JOURNAL ARTICLE

Shaping Police Officer Mindsets and Behaviors: Experimental Evidence of Procedural Justice Training.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 11. P. 8995 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Canales, Rodrigo; Santini, Juan Francisco; González Magaña, Marina; Cherem, Alexis 3 of 3

Abstract

This article examines whether police officers can be effectively trained to incorporate procedural justice principles—voice, neutrality, respect, and trustworthiness—in their interactions with citizens. Through a randomized controlled trial involving 1,854 officers from the Mexico City Ministry of Citizen Security, the study finds that procedural justice training significantly improved officers' mindsets and translated into more procedurally just behaviors in the field, as measured by external observers using a mystery-shopper methodology. The training's impact was generally consistent across officers but was stronger among those with higher prosocial attitudes and more positive views of citizens, while officers patrolling higher-crime areas showed smaller behavioral changes. Additionally, training managers enhanced the effects on subordinate officers, highlighting the importance of managerial alignment. The findings suggest that organizational justice can be taught and sustained through well-designed training programs integrated with supportive managerial practices, although contextual factors such as occupational risk may influence behavioral outcomes.

Additional Information

  • Source:Management Science (INFORMS). 2025/11, Vol. 71, Issue 11, p8995
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2022.03243
  • Accession Number:189064392
  • Copyright Statement:Copyright of Management 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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