IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i22p2904-d679360.html
   My bibliography  Save this article

Platform Revenue Strategy Selection Considering Consumer Group Data Privacy Regulation

Author

Listed:
  • Xudong Lin

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China)

  • Shuilin Liu

    (College of Management, Shenzhen University, Shenzhen 518060, China)

  • Xiaoli Huang

    (College of Management, Shenzhen University, Shenzhen 518060, China)

  • Hanyang Luo

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China)

  • Sumin Yu

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China)

Abstract

In the era of big data, consumer group privacy has become an important source of revenue for the digital platform. Considering the situation that the platform collects consumer group data privacy to generate business revenue, we explore how the service matching level and commission rate affect the platform revenue, social welfare, and seller benefits. Based on the theory of group privacy, the three-party equilibrium evolution is solved by constructing a sequential game model including platform, seller, and consumer alliance. It is found that when the service matching level of the platform is greater than the threshold value, there are two main situations: on the one hand, if using the data privacy of a consumer group is subject to market regulation, the platform will set a high commission rate and service matching level in order to maximize profit. However, social welfare and seller’s business benefit both reach a minimum in this case, and the three-party game cannot attain equilibrium. On the other hand, when the market governor relaxes the platform’s regulation on the use of consumer group privacy data and data revenue efficiency is high enough, the platform can maximize the revenue by increasing the service matching level and reducing the commission rate. The optimal commission rate depends on the data revenue efficiency of the platform. Moreover, when the platform sets the highest commission rate and the service matching level is at a medium level, a stable partial equilibrium among the three-party will be achieved. These conclusions can give some insights into platform’s business model choice decision.

Suggested Citation

  • Xudong Lin & Shuilin Liu & Xiaoli Huang & Hanyang Luo & Sumin Yu, 2021. "Platform Revenue Strategy Selection Considering Consumer Group Data Privacy Regulation," Mathematics, MDPI, vol. 9(22), pages 1-24, November.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2904-:d:679360
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/22/2904/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/22/2904/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ramnath K. Chellappa & Shivendu Shivendu, 2010. "Mechanism Design for "Free" but "No Free Disposal" Services: The Economics of Personalization Under Privacy Concerns," Management Science, INFORMS, vol. 56(10), pages 1766-1780, October.
    2. Joseph R. Buckman & Jesse C. Bockstedt & Matthew J. Hashim, 2019. "Relative Privacy Valuations Under Varying Disclosure Characteristics," Information Systems Research, INFORMS, vol. 30(2), pages 375-388, June.
    3. Lin Tian & Asoo J. Vakharia & Yinliang (Ricky) Tan & Yifan Xu, 2018. "Marketplace, Reseller, or Hybrid: Strategic Analysis of an Emerging E‐Commerce Model," Production and Operations Management, Production and Operations Management Society, vol. 27(8), pages 1595-1610, August.
    4. Avi Goldfarb & Catherine E. Tucker, 2011. "Privacy Regulation and Online Advertising," Management Science, INFORMS, vol. 57(1), pages 57-71, January.
    5. Francis Bloch & Gabrielle Demange, 2018. "Taxation and privacy protection on Internet platforms," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 20(1), pages 52-66, February.
    6. Michael Kummer & Patrick Schulte, 2019. "When Private Information Settles the Bill: Money and Privacy in Google’s Market for Smartphone Applications," Management Science, INFORMS, vol. 65(8), pages 3470-3494, August.
    7. Florian Morath & Johannes Münster, 2018. "Online Shopping and Platform Design with Ex Ante Registration Requirements," Management Science, INFORMS, vol. 64(1), pages 360-380, January.
    8. Chen, Yongmin & Hua, Xinyu & Maskus, Keith E., 2021. "International protection of consumer data," Journal of International Economics, Elsevier, vol. 132(C).
    9. Loertscher, Simon & Marx, Leslie M., 2020. "Digital monopolies: Privacy protection or price regulation?," International Journal of Industrial Organization, Elsevier, vol. 71(C).
    10. Shota Ichihashi, 2020. "Online Privacy and Information Disclosure by Consumers," American Economic Review, American Economic Association, vol. 110(2), pages 569-595, February.
    11. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    12. France Bélanger & Tabitha L. James, 2020. "A Theory of Multilevel Information Privacy Management for the Digital Era," Information Systems Research, INFORMS, vol. 31(2), pages 510-536, June.
    13. Hagiu, Andrei & Hałaburda, Hanna, 2014. "Information and two-sided platform profits," International Journal of Industrial Organization, Elsevier, vol. 34(C), pages 25-35.
    14. Jongwoo Kim & Richard L. Baskerville & Yi Ding, 2020. "Breaking the Privacy Kill Chain: Protecting Individual and Group Privacy Online," Information Systems Frontiers, Springer, vol. 22(1), pages 171-185, February.
    15. Barnett, Jonathan M., 2018. "The costs of free: commoditization, bundling and concentration," Journal of Institutional Economics, Cambridge University Press, vol. 14(6), pages 1097-1120, December.
    16. Hongjun Lv & Yinghong Wan, 2019. "Contracting for online personalisation services: An economic analysis," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(7), pages 1149-1163, July.
    17. H. Li & A. Nill, 2020. "Online Behavioral Targeting: Are Knowledgeable Consumers Willing to Sell Their Privacy?," Journal of Consumer Policy, Springer, vol. 43(4), pages 723-745, December.
    18. Belhadj, Nada & Laussel, Didier & Resende, Joana, 2020. "Marketplace or reselling? A signalling model," Information Economics and Policy, Elsevier, vol. 50(C).
    19. Esther Gal‐Or & Ronen Gal‐Or & Nabita Penmetsa, 2019. "Can platform competition support market segmentation? Network externalities versus matching efficiency in equity crowdfunding markets," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 28(3), pages 420-435, June.
    20. Jentzsch, Nicola, 2016. "State-of-the-Art of the Economics of Cyber-Security and Privacy," EconStor Research Reports 126223, ZBW - Leibniz Information Centre for Economics.
    21. Tamara Dinev & Paul Hart, 2006. "An Extended Privacy Calculus Model for E-Commerce Transactions," Information Systems Research, INFORMS, vol. 17(1), pages 61-80, March.
    22. Ramon Casadesus-Masanell & Andres Hervas-Drane, 2015. "Competing with Privacy," Management Science, INFORMS, vol. 61(1), pages 229-246, January.
    23. Alexandre de Cornière & Romain de Nijs, 2016. "Online advertising and privacy," RAND Journal of Economics, RAND Corporation, vol. 47(1), pages 48-72, February.
    24. Hana Choi & Carl F. Mela, 2019. "Monetizing Online Marketplaces," Marketing Science, INFORMS, vol. 38(6), pages 948-972, November.
    25. Janice Y. Tsai & Serge Egelman & Lorrie Cranor & Alessandro Acquisti, 2011. "The Effect of Online Privacy Information on Purchasing Behavior: An Experimental Study," Information Systems Research, INFORMS, vol. 22(2), pages 254-268, June.
    26. Alexandre de Corniere & Romain De Nijs, 2013. "Online Advertising and Privacy," Economics Series Working Papers 650, University of Oxford, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shuilin Liu & Xudong Lin & Xiaoli Huang & Hanyang Luo & Sumin Yu, 2023. "Research on Service-Driven Benign Market with Platform Subsidy Strategy," Mathematics, MDPI, vol. 11(2), pages 1-21, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    2. Helia Marreiros & Mirco Tonin & Michael Vlassopoulos & M.C. Schraefel, 2016. "“Now that you mention it”: A Survey Experiment on Information, Salience and Online Privacy," BEMPS - Bozen Economics & Management Paper Series BEMPS34, Faculty of Economics and Management at the Free University of Bozen.
    3. Bleier, Alexander & Goldfarb, Avi & Tucker, Catherine, 2020. "Consumer privacy and the future of data-based innovation and marketing," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 466-480.
    4. Esther Gal-Or & Ronen Gal-Or & Nabita Penmetsa, 2018. "The Role of User Privacy Concerns in Shaping Competition Among Platforms," Information Systems Research, INFORMS, vol. 29(3), pages 698-722, September.
    5. Caleb S. Fuller, 2019. "Is the market for digital privacy a failure?," Public Choice, Springer, vol. 180(3), pages 353-381, September.
    6. Chen, Yongmin & Hua, Xinyu & Maskus, Keith E., 2021. "International protection of consumer data," Journal of International Economics, Elsevier, vol. 132(C).
    7. Abrardi, Laura & Cambini, Carlo, 2022. "Carpe Data: Protecting online privacy with naive users," Information Economics and Policy, Elsevier, vol. 60(C).
    8. Florian Morath & Johannes Münster, 2018. "Online Shopping and Platform Design with Ex Ante Registration Requirements," Management Science, INFORMS, vol. 64(1), pages 360-380, January.
    9. Caleb S. Fuller, 2018. "Privacy law as price control," European Journal of Law and Economics, Springer, vol. 45(2), pages 225-250, April.
    10. Anna D’Annunzio & Antonio Russo, 2020. "Ad Networks and Consumer Tracking," Management Science, INFORMS, vol. 66(11), pages 5040-5058, November.
    11. Shuilin Liu & Xudong Lin & Xiaoli Huang & Hanyang Luo & Sumin Yu, 2023. "Research on Service-Driven Benign Market with Platform Subsidy Strategy," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    12. Michael Kummer & Patrick Schulte, 2019. "When Private Information Settles the Bill: Money and Privacy in Google’s Market for Smartphone Applications," Management Science, INFORMS, vol. 65(8), pages 3470-3494, August.
    13. Jean-Marc Zogheib & Marc Bourreau, 2021. "Privacy, Competition, and Multi-Homing," EconomiX Working Papers 2021-15, University of Paris Nanterre, EconomiX.
    14. Morlok, Tina & Matt, Christian & Hess, Thomas, 2017. "Privatheitsforschung in den Wirtschaftswissenschaften: Entwicklung, Stand und Perspektiven," Working Papers 1/2017, University of Munich, Munich School of Management, Institute for Information Systems and New Media.
    15. Marreiros, Helia & Tonin, Mirco & Vlassopoulos, Michael & Schraefel, M.C., 2017. "“Now that you mention it”: A survey experiment on information, inattention and online privacy," Journal of Economic Behavior & Organization, Elsevier, vol. 140(C), pages 1-17.
    16. Sajeesh, S. & Singh, Ashutosh & Bhardwaj, Pradeep, 2022. "Optimal checkout strategies for online retailers," Journal of Retailing, Elsevier, vol. 98(3), pages 378-394.
    17. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
    18. Lefouili, Yassine & Toh, Ying Lei & Madio, Leonardo, 2017. "Privacy Regulation and Quality-Enhancing Innovation," TSE Working Papers 17-795, Toulouse School of Economics (TSE), revised Jul 2023.
    19. Mert Demirer & Diego Jimenez-Hernandez & Dean Li & Sida Peng, 2024. "Data, Privacy Laws and Firm Production: Evidence from the GDPR," Working Paper Series WP 2024-02, Federal Reserve Bank of Chicago.
    20. Didier Laussel & Ngo Van Long & Joana Resende, 2023. "Profit Effects of Consumers’ Identity Management: A Dynamic Model," Management Science, INFORMS, vol. 69(6), pages 3602-3615, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2904-:d:679360. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.