. Pagani 2006 High Speed Data Services

R.G. Saade & D. Kira 2006 PEOU by anxiety

S. Lee et al. 2006 TAM and Object-oriented Technology

Se-Joon Hong et al. 2006 A comparison of 3 models and mobile internet

T.C. Edwin Cheng et al. 2006 Internet Banking

U. Konradt et al. 2006 Corporate web portal

J.-H. Wu et al. 2007 End User Computing

K. C. Lee et al. 2007 User interface

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