Digital Natives' Mobile Learning Adoption in terms of UTAUT-2 Model: a Structural Equation Model

Tarik Talan; Yunus Doğan; Yusuf Kalinkara

doi:10.24310/ijtei.101.2024.17440

Authors

Tarik Talan Gaziantep Islam Science and Technology University, Turkey https://orcid.org/0000-0002-5371-4520
Yunus Doğan Fırat University, Turkey https://orcid.org/0000-0002-5038-3404
Yusuf Kalinkara Gaziantep Islam Science and Technology University, Turkey https://orcid.org/0000-0001-6077-9800

DOI:

https://doi.org/10.24310/ijtei.101.2024.17440

Keywords:

Mobile Learning Adoption, Unified Theory of Acceptance and Use of Technology 2, UTAUT2, Structural Equation Model, Digital Natives

Abstract

This research investigates university students' intentions and behaviors regarding the adoption of mobile learning tools in higher education, with a focus on the Unified Theory of Acceptance and Use of Technology (UTAUT-2) model. A sample of 541 university students from a state university in the Southeastern Anatolia Region of Turkey participated in this study. Structural equation modeling was employed to assess students' mobile learning adoption levels, and statistical analyses were conducted accordingly. The findings indicate a moderate level of mobile learning adoption among the students. The study reveals that students employ various strategies while using mobile tools for learning. Notably, among digital natives, intention to use mobile devices is significantly influenced by habit, hedonic motivation and effort expectancy. Additionally, the study identifies a significant relationship between the use behavior variable and facilitating conditions. The research also examines regulatory effects within the model, demonstrating that age moderates the relationship between habit and use behavior. Furthermore, gender has a moderating effect on the relationship between facilitating conditions and behavioral intention, as well as between hedonic motivation and behavioral intention. Finally, experience moderates the relationship between habit and use behavior, as well as between behavioral intention and use behavior.

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Author Biographies

Tarik Talan, Gaziantep Islam Science and Technology University,

Gaziantep Islam Science and Technology University, Turkey

Yunus Doğan, Fırat University,

Fırat University, Turkey

Yusuf Kalinkara, Gaziantep Islam Science and Technology University,

Gaziantep Islam Science and Technology University, Turkey

References

Açıkgül, K., & Diri, E. (2020). Investigation of prospective mathematics teachers' adoption levels based on diffusion of innovations theory. PAU Journal of Education, 50, 353-373. https://doi.org/10.9779/pauefd.593656.

Açıkgül, K., & Şad, S. N. (2021). High school students’ acceptance and use of mobile technology in learning mathematics. Education and Information Technologies, 26(4), 4181-4201. https://doi.org/10.1007/s10639-021-10466-7

Afacan Adanır, G., & Muhametjanova, G. (2021). University students’ acceptance of mobile learning: A comparative study in Turkey and Kyrgyzstan. Education and Information Technologies, 26, 6163–6181. https://doi.org/10.1007/s10639-021-10620-1

Ahmed, M. S., & Kabir, A. (2018). The acceptance of smartphone as a mobile learning tool: Students of business studies in Bangladesh. Malaysian Online Journal of Educational Technology, 6(2), 38-47. http://dx.doi.org/10.17220/mojet.2018.04.003

Al-Adwan, A. S., Al-Adwan, A., & Berger, H. (2018b). Solving the mystery of mobile learning adoption in higher education. International Journal of Mobile Communications, 16(1), 24-49. https://doi.org/10.1504/IJMC.2018.088271

Al-Adwan, A. S., Al-Madadha, A., & Zvirzdinaite, Z. (2018a). Modeling students’ readiness to adopt mobile learning in higher education: An empirical study. International Review of Research in Open and Distributed Learning, 19(1), 221-241. https://doi.org/10.19173/irrodl.v19i1.3256

Alasmari, T., & Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: An extended framework and a mixed-method study. Education and Information Technologies, 24(3), 2127-2144. https://doi.org/10.1007/s10639-019-09865-8.

Alowayr, A. (2022). Determinants of mobile learning adoption: Extending the unified theory of acceptance and use of technology (UTAUT). The International Journal of Information and Learning Technology, 39(1), 1-12. https://doi.org/10.1108/IJILT-05-2021-0070

Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A. S. (2022). Acceptance of mobile technologies and m-learning by university students: An empirical investigation in higher education. Education and Information Technologies, 27(6), 7805-7826. https://doi.org/10.1007/s10639-022-10934-8

Altunçekiç, A. (2020). Evaluation of mobile learning studies between 2010-2020 using content analysis method: The case of Turkey. Gazi University Journal of Gazi Faculty of Education, 40(3), 1087-1104. https://doi.org/10.17152/gefad.816555

Baraz, B., Atik, E., & Atik. E. (2021). Examination of the use of learning management system within the scope of technology acceptance model: The case of Anadolu University. The Black Sea Journal of Social Sciences, 13(24), 503-522. https://doi.org/10.38155/ksbd.886333

Bauerlein, M. (2008). The dumbest generation: How the digital age stupefies young americans and jeopardizes our future. Pengun Group.

Bervell, B., Kumar, J. A., Arkorful, V., Agyapong, E. M., & Osman, S. (2022). Remodelling the role of facilitating conditions for google classroom acceptance: A revision of UTAUT2. Australasian Journal of Educational Technology, 38(1), 115-135. https://doi.org/10.14742/ajet.7178

Bilgiç, H. G., Duman, D., & Seferoğlu, S. S. (2011, February 2-4). Characteristics of Digital Natives and Their Effects on the Design of Online Environments. XIII Academic Computing Conference Proceedings, İnönü University, Malatya, Turkey.

Bozkurt, A. (2015). Mobile learning: Continuous learning experience anytime, anywhere. Journal of Open and Distance Education Practices and Research, 1(2), 65-81.

Brown, C., & Czerniewicz, L. (2010). Debunking the ‘digital native’: beyond digital apartheid, towards digital democracy. Journal of Computer Assisted Learning, 26(5), 357-369. https://doi.org/10.1111/j.1365-2729.2010.00369.x

Cabero Almenara, J., & Romero Tena, R. (2020). Design of a t-MOOC for training in teaching digital competences: Study under development (DIPROMOOC Project). Innoeduca. International Journal of Technology and Educational Innovation, 6(1), 4–13. https://doi.org/10.24310/innoeduca.2020.v6i1.7507

Cañete Estigarribia, D. L., Torres Gastelú, C. A., Lagunes-Dominguez, A., & Gómez García, M. (2022). Digital competence of future teachers in a Higher Education Institution in Paraguay. Pixel-Bit, Revista de Medios y Educación, (63), 159-195. https://doi.org/10.12795/pixelbit.91049

Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), e10. https://doi.org/10.21512/tw.v13i2.656.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Academic.

Crompton, H. (2013). A historical overview of mobile learning: Toward learner-centered education. In Z. L. Berge, & L. Y. Muilenburg (Eds.), Handbook of Mobile Learning (pp. 3–14). Routledge.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.

Gharrah, A.S.A., & Aljaafreh, A. (2021). Why students use social networks for education: Extension of UTAUT2. Journal of Technology and Science Education, 11(1), 53-66. https://doi.org/10.3926/jotse.1081

Guillén‐Gámez, F. D., Colomo‐Magaña, E., Ruiz‐Palmero, J., & Tomczyk, Ł. (2024). Teaching digital competence in the use of YouTube and its incidental factors: Development of an instrument based on the UTAUT model from a higher order PLS‐SEM approach. British Journal of Educational Technology, 55(1), 340-362. https://doi.org/10.1111/bjet.13365

Gyamfi, S. A. (2021). Influencing factors of students’ smartphones use for academic purposes: A developing country’s perspective. International Journal of Emerging Technologies in Learning, 16(23), 233-246. https://doi.org/10.3991/ijet.v16i23.26675

Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Springer. https://doi.org/10.1007/978-3-030-80519-7

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis. Pearson Prentice Hall.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135.

Hu, S., Laxman, K., & Lee, K. (2020). Exploring factors affecting academics’ adoption of emerging mobile technologies—An extended UTAUT perspective. Education and Information Technologies, 25(5), 4615-4635. https://doi.org/10.1007/s10639-020-10171-x

Joiner, R., Gavin, J., Brosnan, M., Cromby, J., Gregory, H., Guiller, J., Maras, P., & Moon, A. (2013). Comparing first and second generation digital natives’ internet use, internet anxiety, and internet identification. Cyberpsychology Behavior and Social Networking, 16(7), 549-552. https://doi.org/10.1089/cyber.2012.0526

Kalinkara, Y., & Talan, T. (2022). Rethinking evaluating the use of distance learning systems in the context of the unified theory of acceptance and use of technology-2. Journal of Learning for Development, 9(2), 229–252. https://doi.org/10.56059/jl4d.v9i2.617

Karasar, N. (2009). Bilimsel araştırma yöntemi (19. Baskı). Nobel.

Kirk, C.P., Chiagouris, L., Lala, V., & Thomas, J.D. (2015). How do digital natives and digital immigrants respond differently to interactivity online? Journal of Advertising Research, 55(1), 81-94.

Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81-95. https://doi.org/10.1007/s10209-014-0348-1.

Misci Kip, S., & Umul Ünsal, P. (2020). Exploring native advertising in Turkey: insights from digital immigrants and digital natives. Qualitative Market Research: An International Journal, 23(3), 381-406. https://doi.org/10.1108/QMR-01-2018-0016

Mohan, M. M., Upadhyaya, P., & Pillai, K. R. (2020). Intention and barriers to use MOOCs: An investigation among the post graduate students in India. Education and Information Technologies, 25(6), 5017-5031. https://doi.org/10.1007/s10639-020-10215-2

Moorthy, K., Yee, T. T., T’ing, L. C., & Kumaran, V. V. (2019). Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia. Australasian Journal of Educational Technology, 35(4), 174-191. https://doi.org/10.14742/ajet.4432

Onursoy, S. (2018). Digital literacy levels of university youth: A research on the students of Anadolu University. Gümüşhane University E-Journal of Faculty of Cummunication, 6(2), 989-1013. https://doi.org/10.19145/e-gifder.422671

Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies, 27(8), 10705-10730. https://doi.org/10.1007/s10639-022-11047-y

Prensky, M. (2001). Digital natives, digital immigrants part 2: Do they really think differently? On the Horizon, 9(6), 1-6. https://doi.org/10.1108/10748120110424843

Qingyang, L., Yuxuan, X., & Sijia, C. (2018). Y-generation digital natives’ impulsive buying behavior. In IEEE (Ed.), 2018 3rd Technology Innovation Management and Engineering Science International Conference (Times-Icon) (pp. 1-5). IEEE. https://doi.org/10.1109/TIMES-iCON.2018.8621840

Radomir, L., & Moisescu, O. I. (2019). Discriminant validity of the customer-based corporate reputation scale: Some causes for concern. Journal of Product & Brand Management, 29(4), 457–469.

Ringle, C.M., Wende, S., & Becker, J.M. (2015). SmartPLS 3. https://www.smartpls.com.

Rudhumbu, N. (2022). Applying the UTAUT2 to predict the acceptance of blended learning by university students. Asian Association of Open Universities Journal, 17(1), 15-36. https://doi.org/10.1108/AAOUJ-08-2021-0084

Sánchez-Prieto, J. C., Migueláñez, S. O., & García-Peñalvo, F. J. (2015, November). Behavioral intention of use of mobile technologies among pre-service teachers: Implementation of a technology adoption model based on TAM with the constructs of compatibility and resistance to change. In SIIE (Ed.), 2015 International Symposium on Computers in Education (SIIE) (pp. 120-125). IEEE. https://doi.org/10.1109/SIIE.2015.7451660

Sarstedt, M., Ringle, C.M., & Hair, J.F. (2017). Partial least squares structural equation modelling. In C. Hamburg, M. Klarmann, A. Vomberg (Eds.), Handbook of Market Research Heidelberg (pp. 1-47). Springer. https://doi.org/10.1007/978-3-319-05542-8_15-2

Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling. Lawrence Erlbaum Associates.

Sirakaya, M., & Sirakaya, DA. (2017). Examination of associate degree students' attitudes toward mobile learning in terms of various variables. Gazi University Journal of Gazi Faculty of Education, 37(3), 1085-1114. https://doi.org/10.17152/gefad.329512

Smith, K. T. (2019). Mobile advertising to digital natives: Preferences on content, style, personalization, and functionality. Journal of Strategic Marketing, 27(1), 67-80. https://doi.org/10.1080/0965254X.2017.1384043

Šumak, B., Polancic, G., & Hericko, M. (2010). An empirical study of virtual learning environment adoption using UTAUT. In IEEE (Ed.), 2010 Second International Conference on Mobile, Hybrid, and On-Line Learning (pp. 17-22). IEEE. https://doi.org/10.1109/eLmL.2010.11.

Talan, T. (2020). The effect of mobile learning on learning performance: A meta-analysis study. Educational Sciences: Theory and Practice, 20(1), 79 - 103. http://dx.doi.org/10.12738/jestp.2020.1.006.

Thomas, T. D., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development Using Information and Communication Technology (IJEDICT), 9(3), 71-85.

Thompson, P. (2013). The digital natives as learners: Technology use patterns and approaches to learning. Computers & Education, 65(1), 12–33. http://dx.doi.org/10.1016/j.compedu.2012.12.022

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), 425–478. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J. Y., & 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

Wang, Q., Myers, M. D., & Sundaram, D. (2013). Digital natives and digital immigrants towards a model of digital fluency. Business & Information Systems Engineering, 5(6), 409-419. https://doi.org/10.1007/s12599-013-0296-y

Xu, W., Shen, Z.-Y., Lin, S.-J., & Chen, J.-C. (2022). Improving the behavioral intention of continuous online learning among learners in higher education during COVID-19. Frontiers in Psychology, 13, 857709. https://doi.org/10.3389/fpsyg.2022.857709

Yağan, S. A. (2023). Mobile Learning in Higher Education: A Mixed Research Synthesis. Journal of Erzincan University Faculty of Education, 25(1), 108-125. https://doi.org/10.17556/erziefd.884448

Yener, Ö., & Abdulkadir, G. (2007). Sosyal ve eğitim bilimleri araştirmalarinda evren-örneklem sorunu. Atatürk Üniversitesi Kazım Karabekir Eğitim Fakültesi Dergisi, (15), 394-422.

Yosiana, Y., Djuandi, D., & Hasanah, A. (2021). Mobile learning and its effectiveness in mathematics. Journal of Physics: Conference Series, 1806, e012081. https://doi.org/10.1088/1742-6596/1806/1/012081

Yu, C.-W., Chao, C.-M., Chang, C.-F., Chen, R.-J., Chen, P.-C., & Liu, Y.-X. (2021). Exploring behavioral intention to use a mobile health education website: An extension of the UTAUT 2 model. Sage Open, 11(4), e21582440211055720. https://doi.org/10.1177/21582440211055721

Yuliani, R. E. (2010). Pengembangan mobile learning (m-learning) sebagai model pembelajaran alternatif dalam meningkatkan minat dan kemampuan siswa terhadap matematika. Kognisi, 1(1), 52-61.

Zenios, M., & Ioannou, E. (2018). Digital natives and digital immigrants revisited: a case of CALL. In P. Zaphiris, & A. Ioannou (Eds.), Learning and Collaboration Technologies. Learning and Teaching. LCT 2018. Lecture Notes in Computer Science (v. 10925, pp. 99-110). Springer. https://doi.org/10.1007/978-3-319-91152-6_8