درخت حوزه‌های تخصصی

Objective: The purpose of this research is The effect of media tourism and sports diplomacy on product brands. Methodology: This is a correlational study and the statistical population of this study is PhD students in sports in Iran. Based on Morgan's table, 150 Individuals were selected by random method. In order to achieve the research goals, a comprehensive list of sports indicators was prepared and adjusted and a researcher - made questionnaire was developed. The validity of the questionnaire was confirmed by 15 sports management professors and their reliability was in a preliminary study with 30 subjects and Cronbach's alpha was calculated to be 0.909 . Descriptive and inferential statistical methods such as exploratory factor analysis, and confirmatory factor analysis were used to analyze the data using SPSS and LISREL software. Results: The results showed that the main variables have eight general dimensions of sport tourism, sport diplomacy, sports investment, sporting-cultural, sports exports, sports people-sports, sports achievements and media sports, and has a total of 50 indicators. Conclusion: According to the results of the research and the identified indicators, the proposed model is a suitable theoretical model for sport indicators affecting the brand of the country.

The purpose of this article is to measurement and testing a high performance organizational culture model in Iran's petrochemical industries.This research is of quantitative type and in terms of purpose, exploratory and data collection method is survey and from the point of view of cross-sectional time. Data collection tool is a researcher-made questionnaire whose validity has been confirmed by industrial and academic elites. The reliability of the questionnaire was confirmed by Cronbach's alpha test (coefficient of 0.815). The statistical population of the study included 8050 employees of Petropars, Jam, Lavan and Fanavaran petrochemical companies working in Iranian Petrochemical Industries. The sample size was calculated by the Cochran's formula of 385 people. 420 questionnaires were distributed and 400 correct questionnaires were collected. The sampling method is stratified random. Data analysis was performed using structural equation modeling using Amos Software. The results of statistical analysis were confirmed by confirmatory factor analysis and in terms of significance by the method of structural equations up to the level of the main categories and after confirmation of formal and content from 400 experts and managers of petrochemical industries. In the next step with a quantitative approach, the current state of high performance organizational culture according to the pattern the extract was assayed. For this purpose, mean comparison, regression and repetition tests used. The results obtained from field survey indicated that the high-performance organizational culture model was approved and dimensions of Behavioral factors, Internal bedding factors, External bedding factors and Process factors were placed in the first to fourth priorities, respectively.

The main purpose of this study is structural-interpretive modeling of industrial brand competitiveness in the petrochemical industry. This research is a qualitative-quantitative mixed exploratory research. The statistical population in the qualitative part of the research includes faculty members and experts in the field of industrial management, marketing and industrial brand, professors familiar with the subject of research and managers and deputies with experience in petrochemical companies in the country using 16 snowball sampling method were chosen. In a small part, the statistical community includes personnel (managers, deputies and experts) of the marketing and sales department of petrochemical companies in the country. For sampling, due to the small size of the statistical population and the irreversibility of the questionnaires has been used the whole number and the whole population has been considered as a sample in a small part (N = 255). The research tool in the qualitative part of the interview is semi-structured and in the quantitative part the researcher has made a questionnaire. For data analysis in the qualitative part, fuzzy Delphi theme and technique analysis has been used and in the quantitative part, ISM technique has been used for data analysis. In the qualitative part of the research, a total of 14 variables were identified as factors affecting the competitiveness of the industrial brand.

The Forex market is known as the strongest and richest financial market in the world that has been operating continuously. This market is formed based on the exchange rates of different countries, as well as the prices of oil and gold. The price of currency pairs in the Forex market as the largest market for financial transactions has always been of great importance. The purpose of this article is to study the Forex market based on chaos theory. Its statistical population includes three currency pairs Euro / Dollar, Pound / Dollar and Dollar / Yen. The period of prices of the surveyed currency pairs is from January 2017 to December 2021 and its time range includes daily prices. The results showed that after fitting the model and performing the BDS test, on the GARCH model residues in different dimensions and ε of all 6 groups, which are IID residues and there is no dependence on them. The results of Lyapunov's maximalism test showed that for all dimensions, and all time intervals (n), the value of Lyapunov's exponent is a positive and small number, indicating that the time series of the triple currency pair follows a chaotic process.

The issue of transportation, which today as a socio-political phenomenon plays a very sensitive and important role in the quality and socio-economic structure of a society, forms the basis of modern urban life and the needs of human movement. In this research, our goal is to design a system dynamics model in the form of combined transport system to try to reduce costs, time and increase customer satisfaction by taking into account the uncertainty in the combined transport system, especially uncertainty in demand. Considering the productivity required by both of passengers and urban management, the importance of the issue and its impact on other sectors, as well as the lack of sufficient research in this field, in this study, the design of intra-city transportation system will be studied. The purpose of this planning is to determine the location of stations for public transportation as well as to determine the best route to reach the next station. Initially, the IoT system is used to collect and analyze historical data. Then the effective parameters and input values of the mathematical programming model are determined. The implementation of the model determines the appropriate outcomes for the decision variables. In other words, this leads to determining the optimal stations and the appropriate route to transport passengers between stations so that travel time and cost are minimized. In addition to material profitability, this includes passenger satisfaction, increased willingness to use public transport, reduced traffic, and reduced environmental impact, increased reliability of public transport and more accurate forecast of vehicle arrival at the station.

Accurate stock market prediction can assist in an efficient portfolio and risk management. However, accurately predicting stock price trends still is an elusive goal, not only because the stock market is affected by policies, market environment, and market sentiment, but also because stock price data is inherently complex, noisy, and nonlinear. Recently, the rapid development of deep learning can make the classifiers more robust, which can be used to solve nonlinear problems. This study proposes a hybrid framework using Long Short-Term Memory, Autoencoder, and Deep Neural Networks (LSTM-AE-DNNs). Specifically, LSTM-AE is responsible for extracting relevant features, and in order to predict price movement, the features are fed into two deep learning models based on a recurrent neural network (RNN) and multilayer perceptron (MLP). The dataset used for this is Dow Jones daily stock for 2008-2018, which was used in this article. Besides, to further assess the prediction performance of the proposed model, original stock features are fed to the single RNN and MLP models. The results showed that the proposed model gives the more accurate and best results compared to another. In particular, LSTM-AE+RNN shows a better performance than the LSTM-AE+MLP. In addition, hybrid models show better performance compared to a single DNN fed with the all-stock features directly.

This research aims to design an integrated model for digital marketing based on marketing intelligence and validate the model in the ISACO Company. The research uses a mixed-methods descriptive survey design. In the qualitative phase, the model and nine theoretical hypotheses were proposed using grounded theory and a semi-structured questionnaire. Fourteen academic experts and managers in the area of marketing and sales were interviewed until theoretical saturation was achieved. Then, the model and proposed hypotheses were validated using the quantitative method of structural equation modeling. A questionnaire rated by the Likert scale was distributed among 340 active employees in the relevant areas to the research topic with at least a Bachelor’s degree chosen by the convenience sampling method in the ISACO Company and affiliated companies. The structural model and hypotheses related to causal factors, core category, contextual factors, strategies, and consequences (except the moderating role of environmental factors) were validated by the research findings.