Development of a Scale to Measure Memorable Tourism Experiences
Why this work is in the frame
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Bibliographic record
Abstract
Concerning the contention of Pine and Gilmore (1999), experiences are directly related to a business's ability to generate revenue, providing tourist experiences that are more memorable and easier to retrieve would lead to the prosperity of the business. However, extant tourism research has provided little explanation of the factors that characterize memorable tourism experiences. The purpose of this research was: 1) to develop a valid and reliable memorable tourism experience scale; and 2) to examine structural relationships between memorable tourism experience and future behavioral intentions. Following the scale development procedure suggested by Churchill (1979) and Hinkin (1995), the memorable tourist experience scale was developed using a pool of items, expert reviews of the items, and scientific item elimination procedures. Reliability analyses indicated good internal consistency for the 24-item memorable tourism experience scale (Cronbach's alpha= .90). A principal component analysis revealed seven factors, which accounted for 74.63% of the total variance. Components included are hedonics, refreshing, local culture, meaningfulness, knowledge, involvement, and novelty. The finding of the CFA using LISREL program was cross-validated by splitting the total sample into two 250-case sub-samples. All major goodness-of-fit indices indicated the model's good fit to both datasets (CFI: .98, IFI: .98, NNFI: .97, and RMSEA: .05). After aggregating two separate samples (calibration and validation), structural relationships between the memorable tourist experiences and consequent variables (e.g., behavioral intentions) were tested. The findings indicated a good fit of model to the data (CFI: .98, IFI: .98, NNFI: .98, and RMSEA: .04).
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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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it