Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers
Abstract 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...
Ausführliche Beschreibung
Autor*in: |
Lin, Yupeng [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Anmerkung: |
© The Author(s) 2023 |
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Übergeordnetes Werk: |
Enthalten in: International journal of educational technology in higher education - Cham, Switzerland : Springer International Publishing, 2016, 20(2023), 1 vom: 16. Juni |
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Übergeordnetes Werk: |
volume:20 ; year:2023 ; number:1 ; day:16 ; month:06 |
Links: |
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DOI / URN: |
10.1186/s41239-023-00403-8 |
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SPR051916614 |
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10.1186/s41239-023-00403-8 doi (DE-627)SPR051916614 (SPR)s41239-023-00403-8-e DE-627 ger DE-627 rakwb eng Lin, Yupeng verfasserin (orcid)0000-0002-3182-2459 aut Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 ($ R^{2} $ = 64.70–84.20%), which may benefit researchers, instructors, students, and technology designers. Technology acceptance model (dpeaa)DE-He213 Digital reading (dpeaa)DE-He213 Computer-assisted language learning (dpeaa)DE-He213 Academic reading (dpeaa)DE-He213 Yu, Zhonggen (orcid)0000-0002-3873-980X aut Enthalten in International journal of educational technology in higher education Cham, Switzerland : Springer International Publishing, 2016 20(2023), 1 vom: 16. Juni (DE-627)844430722 (DE-600)2843150-9 2365-9440 nnns volume:20 year:2023 number:1 day:16 month:06 https://dx.doi.org/10.1186/s41239-023-00403-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_184 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2023 1 16 06 |
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10.1186/s41239-023-00403-8 doi (DE-627)SPR051916614 (SPR)s41239-023-00403-8-e DE-627 ger DE-627 rakwb eng Lin, Yupeng verfasserin (orcid)0000-0002-3182-2459 aut Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 ($ R^{2} $ = 64.70–84.20%), which may benefit researchers, instructors, students, and technology designers. Technology acceptance model (dpeaa)DE-He213 Digital reading (dpeaa)DE-He213 Computer-assisted language learning (dpeaa)DE-He213 Academic reading (dpeaa)DE-He213 Yu, Zhonggen (orcid)0000-0002-3873-980X aut Enthalten in International journal of educational technology in higher education Cham, Switzerland : Springer International Publishing, 2016 20(2023), 1 vom: 16. Juni (DE-627)844430722 (DE-600)2843150-9 2365-9440 nnns volume:20 year:2023 number:1 day:16 month:06 https://dx.doi.org/10.1186/s41239-023-00403-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_184 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2023 1 16 06 |
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10.1186/s41239-023-00403-8 doi (DE-627)SPR051916614 (SPR)s41239-023-00403-8-e DE-627 ger DE-627 rakwb eng Lin, Yupeng verfasserin (orcid)0000-0002-3182-2459 aut Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 ($ R^{2} $ = 64.70–84.20%), which may benefit researchers, instructors, students, and technology designers. Technology acceptance model (dpeaa)DE-He213 Digital reading (dpeaa)DE-He213 Computer-assisted language learning (dpeaa)DE-He213 Academic reading (dpeaa)DE-He213 Yu, Zhonggen (orcid)0000-0002-3873-980X aut Enthalten in International journal of educational technology in higher education Cham, Switzerland : Springer International Publishing, 2016 20(2023), 1 vom: 16. Juni (DE-627)844430722 (DE-600)2843150-9 2365-9440 nnns volume:20 year:2023 number:1 day:16 month:06 https://dx.doi.org/10.1186/s41239-023-00403-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_184 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2023 1 16 06 |
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10.1186/s41239-023-00403-8 doi (DE-627)SPR051916614 (SPR)s41239-023-00403-8-e DE-627 ger DE-627 rakwb eng Lin, Yupeng verfasserin (orcid)0000-0002-3182-2459 aut Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 ($ R^{2} $ = 64.70–84.20%), which may benefit researchers, instructors, students, and technology designers. Technology acceptance model (dpeaa)DE-He213 Digital reading (dpeaa)DE-He213 Computer-assisted language learning (dpeaa)DE-He213 Academic reading (dpeaa)DE-He213 Yu, Zhonggen (orcid)0000-0002-3873-980X aut Enthalten in International journal of educational technology in higher education Cham, Switzerland : Springer International Publishing, 2016 20(2023), 1 vom: 16. Juni (DE-627)844430722 (DE-600)2843150-9 2365-9440 nnns volume:20 year:2023 number:1 day:16 month:06 https://dx.doi.org/10.1186/s41239-023-00403-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_184 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2023 1 16 06 |
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10.1186/s41239-023-00403-8 doi (DE-627)SPR051916614 (SPR)s41239-023-00403-8-e DE-627 ger DE-627 rakwb eng Lin, Yupeng verfasserin (orcid)0000-0002-3182-2459 aut Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract 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 ($ R^{2} $ = 64.70–84.20%), which may benefit researchers, instructors, students, and technology designers. Technology acceptance model (dpeaa)DE-He213 Digital reading (dpeaa)DE-He213 Computer-assisted language learning (dpeaa)DE-He213 Academic reading (dpeaa)DE-He213 Yu, Zhonggen (orcid)0000-0002-3873-980X aut Enthalten in International journal of educational technology in higher education Cham, Switzerland : Springer International Publishing, 2016 20(2023), 1 vom: 16. Juni (DE-627)844430722 (DE-600)2843150-9 2365-9440 nnns volume:20 year:2023 number:1 day:16 month:06 https://dx.doi.org/10.1186/s41239-023-00403-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_184 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2023 1 16 06 |
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Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers Technology acceptance model (dpeaa)DE-He213 Digital reading (dpeaa)DE-He213 Computer-assisted language learning (dpeaa)DE-He213 Academic reading (dpeaa)DE-He213 |
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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 |
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Abstract 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 ($ R^{2} $ = 64.70–84.20%), which may benefit researchers, instructors, students, and technology designers. © The Author(s) 2023 |
abstractGer |
Abstract 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 ($ R^{2} $ = 64.70–84.20%), which may benefit researchers, instructors, students, and technology designers. © The Author(s) 2023 |
abstract_unstemmed |
Abstract 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 ($ R^{2} $ = 64.70–84.20%), which may benefit researchers, instructors, students, and technology designers. © The Author(s) 2023 |
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