Successful Crisis Recovery in Tourist Resorts From Covid-19: The Case of Xixiakou Village, China
Bibliographic record
Abstract
With the outbreak of COVID-19, a series of measurements, such as lockdowns and requirements of social distancing, were not only greatly impacting consumers, but also placed unprecedented demands on most industries. As an industry focusing on consumer experience, tourism market suffered severe knocks especially. Thus, what is significantly is how to recover customers’ confidence and purchase intention in tourism industry after pandemic. This study employed a case study method, analyzed the successful practices at Xixiakou scenic spot in Shangdong, PR China. A new customer engagement model using new media channels was developed, which combined with the theory of customer engagement, social media engagement and virtual reality. The result indicates that self-driving tours as well as the old-age tourism market will be promising after pandemic. This study will not only enrich the existing theories, but also provide empirical implications for recovery of scenic spot.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".