CentER

Frans Stel

Date of PhD defense:	7 July 2011
Title of thesis:	Improving the performance of co-innovation aliances: Cooperating effectively with new business partners
ISBN:	978 90 5668 285 9
Promotor:	Prof.dr. Arjen van Witteloostuijn Prof.dr. Erik Brouwer

Abstract:
There is little doubt that open Innovation can boost the performance of new business development, where it enables higher R & D output and faster market introduction at lower costs. It is considered to have become the dominant innovation model and therefore a necessity in the competitive world of modern business practice. Unfortunately, open innovation is difficult to implement due to increased complexities and risks. The main objective of this study is to develop and test a theoretical and evidence-based framework in order to improve the performance of co-innovation alliances. The research questions are (1) Which factors and processes are known to diagnose and manage co-innovation alliances? (2) How do they differ in the case of different objectives? (3) Which changes result in higher performance? and (4) How do they relate to one another? In this study, facilitating and blocking factors and processes also specified with contributions of contingency, network, organizational learning, and resource-based theory. Research on joint ventures, strategic alliances and inter-firm cooperation are also summarized. A framework is developed consisting of dependent variables (strategic, learning and financial performance), independent variables (organizational and relationship drivers) and control variables (alliance characteristics, market and strategy). Following principal component analysis, scales are constructed which show the relationship to performance of various drivers: contract, coordination, competences, embeddedness, governance structure, trust, culture, technology transfer, management involvement and personal relations. Based on data from interviews involving 137 co-innovation partnerships in 51 companies, and using multivariate regressions analysis, hypotheses are tested and interaction effects are explored. The study reveals evidence that various organizational and relational drivers are linearly or curvilinearly (U-shaped or hill-shaped) related to performance and differ according to the type of performance, industry and project maturity. Furthermore, optimal levels of the drivers are indicated. Improving the performance of co-innovation is considered to be a multi-level challenge, in which the individual, team, organizational and inter-organizational level interact with one another. By optimizing the relevant drivers at the appropriate time, the performance of co-innovation alliances can be improved. The framework serves as a benchmark tool for co-innovation alliances.

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