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A Cross-Destination Analysis of Country Image: A Key Factor of Tourism Marketing

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  • Enrique Marinao-Artigas

    (Department of Administration, Faculty of Administration and Economics, Universidad de Santiago de Chile, Santiago 9160000, Chile)

  • Karla Barajas-Portas

    (Faculty of Economics and Business, Universidad Anáhuac México, Naucalpan de Juárez 52786, Mexico)

Abstract

The purpose of this study is to check whether the image of a country can be configured by the interaction of cognitive and emotional experiences with the functional, hedonic, and symbolic benefits perceived by the visitor. It is intended to verify whether this multidimensional image facilitates the connection between people’s familiarity with the country, its reputation, and their satisfaction. A non-probabilistic convenience sample has been used. An online questionnaire has been applied. In total, 1812 valid answers have been obtained. Through a structural equations model (SEM), the quantitative analysis has been developed. This study revealed that the country’s image has a multidimensional configuration that positively connects people’s familiarity with a country with its reputation and with visitor satisfaction. This proposed new approach to configure the image of a country based on its visitors can be a key tool for implementing destination marketing strategies.

Suggested Citation

  • Enrique Marinao-Artigas & Karla Barajas-Portas, 2021. "A Cross-Destination Analysis of Country Image: A Key Factor of Tourism Marketing," Sustainability, MDPI, vol. 13(17), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9529-:d:620915
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    1. Beata Jolanta Gierczak-Korzeniowska & Mateusz Stopa & Krzysztof Szpara, 2022. "Methodology of Cyclical Research of the Tourist Image of Podkarpackie Province (Poland)—A Proposal of a Solution and Selected Research Results," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    2. Luis N. Zúñiga-Morales & Jorge Ángel González-Ordiano & J. Emilio Quiroz-Ibarra & César Villanueva Rivas, 2024. "Machine learning framework for country image analysis," Journal of Computational Social Science, Springer, vol. 7(1), pages 523-547, April.

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