Business Students’ Perceptions of Information Systems
Eric Cloete
Department of Information Systems
University of Cape Town
Abstract
There has been much debate about the declining enrolment numbers and low retention of students
within the field of Information Systems. To better understand the reasons behind the decline, there
needs to be an understanding of business students’ perceptions of information systems as a field of
study and work. This paper highlights key factors which influence student perceptions of information
systems. Some of the factors identified are: gender, culture, race, maturity, prior exposure, influences
and job market conditions.
The literature will then outline two models that conceptualise key factors of choice and perceptions. As a
result of the literature review, gaps are identified and analysed. A conclusion is then put forward in the
form of a summary with recommendations to further research in this field. Based on the data from 55
empirical survey respondents, it was found that not all of the abovementioned factors could be analysed.
It was found that gender and race have varied influences on perceptions, and that not all of the
perceptions are aligned to what was previously claimed in similar research
Keywords: Business students’ perceptions, Information Systems, IT/IS job market, Career choices.
Acknowledgements
The author also acknowledge and appreciate the dedicated work done by Ferowza Bowren and
Nadeem Izaacs in 2007, both post-graduate students at the University of Cape Town that have been
involved with this research project (Bowren, Izaacs, 2007).
Introduction
Background to the Research
There has been much debate around the declining enrolment numbers and low retention of students
within the field of Information Systems (IS) (Lomerson, 2006). An important factor attributed to the
steady decline of these numbers is the image of IS as seen by students and fellow professionals. The IS
fraternity are not doing enough to market the discipline, with a distinct lack of exposure being given to
students by social means such as councillors or teachers (Berry, Rettenmayer, & Wood, 2006).
To better understand the reasons behind the decline, there needs to be an understanding of business
students’ perceptions of information systems. Understanding the perceptions of students towards IS will
highlight problem areas around IS as a discipline and profession. This will provide academics and
industry professionals alike with important information that could be used in developing strategies to
improve the image and exposure of IS and thus increasing enrolment numbers.
Literature survey
Introduction
The following section aims at identifying student perceptions about IS as a field of study and work. This
involves outlining various perceptions that students have about certain subjects and also the
identification and explanation of influencing factors that affect student perceptions. The literature review
identifies two models concerning student perceptions; it will then highlight two areas: the job market and
teaching methods. In addition the literature highlights four influencing factors: gender, race, maturity and
prior exposure and experience. These areas and factors will then be summarised in a graphical model
on which the research project will be based.
Conceptual Model of IT Perceptions
The conceptual model of the influence of culture and gender on perceptions of IT studies and careers,
was developed after the investigation into the effects of gender and culture on the perceptions of IT
careers (Nielsen, von Hellens, Greenhill, & Pringle, 1998). Its aim is to better understand IT as a career
and study choice, and determining the factors which leads to declining student retention numbers
(Nielsen, von Hellens, Greenhill, & Pringle, 1998). Fig 2 is a graphical representation of the model.
The main constructs include the cultural background, gender, life history, value of computer skills and
individualism /collectivisms. The cultural background refers to language differences and regional
differences. Gender recognizes different perceptions held by males and females towards IT in general.
The life history is a combination of factors including age, parental influence and prior experience with
computing. The value of computer skills is the relative value attributed to computer skills based on
different cultural backgrounds. Individualism / Collectivism is the cultural traits whereby certain people
prefer to work as a group or individually.
The conceptual model identifies that differences in gender and culture through various factors such as
computer experience and the preference of work practices ultimately lead to differences in the
perceptions of IT as a field of study and career (Nielsen, von Hellens, Greenhill, & Pringle, 1998).
IS Job Market
Acquiring qualified IT/IS personnel is a critical challenge facing organizations today (Goles, 2001).
Information Technology has spread across various sectors of commerce and society, and has therefore
led to the current shortage of IT professionals (Goles, 2001). The Information Technology Association of
America a few years ago forecasted a shortage of more than 800 000 IT persons. Employment statistics
provided by the Bureau of Labour Statistics, employment levels in the computing and IS fields will
continue to grow over the coming years (Rettenmayer, Berry, & Ellis, 2007).
Figure 1: Culture & Gender Perception Model
According to Rettenmayer, Berry and Ellis (2007), males and females have different perceptions of the
IS profession. Males agree more than females that IS professionals work longer hours; whilst females
feel more strongly than males that IS professionals are respected by management (Rettenmayer, Berry,
& Ellis, 2007). Rettenmayer, Berry and Ellis (2007) further point out that females compare IS careers to
the traditional clerical-type positions held by females.
Goles (2001) investigates the factors that influence career choice by looking at Frederick Herzberg’s
Two-Factor Theory of Intrinsic and Extrinsic factors. The factors that influence career choice, as
described by this theory, include: salary, job security and working conditions (extrinsic); and potential for
achievement, career growth, recognition and the job itself (intrinsic). In addition students within the IS
field show particular interest in using new and innovative technologies in their careers (Goles, 2001). In
order for recruiters to attract and attain graduate-level IT students, they need to consider both extrinsic
and intrinsic factors.
The two main factors leading to a student’s decision to partake in IS as a field of work are: higher salary
expectations; and opportunity for greater work challenges (Rettenmayer, Berry, & Ellis, 2007). The
perceptions students have of IT also influences their choice thereof as a career. Three perceptions of IT
careers have been identified in a study by Thomas and Allen (2006): the stereotypical IT programming
nerd; IT is a career for males; and the IT profession is only technically oriented.
Factors Influencing Student Perceptions Of IS
Student perceptions of Information Systems are not uniform. There are various influencing factors that a
review of the literature has identified outlined below. These revolve around gender, racial, prior
exposure and experience influences.
Gender Influences
The general view is that awareness of information technology is low amongst females and that this is a
key factor in the formulation of attitudes towards IT in general (Thomas & Allen, 2006). Despite the
perceived femininity of IS amongst certain scholars due to its social component, the male stereotype
associated with science-orientated subjects is often assumed to exist in the IS field as well (Joshi &
Schmidt, 2006).
The trend of females being less interested in technical majors and careers is not a new concept. The
interest of high school students in IT/IS careers, and therefore in enrolling for IT/IS degrees and
choosing such courses, is significantly lower among females than it is males (Gupta & Houtz, 2000).
The number of degrees obtained in computer related courses has decreased since 1985, and has
decreased at a faster rate for women than for men (Gupta & Houtz, 2000). According to a study by
Camp (2006), the percentage of women in the United States obtaining computing degrees declined from
32.5% in 1980 to 28% in 2000 with similar figures in Australia illustrated a decline in females enrolling
for IT courses from 48.1% in 1994 to 32% in 2003 and a further drop to 21.97% in 2005 (Thomas &
Allen, 2006).
Hart (2002) showed that only 55% of females were familiar with information systems compared to 72%
of male respondents. Female students feel that they are not appropriate candidates for computer-related
courses and choose alternate commerce courses such as accounting or finance (Hart, 2002). These
differences are attributed to the low expectation levels society has of women, and to the maledominated stereotype of technical careers (Joshi & Schmidt, 2006).
The literature also highlighted that perceptions of IT skills differ by gender. Male students tend to rate
themselves as having higher IT skills than females, indicating a difference in the confidence levels of
using information technology (Gupta & Houtz, 2000). Females have also been found to use IT for word
processing as apposed to problem-solving as used by males (Gupta & Houtz, 2000).
However, according to a study by Woszczynski, Myers, and Moody (2006), males and females were
found to have similar perceptions about IT work, and are cognisant of the integration of technical,
systems, social and managerial components in the IS field (Joshi & Schmidt, 2006).
Racial Influences
Students from previously disadvantaged racial groups generally face greater barriers to education and
career success than others (Gupta & Houtz, 2000; Sacks, 1993). These include barriers such as poverty
and limited access to information technology (Gupta & Houtz, 2000; Woszczynski, Myers, Beise &
Moody, 2003). Such racial inequalities exist not only in the availability of technology but also in the way
technology is used, from using it as a tutorial supplement to using it for programming (Gupta & Houtz,
2000).
According to Nielsen, von Hellens, Greenhill, and Pringle (1998), some information systems students
consciously work and interact based on racial backgrounds. Differences in choices also occur along
racial backgrounds, for example, Hispanic students were shown to show greater interest in information
technology degrees and careers compared to other racial backgrounds (Gupta & Houtz, 2000).
Socio-economic circumstances has led to what is known as the “Digital Divide” – a growing gap
between those who have computer and internet access and those who do not (Woszczynski, Myers,
Beise, & Moody, 2003). This access to technology, and the lack thereof, often correlates with racial
background and the barriers faced by the different racial groups (Woszczynski, Myers, Beise, & Moody,
2003).
Higher education institutions need to consider how the digital divide will affect disadvantaged students.
The actual level of technology access should be monitored to alleviate the tension associated with using
IT to attract students with limited access to the technology (Kirkwood & Price, 2005).
Maturity Influences
There is currently very little research to support the notion that age diversity exists in the technology field
(Woszczynski, Myers, Beise, & Moody, 2003). Age discrimination in IT is thought to exist, but is based
more on perceptual models than on reality (Woszczynski, Myers, Beise, & Moody, 2003). A study
conducted by Underwood (as cited in Woszczynski, Myers, Beise & Moody, 2003, p. 118) found no
difference in the job performance of IS professionals as they aged.
Nielson (1998) states that some conclusions on perceptions can be drawn with regards to age. Nielsen
further adds that maturity seems to strengthen with age, with a higher uncertainty avoidance and
emphasis being put on job security.
Prior Exposure & Experience Influences
There is an indication that prior knowledge or education about information systems has an impact on the
perceptions of students towards information systems as a field of study. This is explained below.
According to Hart (2002) there is a noticeable difference in perceptions of information systems as a field
of study between students who have been adequately informed about the subject matter and those who
have not. Students that have been informed show a greater inclination to pursue information systems as
a major (Hart, 2002)
Self efficacy refers to the self impression that one has in being capable to perform a certain task or goal.
This is very important as it influences the choices made by and individual. Self efficacy in its own right
can be seen as a factor that influences whether or not an individual gets involved in computing, and the
attitudes or behaviours that an individual derives from that involvement (Venter, Turner, Sanders, &
Galpin, 2003).
Figure 2: Proposed Research Model from Literature Review
Formulating the Research Model
The review of literature did not produce an adequate model to map out student perceptions. Therefore in
summarising the previous research done on the subject area Fig 3 reflects the model constructed from
the literature that was employed in this research. The key topics identified under the perception areas
are illustrated under perceptions of IS as a field of study and work. These are depicted vertically on the
left. These are the areas of IS job market/career and IS teaching methods. The factors that were
identified as influencers, namely; age, race, gender and experience are depicted at the bottom of the
figure and were then turned into hypothesis shown with arrows.
Research methodology
The literature review highlights various factors that influence student perceptions of IS. These factors
were then formed into various hypotheses whereby a strategy was formulated to thoroughly test these
hypotheses. A quantitative approach using a survey instrument was used to collect and analyse
statistical data collected. The primary objective of this study was to investigate business students’
perceptions of IS.
Therefore the following high level research questions were formulated:
What are business students’ perceptions towards IS as a field of study and work?
What are the underlying influencing factors of these perceptions?
Previous research concerning students’ perceptions of IS has revealed a number of underlying
influencing factors such as gender, race, age and prior computing experience. Therefore the following
research hypotheses were formulated:
H1: Business students’ perceptions of IS influenced by gender
H2: Business students’ perceptions of IS influenced by race
H3: Business students’ perceptions of IS influenced by age
H4: Business students’ perceptions of IS influenced by prior computing experience
In order to gauge the perceptions business students have of IS, and to investigate the relationships
between the various factors and these perceptions, a quantitative research philosophy was used. This
allowed for statistical analysis of data in order to determine significant relationships, and how certain
factors influence students’ perceptions of IS. The strength of the relationships between certain factors
and perceptions could then be measured, allowing for a ranking of the factors that most influence
business students’ perceptions of IS.
Although previous research was available on a similar scale (Atkins, Petkar, & Webber, 1998; Adams,
Menziwa, & Peters, 2001; Haralambous, Natha, & Weng, 2003) and provided previous results and
findings which could have lead to a more positivist approach, the shift in the objectives from the previous
studies done rendered the research in being somewhat exploratory in nature to describe new
relationships in terms of the new objectives
Data collection
The target sample was a group of post graduate business students undertaking a postgraduate course
in the Department of Information Systems at the University of Cape Town. The course, Introduction to
Business Computing (INF4000F), comprises of non-IS major students who will have to complete an IS
course to complete their diploma. These students fit the objective of the research in trying to gauge a
non-IS view. It also addresses a lack of studies done on post graduate students in this regard.
The main data collection tool was in the form of a questionnaire (see questionnaire design for more
information concerning the structure of the questionnaire). The questionnaire will be based on previous
research done on students’ perceptions conducted within the department of Information Systems at the
University of Cape Town. This allowed for comparison of some elements as required. The survey
questionnaires were handed out during the last few lectures of the course when examination content
was to be covered, as the assumption holds that the attendance rate during those few lectures will be
optimal and maximise the number of survey responses. In addition to a questionnaire, a course
evaluation will be used to obtain an overall view of the course.
Data Analysis & Interpretation
The following section presents the data analysis done on the data collected from the survey instruments
and the interpretation of findings thereof.
Demographic Information
The sample from the Information Systems course, Introduction to Business Computing (INF4000F),
yielded 55 respondents. This yielded a total percentage response rate of 53.92% from a class total of
102 students. The following section outlines demographic and summary information from the sample
respondents.
Age:
22.74
Gender:
Female 69.09% Male 30.91%
First Language:
English 78.18% Other 21.82%
Nationality:
RSA 85.45% Other 14.55%
BA 30.91% B Soc Sci 27.27%
Qualification:
Other 41.82%
University:
UCT 72.72% Other 27.28%
Prior Computer Usage:
Yes 100%
Table 1: Summary Information
The following section outlines the summary information as determined by questions from the survey
instrument.
Computer Usage & Experience
All of the respondents indicated that they had prior usage of a computer. This figure was expected to be
high as the students are post graduates and have had some sort of exposure towards computers.
92.73% of respondents indicated that they had access to a computer at home while 71.43% indicated
that they had access to a computer at work. There was an average of 10.63 years of computer
experience amongst respondents.
Figure 3: Summary Information - Computer Usage
Computer usage amongst the target sample predominantly involved word processing and general
administration. Figure 5 below depicts the factors influencing career choice on a gender basis.
Fig 5 illustrates the average ratings of males and females, as well as the average overall rating for each
factor, as seen by the stacked bars. From the above it is evident that Interesting Work and Job Security
& Stability rank as the two main factors that influence a student’s career choice, in terms of what they
rank as the most important factors in a career. These findings differ to that in the literature. Using
Herzberg’s Two-Factor Theory as a basis, previous literature has stated that the two main factors were
Higher Salary Expectations (Goles, 2001) and Opportunities for Greater Work Challenges
(Rettenmayer, Berry, & Ellis, 2007). The two main factors drawn here are Interesting Work and Job
Security & Stability, as previously stated. It should be noted here that the factor, Interesting Work, does
not include work challenges, as this was split into a separate factor, i.e. Interesting Challenges.
Figure 5: Factors Influencing Career Choice – Gender Comparison
The difference in the two main factors could be attributed to the time gap and the evolution of career
development – people have become less concerned with monetary rewards as a factor of career choice
and more concerned with job richness in terms of interesting work. According to Thomas and Allen
(2006), the perceptions students have of IS careers can be noted into three categories. The categories
that encompass stereotypical programming notions and technically-oriented profiles; have already been
discounted in this research, as described under Section 4.2.3, and illustrated by Figure 10. As noted
under Section 4.2.3, the reason for this could be attributed to the timing of the survey instrument. The
remaining perception – that IS is a male-dominated field – is analysed based on whether respondents
indicated Yes (i.e. they perceive IS as a male-dominated field), or No (i.e. they do not perceive IS as a
male-dominated field). This is tested around the following hypothesis:
H0: There is no difference between Gender and the Perception that IS is a Male-Dominated
field of study and work
H1: There is a difference between Gender and the Perception that IS is a Male-Dominated
field of study and work
Note that there is an overlap in this test as it pertains to both gender and career perceptions. For
purposes of determining career perceptions, it is also examined here.
The results below (fig. 6) illustrate the frequencies at which IS is perceived as a male-dominated field.
The frequencies are depicted for both the affirmative and negative responses to the survey question as
described above. The frequencies are grouped by gender. In both figures, the values for each gender
group are quite close together. The p-value further confirms this at p=0.132303. As this p-value is
greater than 0.05, the null hypothesis cannot be rejected at the 95% confidence level. It can therefore be
inferred that there is no difference in the perception of IS as a male-dominated field, between males and
females. This inference is contrary to the perception noted by Thomas and Allen (2006) that IS is a
male-dominated field. The reason behind this could be that more and more females are appreciating
science-oriented degrees. An example of this statement is the University of Cape Town’s Full-Time IS
Honours class of 2007, which has a split of 20 males and 16 females – a proportion of 0.56 and 0.44
respectively.
Gender Influences
The following section provides detail on the influences of gender on perceptions of IS. In analysing the
familiarity males and females have of IS, the following was produced:
Figure 6: Knowledge and Skills Ratings by Gender
Figure 6 shows the ratings of males and females before and after the INF4000F course, with respect to
their perceived knowledge and skills. Whilst there are differences in the ratings of each factor by the
sexes, this difference appears to be minimal. This was statistically tested, under the following
hypothesis:
H0: There is no difference in the Skills and Knowledge ratings between Males and Females
H1: There is a difference in the Skills and Knowledge ratings between Males and Females
Mann-Whitney U Test (Skills and Knowledge Before and After)
By Variable: Gender
Marked tests are significant at p<.05000
variable
Rank Sum Rank Sum
Females
Males
U
Z
p-level
Z
p-level
Skills Before
995.500
489.500
254.500 -0.938 0.348
-1.012
0.312
Skills After
921.000
510.000
218.000 -1.511 0.131
-1.776
0.076
Knowledge Before 1055.000 485.000
275.000 -0.686 0.493
-0.755
0.450
218.500 -1.620 0.105
-1.846
0.065
Knowlede After
959.500
525.500
Table4: Knowledge and Skills Ratings by Gender
The statistical evidence backing fig. 6 indicates that at the 95% confidence level, the null hypothesis
cannot be rejected. It can therefore be inferred that the p-value is not sufficient to conclude that there
are marked differences in the perceptions males and females have of their IS skills and knowledge. This
however, does not agree with the literature that claims that awareness of IS is higher amongst males
(Hart , 2002), nor that males rate their skills significantly higher than females (Gupta & Houtz, 2000).
The difference in these previous findings and the findings presented here could be related to the time
gap, which is over 5 years. The skills and knowledge acquired by males and females could therefore
have become more equally spread over the years. Another factor could be related to the fact that the
sample comprises only post-graduate students, who have all had prior computer exposure to some or
other extent before commencing the INF4000F course.
Whilst no significant differences were found in the skills and knowledge ratings for males and females,
the ratings were tested to determine whether differences exist within male and female subsets in terms
of their perceived skills and knowledge before, and after, the INF4000F course. The matched pairs of
data were tested under the following sets of hypotheses:
H0m: There is no difference in the Skills and Knowledge ratings before and after INF4000F
for Males
H1m: There is a difference in the Skills and Knowledge rating before and after INF4000F for
Males
H0f: There is no difference in the Skills and Knowledge ratings before and after INF4000F for
Females
H1f: There is a difference in the Skills and Knowledge rating before and after INF4000F for
Females
Wilcoxon Matched Pairs Test (Males) Marked tests are significant at p <.05000
Valid
Skills Before & Skills After
T
Z
p-level
16
0.000000 2.934058 0.003346
Knowledge Before & Knowledge After 16
0.000000 3.516196 0.000438
Skills Before & Knowledge Before
16
0.000000 2.934058 0.003346
Skills After & Knowledge After
16
0.000000 2.366432 0.017961
Table 5a: Skills and Knowledge Comparison for Males
Wilcoxon Matched Pairs Test (Females) Marked tests are significant at p <.05000
Valid
Skills Before & Skills After
T
Z
p-level
37
0.00000
4.540725 0.000006
Knowledge Before & Knowledge After 38
0.00000
4.936520 0.000001
Skills Before & Knowledge Before
38
10.00000 3.893114 0.000099
Skills After & Knowledge After
37
9.50000
3.440707 0.000580
Table 5b: Skills and Knowledge Comparison for Females
Tables 5a and 5b illustrate the comparison of the skills and knowledge ratings before and after
INF4000F. This was performed on each gender subset of the sample (i.e. on males and females
separately). Previous research has not covered these types of hypotheses, and so this test is
exploratory. The results highlight the significant p-values at the 95% confidence level. In the case of
both the males and females, the p-value is sufficient to reject the null hypothesis and infer that whilst no
significant differences are evident for the overall skills and knowledge ratings amongst males and
females, they are significant for each gender group. Tables 5a and 5b, in conjunction with Figure 13
described previously, show that the perceived skills and knowledge of IS has increased for males and
females – as separate subsets.
The data analysis in Tables 5a and 5b are typical of most of the tests performed in this research, unless
other statistical methods are pointed out. Due to the similarity of these tests, and to keep the document
within conference length, the rest of the tables are omitted, but are available from the author.
The overall usage of computers has been described below (see Figure 7). This section will look at
whether gender influences computer usage.
Figure 7: Computer Usage by Gender
Figure 4 aligns with Figure 8 in illustrating that the two main uses of computers are for Word Processing
and General Admin purposes. Figure 7 splits the uses by gender, illustrating the proportions of males
and females by their main uses of computers. For both the Word Processing and General Admin uses,
males obtain higher proportions, whilst only females indicated using computers for Programming. This
somewhat contradicts the literature that science-oriented subjects (such as IS or Computer-Science,
where Programming is a component) is stereotypically associated with males (Joshi & Schmidt, 2006).
Here again, the reason could lie in the fact that the students are post-graduate, and have been exposed
to some or other component of IS. The association between gender and whether satisfaction was
derived from studying INF4000F was analysed from the responses of students to a question as to
whether they are happy to have studied the course or not. This test can be marked as exploratory as
there has been very little previous research that links maturity to IS (Woszczynski, Myers, Beise, &
Moody, 2003).
From Figure 8 the following top-three rankings can be determined for males and females:
Males:
(i) Interesting Work;
(ii) Flexible Working Hours; and
(iii) Job Security and Stability.
Females:
(i) Interesting Work;
(ii) Job Security and Stability; and
(iii) Interesting Challenges.
Figure 8: Factors Influencing Career Choice by Gender
Whilst both males and females rank Interesting Work as their top priority, their second and third choices
differ. Whilst there are also differences in the rankings of the other factors between males and females,
these differences are slight.
Racial Influences
This section provides detail on the influences of Race on the perceptions of IS. The racial
denominations that were drawn from the responses to the survey questionnaire included: Black; White;
Coloured; Indian; and Other. Because many of the responses were White, and the others were
scattered across the rest of the racial denominations, the responses were grouped into White and Other.
The graph below depicts the various factors influencing career choice, and illustrates how the two racial
denominations, as described above, rate the factors collectively.
Figure 9: Factors Influencing Career Choice by Race
From Figure 9 it can be determined that both Whites and Other rate Interesting Work as the top factor
influencing career choice. The following are the top-three rated factors for Whites and Other racial
groups:
Whites:
(i) Interesting Work;
(ii) Higher Salary/Job Security & Stability; and
(iii) Interesting Challenges.
Other:
(i) Interesting Work;
(ii) Job Security & Stability; and
(iii) Interesting Challenges.
Figure 10: Frequencies of Perception of IS as a Male-Dominated Field
The familiarity of IS skills and knowledge can be tested by Race, as was tested by Gender. From the
test it appears that there are differences in the ratings before and after the course. This was tested
statistically and also proved that there is no marked significance between the two racial denominations
with respect to skills and knowledge ratings.
Conclusion
This research report has given insight into business students’ perceptions of information systems
through a review of literature and data analysis. It has tested hypotheses of the effects that gender and
race have on various perceptions students have about information systems as a field of study and work.
The holistic analysis of the data showed that students generally used computers for word processing
and general administrative purposes. An overall analysis of the data also showed that skills and
knowledge ratings increased after participation in the INF4000F course. General trends were also
evident in the analysis of IS job market and career perceptions. The analyses showed that the career
functions have more-or-less an equal spread. Furthermore, the two main factors for influencing careers
are: Interesting Work and Job Security & Stability.
In analysing the effects of gender on perceptions, some of the following associations were found to be
insignificant: (i) between gender and the perception that IS is a male-dominated field of work and study;
(ii) between gender and the satisfaction of having studied INF4000F; and (iii) between gender and the
perception that IS knowledge increases with maturity/age. There were also no significant differences in
the rating of skills and knowledge for males and females. However, differences were found to exist in
the skills and knowledge ratings of males and females (separately, as subsets of the sample) before and
after the INF4000F course.
In terms of the factors that influence career choice, both males and females rank Interesting Work as the
most important factor. Both males and females also place Job Security & Stability in the remainder of
the top-three.
Racial denominations were grouped into whites and other for analysis purposes. In analysing the topthree factors that influence career choice, Interesting Work; Job Security & Stability; and Interesting
Challenges were selected by both whites and other racial groups, in the same order of preference. The
only difference was that whites ranked Higher Salary the same as Job Security & Stability. Apart from
the top-three, there was also no significance in the ratings of the other career factors in terms of race.
It was also evident that the ratings of skills and knowledge did not differ by race, as in the case with
gender. There was, however, a significant difference between race and the perception of IS as a maledominated field. This is interesting to note since the test for gender and male-dominated perception did
not yield the same result. The research done gave an overview of non-IS students’ perceptions of
information systems. It tested the effects gender and race had on these perceptions. Although some
conclusions can be drawn from the data analysis, there should be caution when making conclusions
about the population group. The sample of students at UCT was not completely random and was small
in size. A more rigorous approach is needed to confidently make statements about the population group.
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