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Privacy Management in Service Systems

Author

Listed:
  • Ming Hu

    (Rotman School of Management, University of Toronto, Ontario M5S 3E6, Canada)

  • Ruslan Momot

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

  • Jianfu Wang

    (College of Business, City University of Hong Kong, Hong Kong)

Abstract

Problem definition : We study customer-centric privacy management in service systems. Academic/practical relevance : We explore the consequences of extended control over personal information by customers in such systems. Methodology : We adopt a stylized queueing model to capture a service environment that features a service provider and customers who are strategic in deciding whether to disclose personal information to the service provider—that is, customers’ privacy or information disclosure strategy . A customer’s service request can be one of two types, which affects service time but is unknown when customers commit to a privacy strategy. The service provider can discriminate among customers based on their disclosed information by offering different priorities. Results : Our analysis reveals that, when given control over their personal data, strategic customers do not always choose to withhold them. We find that control over information gives customers a tool they can use to hedge against the service provider’s will, which might not be aligned with the interests of customers. More importantly, we find that under certain conditions, giving customers full control over information (e.g., by introducing a privacy regulation) may not only distort already efficiently operating service system but might also backfire by leading to inferior system performance (i.e., longer average wait time), and it can hurt customers themselves. We demonstrate how a regulator can correct information disclosure inefficiencies through monetary incentives to customers and show that providing such incentives makes economic sense in some scenarios. Finally, the service provider itself can benefit from customers being in control of their personal information by enticing more customers joining the service. Managerial implications : Our findings yield insights into how customers’ individually rational actions concerning information disclosure (e.g., granted by a privacy regulation) can lead to market inefficiencies in the form of longer wait times for services. We provide actionable prescriptions, for both service providers and regulators, that can guide their choices of a privacy and information management approach based on giving customers the option of controlling their personal information.

Suggested Citation

  • Ming Hu & Ruslan Momot & Jianfu Wang, 2022. "Privacy Management in Service Systems," Manufacturing & Service Operations Management, INFORMS, vol. 24(5), pages 2761-2779, September.
  • Handle: RePEc:inm:ormsom:v:24:y:2022:i:5:p:2761-2779
    DOI: 10.1287/msom.2022.1130
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    References listed on IDEAS

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    Cited by:

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    2. Xingyu Fu & Ningyuan Chen & Pin Gao & Yang Li, 2026. "Privacy-Preserving Personalized Recommender Systems," Manufacturing & Service Operations Management, INFORMS, vol. 28(1), pages 271-289, January.
    3. Xi Chen & Sentao Miao & Yining Wang, 2023. "Differential Privacy in Personalized Pricing with Nonparametric Demand Models," Operations Research, INFORMS, vol. 71(2), pages 581-602, March.
    4. Yunfei (Jesse) Yao, 2025. "Reputation for Privacy," Marketing Science, INFORMS, vol. 44(5), pages 1145-1162, September.
    5. Fasheng Xu & Xiaoyu Wang & Fuqiang Zhang, 2025. "Consumer Privacy in Online Retail Supply Chains," Management Science, INFORMS, vol. 71(10), pages 8371-8389, October.
    6. Zhi Chen & Jussi Keppo, 2025. "R&D Data Sharing in New Product Development," Manufacturing & Service Operations Management, INFORMS, vol. 27(4), pages 1275-1294, July.
    7. Tommaso Bondi & Omid Rafieian & Yunfei (Jesse) Yao, 2026. "Privacy and Polarization: An Inference-Based Framework," Management Science, INFORMS, vol. 72(2), pages 1389-1409, February.
    8. Shalpegin, Timofey & Browning, Tyson R. & Kumar, Ajay & Shang, Guangzhi & Thatcher, Jason & Fransoo, Jan C. & Holweg, Matthias & Lawson, Benn, 2025. "Generative AI and empirical research methods in operations management," Other publications TiSEM 0eb52ee8-35d0-4c97-8732-8, Tilburg University, School of Economics and Management.
    9. Kostas Bimpikis & Ilan Morgenstern & Daniela Saban, 2024. "Data Tracking Under Competition," Operations Research, INFORMS, vol. 72(2), pages 514-532, March.
    10. Tuoyi Zhao & Wen-Xin Zhou & Lan Wang, 2025. "Private Optimal Inventory Policy Learning for Feature-Based Newsvendor with Unknown Demand," Management Science, INFORMS, vol. 71(7), pages 6092-6111, July.
    11. Xi, Xuan & Zhang, Yulin & Goh, Mark, 2026. "User information control and its strategic implications for information strategies in digital businesses," European Journal of Operational Research, Elsevier, vol. 330(2), pages 608-626.

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