The Dual Effects of Leader Self-Complexity on Leadership Adaptability: The Underlying Mechanisms
Bibliographic record
Abstract
In a complex and dynamic business world, effective leadership requires adaptability. Researchers have long recognized the importance of leader self-complexity in enhancing this adaptability. However, the underlying mechanisms by which leader self-complexity affects adaptability remain unclear. Furthermore, current theories and empirical studies on leader self-complexity mainly emphasize its advantages in facilitating leadership adaptability, while often overlooking potential costs. This research employs conservation of resources theory to uncover two distinct processes linking leader self-complexity to adaptability: a cognitive pathway (i.e., cognitive flexibility) and an affective pathway (i.e., role conflict and well-being), thereby exploring its dual effects. Data were collected from 330 US leaders at three time points. The results demonstrated that different components of leader self-complexity influenced adaptability via cognitive and affective pathways differently. The number of leadership roles did not show any significant positive or negative effect on adaptability through either pathway. Differentiation among roles increased role conflict and decreased well-being, which in turn negatively influenced cognitive flexibility and adaptability. Conversely, integration among roles showed positive effects on adaptability through both pathways.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".