Unfolding e-learning services affecting factors from gender perspectives
DOI:
https://doi.org/10.21556/edutec.2024.88.3099Keywords:
e-learning, gender, online learning, path analysis, TAMAbstract
This research investigated variables affecting the intention to use e-learning services in Indonesian open universities (IOU) perceived by male and female students. We modified the technology acceptance model with six variables: subjective norms, perceived ease of use, usefulness, attitudes, and intention to use; the data from male and female respondents were compared to understand the analysis of the structural model of the study. In the initiation process, we piloted the questionnaire to assess the reliability of the data. Main data were obtained from 419 male and 782 female respondents. We utilized PLS-SEM as the data analysis technique. There is a slight difference in the statistical path analysis results between male and female respondents, in which two hypotheses were rejected from male respondents' data. Meanwhile, all hypotheses were confirmed from male data. In predicting intention to use e-learning services of IOU, attitude is the strongest factor affecting intention to use e-learning services with a t-value of 7.229 at p <.001, perceived by male students. From female students' perspectives, attitude was also the most significant predictor of intention to use, with a t-value of 5.700 at p<001.
Downloads
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbis, N. A. & Habibi, A. (2023). "Extending the Technology Acceptance Model (TAM) to Predict University Students' Intentions to Use Metaverse-Based Learning Platforms". Education and Information Technologies. https://doi.org/10.1007/s10639-023-11816-3
Alsyouf, A., Lutfi, A., Alsubahi, N., Alhazmi, F. N., Al-Mugheed, K., Anshasi, R. J., Alharbi, N. I. & Albugami, M. (2023). The Use of a Technology Acceptance Model (TAM) to Predict Patients' Usage of a Personal Health Record System: The Role of Security, Privacy, and Usability. International Journal of Environmental Research and Public Health, 20(2). https://doi.org/10.3390/ijerph20021347
Baby, A. & Kannammal, A. (2020). Network Path Analysis for developing an enhanced TAM model: A user-centric e-learning perspective. Computers in Human Behavior, 107. https://doi.org/10.1016/j.chb.2019.07.024
Balaman, F. & BaÅŸ, M. (2023). Perception of using e-learning platforms in the scope of the technology acceptance model (TAM): a scale development study. Interactive Learning Environments, 31(8). https://doi.org/10.1080/10494820.2021.2007136
Becker, J. M., Cheah, J. H., Gholamzade, R., Ringle, C. M. & Sarstedt, M. (2023). PLS-SEM's most wanted guidance. In International Journal of Contemporary Hospitality Management (Vol. 35, Issue 1, pp. 321–346). Emerald Publishing. https://doi.org/10.1108/IJCHM-04-2022-0474
Chen, Y., Khalid Khan, S., Shiwakoti, N., Stasinopoulos, P. & Aghabayk, K. (2023). Analysis of Australian public acceptance of fully automated vehicles by extending technology acceptance model. Case Studies on Transport Policy, 14. https://doi.org/10.1016/j.cstp.2023.101072
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
Dong, X., Yu, Z., Cao, W., Shi, Y. & Ma, Q. (2020). A survey on ensemble learning. In Frontiers of Computer Science (Vol. 14, Issue 2). https://doi.org/10.1007/s11704-019-8208-z
Elshareif, E. & Mohamed, E. A. (2021). The effects of E-learning on students' motivation to learn in higher education. Online Learning Journal, 25(3). https://doi.org/10.24059/olj.v25i3.2336
Eynon, R. & Malmberg, L. E. (2021). Lifelong learning and the Internet: Who benefits most from learning online? British Journal of Educational Technology, 52(2). https://doi.org/10.1111/bjet.13041
Fornell, C. & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Habibi, A., Mukminin, A. & Sofyan, S. (2023). Access to the digital technology of urban and suburban vocational schools. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12006-x
Habibi, A., Yaakob, M. F. M. & Sofwan, M. (2022). Student use of digital libraries during COVID-19: structural equation modelling in Indonesian and Malaysian contexts. The Electronic Library, 40(4), 472–485. https://doi.org/10.1108/EL-12-2021-0212
Habibi, A., Yusop, F. D. & Razak, R. A. (2020). The role of TPACK in affecting pre-service language teachers' ICT integration during teaching practices: Indonesian context. Education and Information Technologies, 25(3), 1929–1949. https://doi.org/10.1007/s10639-019-10040-2
Hair, J., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. (2022). PLS-SEM Book: A Primer on PLS-SEM (3rd Ed.). Thousand Oaks: Sage.
Hanham, J., Lee, C. B. & Teo, T. (2021). The influence of technology acceptance, academic self-efficacy, and gender on academic achievement through online tutoring. Computers and Education, 172. https://doi.org/10.1016/j.compedu.2021.104252
He, S., Jiang, S., Zhu, R. & Hu, X. (2023). The influence of educational and emotional support on e-learning acceptance: An integration of social support theory and TAM. Education and Information Technologies, 28(9). https://doi.org/10.1007/s10639-023-11648-1
Hu, L. T. & Bentler, P. M. (1998). Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecification. Psychological Methods, 3(4). https://doi.org/10.1037/1082-989X.3.4.424
Iliyasu, R. & Etikan, I. (2021). Comparison of quota sampling and stratified random sampling. Biometrics & Biostatistics International Journal, 10(1). https://doi.org/10.15406/bbij.2021.10.00326
Jarvis, C. B., Mackenzie, S. B., Podsakoff, P. M., Giliatt, N. & Mee, J. F. (2003). A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research. In Journal of Consumer Research (Vol. 30, Issue 2). https://doi.org/10.1086/376806
Jovanka, D. R., Habibi, A., Mailizar, M., Yaqin, L. N., Kusmawan, U., Yaakob, M. F. M. & Tanu Wijaya, T. (2023). Determinants of e-Learning Services: Indonesian Open University. Cogent Education, 10(1). https://doi.org/10.1080/2331186X.2023.2183703
Kuliya, M. & Usman, S. (2021). Perceptions of E-learning among undergraduates and academic staff of higher educational institutions in north-eastern Nigeria. Education and Information Technologies, 26(2). https://doi.org/10.1007/s10639-020-10325-x
Kumar, J. A., Bervell, B., Annamalai, N. & Osman, S. (2020). Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and whatsapp use habit. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.3037925
Madhubhashini, G. T. (2021). Challenges and Opportunities of E-learning Via Zoom. Proceeding of the 34th Annual Conference of the Asian Association of Open Universities, II(January).
Mailizar, M., Burg, D. & Maulina, S. (2021). Examining university students' behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. In Education and Information Technologies (Vol. 26, Issue 6). https://doi.org/10.1007/s10639-021-10557-5
Muflih, M. (2023). Muzakki's adoption of mobile service: integrating the roles of technology acceptance model (TAM), perceived trust and religiosity. Journal of Islamic Accounting and Business Research, 14(1). https://doi.org/10.1108/JIABR-09-2021-0273
Mutambara, D. & Bayaga, A. (2021). Determinants of mobile learning acceptance for STEM education in rural areas. Computers and Education, 160. https://doi.org/10.1016/j.compedu.2020.104010
Ogbeibu, S., Jabbour, C. J. C., Gaskin, J., Senadjki, A. & Hughes, M. (2021). Leveraging STARA competencies and green creativity to boost green organisational innovative evidence: A praxis for sustainable development. Business Strategy and the Environment. https://doi.org/10.1002/bse.2754
Pannen, P., Widiatmo, H. & Afriani. (2023). The application of the Technology Acceptance Model (TAM) on online learning. In Education Technology in the New Normal: Now and Beyond. https://doi.org/10.1201/9781003353423-21
Rejón-Guardia, F., Polo-Peña, A. I. & Maraver-Tarifa, G. (2020). The acceptance of a personal learning environment based on Google apps: the role of subjective norms and social image. Journal of Computing in Higher Education, 32(2). https://doi.org/10.1007/s12528-019-09206-1
Rizun, M. & Strzelecki, A. (2020). Students' acceptance of the covid-19 impact on shifting higher education to distance learning in Poland. International Journal of Environmental Research and Public Health, 17(18). https://doi.org/10.3390/ijerph17186468
Rokhmawati, I. A. & Nugraha, J. (2021). ANALYSIS OF THE USE OF THE TYPING MASTER APPLICATION IN STUDENTS OF OFFICE ADMINISTRATION EDUCATION AT THE STATE UNIVERSITY OF SURABAYA USING TAM. Jurnal TAM (Technology Acceptance Model), 12(2). https://doi.org/10.56327/jurnaltam.v12i2.1019
Åžahin, F., DoÄŸan, E., Okur, M. R. & Åžahin, Y. L. (2022). Emotional outcomes of e-learning adoption during compulsory online education. Education and Information Technologies, 27(6). https://doi.org/10.1007/s10639-022-10930-y
Scherer, R., Tondeur, J. & Siddiq, F. (2017). On the quest for validity: Testing the factor structure and measurement invariance of the technology-dimensions in the Technological, Pedagogical, and Content Knowledge (TPACK) model. Computers and Education, 112, 1–17. https://doi.org/10.1016/j.compedu.2017.04.012
Schuberth, F., Rademaker, M. E. & Henseler, J. (2022). Assessing the overall fit of composite models estimated by partial least squares path modeling. European Journal of Marketing. https://doi.org/10.1108/EJM-08-2020-0586
Shachak, A., Kuziemsky, C. & Petersen, C. (2019). Beyond TAM and UTAUT: Future directions for HIT implementation research. In Journal of Biomedical Informatics (Vol. 100). https://doi.org/10.1016/j.jbi.2019.103315
Singh, A. S. & Masuku, M. B. (2014). Normality and Data Transformation for Applied Statistical Analysis. International Journal of Economics, Commerce and Management, 2(7).
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). https://doi.org/10.1016/j.heliyon.2020.e05410
Syahruddin, S., Mohd Yaakob, M. F., Rasyad, A., Widodo, A. W., Sukendro, S., Suwardi, S., Lani, A., Sari, L. P., Mansur, M., Razali, R. & Syam, A. (2021). Students' acceptance to distance learning during Covid-19: the role of geographical areas among Indonesian sports science students. Heliyon, 7(9). https://doi.org/10.1016/j.heliyon.2021.e08043
Tawafak, R. M., Al-Rahmi, W. M., Almogren, A. S., Al Adwan, M. N., Safori, A., Attar, R. W. & Habes, M. (2023). Analysis of E-Learning System Use Using Combined TAM and ECT Factors. Sustainability (Switzerland), 15(14). https://doi.org/10.3390/su151411100
Teresi, J. A., Yu, X., Stewart, A. L. & Hays, R. D. (2022). Guidelines for Designing and Evaluating Feasibility Pilot Studies. Medical Care, 60(1). https://doi.org/10.1097/MLR.0000000000001664
Thomas-Francois, K. & Somogyi, S. (2023). Self-Checkout behaviours at supermarkets: does the technological acceptance model (TAM) predict smart grocery shopping adoption? International Review of Retail, Distribution and Consumer Research, 33(1). https://doi.org/10.1080/09593969.2022.2051195
Venkatesh, V., Brown, S. A., Maruping, L. M. & Bala, H. (2008). Predicting different conceptualizations of system USE: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly: Management Information Systems, 32(3), 483–502. https://doi.org/10.2307/25148853
Wang, C., Ahmad, S. F., Bani Ahmad Ayassrah, A. Y. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-Razgan, M., Khan, Y. & Han, H. (2023). An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9(8). https://doi.org/10.1016/j.heliyon.2023.e18349
Wang, Y., Su, Z., Zhang, N., Xing, R., Liu, D., Luan, T. H. & Shen, X. (2023). A Survey on Metaverse: Fundamentals, Security, and Privacy. IEEE Communications Surveys and Tutorials, 25(1). https://doi.org/10.1109/COMST.2022.3202047
Xiao, L. & Hau, K. T. (2023). Performance of Coefficient Alpha and Its Alternatives: Effects of Different Types of Non-Normality. Educational and Psychological Measurement, 83(1). https://doi.org/10.1177/00131644221088240
Zardari, B. A., Hussain, Z., Arain, A. A., Rizvi, W. H. & Vighio, M. S. (2021). Development and validation of user experience-based e-learning acceptance model for sustainable higher education. Sustainability (Switzerland), 13(11). https://doi.org/10.3390/su13116201
Zhang, B., Mildenberger, M., Howe, P. D., Marlon, J., Rosenthal, S. A. & Leiserowitz, A. (2020). Quota sampling using Facebook advertisements. Political Science Research and Methods, 8(3). https://doi.org/10.1017/psrm.2018.49
Zhang, T., Gao, L., He, C., Zhang, M., Krishnamachari, B. & Avestimehr, A. S. (2022). Federated Learning for the Internet of Things: Applications, Challenges, and Opportunities. IEEE Internet of Things Magazine, 5(1). https://doi.org/10.1109/iotm.004.2100182
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Edutec. Revista Electrónica de Tecnología Educativa
This work is licensed under a Creative Commons Attribution 4.0 International License.
By submitting the paper, the authors assign the publication rights to the journal Edutec. For its part, Edutec authorises its distribution as long as its content is not altered and its origin is indicated. At the end of each article published in Edutec, the citation procedure is indicated.
The management and editorial board of Edutec Revista Electrónica de Tecnología Educativa do not accept any responsibility for the statements and ideas expressed by the authors in their work.
Translated with www.DeepL.com/Translator (free version)
