Who Should Own the Data? The Impact of Data Ownership Shift from the Service Provider to Consumers

ABSTRACT

With the wide use of information technologies including Big Data and artificial intelligence (AI), consumers’ personal actions (their search history, transaction records, click-through behaviors, etc.) can be tracked, recorded and analyzed by the service provider (e.g., Google) to provide personalized services. Under the current regime, consumers usually hand over their personal data for free in exchange for high-quality services. As it becomes more and more commonly accepted that “data is property,” should consumers be entitled to claim their property rights over their personal data? New technologies emerge to empower consumers to control their own data, and the service provider may need to compensate for the usage of such data. How consumers and the service provider should react to such technologies, however, is not clear. We build a theoretical model in which consumers have different sensitivities towards their data ownership. We show that the impact of the data ownership shift depends not only on the service provider’s revenue structure and the discount in the service quality offered to non-data-providing consumers, but also on whether and how consumers are compensated. More importantly, if the service provider can endogenously adjust the qualities of services provided to consumers, the shift of data ownership may not necessarily benefit consumers, or harm the service provider. We also offer guidelines for data regulation policy designs.

KEYWORDS:

• Data ownership
• service industry
• service quality
• data compensation
• data regulation
• data as property
• empowering consumers
• service providers
• online services

Acknowledgement

We thank the whole review team at JMIS for their constructive feedbacks, which helped us shape our paper into its published form.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2023.2196775

Notes

¹ There are several similar examples: Permission.io, Oceanprotocol, CitizenMe, SoicalFi, and Wibson.

² This implies that the rate of the revenue from data-providing consumers ($a+b$) is larger than that from non-data-providing consumers ($a$). In this case, all consumers have to provide their personal data in exchange for the service provided by the service provider; as a result, the revenue rate is ($a+b$).

³ This implies that using consumers’ personal data helps to reduce the unit cost of providing better services: if providing a service of the same quality$t$, the cost of providing customized service ($m{t}^2)$is less than that of providing non-customized service ($m{t}^2/{\delta }^2)$.

⁴ Here, Proposition 3 studies the impact of compensation when individualized compensation scheme is adopted (Sections 3, 4, and 5) We study a different scenario “the uniform compensation scheme” in Section 6.1.

⁵ Here${\mathrm{\Delta }}_1=4{\delta }^4-4\left(4a^2+7a+1\right){\delta }^2+{\left(a-1\right)}^2$ and${\mathrm{\Delta }}_2=32a\left(a+1\right)\left(a+2\right)\left(2a+1\right)$.

⁶ In our model, the discount in the service quality offered to non-data-providing consumers ($1-\delta$) is exogenously given. In reality, however, to encourage consumers to provide their data, the service provider may endogenize this and reduce its quality level offered to consumers who refuse to provide their data. For example, in order to reduce , the service provider can increase the response time, place excessive advertisements in the response pages or even halt the services to non-data-providing consumers.

⁷ For example, China has passed the “Personal Information Protection Law” to protect online users’ data privacy, which does not allow price discrimination based on users’ behavioural data.

⁸ This happens when the service provider’s revenue rate from sources where consumers’ private data is NOT needed is relatively smaller than that from sources where consumers’ private data is needed (Proposition 2, Page 14; Proposition 4, Page 19).

⁹ The service provider may be better off from the data ownership shift because it may improve consumers’ demand (Proposition 6, Page 21).</NOTES>

Additional information

Funding

We acknowledge the funding support from the National Natural Science Foundation of China (72171132, 72121001, 71902154, 71871179).

Notes on contributors

Shilei Li

Shilei Li ([email protected]) is a PhD Candidate in the Department of Information Systems, College of Business, City University of Hong Kong. He holds a BA in Mathematics and Applied Mathematics from Beijing Normal University. His research interest is in economics of information systems.

Yang Liu

Yang Liu ([email protected]) is an associate professor in School of Management, Harbin Institute of Technology, China. He holds a PhD in Management Science and Engineering from Xi’an Jiaotong University, China, and a PhD in Information Systems from City University of Hong Kong. Dr. Liu’s research focuses on the economics of data, online reviews, and platform economics. He has published in major journals and conference proceedings in Information Systems, such as Information Systems Research and others.

Juan Feng

Juan Feng ([email protected]; corresponding author) is Hon Hai chair professor in School of Economics and Management & Shenzhen International Graduate School, Tsinghua University, China. She holds a PhD in Business Administration from Pennsylvania State University, with a dual title in Operations Research. She serves as Senior Editor for Information Systems Research, and serves on the Editorial Boards of Journal of Management Information Systems and International Journal of Electronic Commerce. She has published in such journals as Information Systems Research, Journals of Management Information Systems, Management Science, Marketing Science, Production and Operations Management, and Informs Journal on Computing, and others. She serves as vice president of the Association for Information Systems.