The long game of innovation and value creation
Bibliographic record
Abstract
Purpose The purpose of the paper is to emphasize the performance benefits of a long-term innovation and value creation perspective. This paper responds to the recent concept of the imagination premium method for valuing companies. It offers four key takeaways to create a long-term innovation-focused orientation for future value creation. Design/methodology/approach The research is based on both consulting experience and insight from several studies of executives that were supported by the U.S. Conference Board. Findings The research differentiates how high versus low innovators create long-term perspectives and value. High innovators have explicit processes that support innovation, leadership that focuses on long-term performance, resources committed to long-term projects and innovation and knowledge management systems that transfer knowledge throughout the organization. Research limitations/implications The research offers strategic directives aimed at creating long-term value but acknowledges that there are other means to accomplish such objectives. Practical implications This paper offers strategies for executives to create an innovation-focused organizational culture that drives lasting long-term value. Social implications Focusing on long-term innovation prioritizes larger social, environmental and business objectives over superficial short-term stock price changes, leading to greater value-creation. Originality/value This paper advocates that leadership play the long game and adopt a longer-term view of innovation due to its long-term competitive, employee engagement, sustainability and performance benefits.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".