Determinants of perceived e-learning usefulness in higher education: A case of Thailand
-
DOIhttp://dx.doi.org/10.21511/im.18(4).2022.08
-
Article InfoVolume 18 2022, Issue #4, pp. 86-96
- Cited by
- 476 Views
-
130 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Perceived e-learning usefulness as a marketing element has significantly affected student satisfaction, which results in a high propensity to continue using the current e-learning services with their universities. Therefore, this study aims to examine the effects of perceived risk, confirmation, and student motivation on perceived e-learning usefulness. This paper employed a convenience sampling technique to collect opinions from 689 university students at different universities (e.g., Thaksin University, Hatyai University, Prince of Songkla University, and Rajabhat University) around Thailand. Those students were actively using e-learning to access their education. After checking data validity, only 527 valid responses were analyzed through the path analysis method. According to empirical findings, confirmation significantly influenced student motivation, while perceived risk did not significantly impact student motivation. Finally, perceived e-learning usefulness was significantly influenced by confirmation, student motivation, and perceived risk. Furthermore, although these factors significantly influenced perceived e-learning usefulness, attitudes toward perceived e-learning usefulness relied mainly on the degree of confirmation, as this factor highlighted the most substantial effect on perceived e-learning usefulness. Moreover, perceived e-learning usefulness as a marketing element is a promising topic in the e-learning service sector, which requires future studies to examine to which extent the current study findings could apply to other groups of students or practitioners.
- Keywords
-
JEL Classification (Paper profile tab)I23, M30, M31
-
References49
-
Tables4
-
Figures2
-
- Figure 1. Conceptual framework
- Figure 2. Path analysis findings
-
- Table 1. Reliability and convergent validity
- Table 2. Discriminant validity
- Table 3. Model fitness of path analysis
- Table 4. Findings and hypotheses testing
-
- Alhassany, H., & Faisal, F. (2018). Factors influencing the internet banking adoption decision in North Cyprus: an evidence from the partial least square approach of the structural equation modeling. Financial Innovation, 4(1), 29.
- Altin Gumussoy, C., Kaya, A., & Ozlu, E. (2018). Determinants of Mobile Banking Use: An Extended TAM with Perceived Risk, Mobility Access, Compatibility, Perceived Self-efficacy and Subjective Norms. In F. Calisir & H. Camgoz Akdag (Eds.), Industrial Engineering in the Industry 4.0 Era. Lecture Notes in Management and Industrial Engineering (pp. 225-238). Springer.
- Ariffin, S. K., Mohan, T., & Goh, T.-N. (2018). Influence of consumers’ perceived risk on consumers’ online purchase intention. Journal of Research in Interactive Marketing, 12(3), 309-327.
- Baki, R., Birgoren, B., & Aktepe, A. (2018). A meta analysis of factors affecting perceived usefulness and perceived ease of use in the adoption of E-Learning systems. Turkish Online Journal of Distance Education, 19(4), 4-42.
- Bastari, A., Eliyana, A., Syabarrudin, A., Arief, Z., & Emur, A. P. (2020). Digitalization in banking sector: the role of intrinsic motivation. Heliyon, 6(12), e5801.
- Behforouz, B., Al Gaithi, A., & Fekri, N. (2021). Omani efl learner perceptions and motivation toward online learning. Journal of University Teaching and Learning Practice, 18(4), 1-14.
- Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
- Blackwood-Brown, C. G. (2018). An Empirical Assessment of Senior Citizens’ Cybersecurity Awareness, Computer Self-efficacy, Perceived Risk of Identity Theft, Attitude, and Motivation to Acquire Cybersecurity Skills. Nova Southeastern University.
- Cocosila, M., Archer, N., & Yuan, Y. (2009). Early investigation of new information technology acceptance: A perceived risk – Motivation model. Communications of the Association for Information Systems, 25(1), 339-358.
- Cranmer, G. A., Brann, M., & Weber, K. D. (2018). “Challenge Me!”: Using Confirmation Theory to Understand Coach Confirmation as an Effective Coaching Behavior. Communication and Sport, 6(2), 239-259.
- Dai, H. M., Teo, T., Rappa, N. A., & Huang, F. (2020). Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers and Education, 150, 103850.
- Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management and E-Learning, 11(2), 201-214.
- Gagne, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331-362.
- Gao, Y. (2021). Toward the Role of Language Teacher Confirmation and Stroke in EFL/ESL Students’ Motivation and Academic Engagement: A Theoretical Review. Frontiers in Psychology, 12.
- Ge, Y., Yuan, Q., Wang, Y., & Park, K. (2021). The structural relationship among perceived service quality, perceived value, and customer satisfaction-focused on starbucks reserve coffee shops in Shanghai, China. Sustainability, 13(15), 8633.
- Goodboy, A. K., & Myers, S. A. (2008). The effect of teacher confirmation on student communication and learning outcomes. Communication Education, 57(2), 153-179.
- Gupta, A., Dhiman, N., Yousaf, A., & Arora, N. (2021). Social comparison and continuance intention of smart fitness wearables: an extended expectation confirmation theory perspective. Behaviour and Information Technology, 40(13), 1341-1354.
- Gupta, A., Yousaf, A., & Mishra, A. (2020). How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management, 52, 102094.
- Hartelina, Batu, R. L., & Hidayanti, A. (2021). What can hedonic motivation do on decisions to use online learning services? International Journal of Data and Network Science, 5(2), 121-126.
- Horst, M., Kuttschreuter, M., & Gutteling, J. M. (2007). Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in The Netherlands. Computers in Human Behavior, 23(4), 1838-1852.
- Huang, H. M., & Liaw, S. S. (2018). An analysis of learners’ intentions toward virtual reality learning based on constructivist and technology acceptance approaches. International Review of Research in Open and Distance Learning, 19(1), 91-115.
- Ifinedo, P. (2017). Students’ perceived impact of learning and satisfaction with blogs. The International Journal of Information and Learning Technology, 34(4), 322-337.
- Isaac, O., Aldholay, A., Abdullah, Z., & Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Computers & Education, 136, 113-129.
- Kim, L., & Jindabot, T. (2022). Evolution of customer satisfaction in e-banking service industry. Innovative Marketing, 18(1), 131-141.
- Kim, L., Maijan, P., Jindabot, T., & Ali, W. B. (2021). Understanding Customer Trust in Latex Glove Industry: Evidence from Thai Customers. Review of International Geographical Education Online, 11(08), 1014-1022.
- Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54(2), 506-516.
- Lee, Y., & Kwon, O. (2011). Intimacy, familiarity and continuance intention: An extended expectation-confirmation model in web-based services. Electronic Commerce Research and Applications, 10(3), 342-357.
- Li, Z., Yuan, J., Du, B., Hu, J., Yuan, W., Palladini, L., Yu, B., & Zhou, Y. (2020). Customer Behavior on Purchasing Channels of Sustainable Customized Garment With Perceived Value and Product Involvement. Frontiers in Psychology, 11.
- Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information and Management, 42(5), 683-693.
- Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47, 101318.
- Park, S. S., Rhim, Y. T., Kim, M. J., Kim, S. K., & Yoo, J. I. (2014). The influence of perceived risk on participation motivation and re-participation intention in marine sports. Journal of Coastal Research, 72(sp1), 96-100.
- Puntularb, P., Yippikun, C., & Pinchunsri, P. (2021). The Characteristics and Self-Regulation of Undergraduate Students in Online English Learning: A Case Study of A Private University in Thailand. International Journal of Higher Education, 10(7).
- Reuters. (2022, July 15). Covid-19 Tracker: Thailand.
- Sari, E. R. (2012). Online learning community: A case study of teacher professional development in Indonesia. Intercultural Education, 23(1), 63-72.
- Sarkar, S., & Khare, A. (2019). Influence of Expectation Confirmation, Network Externalities, and Flow on Use of Mobile Shopping Apps. International Journal of Human-Computer Interaction, 35(16), 1449-1460.
- Seo, K. H., & Lee, J. H. (2021). The emergence of service robots at restaurants: Integrating trust, perceived risk, and satisfaction. Sustainability, 13(8), 4431.
- Solanki, D. (2020, September 12). Understand the Difference between E-learning & Online Learning. Talentedge.
- Sørebø, Ø., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The role of self-determination theory in explaining teachers’ motivation to continue to use e-learning technology. Computers & Education, 53(4), 1177-1187.
- Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984-990.
- Sun, S., Lee, P. C., Law, R., & Zhong, L. (2020). The impact of cultural values on the acceptance of hotel technology adoption from the perspective of hotel employees. Journal of Hospitality and Tourism Management, 44, 61-69.
- Sun, Y., & Gao, F. (2020). An investigation of the influence of intrinsic motivation on students’ intention to use mobile devices in language learning. Educational Technology Research and Development, 68(3), 1181-1198.
- Tamilmani, K., Rana, N. P., Prakasam, N., & Dwivedi, Y. K. (2019). The battle of Brain vs. Heart: A literature review and meta-analysis of “hedonic motivation” use in UTAUT2. International Journal of Information Management, 46, 222-235.
- Wang, H., Chung, J. E., Park, N., McLaughlin, M. L., & Fulk, J. (2012). Understanding Online Community Participation: A Technology Acceptance Perspective. Communication Research, 39(6), 781-801.
- Wang, S., Wang, J., Li, J., Wang, J., & Liang, L. (2018). Policy implications for promoting the adoption of electric vehicles: Do consumer’s knowledge, perceived risk and financial incentive policy matter? Transportation Research Part A: Policy and Practice, 117, 58-69.
- Wu, H. C., & Cheng, C. C. (2018). What Drives Experiential Loyalty Toward Smart Restaurants? The Case Study of KFC in Beijing. Journal of Hospitality Marketing and Management, 27(2), 151-177.
- Wu, I.-L., Chiu, M.-L., & Chen, K.-W. (2020). Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues. International Journal of Information Management, 52, 102099.
- Yang, H. S., & Park, J. W. (2019). A study of the acceptance and resistance of airline mobile application services: with an emphasis on user characteristics. International Journal of Mobile Communications, 17(1), 24-43.
- Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256-269.
- Zhao, S., Ye, B., Wang, W., & Zeng, Y. (2022). The Intolerance of Uncertainty and “Untact” Buying Behavior: The Mediating Role of the Perceived Risk of COVID-19 Variants and Protection Motivation. Frontiers in Psychology, 13.