Measuring cultural readiness for innovation: six essential questions
Bibliographic record
Abstract
Purpose This paper aims to present a framework that includes six essential factors and four strategic intervention points that provide the necessary context to sustain and support innovation. Design/methodology/approach Based on our academic and consulting experience, this article summarizes our knowledge of what it takes to be a top innovator and how organizations should best pursue innovation agendas. The model presented is supported by our research which considers assessments from 3,642 employee responses assessing the innovation cultures of organizations. Findings We find that companies need to ask six questions to assess their innovation cultures. These questions relate to creativity, incentives, processes, leadership, knowledge management and resources. Our framework presents four intervention points to support implementing and sustaining an innovation culture including objectives, behaviors and actions, context and management for execution. Research limitations/implications Our framework is effective, but we acknowledge that there are other means to creating and sustaining an innovation culture. Practical implications We present six questions that companies need to ask themselves to assess their innovation culture and offer strategies to enhance it. Social implications Given the contribution of innovation culture to competitiveness and performance, our recommendations will allow managers to set themselves apart from their competition and further their financial and nonfinancial corporate objectives. Originality/value Everyone likes the idea of change, but it is the process of change that is difficult. We offer strategies that put such intentions to work.
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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 | 0.044 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".