Middle-Power Inhibition: Uncertainty and Adherence to Organizational Norms
Bibliographic record
Abstract
Decades of research have demonstrated that having or lacking power can influence how people think and behave in organizations. By contrasting high and low power positions, however, this research has neglected the psychological and behavioral correlates of middle-power roles. The current paper addresses this gap in the literature by extending the influential approach/inhibition theory of power to accommodate low, middle, and high power positions. We do this by drawing upon insights from Revised Reinforcement Sensitivity Theory (R-RST), which makes a critical distinction between the experiences of fear and anxiety. Making this distinction allows us to generate novel theoretical propositions about the unique motivational concerns associated with middle-power positions. Specifically, we propose that middle-power individuals are more likely than both low and high-power individuals to experience anxiety and uncertainty. This heightened exposure to uncertainty is in turn proposed to increase behavioral inhibition and uncertainty-reduction motives among middle-power employees. We explore the consequences of these motives for organizational norm adherence, predicting behavioral differences across all three levels of the power hierarchy. Overall, our model highlights the importance of a) considering the distinct psychological experiences associated with middle-power and b) revising the approach/inhibition theory of power with the insights of R-RST.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".