nach oben

Education and Information Technologies

Erschienen in:

23.12.2022

Acceptance of online distance learning (ODL) among students: Mediating role of utilitarian and hedonic value

verfasst von: Syed Shah Alam, Mohammad Masukujjaman, Maisarah Ahmad, Romlah Jaffor

Erschienen in: Education and Information Technologies | Ausgabe 7/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Nowadays, the teaching methods are changed from offline to online primarily for the advent of the internet facility. The Industrial Revolution 4.0 (“Education 4.0”) stresses offering online courses at the university level. The study aims to find out the factors influencing students' intentions to admit to online distance learning courses. In addition, the study wanted to establish the utilitarian and hedonic value construct in mediating the association between attitude and intention. Based on an intensive literature survey, an extended Technology Acceptance Model was proposed including some cognitive and technology-specific factors to test empirically. This is a quantitative study with an exploratory and descriptive scope and cross-sectional design. The information was gathered by applying the convenience sampling method from 293 Malaysian students who participated in anonymous surveys. The obtained data were analyzed using structural equation modeling applying AMOS 21 version. The study reveals that hedonic value, utilitarian value, perceived ease of use, and attitude except for perceived usefulness, affect behavioral intention to accept online distance learning courses except for perceived usefulness construct. The antecedents of utilitarian value are perceived fees, attitude, perceived usefulness, and perceived ease of use, whereas the antecedents of hedonic value are perceived fees, attitude, and perceived usefulness, except for perceived ease of use. Finally, self-efficacy affects perceived ease of use, perceived usefulness, and attitude towards joining online distance learning courses. This study's conclusions will benefit all stakeholders in the education system who are considering or have already adopted e-learning.

Vorheriger Artikel A review of AI teaching and learning from 2000 to 2020

Nächster Artikel Instructors’ presence in instructional videos: A systematic review

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Affouneh, S., Salha, S., & Khlaif, Z. N. (2020). Designing quality e-learning environments for emergency remote teaching in Coronavirus crisis. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 11(2),135–137.

Aguilera-Hermida, A. P., Quiroga-Garza, A., Gómez-Mendoza, S., del Río Villanueva, C. A., AvolioAlecchi, B., & Avci, D. (2021). Comparison of students’ use and acceptance of emergency online learning due to COVID-19 in the USA, Mexico, Peru, and Turkey. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10473-8CrossRef

Ajibade, P. (2018). Technology acceptance model limitations and criticisms: Exploring the practical applications and use in technology-related studies, mixed-method, and qualitative researches. Library Philosophy and Practice. 1941. http://digitalcommons.unl.edu/libphilprac/1941

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Al-Bashayreh, M., Almajali, D., Altamimi, A., Masa’deh, R., & Al-Okaily, M. (2022). An empirical investigation of reasons influencing student acceptance and rejection of mobile learning apps usage. Sustainability, 14(7), 4325.

Alam, S. S., Omar, N. A., Ariffin, A. A. M., & Hashim, N. (2018). Integrating TPB, TAM and DOI theories: An empirical evidence for the adoption of mobile banking among customers in Klang Valley, Malaysia. International Journal of Business and Management Science, 8(2), 385–403.

Albelbisi, N. A., Al-Adwan, A. S., & Habibi, A. (2022). A SWOT analysis on acceptance of MOOC in malaysian higher education: The learners’ perspective. Turkish Online Journal of Distance Education, 23(1), 74–85.

Ali, W. (2020). Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. Higher Education Studies, 10(3). https://doi.org/10.5539/hes.v10n3p16

Allen, I. E., & Seaman, J. (2015). Grade level: Tracking online education in the United States. Babson Survey Research Group. Babson College, 231 Forest Street, Babson Park, MA 02457. https://files.eric.ed.gov/fulltext/ED572778.pdf

Allo, M. D. G. (2020). Is the online learning good in the midst of Covid-19 Pandemic? The case of EFL learners. Jurnal Sinestesia, 10(1), 1–10.

An, M., & Han, S.-L. (2020). Effects of experiential motivation and customer engagement on customer value creation: Analysis of psychological process in the experience-based retail environment. Journal of Business Research, 120, 389–397.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.

Avcilar, M. Y., & Özsoy, T. (2015). Determining the effects of perceived utilitarian and hedonic value on online shopping intentions. International Journal of Marketing Studies, 7, 27–49.

Awang, Z., Afthanorhan, A., Mohamad, M., & Asri, M. A. M. (2015). An evaluation of measurement model for medical tourism research: The confirmatory factor analysis approach. International Journal of Tourism Policy, 6(1), 29–45.

Ayeh, J. K. (2015). Travelers’ acceptance of consumer-generated media: An integrated model of technology acceptance and source credibility theories. Computers in Human Behavior, 48, 173–180.

Baber, H. (2021). Modelling the acceptance of e-learning during the pandemic of COVID-19-A study of South Korea. International Journal of Management Education, 19(2). https://doi.org/10.1016/j.ijme.2021.100503

Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644–656.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327CrossRef

Baheti, R., & Gill, H. (2011). Cyber-physical systems. The Impact of Control Technology, 12(1), 161–166.

Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037//0033-295x.84.2.191CrossRef

Barclay, M. J., Smith, C. W., & Watts, R. L. (1997). The determinants of corporate leverage and dividend policies. Journal of Financial Education, 23, 1–15. https://www.jstor.org/stable/41948238

Basaran, U., & Buyukyilmaz, O. (2015). The effects of utilitarian and hedonic values on young consumers satisfaction and behavioral intentions. Eurasian Journal of Business and Economics, 8(16), 1–18.

Baygin, M., Yetis, H., Karakose, M., & Akin, E. (2016). An effect analysis of industry 4.0 to higher education. In 2016 15th international conference on information technology-based higher education and training (ITHET) (pp. 1–4). IEEE.

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588CrossRef

Bower, M. (2019). Technology-mediated learning theory. British Journal of Educational Technology, 50(3). https://doi.org/10.1111/bjet.12771

Bozkurt, A., & Sharma, R. (2020). Emergency remote teaching in a time of global crisis due to Corona Virus pandemic. Asian Journal of Distance Education, 15(1), 1–6.

Broom, D. R., Lee, K. Y., Lam, M. H. S., & Flint, S. W. (2019). Go ta catch ‘em al or not enough time: Users motivations for playing Pokémon Go^TM and non-users reasons for not installing. Health Psychology Research, 7(1), 7714. https://doi.org/10.4081/hpr.2019.7714

Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258.

Budu, K. W. A., Yinping, M., & Mireku, K. K. (2018). Investigating the effect of behavioral intention on e-learning systems usage: Empirical study on tertiary education institutions in Ghana. Mediterranean Journal of Social Sciences, 9(3), 201.

Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43(6), 706–717.

Chang, H. H., & Wang, I. C. (2008). An investigation of user communication behavior in computer-mediated environments. Computers in Human Behavior, 24(5), 2336–2356.

Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652.

Chawla, D., & Joshi, H. (2020). The moderating role of gender and age in the adoption of mobile wallet. Foresight-the Journal of Future Studies, Strategic Thinking, and Policy, 22(4), 483–504.

Chen, H.-S., Liang, C.-H., Liao, S.-Y., & Kuo, H.-Y. (2020). Consumer attitudes and purchase intentions toward food delivery platform services. Sustainability, 12(23), 10177.

Cheung, M. L., Chau, K. Y., Lam, M. H. S., Tse, G., Ho, K. Y., Flint, S. W., et al. (2019). Examining consumers’ adoption of wearable healthcare technology: The role of health attributes. International Journal of Environmental Research and Public Health, 16(13), 2257.

Chuah, S.H.-W., Rauschnabel, P. A., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016). Wearable technologies: The role of usefulness and visibility in smartwatch adoption. Computers in Human Behavior, 65, 276–284.

Chun, H., Lee, H., & Kim, D. (2012). The integrated model of smartphone adoption: Hedonic and utilitarian value perceptions of smartphones among Korean college students. Cyberpsychology, Behavior, and Social Networking, 15(9), 473–479.

Cicha, K., Rizun, M., Rutecka, P., & Strzelecki, A. (2021). Covid-19 and higher education: First-year students’ expectations toward distance learning. Sustainability (Switzerland), 13(4). https://doi.org/10.3390/su13041889

Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118–143.

Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Burton, R., Magni, P., & Lam, S. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3(1), 1–20.

Cronin, J. J., Jr., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193–218.

Daniel, S. J. (2020). Education and the COVID-19 pandemic. Prospects, 49(1), 91–96.

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology, USA). https://dspace.mit.edu/bitstream/handle/1721.1/15192/14927137-MIT.pdf

Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340.

Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19–45.

Diop, E. B., Zhao, S., & Duy, T. V. (2019). An extension of the technology acceptance model for understanding travelers’ adoption of variable message signs. PLoS One, 14(4), e0216007.

Dutot, V., Bhatiasevi, V., & Bellallahom, N. (2019). Applying the technology acceptance model in a three-countries study of smartwatch adoption. The Journal of High Technology Management Research, 30(1), 1–14.

Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7). https://doi.org/10.1080/10494820.2017.1421560

Estriegana, R., Medina-Merodio, J. A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers and Education, 135. https://doi.org/10.1016/j.compedu.2019.02.010

Fagan, M., Kilmon, C., & Pandey, V. (2012). Exploring the Adoption of a Virtual Reality Simulation: The Role of Perceived Ease of Use, Perceived Usefulness and Personal Innovativeness. Campus-Wide Information Systems, 29(2), 117-127.

Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.

Fatima, J., Ghandforuosh, P., Khan, M., & di Masico, R. (2017). Role of innovativeness and self-efficacy in tourism m-learning. Tourism Review, 72(3), 344–355.

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.

Fishbein, M., & Ajzen, I. (1975). Belief; attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Gansser, O. A., & Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535.

Garača, Ž. (2011). Factors related to the intended use of ERP systems. Management-Journal of Contemporary Management Issues, 16(2), 23–42.

George, M. L. (2020). Effective teaching and examination strategies for undergraduate learning during COVID-19 school restrictions. Journal of Educational Technology Systems, 49(1). https://doi.org/10.1177/0047239520934017

Ghali, Z. Z. (2020). Effect of utilitarian and hedonic values on consumer willingness to buy and to pay for organic olive oil in Tunisia. British Food Journal, 122(4), 1013–1026.

Gonzalez, T., de la Rubia, M. A., Hincz, K. P., Comas-Lopez, M., Subirats, L., Fort, S., & Sacha, G. M. (2020). Influence of COVID-19 confinement on students’ performance in higher education. PLoS ONE, 15(10 October). https://doi.org/10.1371/journal.pone.0239490

Guerrero, F. T. (2019). Aceptación de los estudiantes universitarios en el uso de los sistemas e-learning Moodle desde la perspectiva del modelo TAM. Revista Ciencia Unemi, 12(29), 63–76.

Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.MATH

Hall, T., Connolly, C., Ó Grádaigh, S., Burden, K., Kearney, M., Schuck, S., et al. (2020). Education in precarious times: a comparative study across six countries to identify design priorities for mobile learning in a pandemic. Information and Learning Science, 121(5–6). https://doi.org/10.1108/ILS-04-2020-0089

Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 80, 197–206.

Hanzaee, K. H., & Ghafelehbashi, S. (2012). The Role of Utilitarian and Hedonic Values and Their Antecedents in a Mobile Phone Multimedia Services (Irancell Vitrin^(Tm) Services). Research Journal of Applied Sciences, Engineering, and Technology, 4(13), 1953–1961.

Hanzaee, K. H., & Khonsari, Y. (2011). A review of the role of hedonic and utilitarian values on customer’s satisfaction and behavioral intentions. Interdisciplinary Journal of Research in Business, 1(5), 34–45.

Harman, H. H. (1976). Modern factor analysis. University of Chicago Press.MATH

Hashmi, H., Attiq, S., & Rasheed, F. (2019). Factors affecting online impulsive buying behavior: A stimulus organism response model approach. Market Forces, 14(1), 19–42.

Hayes, A. F., & Preacher, K. J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research, 45(4), 627–660.

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.

Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause.

Holbert, R. L., & Stephenson, M. T. (2002). Structural equation modeling in the communication sciences, 1995–2000. Human Communication Research, 28(4), 1995–2000.

Hong, S.-J., & Tam, K. Y. (2006). Understanding the adoption of multipurpose information appliances: The case of mobile data services. Information Systems Research, 17(2), 162–179.

Hsu, C.-L., & Lin, J.C.-C. (2015). What drives purchase intention for paid mobile apps? –An expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 14(1), 46–57.

Huang, F., Teo, T., & Zhou, M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Educational Technology Research and Development, 68(1), 575–591.

Ibrahim, R., Leng, N. S., M Yusoff, R. C., Samy, G. N., Masrom, S., & Rizman, Z. I. (2017). E-learning acceptance based on the technology acceptance model (TAM). ajol.info, 2017(4S), 871–889. https://doi.org/10.4314/jfas.v9i4s.50

Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International.

Jung, I., & Lee, J. (2020). A cross-cultural approach to the adoption of open educational resources in higher education. British Journal of Educational Technology, 51(1). https://doi.org/10.1111/bjet.12820

Jung, I. (2014). Cultural influences on online learning. Culture and Online Learning. 5–24. http://bi-dam-prod.s3.amazonaws.com/5a4bbbea08fe09bb0a8b59e2/d75d22fdb5dc0c116f9ac2b811bf59bed1788c6b/9781579228552_otherchap_c849eda688575ac74f2985607aa07cc1.pdf

Kalantari, M. (2017). Consumers’ adoption of wearable technologies: Literature review, synthesis, and future research agenda. International Journal of Technology Marketing, 12(3), 274–307.

Karjaluoto, H., & Leppäniemi, M. (2013). Social identity for teenagers: Understanding behavioral intention to participate in the virtual world environment. Journal of Theoretical and Applied Electronic Commerce Research, 8(1), 1–16.

Kemp, A., Palmer, E., & Strelan, P. (2019). A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models. British Journal of Educational Technology, 50(5). https://doi.org/10.1111/bjet.12833

Khalaf, Z. N. (2020). Corona virus and digital equality in tele-teaching in emergency situations. New Education Blog. Available online: https://www.new-educ.com//. Accessed 17 May 2020.

Kim, B., & Han, I. (2009). What drives the adoption of mobile data services? An approach from a value perspective. Journal of Information Technology, 24(1), 35–45.MathSciNet

Kim, B., & Han, I. (2011). The role of utilitarian and hedonic values and their antecedents in a mobile data service environment. Expert Systems with Applications, 38(3), 2311–2318.

Kim, K. J., & Shin, D.-H. (2015). An acceptance model for smartwatches. Internet Research: Electronic Networking Applications and Policy, 25(4), 527–541.

Kim, J.-K., & Yang, H.-C. (2019). Effects of heuristic type on purchase intention in mobile social commerce: Focusing on the mediating effect of shopping value. The Journal of Distribution Science, 17(10), 73–81.

Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126.

Kim, B., Choi, M., & Han, I. (2009). User behaviors toward mobile data services: The role of perceived fee and prior experience. Expert Systems with Applications, 36(4), 8528–8536.

Kim, T., & Chiu, W. (2019). Consumer acceptance of sports wearable technology: The role of technology readiness. International Journal of Sports Marketing & Sponsorship, 20(1), 109–126.

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.

Kleinbaum, D. G., Kupper, L. L., & Muller, K. E. (1988). Applied regression analysis and other multivariable methods. In Applied regression analysis and other multivariable methods (3rd ed., pp. 718). London: Duxbury Press. https://cyberleninka.org/article/n/834508.pdf

Kwon, S. J., & Park, S. C. (2015). Utilitarian value and hedonic value of mobile service: Focusing on mobile addiction. In Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), Portugal. pp. 621–626. https://doi.org/10.5220/0005476106210626

Lee, J., Kim, J., & Choi, J. Y. (2019). The adoption of virtual reality devices: The technology acceptance model integrating enjoyment, social interaction, and strength of the social ties. Telematics and Informatics, 39, 37–48. https://doi.org/10.1016/j.tele.2018.12.006CrossRef

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204.

Liu, Y., & Li, H. (2011). Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China. Computers in Human Behavior, 27(2), 890–898.MathSciNet

Looi, K. H., Wye, C. -K., & Abdul Bahri, E. N. (2022). Achieving learning outcomes of emergency remote learning to sustain higher education during crises: An empirical study of Malaysian undergraduates. Sustainability, 14(3), 1598.

Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114–120.

Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: An empirical study in Vietnam. Education and Information Technologies, 26(6), 6629–6649.

Martin, A. (2020). How to optimize online learning in the age of coronavirus (COVID-19): A 5-point guide for educators. University New South Wales Newsroom. https://newsroom.unsw.edu.au/news/social-affairs/how-optimise-online-learning-age-coronavirus

McDonald, R. P., & Ho, M.-H.R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64.

McFarland, D. J., & Hamilton, D. (2006). Adding contextual specificity to the technology acceptance model. Computers in Human Behavior, 22(3), 427–447.

Mehmood, K. K., & Hanaysha, J. (2015). The strategic role of hedonic value and utilitarian value in building brand loyalty: Mediating effect of customer satisfaction. Pakistan Journal of Social Sciences (PJSS), 35(2), 1025–1036.

Mutahar, A. M., Daud, N. M., Thurasamy, R., Isaac, O., & Abdulsalam, R. (2018). The mediating of perceived usefulness and perceived ease of use: The case of mobile banking in Yemen. International Journal of Technology Diffusion (IJTD), 9(2), 21–40.

Nejati, M., & Moghaddam, P. P. (2012). Gender differences in hedonic values, utilitarian values and behavioural intentions of young consumers: Insights from Iran. Young Consumers: Insight and Ideas for Responsible Marketers, 13(4), 337–344.

Nejati, M., & Moghaddam, P. P. (2013). The effect of hedonic and utilitarian values on satisfaction and behavioural intentions for dining in fast-casual restaurants in Iran. British Food Journal, 115(11), 1583–1596.

Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250–267.

Nguyen, T. T. H., Nguyen, N., Nguyen, T. B. L., Phan, T. T. H., Bui, L. P., & Moon, H. C. (2019). Investigating consumer attitude and intention towards online food purchasing in an emerging economy: An extended TAM approach. Foods, 8(11), 576.

Okada, E. M. (2005). Justification effects on consumer choice of hedonic and utilitarian goods. Journal of Marketing Research, 42(1), 43–53.

Otter, V., & Beer, L. (2021). Alley cropping systems as Ecological Focus Areas: A PLS-analysis of German farmers’ acceptance behaviour. Journal of Cleaner Production, 280, 123702.

Overby, J. W., & Lee, E.-J. (2006). The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59(10–11), 1160–1166.

Ozturk, A. B., Nusair, K., Okumus, F., & Hua, N. (2016). The role of utilitarian and hedonic values on users’ continued usage intention in a mobile hotel booking environment. International Journal of Hospitality Management, 57, 106–115.

Panigrahi, R., Srivastava, P. R., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome—A review of literature. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2018.05.005CrossRef

Parasuraman, A., & Grewal, D. (2000). The impact of technology on the quality-value-loyalty chain: A research agenda. Journal of the Academy of Marketing Science, 28(1), 168–174.

Park, C., Kim, D., Cho, S., & Han, H.-J. (2019). Adoption of multimedia technology for learning and gender difference. Computers in Human Behavior, 92, 288–296.

Pham, Q. T., & Tran, T. P. (2020). The acceptance of e-learning systems and the learning outcome of students at universities in Vietnam. Knowledge Management and E-Learning, 12(1). https://doi.org/10.34105/j.kmel.2020.12.004

Pramana, E. (2018). Determinants of the adoption of mobile learning systems among university students in Indonesia. Journal of Information Technology Education: Research, 17, 365–398.

Prasetyo, Y. T., Roque, R. A. C., Chuenyindee, T., Young, M. N., Diaz, J. F. T., Persada, S. F., et al. (2021). Determining factors affecting the acceptance of medical education e-learning platforms during the covid-19 pandemic in the Philippines: Utaut2 approach. Healthcare (Switzerland), 9(7). https://doi.org/10.3390/healthcare9070780

Qiao, P., Zhu, X., Guo, Y., Sun, Y., & Qin, C. (2021). The development and adoption of online learning in Pre- and Post-COVID-19: Combination of technological system evolution theory and unified theory of acceptance and use of technology. Journal of Risk and Financial Management, 14(4), 162. https://doi.org/10.3390/JRFM14040162CrossRef

Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732.

Rahim, N. N. A., Humaidi, N., Aziz, S. R. A., & Zain, N. H. M. (2022). Moderating effect of technology readiness towards open and distance learning (ODL) technology acceptance curing COVID-19 Pandemic. Asian Journal of University Education, 18(2), 406–421.

Rashid, M. F. A. (2009). The importance of internal migration: In the context of urban planning decision making. In ICBEDC 2009. Penang, Malaysia.

Rizun, M., & Strzelecki, A. (2020). Students’ acceptance of the Covid-19 impacts on shifting higher education to distance learning in Poland. International Journal of Environmental Research and Public Health, 17(18), 6468.

Rogers, E. M. (1983). Diffusion of innovations. New York, NY: Free Press.

Rong-Da Liang, A., & Lim, W. M. (2011). Exploring the online buying behavior of specialty food shoppers. International Journal of Hospitality Management, 30(4), 855–865.

Salloum, S., Alhamad, A., & … M. A.-E. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. ieeexplore.ieee.org. https://ieeexplore.ieee.org/abstract/document/8825866/. Accessed 4 Mar 2022.

Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219–238. https://doi.org/10.1108/ITSE-11-2018-0092/FULL/HTMLCrossRef

Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35.

Selamat, Z., Jaffar, N., & Ong, B. H. (2009). Technology acceptance in the Malaysian banking industry. European Journal of Economics, Finance and Administrative Sciences, 1(17), 143–155.

Seo, K. H., & Lee, J. H. (2021). Understanding Risk Perception toward Food Safety in Street Food: The Relationships among Service Quality, Values, and Repurchase Intention. International Journal of Environmental Research and Public Health, 18(13), 6826.

Shen, J., & Eder, L. B. (2009). Intentions to use virtual worlds for education. Journal of Information Systems Education, 20(2), 225.

Sheth, J. N., Newman, B. I., & Gross, B. L. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159–170.

Singh, S., & Srivastava, R. K. (2020). Understanding the intention to use mobile banking by existing online banking customers: An empirical study. Journal of Financial Services Marketing, 25(3), 86–96.

Šumak, B., Heričko, M., Pušnik, M., & Polančič, G. (2011). Factors affecting acceptance and use of moodle: An empirical study based on TAM. Informatica, 35(1), 91–100.

Sun, H., & Zhang, P. (2006). Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7(1), 24.

Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple-item scale. Journal of Retailing, 77(2), 203–220.

Taat, M. S., & Francis, A. (2020). Factors influencing the students’ acceptance of e-learning at teacher education institute: An exploratory study in Malaysia. International Journal of Higher Education, 9(1), 133–141.

Taber, K. S. (2018). The use of Cronbach’s Alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2

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.

Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: A structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328.

Turel, O., Serenko, A., & Bontis, N. (2007). User acceptance of wireless short messaging services: Deconstructing perceived value. Information & Management, 44(1), 63–73.

Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. Management Information System Quarterly, 25(1), 71–102.

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. Management Information Systems Quarterly, 36(1), 157–178.

Vidaver-Cohen, D. (1998). Moral climate in business firms: A conceptual framework for analysis and change. Journal of Business Ethics, 17(11), 1211–1226.

Vieira, V., Santini, F. O., & Araujo, C. F. (2018). A meta-analytic review of hedonic and utilitarian shopping values. Journal of Consumer Marketing, 35(4), 426–437.

Voss, K. E., Spangenberg, E. R., & Grohmann, B. (2003). Measuring the hedonic and utilitarian dimensions of consumer attitude. Journal of Marketing Research, 40(3), 310–320.

Wu, W.-W. (2011). Developing an explorative model for SaaS adoption. Expert Systems with Applications, 38(12), 15057–15064.

Yadav, R., & Pathak, G. S. (2017). Determinants of consumers’ green purchase behavior in a developing nation: Applying and extending the theory of planned behavior. Ecological Economics. https://doi.org/10.1016/j.ecolecon.2016.12.019CrossRef

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

Yang, K., & Lee, H.-J. (2010). Gender differences in using mobile data services: Utilitarian and hedonic value approaches. Journal of Research in Interactive Marketing, 4(2), 142.

Youn, S., & Lee, K.-H. (2019). Proposing value-based technology acceptance model: Testing on paid mobile media service. Fashion and Textiles, 6(1), 1–16.

Yu, H., Zhang, R., & Liu, B. (2018). Analysis of consumers’ purchase and shopping well-being in online shopping carnivals with two motivational dimensions. Sustainability, 10(12), 4603.

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.

Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service quality delivery through websites: A critical review of extant knowledge. Journal of the Academy of Marketing Science, 30(4), 362–375.

Zhao, X., & Shi, C. (2018). The relationship between regulatory emotional self-efficacy and core self-evaluation of college students: The mediation effects of suicidal attitude. Frontiers in Psychology, 9, 598.

Zheng, J., & Li, S. (2020). What drives students’ intention to use tablet computers: An extended technology acceptance model. International Journal of Educational Research, 102, 101612.

Zhou, S., Zhou, Y., & Zhu, H. (2021). Predicting Chinese University Students’ E-Learning Acceptance and Self-Regulation in Online English Courses: Evidence from Emergency Remote Teaching (ERT) During COVID-19. SAGE Open, 11(4). https://doi.org/10.1177/21582440211061379

Titel: Acceptance of online distance learning (ODL) among students: Mediating role of utilitarian and hedonic value
verfasst von: Syed Shah Alam
Mohammad Masukujjaman
Maisarah Ahmad
Romlah Jaffor
Publikationsdatum: 23.12.2022
Verlag: Springer US
Erschienen in: Education and Information Technologies / Ausgabe 7/2023
Print ISSN: 1360-2357
Elektronische ISSN: 1573-7608
DOI: https://doi.org/10.1007/s10639-022-11533-3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 7/2023

The Effects of Socially Shared Regulation of Learning on the Computational Thinking, Motivation, and Engagement in Collaborative Learning by Teaching

Using a teacher scheme for educational dialogue analysis to investigate student–student interaction patterns for optimal group activities in an artificial intelligence course

Correction to: Mobile learning: Pre-service teachers’ perceptions of integrating iPads into future teaching

Cyberloafing in academia: a sequential exploration into students’ perceptions

Mathematics intelligent tutoring system for learning multiplication and division of fractions based on diagnostic teaching

Promoting programming education of novice programmers in elementary schools: A contrasting cases approach for learning programming

Premium Partner