Representing and evaluating the travel motivations of Pacific islanders

Trupp, Alexander; Pratt, Stephen; Stephenson, Marcus; Matatolu, Ilisapeci; Gibson, Dawn

doi:10.1002/jtr.2528

Cited by 8 publications

(3 citation statements)

References 69 publications

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“…Travel motivation is a prominent factor influencing visitor behaviour and destination selection in tourism research (Su et al, 2020). The literature on travel motivations is extensive, exploring various aspects such as push and pull factors (Dann, 1977), hierarchical needs arrangements and travel careers (Pearce & Lee, 2005), quests for authenticity (MacCannell, 1973), and non-Western tourism mobilities (Trupp et al, 2022).…”

Section: Travel Motivationmentioning

confidence: 99%

Tourism in Times of Crisis and Post-Crisis

Salman,

Trupp,

Chan

2024

Tourism (Zagreb, Online)

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The COVID-19 pandemic has had a profound effect on tourists' travel plans. Whilst the relationship between perceived risk and tourists' decisions and behaviours concerning COVID-19 is already an established research area in tourism studies, the dynamics of post-pandemic travel intentions and how they are influenced by travel motivation, destination safety and the notion of revenge tourism received very little attention. Therefore, this study proposes a model linking revenge tourism, destination safety, and travel motivations, leading to post-pandemic travel intentions. Furthermore, this research note lays a foundation for future research that can guide towards understanding tourists' travel intentions in times of crisis, similar to the COVID-19 pandemic.

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Section: Travel Motivationmentioning

confidence: 99%

Tourism in Times of Crisis and Post-Crisis

Salman,

Trupp,

Chan

2024

Tourism (Zagreb, Online)

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“…Toponyms are the basis of people's understanding of a place, and the names have high cultural, historical and tourism value [1], especially the names of tourist attractions, as a business card for the tourist destination, reflecting the unique charm and characteristics of the place [2]. At the same time, the names of tourist attractions also contribute to the development of the local region [3].…”

Section: Introductionmentioning

confidence: 99%

A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions

Zhang

et al. 2023

IJGI

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In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists’ perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people’s perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley’s K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people’s habitual cognition.

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“…However, after the year 2000, and currently, studies on motivation in tourism tend towards comprehensiveness when analyzing the motivating factors. Thus, there have been multiple studies related to the motivation to carry out various types of tourism such as tourism in exotic places [7], attraction tourism [8], rural tourism [9], sports tourism [10], cultural tourism [11], [12], [13], religious tourism [14], ecotourism or ecological tourism [15], [16], [17], [18] gastronomic tourism [19], sun and beach tourism [20], protest tourism [21] and health tourism [22], [23], among others.…”

Section: Introductionmentioning

confidence: 99%

Tourist motivations: Case Tatacoa desert - Colombia

Andrade¹,

Ramírez²,

Ramírez³

2022

TEM Journal

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This article identified the motivations of national and foreign tourists who visited the Tatacoa desert in Colombia, based on the theory of push and pull motivation factors. For the above, a quantitative instrument was built - Likert-type survey - made up of nine dimensions, between push and pull factors. In this regard, reliability was measured with Cronbach's Alpha and validity through Confirmatory Factor Analysis, both of which were satisfactory. The construct was evaluated through a structural equation model using SPSS AMOS software. As a result, a good fit of the hypothesized model was obtained, while in the evaluation of the motivations, the higher level of motivation stands out in national tourists over international ones. Likewise, a high rating was obtained in the push factors, above the attraction factors, which denotes the strength of the tastes and preferences of tourists in the case.

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Representing and evaluating the travel motivations of Pacific islanders

Cited by 8 publications

References 69 publications

Tourism in Times of Crisis and Post-Crisis

Tourism in Times of Crisis and Post-Crisis

A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions

Tourist motivations: Case Tatacoa desert - Colombia

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