Unveiling Coping Mechanisms in Marketplace Discrimination: The Allure of AI Recommendations
(A2024-118162)

AUTHORS

Kulwinder KAUR, IIM Jammu, India; Nazia Gera, GGDSD Panjab University; Arash Talebi, Assistant Professor of Marketing, EDHEC Business School; Sourjo MUKHERJEE, School of Business, University of Wollongong in Dubai; Gopal Das, Associate Professor of Marketing, Indian Institute of Management Bangalore

ABSTRACT

Despite AI’s increased efficiency and accuracy in many contexts, algorithm aversion is a well-documented phenomenon. We show a reversal of this phenomenon in the prevalent context of marketplace discrimination. Specifically, we document people’s increased propensity to rely on AI-based recommendations over human-based recommendations in the face of marketplace discrimination. Such an increased preference happens because it serves as a coping strategy for consumers faced with marketplace discrimination. A series of four studies provide support for this enhanced reliance on AI (vs. human) recommendations when consumers face marketplace discrimination. The detailed mechanism through perceived embarrassment is also documented in our studies. Using a moderated-mediation model, we further show a boundary condition of the effect by demonstrating that the focal effect remains valid (diminishes) under public (private) consumption. Important managerial implications are offered by our findings.