Developing a new method to extract knowledge from customer satisfaction surveys

  • سال انتشار: 1389
  • محل انتشار: دومین کنفرانس بین المللی مدیریت سرمایه فکری
  • کد COI اختصاصی: IICM02_050
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 2453
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نویسندگان

Alireza Golmohammadi

Amirkabir University of Technology, Iran

Naser Shams Gharneh

Amirkabir University of Technology, Iran

Behrouz Jahandideh

University College of Nabi Akram, Iran

Sayed Alireza Saradjian Tehrani

Islamic azad university- Naragh branch

چکیده

One of the determinants of a firm’s prosperity is the customers’ perceived service quality and satisfaction. While service quality is wide in scope, and consists of various dimensions, there may be differences in the relative importance of these dimensions in affecting customers’ overall satisfaction of service quality. Identifying the relative rank of different dimensions of service quality is very important in that it can help managers to find out which service dimensions have a greater effect on customers’ overall satisfaction. Such an insight will consequently lead to more effective resource allocation which will finally end in higher levels of customer satisfaction. This issue –despite its criticality- has not received enough attention so far. Therefore, using a sample of ۲۴۰ bank customers in Iran, an artificial neural network is developed to address this gap in the literature. As customers’ evaluation of service quality is a subjective process, artificial neural networks –as a brain metaphor- may appear to have a potentiality to model such a complicated process. Proposing a neural network which is able to predict the customers’ overall satisfaction of service quality with a promising level of accuracy is the first contribution of this study. In addition, prioritizing the service quality dimensions in affecting customers’ overall satisfaction –by using sensitivity analysis of neural network- is the second important finding of this paper.

کلیدواژه ها

service quality, customer satisfaction, relative importance, artificial neural network

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