Exploring the development of an innovation metric — from hypothesis to initial use
Notice bibliographique
Résumé
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, R i , 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, R i , 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 ( R i ) 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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».