Exploring factors affecting academics’ adoption of emerging mobile technologies-an extended UTAUT perspective

Abstract

With the proliferation of technology and the Internet, the way education is delivered has undergone a rapid change in different educational settings. Whilst a large amount of research has investigated the implementation of mobile technologies in education, there is still a paucity of research from a teaching perspective across disciplines within higher education. For this reason, this study investigated the acceptance, preparedness and adoption of mobile technologies by academic faculties within higher education, using the context of China. Underpinned by the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) Model, a large-scale quantitative survey investigated the factors affecting academics’ behavioural intentions and use for mobile technologies, and variations between different demographic groups. Findings suggested that the most significant factors affecting academics’ behavioural intention and behaviours of use were their performance expectancy, facilitating conditions, hedonic motivation and habit. Behavioural intention also affected how the faculty staff used their mobile technologies. Moreover, gender, age, teaching experience and discipline were found to be moderating factors. This research provides further verification of the effectiveness of the UTAUT2 Model in the higher education context and the field of new technologies implementation. Findings from this study provide beneficial insights for universities, faculties, and academics in policymaking, faculty management, professional development and lecturer instruction concerning mobile technologies.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. Abdul Rabu, S. N., Hussin, H., & Bervell, B. (2018). QR code utilization in a large classroom: Higher education students’ initial perceptions. Education and Information Technologies, 1–26. https://doi.org/10.1007/s10639-018-9779-2.

  2. Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314. https://doi.org/10.1016/J.CHB.2013.10.035.

    Article  Google Scholar 

  3. 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.

    Article  Google Scholar 

  4. Alshare, K. A., El-Masri, M., & Lane, P. L. (2015). The determinants of student effort at learning ERP: A cultural perspective. Journal of Information Systems Education, 26(2), 117–133.

    Google Scholar 

  5. Alwahaishi, S., & Snásel, V. (2013). Consumers’ acceptance and use of information and communications technology: A UTAUT and flow based theoretical model. Journal of Technology Management & Innovation, 8(2), 61–73. https://doi.org/10.4067/S0718-27242013000200005.

    Article  Google Scholar 

  6. Arteaga Sánchez, R., Cortijo, V., & Javed, U. (2014). Students’ perceptions of Facebook for academic purposes. Computers & Education, 70, 138–149. https://doi.org/10.1016/J.COMPEDU.2013.08.012.

    Article  Google Scholar 

  7. Berger, H., & Symonds, J. (2016). Adoption of bring your own device in HE and FE instituions. In Proceedings of the 11th International Knowledge Management in Organization Conference on The Changing Face of Knowledge Management Impacting Society, 1–6. New York, NY: ACM.

    Google Scholar 

  8. Bidin, S., & Ziden, A. A. (2013). Adoption and application of mobile learning in the education industry. Procedia - Social and Behavioral Sciences, 90(InCULT 2012), 720–729. https://doi.org/10.1016/j.sbspro.2013.07.145.

  9. Cheng, L. (2016). A study of Chinese engineering students’ communication strategies in a mobile-assisted professional development course. The Eurocall Review, 24(2), 24–31.

    Article  Google Scholar 

  10. Cheng, S., Xiong, Z., & Xiang, G. (2016). On the theoretical framework of acceptance of mobile learning and its influence factors. Heilongjiang Researches on Higher Education, 4, 5–8.

    Google Scholar 

  11. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217. https://doi.org/10.1287/isre.14.2.189.16018.

    Article  Google Scholar 

  12. Cochrane, T. (2013). M-learning as a catalyst for pedagogical change. In Z. L. Berge & L. Y. Muilenburg (Eds.), Handbook of mobile learning (pp. 247–258). New York, NY: Routledge.

  13. Dai, L., & Tang, Z. (2012). The design and research of teacher learning community in mobile environment. Modern Educational Technology, 22(10), 90–93. https://doi.org/10.3969/j.issn.1009-8097.2013.12.010.

    Article  Google Scholar 

  14. Domingo, M. G., & Garganté, A. B. (2016). Exploring the use of educational technology in primary education: Teachers’ perception of mobile technology learning impacts and applications’ use in the classroom. Computers in Human Behavior, 56, 21–28. https://doi.org/10.1016/J.CHB.2015.11.023.

    Article  Google Scholar 

  15. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 19(3), 1–16. https://doi.org/10.1007/s10796-017-9774-y.

    Article  Google Scholar 

  16. El-Masri, M., & Tarhini, A. (2017). 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, 65(3), 743–763. https://doi.org/10.1007/s11423-016-9508-8.

    Article  Google Scholar 

  17. Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system (LCS) in executive business studies: Extending UTAUT2. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-06-2016-0015.

  18. Ferreira, J. B., Klein, A. Z., Freitas, A., & Schlemmer, E. (2013). Mobile learning: Definition, uses and challenges. In L. A. Wankel & and P. Blessinger (Eds.), Increasing Student Engagement and Retention Using Mobile Applications : Smartphones, Skype and Texting Technologies (pp. 47–82). https://doi.org/10.1108/S2044-9968(2013)000006D005.

  19. Fetaji, B., Ebibi, M., & Fetaji, M. (2011). Assessing effectiveness in Mobile learning by devising MLUAT (Mobile learning usability attribute Testin ) methodology. International Journal of Computers and Communications, 3(5), 178–187.

    Google Scholar 

  20. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  21. Gaitán, J. A., & Peral, P. B. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet, 20(1), 1–23 Retrieved from https://idus.us.es/xmlui/handle/11441/57220.

    Google Scholar 

  22. Gerhart, N., Peak, D. A., & Prybutok, V. R. (2015). Searching for new answers: The application of task-technology fit to e-textbook usage. Decision Sciences Journal of Innovative Education, 13(1), 91–111. https://doi.org/10.1111/dsji.12056.

    Article  Google Scholar 

  23. Göğüş, A., Nistor, N., & Lerche, T. (2012). Educational technology acceptance across cultures : A validation of the unified theory of acceptance and use of technology in the context of Turkish national culture. The Turkish Online Journal of Educational Technology, 11(4), 394–408.

    Google Scholar 

  24. Goh, W. W., Tang, S. F., & Lim, C. L. (2016). Assessing factors affecting students’ acceptance and usage of X-space based on UTAUT2 model. In S. F. Tang & L. Logonnathan (Eds.), Assessment for Learning Within and Beyond the Classroom (pp. 61–70). https://doi.org/10.1007/978-981-10-0908-2_6.

  25. Gumusoglu, E. K., & Akay, E. (2017). Measuring technology acceptance level of teachers by using unified theory of acceptance and use of technology. International Journal of Languages’ Education and Teaching, 5(4), 378–394. https://doi.org/10.18298/ijlet.1748.

    Article  Google Scholar 

  26. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202.

    Article  Google Scholar 

  27. Hair Jr., J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: Sage.

    MATH  Google Scholar 

  28. 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 

  29. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., et al. (2014). Common beliefs and reality about PLS: Comment on Ronkko and Evermann (2013). Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928.

    Article  Google Scholar 

  30. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(2009), 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014.

    Article  Google Scholar 

  31. Hong, W., Thong, J. Y. L., Chasalow, L. C., & Dhillon, G. (2011). User acceptance of agileinformation systems: A model and empirical test. Journal of Management Information Systems, 28(1), 235–272. https://doi.org/10.2753/MIS0742-1222280108.

    Article  Google Scholar 

  32. Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424.

    Article  Google Scholar 

  33. Huang, Q., & Ou, P. (2018). Influencing factors of mobile learning resources acceptance and use. Information of Chinese Education, 5, 52–58.

    Google Scholar 

  34. Janssen, K. C., & Phillipson, S. (2015). Are we ready for BYOD? A systematic review of the implementation and communication of BYOD programs in Australian schools. Australian Educational Computing, 30(2).

  35. Jung, I., & Lee, Y. (2015). YouTube acceptance by university educators and students: A cross-cultural perspective. Innovations in Education and Teaching International, 52(3), 243–253. https://doi.org/10.1080/14703297.2013.805986.

    Article  Google Scholar 

  36. Kabakçi Yurdakul, I., Ursavaş, Ö. F., & Becit İşçitürk, G. (2014). An integrated approach for preservice teachers’ acceptance and use of technology: UTAUT-PST scale. Eurasian Journal of Educational Research, 55, 21–36. https://doi.org/10.14689/ejer.2014.55.2.

    Article  Google Scholar 

  37. Kang, M., Liew, B. Y. T., Lim, H., Jang, J., & Lee, S. (2015). Investigating the determinants of mobile learning acceptance in Korea using UTAUT2. In G. Chen, V. Kumar, Kinshuk, R. Huang, & S. C. Kong (Eds.), Emerging issue in smart learning (pp. 209–216). https://doi.org/10.1007/978-3-662-44188-6_29.

  38. Lai, K.-W., & Smith, L. (2018). Socio-demographic factors relating to perception and use of mobile technologies in tertiary teaching. British Journal of Educational Technology, 49(3), 492–504. https://doi.org/10.1111/bjet.12544.

    Article  Google Scholar 

  39. Lee, J., Chung, H., Moon, J., & Yoo, Y. (2015). Exploring pre-service teachers’ acceptance of smart learning. In G. Chen, V. Kumar, Kinshuk, R. Huang, & S. C. Kong (Eds.), Emerging issue in smart learning (pp. 175–181). Heidelberg: Berlin: Springer.

  40. Lewis, C. C., Fretwell, C. E., Ryan, J., & Parham, J. B. (2013). 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, 2(2), 22–34. https://doi.org/10.5430/ijhe.v2n2p22.

    Article  Google Scholar 

  41. Li, X., & Song, S. (2018). Mobile technology affordance and its social implications: A case of “Rain Classroom.” British Journal of Educational Technology, 49(2), 276–291. https://doi.org/10.1111/bjet.12586.

  42. Liaw, S. S., Hatala, M., & Huang, H. M. (2010). Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach. Computers and Education, 54, 446–454. https://doi.org/10.1016/j.compedu.2009.08.029.

    Article  Google Scholar 

  43. Lin, S., Zimmer, J. C., & Lee, V. (2013). Podcasting acceptance on campus: The differing perspectives of teachers and students. Computers and Education, 68, 416–428. https://doi.org/10.1016/j.compedu.2013.06.003.

    Article  Google Scholar 

  44. Liu, G., & Wu, F. (2011). Analysis of influence factors on acceptance of mobile learning for college students in WenZhou-empirical research based on the extended technology acceptance model. Modern Educational Technology, 21(6), 109–114. https://doi.org/10.3969/j.issn.1009-8097.2013.02.001.

    Article  Google Scholar 

  45. Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Berlin, Heidelberg: Springer-Verlag.

    Book  Google Scholar 

  46. Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education + Training, 57(7), 738–756. https://doi.org/10.1108/ET-02-2014-0014.

    Article  Google Scholar 

  47. Magsamen-Conrad, K., Upadhyaya, S., Joa, C. Y., & Dowd, J. (2015). Bridging the divide: Using UTAUT to predict multigenerational tablet adoption practices. Computers in Human Behavior, 50, 186–196. https://doi.org/10.1016/j.chb.2015.03.032.

    Article  Google Scholar 

  48. McQuiggan, S., Kosturko, L., McQuinggan, J., & Sabourin, J. (2015). Mobile learning: A handbook for developers, educators, and learners. Hoboken, NJ: John Wiley & Sons.

    Google Scholar 

  49. Mtebe, J. S., Mbwilo, B., & Kissaka, M. M. (2016). Factors influencing teachers’ use of multimedia enhanced content in secondary schools in Tanzania. The International Review of Research in Open and Distance Learning, 17(2), 65–84. https://doi.org/10.19173/irrodl.v17i2.2280.

    Article  Google Scholar 

  50. Nair, P. K., Ali, F., & Leong, L. C. (2015). Factors affecting acceptance & use of ReWIND: Validating the extended unified theory of acceptance and use of technology. Interactive Technology and Smart Education, 12(3), 183–201 https://doi.org/ITSE-02-2015-0001.

    Article  Google Scholar 

  51. Ooms, A., Linsey, T., Webb, M., & Panayiotidis, A. (2008). The in-classroom use of mobile technologies to support diagnostic and formative assessment and feedback, 7th London international scholarship of teaching and learning conference. London: UK.

    Google Scholar 

  52. Pachler, N., Bachmair, B., & Cook, J. (2010). Mobile learning: Structure, agency, practice. https://doi.org/10.1007/978-1-4419-0585-7.

    Book  Google Scholar 

  53. Palalas, A. (2013). Blended mobile learning: Expanding learning spaces with mobile technologies. In A. Tsinakos & M. Ally (Eds.), Global mobile learning implementations and trends (pp. 86–104). Beijing, China: China Central Radio & TV University Press.

    Google Scholar 

  54. Pan, S. (2014). An innovative professional development model. Beijing, China: Tsinghua University Press.

    Google Scholar 

  55. Pilgrim, J., Bledsoe, C., & Reily, S. (2012). New technologies in tlie ciassroom. The Delta Kappa Gamma Bulletin, 78(4), 16–22. https://doi.org/10.1210/endo-110-5-1607.

    Article  Google Scholar 

  56. Qin, C. (2014). Factors affecting M-learning performance of university teachers: A narrative case study. XDJYJS, 24(11), 39–46.

    Google Scholar 

  57. Qu, L., Zhou, Y., Zhao, X., & Zhang, Z. (2018). Research on the effect factors of college students’ MOOC learning. Journal of Hubei University of Education, 35(8), 47–57.

    Google Scholar 

  58. Radovan, M., & Kristl, N. (2017). Acceptance of technology and its impact on teacher’s activities in virtual classroom: Integrating UTAUT and CoI into a combined model. Turkish Online Journal of Educational Technology, 16(3), 11–22.

    Google Scholar 

  59. Raman, A., & Don, Y. (2013). Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157–164. https://doi.org/10.5539/ies.v6n7p157.

    Article  Google Scholar 

  60. Song, Y., & Kong, S. C. (2017). Affordances and constraints of BYOD (bring your own device) for learning and teaching in higher education: Teachers’ perspectives. The Internet and Higher Education, 32, 39–46. https://doi.org/10.1016/j.iheduc.2016.08.004.

    Article  Google Scholar 

  61. Spector, J. M., & Seung, W. P. (2018). Motivation, learning, and technology: Embodied educational motivation. New York, NY: Routledge.

    Google Scholar 

  62. Stevenson, M. E., & Hedberg, J. G. (2017). Mobilizing learning: A thematic review of apps in K-12 and higher education. Interactive Technology and Smart Education, 14(2), 126–137. https://doi.org/10.1108/ITSE-02-2017-0017.

    Article  Google Scholar 

  63. Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre- and post-adopters. Computers in Human Behavior, 64, 602–620. https://doi.org/10.1016/j.chb.2016.07.037.

    Article  Google Scholar 

  64. Sun, Z., Liu, R., Luo, L., Wu, M., & Shi, C. (2017). Exploring collaborative learning effect in blended learning environments. Journal of Computer Assisted Learning, 33(6), 575–587. https://doi.org/10.1111/jcal.12201.

    Article  Google Scholar 

  65. Traxler, J., & Kukulska-Hulme, A. (2016). Conclusion: Contextual challenges for the next generation. In J. Traxler & A. Kukulska-Hulme (Eds.), Mobile learning: The next generation (pp. 208–226). New York, NY: Routledge.

    Google Scholar 

  66. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Article  Google Scholar 

  67. 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. MIS Quarterly, 36(1), 157–178. https://doi.org/10.1111/j.1540-4560.1981.tb02627.x.

  68. 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/10.1080/1097198X.2010.10856507.

    Article  Google Scholar 

  69. Wang, J. (2015). Factors affecting higer education students’ adoption of mobile learning. Distance Education in China, 1, 5–17. https://doi.org/10.13541/j.cnki.chinade.2015.09.006.

    Article  Google Scholar 

  70. Wang, P., & Ryu, H. (2009). Not SMS, but mobile quizzes: Designing a mobile learning application for university students. International Journal of Mobile and Organization, 3(4), 351–365.

    Google Scholar 

  71. Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers and Education, 53(3), 761–774. https://doi.org/10.1016/j.compedu.2009.02.021.

    Article  Google Scholar 

  72. Welsh, K. E., Mauchline, A. L., Powell, V., France, D., Park, J. R., & Whalley, B. W. (2015). Student perceptions of iPads as mobile learning devices for fieldwork. Journal of Geography in Higher Education, 39(3), 450–469.

    Article  Google Scholar 

  73. Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488. https://doi.org/10.1108/JEIM-09-2014-0088.

    Article  Google Scholar 

  74. Xu, L., Lin, J., & Chan, H. C. (2012). The moderating effects of utilitarian and hedonic values on information technology continuance. ACM Transactions on Computer-Human Interaction, 19(2), 1–26. https://doi.org/10.1145/2240156.2240160.

    Article  Google Scholar 

  75. Yang, S. (2013). Understanding undergraduate students’ adoption of mobile learning model : A perspective of the extended UTAUT2. Journal of Convergence Information Technology (JCIT), 8(10), 969–979. https://doi.org/10.4156/jcit.vol8.issue10.118.

    Article  Google Scholar 

  76. Yin, C., Song, Y., Tabata, Y., Ogata, H., & Hwang, G.-J. (2013). Developing and implementing a framework of participatory simulation for mobile learning using scaffolding. Educational Technology & Society, 16(3), 137–150.

    Google Scholar 

  77. You, J., Sun, Z., & Song, W. (2014). The acceptance of digital teaching material and analysis of teachers’ TPACK. Electronic Education Research, 11, 102–108. https://doi.org/10.13811/j.cnki.eer.2014.11.016.

    Article  Google Scholar 

  78. Zhao, G., Pan, J., Yang, L., Li, X., & Ma, L. (2015). Research on the influencing factors of college students MOOC study willingness based on the UTAUT model. JIangsu Science & Technology Information, 25, 18–20.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sailong Hu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hu, S., Laxman, K. & Lee, K. Exploring factors affecting academics’ adoption of emerging mobile technologies-an extended UTAUT perspective. Educ Inf Technol 25, 4615–4635 (2020). https://doi.org/10.1007/s10639-020-10171-x

Download citation

Keywords

  • Academics
  • Adoption
  • Mobile technologies
  • Extended UTAUT model