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Standing Out, Filtering In: Selective Hiring Practices In Atypical Organizations
About the Research
How do atypical organizations, those that deviate from industry norms, sustain their distinctiveness while preserving internal coherence? We argue that hiring represents a key site where atypical organizations grapple with this dilemma. We theorize that atypical organizations’ deviance creates chronic identity threats and heightens concerns about identity boundaries, making their members extra vigilant toward candidates who may blur the organization’s identity. We propose that atypical organizations will be especially likely to reject such identity-threatening candidates, such as those diagnosed with an autism spectrum disorder (ASD), as compared to typical organizations. We test this prediction using a large-scale résumé audit experiment combined with computational embedding-based measures of organizational atypicality derived from organizations’ website texts. Results show that while typical organizations do not discriminate between ASD and non-ASD candidates, atypical organizations are nearly 74% less likely to call back ASD candidates than typical ones. This pattern reveals how especially organizations that challenge institutional conventions tend to act as conformist gatekeepers through their hiring practices. Our study contributes to research on organizational atypicality by shifting the lens from external audience evaluations to internal identity work and by showing how identity-protective processes emerging from atypicality can have unintended but potentially exclusionary labor market consequences.
About Richard Haans
Richard F.J. Haans is an Associate Professor of Strategic Management and Entrepreneurship and the Director of Full-Time Doctoral Education at the Rotterdam School of Management, Erasmus University Rotterdam. His main line of research focuses on the question of how different organizations (should) strive to be different from competitors to attain optimal performance. He is also interested in methodological advances, such as those pertaining to curvilinear relationships, longitudinal web scraping, and text analysis using machine learning.
Date: Thursday 15 January, 2026
Time: 12:45-14:15 CEST
Venue: B2-123, (Zoom link provided to registered attendees)
Reminder: We no longer provide food as per school policy…
Should you want to attend, please register at https://forms.gle/rYxqPmw7cZsCrsHU8
