Disentangling Learning Network Dilemma: A Small-World Effect in a Globalized World

During this pandemic, Research and Development (R&D) firms were faced with the challenge to engage in collaborative networks to immediately find the cure for coronavirus. However, the closed and local model of the innovation ecosystem causes the innovation process carried out by a single laboratory to be slow and ineffective. We study how R&D firms should configure the open innovation ecosystem network for optimal collaborative learning. We argue that value creation in collaborative learning can be influenced by configuring structural connections and relational cohesion in a network of inter-organizational R&D collaborations. A model based on a combination of two network configurations, namely, inter-network connections and intra-network cohesion, was tested on 204 R&D collaborations from the pharmaceutical industry. Our study found an interaction effect between inter-network connections and intra-network cohesion on knowledge acquisition performance. Furthermore, when seeking optimal knowledge transfer, increasing investment commitment in R&D collaboration is more effective than extending the duration of a relationship. This study contributed a dynamic model of collaborative learning by testing the complementary effects between structural and relational configurations in the external and internal firm’s innovation ecosystem for sustainable knowledge acquisition performance.