Effect of Knowledge Sharing and Digital Management to Performance on Ecotourism in Ranong Province, Thailand
Bibliographic record
Abstract
Ecotourism is the combination of ecosystem and tourism. Ecosystem tourism includes travel to destinations where flora, fauna, and cultural heritage are the primary attractions. Present study wants to establish the link between knowledge sharing, information and communication technology (ICT) and ecotourism performance with mediation of tourist attraction and digital management system among employees of ecotourism provider companies in Ranong province of Thailand. Data is collected through questionnaire survey method and via dropdown technique. Partial Least Square (PLS) is used in this study for data analysis. Results indicate that knowledge sharing related to ecotourism and ICT has positive significant impact on ecotourism performance and on tourist attraction and digital management system respectively. Tourist attraction has positive significant impact on ecotourism performance but not mediate the relation. Digital management system mediates the relation and also has the positive significant impact on ecotourism performance. Practitioners should focus on knowledge sharing variable, ICT and digital management system for increasing the ecotourism performance among ecotourism provider companies in Ranong province of Thailand.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".