Management Strategies and Engineering Sciences

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

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.

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.

A Strategic Framework for Managing Smart Contract–Driven Access Control in Industrial Internet of Things Ecosystems

Hourieh Alsadat Hosseini iD , Alireza Hedayati ^* iD , Vahe Aghazarian iD

The objective of this study was to develop and empirically validate a strategic framework for managing smart contract–driven access control in Industrial Internet of Things (IIoT) ecosystems by integrating organizational, technological, governance, identity-management, security, and risk-management dimensions into a comprehensive model. This study employed an exploratory sequential mixed-methods design conducted in Tehran, Iran. In the qualitative phase, 21 Industrial Internet of Things experts were selected using purposive and snowball sampling, and semi-structured interviews were conducted until theoretical saturation was achieved. The qualitative findings were analyzed using thematic analysis through open, axial, and selective coding in MAXQDA. The extracted themes were subsequently transformed into a researcher-developed questionnaire. In the quantitative phase, 246 Industrial Internet of Things experts participated in the survey. The reliability and validity of the measurement instrument were assessed using Cronbach’s alpha, composite reliability, average variance extracted, and discriminant validity. The proposed framework was evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS, while bootstrapping with 5,000 resamples was used to test the significance of the structural relationships. The structural model demonstrated that all hypothesized relationships were statistically significant. Continuous monitoring, auditability, and risk management had the strongest direct effect on effective management of smart contract–driven access control (β = 0.284, p < 0.001), followed by security and privacy assurance (β = 0.257, p < 0.001), smart contract governance and policy management (β = 0.214, p < 0.001), decentralized identity and trust management (β = 0.146, p = 0.003), organizational and regulatory readiness (β = 0.138, p = 0.003), and technological infrastructure and interoperability (β = 0.119, p = 0.012). Organizational and regulatory readiness significantly predicted smart contract governance and policy management (β = 0.412, p < 0.001), while security and privacy assurance (β = 0.391, p < 0.001) and technological infrastructure and interoperability (β = 0.367, p < 0.001) significantly predicted continuous monitoring, auditability, and risk management. Significant indirect effects confirmed the mediating roles of governance and monitoring. The proposed model explained 68.4% of the variance in effective management of smart contract–driven access control. The findings demonstrate that effective smart contract–driven access control in Industrial Internet of Things ecosystems requires an integrated strategic approach that combines decentralized identity management, smart contract governance, organizational readiness, technological interoperability, security and privacy assurance, and continuous monitoring within a unified governance framework.

Design of a Fuzzy Multi-Objective Mathematical Model for Short-Term Preventive Maintenance Scheduling Using a Total Productive Maintenance Approach

Foroud Rajabpour iD , Masoomeh Zeinalnezhad ^* iD , Vahid Hajipour iD

This study aimed to design a fuzzy multi-objective mathematical model for short-term preventive maintenance scheduling based on the Total Productive Maintenance approach to optimize production completion time, total cost, greenhouse gas emissions, and product quality under uncertainty. This applied quantitative study was conducted using mathematical modeling and computational optimization. The proposed model integrated production scheduling and preventive maintenance decisions within a short-term planning horizon and formulated four objective functions: minimization of completion time, total cost, and greenhouse gas emissions, and maximization of product quality. Uncertain parameters were modeled using a fuzzy approach to increase the realism of the scheduling environment. Five small-scale numerical instances with increasing complexity were generated and solved using GAMS as an exact optimization benchmark. The NSGA-II metaheuristic algorithm was implemented in MATLAB to solve the same instances and evaluate the model’s computational efficiency, accuracy, and stability. The inferential comparison between GAMS and NSGA-II indicated that the proposed algorithm generated near-optimal solutions across all objective functions. For the quality objective, deviations ranged from 0.39% to 2.78%, and for completion time, deviations ranged from 0.97% to 2.87%. For the cost objective, deviations ranged from 0.57% to 2.65%, while greenhouse gas emission deviations ranged from 0.36% to 2.93%. Repeated executions of the algorithm showed low dispersion between best, worst, and mean solutions, confirming acceptable convergence and stability. The best–worst deviation for the cost objective reached 3.02% only in the largest instance, while the quality objective remained below 1% across all instances. The proposed fuzzy multi-objective model provides an effective decision-support framework for short-term preventive maintenance scheduling in TPM-based production systems. The results confirm that NSGA-II can generate accurate, stable, and computationally efficient solutions close to exact optimization results while balancing economic, environmental, operational, and quality-related objectives.

The Role of BIM and Artificial Intelligence in Reducing the Environmental Impacts of Construction Projects: A Sustainable Construction Management Approach

Mohammadmoein Zohreh ^* iD

This study aimed to examine the role of Building Information Modeling (BIM) and artificial intelligence in reducing the environmental impacts of construction projects with an emphasis on sustainable construction management. This applied quantitative study was conducted using a descriptive-correlational design. The statistical population consisted of construction professionals, engineers, project managers, BIM specialists, construction management experts, architects, and environmental consultants working in construction projects in Tehran. A total of 284 participants were selected through purposive sampling based on professional experience and familiarity with construction technologies or sustainable construction practices. Data were collected using structured questionnaires measuring BIM application in construction management, artificial intelligence application in construction projects, environmental impact reduction, and sustainable construction management. The questionnaires were scored on a five-point Likert scale, and their content validity was confirmed by experts. Reliability was assessed using Cronbach’s alpha coefficients. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis at a significance level of 0.05. The inferential results showed that BIM application was positively and significantly correlated with environmental impact reduction (r = 0.68, p < 0.01) and sustainable construction management (r = 0.62, p < 0.01). Artificial intelligence application also had significant positive correlations with environmental impact reduction (r = 0.67, p < 0.01) and sustainable construction management (r = 0.64, p < 0.01). Environmental impact reduction was strongly associated with sustainable construction management (r = 0.73, p < 0.01). Regression analysis showed that BIM alone explained 46% of the variance in environmental impact reduction, while the combined model of BIM and artificial intelligence explained 51% of the variance. Both BIM (β = 0.44, p < 0.001) and artificial intelligence (β = 0.42, p < 0.001) significantly predicted environmental impact reduction. In addition, BIM, artificial intelligence, and environmental impact reduction significantly predicted sustainable construction management and explained 62% of its variance. The findings indicate that BIM and artificial intelligence are effective digital enablers for reducing the environmental impacts of construction projects and strengthening sustainable construction management. Their integrated application can improve design coordination, resource optimization, waste reduction, carbon control, and data-driven decision-making in construction projects.

Management Strategies and Engineering Sciences

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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

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