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Do attractions attract tourists?

2019· article· en· W7082416050 sur OpenAlex

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Notice bibliographique

RevueEdinburgh Napier Research Repository (Edinburgh Napier University) · 2019
Typearticle
Langueen
DomaineComputer Science
ThématiqueGeochemistry and Geologic Mapping
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTourismDestinationsTerminologyNoveltyProduct (mathematics)Taxonomy (biology)NationalityPosition (finance)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Many studies revealed travelers’ motives to travel a destination but do not answer explicitly what specific features of a destination influence travelers’ decisions. Dann’s (1976) “push and pull” factors best capture the relationship between the consumer and the destination. But, most work examines only one aspect of the equation. Pearce believed regardless of nationality or experience, all travelers do have the same core motives on traveling to a destination, which is seeking relaxation, novelty and relationship enhancement (appendix 1). As the layer move outward, the importance of motive is getting less mainly due to experience. McKercher developed tourism product taxonomy to standardize the tourism terminology (appendix 2). He further argued that as the tourist’s need becomes more specific, middle and outer, he or she will be attracted to items that appear at the lower end of the taxonomic tree (appendix 3). Alternately, if needs are general (core) then attractions, as defined by a higher tier in the taxonomy, will satisfy these needs. This paper explores the gap by comparing Pearce’s Travel Career pattern model with McKercher’s tourism product taxonomy to look at the relationship between motives and destination attributes. This paper attempted to answer if the destinations position themselves differently for different markets? And if the same markets targeted differently by different destinations? To examine the relationship between tourists’ motivation and attractions, and how attractions attract tourists. The study asks ‘do attractions attract tourists?’ focusses on Chinese, Australian, and Japanese who travel to two identified Asian (Singapore and Hong Kong) and two identified non-Asian (Canada and New Zealand) destinations. An in-depth understanding of the source market (Chinese, Australian and Japanese) is conducted by analysis of the travelling patterns, behaviors and interest activities towards four destinations (Singapore, Canada and New Zealand). Chinese are known to seek shopping and visiting iconic sites, and Australian are more adventure in selecting destinations. Japanese are looking for value when traveling, yet safety is their concern. Findings reveal that Chinese mainly fall under Pearce’s core motives. Australian weighted more towards the outer layer motives compared to Japanese and to avoid over-crowded destinations. Linking with McKercher’s idea of ‘nature of attraction’ in his ‘role of individual attractions in drawing tourists to a destination’, the top 10 attractions for all destinations among all three markets are more or less about the same. One of the reasons is the successful destination promotions and marketing images imprinted in the travelers’ minds (push-pull factors). In conclusion, the answer is ‘it depends’, on what the destination provides in terms of the number of attractions, distance concern, ease of access, safety and etc. It also relates to the complexity of travelers’ experience, expectation, motivation, disposable income and available time. Practical contribution helps the tourism boards and destination markers to set promotion plans according to the potential and target markets’ need and the attractions availability (existing or potential). In terms of theoretical implication, a further assessment of how attractions attract tourists is conducted by comparing the source market’s travelling pattern and experience towards various destinations are placed in Pearce’s three layers of motives, and a further test on McKercher’s framework on attractions and needs relationship. Noting that some data from the tourism boards are not up-to-date, some are dated back in 2013, which market may have changed due to different life stages, hence when comparing the same market’s destination, the consistency of data interpretation is a challenge.This paper focuses on a general overview of each source market and attraction taxonomy. As discussed, large country may have more opportunity to satisfy more motives due to size and available activities. In other words, different parts or regions will have different attributes (including atmosphere, cultures, climates/ seasons) which cater to different tourists’ needs. Further in-depth research is suggested to understand how attractions attract tourist in a specific part of countries, such as provinces or even cities.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,469
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,004
Études des sciences et des technologies0,0010,000
Communication savante0,0010,002
Science ouverte0,0040,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,038
Tête enseignante GPT0,277
Écart entre enseignants0,239 · 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