Research on the influencing factors of tourist experience in smart city landscape based on SEM and fsQCA
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the impact of usability, intelligence, ecology, interactivity, interest, ecology and culture in the landscape of smart city parks on tourist experience and explore the synergistic effects of configurations formed from multiple dimensions on improving the overall tourist experience. Design/methodology/approach The data were collected through field research and internet questionnaires, and participants came from all regions of China. Drawing on 622 valid data samples, structural equation modeling and fuzzy-set qualitative comparative analysis were used for empirical evaluation. Findings The findings reveal that ease of use, ecology, interactivity and enjoyment positively influence the tourist experience. Additionally, three distinct configurations emerged as determinants of tourist experiences: high ease of use, high ecological focus and a combined ease of use-ecology approach. This study highlights the synergistic effects of multiple factors, emphasizing that effective integration enhances the overall tourist experience. Originality/value This study, grounded in the technology acceptance model and the stimulus-organism-response theory, develops a research framework to identify factors influencing the tourist experience through both single- and multi-factor configurations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".