The barriers of responsible agriculture supply chain: The relationship between organization capabilities, external actor involvement, and supply chain integration
Bibliographic record
Abstract
Responsible Supply Chain (RSC) has become an important issue in the agricultural supply chain, along with the development of the sustainable agriculture concept. Furthermore, responsible agriculture helps to ensure the availability of food and maintain land quality, enabling the land to last for a long time. However, in practice, creating a responsible agricultural supply chain is rather difficult, especially in developing countries, where numerous barriers hinder the implementation of RSCs. This study therefore is an exploratory research aimed at finding the barrier factors to implementing responsible action in the agricultural supply chain. Multiple Case study research methods were conducted to provide answers to the research question. Meanwhile, data collection was performed through in-depth interviews with two agribusiness companies, and interpretive Structural Modelling was used to analyse the data obtained. The results showed the barrier factors faced in implementing responsible action within the agricultural supply chain include poor organizational capabilities, external actor involvement, and lack of supply chain integration. Organizational capabilities, including cost management, knowledge management, and organizational policy, play a significant role in implementing a responsible agriculture supply chain. This Implementation is also highly dependent on external involvement by government or non-government organizations (NGOs), as some supply chains take responsible action because of obligations or pressure from these organizations. Another barrier is inadequate supply chain integration, including integration, information sharing, and incentive alignment among supply chain members. Meanwhile, inadequate budget management, as part of organizational capabilities, is the major barrier giving rise to other challenges. The results of this study serve as a reference for supply chains, especially in the agriculture industry, while creating programs in a bid to reduce these inhibiting factors, to ensure responsible actions are well implemented well and able to provide a competitive advantage for all supply chain members.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".