Journal of Resource Management and Decision Engineering
The Journal of Resource Management and Decision Engineering is a pioneering open access, peer-reviewed journal committed to disseminating cutting-edge research and practical insights into the complex world of resource management and decision-making processes. Our mission is to foster a dialogue among professionals, researchers, and practitioners to enhance the understanding and practice of efficient resource allocation and decision-making strategies across various disciplines. Our open access mandate supports a broader global exchange of knowledge, making research freely available to the international community without any barriers. <p style="padding: 0px; margin: 8px 0px 0px; word-break: break-word; font-size: 14px; line-height: var(--cib-type-body1-line-height); font-weight: 400; font-variation-settings: var(--cib-type-body1-font-variation-settings); color: #111111; font-family: -apple-system, Roboto, SegoeUI, 'Segoe UI', 'Helvetica Neue', Helvetica, 'Microsoft YaHei', 'Meiryo UI', Meiryo, 'Arial Unicode MS', sans-serif; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: #f3f3f3; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">This journal adheres to the ethical standards in publications and complies with the regulations of the Committee on Publication Ethics (COPE). It also follows the executive bylaw for the prevention and handling of academic misconduct. Aims and Scopes JRMDE aims to: Advance the theoretical foundations and practical applications of decision engineering and resource management across various engineering disciplines, including computer, electrical, civil, mechanical, and industrial engineering. Promote interdisciplinary research that addresses significant challenges in the allocation and management of resources across diverse sectors. Provide a platform for innovative ideas and novel methodologies that can influence policy-making and operational practices. Encourage the integration of technology and analytical tools in decision-making processes to enhance effectiveness and efficiency within the fields of computer, electrical, civil, mechanical, and industrial engineering. Scope The scope of JRMDE encompasses a wide array of topics within resource management and decision engineering, including but not limited to: Theoretical models and practical applications of decision analysis across computer, electrical, civil, mechanical, and industrial engineering. Resource allocation policies and strategies pertinent to various engineering disciplines. Sustainability and environmental impact assessments in resource management, with applications in civil and environmental engineering. Technological advancements and their impact on resource planning in computer, electrical, mechanical, and industrial engineering. Ethical considerations and societal impacts of decision-making processes in engineering fields. Fields and Areas Covered JRMDE covers a broad spectrum of fields and areas, reflecting the interdisciplinary nature of resource management and decision engineering: Business and Economics: Corporate decision-making processes, economic planning, and financial impacts of resource management strategies. Environmental Sciences: Environmental policy, natural resource management, and strategies for sustainable development. Engineering and Technology: Computer Engineering: Development of algorithms and software for decision support systems, and the role of artificial intelligence in resource management. Electrical Engineering: Optimization of electrical grids, smart grid technologies, and energy resource management. Civil Engineering: Infrastructure planning, construction resource allocation, and sustainable urban development. Mechanical Engineering: Efficiency improvements in mechanical systems, manufacturing resource management, and automation technologies. Industrial Engineering: Process optimization, supply chain management, and integration of industrial systems for resource efficiency. Public Policy and Administration: Governance of resources, public health decision-making, and infrastructure planning. Healthcare and Life Sciences: Resource allocation in healthcare, biomedical decision support systems, and public health logistics. Data Science and Analytics: Big data applications in decision-making, predictive analytics for resource management, and machine learning techniques.
Africa’s Mineral Resources and Global Security: Africa’s Challenges in Relations with Global Powers
This article aims to analyze the complex dynamics of interactions between African countries and these powers, and to identify the challenges arising from extractive models across the continent. Using a qualitative and analytical approach within the framework of geopolitical political economy, the study examines governance structures, foreign investment strategies, and development outcomes associated with resource exploitation in key African countries. The findings reveal that the urgency of global powers to secure exclusive control or reliable access to Africa’s minerals has reinforced neo-colonial dynamics and neglected the priorities of sustainable development in the continent. This global competition not only exacerbates trade inequalities but also deepens the cycles of the resource curse in mineral-exporting countries due to reliance on weak and corrupt governance systems. The central challenge for Africa in these relationships is the absence of a unified and coherent strategy for leveraging its geoeconomic resources to achieve local industrialization, build processing infrastructure, and ensure national sovereignty over resource revenues. To ensure sustainable global mineral security, it is essential to redefine the structure of international relations with Africa. This reconfiguration must prioritize Africa’s full sovereignty over its resources, support the development of raw material processing capacities within the continent, and promote mutually beneficial economic partnerships instead of focusing solely on extraction-oriented and procurement-driven models. Otherwise, the escalation of global competition will inevitably lead to increasing political and social instability in Africa’s mineral-rich countries, ultimately threatening the stability of global supply chains.
Copyright (c) 2024 Majid Rasouli (Author) https://creativecommons.org/licenses/by-nc/4.0
A Sociological Analysis of the Role of Group Norms in Managing Organizational Decision-Making Conflicts
The purpose of this study was to analyze the sociological role of group norms in managing organizational decision-making conflicts, focusing on the structural, communicative, and emotional mechanisms through which norms influence conflict resolution processes. This research employed a mixed-method design combining qualitative and quantitative approaches. In the first phase, a qualitative content analysis was conducted through an extensive literature review of sociological, psychological, and organizational studies, using NVivo 14 software for open, axial, and selective coding until theoretical saturation was reached. Nine main themes and their subcomponents were extracted to represent the sociological structure of group norms in organizational contexts. In the second phase, a quantitative ranking analysis was carried out with 150 participants selected from organizations in Roudbar, Iran. A structured questionnaire based on the qualitative results was used to evaluate the relative significance of each theme. Data were analyzed using SPSS 26, applying descriptive and inferential statistics to prioritize factors influencing decision-making conflict management. The results revealed nine core themes, among which Normative Integration, Conflict Resolution Mechanisms, and Decision-Making Legitimacy achieved the highest mean ranks. These themes emphasize the importance of shared values, reconciliation norms, and perceived procedural fairness in regulating conflict behavior. Communication Patterns and Trust and Reciprocity occupied moderate rankings, indicating their essential but supportive roles in maintaining transparency and cooperation. Lower-ranked factors such as Power Dynamics, Adaptive Norm Evolution, and Cultural Symbolism were also found to indirectly sustain organizational harmony through symbolic and evolutionary mechanisms. The findings underscore that group norms serve as sociological regulators of behavior, ensuring both emotional balance and procedural justice in conflict-laden decision-making environments. Strengthening normative coherence can thus enhance organizational resilience, cooperation, and legitimacy.
Group norms; Organizational conflict; Decision-making; Sociological analysis; Conflict resolution; Organizational behavior; Normative integration.
Copyright (c) 2024 Seyed Mousa Raeisi https://creativecommons.org/licenses/by-nc/4.0
Design and Validation of a Value-Based Human Resource Development Model Grounded in Social Responsibility Using Partial Least Squares Structural Equation Modeling (PLS-SEM): Evidence from the Iranian Banking Industry
This study aimed to identify and design a model of the requirements and pillars for formulating human resource development (HRD) policies grounded in cultural values and social responsibility within the banking industry. The qualitative phase of the research was conducted using a systematic approach and semi-structured interviews with experts and specialists from the banking system and academia. The thematic framework of the study was developed through thematic analysis. The categorization of 1,089 initially identified themes led to the formation of 222 basic themes and 80 organizing themes (main components) grouped into 10 dimensions and 6 overarching themes. The quantitative phase included senior and middle managers of state-owned, semi-state, and private banks in Iran. Based on Krejcie and Morgan’s table, the sample size was estimated at 384 individuals, selected through convenience (accidental) sampling. Out of 440 distributed questionnaires, 410 were deemed valid and included in the statistical analysis. In the quantitative section, data analysis and model fit evaluation were conducted using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) method. The quantitative results indicated a relatively strong structural model fit (R² = 0.041) and high predictive power for the research model. Findings revealed that the relationship between social accountability and responsibility with legal and supportive requirements, despite being ranked lowest in importance by qualitative participants, had the highest path coefficient (0.74). The relationships between the dimensions of enhancing social interaction and participation in the CSR domain with operational and developmental axes, as well as institutionalizing an organizational culture grounded in values and CSR with cultural requirements—both with a path coefficient of 0.65—were jointly ranked second in impact. Moreover, the relationship between legal and programmatic support for CSR and legal-supportive requirements, despite being deemed most important by qualitative participants, exhibited the lowest path coefficient in the formulation of HRD policies within the Iranian banking system.
Copyright (c) 2026 Ahmad Reza Karimi Fard, Akbar Etebarian Khorasgani, Alborz Gheitani, Reza Ebrahimzadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Investment Model in Financial Systems with a FinTech Approach
The purpose of this study is to analyze and design an investment model in financial systems using a FinTech-oriented approach, employing the meta-synthesis method to integrate existing research findings in this field. This method enables researchers to thoroughly examine previous articles and studies to identify and synthesize the dimensions and components of strategic models for investment development. In this research, after identifying and selecting credible articles from scientific databases and journals, the data were analyzed through document analysis and the meta-synthesis process. The findings indicate that strategic investment models in FinTech should focus on utilizing emerging technologies such as artificial intelligence, blockchain, and digital payments in order to effectively manage financial and technological risks inherent in this industry. Furthermore, one of the significant results of this study is the emphasis on training and empowering FinTech managers in financial risk analysis and the adoption of optimal investment strategies. The research also highlights that international cooperation in scientific and technological domains can lead to knowledge exchange and the strengthening of technical infrastructures within the FinTech industry. Ultimately, this study can serve as a theoretical foundation for the design and implementation of strategic models in emerging industries based on information technology—particularly in the FinTech sector—and assist decision-makers and investors in this industry in leveraging optimal investment opportunities.
Copyright (c) 2024 Hamid kalaei (Author); Sina Abouei Mehrizi; Hossein Moinzad, Mehrzad Minouei (Author) https://creativecommons.org/licenses/by-nc/4.0
Identifying Strategies for Positioning Theatre in the Media Consumption Portfolio of Generation Z
This study aims to identify and prioritize strategies for effectively positioning theatre within the media consumption portfolio of Generation Z (Gen Z). Having grown up in the digital age and the realm of social media, Gen Z exhibits a strong preference for instant, interactive, and multi-platform content, demonstrating distinct behaviors in cultural consumption. The study’s findings suggest that the evolution in Gen Z’s media access and interactive experience significantly impacts their cultural and artistic engagement. This necessitates a fundamental re-evaluation of the traditional theatre format to reclaim its desired position. Key challenges identified include limitations related to time, location, cost, and a perceived mismatch between current theatrical content and the concerns of this generation. However, the inherent advantages of the live experience, such as being present in the moment, direct interaction, and the in-depth analysis of topics, remain core strengths of theatre that can effectively attract Gen Z. Essential strategies for successful positioning include leveraging new digital technologies, offering online streaming, integrating hybrid arts, and creating a safe space for the expression of diverse identities. This research proposes that through innovation in content, narrative structure, and audience engagement, theatre can strengthen its educational, social, and cultural role in the lives of Gen Z. This revitalization will foster cultural growth, enhance social participation, and improve the mental well-being of this generation, thus ensuring a dynamic future for the art of theatre in the digital landscape.
Copyright (c) 2026 Mehran Samadi, Azam Hesabi Lashkjani (Author) https://creativecommons.org/licenses/by-nc/4.0
Design of an Optimized Array Antenna in the 64–84 GHz Frequency Band for Application in Autonomous Vehicles
Automotive radar systems require antennas with specific characteristics to achieve optimal performance. One of the main challenges in this field is reducing signal interference and improving beam steering. In this study, a planar array antenna for millimeter-wave automotive radar applications is proposed, which is capable of meeting these requirements. The antenna consists of thirteen array elements with a linear series feed and a coaxial feeding network. Series-fed microstrip patch array antennas are popular options for millimeter-wave radar applications due to their simple structure, high gain, and low cost. However, the limited impedance bandwidth remains one of the main challenges in designing such antennas. In this research, by employing specific techniques in array and transmission line design, not only is the impedance bandwidth increased, but the sidelobe level is also reduced and beam steering is significantly improved. Simulation results demonstrate a maximum sidelobe level of –13 dB in the E-plane and –10 dB in the H-plane. Additionally, an impedance bandwidth of 20 GHz and a gain higher than 14 dB are achieved.
Copyright (c) 2026 Hamidreza Rabiee, Omid Rahmani Seryasat (Author) https://creativecommons.org/licenses/by-nc/4.0
Developing a Model of Green Supply Chain Management Practices with Emphasis on the Behavioral Perspective to Improve Performance
In the modern era, attention to performance considering the environmental impacts of industrial activities has become one of the most important priorities for organizations and governments. Green Supply Chain Management (GSCM), as an innovative approach within supply chain management, plays a pivotal role in reducing negative environmental effects and enhancing organizational performance. This study presents a comprehensive model of green supply chain management practices, analyzing the role of behavioral perspectives in improving performance. In the first phase, a qualitative research design and content analysis method were employed to identify effective behavioral practices of green supply chain management aimed at improving performance through a systematic review of relevant articles. In the next phase, a quantitative approach utilizing the Delphi method was conducted to validate the identified behavioral practices of green supply chain management that contribute to performance enhancement. Finally, the Interpretive Structural Modeling (ISM) method was applied to classify the effective behavioral practices of green supply chain management for performance improvement, and the causal and dependent relationships among these behavioral factors were determined. The findings indicate that factors such as training and empowerment of members, risk-taking, top management support, government legitimization, corporate social responsibility, beliefs and values, as well as commitment and acceptance of the green supply chain, have the strongest direct impacts.
Copyright (c) 2026 Ali Nourizadeh Firouzabadi , Hasan Dehghan Dehnavi, Mohammad Taghi Honary , Mozhde Rabbani, Abolfazl Sadeghian (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification of the Factors and Consequences of Earnings Management in the Chemical Products and Automotive Parts Manufacturing Industries Using the Meta-Synthesis Approach and Expert Opinions
Due to their process complexity and high levels of investment, industries exhibit significant sensitivity to the transparency and accuracy of financial reporting. Earnings management may temporarily enhance the financial health of firms; however, in the long term, it leads to a decline in stakeholder trust, disruption in investors’ decision-making, and reduced efficiency in the capital market. Moreover, identifying the consequences of earnings management contributes to improving regulatory frameworks and enhancing the quality of detecting and reporting financial misconduct within these industries. This, in turn, paves the way for sustainable development and fair competition in both domestic and international markets. The present study was conducted with the objective of identifying the factors and consequences of earnings management in the chemical products and automotive parts manufacturing industries. Data collection was performed through semi-structured interviews with experts. Using the snowball sampling technique, interviews were conducted with 15 academic experts and senior managers from active companies within the chemical products and automotive parts industries. The data were analyzed through a meta-synthesis approach and expert judgment. The Content Validity Ratio (CVR) was used to assess content validity, and Holsti’s coefficient was applied to measure reliability. The identified factors included financial leverage, stock liquidity, free cash flow, information asymmetry, long-term effective tax rate, institutional ownership, executive management ownership, board size, profitability, differentiation strategy, cost leadership strategy, and corporate social responsibility. The identified consequences comprised cash holding level and tax avoidance.
Copyright (c) 2024 Samaneh Elahi Roodposhti (Author); Ebrahim Chirani; Seyed Mozafar Mir Bargkar (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification and Prioritization of Post-Social Responsibility Components of Human Resource Management in the Real Estate and Properties Administration of Iraq
The purpose of this study is to identify and prioritize the post-social responsibility components of human resource management (HRM) in the Real Estate and Properties Administration of Iraq. The statistical population of the study consisted of 350 employees and managers of the Real Estate and Properties Administration of Iraq. Based on the Morgan table and using stratified random sampling, a total of 185 participants were selected as the research sample. Data were collected using a researcher-made questionnaire. Inferential data analysis was performed using the Friedman test. The ranking of the post-social responsibility outcomes of human resource management indicated that human resource–related outcomes, extra-organizational outcomes, and organizational outcomes ranked first to third, respectively, in terms of priority. The human resource–related outcomes included the following components: (1) employee empowerment and development, (2) improvement of employee attitudes, (3) improvement of working conditions, and (4) enhancement of employee performance and productivity. Moreover, the extra-organizational outcomes included, in order of priority, the following components: (1) improvement of socio-cultural conditions, (2) economic development, and (3) environmental development. The organizational outcomes, in order of importance, comprised the following components: (1) impacts on organizational culture, (2) sustainable growth and competitive advantage, and (3) transformation in structure and strategy. Based on the findings, it is recommended that the organization continuously evaluate the activities and effectiveness of policies related to social responsibility within its structure, allocate sufficient financial and human resources for the implementation of social responsibility programs, and ensure full managerial support for these initiatives.
Real Estate and Properties Administration of Iraq
Copyright (c) 2024 Hiba Yasir Taha (Author); Mehraban Hadipeykani; Mohammed Thabit Farawn Shabanh, Badri Shahtalebi (Author) https://creativecommons.org/licenses/by-nc/4.0
Presenting a Model of Factors Affecting on the Productivity of Mining and Mineral Industries Organizations in order to Performance Improvement Using Structural Equation Modeling
The mining and mineral industries organization is one of the vital pillars in job creation and economic growth and development of many countries, including developing countries. Therefore, the presenting research was conducted with the aim of presenting a model of factors affecting on the productivity of mining and mineral industries organizations in order to performance improvement. Methods and Materials: This study was applied in terms of purpose and cross-sectional from type of descriptive in terms of implementation method. The population of the present research was mining and mineral industries civil activists, including experts, managers, and specialists in the Iranian Mineral Processing Research Center and its parent organization (IMIDRO). The sample size based on the Cochran formula was estimated 174 people, who were selected by stratified random sampling method based on key subgroups (management, engineering, human resources, maintenance and repairs, and safety and environment). The instrument of this study was a researcher-made questionnaire of factors affecting on the productivity of mining and mineral industries organizations in order to performance improvement (67 items), which was completed in person and electronically (Porsline system). The research data were analyzed using exploratory factor analysis and structural equation modeling in SPSS and Smart-PLS version 30 software. Findings: The results of exploratory factor analysis showed that 13 items of factors affecting on the productivity of mining and mineral industries organizations in order to performance improvement were removed from the instrument due to factor loadings less than 0.70 and the final questionnaire had 54 items in 12 components and 6 dimensions. The convergent validity was obtained for the dimensions of intervening conditions and strategies less than 0.50 and for other dimensions above 0.50 and for two components of technological infrastructure and economic factors and macro policymaking less than 0.50 and for other components above 0.50. Also, reliability by Cronbach's alpha and combined methods was higher than 0.70 for all dimensions, reliability with Cronbach's alpha was higher than 0.70 for 9 components and lower than 0.70 for three components of technological infrastructure, technology and innovation, and economic factors and macro policymaking, and reliability with combined method was higher than 0.70 for 11 components and lower than 0.70 for one component (technological infrastructure). In addition, the model of factors affecting on the productivity of mining and mineral industries organizations in order to performance improvement had a good fit, and in this model, causal conditions on the central phenomenon, the central phenomenon, background conditions, and intervening conditions on strategies, and the strategies on outcomes had a significant effect (P<0.001). Conclusion: According to the results of this study, in order to increase the productivity of mining and mineral industries organizations in order to performance improvement can be provided the basis for improving the identified valid dimensions and components.
Copyright (c) 2026 Mohammad Homayuni, Davood Gharakhani, Alireza Irajpour (Author) https://creativecommons.org/licenses/by-nc/4.0
Clustering Iranian Railway Transportation Companies Based on Ambidextrous Marketing Dimensions: A Multigroup Structural Equation Analysis
The rapid environmental and competitive transformations in Iran’s railway transportation industry have intensified the necessity of adopting innovative marketing approaches. One such approach is ambidextrous marketing, which enables organizations to exploit existing capabilities while simultaneously exploring new opportunities. The aim of this study is to cluster national railway companies based on ambidextrous marketing dimensions and to compare the structural relationships among the identified groups. Data were collected from 169 valid questionnaires and analyzed using a combination of clustering methods (Ward and K-means) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis identified three distinct clusters: technology leaders, characterized by a focus on innovation and service speed; structured customer-oriented firms, emphasizing organizational culture and human relationship management; and resource-constrained firms, facing challenges related to limited human capital and technological capabilities. The multigroup analysis revealed that the intensity and pattern of variable effects differ across clusters: technology and service speed are key drivers in the technology leaders cluster, organizational culture plays a central role among structured customer-oriented firms, and formal structure along with environmental pressures hinder value creation in resource-constrained firms. Despite these differences, ambidextrous marketing in all clusters led to financial and social outcomes through the pathways of value creation and knowledge management. The findings of this research, while confirming ambidexterity theory, highlight the need for developing strategies tailored to the specific characteristics of each cluster. The study provides practical insights for policymakers and railway managers in designing marketing programs, advancing digitalization, and promoting sustainable industry development.
Copyright (c) 2026 Javad Noroozi (Author); Ali Asghar Eyvazi Heshmat; Rasoul Sanavifard (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification of Ethical Indicators and Components of Human Resource Managers in Employee Separation and Job Placement in Medical Universities of the Western Region of Iran
<a href="https://journalrmde.com/index.php/jrmde/The rapid transformation of organizational environments and the necessity of achieving the objectives outlined in the country’s Twenty-Year Vision Document have compelled human resource management in the public sector to reconsider its ethical policies—particularly in processes of employee separation and job placement. The present study aimed to identify the ethical indicators of human resource managers in employee separation and job placement in medical universities located in the western region of Iran. From the perspective of purpose, this research is applied in nature, and methodologically, it is qualitative, based on the Grounded Theory approach within the Strauss and Corbin framework. The statistical population consisted of human resource managers, experts, and faculty members specializing in administrative ethics and human resource management, from whom 12 experts were selected through purposive and judgmental sampling. Data were collected via semi-structured interviews and analyzed using open, axial, and selective coding methods. The analysis of the data resulted in the extraction of 285 basic themes, 36 organizing themes, and 5 overarching themes, which were classified under five main components: “selection and recruitment,” “motivation and communication,” “performance management,” “compensation,” and “empowerment.” Based on these findings, a conceptual model for developing ethical policymaking among human resource managers was proposed, demonstrating that managerial ethics in employee separation is not merely limited to compliance with legal formalities but is largely influenced by organizational culture, perceived justice, transparent communication, and post-separation support mechanisms. Implementing an ethics-oriented approach in university human resource management requires the formulation of participatory strategies, the establishment of a fair evaluation system, and the institutionalization of ethical behaviors at all managerial levels.">The rapid transformation of organizational environments and the necessity of achieving the objectives outlined in the country’s Twenty-Year Vision Document have compelled human resource management in the public sector to reconsider its ethical policies—particularly in processes of employee separation and job placement. The present study aimed to identify the ethical indicators of human resource managers in employee separation and job placement in medical universities located in the western region of Iran. From the perspective of purpose, this research is applied in nature, and methodologically, it is qualitative, based on the Grounded Theory approach within the Strauss and Corbin framework. The statistical population consisted of human resource managers, experts, and faculty members specializing in administrative ethics and human resource management, from whom 12 experts were selected through purposive and judgmental sampling. Data were collected via semi-structured interviews and analyzed using open, axial, and selective coding methods. The analysis of the data resulted in the extraction of 285 basic themes, 36 organizing themes, and 5 overarching themes, which were classified under five main components: “selection and recruitment,” “motivation and communication,” “performance management,” “compensation,” and “empowerment.” Based on these findings, a conceptual model for developing ethical policymaking among human resource managers was proposed, demonstrating that managerial ethics in employee separation is not merely limited to compliance with legal formalities but is largely influenced by organizational culture, perceived justice, transparent communication, and post-separation support mechanisms. Implementing an ethics-oriented approach in university human resource management requires the formulation of participatory strategies, the establishment of a fair evaluation system, and the institutionalization of ethical behaviors at all managerial levels.
Copyright (c) 2026 Nayere Bakhshian (Author); Reza Taghvaei; Rostam Gharehdaghi, Behrooz Bayat (Author) https://creativecommons.org/licenses/by-nc/4.0
Identifying and Ranking Factors Affecting Personal Branding in the Food Industry Using the Network Analysis Process (ANP) Method
The purpose of this study is to identify and rank the factors influencing personal branding in the food industry. Accordingly, this research is exploratory in nature and was conducted in two stages. The first stage employed the grounded theory approach, which is applied in terms of purpose and qualitative in terms of methodology. Specifically, Strauss and Corbin’s (1998) systematic coding procedure was used, encompassing three main steps: open coding, axial coding, and selective coding. The second stage applied the Analytic Network Process (ANP) method, which is applied in terms of purpose and quantitative and descriptive–survey in terms of method. The statistical population consisted of managers in Iran’s food industry, from whom 19 individuals were selected through non-probability judgmental (purposive) sampling. Using the ANP technique, the relative importance of the identified factors was analyzed, and the prioritization was conducted with the aid of Super Decisions software. The results indicated that, within the variable of causal conditions, individual characteristics ranked first, followed by business characteristics. Within the context variable, social, economic, and political issues, cultural values, global dynamics, and public opinions and beliefs were prioritized. For the intervening conditions, criticism and feedback, audience expectations, the gap between virtual and real environments, and social norms were prioritized. Regarding the strategies variable, the priorities included adopting distinctive approaches, market research, social responsibility, social networking, appropriate visualization tools, goal orientation, and behavioral identity tools. Finally, in the outcome variable, the priorities were advancing company objectives, attracting and retaining audiences, saving time and space, enhancing communication quality, achieving internal satisfaction, and gaining reputation.
Copyright (c) 2024 Mercedeh Mortazavi Rad, Mahmoud Ahmadisharif, Alireza Rousta (Author) https://creativecommons.org/licenses/by-nc/4.0
Formulation and Validation of a Performance Measurement Model in Governmental Organizations Using the Balanced Scorecard (A Mixed-Methods Approach)
The main objective of this study is to formulate and validate a performance measurement model in governmental organizations using the balanced scorecard (a mixed-methods approach). This research employed a mixed-methods design and was conducted in two qualitative and quantitative phases. In the qualitative phase, data were collected through semi-structured interviews and analyzed using thematic analysis. In the quantitative phase, data were gathered through a researcher-made questionnaire and analyzed using structural equation modeling. The statistical population in the qualitative section included academic experts and executive managers in several governmental organizations, from which 14 participants were selected through purposive sampling and theoretical saturation. The statistical population in the quantitative section consisted of employees from selected governmental organizations and institutions, of whom 400 individuals were chosen using simple random sampling. The findings of the qualitative phase produced a thematic network of the research model, including seven organizing themes and 64 basic themes. The results indicate that localization of the balanced scorecard and the definition of indicators consistent with the mission, objectives, structure, and culture of Iran’s public sector have a significant effect on improving transparency, accountability, service quality, and strategic planning in these organizations. Establishing this framework, in addition to measuring financial performance, also encompasses internal processes, stakeholder satisfaction, and the enhancement of organizational learning and growth, thereby enabling better resource allocation and data-driven decision-making for managers. Moreover, challenges such as organizational resistance, weak technological infrastructures, and insufficient training were identified as obstacles to full implementation, for which practical solutions were proposed. In the quantitative section, the path analysis of the model was examined and confirmed.
Performance Measurement, Balanced Scorecard, Governmental Organizations, Financial Dimension.
Copyright (c) 2026 Jafar Heidarian (Author); Gholamreza Farsadamanollahi; Mohammad Ali Bidari (Author) https://creativecommons.org/licenses/by-nc/4.0
Developing a Data-Driven Decision-Making Model for Algorithmic Trading Strategies (Cryptocurrency Market)
The rapid growth of the cryptocurrency market in recent years has created both lucrative opportunities and unprecedented risks for investors. High volatility, the decentralized nature of exchanges, and the massive volume of trading data have challenged human decision-making, highlighting the necessity of employing intelligent and automated systems. In this study, a data-driven model is proposed for decision-making and the implementation of algorithmic trading strategies in the cryptocurrency market. The proposed framework integrates historical price data analysis, technical indicators, and market microstructure data with machine learning methods to forecast price trends and generate buy and sell signals. Subsequently, optimization algorithms are employed to adjust trading parameters in a way that maximizes returns while minimizing risk and transaction costs. Experimental results based on real data from selected cryptocurrencies (such as Bitcoin and Ethereum) demonstrate that the proposed model outperforms traditional strategies in terms of risk-adjusted returns and result stability. Finally, the article provides recommendations for the practical implementation of this framework in intelligent trading systems and suggests future research directions.
Copyright (c) 2026 Alireza Gharibshahian; Mohammad Reza Dehghani (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing a Blockchain-Based Supply Chain Risk Management Model Using a Genetic Algorithm (Case Study: Dairy Industries of Gilan Province)
With the expansion of competition and the growing complexity of supply chains—especially in quality-sensitive food industries such as dairy products—the need for innovative approaches to simultaneously manage cost and risk has become increasingly critical. The present study employed a quantitative and applied research method, and data were collected through fieldwork and official statistical sources. The genetic algorithm was implemented with an initial population of 50 chromosomes and 100 generations, incorporating uniform crossover, binary and real-valued mutations, and local search through the genetic algorithm method. In this research, a three-tier supply chain model was designed for the dairy industry of Gilan Province, including suppliers, industrial and traditional production units, and consumer markets, which was optimized using the genetic algorithm. The objective function of the model was to minimize the combined total cost and supply risk with respective weights of 0.7 and 0.3, while the constraints included supplier capacity, production capability of workshops, and full demand satisfaction. Real-world data on capacity, price, transportation cost, and risk indices from three major suppliers were used as model inputs. Simulation of the genetic algorithm with a population of 50 chromosomes and 100 generations showed that the algorithm rapidly reduced costs during the initial generations and gradually converged around generation 80. The final results indicated that the model achieved an optimal total cost of 454,101,315 tomans and an overall risk of 4.87%, with an allocation pattern in which supplier S3 had the largest share, S2 a moderate share, and S1 the smallest share to control risk. This multi-supplier solution effectively balanced cost and risk, and it can be practically applied as a basis for strategic decision-making in the dairy supply chain.
Copyright (c) 2026 Fariborz Khoshkar Kisomi (Author); Mohammad Ali Enayati Shiraz; Rokhsare Babajafari Esfandabad (Author) https://creativecommons.org/licenses/by-nc/4.0
Application and Management of Intelligent Systems in Construction Projects Using Recycled Materials
Currently, worldwide, modern construction technologies have emerged as one of the most important factors driving transformation and progress in the construction industry. These technologies can serve as cost-effective and environmentally friendly alternatives to conventional building materials. Building Information Modeling (BIM), intelligent systems, and the recycling of construction materials in the city of Qods have been recognized as advanced and innovative technologies in the construction sector. In the future, this system will continue to evolve and develop in response to technological advancements and the dynamic needs of the construction industry. Through intelligent system implementation, project managers and technical teams can address conflicts and technical issues while enhancing the quality and productivity of projects. This technology is also considered a valuable tool for promoting cultural acceptance in construction projects. Overall, BIM, as a powerful technological tool, can contribute to solving common challenges in civil engineering projects. It helps improve the management, quality, and efficiency of such projects while preventing potential interferences and technical complications. In this study, in order to examine the application and management of intelligent systems in the construction of civil projects using recycled materials in the city of Qods, 11 experts were selected. Using a pairwise comparison questionnaire, the identified factors and subgroups were prioritized through the Analytic Hierarchy Process (AHP). Based on the results obtained, “maintenance and repair” was recognized as one of the most important sub-criteria. Subsequently, “precise and transparent design modeling” was identified as the second most significant sub-criterion, followed by “appropriate scheduling.” Furthermore, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to prioritize the implementation tools. According to this analysis, Asta Powerproject software was identified as the top-priority tool for BIM implementation in construction projects utilizing recycled materials in Qods.
Copyright (c) 2025 Aliakbar Bagherloo (Author); Davod Pourian; Mahmood Minavand (Author) https://creativecommons.org/licenses/by-nc/4.0
Investigating communication and security challenges in power microgrids and designing a secure communication network using appropriate protocols and encryption techniques of artificial intelligence technology
With the expansion of the use of renewable energy sources and the need for smart energy distribution systems, microgrids have become one of the vital components of power systems. Considering the increasing importance of power microgrids in modern energy systems and their key role in increasing efficiency and reducing dependence on large power grids, designing a reliable communication network for the utilization of these microgrids is crucial. In this research, a comprehensive framework for simulating, designing, and evaluating the communication network of power microgrids is presented. First, power microgrids were simulated using the Python programming language to enable analysis of the behavior and performance of these systems under different conditions. Then, a communication network based on artificial intelligence algorithms was designed and developed, which ensures the ability to coordinate and manage microgrids optimally. Next, in order to investigate the stability and security of the designed communication network, various types of cyber attacks were simulated. These attacks included data intrusion, disruption of communications, and various cyber-destruction scenarios. Also, smart defense strategies were developed to counter these attacks and their effectiveness in maintaining the performance of the communication network and preventing negative impacts on microgrids and the main power grid during outage conditions was evaluated. The results show that the designed communication network is not only efficient in managing and utilizing microgrids, but also has the ability to resist cyber attacks and maintain system stability. This research can be used as a basis for developing smart and secure systems in energy management and power microgrids and provide an effective solution to address security and stability challenges in power systems.
Copyright (c) 2026 Abolfazl Taleghani (Author); Sepehr Soltani https://creativecommons.org/licenses/by-nc/4.0
Designing a Model to Reduce Brand Hate in the Home Appliance Industry Using Interpretive Structural Modeling Technique
The purpose of this study was to propose a model aimed at reducing brand hate in the country’s home appliance industry. To conduct the present research, ten factors influencing the reduction of brand hate were first identified through a review of the research literature. Then, the interpretive structural modeling (ISM) technique was employed to structure these drivers using the insights of 13 experts. Subsequently, to validate and test the conceptual model derived, a questionnaire containing 33 items with a five-point Likert scale was developed and distributed among 234 managers and employees of the national home appliance industry. Based on the data collected and through the application of structural equation modeling (SEM) and the Smart PLS3 software, the conceptual model of this study was evaluated and tested. The findings revealed an eight-level hierarchical structure, in which customer complaint handling, communication quality, and after-sales services were identified as the primary drivers initiating the model for reducing brand hate. The results of this research can serve as a guiding framework for strategies aimed at mitigating brand hate.
Copyright (c) 2024 Azadeh Zahiri (Author); Ali Pirzad; Seyed Najmeddin Mousavi (Author) https://creativecommons.org/licenses/by-nc/4.0
The Role of Service Quality and Customer Relationship Management in Enhancing Loyalty and Reducing Brand Switching in the Sports Equipment Industry
The aim of this study was to examine the role of service quality and Customer Relationship Management (CRM) in strengthening loyalty and reducing brand switching in Iran’s sports equipment industry. Given the intense competition and high similarity of products in this sector, organizations need to focus on complementary services and effective communication strategies to retain customers. This research is applied in purpose and employs a mixed-method design (qualitative–quantitative). In the qualitative phase, a meta-synthesis approach was used to systematically review the literature, leading to the identification of 37 key indicators categorized into 10 factors and three main dimensions: product and service quality, relationship marketing and CRM, and socio-economic factors. In the quantitative phase, data were collected from 72 experts in the sports equipment industry and analyzed using Fuzzy Cognitive Mapping (FCM) to determine the causal relationships among the identified factors. The findings indicated that service quality—particularly after-sales services and prompt responsiveness—is the strongest factor in retaining customers. Moreover, digital CRM and personalized communication play a key role in fostering trust and customer commitment. Scenario analysis results revealed that the simultaneous improvement of service quality and CRM development can prevent up to 45% of customer brand switching. Based on these findings, it can be concluded that under the volatile economic conditions of Iran, only organizations adopting a comprehensive approach—focusing not only on product quality but also on customer experience, trust-building, and long-term relationships—are likely to succeed. This research fills a gap in domestic studies on the sports equipment sector and can serve as a practical guide for managers seeking to increase customer loyalty and reduce brand switching rates.
Copyright (c) 2024 Zahra Zarafshan (Author); Ali Pirzad; Seyed Najmeddin Mousavi (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing a Portfolio Risk Management Model Using Fundamental Analysis and Multi-Objective Evolutionary Optimization
This study aimed to develop and validate a portfolio risk management model tailored to the Tehran Stock Exchange (TSE) that integrates fundamental analysis with Multi-Objective Evolutionary Optimization to simultaneously enhance return, control risk, and improve downside protection. This quantitative study analyzed all non-financial firms listed on the TSE from July 2023 to July 2024. Firms were first screened using ten fundamental indicators—ROE, ROA, EPS, P/E, P/B, D/E, current ratio, operating cash flow ratio, revenue growth, and net profit margin—and ranked using the Analytic Hierarchy Process (AHP). The top 50 firms were selected as the candidate set for optimization. Portfolios were then constructed and optimized through the Non-dominated Sorting Genetic Algorithm II (NSGA-II), aiming to maximize expected return, minimize variance, and improve Value at Risk (VaR) at the 95% confidence level. Out-of-sample backtesting was conducted, and performance was compared against the market index TEDPIX and a traditional Mean-variance optimization model based on Harry Markowitz’s framework. The optimization produced a well-defined Pareto frontier, with portfolios demonstrating a clear risk-return trade-off. Intermediate-risk portfolios achieved the highest Sharpe Ratio and Sortino Ratio values, indicating optimal risk-adjusted performance. Compared to TEDPIX and the Markowitz model, the optimized portfolios delivered significantly higher average monthly returns (up to 2.7% vs. 1.2%), lower volatility, smaller maximum drawdowns, and higher cumulative wealth. Statistical tests confirmed that these excess returns were significant (p < 0.05), highlighting the superior performance and resilience of the proposed model. Integrating fundamental analysis with multi-objective evolutionary optimization effectively enhances portfolio performance and risk control in the Iranian market.
multi-objective evolutionary optimization
Copyright (c) 2026 Amirhossein Soltanabadi; Hossein Mohseni (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification and Prioritization of Factors Affecting E-Learner Satisfaction in Mathematics Based on the Fuzzy Analytic Hierarchy Process (Case Study: Farhangian University of Mazandaran)
The purpose of this study was to evaluate the factors influencing the satisfaction of mathematics students in an e-learning environment. To achieve this goal, a library-based and descriptive research method was employed. The statistical population consisted of experts, mathematics specialists, and e-learning curriculum planners at Farhangian University of Mazandaran. The sample included seven experts who were purposively selected based on the research objectives and questions. Based on the theoretical literature and in consultation with experts and specialists, the factors and criteria of e-learning environment quality were categorized into four main dimensions—technical system and technology infrastructure quality, educational quality, information and content quality, and service quality—comprising a total of 24 criteria. The weight of each of these indicators within the studied population was determined using an expert questionnaire and the Fuzzy Analytic Hierarchy Process (FAHP). Data analysis was performed using the Super Decisions software. The findings revealed that, in terms of importance, the factors related to technical systems and technology infrastructure, technology and instructional design, content development, and support services were evaluated and ranked, respectively, as the key determinants of e-learning environment quality.
Copyright (c) 2025 Morteza Gorzinnezhad; Davood Darvishi Salookolaei, Mohammad Dehghandar (Author) https://creativecommons.org/licenses/by-nc/4.0
Modeling the Role of Accounting in Enhancing Financial Performance, Technological Innovation, and Sustainability of Knowledge-Based Companies: A Structural Equation Modeling (SEM) Approach
The objective of this study is to model the role of accounting in enhancing financial performance, technological innovation, and sustainability of knowledge-based companies. Considering the importance of these companies in a knowledge-driven economy and the existing challenges related to financial transparency, capital attraction, and sustainable development, examining the role of accounting systems can open new horizons for improving managerial decision-making and strengthening competitive capabilities. This research is applied in purpose and descriptive–survey and quantitative in method. The statistical population in the qualitative phase consisted of accounting faculty members with more than ten years of executive experience, who were selected through purposive sampling. Semi-structured interviews were conducted until theoretical saturation was reached. In the quantitative phase, the statistical population included all accountants working in knowledge-based companies. Using the Krejcie and Morgan table, a sample of 384 participants was selected. The data collection tool was a researcher-designed questionnaire with 25 items across five main dimensions of the study. Its validity was confirmed by subject-matter experts, and its reliability was verified with a Cronbach’s alpha coefficient greater than 0.8. Data analysis was conducted using Structural Equation Modeling (SEM) with SPSS and AMOS software. The findings showed that accounting has a positive and significant effect on the financial performance, technological innovation, and sustainability of knowledge-based companies. In other words, the presence of transparent and efficient accounting systems can not only improve financial management and attract capital but also create conditions for the development of innovative activities and enhancement of organizational sustainability.
Copyright (c) 2025 Moein Ilaghi Hosseini (Author); Abbasali Haghparast; Alireza Hirad, Habib Piri (Author) https://creativecommons.org/licenses/by-nc/4.0
Design of a Fuzzy Expert System for Measuring the Satisfaction of E-Mathematics Learners (Case Study: Farhangian University of Mazandaran)
The issue of student satisfaction with e-learning systems plays a critical role in their academic performance as well as in the success and effectiveness of this educational domain. In this study, a fuzzy expert system was designed using MATLAB software to measure the satisfaction level of e-mathematics learners at Farhangian University of Mazandaran. This fuzzy expert system included four input variables—technical quality of the system and technological infrastructure, educational quality, information and content quality, and service quality—extracted from the theoretical literature of the study; one output variable—e-mathematics learner satisfaction; trapezoidal and triangular membership functions; 50 rules developed based on the input of eight purposively selected experts from Farhangian University; and the centroid defuzzification method. All input and output variables of this system were normalized and converted to a range between 0 and 1, and using the input values, satisfaction levels were estimated with an error margin of less than 0.15. Considering the strong ability of this fuzzy expert system to estimate the satisfaction of e-learners, and given its design based on criteria, sub-criteria, and their relative importance according to ranking results used as input variables, it can provide significant support to educational system managers by enabling timely tracking of feedback and problems in proportion to their level of importance, thereby increasing the satisfaction of e-mathematics learners.
Copyright (c) 2024 Morteza Gorzinnezhad; Mohammad Dehghandar, Davood Darvishi Salookolaei (Author) https://creativecommons.org/licenses/by-nc/4.0
Development of an Analytical Framework for Reverse Logistics Supply Chain in Industrial Waste Management Based on Grounded Theory and Interpretive Structural Modeling (ISM)
The rapid growth of technology and the emergence of new production processes, along with the substitution of synthetic materials and chemical compounds, have resulted in an increased volume of industrial waste and, in some cases, the generation of hazardous waste. Improper handling, transportation, and disposal of this waste, part of which contains dangerous substances, pose serious challenges to human health and the environment. Under such circumstances, establishing an efficient reverse logistics network emerges as an inevitable necessity. With growing social concerns about environmental issues, reverse logistics has become increasingly integrated with waste management, and the management of industrial waste is now considered a core pillar of reverse supply chain management. This study, employing the grounded theory method based on the Strauss and Corbin (1998) model and using the insights of 17 academic and industrial experts in the national gas refining sector, proposes a comprehensive model for the reverse supply chain of waste in this industry. The analysis of data obtained from semi-structured interviews led to the identification of 25 core concepts categorized into six main themes. The application of Interpretive Structural Modeling (ISM) revealed hierarchical and causal relationships among these factors, indicating that regulations and policies, infrastructure, and organizational culture act as fundamental and driving forces with the greatest impact on the success of the system. This paradigmatic model can serve as a roadmap for managers in the gas refining industry to design and implement an effective and sustainable reverse logistics system.
Copyright (c) 2024 Zahra Ramezani (Author); Sadegh Feizollahi; Maryam Daneshmand Mehr (Author) https://creativecommons.org/licenses/by-nc/4.0
Examining the Threshold Effects of Government Governance on the Relationship Between Globalization, Government Effectiveness, and Environmental Pollutant Quality in Selected Developing Countries
Given the significance of the topic and the undeniable role of the environment in human life, this study seeks to examine the threshold effects of government governance on the relationship between globalization, government effectiveness, and the quality of environmental pollutants in selected developing countries during the period 2005 to 2023, using the Panel Smooth Transition Regression (PSTR) model. The research sample includes selected MENA and Middle Eastern countries, namely Saudi Arabia, Qatar, Pakistan, the United Arab Emirates, Algeria, Kuwait, Iran, Egypt, Oman, Morocco, Libya, Yemen, and Sudan. The findings reveal that by employing a PSTR model in which the transition variable is trade volume (EG) in the first model and government governance (INST) in the second model, the functional relationship between economic globalization and environmental quality can be modeled. The estimated results for the linear part of the first and second models (Regime 1) indicate that trade volume (EG) and gross domestic product (GDP) have a positive relationship with nitrogen dioxide (NO₂) emissions. Specifically, a one-unit increase in trade volume and GDP leads to an increase of 0.21 and 0.22 units in greenhouse gas emissions, respectively, in the first model. Furthermore, the variable of governance quality (INST) in the second model results in a reduction of 0.12 units in nitrogen dioxide (NO₂) emissions, and the interaction term of government governance with trade volume (INST × EG) reduces NO₂ emissions by 0.11 units. Additionally, the estimated results for the nonlinear part of the first and second models (Regime 2) confirm the presence of a positive relationship between trade volume (EG) and gross domestic product (GDP) with nitrogen dioxide (NO₂) emissions.
Copyright (c) 2025 Mohamadamin Talebi (Author); Mahnaz Rabiei; Fatemeh Zandi, Mohammad Khezri (Author) https://creativecommons.org/licenses/by-nc/4.0
Futures Studies on Artificial Intelligence in Management Accounting
The present study was conducted with the aim of exploring the future applications of artificial intelligence (AI) in management accounting and is applied and descriptive (non-experimental) in nature. Data collection was carried out using a mixed-methods approach, combining field and library research techniques. The tools employed included expert panels, semi-structured interviews, open-ended questionnaires, the fuzzy Delphi method, observation, and document analysis. The statistical population consisted of professors, doctoral students, members of the Management Accountants Association, and professional specialists in this field in Iran. Sampling was performed using theoretical and snowball methods, and interviews continued until theoretical saturation was reached. Data analysis was conducted both qualitatively and quantitatively, leading to the identification of key drivers in management accounting education and research. Based on the results of the fuzzy Delphi analysis, the technology of deep learning ranked first with a fuzzy mean of 3.7 and was recognized as the most critical driver. Subsequently, natural language processing (NLP) with a mean of 3.4 was also identified among the accepted technologies. Blockchain technology, with a score of 3.1, was evaluated as conditional and requires further investigation. Additionally, explainable artificial intelligence (XAI) was proposed as an emerging driver that could play a significant role in enhancing transparency, trust, and regulatory compliance. The findings of this research indicate that the future of management accounting will be directly influenced by technological advancements in AI. To leverage these technologies effectively, strategic planning and investments in education and infrastructure development are essential.
Futures studies on artificial intelligence
Copyright (c) 2025 Mohsen Aboeuimehrizi (Author); Mahmoud Moeinadin; Akram Taftiyan, Mohammad Sadeghi Sarcheshmeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Modeling and Analysis of Surface Contamination in Ceramic and Polymeric Insulators Using Partial Discharge Signal Characteristics
This study aimed to model and analyze the impact of surface contamination on ceramic and polymeric insulators by characterizing and comparing their partial discharge (PD) signal behavior to support condition-based monitoring and predictive design. An experimental laboratory design was employed to expose medium-voltage ceramic and polymeric insulators to controlled contamination layers prepared with standardized saline and artificial pollutants. PD signals were captured under alternating current stress using an IEC 60270–compliant setup consisting of a high-voltage source, coupling capacitor, and Rogowski coil/high-frequency current transformer sensors. Raw signals underwent wavelet-based denoising to suppress environmental noise and were further analyzed using Fast Fourier Transform (FFT) for spectral features and phase-resolved partial discharge (PRPD) mapping to visualize discharge activity across the AC cycle. Equivalent electrical models of contaminated surfaces were developed to simulate PD inception and growth, and the outputs were validated against laboratory measurements. Ceramic insulators exhibited an abrupt transition from sporadic to high-energy discharges once contamination created a conductive film, reflected by sharp increases in apparent charge and dense PRPD clusters around voltage peaks. Polymeric insulators demonstrated a more gradual rise in PD activity with smaller but persistent pulses distributed across the cycle. FFT analysis revealed dominant low-to-mid frequency peaks intensifying faster in ceramics, while polymeric responses remained broader and less concentrated. The electrical models closely matched experimental PD thresholds and spectral signatures, confirming their ability to predict contamination-driven surface breakdown. Surface contamination strongly influences PD activity, and material type governs the discharge evolution pathway.
Copyright (c) 2025 Nader Tavakol, Mazdak Teimoortashloo, Fatemeh Esmaeelzadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Examining the Impact of Cognitive Processes on Economic Decisions: Simulating the Behavior of Economic Agents under Risk and Uncertainty
This study investigates the influence of cognitive processes on economic decisions and simulates the behavior of economic agents under conditions of risk and uncertainty. Simulation models based on stochastic algorithms were employed to analyze economic decision-making in environments characterized by risk and uncertainty. The simulations were implemented using the Python and R programming languages, and economic behaviors influenced by cognitive factors such as memory constraints, attention, and behavioral biases were examined. The results indicated that when faced with risk, economic agents tend to gravitate toward satisficing options and adopt simpler strategies, even if these decisions are not fully optimal. This research also revealed discrepancies between simulated behaviors and the predictions of rational choice theories, highlighting the impact of cognitive biases such as loss aversion and overconfidence in economic decision-making. The use of diverse simulation techniques and data analysis contributed to a better understanding of decision-making in complex contexts and can inform the improvement of economic and financial policies.
Copyright (c) 2025 Farshid Ghasemi Dijvejin https://creativecommons.org/licenses/by-nc/4.0
Identification and Modeling of Factors Affecting the Effectiveness of Internal Audit and Its Impact on the Quality of Financial Reporting Considering the Role of Senior Management Support
The main objective of this study is to identify and model the factors affecting the effectiveness of internal audit and its impact on the quality of financial reporting, taking into account the role of senior management support. In terms of nature, this research is exploratory, and in terms of purpose, it falls under applied research. The statistical population of this study consists of 179 managers of listed and over-the-counter (OTC) companies in Iran. To collect the required data, interviews and questionnaires were employed. To test the hypotheses, inferential statistics and structural equation modeling (SEM) tests were applied. The results of the study showed that organizational structure (0.803), risk management (0.780), audit committee (0.774), board of directors and managers (0.764), accounting information systems (0.688), working relationships (interactions) between internal and external auditors (0.611), independence (0.536), moral courage (0.527), technological capabilities (0.470), professional commitment (0.440), auditor competence (0.366), and internal control (0.359) had the highest coefficients of impact on the effectiveness of internal audit, respectively. The findings further revealed that the effectiveness of internal audit and senior management support have a positive impact on the quality of financial reporting. Additional results indicated that senior management support has a moderating effect on the relationship between internal audit effectiveness and the quality of financial reporting.
Copyright (c) 2025 Mohammad Bagher Hatami, Rahman Saedi, Mohsen Rahimi Dasjerdi, Hamzeh Mohammadi Khoshouei (Author) https://creativecommons.org/licenses/by-nc/4.0