Developing the return on workplace investment (ROWI) tool
Bibliographic record
Abstract
Facilities managers and the wider corporate real estate (CRE) community have increasingly become focused on cost reduction, with organisations typically viewing property as a cost burden rather than an investment. Consequently, it remains rare for organisations to include performance benefits in financial investment appraisals of workplace projects. A change in narrative is required to one where value can be demonstrated rather than simply costs reduced. Previous attempts have been made to quantify workplace performance, but a tangible tool to assist in recommending major decisions regarding changes to the workplace has eluded discovery. Therefore, the authors joined forces with the Institute of Workplace and Facilities Management (IWFM) to create the Return on Workplace Investment (ROWI) tool. The ROWI tool is a ready reckoner for calculating the impact of workplace projects (including planning, design or operation) on people performance. It can be used as part of a cost-benefit analysis to help professionals build a business case which accounts for positive factors other than cost alone. The initial step to developing the ROWI tool was to conduct an extensive literature review to determine the performance metrics that could be used to calculate a return on workplace investment. Some 105 unique and robust literature sources, with a total of 194 individual assessments of performance, were selected. Five dominant and recurring performance metrics were identified, along with nine recurring broad workplace design elements affecting task performance. Previously, there was little confidence in productivity research due to the range in performance data that various studies produce. A unique aspect of the ROWI tool, however, is that the performance data for each study was weighted to make it more relevant to real office work. The corresponding, more realistic, potential impact of workplace design on each of the performance metrics was calculated using the weighted results from all the research studies.
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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 | 0.040 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.030 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".