The effects of computer anxiety, state anxiety, and computer experience on users' performance of computer based tasks

Person. indiuid. Dijjf Vol. 22, No. 5, pp. 683492,

1997

Q 1997 Elsevier Science Ltd. All rights reserved

Pergamon PII: SO191-8869(96)00260-7

THE EFFECTS OF COMPUTER AND COMPUTER EXPERIENCE OF COMPUTER

Printed in Great Britain 0191-8869197 $17.00+0.00

ANXIETY, STATE ANXIETY, ON USERS’ PERFORMANCE BASED TASKS

Doug Mahar,‘* Ron Henderson’ and Frank Deane3 ‘School of Social Science, Queensland University of Technology, Beams Road, Carseldine, QLD 4034 Australia, ‘Centre for Applied Psychology, University of Canberra, Canberra, ACT, Australia and ‘Department of Psychology. Massey University, New Zealand (Recekd

15 Juiv 1996)

Summary-The construct validity of computer anxiety was explored by assessing the extent to which computer anxiety test scores are predictive of users’ ability to complete basic computer operations accurately and/or swiftly when the level of experience of the user is considered. Undergraduate students’ levels of computer anxiety, computer avoidance. computer experience, state anxiety, and the latency and accuracy with which they could complete a simple data entry task were measured. The data confirmed previous findings that computer anxiety is associated with elevated levels of both computer avoidance and state anxiety. Most importantly, the data revealed that computer anxiety is associated with slower completion of simple computer tasks and that this performance deficit is independent of both the prior level of computer experience and the level of state anxiety of the user. :C;I1997 Elsevier Science Ltd.

INTRODUCTION

The ubiquitous observation that some individuals are unusually anxious about working with computers has led to the proposal of a condition called computer anxiety to describe this state. A substantial number of pen and paper tests have been developed over the last 10 years to measure computer anxiety levels (see LaLomia & Sidowski, 1993 and Woodrow, 1991 for reviews) and the reliability and construct validity of these tests have been subject to close scrutiny (e.g. Deane et al., 1995). Much of the work on the construct validity of these scales has focused on demonstrating relationships between computer anxiety scores and the occurrence of relevant negative behaviour patterns. The types of negative behaviour that may result from a specific anxiety condition, like computer anxiety, range from feelings of distress and lack of control in mild cases to physiological signs of stress and a desire to avoid the anxiety inducing agent in those cases meeting the clinical criteria from a phobic disorder (American Psychiatric Association, 1993)t. Moreover, these behaviours may occur in either the actual or implied presence of the anxiety inducing agent. As Ajzen (1988) explains; “The actual or symbolic presence of an object elicits a generally favourable or unfavourable evaluative reaction, the attitude towards the object. This attitude, in turn, predisposes cognitive, affective, and connotative responses to the object.. .” (pp. 22-23). Empirical studies have confirmed that computer anxiety is associated with all of these types of behaviour. For example, it has been found that high scores on these computer anxiety tests are associated with high trait anxiety scores (Deane et al., 1995; Farina, Arce & Sobral, 1991a, b). Likewise, computer anxious Ss report elevated levels of physical distress consistent with the somatic effects of phobic disorders (Hudiburg, 1990; Rosen, Sears & Weil, 1987). Finally, it has been shown that individuals with high computer anxiety scores report a greater frequency of behaviours associated with avoiding computers (Deane et al., 1995; Harrington, McElroy & Morrow, 1990). The variables used in these studies of the effects of computer anxiety have primarily been either self-report measures scores on other pen and paper tests. Clearly, it is critical to the construct

*To whom all correspondence should be addressed. TVarious researchers have referred to this condition as either computer anxiety or computerphobia. As it seems likely that many individuals exhibit some degree of anxiety regarding computers without meeting the clinical criteria for a phobic disorder, the term computer anxiety will be used throughout this paper. 683

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validity of computer anxiety that scores on computer anxiety scales also relate to directly measurable aspects of the users’ interaction with computers. For example, as anxiety disorders are often associated with reduced performance in the presence of the anxiety inducing agent (e.g. Smock, 1955), high computer anxiety scores should be associated with poor performance when using computers. The majority of studies investigating the relationship between performance on computer tasks and computer anxiety have used students’ grades on computer related courses or course components as the criterion variable. These studies have only yielded equivocal results. For example, some studies have found moderately negative correlations between computer anxiety scores and course grades (e.g. Mawhinney & Saraswat. 1991; Roszkowski et al., 1988). On the other hand, while Kernan and Howard’s (1990) factor analytic study identified a factor associated with computer anxiety, they did not find a bivariate correlation between computer anxiety and course grades. Of course, an individual’s performance in a computer course is due to a large number of personological and environmental variables, thus reducing the ability of these studies to detect specific performance effects due to a computer anxiety. In spite of this, very few studies have employed more direct indices of users’ performance when working with computers. An early study by Paxton and Turner (1984) found that naive users who held negative attitudes towards computers learned editing tasks more slowly and made more errors when using computers than did those with positive attitudes towards computers. Glass and Knight (1988) compared the latency and accuracy with which high and low computer anxious Ss completed a series of computer tasks that were designed to minimise the need for previous experience with computers. They found no significant differences in either the latency or accuracy of performance as a function of computer anxiety; although the effect with respect to latency did approach significance. More recently, Szajna (1994) conducted an extensive study of computer anxiety and computer attitudes as predictors of performance on non-programming related aspects of introductory and intermediate level business computer courses. She found significant negative correlations between computer anxiety and the performance of computer tasks (grades from hands-on exams using several commercial software packages) only early in the course. Once the students had gained more experience with computers, this correlation disappeared. Szajna (1994) thus suggested that computer experience may interfere with attempts to detect the effects of computer anxiety, and hence that there is a need for research on the nature of the relationship between these two variables. In fact, there is already a substantial body of evidence on the relationship between computer anxiety and computer experience. The relevant studies have either measured changes in computer anxiety levels as a consequence of completing some type of computer course or correlated Ss’ reported prior amount of computer experience with their computer anxiety scores. In the former case, there is evidence that undertaking any of a range of different types of computer training leads to lower computer anxiety scores (Reed & Palumbo, 1992; Rosenbluth & Reed, 1992). Likewise, in the case of prior computer experience, a number of studies have found a negative relationship between computer anxiety and amount of prior computer experience (Cohen & Waugh, 1989; Colley, Gale & Harris, 1994; Crable, Brodzinski & Scherer 1994; Deane et al., 1995; Dyck & Smither, 1994; Farina et af., 1991a; Henderson et al., 1995; Igbaria & Chakrabarti, 1990; Loyd, Loyd & Gressard, 1987; Pope-Davis & Twing, 1991; Ray & Minch, 1990; Todman & Monaghan, 1994). In spite of the large number of studies reporting a negative relationship between computer anxiety and computer experience, this result is by no means universal. For example, Rosen et al. (1987) found that computer anxiety did not decrease with computer experience, and at times actually increased. In contrast, while both McInerney, McInerney and Sinclair (1994) and Leso and Peck (1992) found that computer anxiety generally decreases with experience, they also found that high levels of computer anxiety persist in some individuals despite training. Similarly, Temple and Gavillet (1990) found no decrease in computer anxiety as a function of training in older novice computer users. Finally, while Lambert (1991) found a relationship between computer anxiety and computer experience, those Ss with initially low levels of computer anxiety experienced increased levels of state anxiety when faced with a novel task. Thus, the relationship between computer anxiety and computer experience seems to be more complex than a general reduction in anxiety with experience.

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In summary, while computer anxiety is clearly associated both with negative psychological states, such as high trait anxiety, and with avoidance of computers, there is little unequivocal evidence that computer anxiety also impacts on users’ performance when working with computers. While there is some direct evidence that computer anxiety impacts on users’ performance of computer tasks, many of the studies that have revealed performance deficits as a function of computer anxiety have used grades on computer courses as the dependent variable, and thus do not involve an unambiguous index of individuals’ ability to use computers. Even if the results of these studies are taken as evidence for a relationship between computer anxiety scores and ability to use computers, it is unclear whether this relationship is simply due to a lack of experience using computers; a factor that may alter the individuals’ level of computer anxiety. The present study set out to clarify this situation by correlating the latency and accuracy with which Ss completed simple computer operations with their level of computer anxiety while controlling for the Ss’ level of computer experience. If computer anxiety does affect performance on computer tasks independently of experience, then a negative partial correlation between computer anxiety and task performance scores was expected when any effects due to experience were removed. In addition, the present study also included measures of computer avoidance and state anxiety in order to confirm the previous findings that computer anxiety is associated with these two defining indices of specific anxiety disorders.

METHOD

Subjects

A total of 229 first-year undergraduates (153 female, 76 male) participated in this study as part of a scheduled psychology laboratory class. The average age of the students was 22.21 years, and their reported level of experience with computers ranged from 0 to 300 months. In the domain of applied psychology, it is important that the sample is representative of the target population. In the present case, the high frequency of computer use amongst undergraduates, and their high frequency of vocational computer use when they enter the work force, justifies the use of undergraduates in this study. Apparatus andprocedure

Each student first completed the short form of the State-Trait Anxiety Inventory (STAI; Marteau & Bekker, 1992; Spielberger, 1983) using the state protocol in order to assess their initial level of anxiety. They then completed the computer anxiety sub-scale of the Computer Attitudes Scale (CAS; Loyd & Gressard, 1984). This test is one of the most widely used instruments for assessing computer attitudes (Woodrow, 1991) and requires the respondent to use a five-point scale to rate the extent to which they agree or disagree with 30 statements concerning their attitudes towards computers. The test consists of three sub-scales each containing 10 items. These sub-scales assess the respondent’s level of computer confidence, computer anxiety, and the extent to which the respondent likes using computers. The anxiety sub-scale is scored so that high scores reflect low anxiety*, while high scores on the other two sub-scales reflect high confidence and a strong liking, respectively. All three sub-scales have high internal reliabilities as indicated by reported Cronbach alphas ranging from 0.86 to 0.91 (Deane et al., 1995; Loyd & Gressard, 1984). An alpha of 0.90 was obtained for the anxiety sub-scale in the present study. The students’ level of computer avoidance was assessed using a test developed from the sevenitem scale described by Deane et al. (1995). This test requires the respondent to use a five-point scale to rate the extent to which they agree or disagree with 10 statements relating to the extent to which they choose to engage in computer related activities. Sample items from this modified scale include; “I do not enjoy talking with others about computers” and “Once I start to work with

*Although it may be more intuitive to reverse score this scale (yielding high scores for high levels of computer anxiety), the conventional scoring protocol was used here to allow easy comparison between the results of the present study and those of previous studies.

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computers, I find it hard to stop”. Deane et al. (1995) reported a Cronbach alpha of 0.85 for their seven-item version of this test, while an alpha of 0.87 was obtained in the present study. The students next completed a data entry task using a mock database application running on an IBM-PC compatible microcomputer. The program was custom written for this experiment and employed a CUA-like menu interface,* which was navigated via single keystrokes. This task was designed to measure the latency and accuracy with which the students could complete a simple series of computer operations. The students were given a script detailing the tasks they were to complete and the commands that they would need to use. The instructions stressed the need to follow the script exactly; a control included to minimise the extent to which slow latencies and/or high error rates could be attributed to the Ss ‘experimenting’ with the program. The instructions and script are given below; The next task requires you to update a database containing fictitious student’s lab attendance records. Please follow the specified procedure exactly-you should not try and experiment with the various commands and menus. This is not a trick-the experiment is based on the latency and accuracy with which you can complete this designated task!!! Please read points a to h below before beginning this task.

C.

d.

e. f. g. h.

To open either of the menus type the letter beside the menu name. To select a command from a menu once you have opened the menu, type the letter beside the command name. Open the file called “A01 .dat” using the OPEN command in the FILE MENU. Use the NEXT command in the DATA MENU to move through the fiue student records. Whenever you find a student who has only attended nine labs you should use the UPDATE ATTENDANCE command from the DATA MENU to set their attendance to 10 labs. Once you have updated all the necessary records you should save the changes using the SAVE command from the FILE MENU. Once you have saved the file you should exit the data entry task using the QUIT command from the FILE MENU. When either the File or Data menus are open, you can use the CLOSE MENU command to close the menu if you find that it is not the menu you need at that time. In addition to the NEXT command, the DATA MENU has a PREVIOUS command which you can use to go to a previous student record if you find that you have missed one of the two records which need updating.

As the students completed the data entry task the computer recorded each keystroke typed and the time at which it was typed. The temporal resolution of the timer was 55 msec. At the completion of the task the total time taken to complete the task and the number of errors made were calculated. An error was defined as a keystroke other than that required by the script, but did not include keystrokes made in order to correctly recover from a previous error. The students completed this data entry task three times, with a brief distracter task involving typing 12 words and 12 numbers being performed between each repetition. This distracter task was primarily included to break the students’ three attempts at the data entry task into clearly delimited sessions for subsequent analysis. At the completion of the last repetition of the data entry task the students again completed the short form of the STAI. Finally, they were asked to provide biographical details such as age, sex, number of months experience with computers, and their average computer usage per month. RESULTS The data gathered here consisted of the latency and accuracy with which the Ss completed each of their three attempts at the data entry task, their level of computer anxiety as indicated by their score on the computer anxiety sub-scale of the CAS, their reported level of experience with computers

*The CommonUser Access (CUA) style is an industry standard for interface design in which pull-down menus are located along the top of the screen with related commands being contained in each menu and accessed via designated keystrokes or pointing device actions.

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Table 1. Summary statistics for all experimental variables Mean Errors 1

Errors 2 Errors 3 Latency I (WC) Latency 2 (SW) Latency 3 (XC) Computer Anxiety (scaled from IO to 50) Computer Avoidance (scaled from 10 to 50) STAI (pre-test) STAI (post-test) Computer experience (total hours)

2.61 0.89 0.58 128.48 40.22 26.94 41.58 22.92 11.09 I I .03 1813.13

Min. 0 0 0 18.3 11.8 9.2 14 10 6 6 0

Max.

SD

10 8 10 416.2 118.2 86

2.41 1.27 1.10 63.16 16.31 11.03

50 39 22 22 38,400

7.23 6.94 3.05 3.31 3895.96

n = 205.

expressed as the product of the number of months they have used a computer and their average monthly computer usage,* their level of computer avoidance as indicated by their scale score on the computer avoidance scale, and their pre- and post-test levels of state anxiety as indicated by their scores on the short-form STAI. Preliminary inspection of the data revealed several extreme outliers in both the error rate and computer experience data. Twenty-two students recorded more than 10 errors in at least one of their three attempts at the data entry task. Inspection of the keystroke logs for these students revealed that these errors were primarily due to the Ss holding down a key long enough to invoke the auto-repeat function. As such, these keystrokes did not constitute errors on the part of the students. In the case of the experience data, one S reported an average monthly computer usage exceeding the number of hours in a month. Finally, one S did not complete all items on the pre-test STAI. These 24 cases were excluded from all subsequent analyses. Summary statistics for each of the experimental variables were then calculated for the remaining 205 cases. These statistics are presented in Table 1. On average, the students completed the data entry task more rapidly and made fewer errors with each repetition. The variability in the latency and accuracy with which the students completed the task also decreased with practice. The most likely explanation of this decrease in variability is that those students who had initial difficulty with the task benefited most from practice. This explanation was confirmed by a comparison of the practice effects observed between attempt one and attempt three for those students who’s initial completion times fell in either the upper or lower quartiles. This analysis revealed that those students who initially recorded very slow completion times improved their performance by 85.1% with practice. In contrast, the group who initially recorded very fast completion times improved their performance by 69.5%. However, an independent group’s t-test revealed that there was still a significant difference in the completion times of these two groups on the third attempt at the data entry task, t( 101) = 6.05, P < 0.001. These analyses were repeated on the error rate data and revealed a similar pattern of results. Those students who initially had difficulty with the task improved their accuracy by 86.8% compared with only 40.1% for those who initially completed the task very accurately. Again, an independent group’s t-test revealed a significant difference in error rates between these two groups on the third attempt at the task, t(l55) = 2.02, P < 0.05. The average computer anxiety score for the total sample was 41.58 (SD = 7.23), showing that the students generally exhibited low levels of computer anxiety. While this mean level of computer anxiety is comparable with those observed in some other studies (e.g. Henderson et al., 1995; Loyd & Gressard, 1984), many other studies have found substantially higher average levels of computer anxiety in student samples (e.g. Glass & Knight, 1988; Massoud, 1991; Woodrow, 1991). Similarly, the average level of computer avoidance (mean = 22.92, SD = 6.94) was well below the midpoint of the scale. Taken together, these results suggest that this sample was generally comfortable working with computers. * In the absence of a recognised standard procedure to quantify computer experience, this measure seems a sensible choice as it includes equally weighted indices of both longevity and intensity of use; although it does not quantify the difficulty of the tasks experienced by the user.

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Table 2. Pearson’s product-moment El Errors 2 Errors 3 Latency 1 Latency 2 Latency 3 Computer anxiety Computer avoidance STAI I STAI 2 Computer experience Correlations

0.17 0.15 0.29* -0.20 -0.01 0.00

E2

0.14 0.09 0.20 -0.01 0.03

-0.04 -0.02 0.01 0.00

0.07 0.15 0.03

marked with * are significant

et al.

correlations

between all experimental

E3

Ll

L2

L3

0.04 0.10 0.46; - -0.01

0.46* 0.39* -0.25:

0.66’ -0.34’

-0.30*

0.02

0.27*

0.39*

0.36’

0.03 0.25: -0.14

0.14 0.27* -0.11

0.06 0.10 0.05

0.06 0.12 -0.10

CAnx

variables CAvoid

STAI 1

STAI 2

-0.71: -0.24’ -0.29’ 0.27*

0.15 0.33* -0.40’

0.62* -0.04

-0.12

at the 0.001 level (d.f. = 203)

The mean scores on the pre-test (mean = 11.09) and post-test (mean = 11.03) administrations of the short-form STAI were very similar, and both represent a level of state anxiety slightly below the mid point of the 24-point scale used. Although the standard deviation in each case was small (SD = 3.05 and 3.31, respectively), some Ss did record state anxiety rating toward the top of the scale (max. = 22). While the students reported a high average number of hours experience with computers (mean = 1813.13 hours), there was substantial variability in their level of experience (SD = 3895.96 hours). In spite of this, only 5.5% of the sample reported less than 100 hr experience with computers, and so it seems reasonable to describe the group as having considerable experience with computers. Table 2 presents Pearson’s product-moment correlations between all experimental variables. As 55 correlations were calculated, a Bonferroni adjusted alpha level of 0.001 was adopted to control the Type I error rate when assessing the significance of these correlations.* While a number of significant positive correlations were observed between the latency and accuracy with which the data entry task was completed on each attempt, these probably reflect the inherent cost of making errors on the latency of task completion. Likewise, the significant positive correlations observed between the latencies for each attempt at the data entry task simply reflect that individual Ss were either consistently fast or consistently slow at completing the task. The remaining significant correlations are more theoretically interesting. First, significant negative correlations were observed between scores on the computer anxiety sub-scale and the latency of completion of the first attempt, r(203) = -0.25, P < 0.001, the second attempt, r(203) = -0.34, P < 0.001, and the third attempt, r(203) = -0.30, P < 0.001, at the data entry task. As higher scores on the computer anxiety sub-scale reflect lower levels of computer anxiety, these results show that those Ss who were more computer anxious performed the task more slowly. This performanceanxiety relationship was not evident in the error rate data as no significant correlations were observed between computer anxiety scores and the number of errors made on either the first, ~(203) = 0.005, P > 0.001, the second, ~(203) = 0.03, P > 0.001, or the third, ~(203) = -0.01, P > 0.001, attempt at the data entry task. Taken together, the correlations between computer anxiety and latency, and between computer anxiety and errors, suggest that the slow performance of students with higher levels of computer anxiety is not simply due to them making more errors when attempting the task. Significant positive correlations were also observed between computer avoidance scores and the latency ofcompletion of the first, r(203) = 0.27, P -e 0.001, the second, r(203) = 0.39, P < 0.001, and the third, r(203) = 0.36, P < 0.001, attempt at the data entry task. Granted that these correlations are similar in size to those observed between computer anxiety and latency of task completion, and granted the very strong correlation between computer anxiety and computer avoidance scores, r(203) = -0.71, P c 0.001, these results are consistent with computer avoidance being a defining characteristic of computer anxiety. * In line with the correct use of an alpha level as a binary decision point, the significance of all bivariate correlations are reported here with respect to this Bonferroni adjusted alpha level of 0.001.

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Significant correlations were also observed between computer experience and both computer anxiety, r(203) = 0.27, P < 0.001, and computer avoidance, r(203) = -0.40, P < 0.001. These results are consistent with those reported by Deane et al. (1995) although the magnitude of the effects were greater here. The correlation between computer experience and computer avoidance is not surprising as those Ss who prefer to avoid computers are less likely to have gained substantial experience with computers. On the surface, the relationship between computer anxiety and experience suggests that computer anxiety reflects nothing more than a lack of experience with computers, and thus should decrease with practice. As LaLomia and Sidowski (1993) explained; “. . . for the future, the novelty of computer technology should dissipate, and as individuals become more comfortable with computers in their environments, the need for assessing computer anxiety should decrease” (p. 262). In the context of the present study, the key question here is whether the observed relationship between computer anxiety and latency of task completion is independent of any effect due to experience with computers. To test this, partial correlations were calculated between computer anxiety scores and task completion times while controlling for the effects of computer experience. These analyses revealed significant negative correlations between scores on the computer anxiety sub-scale and completion times on the first attempt, r(202) = -0.23, P < 0.001, the second attempt, r(202) = -0.3 1, P < 0.001, and the third attempt, r(202) = -0.28, P < 0.001, at the data entry task. Comparison of these partial correlations with the relevant bivariate correlations between computer anxiety and latency of task completion presented in Table 2 shows that computer experience contributed little to the anxiety-speed relationship. The implication of this is that computer anxiety is more than simply a consequence of lack of experience with computers. Scores on the pre-test administration of the STAI correlated significantly only with computer anxiety scores, r(203) = -0.24, P < 0.001. On the other hand, the post-test STAI scores correlated significantly not only with computer anxiety scores, ~(203) = -0.29, P < 0.001, but also with computer avoidance scores, r(203) = 0.33, P < 0.001, and with the latency of task completion on both the second, r(203) = 0.25, P < 0.001, and the third, r(203) = 0.27, P < 0.001, attempt at the data entry task. One explanation of why the post-test rather than the pre-test state anxiety scores correlated with the latency of completion of the data entry task stems from Deane et al.‘s (1995) state-trait model of computer anxiety, which argues that computer anxiety is a trait that manifests itself as heightened state anxiety in the presence of relevant stressors (for example, a computer). In this case, computer anxiety can be seen as the cause of slow performance on the data entry task through the agency of increased state anxiety. That is, those students with high levels of computer anxiety experienced elevated levels of computer anxiety when faced with the need to use a computer. This increase in state anxiety then impaired the students’ ability to complete the data entry task efficiently. A potential criticism of this explanation is that the average level of state anxiety prior to attempting the data entry task was actually higher than that observed after completing the task. Analysis of the state anxiety data broken down by level of computer anxiety refutes this criticism. The mean pre-test level of state anxiety for students whose computer anxiety scores placed them in the lower quartile (i.e. highest computer anxiety) was 11.92, compared with a score of 10.23 for students whose computer anxiety scores fell in the upper quartile (i.e. lowest computer anxiety). A similar analysis of post-test state anxiety scores revealed that the group with high levels of computer anxiety now exhibited higher levels of state anxiety than at pre-test (mean = 12.16) while the low computer anxiety group now exhibited lower levels of state anxiety that at pre-test (mean = 9.83). Finally, partial correlations were calculated between computer anxiety scores and the latencies with which the three data entry tasks were completed controlling for the effects of both state anxiety and computer experience. Significant negative partial correlations were found between computer anxiety and the latency for completing the second, ~(202) = -0.28, P < 0.001, and the third, ~$202) = -0.23, P c 0.001, attempt at the data entry task. While the negative partial correlation was also observed between computer anxiety scores and the latency for completing the first attempt at the data entry task, this correlation was not significant at the Bonferroni adjusted alpha level used here, r(202) = - 0.21, P > 0.001. A comparison of these partial correlations with the bivariate correlations presented in Table 2 shows that removing the effects of both computer experience and state anxiety had little effect on the strength of the relationships between computer anxiety and

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task completion latency. In summary, it appears that computer anxiety accounts for a significant proportion of the variability in task completion latencies independent of both state anxiety and computer experience levels.

DISCUSSION

This study set out to establish whether computer anxiety is associated with deficits in the latency and/or accuracy with which users complete simple computer operations, and to clarify whether these effects are dependent upon the user’s prior level of experience with computers. The data revealed a moderate negative correlation between computer anxiety scores and the latency with which the data entry task was completed, which was independent of both the level of experience of the operator and of their level of state anxiety. No such relationship was found in the case of the error rate data. Clearly, this issue of the role of experience in computer anxiety needs further clarification. One potentially fruitful avenue is to explore the relative effects of both mere exposure to computers and of specific computer skill training on users’ level of computer anxiety. This may show that the divergent results of studies investigating the computer anxiety-computer experience relationship are due to a failure to discriminate between these two types of experience. A recent study by Martocchio (1994) has highlighted the importance of viewing computer use as an acquirable skill. He told one group of Ss that the ability to use computers was an acquirable skill, while a second group were told that this was a fixed entity. The acquirable skill group exhibited significant reductions in computer anxiety and significant increases in computer efficacy after training, while the fixed entity group exhibited a significant increase in computer anxiety and a significant decrease in efficacy. The data again confirmed the existence of a relationship between computer anxiety and state anxiety. While those Ss with low levels of computer anxiety exhibited a small decrease in state anxiety as a result of completing the data entry task, the highly computer anxious Ss exhibited a slight increase in state anxiety. As was noted earlier, this finding seems consistent with Deane et al.‘s (1995) suggestion that computer anxiety is a trait that is manifested through elevated levels of anxiety in the presence of the anxiety inducing agent (in this case a computer). Granted this, the most parsimonious explanation of the performance deficits observed amongst the computer anxious Ss is that the heightened levels of computer anxiety experienced by these Ss led to their poorer performance of the data entry task. Finally, the data confirmed earlier findings that computer anxiety is strongly associated with computer avoidance (e.g. Deane et al., 1995). Indeed, the Ss’ computer avoidance scores were equally as predictive of performance on the data entry task as were their computer anxiety scores. This is not surprising granted that avoidance behaviour is a defining characteristic of specific anxiety disorders, and that the computer anxiety sub-scale and the computer avoidance scale share several similar items. For example, both scales include an item regarding the extent to which the respondent prefers to talk about computers with others. Subsequent research should thus explore whether it is necessary to treat these as separate constructs, or whether computer avoidance can be regarded as a defining component of computer anxiety. The performance deficit exhibited by the computer anxious may have substantial economic implications as the present data suggests that approximately 8% of the variability in task completion rates may be attributed to the computer anxiety construct alone. These results strongly support Gardner, Young and Ruth’s (1989) assertion that because of computer anxiety; “ . tens of millions of dollars per year in cost of man hours could be lost” (p. 97). As the present results strongly refute LaLomia and Sidowski’s (1993) claim that computer anxiety will dissipate with experience, and show that the performance effects of computer anxiety are independent of the individual’s general level of anxiety, there is a clear need to develop suitable intervention strategies to reduce users’ levels of computer anxiety. While there has been little research conducted on how to treat computer anxiety, there are a range of therapies commonly used to reduce other types of anxiety. As the present results show that computer anxiety is manifest in similar ways to other anxiety disorders it seems likely that these methods may also assist in reducing computer anxiety. These methods include behaviour modi-

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fication therapies like Sharpley’s (1994) biofeedback based behaviour modification technique, relaxation therapies such as Stanton’s (1988) variant of Bandler’s theatre technique, and skill based therapies that are frequently used when the anxiety is triggered by a task-related event as is the case with computer anxiety. As it has been shown that the efficacy of these techniques may increase when used in combination (Farhill, 1985) it may be that the best treatment for computer anxiety will include elements drawn from all three of these types of technique. In summary, the present results show that computer anxious students exhibit many of the behaviours that are characteristic of a specific anxiety disorder. These students tend to avoid computer use more than do students with low computer anxiety scores, they exhibit higher state anxiety at the completion of a computer task, and they perform basic computer operations more slowly than do ‘normal’ students. This performance deficit seems to be independent of both the Ss’ prior level of computer experience and of their level of state anxiety. Granted the growing importance of computer competence as a criterion for employment across a wide range of vocations (Campbell, 1988; Giuliano, 1982), the present results highlight the need for intervention strategies beyond simple exposure to computers if the economic and personal costs of computer anxiety are to be reduced.

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