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12
Chapter II
Exploring Gender Differences in
Computer-Related Behaviour:
Past, Present, and Future
Robin Kay
University of Ontario Institute of Technology, Canada
Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
ABSTRACT
This chapter explores gender differences in three key areas: computer attitude, ability, and use. Past
research (10-25 years ago) is examined in order to provide a framework for a more current analysis.
Seventy-one studies and 644 specic measures are analysed with respect to overall patterns, time, edu-
cation level, and context. Males and females are more similar than different on all constructs assessed,
for most grade levels and contexts. However, males report moderately more positive affective attitudes,
higher self-efcacy, and more frequent use. Females are slightly more positive about online learning
and appear to perform somewhat better on computer-related tasks. The results must be interpreted with
caution because of methodological limitations in many studies reviewed. Finally, a model is proposed
to understand and address gender differences in computer-related behaviour.
INTRODUCTION
A reasonable argument could be made that com-
puters are integrated into every major area of our
lives: art, education, entertainment, business,
communication, culture, media, medicine, and
transportation. It is equally reasonable to as-
sume that considerable power and success rests
with understanding how to use this technology
meaningfully and effectively. Many children
start interacting with computers at three to four
years of age, however, gender-based socialization
begins much earlier when someone asks, “Is it a
boy or a girl?” (Paoletti, 1997). A critical ques-
tion arises as to whether computer behaviour is
inuenced by gender. Given the prominent role
that computers play in our society, it is vital that
males and females have equal opportunity to work
with and benet from this technology.
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Exploring Gender Differences in Computer-Related Behaviour
Numerous studies have investigated the role
of gender in computer behaviour over the past
20 years (see AAUW, 2000; Barker & Aspray,
2006; Kay, 1992; Sanders, 2006; Whitley, 1997
for detailed reviews of the literature) and the
following conclusions can be made. First, most
studies have looked at computer attitude, ability,
and use constructs. Second, clear, reliable, valid
denitions of these constructs are the exception,
rather than the rule. Third, roughly 30% to 50% of
the studies report differences in favour of males,
10% to15% in favour of females, and 40% to 60%
no difference. Fourth, differences reported, while
statistically signicant, are small. Overall, one
could say there is a persistent pattern of small
differences in computer attitude, ability, and use
that favours males; however, considerable vari-
ability exists and has yet to be explained.
There are four main objectives for this chap-
ter. First, past research on computers and gender
will be summarized by examining the results of
ve previous literature reviews. Second, a more
current analysis of gender and technology will
be provided by looking at a comprehensive set of
studies done over the past 10 years. Technology
changes quickly, so might the attitudes and abili-
ties of people who use this technology. Third, a
clear emphasis will be placed on examining the
impact of contextual issues (e.g., type of technol-
ogy used, age group, setting, culture) in order
to explain some of the variability observed in
past research. Finally, a model for understand-
ing gender differences in computer behaviour
will be proposed to help set an agenda for future
research.
BACKGROUND
At least ve comprehensive reviews have been
done examining various aspects of gender and
the use of computers (AAUW, 2000; Barker &
Aspray, 2006; Kay, 1992; Sanders, 2006; Whitley,
1997). Each review is well worth reading and of-
fers detailed, insightful information about gender
and technology. I offer a brief synopsis of key
insights these authors make that help frame the
ideas presented in this chapter.
Whitley Review
In 1997, Whitley did a metaanalysis involving 82
studies and 40,491 American and Canadian re-
spondents from 1973 to 1993. Regarding computer
attitudes, it was found males had more positive
affective attitudes toward computers. Mean effect
sizes ranged from .08 for grammar school students,
.22 to .24 for college students and adults, and .61
for high school students. Note that Cohen (1988,
1992) suggests that an effect size of .10 is small,
.30 is medium, and .50 is large. This means that
gender did play a moderate to signicant role with
respect to liking computers in the early period of
computer use in North America.
General cognitive attitudes or beliefs about
computers appeared to show little gender bias,
with effect size ranging from .04 (college stu-
dents) to .20 (grammar school). However, when
focusing on computer-based stereotypes or sex
biases, males were substantially more biased in
their attitudes with effect size ranging from .44
(college students) to .67 (grammar students).
Self-efcacy toward computers followed a
similar pattern to affective attitudes. Effect size
was not substantial for grammar school students,
but favoured males in high school and beyond
(r=.32 to .66). Even though males reported more
condence in using computers, effect size for
computer experience was relatively small (r=.15 to
.23). Finally, it appears that males used computers
more often than females for all ages groups (r=.24
to .40), although this effect size range would be
considered moderate according to Cohen (1998,
1992).
In summary, Whitley offers a statistical
snapshot of male-female differences in computer
attitude, ability, and use for North Americans
prior to 1993.
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Exploring Gender Differences in Computer-Related Behaviour
Sanders Review
Sanders (2006) took a markedly different approach
to reviewing the literature on gender and technol-
ogy. Over 200 articles were examined, covering
an approximate time period from 1990 to 2001,
but the goal was to offer possible explanations
for why women were so under represented in the
eld of Computer Science. The review, while not
statistically rigorous, offers a multicultural, rich
perspective on the potential factors that contribute
to gender biases in computer-based behavior.
Sanders discusses in some detail the inuence
of society (e.g., parents, media, SES), age, attitude,
ability, use patterns, and the classroom. Regarding
attitude, ability, self-efcacy, and use, her results
are consistent with Whitley. In addition, she offers
the following observations:
• Stereotypes can start with parents
• The media portrays computers as a male
domain
• High SES is associated with greater promo-
tion of computers in girls
• Stereotyped use of computers starts at pre-
school
• Gender differences tend to increase with
age
• Males enroll in more computer-based
courses
• Males play more computer games
• Differences between males and females
in the area of online learning are not as
prominent as in other areas
• The computer curriculum in high school
favours male interests
This review generated some very interesting
and provocative reasons for why males are far
more actively involved in the eld of Computer
Science. Perhaps the most profound statement
that Sanders made was that even with sizeable
gender differences observed in attitude, ability,
and use, almost no effort was being made to in-
vestigate possible interventions that could level
the proverbial “computer” playing eld.
Barker and Aspray Review
Barker and Aspray (2006) provide a methodical
and detailed analysis of research on girls and
computers. Their approach is similar to that of
Sanders (2006) in that they offer a rich discussion
on factors that inhibit girls from pursuing careers
in information technology. Their ndings on com-
puter attitude and use, dating from 1992 to 2004,
are aligned with those reported by Whitley (1997)
and Sanders (2006). They deliberately chose not
to look at differences between girls and boys in
computer literacy or ability.
A number of noteworthy observations were
articulated in this review. First, elementary
teachers, who are primarily females, have lim-
ited computing skills and therefore act as poor
role models for young girls. Second, the gender
gap with respect to computer access and use has
narrowed rapidly over the past 5 years. Third,
boys still use computers more than girls at home,
although each sex uses computers for different
reasons. Boys use games, educational software,
and the Internet more, whereas girls use com-
puters for e-mail and homework. Finally, the
culture of computing is largely driven by males,
and females begin to reject this culture starting
in early adolescence. Females favour careers in
more socially oriented elds such as medicine, law,
or business and see computer-based occupations
as boring and menial. Ultimately, it may do little
good to give females extra resources designed to
increase expertise in computers, when they nd
the IT curriculum to be “out of touch” with their
experiences and interests.
American Association of University
Women (AAUW), 2000
The AAUW (2000) did an extensive review of
the research on gender and technology with the
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Exploring Gender Differences in Computer-Related Behaviour
primary goal of identifying changes needed in the
current computer culture to make it more appeal-
ing to girls and women. They agree with Barker
and Aspray (2006) that girls reject the violence
and tedium of computer games and would prefer
to use technology in a meaningful manner. They
summarize this position by stating that many
girls have a “we can, but don’t want to” attitude
about technology.
The AAUW (2000) offers a poignant expla-
nation of why a commitment to lifelong technol-
ogy learning is important. They note that being
successful in working with technology involves
the ability to adapt to rapid changes, to critically
interpret the morass of electronic information
available, and to experiment without fear.
Kay Review
In 1992, Kay summarized the results of 98 articles
looking at gender and computer attitude, ability,
and use. The familiar pattern of male prevalence
in these areas was observed. Of course, these
results are dated now, but the nal conclusion
of this study is still valid. In the vast majority of
studies analyzed in the past, clear, reliable, valid,
and consistent measures of attitude, ability, and
use constructs are rarely offered. Unfortunately,
measurement problems are still substantial. Out of
644 measures used to analyze gender differences
in this chapter, 24% offered estimates of reliability
and 18% examined validity. The uncertainty and
lack of cohesion with respect to constructs used
to establish gender differences undermines the
process of understanding and addressing gender
differences and technology.
In general, when asked which sex is more
positive toward computers, more apt at using
computers and more likely to use a computer, one
would best be advised to answer, ‘It depends.’ It
depends on what attitudes you are measuring, what
skills you are assessing and what the computer is
being used for. (Kay, 1992, p. 278)
Objectives of this Chapter
The goal of this chapter is to build on previous
research in order to provide a current and cohesive
perspective on gender and technology. This goal
will be achieved by:
• Collecting the most recent empirical data
available after Whitley’s (1997) review
• Focusing on the broader intent of creat-
ing technologically thoughtful, effective,
risk takers (AAUW,2000), as opposed to
IT professionals (Barker & Aspray, 2006;
Sanders. 2006;)
• Determining how current data support the
richer, contextual patterns noted by Sanders
(2006) and Barker & Aspray (2006)
• Proposing a model to explore gender differ-
ences
• Discussing how a coherent model might ad-
vance an intervention program that Sanders
(2006) notes is clearly lacking
• Examining methods and strategies that can
help improve the quality and impact of in-
formation that is needed to develop a more
comprehensive understanding of gender and
technology (Kay, 1992)
Note that the term computer and technology
will be used interchangeably. I realize that “tech-
nology” has a broader focus, but with respect to
research involving gender differences, the sole
area investigated involves computers.
Data Analysis
Seventy-one refereed (see Appendix for full list)
papers were reviewed for this chapter spanning
a decade of research (1997-2007). Each paper
was coded for measures of attitude, ability, self-
efcacy, and use. Each of these constructs was
divided into subcategories to illustrate the wide
range of computer behaviours assessed. This
process resulted in 644 tests of gender differ-
16
Exploring Gender Differences in Computer-Related Behaviour
ences involving more than 380,000 people, from
17 different countries. Computer behaviour was
looked at from four main settings: home, school,
the workplace, and online communication. Eight
distinct populations were examined including
kindergarten, elementary school (Grade 1-5),
middle school (Grade 6-8), high school (grade 9-
12), undergraduates, preservice teachers, graduate
students, and adults. Each of the 644 tests of gender
difference was rated according to whether there
was a signicant male advantage, a signicant
female advantage, or no difference.
In summary, every attempt was made to esh
out as much context as possible with respect to
results reported; however, speculation was kept to
a minimum when there was not enough evidence
to make a rm conclusion. It is also important to
recognize that while an attempt will be made to
nd patterns in gender differences with respect
to attitude, ability, and use, the analysis will be
compromised by the multitude measures used to
address these constructs and the limited reliability
and valid statistics available.
CURRENT RESEARCH ON GENDER
AND COMPUTERS (1997-2007)
Computer Attitudes: Affect and
Cognition
Computer attitudes accounted for 31% (n=202)
of all gender-technology comparisons assessed
for this chapter. It is a daunting task, though, to
sort out the numerous types of attitudes assessed.
Two principle constructs, based on Ajzen and
Fishbein’s (1988) theory of attitude measurement,
provide a schema for understanding the numerous
attitude scales.
The rst construct is based on affect and, to
date, encompasses two basic emotions: anxiety
and happiness. Samples of affective attitudes are
computer anxiety, computer enjoyment, comfort
level, software and activity preferences, liking,
motivation, fun, and sense of achievement. The
second construct looks at cognitions or a person’s
beliefs about computer-related activities and envi-
ronments. Some examples of cognitive attitude are
stereotyping, importance, perceived usefulness,
trust, and acceptance.
When looking at studies measuring affective
attitudes, males had more positive feelings 33%
(n=30) of the time, females had more positive
feelings 15% (n=14) of the time, and there were
no differences 52% (n=64). There appears to be
a moderate male bias with respect to affective
attitude toward computers. The situation, though,
is different for cognitive attitude where males
had more positive thoughts about computers 23%
(n=26) of the time, females more positive thoughts
20% (n=21) of the time, and no differences were
reported 58% (n=64) of the time. Males and
females do not appear to differ with respect to
computer-based cognitions.
valence on affect than cognition.
Digging into the attitude data a little deeper
reveals several patterns. First, there appears to be
no obvious change in male-female differences in
overall attitudes over the past 10 years. While there
is some uctuation in favour of males or females
on a year-by-year basis, males are slightly more
positive than females when it comes to feelings
about computers. One might expect that as the
technology becomes more accessible, easier to
use, and more diverse in application, that com-
puter attitudes might level out between the sexes.
This appears not to be the case. In fact, the most
recent studies (2005-2007) show a strong male
bias for both affective and cognitive attitude (40%
favour males, 15% favour females, 45% show no
difference).
A second pattern looks at the relationship be-
tween grade and gender differences with respect
to overall computer attitudes. In elementary school
(grades 1-5), females appear to have slightly more
positive attitudes about computers, although the
number of tests was small (n=9). In middle school,
females and males have similar attitudes toward
17
Exploring Gender Differences in Computer-Related Behaviour
computers, but in high school males show more
positive attitudes, a bias that continues in uni-
versity. Male and female preservice teachers and
graduate students have similar attitudes toward
computers, but the general adult population shows
a strong male bias. This pattern is consistent with
previous reviews of gender and technology. Males
and females do not start out with different feelings
and thoughts about computers, they emerge over
time and seem to be inuenced by education level
and culture. Nonetheless, it is absolutely critical
to keep these ndings in perspective. Most fe-
males or males do not follow this pattern. Even
in the most extreme case (high school), male bias
means males had more positive attitudes in about
40% of all tests done. Another way of stating this
result is that females have more positive attitudes
or there is no difference in of 60% of the cases
observed.
The third pattern observed with respect to
male-female differences in attitude involves the
setting or context where computers are used. Sur-
prisingly, not one study out of the 71 reviewed for
this chapter looked at attitude toward home use of
computers. When looking at gender differences in
school, males reported more favourable attitudes
46% of the time, females were more positive
16% of the time, and no difference in computer
attitude at school was observed 38% of the time.
However, when one shifts to an online context,
the picture is decidedly different. In this context,
females were more positive 21% of the time, no
difference was observed 76% of the time, leaving
males with having more favorable attitudes only
3% of the time. When context was not mentioned
at all, gender differences in attitude were marginal
(27% favour males, 18% favour females, 55% no
difference).
This analysis of context tells us that setting is
important when looking at attitude toward com-
puters, although a ner granularity is needed in
future research. It is concerning that males are
more positive toward computers in a school setting,
but it is unclear what they are positive about,games,
educational software, presentations, word process-
ing? Azjen and Fishbein (1988) note that the more
specic one is about the object of an attitude, the
higher the predictive value. On the other hand, it
is encouraging that females respond equally if not
more positively to an online environment. Again,
it is important to investigate the explicit nature of
these attitudes in order to understand dynamics
of gender-technology interaction.
In summary, the current analysis of gender and
attitude toward computers offers the following
conclusions. First, computer attitudes appear to be
biased in favour of males with respect to affect but
not cognition. Second, this bias typically means
that males are more positive 30% to 40% of the
time, females are more positive 15% to 25% of
the time, and there is no difference 45% to 55%
of the time. Third, differences do not appear to
be attenuating over time, but are inuenced by
education level and the setting in which comput-
ers are used. Finally, more precise denitions of
attitude and context are needed in order to develop
a more comprehensive understanding of gender
differences in computer technology.
Computer Ability and Self-Efcacy
I have put computer ability and self-efcacy
in the same section because gender-computer
researchers have not been precise when dening
these terms. The rating of one’s actual com-
puter ability and one’s condence or belief in
being able to perform a computer-related task
(computer-self-efcacy) is often blurred. I will
discuss each construct independently, but until
operational denitions, reliability, and validity
are reported more regularly, a sizeable overlap
may exist between the two.
Ability
Differences between males and females regard-
ing computer ability were looked at 93 times
(14% of the total sample). Denitions of ability
18
Exploring Gender Differences in Computer-Related Behaviour
included general knowledge, games, application
software, online tools, programming, and perfor-
mance. Overall, males rated themselves higher in
computer ability 47% of the time, females rated
themselves higher just 9% of the time, and both
sexes reported equal ability 44% of the time.
This difference between males and females is
accentuated if one removes actual performance
estimates of ability. When looking at self-report
measures, males rate themselves as having higher
ability 60% of the time. However, if one looks
at performance measures alone, females exceed
males in computer ability. Five percent of the time
males are better, 24% of time females are better,
and 71% of the time there is no difference. Males
have decidedly greater ability than females when
self-report measures are used, but when actual
performance is assessed, females appear to have
the advantage. This anomaly calls into question
the reliability and validity of using the self-report
technique. More work needs to be done validating
the accuracy of assessment tools used to evaluate
computer ability.
It appears that estimates of computer ability
from 1997 to 2007 are fairly stable (45% favour
males, 10% favour females, 45% show no differ-
ence). One might expect that previously reported
differences in favour of males would lessen over
time given the increased ease of using a computer,
but this expectation is not supported. On the
other hand, the stability may simply mean that
males continue to have inated ratings of their
computer ability.
It is particularly informative to look at com-
puter ability across grade levels. Computer ability
is essentially equal for all grade levels except
university students and general working profes-
sionals, where males show signicantly higher
levels. It is unclear what happens at university
or in the workplace that might cause this differ-
ence. What is clear is that most of the research on
gender disparities in computer ability has focused
on subjects who are 18 years or older. Without a
broader representation of the total population, it
is challenging to give a more informed analysis
with respect to the impact of gender on computer
ability.
Self-Efcacy
Only 6% (n=38) of the gender tests focused on
self-efcacy; therefore, a detailed analysis of this
construct is not possible, nor reliable. Not surpris-
ingly, the overall pattern for computer self-efcacy
is almost the same as that for self-report measures
of computer ability. Fifty-three percent of males
felt more condent than females; the reverse
scenario was observed only 5% of the time. Forty-
percent of the time, males and females reported
equal self-efcacy. As stated earlier, because of
limitations in research methodology, self-rating of
computer ability may be synonymous with self-ef-
cacy. That said, one should not underestimate the
inuence of self-efcacy. It is entirely reasonable
that feelings of condence can inuence computer
behaviour and use. Even though there is evidence
to suggest that females are as good if not better at
using computers, self-efcacy could be holding
them back. The relationship among self-efcacy
and actual computer use has not been examined
in detail; however, it should be placed on future
research agendas.
Computer Use: Frequency
One could argue convincingly that the ultimate
goal of understanding and addressing gender
differences in computer–related behaviour is
to ensure that males and females have the same
choices and opportunities to use computers. This
section will look at both frequency of use and
actual computer behaviours.
Twenty-seven percent (n=172) of tests analysed
for this chapter looked at computer use. This
construct covered a number of domains: general
use, access to computers, application software,
entertainment, programming, and the Internet.
Regardless of the domain, males used computers
19
Exploring Gender Differences in Computer-Related Behaviour
more often than females 40% of the time, females
used computers more than males 5% of the time,
and no difference in use was reported 56% of the
time. Use of application software (74% no differ-
ence) and having access to computers (67% no
difference) were the least gender-biased activities,
whereas using the computer for entertainment
(31% no difference) and general activities (43%
no difference) were the most gender biased. Iden-
tifying the precise area of use, then, is important.
Using computers for games, for example, may be
an activity that is dominated by males, but par-
ticipation will not necessarily be advantageous
with respect to learning new software or making
informed choices using technology. On the other
hand, if females had signicantly less access to
a computer, which does not seem to be the case,
that would be cause for concern because both
opportunity and choice would be restricted.
An encouraging pattern with respect to use is
the trend of decreased gender bias in computer use
over the past 10 years. From 1997 to 2000, males
reported using computers more than females 62%
of the time. This decreased to 37% from 2001 to
2004 and 29% from 2005 to 2007. With respect
to context, general (51% males report more use)
and home (55% males report more use) use indi-
cated signicant male advantages, although no
differences were observed at school or online.
Interestingly, gender bias with respect to home
and general use appears to be decreasing over
time. Use in these two settings was strongly sex
biased with 86% of males reporting signicantly
more use from 1997 to 2000, but from 2005 to
2007, male dominance dropped to 45%.
A closer look at the school context reveals that
while no differences between males and females
exist for elementary school students with respect
to computer use, this pattern changes abruptly for
all other education levels including middle school
and university (38% male dominance), high school
(51% male dominance), and the workplace/gen-
eral population (60% male dominance). If one
takes into account time and education level, high
school and university male students continue to
exceed their female counterparts (between 33%
and 44%), but not at the same level as previous
years. Middle school use of computers, unfortu-
nately, has not been studied in the past 3 years
with respect to gender.
The results regarding computer use and gender
difference offer a somewhat complicated picture.
Clear gender differences in that appeared between
the years of 1997 to 2000, attenuated markedly
from 2005 to 2007. Areas of concern still exist for
home and general use as well as high school and
university settings, but even for these hotspots, a
decreasing gender gap is a promising trend.
Actual Behavior
Actual computer behaviour has been studied
far less than frequency of computer use, yet it is
specic behaviour that can help uncover clues and
nuances with respect to gender differences. While
14% (n=89) of the total sample of tests forged into
an analysis of how males and females behave while
using computers, only 6% (n=37) presented formal
comparisons. No fewer than eight categories of
behaviour were identied including cognitive ac-
tivities, collaboration, communication, learning,
teaching, problem solving, use of software help,
and same vs. mixed-sex behaviour. Overall, no
signicant differences were reported with respect
to specic computer behaviours 76% of the time.
This does not mean that males and females do
not behave differently with respect to computers,
because the vast majority of studies on computer
behaviour are qualitative and present rich descrip-
tions of highly contextualized situations.
There are some interesting and potentially
informative observations made with respect to
actual behaviour. For example, in an online envi-
ronment there is some evidence to suggest males
are more authoritative and assertive (Fitzpatrick
& Hardman, 2000; Guiller & Durndell, 2006)
in online discussions, but are more exible in a
face-to-face conversation. Females, on the other
20
Exploring Gender Differences in Computer-Related Behaviour
hand, change their opinions more in an online
than face-to-face environment. Another study
indicated that girls accounted for nearly twice the
number computer-based interactive behaviours
than boys did (e.g., helping another student, asking
questions) in an elementary school class (Waite,
Wheeler, & Bromeld, 2007). Some researchers
have examined the dynamics that occur in same vs.
mixed-sex groups. Both males and females appear
to do better with their same sex peers (Fitzpatrick
& Hardman, 2000; Jenson, deCastell, & Bryson,
2004; Light, Littleton, Bale, Joiner, & Messer,
2000). Finally, some boys may not take girls se-
riously in terms of computer knowledge. Jenson
et al. (2003) reported that even when girls were
trained and had superior knowledge, boys tended
to rebuff their attempts to help and guide.
These computer behaviour results have to be
interpreted cautiously, as they are often detailed,
but isolated examples collected from small
samples. However, the kind of insight provided
by rich qualitative investigation is critical to de-
velop an effective, workable model to explain the
interaction between gender and technology. We
want males and females to have choice in using
computers, but we do not want to undermine the
learning process, for example, where it may be the
case each sex reacts differently to online discus-
sions. We certainly want to create an atmosphere
of mutual respect and support in a computer-based
classroom, although, there is some evidence to
suggest that effective knowledge building may be
undermined by gender prejudice. In short, while it
is important to examine computer attitude, ability,
self efcacy, and use, it is challenging to build
a cohesive and informative model of gender and
technology using only survey methodology.
FUTURE TRENDS AND RESEARCH
DIRECTIONS
It is clear that more effort needs to be directed
toward developing a model for interpreting and
addressing gender differences in computer-related
behaviour. Isolated studies in this area have taken
place for over 30 years. Solid reviews of the lit-
erature are helpful, but there is an overwhelming
need to organize this data into a more coherent
whole. I propose the model in Figure 1 to help
organize and understand future research. The
model is not empirically supported, but takes into
account the full range offactors that have been
studied to date.
The model is premised on the following as-
sumptions. First, the major components, com-
puter-based attitudes, ability, self-efcacy, use,
and behaviour, occur in a context. The context
may be an elementary or high school classroom,
a business, an online discussion board. This
means there could be slightly or drastically dif-
ferent dynamics in different environments. The
more that one describes a given context, the more
Ability
&
Self-Efficacy
Attitude
Affective Cognitive
Use
&
Behaviour
Context
Figure 1. A model for understanding gender and
computer-related behaviour
21
Exploring Gender Differences in Computer-Related Behaviour
effective the model will be in predicting and
understanding behaviour. Second, attitude needs
to be divided into at least two main constructs:
affect and cognition. This division has been shown
to be meaningful in the present analysis. It is
speculated that attitudes, depending on whether
they are positive or negative, lead to acceptance,
neutrality, or rejection of computers. This, in turn,
will prompt an individual to avoid or learn more
about computers. If the context permits adequate
access to computers, then a user can begin to
develop computer ability and self-efcacy. Once
a person begins using a computer, the type of use
and specic behaviours experienced will have a
direct effect on attitude. If a person experiences
successful use of computers he/she will use a
computer more, develop more positive affect
and cognitions, and increased skills. If a person
experiences challenges and signicant barriers,
attitude may become increasingly negative to the
point of non-use. This model offers opportunities
for future research with respect to improving
methodology, addressing context, shifting focus,
and developing intervention strategies. Each of
these will be discussed in more detail.
Improving Methodology
What is blatantly clear is that researchers need
to be more precise in operationally dening their
constructs, and providing estimates of validity and
reliability. A considerable leap of faith was taken,
perhaps recklessly, in analyzing and reporting
gender and technology research for this study.
Only 17 out of the 71 studies discussed validity
and offered reliability estimates of .70 or better.
This accounted for only 9% (n=64) of all the data.
In 1992, (Kay, 1992) I made the exact same plea to
improve the quality of research simply because it
is difcult to be condent in the results reported.
While most research methodology is awed in
some way, developing solid assessment tools is
fundamental to advance our knowledge on gender
and technology.
Another suggestion, partially based on the
next section on context, is to make scales domain
specic. General measures give us watered-down
estimates of attitude, ability, self-efcacy, and
use. The results of the current review suggest
that context is very important. For example, it
was reported that males have more positive af-
fective attitude toward computers. A closer look
over a broad range of studies reveals that males
may be very positive about using computers for
entertainment, whereas females seem to prefer e-
mail and using computers for a meaningful task.
A general measure of computer affect would not
uncover these important differences.
One nal idea is to study multiple constructs
simultaneously. Many studies focus on one or
two areas of interest, but the ideal study should
examine all of the key areas: attitude, ability, self-
efcacy, and use. This way a statistical analysis
can be done to examine the relationships among
constructs. This kind of analysis is critical for
model building.
Addressing Context
Models for understanding gender and technol-
ogy need to start from a specic context. There
is growing evidence, for example, that females
are responding very positively to online environ-
ments, sometimes more positively than males. In
high school and university settings, males continue
to display more favourable computer attitudes,
ability, and use. Sanders (2006) and Barker and
Aspray (2006) speculate as to why these differ-
ences emerge, but more systematic research and
evidence is needed to discover the mechanisms
of bias. Detailed research looking at a wide range
of behaviours over an extended time period will
help reveal contextual complexities.
Shifting the Focus
The current research menu for investigating gen-
der differences and technology over past decades
22
Exploring Gender Differences in Computer-Related Behaviour
needs to be altered in at least four fundamental
areas. First, more effort needs to be directed
toward understanding computer ability. Self-
report measures do not align well with actual
performance on computers. More effort needs to
be directed to distinguishing self-efcacy from
ability and gathering data that represents an ac-
curate estimate of skill.
Second, researchers need to actively collect
data from younger populations. Most of the cur-
rent data is based on university students and the
general population, age 18 years or older. This
approach may promote the reporting of gender
biases, simply because the subjects involved are
older and may not be reective of the current
computer culture. Computer use for elementary
school students today is far different than it was
for university students 10 to 15 years ago. To get
the most current snapshot of potential gender
disparities, students from K to 8 need to have
a stronger presence. Trends of computer use re-
ecting a reduced gender gap support the need
for this shift.
Third, a research program focusing on gender
and technology with respect to preservice and
experienced teachers needs to be developed. Only
3 out of the 71 studies analysed for this chapter
looked at the preservice population and no studies
examined in-service teachers. Educators, though,
are probably a primary inuence, both indirect
and direct, on students’ behaviours toward com-
puters. If a single teacher has a negative attitude
toward computer use and is unable or unwilling
to integrate computers in the curriculum, it can
affect numerous students over an extended time
period.
Finally, there is a clear need for richer, quali-
tative research all on key areas of computer be-
haviour. Qualitative research accounted for just
2% of the data points collected in this chapter
(Goldstein & Puntambekar, 2004; Jenson et. al.,
2003; Voyles & Williams, 2004; Waite et al.,
2007). We have relatively clear overall patterns
of attitude, ability, and use, but the only way
we are going to understand and address gender
differences is to conduct interviews and observe
actual behaviour. The wealth of survey data has
left us at the mercy of speculation, some of which
may be true, but little of which is supported by
empirical data.
Developing Intervention Strategies
While no comprehensive model exists to explain
the dynamics of gender and technology, the
proposed model in Figure 1, provides a starting
point for intervention research. Only 2 of the 71
studies (Jenson et al., 2003; Kay, 2006) examined
how gender biases might change. Kay (2006)
provided evidence that an 8-month, ubiquitous
laptop program eliminated computer attitude,
ability, and use differences in favour of males.
Establishing a meaningful, supportive culture of
computer use may be a substantial step toward
producing effective intervention.
As Sanders (2006) suggest, more interven-
tion studies are needed, and data from the last
decade indicate that modifying affective attitudes
and self-efcacy in middle school, high school,
and university are reasonable areas to start. Of
course, one could argue that more information
on what causes gender differences is needed to
create effective intervention. On the other hand,
intervention research can provide useful feedback
for understanding and addressing the computer
gender gap in specic areas.
CONCLUSION
As with any large-scale review of the literature,
more questions than answers are generated. In this
specic review of gender and computer-related
behaviour over the past decade, the following
conclusions were made:
• Males have signicantly more positive affec-
tive attitudes toward computers, particularly
23
Exploring Gender Differences in Computer-Related Behaviour
in high school, university, and the general
workplace
• Gender bias in attitudes toward computers
is affected by context – males have more
positive attitude in school, but females have
more positive attitude in an online environ-
ment
• Gender differences with respect to computer
attitude have remained relatively stable for
the past 10 years
• Males in a university or general workplace
setting have consistently reported stronger
computer skills for the past 10 years, how-
ever, these differences disappear when one
looks at actual computer performance
• Males report signicantly higher computer
self-efcacy than females
• Males report higher computer use, however,
this gender bias has decreased markedly over
the past 10 years
• Males report more computer use at home,
but not in school
• When looking at computer behaviour, males
and females appear to act differently, but
there appear to be no signicant advantages
for either sex
It is important to note that when we are talk-
ing about gender bias, even in the most extreme
case, there are no differences between males and
females 50% of the time. In short, male and female
computer attitudes, ability, self-efcacy, and use
are more similar than different.
Only two studies (Anderson & Haddad, 2005;
Ong & Lai, 2006) looked toward developing a
model to understand and explain gender differ-
ences. A model was proposed in this chapter to
help understand current ndings, and to direct and
focus future research efforts. The new research
agenda needs to improve and expand methodology,
include underrepresented populations, consider
the context of computer use, and explore inter-
vention strategies.
The importance of investigating gender and
technology is best illustrated from a comment
made by one of the girls who learned in a same-
sex environment in Jenson et al.’s (2003) study.
She said:
When you taught us, it was simple. And I think one
of the parts about … you … teaching us is that it
feels nice when people go ‘oh you are so smart,
you know how to do this’ …But like… if you ask
a boy, ‘oh could I have some help here,’ they kind
of laugh at you and say ‘You don’t know that?’ …
You are like giving us an opportunity where we
… can say ‘hey this is good maybe I will get into
computers. (p. 569)
If what this girl felt is representative of the
larger high school population where mixed sex
education is the norm, then we cannot start early
enough in rectifying this kind of prejudice. Ulti-
mately, we need to create supportive environments
that do not inhibit learning or choice with respect
to using computers. It is also revealing that when
efforts are made to create technology-rich environ-
ments that emphasize constructive, collaborative,
problem-based learning, looking at authentic ac-
tivities, no differences are reported between males
and females with respect to attitude, self-efcacy,
use, and performance (Mayer-Smith, Pedretti, &
Woodrow, 2002). Perhaps, when technology is
not the main focus but naturally and effectively
integrated into a learning environment, gender
biases are reduced or eliminated.
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ADDITIONAL READING
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aauw.org/member_center/publications/Tech-
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28
Exploring Gender Differences in Computer-Related Behaviour
Barker, L. J., & Aspray, W. (2006). The state of
research on girls and IT. In J. M. Cohoon & W.
Aspray (Eds.), Women and information technology
(pp. 3-54).Cambridge, MA: The MIT Press.
Kay, R. H. (1992). An analysis of methods used to
examine gender differences in computer-related
behaviour. Journal of Educational Computing
Research, 8(3), 323-336.
Kay, R. H. (2006). Addressing gender differ-
ences in computer ability, attitudes and use: The
laptop effect. Journal of Educational Computing
Research, 34(2), 187-211.
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29
Exploring Gender Differences in Computer-Related Behaviour
Authors Location Val Rel Education n Context Constructs
Anderson & Haddad, 2005 US Yes Yes University 109 Online Att, Abil, SE, Beh, M
Andriason, 2001 Sweden No No Graduate 60 Online Att, Beh
Atan et. al., 2002 Malaysia No No University 315 Home Use
Aust et. al., 2005 US No Yes Preservice 265 School Abil
Barrett & Lilly, 1999 US No NA Graduate 14 Online Att, Beh
Broos & Roe, 2006 Belgium Yes Yes Grade 9 -12 1145 School Att, SE
Broos, 2005 Belgium Yes Yes Gen Pop 1058 General Att, Abil
Brosnan & Lee, 1998 HK No No University 286 School Att, Abil
Brosnan, 1998 UK No No University 119 School Att, Beh, Use
Christensen et al., 2005 US Yes Yes Grade 1 - 5 308 to 4632 School Att
Colley & Comber, 2003 UK No Yes Grade 7 & 11 344 to 575 General Att, SE, Use
Colley, 2003 UK No No Grade 7 & 11 213 to 243 General Att
Comber et. al., 1997 UK No Yes Grade 9-12 135-143 General Att, Abil, SE, Use
Crombie et al., 2002 Canada No Yes Grade 9-13 187 SG Att, Abil, SE, Beh,
Durndell & Haag, 2002 Romanian No Yes University 150 School Att, SE, Use
Durndell & Thomson, 1997 UK No No University 165 Home Use
Durndell et. al., 2000 European Yes Yes University 348 General SE
Enoch & Soker, 2006 Israel No No University 36430 General Use
Fan & Li, 2005 Tawain No NA University 940 School Ability
Fitzpatrick & Hardman, 2000 UK No No Grade 1-5 120 School Att, Abil, Beh
Garland & Noyes, 2004 UK No Yes University 250 School Attitude
Goldstein & Puntambekar (2004) US No Yes Grade 6-8 159 School Att, Abil, Beh
Graham et. al., 2003 Canada No No Grade 9 -12 2681 School Use, Beh
Guiller & Durndell, 2006 UK No No University 197 Online Behaviour
Jackson et. al., 2001 US No Yes University 630 General Use
Jenson et. al., 2003 Canada No No Grade 6-8 54 SG Abil, Intervention
Joiner et al., 2005 UK No Yes University 608 Online Att, Use
Joiner, 1998 UK No No Grade 6-8 32 School Attitude
Karavidas et. al., 2005 US No Yes Gen Pop 217 General Abil, Use
Kay, 2006 Canada Yes Yes Preservice 52 School Intervention
Kimbrough, 1999 US No No University 92 Online Abil, Use, Beh
King et. al., 2002 Australia Yes No Grade 6-12 314-372 General Attitude
Lanthier & Windham, 2004 US No Yes University 272 General Att, Use, Beh
Lee, 2003 HK No No University 436-2281 School Att, Abil
Leonard et al., 2005 US No No Grade 1-5 73 School Abil, Use, Beh
Li & Kirkup, 2007 UK & China No Yes University 220 General Att, Abil, Use
Light et. al., 2000 UK No No Grade 6-8 62 SG Att, Abil, Beh
Lightfoot, 2006 US No No University 596 Online Behaviour
APPENDIX
List of Papers Reviewed for this Chapter
continued on following page
30
Exploring Gender Differences in Computer-Related Behaviour
Mayer-Smith et al., 2000 Canada No No Grade 9 -12 81 School Abil, SE, Use
McIlroy et. al., 2001 UK Yes Yes University 193 General Att
Mercier et al., 2006 US No Yes Grade 6-8 86-102 General Att, Abil, SE
Miller et. al., 2001 US No No Grade 6-8 568 Home Att, Abil, Use, Beh
North & Noyes, 2002 UK No No Grade 6-8 104 General Att
O’Neill & Colley, 2006 UK No No Graduate 136 Online Beh
Ong & Lai, 2006 Tawain Yes Yes Gen Pop 156 General Att, SE, M
Ono & Zavodny, 2003 US No No Gen Pop 50000 General Use
Oosterwegel et. al., 2004 UK No No Grade 6-8 73 General Att, SE
Ory et al., 1997 US No No University 1118 Online Att, Abil, Use, Beh
Papastergiou & Solomonidou, 2005 Greece No No Grade 9 -12 340 School Use
Passig & Levin, 1999 Israel No No Kindergarten 90 School Abil, Beh
Price, 2006 UK No No University 268 School Abil
Ray et. al, 1999 US Yes Yes University 62 General Att, Use
Sax et al., 2001 US No No University 272821 General Use
Schumacher et al., 2001 US No No University 225 General Abil, Use
Shapka & Ferrari, 2003 Canada No Yes Preservice 56 School Att, Abil, Use
Shashani & Khalili, 2001 Iran No No University 375 Family Att, SE
Shaw & Marlow, 1999 UK No No University 99 General Att, Beh
Smeets, 2005 Netherlands No No Gen Pop 331 School Beh
Solvberg, 2002 Norway No No Grade 6-8 152 Home SE, Use
Sussman & Tyson, 2000 US No Yes Gen Pop 701 Online Beh
Thayer & Ray, 2006 US No Yes University 174 Online Attitude
Todman & Day, 2006 UK Yes Yes University 138 General Attitude
Todman, 2000 UK No Yes University 166-202 General Attitude
Tsai et. al., 2001 Tawain Yes Yes Grade 9 -12 753 Online Att, SE
Volman et. al., 2005 Netherlands No No Grade 1 -5 94-119 School Att, Abil, Use
Voyles & Williams, 2004 US Yes No Grade 7-8 57 School Abil, SE, Beh
Waite et al., 2007 UK No No Grade 1-5 7 School Behaviour
Wu & Hiltz, 2004 US Yes Yes University 116 School Att, Abil
Young, 2003 US No No Grade 7-8 462 General Att, Abil, SE, Use
Yuen & Ma, 2002 HK Yes Yes Preservice 186 General Attitude
Zhang, 2005 US No Yes Gen Pop 680 General Attitude
