Respond or Forbear? How Stakeholders Influence Rivalry in a Non-Financial Context
Bibliographic record
Abstract
Why and how firms decide to aggressively respond or strategically forbear rival’s attack? Scholars of competitive dynamics typically answered such a question from firm or market perspective, using financial rationality. They ignored the ability of stakeholders to influence competition between rivals in a non-financial context. Responding to the call of Chen and Miller (2015), this study explores stakeholders’ role in rivals’ competition over environmental sustainability issues. Specifically, I survey environmental action/response dyads of Coca-Cola and Pepsico over a period of 15 years (2006-2020). The goal is to build a theory around how external stakeholders’ interests and perceptions affect focal firm’s motivation to aggressively respond or strategically forbear rival’s attack. This study inductively develops a new typology for rival’s actions based on stakeholders’ perception of these actions. In this sense, if rival’s action compels stakeholders to make unfavorable inferences about focal firm, aggressive response becomes inevitable, even if the response is financially infeasible. But if rival’s action fails to impress stakeholders, focal firm remains unmotivated to respond. Contributing to the AMC framework, results suggests that some rival-actions motivate focal firm to respond aggressively (influential actions) than others (skeptical and controversial actions), which is determined by stakeholders’ perception of those actions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".