Open-plan office density and environmental satisfaction
Bibliographic record
Abstract
The open-plan office has been popular among design professionals since the late 1960's. This office design tends to produce higher occupant density than is typically found in traditional enclosed offices, a factor that has the potential to influence environmental satisfaction. Therefore, we conducted a literature review to examine the effects of density on occupants' environmental satisfaction.We selected nineteen studies for this literature review. The first ten studies provided indirect evidence that density might play a role in affecting environmental satisfaction. Specifically, these studies showed that environmental satisfaction decreased in open-plan offices as compared to traditional offices, if offices were considered less private and more crowded, and in high-density laboratory settings.The remaining nine studies directly examined the relationship between density and environmental satisfaction, using either a measure of social density (occupants per office) or spatial density (ft2 per occupant). Two out of four social density studies, which examined a range of 1 to 30+ occupants per office, found that environmental satisfaction decreased as social density increased. Three out of five spatial density studies, which examined a range of 60 to 550 ft2 per person, found that the more space an employee had, the more satisfied they were with their work environment (see figure below). The remaining studies reported that density was not significantly related to occupant satisfaction. However, no studies found that satisfaction increased as density increased.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".