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The purpose of this paper is to address a deficiency in the literature by exploring the impact of negative workplace safety announcements on firm performance. The authors analyze the issue from a corporate social responsibility perspective and explore ways supply chain managers can contribute to improve firm performance through the development of safe working environments.

Utilizing a sample of 227 negative workplace safety announcements, this paper explores the implications of negative workplace safety announcements on the stock price of a firm using event study methodology.

The authors find that negative workplace announcements are associated with an abnormal decrease in shareholder value. Furthermore, the authors find evidence that negative workplace safety announcements have a more pronounced negative effect on firm value in the present environment than in any previous time period.

Operations managers need to play leading roles in ensuring safe working environments. The results provide the support needed to acquire the financial resources necessary to mitigate exposure to unsafe working conditions.

This study explores the impact of negative workplace safety announcements on a firm’s stock performance. It is the first large-scale study to look at public announcements of workplace incidents and to explore the impact of such announcements in the context of time.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

Cost overrun is inherent to project chaos, which is one of the key drivers of project failure. The purpose of this paper is to explore the critical elements of complexity-risk interdependency for cost-chaos in the construction management domain by utilizing a multi-criteria decision model.

A total of 12 complexity and 60 risk attributes are initially identified from the literature and using expert’s judgements. For the development of a structured hierarchy of key complexity and risk drivers, a real-time Delphi process is adopted for recording and evaluating the responses from experts. Afterwards, a pair-wise comparison using analytical network processing is performed to measure complexity-risk interdependencies against cost alternatives.

The findings of the integrated priority decision index (IPDI) suggest that uncertainties related to contingency and escalation costs are the main sources of cost overrun in project drift, along with the key elements such as “the use of innovative technology,” “multiple contracts,” “low advance payment,” “change in design,” “unclear specifications” and “the lack of experience” appear to be more significant to chaos in complexity-risk interdependency network.

This study did not address the uncertainty and vulnerability exit in the judgment process, therefore, this framework can be extended using fuzzy logic to better evaluate the significance of cost-chaos drivers.

These results may assist the management of cost overrun to avoid chaos in a project. The proposed model can be applied within project risk management practices to make better-informed technical decisions in the early phases of the project life cycle where uncertainty is high.

This research addresses the importance of cost overruns as a source of project chaos in dynamic systems where projects reach the edge of chaos and progress stops. A new IPDI index contributes toward evaluating the severity of complexity and risk and their interdependencies which create cost-chaos in infrastructure transport projects.

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to date, there has been no systematic review of applications of Bayesian approaches in existing CM studies. This paper systematically reviews applications of Bayesian approaches in CM research and provides insights into potential benefits of this technique for driving innovation and productivity in the construction industry.

A total of 148 articles were retrieved for systematic review through two literature selection rounds.

Bayesian approaches have been widely applied to safety management and risk management. The Bayesian network (BN) was the most frequently employed Bayesian method. Elicitation from expert knowledge and case studies were the primary methods for BN development and validation, respectively. Prediction was the most popular type of reasoning with BNs. Research limitations in existing studies mainly related to not fully realizing the potential of Bayesian approaches in CM functional areas, over-reliance on expert knowledge for BN model development and lacking guides on BN model validation, together with pertinent recommendations for future research.

This systematic review contributes to providing a comprehensive understanding of the application of Bayesian approaches in CM research and highlights implications for future research and practice.

The turbulent phase of COVID-19 has caused uncertainty as governments fail to develop coherent strategies for cutting emissions and are struggling to match the rhetoric of sustainable activities with actions (Barbier & Burgess, 2020; Cawthorn, Kennaugh, & Ferreira, 2021). In the recent past, firms have failed in their plans to decarbonise their key sectors such as the retail sector in the United Kingdom So far, retailers' commitment to achieving net zero emissions has been an important pledge but delivery is nowhere closer to their promises (Henriques, 2020). The firms' climate targets are not going to be met by magic as serious action is needed to fulfil the promises.

Fossil fuels have led to a drastic increase in carbon emissions in the world over the last decade. Firms championing cleaner energy and low carbon technologies are needed to cut emissions. Renewable energy sources such as wind energy can help reducing the dependency of fossil fuels (Boretti, 2020; Ebhota & Jen, 2020). Wind is an indirect form of solar energy which can provide environment-friendly option in uncertain times and can provide long-term sustainability of global economy. Solar energy technologies have the potential to decrease climate change through energy-related emissions (Li, Dai, & Cui, 2020). Increasing energy demand has initiated a focus on using hydrogen from water as a substitute for oil and fossil fuels (Boretti, 2020).

The first part of the chapter discusses theoretical perspectives of sustainable development and environmental performance with regards to three main issues: energy, water and carbon emissions, whereas the later part highlights the importance of solar technology as a low-polluted alternative to fossil fuels in the retail sector. Sustainable development of energy, water and environmental precautions such as reducing carbon emissions are of interest to wider branches of industries including retail, energy and water sector, governmental policymakers, researchers, educators and society. The purpose of this chapter is to increase the debate of the key issues of sustainable development regarding environment, energy and water in the modern times.

Risk analysis plays a vital role in controlling and managing cost overruns in complex construction projects, particularly where uncertainty is high. This study attempts to address an important issue of cost overrun that encountered by metropolitan rapid transit projects in relation to the significance of risk involved under high uncertainty.

In order to solve cost overrun problems in metropolitan transit projects and facilitate the decision-makers for effective future budgeting, a cost-risk contingency framework has been designed using fuzzy logic, analytical hierarchy process and Monte Carlo simulation.

Initially, a hierarchical breakdown structure of important complexity-driven risk factors has been conceptualized herein using relative importance index. Later, a proposed cost-risk contingency framework has investigated the expected total construction cost in order to consider the additional budgeted cost required to mitigate the risk consequences for particular project activity. The results of cost-risk analysis imply that poor design issues, an increase in material prices and delays in relocating facilities show higher dependency and increase the risk of cost overrun in metropolitan transit projects.

The findings and implication for project managers could possibly be achieved by assuming the proposed cost-risk contingency framework under high uncertainty of cost found in this research. Furthermore, this procedure may be used by experts from other engineering domains by replacing and considering the complex relationship between complexity-risk factors.

This study contributes to the body of knowledge by providing a practical contingency model to identify and evaluate the additional risk cost required to compute total construction cost for getting stability in future budgeting.

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

Supply chain risks (SCRs) have uncertainty and interdependency characteristics that must be incorporated into the risk assessment stage of the SCR management framework. This study aims to develop SCR networks and determine the major risk drivers that impact the performance of the sago starch agro-industry (SSA).

The risk and performance variables were collected from the relevant literature and expert consultations. The Bayesian network (BN) approach was used to model the uncertain and interdependent SCRs. A hybrid method was used to develop the BN structure through the expert’s knowledge acquisitions and the learning algorithm application. Sensitivity analyses were performed to examine the significant risk driver and their related paths.

The analyses of model indicated several significant risk drivers that could affect the performance of the SSA. These SCR including both operational and disruption risks across sourcing, processing and delivery stage.

The implementation of the methodology was only applied to the Indonesian small-medium size sago starch agro-industry. The generalization of findings is limited to industry characteristics. The modelled system is restricted to inbound, processing and outbound logistics with the risk perspective from the industry point of view.

The results of this study assist the related actors of the sago starch agro-industry in recognizing the major risk drivers and their related paths in impacting the performance measures.

This study proposes the use of a hybrid method in developing SCR networks. This study found the significant risk drivers that impact the performance of the sago starch agro-industry.

Supply chain risks (SCRs) do not work in isolation and have impact both on each member of a chain and the performance of the entire supply chain. The purpose of this paper is to quantitatively assess the impact of dynamic risk propagation within and between integrated firms in global fresh produce supply chains.

A risk propagation ontology-based Bayesian network (BN) model was developed to measure dynamic SCR propagation. The proposed model was applied to a two-tier Australia-China table grape supply chain (ACTGSC) featured with an upstream Australian integrated grower and exporter and a downstream Chinese integrated importer and online retailer.

An ontology-based BN can be generated to accurately represent the risk domain of interest using the knowledge and inference capabilities inherent in a risk propagation ontology. In addition, the analyses revealed that supply discontinuity, product inconsistency and/or delivery delay originating in the upstream firm can propagate to increase the downstream firm’s customer value risk and business performance risk.

The work was conducted in an Australian-China table grape supply chain, so results are only product chain-specific in nature. Additionally, only two state values were considered for all nodes in the model, and finally, while the proposed methodology does provide a large-scale risk network map, it may not be appropriate for a large supply chain network as it only follows the process flow of a single supply chain.

This study supports the backward-looking traceability of risk root causes through the ACTGSC and the forward-looking prediction of risk propagation to key risk performance measures.

The methodology used in this paper provides an evidence-based decision-making capability as part of a system-wide risk management approach and fosters collaborative SCR management, which can yield numerous societal benefits.

The proposed methodology addresses the challenges in using a knowledge-based approach to develop a BN model, particularly with a large-scale model and integrates risk and performance for a holistic risk propagation assessment. The combination of modelling approaches to address the issue is unique.

Comprehensive project portfolio risk (PPR) analysis is essential for the success and sustainable development of project portfolios (PPs). However, project interdependency creates complexity for PPR analysis. In this study, considering the interdependency effect among projects, the authors develop a quantitative evaluation model to analyze PPR based on a fuzzy Bayesian network.

In this paper, the primary purpose is to comprehensively evaluate project portfolio risk considering the interdependency effect using a systematical model. Accordingly, a fuzzy Bayesian network (FBN) is developed based on the existing studies. Specifically, first, the risks in project portfolios are identified from the project interdependencies perspective. Second, a fuzzy Bayesian network is adopted to model and quantify the interaction relationships among risks. Finally, the model is implemented to analyze the occurrence situation and characteristics of risks.

The interdependency effect can lead to high-stake risks, including weak financial liquidity, a lack of cross-project members and project priority imbalance. Furthermore, project schedule risks and inconsistency between product supply and market demand are relatively sensitive and should also be prioritized. Also, the validity of this risk evaluation model has been proved.

The findings identify the most sensitive risks for guaranteeing portfolio implementation and reveal interdependency effect can trigger some specific risks more often. This study proposes for the first time to measure and analyze project portfolio risk by a systematical model. It can help systematically assess and manage the complicated and interdependent risks associated with project portfolios.