Analysing Efficiency Change in Working Capital Management using Malmquist Productivity Index

Does the Efficiency of a Firm’s Intellectual Capital and Working Capital Management Affect Its Performance?

This study explores the efficiency of intellectual capital (ICE) and working capital management (WCME) in the GCC industrial sector and its potential impact on firm performance. The data were gathered from Standard & Poor’s database from 2015 to 2019. This study uses data envelopment analysis (DEA), regression analysis, and robustness tests to accomplish its aims. The results indicate that most firms do not employ their intellectual and working capital investments well and need improvement actions to achieve the best practices. The regression model results reveal that ICE and WCME significantly and positively influence firms’ performance. The results of this study support the resource-based, trade-off, and pecking order theories. The study findings have important implications for many stakeholders; for example, they would be helpful for firm decision-makers in managing their investments in intellectual and working capital to achieve the best practices and improve a firm's performance. In addition, the findings would be helpful for financiers, because high-performance firms are likely to have more reasonable valuations that facilitate debt financing. Moreover, the findings have noteworthy implications for trading procedures as investors aspire to attractive economic returns for their investments in corporations that pasture ICE and WCME issues. Additionally, these findings have important implications for employee job satisfaction and retention by improving IC management.

Working Capital Management as Crucial Tool for Corporate Performance in the Transport Sector: A Case Study of Slovakia and the Czech Republic

Working capital management is one of the decisive factors in increasing business performance through the efficient use of current assets such as inventories, receivables, funds, and current liabilities. The primary aim is to identify how working capital management using a wide range of liquidity and activity indicators affects the corporate performance of transport companies broken down by company size into small, medium, large, and very large companies in Slovakia and the Czech Republic using multiple linear regression analysis with achieving competitive R-square as a relevant statistical metric compared to other models from previous research. Our research focuses on a different industry than the traditional production industry. Descriptive statistics show that more than half of the assets are impelled assets in the corporate finances of transport companies. We deal with the impact of working capital management on corporate performance, considering the corporate size. This output delivers specific findings for small, medium, large, and very large businesses separately. All multiple linear regression models for estimating corporate performance are proposed for transport companies in the Czech and Slovak Republics. The results show that liquidity has a negative impact, in contrast to activity indicators except for DPO, on corporate performance in Czech transport companies. On the other hand, Slovak small, medium, and large enterprises must effectively manage free cash and cash equivalents, too. However, activity indicators, except DRO for an aggregated group of large and very large enterprises, also harm business performance. These outputs are beneficial for business management and making relevant decisions to increase business performance, the models identify the strengths and weaknesses of working capital management. In general, this research helps to make specific decisions focused on receivables, inventory management, and cash management as part of working capital management.

Analyzing the Efficiency of Working Capital Management: a New Approach Based on DEA-Malmquist Technology

In this study, we analyze the efficiency of working capital management (WCME) for Gulf companies before and during the coronavirus crisis, then explore the influence of the coronavirus crisis on WCME. This study uses several techniques to achieve its goals, including the Malmquist index (MI), data envelopment analysis (DEA), and Tobit regression. The results demonstrate that most firms (approximately 84%) adopt a conservative strategy for their WCM. The WCME results revealed a statistical difference in the technological and pure efficiency scores for companies before and during the coronavirus crisis, while the results revealed no statistical difference in the technical, scale, and total factor productivity scores. Tobit’s results show that the coronavirus crisis had no significant influence on companies’ WCM performance. Finally, our results indicate that firms that are efficient in terms of WCM have higher sales returns and net income. The findings of this study have important implications for stakeholders to increase their awareness of companies’ WCM performance before and during a crisis. In addition, the results could have implications for trading strategies as investors and financiers seek to invest in companies with good WCM. The implications of WCM performance on social interests would cause decision-makers to use the best strategies and procedures to enhance WCM activities to improve their investments and image in the community in which it operates. We advance a novel contribution to the literature by analyzing and appraising the WCME for companies before and during the coronavirus crisis using a new approach based on DEA-Malmquist technology and then examining whether the coronavirus crisis has affected the WCME.

Exploring predictors of working capital management efficiency and their influence on firm performance: an integrated DEA-SEM approach

Purpose

This study develops an integrated approach combining data envelopment analysis (DEA) and structural equation modeling (SEM) for estimating the working capital management (WCM) efficiency and evaluating the effects of diverse exogenous variables on the WCM efficiency and firms' performance.

Design/methodology/approach

DEA is applied for deriving WCM efficiency for 212 Indian manufacturing firms over a period from 2008 to 2019. Also, the effect of human capital (HC), structural capital (SC), cost of external financing (CEF), interest coverage (IC), leverage (LEV), net fixed asset ratio (NFA), asset turnover ratio (ATR) and productivity (PRD) on the WCM efficiency and firms' performance is examined using SEM.

Findings

The average mean efficiency scores ranging from 0.623 to 0.654 highlight the firms operating at around 60% of WCM efficiency only, which is a major concern for Indian manufacturing firms. Further, IC, LEV, NFA, ATR revealed direct effect on the WCM efficiency as well as indirect effect on firms' performance, whereas CEF had only a direct effect on WCM efficiency. HC, SC and PRD had no effects on WCM efficiency and firms' performance.

Practical implications

The findings offer vital insights in guiding policy decisions for Indian manufacturing firms.

Originality/value

This study is the first to identify the endogenous nature of the relationship of HC, SC, CEF, IC altogether with firms' performance, compounded by the WCM efficiency, by applying a comprehensive methodology of DEA and SEM and provides an efficiency performance model for better decision-making.

Benchmarking the efficiency model for working capital management: data envelopment analysis approach

Purpose

This paper evaluates the working capital management (WCM) efficiency of the Indian manufacturing industries through data envelopment analysis (DEA) and empirically investigates the influence of several exogenous variables on the WCM efficiency.

Design/methodology/approach

WCM efficiency was calculated using BCC input-oriented DEA model. Further, the panel data fixed effect model was used on a sample of 1391 Indian manufacturing firms spread across nine industries, covering the period from 2008 to 2019.

Findings

Firstly, the WCM efficiency of Indian manufacturing industries has been stable over the analysis period. Secondly, the capacity to generate internal resources, size, age, productivity, gross domestic product and interest rate significantly influence WCM efficiency.

Research limitations/implications

First, the selected study period has observed various economic uncertainties including demonetization and recession, so the scenario might differ in normal conditions or country-wise. Second, the findings might not be generalizable to the developed economies, since the current study sample belongs to a developing economy, which further provides scope for comparative study.

Practical implications

An efficient model for managing the working capital comprising most vital determinants could enhance the firms' valuation and goodwill. Also, this study would be helpful for financial executives, manufacturers, policymakers, investors, researchers and other stakeholders.

Originality/value

This study estimates the industry-wise WCM efficiency of the Indian manufacturing sector and suggests measures to the concerned parties on areas to focus on and provide evidence on the estimated relationships of firm-level and macroeconomic determinants with WCM efficiency.