Volume 13 Issue 3 (2024)

Modeling Factors Associated with Continuance Intention to Use E-Learning During and After COVID-19

pp. 116-138  |  Published Online: October 2024  |  DOI: 10.22521/edupij.2024.133.7

Ahmet Murat Uzun , Erhan Ünal , Selcan Kilis

Abstract

Background/purpose. Widespread adoption of e-learning was triggered worldwide during the COVID-19 pandemic and modern higher education institutions dedicated significant resources to the use of diverse e-learning systems. However, to maximize the benefits of these investments, it is crucial that the degree to which end-users accept and continue using these systems is evaluated. Employing an integrative theoretical framework based on the extended Technology Acceptance Model (TAM) and Expectation Confirmation Model (ECM), this study aimed to scrutinize factors affecting students’ continuance intention to use an e-learning platforms following the pandemic.

Materials/methods. The research employed a cross-sectional design, and 343 university students were surveyed. Partial least squares structural equation modeling (PLS-SEM) was employed in the data analysis.

Results. The findings indicate perceived usefulness and satisfaction to be direct predictors of e-learning system usage, whilst confirmation of expectation, perceived usefulness, perceived ease of use, and system interactivity were shown as indirect predictors.

Conclusion. Discussed along with the literature, the study’s results revealed satisfaction to be the most vital indicator of continuance intention. Some suggestions are put forward, aimed towards helping both instructors and system designers.

Keywords: E-learning, learning management system, intention, adoption, structural equation modeling, acceptance, COVID-19

References

Abbad, M. M., Morris, D., & de Nahlik, C. (2009). Looking under the bonnet: Factors affecting student adoption of e-learning systems in Jordan. The International Review of Research in Open and Distributed Learning, 10(2). https://doi.org/10.19173/irrodl.v10i2.596

Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036

Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75-90. https://doi.org/10.1016/j.chb.2016.05.014

Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314. https://doi.org/10.1016/j.chb.2013.10.035

Aguilera-Hermida, A. P., Quiroga-Garza, A., Gómez-Mendoza, S., Del Río Villanueva, C. A., Avolio Alecchi, 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 26(6), 6823-6845. https://doi.org/10.1007/s10639-021-10473-8

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T

Akbaba, B., Kaymakci, S., Birbudak, T. S., & Kilcan, B. (2016). Üniversite öğrencilerinin uzaktan eğitimle Atatürk İlkeleri ve İnkılap Tarihi öğretimine yönelik görüşleri [University students’ perceptions about teaching Atatürk’s Principles and Turkish Revolution History with distance education]. Kuramsal Eğitimbilim Dergisi, 9(2), 285-309. http://doi.org/10.5578/keg.8238

Al-Emran, M., & Teo, T. (2020). Do knowledge acquisition and knowledge sharing really affect e-learning adoption? An empirical study. Education and Information Technologies, 25(3), 1983-1998. https://doi.org/10.1007/s10639-019-10062-w

Al-Fraihat, D., Joy, M., & 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

Ambalov, I. A. (2018). A meta-analysis of IT continuance: An evaluation of the expectation-confirmation model. Telematics and Informatics, 35(6), 1561-1571. https://doi.org/10.1016/j.tele.2018.03.016

Ashrafi, A., Zareravasan, A., Rabiee Savoji, S., & Amani, M. (2022). Exploring factors influencing students’ continuance intention to use the learning management system (LMS): A multi-perspective framework. Interactive Learning Environments, 30(8), 1475-1497. https://doi.org/10.1080/10494820.2020.1734028

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. https://doi.org/10.17718/tojde.471649

Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921

Bozkurt, A., & Sharma, R. C. (2020). Emergency remote teaching in a time of global crisis due to CoronaVirus pandemic. Asian Journal of Distance Education, 15(1), i-vi. https://doi.org/10.5281/zenodo.3778083

Chang, C.-T., Hajiyev, J., & Su, C.-R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010

Chauhan, S., Goyal, S., Bhardwaj, A. K., & Sergi, B. S. (2021). Examining continuance intention in business schools with digital classroom methods during COVID-19: A comparative study of India and Italy. Behaviour & Information Technology, 41(8), 1596-1619. https://doi.org/10.1080/0144929X.2021.1892191

Cheng, E. W. L. (2019). Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM). Educational Technology Research and Development, 67(1), 21-37. https://doi.org/10.1007/s11423-018-9598-6

Cheng, M., & Yuen, A.-H. K. (2018). Student continuance of learning management system use: A longitudinal exploration. Computers & Education, 120, 241-253. https://doi.org/10.1016/j.compedu.2018.02.004

Cheng, Y.-M. (2013). Exploring the roles of interaction and flow in explaining nurses’ e-learning acceptance. Nurse Education Today, 33(1), 73-80. https://doi.org/10.1016/j.nedt.2012.02.005

Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Methodology for business and management. Modern methods for business research (pp. 295-336). Erlbaum.

Choudhury, S., & Pattnaik, S. (2020). Emerging themes in e-learning: A review from the stakeholders’ perspective. Computers & Education, 144, Article 103657. https://doi.org/10.1016/j.compedu.2019.103657

Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.

Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198-211. https://doi.org/10.1016/j.chb.2016.02.066

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

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), 903-1028. https://doi.org/10.1287/mnsc.35.8.982

Eraslan Yalcin, M., & Kutlu, B. (2019). Examination of students’ acceptance of and intention to use learning management systems using extended TAM. British Journal of Educational Technology, 50(5), 2414-2432. https://doi.org/10.1111/bjet.12798

Eroğlu, F., & Kalayci, N. (2020). Üniversitelerdeki zorunlu ortak derslerden Türk Dili dersinin uzaktan ve yüz yüze eğitim uygulamalarının karşılaştırılarak değerlendirilmesi [Comparative evaluation of the distance and face-to-face education practices in the required Turkish language course at universities]. Ana Dili Eğitimi Dergisi, 8(3), 1001-1027. https://doi.org/10.16916/aded.710396

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430-447. https://doi.org/10.1108/IntR-12-2017-0515

Franque, F. B., Oliveira, T., Tam, C., & Santini, F. de O. (2020). A meta-analysis of the quantitative studies in continuance intention to use an information system. Internet Research, 31(1), 123-158. https://doi.org/10.1108/INTR-03-2019-0103

Girish, V. G., Kim, M.-Y., Sharma, I., & Lee, C.-K. (2021). Examining the structural relationships among e-learning interactivity, uncertainty avoidance, and perceived risks of COVID-19: Applying extended technology acceptance model. International Journal of Human–Computer Interaction, 38(8), 742-752. https://doi.org/10.1080/10447318.2021.1970430

Goh, T.-T., & Yang, B. (2021). The role of e-engagement and flow on the continuance with a learning management system in a blended learning environment. International Journal of Educational Technology in Higher Education, 18, Article 49. https://doi.org/10.1186/s41239-021-00285-8

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. https://doi.org/10.1111/bjet.12864

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling. Sage.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202

Han, J.-H., & Sa, H. J. (2021). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID-19 situation in Korea. Asia Pacific Education Review, 23, 403-415. https://doi.org/10.1007/s12564-021-09716-7

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. https://doi.org/10.1007/s11747-014-0403-8

Huang, F., Teo, T., & Scherer, R. (2022). Investigating the antecedents of university students’ perceived ease of using the Internet for learning. Interactive Learning Environments, 30(6), 1060-1076. https://doi.org/10.1080/10494820.2019.1710540

Humida, T., Al Mamun, M. H., & Keikhosrokiani, P. (2022). Predicting behavioral intention to use e-learning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh. Education and Information Technologies, 27(2), 2241-2265. https://doi.org/10.1007/s10639-021-10707-9

Iqbal, S., & Bhatti, A. Z. (2015). An investigation of university student readiness towards m-learning using Technology Acceptance Model. The International Review of Research in Open and Distributed Learning, 16(4), 83-103. https://doi.org/10.19173/irrodl.v16i4.2351

Kocatürk Kapucu, N., & Uşun, S. (2020). Üniversitelerde ortak zorunlu derslerin öğretiminde uzaktan eğitim uygulamaları [Distance education practices for the teaching of common compulsory courses at universities]. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 6(1), 8-27. https://dergipark.org.tr/en/pub/auad/issue/55639/761236

Kondakci, Y., Bedenlier, S., & Aydin, C. H. (2019). Turkey. In O. Zawacki-Richter & A. Qayyum (Eds.), Open and Distance Education in Asia, Africa and the Middle East: National Perspectives in a digital age (pp. 105-119). Springer. https://doi.org/10.1007/978-981-13-5787-9_12

Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193-208. https://doi.org/10.1016/j.compedu.2012.10.001

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. https://doi.org/10.1016/j.compedu.2009.09.002

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

Liao, C., Liu, C.-C., Liu, Y.-P., To, P.-L., & Lin, H.-N. (2011). Applying the expectancy disconfirmation and regret theories to online consumer behavior. Cyberpsychology, Behavior, and Social Networking, 14(4), 241-246. https://doi.org/10.1089/cyber.2009.0236

Liaw, S.-S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. https://doi.org/10.1016/j.compedu.2007.09.005

Liaw, S.-S., & Huang, H.-M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24. https://doi.org/10.1016/j.compedu.2012.07.015

Lockee, B. B. (2021). Online education in the post-COVID era. Nature Electronics, 4(1), 5-6. https://doi.org/10.1038/s41928-020-00534-0

Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077. https://doi.org/10.1007/s10639-021-10557-5

Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173

Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Cengage Learning.

Moreno, V., Cavazotte, F., & Alves, I. (2017). Explaining university students’ effective use of e‐learning platforms. British Journal of Educational Technology, 48(4), 995-1009. https://doi.org/10.1111/bjet.12469

Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33(1), 34-47. https://doi.org/10.1016/j.tele.2015.05.006

Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model: Factors related to use mobile learning. British Journal of Educational Technology, 43(4), 592-605. https://doi.org/10.1111/j.1467-8535.2011.01229.x

Park, Y., Son, H., & Kim, C. (2012). Investigating the determinants of construction professionals’ acceptance of web-based training: An extension of the technology acceptance model. Automation in Construction, 22, 377-386. https://doi.org/10.1016/j.autcon.2011.09.016

Pituch, K. A., & Lee, Y. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244. https://doi.org/10.1016/j.compedu.2004.10.007

Prodanova, J., San Martín, S., & Sánchez-Beato, E. J. (2021). Quality requirements for continuous use of e-learning systems at public vs. private universities in Spain. Digital Education Review, 40, 33-50. https://doi.org/10.1344/der.2021.40.33-50

Rajeh, M. T., Abduljabbar, F. H., Alqahtani, S. M., 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(1), Article 1961348. https://doi.org/10.1080/10872981.2021.1961348

Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683-696. https://doi.org/10.1016/j.ijhcs.2006.01.003

Ros, S., Hernández, R., Caminero, A., Robles, A., Barbero, I., Maciá, A., & Holgado, F. P. (2014). On the use of extended TAM to assess students’ acceptance and intent to use third‐generation learning management systems. British Journal of Educational Technology, 46(6), 1250-1271. https://doi.org/10.1111/bjet.12199

Salomon, G. (1984). Television is “easy” and print is “tough”: The differential investment of mental effort in learning as a function of perceptions and attributions. Journal of Educational Psychology, 76(4), 647–658. https://doi.org/10.1037/0022-0663.76.4.647

Taghizadeh, S. K., Rahman, S. A., Nikbin, D., Alam, M. M. D., Alexa, L., Ling Suan, C., & Taghizadeh, S. (2022). Factors influencing students’ continuance usage intention with online learning during the pandemic: A cross-country analysis. Behaviour & Information Technology, 41(9), 1998-2017. https://doi.org/10.1080/0144929X.2021.1912181

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. https://doi.org/10.1080/10494820.2015.1122635

Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that influence university students’ intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67(3), 749-766. https://doi.org/10.1007/s11423-019-09650-x

Unal, E., & Uzun, A. M. (2021). Understanding university students’ behavioural intention to use Edmodo through the lens of an extended technology acceptance model. British Journal of Educational Technology, 52(2), 619-637. https://doi.org/10.1111/bjet.13046

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872

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

Vladova, G., Ullrich, A., Bender, B., & Gronau, N. (2021). Students’ acceptance of technology-mediated teaching – how it was influenced during the COVID-19 pandemic in 2020: A study from Germany. Frontiers in Psychology, 12, Article 636086. https://doi.org/10.3389/fpsyg.2021.636086

Wang, T., Lin, C.-L., & Su, Y.-S. (2021). Continuance intention of university students and online learning during the COVID-19 pandemic: A modified expectation confirmation model perspective. Sustainability, 13(8), Article 4586. https://doi.org/10.3390/su13084586

Witze, A. (2020). Universities will never be the same after the coronavirus crisis. Nature, 582(7811), 162-164. https://doi.org/10.1038/d41586-020-01518-y

Yan, M., Filieri, R., & Gorton, M. (2021). Continuance intention of online technologies: A systematic literature review. International Journal of Information Management, 58, Article 102315. https://doi.org/10.1016/j.ijinfomgt.2021.102315

Announcement

EDUPIJ News!

► Journal Metrics

  • 8% acceptance rate
  • 3.4 (2023) CiteScore (Scopus)
  • Q2 (2023) CiteScore Best Quartile
  • 0.42 (2023) Scimago Journal & Country Rank (SJR)

EDUPIJ Statistics from Scopus

CiteScore: 3.4, view Scopus page

SCImago Journal & Country Rank

► Educational Process: International Journal is member of the Committee on Publication Ethics (COPE). 

► New issue coming soon! (Volume 13 Issue 4, 2024)