Exploring Factors Affecting the Development of Export-Oriented Garment Industry: Facing the Global Competitiveness Challenges
Bibliographic record
Abstract
The purpose of this study was to explore the factors that affect the development of exported-oriented garment industry facing the global competitiveness challenges.This study examines competitiveness challenges of export-oriented garment industry by testing the research hypotheses.The basis of this study lies in understanding what kind of competitiveness challenges in terms of resource-based, dynamic capabilities and market-based factors. In this study, a purposeful sample of 250 sectors was drawn from total 359 Thailand exported-oriented garment sectors and a total of 211 respondents fully answered the required questions. Study participants were garment supervisors and managers, in garment industry over 5 years. The data collection includes questionnaire investigation for quantitative factor analysis method. Specifically, this study provides empirical evidence on the specific channels and mechanisms through what principal factor within current garment sectors. The findings showed the factors influence the competitiveness challenges of export-oriented garment industry. Therefore, the competitiveness challenges conceptual model emerged with respect to this industry.The model, which identified the main competitive hurdles that export-oriented garment industry faces (i.e., challenges relates to productivity, lead-time, collaboration, opportunity identification, quick response and risk identification factors). Additionally, the author presents Tai export-oriented garment industry current situation, which revealed this strong buy-driven industry needs to restructure their strategies to focus high value added activities and to enhance their competitiveness.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".