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The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

This chapter analyzes the link between the digital divide, infrastructure regulation, and disaster planning and relief through a case study of the flood in San Jose, California triggered by the Anderson dam’s overtopping in February 2017 and an examination of communication failures during the 2018 wildfire in Paradise, California. This chapter theorizes that regulatory decisions construct social and disaster vulnerability. Rooted in the Whole Community approach to disaster planning and relief espoused by the United Nations and the Federal Emergency Management Agency, this chapter calls for leadership to end the digital divide. It highlights the imperative of understanding community information needs and argues for linking strategies to close the digital divide with infrastructure and emergency planning. As the Internet’s integration into society increases, the digital divide diminishes access to societal resources including disaster aid, and exacerbates wildfire, flood, pandemic, and other risks. To mitigate climate change, climate-induced disaster, protect access to social services and the economy, and safeguard democracy, it argues for digital inclusion strategies as a centerpiece of community-centered infrastructure regulation and disaster relief.

Artificial intelligence (AI) is revolutionizing businesses and daily life, with AI-powered technologies like personal assistants and medical diagnostic systems transforming how we interact and make decisions. However, the ethical implications of these technologies cannot be ignored. AI systems can produce biased results and decisions if not designed to be fair and unbiased. Corporate digital responsibility (CDR) provides a valuable framework for addressing these ethical dilemmas. Service organizations need to navigate CDR issues across the data and technology life-cycle stages (e.g., their creation, operation, refinement, and retention) and across its digital service ecosystem (including its external business partners). Despite the risks associated with poor CDR practices, companies may adopt them to benefit from data monetization, enhanced customer experience, and productivity improvement. To mitigate these risks and build a strong CDR culture, organizations need to establish ethical norms, prioritize customer privacy, and ensure equitable power dynamics with business partners. The emergence of generative AI poses enhanced CDR challenges, such as AI complexity, monitoring, accountability, and workforce changes. Going forward, CDR is a crucial framework for firms to address the needs of their multiple stakeholders and to ensure sustainable business practices in the increasingly digital service world.

Purpose: The main aim is to explore and recognize productive ways to create human-centred designs (HCDs) for employee experience (EX). HCD is a concept that prioritizes the needs, preferences, and behaviours of humans using the product or service. EX refers to all interactions an employee has with their employment lifespan – from recruitment to retirement. By taking the HCD approach to EX design, companies can create a work environment tailored to their employees’ needs and preferences.

Design / Methodology: The explorative research design to apply journey maps. By mapping out the employee journey, designers can identify pain points and areas for improvement.

Findings: The findings highlight that artificial intelligence and robotics are core components of designing HCD and can be applied to EX design. By prioritizing EX, companies can attract and retain top talent, increase employee engagement and productivity, and gain a competitive advantage.

Research Limitations: The study is developing and involves detailed insights from different companies, making it difficult and time-consuming to prepare a comprehensive report.

Practical Implications: The findings of the study will add value to other organizations to follow and develop policies and practices that make the employees cherish their work.

Originality: The chapter’s originality lies in providing a comprehensive understanding of HCD and EX. It emphasizes leveraging the strengths of both humans and bots for enhanced workforce experience and business growth. Exploring future automation and technology integration trends adds depth to the chapter’s contribution.

This chapter explores the development of social science visualizations as cultural objects within art worlds. The research examines artworks as social science visualizations to show the importance of conducting analysis within distinctive social, institutional, and cultural environments. To make these arguments, the chapter outlines some of the key features of art worlds as they have been analyzed by cultural sociologists and anthropologists. We point out how cultures of reception and institutional intermediaries, such as museums, have historically shaped the construction of artworks, which are never produced or interpreted in a vacuum. The chapter closes with a call to expand both the application of social science visualizations and our understanding of such visualizations as subject to similar art world dynamics. Such visualizations, it is argued, constitute key components of social research practice increasingly oriented toward a digitally connected public hungry for visual interpretations of contemporary social developments.

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.