Journal of Management and Business Solutions

The present study was conducted with the aim of presenting a conceptual model for sustainability in barter business models, specifically goods and services barter in the consumer-to-consumer (C2C) domain, using a meta-synthesis approach. In terms of purpose, the study is exploratory, and in terms of approach, it is qualitative. The research method employed is meta-synthesis, which is used to integrate, analyze, and interpret the findings of previous studies. The research data were collected through a systematic review of valid scientific articles in the fields of barter and sustainability. Subsequently, using thematic analysis, the sustainability components of barter-based businesses were extracted and categorized. Finally, based on the results, a conceptual model of sustainability for barter-based businesses was developed. The results of data analysis indicate that various elements have been introduced in the scientific literature as sustainability components of barter-based businesses. In the present study, these components were extracted through a systematic review approach by examining scientific studies published between 2010 and 2026. The results show that components such as reducing the need for liquidity, reducing capital pressure, improving non-monetary cash flow, economic resilience under crisis conditions, optimal use of surplus inventory, reducing the risk of inventory accumulation, and reducing operational costs are discussed in the economic domain. In addition, factors such as extending product life, recirculation of goods, reducing waste and losses, reducing the environmental impacts of the supply chain, reducing pollutant emissions, and promoting environmental awareness have been discussed in relation to the environment and environmental issues. The results also indicate that components such as participation and voluntary cooperation, reducing social inequality, equitable access to goods and services, and critical consumer awareness in the institutional domain, as well as components such as the institutional legitimacy of barter-based businesses, social acceptance of barter, an organizational culture supportive of barter, and corporate social responsibility (CSR) in the domain of technology and infrastructure, have been identified in the scientific literature as sustainability components of barter-based businesses.

This study aimed to identify and examine the factors affecting brand image improvement in the automotive industry from the perspective of automobile customers in Tehran. This applied quantitative study was conducted using a descriptive-correlational, cross-sectional survey design. The statistical population consisted of automobile customers, owners, and potential buyers in Tehran who had experience with domestic or foreign automotive brands. The final sample included 384 participants selected through convenience sampling from automobile dealerships, after-sales service centers, automobile exhibitions, and online automotive customer communities. Data were collected using a structured researcher-made questionnaire consisting of demographic items and 48 items measuring product quality, safety and technical performance, design attractiveness, innovation and technology, price fairness, after-sales service quality, customer relationship management, advertising and communication effectiveness, social responsibility, brand trust, perceived value, customer satisfaction, and brand image improvement. The validity of the instrument was confirmed through expert review and pilot testing, and its reliability was supported by Cronbach’s alpha coefficients. Data were analyzed using SPSS and AMOS through descriptive statistics, exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. The inferential findings showed that the proposed measurement and structural models had acceptable fit indices. Exploratory factor analysis extracted 13 factors explaining 68.84% of the total variance. Confirmatory factor analysis confirmed the adequacy of the measurement model, with χ²/df = 1.54, GFI = 0.902, CFI = 0.953, TLI = 0.947, IFI = 0.955, RMSEA = 0.037, and SRMR = 0.041. Structural equation modeling showed that all examined factors had positive and significant effects on brand image improvement. The strongest predictors were brand trust, customer satisfaction, product quality, after-sales service quality, and safety and technical performance. The final model explained 74% of the variance in brand image improvement. The findings indicate that automotive brand image improvement is a multidimensional process shaped by trust, satisfaction, quality, technical reliability, service performance, perceived value, innovation, customer relationships, communication, design, price fairness, and social responsibility.

The first and most fundamental factor in corporate success is having successful managers with distinctive characteristics. Companies with powerful managers are better able to overcome forthcoming crises and make better decisions under risky conditions. When companies have powerful managers, they can better fulfill their responsibilities toward individuals and society. The purpose of this study was to examine the relationship between CEO power and corporate social responsibility, considering the moderating role of political connections. The statistical population of this study included all companies listed on the Tehran Stock Exchange during the period from 2015 to 2024, from which 117 companies were selected as the sample using the systematic elimination method. To analyze the data, multivariate regression using panel data techniques was applied. The results of the study indicate that there is a significant relationship between CEO power and corporate social responsibility. Moreover, political connections can influence the relationship between CEO power and corporate social responsibility.

This paper addresses the critical challenge of manufacturing resource management under inflationary pressures by developing an integrated decision engineering framework. Drawing upon established theories in inventory management, production reliability models, reconfigurable manufacturing systems, and macroeconomics, the proposed hybrid methodological approach combines mathematical optimization, multi-criteria decision-making, system dynamics, and macroeconomic sensitivity analysis within a coherent framework. The model integrates production planning, inventory control, quality management, reliability investments, capacity allocation, and reconfiguration decisions while explicitly considering inflation dynamics through the New Keynesian Phillips Curve framework. The primary contribution lies in bridging the gap between micro-level operational decisions and macro-level inflationary constraints, offering a comprehensive tool for manufacturing decision-makers in unstable economic environments. Numerical analysis using data from the Iranian automotive parts industry demonstrates that the integrated approach achieves 7.2% to 12.1% cost savings compared to sequential decision-making, with reliability and reconfigurability investments gaining significant value as inflation volatility increases. Comprehensive sensitivity analysis with 95% confidence intervals confirms the model's robustness under parameter uncertainty.

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.