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Introduction: Artificial intelligence (AI) is being extensively used to solve complex problems in the industry. AI provides several benefits such as providing visibility in the processes, reducing time, improving accuracy, saving time, helping in the decision-making process, etc. Due to the range of benefits of AI technologies, organisations readily adopt this technology. However, there are several challenges that the organisation faces during the implementation of AI. These challenges are in context to human resource (HR) development for successful implementation of AI across different functions and are discussed in this chapter.

Purpose: Although we know that AI technology is widely accepted in human resource management (HRM) due to its various benefits. But the organisations face many challenges during the implementation of AI. The focus of the study is to explore the literature on AI in HRM, identify the challenges of implementing AI and provide potential future research direction based on a systematic literature review.

Methodology: To explore the literature on AI in HRM, the study undertakes a systematic literature review. The study identifies, analyse and classifies the literature to provide a holistic view of HR challenges in implementing AI. The study is built on a review of 47 documents, including the articles, book chapters and conference papers using the Scopus database for the past 10 years (2012–27 January 2022).

Findings: The study provides an overview of the documents published in Scopus in this area through a systematic literature review. The study reveals that a significant amount of growth in the publication has been shown in the past 10 years. The maximum and continuous growth is shown after 2017. The maximum number of papers are published in India, the USA and China. The study identifies major eight challenges of AI implementation in HRM. The study also provides a secondary case to deep dive in this area based on a systematic literature review.

Research Limitation/Implication: The challenges identified in the study are not empirically tested. Each of the identified challenges should be empirically examined. This study has expanded the body of knowledge of AI in HRM. This study will help the academicians and practitioners work on the identified challenges and help the organisations ease in adopting AI.

Originality/Value: This study represents the first work that integrates AI implementation challenges in HRM.

In the last 10 years, India has amended its laws dealing with sexual offences against women with the changes ranging from increasing terms of imprisonment for the offence of rape to state-funded compensation schemes for women and child victims. In this regard, challenges persist for the agencies of the criminal justice system in India especially the courts to realise the vision of restorative justice as these forums have to navigate the relevant statutory provisions and binding precedents. This chapter seeks to analyse the challenges faced by courts in proper reintegration of victims and offenders of sexual offences, the institutional responses of the courts and suggests reforms to the criminal justice system in India in consonance with the principles of restorative justice acknowledged in the restorative justice movement in the international discourse.

Emerging Market Multinationals (EMNCs) are often seen as firms with singular identity. While they may share certain characteristics, EMNCs are seldom orchestrated and managed in the same manner. Through a cluster analysis of outward foreign direct investment data of EMNCs from India, we propose taxonomy of EMNCs based on their mode of operation, industry in which they operate, region where they invest and the amount invested. We use a dataset spread over 2007–2013, constituting investment data of 4,824 Indian firms into 7,238 foreign entities. Based on a two-step clustering approach, we propose three strategic archetypes of EMNCs: Global Service Providers, Integrated Manufacturers, and Established Internationalizers. The Global Service Providers mainly consists of firms operating in developed markets with an intention to serve their client needs through wholly owned subsidiaries. Integrated Manufacturers are firms that are primarily operating in other developing markets to sell their products through joint ventures and also present in developed markets through wholly owned subsidiaries – to acquire technology and other resources. The Established Internationalizers are large EMNCs with highest levels of investments, and relatively similar to the Western multinationals. We analyze the characteristics of these three groups of EMNCs based on their strategy and investment behavior, to derive insights into the heterogeneity across EMNCs. We discuss our findings and lay out future directions for research in the area.

Purpose: Green computing is a way of using the computer resource in an eco-friendly while maintaining and decreasing the harmful environmental impact. Minimising toxic materials and reducing energy usage can also be used to recycle the product.

Need for the Study: The motivation of the study is to use green computing resources to decrease carbon emissions and their adverse effect on the environment.

Methodology: The study uses a qualitative method of collecting resources and data to address the opportunities, challenges, and future trends in green computing for Sustainable Future Technologies. The study focusses on multiple kinds of cloud computing services collected and executed into single remote servers. The service demand processor offers these services to the client per their needs. The simultaneous requests to access the cloud services, processing and expertly managing these requests by the processors are discussed and analysed.

Findings: The findings suggest that green computing is an upcoming and most promising area. The number of resources employed for green computing can be beneficial for lowering E-waste so that computing can be environmentally friendly and self-sustainable.

Practical Implications: Green computing applies across all industries and service sectors like healthcare, entertainment, tourism, and education. The convergence of technologies like Cloud Computing, AI, and Internet of Things (IoT) is greatly impacting Green Supply Chain Management (GSCM) market.

The purpose of this chapter is to analyze disaster exceptionalism in India, focusing on the case of Kosi river floods in the State of Bihar and their impact on Dalit communities. Data were gathered through document analysis and a qualitative approach based on interviews with national and local leaders and activists of NGOs and Dalit organizations. The main finding is that there are no second-generation social movements related to disasters in India, mainly in what concerns Dalit discrimination. The Disaster Management Act of 2005 reinforced the centralized and top-down nature of the Indian state concerning disasters. On the other hand, national Dalit organizations like National Confederation of Dalit and Adivasi Organisations (NACDAOR) and National Dalit Watch do not possess the expertise to alter the approach to disasters from a contingent and exceptional one to a more structured and long-term perspective.

The chapter shows how extreme events and permanent hazardous situations tend to increase the legitimacy of state intervention, often involving the suspension of social and economic norms, creating a state of exception, which indicates the inevitable presence of the state. The abyssal line that separates those individuals and groups that are integrated from those defined as disposable and invisible crosses through both the Global South and the little colonies of the North, reinforcing the logic of states that want to be bigger and stronger than their own citizens.

Purpose: A cyber insurance policy’s purpose is to help in the recovering of a person or corporation following a cyber breach and to compensate for civil suit expenses stemming from first- and third-party responsibility claims.

Methodology: The usage of cybersecurity spending has forecast a variety of security categories using F&S projection methodology. Each of these is suited to the end-user organisations of in-scope security mechanisms, as well as the particular market circumstances. Critical national infrastructure (CNI), immigration control, big events, first responding, executive branch, infrastructure, and transportation security are among the worldwide forecast categories. This segmentation is further subdivided into 16 subsegments, each with its own security forecasting system. F&S protection marketplaces are anticipated using a bottom-up technique for each nation, which adds up to worldwide market penetration. This covers 177 nations spread throughout seven zones.

Findings: The cybersecurity insurer industry was valued at USD 7.36 billion in 2020 and is predicted to be worth USD 27.83 billion by 2026, growing at a compound annual growth rate (CAGR) of 24.30% during the forecast time frame (2021–2026). The expanding use of digitalisation innovations such as the cloud, big data, mobile computing, internet of things (IoT), and artificial intelligence (AI) across more lines of employment and society, as well as improved connectivity, have enhanced the burden of already overburdened information technology (IT) staff.

Practical implications: Accepted the innovative Insurance Data Security Model Law (#668), which necessitates insurance providers and other agencies registered by government insurance agencies to advance, integrate, and establish an information security management system; start investigating any cybersecurity events; and advise the private insurance superintendent of such happenings. Too far, the approach has been embraced by governorates.

Introduction: The world is passing through a technology explosion phase where one technology is being replaced by another very quickly. Emerging technologies play more important roles in the insurance sector directly or indirectly. These technologies have a high potential to change the insurance paradigm.

Purpose: In this chapter, we discuss emerging technologies such as artificial intelligence (AI), big data, blockchain, the internet of things (IoT), mobile technology, predictive analytics, social media, telematics, chatbots, low codes, and drones in the context of the insurance industry.

Methodology: To carry out our analysis, we searched for data using the keywords for each technology from the Web of Science (WoS) coral database. Certain inclusion and exclusion criteria were followed to select the articles for further analysis. R-studio was used for the data analysis and visualisation.

Findings: It was found that the highest number of research articles published are related to big data, followed by AI and social media. The first article on AI in insurance appeared in 1975. Social media is the highest cited new technology, whereas the low codes are the undiscovered paradigm for the insurance sector with no published research. Research on the impact of chatbots, drones, and mobile technology in the insurance industry is still at a nascent stage. We also noticed that the United States is leading the research on emerging technologies in the insurance sector.

Implications: This chapter audits the emerging technologies in the insurance sector and identifies technological areas with the highest, least, or no research, dominant journals, authors, and countries. This holistic overview empowers managers and academicians to decide the future course of action.