Bibliographic record
Abstract
We investigate the impact that social movements have on firm innovation through private politics. We argue that firms strategically respond to private politics by investing in new technologies that address movement-advocated issues material to firms’ performance. Although both contentious private politics—when activists contentiously target firms—and cooperative private politics—when activists and firms collaborate—catalyze innovation, they do so in different ways. Contentious private politics increases the amount of innovation that firms undertake by drawing managerial attention to movement-advocated issues material to the firm, prompting search for solutions to those issues. Conversely, cooperative private politics provides firms access to new knowledge that encourages firms to search for solutions in areas more distant from their existing knowledge and in so doing, increase innovation involving distant recombination on material issues. We find support for our arguments in a matched sample of firms contentiously targeted and with activist collaborations on climate change issues and firms that were not targets of private politics on those issues but had otherwise similar histories of climate-related innovation and relationships with climate movements and other environmental movements. Supplementary analyses corroborate the mechanisms that undergird our theoretical predictions; contentious private politics is associated with more innovation closer to a firm’s expertise, whereas cooperative private politics is associated with innovations that draw on more distant knowledge. We also find that when collaboration follows contention, their respective impacts on innovation are reduced, which may result from firms seeking collaborations for their legitimacy-granting benefits after contention rather than the learning opportunities they offer. Funding: Funding for this research was provided by the Strategic Management Society Strategy Research Foundation [dissertation grant] and Wharton’s Mack Institute for Innovation Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.17497 .
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".