Virtual Learning Environments: Extending the Technology Acceptance Model
DOI:
https://doi.org/10.24320/redie.2019.21.e22.1866Keywords:
Virtual learning environments, technology acceptance model, higher education, teaching methodology, ICT.Abstract
As part of the ongoing renewal of teaching methodologies, universities are encouraging the use of virtual learning environments as a basic tool in face-to-face teaching settings, as they make it possible to personalize and introduce flexibility into education. The objective of this study is to provide empirical evidence on students’ perception of improvement in their learning by adopting and using virtual environments in traditional classroom settings, on the basis of an extended Technology Acceptance Model. The study population comprises 251 first-year students at the School of Economics of the University of Valencia (Universitat de València). The study results, obtained through structural equations, provide empirical evidence of a relationship in which perceived usefulness and subjective norms positively influence intention to use, which is a determining factor in students’ perceived learning.Downloads
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References
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Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Arteaga, R. y Duarte, A. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computer in Human Behavior, 26(6), 1632-1640. doi:10.1016/j.chb.2010.06.011
Bueno, S. y Salmeron, J. L. (2008). TAM-based success modeling in ERP. Interacting with Computer, 20(6), 515-523. doi:10.1016/j.intcom.2008.08.003
Casalóa, L., Flavián, C. y Guinalíu, M. (2012). Redes sociales virtuales desarrolladas por organizaciones empresariales: antecedentes de la intención de participación del consumidor. Cuadernos de Economía y Dirección de la Empresa, 15(1), 42-51. doi:10.1016/j.cede.2011.06.003
Chen Hsieh, J., Huang, Y-M., Wu, W., (2017) Technological acceptance of LINE in flipped EFL oral training. Computers in Human Behavior, 70, 178-190. doi:10.1016/j.chb.2016.12.066
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. Recuperado de http://www.jstor.org/stable/249008
Deci, E. y Ryan, R. (1995). Human autonomy: the basis for true self-esteem. En M. Kemis (Ed.), Efficacy, agency, and self-esteem (31-49). Nueva York: Plenum.
Del Barrio, S., Romero-Frías, E. y Arquero, J. L. (febrero de 2014). Experiencias y adopción del aprendizaje 2.0. El papel moderador de la necesidad de conocimiento. XXIV Jornadas Luso-Españolas de Gestión Científica. Aveiro, Portugal.
Deng, L. y Tavares, N. (2013). From Moodle to Facebook: exploring students‘ motivation and experiences in online communities. Computers & Education, 68, 167-176. doi:10.1016/j.compedu.2013.04.028
Escobar-Rodríguez, T. y Monge-Lozano, P. (2012). The acceptance of BL technology by business administration students. Computers & Education, 58(4), 1085-1098. doi:10.1016/j.compedu.2011.11.012
Fathema, N., Shannon, D. y Ross, M. (2015). Expanding The Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSS) in higher education institutions. MERLOT Journal of Online Learning and Teaching, 11(2), 210-232. Recuperado de http://jolt.merlot.org/Vol11no2/Fathema_0615.pdf
Gutiérrez, A., Palacios, A. y Torrego, L. (2010). Tribus digitales en las aulas universitarias. Comunicar, 17(34), 173-181. doi:10.3916/C34-2010-03-17
Henrie, C., Halverson, L. y Graham C. (2015). Measuring student engagement in technology-mediated learning: a review. Computers & Education, 90, 36-53. doi:10.1016/j.compedu.2015.09.005
Hu, L.-T. y Bentler, P. M. (1998). Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424-453.
doi:10.1037/1082-989X.3.4.424
Huang, Y-M. (2015). Exploring the factors that affect the intention to use collaborative technologies: the differing perspectives of sequential/global learners. Australasian Journal of Educational Technology, 31(3), 278-292. Recuperado de https://ajet.org.au/index.php/AJET/article/view/1868
Huffman, W. H. y Huffman, A. H. (2012). Beyond basic study skills: the use of technology for success in college. Computers in Human Behavior, 28(2), 583-590. doi:10.1016/j.chb.2011.11.004
Islam, A. (2011). The determinants of the post-adoption satisfaction of educators with an e-learning system. Journal of Information Systems Education, 22(4), 319-331.
Islam, A. (2013). Investigating e-learning system usage outcomes in the university context. Computer & Education, 69, 387-399. doi:10.1016/j.compedu.2013.07.037
Kaiser, H. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23(3), 187-200. doi:10.1007/BF02289233
Lee, D. y Lehto, M. (2013). User acceptance of YouTube for procedural learning: an extension of the technology acceptance model. Computers & Education, 61,193-208. doi:10.1016/j.compedu.2012.10.001
Lee, Y., Kozar, K. y Larsen, K. (2003). The Technology Acceptance Model: past, present, and future. Communications of the Association for Information Systems, 12(50), 752-780. Recuperado de http://aisel.aisnet.org/cais/vol12/iss1/50
Leidner, D. y Jarvenpaa, S. (1995). The use of information technology to enhance management school education: a theoretical view. MIS Quarterly, 19(3), 265-291. doi:10.2307/249596
Liaw, S-S. (2008). Investigating students‘ perceived satisfaction, behavioral intention, and effectiveness of e-learning: a case study of the blackboard system. Computers & Education, 51(2), 864-873. doi:10.1016/j.compedu.2007.09.005
Marcelo, C., Yot, C. y Mayor-Ruiz, C. (2015). Enseñar con tecnologías digitales en la Universidad. Comunicar, 23(45), 117-124. doi:10.3916/C45-2015-12
Ngai, E., Poon, J. y Chan, Y. (2007). Empirical examination of adoption of WebCT using TAM. Computers & Education, 48(2), 250-267. doi:10.1016/j.compedu.2004.11.007
Nunally, J. y Bernstein, I. (1994). Psychometric Theory. Nueva York: Mc Graw Hill.
O’Cass, A. y Fenech, T. (2003). Web retailing adoption: exploring the nature of Internet user’s web retailing behaviour. Journal of Retailing and Consumer Services, 10(2), 81-94.
doi: 10.1016/S0969-6989(02)00004-8
Premkumar G. y Bhattacherjee A. (2008). Explaining information technology usage: a test of competing models. Omega, 36(1), 64-75. Recuperado de https://www.sciencedirect.com/science/article/pii/S0305048305001702
Sánchez-Prieto, J.C.; Olmos-Miguelañez, S. y García-Peñalvo, F.J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519-528. doi:10.1016/j.chb.2015.07.002
Schepers, J. y Werzels, M. (2007). A meta-analysis of the technology acceptance model; investigating subjective norm and moderations effects. Information y Management, 44(1), 90-103. doi:10.1016/j.im.2006.10.007
Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computer & Education, 71, 247-256. doi:10.1016/ j.compedu.2013.09.016
Taylor, S. y Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144-176. Recuperado de https://www.jstor.org/stable/23011007
Tobias, S. y Fletcher, J. D. (2012). Reflections on “A rewiew of trends in serious gaming“. Review of educational research, 82(2), 233-237. doi:10.3102/0034654312450190
Urquidi, A. C. y Calabor, M. S. (2014). Aprendizaje a través de juegos de simulación: un estudio de los factores que determinan su eficacia pedagógica. Revista Electrónica de Tecnología Educativa, (47), 1-15. doi:10.21556/edutec.2014.47.75
Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. doi:10.1287/isre.11.4.342.11872
Venkatesh, V. y Davis F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186-205. doi:10.1287/.mnsc.46.2.186.11926
Venkatesh, V., Morris, M.G., Gordon B. D. y Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. Recuperado de http://www.jstor.org/stable/30036540
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Arteaga, R. y Duarte, A. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computer in Human Behavior, 26(6), 1632-1640. doi:10.1016/j.chb.2010.06.011
Bueno, S. y Salmeron, J. L. (2008). TAM-based success modeling in ERP. Interacting with Computer, 20(6), 515-523. doi:10.1016/j.intcom.2008.08.003
Casalóa, L., Flavián, C. y Guinalíu, M. (2012). Redes sociales virtuales desarrolladas por organizaciones empresariales: antecedentes de la intención de participación del consumidor. Cuadernos de Economía y Dirección de la Empresa, 15(1), 42-51. doi:10.1016/j.cede.2011.06.003
Chen Hsieh, J., Huang, Y-M., Wu, W., (2017) Technological acceptance of LINE in flipped EFL oral training. Computers in Human Behavior, 70, 178-190. doi:10.1016/j.chb.2016.12.066
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. Recuperado de http://www.jstor.org/stable/249008
Deci, E. y Ryan, R. (1995). Human autonomy: the basis for true self-esteem. En M. Kemis (Ed.), Efficacy, agency, and self-esteem (31-49). Nueva York: Plenum.
Del Barrio, S., Romero-Frías, E. y Arquero, J. L. (febrero de 2014). Experiencias y adopción del aprendizaje 2.0. El papel moderador de la necesidad de conocimiento. XXIV Jornadas Luso-Españolas de Gestión Científica. Aveiro, Portugal.
Deng, L. y Tavares, N. (2013). From Moodle to Facebook: exploring students‘ motivation and experiences in online communities. Computers & Education, 68, 167-176. doi:10.1016/j.compedu.2013.04.028
Escobar-Rodríguez, T. y Monge-Lozano, P. (2012). The acceptance of BL technology by business administration students. Computers & Education, 58(4), 1085-1098. doi:10.1016/j.compedu.2011.11.012
Fathema, N., Shannon, D. y Ross, M. (2015). Expanding The Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSS) in higher education institutions. MERLOT Journal of Online Learning and Teaching, 11(2), 210-232. Recuperado de http://jolt.merlot.org/Vol11no2/Fathema_0615.pdf
Gutiérrez, A., Palacios, A. y Torrego, L. (2010). Tribus digitales en las aulas universitarias. Comunicar, 17(34), 173-181. doi:10.3916/C34-2010-03-17
Henrie, C., Halverson, L. y Graham C. (2015). Measuring student engagement in technology-mediated learning: a review. Computers & Education, 90, 36-53. doi:10.1016/j.compedu.2015.09.005
Hu, L.-T. y Bentler, P. M. (1998). Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424-453.
doi:10.1037/1082-989X.3.4.424
Huang, Y-M. (2015). Exploring the factors that affect the intention to use collaborative technologies: the differing perspectives of sequential/global learners. Australasian Journal of Educational Technology, 31(3), 278-292. Recuperado de https://ajet.org.au/index.php/AJET/article/view/1868
Huffman, W. H. y Huffman, A. H. (2012). Beyond basic study skills: the use of technology for success in college. Computers in Human Behavior, 28(2), 583-590. doi:10.1016/j.chb.2011.11.004
Islam, A. (2011). The determinants of the post-adoption satisfaction of educators with an e-learning system. Journal of Information Systems Education, 22(4), 319-331.
Islam, A. (2013). Investigating e-learning system usage outcomes in the university context. Computer & Education, 69, 387-399. doi:10.1016/j.compedu.2013.07.037
Kaiser, H. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23(3), 187-200. doi:10.1007/BF02289233
Lee, D. y Lehto, M. (2013). User acceptance of YouTube for procedural learning: an extension of the technology acceptance model. Computers & Education, 61,193-208. doi:10.1016/j.compedu.2012.10.001
Lee, Y., Kozar, K. y Larsen, K. (2003). The Technology Acceptance Model: past, present, and future. Communications of the Association for Information Systems, 12(50), 752-780. Recuperado de http://aisel.aisnet.org/cais/vol12/iss1/50
Leidner, D. y Jarvenpaa, S. (1995). The use of information technology to enhance management school education: a theoretical view. MIS Quarterly, 19(3), 265-291. doi:10.2307/249596
Liaw, S-S. (2008). Investigating students‘ perceived satisfaction, behavioral intention, and effectiveness of e-learning: a case study of the blackboard system. Computers & Education, 51(2), 864-873. doi:10.1016/j.compedu.2007.09.005
Marcelo, C., Yot, C. y Mayor-Ruiz, C. (2015). Enseñar con tecnologías digitales en la Universidad. Comunicar, 23(45), 117-124. doi:10.3916/C45-2015-12
Ngai, E., Poon, J. y Chan, Y. (2007). Empirical examination of adoption of WebCT using TAM. Computers & Education, 48(2), 250-267. doi:10.1016/j.compedu.2004.11.007
Nunally, J. y Bernstein, I. (1994). Psychometric Theory. Nueva York: Mc Graw Hill.
O’Cass, A. y Fenech, T. (2003). Web retailing adoption: exploring the nature of Internet user’s web retailing behaviour. Journal of Retailing and Consumer Services, 10(2), 81-94.
doi: 10.1016/S0969-6989(02)00004-8
Premkumar G. y Bhattacherjee A. (2008). Explaining information technology usage: a test of competing models. Omega, 36(1), 64-75. Recuperado de https://www.sciencedirect.com/science/article/pii/S0305048305001702
Sánchez-Prieto, J.C.; Olmos-Miguelañez, S. y García-Peñalvo, F.J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519-528. doi:10.1016/j.chb.2015.07.002
Schepers, J. y Werzels, M. (2007). A meta-analysis of the technology acceptance model; investigating subjective norm and moderations effects. Information y Management, 44(1), 90-103. doi:10.1016/j.im.2006.10.007
Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computer & Education, 71, 247-256. doi:10.1016/ j.compedu.2013.09.016
Taylor, S. y Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144-176. Recuperado de https://www.jstor.org/stable/23011007
Tobias, S. y Fletcher, J. D. (2012). Reflections on “A rewiew of trends in serious gaming“. Review of educational research, 82(2), 233-237. doi:10.3102/0034654312450190
Urquidi, A. C. y Calabor, M. S. (2014). Aprendizaje a través de juegos de simulación: un estudio de los factores que determinan su eficacia pedagógica. Revista Electrónica de Tecnología Educativa, (47), 1-15. doi:10.21556/edutec.2014.47.75
Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. doi:10.1287/isre.11.4.342.11872
Venkatesh, V. y Davis F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186-205. doi:10.1287/.mnsc.46.2.186.11926
Venkatesh, V., Morris, M.G., Gordon B. D. y Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. Recuperado de http://www.jstor.org/stable/30036540
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Article abstract page views: 5764
Published
2019-06-28
