Modeling a causality network for tourism development: an empirical analysis
Date
2011
Authors
Assaker, G.
Esposito Vinzi, V.
O'Connor, P.
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Albayrak, T.
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Journal article
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Journal of Modelling in Management, 2011; 6(3):258-278
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Abstract
Purpose: The purpose of this paper is to capture the causal relationships between the primary constituents of the tourism destination paradigm – namely, the economy, society, and the natural and infrastructural environments – and demand for tourism at that destination. Design/methodology/approach: Inspired by prior tourism literature, the study uses structural equation modeling (SEM) methodologies with a cross-sectional data sample from 162 countries, to evaluate a priori proposed measurement and structural models for relationships among the economy, society, environment constructs, and tourism. Findings: The results indicate that although the economy construct was found to have no direct influence on tourism, it does have a mediating, positive impact on tourism through the society and environment constructs, with the society construct paralleling the condition of the infrastructure. Moreover, society and environment were found to have a direct, positive impact on generating tourism activities, and revenues. Research limitations/implications: This study contributes to the literature on tourism demand modeling by verifying causal relationships between tourism constructs and supply variables at the “country” level. In other words, it examines whether relationships among tourism constructs and variables exist and, as such, asks if they are responsible for a destination's success. Practical implications: This study's results provide destination managers with information to help them understand how individual variables affect the economy, society, and tourism industry aggregately, and as such what actions or investments can help to develop a country's tourism industry effectively. Originality/value: By integrating several supply-side factors related to the destination, this paper provides more comprehensive results compared to previous applications of SEM that used a limited number of destination variables and subsequently provided only limited results.
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Copyright 2011 Emerald