Online labor markets (OLM) are increasingly common sources for identifying trained individuals for technological work. Yet, like much of the tech industry, OLMs suffer from under-representation of women. We examine why women may choose not to participate in bidding for software development and analytics projects on OLMs. We theorize that pertinent project factors – project complexity and overall project competition – increase the risk profile of such work and disproportionately dissuade women from bidding for these projects, relative to men. We test these hypotheses using experiments conducted on Amazon Mechanical Turk (AMT). Comparing the effect of higher project complexity, greater boundary spanning requirements, and higher competition on the propensity to bid for riskier projects for women versus men, and on the bid amount issued when they do bid, we find that women are indeed deterred by project complexity in their bid decision (and to bid lower amounts), but are more likely to bid for projects with higher boundary spanning requirements or more competition. We contribute to the IS literature by establishing the specific factors affecting women’s participation and wages in OLMs and suggest several actionable managerial insights to make OLMs more inclusive and attractive to women in IT.
Wang, Yifei; Langer, Nishtha; and Gopal, Anandasivam, "Too Risky to Bid? Women in OLMs and STEM Competitive Environments" (2020). AISWN International Research Workshop on Women, IS and Grand Challenges 2020. 1.