understanding user perceptions of

Journal of Computer Assisted Learning (2002) 18, 137-148

2002 Blackwell Science Ltd 137

Understanding user perceptions ofWorld-wide web environments

S.-S. LiawChina Medical College. Taichung, Taiwan

Abstract The purpose of this study was to develop and test a conceptualmodel of individual perceptions of Web technology as a use and trainingtool. The model presents a perspective of users’ attitudes toward Webenvironments. This model integrates the Technical Acceptance Model,Social Cognitive Theory, individual attitudes, motivation and self-efficacyperspectives to develop a new aspect of users’ perceptions toward Webtechnology acceptance and use. The study provides some evidence thatthe conceptual model helps the understanding of user perceptions to Webenvironments. In addition, training and educational programmes oncomputers may foster a positive feeling towards the Web. Furthermore,the more individuals have self-efficacy towards Web technology, the moreindividuals have motivation to use the Web.

Keywords: Motivation; Self-efficacy; Social cognitive theory;Technology acceptance model; Questionnaire; Undergraduate; World-wide web

Introduction

Despite the realisation that information technology is key to the success and survivalof organisations in a highly competitive environment, the potential benefits ofWorld-wide Web as aids to learning and training may not be fully realised due topoor acceptance by users. Therefore, it is important to understand why certainindividuals jump right onto the information superhighway while others hesitantlystand aside. Understanding Web use and nonuse can be beneficial for the design ofuniversity courses or organisational training programmes. Many authors have studieddifferent aspects of the phenomenon, from a variety of theoretical perspectives,including the Social Cognitive Theory (SCT) (e.g. Bandura, 1977; 1986; Compeau& Higgins, 1995a; 1995b; Compeau et al., 1999) and the Technology AcceptanceModel (TAM) (e.g. Vankatesh & Davis, 1996; Vankatesh, 1999) to individualperceptions, including attitudes (Loyd & Loyd, 1985; Kay, 1989; Igbaria, 1993;Nash & Moroz, 1997; Al-Khaldi & Al-Jabri, 1998), motivation (e.g. Davis et al.,1992; Igbaria, 1993; Vallerand, 1997) and self-efficacy (e.g. Bandura, 1977; 1986;Compeau & Higgins, 1995a; Igbaria & Iivari, 1995; Compeau et al., 1999).

Users are sometimes unwilling to accept and use available technologies andexpress less than enthusiastic response to new technology, even if the technology Accepted 24 October 2001

Correspondence: Shu-sheng Liaw, General Education Center, China Medical College, 91 Shiuesh Rd.,Taichung, 404, Taiwan Email: [email protected]

138 S-S. Liaw

2002 Blackwell Science Ltd, Journal of Computer Assisted Learning, 18, 137-148

may increase their productivity. In general, the acceptance and use of computers byusers appear to be limited due to fear of computers, resistance to new technology,perceived difficulty of use, not understanding the importance of technology, and lackof motivation to adopt a new technology (Igbaria & Iivari, 1995; Thompson et al.1991). The Web is a relative new computer technology and greater attention needs tobe paid to the factors that cause users’ resistance to Web usage.

The current study develops and tests a conceptual model of individualperceptions to Web technology as a use and training tool. The model explores users’attitudes toward Web environments that integrated TAM, SCT, individual attitudes,motivation and self-efficacy perspectives to develop a new aspect of individualperceptions toward Web technology acceptance and use. This helps an understandingof how individual perceptions affect use of the Web and provides stronger causalarguments regarding the observed relationships.

Literature review

AttitudeAttitude can be defined as the way an individual feels about and is disposed towardscertain objects. Gibson et al. (1991) defined attitude as a ‘positive or negativefeeling or mental state of readiness, learned and organised through experience, thatexerts specific influences on a person’s response to people, object, andsituation’(p.70). Triandis (1971) suggested that attitude should consist of affective,cognitive, and behavioural components. The affective component of attitude is theemotion or feeling which includes statements of likes or dislikes about certainobjects. The cognitive component of attitude is statements of beliefs. In other words,an individual holds a belief that a certain object can increase significantly the qualityof her or his output. The behavioural component of attitude is what an individualactually does or intends to do (Al-Khaldi & Al-Jabri, 1998) and is affected byindividuals’ experience.

Culpan (1995) stated that no matter how sophisticated and how capable thetechnology, its effective implementation depends upon users having a positiveattitude towards it. Although the concept of attitude towards computers has gainedrecognition as a critical determinant in use and acceptance of informationtechnology, there is no single, universally accepted definition of the computerattitude construct. Brock & Sulsky (1994) indicated that attitudes toward computerswere composed of two distinct factors; one was the belief that computers were abeneficial tool and second was the belief that computers were autonomous entities.

The Computer Attitude Scale (CAS), developed by Loyd & Loyd (1985),consisted of computer anxiety, computer confidence, computer liking and computerusefulness. Computer anxiety refers to fear of computers or the tendency of a personto be uneasy, apprehensive, and phobic towards current or future use of computers(Igbaria, 1993; Loyd & Loyd 1985). Computer confidence, or computer self-efficacy, refers to the ability to use or learn about the computer. Computer liking, orcomputer enjoyment, refers to liking or enjoying working with computers andcomputer usefulness refers to the degree of perceived usefulness of using computersfor present and future work. In general, enjoyment represents the affective or feelingpart of attitude and self-efficacy, and usefulness represents the cognition or beliefpart of attitude (Thompson et al., 1991). In the CAS, some studies (Kay, 1989; Nash& Moroz, 1997; Al-Khaldi & Al-Jabri, 1998) suggest that computer anxiety and

User perceptions of World-wide web environments 139


computer self-efficacy are part of the same continuum. Additionally, Woodrow(1991) provided the evidence that the three-scale version of the CAS only includedtwo dimensions, affective and behavioural aspects. Moreover, Nash & Moroz (1997)suggested that the attitude toward academic endeavours associated with computertraining should be incorporated into the CAS. This part refers to the learning andtraining of skills in computer courses. Based on these references, the constructs ofWeb attitude can be revised as self-efficacy, enjoyment, usefulness and behaviouralintention (Table 1.).

Table 1 Constructs of the Web attitudes

Construct Description Measurement

Web self-efficacy Refers to the confidence to use or learn about the Internet/WWW CognitiveWeb enjoyment Refers to liking or enjoying working with the Internet/WWW AffectiveWeb usefulness Refers to the degree of perceived usefulness of using the Cognitive

Internet/WWW for present and future workBehavioural intention Refers to the degree of intentions of using the Internet/WWW for Behaviouralto use the Web present and future work

Social Cognitive Theory and self-efficacyGenerally, SCT is based on the premise that environmental influences such as socialpressures or unique situational characteristics, cognitive and other factors includingpersonality, as well as demographic characteristics and behaviour, are reciprocallydetermined (Bandura, 1977; 1986; Compeau & Higgins, 1995a). Thus, individualschoose the environments in which they wish to exist in addition to being influencedby those environments. Additionally, behaviour in a given situation is affected byenvironmental or situational characteristics, which are in turn affected by behaviour.Furthermore, behaviour is influenced by cognitive and personal factors, and in turn,affects those same factors (Compeau & Higgins, 1995a). SCT explicitlyacknowledges the existence of a continuous reciprocal interaction between theenvironment in which an individual operates, his or her cognitive perceptions (suchas self-efficacy and outcome expectation) and his or her behaviour (Bandura, 1986;Compeau & Higgins, 1995a).

Based on SCT, self-efficacy is viewed as an antecedent to use, but successfulinteractions with technology are also viewed as influences on self-efficacy. Thus,SCT incorporates two specific expectations: outcome expectations and expectationsrelated self-efficacy (Igbaria & Iivari, 1995). Outcome expectations are similar to theperceived usefulness in TAM, where users tend to undertake behaviours they believewill help them perform their job better. According to self-efficacy, Wood & Bandura(1989) state that ‘self-efficacy refers to beliefs in one’s capabilities to mobilise themotivation, cognitive resources, and courses of action needed to meet givensituational demands’ (p. 408). SCT claims that both expectations are basicdeterminants of user behaviour.

Bandura (1986) defined self-efficacy as ‘generative capability in whichcognitive, social, and behavioural subskills must organised into integrated coursesof action to serve innumerable purposes’ (p. 391). This definition highlights a keyaspect of the self-efficacy construct. Essentially, if serious uncertainties regardingperformance of necessary activities exist in efficacy expectations, the efficacyexpectations would not impact behaviour. Thus, the greater people perceive theirself-efficacy to be, the more active and longer they persist in their efforts. Kinzie et

140 S-S. Liaw


al. (1994) defined self-efficacy as an individual’s confidence in her or his ability thatmay impact the performance of tasks. They noted that self-efficacy reflected anindividual’s confidence in the ability to perform the behaviour required to producespecific outcomes and was thought to impact directly the choice to engage in a task,the effort that would be expended and the persistence that would be exhibited.Murphy et al. (1989) view computer self-efficacy as an individual’s perception oftheir capabilities regarding specific computer knowledge and skills.

Technology Acceptance Model, usefulness and behavioural intentionTAM, developed from the social psychology Theory of Reasoned Action (TRA)(Ajzen & Fishbein, 1980), explains user acceptance of a technology based on userattitudes. A conspicuous difference between the TAM and TRA is that TAM omitssubjective norms, mostly for methodological reasons and partly because they werenot significant in explaining behavioural intentions. TAM views the causalrelationships as essentially unidirectional, with the environment influencingcognitive beliefs, which influence attitudes and behaviours.

TAM suggests that two specific behavioural beliefs, perceived ease of use (EOU)and perceived usefulness (U), determine an individual’s behavioural intention to usetechnologies. In contrast to perceived ease of use, which is process expectancy,perceived usefulness is outcome expectancy. The behavioural intention to usetechnologies leads to actual system use. Previous research has demonstrated thevalidity of this model across a wide variety of corporate information technologysystems (Szajna, 1996; Taylor & Todd, 1995; Vankatesh & Davis, 1996; Gefen &Straub, 1997; Vankatesh, 1999; Lederer et al., 2000). In many previous studies,behaviour belief is strongly affected by perceived usefulness.

Motivation, usefulness and enjoymentMotivational perspectives, similar to the use of TRA to study human behaviour, havealso been widely used to understand individual behaviour. Davis et al. (1992) foundthat intrinsic and extrinsic motivation are key drivers of behavioural intention to usecomputers. Intrinsic motivation emphasises to the pleasure and inherent satisfactionderived from a specific activity (Vallerand, 1997) while extrinsic motivationhighlights performing a behaviour to achieve a specific goal, such as rewards. Inother words, intrinsic motivation is based on performing an activity purely forenjoyment of the activity itself and extrinsic motivation refers to the performance ofan activity because it is believed to be instrumental in achieving valued outcomesthat are separate from the activity. Recent research that has examined an intrinsicfactor (enjoyment) showed that this had a positive effect on the intention to useinformation technology (Atkinson & Kydd, 1997; Vankatesh, 1999); additionally, anextrinsic factor (usefulness) was also found to have a positive effect on the intentionto use computers (Igbaria, 1993).

Model development

Among the various theoretical models developed to examine users’ intentions to usecomputing technology, TAM has emerged as especially promising. Althoughresearch on TAM has provided insights into computer usage, it has focused onperceived ease of use and perceived usefulness as the determinants of usage ratherthan on other factors affecting these determinants. Additionally, TAM suggests that



users will use computer technology if they believe it will result in positive outcomes.On the other hand, SCT claims that beliefs about outcomes may be insufficient to

influence behaviour if users doubt their capabilities to successfully undertakebehaviours. Self-efficacy, the belief that one has the ability to perform a particularaction, is an important construct of the SCT. Bandura (1977) argues that self-efficacy must be considered to understand users’ behaviours. He states, ‘individualscan believe that a particular course of action will produce certain outcomes, but ifindividuals entertain serious doubts about whether they can perform the necessaryactivities, such information does not influence their behaviour’ (p.193). Thisargument emphasises the impact of the users’ cognitive state on outcomes and theimportance of understanding self-efficacy.

Furthermore, Davis et al. (1992) found that intrinsic motivation (enjoyment) andextrinsic motivation (usefulness) are key drivers of behaviour intention to usecomputers. The perceived usefulness, is constructed by TAM and extrinsic motiv-ation, reflects beliefs (or intentions) about outcomes. From an intrinsic motivationaspect, perceived enjoyment has a positive effect on intention to use computers.

The conceptual model (Technology Use Model) used to guide this study is shownin Fig. 1. The model is derived by integrating TAM, SCT, individual perceptions,motivation perspective, and self-efficacy perspective to develop a new aspect ofindividual intentions toward Web technology acceptance and use. This conceptualmodel includes seven hypotheses which are defined in Table 2.

Research design

InstrumentsThe data for this study was gathered by a questionnaire survey. The questionnaireincluded three major components:Demographic information: The demographic component of the questionnairecovered gender and years of computer-related experience.

Computer experience: In this component, subjects were asked to indicate whetherthey had experiences using the Internet/WWW, experience with word processingpackages, and experience with database packages. These questionnaires are all 7-point Likert scales (from ‘no experience’ to ‘highly experience’).

Web attitude scale: in these four components, subjects were asked to indicate theirperceptions toward Web self-efficacy, enjoyment, usefulness, and intention to use

H6

H5

H7

H4H3

H2

Perceivedusefulness

Perceivedenjoyment

BehaviouralintentionH1

Technologyexperience

Behaviouralself-efficacy

Fig. 1. The conceptual model (Technology Use Model — TUM)

142 S-S. Liaw


the Web. These questionnaires are all 7-point Likert scales (from ‘strongly disagree’to ‘strongly agree’).

Research participantsThe participants were students who studied at a north-west university in the UnitedStates and were chosen by the university’s Web page named ‘White Pages’ forsearching students’ email accounts. The method of selection was to use students’first name as key words for searching. Fifty-eight first names were used in this studyfor selecting samples and 809 participants were chosen. Perceptions were gatheredfrom 263 students through a survey Web page on the Internet; three had no data andso only 260 replies were analysed. The response rate was 32.5%.

Pre-testThe purpose of a pre-test was to examine the reliability of the questionnaire. Theparticipants of the pre-test were doctoral students in the School of Education, in anorth-west university of United States. At first, the whole sample size was 33doctoral students and all of them were in their first or second year of study. Theparticipants returned their surveys from the Web page via the Internet. The totalnumber of respondents was 20 (16 female and 4 male). The response rate was 61%.There were 16 items on the scale, the mean (m) was 88.30, and standard deviation(s.d.) was 16.87. For the split-half coefficient, the first half included first eight itemsand the second half contained last eight items. Cronbach’s α was 0.94 and correcteditem-total correlations ranged from 0.20 to 0.91.

Results

Internal consistencyThe Web attitude scale had 16 items; the mean was 91.88 and standard deviation

Table 2 Hypothesise in the conceptual model

Hypothesis Supporting references since 1995

H1. The higher the individual computer Al-Khaldi & Al-Jabri (1998); Igbaria & Iivari .(1995);and experience, the higher her/his Web Levine & Donitsa-Schmidt (1998); Mitra (1998);self-efficacy Zhang & Espinoza (1998)

H2. The higher the individual Web Compeau & Higgins (1995a); Compeau et al., (1999)self-efficacy, the higher her/his Webusefulness.

H3. The higher the individual Web Compeau & Higgins (1995b); Compeau et al., (1999)self-efficacy, the higher her/his Webenjoyment.

H4. The higher the individual Web Compeau & Higgins (1995b); Compeau et al., (1999);enjoyment, the higher his/her Web Vankatesh & Davis (1996); Vankatesh (1999)usefulness.

H5. The higher the individual Web Gefen & Straub (1997); Szajna (1996);enjoyment, the higher his/her Taylor & Todd (1995); Vankatesh & Davis (1996);intention to use the Web. Vankatesh (1999)

H6. The higher the individual Web Gefen & Straub (1997); Szajna (1996);usefulness, the higher his/her Taylor & Todd (1995); Vankatesh & Davis (1996);intention to use the Web. Vankatesh (1999)

H7. The higher the individual Web Compeau & Higgins (1995b); Compeau et al., (1999)self-efficacy, the higher her/hisintention to use the Web.



14.31. For the split-half coefficient, the first half included the first eight items andthe second half the last eight items. For the first half, the mean was 45.08 andstandard deviation was 7.63. For the second half, the mean was 46.80 and standarddeviation was 7.63. Corrected item-total correlations of the first half were rangedfrom 0.47–0.79 and of the second half were ranged from 0.58 to 0.80. The alphacoefficient was 0.87 and 0.91 for the first and second half, respectively. In addition,Cronbach’s α of the total instrument was 0.93 and corrected item-total correlationswere ranged from 0.47 to 0.80. The item-total correlations are shown in Table 3.

Analysis of relationshipsThe descriptive statistics of years of computer-related experience are shown inTable 4 and the descriptive statistics of computer experience are shown in Table 5with the Pearson correlation coefficients among the variables in Table 6. The bi-variate relationships indicate that most of variables were significantly correlated witheach other and the correlations were all less than 0.80 except the correlation betweenWeb usefulness and behavioural intention to use the Web (r = 0.81).

Analysis of predictionRegarding analytic strategy for assessing the predictive model, path analysis is anappropriate multivariate analytical methodology for empirically examining sets ofrelationships in the form of linear causal models. In general, the value of the pathcoefficient associated with each path represents the strength of each linear influence.Although the path coefficient can be estimated in many ways, multiple regressionanalysis has been used in most empirical applications of this methodology.

Table 3. Mean(m), Standard Deviation(sd) and Corrected Item-Total Correlations

No. Item m sd r*

Web self-efficacy:1 I feel confident using the Internet/World-wide web. 5.88 1.13 0.662 I feel confident using email:. 6.35 0.93 0.543 I feel confident using WWW browsers (Internet Explorer, Netscape). 6.00 1.09 0.684 I feel confident using search engines (i.e. Yahoo, Excite, and Lycos). 5.90 1.23 0.61Web enjoyment:5 I like to use email: to communicate with others. 6.25 1.14 0.476 I enjoy talking with others about the Internet. 3.82 1.79 0.607 I like to work with the Internet/WWW. 5.06 1.62 0.798 I like to use the Internet from home. 5.81 1.45 0.66Web usefulness:9 I believe using the Internet/WWW is worthwhile. 5.85 1.27 0.8010 The Internet/WWW helps me to find information. 6.02 1.13 0.7211 I believe the Internet makes communication with others easier. 6.07 1.20 0.5812 The multimedia environment of WWW(e.g. text, image) is helpful to 5.50 1.37 0.71

understand online information.Behavioural intention to use the Web:13 I believe the Internet/WWW has potential as a learning tool. 5.95 1.14 0.7314 I believe that the Internet/WWW is able to offer online learning 5.71 1.24 0.68

activities.15 I believe that learning how to use the Internet/WWW is worthwhile. 6.12 1.02 0.7416 Learning the Internet/WWW skills can enhance my academic 5.59 1.31 0.64

performance.

r* means item-total correlation

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The stepwise multiple regression results for the path associated with the variables arepresented in Table 7. For examining H1, a regression analysis was performed tocheck the effect of computer experience variables on the Web self-efficacy. Thepredictor variables were experiences using the Internet/WWW, experience withword processing packages and experience with database packages. The resultsindicated the biggest predictor variable was experience using the Internet/WWW andanother predictor was experience with word processing packages (F = 134.54,p = 0.000, R2 = 0.51). For testing H2, a regression analysis was conducted to checkthe effect of the Web self-efficacy on the Web enjoyment. The results show that thepredictor variable had accounted for 37% of the variance in the criterion variable(F = 153.09, p = 0.000, R2 = 0.37). For testing H3, a regression analysis wasconducted to check the effect of the Web self-efficacy on Web usefulness. Theresults show the predictor variable had accounted for 34% of the variance incriterion variable (F = 130.31, p = 0.000, R2 = 0.34). For examining H4, a regressionanalysis was conducted to check the effect of the Web enjoyment on Web usefulness.The results show the predictor variable had accounted for 60% of the variance incriterion variable (F = 390.77, p = 0.000, R2 = 0.60). For examining H5 and H6, aregression analysis was conducted to check the effect of the Web enjoyment and theWeb usefulness on behavioural intention to use the Web. The result which gave thebiggest predictor variable was Web usefulness (F = 256.96, p = 0.000, R2 = 0.67).

Table 6. Correlation analysis

Variables 1 2 3 4 5 6

1. Web self-efficacy 1 0.61* 0.58* 0.50* 0.70* 0.58*2. Web enjoyment 1 0.78* 0.69* 0.59* 0.43*3. Web usefulness 1 0.81* 0.48* 0.40*4. Behavioural intention to use the Web 1 0.41* 0.37*5. Experience using the Internet/WWW 1 0.66*6. Experience with word processing packages 1

*. p < 0.01.

Table 7. Regression results for predicted path relationships

Dependent variable Independent variables, β R2 p

Web self-efficacy Experience using the Internet/WWW 0.55 0.48 0.00Experience with word processing packages 0.22 0.03 0.00

Web enjoyment Web self-efficacy 0.61 0.37 0.00Web usefulness Web self-efficacy 0.58 0.34 0.00Web usefulness Web enjoyment 0.78 0.60 0.00Behavioural intention to use the Web Web usefulness 0.68 0.66 0.00

Web enjoyment 0.16 0.01 0.05Behavioural intention to use the Web Web self-efficacy 0.50 0.25 0.00

Table 4. Years of computer-relatedexperience

Years - Frequency Percentage

6 months or less 7 2.76 months to 1 years 12 4.61–2 years 24 9.22–4 years 58 22.34–6 years 50 19.26 years or more 109 41.9

Table 5. Descriptive statistics ofcomputer experience with:

Variables mean s.d.

Word processing packages. 5.30 1.54Database packages. 2.56 1.69The Internet/WWW. 4.82 1.35



For testing H7, a regression analysis was conducted to check the effect of the Webself-efficacy on behavioural intention to use the Web. The results show the predictorvariable accounted for 25% of the variance in criterion variable (F = 87.28,p = 0.000, R2 = 0.25). Table 6 summarises the results of hypotheses and Fig. 2presents the results of the research model that was based on the hypotheses (seeTable 2) which were all supported by the research data.

Essentially, multicollinearity can becontrolled by two ways: correlationbetween independent variables should allbe less than 0.8 (Emory & Cooper, 1991)and variance inflation factors (VIF) shouldbe less than 10 (Neter & Kutner, 1990). Inthis study, multicollinearity was ruled outbecause the correlation betweenindependent variables, as Table 6 shows,were almost all less than 0.8 and the VIFswere all less than 10. Based on multipleregression analysis, the scatter plots ofstandardised residuals by the standardisedpredicted scores were also examined toverify the assumption of linearity.

Discussion

The purpose of this case study was to explore the role of individual Web attitudesbased on theoretical and personal perceptions. The results of the research providesupport for all hypotheses that were tested. Additionally, all of the three identifiedvariables (Web self-efficacy, Web enjoyment, and Web usefulness) turn out to havesignificantly positive effects on behavioural intention to use the Web. Webusefulness is found to be the most, and Web enjoyment the least, importantdeterminant of behavioural intention to use the Web. Therefore, on a macro level,the present results corroborate previous research thar the Web affects individualbehaviour to use computers and the Internet. On a micro level, the findings provideevidence that the conceptual model has practical value.

The results of the case study confirm earlier research on Social CognitiveTheory. In general, SCT claims that both outcome expectations and expectationsrelated self-efficacy, are basic determinates of user behaviour. Outcome expectationscould be viewed as the perceived usefulness in the Technology Acceptance Model.From the results, self-efficacy was found to play an important role in shapingindividuals’ perceptions and behaviours. The respondents in the case study with highself-efficacy used computers and the Internet more than others. Additionally,perceived usefulness was found to have a significant impact on behavioural intentionto use the Web. Furthermore, consistent with SCT, computer and the Internetexperience affect Web self-efficacy. These results support other researchers’ (e.g.Bandura, 1977; 1986; Igbaria & Iivari, 1995) conjecture of experience as the mostinfluential determinant of self-efficacy. The practical implications of this finding isthat training and educational programmes on computers may foster a feeling of Webself-efficacy. Such training and educational programmes should emphasise user

�=0.68**�=0.16*�=0.78**

�=0.58**�=0.50**

�=0.22**

�=0.61**

�=0.55**

Experience using theInternet/WWW

Experience with wordprocessing packages

Web self-efficacy

Web enjoyment Web usefulness

Behavioural intention touse the Web

Fig. 2. The results of the research model* p<0.05. ** p<0.01.

146 S-S. Liaw


friendliness of currently available microcomputers, and the availability and ease ofuse of software packages which require little or no knowledge of the technicalaspects of computers. In view of the primacy of perceived usefulness, it is vital thateducational programmes emphasise the application of Web technology to authentictasks and work contexts.

The results also support TAM and motivational perspectives which wereintegrated in this research. TAM suggests that behavioural beliefs determine anindividual’s behavioural intention to use technology. On the other hand, motivationis also a key driver of behavioural intention to use technology. Based onmotivational perspectives, perceived enjoyment could be viewed as intrinsicmotivation and perceived usefulness could be viewed as extrinsic motivation. Presentresults show that self-efficacy has significant positive effects on both perceivedenjoyment and usefulness. The more individuals have self-efficacy toward computersand Web technology, the more individuals have motivation to use the Web.

Conclusion

In summary, the present study indicates that teachers, trainers, and instructionaldesigners of computer-based or Web-based instruction would benefit by being moreattentive to learners’ perceptions toward Web-based environments. Based on highCronbach’s coefficients and high test-retest reliability, this research has high internalconsistency, stability and validity. In other words, the conceptual model and thequestionnaire of this research can be used for practical purposes. Further, learners’attitudes toward technology should be considered in many different aspects.Individual computer and Internet experience, self-efficacy and motivation (includingenjoyment and usefulness) are all key factors for individual use of the Web. Ifteachers and trainers have such information when planning their instruction,especially online courses, they may consider allocating some of instructional timeand activities to strengthening the weaker computer-related skills. Given thepredictive utility of perceptions demonstrated by many previous and presentfindings, such effort seems worthy.

LimitationsIn conducting the statistical analysis, it was found that respondents were skewed indistribution by the variable of ‘years of computer-related experience’ (shown inTable 4). This might be a crucial limitation of the Internet survey. This resultsuggests that when individuals have a more positive feeling toward computers theyare more willing to answer the survey. General speaking, users’ computer anxietydecreased with more computing experience and high frequency of using computers(Yaghi & Abu-Saba, 1998). Based on this evidence, participants will be afraid toanswer the questionnaire in a Web format when they use computers infrequently. Inother words, this phenomenon may create the statistical issues of restriction of rangeor skewed distribution.

Another limitation is relatively low response rate which may make generalisationdifficult. The response rate of this study was 32.5% and this also might be due to fearof technology. In general, response rates from the Internet surveys are from 6% to70% (Weible & Wallace, 1998). At the moment, the response rate is unpredictableand that of this study is quite reasonable. Weible & Wallace (1998) also indicatedthat response rates between email and regular mail were not significantly different.



The third limitation is that the results cannot be generalised to all educationalinstitutions. Indeed, this study focused on a university and differences certainly existbetween universities and other schools (such as high schools). Therefore, careful useof the results should be made, especially as to their applicability to different grade-level schools or other organisations (such as business or industry).

Acknowledgement

The author is grateful to the editor, Robert Lewis and the anonymous referees fortheir helpful comments. In addition, the author wishes to acknowledge Dr. Arthur K.Ellis, Dr. Peter Smith, and Dr. Christopher Sink for their helpful suggestions. Thisstudy was partially supported by National Science Council in Taiwan (ProjectNumber: NSC 90-2511-S-039-002) and was partially supported by Ministry ofEducation in Taiwan, Project Number: H045.

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Conference announcementInternational Conference on Computers in Education (ICCE 2002)

Learning communities on the Internet - pedagogy in implementation3-6 December 2002, Auckland, New ZealandFor more information see: http://icce2002.massey.ac.nz

International Conference on Computers in Education (ICCE 2003)The ‘Second Wave’ of ICT in Education: from facilitating teaching and learning toengendering education reform

December 2003, Hong Kong

understanding user perceptions of

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