Competing with Big Data

Jens Prüfer, C. Schottmuller

Research output: Working paperDiscussion paperOther research output

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Abstract

This paper studies competition in data-driven markets, that is, markets where the cost of quality production is decreasing in the amount of machine-generated data about user preferences or characteristics, which is an inseparable byproduct of using services offered in such markets. This gives rise to data-driven indirect network effects. We construct a dynamic model of R&D competition, where duopolists repeatedly determine their innovation investments, and show that such markets tip under very mild conditions, moving towards monopoly. In a tipped market, innovation incentives both for the dominant firm and for competitors are small. We also show under which conditions a dominant firm in one market can leverage its position to a connected market, thereby initiating a domino effect. We show that market tipping can be avoided if competitors share their user information.
Original languageEnglish
Place of PublicationTilburg
PublisherTILEC
Number of pages50
Volume2017-006
Publication statusPublished - 15 Feb 2017

Publication series

NameTILEC Discussion Paper
Volume2017-006

Fingerprint

Dominant firm
Innovation
Monopoly
Indirect network effects
Competitors
Incentives
Leverage
User preferences
By-products

Keywords

  • big data
  • datafication
  • data-driven indirect network effects
  • dynamic competition
  • regulation

Cite this

Prüfer, J., & Schottmuller, C. (2017). Competing with Big Data. (TILEC Discussion Paper; Vol. 2017-006). Tilburg: TILEC.
Prüfer, Jens ; Schottmuller, C. / Competing with Big Data. Tilburg : TILEC, 2017. (TILEC Discussion Paper).
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Prüfer, J & Schottmuller, C 2017 'Competing with Big Data' TILEC Discussion Paper, vol. 2017-006, TILEC, Tilburg.

Competing with Big Data. / Prüfer, Jens; Schottmuller, C.

Tilburg : TILEC, 2017. (TILEC Discussion Paper; Vol. 2017-006).

Research output: Working paperDiscussion paperOther research output

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N2 - This paper studies competition in data-driven markets, that is, markets where the cost of quality production is decreasing in the amount of machine-generated data about user preferences or characteristics, which is an inseparable byproduct of using services offered in such markets. This gives rise to data-driven indirect network effects. We construct a dynamic model of R&D competition, where duopolists repeatedly determine their innovation investments, and show that such markets tip under very mild conditions, moving towards monopoly. In a tipped market, innovation incentives both for the dominant firm and for competitors are small. We also show under which conditions a dominant firm in one market can leverage its position to a connected market, thereby initiating a domino effect. We show that market tipping can be avoided if competitors share their user information.

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Prüfer J, Schottmuller C. Competing with Big Data. Tilburg: TILEC. 2017 Feb 15. (TILEC Discussion Paper).