Ripple effects: How collaboration reduces social movement contention
Bibliographic record
Abstract
Abstract Research Summary Research suggests firms can reduce stakeholder contention (e.g., lawsuits, protests) by collaborating with threatening stakeholders. We propose that by tapping into stakeholder networks and identities, collaborations also produce ripple effects beyond the firm's partner to attenuate contention from a broader set of stakeholders. Using variation in firms' and stakeholders' willingness to collaborate exogenous to contention to account for selection, our examination of contentious and collaborative interactions between 136 environmental movement organizations and 600 US firms corroborates our arguments. Firms face less contention when they collaborate with a better‐connected stakeholder motivated to share affirming information about the firm, or with a more contentious and authentic stakeholder. Our findings generalize to stakeholder criticism beyond movement organizations, suggesting collaborations are powerful tools for fashioning less contentious environments. Managerial Summary Companies can reduce conflict from hostile stakeholders like social activists by collaborating with their friends. We find social movement organizations mount fewer protests, boycotts, lawsuits, and other conflict against a company that collaborates with an organization that is either well connected in the movement or known for mobilizing movement's grassroots. This suggests that cross‐sector collaborations quell conflict through passing affirming information about a company through interorganizational networks or through the broadcast of an affirming signal to the broader stakeholder environment. We find that criticism from a wide range of stakeholders (e.g., media) also abates, suggesting that collaborations are powerful tools for fashioning less contentious environments.
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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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".