Purpose: This study aims to investigate the impact of primary building information modelling (BIM)-related factors, extracted from the literature on the subject, on construction project performance.Design/methodology/approach: Based on data collected from 134 BIM users, this study used structural equation modelling to assess the impact of these factors in five main BIM-related factor clusters.Findings: The results of the analysis confirmed the reliability and validity of the research design and outcomes. The findings indicated that the BIM-related external factors cluster is the most influential cluster affecting construction project performance. BIM-related project factors and BIM-related technological factors also had a significant impact on project performance. These were followed by the BIM-related management factors cluster, while the BIM-related human factors cluster had a low impact on project performance.Research limitations/implications: This study will contribute to fostering BIM adoption and implementation in the construction industry in developing countries.Originality/value: This study has filled a crucial knowledge gap by providing information on manageable primary BIM-related factors affecting construction project performance.

Machine learning plays a vital role in construction industry which could make improve project’s safety, productivity, and quality. Many studies have attempted to explore the potential opportunities to adopt this technology in different aspects of the construction sector. However, no comprehensive study to review the global research trends on this technological advancement in construction management domain. The goal is to investigate and summarize the state-of-the-art knowledge body in this topic in a systematic manner. To achieve this, this paper considered 161 studies on machine learning in construction management related to bibliographic records retrieved from the Scopus database by adopting scientometric analysis approach. This paper found that since 2014, there has been a considerable increase in the number of publications on this domain. Researchers from the United States, China, and Australia have been the main contributors to this research area through regional analysis. This study also revealed that approximately 34% of all countries in the world are engaged in this domain research. In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction, Schedule management, and building energy demand prediction. Furthermore, three potential construction management research areas that can apply this technology were proposed for further studies. The findings will help both professionals and researchers more understanding how machine learning knowledge is evolving and its role played in the construction management domain, and this study thus offers a useful reference point to how can develop this area in the future.

Nowadays, smart city has received a great attention of many countries around the world as a holistic strategy and potential trend for effective urban development that could help to solve a wide range of current urban issues such as population growth, productive land-use, overloaded infrastructure, traffic congestion, social division, etc. While the application of information and communication technology (ICT) was commonly mentioned as hardware aspect integrated in urban infrastructure system of a smart city, particularly around the early 1990s when the ICTs first reached a broad public attention in European countries, in recent years countries supporting a comprehensive approach frequently have considered and complemented the concept of smart city with a software interpretation related to smart vision, smart governance and smart community in order to obtain high quality of life and improve environmental quality. The article discusses smart urban governance - a vital pillar of smart city that addresses urban issues and dynamic collaborations between urban actors towards “smarter” urban growth in the 4.0 era.

Bio-inspired and bio-based materials are increasingly recognized as powerful enablers of climate-responsive and low-carbon architecture. By learning from natural systems, such as adaptability, self-regulation, and resource efficiency, these materials offer promising solutions to the escalating environmental pressures faced by the built environment. However, their systematic integration into building design remains limited, particularly in tropical monsoon climates. To address this gap, this study applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to identify, prioritize, and map the interdependencies among ten bio-based and bio-inspired strategies for sustainable building design. The results highlight five dominant solutions: living building systems, bio-composite exterior cladding for weather resistance, mycelium-based insulation for humidity control, bio-based natural ventilation and passive cooling, and bio-inspired self-shading systems. The causal analysis reveals three key characteristics: (1) living building systems function as a central integrative nexus, (2) bio-composite cladding acts as a primary driver of durability and climate resilience, and (3) bio-based water filtration and local timber exhibit lower systemic leverage despite their environmental benefits. Theoretically, this study advances biomimetic design research by introducing a causal, system-level framework for understanding interactions among nature-inspired strategies. Practically, it provides architects, engineers, and policymakers with an evidence-based decision-support tool to prioritize climate-adapted, bio-inspired solutions, contributing to the development of resilient and regenerative architecture in rapidly changing climates.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies