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In the age of pervasive technologies, marketing strategies are undergoing fundamental transformations, with the alignment of data-driven methodologies and human experience emerging as a pivotal challenge. On one hand, the utilization of big data and advanced algorithms enables precise analyses of market trends and consumer behaviors; on the other, the intuitive and emotional facets of human experience play a decisive role in building deep and meaningful customer connections. This study adopts a critical reappraisal perspective to explore the tensions and potential synergies between these two paradigms, proposing strategies for their optimal integration. The analysis reveals that an exclusive reliance on quantitative data—without acknowledging the psychological and emotional complexities of consumers—can result in superficial decision-making and an inability to adapt flexibly to market shifts. Conversely, a balanced integration of insights derived from cutting-edge technologies with a nuanced understanding of human behavior empowers organizations to optimize their marketing strategies in a multidimensional and comprehensive manner. By reviewing both successful and less effective cases, this research identifies the strengths and weaknesses of current approaches while addressing key challenges such as data privacy, the interpretation of incomplete datasets, and the complexities inherent in behavioral analysis. The findings suggest that intelligently merging data-driven techniques with human-centric insights can significantly enhance the effectiveness of marketing campaigns, boost customer satisfaction, and secure a sustainable competitive edge. Nonetheless, these challenges necessitate innovative solutions and adaptive strategies, ultimately paving the way for fresh perspectives on the evolution and development of marketing strategies in the era of ubiquitous technologies.

In this study, we seek to test the effect of government policy uncertainty on the investment of listed companies in recession and boom regimes. For this purpose, the effect of study variables during the period of 1380 to 1400 is investigated by using the rotation model and the Markov switching (MS) regime change. Based on the results of the model estimation, with the increase of government policy uncertainty, corporate investment decreases. In general, the uncertainty of government policies increases the risk of investment projects and leads to a decrease in corporate investment. Especially in an inflationary situation where the value of money decreases, the nominal interest rates for savings and reserves are low. In this case, investing in currency, real estate, gold, etc., provides a higher interest rate compared to keeping reserves in the banking system. This causes a decrease in the desire to save and reduces the funds available for investment in the banking system, and as a result, corporate investment decreases.

In the era of digital transformation, artificial intelligence systems, especially reinforcement learning, play a vital role in improving marketing processes and enhancing customer experience. Reinforcement learning allows systems to self-learn through continuous interactions with customers and respond more accurately to their changing needs and behaviors. Focusing on reinforcement learning mechanisms, this research analyzes its impact on customer experience in the field of marketing. Self-learning systems are able to provide personalized offers tailored to the specific needs of each customer by analyzing customer behavior, which leads to increased satisfaction and enhanced individual interactions. Also, using big data and advanced algorithms, these systems have the ability to predict the future needs of customers and enable marketers to continuously improve their strategies. This research shows that the application of reinforcement learning in marketing not only improves the customer experience, but also leads to a significant competitive advantage by reducing the costs of customer acquisition and retention and increasing the efficiency of marketing campaigns. However, there are challenges such as managing huge amounts of data, algorithmic complexities, and protecting users' privacy, which require innovative solutions. This research examines these challenges and provides solutions to fully exploit the potential of reinforcement learning in marketing and provides a new perspective for the future of this field.

Considering the multitude of actors in the public sector and the wide scope of government interventions, the coordination of actors is an important challenge to achieve the goals. However, this concept still has theoretical and conceptual ambiguities, which in turn have challenged its realization. To address the coordination challenge, three important questions should be clarified: What is the coordination of public sector activists? Why there is a need for coordination between public sector activists? And how can it be achieved? In previous studies, different answers have been given to these questions. However, there is a lack of a comprehensive approach to coordination, which combines the findings of various studies. In this research, an attempt has been made to provide a comprehensive framework for thinking about coordination in the public sector using the meta-synthesis method. For this purpose, after several stages of screening, 51 final documents were selected from more than 1000 documents, and by analyzing them in MAXQDA software, what, why, and how the phenomenon of coordination of activists in the public sector was identified in the form of a total of 7 main categories. The answer to "what this phenomenon is" was put in the form of three main categories: «Ratio of Actions Together To Achieve Common Goal», «Coordination Is A Continuum, Not A Dichtonomy» and «Dimensions Of Coordination». The answer to why this phenomenon is needed was put in the form of three main categories: «Prior Necessities», «Posterior Necessities» and «Coordination Costs». Finally, the answer to how to achieve it was put in the main category of «Creating A Clear, Common, And Valid Basis For Action».

For many years, the budget deficit has been studied as an important variable in economics, and various schools and scholars have identified its causes and influential factors. However, since the 1990s, increasing attention has been directed toward the effect of institutional and political indicators on the budget deficit, and corruption has been introduced as one of the key contributors. This article, by introducing and calculating a composite index of financial corruption based on the deprivation theory and choosing the four economic dimensions of government spending, investment, income and economic freedom, investigates the effects of financial corruption on the budget deficit in 120 selected countries from 2007 to 2017 using the generalized torque method (GMM). The results show that, assuming other conditions constant, a one- unit increase in the corruption index increases the budget deficit by an average of 49/3 units, which indicates the positive and direct effect of corruption on the budget deficit. Also, according to the significance of the estimation, the variables of inflation, government spending, taxes and oil revenues have a positive effect on the budget deficit, while higher economic growth leads to a reduction in the budget deficit.

Analyzing the role of exchange rate shocks in shaping micro and macro aspects of emerging economies is one of the main topics of academic and policy discussions. The purpose of this research is to analyze the asymmetric effects of currency shocks on the value of industrial production in Iran based on the NARDL model. The results of this research showed that the exchange rate does not have the same effect on the value of industrial products, and there is a great difference between industries in terms of the intensity, direction and significance of its effect. Based on the results of the research in the long-term time horizon of increasing exchange rate shocks in the industries of "other non-metallic mineral products", "coke and refining", "basic metals", "tiles and ceramics (90% confidence level)", "rubber and plastic". "Wood and paper products (90% confidence level)", "food and beverage products except sugar" and "textiles" have a positive and significant effect. Also, in the long-term time horizon, positive exchange rate shocks have a negative and significant effect in the industries of "Automotive and Parts Manufacturing", "Chemical Products", "Pharmaceutical Materials and Products" and "Cement". In the long-term time horizon, negative exchange rate shocks in all investigated industries except "wooden and paper products" have a negative and significant effect on the value of the real products of that sector; However, the effect of negative exchange rate shocks in the "other non-metallic mineral products" sector is significant at the 90% confidence level.

In this article, a new and dynamic metric called Expected Regret of Drawdown (ERoD) is employed to enhance the decision-making quality of managers in forming an optimal portfolio by calculating portfolio risk. This metric essentially represents the average of capital drawdowns that exceed a specific threshold (e.g., 20%). The Expected Regret of Drawdown is somewhat similar to Conditional Drawdown at Risk (CDaR), which is defined as the average of a certain percentage of the largest drawdowns. However, Expected Regret of Drawdown Beta has advantages over CDaR Beta. A negative ERoD Beta identifies securities that generate positive returns during periods when the market experiences drawdowns beyond the threshold. Therefore, ERoD Beta only considers those time periods when the market is in a drawdown state. Conceptually, this differs from standard Beta, which does not distinguish between upward and downward market movements.In this article, the Conditional Drawdown at Risk (CDaR) Beta and the Expected Regret of Drawdown (ERoD) Beta for 30 stocks listed on the Tehran Stock Exchange were compared with the standard Beta. It was found that, in addition to being stable over time, during periods when the Tehran Stock Exchange index experiences declines and capital drawdowns, these betas offer a better measure for calculating risk and forming optimal portfolios compared to the standard Beta. This provides investment managers with a more reliable tool for making higher-quality decisions when constructing optimal portfolios.

A new phenomenon that is derived from the integration of communication technologies of various forms is virtual social networks, whose services are used, and is becoming more popular day by day. In the meantime, making money and doing business through social networks is one of the ways that has opened communication and electronic information systems to organizations. Instagram is particularly popular in this field. The business of influencers and celebrities is advanced, increasingly used by companies and becoming an integral part of digital marketing strategies. The purpose of this research is to provide a model to earn money through Instagram in Iran. For this purpose, the whole number is selected, whose number is 50 samples based on the snowball method and they are completely placed in the customers' placement. Considering that this research is a qualitative analysis based on the Delphi method. Using the Delphi method, the indicators were finalized and then, by integrating the fuzzy network analysis process, the indicators were prioritized. Research studies have shown that the income model through fame on Instagram in the fourth quarter other than support policy, development of relationship with customers, number of customers and trust and role model as a strategy, can have a significant impact on increasing income.

The purpose of this paper is to model the relationship between cognitive abilities and portfolio returns for portfolio managers and investors, with a focus on the dimensions of cognitive bias. The problem addressed in this study is to investigate the effectiveness of decision-making behavior among portfolio managers in the capital market of Iran, based on the dimensions of persistence and representation of cognitive bias in investors.

This study employs an applied research approach with a descriptive objective. Data collection was conducted using a series of tests, including the Cognitive Reflection Test (CRT), where one question (Linda's problem) was utilized to assess communication sophistication bias, and two additional questions were used to evaluate anchor bias. Gerter’s (1992) test was applied to measure long-term bias, while an imagery-based task (a guessing game involving a ball and bag) was administered to a sample of 30 managers from various companies. Additionally, a questionnaire containing 15 questions was given to 302 capital market professionals, including investment managers, analysts, and traders who worked in portfolio management companies and were selected as a statistical sample. The questionnaire included questions about decision-making power, investment activities, organizational position, yield of the past year, and investment strategies. To analyze the data and test the hypotheses, the chi-square test, Mann-Whitney test, and probit regression model were used.

The results showed that a one-unit increase in the decision-making criteria of portfolio management experts leads to a 0.27% increase in investment return. Additionally, the level of cognitive ability and decision-making skills of males and females showed a significant difference, and the level of cognitive ability and decision-making skills of basket operators with different efficiency levels also showed a considerable difference. Furthermore, the results indicated that 40.4% of the participants in the research have high cognitive ability. However, the criteria introduced for cognitive bias, such as representational bias and procrastination, harmed investment returns.

This study evaluated the dimensions of lingering bias, typology, reliance and adjustment, and sophistication in the decision-making of experts. The results showed that the criteria of cognitive ability and decision-making power of expert portfolio managers had a positive and significant effect on investment performance. An increase in the level of cognitive ability leads to an increase in performance by 0.53%, and a one-unit increase in the decision criteria of portfolio management experts leads to a 0.27% increase in investment return. However, the criteria introduced for cognitive bias, such as representational bias and procrastination, hurt investment returns.

The purpose of this research is to implement interpretive structural modelling to determine the role of free zones in the country's economic development. The necessity and importance of conducting research is that considering the role of free zones in the economic development of most countries, our country has taken noteworthy measures with the establishment of the first free zone in 1369 until today, but unfortunately, these zones are due to various reasons. , including the structure of the government administration, lack of requirements and conditions for commercial-industrial activities, inappropriate location, lack of proper implementation of related laws, lack of necessary guarantees for foreign investment, and most importantly, legal obstacles related to the structure of the administration, redundant investment procedures, and  Due to severe bureaucracy, the free zones of our country have not yet reached their true position and have not been able to play a significant role in the economic development of the country, and this research seeks to identify the factors that affect the promotion of the role of free zones in the economic development of the country and provide a model in this regard.

Design/ methodology/ approach:  

In terms of method, this research was qualitative-quantitative research that was conducted in the qualitative part with interpretive structural modeling. The intended statistical population is made up of experts familiar with the subject of the research, including university professors (experts in the economic field) who are among the scientific experts and officials in the field of free zones and the economic field (in the Ministry of Economic Affairs and Finance, the Secretariat of the Supreme Council of Commercial and Industrial Free Zones and the Free Zones faction of the Islamic Council), as well as legal experts in the free zones. And the data was collected and sampled theoretically through scientific interviews with 15 of these experts. In the second step, in order to validate the model obtained from the first step, a questionnaire was designed and distributed in the target statistical community, and after data collection, the validity of the model was investigated using statistical analysis.

The results of the current research show that the status of all indicators except the reform and communication index and the risk reduction index are favorable in the model; According to the influence-dependence diagram, the variable "implementation of policies and knowledge-orientated" has a high influence and has little influence and is placed in the independent variables area. "Development of special advantages of free zones" variables have a high influence and dependence; therefore, they are linked variables. The two variables of "facilitating the process of affairs" also have high dependence but little influence; therefore, they are considered dependent variables. The variable "economic functions and capabilities" is also placed in the first quadrant, which is the autonomous region.

Limitations & Consequences:

 It was not possible to include other important intervening variables in the research due to their great complexity; also, the information was obtained from a single source and it is prudent to generalize it to other areas.Practical Consequences: The implementation of an interpretative structural model to determine the role of free zones in the country's economic development leads to a better understanding of the decision-making environment by managers.

Innovation or value of the Article: 

Despite the great importance of free zones in the economic development of the country, no research has been done on the implementation of interpretive structural modelling of the role of free zones in the economic development of the country. The current research is the first step in this field.

In this research, based on the historical statistics of the deposits of a private bank (Ansar bank before merging) into four different categories (funds), we are looking for the results and prediction of bank deposits in each of the categories, using machine learning prediction methods for the first time in internal studies. In this forecasting method, the superiority of the machine learning method is confirmed due to the examination of all the time changes of the data in a large volume (from the beginning until now). Forecasting the above deposit statistics for bank decision makers in the future according to the economic conditions can be the basis of macro banking decisions. The results show that partly stable long-term deposit and stable deposit will take a downward trend in the future in the Ansar bank, and unstable and interest-sensitive deposit will also take a downward trend. Qarzol-ha-sa-ne deposit has an upward trend, although it has always fluctuations similar to sin-cos fluctuations. It should be noted that this machine learning prediction method is a stable method without sensitivity analysis and has high reliability. According to this research, paying attention to long-term and stable deposits as well as Qarzol-ha-sa-ne is of great importance in the bank’s budget.

In today’s world, digital marketing and artificial intelligence are the cornerstones of modern marketing strategies. However, the rapid advancements in technology and shifts in the business landscape have paved the way for a new and more complex phenomenon: post-human-centric marketing. This emerging concept goes beyond the conventional frameworks of digital marketing and AI, delving into the intricate interactions between humans and technology on deeper, multilayered levels. Post-human-centric marketing not only leverages smart tools and sophisticated algorithms but also seeks to create an unprecedented convergence between the human mind and technology. In this approach, the consumer is viewed as a multi-dimensional entity, where artificial intelligence and digitalization merge with human intelligence and machine-generated data. This article explores the structural and functional changes in marketing, critically examining new concepts such as symbiotic human-machine communication, advanced personalization, and predictive marketing. One of the key questions this article addresses is how this innovative approach will impact the roles of marketers and business models. Can post-human-centric marketing lead to increased efficiency and precision in analytics, or will these advancements result in diminished human interaction and a growing divide between brands and customers? The article also examines the challenges and opportunities businesses face as they navigate this fundamental shift. Ultimately, this article aims to provide a comprehensive and forward-looking perspective, helping readers prepare for the challenges ahead in the post-human-centric marketing world.

In the future world, where self-sustaining artificial ecosystems are designed and developed as sustainable habitats for humans and other creatures, marketing has also undergone fundamental changes. These ecosystems, which are managed using advanced technologies and artificial intelligence, require innovative and intelligent marketing approaches that can adapt to their complex and independent environments. In this short article, we take a deep look at smart marketing strategies in these new ecosystems. From using environmental data to personalize customer experiences to designing campaigns that are synchronized with self-learning algorithms and self-adjusting systems, we will show how marketers can meet the changing and complex needs of the inhabitants of these ecosystems by using advanced technologies and predictive analytics. Also, the ethical and social challenges related to marketing in these self-sufficient environments have been examined and solutions are provided to create a balance between profitability and social responsibility. Finally, we will draw a vision of the future of marketing in a world that moves towards sustainability and self-sufficiency. And the importance of innovation and adaptability will be addressed in this direction.

The purpose of this study is to use a diversified-based model to create optimal stock portfolios and evaluate their superiority by considering sector indicators. The statistical population of this research is all sectors of Iran's economy, where one or more companies belonging to that sector are active in the Tehran Stock Exchange and remain in systematic exclusion. Industry groups active in Tehran Stock Exchange, which during the research process of these industries in the form of different economic sectors such as banking and insurance sector, construction sector, mining sector, etc., have been classified (37 sectors and sub-sectors) into They will be used as investment portfolio components.Since the purpose of this study is to identify the appropriate investment strategies in the stocks of different sectors of the economy, for this purpose, after collecting the data, the descriptive analysis of the data will be done first. In the following, the validity and distribution of the data are evaluated by performing the stationarity test and the normality test to estimate the models. In the next step, using the Hamiltonian approach and the Markov switching method, bull (boom) and bear (stagnation) periods are identified in different parts of the Tehran Stock Exchange market, and then they are analyzed using the logit model of sector investment models. The results show that, along with other macroeconomic factors, the increase in the inflation rate is one of the key factors behind the occurrence of prosperity in economic sectors and has strongly influenced the strategy of investors.

This study measured the investor sentiment to understand its role in the behavior of the Tehran Stock Exchange and its relationship with the stock price during a monthly period from March 2016 to March 2022. The returns on some TSE-listed indices were compared to analyze the relationship between the investor sentiment and the stock price. This study improves our understanding of the price formation process at purchases or sales caused by sentiments acting as the major determinant of the stock price change. Statistical evidence indicates the impact of sentiments on the price formation process in specific TSE-listed indices. The autoregressive integrated moving average (ARIMA) was employed to estimate parameters and the behavior induced by the investor sentiment. According to the results, they respond asymmetrically to sentiment changes and trade more aggressively when sentiments reduce. There are also significant relationships between some of the market variables and the investor sentiment. Finally, comparing the sentiment index with each price index of the market will lead to the conclusion that there is a positive significant relationship between the sentiment tendency and the price formation process at the Tehran Stock Exchange.

This study aimed to evaluate the performance of the gradient boosting decision tree model, the parameters of which were optimized with the improved Gray Wolf Algorithm (GWO) by adding financial network-related variables via the selected models of predicting financial distress. The proposed model of this study was implemented on the data of 123 manufacturing companies admitted to the Tehran Stock Exchange and Iran Fara Bourse Co. (IFB) from 2014 to 2021. Initially, the financial network was formed and then, the financial distress of companies was predicted by integrating the network-based variables with financial ratios and using a gradient boosting decision tree model. The model of the gradient boosting decision tree had better performance in terms of precision and Type I error by adding Financial Network Indicators (FNI) compared to the two models of K-Nearest Neighbor (KNN) and Logistic Regression (LR). Companies with betweenness centrality and high degree centrality were found to be less prone to financial distress and vice versa. This is the first study to predict financial distress by using financial network-related variables integrated with financial ratio variables through the novel gradient boosting decision tree method, the parameters of which were optimized with the improved GWO.

For many years, the economics of corruption has been widely used from political perspective. Scholars identified many variables and determinants of corruption; yet, one could hardly find a comprehensive index of financial corruption that provides a more precise picture of its impacts on the political and economic system. Financial corruption is a hidden variable that cannot be properly observed and measured. Corruption studies face with the conceptual and measurement issues. Most researchers, applied either limited items or some aspects of corruption to represent the entire of concept. Some indicators such as Corruption Perceptions Index (CPI), Corruption Control Index (CCI), etc. are based on expert’s and business executive’s understanding of corruption, instead of actual objective measurement of the phenomenon. Additionally, none of these indicators especially CPI, are adequate for the empirical research of the impact’s corruption on economic variables. To address these shortcomings, it is necessary to create a proper indicator designed to measure corruption. In this paper, we established a new comprehensive Financial Corruption Index (FCI) that has been framed based on “Deprivation Theory”, which measures the shortfall of the nation in each of economic dimensions including government expenditures, investment, income and economic freedom. Applying these four dimensions, in conjunction with a composite index approach to corruption, makes it feasible to create a novel framework for understanding of financial corruption.  The results show during 2007 and 2017, Iran saw its FCI rating increased from 0.475 to 0.535 from 2007 to 2017, which means an improvement in Iran’s rank from 87 in 2007 to 82 in 2017 among 126 countries. The CPI scores for Iran confirms that our results in FCI are valid and accurate.

In the modern world of marketing, the use of emerging technologies plays a vital role in shaping marketing strategies. In this regard, ChatGPT, as a tool based on artificial intelligence, has provided new possibilities for marketers that can create great changes in interacting with customers and personalizing marketing communications. This article examines and analyzes the applications of ChatGPT in marketing and through a case study and data analysis, shows how this technology can help optimize customer-related processes. In this article, a comprehensive review and analysis of scientific articles published in 2023 and 2024 has been done to show what new developments have been experienced in marketing research. The main focus is on the role and importance of using artificial intelligence tools such as GPT chat in this research. The findings show that the use of these technologies in marketing studies has not only helped to form a better understanding of consumer behavior, but has also been effective in improving the level of interaction and responding to their needs. In particular, GPT chat has emerged as a powerful tool for personalizing the customer experience, and significant benefits have been achieved in improving communication and increasing customer loyalty. This research confirms that the integration of advanced technologies in marketing strategies can have positive effects on the overall performance of companies. In addition, this paper addresses the main challenges arising from the implementation of AI-based technologies in the marketing industry, including concerns about customer privacy and data management. Finally, it provides recommendations for overcoming these challenges and optimally exploiting ChatGPT in marketing. This research provides a platform for a better understanding of the uses and potentials of ChatGPT in the field of marketing and can serve as a guide for marketers to use more effective strategies.

Many studies depict marketing, particularly branding, as one of the challenges faced by small and medium-sized enterprises (SMEs). Although branding ranks as a top priority for SMEs, they tend to concentrate more on day-to-day activities like sales rather than on branding endeavors. Consequently, brand management often does not receive high priority among SMEs. Despite the crucial role of branding in SME growth, scant attention has been devoted to it in the literature. Therefore, there is a need for greater focus on branding in SME literature, given its significance in customer outreach, sustainability, and resource utilization. The brand serves as a strategic resource generated within the company, capable of enhancing the company's vision and capabilities and facilitating logical and emotional interactions with customers. Hence, the efficacy of branding strategies hinges on the advantages offered by the brand itself. Given that branding in SMEs remains a relatively underexplored research area, this study aims to contribute new empirical data, particularly in the book industry, to address this gap.On the other hand, while it is evident that branding plays a pivotal role in the success of many businesses, particularly in developing countries like Iran, limited research has been conducted, especially concerning the acquisition and selection of branding strategies in SMEs. Therefore, this research seeks to design entrepreneurial branding strategies tailored to the dynamic capabilities of SMEs in the publishing industry, focusing on areas where knowledge is lacking.

The research adopts a qualitative approach, employing a multiple case study strategy to investigate businesses in the book publishing industry. Given that the unit of analysis is at the company level, the study's statistical population comprises active managers and founders with experience in branding and brand management within the Tehran book publishing industry. Data collection involved the use of an interview protocol with the event-based STAR method. Additionally, multiple data sources, including interviews, documents, evidence, and observation, were utilized. Purposeful sampling continued until theoretical saturation was achieved, ensuring that the obtained information was nearly exhaustive. This saturation was reached after the 14th interview, and to bolster research reliability, semi-structured interviews continued until the 20th interview. The interviews lasted approximately 60 to 120 minutes each. The thematic analysis method was employed to analyze the interview data, with coding performed using Atlas.ti software version 7. The validity and reliability of the coded themes were confirmed through qualitative research validation methods, including review by two coders and peer review, resulting in a validity score of 74%, considered highly desirable according to Chin (1998).

The data obtained from participant interviews were coded using the thematic analysis method as follows: Initial Codes: The researchers initially familiarized themselves with the interview text, identifying basic content and creating initial codes such as copyright infringement, limited book genres, and uniformity across departments.Selective Coding and Theme Creation: Codes with semantic and conceptual affinity were grouped together to form main and sub-themes, generating new meanings and words.Theme Definition and Naming: Main themes were introduced and explained, highlighting the aspects of the data each theme encompassed. Five essential steps and two internal and external components influencing entrepreneurial branding in SMEs were identified based on interviews and documents. These steps include "Triggers of Idea Generation," "Paths of Brand Idea Generation," "Brand Concept Formation," "Brand Evaluation," and "Brand Exploitation and Development."

The process of entrepreneurial branding in SMEs comprises five main steps and two components that influence branding and capability emergence throughout this process. The first step involves primary triggers in ideation to enter the business world, followed by the creation of an idea path through accidental discovery, deliberate search, or legacy. Subsequently, the brand concept is formed, evolving through brand identity and brand imagery. The fourth step entails brand evaluation, measured through desirability and feasibility, leading to brand exploitation and development, where entrepreneurs translate thoughts into action, creating and developing brand-based businesses. This process is influenced by internal components such as human resource characteristics and manager/brand owner personality, as well as external factors like industry and market characteristics and legal and socio-demographic environments. The entrepreneurial process is inherently embedded within the social and environmental context of the entrepreneur.

The current study set out to identify the influential factors involved in implementing public tourism policies in Gilan province, Iran. To this end, this developmental qualitative research applies an inductive approach and follows the interpretive-constructivist philosophy. The study’s population consisted of experts in the field of tourism, public policy, Gilan studies, and geography, out of whom some twenty experts were selected and interviewed using purposive snowball sampling. According to the findings of the study, technological, cultural, social, economic, and political-administrative factors were found to have contributed to the implementation of the public tourism policy in Gilan province. In this regard, the political-administrative factor was found to comprise bureaucracy reduction, education, and human resources development and improvement. Moreover, it was revealed that the social factors included strengthening social responsibility, increasing public and non-governmental organizations' participation, proper communication of the executive department with residents, and enjoying social capital. On the other hand, the results showed that the cultural factor consisted of utilizing cultural symbols, developing localized policies, and emphasizing the cultural structure of each region and that the technological factor included the emphasis on media advertisement and strengthening technological infrastructure. Finally, the economic factor was found to comprise participatory governance, proper financial planning, and proper budgeting. The main achievement of this research is its focus on the implementation aspect of policy-making and the recognition of various cultural, technological, social, economic, and political-administrative factors in the successful implementation of tourism policies, suggesting that in addition to economic factors, some other factors also contribute to the effective policy implementation, a fact which has been neglected in many similar studies.

Considering the ever-increasing inflation of recent years, which came about due to international sanctions in the field of oil and gas condensate sales, and the existence of a large budget deficit, it is necessary for the government to think of ways to improve the negative operational balance of its budget and take effective measures as soon as possible. In order to improve the operational balance of the government budget, drivers of the negative operational balance of the budget should be identified first. The main goal of this research, which was conducted in 1401, is to identify the drivers of the negative operational balance of the government budget. In this qualitative-exploratory research, by conducting 19 interviews with experts (3 faculty members, 6 managers, and 10 experts of plan and budget organization) and analyzing the interviews with thematic analysis, finally the drivers of the negative operational balance of the government budget in the form of 3 categories of contextual, procedural and Programmatic drivers (3 main themes, 8 sub-themes and 83 codes) were identified. Contextual drivers refer to the factors related to the country's economic background and include the undeveloped manufacturing and export industries, monopolies in the country's economic sectors and excessive growth of the public sector in Iran. The programmatic drivers express the factors that are related to lack of planning or the lack of a suitable plan in the field of operational balance management and include the lack of a suitable plan to increase the government's efficiency and the lack of a suitable plan to control the sale of natural resources. Procedural drivers refer to the factors that are related to the processes, procedures and the quality of its implementation in the country and include the weakness in the tax system, Failure to perform performance-based budgeting principles and ineffective monitoring of the government's economic activities.

The purpose of research was to provide a model for the development of the export of Nano products of knowledge-based companies active in the construction field.The present study is a mixed research design.In the first stage, a qualitative study was conducted and by examining the studies conducted in the field of the topic and in-depth interviews with the managers of knowledge-based companies, data was collected and the grounded theory method was used to code and categorize and present the model.The second stage of the study was quantitative and structural equation modeling.The data collected from the researcher-made questionnaire was evaluated using Smart PLS software.After evaluating the internal and external studies, qualitative questions were asked to the statistical population using exploratory interviews, and theoretical saturation was achieved after 15 interviews.The results of GT showed that the export development model knowledge-based products includes 97 open codes,17 core codes and 6 selective codes.In the quantitative part, a researcher-made questionnaire containing 97 items was provided to 217 CEOs, sales managers, experts, and marketing experts of knowledge-based companies.The SEM results revealed that the significance coefficient of the t-statistic related to the relationship between the main variables "causal conditions and central phenomenon, "contextual conditions, intervening conditions, and central phenomenon and strategies," and "strategies and consequences" was more than 1.96 .This indicated the significance of the effect of the relationship between "causal conditions and central phenomenon," "contextual conditions, intervening conditions, central phenomenon and strategies," and "strategies and consequences."Therefore, all six selective codes were identified as the main variables.

Recently, logistics system has increasingly been recognized as one of the main driving forces of economic growth. Petrochemical industry is considered one of the strategic industries and the developments of this industry can have a great impact on the global economy and the countries in this industry. The purpose of this study is to provide an export development model in the Persian Gulf petrochemical industry with a logistics system-based approach using Grounded theory based on Strauss and Corbin method to manage it. The statistical population consisted of 10 interviews with individual experts in the sections of Persian Gulf Petrochemical Business Management, as well as prominent academic professors and experts in this field were selected as participants in the study. The interviews were conducted using the targeted method. Data were collected through interview using the general guidance method in an unstructured manner. Two methods of reviewing the participants as well as reviewing the non-participating experts were used to obtain the validity of the data. The reliability of the model was evaluated using the kappa index. The value of Kappa index was calculated to be 0.63 and was at the level of valid agreement. Interviews and coding were examined using MAXQDA software. In this research, in the open coding section, 76 open codes out of 495 concepts; In the central coding section, 76 initial codes were identified in 10 categories including export market, international marketing, export potentials, export challenges, market competition, organizational factors, macro factors, export sales, export logistics, export product characteristics.

The financial system of an economic enterprise, in simple terms, is a set of financial institutions, financial instruments and financial markets that are responsible for financial decisions. Small and medium-sized enterprises use various financial instruments to provide the financial resources that they need. They also use the surplus funds provided by financial institutions and other economic enterprises for investment purposes and provided to applicants in the form of facilities and loans. This compensates the budget deficit of small and medium enterprises. In this article, the methods of financing small and medium enterprises and the risk associated with each method are studied and analyzed.

This research addresses the comparative analysis of financing methods of small and medium enterprises (SME) with a focus on OECD countries. By reviewing the research literature and using the opinions of experts in the field of financing, the most important risks of small and medium enterprises are identified and then classified. In the final parts of this study, the types of risks are identified through interviews with financing experts and use of a questionnaire. The risks of financing methods are prioritized, and seven financing methods are ranked in terms of credit risk.In the second part of the paper, by using factor analysis and the method of principal components, credit risk indicators are aggregated into a single index (representing the credit risk variable). Then, this variable is included in the decision tree as a dependent variable and the sub-indices are considered as independent variables. Decision tree is one of the most famous and oldest methods of making a classification model. It is a non-parametric method of classifying variables and includes various algorithms. In order to design a decision tree, the CHAID method is used. This method is also known for identifying automatic chi-square interactions in each step. The predictor variable that has the highest correlation with the dependent variable is used in the model and the decision tree.

By reviewing how small and medium enterprises are financed in OECD countries, it was found that those countries use various methods for financing small and medium enterprises. In many countries, there has been an increasing trend in the share of long-term loans compared to short-term credits. Based on the evidence provided, there is a significant difference between alternative financing methods in terms of the average scores of credit, financial, legal, social and environmental risks. Based on these findings, the credit risk in the method of financing small and medium enterprises through the facility channel is higher than in other methods, and the lowest credit risk associated with the method of financing is from the company's internal resources.According to the results of the decision tree, the financing method of small and medium enterprises has also been effective as a control variable on the level of credit risk of these companies. Management skills in small and medium enterprises are the most important factor to explain the credit risk trend of these companies. Therefore, in order to develop a credit risk control strategy in this group of companies, the suppliers of these companies' financial resources should first pay attention to the ability and management skills of these companies. Based on historical records, if the managers of small and medium enterprises do not have acceptable skills in running the company, it is likely that the company will not be able to fulfill its obligations in the near future.The quality of financial statements is also very important in predicting the credit risks of this group of companies. Based on this, banks and other institutions that provide financial resources for small and medium-sized companies should evaluate various indicators of the quality of financial statements in these companies in order to develop a suitable strategy to reduce risks. Dividend policies and current ratio of the company should also be considered in the credit risk control strategy of small and medium enterprises.

Considering that the goal is to reduce credit risk, financiers should try to increase the contribution of small and medium enterprises in financing their costs. If the goal is to reduce the total risk, however, the financing of small and medium-sized enterprises by business angels is less risky, and business angels should be used to finance small and medium-sized enterprises. The government can also help increase the investment of business angels in small and medium enterprises by creating various incentives, including tax incentives.

Partly on the issue that value stocks earn higher returns than growth stocks, there is agreement, but the interpretation of the cause is controversial and there is no clear explanation for this stock feature. According to investment-based asset pricing theory, the financial inflexibility is the reason for expected return of value firms covary more with recessions than the expected returns of growth firms. The present study seeks to determine the effectiveness of the value anomaly of financial inflexibility with attention to the business cycle. In order to achieve the research goals using systematic removal sampling method, the monthly data of 450 year - firm has been used during the period from 2008 to 2017. To test the research hypotheses, regression method using time series and panel data was used. The results of the research show that the financial inflexibility leads to a positive risk premium in the stock level and investment portfolios and value firms gain higher future returns than growth firms due to compensate for the risk of financial inflexibility, and Finally, the effect of financial inflexibility on stock risk premium is not constant during the business cycle, and in times of recession, value companies are more exposed to risk of financial inflexibility than growth companies.Partly on the issue that value stocks earn higher returns than growth stocks, there is agreement, but the interpretation of the cause is controversial and there is no clear explanation for this stock feature. According to investment-based asset pricing theory, the financial inflexibility is the reason for expected return of value firms covary more with recessions than the expected returns of growth firms. The present study seeks to determine the effectiveness of the value anomaly of financial inflexibility with attention to the business cycle. In order to achieve the research goals using systematic removal sampling method, the monthly data of 450 year - firm has been used during the period from 2008 to 2017. To test the research hypotheses, regression method using time series and panel data was used. The results of the research show that the financial inflexibility leads to a positive risk premium in the stock level and investment portfolios and value firms gain higher future returns than growth firms due to compensate for the risk of financial inflexibility, and Finally, the effect of financial inflexibility on stock risk premium is not constant during the business cycle, and in times of recession, value companies are more exposed to risk of financial inflexibility than growth companies.