Measurements of workplace productivity in the office context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a comprehensive survey of workplace productivity key performance indicators (KPIs) used in the office context. Academic literature from the past 10 years has been systematically reviewed and contextualized through a series of expert interviews. Design/methodology/approach The authors present a systematic review of the literature to identify KPIs and methods of workplace productivity measurement, complemented by insights semi-structured interviews to inform a framework for a benchmarking tool. In total, 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, although qualitative questionnaires are more widely adopted than quantitative tools. The diversity of KPIs used in published studies indicates that a multidimensional approach would be the 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 10 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it