Mediation effect of collaborative performance system on fresh produce supply chain performance with a lateral collaboration structure model
Bibliographic record
Abstract
Fresh produce which are part of agricultural products that can survive during the pandemic in Indonesia, there is even an increase in supply, this contribution is important for the availability of these products in maintaining consumption needs in maintaining public health levels in the midst of an unfavourable situation for all parties, including sustainability of business in this chain network. However, the development of this commodity still has many obstacles, especially in the ability to provide high-quality products, resource capabilities and manage existing information, especially the farmers who are involved in cooperation in this supply chain system, so that it can impact their performance. This study explores the mediating effect of collaborative performance systems (CPS) in lateral collaboration structures such as; information sharing (ISH), resource sharing (RSH), contract farming (CTF) and join mode transportation (JTM) in individual companies (CIP) and supply chain performance (SPO) in the fresh produce supply chain (FPSC). The sample in this study was taken based on purposive sampling from the participation of respondents in the FPSC network consisting of farmers producing fresh vegetables and fruits who are members of the Association of the Farmers Groups (Gapoktan), distributors, owners of transportation modes and supermarkets. Respondents consisted of 72 people who had filled out complete questionnaires from their four supply chain channel partners. Data collection methods were analyzed using a structural equation approach. The results of the study that the mediation of CPS on the performance of CIP and SPO in the FPSC was confirmed.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".