Not All Sparks Light a Fire: Stakeholder and Shareholder Reactions to Critical Events in Contested Markets
Bibliographic record
Abstract
This paper examines when and how a critical mass of social and political stakeholders mobilizes against a corporate organization and the impact of such mobilization on the organization’s market value. Our study employs a dataset of more than 51,000 media-reported events describing the interactions among almost 2,300 political, social, and economic stakeholders and 19 gold-mining firms trading on the Toronto Stock Exchange and operating mines in emerging markets around the world. We first examine the conditions and dynamics that explain whether an isolated, stakeholder-initiated negative statement or action—a “spark” or critical event—goes unnoticed or escalates into a cascade of stakeholder reactions targeting the firm. Second, we examine whether such sparks and the ensuing cascades of stakeholder reactions affect shareholders’ valuation of the firm. We argue and show empirically that both stakeholders’ and shareholders’ reactions following critical events are largely influenced by stakeholders’ prior beliefs about the target organization and by peer stakeholders’ reactions to the critical event. Stakeholders with positive beliefs about the firm before the critical event mobilize to defend it, and those with negative prior beliefs reinforce their opposition. Shareholders also take note of the other stakeholders’ prior beliefs and react negatively to critical events if the firm has a history of conflict with its stakeholders. Thus unconnected or loosely connected stakeholders who reveal their beliefs about a firm through public statements and actions influence each other’s reactions to critical events and shareholders’ assessments of the firm’s value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".