درخت حوزه‌های تخصصی

Blockchain technology can bring opportunities, such as reducing transaction business costs and increasing transparency to the oil and gas industry. This model is adopted as a public maturity model in business processes, industry, and IS/IT organizations. This model is classified into five levels of emerging, identified, defined, operational, and mature to identify the applications of this technology toward flourishing and providing the ultimate solution to most of the organization's problems. Therefore, this qualitative research has been designed to identify maturity levels to provide a conceptual model of maturity levels of blockchain technology in grounded theory. First, the initial factors extracted through reviewing the research background; then, by conducting semi-structured interviews with 12 blockchain technology experts, the data were collected. It is noted that the interview was stopped as the duplicated and identical data, based on grounded theory research strategy The meanings and characteristics of the blockchain maturity model challenges presented in three stages of coding. The reliability of the interviews has confirmed by the reliability methods of Test-Retest and intra-subject agreement. The results of data analysis indicate that experts at the emerging level to primary educating and technology monitoring, at the identified level, to recognize the applications of this technology and regulation, at the defined level, to develop a roadmap, proof of concept, and Feasibility, At the operational level, the stakeholder resistance, and at the matured level, the entry new members, the consortium, and increasing attention to maturity requirements, is a spotlight with more frequency than other indicators and elements.

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.

The challenge of environmental pollution and climate change have made countries to develop energy transition progress to move from non-renewable energy sources towards renewable ones. This paper seeks to consider energy transformation process and analyze its pattern in Iran by modeling through the ARDL bounding testing method over the period of 1993-2018. The empirical estimations depicted that in the long-run economic growth and inflation rate negatively impact on energy transformation of Iran, while increase in carbon dioxide emissions and appreciation of Iran’s national currency accelerate the energy transition process in Iran. Regarding the short-run relationship, the major results represented an evidence of positive impact of exchange rate on Iran’s energy transition process, while the other variables have negative coefficient. As a major concluding remark, for the case of Iran, the findings prove that the influential impacts of explanatory variables on energy transformation are stronger in long-run rather than in short-run. Therefore, the presence of efficient long-run energy planning is recommended.

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.