DOI: https://doi.org/10.14710/dijb.4.1.2021.8-19

Technology adoption on online learning during Covid-19 pandemic: implementation of technology acceptance model (TAM)

Universitas Islam Indonesia, Indonesia

Open Access Copyright 2021 Diponegoro International Journal of Business under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract
Individual behavior in technology adoption can be predicted by perception and belief. Implementing TAM, this study aims to analyze the behavioral teaching staff in using Google Apps for online learning. This study examined the effect of perceived usefulness, perceived ease of use, and user training and support on behavioral intention to use technology. We analyzed 108 samples using SPSS Process Model 4 and Model 15 to examine the mediation effect and moderated mediation effect. Our results show that perceived usefulness is confirmed to mediate the perceived ease of use behavioral intention to use technology. However, moderated mediation effect is not supported. This study contributed to advance the empirical evidence on the implications of TAM in analyzing technology adoption.
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Keywords: technology adoption; technology acceptance model; perceived usefulness; perceived ease of use; online learning
Funding: Diploma in Management Program, Department of Management, Faculty of Business and Economics, Universitas Islam Indonesia

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  1. Ajzen, Icek. (1985). From intentions to actions: A theory of planned behavior. Action Control, 11–39
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  3. Al-Suqri, M. N. (2014). Perceived usefulness, perceived ease-of-use and faculty acceptance of electronic books: An empirical investigation of Sultan Qaboos University, Oman. Library Review, 63(4–5), 276–294
  4. Awuah, L. (2015). Supporting 21st-Century teaching and learning: The role of Google Apps for Education. Journal of Instructional Research, 4(2015), 12–22
  5. Basri, H., Hashim, H., & Yunus, M. (2019). Using Google Apps as learning strategy to enhance ESL writing. Creative Education, 10(2008), 2649–2657
  6. Beldarrain, Y. (2006). Distance education trends: Integrating new technologies to foster student interaction and collaboration. Distance Education, 27(2), 139–153
  7. Chen, Ko, H.-C., Kinshuk, & Lin, T. (2005). A model for synchronous learning using the Internet. Innovation in Education and Teaching International, 42(2), 181–194
  8. Chu, T. H., & Robey, D. (2008). Explaining changes in learning and work practice following the adoption of online learning: A human agency perspective. European Journal of Information Systems, 17(1), 79–98
  9. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339
  10. 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
  11. Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human Computer Studies, 45(1), 19–45
  12. Dhawan, S. (2020). Online Learning: A Panacea in the Time of COVID-19 Crisis. Journal of Educational Technology Systems, 49(1), 5–22
  13. Doll, J., & Ajzen, I. (1992). Accessibility and stability of predictors in the theory of planned behavior. Journal of Personality and Social Psychology, 63(5), 754–765
  14. Fagan, M. H., Neill, S., & Wooldridge, B. R. (2008). Exploring the intention to use computers: An empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. Journal of Computer Information Systems, 48(3), 31–37
  15. Grunberg, L., Moore, S., Greenberg, E. S., & Sikora, P. (2008). The changing workplace and its effects: A longitudinal examination of employee responses at a large company. Journal of Applied Behavioral Science, 44(2), 215–236
  16. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis. In Neuromarketing in India: Understanding the Indian Consumer (Seventh). New Jersey: Pearson
  17. Hayes, A. F. (2018). Introduction mediation, moderation, and conditional process analysis: A regression-based approach. The Guildford Press
  18. Hubbard, P. (2013). Making a case for learner training in technology enhanced language learning environments. CALICO Journal, 30(2), 163–178
  19. Hussain, A., Mkpojiogu, E. O. C., & Yusof, M. M. (2016). Perceived usefulness, perceived ease of use, and perceived enjoyment as drivers for the user acceptance of interactive mobile maps. AIP Conference Proceedings
  20. Jahangir, N., & Begum, N. (2007). Effect of perceived usefulness, ease of use, security and privacy on customer attitude and adaptation in the context of E-Banking. Journal of Management Research, 7(3), 147–157
  21. Kanchanatanee, K., Suwanno, N., & Jarernvongrayab, A. (2014). Effects of attitude toward using, perceived usefulness, perceived ease of use and perceived compatibility on intention to use e-marketing. Journal of Management Research, 6(3), 1–13
  22. Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management, 35, 237–250
  23. Keengwee, J., Kidd, T., & Kyei-Blankson, L. (2009). Faculty and technology: Implications for faculty training and technology leadership. Journal of Science Education and Technology, 18(1), 2008–2010
  24. Kuo, Y., & Yen, S. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103–110
  25. Kusumah, E. P. (2018). Technology acceptance model (TAM) of statistical package for the social sciences (SPSS) applications. Integrated Journal of Business and Economics, 2(1), 1–11
  26. Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM - Journal of Information Systems and Technology Management, 14(1), 21–38
  27. Leong, L., Ooi, K., & Lin, B. (2011). Influence of individual characteristics , perceived usefulness and ease of use on mobile entertainment adoption. International Journal of Mobile Communications, 9(4), 359–382
  28. Ni, A. Y. (2013). Comparing the effectiveness of classroom and online learning: Teaching research methods. Journal of Public Affairs Education, 19(2), 199–215
  29. Ramayah, T., & Lo, M.-C. (2007). Impact of shared beliefs on “perceived usefulness” and “ease of use” in the implementation of an enterprise resource planning system. Management Research News, 30(6), 420–431
  30. Ramdhani, N. (2016). Penyusunan alat pengukur berbasis theory of planned behavior. Buletin Psikologi, 19(2), 55–69
  31. Smith, T. J. (2008). Senior citizens and e-commerce websites: The role of perceived usefulness, perceived ease of use, and web site usability. Informing Science: The International Journal of an Emerging Transdiscipline, 11, 59–83
  32. Sugar, W., Crawley, F., & Fine, B. (2004). Examining teachers’ decisions to adopt new technology. Journal of Educational Technology & Society, 7(4), 201–213
  33. Suki, N. M., & Suki, N. M. (2011). Exploring The Relationship Between Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, Attitude and Subscribers’ Intention Towards Using 3G Mobile Services. Journal of Information Technology Management, XXII(1), 1–7
  34. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly: Management Information Systems, 15(1), 125–142
  35. To, P. L., Liao, C., Chiang, J. C., Shih, M. L., & Chang, C. Y. (2008). An empirical investigation of the factors affecting the adoption of Instant Messaging in organizations. Computer Standards and Interfaces, 30(3), 148–156
  36. Tung, F., & Chang, S. (2008). Nursing students’ behavioral intention to use online courses: A questionnaire survey. International Journal of Nursing Studies, 45(18), 1299–1309
  37. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315
  38. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204
  39. 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
  40. Vijayasarathy, L. R. (2004). Predicting consumer intentions to use online shopping: The case for an augmented technology acceptance model. Information and Management, 41(6), 747–762

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