درخت حوزه‌های تخصصی

Spirituality is a universal and human phenomenon which is not restricted to any religions or religious groups. In fact, spiritual experiences are thought of as special human experiences which give meaning to life. Such enlightening experiences make man connect to a higher being. Therefore, spiritual tourism is a journey on which a traveller achieves a perception of spiritual experiences. Such experiences can be gained on a long walk with a group of sympathetic fellow travelers towards religious destinations. They may also be achieved while watching a pristine view of the nature which enables the viewer to understand the creator of the scene and reach deep meanings through a creative insight. Creative tourism is mostly manifested as a form of inner expression and discovery, a tool of improvement in identity and distinction or a search for the expansion of capabilities and experiences as well as an ability to reflect the life purposefully. This type of tourism belongs to self-actualizing individuals. In fact, they are educated people who seek to give meaning to life. Therefore, the creative tourism theory is the closest approach to tourism combined with spirituality. These two categories overlap with each other significantly. In this qualitative study, the two-stage Delphi technique and desk research method were used to verify this hypothesis. Creative tourism can be thought of as a new paradigm of tourism or the third generation after coastal tourism and cultural tourism. It can also be considered an evolved type of cultural tourism which pays more attention to the intangible heritage rather than the tangible one. In either case, it is a form of network tourism in which values are created collectively through available relationships and the flow of social relationships and capital in networks. Therefore, its new management models and strategies should be identified and applied. In the current study, the desk research method was used to identify such models. Then the two-stage Delphi technique was employed to verify them.

This study examines empirically the effect of equity overvaluation on future stock price crash risk in companies with greater information asymmetry. Using the information asymmetry and crash risk indicators, the question that whether overvalued firms are more prone to future crash risk is investigated. Accordingly, the first purpose of this study is to investigate the relation between overvaluation and the future stock price crash risk. With the increase of information asymmetry between a firm and the market, managers have more abilities and opportunities to withhold bad news and accelerating the release of good news. As a result, it is expected that the information asymmetry between managers and investors increases future stock price crash risk. In so doing, we identify one main hypothesis and three subsidiary ones and the data of 111 listed companies of Tehran Stock Exchange for the period between 2009 and 2017 were analyzed and A panel data approach has been used to test of research hypotheses. According to the results, there is a positive and significant relationship between the overvaluation and the future stock price crash risk of companies which are operating in monopolistic markets and this relation intensified by the information asymmetry.

Internet-based social channels have turned into an important information repository for many people to get an idea about current trends and events happening around the world. As a result of Abundance of raw information on these social media platforms, it has become a crucial platform for businesses and individuals to make decisions based on social media analytics. The ever-expanding volume of online data available on the global network necessitates the use of specialized techniques and methods to effectively analyse and utilize this vast amount of information. This study's objective is to comprehend the textual information at the Lexical and Semantic level and to extract sentiments from this information in the most accurate way possible. To achieve this, the paper proposes to cluster semantically related words by evaluating their lexical similarity with respect to feature and sequence vectors. The proposed method utilizes Natural Language Processing, semantic and lexical clustering and hybrid C4.5 algorithm to extract six subcategories of emotions over three classes of sentiments based on word-based analysis of text. The proposed approach has yielded superior results with seven existing approaches in terms of parametric values, with an accuracy of 0.96, precision of 0.92, sensitivity of 0.94, and an f1-score of 0.92.