Ports are vital in the global trading system but are also vulnerable to climate-related threats. This problem has not been widely studied, especially in Egypt. However, there is an urgent need to address climate-related threats to Egyptian ports, which could have significant economic and trade-related consequences. Therefore, exploring the Egyptian port administrations’ and stakeholders’ perceptions regarding climatic hazards is an urgent and essential matter for sustainable and resilient ports, considering their strategic economic importance. Consequently, this article is the first to examine how the port authorities perceive and respond to climate hazards in one of the most important and largest commercial Egyptian ports; it also explores their adaptation strategies and plans, considering the national agenda to develop the coastal ports respecting the sustainable development pillars, and fills the gaps in regional and national studies on seaports and climatic hazards. The paper focuses on the Great Alexandria Port, which includes the Alexandria and El-Dekheila ports. A questionnaire is designed and distributed in different port sectors for gathering relevant data and conducting interviews, discussions, and workshops. The findings declare obvious concern about developing the Egyptian ports under the national projects umbrella. Also, the port authorities are aware of the risks of climate change and believe some adaptation measures are necessary and could be essential soon. They are taking steps to adapt their ports, considering a specific strategy that includes rehabilitating infrastructure (breakwater and berths), functional facilities, and services, improving drainage systems to enhance their port’s resilience, and achieving the 2030 Agenda for Sustainable Development. Our research findings suggest that there are opportunities for scientists to engage with the port stakeholders to proactively develop plans to alleviate climate change’s effects on their ports.

This study compares different machine learning (ML) models, datasets, and dimensionality reduction techniques to determine their effectiveness in detecting the probability of interim financial statements fraud (FSF). Using the design science research (DSR) approach, the study adopts a quantitative approach with a set of secondary data from the financial reports published by non-financial firms listed on the Egyptian Stock Exchange from 2015 to 2022. The research used a set of financial features compromising ratios reflecting the firm’s leverage, profitability, liquidity, and efficiency. Indicators of fraud are based on the Beneish M-score model that demonstrates the possibility of reporting earning manipulations. The findings reveal that the Random Forest classifier outperforms other classifiers, especially with the oversampling dataset after preprocessing using the correlation-based dimensionality reduction method. This study aims to benefit investors, stakeholders, auditors, regulatory bodies, fraud examiners, and academics who pay precise attention to creating new, better methods to detect the probability of FSF. This study introduces novel ML models and dimensionality reduction techniques that have not been previously applied to detect the probability of FSF in an emerging context. The research provides unique insights and evidence on the most effective dimensionality reduction techniques for achieving the best detection results. Additionally, the study introduces innovative solutions to the data imbalance problem. Therefore, the results can enable regulatory bodies and practitioners to detect managerial opportunistic behaviors more accurately in a timely manner and provide a foundation for further academic research in the field.

This study investigates the influence of family-owned firms (FFs) in shaping managerial preferences for earnings management approaches, including accrual-based earnings management (AEM) and real earnings management (REM) within a sample of 109 non-financial firms listed on the Egyptian Stock Exchange. Analyzing a substantial dataset comprising 2,711 quarter-year observations from 2015 to 2022, the research employs the standard Jones (1991) accrual model to identify AEM while utilizing Roychowdhury (2006) approach to measure the utilization of REM through abnormal operating cash flows, discretionary expenses, and production levels. The findings of this study reveal that FFs significantly substitute AEM for REM to meet their pre-established family targets, with a significant inclination toward employing the REM approach. In contrast, the reliance on AEM appears statistically insignificant. These results illuminate the unique dynamics of earnings management within FFs, highlighting a potential oversight by auditors and regulators who may prioritize AEM over REM in their evaluations. Consequently, this research contributes important insights into the complexities of earnings management practices in the context of family dynamics, calling for a more nuanced understanding among stakeholders in the realm of corporate governance and financial monitoring.

This study classifies corporate financial performance in countries in the Middle East and North Africa (MENA) region, addressing the critical need for accurate and early identification of high-, moderate-, and low-performance companies. The selection of the MENA region was driven by its significant economic growth, diverse market structures, and increasing attractiveness for foreign investment, which makes accurate financial performance assessment important. Despite the growing interest in AI applications for corporate financial performance, a research gap still persists. Existing studies focus primarily on bankruptcy and financial distress prediction in developed countries, with rather limited studies on multi-class financial performance classification in the MENA region. This study addresses a significant gap in the corporate financial performance evaluation literature, which is the lack of a robust, comparative evaluation of advanced DL techniques against conventional ML methods for multi-class corporate financial performance prediction using high-dimensional data. This study employs a design science research (DSR) approach by developing an evaluation analytics artifact that integrates structured preprocessing, dimensionality reduction, and comparative ML and DL modeling, following the relevance, design, and rigor cycles. By employing a design science research (DSR) methodology, the research used a dataset from the Compustat database, comprising 7971 firm-year observations from 2013 to 2024. A rigorous dimensionality reduction process, including pairwise correlation filtering, resulted in a final set of 15 key classification features. The study compared three machine learning techniques—random forests (RFs), support vector machines (SVMs), and eXtreme Gradient Boosting (XGBoost), against one deep learning technique, deep neural networks (DNNs), for classifying the corporate financial performance of MENA-region companies. The models were trained to classify corporations into three performance classes (low, moderate, and high), using the earnings per share (EPS) as the target variable. The empirical findings indicate that all four machine learning algorithms achieved meaningful predictive performance in classifying EPS-based corporate performance. Among the benchmark models, the support vector machine (SVM) and random forest (RF) classifiers produced stable and competitive results, indicating strong generalization capabilities across firms and periods. XGBoost consistently outperformed the traditional machine learning models, delivering the highest overall classification accuracy and superior discriminatory power, highlighting its effectiveness in capturing nonlinear relationships and complex feature interactions. Similarly, the deep neural network further improved classification performance relative to the benchmark models and exhibited comparable results to XGBoost, especially in modeling high-dimensional data. This superior performance can substantially enhance earnings performance classification through early performance deterioration and improvement identification, allowing more proactive strategic and operational decisions.

This study examines how assurance level and assurer type jointly influence non-professional investors’ reactions to sustainability disclosures in an emerging market context. It employs a controlled 2 × 2 mixed-design experiment that manipulates assurance level (limited vs. reasonable) and assurer type (audit firm vs. non-audit firm). Data were collected from MBA and DBA students in Egypt as proxies for non-professional investors. Investor reaction is captured through multiple measures, including perceived sustainability performance, reliance on sustainability information, investment intention, stock valuation, and decision confidence. Non-parametric statistical techniques are used to test hypotheses, complemented by exploratory machine learning using SHAP values. The results provide strong and consistent evidence that the assurance level is the dominant factor shaping investor reactions. Reasonable assurance significantly enhances investor judgments across all key measures, whereas the type of assurer does not have a statistically significant independent effect. Additional analyses reveal that reasonable assurance from a non-audit firm elicits more favorable reactions than limited assurance from an audit firm, underscoring the primacy of assurance strength over provider identity. Exploratory findings further indicate that assurance influences investment decisions primarily through perceived sustainability performance and reliance on information. This study contributes to the literature by clarifying the relative roles of assurance level and assurer type and providing novel evidence from an emerging market setting (i.e., Egypt). The findings offer important implications for firms, assurance providers, and regulators seeking to enhance the credibility and decision usefulness of sustainability reporting.

The objectives of this study are to illustrate the evolution of the audit-quality research discipline over the past forty years, determine whether this research discipline is expandable and, if so, what potential future research avenues need further examination, and understand the current knowledge structure of the audit-quality research discipline. To achieve these objectives, we employed bibliometric techniques (keyword-frequency and co-word analyses) to review a dataset of 1,831 articles extracted from the Scopus database between 1981 and 2021. A newly introduced keyword frequency tool (K-indicator) was used to measure the evolutionary stages of the audit-quality research discipline. We then employed co-word analysis visualizations to present the cognitive structure of the audit-quality field. The K-indicator revealed that audit quality had become a mature discipline with established concepts, keywords, and conclusions. Also, it indicated that despite extensive audit-quality research, there is room for further research. The co-word analysis showed that audit quality had reached a tight and coherent status from 1981 to the end of 2021. Co-word visualizations indicated that the audit-quality structure revolves around four main themes: auditor characteristics, client-related factors, audit-firm characteristics, and audit regulations. Therefore, the audit-quality research discipline concentrated on some specific elements and ignored others. To the best of the authors’ knowledge, no similar study was conducted to determine whether the audit-quality notion is still researchable. Therefore, the results of this study would add much value to audit researchers, practitioners, and regulators.