The rapid growth of complex projects in the construction industry worldwide has triggered a growing number of studies over the past two decades, suggesting that understanding project complexity is a key component of successful construction project management. This study aims to investigate the status and trends in project complexity research through a four-stage literature review that can benefit both researchers and practitioners. Seventy-four relevant articles were identified from studies published during the years 1996–2015, and results indicate that research in construction project complexity primarily focuses on four areas: influencing factors contributing to project complexity, the impact of project complexity, complexity measurement methods, and considerations for managing project complexity. Future research should concentrate on specific factors that drive complexity for different types of construction projects and the development of management guidelines for addressing complexity throughout the project lifecycle. This paper provides both a timely summary of literature in the area of project complexity and insights into opportunities for future study of and guidance for successfully managing complexity in construction projects.

This study establishes a model of prefabricated building project risk management system based on the Modified Teaching-Learning-Based-Optimization (MTLBO) algorithm and a prediction model of deep learning multilayer feedforward neural network (Backpropagation, BP neural network) to improve the requirements of risk management during the construction of large prefabricated building projects. First, we introduced the BP neural network algorithm based on deep learning. Second, the traditional Teaching-Learning-Based Optimization (TLBO) algorithm was modified by using information entropy, and the modified algorithm was simulated and tested in five test functions. Then, based on the BP neural network and MTLBO algorithm, we established the MTLBO-BP neural network prediction model and tested its performance. Finally, based on the MTLBO-BP neural network prediction model, MATLAB software was used to establish an intelligent model of the risk management system during the construction of prefabricated building projects, and the example verification was performed. In addition, the MTLBO algorithm was verified by test function simulation and established that global searchability is stronger than the TLBO algorithm. Of note, it is not easy to fall into a local optimum. The test results of the MTLBO-BP neural network prediction model revealed that the prediction model converges faster and exerts a better prediction effect. The example verification of the intelligent model of the risk management system during the construction of prefabricated building projects established in this study revealed that the algorithm proposed is more accurate in the reliability and cost prediction of the risk management of prefabricated building projects. Moreover, the algorithm proposed provides theoretical support for intelligent management and decision-making of prefabricated building projects. Overall, this study validates that this algorithm is essential for construction project management, decision-making, and quality assurance.

This study builds a measurement framework of project governance for mega-infrastructure considering the institutional situation of mega-infrastructure projects in China, including contractual governance, relational governance, and governmental governance. The factors of governmental governance are identified by the method of grounded theory with six cases of megaprojects, and the measures of project governance for mega-infrastructure are refined by expert interviews. The 235 questionnaires are collected, and exploratory factor analysis is used to identify six factors of the governance mechanism for mega-infrastructure projects. The scales are developed, and reliability and validity tests are conducted. Results indicate that (1) the governmental governance mechanism includes government decision, government supervision, and government coordination. (2) The three-dimensional framework of project governance is established as “contractual–relational–governmental” in the field of mega-infrastructure. (3) The measurement scales of project governance are developed and validated for mega-infrastructure, including government regulation, government coordination, risk sharing, revenue distribution, relationship maintenance, and cultural development. This research contributes to (a) the state of the knowledge by gaining a holistic and comprehensive understanding of project governance in mega-infrastructure in China, and (b) the state of the practice by providing a tool for measuring project governance in mega-infrastructure.

Organizational leadership is a key factor affecting the management performance of complex construction projects, but seldom have studies attempted to explore the effect mechanisms of organizational leadership on the project management performance, especially the mediating role of project citizenship behavior. The purpose of this study is to fill this gap by investigating the effects of organizational leadership on project citizenship behavior and management performance in complex construction projects. The theoretical model is constructed based on a literature review, and exploratory factor analyses (EFA) are performed on 169 valid questionnaires collected to measure organizational leadership, then partial least squares-structural equation modeling (PLS-SEM) is used to test the hypotheses. The results show that (i) organizational leadership is measured as vision guiding, context interacting, team building, and systems thinking; (ii) vision guiding and context interacting have both direct and indirect effects on the project management performance, and team building can only improve the project management performance by influencing the project citizenship behavior, whereas systems thinking has no significant effect on project citizenship behavior and the project management performance in complex construction projects; (iii) and project citizenship behavior partly mediates the influence of organizational leadership on the project management performance, and the effect of organizational leadership on the project management performance is more realized through the mediating role of project citizenship behavior. The results have a significant theoretical and practical significance for improving the project management performance.

The “trillion-dollar era” of megaprojects has increased the demand for the scope of mega infrastructure. To address the requirement for high-quality “investment, construction, and operation” integration, the EPC and PPP models must be combined. The complexity of megaprojects has resulted in the complexity of project risk variables under the new model. However, few existing studies have undertaken in-depth studies on the risk of EPC + PPP megaprojects. The interplay and dynamic evolution of risk factors, in particular, have not been taken into account. This research intends to fill this gap by systematically identifying and modeling the risk variables associated with the adoption of the EPC + PPP model for mega infrastructure projects. In this study, the Bayesian network is used to detect decision-making risk variables for large infrastructure projects in China. The findings indicate that (i) 22 influencing factors of megaproject decision making are identified, including organizational decision making, PPP investment and financing, EPC construction, operations management, and policy and law. (ii) Considering the real project decision-making process, a model based on a dynamic Bayesian network is built, and associated governance techniques and early warning protection mechanisms are designed for the decision-making process. (iii) Using the Yiwu Mall Avenue project as an example, the Bayesian simulation model of decision-making risks is applied to a typical case to validate its feasibility and correctness. These findings have significant theoretical and practical implications for research on the identification and governance of decision-making risks in megaprojects using the EPC + PPP model in China.

Purpose: The development of project managers and leadership has been highlighted as crucial for improving project success and performance, resulting in a rise of interest in project leadership research over the last two decades. While several qualitative reviews have been conducted, there have been limited quantitative and systematic reviews on project leadership. This study fills this gap by portraying the knowledge landscape and tracking the evolution of project leadership research from 1998 to 2022 through bibliometric approaches.Design/methodology/approach: Based on 816 records, including 793 articles extracted and selected from the Web of Science database and specific journals, and 23 articles selected from three non-SCI/SSCI indexed journals, the authors used CiteSpace and bibliometrix R-package to depict visualizations of the trajectory of co-cited references, the landscape of co-occurred keywords and emerging trends in project leadership via reference co-citation analysis, keyword co-occurrence analysis and thematic mapping.Findings: The bibliometric analyses enabled the authors to understand the conceptual aspects of project leadership and its theoretical background. Three stages of the intellectual bases were identified and tracked: the infancy phase (1998–2007), the growth phase (2008–2014) and the new development phase (2015–2022). The results of keyword co-occurrence analysis indicated that the research focus evolved from investigating traits and competences to examining the effects of traditional leadership behaviors, and then considering context-specific leadership. The findings of thematic mapping and theoretical interpretation illustrate the potential directions of the competence comparison, new and appropriate leadership, and the interaction between leadership and context.Originality/value: This study advanced the field by providing a systematic review of project leadership, developing potential future directions for project leadership research and providing practical implications for career development and training.