AI Insight
This study examined psychological factors affecting users' intentions to adopt AI virtual companions by surveying 712 Chinese users and analyzing data through both PLS-SEM and artificial neural network methods. The research found that AI trust, perceived anthropomorphism, perceived usefulness, and perceived enjoyment significantly influenced usage intention, with AI trust being the strongest predictor across both analytical approaches. Gender and age differences emerged in how certain factors like AI trust and perceived enjoyment affected adoption intentions, while perceived ease of use and social anxiety showed no direct significant effects.
Why it matters
The findings provide actionable guidance for developers of AI companion technology, suggesting they should prioritize building user trust while balancing technical safety with emotional engagement features. The research also indicates that marketing and design strategies should be tailored to different demographic groups to improve adoption rates of virtual companion products.
IntroductionAI 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.MethodsUsing survey data from 712 users in China, the study employed both PLS-SEM and ANN for empirical analysis.ResultsThe 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.DiscussionThe 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.