A systematic approach for predicting loyalty behavior of tourist destinations

2021

Purpose: Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors. Design/methodology/approach: This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study. Findings: The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts. Originality/value: The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. Th
Ship Design, Production and Operations

Bayesian network; Destination image optimization; Loyalty prediction; journal article

10.1108.JTF-11-2020-0194

Open access content. Open access content
© 2021 Ahmad Reza Talaee Malmiri, Roxana Norouzi Isfahani, Ahmad BahooToroody, M.M. Abaei

English

NLTUD oai:tudelft.nl:uuid:fc9533a3-88fc-4dfb-ba29-58999b120949
doi:10.1108/JTF-11-2020-0194
1284984571

DELFT UNIV OF TECHNOL
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