Through the Lens of Parents: How Preschool Students Adopt U-Learning during COVID-19 in Thailand?
DOI:
https://doi.org/10.14456/shserj.2024.48Keywords:
Ubiquitous Learning, Technology Adoption, Behavioral Intention, Use Behavior, COVID-19Abstract
Purpose: This study ains to examine the factors influencing the acceptance and usage of the ubiquitous learning (u-learning) system among parents of preschool students in a private school in Samutprakarn, Thailand during to the COVID-19 pandemic. The Technology Acceptance Model (TAM) and the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) were used to study the parents’ behavior in the context of technology acceptance and actual use. Research design, data, and methodology: Quantitative research and non-probability sampling techniques were utilized. Item-Objective Congruence and pilot testing were applied to check the content validity and reliability of the questionnaire prior to administering it to 500 respondents via an online survey questionnaire. The data were analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Result: The findings reveal that perceived usefulness influences attitude and behavioral intention to use u-learning. Performance expectancy directly influences the intention to use the U-learning system. On the other hand, perceived ease of use, effort expectancy, and social influence have no significant impact on behavioral intention. Conclusions: The key findings provide technology developers, curriculum designers, and educators with inputs on creating useful and practical strategies to improve the current u-learning system suitable for preschool learners.
References
Abaido, M. A., & Al-Rahmi, W. M. (2021). Investigating the factors affecting university students’ adoption of e-learning during the COVID-19 pandemic: An extended TAM model. Interactive Learning Environments, 17(1), 84-100.
https://doi.org/10.1080/10494820.2021.1900921
Abdelhamid, M. K., Ibrahim, H. M., & Sharaf, S. (2020). E-learning critical success factors during the COVID-19 pandemic: A comprehensive analysis of e-learning managerial perspectives. Journal of Educational Technology Systems, 49(1), 85-106. https://doi.org/10.1177/0047239520968606
Ahsan, M. N., Hoque, R., & Islam, M. R. (2021). An empirical investigation of the adoption of online learning during the COVID-19 pandemic: An extended technology acceptance model. Education and Information Technologies, 20(4), 1-20. https://doi.org/10.1007/s10639-021-10587-6
Akintoye, A. (2015). Developing theoretical and conceptual frameworks. www.jedm.oauife.edu.ng>uploads
Akour, A., Al-Tammemi, A., Barakat, M., & Kanj, R. (2020). The Impact of the COVID-19 Pandemic and Emergency Distance Teaching on the Psychological Status of University Teachers: A Cross-Sectional Study in Jordan. The American journal of tropical medicine and hygiene, 103(6). 2391-2399.
Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2017). Factors influencing the adoption of mHealth apps: A review of the literature. Journal of Health Informatics in Developing Countries, 11(2), 1-17.
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 100-110.
Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Examining factors affecting customer intention to use mobile healthcare services in Saudi Arabia. International Journal of Information Management, 37(3), 127-139.
Al-Fraihat, D., Joy, M., Masadeh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004
Al-Gahtani, S. S. (2016). Empirical Investigation of e-Learning Acceptance and Assimilation: A Structural Equation Model. Applied Computing and Informatics, 12, 27-50. https://doi.org/10.1016/j.aci.2014.09.001
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25(6), 5261-5280. https://doi.org/10.1007/s10639-020-10219-y
Alqahtani, M. A., & Alamri, M. M. (2021). Investigating Students' Perceptions of Online Learning Use as a Digital Tool for Educational Sustainability During the COVID-19 Pandemic. Educational Psychology, 13, 1-10.
https://doi.org/10.3389/fpsyg.2022.886272
Alqahtani, M., Al-Khalifa, H. S., & Al-Qahtani, A. (2021). Primary school teachers’ perceptions of using online learning during the COVID-19 pandemic: An application of the technology acceptance model. Journal of Educational Computing Research, 59(7), 1258-1280. https://doi.org/10.1177/07356331211006436
Alqurashi, E. (2020). What do students engage with the most? A comparative study between high and low achieving students within online learning environments. Open Learning, 37(5), 1-16. https://doi.org/10.1080/02680513.2020.1758052
Al-Somali, S. A., Gholami, R., & Clegg, B. (2009). An Investigation into the Online Banking Acceptance in Saudi Arabia. Tec novation, 29(2), 30-141.
Alzahrani, A. M., Hakami, A., Alhadi, A., & Ali Batais, M. (2020). The interplay between mindfulness, depression, stress, and academic performance in medical students: A Saudi perspective. PLoS ONE, 15(4), e0231088.
Anderson, M., & McKeown, T. (2016). UTAUT: Capturing differences in undergraduate versus postgraduate learning? Education + Training, 58(9), 945-965. https://doi.org/10.1108/ET-07-2015-0058
Arteaga Sánchez, R., Duarte Hueros, A., & García Ordaz, M. (2013). E‐learning and the University of Huelva: A study of WebCT and the technological acceptance model. Campus-Wide Information Systems, 30(2), 135-160.
https://doi.org/10.1108/10650741311306318
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25, 351-370. http://dx.doi.org/10.2307/3250921
Bhattacherjee, A., Perols, J., & Sanford, C. (2008). Information technology continuance:a theoretical extension and empirical test. Journal of Computer Information Systems, 49(1), 17-26. https://doi.org/10.1080/08874417.2008.11645302
Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Publications.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage.
Chang, C. C. (2013). Library mobile applications in university libraries. Library Hi Tech, 31(3), 478-492.
https://doi.org/10.1108/lht-03-2013-0024
Chao, C. M. (2019). Factors determining the behavioural intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(1652), 1-14. https://doi.org/10.3389/fpsyg.2019.01652
Cope, B., & Kalantzis, M. (2013). Towards a New Learning: The Scholar Social Knowledge Workspace, in Theory and Practice. E-Learning and Digital Media, 10(4), 332-356. https://doi.org/10.2304/elea.2013.10.4.332
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approach (3rd ed.). Sage publications.
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
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
De Jong, B., Dirks, K., & Gillespie, N. (2016). Trust and Team Performance: A Meta-Analysis of Main Effects, Moderators and Covariates. Journal of Applied Psychology, 101(8), 1-10. https://doi.org/10.1037/apl0000110
Edgar, T. W., & Manz, D. O. (2017). Exploratory Study (1st ed.). Research Methods for Cyber Security (pp.95-130)
Escobar-Rodríguez, T., Carvajal-Trujillo, E., & Monge-Lozano, P. (2014). Factors that influence the perceived advantages and relevance of Facebook as a learning tool: An extension of the UTAUT. Australasian Journal of Educational Technology, 30(2), 136-151. https://doi.org/10.14742/ajet.585
Fatima, M., Niazi, S., & Ghayas, S. (2017). Relationship between Self-Esteem and Social Anxiety: Role of Social Connectedness as a Mediator. Pakistan Journal of Social and Clinical Psychology, 15(2), 12-17.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research (1st ed.). Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18, 382-388. http://dx.doi.org/10.2307/3150980
Galloway, L., Anderson, M., Brown, W., & Wilson, L. (2005). Enterprise skills for the economy. Education and Training, 47(1), 1-10. https://doi.org/10.1108/00400910510580593
Gao, H., Korn, J. M., Ferretti, S., & Monahan, J. (2015). High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nature Medicine, 21(11), 1318-1325. https://doi.org/10.1038/nm.3954
Garg, R. (2016). Methodology for research I. Indian Journal of Anaesthesia, 60(9), 640.
https://doi.org/10.4103/0019-5049.190619
Grant, C., & Osanloo, A. (2015). Understanding, Selecting, and integrating a theoretical framework in dissertation research: creating the blueprint for your “House”. Administrative Issue Journal, 4(1), 1-25. https://doi.org/10.5929/2014.4.2.9
Gunasinghe, A., Hamid, J. A., Khatibi, A., & Azam, S. M. F. (2020). The adequacy of UTAUT-3 in interpreting academician’s adoption to e-Learning in higher education environments. Interactive Technology and Smart Education, 17(1), 86-106. https://doi.org/10.1108/ITSE-05-2019-0020
Gwebu, K. L., & Wang, J. (2011). Adoption of open-source software: The role of social identification. Decision Support Systems, 51(1), 220-229. https://doi.org/ 10.1016/j.dss.2010.12.010
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. D., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River.
Hew, J.-J., Lee, V.-H., Ooi, K.-B., & Weu, J. (2015). What catalyses mobile apps usage intention: an empirical analysis. Industrial Management & Data Systems, 115(7), 1-10. https://doi.org/10.1108/imds-01-2015-0028
Hopkins, D. (2008). A teacher's guide to classroom research (5th ed.). Open University Press.
Hsiao, C. H., & Tang, K. Y. (2014). Explaining undergraduates’ behavior intention of e-textbook adoption: Empirical assessment of five theoretical models. Library Hi Tech, 32(1), 139-163. https://doi.org/10.1108/LHT-09-2013-0126
Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, 6, 1-55. http://dx.doi.org/10.1080/10705519909540118
Hu, X., & Lai, C. (2019). Comparing factors that influence learning management systems use on computers and on mobile. Information and Learning Sciences, 120(7/8), 468-488. https://doi.org/10.1108/ILS-12-2018-0127
Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning. The Electronic Library, 25(5), 585-598. https://doi.org/10.1108/02640470710829569
Huang, Y. M., & Lin, P. H. (2017). Evaluating students’ learning achievement and flow experience with tablet PCs based on AR and tangible technology in u-learning. Library Hi Tech, 35(4), 602-614. https://doi.org/10.1108/LHT-01-2017-0023
Huang, Y., Hickman, J. E., & and Wu, S. (2018). Impacts of enhanced fertilizer applications on tropospheric ozone and crop damage over sub-Saharan Africa. Atmos Environ., 180, 117-125. https://doi.org/10.1016/j.atmosenv.2018.02.040
Hubert, M., Blut, M., Brock, C., & Backhaus, C. (2017). Acceptance of Smartphone-Based Mobile Shopping: Mobile Benefits, Customer Characteristics, Perceived Risks, and the Impact of Application Context. Psychology and Marketing 34(2), 175-194. https://doi.org/10.1002/mar.20982
Hung, C.-M., Huang, I., & Hwang, G.-J. (2014). Effects of digital game-based learning on students’ self-efficacy, motivation, anxiety, and achievements in learning mathematics. Journal of Computers in Education, 1(2-3), 151-166.
https://doi.org/10.1007/s40692-014-0008-8
Hwang, G. J. (2014). Definition, framework, and research issues of smart learning environments - a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), 1-14. https://doi.org/10.1186/s40561-014-0004-5
Iqbal, S., & Qureshi, I. A. (2021). An assessment of students’ attitudes towards e-learning during the COVID-19 pandemic in Pakistan: A technology acceptance model perspective. Interactive Learning Environments, 11(4), 1-19.
https://doi.org/10.1080/10494820.2021.1891563
Jaiyeoba, O. O., & Iloanya, J. (2019). E-learning in tertiary institutions in Botswana: Apathy to adoption. The International Journal of Information and Learning Technology, 36(2), 157-168. https://doi.org/10.1108/IJILT-05-2018-0058
Kim, C., Kim, M. K., Lee, C., & Spector, J. M. (2013). Teacher beliefs and technology integration. Teaching and Teacher Education, 29(1), 76-85. https://doi.org/10.1016/j.tate.2012.08.005
Kim, S., & Shin, M. (2017). Transformational Leadership Behaviors, the Empowering Process, and Organizational Commitment: Investigating the Moderating Role of Organizational Structure in Korea. International Journal of Human Resource Management, 30, 251-275. https://doi.org/10.1080/09585192.2016.1278253
Kim, Y., & Lee, K. H. (2012). The impact of CSR on relationship quality and relationship outcomes: A perspective of service employees. International Journal of Hospitality Management, 31(3), 745-756. https://doi.org/10.1016/j.ijhm.2011.09.011
Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). Guilford Press, New York.
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Kusuma, P., Pattison, P. M., & Bugbee, B. (2020). From physics to fixtures to food: current and potential LED efficacy. Horticulture Research, 7(1), 1-10. https://doi.org/10.1038/s41438-020-0283-7
Lee, S., Vigoureux, T. F. D., & Hyer, K. (2020). Prevalent insomnia concerns among direct-care workers JAG. Journal of Applied Gerontology, 41(1), 1-10.
Lin, C. Y. (2020). Social reaction toward the 2019 novel coronavirus (COVID-19). Social Health and Behavior, 3(1), 1-2. https://doi.org/10.4103/SHB.SHB_11_20
Lin, H. (2013). The effect of absorptive capacity perceptions on the context‐aware ubiquitous learning acceptance. Campus-Wide Information Systems, 30(4), 249-265. https://doi.org/10.1108/CWIS-09-2012-0031
Lund, B. (2021). The questionnaire method in systems research: An overview of sample sizes, response rates and statistical approaches utilized in studies. VINE Journal of Information and Knowledge Management Systems, 53(1), 1-10.
https://doi.org/10.1108/VJIKMS-08-2020-0156
Marcano Belisario, J. S., Jamsek, J., Huckvale, K., & O’Donoghue, J. (2015). Comparison of self-administered survey questionnaire responses collected using mobile apps versus other methods. Cochrane database of systematic reviews, 7, 1-42. https://doi.org/10.1002/14651858.mr000042.pub2
Marklund, L., & Dunkels, E. (2019). Digital play to develop children's literacy and power in the Swedish preschool: The perspective of preschool teachers. Early Childhood Education Journal, 47(2), 167-178. https://doi.org/10.1080/09575146.2016.1181608
Min, Y., Yang, M., Huang, J., & Duangekanong, S. (2023). Influencing Factors of Behavior Intention of Master of Arts Students Towards Online Education in Chengdu Public Universities, China. Scholar: Human Sciences, 15(1), 1-10. https://doi.org/10.14456/shserj.2023.1
Moghavvemi, S., & Akma Mohd Salleh, N. (2014). Effect of precipitating events on information system adoption and use behaviour. Journal of Enterprise Information Management, 27(5), 599-622. https://doi.org/10.1108/JEIM-11-2012-0079
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web Context. Information and Management, 38, 217-230. http://dx.doi.org/10.1016/S0378-7206(00)00061-6
Murugesan, M., Mathews, P., Paul, H., & Karthik, R. (2021). Protective effect conferred by prior infectionand vaccination on COVID-19 in a healthcare worker cohort in South India. PLOS ONE, 17(5), e0268797.
https://doi.org/10.1371/journal.pone.0268797
Nikou, S., & Maslov, I. (2021). An analysis of students’ perspectives on e-learning participation – the case of COVID-19 pandemic. The International Journal of Information and Learning Technology, 38(3), 299-315.
https://doi.org/10.1108/IJILT-12-2020-0220
Nunnally, J. C., & Bernstein, I. H. (1994). The Assessment of Reliability. Psychometric Theory, 3, 248-292.
Park, D. H., Lee, J., & Han, I. (2007). The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement. International Journal of Electronic Commerce, 11, 125-148.
https://doi.org/10.2753/JEC1086-4415110405
Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: exploration of key determinants and extension of technology acceptance model, Telematics, and Informatics, 31(3), 376-385.
https://doi.org/10.1016/j.tele.2013.11.008
Park, K. H., Kim, D.-H., Kim, S. K., Yi, Y. H., Jeong, J. H., Chae, J., Hwang, J., & Roh, H. (2015). The relationships between empathy, stress, and social support among medical students. Int J Med Educ, 6(1), 103-108.
https://doi.org/10.5116/ijme.55e6.0d44
Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605.
https://doi.org/10.1111/j.1467-8535.2011.01229.x
Plowman, L., Stevenson, O., Stephen, C., & McPake, J. (2012). Preschool children’s learning with technology at home. Computers & Education, 59(1), 30-37. https://doi.org/10.1016/j.compedu.2011.11.014
Reich, J., Buttimer, C. J., Fang, A., Hillaire, G., Hirsch, K., Larke, L. R., & Slama, R. (2020). Remote Learning Guidance from State Education Agencies During the COVID-19 Pandemic: A First Look. Massachusetts Institute of Technology, 4(1), 1-24. https://doi.org/10.35542/osf.io/437e2
Roberts, J. (2010). Designing Incentives in Organizations. Journal of Institutional Economics, 6, 125.
http://dx.doi.org/10.1017/S1744137409990221
Saade, R. G., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in online learning: An extension of the Technology Acceptance Model. Information & Management, 42(2), 317-327.
https://doi.org/10.1016/j.im.2003.12.013
Saleh, A., & Bista, K. (2021). Journal of Interdisciplinary Studies in Education. Journal of Interdisciplinary Studies in Education, 10(1), 1-194.
Salloum, S. A., Gaber, T., Vadera, S., & Shaalan, K. (2021). Phishing Email Detection Using Natural Language Processing Techniques: A Literature Survey. Procedia Computer Science, 189, 19-28. https://doi.org/10.1016/j.procs.2021.05.077
Sitar-Taut, D.-A., Mican, D., & Sarstedt, M. (2021). Digital Socialligators? Social Media-Induced Perceived Support During the Transition to the COVID-19 Lockdown. Social Science Computer Review, 41(3), 748-767.
https://doi.org/10.1177/08944393211065872
Sobti, N. (2019). Impact of demonetization on diffusion of mobile payment service in India: Antecedents of behavioral intention and adoption using extended UTAUT model. Journal of Advances in Management Research, 1(2), 472-497.
https://doi.org/10.1108/jamr-09-2018-0086
Stevens, B. F. (1992). Price Value Perceptions of Travelers. Journal of Travel Research, 31, 44-48.
http://dx.doi.org/10.1177/004728759203100208
Sumak, B., & Sorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre- and post-adopters. Computers in Human Behaviour, 64(2), 602-620.
Talukder, A. K. M. M. H. (2019). Supervisor Support and Organizational Commitment: The Role of Work–Family Conflict, Job Satisfaction, and Work–Life Balance. Journal of Employment Counseling, 56(3), 98-116.
https://doi.org/10.1002/joec.12125
Talukder, M. S., Chiong, R., Bao, Y., & Hayat Malik, B. (2019). Acceptance and use predictors of fitness wearable technology and intention to recommend: An empirical study. Industrial Management & Data Systems, 119(1), 170-188.
https://doi.org/10.1108/IMDS-01-2018-0009
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International journal of medical education, 2, 53. https://doi.org/10.5116/ijme.4dfb.8dfd
Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
Teddlie, C., & Tashakkori, A. (2009). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioural sciences (2nd ed.). Sage.
Trust, T., & Whalen, J. (2020). Should teachers be trained in emergency remote teaching? Lessons learned from the COVID-19 pandemic. Journal of Technology and Teacher Education, 28(2), 189-99.
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., 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
Venkatesh, V., Thong, J. Y. L., & 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. https://doi.org/10.2307/41410412
Wang, X., & Chen, H. (2020). Investigating the use of ubiquitous learning during the COVID-19 pandemic. Education Sciences, 10(9), 259. https://doi.org/10.3390/educsci10090259
Wu, B., & Chen, X. (2017). Continuance Intention to Use MOOCs: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Computers in Human Behavior, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028
Zhang, M., & Deng, Z. (2021). The impact of social influence on users’ online learning behavior during the COVID-19 pandemic: An empirical study in China. Frontiers in Psychology, 12, 580588. https://doi.org/10.3389/fpsyg.2021.580588