Measuring Dimensions of Business Effectiveness in Greek Rural Tourism Areas
Bibliographic record
Abstract
Destination management and business effectiveness (DMBE) is a critical concern in today's volatile and unstable economic environment. The expansion of globalization and integration of markets, the current economic crisis, and the enforcement of regional and/or local administration in the European Union brings to the forefront the need for DMBE. The core business mission of small and middle-size (SMEs) corporations is being competitive and sustainable in an active market. However, in the past, only some dimensions of entrepreneurship and management and business effectiveness in tourism rural areas have been used to measure the DMBE. This study proposes a DMBE model that is based on the theoretical dimensions identified from previous studies and builds on the work of Wilson, Fesenmaier, Fesenmaier, and Van Es (2001 Wilson, F., Fesenmaier, D., Fesenmaier, J. and Van Es, J. 2001. Factors for success in rural tourism development. Journal of Travel Research, 40: 132–138. [Crossref] , [Google Scholar]) on successful factors for rural development. Utilizing data collected from 174 Greek rural tourism enterprises, the proposed DMBE model is tested. Findings identify two subdimensions, namely “local leadership and government synergetic support” and “capable and skilful staff.” Managerial implications of these two dimensions for rural tourism SMEs are discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".