Redline innovations: Strategic responses to stakeholder opposition in innovation management
Bibliographic record
Abstract
Today, it is quite common to see firms facing strong opposition from non-market stakeholders, such as governments, NGOs, and activist groups, when pursuing economically advantageous innovation projects. This paper introduces the concept of a “Redline Innovation,” referring to innovation projects that, while economically advantageous to a firm, encounter substantial resistance from non-market stakeholders. Implementing effective innovation strategies in these contentious scenarios is crucial, as failure can result in erosion of competitive edge, missed economic opportunities, reputational damage, and operational disruptions. Grounded in non-market strategy theorizing, the study offers a strategic framework explaining how firms might respond to pressures against Redline Innovations. Categorizing these responses into three strategic themes (Backing Away, Bridging, and Buffering) across variable engagement levels (None, Reactive, Proactive), the paper identifies nine distinct strategies firms can adopt when confronting non-market stakeholder backlash against an innovative project. The analysis reveals that the choice of strategy significantly impacts a firm’s ability to maintain legitimacy, manage risks, and achieve economic benefits. We provide actionable insights for managers and policymakers, emphasizing the need for a nuanced approach to managing non-market stakeholder opposition in innovation processes, recognizing that innovation contexts are dynamic and can evolve over time. The findings are intended to enhance both strategic decision-making and policy formulation in contentious innovation landscapes.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".