In the search for greener buildings: The role of green human resource management
Bibliographic record
Abstract
Abstract The environmental performance of green buildings is receiving attention from built environment stakeholders. We introduce the concept of green human resource management (GHRM) to analyze how the performance gap in green buildings can be minimized using a human‐focused design perspective. We utilize signaling theory and abilities–motivation–opportunity (AMO) theory to explain the interactions between environmental proactivity, GHRM, pro‐environmental behaviors, job performance, and environmental performance. Survey data were collected from 460 employees working in Leadership in Energy and Environmental Design (LEED)‐certified green buildings in India and analyzed using structural equation modeling (SEM). Findings highlight that GHRM is likely to motivate employees to demonstrate pro‐environmental behaviors and be engaged in their jobs. We also find that when organizational‐level goals are effectively communicated, employees can enhance environmental performance in green buildings. Our study makes several contributions, including a framework that developing countries can use to promote environmental sustainability in the workplace.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".