Journal of Resource Management and Decision Engineering
type: channel
description: 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 a…
language: en
publication name: Journal of Resource Management and Decision Engineering
issn: 3041-9735
items: A Comparative Study of Residential Satisfaction Indicators in Oil Company Towns: A Case Study of Newside and Naft Town, Ahvaz
items: An Infrastructure Model for Lean Banking Human Resource Management in State-Owned Banks
items: Development of an intelligent framework for semantic road segmentation using depth and camera motion constraints: An approach for resource management and engineering decision making in transportation
items: Sustainable Resource Management in Nanofluid Cooling Systems for Electronic Boards: A Systematic Review
items: Presenting a LARG Supply Chain Model with a Hybrid Approach in the Poultry Industry in Northern Iran
items: Integrated Queue Management and Service Composition for Enhancing Manufacturing-as-a-Service Performance
items: Designing an Exploitation-Oriented Digital Innovation Model Based on the Maximum Functional Performance of Mining Industries
items: A Hybrid Statistical–MCDM Framework for Integrated Risk Prioritization and Sustainability Assessment in Construction Supply Chains: Bridging Local Data with Global Theory
items: Explaining the Relationship between Organizational Humanitarianism and Employees’ Social Accountability: The Moderating Role of Employee Self-Leadership
items: Identification of the Components of Environmental Innovation, Green Supply Chain Management, Circular Economy Capabilities, and Financial Performance Through Qualitative Content Analysis
items: Decision-Making Models for Circular Economy Implementation in Waste Management
items: Sentiment Analysis of Customers in Iran’s Automotive Industry Using the LSTM Method
items: Identification of Key Causes of Disputes in Construction Projects and Preventive Strategies: A Systematic Review
items: https://journalrmde.com/index.php/jrmde/article/view/285
items: Investigating the Relationship between Job Characteristics and the Growth of Professional Ethics among Employees of the East Azerbaijan Gas Company
items: Explanation and Prioritization of Factors Influencing the Location of Resilient Schools Against Natural Hazards with an Emphasis on Collective Intelligence (Case Study: Nowshahr City)
items: Detection of Attacks in Internet of Things Devices Using an Optimized Ensemble Classification Based on Deep Transfer Learning and the Harris Hawks Algorithm
items: Comparing the Effectiveness of Supply Chain under Two Contract Mechanisms: Buy-back and Wholesale Price
items: Environmental Life Cycle Assessment of Gasoline Production Process in Khuzestan Using an Ecosystem Approach in Environmental Planning
items: Designing an environmental accounting model with an emphasis on waste management to improve performance sustainability (Case study: Hospitals covered by the Ministry of Health)
items: Validating the Integrated Supply Chain Model Based on the LARG Hybrid Paradigm in the Oil Industry (Case Study: Abadan Oil Refining Company)
items: 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
items: 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
items: Intelligent Harmony: Harnessing Deep Reinforcement Learning for Smart Home Energy Management
items: Presenting a Model for Formulating Sustainable Agricultural Development Policies (A Case Study of the Greenhouse Cultivation Industry in Hamedan Province)
items: Validation of the Model of Human Resource Adherence to Behavioral Values within the Framework of Knowledge Management Implementation
items: Development and Psychometric Validation of an Instrument for Assessing the Quality of Talent Management in the Agricultural Research, Education, and Extension Organization (AREEO)
items: Systematizing Barriers to Open Innovation Adoption: A Multi-Method Approach Based on Meta Synthesis, Fuzzy Interpretive Structural Modeling, and MICMAC Analysis
items: The Role of Blockchain Technology in the Supply Chain Performance of the Oil and Gas Industry
items: Determining the Dimensions, Components, and Indicators of Transformational Digital Leadership in SAIPA Automotive Group

A Comparative Study of Residential Satisfaction Indicators in Oil Company Towns: A Case Study of Newside and Naft Town, Ahvaz
type: item
description: Oil company towns are among the settlements in which housing, in addition to its residential function, is linked to organizational structure, spatial order, and the social relations of residents. Therefore, evaluating residential satisfaction in these settlements requires simultaneous attention to the physical, social, perceptual, and managerial dimensions of residential quality. The present study aims to analyze the components affecting residential satisfaction in the oil company towns of Ahvaz by comparing two residential complexes, namely Newside and Naft Town. The conceptual framework of the study was developed based on human–environment relationship theory, and residential quality was examined across five dimensions: spatial identity and attachment, social and ethical values, functionalism and quality of life, dynamism and physical quality, and urban management and governance. In terms of purpose, this research is applied, and in terms of method, it is quantitative–qualitative. Data were collected using a researcher-made questionnaire based on cluster sampling among 384 residents of the two neighborhoods. The results showed that Naft Town is at a high level in the components of spatial identity and social values, and at a moderate level in the physical, functional, and managerial components. In contrast, Newside is below the moderate level in all components, with its weaknesses being particularly evident in dynamism and physical quality, functionalism, and urban management. The comparison of the two neighborhoods indicates that residential satisfaction in oil company towns is not merely a function of the physical characteristics of housing units, but also depends on spatial legibility, the quality of neighborhood relations, the possibility of developing a sense of belonging, access to services, and the efficiency of urban management. Therefore, achieving desirable housing in these settlements requires a multidimensional and context-sensitive approach to housing…
subject: Articles
  topic: section
subject: Residential Satisfaction
  topic: keywords
subject: Oil Company Towns
  topic: keywords
subject: Newside
  topic: keywords
subject: Naft Town
  topic: keywords
creator: Amin Kamali
creator: Akbar Mousaeijoo
creator: Ahmad Reza Kaboli
creator: Seyedeh Sedigheh Mirgozar Langaroudi
rights: Copyright (c) 2026 Amin Kamali (Author); Akbar Mousaeijoo; Ahmad Reza Kaboli, Seyedeh Sedigheh Mirgozar Langaroudi (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-11-01
publication date: 2026-11-01
starting page: 1
ending page: 13

An Infrastructure Model for Lean Banking Human Resource Management in State-Owned Banks
type: item
description: In an era of intensifying competition and rapid technological transformation, human resource management within the national banking system faces significant challenges related to productivity, service quality, and the elimination of process waste. Despite the importance of lean thinking in enhancing organizational performance, a substantial research gap exists regarding the application of this approach in the human resource management domain of state-owned banking institutions. The present study was conducted with the aim of developing an infrastructure model for lean banking human resource management in the country's state-owned banks. This research employed a qualitative approach and qualitative content analysis method within the framework of the naturalistic paradigm. The statistical population consisted of 20 banking and academic experts who were selected through purposive sampling, and the sample size was determined based on the principle of theoretical saturation. Data were collected through semi-structured and in-depth interviews, with an average duration of 45 to 75 minutes. Data analysis was conducted using thematic analysis and MAXQDA Version 12 software through three stages of open, axial, and selective coding. The data analysis process resulted in the extraction of 189 basic themes, 41 organizing themes, and 10 overarching themes. The identified overarching themes included participatory culture (34 basic themes), technology in banks (31 basic themes), strategy and objectives in banks, banking laws and regulations, employee selection and recruitment in banks, employee empowerment, performance management in banks, motivation and communication in banks, banking compensation management, and organizational design of banks. The findings indicate that the infrastructure model of lean banking human resource management possesses a multidimensional and complex nature that extends beyond the traditional functions of recruitment and compensation. The successful impl…
subject: Articles
  topic: section
subject: Lean Banking
  topic: keywords
subject: Human Resource Management
  topic: keywords
subject: State-Owned Banks
  topic: keywords
subject: Lean Thinking
  topic: keywords
creator: Reza Zarei Einalghasi
creator: Reza Taghvaei
creator: Reza keyhanihekmat
creator: Alireza Eslambolchi
creator: Javad Nik Nafas
rights: Copyright (c) 2026 Reza Zarei Einalghasi, Reza Taghvaei, Reza keyhanihekmat , Alireza Eslambolchi , Javad Nik Nafas (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-11-01
publication date: 2026-11-01
starting page: 1
ending page: 15

Development of an intelligent framework for semantic road segmentation using depth and camera motion constraints: An approach for resource management and engineering decision making in transportation
type: item
description: One of the major challenges in semantic segmentation of road videos is to effectively exploit the 3D structure of the scene and camera motion, while only 2D pixel labeling is available. In this paper, we introduce a semi-supervised learning framework for semantic segmentation that exploits geometric constraints imposed by depth and camera motion in the video. In this approach, for each 15-frame sequence, only the first frame is manually labeled pixel-wise, and for subsequent frames, pseudo-labels are generated by “geometrically mapping” the labeled frame mask to other frames. To estimate monocular depth and relative camera motion between frames, the Monodepth2 model is used, which performs self-supervised learning of depth and camera motion using new view reconstruction and photometric reprojection error. Based on the estimated depth and motion, each pixel from the reference frame mask is mapped to 3D space and then to the destination frame, and by comparing the mapped depth and the estimated depth in the destination frame, a validation mask is constructed to refine the pseudo-labels. These pseudo-labels are used for semi-supervised training of the same LR-ASPP segmentation network. Evaluation on the KITTI-STEP dataset shows that the semi-supervised model based on geometric mapping (SSL-Warp) achieves mIoU of 25.73% and pixel accuracy of 71.42%, providing a slight improvement in performance over the baseline frame-only model. Analysis of the results shows that the depth and camera motion constraints are very useful for static classes such as road and vegetation, but for objects with independent motion, they require combination with explicit motion information.
subject: Articles
  topic: section
creator: Mohammad Mohammadi
rights: Copyright (c) 2026 Mohammad Mohammadi https://creativecommons.org/licenses/by-nc/4.0
date: 2027-03-01
publication date: 2027-03-01
starting page: 1
ending page: 17

Sustainable Resource Management in Nanofluid Cooling Systems for Electronic Boards: A Systematic Review
type: item
description: The present study aimed to systematically review and synthesize the available evidence regarding sustainable resource management in nanofluid cooling systems for electronic boards, with a particular focus on thermal performance enhancement, energy efficiency, resource optimization, environmental sustainability, and future technological development. This study was conducted as a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A comprehensive search was performed in major scientific databases, including Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, Wiley Online Library, and Google Scholar, covering publications from 2010 to 2025. The research team consisted of six researchers from Tehran, Iran, who independently participated in literature screening, quality assessment, and data extraction. A total of 1,284 records were initially identified. After duplicate removal and eligibility assessment, 126 studies met the predefined inclusion criteria and were included in the final synthesis. Data were extracted using a structured review form and evaluated through descriptive and thematic synthesis methods. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT). The findings revealed that nanofluid cooling systems consistently outperformed conventional cooling fluids across diverse electronic thermal management applications. Hybrid nanofluids demonstrated the highest thermal conductivity enhancement (up to 41.3%) and heat transfer improvement (up to 36.8%), particularly in direct liquid cooling and battery thermal management systems. Graphene-based and carbon nanotube-based nanofluids showed superior performance compared with many traditional metal oxide formulations. More than 90% of reviewed studies reported significant improvements in cooling efficiency, while 70.6% identified reductions in energy consumption. Furthermore, 77.8% of studies documented reduction…
subject: Articles
  topic: section
subject: Nanofluids
  topic: keywords
subject: Electronic Board Cooling
  topic: keywords
subject: Sustainable Resource Management
  topic: keywords
subject: Thermal Management
  topic: keywords
subject: Heat Transfer Enhancement
  topic: keywords
subject: Energy Efficiency
  topic: keywords
creator: Sadegh Rasoul Ahari
creator: Mohammad Ali Niroomand
creator: Meysam Mahmoudi
creator: Mahdi Rezaei
rights: Copyright (c) 2026 Sadegh Rasoul Ahari, Mohammad Ali Niroomand, Meysam Mahmoudi (Author); Mahdi Rezaei https://creativecommons.org/licenses/by-nc/4.0
date: 2026-11-01
publication date: 2026-11-01
starting page: 1
ending page: 13

Presenting a LARG Supply Chain Model with a Hybrid Approach in the Poultry Industry in Northern Iran
type: item
description: The aim of the present study is to present a LARG supply chain model with a hybrid approach in the poultry industry in northern Iran. In terms of purpose, the present study is applied, and in terms of data nature, it was conducted using a mixed-methods approach (qualitative–quantitative). The qualitative section was carried out using a multiple fuzzy Delphi analysis approach, both deductively through a systematic review of the theoretical literature and inductively through semi-structured interviews with experts. The participants in the qualitative section included 20 university faculty members and senior managers of poultry industries, who were selected through non-probability theoretical sampling. Interpretive Structural Modeling (ISM) was used to analyze the data. The qualitative findings showed that the final indicators were classified into four main paradigms of the LARG supply chain, namely lean, agile, resilient, and green. To calculate the final weight of each indicator, the fuzzy Analytic Network Process (fuzzy ANP) method was used, and the results showed that just-in-time production, information technology, and continuous improvement obtained the highest final weights among the LARG supply chain indicators, respectively. The DEMATEL results indicate that, in the poultry industry, the lean paradigm has the highest importance with a weight of 35%, reflecting the industry’s focus on cost reduction and efficiency improvement. The agile and resilient paradigms ranked next, whereas the green paradigm has received comparatively less attention in this industry.
subject: Articles
  topic: section
subject: LARG supply chain
  topic: keywords
subject: fuzzy DEMATEL
  topic: keywords
subject: ANP
  topic: keywords
subject: ISM
  topic: keywords
creator: Kaveh Kafili Kasmaei
creator: Arshad Farahmandian
creator: Homa Doroudi
creator: Nabiollah Mohammadi
rights: Copyright (c) 2026 Kaveh Kafili Kasmaei (Author); Arshad Farahmandian; Homa Doroudi, Nabiollah Mohammadi (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2027-01-01
publication date: 2027-01-01
starting page: 1
ending page: 14

Integrated Queue Management and Service Composition for Enhancing Manufacturing-as-a-Service Performance
type: item
description: 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 composit…
subject: Articles
  topic: section
subject: Manufacturing-as-a-Service
  topic: keywords
subject: Queue Management
  topic: keywords
subject: Service Composition
  topic: keywords
subject: Service-Oriented Manufacturing
  topic: keywords
creator: Mina Ravanesh
creator: Emad Roghanian
rights: Copyright (c) 2026 Mina Ravanesh; Emad Roghanian (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-09-01
publication date: 2026-09-01
starting page: 1
ending page: 12

Designing an Exploitation-Oriented Digital Innovation Model Based on the Maximum Functional Performance of Mining Industries
type: item
description: The purpose of this study was to design an exploitation-oriented digital innovation model for Iran’s mining industries, with an emphasis on improving performance, sustainability, and competitiveness. The research adopted a mixed-methods qualitative–quantitative grounded theory approach. The literature review indicated that international models, due to infrastructural and cultural differences, do not fully meet the needs of Iran’s mining industry, highlighting the need for an indigenous model. The statistical population of the qualitative phase consisted of 15 experts and managers in the mining industry, who were selected through purposive sampling based on the criteria of expertise, experience, and diversity. Data were collected through semi-structured interviews and analyzed using grounded theory analysis. In the quantitative phase, data from 220 questionnaires distributed among managers and experts of mines across the country were collected through stratified sampling and analyzed using structural equation modeling (SEM). The qualitative results identified weak technological infrastructures, the absence of an innovation culture, and the lack of data-driven governance frameworks as key barriers. The key factors identified included managerial support, transformational leadership, technology localization, and the integration of managerial software systems. The results of this study show that strengthening human capital, data-driven decision-making, and macro-level policymaking can provide the foundation for achieving the highest level of exploitation, profitability, and sustainability across the entire mining value chain. The quantitative findings confirmed the validity and fit of the proposed model and extracted operational strategies for developing digital innovation. This study provides a novel basis for policymaking and the development of an indigenous digital transformation model in Iran’s mining industry.
subject: Articles
  topic: section
creator: Samira Rahmani Shiviari
creator: Younos Vakil Alroaia
creator: Seyed Hossein Hosseini
creator: Mohammad Reza Rostami
rights: Copyright (c) 2026 Samira Rahmani Shiviari, Younos Vakil Alroaia (Author); Seyed Hossein Hosseini; Mohammad Reza Rostami (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2027-01-01
publication date: 2027-01-01
starting page: 1
ending page: 16

A Hybrid Statistical–MCDM Framework for Integrated Risk Prioritization and Sustainability Assessment in Construction Supply Chains: Bridging Local Data with Global Theory
type: item
description: 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.…
subject: Articles
  topic: section
subject: Sustainable Construction
  topic: keywords
subject: Construction Project Management
  topic: keywords
subject: Sustainable Supply Chain Management
  topic: keywords
subject: Analytic Network Process
  topic: keywords
subject: Risk Prioritization
  topic: keywords
creator: Mahyar Amin Forghani
creator: Sohail Dadkhah
creator: Mohsen Dadras
rights: Copyright (c) 2026 Mahyar Amin Forghani (Author); Sohail Dadkhah; Mohsen Dadras (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-09-01
publication date: 2026-09-01
starting page: 1
ending page: 21

Explaining the Relationship between Organizational Humanitarianism and Employees’ Social Accountability: The Moderating Role of Employee Self-Leadership
type: item
description: 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%.
subject: Articles
  topic: section
subject: Organizational humanitarianism
  topic: keywords
subject: employees’ social accountability
  topic: keywords
subject: employee self-leadership
  topic: keywords
creator: Fatemeh Movahed Nia
creator: Monireh Tahaei
creator: Mahdi Fardi Kalahroudi
creator: Reza Kazemi
rights: Copyright (c) 2026 Fatemeh Movahed Nia; Monireh Tahaei, Mahdi Fardi Kalahroudi, Reza Kazemi (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-09-01
publication date: 2026-09-01
starting page: 1
ending page: 10

Identification of the Components of Environmental Innovation, Green Supply Chain Management, Circular Economy Capabilities, and Financial Performance Through Qualitative Content Analysis
type: item
description: 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…
subject: Articles
  topic: section
subject: Environmental Innovation
  topic: keywords
subject: Green Supply Chain Management
  topic: keywords
subject: Circular Economy
  topic: keywords
subject: Financial Performance
  topic: keywords
subject: Sustainable Development
  topic: keywords
creator: Javad Shomali Nejad
creator: Kiamars Fathi Hafshejani
creator: Nowrouz Nourollahzadeh
rights: Copyright (c) 2026 Javad Shomali Nejad (Author); Kiamars Fathi Hafshejani; Nowrouz Nourollahzadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2027-01-01
publication date: 2027-01-01
starting page: 1
ending page: 13

Decision-Making Models for Circular Economy Implementation in Waste Management
type: item
description: 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.
subject: Articles
  topic: section
subject: waste management
  topic: keywords
subject: circular economy
  topic: keywords
subject: decision-making
  topic: keywords
subject: AHP
  topic: keywords
subject: TOPSIS
  topic: keywords
creator: Amirreza Soleimannezhad Aliabadi
creator: Mohammad Ali Niroomand
creator: Fereshteh Khalili Peykani
rights: Copyright (c) 2026 Amirreza Soleimannezhad Aliabadi; Mohammad Ali Niroomand, Fereshteh Khalili Peykani (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-11-01
publication date: 2026-11-01
starting page: 1
ending page: 14

Sentiment Analysis of Customers in Iran’s Automotive Industry Using the LSTM Method
type: item
description: 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.
subject: Articles
  topic: section
subject: Sentiment analysis
  topic: keywords
subject: automotive industry customers
  topic: keywords
subject: Iran
  topic: keywords
subject: LSTM
  topic: keywords
subject: SMOTE
  topic: keywords
subject: Data Augmentation
  topic: keywords
creator: Lida Shojaei Barjouei
creator: Ghasem Bakhshandeh
creator: Mahmood Daniali Deh houz
rights: Copyright (c) 2026 Lida Shojaei Barjouei (Author); Ghasem Bakhshandeh; Mahmood Daniali Deh houz (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-11-01
publication date: 2026-11-01
starting page: 1
ending page: 13

Identification of Key Causes of Disputes in Construction Projects and Preventive Strategies: A Systematic Review
type: item
description: 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.
subject: Articles
  topic: section
subject: Construction disputes
  topic: keywords
subject: Dispute management
  topic: keywords
subject: Dispute prevention
  topic: keywords
subject: Alternative dispute resolution (ADR)
  topic: keywords
creator: Mohammad Taghi Nazarpour
creator: Negin Kheradmand
rights: Copyright (c) 2026 Mohammad Taghi Nazarpour; Negin Kheradmand (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 13

https://journalrmde.com/index.php/jrmde/article/view/285
type: item
title: 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
description: 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.
subject: Articles
  topic: section
subject: Fake feedback detection
  topic: keywords
subject: clustering with Reptile Search Algorithm
  topic: keywords
subject: enhanced empirical analysis method
  topic: keywords
subject: Particle Swarm Optimization-based Extreme Learning Machine
  topic: keywords
creator: Hussein Kadhim Almamoori
creator: Golnaz Aghaee Ghazvini
creator: Ali Albu-Rghaif
creator: Fariba Majidi
rights: Copyright (c) 2026 Hussein Kadhim Almamoori (Author); Golnaz Aghaee Ghazvini; Ali Albu-Rghaif, Fariba Majidi (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 27

Investigating the Relationship between Job Characteristics and the Growth of Professional Ethics among Employees of the East Azerbaijan Gas Company
type: item
description: 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.
subject: Articles
  topic: section
subject: Job Characteristics
  topic: keywords
subject: Skill Variety
  topic: keywords
subject: Task Identity
  topic: keywords
subject: Task Significance
  topic: keywords
subject: Autonomy and Freedom
  topic: keywords
subject: Job Feedback
  topic: keywords
subject: Professional Ethics
  topic: keywords
creator: Ali Reza Abbasian
creator: Jafar Beikzad
rights: Copyright (c) 2026 Ali Reza Abbasian (Author); Jafar Beikzad https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 12

Explanation and Prioritization of Factors Influencing the Location of Resilient Schools Against Natural Hazards with an Emphasis on Collective Intelligence (Case Study: Nowshahr City)
type: item
description: 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.
subject: Articles
  topic: section
subject: resilient school site selection
  topic: keywords
subject: natural hazards
  topic: keywords
subject: multi-criteria analysis
  topic: keywords
subject: green logistics
  topic: keywords
creator: Mehdi Elkaei Behjati
creator: Seyed Ali Seydian
creator: Mehdi MirzaGoltabar Roshan
rights: Copyright (c) 2026 Mehdi Elkaei Behjati (Author); Seyed Ali Seydian; Mehdi MirzaGoltabar Roshan (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 14

Detection of Attacks in Internet of Things Devices Using an Optimized Ensemble Classification Based on Deep Transfer Learning and the Harris Hawks Algorithm
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Attack Detection
  topic: keywords
subject: Deep Transfer Learning
  topic: keywords
subject: Ensemble Classification
  topic: keywords
subject: Harris Hawks Optimization Algorithm
  topic: keywords
subject: Metaheuristic Optimization
  topic: keywords
creator: Farqad Abdullah Mohammed
creator: Saba Joudaki
creator: Mezher H Mezher
creator: Mahdi Mosleh
rights: Copyright (c) 2026 Farqad Abdullah Mohammed (Author); Saba Joudaki; Mezher H Mezher, Mahdi Mosleh (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 16

Comparing the Effectiveness of Supply Chain under Two Contract Mechanisms: Buy-back and Wholesale Price
type: item
description: 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.
subject: Articles
  topic: section
subject: Supply Chain Coordination
  topic: keywords
subject: Buy-back Contract
  topic: keywords
subject: Wholesale Price
  topic: keywords
subject: Contractual Mechanisms
  topic: keywords
creator: Pooria Shekasteband
creator: Mona Ahmadi rad
creator: Shervin Asadzadeh
rights: Copyright (c) 2026 Pooria Shekasteband (Author); Mona Ahmadi rad; Shervin Asadzadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-12-01
publication date: 2026-12-01
starting page: 1
ending page: 26

Environmental Life Cycle Assessment of Gasoline Production Process in Khuzestan Using an Ecosystem Approach in Environmental Planning
type: item
description: 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.
subject: Articles
  topic: section
subject: Life Cycle Assessment (LCA)
  topic: keywords
subject: Gasoline
  topic: keywords
subject: Ecosystem approach
  topic: keywords
subject: Environment
  topic: keywords
subject: Khuzestan
  topic: keywords
creator: Eghbal Sekhavati
creator: Mehdi Esmaeili Bidhendi
creator: Reza Jalilzadeh-Yengejeh
creator: Farhad Baqi-Asl
rights: Copyright (c) 2026 Eghbal Sekhavati (Author); Mehdi Esmaeili Bidhendi; Reza Jalilzadeh-Yengejeh , Farhad Baqi-Asl (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-03
publication date: 2026-04-03

Designing an environmental accounting model with an emphasis on waste management to improve performance sustainability (Case study: Hospitals covered by the Ministry of Health)
type: item
description: 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 rando…
subject: Articles
  topic: section
subject: Environmental Accounting
  topic: keywords
subject: Waste Management
  topic: keywords
subject: Performance Sustainability
  topic: keywords
subject: Hospitals of the Ministry of Health
  topic: keywords
creator: Mohammad Reza Hosseini
creator: Shiva Hassanpour
creator: Mohammad Tamimi
creator: Hossein Jannat Makan
rights: Copyright (c) 2026 Mohammad Reza Hosseini (Author); Shiva Hassanpour; Mohammad Tamimi, Hossein Jannat Makan (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 1
ending page: 14

Validating the Integrated Supply Chain Model Based on the LARG Hybrid Paradigm in the Oil Industry (Case Study: Abadan Oil Refining Company)
type: item
description: 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.
subject: Articles
  topic: section
subject: LARG paradigm
  topic: keywords
subject: Abadan Oil Refining Company
  topic: keywords
subject: Fuzzy DEMATEL
  topic: keywords
subject: supply chain
  topic: keywords
subject: structural equation modeling
  topic: keywords
creator: Alireza Banisilalavi
creator: Saber Molla Alizadeh Zevardehi
creator: Ali Mahmoodirad
rights: Copyright (c) 2026 Alireza Banisilalavi (Author); Saber Molla Alizadeh Zevardehi; Ali Mahmoodirad (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 13

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
type: item
description: 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 …
subject: Articles
  topic: section
subject: Customer Relationship Management
  topic: keywords
subject: Operational Planning
  topic: keywords
subject: Multi-Criteria Decision Making
  topic: keywords
subject: Quality Function Deployment
  topic: keywords
creator: Hadi Hosseinimanesh
creator: Shahnaz Nayebzadeh
creator: Seyyed Hassan Hataminasab
creator: Mozhde Rabbani
rights: Copyright (c) 2026 Hadi Hosseinimanesh (Author); Shahnaz Nayebzadeh; Seyyed Hassan Hataminasab, Mozhde Rabbani (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 14

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
type: item
description: 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 managemen…
subject: Articles
  topic: section
subject: Portfolio management
  topic: keywords
subject: financial risk control
  topic: keywords
subject: return optimization
  topic: keywords
subject: gated transformer algorithm
  topic: keywords
subject: memory instances
  topic: keywords
creator: Mohammad Haji Ebrahim Tehrani
creator: Amin Safarnejad Borujeni
creator: Mohsen Hashemigohar
creator: Sobhan Zafari
creator: Hossein Alidadi
rights: 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
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 20

Intelligent Harmony: Harnessing Deep Reinforcement Learning for Smart Home Energy Management
type: item
description: 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.
subject: Articles
  topic: section
subject: Deep Reinforcement Learning
  topic: keywords
subject: Smart home
  topic: keywords
subject: Energy management
  topic: keywords
subject: Artificial intelligence
  topic: keywords
creator: Mohammadreza Ganjian
creator: Mohammad Tabrizian
creator: Naser Khodabakhshi-Javinani
creator: Meqdad Ansarian
rights: Copyright (c) 2026 Mohammadreza Ganjian (Author); Mohammad Tabrizian; Naser Khodabakhshi-Javinani, Meqdad Ansarian (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-09-01
publication date: 2026-09-01
starting page: 1
ending page: 18

Presenting a Model for Formulating Sustainable Agricultural Development Policies (A Case Study of the Greenhouse Cultivation Industry in Hamedan Province)
type: item
description: 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.
subject: Articles
  topic: section
subject: Policy
  topic: keywords
subject: sustainable development
  topic: keywords
subject: agriculture
  topic: keywords
subject: greenhouse cultivation industry of Hamedan Province
  topic: keywords
creator: Mohammad Saeid Khaksar
creator: Kambiz Hamidi
creator: Reza Bahramloo
creator: Nasrin Kodabakhsi
creator: Seyed Hamid Hosseini
rights: Copyright (c) 2026 Mohammad Saeid Khaksar, Kambiz Hamidi, Reza Bahramloo, Nasrin Kodabakhsi, Seyed Hamid Hosseini (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 12

Validation of the Model of Human Resource Adherence to Behavioral Values within the Framework of Knowledge Management Implementation
type: item
description: 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, r…
subject: Articles
  topic: section
subject: Behavioral Values
  topic: keywords
subject: Human Resources
  topic: keywords
subject: Knowledge Management
  topic: keywords
subject: Model Validation
  topic: keywords
subject: Organizational Behavior
  topic: keywords
subject: Structural Equation Modeling
  topic: keywords
creator: Seyed Mohammadali Baghernejad
creator: Masoumeh Jafari
creator: Naser Barkhordar
rights: Copyright (c) 2026 Seyed Mohammadali Baghernejad (Author); Masoumeh Jafari; Naser Barkhordar (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2027-09-01
publication date: 2027-09-01
starting page: 1
ending page: 17

Development and Psychometric Validation of an Instrument for Assessing the Quality of Talent Management in the Agricultural Research, Education, and Extension Organization (AREEO)
type: item
description: Talent management is a strategic capability in knowledge-based organizations and plays a vital role in identifying, attracting, developing, and retaining capable human resources. However, indigenous and valid instruments for assessing the quality of talent management are limited in research and public organizations, particularly in the Agricultural Research, Education, and Extension Organization (AREEO). Therefore, this study aimed to develop and psychometrically validate an instrument for measuring talent management quality in this organization. The study was applied in purpose and used a sequential exploratory mixed-methods design. In the qualitative phase, the dimensions and components of talent management quality were identified through a grounded theory approach, and the initial items of the instrument were developed based on expert insights. In the quantitative phase, the instrument was validated among managers, experts, and relevant staff of AREEO. Data were collected through semi-structured interviews and a 30-item researcher-developed questionnaire. Construct validity was assessed using first- and second-order confirmatory factor analysis, reliability was evaluated through Cronbach’s alpha and composite reliability, and convergent and discriminant validity were examined using the average variance extracted (AVE) and the Fornell–Larcker criterion. The findings indicated that the instrument had an appropriate factor structure and satisfactory psychometric properties. Accordingly, the developed instrument can serve as a valid and reliable indigenous tool for evaluating talent management quality and supporting evidence-based decision-making in AREEO.
subject: Articles
  topic: section
subject: AREEO
  topic: keywords
subject: Mixed-Methods Research
  topic: keywords
subject: Psychometric Validation
  topic: keywords
subject: Reliability
  topic: keywords
subject: Talent Management
  topic: keywords
creator: Mohammadreza Bayegan
creator: Naghi Kamali
creator: Nooraldin Mirzaei
creator: Mohammad Mojtabazadeh
rights: Copyright (c) 2026 Mohammadreza Bayegan (Author); Naghi Kamali; Nooraldin Mirzaei, Mohammad Mojtabazadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 13

Systematizing Barriers to Open Innovation Adoption: A Multi-Method Approach Based on Meta Synthesis, Fuzzy Interpretive Structural Modeling, and MICMAC Analysis
type: item
description: Open innovation, as a dominant paradigm in innovation management, encourages organizations to deliberately leverage inbound and outbound knowledge flows to accelerate innovation and create value; however, its adoption and implementation are confronted with multifaceted barriers that are interwoven, multi-level, and dynamic. The central problem of this study is that although the existing literature has identified numerous barriers to open innovation, it has provided limited attention to the causal structure, hierarchical ordering, and the extent of mutual influence among these barriers. Accordingly, this study aims to identify, categorize, and model the structural dynamics of barriers to open innovation adoption. This research employs a sequential multi-method design. In the first phase, a systematic literature review grounded in the PRISMA 2020 framework was integrated with a meta-synthesis approach; relevant studies were extracted and analyzed from six reputable scientific databases. From 612 initial records, after removing duplicates and applying inclusion and exclusion criteria, 24 articles were selected for the final meta-synthesis. This phase yielded 23 barriers organized into six overarching dimensions: individual/behavioral, cultural, structural/managerial, knowledge/capability, relational/governance, and infrastructural/environmental barriers. In the second phase, Fuzzy-ISM and Fuzzy-MICMAC analysis were conducted, drawing on judgments from a panel of 14 academic and industrial experts. The findings reveal that weak knowledge absorptive capacity, misalignment between reward and motivation systems, and insufficient support from top management occupy the lowest hierarchical level, functioning as fundamental root drivers. A control-oriented and low-trust culture, weak learning and knowledge-sharing culture, and insufficient inter-organizational trust constitute linkage variables. By contrast, intellectual property concerns, difficulty in selecting appropriate p…
subject: Articles
  topic: section
subject: Open innovation
  topic: keywords
subject: adoption barriers
  topic: keywords
subject: meta-synthesis
  topic: keywords
subject: Fuzzy-ISM
  topic: keywords
subject: MICMAC
  topic: keywords
creator: Ehsan Afsari
creator: Mahdi Nasrollahi
rights: Copyright (c) 2026 Ehsan Afsari (Author); Mahdi Nasrolahi https://creativecommons.org/licenses/by-nc/4.0
date: 2027-01-01
publication date: 2027-01-01
starting page: 1
ending page: 15

The Role of Blockchain Technology in the Supply Chain Performance of the Oil and Gas Industry
type: item
description: When examining the potential of blockchain technology in the oil and gas industry, one of its most significant application areas is supply chain management. In terms of purpose, this study is applied research, and in terms of methodology, it adopts a causal-correlational approach using a questionnaire as the data collection instrument. A total of 254 questionnaires were distributed through simple random sampling among senior experts and information technology specialists in 2025. Reliability was assessed using Cronbach’s alpha, while face validity was evaluated through pilot testing. Content validity was calculated and confirmed using the Content Validity Index (CVI) and Content Validity Ratio (CVR). To determine the relationships between the independent and dependent variables within the conceptual research model, Structural Equation Modeling (SEM) and the AMOS software package were employed. The findings indicate that blockchain technology, particularly in the oil and gas industry, can facilitate process integration through decentralization and participatory decision-making, foster trust, and enhance transparency. Nevertheless, many large oil and gas companies continue to perceive investment in this technology as risky and have largely limited their actions to monitoring and evaluating the experiences and outcomes of leading organizations in this field.
subject: Articles
  topic: section
subject: nformation and Communication Technology (ICT)
  topic: keywords
subject: Supply Chain
  topic: keywords
subject: Oil and Gas Industry
  topic: keywords
subject: Blockchain Technology
  topic: keywords
creator: Elham Moghadamnia
creator: Abbas Behradfar
creator: Payam Naeini
rights: Copyright (c) 2026 Elham Moghadamnia; Abbas Behradfar, Payam Naeini (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2027-09-01
publication date: 2027-09-01
starting page: 1
ending page: 11

Determining the Dimensions, Components, and Indicators of Transformational Digital Leadership in SAIPA Automotive Group
type: item
description: 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 Creatio…
subject: Articles
  topic: section
subject: Transformational Digital Leadership
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: SAIPA Automotive Group
  topic: keywords
creator: Hamzeh Esmaili
creator: Mohammad Reza Rabiee Mandejin
creator: Vahid Araei
creator: Mohammadreza Saeedi
rights: Copyright (c) 2026 Hamzeh Esmaili (Author); Mohammad Reza Rabiee Mandejin; Vahid Araei, Mohammadreza Saeedi (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2027-09-01
publication date: 2027-09-01
starting page: 1
ending page: 15