An Empirical Study on Resident Engagement in Service Innovation for Wellness Tourism Sustainable Development: An Analysis Based on SEM and fsQCA
Bibliographic record
Abstract
As wellness tourism destinations face increasing competitive pressures and environmental challenges, there is a heightened emphasis on implementing innovative service strategies that promote community engagement and sustainable development. The study proposes an integrated model grounded in generativity and service innovation theory to investigate the factors influencing resident engagement in service innovation performance (SIP). Four hundred-eight valid responses are using a snowball sampling technique. This research employed structural equation modeling (SEM) to assess the impact of individual variables, complemented by fuzzy-set qualitative comparative analysis (fsQCA) to explore the synergistic effects of multiple variables. The SEM results indicate that place attachment, generativity, and digital technology significantly enhance resident engagement in SIP, whereas knowledge management does not exhibit a statistically significant effect. Furthermore, fsQCA identifies three configurations linked to high-quality SIP and two configurations associated with low-quality SIP. The complementary insights gained from SEM and fsQCA enhance theoretical frameworks and provide practical implications for stakeholders in the tourism sector.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".