Inbound Open Innovation and Innovation Performance: A Robustness Study
利用法国、德国和英国的企业创新调查数据，通过贝叶斯模型平均方法评估内向开放创新文献中变量选择对结论稳健性的影响，发现对企业新创新的解释比世界新创新更稳健。
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.