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Working capital efficiency (WCE) is crucial for the sustainability of both large and small firms. This study aims to use the sample of micro, small and medium-sized enterprises (MSMEs) in India and tries to understand the critical determinants of WCE.

Using a fixed effect panel data model on a sample of 578 MSMEs (59 micro, 226 medium and 296 small firms), this study explores the relationship between the predictors of WCE. Additionally, the study adopted two metrics for measuring WCE among each type of firm (micro, small and medium).

Several firm-specific variables, including leverage (lever), firm age (AGE), firm size (Fsiz), profitability (Prof), extended payment terms (EPT), human capital (HCap), asset turnover ratio (ATR), reverse factoring (RF) and firm growth (FG), have a significant effect on working capital management efficiency (WCE). In contrast, tangibility (Tangib) and salary expenses (Sal) had an insignificant effect on working capital management efficiency.

The study is based on secondary data. Future studies may incorporate some primary data, which will facilitate qualitative analysis.

The studies explore the relationship between WCE and expenses in HCap, EPT, RF and Sal as the predictors for WCE, which was not studied earlier in MSMEs scenario, especially in case of developing nation.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to empirically investigate the relationship between working capital management (WCM) efficiency and exogenous variables of the Indian manufacturing sector along with its sub-industries that are involved in export activities.

Panel regression (fixed effects) was used on a sample of 563 Indian manufacturing firms involved in export activities, covering a time period from 2008 to 2018.

Industry-wise results showed a significant relation of leverage, net fixed asset ratio, profitability, asset turnover ratio, total asset growth rate and productivity with cash conversion cycle (CCC).

Firstly, having taken a sample from a developing economy, the results of our study may be generalizable only among developing contexts. Secondly, the time period taken in this study (2008–2018) has witnessed several economic fluctuations such as recession and demonetization which might differ for the firms or countries in normal conditions.

An improved working capital model could advance the firms' performance by reducing the CCC of the firm, thereby creating efficiency in WCM. In addition, the results of this study could be helpful for many stakeholders such as working capital managers, debt holders, investors, financial consultants and others for monitoring the firms.

This study contributes to the existing literature in the relation between WCM efficiency and exogenous variables of the Indian manufacturing firms engaged in the export activities. Moreover, this study is one of the few research studies to investigate this relationship among Indian export firms in different industries, thus filling the gap in similar work done in other countries.

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.

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.

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.

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

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.

This study aims to assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

The purpose of this study is to get insights into working capital management (WCM) practices and the determinants of its efficiency prevailing in the Indian manufacturing sector using firm-specific as well as macro-economic variables by examining three efficiency models, i.e. cash conversion cycle (CCC), cash conversion efficiency (CCE) and net working capital level (NWCL).

The study uses panel data techniques on 1,207 firms of the Indian manufacturing sector, as well as on its nine key manufacturing industries from 2008 to 2018 for the analysis.

Several firm-specific variables such as net fixed asset ratio, size of the firm, profitability, firm’s growth, asset turnover ratio, age of the firm, interest rate and leverage have significant effect on WCM efficiency, whereas total assets growth rate, gross domestic product growth rate and inflation rate have insignificant effect on WCM efficiency.

The study provides new empirical evidence on the short-term liquidity management of manufacturing firms prevailing in the developing countries such as India. The findings are particularly relevant in the present scenario when the liquidity levels are decelerating and there is a marked slowdown in private credit flows to the manufacturing sector due to the problem of burgeoning non-performing assets.

This study examines WCM efficiency exhaustively by incorporating both firm-specific and macro-economic variables using three efficiency measures, i.e. CCC, CCE and NWCL, results of which emerged as an answer to an efficient WCM.

The purpose of this paper is to study the key determinants of capital structure for Indian manufacturing firms and which theory implications, i.e. trade off vs pecking order are more applicable in current Indian manufacturing sector scenario.

A sample size of 422 listed Indian manufacturing companies on Bombay Stock Exchange has been considered to do the empirical evaluation. A ten year period from 2003-2004 to 2012-2013 and annual financial standalone data have been considered for study. Ratio analysis and panel data approach have been applied to perform the empirical evaluation. Total debt to total capital and total debt to total assets are used as the proxy for firm financial leverage.

It was empirically found that size, age, asset tangibility, growth, profitability, non-debt tax shield, business risk, uniqueness and ownership structure are significantly correlated with the firm financial leverage or key determinants of capital structure in Indian manufacturing sector. Also, other variables like dividend payout, liquidity, interest coverage ratio, cash flow coverage ratio (CFCR), India inflation and GDP growth rate are empirically found to be insignificant to determine the capital structure of Indian manufacturing sector. There is no single theory implications, i.e. trade off vs pecking order which can explain the capital structure nature of Indian manufacturing sector and rather it is a mix of both the theories.

The findings of the study would enhance the literature on capital structure and is significant for the Indian manufacturing firm’s decisions as it includes the most recent data and covers the period of both pre- and post-recession of 2008-2009.

General management/strategy.

Case B: On April 4, 2013, the meeting of GMR’s Group Executive Council (GEC) was scheduled to take place. Srinivas Bommidala, G.M. Rao’s son-in-law and Chairman of GMR’s airports business, was gearing up for the meeting. The meeting was called to discuss a proposal for bidding for an upcoming airport project in the Philippines. It had been more than a decade since GMR entered the airport infrastructure sector. The organization had built substantial airport operating expertise during that period. It adopted a joint venture (JV) model for expanding into the airport infrastructure business. Until now the organization had always formed JVs for all its airport projects. JVs, with existing airport operators, were necessitated by the bid conditions that required a certain minimum airport operating experience for qualifying as a bidder for various projects. In some cases, JV with a local player helped GMR with market knowledge for functioning in a foreign market. GMR also used JVs to access the capabilities it lacked for operating in this sector and gradually learnt from its partners for building capabilities in-house. The group now had the required operating expertise in the sector to qualify as a bidder. One of the key issues the GEC was contemplating was: Whether GMR should continue to form JV for bidding for the upcoming project or should it go solo? Further, if it had to form a JV then, in which areas should it seek a partner?

Case B: To help students understand how companies use alliances as growth strategies; to understand the rationale for formation of various alliances; to explore various factors managers consider when deciding on alliance strategy of an organization; to understand the challenges associated with using alliances as a strategic option; and to understand the pros and cons of internal development (i.e. going solo) vis-à-vis strategic alliances.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 11: Strategy.

The purpose of this paper is to explore the role of business intelligence (BI) in a consulting company. The authors propose to analyze quality through data analysis and efficiency under different business contexts. The best processes and tools in data mining are also explored.

Management perspectives of data analysis from Florilla Consulting Company are collected using an inductive research approach. Based on a large sample of qualitative data, cost-and-benefit analysis is used to assess the BI technologies as a strategic necessity to Florilla Consulting Company.

Findings classify the best processes and tools of data analysis under different business scenarios. The authors also propose a revised process and tools for Florilla Consulting Company to be further evaluated by future research.

The insights offered in this paper derives authentic value for any consulting company that is interested to benefit from the opportunities bought by the BI technologies. Quality management also gets a new dimension when technology is integrated into business.

This study has challenged the way quality is managed in Florilla Consulting Company. The connection of BI to quality management is explored via an empirical study of a consulting company by linking theory with practice.