Anglia Ruskin Research Online (ARRO)
Browse

File(s) under embargo

10

month(s)

9

day(s)

until file(s) become available

Platform Competition and Incumbency Advantage under Heterogeneous Lock-in effects

journal contribution
posted on 2023-09-01, 15:24 authored by Emanuele Giovannetti, Paolo Siciliani
Digital platform markets perform a myriad of daily transactions, providing internet-mediated exchange possibilities: between consumers, for peer-to-peer exchanges; between businesses, for digital value chains; and between businesses and consumers, in digital marketplaces. It is essential for competition that new entrants are able to join platform markets. However, these markets are often characterised by proprietary innovations, especially in data analytics applied to existing user data. The algorithmic analysis of user data and information might increase incumbency advantages, creating lock-in effects among users and making them more reluctant to join an entrant platform. The individual costs of these lock-in effects may differ between the sides of a platform, e.g., between sellers and buyers, and across users within each side, e.g., between sellers with different costs and/or propensities to join an entrant platform. Moreover, these costs will interact with cross-group network effects, another well-studied source of incumbency advantage. This paper develops a model exploring how different levels of lock-in effects may favour an incumbent platform. The conditions for platforms’ coexistence, to avoid market tipping, require lock-in effects to be "stronger" than cross-group effects. However, this condition also provides a market advantage to the incumbent platform compared to the entrant's. Therefore, policies aimed at reducing lock-in effects, such as mandating data portability, may counterintuitively impair entry conditions as the incumbent sets its prices more aggressively with lower lock-in effects.

History

Refereed

  • Yes

Publication title

Information Economics and Policy

ISSN

1873-5975

Publisher

Elsevier BV

File version

  • Accepted version

Language

  • eng

Legacy posted date

2023-05-26

Legacy creation date

2023-05-26

Legacy Faculty/School/Department

Faculty of Business & Law

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC