Business-academia (B-A) collaborations have been analysed by an extensive body of literature, taking many different angles, and using various sources and types of information (patent statistics, the Community Innovation Survey data, evidence from specific surveys, interviews, or case studies), but usually a given paper is relying on a single method, addressing one or two major research questions. In contrast, this paper tackles both R&D and innovation collaborations among businesses and academia relying on information from different statistics and interviews. The latter source also allows exploring motivations for, and major features of, business-academia co-operation. The paper argues that mapping B-A collaborations by using multiple methods and multiple sources of information can significantly improve the reliability and richness of our understanding, and can offer insights on dynamics and qualitative features of these co-operation processes. Interviews conducted in Hungary-in line with other research findings-have also confirmed that (i) motivations, incentives for, and norms of, conducting R&D and innovation activities diametrically differ in business and academia; and (ii) different types of firms have different needs. Thus, more refined policy measures are to be devised to promote B-A collaboration more efficiently, better tuned to the needs of the actors, based on a relevant taxonomy of their co-operations. Evaluation criteria for academics should also be revised to remove some major obstacles, currently blocking more effective B-A co-operation. Several findings presented in this paper can be generalised beyond the cases considered, but the research design to analyse B-A collaborations and the concomitant policy recommendations always need to be tailored to the innovation systems in question.
Types of knowledge Business-academia collaboration Multiple methods to map business-academia collaborations STI policy implications