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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 present chapter discusses evolution, definitions, dimensions, capabilities, and the present state of the art of digital supply chain management (DSCM). The objective of the chapter is to offer a detailed understanding of DSCM, by shedding light on its historical development, exploring its multipronged definitions, and highlighting its core dimensions and capabilities in the contemporary business landscape. The evolution of DSCM appears as a central theme, rooted in the background of industrial revolutions. It starts by relooking at the First Industrial Revolution (IR) with its mechanization and steam power, progresses through the Second IR with electrification and mass production, and arrives at the Third IR, characterized by the rise of computers and the internet. The pivoting transition into the Fourth IR, also called Industry 4.0, marks the start of DSCM with its fusion of digital technologies (DTs) in the supply chain (SC) processes. Analysis of key definitions of DSCMs unveils their role as an enabler of SC collaboration, customer-centric nature, having overarching reliance on DTs. Moreover, the chapter explores the core dimensions of DSCM, exposing its ability to improve SC resilience, sustainability, visibility, efficiency, and agility. These capabilities stem from seamlessly woven DT developments into SC: artificial intelligence (AI), machine learning, the Internet of Things (IoT), and advanced analytics. The chapter concludes by highlighting the present state of the art in DSCM, reflecting its indispensable role in the contemporary turbulent business dynamics. In short, this chapter offers a synthesized view of DSCM's definitions, dimensions, evolution, capabilities, and present status within the larger context of supply chain management (SCM) literature.

It is important to implement artificial intelligence (AI) because it can simplify and solve complex problems faster than humans. Because AI learns about people and their behavior from the first purchase, AI marketing can boost marketing efforts by leveraging data to target extremely precise consumer groups. There is a debate about the efficacy of AI marketing due to the constraints and limits imposed by the system's nature. This chapter presents insights from published studies regarding the relationship of AI with marketing and how AI can affect marketing. A real-world example of Netflix's usage of AI in marketing has been demonstrated. Then, consumer attitudes regarding AI were revealed. Then, several ethical considerations concerning AI were highlighted. Finally, the anticipated future of AI marketing was addressed. This chapter demonstrated the significance of firms implementing AI marketing to get a competitive advantage. Although some of the difficulties mentioned in this study need to be resolved, AI marketing has a bright future. There are ethical concerns about bias and privacy that should be addressed further. This chapter will encourage firms to use AI systems in marketing, and it will open the door to concerns that will need to be investigated academically in the future.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.

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.

Introduction: This study investigates the COVID-19 pandemic’s effects on the sustainability of the supply chain. It investigates how modern supply networks and procedures were equipped for such a catastrophe, and the pandemic’s effects on the environment, highlighting the significance of studying resilience and sustainability concurrently.

Purpose: The study acknowledges the importance of environmental sustainability for businesses and the need to examine trends in organisational, customer, policy, and distribution networks.

Need for the Study: The COVID-19 pandemic has significantly impacted supply chains. This study aims to provide insight into the long-term repercussions of the crisis and the importance of incorporating environmental considerations.

Methodology: The study uses a mixed-methods approach to evaluate the effects of the COVID-19 pandemic on supply networks and environmental sustainability indices. Data from industry reports, governmental publications, polls, and qualitative research techniques have been gathered.

Findings: The results of this study advance our understanding of how to preserve supply chains in the wake of the COVID-19 pandemic. It highlights the need for enhanced resilience and sustainability measures, expose the flaws and weaknesses of contemporary supply networks, and uncover developing patterns and tactics in customer behaviour, policy frameworks, distribution networks, and supply chain management.

Practical Implications: The COVID-19 pandemic has provided businesses, decision makers, and researchers with guidance on handling its potential and challenges – increasing the supply chain’s resistance to future interruptions, incorporating environmentally friendly practises, developing policies to support resilient and sustainable supply chains, adapting to changing consumer tastes, increasing effectiveness, and minimising the environmental impact of distribution networks.

Digital technologies (DTs) have emerged as a major driving force, transmuting the ways Supply Chains (SCs) are managed. The integration of DTs in supply chain management (SCM), Digital Supply Chain Management (DSCM), has fundamentally reshaped the SCM landscape, offering new opportunities and challenges for organizations. This chapter provides a comprehensive overview of modern DTs and the way they impact modern SCM. This chapter has twofold objectives. First, it illustrates the major changes that DTs have brought to the supply chain landscape, unraveling their multifaceted implications. Second, it offers readers a deeper and comprehensive understanding of the challenges and opportunities arising from the incorporation of DTs into supply chains. By going through the chapter, readers will be able to have a comprehensive grasp of how DTs are reshaping SCM and how organizations can survive and thrive in the digital age. This chapter commences by shedding light on how DTs have and continue to redefine SCM, improving supply chain resilience, visibility, and sustainability in an increasingly complex and interconnected world. It also highlights the role of DTs in enhancing SC visibility, agility, and customer-centricity. Furthermore, this chapter briefly highlights the challenges related to the adoption (pre and post) of DTs in SCM, elucidating on issues related to talent acquisition, data security, and regulatory compliance. It also highlights the ethical and societal implications of this digital transformation, emphasizing the significance of responsible and sustainable practices. This chapter, with the help of three cases, illustrates how the adoption of DTs in SC can impact the various SC performance indicators.

This chapter provides a comprehensive investigation of the literature on sustainability reporting in Islamic banks using a content analysis of 200 English language articles published between 2000 and 2023. The data were collected from the Web of Science (WOS) database and analyzed using Bibliometrix in R software. This chapter addresses the trends of the most influential authors, institutions, countries, and research hotspots. This chapter fills one of the gaps in sustainability reporting literature, setting up a statistical description of the principal features of sustainable Islamic banking research and carrying out an analysis of its knowledge structure via bibliometric analysis. This chapter found that the number of articles on sustainability reporting in Islamic banks has increased over time; however, it is concentrated on a few core sources and authors, mostly related to Islamic finance, accounting, and ethics, as well as sustainability and social responsibility. The analysis of the co-authorship network shows a limited degree of collaboration between researchers from different clusters. The most productive and collaborative countries are Malaysia, the United Kingdom, and Pakistan, reflecting their well-developed and regulated Islamic banking industries that follow sustainability guidelines and frameworks. Moreover, the co-word analysis identifies 11 clusters and 43 keywords that represent the main topics and themes in the field, such as corporate governance, performance, disclosure, and risk. This chapter suggests some directions for future research and action on sustainability reporting in Islamic banks. This chapter contributes to the literature on sustainability in Islamic banking and the UN 2030 Agenda for Sustainable Development.

This final chapter delves into the future of digital supply chain management (DSCM) amid today's dynamic business environment, shaped by technological advancements and factors like automation, artificial intelligence (AI), sustainability, and resilience. It emphasizes the crucial role of digital technologies (DTs) such as AI, the Internet of Things (IoT), Industrial IoT, and the Internet of Everything (IoET), along with blockchain, in revolutionizing supply chain operations. These technologies enable agility, flexibility, efficiency, and responsiveness, crucial for supply chains to proactively adapt to market changes. The chapter explores the trends in DSCs, focusing on real-time data analytics, end-to-end visibility, sustainability, and resilience. It highlights the growing importance of transparency in supply chains, driven by consumer demand for sustainable practices and product origins. DSCM is identified as pivotal for prioritizing sustainability, leading organizations toward green practices. Despite the opportunities in DSCM, challenges like cybersecurity, data management complexities, geopolitical uncertainties, and talent shortages are acknowledged. To overcome these, the chapter stresses strategic foresight in DSCM and the importance of robust process management, risk management, and talent development. The future-readiness of supply chain professionals is discussed, highlighting the need for change management, development of social and deep work skills, collaboration, and ethical practices. The chapter concludes by underscoring the transformative potential of DTs in the digital era, urging organizations to embrace innovation, transparency, and sustainability in their supply chains, recognizing that the future of DSC is an imminent reality.

Purpose: The study aims to analyse and discuss the effect of COVID-19 on businesses. The chapter discusses the various machine learning (ML) tools and techniques, which can help in better decision making by businesses in the present world.

Need for the Study: COVID-19 has increased the role of VUCA elements in the business environment, and there is a need to address the challenges faced by businesses in such environment. ML and artificial learning can help businesses in facing such challenges.

Methodology: The focus and approach of the chapter are in the context of using artificial intelligence (AI) and ML techniques for decision making during the COVID-19 pandemic in a VUCA business environment.

Findings: The key findings and their implications emphasise the importance of understanding and implementing AI and ML techniques in business strategies during times of crisis.

Practical Implications: The chapter’s content is in the context of using AI and ML techniques during the COVID-19 pandemic and in a VUCA business environment.