Understanding the Competency Levels of Art-Based Students in Utilising Technological Tools for Learning: A Case of a Digital Art Students
Abstract
Using technology as a pedagogical teaching tool is a well-established concept, with numerous studies demonstrating its effectiveness in enhancing learning outcomes. However, few studies have explored the connection between technology and the proficiency of art-based students utilising technology for the purpose of designing and creating digital media, such as video games, comics, and animations. This study explored how facilitating conditions, prior access, and technology usage influenced the competency level of students. A quantitative descriptive approach was used to collect data from 119 students who voluntarily participated by completing an online questionnaire. Structural equation modelling was utilised to process the data obtained from the questionnaire results. The findings indicated that both computer self-efficacy and facilitating conditions had a positive effect on the perceived ease of use within a technological learning environment. This, in turn, influenced the competency of digital art students in utilising technology. Past exposure to technology influenced digital art students’ openness to experimenting with new game design and animation technologies in the technology playroom.
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Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5), 82–107. https://doi.org/10.19173/irrodl.v14i5.1631
Aburub, F., & Alnawas, I. (2019). A new integrated model to explore factors that influence adoption of mobile learning in higher education: An empirical investigation. Education and Information Technologies, 24(3), 2145–2158. https://doi.org/10.1007/s10639-019-09862-x
Adikwu, M. U., Agunbiade, M. O., & Abah, J. M. U. (2017). Science and technology education initiatives in Nigeria: The case of STEP-B. In B. Akpan (Ed.), Science education: A global perspective (pp. 145–173). Springer International. https://doi.org/10.1007/978-3-319-32351-0_8
Agrawal, D., & Madaan, J. (2023). A structural equation model for big data adoption in the healthcare supply chain. International Journal of Productivity and Performance Management, 72(4), 917–942. https://doi.org/10.1108/IJPPM-12-2020-0667
Ahmed, M. S., Everett, J., & Turnbull, W. F. (2017). Extracting best set of factors that affect students’ adoption of smartphone for university education: Empirical evidence from UTAUT-2 model. Journal of Management, Economics, and Industrial Organization, 1(1), 51–64. https://doi.org/10.31039/jomeino.2017.1.1.5
Al-Adwan, A. S., Al-Madadha, A., & Zvirzdinaite, Z. (2018). Modeling students’ readiness to adopt mobile learning in higher education: An empirical study. International Review of Research in Open and Distributed Learning, 19(1), 1–22. https://doi.org/10.19173/irrodl.v19i1.3256
Alhwaiti, M. (2023). Acceptance of artificial intelligence application in the post-COVID era and its impact on faculty members’ occupational well-being and teaching self-efficacy: A path analysis using the UTAUT 2 model. Applied Artificial Intelligence, 37(1), 1–21. https://doi.org/10.1080/08839514.2023.2175110
Alshurafat, H., Al Shbail, M. O., Masadeh, W. M., Dahmash, F., & Al Msiedeen, J. M. (2021). Factors affecting online accounting education during the COVID-19 pandemic: An integrated perspective of social capital theory, the theory of reasoned action and the technology acceptance model. Education and Information Technologies, 26(6), 6995–7013. https://doi.org/10.1007/s10639-021-10550-y
Alyoussef, I. Y. (2021). Factors influencing students’ acceptance of m-learning in higher education: An application and extension of the UTAUT model. Electronics, 10(24), 1–22. https://doi.org/10.3390/electronics10243171
Asrifan, A., Aksan, M., Prastyo, H., Tasni, N., Burhan, M. I., Suparto, W. P., & Pajarianto, H. (2025). From STEM to STEAM: The evolution and its global implications. In S. K. Behera, A. S. Azar, S. Curle, & J. G. Dials (Eds.), Transformative approaches to STEAM integration in modern education (pp. 1–32). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7408-5.ch001
Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165, 120–557. https://doi.org/10.1016/j.techfore.2020.120557
Bernacki, M. L., Greene, J. A., & Crompton, H. (2020). Mobile technology, learning, and achievement: Advances in understanding and measuring the role of mobile technology in education. Contemporary Educational Psychology, 60, 101827. https://doi.org/10.1016/j.cedpsych.2019.101827
Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. University of South Florida.
Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), 106–114. https://doi.org/10.21512/tw.v13i2.656
Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(July), 1–14. https://doi.org/10.3389/fpsyg.2019.01652
Chatterjee, S., & Kar, A. K. (2020). Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India. International Journal of Information Management, 53(March), 102103. https://doi.org/10.1016/j.ijinfomgt.2020.102103
Chen, Y., Li, C., Cao, L., & Liu, S. (2024). The effects of self-efficacy, academic stress, and learning behaviors on self-regulated learning in blended learning among middle school students. Education and Information Technologies, 29(18), 24087–24110. http://dx.doi.org/10.1007/s10639-024-12821-w
Compeau, D., Gravill, J., Haggerty, N., & Kelley, H. (2015). Computer self-efficacy: A review. In P. Zhang, & D. F. Galletta (Eds.), Human-computer interaction and management information systems: Foundations. Routledge. https://doi.org/10.4324/9781315703619
Dingel, J., Kleine, A. K., Cecil, J., Sigl, A. L., Lermer, E., & Gaube, S. (2024). Predictors of health care practitioners’ intention to use AI-enabled clinical decision support systems: Meta- analysis based on the unified theory of acceptance and use of technology. Journal of Medical Internet Research, 26, 57224. https://doi.org/10.2196/57224
Dirgiatmo, Y. (2023). Testing the discriminant validity and heterotrait–monotrait ratio of correlation (HTMT): A case in Indonesian SMEs. In W. A. Barnett, & B. S. Sergi (Eds.), Macroeconomic risk and growth in the Southeast Asian countries: Insight from Indonesia (pp. 157–170). Emerald Publishing. https://doi.org/10.1108/s1571-03862023000033a011
Eneizan, B., Mohammed, A. G., Alnoor, A., Alabboodi, A. S., & Enaizan, O. (2019). Customer acceptance of mobile marketing in Jordan: An extended UTAUT2 model with trust and risk factors. International Journal of Engineering Business Management, 11, 1–10. https://doi.org/10.1177/1847979019889484
Faloye, S. T., Ajayi, N. A., & Raghavjee, R. (2020). Managing the challenges of the digital divide among first year students: A case of UKZN. In 2020 IST-Africa Conference (IST-Africa) (pp. 1– 11). IEEE. https://ieeexplore.ieee.org/abstract/document/9144063
Faloye, S. T., & Faniran, V. (2023). Integrating technology in teaching and learning practices: Students’ competencies. South African Computer Journal, 35(1), 101–114. https://doi.org/10.18489/sacj.v35i1.1111
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 1–13. https://doi.org/10.2307/3151312
Garrison, D. R. (2011). E-learning in the 21st century (2nd ed.). Routledge. https://doi.org/10.4324/9780203838761
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2023). Review of partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Structural Equation Modeling: A Multidisciplinary Journal, 30(1), 165–167. https://doi.org/10.1080/10705511.2022.2108813
Hair, J. F., Ringle, C. M., Gudergan, S. P., Fischer, A., Nitzl, C., & Menictas, C. (2019). Partial least squares structural equation modeling-based discrete choice modeling: An illustration in modeling retailer choice. Business Research, 12(1), 115–142. https://doi.org/10.1007/s40685-018-0072-4
Hall, J. R., Savas-Hall, S., & Shaw, E. H. (2022). A deductive approach to a systematic review of entrepreneurship literature. Management Review Quarterly, 73, 987–1016. https://doi.org/10.1007/s11301-022-00266-9
Heflin, H., Shewmaker, J., & Nguyen, J. (2017). Impact of mobile technology on student attitudes, engagement, and learning. Computers & Education, 107(April), 91–99. https://doi.org/10.1016/j.compedu.2017.01.006
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(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
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
Jang, H. W., & Cho, M. (2022). The relationship between ugly food value and consumers’ behavioral intentions: Application of the theory of reasoned action. Journal of Hospitality and Tourism Management, 50(February), 259–266. https://doi.org/10.1016/j.jhtm.2022.02.009
Jantjies, M. (2020, April 29). How South Africa can address digital inequalities in e-learning. The Conversation. https://theconversation.com/how-south-africa-can-address-digital-inequalities-in-e-learning-137086
Karaca, F., Can, G., & Yildirim, S. (2013). A path model for technology integration into elementary school settings in Turkey. Computers & Education, 68(October), 353–365. https://doi.org/10.1016/j.compedu.2013.05.017
Khayer, A., Talukder, S., Bao, Y., & Hossain, N. (2020). Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach. Technology in Society, 60(April 2019), 101225. https://doi.org/10.1016/j.techsoc.2019.101225
Khlaif, Z. N., & Salha, S. (2022). Exploring the factors influencing mobile technology integration in higher education. Technology, Pedagogy and Education, 31(3), 347–362. https://doi.org/10.1080/1475939x.2022.2052949
Kikawa, C. R., Kiconco, C., Agaba, M., Ntrinampeba, D., Ssematimba, A., & Kalema, B. M. (2022). Social media marketing for small and medium enterprise performance in Uganda: A structural equation model. Sustainability, 14(21), 1–20. https://doi.org/10.3390/su142114391
Kim, H.-J., Lee, J.-M., & Rha, J.-Y. (2017). Understanding the role of user resistance on mobile learning usage among university students. Computers & Education, 113(October), 108–118. https://doi.org/10.1016/j.compedu.2017.05.015
Kwon, K., Ottenbreit-Leftwich, A. T., Sari, A. R., Khlaif, Z., Zhu, M., Nadir, H., & Gok, F. (2019). Teachers’ self-efficacy matters: Exploring the integration of mobile computing device in middle schools. TechTrends, 63(6), 682–692. https://doi.org/10.1007/s11528-019-00402-5
Lee, C. C. E., Leow, S. W. Y., & Kong, X. J. (2020). The use of mobile technologies for learning in higher education: Students’ readiness. In Proceedings of the 6th International SEARCH Conference 2019 (pp. 107–127). Sunway University. http://eprints.sunway.edu.my/1341/
Liao, X., & Cao, P. (2025). Digital media entertainment technology based on artificial intelligence robot in art teaching simulation. Entertainment Computing, 52(January), Article 100792. http://doi.org/10.1016/j.entcom.2024.100792
Liu, L., Miguel Cruz, A., Rios Rincon, A., Buttar, V., Ranson, Q., & Goertzen, D. (2015). What factors determine therapists’ acceptance of new technologies for rehabilitation: A study using the unified theory of acceptance and use of technology (UTAUT). Disability and Rehabilitation, 37(5), 447–455. https://doi.org/10.3109/09638288.2014.923529
Makhado, M. P., & Tshisikhawe, T. R. (2021). How apartheid education encouraged and reinforced tribalism and xenophobia in South Africa. In M. A. Mafukata (Ed.), Impact of immigration and xenophobia on development in Africa (pp. 131–151). IGI Global. https://doi.org/10.4018/978-1-7998-7099-9.ch008
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
Mâță, L., Clipa, O., Cojocariu, V. M., Robu, V., Dobrescu, T., Hervás-Gómez, C., & Stoica, I. V. (2021). Students’ attitude towards the sustainable use of mobile technologies in higher education. Sustainability, 13(11), 5923. https://doi.org/10.3390/su13115923
Murray, C. E. (2009). Diffusion of innovation theory: A bridge for the research-practice gap in counseling. Journal of Counseling & Development, 87(1), 108–116. https://doi.org/10.1002/J.1556-6678.2009.TB00556.X
Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the technology acceptance model (TAM) in combination with the technology–organisation–environment (TOE) framework. Buildings, 12(2), 1–17. https://doi.org/10.3390/buildings12020090
Naicker, V., & Van der Merwe, D. B. (2018). Managers’ perception of mobile technology adoption in the life insurance industry. Information Technology & People, 31(2), 507–526. https://doi.org/10.1108/ITP-09-2016-0212
Nain, H. (2021). Understanding students’ behavioural intentions to use social media learning in education: A UTAUT model approach. In 2021 International Conference on Computational Performance Evaluation (ComPE 2021) (pp. 256–261). IEEE. https://doi.org/10.1109/ComPE53109.2021.9752450
Nakra, P. (2021). COVID-19 disrupts US higher education industry reimagining the future. Journal of Higher Education Theory & Practice, 21(6), 140–147. https://doi.org/10.33423/jhetp.v21i6.4382
Owoseni, A., & Twinomurinzi, H. (2018). Mobile apps usage and dynamic capabilities: A structural equation model of SMEs in Lagos, Nigeria. Telematics and Informatics, 35(7), 2067–2081. https://doi.org/10.1016/j.tele.2018.07.009
Pimmer, C., Mateescu, M., & Gröhbiel, U. (2016). Mobile and ubiquitous learning in higher education settings. A systematic review of empirical studies. Computers in Human Behavior, 63(October), 490–501. https://doi.org/10.1016/j.chb.2016.05.057
Qteishat, M., Alshibly, H., & Al-Ma’aitah, M. (2013). Factors influencing the adoption of e learning in Jordan: An extended TAM model. European Journal of Business and Management, 5(18), 84–100. https://www.researchgate.net/publication/269635743_Factors_Influencing_the_Adoption_of_E-Learning_in_Jordan_an_Extended_TAM_Model
Qureshi, M. A., Khaskheli, A., Qureshi, J. A., Raza, S. A., & Yousufi, S. Q. (2023). Factors affecting students’ learning performance through collaborative learning and engagement. Interactive Learning Environments, 31(4), 2371–2391. http://doi.org/10.1080/10494820.2021.1884886
Ramaila, S., & Molwele, A. J. (2022). The role of technology integration in the development of 21st century skills and competencies in life sciences teaching and learning. International Journal of Higher Education, 11(5), 9–17. https://doi.org/10.5430/ijhe.v11n5p9
Randle, O. (2024a). An indigenized framework for game design curriculum for African universities. Jurnal Bidang Pendidikan Dasar, 8(1), 25–33. https://doi.org/10.21067/jbpd.v8i1.9316
Randle, O. (2024b). Technology mediated bilingual participatory culture framework for delivery of education for students: in disrupted or war zones. Jurnal Bidang Pendidikan Dasar, 8(1), 1–11. https://doi.org/10.21067/jbpd.v8i1.9307
Randle, O. A. (2024c). Factors affecting students’ acceptance of gamified learning (SAGL). Studies in Learning and Teaching, 5(3), 547–569. https://scie-journal.com/index.php/SiLeT/article/view/431
Razali, M. N. M., Hamid, A. H. A., Alias, B. S., & Mansor, A. N. (2025). Validity and reliability: Instruments of teacher competency in the context of small schools in Peninsular Malaysia. Journal of Education and Learning (EduLearn), 19(1), 487–494. http://doi.org/10.11591/edulearn.v19i1.21574
Saleem, A., Aslam, J., Kim, Y. B., Nauman, S., & Khan, N. T. (2022). Motives towards e shopping adoption among Pakistani consumers: An application of the technology acceptance model and theory of reasoned action. Sustainability, 14(7), 1–16. https://doi.org/10.3390/su14074180
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2020). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research. Springer. https://doi.org/10.1007/978-3-319-05542-8
Shiferaw, K. B., & Mehari, E. A. (2019). Modeling predictors of acceptance and use of electronic medical record system in a resource limited setting: Using modified UTAUT model. Informatics in Medicine Unlocked, 17(April), 100182. https://doi.org/10.1016/j.imu.2019.100182
Sinaga, E. K., Suryadi, E., Goeltom, A. D. L., Rakhman, C. U., & Susanto, E. (2024). Personal and organizational factors supporting green HRM performance in the tourism industry. Journal of Eastern European and Central Asian Research, 11(2), 272–289. https://doi.org/10.15549/jeecar.v11i2.1623
Sun, Y., & Gao, F. (2020). An investigation of the influence of intrinsic motivation on students’ intention to use mobile devices in language learning. Educational Technology Research and Development, 68, 1181–1198. https://doi.org/10.1007/s11423-019-09733-9
Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer-based assessment. Computers & Education, 56(4), 1032–1044. https://doi.org/10.1016/J.COMPEDU.2010.11.017
Thongsri, N., Shen, L., & Bao, Y. (2020). Investigating academic major differences in perception of computer self-efficacy and intention toward e-learning adoption in China. Innovations in Education and Teaching International, 57(5), 577–589. https://doi.org/10.1080/14703297.2019.1585904
Timotheou, M., & Hennessy, S. (2021). Understanding technology integration into the classroom as a systemic and socially situated initiative. In G. Marks (Ed.), Proceedings of the International Journal on E Learning (pp. 59–81). Association for the Advancement of Computing in Education. https://doi.org/10.70725/614128pcnooc
Venkatesh, V., Thong, J., & 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, H., Tao, D., Yu, N., & Qu, X. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International Journal of Medical Informatics, 139(July), Article 104156. https://doi.org/10.1016/j.ijmedinf.2020.104156
Yakubu, M. N., & Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492–502. https://doi.org/10.1177/0266666918765907
Yusoff, A. S. M., Peng, F. S., Razak, F. Z and Mustafa, W. (2020). Discriminant validity assessment of religious teacher acceptance: The use of HTMT criterion. Journal of Physics: Conference Series, 1529(4), 042045. https://doi.org/10.1088/1742-6596/1529/4/042045
Zhai, X., & Shi, L. (2020). Understanding how the perceived usefulness of mobile technology impacts physics learning achievement: A pedagogical perspective. Journal of Science Education and Technology, 29, 743–757. https://doi.org/10.1007/s10956-020-09852-6
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