gender differences in computing activities

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Gender Differences in Computing Activities

Allison W. HarrisonDepartment of M anagement and Infonnation Systems

College of Business and Indtistry, Mississippi State University

P.O. Box 9581, Mississippi State, MS 39762

R. Kelly Rainer, Jr.Department of Management, College of Business

Auburn University, Auburn, AL 36849

W ayne A. HochwarterDepartment of Managem ent and M arketing

College of Commerce and Business Administration

University of Alabam a, Tuscaloosa, AL 35487-0225

Over the last fifteen years, more wom en have entered the workplace,

both in general, and in previously male-dominated professions. Themajority of jobs now involve knowledge work and are increasinglyimpacted by computer technology. Many occupations require personnelwho possess technology-related skills. The gender m odel of workpredicts that due to sex role males will be at an advantage in computer-related obs. The job mode l of work predicts that there w ill be no genderdifferences at equivalent jobs. The present study applied the gender andjob models of work to explore gender differences in a variety ofcomputer-related, job-specific tasks. In a discriminant analysis of a

sample consisting of 776 knowledge workers, males experienced morepositive computer-related outcomes than females, supporting the gen-der model. Exam ination w ithin job categories yielded similar resultsexcept for clerical jobs. Males and females reported signiftcantlydifferent computer related outcomes, even when job level was heldconstant. These findings provide support for the gender model of work.

Recently, women have entered the workforce at an increasing rate.

By the year 2000, 47 percent of the workforce will be female and 61



,S?() . lOUKNAL OF SOC IAL BEHAVIOK AND HEKSONALITY

dramatic increases in female participation that have recently taken place.

Computer technology has now becotne impoitant in the work of

tnost etnployees (Igbaria, Parasutatnan, & Baroudi, 1996) atid com-

puter-related work activities have become critical to orgatiizational

success (Keen, l9 9l ;T ur na ge , 1990). As a result, com puter competence

has become a requisite for employee success in many organizations

(Keen ; Ogletree & William s, 1990).

Th ere are a number ot reasons for exam ining the effect of gender on

com pute r performance and skills acqu isition. First, information systems

managers must make effective use of their personnel (Champy, 1992).Competitive factors, such as the cost of personnel relative to hardware

costs, have mandated a reassessment of the contribution of information

system s em ployee s. Second, changes in the workplace and the nature of

work suggest a need to understand differences in male and female

reactions to work and work-related activities. As computer technology

gains importance in the workplace and computer-related skills become

basic job requirements (Keen, 1991), organizations must ensure thatboth m ales and females successfully utilize com puters in their jobs .

Gender differences have been found to exist in role behaviors and

occupations (Deaux, 1995; Deaux & Lewis, 1984; Eagly & Wood,

1991). Gende r research has found, in general, that m ales are perceived as

more independent, masterful, assertive, and instrumentally competent

than females. Correspondingly, females are perceived as more friendly,

unselfish, and concerned with others than males.

Although a substantial body of gender research exists, few studieshave addressed gender differences in the performance of specific job-

relevant tasks. Notably, Igbaria, and Baroudi (1995) looked at the

outcom es of gender participation in information systems activities. They

found that female infonnation systems employees were perceived to

have less favorable chances for prom otion than m ales and that the effects

of job attributions for males were stronger. They also demonstrated that

wom en were m ore likely to be employed at low er levels, received lowerwages, and were m ore apt to leave than their m ale information systems



H;uTison cl al. GENDE R DIFFE KEN CES IN CO M PU TIN G SSI

T H E O R E T I C A L B A C K G R O U N D A N D

P R O P O S I T I O N D E V E L O P M E N T

Changes in the workplace have led to an increased am ount of gender

research (Aven, Parker, & McEvoy, 1993). The m ajority of these studies

have concentrated on gender differences in work values (e.g.. Lacy,

Bok emeier, & Shepard; 1983), jo b satisfaction (e.g., Jurik & Halem ba,

1984), job involvement (e.g., Lorence, 1987), and organizational com-

mitmen t (Aven et al., 1993; de Vaus & M cAllister, 1991; M arsden,

Kalleberg, & Cook, 1993). These studies have used both the gender

model and job model of work introduced by Feldberg and Glenn (1979)to predict employee attitudes and behavioral outcom es.

The Gender Model

The gender model of work (Feldberg & Glenn, 1979) views gender

differences in work based on sex-role stereo types. This m odel states that

females' central life interest is the family, as opposed to work, and that

family roles are the chief source of female identity and fulfillment.Women, therefore, should have a different orientation to work from men

(Loscocco, 1990). That is, personal characteristics associated with gen-

der brought to the job are key determ inants of a variety of work attitudes.

Alternatively, the gender model proposes that men have work as a

central life interest. Human capital economists and those advocating an

econom ic perspective (Becker, 1985; Truman & Barou di, 1994) suggest

that because of greater financial respon sibilities to the family, m en will

have a greater commitment to the job and to activities that result insuccess on the job. Notably, these perspectives contend that females, as

compared to males, have higher turnover rates and career interruptions

while garnering less experience, training, and mobility. In sum, women

acquire less human capital.

Lorence (1987) proposed that the gender model can best be ex-

plained by the sex-role socialization process. Ma les are raised to be lieve

that they should fill the role of econom ic prov ider. Wom en, how ever, areoften trained to accept family roles as their primary concerns. Such



S52 JOURN AL OF SOCIAL BEHAVIOR AND I'ERSO NAL IT'i'

rewarding and punishing lhe appropriate or inappropriate sex role be-

havior through modeling.

The gender model applied to the work activity of computing sug-

gests gender differences will ensue due to different sex roles. The

literature provides several examples of sex role orientation associated

with computing activities. The evidence suggests that computer-related

activities fall within the male domain. For example, the computing

industry is perceived as male dom inated (N ewton, 1991). Cowie (1988)

showed that females represented only 2% of data processing managers

and 12% of prog ram m ers. Although recent research suggests that theseproportions may be improving (e.g., Frenke l, 1990; Goff, 1990; Johnson,

1990), the situation is still male dominated. As a result, females may be

at a disadvantage in understanding and enacting appropriate behaviors

established by males.

Portrayal of males versus females in computing may also lead

females to believe that they are not as capable as males. Ware and Stuck

(1985) provided empirical data indicating that the media depicts males

as the expert computer users. Lloyd and Newell (1985) suggested that

the media represents females in computing environments as purely

decorative, and rarely po sitive. Culley (1986), using observational data,

implied that school computer clubs tend to have a strong male culture

which is derogatory toward females. The study also found that females

who chose to participate in the clubs despite the dominant male culture,

often found the environment uncomfortable.

Studies investigating actual computer usage suggest that femalesuse com puters less than m ales. Culley (1986) reported that only 2 8% of

female students had computers at home, contrasted with 65% of male

students. In another study of computer-related activities amon g second-

ary school children, Hess and Miura (1985) found that males are much

more likely than females to use a computer at home, participate in

computer clubs or activities at school, or attend computer camp. Chil-

dren not having a com puter at hom e are often limited to using com puters

in school (Reece, 1986). School computers are most often found in the



GEND ER DIEFERENC ES IN COiMPU TING S53

more knowledgeable about computer languages. Males also reported a

bcliclthal females were less capable than males in computer usage.

The gender model of work and the evidence from the literatureregarding the sex role orientation of computer-related activities lead to

the following hypothesis:

H I . Across jobs, as well as within jobs, males will experience

significantly more success with com puter-related outcomes

than females.

The Job Model of Work

The jo b model of work (Feldberg & Glenn, 1979) proposes that an

individual's job, not gender, is the primary antecedent of work perfor-

mance . Proponents of the jo b m odel suggest that differences in jo b

conditions for males and females best explain differences in work

performance (Bielby & Baron, 1986; Lorence, 1987; Marsden et al.,

1993). Loscocco (1990) concluded that the basis for gende r differences

in work attitudes and behaviors is the actual work positions of males and

females.

In regard to com puting activities, the jo b m odel im plies that males

and females will differ because they have different jobs and job require-

ments. Studies have found that computing activities have a greater

impact and are considered more important in higher level jobs, such as

managerial positions, than in lower-level jobs, such as clerical work

(e.g., Rolfe, 1990; Sm ith, 1991). The jo b model of work, therefore,

suggests that when job-specific dimensions are controlled, i.e., jobs areheld constant, no gender differences should be observed . The jo b m odel

of work, therefore, predicts that job requirements, not gender, are the

primary determinant of performance differences between males and

females in computer-related activities. The job model of work and the

presented literature lead to the following hypo thesis:

H2. Males and females in the same job category will demon-

strate no significant differences in success with computer-related outcomes.



JOUR NAL OF SOCIAL KRHAVIOK AND I'l-.KSONALITV

M E T H O D O L O G Y

SampleA questionnaire was scnl to 3,48S salaried personnel oF a large

university. The first part o tth e survey included q uestions concerning job

category, gender, education, and years of hands-on experience with

computers. The second part gathered data on respondents' computer

hardware and software usage. The final section included embedded

scales which m easured respond ents' com puter attitudes, com puter anxi-

ety, and computer self-efficacy. Non-users of computers were encour-

aged to complete the demographic portion of the questionnaire. Respon-

dents were assured complete anonymity.

The mailing produced 735 usable responses. Because university

regulations prohibited a follow-up mailing, the authors checked

nonresponse bias in the following m anner. Forty-one peop le who had not

completed the questionnaire were contacted and agreed to complete the

survey. T-tests comparing the demographic variables showed no signifi-

cant differences between the first and second groups of respondents(overall response rate was therefore 22.3%). Tbe conclusion was drawn

that nonresponse bias was not evident and that the results could be

generalized to the university population of salaried employees.

The sample included personnel from every administrative and aca-

demic department in the university. The respondents represented all

ranks in each university job category: clerical, technical, faculty, and

administrative. The sample consisted of respondents in their actual job

settings.

The sample proportions from the four university jo b categories w ere

faculty (43% ), technical (7% ), adm inistrative (2 0% ), and clerical (30% ).

T-tests disclosed no significant differences between sample proportions

and population job-category proportions obtained from the university

personnel office. The sam ple was divided approx imately equally (50.3%

male) between males and females. Seventy-two percent of the sample

held at least a bachelor's degree. Respondents averaged 38 years of ageand 7.5 years of hands-on computer experience.



ll;M Tis()ii cl a l. G E N D E R D I I T E K K N f B S IN C O M l ' i n i N c ;

where males and females dilTer in regard lo computing aelivilies.

Characteri.stics ofthe user. Characterisdcs of the user, also known

as individual differences, are essential determinanls of work behavior(Terborg, 1977). Important user characteristics include computer experi-

ence, com puter attitudes, com puter anxiety, and com puter self-efficacy.

Job category is also included as a user chara cteristic.

Experience. W ilder, Mack ie, and Cooper (1985) found that previous

com puter experience made individuals feel m ore comfortable with com-

puting activities. However, females tended to feel less comfortable than

males. They also found that males with previous computer experiencedemonstrated increased perceptions of competence. Computer experi-

ence was measured in years of hands-on, direct use of computers in

respondents' jobs.

Computer attitude. Several studies have indicated that males have

more favorable attitudes toward computers (e.g., Dambrot, Watkins-

M alek, Silling, Marshal, & G arver, 1985; W ilder et al., 1985). Add ition-

ally, Fetler (1985) indicated that females had less positive attitudes

toward computers than males. Computer attitudes were measured by the20-item Computer Attitude Scale (CAS) (Nickell & Pinto, 1986). The

CAS consists of three stable, underlying factors (Harrison & Rainer,

1992).

The first factor, labeled Control, consists of eight statements and

had a reliability coefficient of .82. This factor contained items that

referred to the belief that computers can dominate and control humans.

The second factor, labeled Positive, consisted of seven items and had areliability coefficient of .79. This factor's items described the belief that

computers are helpful and useful. The third factor, called Perception,

included four statements and had a reliability coefficient of .86. This

factor encompassed items that alluded to the belief that computers are

intimidating.

Computer anxiety. Heinssen, Glass, and Knight (1987) found that

college students with higher compu ter anxiety had lower self confidence

in their abilities and poorer performance outcomes than students with



JOUKNAL OFSOCIAI . HtHAVIOk AND I'EKSt )NALITV

nine s(alcnicnls and liaci a icliabilily coclTicienl of .84. This laclor's

items describe confidence and comfort with the idea of learning and

using computer skills.

Com puter self-efficacy. Co tupu ter self-efficaey is one' s perception

of his or her own computing capability (Murphy, Coover, & Owen,

1989). Males getierally perceive themselves as more competent on

computer-related tasks (Collins, 1985; Miura, 1987). Gattiker (1992)

proposed that females have a lower perception of their computing

abilities than males, which may be linked to performance of computing

activities.The Com puter Self-Efficacy Scale (CSE ) (Murphy et al., 1989) was

used to measure the respondents' perceptions of their capabilities re-

garding specific computer-related knowledge and skills. The 32 state-

ments address specific computer skills ranging from elemental abilities

to more advanced, complex skills. Each item in the CSE begins with "I

feel confident..." For example, "I feel confident adding and deleting

information from a data file." The CS E had a reliability coefficient of .95

in the present sample.

Job category. To test gender differences outlined by the jo b m ode l,

respondents were also classified by jobs. Four job categories were

included in the study: faculty, technical, clerical, and ad m inistrative.

Software. Sein et al. (1987) suggested that the type of software used

is an important determinant of success with computer-related outco m es.

Rainer and Harrison (1993) suggested that the frequency of software

usage may indicate more effective compute.- users. The most commonsoftware applications include word processing, spreadsheets, database

management, and graphics. Respondents were asked to indicate the

extent of their usage for each of these categories. Responses were

measured on 5-point Likert scales ranging from 1 (/ do not use at all) to

5 (/ use many times per day or for extended periods of time).

Hardware. Sein et al. (1987) noted that software may be imple-

mented on many different types of computer hardware, the most com-

mon being microcomputers and mainframes. As with software, the



Hanison ol al, GENDEK DIFFEKENCES IN CO MP UT ING

DATA ANALYSIS

In keeping with the purposes of this sludy, discriminant an alysis w as

used to statistically distinguish between males and fetnales on the hasis

of a variety of computer-related variables. Discriminant analysis is a

robust technique for distinguishing between two groups (Hair, Ander-

son, & Tatham, 1987).

The first discriminant analysis assessed the differences between

males and females with regard to computer-related variables across all

job categories. The analysis included the aggregate sample to examine

for gender differences based on the gender model of work (i.e., to testHy pothesis 1). This discriminan t analysis was performed with a holdou t

sample of 20% ofthe cases for validation purposes as suggested by Hair

etal . (1987).

Second and third discriminant analyses were performed on subsets

of the original sample to determine if differences between males and

females with regard to computer-related variables existed within spe-

cific job categories. These analyses were conducted to examine the

assumptions of the job model of work (i.e., to test Hypothesis 2).

Holdout samples were not used in the second and third discriminant

analyses because sample sizes were too small. The faculty and clerical

job categories were chosen for the second and third discriminant analy-

ses. The technical job category could not be used because females'

participation in this category w as not sufficient. The adm inistrative jo b

category was not analyzed due to a lack of homogeneity in job descrip-

tions.

FINDINGS

Table 1 shows the results ofthe discriminant analysis of gender by

the computer-related variables for the aggregate sam ple. Tables 2 and 3

show the results ofthe discriminant analyses for faculty resp ond ents and

clerical respondents, respectively. All tables include the means for

the computer-related variables for males and females and the Fvalues to determine w here means differ significantly.



,S5.S JOURNAL OFSOCIAL BEHAVIOR AND PERSONALITY-

Discriminanl Analysis: Entire Sample

Variables Witks

Demograph ics

Job Category

AgeEducationExperience

Computer Anxiety

FearAnticipation

Computer Attitudes

PerceptionControlPositive

Computer Self-Efficacy

Hardware UsageMicrocomputerMainframe

Software UsageWord ProcessingSpreadsheet

Database

Graphics

' Lambda

.8192

.9505

.7852

.9169

.9852

.9888

.9967

.9798

.9988

.9812

.9795

.9772

.9956

.9942

.9933

.9793

F

104.80***24.74***

129.90***43.07***

7.12**5 .38*

1.589.79**

.60

9.08**

9.96**11.09***

2.132.77

3.19

10.05**

Group

Men

1.7839.25

6.399.33

1.894.34

1.912.184.14

121.30

4.072.43

3.762.24

1.85

1.89

Means

Women

2.9034.73

4.945.92

2.044.23

2.002.394.18

115.01

3.671.98

3.572.04

1.66

1.58

*p < .05. * *p < .01. *** p < .001.

Canonical Discriminant Function: x^ C^O df) = 185.03; p < .001; Canonical r = .573.

Percent of analysis cases correctly classified: 75.5%.

A priori percentage for analysis c ases: 50.1%.

Percent of validation cases correctly classified: 72.7%.

A priori percentage for validation cases: 49.9%.



llnnison d :il. CENDIER DIF reklEN CE S IN CO MP UTIN G

TA BLE 2 Discriminant Analysis: Faculty

Variables Wilks

Demographics

AgeEducationExperience

Computer Anxiety

FearAnticipation

Computer AttitudesPerceptionControlPositiveComputer Self-Efficacy

Hardware Usage

MicrocomputerMainframe

Software Usage

Word ProcessingSpreadsheetDatabaseGraphics

' Lambda

,96,95,95

,97,99

,97.99,99,97

,96,99

,98,97

,97,97

F

10,25***13,38***

15,23***

8 ,77**,01

7,93**1,231,167,25**

11,07***1,24

5,87**8,95**

7,75**7,52**

Group

Men

42,306,87

10,40

1.804,23

1,852,134,12

121,99

4,321,94

4,162,40

1,942,12

Means

Women

38,226,536,89

2,044,22

2,172,244,21

113,03

3,792,14

3,751,86

1,481,68

*p < .05, **p < .01, ***p < .001.Canonical Discriminant Function: x^ C 4f) = 63.59; p < .001; Canonical r = .469.


A priori percentage for analysis cases: 60.3% .

The job model: Faculty category. The discriminant analysis for the

faculty job category indicates that males and females differ in usercharacteristics, software usage, and hardware usage. Males revealed



JOUK NAL OF SOCIAL BEHAVIOR AND PER,S()NALirV

1 AB LE 3 Dhscriminant

Variables Wilks

Demographics

Age

Education

Experience

Computer Anxiety

Fear

Anticipation

Com puter Attitudes

Perception

Control

Positive

Computer Self-Efficacy

Hardware Usage

Microcomputer

Mainframe

Software Usage

Word Processing

Spreadsheet

Database

Graphics

An a ly s i s :

' Lambda

,92

,98

,98

,97

,94

,9816

,99

,99

,9999

,97

,99

,93

,99

,99

,99

Clerical

F

15,60***

3,92*

3,57*

5,05*

10,70***

3,30

,18

,73

,01

5,30*

,51

13,31***

1,29

,69

1,01

Group

Men

25,72

4,34

4,00

2,30

4,07

2,23

2,44

4,09117,24

3,14

2,10

2,66

1,66

1,93

1,24

Means

Women

33,72

3,88

5,38

2,04

4,40

1,94

2,50

4,20

116,99

3,87

2,33

3,77

1,93

1,72

1,41

*p < .05. **p < .01, ***p < .001.

Canonical Discriminant Function: x^ (l^df) = 5767,- p < .001; Canonical r = .541.


A priori percen tage for clerical cases: 72.7%.



Harrison cl ;il. GENDEK DIFFERENCES IN COMPUTING ,S6I

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JOURNAL OF SOCIAL BEHAVIOR AND PERSONALITY

TA BL E 5 M eans and Standard Deviations lor All Variables

Variable M SD

Age 38,34 10,76

Education 5,67 1,59

Experience 7,51 6,19

Mainframe 2,12 1,47

Microcom puter 3,80 1,51

Word Processing 3,61 1,53Spreadsheet 2,09 1,32

Database 1,73 1,18

Graphics 1,71 1,07

Control 2,37 ,57

Positive 4,17 ,56

Perception 1,95 ,82

Fear 1,96 ,62Anticipation 4,22 ,54

Self-Efficacy 3,88 ,71

The job model: C lerical category. Clerical workers constituted the

second job category examined to test the job model. The discriminant

analysis for the clerical job category indicated that males and femalesdiffer in user chara cteristics, and software and hard w are usa ge. Fem ales

had significantly more years of computer experience, and were older

than males. Females had significantly less fear (computer anxiety), and

significantly more positive anticipation (computer anxiety) of computer

use than males. Females exhibited significantly more microcomputer

usage and significantly more usage of word processing software than

males .

Th e discrim inant function used to differentiate m ales and females in



Hnirison cl ;il GENDEK DITFEKEN CES IN CO M PU TIN G S(i.

across all organizational jobs except clerical. The lindings olTer support

for the gender model of work (H ypo thesis 1), suggesting that the perfor-

mance differences between males and females result from the male sex-role orientation of computing activities. To compound the sex-role

orientation effect, m ale s' success with com puters may further strengthen

the sex-role orientation of computing activities, discouraging females

even more from striving toward success.

Except for clerical workers, analysis of the entire sample showed

that females were more fearful of computer use, had less positive

participation, and viewed computers as more controlling. Because indi-

vidua ls' attitudes toward an object may influence their responses to that

object (Fishbein & Ajzen, 1975; Igbaria et al., 1996), fem ales' negative

feelings may inhibit their use ofthe computer. In fact, across the entire

sam ple, females used the microcomputer and mainframe significantly

less than males. Moreover, females reported using all software applica-

tions and graph ics significantly less than m ales. Learning curve theories

suggest that repetition often improves performance. By utilizing soft-

ware and hardware less than males, females' performance may suffer(Seinetal . , 1987).

Performance differences between m ales and females may also result

from females having lower expectations than males (Ogletree & Will-

iams, 1990). Vollmer (1986) implied that females' lower opinions of

their abilities may lead them to expect less of them selves. Low er expec -

tations were demonstrated over the entire sample as females reported

significantly lower com puter self-efficacy than m ales .

The job model. The findings for the faculty job category did not

provide support for the jo b model of gender differences (Hypothesis 2),

but did provide support for the gender model. The job model predicted

that, in the same job, there would be no significant differences in

computer-related outcomes between males and females. However, the

pattern in the faculty job category closely resembled that found for the

entire sample, with males reporting significantly more successful com-

puter-related outcomes than females. These findings provide furthersupport for the gender model of work.



S64 JOURNAL OK SOCIAL BEHAVIOR AND PERSONALITY

some ca,ses, the job (clerical work) may both be sex-role stereotyped, and

thcrelore impact work outcomes. Several explanations exist to support

(his argument.

One explanation is that the clerical job context is sex typed toward

females (Ogletree & Williams, 1990) and that sex role spillover is

present in the clerical job category (Gutek & Cohen, 1987), Sex role

spillover contends that gender-based roles are brought into the work-

place, often as a result of skewed sex ratios (Nieva & Gutek, 1981).

When spillover ensues, the work environment assumes many elements

of the sex role ofth e majority gender (Gutek & Cohe n). From sex typingand sex role spillover, the clerical job category appears to be socialized

toward females and consist of a majority of females as well.

Two issues arise from sex typing and sex role spillover. First, the sex

typing of clerical work toward females may cause the males in these

positions to view all clerical activities, distastefully as "female work"

and therefore not particularly important. Second, the large majority of

women in the clerical job category may cause men to feel isolated andtherefore uncomfortable, resulting in poorer performance in job-related

activities. Moreover, those in the minority group when the work setting

assumes components of the majority gender often develop problems

such as lower self-esteem and increased job stress (Gutek, Repetti, &

Silver, 1988), which, in turn, may lead to a decrease in performance.

Another explanation is that clerical computing job requirements

may be more basic, well-defined, and repetitive than other job catego-

ries. That is, clerical computing activities may be limited to a clearlydefined, consistent subset of all possible computing activities. Consis-

tently defined computing tasks make it easier for the job incumbent to

master the tasks and improve performance (Gattiker, 1992). Once this

consistent subset of computing activities is mastered, clerical em ployees

may view themselves as competent and have less fear of computing

activities that are unrelated to their jobs. Because females have been

performing clerical tasks longer than m ales, they may be more proficientthan males,



Harrison el al. GEND ER DIFFE REN CES IN CO M PU TING

responsible for the advenl ofdiscriminalory praclices within inlbrma lion

systems environments noted in recent literature (Igbaria & Baroudi,

1995; Truman & Baroudi, 1994). This conclusion has implications lor

females and organizations. Females must be aware that performance of

computer-related activities may constitute yet another barrier to their

oecupational success. Females must exert the effort needed to become

proficient com puter users. These efforts could involve com puter training

and computer courses. Females must consciously cope with the per-

ceived male sex type of computing. For instance, women are setting up

female-oriented forums on the Internet.Organizations must also be aware that a lack of proficiency in

computing may hinder females in responsibility, pay, and career pro-

gression. Organizations should devise methods to overcome sueh barri-

ers. Possibilities include providing additional computer training and

computer courses in-house, recruitment of females who are highly

com petent in com puting to serve as role models for other female em ploy-

ees, and placement of more females in organizational areas concernedwith management of the computing resource (i.e., the infonnation sys-

tems department).

This study has imp ortant implications for further research in the area

of gender differences in w ork activities in general, and com puter-related

activities in particular. The gender and job models should, however, be

examined in different contexts (e.g., Igbaria et al., 1996) and with

different jobs before other conclusions are drawn.

REFERENCES

Aven , F.F., Parker, B., & M cEvoy, G.M. (199 3). Ge nder and attitudinal com mit-ment to organizations: A m eta-analysis. Journal of Business Research, 26,

63-73 .Becker, G.S. (1985). Human capital, effort, and the sexual division of labor.

Joumal of Labor Economics, 3, S33-S58 .Bielby, W., & Baron, J. (198 6). Men and w omen at work: Sex segregation and

statistical discrimination. American Journal of Sociology, 91, 75 9 -79 9 .Champy, J. (1992). Mission: Critical. CIO, I, 18.



Shd JOU UN AL O F .SOCIAL BEHA VIOR AND PIZK.SONALITY

.ships iimong components and gender label. Journal of Personality and

Social P.sycliology. 46. 99\-\004.

de Vaus. D., & McAllister. I. (1991). Gender and work orientation: Values andsatisfaction in Western Europe. Work and Occupation. 18. 7 2 -93 .

Eag ly, A .H .. & Woo d, W. (1991). Explaining sex differences in social beh avior:

A meta-analytic perspective. Personality and Social Psxchologx Bulletin,

7 7 , 3 0 6 - 3 1 5 .Feldberg, R.L., & Glenn, E.N. (1979). Male and female: Job versus gender

models in the sociology of work. Social Problems, 26, 524 -538 .Fetler, M. (1985). Sex differences on the California statewide assessment of

computer literacy. Sex Roles, 13, 181-191.Fishbein, M ., & Ajzen, 1. (197 5). Belief, intentions, and behavior: An introduc-

tion to theory and research. Boston, MA: Addison-Wesley.Frenkel K. (1990 ). W omen and comp uting. Comm unication of the ACM , 11,34-

46.

Gattiker, U.E. (1992). Computer skills acquisition: A review and future direc-tions for research. Journal of Management, 18, 547-574 .

Goff, L. (199 0, April 2 ). Is there a computer gender gap? MIS Week, p. 28.Gutek, B.A., & Cohen, A.G. (1987). Sex ratios, sex role spillover, and sex at

work: A comparison of m en's and wom en's experiences. Human Relations.

4 0 , 9 7 - 1 1 5 .Gutek, B., Repe tti, R., & Silver, D. (1988). Non-work roles and stress at work.

In C. Coope r & R. Payne (E ds.), Current Concerns in Occupational Stress

(pp. 349-388) New York: Wiley.Hair, J.F., Anderson, R.E., & Tatham, R.L. (1987). Multivariate data analysis.

New York, NY: MacMillan Publishing Company.Harrison, A.W., & Rainer, Jr., R.K. (1992). An examination of the factor

structures and concurrent validities for the Computer Attitude Scale, theCom puter A nxiety Rating Scale, and the Com puter Self-Efficacy Scale.Educational and Psychological Measurement, 52, 7 3 5 - 7 4 5 .

Hawkins, J. (1985). Computers and girls: Rethinking the issues. Sex Roles, 13,

165-180.Heinssen, R.K., Glass, C.R., & Knight, L.A. (1987). Assessing computer anxi-

ety: Development and validation of the Computer Anxiety Rating Scale.Computers in Human Behavior, 3, 49-59 .

Hess, R., & Miura, I. (1985). Gender differences in enrollment in computer

camps and class. Sex Roles, 13, 193-203.Igbaria, M., & B aroudi, J. (1995 ). The impact of job performance evaluations on

career advancem ent pro spects: An examination of gender differences in the



H;ini.Miii c l ; i l . G E N D B K D I F F E R H N C E S I N C O M P U T I N G K67

Keen. P.G, (1991), Shapiitii the fiiliire: Business design through informationtechnology. Boston. MA: Hiirvard Bu.sines.s School Prcs.s,

Kie.sler. S,. Sproull. L,. & Eccles. J. (1985). Pool hulls, chips, and war games:women in the culture of computing. Psychology of Women Qttarterlw 9.451-462 .

Kohlberg. L, (1966). A cognitive-developmental analysisof children's sex-roleconcepts and attitudes. In E, Maccoby (Ed,). The developtnetit of sexdifferences. Palo Alto, CA: Stanford University Press,

Lacy. W ,B,, Boke meier, J,L,. & S hepard. J,M, (1983), Job attribute preference,sand work commitment of men and women in the United States, PersontielPsychology, 36, 3\5-329.

Lloyd. A,, & N ew ell, E, (1985), Women and comp uters. In W, Fau lkner and E,Arnold (Eds,), Technology in women's lives: Smothered by invention (pp,238-251). London: Pluto Press.

Lorenc e, J, (1987), A test of "gen der" and "jo b" models of sex differences in jo binvolvement. Social Forces, 66, 121-142,

Loscocco , K,A, (1990), Reactions to blue-collar w ork: A com parison of wom enand m en. Work and Occupations, 17, 152-177,

M arsden, P,, Kalleberg, A,, & Coo k, C, (1993), Ge nder differences in organiza-tional commitment. Work and Occupation, 3, 368-390.

Mischel, W, (1970), Sex-typing and socialization. In P, Mussen (Ed,), CannichaelsManual of Child Psychology (Vol, 2), New York: John Wiley,

M iura, I,T, (198 7), Th e relationsh ip of com puter self-efficacy exp ectation s tocom puter interest and course enrollment in college. Sex Roles, 16,303-311,

Murphy, CA,, Coover, D,, & Owen, S,V, (1989). Development and validationof the Computer Self-Efficacy Scale, Educational and Psychological M ea-surement, 49, 893-899,

Newton, P, (1991), Computing: An ideal occupation for women? In P, Firth-Cozens and M, West (Eds,), Women at Work (pp. 143 -153), P hiladelphia:Open University Press,

Nickell, G .S,, & Pinto, J.N, (198 6), The Com puter Attitude Sca le, Computers inHuman Behavior, 2, 301-306,

Nieva, V .F., & Gutek, B . A, (1981), Woman and work: A psychological perspec-tive. New York: Praeger,

Ogletree, S.M,, & Williams, S.W, (1990). Sex and sex-typing effects on com-puter attitudes and aptitude. Sex Roles, 23, 703-712,

Rainer, Jr., R,K., & Harrison, A,W, (1993), Toward deve lopme nt ofth e end-usercomputing construct in a university setting. Decision Sciences, 24, 1187-1202,

Reece, C.C, (1986), Gender and microcomputers: Implications for the school



S6K J O U R N A L O F S O C I A L B E H A V I O K A N D P E K S O N A L I T Y

Britain. Relations Indiisuielles. 46 . 306-327.Tem ple. L., & Lips. H.M . (1989). GenderdilTeiences and similarities in attitudes

toward computers. Compulers in H iinuin Beluivior, 5. 215-226.Terborg. J.R. (1987). Women in management; A research review, .lonnuil of

Applied Psychology. 62, 647-664.Truman. C . & Baroudi, J. (1994). Gender differences in information systems

managerial ranks: An as.sessment of potential d iscrim inatory practices. MiSQuarterly, 6, 129-141.

Turnage. J.J. (1990). The challenge of new workplace technology for psychol-ogy. American Psychologist, 45, 171-178.

Vollmer,F. (1986). Why do men have higher expectancy than women?5e.ir/fo/e.s,

R 351-362.Ware, M.C., & Stuck. M.F. (1985). Sex-role messages vis-a-vis microcomputer

use: A look at the pictures. Sex Roles, 13, 215-229.Wilder, G., Mackie, D., & Cooper, J. (1985). Gender and computers: Two

surveys of computer-related attitudes. Sex Roles, 13, 215-229.Wilson , F. (1992). Language, technology, gender, and power. Human Relations,

45, 883-904.

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