Challenging conventional wisdom related to defining business metrics: a behavioral approach
Bibliographic record
Abstract
Purpose Measurement drives behavior. Unfortunately, most performance measurement initiatives overlook this fact. Implementations are performed top‐down with strategy as the starting‐point. There needs to be a better understanding of the cultural context of the metrics (What is driving the behaviors?) and a better understanding of what metrics are to define (How do we drive the right behaviors through measurement?). The purpose of this paper is to explore the notion of a context‐based approach to performance metrics – by examining an organization's negative values – and the notion of a content‐based approach – by introducing the concept of business interface metrics. Design/methodology/approach The article analyses business metrics. Findings The paper demonstrates the need to use interface metrics in order to better manager processes and deliver organizational values. Originality/value To get new insights, sometimes conventional wisdom needs to be challenged. Following best practices around metrics can prevent companies from reflecting on the effect of the metrics they are trying to put in place. By coming up with a different approach (business interface metrics and negative values), interesting insights can be gained. Moreover, taking a fresh approach ensures that new thinking takes place and that there are fewer conformist paths to fall back on.
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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 | 0.091 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.077 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.019 |
| 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".