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With an emphasis on the mediating of Perceived Trust (PT) construct, this study examines how perceived Ease of Use (PEOU), and Perceived Usefulness (PU) affect the individual impact (IN) of Artificial Intelligence (AI) technology among UTAS-Muscat IT students. With two independent variables (PEOU and PU), two dependent variables (IN and Intention to Use (ITU), and two mediators (PT and IN) form the basis of our quantitative analysis and testing of nine hypotheses. An online survey using measurement scales taken from earlier research was used to gather the results. Students who are enrolled for the 2024–2025 academic year and extensively use AI applications were the sample. SPSS 22.0 and Smart PLS 24 were used to evaluate the data. The findings show that PEOU and PU both have a considerable impact on IN, with PU having a stronger effect. PEOU has an impact on IN, but it had no discernible direct effect on ITU. Similarly, PT did not directly affect ITU, suggesting that trust plays a more complex, indirect function in figuring out usage patterns. According to mediation analysis, user intentions are a significant predictor of system adoption since IN is essential in connecting PEOU and PU to ITU. These results emphasize how crucial it is to improve students’ impressions of AI’s usability and ease of use, while the role of trust demands more research.

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Impact of Perceived Ease of Use and Usefulness, with Trust and Individual Impact as Mediators on AI Adoption Intentions among IT Students at UTAS-Muscat

  • 会议论文

  • 发表日期：

    2025年

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  • Qasim AlAjmi,
  • Yusra AlRoshdi,
  • Ashish Rostaq

摘要

With an emphasis on the mediating of Perceived Trust (PT) construct, this study examines how perceived Ease of Use (PEOU), and Perceived Usefulness (PU) affect the individual impact (IN) of Artificial Intelligence (AI) technology among UTAS-Muscat IT students. With two independent variables (PEOU and PU), two dependent variables (IN and Intention to Use (ITU), and two mediators (PT and IN) form the basis of our quantitative analysis and testing of nine hypotheses. An online survey using measurement scales taken from earlier research was used to gather the results. Students who are enrolled for the 2024–2025 academic year and extensively use AI applications were the sample. SPSS 22.0 and Smart PLS 24 were used to evaluate the data. The findings show that PEOU and PU both have a considerable impact on IN, with PU having a stronger effect. PEOU has an impact on IN, but it had no discernible direct effect on ITU. Similarly, PT did not directly affect ITU, suggesting that trust plays a more complex, indirect function in figuring out usage patterns. According to mediation analysis, user intentions are a significant predictor of system adoption since IN is essential in connecting PEOU and PU to ITU. These results emphasize how crucial it is to improve students’ impressions of AI’s usability and ease of use, while the role of trust demands more research.