Guidelines for Developing Competencies of Travel Agent Manager: A Comparative Study of Thailand and Laos
Bibliographic record
Abstract
This paper aims to develop guidelines for developing the competency required by a travel agent manager in the leisure and hospitality industry in Thailand and Laos. The conceptual framework is consisted of independent variables (core competency, common competency, and managerial competency) and dependent variable (guideline for Developing the competency of the manager). The data is collected from two sources: 1) 128 travel agents in both Laos and Thailand, and 2) additional 142 responses which are derived from snowball sampling, total 270 samples. Questionnaire is used as the data collecting tool. Multi-Layer Perceptron (MLP), which is a type of neural network statistic, is used for analysis and interpreting the factors to order prioritize of factors. The MLP is a mathematic network model which replicates neural network and it is used for anticipating the pattern of variables. It can be used for single variable analysis and multivariate analysis to describe variable significance and to map the variable linear. The results suggest at least three significant indications for developing the competency of travel agent manager in Thailand and Laos. The findings also indicate that Thailand leisure and hospitality industry has focus on organizational culture development, change management, and self-management, while in Laos there is an emphasis on operation development, personnel administration, and product and service. This study shows what aspects of competency the managers of travel agencies in Thailand and in Laos are possessing and what are lacking. This helps the organizations to initiate the most suitable guideline for development the missing aspects. It can also be seen that both travel agencies in Thailand and in Laos do not pay sufficient attention on working competency nor strengthen their managers’ competency to cope with either domestic competition or the more intense competition within and out of ASEAN region.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".