Consumer behaviour and perceptions evolve over time and affect credit usage from the financial service providers. We use the 2016 FinAccess Household survey data of 2015 from 8665 households to examine how perceptions and behaviour of un(der) banked consumers can shape their dynamics towards credit usage. The perceptions and behaviour are based on source of financial advice, trust of the institutions, characteristics of the financial instrument and cost of credit. The multinomial logistic regression model predicts the odds of credit usage based on perceptions and behaviour of the consumers. The categories for the credit usage are: have credit, used to have credit and never had credit. Consumer perceptions and behaviour based on cost of credit and trust increase credit usage, while source of financial advice had minimal influence on credit usage. The characteristics of the financial instrument are catering to emergencies and being safe to use increased credit usage. The Savings and Credit Cooperative Organizations and microfinance are the most trusted financial institutions by the consumers, while shylock has the highest cost of credit. Radio as a source of financial advice reduced credit usage. The dynamics of credit usage are shaped by the perceptions and behaviour of the consumers.
Cite this paper
Ntwiga, D. B. and Wanyonyi, A. W. (2020). Consumer Perceptions and Behaviour toward Credit Usage in Kenya. Open Access Library Journal, 7, e6204. doi: http://dx.doi.org/10.4236/oalib.1106204.
Allen, F., Demirguc-Kunt, A., Leora, K. and Peria, M. (2016) The Foundations of Financial Inclusion: Understanding Ownership and Use of Formal Accounts. Journal of Financial Intermediation, 27, 1-30. https://doi.org/10.1016/j.jfi.2015.12.003
Demirguc-Kunt, A., Klapper, L., Singer, D. and Oudheusden, P. (2015) The Global Findex Database 2014: Measuring Financial Inclusion around the World. Development Research Group Finance and Private Sector Development Team. Policy Research Working Paper 7255, The World Bank, Washington DC.
https://doi.org/10.1596/1813-9450-7255
Amidzic, G., Massara, A. and Miolou, A. (2014) Assessing Countries’ Financial Inclusion Standing—A new Composite Index. International Monetary Fund, Washington DC, Working Paper, WP/14/36.
Demirguc-Kunt, A., Klapper, L., Singer, D., Ansar, S. and Hess, J. (2017) The Global Findex Database: Measuring Financial Inclusion and the Fintech Ievolution 2017. International Bank for Reconstruction and Development/The World Bank, Washington DC. https://doi.org/10.1596/978-1-4648-1259-0
International Finance Corporation (IFC) (2017) How Fintech Is Reaching the Poor in Africa and Asia: A Start-Up Perspective. International Finance Corporation, World Bank Group, Note 34, Washington DC.
Borovicka, J. (2007) Banking Efficiency and Foreign Ownership in Transition: Is There Evidence of a Cream Skimming Effect? CERGE-EI Working Paper Series, Center for Economic Research and Graduate Education.
International Finance Corporation (IFC) (2018) Digital Access: The Future of Financial Inclusion in Africa. International Finance Corporation, World Bank Group, Washington DC.
Burke, J. and Hung, A.A. (2015) Trust and Financial Advice. RAND Corporation, Santa Monica, Working Paper Series (WR-1075). https://doi.org/10.7249/WR1075
Annim, S., Arun, T. and Koslov, P. (2012) Effect of Perceptions and Behaviour on Access to and Use of Financial Service: Evidence from South Africa. IZA Discussion, Paper No. 7042.
Linciano, N., Gentile, M., Lucarelli, C. and Soccorso, P. (2015) Financial Disclosure, Risk Perception and Investment Choices: Evidence from a Consumer Testing Exercise. CONSOB Working Paper 82. https://doi.org/10.2139/ssrn.2616277
Price Water House Cooper (PWC) (2016) DeNovo Q2 2016 FinTech Recap and Funding Review and the Un(der) Banked Is Fintech’s Largest Opportunity. DeNovo: A Platform to Understand How Disruption Impacts Business Strategy and What Actions to Take.
Financial Sector Deepening (FSD) (2016) 2016 FinAccess Household Survey. Collaboration between Financial Sector Deepening. Kenya National Bureau of Statistics and Central Bank of Kenya Report, Nairobi.
Financial Sector Deepening (FSD) (2014) A Buck Short: What Financial Diaries Tell Us about Building Financial Services That Matter to Low Income Women. Report by Bankable Frontier Associates, Nairobi.
Ntwiga, D.B., Ogutu, C. and Kirumbu, M.K. (2018) Inclusion of Peer Group and Individual Low-Income Earners in M-Shwari Micro-Credit Lending: A Hidden Markov Model Approach. International Journal of Electronic Finance, 9, 121-133.
https://doi.org/10.1504/IJEF.2018.092195
Kranz, N.C. (2005) Indicators of Financial Access: Household-Level Survey. The World Bank, Financial Sector Vice-Presidency. Conference on Measuring Access, Washington DC, October 2004.
http://documents.worldbank.org/curated/en/390821468158076107/Indicators-of-financial-access-household-level-surveys
Ntwiga, D.B. (2017) Credit Risk Analysis for Low Income Earners. Proceedings of the 6th Annual Kenya Bankers Association Banking Research Conference, Nairobi, September 2017, 1-32.
Djankov, S., Miranda, P., Seria, E. and Sharma, S. (2008) Who Are the Unbanked? Policy Research Working Paper 4647, World Bank, Washington DC.
https://doi.org/10.1596/1813-9450-4647
Agaliotis, K. and Hadzic, M. (2015) Predicting Retail Banking Consumer Behaviour Using Statistics. The European Journal of Applied Economics, 12, 43-51.
https://doi.org/10.5937/ejae12-7916
Beckett, A., Hewer, P. and Howcroft, B. (2000) An Exposition of Consumer Behaviour in the Financial Services Industry. International Journal of Bank Marketing, 18, 15-26. https://doi.org/10.1108/02652320010315325
Diacon, S. and Ennew, C. (2001) Consumer Perceptions of Financial Risk. The Geneva Papers on Risk and Insurance, 26, 389-409.
https://doi.org/10.1111/1468-0440.00125
Ntwiga, D.B., Ogutu, C., Kirumbu, M.K. and Weke, P. (2018) A Hidden Markov Model of Risk Classification among the Low Income Earners. Journal of Finance and Economics, 6, 242-249. https://doi.org/10.12691/jfe-6-6-6
Davis, F.D. (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-340.
https://doi.org/10.2307/249008
Chen, S., Li., S. and Li, C. (2011) Recent Related Research in Technology Acceptance Model: A Literature Review. Australian Journal of Business and Management Research, 1, 124-127.
Lai, P.C. (2017) The Literature Review of Technology Adoption Model and Theories for the Novelty Technology. Journal of Information Systems and Technology Management, 14, 21-38. https://doi.org/10.4301/S1807-17752017000100002
