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

Bui, Vinh; Alaei, Alireza; Vu, Huy Quan; Li, Gang; Law, Rob

doi:10.1177/00472875211024749

Cited by 49 publications

(24 citation statements)

References 69 publications

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“…Romanian gastronomy is also an element that catches tourists' attention. Hence, the projected destination image by travellers' blogs on Romania reveals dynamic destination attributes (Bui et al, 2021;Marine-Roig & Clavé, 2016), mainly related to the richness of cultural and natural tourist resources that Romania possesses.…”

Section: Output Results From Content Analysis Of Textual Data Of Trav...mentioning

confidence: 99%

Using travel blogs to explore the image of Romania as an emerging tourism destination

Lupu¹,

Rodrigues²,

Gallarza³

2023

EJTR

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The general objective of this study is to explore and identify the perception about Romania as a tourist destination based on travellers’ blogs; we therefore aim to explore and identify the main elements of the image of Romania as a tourist destination based on these blogs. Data were collected manually during May 2021 using the search keywords “travel blogs” and “Romania” on the Google search engine (201 entries). Further, both a word frequency analysis (quantitative), and a content analysis (qualitative) were conducted to ascertain which places are most frequently mentioned in tourists’ travel posts, and the words most often used to describe their experience. The results shown that both destination-specific and primary image attributes are essential to understand visitors’ perceptions through travel blogs. Managerial and practical implications also emerge for tourism managers to improve the destination image of Romania.

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Section: Output Results From Content Analysis Of Textual Data Of Trav...mentioning

confidence: 99%

Using travel blogs to explore the image of Romania as an emerging tourism destination

Lupu¹,

Rodrigues²,

Gallarza³

2023

EJTR

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“…This work examined the effects of two dimensions of perceived image, but other brand elements representing a broader range of the destinations' characteristics were not investigated. Thus, an avenue of future research is to incorporate other facets of destination CBBE dimensions, such as the people/local residents as service providers and/or as participants in daily life, events/festivals, and other cultural assets (Bui et al , forthcoming; Kladou and Kehagias, 2014; Leicht, 2018; Rodríguez del Bosque and San Martín, 2008).…”

Section: Discussionmentioning

confidence: 99%

Assessing the role of venturesomeness in a destination consumer-based brand equity model

Cruz-Milán

2021

JHTI

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PurposeGiven the lack of research on the nomological validity of tourism destination consumer-based brand equity (CBBE) constructs incorporating core, well-established constructs from the travel and tourism discipline, this research investigates the influence of venturesomeness as a moderator in a model with destination image, satisfaction, and overall CBBE as antecedents of return intentions.Design/methodology/approachThe study uses online panel data of past visitor to the sea-side destination of Corpus Christi, Texas. A sample of 210 residents in Texas and surrounding states was employed to estimate the hypothesized effects through partial least squares-structural equation modeling (PLS-SEM).FindingsResults demonstrate the predictive effects of destination CBBE dimensions on tourists' revisit intention, with the significant moderation effects of venturesomeness through its influence on tourist satisfaction.Research limitations/implicationsFindings provide general support to the nomological validity of the proposed model, highlighting the role of satisfaction as a central dimension to explain destination loyalty, the limitations of generic scales to investigate tourism destination contexts, and the incorporation of consumers' psychographics and lifestyle variables on destination CBBE.Practical implicationsDestination marketers should develop segmentation strategies to target travelers with psychographic profiles that are more responsive to the factors that foster CBBE.Originality/valueThis research provides insights on the nomological validity of a CBBE model by evaluating its integration with a context-specific theoretical domain, which is a condition to increase the explanatory scope of theoretical relations and claims in intermediate theory, and to move the research field forward.

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“…As the internet deepens its influence on tourist behavior, big data research is becoming increasingly important to enhance destination marketing capabilities and understand images from the consumer perspective (Li et al , 2018; Mariani, 2020; Bui et al , 2022). As tourists co-create value for destinations, they should cater to their needs and preferences (Kozak and Buhalis, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Generally, big data analytics is considered a data-driven approach, but it can also guide research design or contribute to improving theories (Chen et al , 2021; Mazanec, 2020). For example, Buhalis et al (2020) combined both deductive and inductive analysis to bridge marketing theory and big data analytics; Bui et al (2022) proposed a holistic measurement framework based on complex textual and visual data.…”

Section: Literature Reviewmentioning

confidence: 99%

Destination image: a consumer-based, big data-enabled approach

Zhong

Morrison

Zheng

et al. 2023

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Purpose This study aims to use a bottom-up, inductive approach to derive destination image attributes from large quantities of online consumer narratives and establish a destination classification system based on relationships among attributes and places. Design/methodology/approach Content and social network analyses were used to explore the consumer image structure for destinations based on online narratives. Cluster analysis was then used to group destinations by attributes, and ANOVA provided comparisons. Findings Twenty-two attributes were identified and combined into three groups (core, expected, latent). Destinations were classified into three clusters (comprehensive urban, scenic and lifestyle) based on their network centralities. Using data on Chinese tourism, the most mentioned (core) attributes were determined to be landscape, traffic within the destination, food and beverages and resource-based attractions. Social life was meaningful in consumer narratives but often overlooked by researchers. Practical implications Destinations should determine into which category they belong and then appeal to the real needs of tourists. Destination management organizations should provide the essential attributes while paying greater attention to highlighting the destinations’ social life atmosphere. Originality/value This research produced empirical work on Chinese tourism by combining a bottom-up, inductive research design with big data. It divided the 49 destinations into three categories and established a new system based on rich data to classify travel destinations.

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

Cited by 49 publications

References 69 publications

Using travel blogs to explore the image of Romania as an emerging tourism destination

Using travel blogs to explore the image of Romania as an emerging tourism destination

Assessing the role of venturesomeness in a destination consumer-based brand equity model

Destination image: a consumer-based, big data-enabled approach

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