Open Access
E3S Web of Conf.
Volume 388, 2023
The 4th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2022)
Article Number 03026
Number of page(s) 9
Section E-Business Sustainability
Published online 17 May 2023
  1. E. C. Fleming, J. Robert, J. Sparrow, J. Wee, P. Dudas, and M. J. Slattery, A digital fluency framework to support 21st-century skills, Chang. Mag. High. Learn., 53, 2, pp. 41–48 (2021). [CrossRef] [Google Scholar]
  2. A. Susanto, The evolution of accounting information systems, Int. J. Sci. Technol. Res., 8, 7 (2019). [Google Scholar]
  3. M. Ghasemi, V. Shafeiepour, M. Aslani, and E. Barvayeh, The impact of information technology (it) on modern accounting systems, Procedia - Soc. Behav. Sci., 28, pp. 112–116 (2011). [CrossRef] [Google Scholar]
  4. T. Tse and K. Soufani, Business strategies for small firms in the new economy, J. Small Bus. Enterp. Dev., 10, 3, pp. 306–320 (2003). [CrossRef] [Google Scholar]
  5. S. Bhavna and P. Patel, Effects of accounting information system on organizational profitability, Int. J. Res. Anal. Rev., 2, 1, pp. 168–174 (2015). [Google Scholar]
  6. L. Marushchak, O. Pavlykivska, G. Liakhovych, O. Vakun, and N. Shveda, Accounting software in modern business., Adv. Sci. Technol. Eng. Syst., 6, 1, pp. 862–870 (2021). [CrossRef] [Google Scholar]
  7. D. Yau-Yeung, O. Yigitbasioglu, and P. Green, Cloud accounting risks and mitigation strategies: evidence from Australia, Account. Forum, 44, 4, pp. 421–446 (2020). [CrossRef] [Google Scholar]
  8. T. Khanom, Cloud Accounting: A Theoretical Overview, IOSR J. Bus. Manag., 19, 6, pp. 31–38 (2017). [CrossRef] [Google Scholar]
  9. P. M. M. and T. G, SP 800-145, The NIST Definition of Cloud Computing. Technical Report, Natl. Inst. Stand. Technol. (2011). [Google Scholar]
  10. M. Talukder, Factors affecting the adoption of technological innovation by individual employees: An Australian study, Procedia - Soc. Behav. Sci., 40, pp. 52–57 (2012). [CrossRef] [Google Scholar]
  11. G. Tornatzky and M. Fleischer, The Processes of Technological Innovation. Lexington: Lexington Books (1990). [Google Scholar]
  12. A. Katebi, P. Homami, and M. Najmeddin, Acceptance model of precast concrete components in building construction based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) framework, J. Build. Eng., 45 (2022). [Google Scholar]
  13. V. Venkatesh, R. H. Smith, M. G. Morris, G. B. Davis, F. D. Davis, and S. M. Walton, User acceptance of information technology: towards a unified view, MIS Quarterly, 27, 3, pp. 425–478 (2003). [Google Scholar]
  14. F. D. Davis, Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Q., 13, 3, pp. 319 (1989). [CrossRef] [Google Scholar]
  15. M. Talukder and A. Quazi, The impact of social influence on individuals’ adoption of innovation, J. Organ. Comput. Electron. Commer., 21, 2, pp. 111–135 (2011). [CrossRef] [Google Scholar]
  16. K. Al-Saedi, M. Al-Emran, T. Ramayah, and E. Abusham, Developing a general extended UTAUT model for M-payment adoption, Technol. Soc., 62 (2020). [Google Scholar]
  17. D. Buckingham, Defining digital literacy – What do young people need to know about digital media?, Nord. J. Digit. Lit., 1, 4, pp. 263–277 (2006). [CrossRef] [Google Scholar]
  18. Q. Wang, M. D. Myers, and D. Sundaram, Digital natives and digital immigrants, WIRTSCHAFTSINFORMATIK, 55, 6, pp. 409–420 (2012). [Google Scholar]
  19. C. Wei, A. H. Pitafi, S. Kanwal, A. Ali, and M. Ren, Improving employee agility using enterprise social media and digital fluency: moderated mediation model, IEEE Access, 8, pp. 68799–68810 (2020). [CrossRef] [Google Scholar]
  20. J. SB, The NMC Horizon Report: 2013 Higher Education Edition. Texas: New Media Consortium (2013). [Google Scholar]
  21. Y. Li, Y., A. H., Liu, X. Yang, and X. Wang, Will digital fluency influence social media use? an empirical study of WeChat users, DATA BASE Adv. Inf. Syst., 49, 4, pp. 30–45 (2018). [CrossRef] [Google Scholar]
  22. T. Tchoubar, T. R. Sexton, and L. L. Scarlatos, Role of digital fluency and spatial ability in student experience of online learning environments: Digital readiness for evolution of educational ecosystem, Adv. Intell. Syst. Comput., 2, pp. 251–264 (2018). [Google Scholar]
  23. V. Peñarroja, J. Sánchez, N. Gamero, V. Orengo, and A. M. Zornoza, The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice, Behav. Inf. Technol., 38, 8, pp. 845–857 (2019). [CrossRef] [Google Scholar]
  24. N. Agarwal, N. Pande, and V. Ahuja, Analysis of employee training needs in the information technology industry, Int. J. Indian Cult. Bus. Manag., 11, 3 (2015). [Google Scholar]
  25. Y. H. Al-Mamary and A. Shamsuddin, The impact of top management support, training, and perceived usefulness on technology acceptance, Mediterr. J. Soc. Sci., 6, 6 (2015). [Google Scholar]
  26. N. Dechow, M. Granlund, and J. Mouritsen, Management control of the complex organization: relationships between management accounting and information technology, Handbooks Manag. Account. Res., 2, pp. 625–640 (2006). [Google Scholar]
  27. X. Yang, Social influence or personal attitudes?: Understanding users’ social network sites continuance intention, Kybernetes, 48, 3, pp. 424–437 (2018). [Google Scholar]
  28. A. A. M. Nassar, K. Othman, and M. A. B. M. Nizah, The impact of the social influence on ICT adoption: behavioral intention as mediator and age as moderator, Int. J. Acad. Res. Bus. Soc. Sci., 9, 11 (2019). [Google Scholar]
  29. A. A. Taiwo and A. G. Downe, The theory of user acceptance and use of technology (UTAUT): a meta-analytic review of empirical findings, J. Theor. Appl. Inf. Technol., 49, 1, pp. 48–58 (2013). [Google Scholar]
  30. A. Bandura, Social Foundations of Thought and Action. New Jersey: Prentice Hall (1986). [Google Scholar]
  31. M. Fishbein and I. Ajzen, Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, Reading, Mass Addison-Wesley Pub. Co (1975). [Google Scholar]
  32. A. G. Ndekwa, E. N. Nfuka, and K. E. John, An analysis of behavioral intention toward actual usage of open source software among students in private high learning institution in tanzania, Int. J. Adv. Eng. Manag. Sci., 4, 3, pp. 175–181 (2018). [Google Scholar]
  33. M. Alharthi, Students’ attitudes toward the use of technology in online courses, Int. J. Technol. Educ., 3, 1 (2020). [Google Scholar]
  34. Y. T. Prasetyo et al., Determining factors affecting acceptance of e-learning platforms during the covid-19 pandemic: Integrating extended technology acceptance model and DeLone and McLean is success model, Sustain., 13, 15 (2021). [Google Scholar]
  35. V. Saprikis, G. Avlogiaris, and A. Katarachia, A comparative study of users versus non-users’ behavioral intention towards m-banking apps’ adoption, Inf., 13, 1 (2022). [Google Scholar]
  36. S. Dias-Trindade and A. G. Ferreira, Digital teaching skills: DigCompEdu CheckIn as an evolution process from literacy to digital fluency, Icono 14, 18, 2, pp. 162–187 (2020). [CrossRef] [Google Scholar]
  37. A. Chigona, Digital fluency: necessary competence for teaching and learning in connected classrooms, African J. Inf. Syst., 10 (2018). [Google Scholar]
  38. National Research Council, Being Fluent with Information Technology. Washington, DC: The National Academies Press (1999). [Google Scholar]
  39. L. R. Machado, T. P. F. Grande, P. A. Behar, and F. de M. R. Luna, Mapeamento de competências digitais: a inclusão social dos idosos, ETD - Educ. Temática Digit., 18, 4, pp. 903 (2016). [CrossRef] [Google Scholar]
  40. J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, When to use and how to report the results of PLS-SEM, Eur. Bus. Rev., 31, 1, pp. 2–24 (2019). [CrossRef] [Google Scholar]
  41. J. Henseler, C. M. Ringle, and M. Sarstedt, A new criterion for assessing discriminant validity in variance-based structural equation modeling, J. Acad. Mark. Sci., 43, pp. 115–135 (2015). [CrossRef] [Google Scholar]
  42. E. A. A. Ghaleb, P. D. D. Dominic, S. M. Fati, A. Muneer, and R. F. Ali, The assessment of big data adoption readiness with a technology– organization–environment framework: A perspective towards healthcare employees, Sustain., 13, 15 (2021). [Google Scholar]
  43. M. Igbaria, S. Parasuraman, and J. J. Baroudi, A motivational model of microcomputer usage, J. Manag. Inf. Syst., 13, 1 (1996). [Google Scholar]
  44. A. Atif, D. Richards, P. Busch, and A. Bilgin, Assuring graduate competency: a technology acceptance model for course guide tools, J. Comput. High. Educ., 27, 2, pp. 94–113 (2015). [CrossRef] [Google Scholar]
  45. Y. C. Chou and C. H. Chiu, The development, and validation of a digital fluency scale for preadolescents, Asia-Pacific Educ. Res., 29, 6, pp. 541–551 (2020). [CrossRef] [Google Scholar]
  46. O. Götz, K. Liehr-Gobbers, and M. Krafft, Evaluation of structural equation models using the partial least squares (PLS) approach, Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds), Handb. Partial Least Squares. Springer Handbooks Comput. Stat. (2010). [Google Scholar]
  47. J. Pallant, SPSS survival manual: A step by step guide to data analysis using SPSS for Windows version 10. Buckingham: Open University Press (2001). [Google Scholar]
  48. M. Sarstedt, C. M. Ringle, and J. F. Hair, Partial least squares structural equation modeling, Handb. Mark. Res., pp. 1–40 (2017). [Google Scholar]
  49. J. E. Cohen, Statistical Power Analysis for the Behavioral Sciences. New Jersey: Lawrence Erlbaum Associates, Inc (1988). [Google Scholar]
  50. E. Yadegaridehkordi, M. Nilashi, L. Shuib, and S. Samad, A behavioral intention model for SaaS-based collaboration services in higher education, Educ. Inf. Technol., 25, 2, pp. 791–816 (2020). [CrossRef] [Google Scholar]

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