Quantifying the impact of environmental conditions on worker performance for inputting to a business case to justify enhanced workplace design features
Bibliographic record
Abstract
Despite the plethora of research showing the impact of environmental conditions on performance, the majority of UK businesses do not accept changes in productivity as part of the business case justification for improvements to the working environment. The authors' intention was to develop a practical methodology to help predict the potential gain in worker productivity that can be expected following design improvements. They carried out a literature review of productivity research and conducted a meta-analysis of 75 studies to quantify the impact of environmental conditions and design factors on performance. The unique aspect of the literature review is that the reported percentage changes in performance were weighted according to the relevance of the research study to real offices and office workers. The weightings converted the widely varying raw research results into what appears to be a more credible range of performance effects. The authors believe that their figures are ones that are more likely to be accepted by financial directors when used in building a business case. Due to the lack of rigorous multiple-factor studies, they proposed that the effect on performance of single factors can be added, but using a relationship based on the law of diminishing returns. Re-analysis of recent research of combined factors indicates that a ‘two-thirds, one-third’ rule of thumb may be appropriate. The authors believe that they have created a robust methodology for quantifying performance effects and using in the business case for workplace improvements.
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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 | 0.075 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".