Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the claim that the application of human factors (HF) knowledge can improve both human well‐being and operations system (OS) performance. Design/methodology/approach A systematic review was conducted using a general and two specialist databases to identify empirical studies addressing both human and OS effects in examining manufacturing OS design aspects. Findings A total of 45 empirical studies were found, addressing both the human and system effects of OS (re)design. Of those studies providing clear directional effects, 95 percent showed a convergence between human effects and system effects (+, + or −,−), 5 percent showed a divergence of human and system effects (+,− or −,+). System effects included quality, productivity, implementation performance of new technologies, and also more “intangible” effects in terms of improved communication and co‐operation. Human effects included employee health, attitudes, physical workload, and “quality of working life”. Research limitations/implications Future research should attend to both human and system outcomes in trying to determine optimal configurations for OSs as this appears to be a complex relationship with potential long‐term impact on operational performance. Practical implications The application of HF in OS design can support improvement in both employee well‐being and system performance in a number of manufacturing domains. Originality/value The paper outlines and documents a research and practice gap between the fields of HF and operations management research that has not been previously discussed in the management literature. This gap may be inhibiting the design of OSs with superior long‐term performance.
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.
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 | 0.037 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".