How to deliver gender diversity education to men: Training algorithms to the rescue

Abstract

Gender diversity training is typically provided to mix-gender audiences. This one-size-fits-all approach may be suboptimal because information about gender bias and inequity is often received differently along gender lines: men are less likely than women to believe it. We argue for tailoring gender diversity training via implementing segmentation and tailoring algorithms in training systems. To develop our theorizing, we integrate a learner-centric approach to diversity training with principles of jiu jitsu persuasion theory. This leads us to test a new approach to diversity training that involves dynamic adaptation and tailoring the training to learners. Specifically, we first identify two distinct segments of men—believers and skeptics—and develop a user-friendly segmentation algorithm that segments men, in real time, using only five items (Study 1). We then use the algorithm to assign segments of men trainees to tailored or non-tailored training and show that presenting skeptic men with a tailored message improves training reactions and increases intentions to support gender diversity efforts (Study 2). Thus, we show that dynamic adaptation and tailoring successfully explain training outcomes, particularly for trainees who are skeptical of the diversity message. Practically, our study demonstrates the functionality and value of segmentation algorithms for organizations' training systems.