The effect of working capital management on profitability: a case of listed manufacturing firms in South Africa

  • Received May 18, 2017;
    Accepted June 27, 2017;
    Published August 8, 2017
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  • Article Info
    Volume 14 2017, Issue #2 (cont. 2), pp. 336-346
  • Cited by
    17 articles

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Working capital management plays a pivotal role in enhancing the operational efficiency of firms and their ultimate profitability. Therefore, the purpose of this study was to examine the trends in working capital management and its impact on the financial performance of listed manufacturing firms on the Johannesburg Securities Exchange (JSE). A panel data methodology was used with different regression estimators to analyze this relationship based on an unbalanced panel of 69 manufacturing firms listed during the period 2007–2016.
The findings revealed that the average collection period and the average payment period are negative and statistically significant for profitability, implying that firms which efficiently manage their accounts receivable and those that pay their creditors on time perform better than those that do not. Additionally, a positive statistically significant relationship between the number of days in inventory and profitability was supported suggesting that firms which stock-up and maintain their inventory levels suffer less from stock-outs and avoid challenges of securing financing when needed. This increases their operational efficiency and ensures profitability in the long run. It could not be ascertained whether a shorter or longer cash conversion cycle enhances firm profitability, since findings to support this premise were weak. However, it was observed that manufacturing firms are on average, carrying lot of debt in their capital structures.
The present study contributes to existing literature by presenting one of the very recent findings on this topic while simultaneously testing the validity of recent local and international methodologies, in order to inform policy change.

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    • Table 1. Descriptive results of all variables over the 10-year period
    • Table 2. Pearson’s correlation analysis
    • Table 3. Multivariate regression estimates for study models using REM
    • Table 4. Multivariate regression estimates using pooled OLS, REM and FEM