Khosravi Ehsan

The present study aimed to present a contextualized model for the development of the university technological entrepreneurship ecosystem at Razi University, Kermanshah. In terms of purpose, the research was applied, and in terms of method, it was qualitative, employing the grounded theory approach (the systematic approach of Strauss and Corbin). Data were collected through semi-structured interviews with 12 academic experts associated with the technological entrepreneurship ecosystem at Razi University and were analyzed through three stages of open, axial, and selective coding. Consequently, 112 initial concepts, 24 subcategories, and 8 main categories were identified. The findings revealed that the development of the university technological entrepreneurship ecosystem, as the core phenomenon, is influenced by a set of interrelated factors. Causal conditions included university policymaking and governance, and financial resources and infrastructure; contextual conditions included organizational culture and attitude, and regional and internal capacities and opportunities; and intervening conditions included legal and institutional support, and organizational and managerial structure. Furthermore, external interactions and networking, and education, innovation, and commercialization were identified as the main strategies. The consequences of this process included strengthening innovation and knowledge commercialization, developing technological and knowledge-based firms, enhancing the skills and employment of graduates, reinforcing entrepreneurial culture, expanding university–industry relations, and increasing the university's role in the economic and social development of the region. Overall, the results indicate that the development of the technological entrepreneurship ecosystem at Razi University requires, more than creating new capacities, the activation, coordination, and interconnection of existing capacities. Therefore, the proposed model can provide a framework for identifying areas requiring intervention and for guiding managerial and policy measures, with the aim of transforming the university's scientific, human, and regional capacities into sustainable entrepreneurial and technological performance.   

Abstract The rapid evolution of digital technologies and the expansion of AI-driven innovations have brought about profound transformations in higher education systems and teaching-learning processes. Entrepreneurship education, characterized by its dynamic, problem-oriented, and experiential nature, is particularly well-suited to leveraging AI capabilities in areas such as opportunity recognition, market analysis, ideation, business model design, and decision-making under uncertainty. However, realizing this potential depends not only on the technology's technical capabilities but also on user attitudes—specifically those of students—regarding its acceptance and effective application. Accordingly, this study aims to examine the attitudes of Razi University students toward the adoption of AI-driven technologies in entrepreneurship education. The research is applied in nature and employs a mixed-methods (quantitative-qualitative) approach. A descriptive-correlational design was used for the quantitative phase. Quantitative data were collected from 52 students using a questionnaire based on an integrated and extended framework combining the TAM, UTAUT, and TOE theories; the data were analyzed using both descriptive statistics (mean, frequency, etc.) and inferential statistics (Structural Equation Modeling via the Partial Least Squares approach) with    (version 24) and SmartPLS (version 4) software. In the qualitative phase, Grounded Theory was employed to identify the factors influencing technology adoption. Data for this section were collected through semi-structured, in-depth interviews with 14 experts and faculty members specializing in management, entrepreneurship, higher education, and information technology at Razi University, employing purposive and snowball sampling until theoretical saturation was reached. Through a three-stage coding analysis process, 423 initial codes, 86 axial codes, and 14 selective categories were extracted and organized into a paradigmatic model. Quantitative results confirmed the overall fit and validity of the research framework. Overall, the findings indicate that the adoption of AI-based technologies in entrepreneurship education cannot be explained solely by individual attitudes or technological capabilities; rather, the interplay among performance expectations, ease of use, behavioral intention, environmental conditions, and organizational readiness plays a crucial role in shaping this process. Consequently, fostering the effective use of AI in entrepreneurship education requires simultaneous attention to empowering students and faculty, upgrading technological infrastructure, formulating tra  arent policies, and redesigning educational methods to align with entrepreneurial needs. Qualitative findings revealed that the adoption of AI-based technologies in entrepreneurship education is a multidimensional, gradual, and context-dependent process shaped by a combination of individual, technological, organizational, and institutional factors. In the derived paradigmatic model, the digital skills gap, the imperative to develop digital skills, and the ineffectiveness of certain traditional teaching methods were identified as causal conditions. Infrastructure limitations, a shortage of specialized digital workforce, and cultural and institutional barriers constituted the contextual conditions. Additionally, policy and legal obstacles, ethical and privacy concerns, and psychological and perceptual resistance were identified as intervening conditions. The core phenomenon of the study was the adoption of new AI-based technologies. Strategies included empowerment and localization, infrastructure development, and appropriate policymaking; the resulting outcomes included enhanced entrepreneurial attitudes, increased self-efficacy, improved learning quality, and the development of an innovation ecosystem.

  The present study aimed to provide a localized model for the application of new artificial intelligence (AI)-based technologies in knowledge-based companies in Kermanshah Province. Given the gap between the theoretical potential of AI and its practical application, especially in developing contexts, this study adopted a sequential exploratory mixed-methods design. In the qualitative phase, using grounded theory and the systematic approach of Strauss and Corbin, data from semi-structured interviews with 9 experts, managers, and specialists from knowledge-based companies were analyzed. The three-stage coding process yielded 376 open codes, 73 axial codes, and 12 selective codes. In the quantitative phase, the extracted model was tested and validated using a researcher-made questionnaire and a sample of 95 employees and managers of knowledge-based companies through partial least squares structural equation modeling (PLS-SEM). The qualitative findings showed that “actual use of new AI-based technologies” as the core phenomenon is influenced by causal conditions (organizational readiness and culture, financial and credit resources, technological and data infrastructure, localization of technology and competitive advantage, and smart knowledge management), contextual conditions (innovation ecosystem and regional infrastructure, human capital and empowerment, economic context and investment), and intervening conditions (government support policies and international interactions). The five strategies included formulating and implementing a technological strategy, developing organizational capacity, networking and technological collaboration, empowering human resources, and implementing technical solutions, leading to organizational, regional, and national/economic outcomes. Quantitative findings indicated acceptable reliability and validity; the model explained 55.4% of the variance in behavioral intention (BI) and 27.7% of the variance in actual use (AB). Of 15 hypotheses, 12 were confirmed. Habit was the strongest predictor of actual AI use (?=0.551, t=5.760). Attitude had a negative and significant effect on behavioral intention (?=-0.300, t=5.344), indicating an attitude-behavior gap. Effort expectancy and subjective norms also negatively affected behavioral intention. The mediating role of behavioral intention was very weak, and the direct path was dominant. Overall, AI adoption in knowledge-based companies is a multifaceted and context-dependent proce   20px;">Keywords: Artificial Intelligence; Knowledge-Based Companies; Technology Adoption; Paradigmatic Model; Kermanshah Province.

   In recent years, economic institutions, particularly formal institutions such as property rights and contractual institutions, have received increasing attention as fundamental determinants of countries' economic growth and development trajectories. Both theoretical and empirical literature suggest that the quality of these institutions plays a critical role in shaping the performance of macroeconomic variables. This study aims to investigate the impact of property rights institutions and contractual institutions on key macroeconomic variables, including gross domestic product (GDP), unemployment rate, inflation rate, trade openness, and investment rate, across the BRICS member countries—namely Brazil, Russia, India, China, South Africa, Egypt, Ethiopia, the United Arab Emirates, Iran, Indonesia, Bolivia, Saudi Arabia, and Nigeria—over the period 2000 to 2023. Given the bidirectional relationship between institutional variables and economic performance, and to address the potential endogeneity problem, this study employs panel data methodology with the two-stage least squares (2SLS) econometric approach. The empirical findings reveal that GDP, trade openness, and investment rate exert a positive and statistically significant effect on the quality of both property rights and contractual institutions, whereas the unemployment rate and inflation rate have a negative and statistically significant impact on these institutions. The results further indicate that improvements in macroeconomic performance can enhance institutional quality in BRICS countries, while strengthening property rights and contractual institutions can, in turn, foster sustainable economic growth and reduce unemployment and inflation in these economies. These findings contribute to a better understanding of the reciprocal mechanisms between the economy and institutions in developing countries and provide valuable insights for policymakers in designing effective institutional frameworks to achieve sustainable economic development.

Therefore, the aim of this study is to identify and prioritize investment opportunities in the field of small industries (case study: Kermanshah Industrial Parks Company). In this regard, this chapter begins with a statement of the problem. In this section, an attempt has been made to clarify the subject under study and enable the reader to understand its meaning. In the second section, the importance and necessity of conducting the research are discussed. In the following sections, the research objectives, questions, and hypotheses are discussed. Then, conceptual and operational definitions and the scope of the research (subject, temporal, and spatial scope) are discussed.  

Abstract This study investigates the impact of fluctuations in the national currency’s value on the level of happiness in Iran. Persistent depreciation of the national currency in recent years—through channels such as rising inflation, declining purchasing power, increasing income inequality, and weakening economic security—has the potential to significantly influence individuals’ subjective well?being and life satisfaction. At the same time, currency devaluation may generate limited positive effects through improved export competitiveness and potential gains in employment.   Using reliable national and international datasets on currency value, key macroeconomic indicators, and happiness measures, this research analyzes the relationship between these variables within a regression?based empirical framework. The findings indicate that currency depreciation has a statistically significant negative effect on happiness in Iran, primarily transmitted through inflationary pressures and the erosion of households’ purchasing power. Although some positive effects related to increased exports and employment appear in certain periods, these effects are insufficient to offset the broader negative consequences of currency devaluation on subjective well?being. Overall, the results highlight that economic stability, inflation control, and preservation of purchasing power play a vital role in enhancing social well?being and improving life satisfaction. Currency instability, therefore, emerges as a key factor contributing to declines in happiness among the Iranian population.   

With the development of new technologies, especially artificial intelligence, the insurance industry, as one of the main pillars of the country's financial system, is on the path of digital transformation. The present study was conducted with the aim of providing a model for improving the attitude towards the acceptance of artificial intelligence in the insurance industry. This study was applied in terms of purpose and was conducted with a qualitative approach and grounded theory research method. The study population included all experts, such as faculty members with relevant resumes and expertise, managers, specialists, and active and expert experts in Kowsar insurance agencies across the country. In terms of data collection method, it was a documentary-field research. Participants were selected using purposive and theoretical sampling methods, and to determine the sample size, sampling continued until theoretical saturation. The data collection tools in this study included reviewing existing documents and evidence, semi-structured interviews, group discussions, and field note-taking. To determine the validity and reliability of the research, triangulation techniques were used, as well as review by key participants and other colleagues of the research team. A three-stage coding technique was used to analyze the data. The results also presented a paradigmatic model consisting of the dimensions of causal conditions (including economic factors, cultural and social factors, etc.), contextual conditions (including managerial and organizational factors, infrastructure), intervening conditions (including legal-supportive factors, educational and research factors), measures (including organizational, educational-research, institutional and governmental factors, etc.), and consequences (including social, technological and innovative factors, etc.). This research states that the adoption of artificial intelligence in the Iranian insurance industry is not only a technological choice, but also a strategic organizational-cultural transformation, the success of which depends on a systemic and comprehensive approach to all influential factors.   

The food industry, as a strategic sector of Iran's economy, plays a crucial role in creating sustainable employment, ensuring food security, and promoting regional development. However, in many less-developed areas, including Kermanshah province, attracting investors in this sector faces structural and institutional challenges. Identifying the factors influencing investment attraction can therefore facilitate economic growth and reduce regional inequalities. This study aims to identify and prioritize methods for attracting investors in small food businesses in Kermanshah. The research is applied in nature and follows a sequential exploratory mixed-methods approach. The population included local experts, managers, and specialists active in the province's industrial towns and zones. A purposeful snowball sampling method was used, and finally, 14 experts participated in a fuzzy Delphi process. Data were collected through interviews and questionnaires and analyzed using content analysis,    23, and Excel 2013. The findings indicate that attracting investors in less-developed food industry areas depends on four main dimensions: economic, political, governmental, and organizational. Among these, the economic dimension carries the greatest weight, with key indicators including improved production infrastructure, targeted financial support, and workforce skill development. The results can serve as a basis for developing investment attraction policies, improving infrastructure, and enhancing government-private sector interaction in Kermanshah province. Additionally, these findings provide a model for other less-developed regions of the country to guide sustainable regional development and investment strategies.   

In the contemporary world, food security, as one of the main pillars of sustainable development and social welfare, plays a central role in the economic and social policies of countries. This concept, which, according to the definition of the Food and Agriculture Organization of the United Nations (FAO), emphasizes universal access to sufficient, safe, and nutritious food for an active and healthy life, is affected by several factors, including exchange rate fluctuations. Exchange rate, as a macroeconomic variable, can affect food access, supply stability, and nutritional quality through its impact on import costs, domestic inflation, and household purchasing power. In developing economies such as Iran, which have a significant dependence on food imports and agricultural inputs, exchange rate fluctuations have become a major challenge for maintaining food security. However, a comprehensive study of the impact of these fluctuations on various dimensions of food security has received less attention. This study aimed to analyze the effect of exchange rate fluctuations on food security in Iran during the period 2010-2013. Using the fully modified least squares (FMOLS) econometric method and time series data, this study seeks to identify the channels of exchange rate impact on the Food Security Index (FSI) and to provide policy solutions to reduce its negative effects. By providing a comprehensive framework, this study fills the gap in the research literature and offers a new perspective for policymaking in the field of food security.   

  Income inequality has always been regarded as one of the most significant political, social, and economic concerns worldwide. It is also one of the challenges that has drawn the attention of policymakers and economic authorities in Iran in recent decades. Therefore, identifying the factors influencing income distribution is crucial in policymaking. Accordingly, the present study seeks to empirically examine the relationship between fiscal policy (government expenditures, tax rates, and financial development) and income inequality (Gini coefficient) using the Markov-Switching approach, with quarterly data from 2001 to 2022. The results of the study indicate that the hypothesis of symmetry regarding the effects of financial development, government expenditures, and income taxes on income distribution in Iran during recessions and booms can be rejected. The impact of financial development, government expenditures, and income taxes—as indicators of fiscal policy—on income distribution is greater during booms than during recessions. These findings suggest that fiscal policy has asymmetric effects on income distribution in Iran.

   In economic literature, financial development is considered one of the prerequisites for the economic growth and development of countries. Financial development significantly impacts economic reconstruction and contributes to long-term economic growth and the performance of relevant institutions. The development of financial markets is considered one of the necessities for the economic prosperity of a country. If financial markets are efficient, all elements of financial markets, including money and capital markets, will also have an efficiency and effectiveness index. Economic growth is known as the main engine of financial development. Increasing GDP and per capita income increase the demand for financial services. As the economy grows, companies and individuals have a greater need for financing; therefore, financial markets develop. Foreign direct investment also plays a key role in financial development as a transfer of capital, technology, managerial knowledge, and experience from developed countries to developing countries. In the most important stage, the effect of the explanatory variables on the dependent variable was analyzed using the generalized moments method, and the results are as follows: All coefficients have an appropriate level of significance, and the sign of the obtained coefficients is consistent with the expected sign of economic theories, and the detection coefficient is at an appropriate level. Keywords: Foreign Direct Investment, Economic Growth, Gross Domestic Product (GNP), Financial Development, Generalized Method of Moments (GMM).

The present study aims to present a model for the adoption of artificial intelligence in human resource management with an emphasis on improving organizational technological entrepreneurship (case study: Kermanshah Provincial Health Insurance Organization) in Kermanshah Province. This study is applied in terms of purpose and is a qualitative research using the grounded theory method. The statistical population of this study was all experts in various relevant communities, such as experts in the field of human resource management, faculty members with relevant resumes and backgrounds, and other experts in this field. 18 people were surveyed in semi-structured in-depth individual interviews using a non-probability purposeful sampling method using the snowball method until saturation was reached. Data analysis was performed using the content analysis method. Based on the results of the study, causal, contextual, intervening factors, main phenomena, strategies, and consequences were identified through three stages of open, axial, and selective coding using MAXQDA 2022 software. The findings showed that the obtained categories were categorized into 26 categories in 6 main categories, which included the following causal factors: competitive advantages in adopting artificial intelligence, managerial and organizational capabilities, technical and technological infrastructure, challenges and barriers to adoption, and factors facilitating adoption. Contextual factors included organizational characteristics, technological characteristics, external environment and market, human and behavioral characteristics. Also, intervening factors included training and skills development, management and leadership support, organizational culture and technology adoption, data and infrastructure quality, communication and information. Strategies included training and development of human resources, management and leadership support, improving infrastructure and data, increasing employee participation, developing evaluation and motivation models, creating a culture of innovation and learning, strengthening internal and external communications, and the consequences were improving human resource performance, strengthening technological entrepreneurship, increasing technology adoption and culture, improving organizational efficiency, and better strategic decision-making. Based on these findings, it can be concluded that for success in adopting new technologies, it is of great importance to develop managerial and organizational capabilities through holding special training courses for managers and clarifying the goals of artificial intelligence for all levels of management.   

   This study investigates the bidirectional relationship between liquidity and the exchange rate in the Iranian economy over the period 1981–2021. Using the quantile regression method in EViews 12, the research aims to explore the mutual effects of these two key macroeconomic variables across different points of their distribution. Two separate models were estimated: the first examines the impact of liquidity growth on the exchange rate, while the second analyzes the effect of the exchange rate on liquidity growth. The results of the first model reveal that liquidity growth has a positive and significant effect on the exchange rate in the lower quantiles (0.125 to 0.375), indicating a higher sensitivity of the exchange rate to liquidity when the exchange rate level is relatively low. The second model shows that the exchange rate also has a positive and significant effect on liquidity in the lower quantiles of the liquidity distribution, confirming the existence of a bidirectional relationship between the two variables. Additionally, the significant effect of GDP on liquidity across all quantiles underscores the importance of real economic variables in monetary developments. However, variables such as trade openness and the government budget deficit did not show significant impacts on liquidity. Moreover, the variable representing the intensity of economic sanctions had a significant effect on the exchange rate only in a specific quantile and failed to demonstrate a consistent structural moderating role in the liquidity–exchange rate relationship. Ultimately, the findings point to the existence of a feedback loop between the monetary and foreign exchange markets in Iran, which, under conditions of inflation and currency instability, can pose serious challenges for economic policymaking. These results highlight the necessity of greater coordination between monetary and exchange rate policies to control fluctuations and enhance macroeconomic stability. This study investigates the bidirectional relationship between liquidity and the exchange rate in the Iranian economy over the period 1981–2021. Using the quantile regression method in EViews 12, the research aims to explore the mutual effects of these two key macroeconomic variables across different points of their distribution. Two separate models were estimated: the first examines the impact of liquidity growth on the exchange rate, while the second analyzes the effect of the exchange rate on liquidity growth. The results of the first model reveal that liquidity growth has a positive and significant effect on the exchange rate in the lower quantiles (0.125 to 0.375), indicating a higher sensitivity of the exchange rate to liquidity when the exchange rate level is relatively low. The second model shows that the exchange rate also has a positive and significant effect on liquidity in the lower quantiles of the liquidity distribution, confirming the existence of a bidirectional relationship between the two variables. Additionally, the significant effect of GDP on liquidity across all quantiles underscores the importance of real economic variables in monetary developments. However, variables such as trade openness and the government budget deficit did not show significant impacts on liquidity. Moreover, the variable representing the intensity of economic sanctions had a significant effect on the exchange rate only in a specific quantile and failed to demonstrate a consistent structural moderating role in the liquidity–exchange rate relationship. Ultimately, the findings point to the existence of a feedback loop between the monetary and foreign exchange markets in Iran, which, under conditions of inflation and currency instability, can pose serious challenges for economic policymaking. These results highlight the necessity of greater coordination between monetary and exchange rate policies to control fluctuations and enhance macroeconomic stability.

   Managers choose different management methods in dealing with different companies, and these management methods cause changes in their basic decisions. Managers who serve on multiple boards (busy managers) are exposed to different situations of direct and indirect decision making. Multiple management increases the workload of managers, reduces the time and attention they give to each company, and causes weak managerial supervision. As a result, the purpose of this research is to investigate the effect of busy managers with an emphasis on the company's life cycle on financial decisions and company performance. Statistical analysis was done using sample data consisting of 171 member companies of Tehran Stock Exchange during the years 1393 to 1401. To test the hypotheses, regression models with the least squares estimator were used in Eviuse 10 software. The results of the hypotheses test showed that the preoccupation of the board members with the variables of cash retention, sales costs, and general obligation has a positive and significant relationship with the capital expenditures and financial performance of the company. Also, the findings indicate the moderating role of the stages of the life cycle in the relationship between the preoccupation of the board members with keeping cash; Sales expenses, general obligation; Capital expenditure and financial performance of the company.

  Tax avoidance is a form of management policy to obtain

 In economic literature, financial development is considered as one of the necessities of economic growth and development of countries. Hence, paying attention to financial development and analyzing its effects on economic growth is of particular importance. Achieving the desired economic growth and development is impossible without the existence of efficient financial institutions and appropriate financial resources. On the other hand, today, the ability to achieve innovations by using creative human resources is known as the first step to turn knowledge into wealth. . Therefore, considering the key importance of innovation and institutional development in the economy of developing countries, the present study examines the impact of institutional development and innovation on financial development in 12 OPEC member countries during the period of 2010-2022 using the fully modified least squares method (FMOLS). ) pays. Based on the results obtained, the effect of institutional development and innovation on financial development in OPEC member countries during the period from 2010 to 2022 has been positive and significant.