Using the Data Envelopment Analysis to Measure and Benchmark the Efficiency of Small-scale Tourism Farms in South Korea
Bibliographic record
Abstract
Although economic viability and low productivity of small-scale tourism farms have been a major concern, there is no information on the economic analysis, including efficiency, of this sector. This exploratory case study aims to demonstrate the value of Data Envelopment Analysis for assessing and benchmarking the efficiency of small-scale tourism farms. Using the case study of 196 small-scale tourism farms in South Korea, the result of analysis indicates that the technical efficiency score is equal to 39.3% and the mean output increase amounting to 60.7%. Most tourism farms (76.0%, 149) were found to be inefficient, indicating an efficiency score lower than .5. The dominant source of inefficiency was found to result from pure technical efficiency involving managerial skills while the scale efficiency of Korean tourism farms has reached a certain level. Implications for farm tourism operators and researchers and directions for future research are discussed. Keywords: tourism farms; agritourism; efficiency; data envelopment analysis (DEA) _________________________________________________ Utilisation de l'Analyse de l'Enveloppement des Donnees pour Mesurer et Etalonner l'Efficacite des Fermes Touristiques de Petite-Echelle en Coree du Sud. Resume Bien que la viabilite economique et la faible productivite des fermes touristiques de petite echelle aient ete une preoccupation majeure, il n'y a aucune information concernant les analyses economiques, incluant l'efficacite de ce secteur. Cette etude de cas exploratoire a pour but de demontrer la valeur des Analyses de l'Enveloppement des Donnees pour evaluer et etalonner l'efficacite des fermes touristiques de petite echelle. A partir de l'etude de cas de 196 fermes touristiques de petite echelle en Coree du Sud, les resultats des analyses indiquent que la note technique d'efficacite est egale a 39.3% et le montant moyen augmente jusqu'a 60.7%. La plupart des fermes (76.0%, 149) se sont revelees etre inefficaces, indiquant une note d'efficacite inferieure a .5. Il fut trouve que la principale source d'inefficacite resulte d'une efficacite technique pure impliquant des competences manageriales alors que l'echelle d'efficacite des fermes touristiques coreennes a atteint un certain niveau. Les implications pour les operateurs des fermes touristiques, les chercheurs et les directions de recherches futures sont discutees.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".