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This paper presents NASA’s experience using a Center of Excellence (CoE) to scale and sustain an open innovation program as an effective problem-solving tool and includes strategic management recommendations for other organizations based on lessons learned.

This paper defines four phases of implementing an open innovation program: Learn, Pilot, Scale and Sustain. It provides guidance on the time required for each phase and recommendations for how to utilize a CoE to succeed. Recommendations are based upon the experience of NASA’s Human Health and Performance Directorate, and experience at the Laboratory for Innovation Science at Harvard running hundreds of challenges with research and development organizations.

Lessons learned include the importance of grounding innovation initiatives in the business strategy, assessing the portfolio of work to select problems most amenable to solving via crowdsourcing methodology, framing problems that external parties can solve, thinking strategically about early wins, selecting the right platforms, developing criteria for evaluation, and advancing a culture of innovation. Establishing a CoE provides an effective infrastructure to address both technical and cultural issues.

The NASA experience spanned more than seven years from initial learnings about open innovation concepts to the successful scaling and sustaining of an open innovation program; this paper provides recommendations on how to decrease this timeline to three years.

The article first examines whether police hiring decisions represent a zero-sum game where hires from one under-represented group (e.g. White women) reduce the number of hires made from other under-represented groups (non-White men and/or non-White women). Second, we explore whether agencies that hire more members of underrepresented groups achieve more diverse applicant pools in future hiring cycles. Negative binomial regression techniques are used in both analyses.

Data for this study come from the Commission on Accreditation for Law Enforcement Agencies (CALEAs) from 2011 to 2016. These data are divided into two periods: Period A (2011–2013) and Period B (2014–2016). The two periods are combined to assess a zero-sum effect. Then, Period A data on hiring decisions is used to estimate the diversity in applicant pools in Period B.

Results from this study provided little evidence of a zero-sum effect. It does not seem that agencies that hire from one under-represented group are less likely to hire from others. Instead, agencies that have shown a commitment to diversification are more likely to make additional hires from under-represented groups. We also found evidence of a relationship between Period A hires and Period B applicant pools for Hispanic women, but not for other groups. Broadly, we found that agencies where a larger share of officers are women were more likely to hire more women applicants.

Previous research examining zero-sum effects in hiring rely on officer rosters rather than specific applicant and hiring data. The data used in this study allows for a more precise examination of hiring decisions, and allows us to link hiring decisions to future applicant pool composition.