Predicting tourism loyalty using an integrated Bayesian network mechanism

Authors:
Chi-I Hsu;Meng-Long Shih;Biing-Wen Huang;Bing-Yi Lin;Chun-Nan Lin
Affiliations:
Dept. of Information Management, Kainan University, No. 1 Kainan Road, Luchu, Taoyuan County 338, Taiwan;Dept. of Social Studies Education, National Taitung University, Taiwan;Dept. of Applied Economic, Chung-Hsing University, Taiwan;Dept. of Information Management, Kainan University, No. 1 Kainan Road, Luchu, Taoyuan County 338, Taiwan;Dept. of Tropical Agriculture and International Cooperation, National Ping-Tung University of Science and Technology, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

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.