EXPLORING FACTORS INFLUENCING THE INTENTION TO USE E-LEARNING MANAGEMENT SYSTEMS AMONG VULNERABLE WOMEN IN DEVELOPING COUNTRIES: A STUDY OF NON-PROFIT ORGANIZATION PARTICIPANTS

Anna Kazanskaia

Authors

Anna Kazanskaia Ph.D. Candidate in Technology, Education and Management, Graduate School of Business and Advanced Technology Management, Assumption University of Thailand.

Keywords:

E-Learning, Non-Profit Organization, Personal Innovativeness, Attitude, Intention to Use

Abstract

This research aims to explore factors that influence the intention to use e-learning management systems among 500 vulnerable women attending non-profit organizations in developing countries. The factors under investigation encompass computer self-efficacy, perceived usefulness, perceived ease of use, user satisfaction, personal innovativeness, and attitude. Employing a quantitative research design, the study involved the distribution of self-administered questionnaires to the target population for data collection. The research employed rigorous methods, including the Item-Objective Congruence (IOC) index, pilot testing, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM), to analyze the data and draw meaningful research conclusions. The study yielded confirmation of hypotheses relating to the significant influence of perceived ease of use, attitude, and user satisfaction on the intention to use e-learning systems. Perceived ease of use has a significant influence on perceived usefulness and attitude. However, computer self-efficacy has no significant influence perceived usefulness. Perceived usefulness and personal innovativeness have no significant influence intention to use. In conclusion, the findings emphasize the paramount importance of fostering a positive attitude towards technology in the context of e-learning implementation.

References

Adewole-Odeshi, E. (2014). Attitude of Students Towards E-learning in South-West Nigerian Universities: An Application of Technology Acceptance Model. Library Philosophy and Practice, 1, 1035.

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. https://doi.org/10.1016/0749-5978(91)90020-T

Al-Busaidi, K. A. (2013). An empirical investigation linking learners’ adoption of blended learning to their intention of full e-learning. Behaviour & Information Technology, 32(11), 1168-1176. https://doi.org/10.1080/0144929X.2013.774047

AlHadi, A., Alarabi, M., & AlMansoor, K. (2021). Mental health and its association with coping strategies and intolerance of uncertainty during the COVID-19 pandemic among the general population in Saudi Arabia: cross-sectional study. BMC Psychiatry, 21, 1-13. https://doi.org/10.1186/s12888-021-03370-4

Al-Momani, M., Pilli, O., & Fanaeian, Y. (2014). Investigating the Students’ Attitude Toward the use of E-Learning in Girne American University. International Journal of Business and Social Science, 5, 169-175.

Ayub, M., Fauzi, A., Syaza, Z., Wong, L., Jaafar, W., & Marzuki, W. (2018). The Influence of Mobile Self-efficacy, Personal Innovativeness and Readiness towards Students’ Attitudes towards the use of Mobile Apps in Learning and Teaching. International Journal of Academic Research in Business and Social Sciences, 7(14), 364-374. https://doi.org/10.6007/IJARBSS/v7-i14/3673

Azzopardi, M. C. (2023, August 9). The Power of Giving: Supporting Nonprofits that Empower Citizens through Donations. Citizens Advice Spain.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.

Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students’ behavioural intentions to use online learning course websites. British Journal of Educational Technology 39(1), 71–78.

Chen, T., Liu, H., & Lin, S. (2014). Construct Of Educational Information System’s Using Willingness Model: An Extended Application of Technology Acceptance Model. International Journal of Organizational Innovation, 18, 1-10.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

DeLone, W., & McLean, E. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19, 9-30. https://doi.org/10.1080/07421222.2003.11045748

DeLone, W., & McLean, E. (2016). Information Systems Success Measurement. Foundations and Trends in Information Systems, 2, 1-116. https://doi.org/10.1561/2900000005

Efthymiou, I.-P. (2023). Building a Sustainable Future: Strategies for Empowering Nonprofit Organizations. In Jaworski, J. A. (Ed.), Advances in Sociology Research (Vol. 41). Nova Science Publishers, Inc.

Elkaseh, A., Wong, K., & Fung, C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192-199. https://doi.org/10.7763/IJIET.2016.V6.683

Eze, S., Chinedu-Eze, V., & Bello, A. (2018). The utilisation of e-learning facilities in the educational delivery system of Nigeria: a study of M-University. International Journal of Educational Technology in Higher Education, 15, 1–20.

Goh, C. F., Hii, P. K., Tan, O. K., & Rasli, A. (2020). Why do University Teachers use E-Learning Systems?. The International Review of Research in Open and Distributed Learning, 21(2), 136-155. https://doi.org/10.19173/irrodl.v21i2.3720

Hadullo, K., Oboko, R., & Omwenga, E. (2017). A model for evaluating e-learning systems quality in higher education in developing countries. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 13(2), 185-204.

Hagger, M. S., & Chatzisarantis, N. L. D. (2005). First- and higher- order models of attitudes, normative influence, and perceived behavioural control in the theory of planned behaviour. British Journal of Social Psychology, 44, 513-535. https://doi.org/10.1348/014466604X16219

Hatcher, L., & O’Rourke, N. (2014). A Step-by-step approach to using SAS for factor analysis and structural equation modeling. SAS Institute.

Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2004). The Role of Social Presence and Moderating Role of Computer Self Efficacy in Predicting the Continuance Usage of E-Learning Systems. Journal of Information Systems Education, 15(2), 139-154.

Holt, L., & Brockett, R. G. (2012). Self-direction and factors influencing technology use: Examining relationships for the 21st century workplace. Computers in Human Behaviour, 28(6), 2075- 2082.

Huang, F., & Teo, T. (2020). Influence of teacher-perceived organisational culture and school policy on Chinese teachers’ intention to use technology: an extension of technology acceptance model. Education Tech Research Dev, 68, 1547–1567. https://doi.org/10.1007/s11423-019-09722-y

Izkair, A. S., & Lakulu, M. M. (2020). Intention to Use Mobile Learning in Higher Education Institutions: Review Paper. International Journal of Education Science Technology and Engineering, 3(2), 78-84. https://doi.org/10.36079/lamintang.ijeste-0302.157

Jung, H. J. (2016). Factors Impacting Learners Satisfaction and Continued Use of E-Learning in an EFL Context. Multimedia-Assisted Language Learning, 19(2), 11-33.

Kim, B., & Park, M. J. (2018). Effect of personal factors to use ICTs on E-learning adoption: comparison between learner and instructor in developing countries. Information Technology for Development, 24(4), 706-732. https://doi.org/10.1080/02681102.2017.1312244

Kisanga, D. (2016). Determinants of Teachers’ Attitudes Towards E-Learning in Tanzanian Higher Learning Institutions. International Review of Research in Open and Distance Learning, 17, 109-125.

Kisanjara, S. B., Tossy, T. M., Sife, A. S., & Msanjila, S. S. (2017). An integrated model for measuring the impacts of e-learning on students’ achievement in developing countries. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 13(3), 109-127.

Laosethakul, K., & Leingpibul, T. (2021). Investigating Student Perceptions and Behavioral Intention to Use Multimedia Teaching Methods for the SAP ERP System. E-Journal of Business Education & Scholarship of Teaching, 15(1), 1–27.

Liaw, S. S. (2004). Considerations for developing constructivist web-based learning. International Journal of Instructional Media, 31(3), 309-321.

Lin, C.-C., Wu, H.-Y., & Chang, Y.-F. (2011). The critical factors impact on online customer satisfaction. Procedia CS, 3(2), 276-281. https://doi.org/10.1016/j.procs.2010.12.047

Liu, I., Chen, M., Chang, S., & Yeali, S., Wible, D., & Kuo, C.-H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education, 54(2), 1-10. https://doi.org/10.1016/j.compedu.2009.09.009

Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245.

Mac Callum, K., Jeffrey, L., & Kinshuk, K. (2014). Factors impacting teachers’ adoption of mobile learning. Journal of Information Technology Education: Research, 13, 141-162. https://doi.org/10.28945/1970

Mislinawati, M. V. L., & Nurmasyitah, N. (2018). Students’ perceptions on the implementation of e-learning: helpful or unhelpful?. Journal of Physics Conference Series, 1088, 1–6.

Muzi, M. N. A., Nadzir, M. N. A., Mutalib, A. S. F., Zukri, M. S., & Fauzi, M. N. Z. (2021). Factors Affecting Academicians’ Acceptance on E-Learning Application. E-BANGI Journal, 18(4), 78–91.

Okazaki, S., & Renda dos Santos, L. (2012). Understanding e-learning adoption in Brazil: Major determinants and gender effects. The International Review of Research in Open and Distributed Learning, 13(4), 91-106.

Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Journal of Educational Technology & Society, 12(3), 150–162.

Pedroso, R., Rosemeri, Z., Luciana, Z., Luciano, G., Marcia, P., Veralice, G., Juliana, S., Felix, K., & Flavio, P. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43, 37-40. https://doi.org/10.1590/0101-60830000000081

Rajeh, M. T., Abduljabbar, F. H., Alqahtani, M. S., Waly, F. J., Alnaami, I., Aljurayyan, A., & Alzaman, N. (2021). Students’ satisfaction and continued intention toward e-learning: a theory- based study. Medical Education Online, 26, 1961348 https://doi.org/10.1080/10872981.2021.1961348

Sam, H. K., Othman, A. E. A., & Nordin, Z. S. (2005). Computer Self-Efficacy, Computer Anxiety, and Attitudes toward the Internet: A Study among Undergraduates in Unimas. Educational Technology & Society, 8(4), 205-219.

Sanchez, R. A., & Hueros, A. D. (2010). Motivational Factors that Influence the Acceptance of Moodle Using TAM. Computers in Human Behavior, 26, 1632-1640.

Sannegadu, R., Seethiah, D., Dookhony-Ramphul, K., Gunesh, R., Seethiah, K., & Jugessur, H. (2018). Investigating the Factors Influencing Students' Intention to Adopt E-Learning in a Small Island Developing State (SIDS) Economy: Evidence from Mauritius. Studies in Business and Economics, 13(3), 135-160. https://doi.org/10.2478/sbe-2018-0040

Schillewaert, N., Ahearne, M., Frambach, R., & Moenaert, R. (2005). The adoption of information technology in the sales force. Industrial Marketing Management, 34, 323-336. https://doi.org/10.1016/j.indmarman.2004.09.013

Sharma, S., Pradhan, K., Satya, S., & Vasudevan, P. (2005). Potentiality of earthworms for waste management and in other uses – a review. The Journal of American Science, 1(1), 4-16.

Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behaviour & Information Technology, 36(10), 1053–1066

Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Behavior Research and Therapy, 45(4), 739-752. https://doi.org/10.1016/j.brat.2006.04.010

Soper, D. S. (2022). A-priori sample size calculator for structural equation models [Software]. http://www.danielsoper.com/statcalc

Twum, K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2021). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management, 13(3), 713-737. https://doi.org/10.1108/JSTPM-12-2020-0168

Udo, G. J., Bagchi, K. K., & Kirs, P. J. (2011). Using SERVQUAL to assess the quality of e-learning experience. Computers in Human Behavior, 27(3), 1272-1283. https://doi.org/10.1016/j.chb.2011.01.009

UNESCO. (2020). COVID-19 educational disruption and response. https://en.unesco.org/themes/education-emergencies/coronavirus-school-closures

Usman, M. B., Septianti, O., Susita, A. D., & Marsofiyati, M. (2020). The Effect of Computer Self-Efficacy and Subjective Norm on The Perceived Usefulness, Perceived Ease of Use and Behavioural Intention to Use Technology. Journal of Southeast Asian Research, 2020, 1-11. https://doi.org/10.5171/2020.753259

Van der Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal in The Netherlands. Information & Management, 40(6), 541-549.

Van Raaij, E. M., & Schepers, J. J. L. (2008). The Acceptance and Use of a Virtual Learning Environment in China. Computers & Education, 50, 838-852. http://dx.doi.org/10.1016/j.compedu.2006.09.001

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.

Vululleh, P. (2018). Determinants of student’s e-learning acceptance in developing countries: An approach based on Structural Equitation Modelling (SEM). International Journal of Education and Development using Information and Communication Technology, 14(01), 141-151.

Wang, Y., Shi, L., Que, J., Lu, Q., Liu, L., Lu, Z., Xu, Y., Liu, J., Sun, Y., Meng, S., Yuan, K., Ran, M., Lu, L., Bao, Y., & Shi, J. (2021). The impact of quarantine on mental health status among general population in China during the COVID-19 pandemic. Molecular Psychiatry, 26, 4813–4822. https://doi.org/10.1038/s41380-021-01019-y

Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. (1977). Assessing Reliability and Stability in Panel Models. Sociological Methodology, 8(1), 84-136.

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS Success: A Respecification of the DeLone and McLean’s Model. Journal of Information & Management, 43, 728-739. http://dx.doi.org/10.1016/j.im.2006.05.002

Yakubu, N., & Dasuki, S. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492–502. https://doi.org/10.1177/0266666918765907