Identifying factors influencing on the profitability of tourist enterprises: Evidence from Vietnam
Bibliographic record
Abstract
The profitability of tourism industry is affected by several factors due to the specificity of tourism business activities such as the season, resources, geographic position, state and local policy, etc.Therefore, determining the groups of factors that affect the profitability and profitability ratios in tourism business activities helps to give synchronous solutions to improve the efficiency of tourism business.This study is based on a survey on the factors affecting the profitability through the questionnaires and interviews of 115 tourist enterprises in BinhDinh, Vietnam.The study conducts Cronbach's Alpha and EFA analysis to determine groups of influencing factors and building regression functions of factors affecting the profitability ratios in tourist enterprises in BinhDinh province, Vietnam.Based on the EFA analysis results, the study has found two main types of factors affecting the profitability of enterprises; namely within and outside the firms.The group of factors within the enterprise includes 3 small groups; namely financial capacity; Enterprise human resources and Enterprise leadership.The external factors include 4 small groups including tourism business market; travel space and support services; political institutions; infrastructure and tourism security.The effects of these factors on financial figures are also represented in regression form.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".