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The objective of this paper is to investigate the effect of working capital efficiency (WCE) and its components on the composite financial performance of a sample of Indian firms.

Our sample includes 796 non-financial listed firms from 2015–16 to 2021–22. Sample firms’ profitability, liquidity, solvency, cash flow management, and financial and operational leverage have been used to classify them into companies with high composite financial performance (HCFP) and with low composite financial performance (LCFP) by using K-Means Clustering technique. A composite financial performance score (CFPS) of 1 has been assigned to HCFP and 0 to LCFP. We have used logistic regression models with fixed effect to estimate the effect of cash conversion cycle (CCC) and its components, i.e. inventory days, accounts receivable days and accounts payable days on CFPS in the presence of control variables such as growth, leverage, firm size, and age.

The study finds that CCC and inventory days are inversely associated with CFPS. This finding shows that the firms’ WCE leads to superior financial performance on a composite basis.

The research findings are based on samples drawn from the population of the listed Indian non-financial companies. Since the operation, financial practices, working capital policies, and management styles of firms vary greatly among nations, the results of this study should be extended to firms in other countries after taking into account the degree of resemblance to the sample firms.

The findings of this study hold significant value for industry practitioners, as they provide guidance in determining the optimal allocation of funds for working capital and devising strategies for effectively managing inventory levels, credit sales, and vendor payments in order to increase the overall value of the company. This study aims to help investors in building their investment portfolios by identifying companies with superior composite financial performance. Investors can enhance the construction of their investment portfolios by strategically selecting companies that demonstrate superior overall performance.

The results of our study will help companies improve their WCM strategies to enhance their overall value, and their significance increases manifold during economic downturns. Business firms that perform well by efficiently managing their working capital have a multiplier effect on the economy and society at large in the form of GDP contribution, labor income, taxes to the government, investment in capital assets, and payments to suppliers.

To understand the impact of WCE on firms’ performance, the extant working capital literature focuses on some specific characteristics such as profitability, valuation, solvency, and liquidity. The limitation of employing a single parameter is its inability to present the comprehensive performance evaluation of firms. This study is among the earliest studies that focus on the holistic evaluation of WCE's impact on the composite performance of a company.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

The purpose of the chapter is to highlight the role of strategic human resources in building resilient organisations. Our research also aims to highlight how resilient organisations are poised better to take on constantly changing business environment and deliver superior products and service experiences to the target market. With the help of an extensive literature review and real-world cases, our research focusses on key elements of strategic human resources and then establishes the linkages between the contribution of individual employees and HR policies towards the achievement of these crucial elements. The findings suggest that the aggregation of individual contributions and HR practices lead to the development of resilience in organisations and helps them to deliver better on all critical indicator of performance. The research has important implications for all important stakeholders including scholars, practitioners, and policymakers, among others. Organisations are also crucial for ensuring the well-being of the masses as their contribution in terms of taxes and duties becomes instrumental in funding various social welfare programs run by governments across the globe. The research is unique in the sense that it draws the linkages between organisations’ resilience and their performance through the creation, development, and management of strategic human resources.

Purpose: This chapter aims to evaluate the impact of money laundering and terrorism financing on the Indian economy and to study the effectiveness of prevention of money laundering acts and terrorist financing as per the guidance of the financial protection task force.

Need for the study: Developing countries like India have been more vulnerable to terrorism and financial scams over the last four decades. Despite the establishment of regulating bodies and anti-money laundering acts, this problem continued to be a national threat. Therefore, examining the impact of money laundering and terrorism finance on the Indian economy is necessary.

Methodology: This study is based on secondary data gathered from the web portals of government agencies and international organisations dealing with money laundering and terror funding. Newspapers, journals, and annual reports are reviewed to identify the modus operandi of money laundering operators and their impact on the economy.

Findings: Money laundering and terrorism financing significantly threaten the Indian economy and national security. Despite different anti-money laundering laws and multiple regulating authorities, the system has pitfalls that allow economic fraud and money transactions for terrorist activities. There is a need for cyber security, and integrated enforcement agencies to combat money laundering at national and international levels.

Practical implications: This study would be helpful for academicians and policymakers to understand the nexus of money laundering and terrorism financing and its impacts on the Indian economy.

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.

India is the birthplace of four major religions which are Hinduism, Jainism, Buddhism, and Sikhism. It is a country where people of all religions live in peace and harmony. Many tourists experience different forms of harassment during their pilgrimage journey, for example, fleecing, extortion of money, harassment by beggars, persistence by vendors and priests, fraud, sexual harassment, and other unacceptable behaviors. In order to appreciate the extent of harassment encountered by tourists, an in-depth study was conducted on the reviews provided by tourists on TripAdvisor's (Indian) website. This study characterizes harassments through ethnographic research approach of published reviews. A total of 260 reviews of 28 top Hindu temples are considered for all the states and union territories where the top Hindu pilgrim centers are located, (excluding Nagaland) according to TripAdvisor. The concerned reviews are categorized and further investigated through a primary data collection in proportion with the reviews received in respective temple sites in the study. through structural equation modeling (SEM). Important factors have been identified for future policy issues and recommendations in these most crowded places with unique mass tourism practices.

Purpose: In this study, an attempt has been made to examine the compliance unit’s role in mediating the electronic government’s role in money laundering. E-government is clarified as the application of Information technology to encourage access and transfer of all aspects of government amenities and operations that impact transparency and accountability for the benefit of the people, trades, workforces and other stakeholders. The current study aims to assess whether the e-government can lessen or counterbalance the risks related to money laundering in the country and the mediating role of the compliance unit in reducing money laundering.

Methodology: This study practices structural modelling to assess the direct linkage between e-government and anti-money laundering and the indirect path between e-government and anti-money laundering that passes through the compliance unit as a mediator.

Findings: The findings prove that the compliance unit fully mediates the relationship between E-government and anti-money laundering. The direct path shows an insignificant relationship between e-government and money laundering, but this association becomes significant when the compliance unit is brought as a mediator.

Originality: This study directs that e-government runs on a sustainable ICT platform to improve transparency and accountability of all aspects of government facilities and actions for sustainable economic goals and help to diminish money laundering by enhancing transparency and accountability of government administration.

The COVID-19 pandemic has recently had an impact on the stock market all over the globe. A thorough review of the literature that included the most cited articles and articles from well-known databases revealed that earlier research in the field had not specifically addressed how the BRIC stock markets responded to the COVID-19 pandemic. The data regarding COVID-19 were collected from the World Health Organization (WHO) website, and the stock market data were collected from Yahoo Finance and the respective country’s stock exchange. A random forest regression algorithm takes the closing price of respective stock indices as target variables and COVID-19 variables as input variables. Using this algorithm, a model is fit to the data and is visualised using line plots. This study’s findings highlight a relationship between the COVID-19 variables and stock market indices. In addition, the stock market of BRIC countries showed a high correlation, especially with the Shanghai Composite Stock Index with a correlation value of 0.7 and above. Brazil took the worst hit in the studied duration by declining approximately 45.99%, followed by India by 37.76%. Finally, the data set’s model fit, which employed the random forest machine learning method, produced R² values of 0.972, 0.005, 0.997, and 0.983 and mean percentage errors of 1.4, 0.8, 0.9, and 0.8 for Brazil, Russia, India, and China (BRIC), respectively. Even now, two years after the coronavirus pandemic started, the Brazilian stock index has not yet returned to its pre-pandemic level.