Impact of Office Modernization on Environmental Satisfaction: A Naturalistic Field Study
Bibliographic record
Abstract
A case study was undertaken on one floor of a multi-floor office building in Seattle, WA. Its aim was to offer a straight-forward example for facilities managers, administrators, and researchers alike wishing to perform systematic, naturalistic, mixed-methods research in office spaces that have recently been retrofitted. Changes were made to the floor’s layout, and to the size of employees’ workspaces. New sound-making technology and a modern lighting framework were added. Objective measurements of lighting, acoustics, and indoor air quality were taken and an online questionnaire was distributed to staff to afford subjective measurements of their perceptions about the previous and new open-plan settings. Items concerning satisfaction with workspace layout, size, lighting, acoustics, air quality, and level of input into the retrofit process were asked. After the new space had been used for 1.5 months, occupants reported being more satisfied, in general, than they recalled being in the original setting. The size of personal workspaces and a sense of privacy were especially important to employees. Despite overhead lighting illuminance levels being below recommended industry standards, occupants were not dissatisfied with light levels. The sound masking system was iteratively commissioned based on negative occupant feedback, resulting in purposely setting some areas to exceed or fall short of acoustical performance guidelines; indoor air quality remained unchanged. Differences in quantitative and qualitative findings highlight the importance of gathering self-reported information from occupants in several ways and exploring them carefully to better understand why environmental satisfaction (or dissatisfaction) exists. Employees’ sense of environmental control remained a prominent theme in the data, supporting existing studies in the field of environmental psychology. While perceptions of control did not improve after the retrofit, occupants’ responses about the level of input they had into the retrofit process correlated significantly and positively with their perceptions of environmental satisfaction after its completion. The nuanced findings from this case study’s customized approach to measuring objective environmental stimuli, along with occupants’ environmental perceptions, add to a growing body of literature merging social scientific methodologies with technical environmental assessments for practical use by decision-makers working to satisfy employee preferences.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".