Understanding Motivation and Social Influence in Stakeholder Prioritization
Bibliographic record
Abstract
Insight into organizational responses to stakeholder claims and influence attempts is critical to understand the challenges currently facing managers and organizations. Drawing on Kelman’s (1958) model of social influence, we advance the field’s understanding of the factors driving firm-level prioritization of competing stakeholder claims by developing a theoretical framework that accounts for both the stakeholder attributes that are important to relevant decision makers, and the decision makers’ motivations for accepting or rejecting the influence attempts of varying stakeholders. Our framework distinguishes itself from existing research by focusing on stakeholder prioritization, not salience, recognizing that stakeholder-related decisions result from group interaction and that important decision makers are not limited to those found within the classic boundaries of the firm. Consequently, we argue that decision makers are simultaneously stakeholders with attributes that might be relevant to other decision makers involved in prioritization. In addition, we identify a more extensive set of stakeholder attributes that includes powerlessness and illegitimacy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".