درخت حوزه‌های تخصصی

Predicting bankruptcy risk is one of the most critical issues in corporate financial decision-making. Investors always try to predict the bankruptcy of a firm to reduce the risk of losing their assets, so they are looking for ways by which they can predict the risk of bankruptcy. We predict the position of companies active in the oil and gas industry based on their financial health in the 2020 ranking of S&P global up to three years before 2020. This study uses three data envelopment analysis models (CCR, BCC, and DDEA) and the traditional Altman model for forecasting. We have shown that dynamic data envelopment analysis is a powerful tool for predicting bankruptcy risk.

Purpose: This research aims to design and explain the process model of content marketing with the approach of strengthening the customers' purchase intention in the field of Keshavarzi bank services.Method: This research is applied in terms of purpose, exploratory in terms of approach, and mixed (qualitative-quantitative) in terms of data analysis. In the qualitative phase, a group of experts was considered as the statistical population including senior managers of the Keshavarzi Bank, university professors in the field of marketing, and marketing consultants familiar with the banking industry. In this phase, using a targeted non-probability sampling method, 11 experts were selected and interviewed in depth. In the quantitative phase, the customers of the Keshavarzi bank in Ahvaz were considered as the population, and out of them, 400 people were selected as a statistical sample based on the available random sampling method. The main tool of data collection in the qualitative phase was in-depth and unstructured interviews with experts. In the quantitative phase, a closed and researcher-made questionnaire consisting of 42 items was used as the main tool for data collection. In the qualitative phase, the data from the questionnaire was analyzed based on the grounded theory method (open, central, and selective coding). Content validity and reliability were obtained with Cronbach's alpha coefficient of 0.7. In the quantitative phase, SPSS and LISREL software were used to perform descriptive and inferential analyses.Findings: The findings showed that the content marketing process model included 14 categories  including causal factors (emphasis on ethics in the design of advertising message; emphasis of advertising message on competitive advantages; persuasive power of advertising message; the power of advertising message to create needs in the customer), contextual and environmental factors (adjustment of the message with the culture of the society; the general inclination of the society to use social media), the main category (attracting the customer's attention to the content of the promotional message and the services of the Keshavarzi bank), Intervening factors (use of specialized media to spread advertising messages; following the example of world-class banks in digital content production), strategic factors (stimulating customers to pass the promotional message by word of mouth; systematic and viral dissemination of the promotional message through digital social media), the consequences (increasing the intention to purchase the services of the Keshavarzi bank; strengthening the brand of the Keshavarzi bank; increasing loyalty to the Keshavarzi bank).Conclusion: The results of the research led to the design of a content marketing process model with the approach of strengthening the purchase intention of customers in the field of Keshavarzi bank services (based on the structure of the paradigm model). The hypothesized relationships of the model were tested and verified in a large community.

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.

  In the modern technological age, laptops are widely used for doing various day-to-day activities and getting updates all around us. The COVID-19 situation is playing a vital role in a dynamic shift in buyer behavior with multiple personal computing devices at home. Prioritizing and selecting appropriate laptop devices is difficult because there are several options of laptops that are available in the market, and these are equipped with the latest features to do gaming, designing, attending online classes, and performing office and other everyday tasks. There are multiple selection criteria that are complex in nature. MCDM (Multiple Criteria Decision Making) approaches can handle and analyze these complicated criteria. By using MCDM techniques, decision-making can be done to select the top-ranked alternative from among the available alternatives. This paper exhibits a group of two MCDM techniques; Best Worst Method (BWM) and Analytical Hierarchy Process (AHP), which have been used to evaluate relative weights of considered conflicting criteria such as brand, price, storage capacity, RAM, processor, weight, touch screen, Bluetooth, and screen size, and these weights are used in the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking and selecting the best product of laptops.