درخت حوزه‌های تخصصی

The religious nationalism in Neo-Taliban era implies a process of distancing from tribal legitimacy toward a more nationalistic narration. The research aims to understand the Religious nationalism in the approach of Neo- Taliban toward state-building and the peace process. In this regard, the social theory of Ibn Khaldun is employed to measure the process, the logic and the consequences of the birth of its religious nationalism. It becomes clear that the group spiced its religious narration with nationalist changes to win more social support and legitimacy by overcoming ethnonationalism and emphasizing on nationalistic notions that ultimately weakened its traditional legitimacy.

In the Industry 4.0 era, many pioneering industries are leveraging emerging technologies such as the Internet of Things (IoT) as solutions in the digital age. One of the largest and most active industries in Iran is the food industry, which stands to benefit significantly from these advancements. Achieving a sustainable competitive advantage is often possible at the level of the supply chain, where companies use information and communication technologies, such as IoT, to coordinate information, finances, and materials among supply chain actors. This research aimed to identify the key success factors (KSFs) for implementing IoT in the food supply chain. Firstly, through a systematic literature review, the KSFs for IoT implementation in the food supply chain were identified. To develop a measurement model, confirmatory factor analysis using structural equation modeling was employed, making the research applied-descriptive. A questionnaire was designed and completed by 142 members of the "Amadeh Laziz" supply chain (a case study), who were selected using a stratified random sampling method. Confirmatory factor analysis and LISREL 8.83 were then used to validate the proposed model. Finally, the cause-and-effect relationship between KSFs in IoT implementation in the food supply chain was analyzed using Grey DEMATEL. Based on the confirmatory factor analysis findings, the KSFs in implementing IoT in the food supply chain were identified as technical, economic, legal, cultural and social, security, applicability of IoT throughout the supply chain, and implementation of IoT applications. Thus, the measurement model included eight factors and 27 measures. According to the cause-and-effect relationship findings, "Implementation of IoT applications" and "Economic" factors were found to be mostly influenced, while "Applicability of IoT throughout the supply chain" and "Technical" factors were recognized as the most influential. The results of this research can guide food producers and technology policymakers in their supply chains and help avoid trial and error in IoT implementation by leveraging global and national experiences.

Nowadays, maritime trade plays an important role in the economy of some countries, including Iran. Especially, some of the ports in Iran that are located on the Persian Gulf border carry out container shipping operations. Therefore, in the present study, we investigate the impact of the most important macroeconomic factors on the container trading in Iranian ports in the Persian Gulf. For this purpose, we employed the autoregressive distributive lag (ARDL) approach for the estimating effect of oil price, gross national production, industrialization and exchange rate on the container trading volume. The used data are quarterly. Container business data is related to the ports of Shahid Rajaei, Bushehr, Imam Khomeini, Khorramshahr, Shahid Bahonar, which are obtained from the Ports and Maritime Organization. The results of this study show a negative impact of increasing oil price on Iran's container trading. Moreover, our empirical findings indicate that the effect of industrial indicator (INDS) on the container trading volume is positively. Also, one percent increases in gross national production (GNP) leads to %0.37 increase  in container trading, and one percent increase in exchange rate (EXH)  leads to %.17 decrease  in container trading, respectively. Finally, coefficient of ECM is -0.54. It means that speed of adjustment in the function of container trading volume is relatively high, and in each period, 54 percent deviation from long run direction of container trading volume to be corrected by variables of model.