Opinion: Gender diversity leads to better science

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Pick up any recent policy paper on women’s participation in science and you will find assurances that gender diversity enhances knowledge outcomes. Universities and science-policy stakeholders, including the European Commission and the US National Institutes of Health, readily subscribe to this argument (13). But is there, in fact, a gender-diversity dividend in science?

The data suggest that there is. Under the right conditions, teams may benefit from various types of diversity, including scientific discipline, work experience, gender, ethnicity, and nationality. In this paper, we highlight gender diversity (Fig. 1). Guided by key research findings, we propose the following “mechanisms for innovation” specifying why gender diversity matters for scientific discovery and what managers should do to maximize its benefits (Fig. 2). Encouraging greater diversity is not only the right thing to do: it allows scientific organizations to derive an “innovation dividend” that leads to smarter, more creative teams, hence opening the door to new discoveries.

Well-run, well-performing research teams have become increasingly crucial to the success of modern scientific investigations. Already, experimental research points to positive links between gender diversity and collective problem solving. In a study of group performance, Anita Woolley et al. (4) randomly assigned 699 participants to teams of varying sizes and asked them to solve a set of both simple and complicated tasks (e.g., visual puzzles, brainstorming, making collective moral judgments, and negotiating over limited resources). Through these experiments, the authors found evidence of a collective intelligence factor that predicts group performance better than the IQ of individual group members. Key components of this factor include the group members’ social perceptiveness and parity in conversational turn-taking. Furthermore, gender plays an important role: women exhibit higher levels of social perceptiveness and teams with more women achieve greater equality in participation (4). The benefits of increasing women’s representation, however, tend to flatten at the extreme (5). Neither all-men nor all-women teams are the most effective in problem solving. Hence, given the persistent gender gap in science, women represent an untapped potential for boosting the collective intelligence in scientific team work.

Recent discoveries in team science also highlight the importance of gender diversity for effectively using the expertise of each team member. Following 60 interdisciplinary teams of more than 500 scientists and engineers across a variety of disciplines, Aparna Joshi (6) shows that women more often than men accurately recognize the expertise of fellow team members. Based on two surveys—one gathering data about the participants’ work-related and educational background, the other asking participants to evaluate fellow team members’ research expertise—Joshi finds that women are more likely to emphasize educational qualifications when evaluating expertise, whereas men tend to be distracted by irrelevant cues, such as gender. By cultivating gender diversity, teams can overcome such biases and reap the full rewards of team expertise.

Gender diversity may also spark new discoveries by broadening the viewpoints, questions, and areas addressed by researchers. Two new large-scale studies shed light on this point*,. Using topic modeling—a form of computational text analysis suitable for studying content variations in large samples of scholarly documents—a new study in management science (7) finds that scholarly contributions written by women-dominated author groups typically pose different questions and engage in different research topics than men-authored studies. Articles with women authors are, for example, more likely to adopt critical and employee-centered perspectives on management, whereas men-dominated studies tend to be more prescriptive and operational in their focus. Although pertaining to the realm of social science research, these findings raise intriguing questions of whether similar variations can be detected in science and engineering.

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doi: 10.1073/pnas.1700616114
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