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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.

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 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.

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.

The workforce management model conceptualised for the effective management of the construction workforce was subjected to expert scrutiny to determine the suitability and applicability of the identified practices and their attributed variables to the construction industry. In achieving this, a Delphi approach was adopted using experts from construction organisations in South Africa. These experts comprised workforce management personnel and construction professionals in senior management positions. The data were analysed using appropriate statistical tools such as interquartile deviation, Kendell’s coefficient of concordance, and chi square to determine consensus among these experts. After a two-round Delphi, the seven constructs proposed in the conceptualised workforce management model were adjudged to be important and worthy of adoption by construction organisations seeking to improve workforce management in the current fourth industrial revolution era.

The importance of humans to the successful delivery of construction projects has led to the emergence of research attention on construction workforce management. As such, this chapter uncovers emotional intelligence (EI) and the external environment as critical aspects of workforce management practices that have not gained substantial attention in past workforce management studies. While some theories and models (existing outside the construction domain) have considered the external environment, none of these models is specific to the construction industry. Furthermore, EI has received less attention within existing workforce management models. Through a review of related studies and theories, this chapter noted that the EI of construction workers and their senior management is crucial to the performance of these workers and the ultimate performance of their organisations. In the same vein, since construction organisations do not operate in silos, the external environment significantly influences the operations of organisations in the construction industry. The environment exact pressures that can influence workforce management practices and technological innovations construction organisations adopt.

As the world experiences a rapid rise in technological advances, the engineering profession will be required to become even more socially responsible and emotionally stable to be able to display higher levels of empathy towards the society they serve. Therefore, the purpose of this study is to evaluate the key emotional competencies (ECs) that engineers need to possess to thrive in the construction industry after graduation.

A quantitative research approach was conducted with close-ended questionnaires developed and administered to built environment professionals such as architects, construction managers, construction project managers, engineers and quantity surveyors.

The results showed that the ability to display a positive outlook on life, understand one's emotional triggers, ability to display resilience in hard times, control one's emotions and ability to show compassion to others were the highest-ranked ECs. Further analysis revealed four clusters such as self-awareness, empathy, self-regulation and self-motivation.

Practical guidance for higher education institutions is offered in terms of revamping and revisiting their engineering education curricula to develop these ECs to produce emotionally sound engineers for the ever-changing world of work.

As the world of work becomes increasingly dynamic and diverse, engineers will be required to possess empathy, social skills and high levels of ECs to be able to seamlessly interact with fellow professionals as well as the society they serve.

This paper presents the findings from the assessment of the determinants of cloud computing (CC) deployment by construction organisations. Using the technology-organisation-environment (TOE) framework, the study strives to improve construction organisations' project delivery and digital transformation by adopting beneficial technologies like CC.

This study adopted a post-positivism philosophical stance using a deductive approach with a questionnaire administered to construction organisations in South Africa. The data gathered were analysed using descriptive and inferential statistics. Also, the fusion of structural equation modelling (SEM) and machine learning (ML) regression models helped to gain a robust understanding of the key determinants of using CC.

The study found that the use of CC by construction organisations in South Africa is still slow. SEM indicated that this slow usage is influenced by six technology and environmental factors, namely (1) cost-effectiveness, (2) availability, (3) compatibility, (4) client demand, (5) competitors' pressure and (6) trust in cloud service providers. ML models developed affirmed that these variables have high predictive power. However, sensitivity analysis revealed that the availability of CC and CC's ancillary technologies and the pressure from competitors are the most important predictors of CC usage in construction organisations.

The paper offers a theoretical backdrop for future works on CC in construction, particularly in developing countries where such a study has not been explored.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

The current adverse changes in climatic conditions have necessitated innovative nature-based solutions like blue-green roofs to ensure sustainable built environments. The use of blue-green roofs in combating climate change issues has continued to grow, and its benefits are showcased in many countries' studies. However, there is an absence of reports on the use of this approach in South Africa. Therefore, in ensuring a sustainable built environment through nature-based solutions, this study explored the built environment professional’s knowledge of blue-green roofs, the hindrances to their use and motivations for much wider use of blue-green roofs in the country.

Based on the nature of the study, a quantitative design was adopted and data were obtained from professionals within the built environment through a questionnaire. Data analyses were conducted using the Cronbach alpha test, Kruskal–Wallis H-Test, exploratory factor analysis and fuzzy synthetic evaluation.

The findings revealed a growing knowledge of blue-green roofs, albeit its slow adoption in the country. Also, five critical clusters of hindrances affecting the use of blue-green roofs were identified. These are understanding the blue-green roof concepts, technical, economic, regulation and client hindrances. Furthermore, the ability to manage stormwater properly, provide climate change adaptation and deliver sustainable buildings were the key motivating factors that could drive the use of this innovative solution.

This study offers actionable insights for built environment professionals and stakeholders to address the hindrances to using blue-green roofs in South Africa. Strategies such as improved education, financial incentives and policy development can help overcome some notable hindrances and promote the widespread adoption of blue-green roofs.

The slow adoption of blue-green roofs and the scant nature of research within the built environment required adequate attention to which this current research contributes. Theoretically, being one of the foremost studies in South Africa to explore blue-green roofs, the findings offer a foundation for future studies seeking to explore this roofing system in the country further.