Technológiaelfogadás a felsőoktatásban

Az interakcióigény és az önszabályozás hatása az online tanulási szándékra

Authors

DOI:

https://doi.org/10.14267/VEZTUD.2022.07.02

Keywords:

technology acceptance, online education, UTAUT-2, personal characteristics

Abstract

In the spring of 2020, the COVID-19 pandemic forced higher education stakeholders worldwide to introduce distance learning, which made it necessary to adopt entirely new teaching and learning strategies. This research explores the personal characteristics, which influence the acceptance of online education, such as the need for personal interaction or the ability for self-regulated learning. In this quantitative research, the authors used data collected from 307 higher education students via an online questionnaire to test their hypotheses. Structural equation modelling (SEM) showed that performance expectancy and hedonic motivation variables (UTAUT-2) directly affected online learning intentions, whereas this was indirectly influenced by self-regulated learning and need for interaction variables. Ultimately, the effort expectancy (UTAUT-2) variable had a non-significant effect on the endogenous variable, while the five constructs explained the intention to use online learning with a variance of 66.4%.

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Author Biographies

Ágnes Halász, Corvinus University of Budapest

PhD student

Zsófia Kenesei, Corvinus University of Budapest

Full Professor

References

Abdul Rabu, S. N., Hussin, H., & Bervell, B. (2019). Qr code utilization in a large classroom: Higher education students’ initial perceptions. Education and Information Technologies, 24(1), 359–384. https://doi.org/10.1007/s10639-018-9779-2

Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036

Al-Adwan, A. S., Al-Madadha, A., & Zvirzdinaite, Z. (2018). Modeling Students’ Readiness to Adopt Mobile Learning in Higher Education: An Empirical Study. The International Review of Research in Open and Distributed Learning, 19(1), 221-241. https://doi.org/10.19173/irrodl.v19i1.3256

Al-Adwan, A. S. (2020). Investigating the drivers and barriers to MOOCs adoption: The perspective of TAM. Education and Information Technologies, 25(6), 5771-5795. https://doi.org/10.1007/s10639-020-10250-z

Alalwan, N., Al-Rahmi, W. M., Alfarraj, O., Alzahrani, A., Yahaya, N., & Al-Rahmi, A. M. (2019). Integrated Three Theories to Develop a Model of Factors Affecting Students’ Academic Performance in Higher Education. IEEE Access, 7, 98725-98742. https://doi.org/10.1109/access.2019.2928142

Alamri, M. M., Almaiah, M. A., & Al-Rahmi, W. M. (2020). The Role of Compatibility and Task-Technology Fit (TTF): On Social Networking Applications (SNAs) Usage as Sustainability in Higher Education. IEEE Access, 8, 161668-161681. https://doi.org/10.1109/access.2020.3021944

Albelbisi, N. A. (2019). The role of quality factors in supporting self-regulated learning (SRL) skills in MOOC environment. Education and Information Technologies, 24(2), 1681-1698. https://doi.org/10.1007/s10639-018-09855-2

Alenazy, W. M., Mugahed Al-Rahmi, W., & Khan, M. S. (2019). Validation of tam model on social media use for collaborative learning to enhance collaborative authoring. IEEE Access, 7, 71550–71562. https://doi.org/10.1109/ACCESS.2019.2920242

Al-Rahmi, W. M., Alias, N., Othman, M. S., Marin, V. I., & Tur, G. (2018). A model of factors affecting learning performance through the use of social media in Malaysian higher education. Computers & Education, 121, 59-72. https://doi.org/10.1016/j.compedu.2018.02.010

Altalhi, M. (2020). Toward a model for acceptance of MOOCs in higher education: The modified UTAUT model for Saudi Arabia. Education and Information Technologies, 26, 1589–1605. https://doi.org/10.1007/s10639-020-10317-x

Atif, A., Richards, D., Busch, P., & Bilgin, A. (2015). Assuring graduate competency: A technology acceptance model for course guide tools. Journal of Computing in Higher Education, 27(2), 94-113. https://doi.org/10.1007/s12528-015-9095-4

Barajas, M. (2002). Restructuring Higher Education institutions in Europe: The case of virtual learning environments. Interactive Educational Multimedia, 5(October), 1-28.

Basak, S. K., 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

Bawack, R., & Kala Kamdjoug, J. R. (2017). Adequacy of UTAUT in clinician adoption of health information systems in developing countries: The case of Cameroon. International Journal of Medical Informatics, 109, 15-22. https://doi.org/10.1016/j.ijmedinf.2017.10.016

Botero, G. G., Questier, F., Cincinnato, S., He, T., & Zhu, C. (2018). Acceptance and usage of mobile assisted language learning by higher education students. Journal of Computing in Higher Education, 30(3), 426-451. https://doi.org/10.1007/s12528-018-9177-1

Byrne, B.M. (2016). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming (3rd ed.). London: Routledge. https://doi.org/10.4324/9781315757421

Chavoshi, A., & Hamidi, H. (2019). Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran. Telematics and Informatics, 38, 133-165. https://doi.org/10.1016/j.tele.2018.09.007

Collier, J. E., & Kimes, S. E. (2012). Only if it is convenient: Understanding How Convenience Influences Self-Service: Technology Evaluation. Journal of Service Research, 16(1), 39-51. https://doi.org/10.1177/1094670512458454

Cosnefroy, L., & Carré, P. (2017). Self-regulated and Self-directed Learning: Why Don’t Some Neighbors Communicate? International Journal of Self-Directed Learning, 11(2), 1-12.

Curran, J. M., & Meuter, M. L. (2005). Self‐service technology adoption: Comparing Three technologies. Journal of Services Marketing, 19(2), 103-113. https://doi.org/10.1108/08876040510591411

Dabholkar, P. A. (1996). Consumer Evaluations of new technology-based self-service options: An investigation of alternative models of service quality. International Journal of Research in Marketing, 13(1), 29-51. https://doi.org/10.1016/0167-8116(95)00027-5

Dabholkar, P. A. & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184- 201. https://doi.org/10.1177/0092070302303001

Dailey-Hebert, A. (2018). Maximizing interactivity in on- line learning: Moving beyond discussion boards. Journal of Educators Online, 15(3), 1199230. https://doi.org/10.9743/jeo.2018.15.3.8

Davis, F. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems (PhD Thesis). Massachusetts Institute of Technology, Sloan School of Management, Cambridge (MA).

Dečman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49, 272–281. https://doi.org/10.1016/j.chb.2015.03.022

Demoulin, N. T., & Djelassi, S. (2016). An integrated model of self-service technology (SST) usage in a retail context. International Journal of Retail & Distribution Management, 44(5), 540-559. https://doi.org/10.1108/ijrdm-08-2015-0122

Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research note - how does personality matter? relating the five-factor model to technology acceptance and use. Information Systems Research, 19(1), 93-105. https://doi.org/10.1287/isre.1070.0153

Doleck, T., Bazelais, P., & Lemay, D. J. (2017). The role of behavioral expectation in technology acceptance: A CEGEP case study. Journal of Computing in Higher Education, 30(3), 407-425. https://doi.org/10.1007/s12528-017-9158-9

Eitel, A., Endres, T., & Renkl, A. (2020). Self-management as a Bridge Between Cognitive Load and Self-regulated Learning: The Illustrative Case of Seductive Details. Educational Psychology Review, 32(4), 1073- 1087. https://doi.org/10.1007/s10648-020-09559-5

Eom, S. B. (2012). Effects of LMS, self‐efficacy, and self‐regulated learning on LMS effectiveness in business education. Journal of International Education in Business, 5(2), 129-144. https://doi.org/10.1108/18363261211281744

Estriegana, R., Medina-Merodio, J., & Barchino, R. (2019). Student acceptance of Virtual Laboratory and practical work: An extension of the Technology Acceptance Model. Computers & Education, 135, 1-14. https://doi.org/10.1016/j.compedu.2019.02.010

Gaskin, J. & Lim, J. (2016). Model fit measures.

Herting, D. C., Pros, R. C., & Tarrida, A. C. (2020). Habit and social influence as determinants of PowerPoint use in higher education: A study from a technology acceptance approach. Interactive Learning Environments, 1-17. https://doi.org/10.1080/10494820.2020.1799021

Hoi, V. N. (2020). Understanding higher education learners acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761. https://doi.org/10.1016/j.compedu.2019.103761

Hu, L.-t., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424 – 453. https://doi.org/10.1037/1082-989X.3.4.424

Ikhsan, R. B., & Prabowo, H. (2021). Drivers of the mobile-learning management system’s actual usage: Applying the utaut model. ICIC Express Letters. Part B, Applications: An International Journal of Research and Surveys, 12(11), 1067-1074.

Jain, N. K., Bhaskar, K., & Jain, S. (2021). What drives adoption intention of electric vehicles in India? An integrated UTAUT model with environmental concerns, perceived risk and government support. Research in Transportation Business & Management, 42, 100730. https://doi.org/10.1016/j.rtbm.2021.100730

Kaushik, M. K., & Verma, D. (2019). Determinants of digital learning acceptance behavior A systematic review of applied theories and implications for higher education. Journal of Applied Research in Higher Education, 12(4), 659-672. https://doi.org/10.1108/JARHE-06-2018-0105

Kazainé Ónodi, A. (2022). Online vagy hagyományos tantermi oktatás? Educatio, 30(3), 508-514. https://doi.org/10.1556/2063.30.2021.3.10

Kemp, N., & Grieve, R. (2014). Face-to-face or face-to- screen? Undergraduates’ opinions and test performance in classroom vs. online learning. Frontiers in Psychology, 5, 1278. https://doi.org/10.3389/fpsyg.2014.01278

Kemény, I., Simon, J., Berezvai, Z., & Kun, Z. (2021). Marketingkutatás kvantitatív módszerei. Segédanyag SPSS program használatához. Budapest: Budapesti Corvinus Egyetem Marketing Intézet.

Kenesei, Z. (2020). A technológia használatának segítő tényezői idős korban. Vezetéstudomány, 51(10), 15-28. https://doi.org/10.14267/VEZTUD.2020.10.02

Kenesei, Zs., & Cserdi, Zs. (2018) A kényszerített önkiszolgálás elfogadásának előzményei és következményei a BKK-automaták példáján keresztül. Vezetéstudomány, 49(12), 4-10. https://doi.org/10.14267/VEZTUD.2018.12.01

Keszey, T. (2018). Bizalom és vezetői információfelhasználás: a hatalom moderáló hatása. Statisztikai Szemle, 96(2), 164–181.

Keszey, T. (2020). Behavioural intention to use autonomous vehicles: Systematic review and empirical extension. Transportation Research Part C: Emerging Technologies, 119, 102732. https://doi.org/10.1016/j.trc.2020.102732

Keszey, T., & Zsukk, J. (2017). Az új technológiák fogyasztói elfogadása: A magyar és nemzetközi szakirodalom áttekintése és kritikai értékelése. Vezetéstudomány, 48(10), 38–47. https://doi.org/10.14267/VEZTUD.2017.10.05

Kuong, H. C. (2015). Enhancing online learning experience: From learners’ perspective. Procedia - Social and Behavioral Sciences, 191, 1002–1005. https://doi.org/10.1016/j.sbspro.2015.04.403

Lazar, I. M., Panisoara, G., & Panisoara, I. O. (2020). Digital technology adoption scale in the blended learning context in higher education: Development, validation and testing of a specific tool. Plos One, 15(7). https://doi.org/10.1371/journal.pone.0235957

Lakhal, S., Khechine, H., & Pascot, D. (2013). Student behavioural intentions to use desktop video conferencing in a distance course: Integration of autonomy to the UTAUT model. Journal of Computing in Higher Education, 25, 93–121.

Lee, J., Kim, J., & Choi, J. Y. (2019). The adoption of virtual reality devices: The technology acceptance model integrating enjoyment, social interaction, and strength of the social ties. Telematics and Informatics, 39, 37-48. https://doi.org/10.1016/j.tele.2018.12.006

Lin, Y., McKeachie, W. J., & Kim, Y. C. (2003). College student intrinsic and/or extrinsic motivation and learning. Learning and Individual Differences, 13(3), 251-258. https://doi.org/10.1016/s1041-6080(02)00092-4

Loyens, S. M., Magda, J., & Rikers, R. M. (2008). Self-Directed Learning in Problem-Based Learning and its Relationships with Self-Regulated Learning. Educational Psychology Review, 20(4), 411-427. https://doi.org/10.1007/s10648-008-9082-7

Maier, C. (2012). Personality within information systems research: A literature analysis. ECIS 2012 Proceedings, 101.

Molnár, É. (2002). Önszabályozó tanulás: Nemzetközi kutatási irányzatok és tendenciák. Magyar Pedagógia, 102(1), 63-79.

Moran, M., Hawkes, M., & Gayar, O. E. (2010). Tablet Personal Computer Integration in higher education: Applying the unified theory of acceptance and use technology model to understand supporting factors. Journal of Educational Computing Research, 42(1), 79-101. https://doi.org/10.2190/ec.42.1.d

Murray, G. (2014). The social dimensions of learner autonomy and self-regulated learning. Studies in Self-Access Learning Journal, 5(4), 320–341.

Ngampornchai, A., & Adams, J. (2016). Students’ acceptance and readiness for E-learning in Northeastern Thailand. International Journal of Educational Technology in Higher Education, 13(1). https://doi.org/10.1186/s41239-016-0034-x

Nistor, N., Stanciu, D., Lerche, T., & Kiel, E. (2019). “I am fine with any technology, as long as it doesn’t make trouble, so that I can concentrate on my study”: A case study of university students’ attitude strength related to educational technology acceptance. British Journal of Educational Technology, 50(5), 2557-2571. https://doi.org/10.1111/bjet.12832

Nunnally, J. C. (1967). Psychometric Theory. New York: McGraw-Hill.

Ongena, G., Staat, S., & Ravesteijn, P. (2020). Factors Affecting the Adoption of Self-Service Technology (SST) in the Public Sector. International Journal of Public Administration in the Digital Age, 7(3), 32-46. https://doi.org/10.4018/ijpada.2020070102

Otter, R. R., Seipel, S., Graeff, T., Alexander, B., Boraiko, C., Gray, J., Petersen, K., & Sadler, K. (2013). Comparing student and faculty perceptions of online and traditional courses. The Internet and Higher Education, 19, 27–35. https://doi.org/10.1016/j.iheduc.2013.08.001

Proháczik, Á. (2020). A tantermi és online oktatás összehasonlító elemzése. Opus et Educatio, 7(3), 208-219. https://doi.org/10.3311/ope.390

Rabu, S. N., Hussin, H., & Bervell, B. (2018). QR code utilization in a large classroom: Higher education students’ initial perceptions. Education and Information Technologies, 24(1), 359-384. https://doi.org/10.1007/s10639-018-9779-2

Raman, P. (2020). Examining the importance of gamification, social interaction and perceived enjoyment among young female online buyers in India. Young Consumers, 22(3), 387-412. https://doi.org/10.1108/yc-05-2020-1148

Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2020). Social Isolation and Acceptance of the Learning Management System (LMS) in the time of COVID-19 Pandemic: An Expansion of the UTAUT Model. Journal of Educational Computing Research, 59(2), 183-208. https://doi.org/10.1177/0735633120960421

Rose, J. & Fogarty, G. (2006). Determinants of perceived usefulness and perceived ease of use in the technology acceptance model: senior consumers’ adoption of self-service banking technologies. Academy of World Business, Marketing & Management Development Conference Proceedings, 2(10), 122-129.

Salloum, S. A., Alhamad, A. Q., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model. IEEE Access, 7, 128445-128462. https://doi.org/10.1109/access.2019.2939467

Saks, K., & Leijen, Ä. (2014). Distinguishing Self-directed and Self-regulated Learning and Measuring them in the E-learning Context. Procedia - Social and Behavioral Sciences, 112, 190-198. https://doi.org/10.1016/j.sbspro.2014.01.1155

Sharif, A., & Raza, S. A. (2017). The influence of hedonic motivation, self-efficacy, trust and habit on adoption of internet banking: A case of developing country. International Journal of Electronic Customer Relationship Management, 11(1), 1-22. https://doi.org/10.1504/ijecrm.2017.086750

Shorfuzzaman, M., Hossain, M. S., Nazir, A., Muhammad, G., & Alamri, A. (2019). Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment. Computers in Human Behavior, 92, 578-588. https://doi.org/10.1016/j.chb.2018.07.002

Sidik, D., & Syafar, F. (2020). Exploring the factors influencing student’s intention to use mobile learning in Indonesia higher education. Education and Information Technologies, 25(6), 4781-4796. https://doi.org/10.1007/s10639-019-10018-0

Sindermann, C., Riedl, R., & Montag, C. (2020). Investigating the Relationship between Personality and Technology Acceptance with a Focus on the Smartphone from a Gender Perspective: Results of an Exploratory Survey Study. Future Internet, 12(7), 110. https://doi.org/10.3390/fi12070110

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), e05410. https://doi.org/10.1016/j.heliyon.2020.e05410

Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077. https://doi.org/10.1016/j.chb.2011.08.005

Sun, P. C., Tsai, R. J., Finger, G., Chen, Y.Y., & Yeh, D. (2008). What drives a successful e-learning?: An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202. https://doi.org/10.1016/j.compedu.2006.11.007

Tamilmani, K., Rana, N. P., Prakasam, N., & Dwivedi, Y. K. (2019). The battle of Brain vs. Heart: A literature review and meta-analysis of “hedonic motivation” use in UTAUT2. International Journal of Information Management, 46, 222-235. https://doi.org/10.1016/j.ijinfomgt.2019.01.008

Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312. https://doi.org/10.1016/j.compedu.2008.08.006

Tu, C.H. & McIsaac, M. (2002). The relationship of social presence and interaction in online classes. American Journal of Distance Education, 16(3), 131–150. https://doi.org/10.1207/S15389286AJDE1603_2

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

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412

Walker, R. H., & Francis, H. (2003). Customer service and relationship management in the context of technology-enabled service delivery systems. Australasian Marketing Journal, 11(2), 23-33. https://doi.org/10.1016/S1441-3582(03)70126-8

Yadegaridehkordi, E., Shuib, L., Nilashi, M., & Asadi, S. (2019). Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities. Education and Information Technologies, 24(1), 79–102. https://doi.org/10.1007/s10639-018-9761-z

Yakubu, M. N., & Dasuki, S. I. (2018). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria. Information Development, 35(3), 492-502. https://doi.org/10.1177/0266666918765907

Yang, H. H., Feng, L., & MacLeod, J. (2019). Understanding college students’ acceptance of cloud classrooms in flipped instruction: Integrating UTAUT and connected classroom climate. Journal of Educational Computing Research, 56(8), 1258–1276. https://doi.org/10.1177/0735633117746084

Young, M. R. (2005). The motivational effects of the classroom environment in facilitating self-regulated learning. Journal of Marketing Education, 27(1), 25- 40. https://doi.org/10.1177/0273475304273346

Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and continuous intention to undertake online courses: A self-regulated learning perspective. Educational Technology Research and Development, 68(3), 1485-1519. https://doi.org/10.1007/s11423-020-09753-w

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Published

2022-07-27

How to Cite

Halász, Ágnes, & Kenesei, Z. (2022). Technológiaelfogadás a felsőoktatásban: Az interakcióigény és az önszabályozás hatása az online tanulási szándékra. Vezetéstudomány / Budapest Management Review, 53(7), 4–18. https://doi.org/10.14267/VEZTUD.2022.07.02

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