Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42415
Title: Predicting thalasso tourist delight: a hybrid SEM-artificial intelligence analysis
Authors: Sánchez-Medina, Agustín J. 
Naranjo-Barrera, Ylenia I.
Alonso, Jesus B. 
Rufo Torres, Julio Francisco 
UNESCO Clasification: 120304 Inteligencia artificial
531290 Economía sectorial: turismo
Keywords: Word-Of-Mouth
Customer Satisfaction
Service Quality
Behavioral Intentions
Destination Loyalty
Neural-Networks
Perceived Value
R Package
Image
Classification
Issue Date: 2018
Publisher: 1076-2787
Journal: Complexity 
Abstract: This study focuses on the influence of the quality of services received by thalassotherapy customers on their global satisfaction and the relationship between this and the word of mouth. This study uses a hybrid SEM-classification tree analysis. The empirical findings reveal a significant relationship between the quality of each offered service and global satisfaction. This study contributes to identify tourist's satisfaction or delight on received thalasso services through a proposed methodology. The main contribution of this work consists of the proposal of a methodology to identify objectively through the opinion of tourists if they were satisfied or had reached delight. This work demonstrates, confirming what has been found in previous literature, that global satisfaction is related to the different experiences provided by the service. Thus, all hypotheses are accepted, supporting the hypotheses that relate the pool, the staff, the treatments, and the environment to satisfaction. In addition, the hypotheses that link satisfaction with the word of mouth are also supported. This theoretical implication has important practical implications for managers of the type of facilities such as those studied in this paper, since it shows that it is not enough to do well in one of the services provided if the environment or the interaction with the staff is not right.
URI: http://hdl.handle.net/10553/42415
ISSN: 1076-2787
DOI: 10.1155/2018/4329396
Source: Complexity [ISSN 1076-2787], article ID 4329396
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