JOURNAL ARTICLE
The Influence of Contextual Bias on Consumers' Usage Intention in Smart Services: The Moderating Effect of Anthropomorphism.
Published In: Journal of Consumer Behaviour, 2025, v. 24, n. 4. P. 1967 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Du, Wei; Gao, Jin; Niu, Jingying; Liu, Shuang 3 of 3
Abstract
The development of artificial intelligence (AI) technology has driven the widespread use of smart (AI) services and significantly enhanced consumer convenience and personalized experiences. However, contextual bias in smart (AI) services can significantly and negatively impact consumers' experiences and hinder the sustainable development of smart (AI) services. To investigate the effect of contextual bias in smart (AI) services on consumers' usage intention and identify its mediating mechanism and ways to mitigate the negative impact, we introduce the explanatory role of information processing fluency to examine the moderating role of anthropomorphism. We adopted an experimental approach, setting up four different experiments. Studies 1 and 2 investigated the effects of different forms of contextual bias on consumers' usage intention; cultural bias impacted consumers more negatively than personal bias did, and information processing fluency mediated the effects of contextual bias on the consumers' usage intention of smart (AI) services. Studies 3 and 4 showed that this process was moderated by anthropomorphism. Social orientation (vs. task orientation) better moderated cultural bias, and low‐form anthropomorphism (vs. high‐form anthropomorphism) better moderated personal bias. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Consumer Behaviour. 2025/07, Vol. 24, Issue 4, p1967
- Document Type:Article
- Subject Area:Communication and Mass Media
- Publication Date:2025
- ISSN:1472-0817
- DOI:10.1002/cb.2506
- Accession Number:186621096
- Copyright Statement:Copyright of Journal of Consumer Behaviour is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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