ANALYZING FACTORS INFLUENCING BENEFICIARIES’ INTENTION TO USE E-LEARNING OF NON-PROFIT ORGANIZATION IN THAILAND

Authors

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

Keywords:

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

Abstract

This study investigates the factors which are computer self-efficacy, perceived usefulness, perceived ease of use, user satisfaction, personal innovativeness, and attitude that influence intention to use e-learning management systems among 500 vulnerable women attending non-profit organizations in developing countries. Employing a quantitative research design, the study involved the distribution of self-administered questionnaires to the target population for data collection. The index of item-objective congruence (IOC), pilot testing, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) were employed to analyze the data to conclude research findings. The study confirmed the hypotheses concerning the significant influence of computer self-efficacy, attitude, user satisfaction, and personal innovativeness on the Intention to use e-learning systems. However, the hypotheses related to the influence of perceived ease of use, perceived usefulness, and the relationship between perceived usefulness and attitude were not supported in this study. In conclusion, findings highlight attitude's significance, necessitating positive technology views. Personal innovativeness and computer self-efficacy emerge as key, emphasizing tailored training. User satisfaction's role in adoption and user-centric design's importance for intuitive platforms were underscored. Contrary to expectations, ease of use's impact was complex. This research guides e-learning implementation in non-profits, promoting empowerment through education for beneficiaries of a non-profit organization.

References

Abdurrahaman, D. T., Owusu, A., & Soladoye, A. B. (2019). Evaluating Factors Affecting User Satisfaction in University Enterprise Content Management (ECM) Systems. The Electronic Journal of Information Systems Evaluation, 23(1), 1-16.

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.

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. Retrieved from https://doi.org/10.1080/0144929X.2013.774047

Awal, M. R., Arzin, T. A., Hasan, M. T., & Islam, M. M. (2023). Understanding railway passengers' E-ticketing usage intention in an e merging economic context: Application of an extended technology acceptance model. Arab Gulf Journal of Scientific Research. Advance Online Publication. Retrieved from https://doi.org/10.1108/AGJSR-12-2022-0294

Barclay, C., Donalds, C., & Osei-Bryson, K. M. (2018). Investigating critical success factors in online learning environments in higher education systems in the Caribbean. Information Technology for Development, 24(3), 582-611. Retrieved from https://doi.org/10.1080/02681102.2018.1476831

Bayraktaroglu, S., Turkey, V., Kahya, V., & Atay, E. (2019). Application of Expanded Technology Acceptance Model for Enhancing the HRIS Usage in SMEs. International Journal of Applied Management and Technology, 18, 48-66. Retrieved from htps://doi.org/10.5590/IJAMT.2019.18.1.04

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

Bentler, P. (2009). Measuring students’ attitude towards E-Learning. A case study [Paper Presentation]. Proceedings of the 5th standing conference on e-learning and software for development held in Bucharest from09-10 April 2009 Bucharist Romania 1-8.

Bubou, G., & Job, G. C. (2021). Individual innovativeness, self-efficacy and e-learning readiness of students of Yenagoa study centre, National Open University of Nigeria. Journal of Research in Innovative Teaching & Learning, 15(1), 2-22. Retrieved from

https://doi.org/10.1108/JRIT-12-2019-0079

Chang, C., Yan, C., & Tseng, J. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809-826. Retrieved from https://doi.org/10.14742/ajet.818

Compeau, D. R., & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189–211.

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

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

Duan, Y., Fu, Z., Alford, P., & Li, Y. (2012). An empirical study on behavioural intention to reuse e-learning systems in rural China. British Journal of Educational Technology, 43(6), 933–948. Retrieved from https://doi.org/10.1111/j.1467-8535.2011.01261.x

Holden, R. J., & Karsh, B.-T. (2010). The Technology Acceptance Model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159. Retrieved from https://doi.org/10.1016/j.jbi.2009.07.002

Hussein, Z. (2017). Leading to Intention: The Role of Attitude in Relation to Technology Acceptance Model in E-Learning. Procedia Computer Science, 105, 159-164.

Ibrahim, M. H., Hussin, S. R., & Hussin, S. H. (2019). Factors Influencing Malaysian Consumers’ Intention to Use Quick Response (QR) Mobile Payment. Jurnal Pengurusan, 57, 1-16.

Keong, Y. C., Albadry, O., & Raad, W. (2014). Behavioral Intention of EFL Teachers to Apply E-Learning. Journal of Applied Sciences, 14, 2561-2569. Retrieved from https://doi.org/10.3923/jas.2014.2561.2569

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. Retrieved from https://doi.org/10.1080/02681102.2017.1312244

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.

Koul, S., & Jasrotia, S. S. (2021). Acceptance of digital payments among rural retailers in India. Journal of Payments Strategy & Systems, 15(2), 201-213.

Kuo, T.-S., Huang, K.-C., & Hsu, K.-F. (2020). Acceptance for Applying Technological Living Assistive Devices in Dignity Care - Evidence from Central Taiwan. The International Journal of Organizational Innovation, 12(3), 278-303.

Lai, J. Y. (2009). How reward, computer self‐efficacy, and perceived power security affect knowledge management systems success: An empirical investigation in high-tech companies. Journal of the American Society for Information Science and Technology, 60(2), 332-347.

Lee, Y.-H., Hsieh, Y. C., & Hsu, C.-N. (2011). Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to use E-Learning Systems. Educational Technology & Society, 14(4), 124–137.

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. Retrieved from https://doi.org/10.1016/j.procs.2010.12.047

Liu, A., & Chou, T. (2020). An Integrated Technology Acceptance Model to Approach the Behavioral Intention of Smart Home Appliance. The International Journal of Organizational Innovation, 13(2), 95-118.

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.

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.

Namungo, H., Haji-Othman, Y., & Cheumar, M. (2023). The Significance of Attitude and Perceived Ease of Use on Intention to Use FinTech by Cash-Waqf givers in Kampala Uganda. Science International, 35, 313-318.

Nathania, L., Indarini, I., & Anandya, D. (2021). The effects of external factors on perceived ease of use, perceived usefulness, attitude towards use, and behavioral intention of older adults in Indonesia. Proceedings of the 18th International Symposium on Management (INSYMA 2021). Atlantis Press. Retrieved from https://doi.org/10.2991/aebmr.k.210628.025.

Park, K., Kwon, D., Park, D., & Kim, J. (2022). Effects of the Basic Psychological Needs Factor of e-book Users on Use Intention through Perceived Usefulness and Perceived Ease of Use. Korean Publishing Science Society, 109, 69-97. Retrieved from

https://doi.org/10.21732/skps.2022.109.69

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. Retrieved from

https://doi.org/10.1590/0101-60830000000081

Podsiad, M., & Havard, B. (2020). Faculty acceptance of the peer assessment collaboration evaluation tool: a quantitative study. Education Tech Research Dev, 68, 1381-1407. Retrieved from https://doi.org/10.1007/s11423-020-09742-z

Rahmiati, R., & Yuannita, I. (2019). The influence of trust, perceived usefulness, perceived ease of use, and attitude on purchase intention. Jurnal Kajian Manajemen Bisnis, 8(1), 27-34. Retrieved from https://doi.org/10.24036/jkmb.10884800

Raksadigiri, M. W., & Wahyuni, S. (2020). Perceived ease of use effect on perceived usefulness and attitude towards use and its impact on behavioural intention to use. International Journal of Advanced Research, 8(12), 439-444.

Roger, P., Berg, G., Boettcher, J., Howard, C., Justice, L., & Schenk, K. (2009). Encyclopedia of distance learning. Idea Group Inc (IGI).

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. Retrieved from https://doi.org/10.2478/sbe-2018-0040

Schlebusch, C. (2018). Computer Anxiety, Computer Self-Efficacy and Attitudes Towards the Internet of First Year Students at a South African University of Technology. Africa Education Review, 15(2), 1-19. Retrieved from https://doi.org/10.1080/18146627.2017.1341291

Shah, H. J., & Attiq, S. (2016). Impact of Technology Quality, Perceived Ease of Use and Perceived Usefulness in the Formation of Consumer’s Satisfaction in the Context of E-learning. Abasyn University Journal of Social Sciences, 9(1), 124–140.

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. Retrieved from https://doi.org/10.1016/j.brat.2006.04.010

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

Sternad Zabukovsek, S., Shah Bharadwaj, S., Bobek, S., & Strukelj, T. (2019). Technology Acceptance Model-Based Research on Differences of Enterprise Resources Planning Systems Use in India and the European Union. Inžinerine Ekonomika-Engineering

Economics, 30(3), 326-338. Retrieved from http://dx.doi.org/10.5755/j01.ee.30.3.21211

Teoh, A., & Tan, Y. (2020). Predicting Behavioural Intention of Manufacturing Engineers in Malaysia to Use E-Learning in the Workplace. International Review of Research in Open and Distributed Learning, 21(4), 20–38. Retrieved from https://doi.org/10.19173/irrodl.v21i4.4919

Teoh, R., Schumann, U., Majumdar, A., & Stettler, M. E. J. (2020). Mitigating the climate forcing of aircraft contrails by small-scale diversions and technology adoption. Environmental

Theresiawati, T., Henki, S., Achmad, H., & Zaenal, A. (2020). Variables Affecting E-Learning Services Quality in Indonesian Higher Education: Students’ Perspectives. Journal of Information Technology Education, 19, 259-286

Thomas, O. A., Adeyanju, J., Popoola, B. G., & Odewale, T. R. (2020). Competency Training Needs of Lecturers for Effective E-Learning Instructional Delivery in Teacher Education Programs in South-West, Nigeria. The Journal of Negro Education, 89(2), 136-145.

Tselios, N.,Daskalakis, S., & Papadopoulou, M. (2011). Assessing the Acceptance of a Blended Learning University Course. Educational Technology & Society, 14(2), 224–235.

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.

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.

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

United Nations Department of Economic and Social Affairs. (2022). World Population Prospects 2022. https://population.un.org/wpp/

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.

Venkatesh, V., & Davis, F. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 186-204.

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.

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.

Wang, D., Xu, L., & Chan, H. C. (2015). Understanding the continuance use of social network sites: a computer self-efficacy perspective. Behaviour & Information Technology, 34(2), 204–216.

Waris, I., Ali, R., Nayyar, A., Baz, M., Liu, R., & Hameed, I. (2022). An Empirical Evaluation of Customers’ Adoption of Drone Food Delivery Services: An Extended Technology Acceptance Model. Sustainability, 14(5), 2922.

Wentling, T. L., Waight, C., Gallagher, J., La Fleur, J., Wang, C., & Kanfer, A. (2000). E-learning - a Review of Literature. Knowledge and Learning Systems Group NCSA, 9, 1–73.

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.

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. Retrieved from https://doi.org/10.1177/0266666918765907

Yalcin, M. G., & Kutlu, D. (2019). Factors affecting mobile banking adoption: A literature review. Journal of Accounting and Finance. 19(87), 437-464.

Downloads

Published

2024-06-30

Issue

Section

Research Articles