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An empirical examination of continuous intention to use m-learning: An integrated model

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Abstract

To the best of our knowledge, much research has been conducted concerning the topic of mobile learning (m-learning) adoption or acceptance. However, examining the continued use of m-learning is still in short supply and calling for further research. To bridge this limitation, this study develops an integrated model through the integration of three different theoretical models, namely technology acceptance model (TAM), theory of planned behavior (TPB), and expectation-confirmation model (ECM). To examine the proposed model, a questionnaire survey was developed to collect data from 273 postgraduate students enrolled at The British University in Dubai in the United Arab of Emirates (UAE). The partial least squares-structural equation modeling (PLS-SEM) is used to analyze the collected data. The empirical results indicated that perceived ease of use, attitude, perceived behavioral control, and subjective norms are significant predictors to explain the continued use of m-learning. Nevertheless, perceived usefulness and satisfaction were shown to be insignificant determinants to continuous intention. Further theoretical and practical implications are also discussed.

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References

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

    Article  Google Scholar 

  • Al-Adwan, A. S., Al-Adwan, A., & Berger, H. (2018). Solving the mystery of mobile learning adoption in higher education. International Journal of Mobile Communications, 16(1), 24–49. https://doi.org/10.1504/IJMC.2018.10007779.

    Article  Google Scholar 

  • Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93–102. https://doi.org/10.1016/j.chb.2015.11.033.

    Article  Google Scholar 

  • Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018a). PLS-SEM in information systems research: A comprehensive methodological reference. In 4th International Conference on Advanced Intelligent Systems and Informatics (AISI 2018) (pp. 644–653). Springer.

  • Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018b). Technology acceptance model in M-learning context: A systematic review. Computers & Education, 125, 389–412.

    Article  Google Scholar 

  • Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453–485. https://doi.org/10.1007/s40692-016-0074-1.

    Article  Google Scholar 

  • Al-Shihi, H., Sharma, S. K., & Sarrab, M. (2018). Neural network approach to predict mobile learning acceptance. Education and Information Technologies., 23, 1805–1824. https://doi.org/10.1007/s10639-018-9691-9.

    Article  Google Scholar 

  • Alzaza, N. S. (2013). Mobile learning services acceptance model among higher education students. Journal of UP for Research and Studies, 5, 1–28.

    Google Scholar 

  • Arpaci, I. (2015). A comparative study of the effects of cultural differences on the adoption of mobile learning. British Journal of Educational Technology. https://doi.org/10.1111/bjet.12160.

  • Arpaci, I. (2019). A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. Computers in Human Behavior, 90, 181–187. https://doi.org/10.1016/j.chb.2018.09.005.

    Article  Google Scholar 

  • Bao, Y., Xiong, T., Hu, Z., & Kibelloh, M. (2013). Exploring gender differences on general and specific computer self-efficacy in Mobile learning adoption. Journal of Educational Computing Research. https://doi.org/10.2190/EC.49.1.e.

  • Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (pls) approach to casual modeling: Personal computer adoption Ans use as an illustration.

  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351–370.

  • Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies – Students’ behavior. Computers in Human Behavior, 72, 612–620. https://doi.org/10.1016/j.chb.2016.05.027.

    Article  Google Scholar 

  • Chen, S.-C., Liu, M.-L., & Lin, C.-P. (2013). Integrating technology readiness into the expectation–confirmation model: An empirical study of mobile services. Cyberpsychology, Behavior and Social Networking, 16(8), 604–612.

    Article  Google Scholar 

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

  • Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064. https://doi.org/10.1016/j.compedu.2012.04.015.

    Article  Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

    Google Scholar 

  • Dalvi-Esfahani, M., Wai Leong, L., Ibrahim, O., & Nilashi, M. (2020). Explaining students’ continuance intention to use Mobile web 2.0 learning and their perceived learning: An integrated approach. Journal of Educational Computing Research Research 57(8), 1956–2005.

  • 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.

    Article  Google Scholar 

  • Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using Davis’s perceived usefulness and ease-of-use instruments for decision making: A confirmatory and multigroup invariance analysis. Decision Sciences, 29(4), 839–869.

    Article  Google Scholar 

  • Erdfelder, E., FAul, F., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*power 3.1: Tests for correlation and regression analyses. Behavior Research Methods. https://doi.org/10.3758/BRM.41.4.1149.

  • 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/10.2307/3151312.

    Article  Google Scholar 

  • Gan, C., Li, H., & Liu, Y. (2017). Understanding mobile learning adoption in higher education: An empirical investigation in the context of the mobile library. Electronic Library, 35(5), 846–860. https://doi.org/10.1108/EL-04-2016-0093.

    Article  Google Scholar 

  • Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have adavantages for small sample size or non-normal data? MIS Quaterly.

  • GSMA. (2018). The mobile economy. Retrieved from https://www.gsma.com/mobileeconomy/wp-content/uploads/2018/02/The-Mobile-Economy-Global-2018.pdf

  • Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

  • Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130.

    Article  Google Scholar 

  • Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101–123. https://doi.org/10.1007/s11423-016-9465-2.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hong, S., Thong, J. Y. L., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834.

    Article  Google Scholar 

  • Hsia, J. W. (2016). The effects of locus of control on university students’ mobile learning adoption. Journal of Computing in Higher Education, 28(1), 1–17. https://doi.org/10.1007/s12528-015-9103-8.

    Article  Google Scholar 

  • Huang, R.-T., Hsiao, C.-H., Tang, T.-W., & Lien, T.-C. (2014). Exploring the moderating role of perceived flexibility advantages in mobile learning continuance intention (MLCI). The International Review of Research in Open and Distributed Learning, 15(3).

  • Iqbal, S., & Qureshi, I. A. (2012). M-learning adoption: A perspective from a developing country. The International Review of Research in Open and Distributed Learning, 13(3), 147–164.

    Article  Google Scholar 

  • Joo, Y. J., Kim, N., & Kim, N. H. (2016). Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educational Technology Research and Development, 64(4), 611–630. https://doi.org/10.1007/s11423-016-9436-7.

    Article  Google Scholar 

  • Karimi, S. (2016). Do learners’ characteristics matter? An exploration of mobile-learning adoption in self-directed learning. Computers in Human Behavior, 63, 769–776. https://doi.org/10.1016/j.chb.2016.06.014.

    Article  Google Scholar 

  • Karjaluoto, H., Mattila, M., & Pento, T. (2002). Factors underlying attitude formation towards online banking in Finland. International Journal of Bank Marketing, 20(6), 261–272.

    Article  Google Scholar 

  • Kim, B. (2010). An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation model. Expert Systems with Applications, 37(10), 7033–7039.

    Article  Google Scholar 

  • Kim, H. J., Lee, J. M., & Rha, J. Y. (2017). Understanding the role of user resistance on mobile learning usage among university students. Computers & Education, 113, 108–118. https://doi.org/10.1016/j.compedu.2017.05.015.

    Article  Google Scholar 

  • Kim-Soon, N., Ibrahim, M. A., Razzaly, W., Ahmad, A. R., & Sirisa, N. M. X. (2017). Mobile Technology for Learning Satisfaction among Students at Malaysian technical universities (MTUN). Advanced Science Letters, 23(1), 223–226.

    Article  Google Scholar 

  • Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications., 156, 278–279. https://doi.org/10.1038/156278a0.

    Article  Google Scholar 

  • Kumar, B. A., & Chand, S. S. (2019). Mobile learning adoption: A systematic review. Education and Information Technologies, 24(1), 471–487. https://doi.org/10.1007/s10639-018-9783-6.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.compedu.2012.07.015.

  • Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55(3), 1211–1219. https://doi.org/10.1016/j.compedu.2010.05.018.

    Article  Google Scholar 

  • Mac Callum, K., & Jeffrey, L. (2013). The influence of students’ ICT skills and their adoption of mobile learning. Australasian Journal of Educational Technology, 29(3), 303–314. https://doi.org/10.1234/ajet.v29i3.298.

    Article  Google Scholar 

  • Mac Callum, K., & Jeffrey, L. (2014). Factors Impacting Teachers’ Adoption of Mobile Learning. Journal of Information Technology Education, 13.

  • Mac Callum, K., Jeffrey, L., & Kinshuk. (2014). Comparing the role of ICT literacy and anxiety in the adoption of mobile learning. Computers in Human Behavior, 39, 8–19. https://doi.org/10.1016/j.chb.2014.05.024.

    Article  Google Scholar 

  • Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76–85. https://doi.org/10.1016/j.compedu.2013.04.021.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Nistor, N., Göǧüş, A., & Lerche, T. (2013). Educational technology acceptance across national and professional cultures: A European study. Educational Technology Research and Development., 61, 733–749. https://doi.org/10.1007/s11423-013-9292-7.

    Article  Google Scholar 

  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. McGraw-Hill, New York. https://doi.org/10.1037/018882.

  • Oghuma, A. P., Chang, Y., Libaque-Saenz, C. F., Park, M.-C., & Rho, J. J. (2015). Benefit-confirmation model for post-adoption behavior of mobile instant messaging applications: A comparative analysis of KakaoTalk and Joyn in Korea. Telecommunications Policy, 39(8), 658–677.

    Article  Google Scholar 

  • Park, S. Y., Lee, H. D., & Kim, S. Y. (2018). South Korean university students’ mobile learning acceptance and experience based on the perceived attributes, system quality and resistance. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2016.1261041.

  • Poong, Y. S., Yamaguchi, S., & Takada, J. I. (2017). Investigating the drivers of mobile learning acceptance among young adults in the world heritage town of Luang Prabang, Laos. Information Development, 33(1), 57–71. https://doi.org/10.1177/0266666916638136.

    Article  Google Scholar 

  • Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS Retrieved from http://www.smartpls.com.

    Google Scholar 

  • Sabah, N. M. (2016). Exploring students’ awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior, 65, 522–533. https://doi.org/10.1016/j.chb.2016.09.009.

    Article  Google Scholar 

  • Sarrab, M., Al Shibli, I., & Badursha, N. (2016). An empirical study of factors driving the adoption of mobile learning in Omani higher education. The International Review of Research in Open and Distance Learning, 17(4), 331–349. https://doi.org/10.19173/irrodl.v17i4.2614.

    Article  Google Scholar 

  • Sarrab, M., Al-Shihi, H., Al-Manthari, B., & Bourdoucen, H. (2018). Toward educational requirements model for Mobile learning development and adoption in higher education. TechTrends., 62, 635–646. https://doi.org/10.1007/s11528-018-0331-4.

    Article  Google Scholar 

  • Seliaman, M. E., & Al-Turki, M. S. (2012). Mobile learning adoption in Saudi Arabia. World Academy of Science, Engineering and Technology, 6(9), 1129–1131.

    Google Scholar 

  • Tan, G. W.-H., Ooi, K.-B., Sim, J.-J., & Phusavat, K. (2012). Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information Systems, 52(3), 82–91.

    Google Scholar 

  • Tan, G. W. H., Ooi, K. B., Leong, L. Y., & Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-neural networks approach. Computers in Human Behavior, 36, 198–213. https://doi.org/10.1016/j.chb.2014.03.052.

    Article  Google Scholar 

  • Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40. https://doi.org/10.1037/0021-9010.90.4.710.

    Article  Google Scholar 

  • Valencia Arias, A., Gonzalez Uribe, G., & Castaneda Riascos, M. (2016). Structural equation model for studying the mobile-learning acceptance. IEEE Latin America Transactions. https://doi.org/10.1109/TLA.2016.7483544.

  • Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540.

    Article  Google Scholar 

  • Wang, R.-B., & Du, C.-T. (2014). Mobile social network sites as innovative pedagogical tools: Factors and mechanism affecting students’ continuance intention on use. Journal of Computers in Education, 1(4), 353–370.

    Article  Google Scholar 

  • Yadegaridehkordi, E., Iahad, N. A., & Baloch, H. Z. (2013). Success factors influencing the adoption of M-learning. International Journal of Continuing Engineering Education and Life Long Learning, 23(2), 167–178.

    Article  Google Scholar 

  • Yeap, J. A. L., Ramayah, T., & Soto-Acosta, P. (2016). Factors propelling the adoption of m-learning among students in higher education. Electronic Markets, 26(4), 323–338.

    Article  Google Scholar 

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Authors and Affiliations

  1. Applied Computational Civil and Structural Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Mostafa Al-Emran

  2. Department of Computer Education and Instructional Technology, Tokat Gaziosmanpasa University, Tokat, Turkey

    Ibrahim Arpaci

  3. Research Institute of Sciences & Engineering, University of Sharjah, Sharjah, UAE

    Said A. Salloum

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  1. Mostafa Al-Emran
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  2. Ibrahim Arpaci
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  3. Said A. Salloum
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Correspondence to Mostafa Al-Emran.

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Appendix A. Constructs and corresponding items

Appendix A. Constructs and corresponding items

1.1 Expectation confirmation

EC1: My experience with using m-learning was better than what I expected.

EC2: The service level provided by m-learning was better than what I expected.

EC3: Overall, most of my expectations from using m-learning were confirmed.

1.2 Perceived ease of use

PEOU1: M-learning is easy to use.

PEOU2: Interaction with m-learning is clear and understandable.

PEOU3: M-learning is convenient and user-friendly.

1.3 Perceived usefulness

PU1: M-learning enhances my efficiency.

PU2: M-learning enables me to accomplish tasks more quickly.

PU3: M-learning improves my performance.

1.4 Satisfaction

SA1: I am satisfied with using m-learning as a learning assisted tool.

SA2: I am satisfied with using m-learning functions.

SA3: I am satisfied with multimedia instructions.

1.5 Attitude

AT1: I like my coursework more when I use m-learning

AT2: Using m-learning in my coursework is a pleasant experience

AT3: Using m-learning in my coursework is a wise idea

1.6 Perceive behavioral control

PBC1: I have a sufficient extent of knowledge to use m-learning.

PBC2: I have a sufficient extent of control to make a decision to use m-learning.

PBC3: I have a sufficient extent of self-confidence to make a decision to use m-learning.

1.7 Subjective norms

SN1: Most people who are important to me think that it would be fine to use m-learning.

SN2: I think other students in my classes would be willing to use m-learning.

SN3: Most people who are important to me would be in favor of using m-learning.

1.8 Continuous intention

CI1: I intend to continue using m-learning rather than discontinue its use.

CI2: I intend to continue using m-learning than other alternative means.

CI3: If I could, I would like to continue my use of m-learning.

1.9 Actual use

AU1: I use m-learning on daily basis

AU2: I use m-learning frequently

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Al-Emran, M., Arpaci, I. & Salloum, S.A. An empirical examination of continuous intention to use m-learning: An integrated model. Educ Inf Technol 25, 2899–2918 (2020). https://doi.org/10.1007/s10639-019-10094-2

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