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The demand-side view of creditor rights posits a negative association between creditor rights and corporate borrowings. The purpose of this paper is as follows: first, the author examines whether the demand-side effect is more pronounced amongst firms with excess promoter shareholding. Subsequently, the authors analyze the impact of high promoter holdings on investment decisions owing to bankruptcy reforms.

To answer the above questions, the authors exploit the passage of the Insolvency and Bankruptcy Code (IBC) (2016) that strengthens the creditor rights of lenders, which impacts the borrowings and financing activities of Indian corporates. Using a panel of listed Indian firms over the period of 2012–2019, the authors analyze how the IBC affects firms’ borrowings and financing decisions with excess promoter holdings.

The authors find that bankruptcy reforms led to a statistically significant decline in the use of borrowed funds (primarily secured and long-term debt) by firms with high concentrated holdings. The analysis also indicates that firms with excess promoter ownership face an increased cost of debt due to bankruptcy reforms. As a result, firms with excess promoter holdings curtail their investments. Overall, the results indicate that India’s bankruptcy reforms significantly affect the firms’ financing and investment decisions with highly concentrated ownership.

While past research has explored the relationship between bankruptcy reforms and demand for/supply of debt, the authors provide novel empirical evidence on the role of promoter holdings that affects the relationship between bankruptcy law and financing and investment decisions. To the best of the author’s knowledge, this study is the first to investigate the role of ownership structure in the context of bankruptcy reforms by using a quasi-natural experiment.

This study examines the association between geopolitical risk (GPR) and corporate tax, which is a major source of revenue for the government and a significant explicit cost for firms. The authors use a comprehensive measure of GPR to study its effects on corporate taxes by using an international sample.

The authors adopt the geopolitical measure constructed by Caldara and Iacoviello (2022) as a proxy for GPR and cash-effective tax rate benchmarked with statutory tax rate to measure corporate tax avoidance. The authors employ panel regression with fixed effects (FEs) to investigate the impact of GPR on corporate tax avoidance. The authors also conduct a battery of robustness tests to ensure the strength of the study’s results.

This study’s empirical results indicate that sample firms increase their tax avoidance amid increasing GPR. Further analyses show that financial constraints incentivize firms to avoid taxes during rising geopolitical tensions. The authors also provide evidence on the role of firm-level and country-level governance in weakening the association between GPR and tax avoidance.

Policymakers and governments may strengthen the enforcement rule to limit aggressive tax practices of corporates during GPR to balance fiscal deficit. In addition, this study sheds light on the debate among administrators and politicians over the efficacy of current tax laws and governance structures in the presence of heightened GPR.

The authors extend the literature on GPR by analyzing its effect on corporate tax avoidance. Unlike existing single-country studies, the authors use a cross-country setup to investigate the impact of GPR on tax avoidance, making this study’s results more generalizable as the authors control for a host of country, industry, and time factors. Apart from political uncertainty, terrorism, and climatic issues, the authors document GPR as a strong macroeconomic driver of corporate tax avoidance. The authors make a new contribution to the literature on the moderating role of governance and institutional factors on the association between tax avoidance and GPR in an international context. The authors also contribute to the literature on macroeconomic determinants of tax avoidance.

This study aims to examine the role of economic political uncertainty (EPU) on various corporate policies, namely, cash reserves, investment, capital structure and operating activities of Indian listed firms.

To assess the influence of policy-related uncertainties, the author uses the India-specific EPU news-based index constructed by Baker et al. (2016) as a proxy for policy uncertainties. This study uses data from listed Indian firms spanning the period 2003 to 2019. The author uses panel regression models with firm-fixed effects to analyze the impact of EPU on corporate policies, including cash reserves, leverage and CAPEX, while considering key control variables.

In response to heightened EPU, firms tend to increase their cash reserves, curtail their investment activities and favour secured financing options. Notably, this study aligns with the “real options” framework, demonstrating that firms with substantial investment irreversibility significantly reduce their capital expenditures during periods of elevated EPU. Additionally, the results reveal that rising EPU corresponds to heightened borrowing costs and increased operating expenses for firms.

In contrast to prior research that predominantly investigated the impact of EPU on the decisions of listed firms in developed markets, this study delves into the role of EPU on corporate policies among listed firms in India. This focus is particularly relevant, given the significant policy changes that have transpired in the Indian business landscape in recent years.

The purpose of this paper is to present a framework for identifying various inbound supply-risk factors and analyzing its indicators considering the contextual relationship between them. This study additionally proposes a framework for developing an overall inbound supply-risk score considering a real-life case of the electronics supply chain (ESC) in the Indian context.

In total, 32 risk indicators are identified by a systematic literature review approach and are validated by supply chain practitioners/experts and further categorized into six main risk factors. A hybrid multi-criteria decision-making-based DANP (DEMATEL and ANP) framework is employed to develop the overall inbound-supply-risk score (ISRS) and to prioritize the risk indicators. Indian ESC is chosen as a viable case study to demonstrate the effectiveness of the proposed framework.

The outcomes from the study reveal that the overall ISRS in the ESC is 36 percent and additionally forewarns critical inbound-supply-risk factors such as supplier performance, product, and buyer organization. Further, the study also identifies the most significant risk indicators such as price margin, investment, on-time delivery, order fulfillment and design changes for ESC.

Supply chain practitioners can adopt this framework as a useful inbound supply-risk assessment tool. Moreover, the hybrid framework will address subjectivity and interrelations among various factors through experts’ judgments. The results will assist the managers to have better insights on the critical risk factors and their complicated interrelationships and further strategize action plans to nullify the impact of incoming risks. This study mainly focused on risk identification and assessment of electronics inbound-supply-risk indicators in the Indian context. The framework can be used for other manufacturing and service industries, albeit the results derived are in the context of a developing country.

This paper provides an effective risk assessment framework for the supply chain practitioners/managers to develop a decision-support system for inbound-supply-risk quantification and prioritization of risk factors in the context of the ESC.

The study analysed the aerospace industry, a traditionally important sector for the topic of risk management, from three complementary perspectives: the supply chain risks present in the sector, the mitigation strategies adopted to face them, and the characteristics (dimensions) observed in the SCRM process of aerospace companies.

The research employed a quali–quantitative method: a survey was carried out, followed by interviews with professionals from companies belonging to different tiers of aerospace supply chains. Interviews helped to interpret the survey data and understand in more detail risk management in aerospace companies.

The study presents a panorama of the aerospace industry in terms of risk management. The sector’s turbulent environment is described as well as the strategies to prevent, minimise or postpone the impact of supply chain risks. In particular, ten dimensions that have been identified in the SCRM process of aerospace firms are discussed. These characteristics influence the objectives of this process and are related to resources, roles and responsibilities, incentives, development of competences and skills, scope (internal and external) and approaches to integrate decisions and actions in the context of the supply chain.

Articles that address the SCRM process usually focus on the process steps, whereas this study investigated dimensions that transcend these steps but whose discussion in the literature is still fragmented. It also analysed a reference sector for the topic from a broader perspective than others available in the literature (supply chain risks, mitigation strategies and characteristics of the SCRM process). Supply chain members with relationships with each other were investigated, a desirable approach for SCRM but still under-explored. The study also answers calls for industry-specific studies and research on emerging countries.

Sustainable supply chain management (SSCM) concepts have received immense attention in the recent past in both academia and industries. Especially, manufacturing industries in developing countries realize the importance of adopting sustainability concepts in their supply chain. The SSCM adoption has not been to the same level across different manufacturing sectors and hence a single implementation framework will not have the same effect across sectors. This paper aims to compare the adoption level of 25 SSCM practices across three major manufacturing sectors, namely, automobile, electronics and textile, in an emerging economy, India.

A questionnaire-based data collection technique is used to obtain adoption levels of each of the identified SSCM practices on a five-point Likert-type scale with “1” representing not considering presently to “5” indicating successful implementation. Second, a hypothesis is framed and tested to compare the adoption levels across sectors using a one-way single-factor ANOVA followed by a post hoc test by Tukey’s test.

The results derived suggest that though the industries across different sectors are in the course of adopting SSCM practices, the level of adoption is found to be not the same. The textile sector has adopted the least, and the electronic sector edges ahead of the automobile sector in terms of successful transformation to SSCM.

This study focuses on the differences and similarities in the adoption of policies in the automobile, electronics and textile sectors using statistical data analysis tools. A total of 25 individual practices are identified from existing literature and classified into six groups, namely, management, supplier, collaboration, design, internal and society, based on their similarities. Based on a detailed questionnaire survey with industrial experts in relevant fields as respondents, the adoption levels of practices are rated individually and categorically.

The financial crisis of 2007-08 originated from the indiscriminate lending by housing finance companies to borrowers, who were in no position to pay back the amount borrowed. Delinquencies arose when the EMIs went up due to rising interest rates and the prices of homes declined. Variable rates of interest often catch the borrower completely unprepared. A part of the blame lies with the housing finance companies that do not explain the implications to borrowers at the time of sanctioning the. This case is based on the actual experience of an HNI (High Net-worth Individual) who is unwittingly caught in the web of high EMI by a bank. He faces the devil's choice. Choose he must, though, between the options presented by the situation. Unwary borrowers may be able to avoid such situations by understanding the various policy prerogatives available.

Automated crop prediction is needed for the following reasons: First, agricultural yields were decided by a farmer's ability to work in a certain field and with a particular crop previously. They were not always able to predict the crop and its yield solely on that idea alone. Second, seed firms frequently monitor how well new plant varieties would grow in certain settings. Third, predicting agricultural production is critical for solving emerging food security concerns, especially in the face of global climate change. Accurate production forecasts not only assist farmers in making informed economic and management decisions but they also aid in the prevention of famine. This results in farming systems’ efficiency and productivity gains, as well as reduced risk from environmental factors.

This research paper proposes a machine learning technique for effective autonomous crop and yield prediction, which makes use of solution encoding to create solutions randomly, and then for every generated solution, fitness is evaluated to meet highest accuracy. Major focus of the proposed work is to optimize the weight parameter in the input data. The algorithm continues until the optimal agent or optimal weight is selected, which contributes to maximum accuracy in automated crop prediction.

Performance of the proposed work is compared with different existing algorithms, such as Random Forest, support vector machine (SVM) and artificial neural network (ANN). The proposed method support vector neural network (SVNN) with gravitational search agent (GSA) is analysed based on different performance metrics, such as accuracy, sensitivity, specificity, CPU memory usage and training time, and maximum performance is determined.

Rather than real-time data collected by Internet of Things (IoT) devices, this research focuses solely on historical data; the proposed work does not impose IoT-based smart farming, which enhances the overall agriculture system by monitoring the field in real time. The present study only predicts the sort of crop to sow not crop production.

The paper proposes a novel optimization algorithm, which is based on the law of gravity and mass interactions. The search agents in the proposed algorithm are a cluster of weights that interact with one another using Newtonian gravity and motion principles. A comparison was made between the suggested method and various existing strategies. The obtained results confirm the high-performance in solving diverse nonlinear functions.

The purpose of the present paper aims to, study the coefficient of friction and wear behavior of nickel based super alloys used in manufacturing of gas and steam turbine blades. In present paper, parametric study focuses on normal load, dry sliding velocity and contact temperature influence on coefficient of friction and wear of a nickel based super alloy material.

Experimental investigation is carried out to know the effect of varying load at constant sliding velocity and varying sliding velocity at constant load on coefficient of friction and wear behavior of nickel based super alloy material. The experiments are carried out on a nickel based super alloy material using pin on disk apparatus by load ranging from 30 N to 90 N and sliding velocity from 1.34 m/s to 2.67 m/s. The contact temperature between pin and disk is measured using K-type thermocouple for all test conditions to know effect of contact temperature on coefficient of friction and wear behavior of nickel based super alloy material. Analytical calculations are carried out to find wear rate and wear coefficient of the test specimen and are compared with experimental results for validation of experimental setup. Regression equations are generated from experimental results to estimate coefficient of friction and wear in the range of test conditions.

From the experimental results, it is observed that by increasing the normal load or sliding velocity, the contact temperature between the pin and disk increases, the coefficient of friction decreases and wear increases. Analysis of variance (ANOVA) is used to study the influence of individual parameters like normal load, dry sliding speed and sliding distance on the coefficient of friction and wear of nickel based super alloy material.

This is the first time to study effect of contact temperature on the coefficient of friction and wear behavior of nickel-based super alloy used for gas and steam turbine blades. Separate regression equations have been developed to determine the coefficient of friction and wear for the entire range of speed of gas turbine blades made of nickel based super alloy. The regression equations are also validated against experimental results.

The purpose of this paper is to study the flow, heat and mass transfer of MHD Casson nanofluid due to an inclined stretching sheet using similarity transformation, the governing PDE’S equations of flow, heat and mass transfer are converted into ODE’S. The resulting non-linear ODE’S are solved numerically using an implicit finite difference method, which is known as Kellor-box method. The effects of various governing parameters on velocity, temperature and concentration are plotted for both Newtonian and non-Newtonian cases. The numerical values of skin friction, Nusselt number and Sherwood number are calculated and tabulated in various tables for different values of physical parameters. It is noticed that the effect of angle of inclination enhances the temperature and concentration profile whereas velocity decreases. The temperature decreases due to the increase in the parametric values of Pr and Gr due to thickening in the boundary layer.

Numerical method is applied to find the results.

Flow and heat transfer analysis w.r.t various flow and temperature are analyzed for different values of the physical parameters.

The numerical values of skin friction, Nusselt number and Sherwood number are calculated and tabulated in various tables for different values of physical parameters.

The study of the boundary layer flow, heat and mass transfer is important due to its applications in industries and many manufacturing processes such as aerodynamic extrusion of plastic sheets and cooling of metallic sheets in a cooling bath.

Here in this paper the authors have investigated the MHD boundary layer flow of a Casson nanofluid over an inclined stretching sheet along with the Newtonian nanofluid as a limited.