https://kes.hmu.ac.ir/ Knowledge Economy Studies en daily 1 Wed, 01 Apr 2026 00:00:00 +0330 Wed, 01 Apr 2026 00:00:00 +0330 https://kes.hmu.ac.ir/article_734950.html This study develops a data-driven framework for financial statement fraud detection by benchmarking machine learning, deep learning, and hybrid classifiers under a unified, leakage-resistant evaluation protocol. Fraud cases are identified from the U.S. Securities and Exchange Commission’s Accounting and Auditing Enforcement Releases (AAERs) and matched with Compustat data over 1991–2014, producing 122,526 firm-year observations, including 902 confirmed fraud cases. Four structured-input configurations are evaluated: 28 raw financial statement items, 14 financial ratios, their combined set (28+14), and a parsimonious seven-feature subset (six ratios plus Altman’s Z-score). Features are selected using minimum redundancy–maximum relevance (mRMR), class imbalance is addressed via cost-sensitive learning, and performance is assessed with a firm-level 80/20 split and stratified group-based five-fold cross-validation within training. Empirical results indicate that deep and hybrid models consistently outperform classical tabular baselines, reflecting non-linear and interaction-driven fraud signals. The Transformer achieves the most stable and highest overall performance, reaching 0.98898 accuracy and a 0.51087 F1-score under the seven-feature configuration. The combined raw-item and ratio inputs outperform ratios alone, implying incremental predictive value in raw accounting items, while the best overall outcomes are obtained with the parsimonious seven-feature subset. Collectively, the findings support the study’s hypotheses and demonstrate the effectiveness of attention-based modeling for financial statement fraud detection. https://kes.hmu.ac.ir/article_733739.html Hierarchical portfolio optimization methods, particularly the Hierarchical Equal Risk Contribution (HERC) approach, have become increasingly prominent in financial research due to their effectiveness in balancing risk and enhancing diversification. Unlike traditional methods such as Equal-Weight (EW) and Inverse Volatility (IV), which rely on oversimplified assumptions and often underperform in volatile markets, HERC allocates capital by distributing risk more efficiently across the assets. This study examined the performance of the HERC model relative to EW and IV to determine its ability to convert risk into investment opportunities under fluctuating market conditions. The methodology followed a structured process that includes deriving variables from multiple data sources, conducting thorough data cleaning and normalization, and implementing traditional allocation models as benchmarks. Advanced hierarchical clustering techniques were then applied to provide a more innovative allocation framework. Rigorous hypothesis testing was used to validate the results, and portfolio performance was evaluated using established statistical metrics. Findings revealed that HERC—especially its single linkage and average linkage versions—delivers substantially higher risk-adjusted returns, as measured by the Sharpe and Sortino ratios, compared to EW and IV. The proposed methodology not only improves overall investment outcomes but also enables more effective risk and return management, making it a strong alternative to conventional portfolio construction and risk evaluation approaches. https://kes.hmu.ac.ir/article_734948.html This study examines how firm-level environmental and social performance relates to stock price volatility in an emerging market characterized by limited transparency and weaker institutional frameworks. While prior research largely relies on linear models and focuses on developed economies, this study adopts a dynamic, data-driven perspective to capture potentially nonlinear and time-dependent sustainability–risk patterns. Using a panel of non-financial firms listed on the Tehran Stock Exchange (TSE) over the period 2011–2023, firm-level environmental and social indicators are constructed based on systematic analysis of sustainability disclosures. Empirical results from conventional linear regressions indicate weak and statistically insignificant average associations between sustainability performance and stock volatility. However, learning-based models, including reinforcement learning and LSTM neural networks, demonstrate superior ability to capture nonlinear and dynamic volatility patterns conditional on sustainability-related information. These findings suggest that sustainability disclosures contain predictive information for volatility dynamics, even when linear risk-reduction effects are not evident. The study highlights the importance of flexible modeling frameworks when assessing the financial implications of environmental and social performance in emerging markets. https://kes.hmu.ac.ir/article_733743.html Enterprise ontology serves as a foundational framework for semantically comprehending the nature of organizations and the essential components that uphold their integrity. The systematic and conceptual understanding of organizations has garnered significant attention from researchers due to its pivotal role in various domains, including business modelling, enterprise architecture, business process management, context-aware systems, application development, interoperability across diverse systems and platforms, knowledge management, organizational learning and innovation, and conflict resolution within organizations. Achieving a consensus on the concepts related to the fundamental elements that constitute an organization is therefore critical. This study aimed to conduct a comprehensive analysis and comparison of the existing conceptual models of enterprises as documented in scholarly articles published over the past decade.The comparison revealed significant variations in coverage, adaptability, and maturity across models, with many lacking completeness or alignment with comprehensive frameworks like Zachman’s framework. The strengths and weaknesses of each model were discussed and a robust framework for their evaluation was introduced. To facilitate this evaluation, we proposed several pertinent criteria derived from established methodologies for assessing the ontologies. Furthermore, we identified contemporary challenges and issues that have been overlooked in prior studies, offering insights and suggestions for future research directions in enterprise modelling. Ultimately, a roadmap for enhancing the systematic understanding of organizations through refined enterprise ontology frameworks was presented. https://kes.hmu.ac.ir/article_734949.html This research examines how external organizational factors influence the acceptance and readiness assessment for integrating big data analytics into citizen relationship management at Tehran Municipality. The study employs Davis's Technology Acceptance Model (TAM) in an applied, field-based design. Data were collected using a standardized questionnaire based on TAM, with a sample of 105 managers and experts from Tehran Municipality, and analyzed using structural equation modeling in SPSS and SmartPLS software. The questionnaire comprises five main dimensions, with validated reliability and validity. Results indicate that external factors (scalability, data storage and processing, data analysis capabilities, flexibility, and reliability), perceived ease of use, and perceived usefulness significantly impact the acceptance of big data analytics. Furthermore, organizational external components such as data storage and processing, flexibility, and reliability lead to satisfaction and intention to utilize big data analytics in managing citizen relations by creating perceived usefulness and confirming user expectations. These findings corroborate previous research and demonstrate that strategic attention to training, expert recruitment, hardware development, infrastructure enhancement, and information security can facilitate effective adoption of big data analytics, thereby creating opportunities for research development in legal, economic, and other fields. https://kes.hmu.ac.ir/article_733744.html The emergence of cryptocurrencies in recent years has been a novel phenomenon in the realm of digital economics, presenting opportunities and challenges for countries. The main aim of this study is to examine the challenges of developing cryptocurrencies with an emphasis on the Government Transformation. From the perspective of purpose, it is applied research, and in terms of subject matter, it falls within descriptive research. The data collection method used in this study was field-based through a questionnaire. In the first stage, challenges related to the development of cryptocurrencies were identified through library research using the Government Transformation document; subsequently, these challenges were formulated into a questionnaire and evaluated by experts using the Analytic Hierarchy Process (AHP). Finally, with the help of Expert Choice software, these challenges were ranked. The study results which is validated by a strong inconsistency rate of 0.06 indicate that, when prioritizing the main challenges, the weakness in macro-management of cryptocurrencies holds the first rank with a coefficient of 0.429, while the increasing share of hidden mining in the total cryptocurrency production market in the country sits at the last rank with a coefficient of 0.114Given the ranking of challenges and the identification of weakness in macro-management as the main problem, policymakers must immediately proceed to establish a unified and powerful command structure and prioritize full clarification of responsibilities. Simultaneously, to mitigate social risks and maintain stability, protecting small capital and safeguarding public trust in the financial system must be integrated as core strategies in planning. https://kes.hmu.ac.ir/article_733745.html Nowadays, in organizations, selecting the appropriate knowledge management (KM) technology has become one of the senior managers’ concerns in the KM area. The purpose of this study is to provide a model to help decision-makers with identifying and selecting the most appropriate KM technologies in‌ an ‌Iranian bank. In this research, KM technologies and criteria to select them are identified and are classified by doing librarian studies. Two questionnaire surveys were conducted to weigh technology selection criteria and determining the value of technologies based on criteria among KM experts of bank. The weight of each criterion was assigned by applying the analytical hierarchy process (AHP) and finally, all the technologies were ranked by using the PROMETHEE II method. The results indicate that in top rank is networking technology followed by organizational group communication/private social network technologies, social networking technologies, web/multimedia presenting technologies, web conferencing technologies, large audience webinars technologies, communications and collaborations technologies, virtual three-dimensional immersive collaboration technologies, and finally groupware.