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Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

Both uncertainty reduction theory and uncertainty management theory suggest that uncertainty reduction during organizational change is imperative as it may influence positively employees’ attitudes and behaviors. By drawing on the theory of planned behavior that links individual’s beliefs and behavior, the study seeks to examine how employees’ self- and other-interest in change may reduce perceptions of change uncertainty and consequently, foster their behavioral change support (i.e. compliance, cooperation and championing).

The study hypothesizes that employees’ self- and other-interest in change mediate the relationship between perceptions of change uncertainty and behavioral change support. The research was conducted in two large IT companies co-located in a big science park in an EU country in South-eastern Europe. Data were collected from 105 employees and their supervisors in three sequential phases.

The research findings suggest that both self- and other-interest in change partially mediate the negative relationship between perceptions of change uncertainty and behavioral change support.

The findings indicate that change management practitioners will benefit considerably if they try to decrease employees’ perceived change uncertainty by increasing their self- and other-interest in change to provoke supportive behaviors. Relevant suggestions are made.

The findings provide new insights into how perceptions of change uncertainty and self- and other-interest in change can affect employees’ change participation. Further, the research findings add to the uncertainty reduction theory and uncertainty management theory as well as, other related notions.

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.

In this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.

Four main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.

The volunteer basis of this study meant that the sample was not representative or generalisable.

Although the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

The purpose of this study is to automatically generate a construction schedule by extracting data from the BIM (Building Information Modeling) model and combining an ontology constraint rule and a genetic algorithm (GA).

This study developed a feasible multi-phase framework to generate the construction schedule automatically through extracting information from the BIM, utilizing the ontology constraint rule to demonstrate the relationships between all the components and finally using the GA to generate the construction schedule.

To present the functionality of the framework, a prototype case is adopted to show the whole procedure, and the results show that the scheme designed in this study can quickly generate the schedule and ensure that it can satisfy the requirements of logical constraints and time parameter constraints.

A proper utilization of conceptual framework can contribute to the automatic generation of construction schedules and significantly reduce manual errors in the Architectural, Engineering, and Construction (AEC) industry. Moreover, a scheme of BIM-based ontology and GA for construction schedule generation may reduce additional manual work and improve schedule management performance.

The hybrid approach combines the ontology constraint rule and GA proposed in this study, and it is an effective attempt to generate the construction schedule, which provides a direct indicator for the schedule control of the project.

In this study, the data application process of the BIM model is divided into four modules: extraction, processing, optimization, and output. The key technologies including secondary development, ontology theory, and GA are introduced to develop a multi-phase framework for the automatic generation of the construction schedule and to realize the schedule prediction under logical constraints and duration interference.

The built environment is taking enormous leaps towards its digitalization. Computer-aided tools such as building information modeling (BIM) are found in the forefront of this evolution, playing a critical role in creating the foundations for the upcoming development of smart low-carbon cities. However, the potential of BIM is still untapped – links will need to be created among the available and forthcoming methodologies under one integral operational system. The purpose of this paper is to present an integrated BIM-based life cycle-oriented framework for achieving sustainable constructions at the pre-construction phase. The developed framework represents an example of the approaches that the construction industry will need to adopt to integrate the different tools under an integrated smart city context.

The methodological approach follows the development of four same-volume different-configuration three-dimensional BIM designs, which are coupled with life cycle assessment (LCA) tools for establishing sustainable building design.

The results of this paper indicated that the choice of building design and shape can play a significant role in reducing the embodied energy and embodied carbon of buildings, achieving a reduction of up to 15% compared to a reference building of same volume and gross floor area.

The originality of this paper is found in its approach application by coupling three-dimensional BIM models with LCA data, the use of reinforcement detailing in an nD BIM study and the employment of country-specific LCA databases.

This study aims to explore the role of frontline service employees’ (FSEs) awareness that their job can be substituted by smart technology, artificial intelligence, robotics and algorithms (STARA) in their job autonomy and proactive service performance and when these relationships can be buffered. Drawing on the cognitive appraisal theory of stress, the study examined the mediating relationship between FSEs’ STARA awareness, job autonomy and proactive service performance and the moderating effects of self-efficacy and resilience on this relationship.

The authors administered two-wave online surveys to 301 South Korean FSEs working in various service sectors (e.g. retailing, food/beverage, hospitality/tourism and banking). The Time 1 survey measured respondents’ STARA awareness, self-efficacy, resilience and job autonomy, and the Time 2 survey assessed their proactive service performance.

FSEs’ STARA awareness negatively affected their subsequent proactive service performance through decreased job autonomy. The negative association between STARA awareness and job autonomy was weaker when FSEs’ self-efficacy was high than when it was low. While the authors observed no significant moderation of resilience, the author found a marginally significant three-way interaction between STARA awareness, self-efficacy and resilience. Specifically, STARA awareness was negatively related to job autonomy only when both self-efficacy and resilience were low. When either self-efficacy or resilience was high, the association between STARA awareness and job autonomy became nonsignificant, suggesting the buffering roles of the two personal resources.

Given that the measurement of variables relied on self-reported data, rater biases might have affected the findings of the study. Moreover, the simultaneous measurement of STARA awareness, self-efficacy, resilience and job autonomy could preclude causal inferences between these variables. The authors encourage future studies to use a more rigorous methodology to reduce rater biases and establish stronger causality between the variables.

Service firms can decrease FSEs’ STARA awareness through training in the knowledge and skills necessary to work with these technologies. To promote FSEs’ proactive service performance in this context, service firms need to involve them in decisions related to STARA adoption and allow them to craft their jobs. Service managers should provide FSEs with social support and exercise empowering and supportive leadership to help them view STARA as a challenge rather than a threat.

Distinct from prior research on STARA awareness and employee outcomes, the study identified proactive service performance as a key outcome in the STARA context. By presenting self-efficacy and resilience as crucial personal resources that buffer FSEs from the deleterious impact of STARA awareness, the study provides practitioners with insights that can help FSEs maintain their job autonomy and proactive service performance in times of digitalization and automation.

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 study aims to identify and evaluate the key strategies to promote the implementation of automation techniques with reference to the Nigerian construction industry.

Pragmatic philosophical thinking using a mixed-method approach (a combination of qualitative and quantitative) was adopted for this study. The qualitative strand of this research was achieved using a Delphi technique while a well-structured questionnaire conducted among 191 construction professionals was adopted to attain the quantitative strand. Obtained data were analyzed using frequencies, percentages, mean item scores, Kruskal–Wallis H test and exploratory factor analysis (FA).

Results revealed that the “provision of funding and subsidies for automation techniques” “mandatory automation policies and regulations,” “creating incentives for adoption,” “formulation of programs to promote awareness” and “deploying gamification to boost employee performance” were the top five strategies to promote the adoption of automation techniques. FA revealed four principal clusters, namely, awareness and publicity programs, government regulations and standards, provision of education and training and awards and recognition.

This study provided a solid theoretical and empirical foundation that can be useful to construction industry stakeholders, decision-makers, policymakers and the government in mapping out strategies to promote the incorporation and deployment of automation and robotics in the construction industry.

To the best of the authors’ knowledge, this study is one of the first in developing countries and Nigeria to establish an ordered grouping structure of the strategies to promote the adoption of automation techniques.

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.