My amazing collaborators: Prof. Ivona Hideg (Oxford University); Prof. Yuval Engel (the University of Amsterdam); Prof. Frédéric Godart (INSEAD Paris).

Published in Entrepreneurship: Theory and Practice (a top-tier management journal part of the Financial Times Top 50 with a 7% acceptance rate), and featured in The Conversation Canada

Github repo here.

"If woman had no existence save in the fiction written by men, one would imagine her a person very various; heroic and mean; splendid and sordid; infinitely beautiful and hideous in the extreme."—Virginia Woolf 

Virginia Woolf’s incisive critique of how women are depicted in literature also applies to how women are depicted in real life as well—through a spectrum of sexism ranging from overt negativity to deceptive positivity. While much research has delved into the traditional overt forms of sexism, I am drawn to a quieter yet no less harmful territory: benevolent sexism. This form of sexism, masquerading as protectiveness and praise, portrays women as wonderful yet fragile creatures in need of safeguarding. I want to know how this socially accepted form of sexism influence evaluations of startups founded by women compared to those founded by men.

To explore this phenomenon, I led an international, interdisciplinary team of experts. We ran a series of three experiments in which participants were randomly assigned to evaluate startups that were identical in every aspect but one: the gender of the founder. Additionally, we assessed the participants' levels of both benevolent and traditional sexism. This approach allowed us to isolate the effect of gender and assess the influence of startup evaluators' sexist attitudes. Our findings uncovered profound, yet less visible and so often-overlooked, inequities introduced by benevolent sexism, prompting a fundamental rethinking of what fairness truly means within the startup ecosystem.

Phases of analysis

On our GitHub repository, you'll find all the R codes behind the analyses and the results from our paper. Each notebook starts with Study 1 and then automates the process through three studies. 

• Descriptive Statistics Analysis: Initial exploration of basic descriptive statistics.

• Correlation Analysis: Examination of linear and partial correlations among key variables.

• Regression Analyses: Comprehensive regression analyses, including hierarchical moderated regression testing two-way and three-way interactions, simple slope analysis, and moderated mediation analysis.

We’re committed to transparency in our research while honoring our participants' privacy. Thus, the raw data is not available for public access due to confidentiality agreements. However, specific requests for data can be addressed to me (the first author).