درخت حوزه‌های تخصصی

Foreign investment contracts are one of the major ways to absorb the technology in the oil and gas industry. Studies show that governments and investing companies prefer to gain more profit, control economic resources and political influence by investing financially and technically in developing countries unless buying foreign technology and intellectual property rights directly. On the other hand, for developing countries, the sum of foreign capital and technology within a contract is a great opportunity for advancement, provided that the choice of technology transfer is made with due regard to the country's needs, requirements and economic future. In this article, after examining the relationship between investment and technology transfer and its various methods in the oil and gas industry, we will analyze the constraints and barriers of technology transfer through foreign investment in developing countries (including Iranian law) and provide a solution. Keywords: Foreign Investment, Technology Transfer, Oil & Gas Industry, Mutual Trade, Intellectual Property Rights.

The main purpose of this research is to explain why and how the stock liquidity affects the default risk of petrochemical and petroleum products companies listed in Tehran Stock Exchange. The study used experimental data and parametric tests in order to estimate the relationship between stock liquidity and default risk through information efficiency and corporate governance roles of stock liquidity. The present research is applied in terms of purpose and is a descriptive-correlative study. All data required regarding the stock liquidity, price, trading volume and days, stockholder ownerships, etc., were extracted from Rahavard Novin database software. To investigate the relationship between variables, the multivariate regression analysis model using OLS method was applied in Eviews platform. The empirical findings showed that there is a significantly negative relationship between stock liquidity and default risk of petrochemical and petroleum product companies listed in TSE and also stock liquidity can affect the default risk through reducing the information asymmetry in the stock pricing process and strengthen the corporate governance. These results could provide visible signals based on the realities of the market in order to improve models to assess and predict the risk of default and lead managers and decision makers of suppliers, customers, partners and stakeholders, to conclude more flexible contracts with petrochemical and petroleum products companies and ultimately better mitigate business risks.

The transformational leadership is known as one of the most important and effective factors on organizational behaviors of the employee. Creation of the motivation and energy in the employees requires a leader with inspirational qualities. On the other hand, the organizational citizenship behavior is a factor in shaping. The current study was aimed at investigating the effects of transformational leadership on the employees’ organizational citizenship behavior. In addition, some personality traits are revealed in this study to be one of the main factors shaping the behaviors and to be moderating variables. The current study is of applied type in terms of the objective and a descriptive-correlational study in terms of data collection procedures. The data needed for the purpose of the study were collected by the use of questionnaire distributed among The 242 persons of statistical population, and these data have been analyzed by Structural Equations Modelling. The results indicated that all six hypotheses of the study were approved. In other words, the transformational leadership has positive and significant effects on both individual and organizational aspects of the organizational citizenship behavior. Also, the positive moderating role of neuroticism and extroversion in effects of transformational leadership on the individual and organizational aspects of organizational citizenship behavior was approved. The results show that the statistical community can utilize its human resources potential to create competitive advantage and develop the voluntary activities of its employees, provided that it is able to optimally implement transformational leadership style.

<span class="H5CharChar"><span>This research identifies decision-making styles and components of succession planning and examines their relationship.The statistical population (190 people) in the present survey research consists of Pars Special Economic Energy Zone (PSEEZ) Organization managers who were assessed using the Census Method. Scott and Bruce’s general decision-making questionnaire and Kim’s succession planning questionnaire were used with a response rate of 90%. Data was analyzed with the SPSS and Smart Planning softwares. In analyzing the obtained results, the dominant decision-making style is rational, followed respectively by the intuitive, dependent, avoidant, and spontaneous decision-making styles. By investigating succession planning capabilities, the results of the research showed that among items in the succession planning system, candidate evaluation ranked first, followed by policy setting, system evaluation, and candidate development respectively as the priorities of the statistical population. The research hypothesis based on a significant relationship between the decision-making styles and organizational succession planning capabilities was confirmed. In order to improve its human resources and bridge the gap with global standards, it is therefore proposed that the senior management of the organization take serious steps to implement the succession planning program. It is also recommended that other esteemed researchers carry out the present research again after the implementation of the succession planning program in the studied organization and compare the results before and after the implementation of the program.</span></span>

Considering global warming and importance of sustainable growth in economic sub-sectors, this paper presents and estimates an empirical model of renewable energy consumption for the industrial sector of selected OPEC and non-OPEC countries over the period 1990-2014. Panel co-integration estimates by Pedroni (1999,2004) and Westerlund (2005,2006) show that, in the long term, increases in industrial value added per capita, real oil prices, and CO2 per capita are found to be major drivers behind per capita renewable energy consumption for both OPEC and Non-OPEC nations. Panel Granger causality by the Dumitrescu and Hurlin (2012) method confirms that there are bi-directional causality relationship between research variables and therefore verify feedback hypothesis. Finally, FMOLS and DOLS results show that when industrial value added per person increases, per capital renewable energy consumption increases by greater magnitude in non-OPEC than OPEC countries; also an increase in CO2 emissions per person increases per capita renewable energy consumption by greater amount in non-OPEC than OPEC nations.

Despite numerous upstream communications, there is still no comprehensive and reliable model for implementing these tasks; neither in the literature nor in oil & gas industry. Previously developed models focus more on the outsourcing process and it seems that there is no outsourcing model that has been developed with an eye to utilizing internal capabilities in a portfolio of alliances and be compatible with the unique characteristics of the Iran's oil and gas industry. In an attempt to validate the components of the "Model of development outsourcing physical assets in the oil and gas industry, with the approach of upgrading internal capacity" this study used a questionnaire in a Likert scale (very high, high, moderate, low, and very low). From the population, 102 people participated and a total of 100 completed questionnaires received. Using confirmatory factor analysis the validity of the variables and the questionnaire were examined. In this research, SPSS and Smart PLS softwares were used and the error probability level was considered α = 0.05. The results showed that the model has a good fit. According to the Geiser index, the fit of the model was medium to high and the goodness of fit criterion was equal to 461.0 and indicates the optimal fit of the model.

Organizations enter international competitive markets because of different motivations such as: gaining international fame, long-term growth, increasing profitability, taking advantages of economies of scale, maturing domestic market, the intensity of competition in a domestic market, governmental rules and regulations; but actually, all of the companies aim to export performance improvement. According to the oil industry situation in Iran's vision, the 4th paragraph of general energy policies and, not having a comprehensive model of Iranian oil products export performance, this research could improve and develop this industry in international markets. This research method is phenomenology and statistical society includes export and marketing managers and experts in the oil industry. Non-probable sampling and snowball sampling method was used in this research. Regarding this qualitative research, semi-structured depth interview was used to gather information and 7 deep interviews were done regarding saturated rule. Structure, internal, interpretive and descriptive validity were used in order to Validation; for surveying reliability, revision while coding and surveying by another informed person were used to insure correct coding. After open, axial and selective coding and using Atlas.ti qualitative software, suggested antecedents of Iranian oil products export performance includes: market orientation, company resources, marketing mix, macro environment and items of Iranian oil products export performance includes: financial performance, customer satisfaction and customer retention. Most of the previous studies have focused on positivism and quantitative research methodology while we used the qualitative method in this research.

Stock price prediction is one of the crucial concepts in finance area. Machine learning can provide the opportunity for traders and investors to predict stock prices more accurately. In this paper, Closing Price is dependent variable and First Price, Last Price, Opening Price, Today’s High, Today’s Low, Volume, Total Index of Tehran Stock Exchange, Brent Index, WTI Index and Exchange Rate are independent variables. Seven different machine learning algorithms are implemented to predict stock prices. Those include Bayesian Linear, Boosted Tree, Decision Forest, Neural Network, Support Vector, and Ensemble Regression. The sample of the study is fifteen oil and gas companies active in the Tehran Stock Exchange. For each stock the data from the September 23, 2017 to September 23, 2019 gathered. Each algorithm provided two metrics for performance: Root Mean Square Error and Mean Absolute Error. By comparing the aforementioned metrics, the Bayesian Linear Regression had the best performance to predict stock price in the oil and gas industry in the Tehran Stock Exchange.