How Robust are the Results? A Bayesian Averaging Approach for Tackling Replication and Model Uncertainty in Research on Inbound Open Innovation

Bernd Ebersberger, Fabrice Galia, Keld Laursen, Ammon Salter

    Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsResearchpeer-review


    In this paper, we explore the effects of inbound open innovation on firm’s
    innovation performance. Empirical strategic management research in general,
    and research on open innovation, in particular, is subject to an important degree
    of model uncertainty. This is because the true model, and therefore the selection
    of appropriate explanatory variables, is essentially unknown. Drawing on the
    literature on the determinants of innovation, and by analyzing innovation
    survey data for France, Germany, and the UK, we conduct a ‘large-scale’
    replication using the Bayesian averaging approach of classical estimators. We
    test a wide range of determinants of innovation performance suggested in the
    prior open innovation literature, and establish a robust set of findings on the
    variables which shape innovation performance. We provide some implications
    for innovation research and explore the potential application of our approach to
    other domains of research in strategic management.
    Original languageEnglish
    Title of host publication15th International Open and User Innovation Conference : Book of Abstracts
    Place of PublicationInnsbruck
    PublisherUniversity of Innsbruck
    Publication date2017
    Publication statusPublished - 2017
    Event15th International Open and User Innovation Conference 2017 - The University of Innsbruck, Innsbruck, Austria
    Duration: 10 Jul 201712 Jul 2017
    Conference number: 15


    Conference15th International Open and User Innovation Conference 2017
    LocationThe University of Innsbruck
    Internet address

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