Examining the minimal combined effects of gender and minoritized racial/ethnic identity among academic entrepreneurs

In: International journal of gender and entrepreneurship, Band 16, Heft 3, S. 367-401

Abstract

PurposeThis study examined differences related to gender and racial/ethnic identity among academic researchers participating in the National Science Foundation's "Innovation-Corps" (NSF I-Corps) entrepreneurship training program. Drawing from prior research in the fields of technology entrepreneurship and science, technology, engineering and mathematics (STEM) education, this study addresses the goal of broadening participation in academic entrepreneurship.Design/methodology/approachUsing ANOVA and MANOVA analyses, we tested for differences by gender and minoritized racial/ethnic identity for four variables considered pertinent to successful program outcomes: (1) prior entrepreneurial experience, (2) perceptions of instructional climate, (3) quality of project team interactions and (4) future entrepreneurial intention. The sample includes faculty (n = 434) and graduate students (n = 406) who completed pre- and post-course surveys related to a seven-week nationwide training program.FindingsThe findings show that group differences based on minoritized racial/ethnic identity compared with majority group identity were largely not evident. Previous research findings were replicated for only one variable, indicating that women report lower amounts of total prior entrepreneurial experience than men, but no gender differences were found for other study variables.Originality/valueOur analyses respond to repeated calls for research in the fields of entrepreneurship and STEM education to simultaneously examine intersecting minoritized and/or under-represented social identities to inform recruitment and retention efforts. The unique and large I-Corps national dataset offered the statistical power to quantitatively test for differences between identity groups. We discuss the implications of the inconsistencies in our analyses with prior findings, such as the need to consider selection bias.