Management Strategies and Engineering Sciences

About the Journal

Management Strategies and Engineering Sciences (MSES) is a cutting-edge, interdisciplinary academic journal committed to advancing the fields of management, engineering, and related sciences. It seeks to publish high-quality, original research that bridges the gap between theory and practice, offering insights that contribute to both academic knowledge and industry applications. As an open-access journal, MSES ensures that all published articles are freely available to the global community without any subscription or access fees. This open-access model promotes greater dissemination and visibility of research findings, supporting a more inclusive and equitable scholarly environment.

All articles published in Management Strategies and Engineering Sciences (MSES) are immediately available under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Authors retain copyright of their work while granting the journal the non-exclusive right to publish and archive the content.

MSES adheres to a double-blind peer-review process to maintain objectivity and impartiality in the evaluation of submissions. The journal encourages contributions from diverse perspectives, methodologies, and disciplines, making it a hub for intellectual growth and innovation. As an open-access journal, JCBL is committed to disseminating knowledge freely to readers worldwide, removing barriers to access, and fostering inclusivity in academic publishing.

The journal welcomes original research articles, review articles, case studies, short communications, and methodological papers that contribute to the advancement of management and engineering sciences.

Open Access Statement

Management Strategies and Engineering Sciences (MSES) is a fully Open Access journal. All articles published in MSES are immediately and permanently available online without subscription or payment.

Users are permitted to read, download, copy, distribute, print, search, or link to the full texts of articles, or use them for any other lawful purpose, provided the original work is properly cited and the applicable Creative Commons license is respected.

The journal supports the principles of the Budapest Open Access Initiative (BOAI) and is committed to the unrestricted dissemination of scholarly knowledge.

Copyright and License

Authors retain the copyright of their published works without restriction.

By submitting and publishing their manuscripts in Management Strategies and Engineering Sciences (MSES), authors grant the journal the non-exclusive right to publish, archive, preserve, and disseminate the article as the original publisher.

All articles published in MSES are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

Under this license, anyone may copy, distribute, display, reproduce, and adapt the published work for non-commercial purposes, provided that appropriate credit is given to the original author(s) and the source, a link to the license is provided, and any modifications are clearly indicated.

Commercial use of the published material requires prior permission from the copyright holder(s).

Current Issue

Vol. 8 No. 3 (2026): May 2026

Published: 2026-05-03

Articles

Analysis of the Impact of Digital Technologies on Supply Chain Optimization in Various Industries

Meisam Karami, Hamid Bouraghi (Author)

1-7
- PDF
https://doi.org/10.61838/msesj.311
A Mutual-Information-Guided and ADASYN-Augmented Machine Learning Framework for Early Prediction of Parkinson’s Disease

Ghadeer Aqil Ali, Leila Sharifi (Author); Parviz Rashidi-Khazaee (Corresponding author); Hossein Nahid-Titkanlue (Author)

1-12
- PDF
https://doi.org/10.61838/msesj.313
Designing a Policy Laboratory for the Center for Strategic Studies of the Presidency

Mohammad Hossein Ali Madadi, Tayebeh Abbas, Ali Asghar Pourezzat (Author)

1-10
- PDF
https://doi.org/10.61838/msesj.315
Analysis of the Limitations of the Earned Value Management Method and Evaluation of Modern Improvement Approaches in Project Management

Amir Mohammad Safari Zarch, Mohammad Mokaberian (Author)

1-14
- PDF
https://doi.org/10.61838/msesj.312
Factors Affecting Economic Resilience for Transition from an Oil-Based Economy

Mohsen Emami (Author); Shahriar Nassabian (Corresponding author); Mahmoud Mahmoudzadeh, Ali Esmaeilzadeh Mogheri (Author)

1-12
- PDF
https://doi.org/10.61838/msesj.320
Designing a Participatory Governance Policy Framework in Iran's Telecommunications Industry

Jafar Movaghar Hoor, Norouz Izadpanah, Seyedeh Fataneh Moghimi (Author)

1-12
- PDF
https://doi.org/10.61838/msesj.316
Prediction of Completion Fluid Stability and Productivity Impact in Challenging Reservoir Environments Using Machine Learning Analysis

Parsa Kazemihokmabad (Corresponding author)

1-9
- PDF
https://doi.org/10.61838/msesj.331
Providing a Continuous Risk-Monitoring Model in the Banking Sector Based on Grounded Data

Soroosh Shanaki Bavarsad, Saeed Nasiri, Ahmad Kaab Omair, Allahkaram Salehi (Author)

1-16
- PDF
https://doi.org/10.61838/msesj.329
Presentation and Validation of a Model of Social Minority Entrepreneurship (Case Study: Socially Harmed Women Entrepreneurs)

Fatemeh Shahnazipour, Mohammad Reza Dalvi Isfahan, Zahra Dashtlaali (Author)

1-9
- PDF
https://doi.org/10.61838/msesj.341
Identification and Evaluation of the Customer Engagement Model with Domestic Home Appliance Brands

Alireza Alikhani (Author); Behrooz Ghasemi (Corresponding author); Soheil Sarmadsaidy (Author)

1-12
- PDF
https://doi.org/10.61838/msesj.321
Comparative Analysis of Hybrid Approaches in Earned Value Management and Proposing an Extended Framework as an Integrated Future Model for Project Control

Amirmohammad Safari Zarch, Mohammad Mokaberian (Author)

1-8
- PDF
https://doi.org/10.61838/msesj.308
Qualitative Analysis of Barriers and Strategies for Fair Resource Allocation in Economic Policymaking in Iran (Case Study: Ministry of Economic Affairs and Finance)

Hossein Matranlooee, Karam Sina, Gholamhossein Agha Golzadeh (Author)

1-12
- PDF
https://doi.org/10.61838/msesj.307
Analysis of Climate Change Impacts on Precipitation and Runoff Using Time Series Models in MATLAB

Soroush Setayeshmanesh (Author); Taghi Ebadi (Corresponding author); Reza Maknoon (Author)

1-16
- PDF
https://doi.org/10.61838/msesj.333
Equilibrium Pricing in Partially Integrated-Direct (PID) Closed Loop Dual channel Supply Chains (CLDSC) Under Symmetric and Asymmetric Information

Saeed Nabizadeh Marziani (Author); Roya M. Ahari (Corresponding author); Mahsa Gandehari, Mahtab Sherafati (Author)

1-17
- PDF
Evaluation of the Application of Smart Contracts in Construction Industry Management: A Systematic Review of Previous Research

Abdul Raqib Amin (Author); Mostafa Ghazi Moradi (Corresponding author); Alireza Rasekhi Sahneh (Author)

1-18
- PDF

View All Issues

Strategic Decarbonization in Mechatronics Supply Chains: Integrating Digital Technologies and Sustainable Operations

Amir Reza Safari ^* iD

This study aimed to investigate the direct and indirect effects of digital technology adoption, sustainable operations, and supply chain integration on strategic decarbonization in mechatronics supply chains. This quantitative, applied, cross-sectional study was conducted among professionals working in mechatronics-related manufacturing, industrial automation, electronics, automotive components, robotics, control systems, and precision manufacturing companies in Tehran, Iran. Using purposive sampling, 348 questionnaires were distributed and 312 valid responses were retained for analysis. Participants included managers, engineers, supply chain specialists, operations personnel, sustainability experts, logistics professionals, procurement specialists, and information technology staff with at least two years of relevant experience. Data were collected using a structured questionnaire measuring digital technology adoption, sustainable operations, supply chain integration, and strategic decarbonization on a five-point Likert scale. Reliability and validity were assessed using Cronbach’s alpha, composite reliability, average variance extracted, indicator loadings, and heterotrait-monotrait ratios. Partial least squares structural equation modeling was used to test direct and indirect relationships, with statistical significance evaluated through bootstrapping with 5,000 resamples. Digital technology adoption significantly predicted sustainable operations (β = 0.61, p < 0.001) and supply chain integration (β = 0.31, p < 0.001). Sustainable operations also significantly predicted supply chain integration (β = 0.45, p < 0.001). Strategic decarbonization was significantly predicted by digital technology adoption (β = 0.18, p < 0.001), sustainable operations (β = 0.42, p < 0.001), and supply chain integration (β = 0.31, p < 0.001). Significant indirect effects were identified through sustainable operations (β = 0.26, p < 0.001), supply chain integration (β = 0.10, p < 0.001), and the sequential pathway through both mediators (β = 0.09, p < 0.001). The model explained 68% of the variance in strategic decarbonization. Strategic decarbonization in mechatronics supply chains depends on the coordinated integration of digital capabilities, sustainable operational practices, and supply chain collaboration, with digital technologies contributing most effectively when translated into operational and interorganizational sustainability mechanisms.

Assessment of the Effects of IT Infrastructure Automation and Technical Web Optimization on Operational Performance and User Experience in Mining and Steel Supply Chain Industries: A Case Study of Bahar Trade Company

Amin Jamali ^* iD , Mohammad Reza Badami iD

https://doi.org/10.61838/msesj.477

This study aimed to assess the effects of IT infrastructure automation, database optimization, Next.js-based web re-architecture, and technical search-engine optimization on operational performance and user experience at Bahar Trade Company. A quantitative, applied, developmental case-study design with a pre-intervention and post-intervention comparison was employed. The study included two consecutive 90-day periods, comprising 90 daily observations before implementation and 90 matched observations after implementation. The intervention consisted of automated Bash-based server monitoring and database backup procedures, PostgreSQL query optimization and indexing, deployment of a headless Payload CMS architecture, development of a Next.js server-side-rendered frontend, and implementation of technical SEO measures. Daily data were collected on SQL query response time, system uptime, Core Web Vitals, organic search traffic, and B2B conversion rate. Data were analyzed in SPSS using descriptive statistics, the Kolmogorov–Smirnov test, and paired-samples (t)-tests at a significance level of (\alpha=0.05). The paired-samples (t)-test showed that heavy SQL query response time decreased significantly after the intervention, (t(89)=24.81), (p<0.001), supporting the first hypothesis. Largest Contentful Paint also improved significantly following the implementation of the Next.js architecture and technical optimization measures, (t(89)=18.45), (p<0.001). In addition, the B2B conversion rate increased significantly in the post-intervention period, (t(89)=-12.32), (p<0.001). The negative sign reflected the order of subtraction between the paired conditions rather than a decline in performance. These inferential results supported the second hypothesis and confirmed statistically significant improvements in database responsiveness, web-loading performance, and commercial conversion. The integrated implementation of infrastructure automation, database engineering, server-side-rendered web architecture, and technical SEO significantly enhanced both internal operational efficiency and external digital performance, indicating that coordinated technical transformation can strengthen reliability, user experience, and commercial effectiveness in mining and steel supply chain companies.

Digital Platform Strategy and Competitive Positioning in Cloud-Based Software Ecosystems

Seyedeh Mina Salimi iD , Babak Nouri Moghaddam ^* iD , Abbas Mirzaei iD

This study aimed to examine how digital platform strategy contributes to competitive positioning in cloud-based software ecosystems by identifying the strategic capabilities, ecosystem mechanisms, and market differentiation practices through which cloud software firms create and sustain competitive advantage. This qualitative study was conducted using an exploratory-descriptive design. The participants consisted of 18 managers, founders, product strategists, cloud solution architects, business development specialists, and digital strategy experts from Tehran who had direct professional experience in cloud-based software ecosystems. Participants were selected through purposive sampling, and data collection continued until theoretical saturation was achieved. Data were collected through semi-structured interviews focused on platform strategy, value proposition, ecosystem orchestration, technological capability, customer retention, governance, and competitive positioning. Interviews were transcribed verbatim and analyzed using inductive thematic analysis with the assistance of NVivo software. Credibility was enhanced through repeated review of transcripts, peer checking of selected codes, member checking, and continuous comparison of emerging themes with participants’ narratives. The thematic analysis indicated that competitive positioning in cloud-based software ecosystems is shaped by the alignment of eight core strategic dimensions: platform value proposition, ecosystem orchestration, technological scalability, competitive differentiation, customer lock-in and retention, platform governance, market expansion strategy, and data-driven competitive advantage. The most central inferred finding was that sustainable competitive positioning does not emerge from technological capability alone; rather, it is produced when customer-centered value, scalable cloud architecture, ecosystem coordination, trust-building, data analytics, and differentiation mechanisms operate as an integrated strategic system. The findings also suggested that niche specialization, interoperability, localization, pricing flexibility, user experience, partner integration, and continuous innovation strengthen the defensibility of cloud-based software platforms. The study concludes that digital platform strategy is a multidimensional and dynamic process through which cloud-based software firms transform technological capabilities into sustainable competitive advantage. Firms that align value proposition, technological scalability, ecosystem governance, and market differentiation are more likely to achieve a durable position in competitive cloud software markets.

The Role of Network Virtualization Strategy in Enhancing Scalability and Cost Efficiency in Cloud Data Centers

Mohammad Alimorad iD , Babak Nouri Moghaddam iD , Abbas Mirzaei ^* iD , Nasser Mikaeilvand iD , Azadeh Zamanifar iD

This study aimed to examine the role of network virtualization strategy in enhancing scalability and cost efficiency in cloud data centers. This applied quantitative study used a descriptive-correlational design. The statistical population consisted of cloud infrastructure specialists, network engineers, virtualization administrators, data center managers, IT project managers, and system architects working in cloud data centers and technology-based organizations in Tehran. A total of 214 participants were selected through purposive sampling based on their professional experience with cloud infrastructure, network virtualization, software-defined networking, or data center management. Data were collected using a structured questionnaire measuring network virtualization strategy, scalability, and cost efficiency. The validity of the questionnaire was confirmed through expert review, and its reliability was assessed using Cronbach’s alpha. Data were analyzed using descriptive statistics, Pearson correlation, multiple regression analysis, and structural equation modeling with SPSS and AMOS software. The inferential findings showed that network virtualization strategy had a positive and significant correlation with scalability (r = 0.68, p < 0.01) and cost efficiency (r = 0.63, p < 0.01). Scalability also had a positive and significant correlation with cost efficiency (r = 0.59, p < 0.01). Regression analysis indicated that network virtualization strategy significantly predicted scalability (β = 0.68, p < 0.001) and explained 46% of its variance. It also significantly predicted cost efficiency (β = 0.63, p < 0.001) and explained 40% of its variance. Structural equation modeling confirmed the proposed model, showing significant direct effects of network virtualization strategy on scalability (β = 0.69, p < 0.001) and cost efficiency (β = 0.61, p < 0.001), as well as a significant direct effect of scalability on cost efficiency (β = 0.28, p < 0.001). The findings indicate that network virtualization strategy is a key technological and managerial capability for improving the scalability and cost efficiency of cloud data centers.

Cognitive and Systems-Theoretic Implications of Using NLP-Based Intelligent Systems in Strategic IT Management and Decision-Making

Abdullateef Haghighat iD , Shady Oyarhossein ^* iD

This study aimed to examine the cognitive and systems-theoretic implications of using NLP-based intelligent systems in strategic IT management and decision-making among managers, experts, and specialists in Tehran. This applied descriptive-analytical study was conducted using a mixed qualitative–quantitative design. The statistical population included senior IT managers, strategic planning managers, digital transformation specialists, business intelligence experts, organizational decision-makers, and academic specialists in information systems and technology management in Tehran. Using purposive sampling, 186 participants were selected, including 24 participants in the qualitative interview phase and 162 participants in the quantitative survey phase. Data were collected through a semi-structured interview protocol and a researcher-made questionnaire developed based on cognitive decision-making, systems theory, strategic IT management, and NLP-based intelligent systems. The validity of the questionnaire was confirmed by experts, and its reliability was assessed using Cronbach’s alpha. Qualitative data were analyzed through thematic analysis, and quantitative data were analyzed using descriptive statistics, Pearson correlation coefficients, and multiple regression analysis. The inferential findings showed that strategic IT decision-making effectiveness had significant positive correlations with cognitive support, strategic alignment, system integration, interpretability, trust in intelligent systems, and decision quality, and a significant negative correlation with perceived risk of excessive reliance. The regression model was significant, F(6, 155) = 41.73, p < .001, and explained 61.8% of the variance in strategic IT decision-making effectiveness. Strategic alignment was the strongest positive predictor, followed by cognitive support, trust in intelligent systems, system integration, and interpretability. Perceived risk of excessive reliance was a significant negative predictor of strategic IT decision-making effectiveness. The results indicated that NLP-based intelligent systems can enhance strategic IT decision-making when they provide cognitive support, align with organizational strategy, integrate with organizational systems, and generate interpretable and trustworthy outputs. However, excessive reliance on automated recommendations may reduce the effectiveness of decision-making by weakening human judgment and critical reflection.

Multi-Objective Optimization of Oil and Gas Project Portfolios Using Metaheuristic Algorithms and Pareto Front

Mahdi Arefnazari iD , Aliasghar Amirkardoust ^* iD , Babak Aminnejad iD , Alireza Lork iD , Mohammad Taghi Banki iD

Portfolio management in the oil and gas industry requires multi-objective optimization to balance economic, social, environmental, and risk criteria while respecting resource constraints. This study aims to optimize a portfolio of 18 oil and gas construction projects using metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). Data were collected through a standardized questionnaire encompassing eight dimensions (technical, spatial, economic, temporal, environmental, risk and safety, social, and operational) and weighted using the Analytic Hierarchy Process (AHP). The Genetic Algorithm generated a Pareto front and identified an optimal portfolio with an overall score of 2.9, economic index of 0.85, social index of 0.75, environmental index of 0.80, and risk index of 0.40. Comparison with a multi-criteria weighting method revealed that metaheuristic algorithms excel in providing non-dominated solutions, whereas the multi-criteria method suggested a portfolio with a higher economic index (0.90) due to its focus on profitability. The Pareto front, visualized using Plotly, illustrates the trade-offs among objectives and supports strategic decision-making. This approach enhances resource allocation and strengthens sustainability. Future research can explore hybrid algorithms and incorporate more real-world data.

Enhancing Interior Design by Exploring Mindset and Preferences of Twitter (X) Users

Seyedeh Leila Omrani Khorasani iD , Peyman Bayat ^* iD , Farzaneh Asadi Malek Jahan iD

Selecting an appropriate interior architectural solution often remains challenging because applicants’ underlying design mindsets are not fully captured, while designers’ stylistic orientations may unintentionally dominate decision-making. As users’ attitudes and preferences toward interior architectural styles evolve over time, ignoring these dynamics can lead to misalignment between applicants and designers, ultimately affecting design outcomes. This research aims to improve interior architecture outcomes by identifying users’ evolving design mindsets and systematically relating them to interior designers whose stylistic orientations are most compatible, before assigning an interior designer to an applicant. To this end, large-scale social media data from Twitter (X) are analyzed using an AI-based analytical approach. User-generated content is examined through sentiment analysis to capture expressed attitudes toward interior architectural styles across time, and these attitudes are represented as temporally informed user profiles. Based on these profiles, an optimized clustering strategy is employed to model both designers’ relatively stable stylistic orientations and users’ evolving design mindsets. By linking users to designers with closely aligned stylistic and cognitive patterns, the proposed approach enables more compatible designer–applicant matching. Overall, the approach supports more informed design decisions and contributes to improved interior architectural outcomes by reducing stylistic mismatch and enhancing alignment between applicants’ mindsets and designers’ design approaches.

Evaluation of Bank Branches Using a Dynamic Network Data Envelopment Analysis Model

Hamid Bahrami iD , Davood Gharakhani ^* iD , Soheila Khishtandar iD

The purpose of this study is to evaluate bank branches using a dynamic network data envelopment analysis model. Bank performance evaluation is a critical aspect of financial and economic management that facilitates the analysis of banks’ efficiency, productivity, and growth potential. Such evaluations are commonly conducted using financial indicators, including liquidity ratios, profitability ratios, and asset quality. Data envelopment analysis is a powerful analytical technique for measuring the relative efficiency of a set of decision-making units based on their inputs and outputs. Data from bank branches in Qazvin Province were used to test the efficiency of and solve the proposed model. The model proposed in this article evaluates decision-making units dynamically and simultaneously calculates the efficiency of the overall system and its constituent divisions. It therefore enables managers to identify inefficient divisions and make appropriate decisions to improve the overall efficiency of the organization.

Management Strategies and Engineering Sciences

About the Journal

Current Issue

Articles

Analysis of the Impact of Digital Technologies on Supply Chain Optimization in Various Industries

A Mutual-Information-Guided and ADASYN-Augmented Machine Learning Framework for Early Prediction of Parkinson’s Disease

Designing a Policy Laboratory for the Center for Strategic Studies of the Presidency

Analysis of the Limitations of the Earned Value Management Method and Evaluation of Modern Improvement Approaches in Project Management

Factors Affecting Economic Resilience for Transition from an Oil-Based Economy

Designing a Participatory Governance Policy Framework in Iran's Telecommunications Industry

Prediction of Completion Fluid Stability and Productivity Impact in Challenging Reservoir Environments Using Machine Learning Analysis

Providing a Continuous Risk-Monitoring Model in the Banking Sector Based on Grounded Data

Presentation and Validation of a Model of Social Minority Entrepreneurship (Case Study: Socially Harmed Women Entrepreneurs)

Identification and Evaluation of the Customer Engagement Model with Domestic Home Appliance Brands

Comparative Analysis of Hybrid Approaches in Earned Value Management and Proposing an Extended Framework as an Integrated Future Model for Project Control

Qualitative Analysis of Barriers and Strategies for Fair Resource Allocation in Economic Policymaking in Iran (Case Study: Ministry of Economic Affairs and Finance)

Analysis of Climate Change Impacts on Precipitation and Runoff Using Time Series Models in MATLAB

Equilibrium Pricing in Partially Integrated-Direct (PID) Closed Loop Dual channel Supply Chains (CLDSC) Under Symmetric and Asymmetric Information

Evaluation of the Application of Smart Contracts in Construction Industry Management: A Systematic Review of Previous Research

Make a Submission

Editorial Board

Keywords

Information Table

Language

Journal Archive

Average time from submission until

Indexing & Abstracting