Inbound Open Innovation and Innovation Performance: A Robustness Study

Bernd Ebersberger, Fabrice Galia, Keld Laursen, Ammon Salter

Research output: Contribution to journalArticlepeer-review

49 Citations (SciVal)

Abstract

In studies of firm's innovation performance, regression analysis can involve a significant level of model uncertainty because the ‘true’ model, and therefore the appropriate set of explanatory variables are unknown. Drawing on innovation survey data for France, Germany, and the United Kingdom, we assess the robustness of the literature on inbound open innovation to variable selection choices, using Bayesian model averaging (BMA). We investigate a wide range of innovation determinants proposed in the literature and establish a robust set of findings for the variables related to the introduction of new-to-the-firm and new-to-the-world innovation with the aim of gauging the overall healthiness of the literature. Overall, we find greater robustness for explanations for new-to-the-firm rather than new-to-the-world innovation. We explore how this approach might help to improve our understanding of innovation.
Original languageEnglish
JournalResearch Policy
Early online date8 May 2021
DOIs
Publication statusPublished - 1 Sept 2021

Funding

Bernd conducted this research as part of the Research Area ?Innovation, Entrepreneurship, and Finance (INEF)? at the University of Hohenheim's Faculty of Business, Economics, and Social Sciences. Fabrice was with Burgundy School of Business when this project started. We thank the participants in the OUI 15th International Open and User Innovation Conference (2017), the SMS Annual Conference (2016), the AOM Annual Meeting (2016), the International Schumpeter Society (ISS) Conference (2016), and the DRUID16 20th Anniversary Conference (2016) for their comments and suggestions on a previous version of the paper. We especially thank the editor and four anonymous referees for providing comments that substantially improved the paper. The usual disclaimer applies.

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