پیش بینی وفاداری گردشگری با استفاده از مکانیزم شبکه های بیزی یکپارچه

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.

With the prevalence of tourism in Taiwan, the development of tourism industry has facilitated local economy and increased the employment opportunity. Thus, tourism becomes an industry that is valued and actively developed by the government. However, when facing a more competitive tourism environment, how to attract the customers and further transform them into loyal ones will be the key for the operation of leisure business. This research seeks to determine the factors that influence tourism loyalty. Moreover, this study proposes an integrated mechanism that combines LISREL (Joreskog & Sorbom, 1993) and BN (Pearl, 1986) to predict a tourist’s loyalty level. Valid samples were collected from 452 tourists with the tour experience of the Toyugi Hot Spring Recreational Village, which is located at the eastern region of Taiwan. The village is managed by Taitung County Farmers Association. With an area of 15 hectare, it is the largest hot spring recreational village in Chihben hot spring area. The village is full of rich ecological resources such as spatial grasslands, varied plants, wild birds and butterflies. Therefore, the village provides the visitors the combined leisure functions such as recreation, conference, experience, education and training by the unique hot spring, landscapes and farm produces. Visitors can enjoy the services, herbs and various agricultural products in hot spring hotels. Besides, the village also provides junior high schools and elementary schools a teaching space for rural village ecology and experience, and the information of agricultural tourism. It is a recreational village with different features. The data analysis was conducted in two stages: verifying relationships in the research model and predicting the level of tourism loyalty. In the first stage, LISREL was used to verify the belief or causal relationships in the research model of tourism loyalty. LISREL is a structural equation modeling (SEM) technique used to determine whether a research model is valid by examining the goodness-of-fit between the model and raw data. It has been widely applied in social-science research. In the second stage, the supported relationships of the LISREL analysis are used as the BN network structure to predict a tourist’s loyalty level. The predicted results were also compared with those generated by BPN and CART.

5.1. Tourism implications According to the LISREL results, we found that: (1) customer service, web function and local characteristics have significant and positive influence on tourism loyalty. Local characteristics are the most important, followed by customer service and web function. In order to increase tourism loyalty to attract visitors’ revisiting or recommendation to others, and increase the number of visitors, the key of tourism management is the reinforcement or use of the characteristics of the spots. With regard to customer service, tourism management should value the quick response to customers’ suggestions and quickly solve the customers’ problems. Finally, with the prevalence of Internet business application, the web function of tourism websites is commonly used by visitors. The tourism administrators should recognize that the enhancement and improvement of web function will increase tourism loyalty. 5.2. Methodology discussion Although LISREL and BN have been widely applied individually in many research studies, few of these studies have investigated combining them for predictive purposes. This research has proposed an integrated mechanism to predict a tourist’s loyalty level. The prediction performance of this approach has also been compared with the predictions of BPN and CART. The three methods LISREL, BN, and BPN represent relationships as networks. However, in terms of explanatory power, the BPN’s internal learning is processed in a black-box mode in which the internal weights are difficult to express in an explicit way which is relevant to real-world problems. Our approach is able to explain the correlative relationships between variables and outperforms BPN and CART by achieving higher predictive accuracy. BPN and CART are appropriate for exploratory studies in which the relationship between variables is unclear, whereas our approach is suitable for data prediction in empirical research with a theoretical basis. Because constructing a BN structure when learning from data presents certain difficulties (Niedermayer, 1998), the approach proposed in this research is demonstrated to be a prospective way to aid a BN in discovering a suitable network architecture with better prediction performance. In the future, a sensitivity analysis would be helpful to understand the approach robustness. Further simulations are needed to be of general interest. As for the contribution of this research, LISREL is a widely used advanced statistical tool in the social and behavioral sciences, but it is seldom combined with other machine-learning algorithms. The proposed approach can be used as a good reference for related research in social-science fields.