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Source: Journal Citation ReportsTM from ClarivateTM 2022

Entrepreneurship and Sustainability Issues Open access
Journal Impact FactorTM (2022) 1.7
Journal Citation IndicatorTM (2022) 0.42
Received: 2021-07-15  |  Accepted: 2021-10-15  |  Published: 2021-12-30

Title

Consumers' perceptions of intention to use a credit card: perceived risk and security


Abstract

The goal of this study is to combine the Technology Acceptance Model (TAM) with the theory of perceived risk to create a hypothetical model for consumer behavioral intention that will be validated using data from Saudi Arabia's intended credit card usage. 217 bank customers were polled via an online survey conducted across the country. Exploratory and confirmatory factor analyses were used to evaluate the factor structure of the measuring items, while structural equation modeling was being used to validate the recommended model and test the assumptions. Social influence (SI), perceived usefulness (PU), perceived ease of use (PEU), and perceived trust (PT) were all significant predictors of perceived risk (PR) and perceived security (PS) to affect consumer intention to use a credit card (IUCC), according to the results of structural equation modeling (SEM). This research might have assisted banks in establishing new customer acquisition tactics and determining how to deploy resources to retain and grow their existing customer base. As a consequence, this study adds to the body of information on consumer behavior by verifying the effects of PR and PS on credit card intention, which most prior studies have not shown. The study also delivers genuine data about Saudi Arabia's e-banking services, particularly in the credit card sector, to an academic research platform.


Keywords

credit cards, perceived security, perceived risk, Technology Acceptance Model, structural equation modeling


JEL classifications

E21 , E40


URI

http://jssidoi.org/jesi/article/898


DOI


Pages

37-49


Funding

The author would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No. R-2021-204

This is an open access issue and all published articles are licensed under a
Creative Commons Attribution 4.0 International License

Authors

Zahrani, Aida Ahmed
Majmaah University, Al Majma'ah, Saudi Arabia https://www.mu.edu.sa
Articles by this author in: CrossRef |  Google Scholar

Journal title

Entrepreneurship and Sustainability Issues

Volume

9


Number

2


Issue date

December 2021


Issue DOI


ISSN

ISSN 2345-0282 (online)


Publisher

VšĮ Entrepreneurship and Sustainability Center, Vilnius, Lithuania

Cited

Google Scholar

Article views & downloads

HTML views: 2014  |  PDF downloads: 930

References


Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.4135/9781412952576.n208

Search via ReFindit


Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 62, 101293. https://doi.org/10.1016/j.techsoc.2020.101293

Search via ReFindit


Ala'raj, M., Abbod, M. F., & Majdalawieh, M. (2021). Modelling customers credit card behaviour using bidirectional LSTM neural networks. Journal of Big Data, 8(1), 1-27. https://doi.org/10.1186/s40537-021-00461-7

Search via ReFindit


Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002

Search via ReFindit


Aydin, A. E. (2021). Psychological and demographic factors influencing responsible credit card debt payment. Journal of Financial Services Marketing, 1-10. https://doi.org/10.1057/s41264-021-00094-0

Search via ReFindit


Aydin, G., & Burnaz, S. (2016). Adoption of mobile payment systems: A study on mobile wallets. Journal of Business Economics and Finance, 5(1), 73-92. https://doi.org/10.17261/pressacademia.2016116555

Search via ReFindit


Bauer, R.A. (1960). Consumer Behavior as Risk Taking, Dynamic Marketing for a Changing World, American Marketing Association, Chicago.

Search via ReFindit


Boden, J., Maier, E., & Wilken, R. (2020). The effect of credit card versus mobile payment on convenience and consumers’ willingness to pay. Journal of Retailing and Consumer Services, 52, 101910. https://doi.org/10.1016/j.jretconser.2019.101910

Search via ReFindit


Cao, Q., & Niu, X. (2019). Integrating context-awareness and UTAUT to explain Alipay user adoption. International Journal of Industrial Ergonomics, 69, 9-13. https://doi.org/10.1016/j.ergon.2018.09.004

Search via ReFindit


Chhonker, M. S., Verma, D., & Kar, A. K. (2017). Review of technology adoption frameworks in mobile commerce. Procedia computer science, 122, 888-895. https://doi.org/10.1016/j.procs.2017.11.451

Search via ReFindit


Cornea, D. (2021). Credit card payments: do cultural values matter? Evidence from the European Union. Managerial Finance. https://doi.org/10.1108/mf-06-2020-0336

Search via ReFindit


Crowe, M., & Tavilla, E. (2012). Mobile Phone Technology: “Smarter than We Thought” How Technology Platforms Are Securing Mobile Payments in the U.S. . echnology-smarter-than-we-thought-how-technology-platforms-are-securing-mobi le-payments-in-the-us.aspx https://www.bostonfed.org/publications/payment-strategies/mobile-phone-t

Search via ReFindit


Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982

Search via ReFindit


Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International journal of human-computer studies, 59(4), 451-474. 00111-3 https://doi.org/10.1016/s1071-5819(03)

Search via ReFindit


Fishbein, M. & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading. https://doi.org/10.2307/2065853

Search via ReFindit


Flavián, C., Guinaliu, M., & Torres, E. (2005). The influence of corporate image on consumer trust: A comparative analysis in traditional versus internet banking. Internet research. https://doi.org/10.1108/10662240510615191

Search via ReFindit


Hair, J. F., Risher, J., Sarstedt, M., & Ringle, C. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203

Search via ReFindit


Hanafizadeh, P., & Khedmatgozar, H. R. (2012). The mediating role of the dimensions of the perceived risk in the effect of customers’ awareness on the adoption of Internet banking in Iran. Electronic Commerce Research, 12(2), 151-175. https://doi.org/10.1007/s10660-012-9090-z

Search via ReFindit


Hassan, M. T., Mukhtar, A., Ullah, R. K., Shafique, H., Rehmna, S. U., & Anwar, A. (2012). Customer service quality perception of internet banking. International Journal of Learning & Development, 2(2), 86-100. https://doi.org/10.5296/ijld.v2i2.1591

Search via ReFindit


Hussein, A. S., Khairy, R. S., Najeeb, S. M. M., & ALRikabi, H. T. (2021). Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression. International Journal of Interactive Mobile Technologies, 15(5). https://doi.org/10.3991/ijim.v15i05.17173

Search via ReFindit


Jamshidi, D., & Kuanova, L. (2020). Investigating the customers’ drivers of Islamic credit card loyalty and word of mouth. Journal of Islamic Marketing. https://doi.org/10.1108/jima-09-2019-0182

Search via ReFindit


Javaria, K., Masood, O., Garcia, F. 2020. Strategies to manage the risks faced by consumers in developing e-commerce. Insights into Regional Development, 2(4), 774-783. http://doi.org/10.9770/IRD.2020.2.4(4)

Search via ReFindit


Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111-122. https://doi.org/10.1016/j.chb.2017.10.035

Search via ReFindit


Jung, K., & Kang, M. Y. (2021). Understanding Credit Card Usage Behavior of Elderly Korean Consumers for Sustainable Growth: Implications for Korean Credit Card Companies. Sustainability, 13(7), 3817. https://doi.org/10.3390/su13073817

Search via ReFindit


Kang, G. D., & James, J. (2004). Service quality dimensions: an examination of Grönroos’s service quality model. Managing Service Quality: An International Journal. https://doi.org/10.1108/09604520410546806

Search via ReFindit


Lebichot, B., Paldino, G. M., Siblini, W., He-Guelton, L., Oblé, F., & Bontempi, G. (2021). Incremental learning strategies for credit cards fraud detection. International Journal of Data Science and Analytics, 1-10. https://doi.org/10.1007/s41060-021-00258-0

Search via ReFindit


Lee, J. M., & Lee, Y. G. (2021). Multidimensional credit attitude and credit card debt behavior in the United States. Review of Behavioral Finance. https://doi.org/10.1108/rbf-09-2020-0239

Search via ReFindit


Liu, Y., & Dewitte, S. (2021). A replication study of the credit card effect on spending behavior and an extension to mobile payments. Journal of Retailing and Consumer Services, 60, 102472. https://doi.org/10.1016/j.jretconser.2021.102472

Search via ReFindit


Liu, Z., Ben, S., & Zhang, R. (2019). Factors affecting consumers’ mobile payment behavior: a meta-analysis. Electronic Commerce Research, 19(3), 575-601. https://doi.org/10.1007/s10660-019-09349-4

Search via ReFindit


Malaquias, R. F., & Hwang, Y. (2019). Mobile banking use: A comparative study with Brazilian and US participants. International Journal of Information Management, 44, 132-140. https://doi.org/10.1016/j.ijinfomgt.2018.10.004

Search via ReFindit


Ngonga, M. (2015). Electronic Banking and Financial Performance: Jomo Kenyatta University of Agriculture and Technology. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.731.2828&rep=rep1&type=pdf

Search via ReFindit


Pelaez, A., Chen, C. W., & Chen, Y. X. (2019). Effects of perceived risk on intention to purchase: A meta-analysis. Journal of Computer Information Systems, 59(1), 73-84. https://doi.org/10.1080/08874417.2017.1300514

Search via ReFindit


Prusti, D., Das, D., & Rath, S. K. (2021). Credit Card Fraud Detection Technique by Applying Graph Database Model. Arabian Journal for Science and Engineering, 1-20. https://doi.org/10.1007/s13369-021-05682-9

Search via ReFindit


Rahman, T., Noh, M., Kim, Y. S., & Lee, C. K. (2021). Effect of word of mouth on m-payment service adoption: a developing country case study. Information Development, 0266666921999702. https://doi.org/10.1177/0266666921999702

Search via ReFindit


Roy, S. K., Balaji, M. S., Kesharwani, A., & Sekhon, H. (2017). Predicting Internet banking adoption in India: a perceived risk perspective. Journal of Strategic Marketing, 25(5-6), 418-438. https://doi.org/10.1080/0965254x.2016.1148771

Search via ReFindit


Trinh, N. H., Tran, H. H., & Vuong, Q. D. H. (2021). Perceived Risk and Intention to Use Credit Cards: A Case Study in Vietnam. The Journal of Asian Finance, Economics and Business, 8(4), 949-958. https://doi.org/10.1108/ajeb-06-2020-0018

Search via ReFindit


Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540

Search via ReFindit


Yuan, Y., Rong, Z., Xu, N., & Lu, Y. (2021). Credit cards and small business dynamics: Evidence from China. Pacific-Basin Finance Journal, 67, 101570. https://doi.org/10.1016/j.pacfin.2021.101570

Search via ReFindit


Zhang, Y., Weng, Q., & Zhu, N. (2018). The relationships between electronic banking adoption and its antecedents: A meta-analytic study of the role of national culture. International Journal of Information Management, 40, 76-87. https://doi.org/10.1016/j.ijinfomgt.2018.01.015

Search via ReFindit