Journal of Management and Business Solutions

This study aimed to develop an integrated framework for the valuation, tokenization, and collateralization of industrial real-world assets (RWAs) in inflationary economies, with particular emphasis on enhancing financing capacity, institutional trust, and collateral recognition through blockchain-enabled financial infrastructure. This research employed a conceptual-developmental design and synthesized principles from corporate finance, asset valuation, secured lending, blockchain finance, tokenization, and institutional governance. The proposed framework consists of three interconnected layers: valuation, tokenization, and collateralization. Industrial asset valuation was performed through a hybrid approach combining net asset value (NAV), discounted cash flow (DCF), market-comparable valuation, and useful-life valuation methods. The resulting integrated asset value was transformed into tokenized units through a Special Purpose Vehicle (SPV) structure. Risk-adjusted token pricing incorporated industrial, liquidity, and legal-operational risk factors, while institutional credibility was measured using a Composite Institutional Trust Index (CITI). The framework further included collateral haircuts, maximum advance rates, collateral coverage monitoring, scenario analysis, and sensitivity testing. An illustrative case involving an Iranian manufacturing production line was used to demonstrate practical implementation. The framework demonstrated that industrial assets can be converted into transparent and collateralizable digital claims while maintaining productive operation. Under the illustrative case assumptions, the hybrid valuation model generated an integrated asset value of approximately USD 52.65 million. After applying aggregate risk adjustments, trust calibration, collateral haircuts, and lending constraints, 500,000 pledged tokens supported an estimated financing capacity of approximately USD 2.01 million. Scenario analyses revealed substantial sensitivity to liquidity conditions, risk loadings, governance quality, inflation, and foreign-exchange shocks. Results further indicated that institutional trust enhances collateral acceptance but should remain bounded to prevent governance factors from overshadowing underlying financial fundamentals. The proposed framework offers a comprehensive architecture that integrates valuation discipline, digital tokenization, institutional trust generation, and prudent collateral engineering for productive industrial assets. The model demonstrates how tokenized RWAs can facilitate access to financing in inflationary and financially constrained economies while maintaining conservative risk management standards. The framework provides a practical foundation for future pilot programs, regulatory sandboxes, banking applications, and Islamic finance implementations involving industrial asset tokenization.

Banking fraud is one of the major challenges that can have significant economic consequences for society. The aim of this study was to classify and detect gambling-related activities using banking transaction data. The dataset consisted of 16,764 banking transactions collected between 2023 and 2024, belonging to 1,857 distinct bank card numbers, with the deposit status column considered as the target label. Following data preprocessing procedures, including data cleaning, normalization, categorical-to-numerical conversion, and standard scaling, statistical features were extracted from two primary variables, resulting in the generation of 44 new features. Due to the high dimensionality of the feature space and the limited dataset size, three dimensionality reduction approaches based on statistical feature-selection tests were employed. The dataset was divided into training and testing subsets using an 80:20 ratio. In the baseline model, the K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN) algorithms were evaluated using the original feature set. The proposed model was an ensemble learning framework that combined XGBoost and Random Forest classifiers through a soft-voting mechanism. Model hyperparameters were optimized using a greedy search strategy. The results demonstrated that the statistical test-based feature selection method achieved the best performance among the dimensionality reduction techniques, with an accuracy of 80.38%. Under this feature-selection approach, the proposed ensemble learning model also achieved an accuracy of 80.38%, representing an improvement of more than 6% compared with the best-performing baseline model (XGBoost with an accuracy of 73.92%). Furthermore, deep neural networks exhibited overfitting throughout all experimental stages, indicating that the available data volume was insufficient for deep learning approaches. Overall, targeted feature engineering, statistical test-based dimensionality reduction, and ensemble learning constitute an effective approach for classifying banking transactions when data availability is limited.

Recent developments in digital media indicate that artificial intelligence, as a strategic technology, can play a significant role in enhancing the production, processing, and management of news content. The present study was conducted with the aim of empirically analyzing and validating a management model for the application of artificial intelligence in online news platforms in Khuzestan Province. This research employed an exploratory mixed-methods design. In the qualitative phase, the components of the model were identified through semi-structured interviews with media managers, editors-in-chief, experienced journalists, and technical experts. Qualitative data were analyzed using a grounded theory approach through open, axial, and selective coding. In the quantitative phase, a researcher-developed questionnaire based on the qualitative findings was designed using a Likert scale and distributed among a purposively selected sample of media professionals. The validity of the instrument was assessed through expert judgment and construct validity indices, while its reliability was evaluated using Cronbach’s alpha and composite reliability. The findings revealed that the management of artificial intelligence applications in online media encompasses multiple dimensions, including content production, data analysis, personalization, publication management, and ethical governance. Consequently, the development of a localized and structured model for the responsible utilization of artificial intelligence in online media is essential.

With the excessive expansion of governments, substantial costs have been imposed on the administrative systems and public management structures of various countries. Consequently, government downsizing has emerged as one of the primary policies and strategic programs aimed at reducing the size of government and controlling public expenditures, attracting the attention of policymakers and scholars. Therefore, the present study was conducted with the objective of explaining a downsizing model for state-owned enterprises in Iran. The research adopted a mixed-methods approach and was carried out in two qualitative and quantitative phases. In the qualitative phase, the study population consisted of academic experts and managers of state-owned enterprises who possessed either research or practical experience in the field of state-owned enterprise downsizing. Sampling was conducted using purposive sampling and theoretical saturation. Accordingly, theoretical saturation was achieved after 17 interviews, and data were analyzed using the coding method proposed by King and Horrocks. Furthermore, to validate and assess the fit of the research model, confirmatory factor analysis and structural equation modeling were employed using SPSS version 19 and SmartPLS version 3 software. The quantitative study population consisted of managers, experts, and employees of the Sistan and Baluchestan Regional Electric Company. Based on Cochran’s formula, a sample size of 148 participants was determined. As a result, 24 interpretive codes identified during the thematic analysis phase were classified into six main dimensions within the framework of the proposed research model. In addition, the findings of the quantitative phase supported the study hypotheses, confirmed the adequacy of the proposed model, and demonstrated an acceptable model fit.

In recent years, and with the rapid advancement of technology, an emerging phenomenon called electronic banking has emerged that affects the lives of all people. Along with the many benefits that electronic banking has brought to people, the field of fraud and money laundering has also entered this field, which helps fraud in banking transactions to be carried out very accurately and systematically in the field of electronic banking. What is important is a precise, fast and calculated confrontation with profiteers who move large amounts of money in this way and enter it into their personal accounts with the utmost intelligence.

In this article, while introducing data mining and its techniques, we analyze and discuss the measures taken in the field of fraud detection in various sectors, especially banking. After that, we introduce 3 types of the most widely used data mining algorithms and implement them on bank transactions in the CCFD benchmark dataset. Finally, after examining the performance of the algorithms, the best algorithm in terms of its performance in fraud detection is introduced.

In today’s turbulent environment, organizational sustainability has become a strategic necessity for survival and competitiveness in large-scale and complex industries such as the petrochemical sector. The primary objective of this study was to analyze the causal relationships between sustainable performance management dimensions and the implementation of innovative and economic production methods in Iran’s petrochemical industry under sanctions conditions. In terms of purpose, this research is applied, and in terms of nature, it is a descriptive–analytical study based on a quantitative approach. The statistical population consisted of senior managers, production experts, and specialists in the fields of sustainability and innovation within the country’s petrochemical companies. Using Cochran’s formula, a sample size of 384 participants was determined. Data were collected through a researcher-developed questionnaire and analyzed using Pearson correlation analysis, multiple regression analysis, and Structural Equation Modeling (SEM). Furthermore, to enhance prediction accuracy and prioritize indicators, the metaheuristic algorithms Adaptive Neuro-Fuzzy Inference System (ANFIS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed. The findings revealed that the environmental dimension, sustainable innovation, economic dimension, and sustainable supply chain management exerted the strongest effects on the implementation of innovative production methods, whereas the human rights and diversity and inclusion dimensions did not demonstrate statistically significant effects. The results further indicate that emphasizing resource management, green technologies, and supply chain resilience can facilitate the advancement of resilience and sustainability in Iran’s petrochemical industry under sanctions conditions.

Structural transformations in the knowledge-based economy and the growing role of intangible assets have turned brand valuation into a strategic issue in both marketing and finance. Despite the expansion of international models and standards, the existing literature still suffers from conceptual and methodological gaps between financial and marketing approaches, ambiguity regarding the mechanism through which perceived value is converted into financial value, and the absence of integrated process-oriented frameworks. The present study aimed to develop a comprehensive model for brand valuation based on a meta-synthesis approach. This research was conducted using a qualitative approach grounded in the interpretive paradigm. The Sandelowski–Barroso meta-synthesis method was employed to systematically analyze 32 specialized brand valuation models and 63 معتبر scientific articles published between 2010 and 2025. The coding process included 215 initial codes, which were refined and merged into 66 open codes, followed by the addition of 15 empirical codes and the final extraction of 82 ultimate codes. Subsequently, 16 axial categories and 5 selective themes were identified. The findings indicate that brand value is a multilayered and process-oriented construct that begins with customer perceptions and experiences, is translated into cash flows through market behavior and competitive advantage mechanisms, and is ultimately reflected in the strategic and financial outcomes of the organization. The proposed model consists of five main layers: institutional infrastructures and brand assets, perceived value, market behavior and performance, financial value and cash flows, and strategic outcomes. By presenting a layered and process-oriented model, the present study reduces the gap between financial and marketing approaches and provides an integrated basis for decision-making by Chief Marketing Officers (CMOs) and Chief Financial Officers (CFOs). In addition to its theoretical coherence, the model also demonstrates operational applicability and the potential for empirical testing in quantitative research.

The present study aimed to develop a model of the factors influencing the achievement of competitive advantage for by-products of the sugar industry. The research was conducted using both qualitative and quantitative approaches. In the qualitative phase, a combined content analysis method (inductive–deductive) was employed, while the quantitative phase utilized a causal-correlational design. The statistical population in the qualitative phase consisted of 20 participants, including senior managers and industry researchers, who were selected through purposive non-probability sampling. In the quantitative phase, 151 experts participated through a complete census approach. The findings of the qualitative phase indicated that competitive intelligence, organizational resources, entrepreneurship, governance, and networking play significant roles in achieving competitive advantage for sugar industry by-products. Subsequently, the causal relationships among these factors were analyzed using Interpretive Structural Modeling (ISM). The results revealed that governance serves as the primary driving factor and exerts influence on both networking and entrepreneurship. Networking and entrepreneurship mutually influence one another, and both affect organizational resources. Organizational resources, in turn, act as a driver of competitive intelligence and competitive advantage. These relationships were further examined through Structural Equation Modeling (SEM). The results demonstrated that governance directly and strongly supports networking and entrepreneurship. However, the relationships between networking and entrepreneurship, as well as between networking and organizational resources, were not confirmed. Organizational entrepreneurship was found to have a direct and positive effect on organizational resources. Competitive intelligence was directly and positively influenced by organizational resources. Ultimately, organizational resources were shown to exert a strong and direct positive effect on enhancing competitive advantage. The measurement model was validated through the assessment of convergent validity (AVE), discriminant validity, and reliability indicators, including factor loadings, Cronbach’s alpha, and composite reliability. Furthermore, the adequacy of the structural model was confirmed using the coefficients of determination (R²), predictive relevance (Q²), and goodness-of-fit (GOF) indices. The findings indicate that the role of government in the development of by-products is highly significant and serves as the primary driving force. In addition, within organizations, competitive advantage depends on marketing strategies and competitive dynamics. Organizational resources can support market development by providing high-quality products at lower production costs.