Designing and implementing performance measurement systems based on enterprise engineering guidelines
Bibliographic record
Abstract
Purpose This conceptual paper presents a proposal for improving a performance measurement (PM) system implementation process based on enterprise engineering (EE) guidelines, which gives the process a sense of completeness. Design/methodology/approach This paper analyzes a well-known process for PM systems implementation organized in two phases: identifying, designing and implementing the top-level performance measures; and cascading the top-level measures and identify appropriate lower-level performance measures. The proposed improvements to the studied process derive from the EE guidelines, which establish a basis for the structure of an organizational management system, the formalization and synchronization of processes, performance expectations, exception handling and change management. Findings The study reveals that not all EE guidelines are covered by the analyzed process, with four of them having no evidence of being adopted: involvement of people in process design and implementation; ensuring interoperability between different systems in the information structure; addressing of all possible exceptions; coherence and consistency of semantics across all processes. Originality/value By the lens of EE guidelines, this paper advances a how-to-guide. This paper can support managers and researchers on PM system design and implementation, given the importance and relevance of EE recommendations having a consistent and well-structured procedure.
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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 | 0.103 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".