The Two Faces of CEO Narcissism and Affordable Loss: The Rivalry-Versus-Admiration Perspective
Bibliographic record
Abstract
Effectuation approach, defined as both process and principles guiding decision-making logic at the firm level, has attracted entrepreneurship scholars’ attention for two decades. The majority of research focuses on effectuation approach as a whole, and few studies have explored related subconstructs, such as affordable loss. However, there is a scanty of empirical exanimations on single principle, which benefits an in-depth understanding of effectuation approach. Hence, based on the rivalry-versus-admiration perspective, this study investigates the relationship between CEO narcissism and affordable loss. On the one hand, the influence of individual personality differences on effectuation principles has been addressed and verified by empirical support. A firm’s affordable loss behaviors indicate the cognitive mechanism underlying effectuation principles. On the other hand, this study discusses the boundary of effectuation approach by including two powerful moderators, namely, perceived uncertainty and slack resources. Using data collected from 126 Chinese enterprises, our results indicate that under low perceived uncertainty and high organizational slack, CEOs high in narcissistic admiration are more likely to take on affordable loss investments, while narcissistic rivalry CEOs prefer to reduce affordable loss activities to secure their status—assertions that fully support our hypotheses. Management implications and future directions are interpreted in the discussion.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".