Unravelling CEO Paranoia Process: From Socio-Cognitive Mechanisms to Organizational Outcomes
Bibliographic record
Abstract
Our paper explores the paradoxical effects of CEO paranoia on organizational decision-making and stakeholder relations by mapping out a three-phase process that illustrates the progression from a CEO's paranoid tendencies to organizational effects. Initially, we identify heightened vigilance and biased interpretation as primary socio-cognitive outcomes of paranoia at both individual and interpersonal levels. These outcomes precipitate a paradoxical approach to decision-making that is marked by both cautious and proactive behaviors. We further suggest that paranoia influences the manner in which CEOs engage with stakeholders, prompting a drive for unity against challenges while maintaining a strategic distance to preserve autonomy. We further theorize that while moderate levels of CEO paranoia can enhance strategic adaptability and stakeholder value appropriation through early threat detection and balanced stakeholder engagement, excessive paranoia may impede strategic change and deteriorate stakeholder trust. We also discuss personal, situational, and organizational factors that moderate the effect of CEO paranoia on socio-cognitive outcomes. This study highlights the need for theoretical frameworks that accommodate both the constructive and destructive potentials of leader psychology. It also aims to inform organizational leaders and policymakers on the optimal calibration of paranoia within leadership to leverage its potential while avoiding its detrimental excesses.
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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 | 0.007 | 0.017 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".