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This paper aims to explore whether firms’ performance determines innovation using a sample of Indian manufacturing firms. The impact of innovation on firms’ performance across specific countries has been discussed in the literature. However, the effect of firms’ performance on innovation output, especially for a developing country like India, remains an open question. Against this backdrop, this paper investigates whether firms’ performance determines innovation in Indian manufacturing firms.

The authors use patent filing information to instrument innovation and total factor productivity to instrument firms’ performance. The patent data are collected from the Patent Search and Analysis Software database and firm-level data from the Centre for Monitoring Indian Economy’s Prowess database. The study uses a sample of 309 Indian manufacturing firms from 2005 to 2021. Given the count nature of the data set used in this study coupled with over-dispersion issues, the authors have used the negative binomial regression to estimate the empirical specification of the models. There could be a possible problem of endogeneity due to the contemporary nature of innovation and firms’ performance. Therefore, to address the possible issues of endogeneity in the model, the authors have used the Generalized Method of Moments (GMM) estimators for more robustness checks of the empirical results.

The empirical results exhibit a positive and significant impact of firms’ performance on the innovation output, validating that firms’ performance determines innovation in Indian manufacturing firms. The posterior estimation results using GMM estimation also corroborate that firms’ productivity is a determining factor for the innovation output of Indian manufacturing firms. Furthermore, empirical results exhibit that the ex ante innovativeness of the firms substantially affects the current innovation. This validates that the firms’ prior experience, learning by doing and past innovative efforts are more likely to precipitate more innovation in the current period.

This paper’s main contribution is empirically estimating whether firms’ performance determines innovation, which is hardly discussed in the existing innovation literature, specifically using Indian manufacturing industries. Further, it adds to the existing literature in two other prominent ways. First, this paper investigates whether firms require ex ante expertise to innovate or if a firm starting from scratch can innovate significantly without any hindrances. Second, it enriches the literature by instrumenting innovation in output terms with the patent application against input measures of innovation, such as research and development expenditures, acquisition of machinery and equipment, while discussing the relationship between firms’ performance and innovation, specifically in the context of a developing economy like India.

The purpose of this paper is to study the influence of inflated energy prices on the capital structure of Indian manufacturing corporations and to investigate whether the capital structure of Indian firms is driven by demand shocks or supply shocks during the study period.

After conducting a thorough review of the capital structure and inflation-based research studies, panel data-based regression model and correlation matrix have been used as statistical tools for Indian manufacturing sector available with the Centre for Monitoring Indian Economy Prowess database.

The results suggest that variables like the presence of inflated energy prices had adversely influenced the capital structure of Indian corporations. Not only this, the study also highlights that factors pertaining to the demand shock had induced Indian corporations to have higher debt levels in the capital structure.

This study has laid some ground work to explore the influence of inflation on capital structure of Indian firms upon which a more detailed evaluation could be based.

To the best of the authors’ knowledge, this study is the first that explores the influence of inflated energy prices on the capital structure of manufacturing firms in India by using the most recent data.

While firms in developing countries are increasingly adopting lean practices of inventory management, there is limited evidence showing the impact of lean practices on firm performance in countries such as India. Lean practices improve the financial performance of the firms through superior cost-reduction measures and operational efficiencies. This paper examines the impact of inventory leanness in Indian manufacturing firms on their financial performance.

The authors measure inventory leanness based on stochastic frontier analysis (SLA), apart from using conventional measures available in the literature. The authors analyze the impact of inventory leanness on the financial performance of firms by examining data for 12,334 unique Indian manufacturing firms for the period 2009–2018. The authors present a comparative analysis using different methods of inventory leanness and study the effects on firm performance.

First, the authors find that only 68 industries out of 411 industries follow lean practices, i.e. most industries do not follow lean practices. Second, the estimation results show that there exists a positive relationship between inventory leanness and firm performance. The results suggest that an inverted U-shaped relationship exists between inventory leanness and firm performance for the entire sample. In particular, 17% of the industries in the sample exhibit such a relationship, and it is sufficiently strong to show up in the average regression results for the entire sample.

The authors introduce a novel measure of inventory leanness named stochastic frontier leanness based on the SFA method used in production economics. It measures leanness by benchmarking the inventory levels against the industry “frontier”. Furthermore, the authors conduct an empirical study of the lean-financial performance relationship with a large panel dataset of Indian firms instead of the survey-based methods that were previously used in the literature.

Transitioning to a low-carbon economy requires a positive response by society, including business organizations, towards the green concept and also requires the implementation of long-term green strategies. These requirements could impose various transition risks on the sustainable development of the firms; hence, the present study aims to examine the impact of climate transition risk on a firm’s financial performance and market value creation from the Indian perspective.

We have considered the firm-level environmental risk score (ERS) to evaluate the sensitivity of a firm’s profitability (measured by ROA & ROE) and market value (measured by Tobin’s Q) towards the climate transition risk. The present study used multiple regression analysis to examine the impact of climate transition risk on the firm’s financial performance and market value creation, as evidenced by Nifty 50 companies.

The empirical results suggested that corporate climate transition risks have been positively associated with the firm’s financial performance indicators but negatively impacted the firm’s market value creation in the case of select Indian-listed firms. Hence, our results indicate that with the increase of firm-level climate transition risk, the firm’s financial performance increases but negatively affects the firm’s market value creation. The robustness tests have also confirmed the same results and supported our analysis.

The present paper contributes to the existing literature on climate risks and firms’ performance by providing insights about firms’ sensitivity towards climate transition risk from the Indian perspective.

Given the dominance of family ownership in India, this paper aims to examine whether the impact of board gender diversity (BGD) on voluntary disclosure (VD) is moderated by family ownership.

Based on a panel data set of the top 100 listed Indian firms for five years, this study examines the impact of BGD on VD by segregating the sample between family-owned and nonfamily firms. For empirical analysis, we use appropriate panel data models. For robustness, we employ a three-stage least square (3SLS) model.

The findings reveal the significant positive impact of BGD in terms of its different measures on VD for family and nonfamily firms. However, the impact becomes insignificant for nonfamily-owned firms when female directors are not substantially represented on the board.

This study extends the ongoing debate about the outcomes of the mandatory gender quota on board by providing novel evidence on the difference between the impact of BGD on VD for family and nonfamily firms in the Indian context.

This paper aims to empirically investigate the moderating role of corporate governance (CG) in the capital structure-performance relationship.

The analysis is based on top Business Today-500 companies and covers a time span of 10 years. The fixed effect panel regression model is used to examine the impact of CG mechanisms on the relationship between capital structure and firm performance.

The core findings of the study indicate significant positive moderating role of board independence, board size and family ownership on the relationship between leverage and performance.

The results enable the managers of Indian firms to comprehend the significance of CG framework while taking financing decisions. The findings encourage managers to raise debt funds in those firms that adhere to good governance norms.

Unlike extant studies that emphasize on the moderating impact of single CG variable in leverage-performance relationship, the current work comprehensively examines the role of many CG factors that moderate the relationship between capital structure and firm performance. To the best of the authors’ knowledge, the present study is the first of its kind with respect to India.

This study examines how intellectual capital (IC) drives firm performance via the lens of dynamic capabilities (DCs). Drawing on resource-based view (RBV) and dynamic capability view (DCV), the authors elaborate the mediating role of learning, integration and reconfiguration DC in the Indian banking context.

A sample of 358 top- and middle-level managers from the Indian banking sector was administered with structured questionnaires for data collection. Structural equation modeling (SEM) and Sobel test were used to analyze the data and test the hypothesized mediating effect.

The findings reveal that learning and integration DCs are key mediators in IC and banks' performance relationships in an emerging economy context. In contrast, the analysis revealed partial mediating role of reconfiguration DC. Furthermore, the learning DC has been identified as the primary mediating mechanism for transforming bank's IC into performance benefits.

This study provides an important implication for the IC and DC link by empirically developing and validating a model in the Indian banking sector and making a several contributions to the related literature. This sector needs to incorporate and strengthen their IC and DCs to attain enhanced performance in today's dynamic environment. Bank managers can use these findings to bring their knowledge-related activities to channelize specific DCs to transform banks' IC when seeking to improve overall performance. Theoretically, this study extends previous research by outlining a set of organizational elements that tend to influence firm performances with the help of IC, learning, integration and reconfigurations DCs.

Although several studies have investigated the links between IC, DC and firm performance, studies on emerging economies are scarce. This study is one of the most in-depth investigations of the relationship between IC, learning, integration and reconfiguration DCs and firm performance in an integrated framework, with a particular focus on the banking sector of an emerging economy.

This study aims to examine the influence of corporate carbon emissions on the financial performance of select Indian companies. It further studies the moderating role of science-based target initiatives (SBTi) in this relationship.

The study is based on 57 Indian SBTi companies and 74 Bombay Stock Exchange-listed non-SBTi companies for the period of four years from 2019–2020 to 2022–2023. The panel data regression models are used to study this association. Furthermore, two-stage least square and generalized method of moments models are used to test the robustness of the results.

There is a negative relationship between corporate carbon emissions and financial performance. The findings support the “win-win” hypothesis and confirm that reducing carbon emissions can improve the financial performance of Indian firms. Furthermore, the SBTi moderate the carbon emission and firm performance nexus.

The findings of the study would provide insights to the policymakers, regulators and managers to mainstream climate change in their core business activities driving sustainability and profitable outcomes.

This study is a noble attempt to study the moderating role of science-based targets in the carbon emissions and firm performance nexus in an emerging market setting. Earlier studies have been conducted in a cross-country context.

This paper analyses and assesses the effect of firm-specific determinants (FSDs) on supply-chain performance (SCP) and export performance (EP). It examines SCP’s influence on EP and its mediating effect on the relationship between FSD and EP.

This paper develops a theoretical framework based on the resource-based view (RBV) and dynamic-capability theory to understand SCP’s role in the FSD-EP link while empirically validating using the Indian automobile industry segments (IAIS) data. The sample frame comprises all listed firms in IAIS between the financial year 2010-11 and 2021–22, with continuous data availability throughout the considered timeline. The paper employs factor analysis for dimension reduction, a panel-data-fixed-effect model to analyze the relationships, bootstrap to test the mediation effect and focus-group discussion for validating the results obtained through statistical analyses.

FSD directly influences SCP’s efficiency aspect and EP. Distribution efficiency and inventory efficiency characteristics of SCP directly impact EP and completely mediate the relationship between FSD and EP.

This study provides significant insights into how firms can increase EP by focusing on firm-specific and SCP-related factors. To improve EP, firms should concentrate on enhancing distribution and inventory efficiencies. Firms must focus on critical firm-level factors like age, size and raw-material import capability to increase their ability to solve SC-specific barriers and improve SCP, resulting in enhanced exports.

This study investigates the impacts of FSD on SCP and EP and examines the mediating effect of SCP on the relationship between FSD and EP. Such a mediating role of SCP has rarely been probed in the literature.

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.