Understanding Congolese Students’ Behavioural Intentions Toward Learning Management Systems: An Extended TAM Approach
Submitted: 2025-11-21
|Accepted: 2025-12-09
|Published: 2026-01-22
Copyright (c) 2025 Rodrigue Kalumendo, Paluku VAGHENI Norbert, Tabiaki Tandele Rufin
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Keywords:
Technology Acceptance Model (TAM), perceived usefulness, perceived ease of use, social norms, behavioural intention, technology adoption, PLS-SEM, digital literacy, structural equation modelling, emerging economies
Supporting agencies:
Abstract:
This study empirically validates a technology acceptance model (TAM)-based framework for predicting technology adoption, integrating core constructs—perceived usefulness (PU), perceived ease of use (PEOU), and social norms (SN)—to examine their influence on behavioural intention and actual usage. Using partial least squares structural equation modelling (PLS-SEM), the measurement model demonstrated strong reliability and validity, while the structural model confirmed all hypothesised relationships. PU emerged as the most influential predictor of intention, and behavioural intention fully mediated the link between perceptual antecedents and usage. The model explained 42.3% of the variance in intention and 34.3% in usage, with Q² values confirming substantial predictive relevance. These findings contribute to the literature by reaffirming the centrality of intention in technology adoption and by highlighting the complementary role of social norms in shaping user behaviour. The study offers theoretical and practical insights for designing inclusive digital strategies, particularly in emerging economies.
References:
Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224. https://doi.org/10.1007/s10639-021-10573-5
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
Akkan, I. N., & Eminoglu Kucuktepe, S. (2024). Distance education practices during the COVID-19 lockdown: Comparison of Belgium, Japan, Spain, and Türkiye. The International Review of Research in Open and Distributed Learning, 25(4), 154–175. https://doi.org/10.19173/irrodl.v25i4.7623
Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27–50. https://doi.org/10.1016/j.aci.2014.09.001
Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1). https://doi.org/10.14569/IJACSA.2014.050120
Alotaibi, N. S. (2024). The impact of AI and LMS integration on the future of higher education: Opportunities, challenges, and strategies for transformation. Sustainability, 16(23), 10357. https://doi.org/10.3390/su162310357
Alshammari, S. H., & Alkhwaldi, A. F. (2025). An integrated approach using social support theory and technology acceptance model to investigate the sustainable use of digital learning technologies. Scientific Reports, 15(1), 342. https://doi.org/10.1038/s41598-024-83450-z
Bajger, T., Khoshnaw, D., Ali, K. A. A., & Mousa, K. M. (2025). Impact of digital transformation on rehabilitating higher education infrastructure in conflict-affected settings. European Journal of Education, 60(3), e70151. https://doi.org/10.1111/ejed.70151
Balaskas, S., Tsiantos, V., Chatzifotiou, S., Lourida, L., Rigou, M., & Komis, K. (2025). Understanding behavioral intention to use Moodle in higher education: The role of technology acceptance, cognitive factors, and motivation. Systems, 13(6), 412. https://doi.org/10.3390/systems13060412
Barz, N., Benick, M., Dörrenbächer-Ulrich, L., & Perels, F. (2024). Students’ acceptance of e-learning: Extending the technology acceptance model with self-regulated learning and affinity for technology. Discover Education, 3(1), 114. https://doi.org/10.1007/s44217-024-00195-7
Bervell, B., & Umar, I. N. (2017). A decade of LMS acceptance and adoption research in Sub-Sahara African higher education: A systematic review of models, methodologies, milestones and main challenges. EURASIA Journal of Mathematics, Science and Technology Education, 13(11). https://doi.org/10.12973/ejmste/79444
Bo, N. S. W. (2025). OECD digital education outlook, 2023: Towards an effective education ecosystem. Hungarian Educational Research Journal, 15(2), 284–289. https://doi.org/10.1556/063.2024.00340
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern methods for business research. Lawrence Erlbaum Associates.
Chu, T. H., & Chen, Y. Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92–93, 37–52. https://doi.org/10.1016/j.compedu.2015.09.013
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263–282. https://doi.org/10.1111/j.1467-8551.2006.00500.x
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
Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447. https://doi.org/10.1108/IntR-12-2017-0515
García, J. A. M., Gallego Gómez, C., Tapia López, A., & Schlosser, M. J. (2024). Applying the technology acceptance model to online self-learning: A multigroup analysis. Journal of Innovation & Knowledge, 9(4), 100571. https://doi.org/10.1016/j.jik.2024.100571
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51. https://doi.org/10.2307/30036519
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.1093/biomet/61.1.101
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications.
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
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
Hsiao, C. H., & Tang, K. Y. (2025). Beyond acceptance: An empirical investigation of technological, ethical, social, and individual determinants of GenAI-supported learning in higher education. Education and Information Technologies, 30(8), 10725–10750. https://doi.org/10.1007/s10639-024-13263-0
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7
Hussein, Z. (n.d.). Subjective norm and perceived enjoyment among students in influencing the intention to use e-learning.
Jiang, S., Li, H., & Gan, D. (2025). Technology acceptance model for online education: Identifying interdisciplinary topics and their evolution based on BERTopic model. Social Sciences & Humanities Open, 12, 101831. https://doi.org/10.1016/j.ssaho.2025.101831
Kalumendo, R. (2022a). Barriers to SME computerization in developing countries: Evidence from SMEs in North Kivu, Democratic Republic of Congo. Texila International Journal of Management, 8(2), 163–169. https://doi.org/10.21522/TIJMG.2015.08.02.Art013
Kalumendo, R. (2022b). Predictive factors of IT systems adoption by SME employees in developing countries: Evidence from SME employees in North Kivu, DRC. Texila International Journal of Academic Research, 9(4), 28–36. https://doi.org/10.21522/TIJAR.2014.09.04.Art003
Kalumendo, R., Kazimoto, P., & Betchoo, N. K. (2025). Information systems’ effectiveness and organisational performance: A study among small and medium-sized enterprises in North Kivu Province, DR-Congo. Journal of Applied Research in Technology & Engineering, 6(1), 51–62. https://doi.org/10.4995/jarte.2025.22609
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705. https://doi.org/10.2307/25148817
Marchewka, J. T., & Kostiwa, K. (2014). An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA, 7(2). https://doi.org/10.58729/1941-6687.1038
Matete, R. E., Kimario, A. E., & Behera, N. P. (2023). Review on the use of eLearning in teacher education during the coronavirus disease (COVID-19) pandemic in Africa. Heliyon, 9(2), e13308. https://doi.org/10.1016/j.heliyon.2023.e13308
Ministère de l’Enseignement Supérieur et Universitaire. (2025). Arrêté ministériel N°004/MINESU/CAB.MIN/SASM/MMK/2025 du 06 février 2025 portant sur l’organisation et le fonctionnement de l’enseignement ouvert et à distance (EAD) au sein des établissements d’enseignement supérieur et universitaire en République Démocratique du Congo. https://www.minesu.gouv.cd/images/A.M%20004.pdf
Munabi, S. K., Aguti, J., & Nabushawo, H. M. (2020). Using the TAM model to predict undergraduate distance learners’ behavioural intention to use the Makerere University learning management system. OALib, 7(9), 1–12. https://doi.org/10.4236/oalib.1106699
Ndejjo, R., Tusubiira, A. K., Kiwanuka, S. N., Bosonkie, M., Bamgboye, E. A., Diallo, I., Kabwama, S. N., Egbende, L., Afolabi, R. F., Mbacké Leye, M. M., Namuhani, N., Kashiya, Y., Bello, S., Babirye, Z., Adebowale, A. S., Sougou, M., Monje, F., Kizito, S., Dairo, M. D., Bassoum, O., Namale, A., Seck, I., Fawole, O. I., Mapatano, M. A., & Wanyenze, R. K. (2023). Consequences of school closures due to COVID-19 in DRC, Nigeria, Senegal, and Uganda. PLOS Global Public Health, 3(10), e0002452. https://doi.org/10.1371/journal.pgph.0002452
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Rhema, A., & Miliszewska, I. (2014). Analysis of student attitudes towards e-learning: The case of engineering students in Libya. Issues in Informing Science and Information Technology, 11, 169–190. https://doi.org/10.28945/1987
Rosli, M. S., Saleh, N. S., Ali, A. M., Bakar, S. A., & Tahir, L. M. (2022). A systematic review of the technology acceptance model for the sustainability of higher education during the COVID-19 pandemic and identified research gaps. Sustainability, 14(18), 11389. https://doi.org/10.3390/su141811389
Saif, N., Khan, S. U., Shaheen, I., Alotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). ChatGPT: Validating technology acceptance model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Treating unobserved heterogeneity in PLS-SEM: A multi-method approach. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling. Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3_9
Srite, M., & Karahanna, E. (2006). The role of espoused national cultural values in technology acceptance. MIS Quarterly, 30(3), 679. https://doi.org/10.2307/25148745
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three-country study. Information & Management, 33(1), 1–11. https://doi.org/10.1016/S0378-7206(97)00026-8
Tshimanga, E. M. (2023). Educational challenges in the Democratic Republic of Congo. https://brokenchalk.org/educational-challenges-in-the-republic-democratic-of-congo/
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. https://doi.org/10.2307/30036540
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
Wang, H., Hou, X., Liu, J., Zhou, X., Jiang, M., & Liao, J. (2025). Framework effect and achievement motivation on college students’ online learning intention: Based on technology acceptance model (TAM) and theory of planned behaviour (TPB) model. Education and Information Technologies, 30(8), 11073–11097. https://doi.org/10.1007/s10639-024-13254-1
World Bank. (2020). Democratic Republic of Congo digital economy assessment. https://thedocs.worldbank.org/en/doc/61714f214ed04bcd6e9623ad0e215897-0400012021/related/DRC-DE4A-EN-Final.pdf
Yang, H. P. (2025). MASEM dataset on educational AI technology adoption among students (from 2020 to May 2025). Mendeley Data. https://doi.org/10.17632/T8NS6FDKY2.1
Zaidi, Z. N. (2025). Des cours en ligne pour les étudiants dans l’est de la RDC. Deutsche Welle (DW). https://www.dw.com/fr/cours-distance-etudiants-rdc-m23/a-71588762
Zulherman, Z., & Zaelani, M. (2023). Students’ acceptance to use Moodle based LMS: Extended TAM model. AIP Conference Proceedings. https://doi.org/10.1063/5.0154384
