Journal of Resource Management and Decision Engineering
The Journal of Resource Management and Decision Engineering is a pioneering open access, peer-reviewed journal committed to disseminating cutting-edge research and practical insights into the complex world of resource management and decision-making processes. Our mission is to foster a dialogue among professionals, researchers, and practitioners to enhance the understanding and practice of efficient resource allocation and decision-making strategies across various disciplines. Our open access mandate supports a broader global exchange of knowledge, making research freely available to the international community without any barriers. <p style="padding: 0px; margin: 8px 0px 0px; word-break: break-word; font-size: 14px; line-height: var(--cib-type-body1-line-height); font-weight: 400; font-variation-settings: var(--cib-type-body1-font-variation-settings); color: #111111; font-family: -apple-system, Roboto, SegoeUI, 'Segoe UI', 'Helvetica Neue', Helvetica, 'Microsoft YaHei', 'Meiryo UI', Meiryo, 'Arial Unicode MS', sans-serif; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: #f3f3f3; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">This journal adheres to the ethical standards in publications and complies with the regulations of the Committee on Publication Ethics (COPE). It also follows the executive bylaw for the prevention and handling of academic misconduct. Aims and Scopes JRMDE aims to: Advance the theoretical foundations and practical applications of decision engineering and resource management across various engineering disciplines, including computer, electrical, civil, mechanical, and industrial engineering. Promote interdisciplinary research that addresses significant challenges in the allocation and management of resources across diverse sectors. Provide a platform for innovative ideas and novel methodologies that can influence policy-making and operational practices. Encourage the integration of technology and analytical tools in decision-making processes to enhance effectiveness and efficiency within the fields of computer, electrical, civil, mechanical, and industrial engineering. Scope The scope of JRMDE encompasses a wide array of topics within resource management and decision engineering, including but not limited to: Theoretical models and practical applications of decision analysis across computer, electrical, civil, mechanical, and industrial engineering. Resource allocation policies and strategies pertinent to various engineering disciplines. Sustainability and environmental impact assessments in resource management, with applications in civil and environmental engineering. Technological advancements and their impact on resource planning in computer, electrical, mechanical, and industrial engineering. Ethical considerations and societal impacts of decision-making processes in engineering fields. Fields and Areas Covered JRMDE covers a broad spectrum of fields and areas, reflecting the interdisciplinary nature of resource management and decision engineering: Business and Economics: Corporate decision-making processes, economic planning, and financial impacts of resource management strategies. Environmental Sciences: Environmental policy, natural resource management, and strategies for sustainable development. Engineering and Technology: Computer Engineering: Development of algorithms and software for decision support systems, and the role of artificial intelligence in resource management. Electrical Engineering: Optimization of electrical grids, smart grid technologies, and energy resource management. Civil Engineering: Infrastructure planning, construction resource allocation, and sustainable urban development. Mechanical Engineering: Efficiency improvements in mechanical systems, manufacturing resource management, and automation technologies. Industrial Engineering: Process optimization, supply chain management, and integration of industrial systems for resource efficiency. Public Policy and Administration: Governance of resources, public health decision-making, and infrastructure planning. Healthcare and Life Sciences: Resource allocation in healthcare, biomedical decision support systems, and public health logistics. Data Science and Analytics: Big data applications in decision-making, predictive analytics for resource management, and machine learning techniques.
Identification of the Factors and Consequences of Earnings Management in the Chemical Products and Automotive Parts Manufacturing Industries Using the Meta-Synthesis Approach and Expert Opinions
Due to their process complexity and high levels of investment, industries exhibit significant sensitivity to the transparency and accuracy of financial reporting. Earnings management may temporarily enhance the financial health of firms; however, in the long term, it leads to a decline in stakeholder trust, disruption in investors’ decision-making, and reduced efficiency in the capital market. Moreover, identifying the consequences of earnings management contributes to improving regulatory frameworks and enhancing the quality of detecting and reporting financial misconduct within these industries. This, in turn, paves the way for sustainable development and fair competition in both domestic and international markets. The present study was conducted with the objective of identifying the factors and consequences of earnings management in the chemical products and automotive parts manufacturing industries. Data collection was performed through semi-structured interviews with experts. Using the snowball sampling technique, interviews were conducted with 15 academic experts and senior managers from active companies within the chemical products and automotive parts industries. The data were analyzed through a meta-synthesis approach and expert judgment. The Content Validity Ratio (CVR) was used to assess content validity, and Holsti’s coefficient was applied to measure reliability. The identified factors included financial leverage, stock liquidity, free cash flow, information asymmetry, long-term effective tax rate, institutional ownership, executive management ownership, board size, profitability, differentiation strategy, cost leadership strategy, and corporate social responsibility. The identified consequences comprised cash holding level and tax avoidance.
Copyright (c) 2024 Samaneh Elahi Roodposhti (Author); Ebrahim Chirani; Seyed Mozafar Mir Bargkar (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification and Prioritization of Post-Social Responsibility Components of Human Resource Management in the Real Estate and Properties Administration of Iraq
The purpose of this study is to identify and prioritize the post-social responsibility components of human resource management (HRM) in the Real Estate and Properties Administration of Iraq. The statistical population of the study consisted of 350 employees and managers of the Real Estate and Properties Administration of Iraq. Based on the Morgan table and using stratified random sampling, a total of 185 participants were selected as the research sample. Data were collected using a researcher-made questionnaire. Inferential data analysis was performed using the Friedman test. The ranking of the post-social responsibility outcomes of human resource management indicated that human resource–related outcomes, extra-organizational outcomes, and organizational outcomes ranked first to third, respectively, in terms of priority. The human resource–related outcomes included the following components: (1) employee empowerment and development, (2) improvement of employee attitudes, (3) improvement of working conditions, and (4) enhancement of employee performance and productivity. Moreover, the extra-organizational outcomes included, in order of priority, the following components: (1) improvement of socio-cultural conditions, (2) economic development, and (3) environmental development. The organizational outcomes, in order of importance, comprised the following components: (1) impacts on organizational culture, (2) sustainable growth and competitive advantage, and (3) transformation in structure and strategy. Based on the findings, it is recommended that the organization continuously evaluate the activities and effectiveness of policies related to social responsibility within its structure, allocate sufficient financial and human resources for the implementation of social responsibility programs, and ensure full managerial support for these initiatives.
Real Estate and Properties Administration of Iraq
Copyright (c) 2024 Hiba Yasir Taha (Author); Mehraban Hadipeykani; Mohammed Thabit Farawn Shabanh, Badri Shahtalebi (Author) https://creativecommons.org/licenses/by-nc/4.0
Identifying and Ranking Factors Affecting Personal Branding in the Food Industry Using the Network Analysis Process (ANP) Method
The purpose of this study is to identify and rank the factors influencing personal branding in the food industry. Accordingly, this research is exploratory in nature and was conducted in two stages. The first stage employed the grounded theory approach, which is applied in terms of purpose and qualitative in terms of methodology. Specifically, Strauss and Corbin’s (1998) systematic coding procedure was used, encompassing three main steps: open coding, axial coding, and selective coding. The second stage applied the Analytic Network Process (ANP) method, which is applied in terms of purpose and quantitative and descriptive–survey in terms of method. The statistical population consisted of managers in Iran’s food industry, from whom 19 individuals were selected through non-probability judgmental (purposive) sampling. Using the ANP technique, the relative importance of the identified factors was analyzed, and the prioritization was conducted with the aid of Super Decisions software. The results indicated that, within the variable of causal conditions, individual characteristics ranked first, followed by business characteristics. Within the context variable, social, economic, and political issues, cultural values, global dynamics, and public opinions and beliefs were prioritized. For the intervening conditions, criticism and feedback, audience expectations, the gap between virtual and real environments, and social norms were prioritized. Regarding the strategies variable, the priorities included adopting distinctive approaches, market research, social responsibility, social networking, appropriate visualization tools, goal orientation, and behavioral identity tools. Finally, in the outcome variable, the priorities were advancing company objectives, attracting and retaining audiences, saving time and space, enhancing communication quality, achieving internal satisfaction, and gaining reputation.
Copyright (c) 2024 Mercedeh Mortazavi Rad, Mahmoud Ahmadisharif, Alireza Rousta (Author) https://creativecommons.org/licenses/by-nc/4.0
Formulation and Validation of a Performance Measurement Model in Governmental Organizations Using the Balanced Scorecard (A Mixed-Methods Approach)
The main objective of this study is to formulate and validate a performance measurement model in governmental organizations using the balanced scorecard (a mixed-methods approach). This research employed a mixed-methods design and was conducted in two qualitative and quantitative phases. In the qualitative phase, data were collected through semi-structured interviews and analyzed using thematic analysis. In the quantitative phase, data were gathered through a researcher-made questionnaire and analyzed using structural equation modeling. The statistical population in the qualitative section included academic experts and executive managers in several governmental organizations, from which 14 participants were selected through purposive sampling and theoretical saturation. The statistical population in the quantitative section consisted of employees from selected governmental organizations and institutions, of whom 400 individuals were chosen using simple random sampling. The findings of the qualitative phase produced a thematic network of the research model, including seven organizing themes and 64 basic themes. The results indicate that localization of the balanced scorecard and the definition of indicators consistent with the mission, objectives, structure, and culture of Iran’s public sector have a significant effect on improving transparency, accountability, service quality, and strategic planning in these organizations. Establishing this framework, in addition to measuring financial performance, also encompasses internal processes, stakeholder satisfaction, and the enhancement of organizational learning and growth, thereby enabling better resource allocation and data-driven decision-making for managers. Moreover, challenges such as organizational resistance, weak technological infrastructures, and insufficient training were identified as obstacles to full implementation, for which practical solutions were proposed. In the quantitative section, the path analysis of the model was examined and confirmed.
Performance Measurement, Balanced Scorecard, Governmental Organizations, Financial Dimension.
Copyright (c) 2026 Jafar Heidarian (Author); Gholamreza Farsadamanollahi; Mohammad Ali Bidari (Author) https://creativecommons.org/licenses/by-nc/4.0
Developing a Data-Driven Decision-Making Model for Algorithmic Trading Strategies (Cryptocurrency Market)
The rapid growth of the cryptocurrency market in recent years has created both lucrative opportunities and unprecedented risks for investors. High volatility, the decentralized nature of exchanges, and the massive volume of trading data have challenged human decision-making, highlighting the necessity of employing intelligent and automated systems. In this study, a data-driven model is proposed for decision-making and the implementation of algorithmic trading strategies in the cryptocurrency market. The proposed framework integrates historical price data analysis, technical indicators, and market microstructure data with machine learning methods to forecast price trends and generate buy and sell signals. Subsequently, optimization algorithms are employed to adjust trading parameters in a way that maximizes returns while minimizing risk and transaction costs. Experimental results based on real data from selected cryptocurrencies (such as Bitcoin and Ethereum) demonstrate that the proposed model outperforms traditional strategies in terms of risk-adjusted returns and result stability. Finally, the article provides recommendations for the practical implementation of this framework in intelligent trading systems and suggests future research directions.
Copyright (c) 2026 Alireza Gharibshahian; Mohammad Reza Dehghani (Author) https://creativecommons.org/licenses/by-nc/4.0
Application and Management of Intelligent Systems in Construction Projects Using Recycled Materials
Currently, worldwide, modern construction technologies have emerged as one of the most important factors driving transformation and progress in the construction industry. These technologies can serve as cost-effective and environmentally friendly alternatives to conventional building materials. Building Information Modeling (BIM), intelligent systems, and the recycling of construction materials in the city of Qods have been recognized as advanced and innovative technologies in the construction sector. In the future, this system will continue to evolve and develop in response to technological advancements and the dynamic needs of the construction industry. Through intelligent system implementation, project managers and technical teams can address conflicts and technical issues while enhancing the quality and productivity of projects. This technology is also considered a valuable tool for promoting cultural acceptance in construction projects. Overall, BIM, as a powerful technological tool, can contribute to solving common challenges in civil engineering projects. It helps improve the management, quality, and efficiency of such projects while preventing potential interferences and technical complications. In this study, in order to examine the application and management of intelligent systems in the construction of civil projects using recycled materials in the city of Qods, 11 experts were selected. Using a pairwise comparison questionnaire, the identified factors and subgroups were prioritized through the Analytic Hierarchy Process (AHP). Based on the results obtained, “maintenance and repair” was recognized as one of the most important sub-criteria. Subsequently, “precise and transparent design modeling” was identified as the second most significant sub-criterion, followed by “appropriate scheduling.” Furthermore, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to prioritize the implementation tools. According to this analysis, Asta Powerproject software was identified as the top-priority tool for BIM implementation in construction projects utilizing recycled materials in Qods.
Copyright (c) 2025 Aliakbar Bagherloo (Author); Davod Pourian; Mahmood Minavand (Author) https://creativecommons.org/licenses/by-nc/4.0
Investigating communication and security challenges in power microgrids and designing a secure communication network using appropriate protocols and encryption techniques of artificial intelligence technology
With the expansion of the use of renewable energy sources and the need for smart energy distribution systems, microgrids have become one of the vital components of power systems. Considering the increasing importance of power microgrids in modern energy systems and their key role in increasing efficiency and reducing dependence on large power grids, designing a reliable communication network for the utilization of these microgrids is crucial. In this research, a comprehensive framework for simulating, designing, and evaluating the communication network of power microgrids is presented. First, power microgrids were simulated using the Python programming language to enable analysis of the behavior and performance of these systems under different conditions. Then, a communication network based on artificial intelligence algorithms was designed and developed, which ensures the ability to coordinate and manage microgrids optimally. Next, in order to investigate the stability and security of the designed communication network, various types of cyber attacks were simulated. These attacks included data intrusion, disruption of communications, and various cyber-destruction scenarios. Also, smart defense strategies were developed to counter these attacks and their effectiveness in maintaining the performance of the communication network and preventing negative impacts on microgrids and the main power grid during outage conditions was evaluated. The results show that the designed communication network is not only efficient in managing and utilizing microgrids, but also has the ability to resist cyber attacks and maintain system stability. This research can be used as a basis for developing smart and secure systems in energy management and power microgrids and provide an effective solution to address security and stability challenges in power systems.
Copyright (c) 2026 Abolfazl Taleghani (Author); Sepehr Soltani https://creativecommons.org/licenses/by-nc/4.0
Designing a Model to Reduce Brand Hate in the Home Appliance Industry Using Interpretive Structural Modeling Technique
The purpose of this study was to propose a model aimed at reducing brand hate in the country’s home appliance industry. To conduct the present research, ten factors influencing the reduction of brand hate were first identified through a review of the research literature. Then, the interpretive structural modeling (ISM) technique was employed to structure these drivers using the insights of 13 experts. Subsequently, to validate and test the conceptual model derived, a questionnaire containing 33 items with a five-point Likert scale was developed and distributed among 234 managers and employees of the national home appliance industry. Based on the data collected and through the application of structural equation modeling (SEM) and the Smart PLS3 software, the conceptual model of this study was evaluated and tested. The findings revealed an eight-level hierarchical structure, in which customer complaint handling, communication quality, and after-sales services were identified as the primary drivers initiating the model for reducing brand hate. The results of this research can serve as a guiding framework for strategies aimed at mitigating brand hate.
Copyright (c) 2024 Azadeh Zahiri (Author); Ali Pirzad; Seyed Najmeddin Mousavi (Author) https://creativecommons.org/licenses/by-nc/4.0
The Role of Service Quality and Customer Relationship Management in Enhancing Loyalty and Reducing Brand Switching in the Sports Equipment Industry
The aim of this study was to examine the role of service quality and Customer Relationship Management (CRM) in strengthening loyalty and reducing brand switching in Iran’s sports equipment industry. Given the intense competition and high similarity of products in this sector, organizations need to focus on complementary services and effective communication strategies to retain customers. This research is applied in purpose and employs a mixed-method design (qualitative–quantitative). In the qualitative phase, a meta-synthesis approach was used to systematically review the literature, leading to the identification of 37 key indicators categorized into 10 factors and three main dimensions: product and service quality, relationship marketing and CRM, and socio-economic factors. In the quantitative phase, data were collected from 72 experts in the sports equipment industry and analyzed using Fuzzy Cognitive Mapping (FCM) to determine the causal relationships among the identified factors. The findings indicated that service quality—particularly after-sales services and prompt responsiveness—is the strongest factor in retaining customers. Moreover, digital CRM and personalized communication play a key role in fostering trust and customer commitment. Scenario analysis results revealed that the simultaneous improvement of service quality and CRM development can prevent up to 45% of customer brand switching. Based on these findings, it can be concluded that under the volatile economic conditions of Iran, only organizations adopting a comprehensive approach—focusing not only on product quality but also on customer experience, trust-building, and long-term relationships—are likely to succeed. This research fills a gap in domestic studies on the sports equipment sector and can serve as a practical guide for managers seeking to increase customer loyalty and reduce brand switching rates.
Copyright (c) 2024 Zahra Zarafshan (Author); Ali Pirzad; Seyed Najmeddin Mousavi (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing a Portfolio Risk Management Model Using Fundamental Analysis and Multi-Objective Evolutionary Optimization
This study aimed to develop and validate a portfolio risk management model tailored to the Tehran Stock Exchange (TSE) that integrates fundamental analysis with Multi-Objective Evolutionary Optimization to simultaneously enhance return, control risk, and improve downside protection. This quantitative study analyzed all non-financial firms listed on the TSE from July 2023 to July 2024. Firms were first screened using ten fundamental indicators—ROE, ROA, EPS, P/E, P/B, D/E, current ratio, operating cash flow ratio, revenue growth, and net profit margin—and ranked using the Analytic Hierarchy Process (AHP). The top 50 firms were selected as the candidate set for optimization. Portfolios were then constructed and optimized through the Non-dominated Sorting Genetic Algorithm II (NSGA-II), aiming to maximize expected return, minimize variance, and improve Value at Risk (VaR) at the 95% confidence level. Out-of-sample backtesting was conducted, and performance was compared against the market index TEDPIX and a traditional Mean-variance optimization model based on Harry Markowitz’s framework. The optimization produced a well-defined Pareto frontier, with portfolios demonstrating a clear risk-return trade-off. Intermediate-risk portfolios achieved the highest Sharpe Ratio and Sortino Ratio values, indicating optimal risk-adjusted performance. Compared to TEDPIX and the Markowitz model, the optimized portfolios delivered significantly higher average monthly returns (up to 2.7% vs. 1.2%), lower volatility, smaller maximum drawdowns, and higher cumulative wealth. Statistical tests confirmed that these excess returns were significant (p < 0.05), highlighting the superior performance and resilience of the proposed model. Integrating fundamental analysis with multi-objective evolutionary optimization effectively enhances portfolio performance and risk control in the Iranian market.
multi-objective evolutionary optimization
Copyright (c) 2026 Amirhossein Soltanabadi; Hossein Mohseni (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification and Prioritization of Factors Affecting E-Learner Satisfaction in Mathematics Based on the Fuzzy Analytic Hierarchy Process (Case Study: Farhangian University of Mazandaran)
The purpose of this study was to evaluate the factors influencing the satisfaction of mathematics students in an e-learning environment. To achieve this goal, a library-based and descriptive research method was employed. The statistical population consisted of experts, mathematics specialists, and e-learning curriculum planners at Farhangian University of Mazandaran. The sample included seven experts who were purposively selected based on the research objectives and questions. Based on the theoretical literature and in consultation with experts and specialists, the factors and criteria of e-learning environment quality were categorized into four main dimensions—technical system and technology infrastructure quality, educational quality, information and content quality, and service quality—comprising a total of 24 criteria. The weight of each of these indicators within the studied population was determined using an expert questionnaire and the Fuzzy Analytic Hierarchy Process (FAHP). Data analysis was performed using the Super Decisions software. The findings revealed that, in terms of importance, the factors related to technical systems and technology infrastructure, technology and instructional design, content development, and support services were evaluated and ranked, respectively, as the key determinants of e-learning environment quality.
Copyright (c) 2025 Morteza Gorzinnezhad; Davood Darvishi Salookolaei, Mohammad Dehghandar (Author) https://creativecommons.org/licenses/by-nc/4.0
Modeling the Role of Accounting in Enhancing Financial Performance, Technological Innovation, and Sustainability of Knowledge-Based Companies: A Structural Equation Modeling (SEM) Approach
The objective of this study is to model the role of accounting in enhancing financial performance, technological innovation, and sustainability of knowledge-based companies. Considering the importance of these companies in a knowledge-driven economy and the existing challenges related to financial transparency, capital attraction, and sustainable development, examining the role of accounting systems can open new horizons for improving managerial decision-making and strengthening competitive capabilities. This research is applied in purpose and descriptive–survey and quantitative in method. The statistical population in the qualitative phase consisted of accounting faculty members with more than ten years of executive experience, who were selected through purposive sampling. Semi-structured interviews were conducted until theoretical saturation was reached. In the quantitative phase, the statistical population included all accountants working in knowledge-based companies. Using the Krejcie and Morgan table, a sample of 384 participants was selected. The data collection tool was a researcher-designed questionnaire with 25 items across five main dimensions of the study. Its validity was confirmed by subject-matter experts, and its reliability was verified with a Cronbach’s alpha coefficient greater than 0.8. Data analysis was conducted using Structural Equation Modeling (SEM) with SPSS and AMOS software. The findings showed that accounting has a positive and significant effect on the financial performance, technological innovation, and sustainability of knowledge-based companies. In other words, the presence of transparent and efficient accounting systems can not only improve financial management and attract capital but also create conditions for the development of innovative activities and enhancement of organizational sustainability.
Copyright (c) 2025 Moein Ilaghi Hosseini (Author); Abbasali Haghparast; Alireza Hirad, Habib Piri (Author) https://creativecommons.org/licenses/by-nc/4.0
Design of a Fuzzy Expert System for Measuring the Satisfaction of E-Mathematics Learners (Case Study: Farhangian University of Mazandaran)
The issue of student satisfaction with e-learning systems plays a critical role in their academic performance as well as in the success and effectiveness of this educational domain. In this study, a fuzzy expert system was designed using MATLAB software to measure the satisfaction level of e-mathematics learners at Farhangian University of Mazandaran. This fuzzy expert system included four input variables—technical quality of the system and technological infrastructure, educational quality, information and content quality, and service quality—extracted from the theoretical literature of the study; one output variable—e-mathematics learner satisfaction; trapezoidal and triangular membership functions; 50 rules developed based on the input of eight purposively selected experts from Farhangian University; and the centroid defuzzification method. All input and output variables of this system were normalized and converted to a range between 0 and 1, and using the input values, satisfaction levels were estimated with an error margin of less than 0.15. Considering the strong ability of this fuzzy expert system to estimate the satisfaction of e-learners, and given its design based on criteria, sub-criteria, and their relative importance according to ranking results used as input variables, it can provide significant support to educational system managers by enabling timely tracking of feedback and problems in proportion to their level of importance, thereby increasing the satisfaction of e-mathematics learners.
Copyright (c) 2024 Morteza Gorzinnezhad; Mohammad Dehghandar, Davood Darvishi Salookolaei (Author) https://creativecommons.org/licenses/by-nc/4.0
Development of an Analytical Framework for Reverse Logistics Supply Chain in Industrial Waste Management Based on Grounded Theory and Interpretive Structural Modeling (ISM)
The rapid growth of technology and the emergence of new production processes, along with the substitution of synthetic materials and chemical compounds, have resulted in an increased volume of industrial waste and, in some cases, the generation of hazardous waste. Improper handling, transportation, and disposal of this waste, part of which contains dangerous substances, pose serious challenges to human health and the environment. Under such circumstances, establishing an efficient reverse logistics network emerges as an inevitable necessity. With growing social concerns about environmental issues, reverse logistics has become increasingly integrated with waste management, and the management of industrial waste is now considered a core pillar of reverse supply chain management. This study, employing the grounded theory method based on the Strauss and Corbin (1998) model and using the insights of 17 academic and industrial experts in the national gas refining sector, proposes a comprehensive model for the reverse supply chain of waste in this industry. The analysis of data obtained from semi-structured interviews led to the identification of 25 core concepts categorized into six main themes. The application of Interpretive Structural Modeling (ISM) revealed hierarchical and causal relationships among these factors, indicating that regulations and policies, infrastructure, and organizational culture act as fundamental and driving forces with the greatest impact on the success of the system. This paradigmatic model can serve as a roadmap for managers in the gas refining industry to design and implement an effective and sustainable reverse logistics system.
Copyright (c) 2024 Zahra Ramezani (Author); Sadegh Feizollahi; Maryam Daneshmand Mehr (Author) https://creativecommons.org/licenses/by-nc/4.0
Examining the Threshold Effects of Government Governance on the Relationship Between Globalization, Government Effectiveness, and Environmental Pollutant Quality in Selected Developing Countries
Given the significance of the topic and the undeniable role of the environment in human life, this study seeks to examine the threshold effects of government governance on the relationship between globalization, government effectiveness, and the quality of environmental pollutants in selected developing countries during the period 2005 to 2023, using the Panel Smooth Transition Regression (PSTR) model. The research sample includes selected MENA and Middle Eastern countries, namely Saudi Arabia, Qatar, Pakistan, the United Arab Emirates, Algeria, Kuwait, Iran, Egypt, Oman, Morocco, Libya, Yemen, and Sudan. The findings reveal that by employing a PSTR model in which the transition variable is trade volume (EG) in the first model and government governance (INST) in the second model, the functional relationship between economic globalization and environmental quality can be modeled. The estimated results for the linear part of the first and second models (Regime 1) indicate that trade volume (EG) and gross domestic product (GDP) have a positive relationship with nitrogen dioxide (NO₂) emissions. Specifically, a one-unit increase in trade volume and GDP leads to an increase of 0.21 and 0.22 units in greenhouse gas emissions, respectively, in the first model. Furthermore, the variable of governance quality (INST) in the second model results in a reduction of 0.12 units in nitrogen dioxide (NO₂) emissions, and the interaction term of government governance with trade volume (INST × EG) reduces NO₂ emissions by 0.11 units. Additionally, the estimated results for the nonlinear part of the first and second models (Regime 2) confirm the presence of a positive relationship between trade volume (EG) and gross domestic product (GDP) with nitrogen dioxide (NO₂) emissions.
Copyright (c) 2025 Mohamadamin Talebi (Author); Mahnaz Rabiei; Fatemeh Zandi, Mohammad Khezri (Author) https://creativecommons.org/licenses/by-nc/4.0
Futures Studies on Artificial Intelligence in Management Accounting
The present study was conducted with the aim of exploring the future applications of artificial intelligence (AI) in management accounting and is applied and descriptive (non-experimental) in nature. Data collection was carried out using a mixed-methods approach, combining field and library research techniques. The tools employed included expert panels, semi-structured interviews, open-ended questionnaires, the fuzzy Delphi method, observation, and document analysis. The statistical population consisted of professors, doctoral students, members of the Management Accountants Association, and professional specialists in this field in Iran. Sampling was performed using theoretical and snowball methods, and interviews continued until theoretical saturation was reached. Data analysis was conducted both qualitatively and quantitatively, leading to the identification of key drivers in management accounting education and research. Based on the results of the fuzzy Delphi analysis, the technology of deep learning ranked first with a fuzzy mean of 3.7 and was recognized as the most critical driver. Subsequently, natural language processing (NLP) with a mean of 3.4 was also identified among the accepted technologies. Blockchain technology, with a score of 3.1, was evaluated as conditional and requires further investigation. Additionally, explainable artificial intelligence (XAI) was proposed as an emerging driver that could play a significant role in enhancing transparency, trust, and regulatory compliance. The findings of this research indicate that the future of management accounting will be directly influenced by technological advancements in AI. To leverage these technologies effectively, strategic planning and investments in education and infrastructure development are essential.
Futures studies on artificial intelligence
Copyright (c) 2025 Mohsen Aboeuimehrizi (Author); Mahmoud Moeinadin; Akram Taftiyan, Mohammad Sadeghi Sarcheshmeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Modeling and Analysis of Surface Contamination in Ceramic and Polymeric Insulators Using Partial Discharge Signal Characteristics
This study aimed to model and analyze the impact of surface contamination on ceramic and polymeric insulators by characterizing and comparing their partial discharge (PD) signal behavior to support condition-based monitoring and predictive design. An experimental laboratory design was employed to expose medium-voltage ceramic and polymeric insulators to controlled contamination layers prepared with standardized saline and artificial pollutants. PD signals were captured under alternating current stress using an IEC 60270–compliant setup consisting of a high-voltage source, coupling capacitor, and Rogowski coil/high-frequency current transformer sensors. Raw signals underwent wavelet-based denoising to suppress environmental noise and were further analyzed using Fast Fourier Transform (FFT) for spectral features and phase-resolved partial discharge (PRPD) mapping to visualize discharge activity across the AC cycle. Equivalent electrical models of contaminated surfaces were developed to simulate PD inception and growth, and the outputs were validated against laboratory measurements. Ceramic insulators exhibited an abrupt transition from sporadic to high-energy discharges once contamination created a conductive film, reflected by sharp increases in apparent charge and dense PRPD clusters around voltage peaks. Polymeric insulators demonstrated a more gradual rise in PD activity with smaller but persistent pulses distributed across the cycle. FFT analysis revealed dominant low-to-mid frequency peaks intensifying faster in ceramics, while polymeric responses remained broader and less concentrated. The electrical models closely matched experimental PD thresholds and spectral signatures, confirming their ability to predict contamination-driven surface breakdown. Surface contamination strongly influences PD activity, and material type governs the discharge evolution pathway.
Copyright (c) 2025 Nader Tavakol, Mazdak Teimoortashloo, Fatemeh Esmaeelzadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Examining the Impact of Cognitive Processes on Economic Decisions: Simulating the Behavior of Economic Agents under Risk and Uncertainty
This study investigates the influence of cognitive processes on economic decisions and simulates the behavior of economic agents under conditions of risk and uncertainty. Simulation models based on stochastic algorithms were employed to analyze economic decision-making in environments characterized by risk and uncertainty. The simulations were implemented using the Python and R programming languages, and economic behaviors influenced by cognitive factors such as memory constraints, attention, and behavioral biases were examined. The results indicated that when faced with risk, economic agents tend to gravitate toward satisficing options and adopt simpler strategies, even if these decisions are not fully optimal. This research also revealed discrepancies between simulated behaviors and the predictions of rational choice theories, highlighting the impact of cognitive biases such as loss aversion and overconfidence in economic decision-making. The use of diverse simulation techniques and data analysis contributed to a better understanding of decision-making in complex contexts and can inform the improvement of economic and financial policies.
Copyright (c) 2025 Farshid Ghasemi Dijvejin https://creativecommons.org/licenses/by-nc/4.0
Identification and Modeling of Factors Affecting the Effectiveness of Internal Audit and Its Impact on the Quality of Financial Reporting Considering the Role of Senior Management Support
The main objective of this study is to identify and model the factors affecting the effectiveness of internal audit and its impact on the quality of financial reporting, taking into account the role of senior management support. In terms of nature, this research is exploratory, and in terms of purpose, it falls under applied research. The statistical population of this study consists of 179 managers of listed and over-the-counter (OTC) companies in Iran. To collect the required data, interviews and questionnaires were employed. To test the hypotheses, inferential statistics and structural equation modeling (SEM) tests were applied. The results of the study showed that organizational structure (0.803), risk management (0.780), audit committee (0.774), board of directors and managers (0.764), accounting information systems (0.688), working relationships (interactions) between internal and external auditors (0.611), independence (0.536), moral courage (0.527), technological capabilities (0.470), professional commitment (0.440), auditor competence (0.366), and internal control (0.359) had the highest coefficients of impact on the effectiveness of internal audit, respectively. The findings further revealed that the effectiveness of internal audit and senior management support have a positive impact on the quality of financial reporting. Additional results indicated that senior management support has a moderating effect on the relationship between internal audit effectiveness and the quality of financial reporting.
Copyright (c) 2025 Mohammad Bagher Hatami, Rahman Saedi, Mohsen Rahimi Dasjerdi, Hamzeh Mohammadi Khoshouei (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing a Green Human Resource Management Policy-Making Model in Municipalities of the Country
The purpose of this study was to design a model for green human resource management (GHRM) policy-making in the country’s municipalities, using a mixed-method approach. The statistical population in the design phase included faculty members specializing in public administration and urban planning in higher education institutions, mayors and senior managers of the country’s municipalities, as well as experts and specialists in the research topic. In the validation phase, professors in the fields of public administration and urban planning at higher education institutions in Mashhad were included, and in the quantitative phase, all senior and middle managers from 13 municipal districts of Mashhad and 22 affiliated municipal organizations totaling 354 individuals participated. In the qualitative phase, 19 experts were selected through snowball sampling. In the validation phase, 17 experts were chosen using purposive sampling, and in the quantitative phase, 185 participants were selected by cluster random sampling based on Cochran’s formula. For data analysis, the qualitative phase employed the grounded theory method following Corbin and Strauss (1998), using open, axial, and selective coding with semi-structured interviews as the data collection tool. In the validation phase, the Delphi method was applied in three stages using an expert evaluation checklist analyzed in SPSS. In the quantitative phase, structural equation modeling was conducted using a 118-item questionnaire in SmartPLS software. To ensure validity and reliability in the qualitative section, necessary checks such as credibility (expert review), confirmability (expert audit), and consensus methods were applied. During the validation stage, the content of the expert evaluation checklist was confirmed for clarity and comprehensibility by several academic and organizational experts, and its reliability was established through the test–retest method with a coefficient of 0.88. In the quantitative section, questionnaire validity was confirmed through face, content, and construct validity (with convergent validity ranging from 0.557 to 0.736 and discriminant validity greater than inter-construct correlations). Reliability was verified using factor loadings of items, Cronbach’s alpha coefficients for components (ranging from 0.734 to 0.835), and composite reliability (ranging from 0.834 to 0.893). According to the findings from the qualitative and validation phases, the final model comprised 11 main categories and 27 subcategories, as follows: causal conditions (1. value-orientation weakness, 2. policy vacuum, 3. managerial unawareness, 4. cultural resistance, 5. lack of infrastructure); contextual conditions (1. organizational structure, 2. institutional capacity, 3. municipal economic situation, 4. organizational culture, 5. influence of stakeholder groups); core category (central phenomenon) (1. the process of designing green HRM policy, 2. mechanisms for implementing green HRM policy); intervening conditions (1. top management support, 2. information technology infrastructure, 3. legal and regulatory support, 4. acceptance of organizational changes, 5. HR experts’ competencies); strategies (1. green training and empowerment, 2. green recruitment and selection, 3. green performance evaluation, 4. green rewarding and motivation, 5. employee green commitment and participation); and consequences (1. effectiveness of green policy, 2. urban environmental sustainability, 3. employee organizational commitment, 4. municipal reputation and public image, 5. organizational innovation in HRM), with 118 indicators. The results of the quantitative phase confirmed that the dimensions and components of the research model were validated in the real statistical population (Mashhad Municipality).
Copyright (c) 2025 Hossein Hesami, Changiz Mohammadizadeh, Mahsa Gholamhosseinzadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing a Blockchain Technology Model in the Sustainable Supply Chain Finance of the Automotive Parts Manufacturing Industry (Quantitative and Qualitative Approach)
This study developed a blockchain technology model for sustainable supply chain finance in the automotive parts manufacturing industry, using a mixed-methods approach. The qualitative phase, involving 10 experts, utilized grounded theory to identify causal, contextual, and intervening factors. The quantitative phase involved 11 experts for initial model design and 214 managers for evaluation. Fuzzy Delphi analysis identified 13 key components for the financial sustainability of automotive parts supply chains. Interpretive Structural Modeling (ISM) revealed independent dimensions like technology, infrastructure, standardization, policymaking, and legal/regulatory frameworks. Dependent dimensions included financial system development, sustainable goals, and supply chain integration. Interconnected dimensions were financial flexibility, a centralized (blockchain-centric) business model, and blockchain information management. The designed model was tested using Structural Equation Modeling (SEM), demonstrating strong fit and performance. SEM confirmed positive relationships among the legal/regulatory framework, standardization, blockchain information management, and the centralized business model. Organizational policymaking positively influenced technology, infrastructure, and blockchain information management. Furthermore, the centralized business model significantly impacted supply chain integration and sustainable financial flexibility. The study concludes that novel blockchain-centric business models, robust integration, and financial flexibility are crucial within sustainable supply chains for enhancing financial performance, mitigating risks, and achieving broader sustainability objectives.
Copyright (c) 2025 Majid Zamani Khormandichali (Author); Mahmoud Modiri; Kiamars Fathi Hafshejani , Norouz Nourollah Zadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Application of Grounded Theory for Developing an Investment Strategy in the Upstream Oil Industry under Sanctions
Part of the development of the oil industry requires technology, while another part depends on financing. The intensification of sanctions and international restrictions has had a significant impact on the process of selecting and proposing financing mechanisms. The purpose of this study is to develop an investment strategy in the upstream oil industry aligned with the approach of sustainable (conservation-oriented) production under sanction conditions. The research method is qualitative–quantitative. For data collection in the qualitative section, semi-structured interviews were used. The interviewees were experts employed in one of the active companies in the national oil industry, selected through purposive sampling. Among 14 interviewees, theoretical saturation was achieved with the tenth participant. Data analysis was conducted using grounded theory and, based on Strauss and Corbin’s approach, the final model was developed. In this process, 26 concepts were identified from verbal statements, categorized into 5 themes. For the quantitative validation of the model, structural equation modeling (SEM) was employed. First, questionnaires were designed, and their validity and reliability were examined. Construct, content, convergent, and discriminant validity were confirmed, while reliability was assessed using Cronbach’s alpha and composite reliability, all of which were verified. The questionnaires were then distributed among the statistical sample. The sample consisted of 135 managers and experts from the studied company. Based on the questionnaire data analysis, and given the non-normal distribution of data, the Partial Least Squares (PLS) method was applied using Smart PLS version 2 software. The research model was tested and confirmed. According to these analyses, the impact of causal conditions on the main phenomenon was 0.904, the impact of the main phenomenon on strategy was 0.579, the impact of contextual conditions on strategy was 0.756, the impact of intervening conditions on strategy was 0.628, and the impact of strategy on outcomes was 0.913.
Copyright (c) 2025 Iman Mohammadi (Author); Hossein Adab; Jalal Haghighat Monfared (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing an Interpretive Structural Model of Organizational Generosity in Governmental Organizations
Organizational generosity, as one of the moral virtues and fundamental values in workplace environments, plays a pivotal role in improving human relations, enhancing social capital, and increasing organizational efficiency. In governmental organizations, which due to the nature of their public services require greater trust, transparency, and accountability, attention to the concept of organizational generosity is of particular importance. The present study aimed at designing a model of organizational generosity in governmental organizations based on interpretive structural modeling. This research, in terms of purpose, is exploratory, and in terms of approach, qualitative. In this study, 15 experts were interviewed. A snowball sampling method was used to collect information, and then, through thematic analysis, the data extracted from texts and interviews were categorized and analyzed. Considering the intended objectives, semi-structured interviews and literature reviews were applied based on a thematic analysis strategy for data collection. Ultimately, the findings were classified into one overarching theme, five organizing themes, twelve basic themes, and fifty-five initial codes. The organizing themes included cultural-leadership factors, organizational credibility factors, political-economic factors, legal-social factors, and participatory-competitive educational factors, which together formed the final model using the interpretive structural modeling method. It can be concluded that the proposed model can serve as a strategic framework for managers and policymakers to foster a culture of generosity, improve human interactions, and enhance efficiency in governmental organizations.
Copyright (c) 2025 Seyed Mohammad Shamszadeh Alavi, Davood Kia Kojouri, Mehran Mokhtari Bayekolaei , Mahnaz Rahmati Kazaz (Author) https://creativecommons.org/licenses/by-nc/4.0
An Optimal Model for Managing Assets, Liabilities, and Equity in Commercial Banks under the Supervisory Regulations of the Central Bank of Iran
This study aimed to develop an optimal model for managing assets, liabilities, and equity in Iranian commercial banks in compliance with Central Bank supervisory regulations. This applied research used audited financial statements of ten listed commercial banks (Mellat, Tejarat, Saderat, Parsian, Pasargad, Eghtesad Novin, Sina, Dey, Karafarin, and Middle East) during 2019–2023. Data were collected from CODAL, the Central Bank of Iran, and related financial databases. The methodology integrated the Best-Worst Method (BWM) for weighting decision variables and a fuzzy goal programming approach to manage uncertainty and set realistic target ranges. Six key decision criteria were evaluated: return on assets (ROA), return on equity (ROE), liquidity risk ratio (LRR), capital adequacy ratio (CAR), non-performing assets (NPA), and market share of deposits and credits (MSDL). The findings revealed that “capital adequacy ratio” ranked as the most critical criterion (average weight 0.4699), followed by liquidity risk ratio and reduction of non-performing assets. In contrast, market share of deposits and credits had the lowest priority. Results of the optimization model indicated that all banks achieved full compliance with Central Bank requirements after modest adjustments. The most recurrent deviations were observed in return on equity, which consistently required reduction across banks, averaging 1.1 percentage points below optimal values. Liquidity ratios and capital adequacy remained within acceptable ranges across institutions, while non-performing assets showed only minor deviations. Banks such as Mellat and Sina required minimal reforms, whereas Parsian, Middle East, and Eghtesad Novin demanded more extensive adjustments to balance their financial structures. The proposed fuzzy goal programming model provides a robust framework for balancing profitability, risk, and compliance.
Copyright (c) 2025 Ebrahim Azarkamand (Author); Seyed Rasoul Hosseini; Vahid Mahmoudi, Habib Amirbeiki Langroodi (Author) https://creativecommons.org/licenses/by-nc/4.0
Presenting a Smart Manufacturing Model Using Decision Tree (Case Study: Mineral Processing Industry)
This study was conducted with the aim of presenting and evaluating a smart manufacturing model using a decision tree (case study: the mineral processing industry). Given the necessity of responding to environmental and competitive pressures, the integration of modern technologies—including the Industrial Internet of Things (IIoT), automation, and data mining—within the framework of smart manufacturing was considered. The required data were collected through questionnaires and extraction of operational information from mineral processing units. After initial data cleansing, seven key variables—including energy consumption, pollution level, technological status of production lines, presence of an Information Technology (IT) system, green planning, technical readiness, and production efficiency—were entered into the model. Modeling was performed using the Classification and Regression Tree (CART) method, and the factors affecting unit productivity were analyzed. The results showed that the technological status of production lines and the technical readiness of employees had the greatest weight in determining production efficiency, while green planning and the use of IT systems played complementary and influential roles in enhancing production and reducing pollution. It was also found that implementing green policies and improving technical knowledge levels can enhance process efficiency even in units with outdated technology. Comparing the decision tree model with other machine learning methods demonstrated its superior interpretability and suitable accuracy for industrial applications. Overall, the findings indicated that the use of data-driven smart models based on decision trees can provide an effective decision-support tool for improving productivity, reducing energy consumption, and achieving sustainability goals in the mineral processing industry. It is recommended that strategies for technology upgrading, continuous technical training of employees, and expanding green management be prioritized in future policymaking.
Smart manufacturing, Decision tree, Machine Learning, Mineral processing industry
Copyright (c) 2025 Alireza Ahrari (Author); Nasser Mikaeilvand; Peyman Hajizade (Author) https://creativecommons.org/licenses/by-nc/4.0
Investigating the Opportunities and Threats of the Iranian Startup Ecosystem
This study aimed to qualitatively explore and analyze the main opportunities and threats shaping the development of the startup ecosystem in Iran, with the goal of informing strategies to strengthen its entrepreneurial infrastructure, policies, and cultural foundations. This research employed a qualitative design using library-based data sources, including academic articles, policy documents, institutional reports, and national statistics related to the Iranian startup ecosystem. A purposive sampling strategy guided the selection of credible and thematically relevant documents, and data collection continued until theoretical saturation was achieved. All textual data were imported into NVivo 14 for systematic analysis through a three-stage coding process—open, axial, and selective coding—based on thematic content analysis. Emerging codes were constantly compared to ensure consistency and reliability in theme development. The analysis revealed two overarching categories—opportunities and threats—comprising multiple subthemes. Opportunities included the expansion of digital infrastructure, the availability of youthful human capital, the rise of institutional support structures, growing domestic market potential, increasing cultural acceptance of entrepreneurship, emerging access to risk capital, and policy-level encouragement. Conversely, key threats involved financial and investment barriers, legal and regulatory uncertainty, weak technical infrastructure, human resource constraints, cultural and social risk aversion, limited access to international markets, and fragmented policy governance. These findings highlight a dual reality in which promising enablers coexist with structural constraints that impede ecosystem maturation. The Iranian startup ecosystem is in a transitional stage, endowed with significant assets yet constrained by systemic weaknesses. Strategic interventions addressing funding gaps, regulatory reform, infrastructure upgrading, talent development, and cultural change are essential to transform Iran’s entrepreneurial potential into a sustainable engine for innovation-driven economic growth.
Copyright (c) 2025 Payam Khodagholi (Author); Soheila Sardar; Seyed Abdollah Amin Mousavi, Nazi Mohamadzadeh Asl (Author) https://creativecommons.org/licenses/by-nc/4.0
Presenting a Model for Effective Internal Audit and its Impact on Good Governance in Iran's Public Sector Using Grounded Theory
This research aims to present an effective internal audit model and its impact on good governance in Iran's public sector. The present study is an applied research applying mixed (qualitative and quantitative) methods. The statistical population of the qualitative section included all Iranian experts, academic professors, managers, and audit experts. The data were collected through semi-structured interviews and analyzed using the Strauss and Corbin coding method. In the second stage, regarding the assumed standard sample, the research sample was determined by applying the Cochran formula, and 183 experts and connoisseurs were selected through the Convenience judgmental Sampling method. The results revealed that the experts agreed upon key dimensions and components for auditing activity using the data-based method, which included 104 indicators, 14 components, and 5 dimensions. According to the results, internal auditing plays a significant role in increasing the internal control system, improving the risk management process, and the regulatory agencies and people's satisfaction, and promoting the executive managers' and employees' accountability against the public. Internal auditors can play a prominent role in establishing and promoting good governance indicators in the public sector, but implementing this requires reforms in the current internal audit oversight system.
Copyright (c) 2025 Ali Nikpour, Mahmoud Lari Dasht Biyaz, Habibollah Nakhaei (Author) https://creativecommons.org/licenses/by-nc/4.0
Firefly Optimization Algorithm for Multi-Objective Job Scheduling in Cloud Computing
Due to the increasing use of the Internet of Things, efficient task scheduling in cloud computing has become increasingly important with the aim of maximizing the use of available resources, reducing energy consumption, and enhancing the quality of service (QoS). In this paper, we use the Firefly Optimization (FFO) algorithm to improve scheduling efficiency and minimize the overall completion time in cloud environments. For this purpose, twelve distinct scenarios were designed in the Cooja Contiki simulator environment with the perspective of computationally intensive, input/output intensive, and mixed workloads, and the overall completion time results obtained with the Min-Min and GA-PSO-Min methods were compared and the better performance of the method was confirmed.
Scalability and Multi-Objective Optimization
Copyright (c) 2025 Vahid Mokhtari (Author); Nasser Mikaeilvand; Abbas Mirzaei, Babak Nouri-Moghaddam, Sajjad Jahanbakhsh Gudakahriz (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing a Knowledge-Based Business Model in a VUCA environment Using Artificial Intelligence and the Grounded Theory Approach
This study aimed to develop a conceptual model for designing knowledge-based business models in VUCA environments through the integration of artificial intelligence, using a grounded theory methodology. This qualitative study employed the grounded theory approach to construct a model for knowledge-based business operations in VUCA environments. Data were collected through in-depth interviews with 25 experts (18 male and 7 female), including both academic and industry specialists. Participants ranged from under 35 to over 45 years old, and possessed master’s (n = 11) or doctoral degrees (n = 14) with 10–20 years (n = 13) or over 20 years (n = 12) of work experience. Data analysis followed the systematic coding framework of Anselm Strauss and Juliet Corbin (1998) using ATLAS.ti software, progressing through open, axial, and selective coding stages. The results revealed six main categories: causal conditions (advanced data analytics, rapid competitive innovation, AI growth, advanced knowledge management, short technology lifecycles, customization demand); contextual conditions (technological, legal–ethical, risk-taking, cybersecurity, collaborative innovation infrastructures); intervening conditions (unstable economy, shortage of skilled workforce, weak digital maturity, lack of policymaker support, absence of a learning organization, lack of an innovative culture); strategies (AI effectiveness, knowledge dynamism, customer-centric innovation, continuous organizational learning, decision-making optimization); and consequences (sustainable competitive advantage, intelligent organizational agility, knowledge-based value creation, decision-making risk management, effective customer management, digital organizational resilience). These components were structured into a theoretical model that illustrates the process of embedding AI into knowledge-based business design for turbulent environments. Integrating AI into knowledge-based business models enables organizations to enhance strategic agility, operational efficiency, and resilience in VUCA contexts. The grounded theory model offers a comprehensive framework for aligning causal drivers, contextual enablers, and strategic mechanisms to achieve sustainable competitive outcomes.
Copyright (c) 2024 Majid Robat Sarpoosh (Author); Seyed Abbas Heydari; Majid Fattahi (Author) https://creativecommons.org/licenses/by-nc/4.0
Forecasting the Air Quality Index Using Machine Learning Models, Bayesian Optimization, and the Development of the S-GBR Model Incorporating Seasonal Variables
Air pollution is considered one of the most serious environmental and public health challenges in urban communities, and accurately forecasting the Air Quality Index (AQI) plays a crucial role in mitigating its negative impacts and supporting data-driven decision-making. Given the complexity and nonlinear nature of factors influencing air quality, the use of machine learning methods has attracted widespread attention in recent years. However, a review of previous studies reveals two major shortcomings: first, many models have been implemented based on default hyperparameter values, which has led to reduced accuracy and generalizability; second, temporal and seasonal components have often been overlooked, even though they play a decisive role in variations in air quality. To address these shortcomings, this study proposes a novel framework called the Seasonal Gradient Boosting Regressor (S-GBR). In this model, the Bayesian optimization search method is used for hyperparameter optimization, and the seasonal feature is incorporated as an input to the Gradient Boosting Regressor algorithm. In addition, baseline models such as Random Forest and XGBoost were also simulated and compared to determine the standing of the proposed model. Empirical findings show that the proposed model achieved a coefficient of determination of 0.9686 and significantly reduced errors, performing almost as well as the most accurate baseline model (Random Forest with 0.9796) while outperforming XGBoost. These results demonstrate that combining Bayesian optimization with the inclusion of seasonal components can raise prediction accuracy to the level of rich and complex datasets, even under limited data conditions. Such an achievement highlights the high potential of the proposed model for use in practical air quality monitoring and management.
Copyright (c) 2025 Mahtab Mahbodi (Author); Babak Karasfi https://creativecommons.org/licenses/by-nc/4.0