JOURNAL ARTICLE
Two-Sided Impacts of Service Provider's Identity Disclosure in e-Customer Service Platforms: Evidence from Two Field Experiments.
Published In: Information Systems Research (INFORMS), 2025, v. 36, n. 3. P. 1631 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Chung, Sunghun; Jung, Jaehwuen; Park, Jooyoung; Lee, Chul Ho; Ceran, Yasin 3 of 3
Abstract
This article investigates the effects of disclosing service provider identities on service performance, customer satisfaction, and ethnic biases within an online customer complaint management (CCM) platform. Through two large-scale randomized field experiments involving over 75,000 customers and 1,200 providers across multiple companies, the research finds that revealing provider identities improves service performance by reducing anonymity and increasing accountability, particularly among less experienced providers handling discretionary complaints. Additionally, disclosing ethnic identity influences customer satisfaction: customers generally prefer providers from majority ethnic groups, while minority customers report lower satisfaction when served by providers of the same ethnicity, suggesting in-group derogation. Four supplementary studies further explore psychological mechanisms, highlighting the complex interplay of transparency, accountability, and social identity in shaping customer-provider interactions on two-sided digital platforms.
Additional Information
- Source:Information Systems Research (INFORMS). 2025/09, Vol. 36, Issue 3, p1631
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:1047-7047
- DOI:10.1287/isre.2023.0499
- Accession Number:188497600
- Copyright Statement:Copyright of Information Systems Research (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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