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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 current study aims to explore the relationship between time perspective and knowledge hiding at the workplace.

The research adopts a cross-sectional design. Data were collected using purposive sampling technique from (n = 425) employees representing five sectors of companies.

Holding onto negative experiences of the past (past negative TP) and perceiving that one cannot control the outcomes of a circumstance (fatalistic TP) predict all three types of knowledge hiding. Staying in a perspective of working towards achieving a goal (futuristic TP) was negatively (although insignificantly) associated with knowledge hiding.

If organizations focus on shifting the time perspective of employees towards the future, KH can be curtailed. Consequently, knowledge management can be enhanced.

A decade of research has focused upon identifying dynamics of knowledge hiding at multiple levels, yet, the influence of TP on knowledge hiding is a novel empirical contribution to literature.

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.

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.

Time and again, scholars have emphasized the vitality of mental toughness for success in performance-oriented contexts. Despite the awareness about the significance of mental toughness, there is ambiguity in the conceptual consensus of the factors that comprise of the construct in an organizational setup. Second, there is a dearth of a psychometrically sound measure that assesses mental toughness among employees.

The study follows a multi-method approach to develop a mental toughness questionnaire. First, to arrive at a consensus of the factors that construe mental toughness, a meta-ethnography was done. Subsequently, a measure of mental toughness was developed and tested following scale development norms.

Drawing from the results of qualitative inquiry, four factors of mental toughness were derived, namely, perseverance, control, challenge and commitment. Then, the scale development process was followed. Results of psychometric testing using three samples were above the acceptable range, justifying the use of developed scale for academic and professional purposes.

This study is a novel attempt in the literature to extract factors of mental toughness through meta-ethnography and consequently develop a scale.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

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.

After reading and discussing the case, the participant would be able to: comprehend the ethics in marketing strategies that were displayed by an entrepreneur in the challenging times of the Covid-19 pandemic; appreciate how the marketing was executed in a socially responsible manner through digital means when there was a conflict between the business and the social interest; and understand how the innovative services or products can be developed that turn the trouble into an opportunity. The case shall also enlighten the students on how to create suitable marketing messages in digital times. The purpose of this paper is to let the students appreciate how they can carry out marketing efforts for a business while keeping customer needs and aspirations at the core, how the marketing campaigns can be designed and executed in a socially responsible manner and how the product portfolio can be suitably altered to make it more valued to the customer. After reading and discussing this case, the students will also be able to appreciate that it is important to identify and address the customer’s pain which may be stated or unstated by the customer. The case intends to teach students how to identify and reap the opportunities that get created from time to time and to make them appreciate that businesses can contribute significantly towards societal gains by committing minimal resources.

The mainstream marketing discipline focuses on excessive consumerism as opposed to the concept of “socially responsible marketing” which advocates that business initiatives should be supported by ethical considerations. The coworking industry was one of the worst affected industries by the pandemic since their customers started working from their homes during and post the lockdowns, leading to a loss in revenues. The protagonist in the case had a strong belief that the business interests would be secondary to the overall interest of society, and therefore, he advised the customers on how they could work productively, safely and stress-free from their homes. As the lockdown was un-eased, the marketing campaigns were launched and executed in a very ethical manner, while designing innovative service offerings were designed.

The case is relevant for the students pursuing undergraduate and graduate studies in the field of business and management.

Teaching notes are available for educators only.

CSS 8: Marketing.

Millennial managers are required to adopt to contemporary management practices and continually evolve to manage the workforce. To help them evolve and create positive workplaces, this study aims to extrapolate the associations between job crafting, mental toughness and authentic happiness (AH) among millennials and derive implications.

Data was collected from 496 millennial employees at Time-1 (March 2018), Time-2 (November 2018) and Time-3 (August 2019) from multiple sectors. A combined structural equation modelling and artificial neural networks approach was implied to test the strength of the proposed associations.

With reference to the relationships thus obtained between job crafting, mental toughness and AH, the results specifically conclude that challenging job demand is significant predictor of AH. Mental toughness partially mediates the relationship between challenging job demands and AH, indicating that mental toughness is an essential component for employee happiness.

Millennial managers and policymakers must challenge the skills of employees for obtaining optimal performance. When employees perceive adequate workload and have a set deadline for the completion of tasks assigned to them, they carry out the tasks with greater efficacy, in turn, leading them to find greater meaning (focusing on essential tasks), purpose and engagement in life (enthusiastic, interested, engaged). Overall, when millennial managers focus on such associations, they can help enhance productivity and constitute happier workplaces.

This study explores constructs such as mental toughness to support the job crafting and happiness relationship. A hybrid statistical analysis strengthens the accuracy of the proposed model and enhances its empirical and implied value.

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.