Integration between radical innovation and incremental innovation to expedite supply chain performance through collaboration and open-innovation: A case study of Indonesian logistic companies
Bibliographic record
Abstract
During the past few decades, logistic industry has grown rapidly worldwide, however, the performance of Indonesian logistic industry is decreasing due to various supply chain issues.Logistic companies are suffering with low performance which has negative consequence on gross domestic product (GDP).To address this issue, the primary objective of the current study is to investigate the role of innovation in supply chain management.By using the cross-sectional research design, 300 questionnaires were distributed among the employees of logistic companies.All the questionnaires were distributed by using area cluster sampling.PLS-SEM was preferred to achieve the objectives of the current study.Findings of the study have revealed that collaboration with supplier, customers and external partners had significant positive relationship with radical and incremental innovation.Moreover, radical and incremental innovation maintained significant positive relationship with open-innovation performance.An increase in open-innovation increases the supply chain performance among Indonesian logistic companies.Therefore, logistic companies must focus on innovation to boost their performance.This study contributed in the body of literature by examining the important role of radical and incremental innovation in supply chain performance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".