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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Marques, C., Mohsin, A. & Lengler, J. (2018). A multinational comparative study highlighting students' travel motivations and touristic trends. Journal of Destination Marketing and Management. 10, 87-100
Exportar Referência (IEEE)
C. M. Marques et al.,  "A multinational comparative study highlighting students' travel motivations and touristic trends", in Journal of Destination Marketing and Management, vol. 10, pp. 87-100, 2018
Exportar BibTeX
@article{marques2018_1732206506049,
	author = "Marques, C. and Mohsin, A. and Lengler, J.",
	title = "A multinational comparative study highlighting students' travel motivations and touristic trends",
	journal = "Journal of Destination Marketing and Management",
	year = "2018",
	volume = "10",
	number = "",
	doi = "10.1016/j.jdmm.2018.06.002",
	pages = "87-100",
	url = "https://www.sciencedirect.com/science/article/pii/S2212571X17303773?via%3Dihub"
}
Exportar RIS
TY  - JOUR
TI  - A multinational comparative study highlighting students' travel motivations and touristic trends
T2  - Journal of Destination Marketing and Management
VL  - 10
AU  - Marques, C.
AU  - Mohsin, A.
AU  - Lengler, J.
PY  - 2018
SP  - 87-100
SN  - 2212-571X
DO  - 10.1016/j.jdmm.2018.06.002
UR  - https://www.sciencedirect.com/science/article/pii/S2212571X17303773?via%3Dihub
AB  - The aim of this study is to assess differences and commonalities in the student travel market across different countries and to determine typologies based on touristic attractions/activities. The study generates groups based on travel motivations largely drawn from the Leisure Motivation Scale and other relevant tourism literature. To achieve the aim, data is obtained from a sample of 3,431 respondents from eight countries i.e. Brazil, India, Malaysia, Pakistan, Portugal, Spain, Thailand, and the USA. The data are analysed using two Principal Component Analyses (PCA), a combination of two clustering methods - the Ward method, and an optimal solution method, the K-Means method. Seven clusters based on touristic attractions/activities emerged. The findings from the current study suggest that perceptions of touristic attractions/activities are different by country although some similarities do exist. Besides providing important new insights for theory, this large comparative study also suggests synergies that could be generated from the information for both destination marketers and managers. 
ER  -