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Record W4309698250 · doi:10.1111/peps.12557

The paradoxical relationship between sense of power and creativity: Countervailing pathways and a boundary condition

2022· article· en· W4309698250 on OpenAlexaff
Federico Magni, Yaping Gong, Jie Li, Jingzhou Pan, Mingjian Zhou

Bibliographic record

VenuePersonnel Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCreativityOpenness to experiencePsychologyModerationPerspective (graphical)Power (physics)Social psychologyPath analysis (statistics)Computer science

Abstract

fetched live from OpenAlex

Abstract When sense of power helps or hinders creativity remains an unresolved question. Drawing upon the approach‐inhibition theory of power and its extensions, we integrate two different predictions into a dual‐pathway model, showing the paradoxical role that sense of power—one's perceived ability to influence others—plays in predicting creativity. Specifically, sense of power helps creativity through increased risk‐taking and simultaneously hinders it through reduced perspective taking. We further propose openness to experience as a moderator of the countervailing mechanisms, such that the positive path through risk‐taking is stronger and the negative path through perspective taking is weaker for individuals with higher (vs. lower) levels of openness. We test our hypotheses with three multisource and multi‐wave field studies (Study 1: n = 181 part‐time MBAs paired with peers; Study 2: n = 128 sales employees paired with store managers; Study 3: n = 153 sales employees paired with store managers). The results support our theoretical model, showing that sense of power and creativity are simultaneously connected positively through risk‐taking and negatively through perspective taking, and that the overall indirect effect of sense of power on creativity is more positive for individuals with higher (vs. lower) levels of openness to experience.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.391
Teacher spread0.302 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2022
Admission routes1
Has abstractyes

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