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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.

In an era where sustainability and digital transformation are becoming indispensable pillars of successful business operations, this chapter explores the potent synergy between these two paradigms. As businesses strive to align their operations with Environmental, Social, and Governance (ESG) goals, digital transformation emerges as a powerful enabler. This chapter delves into how digital technologies are not only revolutionizing traditional business models but are also paving the way toward more sustainable practices. From data-driven decision-making to improved resource management, this chapter discusses the diverse ways in which digital transformation contributes to sustainability. It also offers an in-depth analysis of real-world case studies, illustrating how businesses have successfully integrated digital transformation in their pursuit of sustainability. Recognizing the potential roadblocks, this chapter also addresses the challenges businesses may face in this journey, including cybersecurity risks, data privacy issues, and the need for technological literacy. It further presents strategies to navigate these challenges and underscores the importance of preparedness in managing potential risks. Finally, this chapter ventures into the future of digital transformation, evaluating current trends and predictions, and their potential impact on sustainable business practices.

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.

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.

In this final chapter, we explore the ever-evolving 21st century landscape where ethics drive community development toward resilience and progress. Drawing inspiration from the subheadings mapping our journey, we traverse international case studies spanning Canada, Brazil, Sweden, Kenya, China, Australia, Antarctica, and India. Through these global insights, we uncover the impacts of dynamic forces on communities worldwide, navigating ethical dilemmas and opportunities. We present strategies tailored to diverse continent-specific needs, explore inclusive governance models, and highlight the transformative power of ethical engagement. This journey underscores the vital role of resilience and concludes with a global call to embrace ethical approaches for inclusive community development and a sustainable future.