Evidence map›Paper›PMID 42344986›Full record

ArticleFrontiers in psychology2026

Exploring the psychological factors influencing intention to use AI virtual companions: an SEM-ANN study.

Yue Sun, Jun Liu, Ting Liu, Zhengqi Wei, Huajie Shen

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yue Sun *School of Design, Fujian University of Technology, Fuzhou, China.
Jun Liu *College of Art, Cheongju University, Cheongju, Republic of Korea.
Ting LiuSchool of Design, Fujian University of Technology, Fuzhou, China.
Zhengqi WeiSchool of Design, Fujian University of Technology, Fuzhou, China.
Huajie ShenSchool of Design, Fujian University of Technology, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: AI virtual companion products have shown promise in providing emotional support and interactive experiences, but their user acceptance and broader adoption still face challenges. Building on the TAM3 model, this study introduced AI trust, perceived anthropomorphism, and social anxiety to construct a theoretical model of usage intention. Methods: Using survey data from 712 users in China, the study employed both PLS-SEM and ANN for empirical analysis. Results: The results showed that perceived usefulness, perceived enjoyment, AI trust, and perceived anthropomorphism were all significantly and positively associated with usage intention, whereas the direct associations of perceived ease of use and social anxiety with usage intention were not significant. In addition, gender showed significant differences in the paths linking AI trust and perceived enjoyment to usage intention, while age showed significant differences in the paths linking AI trust and perceived anthropomorphism to usage intention. Both SEM and ANN indicated that AI trust was the most critical predictor, whereas perceived usefulness had the lowest relative importance. However, the two methods differed in their ranking of the intermediate predictors: in SEM, perceived enjoyment was more important than perceived anthropomorphism, whereas the opposite pattern emerged in ANN. Discussion: The originality of this study lies in its theoretical extension of the user acceptance framework, its methodological demonstration of the advantages of integrating SEM and ANN, and its practical implications for optimizing virtual companion products. Specifically, the findings suggest the need to balance technical safety with emotional experience and to adopt differentiated design and promotion strategies for different user groups.

Indexed as

AI trustartificial intelligence (AI)artificial neural network (ANN)structural equation modeling (SEM)virtual companion

Identifiers

PMID42344986
PMCPMC13287140

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