Searching for Partners in Open Innovation Settings: How to Overcome the Constraints of Local Search

What is it about?

The search for partners in open innovation settings often consumes substantial time and managerial attention. Yet, organizations tend to get trapped in local search, which typically leads to collaboration with partners already known to them. To improve the search for partners, this article develops a tool that exploits the power of state-of-the-art information technology. In a sample of 33 search queries conducted in six innovation intermediaries, it studies differences between search with and without the use of our tool. The tests confirm the tool’s effectiveness and efficiency, and highlight the importance of searching with keywords that represent the core roles and activities of a firm, next to keywords referring to market and technology characteristics. Network visualization and semantic algorithms thus appear to facilitate the effort to identify distant partners. The article also finds that local partners are not that easy to find as commonly assumed.

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Why is it important?

------------------------------------------------------- Contribution to Academic Scholarship ------------------------------------------------------- Organization's core activities and roles were identified as an important notion in conceptualizing local and distant search, one that helps understand how OI managers identify and select new partners to align their ecosystems and produce new value propositions. Second, established theories of search and OI (implicitly) depict the search for local partners as relatively easy. Yet, our results imply that OI managers often overlook many potential local partners. This underlines the role of the availability heuristic as one of the drivers of the local search trap. ------------------------------------------------------- Contribution to Management Practice ------------------------------------------------------- This study provides actionable knowledge for innovation managers who seek to prevent the local search trap and improve the identification of potential partners. In particular, (1) the smart use of keywords serves to identify innovative partners; (2) network visualization and semantic algorithms help identify distant options; and (3) while searching for partners in markets and technologies already known to managers, often many local fir