Bibliographic record
Abstract
Purpose This interview of C.K. Prahalad, one of the world's leading strategic thinkers, aims to offer corporate leaders a practical look at the radical concepts presented in his The New Age of Innovation (HBP, 2008), written with M.S. Krishnan. A lengthy review of the book is also in this issue. Design/methodology/approach The questions for this interview were researched by a team of Strategy & Leadership contributing editors. The interview was conducted by Robert J. Allio, a consultant who has previously been a senior executive at major US and Canadian corporations and a business school dean. Practical implications Prahalad believes that many businesses will undergo a transformation in the near future as value shifts from offering products to providing co‐created personalized experiences. Originality/value Because his new ideas explore the cutting‐edge of management innovation, managers will likely appreciate having Prahalad explain how his new model works. It posits that value will be determined by one customer co‐created experience at a time, defined as n=1; and to compete successfully in this environment, firms must access resources from multiple outside sources, either local or global, defined as R=G. In this interview he discusses the practical steps needed to ready a company to compete in this new business landscape.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".