New Approaches in Management and Marketing

Abstract The aim of this study is to present a model of factors affecting investors' intention to participate in crowdfunding projects and is mixed in terms of methodology. In the qualitative part, data were collected through interviews with 14 experts and specialists in the field under study and analyzed using the content analysis method and ATLAS.ti software, which resulted in the extraction of 14 main themes and 46 sub-themes. Next, in the quantitative part, by designing a questionnaire and collecting 200 samples of active investors, the proposed model was tested using structural equation modeling using Smart Pls software. The research findings showed that all identified factors, including motivation and desire, trust, risk perception, familiarity and recognition, altruism, financial and information resources available, shared values, network size, innovation and quality of the project, rate of return, geographical location, expertise and experience of the project owners, and product or service characteristics, have a positive and significant effect on investing in crowdfunding. The results also indicate that investor behavior in this area is influenced by a combination of economic, social, and psychological factors and is not limited to financial dimensions. Finally, the conceptual framework presented in this study can be used as a practical basis for improving the performance of crowdfunding platforms, enhancing investors' decision-making processes, and increasing the success of entrepreneurial projects.

Abstract The aim of this research was to identify the dimensions and components of a service delivery model based on Financial Technology (Fintech) with a customer-centric approach in the Iranian banking system. In terms of purpose, this research is applicable-developmental; and in terms of research method, it is qualitative. The data collection tools consisted of two parts: an investigation and review of research literature in the library section, and semi-structured interviews in the field section. The participants in the field section of this study were managers within the banking system. The selection of participants was conducted through purposive sampling. Semi-structured interviews with the participants continued until the stage of theoretical saturation. The coding process and textual analysis of the interviews were performed via MAXQDA 2020 software.
The research findings indicated that the dimensions and components of the presented model include: Technology Infrastructure and Capabilities (digital infrastructure, emerging financial technologies, cybersecurity and privacy, technological integration); Customer Centricity (customer knowledge, customer experience, service personalization, communication and interaction with the customer); Digital Financial Services (diversity of Fintech services and modern tools, digital service quality, service accessibility, innovation in financial services); Banking Structure and Management (digital transformation strategy, organizational innovation culture, social responsibility and banking sustainability); and Institutional and Regulatory Environment (Fintech laws and regulations, oversight and regulation, institutional support).
Introduction
In an environment where customers are becoming increasingly informed and empowered with choice, neglecting their needs is no longer possible (Doumpos et al., 2024). On the other hand, electronic banking and the development of financial technology (Fintech) are among the most critical contemporary topics garnering attention within the banking industry. Electronic banking, and subsequently Fintech, as one of the most vital infrastructures for e-commerce, can further facilitate the growth and development of electronic trade by streamlining payments and the electronic transfer of funds for internet transactions. Fintech, or financial technology, is a term referring to the innovative application of technology in the delivery of financial services. In simpler terms, Fintech encompasses a set of activities in which financial services are provided with greater ease and speed through the aid of technology (Bakhshayesh Ardestani & Radfar, 2025).
As one of the most significant emerging trends in the financial industry, financial technology (Fintech) has successfully transformed the banking structure by providing faster, cheaper, more transparent, and customer-centric services. By leveraging technologies such as Artificial Intelligence (AI), Big Data, Blockchain, Open Banking, and smart applications, Fintech firms have redesigned financial services to match the evolving needs of customers (Karami & Esmaeilpour, 2025). Conversely, to maintain their market position and respond to customer expectations, banks have been forced to adopt new technologies and implement fundamental changes in their business models. Consequently, banks have been at the forefront of technology adoption for the past three decades; as other sectors are influenced by financial technology (Information and Communication Technology), banks—acting as the foundation and prerequisite for development—must synchronize their strategies and policies with these transformations to mitigate upcoming challenges and remain competitive. Undoubtedly, the financial services sector is recognized today as one of the most successful and influential areas of activity globally, and focusing on marketing within financial services will lead to greater productivity and profitability for banks (Li et al., 2023). Therefore, the primary problem of this research is: What are the dimensions and components of the fintech-based service delivery model with a customer-centric approach in the Iranian banking system?
Theoretical Framework
Banking Service Delivery
Modern banks must pay special attention to the quality of their services to remain competitive in today’s turbulent organizational environment. This focus leads to increased customer retention, the acquisition of new customers, and the improvement of financial performance and profitability. In recent decades, intensifying competition among economic enterprises to gain greater market share has driven them toward a more precise and profound understanding of customer needs and desires (Bakhtiari & Yaghoubpour, 2025).
Financial Technology (Fintech)
Financial technology (Fintech) refers to the innovative application of technology in the provision of financial services. Fintech is an industry within the economic landscape that encompasses companies striving to make financial services more efficient through the use of technology. Firms active in the field of financial technology are generally startups that attempt to establish themselves within financial systems and challenge traditional institutions (Rahman et al., 2024).

Customer Centricity
Customer centricity is a state in which a business prioritizes a positive customer experience across all stages of the customer journey. A business is considered customer-centric when it recognizes the needs, wants, and preferences of its valued customers and adapts its products and services accordingly (Gordi Baghcheh mishe et al., 2025).
Zandi et al. (2026) investigated the provision of a Fintech model in platform companies using an interpretive structural modeling (ISM) approach within the Iranian environmental context. The findings from the interpretive structural modeling revealed that the exploratory research model identifies five main components and twenty sub-dimensions across three hierarchical levels. At the first level, the “Fintech Execution and Implementation” component—comprising the dimensions of current state analysis, scenario design and roadmap, architecture and execution, and monitoring and learning—is situated at the top of the ISM graph as the most influenced and dependent component. The second level includes three components: “Fintech Opportunities and Benefits for Platform Companies” (improving user experience, increasing customer acquisition and loyalty, reducing operational and transactional costs, and developing complementary services within the platform ecosystem), “Challenges and Obstacles” (technical, legal/regulatory, and economic barriers, as well as a shortage of specialist labor), and “Infrastructure and Basic Requirements” (IT infrastructure, open and standard APIs, cybersecurity and data management, and supportive laws and regulatory frameworks). These components are influenced by the third-level component and, in turn, impact the execution and implementation phase.
Ally et al. (2025) examined bank efficiency in the digital age, focusing on the role of financial technology in Tanzanian banks. The results of this research indicated that the Fintech index, which measures the development of financial technology in banks, significantly increases efficiency across all banks. It has the greatest impact on large banks due to their advanced financial technology development. However, medium and small banks face challenges in developing financial technology, leading to a negative relationship between the Fintech index and bank efficiency.
Research Methodology
This research is applicable-developmental in terms of its objective, and qualitative in terms of its research methodology. The data collection tools consisted of two parts: a library-based review and exploration of the research literature, and semi-structured interviews in the field section. The participants in the field phase of the study were managers within the banking system. Participant selection was conducted by purposive sampling. The semi-structured interviews with participants continued until the stage of theoretical saturation was reached.
Research Findings
The process of coding and textual analysis of the interviews was conducted by MAXQDA 2020 software. The research findings revealed that the dimensions and components of the proposed model include:

Technology Infrastructure and Capabilities: (Digital infrastructure, emerging financial technologies, cybersecurity and privacy, technology integration).
Customer Centricity: (Customer knowledge, customer experience, service personalization, customer interaction and engagement).
Digital Financial Services: (Diversity of Fintech services and modern tools, digital service quality, service accessibility, innovation in financial services).
Banking Structure and Management: (Digital transformation strategy, organizational innovation culture, social responsibility, and banking sustainability).
Institutional and Regulatory Environment: (Fintech laws and regulations, supervision and regulation, institutional support).

Conclusion
The present study was conducted with the objective of identifying the dimensions and components of a Fintech-based service delivery model with a customer-centric approach in the Iranian banking system. The results of this research are consistent with the findings of Zandi et al. (2026), Ally et al. (2025), Farmahini Farahani et al. (2025), Kaartti et al. (2025), Schreiber (2024), Rizvi et al. (2024), Sadraee et al. (2024), Moradi et al. (2024), and Kumar (2024).
Specifically, Ally et al. (2025) examined bank efficiency in the digital age and the role of financial technology in Tanzanian banks. Their results indicated that the Fintech index, which measures the development of financial technology in banks, significantly increases efficiency across all banks. It has the greatest impact on large banks due to their high level of financial technology development. However, medium and small banks face challenges in developing financial technology, which leads to a negative relationship between the Fintech index and bank efficiency.
Based on the research findings, it is recommended that banks pay special attention to the development and integration of Information Technology infrastructure and the enhancement of cybersecurity. They should utilize customer data analytics to personalize services and improve the customer experience, while expanding diversity and innovation in digital financial services. Furthermore, banks should strengthen digital transformation strategies and a culture of innovation within their organizational structures. Finally, through close cooperation with regulatory bodies, they should assist in creating flexible regulatory frameworks that support the development of Fintech within the country’s banking system.

Abstract The purpose of this study was to explore the dimensions and components of a data governance model for the National Organization for Civil Registration, using a qualitative approach based on experts’ perspectives. In terms of its purpose, the study was applied-developmental, and methodologically, it was a qualitative study based on thematic analysis. Data were collected through document review, library research, and semi-structured interviews with 15 experts and managers from the National Organization for Civil Registration. Participants were selected purposively based on criteria including at least 10 years of managerial or specialized experience and holding a master’s degree or higher. The sampling process continued until theoretical saturation was achieved.The data were analyzed using thematic analysis in MAXQDA 2020, and themes were extracted at three levels: basic, organizing, and global themes. The findings indicated that the data governance model in the National Organization for Civil Registration is based on five main dimensions: data policymaking and stewardship, data quality, data security and privacy, data life-cycle management, and data infrastructure and utilization.These dimensions provide an integrated framework for data management in civil registration organizations and can contribute to improving data quality, enhancing accountability, increasing public trust, and supporting data-driven decision-making in public-sector organizations. The findings provide a foundation for developing data governance policies and strategies in government organizations responsible for managing identity-related data.

Abstract Abstract The aim of the present research is to study the iron ore pricing forecasting using dynamic neural network method and factors’ influence and effectiveness trend. The present study is applicable in terms of its purpose, and survey in terms of data. The statistical population includes daily iron ore stock prices for 2058 working days. Given that severe stock price fluctuations will affect the forecast; the statistical sample used in this study includes daily iron ore stock prices during the period of companies’ entry into the stock exchange from 21/03/2016 to 20/03/2023. Python programming language was used to model the dynamic neural network, and DEMATEL software was used for the influence and effectiveness of factors. The results showed that the dynamic neural network model (LSTM) with its high ability to model the nonlinear effects of macroeconomic variables showed the best performance in predicting iron ore prices. After optimizing the parameters (3 layers and 64 neurons), this model achieved the highest coefficient of determination (R2) of 0.985 and the lowest root mean square error (RMSE) of 0.051. Sensitivity analysis indicated that steel prices were the most important variables for predicting iron ore prices. The results of DEMETL also showed that interest rates were the strongest antecedent (influencing) factor in the economic system, while iron ore production was the strongest a posteriori (influencing) factor. These findings emphasize that iron ore prices are highly dependent on macroeconomic and financial conditions and that the dynamic neural network is a superior tool for predicting them. Introduction Today, the rate of economic growth and development depends on capital accumulation on the one hand, and on the productivity factor in economic activities on the other. These two basic factors depend on the nature of the investment process; therefore, one of the most important tasks of financial markets is to facilitate capital formation. Capital markets can well handle both of the aforementioned tasks of capital accumulation and increasing economic productivity (Farajian & Farajian, 2022). Given the key impact of mineral product prices on calculating the cut-off grade and net present value of mining projects, reliable forecasting of mineral product prices is an important and fundamental issue in economics and the design and planning of open-pit metal mining. Given the high volatility of iron ore prices, its accurate forecasting is one of the critical issues in the design of open-pit mines to increase decision-making certainty (Sadegh Beigi Aliayee et al., 2025). Iron ore is the main raw material for steel production. The iron ore market has always been affected by different and variable conditions. There are many large and small producers and exporters active in this industry (Jan Nesari & Aghajani Bazazi, 2023). Iron is one of the most useful metals in the world. The global price of iron ore is determined by supply and demand. There are several variables, including steel prices, steel production, oil prices, gold prices, interest rates, inflation rates, iron production, and aluminum prices, that affect the global price of iron ore (Mehrdanesh et al., 2021). Iron ore does not have a direct substitute, but steel produced from iron ore has substitutes such as aluminum. On the other hand, any planning for the future requires predicting the future situation. Manufacturing companies need product price forecasts to plan, produce economic analysis of projects, review new investments for development, and so on. Steel production and consumption is today one of the main branches of development of countries and societies. The presence of the steel industry in a region has a significant impact on the process of development of culture, knowledge level, employment generation, research, education and trade of that region. Human daily life is mixed with steel, and steel industries play an important role in the construction, reconstruction and development of the country. Considering that the difference between the consumption and production of crude steel in the country in 2009 was more than 8.9 million tons, investment and growth of the steel industry in the country seems economical and logical; provided that the location of the process, production, supply of natural resources and energy, and project management are selected correctly (Azimi & Afrogh, 2015). Accordingly, the present study seeks to answer the question: how to predict iron ore pricing using the dynamic neural network method and the process of influence and effectiveness of factors? Theoretical Framework Iron Ore Pricing The iron ore industry plays a key and influential role in the growth and development of a country. On the one hand, this industry is a fundamental industry in development, and on the other hand, this industry is considered a benchmark for the industrialization of countries. Therefore, its improvement and development is of particular importance. Basic industries such as transportation, construction, machinery manufacturing, mining and other industries related to the production and transmission of energy are dependent on products produced from iron ore. Therefore, the global demand for iron ore is high and will remain stable in the future, if not increase (Hao et al., 2018). Sadegh Beigi Aliayee et al. (2025) studied the optimization of global iron ore price forecasting using intelligent methods. The main features of the forecasting model were based on the analysis of the correlation coefficients of iron ore prices and the dependent variables of six parameters including price, copper, gold, silver, oil, crude, transportation cost and iron ore demand. They were limited and normalized to improve the performance of intelligent algorithms. Then, a multivariate linear regression model of iron ore price forecasting based on the features was obtained with a coefficient of determination of 0.85. Finally, the frog leap metaheuristic algorithm was used to optimize the model, which led to an increase in the value of R2 and a decrease in RMSE and MSE. Souza et al. (2024) presented a new approach to predicting iron ore prices using weighted fuzzy time series analysis. Given the large number of effective parameters and the complex relationships between them, artificial intelligence-based approaches can be used to predict iron ore prices.  Research Methodology The present study is applicable in terms of purpose, and survey in terms of data. The statistical population includes the daily price of iron ore stocks for 2058 working days. Given that the strong fluctuations in stock prices will affect the forecast; therefore, the statistical sample used in this study includes the daily prices of iron ore stocks in the period of companies entering the stock exchange from 21/03/2016 to 20/03/2023. Research findings For modeling the dynamic neural network, the Python programming language was used, and DEMATEL software was used for the effects and effectiveness of factors. The results showed that the dynamic neural network model (LSTM) with its high ability to model the nonlinear effects of macroeconomic variables showed the best performance in predicting iron ore prices. After optimizing the parameters (3 layers and 64 neurons), this model achieved the highest coefficient of determination (R2) equal to 0.985 and the lowest root mean square error (RMSE) equal to 0.051. Sensitivity analysis indicated that steel prices are the most important variable for predicting iron ore prices. The results of DEMETL also showed that the interest rate is the strongest antecedent factor in the economic system, while iron ore production is the strongest adversarial factor. These findings emphasize that iron ore prices are highly dependent on macroeconomic and financial conditions, and that the dynamic neural network is a superior tool for predicting them. Conclusion The present study aimed to investigate the prediction of iron ore pricing using the dynamic neural network method and the trend of factors’ effectiveness and effectiveness. The results of this study are consistent with the results of Karami Moghaddam & Vishlaghi (2025), Hadian et al. (2025), Isiaka (2025), Bamiri et al. (2025), Safarloo et al. (2024), Suryani & Syamsulbahri (2024), Butson et al. (2023), Bekhit et al. (2023), and Adibzadeh & Roknabadi (2023). Karami Moghaddam & Vishlaghi (2025) showed that a fair payment system in laws, the right to legal promotion and advancement, job security based on the law, determining salaries and benefits based on the approved and unified government table, legal protections in critical situations, and equality and prohibition of legal discrimination were raised as the most important legal factors. According to the results of the study, it is proposed to replace the core of traditional predictive models with deep learning architecture (LSTM), a review of risk management systems, focusing on key nonlinear drivers and implementing metaheuristic optimization methodologies and hybrid models should be implemented.

Abstract Abstract The aim of the present study is to design a sustainable business model based on circular economy in the Iranian oil industry. This study is applicable in terms of purpose, and mixed in terms of implementation (qualitative-quantitative). The research community in the qualitative section includes 12 experts, based on snowball sampling, and the statistical community in the quantitative section includes 220 experts, managers, and specialists active in subsidiaries of the National Iranian Oil Company, universities, and research centers related to the oil industry and the environment. Data collection was carried out using semi-structured interviews and questionnaires. Data analysis was performed using meta-synthesis in the qualitative section and PLS in the quantitative section. The results in the qualitative section showed that 142 basic themes were categorized into 34 organizing themes and 11 overarching themes. The overarching themes are: diversification strategy, smart production and operations, strategic collaboration and partnership, value creation, resource management, risk management and resilience, dynamic capabilities, sustainability culture, innovation and technology, flexibility and organizational, establishment of international standards and requirements. The results in the quantitative section showed that all the identified factors were confirmed and the desirability of the extracted model was also confirmed. According to the research findings, the general management suggestion is that policymakers and oil industry managers establish and institutionalize the principles of the circular economy in all parts of the oil value chain by developing a comprehensive strategic framework. Introduction Sustainability has increased significantly in recent years. To move towards sustainability, industries and companies need to look at everything from a holistic perspective and in order to understand the interplay between economic growth and environmental and social sustainability (Sari et al., 2024). The one-dimensional business approach to developing institutions and business units and increasing profits without considering their surrounding environment has led to negative environmental consequences such as climate pollution, global warming, reduction in animal and plant diversity, and others (Antony et al., 2023). Given the emergence of such issues, many researchers and experts believe that business units must consider measures that take into account not only the interests of shareholders, but also the interests of other stakeholders such as employees, suppliers, customers, government, non-governmental organizations, and even the environment, in order to ensure their survival in society. Such businesses are considered sustainable enterprises that, in addition to maintaining their activities at a profitable level, consider the limitations of the socio-ecological system of which they are a part (KHayatian Yazdi et al., 2016). The circular economy is a two-dimensional approach to the industrial economy that aims to recycle industrial waste to protect the environment and achieve a sustainable economy. The circular economy is more than recycling. The linear economy uses materials and ultimately throws them away. However, the circular economy seeks all the options available in this chain so that the least resources are used initially and these resources and materials have the greatest circulation in the economy, and as a result, the most added value is extracted from them during consumption, and finally the products are revived or recycled (Gomez et al., 2024). Circular business models can be defined as those that make the loops smaller, smaller, slower, denser, and dematerialized (raw materials) in order to minimize the input and output sources of waste and energy leakage. Replacing the current linear economic model with a circular economy model not only saves costs, but also significantly reduces the negative impacts of industries on the natural environment. This is why the circular economy has attracted increasing attention as one of the strongest and most recent steps towards a sustainable economy (Hengyu et al., 2019). Aiming to fill this gap, this research seeks to answer the main question: “How is a sustainable business model based on a circular economy designed in the Iranian oil industry?” Theoretical Framework Business Sustainability Business sustainability refers to the ability of an organization or company to continue its activities in the long term, while maintaining a balance between economic success, environmental protection and promotion of social welfare. In fact, business sustainability means continuing to be financially, environmentally and socially acceptable (Hina et al., 2024). Circular Economy A circular economy is an economic system that aims to minimize waste and maximize resource use. This regenerative approach contrasts with the traditional linear economy approach, in which the production model is “receipt of raw materials, production, and disposal.” In a circular system, input resource consumption, output waste, and energy loss are minimized by closing or shrinking material and energy loops. These goals can be achieved in the long term through design, maintenance, repair, reuse, reproduction, and recycling (Marina et al., 2019). Torabi et al. (2024) conducted a research. The results showed that the meta-synthesis method was obtained in the form of a comprehensive questionnaire, which was approved after screening by the fuzzy Delphi method in two stages, to provide the main research question, which is the circularization of the industrial innovation process in 4 stages and 12 dimensions based on the circular economy as an effective framework for expanding the results in line with the sustainable development goals in this research. The results of this study can be a basis for operationalizing circular economy strategies in the industrial innovation ecosystem and lead to new innovation opportunities in industrial organizations, especially start-ups. Colabi (2020) examined the modeling of factors affecting business model sustainability. It has been stated that the factors affecting business model sustainability are innovation, shared value creation, participatory leadership, economic factors, and corporate governance, respectively. Research Methodology This study is applicable in terms of purpose, and mixed (qualitative-quantitative) in terms of implementation. The research community in the qualitative section includes 12 experts, based on snowball sampling, and the research statistical community in the quantitative section includes 220 experts, managers, and specialists active in subsidiaries of the National Iranian Oil Company, universities, and research centers related to the oil industry and the environment. Data collection was carried out using semi-structured interviews and questionnaires. Research findings Data analysis in the qualitative part is meta-synthesis method, and in the quantitative part is PLS. The results in the qualitative part showed that 142 basic themes were categorized into 34 organizing themes and 11 overarching themes. Overarching themes include: diversification strategy, smart production and operations, strategic collaboration and partnership, value creation, resource management, risk management and resilience, dynamic capabilities, sustainability culture, innovation and technology, flexibility and organizational, establishment of international standards and requirements. The results in the quantitative part showed that all identified factors were confirmed and the desirability of the extracted model was also confirmed. According to the research findings, the general management suggestion is that policymakers and managers of the oil industry establish and institutionalize the principles of circular economy in all parts of the oil value chain by developing a comprehensive strategic framework. Conclusion The present study was conducted with the aim of designing a sustainable business model based on circular economy in the Iranian oil industry. The findings of this research are in line with studies such as Mattera et al. (2021), Leal et al. (2024), Torabi et al. (2024), Colabi (2020), Sharafi et al. (2019), Tunn et al. (2019), Meike et al. (2018), Angus&Nancy (2018), Maghsoudi (2017), Parsapour et al. (2017), KHayatian yazdi et al. (2016), Benzazoua et al. (2015), and Regasa (2015) that emphasize the role of strategic collaboration and sustainable innovation in circular business models. However, while Western studies mainly focus on technical recycling or supply chain optimization in open environments, this research shows that in the context of sanctions and the monopolistic structure of the Iranian oil industry, factors such as energy diversification and ethical value leadership become more important. This shows that the circular economy in sanctioned countries is not only a technical-environmental issue, but also a survival strategy. The presence of “risk management and resilience” as one of the key factors is a reflection of the structural instability of the Iranian business environment. 

Abstract The aim of this research is to identify the factors affecting the energy market ecosystem (Case Study of the Iranian Electricity Market). The research method is applicable-exploratory in terms of its purpose, and qualitative in terms of its implementation method, based on the data-based method. The statistical population of the research includes 22 market management experts, as well as energy industry activists. The sample size was carried out using the purposive sampling method and the snowball method, and the interviews continued until theoretical saturation. Semi-structured interviews were used to collect information. The data-based method and Maxqda software were used to analyze the data. According to the findings, the concepts in the subcategories include: causal conditions (power grid instability, non-integrated governance); background conditions (energy exchange, power plant capacity money (generation) and technology, modularity, shared production and trust, cooperative stability); Intervening conditions (governance organizations/policymakers, production and consumption core, production cycle completers); strategies (decentralization, orchestration, energy pricing, new development investments, diversified financing, foreign energy trade and win-win self-regulation); and consequences (network, ecosystem attractiveness, multi-dimensionality of low-cost and network interactions and multiple satisfaction (governance, business, social) and development synergies and increased regional power).
Introduction
Today, ecosystems are developing and becoming widespread as a new method of value creation with an approach to creating shared and focal value resulting from the performance and cooperation of different institutions (Ghods et al., 2023). Most businesses have paid attention to ecosystems in their macro strategy in two ways: creating their own business ecosystem or connecting to existing ecosystems. In the organizational literature, the term ecosystem was first used in business by James Moore in 1993. He described an ecosystem as “a complex network of interconnected businesses that depend on and feed off each other to deliver value to their customers, end users, and key stakeholders. This concept reflects the emergence of business environments that are characterized by co-evolution and complexity, which is in contrast to the traditional hierarchical organizational forms that have dominated markets and industries for many years. Recently, there have been efforts to systematize the understanding of ecosystems (Jacobides et al., 2018). Jacobides & Lianos (2021) consider ecosystems as communities of cooperating companies that collectively produce a good, service, or solution and co-evolve their products under a common vision that requires the creation of a specific structure of relationships and alignment to create value.” Mitleton-Kelly (2003) consideres that an ecosystem consists of organizations and companies that both affect and are affected by it. Due to the breadth of the meaning, multiple interpretations of this concept have been made according to the need and type of its function and application. Platform ecosystems are one of the definitions of the ecosystem in the literature (the definition intended for this study). In this ecosystem, a company uses a platform to utilize the capabilities of other companies as a complement to create a specific value. In this case, the ecosystem is a model and method of organization (Jacobides et al., 2018).
Electricity, as a key infrastructure, plays a prominent role in economic growth and development. Sustainable electricity supply is a prerequisite for growth (KHosravani et al., 2023). This research aims to identify the dimensions and provide a framework for the B2B platform ecosystem in the electricity market, providing a tool for organizations in this industry to become aware of creating or joining an ecosystem. The results of this research can help increase the literature in this field, as well as fill existing gaps and provide practical solutions in this field. Therefore, the main research question: What are the factors affecting the energy market ecosystem?
Theoretical Framework
Ecosystem
The concept of ecosystem emerged from biological sciences and generally means “an interactive system created between living organisms and the environment in which they live” (Jucevičius & Grumadaitė, 2014). In fact, an ecosystem is considered an environment in which different factors exist and interact with each other (Abdi et al., 2023).
Ghavidast Kouhpayeh et al. (2024) examined the role of blockchain technology in creating trust in customers in the marketing ecosystem. The results of the present study showed that trust resulting from transparency arising from blockchain technology is able to have significant effects on various financial and non-financial outcomes for businesses that use this technology in their marketing ecosystem.
Mir et al. (2024) examined the design of an entrepreneurial ecosystem model in a university with a knowledge-based approach. The results in the qualitative section showed that 67 initial codes, 11 basic themes and 5 constructive themes in the main clusters of infrastructure and support (2 themes), technology and knowledge integration (2 themes), education and culture (3 themes), policy and planning (2 themes) and integrated management (2 themes) were identified and the relationships between them were drawn and presented in the form of a paradigm model. The results of the quantitative section showed that 5 indicators and 11 components with factor loading, average variance extracted and high convergent validity (0.4), Cronbach's alpha coefficient and composite reliability (0.7), and high t-significance coefficients (1.96) were confirmed, and the model has a strong fit.
Research Methodology
The research method is applicable-exploratory in terms of its purpose, and qualitative in terms of its implementation method, based on the data-based method. The statistical population of the research includes 22 market management experts and energy industry activists. The sample size was determined using purposive sampling and snowball method, and the interviews continued until theoretical saturation was achieved. Semi-structured interviews were used to collect information.
Research Findings
The data-driven method and Maxqda software were used to analyze the data. According to the findings, the concepts in the subcategories include: causal conditions (power grid instability, non-integrated governance); contextual conditions (energy exchange, power plant capacity money (generation) and technology, modularity, shared production and trust, participatory stability); intervening conditions (governance organizations/policymakers, production and consumption core, production cycle completers); Strategies (decentralization, orchestration, energy pricing, new development investments, diversified financing, foreign energy trade, and win-win self-regulation) and consequences (network attractiveness, ecosystem attractiveness, multi-dimensionality of low-cost and network interactions, and multiple satisfaction (sovereignty, business, social), and synergistic development flows and increased regional power).
Conclusion
The present study was conducted with the aim of identifying the factors affecting the energy market ecosystem (case study of the Iranian electricity market). The results of this study are in line with the research of Ghavidast Kouhpayeh et al. (2024), Mir et al. (2024), Suuronen et al. (2023), Bonina & Eaton (2023), Wecht et al. (2021), Gawer (2021), Hadad et al. (2021), Hein Andreas et al. (2020), and Aulkemeier et al. (2019). Suuronen et al. (2023) introduced digital business ecosystems as networks of shared value creation and the importance of digitalization in how organizations collaborate and compete in the current era, and identified the opportunities and challenges that these ecosystems create by providing a systematic review of the prerequisites, challenges, and benefits of DBEs.
According to the research results, the following suggestions were made:
Creating ecosystems as new organizational types and alternatives to traditional organizations and management methods requires a change in the thinking, outlook, and performance of influential managers. Based on the research findings, creating ecosystems basically requires creating new paradigms such as decentralization, transferring activities to the private sector, non-interference in buying and selling processes, and eliminating rent and monopoly. At first glance, this seems to be very difficult and complicated, but it is one of the requirements of B2B platform ecosystems and plays an important role in its fundamental formation. The independence of actors must be accepted and the role of guidance should be performed by the government and the government instead of control and policymaking instead of mediation.
Providing new capital attraction platforms as a factor for the continued health of the energy ecosystem should be considered. Investment facilities, the challenge of exchange rate and its fluctuations and its allocation, lack of incentive for private sector investment due to lack of profitability, return on investment, contractual attractiveness, high investment costs of power plants and facilitating laws including the Electricity Industry Barrier Removal Law, new and attractive investment models and attraction of foreign capital are among the important factors on investment in an electricity energy ecosystem that should be given special attention. Governance and the government will play the most important role in this category. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract This study was conducted with the aim of identifying and analyzing the variables affecting the risk management model in the banking industry and examining the role of environmental uncertainty and legal transparency. The research method is applicable in terms of its purpose, quantitative in terms of implementation method, and descriptive-correlational in terms of nature and method. A standard questionnaire based on a 5-point Likert scale was used to collect research data. The content validity of the tool was confirmed by experts and specialists, and Cronbach's alpha and composite reliability were used to measure the reliability of the tool. By distributing the questionnaire, the validity of the tool was measured with three methods: construct validity (external model), convergent validity (AVE), and divergent validity. The AVE value for all variables should be greater than 0.5. SPSS and PLS software were used to analyze the data. The results showed that strong supervisory management as the main driving factor has the greatest impact on other variables, and other dimensions including accountability and responsibility, credit risk monitoring, business environment control, transparency of macro banking facilities, and protection of the bank's image are at a linked level and have a complex interaction with each other. The findings show that these variables play a key role in improving risk management performance and reducing the negative effects of environmental uncertainty and lack of legal transparency.
Introduction
The increasing expansion of business activities has made financial relations, processes, and methods of organizational and people's financial management more complex. Thus, the continuity of the activities of companies and economic enterprises must be sought in having sufficient financial resources (Khanboubi & Boulmakoul, 2020). Without access to financial resources, many activities cannot be implemented and, as a result, the achievement of goals is impossible (Vaziri et al., 2025).
In the meantime, in order to achieve the goals of the program (development), various strategies such as export development policy, import substitution policy, and the like have been considered; but it is obvious that the trend of changes in interest rate indices, exchange rates, and bank facility interest rates show the basis of the economic growth and development of society, and that the goals of the programs and development achievement patterns will be realized in practice according to the predicted plan (Syadali et al, 2023).
The occurrence of financial crises in the late seventies, early eighties, and late nineties created a huge wave of changes in the international arena (shahrzadi et al., 2022). These crises increased the awareness of banks and regulators to monitor more types of risks in financial and banking institutions. With the globalization of the economy and the intensification of competition between banks, the profit margin of traditional banking activities has decreased and increased risk in banks. Risk management, as a factor that plays an effective role in gaining competitive advantage, should be given serious attention in these and other financial and credit institutions (Rezaei et al., 2025). One of the factors that disrupt the risk management process in an organization is environmental uncertainty, which confronts the company with an unpredictable environment characterized by rapid changes in technology, extreme diversity in customer demand, and severe fluctuations (Huynh & Phan, 2024). Unpredictable changes and actions in the environment may change the nature of competition by creating new opportunities or threats for the company (Dai & Zhang, 2023). The most important feature of the current era of environmental uncertainty is complexity, globalization, and increasing competition, which affect the success of any organization. Environmental uncertainty is defined as the rate of variability in the external environment of organizations, which includes major customers, competitors, government regulations, and labor unions (Lu et al., 2023). High environmental uncertainty increases the risk of accurate estimation of future profits by shareholders and makes it a complex issue for them. If management does not take appropriate action to reduce this volatility, the information asymmetry between management and shareholders becomes more acute (Racicot et al., 2023). Environmental uncertainty creates serious limitations for the company and affects the risk control strategy and managers' decisions in the company (Harb et al., 2023). Another effective factor in controlling risk and uncertainty in the environment is the existence of transparency in relevant laws. In this regard, the main research question is posed as follows: What is the comprehensive risk management model considering the role of environmental uncertainty and legal transparency in the banking industry?
Theoretical foundations
Maintaining the health of the economic system and creating strategic opportunities for the banking system
Maintaining the health of the economic system is one of the most important macro-goals of financial and banking policymaking in any country. Banks, as the main pillars of the financial system, play a vital role in economic stability and sustainable development. By utilizing comprehensive risk management and efficient supervisory systems, banks can prevent credit, currency, and liquidity crises and contribute to financial balance in the economy. The “risk-based” approach in banking management not only focuses on reducing threats, but is also effective in identifying and exploiting strategic opportunities. Such opportunities include the development of digital services, the design of new financial products, the expansion of electronic banking, and entry into new investment markets, which, while increasing bank profitability, dynamize the national economy (Asteriou et al., 2021).
Accountability, Responsibility, and Trust in the Banking System
Trust in the banking system is one of the most fundamental components of financial stability and the effective functioning of money markets. When customers and stakeholders believe in the honesty, transparency, and accountability of banks, they are more willing to deposit, use financial services, and cooperate with the banking system. Therefore, accountability and responsibility are essential elements in the formation and maintenance of public trust (Malahim et al., 2023).
Accountability refers to the commitment of managers and employees to explain and defend their decisions, policies, and performance to stakeholders. In the banking system, accountability not only increases the transparency of financial information, but also improves public oversight and reduces the likelihood of opportunistic behavior. The higher the level of accountability in the banking structure, the lower the probability of corruption, inefficiency, and risky decisions, which strengthens institutional trust among customers and investors (natufe et al., 2023).
Research Background
Rezaei et al. (2025) studied “Identifying Uncertainty and Risk and Increasing Flexibility in Capital Budgeting Decisions with an Investment Discretionary Approach”. This research was applicable-developmental in terms of its purpose and qualitative in terms of the nature of the data. The results showed that 6 constitutive themes and 14 basic themes were identified, which include political and international factors, legal and regulatory, financial and budgetary, technology and information, organizational structure and culture, and economic factors.
Huynh & Phan (2024) studied “Bank Uncertainty and Risk in an Emerging Market: The Moderating Role of Business Models”. This study was conducted by analyzing a panel of Vietnamese commercial banks between 2007 and 2019. The results showed that higher levels of banking uncertainty can increase banking risk, but the diversity of bank income has a moderating role and can reduce adverse effects.
Research Method
This study is applicable in terms of purpose and descriptive-correlational in terms of method. The statistical population of this study includes managers and experts of Bank Mellat branches in the southeast of the country, including Kerman, Sistan and Baluchestan, Yazd and Hormozgan provinces, and their total number is estimated to be about 1,125 people. For structural analysis, 400 questionnaires were distributed among the statistical population and 373 valid questionnaires were collected, which were used as the basis for statistical analysis. The findings from the Cronbach's alpha test and composite reliability to measure the reliability of the research instrument are reported in Table 2. To examine the validity of the instrument, content validity (expert opinion poll) was used and its validity was confirmed. Then, by distributing the questionnaire, the validity of the instrument was measured with three methods: construct validity (external model), convergent validity (AVE), and divergent validity. The AVE value for all research variables must be greater than 0.5. In order to test the research hypotheses, structural equation modeling was used in the context of smart pls2 statistical software.
Research findings
Research findings showed that “strong supervisory management” has the greatest impact on other dimensions of banking risk management, including “accountability and responsibility” and “protecting the bank’s image and relationships with stakeholders.” “Accountability and responsibility” plays an important mediating role in transferring the effects of supervisory management to “control of the business environment”, “care of credit risks”, and “building trust in the banking system”. The results of the structural equation model confirmed that the relationships between the dimensions of banking risk management are meaningful and effective. These findings emphasize the importance of establishing strong supervision and legal transparency in reducing the adverse effects of environmental uncertainty and strengthening the stability of the economic system.
Conclusion and Discussion
The results show that strong supervisory management in the banking system plays a key role in promoting the accountability and responsibility of managers and employees. This finding is consistent with the studies of Shabir et al. (2023), which show that the power of the CEO and the board of directors can reduce the adverse effects of economic and geopolitical uncertainty. The results of Huynh & Phan (2024) also indicate that banks with strong managerial supervision are more resilient in the face of environmental fluctuations and have the ability to manage credit risk and more sustainable performance.
The findings also show that organizational accountability and responsibility have a direct impact on controlling the business environment and managing credit risks. Banks that document and clarify their decision-making processes can better manage the business environment and prevent risky decisions from occurring. This is consistent with the studies of Syadali et al. (2023) and Rezaei et al. (2025), which show that accountability and responsibility increase decision-making flexibility and reduce risk in conditions of environmental uncertainty. In addition, Charles et al. (2016) and OECD (2021) have emphasized that transparency and accountability of banks are the basis for building investor confidence and maintaining financial stability.
The results of the study show that protecting the image, reputation, and relationships of the bank with stakeholders has a direct impact on public trust and the health of the economic system. Banks with high and positive reputation are able to direct financial resources to productive sectors and prevent financial crises. The findings of Asteriou et al. (2021) and Bhatt et al. (2023) also confirm that public trust and a positive image of the bank are the key to economic stability and growth.
Controlling the business environment and taking care of credit risks also play a vital role in reducing legal ambiguity and increasing transparency. Banks with a transparent legal environment can make faster and more effective decisions and reduce credit and operational risks. These findings are consistent with the studies of Chen et al. (2023) and Markaz Malmiri et al. (2022), which have introduced the transparency of laws and regulations as the main factor in reducing banking risks. Rezaei et al. (2025) also showed that transparent rules and management structure enhance decision-making flexibility and risk control and empower banks in facing high-risk environments. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract Abstract
The aim of this research is to present a study and analysis of consumer behavior through social media analytical models to improve reverse logistics decisions. The research method is fundamental and applicable research in terms of its purpose, and descriptive-survey in terms of research method. The statistical population of the study is SAMSUNG mobile phone users. To analyze the findings, first programming for social media data through word labeling using LAD, word stemming with Porter STEMMER, data feature identification, and in the case of website and Instagram data; entity identification with NER, stop word removal, latent dirichlet thematic modeling, word classification based on determined keywords, and finally analyzing the sentiments of SAMSUNG mobile phone users with the Naive Bayes algorithm was carried out. The findings showed that sentiment analysis has a positive effect on reverse logistics decisions. By providing real-time insight into customer sentiment, it helps managers and decision makers identify potential issues and facilitate rapid resolution. In addition, sentiment analysis helps predict demand, increase resource allocation, and improve overall operational efficiency. By incorporating sentiment analysis into the reverse logistics decision-making process, organizations can gain a competitive advantage by understanding and responding appropriately to customer sentiment.
Introduction
Anything that allows users to participate, create, and share information from anywhere is a social media (Berthon et al., 2012). Looking at the breadth of research conducted in various sciences, the importance and role of social media in various product-oriented and service-oriented industries cannot be denied (Garant, 2017), because this platform directly and indirectly affects user behavior or the way they make decisions (Schivinski & Dabrowski, 2016). Today, users' interactive behaviors on social media have become an important source and influence on marketing activities in various businesses.
The availability of interactive tools such as mobile phones, tablets and personal computers, along with the wide variety of social media, has made it possible to make user experience and information available to others in the shortest possible time. Companies (or organizations) obtain this information through the most important competitive analysis techniques in social media, including text mining, trend analysis, sentiment analysis, opinion analysis and visualization techniques, which are used to discover hidden concepts and patterns in large volumes of data (Kim et al., 2016). Collecting data on social media and analyzing data to help decision makers address specific problems refers to social media analytics (Lee, 2019).
In the era of information and electronic communications, all mobile phone manufacturing companies are also subject to challenges such as changing customer expectations, expanding competition, technological advances, and extensive economic, social, demographic, cultural, etc. developments, and must choose strategies to continue their lives in turbulent markets. The huge popularity of social media, especially mobile phones, in recent years has attracted more attention from society and, with the large volume of user-generated content, including their opinions, feelings, and experiences, has become one of the most important and up-to-date sources of information for manufacturers, suppliers, consumers, and other active factors in the economy and industry, and has brought new opportunities and challenges to organizations (Kim et al., 2016; Zarco et al., 2019). Social network analysis and big data confront today's digital world with issues that it has not encountered before. The influx of information allows businesses to better understand customer behavior and purchasing patterns. However, the benefits of big data go beyond these issues and can help scientists solve global issues (Talón et al., 2018). With the growing world population and ever-increasing technologies, more electronic devices are being produced than ever before (Balde et al., 2017) and recently, more than 145,000 tons of natural resources - some of which are very scarce - are consumed each year to produce mobile phones (Statista, 2020). Considering the above, the main research question is as follows: How can consumer behavior analysis through social media analytics models improve reverse logistics decisions?
Theoretical Framework
Social Media
Social media are websites and services that shape users' social connections in the form of user-generated content; they are sharing their content in small and large groups (Marwick & Boyd, 2014).
Reverse Logistics
Reverse logistics is the process by which manufacturers receive their previously shipped products from end consumers or recipients for re-sending to another customer, recycling, remanufacturing, repair, or disposal. The principles of reverse logistics will increase customer service and loyalty, restore the value of assets faster, improve customers' mental image of the brand, better communication with shareholders, increase personal motivation, and ultimately achieve the organization's sustainable vision and goals (Eidi & Rezaei, 2017).
Mohades deilami et al. (2021) studied the presentation of an intelligent model for multi-faceted recognition of user personality using deep learning methods. They combined the sand convolutional neural network with the Adabost model for data classification so that they could classify the experimental data that were classified with errors in the second stage of classification with higher accuracy by assigning an alpha coefficient. The proposed model was tested on two datasets, ISIS and YouTube; and based on the results obtained, the proposed model has higher accuracy than other existing methods on both datasets.
Mohammadi & Khalaj (2021) studied the presentation of a model for opinion mining at the document feature level for hotel user reviews. The results showed that the proposed method performed better than the basic methods based on sentiment dictionaries on this dataset, and the accuracy, precision, recall, and F-criterion evaluation criteria using the proposed method were 65, 94, 53, 94, 89, 93, 17, and 95, respectively. The diversity of ways in which customers engage with brands through social networks has become a challenge for brand managers; such as how social networks are used with a strategic approach and how much content should be produced so that company-customer interactions lead to value creation for the organization.Research Methodology
The research method is fundamental and applicable in terms of research method, and descriptive-survey. The statistical population of the study is: SAMSUNG mobile phone users.
Research Findings
To analyze the findings; first, by programming for social media data through word tagging using LAD, word stemming with Porter STEMMER, data feature identification, and in the case of website and Instagram data; entity identification with NER, stop word removal, Latent Dirichlet thematic modeling, word classification based on determined keywords, and finally analyzing the sentiments of SAMSUNG mobile phone users with the Naive Bayes algorithm was carried out. The findings showed that sentiment analysis has a positive effect on reverse logistics decisions. By providing real-time insight into customer sentiments, it helps managers and decision makers identify potential issues and facilitate quick resolution. In addition, sentiment analysis helps to predict demand, increase resource allocation, and improve overall operational efficiency. By incorporating sentiment analysis into the reverse logistics decision-making process, organizations can gain a competitive advantage by understanding and responding appropriately to customer sentiment.
Conclusion
The present study aimed to provide a review and analysis of consumer behavior through social media analytical models to improve reverse logistics decisions. The results of this study are consistent with the results of Shahsavandi et al. (2025), Mashhadizadeh et al. (2024), Ghanbarian et al. (2023), Hahighhinia et al. (2021), Malekakhlagh et al. (2021), Mohades deilami et al. (2021), Mohammadi & Khalaj (2021), Ahmadia et al. (2020), Abdul Waheed et al. (2019), Zhang et al. (2019), and D. Xue et al. (2018). Hahighhinia et al. (2021) showed that a number of units were jointly efficient by implementing the CCR and BCC models at the levels of organizational activity and audience response; but other units were also identified as efficient by implementing the BCC model. Then, using hyperefficiency analysis, the efficient units were ranked. Finally, inefficient units in each of the social networks are identified and appropriate strategies are proposed to improve the performance of the inefficient units. This model will help media managers to identify their inefficiency compared to competitors and become an efficient unit.