a field study of computer efficacy beliefs as an outcome of training: the role of computer...

A field study of computer efficacy beliefs as anoutcome of training: the role of computerplayfulness, computer knowledge, and

performance during training

Denise Potosky*

The Pennsylvania State University, Great Valley School of Graduate Professional Studies,

30 East Swedesford Road, Malvern, PA 19355, USA

Abstract

Prior research has established the importance of enhanced task-specific self-efficacy (SSE)as an outcome of training, including computer-related training. This field study of 56 newlyhired computer programmers explored potential antecedents of post-training computer self-efficacy beliefs regarding trainees’ programming capabilities. Specifically, the relationship

between computer playfulness, computer knowledge and experience, performance duringtraining, and post-training efficacy was explored. Self-rated knowledge of computers andperformance during training were positively correlated with post-training programming effi-

cacy. After controlling for general pre-training computer efficacy, there were no main effectsfor the independent variables studied. However, the three antecedent variables included in aregression model explained a significantly greater portion of the variance in post-training

efficacy beyond the control measure of pre-training computer efficacy, suggesting that post-training SSE may depend on a composite of relevant individual characteristics and trainingexperiences. Interestingly, results suggested a significant interaction effect between computer

playfulness and performance during training, such that more playful individuals who per-formed well on hands-on exercises during training made the highest post-training program-ming efficacy judgments. Suggestions for future research on computer efficacy are discussed.# 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Self-efficacy; Computer efficacy; Computer playfulness; Computer experience; Training

Computers in Human Behavior 18 (2002) 241–255

www.elsevier.com/locate/comphumbeh

0747-5632/02/$ - see front matter # 2002 Elsevier Science Ltd. All rights reserved.

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* Tel.: +1-610-648-3375; fax: +1-610-725-5224.

E-mail address: [email protected] (D. Potosky).

1. Introduction

Some people attempt to avoid the seemingly constant push to learn new computertechnology, while others seem to embrace the opportunity. Some people seem tocatch on quickly and with confidence when faced with new software or updatedprocedures, but others seem to need more time and training. A compelling questionin many organizations is, which people?Fortunately, the research literature on training and performance in general, and on

computer training and use in particular, has provided at least one answer to the ques-tion of who is likely to learn to use computers and new software: People who think theycan. That is, there is extensive evidence that self-efficacy, or ‘‘the beliefs in one’s capa-bilities to mobilize the motivation, cognitive resources, and courses of action needed tomeet given situational demands’’ (Wood & Bandura, 1989, p. 408), is associated withhigher levels of motivation and performance (Bandura, 1997; Stajkovic & Luthans,1998). Bandura (1991) has argued that self-efficacy may be one of the most importantfactors influencing a person’s activity and efforts toward goal attainment.Several studies have shown that training increases self-efficacy, and that post-

training self-efficacy is related to performance (e.g. Frayne & Latham, 1987; Gau-dine & Saks, 1998; Gist, 1989; Gist, Schwoerer, & Rosen, 1989; Latham & Frayne,1989; Martocchio & Webster, 1992; Saks, 1995; Tannenbaum, Mathieu, Salas, &Cannon-Bowers, 1991). As such, self-efficacy is itself an important outcome of thesuccessful training process. Self-efficacy may be especially relevant in more complextraining programs, in which trainees may face more obstacles and frustrations asthey learn new technology and skills (Davis, Fedor, Parsons, & Herold, 1998). Inaddition, self-efficacy has been shown to be associated with higher levels of persis-tence in the event of failure (Bandura & Cervone, 1983), and may enable resilienceduring stressful or difficult times (Gist & Mitchell, 1992; Gist et al., 1989).Relatively few studies have focused on self-efficacy in field settings (e.g. Davis et

al., 1998; Saks, 1995), and/or in extended computer training situations, however.Much of the research on self-efficacy judgments regarding computers has focused onnovice computer users in training focused on introductory or rather basic computerapplications (e.g. word processing or spreadsheet manipulation) that occurred in afew hours or perhaps one day. In contrast, in field settings, weeks or months may berequired for learning and computer skill acquisition, especially for more complexsets of tasks. Less is known about efficacy beliefs in the increasingly common situa-tion in which an organization introduces new programming languages or softwareapplications, advanced features, and computer-related procedures, and expects itsemployees to use the technology made available to them.Computer efficacy refers to a person’s belief in his or her own ability to use com-

puters. Trainees’ beliefs in their ability to perform computer tasks appears to be animportant predictor of their willingness to continue to learn and perform suchcomputer tasks after training. For example, computer efficacy beliefs have beenshown to influence individuals’ decisions to use computers (Hill, Smith, & Mann,1987). In addition, Gist et al. (1989) reported that trainees who scored high incomputer self-efficacy performed significantly better than trainees with low computer

242 D. Potosky / Computers in Human Behavior 18 (2002) 241–255

efficacy scores in a 3-hour training course in using financial analysis/spreadsheetsoftware. Martocchio and his colleagues have demonstrated that individuals’ withhigh computer/software efficacy beliefs performed better on declarative knowledgetests following computer training courses than did individuals with low efficacybeliefs (Martocchio & Dulebohn, 1994; Martocchio & Judge, 1997; Martocchio &Webster, 1992). Overall, one can conclude from these studies that computer efficacyis important to learning to use computers, and that enhanced specific computerefficacy beliefs are an expected outcome of computer training.One research issue relevant to organizations endeavoring to train computer users

and programmers concerns the role of trainees’ experiences during the training itselfin the development of computer self-efficacy. For example, Davis et al. (1998)showed that performance on trials on a computerized flight training system during apilot training course were positively related to post-training efficacy beliefs aboutflight maneuvers. Investigation of these issues can contribute not only to ourunderstanding of self-efficacy and computer playfulness, but also to the design ofcomputer training programs.A related question concerns the extent to which prior computer knowledge and

experience influences post-training computer efficacy. Prior studies have examinedthe effect of prior computer experience on learning new computer applications andon general computer use (Gardner, Dukes, & Discenza, 1993; Hill et al., 1987; Nel-son & Cheney, 1987). In fact, individuals’ levels of computer experience, which is apotential source of enactive mastery (see later), have been shown to be related totheir computer efficacy beliefs. Computer self-efficacy beliefs may also mediate therelationship between prior computer experience and future adoption of computertechnology (Hill et al., 1987). It seems appropriate to consider the potential effect ofindividuals’ knowledge of computers on self-efficacy beliefs.One issue that has received less research attention is the antecendent role of indi-

vidual characteristics (other than prior computer experience) in the observed chan-ges in efficacy beliefs observed during computer training. That is, it is not clearwhether training uniformly increases efficacy beliefs for all trainees, or perhaps ifsome trainees exhibit greater increases than others. For example, Webster andMartocchio (1992) found that microcomputer (or cognitive) playfulness, whichrefers to an individual’s tendency to interact spontaneously, inventively, and imagi-natively with computers, was positively related to computer efficacy beliefs. Theseauthors also demonstrated that computer playfulness is a relatively stable trait,whereas efficacy beliefs regarding specific computer applications have been shown toincrease with training. Further, Martocchio and Webster (1992) reported positivecorrelations between cognitive playfulness and post-training software efficacybeliefs. Based upon these findings, it appears that increases in efficacy beliefs may begreater for playful individuals than for individuals low in computer playfulness.This field study examined the antecedent role of computer playfulness, prior

computer knowledge and experience, and performance during training on thedevelopment of post-training efficacy beliefs associated with a four-day trainingcourse in computer programming. More detailed discussion of these potential influ-ences and hypotheses about their effects are presented below.

D. Potosky / Computers in Human Behavior 18 (2002) 241–255 243

1.1. The development of specific efficacy beliefs

The construct of self-efficacy has been shown in prior research to act as a moti-vational mechanism that leads to desirable performance outcomes (Gist & Mitchell,1992; Kanfer, 1987). Generalized self-efficacy refers to an individual’s belief in his orher capabilities to meet the demands of various goals across situations (Eden, 1988),and as such reflects the overall level of ‘‘I think I can’’ an individual possesses.However, within the context of the social-cognitive theory of self-regulation (Ban-dura, 1977, 1986, 1991), it is important to consider ‘‘I think I can what?’’ That is,Bandura (1997) has argued that it is more meaningful to consider task-specific self-efficacy beliefs (SSE), which refer to an individual’s beliefs and intentions to allocateeffort in order to achieve some targeted level of performance (Kanfer, 1987). Withthis in mind, computer efficacy as well as language-specific, software-specific, orcomputer application-specific efficacy beliefs are conceptualized.When considering specific efficacy beliefs, it is also interesting to ask ‘‘What makes

you think you can?’’ Bandura (1977) suggested that self-efficacy comes from fourdifferent sources: enactive mastery, vicarious experiences, verbal persuasion, andpsycho/physiological arousal. Of these, the most powerful source of SSE is enactivemastery, which refers to repeated task-related experiences (Bandura, 1977; Gist,1987). When a trainee makes post-training efficacy judgments, two sources of enac-tive mastery are possible: (1) prior experience with the computer-related tasks; and(2) performance during training.With regard to prior experience, if trainees have knowledge and experience in using

computers in a way that is related to the focus of the training, they may rely on those pastexperiences when determining their capabilities regarding the computer programmingtasks. Judgments of SSE are based, at least in part, on individuals’ assessments of theirskills (Gist & Mitchell, 1992). For example, learning to use new computer softwareinitially consists of acquiring declarative knowledge, i.e. knowledge about particularfacts (Anderson, 1985; Martocchio & Dulebohn, 1994). This declarative knowledgeincludes basic commands about creating and saving files, printing, using default settings,etc. Declarative knowledge is not only a prerequisite to higher order learning, e.g.compilation, in which computer trainees might combine various aspects of theirknowledge to sequence commands (Ackerman, 1987; Anderson, 1982), but it hasalso been shown to be an antecedent to software efficacy (Martocchio & Dulebohn,1994). Trainees who lack declarative knowledge of computers are unable to look toprior task accomplishments when making self-efficacy judgments, and must then relyon less powerful sources of efficacy information. As a result, it follows that individualswith less computer knowledge and experience will have lower post-training SSE.

Hypothesis 1. There will be a positive relationship between prior general computerknowledge and experience and post-training programming efficacy.

In addition to prior knowledge and experience, performance during the trainingitself also provides information that each trainee can use to estimate one’s post-trainingcapabilities, which, in turn, motivates post-training performance. That is, performance


during training serves as another source of enactive mastery that can influence post-training judgments. In a computer programming course trainees learn and practice task-relevant skills, and the feedback they receive about their hands-on training performancein particular should be related to their post-training computer efficacy beliefs. Traineeswho perform well on practice exercises during the training should have higherpost-training programming SSE than trainees who perform poorly during training.

Hypothesis 2. Training performance will be positively related to post-training com-puter efficacy.

Reviews of the research literature concerning self-efficacy have called for moreresearch on the antecedents of processes involving SSE (Kanfer 1990, 1992). Asnoted earlier, one individual difference that may have implications for computertraining is computer playfulness (Martocchio & Webster, 1992; Webster & Martoc-chio, 1992). Computer playfulness has been defined by these researchers as a moti-vation-related, but relatively stable individual attribute that represents anindividual’s capacity for cognitive spontaneity when interacting with computers.Drawing upon the theoretical framework regarding intellectual playfulness (e.g.Barnett, 1990, 1991; Csikszentmihayli, 1990; Liebermen, 1977) as well as research oncognitive playfulness (e.g. Miller, 1973), Martocchio and Webster (1992) argued thattrainees who are higher in cognitive playfulness would be more spontaneous, inventive,and imaginative when interacting with computers. Their research has also suggested thatan individual’s tendency to interact playfully with computers is positively related tocomputer/software efficacy beliefs (Martocchio & Webster, 1992; Webster &Martocchio, 1992). In addition, Webster and Martocchio (1992) reported that computerplayfulness is not only correlated with positive computer attitudes, less computeranxiety, and positive mood when using computers, but that computer playfulnesswas more strongly related to outcomes such as learning, mood, and satisfaction thancomputer anxiety or computer attitudes. Computer playfulness may have explanatorypower regarding which individuals will show greater increases in SSE.

Hypothesis 3. There will be a positive relationship between computer playfulness andpost-training self-efficacy beliefs.

In addition to a potential main effect for computer playfulness, it is plausible thatcomputer playfulness will interact with performance during training. Webster andMartocchio (1992) explained that trainees who are highly playful with computersshould experience higher involvement, satisfaction, and learning during trainingthan individuals low in playfulness. In addition, trainees who are more playful wheninteracting with computers might explore their capabilities more and perhaps gain abetter understanding of them (Malone, 1980; Miller, 1973). With this in mind, itseems logical to expect that individuals who respond to computer training playfullymay actually perform better on training activities and hands-on exercises during thetraining. This interaction should lead to increased post-training SSE judgmentsabout performance of the training tasks.


Hypothesis 4. Computer playfulness will interact with performance during trainingsuch that more playful trainees who perform well during training will have increasedpost-training software efficacy beliefs.

2. Method

2.1. Participants

The data for this study were collected from 56 newly hired employees at a mid-sized (about 3000 employees) software development firm. These individuals wereselected by the company to be trained as computer programmers and analysts.Seventy-one percent of the participants were male, and they ranged in age from 21to 40 (mean age=26 years, S.D.=4 years). For the most part, these individuals wererecent college graduates from computer science undergraduate majors recruitednationally as well as internationally to work in entry-level positions as programmer-analysts. They had been screened during the company’s recruitment and selectionprocess on the basis of their computer programming experience as well as theirinterest in becoming a programmer. As such, these were not novice computer users,but they were individuals with relatively little work experience (e.g. more than halfof these new employees reported having less than 2 years of any type of workexperience). According to company representatives involved in hiring and trainingthese individuals, it was unlikely that these new employees would have advancedSQL-Oracle programming language experience, or that they would have written thetype of programs used by the company in its software development.

2.2. Measures

2.2.1. Computer efficacyGiven that the dependent measure of interest is post-training computer efficacy, it

would be appropriate to control for the effects of pre-training self-efficacy inthe design of the study. However, given the nature of the field setting in which thetraining occurred, it was not appropriate to ask trainees to make efficacy judgmentsabout the company-specific programming tasks taught in the training prior to thetraining itself. Instead, a more general measure of computer efficacy, which focusedon more basic uses of the company’s computer system, was administered prior totraining. It was expected that there would be a positive relationship between thesecomputer efficacy beliefs, and post-training programming efficacy.Computer efficacy was measured using a scale format adapted from Gist et al.

(1989), which asked study participants to rate their confidence in performing (i.e.‘‘can do’’) three different computer operations over five levels of difficulty. Thismethod of measuring self-efficacy, described by Gist and Mitchell (1992) and dis-cussed by Bandura (1984, 1986) entails having individuals respond yes or no towhether they are capable of performing tasks at each of several specific levels.Although study participants were new employees at the software company, it was


fairly safe to assume some level of basic familiarity with desktop computers, e-mail,and general use of a networked system. The three computer tasks incorporated intothis measure pertained to: (1) operating a desktop computer within the company; (2)Accessing e-mail using the company’s internal application; and (3) Using the com-pany’s networked computer system, which entailed navigating through a Windowsenvironment. Participants responded ‘‘yes’’ or ‘‘no’’ to the ‘‘can do’’ portion at eachof the five levels of difficulty (which ranged from ‘‘When I am provided with writteninstructional material’’ to ‘‘When there is an instructor to guide me by telling meeach step as I proceed, and explaining any errors I make’’) and, if their response was‘‘yes,’’ rated their confidence on a scale of 1 (not at all) to 10 (totally confident). Theinternal consistency estimate obtained for this measure was �=0.94.

2.2.2. Computer experienceComputer experience was assessed in two ways. General computer ‘‘know how’’

was assessed using the Computer Understanding and Experience (CUE) scaledeveloped by Potosky and Bobko (1998). This measure consists of 12 items pre-sented with a five-point, Likert-type scale ranging from 1 (strongly disagree) to 5(strongly agree). Sample items are ‘‘I know how to write computer programs’’ and‘‘I know what an operating system is.’’ The internal consistency estimate for theoverall CUE measure was �=0.79. The CUE has been shown to provide two sub-scales, which assess general computer knowledge and programming experience(Potosky & Bobko, 1998). Because the participants’ in this study were essentiallyprogrammers-in-training, responses to the six items pertaining to the programmingsubscale of the CUE were summed to create a CUE Programmer score (�=0.77).Study participants were also asked to provide a single item, self-rating of their

knowledge of computers on a scale of 1 (very minimal) to 5 (extensive). Responsesobtained for this scale ranged from 3 to 5, indicating that all study participantsreported having at least some knowledge of computers.

2.2.3. Computer playfulnessThe 22-item, seven-point microcomputer playfulness scale administered in this

study was developed by Webster and Martocchio (1992). In accordance with therecommendations of these authors, seven items from the larger scale (spontaneous,flexible, creative, playful, unimaginative, unoriginal, and uninventive) were used toconstruct the measure of computer playfulness used in the primary analyses of thisstudy. The last three of the seven items listed were reverse-scored prior to construc-tion of the measure. The internal consistency estimate for this seven-item scale was�=0.71.

2.2.4. Training performanceStudy participants were required to work independently on end-of-chapter exer-

cises throughout the training. These exercises represented a ‘‘hands on’’ componentof the training. Performance on these exercises formed the basis of the performance-during-training measure obtained for this study. Each trainer rated the performanceof the trainees on each chapter, and trainees were provided with information from


their trainers about how well they did on the exercises. An overall mean exerciseperformance rating was calculated for ratings on the two most advanced chaptersthat both training groups covered (i.e. chapters 5 and 6). This gave each study par-ticipant an overall training performance score, which ranged from 1 (poor perfor-mance on training exercises) to 5 (excellent performance on training exercises).

2.2.5. Post-training efficacyStudy participants’ efficacy beliefs about performing job-related tasks associated

with SQL-Oracle programming were assessed using a format similar to the one usedto assess computer efficacy described above. The form given to trainees explainedthat ‘‘using Structured Query Language (SQL) involves creating tables, writing sub-queries to retrieve data, creating data views of the database, and grouping data byfunctions such as SUM, AVG, and COUNT’’, and trainees were then asked to indi-cate their beliefs about their ability to use SQL. Specifically, trainees indicatedwhether or not they believed they could perform each of five different tasks (e.g. ‘‘Iam capable of writing subqueries in SQL to retrieve data’’) and then rated theirconfidence (on a scale of 1–10) in performing each task. The internal consistencyestimate for this measure was �=0.93.

2.3. Procedure

Before being deployed to their assignments within the company, new employeeswere required to participate in a 3-day company orientation program and a 6-weektraining academy. During this company orientation, after signing an informedconsent form, study participants completed a survey that measured several indivi-dual characteristics, including general computer efficacy, computer understandingand experience, computer knowledge, computer playfulness, and demographiccharacteristics.Approximately 2 weeks after the orientation program and initial survey, study

participants were sent to one of two company sites within the USA to begin a 4-daycourse in SQL-Oracle programming. Two different trainers provided instruction foreach of the two training groups. The course was divided into eight chapters ofincreasing complexity regarding SQL programming. The first few chapters of thetraining materials essentially covered the basics of the programming language andprotocols, and the remaining chapters addressed more specialized topics relevant toeach location. The final two chapters covered at each training location differed incontent, and thus were not used in compiling the performance during trainingmeasure for the study. Instead, training performance was measured using trainerevaluations of trainees’ performance on end-of-chapter exercises for the mostadvanced chapters that both training groups covered (i.e. chapters five and six).SQL-Oracle programming-specific efficacy beliefs were measured after trainees hadcompleted the first six chapters of the course, using the self-report post-trainingmeasure described in the measures section.Unfortunately, scores for some measures used in this field study were unavoidably

incomplete (e.g. some participants may have been absent during the hands-on


training exercises or may have opted not to respond to certain items). Pre-trainingcomputer efficacy measures obtained for four study participants were incompleteand were dropped from analyses utilizing this measure; 52 computer efficacy scoreswere used in the analyses. Similarly, post-training efficacy measures obtained forfour different study participants were also incomplete and were dropped from ana-lyses; 52 software efficacy scores were used in the analyses. Examination of thedemographic information and scores on the other measures provided by these indi-viduals suggested that these trainees were not substantively different from theindividuals for whom complete post-training SSE data were available. In addition,one trainee was absent from the hands-on exercises during training that formed thebasis for the training performance scores. This same trainee was one of the four whomissed the post-training software efficacy measure. In sum, a total of 55 trainingperformance scores were available for the analyses.

2.4. Data analyses

Two sets of analyses were performed. First, correlations between all measureswere computed and analysed in the context of the first three hypotheses for thisstudy. Second, a hierarchical regression analysis was performed (cf. Cohen &Cohen, 1983). In the first step of the regression analysis, generalized computer effi-cacy was entered into the model as a control variable. In the second step, the threeindependent variables (computer knowledge, computer playfulness, and perfor-mance during training) were entered as a block of variables. Step 2 allowed forevaluation of parameter estimates in order to test for main effects, and also for thedetermination as to whether this block of variables, taken as a group, offered addi-tional significant explanatory power in terms of the variance in the dependentmeasure beyond that explained by the control variable. In the third step, the twohypothesized interaction terms (cross-products) were entered into the regressionmodel, again allowing for the determination of the significance of the interactionterm as well as the amount of additional variance explained.It may be important to note that although training materials were consistent

across training groups, there may have been differences in delivery style, emphasison certain points, etc., between the two different trainers who conducted the trainingat two separate company locations. T-test comparisons were conducted to ensurethat the background characteristics of the two groups of trainees did not differ sig-nificantly, and there was no significant relationship between the two training groupsin terms of the dependent variable. Although it was not possible to accuratelymeasure and account for trainer differences in the analyses, it is important toacknowledge that there may be some non-random bias in the results obtained.

3. Results

Means, standard deviations, reliabilities, and intercorrelations of all measures areshown in Table 1. As anticipated, correlation analysis results indicated a significant


relationship between computer efficacy beliefs and post-training, SQL-specific effi-cacy beliefs (r=0.37, P<0.01). Table 1 also indicates tentative support for hypoth-esis 1, which proposed that prior computer knowledge and experience would berelated to post-training software efficacy. Respondents’ single-item self-ratings oftheir knowledge of computers were positively related to post-training efficacy(r=0.28, P<0.05). However, although the relationship was positive, post-trainingefficacy was not significantly correlated with scores on the Computer Understandingand Experience measure (r=0.22, P=0.12). Given that the training was a course incomputer programming, the correlation between individuals’ scores on the pro-gramming experience subscale of the CUE and post-training SSE were examined.This correlation is indeed higher (r=0.26, P=0.07) than the correlation betweenoverall CUE scores and SSE, but was not significant. Since CUE scores were highlycorrelated with trainees’ self-rated knowledge of computers (r=0.58, P<0.001),only the latter rating were used in the subsequent regression analysis.Correlation results shown in Table 1 lend support to Hypothesis 2, which proposed

that good performance during training would be positively related to post-trainingSSE (r=0.28, P<0.05). Although the correlation between computer playfulness andpost-training efficacy (Hypothesis 3) was positive, it was not significant (r=0.21,P=0.12).Table 2 shows hierarchical regression analysis results. In step one of the analysis,

generalized computer self-efficacy, entered as a control variable, was significantlyrelated to software self-efficacy. In step two, main effects were entered into the

Table 1

Descriptive statistics and intercorrelationsa

Measure Mean S.D. n � 1 2 3 4 5 6 7

1. Computer efficacy 141.31 12.89 52 0.93 1.00

2. CUEb 53.09 4.66 56 0.79 0.14 1.00

3. CUE programmerb 26.05 3.49 56 0.77 0.11 0.91*** 1.00

4. Computer knowledgec 3.95 0.67 56 n/a 0.28* 0.58*** 0.54*** 1.00

5. Computer playfulness 38.48 5.16 56 0.71 0.14 0.18 0.12 0.17 1.00

6. Training performanced 3.99 0.75 55 n/a 0.25 0.07 0.12 0.07 0.09 1.00

7. Post-training software efficacy 41.44 7.29 52 0.93 0.37** 0.22 0.26 0.28* 0.21 0.28* 1.00

a Overall number of trainees=56. Computer efficacy measures obtained for four individuals, and soft-

ware efficacy measures for four different individuals, were incomplete. Observations pertaining to these

individuals’ responses on other measures were dropped from analyses.b CUE=Computer Understanding and Experience scale (Potoksy & Bobko, 1998); CUE Programmer

represents the programming experience subscale (six items) of the CUE scale.c Computer knowledge was assessed using a self-rated single-item of respondents’ knowledge of com-

puters.d The measure of performance during training reflects mean performance on chapter exercises per-

taining to two chapters of training content. Evaluation points ranged from 1 (poor performance) to 5

(good performance). One trainee was absent during the hands-on exercises used to determine training

performance scores, and therefore n=55 for this measure.

* P<0.05.

** P<0.01.

*** P<0.001.


regression model. Although their parameter estimates were positive, there were nosignificant main effects for the three independent variables (computer playfulness,computer knowledge, and performance during training) entered into the model,suggesting no unique, independent contribution of any one of these variables inrelation to post-training SSE. The R2 for the overall model increased significantlyin step two, suggesting that this block of variables made a significant contribution tothe regression equation by explaining an additional 10% of the variance (P<0.01) inpost-training SSE.In step three, the interaction term was entered. This variable made a significant

contribution to the regression equation by explaining an additional 8% of the vari-ance (P<0.01) in post-training SSE. In support of Hypothesis 4, the interaction ofcomputer playfulness and performance during training was significantly related topost-training SSE (P<0.05). This interaction was graphed in order to interpret itsmeaning. Fig. 1 shows the mean values of post-training SQL-specific efficacy (SSE)for individuals with high computer playfulness versus low playfulness (based upon amedian split of the computer playfulness variable), plotted against training perfor-mance scores. As a check to ensure that highly playful individuals did not simplyperform better during training than less playful individuals, t-test analysis compari-son of high versus low computer playful groups was performed. Consistent with thecorrelation results that showed the computer playfulness was not related to perfor-mance during training (Table 1), training performance scores did not differ sig-nificantly between these two classifications (t=1.35, d.f.=50). Thus, the graph ofthe significant interaction between computer playfulness and training performancesuggests that individuals high in computer playfulness who performed well duringthe training had higher post-training SSE scores than less playful individuals whoperformed well.

Table 2

Results of hierarchical regression of software efficacy on computer playfulness, performance during

training, and interactionsa

Step � Step R2 �R2

Step 1—controls

General computer efficacy 0.21*** 0.14**

Step 2—main effects 0.24* 0.10**

Computer playfulness 0.15

Computer knowledge 1.73

Performance during training 2.41

Step 3—interactions 0.32** 0.08**

Playfulness � performance 0.69*

a F (5, 41)=3.91**; Final Adj. R2=0.24; n=47.

* P<0.05.

** P<0.01.

*** P<0.001.


4. Discussion

The motivation and training research literatures has suggested that self-efficacy isrelated to post-training task performance, and is an important outcome of thetraining process. In this field study, the development of task-specific self-efficacy(SSE) was examined in the context of a 4-day training course in SQL programmingconducted for newly hired computer programmers at a software development com-pany. Several hypotheses regarding the relationship between post-training SSE andtwo individual characteristics (computer playfulness and computer knowledge/experience), as well as one contextual source of SSE (performance during training),were examined. The results indicated that self-rated computer knowledge and per-formance during training were significantly and positively correlated with post-training SQL programming SSE. The relationship between computer playfulnessand post-training SSE was also positive, though not significant. After controlling forthe effects of more general computer efficacy, which was assessed several days priorto the start of the SQL training course, the three independent variables underinvestigation had no significant, unique effect in terms of explaining the variance inpost-training SSE. Entering these three variables as a block did make a modestcontribution to the regression equation by significantly increasing the amount of

Fig. 1. Interaction of computer playfulness and training performance.


overall reliably measured variance explained in post-training SSE by 0.10. In addi-tion, the hypothesized interaction between computer playfulness and performanceduring training was supported, suggesting that computer playful individuals whoperformed well during training made higher (more positive) post-training SQL effi-cacy judgments.The finding that computer playfulness interacted with performance during train-

ing is quite interesting, because it suggests that trainees who successfully completetraining exercises may not necessarily make strong positive judgments about theirtask-specific capabilities. Instead, it was the more playful individuals, i.e. those witha tendency to explore how a program or computer system works and who may feelmore relaxed and playful when using computers, and who also performed well dur-ing training, were more confident in their post-training programming capabilities.Consistent with Webster and Martocchio’s (1992) and Malone’s (1980) expectations,it seems that trainees who were highly playful with computers may have been able toexplore their abilities more during the hands-on exercises performed during thetraining. In sum, all trainees attempted the exercises, several trainees did well onthe exercises, but the computer playful trainees who did well on the exercises repor-ted higher post-training SQL SSE.This field study was not without limitations, which temper the conclusions that

can be drawn. One obvious limitation in this study was its sample size. Althoughtraining 56 employees over 4 days is a considerable investment from a company’sperspective, clearly results obtained in this study must be interpreted with cautiongiven its small sample. A related problem concerned attrition over the course of datacollection, which occurred over three periods. Study participants completed allmeasures on a voluntary basis, and as a result the regression analyses were basedupon 47 complete observations.Despite these limitations, even interpreted tentatively, the results of this study

contribute to the current body of research on the broad area of self-efficacy as wellas the specific areas of computer-related training and computer efficacy. First, thisstudy provides one response to the call for more investigation of the individualcharacteristics that may influence SSE (Kanfer, 1990, 1992). Integrating priorresearch on computer training and computer efficacy, this study identified a combi-nation of individual differences that may be important to the development of post-training SSE. Future research might explore the predictive ability of these variableson computer efficacy in settings associated with complex computer skill acquisitionand self-efficacy beliefs associated with challenging, occupationally relevant compu-ter work. From a practical standpoint, the results of this study suggest that softwareself-efficacy may not increase uniformly for all trainees, and organizations may takean interest in future related research that would allow them to tailor their computertraining to trainees’ relevant individual differences. Second, this study providedsome evidence of how performance during training (a source of enactive mastery)and computer playfulness (a motivation-related predisposition) interact in relationto post-training programming SSE. Future research on computer-related trainingmight consider aspects of training design that compliment high computer playfulindividuals, but should also consider ways to engage less playful trainees. That is,


future studies may wish to distinguish between computer playfulness as a predis-position versus playfulness as a motivational state that can be manipulated by givingtrainees’ varying opportunities to play and explore. It may be rather useful iforganizations could encourage playfulness during training in order to stimulateexploration during practice activities. Future research may wish to investigate thepotential effects of computer playfulness on outcomes associated with computertraining and computer efficacy.

Acknowledgements

The author gratefully acknowledges a Summer Research Stipend (SRS) awardfrom Penn State Great Valley, which supported the data collection for this study.The author also thanks Jerry McClaughlin, Eric Crist, Lisa Abramson, and SusanKnoble for their time and cooperation in this project.

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