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Artificial intelligence or human: when and why consumers prefer AI recommendations

Fei Jin (Sichuan University, Chengdu, China)
Xiaodan Zhang (University of Science and Technology Beijing, Beijing, China)

Information Technology & People

ISSN: 0959-3845

Article publication date: 6 October 2023

1165

Abstract

Purpose

Artificial intelligence (AI) is revolutionizing product recommendations, but little is known about consumer acceptance of AI recommendations. This study examines how to improve consumers' acceptance of AI recommendations from the perspective of product type (material vs experiential).

Design/methodology/approach

Four studies, including a field experiment and three online experiments, tested how consumers' preference for AI-based (vs human) recommendations differs between material and experiential product purchases.

Findings

Results show that people perceive AI recommendations as more competent than human recommendations for material products, whereas they believe human recommendations are more competent than AI recommendations for experiential products. Therefore, people are more (less) likely to choose AI recommendations when buying material (vs experiential) products. However, this effect is eliminated when is used as an assistant to rather than a replacement for a human recommendation.

Originality/value

This study is the first to focus on how products' material and experiential attributes influence people's attitudes toward AI recommendations. The authors also identify under what circumstances resistance to algorithmic advice is attenuated. These findings contribute to the research on the psychology of artificial intelligence and on human–technology interaction by investigating how experiential and material attributes influence preference for or resistance to AI recommenders.

Keywords

Citation

Jin, F. and Zhang, X. (2023), "Artificial intelligence or human: when and why consumers prefer AI recommendations", Information Technology & People, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ITP-01-2023-0022

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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