Kısmi en küçük kareler yapısal eşitlik modellemesiyle mobil bankacılık kullanımının araştırılması

Yıl 2021, Cilt: 7 Sayı: 2, 133 - 149, 19.06.2021

Veysel Yılmaz Yasemin Kinaş

https://doi.org/10.30855/gjeb.2021.7.2.004

Öz

Son yıllarda yaşanan teknolojik gelişmeler finans sektöründe devrim yaratmış ve finansal hizmetlerde büyük değişikliklere yol açmıştır. İnternet özellikli mobil telefon, tablet gibi teknolojik aletlerin hızlı ve güvenilir iletişim ağlarıyla birlikte yayılması, bankaları ve finansal kurumları mobil bankacılık (m-bankacılık) uygulamalarını geliştirmeye teşvik etmiştir. Bu durum, iş dünyası ve sistemlerin küreselleşmesi ile birleştiğinde, m-bankacılık hizmetlerinin kabulünün etkisi hakkında daha derin bir anlayış edinme ihtiyacını güçlendirmiştir. Bu nedenle bu çalışmada, Teknoloji Kabul Modeli (TKM) ile m-bankacılık kullanımı araştırılmıştır. Modelin uyumunda, küçük örneklemlerde kullanılabilen ve çok değişkenli normal dağılım varsayımı gerektirmeyen kısmi en küçük kareler yapısal eşitlik modellemesi (KEKK-YEM) kullanılmıştır. TKM’de yer alan faktörler kullanışlılık, algılanan kullanım kolaylığı, m-bankacılığa ilişkin tutum ve m-bankacılığı kullanma niyetidir. Çalışmada geliştirilen anket Ankara’da madencilik alanında faaliyet gösteren bir kamu kurumunda 300 katılımcıyla yüz yüze görüşülerek uygulanmıştır. Analiz sonuçlarına göre, m-bankacılığa yönelik algılanan kolaylık bir birim arttıkça algılanan kullanışlılık 0,837 birim artacağı belirlenmiştir.

Anahtar Kelimeler

Mobil Bankacılık, Teknoloji Kabul Modeli, Kısmi En Küçük Kareler, Yapısal Eşitlik Modellemesi

Destekleyen Kurum

Eskişehir Osmangazi Üniversitesi Bilimsel Araştırma Projesi (Proje Kodu: 2019-2649)’nden üretilmiştir.

Proje Numarası

2019-2649

Investigation of mobile banking usage with partial least squares structural equation modeling

Öz

Technological developments in recent years have revolutionized the financial sector and have led to major changes in financial services. The spread of technological devices such as internet-enabled mobile phones and tablets together with fast and reliable communication networks encouraged banks and financial institutions to develop mobile banking (m-banking) applications. This, combined with the globalization of business and systems, has reinforced the need for a deeper understanding of the impact of adopting m-banking services. Therefore, in this study, m-banking usage was investigated with Technology Acceptance Model (TAM). Partial least squares structural equation modeling (PLSYEM), which can be used in small samples and does not require the assumption of multivariate normal distribution, was used in the fit of the model. Factors in TAM are usefulness, perceived ease of use, attitude towards m-banking and intention to use m-banking. The questionnaire developed in the study was applied in a face-to-face interview with 300 participants in a public institution operating in the mining field in Ankara. According to the analysis results, it was determined that perceived usefulness will increase by 0,837 unit as perceived ease of m-banking increases by one unit.

Anahtar Kelimeler

Mobile Banking, Technology Acceptance Model, Partial Least Squares, Structural Equation Modeling

Proje Numarası

2019-2649

Kaynakça

• Abbas, M., Zaman, U., Ahmad, J., Nawaz, M. S. ve Ahraf, M. (2019). Diffusion of mobile banking in Pakistan. Smart Journal of Business Management Studies, 15(1), 10-19.
• Adzima, F. ve Ariyanti, M. (2018). Analysis of factors influencing interest in using mobile banking application on the customer bank BRI Purwakarta. E-Proceeding of Management, 5(2), 1584-1592.
• Baptista, G. (2017). Mobile banking and mobil payment acceptance. NOVA Information Management School, Doktorate Program, Lizbon.
• Belousova, V. ve Chichkanov, N. (2015). Mobile Banking Adoption in Russia: What Incentives Matter? National Research University Higher School of Economics, 1-24.
• Bilici, F. ve Özdemir, E. (2019). Tüketicilerin artırılmış gerçeklik teknolojilerini kullanmaya yönelik tutum ve niyeti üzerine bir araştırma. BMIJ, 7(5), 2011-2033 doi: http://dx.doi.org/10.15295/bmij.v7i5.1252.
• Can, Y. (2013). Sürekli regresyon ve ilişkili regresyon modellerinin incelenmesi. Yüksek lisans tezi, Çukurova Üniversitesi, Adana.
• Cohen, J. (1988). Statistical Power Analysis For The Behavioral Sciences, Lawrence Erlbaum, Mahwah, NJ.
• Çelik, H. ve Başaran, B. (2008). ‘’Bireysel Müşteriler Tarafından Algılanan Elektronik Hizmet Kalitesi’’. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 8(2), 129-152.
• Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
• Doğan, H. ve Burucuoğlu, M. (2018). Tüketicilerin mobil bankacılık hizmet kalitesi algıları ve tekrar kullanma niyetleri: Ampirik bir araştırma. Interntional Journal of Management Economics and Business, 14(4), 1183-1198.
• Fornell, C. ve Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS appliead to consumer exit-voice theory. Journal of Marketing Research, 19, 440-452.
• Fornell, C. ve Larcker, D.F. (1981). Evaluating Structural Equation Models With Unobservable Variables And Measurement Error, Journal of Marketing Research, 18 (1), 39-50.
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. ve Tatham, R. L. (2006). Multivariate Data Analysis, Upper Saddle River, NJ: Pearson Prentice Hall.
• Hair, J.F., Tomas, G., Hult, M., Ringle, C.M. ve Sarstedt, M. (2014). A Primer on Partial Least Square Structural Equations Modeling (PLS-SEM), Los Angeles: Sage.
• Hair, J. F., Sarstedt, M., Ringle, C. M. ve Gudergan, S. P. (2018). Advanced issues in Partial Least Squares Structural Equation Modelling. SAGE Publications, USA.
• Hanif, M. (2018). Analysis Technology Acceptance Model (TAM) pada aplikasi mobil banking jenius Di Kota Jakarta. Universitas Katolik Parahyangan Fakultas Ekonomi Program Sarjana Manajemen, Undergraduate thesis
• Henseler, J., Ringle, C.M. ve Sarstedt, M. (2015). A New Criterion For Assessing Discriminant Validity in Varience-Based Structural Equation Modelling, Journal of the Academy of Marketing Science, 43, 115-135.
• Höskuldsson, A. (1988). PLSR regression methods. Journal of Chemometrics, 2, 211-228.
• Jusuf, M. B., Utami, N. P., Hidayanto, A. N. ve Shihab, M. R. (2017). Analysis of intrinsic factors of mobile banking application users' continuance intention: An evaluation using an extended Expectation Confirmation Model. DOI: 10.1109/IAC.2017.8280589.
• Kim, M. ve Kim, M. (2016). Behavioral intention on smartphone banking focused on non-users. The Business and Management Review, 8(3), 66.
• Klingler, K. (2014). Structural Equation Modelling with Latent Variables. der Wirtschaftswissenschaften (Dr. rer. pol.) der Wirtschaftswissenschaftlichen Fakultät der Heinrich-Heine-Universität
• Koo, C. ve Wati, Y. (2010). Toward an understanding of the mediating role of “Trust” in mobile banking service: An empirical test of Indonesia case. Journal of Universal Computer Science, 16(13), 1801-1824.
• Kurniawan, T. A. (2011). Pengujian DeLone & McLean pada mobile banking Bank Rakyat Indonesia. Journal Akuntansi Keuangan Dan Bısnıs, 4, 32-39.
• Lohmöller, J. B. (1989). Latent variables path modeling with partial least squares. PhysicaVerlag, Heildelberg. PhysicaVerlag, Heildelberg.
• Magdelena, R. ve Baridwan, Z. (2015). The analysis of individuals’ behavioral intention in using mobile banking based on TPB, TAM and perceived risk. International Undergraduate Program in Accounting Faculty of Economics and Business, University of Brawijaya, 4(1), 1-13.
• Massy, W. F. (1965). Principal components regression in explanatory statistical research. Journal of the American Statistical Association, 60, 234-246.
• Mateos-Aparicio, G. (2011). Partial least squares (PLS) methods: Origins, evolution, and application to social sciences. Communications in Statistics-Theory and Methods, 40, 2305-2317.
• Mohammadi, H. (2015). A study of mobile banking loyalty in Iran. Computers in Human Behavior, 44, 35-47.
• Özkan, S., Baykal, N., Alaşehir, O., Alkış, N., Kanat, İ. E. ve Sezgin, E. (2011). Kullanıcı teknoloji benimseme faktörleri: Yapısal eşitlik modeli yaklaşımı ile farklı bağlamlarda ampirik incelemeler. Proje No: 109K394.
• Purwanegara, M., Apriningsih, A. ve Andika, F. (2014). Snapshot on Indonesia regulation in mobile internet banking users attitudes. Procedia-Social and Behavioral Sciences, 115, 147-155.
• Raza, S. A., Umer, A. ve Shah, N. (2017). New determinants of ease of use and perceived usefulness for mobile banking adoption. International Journal Electronic Customer Relationship Management, 11(1), 44-65.
• Ringle, C.M., Wende, S. ve Becker, J.M. (2015). SmartPLS 3. www.smartpls.com.
• Salsabilla, S. ve Zuliestiana, D. A. (2019). Analysis of intention use BRI mobile banking in Indonesia, from perceived usefulness, perceived ease of use and perceived risk. E-Proceeding of Management, 6(2), 1-8.
• Sarstedt, M., Ringle, C.M. ve Hair, J.F. (2017), Partial Least Squares Structural Equation Modelling, In C. Homburg, M.Klarmann, A.Vomberg (Eds.), Handbook of Market Research, Heidelberg: Springer.
• Schneeweiß, H. (1991). Modelswithlatentvariables: LISREL versus PLS. Statistica Neerlandica, 45(2),145-157.
• Schierz, P., Schilke, O. ve Wirtz, B. (2010). Understanding customer acceptance of mobile payment services: An empirical analysis. Journal of Electronic Commerce Research and Application, 9, 209-216.
• Shaikh, A. A. ve Karjaluoto, H. (2015). Mobile banking adoption: A literatüre review. Telematics and Informatics, 32, 129-142.
• Taşkın, Ç. ve Gülerhocaoğlu, T. (2018). Mobil pazarlama uygulamalarına yönelik tüketici tutumlarının öncüllerinin etkisinin PLS-SEM ile araştırılması. Uludağ Journal of Economy and Society / B.U.Ü. İktisadi ve İdari Bilimler Fakültesi Dergisi, 37(1), 29-51.
• Thakur, R. (2014). What keeps mobile banking customers loyal? International Journal of Bank Marketing, 32(7), 628-646.
• Trinchera, L. ve Russolillo, G. (2010). On the use of structural equation models and PLS path modeling to build composite ındicators. University of Macerata, Italy.
• Tumewah, E. ve Juniarta, Kurniawan, Y. (2020). The effect of m-banking service quality and customer perceived value to satisfaction and loyalty of Bank XYZ customers. International Journal of Management and Humanities, 4(6), 132-138.
• Vinzi, V. E., Trinchera, L. ve Amato, S. (2010). PLS path modelling: from foundations to recent developments and open issues for model assessment and improvement. In Handbook of partial least squares, 47-82. Springer, Berlin, Heidelberg.
• Wang, Y. S. ve Liao, Y. W. (2007). The conceptualization and measurement of m-commerce user satisfaction. Computers in Human Behavior, 23(1), 381-398.
• Wetzels, M., Odekerken-Schroder, G. ve Van Oppen, C. (2009). “Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration”, MIS Quarterly, 3(1), 177-196.
• Wold, H. (1966). Estimation of principal components and related models by ıterative least squares. New York: Academic Press.
• Wold, H. (1973). Nonlinear iterative partial least squares (NIPALS) modelling: Some current developments. In P. R. Krishnaiah (Ad.), Multi-variate analysis III, 383-407. New York: Academic Press.
• Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce: Theory and application of partial least squares. In J. Kmenta&J. B. Ramsey (Eds.), Evaluation of econometric models, 47-74. New York, NY: Academic Press.
• Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Joreskog & H. Wold (Eds.), Systems under indirect observations: Part II:1-54. Amsterdam: North-Holland.
• Wold, H. (1985). Partial least squares. In S. Kotz&N. L. Johnson (Eds.), Encyclopedia of statistical sciences, 581-591. New York, NY: John Wiley.
• Yılmaz, Ö. (2018). Tüketicilerin online alışveriş niyetlerinin teknoloji kabul modeli bağlamında incelenmesi. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 20(3), 331-346.
• https://www.smartpls.com/documentation/algorithms-and-techniques/model-fit-16-12- 2019