Effect of strategic agility, innovation capability, and technology adoption through supply chain integration on the firm performance moderated by environmental turbulence in Indonesia’s textile industry
Bibliographic record
Abstract
Textile industry involves a lengthy process from upstream to downstream, making supply chain integration crucial for enhancing firm performance. This study explores various factors that can boost supply chain integration and company performance in Indonesia's textile sector, including strategic agility, innovation capability, and technology adoption. The research is grounded in resource-based-view and market-based-view theories, suggesting that companies can optimize their resources and collaborate effectively with supply chain partners to enhance industry performance. Additionally, the study considers environmental turbulence as a moderating variable. Utilizing a quantitative approach with judgmental sampling, the research collected data through a structured questionnaire, resulting in 270 valid responses. The data was analyzed using the partial least squares structural equation modeling (PLS-SEM) method with SmartPLS 4.0 software. Findings indicate that strategic agility, innovation capability, and technology adoption significantly influence firm performance through supply chain integration, while environmental turbulence notably moderates the relationship between innovation capability and supply chain integration on firm performance. The study recommends that textile companies prioritize agility, strategic innovation, and technology adoption to enhance their integration with supply chain partners. It underscores the critical role of supply chain integration in improving company performance and the impact of environmental turbulence as a moderating factor.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".