Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827218120260101Nonlinear Seismic Modeling of Soil Behavior in Interaction with Foundation for Heavy Structures115244404ENMohammadAghaei AslDepartment of Technology and Engineering, Payame Noor University, Tehran, Iran0009-0006-1667-6373Journal Article20250621In this research, the nonlinear seismic behavior of the soil–foundation interaction system under heavy structures has been investigated. A numerical model was developed in Abaqus software, where the contact behavior between soil and foundation was simulated considering slippage and shear yielding. To examine the effect of structure mass, analyses were conducted for masses ranging from 200 to 1000 tons under the recorded El Centro earthquake excitation. Output parameters, including horizontal displacement, foundation rotation, interface shear stress, and foundation settlement, were extracted and analyzed as functions of time. The results indicated that as the structure mass increases, the amplitude of horizontal displacement and foundation rotation decreases, while the static settlement and interface shear stress significantly rise. This response stems from the increased effective vertical force and contact pressure beneath the foundation, leading to enhanced frictional resistance and consequently reduced relative movement. The hysteresis loops obtained from the analyses demonstrate considerable energy dissipation at the soil–foundation interface, with greater intensity observed in heavier structures. Ultimately, by performing sensitivity analyses over the mass range of 200 to 1000 tons, power regression relations were derived between seismic responses and structure mass. These relationships can be utilized for rapid estimation of soil–foundation behavior during the preliminary design phases of heavy structures. The findings highlight that incorporating nonlinear soil–structure interaction plays a critical role in realistic prediction of dynamic response and long-term settlement control in the seismic design of deep foundations.https://www.jise.ir/article_244404_d742feafc35038e6887da329f0d9acdf.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827218120260101Productivity Analysis in the Banking System with a Short-Run and Long-Run Causality Approach1627244405ENAhooFamil BakhtiyariDepartment of Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.KambizHojabrkianiDepartment of Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.AbbasMemarnegadDepartment of Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.FarhadGhaffariDepartment of Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.Journal Article20250713Enhancing the productivity of financial markets plays a key role in the economic development of countries. Considering the bank-oriented nature of the financial systems in most nations, including Iran, identifying effective methods for measuring and improving productivity levels in banks is of great importance. Accordingly, the present study aims to examine the short-run and long-run causal relationships among capital adequacy, labor force, and total factor productivity (TFP) within a selected sample of ten banks listed on the Tehran Stock Exchange over the period 2010–2017 (1389–1396 in the Iranian calendar). The research results, obtained using the Dynamic Ordinary Least Squares (DOLS), the Vector Error Correction Model (VECM), and the Wald test, reveal a one-way causal relationship from capital adequacy to total factor productivity in the short run. In the long run, however, bidirectional and statistically significant positive relationships are found between labor force and capital adequacy with total factor productivity. Moreover, the error correction term, which represents the speed of short-run adjustment toward long-run equilibrium, is also evaluated.https://www.jise.ir/article_244405_dd4c6e635f2e4062fa89c334820ee96d.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827218120260101The Impact of Foreign Trade on the Mobility of Factors of Production in BRICS Countries2841244407ENShahabKazemiDepartment of Economics, Ab.c., Islamic Azad University, Abhar, IranAliEmami MeybodiDepartment of Energy Economics, Faculty of Economics, Allameh Tabatabai University, Tehran, Iran0000-0002-4823-4151AliNazariDepartment of Statistics, Ab.c., Islamic Azad University, Abhar, Iran0000-0002-8454-2311FaridAskariDepartment of Economics, Ab.c., Islamic Azad University, Abhar, IranJournal Article20240227In recent decades, alongside the expansion of globalization, examining relationship between foreign trade and the mobility of factors of production has become a central issue in international economics. According to Mundell’s theory, trade and factor mobility can be substitutes for one another; however, recent empirical evidence, particularly in emerging economies, points to the existence of a complementary relationship between the two. objective of this study is to investigate the impact of foreign trade on labor and capital mobility in the BRICS member countries over the period 2000–2025 and to empirically test the validity of Mundell’s theory in these countries. To achieve this objective, annual data extracted from the World Development Indicators (WDI) database are employed, and panel econometric methods are used. After testing for stationarity and cointegration among the variables, long-run coefficients are estimated using Fully Modified Ordinary Least Squares (FMOLS) method to analyze the long-term effects of trade liberalization and tariffs on factor mobility. results indicate that trade tariffs have a positive effect on labor mobility and a negative and statistically significant effect on capital mobility, while an increase in trade openness leads to a reduction in labor mobility and a strengthening of foreign direct investment flows. These findings suggest that the relationship between foreign trade and factor mobility in BRICS countries is not necessarily substitutive and exhibits a complementary nature in the case of capital. results emphasize the key role of trade policies in shaping factor mobility and need for coordination among trade, labor market, and investment policies.https://www.jise.ir/article_244407_e2aebd9710f920fb7393504cf68893f1.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827218120260101Artificial Intelligence-Driven Personalization and Its Effects on Consumer Behavior4256245163ENParvanehZeraati FoukolaeiDepartment of management,Jo.C, Islamic Azad University, Jouybar, iran0000-0003-2518-2982Journal Article20250104Artificial intelligence (AI) has emerged as a transformative technology that enables organizations to deliver highly personalized marketing experiences and improve customer interactions in digital environments. This study investigates the effects of AI-driven personalization on consumer behavior, focusing on customer engagement, purchase intention, customer satisfaction, and brand loyalty. A quantitative research approach was employed using survey data collected from consumers who regularly interact with AI-enabled digital marketing platforms. The proposed conceptual framework was tested using Structural Equation Modeling (SEM) to examine the relationships among the study variables. The findings reveal that AI-driven personalization has significant positive effects on customer engagement, purchase intention, and customer satisfaction. Furthermore, customer engagement, purchase intention, and customer satisfaction were found to positively influence brand loyalty, with customer satisfaction demonstrating the strongest impact. The results suggest that personalized experiences generated through artificial intelligence technologies enhance consumer perceptions, improve purchasing decisions, and strengthen long-term relationships between consumers and brands. The study contributes to the growing literature on artificial intelligence and marketing by providing empirical evidence regarding the behavioral outcomes of AI-based personalization. In addition, the findings offer practical implications for organizations seeking to leverage AI technologies to improve customer experiences and achieve sustainable competitive advantages in increasingly competitive digital marketplaces.https://www.jise.ir/article_245163_f1651783c4bd44c9fc75b2e7adf81511.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827218120260101Environmental Sustainability Business Model with an Emphasis on Circular Economy in Small and Medium-Sized Technology Enterprises(SMEs)5766246087ENMeisamMarbaghiDepartment of technology Management, Ro.C., Islamic Azad University, Roudehen, IranSirousTadbiriDepartment of technology Management, Ro.C., Islamic Azad University, Roudehen, IranAbasAsadiDepartment of Business Management, VaP.C., Islamic Azad University, Varamin, IranMahmonirBayanatiDepartment of technology Management, Health and Industry Research Centre, WT.C., Islamic Azad University, Tehran, Iran0000-0002-4236-6110Journal Article20250110The increasing environmental challenges associated with resource depletion, waste generation, and climate change have intensified the need for sustainable business practices across industries. Small and medium-sized enterprises (SMEs) in the technology sector face unique sustainability challenges due to rapid technological advancement, resource-intensive operations, and growing environmental expectations from stakeholders. This study aims to develop an environmental sustainability business model with an emphasis on circular economy principles for technology-oriented SMEs. Drawing upon the concepts of sustainable business models and circular economy, a conceptual framework was developed and validated through expert evaluation using a Design Science Research approach. The proposed model integrates four key dimensions: organizational enablers, circular economy practices, environmental sustainability outcomes, and business performance outcomes. The findings indicate that effective implementation of circular economy practices contributes to waste reduction, resource efficiency, energy optimization, and carbon reduction while simultaneously enhancing innovation capability, competitive advantage, organizational resilience, and sustainable growth. The study provides both theoretical and practical contributions by offering an integrated framework that supports the transition of technology SMEs toward environmentally sustainable and economically viable business operationshttps://www.jise.ir/article_246087_0cd22b0c3ede085c770c6a4fa4848f6e.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827218120260701Developing a Model of Philosophical Mindset and Mindfulness on Information Processing Styles in Problem-Solving Skills of Mathematics Teachers6782209778ENElhamGolijani MoghadamDepartment of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.MohsenRostamy-MalKhalifehDepartment of Mathematics and Computer Science, School of Basic Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran.Mohammad HasanBehzadiDepartment of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran.Journal Article20241030Information processing styles play a crucial role in mathematics teachers' problem-solving skills, as they directly impact their ability to teach effectively and address educational challenges. A precise understanding of these styles can enhance teaching methods and improve students' problem-solving skills, necessitating the development of a localized model. This article aims to formulate a model of philosophical mindset and mindfulness and examine their effects on information processing styles in mathematics teachers' problem-solving abilities. Developing this model requires an in-depth analysis to assess how philosophical beliefs, attention quality, and focus relate to problem-solving approaches. The study employs a mixed-methods approach (qualitative and quantitative) to explore, describe, interpret, and explain the research topic. Initially, qualitative interviews were conducted to identify model components. The research population comprises experts from the education departments of Districts 1, 2, and 3 in Tehran, who participated in interviews and completed a structured interpretive questionnaire. Data were analyzed using MAXQDA and ISM software. The results indicate that philosophical mindset, mindfulness, and their components significantly impact mathematics teachers' information processing styles in problem-solving. Based on these findings, practical recommendations are provided.https://www.jise.ir/article_209778_6b51685127d187f136edb31ff5ea5e8a.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827218120260101Fuzzy Multi-Objective Optimization Model for Online Businesses in International Markets: Reducing Response Time and Managing Inventory Uncertainty83104246716ENAnooshehCheginiDepartment of Management, Cha.C., Islamis Azad University, Chalous, Iran.MaryamRahmatyDepartment of Management, Chalous Branch, Islamic Azad University, Chalous, Iran.Mohammad JavadTaghipourianDepartment of Management, Cha.C., Islamic Azad University, Chalus,Iran.JavadGilanipourDepartment of Management, Cha.C., Islamis Azad University, Chalous, Iran.Journal Article20251029International online businesses face several challenges in supply chain management, including reducing customer response time and managing uncertainty in inventory levels. This study proposes a fuzzy multi-objective optimization model to improve supply chain performance in international environments. This model uses fuzzy numbers to handle demand fluctuations, transportation costs, and delivery time, and provides a flexible decision-making framework. To solve this model, four meta-heuristic algorithms, including NSGA-II, PSO, GOA, and GA, are used, and their performance in terms of reducing supply chain costs, optimizing delivery time, and increasing inventory stability is investigated. The results show that PSO and GOA provide the shortest response time, while NSGA-II significantly reduces overall costs. Also, sensitivity analysis showed that NSGA-II and GOA are more stable regarding demand fluctuations, while GA has the least flexibility. This research presents a novel framework for supply chain optimization in international digital businesses that can help increase competitiveness and improve service levels in global markets.https://www.jise.ir/article_246716_ab0873a38381dd139895baa3e672e0c0.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827218120260101Explaining the Conceptual Model of Financial Fraud Detection Based on Transparency and Financial Discipline Using Artificial Intelligence105116246777ENZeinabNateghi RostamiDepartment of Accounting, ST.C., Islamic Azad University, Tehran, IranRoyaDarabiDepartment of Accounting, ST.C., Islamic Azad University, Tehran, IranZohrehHajihahDepartment of Accounting, ST.C., Islamic Azad University, Tehran, IranJournal Article20250205This study develops and validates a conceptual model for detecting financial fraud in the financial reporting of firms listed on the Tehran Stock Exchange, emphasizing transparency and financial discipline through artificial intelligence. Based on established theoretical foundations, the model incorporates auditing, corporate governance, managerial, and financial indicators as the principal determinants of fraudulent reporting. Panel data covering the period 2013–2024 were collected and labeled using the adjusted Beneish M-Score (Adj-M-Score). Both conventional statistical methods and machine learning algorithms were applied to assess predictive performance. The results demonstrate that tree-based models, particularly XGBoost, achieve the highest predictive accuracy (AUC ≈ 0.85). Feature importance and SHAP analyses indicate that governance- and behavior-related variables, together with liquidity indicators such as the current ratio and operating cash flow to total assets, are the most influential predictors of fraud. Overall, integrating behavioral, financial, and governance dimensions within an explainable AI framework provides a robust and effective approach for improving financial transparency and detecting fraudulent reporting.https://www.jise.ir/article_246777_511fbe589820f66c7e1f7dca239dd785.pdf