Organizational Selection of Innovation

Abstract

Budgetary constraints force organizations to pursue only a subset of possible innovation projects. Identifying which subset is most promising is an error-prone exercise, and involving multiple decision makers may be prudent. This raises the question of how to most effectively aggregate their collective nous. Our model of organizational portfolio selection provides some first answers. We show that portfolio performance can vary widely. Delegating evaluation makes sense when organizations employ the relevant experts and can assign projects to them. In most other settings, aggregating the impressions of multiple agents leads to better performance than delegation. In particular, letting agents rank projects often outperforms alternative aggregation rules—including averaging agents’ project scores and counting their approval votes—especially when organizations have tight budgets and can select only a few project alternatives out of many. Funding: L. Böttcher acknowledges financial support from hessian.AI and the Army Research Office [Grant W911NF-23-1-0129]. Supplemental Material: The data files are available at https://doi.org/10.1287/orsc.2023.17357 .