Algorithm Aversion: Evidence from Ridesharing Drivers

28 Pages Posted: 30 Aug 2022

See all articles by Meng Liu

Meng Liu

Washington University in St. Louis

Xiaocheng Tang

Meta Platforms Inc

Siyuan Xia

Shanghai Jiaotong University

Shuo Zhang

Shanghai Jiao Tong University (SJTU) - Shanghai Jiao Tong University, Antai College of Economics and Management

Yuting Zhu

National University of Singapore

Date Written: August 19, 2022

Abstract

AI algorithms often cannot realize their intended efficiency gains because of their low adoption by human users. We uncover various factors that explain ridesharing drivers’ aversion to an algorithm designed to help them make better location choices. By leveraging an algorithmic recommendation rollout on a large ridesharing platform, we find that drivers are more averse to the algorithm when they face a higher cost of implementing its instructions, when their experience suggests a greater opportunity cost of following the algorithm, and when their peers’ actions contradict the algorithmic recommendations. We discuss the managerial implications of these findings.

Keywords: Algorithm Aversion, AI Algorithms, Human Experience, Herding, Ridesharing

Suggested Citation

Liu, Meng and Tang, Xiaocheng and Xia, Siyuan and Zhang, Shuo and Zhu, Yuting, Algorithm Aversion: Evidence from Ridesharing Drivers (August 19, 2022). Available at SSRN: https://ssrn.com/abstract=4194660 or http://dx.doi.org/10.2139/ssrn.4194660

Meng Liu

Washington University in St. Louis ( email )

Xiaocheng Tang

Meta Platforms Inc

Siyuan Xia

Shanghai Jiaotong University

Shuo Zhang

Shanghai Jiao Tong University (SJTU) - Shanghai Jiao Tong University, Antai College of Economics and Management ( email )

1954 Huashan Road
Shanghai, Shanghai 200030
China

Yuting Zhu (Contact Author)

National University of Singapore ( email )

15 Kent Ridge Dr
BIZ 1 8-14
Singapore, 119245

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