Abstract
In recent years, the rapid advancement of artificial intelligence has led to its widespread adoption, with applications expanding across various fields. In the domain of information retrieval, artificial intelligence–assisted search tools demonstrate considerable potential because of their ability to efficiently analyze and process large volumes of data. Despite these advantages, the key factors influencing users’ continuance intention toward such tools remain insufficiently understood. To address this gap, this study investigates the determinants of users’ continuance intention toward artificial intelligence–assisted search tools empirically by integrating the expectation confirmation model and the technology acceptance model. In a survey-based study, data were collected from 306 participants (36.60% male and 63.40% female). The results indicate that expectation confirmation, perceived usefulness, and perceived ease of use significantly increase user satisfaction. Furthermore, both satisfaction and perceived ease of use influence continuance intention positively, whereas the direct effect of perceived usefulness is not statistically significant. With respect to the external variables, perceived benefit affects perceived usefulness and perceived ease of use positively, whereas perceived risk affects expectation confirmation positively. The possible explanations for these findings, along with their theoretical and practical implications, are discussed.
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The datasets generated and/or analyzed during the current study are provided in the Supplementary Data.
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Acknowledgements
We sincerely thank Yujiao Wu for her valuable assistance in revising the manuscript of this study. This research was funded by grants from the National Social Science Fund(24FJKB021); the National Social Science Fund of China–Arts Program (Grant No. 23BC048); the Smart Medical Innovation Technology Center, GDUT (Project Number: ZYZX24-023); and the Undergraduate Teaching Quality and Teaching Reform Project of Guangdong Province “Teaching Reform for Industrial Design Specialty Curriculum Based on Brain Science and Artificial Intelligence” (Document No. Yue Jiao Gao Han [2024] 30).
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Conceptualization, CY and TX; methodology, CY and XP; validation, CY, TX and XP; formal analysis, CY and XP; resources, TX; data curation, CY; writing—original draft preparation, CY; writing—review and editing, TX, YC; project administration, YC; funding acquisition, YC. All authors have read and agreed to the published version of the manuscript.
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All procedures in this study were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the Departmental Ethics Committee and the Institutional Review Board of the university where the first author is affiliated (No. GDUTXS2024170; date of approval: 01.09.2024).
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Xia, T., Yu, C., Pan, X. et al. An empirical study of user willingness to continuously use AI-assisted search tools: an extension based on the ECM and TAM theoretical models. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-06711-4
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DOI: https://doi.org/10.1057/s41599-026-06711-4
