DAO governance model for open access sports science databases: a study on decentralized autonomous organizations

Authors

DOI:

https://doi.org/10.47197/retos.v80.119245

Keywords:

DAO Governance Model, Tokenomic Dual-Anchoring, Cross-Chain Interoperability, Biometric Streaming Data, Dynamic NFT Rights Allocation

Abstract

Introduction, Sports science data governance is characterized by persistent tensions between data sharing, stakeholder incentives, and regulatory constraints. These challenges are amplified by fragmented data infrastructures and competing interests among stakeholders, limiting the effective use of data in performance optimization and research.

Objective, This study aims to develop and theoretically ground a decentralized autonomous organization (DAO)-based governance framework for sports science data ecosystems, focusing on how decentralized mechanisms can enhance coordination, participation, and compliance.

Methodology, A multi-method research design is employed, integrating conceptual case analysis, agent-based modeling (ABM), and survey-based empirical analysis. Structural equation modeling (SEM) is used to examine the relationships between governance perceptions, incentives, and data-sharing intentions.

Results, The findings indicate that DAO-based mechanisms can support more distributed and transparent data-sharing processes. Simulation results suggest that participation dynamics follow non-linear patterns, with incentive and reputation mechanisms contributing to system stabilization. Empirical results identify technical usability, perceived regulatory compliance, and incentive structures as significant predictors of stakeholder participation.

Discussion, The study contributes to platform governance and institutional theory by conceptualizing a hybrid decentralized governance model for data-intensive environments. The findings highlight the importance of aligning technological design with usability and regulatory requirements. However, limitations related to model assumptions, perception-based data, and interoperability challenges remain. Future research should focus on real-world implementation and the development of standardized governance frameworks.

References

Abraham, R., Schneider, J., & Vom Brocke, J. (2019). Data governance: A conceptual framework, struc-tured review, and research agenda. International journal of information management, 49, 424-438. https://doi.org/10.1016/j.ijinfomgt.2019.07.008

Bandyopadhyay, K., & Bandyopadhyay, S. (2010). User acceptance of information technology across cultures. International Journal of Intercultural Information Management, 2(3), 218-231. https://doi.org/10.1504/IJIIM.2010.037862

Beck, R., Müller-Bloch, C., & King, J. L. (2018). Governance in the blockchain economy: A framework and research agenda. Journal of the association for information systems, 19(10), 1. https://aisel.aisnet.org/jais/vol19/iss10/1

Bena, J., & Zhang, S. (2023). Token-based decentralized governance, data economy and platform busi-ness model. Available at SSRN 4248492. http://dx.doi.org/10.2139/ssrn.4248492

Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences, 99(suppl_3), 7280-7287. https://doi.org/10.1073/pnas.082080899

Constantinides, P., Henfridsson, O., & Parker, G. G. (2018). Introduction—platforms and infrastructures in the digital age. Information systems research, 29(2), 381-400. https://doi.org/10.1287/isre.2018.0794

da Silva, L. (2024). Wearable technology in sports monitoring performance and health metrics. Revista De Psicología Del Deporte (Journal of Sport Psychology), 33(2), 250-258. https://rpd-online.com/manuscript/index.php/rpd/article/view/1718

De Filippi, P., Mannan, M., & Reijers, W. (2024). Blockchain technology and the rule of code: Regulation via governance. Geo. Wash. L. Rev., 92, 1229.

Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149.

Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., ... & Li, Y. (2024). Large language models empowered agent-based modeling and simulation: A survey and perspectives. Humanities and Social Scien-ces Communications, 11(1), 1-24. https://doi.org/10.1057/s41599-024-03611-3

Hassan, S., & De Filippi, P. (2021). Decentralized autonomous organization. Internet Policy Review, 10(2), 1–10. https://doi.org/10.14763/2021.2.1556

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8

Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). https://doi.org/10.1080/1743727X.2015.1005806

Momani, A. M. (2020). The unified theory of acceptance and use of technology: A new approach in te-chnology acceptance. International Journal of Sociotechnology and Knowledge Development (IJSKD), 12(3), 79-98. https://doi.org/10.4018/IJSKD.2020070105

Nokkala, T., Salmela, H., & Toivonen, J. (2019). Data governance in digital platforms. https://aisel.aisnet.org/amcis2019/ebusiness/ebusiness/12

Obi, O. C., Dawodu, S. O., Onwusinkwue, S., Osasona, F., Atadoga, A., & Daraojimba, A. I. (2024). Data science in sports analytics: A review of performance optimization and fan engagement. World Journal of Advanced Research and Reviews, 21(1), 2663-2670. https://doi.org/10.30574/wjarr.2024.21.1.0370

Ramachandran, R., Bugbee, K., & Murphy, K. (2021). From open data to open science. Earth and Space Science, 8(5), e2020EA001562. https://doi.org/10.1029/2020EA001562

Schweik, C. M., & English, R. C. (2012). Internet success: a study of open-source software commons. MIT Press.

Seçkin, A. Ç., Ateş, B., & Seçkin, M. (2023). Review on wearable technology in sports: concepts, challen-ges and opportunities. Applied sciences, 13(18), 10399. https://doi.org/10.3390/app131810399

Tiwana, A. (2021). Platform ecosystems: Aligning architecture, governance, and strategy. MIS Quar-terly Executive, 20(4), 1–18.

Vanhaverbeke, W. (2025). From network effects to data network effects: Enabling ecosystemic inno-vation for sustainability. M@ n@ gement, 28(5), 143-151. https://doi.org/10.37725/mgmt.2025.13682

Voigt, P., & Von dem Bussche, A. (2017). The eu general data protection regulation (gdpr). A practical guide, 1st ed., Cham: Springer International Publishing, 10(3152676), 10-5555.

https://doi.org/10.1007/978-3-319-57959-7

Zhang, D. (2021). Interoperability technology of sports health monitoring equipment based on multi-sensor information fusion. EURASIP Journal on Advances in Signal Processing, 2021(1), 62. https://doi.org/10.1186/s13634-021-00775-x

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Published

21-05-2026

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Original Research Article

How to Cite

Qingsheng, W., Wu, R., & Zhanguo, S. (2026). DAO governance model for open access sports science databases: a study on decentralized autonomous organizations. Retos, 80, 847-865. https://doi.org/10.47197/retos.v80.119245