This chapter explores the integration of circular economy principles with artificial intelligence (AI) to transform waste into valuable business assets. It outlines the frameworks and business relevance of circularity, emphasizing how waste can be redefined as a resource for innovation. The chapter highlights AI's role in enabling circular strategies, from waste detection and sorting to predictive analytics and lifecycle tracking. Through case studies from companies like Adidas, Unilever, and IKEA, it illustrates how AI-driven circular models create brand value. Ethical considerations and challenges, including data privacy and environmental impacts, are also discussed. Finally, the chapter examines future directions, emphasizing the potential of emerging technologies to further align AI with circular economy goals and revolutionize green marketing strategies.

This chapter explores how artificial intelligence (AI) is reshaping corporate social responsibility (CSR) and purpose-driven marketing to build sustainable brand equity. As consumers increasingly demand ethical and environmentally responsible practices, AI offers powerful tools to decode consumer sentiment, personalize CSR messaging, and amplify sustainability narratives with precision and authenticity. Through real-world examples and a strategic framework, the chapter illustrates how AI supports transparent communication, stakeholder engagement, and ethical decision-making in CSR initiatives. It also addresses the ethical challenges of AI use, emphasizing responsible practices to avoid bias and greenwashing. Finally, this chapter highlights AI's role as a catalyst for integrating purpose and performance, enabling brands to align business success with social and environmental commitments in an evolving sustainable ecosystem.

This chapter explores the vital role of Customer Relationship Management (CRM) in improving client loyalty and retention. It analyzes the fundamental components of effective CRM implementation, including customer data management, personalization, and interaction with corporate operations. The chapter emphasizes technology-driven advancements, including artificial intelligence and predictive analytics, that are revolutionizing CRM processes. Additionally, it examines ways for cultivating client loyalty, assessing CRM efficacy using key performance indicators, and extracting lessons from practical case studies. Emerging trends, such as omni-channel engagement and data privacy issues, are discussed, highlighting the need for firms to adjust to evolving consumer expectations. By implementing best practices and prioritizing customer-centric strategies, firms may cultivate significant connections that promote enduring success in a more competitive market. This chapter functions as a detailed manual for firms aiming to refine their CRM strategies and improve overall customer experiences.

This chapter explores the critical privacy and security challenges posed by AI-powered education tools. It examines how AI enhances student support and personalized learning while highlighting risks associated with chatbots, data collection, and processing. Ethical concerns, including consent and fairness, are discussed alongside the complexities of global regulations such as GDPR and FERPA. The chapter also addresses cybersecurity threats targeting educational AI systems and presents strategies to mitigate these risks. Emerging privacy-preserving techniques like federated learning and differential privacy are evaluated for their potential to safeguard student data. Drawing on case studies, it identifies best practices for ethical AI implementation and offers actionable recommendations for educators and policymakers. Finally, this chapter underscores the need for a balanced approach that protects student privacy without stifling innovation in AI-driven education.

This chapter explores the transformative role of artificial intelligence in advancing environmental sustainability amid escalating climate challenges. It examines how AI-driven technologies optimize resource management, enhance climate monitoring, and promote green innovations across sectors such as energy, agriculture, and waste management. Through predictive analytics, machine learning, and real-time data integration, AI supports more precise climate modeling, disaster early warning systems, and efficient energy use. The chapter also addresses ethical and practical concerns, including algorithmic bias, data privacy, and the digital divide, emphasizing the need for inclusive and responsible AI deployment. By highlighting real-world applications and offering policy recommendations, it underscores the potential of AI to balance technological progress with ecological stewardship. Finally, this work advocates for interdisciplinary collaboration and equitable governance to ensure AI contributes meaningfully to a sustainable and just environmental future.

This chapter critically explores the integration of conversational AI in addressing student mental health within educational settings. Drawing on recent empirical studies and real-world applications, it examines how AI-powered chatbots are being utilized to offer scalable, accessible, and timely psychological support. While highlighting the promise of such technologies in reducing anxiety, promoting resilience, and supplementing overburdened counseling services, the chapter also scrutinizes key ethical concerns, including privacy, algorithmic bias, and the risk of emotional dehumanization. Through a nuanced discussion of case studies, challenges, and governance imperatives, the chapter underscores the need for human-AI collaboration grounded in empathy, equity, and accountability. It concludes by proposing a forward-looking policy and research agenda to ensure that AI serves as a complement not a substitute to human care in fostering student well-being.

This chapter explores the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in revolutionizing logistics and supply chain management. It examines how the integration of these technologies enhances operational efficiency, end-to-end visibility, and sustainability. Key applications such as AI-powered demand forecasting, route optimization, and IoT-driven real-time monitoring are highlighted, demonstrating their capacity to reduce costs, improve decision-making, and minimize environmental impact. Furthermore, the chapter addresses the challenges associated with AI and IoT adoption, including data privacy, cybersecurity, and integration with legacy systems. Through industry case studies, the chapter illustrates the tangible benefits and real-world impact of smart logistics. Finally, it outlines future research directions and prospects for further advancements in these transformative technologies.

The integration of smart logistics technologies is revolutionizing supply chains by enhancing operational efficiency and promoting sustainability. However, the path to adoption is fraught with technological, financial, organizational, regulatory, and security challenges. This chapter critically examines these hurdles, offering strategic frameworks for overcoming them through targeted training, phased implementation, collaborative partnerships, robust business cases, and stringent cybersecurity measures. Drawing on empirical evidence and real-world case studies, it highlights the importance of leadership, organizational culture, and adaptive strategies. It also outlines future directions shaped by advancements such as autonomous systems and AI-driven logistics, emphasizing the need for interdisciplinary research and ethical governance. By addressing these complexities, the chapter aims to equip practitioners, researchers, and policymakers with actionable insights for fostering resilient and future-ready smart logistics ecosystems.

The circular economy (CE) provides a transformational strategy for alleviating inflationary pressures via resource dependence reduction, supply chain optimization, and waste minimization. The contributions of circular business models, technical developments, and changing consumer behavior to economic stability are investigated in this chapter. Blockchain for supply chain transparency, predictive analytics powered by artificial intelligence, and IoT-enabled resource efficiency are among the fundamental technologies. Furthermore, helping to reduce price volatility and stabilize production costs are remanufacturing, recycling, and second-hand markets. Businesses may increase supply chain resilience, lower running costs, and provide consumers with reasonably priced, environmentally friendly choices by using circular economy concepts. The importance of using circular solutions to solve inflationary issues and promote long-term economic sustainability is underlined in this chapter.

This chapter, titled “Preparing for the Next Disruption: Lessons Learned from Past Crises and Building a Playbook for Future Supply Chain Shocks,” offers a critical examination of historical supply chain disruptions and the strategic lessons they impart for enhancing future resilience. It systematically traces the chronology of major global crises, analyzing their economic and inflationary consequences, and draws on illustrative case studies that highlight effective organizational responses. The chapter further investigates the transformative role of digitalization and artificial intelligence in the early detection and mitigation of supply chain risks, while exploring how sustainable branding practices can foster long-term consumer trust. A structured framework for developing a proactive disruption response playbook is proposed, alongside targeted policy recommendations aimed at strengthening both national and global supply chain resilience. The chapter concludes by outlining key avenues for future scholarly inquiry in this increasingly vital domain.

This chapter critically examines the evolving landscape of personal data privacy and security amid rapid digital transformation. It explores the multifaceted challenges faced by individuals and organizations, including asymmetries in data control, cyber vulnerabilities, and systemic governance failures. The chapter provides a comprehensive overview of global regulatory frameworks such as the GDPR, CCPA, and emerging policies in the Global South, highlighting both progress and persistent compliance gaps. Emphasis is placed on best practices risk assessments, employee training, incident response, and data minimization as well as the role of cutting-edge technologies like AI, blockchain, and encryption in strengthening data protection. Ultimately, the chapter underscores the necessity of cultivating a security-oriented culture that embeds privacy into organizational ethos, urging a shift from reactive compliance to proactive responsibility in managing digital trust.

This chapter examines the critical challenge of balancing usability and security in digital system design. As organizations increasingly rely on digital platforms, the tension between creating user-friendly interfaces and ensuring robust security measures has become more pronounced. By exploring human-centered design principles, behavioral insights, and adaptive technologies, the chapter highlights strategies for integrating security seamlessly into user workflows without sacrificing ease of use. Drawing on interdisciplinary research, case studies, and best practices, the chapter emphasizes the importance of transparency, user trust, and iterative design. It concludes by proposing a framework for developing systems that not only protect privacy but also enhance user experience, offering a roadmap for sustainable and secure digital transformations.

Traditional media is essential for distributing information during supply chain problems; nevertheless, it may unintentionally reinforce misinformation, therefore increasing interruptions. This chapter explores the role of mainstream media in increasing crisis via sensationalism, insufficient fact-checking, and the requirements of real-time reporting. The study explores ethical challenges in crisis journalism, focusing on the balance between public awareness and responsible reporting. This chapter discusses psychological and socio-economic effects and suggests measures including media literacy, ethical frameworks, and AI-based fact-checking to combat misinformation. Enhancing cooperation between media and supply chain stakeholders is crucial for reducing volatility caused by crisis.

This chapter explores the impact of misinformation on supply chain resilience, analyzing historical crises to identify key lessons and strategies for building robust supply chains. By examining case studies, such as the COVID-19 toilet paper shortage and semiconductor supply chain disruptions, the chapter highlights effective approaches companies can adopt, including technological innovations, proactive communication, and stakeholder collaboration. It emphasizes the importance of predictive analytics, regulatory frameworks, and consumer education in mitigating misinformation risks and enhancing overall supply chain integrity. Finally, the chapter provides actionable insights for organizations seeking to strengthen their supply chains against future disruptions.

This chapter explores the integration of artificial intelligence (AI) within social media and digital platforms to enhance English Language Teaching (ELT). It examines AI-driven tools like chatbots, conversational agents, and content curation algorithms that facilitate personalized, interactive, and engaging learning experiences. By analyzing platforms such as Facebook, Instagram, Duolingo, and TikTok, the chapter highlights how AI fosters dynamic English learning communities and enables real-time language practice. It also addresses ethical considerations, such as data privacy, accessibility, and algorithmic bias, while emphasizing the need for inclusive and equitable AI deployment. Through case studies and real-world examples, the chapter illustrates AI's transformative potential and its challenges in reshaping traditional ELT methodologies, offering insights into how educators can effectively leverage these innovations.

The GreenTech revolution is reshaping the global landscape, offering innovative solutions to the pressing environmental challenges of our time. This chapter explores the concept of Green Technology (GreenTech), tracing its historical evolution and highlighting its key sectors and applications. GreenTech encompasses a wide array of technologies and practices aimed at reducing environmental impact, conserving natural resources, and promoting sustainability across various industries, including energy, transportation, agriculture, and waste management. Despite its transformative potential, the widespread adoption of GreenTech faces significant challenges, including technological barriers, financial constraints, regulatory complexities, and societal resistance. These obstacles, however, also present opportunities for innovation, market growth, and collaboration. The chapter examines these challenges and opportunities, emphasizing the role of technological advancements, emerging markets, and strategic partnerships in driving the GreenTech revolution forward.

This chapter, titled “Bridging the Gap: Realizing GreenTech Potential,” explores the critical barriers hindering the widespread adoption and implementation of Green Technology (GreenTech) and proposes strategic frameworks to overcome them. The discussion covers key challenges, including technological innovation gaps, financial constraints, regulatory hurdles, and societal resistance, which collectively impede GreenTech's progress. The chapter emphasizes the importance of collaborative innovation, financial incentives, supportive regulatory frameworks, and public engagement in accelerating GreenTech deployment. Through case studies of successful renewable energy projects, sustainable agriculture, and green building initiatives, the chapter provides practical insights and lessons learned. By addressing these barriers and leveraging strategic approaches, the chapter outlines a pathway for realizing the full potential of GreenTech, contributing to a more sustainable and environmentally responsible future.

This chapter, titled “Eco-Intelligence: AI's Contribution to a Sustainable World,” explores the significant influence of artificial intelligence (AI) on environmental sustainability. The text examines the impact of AI in several areas such as climate change mitigation, biodiversity protection, smart agriculture, urban sustainability, and circular economy projects. The chapter focuses on the use of AI in predictive modeling for climate scenarios, the optimization of renewable energy systems, and the real-time monitoring of emissions. These applications together contribute to the reduction of greenhouse gas emissions. The emphasis on biodiversity conservation is on AI's skills in species identification, habitat mapping, and anti-poaching initiatives. The chapter also explores the use of AI in augmenting precision farming, boosting crop monitoring, and managing sustainable fisheries. The integration of AI technology revolutionizes urban planning and waste management by enhancing the optimization of resource use and energy efficiency.