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Advanced education is often thought to respond to the demands of the economy, market forces create new occupations, and then universities respond with new degrees and curricula aimed at training future workers with specific new skills. Presented here is comparative research on an underappreciated, yet growing, concurrent alternative process: universities, with their global growth in numbers and enrollments, in concert with expanding research capacity, create and privilege knowledge and skills, legitimate new degrees that then become monetized and even required in private and public sectors of economies. A process referred to as academization of occupations has far-reaching implications for understanding the transformation of capitalism, new dimensions of social inequality, and resulting stratification among occupations. Academization is also eclipsing the more limited professionalization processes in occupations. Additionally, it fuels further expansion of advanced education and contributes to a new culture of work in the 21st century. Commissioned detailed German and US case studies of the university origins and influence on workplace consequences of seven selected occupations and associated knowledge, skills, and degrees investigate the academization process. And to demonstrate how universal this could become, the cases contrast the more open and less-restrictive education and occupation system in the US with the centralized and state-controlled education system in Germany. With expected variation, both economies and their occupational systems show evidence of robust academization. Importantly too is evidence of academic transformations of understandings about approaches to job tasks and use of authoritative knowledge in occupational activities.

Purpose of This Chapter: This research aims to study the need for more age of qualified talent. The evolving corporate needs, education, and curriculum require urgent reform. Current university methods do not align with corporate demands due to outdated content and ineffective pedagogy.

Design / Methodology / Approach: Drawing on established research, this study delves into 7 prominent training strategies across 14 sectors. A survey of 53 HR professionals and managers forms the basis for employing the non-linear best–worst method (BWM) and the Fuzzy BWM to discern the most effective training and development (T&D) modules. This comprehensive methodology ensures a nuanced analysis of T&D practices and insights for businesses seeking to align with Industry 5.0 demands.

Findings: On-the-job training emerges as the most impactful method, followed by case studies, interactive group learning, and more. These methods enhance employee skills in Industry 5.0.

Research Limitations: Limited by a small sample, future research should expand participant diversity for robustness.

Practical Implications: The study holds significance for bridging the skill gap between academic institutions and Industry 5.0. Aligning strategies with industry needs reduces skill disparities, fuels growth, and addresses employability. The study’s impact extends to society, lowering unemployment and shaping a resilient, adaptable workforce for Industry 5.0.

Originality: This innovative research examines decision-making for implementing T&D strategies in the emergence of Industry 5.0, aligning with a human-centric approach to business transformation.

In the quest for better construction workforce management, this chapter explored the background of workforce management and related theories, models, and practices. Through a review, the chapter provided meaning to the concept of construction and workforce management. The chapter concluded that while the construction industry worldwide is important to the economic growth of the countries where it operates, the industry’s management of its workforce is challenged by several problems. These problems include the nature of the industry, skill shortage, unhealthy working environment, and poor image of the industry, among others. Also, while the construction industry is rich in diversity, this has been a major source of problems for workforce management. The chapter further revealed that to improve workforce management and attain better-performing construction organisations, careful recruitment, effective training, providing a safe working environment, putting policies to promote diversity, and ensuring innovativeness, among others, are essential.

The consideration of alternative sources of material for construction is imperative to reduce the environmental impacts as two-fifths of the carbon footprint of materials is attributed to the construction industry. One alternative material with improved biodegradable attributes which can contribute to carbon offset is bamboo. The commercialisation of bamboo in modern infrastructures has significant potential to address few of the Sustainable Development Goals (SDGs) itemised by the United Nations, namely SDG 9 about industry, innovation and infrastructure. Other SDGs covering sustainable cities and communities, responsible consumption and production and climate action are also indirectly addressed when utilising sustainable construction materials. Being a natural material however, the full commercialisation of materials such as bamboo is constrained by a lack of durability. Besides fracture mechanisms arising from load-induced cracks and thermal modification, the durability of bamboo material is greatly impaired by biotic and abiotic factors, which equally affect its natural rate of degradation, hence fracture behaviour. In first instance, this chapter outlines the various factors leading to the durability limitations in bamboo material due to load-induced cracks and natural degradation based on recent findings in this field from the author's own work and from past literature. Secondly, part of this chapter is devoted to a new approach of processing the surge of information about the varied aspects of bamboo durability by considering the powerful technique of artificial intelligence (AI), specifically the artificial neural network (ANN) for prediction modelling. Further use of AI-enabled technologies could have an impactful outcome on the life cycle assessment of bamboo-based structures to address the growing challenges outlined by the United Nations.

The construction workforce plays a crucial role in the successful delivery of any construction project and, eventually, the performance of any construction organisation. Effectively managing these workforces becomes crucial. However, past studies have shown that workforce management within the construction industry has been on the back foot, with workers being seen as resources required to deliver construction projects. This situation begs the need for a construction workforce management model that can be tailored to an organisation’s situation and adopted to manage workers and improve organisational performance effectively. To this end, this chapter reviewed existing workforce management theories, models, and practices to develop a suitable approach towards managing the construction workforce. Ultimately, a strategic workforce management with a classical view using a soft workforce management approach that embraces employees’ empowerment and development through trust was proposed. Five major practices that best suit the soft workforce management approach were identified as key constructs in the proposed construction workforce management model.

Research with vulnerable groups is crucial to get their input into public policy design that will directly impact on them. However, there are many methodological and ethical challenges involved in encouraging participation from groups with a wide range of intellectual, cognitive and physical capacities while ensuring that the rights and well-being of participants are protected. Rather than exploring ethical theories, this chapter is a case study describing the practical ethical considerations that were involved in designing and holding a series of focus groups with adult health and social care service users from vulnerable cohorts. It is based on a series of focus groups which the Institute of Public Health (IPH) held with specified cohorts as part of a policy development process on adult safeguarding for the Department of Health (DOH) in Ireland. The four cohorts were people with intellectual disability, cognitive impairments, significant mental health challenges and nursing home residents. This chapter does not describe the findings of the focus groups but outlines the ethical and methodological considerations that arose in designing and conducting this research, and the practical ethical safeguards employed to mitigate risk and comply with Irish and EU General Data Protection Regulation (GDPR) legislation governing health research. It outlines the ethical issues around protecting confidentiality and using incentives to encourage participation, how individuals' capacity to give informed consent was maximized, the risk-assessment and mitigation procedures used to prevent harms arising and the measures put in place to provide follow-up emotional support to participants.

Purpose: This chapter aims to unveil the challenges of adopting and using banking chatbots in India and identify the challenges of Chat Generative Pre-trained Transformer (ChatGPT) for future banking.

Need for the study: Unveiling the challenges of chatbots and ChatGPT in the banking industry in India is crucial to understand the limitations and areas of improvement to enhance customer experience, ensure data security, and maintain regulatory compliance.

Methodology: The researchers conducted a narrative review systematically summarising and analysing existing literature on chatbots and ChatGPT, providing a comprehensive overview of the challenges faced in the industry.

Findings: The authors identify perceived risk, platform quality, connectivity and infrastructure, data privacy and security, user education and acceptance, existing legacy systems, and regulatory guidelines as the challenges of adopting chatbots. Additionally, the findings reveal that the challenges posed by ChatGPT in future banking include the potential reduction of traditional banking jobs, linguistic diversity, data privacy and security, ethical considerations and bias mitigation, explainability and accountability, integration with existing banking systems, and user trust and acceptance. However, implementing these new technologies also presents opportunities for individuals with unique human skills, such as critical thinking, empathy, and creativity, which are difficult to replace with technology.

Practical implications: By minimising the challenges of ChatGPT and chatbots, the banking industry could achieve improved customer service, cost efficiency, automation of routine tasks, and 24/7 availability, leading to enhanced customer satisfaction and operational efficiency in the banking industry. Additionally, these artificial intelligence (AI) tools enable data-driven insights, personalised experiences, scalability, and efficient handling of large customer volumes, contributing to better decision-making and enhanced customer engagement.