Combining LISREL and Bayesian network to predict tourism loyalty

This study proposes an analytic approach that combines LISREL and Bayesian networks (BN) to examine factors influencing tourism loyalty and predict a touristpsilas loyalty level. LISREL is used to verify the hypothesized relationships proposed in the research model. Subsequently, the supported relat...

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Bibliographic Details
Main Authors: Hsu, Chi-I, Shih, Meng-Long, Huan, Biing-Wen, Li, Bing-Yi, Lin, Chun-Nan
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Bayesian methods
Conferences
Joints
Prediction methods
Regression tree analysis
Springs
Online Access:Request full text
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Summary:This study proposes an analytic approach that combines LISREL and Bayesian networks (BN) to examine factors influencing tourism loyalty and predict a touristpsilas loyalty level. LISREL is used to verify the hypothesized relationships proposed in the research model. Subsequently, the supported relationships are used as the BN network structure for prediction. 452 valid samples were collected from tourists with the tour experience of the Toyugi hot spring resort, Taiwan. Compared with other prediction methods, our approach yielded better results than those of back-propagation neural networks (BPN) or classification and regression trees (CART) for 10-fold cross-validation.
ISSN:2161-4393
1522-4899
2161-4407
DOI:10.1109/IJCNN.2008.4634220