Cognitive Diversity Within Entrepreneurial Teams: Contingencies of Their Cost and Benefit

Academy of Management Proceedings · 2023

• Sociology
• Computer Science
• Psychology

This AoM symposium advances the debate on whether cognitive diversity - differences in how people think, perceive, and process information - facilitates or impedes the performance of entrepreneurial teams. The formation of entrepreneurial teams involves the decision by the first founder(s) to recruit other cofounders and early joiners. Conventional wisdom in the business world is that teams perform best when bringing different perspectives and ideas to their collective decision making (Bunderson & van der Vegt 2018). Indeed, recent reports by McKinsey (Barta, Kleiner, & Neumann 2012) and Deloitte (Bourke & Dillon 2018) suggest that different cognitive perspectives among senior leaders create substantial value for firms. The symposium aims to add an understanding of the circumstances whereby cognitive diversity facilitates the performance of entrepreneurial teams. It builds on a growing literature that recognizes that cognitive diversity is not unconditionally better in all contexts (Eesley, Hsu, & Roberts 2013; Ter Wal et al. 2016). With the assembled thought leaders on cognitive diversity and entrepreneurial teams, the panel addresses what we know and the most pressing research directions ahead.

Hybrid Predictive Ensembles: Synergies Between Human and Computational Forecasts

Journal of Social Computing · 2021 · 15 citations

• Artificial Intelligence
• Artificial Intelligence
• Machine Learning

An increasing proportion of decisions, design choices, and predictions are being made by hybrid groups consisting of humans and artificial intelligence (AI). In this paper, we provide analytic foundations that explain the potential benefits of hybrid groups on predictive tasks, the primary use of AI. Our analysis relies on interpretive and generative signal frameworks as well as a distinction between the big data used by AI and the thick, often narrative data used by humans. We derive several conditions on accuracy and correlation necessary for humans to remain in the loop. We conclude that human adaptability along with the potential for atypical cases that mislead AI will likely mean that humans always add value on predictive tasks.