Mediation effect of collaborative performance system on fresh produce supply chain performance with a lateral collaboration structure model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it