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Record W4200284021 · doi:10.1017/jmo.2021.65

So tired, I can't even help you: how work-related sleep deprivation evokes dehumanization of organizational leaders and less organizational citizenship behavior

2021· article· en· W4200284021 on OpenAlexaff
Dirk De Clercq

Bibliographic record

VenueJournal of Management & Organization · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsBrock University
Fundersnot available
KeywordsDehumanizationOrganizational citizenship behaviorOrganizational commitmentWork (physics)PsychologySocial psychologyOrganizational studiesPerceptionPublic relationsSociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract To unpack the relationship between employees' work-induced sleep deprivation and their organizational citizenship behavior, this study details a mediating role of their propensities to dehumanize their organizational leaders, as well as a moderating role of perceived job formalization. Survey data collected from employees who work in the oil distribution sector show that a critical reason that persistent sleep problems, caused by work, reduce the likelihood that they engage in voluntary work efforts is that they treat organizational leaders as impersonal objects. Perceptions of the presence of job formalization or red tape invigorate this detrimental effect. For organizational practitioners, this study accordingly reveals a notable danger for employees who have trouble sleeping due to work: They do not take on extra work that otherwise could add to their organizational standing. This counterproductive dynamic is particularly salient when employees believe that their work functioning is constrained by strict organizational policies and guidelines.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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 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

Citations11
Published2021
Admission routes1
Has abstractyes

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