Predicting tourism loyalty using an integrated Bayesian network mechanism

For effective Bayesian networks (BN) prediction with prior knowledge, this study proposes an integrated BN mechanism that adopts linear structural relation model (LISREL) to examine the belief or causal relationships which are subsequently used as the BN network structure for predicting tourism loya...

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Bibliographic Details
Published in:Expert systems with applications 2009-11, Vol.36 (9), p.11760-11763
Main Authors: Hsu, Chi-I, Shih, Meng-Long, Huang, Biing-Wen, Lin, Bing-Yi, Lin, Chun-Nan
Format: Article
Language:English
Subjects:
Bayesian networks
Linear structural relation model
Loyalty
Tourism management
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Summary:For effective Bayesian networks (BN) prediction with prior knowledge, this study proposes an integrated BN mechanism that adopts linear structural relation model (LISREL) to examine the belief or causal relationships which are subsequently used as the BN network structure for predicting tourism loyalty. Four hundred and fifty-two valid samples were collected from tourists with the tour experience of the Toyugi hot spring resort, Taiwan. The proposed mechanism is compared with back-propagation neural networks (BPN) or classification and regression trees (CART) for 10-fold cross-validation. The results indicate that our approach is able to produce effective prediction outcomes.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2009.04.010