Bibliographic record
Abstract
Purpose The purpose of this paper is to explain how, in the future, the most successful business innovation efforts will balance analytical mastery and intuitive originality in a dynamic interplay that the author calls “design thinking.”” Design/methodology/approach As a useful way to think about how to do this the paper takes the reader step‐by‐step through the “knowledge funnel” concept. Findings Design thinking empowers the design of business, the directed movement of a business through the knowledge funnel – from mystery to heuristic to algorithm – and then the utilization of the resulting efficiency to tackle the next mystery and the next and the next. Practical implications The apaper suggests that the velocity of movement through the knowledge funnel, powered by design thinking, is the most powerful formula for competitive advantage in the twenty‐first century. Originality/value The paper has a radical thesis: to advance knowledge, we must turn away from our standard definitions of proof – and from the false certainty of the past – and instead stare into the mystery of what could be.
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 | 0.052 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".