A case study of global agency innovation rankings: implications on current definitions of innovation
Bibliographic record
Abstract
In this paper, the authors analyze global innovation rankings as provided by Strategy& over the last 7 years. They first explore the raw ranks and report variations in year-over-year (YOY) ranks for top ten ranking companies. The normalized innovation ranks are then used to calculate the Innovation Power (inP) to assess if these companies maintained or improved their ranks over time. An interesting classification of innovations for the top 10 emerges from this analysis. The constant top innovators were Apple and Google. The rising innovators were Tesla, 3M and Facebook. Other classifications are discussed. The authors propose a non-statistical predictive model, which is reminiscent of a kinematic model using a novel concept of Innovation Momentum (inM) and predict that for 2017, Apple and Google will hold their first and second place, followed by Amazon, Samsung and Tesla. Facebook is also expected to rise in its rank. Companies that reach out and serve end-user needs with service innovations appear rising in ranks, far more than R&D intensive patent filing innovators in these ranks. Tesla is an interesting top ranker to watch. There are implications for software focused companies gaining importance given their flexibility over hardware dominant ones. Some bottom innovators are further declining. Although the rankings are perception-based, there is a pattern that implies it is not random or merely subjective. The analysis highlights the need for leaders and consultants to put in perspective the complex management problem of measuring 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".