Comparing better building design and operation to other corporate strategies for improving organizational productivity: a review and synthesis
Bibliographic record
Abstract
‘Better buildings’ are intended to improve employee well-being and other important organizational productivity metrics, but such effects have been notoriously difficult to quantify convincingly. This paper uses new, multi-metric approaches to develop a framework for valuing these effects. The organizational productivity metrics considered are: absenteeism, employee turnover intent, self-assessed performance, job satisfaction, health and well-being, and complaints to the facilities manager. The effects of several ways of improving building design and operation (improved ventilation, enhanced lighting conditions, green building certification measures) are compared to the effects of other corporate strategies also employed with the intent of influencing employees to improve organizational productivity: office type (private vs open-plan), workplace health programs, bonuses, and flexible work options. Results were derived from a broad search and synthesis of published information from several disciplines: business, medicine, psychology, engineering, and facilities management. The scope was limited to studies conducted in real organizations in large office buildings, with a geographic focus on studies from North America, Europe, and Australia/New Zealand. In summary, better buildings strategies provided benefits on multiple organizational productivity metrics at levels similar to other corporate strategies. This supports greater consideration being given to better buildings strategies to improve organizational productivity beyond energy savings. In this paper, and for want of more primary research, the ‘better buildings’ category blends the effects of different improvements; this synthesis is proposed as a starting point to encourage more buildings research in this context, allowing future differentiation of the effects of specific interventions.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".