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Introduction: Commercial banks play a vital role in the global economy, facilitating economic growth and providing essential financial services. As key intermediaries between savers and borrowers, these institutions operate in a dynamic and complex environment characterised by various risk factors that can significantly impact their profitability and overall stability. Understanding the interconnected relationships between credit risk, interest rate risk, liquidity risk, and profitability is crucial for effective risk management strategies and the development of appropriate regulatory frameworks.

Purpose: Commercial banks play a critical role in the global economy by facilitating economic growth and providing financial services. This study examines the interconnected relationships between credit risk, interest rate risk, liquidity risk, and profitability in commercial banking.

Methodology: The sample consists of licenced scheduled commercial banks on the Bombay Stock Exchange (BSE) from 2015 to 2022. Using the Smart PLS-SEM 3.0 path analysis technique, the study evaluates the combined influence of these risk factors on profitability and provides evidence-based recommendations for risk management strategies.

Findings: The findings can assist banks in enhancing their risk management practices, and regulators in developing appropriate regulatory frameworks. By understanding the key risk factors and their impact on profitability, banks and regulators can mitigate risks, enhance transparency, and promote stability within the banking sector.

Significance/value: The value of this study lies in its focus on the interconnectedness of risk factors, profitability, and the potential implications for decision-making, risk management strategies, regulatory frameworks, and the overall stability of the commercial banking sector.

COVID-19 has been the subject of a number of inquiries recently. All country's capital market practices have been affected by the COVID-19 outbreak. Economic woes, along with the stock market crash, have hit emerging markets and developing economies in a variety of directions.

This study is an attempt to focus on the Indian economy to provide the gist of the situation and recovery mode of an economy with the help of growth indicators of the economy.

This study is based on secondary data. The researchers applied some econometric tools, viz, unit root test Augmented Dickey-Fuller (ADF), Panel Granger Causality, and Panel ARDL Bound Test were applied to examine the relationship of economic indicators and stock market benchmark in two periods: March 2020–June 2021 (during period) and July 2021 to March 2022 (post period).

The findings of this study explored the different causal relationships for the selected variables in both periods. The study discussed the reasons for ARDL (Auto Regressive Distributed Lag) bound for all selected factors. The study revealed the story of crude oil prices and Gold as trusted investment avenues during the crises.

As we know, the capital market's backlash is reflected in movements in stock prices and stock exchange volume, which are concerned with the economic effects of the pandemic and urged the segment to react. Investors can use the information in the event to make investment decisions.

Purpose: The study aims to analyse and discuss the effect of COVID-19 on businesses. The chapter discusses the various machine learning (ML) tools and techniques, which can help in better decision making by businesses in the present world.

Need for the Study: COVID-19 has increased the role of VUCA elements in the business environment, and there is a need to address the challenges faced by businesses in such environment. ML and artificial learning can help businesses in facing such challenges.

Methodology: The focus and approach of the chapter are in the context of using artificial intelligence (AI) and ML techniques for decision making during the COVID-19 pandemic in a VUCA business environment.

Findings: The key findings and their implications emphasise the importance of understanding and implementing AI and ML techniques in business strategies during times of crisis.

Practical Implications: The chapter’s content is in the context of using AI and ML techniques during the COVID-19 pandemic and in a VUCA business environment.

The purpose of this study is to find out the negative environmental, cultural and socio-economic effects of overtourism at prominent tourist destination in India focussing upon Shimla. As well, the study will also try to identify the changes in the residents’ perceptions at these tourism sites experiencing overtourism. It will be based on the review of previous literature to find out the significant negative effects of tourism-related activities on the residents' attitudes and perceptions related to tourists. It will also discuss the negative impacts of overtourism on the overall destination image using the case study approach. It is confirmed through the study findings that overtourism has caused severe environmental, economic and socio-cultural impressions at the tourist destinations under study. It is also revealed that overtourism at these destinations has caused the increased water consumption, collection of huge litter masses, improper waste disposal and air pollution due to vehicular and other sources owing to upsurge in tourist activities. In the tourist destinations under study, overtourism has caused difficulties for the locals as the prices have risen and short-term rentals have caused housing shortages. These findings will be useful for the stakeholders in the tourism sector in India to devise strategies to prevent the over-exploitation of resources and take sufficient measures to reverse the negative impacts and simultaneously promote these destinations for sustainable tourism growth.

The case pertains to entrepreneurship and the struggles of microentrepreneurs in raising adequate funds. It highlights the insufficiencies in implementation of government’s financing schemes for microenterprises. The case also throws light on the difficulties faced by microentrepreneurs in raising capital through banks and more so if the entrepreneur happens to be a woman. This case revolves around a young woman microentrepreneur, from a humble background, who is determined to expand her orthopaedic-support manufacturing unit. It brings out the enormous difficulties faced by her in obtaining an adequate financing through banks despite many laid out government policies to provide relief and stability to microenterprises. The ardent pursuit and the innovatively designed marketing strategy helped the entrepreneur achieve a reasonable success, in spite of the lack of capital. Her revenue for the financial year 2014–2015 rose to Rs. 9 lakhs/per annum. Her projected revenue for 2015–2016 was Rs. 24 lakhs. The ambitious target seemed difficult if additional funds were not forthcoming. With no help from the banks, the entrepreneur was in a dilemma where to raise the funds from. Her long-term plans for growth would be badly affected.