IGL Annual Research Prizes

The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise

Fabrizio Dell’Acqua, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stew Taub, and Karim R. Lakhani

Read more

Abstract: We examine how artificial intelligence transforms the core pillars of collaboration— performance, expertise sharing, and social engagement—through a pre-registered field experiment with 776 professionals at Procter & Gamble, a global consumer packaged goods company. Working on real product innovation challenges, professionals were randomly assigned to work either with or without AI, and either individually or with another professional in new product development teams. Our findings reveal that AI significantly enhances performance: individuals with AI matched the performance of teams without AI, demonstrating that AI can effectively replicate certain benefits of human collaboration. Moreover, AI breaks down functional silos. Without AI, R&D professionals tended to suggest more technical solutions, while Commercial professionals leaned towards commerciallyoriented proposals. Professionals using AI produced balanced solutions, regardless of their professional background. Finally, AI’s language-based interface prompted more positive selfreported emotional responses among participants, suggesting it can fulfill part of the social and motivational role traditionally offered by human teammates. Our results suggest that AI adoption at scale in knowledge work reshapes not only performance but also how expertise and social connectivity manifest within teams, compelling organizations to rethink the very structure of collaborative work.

Moving to Profitability? Alleviating Constraints on Microentrepreneur Location

Carolyn Pelnik

Read more

Abstract: Observationally similar business owners earn vastly different profits across city locations within Kampala, Uganda. This variation may reflect spatial misallocation, aspects of entrepreneurs’ objective function other than profits (e.g., amenities or risk), or sorting on unobservables. I first quantify the extent
of profit variation using primary surveys of microentrepreneurs. I then quantify the extent of spatial misallocation using a field experiment with nearly three thousand microentrepreneurs and a structural model. I experimentally allocate a moving subsidy, cross-randomized with an information intervention, and
estimate the effect of moving business location on profits. Entrepreneurs realize 45% higher profits—net of the value of the subsidy—as the result of moving, but only when they receive both the subsidy and information. The subsidy serves as insurance for profitable but risky moves, and information induces high-return entrepreneurs to select into moving. Impacts fade throughout the month after the intervention ends, consistent with risk-averse entrepreneurs gradually losing the ability to self-insure. I rationalize these results in a structural model where entrepreneurs choose locations and realize profit in equilibrium. Using data from the experiment to estimate the model, I show that aggregate income increases by 37% when financial and information constraints are relieved at scale. The results demonstrate spatial misallocation of entrepreneurs within the city, as well as complementarities between liquidity and information in overcoming frictions that otherwise inhibit the mobility of entrepreneurs.

Learning to Quit? A Multi-year, Multi-site Field Experiment With Innovation-driven Entrepreneurs

Esther Bailey-Rihawi, Daniel C. Fehder, Eric Floyd, Yael V. Hochberg, and Daniel Lee

Read more

Abstract: We use a randomized experiment with 553 science- and technology-based startups in 12 co-working spaces across the US to evaluate the effects of intensive, short-term entrepreneurial training programs on survival and performance for innovation-driven startups. Treated startups are more likely to shut down their businesses and do so sooner than control startups. Conditional on survival, however, treated startups are more likely to raise external funding for their ventures, raise funding faster, and raise more funding than the control group; they also exhibit higher employment and revenue. Treated founders are less likely to found a new startup after shutdown. Our findings are consistent with practitioner arguments that early entrepreneurship training interventions can help entrepreneurs with less viable ventures “rationally quit” (“fail fast”). We use machine learning techniques (causal random forest) to provide exploratory insights on the most impacted subgroups.