eChapters

Introduction

Work-dwelling or workplace well-being describes all aspects of the working lifespan of workers from the point of view of quality, safety and physical environment. It pronounces the workers’ feelings about their work, their working environment, the climate at work and work organization. According to WHO (2010), a healthy workplace environment not only merely finds the absence of illness but also a state of comprehensive physical, mental and social comfort. Work and health hence have a reciprocal relationship that concerns occupational health wellbeing and confers decent work environments i.e. (i) payment of reasonable income; (ii) safety-security in employment and working environment; (iii) guarantee of equal opportunities and treatment for all; (iv) enclosure of social protection for the workers and their families; (v) offering prospects for personal development and inspiring social integration and (vi) allowance of workers to express their apprehensions and to organize (ILO, 1999). In the entrepreneurial sphere, globalization along with fast industrial progress is supposed to be accountable for occupational health hazards and related workplace ill-being, work insecurity, the lack of protection for workers’ health and well-being in the form of work-related accidents, illness and stress, lack of workers’ compensation and paid sick leave, lack of maternity protection, excessive hours of work, etc.(https://www.ilo.org/). This was evidenced in the recent report of ILO (2023) where it was found that 2.3 million people around the world surrender to work-related accidents or diseases every year which links to over 6000 deaths every solitary day (www.ilo.org). Subsequently, there are about 340 million occupational accidents where 160 million are fatalities of work-related illnesses annually in the World.

Introduction

Financial inclusion has become a global agenda because it is now well recoganised that a nation can prosper only if it ensures inclusive growth. Financial access and use of formal financial system, the means of financial inclusion, have positive association with inclusive growth (Levine and Demirguc-Kunt, 2008). The financial systems which are more inclusive have provided broader access to appropriate financial services to ensure that the benefit can be availed by poor and disadvantaged sections of the society (Akter, 2016). Since, financial inclusion is the process of ensuring availability of f inancial services and well-timed and sufficient credit as required by vulnerable groups such as low income segment and weaker segment of the society at an affordable cost (Rangarajan Committee on financial inclusion, 2005). Thus, the study of financial inclusion on the basis of socio economic factors is the key to investigate individual characteristics associated with financial inclusion (Allen, Kunt, Klapper, Peria, 2012). Understanding financial usage on the basis of socio economic factors enables framing appropriate financial inclusion strategies to achieve financial inclusion in real sense. Many international agencies like International Monetary Fund, Asian Development Bank, World Bank release data on financial inclusion around the world. Global findex report is published by World Bank after the interval of every three years to present the picture of financial inclusion on various socio economic parameters. Around the world, 74% adults, 72% female, 70% income poorest population, 63% out of labour force population and 66% rural population report to have account at formal financial system (World Bank Global findex report, 2021).

Introduction

No revolution occurs without disruption. Its effects break down traditions, behavior, relationships, ways of thinking, and methods of education. (Barreiro, 2021) throughout history, from the hunter-gatherer era to the present day humanity has experienced multiple industrial revolutions impacting both social and economic domains. The latest industrial revolution, which has ushered in digital transformation and enhanced productivity at reduced costs, introduces a new lifestyle distinct from our traditional daily routines. It also aims to foster a more sustainable overall environment. (Javaid et al., 2022).

The rise of Industry 4.0 is fueled by both rapid technological advancements and various social and economic factors. Central technologies propelling Industry 4.0 include wearable, augmented reality, simulation, autonomous vehicles and robots, additive manufacturing, distributed ledger systems like block chain, big data analytic, mobile computing, and cloud computing. These technologies influence and facilitate the development of new and innovative business models. (Industry 4.0 Implications for Higher Education Institutions 2019)

The advent of Industry 4.0, characterized by the integration of advanced technologies has revolutionized various sectors, including higher education. As these technologies reshape industries, they also redefine educational paradigms, creating a more interconnected, efficient, and personalized learning environment. This transformation, known as Education 4.0, responds to the demands of the Fourth Industrial Revolution by preparing students with the skills and knowledge necessary to thrive in an increasingly digital and automated world.

Introduction

Indian economy is basically an agrarian economy. In spite of all challenges and difficulties faced over the years, the sector still serves as the lifeblood of the nation, by standing as the major source of livelihood for over half of the population by guaranteeing food security for its teeming millions. A significant contribution to the GDP of the nation is still ensured by the agriculture sector. The development of a nation highly centres on its primary agriculture sector and Indian economy have a much diverse dependence on the agriculture sector in terms of its growth and development. The nation straightaway necessitates a tremendous shift entirely to sustainable agricultural practices in its development trajectory.

Despite its undeniable significance and praised role, Indian agriculture currently faces multifaceted challenges, which significantly restrict the production and productivity level of the sector as a whole and the farmers in particular. Its high time that the Indian agriculture sector, which stands as the backbone of the nation needs to go through a looming transformation. The industry 4.0 revolution is a vibrant platform for the sector to make a complete transformation. The traditional face of the sector will transform towards a modern makeover by taking up technological advancement and digitalisation. The wider transformative opportunities will ensure a return of the Indian agriculture sector to the forefront in the nations growth and development.

Financial inclusion, which is the process of accessing and properly utilizing formal financial services, is an important factor in powering economic development and poverty reduction. Study undertakes a broad cross- country analysis of financial inclusion and its impact on social and economic factors. Applying data from respectable bodies like World Bank, International Monetary Fund, Eurostat and United Nations Development Programme, the study applies regression analysis and fixed effects models to determine how income, employment, income inequality, urbanization and human development affect financial inclusion. The outcomes show the complicated interplay between these actors and financial inclusion over various national situations.Financial inclusion is becoming a key component of equitable economic development and poverty alleviation worldwide. The importance of financial inclusion is undisputed, but its causes must be identified. This research article uses a comprehensive cross- country analysis to explain how various civilizations utilize financial services. The study will examine the relationship between financial inclusion and its key socio-economic factors— income levels, employment dynamics, income inequality, urbanization trends, and human development—to inform inclusive economic development policymakers, researchers, and practitioners. The data demonstrates that financial inclusion promotes economic development by helping people save, invest, and manage risk. Additionally, academics have focused on socio-economic issues including income distribution, urban-rural divide, and education level in shaping financial inclusion patterns within and within nations. These characteristics’ effects on financial literacy are yet unknown, thus more empirical study is needed to develop evidence-based policy.

Introduction

A vision of the industrial landscape of the future that emphasises safeguarding society and the environment has lately surfaced as the “Industry 5.0” idea. People collaborating with robotics and intelligent machines are referred to as “Industry 5.0.” By utilising cutting-edge technologies like big data, Internet of Things (IoT) and robots can help people operate more efficiently and quickly. The Industry 4.0 pillars of efficiency and automation are given a more human touch through this. It aims to combine the inventiveness and problem solving abilities of human employees with the powers of mechanisation and artificial intelligence, ultimately producing a more productive and adaptable production environment. This paradigm shift emphasises the value of human decision-making and intervention in the industrial context while addressing the drawbacks of complete automation. By fusing the strengths of both robots and humans, Industry 5.0, which is still in its early stages, has the potential to revolutionise several industries. In 1987, the United Nations Commission defined sustainability as addressing the requirements of the present without compromising the capacity of people in the future to address their issues. Sustainability is essential in Industry 5.0, the next stage of industrial development characterised by growing digitization and automation. Within this paradigm, sustainable practices reduce resource consumption, trash, and emission levels, leading to more effective utilisation of resources like materials and energy. Industry 5.0’s commitment to sustainability reduces waste, carbon dioxide emissions, and environmental impacts. Utilising green technologies and eco-friendly practices helps reduce environmental impact

Introduction

Given the interdependence of all ecosystem units and humans, education for supportable sustainable development (ESD) is a crucial educational tool. The Brundtland Report of 1987 also highlights a crucial element when it states that “everyone must have access to the basic necessities of life and the opportunity to realize their dreams of a better life for sustainable development.” In order to alter the prevailing beliefs and attitudes which people have about themselves, society, and the environment, ESD — which is mapped out on the three pillars of the economy, society, and environment—plays a crucial role. The core tenets of ESD are those that support sustainability, including social tolerance, gender equity, intergenerational equity, poverty reduction, and environmental preservation and renovation.

The Rio Declaration, which outlines 27 sustainability principles, makes this claim. As they support the development which is related to education and reorient current education to promote sustainability, these principles can assist governments, society, and educational institutions in routinely classifying information, principles, skills, and values. Globally, education which is related to sustainable development, or ESD, is widely regarded for its substantial contribution to improving livelihoods and advancing society’s transition to sustainability. Although opinions on the viability of sustainable development vary, it is generally agreed that moving toward a sustainable future requires increasing public awareness, education, and training. Notwithstanding the difficulties in defining sustainability, it is evident that many social behaviours— such as those involving pollution, inefficient energy use, water conservation, and other issues—are not sustainable.

The digital age has revolutionized the way we live, work, and interact with the world around us. Digital transformation has become a crucial imperative for businesses of all sizes and industries, enabling them to not only adapt to the changing landscape but also thrive in it. (De Lucas,2021). The rapid advancement of technologies such as 5G, cloud computing, big data, artificial intelligence, IoT, and blockchain has opened up a plethora of opportunities for enterprises to enhance their operations, improve customer experiences, and gain a competitive edge. (Anjali et al, 2023). Businesses can increase their reach, automate jobs, obtain insights from data, personalize services, and streamline procedures by utilizing these technologies. They must reconsider their business models and implement more flexible and agile strategies in response to these exponential changes. (Bansal et al, 2020, Fenech et al, 2019).

Introduction

In the annals of history, the term “Renaissance” evokes images of a time marked by profound cultural, artistic, and scientific rebirth. The Renaissance of the 14th to the 17th century was characterized by an extraordinary surge in creativity and intellectual achievement, which laid the groundwork for the modern world. Today, we find ourselves at the cusp of a new Renaissance—a Digital Renaissance—propelled by the rapid and relentless advancements in technology. This new era, like its historical counterpart, is marked by a transformative shift that is redefining every aspect of human life, from how we work and communicate to how we learn and govern.

This chapter delves into the essence of this transformative era, exploring the fundamental technological innovations reshaping our world and their far-reaching impacts on society. We will examine the pivotal technologies driving the Digital Renaissance, including artificial intelligence, blockchain, the Internet of Things (IoT), and other emerging technologies such as virtual and augmented reality and 5G networks. By understanding these technologies and their applications, we can appreciate how they collectively contribute to a digital transformation that is as revolutionary as the artistic and scientific achievements of the past.

Introduction

Artificial intelligence (AI) became one of the 21st century’s most technologically disruptive innovations very quickly. The effort to be at the leading edge of AI discovery and development is becoming more intense as the field develops. The rapid advancement of artificial intelligence (AI) technology has sparked significant interest and debate across various sectors, including higher education. From personalized learning experiences to data-driven insights, AI offers a multitude of opportunities for optimizing educational processes and enriching student outcomes. In this reframed exploration, we delve into the transformative potential of AI within higher education and its multifaceted utilization across various domains. AI accelerates scientific research and knowledge discovery by analyzing vast datasets, identifying patterns, and generating insights that fuel innovation. Natural language processing (NLP) algorithms facilitate literature reviews, automate data analysis, and assist in hypothesis generation, empowering researchers to make breakthroughs across disciplines. AI-driven tools empower educators with innovative methods for teaching and assessing student progress. Automated grading systems, virtual tutors, and intelligent tutoring systems streamline administrative tasks, provide real-time feedback, and enable instructors to focus more on personalized instruction. AI streamlines administrative processes within higher education institutions, from admissions to alumni engagement. Chatbots handle inquiries, virtual assistants aid in course scheduling, and predictive analytics optimize resource allocation, enhancing operational efficiency and cost-effectiveness.

Introduction

The successful combination of technology and finance, commonly referred to as Financial Technology (henceforth, FinTech), can be understood as “technology enabled financial innovation” that may result in new business applications, models, products and/or processes, that create manifold material impact on f inancial markets and provision of financial services (Schueffel, 2016).

FinTech presents a promising prospective of bettering the situation of financial inclusion in developing countries like India by leveraging on technology in an innovative manner. Inclusive growth can be achieved through FinTech by means of broad-based prosperity, reduction in transaction costs and better access to financial services to the underserved (Sahay et al., 2020). In this context, Fintech companies are institutions applying technological innovations to increase efficiency and create enhanced access to the finance industry. There has been tremendous growth of such companies in recent years and it has also captured the interest of investors alike.

17.1. Introduction

Grounded Theory (GT) is a systematic methodology in the social sciences that involves the construction of theories through methodical gathering and analysis of data. Developed by Barney Glaser and Anselm Strauss in 1967, GT is widely used in qualitative research to generate theories that are grounded in empirical data. Unlike traditional research methods that test hypotheses, GT allows theories are to emerge from the data itself, making it a valuable tool for exploring complex social phenomena.

This chapter provides an in-depth exploration of Grounded Theory, including its origins, key principles, methodologies, applications, and criticisms. By the end of this article, readers will have a thorough understanding of GT and its significance in qualitative research.

15.1. Introduction

Meta-analysis is a statistical technique used to combine the results of multiple studies to produce a single, more precise estimate of the effect size of a particular intervention or association. It is a powerful tool in research, particularly in f ields such as medicine, psychology, education, and social sciences, where it is often necessary to synthesize evidence from numerous studies to draw meaningful conclusions. This chapter provides a comprehensive overview of meta-analysis, including its history, methodology, advantages, limitations, and applications.

15.2. Historical Background

The concept of meta-analysis dates back to the early 20th century, but it was not until the 1970s that the term was coined by Gene V. Glass. Glass defined meta-analysis as “the statistical analysis of a large collection of analysis results from individual studies for the purpose of integrating the findings” (Glass, 1976). Since then, meta-analysis has evolved significantly, with advancements in statistical methods and software making it more accessible and robust.

15.3. The Need for Meta-Analysis

15.3.1. Accumulation of Evidence

In many fields, particularly in medicine and psychology, a large number of studies are conducted on the same or similar research questions. However, individual studies may have small sample sizes, leading to low statistical power and inconclusive results. Meta-analysis allows researchers to pool data from multiple studies, increasing the overall sample size and statistical power, thereby providing a more reliable estimate of the effect size.

Project managers need to make sense on the many data that they have. Analytical tools are used in project management to achieve such need. Such tools are used to create a forecast of potential outcomes based on the variations present in the environmental and project variables. There are different types of analytical tools used and one of the most common tools is regression analysis.

14.1. Multiple Regression

Concept: Multiple regression is the most commonly utilized multivariate technique. It examines the relationship between a single metric dependent variable and two or more metric independent variables. Multiple regressions are often used as a forecasting tool. Goal is to use the linear composite of two or more continuous and/or categorical variables (predictors) to: 1) predict scores on a single continuous variable (criterion), or to 2) explain the nature of the single continuous criterion variable from what is known about the predictor variables. In prediction, the criterion is the main emphasis because decisions are made on its value, but often times, it is difficult to directly measure or obtain a subject’s actual score on the criterion; therefore, it is important to estimate or predict one’s criterion score based on the value of the predictor scores.

13.1. Introduction

The Delphi technique is a systematic, interactive forecasting method that involves gathering expert opinions to make informed decisions, predict future trends, or address complex problems. The core idea behind this technique is to use the knowledge and insights of experts in a structured manner to produce well-informed and reliable outcomes. By combining expert judgment with iterative feedback, the Delphi method allows for deeper analysis, greater accuracy, and a better understanding of complex issues that cannot be easily solved through traditional data analysis or quantitative methods (Ismail and Taliep, 2023).

Originally designed for defence-related forecasting, the Delphi technique has since evolved into a versatile tool used across a wide range of fields, such as business, healthcare, education, public policy, environmental science, and social research. Its versatility stems from its ability to gather expert opinions in a manner that allows for rigorous analysis of complex issues, without the constraints of traditional group meetings or face-to-face discussions.

12.1. Introduction

Artificial Neural Networks (ANNs) are computational models inspired by the way biological neural networks in the human brain process information. They consist of interconnected nodes or “neurons” arranged in layers: an input layer, one or more hidden layers, and an output layer. Each neuron in the network processes data and passes the results to the next layer. ANNs are designed to recognize patterns, learn from data, and make predictions or classifications based on that learning.

In an ANN, the connections between neurons are weighted, meaning each connection has a strength or value that determines the influence of one neuron on another. Through a process called training, the network adjusts these weights to minimize errors in its predictions. This is typically done using techniques like backpropagation, where the error is propagated backward through the network to update the weights (Abiodunet al., 2018).

ANNs are highly versatile and have been applied across various domains, including image and speech recognition, natural language processing, and decision-making systems. Their ability to model complex relationships in large datasets makes them a crucial component in the field of machine learning and artificial intelligence (Ahmed et al., 2023).

11.1. Introduction

Fuzzy Cognitive Mapping (FCM) is a powerful soft computing technique that integrates elements of fuzzy logic and cognitive mapping to model complex systems. It allows for the representation of causal relationships between variables in an interpretable and flexible manner, making it particularly valuable for analyzing and simulating real-world dynamic systems. FCM has its roots in cognitive science, systems thinking, and artificial intelligence, enabling researchers and practitioners to model uncertainties and interdependencies effectively (Tyrovolas et al., 2023). The approach is particularly advantageous when dealing with complex, ill-structured problems where traditional mathematical modeling techniques fall short. By capturing expert knowledge and combining it with computational techniques, FCM helps in making informed predictions and policy decisions.

It is widely applied across multiple domains, including environmental management for sustainability assessments (Özesmi and Özesmi, 2004), decision support systems in business and governance, healthcare for medical diagnostics and treatment planning (Apostolopoulos et al., 2024), and socio economic planning for policy analysis and forecasting (Gray et al., 2015). Furthermore, its adaptability has led to its increasing integration with artificial intelligence, machine learning, and big data analytics, making it a promising approach for tackling contemporary challenges.

10.1. Introduction

Decision-making is a fundamental aspect of human life, influencing choices in both personal and professional settings. Every individual, whether consciously or unconsciously, engages in decision-making daily, ranging from simple tasks like selecting a meal to complex strategic planning in organizations. However, decision-making becomes increasingly challenging when multiple conflicting criteria are involved. The presence of numerous factors, each with varying degrees of importance, often creates ambiguity and uncertainty, making it difficult to arrive at the most suitable option. To address these challenges, structured decision-making methodologies have been developed to provide a systematic and logical framework for evaluating alternatives.

One such widely recognized method is the Analytical Hierarchy Process (AHP), introduced by Thomas L. Saaty in the 1970s. AHP was designed to assist decision-makers in breaking down complex problems into a structured and systematic hierarchy. This approach allows for a step-by-step evaluation of different factors, making it easier to analyze their relative importance. By structuring a problem into different levels - such as goal, criteria, sub-criteria, and alternatives - AHP provides clarity in decision-making and enhances consistency in judgments. The method is particularly beneficial when dealing with decisions that involve both tangible and intangible factors, as it allows subjective preferences to be quantified systematically (Saaty, 2008).

9.1. Introduction

The human population of the world will reach nearly 10 billion by 2050, boosting agricultural order-in a situation of humble financial development (FAO, 2017). At present, approximately 37.7% of total land surface is being utilized for crop production. From employment generation to contribution to National Income, agriculture sector is contributing a significant role in economic prosperity of the developed nations and is playing an active part in the economy of the developing countries as well. The growth of agriculture has resulted in a significant increase in the per-capita income of the rural community. Thus, a greater emphasis on agricultural sector is the need of the hour. For countries, like India, the agricultural sector accounts for 18% of GDP and provides employment to 50% of the country’s workforce. Development in the agricultural sector will enrich the rural development which will lead toward rural transformation and ultimately the desired structural transformation (Mogili and Deepak, 2018; Shah et al., 2019). The role of Artificial Intelligence (AI) has been obvious in the agricultural sector recently. The sector needs the impetus from AI based technology to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology.

8.1. Introduction

Sometimes writing reference list could be so confusing since there are a lot of different rules. Each universities or even each faculty use different rule in writing reference list. The different rule of writing reference list is normal because they refer to different style guide. There are a lot of styles guide available. MLA (Modern Language Association of America) style; the Chicago Manual of Style; the Oxford Guide to Style are some examples of style guide. Difference reference of style guides chosen will result in totally different way of writing reference list. The most widely accepted style guides in social science is the APA (American Psychological Association) style. In some higher education journals (e.g. Journal of Studies in International Education, Higher Education) it is stated clearly that author must submit their paper according to APA style. Referencing in a general sense means to give credit to someone for using his or her own ideas or thoughts in a research activity. Referencing helps in gaining the originality of the ideas and thoughts used in the research activity. Failure to reference is treated as disrespect to the original author or writer and seen as a major misconduct in the area of academic research writing. Generally students made the mistake of not mentioning proper referencing at the end of their research projects, essays or any other piece of work. This may lead to cancellation of the written matter. If we do not reference properly the written matter is treated as if it is copied from somewhere. The research paper or material cannot be submitted for further research or reading purpose. We find a lot of data when we search for something.