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Modeling technophobia: a case for word processing
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Computers in Human Behavior 15 (1999) 105±121
Modeling technophobia: a case for word processing M.J. Brosnan* Division of Psychology, School of Social Sciences, University of Greenwich, Southwood Site, Avery Hill Campus, Avery Hill Road, Eltham, London SE9 2HB, UK
Abstract After theory of reasoned action, Davis (1986, 1993) and Davis, Bagozzi, and Warshaw (1989) proposed the Technology Acceptance Model to account for how perceived ease of use, perceived usefulness, and attitudes predict behavioral intention to use computers. This study combined these factors with measures from Bandura's self ecacy theory (computer self ecacy and computer anxiety; Bandura, 1977, 1986) in conjunction with assessments of current computer experience. A total of 147 undergraduates completed a series of questionnaires at the beginning and end of a 13-week semester. A multiple regression analysis revealed that self-reported word-processor usage over a 13-week period was predicted by levels of usage at the beginning of the semester, expected usage, and perceived usefulness. Initial levels of usage and perceived usefulness were both predicted by levels of computer anxiety. A combination of the variables formulated by the Technology Acceptance Model and self ecacy theory account for 45% of the variance in self-reported computing behavior over a 13-week period. The theoretical implications are discussed. # 1999 Elsevier Science Ltd. All rights reserved. Keywords: Computer anxiety; Word processing; Self ecacy theory; Technology Acceptance Model
*Requests for reprints should be addressed to Mark J. Brosnan, Division of Psychology, School of Social Sciences, University of Greenwich, Southwood Site, Avery Hill Campus, Avery Hill Road, Eltham, London SE9 2HB, UK. E-mail: [email protected] 0747-5632/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved P II: S074 7- 5632( 98) 0002 0-X
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1. Introduction Technophobia has been described in terms of aversive behavioral, aective, and attitudinal responses to technology (Jay, 1981; Rosen & Maguire, 1990). This re¯ects a large body of research which has demonstrated an association between these computer-based attitudes and anxieties and computer-related behavior. Maurer (1994) reviewed the correlates of computer anxiety, which include experience, gender, age, and academic major (see also Rosen & Maguire, 1990). Maurer (1994, p. 374) argues that the only conclusion that can be drawn from this myriad of (largely correlational) data is that ``We do not know what to do about computer anxiety!''. This is not to argue that computer anxiety is not a salient concept. On the contrary, Moldafsky and Kwon (1994, p. 302) report that ``computer anxiety is a real phenomenon''. Rather Maurer's critique concerns the lack of focused research goals within this body of literature. The author agues ``The development of a model of the development of computer anxiety may help in providing this focus'' (Maurer, 1994, p. 374). It can be argued, therefore that much technophobia research lacks a theoretical underpinning to the identi®cation of (an increasing number of) correlates of computer anxiety. This paper describes two theoretical approaches to computer attitudes and anxiety (respectively) which can be combined into a model of how the psychological and demographic factors associated with technophobia impact upon computer usage. The application of these theoretical models to predicting computer usage will then be empirically evaluated. 1.1. The Technology Acceptance Model Davis (1986) introduced the Technology Acceptance Model (TAM) to account for the attitudinal factors that are postulated to aect computer acceptance. As an example of this, Desmond (1984) found a high correlation between computer misuses and people with negative attitudes towards information technology. In addition to negative attitudes being associated with resistance to using computers, Loyd and Gressard (1984) found that more positive attitudes toward computers were related to positive computer experience. Munger and Loyd (1989) con®rm that students with positive attitudes to computers perform better than students with more negative attitudes. TAM is based upon theory of reasoned action (TRA; Ajzen, 1987; Ajzen & Fishbein, 1977; Fishbein & Ajzen, 1975). TRA posits that behavioral intention is a measure of one's intention to perform a speci®ed behavior and represents the primary predictor of actual behavior. Behavioral intention is itself predicted by an attitudinal component which represents an individual's feelings about performing the behavior (Fishbein & Ajzen, 1975). This pathway was incorporated into TAM which postulates that computer-related attitudes in¯uence behavioral intention to use computers (and subsequently usage). TAM combines these two concepts with perceived usefulness and perceived ease of use. Perceived usefulness is de®ned as the prospective user's subjective probability
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that using a speci®c application system will increase his or her task performance. Perceived ease of use refers to the degree to which the prospective user expects the target system to be free of eort (Davis, Bagozzi & Warshaw, 1989). Perceived usefulness and perceived ease of use are both psychologically and statistically distinct (Davis et al., 1989) enabling a system to be perceived as useful but not easy to use, and vice versa. Todman and Dick (1994) developed and validated a computer attitude scale which incorporated perceived usefulness and a perceived ease of use subscales. Combining these two subscales with a subscale which assessed the amount of ``fun'' a computer was perceived to be, represented the composite attitudinal scale. This is consistent with TAM which identi®ed perceived usefulness and perceived ease of use as the factors determining the attitudinal component. Combining the work of Todman and Dick with that of Davis suggests that the attitudinal scale, speci®cally the perceived usefulness and perceived ease of use subscales, should predict the behavioral intention to use computersÐthe relative weights being obtained through regression (Davis et al., 1989). Davis (1993) reported that TAM accounts for 36% of the variance in computer usage. Although signi®cant, the correlations between computer attitudes and predictor variables of computer use (such as computer aptitude) have been found to be low (Dambrot, Watkins-Malek, Silling, Marshall & Graver, 1985). Dambrot et al. compared those who dropped out of a computer course and those who did not. The authors concluded that computer attitudes were, at best, a moderate predictor of actual use. This poor predictive validity is consistent with the conceptualization of technophobia as multifaceted. Both Jay (1981) and Rosen and Maguire (1990) postulate an aective element to technophobia which determines computer usage, namely computer anxiety. This is consistent with the TAM as both perceived usefulness and perceived ease of use are proposed to be determined by variables external to the model. Davis et al. (1989) explicitly suggest that levels of computer anxiety and computer self ecacy determine levels of perceived ease of use. 1.2. Computer anxiety Computer anxiety has been found to relate to computer avoidance (Hess & Miura, 1985; Scott & Rockwell, 1997). Consequently, the relationship between experience and anxiety is likely to be confounded by extremely anxious individuals avoiding computer interaction (Rosen & Maguire, 1990). Arch and Cummins (1989) report that a compulsory element to the computer-based interaction reduces levels of anxiety. The fact that computer-anxious individuals are often required to use technology has lead some researchers to ®nd no relationship between computer anxiety and computer-based performance (Kernan & Howard, 1990; Munger & Loyd, 1989; Szajna, 1994; Szajna & Mackay, 1995). Where dierences do occur, the performance of an anxious individual can be expected to be slower and less accurate (cf. Rosen & Weil, 1995, p. 13) due to more o-task (negative) cognitions (Heinssen, Glass & Knight, 1987; Weil, Rosen & Sears, 1987). This is con®rmed by Mahar, Henderson, and Deane (1997) who found that, whilst computer anxiety is associated with slower completion of computer tasks, this performance de®cit is independent of the prior
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level of computer experience of the user. This concurs with Whitely's conclusions that whilst prior experience does not mediate gender dierences in computer anxiety, computer anxiety does mediate gender dierences in computer-related behavior (Whitely, 1996). Thus, both Mahar et al. and Whitley argue that the impact of computer anxiety upon behavior is independent of prior experience. This is consistent with self ecacy theory which conceives experience and anxiety as independent factors. 1.3. Self ecacy theory Meier (1985, 1988) has proposed that self ecacy theory (Bandura, 1977, 1982, 1986) can provide a theoretical framework to account for how computer anxiety can aect computer usage. Self ecacy is de®ned as a judgment of ``how well one can execute courses of action required to deal with prospective situations'' (Bandura, 1982, p. 122). Perceptions of one's own physiological state are used in assessing performance capability. An individual in an aroused state may interpret the arousal as debilitating fear and feel vulnerable to failure. Those with low self ecacy expectations in a particular situation will experience unpleasant feelings, such as anxiety, and will behave in unproductive ways, such as avoiding work, and may lack persistence (Bandura, 1977). Hill, Smith, and Mann (1987) developed a measure that assessed self ecacy with respect to computers, termed ``computer self ecacy''. Hill et al. demonstrated a direct relation between computer self ecacy and a decision to use computers that is independent of people's beliefs about the instrumental value of doing so. The authors also found that previous experience is related to self ecacy with respect to computers, but that experience does not exert an independent in¯uence on the decision to use computers. Other research has also suggested that computer self ecacy is a salient concept, mediating the eects of computer anxiety upon computer usage. Kernan and Howard (1990), for example, demonstrate how anxiety eects expectation for success. The authors found that those subjects who saw themselves as more anxious about computers expected to be less skilled at the end of the course, expected a lower grade, and expected to like the course less than subjects who were less anxious. In a meta-analysis of over 4000 subjects, Sadri and Robertson (1993) con®rm that self ecacy predicts both performance and behavior choice. Additionally, experience gained through computer training has been found to increase levels of self ecacy (Torkzada & Koufteros, 1994). The mediation of the relationship between anxiety and avoidance by self ecacy may explain why the direct relationship between these two factors has been found to be seriously wanting (Bandura, 1986). This is consistent with an observation by both Rosen, Sears, and Weil (1987) and Marcoulides (1988) that computer users can be anxious, thereby reinforcing the point that anxiety reduction may not reduce avoidance (Bandura, 1988). Rather, computer anxiety will cause the individual to experience the interaction as unpleasant and terminate the interaction as soon as possible, having made more errors and performed more poorly (Rosen & Weil, 1995).
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The directionality of relationship between anxiety and self ecacy has been called into question, however. Krampen (1988) found that in prospective studies, perceived self ecacy predicts subsequent levels of anxiety but anxiety level does not predict subsequent perceptions of subsequent personal ecacy. Henderson, Deane, and Ward (1995) also report that self ecacy is the best predictor of anxiety, suggesting that the directionality of self ecacy in¯uencing anxiety is more salient. The present study aims to conduct a path analysis upon the factors theorised to aect word-processor usage. Whilst word processing is one of the most common computer-based activities, courses in word processing can result in subjects liking computers less after the course (Dyck & Smither, 1996). Assessments of wordprocessing experience will be placed in a (stepwise) multiple regression analysis to identify which factors are most predictive of these levels of experience. These predictive factors will then, in turn, be placed into a (stepwise) multiple regression themselves, to identify which remaining factors predict them. In addition to the anxiety, self ecacy, and attitudinal factors, certain demographic variables have also been found to be salient. 1.4. Demographic variables As mentioned already, many researchers have found females to be higher in computer anxiety and to obtain less computer-related experience than males (see Brosnan & Davidson, 1994; Whitely, 1997, for reviews). As Bandura would predict, these sex dierences in anxiety and experience have transferred to self ecacy. Sex dierences in perceived self ecacy for computer-related activities have been reported in adolescents in Grades 6±8 (age 11±13 years; Miura, 1987) and for students undertaking word-processing tasks (Busch, 1995). Other external variables that have been found to aect computer attitudes and anxiety will also be assessed. Rosen et al. (1987) found a positive correlation between student age and computer anxiety. Todman and Monaghan (1994) also report that age of ®rst computer interaction is signi®cant. Both computer ownership with software and programming experience were requested and these have also been found to be signi®cant variables (Brosnan & Davidson, 1996; Gilroy & Desai, 1986). 2. Method 2.1. Population/sample A total of 147 psychology freshmen were monitored over a period of a 13-week semester. There were 41 males and 104 females (2 unstated). The high proportion of females in the psychology sample is consistent with psychology courses throughout Europe and the US (Radford & Holdstock, 1995, 1996). Ages ranged from 18 to 45 years, with a mean age of 21.55 years (SD=5.61 years). The sample
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is therefore representative of psychology undergraduates with respect to these demographics. The sample was made up from the entire intake to the 1996/7 psychology degree program at the University of Greenwich, London. The program involves a 75% psychology component in the ®rst year with the remaining 25% open to selection of an option (sociology, economics, physiology, computing, for example). Many students from these other programs select to take psychology as their option, indicating a degree of overlap in the teaching received by students across the campus. All students were given the opportunity to withdraw (or withdraw their data) from the study at any time without the need for giving a reason for doing so and without the fear of repercussion. The researcher emphasized their own role within the University, making explicit that they were not involved in any form of tuition or assessment and that the data would only be used for research purposes, as part of an on-going University research program. No student requested withdrawing at any stage. 2.2. Procedure and measures The participants were all freshmen who had just experienced an orientation/ induction week at a University in which the coursework requirements were clearly stipulated. Participants were also informed that all coursework was required to be word processed and that an introductory session to the University network would be held at the end of the week. Attendance was mandatory as network IDs would also be allocated at this session. Eight 1-hr sessions ran with up to 20 students in each. After instruction on how to log on, demonstrations were given on how to access the word-processing package and other network packages (actual tuition on word processing was not given at this session, but was available to students throughout the semester). Students were then asked to complete a battery of questionnaires which were rotated in a Latin square design. The battery contained the Computer Anxiety Rating Scale (CARS; Heinssen et al., 1987). The CARS comprises of 9 ``negative'' anxiety-laden statements (e.g., ``I feel apprehensive about using computers'') and 10 ``positive'', nonanxiety statements (e.g., ``I am con®dent that I can learn computer skills''). Participants endorse the items on a 5point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Responses to nonanxiety items are later reversed before obtaining a total score which can range from 19 (not anxious) to 95 (very anxious). The authors report a high level of internal consistency for the overall scale with an alpha coecient of .87. The computer self ecacy (Hill et al., 1987) is a 4-item scale which is endorsed upon a 5-point Likert type scale as previous. The scale asks speci®cally about con®dence with computers (e.g., ``Only a few experts really understand how computers work''), and levels of computer self ecacy can range from 4 (low) to 20 (high). Computer attitudes were assessed using Todman and Dick's 11-item questionnaire (Todman & Dick, 1994) endorsed upon the 5-point Likert scale described already. The scale comprises of three subscales: namely, perceived usefulness (4 items, e.g., ``Computing is a useful hobby''), perceived ease of use (3 items, e.g., ``It
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is dicult to use a computer''), and perceived fun (4 items, e.g., ``Computers are fun''). The alpha coecient of item consistency for the full scale was .74. Alpha coecients for the three subscales were .53 (fun), .69 (perceived ease of use), and .62 (perceived usefulness). Estimates of current word-processing usage and intended word-processing usage over the following 13-week semester were also obtained on a 7-point scale ranging from not at all to at least once a day (computing facilities were accessible to students 7 days a week). Previous research has identi®ed that this measure is most accurate and meaningful to participants when estimating future usage and past usage over a long period of time. Davis et al. (1989) used this self-report measure and found that it signi®cantly correlated with system-based measures of usage, which suggests validity for this measure. The relationship between these measures and computer anxiety is con®rmed by Harris and Grandgenett (1996). The demographic variables of sex, age, age of ®rst computer interaction, nonword-processing software, and programming experience were requested together with whether the subject owned a computer or not. At the end of the 13-week semester, subjects were asked to report how often they had used a computer during the semester, on the scale described already. The study was a within-participant repeated measures design. A path-analysis was conducted with the self-reported measure of computer usage as the dependant measure and the remaining variables as the independent measures. A stepwise multiple regression placed all the variables listed earlier as independent variables in the equation, the variable sharing the largest amount of variance being selected as the most predictive. Having accounted for this variance, the variable sharing the greatest proportion of the remaining variance was then selected, and so on. The total amount of shared variance between the dependent and independent variables is represented by the percentages reported in Appendix A (adjusted R2). 3. Results This study assessed the variables predicted by TAM and self ecacy theory to in¯uence computer usage, to identify which of these variables best predicted wordprocessor usage over a 13-week semester. The means and ranges of all the variables assessed are reported in Table 1. The full range of word-processing behavior was reported from not at all to more than once a day. The age of ®rst interaction ranged from 5 to 44 years, with an average of just under 15 years. The self-reports of computer attitudes and anxiety were also spread across almost the entire range, with averages around the midpoint and were normally distributed. Within the present sample, no sex dierences in computer anxiety were identi®ed. Whilst this is inconsistent with previous research identifying females as possessing higher levels of computer anxiety, the inequality in the number of males and females should be borne in mind. The means and standard deviations for males and females separately are reported in Table 2. Table 2 shows that there were also
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Table 1 Distributions of experimental variables (n=147) Variable Age ®rst used a computer Computer self ecacy Computer anxiety AttitudeÐperceived fun AttitudeÐperceived usefulness AttitudeÐperceived ease of use Current word-processor usage Expected word-processor usagea Reported word-processor usagea a
M
SD
Minimum
Maximum
14.79 14.53 45.25 13.74 14.94 10.21 2.54 3.99 3.34
6.93 2.95 11.51 3.10 2.34 2.91 1.62 1.24 1.41
5 6 22 4 7 3 1 1 1
44 20 81 20 22 15 7 7 7
Over a 13-week semester.
Table 2 Sex dierences in the experimental variables Variable
Age ®rst used a computer Computer self ecacy Computer anxiety AttitudeÐperceived fun AttitudeÐperceived usefulness AttitudeÐperceived ease of use Current word-processor usage Expected word-processor usagea Reported word-processor usagea
Males (n=41)
Females (n=104)
M
SD
M
SD
12.7 14.7 43.1 14.8 14.5 10.4 2.6 4.0 3.0
5.2 2.9 13.0 3.1 2.4 2.9 1.7 1.4 1.3
15.4 14.4 46.4 13.1 15.1 10.1 2.5 4.0 3.4
7.3 3.0 11.0 3.2 2.3 2.9 1.6 1.2 1.4
t
t(136):2.05* t(146):0.55 (ns) t(146):1.55 (ns) t(145):2.80** t(145):1.34 (ns) t(145):0.37 (ns) t(145):0.45 (ns) t(146):0.02 (ns) t(134):0.89 (ns)
Note. Slight variation in the degrees of freedom re¯ects some students not answering some questions. a Over a 13-week semester. *p<.05; **p<.01.
no sex dierences in levels of computer self ecacy, perceived usefulness, or perceived ease of use. Additionally, levels of current word-processor usage and expected word-processor usage did not dier signi®cantly between males and females. The only signi®cant sex dierences were in age of initial computer experience and the perception of computers as ``fun''. Males' initial computer experience typically occurred 3 years before females' and males also reported perceiving computers as being more fun than females did. 3.1. Multiple regression Initially, the self-reported measure of word-processing usage over the 13-week semester was placed as the dependent variable in a (stepwise) multiple regression
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analysis. A path analysis then followed, replacing the dependent variable with the factor which had been identi®ed as predicting the dependent variable (and so on, see Appendix A). The beta values are reproduced in Fig. 1. The percentage before the variable name in Fig. 1 represents the amount of variance explained by the related variables (adjusted R2). Thus, 45% of the variance in the self-reported usage of word processing over the semester (``Reported WP usage'') was predicted by self-reports of current levels of word processing at the beginning of the semester (``Current WP usage'') combined with expected levels of word processing for the semester (``Expected WP usage''), and perceived usefulness. This represents an increase in 9% of the variance accounted for over Davis et al.'s (1989) initial ®ndings for TAM. The inclusion of current usage as a variable would therefore appear prudent. Behavioral intention and current usage were interdependent, such that those who word processed more at the beginning of the semester, expected to word process more in the future (and vice versa). Greater perceived usefulness also related to a greater behavioral intention to word process whereas owning a computer and being older related to greater actual computer use at the beginning of the semester. Level of computer anxiety also had a signi®cant impact upon current (at the beginning of the semester) word-processing usage and perceived usefulness, sharing around half its variance with perceived ease of use. Perceived ease of use, in turn, negatively aected perceived usefulness suggesting that those who perceived computers to be easier to use, perceived computers to be less useful. Computer self ecacy and perceived fun were inter-related and both predicted levels of computer anxiety. Perceived fun was also aected by sexÐbeing male associated with greater perceived fun. Finally, sex also related to perceived usefulness, being female associated with greater perceived usefulness. 4. Conclusions The aim of the present study was to identify which factors best predicted wordprocessor usage over a 13-week semester. TAM proposes that usage will be predicted by behavioral intention which will, in turn, be predicted by perceived usefulness and perceived ease of use. TAM also predicts that self ecacy and anxiety will predict perceived ease of use. Self ecacy theory postulates a more direct relationship, with anxiety and experience predicting computer self ecacy which in turn predicts usage. 4.1. Technology Acceptance Model TAM does not explicitly theorise a measure for current levels of experience; however, including current usage as a variable increased (by 9%) the variance accounted for in the self-reporting of word-processor usage over a 13-week period to 45%. Thus, we can conclude that the TAM and demographic variables accounted for almost half the variation in word-processor usage.
Fig. 1. A model of word-processor usage. 1Measure taken at the end of a 13-week semester. All other measures taken at the beginning of the semester.
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Perhaps unsurprisingly, the end of semester report of that semester's usage was predicted by how much word processing was expected and how much word processing was being undertaken at the beginning of the semester. Thus, those who were engaging in word processing more often at the beginning of the semester, expected to continue engaging in word processing more often than those who were engaging in word processing less often. This expectation was borne out, emphasizing the role of behavioral intention (in addition to current behavior) in predicting future usageÐ as postulated by TAM. The perceived usefulness of word processing also aected the reported usage over the semester. This highlights the signi®cance of this psychological variable in in¯uencing both expected future usage and actual future usage (retrospectively reported). Davis (1993) also reported a direct relationship between perceived usefulness and usage which, although not consistent with TRA, appears to be a consistent ®nding with respect to technology uptake. Thus, although the direct relationship between perceived usefulness and usage is not theoretically consistent with TAM, this relationship is consistent with the empirical ®ndings of other researchers. Perceived usefulness itself was determined by three factors, computer anxiety, perceived ease of use, and sex. Less anxious individuals perceived computers as more useful. In this way, computer anxiety indirectly reduces expectations to word process and future word processing, whilst directly reducing current levels of word processing. Computer anxiety shares around half of its variance with perceived ease of use, again the less anxious individual perceiving computers to be easier to use (and vice versa). In addition to perceived ease of use indirectly (via computer anxiety) relating to increased perceived usefulness, perceiving computers to be easier to use also related directly to lower levels of perceived usefulness. Thus, if a system is perceived to be easy to use, it is perceived to be less useful and used less. This embraces what Norman (1990, p. 31) terms ``the paradox of technology'', arguing that ``The same technology that simpli®es life by providing more functions in each device also complicates life by making the device harder to learn, harder to use''. This suggests an implicit antagonism between perceived usefulness and perceived ease of use (not theorised within TAM) in which computers have to be complex, and if they are not complex, they are not perceived as computers. Norman argues that the computers of the future will be ``invisible'' in the sense that we will be unaware that we are using computers. This is already true as computers are embedded within many modern automobiles, microwave ovens, games, CD players and calculators. Norman argues that ``You don't notice the computer because you think of yourself as doing the task, not as using the computer'' (p. 185). This latter point is crucial. Technophobes are anxious about using the computer, not doing the taskÐusing the word processor, not writing the document. The perceived usefulness measure, for example, does not explicitly refer to word processing, rather computers generally. Thus, a general ``perceived usefulness of computers'' related speci®cally to word-processor usage, suggesting that whilst word processing, participants perceive themselves as using technology in a manner that does not occur when using other technologies such as calculators. This suggests that to encourage technology usage, the usefulness of the technology in facilitating the
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completion of the task, should represent the primary emphasis for any program introducing technology. The relationship between sex and perceived usefulness is also interesting as being male related to lower levels of perceived usefulness. Other research has identi®ed that perceived usefulness may be particularly salient to females; females waiting until technology is perceived to be useful before using it (Popovich, Hyde, Zakrajsek & Blumer, 1987; Siann, MacLeod, Glissov & Durndell, 1990). 4.2. Self ecacy theory The self ecacy factors were found to be directly predictive of current word-processor usage (at the beginning of the semester) and indirectly predictive of future computer usage (at the end of the semester). In the present study, self ecacy predicted computer anxiety which predicted current usage. This directionality of the relationship between computer self ecacy and computer anxiety re¯ects that suggested by previous authors (Henderson et al., 1995; Krampen, 1988). Whilst self ecacy theory postulates that anxiety should predict self ecacy, the present model suggests that increasing levels of self ecacy will reduce levels of anxiety. Self-perceptions of self ecacy have been found to be extremely malleable across experimental conditions (Cole & Hopkins, 1995) suggesting that this should be the focus of encouraging anxious individuals to use technology. The importance of introducing a ``fun'' element into computer-based tuition is also highlighted in the present study as perceived fun and computer self ecacy were found to be interdependent. Instigating a perception of fun may be particularly important for female technophobes as this is a consistent area of sex dierence from 5-year-olds (Todman & Dick, 1994) to undergraduates and relates to both increased levels of computer self ecacy and decreased levels of computer anxiety. Whilst tuition should be ``fun'', the game format should be avoided as labeling an activity a ``game'' has a detrimental impact upon female performance (Artis, Ashman & Roberts, 1996). Interestingly, computer anxiety determined current (at the beginning of the semester), rather than expected or future, levels of word-processor usage. As the current levels of usage in¯uenced both expected and future usage, computer anxiety is a particularly salient variable. A great proportion of the variance in computer anxiety is shared with perceived ease of use. Referring to Maurer's conclusions, this would suggest that anxiety reduction should focus upon making the hardware and software seem easy to use. Paradoxically, this may relate to a reduction in the perceived usefulness of the technology, highlighting (again) that the usefulness of the technology should always be emphasized to the user. The present study proposes a framework to account for how the aective attitudinal and behavioral facets of technophobia can account word-processing usage over a semester. As mentioned earlier, the attitudinal and aective assessments referred to computers generally, suggesting that further research could apply this model to a broader range of tasks. Future research should assess this possibility. The relatively compulsory nature of word processing within the present study is likely also to be a factor (Arch & Cummins, 1989) as is the related issue of examining
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the factors related to the future usage of a computer activity that the participants were largely already familiar with to some degree. However, by the age of 21 years, most potential participants are likely to have experienced computer interaction and a requirement to use computers in their studies or work. Whilst the present sample was representative of psychology majors in Europe and the USA, the female domination of psychology undergraduate programs may represent a feature aecting the generalizability of the present model. As highlighted already, however, the model is consistent with previous research. The ®nding that females were not signi®cantly higher in computer anxiety than males may be related to the sample consisting of social science students in particular (Bowers & Bowers, 1996). Sex dierences have been identi®ed in other populations which may impact upon the model. The sample also consists of students whose ®rst computer interaction occurred around 14±15 years of age (on average). As future students will undoubtedly have much earlier initial experience, it could be that (for example) sex dierences will be diminished (Markert, 1996) or enhanced (Brosnan, 1997; Rosen & Weil, 1995) in the model. Research is therefore required to assess the validity of the proposed model for younger students earlier in the educational system and older subjects who have left the educational system.
Appendix A. Multiple regression
B
se B
Beta
T
45% reported WP usage Current WP usage Expected WP usage Perceived usefulness (Constant)
.3180 .3161 .1297 ÿ.7121
.0960 .1477 .0635 .9339
.3981 .2689 .2070
3.310** 2.140* 2.045* ÿ.763
28% expected WP usage Current WP usage Perceived usefulness (Constant)
.3792 .0811 1.878
.0555 .0369 .5721
.5067 .1630
6.828*** 2.197* 3.282**
36% current WP usage Expected WP usage Computer anxiety Age Own (Constant)
.5203 ÿ.0292 .0463 .5193 .5781
.1009 .0099 .0195 .2436 .7651
.3894 ÿ.2144 .1695 .1600
5.159*** ÿ2.942** 2.367* 2.132* .756
(Table continued on next page)
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Appendix A (continued) B
se B
Beta
T
18% perceived usefulness Computer anxiety Perceived ease of use Sex (Constant)
ÿ.1232 ÿ.2946 ÿ.8925 23.8033
.0224 .0893 .4332 1.7984
ÿ.6013 ÿ.3591 ÿ.1638
ÿ5.496*** ÿ3.298** ÿ2.060* 13.235***
65% computer anxiety Perceived ease of use Perceived fun Computer self ecacy (Constant)
ÿ1.6294 ÿ1.1364 ÿ1.2874 96.1769
.2484 .2192 .2491 3.4547
ÿ.4065 ÿ.2936 ÿ.3262
ÿ6.561*** ÿ5.169*** ÿ5.169*** 27.839***
47% perceived ease of use Computer anxiety (Constant)
ÿ.1716 18.0131
.0158 .7377
ÿ.6878
ÿ10.843*** 24.418***
15% computer self ecacy Perceived fun (Constant)
.3885 9.0481
.0787 1.1154
.3961
4.938*** 8.112***
20% perceived fun Computer self ecacy Sex (Constant)
.4039 1.5612 7.611
.0796 .5365 1.1805
.3961 .2271
5.076*** 2.910** 6.448***
Note. Age of ®rst computer interaction was the only remaining variable in the regression analysis that never reached signi®cance. *p<.05; **p<.01; ***p<.001.
References Ajzen, I. (1987). Attitudes, traits and actions: Dispositional prediction of behavior in personality and social psychology. Advances in Experimental Social Psychology, 20, 1±63. Ajzen, I., & Fishbein, M. (1977). Attitude behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888±918. Arch, E., & Cummins, D. (1989). Structured and unstructured exposure to computers: Sex dierences in attitude and use among college students. Sex Roles, 20, 245±254. Artis, J., Ashman, H., & Roberts, A. (1996) Curricular contexts and signi®cance of computer-based tasks. Paper presented at the British Psychological Conference Education Section Annual Conference, Wycombe.
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Bandura, A. (1977). Self-ecacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191±215. Bandura, A. (1982). Self-ecacy mechanism in human agency. American Psychologist, 37, 122±147. Bandura, A. (1986). Social foundations of thought and actionÐa social cognitive theory. Englewood Clis, NJ: Prentice Hall. Bandura, A. (1988). Self-ecacy conception of anxiety. Anxiety Research, 1, 77±98. Bowers, D., & Bowers, V. (1996). Assessing and coping with computer anxiety in the social science classroom. Social Science Computer Review, 14 (4), 439±443. Brosnan, M. (1997). The fourth ``R'': Are teachers hindering computer literacy in school children? Educational Review, 21 (1), 29±37. Brosnan, M., & Davidson, M. (1994). Computerphobia: Is it a particularly female phenomenon? The Psychologist, 7, 73±78. Brosnan, M., & Davidson, M. (1996). Psychological gender issues in computing. Journal of Gender, Work and Organization, 3 (1), 13±25. Busch, T. (1995). Gender dierences in self ecacy and attitudes towards computers. Journal of Educational Computing Research, 12, 147±158. Cole, B., & Hopkins, B. L. (1995). Manipulations of the relationship between reported self-ecacy and performance. Journal of Organizational Behavior Management, 15 (1±2), 95±135. Dambrot, F. H., Watkins-Malek, M. A., Silling, M. S., Marshall, R. S., & Graver, J. A. (1985). Correlates of sex dierences in attitudes toward involvement with computers. Journal of Vocational Behavior, 27, 71±86. Davis, F. (1986) A Technology Acceptance Model for empirically testing new end-user information systems: Theory and results. Doctoral Dissertation, Sloan School of Management, MIT. Davis, F. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man±Machine Studies, 38, 475±487. Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982±1003. Desmond, J. (1984). Computer crashes: A case of mind over matter. Computer World, 29, 1. Dyck, J., & Smither, J. (1996). Older adult's acquisition of word processing: The contribution of cognitive abilities and computer anxiety. Computers in Human Behavior, 12, 107±119. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison Wesley. Gilroy, F., & Desai, H. (1986). Computer anxiety: Sex, race and age. International Journal of Man± Machine Studies, 25, 711±719. Harris, J., & Grandgenett, N. (1996). Correlates among teachers anxieties, demographics and telecomputing activity. Journal of Research on Computing in Education, 28, 300±317. Heinssen, R. K., Jr., Glass, C. R., & Knight, L. A. (1987). Assessing computer anxiety: Development and validation of the Computer Anxiety Rating Scale. Computers in Human Behavior, 3, 49±59. Henderson, R., Deane, F., & Ward, M. (1995). Occupational dierences in computer-related anxiety: Implications for the implementation of a computerized patient management information system. Behavior and Information Technology, 14, 23±31. Hess, R., & Miura, I. (1985). Gender dierences in enrolment in computer camps and classes. Sex Roles, 13, 193±203. Hill, T., Smith, N. D., & Mann, M. F. (1987). Role of ecacy expectations in predicting the decision to use advanced technologies: The case of computers. Journal of Applied Psychology, 72, 307±313. Jay, T. (1981). Computerphobia. What to do about it. Educational Technology, 21, 47±48. Kernan, M. C., & Howard, G. S. (1990). Computer anxiety and computer attitudes: An investigation of construct and predictive validity issues. Educational and Psychological Measurement, 50, 681±690. Krampen, G. (1988). Competence and control orientations as predictors of test anxiety in students: Longitudinal results. Unpublished manuscript, University of Trier.
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Loyd, B. H., & Gressard, L. P. (1984). The eects of sex, age and computer experience on computer attitudes. AEDS Journal, 18, 67±77. Mahar, D., Henderson, R., & Deane, F. (1997). The eects of computer anxiety, state anxiety and computer experience on users performance of computer based tasks. Personality and Individual Dierences, 22 (5), 683±692. Marcoulides, G. A. (1988). The relationship between computer anxiety and computer achievement. Journal of Educational Computing Research, 4, 151±158. Markert, L. (1996). Gender related to success in science and technology. Journal of Technology Studies, 22 (2), 21±29. Maurer, M. M. (1994). Computer anxiety correlates and what they tell us: A literature review. Computers in Human Behavior, 10, 369±376. Meier, S. (1985). Computer aversion. Computers in Human Behavior, 1, 171±179. Meier, S. (1988). Predicting individual dierences in performance on computer administered tests and tasks: Development of the Computer Aversion Scale. Computers in Human Behavior, 4, 175±187. Miura, I. T. (1987). The relationship of computer self ecacy expectations to computer interest and course enrolment in college. Sex Roles, 16 (5/6), 303±311. Moldafsky, N. I., & Kwon, I. W. (1994). Attributes aecting computer aided decision makingÐa literature survey. Computers in Human Behavior, 10, 299±323. Munger, C. F., & Loyd, B. H. (1989). Gender and attitudes towards computers and calculators: The relationship to math performance. Journal of Educational Computing Research, 5, 167±177. Norman, D. (1990) The design of everyday things. Doubleday: New York. Popovich, P. M., Hyde, K. R., Zakrajsek, T., & Blumer, C. (1987). The development of the Attitudes Toward Computer Usage Scale. Educational and Psychological Measurement, 47, 261±269. Radford, J., & Holdstock, L. (1995). Does psychology need more boy appeal? The Psychologist, 8, 21±24. Radford, J., & Holdstock, L. (1996). The growth of psychology. The Psychologist, 9, 548±550. Rosen, L., & Maguire, P. (1990). Myths and realities of computerphobia: A meta-analysis. Anxiety Research, 3, 175±191. Rosen, L. D., Sears, D. C., & Weil, M. M. (1987). Computerphobia. Behavior Research Methods, Instruments and Computers, 19, 167±179. Rosen, L., & Weil, M. (1995). Computer availability, computer experience and technophobia among public school teachers. Computers in Human Behavior, 11, 9±31. Sadri, G., & Robertson, I. (1993). Self ecacy and word-related behavior: A review and meta-analysis. Applied Psychology: An International Review, 42, 139±152. Scott, C., & Rockwell, S. (1997). The eect of communication, writing and technology apprehension on likelihood to use new communication technologies. Communication Education, 46, 44±62. Siann, A., MacLeod, H., Glissov, P., & Durndell, A. (1990). The eect of computer use on gender differences in attitudes towards computers. Computers and Education, 14, 183±191. Szajna, B. (1994). An investigation into the predictive validity of computer anxiety and computer attitude. Educational and Psychological Measurement, 54, 926±934. Szajna, B., & Mackay, J. M. (1995). Predictors of learning performance in a computer-user training environment: A path-analytic study. International Journal of Human±Computer Interaction, 7, 361± 369. Todman, J., & Dick, G. (1994). Primary children and teacher's attitudes to computers. Computers and Education, 20, 199±203. Todman, J., & Monaghan, E. (1994). Qualitative dierences in computer experience, computer anxiety and students' use of computers: A path model. Computers in Human Behavior, 10, 529±539. Torkzada, G., & Koufteros, X. (1994). Factorial validity of a computer self ecacy scale and the impact of computer training. Educational and Psychological Measurements, 54, 813±821. Weil, M. M., Rosen, L. D., & Sears, D. C. (1987). The Computerphobia Reduction Program. Year 1. Program development and preliminary results. Behavior Research Methods, Instruments and Computers, 19, 180±184.
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Whitely, B. (1996). Gender dierences in computer related attitudes. It depends on what you ask. Computers in Human Behavior, 12, 275±289. Whitely, B. (1997). Gender dierences in computer-related attitudes and behavior: A meta-analysis. Computers in Human Behavior, 13, 1±22.
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Modeling technophobia: a case for word processing M.J. Brosnan* Division of Psychology, School of Social Sciences, University of Greenwich, Southwood Site, Avery Hill Campus, Avery Hill Road, Eltham, London SE9 2HB, UK
Abstract After theory of reasoned action, Davis (1986, 1993) and Davis, Bagozzi, and Warshaw (1989) proposed the Technology Acceptance Model to account for how perceived ease of use, perceived usefulness, and attitudes predict behavioral intention to use computers. This study combined these factors with measures from Bandura's self ecacy theory (computer self ecacy and computer anxiety; Bandura, 1977, 1986) in conjunction with assessments of current computer experience. A total of 147 undergraduates completed a series of questionnaires at the beginning and end of a 13-week semester. A multiple regression analysis revealed that self-reported word-processor usage over a 13-week period was predicted by levels of usage at the beginning of the semester, expected usage, and perceived usefulness. Initial levels of usage and perceived usefulness were both predicted by levels of computer anxiety. A combination of the variables formulated by the Technology Acceptance Model and self ecacy theory account for 45% of the variance in self-reported computing behavior over a 13-week period. The theoretical implications are discussed. # 1999 Elsevier Science Ltd. All rights reserved. Keywords: Computer anxiety; Word processing; Self ecacy theory; Technology Acceptance Model
*Requests for reprints should be addressed to Mark J. Brosnan, Division of Psychology, School of Social Sciences, University of Greenwich, Southwood Site, Avery Hill Campus, Avery Hill Road, Eltham, London SE9 2HB, UK. E-mail: [email protected] 0747-5632/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved P II: S074 7- 5632( 98) 0002 0-X
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1. Introduction Technophobia has been described in terms of aversive behavioral, aective, and attitudinal responses to technology (Jay, 1981; Rosen & Maguire, 1990). This re¯ects a large body of research which has demonstrated an association between these computer-based attitudes and anxieties and computer-related behavior. Maurer (1994) reviewed the correlates of computer anxiety, which include experience, gender, age, and academic major (see also Rosen & Maguire, 1990). Maurer (1994, p. 374) argues that the only conclusion that can be drawn from this myriad of (largely correlational) data is that ``We do not know what to do about computer anxiety!''. This is not to argue that computer anxiety is not a salient concept. On the contrary, Moldafsky and Kwon (1994, p. 302) report that ``computer anxiety is a real phenomenon''. Rather Maurer's critique concerns the lack of focused research goals within this body of literature. The author agues ``The development of a model of the development of computer anxiety may help in providing this focus'' (Maurer, 1994, p. 374). It can be argued, therefore that much technophobia research lacks a theoretical underpinning to the identi®cation of (an increasing number of) correlates of computer anxiety. This paper describes two theoretical approaches to computer attitudes and anxiety (respectively) which can be combined into a model of how the psychological and demographic factors associated with technophobia impact upon computer usage. The application of these theoretical models to predicting computer usage will then be empirically evaluated. 1.1. The Technology Acceptance Model Davis (1986) introduced the Technology Acceptance Model (TAM) to account for the attitudinal factors that are postulated to aect computer acceptance. As an example of this, Desmond (1984) found a high correlation between computer misuses and people with negative attitudes towards information technology. In addition to negative attitudes being associated with resistance to using computers, Loyd and Gressard (1984) found that more positive attitudes toward computers were related to positive computer experience. Munger and Loyd (1989) con®rm that students with positive attitudes to computers perform better than students with more negative attitudes. TAM is based upon theory of reasoned action (TRA; Ajzen, 1987; Ajzen & Fishbein, 1977; Fishbein & Ajzen, 1975). TRA posits that behavioral intention is a measure of one's intention to perform a speci®ed behavior and represents the primary predictor of actual behavior. Behavioral intention is itself predicted by an attitudinal component which represents an individual's feelings about performing the behavior (Fishbein & Ajzen, 1975). This pathway was incorporated into TAM which postulates that computer-related attitudes in¯uence behavioral intention to use computers (and subsequently usage). TAM combines these two concepts with perceived usefulness and perceived ease of use. Perceived usefulness is de®ned as the prospective user's subjective probability
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that using a speci®c application system will increase his or her task performance. Perceived ease of use refers to the degree to which the prospective user expects the target system to be free of eort (Davis, Bagozzi & Warshaw, 1989). Perceived usefulness and perceived ease of use are both psychologically and statistically distinct (Davis et al., 1989) enabling a system to be perceived as useful but not easy to use, and vice versa. Todman and Dick (1994) developed and validated a computer attitude scale which incorporated perceived usefulness and a perceived ease of use subscales. Combining these two subscales with a subscale which assessed the amount of ``fun'' a computer was perceived to be, represented the composite attitudinal scale. This is consistent with TAM which identi®ed perceived usefulness and perceived ease of use as the factors determining the attitudinal component. Combining the work of Todman and Dick with that of Davis suggests that the attitudinal scale, speci®cally the perceived usefulness and perceived ease of use subscales, should predict the behavioral intention to use computersÐthe relative weights being obtained through regression (Davis et al., 1989). Davis (1993) reported that TAM accounts for 36% of the variance in computer usage. Although signi®cant, the correlations between computer attitudes and predictor variables of computer use (such as computer aptitude) have been found to be low (Dambrot, Watkins-Malek, Silling, Marshall & Graver, 1985). Dambrot et al. compared those who dropped out of a computer course and those who did not. The authors concluded that computer attitudes were, at best, a moderate predictor of actual use. This poor predictive validity is consistent with the conceptualization of technophobia as multifaceted. Both Jay (1981) and Rosen and Maguire (1990) postulate an aective element to technophobia which determines computer usage, namely computer anxiety. This is consistent with the TAM as both perceived usefulness and perceived ease of use are proposed to be determined by variables external to the model. Davis et al. (1989) explicitly suggest that levels of computer anxiety and computer self ecacy determine levels of perceived ease of use. 1.2. Computer anxiety Computer anxiety has been found to relate to computer avoidance (Hess & Miura, 1985; Scott & Rockwell, 1997). Consequently, the relationship between experience and anxiety is likely to be confounded by extremely anxious individuals avoiding computer interaction (Rosen & Maguire, 1990). Arch and Cummins (1989) report that a compulsory element to the computer-based interaction reduces levels of anxiety. The fact that computer-anxious individuals are often required to use technology has lead some researchers to ®nd no relationship between computer anxiety and computer-based performance (Kernan & Howard, 1990; Munger & Loyd, 1989; Szajna, 1994; Szajna & Mackay, 1995). Where dierences do occur, the performance of an anxious individual can be expected to be slower and less accurate (cf. Rosen & Weil, 1995, p. 13) due to more o-task (negative) cognitions (Heinssen, Glass & Knight, 1987; Weil, Rosen & Sears, 1987). This is con®rmed by Mahar, Henderson, and Deane (1997) who found that, whilst computer anxiety is associated with slower completion of computer tasks, this performance de®cit is independent of the prior
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level of computer experience of the user. This concurs with Whitely's conclusions that whilst prior experience does not mediate gender dierences in computer anxiety, computer anxiety does mediate gender dierences in computer-related behavior (Whitely, 1996). Thus, both Mahar et al. and Whitley argue that the impact of computer anxiety upon behavior is independent of prior experience. This is consistent with self ecacy theory which conceives experience and anxiety as independent factors. 1.3. Self ecacy theory Meier (1985, 1988) has proposed that self ecacy theory (Bandura, 1977, 1982, 1986) can provide a theoretical framework to account for how computer anxiety can aect computer usage. Self ecacy is de®ned as a judgment of ``how well one can execute courses of action required to deal with prospective situations'' (Bandura, 1982, p. 122). Perceptions of one's own physiological state are used in assessing performance capability. An individual in an aroused state may interpret the arousal as debilitating fear and feel vulnerable to failure. Those with low self ecacy expectations in a particular situation will experience unpleasant feelings, such as anxiety, and will behave in unproductive ways, such as avoiding work, and may lack persistence (Bandura, 1977). Hill, Smith, and Mann (1987) developed a measure that assessed self ecacy with respect to computers, termed ``computer self ecacy''. Hill et al. demonstrated a direct relation between computer self ecacy and a decision to use computers that is independent of people's beliefs about the instrumental value of doing so. The authors also found that previous experience is related to self ecacy with respect to computers, but that experience does not exert an independent in¯uence on the decision to use computers. Other research has also suggested that computer self ecacy is a salient concept, mediating the eects of computer anxiety upon computer usage. Kernan and Howard (1990), for example, demonstrate how anxiety eects expectation for success. The authors found that those subjects who saw themselves as more anxious about computers expected to be less skilled at the end of the course, expected a lower grade, and expected to like the course less than subjects who were less anxious. In a meta-analysis of over 4000 subjects, Sadri and Robertson (1993) con®rm that self ecacy predicts both performance and behavior choice. Additionally, experience gained through computer training has been found to increase levels of self ecacy (Torkzada & Koufteros, 1994). The mediation of the relationship between anxiety and avoidance by self ecacy may explain why the direct relationship between these two factors has been found to be seriously wanting (Bandura, 1986). This is consistent with an observation by both Rosen, Sears, and Weil (1987) and Marcoulides (1988) that computer users can be anxious, thereby reinforcing the point that anxiety reduction may not reduce avoidance (Bandura, 1988). Rather, computer anxiety will cause the individual to experience the interaction as unpleasant and terminate the interaction as soon as possible, having made more errors and performed more poorly (Rosen & Weil, 1995).
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The directionality of relationship between anxiety and self ecacy has been called into question, however. Krampen (1988) found that in prospective studies, perceived self ecacy predicts subsequent levels of anxiety but anxiety level does not predict subsequent perceptions of subsequent personal ecacy. Henderson, Deane, and Ward (1995) also report that self ecacy is the best predictor of anxiety, suggesting that the directionality of self ecacy in¯uencing anxiety is more salient. The present study aims to conduct a path analysis upon the factors theorised to aect word-processor usage. Whilst word processing is one of the most common computer-based activities, courses in word processing can result in subjects liking computers less after the course (Dyck & Smither, 1996). Assessments of wordprocessing experience will be placed in a (stepwise) multiple regression analysis to identify which factors are most predictive of these levels of experience. These predictive factors will then, in turn, be placed into a (stepwise) multiple regression themselves, to identify which remaining factors predict them. In addition to the anxiety, self ecacy, and attitudinal factors, certain demographic variables have also been found to be salient. 1.4. Demographic variables As mentioned already, many researchers have found females to be higher in computer anxiety and to obtain less computer-related experience than males (see Brosnan & Davidson, 1994; Whitely, 1997, for reviews). As Bandura would predict, these sex dierences in anxiety and experience have transferred to self ecacy. Sex dierences in perceived self ecacy for computer-related activities have been reported in adolescents in Grades 6±8 (age 11±13 years; Miura, 1987) and for students undertaking word-processing tasks (Busch, 1995). Other external variables that have been found to aect computer attitudes and anxiety will also be assessed. Rosen et al. (1987) found a positive correlation between student age and computer anxiety. Todman and Monaghan (1994) also report that age of ®rst computer interaction is signi®cant. Both computer ownership with software and programming experience were requested and these have also been found to be signi®cant variables (Brosnan & Davidson, 1996; Gilroy & Desai, 1986). 2. Method 2.1. Population/sample A total of 147 psychology freshmen were monitored over a period of a 13-week semester. There were 41 males and 104 females (2 unstated). The high proportion of females in the psychology sample is consistent with psychology courses throughout Europe and the US (Radford & Holdstock, 1995, 1996). Ages ranged from 18 to 45 years, with a mean age of 21.55 years (SD=5.61 years). The sample
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is therefore representative of psychology undergraduates with respect to these demographics. The sample was made up from the entire intake to the 1996/7 psychology degree program at the University of Greenwich, London. The program involves a 75% psychology component in the ®rst year with the remaining 25% open to selection of an option (sociology, economics, physiology, computing, for example). Many students from these other programs select to take psychology as their option, indicating a degree of overlap in the teaching received by students across the campus. All students were given the opportunity to withdraw (or withdraw their data) from the study at any time without the need for giving a reason for doing so and without the fear of repercussion. The researcher emphasized their own role within the University, making explicit that they were not involved in any form of tuition or assessment and that the data would only be used for research purposes, as part of an on-going University research program. No student requested withdrawing at any stage. 2.2. Procedure and measures The participants were all freshmen who had just experienced an orientation/ induction week at a University in which the coursework requirements were clearly stipulated. Participants were also informed that all coursework was required to be word processed and that an introductory session to the University network would be held at the end of the week. Attendance was mandatory as network IDs would also be allocated at this session. Eight 1-hr sessions ran with up to 20 students in each. After instruction on how to log on, demonstrations were given on how to access the word-processing package and other network packages (actual tuition on word processing was not given at this session, but was available to students throughout the semester). Students were then asked to complete a battery of questionnaires which were rotated in a Latin square design. The battery contained the Computer Anxiety Rating Scale (CARS; Heinssen et al., 1987). The CARS comprises of 9 ``negative'' anxiety-laden statements (e.g., ``I feel apprehensive about using computers'') and 10 ``positive'', nonanxiety statements (e.g., ``I am con®dent that I can learn computer skills''). Participants endorse the items on a 5point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Responses to nonanxiety items are later reversed before obtaining a total score which can range from 19 (not anxious) to 95 (very anxious). The authors report a high level of internal consistency for the overall scale with an alpha coecient of .87. The computer self ecacy (Hill et al., 1987) is a 4-item scale which is endorsed upon a 5-point Likert type scale as previous. The scale asks speci®cally about con®dence with computers (e.g., ``Only a few experts really understand how computers work''), and levels of computer self ecacy can range from 4 (low) to 20 (high). Computer attitudes were assessed using Todman and Dick's 11-item questionnaire (Todman & Dick, 1994) endorsed upon the 5-point Likert scale described already. The scale comprises of three subscales: namely, perceived usefulness (4 items, e.g., ``Computing is a useful hobby''), perceived ease of use (3 items, e.g., ``It
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is dicult to use a computer''), and perceived fun (4 items, e.g., ``Computers are fun''). The alpha coecient of item consistency for the full scale was .74. Alpha coecients for the three subscales were .53 (fun), .69 (perceived ease of use), and .62 (perceived usefulness). Estimates of current word-processing usage and intended word-processing usage over the following 13-week semester were also obtained on a 7-point scale ranging from not at all to at least once a day (computing facilities were accessible to students 7 days a week). Previous research has identi®ed that this measure is most accurate and meaningful to participants when estimating future usage and past usage over a long period of time. Davis et al. (1989) used this self-report measure and found that it signi®cantly correlated with system-based measures of usage, which suggests validity for this measure. The relationship between these measures and computer anxiety is con®rmed by Harris and Grandgenett (1996). The demographic variables of sex, age, age of ®rst computer interaction, nonword-processing software, and programming experience were requested together with whether the subject owned a computer or not. At the end of the 13-week semester, subjects were asked to report how often they had used a computer during the semester, on the scale described already. The study was a within-participant repeated measures design. A path-analysis was conducted with the self-reported measure of computer usage as the dependant measure and the remaining variables as the independent measures. A stepwise multiple regression placed all the variables listed earlier as independent variables in the equation, the variable sharing the largest amount of variance being selected as the most predictive. Having accounted for this variance, the variable sharing the greatest proportion of the remaining variance was then selected, and so on. The total amount of shared variance between the dependent and independent variables is represented by the percentages reported in Appendix A (adjusted R2). 3. Results This study assessed the variables predicted by TAM and self ecacy theory to in¯uence computer usage, to identify which of these variables best predicted wordprocessor usage over a 13-week semester. The means and ranges of all the variables assessed are reported in Table 1. The full range of word-processing behavior was reported from not at all to more than once a day. The age of ®rst interaction ranged from 5 to 44 years, with an average of just under 15 years. The self-reports of computer attitudes and anxiety were also spread across almost the entire range, with averages around the midpoint and were normally distributed. Within the present sample, no sex dierences in computer anxiety were identi®ed. Whilst this is inconsistent with previous research identifying females as possessing higher levels of computer anxiety, the inequality in the number of males and females should be borne in mind. The means and standard deviations for males and females separately are reported in Table 2. Table 2 shows that there were also
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Table 1 Distributions of experimental variables (n=147) Variable Age ®rst used a computer Computer self ecacy Computer anxiety AttitudeÐperceived fun AttitudeÐperceived usefulness AttitudeÐperceived ease of use Current word-processor usage Expected word-processor usagea Reported word-processor usagea a
M
SD
Minimum
Maximum
14.79 14.53 45.25 13.74 14.94 10.21 2.54 3.99 3.34
6.93 2.95 11.51 3.10 2.34 2.91 1.62 1.24 1.41
5 6 22 4 7 3 1 1 1
44 20 81 20 22 15 7 7 7
Over a 13-week semester.
Table 2 Sex dierences in the experimental variables Variable
Age ®rst used a computer Computer self ecacy Computer anxiety AttitudeÐperceived fun AttitudeÐperceived usefulness AttitudeÐperceived ease of use Current word-processor usage Expected word-processor usagea Reported word-processor usagea
Males (n=41)
Females (n=104)
M
SD
M
SD
12.7 14.7 43.1 14.8 14.5 10.4 2.6 4.0 3.0
5.2 2.9 13.0 3.1 2.4 2.9 1.7 1.4 1.3
15.4 14.4 46.4 13.1 15.1 10.1 2.5 4.0 3.4
7.3 3.0 11.0 3.2 2.3 2.9 1.6 1.2 1.4
t
t(136):2.05* t(146):0.55 (ns) t(146):1.55 (ns) t(145):2.80** t(145):1.34 (ns) t(145):0.37 (ns) t(145):0.45 (ns) t(146):0.02 (ns) t(134):0.89 (ns)
Note. Slight variation in the degrees of freedom re¯ects some students not answering some questions. a Over a 13-week semester. *p<.05; **p<.01.
no sex dierences in levels of computer self ecacy, perceived usefulness, or perceived ease of use. Additionally, levels of current word-processor usage and expected word-processor usage did not dier signi®cantly between males and females. The only signi®cant sex dierences were in age of initial computer experience and the perception of computers as ``fun''. Males' initial computer experience typically occurred 3 years before females' and males also reported perceiving computers as being more fun than females did. 3.1. Multiple regression Initially, the self-reported measure of word-processing usage over the 13-week semester was placed as the dependent variable in a (stepwise) multiple regression
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analysis. A path analysis then followed, replacing the dependent variable with the factor which had been identi®ed as predicting the dependent variable (and so on, see Appendix A). The beta values are reproduced in Fig. 1. The percentage before the variable name in Fig. 1 represents the amount of variance explained by the related variables (adjusted R2). Thus, 45% of the variance in the self-reported usage of word processing over the semester (``Reported WP usage'') was predicted by self-reports of current levels of word processing at the beginning of the semester (``Current WP usage'') combined with expected levels of word processing for the semester (``Expected WP usage''), and perceived usefulness. This represents an increase in 9% of the variance accounted for over Davis et al.'s (1989) initial ®ndings for TAM. The inclusion of current usage as a variable would therefore appear prudent. Behavioral intention and current usage were interdependent, such that those who word processed more at the beginning of the semester, expected to word process more in the future (and vice versa). Greater perceived usefulness also related to a greater behavioral intention to word process whereas owning a computer and being older related to greater actual computer use at the beginning of the semester. Level of computer anxiety also had a signi®cant impact upon current (at the beginning of the semester) word-processing usage and perceived usefulness, sharing around half its variance with perceived ease of use. Perceived ease of use, in turn, negatively aected perceived usefulness suggesting that those who perceived computers to be easier to use, perceived computers to be less useful. Computer self ecacy and perceived fun were inter-related and both predicted levels of computer anxiety. Perceived fun was also aected by sexÐbeing male associated with greater perceived fun. Finally, sex also related to perceived usefulness, being female associated with greater perceived usefulness. 4. Conclusions The aim of the present study was to identify which factors best predicted wordprocessor usage over a 13-week semester. TAM proposes that usage will be predicted by behavioral intention which will, in turn, be predicted by perceived usefulness and perceived ease of use. TAM also predicts that self ecacy and anxiety will predict perceived ease of use. Self ecacy theory postulates a more direct relationship, with anxiety and experience predicting computer self ecacy which in turn predicts usage. 4.1. Technology Acceptance Model TAM does not explicitly theorise a measure for current levels of experience; however, including current usage as a variable increased (by 9%) the variance accounted for in the self-reporting of word-processor usage over a 13-week period to 45%. Thus, we can conclude that the TAM and demographic variables accounted for almost half the variation in word-processor usage.
Fig. 1. A model of word-processor usage. 1Measure taken at the end of a 13-week semester. All other measures taken at the beginning of the semester.
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Perhaps unsurprisingly, the end of semester report of that semester's usage was predicted by how much word processing was expected and how much word processing was being undertaken at the beginning of the semester. Thus, those who were engaging in word processing more often at the beginning of the semester, expected to continue engaging in word processing more often than those who were engaging in word processing less often. This expectation was borne out, emphasizing the role of behavioral intention (in addition to current behavior) in predicting future usageÐ as postulated by TAM. The perceived usefulness of word processing also aected the reported usage over the semester. This highlights the signi®cance of this psychological variable in in¯uencing both expected future usage and actual future usage (retrospectively reported). Davis (1993) also reported a direct relationship between perceived usefulness and usage which, although not consistent with TRA, appears to be a consistent ®nding with respect to technology uptake. Thus, although the direct relationship between perceived usefulness and usage is not theoretically consistent with TAM, this relationship is consistent with the empirical ®ndings of other researchers. Perceived usefulness itself was determined by three factors, computer anxiety, perceived ease of use, and sex. Less anxious individuals perceived computers as more useful. In this way, computer anxiety indirectly reduces expectations to word process and future word processing, whilst directly reducing current levels of word processing. Computer anxiety shares around half of its variance with perceived ease of use, again the less anxious individual perceiving computers to be easier to use (and vice versa). In addition to perceived ease of use indirectly (via computer anxiety) relating to increased perceived usefulness, perceiving computers to be easier to use also related directly to lower levels of perceived usefulness. Thus, if a system is perceived to be easy to use, it is perceived to be less useful and used less. This embraces what Norman (1990, p. 31) terms ``the paradox of technology'', arguing that ``The same technology that simpli®es life by providing more functions in each device also complicates life by making the device harder to learn, harder to use''. This suggests an implicit antagonism between perceived usefulness and perceived ease of use (not theorised within TAM) in which computers have to be complex, and if they are not complex, they are not perceived as computers. Norman argues that the computers of the future will be ``invisible'' in the sense that we will be unaware that we are using computers. This is already true as computers are embedded within many modern automobiles, microwave ovens, games, CD players and calculators. Norman argues that ``You don't notice the computer because you think of yourself as doing the task, not as using the computer'' (p. 185). This latter point is crucial. Technophobes are anxious about using the computer, not doing the taskÐusing the word processor, not writing the document. The perceived usefulness measure, for example, does not explicitly refer to word processing, rather computers generally. Thus, a general ``perceived usefulness of computers'' related speci®cally to word-processor usage, suggesting that whilst word processing, participants perceive themselves as using technology in a manner that does not occur when using other technologies such as calculators. This suggests that to encourage technology usage, the usefulness of the technology in facilitating the
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completion of the task, should represent the primary emphasis for any program introducing technology. The relationship between sex and perceived usefulness is also interesting as being male related to lower levels of perceived usefulness. Other research has identi®ed that perceived usefulness may be particularly salient to females; females waiting until technology is perceived to be useful before using it (Popovich, Hyde, Zakrajsek & Blumer, 1987; Siann, MacLeod, Glissov & Durndell, 1990). 4.2. Self ecacy theory The self ecacy factors were found to be directly predictive of current word-processor usage (at the beginning of the semester) and indirectly predictive of future computer usage (at the end of the semester). In the present study, self ecacy predicted computer anxiety which predicted current usage. This directionality of the relationship between computer self ecacy and computer anxiety re¯ects that suggested by previous authors (Henderson et al., 1995; Krampen, 1988). Whilst self ecacy theory postulates that anxiety should predict self ecacy, the present model suggests that increasing levels of self ecacy will reduce levels of anxiety. Self-perceptions of self ecacy have been found to be extremely malleable across experimental conditions (Cole & Hopkins, 1995) suggesting that this should be the focus of encouraging anxious individuals to use technology. The importance of introducing a ``fun'' element into computer-based tuition is also highlighted in the present study as perceived fun and computer self ecacy were found to be interdependent. Instigating a perception of fun may be particularly important for female technophobes as this is a consistent area of sex dierence from 5-year-olds (Todman & Dick, 1994) to undergraduates and relates to both increased levels of computer self ecacy and decreased levels of computer anxiety. Whilst tuition should be ``fun'', the game format should be avoided as labeling an activity a ``game'' has a detrimental impact upon female performance (Artis, Ashman & Roberts, 1996). Interestingly, computer anxiety determined current (at the beginning of the semester), rather than expected or future, levels of word-processor usage. As the current levels of usage in¯uenced both expected and future usage, computer anxiety is a particularly salient variable. A great proportion of the variance in computer anxiety is shared with perceived ease of use. Referring to Maurer's conclusions, this would suggest that anxiety reduction should focus upon making the hardware and software seem easy to use. Paradoxically, this may relate to a reduction in the perceived usefulness of the technology, highlighting (again) that the usefulness of the technology should always be emphasized to the user. The present study proposes a framework to account for how the aective attitudinal and behavioral facets of technophobia can account word-processing usage over a semester. As mentioned earlier, the attitudinal and aective assessments referred to computers generally, suggesting that further research could apply this model to a broader range of tasks. Future research should assess this possibility. The relatively compulsory nature of word processing within the present study is likely also to be a factor (Arch & Cummins, 1989) as is the related issue of examining
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the factors related to the future usage of a computer activity that the participants were largely already familiar with to some degree. However, by the age of 21 years, most potential participants are likely to have experienced computer interaction and a requirement to use computers in their studies or work. Whilst the present sample was representative of psychology majors in Europe and the USA, the female domination of psychology undergraduate programs may represent a feature aecting the generalizability of the present model. As highlighted already, however, the model is consistent with previous research. The ®nding that females were not signi®cantly higher in computer anxiety than males may be related to the sample consisting of social science students in particular (Bowers & Bowers, 1996). Sex dierences have been identi®ed in other populations which may impact upon the model. The sample also consists of students whose ®rst computer interaction occurred around 14±15 years of age (on average). As future students will undoubtedly have much earlier initial experience, it could be that (for example) sex dierences will be diminished (Markert, 1996) or enhanced (Brosnan, 1997; Rosen & Weil, 1995) in the model. Research is therefore required to assess the validity of the proposed model for younger students earlier in the educational system and older subjects who have left the educational system.
Appendix A. Multiple regression
B
se B
Beta
T
45% reported WP usage Current WP usage Expected WP usage Perceived usefulness (Constant)
.3180 .3161 .1297 ÿ.7121
.0960 .1477 .0635 .9339
.3981 .2689 .2070
3.310** 2.140* 2.045* ÿ.763
28% expected WP usage Current WP usage Perceived usefulness (Constant)
.3792 .0811 1.878
.0555 .0369 .5721
.5067 .1630
6.828*** 2.197* 3.282**
36% current WP usage Expected WP usage Computer anxiety Age Own (Constant)
.5203 ÿ.0292 .0463 .5193 .5781
.1009 .0099 .0195 .2436 .7651
.3894 ÿ.2144 .1695 .1600
5.159*** ÿ2.942** 2.367* 2.132* .756
(Table continued on next page)
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Appendix A (continued) B
se B
Beta
T
18% perceived usefulness Computer anxiety Perceived ease of use Sex (Constant)
ÿ.1232 ÿ.2946 ÿ.8925 23.8033
.0224 .0893 .4332 1.7984
ÿ.6013 ÿ.3591 ÿ.1638
ÿ5.496*** ÿ3.298** ÿ2.060* 13.235***
65% computer anxiety Perceived ease of use Perceived fun Computer self ecacy (Constant)
ÿ1.6294 ÿ1.1364 ÿ1.2874 96.1769
.2484 .2192 .2491 3.4547
ÿ.4065 ÿ.2936 ÿ.3262
ÿ6.561*** ÿ5.169*** ÿ5.169*** 27.839***
47% perceived ease of use Computer anxiety (Constant)
ÿ.1716 18.0131
.0158 .7377
ÿ.6878
ÿ10.843*** 24.418***
15% computer self ecacy Perceived fun (Constant)
.3885 9.0481
.0787 1.1154
.3961
4.938*** 8.112***
20% perceived fun Computer self ecacy Sex (Constant)
.4039 1.5612 7.611
.0796 .5365 1.1805
.3961 .2271
5.076*** 2.910** 6.448***
Note. Age of ®rst computer interaction was the only remaining variable in the regression analysis that never reached signi®cance. *p<.05; **p<.01; ***p<.001.
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