New Approaches in Management and Marketing

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 purpose of this study is to identify the dimensions and components of competitive advantage and innovation within the policy framework for creating and developing digital entrepreneurship in knowledge‑based companies. This research is applicable in terms of purpose, and qualitative in terms of methodology. The statistical population consists of 15 experts, including university faculty members in management and managers of knowledge‑based firms located in Kerman. Considering participants’ diversity (managers, faculty members, and employees), a purposive sampling method was employed, and interviews continued until theoretical saturation was reached. Data were collected through semi‑structured interviews and analyzed using coding and thematic analysis, facilitated by MAXQDA software. The findings reveal that the most significant themes in this domain include research and development, adoption of emerging technologies, creation of digital business models, data‑driven decision‑making, digital networking, cybersecurity, development of e‑commerce, digital management and online human‑resource systems, development of digital markets, digital investment, and attraction of digital financial resources. These results indicate that digital entrepreneurship in knowledge‑based firms encompasses multiple dimensions that can contribute to enhanced performance and competitiveness. Introduction With the expansion of digital technologies and their prominent role in the global economy, digital entrepreneurship has emerged as a key driver of economic and social development. In this context, knowledge‑based businesses—relying on innovation and competitive advantages—possess significant potential for value creation and sustainable development. However, the absence of a comprehensive and effective policy model for establishing and developing digital entrepreneurship in such firms represents a major challenge (Bagaini et al., 2022). As an emerging and rapidly evolving field, digital entrepreneurship requires specialized operational models tailored to the needs and characteristics of knowledge‑based enterprises. Many existing models have been designed in a general form and do not sufficiently address the specific context of digital entrepreneurship or the distinct nature of knowledge‑based firms. This lack of operational models often leads to confusion and difficulties in implementing digital entrepreneurship strategies (Merín‑Rodrigáñez et al., 2024). Innovation and competitive advantage are two critical factors in the success of knowledge‑based companies within the realm of digital entrepreneurship. Innovation is a key driver of growth for firms, enabling them to achieve future successes and providing a mechanism through which businesses can sustain their presence in the global economy (Sung & Kim, 2021). It helps companies secure competitive advantages in uncertain environments, outperform competitors; and ultimately influences long‑term organizational performance. Innovation is also the primary factor contributing to business growth (Eshkor Vakili & Nojabaei, 2022). The innovation process requires effective management and efficient use of resources and technologies. The lack of an effective model for managing and implementing innovation can diminish competitive capability and hinder the realization of a firm’s full potential. Competitive advantage refers to a firm’s ability to, compared to its competitors, deliver greater value to customers. Knowledge‑based businesses, particularly those engaged in technology and innovation, must identify and leverage their competitive advantages. However, many such firms face challenges in recognizing, analyzing, and operationalizing these advantages. These challenges often stem from the absence of an integrated and practical framework for analyzing and exploiting competitive advantages (Hoang & Böckel, 2024). Accordingly, the central question of this study is: How can the dimensions and components of competitive advantage and innovation be identified within the policy framework for creating and developing digital entrepreneurship in knowledge‑based companies? Theoretical Framework Digital Entrepreneurship Digital entrepreneurship refers to the process of creating and developing businesses that primarily focus on digital technologies and technology-based business models. This type of entrepreneurship involves utilizing the internet, digital software, online platforms, and emerging technologies to establish, launch, and grow new ventures (Sharma, 2022). Competitive Advantage Competitive advantage refers to an organization’s ability to create higher value than its competitors and achieve market superiority. This advantage enables organizations to differentiate themselves from rivals by leveraging unique resources and capabilities; thereby gaining a larger market share (Teece, 2020). Mir Jalali et al. (2025) investigated the design of a customer relationship management development model and the role of sustainable competitive advantage in sports clubs in Gilan province. The research findings indicated that 469 indicators could be effective in customer relationship management with an emphasis on sustainable competitive advantage in sports clubs in Gilan province. Subsequently, in the axial coding of indicators, they were categorized into 105 concepts and 41 categories. The data obtained from interviews were analyzed using the grounded theory approach. The results showed that seven main categories—Marketing, Organizational Environment, Infrastructure, Performance, Management and Planning, Service Quality, and Relationship Management—are important for optimizing the customer relationship management system to develop CRM with an emphasis on sustainable competitive advantage. Managers of sports clubs can utilize the identified indicators, concepts, and categories in their future planning for effective customer engagement. Abhkiz et al. (2024) examined the presentation of a competitive advantage model with a pioneering approach in Iran’s aviation industry. The results indicated that having an appropriate network and scope of air routes, flights, and airport services in the country; the possibility (capability) of technological sharing for producing modern aircraft; the technical and technological capabilities of the industry for pioneering; possessing strategic management vision and abilities for the industry to lead; having experience in joint cooperation with international consultants, companies, and governments; the willingness of statesmen and decision-makers in the industry to be pioneers; commitment to strategic plans by industry managers for pioneering; the aviation industry’s learning, adaptation, and future-gazing capabilities for pioneering; possessing skills and expertise among managers and human resources in the industry for pioneering; the number and composition of the air transport fleet; and having cohesive, integrated, and strategic marketing and branding plans in the industry have the most influence among variables on competitive advantage for pioneering. Research Methodology In terms of its objective, the research methodology is applicable; and in terms of execution, it is qualitative. The statistical population of the research includes 15 experts, university professors in management, and managers of knowledge-based companies in Kerman city. For sample selection, considering the diversity of experts (managers, professors, and staff), a purposive sampling method was used, and interviews continued until theoretical saturation was achieved. Semi-structured interviews were used for data collection. Research Findings For data analysis, coding and thematic analysis methods were employed, utilizing the Maxqda software. The results indicated that the most significant themes in this domain include Research and Development (R&D), utilization of new technologies, creation of digital business models, data-driven decision-making, digital networking, cybersecurity, e-commerce development, digital management and online human resources, digital market development, digital investment, and digital fundraising. These findings suggest that digital entrepreneurship in knowledge-based companies encompasses various dimensions that can contribute to improving the performance and competitiveness of these firms. Conclusion The present research was conducted with the aim of identifying the dimensions and components of competitive advantage and innovation in the policy-making for the creation and development of digital entrepreneurship in knowledge-based companies. These findings are consistent with the results of previous studies by Mir Jalali et al. (2025), Abhkiz et al. (2024), Hosseini et al. (2023), Masah Choolabi et al. (2023), Porter & Kramer (2023), Teece (2022), Snihur et al. (2022), Fartash (2022), Nambisan & Baron (2021), and Ciriello et al. (2021). Hosseini et al. (2023) have shown that challenges such as technological limitations and legal institutions, along with digital market opportunities, are considered the main barriers and opportunities for digital entrepreneurship in Iran. Based on the research findings, the following suggestions are provided: Establish digital infrastructures for online sales and customer acquisition through various platforms. These models should be designed to cover the specific needs of particular customers and markets. Create an online system for collecting, evaluating, and implementing suggestions. This system can assist companies in identifying new market needs and improving internal services and processes.

Abstract Abstract
The aim of this study is to present a model for developing social entrepreneurship policies in the Imam Khomeini Relief Committee. The research method is developmental-applicable in terms of its purpose, and qualitative in terms of its implementation method. The statistical population of this study includes 17 university professors, social entrepreneurship experts, and senior managers of the Imam Khomeini Relief Committee (RA), selected through purposive sampling. The data collection tool is a semi-structured interview, and the interviews continued until theoretical saturation. Data analysis was performed through coding using the content analysis method and using NVIVO version 11 software. The research findings showed that the extracted concepts related to the model for developing social entrepreneurship policies in the Imam Khomeini Relief Committee include 94 themes, 30 basic themes, and 10 organizing themes (drawing a vision supporting social entrepreneurship, improving entrepreneurial capacities in the organization, utilizing economic activity, creating an entrepreneurial organizational culture, service-oriented human resources, social responsibility of the relief committee, creating sustainable value in the organization, creating organizational interactions, promoting social justice and reducing poverty, and improving the organization's entrepreneurial performance).
Introduction
Today, a new topic that has been considered in the field of entrepreneurship, along with organizational entrepreneurship, is social entrepreneurship. This type of entrepreneurship expresses the benefit of a social institution with an image of discipline, innovation, and social dependence and is rooted in human interactions and human social relations (Gelard et al., 2022). Social entrepreneurship is recognized as the most important tool for addressing social challenges and whenever the market and the public sector cannot cope with these challenges, social entrepreneurship emerges as an agent of change and a social organization as an organizational entity that considers the separatist sector as a customer, while being guided by the philosophy of harmonizing social and economic value (Choi et al., 2024).
Entrepreneurship is a process that involves recognizing, evaluating and exploiting opportunities to introduce new products, access new markets or raw materials by organizing efforts that did not exist before (Simeone et al., 2018). Social entrepreneurship is proposed as an innovative approach to addressing social needs with an emphasis on problem solving and social innovations that eliminate traditional boundaries between the private, public and non-governmental sectors. Defining social entrepreneurship and its conceptual boundaries is not an easy task; because the concept is inherently complex and the literature in this field is very new. The term, used in both the general and academic literature, covers a wide range of activities and innovations (Seifi et al., 2024).
According to the latest estimates, there were approximately ten million NGOs in the world in 2015. There are also about fourteen thousand NGOs in Iran. Of the total number of NGOs in the country, 5,409 operate under the title of People’s Charity Centers with the license of the Imam Khomeini Relief Committee. These centers consist of a number of local benefactors and trustees managed voluntarily and in a completely popular manner with a board of directors structure and usually in the manner of traditional charities. However, if we define social entrepreneurship as an effective, innovative, sustainable and voluntary response of individuals or social institutions to one or more social needs, then undoubtedly the charity centers of the Imam Khomeini Relief Committee, which are often providing social services in old and traditional ways, are still a long way from reaching the stage of social entrepreneurship. For this reason, one of the solutions to promote social entrepreneurship in the charity centers of the Imam Khomeini Relief Committee is to develop and present a desirable model of social entrepreneurship to transform the management methods of these centers. Therefore, in this research, we seek to answer the question: what is the model for developing social entrepreneurship policies in the Imam Khomeini Relief Committee organization?
Theoretical Framework
Social Entrepreneurship
"Social entrepreneurship is the activities and processes committed to discovering, defining, and exploiting opportunities to enhance social values, which are carried out through new actions and the management of existing organizations in an innovative manner" (Shahvand et al., 2022).
Bazoukar & Bagheri (2025) examined the presentation of the Islamic jurisprudence support model of social and economic entrepreneurship. The findings were extracted based on the research approach of 6 aspects, 26 components, and 76 codes. Managers and entrepreneurs should adopt and implement effective principles and strategies to achieve positive outcomes. The results showed that the rights support model of social and economic entrepreneurship was presented in the form of 26 components. Doozandeh Ziabari et al. (2025) examined the presentation of a social entrepreneurship model in the development of rural tourism in the tourism target villages of Gilan province. The results showed that traditional tourism entrepreneurship follows a capitalist approach, is highly profit-oriented, and ignores the social aspects of doing business, which may in turn cause further harm to already disadvantaged communities. Social entrepreneurship in tourism, referred to here as tourism social entrepreneurship, is positioned as a market-oriented approach to addressing various social problems through tourism entrepreneurship.
Research Methodology
The research method is developmental-applicable in terms of its purpose, and qualitative in terms of its implementation method. The statistical population of this study includes 17 university professors, social entrepreneurship experts, and senior managers of the Imam Khomeini Relief Committee, selected through purposive sampling. The data collection tool is a semi-structured interview, and the interviews continued until theoretical saturation was achieved.
Research Findings
Data analysis was carried out through coding using the content analysis method and using NVIVO version 11 software. The research findings showed that the extracted concepts related to the model for developing social entrepreneurship policies in the Imam Khomeini Relief Committee included 94 themes, 30 basic themes, and 10 organizing themes (drawing a vision supporting social entrepreneurship, improving entrepreneurial capacities in the organization, utilizing economic activity, creating an entrepreneurial organizational culture, service-oriented human resources, social responsibility of the relief committee, creating sustainable value in the organization, creating organizational interactions, promoting social justice and reducing poverty, and improving the organization's entrepreneurial performance).
Conclusion
The present study was conducted with the aim of presenting a model for developing social entrepreneurship policies in the Imam Khomeini Relief Committee. The results of this study are consistent with the studies of Bazoukar & Bagheri (2025), Doozandeh Ziabari et al. (2025), Blanco-Jiménez et al. (2024), Abhari & Hashempour (2024), Viswanath et al. (2024), Azizi & Mohammadi (2024), Chichevaliev et al. (2023), Cortes & Lee (2022), Nourpour et al. (2023), and Gelard et al. (2022). Abhari & Hashempour (2024) showed that there is a direct and significant relationship between social entrepreneurship and its various dimensions and components based on the multidimensional model of social entrepreneurship with environmental components that affect this concept; accordingly, the environmental component corresponding to the "risk-taking" element of "innovative behavior"; the concept of "creative and innovative spaces" and the pillar of "power of wisdom"; the concept of "coherence and integrity" and the pillar of "entrepreneurial virtue"; the concept of "moral values"; and finally the pillar of "identifying social opportunities" which is one of the pillars of social entrepreneurship is the concept of "common norms".
According to the results of the research, the following suggestions were made:
Participation in social projects Cooperation with non-profit organizations and charities to improve social conditions.
Creating awareness campaigns and launching educational campaigns to make people aware of social and environmental issues.

Abstract 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.

Abstract Abstract The purpose of the present study is to interpretive-structural modeling of uncertainty and risk factors and increasing flexibility in capital budgeting decisions with an investment discretionary approach. The method of this research is applicable in terms of purpose, and qualitative in terms of method type. The statistical population of this study included 18 power plant experts from Mejmar who had at least 10 years of teaching, research, and management experience in power plants. Purposive sampling was used in this study. The data collection method was referring to documents and semi-structured interviews. The fuzzy Delphi method was used to analyze the findings, and the MICMAC software was used for influence and impact. The results showed that the variables of technology and information factors, organizational structure and culture, and economic factors had high influence and had low impact and were in the area of ​​independent variables. Political and international factors, as well as legal and regulatory factors, have similar influence and degree of dependence, so it is a linked variable. Financial and budgetary factors also have high dependence but low influence, so they are considered dependent variables. No variable is in the first quadrant, i.e. the autonomous area. Introduction Since the late 1970s, along with the spread of advanced financial techniques such as internal rate of return and net present value at the company level, many researchers have offered criticisms regarding the use of these techniques in valuing strategic investment decisions (Dai et al., 2021). The first criticism is related to the inability of these techniques to correctly value uncertain investments, in situations where the company has a degree of flexibility in decision-making (Alipour & Behdadian, 2020). In particular, early proponents of the discretionary theory of capital assets criticized the NPV method for considering decisions purely as if-or-not, without considering the value of flexibility (choosing between different options when receiving new information). By applying the theoretical framework presented for pricing contingent claims, financial researchers have proposed the capital asset investment option theory as an alternative to the net present value method to overcome its shortcomings. Since the aforementioned conditions are often related to the structure of strategic decisions, the capital asset investment option theory claims to be the best method for valuing such decision-making issues (Cheong, 2021). Strategic cost management attempts to bridge the gap between financial techniques and strategy by introducing strategic insights into the evaluation of investment decisions; because strategic cost management as a theoretical framework is able to consider the aspects of strategic positioning, value chain, and cost accounting and management. Although the theory of investment discretion in capital assets and risk management belong to two distinct scientific worlds (i.e., financial management and cost accounting), it seems that they both address the same problem, which is how to improve the effectiveness of the techniques used in making strategic investment decisions. It is worth noting that the theory of investment discretion in capital assets and strategic risk management differ in the way they approach this problem. At first glance, it seems that the discretionary theory of capital assets focuses more on the adequacy of the accounting algorithm with respect to the intra-organizational structure of the decision (i.e., uncertainty, risk, and flexibility), while strategic risk management places more emphasis on the relationship of the decision with the company's strategy. Therefore, it would be useful to compare these two methods in order to identify commonalities and the possibility of integrating them into a single framework (Deng & Lin, 2021). Therefore, this research seeks to answer the question: what is the interpretative-structural modeling of uncertainty and risk factors and increasing flexibility in capital budgeting decisions with the discretionary approach?  Theoretical Framework Investment Options Investment options imply that in many project evaluation scenarios, firms will have one or more options to make strategic changes to the project during its life. This strategic option, known as investment options, is generally ignored in standard discounted cash flow analysis techniques. However, these options can increase the value of a project by eliminating undesirable returns and creating new opportunities in the future (Makarem et al., 2023).  Environmental Uncertainty Environmental uncertainty is a type of inability to predict the possible outcomes of a decision. Chandler states that environmental uncertainty is a condition under which organizations adjust their framework and is caused by environmental factors of the organization that relate to the degree of change that characterizes the environmental activities related to the organization's operations, including the unpredictability of the activities of customers, suppliers, competitors, and legal entities (Hamidian et al., 2023). Capital Budgeting Capital budgeting is the process of identifying, evaluating, planning, and financially supporting major investment projects in organizational units. Decisions made in connection with capital budgeting largely affect the success of the organization in achieving its set goals. Therefore, capital budgeting plays a major role in the long-term success of these units (Rezaei et al., 2025). Rezaei et al. (2025) examined a model for identifying uncertainty and risk and increasing flexibility in capital budgeting decisions with an investment discretion approach. Based on the results obtained, 6 constitutive themes and 14 basic themes were identified. The 6 constitutive themes are political and international factors, legal and regulatory factors, financial and budgetary factors, technology and information factors, organizational structure and culture, and economic factors. The dimensions of the organizational structure and culture factors are: structure and organization, human resources, and management. The dimensions of technology and information factors are: information technology environment and information sharing. The dimensions of economic factors include: economic structure and economic environment. The dimensions of political and international factors include: political structure and environment and transnational and international factors. The dimensions of legal and regulatory factors include: general legal factors and specific legal factors. The dimensions of financial and budgetary factors include: human factors and individual capabilities, intra-organizational requirements and factors, and exogenous factors and requirements. Hirota & Yunoue (2022) examined fictitious financial laws and accounting: Evidence from capital budgeting in Japan. The results showed that municipalities participate in stock flow adjustments by increasing their expenditures and revenues through intergovernmental transfers, which indicates creative accounting because it allows municipalities to delay the improvement of their financial conditions. To evaluate a new financial law, we should examine not only targeted indicators, but also non-targeted ones that municipalities have no incentive to control. Research Methodology The method of this research is applicable in terms of purpose, and qualitative in terms of method type. The statistical population of this research includes 18 power plant experts from Mejmar who have at least 10 years of teaching, research and management experience in power plants. In this research, purposive sampling method was used. The data collection method is referring to documents and semi-structured interviews. Research findings The fuzzy Delphi method was used to analyze the findings, and MICMAC software was used for influence and impact. The results showed that the variables of technology and information factors, organizational structure and culture, and economic factors have high influence and low influence and are in the area of ​​independent variables. Political and international factors, and legal and regulatory factors have similar influence and level of dependence, so they are linked variables. The variables of financial and budgetary factors also have high dependence but low influence, so they are considered dependent variables. No variable is in the first quadrant, i.e. the autonomous area. Conclusion The present study aimed to interpretative-structural modeling of uncertainty and risk factors and increase flexibility in capital budgeting decisions with an investment discretionary approach. The results of this study are consistent with the results of Rezaei et al. (2025), Hirota & Yunoue (2022), Frost & Rooney (2021), Dai et al. (2021), Zhang (2021), Cheong (2021), Vatanparast & Maleki (2020), Khoshkar Hassankiadeh et al. (2020), and Mashhadizadeh et al. (2020). Mashhadizadeh et al. (2020) showed that the economic evaluation of investment projects, given the uncertainty prevailing in their financial conditions, requires a new thinking called investment discretion theory to cover the shortcomings of traditional capital budgeting methods, including being static and not considering uncertainty. The results indicate the impact of uncertainty factors on the investment environment and the existence of a significant relationship between the use of investment flexibility and discretion with increasing the value of investment in this power plant. In addition, the experts positively evaluated the effect of using expansion, patience, and delegation options to increase the flexibility of investment decisions in this power plant. Considering the results obtained, the following suggestion is made: By planning and implementing training and professional development courses for employees, they can be improved and provided with the necessary capabilities to face changes and risks.

Abstract Abstract The aim of this research is to design a portfolio management model for investment funds with an emphasis on behavioral finance. The research method is applicable in terms of its purpose, qualitative in terms of implementation method and time of data collection, and descriptive-survey in terms of nature and research method. The statistical population of the research includes managers of 329 investment funds present in the Tehran Stock Exchange, selected using purposive sampling and snowball method. Interviews continued until theoretical saturation. Semi-structured interviews were used to collect information. Thematic analysis technique was used to analyze the data. The results showed that the most important behavioral finance variables affecting portfolio management in investment funds included behavioral optimism and pessimism, controlling the fear of profit retention, paying attention to risk-taking and risk-averse behavior, paying attention to self-control behaviors, normalizing conservative behaviors, controlling herd behavior, having a written strategy, controlling regret-avoidance behaviors, propensity behavior, and mental accounting. It is suggested that investment funds as well as real individuals pay attention to these behavioral finance variables in order to better manage their portfolios. Introduction In recent years, a new field of research in finance has emerged that has transformed financial theory. In the wake of the anomalies found in financial markets, it was concluded that markets are not as efficient as previously thought in the last century, but are affected by several biases that indicate their imperfection, and as a result, a new theory called behavioral finance theory is presented (Paule-Vianez et al., 2020). Behavioral finance suggests that human decision-making involves a combination of cognitive and emotional dimensions. Behavioral finance believes that human decision-making can be explained from cognitive, emotional, and social dimensions. Personal values, emotions, personality traits, and social influence affect investors’ subjective perceptions of reality in financial decision-making (Karimkhani & Karimkhani, 2021). One of the keys to success in the capital market is for investors to adopt an appropriate approach to managing the stock portfolios they have built. A portfolio is actually a collection of different securities and assets formed by an individual investor or an investment fund. As previously mentioned, the main question in this area is how to distribute and allocate capital to assets and form a portfolio of securities and then manage it (Hassanloo, 2017). A stock portfolio is a suitable combination of stocks or other assets that an investor has purchased. The purpose of forming a stock portfolio is to divide the investment risk among several stocks; the profit of one stock can compensate for the loss of another stock. The financial and cash value of any individual or legal entity is called portfolio value. For pricing investment companies listed on the stock exchange, the most important factor is the portfolio value of these companies. The first investment fund in its current form was formed in 1924 in Boston, USA. Since that year, investment funds have continued their activities successfully in the world, especially in the United States. Therefore, in this research, we seek to answer the question of what the portfolio management model of investment funds with an emphasis on behavioral finance is like? Theoretical Framework Portfolio Management Portfolio management is the process of guiding the investor to select the best available securities that provide the expected rate of return for each degree of risk and also reduce risk. This is a strategic decision that is considered by top management (Kapoor, 2014). Mutual Funds Mutual funds have continued to operate successfully in the world, especially in the United States. These funds, which are one of the most important capital market institutions for raising small but large-scale capital and have significantly reduced investment risk, have led to a boom in the stock market and management and have attracted more ordinary people unfamiliar with the market to the capital market (Asadi Gharehjeloo & Abdo Tabrizi, 2020). Behavioral Finance Behavioral finance studies how psychological phenomena affect financial behavior. Financial behavior studies how humans behave in determining financial matters. Behavioral finance is a new theoretical branch in finance defined by combining the knowledge of psychology, sociology, and other social sciences (Meisa Dai et al, 2021). Moghdisi et al. (2025) studied the modeling of factors affecting the profit response coefficient of companies by combining behavioral finance components using the structural equation modeling method. The results of the study showed that the financial condition and performance factor with a path coefficient of -0.19, the capital market condition and performance factor with a path coefficient of 0.167, and the investment environment factor with a path coefficient of 0.12 have a significant effect on the profit response coefficient at the 0.05 error level. Accordingly, the structures of earnings per share, financial leverage, information asymmetry, market index return, inflation rate, free float, stock turnover rate, and stock trading frequency were identified as structures affecting the profit response coefficient. Among the aforementioned structures, the absolute value of the coefficients of the structures showed that the inflation rate has the highest and the profit per share has the lowest impact on the profit response coefficient. Shahamat et al. (2025) studied the presentation of a portfolio management model in investment funds based on behavioral financial variables. The results showed that at the first level, the most influential components included: controlling the fear of profit retention, normalizing conservative behaviors, and controlling regret-avoidant behaviors. At the second level, the components affecting the first level were attention to self-control behaviors, having a documented investment strategy, and attention to mental accounting principles, and at the third level, the components affecting the second level included behavioral optimism and pessimism, attention to risk-averse and risk-taking behaviors, and control of herd behaviors. At the fourth level, there is the most influential component, which includes the tendency effect. Research Methodology The research method is applicable in terms of its purpose, qualitative in terms of implementation method and time of data collection, and descriptive-survey in terms of nature and research method. The statistical population of the research includes managers of 329 investment funds listed on the Tehran Stock Exchange selected using purposive sampling and snowball method. Interviews continued until theoretical saturation. Semi-structured interviews were used to collect information. Research findings Thematic analysis technique was used to analyze data. The results showed that the most important behavioral financial variables affecting portfolio management in investment funds included behavioral optimism and pessimism, controlling fear of profit retention, paying attention to risk-taking and risk-averse behavior, paying attention to self-control behaviors, normalizing conservative behaviors, controlling herd behavior, having a documented strategy, controlling regret-avoidance behaviors, propensity behavior, and mental accounting. It is recommended that investment funds as well as individuals pay attention to these behavioral finance variables in order to better manage their portfolios. Conclusion The present study was conducted with the aim of designing a portfolio management model for investment funds with an emphasis on behavioral finance. The results of this study are consistent with previous studies, including Moghdisi et al. (2025), Shahamat et al. (2025), Mousavi Kakhki & Khatabi (2024), Aurengzeb & Maqbool Shah (2022), Chung Wu et al. (2022), Nassim Mellem et al. (2022), Betancourt & Chen (2021), Gruszka & Szwabiński (2021), Karimkhani & Karimkhani (2021), Ebrahim Nejad et al. (2021), Ranjbari Vahid et al. (2020), and Risboff Fakur (2020). Shahamat et al. (2025) showed that at the first level, the most influential components included: controlling the fear of profit loss, normalizing conservative behaviors, and controlling regret-avoidance behaviors. At the second level, the components affecting the first level were attention to self-control behaviors, having a well-documented investment strategy, and paying attention to mental accounting principles. At the third level, the components affecting the second level included behavioral optimism and pessimism, attention to risk-averse and risk-taking behaviors, and controlling herd behaviors. At the fourth level, there is the most influential component, which includes the tendency effect. In the MicMac model, most of the variables were placed in the linked variables section, which have a strong influence force and also a strong dependency force. According to the results of the research, it is suggested that: Investors should be aware of their behavioral biases towards optimism or pessimism and try to have a balanced and realistic perspective when making investment decisions. Investment funds and individuals should develop strategies to manage and reduce fear of possible losses and ensure that decisions are not simply driven by fear.