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To study the key determinants of chronically high inflation in Iran.

Relying on annual data from 1978 to 2019, the authors employ an Auto-Regressive Distributed Lag (ARDL) model and Error Correction Model (ECM) to study the inflationary effects of monetary and fiscal policies as well as exchange rate swings and sanctions intensification.

The authors find that increase in money supply, depreciation of nominal exchange rate, increase in fiscal deficit and intensification of sanctions are among the key drivers of inflation in Iran. Their impact is profound in the long run, but in the short run only money supply and currency depreciation are significant. Also, when exploring the inflation in different components of Consumer Price Index (CPI), we find robust long- and short-run effects from money supply and exchange rate, while the effects of fiscal deficit and sanctions vary across different components.

The authors contribute to the literature by setting apart the long-vs short-run effects of key variables on inflation in Iran. The authors also employ improved measures of fiscal deficit and sanctions that are shown to be of significance in the long run. Lastly, the authors go beyond the aggregate index and examine the variations in different CPI components.

This paper aims to develop a framework for competitive advantage by systematic quantitative methodology based on resource-based view and dynamic capability theory. Strategic agility was used as a dynamic capability.

Data were collected from a survey aimed at manufacturing companies from five manufacturing industry in Semnan, Iran. A total of 102 questionnaires were received from 13 companies using convenience sampling. Fuzzy two-stage data envelopment analysis model (DEA) was used to analyse the data collected.

The results indicate that there is close internal relationship among firm resources, strategic agility and competitive advantage, and their inherent relationship makes constant returns to scale (CRS) scores closer to 1. In most of the companies, the second process which transforms strategic agility to competitive advantage is the main cause for unsatisfactory performance in gaining competitive advantage.

The innovation of this paper is in its model and method. There is no research has been ever done on the relationship among firm resources, strategic agility and competitive advantage. Moreover, to obtain a competitive advantage structure, DEA technique was adopted which is a new approach in this area.

This study aims to investigate the impact of message framing (emotional vs rational) on social media users' ability to accurately detect information and their intention to share messages about the COVID-19 vaccine.

Using an experimental design approach, the authors recruited 600 adult participants via a crowdsourcing platform. Participants were randomly assigned to receive emotional or rational messages and their ability to accurately detect information and intention to share messages were assessed.

The results showed a significant multivariate effect of message framing on both the detection of accurate information and intention to share (p < 0.001). Participants who received emotional messages demonstrated better performance in the detection and sharing task than those who received rational messages. Gender and age also had significant main effects on the outcomes, with women performing better than men and younger participants performing better than older participants in detecting the accuracy of information. The interaction effects of the independent variables were not statistically significant (p = 0.098).

The findings highlight the importance of considering emotional factors in combating the spread of messages about the COVID-19 vaccine on social media. Practitioners responsible for social media content should strengthen the content review mechanism, with an emphasis on screening content with high emotional arousal.

The purpose of this paper is to explore the ability of capillary‐attached gas sensor (CGS) in detecting components of gas mixtures, including a volatile organic gas and hydrogen in a wide range of concentrations.

Diverse feature extraction and classification techniques were employed to analyze the response of CGS when applied to different mixtures.

It was observed that the response of CGS to the above gas mixtures could be distinguishable. While evaluating the results of the classification technique, it was implied that hydrogen, in the presence of the volatile organic gases, could be detected perfectly by analyzing the response of the CGS. Separating techniques, which yielded a high rate of classification, were used to separate mixtures containing hydrogen and organic gases from other organic gas mixtures without hydrogen.

The results presented in this paper prove the ability of CGS in fabricating an olfactory machine for analyzing the components of gas mixtures.