Political skill reduces the negative impact of distrust
Bibliographic record
Abstract
Purpose A number of studies have explored the benefits (e.g. enhanced job performance and reduced strain), of being politically skilled. Within the framework of uncertainty management theory, the purpose of this paper is to explore the benefits of high political skill to affective commitment, job satisfaction, and perceived job mobility, under conditions of distrust in management. Design/methodology/approach Sales representatives were surveyed and moderated multiple regression analyses were conducted to analyze the data. Findings The authors found that as distrust increased, affective commitment decreased for all persons, but was most pronounced for persons low on political skill. However, distrust in management had no impact on job satisfaction for those high on political skill, allowing persons high on political skill to enjoy their jobs despite high levels of distrust (an intrapsychic benefit of political skill). Finally, as distrust in management increased, persons high on political skill had increased perceived job mobility. Research limitations/implications This study is cross-sectional, limiting conclusions about causality in the relationships studied and leaving open the possibility of reverse causation. Practical implications This research has important implications, such that, under conditions of distrust, persons low on political skill are less committed, more dissatisfied, and feel a sense of job immobility, which could lead to poor work outcomes, such as decreased job performance. Originality/value The study is the first to examine how being politically skilled benefits employee outcomes when the employee distrusts management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".