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In this research, the authors distributed a survey to 156 residential construction developers and 468 buyers to assess the level of perceived agreement of developers and buyers on the key indicators for measuring stakeholder management, project performance and stakeholder satisfaction. Following this, a partial least squares structural equation modelling (PLS-SEM) model was developed to quantitatively analyse the direct impacts of stakeholder management on both project performance and stakeholder satisfaction, and to analyse the mediating role of stakeholder satisfaction in enhancing project performance.

This paper seeks to investigate the direct effects of stakeholder management on project performance and stakeholder satisfaction within residential construction projects, and also examine the mediating role of stakeholder satisfaction in enhancing project performance by surveying residential buildings’ developers and buyers in Malaysia.

This research found that effective stakeholder management directly improves project performance and stakeholder satisfaction within Malaysian residential construction projects. It further identified that stakeholder satisfaction significantly enhances project performance, serving as a critical mediator in the relationship between stakeholder management and project performance.

This study enhances the understanding of stakeholder management, stakeholder satisfaction and project performance in the Malaysian construction industry, offering strategic insights that emphasise the importance of stakeholder-centric practices for improving project outcomes, ensuring better collaboration and fostering enhanced project performance. Integrating these insights with digital technologies like building information modelling can lead to clearer communication, more informed stakeholder engagement, and, ultimately, enhanced project efficiency and satisfaction.

This paper offers empirical evidence on the critical role of stakeholder management and satisfaction in enhancing project performance in Malaysia’s residential construction projects, providing novel insights into stakeholder-centric approaches that contribute to improved project outcomes.

This study aims to explore the interrelationships among quality and safety metrics within the Global Food Security Index (GFSI). Its primary objective is to identify key indicators and their respective influences on food security outcomes, thereby enriching comprehension of the intricate dynamics within global food security.

The analysis encompasses data from 113 countries for the year 2022, utilizing Bayesian Belief Network (BBN) models to identify significant drivers of both the GFSI and quality and safety dimensions. This methodological approach enables the examination of probabilistic connections among different indicators, providing a structured framework for investigating the complex dynamics of food security.

The study highlights the critical role of regulatory frameworks, access to clean drinking water, and food safety mechanisms in fostering food security. Key findings reveal that “nutrition monitoring and surveillance” has the highest probability (75%) of achieving a high-performance state, whereas “national dietary guidelines” have the highest probability (41%) of achieving a low-performance state. High GFSI performance is associated with excelling in indicators such as “access to drinking water” and “food safety mechanisms”, while low performance is linked to underperformance in “national dietary guidelines” and “nutrition labeling”. “Protein quality” and “dietary diversity” are identified as the most critical indicators affecting both the GFSI and quality and safety dimensions.

This research operationalizes a probabilistic technique to analyze the interdependencies among quality and safety indicators within the GFSI. By uncovering the probabilistic connections between these indicators, the study enhances understanding of the underlying dynamics that influence food security outcomes. The findings highlight the critical roles of regulatory frameworks, access to clean drinking water, and food safety mechanisms, offering actionable insights that empower policymakers to make evidence-based decisions and allocate resources effectively. Ultimately, this research significantly contributes to the advancement of food security interventions and the achievement of sustainable development goals related to food quality and safety.

This research aims to systematically review studies on significant risks for Critical Infrastructure Projects (CIPs) from selected top-tier academic journals from 2011 to 2023.

In this research, a three-step systematic literature review methodology was employed to analyse 55 selected articles on Critical Infrastructure Risks (CIRs) from well-regarded and relevant academic journals published from 2011 to 2023.

The findings highlight a growing research focus on CIRs from 2011 to 2023. A total of 128 risks were identified and grouped into ten distinct categories: construction, cultural, environmental, financial, legal, management, market, political, safety and technical risks. In addition, literature reviews combined with questionnaire surveys were more frequently used to identify CIRs than any other method. Moreover, oil and gas projects were the subjects most often explored in the reviewed papers. Furthermore, it was observed that publications from Iran, the USA and China dominated CIRs research, making significant contributions, accounting for 49.65% of the analysed articles.

This research specifically focuses on five types of CIPs (i.e. roadways, bridges, water supply systems, dams and oil and gas projects). Other CIPs like cyber-physical systems or electric power systems, were not considered in this research.

Governments and contracting firms can benefit from the findings of this study by understanding the significant risks associated with the execution of CIPs, irrespective of the nation, industry or type of project. The results of this investigation can offer construction professionals valuable insights to formulate and implement risk response plans in the early stages of a project.

As a novel literature review related to CIRs, it lays the groundwork for future research and deepens the understanding of the multi-faceted effects of these risks, as well as sets practical response strategies.

This study aims to address a gap in traditional university ranking methodologies by investigating the interrelations among key indicators featured in the QS rankings, within the broader context of benchmarking in higher education.

Utilizing the 2024 QS ranking data and a Bayesian Belief Network (BBN) model, this research explores the interconnected relationships among indicators such as “academic reputation,” “employer reputation,” “faculty-to-student ratio,” “sustainability” and others to predict university rankings.

The developed model achieves 80% predictive accuracy and shows that strong performance in “employment outcomes,” “academic reputation” and “employer reputation” contributes to higher overall scores. In contrast, weaker performance in “academic reputation” and “sustainability” is associated with lower scores. Among these factors, “academic reputation” is the most informative indicator for predicting the overall score.

This research contributes to the literature by emphasizing the interconnections among ranking criteria and advocating for network-based models for benchmarking in higher education. Particularly, it underscores the importance of “sustainability” in forecasting rankings, aligning well with the broader theme of predicting university performance and societal impact. This study offers valuable insights for researchers and policymakers, promoting a comprehensive approach that considers the interdependencies among criteria to enhance educational quality and address societal change within the framework of benchmarking in university rankings.

Safety program initiatives in the construction industry remain below the desired level, especially in developing countries, and the reasons for low adoption are not well established. This study aims to investigate and quantify the impact levels of the key barriers to safety program initiatives in the construction industry in Iraq.

The study used a multi-method research design to assess the barriers to safety program initiatives in the construction industry in Iraq. Initially, a comprehensive literature review was conducted to identify potential barriers. Following this, semi-structured interviews with construction professionals were conducted to gather in-depth insights. Thematic analysis was then applied to analyze the results of these interviews. Subsequently, a questionnaire survey was developed based on the findings from the literature review and interviews to quantitatively measure the impact of the identified barriers among a broader group of construction professionals. Finally, a fuzzy synthetic evaluation (FSE) was used to assess the overall impact of these barriers comprehensively.

The results showed that the top three key barriers to the implementation of construction safety programs in Iraq include tight project schedules, inadequate commitment to safety and the lack of safety rules and policies. A FSE indicated that each group of barriers, including unsupportive industry norms, poor safety awareness, an unconducive work climate and poor governance, significantly hinders safety program implementation in the Iraqi construction industry.

The study was conducted in the construction industry of Iraq, specifically focusing on Baghdad, Anbar, Basra and Erbil governorates, as these cities are prominent within the country. In addition, this research exclusively captured the perspectives of construction consultants, contractors and clients.

The findings suggest that improving supportive and safety-aware industry norms, increasing safety awareness, generating a safe work climate and culture and improving safety governance can significantly influence the widespread adoption of safety programs in the construction industry. To the best of the authors’ knowledge, this constitutes the first study to quantify the impact levels of barriers to construction safety program initiatives in developing countries. It provides additional insight to promote a safer construction industry. Moreover, the findings can notably be used by construction practitioners and policymakers to develop and successfully implement safety programs in construction.

This study aims to develop an assessment model for identifying the primary barriers to safety program initiatives within the construction industry of developing countries, exploring deeper into the barriers that impede the adoption of safety programs in construction, and providing valuable insights that could serve as a foundation for crafting strategies to enhance safety program initiatives within the industry.

The Logistics Performance Index (LPI), published by the World Bank, is a key measure of national-level logistics performance. It comprises six indicators: customs, infrastructure, international shipments, service quality, timeliness, and tracking and tracing. The objective of this study is to explore temporal dependencies among the six LPI indicators while operationalizing the World Bank’s LPI framework in terms of mapping the input indicators (customs, infrastructure, and service quality) to the outcome indicators (international shipments representing cost, timeliness, and tracking and tracing representing reliability).

A Bayesian Belief Network (BBN)-based methodology was adopted to effectively map temporal dependencies among variables in a probabilistic network setting. Using forward and backward propagation features of BBN inferencing, critical variables were also identified. A BBN model was developed using the World Bank’s LPI datasets for 2010, 2012, 2014, 2016, 2018, and 2023, covering the six LPI indicators for 118 countries.

The prediction accuracy of the model is 88.1%. Strong dependencies are found across the six LPI indicators over time. The forward propagation analysis of the model reveals that “logistics competence and quality” is the most critical input indicator that can influence all three outcome indicators over time. The backward propagation analysis indicates that “customs” is the most critical indicator for improving the performance on the “international shipments” indicator, whereas “logistics competence and quality” can significantly improve the performance on the “timeliness” and “tracking and tracing” indicators. The sensitivity analysis of the model reveals that “logistics competence and quality” and “infrastructure” are the key indicators that can influence the results across the three outcome indicators. These findings provide useful insights to researchers regarding the importance of exploring the temporal modeling of dependencies among the LPI indicators. Moreover, policymakers can use these findings to help their countries target specific input indicators to improve country-level logistics performance.

This paper contributes to the literature on logistics management by exploring the temporal dependencies among the six LPI indicators for 118 countries over the last 14 years. Moreover, this paper proposes and operationalizes a data-driven BBN modeling approach in this unique context.

This study aims to design and validate a decision support system (DSS), named preliminary risk assessment of brownfield sites (PRABS). It is intended that the proposed DSS will aid the identification of potential hazards and, in doing so, highlight challenges facing those stakeholders dealing with the decision-making on brownfield site redevelopments, where the examples of diverse stakeholders would include, for instance, risk assessors, local planning authorities, regulator, developers, civil engineers, architectures, landowners, investors and alike. Moreover, the DSS will enable them to promote safer redevelopment and minimise the risks to future occupants of brownfield sites and neighbouring lands, on the top of the tool being communal platform of an effective communication between them as it is for both experts and non-experts.

This research employs a comprehensive five-stage process, integrating both quantitative and qualitative methods and utilizing mixed methods for a nuanced exploration of data. The initial stage involves an in-depth examination of contemporary risk assessment tools for contaminated sites, setting the foundation and benchmarks for subsequent stages. Stage two focuses on creating a conceptual framework using insights from existing literature to guide the development of the DSS tool. Stage three introduces a validation mechanism through a questionnaire administered to experts. Stage four involves the active development of the DSS tool, transforming theoretical constructs into a practical application. The final stage, stage five, employs quantitative data analysis and case studies to validate, refine and enhance the DSS tool’s applicability in real-world scenarios, ensuring its approval.

This study presents PRABS, a user-friendly DSS for the PRABS. Validation through a quantitative online survey indicates strong support for PRABS, with around 80% of participants willing to recommend it due to its ease of use and information quality. Qualitative data analysis using real-life case studies further demonstrates the tool’s effectiveness. PRABS proves valuable in identifying hazards during the preliminary stage, accurately predicting potential contaminants despite limited input data in the case studies. The tool’s hazard identification aligns well with expert judgments and case study reports, confirming its practical utility.

This study has several limitations. First, the DSS identifies only hazards associated with one layer of site geology, even though sites may include multiple layers, which limits the comprehensiveness of the hazard identification process. Second, adopting an online survey approach posed challenges in achieving a high response rate and gathering a representative sample, making it uncertain how the results might vary with a higher number of professional participants. This limitation affects the generalisability of the findings. Finally, while this study identified 65 potential hazards associated with brownfield sites, this number could be expanded to include hazards related to plants, animals and air, indicating the need for a more inclusive approach to hazard identification. Given these limitations, future research should focus on addressing these gaps.

The contributions of this study offer practical benefits. Firstly, it enables the initial risk assessment process to be more comprehensive and integrated and reduces complexity in the risk assessment process by ensuring that all probabilities, along with their significance, are identified at the initial stage of the risk assessment. This could be a strong starting point for successfully conducting a more detailed risk assessment and remediation. Secondly, the developed PRABS can promote effective environmental communication among stakeholders, which should speed up the planning process and help develop brownfield sites more efficiently and effectively, while preserving the natural environment.

This essay contributes to the ongoing exposition of a project management-focused understanding of “public policy” implementation. Distinct from previous studies that take a predominantly administrative sciences perspective, the delivery and implementation of publicly funded infrastructure projects as an instrument of public policy is explored through the lens of legal frameworks.

We adopt the explanation-building review approach to provide descriptions and explanations of the relevant enablers and context necessary for the successful delivery and implementation of publicly funded infrastructure projects.

The ambiguity associated with public policy is more likely to hinder than facilitate the use of publicly funded infrastructure projects as preferred instruments of choice for implementing public policy.

Despite substantial interest among academics and practitioners in utilising projects as tools for public policy implementation, thorough discussions on the legal complexities inherent in these projects remain scarce.

This study investigates the dependencies between the Global Food Security Index (GFSI) and its affordability-related indicators using Bayesian belief network (BBN) models. The research also aims to prioritise these indicators within a probabilistic network setting.

The research utilises BBN models to analyse data from 113 countries in 2022. Nine indicators related to food affordability, including income inequality, safety net programmes and trade freedom, are examined to understand their impact on food security. The methodology involves statistical modelling and analysis to identify critical factors influencing food security and to provide a comprehensive understanding of the global food affordability landscape.

The study reveals that income inequality, the presence and efficacy of safety net programmes and the degree of trade freedom are significant determinants of food affordability and overall food security outcomes. The analysis reveals marked disparities in performance across different countries, highlighting the need for context-specific interventions. The findings suggest that improving safety net programmes, implementing trade policy reforms and addressing income inequality are crucial for enhancing food affordability and security.

This research contributes to the literature by using BBN models to comprehensively analyse the relationship between the GFSI and affordability-related indicators. The study provides novel insights into how different socioeconomic factors influence food security across a diverse range of countries. The study offers actionable recommendations for policymakers to address food security challenges effectively, thereby supporting the development of more equitable and resilient food systems globally.

This study underscores the critical importance of well-functioning sewer systems in achieving smart and sustainable urban drainage within cities. It specifically targets the pressing issue of sewer overflows (SO), widely recognized for their detrimental impact on the environment and public health. The primary purpose of this research is to bridge significant research gaps by investigating the root causes of SO incidents and comprehending their broader ecological consequences.

To fill research gaps, the study introduces the Multi-Phase Causal Inference Fuzzy-Based Framework (MCIF). MCIF integrates the fuzzy Delphi technique, fuzzy DEMATEL method, fuzzy TOPSIS technique and expert interviews. Drawing on expertise from developed countries, MCIF systematically identifies and prioritizes SO causes, explores causal interrelationships, prioritizes environmental impacts and compiles mitigation strategies.

The study's findings are multifaceted and substantially contribute to addressing SO challenges. Utilizing the MCIF, the research effectively identifies and prioritizes causal factors behind SO incidents, highlighting their relative significance. Additionally, it unravels intricate causal relationships among key factors such as blockages, flow velocity, infiltration and inflow, under-designed pipe diameter and pipe deformation, holes or collapse, providing a profound insight into the intricate web of influences leading to SO.

This study introduces originality by presenting the innovative MCIF tailored for SO mitigation. The combination of fuzzy techniques, expert input and holistic analysis enriches the existing knowledge. These findings pave the way for informed decision-making and proactive measures to achieve sustainable urban drainage systems.