Open Journal Systems

Understanding mobile learning continuance after the COVID-19 pandemic: Deep learning-based dual stage partial least squares-structural equation modeling and artificial neural network analysis

Yakup Akgul, Ali Osman Uymaz, Pelin Uymaz

Article ID: 2307
Vol 9, Issue 4, 2024, Article identifier:

VIEWS - 267 (Abstract) 90 (PDF)


The influence of COVID-19 on educational processes has halted physical forms of teaching and learning and initiated online and mobile learning systems in most countries. The provision and usage of online and e-learning systems are becoming the main challenge for many universities during the COVID-19 pandemic. Due to the novelty of this situation, a substantial amount of research has been carried out to investigate the issue of m-learning adoption or acceptance. Nevertheless, little is known about studying to examine the continued use of m-learning, which is still in short supply and calls for further research. Five different theoretical models are integrated into this study to develop an integrated model that overcomes this limitation, including the technology acceptance model, the theory of planned behavior, the expectation-confirmation model, the Delone and McLean Information System Success Model, and the Unified Theory of Acceptance and Utilization of Technology 2. This conceptual framework shows novel relationships between variables by integrating trust, personal innovation, learning value, instructor quality, and course quality. Unlike extant literature, this study utilized a hybrid analysis methodology combining two-stage analysis using partial least squares structural equation modeling (PLS-SEM) and evolving artificial intelligence named deep learning (Artificial Neural Network [ANN]) on 250 usable responses. The sensitivity analysis results revealed that attitude has the most considerable effect on the continued use of m-learning, with 100% normalized importance, followed by perceived usefulness (88%), satisfaction (77%), and habit (61%). This research reveals that a “deep ANN architecture” may determine the non-linear relationships between variables in the theoretical model. Further theoretical and practical implications are also discussed.


deep learning; non-linearity; artificial neural network; mobile learning; partial least squares-structural equation modeling

Full Text:



1. International Telecommunication Union. Individuals using the Internet 2005–2019. Available online: (accessed on 5 January 2024).

2. Statista. Global smartphone penetration rate as share of population from 2016 to 2022. Available online: (accessed on 5 January 2024).

3. Almaiah MA, Al-Khasawneh A, Althunibat A. Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies. 2020, 25(6): 5261-5280. doi: 10.1007/s10639-020-10219-y

4. Vladova G, Ullrich A, Bender B, et al. Students’ Acceptance of Technology-Mediated Teaching – How It Was Influenced During the COVID-19 Pandemic in 2020: A Study From Germany. Frontiers in Psychology. 2021, 12. doi: 10.3389/fpsyg.2021.636086

5. Kumar BA, Chand SS. Mobile learning adoption: A systematic review. Education and Information Technologies. 2018, 24(1): 471-487. doi: 10.1007/s10639-018-9783-6

6. Acikgoz F, Vega RP. The Role of Privacy Cynicism in Consumer Habits with Voice Assistants: A Technology Acceptance Model Perspective. International Journal of Human–Computer Interaction. 2021, 38(12): 1138-1152. doi: 10.1080/10447318.2021.1987677

7. El-Masri M, Tarhini A. Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development. 2017, 65(3): 743-763. doi: 10.1007/s11423-016-9508-8

8. Türker C, Altay BC, Okumuş A. Understanding user acceptance of QR code mobile payment systems in Turkey: An extended TAM. Technological Forecasting and Social Change. 2022, 184: 121968. doi: 10.1016/j.techfore.2022.121968

9. Wong KT, Teo T, Goh PSC. Understanding the intention to use interactive whiteboards: model development and testing. Interactive Learning Environments. 2013, 23(6): 731-747. doi: 10.1080/10494820.2013.806932

10. Cheng Y. Effects of quality antecedents on e‐learning acceptance. Internet Research. 2012, 22(3): 361-390. doi: 10.1108/10662241211235699

11. Ain N, Kaur K, Waheed M. The influence of learning value on learning management system use. Information Development. 2016, 32(5): 1306-1321. doi: 10.1177/0266666915597546

12. Lwoga ET. Critical success factors for adoption of web-based learning management systems in Tanzania. International Journal of Education and Development using ICT. 2014; 10(1): 4-21.

13. Al-Azawei A, Alowayr A. Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society. 2020, 62: 101325. doi: 10.1016/j.techsoc.2020.101325

14. Al-Emran M, Arpaci I, Salloum SA. An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies. 2020, 25(4): 2899-2918. doi: 10.1007/s10639-019-10094-2

15. Alzahrani L, Seth KP. Factors influencing students’ satisfaction with continuous use of learning management systems during the COVID-19 pandemic: An empirical study. Education and Information Technologies. 2021, 26(6): 6787-6805. doi: 10.1007/s10639-021-10492-5

16. Yuan YP, Wei-Han Tan G, Ooi KB, et al. Can COVID-19 pandemic influence experience response in mobile learning? Telematics and Informatics. 2021, 64: 101676. doi: 10.1016/j.tele.2021.101676

17. Sim JJ, Tan GWH, Wong JCJ, et al. Understanding and predicting the motivators of mobile music acceptance – A multi-stage MRA-artificial neural network approach. Telematics and Informatics. 2014, 31(4): 569-584. doi: 10.1016/j.tele.2013.11.005

18. Wong TC, Wong SY, Chin KS. A neural network-based approach of quantifying relative importance among various determinants toward organizational innovation. Expert Systems with Applications. 2011, 38(10): 13064-13072. doi: 10.1016/j.eswa.2011.04.113

19. AlHamad AQM. Predicting the intention to use Mobile learning: A hybrid SEM-machine learning approach. International Journal of Engineering Research & Technology. 2020; 9(3): 275-282.

20. Alshurideh M, Al Kurdi B, Salloum SA, et al. Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments. 2020, 31(3): 1214-1228. doi: 10.1080/10494820.2020.1826982

21. Akour I, Alshurideh M, Al Kurdi B, et al. Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach. JMIR Medical Education. 2021, 7(1): e24032. doi: 10.2196/24032

22. Thongsri N, Chootong C, Tripak O, et al. Predicting the determinants of online learning adoption during the COVID-19 outbreak: a two-staged hybrid SEM-neural network approach. Interactive Technology and Smart Education. 2021, 18(3): 362-379. doi: 10.1108/itse-08-2020-0165

23. Yakubu MN, Dasuki SI, Abubakar AM, et al. Determinants of learning management systems adoption in Nigeria: A hybrid SEM and artificial neural network approach. Education and Information Technologies. 2020, 25(5): 3515-3539. doi: 10.1007/s10639-020-10110-w

24. Kumar JA, Bervell B, Annamalai N, et al. Behavioral Intention to Use Mobile Learning: Evaluating the Role of Self-Efficacy, Subjective Norm, and WhatsApp Use Habit. IEEE Access. 2020, 8: 208058-208074. doi: 10.1109/access.2020.3037925

25. Shukla S. M-learning adoption of management students’: A case of India. Education and Information Technologies. 2020, 26(1): 279-310. doi: 10.1007/s10639-020-10271-8

26. Alhumaid K, Habes M, Salloum SA. Examining the Factors Influencing the Mobile Learning Usage During COVID-19 Pandemic: An Integrated SEM-ANN Method. IEEE Access. 2021, 9: 102567-102578. doi: 10.1109/access.2021.3097753

27. Songkram N, Chootongchai S. Adoption model for a hybrid SEM-neural network approach to education as a service. Education and Information Technologies. 2022, 27(5): 5857-5887. doi: 10.1007/s10639-021-10802-x

28. Huang W, Stokes JW. MtNet: A multi-task neural network for dynamic malware classification. In: Caballero J, Zurutuza U, Rodríguez RJ (editors). Detection of Intrusions and Malware, and Vulnerability Assessment, Proceedings of the 13th International Conference, DIMVA 2016, 7–8 July 2016; San Sebastián, Spain. Springer; 2016. pp. 399–418. doi: 10.1007/978-3-319-40667-1_20

29. Akgül Y. Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. In: Patel HS, Kumar AVS (editors). Applications of Artificial Neural Networks for Nonlinear Data. ‎Engineering Science Reference; 2021. doi: 10.4018/978-1-7998-4042-8.ch006

30. Lee VH, Hew JJ, Leong LY, et al. Wearable payment: A deep learning-based dual-stage SEM-ANN analysis. Expert Systems with Applications. 2020, 157: 113477. doi: 10.1016/j.eswa.2020.113477

31. Leong LY, Hew TS, Ooi KB, et al. A hybrid SEM-neural network analysis of social media addiction. Expert Systems with Applications. 2019, 133: 296-316. doi: 10.1016/j.eswa.2019.05.024

32. Ashaari MA, Singh KSD, Abbasi GA, et al. Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective. Technological Forecasting and Social Change. 2021, 173: 121119. doi: 10.1016/j.techfore.2021.121119

33. Akgül Y, Uymaz AO. Facebook/Meta usage in higher education: A deep learning-based dual-stage SEM-ANN analysis. Education and Information Technologies. 2022, 27(7): 9821-9855. doi: 10.1007/s10639-022-11012-9

34. Uymaz P, Uymaz AO, Akgül Y. Assessing the Behavioral Intention of Individuals to Use an AI Doctor at the Primary, Secondary, and Tertiary Care Levels. International Journal of Human–Computer Interaction. 2023, 1-18. doi: 10.1080/10447318.2023.2233126

35. Uymaz P, Uymaz AO. Assessing acceptance of augmented reality in nursing education. PLoS ONE. 2022, 17(2): e0263937. doi: 10.1371/journal.pone.0263937

36. Abbasi GA, Tiew LY, Tang J, et al. The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis. PLoS ONE. 2021, 16(3): e0247582. doi: 10.1371/journal.pone.0247582

37. Liébana-Cabanillas F, Marinković V, Kalinić Z. A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management. 2017, 37(2): 14-24. doi: 10.1016/j.ijinfomgt.2016.10.008

38. Kwak Y, Seo YH, Ahn JW. Nursing students’ intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. Nurse Education Today. 2022, 119: 105541. doi: 10.1016/j.nedt.2022.105541

39. Ajzen I, Fishbein M. Understanding Attitudes and Predicting Social Behavior. Prentice-Hall Englewood Cliffs, N.J.; 1980.

40. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. 1989, 13(3): 319. doi: 10.2307/249008

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

42. Fishbein MA, Ajzen I. Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research. Addison-Wesley; 1975.

43. Rogers EM. Diffusion of innovations: Modifications of a model for telecommunications. In: Stoetzer MW, Mahler A (editors). The Diffusion of Innovations in Telecommunications (German). Springer; 1995. Volume 17. pp. 25-38. doi: 10.1007/978-3-642-79868-9_2

44. Venkatesh, Morris, Davis, et al. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly. 2003, 27(3): 425. doi: 10.2307/30036540

45. The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems. 2003, 19(4): 9-30. doi: 10.1080/07421222.2003.11045748

46. Berger H, Al Adwan A, Al Adwan AS. Solving the mystery of mobile learning adoption in higher education. International Journal of Mobile Communications. 2018, 16(1): 24. doi: 10.1504/ijmc.2018.10007779

47. Park SY, Lee HD, Kim SY. South Korean university students’ mobile learning acceptance and experience based on the perceived attributes, system quality and resistance. Innovations in Education and Teaching International. 2016, 55(4): 450-458. doi: 10.1080/14703297.2016.1261041

48. Al-Hamad MQ, Mbaidin HO, AlHamad AQM, et al. Investigating students’ behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic. International Journal of Data and Network Science. 2021, 5: 321-330. doi: 10.5267/j.ijdns.2021.6.001

49. Alzaidi MS, Shehawy YM. Cross-national differences in mobile learning adoption during COVID-19. Education + Training. 2022, 64(3): 305-328. doi: 10.1108/et-05-2021-0179

50. Zhou M, Dzingirai C, Hove K, et al. Adoption, use and enhancement of virtual learning during COVID-19. Education and Information Technologies. 2022, 27(7): 8939-8959. doi: 10.1007/s10639-022-10985-x

51. Chahal J, Rani N. Exploring the acceptance for e-learning among higher education students in India: combining technology acceptance model with external variables. Journal of Computing in Higher Education. 2022, 34(3): 844-867. doi: 10.1007/s12528-022-09327-0

52. Aloqaily A, Nawayseh MKA, Baarah AH, et al. A neural network analytical model for predicting determinants of mobile learning acceptance. International Journal of Computer Applications in Technology. 2019, 60(1): 73. doi: 10.1504/ijcat.2019.099502

53. Al-Shihi H, Sharma SK, Sarrab M. Neural network approach to predict mobile learning acceptance. Education and Information Technologies. 2018, 23(5): 1805-1824. doi: 10.1007/s10639-018-9691-9

54. Tan GWH, Ooi KB, Leong LY, et al. Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior. 2014, 36: 198-213. doi: 10.1016/j.chb.2014.03.052

55. Sharma SK, Sarrab M, Al-Shihi H. Development and validation of Mobile Learning Acceptance Measure. Interactive Learning Environments. 2016, 25(7): 847-858. doi: 10.1080/10494820.2016.1224250

56. Elnagar A, Alnazzawi N, Afyouni I, et al. An empirical study of e-learning post-acceptance after the spread of COVID-19. International Journal of Data and Network Science. 2022, 6(3): 669-682. doi: 10.5267/j.ijdns.2022.4.005

57. Zhang M, Chen Y, Zhang S, et al. Understanding mobile learning continuance from an online-cum-offline learning perspective: a SEM-neural network method. International Journal of Mobile Communications. 2022, 20(1): 105. doi: 10.1504/ijmc.2022.119995

58. Al-Emran M, Mezhuyev V, Kamaludin A. Technology Acceptance Model in M-learning context: A systematic review. Computers & Education. 2018, 125: 389-412. doi: 10.1016/j.compedu.2018.06.008

59. Karjaluoto H, Mattila M, Pento T. Factors underlying attitude formation towards online banking in Finland. International Journal of Bank Marketing. 2002, 20(6): 261-272. doi: 10.1108/02652320210446724

60. Cheon J, Lee S, Crooks SM, et al. An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education. 2012, 59(3): 1054-1064. doi: 10.1016/j.compedu.2012.04.015

61. Yadegaridehkordi E, Iahad NA, Baloch HZ. Success factors influencing the adoption of M-learning. International Journal of Continuing Engineering Education and Life-Long Learning. 2013, 23(2): 167. doi: 10.1504/ijceell.2013.054290

62. Yeap JAL, Ramayah T, Soto-Acosta P. Factors propelling the adoption of m-learning among students in higher education. Electronic Markets. 2016, 26(4): 323-338. doi: 10.1007/s12525-015-0214-x

63. Bhattacharya S. Artificial intelligence, human intelligence, and the future of public health. AIMS Public Health. 2022, 9(4): 644-650. doi: 10.3934/publichealth.2022045

64. Cheng M, Yuen AHK. Student continuance of learning management system use: A longitudinal exploration. Computers & Education. 2018, 120: 241-253. doi: 10.1016/j.compedu.2018.02.004

65. Joo YJ, Kim N, Kim NH. Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educational Technology Research and Development. 2016, 64(4): 611-630. doi: 10.1007/s11423-016-9436-7

66. DeLone WH, McLean ER. Information Systems Success: The Quest for the Dependent Variable. Information Systems Research. 1992, 3(1): 60-95. doi: 10.1287/isre.3.1.60

67. Kim TG, Lee JH, Law R. An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management. 2008, 29(3): 500-513. doi: 10.1016/j.tourman.2007.05.016

68. Chen SC, Liu ML, Lin CP. Integrating Technology Readiness into the Expectation–Confirmation Model: An Empirical Study of Mobile Services. Cyberpsychology, Behavior, and Social Networking. 2013, 16(8): 604-612. doi: 10.1089/cyber.2012.0606

69. Hong S, Thong JYL, Tam KY. Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems. 2006, 42(3): 1819-1834. doi: 10.1016/j.dss.2006.03.009

70. Oghuma AP, Chang Y, Libaque-Saenz CF, et al. Benefit-confirmation model for post-adoption behavior of mobile instant messaging applications: A comparative analysis of KakaoTalk and Joyn in Korea. Telecommunications Policy. 2015, 39(8): 658-677. doi: 10.1016/j.telpol.2015.07.009

71. Venkatesh, Thong, Xu. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly. 2012, 36(1): 157. doi: 10.2307/41410412

72. Yadav R, Sharma SK, Tarhini A. A multi-analytical approach to understand and predict the mobile commerce adoption. Journal of Enterprise Information Management. 2016, 29(2): 222-237. doi: 10.1108/jeim-04-2015-0034

73. Zuiderwijk A, Janssen M, Dwivedi YK. Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly. 2015, 32(4): 429-440. doi: 10.1016/j.giq.2015.09.005

74. Sharma SK, Al-Badi AH, Govindaluri SM, et al. Predicting motivators of cloud computing adoption: A developing country perspective. Computers in Human Behavior. 2016, 62: 61-69. doi: 10.1016/j.chb.2016.03.073

75. Gruzd A, Staves K, Wilk A. Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior. 2012, 28(6): 2340-2350. doi: 10.1016/j.chb.2012.07.004

76. Teo T, Fan X, Du J. Technology acceptance among pre-service teachers: Does gender matter? Australasian Journal of Educational Technology. 2015, 31(3). doi: 10.14742/ajet.1672

77. Masa’deh R (Moh’d T, Tarhini A, Bany Mohammed A, et al. Modeling Factors Affecting Student’s Usage Behaviour of E-Learning Systems in Lebanon. International Journal of Business and Management. 2016, 11(2): 299. doi: 10.5539/ijbm.v11n2p299

78. Tarhini A, Masa’deh R, Al-Busaidi KA, et al. Factors influencing students’ adoption of e-learning: a structural equation modeling approach. Journal of International Education in Business. 2017, 10(2): 164-182. doi: 10.1108/jieb-09-2016-0032

79. Tarhini A, Teo T, Tarhini T. A cross-cultural validity of the E-learning Acceptance Measure (ElAM) in Lebanon and England: A confirmatory factor analysis. Education and Information Technologies. 2015, 21(5): 1269-1282. doi: 10.1007/s10639-015-9381-9

80. García Botero G, Nguyet DA, García Botero J, et al. Acceptance and Use of Mobile-Assisted Language Learning by Higher Education Language Teachers. Lenguaje. 2022, 50(1): 66-92. doi: 10.25100/lenguaje.v50i1.11006

81. Moran M, Hawkes M, Gayar OE. Tablet Personal Computer Integration in Higher Education: Applying the Unified Theory of Acceptance and use Technology Model to Understand Supporting Factors. Journal of Educational Computing Research. 2010, 42(1): 79-101. doi: 10.2190/ec.42.1.d

82. Blaise R, Halloran M, Muchnick M. Mobile Commerce Competitive Advantage: A Quantitative Study of Variables that Predict M-Commerce Purchase Intentions. Journal of Internet Commerce. 2018, 17(2): 96-114. doi: 10.1080/15332861.2018.1433911

83. Dwivedi YK, Rana NP, Jeyaraj A, et al. Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers. 2017, 21(3): 719-734. doi: 10.1007/s10796-017-9774-y

84. Li W, Yuan K, Yue M, et al. Climate change risk perceptions, facilitating conditions and health risk management intentions: Evidence from farmers in rural China. Climate Risk Management. 2021, 32: 100283. doi: 10.1016/j.crm.2021.100283

85. Venkatesh V, Davis FD. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science. 2000, 46(2): 186-204. doi: 10.1287/mnsc.

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

87. Alalwan AA, Dwivedi YK, Rana NP, et al. Consumer adoption of Internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing. 2015, 20(2): 145-157. doi: 10.1057/fsm.2015.5

88. Escobar-Rodríguez T, Carvajal-Trujillo E. Online drivers of consumer purchase of website airline tickets. Journal of Air Transport Management. 2013, 32: 58-64. doi: 10.1016/j.jairtraman.2013.06.018

89. Lee YH, Hsiao C, Hadi S. Enhancing e-learning Acceptance: An Empirical Examination on individual and system characteristics. Academy of Management Proceedings. 2012, 2012(1): 15828. doi: 10.5465/ambpp.2012.15828abstract

90. Lewis CC, Fretwell CE, Ryan J, et al. Faculty Use of Established and Emerging Technologies in Higher Education: A Unified Theory of Acceptance and Use of Technology Perspective. International Journal of Higher Education. 2013, 2(2). doi: 10.5430/ijhe.v2n2p22

91. Raman A, Don Y. Preservice Teachers’ Acceptance of Learning Management Software: An Application of the UTAUT2 Model. International Education Studies. 2013, 6(7). doi: 10.5539/ies.v6n7p157

92. Brown, Venkatesh. Model of Adoption of Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle. MIS Quarterly. 2005, 29(3): 399. doi: 10.2307/25148690

93. Zhou T, Lu Y, Wang B. Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior. 2010, 26(4): 760-767. doi: 10.1016/j.chb.2010.01.013

94. Ozkan S, Koseler R. Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education. 2009, 53(4): 1285-1296. doi: 10.1016/j.compedu.2009.06.011

95. Petter S, McLean ER. A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level. Information & Management. 2009, 46(3): 159-166. doi: 10.1016/

96. Hassanzadeh A, Kanaani F, Elahi S. A model for measuring e-learning systems success in universities. Expert Systems with Applications. 2012, 39(12): 10959-10966. doi: 10.1016/j.eswa.2012.03.028

97. Kim K, Trimi S, Park H, et al. The Impact of CMS Quality on the Outcomes of E‐learning Systems in Higher Education: An Empirical Study. Decision Sciences Journal of Innovative Education. 2012, 10(4): 575-587. doi: 10.1111/j.1540-4609.2012.00360.x

98. Roca JC, Chiu CM, Martínez FJ. Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies. 2006, 64(8): 683-696. doi: 10.1016/j.ijhcs.2006.01.003

99. Saba T. Implications of E-learning systems and self-efficiency on students outcomes: a model approach. Human-centric Computing and Information Sciences. 2012, 2(1). doi: 10.1186/2192-1962-2-6

100. Wang HC, Chiu YF. Assessing e-learning 2.0 system success. Computers & Education. 2011, 57(2): 1790-1800. doi: 10.1016/j.compedu.2011.03.009

101. Ramayah T, Ahmad NH, Lo MC. The role of quality factors in intention to continue using an e-learning system in Malaysia. Procedia - Social and Behavioral Sciences. 2010, 2(2): 5422-5426. doi: 10.1016/j.sbspro.2010.03.885

102. Kim B. An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation model. Expert Systems with Applications. 2010, 37(10): 7033-7039. doi: 10.1016/j.eswa.2010.03.015

103. Al-Gahtani SS. Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics. 2016, 12(1): 27-50. doi: 10.1016/j.aci.2014.09.001

104. Mohammadi H. Social and individual antecedents of m-learning adoption in Iran. Computers in Human Behavior. 2015, 49: 191-207. doi: 10.1016/j.chb.2015.03.006

105. Sabah NM. Exploring students’ awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior. 2016, 65: 522-533. doi: 10.1016/j.chb.2016.09.009

106. Tan GWH, Ooi KB, Sim JJ, Phusavat K. Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information System. 2012, 52(3): 82–91. doi: 10.1080/08874417.2012.11645561.

107. Iqbal S, Qureshi IA. M-learning adoption: A perspective from a developing country. The International Review of Research in Open and Distributed Learning. 2012, 13(3): 147. doi: 10.19173/irrodl.v13i3.1152

108. van Raaij EM, Schepers JJL. The acceptance and use of a virtual learning environment in China. Computers & Education. 2008, 50(3): 838-852. doi: 10.1016/j.compedu.2006.09.001

109. Thompson R, Compeau D, Higgins C, et al. Intentions to use information technologies. In: Clarke S (editor). End User Computing Challenges and Technologies: Emerging Tools and Applications. Information Science Reference; 2008. pp. 79-101. doi: 10.4018/978-1-59904-295-4.ch006

110. Doll WJ, Hendrickson A, Deng X. Using Davis’s Perceived Usefulness and Ease‐of‐use Instruments for Decision Making: A Confirmatory and Multigroup Invariance Analysis. Decision Sciences. 1998, 29(4): 839-869. doi: 10.1111/j.1540-5915.1998.tb00879.x

111. Wang RB, Du CT. Mobile Social Network Sites as innovative pedagogical tools: factors and mechanism affecting students’ continuance intention on use. Journal of Computers in Education. 2014, 1(4): 353-370. doi: 10.1007/s40692-014-0015-9

112. Bandura A, Schunk DH. Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. Journal of Personality and Social Psychology. 1981, 41(3): 586-598. doi: 10.1037/0022-3514.41.3.586

113. Bandura A. Social Cognitive Theory: An Agentic Perspective. Annual Review of Psychology. 2001, 52(1): 1-26. doi: 10.1146/annurev.psych.52.1.1

114. Compeau DR, Higgins CA. Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly. 1995, 19(2): 189. doi: 10.2307/249688

115. Downey J. Measuring general computer self-efficacy: The surprising comparison of three instruments in predicting performance, attitudes, and usage. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06). 4–7 January 2006; Kauai, HI, USA. p. 210a. doi: 10.1109/hicss.2006.268

116. Hernandez B, Jimenez J, Jose Martin M. The impact of self-efficacy, ease of use and usefulness on e-purchasing: An analysis of experienced e-shoppers. Interacting with Computers. 2009, 21(1-2): 146-156. doi: 10.1016/j.intcom.2008.11.001

117. Yuen AHK, Ma WWK. Exploring teacher acceptance of e‐learning technology. Asia-Pacific Journal of Teacher Education. 2008, 36(3): 229-243. doi: 10.1080/13598660802232779

118. Park SY. An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology & Society. 2009, 12(3): 150-162.

119. Petter S, DeLone W, McLean E. Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems. 2008, 17(3): 236-263. doi: 10.1057/ejis.2008.15

120. Poulova P, Simonova I. E-learning Reflected in Research Studies in Czech Republic: Comparative Analyses. Procedia - Social and Behavioral Sciences. 2014, 116: 1298-1304. doi: 10.1016/j.sbspro.2014.01.386

121. Tajuddin RA, Baharudin M, Hoon TS. System Quality and its Influence on Students’ Learning Satisfaction in UiTM Shah Alam. Procedia - Social and Behavioral Sciences. 2013, 90: 677-685. doi: 10.1016/j.sbspro.2013.07.140

122. Xu D, Huang WW, Wang H, et al. Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: An empirical investigation. Information & Management. 2014, 51(4): 430-440. doi: 10.1016/

123. Li Y, Duan Y, Fu Z, et al. An empirical study on behavioural intention to reuse e‐learning systems in rural China. British Journal of Educational Technology. 2011, 43(6): 933-948. doi: 10.1111/j.1467-8535.2011.01261.x

124. Park SY, Nam M, Cha S. University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology. 2011, 43(4): 592-605. doi: 10.1111/j.1467-8535.2011.01229.x

125. Wang Y, Wu M, Wang H. Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology. 2008, 40(1): 92-118. doi: 10.1111/j.1467-8535.2007.00809.x

126. Dalvi-Esfahani M, Wai Leong L, Ibrahim O, et al. Explaining Students’ Continuance Intention to Use Mobile Web 2.0 Learning and Their Perceived Learning: An Integrated Approach. Journal of Educational Computing Research. 2018, 57(8): 1956-2005. doi: 10.1177/0735633118805211

127. Huang RT, Hsiao CH, Tang TW, et al. Exploring the moderating role of perceived flexibility advantages in mobile learning continuance intention (MLCI). The International Review of Research in Open and Distributed Learning. 2014, 15(3). doi: 10.19173/irrodl.v15i3.1722

128. Alsabawy AY, Cater-Steel A, Soar J. IT infrastructure services as a requirement for e-learning system success. Computers & Education. 2013, 69: 431-451. doi: 10.1016/j.compedu.2013.07.035

129. Gefen D, Straub D, Boudreau MC. Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems. 2000, 4. doi: 10.17705/1cais.00407

130. Chu TH, Chen YY. With Good We Become Good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education. 2016, 92-93: 37-52. doi: 10.1016/j.compedu.2015.09.013

131. Alalwan AA, Dwivedi YK, Williams MD. Customers’ Intention and Adoption of Telebanking in Jordan. Information Systems Management. 2016, 33(2): 154-178. doi: 10.1080/10580530.2016.1155950

132. Iqbal MS, Khan SUD, Iqbal MZ. University students’ perception of Ebola virus disease. Journal of Pharmaceutical Research International. 2020, 32(34): 132-140. doi: 10.9734/jpri/2020/v32i3430989

133. Myers ND, Ahn S, Jin Y. Sample Size and Power Estimates for a Confirmatory Factor Analytic Model in Exercise and Sport. Research Quarterly for Exercise and Sport. 2011, 82(3): 412-423. doi: 10.1080/02701367.2011.10599773

134. Bentler PM, Chou CP. Practical Issues in Structural Modeling. Sociological Methods & Research. 1987, 16(1): 78-117. doi: 10.1177/0049124187016001004

135. Hair J, Hollingsworth CL, Randolph AB, et al. An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems. 2017, 117(3): 442-458. doi: 10.1108/imds-04-2016-0130

136. Shmueli G, Sarstedt M, Hair JF, et al. Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing. 2019, 53(11): 2322-2347. doi: 10.1108/ejm-02-2019-0189

137. Podsakoff PM, MacKenzie SB, Lee JY, et al. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology. 2003, 88(5): 879-903. doi: 10.1037/0021-9010.88.5.879

138. Ooi KB, Tan GWH. Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications. 2016, 59: 33-46. doi: 10.1016/j.eswa.2016.04.015

139. Tan GWH, Ooi KB. Gender and age: Do they really moderate mobile tourism shopping behavior? Telematics and Informatics. 2018, 35(6): 1617-1642. doi: 10.1016/j.tele.2018.04.009

140. Hair JF, Hult GT, Ringle CM, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed. Sage; 2016.

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

142. Al-Busaidi KA. Learners’ Perspective on Critical Factors to LMS Success in Blended Learning: An Empirical Investigation. Communications of the Association for Information Systems. 2012, 30. doi: 10.17705/1cais.03002

143. Al-Busaidi KA. An empirical investigation linking learners’ adoption of blended learning to their intention of full e-learning. Behaviour & Information Technology. 2013, 32(11): 1168-1176. doi: 10.1080/0144929x.2013.774047

144. Schillewaert N, Ahearne MJ, Frambach RT, et al. The adoption of information technology in the sales force. Industrial Marketing Management. 2005, 34(4): 323-336. doi: 10.1016/j.indmarman.2004.09.013

145. Zhang S, Zhao J, Tan W. Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science and Technology. 2008, 13(3): 312-317. doi: 10.1016/s1007-0214(08)70050-6

146. Kim H, Niehm LS. The Impact of Website Quality on Information Quality, Value, and Loyalty Intentions in Apparel Retailing. Journal of Interactive Marketing. 2009, 23(3): 221-233. doi: 10.1016/j.intmar.2009.04.009

147. Wan Z, Fang Y. The role of information technology in technology-

148. mediated learning: A review of the past for the future. In: Proceedings of the 12th Americas Conference on Information Systems, AMCIS 2006; 4–6 August 2006; Acapulco, México. Volume 4, pp. 2018–2025.

149. Fornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research. 1981, 18(1): 39. doi: 10.2307/3151312

150. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science. 2014, 43(1): 115-135. doi: 10.1007/s11747-014-0403-8

151. Dijkstra TK, Schermelleh-Engel K. Consistent Partial Least Squares for Nonlinear Structural Equation Models. Psychometrika. 2013, 79(4): 585-604. doi: 10.1007/s11336-013-9370-0

152. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. 1999, 6(1): 1-55. doi: 10.1080/10705519909540118

153. Tenenhaus M, Vinzi VE, Chatelin YM, et al. PLS path modeling. Computational Statistics & Data Analysis. 2005, 48(1): 159-205. doi: 10.1016/j.csda.2004.03.005

154. Wetzels, Odekerken-Schröder, van Oppen. Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly. 2009, 33(1): 177. doi: 10.2307/20650284

155. Cohen J. A power primer. Psychological Bulletin. 1992, 112(1): 155-159. doi: 10.1037/0033-2909.112.1.155

156. Hew JJ, Tan GWH, Lin B, et al. Generating travel-related contents through mobile social tourism: Does privacy paradox persist? Telematics and Informatics. 2017, 34(7): 914-935. doi: 10.1016/j.tele.2017.04.001

157. Shmueli G, Ray S, Velasquez Estrada JM, et al. The elephant in the room: Predictive performance of PLS models. Journal of Business Research. 2016, 69(10): 4552-4564. doi: 10.1016/j.jbusres.2016.03.049

158. Ringle CM, Sarstedt M. Gain more insight from your PLS-SEM results. Industrial Management & Data Systems. 2016, 116(9): 1865-1886. doi: 10.1108/imds-10-2015-0449

159. Teo AC, Tan GWH, Ooi KB, et al. The effects of convenience and speed in m-payment. Industrial Management & Data Systems. 2015, 115(2): 311-331. doi: 10.1108/imds-08-2014-0231

160. Teo AC, Tan GW, Ooi KB, et al. The effects of convenience and speed in m-payment. Industrial management & data systems, 115(2): 311-331. doi: 10.1108/IMDS-08-2014-0231.

161. Talukder MdS, Sorwar G, Bao Y, et al. Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technological Forecasting and Social Change. 2020, 150: 119793. doi: 10.1016/j.techfore.2019.119793

162. Liébana-Cabanillas F, Molinillo S, Ruiz-Montañez M. To use or not to use, that is the question: Analysis of the determining factors for using NFC mobile payment systems in public transportation. Technological Forecasting and Social Change. 2019, 139: 266-276. doi: 10.1016/j.techfore.2018.11.012

163. Asadi S, Abdullah R, Safaei M, et al. An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices. Mobile Information Systems. 2019, 2019: 1-9. doi: 10.1155/2019/8026042

164. Yee-Loong Chong A, Liu MJ, Luo J, et al. Predicting RFID adoption in healthcare supply chain from the perspectives of users. International Journal of Production Economics. 2015, 159: 66-75. doi: 10.1016/j.ijpe.2014.09.034

165. Sternad Zabukovšek S, Kalinic Z, Bobek S, et al. SEM–ANN based research of factors’ impact on extended use of ERP systems. Central European Journal of Operations Research. 2018, 27(3): 703-735. doi: 10.1007/s10100-018-0592-1

166. Mac Callum K, Jeffrey L. The influence of students’ ICT skills and their adoption of mobile learning. Australasian Journal of Educational Technology. 2013, 29(3). doi: 10.14742/ajet.298

167. Mac Callum K, Jeffrey L, NA K. Factors Impacting Teachers’ Adoption of Mobile Learning. Journal of Information Technology Education: Research. 2014, 13: 141-162. doi: 10.28945/1970

168. Kim-Soon N, Ibrahim MA, Razzaly W, et al. Mobile Technology for Learning Satisfaction Among Students at Malaysian Technical Universities (MTUN). Advanced Science Letters. 2017, 23(1): 223-226. doi: 10.1166/asl.2017.7140

169. Oghuma AP, Libaque-Saenz CF, Wong SF, et al. An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics. 2016, 33(1): 34-47. doi: 10.1016/j.tele.2015.05.006

170. Cheung R, Vogel D. Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education. 2013, 63: 160-175. doi: 10.1016/j.compedu.2012.12.003

171. Mohd Alwi NH, Fan IS. E-Learning and Information Security Management. International Journal for Digital Society. 2010, 1(2): 148-156. doi: 10.20533/ijds.2040.2570.2010.0019

172. El-Khatib K, Korba L, Xu Y, et al. Privacy and Security in E-Learning. International Journal of Distance Education Technologies. 2003, 1(4): 1-19. doi: 10.4018/jdet.2003100101

173. Chang S, Tung F. An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology. 2007, 39(1): 71-83. doi: 10.1111/j.1467-8535.2007.00742.x

174. Chatzoglou PD, Sarigiannidis L, Vraimaki E, et al. Investigating Greek employees’ intention to use web-based training. Computers & Education. 2009, 53(3): 877-889. doi: 10.1016/j.compedu.2009.05.007

175. Tarhini A, Arachchilage NAG, Masa’deh R, et al. A Critical Review of Theories and Models of Technology Adoption and Acceptance in Information System Research. International Journal of Technology Diffusion. 2015, 6(4): 58-77. doi: 10.4018/ijtd.2015100104

176. Tarhini A, Arachchilage NA, Abbasi MS. A critical review of theories and models of technology adoption and acceptance in information system research. International Journal of Technology Diffusion. 2015, 6(4): 58-77. doi: 10.4018/ijtd.2015100104

177. Al-Hujran O, Al-Lozi E, Al-Debei MM. Get ready to mobile learning: Examining factors affecting college students’ behavioral intentions to use m-learning in Saudi Arabia. Journal of Business Administration. 2014, 10(1): 111-128. doi: 10.12816/0026186

178. Deng S, Liu Y, Qi Y. An empirical study on determinants of web based question‐answer services adoption. Online Information Review. 2011, 35(5): 789-798. doi: 10.1108/14684521111176507

179. Tarhini A, Hone K, Liu X. Measuring the Moderating Effect of Gender and Age on E-Learning Acceptance in England: A Structural Equation Modeling Approach for An Extended Technology Acceptance Model. Journal of Educational Computing Research. 2014, 51(2): 163-184. doi: 10.2190/ec.51.2.b

180. Tarhini A, Hone K, Liu X. The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior. 2014, 41: 153-163. doi: 10.1016/j.chb.2014.09.020

181. Teo T. A path analysis of pre-service teachers’ attitudes to computer use: Applying and extending the technology acceptance model in an educational context. Interactive Learning Environments. 2010, 18(1): 65-79. doi: 10.1080/10494820802231327

182. Chao CM. Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology. 2019, 10. doi: 10.3389/fpsyg.2019.01652

(267 Abstract Views, 90 PDF Downloads)


  • There are currently no refbacks.

Copyright (c) 2024 Yakup Akgul, Ali Osman Uymaz, Pelin Uymaz

License URL: