N Kourakos Abstract for ICTPI 2006

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Technology Acceptance for a Sustainable E-learning

Nikolaos Kourakos

Phd Candidate SOI, Centre for HCI Design, City University, London, UK.e-mail: [email protected]

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. According to E. Masie (2001)

'The real truth about e-learning's future," in a few years "there will not be a division

between e-learning and traditional learning, as learning will naturally evolve toutilise technological progress to improve learning efficiency".

As Zhang (Zhang et al, 2004) claims the todays economy, the knowledge-based

economy has an ever-increase demand for new ways of delivery education. This issue

has led to very dynamic changes in learning activities. The new K-economy requires

people and especially working staff to acquire knowledge and skills in a timely

manner and to make decisions under various circumstances. So the issues of life long

learning and continuous training is a major issue. The delivery of instruction and

teaching materials electronically to remote students via internet is what we call e-

learning.

After four decades of e-learning initiatives, the crucial point for todays 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 learners 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.

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.
mailto:[email protected]:[email protected]


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The Theory of Reasoned Action (TRA) proposed by Fishbein and Ajzen

(1975) to explain and predict the peoples behavior in a specific situation (figure1 &

table1 in appendix)

The Ajzens 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)

(figure2, table2, appendix).

More specifically, according to Ajzen (Ajzen, 1985, 1991), intension is an

immediate predictor of behavior. This intension is loaded by SN (i.e. perceived social

pressure), PBC (the beliefs about the ability to control the behavior) and ones 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).

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 didnt 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.

Innovation diffusion theory (IDT) (Rogers, 1993), is another model also

grounded in social psychology. Since 1940s 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.

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.


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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 andacceptance 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

Daviss model is shown in figure5, appendix).

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 ofphysical and mental effort, and PU as the degree to which an individual believes thatusing 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 andPU 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.

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

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).

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.


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Appendix

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

Table1.Structure of TRA.

Behavior Intension

Attitude

Toward

Behavior

Behavioral

Beliefs

an individuals feelings about

performing the target behavior

(Fishbein and Ajzen (1975, p.

216)

Subjective

Norm

Normative

Beliefs

the persons perception that

most people who are important

to him think he should or

should not perform thebehavior in question (Fishbein

and Ajzen (1975, p. 302)

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


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Table3 Tam extensions / implementations.


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

References

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