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Exploring Psychographic Traits in Tourism Maketing: Causal Relationship from Destination Image to Loyalty
Author(s) -
Abduljalil Sarli,
Rohaizat Baharun
Publication year - 2013
Publication title -
jurnal teknologi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.191
H-Index - 22
eISSN - 2180-3722
pISSN - 0127-9696
DOI - 10.11113/jt.v64.2211
Subject(s) - psychographic , loyalty , structural equation modeling , tourism , marketing , personality , psychology , construct (python library) , purchasing , product (mathematics) , big five personality traits , consumer behaviour , advertising , social psychology , business , computer science , geography , mathematics , geometry , archaeology , machine learning , programming language
Psychographics has been proposed as a valuable topic in the marketing literature. Also, it can be represented as latent variables which are related to the behaviors not only in the product or service discussions but also in the tourism activities. Besides, the tourism as the globally business can be understood in terms of the tourists’ reflections in the different ways. Hence, the survey of original intentions of tourists is very precious subjects by regarding the causal relationship for exploring phenomena from their behavioral intentions. As psychographic constructs, which can impact in the different glances of the affect and cognitive systems, create new consumption patterns for purchasing and repurchasing tourism packages. Therefore, loyalty is significantly considered as a valuable construct for stakeholders and academic researchers. Consequently, the aim of this study is to find out loyalty from psychographic facet. To date, there is a bridge gap through the intervening psychographic constructs like lifestyle, personality, and travel satisfaction to achieve loyalty. Additionally, the current study suggested an integrated model through the contemplated constructs by employing Structural Equation Modeling (SEM) technique to ascertain an appropriate model by Goodness-Of-Fit (GOF) indices.

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