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This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

The rapid expansion of low-cost carriers (LCCs) in East and Southeast Asia has brought fierce competition to full-service carriers (FSCs). Competition in the air transport market is at an all-time high, thanks to the ongoing liberalization in air transport in the last several decades. This chapter assesses the efficiency performance of major FSCs in this region. It provides indicative evidence of the close association between FSCs' efficiency, and air transport liberalization and LCCs penetration. Singapore Airlines and Asiana are identified as the star companies in this region for their ability to achieve higher efficiency and, at the same time, report positive growth in productivity.

This first chapter provides a general overview of the book. The chapter describes workforce management and the lack of adequate attention to this concept within construction organisations. In the fourth industrial revolution era, where emerging technologies have continued to change how business functions, the need for an effective approach towards managing the construction workforce becomes crucial. Having a construction workforce management practice model was deemed vital at this point, where technological advancement is rapidly changing the construction environment, and skill shortage is evident in the construction industry of most countries around the world. As such, this chapter establishes that the development of a construction workforce management model that will help improve the management of construction workers serves a critical value of the book.

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.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

In recent years, scholarly focus has shifted towards exploring the applications of disruptive technologies in professional services. These studies emphasise the need for further research in this domain. This research aims to comprehensively review the existing literature on the uses of blockchain, artificial intelligence (AI) and machine learning (ML) algorithms in professional services such as higher education, healthcare, financial securities firms and smart energy consulting. The rapid innovation and advancement in technology have led to substantial improvements in work efficiency and productivity. As industries transition towards sustainability and digitalisation, the role of energy-efficient systems becomes important in shaping smart factory designs and in further implementations. The uses of AI and other disruptive technologies for business operations not only boost production efficiency but also enhance customer satisfaction. Central to this transformation are strategies like deep learning and data/text mining, which facilitate the shift from conventional manufacturing practices to smart manufacturing. Apart from smart energy systems, the fields of higher education, healthcare and financial securities are witnessing a surge in the applications of AI, ML algorithms and blockchain technology and their contributions in emerging service economy. This study undertakes a comprehensive investigation into various factors associated with the application of disruptive technologies, evaluating their impact on the operational efficiency of professional service firms (PSFs). Through this research, we aim to identify gaps in the current literature and will suggest the directions for future research.