A detailed method for destination image analysis using user-generated content

Mariné-Roig, Estela; Clavé, Salvador Antón

doi:10.1007/s40558-015-0040-1

Cited by 63 publications

(37 citation statements)

References 29 publications

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“…The seasonality problem shown in Figure 4 is present in other countries of the Mediterranean coast with an increased tourist influx during the summer [105]. Regarding sustainability, the frequency of keywords (Table A10) related to crowdedness in visits to the attractions is much lower than that of other attractions such as the Louvre Museum in Paris [71] or the Basilica of the Sagrada Familia in Barcelona [104].…”

Section: Discussionmentioning

confidence: 99%

“…Among research on TDIs stemming from TGC, the results of which can be compared with those of the study presented here, four studies warrant mention. The first involved collecting and analysing 18,884 travel blogs and OTRs on VirtualTourist and TripAdvisor of the Basilica of La Sagrada Familia in Barcelona [104]; the second did the same with 132,502 travel blogs and OTRs on TravelBlog, VirtualTourist and TripAdvisor addressing Catalonia in general [105]; the third did that with 387,414 OTRs about "Things to Do" in Île de France on TripAdvisor [71]; and the fourth did it with 330,000 OTRs on TripAdvisor regarding Catalan territorial brands [106].…”

Section: Use Of Big Data Analytics In Hospitality and Tourism Researchmentioning

confidence: 99%

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Destination Image Analytics Through Traveller-Generated Content

Mariné-Roig

2019

Sustainability

109

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The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users’ opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability.

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Section: Discussionmentioning

confidence: 99%

Section: Use Of Big Data Analytics In Hospitality and Tourism Researchmentioning

confidence: 99%

Destination Image Analytics Through Traveller-Generated Content

Mariné-Roig

2019

Sustainability

109

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“…In [56], internal factors influencing the image construct include socio-demographic factors. Specifically, the social and cultural environment relate to socio-demographic aspects of a human being [57]. It is postulated that today's tourists play a leading role in image projection [58].…”

Section: Demand (Tourist) Factors As Determinants Of DI and Performancementioning

confidence: 99%

“…A Visitor Exit Survey which was conducted at Zimbabwe's ports of entry by [83] revealed that the majority of visitors to Zimbabwe were young (35-39) years (16.4%) and middle-aged (40-49) years (13.9%). A study which was conducted in Egypt by [84] focusing on cultural dimensions, demographics, and information sources as antecedents to cognitive and affective DI found out that tourists in the age ranges 26-35 and 36-50 were more likely to use the Internet, while younger (aged 18-25) and older (51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62)(63)(64)(65) were less likely to use it. This finding in terms of age was similar to that of tourists in that most of the international tourists were fairly young.…”

Section: Demographic Characteristics Of Respondentsmentioning

confidence: 99%

Development of a Destination Image Recovery Model for Enhancing the Performance of the Tourism Sector in the Developing World

Kanokanga¹,

Tukuta²,

Chikuta³

2021

Tourism

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This chapter is based on a doctoral thesis on the development of a destination image (DI) recovery model for enhancing the performance of the tourism sector in Zimbabwe. The study was prompted by the failure of African destinations to develop DI image recovery models. A pragmatist paradigm, a convergent parallel mixed methodology research approach and a cross sectional survey were adopted. A sample of three hundred and nineteen comprising international tourists, service providers and key informants was used. A structured, semi-structured questionnaire and semi-structured interview guide were used respectively. Quantitative data was analyzed using the Statistical Package for Social Sciences (SPSS) and AMOS version 25 while qualitative data was analyzed using NVivo version 12. Tests were conducted using descriptive statistics, exploratory factor analysis, and confirmatory factor analysis. Structural Equation Modeling (SEM) was used to analyze the multiple independent variables. The major findings were that price, ancillary services and amenities significantly influenced affective image while ancillary services significantly influenced destination performance. The study recommended that the Ministry of Environment, Climate, Tourism and Hospitality Industry trains tourism stakeholders including the host community in order to achieve sustainable destination image recovery.

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“…Key-words analysis methodology has been used to identify the attributes of tourist destinations (Y. Pang et al 2011; Youn Kim and Yoon 2013) or the patterns concerning the distribution of destination brands (Marine-Roig and Clavé 2016), as the classes are given by the considered destinations.…”

Section: Related Workmentioning

confidence: 99%

Identification of the Unique Attributes of Tourist Destinations from Online Reviews

Toral

Torres

González‐Rodríguez

2017

Journal of Travel Research

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Online reviews have become increasingly important in tourist destinations and they affect the way in which they are perceived by potential consumers. They represent an overabundant source of information in the form of open answers, where customers freely express their experience regarding the tourist destination. Previous studies have made use of the shared experiences of visitors to obtain the main attributes of a tourist destination. This article goes one step further and proposes the identification of those attributes that are uniquely associated with a tourist destination when compared with some other destinations, using a combination of analysis of variance (ANOVA) and Tukey’s honestly significant difference. Findings reveal that the unique attributes are the best predictors of tourist destinations when compared with other destinations. Additionally, they represent a competitive advantage that could be reinforced by planners or agencies while minimizing the effect of the unique attributes of competitors.

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A detailed method for destination image analysis using user-generated content

Cited by 63 publications

References 29 publications

Destination Image Analytics Through Traveller-Generated Content

Destination Image Analytics Through Traveller-Generated Content

Development of a Destination Image Recovery Model for Enhancing the Performance of the Tourism Sector in the Developing World

Identification of the Unique Attributes of Tourist Destinations from Online Reviews

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