As one of the emerging digital technologies, building information modeling (BIM) has been increasingly used in the architecture, engineering, and construction (AEC) industry of many countries. One of the core benefits associated with BIM is facilitating collaboration among project teams, thereby improving information sharing and project performance by providing BIM-based construction networks (BbCNs). Nevertheless, it is still challenging to ensure an effective collaboration process in BIM-enabled projects. Several studies have investigated the factors that influence collaboration in BIM-enabled projects; however, most of these studies focus on technical matters of BIM, while the perspectives of socio-organization and processes have received less attention and are still in the conceptual stage, especially for the role of people management. Thus, an exploratory study was proposed to explore how key factors influence the professionals’ willingness to collaborate in BIM-enabled projects. A total of 10 hypotheses were established based on literature review and expert verification. Following that, a questionnaire survey was administered in China to solicit the opinions of 273 BIM professionals for data collection. The hypotheses were then examined on the basis of the predictive capacity of regression analysis (binary logistic regression), with the findings validated using neural network analysis (multilayer perceptron). It was found that seven independent variables have a statistically significant influence on professionals’ willingness to collaborate in BIM-enabled projects, namely, professional knowledge, skill, training, investment, BIM tools, BIM ownership, and standards and regulations. Among them, the variable of professional knowledge was ranked as the most influential factor. While extending the existing knowledge of literature, the findings of this study also deliver insights for stakeholders by enhancing their understanding of the BIM collaboration process and its influence factors.

The construction industry utilizes a substantial number of resources, which has negative impacts on both environmental and socioeconomic aspects. Therefore, it is important to reduce these negative impacts and maintain sustainable development (SD). Recent studies suggest that integrating Industry 4.0 (also called Construction 4.0 (C4.0) in the construction industry) and SD may help address these concerns, which is a new and ever-evolving field. In order to fully understand SD in the C4.0 context, this paper offers a verifiable and reproducible systematic literature review and bibliometric analysis of associated topics. Through a review of 229 works, this article presents the publication trend, the most prolific journals, countries, institutions, researchers, and keywords analysis, as well as the content analysis of C4.0 impacts on SD based on triple-bottom-line (TBL) dimensions. The authors also identify and summarize the critical success factors (CSFs) of C4.0 toward SD. Overall, findings reveal the potential benefits of C4.0 on SD and contribute to the evaluation of sustainable C4.0 innovations. The key topics and CSFs identified in this work could potentially serve as the basis for future investigations, encouraging and directing interested researchers, and thus supporting both theoretical and practical progress in this evolving research area.

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.

Purpose: In the digital economy, the adoption and implementation of digital transformation (DX) is essential for small and medium-sized enterprises (SMEs) in the construction industry to secure a competitive advantage and achieve sustainability. This study examines the dynamic managerial capabilities of SME leaders and the organizational readiness required to facilitate DX. Design/methodology/approach: Drawing on insights from 33 managers across six SMEs, the study identifies key dynamic capabilities, organizational readiness factors, and transformation challenges. The framework was further refined and validated through a Delphi process, resulting in a four-layer model that links internal capabilities to specific DX challenges and provides a structured roadmap for SME transformation. Findings: Our findings reveal that DX in construction SMEs is driven by five dynamic capabilities: environmental sensing, strategic agility, resource mobilization, digital ecosystem building and organizational learning. In addition, organizational readiness serves as a critical enabler, shaped by leadership and cultural readiness, policy and stakeholder alignment and technological preparedness. Building on these insights, we propose a four-layer integrative framework that links these capabilities and readiness factors to specific internal and external challenges, providing a structured pathway for overcoming barriers and facilitating effective DX in construction SMEs. Originality/value: This study provides actionable insights by clarifying how construction SMEs strategically organize and leverage DX-related resources and capabilities to drive change and maintain a competitive edge. It contributes to theory by introducing an integrative framework that illuminates the interrelationships among these resources, managerial competencies and readiness factors, offering a detailed roadmap for implementing and sustaining DX.

Purpose: The Internet of Things (IoT) offers substantial potential for improving efficiency and effectiveness in various applications, notably within the domain of smart construction. Despite its growing adoption within the Architecture, Engineering, and Construction (AEC) industry, its utilization remains limited. Despite efforts made by policymakers, the shift from traditional construction practices to smart construction poses significant challenges. Consequently, this study aims to explore, compare, and prioritize the determinants that impact the acceptance of the IoT among construction practitioners. Design/methodology/approach: Based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT2), Task-Technology Fit (TTF), and perceived risk. A cross-sectional survey was administered to 309 construction practitioners in China, and the collected data were analyzed using structural equation modeling (SEM) to test the proposed hypotheses. Findings: The findings indicate that TTF, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and perceived risk exert significant influence on construction practitioners’ intention to adopt IoT. Conversely, social influence and habit exhibit no significant impact. Notably, the results unveil the moderating influence of gender on key relationships – specifically, performance expectancy, hedonic motivation, and habit – in relation to the behavioral intention to adopt IoT among construction practitioners. In general, the model explains 71% of the variance in the behavioral intention to adopt IoT, indicating that the independent constructs influenced 71% of practitioners’ intentions to use IoT. Practical implications: These findings provide both theoretical support and empirical evidence, offering valuable insights for stakeholders aiming to gain a deeper understanding of the critical factors influencing practitioners’ intention to adopt IoT. This knowledge equips them to formulate programs and strategies for promoting effective IoT implementation within the AEC field. Originality/value: This study contributes to the existing literature by affirming antecedents and uncovering moderators in IoT adoption. It enhances the existing theoretical frameworks by integrating UTAUT2, TTF, and perceived risk, thereby making a substantial contribution to the advancement of technology adoption research in the AEC sector.

In recent years, some studies have explored the determinants of Building Information Modeling (BIM) adoption. However, the findings of these studies are varied and sometimes contradicting. Consequently, this study undertakes an in-depth exploration of the relationship between influencing factors and behavioral intention. This analysis is achieved through a synthesis of findings from prior empirical studies, considering the nuanced impacts of specific contextual factors, including sample size, national culture, and economic level, on these relationships. In total, this meta-analysis encompasses 57 articles, and as of 31 December 2023, incorporates 63 datasets comprising a collective sample size of 13,301. An extended Unified Theory of Acceptance and Use of Technology (UTAUT) model was developed based on the most frequently studied constructs relevant to BIM adoption. The analysis reveals that BIM adoption is primarily affected by performance expectancy, social influence, facilitating conditions, effort expectancy, and perceived value. The moderator analysis indicates that sample size statistically significantly moderates the relationships between facilitating conditions and use behavior. Moreover, the extent of individualism in each national culture significantly moderates the associations between facilitating conditions and user behavior. The research serves to enrich the existing body of literature on BIM acceptance by addressing contradictory and mixed results found in empirical studies. It represents one of the first attempts to explore the influence of sample size, economic level, and Hofstede’s six cultural dimensions as moderators in the field of BIM utilizing meta-analytic techniques.