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The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.

The aim of this paper is to study the effect of strategic and project related potential risks on project delivery in Qatar. Two objectives have been defined. The first is to identify potential risk indicators (manifest variables) and categorize them (constructs/latent variables) based on a literature review, while the second is to examine and rank the relationships between the indicators and constructs by developing a structural equation model.

Twenty-five indicators were identified from the literature review and categorized into five groups. To collect the data, an online questionnaire was distributed in Qatar, and 116 responses were obtained. Structural equation modeling (SEM) was used to examine the model. The model that was developed based on the research hypothesis met goodness-of-fit, reliability and validity requirements.

The results showed that all constructs contributed well to the model and that the project parties (PPs) have the highest contribution with an effect weight of 0.209 followed by economic and legal (EL) conditions with an effect weight of 0.205. Site and safety (SS) conditions were third with an effect weight of 0.200 while environmental, natural and technological (ENT) conditions were fourth with an effect weight of 0.1989. The last ranked construct is political and social (PS) conditions with an effect weight of 0.186. Based on the outcome of the SEM, recommendations were provided to industry professionals in Qatar about mitigating the impact of potential risks on construction project.

To the authors' best knowledge, this is the first study to quantify the effects of strategic and project related risks on a construction project using SEM, considering the risk management indicators of SS, EL, ENT, PS in Qatar. The study's practical implications are to enlarge the project's risk management plan by considering the strategic and project related risks to enhance the project performance for the cost overrun and delay. The study is intended for construction projects in Qatar, but it can easily be adapted to other parts of the world given the local circumstances.

This study aims to assess both internal and external factors that impact consumer attitudes and intentions with regard to the purchase of non-deceptive counterfeits. More specifically, this study examines the impact of integrity, brand consciousness, performance risk and social risk on the attitude and in turn on the purchase intention of consumers towards non-deceptive counterfeits.

A total of 679 valid responses from the university students in two different Gulf countries, namely, Oman (264) and Qatar (415) were gathered through a self-administered structured questionnaire and analysed through partial least square‐structural equation modeling.

All the predictors of consumer attitude appeared significant in both country samples except integrity. However, brand consciousness appeared insignificant in the sample of Oman. In addition, Purchase intention towards the non-deceptive counterfeits was significantly predicted by attitude and subjective norm in both samples.

In the domain of non-deceptive counterfeit literature, the findings of the study will substantially add value. Particularly, in the Gulf country context, the impact of internal psychological and external risk factors on the attitude and purchase intention of non-deceptive counterfeits will enhance the insights of existing literature and extend and proof the robustness of the theory of reasoned action.

This paper aims to explore the risk and benefit perceptions influencing beer patrons’ purchase and consumption decisions in India. It delves into the drivers behind consumers’ consumption patterns and attitudes towards beer, which subsequently impact their behavioural intentions, including word-of-mouth recommendations and repurchase intentions.

The study uses a structured questionnaire to collect data from 306 beer patrons in Delhi and the National Capital Region. Through exploratory factor analysis and structural equation modelling, the research examines the risk (5) and benefit (3) factors associated with beer consumption using a factor model consisting of 29 constructs. A conceptual framework illustrates the interrelationships between variables, which are subsequently validated empirically.

The findings reveal that patrons’ attitudes towards beer are influenced by their perceptions of risk and benefit. Specifically, health and socio-psychological risks are identified as the most significant and negatively impacting factors on patrons’ attitudes. Factorial analysis demonstrates that patrons’ attitudes towards beer are positively influenced by their perception of benefits such as value for money, sensory appeal and convenience. Furthermore, the study highlights that an increase in benefit perception or a decrease in risk perception leads to a favourable shift in patrons’ attitudes towards beer.

To the best of the authors’ knowledge, this study will be the first to investigate how beer consumers’ consumption patterns and purchase decisions are impacted by assessing consumer risk and benefit perceptions. This study will also aid stakeholders in tailoring their beer offerings better to meet the desires and requirements of their customers.

As public awareness of the concept of sustainable development has increased, a new investor market has appeared. These investors will only make investments in sustainable financial instruments. Yet, how corporate managers can effectively exploit this new financing concept to make their companies risk resilient remains unaddressed. This study, a conceptual research, aims to examine the impact of sustainable finance (SF) on business risk resilience (BR) and the impact of SF on risk management infrastructure (RI). It also addresses the impact of RI on BR and the mediating effect of the former between SF and BR in the corporate world. Finally, this research explores the moderating effect of managerial capability (MC) and firm technology-focused innovation capability (IC) between SF and RI.

This study incorporates both theoretical and empirical works in the sustainability, innovation, risk management and HRM fields. Afterwards, it constructs a conceptual model alongside suppositions that can be tested in further studies.

This study proposes that SF will enhance BR and RI. Moreover, RI will promote BR and positively intervene between SF and BR. Furthermore, MC and IC will reinforce the SF–RI impact such that the SF–RI impact will be strengthened for companies whose MCs and ICs are high than low.

This research affords suggestions for researchers in multidisciplinary fields. It reinforces BR and RI by introducing SF, MC and IC as tactical devices. It also serves as a reference point for forthcoming academics to investigate this conceptual model, empirically, in diverse industries worldwide.

Practical lessons for finance, investment and risk managers, as well as corporate investors are discussed.

This study provides a new research model that demonstrates how SF can be exploited to promote BR and build RI. It also shows how RI can bolster BR and how RI can connect SF to BR. This new model also exhibits how MC and IC moderate the impacts of SF and RI. Thus, it attempts to advance existing knowledge and theoretical frameworks.

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.

The Scottish economy was experiencing steady growth prior the hit of the COVID-19, with the pandemic causing the government to announce extreme lockdown measures with unprecedented impact on small and medium-sized enterprises (SMEs). Whilst some of the industry sectors in Scotland took a lighter hit, a large part of the organizations had to either adapt or completely disrupt their business. An essential aspect of their survival, risk management (RM) was among the areas requiring the most significant acceleration. This study compared the RM practices implemented by Scottish SMEs prior and after the outbreak as well as examined the attitudes of key decision-makers in the SMEs in relation to risk, including their perceived readiness for another crisis of similar significance.

An online survey based on ISO 31000 RM guidelines was distributed to 232 Scottish SMEs. Based on the official government reports and existing knowledge on how SMEs around the world have handled crisis events within the past 20 years, the authors developed the hypothesis that crisis events significantly accelerate SMEs' RM implementation. Around 13 items were tested in relation to the hypothesis and responses were tested via two-tailed T-test to establish significant statistical difference.

The research provides insight into the current state of risk management practices implemented by Scottish SMEs. As expected, SMEs showed significant difference in their RM implementation prior and after the COVID-19 outbreak. Whilst this has been viewed as a positive, motivations, priorities and approaches in managing risk demonstrated by the SMEs is questionable with views to their sustainable long-term recovery. The study highlights the lack of confidence instilled within the SMEs that they can handle another crisis of similar significance and provides directions for further investigation and improvements with the aim of helping the SMEs prepare better to mitigate the consequences of future crisis events.

In academic sense, the study offers a tested universal framework and a detailed questionnaire for assessment of RM strategy, applicable to organisations of various type, size and geography. Several implications with regards to managerial practices have been highlighted, including the neglect of the SMEs’ own internal environments and its significance in their risk strategies, the predominantly reactive approach to RM displayed by most Scottish SMEs as well as the neglect of compliance risk leading to potential quality and customer satisfaction issues preventing SMEs from full post-crisis recovery.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

This study aims to investigate the effect of accounting conservatism and business strategies as mitigating tools for bankruptcy risk. It determines the association among these factors and provides insights into the effectiveness of accounting discretion and business strategies in decision-making.

The study uses a sample of 83 nonfinancial listed firms in ASE for the period from 2013 to 2019. Bankruptcy risk is measured using the Altman Z-score (1968). Accounting conservatism is measured using the accrual-based approach, and optimal business strategies are identified through cluster analysis.

The results indicate that accounting conservatism has a significant negative effect on bankruptcy risk. Increased application of accounting conservatism practices leads to a decrease in the level of bankruptcy risk. However, the type of business strategy adopted by firms does not have a significant impact on bankruptcy risk, suggesting that firms are not effectively implementing their strategies to mitigate this risk.

This study focuses on nonfinancial listed firms in the ASE, limiting the generalizability of the findings to other contexts. The study's findings contribute to the understanding of the role of accounting conservatism in reducing bankruptcy risk but highlight the need for further research on the effectiveness of business strategies in mitigating this risk.

This study lies in understanding of the role of accounting discretion in financial evaluations and emphasizes the importance of accounting conservatism as a tool for mitigating bankruptcy risk. The study's insights provide valuable guidance to practitioners, regulators and researchers in this field.