Integrate the adoption and readiness of digital technologies amongst accounting professionals towards the fourth industrial revolution

References

• Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 62 (2020) , 101293. https://doi.org/10.1016/j.techsoc.2020.101293
   Google Scholar
• Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50 (2015) , 418–14. https://doi.org/10.1016/j.chb.2015.04.024
   Google Scholar
• Chang, C. C., Yan, C. F., & Tseng, J. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809–826. https://doi.org/10.14742/ajet.818
   Web of Science ®Google Scholar
• Chang, Y. Z., Yu, C. W., Chao, C. M., & Lin, F. C. (2020). Influences on medical app adoption by patients: The unified theory of acceptance and use of technology model and the moderating effects of technology readiness. The Social Science Journal, 1–14. https://doi.org/10.1080/03623319.2020.1848338
   Web of Science ®Google Scholar
• Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly 22 (1) , vii–xvi https://jstor.org/stable/249674.
   Web of Science ®Google Scholar
• Chiu, W., & Cho, H. (2020). The role of technology readiness in individuals’ intention to use health and fitness applications: A comparison between users and non-users. Asia Pacific Journal of Marketing and Logistics, 33(3), 807–825. https://doi.org/10.1108/APJML-09-2019-0534
   Web of Science ®Google Scholar
• Cruz-Cárdenas, J., Zabelina, E., Deyneka, O., Guadalupe-Lanas, J., & Velín-Fárez, M. (2019). Role of demographic factors, attitudes toward technology, and cultural values in the prediction of technology-based consumer behaviors: A study in developing and emerging countries. Technological Forecasting and Social Change, 149 (2019) , 119768. https://doi.org/10.1016/j.techfore.2019.119768
   Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1006/ijhc.1996.0040
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M 2nd Ed. . (2017). A primer on partial least squares structural equation modeling SAGE Publication. .
   Google Scholar
• Hair, J. F.,sJr, Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review 26 (2) 106–121 https://doi.org/10.1108/EBR-10-2013-0128. .
   Google Scholar
• Hart, L. (2017). How Industry 4.0 will change accounting. here’s what CPAS need to know about the next industrial revolution. Journal of Accountancy. AICPA https://www.journalofaccountancy.com/newsletters/2017/sep/industry-4-0-change-accounting.html
   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
   Web of Science ®Google Scholar
• Howard, R., Restrepo, L., & Chang, C. Y. (2017). Addressing individual perceptions: An application of the unified theory of acceptance and use of technology to building information modelling. International Journal of Project Management, 35(2), 107–120. https://doi.org/10.1016/j.ijproman.2016.10.012
   Web of Science ®Google Scholar
• Jin, C. H. (2020). Predicting the use of brand application based on a tram. International Journal of Human–Computer Interaction, 36(2), 156–171. https://doi.org/10.1080/10447318.2019.1609227
   Web of Science ®Google Scholar
• Kaushik, M. K., & Agrawal, D. (2021). Influence of technology readiness in adoption of e-learning. International Journal of Educational Management, 35(2), 483–495 https://doi.org/10.1108/IJEM-04-2020-0216.
   Web of Science ®Google Scholar
• Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and useof technology and perceived risk application. International Journal of Information Management, 34(1), 1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
   Web of Science ®Google Scholar
• Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal Of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
   Google Scholar
• Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research 18 (1) , 59–74. https://doi.org/10.1177/1094670514539730
   Web of Science ®Google Scholar
• Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal Of Educational Technology, 43(4), 592–605. https://doi.org/10.1111/j.1467-8535.2011.01229.x
   Web of Science ®Google Scholar
• Qasem, Z. (2021). The effect of positive TRI traits on centennials adoption of try-on technology in the context of E-fashion retailing. International Journal of Information Management, 56 (2021) , 102254. https://doi.org/10.1016/j.ijinfomgt.2020.102254
   PubMedGoogle Scholar
• Rahi, S., Mansour, M. M. O., Alghizzawi, M., & Alnaser, F. M. (2019). Integration of UTAUT model in internet banking adoption context: The mediating role of performance expectancy and effort expectancy. Journal of Research in Interactive Marketing, 13(3), 411–435. https://doi.org/10.1108/JRIM-02-2018-0032
   Web of Science ®Google Scholar
• Reyes-Mercado, P., & Barajas-Portas, K. (2020). Analysis of the usage intensity of digital advertising platforms by SMEs using an integrated models. Journal of Business-to-Business Marketing, 27(4), 407–417. https://doi.org/10.1080/1051712X.2020.1831215
   Web of Science ®Google Scholar
• Shibl, R., Lawley, M., & Debuse, J. (2013). Factors influencing decision support system acceptance. Decision Support Systems, 54(2), 953–961. https://doi.org/10.1016/j.dss.2012.09.018
   Web of Science ®Google Scholar
• Sinha, A., Kumar, P., Rana, N. P., Islam, R., & Dwivedi, Y. K. (2019). Impact of internet of things (IoT) in disaster management: A task-technology fit perspective. Annals of Operations Research, 283(1), 759–794. https://doi.org/10.1007/s10479-017-2658-1
   Web of Science ®Google Scholar
• Succi, M. J., & Walter, Z. D. (1999). Theory of user acceptance of information technologies: An examination of health care professionals. Proceedings of the 32nd Annual Hawaii international conference on systems sciences.IEEE. 7, https://doi.org/10.1109/HICSS.1999.773013
   Google Scholar
• Sun, S., Lee, P. C., Law, R., & Hyun, S. S. (2020). An investigation of the moderating effects of current job position level and hotel work experience between technology readiness and technology acceptance. International Journal of Hospitality Management, 90 (2020) , 102633. https://doi.org/10.1016/j.ijhm.2020.102633
   Google Scholar
• Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64(2), 53–78. https://doi.org/10.1016/j.ijhcs.2005.04.013
   Web of Science ®Google Scholar
• Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb01822.x
   Web of Science ®Google Scholar
• 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. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J. Y., & 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/10.2307/41410412
   Web of Science ®Google Scholar
• Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information and Management, 44(2), 206–215. https://doi.org/10.1016/j.im.2006.12.005
   Web of Science ®Google Scholar
• Wang, H., Tao, D., Yu, N., & Qu, X. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International Journal of Medical Informatics, 139 (2020) , 104156. https://doi.org/10.1016/j.ijmedinf.2020.104156
   PubMedGoogle Scholar
• Warden, C. A., Yi-Shun, W., Stanworth, J. O., & Chen, J. F. (2022). Millennials’ technology readiness and self-efficacy in online classes. Innovations in Education and Teaching International, 59(2), 226–236. https://doi.org/10.1080/14703297.2020.1798269
   Web of Science ®Google Scholar
• Wiese, M., & Humbani, M. (2020). Exploring technology readiness for mobile payment app users. The International Review of Retail, Distribution and Consumer Research, 30(2), 123–142. https://doi.org/10.1080/09593969.2019.1626260
   Web of Science ®Google Scholar
• Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102. https://doi.org/10.1287/isre.1050.0042
   Web of Science ®Google Scholar
• Zuiderwijk, A., Janssen, M., & Dwivedi, Y. K. (2015). Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly, 32(4), 429–440. https://doi.org/10.1016/j.giq.2015.09.005
   Web of Science ®Google Scholar