Transition Thinking for Circular Agrobusiness Innovation - A Study Case on Agroindustry Company at Sumatera Island, Indonesia
Bibliographic record
Abstract
The Circular Economy (CE) transition toward sustainable business needs a fundamental change in business culture, production process and resource management.The business sector must understand their own capacity to overcome business challenges and put the CE concept as a long-term investment that requires continuous commitment.This research aims to analyze and evaluate the business transition to CE adoption to obtain a sustainable purpose.This qualitative research with narrative analysis method focuses on identifying corporate CE management transition that comprises innovation in business process, strategic integration, and transformation process.The socio-technical transition theory is used as a research framework to identify the technical aspects of CE implementation and further a deeper analysis of social dimension amidst the perspective of people's interaction.The data collection, analysis, and validation approach, which included FGD, semi-structured interviews and documentary analysis, examined the nuances of the actor-related strategies and institutional enabling processes of CE transition in the study case company.The research also gained valuable insight into seeing the internal dynamics of company management utilize their influence in managing resources aligned with CE principles through open innovation and people interaction.The outcome of this study showed that company's shift towards the circular business could be achieved through an organizational learning process, with particular attention to strengthening internal transformation by actively involving employees.The open innovation approach is an excellent way to utilize a collaborative culture by combining existing technologies that could drive innovation in the industry.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".