Rattan Sharma and Priti Aggarwal
The purpose of this paper is to investigate the impact of mandatory corporate social responsibility (CSR) expenditure on the firm’s financial performance in the aftermath of…
Abstract
Purpose
The purpose of this paper is to investigate the impact of mandatory corporate social responsibility (CSR) expenditure on the firm’s financial performance in the aftermath of insertion of Section 135 in the Companies Act, 2013 for Indian listed companies.
Design/methodology/approach
The paper uses independent sample t-test, one-way ANOVA, fixed effect panel regression model and principal component analysis on a data set of 153 non-financial companies listed in BSE-500 companies for a period of 2015–2019.
Findings
The empirical results of the paper suggest that the mandatory CSR expenditure negatively impacts the company’s profitability.
Practical implications
The study has important implications for regulators and listed companies. Firstly, the mandatory CSR expenditure acts as a burden onto the on-going activities of the firms. CSR activities, therefore, should be integrated with the existing skillsets and expertise of the firms. Secondly, the government can encourage CSR activities by making the expenditure tax deductible. Moreover, the Schedule VII list of activities has a scope to become more inclusive rather than the present exhaustive list.
Originality/value
The paper highlights the gap in the expectation and actualisation of the CSR mandate by studying the recent data of the sample companies of the BSE-500 index. The paper adds to the CSR literature in the emerging market context.
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Tarun K. Soni, Vikas Pandey and Priti Aggarwal
The paper analyzes the volatility transmission within the cotton markets by utilizing commodity futures prices from the USA, China and India, encompassing important global events…
Abstract
Purpose
The paper analyzes the volatility transmission within the cotton markets by utilizing commodity futures prices from the USA, China and India, encompassing important global events that have significantly influenced the global cotton markets, like the China-USA trade dispute, the COVID-19 outbreak and the Russia–Ukraine conflict.
Design/methodology/approach
The authors employ a volatility spillover measure developed by Diebold and Yilmaz (2009, 2012, 2014). Additionally, the methodology proposed by Baruník and Křehlík (2018), which divides the overall volatility spillover into short, medium and long-term segments has been used. To investigate the volatility connectedness, weekly (close-to-close) returns of the cotton futures contracts that are traded on the Chicago Board of Trade Dalian Commodity Exchange National Commodity Exchange of India (NCDEX), and Multi Commodity Exchange (MCX) are considered.
Findings
The paper identifies the presence of long-term volatility transmission among the three cotton futures markets. It demonstrates that a global shock like the Russia–Ukraine conflict has a greater impact on volatility in other markets than USA–China trade disputes. It also highlights the weakening role of the US cotton futures markets as a price leader for Indian and Chinese markets.
Research limitations/implications
Since only three major markets have been studied, the future studies can explore the interconnectedness by including other important markets including Brazil, Turkey, Bangladesh, etc. Further, the moderating role of relationship between other important variables such as cotton production, harvest, inventory, exchange rate, oil price, trade policies, etc. can be examined. Furthermore, the interconnectedness with the regional spot markets in India can also be examined to study how the volatility from the futures market can affect the volatility in the spot markets and vice-versa.
Practical implications
The understanding of domestic food price volatility and its transmission from international to domestic markets is crucial for designing effective policies to address excessive volatility and protect vulnerable groups like producers, consumers, etc.
Social implications
The findings emphasize on the substantial market dependence with the US and the Chinese markets which have a significant impact on the Indian markets with considerable implications for hedgers, producers and exporters, particularly during periods of higher volatility.
Originality/value
This study assesses the interdependencies among three major cotton-producing countries and the influence of factors like the USA–China trade tensions in 2018, the COVID-19 crisis and the Russia–Ukraine conflict in order to gauge the degree of volatility interconnection among these key players in the cotton market.
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Vanita Tripathi and Priti Aggarwal
This paper is an attempt to explore the fact that whether the literature-promised value premium has any sector orientation. The paper tests the relationship between the value…
Abstract
Purpose
This paper is an attempt to explore the fact that whether the literature-promised value premium has any sector orientation. The paper tests the relationship between the value premium and Indian sectors: fast-moving consumer goods (FMCG), financials, healthcare, information technology (IT), manufacturing and miscellaneous.
Design/methodology/approach
The paper analyses around 210–480 companies listed on BSE-500 for the period of the recent 18 years ranging from March 1999 to March 2017. The paper employed Welch's ANOVA to examine whether the price-to-book market ratio is significantly different across sectors. Two prominent asset pricing models – single factor market model and Fama–French three-factor model – were used to examine the existence of value premium within sectors for full period and two sub-periods.
Findings
The empirical results of the paper suggest that the difference in the P/B ratio both between sectors and within sectors is statistically significant. The results further suggest that the value premium exists within the sectors irrespective of their value-growth orientation.
Research limitations/implications
The paper is not free from certain limitations. Firstly, due to the non-availability of data in the public domain, the time period before 1999 could not be considered. Secondly, the study has used data pertaining to the Indian stock market only. To add to it, our study has concentrated on BSE-500 companies only; however, the future researchers can include both NSE and BSE companies.
Practical implications
The paper has important implications for portfolio managers and retail investors following a top-down approach of investing. The portfolio manager can strategically build up the portfolios to concentrate more on the companies belonging to sectors like healthcare, manufacturing and FMCG. Investors following the top-down approach should avoid the underperforming growth stocks belonging to the growth sectors and allocate their funds to value stocks in the growth sector.
Originality/value
The paper is first of its kind to study the relationship between the value premium and Indian sectors. The paper contributes to portfolio management and asset pricing literature for an emerging market.
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Ashu Lamba, Priti Aggarwal, Sachin Gupta and Mayank Joshipura
This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms…
Abstract
Purpose
This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).
Design/methodology/approach
This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.
Findings
The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.
Originality/value
The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.
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Arash Heidari and Nima Jafari Navimipour
The main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized…
Abstract
Purpose
The main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized, decentralized, and hybrid. Moreover, in this classification, the peer-to-peer (P2P) and agent-based mechanisms are considered the parts of the decentralized mechanism. This paper investigates the main improvements in these three main categories and outlines new challenges. Moreover, the other goals are analyzing the current challenges in a range of problem areas related to cloud discovery mechanisms and summarizing the discussed service discovery techniques.
Design/methodology/approach
Systematic literature review (SLR) is utilized to detect, evaluate and combine findings from related investigations. The SLR consists of two key stages in this paper: question formalization and article selection processes. The latter includes three steps: automated search, article selection and analysis of publication. These investigations solved one or more service discovery research issues and performed a general study of an experimental examination on cloud service discovery challenges.
Findings
In this paper, a parametric comparison of the discovery methods is suggested. It also demonstrates future directions and research opportunities for cloud service discovery. This survey will help researchers understand the advances made in cloud service discovery directly. Furthermore, the performed evaluations have shown that some criteria such as security, robustness and reliability attained low attention in the previous studies. The results also showed that the number of cloud service discovery–related articles rose significantly in 2020.
Research limitations/implications
This research aimed to be comprehensive, but there were some constraints. The limitations that the authors have faced in this article are divided into three parts. Articles in which service discovery was not the primary purpose and their title did not include the related terms to cloud service discovery were also removed. Also, non-English articles and conference papers have not been reviewed. Besides, the local articles have not been considered.
Practical implications
One of the most critical cloud computing topics is finding appropriate services depending on consumer demand in real-world scenarios. Effective discovery, finding and selection of relevant services are necessary to gain the best efficiency. Practitioners can thus readily understand various perspectives relevant to cloud service discovery mechanisms. This paper's findings will also benefit academicians and provide insights into future study areas in this field. Besides, the drawbacks and benefits of the analyzed mechanisms have been analyzed, which causes the development of more efficient and practical mechanisms for service discovery in cloud environments in the future.
Originality/value
This survey will assist academics and practical professionals directly in their understanding of developments in service discovery mechanisms. It is a unique paper investigating the current and important cloud discovery methods based on a logical categorization to the best of the authors’ knowledge.
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Ambika Aggarwal, Priti Dimri, Amit Agarwal and Ashutosh Bhatt
In general, cloud computing is a model of on-demand business computing that grants a convenient access to shared configurable resources on the internet. With the increment of…
Abstract
Purpose
In general, cloud computing is a model of on-demand business computing that grants a convenient access to shared configurable resources on the internet. With the increment of workload and difficulty of tasks that are submitted by cloud consumers; “how to complete these tasks effectively and rapidly with limited cloud resources?” is becoming a challenging question. The major point of a task scheduling approach is to identify a trade-off among user needs and resource utilization. However, tasks that are submitted by varied users might have diverse needs of computing time, memory space, data traffic, response time, etc. This paper aims to proposes a new way of task scheduling.
Design/methodology/approach
To make the workflow completion in an efficient way and to reduce the cost and flow time, this paper proposes a new way of task scheduling. Here, a self-adaptive fruit fly optimization algorithm (SA-FFOA) is used for scheduling the workflow. The proposed multiple workflow scheduling model compares its efficiency over conventional methods in terms of analysis such as performance analysis, convergence analysis and statistical analysis. From the outcome of the analysis, the betterment of the proposed approach is proven with effective workflow scheduling.
Findings
The proposed algorithm is more superior regarding flow time with the minimum value, and the proposed model is enhanced over FFOA by 0.23%, differential evolution by 2.48%, artificial bee colony (ABC) by 2.85%, particle swarm optimization (PSO) by 2.46%, genetic algorithm (GA) by 2.33% and expected time to compute (ETC) by 2.56%. While analyzing the make span case, the proposed algorithm is 0.28%, 0.15%, 0.38%, 0.20%, 0.21% and 0.29% better than the conventional methods such as FFOA, DE, ABC, PSO, GA and ETC, respectively. Moreover, the proposed model has attained less cost, which is 2.14% better than FFOA, 2.32% better than DE, 3.53% better than ABC, 2.43% better than PSO, 2.07% better than GA and 2.90% better than ETC, respectively.
Originality/value
This paper presents a new way of task scheduling for making the workflow completion in an efficient way and for reducing the cost and flow time. This is the first paper uses SA-FFOA for scheduling the workflow.
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Nafisa Priti Sanga and Rajeev Kumar Ranjan
The purpose of this paper is to study Indian aspects of policy convergence in the context of budgetary linkage of two nationalized flagship programs – Mahatma Gandhi National…
Abstract
Purpose
The purpose of this paper is to study Indian aspects of policy convergence in the context of budgetary linkage of two nationalized flagship programs – Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) and Integrated Watershed Management Program (IWMP). Therefore, in lieu of inter-departmental convergences; issues related to water resource development of Jharkhand’s (India) rain-fed areas were addressed.
Design/methodology/approach
Centered on policy convergence strategy, present study applied comprehensive review and analysis approach for formulation of research base. A conceptual framework was thus designed for analytical purposes and therefore advancing toward conjectural knowledge base.
Findings
Application of inter-departmental policy convergence strategy suggested ample opportunities for optimal water resource development. Presence of abundant wage labor, rich indigenous water management techniques, tested replicable models, under-harvested rainwater potential, etc., appeared as catalysts of policy convergence. Yet, State’s lack of inter-departmental coordination and grass-root institutional framework will continually challenge policy convergences in absence of good governance.
Originality/value
An initiative of Indian government; MGNREGA has received international attention due to its wider coverage including natural resource management, besides guaranteed wage employment. Targeted at freshwater management discourse of Jharkhand; present paper reviewed prospective inter-departmental policy convergence strategy within various arena of MGNREGA, by exploring associated scopes and challenges. Similarly for cost effectiveness, related to maintenance and lift-irrigation demands of rain-fed area development; the present study suggested optimum utilization of inter-departmental funding linkages for development of sustainable water resources.
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Özge H. Namlı, Seda Yanık, Aslan Erdoğan and Anke Schmeink
Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is…
Abstract
Purpose
Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.
Design/methodology/approach
In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.
Findings
The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.
Originality/value
This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.