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This article’s purpose is to develop The Model for Smart, Self-learning and Adaptive Resilience Building (SARB).

Products and patents of methods and systems analysis was carried out in the fields of BIM application, Smart, Self-learning and Adaptive Resilience Building. Based on other researchers’ findings, The SARB Model was proposed.

Analysis of the literature showed that traditional decisions on the informational modelling do not satisfy all the needs of smart building technologies owing to their static nature. The SARB Model was developed to take care of its efficiency from the brief stage to the end of its service life.

The SARB Model was developed to take care of its efficiency from the brief stage to the end of its service life. The SARB Model does have some limitations: (1) the processes followed require the collection of much unstructured and semi-structured data from many sources, along with their analyses to support stakeholders in decision-making; (2) stakeholders need to be aware of the broader context of decision-making and (3) the proposal is process-oriented, which can be a disadvantage during the model’s implementation.

Two directions can be identified for the practical implications of the SARB Model. The initial expectation is the widespread installation of SARB Model within real estate and construction organisations. Furthermore, development of the SARB Model will be used to implement the ERASMUS+ project, “Advancing Skill Creation to ENhance Transformation—ASCENT” Project No. 561712-EPP-1-2015-UK-EPPKA2-CBHE-JP.

The practical implications of this paper are valuable.

This chapter aims to (1) examine the effect of full-time employees’ STARA awareness on innovative work behavioural intentions in US casual dining restaurants; (2) investigate the mediating roles of employees’ challenge–hindrance appraisals of STARA awareness on the relationship between their STARA awareness and innovative work behavioural intentions; (3) compare the group differences between management employees and non-management employees; and (4) provide recommendations for the casual dining restaurants.

This chapter employed an online survey to collect data from 609 full-time employees in US casual dining restaurants, including 306 management employees and 303 non-management employees. Partial least squares–structural equation modelling was applied for data analysis. The results reveal that the high levels of employees’ STARA awareness raise innovative work behavioural intentions through the mediations of challenge appraisal of STARA awareness.

The proposed conceptual framework and empirical findings in this chapter enrich the literature of cognitive appraisal theory, transactional model and stress, two-dimensional stressor framework, and person-environment fit theory. Employees’ challenge appraisal of STARA awareness makes the job insecurity stressor to drive innovative work behavioural intentions. As STARA adoption deepens in casual dining restaurants, managers need to be aware of full-time employees’ stress and psychological responses towards STARA adoption. Restaurants are suggested to provide employees with adequate resources and support to help employees’ professional competency growth. The capable employees will appraise the job insecurity stressor induced by STARA adoption as an opportunity and be motivated to perform innovatively in the workplace. The casual dining restaurants may enjoy a competitive advantage in the market through value-added innovative activities.

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.

This chapter focuses upon operators' automation usage decisions (AUDs), choices in which people have the option of relying on automation or employing a manual or less technologically advanced means of control. Misuse, the over-utilization of automation, and disuse, the under-utilization of automation, result from inappropriate AUDs. Three causes of misuse and disuse were identified. Operators may: (1) not recognize that both automated and manual alternatives are available (recognition errors), (2) inaccurately estimate the utilities of the automated and/or manual options (appraisal errors), or (3) knowingly select the alternative with the lowest expected outcome (action errors). A decision-making model was used to organize the literature, identify new areas of inquiry, and examine the effects of a number of variables known to affect AUDs. These included machine reliability and consistency, performance feedback, trust in automation, self-confidence in manual control, perceived utility, and the automation bias.

Businesses are rapidly automating workplaces with new technologies (e.g., driverless cargo trucks, artificially intelligent mortgage approvals, machine-learning-based paralegals, algorithmic managers). Such technological advancement raises a host of questions for business and society. As Thomas Donaldson recently remarked, “It’s instance of a problem that more sophisticated engineering cannot solve, and that requires a more sophisticated approach to values” (Ufberg, 2017). In this chapter, we explore the value questions as follows: What is the purpose of business in the machine age? What model for business will best serve society in coming decades: profit maximization, stakeholder theory, or another conception entirely? Is it time for a new social contract between business and society? Do firms have a natural duty to offer employment? Are existing concepts of responsibility/liability adequate for an age in which companies use autonomous robots as scapegoats? How can we protect our humanity and dignity in an algorithm-based society? Do we need to teach ethics to robots?

In this chapter, the authors offer a critical appraisal of predictions of a jobless future due do rapid technological change, as well as provide evidence on whether the rate of occupational change has been increasing. The authors critique the “task replacement” methodology that underlies the most powerful and specific predictions about the impact of technology on employment in particular occupations. There are a number of reasons why assuming a correspondence between task replacement and employment declines is not warranted. The authors also raise questions about how rapidly the development, acceptance, and diffiusion of labor-displacing technologies is likely to occur. In the empirical portion of the chapter, the authors compare the current rate of employment disruption with those observed in earlier periods. This analysis is based on an analysis of occupation data in the US covering the period 1870–2015. Using an index of dissimilarity as the metric, the authors find that the rate of occupational change from 1870 to 2015 does not provide evidence of a sharp uptick in the rate of occupational shifts in the information age. Instead, the rate of occupation shifts has been declining slowly throughout the second half of the twentieth century. Thus, the issues and results discussed here suggest that imminent massive employment displacement is not a foregone conclusion.

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.

Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.

Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.

Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.

Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.

Need of the Study: In an ever-changing environment, the use of artificial intelligence (AI) to accelerate the business is inevitable. By introducing various advanced technologies to improve productivity, technology users are well aware of the challenges ahead.

Purpose: This chapter aims to understand AI technology and the challenges it faces in noted domains.

Methodology: This chapter is based on secondary research, and relevant information has been gathered from various secondary sources such as research articles, newspaper articles, books, and websites. There is a considerable gap between the expected outcomes of AI and the reality of AI in human resource (HR) practice.

Findings: The study’s outcome focuses on AI challenges in human resource management (HRM) functions such as recruitment and selection, learning and development, and performance appraisal. Considering the numerous benefits, it becomes essential to understand these issues/challenges so that they can be adequately addressed.

Practical Implications: This study highlights the issues such as complexity of HR practices, organisation readiness, staff acceptability, and responsibility for AI implementation in HRM, and other related issues and proposes prudent response to these challenges that will be embraced by both employees and employers, thereby adding novelty to this research.