Uncorking the potential of wine: an empirical prediction of consumers’ intention to visit wine tourism destinations (WTDs) post-COVID-19
Bibliographic record
Abstract
Purpose The study aims to examine consumers' intentions to visit wine tourism destinations (WTDs) in Canada post-COVID-19 by combining destination-related constructs with the theory of planned behavior (TPB). Design/methodology/approach Convenience sampling was employed in the online survey method to gather data. Using AMOS and SPSS software, structural equation modeling (SEM) was used to analyze the data. Findings The outcomes of the SEM show that a powerful model for predicting consumers’ intention to visit WTDs was developed by combining the TPB with additional variables. More precisely, the study identified that consumers' attitudes, perceived behavioral control, wine product involvement and motivation exhibit positive influences on their intention to visit WTDs. Conversely, subjective norms and the destination wine image did not influence the intention. Research limitations/implications The findings have important ramifications for various parties involved, including the government, travel agencies, tourism associations and wine producers. This research's emphasis on consumer behavior enables practitioners to adjust to the changing needs of consumers in the post-pandemic environment. Originality/value The drawn-out model gives an improvised view of consumers’ behavioral intentions to visit WTDs post-COVID-19 by testing an integrated structural model comprising TPB and destination-related constructs. As far as the authors are aware, this research represents the first-ever effort to predict consumer's intentions to visit WTDs post-COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".