درخت حوزه‌های تخصصی

Achieving e-commerce success is influenced by a variety of interrelated activities. Identifying, analyzing and prioritizing these activities enable decision-makers firstly to evaluate the current success state of Electronic Commerce (EC) businesses more accurately and then to develop an efficient improvement plan to allocate limited resources to achieve a higher level of success. In this research, a Fuzzy Cognitive Maps- analytical hierarchy process (FCM-AHP) approach is used to address this issue by (1) determining interrelationships between influential activities, (2) determining how much these activities influence each other and the overall success, and (3) prioritizing activities to develop a sound improvement plan. In this approach, the FCM technique is used to indicate all possible causal interrelationships between activities and consider all feedback loops between them. Then, the AHP technique is used to determine the contribution weights of activities to the overall success. The obtained management matrices that make it possible to categorize influential activities and the results of improvement scenarios help the decision-makers to develop the most efficient improvement plan and gain the maximum benefit from the allocation of limited resources. An empirical study is implemented to serve as an example to indicate how the approach works.

One of the most important risks that the banks and financial institutes face, is credit risk which is related to not-paid instalments or the instalments paid with delay by borrowers. Banks use credit scoring models In order to prevent this type of risk. The goal of this research is to investigate the joint effect of economic time cycles and the industry sector on credit scoring models we are seeking to answer the key question: “when should bank change their credit scoring models based on economic time cycles and for which industry sectors?”. The dataset of the research involves all companies that were applied for a loan in one of the Iranian major banks during the years 2008-2011. The companies have been divided into four industry sectors including “Industry and Mine”, “Oil and chemical”, “Service and Infrastructure” and finally “Agriculture”. Based on the sector of the industry and year, 54 explanatory variables, both financial and non-financial, 12 distinct industry sectors and time-specific data sets are built then classification methods were used to classify customers into two groups of defaults and non-defaults. Finally, we compared the results by Wilcoxon Test. The results show that the companies that are in the groups of Industry and Mine and Agriculture, need their own special credit scoring based on industry type model and time but two other groups don’t need of course in the studies dataset duration. Finally, the study concluded by introducing the credit scoring strategies for different four-cycle of economies