Computer-related success and failure: a longitudinal field study of the factors influencing computer-related performance

Computer-related success and failure: alongitudinal ®eld study of the factors in¯uencing

computer-related performance

E.J. Rozell a,*, W.L. Gardner III baDepartment of Management, College of Business Administration, Southwest Missouri State University,

Spring®eld, MO 65804, USAbDepartment of Management, College of Business Administration, University of Mississippi, University,

MS 38677, USA

Abstract

This study used a path analysis of longitudinal data collected from 75 manufacturingemployees participating in a computer training course, to test a model of the intrapersonalprocesses impacting computer-related performance. Gender, computer experience, and attri-

butional style were found to be predictive of computer attitudes, which were in turn related tocomputer e�cacy, task-speci®c performance expectations, and post-performance anxiety.Computer training was e�ective in raising user e�cacy levels and improving computer per-

formance. In addition, post-training e�cacy was predictive of subsequent computer perfor-mance. Finally, performance outcomes and future performance expectations were predictiveof users' a�ective reactions. # 1999 Elsevier Science Ltd. All rights reserved.

Keywords: Computer-related performance; Attributional style; Computer training

As the in¯uence of computers grows increasingly pervasive, it has becomeimperative for organizations to address the individual needs of a diverse group ofusers. Despite e�orts to ease users into computer-intensive environments, negativeexperiences arise far too often. Aversive experiences may in turn cause organiza-tional problems, such as high training costs, alienation, absenteeism, turnover, and acommiserate decline in productivity (Williams, 1991). Although several authors haveattempted to identify the major causes of MIS success and failure (e.g. Zmud, 1979),this literature lacks an integrative model of the intrapersonal processes that impactuser performance. This ®eld study seeks to ®ll this void by using a longitudinaldesign to test such a model.

Computers in Human Behavior 15 (1999) 1±10

0747-5632/99/$Ðsee front matter # 1999 Elsevier Science Ltd. All rights reserved.

PII: S0747-5632(98)00030-2

*Corresponding author.

1. Literature review and hypotheses

1.1. Predictors of computer attitudes

Based on prior research (Chen, 1986; Igbaria, Pavri, & Hu�, 1989; Levin &Gordon, 1989; Rosen & Maguire, 1990; Sigurdsson, 1991; Wilder, Mackie,& Cooper, 1985), we predict that male, younger, and more experienced users willexhibit more favorable computer attitudes. Moreover, based on the work ofSeligman (1990) and preliminary research by Rozell and Gardner (1995), we expectattributional style to be directly related to computer attitudes. Note that genderand age e�ects are posited, despite inconsistent ®ndings (Marakas, Yi, & Johnson,1998), because we believe the balance of evidence indicates their inclusion in themodel is warranted.

1.2. Predictors of performance expectations

For computer-related tasks, e�cacy expectations have been shown to be impactedby computer experience, gender, and age (Busch, 1995; Carlson & Grabowski, 1992;Chen, 1986; Igbaria et al., 1989; Loyd & Gressard, 1984; Marakas et al., 1998;Wilder et al., 1985), as well as attributional style (Rozell & Gardner, 1995), withmore experienced, male, younger, and optimistic users exhibiting greater e�cacy.We also expect persons with positive as opposed to negative computer attitudes(Chen, 1986; Harrison & Ranier, 1992; Kinzie, Delcourt, & Powers, 1994; Rozell &Gardner, 1995), and computer training (Gist, Schwoerer, & Rosen, 1989; Torkzadeh& Koufteros, 1994), to be more con®dent of their computer abilities.Because computer e�cacy measures (Murphy, Coover, & Owens, 1989) tap e�cacy

expectations for a wide variety of computer tasks, we also measured the subjects'performance expectations regarding the speci®c tasks for which they were trained, asMarakas et al. (1998) recommend. We expect users' task-speci®c expectations to be afunction of the variables already listed, as well as computer e�cacy in general.

1.3. Predictors of user a�ect

The available research suggests that a set of relationships exist between gender,age, computer experience, and attributional style and user a�ect which parallel thosedescribed above for computer attitudes (Chen, 1986; Igbaria et al., 1989; Rosen &Maguire, 1990). In general, when these variables are directly related to computerattitudes, they also tend to be directly related to positive a�ect, and inversely relatedto negative a�ect.There is ample evidence that individuals who are anxious about computers experi-

ence feelings of ine�cacy (Harrison & Ranier, 1992; Martocchio, 1992). However,research on computer self-e�cacy suggests that perceptions of computer ine�cacycontribute to computer anxiety, rather than vice versa (Compeau & Higgins, 1995;Marakas et al., 1998). Accordingly, we assert that users' performance expectationswill predict their a�ective state.

2 E.J. Rozell, W.L. Gardner III/Computers in Human Behavior 15 (1999) 1±10

1.4. Predictors of computer-related e�ort

E�cacy judgements in¯uence one's tendency to engage in a task, as well as exer-tion and endurance levels (Bandura, 1997). It follows that computer-related e�ort islikewise impacted by user e�cacy (Igbaria & Parasuraman, 1989; Marakas et al.,1998; Muira, 1987), as well as task-speci®c expectations. Support for this assertion isprovided by Hill, Smith and Mann (1987), who showed that the more people viewcomputers as controllable, the more likely they are to use them.

1.5. Predictors of computer-related performance

We consider computer-related performance to be a function of selected attrib-utes of the user [e.g. ability (not measured in this study), computer experience,and attributional style; Marakas et al., 1998; Rozell & Gardner, 1995] and thework environment (e.g. training; Gist, Rosen, & Schwoerer, 1988; Gist et al., 1989;Torkzadeh & Koufteros, 1994), as well as the user's performance expectations (Gistet al., 1989; Marakas et al., 1998), and e�ort (Weiner, 1979, 1985). Based on priorresearch, we do not expect gender, age, or computer attitudes to be directly relatedto performance (Dambrot, Silling, & Zook, 1988; Igbaria & Parasuraman, 1989;Kinzie et al., 1994; Marakas et al., 1998; Rosen & Maguire, 1990).

1.6. Predictors of causal attributions

We predict that users' attributions will be a function of their attributional styleand performance (Peterson, Maier, & Seligman, 1993; Seligman, 1990; Silver,Mitchell, & Gist, 1995; Weiner, 1979, 1985).

1.7. Feedback e�ects

Drawing on the work of Weiner (1979, 1985), we predict that performance will bedirectly related to future performance expectations and positive a�ect, and inverselyrelated to negative a�ect. However, we also anticipate two interaction e�ects, suchthat performance will have greater e�ects on: (1) expectations when it is attributedto stable rather than unstable causes, and (2) a�ect when it is attributed to internalversus external causes (Peterson et al., 1993).

2. Methods

2.1. Participants

Data were collected from 75 assembly-line employees of a mid-size manufacturing®rm in the midwest. As part of a pay-for-knowledge program, the subjects com-pleted an introductory course on DOS, WordPerfect, and Lotus 123. The vastmajority had little or no experience with personal computers, and most were males(77%). A total of 17% were between 21 and 30 years old, 29% were between 31 and

E.J. Rozell, W.L. Gardner III/Computers in Human Behavior 15 (1999) 1±10 3

40, 33% were between 41 and 50, and 13% were between 51 and 60. Eight percent ofthe respondents did not provide data regarding their age. With respect to companytenure, 65% had 10 years or less, while 23% had between 11 and 20 years, and 12%had over 20 years. Most subjects had a high-school degree (57%), followed by thosewith some college training (34%) or a college degree (9%).

2.2. Procedure

The research instruments were administered across six time periods. The ®rst ques-tionnaire was completed during an initial training session (T1). It included the attribu-tional style, computer attitudes, computer experience, and computer e�cacymeasuresdescribed in the next section, plus the demographic items. The pre-test was given next(T2). After the pre-test results were returned, a second questionnaire was administeredto measure users' performance attributions, task-speci®c expectations, and a�ectivereactions as described below (T3). Following the training period, Questionnaire 3 wasemployed to measure the amount of self-reported e�ort users expended preparing forthe exam, while obtaining more current measures of their task-speci®c expectationsand computer e�cacy (T4). The post-test was then given (T5). Finally, Questionnaire 4was administered after the trainees received the post-test results (T6). This instrumentincluded the measures of subjects' attributions, task-speci®c expectations, and a�ect.

2.3. Measures

Existing measures of computer experience (Lee, 1986), computer attitudes(Popovich, Hyde, & Zakrajsek, 1987), and computer e�cacy (Murphy et al., 1989)were employed. A�ect was measured using 21 items from the anxiety, depression,and positive a�ect scales of the Multiple A�ect Adjective Checklist (Zuckerman &Lubin, 1965). Nine and four items, respectively, were developed to measure self-reported e�ort and performance expectations. Scores on the pre-test and post-test were used as measures of computer-related performance. These tests appraisedtrainees' knowledge of DOS, WordPerfect, and Lotus 123.To measure attributional style, a modi®ed version of an instrument developed by

Seligman (1990) was used. The 32 items tap the respondents' attributions for posi-tive and negative events. Subscales were not used because the reliabilities for mostwere below 0.4. Instead, we scored each item in the direction of optimism to producean overall scale ranging from extreme pessimism to extreme optimism. Based on anitem analysis, ®ve items which lowered the scale's reliability were dropped. Thecoe�cient alpha of the resultant scale is 0.61.The users' attributions for their pre- and post-test performance were measured

using two items. The ®rst was an open-ended question which asked the trainees toindicate the reason(s) for their test success or failure. The second asked the subjectsto ``check the most important reason for your performance on the test'' from achecklist of eight potential reasons developed by Bartol and Darom (in press). Eachcause was then classi®ed as internal versus external, and stable versus unstable toproduce the locus and stability dimensions.


Table 1 summarizes the coe�cient alphas obtained for these measures, whereappropriate, as well as the means and standard deviations for each scale. Most ofthe reliability coe�cients are at appropriate levels of 0.7 or above.

2.4. Analysis

To test most of our assertions, a path analysis was conducted using a series ofstepwise multiple regressions. To explore the posited e�ects of causal attributions onperformance expectations, a dummy variable, locus (1=internal, 0=external), alongwith a performance�locus interaction term, were included in the appropriateregressions. Other assertions were tested using t-tests.

3. Results

3.1. Path analysis of the research model

3.1.1. Predictors of computer attitudesFemales, more experienced, and optimistic users expressed more positive comput-

er attitudes (R2 � 0:46, p < 0:0001). Thus, partial support for our assertion was

Table 1

Psychometric measures: reliabilities, means and standard deviations

Scale No. of items Coe�cient alpha Mean SD

Computer experience 8 0.82 1.54 0.60

Computer attitudes 19 0.84 2.16 0.55

Attributional style

Overall: pessimism±optimism 27 0.61 12.22 3.77

Computer e�cacy

1st administration 20 0.98 2.59 1.05

2nd administration 20 0.98 3.26 0.98

Task-speci®c expectations

1st administration 4 0.95 2.75 0.73

2nd administration 4 0.97 2.87 0.94

3rd administration 4 0.95 2.73 0.86

A�ective state

1st administration

Positive a�ect 10 0.85 1.29 0.28

Negative a�ect:

Anxiety 5 0.85 1.59 0.37

Depression 6 0.69 1.78 0.24

2nd administration

Positive a�ect 10 0.74 1.08 0.15

Negative a�ect:

Anxiety 5 0.82 1.79 0.31

Depression 6 0.73 1.92 0.18

E�ort 9 0.88 2.89 0.88


obtained; however, the posited e�ect of age was not supported, and the genderresults were the opposite of those predicted.

3.1.2. Predictors of performance expectationsBefore training, computer attitudes were positively related to task-speci®c expec-

tations (R2 � 0:25, p < 0:001) and e�cacy levels (R2 � 0:29, p < 0:0001). Aftertraining, similar results emerged, with computer attitudes and task-speci®c expecta-tions relating directly to computer e�cacy (R2 � 0:47, p < 0:0001), which in turnpredicted subsequent task expectations.To compare users' performance expectations before and after training, t-tests were

used. The means (Table 1) and t-test results indicate that users' e�cacy levels(t � ÿ4:9, p < 0:001) were higher after training than before, as predicted. However,task-speci®c expectations were not signi®cantly elevated immediately followingtraining (t � ÿ1:54, p � 0:12). Moreover, it is interesting to note that task-speci®cexpectations signi®cantly declined following the post-test (t � 2:51, p � 0:01).Hence, while our assertion was supported for computer e�cacy, the ®ndings fortask-speci®c expectations were unexpected.While knowledge of computer attitudes alone was su�cient to predict task-speci®c

expectations prior to training, computer e�cacy was the sole predictor variable toemerge immediately following training (R2 � 0:59, p < 0:0001) and after the post-test (R2 � 0:55, p < 0:0001).To test for the posited e�ects of stability on user expectations, t-tests were used.

Contrary to our assertions, unstable attributions were not associated with greaterchanges in computer e�cacy or task-speci®c expectations.

3.1.3. Predictors of user a�ectBefore training, partial support was found for our assertions. Speci®cally, as pre-

dicted (1) more experienced users exhibited lower general anxiety levels (R2 � 0:21,p < 0:01) and (2) higher task-speci®c expectations led to higher levels of positive a�ect(R2 � 0:15, p < 0:01). In addition, higher pre-test scores were predictive of lowerlevels of depression (R2 � 0:13, p < 0:05), as predicted.Predictors of anxiety following training included scores on the post-test, com-

puter attitudes, gender, and e�ort (R2 � 0:42, p < 0:0001). Our ®ndings revealedthat females, and users who exhibit more negative computer attitudes, greatere�ort, and lower post-test performance, tend to experience greater anxiety follow-ing training. Moreover, higher post-test scores and task-speci®c expectations werelinked to lower levels of depression (R2 � 0:46, p < 0:0001). Lastly, users' ®naltask expectations were directly related to positive a�ect (R2 � 0:26, p < 0:001)following training. Each of these results, with the exception of the relationshipbetween e�ort and anxiety, were predicted a priori. However, the anticipatede�ects of age, experience, attributional style, and computer e�cacy failed toemerge.Multiple regression was used to test for the feedback e�ects of the locus dimen-

sion. Because all of the users' post-test attributions were internal, our assertioncould only be tested using pre-test data. No support was obtained.


3.1.4. Predictors of computer-related e�ortNo support was obtained for the predictors of computer-related e�ort.

3.1.5. Predictors of computer-related performanceRegression was used to test the predictors of performance before and after train-

ing. Because computer e�cacy and task-speci®c expectations are highly correlated,separate regressions were run including one or the other, but not both variables, toavoid multicollinearity. Before training, it was found that computer experience waspredictive of pre-test performance (R2 � 0:28, p < 0:0001). After training, highercomputer e�cacy led to higher post-test scores (R2 � 0:10, p < 0:05).A t-test was used to test the training e�ects by comparing subjects' pre- and post-

test scores. As expected, users' post-test scores were signi®cantly greater than theirpre-test scores (t � ÿ14:21, p < 0:001). Thus, training was e�ective at raising userperformance on a computer-related task.

3.1.6. Predictors of casual attributionsWe intended to test the predictors of causal attributions by dividing the subjects

into success and failure groups based on their pre- and post-test scores, and thenexamining the attributional style of users making internal versus external, and stableversus unstable attributions. However, several factors combined to produce inade-quate cell sizes, including (1) missing values (23 nonresponses for the pre-test at-tributions; 10 for post-test attributions), (2) restrictions in the ranges of someattributional dimensions (e.g. all of the subjects made internal attributions for post-test performance), and (3) the restricted range of the post-test data [M � 91:5%correct; most (91%) of the subjects scored above 75%]. Thus, it was impossible tostatistically test our assertion.

4. Discussion

4.1. Determinants and e�ects of computer attitudes

The roles that one's background and outlook towards computers can play inshaping one's interactions with computers are readily apparent from this study.Indeed, a key contribution is that it traces the major determinants and pervasivee�ects of computer attitudes across time. Consistent with prior studies (Busch, 1995;Chen, 1986; Kinzie et al., 1994; Levin & Gordon, 1989; Loyd & Gressard, 1984;Pope-Davis & Twing, 1991; Sigurdsson, 1991; Wilder et al., 1985), computerexperience contributed strongly to favorable attitudes. Importantly, experience alsoserved as the major predictor of initial user performance and anxiety. Thus, theimpact of experience on users' reactions to, and performance with, computers isconsiderable.Gender was also related to computer attitudes, with females expressing more

favorable attitudes than males. Interestingly, this ®nding is the opposite of those ofmany earlier studies (Busch, 1995; Chen, 1986; Levin & Gordon, 1989; Rosen &


Maguire, 1990; Wilder et al., 1985). Perhaps men's and women's attitudes towardscomputers are changing; if so, continuing assumptions that women dislike comput-ers may be misguided. Still, it is interesting that females expressed greater anxietyfollowing the post-test than males. Thus, despite women's relatively positive atti-tudes, computer usage appeared to create greater anxiety among women than men.The ®nal signi®cant predictor of computer attitudes is attributional style. As

expected, pessimists tended to dislike computers, whereas optimists viewed themmore favorably. Given the research by Seligman (1990) which suggests that pessi-mists have less satisfying life experiences than optimists, this ®nding is consistentwith that research. It does suggest, however, that one's general outlook toward lifeplays a key role in determining one's attitudes towards computers.Our results also suggest that, to a large degree, one's outlook towards computers

serves as a self-ful®lling prophecy. Users with positive versus negative computerattitudes displayed higher levels of computer e�cacy and task-speci®c expectations,as well as less anxiety following task completion. Moreover, users who expectedto succeed, reported more positive a�ect, while those with greater e�cacy levels ex-celled at the task. Finally, computer-related performance was inversely related tonegative a�ect. Thus, it appears that one's outlook toward life in general, and one'sexperiences with and attitudes towards computers in particular, foster either positiveor negative expectations about upcoming computer interactions, which tend to cometrue. The degree of one's success or failure, coupled with expectations for futureperformance, in turn determines whether one experiences joy or sorrow.

4.2. Training e�ects

Our results also highlight the impact that training can have on computer e�cacy.Speci®cally, training raised computer e�cacy levels, which were, in turn, predictiveof subsequent performance. These results suggest that computer training can payimportant dividends for organizations who make the investment.Two sets of ®ndings suggest that this inference should be quali®ed, however. First,

it is important to note that training did not signi®cantly raise users's task-speci®cexpectations. While users' expectations (Table 1) immediately following trainingwere raised, the di�erence is not signi®cant. Moreover, mean expectations followingthe post-test declined signi®cantly to fall just below initial levels, suggesting that thee�ects of training on task expectations were temporary, at best. Second, while e�-cacy levels were elevated following training, initial computer attitudes continued toaccount for a signi®cant amount of the variance. Therefore, persons with negativeattitudes tend to continue to possess lower levels of computer e�cacy, despite com-puter training, than those with more favorable attitudes.

5. Practical implications

Given the prominent roles that computer experience, attitudes, and e�cacyplay in determining users' performance and a�ective reactions, two primary


recommendations are o�ered for organizations. The ®rst is to carefully evaluateeach of these factors when selecting employees for computer-related positions. Thesecond is to supply new and existing employees with computer training which goesbeyond imparting computer skills; attitudes towards computers and e�cacy expec-tations should be speci®cally targeted for improvement as well. One promisingapproach is suggested by Martinko and Gardner (1982), who advocate ensuringthat trainees experience some initial task success to boost their con®dence. In acomputer-related context, anxious workers could secure initial success at relativelysimple computer tasks (e.g. basic word processing). As they gain con®dence, theycan be exposed to increasingly challenging tasks. For persons with especially nega-tive attitudes, exposure to models who demonstrate the utility and usability ofcomputers may be required as well. To the extent that these techniques prove to bee�ective, they can help organizations to more fully capitalize on both their computerand human resources.

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Documents

Computer-related success and failure: a longitudinal field study of the factors influencing computer-related performance