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Record W1573560126 · doi:10.1080/1359432x.2015.1061999

One size doesn’t fit all: the influence of supervisors’ power tactics and subordinates’ need for cognitive closure on burnout and stress

2015· article· en· W1573560126 on OpenAlexaff
Jocelyn J. Bélanger, Antonio Pierro, Barbara Barbieri, Nicola Alberto De Carlo, Alessandra Falco, Arie W. Kruglanski

Bibliographic record

VenueEuropean Journal of Work and Organizational Psychology · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBurnoutAmbiguityPsychologyPower (physics)Social psychologyClosure (psychology)AutonomyStress (linguistics)Role conflictCognitionApplied psychologyClinical psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The present research investigated the notion of fit between supervisors’ power tactics and subordinates’ need for cognitive closure (NFCC) on subordinates’ burnout and stress. Subordinates who tend to avoid ambiguity (high NFCC) were expected to experience relatively less burnout (Study 1) and stress (Study 2) if their supervisors utilize harsh (controlling and unequivocal) power tactics and more burnout and stress if their supervisors utilize soft (autonomy-supportive and equivocal) power tactics. In contrast, it was expected that subordinates who avoid firm and binding conclusions (low NFCC) would experience relatively less burnout and stress if their supervisors use soft power tactics and more burnout and stress if they use harsh power tactics. Two studies conducted in diverse organizational settings supported these hypotheses. Collectively, these results support the conclusion that soft (vs. harsh) power tactics are not always associated with better (vs. worse) organizational outcomes. Theoretical and practical implications for organizations 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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.028
GPT teacher head0.271
Teacher spread0.243 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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