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Factors Affecting Internet Banking Adoption: An Application of Adaptive LASSO

by Hatice Jenkins1, Siamand Hesami1,*, Fulden Yesiltepe2

1 Department of Banking and Finance, Eastern Mediterranean University, Famagusta, Mersin 10, 99628, Turkey
2 Finnprints Inc., Lefkosa, Mersin 10, Turkey

* Corresponding Author: Siamand Hesami. Email: email

Computers, Materials & Continua 2022, 72(3), 6167-6184. https://doi.org/10.32604/cmc.2022.027293

Abstract

This research investigates a broad range of possible factors affecting the adoption of new technology in the banking industry using adaptive LASSO and a standard logit model. The research integrated the adoption of the innovation framework and the technology acceptance theory to develop a conceptual framework for the analysis. Primary data was collected from 400 bank customers in North Cyprus. Risk perception and other customer-specific factors such as perceived risk index and negative attitude toward new technologies index were formulated for the proposed conceptual model. The findings indicated that individuals with a negative attitude toward new technology are least likely to adopt internet banking. In addition, the logit model suggested that age, education level, and general (innate) innovativeness significantly impact the adoption of internet banking. However, gender, income, occupation, perceived risk, familiarity with the internet, and social inclusion have no significant impact on internet banking adoption in North Cyprus.

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APA Style
Jenkins, H., Hesami, S., Yesiltepe, F. (2022). Factors affecting internet banking adoption: an application of adaptive LASSO. Computers, Materials & Continua, 72(3), 6167-6184. https://doi.org/10.32604/cmc.2022.027293
Vancouver Style
Jenkins H, Hesami S, Yesiltepe F. Factors affecting internet banking adoption: an application of adaptive LASSO. Comput Mater Contin. 2022;72(3):6167-6184 https://doi.org/10.32604/cmc.2022.027293
IEEE Style
H. Jenkins, S. Hesami, and F. Yesiltepe, “Factors Affecting Internet Banking Adoption: An Application of Adaptive LASSO,” Comput. Mater. Contin., vol. 72, no. 3, pp. 6167-6184, 2022. https://doi.org/10.32604/cmc.2022.027293



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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