A Study on Chinese College Students' Intention to Use Toward AI Chatbots: Integrating Innovation Diffusion Theory and Technology Acceptance Model

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Technology Adoption and User Behaviour Artificial Intelligence in Education AI in Service Interactions

Abstract

ABSTRACT Despite the considerable potential of artificial intelligence ( AI) chatbots, existing research has predominantly emphasised technical aspects, lacking empirical investigation from the perspective of user willingness, particularly among college students. To address this gap, this study integrates the Technology Acceptance Model and the Innovation Diffusion Theory to explore the key factors influencing Chinese college students' intention to use AI chatbots. Specifically, it examines how the features of AI chatbots, such as relative advantage, compatibility, complexity, trialability, observability, perceived usefulness, perceived ease of use and interactivity, affect college students' attitude (ATT) and intention to use. Data from 1533 valid responses, collected via an online survey in China's Hunan Province, were analysed using Structural Equation Modelling ( SEM ) via Smart‐ PLS 4 software. The results indicated that the proposed factors collectively explain 70.2% of the variance in intention to use, with ATT exerting a significant positive influence. Perceived usefulness and perceived ease of use emerged as the strongest predictors of ATT, while the features of AI chatbots (compatibility, trialability, observability, interactivity) significantly affected perceived usefulness and perceived ease of use. In contrast, relative advantage showed no significant impact. This study empirically validates an integrated model of Innovation Diffusion Theory and Technology Acceptance Model, highlighting the driving effects of features such as perceived usefulness and perceived ease of use on ATT and intention to use. Based on these findings, strategic recommendations are provided for developers, higher education institutions, and educators to optimise AI chatbot design and facilitate their deeper integration into pedagogical contexts.

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References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error 1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology 2001Knowledge Management: An Organizational Capabilities Perspective 2012Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations 2013Structural Equation Modeling with Mplus 1999Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology 2012Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning 2020Understanding the attitude and intention to use smartphone chatbots for shopping

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