Bibliographic record
Abstract
Purpose A survey by Gary Hamel's company (Strategos) identified that over 80 percent of senior managers agreed that innovation creates a strong source of competitive advantage, and 90 percent indicated that innovation is highly valued. Yet these same companies rated themselves poorly at innovation. This paper sets out to consider behaviors and traits that will help organizations to successfully innovate. Design/methodology/approach Recent articles have attempted to use the concept of scientific DNA as a metaphor to describe characteristics of an organization. Many of these are descriptive and refer to basic core activities that managers need to concern themselves with. This article presents an analogy of DNA for the business perspective. There are certain behaviors and traits – call them innovation genes – that are foundational to innovation. It is believed that the sequence presented is this paper represents the basic building blocks for organizational innovation. Findings The paper finds that the innovation DNA sequence includes employee centric traits of knowledge management, cluster management, value management, and alignment. The context shaping innovation includes employee constituency and empowerment. The outcomes include strategic architecture to support innovation, innovation mapping of strategic initiatives, and value creation. There are competitive and positioning advantages of innovation DNA that promote a sustainable competitive advantage. Originality/value Embedding innovation DNA into the organization's fabric elevates organizations to being innovative in everything they do ‐ from knowledge management to value creation, and execution. Its application is universal as it elevates the least common denominator respecting how employees think and act; behaviors which lend life to innovation. As a result, the innovation imperative will only be as good as the organization's lowest common denominator in this respect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".