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Inbound openness and its impact on innovation performance: an agent‐based and simulation approach
Author(s) -
Cheng Lu,
Lyu Yibo,
Su Jingqin,
Han Shaojie
Publication year - 2020
Publication title -
randd management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.253
H-Index - 102
eISSN - 1467-9310
pISSN - 0033-6807
DOI - 10.1111/radm.12391
Subject(s) - openness to experience , variance (accounting) , benchmark (surveying) , embodied cognition , industrial organization , business , distribution (mathematics) , semiconductor industry , open innovation , marketing , econometrics , knowledge management , psychology , computer science , economics , social psychology , mathematics , engineering , manufacturing engineering , artificial intelligence , mathematical analysis , accounting , geodesy , geography
A firm’s superior innovation performance is embodied not only by its average innovation performance but also by the likelihood of extremely high innovative outcomes. The former benchmark is associated with the mean of the performance distribution, while the latter is associated with the variance, both of which play an important role in instructing the achievement of superior innovation performance. In this paper, we explored how inbound open innovation impacts superior innovation performance by considering both the average and variance effect s of inbound openness. We conducted agent‐based modeling and simulation research to untangle the relationship between inbound openness and superior innovation performance and how the relationship is moderated by the disruptiveness of industrial innovation. We found that inbound openness significantly influences both the benchmarks. Specifically, search breadth positively influences the likelihood of extremely high innovative outcomes in general, whereas search depth positively influences average innovation performance; and the strength of these effects varies under different disruptiveness.