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Uniqueness neglect on consumer resistance to AI

Yupeng Mou (China University of Mining and Technology, Xuzhou, China)
Tianjie Xu (China University of Mining and Technology, Xuzhou, China)
Yanghong Hu (University of Aberdeen, Aberdeen, UK)

Marketing Intelligence & Planning

ISSN: 0263-4503

Article publication date: 21 July 2023

Issue publication date: 23 August 2023

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Abstract

Purpose

Artificial intelligence (AI) has a large number of applications at the industry and user levels. However, AI's uniqueness neglect is becoming an obstacle in the further application of AI. Based on the theory of innovation resistance, this paper aims to explore the effect of AI's uniqueness neglect on consumer resistance to AI.

Design/methodology/approach

The authors tested four hypothesis across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI's uniqueness neglect leads to consumer resistance to AI; Studies 2 focused on the role of human–AI interaction trust as an underlying driver of resistance to medical AI. Study 3–4 provided process evidence by way of a measured moderator, testing whether participants with a greater sense of non-verbal human–AI communication are more reluctant to have consumer resistance to AI.

Findings

The authors found that AI's uniqueness neglect increased users' resistance to AI. This occurs because the uniqueness neglect of AI hinders the formation of interaction trust between users and AI. The study also found that increasing the gaze behavior of AI and increasing the physical distance in the interaction can alleviate the effect of AI's uniqueness neglect on consumer resistance to AI.

Originality/value

This paper explored the effect of AI's uniqueness neglect on consumer resistance to AI and uncovered human–AI interaction trust as a mediator for this effect and gaze behavior and physical distance as moderators for this effect.

Keywords

Citation

Mou, Y., Xu, T. and Hu, Y. (2023), "Uniqueness neglect on consumer resistance to AI", Marketing Intelligence & Planning, Vol. 41 No. 6, pp. 669-689. https://doi.org/10.1108/MIP-11-2022-0505

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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