Effort expectancy and social influence factors as main determinants of performance expectancy using electronic banking
-
DOIhttp://dx.doi.org/10.21511/bbs.16(2).2021.03
-
Article InfoVolume 16 2021 , Issue #2, pp. 27-37
- Cited by
- 1408 Views
-
483 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This study is aimed at determining the effect of expected effort and social influence factors on expected performance when using internet banking. The study adapts the constructs and definitions from the UTAUT model in the context of the adaptation of online banking technology. With regard to the nature of the variables analyzed, the following statistical tests and methods were used: calculation of average values using descriptive statistics; multiple linear regression analysis – to interpret associations between quantitative variables. Banks, as well as users of these banking services in the online environment, are the subject of research. The survey sample consists of 454 men and women and reflects the profile of online consumers across different countries of the European Union. The results of this study show the impact of the social influence construct on the respondents’ behavior when using electronic banking. The expected effort factor in the study significantly affects the expected performance factor, which can be characterized by original research, which showed that the effect of perceived ease of use on behavioral intent and use is incompatible with the degree of system complexity.
- Keywords
-
JEL Classification (Paper profile tab)M31, M50, M15
-
References26
-
Tables8
-
Figures3
-
- Figure 1. Results for the factor of expected performance
- Figure 2. Summary of results for the expected effort factor
- Figure 3. Overview of results for the social influence factor
-
- Table 1. Results for the expected performance factor
- Table 2. Summary of results for the expected effort factor
- Table 3. Results for the social influence factor
- Table 4. SEM for the PE factor – Overview of results for latent variables
- Table 5. SEM for the EE factor – Overview of results for latent variables
- Table 6. SEM for the SI factor– Overview of results for latent variables
- Table 7. SEM when estimating ML – Overview of results for a structural regression model
- Table 8. Overview of statistical evaluation of research hypotheses
-
- Al-Qeisi, K., Dennis, Ch., Alamanos, E., & Jayawardhena, Ch. (2014). Website design quality and usage behavior: Unified Theory of Acceptance and Use of Technology. Journal of Business Research, 67(11), 2282-2290.
- Belas, J., Koraus, M., & Gabcova, L. (2015). Electronic Banking, Its Use And Safety. Are There Differences In The Access Of Bank Customers By Gender, Education And Age. International Journal of Entrepreneurial Knowledge, 3(2).
- Bilan Y., Mishchuk, H., Roshchyk, I., & Joshi, O. (2020a). Hiring and retaining skilled employees in SMEs: problems in human resource practices and links with organizational success. Business: Theory and Practice, 21(2), 780-791.
- Bilan, Y., Mishchuk, H., Samoliuk, N., & Mishchuk, V. (2020b). Gender discrimination and its links with compensations and benefits practices in enterprises. Entrepreneurial Business and Economics Review, 8(3), 189-204.
- Brown, S. A., & Venkatesh, V. (2005). A model of adoption of technology in the household: A baseline model test and extension incorporating household life cycle. Management Information Systems Quarterly, 29(3), 11.
- Budiarto, D. S., Rahmawati, Bandi, & Prabowo, M. A. (2019). Accounting information system and non-financial performance in small firm: Empirical research based on ethnicity. Journal of International Studies, 12(1), 338-351.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 319-340.
- Heshan, S., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64(2), 53-78.
- Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal computing acceptance factors in small firms: a structural equation model. Management Information Systems Quarterly, 21(3), 279-305.
- Jhumkee, I., & Belvelkar, M. (2009). Case study of online banking in India: User behaviors and design guidelines. IFIP Working Conference on Human Work Interaction Design (pp. 180-188). Springer, Berlin, Heidelberg.
- Karahanna, E., & Detmar W. S. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management, 35(4), 237-250.
- Kočišová, K. (2020). Two-Stage DEA: An Application in Banking. Lecture Notes in Networks and Systems, 129 LNNS.
- Majetić, F., Makarovič, M., Šimleša, D., & Golob, T. (2019). Performance of work integration social enterprises in Croatia, Slovenia, and Italian regions of Lombardy and Trentino. Economics and Sociology, 12(1), 286-301.
- Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2), 375-403.
- Pakurár, M., Haddad, H., Popp, J., Khan, T., & Oláh, J. (2019). Supply chain integration, organizational performance and balanced scorecard: An empirical study of the banking sector in Jordan. Journal of International Studies, 12(2), 129-146.
- Pingjun, J. (2009). Consumer adoption of mobile internet services: An exploratory study. Journal of Promotion Management, 15(3), 418-454.
- Rahman, A., Belas, J., Rosza, Z., & Kliestik, T. (2017). Does bank ownership affect relationship lending: A developing country perspective. Journal of International Studies, 10(1), 277-288.
- Rahmatiah, Wiroto, D. W., & Taan, H. (2019). Business continuity, motivation, and social conditions of young entrepreneurs. Economics and Sociology, 12(4), 166-182.
- Sang-Hoon, K., & Jung Park, H. (2011). Effects of social influence on consumers’ voluntary adoption of innovations prompted by others. Journal of Business Research, 64(11), 1190-1194.
- Shirley, T., & Todd, P. (1995). Assessing IT usage: The role of prior experience. Management Information Systems Quarterly, 19(4), 561-570.
- Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of Internet banking. Journal of the Association for information Systems, 1(1), 5.
- Tung, H. T., Belas, J., & Baideldinova, T. (2018). How do banks implement the capital regulation requirement? Journal of International Studies, 11(3), 161-175.
- Venkatesh, M., & Davis, D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425-478.
- Venkatesh, V., & Fred, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
- Wang, Yi-Shun, Yu-Min, W., Hsin-Hui, L., & Tzung-I, T. (2003). Determinants of user acceptance of Internet banking: an empirical study. International Journal of Service Industry Management, 14(5), 501-519.
- Zamir, Z. (2019). The Impact of Knowledge Capture and Knowledge Sharing on Learning, Adaptability, Job Satisfaction and Staying Intention. International Journal of Entrepreneurial Knowledge, 7(1), 46-64.