A reassessment of the relationship between working capital management and firm performance: evidence from non-financial companies in Nigeria

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This paper reassesses the relationship between working capital management (WCM) and firm performance in the Nigerian context. The study is motivated by the limited insights available on the impacts of WCM on firm performance in the country. To date, most studies from Nigeria have been largely descriptive and focused on a small sample size that is non-representative of the population. In addition, there are limited rigorous statistical analyses involved in such studies. This paper addresses the methodological limitations apparent in prior literature and provides a better understanding of the relationship between WCM and firm performance, revealing how firms can manage their operations more profitably. The paper adopts a panel data regression analysis on a sample of 75 non-financial firms listed on the Nigerian Stock Exchange from 2007 to 2015. The results of the analyses showed that WCM variables have an inconsistent relationship with the measures of performance adopted, which were return on assets and Tobin’s Q. Specifically, accounts receivable management and inventory management were negatively associated with the return on assets, while accounts payable management, cash conversion cycle and cash conversion efficiency were positively associated with return on assets. Additionally, accounts receivable management and inventory management were positively associated with Tobin’s Q, whereas accounts payable management, cash conversion cycle and cash conversion efficiency were negatively associated with Tobin’s Q. These results were found to be robust using quantile regression. The results of the quantile regression showed inconsistency across the various quantiles used (0.10, 0.25, 0.50 and 0.75). These findings have two important implications. The first is that WCM variables influence the performance of firms. The second is that the mixed findings partly indicate that firms and managers must understand and formulate WCM policies that reflect their peculiar conditions.

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    • Table 1. Sample selection distribution
    • Table 2. Summary of variables and measurements
    • Table 3. Descriptive statistics
    • Table 4. Descriptive statistics of logged variables
    • Table 5. Correlations
    • Table 6. Regression results of WCM and firm performance
    • Table 7. Result of quantile regression (QR) for the ROA model
    • Table 8. Result of quantile regression (QR) for the TQ model
    • Table A1. Industry classification of a sample