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This study aims to investigate the effect of monetary policy transmission on the use-based classification of manufacturing industries in India, an integral aspect influencing the overall economic growth of the nation.

The empirical study applies a panel autoregressive distributed lag model to examine the relationship/association between monetary policy transmission mechanism and the output of manufacturing industries in the long run and short run.

In the long run, the findings reveal a negative association between money supply and manufacturing industries’ output, indicating that an increase in money supply corresponds to a decrease in manufacturing output. Conversely, a positive relationship is observed between manufacturing industries’ output and banks’ credit, indicating that an increase in bank credit leads to a corresponding increase in manufacturing output. In the short run, the results highlight a significant positive relationship between manufacturing output and monetary policy transmission variables, including money supply, statutory liquidity ratio, real exchange rate and foreign direct investment. The use-based classification of manufacturing industries such as primary goods, capital goods and intermediate goods exhibits greater responsiveness to monetary policy shocks than consumer durables and non-durables goods.

Policymakers are advised to regulate credit expansion to support the industry without risking financial instability, with key recommendations including stimulating consumer demand and adopting sector-specific policies to promote sustainable growth across diverse manufacturing sectors.

India, being a developing economy, efficient monetary policy transmission is crucial for boosting manufacturing output and employment. Nevertheless, there has been a scarcity of research concentrated on this pivotal intersection. This study aims to fill that gap, providing fresh insights into how monetary policy affects the growth of the manufacturing industry.

The focus of this paper is to provide an assessment of the impact of imports from China on Indian manufacturing and capture the multifarious dimensions of India–China bilateral trade flows. By examining the comparative disadvantage imports (RCA<1), the paper critically examines their significance on India's industry output and performance and underlines factors beyond trade competitiveness.

For examining the impact of India's manufacturing imports from China on industry performance, four stages of analysis is adopted. First, the imports with RCA <1 have been identified. For these, BRCA was also computed. Second, trends in industry performance associated with high imports from China. Third, for estimating the impact of imports on industry output, augmented production function was specified and estimated with imports from China as a potential determinant. And fourth, comparison of industry performance between India and China.

The impact of imports from China on industry output is positive and significant. A 1% increase/decrease in the share of China in world imports will result in output increasing by 0.31%. The rise in imports from China seems to be on account of non-availability of necessary intermediate and capital goods domestically, thereby making these imports critical and complementary for production. This negates the threat perception of imports from China.

The paper recognizes the need for understanding the firm heterogeneity in import decisions and R&D intensity of imports. Across industries, the drivers for firms' decisions to import are “learning by importing’ and “self-selection” (Camino-Magro et al., 2020). Also, another important dimension at the firm-level analysis is the elasticity of substitution between foreign and domestic inputs. If the elasticity of substitution is low then high import barriers will lead to reduction of domestic output. These firm-level issues are important for effective policy interventions.

One, the inward looking focus of the industry which is exhibited in low export intensity will not provide the necessary impetus to propel the manufacturing sector to a higher technology frontier and translate the productivity gains to export competitiveness. Two, unless the domestic manufacturing is propelled from the current low/medium technology to high technology products, the current policy thrust on “self-reliance” cannot be realized.

Analysis is based on manufacturing imports with RCA<1 from China thereby underlining factors beyond trade competitiveness not covered by RCA methodology. Complementing the quantitative analysis with economic policy developments in China and India and contrasting the same has provided insights into the real factors determining India–China bilateral trade.

The study of the relationship between external auditing services and investment deviation in a French setting has received relatively little research attention thus far. There are insufficient indicators to measure audit quality and then have a measurable link to investment efficiency. This study is motivated by such a research gap as well as the important role of auditing services in assuring investment efficiency. The purpose of this study is to test whether a good audit quality service improves corporate investment awareness in French-listed companies and contributes to establishing a comprehensive analysis framework for inefficient investment and how audit services have become an important tool to reduce the investment deviation of listed companies in France.

Based on a sample of 89 non-financial French firms listed on the Stoxx 600 Index from 2015 to 2021, this study uses feasible generalised least squares (FGLS) regressions to study the relationship between investment deviation and auditing service quality.

After running an FGLS regression model for two firm groups (overinvestment and overinvestment groups) and testing for a set of control variables, especially COVID-19, the findings show a non-linear correlation between audit service and corporate investment deviation. Both underinvestment and overinvestment decisions are negatively and statistically significantly impacted by audit indicators. Furthermore, involving a high-quality specialised auditor may enhance overall monitoring and lead to a lower investment deviation level. Overall, the empirical results show that a high-quality audit service enhances the investment efficiency of French-indexed companies.

This study offers crucial information that audit regulators can use to better appreciate the advantages of high audit quality and to take seriously the policy issues that affect it. Board members are urged to provide excellent audit quality that improves investment efficiency with careful consideration.

This study contributes to the existing audit literature by illuminating the effect of audit quality services on investment deviation to show a deeper understanding of the factors that contribute to the differences in prior studies’ findings in the field of audit quality impacts.

The connection between digital manufacturing technologies (Industry 4.0) and the environment has sparked discussions on firms' disclosure of negative information on pollutant emissions and the pursuit of positive environmental outcomes. However, very few studies explore how it relates to a firm's robot usage and its mechanism. The purpose of this paper is to investigate the impacts of robot penetration on firms' environmental governance in China.

The ordered probit model (and probit model) are employed and empirically tested with a sample of 1,579 Chinese listed firms from 2010 to 2019.

The study reveals a negative relationship between robot usage and the disclosure of negative indicators and a U-shaped relationship between robot usage and positive environmental outcomes. Among the sample, nonstate-owned enterprises (SOEs) display unsatisfactory performance, while heavily polluting industries disclose more information on pollutant emissions. The robot–environmental governance nexus is conditional on firm size, capital intensity and local economic development.

The study proposes a fresh view of corporate environmental governance to assess the environmental implications of robot adoption. It also contributes to identifying the curvilinear, moderating and heterogenous effects in the robot–environment nexus. The results provide rich policy implications for the development of industrial intelligence and corporate environmental governance in the circular economy (CE) context.

This study aims to contribute to the ongoing assessment of executive compensation by investigating the nexus between managerial entrenchment factors, adopting a multifaceted perspective encompassing both economic and non-economic dimensions.

This research employs pooled cross-sectional Ordinary Least Squares (OLS) regression and Least Squares with Dummy Variables (LSDV) models with fixed effects to examine the determinants of Chief Executive Officer (CEO) compensation.

This research identifies firm size, performance (via ROA and Tobin’s Q), and CEO characteristics (age, tenure, stock ownership, MBA degree) as significant determinants of executive compensation at the 0.05 level. In contrast, the prestige of educational institutions, doctoral degrees, and the MBA’s relevance to short-term performance, along with CEO tenure, do not significantly affect pay. Additionally, the study highlights the significance of industry type (manufacturing vs technology) in shaping compensation, emphasizing the role of firm metrics and CEO credentials in designing executive pay packages.

This research introduces an innovative approach to controlling unobserved heterogeneity and adjusting for the dynamic nature of CEO compensation attributes across diverse CEO characteristics. By integrating both pooled Ordinary Least Squares (OLS) and Least Squares Dummy Variable (LSDV) models, the study addresses the challenges posed by time-invariant variables and unobservable heterogeneity. Such issues have historically skewed the accuracy of traditional OLS models in identifying the comprehensive array of factors—both economic and non-economic—that influence CEO compensation. This novel methodological framework significantly advances the examination of unobservable variables that may vary not only across the firms selected for analysis but also over time periods, thereby offering a more detailed understanding of the determinants of CEO pay.

The link between interlocking directors and mergers and acquisitions (M&A) efficiency has been analyzed in an information asymmetry environment. Despite an abundance of evidence highlighting that interlocking directors do contribute to M&A efficiency in an acquirer-target binary relationship, the target is embedded in a complex network of supplier-customer relationships, which implies that the acquirer needs to consider the value of suppliers, distributors and retailers in the target’s supply chain in improving M&A efficiency. Through the lenses of acquirer-target multivariate relationships, this paper aims to examine how directors with supply chain experience (DSCs) act as heterogeneous network pipes to affect M&A efficiency.

Using a sample of 311 A-share listed firms on the Shanghai and Shenzhen stock exchanges in China during 2011–2020, this paper investigates the relationship between DSCs and M&A efficiency by using ordinary least squares (OLS) regression.

Through empirical research, we verify a negative relationship between DSCs and M&A duration and an inverted U-shaped relationship between both DSCs and M&A performance, revealing the complexity of the relationship between experience and efficiency. Furthermore, drawing on upper echelon theory, the information value of DSCs will be greatly reduced when executives have overconfident psychological characteristics, which are mainly shown to negatively moderate the relationship between DSCs and M&A performance. We also conduct multiple robustness tests and supplemental analyses to illustrate the robustness and boundaries of our findings. Finally, DSCs are likely more important in environments among growth and mature firms as well as high-growth industries.

We break through the assumption that interlocking directors contribute to M&A efficiency in an acquirer-target binary relationship and examine the impact of DSCs on M&A efficiency based on micro-empirical evidence from the value of target-related upstream or downstream industries, which extends the connotation of interlocking directors and enriches the study related to factors influencing M&A efficiency.

The purpose of this paper is to form propositions about the relationship between top management team (TMT) heterogeneity and peer effects in investment decision-making and explore the mediating role of social learning processes.

To investigate the correlations between TMT heterogeneity and investment peer effects, we considered the TMT heterogeneity category, team process and contextual factors. With a sample of 8,467 firm-year observations from Chinese listed companies, we used the mean linear model and instrumental variable method to empirically examine their relationships. To identify the mediating role of social learning processes, we introduced a social learning model to find out the contextual factors influencing corporate social learning demands from three aspects and subsequently used comparative statics analysis to explore the variations in the main effect under these contextual factors.

For task-oriented heterogeneity (e.g. functional background, education and tenure heterogeneity), the opposite effects of information elaboration and social categorization processes make it a nonlinear multiplex correlation with investment peer effects. For relation-oriented heterogeneity (e.g. age and gender heterogeneity), the sole effect of social categorization processes leads to a negative linear correlation. Further, we identify the mediating role of social learning processes. In summary, we established a connection from the TMT heterogeneity, to information elaboration theory or social categorization theory, to social learning processes and ultimately to investment peer effects.

The results of this study provide a comprehensive perspective to predict the decision-making outcomes of team heterogeneity and contribute to heterogeneity research and practice.

The lack of transparency contributes to the growing corruption problem in various spheres of society. This paper aims to analyse the sustainability report disclosures published by Czech companies in 2021 and registered by the Association of Social Sustainability of the Czech Republic.

Based on three hypotheses, the relationships between the level of disclosed anti-corruption information and selected variables related to the corporate environment are tested using content analysis and the Mann–Whitney test.

This paper reveals that Czech firms provide more information if they operate in a higher-risk environment (energy, materials and financial services) or are state-owned (or with a state ownership stake). It also reveals that companies participating in corporate social responsibility (CSR) initiatives (UN Global Compact and Global Reporting Initiative) increase their credibility and social responsibility with more disclosed information.

A limitation of this paper is the smaller number of selected companies matching the chosen criteria. In addition, a certain degree of subjectivity is likely to have manifested in the process of coding the reports and in the use of the content analysis method.

The paper contributes to research that addresses the fight against corruption and CSR issues with a specific study in a small, Central European country and provides new empirical data on the anti-corruption fight problem.

The purpose of this study is to explore the relationship between digital transformation and excess employees. This research investigates the questions of when human−machine synergy can be achieved after a firm goes through digital transformation and whether there will be excess employees in the interim.

This paper takes A-share listed companies as research object in the period of 2011−2020 and a total of 24,718 samples are obtained. Hypothesis testing and regression analysis are performed in STATA.

This paper finds a human−machine mismatch in the short term, as evidenced by an increase in the rate of excess employees; however, with the progress of digital transformation, it can drive the achievement of human−machine synergy in the long term, and management efficiency plays a mediating role in this process. Further research showed that the effects of digital transformation on the number of employees, revenue generation per capita and profit generation per capita varied in both the short and long term. In addition, the characteristics of the company affect the relationship between digital transformation and excess employees.

This paper contributes to the understanding of the impacts of digital transformation on the human capital structure of companies at a micro level. It also provides insights into how to improve human capital demand structure through digitalization, thus providing insights into labor market changes.

The purpose of this paper is to explore the factors that influence migrant workers' household registration transfer willingness at both individual and urban levels and to provide empirical evidence on adjusting the household registration system to accommodate economic development and migrant workers' imbalances.

This paper adopts a hierarchical nonlinear model and examines individual and urban influencing factors of migrant workers' household registration transfer willingness, based on the data from China Migrants Dynamic Survey (CMDS) and the Urban Statistical Yearbooks.

This paper shows that: (1) multi-factors, such as age, education, marital status, household demographics, industry and migrant workers' contract coverage, have significant effects on migrant workers' household registration transfer willingness; (2) The urban public service equalization indicators, such as regional economic, educational resources, medical care and ecological quality, have significant effects on migrant workers' willingness to transfer household registration; (3) The heterogeneity of migrant workers' willingness to transfer household registration is significant in central, eastern and western China.

The authors provide a fresh perspective on population migration research in China and other countries worldwide based on the pull–push migration theory, which incorporates both individual and macro (urban) factors, enabling a comprehensive examination of the factors influencing household registration transfer willingness. This hierarchical ideology and approach (hierarchical nonlinear model) could be extended to investigate the influencing factors of various other human intentions and behaviors.

Micro approaches (individual perspective) have dominated existing studies examining the factors influencing migrant workers' household registration transfer willingness. The authors combine individual and urban perspectives and adopt a more comprehensive hierarchical nonlinear model to extend the empirical evidence and provide theoretical explanations for the above issues.