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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 study aims to evaluates a public policy program that finances projects for the development of innovative technological solutions. This paper analyzed the influence of human and social capital on the development of the projects, under the perspective of the policy’s effectiveness and efficiency. This specific policy adopted the funding model of economic subsidy by means of grants, which shows the significant engagement of the public sector in applying nonrefundable resources more directly through loans, assuming the role of an entrepreneurial state, according to Mazzucato (2011, 2018) and Tavani and Zamparelli (2020).

This is a quantitative-descriptive study, according to Marconi and Lakatos (2017). This study is descriptive, for presenting information on innovation projects funded by FUNCAP (Ceará Foundation for Support to Scientific and Technological Development). In addition, this study is quantitative, by establishing multivariate relationships among the variables that relate to human capital and social capital, which are relevant to technological and innovative development, and by introducing variables on technological evolution, proposed as measures of the program’s effectiveness (DTRL, MkTRL) and efficiency (ETRL).

This paper sought to contribute on public policies for innovation, more specifically on analyzing variables that may affect the development of technological and innovative projects in knowledge-intensive companies. The authors studied capitals potentially important for these companies in the development of innovative projects. Specifically, the authors sought to understand the importance of human capital and how it reflects in technical and scientific knowledge of the project team and of social capital and how it reflects the connection and social relationship among different team members. The results presented that the degree of efficiency of the public funding program depends on how much the teams of the benefited projects have accumulated knowledge, skills and technical capacities – the so-called teams’ human capital.

It is important to address the research sample as a research limitation, which had 72 responses obtained, from a submission rate of 284. Another study limitation is on the qualitative analysis of the topics addressed from the companies and policymakers perspectives, considering that the quantitative nature of the study does not allow for a deeper understanding of the qualitative perspective of the actors involved in the phenomenon studied. As recommendations for future studies, it is suggested to conduct qualitative studies on the aspects studied here. In this sense, it is possible to conduct case studies for specific companies, or policymakers, to clarify and deepen the relationships between the themes addressed here.

As for the practical implications of the research, both for managers of public funding programs and for company managers, the benefits of human capital, related to innovative project development teams, are important in programs that deal with technological development projects. In practice, this means that the greater the human capital of academic background of the members of the supported project teams, the more efficient the projects are in the process of developing their technologies by using the resources provided (Ashford, 2000; Chen et al., 2008; Lerro et al., 2014).

Hence, the authors conclude that the evaluated innovation-funding program through grants achieved acceptable results in terms of promoting the technological evolution of the benefited projects and bringing the technologies closer to the market. Its efficiency was the least favorable result, showing that the program needs to focus on improving this specific aspect. Within the investigated program, the issue that needs enhancement (efficiency – ETRL) was the one that presented significant relationships with the human and social capital of the benefited projects’ teams. Thus, it is possible that, by selecting more projects that have teams with high capital, the efficiency of the public policy, in this case the development of projects with high technological and innovative potential, will be possibly reached.

The findings strengthen the need for innovation public policies designed and implemented in a systemic way in the science, technology and innovation ecosystem, to provide a technological infrastructure and human capital necessary for developing projects with high technological and innovative potential (Ergas, 1987; Audretsch and Link, 2012; Caloghirou et al., 2015; Edler and Fagerberg, 2017; Silvio et al., 2019).

This study aims to examine hotel guests’ acceptance of technology agency – the extent to which they would let artificial intelligence (AI)-based systems make decisions for them when staying in hotels. The examination was conducted through the prism of several antecedents of acceptance of technology agency, including perceived ethics, benefits, risks and convenience orientation.

A thorough literature review provided the foundation of the structural model, which was tested using confirmatory factor analysis, followed by structural equation modeling. Data were collected from 400 US hotel guests.

The most important determinant of acceptance of technology agency was perceived ethics, followed by benefits. Risks of using AI-based systems to make decisions for consumers had a negative impact on acceptance of technology agency. In addition, perceived loss of competence and unpredictability had relatively strong impacts on risks.

The results provide a conceptual foundation for research on systems that make decisions for consumers. As AI is increasingly incorporated in the business models of hotel companies to make decisions, ensuring that the decisions are perceived as ethical and beneficial for consumers is critical to increase the utilization of such systems.

Most research on AI in hospitality is either conceptual or focuses on consumers’ intentions to stay in hotels that may be equipped with AI technologies. Occupying a unique position within the literature, this study discusses the first time AI-based systems that make decisions for consumers. The value of this study stems from the examination of the main concept of technology agency, which was never examined in hospitality.

This book aimed to conceptualise a construction workforce management model suitable for effectively managing workers in construction organisations. To this end, this chapter presents the conceptualised model, which consists of seven workforce management practices with their respective measurement variables. Drawing from existing theories, models, and practices, the chapter concludes that a construction organisation that will attain its strategic objectives in the current fourth industrial revolution era must be willing to promote effective recruitment and selection, compensation and benefits, performance management and appraisal, employee involvement and empowerment, training and development, as well as improving workers emotional intelligence and handling external environment pressure. These practices can promote proactiveness, participation, and improved skills and can lead to effective commitment, better quality, and flexibility within the organisation.

The purpose of our study was two-fold: (1) to examine the effect of perceived value derived from perceived benefits and sacrifices in the adoption of artificial intelligence (AI) in talent acquisition and (2) to investigate the moderating role of human resource (HR) readiness in the association between perceived value and AI adoption intention.

A structured questionnaire was administered to 198 talent acquisition executives and HR professionals of Indian IT companies based on a purposive sampling technique. Partial least squares structural equation modeling (PLS-SEM) was used on the Smart PLS 2.0 platform to analyse the data and test the model.

Results revealed that perceived benefits and sacrifices significantly predict perceived value which significantly affects the HR professional’s AI adoption intention. The study further found that HR readiness moderates the link between perceived value and the intention of HR professionals to adopt AI in the talent acquisition process in the Indian IT industry.

IT companies are advised to continuously monitor and evaluate the performance of AI tools to ensure that they are meeting the recruitment process needs to leverage AI’s benefits in talent acquisition. This study seeks to provide the impetus for a planned AI adoption in talent acquisition.

This research provides ample evidence for the existing technology adoption theories. It explored the predictors of adoption by validating the value-based adoption model in the Indian context. It provides valuable insights into the practice of acquiring talents in the IT sector using artificial intelligence.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

The purpose of this research is to propose and empirically validate a theoretical framework to investigate the willingness of the elderly to disclose personal health information (PHI) to improve the operational efficiency of AI-integrated caregiver robots.

Drawing upon Privacy Calculus Theory (PCT) and the Technology Acceptance Model (TAM), 274 usable responses were collected through an online survey.

Empirical results reveal that trust, privacy concerns, and social isolation have a direct impact on the willingness to disclose PHI. Perceived ease of use (PEOU), perceived usefulness (PU), social isolation, and recognized benefits significantly influence user trust. Conversely, elderly individuals with pronounced privacy concerns are less inclined to disclose PHI when using AI-enabled caregiver robots.

Given the pressing need for AI-enabled caregiver robots due to the aging population and a decrease in professional human caregivers, understanding factors that influence the elderly's disclosure of PHI can guide design considerations and policymaking.

Considering the increased demand for accurate and comprehensive elder services, this is the first time that information disclosure and AI-enabled caregiver robot technologies have been combined in the field of healthcare management. This study bridges the gap between the necessity for technological improvement in caregiver robots and the importance of transparent operational information by disclosing the elderly's willingness to share PHI.

This study aims to examine the perceived values of the metaverse when adopting it in the luxury hospitality business. Based on the cost–benefit perspective, this research provides solid theoretical contributions and actionable managerial recommendations.

An exploratory sequential mixed-method design was used. For the qualitative phase, 21 hotel managers and 24 hotel guests (who often stay in four-star and five-star hotels and resorts) were interviewed after showing them a series of videos about using the metaverse in the hotel business. Based on the results of the qualitative phase, the analytic hierarchy process method was used, and 476 valid questionnaires were analyzed.

The results highlight the perceived benefits (personalized services, immersive experience and positive brand image) and costs (lack of human touch, time and effort and security and privacy) of metaverse adoption for hotel managers and their guests. In addition, the study determines the weight of each value attribute of metaverse adoption for each travel stage (pre-travel, during travel and post-travel).

Regarding metaverse adoption, the research offers practical suggestions for luxury hotels. For instance, the cost of equipment and the time and effort required are perceived costs of metaverse adoption. To address these challenges, hotels may offer free equipment (e.g. VR headsets) and training to their guests to stimulate the use of the metaverse.

This study addresses a gap in the literature by presenting a conceptual framework for examining metaverse adoption in the luxury hotel scenario. Unlike using conventional models like the technology acceptance model or the unified theory of acceptance and use of technology to investigate a technology’s adoption, this study stands out by unraveling the topic through the lens of value proposition. The latter often comes from an efficient value co-creation process, which is indeed shaped by an adequate appreciation of the congruence of perceived values (i.e. perceived benefits and costs) of metaverse from hotel manager and guest perspectives.