Adapting the Technology Acceptance Model to Explore College Students' Intentions to Use Technology in Virtual Education

Authors

DOI:

https://doi.org/10.1344/der.2023.44.13-22

Keywords:

ICT, e-learning, digital technologies, education, learning

Abstract

The use of technologies in educational contexts has been increasing due to the COVID-19 pandemic. However, the use of models that can explain a student's intention to use certain technologies has not been adequately researched, much less has it been possible to identify the validity of a model that maintained his factorial structure in different contexts. Therefore, this study seeks to adapt the technology acceptance model to Spanish, to be used with university students. Thus, a questionnaire was applied to 297 students belonging to a private university. The results indicate that the original factorial structure of the model is maintained when it is adapted to Spanish. Likewise, it was identified that there were no differences in the model according to the gender of the participant, which corroborates a factorial invariance of the model. Also, causal relationships within the model with the intention of using technologies are corroborated. We concluded that the technology acceptance model is maintained in a different context and can be used with university students to evaluate possible educational interventions in virtuality.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Alfadda, H. A., & Mahdi, H. S. (2021). Measuring Students’ Use of Zoom Application in Language Course Based on the Technology Acceptance Model (TAM). Journal of Psycholinguistic Research, 50(4), 883–900. https://doi.org/10.1007/S10936-020-09752-1/TABLES/6

Al-Kurdi, B., Alshurideh, M., & Salloum, S. A. (2020). Investigating a theoretical framework for e-learning technology acceptance. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6484–6496. https://doi.org/10.11591/ijece.v10i6.pp6484-6496

Al-Kurdi, B., Alshurideh, M., Salloum, S. A., Obeidat, Z. M., & Al-dweeri, R. M. (2020). An Empirical Investigation into Examination of Factors Influencing University Students’ Behavior towards Elearning Acceptance Using SEM Approach. International Journal of Interactive Mobile Technologies (IJIM), 14(02), 19. https://doi.org/10.3991/ijim.v14i02.11115

Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2020). Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2020.1830121

Alshare, K. A., Mesak, H. I., Grandon, E. E., & Badri, M. A. (2011). Examining the Moderating Role of National Culture on an Extended Technology Acceptance Model. Journal of Global Information Technology Management, 14(3), 27–53. https://doi.org/10.1080/1097198X.2011.10856542

Alshurideh, M. T., al Kurdi, B., AlHamad, A. Q., Salloum, S. A., Alkurdi, S., Dehghan, A., Abuhashesh, M., & Masa’deh, R. (2021). Factors Affecting the Use of Smart Mobile Examination Platforms by Universities’ Postgraduate Students during the COVID-19 Pandemic: An Empirical Study. Informatics, 8(2), 32. https://doi.org/10.3390/informatics8020032

Alturki, U., & Aldraiweesh, A. (2021). Application of Learning Management System (LMS) during the COVID-19 Pandemic: A Sustainable Acceptance Model of the Expansion Technology Approach. Sustainability, 13(19), 10991. https://doi.org/10.3390/su131910991

Alyoussef, I. Y. (2021). Massive Open Online Course (MOOCs) Acceptance: The Role of Task-Technology Fit (TTF) for Higher Education Sustainability. Sustainability, 13(13), 7374. https://doi.org/10.3390/su13137374

Ameen, N., Willis, R., Abdullah, M. N., & Shah, M. (2019). Towards the successful integration of e-learning systems in higher education in Iraq: A student perspective. British Journal of Educational Technology, 50(3), 1434–1446. https://doi.org/10.1111/BJET.12651

An, F., Xi, L., Yu, J., & Zhang, M. (2022). Relationship between Technology Acceptance and Self-Directed Learning: Mediation Role of Positive Emotions and Technological Self-Efficacy. Sustainability, 14(16), 10390. https://doi.org/10.3390/su141610390

An, F., Yu, J., & Xi, L. (2022). Relationship between perceived teacher support and learning engagement among adolescents: Mediation role of technology acceptance and learning motivation. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.992464

Baber, H. (2020). Determinants of students’ perceived learning outcome and satisfaction in online learning during the pandemic of COVID19. Journal of Education and E-Learning Research, 7(3), 285–292. https://doi.org/10.20448/JOURNAL.509.2020.73.285.292

Baki, R., Birgoren, B., & Aktepe, A. (2018). A Meta Analysis of Factors Affecting Perceived Usefulness and Perceived Ease of Use in The Adoption of E-Learning Systems. Turkish Online Journal of Distance Education, 4–42. https://doi.org/10.17718/tojde.471649

Cabero-Almenara, J., Fernández-Batanero, J. M., & Barroso-Osuna, J. (2019). Adoption of augmented reality technology by university students. Heliyon, 5(5), e01597. https://doi.org/10.1016/J.HELIYON.2019.E01597

Chaveesuk, S., & Chaiyasoonthorn, W. (2022). COVID-19 in Emerging Countries and Students’ Intention to Use Cloud Classroom: Evidence from Thailand. https://doi.org/10.1155/2022/6909120

Cohen, J. (1998). Statistical Power Analysis for the Behavioral Sciences. In Biometrics (Second Edition). Lawrence Erlbaum Associates.

Dai, H. M., Ju, B., Teo, T., & Rappa, N. A. (2021). Understanding Chinese female university teachers’ intention to pursue a PhD degree: some insights from a Chinese university. Higher Education, 81(6), 1347–1366. https://doi.org/10.1007/S10734-020-00616-0/FIGURES/5

Daraghmi, E. (2023). The Adoption of Augmented Reality Technology in e-learning: Case of a Mobile Application During Covied-19. Lecture Notes in Networks and Systems, 488, 915–936. https://doi.org/10.1007/978-3-031-08090-6_59/COVER

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems : theory and results [Massachusetts Institute of Technology, Sloan School of Management]. http://hdl.handle.net/1721.1/15192

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

Dwivedi, Y. K., Rana, N. P., Tamilmani, K., & Raman, R. (2020). A meta-analysis based modified unified theory of acceptance and use of technology (meta-UTAUT): a review of emerging literature. Current Opinion in Psychology, 36, 13–18. https://doi.org/10.1016/j.copsyc.2020.03.008

Elacqua, G., Navarro-Palau, P., Prada, M. F., & Soares, S. (2020). Hablemos de política educativa en América Latina y el Caribe #5: Educación a distancia, semipresencial o presencial: ¿Qué dice la evidencia? In División de Educación – Sector Social. https://doi.org/10.18235/0002998

European Federation of Psychologists Associations. (2013). EFPA Review model for the description and evaluation of psychological and educational tests: Test review form and notes for reviewer Version 4.2.6. In EFPA Board of Assessment Document (pp. 1–72). https://mlp.fi/wp-content/uploads/2020/09/4.-DISC-EFPA_TestReviewModel2020_Report.pdf

Feng, G. C., Su, X., Lin, Z., He, Y., Luo, N., & Zhang, Y. (2021). Determinants of Technology Acceptance: Two Model-Based Meta-Analytic Reviews. Journalism and Mass Communication Quarterly, 98(1), 83–104. https://doi.org/10.1177/1077699020952400/ASSET/IMAGES/LARGE/10.1177_1077699020952400-FIG2.JPEG

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

Galvis, Á. H., & Carvajal, D. (2022). Learning from success stories when using eLearning and bLearning modalities in higher education: a meta-analysis and lessons towards digital educational transformation. International Journal of Educational Technology in Higher Education, 19(1), 1–31. https://doi.org/10.1186/S41239-022-00325-X/FIGURES/12

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: a global perspective (7th ed.). Pearson.

Han, J.-H., & Sa, H. J. (2021). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): the COVID-19 situation in Korea. Asia Pacific Education Review, 1, 3. https://doi.org/10.1007/s12564-021-09716-7

Hanham, J., Lee, C. B., & Teo, T. (2021). The influence of technology acceptance, academic self-efficacy, and gender on academic achievement through online tutoring. Computers & Education, 172, 104252. https://doi.org/10.1016/J.COMPEDU.2021.104252

Hernández, R., Fernández, C., & Baptista, M. del P. (2010). Metodología de la Investigación. McGraw-Hill.

Hofstede, G. (1991). Cultures and Organizations. Software of the mind. McGrawHill.

Hofstede, G. (2022, November 27). Hofstede Insights: Country Comparision. Https://Www.Hofstede-Insights.Com/Country-Comparison/.

Hogan, T. P. (2015). Pruebas psicológicas: Una introducción práctica. El Manual Moderno.

Huang, F., Sánchez-Prieto, J. C., Teo, T., García-Peñalvo, F. J., Olmos-Migueláñez, S., & Zhao, C. (2021). A cross-cultural study on the influence of cultural values and teacher beliefs on university teachers’ information and communications technology acceptance. Educational Technology Research and Development, 69(2), 1271–1297. https://doi.org/10.1007/S11423-021-09941-2/TABLES/9

Huang, F., & Teo, T. (2021). Examining the role of technology‐related policy and constructivist teaching belief on English teachers’ technology acceptance: A study in Chinese universities. British Journal of Educational Technology, 52(1), 441–460. https://doi.org/10.1111/bjet.13027

Huang, F., Teo, T., & Guo, J. (2021). Understanding English teachers’ non-volitional use of online teaching: A Chinese study. System, 101, 102574. https://doi.org/10.1016/j.system.2021.102574

Huang, F., Teo, T., Sánchez-Prieto, J. C., García-Peñalvo, F. J., & Olmos-Migueláñez, S. (2019). Cultural values and technology adoption: A model comparison with university teachers from China and Spain. Computers & Education, 133, 69–81. https://doi.org/10.1016/j.compedu.2019.01.012

Huang, F., Teo, T., & Zhou, M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Educational Technology Research and Development, 68(1), 575–591. https://doi.org/10.1007/s11423-019-09695-y

Instituto Internacional para la Educación Superior en América Latina y el Caribe. (2003). La Educación Superior Virtual en América Latina y el Caribe. http://hdl.handle.net/20.500.12799/527

Jadil, Y., Rana, N. P., & Dwivedi, Y. K. (2021). A meta-analysis of the UTAUT model in the mobile banking literature: The moderating role of sample size and culture. Journal of Business Research, 132, 354–372. https://doi.org/10.1016/j.jbusres.2021.04.052

Jamalova, M., & Bálint, C. (2022a). Modelling Students’ Adoption of E-Learning During the COVID-19 Pandemic. International Journal of Emerging Technologies in Learning (IJET), 17(07), 275–292. https://doi.org/10.3991/ijet.v17i07.29243

Jamalova, M., & Bálint, C. (2022b). Modelling Students’ Adoption of E-Learning During the COVID-19 Pandemic. International Journal of Emerging Technologies in Learning (IJET), 17(07), 275–292. https://doi.org/10.3991/ijet.v17i07.29243

Jun, J. S., Lee, K. H., & Roh, S. (2021). Technology acceptance, social support, and life satisfaction among rural American Indian Elders. Journal of Ethnic & Cultural Diversity in Social Work, 30(5), 398–412. https://doi.org/10.1080/15313204.2019.1702606

Kamin, S. T., Beyer, A., & Lang, F. R. (2020). Social support is associated with technology use in old age. Zeitschrift Für Gerontologie Und Geriatrie, 53(3), 256–262. https://doi.org/10.1007/s00391-019-01529-z

Khan, F. M., Singh, N., Gupta, Y., Kaur, J., Banik, S., & Gupta, S. (2022). A Meta-analysis of Mobile Learning Adoption in Higher Education Based on Unified Theory of Acceptance and Use of Technology 3 (UTAUT3). Vision: The Journal of Business Perspective, 097226292211011. https://doi.org/10.1177/09722629221101159

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003

Kline, R. (2011). Principles and practice of structural equation modeling (3rd ed). The Guilford Press.

Kumar Basak, S., Wotto, M., & Bélanger, P. (2018). E-learning, M-learning and D-learning: Conceptual definition and comparative analysis. E-Learning and Digital Media, 15(4), 191–216. https://doi.org/10.1177/2042753018785180

Kyriazos, T. A. (2018). Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General. Psychology, 9(8), 2207–2230. https://doi.org/10.4236/psych.2018.98126

Lacasa, P., Nieto, J. J., Radanliev, P., Vladova, G., Ullrich, A., Bender, B., & Gronau, N. (2021). Students’ Acceptance of Technology-Mediated Teaching – How It Was Influenced During the COVID-19 Pandemic in 2020: A Study From Germany. https://doi.org/10.3389/fpsyg.2021.636086

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4

Li, W., Shen, S., Yang, J., & Tang, Q. (2021). Internet-Based Medical Service Use and Eudaimonic Well-Being of Urban Older Adults: A Peer Support and Technology Acceptance Model. International Journal of Environmental Research and Public Health, 18(22), 12062. https://doi.org/10.3390/ijerph182212062

Manzano, A. (2017). Introducción a los modelos de ecuaciones estructurales. Inv Ed M, 7(25), 67–72.

Marandu, E. E., Makudza, F., & Ngwenya, S. N. (2019). Predicting Students’ Intention and Actual Use of E-Learning Using the Technology Acceptance Model: A Case from Zimbabwe. International Journal of Learning, Teaching and Educational Research, 18(6), 110–127. https://doi.org/10.26803/ijlter.18.6.7

Mardia, K. v. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530. https://doi.org/10.1093/biomet/57.3.519

McDermott, M. S., Oliver, M., Simnadis, T., Beck, E. J., Coltman, T., Iverson, D., Caputi, P., & Sharma, R. (2015). The Theory of Planned Behaviour and dietary patterns: A systematic review and meta-analysis. Preventive Medicine, 81, 150–156. https://doi.org/10.1016/j.ypmed.2015.08.020

Neciosup-La Rosa, F. L. (2006). La educación superior virtual. Un reto para la universidad latinoamericana. In Escenarios mundiales de la educación superior. Análisis global y estudios de casos. CLACSO, Consejo Latinoamericano de Ciencias Sociales Editorial.

Nguyen, T.-M., Nham, P. T., & Hoang, V.-N. (2019). The theory of planned behavior and knowledge sharing. VINE Journal of Information and Knowledge Management Systems, 49(1), 76–94. https://doi.org/10.1108/VJIKMS-10-2018-0086

Organisation for Economic Co-operation and Development. (2015). E-learning in Higher Education in Latin America.

Pal, D., & Vanijja, V. (2020). Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Children and Youth Services Review, 119. https://doi.org/10.1016/J.CHILDYOUTH.2020.105535

Pan, X. (2020). Technology Acceptance, Technological Self-Efficacy, and Attitude Toward Technology-Based Self-Directed Learning: Learning Motivation as a Mediator. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.564294

Pinho, C., Franco, M., & Mendes, L. (2020). Acceptance and use of information technology: context of Portuguese universities. Information and Learning Science, 121(11–12), 869–887. https://doi.org/10.1108/ILS-02-2020-0030/FULL/XML

Rahimi, B., Nadri, H., Lotfnezhad Afshar, H., & Timpka, T. (2018). A Systematic Review of the Technology Acceptance Model in Health Informatics. Applied Clinical Informatics, 09(03), 604–634. https://doi.org/10.1055/s-0038-1668091

Reddy, P., Chaudhary, K., Sharma, B., & Chand, R. (2021). The two perfect scorers for technology acceptance. Education and Information Technologies, 26(2), 1505–1526. https://doi.org/10.1007/s10639-020-10320-2

Riebl, S. K., Estabrooks, P. A., Dunsmore, J. C., Savla, J., Frisard, M. I., Dietrich, A. M., Peng, Y., Zhang, X., & Davy, B. M. (2015). A systematic literature review and meta-analysis: The Theory of Planned Behavior’s application to understand and predict nutrition-related behaviors in youth. Eating Behaviors, 18, 160–178. https://doi.org/10.1016/j.eatbeh.2015.05.016

Romero-Sanchez, D., & Barrios, D. (2022). Technological Acceptance of Virtual Platforms in University Students: An Analysis in Times of Pandemic. IEEE Revista Iberoamericana de Tecnologias Del Aprendizaje, 17(1), 17–20. https://doi.org/10.1109/RITA.2022.3149782

Rutkowski, L., & Svetina, D. (2014). Assessing the Hypothesis of Measurement Invariance in the Context of Large-Scale International Surveys. Educational and Psychological Measurement, 74(1), 31–57. https://doi.org/10.1177/0013164413498257

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514. https://doi.org/10.1007/BF02296192

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007

Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review. The Journal of Educational Research, 99(6), 323–338. https://doi.org/10.3200/JOER.99.6.323-338

Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11). https://doi.org/10.1016/j.heliyon.2020.e05410

Sykes, Venkatesh, & Gosain. (2009). Model of Acceptance with Peer Support: A Social Network Perspective to Understand Employees’ System Use. MIS Quarterly, 33(2), 371. https://doi.org/10.2307/20650296

Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2021). Consumer Acceptance and Use of Information Technology: A Meta-Analytic Evaluation of UTAUT2. Information Systems Frontiers, 23(4), 987–1005. https://doi.org/10.1007/s10796-020-10007-6

Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144

Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/J.COMPEDU.2011.06.008

Teo, T. (2012). Examining the intention to use technology among pre-service teachers: an integration of the Technology Acceptance Model and Theory of Planned Behavior. Interactive Learning Environments, 20(1), 3–18. https://doi.org/10.1080/10494821003714632

Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540

Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Waris, I., & Hameed, I. (2022). Modeling teachers acceptance of learning management system in higher education during COVID ‐19 pandemic: A developing country perspective. Journal of Public Affairs. https://doi.org/10.1002/pa.2821

Xu, F., & Du, J. T. (2018). Factors influencing users’ satisfaction and loyalty to digital libraries in Chinese universities. https://doi.org/10.1016/j.chb.2018.01.029

Yamakawa, P., Delgado, C., Díaz, E., Garayar, E., & Laguna, H. (2013). Factors Influencing the Use of Mobile Technologies in a University Environment. International Journal of Information and Communication Technology Education, 9(2), 24–38. https://doi.org/10.4018/jicte.2013040103

Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta‐analysis of the TAM: Part 2. Journal of Modelling in Management, 2(3), 281–304. https://doi.org/10.1108/17465660710834462

Zhang, L., Nyheim, P., & S. Mattila, A. (2014). The effect of power and gender on technology acceptance. Journal of Hospitality and Tourism Technology, 5(3), 299–314. https://doi.org/10.1108/JHTT-03-2014-0008

Zhu, X., & Cheng, X. (2022). Staying connected: smartphone acceptance and use level differences of older adults in China. Universal Access in the Information Society. https://doi.org/10.1007/s10209-022-00933-4

Published

2023-12-29

Issue

Section

Peer Review Articles