Modeling Factors Affecting Vocational School Students’ Perceived Ease of Use of Educational Technology
DOI:
https://doi.org/10.24246/ijiteb.812025.32-39Keywords:
Educational Technology, Perceived Ease of Use, Technology Acceptance Model (TAM), PLS-SEM, Vocational EducationAbstract
The integration of educational technology has become increasingly important in supporting effective learning processes. Google Drive is one of the widely used cloud platforms for online storage, access, and collaboration in educational activities. This study aims to examine the factors influencing vocational school students’ perceived ease of use (PEoU) of Google Drive as a learning support tool. The research model was adapted from the Technology Acceptance Model (TAM) by incorporating external variables, including Computer Self-Efficacy (CSE), Perceived Enjoyment (PE), Perceptions of External Control (PEC), Technological Complexity (TC), and Facilitating Conditions (FC). Data were collected from 274 students at a public vocational school in Central Java using an online questionnaire. The data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS. The findings reveal that Computer Self-Efficacy, Perceived Enjoyment, and Perceptions of External Control significantly influence students’ perceived ease of use of Google Drive. In contrast, Technological Complexity and Facilitating Conditions do not significantly affect perceived ease of use. These findings highlight the importance of internal user perceptions in supporting educational technology adoption among vocational school students.
Downloads
References
M. Alharthi, “Students’ Attitudes toward the Use of Technology in Online Courses,” International Journal of Technology in Education, vol. 3, no. 1, pp. 14–23, 2020, doi: 10.46328/ijte.v3i1.18.
A. M. Al-Ghurbani, F. Jazim, M. Abdulrab, Y. H. S. Al-Mamary, and I. Khan, “The impact of internal factors on the use of technology in higher education in Saudi Arabia during the COVID-19 pandemic,” Journal of Applied and Advanced Research, vol. 41, no. 1, pp. 283–302, 2021, doi: 10.3233/HSM-211219.
R. Raja and P. C. Nagasubramani, “Impact of modern technology in education,” Journal of Applied and Advanced Research, vol. 3, no. 1, pp. S33–S35, 2018, doi: 10.21839/jaar.2018.v3S1.165.
M. D. Solviana, “Utilization of Educational Technology During the Covid-19 Pandemic: Use of Online Gamification Features at the University of Muhammadiyah Pringsewu Lampung,” Al-Jahiz: Journal of Biology Education Research, vol. 1, no. 1, pp. 1–14, 2020.
D. Suminar, “Application of Technology as a Learning Media in Sociology Subjects,” in Proceedings of the National Seminar on Education FKIP, vol. 2, 2019.
I. N. Abidah, M. A. Hamdani, and Y. Amrozi, “Implementation of Cloud Computing Database System in the Education Sector,” KELUWIH: Journal of Science and Technology, vol. 1, no. 2, pp. 77–84, 2020, doi: 10.24123/saintek.v1i2.2868.
A. Rashid and A. Chaturvedi, “Cloud Computing Characteristics and Services: A Brief Review,” International Journal of Computer Sciences and Engineering, vol. 7, no. 2, pp. 421–426, 2019, doi: 10.26438/ijcse/v7i2.421426.
D. C. Marinescu, Cloud Computing Theory and Practice, 3rd ed. Waltham, MA, USA: Morgan Kaufmann, 2013.
K. Jamsa, Cloud Computing: SaaS, PaaS, IaaS, Virtualization, Business Models, Mobile, Security and More. Burlington, MA, USA: Jones & Bartlett Learning, 2013.
A. Sari, “A Review of Anomaly Detection Systems in Cloud Networks and Survey of Cloud Security Measures in Cloud Storage Applications,” Journal of Information Security, vol. 6, no. 2, pp. 142–154, 2015, doi: 10.4236/jis.2015.62015.
A. J. M. Guerrero, C. R. Jimenez, M. R. N. Parejo, R. S. Costa, and J. L. Belmonte, “WhatsApp and Google Drive Influence on Pre-service Students’ Learning,” Frontiers in Education, vol. 5, no. 152, pp. 1–10, 2020, doi: 10.3389/feduc.2020.00152.
I. Muhammad et al., “Using Google Form for Student Worksheet as Learning Media,” International Journal of Engineering & Technology, vol. 7, no. 3, pp. 321–324, 2018.
M. Okmawati, “The use of Google Classroom During Pandemic,” Journal of English Language Teaching, vol. 9, no. 2, pp. 438–443, 2020.
M. M. Salih, “A Comparative Study Between Google Workspace and Microsoft Office 365 Productivity Services in Iraqi Educational Institutions,” International Journal of Humanities and Educational Research, vol. 3, no. 5, pp. 123–135, 2021, doi: 10.47832/2757-5403.5-3.11.
X. Wu, S. S. Maidin, and M. N. Abdul Rahman, “Integrating the technology acceptance model on online learning effectiveness among emerging adult learners,” International Journal of Education and Practice, vol. 11, no. 2, pp. 129–140, 2023.
L. Zhou, F. Li, S. Wu, and M. Zhou, “Extending the technology acceptance model to explore students’ intention to use an online education platform,” SAGE Open, vol. 12, no. 2, 2022, doi: 10.1177/21582440221085259.
A. Prabowo and P. Ninghardjanti, “Perceived usefulness and perceived ease of use toward students’ behavioral intention in vocational schools,” Jurnal Informasi dan Komunikasi Administrasi Perkantoran, 2025.
M. R. Hasan, N. I. Chowdhury, M. H. Rahman, M. A. B. Syed, and J. Ryu, “Analysis of the user perception of chatbots in education using a partial least squares structural equation modeling approach,” arXiv preprint arXiv:2311.03636, 2023.
V. Venkatesh and H. Bala, “Technology Acceptance Model 3 and a Research Agenda on Interventions,” Decision Sciences, vol. 39, no. 2, pp. 273–315, 2008, doi: 10.1111/j.1540-5915.2008.00192.x.
S. Sugiyono, Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Bandung, Indonesia: Alfabeta, 2019.
R. V. Krejcie and D. W. Morgan, “Determining Sample Size for Research Activities,” Educational and Psychological Measurement, vol. 30, no. 3, pp. 607–610, 1970.
R. H. Hoyle, Structural Equation Modeling: Concepts, Issues, and Applications. Thousand Oaks, CA, USA: SAGE Publications, 1995
F. Huang, T. Teo, and R. Scherer, “Investigating the antecedents of university students’ perceived ease of using the Internet for learning,” Interactive Learning Environments, pp. 1–17, 2020, doi: 10.1080/10494820.2019.1710540.
B. Mei, G. T. L. Brown, and T. Teo, “Toward an Understanding of Preservice English as a Foreign Language Teachers’ Acceptance of Computer-Assisted Language Learning 2.0 in the People’s Republic of China,” Educational Technology Research and Development, vol. 56, no. 1, pp. 1–31, 2018.
F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, vol. 13, no. 3, pp. 319–340, 1989, doi: 10.2307/249008.
J. Khlaisang, T. Teo, and F. Huang, “Acceptance of a flipped smart application for learning: A study among Thai university students,” Interactive Learning Environments, vol. 29, no. 5, pp. 772–789, 2019, doi: 10.1080/10494820.2019.1612447.
V. Peñarroja, J. Sánchez, N. Gamero, V. Orengo, and A. M. Zornoza, “The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice,” Behaviour & Information Technology, vol. 38, no. 8, pp. 845–857, 2019, doi: 10.1080/0144929X.2018.1564070.
R. Ambarwati, Y. D. Harja, and S. Thamrin, “The Role of Facilitating Conditions and User Habits: A Case of Indonesian Online Learning Platform,” The Journal of Asian Finance, Economics and Business, vol. 7, no. 10, pp. 481–489, 2020, doi: 10.13106/jafeb.2020.vol7.no10.481.
K. K. Fung So, H. Kim, and H. Oh, “What makes Airbnb experiences enjoyable? The effects of environmental stimuli on perceived enjoyment and repurchase intention,” Journal of Travel Research, vol. 60, no. 5, pp. 1–15, 2020, doi: 10.1177/0047287520921241.
R. Baki, B. Birgoren, and A. Aktepe, “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, vol. 19, no. 4, pp. 4–42, 2018, doi: 10.17718/tojde.471649.
N. Zeng, Y. Liu, P. Gong, M. Hertogh, and M. König, “Do right PLS and do PLS right: A critical review of the application of PLS-SEM in construction management research,” European Business Review, vol. 31, no. 1, pp. 356–369, 2021, doi: 10.1108/EBR-11-2018-0203.
J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to use and how to report the results of PLS-SEM,” European Business Review, vol. 31, no. 1, pp. 2–24, 2019, doi: 10.1108/EBR-11-2018-0203.
C. Fornell and D. F. Larcker, “Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics,” Journal of Marketing Research, vol. 18, no. 3, pp. 382–388, 1981.
J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” Journal of the Academy of Marketing Science, vol. 43, no. 1, pp. 115–135, 2015, doi: 10.1007/s11747-014-0403-8.
B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap. New York, NY, USA: Chapman & Hall/CRC, 1998.
F. Arianto et al., “Technology Acceptance and Utilization Model: E-Learning in Higher Education,” Kwangsan: Jurnal Teknologi Pendidikan, vol. 8, no. 1, pp. 110–121, 2020, doi: 10.31800/jtp.kw.v8n1.p110-121.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Information Technology and Business
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
