Knowledge integration and entrepreneurial capabilities for sustainable competitive advantage through supply chain management
Bibliographic record
Abstract
Sustainable Competitive Advantage (SCA) is very much needed in the development of the business world. This study aims to determine the model of increasing the SCA variable with Entrepreneurial Capability (EC) and Knowledge Integration Capability (KIC) directly or through Supply Chain Management (SCM) variables indirectly so that the objectives of SCA in small and medium enterprises (SMEs) can be achieved effectively. The research method used is a quantitative method with a structural model type using the SmartPLS version 3.2 program. The population in this study were all 2,296 administrators and members of IPEMI West Java. The sampling method used is random sampling. Data collection techniques using questionnaires were addressed to 360 respondents and 344 respondents were properly collected. The results show that EC influenced SCA with a T statistics score of 3.971, EC for SCM was 4.858, KIC for EC was 13.874, KIC for SCA was 1.886, KIC against SCM was 7.876, and SCM against SCA was 7.796 and it can be concluded that KIC to SCA can be significant if it is through SCM.
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 | 0.002 | 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".