Mobile Learning Adoption: Using Composite Model Measurement Invariance to Assess Gender Differences

Document Type : Research Paper

Authors

1 University of Professional Studies, Accra, P. O. Box LG149 Legon, Accra, Ghana.

2 Accra Technical University, P. O. Box GP561, Accra, Ghana.

Abstract

This study investigates Ghanaian students' adoption of Mobile Learning (ML) by extending the technology acceptance model with a subjective norm variable. Specifically, this study focuses on the moderating effect of gender using the Measurement Invariance of Composite Models for the analysis. The study used a purposive sampling technique to collect the data for the study from sec-ond-year diploma students at the University of Professional Studies in Accra. SmartPLS 3.3.3 was used to analyze the data from 330 respondents. The findings of the study suggest that perceived ease of use, perceived usefulness, and subjective norm have a significant influence on the behavior-al intention to adopt mobile learning for the complete data set. In addition, the results suggest that the impact of the subjective norm was not significant for female students but for male students. Also, the impact of perceived ease of use and perceived usefulness on behavioral intention were insignificant. Furthermore, the findings suggest that behavioral intention influences students' actual use of mobile devices to access learning materials. Finally, gender moderates the relationship be-tween subjective norms and behavioral intention. The findings demonstrate group heterogeneity, therefore, investigations on technology adoption must always incorporate group dynamics to un-derstand how different groups respond to its adoption. The findings of the study hold significance for both policy and research implications.

Keywords

Main Subjects

References

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179–211
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), 222-241
Alowayr, A. (2021). Determinants of mobile learning adoption: extending the unified theory of acceptance and use of technology (UTAUT). International Journal of Information and Learning Technology. 39(1), 1-12. https://doi.org/10.1108/IJILT-05-2021-0070
Alrajawy, I., Isaac, O., Ghosh, A., Nusari, M., Al-Shibami, A. H., & Ameen, A. (2018). Determinants of student's intention to use mobile learning in Yemeni public universities: Extending the technology acceptance model (TAM) with anxiety. International Journal of Management and Human Science (IJMHS), 2(2), 1–9.
Amirian, P., & Basiri, A. (2016). Landmark-based pedestrian navigation using augmented reality and machine learning. Progress in Cartography: EuroCarto 2015, 451-465.
Ananto, P., & Ningsih, S. K. (2020). Incorporation of smartphones and social media to promote mobile learning in an Indonesian vocational higher education setting. International Journal of Interactive Mobile Technologies, 14(19), 66–81.
Arkorful, V., & Abaidoo, N. (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. International journal of instructional technology and distance learning, 12(1), 29-42.
Asabere, N. Y. (2013). Benefits and challenges of mobile learning implementation: Story of developing nations. International Journal of Computer Applications, 73(1), 23-27.
Asabere, N. Y., Acakpovi, A., Torgby, W., Sackey, J. Y. A., & Kwaikyi, S. (2019). Exploiting the adoption and implementation of electronic learning in a technical university in Ghana. International Journal of Online Pedagogy and Course Design (IJOPCD), 9(4), 44-67.
Azizi, S. M., & Khatony, A. (2019). Investigating factors affecting on medical sciences students' intention to adopt mobile learning. BMC Medical Education, 19 (1), 1–10.
Bali, S., & Liu, M. C. (2018, November). Students’ perceptions toward online learning and face-to-face learning courses. In Journal of Physics: Conference Series (110(1), 012094. IOP Publishing.
Bao, Y., Xiong, T., Hu, Z., & Kibelloh, M. (2013). Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption. Journal of Educational Computing Research, 49 (1), 111–132. https://doi.org/10.2190/EC.49.1.e
Binyamin, S. S., Rutter, M. J., & Smith, S. (2020). The moderating effect of gender and age on the students' acceptance of learning management systems in Saudi higher education. Knowledge Management and E-Learning, 12(1), 30–62. https://doi.org/10.34105/j.kmel.2020.12.003
Buabeng-Andoh, C. (2018). Predicting students' intention to adopt mobile learning. Journal of Research in Innovative Teaching & Learning, 11(2), 178–191. https://doi.org/10.1108/jrit-03-2017-0004
Buabeng-Andoh, C. (2021). Exploring University students' intention to use mobile learning: A research model approach. Education and Information Technologies, 26 (1), 241–256. https://doi.org/10.1007/s10639-020-10267-4
Cacciamani, S., Villani, D., Bonanomi, A., Carissoli, C., Olivari, M. G., Morganti, L., Riva, G., & Confalonieri, E. (2018). Factors affecting students' acceptance of tablet PCs: A study in Italian high schools. Journal of Research on Technology in Education, 50(2), 120–133. https://doi.org/10.1080/15391523.2017.1409672
Criollo-C, S., Guerrero-Arias, A., Jaramillo-Alcázar, Á., & Luján-Mora, S. (2021). Mobile learning technologies for education: Benefits and pending issues. Applied Sciences, 11(9), 4111.
Carranza, R., Díaz, E., Martín-Consuegra, D., & Fernández-Ferrín, P. (2020). PLS–SEM in business promotion strategies. A multigroup analysis of mobile coupon users using MICOM, Industrial Management & Data Systems, 120(12), 2349-374.  https://doi.org/10.1108/IMDS-12-2019-0726
Chang, C. C., Liang, C., Yan, C. F., & Tsen, J. S. (2013). The impact of college students' intrinsic and extrinsic motivation on continuance intention to use English mobile learning systems. Asia-Pacific Education Researcher, 22 (2), 181–192. DOI: 10.1007/s40299-012-0011-7
Cheng, Y. M. (2015). Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pacific Management Review, 20(3), 109–119. https://doi.org/10.1016/j.apmrv.2014.12.011
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https//www.jstor.org/stable/249008
Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). Approach to M-learning acceptance among university students: An integrated model of TPB and TAM. International Review of Research in Open and Distance Learning, 20(3), 141–164. https://doi.org/10.19173/irrodl.v20i4.4061
Hair, J. F. Jr, Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications
Habibi, A., Yaakob, M. F. M., & Al-Adwan, A. S. (2021). m-Learning management system use during Covid-19. Information Development. https://doi.org/10.1177/02666669211035473
Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053-1070.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International marketing review.
Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model, Electronic Library, 25 (5), 585–598. DOI: 10.1108/02640470710829569
Iqbal, S., & Ahmed, B. Z. (2015). An investigation of university student readiness towards m-learning using technology acceptance model. International Review of Research in Open and Distributed Learning, 16(4), 83-103.
Iskander, M. (2008). Innovative techniques in instruction technology, e-learning, e-assessment and education, Springer Netherlands
Jaradat, M. I. R. M., & Faqih, K. M. (2014). Investigating the moderating effects of gender and self-efficacy in the context of mobile payment adoption: A developing country perspective. International Journal of Business and Management, 9(11), 147.
Joo, Y. J., Joung, S., Shin, E. K., Lim, E., & Choi, M. (2014). Factors influencing actual use of mobile learning connected with e-learning. Computer Science & Information Technology, 4(11), 169-176.
Kankam, P. K. (2020). Mobile information behavior of sandwich students towards mobile learning integration at the University of Ghana. Cogent Education, 7(1), 1796202
Kanwal, F., Rehman, M., & Malik, M. A. (2020). E-Learning adoption and acceptance in Pakistan: Moderating effect of gender and experience. Mehran University Research Journal of Engineering and Technology, 39(2), 324–341. https://doi.org/10.22581/muet1982.2002.09
Kemp, S. (2022, July 22). Digital in Ghana: All the Statistics You Need in 2021.  DataReportal. https://datareportal.com/reports/digital-2021-ghana
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, 208058–208074. https://doi.org/10.1109/ACCESS.2020.3037925
Kuadey, N. A., Bensah, L., Ankora, C., Mahama, F., Agbesi, V. K., & Newman, N. K. (2020). Adoption of mobile technology application at a technical university in Ghana. International Journal of Computer Applications, 175 (31), 7–13. https://doi.org/10.5120/ijca2020920871
Li, P. C., Kong, W. J., & Zhou, W. L. (2020). Research on the mobile learning adoption of college students based on TTF and UTAUT. In Proceedings of the 5th International Conference on Distance Education and Learning. Pervasive Computing Technologies for Healthcare.
Lingga, I. S., Eddy, E. P. S., Dewi, N. L., & Saputra, C. A. R. (2021). Analysis of Using e-Filing with the Implementation of Theory of Planned Behavior. KINERJA, 25(2), 192-204.
Oluwajana, D., & Adeshola, I. (2021). Does the student's perspective on multimodal literacy influence their behavioral intention to use collaborative computer-based learning?. Education and information technologies, 26(5), 5613-5635
Madlala, M., Civilcharran, S., & Singh, U. G. (2020). Understanding students' usage of smartphone applications for learning purposes: A case study. In Proceedings of the 2020 International Conference on Advances in Computing and Communication Engineering, ICACCE 2020. https://doi.org/10.1109/ICACCE49060.2020.9154920
Maldonado, U. P. T., Khan, G. F., Moon, J., & Rho, J. J. (2011). E-learning motivation and educational portal acceptance in developing countries. Online Information Review, 35(1), 66–85. https://doi.org/10.1108/14684521111113597/FULL/HTML
Mehdi, K. D. B. A. (2020). Handbook of research on modern educational technologies, applications, and management, IGI Global
Mpungose, C. B. (2020). Emergent transition from face-to-face to online learning in a South African University in the context of the Coronavirus pandemic. Humanities and Social Sciences Communications, 7(1), 1-9.
Pagani, M. (2008). Encyclopedia of Multimedia Technology and Networking, (2nd Ed), Information Science Reference
Saroia, A. I., & Gao, S. (2019). Investigating university students' intention to use mobile learning management systems in Sweden. Innovations in Education and Teaching International, 56(5), 569–580. https://doi.org/10.1080/14703297.2018.1557068
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John wiley & sons.
Sultana, J. (2020). Determining the factors that affect the uses of mobile cloud learning (MCL) platform Blackboard- a modification of the UTAUT model. Education and Information Technologies, 25(1), 223–238. https://doi.org/10.1007/s10639-019-09969-1
Tagoe, M., & Abakah, E. (2014). Determining distance education students' readiness for mobile learning at the University of Ghana using the Theory of Planned Behavior. International Journal of Education and Development Using Information and Communication Technology, 10 (1), 91–106.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540‑5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly: Management Information Systems, 24(1), 115–136. https://doi.org/10.2307/3250981
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
Wai, I. S. H., Ng, S. S. Y., Chiu, D. K., Ho, K. K., & Lo, P. (2018). Exploring undergraduate students' usage pattern of mobile apps for education. Journal of Librarianship and Information Science, 50(1), 34–47. DOI: 10.1177/0961000616662699
Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
Zaidi, S. F. H., Osmanaj, V., Ali, O., & Zaidi, S. A. H. (2021). Adoption of mobile technology for mobile learning by university students during COVID-19. International Journal of Information and Learning Technology, 38 (4), 329–343.
Journal of Information Technology Management
Volume 16, Issue 2
2024
Pages 35-61

Files

Share

How to cite

Statistics