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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.
data-driven indirect network effects, 330, big data, regulation, dynamic competition, datafication
data-driven indirect network effects, 330, big data, regulation, dynamic competition, datafication
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 91 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |