جستجوی مقالات مرتبط با کلیدواژه « خروجی نامطلوب » در نشریات گروه « مالی »

The purpose of this research is to provide a model for evaluating the performance of bank branches using the network coverage analysis model using fuzzy data. In this research, an attempt was made to use a model based on the traditional model of data envelopment analysis, taking into account factors such as the network nature and internal relationships of each decision-making unit, undesirable output, non-optional input, and the variables having a fuzzy nature. which is more consistent with the actual conditions related to the decision-making units and calculates the efficiency in a more accurate way.After presenting the research model, first, based on the studies conducted in the past researches and field studies and obtaining opinions from experts in this industry, indicators were considered as input and output. After identifying the indicators, fuzzy Delphi method was used for initial screening of the indicators. In the following, confirmatory factor analysis technique was used to finalize the screened indicators. At the end of the research, the developed model was solved using the data collected from the studied branches, by Gams software and with the alpha cutting approach. The results indicate that among the 34 investigated branches, 8 are efficient and 26 are ineffective.

The issue of efficiency evaluation is one of the most important challenges facing managers in the dynamic and vital banking industry. Due to the very important and fundamental role of banks in most economic activities, the study of bank efficiency has a special place. Models based on data envelopment analysis had wide applications in the field of measuring and evaluating the efficiency of banks. In this study, we tried to add other assumptions to the traditional model of data envelopment analysis, using a model that is more consistent with the real conditions related to decision-making units and calculates the efficiency more accurately. In designing the data envelopment analysis model in the present study, attention has been paid to the networking and internal relations of each decision-making unit, undesirable output, involuntary input and the fuzzy nature of the variables. After developing the model with the mentioned assumptions, first, based on reviewing previous studies as well as field studies and obtaining opinions from industry experts, indicators considered as input and output. After identifying the indicators, fuzzy Delphi method used for initial screening of the identified indicators from the research literature. After the initial approval of the indices, the indices analyzed using the confirmatory factor analysis technique. Finally, the research model solved using data collected by GAMS software and with alpha-cutting approach. The results show that out of 38 branches examined, 8 branches are efficient and another 30 branches are inefficient.

Insurance industry is one of the most influential economic institutions and is considered to support other economic institutions and families. Insurance industry has been facing changes that lead it to becoming a competitive industry. Therefore, we can say insurance companies that are active in Iran insurance industry must constantly monitor performance of their branches and agencies. Ongoing problems in existing assessing methods of organizations are their emphasis on a single index and subjective judgment. Therefore, the assessment should comprehensively take all aspects into account. Subjective judgment should be reduced as much as possible. Thus, in this study we evaluate the performance of Iran Stock Corporation and its branches by using data envelopment analysis (DEA) technique. Meanwhile, in the classical applications of DEA models typically problems occur such as ignoring undesirable outputs and non-discretionary inputs. Accordingly, in this study undesirable outputs and non-discretionary inputs have been investigated. The results show that 50 and 36 percent of the branches are efficient under variable and constant returns to scale respectively in the presence of non-discretionary inputs and undesirable outputs.