Competing with Big Data

Jens Prüfer and Christoph Schottmüller

Journal of Industrial Economics, 2021, vol. 69, issue 4, 967-1008

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.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://doi.org/10.1111/joie.12259

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jindec:v:69:y:2021:i:4:p:967-1008

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0022-1821

Access Statistics for this article

Journal of Industrial Economics is currently edited by Pierre Regibeau, Yeon-Koo Che, Kenneth Corts, Thomas Hubbard, Patrick Legros and Frank Verboven

More articles in Journal of Industrial Economics from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().