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Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

This paper aims to explore the challenges and opportunities faced by buying centers in small and medium enterprises (SME) manufacturing companies in view of recent technological changes and the virtualization of communication.

This study uses a qualitative approach with multiple case studies to portray complex realities within the buying centers in the manufacturing SME context. The authors selected five Italian companies to portray the diverse characteristics, practices and policies of relevant stakeholders before reaching saturation with the issues explored.

The authors find that interactions among buying center members are more effective with greater collaboration and exchange (as opposed to competition and a struggle for power). Virtual/hybrid relations require greater intra-group cooperation, whereas diverse backgrounds and collaborative interactions help the flexibility and performance of the buying center. Greater use of technology produces certainty and automation, but it may also cause overload and biases that can be solved with the ability to analyze and clear responsibility for decisions.

The authors studied only five Italian companies in this study. Future research in other countries with diverse cultural and socio-economic conditions and methods would help extend this research.

The findings would improve the understanding of the challenges of adopting new purchase process technologies that would help automate routine tasks, produce useful data and support decision-making.

Unlike prior studies, this study uses an exploratory design to study the evolution of buying centers in SMEs to seek deeper insights into the challenges and opportunities faced by SMEs because of the growing use of emerging technologies.

The advent of Internet of Things, cloud computing and advanced computing has endowed smart manufacturing environments with resilience, reconfigurability and intelligence, resulting in the emergence of novel capabilities. These capabilities have significantly reshaped the manufacturing ecosystem, enabling it to effectively navigate uncertainties. The purpose of this study is to assess the operational transformations resulting from the implementation of smart manufacturing, which distinguish it from conventional systems.

A list of qualitative and quantitative smart manufacturing performance metrics (SMPMs) are initially suggested and categorized into strategic, tactical and operational levels. The SMPMs resemble the capabilities of smart manufacturing systems to manage disruptions due to uncertainties. Then, industry and academia experts validate the SMPMs through the utilization of the Delphi method, enabling the ranking of the SMPMs.

The proposition of the SMPMs serves as a metric to assess the digital transformation capabilities of smart manufacturing systems. In addition, the ranking of the proposed SMPMs shows a degree of relevance of the measures in smart manufacturing deployment and managing the disruptions caused due to the COVID-19 pandemic

The findings benefit managers, consultants, policymakers and researchers in making appropriate decisions for deploying and operationalizing smart manufacturing systems by focusing on critical SMPMs.

The research provides a metric to assess the operational transformations during the deployment of smart manufacturing systems. Also, it states the role of the metric in managing the potential disruptions that can alter the performance of the business due to the COVID-19 pandemic.

This conceptual paper aims to develop the vision of a novel policy tool called the digital industrial cluster (DIC) thought for a future context shaped by digital economies and Industry 4.0. The DIC departs from the industrial cluster’s precepts and take them into the digital agglomeration phenomenon.

This research uses a discursive and theoretical approach. To create a clear picture related to the research topic, this paper reviews the literature on how industrial clusters deal with digital transformation. The review takes a particular interest in industrial clusters, information and communication technologies, Industry 4.0 and policy tools for digital agglomeration.

The research develops the vision of the DIC as a new policy tool, building its foundations on both industrial clusters and digital economies; furthermore, the DIC was compared with other policy tools based on digital agglomeration to understand the similarities, differences and advantages of the former. Finally, this paper discussed where the DIC could find its way toward implementation and what externalities could be expected from doing so.

This research could be useful for policymakers and cluster organizations looking to deploy policy tools that take advantage of industrial clusters and digital transformation. Besides, the theoretical foundations presented could lead researchers to empirical identification of early incarnations of the DIC.

This paper develops theoretical principles for a new policy tool that connects industrial clusters, digital agglomeration and Industry 4.0 for the first time.

Preventive ergonomics is essential to protecting the health and safety of workers as is recognizing human variability. The purpose of this paper is to describe a Unity-based application designed for three-dimensional postural analysis and visualizations using motion capture data. Integration with virtual reality (VR) technologies allows the user to be immersed in the simulated working environment without the need for a physical prototype. The proposed application aims to facilitate the application of ergonomic principles in workplace design and assessment for a proactive, participatory and inclusive approach to worker well-being.

The authors developed an application that leverages motion capturing techniques and VR technologies and aims to support the analysts in the ergonomic assessment of physical prototypes as well as future workplaces. An innovative postural prediction module helps the analyst understanding what postures different users are likely to assume in the interaction with the workplace from a single data record.

The functionalities of the proposed application are illustrated on some case studies, presenting how different information is made available and can support workplace analysts and designers in an industrial context.

This paper provides insights into the experience and research carried out by an automotive company in the application of wearable sensors and VR to support a proactive and participatory approach to workplace ergonomics.

This paper aims to consider the relationship between urban events and urban public space, asking whether cities have enough space for events and whether events have enough space in cities.

Policy analysis surrounding events and festivals in the Netherlands is used to understand the dynamics of urban events, supported by content analysis of policy documents. A vignette of event space struggles in Amsterdam illustrates the contradictions of the event/space relationship.

The research identifies a policy shift in the Netherlands towards urban events from expansive, festivalisation strategies to defensive, NIMBYist policies. It exposes contradictions between protecting space as a living resource and the exploitation of space for regenerative purposes. Three future scenarios for urban events are outlined: conflict and competition, growth and harmony and digitalisation and virtualisation.

Develops scenarios for the future relationship between events and urban space.

Provides an analysis of the recursive spatial implications of the growth of the events sector for cities and the growth of cities for events.

This study aims to investigate technology-based health and safety (H&S) management to control the spread of disease on construction sites using a partial least squares structural equation modelling (PLS-SEM) approach.

An extensive literature review is conducted to develop a conceptual framework. The variables identified from the literature review are included in a cross-sectional survey which gathered a total of 203 valid feedback. The variables for challenges are grouped under their relevant construct using exploratory factor analysis. Then, a hypothesized model is developed for PLS-SEM analysis using Smart PLS software. Later, the outcome of the model is further validated by nine construction experts using a semi-structured questionnaire survey.

The results rationalized the relationships between the COVID-19 H&S measures, challenges in implementing COVID-19 H&S measures on construction sites and the innovative technologies in transforming construction H&S management during the COVID-19 pandemic. The possible challenges that obstruct the implementation of H&S measures are highlighted. The potential technologies which can significantly transform H&S management by reducing the impact of challenges are presented.

The findings benefited the industry practitioners who are suffering disruption in construction operations due to the pneumonic plague.

By developing a conceptual model, this study reveals the contribution of technology-based H&S management for construction projects during the COVID-19 pandemic, which remains under-studied, especially in the context of the developing world.

This article aims to understand the challenges and key takeaways of implementing total quality management (TQM) in a virtual organisation.

An interventionist research (IVR) methodology combined with a qualitative critical event analysis was used to evaluate the challenges and concerns faced during the company’s adoption of TQM and understand the roles of the key players involved.

Standard process tools such as desktop procedures (DTP), focused teams, and service-level agreements (SLAs) were fundamental to implementing TQM in the company. These processes require the right leaders, but external agents may also be influential, acting as accelerators of change in adopting and using management practices in small companies. Indeed, the researcher acted as a problem solver, bringing innovative solutions to the firm using a hands-on iterative approach.

This research underscores the importance of critical success factors (CSF), such as employee engagement, training, and project management tools. These factors are not just important but crucial for the success of TQM in organisations seeking to adopt the industry’s best practices.

This study, conducted as a virtual IVR for TQM implementation, provides novel insights for practitioners and academics. It elucidates the pivotal role of some quality management tools in the journey towards TQM and the role of both internal and external critical players in the process, particularly in small virtual organisations based on innovative business models.

The purpose of this paper is to investigate the research landscape of LSS 4.0 papers published in two well-known repositories, Scopus and Web of Science (WoS), in terms of publication trends, article distribution by author, journal, affiliations and country, and article clustering based on keywords, authors and countries. In addition, a literature review was carried out to build a conceptual framework of integrated Lean Six Sigma and Industry 4.0 (LSS 4.0) that encompasses operational, sustainability and human factors or ergonomics aspects.

The literature review of integrated Lean Six Sigma and I4.0 publications published in Scopus and WoS databases in the current decade was conducted for the present study. This study categorizes LSS, I4.0 and related research articles based on publication patterns, journals, authors and affiliations, country and continental-wise distribution and clustering the articles based on keywords and authors from the Scopus and WoS databases from 2011 to 2022 using the search strings “Lean”, “Six Sigma”, “Lean Six Sigma” and “Industry 4.0” in the Title, Abstract and Keywords using Biblioshiny, VOS viewer and Microsoft Excel.

In the recent three years, from 2020 to 2022, LSS 4.0 has been substantially increasing and is seen as an emerging and trending area. This research identifies the most influential authors, most relevant affiliations, most prolific countries and most productive journals and clusters based on keywords, authors and countries. Further, a conceptual framework was developed that includes the impact of operational, sustainability and ergonomic or human factors in LSS 4.0.

This article assists in comprehending the trends and patterns of LSS 4.0. Further, the conceptual framework helps professionals and researchers understand the significance and impact of integrating LSS and Industry 4.0 in the aspects of human factors/ergonomic, sustainability and operations. Also, the research induce professionals to incorporate all these factors while designing and implementing LSS 4.0 in their organization.

This conceptual framework and bibliometric analysis would aid in identifying potential areas of research and providing future directions in the domain of LSS 4.0. It will be beneficial for academicians, professionals and researchers who are planning to apply and integrate techniques of LSS and technologies of I4.0 in their organizations and research.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.