Exploring shopping tourism as an adjunct therapy to improve mental health: Evidence from <scp>PLS‐SEM</scp> and <scp>NCA</scp>
Bibliographic record
Abstract
Abstract While previous tourism studies have examined mental health, additional research is necessary. Drawing on Rogers' theory of person‐centered therapy and self‐determination theory, this study explores shopping tourism as an adjunct therapy to improve mental health. 309 residents from Hong Kong who had shopping tourism experiences were surveyed. Partial Least Squares‐Structural Equation Modeling (PLS‐SEM) and Necessary Condition Analysis (NCA) were adopted. The results showed that shopping hedonism, consisting of memorable and fashion shopping, and tourism escapism had specific effects on tourists' self‐congruence and eventually enhanced mental health. While tourism escapism was shown through PLS‐SEM to be non‐significant in driving shopping tourists' self‐congruence, it proved to be a necessary condition of such self‐congruence in NCA. This study recommends that government agencies promote shopping tourism as a non‐conventional way of enhancing people's mental health. Destinations can also attract shopping tourists from the perspective of promoting their mental health.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".