The Effect of Perceived Security Risks on Mobile Banking Adoption in Fashion Retail Industry. A Case Study of Mwanza Region, Tanzania
Albert Moshi
Tanzania Institute of Accountancy, P.O. Box 9522, Dar es Salaam, Tanzania
https://doi.org/10.47191/jefms/v7-i12-28ABSTRACT:
The adoption of mobile banking among fashion retailers is increasingly recognized as a powerful catalyst for economic empowerment and financial inclusion. It reduces reliance on traditional banking methods, allowing consumers and suppliers to engage in transactions without the constraints of banking hours or physical locations. This flexibility enables online transactions to occur anytime and anywhere, fostering a more dynamic and responsive shopping environment. However, its adoption is very limited in fashion industry. This study was set to explore security issues that affect adoption of mobile banking by fashion retailers in Mwanza municipal councils. The study is grounded on perceived risk theory which explain how individuals assess risk and make decisions under uncertainty. The data for this study were collected using a close-ended questionnaire distributed to a sample of 200 respondents, who were selected through a simple random sampling technique. The study employed a combination of content analysis and binary logistic regression analysis as its analytical approach. The study revealed a negative effect of fraud, phishing attacks, and transaction errors on the adoption of mobile banking, while highlighting the positive influence of security literacy in encouraging this adoption. The study concluded that that mobile banking service providers should implement comprehensive user education programs focused on security best practices, including the importance of strong passwords and awareness of phishing schemes. Additionally, enhancing authentication methods and establishing robust transaction error resolution processes that can mitigate risks and foster greater trust in mobile banking among fashion retailers.
KEYWORDS:
mobile banking, perceived security risk, fashion industry, perceived risk theory
REFERENCES:
1) Abdul-Sater, Z., Menassa, M., El Achi, N., Abdul-Khalek, R. A., Abu-Sittah, G., & Mukherji, D. (2020). Strengthening capacity for cancer research in conflict settings: key informant insights from the Middle East. ecancermedicalscience, 14.
2) Ali, M., Raza, S. A., Khamis, B., Puah, C. H., & Amin, H. (2021). How perceived risk, benefit and trust determine user Fintech adoption: a new dimension for Islamic finance. Foresight, 23(4), 403-420.
3) Alkhalil, Z., Hewage, C., Nawaf, L., & Khan, I. (2021). Phishing attacks: A recent comprehensive study and a new anatomy. Frontiers in Computer Science, 3, 563060.
4) Andrade, C. (2020). Understanding the difference between standard deviation and standard error of the mean, and knowing when to use which. Indian Journal of Psychological Medicine, 42(4), 409-410.
5) Aziz, F., Sheikh, S. M., & Shah, I. H. (2022). Financial inclusion for women empowerment in South Asian countries. Journal of Financial Regulation and Compliance, 30(4), 489-502.
6) Banerjee, R. (2024). Corporate Frauds: Now Bigger, Broader and Bolder. Penguin Random House India Private Limited.
7) Bhardwaj, A., Al-Turjman, F., Sapra, V., Kumar, M., & Stephan, T. (2021). Privacy-aware detection framework to mitigate new-age phishing attacks. Computers & Electrical Engineering, 96, 107546.
8) Bitzer, M., Stahl, B., & Strobel, J. (2021). Empathy for Hackers-an IT Security Risk Assessment Artifact for Targeted Hacker Attacks. ECIS,
9) Bojjagani, S., Sastry, V., Chen, C.-M., Kumari, S., & Khan, M. K. (2023). Systematic survey of mobile payments, protocols, and security infrastructure. Journal of Ambient Intelligence and Humanized Computing, 14(1), 609-654.
10) Burda, P., Allodi, L., & Zannone, N. (2024). Cognition in social engineering empirical research: a systematic literature review. ACM Transactions on Computer-Human Interaction, 31(2), 1-55.
11) Chanti, S., & Chithralekha, T. (2022). A literature review on classification of phishing attacks. International Journal of Advanced Technology and Engineering Exploration, 9(89), 446-476.
12) Colasante, A., & D'Adamo, I. (2021). The circular economy and bioeconomy in the fashion sector: Emergence of a “sustainability bias”. Journal of Cleaner Production, 329, 129774.
13) Desolda, G., Ferro, L. S., Marrella, A., Catarci, T., & Costabile, M. F. (2021). Human factors in phishing attacks: a systematic literature review. ACM Computing Surveys (CSUR), 54(8), 1-35.
14) Dhobe, S. D., Tighare, K. K., & Dake, S. S. (2020). A review on prevention of fraud in electronic payment gateway using secret code. Int. J. Res. Eng. Sci. Manag, 3(1), 602-606.
15) Dzidzah, E., Owusu Kwateng, K., & Asante, B. K. (2020). Security behaviour of mobile financial service users. Information & Computer Security, 28(5), 719-741.
16) Ebert, F., Castor, F., Novielli, N., & Serebrenik, A. (2021). An exploratory study on confusion in code reviews. Empirical Software Engineering, 26, 1-48.
17) Han, H., & Dawson, K. J. (2021). Applying elastic-net regression to identify the best models predicting changes in civic purpose during the emerging adulthood. Journal of adolescence, 93, 20-27.
18) Hanif, Y., & Lallie, H. S. (2021). Security factors on the intention to use mobile banking applications in the UK older generation (55+). A mixed-method study using modified UTAUT and MTAM-with perceived cyber security, risk, and trust. Technology in Society, 67, 101693.
19) Hultberg, E., & Pal, R. (2021). Lessons on business model scalability for circular economy in the fashion retail value chain: Towards a conceptual model. Sustainable Production and Consumption, 28, 686-698.
20) Ibrahimnur, A. A. (2023). Impact of Cybercrime on the Finance Sector: a Case of Banks in Nairobi County, Kenya (2008-2022) University of Nairobi].
21) Iyelolu, T. V., Agu, E. E., Idemudia, C., & Ijomah, T. I. (2024). Conceptualizing mobile banking and payment systems: Adoption trends and security considerations in Africa and the US. International Journal of Science and Technology Research Archive, 7(1), 001-009.
22) Jadil, Y., Rana, N. P., & Dwivedi, Y. K. (2021). A meta-analysis of the UTAUT model in the mobile banking literature: The moderating role of sample size and culture. Journal of Business Research, 132, 354-372.
23) Jain, S., Basu, S., Ray, A., & Das, R. (2023). Impact of irritation and negative emotions on the performance of voice assistants: Netting dissatisfied customers’ perspectives. International Journal of Information Management, 72, 102662.
24) Jibril, A. B., Kwarteng, M. A., Pilik, M., Botha, E., & Osakwe, C. N. (2020). Towards understanding the initial adoption of online retail stores in a low internet penetration context: An exploratory work in Ghana. Sustainability, 12(3), 854.
25) Kasowaki, L., & William, J. (2024). Digital Dollars: Maximizing the Power of Internet Banking for Seamless E-Commerce Transactions (2516-2314).
26) Kennedy, I. (2022). Sample size determination in test-retest and Cronbach alpha reliability estimates. British Journal of Contemporary Education, 2(1), 17-29.
27) Kocabas, H., Nandy, S., Tamanna, T., & Al-Ameen, M. N. (2021). Understanding user’s behavior and protection strategy upon losing, or identifying unauthorized access to online account. International Conference on Human-Computer Interaction,
28) Lestari, S., Adawiyah, W. R., Alhamidi, A. L., Prayogi, J., & Haryanto, R. (2024). Navigating perilous seas: unmasking online banking frauds, perceived usefulness, fear of cybercrime and distrust in online banking. Safer Communities, 23(4), 444-464.
29) Macha, D. P., & Massawe, N. M. (2023). Financial Technology in Tanzania: Assessment of Growth Drivers.
30) Mapunda, E. F. (2022). Influence of Service Digitalization on the Performance of Commercial Banks in Tanzania: A Case of CRDB Bank Plc Headquarters The Open University of Tanzania].
31) McCray, K. L. (2023). Vulnerabilities and Threats in Mobile Banking that Financial Institutions Must Understand to Reduce Mobile Banking Fraud Marymount University].
32) Msengi, Y. D. (2022). Factors Affecting Customers Loyalty towards Mobile Telecommunication Service Providers in Dar es Salaam The Open University of Tanzania].
33) Nadeem, M., Zahra, S. W., Abbasi, M. N., Arshad, A., Riaz, S., & Ahmed, W. (2023). Phishing attack, its detections and prevention techniques. International Journal of Wireless Security and Networks, 1(2), 13-25p.
34) Nattino, G., Pennell, M. L., & Lemeshow, S. (2020). Assessing the goodness of fit of logistic regression models in large samples: a modification of the Hosmer-Lemeshow test. Biometrics, 76(2), 549-560.
35) Nish, A., Naumann, S., & Muir, J. (2022). Enduring cyber threats and emerging challenges to the financial sector. Carnegie Endowment for International Peace.
36) Rajendran, R. (2024). Data Breach Fraudulence and Preventive Measures in E-Commerce Platforms. In Advancements in Cybercrime Investigation and Digital Forensics (pp. 161-184). Apple Academic Press.
37) Razaq, L., Ahmad, T., Ibtasam, S., Ramzan, U., & Mare, S. (2021). "We Even Borrowed Money from Our Neighbor" Understanding Mobile-based Frauds through Victims' Experiences. Proceedings of the ACM on human-computer interaction, 5(CSCW1), 1-30.
38) Roszkowska, P. (2021). Fintech in financial reporting and audit for fraud prevention and safeguarding equity investments. Journal of Accounting & Organizational Change, 17(2), 164-196.
39) Sandell, S. (2021). Risk management for universities in the age of cybercrime S. Sandell].
40) Sanni, M. L., Akinyemi, B. O., Akinwuyi, D., Olajubu, E. A., & Aderounmu, G. A. (2023). A Predictive Cyber Threat Model for Mobile Money Services. Annals of Emerging Technologies in Computing (AETiC), 7(1), 40-60.
41) Schreiber, J. B. (2021). Issues and recommendations for exploratory factor analysis and principal component analysis. Research in Social and Administrative Pharmacy, 17(5), 1004-1011.
42) Shankar, A., Tiwari, A. K., & Gupta, M. (2022). Sustainable mobile banking application: a text mining approach to explore critical success factors. Journal of Enterprise Information Management, 35(2), 414-428.
43) Sharma, A., Singh, S. K., Kumar, S., Chhabra, A., & Gupta, S. (2021). Security of android banking mobile apps: Challenges and opportunities. International conference on cyber security, privacy and networking,
44) Siano, A., Raimi, L., Palazzo, M., & Panait, M. C. (2020). Mobile banking: An innovative solution for increasing financial inclusion in Sub-Saharan African Countries: Evidence from Nigeria. Sustainability, 12(23), 10130.
45) Souiden, N., Ladhari, R., & Chaouali, W. (2021). Mobile banking adoption: a systematic review. International Journal of Bank Marketing, 39(2), 214-241.
46) Wallisch, C., Dunkler, D., Rauch, G., De Bin, R., & Heinze, G. (2021). Selection of variables for multivariable models: Opportunities and limitations in quantifying model stability by resampling. Statistics in Medicine, 40(2), 369-381.
47) Zhang, W., Jin, Y., Liu, N., Xiang, Z., Wang, X., Xu, P., Guo, P., Mao, M., & Feng, S. (2022). Predicting physical activity in Chinese pregnant women using multi-theory model: a cross-sectional study. International Journal of Environmental Research and Public Health, 19(20), 13383.
48) Zhang, Z. (2021). Designing an Autoresponder for Phishing Email Reports PhD thesis, University of Edinburgh].