Computer Self-Efficacy, Computer Anxiety, and Atti

Computer Self-Efficacy, Computer Anxiety, and Attitudes toward the Internet: A Study among Undergraduates in Unimas

Hong Kian Sam, Abang Ekhsan Abang Othman and Zaimuarifuddin Shukri Nordin

Faculty of Cognitive Sciences and Human Development

Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia

Fax: +60 82-672281

hksam@fcs.unimas.my

ABSTRACT

Eighty-one female and sixty-seven male undergraduates at a Malaysian university, from seven faculties and

a Center for Language Studies completed a Computer Self-Efficacy Scale, Computer Anxiety Scale, and an

Attitudes toward the Internet Scale and give information about their use of the Internet. This survey

research investigated undergraduates’ computer anxiety, computer self-efficacy, and reported use of and

attitudes toward the Internet. This study also examined differences in computer anxiety, computer self-

efficacy, attitudes toward the Internet and reported use of the Internet for undergraduates with different

demographic variables. The findings suggest that the undergraduates had moderate computer anxiousness,

medium attitudes toward the Internet, and high computer self-efficacy and used the Internet extensively for

educational purposes such as doing research, downloading electronic resources and e-mail communications.

This study challenges the long perceived male bias in the computer environment and supports recent studies

that have identified greater gender equivalence in interest, use, and skills levels. However, there were

differences in undergraduates’ Internet usage levels based on the discipline of study. Furthermore, higher

levels of Internet usage did not necessarily translate into better computer self-efficacy among the

undergraduates. A more important factor in determining computer self-efficacy could be the discipline of

study and undergraduates studying computer related disciplines appeared to have higher self-efficacy

towards computers and the Internet. Undergraduates who used the Internet more often may not necessarily

feel more comfortable using them. Possibly, other factors such as the types of application used, the purpose

for using, and individual satisfaction could also influence computer self-efficacy and computer anxiety.

However, although Internet usage levels may not have any impact on computer self-efficacy, higher usage

of the Internet does seem to decrease the levels of computer anxiety among the undergraduates.

Undergraduates with lower computer anxiousness demonstrated more positive attitudes toward the Internet

in this study.

Keywords

Computer self-efficacy, Computer anxiety, Internet attitudes, Internet experience

Introduction

The teaching and learning process has been altered by the convergence of a variety of technological, instructional, and pedagogical developments in recent times (Bonk & King, 1998; Marina, 2001). Technology is challenging the boundaries of the educational structures that have traditionally facilitated learning. Recent advances in computer technology and the diffusion of personal computers, productivity software, multimedia, and network resources over the last decade, heralded the development and implementation of new and innovative teaching strategies. Educators who advocate technology integration in the learning process believe it will improve learning and better prepare students to effectively participate in the 21st century workplace (Butzin, 2000; Hopson, Simms, & Knezek, 2002; Reiser, 2001).

The Campus Computing Project’s survey shows that the computer technologies have become core components of the campus environment and the college experience (Green, 1998) while a survey of first-year students by Sax, Astin, Korn, and Mahoney (1998) indicated that computer network use has become a way of life for the majority of the students. They use computers around the clock to accomplish a wide range of academic tasks (Green, 1998; Romiszowski & Mason, 1996). Many prepare course assignments, make study notes, tutor themselves with specialized multimedia, and process data for research projects. Most exchange e-mails with faculty, peers, and remote experts. They keep up-to-date in their fields on the Internet, accessing newsgroups, bulletin boards, listservs, and web sites posted by professional organizations. Most access library catalogs, bibliographic databases, and other academic resources in text, graphics, and imagery on the World Wide Web (Green, 1998).

Furthermore, “information technology literacy” has become the centerpiece of “professional literacy” and “workforce readiness” (Resnick & Wirt, 1996). Workforce readiness includes communication skills, competencies in emerging technologies, and critical thinking skills. Given the certainty of technological change,far more desirable than competencies in a limited number of specific applications are broad flexible skills, transferable skills and the related confidence to adapt to new applications and environments (Rush, 1998). Romiszowski and Mason (1996) conclude that higher education will expand academic computing resources not only for their pedagogical benefits but also “because it will be seen to be the duty of education to use such systems in order to prepare its graduates for the realities of a workplace where they will be obliged to use them” (p. 449).

However, in integrating computers in higher education, researchers have proposed that positive attitudes toward computers and high computer self-efficacy and lower computer anxiety levels could be important factors in helping people learn computer skills and use computers (e.g., Busch, 1995). Sproull, Zubrow, and Kiesler (1986) recognized that some college students felt confused and a loss of personal control when they encountered technology. DeLoughry (1993) also cited that “as many as one-third of the 14 million college students in the United States suffer from ‘technophobia’” (p. A25) and implied that the effectiveness for the use of computers in higher education might not be realized without research foundations and corresponding planning.

Kinzie, Delcourt, and Powers (1994) defined self-efficacy as an individual’s confidence in his or her ability, which may impact the performance of tasks:

“Self-efficacy reflects an individual’s confidence in his/her ability to perform the behavior

required to produce specific outcome and it’s thought to directly impact the choice to engage in a

task, as well as the effort that will be expended and the persistence that will be exhibited.” (p. 747)

Self-efficacy has been shown to influence choice of whether to engage in a task, the effort expended in performing it, and the persistence shown in accomplishing it (Bouffard-Bouchard, 1990). The greater people perceived their self-efficacy to be, the more active and longer they persist in their effort (Bandura, 1986).

Miura (1987) has suggested that self-efficacy may be an important factor related to the acquisition of computing skills. Computer self-efficacy is a specific type of self-efficacy. Specific self-efficacy is defined as belief in one’s ability to “mobilize the motivation, cognitive resources, and courses of action needed to meet given situational demands” (Wood & Bandura, 19, p. 408). Thus, computer self-efficacy is a belief of one’s capability to use the computer (Compeau & Higgins, 1995) and participants with little confidence in their ability to use computers might perform more poorly on computer-based tasks. On the other hand, previous computer experience may lead students to believe computer applications courses are easy. Heightened self-efficacy may cause students to expend little effort toward learning new computer concepts. On the other hand, Brosnan (1998) argued that better computer self-efficacy could increase persistence in studying computing.

Computer anxiety has been defined as a fear of computers when using one, or fearing the possibility of using a computer (Chua, Chen, & Wong, 1999). It is different from negative attitudes toward computers that entail beliefs and feelings about computers rather than one’s emotional reaction towards using computers (Heinssen, Glass, & Knight, 1987). Computer anxiety is characterized as an affective response, an emotional fear of potential negative outcomes such as damaging the equipment or looking foolish. From an information processing perspectives, the negative feelings associated with high anxiety detract cognitive resources from task performance (Kanfer & Heggestad, 1997). Thus the performance of participants with higher computer anxiety might be poorer than those with little or no computer anxiety.

Woodrow (1991) claimed that students’ attitudes toward computers were critical issues in computer courses and computer-based curricula. Monitoring the user’s attitudes toward computers should be a continuous process if the computer is to be used as a teaching and learning tool. Other attributes, such as the relationship with gender and age (Morris, 1988-19), the effects of training and learning (Ford & Noe, 1987), and computer anxiety (Paxton & Turner, 1984) were also related to attitudes toward computers.

Purpose of the research

This research looked at two research objectives. Firstly, are computer anxiety and computer self-efficacy related to the reported use of and attitudes toward the Internet among undergraduates in Universiti Malaysia Sarawak (Unimas), and secondly, are there any differences in computer anxiety, computer self-efficacy, attitudes toward the Internet and reported use of the Internet based on gender and faculty for these undergraduates? Specifically, this research investigated the following research questions:

¾What is the Internet use pattern of Unimas undergraduates?¾Are there differences in the Internet use pattern based on gender and faculty?

¾What are the Unimas undergraduates’ attitudes toward the Internet and computer anxiety and computer self-efficacy levels?

¾Are there differences in attitudes toward the Internet and computer anxiety and computer self-efficacy levels based on gender and faculty?

¾Are there differences in Internet use pattern based on the Unimas undergraduates’ attitudes toward the Internet and computer anxiety and computer self-efficacy?

¾Are there relationships between time spent on Internet use, attitudes toward the Internet, computer anxiety, and computer self-efficacy?

Review of related literature

Computer anxiety, computer self-efficacy, and attitudes toward computers

Several studies have demonstrated the effect of computer anxiety and computer self-efficacy on computer-related behaviors. Computer self-efficacy has been shown to be positively related to performance during computer training (Webster & Martocchio, 1992). A student’s confidence about computer skills may affect the willingness to learn about computer skills. The less confident a student feels about computer skills, the more he or she desires to learn about computer technology (Zhang & Espinoza, 1998).

Computer self-efficacy was also found to be associated with attitudes toward computer technologies (Zhang & Espinoza, 1998). Furthermore, Zhang and Espinoza (1998) also reported that past enrollment in computer programming courses was found to be positively related to self-efficacy and computer self-efficacy positively related to plans to take more computer related courses.

A high level of computer anxiety, on the other hand, has been negatively related to learning computer skills (Harrington, McElroy, & Morrow, 1990), resistance to the use of computers (Torkzadeh & Angula, 1992; Weil & Rosen, 1995), and poorer task performance (Heinssen et al., 1987).

Taken together, these studies show that these three characteristics can have an important impact on computer use and ability to learn to use computers.

Computer anxiety, computer self-efficacy, and attitudes toward computers with gender and computer use In this age of all-pervading use of computers in most parts of the world, the issue of gender and computer use should be redundant. Nonetheless, as recently as the year 2000, in the United Kingdom, HESA (2000) reported that only 17% of enrollment to study computing at universities was female. Balka and Smith (2000) likewise reported that in the United States of America, the proportion of females studying computing was also getting less in recent years. Thus gender differences in computer use are still relevant, especially with the advent of the Internet to continue to study the genderisation of computing as proposed by Gackenbach (1998).

The research on gender and computing has often, although not conclusive, reported that males have more experience and use of computers (Brosnan & Lee, 1998; Balka & Smith, 2000). For example, Chua et al. (1999) and Coffin and Mackintyre (2000) in their meta analyses on the relationships between computer anxiety, computer attitudes, computer self-efficacy and computer experience state that most findings usually reinforce the gender effects and suggest that greater levels of computer experience are associated with lower computer experience and more positive computer attitudes.

Females also usually have more negative attitudes toward computers (Durndell & Thompson, 1997; Whitely, 1997) and greater computer anxiety (McIlroy, Bunting, Tierney, & Gordon, 2001) than males. Research on computer self-efficacy in general also revealed that males on average have better computer self-efficacy than females (Torkzadeh & Koufteros, 1994). Several studies have investigated female students’ choice of courses and careers, and self-efficacy has turned out to be a critical predictor. Female students have significantly lower self-efficacy than male students regarding math-related and traditionally male-dominated subjects, including computer science (Hackett, 1985).

However, controlling for computer experience, men and women had similar interest toward computers (Badagliacco, 1990). Loyd, Loyd, and Gressard (1987) reported that female students had less computer anxietythan male students, and female students liked working with computers more than male students. Rosen, Sears, and Weil (1987) on the other hand, found that gender was not related to computer anxiety, but was significantly related to computer attitudes, with women having more negative attitudes.

Furthermore, there are few examples of study to the contrary on the gender issue in computing. For example, Brosnan and Lee (1998) found that males were more computer anxious than females in a study in Hong Kong. Recently, it has also been suggested that the contemporary male and female students alike are pragmatic; their sights are set less on intellectual development than professional advancement and the utilitarian promise of higher education appeals to their desire to remain competitive and to increase personal income (Fulkerth, 1998; Sax et al., 1998). Shaw and Giacquinta (2000) reported that their findings suggested two frequently held beliefs, that older adult students showed more resistance than do younger students toward computing for academic purposes and that males are more involved with, interested and skilled in the use of computers than females, are no longer accurate. Pervasive use and importance of computers among undergraduates (Green, 1998; Sax et al., 1998) and striving for professional advancement (Fulkerth, 1998; Sax et al., 1998) have been suggested as possible reasons to account for these findings.

On the other hand, Shaw and Giacquinta (2000) discovered that educational technology students reported using computers more frequently, for a wider array of purposes, and for greater number of hours each week than students in the Educational Administration, Business Education, and Higher Education programs. They also reported completing more formal instruction and more positive attitudes toward the value of computers in academic studies.

Nearer at home, in a study conducted in Unimas, Hong (1998) reported that there were no significant differences in undergraduates’ attitudes toward computers and computer anxiety for male and female undergraduates and their different fields of study. However, low computer anxiety level and high self-efficacy with computer skills were significant predictors of success in computer-related courses.

The rapid growth of the use of the Internet brings up the question of whether the gender, age, and computer use issues reported earlier would be present with regard to the Internet. Furthermore, Schumacher and Morahan-Martin (2001) commented on the limited research comparing computer and Internet use. Gackenbach (1998), however, commented that the findings from the few studies on Internet use and attitudes suggest a parallel between computers and the Internet. For example, Kraut, Patterson, Lundmark, Kiersley, Mukopadhyay, and Scherlis (1998) found that more males than females use the Internet. Furthermore, males access more domains and use it more often and for longer periods of time than females. There were also differences in Web navigation strategies (Balka & Smith, 2000) and communication styles on the Internet (Sussman & Tyson, 2000) based on gender. These studies indicated a continuation of the computer literature in the study on Internet use (Morahan-Martin, 1998). Would this apparent trend be valid for undergraduates in Unimas?

Methodology

This study employed a survey research design to investigate undergraduates’ computer anxiety, computer self-efficacy, and reported use of and attitudes toward the Internet. This study also examined differences in computer anxiety, computer self-efficacy, attitudes toward the Internet and reported use of the Internet for undergraduates with different demographic variables in Universiti Malaysia Sarawak (Unimas).

Sample

The subjects for this study were 148 undergraduates at Universiti Malaysia Sarawak (Unimas). The mean age of the subjects was 23.8 years old (standard deviation = 4.06), ranging from 19 to 43 years old. Majority of the subjects were in the 19-23 age group. The demographic characteristics of the subjects are shown in Table 1. Research instruments

A questionnaire was used to collect data for this study. The questionnaire was divided into five sections. The first section collected demographic characteristics such as age, race, gender, and faculty/ center. The secondsection of the questionnaire required the subjects to report how much time in a week they used the Internet and

the uses to which the Internet was used for.

The third section of the questionnaire was the Computer Anxiety Rating Scales (CARS). CARS was used to

assess the subjects’ level of computer anxiety. CARS is a 19 items self-report inventory, designed and validated

by Heinssen et al. (1987). The subjects responded on a five-point Likert type scale (1=strongly disagree,

2=disagree, 3=undecided, 4=agree, and 5=strongly agree). Total scores ranged from 19, indicating a low level of

computer anxiety, to 95, which would indicate a high degree of computer anxiety.

Table 1. The subjects’ demographic characteristics

%

N

45.3 Male 67

Ethnicity Chinese 66 44.6

29.1 Malay 43

Bumiputeras 26

17.6 Sarawak

8.7 Others 13

Faculty/Centre Faculty of Computer Science and Information Technology 32 21.7

Faculty of Resource Sciences and Technology 27 18.2

Faculty of Engineering 23 15.5

Faculty of Social Sciences 20 13.5

Faculty of Economic and Business 17 11.5

Centre for Language Studies 12 8.1

Faculty of Applied and Creative Arts 11 7.4

Faculty of Cognitive Sciences and Human Development 6 4.1

The fourth section was the Internet Attitude Scale (IAS). IAS was modified from the Computer Attitude Scale,

developed and validated by Nickell and Pinto (1986). In the IAS, used to measure attitudes toward the Internet,

the word “computer” was replaced with “the Internet” throughout the scale. The IAS is a 20-item self-report

inventory, rated on a five point Likert type scale (1=strongly disagree, 2=disagree, 3=undecided, 4=agree, and

5=strongly agree). Total scores on IAS ranged from 20, indicating an extremely negative attitude toward the

Internet, to a score of 100, which would imply an extremely positive attitude toward the Internet.

The fifth section was the Computer Self-Efficacy Scale (CSE) (Torkzadeh & Koufteros, 1994; Murphy, Coover,

& Owen, 19). CSE has 29 items, each item preceded by the phrase “I feel confident”. The subjects responded

to a five-point Likert type scale (1=strongly disagree, 2=disagree, 3=undecided, 4=agree, and 5=strongly agree).

Total scores for CSE ranged from 29 to 145, with high scores indicating a high degree of confidence in a

subject’s ability to use computers (Durndell, Haag, & Laithwaite, 2000).

The reliability for sections three, four and five of the questionnaire was acceptable, with Cronbach alpha values

of 0.6334, 0.7186, and 0.9049 respectively for CARS, IAS, and CSE. The questionnaire is appended in

Appendix 1.

Data collection and data analysis procedures

The questionnaire was distributed to the subjects at the end of the academic year 2002/2003. All subjects were volunteers. Data analyses were carried out with the Statistical Packages for Social Sciences using frequencies, percentages, cross-tabulations and chi-square tests, t-tests, One-Way ANOVAs and Pearson’s correlations

Results

Results in Table 2 showed that most of the undergraduates have used the Internet for e-mail services (98.6%),

research purposes (95.9%), downloading electronic papers (95.3%), entertainment (85.1%), and gathering

product and service information (82.4%). However, only 66.2%, 56.8%, 50.0%, and 46.6% of theundergraduates used the Internet for downloading software and games, assessing newsgroups, chat room, and

games respectively. Only 6.8% of the undergraduates have conducted purchase over the Internet.

Table 2. Distribution of activities subjects’ conducted over the Internet

No Activities: Yes

I have used the Internet for

1. downloading software and games 98 (66.2%) 50 (33.5%)

2. shopping 10 (6.8%) 138 (9

3.2%)

3. research 142 (95.9%) 6 (

4.1%)

4. newsgroups 84 (56.8%) (43.2%)

5. games 69 (4

6.6%) 79 (53.4%)

6. product and service information 122 (82.4%) 26 (1

7.6%)

7. entertainment 126 (85.1%) 22 (14.9%)

8. education (electronic papers etc) 141 (95.3%) 7 (4.7%)

9. e-mail 146 (98.6%) 2 (1.4%)

10. chat room 74 (50.0%) 74 (50.0%)

On average, the undergraduates spent 9.2 hours in a week using the Internet (standard deviation = 1.2 hours).

Twenty-three of the undergraduates (15.5%) reported using the Internet on average 10 hours in a week while 11

undergraduates (7.4%) used the Internet for 14 hours in a week. Most of the undergraduates used the Internet for

three to five hours in a week (N=68, 45.9%)

Differences in the Internet use pattern and use levels based on race, gender, and faculty

There were no differences in the undergraduates’ usage pattern for the ten common activities with the Internet

based on gender. However, significantly more undergraduates from the Faculty of Computer Science and

Information Technology have used the Internet for downloading software and games as compared to

undergraduates from the Faculty of Applied and Creative Arts (refer to Table 3).

Table 3. Differences in using the Internet for downloading software and games based on faculty

Faculties I have used the Internet for

downloading software and games

Yes No

Faculty of Economics and Business (FEB) 10 (-0.4) 7 (0.5)

Faculty of Engineering (FE) 15 (-0.1) 8 (0.1)

Faculty of Applied and Creative Arts (FACA) 1 (-2.3) 10 (3.3)

Faculty of Social Sciences (FSS) 15 (0.5) 5 (-0.7)

Faculty of Resource Science and Technology (FRST) 15 (-0.7) 12 (1.0)

Faculty of Computer Science and Information Technology (FCSIT) 31 (2.1) 1 (-3.0)

Note: 1. χ = 32.1, df = 6, p < 0.0005

2. numbers in brackets refer to standardized residuals

There were no differences in the undergraduates’ Internet usage levels, as measured by the time they spent on

using the Internet, based on gender (t=1.413, df=145, p=0.160). However, there were differences in

undergraduates’ usage levels based on Faculty (F=2.509, df=6/146, p=0.025). Post-hoc analyses showed that

undergraduates at Faculty of Computer Science and Information Technology and Faculty of Applied and

Creative Arts had significantly higher usage time than the other faculties.

Computer anxiety, attitudes toward the Internet and computer self-efficacy

Based on the undergraduates’ responses to the CARS, they showed moderate computer anxiousness Likewise,

the undergraduates had moderate attitudes toward the Internet based on their responses to the IAS. However, the

undergraduates had high computer self-efficacy.

Table 4. Means and standard deviations for computer anxiety, attitudes toward the Internet and computer self-

efficacy

deviation

Standard

Mean

(1=low computer anxiety, 5=high computer anxiety)

Attitudes toward the Internet (based on IAS) 3.2081 0.33

(1=negative attitudes toward the Internet,

5=positive attitudes toward the Internet)

Computer self-efficacy (based on CSE) 3.8656 0.5955

(1=low computer self-efficacy, 5=high computer self-efficacy)

Differences in computer anxiety, attitudes toward the Internet and computer self-efficacy based on gender and

faculty

With reference to Table 5, there were no significant differences in computer anxiety levels, attitudes toward the

Internet, and computer self-efficacy based on gender. Undergraduates from the seven faculties and one centre

also did not show significant differences in their computer anxiety levels and attitudes toward the Internet (refer

Table 6). There was however differences in computer self-efficacy among the undergraduates based on faculty.

Undergraduates from the Faculty of Computer Science and Information Technology (Mean=4.154) have

significantly better computer self-efficacy than undergraduates from the Faculty of Creative and Applied Arts

(Mean=3.574).

Table 5. t-tests results for differences based on gender

Dev

df

p

t

Std

N

Mean

0.297

3.339

80

Female

Attitudes toward the Internet (based on IAS) Male 67 3.199 0.359 0.312 146 0.755

3.222

0.323

81

Female

3.902

0.678 0.680 146 0.498

67

Computer self-efficacy (based on CSE) Male

0.520

3.835

Female

81

Table 6. One-Way ANOVA results for differences based on faculty

F

P

Df

MS

SS

Computer anxiety (based on CARS)

Between group 0.821 6 0.137 1.496 0.184

Error 12.806 140 0.091

Total 13.627 146

Attitudes toward the Internet (based on IAS)

Between group 0.588 6 0.098 0.848 0.535

Error 16.297 141 0.116

Total 16.885 147

Computer self-efficacy (based on CSE)

Between group 5.321 6 0.887 2.671 0.017*

Error 46.812 141 0.332

Total 52.133 147

Note: *p<0.05

Differences in Internet use based on computer anxiety, computer self-efficacy, and attitudes toward the

Internet

The findings from this study (refer Table 7) showed that undergraduates with better attitudes toward the Internet

did more “downloading of software and games” activities. Likewise, undergraduates who had higher computer

self-efficacy were more likely to “use the Internet for product and service information.” The findings also

showed that undergraduates “used the Internet for educational purposes (electronic papers etc)” regardless of

their computer self-efficacy and computer anxiety levels. Likewise, no matter what their levels of computer

anxiety, attitudes toward the Internet, and computer self-efficacy may be, many of the undergraduates “used the

Internet mainly for emails.”

Table 7. χ2 tests results for differences in Internet use based on attitudes toward the Internet, computer self-

efficacy, and computer anxiety

Attitudes toward the Internet Computer self-efficacy Computer anxiety

Low High Low High Low High

Downloading of software and games

Yes 12 57

No 30 49

χ2=7.677, df=1, p=0.006

Used the Internet for product and service information

Yes 5 117 No 6 20

χ2=11.220, df=1, p=0.001

Used the Internet for educational purposes (electronic papers etc)

Yes 9 132 16 124

No 2 5 3 4

χ2=4.772, df=1, p=0.029 χ2=5.851, df=1, p=0.016

Used the Internet mainly for emails

Yes 40 106 10 136 17 128

No 2 0 1 1 2 0

χ2=5.117, df=1, p=0.024 χ2=5.340, df=1, p=0.021 χ2=13.3660, df=1, p<0.0005

Note: Only significant results are shown in the table above.

Relationships between times spent on using the Internet, computer anxiety, attitudes toward the Internet,

and computer self-efficacy

The results shown in Table 8 indicated that there were no significant relationship between time spent in a week

using the Internet and the undergraduates’ attitudes toward the Internet and computer self-efficacy. However,

undergraduates who spend longer hours using the Internet for educational purposes generally had lower

computer anxiety. The relationship, however, was not strong.

Although there were no significant relationships between computer anxiety and attitudes toward the Internet with

computer self-efficacy, there was, however, a significant relationship between computer anxiety and attitudes

toward the Internet. Undergraduates who were highly computer anxious generally have more negative attitudes

toward the use of the Internet.

Table 8. Correlations between time spent on using the Internet, attitudes toward the Internet, computer self-

efficacy, and computer anxiety

Time spent on using the Internet Attitudes toward the Internet Computer self-efficacy Computer anxiety

Time spent on using the Internet

0.056 0.125 0.166*

Attitudes toward the Internet

0.005 -0.454***

Computer self-efficacy 0.038

Computer anxiety

In general, the results suggest that the respondents had moderate computer anxiousness, medium attitudes toward the Internet, and high computer self-efficacy. Similar to findings reported by Green (1998) and Romiszowski and Mason (1996), the undergraduates at Unimas also use the Internet extensively for educational purposes such as doing research, downloading electronic resources and e-mail communications.

This study challenges the long perceived male bias in the computer environment (Chen, 1986; Balka & Smith, 2000; Durndell & Thompson, 1997; McIlroy et al., 2001; Torkzadeh & Koufteros, 1994; Whitely, 1997) and instead supports recent studies that have identified greater gender equivalence in interest, opportunity, use, and skills levels (Green, 1998; Shaw & Giacquinta, 2000). Gender, at least among the undergraduates in this study, did not account for differences in the Internet use pattern, computer self-efficacy, computer anxiety, and attitudes toward the Internet. Female as well as male undergraduates seem to be equal in their receptivity to the use of the Internet, the extent of their use of the Internet, and the purposes for which they use the Internet. These findings seem to support the profile of contemporary undergraduates in the literature (Fulkerth, 1998; Green 1998; Sax et al., 1998) and their mindfulness of the role of computer-based technologies across professions and industries (Callan, 1998; Rush, 1998).

There were differences in undergraduates’ usage levels based on the discipline of study. Undergraduates from the Faculty of Computer Science and Information Technology (FCSIT) and Faculty of Applied and Creative Arts (FACA) were found to use the Internet longer than those from other faculties. Although undergraduates from these two faculties recorded the highest usage levels compared to undergraduates from other faculties, the only differences in computer self-efficacy levels were between undergraduates from these two faculties. FCSIT undergraduates had significantly better computer self-efficacy than undergraduates from FACA. These two findings seemed to indicate that higher levels of Internet usage did not necessarily translate into better computer self-efficacy among the undergraduates. A more important factor in determining computer self-efficacy could be the discipline of study (Shaw & Giaquinta, 2000) and undergraduates studying computer related disciplines may in general have higher self-efficacy towards computers and the Internet.

Although the general belief is that “the more is better”, in this study there is no empirical evidence to support this assumption in contradiction of a positive relationship between the Internet usage levels and self-efficacy (Seyal, Rahim, & Rahman, 2002). Undergraduates who used computers often may not necessarily feel more comfortable using them. Possibly, other factors such as the types of application used, the purpose for using, and the role of satisfaction, could also influence computer self-efficacy and computer anxiety. Nonetheless, although the Internet usage levels may not impact on computer self-efficacy, higher use of the Internet does seem to decrease the levels of computer anxiety among the undergraduates. Undergraduates with lower computer anxiousness demonstrated more positive attitudes toward the Internet, in this study.

Conclusions

It is believed that gender would not be a factor influencing undergraduates’ attitudes toward computers, computer self-efficacy, and attitudes toward the Internet in the near future, as computers become a prevalent tool in our daily lives, regardless of whether one likes to use it or not.

The findings on this study, however, indicate that learning in the computer environment requires the special challenge of developing a mix of declarative, procedural, conceptual, and logical knowledge (Johnson & Johnson, 1996) as suggested by the theories of learning in general (Farnham-Diggory, 1992). While successful learning is always a function of the interaction of many factors, those known to be essential for cultivating computer skills include extensive practice (Anderson, 1990), experimentation with many “instances” or “examples” of applications (Brown, Collins, & Duguid, 19), a positive attitude, motivation, and the sense of satisfaction that attends accomplishment (Brown et al., 19; Farnham-Diggory, 1992). These factors clearly interact in a circular fashion, for example, the more one has or take the opportunity for instruction and practice, the more time one will devote, this supports motivation and satisfaction which, in turn, extend one’s use and thirst for more.

Thus, as suggested by Shaw and Giacquinta (2000), faculty should in addition to integrating computer use in their courses, make regularly available a wide range of short-format, hands-on workshops and demonstrations in which undergraduates can be given individual attention. The subjects of the workshops and demonstrationsshould parallel applications being integrated into course activities, in order to enhance exposure and high levels of practice.

In addition to allocating fiscal resources to on-campus hardware and infrastructure, universities should also provide for upgrading of users’ skills and user support (Green, 1998;; Shaw & Giacquinta, 2000), opportunities for undergraduates to purchase affordable software and hardware for use at home, and remote connectivity to the campus network for all students. This is view of the limitations in the ability of university to put in place adequate and up-to-date computer facilities on-campus and as suggested by Shaw and Giacquinta (2000) that undergraduates’ generally prefer to do academic computing at home rather than at the universities.

Furthermore, students who are going to participate in courses that require the use of the Internet would benefit if offered technology literacy courses prior to enrolling in courses that require its use (Hong, 2002). One may conclude that these courses would increase computer literacy, consequently improving attitudes toward learning. Acknowledgements

We would like to thank Lily Law for her assistance in editing the paper and the reviewers for providing guidance and useful suggestions in improving the paper.

References

Anderson, J. R. (1990). Cognitive psychology and its implications,New York: Freeman.

Badagliacco, J. M. (1990). Gender and race differences in computing attitudes and experience. Social Science Computer Review, 8, 42-.

Balka, E., & Smith, R. (2000). Women work and computerization, Boston: Kluwer.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory, Englewood Cliffs, NJ: Prentice-Hall.

Bonk, C. J., & King, K. S. (1998). Electronic collaborators: Learner centered technologies for literacy, apprenticeship, and discourse, Mahwah: Lawrence Erlbaum.

Bouffard-Bouchard, T. (1990). Influence of self-efficacy on performance in a cognitive task. The Journal of Social Psychology, 130, 353-363.

Brosnan, M. (1998). Technophobia, London: Routledge.

Brosnan, M., & Lee, W. (1998). A cross-cultural comparison of gender differences in computer attitudes and anxiety: The UK and Hong Kong. Computers in Human Behavior, 14 (4), 559-577.

Brown, J. S., Collins, A., & Duguid, P. (19). Situated cognition and the culture of learning. Educational Researcher, 18 (1), 32-41.

Busch, T. (1995). Gender differences in self-efficacy and attitudes toward computers. Journal of Educational Computing Research, 12, 147-158.

Butzin, S. M. (2000). Using instructional technology in transformed learning environments: An evaluation of project child. Journal of Research in Educational Computing Education, 33 (4), 367-384.

Callan, P. M. (1998). A national center to address higher education policy, Retrieved October 25, 2005, from http://www.highereducation.org/reports/concept/concept.shtml.

Chua, S. L., Chen, D., & Wong, A. F. L. (1999). Computer anxiety and its correlates: A meta-analysis. Computers in Human Behavior, 15, 609-623.Coffin, R., & Mackintyre, P. (2000). Cognitive, motivation, and affective processes associated with computer related performance: A path analysis. Computers in Human Behavior, 16 (2), 199-222.

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19, 1-211.

DeLoughry, T. J. (1993). Two researchers say ‘technophobia’ may afflict millions of students. Chronicle of Higher Education, A25-A26.

Durndell, A., Haag, Z., & Laithwaite, H. (2000). Computer self-efficacy and gender: A cross cultural study of Scotland and Romania. Personality and Individual Differences, 28, 1037-1044.

Durndell, A., & Thompson, K. (1997). Gender and computing: A decade of change? Computers and Education, 28 (1), 1-9.

Farnham-Diggory, S. (1992). Cognitive processes in education, New York: Harper Collins.

Ford, K. J., & Noe, R. A. (1987). Self-assessed training needs: The effects of attributes toward training, managerial level, and function. Personnel Psychology, 40, 39-53.

Fulkerth, B. (1998). A bridge for distance education: Planning for the information age student. Syllabus, 12 (4), 3-5.

Gackenbach, J. (1998). Psychology and the Internet: Intrapersonal, interpersonal and transpersonal implications, New York: Academic Press.

Green, K. C. (1998). Campus computing 1998: The ninth annual survey of desktop computing and information technology in higher education, Encino, CA: The Campus Computing Project.

Hackett, G. (1985). The role of mathematics self-efficacy in the choice of math-related majors of college women and men: A path analysis. Journal of Counseling Psychology, 32, 47-56.

Harrington, K. V., McElroy, J. C., & Morrow, P. C. (1990). Computer anxiety and computer-based training: A laboratory experiment. Journal of Educational Computing Research, 6, 343-358.

Heinssen, R. K., 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.

HESA (Higher Education Statistics Agency) (2000). Higher Education Statistics for the UK, 1998/9. London: HESA.

Hong, K. S. (1998). Predictors of computer anxiety and performance in an Introductory Information Technology course. Journal of Science and Mathematics Education in Southeast Asia, XXI (2), 1-18.

Hong, K. S. (2002). Relationships between students' and instructional variables with satisfaction and learning from a Web-based course. The Internet & Higher Education, 5 (3), 267-281.

Hopson, M. H., Simms, R. L., & Knezek, G. A. (2002). Using a technologically enriched environment to improve higher-order thinking skills. Journal of Research on Technology in Education, 34 (2), 109-119.

Johnson, D. W., & Johnson, R. T. (1996). Cooperation and the use of technology. In D. H. Jonassen (Ed.), Handbook of research for educational communication and technology, New York: Macmillan Library Reference, 1017-1044.

Kanfer, R., & Heggestad, E. D. (1997). Motivational traits and skills: A person-centered approach to work motivation. Research in Organizational Behavior, 19, 1-56.

Kay, R. H. (1990). Predicting student teacher commitment to the use of computers. Journal of Educational Computing Research, 6, 299-309.Kinzie, M. B., Delcourt, M. A. B., & Powers, S. M. (1994). Computer technologies: Attitudes and self-efficacy across undergraduate disciplines. Research in Higher Education, 35, 745-768.

Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukopadhyay, T., & Scherlis, W. (1998). Internet paradox:

A social technology that reduces social involvement and psychological well-being? American Psychologist, 53

(9), 1017-1031.

Loyd, B. H., Loyd, E. L., & Gressard, C. (1987). Gender and computer experience as factors in the computer attitudes of middle school students. Journal of Early Adolescence, 7, 13-19.

Marina, S. T. (2001). Facing the challenges, getting the right way distance learning. Education at a Distance, 15 (30), 1-8, Retrieved October 25, 2005 from http://www.usdla.org/html/journal/MAR01_Issue/article03.html.

McIlroy, D., Bunting, B., Tierney, K., & Gordon, M. (2001). The relation of gender and background experience to self-reported computing anxiety and cognitions. Computers in Human Behavior, 17 (1), 21-33.

Miura, I. T. (1987). The relationship of computer self-efficacy expectations to computer interest and course enrollment in college. Sex Roles, 16, 303-311.

Morahan-Martin, J. (1998). Males, females, and the Internet. In J. Gackenbach (Ed.), Psychology and the Internet: Intrapersonal, interpersonal and transpersonal implications, New York: Academic Press, 169-184. Morris, D. C. (1988-19). A survey of age and attitudes toward computers. Journal of Educational Technology Systems, 17, 73-78.

Murphy, C. A., Coover, D., & Owen, S. V. (19). Development and validation of the Computer Self-Efficacy Scale. Educational and Psychological Measurement, 49, 3-9.

Nickell, G. S., & Pinto, J. N. (1986). The computer attitude scale. Computers in Human Behavior, 2, 301-306.

Paxton, A. L., & Turner, E. J. (1984). The application of human factors to the needs of novice computer users. International Journal of Man-Machine Studies, 20, 137-156.

Reiser, R. A. (2001). A history of instructional design and technology: Part 1: A history of instructional media. Educational Technology Research and Development, 49 (1), 53-.

Resnick, L., & Wirt, J. (1996). Linking school and work: Roles for standards and assessment,San Francisco: Jossey-Bass.

Romiszowski, A. J., & Mason, R. (1996). Computer-mediated communication. In D.H. Jonassen (Ed.), Handbook of research for educational communication and technology, New York: Macmillan Library Reference, 438-456.

Rosen, L. D., Sears, D. C., & Weil, M. M. (1987). Computerphobia. Behavior Research Methods, Instruments, & Computers, 19, 167-179.

Rush, S. (1998). Building the 21st century information technology workforce: Upgrading IT skills of the current workforce, Washington, DC: Information Technology Society of America.

Sax, L. J., Astin, A. W., Korn, W. S., & Mahoney, K. M. (1998). The American freshman: National norms for Fall 1998,Los Angeles, CA: Higher Education Research Institute, University of California at Los Angeles Graduate School of Education and Information Studies.

Schumacher, P., & Morahan-Martin, J. (2001). Gender, Internet, and computer attitudes and experiences. Computers in Human Behavior, 17 (1), 95-110.

Seyal. A. H., Rahim, M., & Rahman, M. N. A. (2002). A study of computer attitudes of non-computing students of technical colleges in Brunei Darussalam. Journal of End User Computing, 14 (2), 40-47.

Shaw, F. S., & Giacquinta, J. B. (2000). A survey of graduate students as end users of computer technology: New roles for the faculty. Information Technology, Learning, and Performance Journal, 18 (1), 21-39.

Sproull, L., Zubrow, D., & Kiesler, S. (1986). Cultural socialization to computing in college. Computers in Human Behavior, 2, 257-275.

Sussman, N., & Tyson, D. (2000). Sex and power: Gender differences in computer mediated interactions. Computers in Human Behavior, 16 (4), 381-392.

Torkzadeh, G., & Angula, I. E. (1992). The concept and correlates of computer anxiety. Behavior and Information Technology, 11, 99-108.

Torkzadeh, G., & Koufteros, X. (1994). Factorial validity of a computer self-efficacy scale and the impact of computer training. Educational and Psychological Measurement, 54 (3), 813-921.

Webster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS Quarterly, 16 (2), 201-226.

Weil, M. M., & Rosen, L. D. (1995). The psychological impact of technology from a global perspective: A study of technological sophistication and technophobia in university students from twenty three countries. Computers in Human Behavior, 11 (1), 95-133.

Whitely, B. (1997). Gender differences in computer related attitudes and behavior: A meta analysis. Computers in Human Behavior, 13 (1), 1-22.

Wood, R., & Bandura, A. (19). Impact of conceptions of ability on self-regulatory mechanism and complex decision making. Journal of Personality and Social Psychology, 56 (3), 407-415.

Woodrow, J. J. (1991). A comparison of four computer attitudes scales. Journal of Educational Computing Research, 7, 165-187.

Zhang, Y., & Espinoza, S. (1998). Relationships among computer self-efficacy, attitudes toward computers, and desirability of learning computing skills. Journal of Research on Technology in Education, 30 (4), 420-436.Appendix 1

Computer Anxiety Scale (CARS)

Item

1 I feel insecure about my ability to interpret a computer printout

2 I look forward to using a computer on my job

3 I do not think I would be able to learn a computer programming language

4 The challenge of learning about computers is exciting

5 I am confident that I can learn computer skills

6 Anyone can learn to use a computer is they are patient and motivated

7 Learning to operate computers is like learning any new skill, the more you practice, the better you

become

8 I am afraid that if I begin to use computer more, I will become more dependent upon them and lose

some of my reasoning skills

9 I am sure that with time and practice I will be as comfortable working with computers as I am in

working by hand

10 I feel that I will be able to keep up with the advances happening in the computer field

11 I would dislike working with machines that are smarter than I am

12 I feel apprehensive about using computers

13 I have difficulty in understanding the technical aspects of computers

14 It scares me to think that I could cause the computer to destroy a large amount of information by hitting

the wrong key

15 I hesitate to use a computer for fear of making mistakes that I cannot correct

16 You have to be a genius to understand all the special keys contained on most computer terminals

17 If given the opportunity, I would like to learn more about and use computers more

18 I have avoided computers because they are unfamiliar and somewhat intimidating to me

19 I feel computers are necessary tools in both educational and work settings

Internet Attitudes Scale (IAS)

Item

1 The Internet will never replace human life

2 The Internet makes me uncomfortable because I don’t understand it

3 People are becoming slaves to the Internet

4 The Internet is responsible for many good things we enjoy

5 Soon our lives will be controlled by the Internet

6 I feel intimidated by the Internet

7 There are unlimited possibilities of Internet applications that have not been thought of yet

8 The overuse of the Internet may be harmful and damaging to humans

9 The Internet is dehumanizing to society

10 The Internet can eliminate a lot of tedious work

11 The use of the Internet is enhancing our standard of living

12 The Internet turns people into just another number

13 The Internet is lessening the importance of too many jobs done now by humans

14 The Internet is a fast and efficient means of gaining information

15 The Internet’s complexity intimidates me

16 The Internet will replace the working human

17 The Internet is bringing us into a bright new era

18 Soon our worlds will be run by the Internet

19 Life will be easier and faster with the Internet

20 The Internet is difficult to understands and frustrating to work withComputer Self-Efficacy Scale (CSE)

Item

I feel confident:

1 working on a personal computer

2 getting software up and running

3 using the users guide when help is needed

4 entering and saving data (numbers and words) into a file

5 escaping (exiting) from the program (software)

6 calling up a data fie to view on the monitor screen

7 understanding terms/ words relating to computer hardware

8 understanding terms/words relating to computer software

9 handling a floppy disc correctly

10 learning to use a variety of programs (software)

11 learning advanced skills within a specific program (software)

12 making selections from an onscreen menu

13 using the computer to analyze number data

14 using a printer to make “hardcopy” of my work

15 copying a disc

16 copying an individual file

17 adding and deleting information from a data file

18 moving the cursor around the monitor screen

19 writing simple programs for the computer

20 using the computer to write a letter or essay

21 describing the function of computer hardware (e.g. keyboard, monitor, disc drives, computer processing

unit)

22 understanding the 3 stages of data processing: input, processing, output

23 getting help for problems in the computer system

correctly

software

24 storing

25 explaining why a program (software) will or will not run on a given computer

26 using the computer to organize information

27 getting rid of files when they are no longer needed

28 organizing and managing files

29 troubleshooting computer problems