effects of training method and computer anxiety on learning performance and self-efficacy

E�ects of training method and computer anxietyon learning performance and self-e�cacy

H.-W. Chou *

Department of Information Management, National Central University, #38, Wu-Chuan Li, Chungli,

32054, Taiwan

Abstract

The present study compared the e�ects of training method and computer anxiety on learners'

computer self-e�cacy and learning performance. The study results indicated that the behavior-modeling training method yielded consistently superior performance and higher computer self-e�cacy. The signi®cant two-way interaction con®rmed the importance of person±situation

adaptation. In addition, the adaptation is task dependent. Findings of the study contribute toan expanded understanding of the factors that in¯uence learning performance and self-e�cacyand also have important implications for the management of information systems. Futureresearch directions conclude the paper. # 2001 Elsevier Science Ltd. All rights reserved.

Keywords: End-user training; Behavior modelling; Computer anxiety; Gender di�erence; Self-e�cacy

Computer training is considered an essential contributor to the success of orga-nizational computing in the information age. Information system managers have aconcurrent need to ensure that end users acquire adequate computing skills toassume their end-user roles. Nevertheless, broad diversity of individual di�erencesamong potential trainees could call for one or another training method of instruc-tion. Maier (1973) suggested that the result of training is a multiplicative product ofa learner's ability, his or her motivation level, and the training environment. Davisand Davis (1990) ®rst explored the e�ects of training techniques and personal char-acteristics on training end users of information systems. The results showed that themost e�cient and e�ective training method partially depends on one or moreemployee characteristics. With solid knowledge about end-user training potential,educators or trainers can develop programs more suitable for individuals (Bostrom,Olfman & Sein, 1990; Davis & Davis, 1990; Sein, Bostrom & Olfman, 1987).

Computers in Human Behavior 17 (2001) 51±69

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E-mail address: [email protected] (H.-W. Chou).

Three objectives are attempted in this paper. Developing a conceptual model toevaluate how training method, student's gender and computer anxiety level in¯uencelearning performance and computer self-e�cacy is the ®rst objective. The secondobjective is to compare the relative e�ectiveness of instruction-based and behavior-modeling approaches with respect to learning performance and computer self-e�cacy. Research in instructional psychology has demonstrated that adaptinginstructional methods and teaching strategies to accommodate key individual di�er-ences improves learning performance (Snow, 1986). The third objective then is tostudy if individuals with di�erent traits perform di�erently in two training conditions.The rest of this paper is organized as follows. Section 1 reviews the literature

on the variables included in the proposed model. It also provides the theoreticalframework and hypotheses for the model. Section 2 presents the study method,including subjects, experimental design, procedure, training approaches, and soforth. Section 3 introduces data analysis and results, followed by a discussion inSection 4. Section 5 summarizes the research ®ndings and concludes the paper withlimitations and implications.

1. Literature review

1.1. Training method

As organizations use training to help individuals reach their potential and meetthe demands of technologically complex jobs, the search for e�ective and e�cienttraining methods is a continuing concern for both academic sta� and practitioners.The advent and proliferation of end-user computing makes the training issue evenmore salient. Cheney, Mann and Amoroso (1986) identi®ed end-user computing(EUC) training as one of the controllable variables that could a�ect the success orfailure of EUC. Nelson and Cheney (1987) and Simon, Grover, Teng and Whitcomb(1996) have proved training as a critical factor in information system implementa-tion. Santhanam and Sein (1994) found that training methods providing good con-ceptual models would enhance performance.Bostrom et al. (1990) suggested that training method be examined by the follow-

ing dimensions: method employed and conceptual model. The former consists ofapplication-based and construct-based approaches. While the application-based ap-proach focuses on tasks and is exploration-oriented, the construct-based approachfocuses on features and is instruction-oriented. The conceptual model di�erentiatesthe ways to present the instructional content, that is, either by giving examples or byproviding synthetic representations. Based on Bostrom et al.'s theory, two trainingmethods are developed in the present study (see Fig. 1).

1.2. Instruction-based approach

The instruction-based method o�ers a traditional approach that is appropriate foralmost all training (Simon et al., 1996). This approach teaches primarily by lectures

52 H.-W. Chou /Computers in Human Behavior 17 (2001) 51±69

and follows a deductive way to learning, where learners proceed from general rulesto speci®c examples. Davis and Davis (1990) found that the instruction conditionwas superior to a self-directed approach. Hall and Freda (1982) found that instruc-tion training was more e�ective than exploratory training in courses that teach rulesor general tasks. Overall, the literature suggests that the instruction technique issuperior for retention of information.

1.3. Behavior-modeling approach

The behavior-modeling method, which means behavior is a function of personand environment, is originated in Lewin's (1951) equation B=f(P,E), The methoddeveloped in the 1970s was grounded on Bandura's (1969) principles of SocialLearning Theory (STL). This task-focused method involves a visual observationof the behaviors of a model performing a task. Learners then imitate and extendthe model's behavior in practice and experimentation to master the task. Thebehavior-modeling method employs an inductive approach that teaches by hands-ondemonstrations ®rst followed by complimentary lectures.Gist, Rosen and Schwoerer (1988) examined the e�ect of training method on the

acquisition of computer skills. They found that students in the modeling conditionhad better computer software mastery than those in the tutorial approach. Gist,Schwoerer and Rosen's (1989) further study found students in the behavior-modelingcondition had superior performance and higher self-e�cacy scores over a tutor-ial approach in learning a computer spreadsheet. Compeau and Higgins (1995)compared the instruction condition with behavior modeling in a study of self-e�cacy and outcome expectation using two software packages. The results partiallysupported that behavior modeling could enhance self-e�cacy and performance.Simon and Werner (1996) compared the e�ectiveness of three computer-trainingmethods and found that cognitive learning and skill demonstration in behaviormodeling was the best. Simon et al. (1996) conducted a longitudinal ®eld study to

Fig. 1. Training methods based on Bostrom et al.'s theory.

H.-W. Chou /Computers in Human Behavior 17 (2001) 51±69 53

examine the relationship of training methods and cognitive ability to end-usersatisfaction, comprehension, and skill transfer. The researchers found that subjects inthe behavior-modeling condition outperformed those in the other three conditionsin retention of knowledge, transfer of learning, and end-user satisfaction.

Hypothesis 1: Students in the behavior-modeling group will score higher onboth computer self-e�cacy and learning performance measures than those inthe instruction-based group.

1.4. Self-e�cacy

Self-e�cacy emanates from social learning theory and refers to self-assessedexpectations of performance. Christoph, Schoenfeld and Tansky (1998) studied thein¯uence of self-e�cacy on multimedia-based training receptiveness. They foundthat the training e�ectiveness was determined partly by the level of students' self-e�cacy ability. Gist et al. (1989) and Gist, Stevens and Bavetta (1991) found thatwork-related performance is associated with self-e�cacy. The authors suggestedthat initial computer self-e�cacy moderated the e�ect of training method ontraining outcome.

1.5. Individual di�erences

Individual di�erences play a critical role in learning and instruction. Being awareof individual di�erences will make educators more sensitive to their role in instruc-tion. Posner and McLeod (1982) suggested a model to explain individual di�erencesin mental information-processing operations. Speci®city and dynamics are the twodimensions employed in the model. Dichotomizing these two dimensions gives thefour-fold classi®cation: strategies, structures, traits, and states. Strategies aredynamic processes that perform speci®c tasks, including learning strategies and sol-ving problems, whereas structures process a speci®c function that is endured, such ashuman sensory systems.

1.5.1. GenderThe third type of individual di�erences, traits, denotes static aspects of informa-

tion processing. Traits can be descriptive or cognitive. The former is described byage, gender, education, experience, and prior achievement. Harrison and Rainer(1992) found the individual di�erences variables, such as male gender, lower com-puter anxiety, and so forth, accounted for 56% of the variance associated with end-users computing skills.Gender is a biological classi®cation and belongs to biographical data. The variable

is introduced in this study because it may contribute to the understanding of self-e�cacy exerted in improving the training bene®ts of computer skills (Rattanapion &Gibbs, 1995). Gender may also be a signi®cant variable that moderates the e�ects oftraining method and computer anxiety on both learning performance and computerself-e�cacy.


Hypothesis 2: Male students will score signi®cantly higher on learning perfor-mance and computer self-e�cacy measures, and score lower on computeranxiety scale than female students.

1.5.2. AnxietyThe fourth category in Posner and McLeod's (1982) taxonomy on individual

di�erences is states. States, consisting of emotion, feeling, motivation, anxiety, andattitude, have general in¯uences on learning performance. Jonassen and Grabowski(1993) suggested that individuals also di�er in their personality, which is describedas another type of learner traits. They include anxiety as one construct of person-ality. In the literature, computer anxiety and attitudes toward computers have oftenbeen identi®ed as the two critical factors in¯uencing computer learning perfor-mance (Amdt, Clevenger & Meiskey, 1985; Sein & Bostrom, 1989). Nevertheless,studies on the relationship between computer anxiety and learning performanceobtained mixed results. Marcoulides (1988), and Mawhinney and Saraswat (1991)found a signi®cant inverse correlation between anxiety and course performance.Brosnan's (1998) study found that computer anxiety directly in¯uenced the perfor-mance on database learning. On the other hand, the computer anxiety factordetermined by Kernan and Howard (1990) showed no association with coursegrade, neither did Szajna and Mackay (1995). These studies suggest that the rela-tionship between computer anxiety and computer performance needs to be furtheranalyzed.

Hypothesis 3: Students with high pre-training computer anxiety will scorelower on learning performance and computer self-e�cacy measures than thosewith low pretraining computer anxiety.

1.6. Person±situation adaptation

Research in instructional psychology has demonstrated that adapting instruc-tional methods and teaching strategies to accommodate key individual di�erenceshas led to improved performance (Snow, 1986). Pintrich, Cross, Kozma andMcKeachie (1986) con®rmed the above rationales in these terms: ``What the learnerbrings to the instruction situation in prior knowledge and cognitive skills is of cru-cial importance'' (p. 613). They both stress the need to tailor instructional methodsto accommodate individual uniqueness.Aptitude treatment interaction (ATI) is a research methodology that explores

interactions between various traits, aptitudes, or attributes and alternative instruc-tional methods (Snow, 1989). Its aim is to predict performance from combinationsof variables from aptitude and treatment so that optimal person±situation matchescan be identi®ed. Among the traits variables, anxiety and achievement motivationwere the most signi®cant variables that resulted in the strongest interactions (Snow,1989). In the present study, computer anxiety and training method were chosen tostudy plausible interaction e�ects on learning performance and/or computer self-e�cacy. Whether the combined e�ects of computer anxiety and training method on


learning performance and computer self-e�cacy were moderated by gender was alsostudied. The following hypotheses are derived.

Hypothesis 4: Computer anxiety has a stronger negative e�ect on learningperformance and computer self-e�cacy for students in the instruction-basedcondition than for students in the behavior-modeling group.Hypothesis 5: Computer anxiety has a stronger negative e�ect on learning per-formance and computer self-e�cacy for male students than for female students.Hypothesis 6: The behavior-modeling method has a stronger positive e�ect onlearning performance and computer self-e�cacy for male students than forfemale students.Hypothesis 7: Computer anxiety has a stronger negative e�ect on learningperformance and computer self-e�cacy for male students in the instruction-based group than for male students in the behavior-modeling condition, orfemale students in either training condition.

One of the main criticisms of ATI, according to Snow (1989), is the failure ofthe results to be generalizable. Although investigators have obtained replicationsof results, the subtle and changing complexities of educational contexts make thegeneralizations problematic. Nevertheless, such di�culty is common to educationalresearch, and is no more di�cult for ATI research. Since the theoretical frameworkof the present study is built on ATI, it would be a good contribution to observe howinstructional treatments facilitate or inhibit learning performance and self-e�cacy,depending on learners' anxiety levels.

1.7. Conceptual research model

The conceptual model including the following variables is depicted as Fig. 2.

Fig. 2. Conceptual research model.


1. Gender: was suggested as one of the descriptive traits in Posner and McLeod's(1982) taxonomy on individual di�erences. Gender was proposed as a moder-ating variable that moderates the e�ects of training method and computeranxiety on both learning performance and computer self-e�cacy.

2. Training method: was adopted from Bostrom et al.'s theory and was manipu-lated into two levels: instruction-based and behavior modeling.

3. Anxiety: was suggested as one of the ``states'' in Posner and McLeod's (1982)taxonomy on individual di�erences and was indicated by computer anxiety.

4. Self-e�cacy: was adopted from Bandura (1986) and was indicated by computerself-e�cacy.

5. Learning performance: was adopted from Kirkpatrick (1994) and wasindicated by TASK1 and TASK2. Both tasks were measured on general andprocedural knowledge and were graded by correctness and problem-solvingskills.

2. Methods

2.1. Subjects

Data were collected from a local senior high school in Chungli, Taiwan. Two10th-grade classes were randomly selected to participate in the experiment. Eachclass, with 53 and 55 students, respectively, was randomly assigned to one of thetwo training methods. Among the 101 subjects who successfully completed the entiretraining process, 92 provided valid data that were used for statistical analysis.According to the school system, the ®rst formal computer course was o�ered in thesecond semester of the 10th grade.

2.2. Experimental design

A ®eld experiment consisting of three training sessions was conducted to test thehypotheses. Because of practical limitations, a ®eld experiment was employed,instead of a laboratory experiment. One of the major critics of ®eld experimentcomes from the unit of analysis problem, where treatments are randomly assigned tointact classes rather than individuals. Since random assignment of subjects was notapplicable in the present study, t-test results on last semester's mathematics grades(P=0.798) and pretraining computer anxiety (P=0.313 for CAS.b), and genderdistribution (approximately 54, 62% of male and 46, 38% of female; for each class),proved that the two classes are comparable and homogeneous.Gender, training method, and computer anxiety are treated as the independent

variables. Training method includes instruction-based and behavior-modelingapproaches. TASK1 and TASK2, including objective questions and hands-on prob-lems to test general and procedural knowledge, were self-developed to evaluatelearning performance. The computer self-e�cacy measure was used as the otherdependent variable.


2.3. Procedure

In each class the experiment began with a brief introduction. Subjects then wereinstructed to ®ll out the computer anxiety scale, the computer self-e�cacy measure,and the background questionnaire. The background questionnaire included ques-tions about student prior experience in web design, Internet usage, applicationpackages such as word processor or spreadsheet, and computer training course.Three training sessions were held in the following three weeks with each sessionlasting for 2 h. A second computer self-e�cacy measure was given at the end of thethird training session. The di�erences between the two computer self-e�cacy meas-ures indicate the changes during the experiment.The ®rst session contained instruction and exercise, with each lasted for 1 h.

In the second and third sessions, the ®rst hour was for instruction, and thesecond hour was for exercise and a learning performance test. During the 30-minpractice time subjects were instructed to review what they had learned. Studentsin the behavior-modeling group were encouraged to imitate the model's behav-ior in practice and experimentation to familiarize the procedures and functions.Students in the instruction-based group were encouraged to experiment andpractice the procedures and functions that they had learned from the lecture, onthe computer. Each student was provided with a ¯oppy disk to save his or herwork.During the 30-min learning performance test time, students were asked to

apply whatever they learned in the training session to solve the objective questionsand hands-on problems. Upon completion of the test, a ®le containing theirperformance on the test items was saved into the diskette and handed in to thetrainer.

2.4. Training material

Three sets of training materials were developed based on a commercialized refer-ence book for WWW homepage design (Horton, Taylor, Ignacio & Hoft, 1996). The®rst set provides the participants with WWW and HTML orientation, includingintroduction of Netscape Composer, primary attributes, and document background.The second set contains paragraph de®nition and image insertion. The ®nal setincludes hyperlink and table manipulation. To ensure that the main aspects ofcomputer training are covered, each session includes both general and proceduralknowledge.

2.5. Training approaches

Uniform content of information, including general knowledge and proceduralknowledge, was provided to both training groups. Procedural knowledge refersto speci®c instructions or step-by-step operations, whereas general knowledgemeans factual or universal knowledge. All training was conducted using a singleinstructor, providing continuity throughout all training sessions. The two training


approaches di�er in terms of the presentation sequence and instructionalmedia. Both treatments were conducted in the same computer room. Students werefree to take notes and were encouraged to ask questions at any time during thepresentation.In the instruction-based condition, a deductive approach was employed in the

lecture format. At the beginning of each session, the instructor brie¯y outlinedthe key learning points followed by descriptions on general knowledge includingconcepts, general rules, and command features. Procedural knowledge and theaccompanying examples were orally presented step-by-step using the computer-driven transparencies as the principal instructional media.In the behavior-modeling group, an inductive approach was employed where the

learner starts with speci®c examples and proceeds to general rules. Students watchedthe instructor demonstrating examples and executing corresponding step-by-stepprocedures on the computer from their computer monitors. Then the instructor gavelectures on general knowledge including concepts, general rules, and command fea-tures, followed by a summary on key learning points. The computer-driven demon-stration was the principal instructional media.

2.6. Measures

2.6.1. Learning performanceTASK1 and TASK2 were self-developed; objective questions to test general

knowledge and hands-on tasks to test procedural knowledge. They were delivered atthe last 30 min of the second and the third training sessions. Participants wereinstructed to complete the test by applying whatever they had learned from instruc-tion. The range of possible total scores for each task is 0 to 100, with higher scoresindicating better learning performance.

2.6.2. Computer self-e�cacyThis ®ve-point Likert-type measure of computer self-e�cacy (CSE; Murphy,

Coover & Owen, 1989) was employed in the study. The 32-item scale was given twiceto the subjects, before and after the experiment. CSE.b and CSE.a represent the pre-and post-training computer self-e�cacy scores. Possible total scores range from 32to 160, with higher scores indicating more self-reported computer self-e�cacy. Thedi�erences between CSE.a and CSE.b, represented by CSE, indicate the computerself-e�cacy change during the experiment.

2.6.3. Computer anxietyA Chinese version of the Computer Anxiety Scale (CAS), was translated from

Marcoulides and Wang (1990). Minor semantic changes were made by two expertsto ®t in subjects' background. The scale is a measure of subjects' perception of theiranxiety in di�erent situations related to computers. The scale contains 20 items thatare rated on a ®ve-point scale re¯ecting a level of anxiety ranging from ``not at all''to ``very much''. The range of possible total scores is 20 to 100, with higher scoresindicating more self-reported computer anxiety.


2.7. Statistical techniques

t-Test, correlation, and the analysis of variance (ANOVA) procedure were used toanalyze data. In the present study, gender is used as a moderating variable becausetraining method, computer anxiety, or both may have di�erential e�ects on learningperformance and/or computer self-e�cacy, depending on the subject's gender.

3. Analysis and results

Reliability measures for CAS, CSE.b, CSE.a, assessed by Cronbach � coe�cients,were 0.9403, 0.9715, and 0.9768, respectively. The background questionnaire con-cluded most subjects had very little experience in web design, Internet usage, orapplication packages (about 90% of subjects rated their experiences in the aboveitems as ``not at all'' and ``very little'' on a ®ve-point Likert scale, another 5% ofthem rated themselves as ``some'', the remaining 5% of data was missing). Onlythree subjects attended a private computer training course.

3.1. Correlation analysis

A correlation matrix among variables (see Table 1) showed very signi®cant nega-tive associations between TASK1, TASK2 and CAS (ÿ0.238, ÿ0.189), and betweenCAS and CSE.b, CSE.a (ÿ0.565, ÿ0.434). That implies students with lower com-puter anxiety performed better and had higher computer self-e�cacy scores. Thepositive, although not signi®cant, correlation between CAS and CSE (0.127) impliesthat students with lower computer anxiety gained less in computer self-e�cacy

Table 1

Correlation among variables studieda

Variable

1 2 3 4 5

1. CAS ±

2. TASK1 ÿ238 ±

ÿ280 (ÿ178)3. TASK2 ÿ189 580 ±

ÿ220 (ÿ103) 578 (603)

4. CSE.b ÿ565 153 198 ±

ÿ506 (ÿ618) 184 (109) 177 (102)

5. CSE.a ÿ434 075 197 794 ±

ÿ287 (ÿ558) 122 (011) 078 (181) 739 (800)

6. CSE 127 ÿ103 028 ÿ184 451

305 (ÿ015) ÿ088 (ÿ141) ÿ131 (149) ÿ366 (ÿ144) 478 (356)

a Decimals are omitted for the coe�cients. For 0.1734 |r| 4 0.205, P40.1; for 0.2064 |r| 40.267,

P40.05, for |r|50.268, P40.01. The ®rst row of each block represents the overall group. The second row

represents instruction-based and behavior-modeling groups (in parenthesis).


during the experiment. Table 1 also revealed di�erent correlation patterns for twotreatment groups. A detailed description of the correlation pattern for each group isprovided in the next session.

3.1.1. Individual group correlation analysisOpposite correlation patterns were found between the two treatments in the fol-

lowing variable sets.

3.1.1.1. CAS and CSE. The CAS is signi®cantly correlated with CSE (0.305) in theinstruction-based group, whereas the correlation is negative and not signi®cant(ÿ0.015) in the behavior-modeling group. This means that in the former, studentswith higher CAS gained more CSE during the experiment. In other words, theinstruction-based approach was an e�ective method for high computer anxiety stu-dents to gain computer self-e�cacy.

3.1.1.2. Three CSE variables and TASK2. In the behavior-modeling group, the cor-relation between computer self-e�cacy and TASK2 increased from pretraining topost-training (0.102±0.181), whereas it decreased in the instruction-based group(0.177±0.078). The much stronger association between CSE.a and TASK2 in thebehavior-modeling group (0.181 vs. 0.078) implies that this training approach wasmore e�ective in enhancing students' accurate perception of their computer cap-ability. The directions of the correlation between the CSE and TASK2 in twogroups were opposite (0.149 in behavior-modeling, ÿ0.131 in instruction-based),although none was signi®cant. This showed that in the behavior-modeling group,students who performed better in TASK2 also scored higher on CSE, but theinstruction-based group students who performed better in TASK2 gained less inCSE.

3.2. Group di�erences on learning performance, computer anxiety, and computer self-e�ecacy

Table 2 shows group means of study variables by di�erent classi®cations. t-Testresults showed di�erent training methods and personal traits such as gender andcomputer anxiety had signi®cant impacts on TASK1, TASK2, and CSE.

3.2.1. Training method e�ectsThe signi®cant training method e�ects showed that students in the behavior-

modeling group performed better in TASK1, TASK2 (P<0.01), CSE.b (P<0.01),CSE.a (P<0.01), and CSE (P<0.05).

3.2.2. Gender di�erencesSigni®cant gender e�ects were also found in computer self-e�cacy variables.

Female students had much lower CSE.b (P<0.01) and CSE.a (P<0.01) scores butscored higher on CSE (P<0.1) than male students. Male students had higher scoreson TASK2 whereas female students had higher TASK1 scores, although none of the


Table

2

Groupmeansonstudyvariablesbydi�erentclassi®cations

Variable

Means(S.D

.)Means(S.D

.)Means(S.D

.)Means(S.D

.)

Overall

Instruction±B-M

aMale±Fem

ale

Low

CAS±HighCAS

n=

92

n=

46

n=

46

n=

54

n=

38

n=

48

n=44

TASK1

65.4

(31.1)

64.1

(34.1)

66.6

(28.2)

62.8

(32.1)

68.9

(29.8)

68.2

(31.2)

62.3

(31.1)

TASK2

54.7

(26.0)

48.7

(28.7)***

60.7

(21.7)

57.4

(26.2)

50.9

(25.5)

57.2

(25.1)

52.0

(26.9)

CSE.b

89.2

(26.7)

81.5

(24.7)***

96.9

(26.6)

99.7

(27.3)***

74.4

(17.2)

101.2

(24.4)

75.6

(22.3)

CSE.a

97.3

(29.4)

86.4

(24.6)***

108.1

(29.9)

105.4

(31.8)***

85.7

(21.0)

108.2

(29.9)***

84.8

(23.2)

CSE

8.0

(18.2)

4.9

(17.8)**

11.1

(18.1)

5.7

(19.0)*

11.3

(16.6)

7.0

(19.1)

9.2

(17.3)

CAS

47.1

(16.4)

48.8

(16.9)

45.3

(15.9)

45.7

(15.9)

49.0

(17.2)

33.9

(7.3)***

61.4

(10.5)

aB-M

forbehavior-modelingcondition.

*P<

0.1.

**P<

0.05.

***P<

0.01.


above di�erences was signi®cant. Male students also scored lower on CAS. Thisshowed that male students generally performed better in computer learning, hadhigher perception of their computer capabilities, and were lower in computer anxietythan female students.

3.2.3. Computer anxiety e�ectsStudents were grouped into low and high levels by the CAS mean scores. The

results indicated that the two groups signi®cantly di�ered in CSE.a (P<0.01). Stu-dents in low computer anxiety group generally scored higher on learning perfor-mance and had higher perception on their computer capabilities than students inhigher computer anxiety group.

3.2.4. ANOVAANOVA technique was employed to study plausible interaction e�ects of com-

puter anxiety, treatment, and gender, on learning performance and computer self-e�cacy (see Table 3).

3.2.5. Interaction e�ects on learning performanceThe computer anxiety e�ect on learning performance, moderated by gender, was

signi®cant on TASK1 (P<0.05) and TASK2 (P<0.05). A stronger negative e�ect ofcomputer anxiety for male students was found only on TASK1, as hypothesized.The e�ect of training method, moderated by gender, was signi®cant on TASK1

(P<0.05) and TASK2 (P<0.01). Table 4 shows male students performed better inthe instruction-based condition whereas female students consistently bene®ted morefrom the behavior-modeling condition. The training method e�ect on learning per-formance moderated by gender was signi®cant in an opposite direction as hypothe-sized.The signi®cant three-way interaction e�ect on TASK2 (P<0.01) indicated that

gender together with computer anxiety signi®cantly interacted with training condi-tion. Table 4 and Fig. 3 show the pattern that training method had di�erentiale�ects for students with di�erent traits. Contrary to Hypothesis 7, computer anxietywas found to have a stronger negative e�ect on TASK2 for female students, ratherthan for male students, in the instruction-based condition (35.5 vs. 57.1). Regarding

Table 3

F-ratios of interaction e�ects on TASK1, TASK2, and CSE

Source TASK1 TASK2 CSE

Anxiety�Training method 0.6 0.2 2.4*

Anxiety�Gender 2.2* 2.5* 2.7*

Training method�Gender 5.3** 12.0*** 3.9**

Anxiety�Training�Gender 0.3 2.2* 5.9***

*P<0.1.

**P<0.05.

***P<0.01.


TASK2, the behavior-modeling approach is more bene®cial to male students withlow computer anxiety than for those with high computer anxiety (63.2 vs. 47.1),whereas the same approach is more suitable for female students with high computeranxiety than for those with low computer anxiety (77.6 vs. 60.8). In the instruction-based group, male and female students followed a much similar pattern where lowcomputer anxiety subjects performed better. Besides, high computer anxiety femalesperformed at the extremes; that is, they performed the worst in the instruction anddid the best in the behavior-modeling condition among eight subgroups (77.6 vs.35.5).

3.2.6. Interaction e�ects on computer self-e�cacyTable 3 shows all two- and three-way interaction e�ects on computer self-e�cacy

were signi®cant. Table 5 and Fig. 3 show the pattern that training method had dif-ferential e�ects for students with di�erent traits.The signi®cant interaction e�ects of computer anxiety by training method on CSE

suggested that low computer anxiety students bene®ted more from the behavior-modeling condition than from the instruction-based approach (11.8 vs. 1.4). Theinstruction-based approach was more appropriate for high computer anxiety stu-dents than for low computer anxiety ones (8.4 vs. 1.4).The e�ect of computer anxiety on CSE was signi®cantly moderated by gender.

The result showed that high computer anxiety female students improved more thanmale students with the same high level of computer anxiety (15.8 vs. 4.0).The treatment e�ect on CSE was signi®cantly moderated by gender. It showed

that male students bene®ted more from the behavior-modeling approach whereasfemale students gained more in the instruction-based condition. For male studentsin the instruction-based condition, their gain in CSE was negative (ÿ1.6).The signi®cant three-way interaction on CSE indicated that gender together

with computer anxiety interacted with training condition. Computer anxiety had

Table 4

Means in TASK1 and TASK2 among subjects in di�erent groupsa

Low computer anxiety High computer anxiety

Male 67.8 (61.4) n=31 56.1 (52.1) n=23

Female 66.7 (49.3) n=18 71.0 (52.4) n=20

Instruction-based Behavior-modeling

Male 67.8 (59.1) n=25 58.6 (56.0) n=29

Female 59.8 (36.5) n=21 80.3 (68.7) n=17

I: Male I: Female B-M: Male B-M: Female

Low computer anxiety (60.6) n=14 (37.8) n=9 (63.2) n=16 (60.8) n=9

High computer anxiety (57.1) n=11 (35.5) n=12 (47.1) n=13 (77.6) n=8

a Number in parenthesis is for TASK2.


Table 5

Means in CSE among subjects in di�erent groups


Instruction-based 1.4 n=23 8.4 n=23

Behavior-modeling 11.8 n=25 10.4 n=21


Male 7.1 n=30 4.0 n=24

Female 6.3 n=18 15.8 n=20

Instruction-based Behavior-modeling

Male ÿ1.6 n=25 12.03 n=29

Female 12.5 n=21 9.71 n=17

I: Male I: Female BM: Male BM: Female

Low computer anxiety ÿ1.4 n=14 5.6 n=9 14.5 n=16 7.0 n=9

High computer snxiety ÿ1.9 n=11 17.8 n=12 9.0 n=13 12.8 n=8

Fig. 3. Three-way interaction on TASK2 and CSE.


e�ects on CSE for all subjects across training conditions, except for males in theinstruction-based group (ÿ1.4 vs. ÿ1.9). Besides, female students with high com-puter anxiety gained the most CSE in the instruction-based condition (17.8),whereas male students with the same computer anxiety gained the worst in the samecondition (ÿ1.9).

4. Discussion

The purpose of the present study was to explore the role of training method andpersonal traits in computer learning behaviors.The results supported Hypothesis 1 and con®rmed with earlier research on beha-

vior modeling, which found the superiority of behavior-modeling to instruction-based approaches on learning performance and self-e�cacy (Compeau & Higgins1995; Simon et al., 1996; Simon & Werner, 1996).The results of the present study also partly supported Hypothesis 2 that male

students had better learning performance, higher computer self-e�cacy, and lowercomputer anxiety. The result that female students had signi®cantly lower self-e�cacy is consistent with Chou and Wang's (2000) research, which found femalestudents had lower self-image about their computer learning capabilities.Students with low computer anxiety scored higher on TASK1, TASK2, CSE.b,

and CSE.a, but scored lower on CSE than students with high computer anxiety.Nevertheless, none of the di�erences was signi®cant. Hypothesis 3 was not sup-ported.The signi®cant computer anxiety by training method e�ects on CSE indicated

that low computer anxiety students did the worst in the instruction-based andthe best in the behavior-modeling condition. Hypothesis 4 was therefore partlysupported.A stronger negative e�ect of computer anxiety for male students was found on

TASK1 and CSE, but not on TASK2. High computer anxiety male students gainedthe least in TASK1 and CSE. The result partly supported Hypothesis 5.It was con®rmed as hypothesized that male students had higher computer self-

e�cacy in the behavior-modeling condition, whereas female students bene®ted morefrom the instruction-based condition. The direction of the moderating e�ects wasreversed on learning performance; that is, the behavior-modeling condition was moresuitable for female students whereas the instruction-based method was preferred tomale students. The result partly supported Hypothesis 6, which describes a strongerpositive e�ect of behavior-modeling method on learning performance and computerself-e�cacy for male students than for female students.The signi®cant three-way interaction on TASK2 and CSE partly supported

Hypothesis 7. The hypothesis describes a stronger negative e�ect of computer anxi-ety on learning performance and computer self-e�cacy for male students in theinstruction-based group.Regarding TASK2, a signi®cantly stronger negative e�ect of computer anxiety for

female students, rather than for male students, in the instruction-based group.


Regarding CSE, a stronger negative e�ect of computer anxiety for male studentscame from the instruction-based group, as hypothesized.Computer anxiety was signi®cant on CSE for all subjects across training condi-

tions, except for males in the instruction-based group. Besides, female students withhigh computer anxiety gained the most CSE in the instruction-based condition,whereas male students with the same computer anxiety gained the least in the samecondition.

5. Conclusions

The study provides some tentative answers to the questions concerning end-user training. The study results showed the behavior-modeling training methodis superior with respect to learning performance and computer self-e�cacy. Gendere�ects in general were found to be signi®cant: male subjects performed betterthan female subjects. The computer anxiety e�ect was signi®cant only on post-training computer self-e�cacy. The signi®cant two-, three-way interaction indicatesthe critical roles of gender and computer anxiety level in interacting with trainingmethod.The interaction e�ects were more signi®cant on computer self-e�cacy than on

learning performance. The training method by gender interaction on learningperformance and computer self-e�cacy proved the signi®cant moderating e�ectsofgender. Nevertheless, the di�erential moderating e�ects on learning perform-ance and computer self-e�cacy are worth further study. Regarding learningperformance, male subjects preferred an instruction-based approach and femaleliked the behavior-modeling condition better. Regarding computer self-e�cacy,male bene®ted more from the behavior-modeling condition whereas female stu-dents preferred the instruction-based condition. How and why the moderatinge�ects of gender had di�erential e�ects on various learning contents is worthfurther study.The computer anxiety by gender interaction on computer self-e�cacy again con-

®rmed the signi®cant moderating e�ects of gender. For high computer anxiety lear-ners, females improved the most whereas males gained the least. The computeranxiety by training method interaction e�ects on computer self-e�cacy showed thatlearners with low computer anxiety learned the most in the behavior-modelingmethod and gained the least in the instruction condition.Further, the three-way interaction on learning performance indicated that high

computer anxiety females performed at the extremes; that is, they did either theworst in the instruction-based or the best in the behavior-modeling condition. Thethree-way interaction on computer self-e�cacy showed that high computer anxietyfemale students improved the best in the instruction condition whereas high com-puter anxiety male students performed the worst in the same condition.The above results con®rmed that matching person±situation is critical to the suc-

cess of computer learning outcomes since each training method has its unique meritto meet designated training objectives for learners with speci®c traits.


5.1. Limitation and implications

Speci®c weaknesses of the present study include the use of students instead ofemployed individuals, and a relatively small sample. The scope of the study needs tobe extended and replicated with real-world end users.Several practical and theoretical implications arise from these ®ndings. First,

additional training techniques need to be studied with di�erent computer trainingcontent, such as languages programming and application software. Another avenueof research is to explore and compare the e�ects of di�erent types of learning styles,as well as other potentially important individual di�erences.

Acknowledgements

This research was partially supported by the National Science Council, Taiwan.Grant Number NSC89-2511-C-008-001.

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