Abstract:

How can organizations structure their selection of innovation projects to reduce errors in the form of false positives (investments that should not have been made) and false negatives (investments that should have been made but weren’t)? Although simulations and case studies exist, our understanding of the effects of selection regimes on both types of errors has been limited due to a lack of decision and outcome data over a large set of projects. We first use 121 interviews and secondary material from AppCampus to understand how three selection regimes unfolded, and map these to existing literature. We complement this with unique data of 3,611 innovation projects where we hand-code the outcomes for both funded and rejected projects to measure false positives and false negatives at the project-level. This is joint work with Dmitry Sharapov from Imperial College Business School.

Dmitry Sharapov, from Imperial College Business School, collaborated with Linus Dahlander on this research.

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House of Innovation and the Stockholm School of Entrepreneurship jointly organized this seminar.