Warmth trumps competence? Uncovering the influence of multimodal AI anthropomorphic interaction experience on intelligent service evaluation: Insights from the high-evoked automated social presence

Shizhen Bai, Dingyao Yu, Chunjia Han, Mu Yang, Brij B. Gupta, Varsha Arya, Prabin Kumar Panigrahi, Rui Tang, Hao He and Jiayuan Zhao

Technological Forecasting and Social Change, 2024, vol. 204, issue C

Abstract: The rapid rise of multimodal AI anthropomorphic interaction assistants has subverted the traditional human-computer interaction. Based on social cognitive theory, our study aims to validate the effectiveness of the strategy to improve users' intelligent service experience and service outcomes by evoking a high level of automated social presence through revealing the effects of both warmth perception and competence perception from multimodal AI anthropomorphic interactions on users' intelligent service evaluation. The conclusions show that: (1) Both warmth perception and competence perception can improve users' intelligent service evaluations; (2) At the beginning of car use, users are more likely to give positive evaluations for competence perception (safety), while the longer they use the car, the more likely they are to give positive evaluations for warmth perception (intimacy and friendliness); (3) Warmth perception can significantly enhance the positive impact of competence perception (safety and entertainment) on users' intelligent service evaluations. The findings of this study enrich the social cognition theory and the social presence theory, which have significant implications for the management of user experience in anthropomorphic AI interactions as well as the improvement of intelligent service quality.

Keywords: Artificial intelligence interaction; Automated social presence; Multimodal; Anthropomorphic; Social recognition; Structural topic modelling (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:204:y:2024:i:c:s0040162524001914

DOI: 10.1016/j.techfore.2024.123395

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