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Mixed-methods analysis shows social influence impacts behavioral intention to use ChatGPT among Chinese users.
As utilization of generative artificial intelligence increases, understanding user behavioral intentions that influence its use is crucial. We surveyed 359 ChatGPT users and analyzed their responses using partial least squares structural equation modeling to test hypotheses based on the unified theory of acceptance and use of technology. We found that performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived enjoyment were all positively related to the behavioral intention to use ChatGPT, and perceived risk was negatively related to the behavioral intention to use ChatGPT. The results of fuzzy-set qualitative comparative analysis were categorized into the following antecedent configurations affecting the behavioral intention to use ChatGPT: influence-driven performance, influence-facilitating enjoyment, and hybrid. These findings not only enrich and develop the unified theory of acceptance and use of technology framework but also provide insights for ChatGPT designers to promote the product.
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Wang et al. (2026) studied this question.
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