Nina Gerber and Alina Stöver, Technical University of Darmstadt; Peter Mayer, University of Southern Denmark
Gender imbalances are prevalent in computer science and the security and privacy (S&P) field in particular, giving rise to gender stereotypes. The existence of such stereotypes might elicit the stereotype threat effect well-known from research in math settings: mere exposure to stereotypes can decrease the performance in and attitude towards specific fields. In this work, we investigate whether the stereotype threat effect influences women and men in the S&P field. We conducted an online experiment with multiple groups to explore whether videos that depict and counteract gender stereotypes influence S&P attitudes and intentions (RQ1), and (self-assessed) S&P knowledge (RQ2). We find overall little evidence for the stereotype threat effect, but our results show that women in the condition actively counteracting gender stereotypes report a higher interest in preventing hacker access to their devices than women in the stereotype conditions. In addition, we find that men score higher than women in a variety of self-report measures, except for security and privacy concerns. These results indicate that stereotypes might need to be addressed early on to prevent stereotypes from becoming social norms and a self-fulfilling prophecy of gender imbalance in the S&P field.
Open Access Media
USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.
![](https://www.usenix.org/sites/all/modules/usenix/usenix_files/images/usenix-locked.png)
author = {Nina Gerber and Alina St{\"o}ver and Peter Mayer},
title = {Of Mothers and Managers {\textendash} The Effect of Videos Depicting Gender Stereotypes on Women and Men in the Security and Privacy Field},
booktitle = {Twentieth Symposium on Usable Privacy and Security (SOUPS 2024)},
year = {2024},
isbn = {978-1-939133-42-7},
address = {Philadelphia, PA},
pages = {547--566},
url = {https://www.usenix.org/conference/soups2024/presentation/gerber},
publisher = {USENIX Association},
month = aug
}