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The effect of cultural factors, activities characteristics and technology features on the appropriateness and usage of technology regarding customer personality | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 05 دی 1403 اصل مقاله (514.17 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.30946.4522 | ||
| نویسندگان | ||
| Reza Abbasi1؛ Morteza Mohammadi* 2 | ||
| 1Department of Accounting, Birjand Branch, Islamic Azad University, Birjand, Iran | ||
| 2Department of Economics, Faculty of Literature and Humanities, Hakim Sabzevari University, Sabzevar, Iran | ||
| تاریخ دریافت: 24 اردیبهشت 1402، تاریخ پذیرش: 25 تیر 1402 | ||
| چکیده | ||
| This study investigated the relationship between Task-Technology Fit (TTF), usage, and performance with the moderating role of culture on adoption and retention of mobile banking technology; and the case study was Melli Bank of Iran. To test the hypotheses, first, the data was collected by questionnaires containing 24 questions (Likert scale) from 200 customers of the National Bank of Iran. Then data reliability (Korbach's alpha test) and construct validity (confirmatory factor analysis) were checked. Finally, the conceptual model was estimated using the Structural Equation Model (SEM) and Amos statistical software. The findings showed that, firstly, the TTF theory is well explained by the two dimensions of task characteristics and technology characteristics. Secondly, there is a significant positive relationship between TTF, use, and individual performance. Thirdly, individualism as a cultural dimension weakens the relationship between TTF and Use, while it strengthens the relationship between use and individual performance. Fourth, avoiding uncertainty as another cultural dimension weakens the relationship between TTF and Use; while it strengthens moderates the relationship between TTF and individual performance, as well as between use and individual performance. The results indicate that providing services that match the needs increases their use, and it can also improve performance. In addition, more use improves people's performance, which emphasizes the theory of learning by doing. Individualism as a cultural factor can hurt TTF theory, which can be reduced through advertising and branding. Also, customer psychology and providing special services increase their use. Avoiding uncertainty makes customers use it more cautiously, so the bank should improve their confidence in using mobile banking. | ||
| کلیدواژه‌ها | ||
| cultural factors؛ activities’ appropriateness؛ technology features | ||
| مراجع | ||
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[1] I. Ajzen, The theory of planned behavior, Handbook of Theories of Social Psychology: Volume 1, Chapter 21, Edited by: P.A.M. Van Lange, A.W. Kruglanski, and E. Tory Higgins, SAGE Publications Ltd, 2012. [2] G. Baptista and T. Oliveira, Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators, Comput. Human Behav. 50 (2015), 418–430. [3] A. Burton-Jones and G.S. Hubona. The Mediation of external variables in the technology acceptance model, Inf. Manag. 43 (2006), 706–717. [4] K.J. Calhoun, J.T.C. Teng, and M.J. Cheon, Impact of national culture on information technology usage behaviour: An exploratory study of decision making in Korea and the USA, Behav. Inf. Technol. 21 (2002), 293–302. [5] B. Choi, I. Lee, J. Kim, and Y. Jeon, A qualitative cross-national study of cultural influences on mobile data service design, Proc. SIGCHI Conf. Human Factors Comput. Syst. ACM, 2015, pp. 661–670. [6] F.D. Davis, User acceptance of information technology: System characteristics, user perceptions and behavioral impacts, Int. J. Man-Machine Stud. 38 (1993), no. 3, 475–487. [7] S.M. Dewan, Past, present and future of m-banking research: A literature review, 21st Aust. Conf. Inf. Syst., 2010. [8] A. Dillon and M.G. Morris, User acceptance of information technology: Theories and models, Ann. Rev. Inf. Sci. Technol. 31 (1996), 3–32. [9] M.T. Dishaw and D.M. Strong, Extending the technology acceptance model with task–technology fit constructs, Inf. Manag. 36 (1999), 9-–21. [10] D.L. Goodhue and R.L. Thompson, Task-technology fit and individual performance, MIS Quart. 19 (1995), 213–236. [11] H. Hoehle and S. Huff, Advancing task-technology fit theory: a formative measurement approach to determining task-channel fit for electronic banking channels, D.N. Hart, S.D. Gregor (Eds.), Information Systems Foundations: Theory Building in Information Systems, ANUE Press, Canberra, Australia, 2012, pp. 133–169. [12] H. Hoehle, E. Scornavacca, and S. Huff, Three decades of research on consumer adoption and utilization of electronic banking channels: a literature analysis, Decis. Support Syst. 54 (2012), 122–132. [13] G. Hofstede, Culture’s Consequences: International Differences in Work-related Values, Sage, Beverly Hills, 1980. [14] G. Hofstede, Dimensionalizing cultures: The Hofstede model in context, Online Read. Psychol. Cult. 2 (2011), no. 1, 8. [15] S.J. Hong, J.Y.L. Thong, and K.Y. Tam, Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet, Decis. Support Syst. 42 (2006), 1819–1834. [16] I. Im, Y. Kim, and H.J. Han, The effects of perceived risk and technology type on users’ acceptance of technologies, Inf. Manag. 45 (2008), 1–9. [17] I. Junglas, C. Abraham, and R.T. Watson, Task-technology fit for mobile locatable information systems, Decis. Support Syst. 45 (2008), 1046–1057. [18] J. Kim, I. Lee, B. Choi, S.-J. Hong, K.Y. Tam, and K. Naruse, Towards reliable metrics for cultural aspects of human-computer interaction, Human-Computer Interaction and Management Information Systems: Applications in Management Information Systems, ME Sharpe, Inc., Armonk, NY, 2006, pp. 173. [19] P.E. Kourouthanassis and G.M. Giaglis, Introduction to the special issue mobile commerce: The past, present, and future of mobile commerce research, Int. J. Electron. Commer. 16 (2012), 5–18. [20] C.-C. Lee, H.K. Cheng, and H.-H. Cheng, An empirical study of mobile commerce in insurance industry: Task–technology fit and individual differences, Decis. Support Syst. 43 (2007), 95–110. [21] I. Lee, B. Choi, J. Kim, and S.-J. Hong, Culture-technology fit: Effects of cultural characteristics on the postadoption beliefs of mobile internet users, Int. J. Electron. Commer. 11 (2007), 11–51. [22] D.E. Leidner and T. Kayworth, Review: a review of culture in information systems research: toward a theory of information technology culture conflict, MIS Quart. 3 (2006), 357–399. [23] T.C. Lin and C.-C. Huang, Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit, Inf. Manag. 45 (2008), 410-–417. [24] P. Longstreet, X. Xiao, and S. Sarker, Computer-Related task performance: A new perspective, Inf. Manag. 53 (2016), 517–527. [25] P. Luarn and H.-H. Lin, Toward an understanding of the behavioral intention to use mobile banking, Comput. Human Behav. 21 (2005), 873–891. [26] T.J. McGill and J.E. Klobas, A task–technology fit view of learning management system impact, Comput. Educ. 52 (2009), 496–508. [27] R.L. Oliver, A cognitive model of the antecedents and consequences of satisfaction decisions, J. Market. Res. 17 (1980), no. 4, 460–469. [28] T. Oliveira, M. Faria, M.A. Thomas, and A. Popovic, Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM, Int. J. Inf. Manag. 34 (2014), 689–703. [29] E.M. Rogers, A. Singhal, and M.M. Quinlan, Diffusion of innovations, An integrated approach to communication theory and research, Routledge, 2014, pp. 432–448. [30] G.M. Rose, R. Evaristo, and D. Straub, Culture and consumer responses to web download time: A four-continent study of mono and polychronism, Eng. Manag. IEEE Trans. 50 (2003), no. 1, 31–44. [31] A.A. Shaikh and H. Karjaluoto, Mobile banking adoption: A literature review, Telemat. Inf.32 (2015), 129–142. [32] S. Sonnentag and M. Frese,. Performance concept and performance theory, S. Sonnentag (Ed.), Psychological Management of Individual Performance, John Wiley & Sons, Ltd, Germany, 2012, pp. 3–25. [33] H. Sun and P. Zhang. The role of moderating factors in user technology acceptance, Int. J. Human-Computer Stud. 64 (2006), 53–78. [34] C. Tam and T. Oliveira, Literature review of mobile banking and individual performance, Int. J. Bank Mark. 35 (2017), no. 7, 1044–1067. [35] L.G. Tornatzky and M. Fleischer, The processes of technological innovation Lexington, MA: Lexington Books, 1990. [36] V. Venkatesh, M.G. Morris, G.B. Davis, and F.D. Davis, User acceptance of information technology: Toward a unified view, MIS Quart. 27 (2003), no. 3, 425–478 [37] D.-C. Yen, C.-S. Wu, F.-F. Cheng, and Y.-W. Huang, Determinants of user’s intention to adopt wireless technology: An empirical study by integrating TTF with TAM, Comput. Human Behav. 26 (2010), 906–915. [38] Y. Yuan, N. Archer, C.E. Connelly, and W. Zheng, Identifying the ideal fit between mobile work and mobile work support, Inf. Manag. 47 (2010), 125–137. [39] T. Zhou, Y. Lu, and B. Wang, Integrating TTF and UTAUT to explain mobile banking user adoption, Comput. Human Behav. 26 (2010), 760–767. | ||
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