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Record W4409483944 · doi:10.1177/01461672251324813

The Hidden Cost of Decision-Making Autonomy at Work: How Task Reflexivity and Construal Level Induce Mental Fatigue

2025· article· en· W4409483944 on OpenAlexaff
Hun Whee Lee, Zhenyu Liao, Henry Robin Young, D. Lance Ferris, Nan Wang, Yifeng Chen

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of Ottawa
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsAutonomyPsychologyConstrual level theoryReflexivitySocial psychologyCognitionTask (project management)Iowa gambling taskTraitPolitical science

Abstract

fetched live from OpenAlex

Contrary to the traditional belief that decision-making autonomy enhances employee well-being, we investigate the cognitive circumstances and mechanisms through which daily decision-making autonomy leads to mental fatigue. Integrating self-regulation theory with construal-level theory, we propose that daily decision-making autonomy triggers cognitive activities related to task reflexivity, which subsequently results in next-day mental fatigue. We identify trait construal level as a key moderating factor, arguing that the indirect effect of decision-making autonomy on mental fatigue through task reflexivity is particularly pronounced when employees have a low (vs. high) trait construal level. Our hypotheses received support from two experience sampling studies in the United States and China. Specifically, we found that the detrimental effects of decision-making autonomy are indirect by nature and only manifest in certain employees.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.189
GPT teacher head0.490
Teacher spread0.301 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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