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Steam explosions are a major safety concern in many modern furnaces. The explosions are sometimes caused by water ingress into the furnace from leaks in its high-pressure (HP) cooling water system, coming into contact with molten matte. To address such safety issues related to steam explosions, risk based inspection (RBI) is suggested in this paper. RBI is presently one of the best-practice methodologies to provide an inspection schedule and ensure the mechanical integrity of pressure vessels. The application of RBIs on furnace HP cooling systems in this work is performed by incorporating the proportional hazards model (PHM) with the RBI approach; the PHM uses real-time condition data to allow dynamic decision-making on inspection and maintenance planning.

To accomplish this, a case study is presented that applies an HP cooling system data with moisture and cumulated feed rate as covariates or condition indicators to compute the probability of failure and the consequence of failure (CoF), which is modelled based on the boiling liquid-expanding vapour explosion (BLEVE) theory.

The benefit of this approach is that the risk assessment introduces real-time condition data in addition to time-based failure information to allow improved dynamic decision-making for inspection and maintenance planning of the HP cooling system. The work presented here comprises the application of the newly proposed methodology in the context of pressure vessels, considering the important challenge of possible explosion accidents due to BLEVE as the CoF calculations.

This paper however aims to optimise the inspection schedule on the HP cooling system, by incorporating PHM into the RBI methodology, as was recently proposed in the literature by Lelo et al. (2022). Moisture and cumulated feed rate are used as covariate. At the end, risk mitigation policy is suggested.

In this paper, the proposed methodology yields a dynamically calculated quantified risk, which emphasised the imperative for mitigating the risk, as well as presents a number of mitigation options, to quantifiably affect such mitigation.

Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.

This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.

The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.

This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.

Construction fall-from-height accidents are not only caused by a single factor but also by the risk coupling between two or more factors. The purpose of this paper is to quantitatively analyze the risk coupling relationships between multiple factors and identify critical factors in construction fall-from-height accidents.

A cause analysis framework was established from the perspective of human, machine, material, management and environmental factors. The definition, the classification and the process of risk coupling were proposed. The data from 824 historical accident reports from 2011 to 2021 were collected on government websites. A risk coupling analysis model was constructed to quantitatively analyze the risk coupling relationships of multiple factors based on the N-K model. The results were classified using K-means clustering analysis.

The results indicated that the greater the number of causal factors involved in risk coupling, the higher the risk coupling value and the higher the risk of accidents. However, specific risk coupling combinations occurred when the number of their coupling factors was not large. Human, machine and material factors were determined to be the critical factors when risk coupling between them tended to pose a greater risk of accidents.

This study established a cause analysis framework from five aspects and constructed a theoretical model to quantitatively analyze multi-factor coupling. Several suggestions were proposed for construction units to manage accident risks more effectively by controlling the number of factors and paying more attention to critical factors coupling and management and environmental factors.

Project information is widely and thoroughly exchanged within construction projects. However, the risk management process exhibits deficiencies in coordination and visibility, particularly in developing countries. Practitioners in developing countries often engage in Project Risk Management (PRiM) using practices that inadvertently hinder project success, frequently resulting in suboptimal outcomes. Therefore, this research explores practices within Project Integration Management (PIM) and Project Communication Management (PCmM) that could enhance PRiM and improve managerial proficiency to achieve project success in developing countries.

The PIM, PCmM and PRiM processes were explored from the literature; the data was gathered initially by close-ended interviews conducted with a panel of twelve experts followed by a well-structured questionnaire. Structural Equation Modeling (SEM) was employed via AMOS to analyze the data and construct a model representing the intricate relationships between the processes. Additionally, the validity and reliability of the method were inspected.

The data analysis confirmed that PIM and PCmM were correlated according to contractors and consultants in the Jordanian construction industry. Furthermore, both have a positive influence on the PRiM. In addition, a 13-step process was developed to apply extra processes and practices to ensure better implementation of the PRiM in developing countries.

The literature highlights that integration and communication management influence project performance. However, there is a lack of research utilizing practices from these knowledge areas to achieve better project risk management implementation. This research highlights two of the most underrated knowledge areas in project management. Therefore, a framework was devised, comprising processes that practitioners should take into account during the planning phase leading to efficient PRiM to enhance their managerial proficiency.

From the buying club’s perspective, the transfer of a player can be interpreted as an investment from which the club expects uncertain future benefits. This paper aims to develop a decision-oriented approach for the valuation of football players that could theoretically help clubs determine the subjective value of investing in a player to assess its potential economic advantage.

We build on a semi-investment-theoretical risk-value model and elaborate an approach that can be applied in imperfect markets under uncertainty. Furthermore, we illustrate the valuation process with a numerical example based on fictitious data. Due to this explicitly intended decision support, our approach differs fundamentally from a large part of the literature, which is empirically based and attempts to explain observable figures through various influencing factors.

We propose a semi-investment-theoretical valuation approach that is based on a two-step model, namely, a first valuation at the club level and a final calculation to determine the decision value for an individual player. In contrast to the previous literature, we do not rely on an econometric framework that attempts to explain observable past variables but rather present a general, forward-looking decision model that can support managers in their investment decisions.

This approach is the first to show managers how to make an economically rational investment decision by determining the maximum payable price. Nevertheless, there is no normative requirement for the decision-maker. The club will obviously have to supplement the calculus with nonfinancial objectives. Overall, our paper can constitute a first step toward decision-oriented player valuation and for theoretical comparison with practical investment decisions in football clubs, which obviously take into account other specific sports team decisions.

The study aims to analyze how the characteristics of intellectual capital (IC) facilitate green entrepreneurship development in the context of ecology, environment and sustainability. Specifically, the evolution of IC and green entrepreneurship was explored through a systematic review, including the relationships and interactions between human, structural and relational capital and green entrepreneurship.

Meticulously combing the Web of Science Core Collection, the researcher conducted a bibliometric analysis of 800 English-language articles from 2002 to 2023. Employing co-word analysis and visualization, the literature on IC and green entrepreneurship was synthesized and systematized, exploring core topics, knowledge architectures and their evolutionary trajectories.

The IC elements such as human, structural and relational capital interact with green entrepreneurship; IC enhances the innovation and competitiveness of green entrepreneurship, while green entrepreneurship orientation influences the accumulation and reshaping of IC. The flow of IC impacts the establishment of green start-ups and the emergence of green industries, promoting sustainable growth.

The dynamic interplay between IC and green entrepreneurship is marked by intricate relationships and diverse attributes. Currently, no comprehensive theoretical model has been established to address the complexities intrinsic to this study. The evidence suggests that the green entrepreneurial orientation influences corporate initiatives to bolster human and structural capital, with structural capital serving as both a constraint and catalyst for human capital. The paper presents an embryonic framework of IC for green entrepreneurship, highlighting its critical role in the aggregation and reconfiguration of IC or venture creation and industry evolution. This contributes to a more profound understanding of IC in entrepreneurial contexts, providing a basis for future research and practical strategy.

The main purpose of this study is to reveal the degree of association between lean, building information modeling (BIM) and construction project success. The study further intends to provide strategies for high and low associations of the factors.

Lean construction and BIM are two important applications that have recently gained popularity in terms of enhancing project success. Considering this impact, this study investigates the synergy between lean construction and BIM and determines to what extent these two contribute to the success of the projects. As a first step, lean, BIM and project success were examined based on an in-depth literature review. In the second stage, a structural equation model (SEM) was established to reflect the relationship among these three through hypotheses. Then, a questionnaire was designed and administered to the construction professionals experienced in both lean and BIM implementation. The SEM was tested using Analysis of Moment Structures (AMOS), an SPSS tool.

The results indicated that lean implementation has a significant and positive impact on BIM implementation and project success. On the other hand, BIM implementation had a lower significant impact on project success than lean implementation construct.

The results of this study can be used by both policymakers and industry practitioners in terms of developing strategies for effectively using both lean and BIM. The researchers can further develop other implementation models to investigate whether these concepts are more effective in increasing project success when used integratively.

This study considers both the impact of lean and BIM on project success through input from construction practitioners working on large projects. This way, the study fosters the use of lean, BIM or lean–BIM together in construction projects to enhance project success.

The current research aims to investigate the impact of coronavirus 2019 (COVID-19) evolution, government stringency measures and economic resilience on stock market volatility in the Middle East and North African (MENA) emerging markets. Other macroeconomic factors were also taken into account.

Based on financial data from 10 selected MENA countries, we tested an integrated framework that has not yet been explored in prior research. The exponential generalized autoregressive conditional heteroskedasticity (E-GARCH) was adopted to analyze data from March 2020 to February 2022.

Our research illustrates the direct and indirect effects of the virus outbreak on stock market stability and reports that economic resilience could alleviate the volatility shock. This finding is robust across the various proxies of economic resilience used in this study. We also argue that the negative impact of the pandemic on equity market variation gets more pronounced in countries with higher level of stringency scores.

Policymakers ought to strengthen their economic structures and reinforce the economic governance at the national level to gain existing and potential investors’ trust and ensure lower stock market volatilities in times of crisis. Our study also recommends some key economic factors to consider while establishing efficient policies to tackle unexpected shocks and prevent financial meltdowns.

Our findings add to the evolving literature on the reaction of economic and financial markets to the sanitary crisis, particularly in developing countries where research is still scarce. This study is the first of its kind to investigate the stock market reaction to stringency measures in the understudied MENA region.

The purpose of this study is to examine the differential impact of qualitative and quantitative informational signals within the merger and acquisition (M&A) press releases on deal completion and duration. A significant percentage of deals by emerging market acquirers get abandoned before completion, and those that are completed have a longer duration. The limited information about the operations of acquirers from emerging markets creates suspicion among the stakeholders involved in deal resolution, hindering the completion of deals. Thus, using the signal-feedback paradigm, authors investigate how informational signals in the M&A press release impact the deal resolution.

The study employs content analysis on M&A press releases announced by firms from five emerging economies: Brazil, Russia, India, China and South Africa. The technique is applied based on the exploration-exploitation framework developed by March (1991) to categorize the announced deal motives (qualitative information). Next, the authors identify the percentage of relevant quantitative information disclosed in the press release, following which results are obtained using logistic and ordinary least square regressions.

The study reports that deals with declared exploratory motives take longer to complete. Additionally, deals disclosing higher percentage of quantitative disclosure exhibit lower completion rate and increased deal duration.

This is the first study to provide evidence that familiarity bias impacts deal duration as relative to exploitation deals that are familiar to the stakeholders; exploratory deals take longer to conclude. Further, our analysis indicates that a greater percentage of quantitative disclosure may not always reduce information risk but rather be interpreted negatively in the form of the acquirer’s overconfidence in the deal’s potential.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.