Adressing Replication and Model Uncertainty: A Bayesian Averaging Approach Applied to Innovation Survey Data

Bernd Ebersberger, Fabrice Galia, Keld Laursen, Ammon Salter

    Research output: Contribution to conferencePaperResearchpeer-review

    Abstract

    Many fields of strategic management are 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. Our method tests a wide range of determinants of innovation suggested in the prior literature, and establishes a robust set of findings on the variables which shape the introduction of new to the firm and new to the world innovations. We provide some implications for innovation research, and explore the potential application of our approach to other domains of research in strategic management.
    Many fields of strategic management are 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. Our method tests a wide range of determinants of innovation suggested in the prior literature, and establishes a robust set of findings on the variables which shape the introduction of new to the firm and new to the world innovations. We provide some implications for innovation research, and explore the potential application of our approach to other domains of research in strategic management.

    Conference

    ConferenceThe DRUID 20th Anniversary Conference 2016
    Number38
    LocationCopenhagen Business School
    CountryDenmark
    CityKøbenhavn
    Period13/06/201615/06/2016
    OtherThe DRUID Society Conference 2016
    SponsorCopenhagen Business School
    Internet address

    Keywords

    • Model uncertainty
    • Replication
    • Innovation
    • Strategic management

    Cite this

    Ebersberger, B., Galia, F., Laursen, K., & Salter, A. (2016). Adressing Replication and Model Uncertainty: A Bayesian Averaging Approach Applied to Innovation Survey Data. Paper presented at The DRUID 20th Anniversary Conference 2016, København, Denmark.
    Ebersberger, Bernd ; Galia, Fabrice ; Laursen, Keld ; Salter, Ammon. / Adressing Replication and Model Uncertainty : A Bayesian Averaging Approach Applied to Innovation Survey Data. Paper presented at The DRUID 20th Anniversary Conference 2016, København, Denmark.36 p.
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    abstract = "Many fields of strategic management are 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. Our method tests a wide range of determinants of innovation suggested in the prior literature, and establishes a robust set of findings on the variables which shape the introduction of new to the firm and new to the world innovations. We provide some implications for innovation research, and explore the potential application of our approach to other domains of research in strategic management.",
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    Ebersberger, B, Galia, F, Laursen, K & Salter, A 2016, 'Adressing Replication and Model Uncertainty: A Bayesian Averaging Approach Applied to Innovation Survey Data' Paper presented at, København, Denmark, 13/06/2016 - 15/06/2016, .

    Adressing Replication and Model Uncertainty : A Bayesian Averaging Approach Applied to Innovation Survey Data. / Ebersberger, Bernd; Galia, Fabrice; Laursen, Keld; Salter, Ammon.

    2016. Paper presented at The DRUID 20th Anniversary Conference 2016, København, Denmark.

    Research output: Contribution to conferencePaperResearchpeer-review

    TY - CONF

    T1 - Adressing Replication and Model Uncertainty

    T2 - A Bayesian Averaging Approach Applied to Innovation Survey Data

    AU - Ebersberger,Bernd

    AU - Galia,Fabrice

    AU - Laursen,Keld

    AU - Salter,Ammon

    PY - 2016

    Y1 - 2016

    N2 - Many fields of strategic management are 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. Our method tests a wide range of determinants of innovation suggested in the prior literature, and establishes a robust set of findings on the variables which shape the introduction of new to the firm and new to the world innovations. We provide some implications for innovation research, and explore the potential application of our approach to other domains of research in strategic management.

    AB - Many fields of strategic management are 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. Our method tests a wide range of determinants of innovation suggested in the prior literature, and establishes a robust set of findings on the variables which shape the introduction of new to the firm and new to the world innovations. We provide some implications for innovation research, and explore the potential application of our approach to other domains of research in strategic management.

    KW - Model uncertainty

    KW - Replication

    KW - Innovation

    KW - Strategic management

    KW - Model uncertainty

    KW - Replication

    KW - Innovation

    KW - Strategic management

    M3 - Paper

    ER -

    Ebersberger B, Galia F, Laursen K, Salter A. Adressing Replication and Model Uncertainty: A Bayesian Averaging Approach Applied to Innovation Survey Data. 2016. Paper presented at The DRUID 20th Anniversary Conference 2016, København, Denmark.