Abstract
Several cultural and psychological factors can affect the behavior of users towards the use and acceptance of mobile-based educational applications. One of the methods to measure the factors resulting in the acceptance of mobile-based educational applications is Unified Theory of Acceptance and Use of Technology (UTAUT). The objective of this study was to evaluate the behavioral intention of the pharmacy students for acceptance and long-term use of the mobile-based application for educating safety measures in pharmaceutical laboratories (LabSafety) using UTAUT2 in 2017–2018. The research population was all pharmacy students (n = 241) who had experience of using the LabSafety application. Data were collected using a translated and modified version of the UTAUT2 questionnaire. The Partial Least Squares Structural Equation Modelling (PLS-SEM) was used for statistical analysis. Based on the obtained results, “Performance Expectancy”, “Social Influence” and “Habit” had positive effects on “Behavioral Intention”. “Behavioral Intention” had significant positive effects on “Use Behavior”. The effect of “Habit” on “Use Behavior” in men was higher than women. As a result, the usefulness of educational applications such as LabSafety, their positive impact on the improvement of students’ efficiency, and the influence of the faculty member’ viewpoints on their use can result in frequent and daily use of these applications.
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Ain, N. U., Kaur, K., & Waheed, M. (2015). The influence of learning value on learning management system use: An extension of UTAUT2. Information Development, 32, 1–16. https://doi.org/10.1177/0266666915597546.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T.
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.2018.088271.
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Lal, B., & Williams, M. D. (2015). Consumer adoption of internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing, 20(2), 145–157. https://doi.org/10.1057/fsm.2015.5.
Alasmari, T., & Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: An extended framework and a mixed-method study. Education and Information Technologies, 24, 1–18. https://doi.org/10.1007/s10639-019-09865-8.
Al-Gahtani, S. S. (2016). Empirical investigation of E-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27–50. https://doi.org/10.1016/j.aci.2014.09.001.
Alquraini, H., Alhashem, A. M., Shah, M., & Chowdhury, R. (2007). Factors influencing nurses’ attitudes towards the use of computerized health information systems in Kuwaiti hospitals. Journal of Advanced Nursing, 57(4), 375–381. https://doi.org/10.1111/j.1365-2648.2007.04113.x.
Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human Behavior, 52, 65–71. https://doi.org/10.1016/j.chb.2015.05.046.
American Chemical Society. (2016). Guidelines for chemical laboratory safety in academic institutions. Washington: American Chemical Society.
Anshari, M., & Almunawar, M. N. (2017). Smartphones usage in the classrooms: Learning aid or interference? Education and Information Technologies, 22, 3063–3079. https://doi.org/10.1007/s10639-017-9572-7.
Arenas-Gaita´n, J. O. R. G. E. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20, 1), 1–1),23.
Attuquayefio, S. N., & Addo, H. (2014). Using the UTAUT model to analyze students’ ICT adoption. International Journal of Education and Development Using Information and Communication Technology, 10(3), 75–86.
Briz-Ponce, L., & García-Peñalvo, F. J. (2015). An empirical assessment of a technology acceptance model for apps in medical education. Journal of Medical Systems, 39(176), 1–5. https://doi.org/10.1007/s10916-015-0352-x.
Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies e students’ behavior. Computers in Human Behavior, 72, 612–620. https://doi.org/10.1016/j.chb.2016.05.027.
Chu, T. H., & Chen, Y. Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92(1), 37–52. https://doi.org/10.1016/j.compedu.2015.09.013.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13, 319–340. https://doi.org/10.2307/249008.
Davis, F. D., & Venkatesh, V. (2004). Toward pre-prototype user acceptance testing of new information systems: Implications for software project management. IEEE Transactions on Engineering Management, 51(1), 31–46. https://doi.org/10.1109/TEM.2003.822468.
de Witt, C., & Gloerfeld, C. (2018). Mobile learning and higher education. In D. Kergel, B. Heidkamp, P. Telléus, T. Rachwal, & S. Nowakowski (Eds.), The digital turn in higher education (pp. 61–79). Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-19925-8_6.
El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the unified theory. Educational Technology Research and Development, 65(3), 743–763. https://doi.org/10.1007/s11423-016-9508-8.
Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. https://doi.org/10.2307/3151312.
Hair, J., Anderson, R., Tatham, R., & Black, C. (1995). Multivariate data analysis. 4th ed. USA. Upper Saddle River: Prentice-Hall, Inc..
Hair, J., Hult, G. T., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). California, Thousand Oaks: SAGE Publications.
Hsueh, W. D., Ben, J. P., & Moskowitz, H. S. (2017). An app to enhance resident education in otolaryngology. Laryngoscope, 128, 1–6. https://doi.org/10.1002/lary.27040.
Jakober, C. (2014). Laboratory safety manual. California: University of California, Davis, Environmental Health and Safety.
Kim, S. (2016). The study on the path of using educational applications-focusing on the technology acceptance model. Asian Journal of Information Technology, 15(22), 4678–4680. https://doi.org/10.3923/ajit.2016.4678.4680.
Kim, H., & Suh, E. E. (2018). The effects of an interactive nursing skills mobile application on nursing students’ knowledge, self-efficacy, and skills performance: A randomized controlled trial. Asian Nursing Research, 12, 17–25. https://doi.org/10.1016/j.anr.2018.01.001.
Kowitlawakul, Y., Chan, S. W. C., Pulcini, J., & Wang, W. (2014). Factors influencing nursing students’ acceptance of electronic health records for nursing education (EHRNE) software program. Nurse Education Today, 35(1), 189–194. https://doi.org/10.1016/j.nedt.2014.05.010.
Kumar, J. A., & Bervell, B. (2019). Google classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies, 24, 1–25. https://doi.org/10.1007/s10639-018-09858-z.
Lei, P. W., & Wu, Q. (2007). Introduction to structural equation modeling: Issues and practical considerations. Educational Measurement: Issues and Practice, 26, 33–43. https://doi.org/10.1111/j.1745-3992.2007.00099.x.
Lin, P. C., Lu, H. K., & Liu, S. C. (2013). Towards an education behavioral intention model for e-learning systems: An extension of UTAUT. Journal of Theoretical and Applied Information Technology, 47(3), 1120–1127.
Liu, R. F., Wang, F. Y., Yen, H., Sun, P. L., & Yang, C. H. (2018). A new mobile learning module using smartphone wallpapers in identification of medical fungi for medical students and residents. International Journal of Dermatology, 57, 1–5. https://doi.org/10.1111/ijd.13934.
Loraas, T., & Wolfe, C. (2006). Why wait? Modeling factors that influence that decision of when to learn a new use of technology. Journal of Information Systems, 20(2), 1–23. https://doi.org/10.2308/jis.2006.20.2.1.
Marchewka, J. T., & Kostiwa, K. (2007). An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA, 7(2), 93–104.
Merhi, M. I. (2015). Factors influencing higher education students to adopt podcast: An empirical study. Computers & Education, 83(2), 32–43. https://doi.org/10.1016/j.compedu.2014.12.014.
Mosunmola, A., Mayowa, A., Okuboyejo, S., and Adeniji, C. (2018). Adoption and use of mobile learning in higher education: The UTAUT model. In IC4E 2018, 20–25. San Diego, CA, USA. https://doi.org/10.1145/3183586.3183595.
Mpotos, N., Lemoyne, S., Calle, P. A., Deschepper, E., Valcke, M., & Monsieurs, K. G. (2011). Combining video instruction followed by voice feedback in a self-learning station for acquisition of basic life support skills: A randomised non-inferiority trial. Resuscitation, 82(7), 896–901. https://doi.org/10.1016/j.resuscitation.2011.02.024.
Nunnally, J. C. (1978). Psychometric theory. New York: McGraw Hill.
Olasina, G. (2018). Human and social factors affecting the decision of students to accept e-learning. Interactive Learning Environments, 27, 1–14. https://doi.org/10.1080/10494820.2018.1474233.
Raman, A., & Don, Y. (2013). Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model. Journal of Studies in International Education, 6(7), 157–160. https://doi.org/10.5539/ies.v6n7p157.
Rogers, E. M. (2003). Diffusion of innovations (4th ed.). New York: Free Pres.
Shen, C., Ho, J., Minh, P. T., & Kuo, T. (2018). Behavioural intentions of using virtual reality in learning: Perspectives of acceptance of information technology and learning style. Virtual Reality, 1–12. https://doi.org/10.1007/s10055-018-0348-1.
Statista. (2018). “Mobile phone users worldwide 2015-2020.” 2018. https://www.statista.com/statistics/274774/forecast-of-mobile-phone-users-worldwide/. Accessed 20 Sept 2018.
Thakre-Subhash, S., & Thakre-Bapurao, S. (2015). Perception of medical students for utility of mobile technology use in medical education. International Journal of Medicine and Public Health, 5(4), 305–311. https://doi.org/10.4103/2230-8598.165959.
Tosuntas, S. B., Karadag, B. E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the unified theory of acceptance and use of technology. Computers & Education, 81(2), 169–178. https://doi.org/10.1016/j.compedu.2014.10.009.
Uğur, N. G., & Turan, A. H. (2018). E-learning adoption of academicians: A proposal for an extended model. Behaviour & Information Technology, 37(4), 393–405. https://doi.org/10.1080/0144929X.2018.1437219.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal a theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926.
Venkatesh, V., Morris, M. G., Hall, M., Davis, G. B., Davis, F. D., & Walton, S. M. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540.
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 technology. Management Information Systems Quarterly, 36(1), 157–178.
Venkatesh, V., Thong, J. Y., & 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.
Vogel, A. L. (1954). A textbook of practical organic chemistry including Qualitive organic analysis (2nd ed.). London: Longman, Green and Co.
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. https://doi.org/10.1007/s12525-015-0214-x.
Yousafzai, A., Chang, V., Gani, A., & Noor, R. M. (2016). Multimedia augmented m-learning: Issues, trends and open challenges. International Journal of Information Management, 36(5), 784–792. https://doi.org/10.1016/j.ijinfomgt.2016.05.010.
Zubrick, J. W. (2016). The organic Chem lab survival manual: A Student’s guide to techniques. Hoboken, New Jersey: John Wiley & Sons.
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We are grateful to pharmacy students of KMU for their contributions to the study. To meet the ethical considerations, this research was approved by the Ethics Committee of KMU (IR.KMU.REC.1396.1938).
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Ameri, A., Khajouei, R., Ameri, A. et al. Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Educ Inf Technol 25, 419–435 (2020). https://doi.org/10.1007/s10639-019-09965-5
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DOI: https://doi.org/10.1007/s10639-019-09965-5