Efficiency Measurement of Tourism and Recreation Companies (Industry Code E51) Listed on the Indonesia Stock Exchange
Bibliographic record
Abstract
COVID-19 pandemic which the first outbreak was found on December 2019 in Wuhan, China, has given great impact to tourism industries worldwide.Since then, most countries implemented lockdown and quarantine system, issued tight regulations about travel restriction.In order to survive the COVID-19 pandemic, which the ending has yet to be determined, every tourism industry must be able to work efficiently to maintain the usage of operating costs as low as possible since the revenue could not be optimized.This research aims to measure efficiency score of 41 companies in Tourism and Recreation Industry (code E51) listed on Indonesia Stock Exchange (IDX) from 2018 to 2021.At the first stage, data envelopment analysis (DEA) method with variable-return-to-scale (VRS) input-oriented approach is employed to estimate technical efficiency scores.At the second stage, left-truncated regression estimation with double-bootstrap is employed to test the significance of some explanatory factors.Cost of Sales and Revenue, Operating expenses, Interest expenses, and Fixed Assets are chosen as input variables, while Sales and Revenue, Profit (Loss) from Operation, and Asset Turnover Ratio as output variables of DEA.The result shows that efficiency score dropped by 20.42% in 2020 compared to the score in 2019.A slight increase of 2.39% in 2021 compared to the score in 2020.Another result also denotes that several explanatory factors such as Stock Price positively affected efficiency score of, meanwhile Liability to Asset Ratio gave negative influences.Finally, this research may contribute to the development of operation and management science in hospitality and tourism field as well as to support the business operators to adjust their strategic plans, especially in financial budgeting, to face the longimpact of COVID-19 pandemic.Efficiency measurement using advanced DEA Double Bootstrap method with selected financial parameters that are different from any previous studies in tourism provides novelty to this research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.002 | 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".