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The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

This research focuses on the Metaverse's evolving trend and the potential application of blockchain technology in the accounting of virtual assets in this digital domain. The Metaverse introduces a new economy in which users may earn real-world revenue through virtual activities, necessitating the need for efficient and dependable virtual asset accounting. Blockchain technology, with its decentralized and immutable record, appears to be a viable answer to these problems. This chapter discusses the present status of blockchain technology for accounting for virtual assets in the Metaverse as well as its potential role for businesses and the economy. It also determines the technology's issues and limits and makes recommendations for further development. The findings indicate that blockchain technology has the potential to transform virtual asset accounting in the Metaverse by improving security, transparency, and consistency. However, scalability and legal/regulatory issues must be overcome before it can completely achieve its promise. Accounting experts, developers, and stakeholders interested in the convergence of blockchain technology and the Metaverse economy will find this chapter useful.

Introduction: Historically, the corporate governance (CG) framework was designed primarily to safeguard the economic interests of shareholders, as a result of political and legal interventions, developing into an effective instrument for stakeholders and society in general.

Purpose: The core objectives of the study include: identifying journals/publications responsible for publishing CG studies in India, key CG issues covered by CG researchers, the amount of high-impact CG literature across different time periods, sectors/industries covered by CG researchers and different research instruments (quantitative or qualitative) used in CG studies in India.

Design/methodology: The chapter used a sample of 130 corporate governance studies that fulfil the selection criteria, drawn from the repository of over 100 reputed journals that are either recognised by the Australian Business Deans Council (ABDC) or indexed by SCOPUS. A systematic literature review has been carried out pertaining to CG issues in India, based on various statistical tools, data, industries, research outlets & citations, etc.

Findings: The results show an overwhelming number of studies have assessed the relationship between CG variables and firm performance, which could be measured through a variety of performance metrics such as ROA and ROI. Apart from empirical analysis, many conceptual studies use repetitive basic statistical tools like descriptive statistics or regression analysis. The chapter offers insights into current achievements and future development.

Originality/value: This bibliometric study is a useful guide for policymakers, corporate leaders, research organisations and management faculty to draw insights from work produced by eminent researchers in GC in India.

The context of sustainable development dwells, quite significantly, upon the one of gender parity in a society and nation. This is so because the issue of gender equality is key to distributive justice, which is in turn much essential for creating a good amount of precondition for sustainable development. Academic inquests into the problem into the gender disparity are indicative of how gender disparity on economic and social parameter triggers a negative productivity change over time and space. Thus, the current chapter brings forth an analytical approach to contemplating into the above-mentioned narratives in both theoretical and empirical terms. The tack of our analysis is as follows. To begin with, this chapter develops an index to determine the extent of gender disparity in health, education and participation in workforce (namely, Gender Gap Index or GGI). Moving on, the study extends to looking at India changing dynamics on gender gap vis-à-vis developing and less-developed countries. Besides, a general equilibrium model has been developed to unfurl the fallout of gender disparity in terms of the wage gap between male and female workers in the labour force, extant and conditioned further by demographic and sociopolitical factors.

High turnover rates, delay and dissatisfaction among PhD students about the high efforts and low rewards are common problems in doctoral education. Research shows that many different factors are associated with the mental health crisis in graduate education, but these diverse aspects have not often been studied in relation to talent management and human resource management (HRM) strategies. Based on questionnaires and in-depth interviews, this chapter critically assesses the factors that influence doctoral students’ well-being, using as theoretical framework the self-determination theory, concerned with the social and other conditions that facilitate or hinder human well-being and flourishing, and the job demands–resources model, an occupational stress model that suggests strain is a response to imbalance between demands on the individual and the resources he or she has to deal with those demands. These theoretical frameworks help to explore the perceived job demands and resources, and motivations of a sample of 25 PhD students in the Netherlands, in order to recommend adequate talent management strategies to improve PhD work conditions at universities and reduce the increasing levels of ill-being. The study proposes a collegial model, focussing on the enjoyment of work, instead of the current managerial model, which focusses on strengthening knowledge and skills, and stimulating performance-oriented behaviour. A differentiated approach is needed, offering customized talent development for each PhD student in order to respond to his or her specific qualities, improving general well-being. This radical shift in talent management is needed to counter the mental health crisis in doctoral studies.

Industry 4.0 and the digital supply chain (DSC) are changing how things are made and moved around the world. This change is all about how smart technologies like the Internet of Things (IoT), artificial intelligence (AI), and blockchain are making supply chains work better. These tools help companies react faster and more clearly to what's needed. By using these new technologies, businesses can get better at guessing what customers want, keeping the right amount of stock, and quickly adjusting to new market trends. With these advanced technologies, companies can see big improvements, like being able to match supply with demand more closely and change their plans fast when things in the market change. It is really important for businesses to get how these tech tools work together as the world of making and selling things keeps changing. This chapter examines the convergence of traditional supply chain systems with Industry 4.0, focusing on the transformative impact of technologies such as the IoT, AI, and blockchain.

The convergence of the digital supply chain (DSC) with sustainability presents promising opportunities and notable challenges for today's enterprises. This chapter explores the relationship between digital supply chain management (DSCM) and sustainability within the context of supply chain management. It begins by providing a detailed overview of the digital transformation in supply chain management, emphasizing its rapid evolution and its profound impact on sustainability. The chapter then delves into the various sustainability challenges that manifest within DSCs, including issues related to energy consumption, e-waste management, and environmental impact. It goes beyond environmental considerations, exploring social and ethical dimensions, such as the potential consequences of digitalization on socioeconomic disparities. Moreover, the chapter presents a comprehensive framework of best practices and strategies aimed at navigating these challenges. It highlights the utilization of digital technologies like artificial intelligence (AI), Internet of Things (IoT), and blockchain to enhance transparency, efficiency, and responsible sourcing. Real-world case studies are included to exemplify successful implementations of sustainable practices across diverse industries. In conclusion, the chapter emphasizes the imperative of a holistic, authentically sustainable supply chain transformation, underpinned by digital technologies, to advance sustainability objectives while adeptly addressing emerging challenges in the ever-evolving landscape of supply chain management.