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Middle-Power Inhibition: Uncertainty and Adherence to Organizational Norms

2016· article· en· W2765111473 on OpenAlexaff
Eric M. Anicich, Jacob B. Hirsh

Bibliographic record

VenueAcademy of Management Proceedings · 2016
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPower (physics)PsychologySocial psychologyNorm (philosophy)AnxietyBehavioral inhibitionHierarchyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.060
GPT teacher head0.347
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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