Factors influencing supply chain agility to enhance export performance: case of export-oriented textile sector
Bibliographic record
Abstract
Purpose This study aims to examine the effect of supply chain agility (SCA) on the export performance of the Pakistani textile industry. Despite being one of the leading manufacturing and exporting sectors, only a handful of the extant literature is found on the textile industry. Design/methodology/approach A structured questionnaire was prepared using the extant literature. Data was gathered from 146 respondents associated with the textile industry of Pakistan. Hypotheses were tested using structural equation modeling after ensuring the reliability and validity of the data collected for this study. Findings This study provides several crucial insights for export-oriented firms. International entrepreneurial orientation and domestic competition are the crucial drivers for a firm’s agility. This study confirms that SCA has a significant impact on escalating export performance of the Pakistani textile industry in the international market. Originality/value To the best of the authors’ knowledge, the theoretical framework developed for this study is original and drawn from the extant literature. The findings of resulted from empirical testing of the theoretical model in the context of developing countries provide new information in the knowledge body.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".