Unveiling business environment and digital entrepreneurial activity in the European tourism industry
Bibliographic record
Abstract
Purpose This article assesses the impact of the internal and external Business Environment on the Digital Entrepreneurial Activity (DEA) in the European tourism industry. Design/methodology/approach A sample of 125 European tourism entrepreneurs in Germany and France was studied. Data were analyzed using quantitative methods. Findings The results indicate that a firm experiencing losses due to theft and vandalism has a positive relationship with the DEA, and there is Liquidity or Cash flow that contributes positively to DEA. The outcomes shows that there is a specific limit to the institution having liquidity or cash flow, the costs of inspection by tax officials, and the average management time with government regulations that affect digital entrepreneurs. The total cost of labor contributes significantly to the digital productivity of entrepreneurs in the tourism sector. Originality/value These findings have significant and practical implications for entrepreneurs and academics in the tourism industry, providing them with valuable insights for decision-making.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".