Araştırma Makalesi
BibTex RIS Kaynak Göster

Rethinking E-learning Continuance Intention for Turkish Adult Learners: A Combined Model

Yıl 2025, Cilt: 13 Sayı: 26, 743 - 762

Öz

This research aims to determine the factors affecting the e-learning continuance intention of adult learners by testing the model established by combining the variables frequently found in the literature and the factors influencing adult education. Convenience sampling, a non-random sampling method, was used; 372 graduate students of non-thesis programs from a state university in Türkiye constituted the sample. The model, tested with R, revealed that usability, outcome expectations, perceived value, confirmation, and satisfaction all have similar positive effects on continuance intention and predicts 69 percent. Usability and outcome expectation account for 68 and 69 percent of the explanation for confirmation and satisfaction, respectively. In consideration of the existing limitations, it is notable that the tested model differs from the existing literature in in some major aspects. Confirmation has no effect on satisfaction, while perceived value has a positive effect only on continuance intention.

Etik Beyan

Ethical Committee Permission Information Name of the board that carries out ethical assessment: Aydın Adnan Menderes University Scientific Research Ethics Committee for Social and Human Sciences The date and number of the ethical assessment decision: 22.12.2022/289121

Kaynakça

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Process, 50(2), 179–211.
  • Alshehri, A.M., Rutter, M.J., & Smith, S.G. (2019). Assessing the relative importance of an e-learning system’s usability design characteristics based on students' preferences. European Journal of Educational Research, 8(3), 839-855.
  • Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148.
  • Ambolov, I. A. (2018). A meta-analysis of it continuance: An evaluation of the expectation-confirmation model. Telematics and Informatics, 35(6), 1561–1571.
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc.
  • Bhattacherjee, A. (2001a). Understanding information system continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  • Bhattacherjee, A. (2001b). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214. https://doi.org/10.1016/S0167-9236(01)00111-7
  • Bellare, Y., Smith, A., Cochran, K., & Lopez, S. G. (2023). Motivations and barriers for adult learner achievement: Recommendations for institutions of higher education. Adult Learning, 34(1), 30–39. https://doi.org/10.1177/10451595211059574
  • Bervell, B., & Naufal, U. (2020). Blended learning or face-to face? Does tutor anxiety prevent the adoption of learning management systems for distance education in Ghana?. Open Learning: The Journal of Open, Distance and e-Learning, 35(2), 159-177.
  • Bismala, L., & Manurung, Y. (2021). Student satisfaction in e-learning along the covid-19 pandemic with importance performance analysis. International Journal of Evaluation and Research in Education, 10(3), 753-759. http://doi.org/10.11591/ijere.v10i3.21467
  • Botha, J-A., Coetzee, M., & Coetzee, M. (2015). Exploring adult learners’ self-directedness in relation to their employability attributes in open distance learning. Journal of Psychology in Africa, 25(1), 65–72. https://doi.org/10.1080/14330237.2015.1007603
  • Cheng, M. (2020). Students' satisfaction and continuance intention of the cloud-based e-learning system: Roles of interactivity and course quality factors. Education & Training, 62(9), 1037-1059. https://doi.org/10.1108/ET-10-2019-0245
  • Daghan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198-211.
  • Dangiso, P., Makudza, F., & Hogo, H. (2022). Modelling perceived e-learning service quality, student satisfaction and loyalty. A higher education perspective, Cogent Education, 9(1), Article 2145805. https://doi.org/10.1080/2331186X.2022.2145805
  • Das, L., & Kumar, P. (2022). Motivational orientation for adult learners. Journal of Adult and Continuing Education, 28(2), 615–633. https://doi.org/10.1177/14779714211043903
  • Davids, M.R., Chikte, U.M., & Halperin, M.L. (2014). Effect of improving the usability of an e-learning resource: A randomized trial. Adv Physiol Educ, 38(2), 155-60.
  • 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), 982–1002.
  • Davvetas, V., Diamantopoulos, A., Zaefarian, G., & Sichtmann, C. (2020). Ten basic questions about structural equations modeling you should know the answers to – But perhaps you don't. Industrial Marketing Management, 90, 252-263.
  • Delone, W.H. & Mclean, E.R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95.
  • Dzubinski, L., Hentz, B., Davis, K., & Nicolaides, A. (2012). Envisioning an adult learning graduate program for the early 21st century. Adult Learning, 23(3), 103-110.
  • Dziuban, C., Moskal, P., Thompson, J., Kramer, L., DeCantis, G., & Hermsdorfer, A. (2015). Student satisfaction with online learning: Is it a psychological contract?. Journal of Asynchronous Learning Network, 19(2), 1-15. https://doi.org/10.24059/olj.v19i2.496
  • El-Sayad, G., Md-Saad, N.H. & Thurasamy, R. (2021). How higher education students in Egypt perceived online learning engagement and satisfaction during the covid-19 pandemic. J. Comput. Educ. 8, 527–550. https://doi.org/10.1007/s40692-021-00191-y
  • Esteban-Millat, I., Martínez-López, F. J., Huertas-García, R., Meseguer, A., & Rodríguez-Ardura, I. (2014). Modelling students’ flow experiences in an online learning environment. Computers & Education, 71, 111–123. Fasbender, U. (2020). Outcome Expectancies. In V. Zeigler-Hill & T. K. Shackelford (Eds.) Encyclopedia of personality and individual differences (pp. 3377–33379). Springer.
  • Fiorini, L. A., Borg, A., & Debono, M. (2022). Part-time adult students’ satisfaction with online learning during the covid-19 pandemic. Journal of Adult and Continuing Education, 28(2), 354–377. https://doi.org/10.1177/14779714221082691
  • Finn, D. (2011). Principles of adult learning: An ESL context. Journal of Adult Education, 40(1), 34–39.
  • Ginsberg, M. B. & Wlodkowski, J. R. (2020). Motivation. In Rocco, T. S., Smith, M. C., Mizzi, R. C., Merriweather, L. R., & Hawley, J. D. (Eds.) The handbook of adult and continuing education (pp. 91–99). Stylus Publishing.
  • Gunesekera, A., Bao, Y., & Kibelloh, M. (2019). The role of usability on e-learning user interactions and satisfaction: A literature review. Journal of Systems and Information Technology, 21(3), 368-394. http://dx.doi.org/10.1108/JSIT-02-2019-0024
  • Hsu, C-L., & Lin, 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. https://doi.org/10.1016/j.elerap.2014.11.003
  • Ilgaz, H., & Gülbahar, Y. (2015). A snapshot of online learners: E-readiness, e-satisfaction and expectations. International Review of Research in Open and Distributed Learning, 16(2), 171–187. https://doi.org/10.19173/irrodl.v16i2.2117
  • Islam, A. N., Mäntymäki, M., & Bhattacherjee, A. (2017). Towards a decomposed expectation confirmation model of it continuance: The role of usability. Communications of the Association for Information Systems, 40. https://doi.org/10.17705/1CAIS.04023
  • International Organization for Standardization. (2018). Ergonomics of human-system interaction (ISO Standard No. 9241-11:2018). https://www.iso.org/obp/ui/#iso:std:iso:9241:-11:ed-2:v1:en
  • İlhan, M., & Çetin, B. (2014). Lisrel ve amos programları kullanılarak gerçekleştirilen yapısal eşitlik modeli (yem) analizlerine ilişkin sonuçların karşılaştırılması. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 5(2), 26–42.
  • Karakis, O. (2022). Factors affecting the behaviors of teachers towards technology integration teaching via distance education during covid-19 pandemic: A path analysis. International Journal of Curriculum and Instruction, 14(1), 814-843.
  • Kilburn, B., Kilburn, A., & Davis, D. (2016). Building collegiate e-loyalty: The role of perceived value in the quality-loyalty linkage in online higher education. Contemporary Issues in Education Research, 9(3), 95-102.
  • Knightley, W. (2007) Adult learners online: Students’ experiences of learning online. Australian Journal of Adult Learning, 47(2), 264-288.
  • Knowles, M. S., Holton, E. F., & Swanson, R. A. (2005). The adult learner. The definitive classic in adult education and human resource development (6th ed.). Elsevier.
  • Koohang, A. & Paliszkiewicz, J. (2014). Empirical validation of an learning courseware usability model. Issues in Information Systems, 15(2), 270-275.
  • Kumar, R. (2022). E-learning programs in executive education: Effects of perceived quality and perceived value on self-regulation and motivation. Higher Education, Skills and Work-Based Learning, 12(6), 1025-1039. https://doi.org/10.1108/HESWBL-07-2022-0149
  • Lanford, M. (2021). In pursuit of respect: The adult learner attending community college in the “new economy”. The Educational Forum, 85(1), 34–48.
  • LeNoue, M., Hall, T., & Eighmy, M. (2011). Adult education and the social media revolution. Adult Learning, 22(2), 4-12. https://doi.org/10.1177/104515951102200201
  • Li, L., Wang, Q., & Li, J. (2022). Examining continuance intention of online learning during COVID-19 pandemic: Incorporating the theory of planned behavior into the expectation–confirmation model. Front. Psychol., 13, Article 1046407.
  • Liao, C., Palvia, P., & Chen, J. (2009). Information technology adoption behaviour life cycle: Toward a technology continuance theory. International Journal of Information Management, 29(4), 309-320. https://doi.org/10.1016/j.ijinfomgt.2009.03.004
  • Liao, Y.-K. Wu, W.-Y. Le, T.Q.& Phung, T.T.T. (2022). The integration of the technology acceptance model and value-based adoption model to study the adoption of e-learning: the moderating role of e-wom. Sustainability, 14(2), Article 815.
  • Y. Liu., H. Li., & A. Zhang. (2022, June 25-27). Exploring factors affecting online learners' intention to continue learning in e-learning: A meta-analysis. IEEE 2nd International Conference on Educational Technology (ICET), Beijing, China.
  • Lu, Y., Hong, X., & Xiao, L. (2022). Toward high-quality adult online learning: A systematic review of empirical studies. Sustainability, 14(4), Article 2257.
  • Lucas, R., & Moll, B. (2014). Knowledge growth and the allocation of time. Journal of Political Economy, 122(1), 1-51. https://doi.org/10.1086/674363
  • Marikyan, D., Papagiannidis, S., & Stewart, G. (2023). Technology acceptance research: Meta-analysis. Journal of Information Science. https://doi.org/10.1177/01655515231191177
  • Martin, F., & Bolliger, D.U. (2022). Developing an online learner satisfaction framework in higher education through a systematic review of research. Int J Educ Technol High Educ, 19, Article 50. https://doi.org/10.1186/s41239-022-00355-5
  • Mason, R. (2006). Learning technologies for adult continuing education. Studies in Continuing Education, 28(2), 121-133. https://doi.org/10.1080/01580370600751039
  • Ming-Chi, L. (2010). Explaining and predicting users’ continuance intention toward e-earning: An extension of the expectation–confirmation model. Computers & Education, 54, 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
  • Mohammed, M., Liu, P., & Nie, G. (2022). Do knowledge economy indicators affect economic growth? Evidence from developing countries. Sustainability, 14(8), Article 4774.
  • Mohammed, L.A. Aljaberi, M.A. Amidi, A. Abdulsalam, R. Lin, C.-Y. Hamat, R.A. Abdallah, A.M. (2022). Exploring factors affecting graduate students’ satisfaction toward e-learning in the era of the covid-19 crisis. Eur. J. Investig. Health Psychol. Educ., 12, 1121–1142. https://doi.org/10.3390/ejihpe12080079
  • Morris, T. H., & Rohs, M. (2021). Digitization bolstering self-directed learning for information literate adults–A systematic review. Computers and Education Open, 2, Article 100048 1–11. https://doi.org/10.1016/j.caeo.2021.100048
  • Oliver, R. L. (1980). A cognitive model for the antecedents and consequences of satisfaction. Journal of Marketing Research, 17, 460–469.
  • Osam, E. K., Bergman, M., & Cumberland, D. M. (2017). An integrative literature review on the barriers impacting adult learners’ return to college. Adult Learning, 28(2), 54-60.
  • Öz, E. (2022). The impact of gender differences on lifelong learning tendencies in turkey: A meta-analysis. SAGE Open, 12(2), 1-15. https://doi.org/10.1177/21582440221099528
  • Preece, J., Rogers, Y., & Sharp, H. (2019). Interaction design: Beyond human–computer interaction. (5th ed). John Wiley & Sons.
  • Ramadhan, A., Hidayanto, A.N., Salsabila, G.A., Wulandari, I., Jaury, J. A., & Anjani, N. N. (2022). The effect of usability on the intention to use the e-learning system in a sustainable way: A case study at universitas Indonesia. Educ Inf Technol 27, 1489–1522.
  • Robinson, O. (2014) Sampling in interview-based qualitative research: A theoretical and practical guide. Qualitative Research in Psychology, 11(1), 25-41.
  • Rogers, E.M. (1995) Diffusion of Innovations. (4th ed), The Free Press.
  • Seo, Y.J., & Um, K.H. (2023). The role of service quality in fostering different types of perceived value for student blended learning satisfaction. Journal of Computing in Higher Education, 35, 521-549. https://doi.org/10.1007/s12528-022-09336-z
  • Shi, Y., & Lin, X. (2021). Exploring the characteristics of adults’ online learning activities: A case study of edx online institute. Research in Learning Technology, 29, Article 2622.
  • Sorebo, O., Halvari, H., Gulli, V., & 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. https://doi.org/10.1016/j.compedu.2009.06.001
  • Toufaily, E., Zalan, T., & Lee, D. (2018). What do learners value in online education? Anemerging market perspective. e-Journal of Business Education & Scholarship of Teaching, 12(2), 24-39.
  • Wang, L-Y., Lew, S-L., Lau, S-H., & Chew, L. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6).
  • Zaharias, P. (2004). Usability and e-Learning: The road towards integration. ACM eLearn Magazine, 6. https://doi.org/10.1145/998337.998345
  • Zalazar-Jaime, M. F., Moretti, L.S., García-Batista, Z. E., & Medrano, L.A. (2023). Evaluation of an academic satisfaction model in e-learning education contexts. Interactive Learning Environments, 31(7), 4687-4697. https://doi.org/10.1080/10494820.2021.1979047
  • Zhang, M., & Su, C-Y., & Li, Y., & Li, Y-Y. (2020). Factors affecting Chinese university students’ intention to continue using virtual and remote labs. Australasian Journal of Educational Technology, 36(2), 169-185. https://doi.org/10.14742/ajet.5939
  • Zeithaml, A. V. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.

Rethinking E-learning Continuance Intention for Turkish Adult Learners: A Combined Model

Yıl 2025, Cilt: 13 Sayı: 26, 743 - 762

Öz

This research aims to determine the factors affecting the e-learning continuance intention of adult learners by testing the model established by combining the variables frequently found in the literature and the factors influencing adult education. Convenience sampling, a non-random sampling method, was used; 372 graduate students of non-thesis programs from a state university in Türkiye constituted the sample. The model, tested with R, revealed that usability, outcome expectations, perceived value, confirmation, and satisfaction all have similar positive effects on continuance intention and predicts 69 percent. Usability and outcome expectation account for 68 and 69 percent of the explanation for confirmation and satisfaction, respectively. In consideration of the existing limitations, it is notable that the tested model differs from the existing literature in in some major aspects. Confirmation has no effect on satisfaction, while perceived value has a positive effect only on continuance intention.

Kaynakça

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Process, 50(2), 179–211.
  • Alshehri, A.M., Rutter, M.J., & Smith, S.G. (2019). Assessing the relative importance of an e-learning system’s usability design characteristics based on students' preferences. European Journal of Educational Research, 8(3), 839-855.
  • Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148.
  • Ambolov, I. A. (2018). A meta-analysis of it continuance: An evaluation of the expectation-confirmation model. Telematics and Informatics, 35(6), 1561–1571.
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc.
  • Bhattacherjee, A. (2001a). Understanding information system continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  • Bhattacherjee, A. (2001b). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214. https://doi.org/10.1016/S0167-9236(01)00111-7
  • Bellare, Y., Smith, A., Cochran, K., & Lopez, S. G. (2023). Motivations and barriers for adult learner achievement: Recommendations for institutions of higher education. Adult Learning, 34(1), 30–39. https://doi.org/10.1177/10451595211059574
  • Bervell, B., & Naufal, U. (2020). Blended learning or face-to face? Does tutor anxiety prevent the adoption of learning management systems for distance education in Ghana?. Open Learning: The Journal of Open, Distance and e-Learning, 35(2), 159-177.
  • Bismala, L., & Manurung, Y. (2021). Student satisfaction in e-learning along the covid-19 pandemic with importance performance analysis. International Journal of Evaluation and Research in Education, 10(3), 753-759. http://doi.org/10.11591/ijere.v10i3.21467
  • Botha, J-A., Coetzee, M., & Coetzee, M. (2015). Exploring adult learners’ self-directedness in relation to their employability attributes in open distance learning. Journal of Psychology in Africa, 25(1), 65–72. https://doi.org/10.1080/14330237.2015.1007603
  • Cheng, M. (2020). Students' satisfaction and continuance intention of the cloud-based e-learning system: Roles of interactivity and course quality factors. Education & Training, 62(9), 1037-1059. https://doi.org/10.1108/ET-10-2019-0245
  • Daghan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198-211.
  • Dangiso, P., Makudza, F., & Hogo, H. (2022). Modelling perceived e-learning service quality, student satisfaction and loyalty. A higher education perspective, Cogent Education, 9(1), Article 2145805. https://doi.org/10.1080/2331186X.2022.2145805
  • Das, L., & Kumar, P. (2022). Motivational orientation for adult learners. Journal of Adult and Continuing Education, 28(2), 615–633. https://doi.org/10.1177/14779714211043903
  • Davids, M.R., Chikte, U.M., & Halperin, M.L. (2014). Effect of improving the usability of an e-learning resource: A randomized trial. Adv Physiol Educ, 38(2), 155-60.
  • 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), 982–1002.
  • Davvetas, V., Diamantopoulos, A., Zaefarian, G., & Sichtmann, C. (2020). Ten basic questions about structural equations modeling you should know the answers to – But perhaps you don't. Industrial Marketing Management, 90, 252-263.
  • Delone, W.H. & Mclean, E.R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95.
  • Dzubinski, L., Hentz, B., Davis, K., & Nicolaides, A. (2012). Envisioning an adult learning graduate program for the early 21st century. Adult Learning, 23(3), 103-110.
  • Dziuban, C., Moskal, P., Thompson, J., Kramer, L., DeCantis, G., & Hermsdorfer, A. (2015). Student satisfaction with online learning: Is it a psychological contract?. Journal of Asynchronous Learning Network, 19(2), 1-15. https://doi.org/10.24059/olj.v19i2.496
  • El-Sayad, G., Md-Saad, N.H. & Thurasamy, R. (2021). How higher education students in Egypt perceived online learning engagement and satisfaction during the covid-19 pandemic. J. Comput. Educ. 8, 527–550. https://doi.org/10.1007/s40692-021-00191-y
  • Esteban-Millat, I., Martínez-López, F. J., Huertas-García, R., Meseguer, A., & Rodríguez-Ardura, I. (2014). Modelling students’ flow experiences in an online learning environment. Computers & Education, 71, 111–123. Fasbender, U. (2020). Outcome Expectancies. In V. Zeigler-Hill & T. K. Shackelford (Eds.) Encyclopedia of personality and individual differences (pp. 3377–33379). Springer.
  • Fiorini, L. A., Borg, A., & Debono, M. (2022). Part-time adult students’ satisfaction with online learning during the covid-19 pandemic. Journal of Adult and Continuing Education, 28(2), 354–377. https://doi.org/10.1177/14779714221082691
  • Finn, D. (2011). Principles of adult learning: An ESL context. Journal of Adult Education, 40(1), 34–39.
  • Ginsberg, M. B. & Wlodkowski, J. R. (2020). Motivation. In Rocco, T. S., Smith, M. C., Mizzi, R. C., Merriweather, L. R., & Hawley, J. D. (Eds.) The handbook of adult and continuing education (pp. 91–99). Stylus Publishing.
  • Gunesekera, A., Bao, Y., & Kibelloh, M. (2019). The role of usability on e-learning user interactions and satisfaction: A literature review. Journal of Systems and Information Technology, 21(3), 368-394. http://dx.doi.org/10.1108/JSIT-02-2019-0024
  • Hsu, C-L., & Lin, 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. https://doi.org/10.1016/j.elerap.2014.11.003
  • Ilgaz, H., & Gülbahar, Y. (2015). A snapshot of online learners: E-readiness, e-satisfaction and expectations. International Review of Research in Open and Distributed Learning, 16(2), 171–187. https://doi.org/10.19173/irrodl.v16i2.2117
  • Islam, A. N., Mäntymäki, M., & Bhattacherjee, A. (2017). Towards a decomposed expectation confirmation model of it continuance: The role of usability. Communications of the Association for Information Systems, 40. https://doi.org/10.17705/1CAIS.04023
  • International Organization for Standardization. (2018). Ergonomics of human-system interaction (ISO Standard No. 9241-11:2018). https://www.iso.org/obp/ui/#iso:std:iso:9241:-11:ed-2:v1:en
  • İlhan, M., & Çetin, B. (2014). Lisrel ve amos programları kullanılarak gerçekleştirilen yapısal eşitlik modeli (yem) analizlerine ilişkin sonuçların karşılaştırılması. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 5(2), 26–42.
  • Karakis, O. (2022). Factors affecting the behaviors of teachers towards technology integration teaching via distance education during covid-19 pandemic: A path analysis. International Journal of Curriculum and Instruction, 14(1), 814-843.
  • Kilburn, B., Kilburn, A., & Davis, D. (2016). Building collegiate e-loyalty: The role of perceived value in the quality-loyalty linkage in online higher education. Contemporary Issues in Education Research, 9(3), 95-102.
  • Knightley, W. (2007) Adult learners online: Students’ experiences of learning online. Australian Journal of Adult Learning, 47(2), 264-288.
  • Knowles, M. S., Holton, E. F., & Swanson, R. A. (2005). The adult learner. The definitive classic in adult education and human resource development (6th ed.). Elsevier.
  • Koohang, A. & Paliszkiewicz, J. (2014). Empirical validation of an learning courseware usability model. Issues in Information Systems, 15(2), 270-275.
  • Kumar, R. (2022). E-learning programs in executive education: Effects of perceived quality and perceived value on self-regulation and motivation. Higher Education, Skills and Work-Based Learning, 12(6), 1025-1039. https://doi.org/10.1108/HESWBL-07-2022-0149
  • Lanford, M. (2021). In pursuit of respect: The adult learner attending community college in the “new economy”. The Educational Forum, 85(1), 34–48.
  • LeNoue, M., Hall, T., & Eighmy, M. (2011). Adult education and the social media revolution. Adult Learning, 22(2), 4-12. https://doi.org/10.1177/104515951102200201
  • Li, L., Wang, Q., & Li, J. (2022). Examining continuance intention of online learning during COVID-19 pandemic: Incorporating the theory of planned behavior into the expectation–confirmation model. Front. Psychol., 13, Article 1046407.
  • Liao, C., Palvia, P., & Chen, J. (2009). Information technology adoption behaviour life cycle: Toward a technology continuance theory. International Journal of Information Management, 29(4), 309-320. https://doi.org/10.1016/j.ijinfomgt.2009.03.004
  • Liao, Y.-K. Wu, W.-Y. Le, T.Q.& Phung, T.T.T. (2022). The integration of the technology acceptance model and value-based adoption model to study the adoption of e-learning: the moderating role of e-wom. Sustainability, 14(2), Article 815.
  • Y. Liu., H. Li., & A. Zhang. (2022, June 25-27). Exploring factors affecting online learners' intention to continue learning in e-learning: A meta-analysis. IEEE 2nd International Conference on Educational Technology (ICET), Beijing, China.
  • Lu, Y., Hong, X., & Xiao, L. (2022). Toward high-quality adult online learning: A systematic review of empirical studies. Sustainability, 14(4), Article 2257.
  • Lucas, R., & Moll, B. (2014). Knowledge growth and the allocation of time. Journal of Political Economy, 122(1), 1-51. https://doi.org/10.1086/674363
  • Marikyan, D., Papagiannidis, S., & Stewart, G. (2023). Technology acceptance research: Meta-analysis. Journal of Information Science. https://doi.org/10.1177/01655515231191177
  • Martin, F., & Bolliger, D.U. (2022). Developing an online learner satisfaction framework in higher education through a systematic review of research. Int J Educ Technol High Educ, 19, Article 50. https://doi.org/10.1186/s41239-022-00355-5
  • Mason, R. (2006). Learning technologies for adult continuing education. Studies in Continuing Education, 28(2), 121-133. https://doi.org/10.1080/01580370600751039
  • Ming-Chi, L. (2010). Explaining and predicting users’ continuance intention toward e-earning: An extension of the expectation–confirmation model. Computers & Education, 54, 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
  • Mohammed, M., Liu, P., & Nie, G. (2022). Do knowledge economy indicators affect economic growth? Evidence from developing countries. Sustainability, 14(8), Article 4774.
  • Mohammed, L.A. Aljaberi, M.A. Amidi, A. Abdulsalam, R. Lin, C.-Y. Hamat, R.A. Abdallah, A.M. (2022). Exploring factors affecting graduate students’ satisfaction toward e-learning in the era of the covid-19 crisis. Eur. J. Investig. Health Psychol. Educ., 12, 1121–1142. https://doi.org/10.3390/ejihpe12080079
  • Morris, T. H., & Rohs, M. (2021). Digitization bolstering self-directed learning for information literate adults–A systematic review. Computers and Education Open, 2, Article 100048 1–11. https://doi.org/10.1016/j.caeo.2021.100048
  • Oliver, R. L. (1980). A cognitive model for the antecedents and consequences of satisfaction. Journal of Marketing Research, 17, 460–469.
  • Osam, E. K., Bergman, M., & Cumberland, D. M. (2017). An integrative literature review on the barriers impacting adult learners’ return to college. Adult Learning, 28(2), 54-60.
  • Öz, E. (2022). The impact of gender differences on lifelong learning tendencies in turkey: A meta-analysis. SAGE Open, 12(2), 1-15. https://doi.org/10.1177/21582440221099528
  • Preece, J., Rogers, Y., & Sharp, H. (2019). Interaction design: Beyond human–computer interaction. (5th ed). John Wiley & Sons.
  • Ramadhan, A., Hidayanto, A.N., Salsabila, G.A., Wulandari, I., Jaury, J. A., & Anjani, N. N. (2022). The effect of usability on the intention to use the e-learning system in a sustainable way: A case study at universitas Indonesia. Educ Inf Technol 27, 1489–1522.
  • Robinson, O. (2014) Sampling in interview-based qualitative research: A theoretical and practical guide. Qualitative Research in Psychology, 11(1), 25-41.
  • Rogers, E.M. (1995) Diffusion of Innovations. (4th ed), The Free Press.
  • Seo, Y.J., & Um, K.H. (2023). The role of service quality in fostering different types of perceived value for student blended learning satisfaction. Journal of Computing in Higher Education, 35, 521-549. https://doi.org/10.1007/s12528-022-09336-z
  • Shi, Y., & Lin, X. (2021). Exploring the characteristics of adults’ online learning activities: A case study of edx online institute. Research in Learning Technology, 29, Article 2622.
  • Sorebo, O., Halvari, H., Gulli, V., & 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. https://doi.org/10.1016/j.compedu.2009.06.001
  • Toufaily, E., Zalan, T., & Lee, D. (2018). What do learners value in online education? Anemerging market perspective. e-Journal of Business Education & Scholarship of Teaching, 12(2), 24-39.
  • Wang, L-Y., Lew, S-L., Lau, S-H., & Chew, L. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6).
  • Zaharias, P. (2004). Usability and e-Learning: The road towards integration. ACM eLearn Magazine, 6. https://doi.org/10.1145/998337.998345
  • Zalazar-Jaime, M. F., Moretti, L.S., García-Batista, Z. E., & Medrano, L.A. (2023). Evaluation of an academic satisfaction model in e-learning education contexts. Interactive Learning Environments, 31(7), 4687-4697. https://doi.org/10.1080/10494820.2021.1979047
  • Zhang, M., & Su, C-Y., & Li, Y., & Li, Y-Y. (2020). Factors affecting Chinese university students’ intention to continue using virtual and remote labs. Australasian Journal of Educational Technology, 36(2), 169-185. https://doi.org/10.14742/ajet.5939
  • Zeithaml, A. V. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.
Toplam 69 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Öğretim Teknolojileri, Hayat Boyu Öğrenme
Bölüm Araştırma Makalesi
Yazarlar

Taner Arabacıoglu 0000-0003-1116-1777

Erken Görünüm Tarihi 14 Temmuz 2025
Yayımlanma Tarihi 19 Ekim 2025
Gönderilme Tarihi 18 Şubat 2025
Kabul Tarihi 16 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 13 Sayı: 26

Kaynak Göster

APA Arabacıoglu, T. (2025). Rethinking E-learning Continuance Intention for Turkish Adult Learners: A Combined Model. Journal of Computer and Education Research, 13(26), 743-762.

Creative Commons Lisansı


Bu eser Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır.


Değerli Yazarlar,

JCER dergisi 2018 yılından itibaren yayımlanacak sayılarda yazarlarından ORCID bilgilerini isteyecektir. Bu konuda hassasiyet göstermeniz önemle rica olunur.

Önemli: "Yazar adından yapılan yayın/atıf taramalarında isim benzerlikleri, soyadı değişikliği, Türkçe harf içeren isimler, farklı yazımlar, kurum değişiklikleri gibi durumlar sorun oluşturabilmektedir. Bu nedenle araştırmacıların tanımlayıcı kimlik/numara (ID) edinmeleri önem taşımaktadır. ULAKBİM TR Dizin sistemlerinde tanımlayıcı ID bilgilerine yer verilecektir.

Standardizasyonun sağlanabilmesi ve YÖK ile birlikte yürütülecek ortak çalışmalarda ORCID kullanılacağı için, TR Dizin’de yer alan veya yer almak üzere başvuran dergilerin, yazarlardan ORCID bilgilerini talep etmeleri ve dergide/makalelerde bu bilgiye yer vermeleri tavsiye edilmektedir. ORCID, Open Researcher ve Contributor ID'nin kısaltmasıdır.  ORCID, Uluslararası Standart Ad Tanımlayıcı (ISNI) olarak da bilinen ISO Standardı (ISO 27729) ile uyumlu 16 haneli bir numaralı bir URI'dir. http://orcid.org adresinden bireysel ORCID için ücretsiz kayıt oluşturabilirsiniz. "