From Malls to Markets: What Makes Shopping Irresistible for Chinese Tourists?
Bibliographic record
Abstract
This study investigates how multidimensional value and experience quality shape satisfaction and loyalty in shopping tourism. We extend the QVSL tradition by (i) specifying three hedonic value dimensions (entertainment, exploration, escapism), (ii) differentiating functional value into performance-oriented and money-saving facets, and (iii) incorporating epistemic value and experience quality as additional antecedents. We also model immediate behavioral outcomes (i.e., money spent and time spent) and test involvement as a moderating condition. Using path analysis on data from 413 mainland Chinese tourists in Japan, findings confirm that entertainment, functional value (for performance and money), epistemic value, and experience quality enhance shopping satisfaction. Functional values, epistemic value, and satisfaction drive destination loyalty. Money and time spent are additional outcomes of satisfaction. Involvement moderates the link between satisfaction and money spent. These insights offer strategic implications for Destination Marketing Organizations (DMOs) and retailers to optimize shopping environments and employee services, increasing tourist satisfaction, loyalty, and both time and money spent in the competitive shopping tourism market. Limitations include the cross-sectional design and the use of composite-indicator path analysis; future research could apply longitudinal or full SEM approaches, broaden contexts, and test additional constructs.
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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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".