Competing with Big Data

Prüfer, Jens ORCID: https://orcid.org/0000-0001-7203-9711 and Schottmüller, Christoph (2021) Competing with Big Data. Journal of Industrial Economics, 69 (4). pp. 967-1008. ISSN 0022-1821

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Abstract

We study competition in data-driven markets, where the cost of quality production decreases in the amount of machine-generated data about user preferences or characteristics. This gives rise to data-driven indirect network effects. We construct a dynamic model of R&D competition, where duopolists repeatedly determine innovation investments. Such markets tip under very mild conditions, moving towards monopoly. After tipping, innovation incentives both for the dominant firm and the competitor are small. We show when a dominant firm can leverage its dominance to a connected market, thereby initiating a domino effect. Market tipping can be avoided if competitors share their user information.

Item Type: Article
Additional Information: First published online: 02 February 2022
Uncontrolled Keywords: accounting,business, management and accounting(all),economics and econometrics ,/dk/atira/pure/subjectarea/asjc/1400/1402
Faculty \ School: Faculty of Social Sciences > School of Economics
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 12 Sep 2022 10:31
Last Modified: 29 Sep 2022 00:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/88106
DOI: 10.1111/joie.12259

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