Conversational Receptiveness: Improving Engagement With Opposing Views
We develop a machine learning algorithm to detect "conversational receptiveness” – language that communicates thoughtful engagement during disagreement. We show that receptiveness makes writers more persuasive, builds reader trust, and prevents conflict escalation among Wikipedia editors. We also develop a short "receptiveness recipe" intervention from our algorithm.
Citation:
Michael Yeomans, Julia Minson, Hanne Collins, Frances Chen, and Francesca Gino (2020) ,"Conversational Receptiveness: Improving Engagement With Opposing Views", in NA - Advances in Consumer Research Volume 48, eds. Jennifer Argo, Tina M. Lowrey, and Hope Jensen Schau, Duluth, MN : Association for Consumer Research, Pages: 1017-1021.
Authors
Michael Yeomans, Harvard Business School, USA
Julia Minson, Harvard Business School, USA
Hanne Collins, Harvard Business School, USA
Frances Chen, University of British Columbia, Canada
Francesca Gino, Harvard Business School, USA
Volume
NA - Advances in Consumer Research Volume 48 | 2020
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