Portfolio manager’s perception of the determinants of digital credit repayment in Kenya

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dc.contributor.authorNdung’u, Eva Wangari
dc.date.accessioned2022-10-05T06:41:28Z
dc.date.available2022-10-05T06:41:28Z
dc.date.issued2021
dc.descriptionA Dissertation submitted to Strathmore Business School in Partial fulfillment of the requirements for the award of Master of Science in Development Finance Degree of Strathmore Universityen_US
dc.description.abstractThe critical problem most digital credit-lending agencies face is poor loan repayment. Statistics show that loan default has been a tragedy and loan repayment problem is an unsolved issue faced by the majority of digital lending institutions. The study sought to establish the perception of the determinants of digital credit repayment in Kenya. The study specifically looked into the effect of individual/borrowers factors, loan factors and lender factors on digital credit repayment in Kenya. The research was based on prospect theory and the theory of delegated borrowers monitoring. To determine and be able to characterize the features of variables of interest, a descriptive research design was used. The study targeted all the main credit digital lenders in Kenya but the unit of observation was the credit managers, credit analysts and account relationship managers. The study adopted stratified sampling and employed the Yamane (1967) formula below to calculate the sample size of 204 respondents. The study relied on primary data gathered through questionnaires. The questionnaires were self-administered using a drop-and-pick method. Both descriptive and inferential statistical approaches were used to analyze the data. For simplicity of analysis, the data was sorted, categorized, and coded before being tabulated. The information was grouped and summarized based on common topics. The data was analyzed using descriptive statistics. The Statistical Package for Social Sciences (SPSS) was used to conduct the analysis (SPSS Version 25.0). The qualitative data from the open-ended questions was evaluated and presented in prose using content analysis. Further, inferential statistics was done using multiple regression and correlation analysis. Tables and other graphical presentations as appropriate were used to present the data collected for ease of understanding and analysis. The study established that the number of dependants; marital status; level of education; and gender affect digital credit repayment to a great extent. The study also found that repayment period and type of loan/security provided affect digital credit repayment to a great extent. The study found that number of loan installments affect digital credit repayment to a moderate extent. The study concludes that individual/borrowers factors positively and significantly affect digital credit repayment in Kenya (β=0.792, p=.000<0.05). The study further deduced that there is a negative but significant relationship between the loan factors and digital credit repayment (β=-0.229, p=.006>0.05). The study also concluded that there was a negative but significant relationship between lender factors and digital credit repayment in Kenya (β=0.457, p=.000<0.05). The study therefore concluded that individual/borrowers had the greatest effect on the digital credit repayment in Kenya, followed by lender factors while loan factors had the least effect on the Digital credit repayment in Kenya. When building loan products for the Kenyan market, digital credit lenders should take into account borrowers' demographic factors such as age, gender, marital status, occupation, education, and income, according to the study. This is because demographic elements are important and measurable population data that aid in the identification of target markets, are easier to quantify, and are appropriate for psychographic and sociocultural research. Furthermore, Kenyan digital credit lenders should take more steps to perform broad market surveys so that they can better understand the regions where they can tap into and produce lending products that are relevant to market needs. Lenders should do a better job of reporting and clarifying key loan elements so that borrowers have a clear understanding of the loan's cost, payment due dates, and the repercussions of late repayment and default.en_US
dc.identifier.urihttp://hdl.handle.net/11071/12945
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectDigital credit repaymenten_US
dc.subjectIndividual/borrowers factorsen_US
dc.subjectLoan factorsen_US
dc.subjectLender factorsen_US
dc.titlePortfolio manager’s perception of the determinants of digital credit repayment in Kenyaen_US
dc.typeThesisen_US
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MDF Theses and Dissertations (2021)