Inbound Open Innovation and Innovation Performance
| Autor: | Keld Laursen, Bernd Ebersberger, Ammon Salter, Fabrice Galia |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Strategy and Management 05 social sciences Feature selection Regression analysis Model Uncertainty Management Science and Operations Research 050905 science studies Bayesian inference Variable Robustness Open Innovation Range (mathematics) Management of Technology and Innovation 0502 economics and business Econometrics Survey data collection Innovation Surveys 0509 other social sciences Set (psychology) Robustness (economics) Innovation 050203 business & management Innovation Studies Open innovation |
| Zdroj: | Research Policy. 50(7) |
| ISSN: | 0048-7333 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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