Political Skill and Manager Performance: Exponential and Asymptotic Relationships Due to Differing Levels of Enterprising Job Demands
Bibliographic record
Abstract
Political skill, a social competence that enables individuals to achieve goals due to their understanding of and influence upon others at work, can play an important role in manager performance. We argue that the political skill–manager performance relationship varies as a nonlinear function of differing levels of enterprising job demands (i.e., working with and through people). A large number of occupations have some enterprising features, but, across occupations, management roles typically contain even greater enterprising expectations. However, relatively few studies have examined the enterprising work context (e.g., enterprising demands) of managers. Specifically, under conditions of high enterprising job demands, we argue and find that, as political skill increases, there is an associated exponential increase in enterprising performance, with growth beyond the mean of political skill resulting in outsized performance gains. Whereas, under conditions of low (relative to other managers) enterprising job demands, political skill will have an asymptotic relationship with enterprising job performance, such that the positive relationship becomes weaker as political skill grows, with increases on political skill beyond the mean resulting in minimal performance improvements. Our hypotheses are generally supported, and these findings have important implications for managers, as the performance gains in managerial roles were shown to be a joint function of manager political skill and enterprising job demands.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".