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This study aims to examine the extent to which external auditors (EAs) use the work of the internal audit function (IAF) based on the purpose of its primary activities. The authors rely on attribution theory, which suggests that individuals search for meaning when an event occurs. In this setting, the authors explore how the overall (assurance vs advisory) or specific (e.g. risk management and evaluating internal controls) focus of IAF activities influences perceived EA reliance on the IAF’s work.

The authors first explore the research question with data extracted from a broad, longitudinal survey conducted triennially by the national chapters of the Institute of Internal Auditors in Austria, Germany and Switzerland. The data includes responses from 2014, 2017 and 2020 administrations of the survey. The authors conduct a parallel survey with practicing EAs attending two training sessions of a European office of a global network firm. Hypotheses were tested using ordered logistic regression.

Among the chief audit executive (CAE) participants, the authors observe that a balanced or primarily assurance-related purpose of the IAF, relative to a primarily advisory-related purpose, is associated with higher perceived EA reliance. The authors observe similar perceptions of the extent of reliance among the EA participants.

With a unique data set of practicing internal auditors from three countries, coupled with a sample of EAs, to the best of the authors’ knowledge, this study is the first to examine differences in EA reliance across the IAF’s primary roles. The study relies on data from three European countries, which differs from prior EA reliance literature with a largely North American focus. Further, comparison between perceptions of EAs and CAEs is a novel approach and this paper’s findings suggest that perceptions of CAEs could be a reliable proxy for EA-intended behavior.

Proactive risk assessment suggests that risk assessment should emphasize the consequences that it might cause and the opportunities it might create for firms. Hence, this study aims to validate risk impact on supply chain performance in the context of the Vietnamese construction sector. Also, a complex network, in which multiple risk factors mutually affect, impede or promote each other, is developed to assist managers in tackling unpredictable risks proactively. In particular, the authors investigate whether certain risks could be considered either challenges or opportunities for businesses in turbulent times to improve SC performance.

The construction industry is the focal study context as it is one of the most essential industries in charge of providing accommodations, infrastructures and employment for society. 289 valid responses used in this research are from a large-scale survey result, supported by a Japanese government project promoting sustainable socio-economic development in Vietnam.

From the study findings, the authors find that external risk brings opportunities for supply chain performance. Meanwhile, demand risk, when it occurs, can reduce the danger level of operational risk, which is an interesting finding of this research. It is evident that when multiple risk factors mutually affect, impede or promote each other, it provides a more meaningful examination of mutually interconnected supply chain risks.

Practitioners should perceive risks as an opportunity than a threat. This study contributes to preventing risks and guaranteeing an effective and efficient supply chain by tackling unpredictable risks in a disruptive period. Moreover, data on validating research models collected during the Covid-19 pandemic and Ukraine and Russia conflicts reflect the topicality of this study.

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 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.

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.

Introduction: Commercial banks play a vital role in the global economy, facilitating economic growth and providing essential financial services. As key intermediaries between savers and borrowers, these institutions operate in a dynamic and complex environment characterised by various risk factors that can significantly impact their profitability and overall stability. Understanding the interconnected relationships between credit risk, interest rate risk, liquidity risk, and profitability is crucial for effective risk management strategies and the development of appropriate regulatory frameworks.

Purpose: Commercial banks play a critical role in the global economy by facilitating economic growth and providing financial services. This study examines the interconnected relationships between credit risk, interest rate risk, liquidity risk, and profitability in commercial banking.

Methodology: The sample consists of licenced scheduled commercial banks on the Bombay Stock Exchange (BSE) from 2015 to 2022. Using the Smart PLS-SEM 3.0 path analysis technique, the study evaluates the combined influence of these risk factors on profitability and provides evidence-based recommendations for risk management strategies.

Findings: The findings can assist banks in enhancing their risk management practices, and regulators in developing appropriate regulatory frameworks. By understanding the key risk factors and their impact on profitability, banks and regulators can mitigate risks, enhance transparency, and promote stability within the banking sector.

Significance/value: The value of this study lies in its focus on the interconnectedness of risk factors, profitability, and the potential implications for decision-making, risk management strategies, regulatory frameworks, and the overall stability of the commercial banking sector.

This study aims to analyze how risk tolerance is influenced by bull and bear market phases, age and professional work experience (PWE) of investors in emerging economies. The authors also analyze how different market phases (bull and bear) influence risk tolerance of investors in emerging economies for different age groups and with varying PWE.

The study uses two quantitative methods, one-way ANOVA and hierarchical regression model (HLM) to analyze individual investors' financial risk tolerance (FRT) in India.

The authors find that age and PWE have positive relationship with FRT behavior. However, interactions of these variables with market phase variable indicate that risk tolerance has nonlinear increasing relationship with investor's age and PWE. The risk tolerance of older investors is consistently high in both bull and bear market conditions, while young investors display a nonlinear risk behavior in different market conditions.

The study suggests that financial planners should include a longitudinal risk profiling of investors based on age groups, PWE and the current market phase to better understand investors' FRT and also to prefer more context-specific advice to investors in emerging economies, which, consequently, result in increasing the retail investors' interest in otherwise sparsely participated equity market.

Interaction effect of bull and bear market phases on relationship between age and PWE and FRT has been scantly studied.

Because the construction industry is one of the largest waste producers, understanding the primary reasons for waste production is essential. The goal of this study is to identify the major causes of waste production over the project life cycle in Iran's construction industry and to propose effective solutions based on modern technologies like BIM.

After identifying the primary causes of construction and demolition waste production through interviews and literature analysis, solutions based on building information modeling (BIM) were provided. Then, using questionnaires and exploratory factor analysis (EFA), the areas impacting waste reduction were found.

The findings suggest that “prefabrication” is the best approach for improving time and quality, while “detection and prediction of errors in the design and construction phases” is the most cost-effective technique for addressing cost and environmental issues.

Cost, time, quality and environmental concerns may all be influenced by effective waste management throughout the project life cycle. Furthermore, utilizing state-of-the-art technologies has far-reaching implications for reducing material waste, resulting in more environmental-friendly construction.

This study aims to investigate the relationship between political instability and the performance of Islamic banks in emerging countries.

For a data sample of 93 Islamic banks in 20 emerging countries during the period from 2011 to 2016, the authors identify indicators that matter most for the activities of Islamic banks.

The study finds that a stable government and law and order are positively correlated with the health of Islamic financial institutions. On the other hand, corruption and military involvement in politics can create an unstable environment for businesses, leading to uncertainty and risk. The study also reveals that Islamic banks operating in regions or communities with lower risk of socio-economic conditions tend to exhibit higher levels of profitability.

Overall, the study provides valuable insights into the impact of political instability on Islamic banks in emerging countries.