JOURNAL ARTICLE

Race, Sexual Orientation, and Intersectionality in Distributive Negotiation Outcomes for Men.

  • Published In: Organization Science (INFORMS), 2026, v. 37, n. 2. P. 750 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Chang, Edward H.; Kirgios, Erika L.; Zlatev, Julian J. 3 of 3

Abstract

This article investigates how race (Black, East Asian, White) and sexual orientation (gay, straight) influence distributive negotiation outcomes for men, using a large-scale (n = 3,000) preregistered audit experiment involving fictitious buyers negotiating car prices on Craigslist. Findings reveal that sellers were 7.7 percentage points (22.4%) less likely to respond to gay White men than straight White men, while Black and East Asian men—regardless of sexual orientation—received similarly polite but significantly less polite responses compared to straight White men. A follow-up experiment demonstrated that experiencing impoliteness reduces negotiators' positive expectations and intentions to negotiate in the future, suggesting that subtle demand-side biases may contribute to supply-side differences in negotiation behavior. The results support a lens-based account of intersectional stereotyping, indicating that race predominantly shapes negotiation outcomes in this context, and highlight the importance of addressing incivility to promote equitable negotiation experiences for marginalized groups.

Additional Information

  • Source:Organization Science (INFORMS). 2026/03, Vol. 37, Issue 2, p750
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2026
  • ISSN:1047-7039
  • DOI:10.1287/orsc.2023.18464
  • Accession Number:192562415
  • Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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