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Enregistrement W2954069994 · doi:10.25904/1912/910

The Measurement of Cultural Influence on Travel Lifestyle and Behaviour using Personal Values and Sensation-Seeking Behaviour: A Comparison of Koreans in Korea and Australia

2007· dissertation· en· W2954069994 sur OpenAlexaboutno aff
Sunhee Lee

Notice bibliographique

RevueGriffith Research Online (Griffith University, Queensland, Australia) · 2007
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueDiverse Aspects of Tourism Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAcculturationEthnic groupImmigrationPsychologySensation seekingNationalityTourismGeographyDemographySocial psychologySociologyPersonality

Résumé

récupéré en direct d'OpenAlex

Immigration and international “floating populations” have increased in the past decade, arousing marketers’ interest in culture. In particular, culture has been used for analysing the direction of market trends, as well as providing a better understanding of consumers’ needs, expectations and preferences within and across countries. However, cross-cultural research has been hampered by the common use of nationality as a surrogate for cultural affiliation, even though a variety of ethnic, social class, lifestyle, and subcultures exist, particularly in countries that have been built from large immigrant populations such as the United States, Canada, Australia and New Zealand (Tan, McCullough, and Teoh, 1987). Furthermore, within the tourism discipline, the study of ethnic minorities has not received as much attention as specific populations of ethnic groups such as black American, Hispanic, and Chinese in the United States. This study investigated the influences of culture on travel lifestyle and behaviour, through the comparison of Korean Australians and Koreans in Korea. Korean residents who live in Korea (N = 325) and Koreans who live in Australia (N = 306) completed a self-administered questionnaire. The questionnaire sought information in regard to travel lifestyle and behaviour as well as personal values, sensation-seeking behaviour and travel experiences, in order to investigate factors associated with the differences between the two groups in travel lifestyle and behaviour. Socio-demographic characteristics of the sample were also measured. Furthermore, a measure of acculturation was taken for the Korean Australians. Data were analysed using factor analysis, t-test, one-way ANOVA, multiple regression analysis, chi-square and cluster analysis to test the proposed hypotheses. The results of this study showed differences between the two groups of Koreans in respect of travel lifestyle and behaviour, personal values, sensationseeking behaviour and travel experiences. Cluster analysis identified five distinct groups: “affluent group travellers”, “low resource group travellers”, “active independent travellers”, “non- active independent travellers”, and “low-interest travellers”. Korean Australians were more likely than Koreans to be represented in the clusters of “active independent travellers”, “non-active independent travellers”, and “low-interest travellers”. Koreans were more likely to want to travel as part of a group than Korean Australians. Relationships between personal values, sensation-seeking behaviour and travel experiences, as well as travel lifestyle and behaviour were also found. The “low resource group traveller” within Korean Australians was likely to report having low “community values” and “life values”, while the same cluster within Koreans was likely to report having low “social values” and “life values” as opposed to the “active independent traveller” for both samples of Koreans. The “active independent traveller” in both Korean groups was more likely to report holding a strong preference for “novelty” seeking than the other cluster travellers. Korean Australians who undertake frequent international travel were more likely to be in the “active independent travellers” cluster, whereas Koreans who are frequent international travellers were more likely to be in the “affluent group travellers” cluster. Acculturation also predicted, to some extent, the travel lifestyle and behaviour of Korean Australians. Demographic variables were mostly irrelevant to travel lifestyle and behaviour in each group. This study confirmed that personal values and sensation-seeking behaviour seemed to be associated with the different travel lifestyles and behaviour of Korean Australians compared to Koreans. The differences of travel lifestyle and behaviour may be caused by different cultures. Findings of the current study reinforce the importance of culture in the tourism market. The results revealed that overall predictability of travel lifestyle and behaviour by sensation-seeking behaviour was stronger than the predictability of travel lifestyle and behaviour by personal values. However, variables such as personal values, sensation-seeking behaviour, travel experiences and demographic characteristics, which were expected to predict travel lifestyle and behaviour, did not appear to explain much variance. Future research is recommended to investigate other factors that may predict travel lifestyle and behaviour among other ethnic groups. This study suggests that marketers should acknowledge that consumers in countries with diverse cultural backgrounds also need differentiated services and products. Crosscultural insights provide opportunities for marketers to develop and extend markets in multicultural countries.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,195
Tête enseignante GPT0,463
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2007
Routes d'admission1
Résumé présentoui

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