Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy: Volume 110B

Purpose: The purpose of the study is to determine the significant difference between the performance of the Indian banks in pre coronavirus disease (COVID 19) and post COVID 19 periods. Further, it explores the impact of COVID 19 on the profitability of the Indian banks by investigating variation between the non-performing assets (NPAs) and the net profit of the banks during pre and post COVID 19 periods.

Need of the study: The COVID 19 outbreak has affected various industries including Indian banks which reported an increase in NPAs, and demand for credit which in turn impacted profitability. This study was carried out to examine the impact of COVID 19 outbreak on Indian banking sector.

Methodology: This study uses different banks’ NPA and net profits performance to examine the effect of COVID 19 on banks’ overall performance. The data have been collected from secondary sources, commercial websites, and websites of Indian banks (private and public sectors). t-Test was used to analyse the data.

Findings: Among public sector banks, Canara Bank was found to have a significant difference in net profit in the pre and post COVID 19 periods. In private sector banks, HDFC Bank showed a significant difference in the net profit in pre and post COVID 19 periods. For NPAs, all private banks showed no significant difference in pre and post COVID 19 period results.

Implications: The study revealed that both private and public sector banks in India were mildly affected by pandemic and most of them are significantly reporting no difference in net profit and NPAs during pre and post COVID 19 periods.

Purpose: Small and medium enterprises (SMEs) are the most significant contributors to maximum employment generation, the gross domestic product (GDP) of many countries, and the overall global economy. It is also evident that cyber threats are becoming a big challenge for SMEs, which is directly impacting global economy.

Methodology: Existing research inputs were accessed to understand current cyber threats for SMEs and their cybersecurity posture. Additionally, this research has collected the latest insights by taking direct inputs from SMEs and conducting a well-designed research survey. It has provided a few direct inputs to designing solutions for the SME segment. For analysis and recommendations, cybersecurity best practices and core cybersecurity concepts are considered at the centre of the solution.

Findings: Implementing existing cybersecurity standards or frameworks is not easy for SMEs, as they generally have limited resources and different priorities for their business when it comes to the implementation of any cybersecurity controls. Currently, many cybersecurity standards are not able to support the implementation of business domain-specific controls.

Practical implications: Along with the research findings shared in this chapter, as a resolution to the problems faced by SMEs, the authors will propose a new framework as a solution. This framework is designed using core concepts of cybersecurity such as confidentiality, integrity, and availability (CIA triad) as well as defence in depth (DiD) mechanisms in each layer of organisation. The authors will also share a high-level idea about how reliable artificial intelligence-based software can help identify recommended controls for particular SMEs.

Purpose: This study aims to employ bibliometric analysis to condense multiple studies into a single publication that not only gives insights into the growth and advancement of the research area but also establishes a future research agenda. This study provides a summary of advances in academic research on money laundering. The research includes bibliometric analysis and visualisation of bibliographic data using the Scopus database. The results of the study show that there has been a significant increase in the number of publications in the field of money laundering research, with topics focussed on specific areas. This study will also benchmark existing and preliminary themes, designs, and methodological choices for future money laundering research.

Methodology: With the help of the ‘visualisation of similarities’ (VOS) viewer open-source software, bibliometric analysis was performed using Scopus data. Citation analysis, topic mapping, country collaboration, co-citation analysis, and keyword co-occurrence analysis are some of the approaches used in bibliometric analysis.

Findings: Based on a bibliometric analysis of 1,391 research papers retrieved from the Scopus database over the past three decades (1990–2021), the study identified the most prominent authors, studies, journals, affiliations, and countries in the field of money laundering, as well as the most co-cited authors and journals. The writers also highlight future study issues in the field of money laundering.

Practical implications: The study’s findings might provide academics and practitioners with information on the present state of money laundering research and trend subjects. It can also be used as a guideline for identifying possible research gaps in the existing literature.

Introduction: This chapter is intended to link the embracing strategy of ‘socially responsible investment’ with the apparent cause of economic destruction ‘financial crimes’. Today’s financial world is not always associated with ethics and morality, but it does not mean rising investments cause rising financial crimes. Socially responsible investing (SRI) has been rising, and many of today’s investors are interested in tracking ethically sound companies. Investors find a great way to invest around many investment opportunities, while socially responsible investors work with little social cause. This increasing literacy over SRI notably helps to reduce investments in unethical grounds which in turn reduces financial crimes.

Design/methodology: This work is premised on desk research. Conceptual and documentary methods were used in the study. The tertiary data source has been used in the study to develop a template describing the working of SRI in fixing financial crimes.

Findings: Findings of this study detail: a breakdown of industries that comes under SRI, channels of financial crimes, impact of SRI on financial crimes, and design an action plan for more effective environmental, social, and governance (ESG)-based investments to fix problems of financial crimes in the Indian economy.

Practical implications: The model of SRI has unfolded these days. While the purpose of these funds differs, they generally swear off the weapons industry and avoid ‘sin stocks’. In-depth analysis of this study area enables building quality investment strategy among investors and thereby helps to combat financial crimes.

Central theme: The present chapter discusses the integration of data science methods in devising economic policies in different countries with special reference to India.

Purpose: It explains how the policy-making process in countries can be transformed from estimate-based policies to evidence-based policies with the help of techniques such as artificial intelligence (AI), big data, and data analytics. It answers the research question of whether the data science techniques can make the economic policy process efficient or not in developing countries like India.

Research methodology: Data are collected from secondary sources such as government websites, journals, corporate reports, and research databases to conduct this descriptive analysis. Research papers from Scopus/Web of Science (WoS) database are extracted, and exclusion/inclusion criteria are applied for extracting papers relevant to this research.

Findings: The chapter found out various opportunities which India can tap by gaining new insights on critical macroeconomic issues such as unemployment, labour markets, and water crises and would be able to resolve the problems with the help of predictive modelling. The findings exhibit the possibility of building models that could explain how to integrate data science techniques into the policy-making process. It also highlights the challenges that Indian economy is facing in incorporating these techniques in its policy-making process. It states the need to design different evaluation schemes based on information and communication technology (ICT) and data science for different policies, since one methodology does not suit all.

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.

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: As the human population grows, consumer demand for digital services tailored to their specific needs also increases. To improve the financial performance of farms and meet the need for food of a growing population, farmers and agribusinesses have started incorporating distributed ledger technology into agricultural and farm management software. These developments in the agriculture sector may lead to realising sustainable development goals.

Purpose: Several researchers have done studies to explore the features and benefits of blockchain technology in the field of agriculture. There is a need to analyse the available literature to identify the use of this technology in agriculture and the scope of further research. This chapter will mainly focus on its publication trend, journal productivity and impact, prolific studies, and coherent themes.

Methodology: For a comprehensive review, bibliometric and content analysis of 71 open-access articles collected through a structured database of Mendeley is done. These articles were published during 2017–2021.

Findings: The execution of blockchain is continuously increasing in the agriculture sector, which has resulted in automation in supply chain management, land registrations, and crop insurance. The study revolves around supply chain management, digitisation of agriculture, and sustainable economic development. This study’s conclusions can help agriculturalists improve their understanding of blockchain implementation in agriculture. The study also gives directions for future research.

Purpose: The chapter’s objective is to develop a new model or approach to earnings management for sustainability. The challenges posed by climate change and environmental degradation have stimulated interest in sustainability. However, such interest has not led to the development of new models demonstrating how firms’ earnings management can contribute to sustainability and sustainable development.

Methodology: The chapter develops a model demonstrating how earnings management can contribute to sustainability. The surplus income model uses income targeting as a channel through which the surplus income generated by a firm is allocated to a relevant sustainability activity or project. The author shows that a firm’s total income can be divided into the target and surplus income components. The author then explores the possible activities that firms may allocate surplus income to in the interest of sustainability.

Finding: The surplus income model or approach allows a firm to contribute or donate to a relevant sustainability activity or project out of its surplus income. Under this model, managers are incentivised to generate surplus income from which they can contribute to a relevant sustainability activity or project, thereby making the firm a champion of sustainability.

Originality: Previous studies have not examined how earnings management by firms can contribute to sustainability. This chapter fills this gap in the literature.

Purpose: This chapter presents a discussion of the COVID-19 global debt crisis.

Methodology: The chapter uses the discourse analysis method to identify the cause of the COVID-19 global debt crisis and suggests ways to overcome the crisis.

Findings: The chapter argues that the high debt incurred by many countries during the COVID1-19 pandemic, combined with tightening global financial conditions, led to a significant increase in global debt. The author suggests ideas to avert a debt crisis. It was argued that rich countries could forgive the debt owed to them by heavily indebted countries or consider interest repayment holidays, debt-for-green swaps, or other debt-relief options. Heavily indebted countries can consider restructuring their debt, reevaluating their economic policy priorities, and raising taxes. Multilateral organisations can assist heavily indebted countries and engage in debt forgiveness advocacy.

Implication: There is a need for rich countries and creditor organisations to offer some relief to heavily indebted countries to help them meet their debt repayment obligations during and after the pandemic.

Originality: The chapter is one of the first to analyse the global COVID-19 debt situation.

Purpose: This chapter aims to perform text analysis to investigate the academic area delimitated by economic and financial performance and money laundering.

Need for the study: The findings contribute to the body of literature by providing important insights in terms of money laundering and financial performance.

Methodology: In order to achieve the research objective, further than 640 papers were retrieved from the Web of Science from 1994 to 2022, concentrating on the most referenced documents found in the superior quartile.

Findings: The empirical findings emphasise that the article with the unique words Fraud Detection System: A Survey by Abdallah A., Maarof M. A., and Zainal A., examines a complete and systematic assessment of the concerns and obstacles that impede the performance of fraud detection systems. Furthermore, topic modelling findings highlighted the presence of four main topics: topic 1 – identified by ‘performance’, ‘firms’, ‘financial’, ‘fraud’, and ‘board’; topic 2 – described in terms of ‘fraud’, ‘accounting’, ‘evidence’, ‘audit’, and ‘research’; topic 3 – identified by ‘firms’, ‘fraud’, ‘financial’, ‘CEO’, and ‘results’ while topic 4 – identified through ‘fraud’, ‘detection’, ‘data’, ‘cost’, and ‘card’.

Practical implications: This study will act as a guide for researchers of the financial performance field to explore the scientific publications in the field of money laudering.

Purpose: Central bank digital currency (CBDC) is non-physical or the digital equivalent of physical money issued by a central bank. Nigeria became the first African country to issue a CBDC, popularly known as the eNaira. This chapter highlights the redesign features that eNaira should possess to offer payment solutions and macroeconomic stability effectively.

Methodology: The chapter used discourse analysis to highlight the features the eNaira should possess.

Findings: The chapter suggests that the eNaira should have an interest-bearing status, have enhanced security features, and offer zero transaction costs on eNaira transactions. These are design features which the eNaira presently lacks.

Originality: This chapter is the first to suggest redesign features for an already issued CBDC. It is also the first to highlight the design features of a CBDC in the African continent.

Introduction: Banking institutions are instrumental for lending support and steering the economy in the planned direction to achieve long-term goals. Sustainable development has become the focal point of new policies so that the economies attain inclusive growth. This needs substantial funding to accelerate industrial activity; hence, banks have to play a dominant role in helping such plans succeed. Banks need to look beyond their current framework and play a proactive role in promoting sectors focussed on sustainable development. Banks can prioritise lending to green initiatives to reduce carbon footprint, which will provide impetus to the goals laid out in the COP 26 United Nations (UN) Climate Change Conference. The chapter aims to identify the gap in investment for sustainable development and the funding support required from banks to help India achieve the desired sustainable goals. The chapter recommends that banks increase their green financing to provide the impetus for creating sustainable infrastructure.

Purpose: The present study aims to understand the banking sector’s importance in developing sustainable economic growth through lending practices. The study recommends certain practices for increasing focussed lending towards sustainable projects.

Methodology: In this study, the authors developed prepositions based on a literature review. Significant issues were raised based on the lending policies per the guidelines of Reserve Bank of India (RBI), and a solution was proposed by preparing a conceptual model.

Findings: The study offers a lending technique that can assist the financial sector in supporting sustainable economic growth.

Introduction: Artificial intelligence (AI) and digitisation offer substantial human potential and profit margins, making them promising retail solutions. Retail leaders have successfully integrated comprehensive uses into their daily operations, while competitors heavily invest in new projects. The Indian retail sector is undergoing a significant transformation, which can be attributed to factors such as growing income, demographic characteristics, and enhanced consumerism, as well as the rapid development of new technologies such as digitisation and AI, which is changing both consumers’ and retailers’ buying behaviour.

Purpose: This study aims to determine the influence of AI on elements that drive digitisation in the retailing sector, as well as the factors that lead to organised retailers adopting digitisation and its impact on their business.

Methodology: The study employs a standardised questionnaire distributed to organised stores via an online link, and the data are analysed with SmartPLS software 3.0.

Finding: The retail sector is driven by elements that promote digitalisation in food and groceries retailing, such as simplicity of operation, adoption of digital payment, quicker internet connection, retailer consumer interface, and the involvement of AI.

Research implication: AI has significant consequences for retailing, which serves as the interface between marketers and customers.

Theoretical implication: The study’s findings reflect the perspectives of retailers, store managers, and entrepreneurs on how digitalisation and AI are crucial for the creation and growth of long-term competitive advantages in retail.

Purpose: Due to COVID-19, tourism to Tokyo Olympic is totally banned and sports enthusiasts are not allowed to view any sport event.

Need of the study: The need of this chapter is to explore Twitter content and analyse its sentiments towards the mega sporting event #Tokyo2020 Olympic.

Methodology: This research has adopted the behaviour of players and sport fans to analyse their Twitter messages using netnographic approach. Sample of 7,475 tweets of #Tokyo2020 an official Twitter online Olympic campaign was collected, and these tweets were categorised with frequency analysis, sentiment analysis, and context analysis using NVivo.

Findings: Results highlighted that majority of tweets are positive towards Tokyo Olympic even in COVID-19 scenario. Findings of the study showed that Tokyo Olympic had favourable emotions, sporting sprit, positive state of energy, and players’ lifestyles.

Practical implications: This research explored the dynamics of engagement practices and can be extended to other fields of study and is useful to sports authorities to strategize their forthcoming sporting events.

Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.

Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.

Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.

Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.