Abstract: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 loca… Show more
“…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
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.
“…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
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.
“…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).…”
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.
“…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.…”
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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