Datasets Comparison

This dataset contains survey data examining the influence of online user-generated content (UGC) characteristics on tourist engagement, self-congruity, revisit intention, and destination brand evangelism in Vietnam. The study investigates how perceived informativeness, authenticity, vividness, and relevance of UGC affect engagement behavior and subsequent loyalty-related outcomes, while also incorporating travel constraints as a contextual factor. Data were collected from domestic and international tourists at major tourism destinations across Northern, Central, and Southern Vietnam between December 2025 and February 2026. A structured questionnaire using five-point Likert scales was administered on-site with the support of trained research assistants. A total of 1,500 tourists were approached, resulting in 1,347 valid responses after screening. The dataset includes raw survey responses, recoded variables, and processed outputs used for Partial Least Squares Structural Equation Modeling (PLS-SEM). The data can be used for replication studies, comparative tourism research, and further investigation into digital engagement, destination branding, and behavioral intention models in emerging tourism markets

1. Download the raw dataset file from Mendeley Data. 2. Import the dataset into statistical software (e.g., SmartPLS 4, SPSS, or R). 3. Recode items where necessary according to the codebook (all constructs measured using five-point Likert scales). 4. Specify the reflective measurement model including the following constructs: Informativeness (INF), Authenticity (AUT), Vividness (VID), Relevance (REL), UGC Engagement (ENG), Self-Congruity (SC), Travel Constraints (TCN), Revisit Intention (RET), and Destination Brand Evangelism (EVA). 5. Assess the measurement model by evaluating outer loadings (>0.70), Cronbach’s alpha (>0.70), Composite Reliability (>0.70), and Average Variance Extracted (>0.50). 6. Evaluate discriminant validity using the HTMT criterion (<0.85). 7. Assess the structural model using bootstrapping (5,000 resamples) to obtain path coefficients, t-values, and p-values. 8. Examine R², f² effect sizes, and Stone–Geisser Q² values for predictive relevance. 9. Run PLSpredict to compare PLS-SEM prediction errors (RMSE, MAE) with linear model benchmarks.

Tourism, Consumer Behavior, Destination Marketing, Hospitality Management, User-Generated Content

Creative Commons Attribution 4.0 International

This dataset contains survey data on tourists’ perceptions of online user-generated travel content (UGC) and related behavioral responses toward tourism destinations in Vietnam. The dataset captures tourists’ evaluations of UGC characteristics, including informativeness, authenticity, vividness, and relevance, as well as their engagement with online travel content and subsequent behavioral responses such as revisit intention and destination brand evangelism. Variables related to self-congruity and travel constraints are also included. Data were collected from domestic and international tourists at major tourism destinations across Northern, Central, and Southern Vietnam between December 2025 and February 2026. A structured questionnaire with five-point Likert scale items was administered on-site using a time-location intercept approach with the support of trained research assistants. A total of 1,500 tourists were approached, resulting in 1,347 valid responses after eligibility screening. The repository includes the raw survey dataset, a codebook describing variables and coding schemes, the questionnaire in English and Vietnamese, and the informed consent form used during data collection. The dataset allows replication of statistical analyses using structural equation modeling or other quantitative methods and can support research on digital tourism behavior, online engagement, and destination branding in emerging tourism markets.

1. Download the dataset file “Dataset on tourists’ perceptions of online user-generated travel content in Vietnam (2025–2026).xlsx” from this repository. 2. Open the dataset using statistical software such as SmartPLS 4, SPSS, or R. 3. Refer to the Codebook.xlsx file to identify variable names, coding schemes, and construct definitions. 4. Specify the reflective measurement model including the following constructs: Informativeness (INF), Authenticity (AUT), Vividness (VID), Relevance (REL), UGC Engagement (ENG), Self-congruity (SC), Travel Constraints (TCN), Revisit Intention (RET), and Destination Brand Evangelism (EVA). 5. Evaluate the measurement model using reliability and validity indicators (outer loadings, Cronbach’s alpha, composite reliability, and AVE). 6. Assess discriminant validity using the HTMT criterion. 7. Evaluate the structural model using bootstrapping (5,000 resamples) to obtain path coefficients and significance levels. 8. Examine model explanatory and predictive power using R², effect sizes (f²), and Stone–Geisser Q² values. 9. Run PLSpredict to assess out-of-sample predictive performance.

Tourism, Consumer Behavior, Destination Marketing, Hospitality Management, User-Generated Content

Creative Commons Attribution 4.0 International