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<p>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.</p>

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An empirical study of user willingness to continuously use AI-assisted search tools: an extension based on the ECM and TAM theoretical models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月6日

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  • Tiansheng Xia,
  • Chenxi Yu,
  • Xinyi Pan,
  • Yibing Chen

摘要

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.