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Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers
Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers
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Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers
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Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers
Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers

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Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers
Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers
Journal Article

Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers

2023
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Overview
Digital academic reading tools on computers bring multiple benefits to higher-education students. Through structural equation modeling methods, this study contributes to the following findings: (1) Perceived ease of use, perceived usefulness, and lecturers’ positive responses significantly predict students’ positive attitudes toward digital academic reading tools on computers; (2) perceived ease of use, lectures’ positive responses, and expectations of academic achievement are significantly positive predictors of students’ perceived usefulness of these tools; (3) attitudes and expectations of academic achievement significantly predict students’ positive intentions to use these tools; (4) academic experience significantly predicts students’ negative attitudes toward these tools; (5) perceived ease for collaborative learning and self-efficacy are significantly positive predictors of students’ perceived ease of using these tools. Findings in this study may contribute to understanding the external factors influencing students’ acceptance and use of digital academic reading tools on computers with a substantial explanatory power of the proposed model (R2 = 64.70–84.20%), which may benefit researchers, instructors, students, and technology designers.