Proactive Environmental Strategies and Their Impact on Hotel Competitiveness During Crisis: The Case of the Czech Hotel Industry
Bibliographic record
Abstract
This research focuses on hotel competitiveness in the Czech Republic. It provides evidence that proactive environmental strategies implemented in hotel operations are a competitive advantage for hotels, especially during a crisis. This research determined the impact of proactive environmental strategies on hotel competitiveness in the period before and during the COVID-19 pandemic. Competitiveness was assessed based on the occupancy rate and hotel experience. Data were obtained through quantitative research, which involved 110 accommodation facilities from the Czech Republic, of which 51 were common hotels and 59 were green hotels. The research yields two groups of results. The first group is the results of testing the dependence between the type of hotel and changes in hotel competitiveness. These results did not confirm the relationship between hotel type according to implementation of proactive environmental strategies and competitiveness based neither on evaluation of hotel experience nor on the occupancy rate. According to these findings, it does not matter whether the hotel is green during a crisis. The second group of results includes concrete values of changes in occupancy in the second and third quarters of 2019 and 2020, as well as specific changes in the clientele. The decrease in occupancy in the second quarter of 2020 compared with 2019 was not as significant as expected. In the third quarter of 2020, the year-on-year change was minimal, and some accommodation facilities experienced an increase in occupancy because Czech visitors embraced domestic tourism. In this case, the absolute indicators show that green hotels had an advantage.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".