This research aims to provide a hybrid model based on econometrics and clustering to analyze customer satisfaction data. The statistical population of the research,Logan car owners and a sample of 177 customers from Pars Khodro agencies. The researcher, along with ISQI' More
This research aims to provide a hybrid model based on econometrics and clustering to analyze customer satisfaction data. The statistical population of the research,Logan car owners and a sample of 177 customers from Pars Khodro agencies. The researcher, along with ISQI's research team, identified the current needs of customers of all vehicles from post-sales services and by designing a customer satisfaction questionnaire, the effect of each variable on the overall satisfaction of customers according to the high-income group, middle income and low income is measured. Factors affecting overall customer satisfaction based on all income groups include six variables: Provision of parts on time; Description presented when vehicle clearance; Ease of access to dealers; periodic service quality; Quality of repairs; Cost of payment and in the high-income group, three variables are described: Cost of payment; Record mentioned Items by the receptionist at the time of admission; Description presented when vehicle clearance and In the middle income group, four variables are described: Provision of parts on time; Quality of repairs; Description presented when vehicle clearance; Cost of payment and and in the low-income group, it includes four variables: Description presented when vehicle clearance; Provision of parts on time; Quality of repairs; Ease of access to dealers. To run this research of software EViews and Spss is used.
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Investment decision, have always has been one of the most important issues. Investors are trying to achieve the highest efficiency and the least risk by selecting the best companies from Among a wide variety of companies considering to various financial indicators. Acco More
Investment decision, have always has been one of the most important issues. Investors are trying to achieve the highest efficiency and the least risk by selecting the best companies from Among a wide variety of companies considering to various financial indicators. Accordingly, today, there are many ways to analyze the data from this company. One of the ways is clustering that classification of the companies. However, the present study aimed to identify and distinguish successful from unsuccessful companies in Tehran Stock Exchange has been done using K-means clustering. Then this problem is solved using meta-heuristic algorithms. The results indicate that meta-heuristic algorithms compared with conventional methods, more efficient and have led to a global optimum. Also these results of Altman’s bankruptcy model were confirmed results of meta-heuristic algorithms.
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