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Despite the significance of the construction industry to the nation's economic growth, there is empirical evidence that the sector is lagging behind other industries in terms of productivity growth. The need for improvements inspired the industry's stakeholders to consider using emerging technologies that support the enhancement. This research aims to report augmented reality applications essential for contractors' productivity improvement.

This study systematically reviewed academic journals. The selection of journal articles entailed searching Scopus and Web of Science databases. Relevant articles for reviews were identified and screened. Content analysis was used to classify key applications into six categories. The research results were limited to journal articles published between 2010 and 2021.

Augmented reality can improve construction productivity through its applications in assembly, training and education, monitoring and controlling, interdisciplinary function, health and safety and design information.

The research provides a direction for contractors on key augmented reality applications they can leverage to improve their organisations' productivity.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

The purpose of this paper is to fill a gap in the literature regarding the open innovation management approaches that small and medium-sized enterprises (SMEs) can use to access digital technologies and incorporate them into their organizational processes. The research question is: What organizational and process-level managerial actions do SMEs take to successfully access and implement digital technologies within their organizational processes?

Using Guertler et al.'s (2020) Action Innovation Management Research (AIM-R) framework, this study examines the digital transformation experiences of 10 European SMEs to gain insights into the managerial actions that foster successful digital transformation.

The findings of the paper reveal two major contributions. First, a digital transformation roadmap for SMEs is proposed, with a focus on accessing external resources and reconfiguring internal ones to ease their digital transformation journey. Second, three distinct paradoxes that influence the digital transformation process in SMEs are highlighted, providing useful insights into the challenges and tensions SMEs face during this journey.

This paper provides a unique perspective on the digital transformation of SMEs by examining the managerial actions required for successful technology adoption and revealing the paradoxes that may emerge during this transformative process. The findings form the basis for future research, deepening our understanding of digital transformation in SMEs and providing actionable advice to managers and practitioners navigating this journey.

Railways are a well-known example of complex critical infrastructure, incorporating socio-technical systems with humans such as drivers, signallers, maintainers and passengers at the core. The technological evolution including interconnectedness and new ways of interaction lead to new security and safety risks that can be realised, both in terms of human error, and malicious and non-malicious behaviour. This study aims to identify the human factors (HF) and cyber-security risks relating to the role of signallers on the railways and explores strategies for the improvement of “Digital Resilience” – for the concept of a resilient railway.

Overall, 26 interviews were conducted with 21 participants from industry and academia.

The results showed that due to increased automation, both cyber-related threats and human error can impact signallers’ day-to-day operations – directly or indirectly (e.g. workload and safety-critical communications) – which could disrupt the railway services and potentially lead to safety-related catastrophic consequences. This study identifies cyber-related problems, including external threats; engineers not considering the human element in designs when specifying security controls; lack of security awareness among the rail industry; training gaps; organisational issues; and many unknown “unknowns”.

The authors discuss socio-technical principles through a hexagonal socio-technical framework and training needs analysis to mitigate against cyber-security issues and identify the predictive training needs of the signallers. This is supported by a systematic approach which considers both, safety and security factors, rather than waiting to learn from a cyber-attack retrospectively.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

The purpose of this paper is to identify the areas and logic of integration of different stakeholders using different methods and to analyse their applicability and challenges in practical projects. The main aim is to describe how these different methods impact value creation.

Action design research was carried out in a large hospital construction project where the first author acted as an “involved researcher” and the second author acted as an “outside researcher”. Two workshops were organised to evaluate the direct and indirect challenges and benefits of the applied four methods and to explain how different methods enable value creation.

All the studied methods provide good results in terms of usability and commitment to the aims of the project, thus delivering the direct benefits expected. Process, people and tools logic works well in this case project when applying the methods properly. Significant evidence was provided on secondary deliverables of the methods, and all analysed methods had a significant impact in the area of leading people, clarifying what “focus on people” means and how it is enabled.

Focus on people can be achieved through different operative methods if applied in the right way. It is necessary to select the most suitable methods based on all the direct and indirect deliverables.

This case project offered a platform to analyse integration methods in a real-life project using the collaborative contract method. The authors were able to participate in the analysis by taking action from the very beginning of the project in terms of training, learning, continuous development and coaching of these methods and evaluating the applicability.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

There is a renowned interest in adaptability as an important principle for achieving circularity in the built environment. Circular building adaptability (CBA) could enable long-term building utilisation and flexible use of space with limited material flows. This paper identifies and analyses design strategies facilitating CBA to propose a framework for enhancing the implementation of the concept.

Interviews were conducted with professionals experienced in circular building design to explore the questions “How do currently applied design strategies enable CBA?” and “How can CBA be implemented through a conceptual design framework?”. The interviews encircled multi-residential building examples to identify currently applied circular design strategies. The interviews were analysed through qualitative content analysis using CBA determinants as a coding framework.

The results show that all ten CBA determinants are supported by design strategies applied in current circular building design. However, some determinants are more supported than others, and design strategies are often employed without explicitly considering adaptability. The design strategies that enable adaptability offer long-term solutions requiring large-scale modifications rather than facilitating low-impact adaptation by dwelling occupants. The proposed conceptual design framework could aid architects in resolving these issues and implementing CBA in their circular building design.

This paper’s contribution to CBA is threefold. It demonstrates design strategies facilitating CBA, proposes a conceptual design framework to apply the concept and identifies the need for a more comprehensive application of available adaptability strategies.

The aim of this study is to examine the behavioral intention (BI) to adopt the Coinbase Wallet by Spanish users.

A survey was administered to individuals residing in Spain between March and April 2021. There were 301 questionnaires analyzed. This research applies a new predictive model based on technology acceptance model (TAM) 2, the unified theory of acceptance and use of technology (UTAUT) model, the theory of perceived risk and the commitment trust theory. A mixed partial least squares structural equation modeling (PLS-SEM)/fuzzy-set qualitative comparative analysis (fsQCA) methodology was employed for the modeling and data analysis.

The results showed that all the variables proposed have a direct and positive influence on the intention to use a Coinbase Wallet. The findings present clear directions for traders, investors and academics focused on improving their understanding of the characteristics of these markets.

First, this study addresses important concerns relating to the adoption of crypto-wallets during the global pandemic. Second, this research contributes to the existing literature by adding electronic word of mouth (e-WOM), trust, web quality and perceived risk as new drivers of the intention to use the Coinbase Wallet, providing unique and innovative insights. Finally, the study offers a solid methodological contribution by integrating linear (PLS) and nonlinear (fsQCA) techniques, showing that both methodologies provide a better understanding of the problem and a more detailed awareness of the patterns of antecedent factors.