A blessing and a curse: How <scp>chief executive officer</scp> cognitive complexity influences firm performance under varying industry conditions
Bibliographic record
Abstract
Abstract Research Summary How CEO cognitive complexity influences firm outcomes raises an intriguing theoretical tension. While more cognitively complex CEOs can potentially bolster firm performance through their more elaborate and multifaceted information processing, those tendencies can also hurt performance because they require more time and energy, delaying decision making. We posit and show a nuanced effect of CEO cognitive complexity on firm performance, contingent on industry conditions. CEO cognitive complexity benefits performance under more complex, stable, and munificent industry conditions, but hurts performance under simpler, more dynamic, and more constrained conditions. Post‐hoc analyses further show that these effects are similar when considering firm‐level factors reflecting munificent and dynamic internal conditions. Our study highlights the boundary conditions under which CEO cognitive complexity may be beneficial or detrimental for firms. Managerial Summary CEOs have different cognitive styles that can impact how they approach decision making. Whereas some exhibit greater cognitive complexity, that is, by engaging in broader and deeper information search and considering more differentiated and nuanced perspectives and alternatives, others engage in simpler and less comprehensive information processing when making decisions. While it seems intuitive to assume that CEOs' cognitive complexity should be beneficial for firms, collecting and processing a large amount of complex information can also complicate and delay decision‐making. Our results show that S&P 1500 CEOs who are more cognitively complex improve firm performance when their firms operate in more complex, stable, and resource‐rich environments but hurt firm performance when their firms operate in simpler, more dynamic, and resource‐constrained environments.
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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 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".