When Wrong Is Right: Leaving Room for Error in Innovation Measurement
Bibliographic record
Abstract
To date, measuring innovation has not been an exact science. As in many areas of organizational life, errors in measuring innovation are a recurring fact. Innovation researchers and practitioners alike have become increasingly interested in understanding the occurrence of organizational errors and how these errors affect innovation and its measurement. This empirical study aims to address this under-explored area by utilizing a qualitative in-depth case study at the innovation department of an organization with production sites and sales organizations worldwide. A total of 28 semi-structured interviews at several organizational levels were conducted, with innovation managers, project managers, senior managers, and staff. Based on the findings in this case study, three explanations are presented on how organizational errors occur when using innovation KPIs (key performance indicators). The first explanation can be connected to the increasing complexity of innovation and its intangible nature. Another explanation can be traced to the difference between innovation strategy and innovation KPIs. Lastly, room for organizational errors can be related to the multitude of individuals and organizational levels involved in innovation and its measurement. The implications for practitioners are that innovation KPIs are not precise metrics but should be seen as estimates with organizational errors. Whether or not these innovation KPIs can be used as tools to turn innovation into competitive advantages largely depends on whether wrong is right. Future research should focus on the metrics that are implemented and actually in use, as this future path would highlight the function and dysfunction that organizational errors in innovation KPIs can have.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".