This paper aims to systematically analyze the reasons for the differences in the relationship between transformational leadership (TL) and project success and apply meta‐analysis to summarize which dimensions of TL are the main driving forces for project success. Adopting the meta‐analysis approach, we investigated 31 independent studies (N = 6475) and studied the theoretical moderators of this relationship from the perspectives of mediating variables, cultural background, and document type to test whether the moderating effects can explain the inconsistent research results. The results reveal that TL positively affects project success and leadership charm is the primary driver of TL. Also, the existence of a mediating mechanism has a more significant impact on the success of the leading project. Meanwhile, compared with project construction under the Western cultural background, countries with Eastern culture are more inclined to use a people‐oriented philosophy for project management to promote project success. This research provides an empirical perspective to help project leaders select management talents, regulate leaders’ words and deeds, and cultivate technical and soft leadership skills. Besides, this paper proposes a unique and nuanced view of the relationship between TL and project success, enhancing people’s understanding of the TL’s role in influencing project success.

Transmission lines are often located in complex environments and are susceptible to the presence of foreign objects. Failure to promptly address these objects can result in accidents, including short circuits and fires. Existing foreign object detection networks face several challenges, such as high levels of memory consumption, slow detection speeds, and susceptibility to background interference. To address these issues, this paper proposes a lightweight detection network based on deep learning, namely YOLOv5 with an improved version of CSPDarknet and a Swin Transformer (YOLOv5-IC-ST). YOLOv5-IC-ST was developed by incorporating the Swin Transformer into YOLOv5, thereby reducing the impact of background information on the model. Furthermore, the improved CSPDarknet (IC) enhances the model’s feature-extraction capability while reducing the number of parameters. To evaluate the model’s performance, a dataset specific to foreign objects on transmission lines was constructed. The experimental results demonstrate that compared to other single-stage networks such as YOLOv4, YOLOv5, and YOLOv7, YOLOv5-IC-ST achieves superior detection results, with a mean average precision (mAP) of 98.4%, a detection speed of 92.8 frames per second (FPS), and a compact model size of 10.3 MB. These findings highlight that the proposed network is well suited for deployment on embedded devices such as UAVs.

As the complexity, breadth of expertise and number of agents involved in megaprojects grow, collaborative innovation models become invaluable for helping to achieve sustainable project development. On this basis, the purpose of this study is to explore the innovation output mechanisms used for collaborative innovation in megaproject (CIMP) systems by the agent-based modelling (ABM) approach, and to promote the efficiency and effectiveness of organisational collaborative innovation through variable controls. A multi-agent simulation CIMP model was developed using the NetLogo tool. The model encompasses the behavioural factors and interaction rules that affect organisational CIMP. Four simulations were conducted, and the results showed that (1) the innovation environment, including policy environment, cultural climate, and engineering demand, has a positive effect on the output rate of CIMP; (2) a larger scale of innovative network organisation accelerates innovation output; (3) innovative organisations must avoid enforcing high standards for cooperation, communication, and recognition abilities when selecting partner organisations; (4) innovative organisations’ ability to absorb technology, information, and knowledge is positively related to output, while an increase in behavioural costs reduces the scale of innovative network organisations, thereby affecting their output. This study developed its CIMP theory from the perspective of organisational behaviour. The findings are expected to provide methodological and practical guidance for the selection of innovation agents, behavioural patterns, and for guaranteeing efficient innovation in collaborative megaproject organisations.

Building information modeling (BIM) is evolving as a digital infrastructure model for innovation in the construction field. The innovation-enabling potential of BIM has been highly neglected in the literature. This study explores the innovative potential of BIM, specifically its value in enabling construction innovation (CI). Through reflective research and a literature review, the relationship between BIM and CI is redefined, BIM-CI’s value spectrum and underlying mechanisms are mapped and their required resources and activities are illustrated. The results indicate that different BIM applications provide various proinnovation environments wherein CI may flourish. Extra attention should be paid to BIM-enabled systematic collaborative innovation and digital innovation ecosystems with BIM as the core infrastructure that integrates the physical space with cyberspace to accelerate radical innovation. This study extends BIM management research by considering digital innovation and providing a new perspective for CI management theory and practice. The results will provide academics with a solid point of departure for developing relevant research and serve as a reference for practitioners who intend to utilize BIM for efficient innovation in construction projects.

This research endeavors to delve comprehensively into the ramifications of behavioral guidance policies pertaining to construction waste resource utilization (CWRU) on the behavioral awareness and attitudes of Chinese construction contractors. The implicit attitudes of 98 construction contractor workers toward construction waste collection, sorting, and reuse were quantitatively assessed by simulating different types of behavior-guided policies using the External Affective Simon Task (EAST) method with unconscious reaction time as an indicator. At the same time, the questionnaire survey was combined to collect and analyze the scores of these workers’ exogenous attitudes toward CWRU, with a view to revealing the contractors’ intrinsic behavioral awareness and exogenous attitudinal tendencies. The study reveals that economic incentive behavioral guidance policies are pivotal in fostering the collection, sorting, and reuse of construction waste. Furthermore, it novelly uncovers the distinct advantages of laws and regulations frameworks in safeguarding the fundamental aspects of construction waste collection. Notably, publicity and education behavioral policies emerge as a cornerstone in elevating the significance of construction waste collection and sorting, highlighting the cruciality of enhancing public awareness and knowledge to propel CWRU. The results of the study not only provide empirical evidence to understand the inherent attitudes of construction contractor workers toward CWRU behavior but also provide practical support for future policymakers. By advocating for economic incentives, strengthening regulations, and leading education, the multi-pronged approach promotes China’s CWRU accelerates the green transformation of the construction industry.

随着建设行业的数字化转型，融合传统工程建设与人工智能技术已成为迫切需求。本研究以技术接受模型和技术-组织-环境框架为理论基础，运用结构方程模型分析了建设行业中人工智能技术采纳行为的影响因素。结果表明，在技术层面，人工智能技术的特性显著影响用户的感知易用性和有用性，进而直接影响采纳行为；而技术复杂性虽对感知有用性影响不显著，但明显增加了感知易用性的难度。在环境方面，外部环境对感知有用性有显著正向影响，但对感知易用性影响较弱，同时也直接影响采纳行为。组织层面上，针对人工智能技术的充分准备能显著提升员工的感知易用性、感知有用性并促进采纳行为，而领导层的支持则主要增强员工的感知易用性，进而推动其采纳行为。这表明，工程建设企业在推动人工智能技术应用时，应综合考虑技术、组织和环境三方面的关键因素，以制定更有效的战略。