Analysis of Technology Adoption Factors in Learning among Vocational Students using UTAUT2 Model
DOI:
https://doi.org/10.52436/1.jutif.2025.6.5.4940Keywords:
Acceptance Technology, UTAUT2, Learning Vocation, Behavioral Intention, Actual UsageAbstract
Technology acceptance in vocational education is a key factor in supporting the effectiveness of teaching and learning processes in the digital era. This study aims to analyze the factors influencing technology acceptance among students of the Computer and Network Engineering (TKJ) Department at SMK Ma'arif 1 Kroya using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The model includes the variables Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, Habit, Behavioral Intention, and Actual Usage. The results reveal that five key variables—Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, and Price Value—significantly influence Behavioral Intention, while Habit, Facilitating Conditions, and Behavioral Intention directly affect Actual Usage. All constructs in the model meet validity and reliability criteria, and no multicollinearity was detected (VIF < 3.3). The coefficient of determination (R²) values of 0.612 for Behavioral Intention and 0.673 for Actual Usage indicate strong predictive power of the model. These findings confirm the relevance of the UTAUT2 framework for understanding and enhancing technology acceptance in vocational education settings and provide valuable insights for improving technology integration in technical learning environments.
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A. M. Baabdullah, A. A. Alalwan, N. P. Rana, H. Kizgin, and P. Patil, “Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model,” Int. J. Inf. Manage., vol. 44, no. August 2018, pp. 38–52, 2019, doi: 10.1016/j.ijinfomgt.2018.09.002.
I. Im, S. Hong, and M. S. Kang, “An international comparison of technology adoption,” Inf. Manag., vol. 48, no. 1, pp. 1–8, 2011, doi: 10.1016/j.im.2010.09.001.
E. Tan and J. Leby Lau, “Behavioural Intention to Adopt Mobile Banking Among the Millennial Generation,” Young Consum., vol. 17, no. 1, pp. 18–31, Jan. 2016, doi: 10.1108/YC-07-2015-00537.
M. Farzin, M. Sadeghi, F. Yahyayi Kharkeshi, H. Ruholahpur, and M. Fattahi, “Extending UTAUT2 in M-banking adoption and actual use behavior: Does WOM communication matter?,” Asian J. Econ. Bank., vol. 5, no. 2, pp. 136–157, Jan. 2021, doi: 10.1108/AJEB-10-2020-0085.
M. Al-Emran, V. Mezhuyev, and A. Kamaludin, “Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance,” Technol. Soc., vol. 61, no. February, p. 101247, 2020, doi: 10.1016/j.techsoc.2020.101247.
C. H. A. Tam, Y. C. Chan, Y. Law, and S. W. K. Cheng, “The Role of Three-Dimensional Printing in Contemporary Vascular and Endovascular Surgery: A Systematic Review,” Ann. Vasc. Surg., vol. 53, pp. 243–254, 2018, doi: 10.1016/j.avsg.2018.04.038.
T. Hariguna, “An Empirical Study to Understanding Students’ Continuance Intention Use of Multimedia Online Learning,” Int. J. Appl. Inf. Manag., vol. 1, no. 2, pp. 1–10, 2021, doi: 10.6025/jitr/2018/9/2/60-69.
N. Q. Huy, L. P. Nga, and P. T. Tam, “Applied Simulation Modeling for Promoting Policy Recommendations for Microfinance Activity Development : a Case Study in Vietnam,” vol. 4, no. 4, pp. 333–345, 2023.
L. Lennita, A. R. Condrobimo, and S. Candra, “Integrating TAM and ISSM Theories to Investigate Social Media Factors on Students’ Satisfaction and Academic Performance,” in 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), IEEE, 2024, pp. 1117–1122.
C. Lazim, N. D. B. Ismail, and M. Tazilah, “Application of technology acceptance model (TAM) towards online learning during covid-19 pandemic: Accounting students perspective,” Int. J. Bus. Econ. Law, vol. 24, no. 1, pp. 13–20, 2021.
S. Krisdina, O. D. Nurhayati, and D. M. K. Nugraheni, “Hybrid Model Based on Technology Acceptance Model (TAM) & Information System Success Model (ISSM) in Analyzing the Use of E-Health,” in E3S Web of Conferences, EDP Sciences, 2022, p. 5003.
D. Novita and F. Helena, “Analisis Kepuasan Pengguna Aplikasi Traveloka Menggunakan Metode Technology Acceptance Model (TAM) Dan End-User Computing Satisfaction (EUCS),” J. Teknol. Sist. Inf., vol. 2, no. 1, pp. 22–37, 2021.
A. Astari, N. Yasa, I. Sukaatmadja, and I. Giantari, “Integration of technology acceptance model (TAM) and theory of planned behavior (TPB): An e-wallet behavior with fear of COVID-19 as a moderator variable,” Int. J. Data Netw. Sci., vol. 6, no. 4, pp. 1427–1436, 2022.
C. K. Dewi, Z. Mohaidin, and M. A. Murshid, “Determinants of online purchase intention: a PLS-SEM approach: evidence from Indonesia,” J. Asia Bus. Stud., vol. 14, no. 3, pp. 281–306, Jan. 2020, doi: 10.1108/JABS-03-2019-0086.
V. Saprikis, G. Avlogiaris, and A. Katarachia, “A Comparative Study of Users Versus Non-Users’ Behavioral Intention Towards M-Banking Apps’ Adoption,” Inf., vol. 13, no. 1, 2022, doi: 10.3390/info13010030.
J. Mou and M. Benyoucef, “Consumer behavior in social commerce: Results from a meta-analysis,” Technol. Forecast. Soc. Change, vol. 167, no. July 2020, 2021, doi: 10.1016/j.techfore.2021.120734.
E. Purwanto and J. Loisa, “The Intention and Use Behaviour of the Mobile Banking System in Indonesia: UTAUT Model,” Technol. Reports Kansai Univ., vol. 62, no. 6, pp. 2757–2767, 2020.
K. Shahzad, Q. Zhang, and M. K. Khan, “Blockchain technology adoption in supply chain management: an investigation from UTAUT and information system success model,” Int. J. Shipp. Transp. Logist., vol. 18, no. 2, pp. 165–190, 2024.
H. Khazaei, “Integrating cognitive antecedents to UTAUT model to explain adoption of blockchain technology among Malaysian SMEs,” JOIV Int. J. Informatics Vis., vol. 4, no. 2, pp. 85–90, 2020.
L. Beggel, M. Pfeiffer, and B. Bischl, “Robust Anomaly Detection in Images Using Adversarial Autoencoders,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11906 LNAI, pp. 206–222, 2020, doi: 10.1007/978-3-030-46150-8_13.
A. M. Karim, H. Kaya, M. S. Güzel, M. R. Tolun, F. V. Çelebi, and A. Mishra, “A novel framework using deep auto-encoders based linear model for data classification,” Sensors (Switzerland), vol. 20, no. 21, pp. 1–21, 2020, doi: 10.3390/s20216378.
R. S. Wafiyyah and N. M. W. Kusumadewi, “The Effect of Perceived Usefulness, Perceived Ease Of Use, And Trust On Repurchase Intention On E-Commerce Shopee,” IJISET-International J. Innov. Sci. Eng. Technol., vol. 8, no. 7, pp. 428–434, 2021, [Online]. Available: www.ijiset.com
J. Chen, D. Tam, C. Raffel, M. Bansal, and D. Yang, “An Empirical Survey of Data Augmentation for Limited Data Learning in NLP,” Accessed Jan. 08, 2022, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137119577&partnerID=40&md5=214257638eda07faf9e536900297f25e
W. Rafdinal and W. Senalasari, “Predicting the adoption of mobile payment applications during the COVID-19 pandemic,” Int. J. Bank Mark., vol. 39, no. 6, pp. 984–1002, Jan. 2021, doi: 10.1108/IJBM-10-2020-0532.
A. S. Mustafa and M. B. Garcia, “Theories integrated with technology acceptance model (TAM) in online learning acceptance and continuance intention: A systematic review,” in 2021 1st Conference on online teaching for mobile education (OT4ME), IEEE, 2021, pp. 68–72.
M. H. Kalayou, B. F. Endehabtu, and B. Tilahun, “The applicability of the modified technology acceptance model (TAM) on the sustainable adoption of eHealth systems in resource-limited settings,” J. Multidiscip. Healthc., pp. 1827–1837, 2020.
S. Na, S. Heo, S. Han, Y. Shin, and Y. Roh, “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, vol. 12, no. 2, p. 90, 2022.
A. Katebi, P. Homami, and M. Najmeddin, “Acceptance model of precast concrete components in building construction based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) framework,” J. Build. Eng., vol. 45, p. 103518, 2022.
J. Jagdale and E. M., “Optimization driven actor-critic neural network for sentiment analysis in social media,” VINE J. Inf. Knowl. Manag. Syst., vol. 49, no. 4, pp. 457–476, Jan. 2019, doi: 10.1108/VJIKMS-12-2018-0116.
J. Wen, Y. Li, and P. Hou, “Customer mistreatment behavior and hotel employee organizational citizenship behavior,” Nankai Bus. Rev. Int., vol. 7, no. 3, pp. 322–344, Jan. 2016, doi: 10.1108/NBRI-02-2016-0009.
M. Tajvidi, M. O. Richard, Y. C. Wang, and N. Hajli, “Brand co-creation through social commerce information sharing: The role of social media,” J. Bus. Res., vol. 121, no. June, pp. 476–486, 2020, doi: 10.1016/j.jbusres.2018.06.008.
M. Sarstedt and J. H. Cheah, “Partial least squares structural equation modeling using SmartPLS: a software review,” J. Mark. Anal., vol. 7, no. 3, pp. 196–202, 2019, doi: 10.1057/s41270-019-00058-3.
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