Green competitive advantage: Examining the role of environmental consciousness and green intellectual capital
Bibliographic record
Abstract
The purpose of this study was to examine the role of environmental consciousness and green intellectual capital (GIC) for green competitive advantage. The association between environmental consciousness and each component of GIC—green human capital, green relational capital, green structural capital—is tested in this study. Tests are also carried out to examine the association of each GIC element with green competitive advantage. Data were collected using an online questionnaire. A total of 237 questionnaires were sent to the CEOs of medium manufacturing companies in Bali Province, Indonesia. There were 72 returned questionnaires that could be analyzed (a 30.37% usable response rate). Data analysis was performed using variance-based structural equation modelling with the partial least square (SEM-PLS) approach with WarpsPLS 7.0. The findings show that there is a positive and significant association between environmental consciousness and each component of GIC: environmental consciousness with green human capital, environmental consciousness with green relational capital and environmental consciousness with green structural capital. The findings also demonstrate that each component of GIC has a significant positive association with green competitive advantage: green human capital with green competitive advantage, green relational capital with green competitive advantage and green structural capital with green competitive advantage. This research implies that going green through the adoption of green practices can contribute to green competitive advantage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".