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Record W4400187702 · doi:10.1108/lodj-07-2022-0319

How sense of power influence exploitative leadership? A moderated mediation framework

2024· article· en· W4400187702 on OpenAlexaff
Zhining Wang, Fengya Chen, Shaohan Cai, Yuhang Chen

Bibliographic record

VenueLeadership & Organization Development Journal · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsCarleton University
Fundersnot available
KeywordsMediationPsychologySocial psychologyPower (physics)Moderated mediationSense (electronics)Political scienceLaw

Abstract

fetched live from OpenAlex

Purpose Based on the approach/inhibition theory of power, this study explores the relationship between sense of power and exploitative leadership. We particularly examine the role of self-interest as a mediator and the role of ambition at work as a moderator. Design/methodology/approach The data were collected from 189 supervisors and 702 employees. We analyzed the data using path analysis to test the research model. Findings The results show the following: (1) sense of power positively affects exploitative leadership; (2) the effects of sense of power on exploitative leadership are mediated by self-interest; (3) the effects of self-interest on exploitative leadership are moderated by ambition at work. Originality/value The current study identifies self-interest as a key mediator that links sense of power to exploitative leadership and demonstrates that ambition at work moderates the process of self-interest to exploitative leadership.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.000

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 head0.055
GPT teacher head0.245
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations7
Published2024
Admission routes1
Has abstractyes

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