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Beyond the Full-range Leadership:Incremental Effects of Machiavellian Leadership in Predicting Trust

2019· article· en· W2966072727 on OpenAlexaff
Tatjana Llic-balas, Amy Wei Tian, Susan Pepper

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsWestern UniversityHusky Energy (Canada)
Fundersnot available
KeywordsTransformational leadershipPsychologyLeadership styleOperationalizationSocial psychologyTransactional leadershipTrustworthinessEpistemology

Abstract

fetched live from OpenAlex

A perennial challenge facing leaders is gaining their followers' trust. The present research investigates the relationship between leadership style and trust in the leader as mediated by employees’ affective reactions and perceptions of leader trustworthiness. Leadership style was operationalized in terms of the full-range leadership model (FRLM, Bass & Avolio, 2004) augmented with an author-developed measure of Machiavellian leadership to aid in distinguishing between transformational and pseudo-transformational leadership (Bass & Steidlmeier, 1999; Christie et al., 2011). Following a preliminary measure development study, we conducted a field survey study with employees (N = 292) and an experimental study with working students (N = 392) to test our mediation model. Both studies provided evidence for the mediating effect of perceived trustworthiness and the incremental contribution of Machiavellian leadership in the prediction of trust; positive affect also mediated the link between leadership and leader trust in the experimental study. The experimental study also extended earlier findings by Christie et al. by demonstrating that Machiavellian leadership behaviors complement the facets of transformational leadership identified in the FRLM in distinguishing the behavioral profiles of transformational and pseudo-transformational leaders. Implications for the leadership theory and practice (e.g., leader selection and development) are discussed.

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 score0.071
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.230
Teacher spread0.192 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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