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From Crisis to Digital Competence: Investigating the Impact of Self-Efficacy on Perceived Technology Use through the Technology Acceptance Model in Undergraduate Business Education

Abstract

The visible global fast expansion of Coronavirus outbreak has deeply affected education, speeding up the shift toward digital learning and technology. Universities are now operating in a post-pandemic world with hybrid teaching and AI-based platforms. Understanding what motivates students to accept technology is more important than ever. The main aim of the present study is to investigate the connection involving undergraduate business higher education students’ self-efficacy (SE) and their perceived value of how beneficial technology is (usefulness) through the application of TAM (Technology Acceptance Model). Students’ gender, self-efficacy, and fundamental TAM components were among the thirty-six questions included in a validated survey that collected data. Undergraduates with familiarity with various forms of interactive learning technology served as the focus groups of this research study. The outcomes of the study indicate that students’ beliefs in their own abilities have a major impact on how they rate the practicality and simplicity of Edu-tech (educational technology) and AI-enhanced learning tools. Results show a favourable correlation between self-efficacy and three measures of technology adoption intent: behavioural intention (BI), perceived ease of use (PEOU), and perceived usefulness (PU). Educational policymakers and practitioners can benefit from these insights, which underscore the need for initiatives that build digital confidence and skills for lasting academic and career success. A new viewpoint is offered by this current study, which expands upon TAM (Technology Acceptance Model), considering the changes in education following Coronavirus. It highlights how self-efficacy contributes as a key determinant of technology acceptance in undergraduate business education.

Research topics

  • Technology Adoption and User Behaviour

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DOI: 10.21608/aja.2025.397869.1884

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