Open Access Peer-reviewed Research Article

Expected Performance, Habit, and Enjoyment as Key Factors Influencing Teachers' Intentions to Use Personal Learning Environments for Educational Purposes

Main Article Content

Konstantinos Lavidas corresponding author
George Koutromanos
Vassilis Komis
Crossmark logo

Abstract

Recent research emphasizes the importance of Personal Learning Environments (PLEs) in the educational process. However, before their integration into school classrooms, it is important to investigate the extent to which teachers accept PLEs for educational purposes---an area that has received limited attention in the existing literature. To this end, this study empirically investigates teachers' intention to use and Use Behavior of PLEs. Drawing on the UTAUT2 model, data from 540 Greek primary and secondary school teachers were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings showed that Performance Expectancy, Habit, and Hedonic Motivation were significantly associated with Behavioral Intention, while Behavioral Intention, Habit, and Facilitating Conditions were significantly associated with Use Behavior. Mediation analysis further showed significant indirect effects of Performance Expectancy, Hedonic Motivation, and Habit on Use Behavior through Behavioral Intention. Behavioral Intention partially mediated the relationship between Habit and Use Behavior, indicating complementary partial mediation. No significant moderating effects of gender, age, or classroom technology-integration experience were found. The findings offer further insight into the mechanisms underlying teachers' use of PLEs and inform their integration into educational practice.

Keywords
ICT integration, e-me, e-learning, teachers' professional development program

Article Details

How to Cite
Lavidas, K., Koutromanos, G., & Komis, V. (2026). Expected Performance, Habit, and Enjoyment as Key Factors Influencing Teachers’ Intentions to Use Personal Learning Environments for Educational Purposes. Advances in Mobile Learning Educational Research, 6(2), 1929-1939. https://doi.org/10.25082/AMLER.2026.02.006

References

  1. Attwell, G. (2007). Personal Learning Environments—the future of e-Learning. Elearning papers, 2(1), 1–8.
  2. Benhamdi, S., Babouri, A., & Chiky, R. (2016). Personalized recommender system for e-Learning environment. Education and Information Technologies, 22(4), 1455–1477. https://doi.org/10.1007/s10639-016-9504-y
  3. Cohen, J. (1977). Statistical power analysis for the behavioral sciences. (Revised). Academic.
  4. Creswell, J.W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Boston: Pearson.
  5. 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
  6. 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), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
  7. Del Barrio-García, S., Arquero, J. L., & Romero-Frías, E. (2015). Personal Learning Environments Acceptance Model: The Role of Need for Cognition, e-Learning Satisfaction and Students’ Perceptions. Educational Technology & Society, 18(3), 129–141.
  8. Drolia, M., Sifaki, E., Papadakis, S., & Kalogiannakis, M. (2020). An Overview of Mobile Learning for Refugee Students: Juxtaposing Refugee Needs with Mobile Applications’ Characteristics. Challenges, 11(2), 31. https://doi.org/10.3390/challe11020031
  9. Elfeky, A. I. M. (2019). The effect of personal learning environments on participants’ higher order thinking skills and satisfaction. Innovations in Education and Teaching International, 56(4), 505–516. https://doi.org/10.1080/14703297.2018.1534601
  10. Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423–435. https://doi.org/10.1016/j.compedu.2012.02.001
  11. Gunasinghe, A., Hamid, J. A., Khatibi, A., & Azam, S. M. F. (2020). The viability of UTAUT-3 in understanding the lecturer’s acceptance and use of virtual learning environments. International Journal of Technology Enhanced Learning, 12(4), 458. https://doi.org/10.1504/ijtel.2020.110056
  12. Hair, J.F., Hult, G.T.M., Ringle, C.M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage.
  13. Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). 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
  14. Karakose, T., Tülübaş, T., Papadakis, S., & Yirci, R. (2023). Evaluating the Intellectual Structure of the Knowledge Base on Transformational School Leadership: A Bibliometric and Science Mapping Analysis. Education Sciences, 13(7), 708. https://doi.org/10.3390/educsci13070708
  15. Kim, J., & Lee, K. S.-S. (2020). Conceptual model to predict Filipino teachers’ adoption of ICT-based instruction in class: using the UTAUT model. Asia Pacific Journal of Education, 42(4), 699–713. https://doi.org/10.1080/02188791.2020.1776213
  16. Kock, N. (2015). Common Method Bias in PLS-SEM. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
  17. Lavidas, K., Papadakis, S., Manesis, D., Grigoriadou, A. S., & Gialamas, V. (2022). The Effects of Social Desirability on Students’ Self-Reports in Two Social Contexts: Lectures vs. Lectures and Lab Classes. Information, 13(10), 491. https://doi.org/10.3390/info13100491
  18. Lavidas, K., Petropoulou, A., Papadakis, S., Apostolou, Z., Komis, V., Jimoyiannis, A., & Gialamas, V. (2022). Factors Affecting Response Rates of the Web Survey with Teachers. Computers, 11(9), 127. https://doi.org/10.3390/computers11090127
  19. Nan Cenka, B. A., Santoso, H. B., & Junus, K. (2022). Using the personal learning environment to support self-regulated learning strategies: a systematic literature review. Interactive Learning Environments, 32(4), 1368–1384. https://doi.org/10.1080/10494820.2022.2120019
  20. Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2, 100041. https://doi.org/10.1016/j.caeo.2021.100041
  21. Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 116(9), 1849–1864. https://doi.org/10.1108/imds-07-2015-0302
  22. Papadakis, S., & Kalogiannakis, M. (2019). Evaluating the effectiveness of a game-based learning approach in modifying students’ behavioural outcomes and competence, in an introductory programming course. A case study in Greece. International Journal of Teaching and Case Studies, 10(3), 235. https://doi.org/10.1504/ijtcs.2019.102760
  23. Pynoo, B., Devolder, P., Tondeur, J., van Braak, J., Duyck, W., & Duyck, P. (2011). Predicting secondary school teachers’ acceptance and use of a digital learning environment: A cross-sectional study. Computers in Human Behavior, 27(1), 568–575. https://doi.org/10.1016/j.chb.2010.10.005
  24. R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org
  25. Raman, A., & Rathakrishnan, M. (2018). FROG VLE: Teachers’ technology acceptance using UTAUT model. International Journal of Mechanical Engineering and Technology, 9(3), 529–538.
  26. Rejón-Guardia, F., Polo-Peña, A. I., & Maraver-Tarifa, G. (2019). The acceptance of a personal learning environment based on Google apps: the role of subjective norms and social image. Journal of Computing in Higher Education, 32(2), 203–233. https://doi.org/10.1007/s12528-019-09206-1
  27. Revelle, W. (2023). Psych: Procedures for Psychological, Psychometric, and Personality Research. R package. https://CRAN.R-project.org/package=psych
  28. Sanchez, G., Trinchera, L., & Russolillo, G. (2025). plspm: Partial Least Squares Path Modeling (PLS-PM) [Data set]. CRAN: Contributed Packages. The R Foundation. https://doi.org/10.32614/cran.package.plspm
  29. Serhan, S. A. L., & Yahaya, N. (2022). A Systematic Review and Trend Analysis of Personal Learning Environments Research. International Journal of Information and Education Technology, 12(1), 43–53. https://doi.org/10.18178/ijiet.2022.12.1.1585
  30. Shaikh, Z. A., & Khoja, S. A. (2012). Role of Teacher in Personal Learning Environments. Digital Education Review, 21, 23–32.
  31. Teo, T. (2010). Development and validation of the E-learning Acceptance Measure (ElAM). The Internet and Higher Education, 13(3), 148–152. https://doi.org/10.1016/j.iheduc.2010.02.001
  32. Torres Kompen, R., Edirisingha, P., Canaleta, X., Alsina, M., & Monguet, J. M. (2019). Personal learning Environments based on Web 2.0 services in higher education. Telematics and Informatics, 38, 194–206. https://doi.org/10.1016/j.tele.2018.10.003
  33. Tzavara, A., Lavidas, K., Komis, V., Misirli, A., Karalis, T., & Papadakis, S. (2023). Using Personal Learning Environments before, during and after the Pandemic: The Case of “e-Me”. Education Sciences, 13(1), 87. https://doi.org/10.3390/educsci13010087
  34. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward A Unified View1. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  35. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology1. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
  36. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428
  37. Yee, M. L. S., & Abdullah, M. S. (2021). A Review of UTAUT and Extended Model as a Conceptual Framework in Education Research. Jurnal Pendidikan Sains Dan Matemaitk Malaysia, 11(Sp), 1–20. https://doi.org/10.37134/jpsmm.vol11.sp.1.2021