Patterns of Open Innovation Between Industry and University: A Fuzzy Cluster Analysis Based on the Antecedents of Their Collaboration

Competing in a complex and interconnected environment, firms are increasingly employing open innovation to search for and collaborate with different partners for better performance. While universities are considered an important source of knowledge for industry, there has been limited literature that investigates patterns of their collaboration in an open innovation context. Moreover, the influence of contextual characteristics such as size and industry classes on these patterns has also received little attention. Aiming to address these research gaps, a research framework was developed from the extant literature. Taking into account the main antecedents integrated into this framework, a fuzzy c-means clustering approach was employed to find a typology of open innovative firms in their collaboration with universities. Using the typical value of the fuzzifier factor of this algorithm equal to 2, three distinct clusters were identified with respect to these antecedents as low, insecure, and responsive open innovators. Then, an econometric model using a multinomial logistic regression was constructed to explore the influence of firms’ size and industry type on the identified patterns of such collaboration. Based on the marginal effects analysis, mixed evidence was found regarding the influence of the firm’s size on the identified clusters, while the impact of industry intensity was in line with other prior studies in the extant literature. The results of our study lead to some meaningful implications from both an empirical and managerial point of view that are discussed alongside with future research recommendations.