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This paper aims to investigate the association of COVID-19 confirmed cases and deaths with mental health, unemployment and financial markets-related search terms for the USA, the UK, India and worldwide using Google Trends.

The authors use Spearman’s rank correlation coefficients to assess the relationship between relative search volumes (RSVs) and mental health, unemployment and financial markets-related search terms, with the total confirmed COVID-19 cases as well as deaths in the USA, UK, India and worldwide. The sample period starts from the day 100 cases were reported for the first time, which is 7 March 2020, 13 March 2020, 23 March 2020 and 28 January 2020 for the US, the UK, India and worldwide, respectively, and ends on 25 June 2020.

The results indicate a significant increase in anxiety, depression and stress leading to sleeping disorders or insomnia, further deteriorating mental health. The RSVs of employment are negatively significant, implying that people are hesitant to search for new jobs due to being susceptible to exposure, imposed lockdown and social distancing measures and changing employment patterns. The RSVs for financial terms exhibit the varying associations of COVID-19 cases and deaths with the stock market, loans, rent, etc.

This study has implications for the policymakers, health experts and the government. The state governments must provide proper medical facilities and holistic care to the affected population. It may be noted that the findings of this study only lead us to conclude about the relationship between COVID-19 cases and deaths and Google Trends searches, and do not as such indicate the effect on actual behaviour.

To the best of the authors’ knowledge, this is the first attempt to investigate the relationship between the number of COVID-19 cases and deaths in the USA, UK and India and at the global level and RSVs for mental health-related, job-related and financial keywords.

The purpose of this paper is to investigate the stock market performance of companies featured in the survey “Best Companies to Work For” as a proxy for corporate culture.

The authors employed the portfolio formation and event study methods from finance to examine the linkage between corporate culture and future stocks returns. The lists of India’s best place to work for by Great Place to Work® Institute and Business Today (BT), India’s leading business magazine, form the primary surrogate for a great corporate culture. The authors compared the stock market performance of the culture portfolio vis-à-vis market index, in addition to using Carhart’s (1997) four-factor model.

A portfolio of Indian firms that featured in the “Best Companies to Work For” by Great Place to Work© Institute and BT magazine provides a higher return than the market index Sensex both on an ordinary return and on a risk-adjusted basis. The four-factor αs of the value-weighted culture portfolios are significant, implying that these portfolios have provided abnormal returns during the sample period. Moreover, the findings suggest a positive drift in the abnormal returns after inclusion in the “Best Companies to Work For” list.

The results are largely in conformity with the prediction of the theory that states that corporate culture is an economic asset for a firm that increases its value.

From an investor’s point of view, the study indicates that investment in “Best Companies to Work For” is a better alternative than passive index investing.

This study fills the empirical void in the relationship between corporate culture and stock market performance in the Indian context.

Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization.

Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing.

To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time.

The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

This paper aims to investigate the role of corporate governance on the bank profitability of Indian banks vis-à-vis South Asian Association for Regional Cooperation (SAARC) nations.

For the Corporate Governance Index, the authors examined board accountability, transparency and disclosure and audit committee, while Tobin’s Q, return on equity and return on assets are used to measure the bank’s profitability. The study used a two-stage analysis based on balanced panel data for robust findings. Sample of this study consists of 60 commercial banks from India and 60 banks from SAARC nations for the period of 2009–2021. This study used panel regression and a generalized method of moment approach using the CAMELS framework on banking industry-specific variables to determine their respective impacts.

The findings of this study suggest that board accountability is positive and significantly affects the profitability of banks as indicated by return on assets, return on equity and Tobin’s Q. In contrast, the audit committee has a positive and insignificant impact on return on assets, return on equity and Tobin’s Q, while transparency and disclosure have a negative and significant impact on these metrics. Furthermore, the country dummy result shows a significant positive impact on all the bank performance parameters, implying that Indian banks have the highest degree of convergence with corporate governance as compared to other SAARC nations.

This study provides insight to the regulators, policymakers and financial institutions to evaluate the role of corporate governance in emerging economies. However, the findings of the study should be interpreted with caution, as the results are sensitive to the disparity between India and other SAARC nations' government policies, climatic circumstances and cultural or religious traditions.

To the best of the authors’ knowledge, this is the first attempt to gauge the performance of Indian banks vis-à-vis SAARC nations using the CAMELS framework approach. Further, findings of this study suggest some novel evidence tying corporate governance quality with the profitability of banks among SAARC nations.

This study aims to empirically compare the performance of Islamic indices vis-à-vis to their conventional counterparts in India.

The performance of the Islamic and selected conventional indices is evaluated using various risk-adjusted performance measures such as Sharpe ratio, Treynor ratio, M-square (M²) ratio, information ratio, capital asset pricing model (CAPM), Fama-French three-factor model and Carhart four-factor model in India context. The period of study is from December 2006 to 2018.

The risk-adjusted performance measures based on the Sharpe ratio, Treynor ratio, information ratio, the M² ratio show that the return of Islamic indices provides slightly superior performance. However, performance investigated using CAPM, Fama-French and Carhart benchmarks produce a statistically insignificant differences in return of the Islamic and conventional benchmarks.

The Sharīʿah-compliant indices can provide a viable, ethical and alternative investment avenue for faith-based investors as it will not make them worse off in comparison to the conventional benchmarks. This also offers opportunity to conventional investors for portfolio diversification. The promotion of faith-based investment can serve as a tool for financial inclusion to attract a huge segment of Indian population in the formal financial system. The findings of the study suffer from the limitation of small sample size and empirical methods used.

This study contributes to the literature on the comparative performance of Islamic and conventional indices in general and emerging markets, in particular, using most recent data and covering a relatively long span of time. To the best of the knowledge, this is the first comprehensive study examining the performance of Islamic indices, using multiple Islamic indices and various risk-adjusted measures in the Indian context.

Load balancing is an important issue for a heterogeneous distributed computing system environment that has been proven to be a nondeterministic polynomial time hard problem. This paper aims to propose a resource-aware load balancing (REAL) model for a batch of independent tasks with a centralized load balancer to make the solution appropriate for a practical heterogeneous distributed environment having a migration cost with the objective of maximizing the level of load balancing considering bandwidth requirements for migration of the tasks.

To achieve the effective schedule, load balancing issues should be addressed and tackled through efficient workload distribution. In this approach, the migration has been carried out in two phases, namely, initial migration and best-fit migration. Using the best-fit policy in migrations helps in the possible performance improvement by minimizing the remaining idle slots on underloaded nodes that remain unentertained during the initial migration.

The experimental results reveal that the proposed model exhibits a superior performance among the other strategies on considered parameters such as makespan, average utilization and level of load balancing under study for a heterogeneous distributed environment.

Design of the REAL model and a comparative performance evaluation with LBSM and ITSLB have been conducted by using MATLAB 8.5.0.

This paper investigates the style timing and liquidity style timing vis-à-vis the market, size, value and momentum factors of the actively managed Indian equity mutual funds.

We examine the style timing of the funds using the augmented Carhart four-factor model by incorporating timing measures (Treynor and Mazuy; Henriksson and Merton). Based on this, the study explores the four-factor liquidity and volatility style timing exhibited by fund managers. The sample is from April 2000 to March 2018 and spans the volatile 2008 subprime economic crises. The sample comprised 182 actively managed equity funds from various sizes and was considered to be a well-diversified sample.

The results of our study provide strong evidence of market liquidity timing in India. No other style timing skills are observed in our analysis. Our results also imply that the fund managers might misidentify size timing as market timing if integrated liquidity timing measures are not employed, leading to false conclusions.

The findings of our study imply that the fund managers might misidentify size timing as market timing if integrated liquidity timing measures are not employed, leading to false conclusions.

This study, to our knowledge, is the first attempt to investigate the portfolio-based style timing in the Indian context.

With the advancement in technology, the day-to-day life of people has gone through an immense transformation. The use of smart devices for day-to-day life is greater than before, and people are moving towards smart work rather than doing hard work. In this paper, a novel framework is proposed named Online Service Provider in Metro City (OSPMC) for IoT. The purpose of this study is to provide a theoretical framework for the E-Government in order to sustain or minimize the unemployment rate.

The utilization of the Web in the upcoming years would create further opportunities for smart work. Internet of Things (IoT) plays an essential part in a system of multiple networks that aims to connect all things in the world that are capable of being connected through the internet. OSPMC framework can be developed on ASP.NET through (visual C#) 3.0 and Microsoft SQL Server with frontend and backend languages, respectively, under a web-based environment built on .Net platform. This lucrative facility is available mainly for the people living in the smart city.

Rural people are coming to smart cities in search of jobs, better education and a healthy lifestyle. India is also coping up with the modern world. The Government of India has taken an initiative for the making of 100 smart cities where the residents are relied upon to use Information and Communication Technology with the assistance of web.

OSPMC promises to eliminate multiple evils like social injustice, crime, unemployment, tax fraud and would promote women empowerment. Also, provide opportunities to start-ups in order to grow and enhance their skill work.

The objective of OSPMC can be found useful for opening new job opportunities for urban/rural people while also encouraging people to learn skill work. For smart cities’ success in developing nations, it is important to recognize the elements influencing it. The motivation behind OSPMC is to identify those variables influencing the successful usage of allowing IoT in the smart cities by E-Government of India and to use IoT to help urban smart cities.

This study aims to empirically evaluate the performance of commercial banks operating in India.

The efficiency of the commercial banks is evaluated using the data envelopment analysis (DEA) approach. We measure the technical, pure technical and scale efficiency of the sampled conventional banks using the input-oriented model. We employed an extended DEA window analysis approach based on a panel sample of 47 banks in the Indian scenario. The period of study is from 2009 to 2018.

The results obtained from CRS and VRS measures envisage that Indian banks have failed to manage their inputs efficiently and convert them into outputs. It implies that Indian banks do not operate at an optimum level. Moreover, the results show that public banks exhibit superior efficiency scores followed by private and foreign banks. Apart from the aggregate sector level, we also investigate the performance of Indian banks at the individual level for in-depth analysis. The individual bank-level analysis reports that the public sector banks (PSBs) are the most efficient followed by foreign banks, whereas, the least efficient are the private banks.

The findings of our study have implications for government, financial institutions and policymakers to access the verve and flexibility of the Indian banking system. The government should consider restructuring inefficient banks to enhance overall performance. This can be considered by improvement in managerial efficiency, efficient allocation of scarce resources and appropriate scale of operation. However, the findings of the study should be interpreted in light of the period of study for the banks being operational (as we filter out banks that ceased to exist) in India and empirical methods employed. The results may vary if alternative measures are used.

The present paper investigates the efficiency of the Indian banking sector employing the Data Envelopment Window Analysis (DEWA) technique. To the best of our knowledge, the present study is perhaps the first one to employ the DEWA measure on the Indian banking industry to gauge their performance over time.