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Student Acceptance of Web-based Learning

SystemDin Jong 1 , Tzong-Song Wang 21Chung Hwa University of Medical Technology, Dept. of MIS, Tainan, Taiwan

Email: djong@mail.hwai.edu.tw2Tajen University, Dept. of MIS, Pingtung, Taiwan

Email: tswang@ccsun.tajen.edu.tw

AbstractThe study has modified UTAUT (Venkatesh et.

al., 2003) to determine technology acceptance of web-based

learning system of Taiwan technical university students.

This study used cluster sampling and stepwise regression

analysis to determine the relationships among constructs.

Research results showed that performance expectancy,

attitude toward using technology, facilitating conditions,self-efficacy, and social influence have significant influence

on behavior intention. Additionally, only behavior intention,

attitude toward using technology, and social influence have

direct impact on system usage.

Index Termsweb-based learning, e-learning, learning

system, technology acceptance, UTAUT

I.INTRODUCTION

In educational setting, Information technology (IT) hasbegun gradually to play an indispensable role. Applying

IT in instruction can change education significantly

[1][2]. The adoption of IT can also improve studentslearning, problem solving and creativity [3]. Web-based

learning system such as WebCT, Blackboard, or Moodle

has been widely adopted by many educational

institutions for online course-building [4]. According to

Allen and Seaman [5], around 90% of all public

institutions have provided online courses at the

postsecondary level.

II.LITERATUREREVIEW

A. Web-based learning systems

A Web-based learning system allows instructors to

create individualized web pages based on students

competencies and students can control the phase of acourse according to their learning abilities. As opposed

to instructional television (ITV) that is able to deliver

audio and video, the most common form of Web-based

learning is text-based asynchronous learning [6].

Asynchronous learning has the advantage of flexibility in

time and location for both instructors and learners. They

are not required to be present in the system at the same

time. Once connected to the Internet with access to thelearning system, students can learn from anywhere in the

world at any time. The disadvantage of asynchronous

learning is that the interaction among instructor and

students might not be as effective as face-to-faceinstruction because they are not able to communicate

with each other directly [7].Due to increase of Internet bandwidth and advance of

Information Technology, web-based learning system isnow capable of integrating media of many different typesincluding text, graphic, audio, and video. Real timefunctions such as chat, online lectures, or online

assessments have been integrated into web-based learningsystem to enable online learning [8][9]. Users havecontrol over or interact with the system.

The purpose of adopting Web-based learning systemis not only to improve learning effectiveness andefficiency, but also to provide an alternative approach ininstruction. Altekruse and Brew [10] pointed out thatWeb-based instruction is not capable of duplicating thelive instructor, but would probably be second best toface-to-face instruction. McArthur, Parker, and Giersch[11] believed that use of online functions can enhancetraditional teacher-centered courses. Some studies evenshowed that students in e-learning outperformed those in

traditional classes [12][13]. Nanayakkara [14] has foundthat release time, the ease of use, perceived usefulness,training and support, and reliability are the five mostessential factors for e-learning systems. Thoughweb-based learning is increasingly common in tertiaryeducation [15], Hill [16] suggested that new users whofirst received Web-based instruction might have the needfor more initial training and more feedback. It must beprevented that technology used in the classroomsometime was unrelated or even became a barrier to acourse [17].

B. Technology acceptance theories and UTAUT

Users acceptance of Information Technology (IT) is aprecondition before users can recognize ITs value andthen utilize it [18]. As early as mid-1980s, many modelsor theories tried to explain users technology acceptancehave been proposed and widely discussed. The mostimportant ones are the Theory of Reasoned Action (TRA)[19], the Technology Acceptance Model (TAM) [20], theTheory of Planned Behavior (TPB) [21], the extendedtechnology acceptance model (TAM2) [22], and the mostrecent Unified Theory of Acceptance and Use ofTechnology (UTAUT) [23].

Theories like TAM, TPB and UTAUT are alloriginated from TRA, which explain human behaviorfrom social psychologys view point. TRA was verygeneral in nature and tried to explain almost any humanbehavior. TRA suggests that social behavior is motivatedby an individuals attitude toward carrying out thatbehavior. An individuals actual behavior can be

Tzong-Song Wang is the author to whom correspondence should be

addressed.

ISBN 978-952-5726-00-8 (Print), 978-952-5726-01-5 (CD-ROM)

Proceedings of the 2009 International Symposium on Web Information Systems and Applications (WISA09)

Nanchang, P.R. China,May 22-24, 2009,pp. 533-536

2009 ACADEMY PUBLISHER

AP-PROC-CS-09CN001

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predicted by behavior intention, which is determined byboth the attitude towards a specific behavior andsubjective norm concerning the behavior in question. Inother words, behavior is the result of ones beliefs aboutthe outcomes of performing that behavior after evaluating

each of those outcomes.TAM (Figure 1) adopts TRAs causal linkages, a

sequence of beliefs, attitudes, intentions, and behaviors,to explain the individuals IT acceptance behaviors. InTAM, perceived usefulness (PU) and perceived ease ofuse (PEOU) are hypothesized as fundamentaldeterminants of user acceptance of a given IT. Davis(1989) defined perceived usefulness as the prospectiveusers subjective probability that using a specificapplication system will increase his or her jobperformance within an organizational context (p.985)and perceived ease of use as the degree to which theprospective user expects the target system to be free of

effort (p. 985). PU and PEOU have a direct influence onan individuals attitudes toward the use of IT. AnIndividuals attitude impacts ones behavioral intentionsto use IT. Behavioral intentions, in turn, directly affect

actual IT usage.

In TAM2 (Figure 2), an extended TAM, social andorganizational variables such as subjective norm, image, job relevance, output quality, and result demonstrabilitywere included in the model. All these factors have directimpact on PU. In addition, subjective norm not onlyinfluence PU, but also affect user intention. Either TAMor TAM2 has been widely adopted to explain individualstechnology acceptance in various consumer ororganization settings [24].

TAM is the first and the most influential research

models in studies of the determinants of IT or InformationSystem acceptance. The model is empirically provensuccessful in predicting about 40% of a systems use[25].However, a meta-analysis performed by Legris, Ingham

and Collerette [26] pointed out three shortcomings ofTAM research to date. First, nine out of 22 TAM studiesinvolved students. They argued that it would be better ifthe research had been conducted in a businessenvironment. Secondly, most studies examined the

introduction of office automation software or systemdevelopment applications. It would be beneficial if theresearch examined the introduction of business processapplications. Third, most studies did not measure systemuse intended in TAM but measured the variance ofself-reported use instead. Self-reported use should serveas a relative indicator only.

After reviewing 8 technology acceptance theories,Venkatesh [23] proposed UTAUT (Figure 3) whichconsists of four constructs, performance expectancy,effort expectancy, social influence, and facilitatingconditions, that have direct impact on behavior intentionand usage. UTAUT is the best model so for to predict

individuals technology acceptance because it can explain70% of depend variable variance comparing with TAMonly 40%. In UTAUT, performance expectancy is similarto TAMs perceived usefulness, and is defined as thedegree to which an individual believes that using thesystem will help him or her to attain gains in jobperformance. Like perceived ease of use in TAM, effortexpectancy is refers to the degree of ease associated withthe use of the system. Social influence is the degree towhich an individual perceived that important othersbelieve he or she should us the new system. Socialinfluence refers to the degree to which an individualbelieves that an organizational and technicalinfrastructure exists to support use of the system.

Unlike TAM, UTAUT introduced 4 moderators.There are gender, age, experience, and voluntariness ofuse. These factors can help to explain the behaviordifferentiation on different relationships. For example, theeffect of performance expectancy on behavior intention ismoderated by gender, age, and experience. The effect ofeffort expectancy on behavior intention is moderated bygender, age, and experiences. The effect of socialinfluence on behavior intention is moderated by all 4

variables while the effect of facilitating conditions on usebehavior is moderated by age and experience, 2 variablesonly.

Figure 1. Technology Acceptance Model (TAM)

Figure 2. Extended Technology Acceptance Model (TAM 2)

Figure 3. Unified Theory of Acceptance and Use of Technology

(UTAUT)

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III.THE RESEARCH

A. Research model

Seven constructs that have direct impact on userintention or usage [23] will be included in the initial

research model. These constructs are performanceexpectancy, effort expectancy, attitude toward usingtechnology, facilitating conditions, self-efficacy, socialinfluence, and anxiety. Proposed research model is shownas Figure 4.

B. Population and sample

The target population contained all postsecondarystudents who take e-learning classes and were enrolled

technical universities in Taiwan during the spring

semester of 2009.

Cluster sampling was used to select students in thestudy. Samples were collected form 4 technicaluniversities located in northern, middle, and southernTaiwan. In total, there were 22 classes of students whowere over 18 years old and from different academicmajors, such as Management Information Systems,Healthcare Administration, and Pharmacy, participated inthis study.

C. The survey instrument

An initial survey instruments with 23 questions wasdeveloped based upon the work of Venkatesh et. al. [23].The questionnaire contains 2 questions each for variables

such as performance expectancy, effort expectancy,attitude toward using technology, social influence,facilitating conditions, self-efficacy, anxiety, behaviorintention, and system usage. Remaining 5 questions arefor moderators such as gender, computing skills,computer ownership, accessibility outside campus, andfrequency of using Interne.

Fifty two students participated a pilot study to assurethe reliability and validity of the instrument. Questionsfor testing facilitating conditions and self-efficacy wereremoved from the questionnaire due to their low alphacoefficients. Finally, there are 19 questions in thequestionnaire.

D. Instrument Validity and ReliabilityValidity is the strength of conclusions, inferences, or

propositions. More formally, Cook and Campbell (1979)defined it as the "best available approximation to the truth

or falsity of a given inference, proposition, or conclusion"(Trochim, 1991, p. 33). The questions in the surveyinstruments were sifted from items selected by Venkateshet. al. [23] to estimate UTAUT. Thus, validity of thesurvey instrument has been established.

Reliability is the consistency of the measurement orthe degree to which an instrument measures the same wayeach time it is used under the same conditions with thesame subjects. Reliability of the instrument may bemeasured by test/retest or internal consistency. Internalconsistency can be determined by the proceduredeveloped by Cronbach (1951). Cronbach's alpha splitsall the questions in the instrument every possible way andcomputes correlation values for them all. Alphacoefficients for all the constructs ranged from .768 to .878,all well above the .70 standard of reliability as suggestedby Nunnally and Bernstein (1994). The reliability for thesurvey instruments as a whole is .860. Thus, internal

consistency of the instrument was determined.E. Data analysis

A total of 606 valid surveys were collected. Thestatistical methods of stepwise regression analyses wereperformed to check the effects among various constructs.SPSS 15 and AMOS 7 were used to perform all statisticalanalysis.

IV.FINDINGS

A. Analysis of the research model

A series of stepwise regression analyses wereperformed to examine the effects of performance

expectancy, effort expectancy, attitude toward usingtechnology, facilitating conditions, self-efficacy, socialinfluence, and anxiety on the behavior intention andsystem usage. The results indicated that performanceexpectancy, attitude toward using technology, facilitatingconditions, self-efficacy, and social influence can predictbehavior intention. The analysis of variance shows thatthe model is significant, F(5, 575) = 78.72, p = .000. Thecoefficient of determination (R2) was computed as .406and the adjusted R2 was .401, indicating that theregression model accounted for 40.1% of the totalvariance in behavior intention.

In addition, behavior intention, attitude toward usingtechnology, and social influence can predict system usage.The analysis of variance shows that the model issignificant, F(3, 577) = 214.25, p = .000. The coefficient

Figure 4. Proposed research model for student acceptance of

web-based learning system

Figure 5. Final model for student acceptance of web-based learning

system

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of determination (R2) was computed as .527 and theadjusted R2 was .524, indicating that the regression modelaccounted for 52.4% of the total variance in system usage.The result of research is shown as Figure 5.

V.DISCUSSION AND CONCLUSIONSThe study has modified UTAUT (Venkatesh et. al.,

[23] to determine technology acceptance of web-basedlearning system of Taiwan technical university students.Performance expectancy, attitude toward usingtechnology, facilitating conditions, self-efficacy, andsocial influence have significant influence on behaviorintention. Additionally, only behavior intention, attitudetoward using technology, and social influence have directimpact on system usage.

Except for the influence of constructs on system usage,the final model differs from UTAUT in 2 ways. First,effort expectancy was not included in the final model.Second, attitude and self-efficacy reenter the final model.The researcher believes that the differences are causedmainly by different experimental setting. The studysampled students enrolled in distance courses fromdifferent schools while Venkatesh et. al. sampled IT usersfrom different organizations.

Hindess [27] studied regarding a Web-based course

indicated that participants attitudes toward Web-based

instruction are positive, and Web-based instruction

provides a learning environment for participants to

develop electronic literacy skills and share their ideas

and projects. Individual reasons such as negative

attitudes could influence the technology use in theclassroom [28]. These studies indicated that student

attitude have positive impact on both behavior intention

and actual usage of web-based learning system.Due to time constrain, the effect of moderators on

constructs were not discussed in the paper. Futureresearch should explore the influence of gender,computing skills, computer ownership, accessibilityoutside campus, and frequency of using Interne on themodel.

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