Measurements of workplace productivity in the office context: A systematic review and current industry insights
Bibliographic record
Abstract
Purpose – The aim of this paper is to present a comprehensive survey of workplace productivity key performance indicators used in the office context. Academic literature from the past ten years is systematically reviewed and contextualized through a series of expert interviews. Design/methodology/approach – The authors present a systematic review of literature to identify Key Performance Indicators (KPIs) and methods of workplace productivity measurement, complemented by insights semi-structured interviews to inform a framework for a benchmarking tool. 513 papers published since 2007 were considered, of which 98 full-length papers were reviewed, and 20 were found to provide significant insight and are summarized herein. Findings – Currently, no consensus exists on a single KPI suitable for measuring workplace productivity in an office environment, though qualitative questionnaires are more widely adopted than quantitative tools. The diversity of KPIs used in published studies indicates that a multidimensional approach would be most appropriate for knowledge-worker productivity measurement. Expert interviews further highlighted a shift from infrequent, detailed evaluation to frequent, simplified reporting across human resource functions and this context is important for future tool development. Originality/value – This paper provides a summary of significant work on workplace productivity measurement and KPI development over the past ten years. This follows up on the comprehensive review by B. Haynes (2007a), providing an updated perspective on research in this field with additional insights from expert interviews.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".