Assessing Gender Bias in the Information Systems Field: An Analysis of the Impact on Citations

TL;DR

This study investigates gender bias in the academic information systems field by analyzing citation disparities between male and female scholars in top IS journals using panel regression.

Contribution

It provides the first empirical analysis of gender bias in IS citations, filling a significant research gap in this academic domain.

Findings

Female academics receive fewer citations than male counterparts.

Gender bias in citations persists even after controlling for other factors.

The study highlights the need for awareness and policies to address gender disparities.

Abstract

Gender bias, a systemic and unfair difference in how men and women are treated in a given domain, is widely studied across different academic fields. Yet, there are barely any studies of the phenomenon in the field of academic information systems (IS), which is surprising especially in the light of the proliferation of such studies in the Science, Technology, Mathematics and Technology (STEM) disciplines. To assess potential gender bias in the IS field, this paper outlines a study to estimate the impact of scholarly citations that female IS academics accumulate vis-\`a-vis their male colleagues. Drawing on a scientometric study of the 7,260 papers published in the most prestigious IS journals (known as the AIS Basket of Eight), our analysis aims to unveil potential bias in the accumulation of citations between genders in the field. We use panel regression to estimate the gendered citations accumulation in the field. By doing so we propose to contribute knowledge on a core dimension of gender bias in academia, which is, so far, almost completely unexplored in the IS field.