The Influence of Contextual Bias on Consumers' Usage Intention in Smart Services: The Moderating Effect of Anthropomorphism

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.

Authors

Institutions

Publication Details

Journal
Journal of Consumer Behaviour
Published
2025-04-29
DOI
https://doi.org/10.1002/cb.2506
Citations
1
Primary Topic
Behavioral Health and Interventions
Type
article
Field-Weighted Citation Impact
1.54
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Influence of Contextual Bias on Consumers' Usage Intention in Smart Services: The Moderating Effect of Anthropomorphism

Shuang Liu, Wei Du, Jin Gao, Jingying Niu
1 citations
Journal of Consumer Behaviour
Behavioral Health and Interventions
1.54
article

The Influence of Contextual Bias on Consumers' Usage Intention in Smart Services: The Moderating Effect of Anthropomorphism

Shuang Liu, Wei Du, Jin Gao, Jingying Niu
article en
1 citations

Abstract

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.

Journal of Consumer BehaviourVol. 24(4)
Chongqing University of Posts and Telecommunications (CN)
Openalex Percentile: Top 18%
Behavioral Health and Interventions
1.54
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.