The Effects of Open Innovation at the Network Level

To open the black box of how open innovation works at the network level, we particularly focused on the effects of TFs’ collective openness of external knowledge search on RIN innovation performance in different innovation environments of disruptiveness. To conduct the research, we adopted a bottom-up research approach and designed an agent-based simulation model. The simulation results show that either collective breadth or depth exerts significant effects on the RIN innovation performance, and their effectiveness is significantly moderated by disruptiveness. Our results reveal the followings: (1) RIN innovation performance can be considerably enhanced by high collective openness, but it is not necessarily true that more collective openness is better, which reflects that the “inverted U-shaped” relationships broadly argued in firm-level open innovation studies also exists at the network level. (2) The effect of collective openness depends on the disruptiveness of innovation environment. The likelihood of a positive effect of collective openness on RIN innovation performance increases as the disruptiveness is enhanced. The trends of the polarization of individual TFs’ innovation performance in an RIN is alleviated as the disruptiveness is enhanced. Based on these findings, we give some guidance of innovation policymaking. When the industry is in its emerging stage, aggressive collective openness—high collective breadth and depth—aiming at achieving high RIN innovation performance is recommended. When the industry steps into its developing stage, directed collective openness—high collective depth and moderate collective breadth—aiming at fostering future industry leaders in the RIN is recommended. When the industry reaches its maturity, conservative collective openness—low collective depth and moderate collective breadth—aiming at maximum utilization of current RIN’s competitive advantages is recommended.