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doi:10.1016/S0957-4174(02)00157-4    
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Copyright © 2003 Elsevier Science Ltd. All rights reserved.

Exploring artificial intelligence-based data fusion for conjoint analysis

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Sungbin ChoCorresponding Author Contact Information, E-mail The Corresponding Author, a, Seung Baekb and Jonathan S. Kimb

a Department of Industrial Engineering, Konkuk University, 1 Hwayang-dong, Gwangjin-gu, Seoul 143-701, South Korea

b School of Business Administration, Hanyang University, 17 Haengdang-dong, Sungdong-gu, Seoul 133-791, South Korea


Accepted 18 September 2002. ;
Available online 22 October 2002.

Abstract

Conjoint analysis is used to understand how consumers develop preferences for products or services, which encompass, as usual, multi-attributes and multi-attribute levels. Conjoint analysis has been one of the popular tools for multi-attribute decision-making problems on products and services for consumers over the last 30 years. It has also been used to market segmentation and optimal product positioning. In spite of its popularity and commercial success, a major weakness of conjoint analysis has been pointed such that respondents participating in conjoint experiment have to evaluate a number of hypothetical product profiles. To reduce the number of hypothetical products, this paper proposes a systematic method, called data fusion, and explores the usability of various data fusion techniques. The paper evaluates traditional data fusion (correlation-based), hierarchical Bayesian-based data fusion, and neural network-based data fusion.

Author Keywords: Data fusion; Artificial intelligence techniques; Conjoint analysis

Article Outline

1. Introduction
2. Data fusion
2.1. Description of data fusion
2.2. Application of data fusion to conjoint analysis
2.3. An overview of methodologies for handling missing data
2.4. Exploring the possibility of artificial intelligence techniques as a data fusion tool
3. Research methodology
3.1. Data description
3.2. Design of data missing pattern
3.3. Correlation approach
3.4. Hierarchical Bayesian approach
3.5. Neural network approach
4. Evaluation results
5. Concluding remarks
Acknowledgements
References




Corresponding Author Contact InformationCorresponding author. Tel./fax: +82-2-450-3527


 
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