The long game of innovation and value creation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it