Psychological consequences of tourism ideal affect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Guided by the affect valuation theory, this study examined the relationships among tourism ideal affect (i.e. how people want to feel at tourism destinations), tourism actual affect (i.e. how people really feel at tourism destinations), and tourism satisfaction. Online surveys were conducted before and after travel, and 418 Japanese adults provided usable data. Our SEM results indicated that (a) tourism ideal affect, not global ideal affect, influenced tourism actual affect; (b) tourism ideal affect influenced tourism actual affect with matching arousal levels (high- vs. low-arousal); and (c) tourism ideal affect influenced tourism satisfaction via tourism actual affect, but only for high-arousal levels. Our research extends ideal affect to the tourism context, which has motivational force and better explains tourism experience and satisfaction than global ideal affect. Tourism federations and agencies need to know that tourism and global ideal affect is distinct, and the former predicts actual emotional experiences in tourism. Providing people with tourism experiences that match their tourism ideal affect is the key to tourism development and planning/managing tourism destinations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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