A Model of environmental and job satisfaction in open-plan offices: COPE field findings
Bibliographic record
Abstract
As part of NRC's Cost-effective Open-Plan Environments project, a field study was conducted to examine occupants' satisfaction with their physical environments. The questionnaire, including satisfaction ratings of 18 environmental features, 2 overall environmental satisfaction items, and 2 job satisfaction items, was administered to 779 US and Canadian office workers, from public and private sector organizations. (Two presentations at CPA 2002 reported on a subset of these data.) This paper focuses on the factor structure of the 18 environmental features, and examines a model combining these factors, overall environmental satisfaction, and job satisfaction. Exploratory and confirmatory factor analyses, conducted on 3 subsets of the data, supported a 3-factor structure: satisfaction with lighting, satisfaction with privacy and acoustics, and satisfaction with ventilation. The models showed acceptable fit to data from different geographical locations and organizational sectors, showing their generalisability. Structural equation modelling also confirmed a model in which these 3 factors were jointly related to overall environmental satisfaction, which in turn was related to job satisfaction. Occupants who were more satisfied with their environment also reported greater job satisfaction, suggesting a role for the physical environment in promoting organizational well-being and effectiveness.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".