delphi-fuzzy

Contrary to both the banks’ outstanding positions in financing and availability of unhealthy competition environment in the country banking industry in the recent years, little or no research was conducted around effective competition. The core objective of the current research is to design the competition model in banking industry with the means of DEMATEL technique. The data collection was organized through document-based analysis and semi-structured interviews with financial-banking elites. Sampling was in judgmental and snowball version. In order to analyze the data, coding was carried out by the thematic analysis and in order to approve of and screen the data, Fuzzy two stages-approach was employed and in order to assess the cause-and-effect relation among model dimensions, DEMATEL approach was applied. The result depicts economic, political, environmental and social factors as of causes, influence structural, legal, managemental, financial features. Furthermore, environmental factors were the most influential parameters and structural factors were on easy verge of impact. Additionally, legal and environmental components were of higher significance due to the broad interaction with the alternative variables. Additionally, in accordance with the result attained, legal factors were considered as the principal Achille Hills in having effective competition and environmental factors could be considered as the solutions for the legal factors.

The first part of the economic system that was affected by the outbreak of the Corona pandemic was the banking system of countries. Therefore, the aim of this study was to design a social banking model with a post-corona approach in the country's banking industry, which uses a combination of Delphi-fuzzy method and interpretive structural modeling. In this study, the opinions of university professors and managers in the field of banking industry, which included 20 people, were used. For component screening, Delphi-fuzzy method was used, in which the diffused mean of 44 components was more than 0.7, so 44 components were identified in 5 dimensions in this industry. Then, structural-interpretive modeling and MICMAC analysis were used to level the dimensions. The results of ISM showed that the social banking model has four levels, so that the social banking infrastructure is firstly factor (bank entry into social networks for marketing purposes) and then secondly factor (possibility of creating non-bank social financial platforms), third factor (Bank entry into social networks for banking operations) and in the fourth degree are factors (customer service privatization - the use of collective wisdom to create and develop products and services). MICMAC results also showed the dimension (bank entry into social networks for marketing purposes) in the independent cluster and the dimension (using collective wisdom to create and develop products and services) in the link cluster and dimensions (possibility of creating non-bank social financial platforms - bank entry Social networks for banking operations are located in the autonomous cluster and then (customer service privatization) in the dependent cluster. Finally, it can be concluded that although the negative effects of Covid-19 on the banking industry are inevitable, but if there is a solution, it can be a good opportunity to improve the banking system and use more appropriate methods through research in new technologies to attract customers. Innovate in providing complete and diverse products and services with the aim of creating value for customers and improving the performance of banks and creating new opportunities and infrastructure in the banking industry.

Banking capital and risk decisions are affected by laws, private incentives and capital market pressure. Focusing on examining these cases will explicitly provide an updated insight into empirical evidence on the impact of auditors 'regulations on capital and risk, and provide an assessment of the evolution of banks' decision making mechanisms. Therefore, this research has determined the factors affecting the optimal level of risk and optimal capital structure in Iranian banks using Delphi-Fuzzy approach based on in-depth interview with experts and content analysis. After identifying the effective factors using content analysis and content analysis, 15 experts were interviewed. The Delphi-Fuzzy approach was used to determine the effect of the final factors. The results show that the most important factors affecting efficiency are physical capital cost, manpower cost, cost of funds, investment in securities and net facility. In addition, among the various factors, the variables of facility coverage rate, risk capacity and legal requirement will be the most important factors affecting the optimal level of risk in banks. Finally, minimum capital, minimum capital risk and minimum leverage ratio were considered as the most effective factors in determining optimal capital structure.

The use of artificial neural networks (ANN) in forecasting has many applications. Appropriate design of ANN parameters enhances the performance and accuracy of neural network models.  Most studies use a trial and error approach in setting the value of ANN parameters. Other methods used to determine the best structure of a neural network only use a single evaluation criterion to determine the appropriate structure. In this study, the authors provide a new method to design the network structure. In this method, we use a combination of Taguchi experimental design and TOPSIS methods, to determine he optimal ANN structure, taking into account three evaluation criteria simultaneously. The  estimated demand for gasoline in the Hormozgan province produced using this method, confirms its efficiency and effectiveness. Analysis of variance (ANOVA) of the ANN variables indicates that contribution of the number of neurons in the first hidden layer to the changes in the network performance is about 54% while the contribution of the learning algorithm is about 27%.