From international travelling consumer to place ambassador
Bibliographic record
Abstract
Purpose The purpose of this paper is to extend international marketing theory by examining country image effects simultaneously from the perspectives of Product-Country Image (PCI), Tourism Destination Image (TDI), and General Country Image (GCI), and by using tourism satisfaction as the central construct in a comprehensive model that investigates post-visit effects in both the product and tourism domains. Design/methodology/approach International tourists from multiple countries were intercepted at the end of a tourism trip and interviewed in-person using a structured questionnaire, resulting in 498 usable responses for data analysis. The model comprised seven constructs measured with 28 variables and was tested with structural equation modelling. Findings The study uncovers a number of cross-effects between a country as destination and as producer, and establishes tourism satisfaction as a core construct that is relevant to both the tourism and product facets of place image. Practical implications Above all, the study’s findings argue strongly in favour of greater coordination between the “product” and “tourism” sides of place marketing. Originality/value The study is original in its integrative analysis of GCI, PCI, and TDI constructs as antecedents and consequences of the tourism experience and, among other original contributions, is the first to investigate the direct link between product beliefs, tourism satisfaction, and post-visit product-related intentions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".