Sustainability, Risk Management, and Innovation: Enhancing Performance in Indonesian Social Enterprises
Bibliographic record
Abstract
This study investigates the integration of sustainability practices and risk management in Indonesian social enterprises, emphasizing the role of innovation as a mediator and operational type as a moderator. Social enterprises face unique challenges in balancing economic sustainability with social impact, especially in emerging markets like Indonesia. A structured survey was conducted with 118 social enterprises to assess their sustainable practices, risk management procedures, innovation scores, and operational models (permanent vs. project-based). Using Structural Equation Modeling (SEM) and Partial Least Squares (PLS) analysis, the results show that sustainability practices positively influence innovation, while both innovation and risk management significantly improve sustainable performance. Additionally, innovation mediates the relationship between sustainability practices, risk management, and performance. The operational type moderates the link between risk management and sustainable performance but does not influence the connection between sustainability practices and performance. These findings suggest that innovation is crucial for improving the sustainability of social enterprises and that risk management strategies should be tailored to the operational model. Social enterprises in Indonesia should prioritize innovative approaches and effective risk management to enhance their long-term sustainability and social impact.
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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 | 0.003 | 0.005 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".