How does CEO narcissism affect strategic consensus? A serial mediation model linking CEO narcissism, middle manager engagement, market culture and strategic consensus
Bibliographic record
Abstract
Research suggests that CEO narcissism has a significant impact on various firm outcomes such as innovation, growth, and financial performance. However, some of the findings have been mixed. These inconclusive findings indicate a need to better understand how CEO narcissism affects the key antecedents to firm performance, such as strategic consensus, and the intervening processes through which this effect occurs. This study addresses this issue by developing a serial mediation model of the effect of two underlying types of narcissism—rivalry and admiration—on strategic consensus. Specifically, I propose that CEO narcissistic rivalry and CEO narcissistic admiration have opposite indirect effects on strategic consensus through two mediating variables: (1) middle manager engagement and (2) market culture. Using survey data collected from 96 Chinese firms (including responses from 96 CEOs and 503 middle managers) in 2018 and 2019, I found that CEO narcissistic rivalry increases the degree of strategic consensus through reduced middle manager engagement and, in turn, increased market culture. Meanwhile, CEO narcissistic admiration reduces the degree of strategic consensus by increasing middle manager engagement and, in turn, reducing market culture. This research helps refine our understanding of the organizational impact of CEO narcissism by examining the different types of narcissism (rivalry and admiration), providing empirical support for the differential impact of the two subtypes of narcissism on strategic consensus, and theorizing the mediating process through which CEO narcissism impacts strategic consensus.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".