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This systematic literature review used bibliometric and science mapping as a means of exploring and understanding the evolution and landscape of research in the counterfeiting of products and goods. The review sought to document the size, growth trajectory and geographic distribution of counterfeiting research, identify high-impact scholars and documents, and explore the intellectual structure of the field. We identified 403 peer-reviewed articles published in the SCOPUS database, within subject areas of business, social sciences, economics, decision-making, arts and humanities, and psychology. We used VOSviewer software to analyse the data set of SCOPUS-indexed articles. Using citation analysis, the review identified the most cited scholars, documents, journals and most productive countries publishing research on counterfeiting. Aiming to identify highly influential documents whose impact in counterfeiting research has been sustained over time, we conducted a co-citation analysis. Apart from identifying main aspects of knowledge production through citation and co-citation analysis, we employed keyword co-occurrence analysis to illuminate research fronts in counterfeiting research, notably: anti-counterfeiting strategies for combating crime; counterfeiting and intellectual property rights; counterfeiting of luxury products; consumer ethics; consumer psychology and brand protection. We conclude that bibliometric analysis and science mapping offer a novel and useful means of investigating the development of this field of study.

COVID entered the world in 2019 as a pandemic and the intensity of this health crisis is only increasing in several regions. Therefore, it is critical to study and detect the public's frame of mind, government and economists' perception regarding the COVID crisis, as well as the primary worries that the public has expressed, and how this evolves over time. Responsive measures towards COVID-19 from the Indian economy have been explored as a key objective. Moreover, efforts have been made to explore recovery in India through economists and policymakers. Data have been explored through online interviews of key economists which were published in leading newspapers and covered through media channels such as NDTV, CNBC, etc. Moreover, various newspapers and reports were explored to understand government initiatives to address COVID-19 in India. The study's findings show how essential economic recovery from the second wave is in India, and how it may be achieved by strong fiscal and monetary policies, as well as specific attention to impoverished households, small and micro-businesses and increased employment. The short-term focus of the developing economic strategy must be on giving crisis relief to the most unprotected segments of society since long-term system stimulation is impossible.

Purpose: Worldwide economies have been shattered by the alarming increase in Non-Performing Assets (NPAs) in Banking Sector. In India, the rise in NPA levels gives a clear insight into the health of industry and state. This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: Punjab National Bank (PNB), Bank of India (BOI), UCO Bank, Punjab and Sind Bank (PSB), HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Design/methodology/approach: The study utilised IBM SPSS version 20 application to carry out our statistical analysis of measures of central location (mean and median), measures of dispersion (standard deviation), to carry out the Kolmogorov–Smirnov test to check the normality of data, the Mann–Whitney U test (for two groups) for median comparison between private and public sector banks and the Kruskal–Wallis test (for more than two groups) for median comparison for more than two banks. p ≤0.01 and p ≤0.05 were the two-tailed significance level used for determining the significance of all statistical tests.

Findings: Trend analysis and statistical tests show that the trend in public sector banks to have NPAs is higher compared to private sector banks, and losses arising from NPA impact the banks’ profitability.

Practical implications: It is apparent that NPAs are a large threat to banks in India as it reflects the state of the Indian economy. The growth of the economic cycle is predominantly dependent on the smooth and profitable functioning of private and public sector banks. This current study focusses on and compares the impact of NPAs on the profitability of public and private sector banks. NPAs have grown exponentially more in the case of public sector banks than private sector banks, which has affected the former banks’ financial health and performance. Increases in the level of NPAs adversely affect the working style and long-term stability of public and private sector banks in the economy.

Social Implications: NPAs have a negative influence on the profitability of the banks as well as on the economic growth of the country too. However, it is recommended that management in the banking sector, particularly the public banks, should use various preventive and recovery strategies to reduce the risk of failure and to keep track of NPAs to stay safe.

Originality/value: This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: PNB, BOI, UCO Bank, PSB, HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Introduction: Lending is an age-old concept, and Peer-to-Peer (P2P) lending is not new. The reduction in the issuing of loans by banks has made people switch from traditional to online mode. The introduction of the online P2P lending industry is in its nascent stage of growth. As this industry is relatively new, understanding user experience, sentiments, and emotions would be helpful for the industry to innovate as per customer requirements.

Purpose: To explore the patterns in the sentiments expressed by users of ‘Cashkumar’ based on Google reviews.

Methodology: Sentiments have been analysed using user experience in risk, cost, ease of use, and loan processing time. Python application was used for sentiment analysis of Google reviews.

Findings: The sentiment analysis results showed that the average sentiment score was 0.7144, which indicates that the user sentiment towards ‘Cashkumar’ is positive. The reviews reflect that the users, especially borrowers were satisfied with the platform’s services and happy with loan processing time. The other factors – ease of use, cost, and risk – were not given much importance by users. Both lenders and borrowers faced a few issues, but the results of the lender’s sentiment analysis could not be generalised due to a smaller number of posted reviews.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.