Firm Performance and the Determinants in the Textile and Textile Product Industry of Indonesia Pre- and Post-COVID-19 Pandemic
Bibliographic record
Abstract
This research aimed to examine firm performance and its determinants in the textile and textile product (TPT) industry of Indonesia before and after the COVID-19 pandemic. The analysis used data from the manufacturing survey conducted by Indonesia’s Bureau of Central Statistics (BPS) for the period 2018–2021. It further incorporated the fixed-effect model on the subsectors by applying least-square dummy variables. The results show that firm performance declined during the COVID-19 pandemic while the price–cost margin was affected by firm size, export orientation, foreign ownership, and the pandemic. However, the Herfindahl–Hirschman index did not have a significant influence on firm performance. This research addresses the gaps identified in previous publications, which had limitations regarding sample data. It further contributed to the literature by applying price–cost margin (PCM) as a proxy for firm performance and investigating the determining factors in the TPT industry before and after the COVID-19 pandemic, particularly in Indonesia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".