ORIGINAL RESEARCH article
Front. Psychol.
Sec. Cognitive Science
This article is part of the Research TopicHuman Reactions to Artificial Intelligence with Anthropomorphic FeaturesView all 6 articles
Decoding the Duality of GAI Anthropomorphism and Its Joint Effects—A Sequential Mixed-Methods Approach
Provisionally accepted
- 1Southwestern University of Finance and Economics, Chengdu, China
- 2Southwest Petroleum University, Chengdu, Sichuan Province, China
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The advancement of anthropomorphic generative artificial intelligence, especially in large language models and multimodal capabilities, has developed its two dimensions: functional anthropomorphism and interactional anthropomorphism. Despite this progress, prior research has predominantly emphasized interactional anthropomorphism, neglecting a holistic understanding of the dual dimensions and their combined effects. This research utilizes a sequential mixed-methods approach, starting with qualitative interviews (n = 15) to explore the joint effects of dual anthropomorphism. The qualitative results were incorporated into a subsequent series of experiments aimed at testing the joint effects, their underlying mechanisms, and boundary conditions. By extending the Expectation Confirmation Model (ECM), this research integrates the dual anthropomorphic features of GAI into a dynamic process that links users' initial expectations—both cognitive and emotional—to their subsequent experiences, evaluations, and continuance intentions. This user-centered approach addresses the growing demand in IS research to focus on not only technological features but also on how these features influence user experiences. The findings provide practical recommendations to GAI service designers and deployers, offering strategies to enhance user experiences and improve the effectiveness of GAI applications.
Keywords: Generative Artificial Intelligence1, dual anthropomorphism2, failure3, continuance intention4, Expectation Confirmation Mode5, Mixed-Methods6
Received: 21 Apr 2025; Accepted: 27 Oct 2025.
Copyright: © 2025 Zhang, Yang, Yu and Diao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Cuiting Yu, yu.cuiting@163.com
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