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Managing megaprojects is challenging due to their inherent complexity and uncertainty. Collaborative project delivery models have been introduced as an alternative to traditional project management in public infrastructure megaprojects and are often realized through collaborative contracts. These project organizations act as institutional arenas for logic interaction as actors with differing institutional backgrounds interact within the project. This paper aims to study the delivery phase of three megaprojects through an institutional lens, investigating the institutional interaction and alignment of logics therein.

A multiple case study was employed to reach deep insight into the phenomenon. Sixty-one interviews were conducted over 3 cases with representatives from all levels of the project hierarchy. Respondents were selected through snowball sampling. In two cases, observations of the shared project office were conducted. Data analysis built on first-order codes and second-order themes, collected into a theoretical framework.

The empirical evidence demonstrates the dynamics shaping institutional logics and gives evidence for changing logics in projects with a well-applied collaborative contract. However, there is a risk of resistance and a return to traditional logics since institutional change is slow and an unsuitably applied collaborative contract can lead to adherence to the conventional way of work.

Current research has focused on the regulatory framework and procurement phase of such models, but little attention has been given to the delivery phase and the interaction of conflicting logics. This paper can serve as an exemplar of the different logics found within public infrastructure projects and their interaction and alignment. Contributions include a heightened emphasis on the start of the project as a meeting point for differing institutional logics and the role change necessary when using a collaborative contract.

Organizations are full of contradictions and leadership dilemmas. Managers often face challenges such as selecting between two contradicting options such that which one is more important can hardly be judged. To manage contradicting dynamics, today’s managers can adopt the paradoxical leadership approach. We build a theoretical model to investigate the influence of paradoxical leadership on multi-dimensional project agility (proactivity, adaptability, and resilience), and multi-dimensional project success (management, investment, and ownership success).

Drawing on survey-based data from the China–Pakistan Economic Corridor (CPEC) megaproject (N = 209), we performed covariance-based structural equation modeling to test the conceptual model.

The findings show that (1) paradoxical leadership has a significant positive impact on megaproject success, (2) paradoxical leadership has a significant positive influence on project agility, (3) project agility has a significant positive effect on megaproject success, and (4) project agility has a significant effect that mediates the link between paradoxical leadership and megaproject success. This research provides a theoretical and practical comprehension of paradoxical leadership with a new perspective on megaprojects.

This study provides an extension of the existing studies on paradoxical leadership and identifies the role of contradicting dynamics and their impact on multiple facets of megaproject success. It not only clarifies the relationship between paradoxical leadership and megaproject success, but also identifies the mediating role of project agility that can play an effective role in mobilizing success in megaprojects.

This study aims to obtain governance strategies for managing the complexity of megaprojects by analyzing the impact of individual factors and their configurations using the fuzzy-set qualitative comparative analysis (fsQCA) method and to provide references for project managers.

With the continuous development of the economy, society and construction industry, the number and scale of megaprojects are increasing, and the complexity is becoming serious. Based on the relevant literature, the factors affecting the complexity of megaprojects are determined through case analysis, and the paths of factors affecting the complexity are constructed for megaprojects. Then, the fsQCA method is used to analyze the factors affecting the complexity of megaprojects through 245 valid questionnaires from project engineers in this study.

The results support the correlation between the complexity factors of megaprojects, with six histological paths leading to high complexity and seven histological paths leading to low complexity.

It breaks the limitations of the traditional project complexity field through a “configuration perspective” and concludes that megaproject complexity is a synergistic effect of multiple factors. The study is important for enriching the theory of megaproject complexity and providing complexity governance strategies for managers in megaproject decision-making.

Due to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects.

This study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process.

This study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming.

APMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste.

Megaprojects have large impact on the environment and stakeholders should take collective action to ensure that these projects are developed in a socially responsible manner. Hitherto, it is not known whether group and subjective norms and social identity could compel stakeholders to take socially responsible collective actions in megaprojects. The aim of this study is to design and test a model to boost stakeholders' intention to take socially responsible collective action in the context of mega water transfer projects in China.

A quasi-experimental causal research design was adopted to establish cause–effect relationships among the dependent variable (we-intention) and independent variables (subjective norms, group norms, social identity and desire). This study adopts the belief–desire–intention model and social influence theory to empirically investigate how to boost the stakeholders' intention to participate in socially responsible collective action. An online questionnaire survey was conducted and data was collected from 365 respondents who were involved in mega water transfer projects in China. The partial least squares structural equation modeling technique was employed to analyze the data.

The results from partial least squares analyses indicate that the presence of subjective norms, group norms and social identity (collectively known as social influence process) could increase stakeholders' intention to take socially responsible collective action. In addition, the desire to be socially responsible also boosts stakeholders' intention to take collective action. Desire partially mediates the relationship between social influence process and intention to take socially responsible collective action.

This study adds to existing knowledge by discovering social influence process as an antecedent to taking socially responsible collective action in megaprojects. Strong group norms and subjective norms could propel stakeholders to be more socially responsible. The study also adds to knowledge by discovering that stakeholders' desire to fulfill social responsibility also leads them to take concrete actions. Implications and recommendations are provided on how to manipulate different types of social influence processes to facilitate stakeholders to adopt socially responsible collective action in the process of managing megaprojects.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

The traditional Chinese culture has always emphasized the authority of leaders and their “top-down” influence over subordinates tangled with “bottom-up” management. Paternalistic leadership can both nurture and restrict growth in mega-construction projects, due to the unique consequences (i.e. positive vs negative implications) for project teams. Hence, the present study aimed to explore the impact of paternalistic leadership (PL), team members’ voice (TMV) and team resilience (TR) on the mega-construction project success (MPS) in China's Belt and Road Initiative (BRI).

A surveyed-based sample of project professionals (N = 563) directly linked with the BRI was employed for statistical estimations with partial least squares (PLS) structural equation modeling (SEM).

Paternalistic leadership styles, including authoritarian leadership (AL), moral leadership (ML) and benevolent leadership (BL), significantly influence the mega-construction project success in BRI. The findings empirically validated that both BL and ML increase the likelihood of mega-construction project success. However, AL could impose a threat through its underlying negative influence. In addition, leaders with benevolence and morality have a positive influence on TMV and TR, while leaders with authoritarianism signal a negative impact. Furthermore, both TMV and TR significantly and positively mediate the relationships between AL-MPS (Model-1), BL-MPS (Model-2) and ML-MPS (Model-3), respectively.

The present study is a groundbreaking endeavor that fills a crucial research gap by investigating mega-construction project success in the BRI through paternalistic leadership, project team members' voice and team resilience in a multi-mediation model. These novel findings offer valuable strategic insights for managing mega-construction projects in countries with paternalistic solid cultural foundations, enabling project managers to navigate cultural nuances and optimize megaproject outcomes.

Managing mega infrastructure projects (MIPs) is more complex because of time, size, social, environmental and financial implications. This study aims to address the management approaches, complexity and risk factors involved in MIPs. The study focuses on project success criteria and their individual effects on the success of MIPs.

To address the challenges and identify the most influencing factor for the success of MIPs, the study deployed a cross-sectional survey approach. Six hundred eighty-two usable samples were collected from the respondents to understand the impact of predetermined factors on the success of MIPs. The structural equation model and artificial neural network approach were used to derive the importance of factors affecting the success of MIPs.

The study's outcome confirms that all three influencing factors: feasibility studies, community engagements and contract selection, have a significant positive impact on the success of MIPs. Community engagement amongst all three has the most influential predictor for the success of MIPs.

The developed model will enable practitioners and policymakers from Indian construction companies and other emerging nations to concentrate on recognized risk reduction variables to enhance project success criteria and project management success, especially for MIPs.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.