Framework for a Bayesian Network version of IDHEAS

Zwirglmaier, Kilian; Groth, Katrina M.; Straub, Daniel

doi:10.1201/b19094-416

Cited by 1 publication

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, it is a promising tool that allows the comprehensive reflection of available knowledge about the process (Abaei et al, 2018;Arzaghi et al, 2018;BahooToroody et al, 2019;Leoni et al, 2019). Secondly, in comparison to other tools such as the analytic hierarchy process (AHP) and factor analysis (FA), BN performs better in quantifying the uncertainty and solving decision-making problems when extended to an influence diagram (Friis-Hansen, 2000;Zwirglmaier et al, 2015). Thirdly, in a Bayesian approach, it is also possible to convert continuous random variables into a discrete space, enabling the inference of more complicated stochastic relationships amongst many parameters (Friis-Hansen, 2000).…”

Section: Introductionmentioning

confidence: 99%

A systematic approach for predicting loyalty behavior of tourist destinations

Malmiri

Isfahani

BahooToroody

et al. 2021

JTF

View full text Add to dashboard Cite

PurposeDestinations 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/approachThis 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.FindingsThe 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/valueThe 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. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.

show abstract