Activist engagement and industry-level change: Adoption of new practices by observing firms
Bibliographic record
Abstract
Activists strive to create industry-level change and institutionalize socially and environmentally responsible practices by engaging with high profile and legitimate firms. In doing so, they reach a broad audience of firms who carefully observe and evaluate the activists-target interaction. Drawing on legitimacy theory and using a vignette-based roleplaying experiment, this paper investigates how different characteristics of both the activists' campaign and the targeted firms' response jointly affect the likelihood that observing firms' decision makers will support the sustainability practice recommended by the activists. Specifically, we assess how observers' evaluations of both the legitimacy of the target firms' decision and the legitimacy of activists' recommendation affect their support for the activists' recommendations. Subsequently, we evaluate the ways in which different characteristics of the activists-target interaction (including activists' engagement style, their evidence quality, and target firm adoption decision) drive these two legitimacy evaluations. Our results highlight the importance of both target decision legitimacy and activists' recommendation legitimacy on the relationship between target's practice adoption and observer support for the practice.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".