Determinants of credit risk management techniques among automotive companies in Nairobi City County

Abstract

The practice of purchasing vehicles on credit has introduced a dynamic element to the industry, with inherent risks associated with default. However, this rapid growth, spurred by rising car ownership and fueled by loan-based purchases, rests on a delicate foundation: credit risk. This high loan volume amplifies the potential impact of loan defaults. Effective credit risk management can reduce defaults by unlocking further expansion and financial opportunities for the industry. As a result, the purpose of this research is to investigate the factors that determine credit risk management among automotive firms in Nairobi City County. The specific objectives of the study were to evaluate the effect of firm specific determinants, industry- specific determinants, macroeconomic determinants on credit risk management of automobile firms in Nairobi City County, and to investigate the moderating effect of corporate governance on the relationship between determinants and credit risk management of automobile firms in Nairobi City County. Agency, resource dependency and information asymmetry theories. The positivist methodology is appropriate for this research because of the quantitative character of the data. The study adopted a descriptive survey design. The study targeted 51 registered motor vehicle dealers and assemblers in Nairobi City County. The unit of analysis was motor vehicle dealers and assemblers while the unit of inquiry was finance managers The study utilized census sampling technique, and the sample size is 51. The study was collected primary data through a structured questionnaire. The study period was between March and April 2025. To ensure data quality, the study utilized content validity to establish suitability of the instrument and Cronbach alpha to establish internal consistency. The study used descriptive statistics for quantitative data that shall include means generation, frequencies, standard deviation and percentages to help generalize the findings. Besides, the study used inferential statistics such Pearson correlation and inferential statistics with aid of SPSS version 26.0. Tables and figures in the form of pie charts and bar graphs were adopted for presentation of data. The analysis revealed that the firm specific factors had significant impact on the model, with a coefficient (B) of 0.287 and a p-value of 0.000. The beta coefficient for Industry specific factors was 0.221. The observed values are both statistically significant (B=.207, p=.013). Another variable that made a distinct and important contribution to the model was the Macro-economic determinants (B=.234, p=.001). The introduction of corporate governance as a moderator increased the R square from 60.5% to 79.8%, this change was significant at P<0.05. The study concluded that three determinants had significant effect on credit risk management while corporate governance is a significant moderator on the relationship between determinant and credit risk management. This study contributes to both theoretical and practical knowledge by demonstrating how firm-specific, industry-specific, and macroeconomic determinants influence credit risk management in the automotive sector. It highlights the significant moderating role of corporate governance and offers policy and managerial recommendations to enhance credit risk practices through governance and technological integration. However, the study is limited to Nairobi City County, which may restrict the generalizability of findings to other regions. Its cross-sectional design limits causal inference over time, and reliance on self-reported questionnaire data may introduce respondent bias. Additionally, the study excludes qualitative insights that could offer deeper contextual understanding.