Bibliographic record
Abstract
Purpose This paper aims to clarify the notions that underlie performance measurement systems (PMS) and to propose an information systems (IS)‐based characterisation and definition of PMS, that is, as a performance management information system (PMIS). Design/methodology/approach Research on PMS can be enhanced by a clear, precise and uniform characterisation of this research object in IS terms A classification scheme is developed and the contribution areas of an IS perspective to PMS research are presented and exemplified. Findings The knowledge developed in IS research in the form of IS theories, models and methods can be applied in research on PMS, particularly in empirical studies that analyse the individual and organisational behaviours associated with the PMS phenomenon. Research limitations/implications The conceptualisation and definition of PMS, as found in the literature, have not truly reflected their basic nature and characterisation as IS. Practical implications The research benefits of an IS‐based approach are illustrated through a PMS usage model founded on IS theory. In so doing, a contribution is made to the PMS research field by reinforcing its theoretical and empirical foundations. Originality/value This study proposes a novel and demonstrably useful IS‐based perspective, including an improved conceptualization and definition of PMS.
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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 | 0.044 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".