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: Central Asia Oil and Gas Transfer to China, Cooperative Game Theory Approach
items: Designing a Risk Management Model for Power Plant Maintenance Using a Grounded Theory Approach
items: Designing a Political Marketing Management Model
items: https://journalrmde.com/index.php/jrmde/article/view/392
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: The Role of Moral Responsibility in Decision-Making Models Inspired by Islamic Religious Traditions
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: Identifying Components and Indicators of Social Media Usage Among Bank Users Based on the Consequences Element of Means-End Chain Theory
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: Exploring the Meta-Synthesis Model of the Green Supply Chain in the Power Industry: An Industry 4.0 and Smart Sustainability Approach
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: The Role of Digital Human Resource Management in Productivity and Operational Risk Reduction in the Performance of Marketing and Customer Relationship Management (CRM) Teams
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

Central Asia Oil and Gas Transfer to China, Cooperative Game Theory Approach
type: item
description: International institutions project a substantial increase in China's demand for oil and gas in the coming years. To secure its energy supply, China has pursued a strategy of diversifying import sources. Landlocked Central Asian nations and energy-rich Iran aim to capitalize on this demand by expanding their export capabilities. This study employs cooperative game theory to analyze the bargaining power of countries in potential oil and gas transmission scenarios from Central Asia and Iran to China through pipeline. By developing future scenarios and evaluating their economic feasibility, we assess the bargaining power of each country using the Shapley value. Our findings indicate that a country's geographic location and production capacity are key determinants of its bargaining power in energy geopolitics. Additionally, if Iran significantly increases its production and export capacity, and its pipeline network connects with Turkmenistan's, it could economically justify joining the coalition for natural gas exports through the Central Asia-China pipeline by supplying approximately 12 billion cubic meters of natural gas annually.
subject: Articles
  topic: section
subject: Oil
  topic: keywords
subject: Natural Gas
  topic: keywords
subject: Cooperative games
  topic: keywords
subject: Bargaining Power
  topic: keywords
subject: Central Asia
  topic: keywords
creator: Touraj Dehghani
rights: Copyright (c) 2026 Touraj Dehghani https://creativecommons.org/licenses/by-nc/4.0
date: 2027-03-01
publication date: 2027-03-01
starting page: 1
ending page: 13

Designing a Risk Management Model for Power Plant Maintenance Using a Grounded Theory Approach
type: item
description: Maintenance and repair processes in thermal power plants continually face serious risk management challenges because of equipment complexity and the high-risk nature of operations. The present study aimed to develop a comprehensive risk management model in this field to enhance reliability and improve managerial decision-making. In terms of purpose, this study was applied, and methodologically, it employed a qualitative grounded theory approach based on the Strauss and Corbin framework. Data were collected through semi-structured interviews with 14 academic and industry experts in the thermal power plant sector and analyzed in MAXQDA using the three stages of open, axial, and selective coding. The findings resulted in the identification of 122 initial codes and 41 key components, which were ultimately explained within a paradigmatic model comprising six main categories: causal conditions, contextual conditions, the central phenomenon, intervening conditions, strategies, and consequences. By explaining the actions and interactions among technical, behavioral, structural, and environmental factors, the resulting model provides a comprehensive perspective on the logic of risk management within the indigenous context of Iran’s power generation industry. The results not only enrich the risk management literature but also provide an operational framework that can assist power plant managers in designing effective risk management systems and optimizing resource allocation.
subject: Articles
  topic: section
subject: Risk management
  topic: keywords
subject: maintenance and repair
  topic: keywords
subject: power plant
  topic: keywords
subject: grounded theory
  topic: keywords
creator: Mehdi Baghandeh
creator: Arshad Farahmandian
creator: Firouzeh Haj Ali Akbari
creator: Mohammad Hassan Abdollahi
rights: Copyright (c) 2026 Mehdi Baghandeh (Author); Arshad Farahmandian; Firouzeh Haj Ali Akbari, Mohammad Hassan Abdollahi (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2027-03-01
publication date: 2027-03-01
starting page: 1
ending page: 16

Designing a Political Marketing Management Model
type: item
description: This study aimed to develop a comprehensive, context-sensitive model of political marketing management grounded in the institutional, cultural, communicative, and ethical characteristics of Iran’s political environment. This qualitative study was conducted within an interpretive-constructivist paradigm using an inductive approach. Data were collected through in-depth, semi-structured interviews with 10 experts, including university professors, political strategists, and former members of parliament. Participants were selected purposively based on their theoretical knowledge and practical experience in political and electoral affairs. The interview data were analyzed using Braun and Clarke’s six-phase thematic analysis method. Data management and coding were performed in MAXQDA software through open, axial, and selective coding. The analytical process included repeated familiarization with the transcripts, generation of initial codes, categorization of conceptually related codes, review and refinement of themes, definition of organizing and global themes, and development of the final conceptual model. The analysis generated 33 open codes that were integrated into six organizing themes: structural and institutional ecology, voter identity dynamics, social capital and the authority of traditional networks, localized political marketing-mix and brand architecture, organizational agility and media defense, and ethical and normative governance. These themes were subsequently organized into three overarching dimensions. The analytical-cognitive dimension comprised environmental intelligence and indigenous voter analysis; the design-strategic dimension incorporated value architecture, political branding, and strategic configuration; and the executive-normative dimension encompassed agile campaign operations, rapid media response, performance monitoring, and ethical governance. The resulting model indicated that political marketing management is shaped by reciprocal relation…
subject: Articles
  topic: section
subject: Political marketing management
  topic: keywords
subject: thematic analysis
  topic: keywords
subject: indigenous political marketing
  topic: keywords
subject: political branding
  topic: keywords
subject: ethical governance
  topic: keywords
subject: traditional reference networks
  topic: keywords
creator: Maryam Sayedan Ghaleh Jourtan
creator: Mohammad Reza Dalvi
creator: Zahra Dashtlaali
rights: Copyright (c) 2026 Maryam Sayedan Ghaleh Jourtan (Author); Mohammad Reza Dalvi; Zahra Dashtlaali (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2027-03-01
publication date: 2027-03-01
starting page: 1
ending page: 18

https://journalrmde.com/index.php/jrmde/article/view/392
type: item
title: Designing an Intelligent Decision-Support Model for Risk and Delay Management in Industrial Projects Using an Integration of Monte Carlo Simulation and Machine Learning in Dynamic Uncertainty Environments
description: In recent years, industrial projects have faced increasing complexity, uncertainty, and interdependence among project activities. According to international project management reports, approximately 60% to 80% of large-scale industrial projects experience schedule delays, and nearly 45% encounter cost overruns of more than 20% compared with the initial budget. The objective of this study is to design an intelligent decision-support model for predicting and managing risk and delay in industrial projects by integrating Monte Carlo simulation and machine learning algorithms. In this study, performance data from previous projects, including activity schedules, resource consumption levels, cost variations, and risk indicators, are first collected. Then, using Monte Carlo simulation, more than 10,000 probabilistic project execution scenarios are generated to model uncertainty in time and cost from a probabilistic perspective. Subsequently, machine learning algorithms, such as Random Forest and Gradient Boosting, are applied to identify delay patterns and predict schedule deviation with a targeted accuracy of approximately 85% to 92%. The expected results indicate that the proposed hybrid model can improve the accuracy of project delay prediction by up to 30% compared with traditional project planning methods, including CPM/PERT, and reduce the mean error in estimating project completion time from approximately 18% to less than 10%. Furthermore, the model is capable of identifying critical risks at least 2 to 4 weeks earlier than conventional methods, which can play an important role in reducing additional costs and improving resource productivity.
subject: Articles
  topic: section
subject: Project management
  topic: keywords
subject: Monte Carlo simulation
  topic: keywords
subject: machine learning
  topic: keywords
subject: project risk
  topic: keywords
subject: project delay
  topic: keywords
subject: intelligent decision-support system
  topic: keywords
subject: uncertainty
  topic: keywords
subject: schedule optimization
  topic: keywords
subject: project data analytics
  topic: keywords
subject: industrial engineering
  topic: keywords
creator: Ehsan Najafi Chimeh
rights: Copyright (c) 2026 Ehsan Najafi Chimeh https://creativecommons.org/licenses/by-nc/4.0
date: 2027-01-01
publication date: 2027-01-01
starting page: 1
ending page: 16

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

The Role of Moral Responsibility in Decision-Making Models Inspired by Islamic Religious Traditions
type: item
description: Despite the proliferation of ethical decision-making frameworks in contemporary management literature, the integration of religiously grounded moral responsibility remains underexplored, particularly within Islamic traditions. This paper addresses this lacuna by developing a comprehensive conceptual model of decision-making that foregrounds the Islamic conception of moral responsibility (al-mas’uliyyah al-akhlaqiyyah). Drawing on an integrative review of Islamic theology, jurisprudence, and business ethics literature, the study synthesises foundational Islamic principles—amanah (trust), taklif (moral obligation), khilafah (vicegerency), shura (consultation), and maqasid al-shari’ah (higher objectives of Islamic law)—into a phased, processual framework termed the Islamic Moral Responsibility-Driven Decision-Making (IMR-DM) model. The model comprises four interconnected stages: ethical intentionality and framing, deliberative consultation, maqasid-based assessment, and post-decision moral reflection. A qualitative conceptual analysis was conducted using primary Islamic sources and secondary scholarly works, guided by an interpretivist epistemology. The findings reveal that Islamic moral responsibility operates as a dynamic, spiritually informed meta-norm that permeates every phase of decision-making, embedding a transcendent accountability absent in secular models. The paper contributes to the fields of Islamic business ethics, religious-based management, and ethical decision-making by providing a theoretically robust and practically adaptable framework. It also highlights how the vertical dimension of divine accountability, combined with the horizontal obligations of communitarian welfare, offers a unique alternative to purely rational-choice or stakeholder-centric models. Implications for organisational practice, leadership development, and corporate governance in Islamic contexts are discussed, along with the challenges of operationalising such a model in pluralist…
subject: Articles
  topic: section
subject: moral responsibility
  topic: keywords
subject: Islamic ethics
  topic: keywords
subject: decision-making models
  topic: keywords
subject: maqasid al-shari’ah
  topic: keywords
creator: Zeinab Sedaghatian
rights: Copyright (c) 2026 Zeinab Sedaghatian https://creativecommons.org/licenses/by-nc/4.0
date: 2026-11-01
publication date: 2026-11-01
starting page: 1
ending page: 14

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

Identifying Components and Indicators of Social Media Usage Among Bank Users Based on the Consequences Element of Means-End Chain Theory
type: item
description: The present study aimed to validate the components and indicators of social media usage among bank users based on the consequences element of the Means–End Chain (MEC) theory (attributes, consequences, and values). The expansion of social media and the transformation of user behavior have compelled banks to utilize these platforms to establish effective interactions, enhance customer satisfaction, and create value for customers. Despite extensive research on technology adoption and social media usage, the components and indicators of the Means–End Chain theory have not yet been comprehensively developed within the context of banking users. In terms of purpose, this study is applied research, and in terms of methodology, it is a quantitative descriptive-survey study. The statistical population consisted of social media users of Bank Sepah across the four regions of Tehran Province. Using Cochran’s formula, a sample size of 384 participants was determined, and 362 valid questionnaires were ultimately collected and analyzed. Data were gathered through a researcher-developed questionnaire based on the dimensions of the Means–End Chain theory, consisting of 86 items measured on a five-point Likert scale. The validity of the instrument was confirmed through content validity, confirmatory factor analysis, and convergent and discriminant validity assessments. Reliability was also verified using Cronbach’s alpha and composite reliability coefficients. Data were analyzed using SPSS and SmartPLS software through Structural Equation Modeling (SEM). The findings indicated that the chain of “Attributes → Consequences → Values” was significant in explaining bank users’ social media usage behavior. The effect of perceived attributes on usage consequences was confirmed with a path coefficient of 0.532, while the effect of consequences on perceived values was confirmed with a path coefficient of 0.647. The most influential attributes included ease of access to services, up-to-date in…
subject: Articles
  topic: section
subject: Social media
  topic: keywords
subject: Social banking
  topic: keywords
subject: Means–End Chain
  topic: keywords
subject: Bank users
  topic: keywords
creator: Amirhossein Tavaf
creator: Reza Ebrahimzadeh Dastjerdi
creator: Mehrdad Sadeghi
creator: Mehraban Hadi Peykani
rights: Copyright (c) 2026 Amirhossein Tavaf (Author); Reza Ebrahimzadeh Dastjerdi; Mehrdad Sadeghi, Mehraban Hadi Peykani (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-11-01
publication date: 2026-11-01
starting page: 1
ending page: 11

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

Exploring the Meta-Synthesis Model of the Green Supply Chain in the Power Industry: An Industry 4.0 and Smart Sustainability Approach
type: item
description: The establishment of a green and sustainable supply chain in the power industry particularly in the manufacturing sector of electrical equipment and transmission/distribution facilities plays a vital role in supporting sustainable development and ensuring national energy security. This study aims to analyze and identify the primary and secondary variables of the green supply chain within the framework of Industry 4.0 and smart sustainability in companies producing electrical equipment and infrastructure in Mazandaran Province, Iran. The research employs a meta-synthesis technique combined with the Delphi method. Following the seven-step process of Sandelowski and Barroso (2007), theoretical foundations and prior research published between 2014–2025 (international sources) and 1393–1404 (Persian sources) were systematically reviewed. In total, 248 documents were initially identified, from which 34 core studies remained after a rigorous critical appraisal process. Through open, axial, and selective coding, five main categories and ten axial codes were extracted, including Smart Ecological Design, Digital Green Processes, Data-Driven Waste Management, AI-Based Green Marketing, and Smart Ecological Governance. The axial codes encompass Ecological Product Design, Eco-Friendly Raw Material Procurement, Ecological Production, Green Procurement, Primary and Secondary Waste Management, Ecological Information Exchange, and Green Marketing Contexts, originating from 46 open codes derived during the synthesis phase.
subject: Articles
  topic: section
subject: Green Supply Chain
  topic: keywords
subject: Power Industry
  topic: keywords
subject: Industry 4
  topic: keywords
subject: Smart Sustainability
  topic: keywords
subject: Meta-Synthesis
  topic: keywords
creator: Shafagh Shariati
creator: Yousef Gholipour Kanani
creator: Fatemeh Harsej
rights: Copyright (c) 2026 Shafagh Shariati (Author); Yousef Gholipour Kanani; Fatemeh Harsej (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2027-01-01
publication date: 2027-01-01
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

The Role of Digital Human Resource Management in Productivity and Operational Risk Reduction in the Performance of Marketing and Customer Relationship Management (CRM) Teams
type: item
description: This study examines how digital human resource management (DHRM) enhances productivity and reduces operational risk to improve the performance of marketing and customer relationship management (CRM) teams. A quantitative, cross-sectional design was employed using structural equation modeling (SEM) to analyze data from 348 employees working in marketing and CRM teams across banking, insurance, telecommunications, and online retail organizations. Participants were selected through proportional stratified random sampling, ensuring representation across functional roles and organizational levels. Four validated multidimensional scales measured key constructs: digital HRM practices, team productivity, operational risk (reverse-coded to reflect lower risk), and marketing/CRM performance. Data were screened for normality and missingness and analyzed using confirmatory factor analysis (CFA) to validate the measurement model, followed by SEM to test direct and indirect relationships among variables. Bootstrapping (5,000 samples) was used to examine mediation effects. Digital HRM significantly predicted higher team productivity (β = 0.54, p < .001) and lower operational risk (β = 0.47, p < .001). Marketing and CRM performance was positively predicted by digital HRM (β = 0.26, p < .001), productivity (β = 0.33, p < .001), and operational risk (β = 0.24, p < .001). Mediation analysis showed that productivity (indirect effect = 0.18, 95% CI [0.11, 0.26]) and operational risk (indirect effect = 0.11, 95% CI [0.06, 0.18]) significantly mediated the relationship between DHRM and performance. The full model explained 39% of productivity variance, 32% of operational risk variance, and 58% of performance variance. Digital HRM substantially enhances marketing and CRM performance through its dual impact on productivity and operational risk, underscoring the strategic role of human-centered digital transformation in customer-facing organizational functions.
subject: Articles
  topic: section
subject: Digital HRM
  topic: keywords
subject: operational risk
  topic: keywords
subject: productivity
  topic: keywords
subject: CRM performance
  topic: keywords
subject: marketing teams
  topic: keywords
subject: digital transformation
  topic: keywords
creator: Alireza Tafakori
creator: Yaghoub Ansari
rights: Copyright (c) 2026 Alireza Tafakori; Yaghoub Ansari (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 1
ending page: 14

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
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subject: Waste Management
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subject: Performance Sustainability
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subject: Hospitals of the Ministry of Health
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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
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subject: Fuzzy DEMATEL
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subject: supply chain
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subject: structural equation modeling
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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
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subject: Multi-Criteria Decision Making
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subject: Quality Function Deployment
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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