Governance of big data collaborations: How to balance regulatory compliance and disruptive innovation

Tijs van den Broek and Anne Fleur van Veenstra

Technological Forecasting and Social Change, 2018, vol. 129, issue C, 330-338

Abstract: Big data is an important driver of disruptive innovation that may increase organizations' competitive advantage. To create innovative data combinations and decrease investments, big data is often shared among organizations, crossing organizational boundaries. However, these big data collaborations need to balance disruptive innovation and compliance to a strict data protection regime in the EU. This paper investigates how inter-organizational big data collaborations arrange and govern their activities in the context of this dilemma. We conceptualize big data as inter-organizational systems and build on IS and Organization Theory literature to develop four archetypical governance arrangements: Market, Hierarchy, Bazaar and Network. Subsequently, these arrangements are investigated in four big data collaboration use cases. The contributions of this study to literature are threefold. First, we conceptualize the organization behind big data collaborations as IOS governance. Second, we show that the choice for an inter-organizational governance arrangement highly depends on the institutional pressure from regulation and the type of data that is shared. In this way, we contribute to the limited body of research on the antecedents of IOS governance. Last, we highlight with four use cases how the principles of big data, specifically data maximization, clash with the principles of EU data protection regulation. Practically, our study provides guidelines for IT and innovation managers how to arrange and govern the sharing of data among multiple organizations.

Keywords: Disruptive innovation; Data protection regulation; Big data; Governance; Inter-organizational collaboration (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (28)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:129:y:2018:i:c:p:330-338

DOI: 10.1016/j.techfore.2017.09.040

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