The decade of innovation: from benchmarking to execution
Bibliographic record
Abstract
Purpose This article aims to highlight the results of a Global Innovation Survey from 407 organizations representing 33 countries. This was the third of three surveys conducted by the researchers since 2011. Ten key insights were formulated to gauge the progress of innovation in organizations as well as the practice and success of nine innovation methods (data analytics, design thinking, innovation metrics, etc.) used to support innovation execution. Design/methodology/approach The survey data was bifurcated into two groups, high and low innovators, by analyzing their innovation scores using a K-means cluster analysis. This was followed by correlational analysis with the innovation practices by these groups. Qualitative survey data was also collected and used to interpret the results. Findings Overall innovation scores have improved over the decade. Organizations are still struggling with process drivers such as idea management and innovation measures. High innovators are pervasively using innovative methods to advance innovation execution much more than low innovators. The two methods that showed the highest correlation to an innovative culture were design thinking and open innovation. Originality/value Comparing the Global Innovation Survey to two other surveys, 2011 Canadian Executives (n = 605) and 2013 US Fortune 1000 (n = 1,203) that use the same innovation measurement scale, provides a unique longitudinal perspective. The nine innovation methods investigated in the Global Innovation Survey provide original insight into how high and low innovative organizations are using methods to advance innovation execution.
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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 | 0.026 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".