Role of entrepreneur’s perspective of waste management for coffee shop sustainability
Bibliographic record
Abstract
The development of the coffee business as a small and medium enterprise (SME) shows positive things with the increasing number of coffee, which means an abundance of existing coffee grounds. There is a lack of research on entrepreneur perspectives on coffee waste management. This study aims to analyze the perspective of coffee shop entrepreneurs in managing coffee waste and converting waste into alternative energy for sustainable and environmentally friendly prospects. Respondents in this study were 201 coffee shop owners in Bekasi, Indonesia, who received questionnaires using the snowball sampling technique; for data analysis, the paper used PLS-SEM. This study found that the entrepreneur perspective significantly affects coffee waste management, encouraging the sustainability of coffee shops. The results of the R-Square analysis show that coffee shop owner awareness is strongly influenced by coffee waste management knowledge (93.6%); the attitude of coffee shop owners is influenced by coffee waste management knowledge and coffee shop owner awareness (92.5%); and the coffee waste management behavior is influenced by coffee shop owner awareness and attitude of coffee shop owners (97.8%). In addition, entrepreneurs’ excellent attitudes and awareness toward the potential of coffee grounds encourage them to carry out better waste management through sorting procedures to convert coffee grounds into alternative energy. AcknowledgmentThis study is funded by the Directorate of Research and Development, Universitas Indonesia, under Hibah PUTI 2022 (Grant No. NKB-1364/UN2.RST/HKP.05.00/2022).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".