The mediating effect of innovative behavior on supply chain performance of water supply companies
Bibliographic record
Abstract
This research investigates the mediating effect of Innovative Behavior on the relationship between Emotional Intelligence, Intellectual Intelligence, and supply chain performance. Data were collected from 198 employees of water supply companies across five major cities in Central Java, using purposive sampling and analyzed through structural equation modeling. The findings indicate that both Emotional Intelligence and Intellectual Intelligence positively and significantly impact supply chain performance. Additionally, Innovative Behavior not only positively influences supply chain performance but also mediates the relationship between Emotional and Intellectual Intelligence with supply chain performance. The study suggests that enhancing Emotional and Intellectual Intelligence through Innovative Behavior can significantly improve supply chain performance in water supply companies. Organizations are recommended to incorporate Emotional Intelligence into selection and training programs, develop initiatives to boost Emotional and Intellectual Intelligence through training in areas such as emotional management, interpersonal communication, problem-solving, critical thinking, and creativity, and create a supportive work environment that encourages innovative behavior. Implementing these strategies can lead to more efficient operations, better resource management, and increased customer satisfaction, thereby enhancing overall supply chain performance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".