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Debt Financing and Operational Efficiency of Companies listed at Nairobi Stock Exchange, Kenya
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Background: As companies evolve, they confront a myriad of challenges, opportunities, and risks, demanding a laser focus on financial strategies. Within this landscape, the intertwined concepts of debt financing and operational efficiency emerge as critical determinants of a firm's performance and longevity. Debt financing sources presents an intricate landscape in which companies make their financial decisions. However, the existing literature often treats debt financing as a homogenous entity, lacking the granularity required to discern how the specific composition of debt, including the reliance on trade credit versus traditional short-term and long-term bank borrowings, might influence operational efficiency. Thus, this study aimed at identifying the effect of debt financing composition on operational efficiency of companies listed in NSE.
Methods: The research focused on examining the link between debt financing structures and operational efficiency in 50 firms listed on the Nairobi Securities Exchange (NSE). The study collected panel data ranging between 2015 and 2022. A positivist research philosophy was employed, utilizing quantitative analysis techniques, specifically descriptive statistical analysis and random effects regression model.
Findings: The regression results of the random effects model reveal the relationship between debt financing constructs and operational efficiency. The overall R-squared value for the model is 0.176, suggesting that around 17.6% of the variation in operational efficiency can be explained by the debt financing sub-variables. Furthermore, the model’s goodness of fit is assessed through the Wald chi-square test (χ² =72.61, p = 0.000), confirming its statistical significance. The coefficient for the independent variables trade credit, short-term borrowings, and long-term borrowings are (β = 0.331, p = 0.002), (β = 0.642, p = 0.001), and (β = 0.349, p = 0.001), respectively. The p-values are less than 0.05, indicating that the effects of trade credit, short-term borrowings, and long-term borrowings on the operational efficiency of firms listed at the NSE are statistically significant at the 5% level.
Conclusion and Implications: The study concludes that debt financing comprising of trade credit, short-term borrowing and long-term borrowing have beneficial effects on operational efficiency. While all three sub-variables of debt financing contribute to enhanced operational efficiency, their application in corporate financing practices varies, with long-term borrowing being a common choice for funding total assets. In light of these findings, company managers should recognize debt financing as a strategic tool for improving operational efficiency. This recognition should be accompanied by a careful and thoughtful approach to utilizing the three components of debt: trade credit, short-term borrowing, and long-term borrowing.
Sciencedomain International
Title: Debt Financing and Operational Efficiency of Companies listed at Nairobi Stock Exchange, Kenya
Description:
Background: As companies evolve, they confront a myriad of challenges, opportunities, and risks, demanding a laser focus on financial strategies.
Within this landscape, the intertwined concepts of debt financing and operational efficiency emerge as critical determinants of a firm's performance and longevity.
Debt financing sources presents an intricate landscape in which companies make their financial decisions.
However, the existing literature often treats debt financing as a homogenous entity, lacking the granularity required to discern how the specific composition of debt, including the reliance on trade credit versus traditional short-term and long-term bank borrowings, might influence operational efficiency.
Thus, this study aimed at identifying the effect of debt financing composition on operational efficiency of companies listed in NSE.
Methods: The research focused on examining the link between debt financing structures and operational efficiency in 50 firms listed on the Nairobi Securities Exchange (NSE).
The study collected panel data ranging between 2015 and 2022.
A positivist research philosophy was employed, utilizing quantitative analysis techniques, specifically descriptive statistical analysis and random effects regression model.
Findings: The regression results of the random effects model reveal the relationship between debt financing constructs and operational efficiency.
The overall R-squared value for the model is 0.
176, suggesting that around 17.
6% of the variation in operational efficiency can be explained by the debt financing sub-variables.
Furthermore, the model’s goodness of fit is assessed through the Wald chi-square test (χ² =72.
61, p = 0.
000), confirming its statistical significance.
The coefficient for the independent variables trade credit, short-term borrowings, and long-term borrowings are (β = 0.
331, p = 0.
002), (β = 0.
642, p = 0.
001), and (β = 0.
349, p = 0.
001), respectively.
The p-values are less than 0.
05, indicating that the effects of trade credit, short-term borrowings, and long-term borrowings on the operational efficiency of firms listed at the NSE are statistically significant at the 5% level.
Conclusion and Implications: The study concludes that debt financing comprising of trade credit, short-term borrowing and long-term borrowing have beneficial effects on operational efficiency.
While all three sub-variables of debt financing contribute to enhanced operational efficiency, their application in corporate financing practices varies, with long-term borrowing being a common choice for funding total assets.
In light of these findings, company managers should recognize debt financing as a strategic tool for improving operational efficiency.
This recognition should be accompanied by a careful and thoughtful approach to utilizing the three components of debt: trade credit, short-term borrowing, and long-term borrowing.
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