Analysis of the supply chain response power, practices and firm capabilities on competitive advantage and performance
Bibliographic record
Abstract
This research was conducted by analyzing the factors that affect company performance with supply chain management practices, supply chain responsiveness, company capabilities and competitive advantage variables as intervening variables. This research method was a quantitative survey and the object of this research consisted of 460 distributors, at the level of lubricants retailers, in Indonesia determined by simple random sampling. The research data was obtained by distributing online questionnaires through social media and the questionnaire was designed using open statements with a Likert scale of 2 to 7. The analysis technique used was Structural Equation Modeling with validity test analysis, hypothesis testing using structural equation modeling techniques with smartPLS 3.0 software as a tool to assist data processing. The results of this study indicate that the company's ability had a positive and significant effect on the company's competitive advantage, supply chain management practices had a positive and significant effect on advantage, supply chain responsiveness had a positive and significant effect on competitive advantage, competitive advantage had a positive and significant effect on company performance and finally supply chain management had a positive and significant impact on company performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".