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The aim is to provide a framework for statement organizational research problems. State the problem is the most important reason for the researcher to choose the subject. Although research has been done on the characteristics of expression of research problems, but in organizational research, no framework for expressing the problem has been provided. The method used in this research is grounded theory followed by the ISM-MICMAC approach for modelling. The findings of this study identify the components of problem statement in organizational research and modelling them. Due to the large number of indicators, first nine indicators of higher importance were identified, which had much more weight than other indicators. These indicators were then compared and prioritized again by other experts. Based on the level of importance of these indicators, a model is presented and it is determined what position each of these indicators is in terms of Degree of dependence and Influence rate. The researcher can easily use this information to provide an acceptable problem statement and the editors will have a good assessment tool.

Workplace injuries and accidents have an adverse effect on the lives of textile workers. The research study investigates the moderating effect of ethical climate on the relationship between safety management practices and safety behaviour. A simple random sampling technique was employed to collect data from 12 textile companies. A total of 384 textile workers participated in this study. Results revealed that management commitment, safety training, workers involvement, safety communication & feedback and safety promotion policies have a significant and positive influence on safety behaviour. While safety rules and procedure are failed to predict safety behaviour. Moreover, ethical climate positively moderate the relationship among management commitment, safety training, workers’ involvement, safety communication & feedback and safety behaviour. Whereas, the ethical climate failed to moderate safety rules and procedure, safety promotion policies and safety behaviour. It is recommended that Textile companies.

This research aims to quantify the linear and non-linear relationship between debt and economic growth in selected developing economies. Based on theoretical arguments and annual data considerations in modelling the debt and growth as a complex relationship across countries, our panel methodology is based on the fixed effect technique. Our core finding indicates that government debt lowers the GDP in selected developing economies. We also find that the urban population is a key factor that improves economic growth. Moreover, government expenditures on health and industrialization are helpful to enhance the growth of the economies. Our study also suggests increased exports, industrial development, and investment in education for growth. We also propose certain supporting strategies to reduce the adverse effects of debt-growth relation in the considered economies. Given developing economies’ status, the prerequisite for broad, dynamic and rule-based debt policy is of paramount importance, ensuring the factual choices among numerous possibilities, addressing financial constraints and ensuring intergenerational welfare impact.

This paper estimates NIFTY index from Indian stock market by considering a cluster of MSCI European, Middle East and Asian stock market indices. In the forecasting process, we obtain group of independent variables to test its relative impact over dependent variable (NIFTY) considering a sample size of daily observations from January 2000 to December 2021 abstracted from Bloomberg. We run OLS regression, Quantile estimations with additional parameter of VIF and BKW. We found significant impact association with China (Asian index) and Saudi Arabia (Middle East index) during the forecasting process compared to rest of sample indices that exceed unexpectedly out of VIF limits. Further, we recorded strong association of independent variables despite of statistical significance (

This study aimed to analyse the normal and abnormal loss of a jeans manufacturing company in India. Personal interview and observation method are used in this study. Abnormal loss in quantity and rupee value is computed for 40 days of production based on the observed data. Mean abnormal losses are computed and one sample t-test is applied to test the hypotheses that the mean abnormal loss is not equal to zero. The study revealed that a normal loss of 3 to 5% is expected in any garment manufacturing company due to loss during the cutting and shrinkage process. The p-values of one sample t-test were less than 0.05 for all the tested hypotheses, hence, all the null hypotheses (H01 to H05 mean abnormal losses equal to zero) were rejected. Further, it was found that fabric is the big contributor in terms of abnormal loss. Hence, proper training for workers and recruiting of trained workers are advised to reduce abnormal losses

Education and health are considered a cornerstone for obtaining targeted development in any society. Moreover, both sectors promote prosperity greatly. In this changeable epoch, people are thought out as the real wealth of any nation and this wealth with good human capital serves the economy very efficiently and productively. This research study aims to analyse how Development Assistance Committee (DAC) aid commitment for education along with institutional quality is effective for the human development of selected Asian economies. A panel data set over 2011–2018 is used for this analysis in Asian countries. GMM results show a significant and positive relationship between aid commitment for education and the human development of these economies. A more interesting result is that financial development seems to boost up human deployment in the selected Asian economies. The development of the textile industry is significantly influenced by education, especially considering the effects of OECD's Development Assistance Committee (DAC) Aid Commitments for education on human development in Asian countries. There is a dire need to reconsider more allocation of resources and aid to education and health to utilize these inflows at the maximum level for targeted development.

This research paper aims to examine the impact of the COVID-19 pandemic on volatility patterns and its global implication for the textile industry in China. The COVID-19 pandemic has generated a global health crisis with profound economic, social and financial implications, but also has triggered a ruthless global recession. The global economic recovery as a result of the COVID-19 pandemic can also generate significant investment opportunities for the textile industry in China. In this paper, the application of empirical methods could explain historical prices, the movement dynamics of financial assets, and investigate various important characteristics of asset pricing that explore details of the Chinese stock market. The econometric framework includes the following: symmetric Generalize Autoregressive Conditional Heteroscedastic GARCH (1, 1) model, asymmetric GARCH models such as EGARCH and GJR models. The main aim is to identify the asymmetric volatility effect, and impact of news on the SSE Composite Index and investigate long memory properties in volatility using daily data for the sample period from 19th December 1990 to 31st December 2020. This empirical study contributes to the existing literature on the impact of the COVID-19 pandemic on international stock markets, by investigating symmetric and asymmetric volatility patterns in the case of the Shanghai Stock Exchange from China

The textile sector in India is the oldest manufacturing sector. As the raw materials for this sector are sourced from the petrochemical industries, the earnings of Indian textile companies are dependent on the crude oil price. The crude price in the international market has become more volatile and hence, the equity price of Indian textile companies has become more volatile. This study aims to develop two price risk management strategies for Indian textile equities. Using the vector autoregressive (VAR) model, a price forecast model, further the possibility of cross hedge for textile equities with the help of crude futures is examined using the Granger causality test and Pearson correlation statistics. The results of the study showed that crude futures price in India is one of the price determinants of textile industry stock prices.

Today, the importance of customer relationship is not hidden from anyone, and predicting the value of customer life can help organizations to create an optimal relationship with their customers. The concept of industrial society represents a symbiosis between social and industrial activities using mass-production technologies. A sustainable CRM approach can generate significant benefits for the development of the textile industry. This paper compares ARIMA and neural network models in predicting customer lifetime value. The time-domain of the research is related to the year 2021 in the Lojoor company. To identify the variables needed to predict the value of customer longevity, experts in this field and university professors were used through descriptive survey method and using databases to collect other data. After collecting the data, the required variables were first identified by the Delphi method and then the databases were analysed using the artificial neural network method and the ARIMA model, for which MATLAB software was used. The results showed that both ARIMA and artificial neural network models can be used to predict customer lifetime value. In the case of the artificial neural network, it was observed that in addition to better prediction of the relationship between variables, which assumes them to be nonlinear, the artificial neural network model also performed better in terms of prediction results. In total, the values of MAPE error are 10.3% and MSE error is 11.6% for the neural network model. The neural network model is acceptable.

The recent exponential rise in the number of cyber-attacks has demanded intensive study into community intrusion detection, prediction, and mitigation systems. Even though there are a variety of intrusion detection technologies available, predicting future community intrusions is still a work in progress. Existing approaches rely on statistical and/or superficial device mastery techniques to solve the problem, and as a result, feature selection and engineering are required. The truth is that no single classifier can provide the highest level of accuracy for all five types of training data set. Cyber-attack detection is a technique for detecting cyber-attacks as they emerge on a laptop or network device, intending to compromise the gadget's security. As a result, using a novel type and encryption mechanism, this paper offered a unique architecture for attack node mitigation. The input UNSW-NB15 dataset is first acquired and divided into training and testing statistics. First and foremost, the information is pre-processed and capabilities are retrieved in the training section. The Taxicab Woodpecker Mating Algorithm (TWMA) is then used to select the critical characteristics. The attacked and non-attacked information are then classified using the BRELU-ResNet (Bernoulli's Leaky Rectified Linear Unit - Residual Neural Community) classifier. The encrypted at Ease Hash Probability-Based Elliptic-Curve Cryptography (ESHP-ECC) technique is used to encrypt the ordinary facts, which are subsequently kept in the security log report. Following that, using Euclidean distance, the shortest course distance is estimated. Finally, the records are decrypted using a set of principles known as Decrypted Relaxed Hash Probability-Based Elliptic-Curve Cryptography (DSHP-ECC). If the input appears in the log file during testing, it is regarded as attacked data and is prevented from being transmitted. If it isn't found, the procedure of detecting cyber-attacks continues.

This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other words, the sample period covers both the period of the global financial crisis (GFC) and the COVID-19 pandemic. The econometrics includes GARCH (1,1), GJR (1,1), and EGARCH (1,1) models. By applying GARCH family models, this empirical study also examines the long-term behavior of the Japanese stock market.The Japanese stock market is much more stable and efficient than emerging or frontier markets characterized by higher volatility and lower liquidity. The paper establishes that NIKKEI 225 index dynamics is different in intensity in the case of the two most recent extreme events analyzed, namely the global financial crisis (GFC)of 2007–2008 and the COVID-19 pandemic. The findings confirmed the presence of the leverage effect during the sample period. Moreover, the empirical results identified the presence of high volatility in the sample returns of the selected stock market. Nevertheless, the econometric framework showed that the negative implications of the GFC were much more severe and caused more significant contractions compared to the COVID-19 pandemic for the Japanese stock market. This study contributes to the existing literature by providing additional empirical evidence on the long-term behavior of the stock market in Japan, especially in the context of extreme events.

The main aim of this research paper is to examine the linkage between safety training, safety rules and procedures, safety performance and protection against hazards in Pakistani construction companies related to its effects on the textile industry. The primary responsibility of the organization is to provide a safe workplace to the workers where workers do their work safely. The current study examines the relationship between safety training, safety rules & procedures and safety performance. A total of 450 workers from 15 companies participated in the study. A questionnaire survey was used to collect the data. The findings revealed that both safety training and safety rules and procedure were significantly and positively associated with safety compliance. The results propose that construction companies should give proper training to their worker in order to avoid any bad incidents. Similarly, adequate safety rules and procedures are essential for a safer work environment. The textile industry is a very important sector in Pakistan with a significant impact on employment and the labour market.

This paper investigates the benefits of forming an internationally diversified portfolio in the stock markets of Bangladesh, India and Pakistan using the stock market indices data from April 2013 to March 2020. The portfolio comprises of three stock market indices from Pakistan, India and Bangladesh. The goal is to identify financial opportunities for traditional clothing industry in South Asia. Bangladesh, India and Pakistan are neighbouring countries in South Asia. Tradition, culture and specific ethnic elements influence traditional clothing in the case of the selected country cluster consisting of Bangladesh, India and Pakistan. Our empirical results indicate that internationally diversified portfolio does not reduce the risk due to global market integration in the background. Furthermore, ARCH and GARCH models reveal that large change in conditional variance is followed by large changes in conditional variance whereas small change in conditional variance is followed by small changes in conditional variance.

The trading of natural rubber derivatives in the Indian commodity exchanges was banned several times in the past. Hence, in India, the derivatives on natural rubber are not traded actively and regularly. We have examined the possibility of a forecast model and across hedge tool for the natural rubber price by using crude oil futures in India. Results of the Johansen cointegration test proved that there is no cointegration equation in the model; hence, there is no scope to develop long-run models or error, correction models. We have developed a vector autoregressive [VAR(2)] model to forecast the rubber price, and we examined the possibility of a cross hedge for natural rubber further by using the Pearson correlation coefficient and Granger causality test. We have extended our research to a structural VAR analysis to examine the effect of crude futures and exchange rate shocks on the natural rubber price. Our results showed that there is a short-term relationship between the crude oil futures price, the exchange rates of the US dollar to the Indian rupee, the Malaysian ringgit to the Indian rupee, and the Thai baht to the Indian rupee; and the natural rubber price in India. The effort of policymakers to cause the Indian rupee to appreciate against the Thai baht and Malaysian ringgit may increase the natural rubber price in India. Natural rubber traders, growers, and consumers can use crude futures to hedge the price risk. The Indian Rubber Board can suggest the VAR(2) model predict the short-run price for natural rubber.

The purpose of this paper is to predict the financial distress of companies of the Indian textile sector using Altman Z score. The analysis conducted on 161 listed textile companies in India for a period of 10 years from 2009 to 2018. All the listed companies are categorized into large, medium, and small using the median split method based on the size of total assets. Kruskal Wallis test is applied to test whether the mean z-score is different for each category of companies. This research study shows that majority of the companies in the Indian textile sector are facing financial distress. Further, it shows that the z score of small, medium, and large-scale textile companies in India is significantly different.

The main objective of this empirical study is to investigate the impact of tax revenue on GDP dynamics at EU-28 level based on structural equation modeling (SEM). We applied structural equation modeling (SEM) which represents a multivariate statistical analysis technique used especially for analyzing structural relationships between measured variables and latent structures. Selected taxes are the following: environmental taxes, indirect taxes, social contributions, taxes on capital, taxes on labour, taxes on property, and direct taxes. The sample period includes a long time horizon during 2005-2017 for each member states of EU-28. Our empirical findings revealed the level of taxation exhibits an increased influence on GDP dynamics in case of EU-28 member states. JEL Codes - C3; E62; O47; O52

This empirical study investigates the financial integration linkages among the sample stock markets of Canada, Mexico,United States (for both New York Stock Exchange, i.e. NYSE and NASDAQ), Panama, Brazil, Chile, Peru, Venezuela,Jamaica, Trinidad, and Tobago during the period from January 2001 to April 2019. This research study also examinesthe impact of selected stock market dynamics on the textile sector. International portfolio diversification has been animportant subject of research in financial fraternity since the emergence of Modern Portfolio Theory in 1952. This studyexamines the portfolio diversification opportunities in the 11 stock markets of Americas.International diversificationamong stock market indices has proven to be fruitful in the past. Certain tests have been used to determine opportunitiesfor diversification are correlation test, pairwise co-integration test, multiple co-integration test and granger causality test.The empirical results show that stock market indices share low correlation among other and they are not highlyco-integrated whereas results of Granger causality test exhibit an unidirectional relationship among few stock marketsin short run.

Nowadays, ECAs have a crucial role in the export of production, creating job opportunities for countries and growth of economic indicators.This research aims first to estimate the performance of ECAs based on covering all countries of the world and ranking the countries based on the issue of export credit according to their performance and clustering techniques. For evaluation performance of these ECAs, clustering techniques are used to put them in the categories according to their performance between 2005 to 2020 in the fourth quarter. The context of clustering shows the rank of each cluster, and then exporters can choose a better choice from them. Moreover, for reinsurance,other ECAs can find out which ECAs have high performance. The result indicates that ranking the ECAs and show the performance of each cluster.

This empirical study investigates the leverage effect in six Eastern European countries under normal and non-normaldistribution densities for the sample period from January 2020 to August 2020. We find three countries, Bulgaria, CzechRepublic and Russia which are subject to ARCH effect whereas Poland, Romania and Hungary do not exhibit ARCHeffect in daily stock returns. Further, our study finds leverage effect, where past bad news affects is asymmetrical, pastnegative returns cause more volatility in current stock returns as compared to past positive returns, in three EasternEuropean countries. Based on the AIC and BIC model selection criteria we find that the non-normal student t-distributionand GED produce reliable estimates for Bulgaria, Czech Republic and Poland, respectively. The autocorrelation functionQ1 statistic confirms the insignificance of autocorrelation in residuals of TGARCH model. The impact of stock marketdynamics on other industries, such as pharmaceutical industry, textile and clothing industry, automotive industry issignificant, especially in the conditions of COVID-19 pandemic

Following the work of Kaminsky, Lizondo, and Reinhart (1997), Signals Extraction Approach has been adopted with some extensions for South-East Asian (SEA) region to investigate the performance of the technique as an Early Warning System (EWS) during Asian Financial Crisis (AFC) and Global Financial Crisis (GFC). This approach is very original in the context of investigating the impact on the dynamics of the textile industry in South-East Asia. Two additional approaches namely Signal to Noise Balance (STNB) and Kuipers Score (KS) have also been utilised. Outcome suggested that variables performed well both during AFC and GFC. However, predictive ability of variables was less during GFC compared to the AFC indicating that there may exist some complex phenomenon which requires composite statistical methods.

This chapter aims to provide an elaborate empirical analysis of the monetary policy dynamics in Romania using a structural vector autoregressive model. This chapter contributes to literature based on an empirical framework regarding the implications of exchange rate channel within the monetary policy, and the impact of the monetary aggregates channels in order to explain the evolution of the prices level in Romania.

The main purpose of this chapter is to investigate monetary policy dynamics, as well as the inflation inertia and inflation persistence in Romania using a DSGE approach. The empirical findings revealed that the price evolution reflects the difficulties of eliminating the inflation inertia. Moreover, in Romania, the historic inflation evolution has a significant influence in terms of inflation expectation patterns. Inflation is a negative phenomenon with dramatic consequences for Romania's economic development on long term.

This chapter aims to provide additional empirical evidence on monetary policy transmission mechanism in Romania over the period 2001 to 2012 based on a BVAR analysis with a KoKo Minnesota/Litterman prior. The importance of the central bank is rising in Romania considering its main attribution to control the interest rate in accordance with its objectives. The empirical evidence provides a significant contribution to literature taking into account the characteristics of the selected emerging country, i.e. Romania, a former communist country in Central and Eastern Europe.

This study has attempted to investigate the consequences of CO2 emissions on infants’ health in Pakistan over the period of 1975 to 2013. Several economic factors have been employed in our analysis and the estimates show insignificant impact of CO2 emissions in affecting children mortality. Increasing health facilities lowers children mortality over a short period were also observed but the relationship inversed in the long-run. In short-run, urbanization appeared as a decreasing factor to children mortality. While income inequality remains inversely related with children mortality. Both poverty and fertility are found enhancing factors to children deaths. The poor sector of the economy seemed to observe higher children mortality due to inadequate health facilities and low standards of living. Overall, we have observed greater impact of economic factors in explaining children mortality than CO2 emissions in case of Pakistan. These issues have a significant impact on the representative industries in Pakistan, such as the cotton textile and traditional clothing industry (apparel manufacturing).

Government expenditure is linked to the economic growth and is the driving force of the every country. In the post liberalization era, India has been exposed to the dynamics of the world economy due to which India has witnessed a significant impact of Government spending on its economic growth. The objective of this paper is to investigate the effects of the Central Government spending on the growth of the Indian economy over a period, from 2006 to 2016. The online data disclosures of the various ministries have been the major source of secondary data. Co-integration analysis is adopted to evaluate the effect of individual sectorial spending on the economic growth and gross domestic product. The economic spending is classified into 5 sectors namely: General Services, Social Services, Economic Services, Grants in Aid & Contribution and Public debt & Loans for analysis, as disclosed by the sources. The analysis gives us an idea of the various sectors which have a positive impact and the sectors which have a negative impact. The results would play an instrumental role in exploring the sectors in which the government should invest more, thereby contributing to an enhancement in the country’s growth.

Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A sample size of 782 time-series observations from January 1, 2017 to December 31, 2019 for each selected pharmaceutical firm has been considered to frame the ARIMA model. ADF test is used to verify whether the data are stationary or not. For ARIMA model estimation, significant spikes in the correlogram of ACF and PACF have been observed, and many models have been framed taking different AR and MA terms for each selected company. After that, 5 best models have been selected, and necessary inculcation of various AR and MA terms has been made to adjust the models and choose the best adjusted ARIMA model for each firm based on Volatility, adjusted R-squared, and Akaike Information Criterion. The results could be used to analyze the stock prices and their prediction in-depth in future research efforts.

Local apparel and textile manufacturing industry in Taiwan is a sector of great importance for sustainable economicgrowth. A stock market is an effective barometer indicating the economic health of a country and Taiwan is a case evenmore special. However, is Taiwan a black swan phenomenon for local apparel and textile manufacturing industryconsidering its economic growth and financial perspectives? In addition to existing literature, this research articleprovides a new robust nonlinear regression-based model for stock exchange prediction for Taiwan stock market. Thefinancial data series used for the econometric analysis include the period from January 2000 to July 2018 for 13 mainstock markets from countries all around the globe, such as: Taiwan, Spain, Poland, Hungary, Romania, Canada, USA,Japan, Germany, France, UK, India, and China. The final multiple regression equation provides a new prediction modelfor Taiwan’s main stock market index. A sustainable economic growth in Taiwan is necessary to achieve major objectivessuch as social justice, poverty alleviation and natural environment protection. The stock market in Taiwan plays anessential role in order to stimulate economic growth and technological progress by attracting foreign investment andforeign capital. In a globalized economy, the inter-linkages between stock markets are complex and can significantlyinfluence Taiwan’s sustainable development.

This research paper examines the holiday effects presence on the Bombay Stock Exchange (BSE), which is a major Indian stock exchange. Textile and clothing industry in India is one of the most important producers in the world, but also the second exporter of textile and apparels globally. The empirical analysis investigates the impact of holiday effect on the development of textile and clothing industry in India. The holiday effect is one of the most important calendar anomalies identified in the financial markets. The methodological approach includes the non-parametric Mann-Whitney U-test used to test the equality of means for different sub-sets. The findings revealed that the mean returns for pre-holiday and post holidays were greater compared to that of remaining days, but the empirical results showed that they were not statistically significant for selected stocks of BSE based on daily stock returns data for Ruby Mills and Mafatlal Industries

The aim of this research study is to examine the impact of domestic portfolio diversification strategies in Toronto Stock Exchange (TSX) on Canadian textile manufacturing industry in order to obtain attractive investment opportunities. Dissipation of benefits of globally diversified portfolios due to overwhelming convergence among the international and regional stock markets around the globe have given rebirth to the idea of domestic portfolio diversification particularly after the global financial crisis of 2008. Textile industry in Canada is challenging but can achieve higher performance based on Toronto Stock Exchange behavior. Therefore, this is a complex applied research focused on investigating TSX as standalone stock market for domestic diversification opportunities. For this purpose, correlation coefficients, pairwise cointegration, multiple cointegration and causality of sectors in TSX have been examined. The empirical results show that majority of the sectors in TSX do not share high correlation with each other and they are also not highly cointegrated. These empirical findings indicate that TSX presents attractive opportunities for domestic portfolio diversification.

Sustainability reporting and disclosure in India have received a significant attention over the most recent few years propelled to a large extent by investors and policy makers. The Sustainable Business Leadership Forum (SBLF) has been closely working with many firms, owners of the companies and policy makers to single out the relationship between investment and environmental, social and governance (ESG) disclosure. Besides that, SBLF has had a coordinated conversation about the anticipations, concerns, difficulties and realities surrounding ESG estimation. This ESG criteria refers to three important elements which are considered by investors with regards to an ethical impact of firms and sustainable practices. As per the literature companies with higher ESG scores are better investment picks. This paper attempts to assess the volatility and returns of Indian companies and to measure the impact of ESG scores on returns and volatility with the help of panel regression.Keywords: ESG, ESG Scores, Sustainability, Panel Regression, Investment returns.JEL Classifications: D22, G11, G14, G32DOI: https://doi.org/10.32479/ijeep.9311

The main purpose of this research article is to provide a comparative framework on various implications of risk anomalies on Indian stock market based on an empirical study for the following sectors: Pharmaceutical, Fast-Moving Consumer Goods (FMCG) and IT. Risk anomaly is a notable anomaly because it is continual and all-inclusive. This research study aims to examine the existence of risk anomaly in the National Stock Exchange, India, and in particular providing a comparative analysis on the behavior of the pharmaceutical sector in India.

Nowadays, environmental protection involves many issues and problems, among which the waste generated by various human activities makes up a significant share, which is becoming newer day by day. Moreover, the production of normal, industrial, special, hospital, and agricultural waste and improper management of these materials has created many health, safety, and environmental problems. Based on this approach, this research study aims to determine the model of waste management and energy efficiency in smart homes using the Internet of Things (IoT). The research method used by this study is estimative-computational. For this purpose, the required data were collected using a computational approach. For this purpose, the required views and data were collected through experts in this field and calculated in MATLAB and STATA software. The data analysis tool was represented by fuzzy calculations and for this purpose MATLAB software was used. The study revealed that energy costs in smart homes using the IoT technology are impressive. The number of home residents in smart homes using the IoT is impressive. Home area in smart homes using the innovative technology of IoT is also impressive.

The COVID-19 pandemic has brought rampant changes in skill needed in the labor market. It has accentuated technological disruption leaving millions in dire need of reskilling and upskilling. In this paper, we empirically analyze the impact of the COVID-19 pandemic related lockdown on the thrust of skills upgradation among people. By analyzing the Google trends data of 13 countries, we test the effect of the lockdown implementations on the urge to upgrade the skills through online searches for skills enhancement. Using difference-in-difference estimation approach, we found a substantial hike in the frequency of search terms related to skills upgradation. Our results suggest that people are utilizing the excess time, made available due to lockdowns, by exploring avenues to enhance their skills to accumulate human capital. The online educational platforms have been proven vital. The findings of this study establish the causal link between use of online education platforms and human capital development.

There are two kinds of strategies to control Non Performing Assets i.e. curative and preventive. The paper is an attempt to focus on the effectiveness of various preventive strategies in controlling NPA in future.For this study primary data have been collected from 82 branches out of 138 branches of Sagar District in Madhya Pradesh of India. The respondents are the branch managers or recovery officer of each branch.The primary data are related to the causes of NPA, actual usage of preventive measures and effectiveness of each preventive strategy. It highlights few new causes which are barely covered by the earlier researches. The study also represents the actual usage of various preventive measures along with the effectiveness of preventive measures in averting NPA to be occurred in future. The outcome of this study could provide a valuable insight about which strategy is more effective to prevent these stressed assets. Besides that, it could aware the banking authorities regarding the problems faced by the managers in using the preventive measures.JEL Codes - G21; G23, G24

This research makes an attempt to understand various factors that influence the adoption of mobile applications. Within the context of the “Unified theory of acceptance and use of technology” (UTAUT) modified model, considering the upcoming demand and increase in demand for mobile-banking applications, the researcher tried to explore the theoretical concept between random people of various states in India. The primary data was collected by preparing a questionnaire and circulating it using Google Forms. The collected data was further coded into Smart PLS 4 to understand the model and structural equation with reference to mobile-banking technological adoption and factors that had a significant impact. The conclusions derived from the study is that social influence, “effort expectancy”, and “trust” factors had a very strong influence on the “purchase intention”, whereas “effort” and “risk” factors had a negligible impact on purchase intent. It was also found that the UTAUT model is appropriate for evaluating the technological adoption of mobile-banking applications. With the advent of many players in the market and their unique banking management applications on mobile platforms, consumers are moving towards different third-party app than their origin bank in which they hold account. This has forced banking institutions to up the pace in the competition, introducing a lot of new features. It is also important to understand that, as a customer, there are a lot of attributes that he would be looking into for adoption. This paper is an attempt to understand the advancements in various variables that consumers would look at in the area of mobile-banking applications.

The oil and gas industry plays a significant role in the economies of many countries today. Due to various factors, including oil price fluctuations, wars, sanctions, and many other instances, selling and supplying these products at low prices is necessary. As a result, the global economy may suffer as well. Supply chain management is one way to reduce the prices of these products. This study was conducted to identify supply chain management contracts in the oil and gas industry. The paper presents an application of multi-criteria decision-making (MCDM) for coping with uncertainty. We contribute to the literature by proposing a new hybrid MCDM method with gray numbers for ranking supply chain management contracts in the oil and gas industry. The results show that the factors for evaluating supply chain management contracts must be selected, and then according to these factors, the supply chain management contracts must be chosen. As a result, we provide our customers with the best deals and help oil and gas companies minimize their costs.

In the present economic context, one of the most important topics of discussion is that regarding sustainable development. According to the agenda developed by the United Nations, one of the most important objectives for the present decade is represented by the list of the Sustainable Development Goals. The Sustainable Development Goals can be divided into five pillars: people, planet, prosperity, partnership and peace. One of the first stipulated goals of the UN agenda is the eradication of poverty and famine. We consider that a significant influence on the eradication of poverty is represented by the development of technology. In this paper, the authors aim to establish a connection between the rate of technological development and the poverty headcount rate. To measure the digital development of the analyzed countries, we decided to compose an index of digital development by taking into account indicators made available by the International Telecommunication Union and the poverty headcount ratio, as was calculated by the World Bank database. This empirical study is of interest for the implications that it has in shaping governmental policies regarding easing the access to digital technology. The method used to quantify the influence of digital development on poverty was the panel data GMM vector autoregressive model for a dataset composed of 35 countries for the period between 2005 and 2018. The results indicate that an increase in digital development will lead to a reduction in the poverty headcount rate. These results imply that by increasing access to technology, countries could help reduce their level of poverty. In this paper, we will also analyze the way in which adopting digital development leads to better economic performance when faced with the COVID-19 pandemic. The results of the present study are of great interest to the scientific community and the public due to the implications of digital development in the field of economics and the combined effect of this phenomenon and the COVID-19 pandemic. We thus conclude that by encouraging digital development and through adopting new technologies, the government can lead to the eradication of poverty. This seems counterintuitive due to the fact that investment in shelter and primary goods can be seen as one of the primary ways of developing the economy. We conclude that better and more consistent results regarding the reduction of poverty can be obtained by increasing the digital development of a country.

Project risk is an uncertain situation or event that, if it occurs, may have a negative or positive effect on one or more project objectives, such as scope, schedule, cost, and quality. Major industrial projects are increasingly facing complexity and uncertainty. The scope of this paper is related to petrochemical projects, in which risks directly affect the approved time, cost, and quality of the project. In such projects, there are risks that neither the owner nor the contractor has the main role in the occurrence or prevention of, and it is not easy to determine who is responsible for them. In such projects, there are risks that neither the owner nor the contractor has the main role in the occurrence or prevention of, and for which it is not easy to determine responsibility. Therefore, predicting, identifying, analyzing, and determining of the optimal allocation of risk responsibility between contracting parties is one of the most important steps before the start of the project. Suppose it is not correctly allocated among project stakeholders, then, in that case, risk responsibility imposes costs on the project that must be paid by the owner, contractor, and partnership, causing, in general, many problems for project management. Therefore, this paper presents a model to calculate the optimal ratio of risk allocation between the project parties in the concluding contract stage, using the UTA-STAR technique to obtain the owner and contractor utility function to create as much of a win-win relationship between them as possible.

The significance of Institutional Effectiveness is pivotal to the functioning of an academic institution. The mushrooming of private institutions in the Indian higher education space necessitates exploring its antecedents to ensure quality higher education is imparted by the institution. The purpose of this research endeavor is to explore the impact of Academic Leadership on Institutional Effectiveness with a mediating role of Campus Culture and Faculty Involvement in Decision Making. The study employed a cross-sectional research design and implemented a purposive sampling technique to collet primary data from 80 permanent faculties and 249 students of private engineering colleges located in the Karnataka state of India. Data was collected through a structured questionnaire and analyzed using Partial Least Square-Structural Equation Modeling. Hypothesis shows academic leadership and campus culture has high influence on institutional effectiveness. Faculty in decision making and campus culture partially mediates between academic leadership and institutional effectiveness. The results of FIMIX-PLS and PLS-MGA analysis shows the similarity in the results of total effect and path relationships. This paper provides theoretical foundations and empirical findings on conceptualizing the antecedents of institutional effectiveness. The outcomes of this research serve as significant input to policy makers and higher education institutions to facilitate enhancement of institutional effectiveness.