JOURNAL ARTICLE

Can corporate social responsibility reduce customer mistreatment? A contingent dual‐process model.

  • Published In: Business Ethics, the Environment & Responsibility, 2025, v. 34, n. 4. P. 1225 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Zhan, Xiaojun; Lu, Na; Lin, Weipeng; Luo, Wenhao; Zhang, Xixia 3 of 3

Abstract

Although corporate social responsibility (CSR) has been widely studied, little is known about whether it has implications for customer mistreatment. In this study, we aim to understand how and when CSR is related to customer incivility, a typical type of mistreatment in service contexts. Integrating the perspectives of social exchange theory and social identity theory, we theorize that CSR influences customer incivility via customer trust and customer identification, which are contingent on front‐line employees' emotional labor (i.e., surface acting and deep acting) during service interactions. In our two‐source field study involving 332 employee–customer dyads, CSR promoted both customer trust and customer identification, which in turn reduced customer incivility. Moreover, the indirect relationship between CSR and customer incivility via customer trust (but not customer identification) was weakened by employee surface acting but strengthened by employee deep acting. Taken together, this study uncovers the relationship between CSR and customer mistreatment by exploring how and when CSR influences customer incivility, which advances the understanding of the customer‐related implications of CSR. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Business Ethics, the Environment & Responsibility. 2025/10, Vol. 34, Issue 4, p1225
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2025
  • ISSN:2694-6416
  • DOI:10.1111/beer.12709
  • Accession Number:187392531
  • Copyright Statement:Copyright of Business Ethics, the Environment & Responsibility is the property of Wiley-Blackwell 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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