Proximity Metrics for Selecting R&D Partners in International Open Innovation Processes

The implementation of open innovation models for R&D activities has been extensively tested by many public and private institutions by using several models; however, both lessons learned and rationale for internal decision-making have not been systematically analyzed to serve as a basis for future institutional process management improvements, which increase the success rate. The large diversity of cases, firms, sectors and open activities, and the multiple factors affecting success has made it difficult to elaborate a general framework where to extract from some policy design rules adapted to specific contexts; thus, involved firms do not have a consolidated framework to measure the effectiveness of the open innovation practices in use. Open innovation for R&D activities conducted by multinational firms is usually carried out at the international level looking for specific knowledge and complementarity of partners. One of the critical factors for success in open innovation activities is the “right selection of adequate partners”. This paper postulates that the multi-variable selection based on the previous characterization of the relationship between the firm and one potential international partner constitutes a key factor for success. In addition, the article presents a semi-quantitative approach to measure the “differences” (a metrics based on “proximity” is defined) to analyze and compare between different partners from several dimensions. The key goal is to select external partners with the “minimum distance” to the firm. The proposed selection metrics were applied to various open research projects launched with university partners in the three Latin American countries (Mexico, Chile, and Peru).