Developing and validating a multidimensional tourist engagement scale (TES)
Bibliographic record
Abstract
Customer engagement (CE) narrows the focus of experience to the interactive, value co-creative process between customers and company. Despite a few attempts measuring online engagement, we found no studies on fully operationalized engagement in the tourism destination. Considering the existing CE scales might not fully capture the scope of value co-creation process and its context-dependent nature, we proposed to develop a tourist engagement scale (TES) which incorporates more actors in value co-creation at the destination. Following the procedures of item generation, scale purification, and scale validation, we identified a 16-item, four-dimensional, second-order model of TES: social interaction, interaction with employees, relatedness, and activity-related tourist engagement (including immersed involvement and novelty-seeking). It provides various uses to DMOs and service providers, e.g. an assessment tool for tourist experience, a market segment tool, and a predictor to tourists’ behavioral intention. We contribute to the literature of tourist experience, value co-creation, and engagement study.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".