Picture credit: Harvey Mudd College

While the emerging knowledge- and service industries of the 21^st-century is a continuing discussion topic of public discourse, the minuscule percentage of women embarking on careers in IT is deeply troubling. The IT industry struggles to find enough applicants to fill open positions as women are losing out on a personal and societal level by not embarking on beneficial career paths, effectively excluding themselves from Economy 4.0.

Problem Definition

Across Western cultures, very few women choose careers in IT. According to a recent study conducted by COMPTIA (2017), girl’s consideration of IT jobs wanes with age. While in Middle School 27% of girls still consider a career in IT, this number dwindles to a meagre 18% in High School. Only 7% of girls consider a career as a programmer (versus 25% of boys) and only 3% imagine a career as a software developer (versus 23% of boys). In Germany, only 17,4% of new job applications in IT are women according to a recent study ‘Recruiting Trends 2017’. Among the top 1000 IT companies in Germany, this number comes down to 13,2% of female applicants, constituting IT as a highly male-dominated industry.

The cited reasons for the low uptake are manifold. Early childhood socialisation favours boys to be associated with technology as compared to girls. As a result of this stereotypical expectation which is supported by many parents, boys are more encouraged to explore technology and science as compared to girls. Many women also feel uncomfortable to work as a minority in an already male-dominated field. In the social sphere, there are too few female role models available to guide and mentor younger girls. Many schools offer only rudimentary IT classes that do not inform and prepare students adequately for a future career. At the workplace, reports of unequal pay, unequal career development options and widespread gender bias within IT companies keep many women from joining. On top, very little useful information appears to be provided to young women about the diversity of careers in IT in general. We cannot dream about things that we don’t know about.

Although the general ‘acupuncture points‘ to why women do not take up jobs in IT have been identified in numerous international surveys, obstacles have not yet been specified by more thorough empirical research including validated models. What specifically keeps girls and women from embarking on careers in IT? How do factors interact? Can we draw inferences across cultural contexts? To dig deeper and go beyond generalised gender statements, we need to take a look at some of the leading theoretical frameworks in educational sociology and psychology.

Approaches in Educational Sociology

Raymond Boudon’s approach follows, as an external methodology, a rational choice approach based on an individual and situated cost-benefit calculus. In our case, young girls and their parents evaluate the costs, payoffs and likeliness of succeeding in careers in IT. The question is if and how young women from disadvantaged socioeconomic backgrounds make different educational decisions after finishing school or college as compared to women from academic- and well-off families. The data gained from studies on the more fragile joints of education systems can inform interventions and communication strategies based on the attitudes and ways of life within a specific social milieu or class. Rational choice theories such as Boudon’s are good at examining the joints of educational trajectories such as transitions from secondary to tertiary and from tertiary to university- or polytechnic education.

Pierre Bourdieu’s Cultural Deprivation Theory, by comparison, looks at the ‘habitus‘, internalised patterns of socialisation, dispositions, daily routines and the in situ constitution of life-planning. His complementary concept of ‘doxa’ refers to the attempt of social members to bring subjective cognitive convictions into congruence with given (objective) social settings. In psychology, Cognitive Dissonance Theory (Festinger, 1957) deals with similar conflicts of cognitive non-congruence and scenarios where issues such as forced compliance behaviour, decision-making and effort play a central role. In our case, we are interested to find out how habitus and doxa affect the motivation of young women to engage in IT.

Psychological Approaches

Regarding observational learning and learning from social role models, Albert Bandura’s Social Learning Theory can inform our problem. What are the characteristics of role models in IT that inform and motivate young women? Which are the key predictors provided by role models for a behavioural engagement in IT?

As a motivational theory, Self-Determination Theory (Deci & Ryan, 2012) offers a useful framework to identify the levels of personal autonomy, competencies and social relations in IT careers. As a motivational theory, SDT differentiates between extrinsic motivation (which is e.g., reward dependent) and intrinsic motivation which is self-sustaining. Analogously to Bourdieu’s notion of internalised life practice, it would be illuminating to examine to which extent life-planning, given a specific socioeconomic background, is constructed by more intrinsic or extrinsic types of motivation. There is an interesting conflict here: given that IT careers are based on the prerequisite of lifelong learning skills, an entirely extrinsic motivation orientation (e.g., a focus on above-average salaries and material benefits) that may prompt some women into taking up IT careers might not be the same motivation that is needed to sustain long-term growth within the field.

As in rational choice theories in sociology, we find similar approaches in psychology. Well-supported rational choice theories such as the Theory of Planned Behavior (Ajzen, 1991) examine attitudes towards planned behaviour, normative beliefs and perceived behavioural control analogous to Boudon’s framework. The theory’s assumption is that actors make rational, individual decisions in favour of personal benefit and estimate their chances at achieving payoffs.

Badura’s Social Cognitive Theory (Bandura, 2002) focusses, by contrast, on the social context at hand and it researches the interplay between social modelling (such as by role-models and tutors), individual outcome expectations and the development of Self-Efficacy. The relevance of the latter for women’s interest in IT has been confirmed by previous studies. Social Cognitive Theory and Social Identity Theory (Tajfel & Turner, 1979) may also explain how the discrimination of women in IT, as illustrated by a recent example of open gender discrimination at Google, (a ten-page manifesto, by a software engineer, circulated internally and ranting against women’s capability in IT) discourages women to take up technology jobs. Why should women work in an environment psychologically hostile to them, objectively pays women less than men and progresses men faster than women in their careers, as claimed by many Google employees? Social Identity Theory can explain stereotype formation by the subsequent processes of social categorization (such as gender-based categorization), social identification (such as traits identified as distinctively male or female) and social comparison (such as concluding males more capable than women).

Strong Empirical Support

There appears to be solid empirical support for Cultural Deprivation Theory as well as the cited psychological theories, especially SDT and Social Cognitive Theory. Boys have earlier profound computer experiences, such as e.g., through computer games (Oosterwegel, Littleton, & Light, 2004) and display a more autonomous acquisition of technology as compared to girls. Fathers are reported to be stronger roles models for both male and female students (Turner et al., 2002). Girls tend to rate their computer skills generally far lower than boys (Young, 2000; McCoy & Heafner, 2004). Girls attend fewer computer classes and display lower self-confidence in the use of computers (Beyer et al., 2002; Durndell & Haag, 2002; Lee, 2003). Regarding the cultural value underlying IT, women in Applied IT “rated helping others as an important reason for choosing an IT major more often than did any other group.” (Organ et al., 2005, pg.20), suggesting a different set of underlying motivations for engaging in IT as compared to boys. Most of the recent studies such as COMPTIA (2017) and ‘Recruiting Trends 2017’ confirm findings of these earlier studies.

Graphic: Top Barriers identified by ISACA Study (2016). Workplace disadvantages have a significant negative correlation to IT enrollment by women. 

Defining the Acupuncture Points for Potential Studies

From a psychological perspective, there are at least three areas of interest that we could preliminarily define as a 3-factor model, following Bandura’s Triadic reciprocal causation model, consisting of the independent variables of  (a) social predictors, (b) workplace predictors and (c) intrapersonal predictors on the dependent variable of women to enroll in IT careers. As we know from the studies cited above, all of these factors influence women’s motivation to enrol in IT jobs. Under factor (a) items of interest are measures such as the availability of female mentors, female role models, early childhood socialisation towards technology and social milieu. Under factor (b) fall items such as gender bias/ gender fairness in the workplace, the perception of equal growth opportunities, equal pay as well as the prospect to work with other women and not only men. Under factor (c) items of interest might be tried-and-tested psychological variables such as the prevalence of traditional gender role beliefs, goal orientation (extrinsic versus intrinsic goal-orientation), control beliefs and attitudes towards IT. A working hypothesis as the basis for an Exploratory and Confirmatory Factor Analysis is sketched out below (Fig.1)

Figure 1: The supervening working hypothesis for the factors predicting women’s enrollment in IT. We assume that social scaffolding, workplace support and intrapersonal competencies have an equal influence on women enrolling in IT jobs. Factor loadings of items would reveal in greater detail the more significant predictors.

More informative, from a social psychology perspective, would be multi group comparisons, e.g. between male and female students prior to applying for IT jobs (Study Design Option I – Differences between male and female profiles predicting engagement in IT careers) or between undecided female students and young women who have already embarked on a career in IT (Study Design Option II – Differences between female IT professionals and average female student population: Which factors predict enrollment in IT ?) in order to gain deeper insights on the most significant factors that keep women from joining careers in IT.

Another unresolved issue is if and how IT has been influenced by predominantly male concepts and values (Study Design Option III – Relation between IT, male constructs and values: Are IT constructs excluding female values and perspectives?). For example, current enrollment data suggests that women are more attracted to IT jobs that involve the motif of caring such as in biomedical research, environmental- or socioeconomic development. Which cultural values does IT represent and how is it related to gender constructs? Has IT been conceptualized, as suggested by Clegg (2001), as an obsessively masculine construct that lacks appeal to women? Ideological gender constructs of computing technology are no trivial matter. Clegg points out that most action-packed and competitive games have been designed and marketed by men for men. Computing has been widely associated in public discourse and media with military technology, cyber warriors and a competitive display of power – a technological machismo that few women find appealing and are able to identify with. Women, by contrast, have been marginalized in IT as secretaries, low-paid administerial workers or staff in online call-centers. New domains, such as in computerised lifelong learning, however, may offer a more fitting identification for many women.

Lastly, retaining female IT personnel and offering women long-term prospects in IT would be another relevant area of research (Study Design Option IV: How can employers retain female IT staff?). The outcomes of gender-based IT studies are useful for designing more efficient information campaigns, communication strategies, e-platforms, school- and college-initiatives as well as developing institutional policies for employers to motivate young women to join IT-related careers.

Conclusion

As Economy 4.0 embraces collaboration and cooperation, it opens the female notion of caring which appeared as a strong motif in all of the studies. From a caring female perspective, IT can be reformulated as a means to re-establish our connections to nature, to others and our future potentials. It is not so much that women have an issue with IT, but that IT has been widely cultivated and advertised as a predominantly male domain. New developments such as in online learning, computer science or AI development offer new role identification opportunities for young women.

The illustrated working hypothesis suggests that multiple factors predict the involvement of women in IT rather than a single argument. In addition, media images and stereotype clichées of the lonely nerd or socially deprived hacker are not helpful to any gender since IT relies heavily on the ability to work in teams. Instead, it would be more productive to develop cooperative spaces within IT where gender domination does not obscure the love for creating technology that is beneficial to all.

 

References

Ajzen, I. (1991). “The theory of planned behavior”. Organizational Behavior and Human Decision Processes. 50 (2): 179–211.

Bandura, A. (2002). Social cognitive theory of mass communication. In J. Bryant & M. B. Oliver (Eds.), Media Effects: Advances in Theory and Research (pp. 94-124). New York, NY: Routledge.

Beyer, S., Chavez, M., and Rynes, K. (2002, May). Gender differences in attitudes
toward and confidence in computer science. Paper presented at the annual
meeting of the Midwestern Psychological Association, Chicago, IL.

Clegg, S. (2001). Theorising the machine: Gender, education and computing. Gender and
Education, 13, 307-324.

Deci, E. L., & Ryan, R. M. (2012). Motivation, personality, and development within embedded social contexts: An overview of self-determination theory. In R. M. Ryan (Ed.), Oxford handbook of human motivation (pp. 85-107). Oxford, UK: Oxford University Press.

Durndell, A., and Haag, Z. (2002). Computer self-efficacy, computer anxiety, attitudes
towards the internet and reported experience with the Internet, by gender, in an
East European sample. Computers in Human Behavior, 18, 521-535.

McCoy, L.P., and Heafner, T.L. (2004). Effect of gender on computer use and attitudes of
college seniors. Journal of Women and Minorities in Science and Engineering,
10, 55-66

Lee, A.C.K. (2003). Undergraduate students’ gender differences in IT skills and attitudes.
Journal of Computer Assisted Learning, 19, 488-500.

Ogan, C., Herringg, S., Robinson, J.C. & Ahuya M. (2005). The More Things Change, the More They Stay the Same: Gender Differences in Attitudes and Experiences Related to Computing Among Students in Computer Science and Applied Information
Technology Programs. Paper presented at the 2005 International Communication Association Conference, New York, NY

Oosterwegel, A., Littleton, K., and Light, P. (2004). Understanding computer-related
attitudes through an idiographic analysis of gender- and self-representations.
Learning and Instruction, 14, 215-233.

Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. The social psychology of intergroup relations?, 33, 47.

Turner, S.V., Bernt, P.W., and Pecora, N. (2002, April). Why women choose information
technology careers: Educational, social, and familial influences. Paper presented
at the annual meeting of the American Educational Research Association, New
Orleans, LA.

Young, B.J. (2000). Gender differences in student attitudes toward computers. Journal of
Research on Computing in Education, 33, 204-216.