Metric intensity and innovation dependency
Bibliographic record
Abstract
Abstract We examine how metric intensity—that is, the quantity, frequency, and extent to which performance metrics are tracked and used—varies with a firm's dependency on innovation for business success. Although performance metrics are essential in an organization's management control system, little is known about how the use of metrics differs in organizations with varying dependencies on incremental and radical innovation. Drawing on data from a sample of small‐ and medium‐sized enterprises (SMEs), we hypothesize and find that firms' dependency on incremental (radical) innovation is positively (negatively) associated with metric intensity. Furthermore, we find that (1) the positive relationship between the dependency on incremental innovation and metric intensity is stronger when the organizational culture is focused more on “control” and (2) the negative relationship between the dependency on radical innovation and metric intensity is mitigated when the organizational culture is focused more on “flexibility.” Additional analysis shows that the positive (negative) relationship between incremental (radical) innovation dependency and metric intensity can be mitigated by greater use of metrics for decision‐facilitating purposes. Our findings suggest that metrics‐based formal controls are designed to match the types of innovation dependencies and pre‐existing informal controls such as organizational culture. This study highlights the importance of distinguishing different types of innovation dependency in studying management control systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".