Exploring the development of an innovation metric — from hypothesis to initial use
Bibliographic record
Abstract
Abstract Purpose The challenge to improving innovation in an organisation is limited by the metrics used to measure it. Dimensions related to where an organisation is on an innovation spectrum, how fast is that organisation innovating and what is holding it back are key elements that could be used to adequately measure innovation. The objective of this work is to explore the development of an innovation metric based on a pipe flow analogy that has the potential to provide insights into these key elements describing innovation. Methodology This work follows three steps: establishing a hypothesis, testing the hypothesis and applying the hypothesis. The proposed hypothesis suggests that an innovation metric, Ri, can be developed based on a pipe flow analogy. This hypothesis is tested qualitatively and quantitatively. The quantitative assessment is accomplished by populating the innovation metric, Ri, with data mainly from the World Bank, comparing the results with established innovation and competitiveness metrics and examining if the metric confirms the trends suggested by the qualitative assessment. Using an illustrative case drawn from the quantitative assessment, the resulting innovation metric is used to indicate possible avenues for innovation performance improvement. Statistical analysis is limited to describing the goodness of fit of different trend line relationships. Results A qualitative assessment indicates that the innovation metric (Ri) behaves as illustrated in the literature. The quantitative assessment confirms the qualitative assessment results. The illustrative case demonstrates how the innovation metric can be used to potentially orient innovation performance improvement. The paper closes with a discussion addressing issues and limitations of the metric. Research limitations/implications The validity of this innovation metric is limited by the variables defining it and the quality of the data input. Consequently, the variables used are limited to the analogous versions of fluid mechanics variables used to describe fluid flow in a pipe. The variables used require both hard and soft data which was obtained from the data sources cited as related to nations. On the other hand, the subsequent challenge is related to applying this model to ever smaller organisations especially with respect to gathering soft data related to trust and ease of communication. Practical implications Keeping in mind the limitations mentioned, the innovation metric, as it stands, can be used to describe an organisation’s innovation performance, the speed of innovation and resistance to innovation with the data available from the sites indicated. As a result, the model also can be used to see how an organisation’s innovation performance evolves over time as well as indicate possible avenues to improve innovation performance. Originality/value This is the first application of the fluid mechanics analogy to describe innovation performance. Its main value is related to contributing to the global conversation on innovation.
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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 | 0.039 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".