Theorizing CEO Loneliness: A Behavioral Strategy Perspective
Bibliographic record
Abstract
Claiming that “it is lonely at the top” has become somewhat of a cliché in the popular press; yet there are very few scientific studies that investigate this topic, and none of them, to our knowledge, has specifically tackled CEO loneliness. This article provides empirical evidence from in-depth interviews with 46 CEOs of leading Canadian organizations. We found that CEOs do experience feelings of loneliness in their job, but in a way that differs from dominant psychological and sociological accounts—e.g. as a subjective negative emotions deriving from a lack of meaningful relationships. Building on the behavioral strategy literature, we show that CEO loneliness stems from an assessment of organizational vulnerability coupled with a sense of uselessness in proximal task-network, which translates into increased task-challenges. We also argue that CEOs thwart their feeling of loneliness through two dominant strategies that aim at increasing their self-agency and reinforcing their (proximal and distal) task-networks. We propose a task-related model of CEO loneliness that unpacks its organizing effects, thereby offering a perspective that nuances former psychological and sociological accounts, as well as contributes to the behavioral strategy literature.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".