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.
Integrated Queue Management and Service Composition for Enhancing Manufacturing-as-a-Service Performance
This study aimed to develop and empirically evaluate an integrated framework examining the effects of integrated queue management and service composition capability on Manufacturing-as-a-Service (MaaS) performance, while assessing the enabling role of technological support factors within service-oriented manufacturing environments. This applied quantitative study employed a descriptive-correlational design using structural equation modeling. The statistical population consisted of manufacturing managers, industrial engineers, production planners, and digital manufacturing specialists working in manufacturing organizations in Tehran, Iran. A total of 384 participants were selected through stratified random sampling. Data were collected using a researcher-developed questionnaire measuring integrated queue management, service composition capability, technological support factors, and MaaS performance. Content validity was confirmed by an expert panel, while construct validity was assessed through confirmatory factor analysis. Reliability was established using Cronbach’s alpha, composite reliability, and average variance extracted indicators. Data analysis was performed using SPSS 27 and SmartPLS 4. The measurement model demonstrated satisfactory reliability and validity, with Cronbach’s alpha values ranging from 0.887 to 0.925, composite reliability values between 0.909 and 0.938, and average variance extracted values exceeding 0.50 for all constructs. Structural equation modeling revealed that integrated queue management had a significant positive effect on MaaS performance (β = 0.421, t = 8.974, p < 0.001). Service composition capability also exerted a significant positive effect on MaaS performance (β = 0.366, t = 7.852, p < 0.001). Technological support factors significantly influenced MaaS performance directly (β = 0.214, t = 4.986, p < 0.001) and indirectly through their effects on integrated queue management (β = 0.492, t = 9.843, p < 0.001) and service composition capability (β = 0.547, t = 11.308, p < 0.001). The proposed model explained 68.7% of the variance in MaaS performance (R² = 0.687), indicating substantial explanatory power and predictive relevance. The findings demonstrate that integrated queue management and service composition capability are critical drivers of Manufacturing-as-a-Service performance, while technological support factors provide the essential infrastructure enabling their effectiveness.
Copyright (c) 2026 Mina Ravanesh; Emad Roghanian (Author) https://creativecommons.org/licenses/by-nc/4.0
A Hybrid Statistical–MCDM Framework for Integrated Risk Prioritization and Sustainability Assessment in Construction Supply Chains: Bridging Local Data with Global Theory
This study aimed to develop and empirically validate a hybrid statistical–multi-criteria decision-making (MCDM) framework for identifying, prioritizing, and managing sustainability-related risks and strategic interventions in construction supply chains within the context of the Qeshm Free Trade Zone. A practical descriptive–inferential research design was employed using a structured Delphi survey administered to 33 experts, including project managers, engineers, contractors, consultants, and supply chain professionals involved in major construction projects in Qeshm. Following expert consensus, a questionnaire measuring economic, social, environmental, and structural risks, as well as key supply chain success drivers, was developed and validated. Reliability was confirmed through Cronbach’s alpha coefficients. One-sample t-tests were conducted to determine statistically significant risk factors. The Analytic Network Process (ANP) was applied to prioritize critical supply chain drivers and capture interdependencies among criteria, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank sustainability-oriented management strategies based on multiple performance criteria. The results revealed that economic risks, including supplier financial challenges, material price volatility, exchange-rate fluctuations, transportation costs, and delay-related expenses, were statistically significant. Environmental risks, such as pollution, resource depletion, non-renewable resource usage, and environmental compliance issues, also demonstrated significant effects. ANP results indicated that Material Supply Management was the most influential driver (weight = 0.553), followed by Design and Engineering Management (0.294), Manufacturing and Production Management (0.094), and Workforce Supply and Safety Management (0.059). At the sub-criterion level, provision of high-quality domestic building materials (0.304) and optimal foundation design (0.215) received the highest priorities. TOPSIS analysis identified sustainable supply chain management software (0.8962) and sustainable material selection (0.8664) as the most effective strategic interventions. The integration of statistical analysis, ANP, and TOPSIS provides a robust decision-support framework for sustainable construction supply chain management. The findings demonstrate that economic volatility, environmental pressures, material quality, and engineering effectiveness are dominant determinants of sustainable performance.
Copyright (c) 2026 Mahyar Amin Forghani (Author); Sohail Dadkhah; Mohsen Dadras (Author) https://creativecommons.org/licenses/by-nc/4.0
Explaining the Relationship between Organizational Humanitarianism and Employees’ Social Accountability: The Moderating Role of Employee Self-Leadership
This study seeks to demonstrate how employee self-leadership, as an individual facilitating factor, can moderate and strengthen the effect of organizational humanitarianism on social accountability within Iran Khodro Company. Therefore, the present study aimed to explain the relationship between organizational humanitarianism and employees’ social accountability, emphasizing the moderating role of employee self-leadership in Iran Khodro Company. The present research is descriptive–analytical in nature, applied in terms of purpose, and categorized as a survey study in terms of methodology. The statistical population included all employees of Iran Khodro Company, comprising 10,000 official employees. Among them, 269 individuals were selected through simple random sampling. A total of 300 questionnaires were distributed, of which 190 valid questionnaires were returned. To conduct the study, a 51-item closed-ended questionnaire based on a five-point Likert scale was distributed among employees, and the questionnaire measured the study variables. Content validity was confirmed through expert opinions, construct validity was assessed using confirmatory factor loading analysis, and reliability was verified through Cronbach’s alpha and composite reliability coefficients. After data collection and classification, the data were analyzed using SPSS version 24 and Smart PLS software. The findings indicated that among the variables examined in the present study, employee self-leadership obtained the highest mean score (3.94), whereas organizational humanitarianism obtained the lowest mean score (3.30). The results of structural equation modeling using PLS software demonstrated that organizational humanitarianism had a 55% effect on employees’ social accountability. Furthermore, employee self-leadership moderated the relationship between organizational humanitarianism and employees’ social accountability by 3%.
Copyright (c) 2026 Fatemeh Movahed Nia; Monireh Tahaei, Mahdi Fardi Kalahroudi, Reza Kazemi (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification of the Components of Environmental Innovation, Green Supply Chain Management, Circular Economy Capabilities, and Financial Performance Through Qualitative Content Analysis
The present study aimed to identify and explain the components of environmental innovation, green supply chain management, circular economy capabilities, and financial performance through qualitative content analysis using the multi-grounded theory approach. This study was conducted using a qualitative exploratory design based on multi-grounded theory. The statistical population included university experts, senior managers, sustainability specialists, and experienced professionals in environmental management, green innovation, and supply chain systems associated with airport organizations in Tehran. Participants were selected through purposive and snowball sampling, and sampling continued until theoretical saturation was achieved. Finally, 17 experts participated in semi-structured in-depth interviews. In addition to field interviews, 40 scientific studies published between 2015 and 2025 were systematically reviewed through meta-synthesis to strengthen the theoretical grounding of the extracted concepts. Data analysis was conducted through open, axial, and selective coding using continuous comparative analysis. To ensure trustworthiness, member checking, peer review, rich description, and inter-coder reliability assessment based on Cohen’s Kappa were employed. The findings led to the extraction of a comprehensive qualitative model consisting of causal conditions, contextual conditions, enabling factors, strategies, the central category, and multidimensional consequences. Environmental innovation in the context of green supply chain management emerged as the central category of the model. The main causal conditions included institutional sustainability requirements, organizational environmental responsibility, and competitive green market pressures. Contextual conditions involved green governance, environmental culture, technological infrastructure, and socio-environmental justice. Knowledge-based, technological, digital, and financial capabilities were identified as major enabling factors. Strategic responses included green product design, reverse logistics, green procurement, smart environmental monitoring, and sustainability-oriented governance systems. The consequences of the model included improved financial performance, operational efficiency, green competitiveness, social sustainability, environmental performance, and institutional learning.
Copyright (c) 2026 Javad Shomali Nejad (Author); Kiamars Fathi Hafshejani; Nowrouz Nourollahzadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Decision-Making Models for Circular Economy Implementation in Waste Management
The transition from linear waste management systems to circular economy (CE) models creates the demand for advanced decision-making frameworks designed to tackle environmental, economic, social, and technical issues. This research presents the systematic literature review on decision-making models for the application of circular economy in waste management. The review processes the PRISMA framework and studies obtained from Scopus, Web of Science, and Google Scholar. Search terms included circular economy, waste management, sustainability, and MCDM, and were applied to articles from 2020 to 2026. Following the application of predefined criteria and title, abstract, and full text screenings, nine articles were chosen for qualitative synthesis. Studies were found to be moving away from classical MCDM methods, like AHP and TOPSIS, toward fuzzy-set and hybrid complex environmental system decision-making frameworks. The analyzed studies examined a variety of waste types, including medical, electronic, construction, food, organic, and plastic waste. Among these, construction and demolition waste was the most researched category. The most recent studies found that environmental factors, particularly regarding pollution, public health, and sustainability, were increasingly found among the most prominent factors of the decision-making process. Furthermore, the analysis demonstrated that hybrid and uncertainty-oriented models provide more robust and reliable support for circular economy implementation in waste management systems.
Copyright (c) 2026 Amirreza Soleimannezhad Aliabadi; Mohammad Ali Niroomand, Fereshteh Khalili Peykani (Author) https://creativecommons.org/licenses/by-nc/4.0
Sentiment Analysis of Customers in Iran’s Automotive Industry Using the LSTM Method
Given the large volume of customer opinions and complaints regarding automobiles in Iranian virtual platforms, this study aimed to develop an accurate sentiment analysis model based on Long Short-Term Memory (LSTM) neural networks for processing Persian texts. The primary objective was to improve the accuracy of sentiment classification (positive, negative, and neutral) by overcoming the challenges associated with imbalanced data and linguistic limitations. Data were collected from 12 valid online sources, including 4,348 customer comments regarding various automobile models. Three LSTM architectures (single-layer, two-layer, and three-layer) were implemented and compared using fixed parameters (Embedding = 128, Hidden Units = 64, Dropout = 0.3). To enhance performance, Data Augmentation, the SMOTE algorithm, and FastText word embeddings were employed. The three-layer LSTM model outperformed the shallower models, achieving a validation accuracy of 62.01%, which increased to 95.45% after applying optimization techniques. The two-layer model demonstrated superior performance in identifying positive comments, whereas the three-layer model showed a more balanced performance across all sentiment classes. The issue of overfitting in simpler models was mitigated through the addition of layers and the use of Dropout. The findings indicated that the two-layer model improved overall accuracy, particularly in recognizing positive sentiments, compared to the single-layer model. Furthermore, the three-layer LSTM model demonstrated greater balance and robustness across all classes. To further improve performance, the three-layer model was retrained using Data Augmentation techniques and the SMOTE algorithm for data balancing. The modified version substantially improved overall accuracy and validation performance.
Copyright (c) 2026 Lida Shojaei Barjouei (Author); Ghasem Bakhshandeh; Mahmood Daniali Deh houz (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification of Key Causes of Disputes in Construction Projects and Preventive Strategies: A Systematic Review
Construction disputes affect costs, timelines, and relationships among stakeholders. Therefore, the purpose of this study to collate the latest strategies for construction dispute resolution from various primary sources, using a systematic literature review method. The author conducted a structured query on Scopus, Web of Science, and Google Scholar for literature published for the years 2023-2026. Overall, the query returned 518 records, which after multiple iterations, were narrowed to 12 studies, which formed the basis of this review. The study identifies five predominant categories of dispute management strategies: preventive strategies, contractual and management strategies, alternative dispute resolution (ADR), technological strategies, and risk and context components. The findings suggest that the literature is moving from a focus on reactive dispute resolution to a focus on proactive dispute prevention, especially in the pre-contract and early phases of a project. The ability to reduce disputes was linked to communication, documentation, and a clearly defined scope. Concern around standardization and contractual risk allocation remains valid given that contractual issues continue to be a source of conflict. In addition, this study validates the increased use of negotiation and adjudication as alternative methods of dispute resolution that provide varying degrees of flexibility and legal certainty. Emerging technologies, including smart contracts and artificial intelligence, have the potential to improve transparency, automate parts of contracts, and address the issues of payment delays and unclear contractual obligations. Additionally, macro-level factors such as regional legal risks and technical uncertainties, particularly ground conditions, were found to significantly influence dispute occurrence.
Copyright (c) 2026 Mohammad Taghi Nazarpour; Negin Kheradmand (Author) https://creativecommons.org/licenses/by-nc/4.0
An Enhanced Hybrid Statistical–Learning Approach for Detecting Fake Feedback in Cloud Environments Using Hierarchical Clustering with Reptile Search Algorithm and Particle Swarm Optimized Extreme Learning Machine
The proliferation of fake feedback in cloud environments represents a major challenge in maintaining user trust and ensuring service quality. Accordingly, the development of intelligent approaches for distinguishing genuine feedback from fraudulent instances has become increasingly important. In this study, a hybrid framework based on statistical analysis and machine learning is proposed for the detection of fake feedback. The framework integrates improved hierarchical clustering using the Reptile Search Algorithm (AHC-RSA), the E-EDA statistical model for anomaly filtering, and an Extreme Learning Machine optimized with Particle Swarm Optimization (ELM-PSO). This integrated approach reduces intra-cluster dispersion, enhances data separability, improves prediction accuracy, and accelerates model convergence. Experimental evaluations conducted on the CloudArmor and Epinions datasets demonstrated that the proposed model achieved accuracies of 99.83% and 99.84%, respectively. The results indicate that the proposed model outperforms SVM, ANN, LSTM, and GRU algorithms in terms of accuracy, stability, and convergence speed. On average, the proposed model shows an improvement of approximately 5% to 8% in accuracy and F1-score, along with a significant reduction in MSE and RMSE compared to the best existing methods. Therefore, the proposed framework can be considered an effective and reliable approach for detecting fake feedback and enhancing trust in cloud services.
clustering with Reptile Search Algorithm
Particle Swarm Optimization-based Extreme Learning Machine
Copyright (c) 2026 Hussein Kadhim Almamoori (Author); Golnaz Aghaee Ghazvini; Ali Albu-Rghaif, Fariba Majidi (Author) https://creativecommons.org/licenses/by-nc/4.0
Investigating the Relationship between Job Characteristics and the Growth of Professional Ethics among Employees of the East Azerbaijan Gas Company
The aim of this study is to investigate the relationship between job characteristics and the growth of professional ethics among employees of the East Azerbaijan Gas Company. For this purpose, job characteristics were defined based on the theory of Richard Hackman and Greg Oldham across five dimensions: skill variety, task identity, task significance, autonomy and freedom, and job feedback. Professional ethics were defined based on the code of ethics of the East Azerbaijan Gas Company. Accordingly, one main hypothesis and five sub-hypotheses were formulated. The statistical population of this study consists of all employees of the East Azerbaijan Gas Company, totaling 380 individuals. The statistical sample size was estimated to be 192 individuals based on the Cochran formula, and participants were selected using stratified random sampling. Data collection tools included two questionnaires: the Hackman and Oldham Job Characteristics Questionnaire and a Professional Ethics Questionnaire based on the code of ethics of the East Azerbaijan Gas Company. After assessing and confirming their validity and reliability, the questionnaires were distributed to the statistical sample. Descriptive and inferential statistical methods were used to analyze the data. Specifically, descriptive statistics were employed for classification, summarization, and interpretation of the data, while the Kolmogorov-Smirnov test and Spearman's correlation test were used at the inferential level to test the research hypotheses. The results of hypothesis testing confirmed all hypotheses, indicating that there is a significant relationship between job characteristics and the growth of professional ethics among employees of the East Azerbaijan Gas Company.
Copyright (c) 2026 Ali Reza Abbasian (Author); Jafar Beikzad https://creativecommons.org/licenses/by-nc/4.0
Explanation and Prioritization of Factors Influencing the Location of Resilient Schools Against Natural Hazards with an Emphasis on Collective Intelligence (Case Study: Nowshahr City)
This study was conducted with the aim of identifying and prioritizing the factors influencing the location of resilient schools in the face of natural hazards. The research adopted a mixed-methods approach, and data were analyzed using the Analytic Network Process (ANP), Delphi method, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Geographic Information System (GIS). The findings indicated that out of 22 initial items, 14 items were identified as key factors agreed upon by experts, encompassing physical, environmental, safety, accessibility, and socio-managerial dimensions. The results of spatial analysis, combined with collective intelligence algorithms, enabled the identification of safe and high-risk zones, as well as the optimization of evacuation routes and school site allocation. This framework can serve as a practical tool for urban planners, educational administrators, and designers of educational spaces to enhance the resilience and safety of schools.
Copyright (c) 2026 Mehdi Elkaei Behjati (Author); Seyed Ali Seydian; Mehdi MirzaGoltabar Roshan (Author) https://creativecommons.org/licenses/by-nc/4.0
Detection of Attacks in Internet of Things Devices Using an Optimized Ensemble Classification Based on Deep Transfer Learning and the Harris Hawks Algorithm
With the rapid expansion of the Internet of Things (IoT) and the increasing number of cyberattacks targeting these devices, the use of efficient methods for attack detection has become increasingly important. In this study, a novel approach based on ensemble deep transfer learning optimized by the Harris Hawks Optimization (HHO) algorithm is proposed for detecting attacks in IoT devices. In this method, multiple deep transfer learning models are employed, which are capable of transferring learned knowledge from data in other domains to IoT-related data. Subsequently, the Harris Hawks algorithm is utilized to optimize the model parameters and integrate them into an ensemble classifier. The Edge-IIoTset dataset is used to evaluate the performance of the proposed method. The obtained results indicate that the detection accuracy reaches 99.8%, while the false alarm rate is significantly reduced. These findings demonstrate the high effectiveness of the proposed method in enhancing the security level of IoT devices.
Copyright (c) 2026 Farqad Abdullah Mohammed (Author); Saba Joudaki; Mezher H Mezher, Mahdi Mosleh (Author) https://creativecommons.org/licenses/by-nc/4.0
Comparing the Effectiveness of Supply Chain under Two Contract Mechanisms: Buy-back and Wholesale Price
Buy-back and wholesale price contracts represent critical coordination mechanisms in supply chains, playing a significant role in enhancing supply chain effectiveness. This research, through a comparative analysis of theoretical frameworks and practical evidence, demonstrates that buy-back contracts generally outperform wholesale price contracts, particularly in reverse supply chains and for perishable products. This superiority originates from the higher flexibility of buy-back contracts in allocating risks among supply chain partners. In high-uncertainty environments, buy-back contracts exhibit stronger effectiveness through risk-sharing mechanisms, whereas in supply chains with stable demand, wholesale price contracts may be more suitable due to their simplicity in implementation. On the other hand, adopting a hybrid mechanism that combines both contract types can lead to improved coordination and greater overall effectiveness of the supply chain. Future studies may focus on behavioral factors, the influence of emerging technologies, and the adaptation of these mechanisms within more complex supply chain structures.
Copyright (c) 2026 Pooria Shekasteband (Author); Mona Ahmadi rad; Shervin Asadzadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Environmental Life Cycle Assessment of Gasoline Production Process in Khuzestan Using an Ecosystem Approach in Environmental Planning
In the present study, a comprehensive investigation and analysis of the environmental impacts of the gasoline production process in Khuzestan province were conducted using the Life Cycle Assessment (LCA) method and an ecosystem approach. The system boundary included all stages from exploration and drilling to refining and transportation, and the analyses were performed utilizing SimaPro software and the Ecoinvent database. In the Life Cycle Inventory (LCI) phase, data concerning resource consumption and pollutant emissions for each stage were collected. Subsequently, in the Life Cycle Impact Assessment (LCIA) phase, indicators such as Global Warming Potential (GWP), human toxicity, acidification, ozone layer depletion, water pollution, and photochemical air pollution were examined. The results indicated that the refining and drilling stages account for the largest share in creating negative environmental impacts. The GWP indicator, with a value of 3.62kg 〖"CO" 〗_2equivalent per liter of gasoline, assigned the highest weight to itself. Furthermore, the weighting and normalization phase was carried out using the EPM method, which determined the priority of the indicators with an emphasis on ecosystem importance. Sensitivity analysis also revealed that substituting the refinery’s fossil-fuel electricity with solar energy, utilizing recycled drilling mud, and monitoring methane leakage can significantly reduce the total environmental burden.
Copyright (c) 2026 Eghbal Sekhavati (Author); Mehdi Esmaeili Bidhendi; Reza Jalilzadeh-Yengejeh , Farhad Baqi-Asl (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing an environmental accounting model with an emphasis on waste management to improve performance sustainability (Case study: Hospitals covered by the Ministry of Health)
In recent years, environmental accounting has become a strategic tool for managing sustainability costs in organizations. Hospitals face a serious challenge in the systematic recording and reporting of environmental costs due to the production of hazardous waste. Lack of native accounting model in public hospitals has led to the concealment of real waste management costs and reduced financial transparency in sustainable performance appraisal, so the purpose of this study is to design an environmental accounting model with emphasis on waste management to improve performance sustainability (Case study: hospitals covered by the Ministry of Health). The research method was mixed, qualitative and quantitative and first, qualitative data were collected. Then, based on the findings of the qualitative stage, a tool was made to enable quantitative data collection and generalization. In order to obtain qualitative data, according to targeted sampling of snowball type, 16 elites, accounting professors, accountants, managers and doctors of hospitals under the auspices of the Ministry of Health were selected and conducted by semi-in-deep interviews and open-ended questions and based on thematic analysis and findings. At this stage, the model of environmental accounting model was developed with emphasis on waste management to improve performance sustainability (Case study: hospitals covered by the Ministry of Health). In order to explain the model based on the findings of the qualitative method, the initial questionnaire in the quantitative design, validity and reliability of this tool was reviewed by referring to the opinion of experts and managers and approved after corrections. Statistical population in the quantitative section, accountants, managers and physicians of hospitals under the auspices of the Ministry of Health were selected as 106396 and the statistical sample according to Cochran's formula, a sample of 383 people was selected and distributed and collected by random sampling method. Quantitative data were analyzed and processed with the help of PLS software. According to the paradigm model, environmental accounting components with emphasis on waste management to improve the sustainability of hospital performance in six categories of causal factors including 6 components; Factors affecting environmental accounting with 3 dimensions (strategic indicators, uncertainty indicators, commitment indicators and senior management); Environmental accounting with 3 dimensions (social legitimacy, accounting information system, control and monitoring); Waste management with 6 dimensions (separation of waste at the source of production, collection of waste sectors, waste transportation, temporary waste storage, waste disposal, manpower status); Factors affecting performance stability with 7 indicators; Performance stability with 4 indicators; and environmental accounting results with emphasis on waste management to improve performance sustainability with 3 dimensions (improving and optimizing environmental processes with an emphasis on waste management, cooperation with HSE (Health, Safety and Environment), Financial Implications); were identified.
Copyright (c) 2026 Mohammad Reza Hosseini (Author); Shiva Hassanpour; Mohammad Tamimi, Hossein Jannat Makan (Author) https://creativecommons.org/licenses/by-nc/4.0
Presenting an Operational Planning Framework for Customer Relationship Management Using a Multi-Objective Decision-Making Approach:A Case Study of Gol Gohar Mining and Industrial Company
The aim of this study is to develop an operational planning framework for the development of Customer Relationship Management (CRM) strategies in the Gol Gohar Mining and Industrial Company. This research is grounded in the pragmatism paradigm with a developmental objective and is conducted as a mixed-method (qualitative–quantitative) study using a cross-sectional, survey-based approach (interviews and questionnaires), implemented through field studies in two stages.In the first qualitative stage, the research strategy was based on thematic analysis. Data were collected through semi-structured, in-depth interviews with experts, along with a review of internal company documents and scientific sources. A total of 15 experts in sales, marketing, and customer service at the Gol Gohar Mining and Industrial Company were selected using purposive sampling. The sample size was determined based on the principle of theoretical saturation. Data coding was performed at open, axial, and selective levels using MAXQDA software. As a result, 18 key challenges, 18 corresponding strategies to address them, and 11 developmental objectives were identified and organized within a challenge–strategy–objective conceptual framework. In addition, 14 key success factors in CRM and major resource constraints, including budget and time limitations, were identified to establish a coherent and realistic framework for operational CRM planning. At the end of this stage, the extracted themes and categories were converted into measurable indicators to serve as inputs for the quantitative analysis in the second stage. Research validity was ensured through expert review of the items and thematic analysis of qualitative data, while reliability was confirmed through expert consensus and independent review of the coding process.In the second stage, the research strategy involved quantitative analysis based on the data extracted from the first stage. Quantitative data were collected through a questionnaire derived from the identified themes and strategies and distributed among 15 experts and managers in the mining industry with at least ten years of professional experience, selected through purposive sampling. Data analysis was conducted using Excel and R software and multi-criteria decision-making methods, including the House of Quality matrix, Analytic Hierarchy Process (AHP), Simple Additive Weighting (SAW), and Goal Programming. The results indicate that successful CRM implementation in the Gol Gohar Mining and Industrial Company requires the simultaneous integration of technological, data-driven, and human-centered initiatives. The findings show that strategies such as CRM system integration, development of managerial dashboards, and establishment of interdepartmental committees exhibit the highest alignment with organizational development objectives and generate the greatest effectiveness under resource constraints.By adopting a mixed qualitative–quantitative approach and integrating thematic analysis with multi-criteria decision-making techniques and goal programming, this study contributes to the existing body of knowledge on CRM. With a specific focus on the mining industry, it provides managers with a structured and evidence-based understanding of CRM challenges, strategies, and success factors, enabling more effective operational decision-making and strategic planning through optimal resource allocation and alignment of technological and human objectives.
Copyright (c) 2026 Hadi Hosseinimanesh (Author); Shahnaz Nayebzadeh; Seyyed Hassan Hataminasab, Mozhde Rabbani (Author) https://creativecommons.org/licenses/by-nc/4.0
Validating the Integrated Supply Chain Model Based on the LARG Hybrid Paradigm in the Oil Industry (Case Study: Abadan Oil Refining Company)
Supply chain management has become one of the key determinants of organizational success in competitive markets, and the LARG hybrid paradigm (Lean, Agile, Resilient, and Green) has been introduced as a comprehensive framework for supply chain optimization. The Iranian oil industry faces challenges such as aging infrastructure, economic sanctions, and environmental requirements, which underscore the necessity of designing an integrated and localized model. The objective of this study was to design and validate an integrated supply chain model based on the LARG paradigm at Abadan Oil Refining Company. The research was conducted using a mixed-methods approach (qualitative–quantitative). In the qualitative phase, meta-synthesis and interviews with 10 experts were employed, while in the quantitative phase, a questionnaire was administered to a sample of 385 participants. Data were analyzed using Fuzzy Delphi, Fuzzy DEMATEL, F.D.ANP, and Structural Equation Modeling techniques. The findings indicated that out of 126 initial indicators, 119 were confirmed. The dimensions of leadership and organizational culture were positioned as causal factors and had significant effects on processes and innovation. All hypotheses were confirmed, and the strongest path coefficient was related to the effect of leadership on processes (β = 0.510). The ranking of LARG components revealed that the Green component (28.1%), Agile (26.3%), Lean (24.5%), and Resilient (21.1%) were prioritized, respectively. The proposed model can serve as a practical tool for enhancing sustainable supply chain performance in the oil industry.
Copyright (c) 2026 Alireza Banisilalavi (Author); Saber Molla Alizadeh Zevardehi; Ali Mahmoodirad (Author) https://creativecommons.org/licenses/by-nc/4.0
A Hybrid Approach Based on a Memory-Instance–Based Gated Transformer (MIGT) Algorithm and Metaheuristic Optimization for Portfolio Management with the Aim of Return Optimization and Risk Control
The objective of this study is to propose an efficient hybrid framework for portfolio management that can simultaneously optimize investment returns and effectively control risk under both normal and turbulent market conditions. The primary focus is on improving the accuracy of asset return forecasting and transforming these forecasts into optimal portfolio weighting decisions. In this research, a hybrid approach is employed that combines a memory-instance–based gated transformer model for forecasting asset returns with a hybrid metaheuristic algorithm based on adaptive differential evolution and particle swarm optimization for portfolio optimization. Financial data from the Iranian capital market covering the period from 2016 to 2024 were collected and, after preprocessing, cleaning, and feature extraction, were entered into the modeling process. The mean absolute error in the test period was 0.0068, and the root mean square error was 0.0100; these values exhibited a standard deviation of less than 0.0004 in cross-validation, indicating prediction stability. The optimal portfolio obtained by integrating these forecasts with the hybrid metaheuristic algorithm achieved an annualized return of 0.325 and an annualized standard deviation of 0.238, resulting in a Sharpe ratio of 1.16 and a Sortino ratio of 1.61. Tail risk measures also remained at controlled levels, such that the value at risk (VaR) at the 0.95 confidence level was 0.021 and the conditional value at risk (CVaR) was calculated as 0.0316. The paired Wilcoxon test conducted to compare the proposed model with benchmark methods yielded statistics above the significance threshold (for the Sharpe ratio, z = 2.51 and p = 0.012; for the Sortino ratio, z = 2.78 and p = 0.005), indicating a statistically significant improvement in performance. Based on the findings, it can be concluded that the proposed hybrid framework is capable of establishing an appropriate balance between return and risk in portfolio management by leveraging deep learning and metaheuristic optimization. The stability of the results across different temporal subsamples and the maintenance of acceptable performance under turbulent market conditions indicate that this approach possesses strong generalizability and practical applicability.
Copyright (c) 2026 Mohammad Haji Ebrahim Tehrani (Author); Amin Safarnejad Borujeni; Mohsen Hashemigohar, Sobhan Zafari, Hossein Alidadi (Author) https://creativecommons.org/licenses/by-nc/4.0
Intelligent Harmony: Harnessing Deep Reinforcement Learning for Smart Home Energy Management
This review examines the intricate developments and challenges in smart home energy management systems, with a particular focus on the integration of cutting-edge technologies and innovative methodologies aimed at addressing the rapidly evolving landscape of energy use. Despite significant strides in the field, there remain critical gaps that require further attention. Chief among these are ensuring data privacy and security, which have become increasingly important with the proliferation of interconnected devices, managing the complexity of heterogeneous data from diverse sources, and overcoming persistent interoperability issues that hinder seamless communication among various smart devices. To address these challenges, this review outlines several promising directions for future research. These include the implementation of robust encryption techniques to protect sensitive information, the development of scalable storage solutions to handle the ever-growing volume of data, and the deployment of advanced analytics algorithms capable of real-time processing and decision-making. Additionally, the review emphasizes the need to explore the socio-economic implications of smart energy management systems, particularly in light of global crises such as the COVID-19 pandemic, which have underscored the importance of resilient and sustainable energy infrastructures. By providing a comprehensive overview of both the current state of smart home energy management and the areas that require further exploration, this review aims to foster advancements that will lead to the creation of more efficient, secure, and adaptable systems. Ultimately, addressing these challenges will contribute to the development of sustainable solutions that align with broader societal goals, enhancing the resilience of energy infrastructures while promoting environmental stewardship.
Copyright (c) 2026 Mohammadreza Ganjian (Author); Mohammad Tabrizian; Naser Khodabakhshi-Javinani, Meqdad Ansarian (Author) https://creativecommons.org/licenses/by-nc/4.0
Presenting a Model for Formulating Sustainable Agricultural Development Policies (A Case Study of the Greenhouse Cultivation Industry in Hamedan Province)
Sustainable agricultural development, given the scarcity of water resources in the country, is one of the most important issues in water and agricultural management and requires the formulation of prudent strategies and policies. This study was conducted with the aim of presenting a model for formulating sustainable agricultural development policies (a case study of the greenhouse cultivation industry in Hamedan Province). In terms of purpose, the present research is applied; in terms of data collection, it is descriptive–exploratory; with respect to the logic of inference, it follows an inductive–deductive research approach; and in terms of nature, it is qualitative. In estimating the size of the first statistical population (the academic community), through examination and review of the Planning Deputy of the Agricultural Jihad Organization, the Agricultural Research Center, and university systems, a list of faculty members who collaborated with the greenhouse cultivation industry was used for data collection, and themes were identified and refined through thematic analysis. The primary data collection instrument in this research was interviews. To enhance accuracy, the coding process and aggregation of themes were supported using MAXQDA software, and through repeated reviews, overlaps were reduced and thematic boundaries were clearly delineated. The findings indicated that 86 themes were extracted from the interviews, which were categorized into five levels and nine main components, including the role of exploitation, geographical factors, government policymaking, influential internal and external environmental factors, economic factors, cultural factors, social factors, and technical factors.
greenhouse cultivation industry of Hamedan Province
Copyright (c) 2026 Mohammad Saeid Khaksar, Kambiz Hamidi, Reza Bahramloo, Nasrin Kodabakhsi, Seyed Hamid Hosseini (Author) https://creativecommons.org/licenses/by-nc/4.0
Determining the Dimensions, Components, and Indicators of Transformational Digital Leadership in SAIPA Automotive Group
The purpose of this study was to identify and develop the dimensions, components, and indicators of transformational digital leadership in SAIPA Automotive Group. This study employed a qualitative developmental research design grounded in the interpretive paradigm and guided by an inductive approach. Data were collected through semi-structured interviews with 17 experts, senior and middle managers, policymakers, consultants, and specialists with substantial experience in digital transformation, leadership, information technology, innovation, and automotive industry management. Participants were selected using purposive criterion-based sampling, and interviews continued until theoretical saturation was achieved. The collected data were transcribed and analyzed using Braun and Clarke’s six-phase thematic analysis method with the support of MAXQDA software. Trustworthiness was enhanced through repeated data review, continuous comparison of codes and themes, expert participation, and systematic documentation of the analytical process. The analysis resulted in the extraction of 570 initial concepts, which were reduced to 354 basic codes after removing overlaps and redundancies. These codes were subsequently organized into 26 subthemes and 7 overarching themes. The final model of transformational digital leadership consisted of: (1) Digital Transformation Leadership and Strategy, including transformational leadership, digital vision, strategic alignment, and dynamic capabilities; (2) Data Governance and Digital Technologies, encompassing data governance, analytics, artificial intelligence, and digital infrastructure; (3) Digital Transformation in Production and Operations, including smart manufacturing, digital operations, supply chain digitalization, and digital product management; (4) Digital Innovation and Technology Ecosystem, comprising innovation capability, knowledge management, ecosystem collaboration, and digital R&D; (5) Digital Market and Customer Value Creation, including digital customer experience, digital marketing, connected services, and customer analytics; (6) Human Capital and Digital Culture, involving digital culture, talent development, employee empowerment, and change management; and (7) Security, Risk, and Macro Digital Governance, including cybersecurity, digital governance, risk management, and digital sustainability. The findings demonstrate that transformational digital leadership in the automotive industry is a multidimensional and integrative phenomenon extending beyond technological implementation.
Copyright (c) 2026 Hamzeh Esmaili (Author); Mohammad Reza Rabiee Mandejin; Vahid Araei, Mohammadreza Saeedi (Author) https://creativecommons.org/licenses/by-nc/4.0
Validation of a Leadership Behavior Model with an Information and Communication Technology Approach in Government Organizations
This study aimed to validate a comprehensive leadership behavior model with an information and communication technology (ICT) approach in government organizations to identify key dimensions and relationships influencing digital transformation and organizational performance. A mixed-methods design was employed, combining qualitative and quantitative approaches. The qualitative phase involved semi-structured interviews with 14 experts, including seven university professors and seven experienced government specialists, to identify main and subthemes of ICT-based leadership behavior. The quantitative phase used a researcher-made questionnaire distributed to 260 employees of the State Organization for Registration of Deeds and Properties, selected via stratified random sampling. Qualitative data were analyzed using thematic analysis, and quantitative data were examined using descriptive statistics, reliability analysis, exploratory factor analysis, and structural equation modeling with SmartPLS. Measurement validity was confirmed through Cronbach’s alpha, composite reliability, average variance extracted (AVE), and discriminant validity (HTMT). Structural model evaluation assessed path coefficients, t-values, R², and overall fit indices (SRMR, NFI, chi-square). Inferential analysis confirmed that all hypothesized paths were statistically significant (t > 1.96, p < 0.001), indicating meaningful relationships between the ICT-oriented leadership model and its components. R² values demonstrated moderate explanatory power across endogenous variables, confirming that the model effectively explains variance in leadership dimensions such as digital competency, employee empowerment, administrative process transformation, participatory leadership, ethical governance, cybersecurity, and data-driven decision-making. The measurement model demonstrated high reliability (Cronbach’s alpha and CR > 0.70), convergent validity (AVE > 0.50), and discriminant validity (HTMT < 0.90). Overall model fit was acceptable, with SRMR = 0.076, NFI = 0.873, and a satisfactory chi-square statistic. The study validated a multidimensional ICT-based leadership behavior model for government organizations, highlighting that effective digital leadership requires integration of technological competence, ethical governance, participatory management, data-driven decision-making, and knowledge management to enhance organizational performance and facilitate sustainable digital transformation.
Copyright (c) 2026 Ashraf Jebeli (Author); Masoumeh Jafari; Tahereh hasoomi (Author) https://creativecommons.org/licenses/by-nc/4.0
Validation of the Model of Human Resource Adherence to Behavioral Values within the Framework of Knowledge Management Implementation
This study aimed to validate the model of human resource adherence to behavioral values within the framework of knowledge management implementation. This applied study was conducted using an exploratory sequential mixed-methods design. In the qualitative phase, semi-structured interviews were conducted with 19 experts, including university faculty members, managers, and specialists in management, organizational behavior, and knowledge management. Participants were selected through purposive and criterion-based sampling, and interviews continued until theoretical saturation was achieved. The qualitative data were analyzed through thematic analysis using MAXQDA software. In the quantitative phase, the statistical population included approximately 800 employees of Tehran Cement Company, from whom 260 participants were selected using the Krejcie and Morgan table and simple random sampling. Data were collected using a researcher-made questionnaire developed from the qualitative findings and analyzed through structural equation modeling using SmartPLS version 4. The inferential findings confirmed the validity and reliability of the proposed model. Cronbach’s alpha values ranged from .753 to .965, and composite reliability coefficients ranged from .859 to .967, confirming acceptable internal consistency. The AVE values for all constructs were higher than .50, indicating convergent validity, and all HTMT values were below .90, confirming discriminant validity. The overall model fit was acceptable, with SRMR = .076 and NFI = .873. The R² values ranged from .491 to .614, indicating strong explanatory power for the endogenous constructs. All 18 structural paths were statistically significant at p < .001, with path coefficients ranging from .703 to .785 and t-values exceeding 1.96. The validated model demonstrated that adherence to behavioral values in knowledge management implementation is a multidimensional construct shaped by ethical culture, trust, leadership, motivation, responsibility, teamwork, innovation, organizational support, justice, reward systems, conflict management, knowledge-behavioral empowerment, and continuous organizational learning.
Copyright (c) 2026 Seyed Mohammadali Baghernejad (Author); Masoumeh Jafari; Naser Barkhordar (Author) https://creativecommons.org/licenses/by-nc/4.0
Evaluation of the Impact of Sustainable Architecture on Reducing Environmental Pollution and Improving Energy Efficiency in Residential Buildings
The present study aimed to evaluate the impact of sustainable architecture on reducing environmental pollution and improving energy efficiency in residential buildings through a comparative analysis of physical-spatial and environmental components in selected urban neighborhoods. This study employed a mixed-methods approach combining qualitative and quantitative analyses to assess sustainability indicators in two neighborhoods, El-Goli and Khiyaban, in Tabriz. Data were collected through structured questionnaires, expert interviews, field observations, and document analysis. The Analytic Hierarchy Process (AHP) was utilized to determine the relative importance of criteria and sub-criteria related to physical-spatial and environmental components. Pairwise comparison matrices were developed using Saaty’s 9-point scale, and the data were analyzed using Expert Choice software to compute weights, priorities, and consistency ratios. The evaluation framework included indicators such as accessibility, housing quality, environmental cleanliness, air quality, infrastructure efficiency, and resource management. The results indicate that the El-Goli neighborhood demonstrates significantly higher performance compared to the Khiyaban neighborhood across both physical-spatial and environmental components. The relative weight of the physical-spatial component for El-Goli was 0.723, compared to 0.277 for Khiyaban, while in the environmental component, El-Goli achieved a weight of 0.766 versus 0.234 for Khiyaban. The consistency ratios of the AHP analysis were below 0.10, confirming the validity of the comparisons. Mean scores of environmental and physical-spatial indicators were also higher in El-Goli, indicating superior environmental quality, better infrastructure efficiency, and improved spatial organization. The findings confirm that the application of sustainable architectural and urban planning principles significantly enhances environmental quality and energy efficiency in residential areas, and the superior performance of the El-Goli neighborhood highlights the effectiveness of integrated design, modern infrastructure, and sustainable resource management in achieving these outcomes.
Copyright (c) 2026 Yosef Asadi Khanegah https://creativecommons.org/licenses/by-nc/4.0
Modeling and Analysis of Energy Consumption Reduction Strategies in Sustainable Buildings in Hot and Humid Regions Using Simulation and Artificial Neural Networks (Case Study: Hormozgan)
This study was designed to model and analyze effective strategies for reducing energy consumption in sustainable buildings in hot and humid regions, with a specific focus on Hormozgan Province. Given the rapid growth of energy use in the building sector and the region’s climatic challenges, the development of low-energy and climate-responsive solutions has become imperative. The research adopts an applied and hybrid methodology (analytical–simulation and machine learning), in which building energy simulation and artificial neural networks (ANNs) were employed to predict energy consumption. Ten sustainable design strategies were evaluated, including advanced thermal insulation, green roofs, passive cooling design, reduction of the window-to-wall ratio (WWR), variable refrigerant flow (VRF) smart ventilation systems, low-emissivity glazing, optimal building orientation, and hybrid ventilation. The results indicated that the strategy of advanced insulation using mineral wool was the most effective solution for hot and humid climates, achieving an energy savings of 75.1%. This was followed by the combined strategy of green roof and insulation (58.7% savings) and passive cooling design (57.3%), which ranked second and third, respectively. The findings further demonstrated that building-envelope-based strategies (insulation, WWR reduction, orientation) exhibit substantially higher effectiveness compared to active systems (such as hybrid ventilation). The ANN model achieved a coefficient of determination (R²) exceeding 0.95 and a mean absolute percentage error (MAPE) below 2%, indicating a high level of predictive accuracy. By providing a quantitative ranking of energy consumption reduction strategies, this research offers a scientifically grounded and operational framework for designers, policymakers, and urban planners working in hot and humid regions of the country.
Copyright (c) 2026 Seyed Shahab Sadri (Author); Mohammad Behzadpour; Cyrus Bavar, Hossein Aali (Author) https://creativecommons.org/licenses/by-nc/4.0
An Engineering and Management Approach to Optimizing the Behavior of Strip Footings on Geocell-Reinforced Saturated Sand: Analyzing the Effect of Embedment Depth, Reinforcement Width, and Geocell Geometry
Improving the performance of shallow foundations on problematic soils such as saturated sand requires integrating precise engineering design with informed management of influential parameters. This research examines geocell systems from an engineering-management perspective to provide solutions that simultaneously enhance bearing capacity and optimize economic and executional efficiency. In a systematic laboratory study, the effect of three key parameters with engineering significance (embedment depth, reinforcement width, and geocell geometry) on the behavior of a strip footing on saturated sand was investigated. These parameters were considered as design management variables to quantify their impact on the system's performance and economic indicators. Forty-nine tests were conducted, including an unreinforced reference case, under uniform loading. The results showed that optimal management of embedment depth at shallow depths (u/B = 0.25) combined with engineered selection of reinforcement width (L/B ≥ 3) can increase the Bearing Capacity Ratio (BCR) up to 4.2 times. From the perspective of material management and technical selection, geocells with greater height and smaller cell aperture (G3) were identified as the superior engineering option. This study demonstrates that through engineering design based on testing and intelligent management of effective parameters, reinforced systems can be achieved that are both technically efficient and economically and executionally optimal. The findings provide a framework for managerial decision-making in selecting configuration, materials, and execution methods in real-world projects.
Copyright (c) 2026 Manesht Maleki Vasegh (Author); Vahid Rostami; Hamidreza Rabieefar (Author) https://creativecommons.org/licenses/by-nc/4.0
Modeling the Mechanisms and Consequences of Credit Shocks, Including Shocks to Deposit Profit Rates and Bank Loan and Credit Interest Rates, and Financial Shocks, Including Asset Price Shocks, Using a Dynamic Stochastic General Equilibrium Model
The objective of this study is to investigate the transmission mechanisms and macroeconomic effects of credit shocks (including deposit and lending rate shocks) and financial shocks (including asset price shocks) on key macroeconomic variables in Iran within a dynamic stochastic general equilibrium framework. This study employs a quantitative and analytical approach based on a dynamic stochastic general equilibrium (DSGE) model tailored to the structural characteristics of the Iranian economy. The model incorporates key sectors including households, firms, the banking system, government, and the external sector, allowing for the integration of financial frictions, credit markets, and asset price dynamics. Empirical support is provided through descriptive-comparative analysis across selected countries and time-series analysis for Iran over the study period. Key variables include financial development indicators (such as trading value to GDP, number of listed firms, turnover ratio, bank deposits, private sector credit, and market capitalization), macroeconomic indicators (real GDP, inflation, consumption, investment), and nominal variables (interest rates, exchange rates, liquidity). Bivariate regressions and correlation analyses are used to examine relationships between financial variables and macroeconomic performance. The findings indicate that higher levels of financial development are positively associated with real per capita income and negatively associated with inflation across countries. In Iran, financial market expansion and increased liquidity are positively correlated with consumption, investment, and GDP, while also showing a positive but weaker relationship with inflation. Banking sector development and private sector credit exhibit a positive relationship with real economic performance and an inverse relationship with inflation. Asset price shocks and financial market growth significantly influence macroeconomic variables through wealth, expectations, and financing channels. Additionally, liquidity growth and exchange rate fluctuations are found to be strongly associated with financial market dynamics, indicating the presence of intertwined nominal and real transmission mechanisms. The study concludes that credit and financial shocks play a fundamental role in shaping macroeconomic dynamics in Iran, with both banking and capital market channels jointly influencing economic performance.
Copyright (c) 2026 Mohammad Ranjbari, Hossein Izadi, Hossein Jahangirnia, Majid Zanjirdar (Author) https://creativecommons.org/licenses/by-nc/4.0
The Isfahan Water Crisis, Zayandeh Rud River Basin: Sustainable Governance in Central Iranian Plateau
Water is a social element that is essential to the existence of human communities, not just a pure chemical or molecule. This issue is critical for Iran, a nation situated in a largely dry and semi-arid region. With only 5.2% of the globe's total freshwater resources, Iran encounters serious challenges concerning water scarcity. Scholars such as Witfogel and Marx have suitably articulated the "tyranny of water" concept. This study uses a secondary research methodology, applying qualitative content analysis to methodically examine 211 online works, news stories, and publications produced between 2001 and 2023. It delineates the principal themes that characterize the water issue as a social concern, referencing definitions by Earl R. Rabington and Martin Weinberg. Sustainable development necessitates a cohesive strategy for governance and administration, utilizing historical, natural, and regional infrastructures while safeguarding the environment. In this context, analyzing water as a social issue allows us to formulate a novel idea and definition of social concerns, informed by Ulrich Beck's viewpoints in risk society theory. This notion rekindles our optimism that cities in the Central Plateau, including Isfahan and Yazd, are water-sensitive urban areas necessitating water-sensitive strategies for effectively managing water supply and demand focused on efficiency.
Copyright (c) 2026 Hadi Zamani (Author); Mohammad Reza Hosseini https://creativecommons.org/licenses/by-nc/4.0
Presenting an Integrated Quasi-Data Envelopment Analysis Model Based on Fuzzy Goal Programming for Performance Evaluation of Iran’s Petrochemical Industries
The importance of the petrochemical industry as a dynamic and leading sector in the economies of countries producing these products is undeniable. According to experts, this industry generates an added value approximately ten to thirty times greater than that obtained from the export of crude oil and natural gas. Nevertheless, examining the optimal combination of input factors in these industries, including raw materials and feedstock consumption, human resources, and investment costs, in order to achieve optimal production and, in summary, to evaluate the efficiency of these industries in the country, is an issue that has not yet been comprehensively assessed. The present study was conducted with the aim of presenting an integrated quasi-data envelopment analysis model based on fuzzy goal programming for evaluating the performance of Iran’s petrochemical industries. To achieve this objective, four research questions and subsidiary objectives were investigated. This study is applied and descriptive in nature, and the statistical population consisted of petrochemical companies located in Assaluyeh, Iran. The data collection instruments used in this study included documents and records, interviews, and questionnaires. Based on the results obtained from the interviews (qualitative research for identifying efficiency indicators from the perspective of data envelopment analysis), as well as questionnaires and documents (quantitative research for obtaining cost, time, profit, production, and related data), the statistical sample size comprised 43 participants. Confirmatory factor analysis was employed as the analytical tool for interview results, and the findings indicated appropriate validity and reliability of the measurement model for the identified indicators. The mathematical model of quasi-data envelopment analysis and fuzzy goal programming was designed and solved. Based on the results, twenty-two petrochemical companies were evaluated in terms of efficiency, ranked according to performance and efficiency, and their deviations from predetermined goals were identified. The proposed model was also validated, and practical recommendations were presented at the conclusion of the study.
Copyright (c) 2026 Rahim Abdollahi (Author); Mir Hossein Seyyedi; Kamaladin Rahmani (Author) https://creativecommons.org/licenses/by-nc/4.0
The Impact of Product Creativity on the Dimensions of Broad Scope, Timeliness, Aggregation, and Integration in the Use of Management Accounting System Information
The present study aimed to investigate the effect of product creativity on the dimensions of broad scope, timeliness, aggregation, and integration in the use of management accounting system information among managers of small and medium-sized enterprises in Mashhad. This applied study was conducted using a descriptive-survey research design. The statistical population consisted of managers of small and medium-sized companies in Mashhad, Iran, totaling 1,560 individuals, from which 384 participants were selected based on Morgan’s table using convenience sampling. Data were collected through a standardized questionnaire adapted from previous studies, including 18 items measuring product creativity, broad scope of information, timeliness, aggregation, and integration of management accounting system information. The questionnaire employed a five-point Likert scale ranging from completely disagree to completely agree. Data analysis was performed using Structural Equation Modeling (SEM) with Smart PLS 3 software. Reliability and validity of the constructs were confirmed through Cronbach’s alpha, composite reliability, convergent validity, and discriminant validity indices. The results of structural equation modeling demonstrated that product creativity had a positive and statistically significant effect on all four dimensions of management accounting system information usage. Specifically, product creativity significantly affected broad scope information (β=0.576, t=16.530, p<0.05), timeliness of information (β=0.531, t=14.252, p<0.05), information aggregation (β=0.575, t=17.383, p<0.05), and information integration (β=0.523, t=17.457, p<0.05). In addition, the coefficient of determination values indicated moderate explanatory power for the endogenous variables, with R² values of 0.332 for broad scope information, 0.282 for timeliness, 0.331 for aggregation, and 0.273 for integration. The overall model fit indices, including GOF and SRMR, confirmed the acceptable fit and predictive quality of the proposed structural model. The findings indicate that effective utilization of management accounting system information significantly enhances product creativity in organizations.
Management accounting system information
Copyright (c) 2026 Amir Ghafourian Shagerdi; Maryam Nojavan (Author) https://creativecommons.org/licenses/by-nc/4.0
Application of Deep Learning Algorithms in the Optimization of Banking Asset–Liability Portfolios: A Case Study of Bank Melli Iran
The present study was conducted to investigate the application of deep learning algorithms in the optimization of banking asset–liability portfolios, with the case study focusing on real-world data from Bank Melli Iran. In a context where the national banking system is confronted with challenges such as liquidity fluctuations, high inflation, and credit risk, the design of predictive and decision-support models can play a significant role in improving the efficiency of asset–liability management (ALM). In this regard, the study proposes a bidirectional artificial intelligence-based model in which, at the first stage, recurrent neural networks based on Long Short-Term Memory (LSTM) architectures are employed to forecast temporal patterns of deposits and loans, while in the second stage, the Proximal Policy Optimization (PPO) reinforcement learning algorithm is utilized to determine the optimal portfolio composition within a dynamic and uncertain environment. Empirical findings derived from data collected from selected bank branches indicate that the proposed hybrid model demonstrates more accurate and stable performance compared to conventional methods such as the ARIMA model and the Markowitz mean–variance framework. Specifically, the prediction error (RMSE) was reduced by up to 55%, while capital adequacy ratios were maintained under critical scenarios. The findings suggest that deep learning and reinforcement learning techniques can be effectively utilized in the financial decision-making processes of banks.
Copyright (c) 2026 Behzad Abdi (Author); Afshin Baghfalaki; Alireza Moradi, Adel Fatemi (Author) https://creativecommons.org/licenses/by-nc/4.0