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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.

As e-HRM systems move into the ‘smart’ technology realm, expectations and capabilities for both the automational and informational features of e-HRM systems are increasing. This chapter uses the well-established DeLone and McLean (D&M) model from the information systems literature to analyze how a smart workforce management system can create value for an organization.

The chapter is based on an exploratory case study conducted with a North American industrial products firm. We review three systems-level predictors of success from the D&M model (system quality, information quality, and service quality) and evaluate the company’s systems on these attributes.

The company’s e-HRM systems fall short on the information quality dimension, which limits potential for overall system success related to smart workforce management.

The e-HRM literature focuses on individual-level factors of system success, while the D&M model uses more macro factors. Blending these may help researchers and practitioners develop a more complete view of e-HRM systems. Conclusions from this chapter are limited due to the use of a single, exploratory case study.

Companies must pay attention to all three predictors of system quality when developing smart workforce management systems. In particular, implementation of a data governance program could help companies improve information quality of their systems.

This chapter adds to the literature on smart workforce management by using a model from the information systems literature and a practical example to explore how such a system could add value.

In a world where the continual combining of computer applications and the expansion of artificial intelligence is already necessarily changing the world of work for people, an education system that does not adequately respond to these trends and changes will render itself irrelevant. Education policy and regulation may suffer at the hand of such accelerations due to unexpected consequences and developments. However, the rapid, exponential improvements in computer hardware and software that have enhanced the rate and our ability to gather, transform, manipulate, and interpret these data in an ongoing fashion also present myriad educational opportunities. The so-called Fourth Industrial Revolution offers societies data and information capabilities previously unimagined, making it possible to learn how to combine, innovate, and imagine entirely new avenues for building responsive and intelligent education policies and systems that promote the education and wellbeing of citizens as well as improving their economic participation. These advances necessitate a growing number of educators and education systems who can intelligently respond to Industry 4.0 trends. In this chapter, some considerations regarding the use of large-scale, international datasets and emerging data analytics for analyzing policy for the governance of education are offered, and a discussion of the need for the more systematic use of data analytics as a mechanism for developing socially responsive adult learning and workforce education policy and programing.

This paper discusses how educational policies have shaped the development of large-scale educational data and reviews current practices on the educational data use in selected states. Our purposes are to: (1) analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads; (2) propose the concept of Data Literacy (DL) for teaching (Mandinach & Gummer, 2013a) and its relevance to researchers and stakeholders in postsecondary education; and (3) provide future implications for practices and research to increase educational DL among administrators, practitioners, and faculty in postsecondary education.

We used two guiding conceptual frameworks to analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads. First, we used the 4Vs of Big Data by Rajan (2012) to examine the misalignment between the policy mandate and the practices. The elements of the 4Vs of Big Data – volume, velocity, variety, and veracity – help us to depict how Big Data enables educators to organize, store, manage, and manipulate vast amounts of educational data at the right moment and at the right time. Second, we used the conceptual framework for DL proposed by Gummer and Mandinach (in press). They interpret DL “as the collection, examination, analysis, and interpretation of data to inform some sort of decision in an educational setting” (p. 1, in press).

Using the guiding frameworks, we identified four educational data crossroads as follows:

Crossroad 1: Unintended Increase in Workload Volume;

Crossroad 2: Unrealistic Expectations of Data Velocity;

Crossroad 3: Data Variety in Silos; and

Crossroad 4: Data Veracity and Policy Agenda Mismatch.

In this paper, we explain each of these crossroads in more detail with some examples.

Much of the existing body of literature, exemplary practices, as well as federal and state funding has been focused on K-12 education contexts. In this paper, we identify current practices and challenges of educational data in the institutions of higher education. Additionally, this paper presents the application of the exemplary practices of data literacy development in postsecondary education and implications for future practices of data literacy development in postsecondary education.

In this chapter, we apply diffusion of innovation theory and the theory of management fashion to examine the diffusion trajectory of human resource (HR) analytics in a U.S. context. We focus on the role mass media plays in influencing the diffusion process and address two research questions. First, does the mass media on HR analytics make observable the positive outcomes of HR analytics and is this related to increasing HR analytics adoption over time? Second, does the mass media on HR analytics show evidence of management trendsetting rhetoric?

We analyze published popular trade, business press, and peer-reviewed academic articles over a decade using a big data discourse analytical technique, natural language processing.

We find preliminary evidence that suggests that although the media has broadcasted positive outcomes of HR analytics, adoption has tailed off. In concert with the tailing off of HR analytic adoptions, the media appears to be recasting HR analytics as solving newer problems such as managing talent. Whether this shift makes a difference has yet to be determined.

Business press appears to influence the adoption process, both by broadcasting positive outcomes and through creating management fashion trendsetting rhetoric.

To promote the use of HR analytics, academic institutions and the HR profession need to train HR professionals in the use and benefits of HR analytics.

We lay the groundwork to improve our understanding of the role media plays in influencing how new HRM practices spread across organizations. We introduce the application of an emerging big data analytic technique, natural language processing, to analyze published media on HR analytics.

The chapter assesses the state of research and gender-specific data in maritime shipping and ports. This is crucial for implementing and promoting Sustainable Development Goal 5 ‘Achieve gender equality and empower all women and girls’ in the sector under study. Moreover, the present chapter examines whether progress has been made regarding the availability of gender-related data and research in the port sector.

An extensive review of the literature covering gender-related research in the shipping and port sectors going back to the pre-1990 period has been carried out. Next, data from the Port Performance Scorecard established under the United Nations Conference for Trade and Development (UNCTAD) TrainForTrade (TfT) Port Management Programme (PMP) is analysed, covering the 2015–2020 period. Last, this research is complemented by the results of a survey conducted in 2021 across UNCTAD’s TfT port network.

The literature review identified the main gender dimensions affecting maritime shipping and ports, namely health, empowerment, economic, and education. These results were completed by insights generated by the analysis of gender-related data obtained through selected member ports of the UNCTAD TfT PMP. Overall, the data provided information about the state of play and the existing data gaps and limitations relating to mainstreaming the gender dimension in maritime shipping and ports. It confirmed that traditional stereotypes about the type of job functions that can be fulfilled by women remain prevalent in these two sectors. In this context, the chapter concludes that improving data availability and accessibility in maritime shipping and ports is imperative to advance research and evidence-based policymaking that promote gender equality in maritime transport. Closing the data gap and refining data on gender across the maritime supply chain sector is therefore key. As available data are insufficiently accessible and highly aggregated and which in turn limits the analytical scope, the present chapter recommends that a framework for a Maritime Shipping and Port Gender Equality and Diversity Index be established while leveraging existing data and efforts, including by UNCTAD.

This chapter addresses the grand challenge of an aging society and the subsequent growing demand for in-home care for the elderly – often referred to as homecare. It examines how emergent homecare models in England differ from the “time and task” model and how they are shaping the care market. These models offer new approaches regarding what, how, and when care is delivered at home. Homecare providers face rising demand driven not only by population aging but also by market demand for personalized care, choice, continuity of care, and real-time availability. The landscape presents an opportunity for innovative models to become established, by offering a more inducing service design and value propositions that respond to customers' needs. Using the “business model canvas” to guide data collection, this study presents an ethnographic case analysis of four homecare organizations with distinct emergent homecare models. The study includes 14 months of field observation and 33 in-depth interviews. It finds that providers are becoming increasingly aware of evolving customer needs, establishing models such as the “uberization,” “community-based,” “live-in,” and “preventative” described in the chapter. These models are becoming more pervasive and are mostly market-driven; however, some of their innovations are market shaping. The major innovations are in their value propositions, partnership arrangements, and customer segments. Their value propositions focus on well-being outcomes, including choice and personalization for care users; their workforces are perceived to be a major stakeholder segment, and their networks of partners offer access to complementary services, investments, and specialist knowledge.