An Orchestration Perspective on Open Innovation between Industry–University: Investigating Its Impact on Collaboration Performance

Abstract

Since open innovation between industry–university is a highly complex phenomenon, its orchestration may be of great support for better collaboration between these organizations. However, there is a lack of evidence on how an orchestration framework impacts the collaboration performance between these organizations in such a setting. Based on a research model that investigates the influence of the main orchestration dimensions on the performance of collaboration, this study offers one of the first perspectives of an orchestration process between the industry and university actors in open innovation. The developed research model was assessed using a deep learning dual-stage PLS-SEM and artificial neural network (ANN) analysis. In the first stage, the hypotheses of the research model were tested based on a disjoint two-stage approach of PLS-SEM, and the results reveal the orchestration dimensions that have a significant impact on collaboration performance. In the second stage, a deep learning network approach was successfully employed to capture the complex relationships among the significant orchestration dimensions identified through the PLS-SEM analysis. An importance–performance map analysis provided useful insights into the relative importance of the components of each orchestration dimension based on their effects on the collaboration performance.