درخت حوزه‌های تخصصی

According to published reports on the level of internalization of commercial vehicles, it shows that most carmakers pursue a minimal domestic manufacturing policy and less have a policy of increasing domestic manufacturing on their agenda. The most important research gaps are need to study R&D activities in the form of a dynamic system in the automotive industry based on the three variables of supply network capabilities, product innovation and process. The purpose of this study is to provide a dynamic model to investigate the behavior of a commercial vehicle R&D system. The present study is applied in terms of purpose. The basis of the system analysis is previous research as well as information obtained from interviews with automotive industry experts. For this purpose, first the variables affecting the cycle of R&D activities are studied and modeling is presented using the dynamics approach. Also, various policies on the performance of the development research cycle based on the time of knowledge acquisition and product introduction have been simulated and analyzed. The results of system performance simulation show that the weak share of research and development budget along with low and limited market share and lack of proper investment makes the research and development capability at an unfavorable level during the next 10 years. Therefore, it is suggested that the necessary measures in this regard be taken in the fields of product sales marketing along with appropriate investment in research and development.

In the current situation, one of the concerns in the fight against smuggling of goods and currency is the improvement of the inter-organizational network. The purpose of this research is to design and implement organizational architecture to improve the management of organizational networks with SWOT approach, in this area using the artificial neural network toolbox and fuzzy logic in Matlab. This research is applied-modeling in terms of purpose. The statistical population includes expert professors and experts of organizations in charge of combating smuggling of goods and currency. After distributing 100 questionnaires, the sample size of this study is equal to 96 experts who were selected by a combination of two methods of non-probabilistic purposive sampling and snowball sampling. The results show that using the intelligent system, the status of "success of the organization's network management" can be examined numerically and more accurately: In terms of ideal importance, if; The "Network Management Based on EA Application Layer" status is good, ie exactly 0.813, and "Network management based on EA data source layer" is good, ie exactly 0.824, and "Network Management Based on EA Central Component Layer" is good, ie exactly 0.819, and "Network Management Based on EA Data Preparation Layer" is good, ie exactly 0.812, and "Network management based on EA service quality layer" in good condition, ie exactly 0.815;Then; The status of "successful implementation of the organization's network management" is at the top level (fifth level), ie exactly 0.952.According to the membership functions of language variables by experts, the value of 4.76 within the 5-value range in the range defined for the "excellent" language variable, ie the success status of the organization's network management, has been calculated exactly 0.952.

Several theories have been presented about the corporate capital structure; some researchers believe that the structure of capital and firm value depend on each other. Moreover, others believe that the firm value is not influenced by any changes in the structure of capital, and with the change in the structure of capital, the value of the firm remains constant. The present research studies the historical theories of capital structure and presents the classification of existing theories in terms of the optimal capital structure. This classification is subdivided into two categories: the presence of an optimal capital structure and the absence of an optimal capital structure. With the historical review of theories of capital structure and the presence of an optimal capital structure or the lack of an optimal capital structure, it seems that none has been a complete model and so far, no one has been able to provide an optimal capital structure. In this research, from the system dynamics approach and using VENSIM software, the capital structure and share price of the company have been studied and the results of model simulation with real data are to ensure the correct performance of the model behavior. And the results show that financing through debt in the capital structure of the company has a significant effect on the debt cost rate and capital cost rate and does not have a significant effect on stock prices. Finally, the share price shows little sensitivity to the company's financial structure.

Evaluating the performance of companies using their financial ratios is a challenging task that is expected to become more straightforward by reducing the dimensionality of the data. The purpose of this study is to evaluate the performance of companies using a hybrid model for investment-related decision making through which the mean value of various financial ratios are calculated based on the investor's risk-taking behavior so that the number of all criteria is reduced to one single value for each alternative. To do so, a sample of 172 companies listed in Tehran Stock Ex-change was selected from 2008 to -2018. Firstly, the financial ratios were prioritized using decision trees regression analysis (type CART) and TOPSIS Technique. The results showed that Gross Profit Margin and Debt to Equity Ratio are the most and the least important factors, respectively. Then, using OWA (Ordered Weighted Averaging Aggregation) operator, the role of investor’s risk-taking behavior was investigated, and the results showed that investor’s risk-taking behavior changes the outcome of the decision-making process significantly.

Resource-based view (RBV) theory analyzes and interprets company's resources in order to find out how organizations gain a competitive advantage. This theory fo-cuses on the implications of the complicated features of a company as the resources for excellent performance and comparative advantage. According to this view, capa-bilities and resources of the company are the main factors in explaining the func-tional results or competitive advantage. Resources can be considered as inputs which enable companies to do their activities. The purpose of this research is to introduce a resource-based theory for calculating the efficiency score of accepted companies in Tehran Securities Exchange by using Data Envelopment Analysis (DEA). In this regard, the financial statements of 190 companies accepted in the exchange for the 2009 – 2018 period have been analysed. Efficiency indicators, which include 4 categories of resources (10 inputs) and 5 outputs, have formed the axis of the mentioned technique. The results of implementing this model for compa-nies with the efficiency score of one, indicates first a minimal input consumption compared to competing companies in the same industry while producing more out-put, and second, using the resource-based view theory (integration of tangible and intangible resources) enables the company to push the boundaries of efficiency. Finally, it can be said that utilizing minimum and maximum resources simultaneous-ly leads to a focus strategy-type competitive advantage.

This study, attempts to estimate and compare four different models of jump-diffusion class combined with stochastic volatility that are based on stochastic differential equations, and their parameters latent variables are estimated by Markov chain Monte Carlo (MCMC) methods. In the Stochastic Volatility with Correlated Jumps (SVCJ) model, volatilities are scholastic, and the term jump is added to both scholastic prices and volatilities. The results of this study showed that this model is more efficient than the others are, as it provides a significantly better fit to the data, and therefore, corrects the shortcomings of the previous models and that it is closer to the actual market prices. Therefore, our estimating model under the Monte Carlo simulation allows an analysis on oil prices during certain times in the periods of tension and shock in the oil market