IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v99y2025ics1059056025002060.html

Analyzing the game pricing mechanism of data assets: Theoretical evidence considering market structure and competitive characteristics

Author

Listed:
  • Li, Wenchang
  • Yang, Weiqing
  • Zhou, Zhongsheng
  • Wu, Shuijuan
  • Mo, Changwei

Abstract

The trade of data assets is increasingly serving as a central impetus for economic progress. By implementing the strategy of “data assetisation”, companies not only increase their financial strength, but also pose new challenges for the valuation and pricing of data assets. In this paper, we use game theory to explore the impact of different market structures and the competitive characteristics of market participants on the pricing of data assets, with the aim of determining an appropriate price that can maximize the profits of both the supply and demand sides of data assets. It is found that the competitive characteristics of the demand side of data assets, such as the ability to exploit data and the ability to control costs, indirectly influence the pricing of data assets by regulating the level of data services. In addition, the study shows that the introduction of competition into the market structure helps to improve the pricing of data assets and the level of data services, thereby stimulating market dynamics and creating larger profit margins for data asset suppliers. Finally, the supply side of data assets needs to take into account the data needs of different demand sides when pricing, and ignoring vulnerable customers can harm the profits of both the supply and demand sides. Numerical analyses further support these conclusions. This study provides strong theoretical support and practical guidance for value measurement and transaction pricing of data assets.

Suggested Citation

  • Li, Wenchang & Yang, Weiqing & Zhou, Zhongsheng & Wu, Shuijuan & Mo, Changwei, 2025. "Analyzing the game pricing mechanism of data assets: Theoretical evidence considering market structure and competitive characteristics," International Review of Economics & Finance, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:reveco:v:99:y:2025:i:c:s1059056025002060
    DOI: 10.1016/j.iref.2025.104043
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056025002060
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2025.104043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Post-Print hal-03032504, HAL.
    2. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    3. Sun, Xiaowen & Du, Zhenhua, 2024. "Enhancing capital market efficiency: The role of data assets disclosure in reducing stock price synchronicity," International Review of Economics & Finance, Elsevier, vol. 94(C).
    4. Laura Veldkamp, 2023. "Valuing Data as an Asset," Review of Finance, European Finance Association, vol. 27(5), pages 1545-1562.
    5. Nakamura, Yasuhiko, 2019. "Combining the endogenous choice of the timing of setting incentive parameters and the contents of strategic contracts in a managerial mixed duopoly," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 207-233.
    6. Wang, Sujuan & Hu, Qiying & Liu, Weiqi, 2017. "Price and quality-based competition and channel structure with consumer loyalty," European Journal of Operational Research, Elsevier, vol. 262(2), pages 563-574.
    7. Brito, Duarte, 2003. "Preemptive mergers under spatial competition," International Journal of Industrial Organization, Elsevier, vol. 21(10), pages 1601-1622, December.
    8. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Grenoble Ecole de Management (Post-Print) hal-03032504, HAL.
    9. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Post-Print hal-03511355, HAL.
    10. Cheng, Yiran & Zhou, Xiaorui & Li, Yongjian, 2023. "The effect of digital transformation on real economy enterprises’ total factor productivity," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 488-501.
    11. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Post-Print hal-03511381, HAL.
    12. Tseng, Hsiao-Ting, 2023. "Customer-centered data power: Sensing and responding capability in big data analytics," Journal of Business Research, Elsevier, vol. 158(C).
    13. Tao Xu & Hanning Shi & Yongjiang Shi & Jianxin You, 2023. "From data to data asset: conceptual evolution and strategic imperatives in the digital economy era," Asia Pacific Journal of Innovation and Entrepreneurship, Emerald Group Publishing Limited, vol. 18(1), pages 2-20, December.
    14. Raguseo, Elisabetta & Vitari, Claudio & Pigni, Federico, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," International Journal of Production Economics, Elsevier, vol. 229(C).
    15. Ouyang, Yingying & Hu, Mingzheng, 2024. "The impact of data elements marketization on corporate financing constraints: Quasi-experimental evidence from the establishment of data trading platforms in China," Finance Research Letters, Elsevier, vol. 69(PA).
    16. Pedota, Mattia, 2023. "Big data and dynamic capabilities in the digital revolution: The hidden role of source variety," Research Policy, Elsevier, vol. 52(7).
    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. Shi, Huiyi & Xia, Yufei & Li, Yawen & Hua, Yifan & Fu, Yating, 2025. "Data asset information disclosure divergence and green innovation: causal evidence from double-debiased machine learning," Finance Research Letters, Elsevier, vol. 84(C).

    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. Lin, Boqiang & Zhu, Yitong, 2025. "Supply chain configuration and total factor productivity of renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 209(C).
    2. Fang Tang & Longpeng Zhang, 2025. "Impact of data element development on the application of artificial intelligence in enterprises," Empirical Economics, Springer, vol. 69(6), pages 3589-3634, December.
    3. Yi Jin & Bo Liu, 2025. "Industry Concentration and Digital Process Innovation: Evidence from Chinese Rail Transit Firms," Sustainability, MDPI, vol. 17(9), pages 1-15, May.
    4. Surajit Bag & Tsan-Ming Choi & Muhammad Sabbir Rahman & Gautam Srivastava & Rajesh Kumar Singh, 2025. "Examining collaborative buyer–supplier relationships and social sustainability in the “new normal” era: the moderating effects of justice and big data analytical intelligence," Annals of Operations Research, Springer, vol. 348(3), pages 1235-1280, May.
    5. Hossein Tarighi & Zeynab Nourbakhsh Hosseiny & Mohammad Reza Abbaszadeh & Grzegorz Zimon & Darya Haghighat, 2022. "How Do Financial Distress Risk and Related Party Transactions Affect Financial Reporting Quality? Empirical Evidence from Iran," Risks, MDPI, vol. 10(3), pages 1-23, February.
    6. Egor Ivanov & Mariia Molodchik, 2026. "Industry-specific effects of IT systems: uncovering the winners and losers," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 16(1), pages 95-122, March.
    7. Abdul-Hamid, Asma-Qamaliah & Ali, Mohd Helmi & Osman, Lokhman Hakim & Tseng, Ming-Lang & Lim, Ming K., 2022. "Industry 4.0 quasi-effect between circular economy and sustainability: Palm oil industry," International Journal of Production Economics, Elsevier, vol. 253(C).
    8. Saeed, Abubakr & Riaz, Hammad & Baloch, Muhammad Saad, 2022. "Does big data utilization improve firm legitimacy?," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    9. Ponta, Linda & Puliga, Gloria & Manzini, Raffaella & Cincotti, Silvano, 2022. "Sustainability-oriented innovation and co-patenting role in agri-food sector: Empirical analysis with patents," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    10. Oleksandr Melnychenko, 2020. "Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?," JRFM, MDPI, vol. 13(9), pages 1-19, August.
    11. Bodendorf, Frank & Xie, Qiao & Merkl, Philipp & Franke, Jörg, 2022. "A multi-perspective approach to support collaborative cost management in supplier-buyer dyads," International Journal of Production Economics, Elsevier, vol. 245(C).
    12. Mila Austria Reyes & Monika Kussetya Ciptani & Teuku Ismail Fahmi, 2025. "Intellectual Capital Effects on Firm’s Profitability with Industry Types as Moderating Variable," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 3390-3405, January.
    13. Mark Rodgers & Sayan Mukherjee & Benjamin Melamed & Alok Baveja & Ajai Kapoor, 2024. "Solving business problems: the business-driven data-supported process," Annals of Operations Research, Springer, vol. 332(1), pages 705-741, January.
    14. Feng, Yongjie & Zhang, Zenglian & Zhang, Jingxian & Zhou, Wenjie, 2025. "More authentic data asset disclosure by myopic managers: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 105(C).
    15. Huang, Zhehao & Dong, Hao & Liu, Zhaofei & Albitar, Khaldoon, 2025. "Unleashing the empowered effect of data resource on inclusive green growth: Based on double machine learning," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 1270-1290.
    16. Ji, Ruibing & Zhang, Shengling & Cao, Wenxuan & Hao, Yu & Wang, Nuo, 2025. "Data assets, supply chain spillovers, and corporate ESG development — Evidence from Chinese listed companies," Structural Change and Economic Dynamics, Elsevier, vol. 75(C), pages 880-894.
    17. Xu, Ning & Xu, Longchao & Yan, Xiang-Wu, 2025. "Data factor marketization empowering enterprise innovation quality: New evidence from Chinese patent citations," International Review of Economics & Finance, Elsevier, vol. 103(C).
    18. Wu, Haoyue & Yin, Yingkai, 2025. "Data trading platforms and total factor productivity: Insights from biased technical progress," Economic Analysis and Policy, Elsevier, vol. 88(C), pages 377-398.
    19. Florentina PANĂ-MICU, 2025. "The economic value of data. From data to decisions in the public sector," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(644), A), pages 337-347, Autumn.
    20. Jing Ma & Huaichao Chen & Kai Wang & Yifan Ma, 2025. "Research on the Impact of Digital Transformation on Carbon Performance of Chinese Manufacturing Enterprises," SAGE Open, , vol. 15(2), pages 21582440251, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:reveco:v:99:y:2025:i:c:s1059056025002060. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    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.