JOURNAL ARTICLE

The Impact of Customer Information on Service Supply and Demand: Evidence from a Large Live-Streaming Experiment.

  • Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2026, v. 28, n. 1. P. 212 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Zeng, Zhiyu; Clyde, Nicholas; Dai, Hengchen; Zhang, Dennis J.; Xu, Zhiwei; Shen, Zuo-Jun Max 3 of 3

Abstract

This article investigates how providing customer-related information at the start of a service encounter affects both service supply and demand on digital entertainment platforms. Through a randomized field experiment on a large Chinese live-streaming platform ("Platform L"), the study found that displaying simple viewer information (username and last visited page) upon entry increased broadcasters' service supply by 12.62% and viewers' watch time by 4.51%, leading to a 10.44% increase in broadcasters' earnings. Surveys and three preregistered online experiments further revealed that this intervention enhances broadcasters' motivation by increasing customer vividness and feelings of appreciation, while viewers respond positively to personalized service and increased social interaction. The findings suggest that low-cost, information-based interventions can effectively boost engagement and revenue on digital service platforms, with potential applicability beyond entertainment to sectors like online education and telemedicine, while highlighting the need to balance personalization with privacy concerns.

Additional Information

  • Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2026/01, Vol. 28, Issue 1, p212
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2026
  • ISSN:1523-4614
  • DOI:10.1287/msom.2022.0224
  • Accession Number:190748627
  • Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (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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