Organizational Selection of Innovation
Lucas Böttcher, Ronald Klingebiel
TL;DR
The paper tackles how to optimally aggregate multiple decision-makers’ evaluations to fund a limited number of innovation projects under budget and uncertainty. It extends a prior organizational-aggregation framework to portfolio selection, comparing five aggregation rules (Individual, Delegation, Voting, Averaging, Ranking) via Monte Carlo simulations with domain-specific noise and expertise. The main finding is that Ranking (Borda-count-like aggregation) often delivers the best portfolio performance, particularly under tight budgets and realistic expertise distributions, while Delegation can outperform Ranking when expert alignment is strong and budgets permit, though delegation susceptibility to errors can erode this advantage. The work provides normative guidance for resource allocation in innovation portfolios and highlights the conditions under which crowds can outperform expert delegation, informing managerial practice and future research on decision aggregation under uncertainty.
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 as well as counting their approval votes -- especially when organizations have tight budgets and can select only a few project alternatives out of many.