Measuring the effect of outbound Chinese tourists travel decision-making through tourism destination image and travel safety and security

2017 ◽  
Vol 38 (3-4) ◽  
pp. 559-584 ◽  
Author(s):  
Shih-Chieh Hsu ◽  
Chin-Tsai Lin ◽  
Chuan Lee
Keyword(s):  
Decision Making ◽  
Destination Image ◽  
Tourism Destination ◽  
Chinese Tourists ◽  
Travel Decision

South Korea's Destination Image: Comparing Perceptions of Film and Nonfilm Chinese Tourists

2015 ◽  
Vol 43 (9) ◽  
pp. 1453-1462 ◽  
Author(s):  
Shiheng Zeng ◽  
Weisheng Chiu ◽  
Chul Won Lee ◽  
Hyun-Wook Kang ◽  
Chanmin Park
Keyword(s):  
South Korea ◽  
Destination Image ◽  
Tourism Destination ◽  
Positive Image ◽  
Significant Difference ◽  
Chinese Tourists ◽  
Food Variety ◽  
And Behaviors

We examined South Korea's destination image for Chinese tourists and compared the differences between visitors who had come to that destination because of exposure to movies or television dramas filmed at their destination (film tourists) and those who were nonfilm tourists. A survey of 311 Chinese tourists, consisting of film tourists (n = 132) and nonfilm tourists (n = 179) revealed that South Korea is perceived as a safe, friendly, and clean tourism destination, and that Chinese tourists feel happy and relaxed during their trip. We also found that Chinese tourists believe that Korea lacks food variety and historical attractions, and is not easy to get around. Moreover, we also found that there was a difference between film and nonfilm tourists in regard to cognitive image of the destination, in that film tourists had a more positive image than did nonfilm tourists. However, there was no significant difference in affective destination image between film and nonfilm tourists. Our findings contribute to understanding of Chinese tourists' perceptions and behaviors in regard to South Korea as a tourism destination. In addition, the implications for film and tourism destination marketers are discussed.

How does familiarity shape destination image and loyalty for visitors and residents?

2020 ◽  
pp. 135676672096974
Author(s):  
Gian Luca Casali ◽  
Yulin Liu ◽  
Angelo Presenza ◽  
Char-Lee Moyle
Keyword(s):  
Decision Making ◽  
Destination Image ◽  
Future Research ◽  
Tourism Destination ◽  
Holistic Model ◽  
Behavioural Intentions ◽  
Place Image ◽  
Decision Making Processes

Destination familiarity is thought to critically influence tourists’ decision-making processes. Yet the role of familiarity in shaping tourists’ and residents’ image of, and loyalty to, a destination remains uncertain. This research tests a complex and holistic model of familiarity, affective, cognitive and overall images, and the conative behavioural intentions of visiting and recommending the destination for both residents and visitors in the context of the emerging tourism destination of Molise, Italy. The results reveal that residents and visitors differ in terms of their familiarity and intention to visit a place, with familiarity being less likely to influence residents’ intentions. There is heterogeneity between residents and visitors’ affective image and intention to visit, as well as between their overall image and intention to recommend Molise. Hence, unlike visitors, residents are more likely to respond to factual cognitive imaging, rather than emotional messaging, suggesting that shifting residents’ perceptions of place image requires a different approach to that of visitors. Future research should seek to confirm the relationships in a multi-destination study.

Exploring destination image through online reviews: an augmented mining model using latent Dirichlet allocation combined with probabilistic hesitant fuzzy algorithm

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyan Luo ◽  
Tao Tong ◽  
Xiaoxu Zhang ◽  
Zheng Yang ◽  
Ling Li
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Small Sample ◽  
Destination Image ◽  
Tourism Destination ◽  
Fuzzy Algorithm ◽  
Content Type ◽  
Dirichlet Allocation

PurposeIn the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.Design/methodology/approachThe study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.FindingsIn the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.Originality/valuePrevious research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

Revisiting Tourism Destination Image: A Holistic Measurement Framework Using Big Data

2021 ◽  
pp. 004728752110247
Author(s):  
Vinh Bui ◽  
Ali Reza Alaei ◽  
Huy Quan Vu ◽  
Gang Li ◽  
Rob Law
Keyword(s):  
Large Scale ◽  
Structural Model ◽  
Destination Image ◽  
Tourism Destination ◽  
Computational Framework ◽  
Four Dimensions ◽  
Tourism Management ◽  
Measurement Framework ◽  
Compare And Contrast

Understanding and being able to measure, analyze, compare, and contrast the image of a tourism destination, also known as tourism destination image (TDI), is critical in tourism management and destination marketing. Although various methodologies have been developed, a consistent, reliable, and scalable method for measuring TDI is still unavailable. This study aims to address the challenge by proposing a framework for a holistic measure of TDI in four dimensions, including popularity, sentiment, time, and location. A structural model for TDI measurement that covers various aspects of a tourism destination is developed. TDI is then measured by a comprehensive computational framework that can analyze complex textual and visual data on a large scale. A case study using more than 30,000 images, and 10,000 comments in relation to three tourism destinations in Australia demonstrates the effectiveness of the proposed framework.

Like, tag and share: bolstering social media marketing to improve intention to visit a nature-based tourism destination

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vanessa Gaffar ◽  
Benny Tjahjono ◽  
Taufik Abdullah ◽  
Vidi Sukmayadi
Keyword(s):  
Social Media ◽  
Structural Equation ◽  
Botanical Garden ◽  
Equation Model ◽  
Destination Image ◽  
Social Media Marketing ◽  
Tourism Destination ◽  
Content Type ◽  
Future Value ◽  
Tourism Destinations

Purpose This paper aims to explore the influence of social media marketing on tourists’ intention to visit a botanical garden, which is one of the popular nature-based tourism destinations in Indonesia. Design/methodology/approach This study sent questionnaires to 400 followers of the botanical garden’s Facebook account who responded to the initial calls for participation and declared that they have not visited the garden before. Analyses were conducted on 363 valid responses using the structural equation model. Findings The findings revealed several key determinants influencing the image of the botanical garden and its future value proposition, particularly in supporting the endeavour to shift from a mere recreational destination to a nature-based tourism destination offering educational experiences. Originality/value This paper offers a fresh look into the roles of social media marketing in increasing the intention to visit a tourism destination that is considerably affected by the destination image.

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