肯尼亚内罗毕证券交易所上市公司的债务融资与运营效率

Joseph Kimathi James, Patricia Gachambi Mwangi, Henry Kimathi Mukaria
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引用次数: 0

摘要

背景:随着公司的发展,他们面临着无数的挑战、机遇和风险,需要专注于财务战略。在这种情况下,债务融资和运营效率的概念交织在一起,成为公司业绩和寿命的关键决定因素。债务融资来源呈现出公司做出财务决策的复杂局面。然而,现有文献往往将债务融资视为一个同质实体,缺乏必要的粒度来辨别债务的具体构成,包括对贸易信贷的依赖与传统的短期和长期银行借款的依赖,可能如何影响运营效率。因此,本研究旨在确定债务融资构成对NSE上市公司运营效率的影响。方法:研究的重点是检查在内罗毕证券交易所(NSE)上市的50家公司的债务融资结构和运营效率之间的联系。该研究收集了2015年至2022年间的面板数据。采用实证主义的研究理念,运用定量分析技术,特别是描述性统计分析和随机效应回归模型。研究发现:随机效应模型的回归结果揭示了债务融资结构与经营效率之间的关系。该模型的总体r平方值为0.176,表明约17.6%的运营效率变化可以由债务融资子变量解释。进一步,通过Wald卡方检验评估模型的拟合优度(χ²=72.61,p = 0.000),证实其具有统计学意义。自变量贸易信用、短期借款和长期借款的系数分别为(β = 0.331, p = 0.002)、(β = 0.642, p = 0.001)和(β = 0.349, p = 0.001)。p值小于0.05,表明在5%的水平下,贸易信贷、短期借款和长期借款对NSE上市公司经营效率的影响具有统计学意义。结论与启示:本研究认为由贸易信贷、短期借贷和长期借贷组成的债务融资对企业的经营效率有有益的影响。虽然债务融资的所有三个子变量都有助于提高运营效率,但它们在企业融资实践中的应用各不相同,长期借款是为总资产融资的常见选择。鉴于这些发现,公司管理者应该认识到债务融资是提高运营效率的战略工具。在认识到这一点的同时,应采取谨慎和深思熟虑的办法,利用债务的三个组成部分:贸易信贷、短期借款和长期借款。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Debt Financing and Operational Efficiency of Companies listed at Nairobi Stock Exchange, Kenya
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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