درخت حوزه‌های تخصصی

This study examines the effect of Iranian oil sanctions on the International oil market network for the first time with Complex Network Analysis (CAN) with Diebold -Yilmaz and Arch indexes from 1991:01 to 2019:12. The analysis was performed for two periods before and after the sanctions, and the results were compared. Results showed that the Iranian oil market in both networks before and after the sanction is one of the influential nodes in the oil network. The volatility spillover of the Iranian oil market in the oil network market has increased after the sanctions. Also, on Iranian oil, volatility spillover from other oil markets has to Iran increased after the sanction. But overall, the sanction has not had a significant impact on the oil market network. The Iranian oil market volatility was receiver before the sanction in the network, but its role changed after the sanction, and it became a sender node.

Purpose: This paper mainly aimed to provide a model for policy-making in the private school system. Methodology: Based on the literature review, 17 influential factors were identified with the mixed method of survey and theme analysis. In the qualitative part, the semantic pattern was discovered and analyzed with the RQDA qualitative analysis software, and in the quantitative part, the Interpretive Structural Modeling (ISM) method was used to determine relationships. Statistical population in the qualitative part were 20 people (policy-making experts, professors and senior managers) were selected and participated in a semi-structured and quantitative interview. A number of 1140 schools were selected from Northern, Central and Southern Khorasan using a simple random sampling method with proof of validity and reliability. Findings: The results of factors affecting policy-making based on fuzzy numerical average are: Investment 0.72, government aid 0.87, managers 0.74, teachers 0.73, executive agents 0.75, educational equipment 0.81, government-approved laws 0.89, banking system 0.88, tax and insurance 0.73, municipal, housing and urban organizations 0.33, community members 0.65, experts 0.8, Farhangian University training 0.8, coordination council 0.81, specialized founders 0.78, special schools 0.78, supervisory council 0.88, which because municipal organizations, housing and urban development 0.33, and community members 0.65 were lower than the average, they were rejected and the rest were accepted. Strategies in the first level (managers, executives and teachers) are the most effective and the sixth level (government laws) is the most effective. Conclusion: Based on the results, the other 15 factors that have a great impact on policy-making should be considered.