Performance measurement and management systems as IT artefacts
Bibliographic record
Abstract
Purpose Considering performance measurement and management systems (PMMS) to be “mission-critical” information systems for many business organisations, calls have been made for researchers to shift from studying the use of such systems to studying their “effective” use, and in so doing to focus on their characterisation as information technology (IT) artefacts. The paper aims to discuss this issue. Design/methodology/approach In seeking to answer these calls, the authors apply Burton-Jones and Grange’s theoretical framework to study the dimensions, contextual drivers and benefits of the effective use of PMMS. This is done through a field study of 16 PMMS artefacts as used in small- and medium-sized enterprises (SMEs). Findings In characterising, contextualising and valuing the effective use of PMMS, this study provides answers to the following questions: What constitutes the effective use of PMMS? What are the user, artefactual and task-related drivers of such use? And what are the benefits for SMEs of using performance measurement and management (PMM) systems effectively? Practical implications With regard to the design of a PMMS artefact, the findings imply that one should concentrate on those artefactual attributes that most enable informed action on the part of owner-managers, as it is these actions have the greater consequences for the realisation of IT business value in SMEs. Moreover, the nomological network resulting from this research provides the theoretical and methodological underpinnings of a diagnostic tool meant to develop the PMM function in SMEs. Originality/value This study provides further empirical grounding and understanding. This study provides further empirical grounding and understanding of the concept of effective use, as well as further applicability and actionability to this concept and to the nomological network of its dimensions, contextual drivers and benefits in the case of PMMS and in the context of SMEs.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".