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Competing with Big Data *
Author(s) -
Prüfer Jens,
Schottmüller Christoph
Publication year - 2021
Publication title -
the journal of industrial economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.93
H-Index - 77
eISSN - 1467-6451
pISSN - 0022-1821
DOI - 10.1111/joie.12259
Subject(s) - leverage (statistics) , competitor analysis , monopoly , incentive , industrial organization , competition (biology) , dominance (genetics) , business , construct (python library) , market share , microeconomics , big data , economics , marketing , computer science , ecology , biochemistry , chemistry , machine learning , gene , programming language , biology , operating system
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.