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Record W4200589541 · doi:10.1027/2151-2604/a000481

Distinguishing the Explicit Power Motives

2021· article· en· W4200589541 on OpenAlexaff
Kaspar Schattke, Ariane S. Marion-Jetten

Bibliographic record

VenueZeitschrift für Psychologie · 2021
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMachiavellianismSocial psychologyPsychologyNarcissismTransactional leadershipTransformational leadershipPrestigeOrganizational citizenship behaviorDominance (genetics)Leadership stylePower (physics)Counterproductive work behaviorPersonalityBig Five personality traitsOrganizational commitment

Abstract

fetched live from OpenAlex

Abstract. Power is an important motivator at work, particularly for leaders. However, power also relates to dark personality traits, which negatively affect employees and organizations. Therefore, we argue that a high explicit power motive is a double-edged sword depending on whether people desire power for dominance, prestige, or leadership. We explored these research questions in a cross-sectional ( N = 151 employees) and a prospective study ( N = 371 leaders). Both studies revealed that dominance is most strongly related to Machiavellianism and moderately to narcissism and psychopathy. Prestige related strongly to narcissism and weakly to Machiavellianism, while leadership only weakly related to narcissism. Dominance best predicted counterproductive work behavior (CWB), while leadership best-predicted organizational citizenship behavior (OCB). In addition, Study 2 showed that transformational and, to a lesser extent, transactional leadership styles mediated the relations between the three power motives with OCB and CWB, respectively. Thus, promoting transformational leadership might be a fruitful way of channeling leaders’ power motives into pro-social actions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.071
GPT teacher head0.404
Teacher spread0.333 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations14
Published2021
Admission routes1
Has abstractyes

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