Knowledge governance is a critical factor to play and manage the knowledge related activities in projects and consequently the overall success of a project. This research paper aims to investigate the role of Knowledge Governance (KG) on Project Success (PS) in Pakistani major construction projects. Further, the paper investigates the mediating role of Efficient Team Leadership (ETL) and Knowledge Sharing (KS), and the moderating role of Organizational Culture (OC) between KG and PS. A quantitative study design was carried out using survey data collected through construction professionals in Pakistan. A total of 374 valid replies were examined using Structural Equation Modeling (SEM) via SmartPLS 4 to evaluate the potential relationship among the studied variables. The results demonstrate that KG substantially enhances PS. The exchange of knowledge, encompassing explicitly stated and implicit aspects, partially mediates this relationship with differing degrees of impact. ETL partially mediates by enhancing coordination, decision-making, and project execution. Furthermore, OC favorably influences the relationship between KG and PS, hence augmenting the efficacy of approaches to governance. This research expands contingency theory and knowledge-based theory by introducing a comprehensive framework that elucidates the mediating and regulating mechanisms connecting knowledge governance to project success within Pakistan's construction industry
Despite increasing attention to sustainable roofing systems, the integration of sustainability across the roofing supply chain remains fragmented. This study addresses this gap by investigating how sustainability is defined, prioritized, and operationalized by supply chain stakeholders, specifically manufacturers, distributors, and contractors within the U.S roofing industry. Using a qualitative, interview-based approach with seventeen in-depth stakeholder interviews, the study identifies role-specific responsibilities and perceptions, revealing that sustainability is largely driven by interdependent actions rather than isolated initiatives. Recycling and long-lasting performance emerged as the most frequently cited sustainability priorities, particularly among manufacturers and contractors. In contrast, distributors primarily focused on social stewardship, reflecting their distinct sustainability priorities. Additional themes, such as innovation, collaboration, safety, renewable energy, proximity, and Research and Development (R&D), emerged as shared priorities among these stakeholders in the roofing supply chain. These insights highlight how communication, feedback, and shared priorities contribute to coordinated sustainability practices across the roofing supply chain. The findings offer practical value for practitioners seeking to benchmark progress, design targeted training, and strengthen collaboration. They also set the stage for future quantitative research, such as multi-criteria analysis (MCA) or analytical hierarchy process (AHP), to assess the relative influence of stakeholder actions.
Effective risk management is essential for the successful delivery of Public-Private Partnership (PPP) projects. While China has become a global leader in PPP implementation, sector-specific research on China's metro transportation PPP projects (CMTPPs) remains insufficient, especially regarding the nuances of the Chinese institutional context. This study aims to investigate critical risk factors for CMTPPs and provide a holistic analysis of risk sources, impacts, and mitigation. Following a systematic literature review and validation from senior experts, the Analytic Hierarchy Process (AHP) was utilized to prioritize 11 primary and 45 secondary risk indicators. The findings reveal that "Interest rate changes" and "Financing feasibility" are the most critical secondary risks, reflecting the high sensitivity of this sector to the evolving financial regulations in China. Additionally, "Political Risks," "Financing Phase Risks," and 'Economic Risks' were identified as the top three primary risk categories. This study further integrates expert interpretations to explain these results within the framework of China's administrative culture, structural disparity between state-owned and private-owned enterprises, and regulatory shifts. By visualizing the interrelationships of key risks through influence diagrams, this research offers practical insights for improving the sustainability and governance of metro PPP projects in transition economies.
Road Maintenance Expenditure (RME) forecasting is essential for strategic budgeting, proactive asset management, and long-term infrastructure resilience. However, existing approaches often treat expenditure drivers in isolation and inadequately account for socio-economic conditions, pavement condition metrics, economic disruptions, natural shocks, and their delayed or compounded impacts. This study addresses this gap by developing a domain-integrated forecasting framework to model nonlinear, lagged, and shock-driven RME dynamics using a New Zealand case study. The framework combines Variational Autoencoder (VAE)-based latent feature extraction and nonlinear dimensionality reduction with a fine-tuned Random Forest regressor. Bayesian optimisation is used to identify optimal latent representations, while lagged variables and interaction terms explicitly represent delayed and compounded effects of external shocks. Comparative benchmarking across Local, Total, and New Zealand Transport Agency expenditure categories demonstrates strong in-sample modelling performance, with R2 values of 0.9818, 0.9842, and 0.9800, respectively, outperforming conventional linear, ensemble, and neural network-based models. A supplementary forward-rolling validation under chronological forecasting conditions showed reduced predictive performance, highlighting the challenge of temporal generalisation amid residual uncertainty, decentralised decision-making, and evolving external conditions. The study contributes a robust methodological framework for uncertainty-aware maintenance expenditure forecasting and demonstrates the importance of distinguishing structural relationship learning from realistic temporal prediction.
