Regulating Algorithmic Learning in Digital Platform Ecosystems through Data Sharing and Data Siloing: Consequences for Innovation and Welfare

Kraemer, Jan; Shekhar, Shiva; Hofmann, Janina

doi:10.2139/ssrn.4163171

Cited by 1 publication

(1 citation statement)

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“…Data sharing as part of data-driven business models is often associated with knowledge risks (Fruhwirth et al, 2019). For these reasons, among others, data silos occur, which on the other hand, is also legally prescribed by politics in the case of mergers of companies (Kraemer et al, 2021). Sharing data with others can ensure that companies can extract more information from data than companies can do on their own (Richter & Slowinski, 2019;van den Broek & van Veenstra, 2015).…”

Section: Data Sharingmentioning

confidence: 99%

Data Sharing Fundamentals: Definition and Characteristics

Jussen,

Schweihoff,

Dahms

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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The importance of data as a key resource is a universal theme dominating social and business life. In this regard, inter-organizational data sharing shines in a new light prompting businesses to leverage the potential of their own data. However, it is still unclear what data sharing actually entails, i.e., what it means, what its potentials are, and what barriers organizations must overcome. In short, it lacks conceptual clarity and a clear description of its characteristics. The conceptual ambiguity and the synonymous use with data exchange in the literature are particularly problematic, which prevents a targeted conceptualization and use. The paper starts precisely at this point as it proposes a unifying definition and characteristics of data sharing. We report on a systematic literature review characterizing data sharing and delineating it from data exchange.

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Section: Data Sharingmentioning

confidence: 99%