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Knowledge Extraction from a Computational Consumer Model Based on Questionnaire Data Observed in Retail Service
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Author(s): Tsukasa Ishigaki (National Institute of Advanced Industrial Science and Technology, Japan), Yoichi Motomura (National Institute of Advanced Industrial Science and Technology, Japan), Masako Dohi (Otsuma Women’s University, Japan), Makiko Kouchi (National Institute of Advanced Industrial Science and Technology, Japan)and Masaaki Mochimaru (National Institute of Advanced Industrial Science and Technology, Japan)
Copyright: 2012
Pages: 15
Source title:
Theoretical and Analytical Service-Focused Systems Design and Development
Source Author(s)/Editor(s): Dickson K. W. Chiu (Dickson Computer Systems and The Hong Kong Polytechnic University, Hong Kong)
DOI: 10.4018/978-1-4666-1767-4.ch013
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
In service industries, matching the level of demand of the consumer and the level of service of the provider is important because it requires the service provider to have knowledge of consumer-related factors. Therefore, an intelligent model of the consumer is needed to estimate such factors because they cannot be observed directly by the service provider. This paper describes a method for computational modeling of the consumer by understanding his or her behavior based on datasets observed in real services. The proposed method constructs a probabilistic structure model by integrating questionnaire data and a Bayesian network, which incorporates nonlinear and non-Gaussian variables as conditional probabilities. The proposed method is applied to an analysis of the requested function from customers regarding the continued use of an item of interest. The authors obtained useful knowledge for function design and marketing from the constructed model by a simulation and sensitivity analysis.
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