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Record W2802625589 · doi:10.5539/ijbm.v13n5p61

Promoting Autonomy to Reduce Employee Deviance: The Mediating Role of Identified Motivation

2018· article· en· W2802625589 on OpenAlexaff
Julien S. Bureau, Geneviève A. Mageau, Alexandre J. S. Morin, Marylène Gagné, Jacques Forest, Konstantinos Papachristopoulos, A.T. Lucas, Anaïs Thibault Landry, Chloé Parenteau

Bibliographic record

VenueInternational Journal of Business and Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversité du Québec à MontréalConcordia UniversityUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsAutonomyDeviance (statistics)PsychologySocial psychologyPositive devianceOrganizational commitmentOrganizational culturePublic relationsPolitical science

Abstract

fetched live from OpenAlex

The organizational environment is purported to have a profound impact on how employees behave at work. In particular, the extent to which the work environment can foster autonomy in employees has been shown to predict several positive outcomes for employees and organizations. This research explores the associations between employees’ experiences of autonomy at work and organizational deviance. We also investigate the mechanisms underlying this association and the possible role of identified motivation as a mediator of this relation. Three studies conducted in a variety of settings, countries, populations and assessment methods showed that employees who experience more autonomy at work tend to engage in lower levels of organizational deviance. Two studies also showed that this relation was mediated by identified motivation. Thus, employees’ experiences of autonomy at work seemed to foster higher levels of identified motivation towards work, which in turn predicted lower levels of organizational deviance. The present results may help guide managerial training and promote organizational cultures that are respectful of employee autonomy, potentially reducing the costs associated with organizational deviance.

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)0.000
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 score0.455
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.254
Teacher spread0.240 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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