Platform Neutrality and Data-Driven Business Models: B2B Data Sharing as Payment for Prominence of Content Providers on Online Platforms
Final Report Abstract
Online platforms (e.g., Amazon, Facebook, or Google) play a key role in the Internet ecosystem because, as intermediaries, they mediate business relationships between users and content providers (CPs). Platforms create added value for their customers by organizing the available content and preparing it so that it can be found quickly. In this function, platforms also receive a variety of data, not only from users but also from content providers. This data can be used by platforms to gain a competitive advantage. The project examined two manifestations of potential advantage creation through privileged access to data and determined the implications of platform behavior that is not "competition neutral" with respect to the use of this data. In the first scenario examined, platforms can potentially demand more valuable data from CPs (e.g., to build customer profiles) in exchange for offering higher visibility on the platform. Using a game-theoretic model, it can be shown that this in turn leads to increased data collection from CPs and in some cases also increases the bias of the platform, i.e., users are no longer directed to the best offer for them. Both effects reduce consumer surplus. In the second scenario, it is assumed that a platform has already achieved dominance in a (primary) market (e.g., the search engine market) and is therefore able to collect comprehensive data about users. However, this data may not only be of importance in the primary market, but may also lead to competitive advantages in a secondary market, e.g. better estimation of customer risk in an insurance market). In the project, a game-theoretical model was used to investigate whether sharing data with competitors in the secondary market or prohibiting the use of data in the secondary market leads to better market results. The results tend to favor data sharing, but also show that there may be interactions between the two measures that need to be taken into account in any potential regulation. Overall, the project contributed to a better understanding of the competitive and societal implications of data use by platforms, which are important in the context of global efforts to strengthen regulation of market-dominating platforms.
Publications
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Prominence-for-Data Schemes in Digital Platform Ecosystems: Implications for Platform Bias and Consumer Data Collection. SSRN Electronic Journal.
Bourreau, Marc; Kraemer, Jan & Hofmann, Janina
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Regulating Algorithmic Learning in Digital Platform Ecosystems through Data Sharing and Data Siloing: Consequences for Innovation and Welfare. Proceedings of the Forty-Second International Conference on Information Systems, Austin 2021.
Krämer, J.; Shekhar, S. & Hofmann, J.
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Regulating Algorithmic Learning in Digital Platform Ecosystems through Data Sharing and Data Siloing: Consequences for Innovation and Welfare. SSRN Electronic Journal.
Kraemer, Jan & Shekhar, Shiva
