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Knowledge Extraction from a Computational Consumer Model Based on Questionnaire Data Observed in Retail Service

Knowledge Extraction from a Computational Consumer Model Based on Questionnaire Data Observed in Retail Service

Tsukasa Ishigaki, Yoichi Motomura, Masako Dohi, Makiko Kouchi, Masaaki Mochimaru
Copyright: © 2010 |Volume: 1 |Issue: 2 |Pages: 15
ISSN: 1947-3052|EISSN: 1947-3060|EISBN13: 9781609603724|DOI: 10.4018/jssoe.2010040103
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MLA

Ishigaki, Tsukasa, et al. "Knowledge Extraction from a Computational Consumer Model Based on Questionnaire Data Observed in Retail Service." IJSSOE vol.1, no.2 2010: pp.40-54. http://doi.org/10.4018/jssoe.2010040103

APA

Ishigaki, T., Motomura, Y., Dohi, M., Kouchi, M., & Mochimaru, M. (2010). Knowledge Extraction from a Computational Consumer Model Based on Questionnaire Data Observed in Retail Service. International Journal of Systems and Service-Oriented Engineering (IJSSOE), 1(2), 40-54. http://doi.org/10.4018/jssoe.2010040103

Chicago

Ishigaki, Tsukasa, et al. "Knowledge Extraction from a Computational Consumer Model Based on Questionnaire Data Observed in Retail Service," International Journal of Systems and Service-Oriented Engineering (IJSSOE) 1, no.2: 40-54. http://doi.org/10.4018/jssoe.2010040103

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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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