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.

BIM exhibits strong potential to become a core technological advancement adopted in construction projects. However, the process of BIM implementation is being affected by various factors depending on specific circumstances. This study aims to identify global factors influencing BIM adoption in construction projects. By a comprehensive review of the previous literature, this study managed 39 critical factors impacting construction labor productivity, which were categorized as primary 5 groups, namely, human, management, technology, project, and external. A total of 159 valid samples were collected by respondents who completed a questionnaire survey according to their previous direct or indirect participation in the implementation of construction projects. These factors were ranked based on their Relative Important Index (RII) and descriptive statistics. The findings indicated that the most significant factors affecting BIM adoption in construction project implementation consist of (1) “perceived usefulness,” (2) “speed of BIM tools,” (3) “perceived benefits of BIM for organization,” (4) “technology quality,” and (5) “experience and skills”.

Labour productivity of Vietnamese economy in general and of Vietnamese construction industry in particular is low in comparison with other countries. Boosting labour productivity is becoming an urgency and is attracting much attention from both domestic and foreign researchers. This paper focuses on a series of factors affecting labour productivity on construction site in Hanoi. The research aims to evaluate and rate the extent of impact of each factor to labour productivity. By referring to the outcomes of this study, Vietnamese construction contractors will be able to come up with workable solutions which will contribute towards a better performance of construction workers. On that basis, the productivity of construction firms and national economy will be improved correspondingly. Keywords: productivity; labour productivity; factors affecting; construction worker.

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.

The COVID-19 pandemic has generated a wide range of socio-economic disruption, which causes devastating in numerous aspects. Our knowledge of the true health of the construction industry under the ravage of COVID-19 outbreak is largely based on very limited data. This study aims to assess the impact of pandemic on the construction industry through an investigation in Vietnam. Data were collected through 129 respondents whose online questionnaire survey completed according to their recent direct or indirect participation in delivering construction projects during the spread. The implications of COVID-19 on the construction industry were examined based on simple percentage analysis and Relative Importance Index approaches. Three principal facets of the construction industry were considered: firms' business activities, project performance, and workforce demand. The findings highlighted the multilevel, multidimensional nature of the epidemic consequences on the construction sector. Notably, the revenue and profitability, in a general sense, have decreased during the COVID-19 period, while most of the production and business costs had remained unchanged. Further, the pandemic was argued to impair construction practitioners' incomes and mental health and sabotage projects' schedule and cost.