Green innovative work behaviour model on generation z employees in the manufacturing industry: An empirical evidence from Indonesia
Bibliographic record
Abstract
With increasing global attention to environmental issues , companies are required to improve all aspects of their operations to become more environmentally friendly. This is essential to minimizing the negative impact on the environment, particularly in the manufacturing industry . The green innovative work behaviour of employees plays a key role in enhancing sustainability. To ensure the success of sustainable development practices, companies must actively encourage and support employees' green innovative work behaviour. This study aims to analyze the factors influencing green innovative work behaviour among employees in Indonesia's manufacturing industry , with a specific focus on Generation Z employees. This generation is widely known for its unique characteristics, which differ from those of previous generations. Additionally, they are expected to become the largest workforce in the manufacturing industry within the next few years. Data for this study was collected through a survey using a questionnaire. The sample consisted of 200 Generation Z employees working in the manufacturing industry. The results of this study indicate that green human resource management practices have a significant and positive impact on green innovative behaviour, both directly and indirectly through green empowerment. Other factors were not found to have a significant influence, either directly or indirectly. The theoretical and practical implications of these findings are discussed in this paper.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".