Indonesia in the Headlight: Fighting Sustainability Through the Implementation of the Product-Oriented Product-Service Systems
Bibliographic record
Abstract
This research focuses on Indonesia's efforts to promote sustainability through the implementation of product-oriented Product-Service Systems (PSS).The study first explores the Indonesian government's support for sustainability and then examines the existing implementation of product-oriented PSS in the Indonesian motorcycle network.The paper investigates the drivers and barriers of product-oriented PSS implementation to improve sustainability through semi-structured interviews with thirteen senior managers from five Indonesian motorcycle companies, including manufacturers, intermediaries, and service partners.The study identifies key criteria for manufacturers to meet sustainability, such as waste management, reducing hazardous material usage, designing for disassembly, increasing the use of recyclable materials, and implementing take-back and recycling policies.For intermediaries and service partners, the key criteria include providing services, repairs, and maintenance, as well as reducing the use of hazardous materials.Finally, the study offers recommendations based on previous research on the role of government in overcoming barriers to product-oriented PSS implementation.This study contributes to the body of knowledge on improving sustainability through product-oriented PSS implementation by identifying drivers and barriers.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".