Avoiding Bad Press: Interpersonal Influence in Relations between CEOs and Journalists and the Consequences for Press Reporting About Firms and Their Leadership
Bibliographic record
Abstract
In this study we consider how and when interpersonal relations between chief executive officers (CEOs) and journalists can influence the content of journalists' reporting about corporate leaders and their firms. Specifically, we draw from the social psychological literature on interpersonal influence and social exchange to suggest (i) how the disclosure of relatively low corporate earnings may prompt the CEO to engage in ingratiatory behavior toward journalists, and (ii) how such behavior may be effective in prompting journalists to issue relatively positive reports about the CEO's firm. We also extend our theory to consider how relatively negative journalist reports may prompt CEOs to retaliate against individual journalists by limiting or cutting off communication with the offending journalist, and how such retaliation may deter other journalists from issuing negative reports about the firm in the future. We find support for our hypotheses in a unique data set that includes large-sample survey data on CEO-journalist relations. We discuss how our research contributes to the growing literature in organization theory and strategy on the social processes by which corporate leaders influence the behavior of information intermediaries and other external constituents toward their firms. Moreover, we suggest that an implication of our findings is that top executives can actively influence the reputation of their firms, as well as their own reputations as corporate leaders, by engaging in interpersonal influence processes toward journalists.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".