Profitability has always been considered as a primary indicator of dividend payout by a company. There are factors other than profitability namely cash flows, debt equity ratio, retained earnings, sales growth, share ...Profitability has always been considered as a primary indicator of dividend payout by a company. There are factors other than profitability namely cash flows, debt equity ratio, retained earnings, sales growth, share prices of a company, capital expenditure and beta etc. that also affect dividend decisions of an organization. Existing literature suggests that dividend payout is positively related to profits, cash flows while CAPEX （capital expenditure） retained earnings, sales growth, share prices, beta, interest paid and debt equity ratio have inverse relationship. A set of 21 key variables have been identified that affect the dividend payout of a firm. Researchers in the past have used several proxies to represent these determinants. Authors have tried to find out which proxy variable is most relevant in the present scenario. The paper attempts to give a focused overview of the important dividend theories and empirically analyze the determinants of dividend behavior of Indian FMCG （Fast moving consumer goods） sector. The relationship between key variables has been explored with the aid of statistical techniques of factor analysis. Thus, the main theme of this study is to examine the various factors that influence the dividend policy decisions of FMCG firms in India.展开更多
The current study attempts to compare the hybrid artificial intelligence models to forecast the environmental externalities in Saudi Arabia.We have used the denoising based artificial intelligence models to construct ...The current study attempts to compare the hybrid artificial intelligence models to forecast the environmental externalities in Saudi Arabia.We have used the denoising based artificial intelligence models to construct hybrid models.While comparing the denoising techniques,the CSD-based denoising has outperformed.However,we have used the CSD-based hybrid models.CSD-ANN and CSD-RNN are used for denoising-based artificial intelligence models,whereas CSD-ARIMA is used for denoising-based traditional models.All these models are used to check and compare their performance in terms of level and direction of prediction for PM_(10).The results show that the CSD-based ANN model has a higher predictability for PM_(10) levels in Saudi Arabia due to low error values and higher Dstat values.In comparing original and forecasted data,the superiority of CSD-ANN is evident in predicting the PM_(10) in Saudi Arabia.Hence,this hybrid model can predict the environmental externalities for non-linear and highly noised data.Moreover,the findings can be useful in achieving the sustainable development goal.展开更多
Countries face the risk of natural resource curse because of making their economic growth excessively dependent on natural resources.Although excessive resource dependence causes such a risk,it is inevitable that reso...Countries face the risk of natural resource curse because of making their economic growth excessively dependent on natural resources.Although excessive resource dependence causes such a risk,it is inevitable that resource-rich countries will need resource rent up to a certain level of economic maturity.On the other hand,transferring the wealth achieved after this maturity level to productive investment areas also reduces the resource dependency levels of countries.In this context,countries that capture the possible inverted U-shaped relationship between economic growth and resource dependence can escape the curse.Based on this,the aim of this research is to determine the validity of the Kuznets type relationship between resource dependence and economic growth for the first time in the literature.Nine nations that rely heavily on natural resources are used as a sample for this.The countries with a share of total resource rent in national revenue greater than 25%are taken into consideration throughout the selection process for these countries.Using novel panel data methodologies,the effects of capital accumulation,public spending,foreign direct investment,and economic growth on the dependence on natural resources is examined from 1993 to 2021.The results reveal that capital accumulation reduces resource dependency while foreign investments and government size increases it.In addition,the Resource-Based Kuznets curve concept is supported by empirical data demonstrating an inverted-U-shaped relationship between economic growth and resource dependence for these nations.The thresholds derived from the parameters show that Saudi Arabia and Kazakhstan are well beyond this cutoff.The Democratic Republic of the Congo and the Republic of the Congo,on the other hand,remain a long way from this threshold.Furthermore,Iraq,Mongolia,Iran,and Azerbaijan have national incomes that are close at the threshold.展开更多
This study aims to highlight the need for Industry 4.0 in a manufacturing system and explore the importance of the barriers to adopting Industry 4.0 technologies in Indian SMEs.Many barriers to implementing Industry 4...This study aims to highlight the need for Industry 4.0 in a manufacturing system and explore the importance of the barriers to adopting Industry 4.0 technologies in Indian SMEs.Many barriers to implementing Industry 4.0 were explored through a literature review.These barriers are prioritized using the Best-Worst Method(BWM).The framework has illustrated the approach to exploring the barriers and ranking these barriers based on feedback from industry experts.Some of the barriers such as lack of infrastructure,lack of financial resources,lack of government initiatives,high complexity,and cyber security and data ownership issues are observed to be very influential in SMEs to adopting Industry 4.0.The proposed framework can also be used in other industries for implementing Industry 4.0 technologies.Prioritizing and overcoming the barriers step-by-step may help the manager to digitalize the systems.展开更多
About two thirds of the population in India lives in villages.There is an acute shortage of health centers in rural areas.Hospitals are not located uniformly across different regions of country.Rural areas are also no...About two thirds of the population in India lives in villages.There is an acute shortage of health centers in rural areas.Hospitals are not located uniformly across different regions of country.Rural areas are also not well connected with cities due to a lack of infrastructure.Therefore,the demand for super specialty hospitals is greater in rural areas.This paper has analyzed the health requirement in a prominent Indian state,Bihar,in terms of population density.The purpose of this study is to illustrate the hospital site-selection problem by using the fuzzy extended elimination and choice expressing reality(ELECTRE)approach.Different attributes considered for site selection in this paper are cost,proximity,population characteristics,availability of human resources,accessibility,environment,etc.The findings of the study will be of great value to the health ministry and policy makers in taking judicious decision s while selecting the site for a new hospital or health center.展开更多
The aim of the paper is to benchmark the performance of the Indian fertilizermanufacturing organizations based on the ranking of efficiencies using a fuzzy data envelopment analysis(FDEA).FDEA has been used to find t...The aim of the paper is to benchmark the performance of the Indian fertilizermanufacturing organizations based on the ranking of efficiencies using a fuzzy data envelopment analysis(FDEA).FDEA has been used to find the relative efficiency and ranking of the fertilizer-manufacturing organizations.The last few years’data have been converted into the fuzzy inputs and outputs as minimum,mean,and maximum values,respectively.The performance of the fertilizer manufacturing organizations is based on the output maximization model of DEA.The frontier organizations set the benchmark for the lagging organizations for further improvement in the performance.This method can also be used to incorporate the data of the several years for multiple inputs and outputs instead of consideration of data of only one year.The proposed approach in this study may help organizations to improve its efficiency to fulfill its goal.展开更多
The study examine the efficacy of government policy interventions initiated since 2014 to curb FCRB in NW Indian states and the related air pollution in Delhi-NCR during 2014-2019.The regression analysis suggests an i...The study examine the efficacy of government policy interventions initiated since 2014 to curb FCRB in NW Indian states and the related air pollution in Delhi-NCR during 2014-2019.The regression analysis suggests an increase in PM2.5 of~69μg/m3/1000 surge in fires.VIIRS retrieved data suggest an overall declining trend of~1606 and~4308 fire counts per year across NW states during October-November respectively.The monthly PM2.5 concentrations in New Delhi exhibits a decline of~2.18μg/m3 and~5.17μg/m3 per year over the same period.Despite an overall increase of~17.6%rice productivity,a noted decrease in fire activity over the period is an encouraging move,likely a result of some control imposed by authorities on FCRB.Owing to a significant~35.5%rise in wheat productivity,data records rising trend in wheat residue burning activities in April(~1298/year)and May(~2402/year)but do not trigger extreme pollution due to difference in intensity of fires across harvesting seasons and relatively weak northwesterly wind direction.Nevertheless,the overall high PM2.5 levels in October-November and April-May compared to NAAQS 24-hour average of 60μg/m3,disproves the overall efficacy of government policies to curb FCRB and related air pollution in IGP region.展开更多
文摘Profitability has always been considered as a primary indicator of dividend payout by a company. There are factors other than profitability namely cash flows, debt equity ratio, retained earnings, sales growth, share prices of a company, capital expenditure and beta etc. that also affect dividend decisions of an organization. Existing literature suggests that dividend payout is positively related to profits, cash flows while CAPEX （capital expenditure） retained earnings, sales growth, share prices, beta, interest paid and debt equity ratio have inverse relationship. A set of 21 key variables have been identified that affect the dividend payout of a firm. Researchers in the past have used several proxies to represent these determinants. Authors have tried to find out which proxy variable is most relevant in the present scenario. The paper attempts to give a focused overview of the important dividend theories and empirically analyze the determinants of dividend behavior of Indian FMCG （Fast moving consumer goods） sector. The relationship between key variables has been explored with the aid of statistical techniques of factor analysis. Thus, the main theme of this study is to examine the various factors that influence the dividend policy decisions of FMCG firms in India.
文摘The current study attempts to compare the hybrid artificial intelligence models to forecast the environmental externalities in Saudi Arabia.We have used the denoising based artificial intelligence models to construct hybrid models.While comparing the denoising techniques,the CSD-based denoising has outperformed.However,we have used the CSD-based hybrid models.CSD-ANN and CSD-RNN are used for denoising-based artificial intelligence models,whereas CSD-ARIMA is used for denoising-based traditional models.All these models are used to check and compare their performance in terms of level and direction of prediction for PM_(10).The results show that the CSD-based ANN model has a higher predictability for PM_(10) levels in Saudi Arabia due to low error values and higher Dstat values.In comparing original and forecasted data,the superiority of CSD-ANN is evident in predicting the PM_(10) in Saudi Arabia.Hence,this hybrid model can predict the environmental externalities for non-linear and highly noised data.Moreover,the findings can be useful in achieving the sustainable development goal.
文摘Countries face the risk of natural resource curse because of making their economic growth excessively dependent on natural resources.Although excessive resource dependence causes such a risk,it is inevitable that resource-rich countries will need resource rent up to a certain level of economic maturity.On the other hand,transferring the wealth achieved after this maturity level to productive investment areas also reduces the resource dependency levels of countries.In this context,countries that capture the possible inverted U-shaped relationship between economic growth and resource dependence can escape the curse.Based on this,the aim of this research is to determine the validity of the Kuznets type relationship between resource dependence and economic growth for the first time in the literature.Nine nations that rely heavily on natural resources are used as a sample for this.The countries with a share of total resource rent in national revenue greater than 25%are taken into consideration throughout the selection process for these countries.Using novel panel data methodologies,the effects of capital accumulation,public spending,foreign direct investment,and economic growth on the dependence on natural resources is examined from 1993 to 2021.The results reveal that capital accumulation reduces resource dependency while foreign investments and government size increases it.In addition,the Resource-Based Kuznets curve concept is supported by empirical data demonstrating an inverted-U-shaped relationship between economic growth and resource dependence for these nations.The thresholds derived from the parameters show that Saudi Arabia and Kazakhstan are well beyond this cutoff.The Democratic Republic of the Congo and the Republic of the Congo,on the other hand,remain a long way from this threshold.Furthermore,Iraq,Mongolia,Iran,and Azerbaijan have national incomes that are close at the threshold.
文摘This study aims to highlight the need for Industry 4.0 in a manufacturing system and explore the importance of the barriers to adopting Industry 4.0 technologies in Indian SMEs.Many barriers to implementing Industry 4.0 were explored through a literature review.These barriers are prioritized using the Best-Worst Method(BWM).The framework has illustrated the approach to exploring the barriers and ranking these barriers based on feedback from industry experts.Some of the barriers such as lack of infrastructure,lack of financial resources,lack of government initiatives,high complexity,and cyber security and data ownership issues are observed to be very influential in SMEs to adopting Industry 4.0.The proposed framework can also be used in other industries for implementing Industry 4.0 technologies.Prioritizing and overcoming the barriers step-by-step may help the manager to digitalize the systems.
文摘About two thirds of the population in India lives in villages.There is an acute shortage of health centers in rural areas.Hospitals are not located uniformly across different regions of country.Rural areas are also not well connected with cities due to a lack of infrastructure.Therefore,the demand for super specialty hospitals is greater in rural areas.This paper has analyzed the health requirement in a prominent Indian state,Bihar,in terms of population density.The purpose of this study is to illustrate the hospital site-selection problem by using the fuzzy extended elimination and choice expressing reality(ELECTRE)approach.Different attributes considered for site selection in this paper are cost,proximity,population characteristics,availability of human resources,accessibility,environment,etc.The findings of the study will be of great value to the health ministry and policy makers in taking judicious decision s while selecting the site for a new hospital or health center.
文摘The aim of the paper is to benchmark the performance of the Indian fertilizermanufacturing organizations based on the ranking of efficiencies using a fuzzy data envelopment analysis(FDEA).FDEA has been used to find the relative efficiency and ranking of the fertilizer-manufacturing organizations.The last few years’data have been converted into the fuzzy inputs and outputs as minimum,mean,and maximum values,respectively.The performance of the fertilizer manufacturing organizations is based on the output maximization model of DEA.The frontier organizations set the benchmark for the lagging organizations for further improvement in the performance.This method can also be used to incorporate the data of the several years for multiple inputs and outputs instead of consideration of data of only one year.The proposed approach in this study may help organizations to improve its efficiency to fulfill its goal.
文摘The study examine the efficacy of government policy interventions initiated since 2014 to curb FCRB in NW Indian states and the related air pollution in Delhi-NCR during 2014-2019.The regression analysis suggests an increase in PM2.5 of~69μg/m3/1000 surge in fires.VIIRS retrieved data suggest an overall declining trend of~1606 and~4308 fire counts per year across NW states during October-November respectively.The monthly PM2.5 concentrations in New Delhi exhibits a decline of~2.18μg/m3 and~5.17μg/m3 per year over the same period.Despite an overall increase of~17.6%rice productivity,a noted decrease in fire activity over the period is an encouraging move,likely a result of some control imposed by authorities on FCRB.Owing to a significant~35.5%rise in wheat productivity,data records rising trend in wheat residue burning activities in April(~1298/year)and May(~2402/year)but do not trigger extreme pollution due to difference in intensity of fires across harvesting seasons and relatively weak northwesterly wind direction.Nevertheless,the overall high PM2.5 levels in October-November and April-May compared to NAAQS 24-hour average of 60μg/m3,disproves the overall efficacy of government policies to curb FCRB and related air pollution in IGP region.
