درخت حوزه‌های تخصصی

This study is a structural equation modeling study. It has investigated the effect of literary tourism during the Covid-19 virus pandemic on tourists' behavioral goals through the attitude of mental norms. The present study is a one-section survey. The statistical population of this study includes all tourists who visited the cultural and spiritual sites of Shiraz, like Hafez and Saadi tombs. Literary tourism is a concept that praises the culture and heritage of a destination. The concept is to go to locations worldwide highlighted in some non/fictional novels. They can fascinate new tourists, increase the number of domestic travelers, and promote tourism destinations. Because the sample size is unknown through Cochran's formula with a 95% confidence level, 384 people were selected by the available sampling method. They answered the questionnaire about the effect of literary tourism during the Covid-19 virus pandemic on tourists' behavioral goals. The collected data were analyzed by using Amos software and SPSS. Confirmatory factor analysis, the reliability and validity of the contract measurement model, Cronbach's alpha results (above 0.7), and composite reliability (greater than 0.7) for the reliability and mean of the extracted variance (greater than 0.5) were confirmed. The structural equation model was used to test research hypotheses and the relationships between research variables (spiritual factors, behavioral purpose). The results showed that spiritual variables, in general, have a positive effect on the behavioral goal of tourists through the attitude of tourists' mental norms. This attitude was also recognized as a significant mediator between spiritual perception and behavioral goals. The findings of this study indicate that literary tourism has a positive, significant effect on increasing re-visit tourism destinations through attitude, mental norms, and perceived behavioral control.

Due to the rapid advancements in computer technology, researchers are attracted to solving challenging problems in many different fields. The price of rainbow options is an interesting problem in financial fields and risk management. When there is no closed-form solution to some options, numerical methods must be used. Choosing a suitable numerical method involves the most appropriate combination of criteria for speed, accuracy, simplicity and generality. Monte Carlo simulation methods and traditional numerical methods have expensive repetitive computations and unrealistic assumptions on the model. Deep learning provides an effective and efficient method for options pricing. In this paper, the closed-form formula or Monte-Carlo simulation are used to generate data in European and Asian rainbow option prices for the deep learning model. The results confirm that the deep learning model can price the rainbow options more accurately with less computation time than Monte-Carlo simulation.

Bank efficiency is essential in the establishment of healthy financial systems in countries. In this respect, bank managers are expected to respond correctly to questions raised about the financial performance of banks, which is practically impossible without examining the efficiency of the branches under their oversight. In most previous studies, Data Envelopment Analysis was used for evaluating the efficiency of financial branches. The large number of evaluation factors in the analysis leads to an increase in the number of efficient units and thus a decrease in the power of discrimination. Taking a systematic view into consideration, in this study, a step-by-step method was developed for selecting the effective factors in the efficiency of different branches of one of the Iranian Banks based on the effect of each factor or indicator on the whole system’s efficiency including the branches under evaluation. To this end, a new method was proposed for the evaluation of system’s efficiency and some of its properties were stated.