INFLUENCE OF MOBILE-BASED LENDING PRACTICES ON CREDIT CONSUMER BEHAVIOUR IN EMBU COUNTY, KENYA
Purpose of the study: This study sought to assess the influence of mobile-based lending practices on credit consumer behaviour in Embu County, Kenya. The objectives were to determine the influence of mobile-based loan application, receipts and repayment practices on credit consumer behaviour.
Problem statement: Mobile lenders have exponentially increased in the market. Some of the lending platforms are, however, not regulated by the Central Bank of Kenya and thus operate with potential of exploitation to customers who have shown a great appetite for loan products. Studies have shown the use of available credit products offered as digital finance has not in any way been able to improve consumers’ lives and their livelihoods in Kenya. Further, about 1 million credit consumers or borrowers do not understand the applied interest rates and regime but are willing to borrow digitally or via mobile. The mobile-based credit decision is influenced by some activities in the value chain that make consumers make decisions that are not rational.
Methodology: A mixed methodology of research was adopted together with a survey design, which was the most appropriate for the methods chosen. The target population of 549,098 people with mobile phones living in Embu County was identified. A sample of 100 respondents was interviewed randomly using a semi-structured questionnaire. Using simple comma-separated values (CSV) file for recording results, Python version 3.7 was used to analyze the data.
Results of the study: The results showed all the three variables have a significant influence on credit consumer behaviour. The key findings of this study were loan application practice is the most influential activity in credit consumer behaviour. Additionally, there is a significant number of credit consumers in Embu county, majority who are young male consumers.
Conclusion: Cash urgency and speed of the process influence consumers to make decisions that are not rational. Mobile credit consumers are seeking faster and convenient practices
Recommendations: The researcher recommended activities that make up the loan application, receipt and repayment should be made more efficient to enhance speed, convenience and simplicity. Innovative products to attract other segments of consumers should be developed.
Keywords: Loan application, Loan appraisal, Loan repayment, Credit Consumer behaviour.
Ahmad, A., & Adnan, A. (2019). Lifestyles Concepts and Ecological Behavior: An Empirical Study In India. Serbian Journal of Management, 14(2).
Amiram, D., Beaver, W. H., Landsman, W. R., & Zhao, J. (2017). The effects of credit default swap trading on information asymmetry in syndicated loans. Journal of Financial Economics, 126(2), 364-382.
Berg, T. (2015). Playing the devil's advocate: The causal effect of risk management on loan quality. The Review of Financial Studies, 28(12), 3367-3406.
Bhardwaj, M., & Aggarwal, R. (2016). Key Drivers for Technology Adoption: A Case of Mobile Banking Adoption. Available at SSRN 2746343.
Blechman, J. G. (2016). Mobile credit in Kenya and Tanzania: Emerging regulatory challenges in consumer protection, credit reporting and use of customer transactional data. The African Journal of Information and Communication (AJIC), 17, 61-88.
Central Bank of Iraq (2016). The Credit Process. Retrieved from http://pdf.usaid.gov/pdf_docs
Chakrabarty, A. K., & Ghosh, K. (2009). Appraisal of a rural co‐operative with the thrust on rural development: an empirical study. International Journal of Social Economics.
Check, J., & Schutt, R. K. (2012). Teacher research and action research. Research methods in education, 255-271.
Cole, S., Kanz, M., & Klapper, L. (2012). Incentivizing calculated risk-taking: Evidence from an experiment with commercial bank loan officers. The World Bank.
Edoardo T. (2018). The digital credit revolution in Kenya: An assessment of market demand, 5 years on (pp. 1-18). Nairobi: FSD.
Embu County Government. (2017). County Integrated Development Plan 2018-2022. Retrieved from http://www.embu.go.ke/wp-content/uploads/2017/09/EMBU-COUNTY-DRAFT-CIDP.pdf
Frank, L. (2018). How banks and fintechs create and capture value with mobile payment via their business value network: A value network case study of ING’s Mobiel Betalen and WeChat Pay’s Quick Pay (Bachelor's thesis, University of Twente).
Gikunda, R. M., Abura, G. O., & Njeru, S. G. (2014). Socio-economic effects of Mpesa adoption on the livelihoods of people in Bureti Sub County, Kenya. International Journal of Academic Research in Business and Social Sciences, 4(12), 348.
Graziano, M. G., Meo, C., & Yannelis, N. C. (2017). Stable sets for exchange economies with interdependent preferences. Journal of Economic Behavior & Organization, 140, 267-286.
Groppa, O., & Curi, F. (2012). Mobile Money Regulation: Kenya, Ecuador and Brazil Compared. Ecuador and Brazil Compared (September 20, 2012).
Gupta, A., & Arora, N. (2017). Consumer adoption of m-banking: a behavioral reasoning theory perspective. International Journal of Bank Marketing.
Hashimov, E. (2015). Qualitative Data Analysis: A Methods Sourcebook and The Coding Manual for Qualitative Researchers: Matthew B. Miles, A. Michael Huberman, and Johnny Saldaña. Thousand Oaks, CA: SAGE, 2014. 381 pp. Johnny Saldaña. Thousand Oaks, CA: SAGE, 2013. 303 pp.
Huh, K. (2016). The Effects of Consumer’s Rational, Fashion-Oriented, Demonstrative Purchase Behavior on the Korean Famous Brand and Imported Famous Brand Purchase Behavior. Consumer Policy And Education Review, 12(4), 249-268.
Hunter, J. (2014). “All Data is Credit Data,” or, On Close Reading as a Reciprocal Process in Digital Knowledge Environments. Scholarly and Research Communication, 5(2).
Jones, B., & Miller, B. (2009). Innovation diffusion in the new economy: The tacit component. London: Routledge.
Jumia Kenya. (2018). Smartphones: The gateway to a better life. Retrieved from https://jumia.co/nl-templates-kenya/uploads/mobile-report/Jumia_MW18_White_Paper.pdf
Kenya information guide. (2018). About Embu County in Kenya. Retrieved from http://www.kenya-information-guide.com/embu-county.html
Kircova, I., & Esen, E. (2018). The Effect of Corporate Reputation on Consumer Behaviour and Purchase Intentions. Management Research and Practice, 10(4), 21-32.
Koksal, M. (2016). The intentions of Lebanese consumers to adopt mobile banking. International Journal Of Bank Marketing, 34(3), 327-346.
Kombo, D. K., & Tromp, D. L. A. (2010). Proposal and Project Writing, An Introduction.
Kubota, K., & Fukushige, M. (2016). Rational consumers. International Economic Review, 57(1), 231-254.
Lee, N., & Lee, V. (2018). Bank Lending: Principles and practice. Reading: Gosbrook Professional Publishing Ltd.
Manyika, J., Lund, S., Singer, M., White, O., & Berry, C. (2016). Digital finance for all: Powering inclusive growth in emerging economies. McKinsey Global Institute.
Mills, K., & McCarthy, B. (2014). The state of small business lending: Credit access during the recovery and how technology may change the game. Harvard Business School General Management Unit Working Paper, (15-004).
Moti, H. O., Masinde, J. S., Mugenda, N. G., & Sindani, M. N. (2012). Effectiveness of credit management system on loan performance: empirical evidence from micro finance sector in Kenya. International Journal of Business, Humanities and Technology, 2(6), 99-108.
Muigai, R. (2018). Effect of Credit Risk Management Practices on Performance of Commercial Banks in Kenya. International Journal Of Finance And Banking Research, 4(3), 57.
Mwiti, L. (2018, August 3). Central Bank moves to rein in 'exploitative? mobile lenders : The Standard. Retrieved from https://www.standardmedia.co.ke/article/2001290469/central-bank-moves-to-rein-in-exploitative-mobile-lenders
Naicker, V., & Van Der Merwe, D. B. (2018). Managers’ perception of mobile technology adoption in the Life Insurance industry. Information Technology & People.
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404-414.
Ooko, D., Nzomoi, J., & Mumo, R. (2014). Determinants of consumer switching behavior in mobile telephony industry in Kenya. International Journal of Business and Commerce, 3(5), 82-98.
Orodho, J. (2010). Techniques of writing research Projects and reports in education and social sciences. Nairobi: Kanezja HP Enterprises.
Osman, F. (2018). Mobile Money Challenges On Policies, Regulation And Security Frauds In East Africa. Mobile Money development, 5(3)21-25
Ouhibi, S., Ghabri, M., & Hammami, S. (2017). The impact of macroprudential supervision tools on financial soundness in the Southern Mediterranean countries. Asian Journal of Empirical Research, 7(4), 84-95.
Paul, J., Modi, A., & Patel, J. (2016). Predicting green product consumption using theory of planned behavior and reasoned action. Journal of retailing and consumer services, 29, 123-134.
Ponto, J. (2015). Understanding and evaluating survey research. Journal of the advanced practitioner in oncology, 6(2), 168.
Ross, V., & Bloomberg News (Firm). (2012). Ross: EU needs stable financial markets. New York: Bloomberg.
Schulte, P. (2018). Mobile Technology: The New Banking Model Connecting Lending to the Social Network. In Handbook of Blockchain, Digital Finance, and Inclusion, Volume 2 (pp. 331-359). Academic Press.
Shrestha, R. (2017). The Impact of Credit Risk Management on Profitability: Evidence from Nepalese Commercial Banks. Available at SSRN 2938546.
Simiyu, F., Namusonge, P., & Sakwa, P. (2017). Influence of Strategic Investment Management Practices on Financial Performance of Sugar Manufacturing Companies in Kenya. IOSR Journal Of Business And Management, 19(01), 05-14.
Stephens, E., & Thompson, J. R. (2017). Information asymmetry and risk transfer markets. Journal of Financial Intermediation, 32, 88-99.
Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288-1292.