Predictors of computer anxiety and performance in information systems

Pergamon

Computers in Human Behavior, Vol. 12, No. 1, pp. 61-77, 1996 Copyright © 1995 Elsevier Science Ltd Printed in Great Britain. All fights reserved 0747-5632/96 $15.00 + .00

0747-5632(95)00019-4

Predictors of Computer Anxiety and Performance in Information Systems Alastair A. Anderson School of Management Information Systems, Deakin University

Abstract - - This paper reports on the results of a study of business undergraduates undertaking an introductory unit in information systems. The focus of the study was to determine whether or not perceived knowledge of software, microcomputer experience, overall knowledge of computers, programming experience, and biological sex were predictors of computer anxiety. Analysis was carried out to assess the factor structure and the discriminatory power of the Computer Anxiety Rating Scale (CARS). Previous computer experience is an important element of success in undergraduate courses in information systems. Computer anxiety is definitely implicated in performance. Sex in general was not found to be a predictor of computer anxiety. However, females with low levels of perceived knowledge of software and limited experience with computers were predominant in the failing subgroup. The CARS appears to have a very high discriminatory capability. The CARS is easy to use and efficient. It can be used to identify and assist students who present with significant anxiety about using microcomputers.

The study reported in this paper sought to examine the effects of microcomputer experience, perceived knowledge of software, overall knowledge of microcomputers, programming experience, and biological sex on computer anxiety and performance in an introductory unit in information systems. Computer anxiety is "the fear of impending interaction with a computer that is disproportionate to the actual threat presented by the computer" (Howard, Requests for reprints should be addressed to Alastair Anderson, School of Management Information Systems, Faculty of Management, Deakin University, 221 Burwood Highway, Burwood, Victoria, Australia 3125. 61

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Murphy, & Thomas, 1986, p. 630). For many, computer anxiety is a transient condition which has a peak intensity when experience with computers is limited. The moderation of that anxiety is largely beyond the jurisdiction of the novice, simply because the novice lacks sufficient procedural knowledge. Research on the psychological effects of computers has important practical implications but not a long tradition (Rosen & Maguire, 1990). Prior to the mid1980s, much of the writing about computer anxiety and attitudes towards computers was concentrated in trade and business publications (Howard, 1986; Igbaria & Parasuraman, 1989). Raub (1981) developed an instrument to measure computer anxiety. Raub's work is one of the most frequently cited in the literature on computer anxiety. Her anxiety scale has been employed extensively in studies on computer anxiety (e.g., Howard, 1986; Igbaria & Parasuraman, 1989; Morrow, Prell, & McElroy, 1986; Ray & Minch, 1990). The research of Howard (1986) addressed microcomputer anxiety and the use of microcomputers in management. His research was a welcome addition to literature. Howard sought to uncover the psychological mechanisms which trigger computer anxiety. He developed a model of computer anxiety which lends itself to a more formal investigation of computer anxiety and the remedies for it. Howard's work is distinctive in this regard. The work of Howard (1986) is similar to more recent research on computer anxiety. He drew a clear distinction between microcomputer anxiety and computer anxiety in the more general sense. This illustrates the increasing penetration and importance of the microcomputer in business, education and everyday life. In the USA, the use of microcomputers in the workplace has reached a per capita penetration equal to that which took 75 years to achieve for the telephone (Webster & Martocchio, 1992). In many environments the microcomputer has eclipsed the mainframe. Increasingly, a person's first contact with a computer is likely to be with a microcomputer. The microcomputer is regarded as more accessible than the traditional mainframe. Therefore, the penetration of the microcomputer may have reduced the overall incidence of computer anxiety. However, it could also be argued that the ever-expanding technological web will increase the incidence and perhaps the severity of microcomputer anxiety. Microcomputers will become all pervasive. It will be almost impossible to avoid them (Howard, 1986).

RESEARCH ON COMPUTER ANXIETY

Research on computer anxiety has focused on computer experience, computer knowledge, sex, age, mathematics anxiety, the social impact of computers, and playfulness as major factors affecting computer anxiety and computer attitudes (Clarke & Chambers, 1989; Cohen & Waugh, 1989; Dambrot, Watkins-Malek, Silling, Marshall, & Garver, 1985; Farina, Arce, Sobral, & Carames, 1991; Gilroy & Desai, 1986; Howard, 1986, Igbaria & Parasuraman, 1989; Kernan & Howard, 1990; Linn, 1985; Loyd, Loyd, & Gressard, 1987; Morrow et al., 1986; Orpen & Ferguson, 1991; Pope-Davis & Twing, 1991; Ray & Minch, 1990; Webster & Martocchio, 1992). The important factors to emerge from this research are discussed.

Predictors of computer anxiety

63

Locus of Control Rotter (1966) introduced the term "locus of control". Locus of control refers to a person's perspective on the world. Internals are very much self-directed individuals who attribute outcomes to their own efforts. Externals see themselves as somewhat helpless and governed by forces and circumstances beyond their control. Howard (1986) found that external locus of control is a significant correlate of attitudes towards microcomputers but not directly correlated with computer anxiety. In contrast, Dambrot et al. (1985) and Morrow et al. (1986) found that external locus of control was related to higher levels of computer anxiety. Igbaria and Parasuraman (1989) also found that external locus of control was correlated with computer anxiety and, therefore, indirectly related to attitudes towards computers.

Playfulness Playfulness may be one of the most important aspects of human-computer interaction. Webster and Martocchio (1992) determined that there was an inverse relationship between computer anxiety and playfulness. They have emphasized the importance of playfulness as a character trait which fosters a greater degree of cognitive spontaneity, inquisitiveness and creativity with computers (Webster & Martocchio, 1992, p. 202). However, Webster and Martocchio have also characterized playfulness as a state. A person who is not playful by nature may exhibit playfulness with a computer in particular situations, such as when using software which is particularly user friendly (Webster & Martocchio, 1992, p. 204). Perkins and Simmons (1988) have depicted four frames for understanding in science, maths, and computer programming. These are the content frame, the problem-solving frame, the epistemic frame and the inquiry frame (Perkins & Simmons, 1988, p. 305). In computing, the content frame for understanding is typical of the novice who seeks to understand basic procedures and the more mechanical aspects of computer operation. The inquiry frame is characterized by higher level interactions with the computer. These interactions are self-initiated and self-directed and are primarily directed at broadening and deepening knowledge about computing (Mandinach & Corno, 1985). Playfulness as a dimension of human-computer interaction is strongly linked to this inquiry frame for understanding (Webster & Martocchio, 1992, p. 204).

Sex There have been a number of extensive studies on sex as it relates to computer anxiety. Studies by Howard (1986) (N = 111:90 male, 19 female, 2 sex unknown), Igbaria and Parasuraman (1989) (N = 166:115 male, 51 female), Ray and Minch (1990) (N = 114:68 male, 46 female), Cohen and Waugh (1989) (N = 43, 17 male, 26 female), and Morrow et al. (1986) (N = 173, 108 male, 65 female) each found that sex was not a significant factor in explaining differences in computer anxiety and attitudes towards computers. In contrast, Gilroy and Desai (1986) (N = 326, 136 male, 190 female) reported differences in the level of computer anxiety according to sex. Dambrot et al. (1985) determined from a sample of 599 female and 342 male undergraduates that females held more negative attitudes towards computers, scored lower in a computer aptitude test and had less prerequisite ability and background in mathematics (Dambrot et

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al., 1985, p. 83). In a meta-analysis of research on computer anxiety, Rosen and Maguire (1990) found that although a difference in computer anxiety according to sex had been established the difference was minimal (Rosen & Maguire, 1990, p. 180). Ware and Stuck (1985) undertook a content analysis of illustrations in popular computer magazines. Their analysis revealed that males were predominant in the illustrations. Where women appeared they were depicted in the less powerful and often passive role, or as sex objects. There was considerable evidence of stereotyping in that men were usually depicted as the expert, the manager and the decision maker whereas women were more often shown in clerical and supportive roles. Women were more frequently depicted in illustrations connected to computer phobia. The significance of these mediated precepts is that women often take these on board and come to see themselves in the same way. Stereotyping in the media has cultivated the view that women are more technophobic than men. Societal norms about the role of women have led to differential opportunities for women in education, particularly in mathematics, science, and engineering. Computer science and computing in general is strongly aligned with the hard sciences. Computer anxiety may be more prevalent amongst women, because they perceive the computer as relating to discipline areas with which they are less familiar (Fetler, 1985; Hawkins, 1985). Studies have shown that girls view science and computers as being more male-appropriate than female-appropriate (Dambrot et al., 1985; Wilder, Mackie, & Cooper, 1985). Hess and Miura (1985) undertook a study of sex differences in enrollments in computer camps and classes. In a sample of 5,533 they determined that the ratio of boys to girls in computer camps was about 3:1 (4083 boys, 1450 girls) and that the proportion of girls enrolled declined with age. Females were much less likely to enroll in advanced classes such as those on the assembly language. Hess and Miura (1985) uncovered significant evidence of stereotyping through an examination of computer games. They found that these games were primarily directed at males and that boys, girls, and adult males and females perceived these games to be directed at the male market. Turkle (1984) captured the male and female orientations towards the computer when she described boys as "hard masters" and girls as "soft masters" (Turkle, 1984, p. 107). These characterizations contrast the intensely competitive behavior which males often display when in contact with computers with the more accommodating behavior of females. Turkle notes that there are few female hackers (Turkle, 1984, p. 216).

Mathematics Anxiety and Age Mathematics anxiety has been reported in a number of studies as a significant correlate of computer anxiety. Howard (1986), Igbaria and Parasuraman (1989), Morrow et al. (1986), and Raub (1981) each reported mathematics anxiety as a significant correlate of computer anxiety. Rosen and Maguire (1990) reported results from 10 research reports which found that mathematics anxiety and computer anxiety were positively correlated. However, the relationship, although significant, accounted for only a small proportion (6-20%) of the variation in computer anxiety among the adult populations studied. Computer anxiety is quite distinct from mathematics anxiety (Rosen & Maguire, 1990, p. 181).

Predictors of computer anxiety

65

Igbaria and Parasuraman (1989) reported age as correlated with attitudes towards microcomputers. Older managers were found to have more unfavorable attitudes towards them than their younger colleagues (Igbaria & Parasuraman, 1989, p. 384). In contrast to this, Rosen and Maguire (1990) reported that the results of 17 studies (8 of which were statistical) did not support the contention that age (or grade level in the case of students) was a significant correlate of computer anxiety (Rosen & Maguire, 1990, p. 181).

Experience In almost all research on computer anxiety, experience with computers has been found to be an inverse correlate of computer anxiety (Ray & Minch, 1990, p. 485; Rosen & Maguire, 1990, p. 183). Howard (1986) has drawn a distinction between experience and knowledge insofar as one can have significant experience with computers and relatively little knowledge. Such outcomes can occur in environments where microcomputers are networked or operated through menus which make the mechanics of operation invisible to the user. Recovery from errors is usually an easy task in such environments as user prompts and help facilities are generally available. However, a person with extensive experience in a highly automated environment may flounder when faced with the DOS prompt.

Hassles Hudiberg (1989a, 1989b) has demonstrated that hassles with computers are the norm. The experienced user can often rectify hassles by recourse to a more extensive and integrated personal knowledge of computers. For the novice, hassles such as the breakdown of a printer or the corruption of a floppy disk may c o m p o u n d an already anxious state by generating frustration.

METHOD

Subjects and Procedure The subjects were l st-year business students. They were enrolled in an introductory computing unit (Information Systems 1) in the management faculty of a large university in Melbourne, Australia. The primary objective in teaching the unit was to acquaint students with the microcomputer, the DOS operating system, Microsoft Word and Lotus 123. The method of instruction comprised one 2-hr lecture and one 2-hr tutorial each week for a 13-week semester. No programming was taught in the unit. A questionnaire was prepared and pilot tested. It was then mailed, complete with a postage-paid envelope for return, to a random sample of 200 students who were enrolled for the second semester in 1992. This was done in late September 1992 following the release of results for a test which was conducted in August. The test was structured to assess the students ability to manipulate the computer and included a number of DOS exercises. At the commencement of the semester there were 510 students enrolled in the unit. In all, 79 questionnaires were returned which represents a response rate of 40%. After coding and

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checking by an independent person, 64 useable surveys were retained for analysis. The questionnaire included the Computer Anxiety Rating Scale, (CARS; Raub, 1981). It also contained questions about ownership of computers, experience with computers, and the respondents' perceived knowledge of various computer software packages. The respondents were asked to indicate the grade that they had achieved in the test conducted in the previous month. A number of other questions in the questionnaire sought information about the students rating of lectures as against tutorials and their perception of their ability to manage their own learning.

Measures

CARS. The CARS (Appendix A) is a self-report inventory consisting of 10 statements designed to measure computer anxiety. The scale comprises a mix of anxiety-specific statements (e.g., "I feel apprehensive about using the microcomputer") and positive statements (e.g., "I am confident that I could learn microcomputer skills"). The scale was scored on a 5-point scale (1 -- "strongly agree", 5 = "strongly disagree"). The anxiety-specific statements were subsequently reverse scored before calculating anxiety scores. The maximum achievable score was 50. A score of 30 was used as the breakpoint between low scores and high scores on the computer anxiety scale. Those who scored 30 or less were adjudged as not anxious about using the microcomputer. Scores in excess of 30 were taken to indicate moderate to extreme anxiety about using the microcomputer.

Computer knowledge. Several questions were asked to elicit the respondents perceptions of their computer knowledge before they had commenced studying for the unit. They were asked to indicate their knowledge of DOS, Lotus 123, DBASE, Microsoft Word, and Microsoft Works on a 5-point scale (1 = "none", 2 = "minimal", 3 = "adequate", 4 = "more than adequate", 5 = "superior"). The students were also asked to give a global rating in relation to their overall knowledge of computing on a 5-point scale (1 = "none", 2 = "minimal", 3 = "adequate", 4 = "more than adequate", 5 = "superior"). Computer experience. A number of questions were used to elicit information about the computer experience of respondents. The students were asked to indicate if they had owned a personal computer prior to enrolling in the unit and, if not, whether or not they had subsequently acquired one. They were asked to indicate if they had ever written a computer program and, if so, in what language. The languages from which to choose were Basic, Fortran, Cobol, Pascal, and C. Respondents were asked to check as many languages as they had used. The students were asked to indicate if they used computers in any paid employment and, if so, what their level of involvement was. Involvement was measured on three levels (1 = "user", 2 = "programmer", 3 = "systems analyst").

Learning satisfaction and confidence. The students were asked to rate the benefit of lectures and tutorials in terms of their own learning (1 = "of little benefit", 5 = "very beneficial"). To assess the students' perceptions of their ability to learn by themselves, students were asked to indicate on a dichotomy whether or not they could manage their own learning (0 = "no", 1 = "yes").

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67

Performance. R e s p o n d e n t s were asked to indicate w h a t grade they h a d achieved on the test c o n d u c t e d in the previous m o n t h (1 = "fail", 2 = " p a s s " , 3 = " c r e d i t " , 4 = " d i s t i n c t i o n " , 5 = " h i g h distinction"). Only grades a n d n o t actual scores were available. RESULTS AND DISCUSSION

Summary Results by Sex The results in Table 1 indicate that there were no significant age differences a m o n g the respondents (;(2 = 2.01, p = .7347). 1 The students were similar in t h a t the m a j o r i t y were full-time students (?C2 = 0.16, p = .6859, Fisher's Exact Test, p = .4921) w h o were a t t e m p t i n g the unit for the first time (Z2 = 0.00, p = .9232, Fisher's Exact Test p = .7085). Sex was n o t a significant factor in relation to current ownership o f c o m p u t e r s (Z2 = 1.51, p = .2189) or ownership o f c o m p u t e r s prior to c o m m e n c i n g studies at the university, (Z 2 = 2.11, p = . 1465). There a p p e a r e d to be no significant differences in the grades achieved according to s e x (~2 = 7.90, p = .0952). 2

Computer Knowledge and Experience by Sex Table 2 d e m o n s t r a t e s t h a t the male students perceived their knowledge o f D O S a n d L o t u s 123 as greater t h a n did the female students (t = 2.24, p = .01, a n d t = 2.62, p = .00, for DOS a n d L o t u s 123, respectively). Male a n d female Table 1. Summary Results by Sex Male (N = 23; 35.9%) Item

%

n

%

14 8 1 0

60.9 34.8 4.3 0.0

25 11 2 3

61.0 26.8 4.9 7.3

Status Full-time Part-time

20 3

87.0 13.0

37 4

90.2 9.8

First Enrollment Yes No

22 1

95.7 4.3

39 2

95.1 4.9

18 5

78.3 21.7

26 15

63.4 36.6

15 8

65.2 34.8

19 22

46.3 53.7

1 3 15 2 2

4.4 13.0 65.2 8.7 8.7

9 3 18 1 10

22.0 7.3 43.9 2.4 24.4

Age (Years) < 20 20 to < 25 25 to < 30 > 30

Computer Ownership Have own computer Yes No Had one prior to enrollment Yes No Grade Achieved For Test Fail ( < 50 marks) Pass (50-64 marks) Credit (65-74 marks) Distinction (75-84 marks) High distinction (>~ 85 marks)

n

Female (N = 41; 64.1%)

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Anderson Table 2. Computer Knowledge and Experience by Sex

Category Computer Knowledge Software DOS Ma SD Lotus 123 Ma SD DBASE Ma SD Word Ma SD Works Ma SD Overall Knowledge Ma SD Overall instruction preferences Lectures Ma SD Tutorials Ma SD Computer Experience Programming Yes No Languages Basic Yes No Fortran Yes No Cobol Yes No Pascal Yes No C Yes No Employment Experience With Computers Yes No

Males

Females

2.17 1.07

1.63 0.83

1.96 0.93

1.39

t

p

2.24

.01"*

2.62

.00"*

0.77

1.39 0.50

0.93

2.13 1.14

2.24 1.36

1.35 0.57

1.59 0.97

2.43 0.78

2.20 0.90

-0.35

.37

- 0.34

.37

-1.23

.11

1.07

.15

5.68

.00"*

1.46

2.52 1.02 3.59 1.20

11 (47.8) b 12 (52.2) b

9 (22.0) b 32 (78.0) b

9 (39.1) b 14 (60.9) °

6 (14.6) b 35 (85.4) b

0 (0.0) ° 23 (100.0) b

o (0.0) ° 41 (100.0) b

0 (0.00) b 23 (100.0) °

0 (0.00) ° 41 (100.0) °

5 (21.7) b 18 (78.3) b

5 (12.2) b 36 (87.8) b

0 (0.0) b 23 (100.0) b

0 (0.o) ° 41 (100.0) b

6 (26.1)° 17 (73.9) b

10 (24.4) b 31 (75.6) b

aMean scores on a 5-point scale; bValues in parentheses indicate percentage of total respondents in group (n = 64, males = 23, females = 41). p < .05, p < .01; all t tests one-tailed.

students i n d i c a t e d a clear preference for tutorials as o p p o s e d to lectures as a m e t h o d o f i n s t r u c t i o n (t = 5.68, p = .00). Males a p p e a r e d to have m o r e experience with c o m p u t e r p r o g r a m m i n g (Z 2 = 4.59, p = .0321). Significant sex effects were f o u n d in relation to experience with Basic (Z 2 = 4.93, p = .0264) b u t n o t Pascal (Z 2 = 1.01, p = .3129, Fisher's E x a c t Test, p = .2541). Pascal is the usual l a n g u a g e e m p l o y e d for teaching p r o g r a m m i n g at s e c o n d a r y school level.

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69

Therefore, those who had written programs would more than likely have used this language. The students, male and female, were not likely to have used computers in paid employment. If they had, they exhibited similar levels of exposure (;(2 = 0.02, p = .8804).

Computer Anxiety Table 3 shows the scores on the CARS scale. Respondents can be characterized as fairly comfortable with computers as indicated by the mean CARS score of 24.84. N o significant difference was found in the mean CARS scores by sex (t = 0.62, p = .27) or between students passing the test according to sex (t = - 0 . 4 4 , p = .33). However, those students who failed the test had a mean anxiety score which was significantly higher than that for passing students (t = 5.16, p = .00).

Factor Analysis The results of a factor analysis of responses are shown in Table 4. For this sample the CARS scale exhibits excellent reliability (Cronbach's Alpha = Table 3. Computer Anxiety Scores CARS Score Category Overall Anxiety Score Passes Versus Fails Overall Passes Fails Overall By Sex Males Females By Grade By Sex Passes Males Females Fails Males Females

M

SD

24.84

8.27

22.91 35.30

6.87 9.53

24.09 25.27

5.78 9.43

23.36 22.59

4.74 8.08

40.00 34.78

-7.79

t

p

5.16

.00"*

0.62

.27

-0.44

.33

- - Standard deviation could not be calculated as only one observation in subsample. *p < .05; **p < .01; all t tests one-tailed.

Table 4. Factor Analysis of Responses Items Factor 1 4 5 6 7

Loadings

8

.65 .72 .80 .85 .75

Factor 2 1 2 3 9 10

.59 .69 .78 .68 .71

Total Variance Explained

Eigenvalues

Variance (%)

3.44

34.33

2.83

28.28

62.61

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.8917). A factor analysis o f the scale using Principal Components extraction with a Varimax rotation yielded two orthogonal factors which in the aggregate explained 62.61% o f the variance in responses. Both of these factors are significant and explain 34.33% and 28.28% of the variance respectively. The items which load above .4 on the first factor are items 4, 5, 6, 7, and 8. These items concern fear, hesitancy, avoidance, and a lack of confidence about using microcomputers. The dominance of affect in this factor leads to the label "apprehension". Items 1, 2, 3, 9, and 10 all loaded above .4 on Factor 2. These items concern understanding microcomputers, computer programming, and the ability to use microcomputers to perform tasks. The dominance of interaction on this factor leads to the label "hands-on". The CARS scale appears to be a good instrument for assessing computer anxiety. Following Chu and Spires (1991, p. 20), factor scores were computed according to whether respondents had passed or failed the test. Mean scores and standard deviations were then calculated and hypothesis tests for mean differences were carried out. The purpose in carrying out this analysis was to ascertain if failing students recorded higher m e a n scores on both the apprehension and hands-on factors. Failing students did record a significantly higher mean score on the apprehension factor (failing students: M = 19, S D = 5.12; passing students: M = 10.46, S D = 4.11; t = 5.80, p = .00). Failing students also recorded a significantly higher mean score on the hands-on factor (failing students: M = 16.30, S D = 3.53; passing students: M = 12.44, S D = 3.48; t = 3.21, p = .00). Overall the results indicate that lower performing students can be expected to be more anxious about using the computer. Apprehension and a lack of confidence in hands-on ability with computers appear to go hand in hand.

Predicting ComputerAnxiety Rosen and Maguire (1990), in their meta-analysis of computer anxiety research, reported that 17 statistical and 6 nonstatistical reports unanimously supported the contention that computer experience is negatively related to computer anxiety. However, the explanatory power of computer experience was minimal with correlations from research studies accounting for between 5% and 9% of the variance (Rosen & Maguire, 1990, p. 183). Rosen and Maguire also reported that 25 statistical studies had reported sex as a correlate of computer anxiety. This trend was supported by another 13 nonstatistical reports. However, although sex was found to be a significant correlate of computer anxiety the magnitude of that effect was slight (Rosen & Maguire, 1990, p. 181). The current study sought to uncover the explanatory power of computer knowledge, computer experience, and sex in regard to computer anxiety. Initial data analysis revealed that sex was not a significant correlate of computer anxiety (r = -.0691, p = .588). However, further analysis indicated that computer anxiety and sex were related at higher levels of anxiety. The CARS scores were recoded so that scores of less than 30 were assigned to the low anxiety condition and scores greater than 30 were assigned to the high anxiety condition. A cross tabulation of this data with sex revealed a significant relationship (Xz = 3.36, p = .0667, ~b = -.2293. O f the 10 students who failed the test 9 were female. These students reported low scores on their perceived knowledge of computers (mean on a m a x i m u m of 5 = 1.56), low scores on their perceived knowledge o f software (mean on a m a x i m u m of 5 = 1.22), low scores

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71

on microcomputer experience (5 students recorded 0 and the mean on a maximum of 3 was 0.79) and low scores on programming experience (7 students recorded 0 and the mean on a maximum of 3 was 0.44). All except 1 student recorded scores on the CARS scale of 30 or more (M = 34.78). These results accord with those of Farina et al. (1991), Gilroy and Desai (1986), and Nelson, Wiese, and Cooper (1991). Gilroy and Desai (1986) noted that women were overrepresented in the high anxiety categories. Nelson et al. (1991) observed that females who dropped out of an introductory computer class had higher anxiety than females who remained in the classes. What has emerged from this research is that, in the aggregate, sex does not rate as a factor in predicting computer anxiety. However, when the data is examined more carefully females are overrepresented in the higher anxiety groups. This subgroup is characterized by low levels of experience and perceived knowledge of microcomputers and poor performance. Lack of knowledge and experience contribute to computer anxiety and appear to affect performance. Table 5 shows knowledge and experience variables which achieved correlations above .3 with the CARS scale. Each of the variables had a negative sign, as was expected, and each was statistically significant. A stepwise multiple regression was performed using CARS as the dependent variable and DBASE, DOS, LOTUS 123, WORD, WORKS, OWNPC (indicating ownership of a personal computer), M A N O W N (indicating the students' perception as to whether or not they could manage their own learning in the unit), and K N O W (indicating the respondents' assessment of their overall computer knowledge) as independent variables. An extract of results produced by SPSS for Windows (Version 6.0) is shown in Figure 1. As can be seen from the results the variable K N O W predominated in the multiple regression analysis. Its coefficient is negative as expected ( - 5.7989, t = - 5.985, p = .0000). This variable explained approximately 37% of the variance in the CARS scores. Further analysis was undertaken to ascertain the influences which were underlying this variable. Table 6 shows correlations of the knowledge and experience variables including programming knowledge and Table 5. Correlations of Knowledge and Experience Variables With CARS Scores Variable

CARS

CARS DBASE a DOS a LOTUS 123 a WORD a WORKS a OWNPC a MANOWN a KNOW b

1.0000 - .3277 - .4952 -.3805 -.3733 -.3301 -3806 - .3282 - .6051

p (2-tailed significance) .008 .000 .002 .002 .008 .013 .008 .000

aThese variables are all dichotomous variables. The correlations reported are therefore Biserial correlations (for a theoretical account see Welkowitz, Ewen, & Cohen, 1976, pp. 188-189). bKNOW stands for the respondents' assessment of their overall computer knowledge and was measured on a 5-point scale. Conservatism would suggest that Spearman's r be used as the measure of correlation. For convenience the correlation reported is Pearson's r (Spearman's r = - . 6 0 1 1 ) .

72

Anderson Equation Number 1 Dependent Variable is" CARS Independent Variables are: DBASE DOS LOTUS 123 WORD

WORKS

OWNPC

MANOWN

KNOW

Variable(s) Entered on Step Number 1 KNOW Multiple R .60513 R Square .36618 Adjusted R Square .35596 Standard Error 6.63816

Analysis of Variance df Sum of Squares Regression 1 1578.39755 Residual 62 2732.03995 F = 35.81963 Significance F = .0000

Mean Square 1578.39755 44.06516

Variables in the Equation Variable KNOW (Constant)

B -5.798935 38.072570

SE B 968920 2.360965

Beta -.605128

T

Sig T

-5.985 16.126

.0000 .0000

T -.656 -1.013 -1.453 -.742 -1.027 -1.086 -1.080

Sig T .5145 .3152 .1514 .4611 .3084 .2817 .2843

Variables not in the Equation Variable DBASE DOS LOT WORD WORK OWNPC MANOWN

Beta In -.074283 -.142786 -.159573 -.087507 -.112610 -.116575 -.117673

Pa~ial -.083662 -.128583 -.182880 -.094553 -.130403 -.137731 -.136999

Min Toler .803960 .514000 .832492 .739997 .849939 .884736 .859111

Figure 1. Stepwise multiple regression (CARS).

experience with KNOW. The criterion for selecting variables was that the correlation coefficients had to be at least .3 and have a positive sign. To simplify the analysis, three independent variables were constructed from those in Table 6. A variable SOFTKNOW was constructed by summing the responses on the variables DBASE, DOS, LOTUS 123, WORD, and WORKS and then dividing this result by 5 so as to scale the variable on a score of 1-5. This variable was intended to reflect the respondents' assessment of their knowledge of mainstream software. The second variable designed to reflect experience with computers was constructed by summing responses to the variables OWNPC and MANOWN. This variable was named EXP. The third variable was constructed to reflect the programming experience of the respondents. This was done by summing responses on the PROG, PASCAL, and BASIC variables. This variable was named PROGRAM. A stepwise multiple regression was then conducted with KNOW as the dependent variable and SOFTKNOW, EXP, and P R O G R A M as independent variables. The results of this procedure are shown in Figure 2. The results in Figure 2 indicate that knowledge of software and experience with computer programming are good predictors of the variable KNOW. In the aggregate these variables explain 58% of the variance in KNOW. The dominant variable is SOFTKNOW. This variable accounted for 51% of the variance by

Predictors of computer anxiety

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Table 6. Correlations of Knowledge and Experience Variables With KNOW Scores Variable

KNOW

KNOW DBASE DOS LOTUS 123 WORD WORKS OWNPC MANOWN PROG a PASCAL a BASIC a

1.0000 .4428 .6971 .4093 .5099 .3874 .3806 .3754 .4084 .3109 .3782

p (2-tailed significance) .000 .000 .001 .000 .002 .013 .002 .001 .012 .002

aThese variables are each a dichotomous variable indicating whether or not respondents had ever written a computer program and if so whether that experience was with Pascal or Basic or both. The correlations coefficients are Biserial.

itself before the inclusion of PROGRAM. As expected, the coefficients in the regression equation are each positive. On the basis of the results emanating from the correlation and regression analysis, it could be posited that computer anxiety is a function of knowledge of microcomputers and that knowledge of software, in particular, is a good way of acquiring knowledge of microcomputers. Microcomputer experience is only of value if it leads to a good working knowledge of microcomputers. The results indicate that research on computer anxiety should focus on specific skills and knowledge and not just on whether a person has had experience with a Equation Number 1 Dependent Variable is: KNOW Independent Variables are:

SOFTKNOW

EXP

PROGRAM

Variable(s) Entered on Step Number 2 PROGRAM Multiple R .76302 R Square .58221 Adjusted R Square.56851 Standard Error .56699

Analysis of Variance df Sum of Squares Regression 2 27.32727 Residual 61 19.61023 F = 42.5023 Significance F = .0000

Mean Square 13.66363 .32148

Variables in the Equation Variable

B

SOFTKNOW PROGRAM (Constant)

.854762 .215703 .665803

SE B

Beta

T

Sig T

.112460 .068365 199498

.649606 .269665

7.601 3.155 3.337

.0000 .0025 .0014

Variables not in the Equation Variable EXP

Beta ln .142710

Pa~ial

Min Toler

T

Sig T

.195312

.782552

1.543

.1282

Figure 2. Stepwise multiple regression (KNOW).

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Anderson

microcomputer. This point was made by Howard, "knowledge and experience are not the same thing" (Howard, 1986, p. 97). The final analysis undertaken was to determine whether or not computer anxiety (CA), perceived software knowledge (SOFTKNOW), microcomputer experience (EXP), programming experience (PROGRAM), and sex (GEN) were all predictors of performance in information systems. To do this a discriminant analysis was undertaken with G R A D E (scored as a dichotomy, 0 = "fail", 1 = "pass") as the dependent variable and CA, SOFTKNOW, EXP, PROGRAM, and G E N as the independent variables. The results of this procedure are shown in Table 7. Table 7 indicates that scores on the CARS scale are a significant predictor of performance in information systems. Overall, the percentage of correctly classified cases on the pass-fail dichotomy was 85.94%. When each of the other variables were simultaneously dropped from the discriminant analysis, the percentage of correctly classified cases dropped only marginally to 84.38%. Of the variables examined, CA has the greatest discriminatory power in regard to test performance. It encompasses other variables, in particular, SOFTKNOW and P R O G R A M (Refer to the earlier analysis in Figures 1 and 2 and Table 6).

CONCLUSION Perceived knowledge rather than experience is a predictor of microcomputer anxiety. Although previous research has found that microcomputer experience is a negative correlate of computer anxiety, the results have not been spectacular (Rosen & Maguire, 1990, p. 183). The research on a random sample of university students reported in this paper indicates that a more careful look at the outcomes of experience with computers must be taken. The perceived knowledge acquired as a consequence of that experience must be investigated. Care should be taken, however, not to view programs aimed at increasing knowledge of computers as the panacea for computer anxiety. Research has shown that programs which use cognitive behavior therapy can be very effective in alleviating and/or eliminating computer anxiety. In fact, prescribing more Table 7. Discriminant Analysis Independent Variables by Size of Correlation With Function

Value

CA SOFTKNOW EXP GEN PROGRAM

0.89582 - 0.44937 -0.35314 - 0.32682 - 0.16773

Eigenvalue Canonical Correlation Chi-square

0.5356 0.5906 25.522

df

5

Significance Level Percentage of Correctly Classified Cases Centroids (%)

0.0001 85.94

GRADE (Grouping Variable) Group 0 (Fails) Group 1 (Passes)

1.67391 -.30998

Predictors of computer anxiety

75

experience for a person who is extremely computer-phobic may only exacerbate the situation (Rosen & Maguire, 1990, p. 184). The results of this study do not support the contention that women in general exhibit higher levels of computer anxiety than men. This is in keeping with the research by Parasuraman and Igbaria (1990) and Pope-Davis and Twing (1991). Rosen and Maguire (1990) reported that nearly half of 25 statistical studies had found that women were more computer-phobic than men. However, their overall conclusion based on an analysis of a further 13 nonquantitative studies was that the differences are slight (Rosen & Maguire, 1990, p. 180). The results in this paper show that at higher levels of computer anxiety women are overrepresented. Higher computer anxiety is accompanied by less experience and less perceived knowledge of microcomputers. These results are in agreement with those of Farina et al. (1991), Gilroy and Desai (1986) and Nelson et al. (1991). Computer anxiety does affect the ability of individuals to use computers. The students who failed the test reported in this study had substantially higher levels of computer anxiety (failing students: M = 35.30; passing students: M = 22.91; t = 5.16, p = .00). The incidence of computer anxiety is estimated to be of the order of 25% (Rosen & Maguire, 1990, p. 180). Managers and educators should use simple methods to assess whether or not a person is anxious about using a microcomputer. The CARS scale employed in this paper appears to be a good measure. More research is needed on computer anxiety because of its ramifications in the home, workplace and in education. Managers, educators, and citizens need to know how to recognise computer anxiety and the strategies which they can use to help alleviate or eliminate it.

NOTES 1Some caution should be exercised in interpreting this result. In the original formulation of the cross tabulation there were eight categories for age. As a result, 60% of the cells had expected values less than 5. This means that the chi-square statistic reported has an upward bias. When the eight age categories were collapsed into three categories (i.e., < 20, 20 to < 25, and >/ 25) the chisquare statistic was (Z2 = 1.28, p = .5268). In this instance, 33% of the cells had expected values less than 5. However, no further aggregation was done since this would have resulted in loss of specificity in the data. Where possible (i.e., when the contingency table is a 2 x 2) Fisher's Exact Test has been calculated and reported as an adjunct to the chi-square statistic. 2When G R A D E was collapsed to a dichotomy (0 = "fail", 1 = "pass"), Z2 = 3.46, p = .0627, Fisher's Exact Test p = .0606, indicating a significant relationship between grade achieved and gender at a significance level of 10%.

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Wilder. G., Mackie, D., & Cooper, J. (1985). Sex and computers: Two surveys of computer-related attitudes. Sex Roles, 13, 215-228.

APPENDIX A The Computer Anxiety Rating Scale shown is based on that developed by Raub (1981). The word microcomputer has been substituted for computer to reflect the predominance of microcomputers. The responses are scaled as follows: 1= 2= 3= 4= 5=

Strongly Agree Agree Unsure Disagree Strongly Disagree

Items 2, 4, 5, 6, 7, 8, 9, and 10 are reverse scored so that high scores indicate high levels of computer anxiety. 1. I am confident that I could learn microcomputer skills. 2. I am unsure of my ability to learn a computer programming language. 3. I will be able to keep up with the important technological advances of computers. 4. I feel apprehensive about using the microcomputer. 5. If given the opportunity to use a microcomputer, I'm afraid that ! might damage it in some way. 6. ! have avoided microcomputers because they are unfamiliar to me. 7. I hesitate to use the microcomputer for fear of making mistakes that I cannot correct. 8. ! am unsure of my ability to interpret a computer print-out. 9. I have difficulty understanding most technological matters. 10. Computer terminology sounds like confusing jargon to me.