Dijital Bankacılık Kullanımına Teknoloji Kabulü Temelli Bir Yaklaşım

Year 2020, , 401 - 410, 30.10.2020

Fatih Sinan Esen

https://doi.org/10.17671/gazibtd.664854

Cited By: 2

Abstract

Teknolojinin önceki yüzyıllara göre çok daha hızlı geliştiği ve yayıldığı günümüz şartlarında, teknolojik araçların, makinelerin, yazılımların ve gelişen imkânların kitleler arasında kullanımı da artış göstermiştir. Dijital bankacılık da bu teknolojik araçlardan birisi olarak kabul edilmektedir. Türkiye’de dijital bankacılık kullanan kişi sayısında son yıllarda kayda değer bir artış gerçekleşmiştir. Bu artışı etkileyen faktörlerin bilinmesi süreci hızlandırabilir. Çalışmada bireysel yenilikçiliğin dijital bankacılık kullanımına etkisi, Teknoloji Kabul Modeli üzerinden araştırılmış ve öngörülen etkiler, dijital bankacılığı aktif olarak kullanan toplam 302 kişiden toplanan verilerle, Yapısal Eşitlik Modellemesi kullanılarak test edilmiştir. Kişilerin bireysel yenilikçilik düzeyinin dijital bankacılık kullanımlarına sırasıyla algılanan kullanım kolaylığı, algılanan kullanışlılık-kullanma niyeti üzerinden dolaylı bir etkisinin olduğu görülmüştür. Yaş, eğitim durumu ve gelir durumu gibi demografik değişkenlerin de bu etkiler üzerinde yönetici etkisi olduğu saptanmıştır. Cinsiyet ise tüm değişkenler üzerinde farklılaştırıcı bir etkiye sahiptir. Bu çalışmanın katkısı, teknolojinin yayılmasında etki eden faktörlerin belirlenmesi olmuştur.

Keywords

Bilişim teknolojilerinde finans, teknoloji kabul modeli, dijital bankacılık, internet bankacılığı, bireysel yenilikçilik

A Technology Acceptance Based Approach To Digital Banking Use

Abstract

The use of technological tools, machines, software and developing facilities among the masses has increased in the present conditions where technology has developed and spread much faster than previous centuries. Digital banking is also considered as one of these technological tools. There ise a noteworthy increase in the number of people using digital banking in Turkey. Knowing the factors affecting this increase can speed up the process. In this study, the effect of personal innovation on digital banking use was investigated through Technology Acceptance Model and the predicted effects were tested using Structural Equation Modeling with data collected from 302 people who actively use digital banking. Individuals' level of personal innovation has an indirect effect on digital banking usage, perceived ease of use, perceived usefulness and intention to use, respectively. Demographic variables such as age, educational level and income level also had a managing effect on these effects. Gender has a differentiating effect on all variables. The contribution of this study is to determine the factors affecting the spread of technology.

Keywords

Finance in information systems, technology acceptance model, digital banking, internet banking, personal innovativeness

References

• Internet: Türkiye Bankalar Birliği-Bankalarımız, Aralık 2018 Raporu, https://www.tbb.org.tr/Content/Upload/istatistikiraporlar /ekler/1151/Bankalarimiz_2018.pdf, 10.08.2019.
• Internet: Türkiye Bankalar Birliği Dijital, İnternet ve Mobil Bankacılık İstatistikleri Aralık 2018 Raporu, https://www.tbb.org.tr/ Content/Upload/istatistikiraporlar/ekler/1108/Dijital-Internet-Mobil_ Bankacilik_Istatistikleri-Aralik_2018.pdf, 10.08.2019.
• I. Ajzen, From intentions to actions: A theory of planned behavior Action control, Springer, ABD, 1985.
• F. D. Davis, A technology acceptance model for empirically testing new end-user information systems: Theory and results, Doktora Tezi, Massachusetts Institute of Technology, 1986.
• F. D. Davis, V. Venkatesh, “A critical assessment of potential measurement biases in the technology acceptance model: Three experiments.”, International Journal of Human Computer Studies, 45(1), 19–45, 1996.
• E. B. Diop, S. Zhao, T. Van Duy, “An extension of the technology acceptance model for understanding travelers’ adoption of variable message signs”, PLOS one, 14(4), 112-126, 2019.
• V. Venkatesh, H. Bala, "Technology Acceptance Model 3 and a Research Agenda on Interventions", Decision Sciences, 39(2), 273–315, 2008.
• V. Venkatesh, F. D. Davis, "A theoretical extension of the technology acceptance model: Four longitudinal field studies", Management Science, 46(2), 186–204, 2000.
• Jr. L. Robinson, G. W. Marshall, M. B. Stamps, “Sales force use of technology: antecedents to technology acceptance”, Journal of Business Research, 58(12), 1623-1631, 2005.
• R. Agarwal, J. Prasad, “A conceptual and operational definition of personal innovativeness in the domain of information technology”. Information systems research, 9(2), 204-219, 1998.
• F. D. Davis, R. P. Bagozzi ve P. R. Warshaw, “User acceptance of computer technology: A comparison of two theoretical models”, Management science, 35(8), 982- 1002, 1989.
• I. Ajzen, M. Fishbein, Understanding Attitudes and Predicting Social Behavior, Prentice-Hall, Englewood Cliffs, NJ, ABD, 1980.
• J. Lu, J. E. Yao, C. S. Yu, “Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology” The Journal of Strategic Information Systems, 14(3), 245-268, 2005.
• R. Walczuch, J. Lemmink, S. Streukens, “The effect of service employees’ technology readiness on technology acceptance”, Information & Management, 44(2), 206-215, 2007.
• O. Nov, C. Ye, “Personality and technology acceptance: Personal innovativeness in IT, openness and resistance to change” In Proceedings of the 41st annual IEEE Hawaii international conference on system sciences (HICSS 2008), 448-448, 2008.
• Ç. Altın Gümüşsoy, A. Yeterel, “Fırsat sitelerinden tekrar satın alma kararını etkileyen faktörlerin araştırılması”, Bilişim Teknolojileri Dergisi, 9(3) , 275-284, 2016.
• Ç. Güler, “A structural equation model to examine mobile application usability and use”, Bilişim Teknolojileri Dergisi, 12(3) , 169-181, 2019.
• A. Özduman, B. Gök, H. Gökçen, “Mobil telefon kullanıcılarının mobil bağımlılık durumu ve 5G teknolojisi kabullenme niyeti modellerinin geliştirilmesi”, Bilişim Teknolojileri Dergisi, 13(3) , 269-288, 2020.
• W. M. Lassar, C. Manolis, S. S. Lassar, “The relationship between consumer innovativeness, personal characteristics, and online banking adoption”, International Journal of Bank Marketing, 23(2), 176-199, 2005.
• C. S. Yiu, K. Grant, D. Edgar, “Factors affecting the adoption of Internet Banking in Hong Kong-implications for the banking sector”, International journal of information management, 27(5), 336-351, 2007.
• S. K. Chitungo, S. Munongo, “Extending the technology acceptance model to mobile banking adoption in rural Zimbabwe”, Journal of Business Administration and Education, 3(1), 2013.
• H. S. Kwon, L. Chidambaram, “A test of the technology acceptance model: The case of cellular telephone adoption”. In Proceedings of the 33rd Annual Hawaii IEEE International Conference on System Sciences, 7, 2000.
• J. H. Wu, S. C. Wang, “What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model”. Information & management, 42(5), 719-729, 2005.
• B. Hernandez, J. Jimenez, M. J. Martín,. “Extending the technology acceptance model to include the IT decision-maker: A study of business management software”, Technovation, 28(3), 112-121, 2008.
• M. Chow, D. K. Herold, T. M. Choo, K. Chan, “Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education”, Computers & Education, 59(4), 1136-1144, 2012.
• M. Ghazizadeh, J. D. Lee, L. N. Boyle, “Extending the Technology Acceptance Model to assess automation”, Cognition, Technology & Work, 14(1), 39-49, 2012.
• S. Y. Park, M. W. Nam, S. B. Cha, “University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model”, British journal of educational technology, 43(4), 592-605, 2012.
• H. Son, Y. Park, C. Kim, J. S. Chou,. “Toward an understanding of construction professionals' acceptance of mobile computing devices in South Korea: An extension of the technology acceptance model”, Automation in construction, 28, 82-90, 2012.
• L. G. Wallace, S. D. Sheetz, “The adoption of software measures: A technology acceptance model (TAM) perspective”, Information & Management, 51(2), 249-259, 2014.
• N. M. Yaghoubi, E. Bahmani, “Factors affecting the adoption of online banking-an integration of Technology Acceptance Model and Theory of Planned Behavior”, International journal of business and management, 5(9), 159-165, 2010.
• S. Y. Yousafzai, G. R. Foxall, J. G. Pallister, “Explaining internet banking behavior: theory of reasoned action, theory of planned behavior, or technology acceptance model”, Journal of applied social psychology, 40(5), 1172-1202, 2010.
• A. Kesharwani, S. Singh Bisht, “The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model”, International journal of bank marketing, 30(4), 303-322, 2012.
• I. Lule, T. K. Omwansa, T. M. Waema, “Application of technology acceptance model (TAM) in m-banking adoption in Kenya”, International journal of computing & ICT research, 6(1), 2012.
• Internet: C. Yu, Structural equation model, http://www.creative-wisdom.com/teaching/WBI/SEM.shtml, 17.10.2016.
• C. Meydan, H. Şeşen, Yapısal eşitlik modellemesi AMOS uygulamaları, Detay Yayıncılık, Ankara, 2011.
• D. W. McCloskey, “The importance of ease of use, usefulness, and trust to online consumers: An examination of the technology acceptance model with older customers”, Journal of organizational and end user computing, 18(3), 47-65, 2006.
• A. Field, Discovering statistics using IBM SPSS statistics, 4 Baskı, Sage, ABD, 2013.
• T. A. Shao, Marketing Research: An aid to Decision Making, SouthWestern, Thomson Learning, ABD, 2002.
• H. F. Kaiser, “The varimax criterion for analytic rotation in factor analysis”, Psychometrika, 23(3), 187-200, 1958.
• G. Darren, P. Mallery, SPSS for windows step by step: A simple guide and reference, 4 Baskı, Allyn & Bacon, Boston, ABD, 2003.
• C. Fornell, D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error”, Journal of marketing research, 18(1), 39-50, 1981.
• J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, Multivariate data analysis,7 Baskı, Pearson Education, Londra, İngiltere, 2014.
• K. Schermelleh-Engel, H. Moosbrugger, H. Müller, “Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures”, Methods of psychological research online, 8(2), 23-74, 2003.
• R. E. Schumacker, R. G. Lomax, A beginner's guide to structural equation modeling, Taylor & Francis, ABD, 2004.
• L. T. Hu, P. M. Bentler, “Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification”, Psychological methods, 3(4), 424, 1998.
• J. Jaccard, C. K. Wan, Lisrel approaches to interaction effects in multiple regression, Sage, ABD, 1996.
• R. B. Kline, Principles and practice of structural equation modeling, Guilford publications, New York, ABD, 2015.
• N. Görz, L. Hildebrandt, D. Annacker, “Analyzing multigroup data with structural equation models”, Proceedings of the 23rd Annual Conference of the GfKl, 312-319, 2000.