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The present study aims to identify the factors affecting the behavior of customers’ use of ebanking services of Tejarat Bank in Tehran. A qualitative method and an in-depth interview have been applied to achieve the research goal. The information and data collected from the interviews have been analyzed using open coding and axial coding. Then, the initial indicators of the model of using e-banking services were identified. In the next step, e-banking experts and specialists were asked to comment on the indicators obtained from the interviews, using the Delphi technique. The final results demonstrated that trust in banks, perceived security, ease of use, perceived utility, the impact of society, and perceived risk are considered as indicators affecting users’ behavioral intentions and ultimately their use behavior.
 

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This study was conducted with the aim of designing and explaining a content marketing pattern with a brand reinforcement approach in the country's banking industry. This research is applied in terms of objective, exploratory in terms of approach, and mixed in terms of data analysis. In the qualitative phase of the research and in order to  design a model based on methodology of data foundation theory, a group of experts including senior managers of the banking industry, university professors in the field of marketing, and marketing consultants familiar with the banking industry were considered as the statistical population. Snowball sampling method was used in this phase, and this process continued until reaching the theoretical saturation. 9 interviews were conducted in total. Also in quantitative phase, the customers of the banking industry in the city of Tehran were considered as population and 450 people were selected among them as the statistical sample based on equal size cluster sampling. In the qualitative phase of the research, due to using the data foundation theory, the main data collection tool was unstructured in-depth interviews with experts. In the quantitative phase of the research, the main data collection tool was a closed-ended researcher-made questionnaire consisting of 37 items that were designed based on the initial conceptual model. The SPSS, LISREL, and smart-PLS pieces of software were used to perform descriptive and inferential analyzes in the quantitative phase of the research. Finally, the research results led to designing a content marketing pattern with a brand reinforcement approach in the country's banking industry with 11 main variables and the hypothetical relationships of the model were tested and approved in a large population.

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According to current development in credit allocation and recent economic crises, planning for identification of credit risk has found special importance for investors, banks, shareholders and financial analysts, so that they are able to make proper decisions. Although credit loss is a common cost in banking industry, however, increase in this loss might affect the bank performance. Therefore, there is a strong need to reassess current approaches in risk evaluation of each loan and default rate of loan portfolios. Banks usually have their own internal validation models for loan risk measurement but these approaches are inappropriate and utilize simple mathematical approaches based on incomplete premises. In this paper, we have tried to estimate the possibility of default for legal customers using 20 financial ratios for 200 healthy and 200 unhealthy companies receiving civil participation facilities from Eghtesad Novin (EN) Bank in 2009 and 2010 and 4 approaches for choosing financial ratios including remarks from credit experts of Raah Eghtesad Novin Co., Altman, comparison between averages and choosing correlation attribute. Results show that Support Vector Machine approach can differentiate between healthy and unhealthy companies with average accuracy of 84.63% using all chosen ratios.