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Record W4313573806 · doi:10.1111/jems.12511

Worker autonomy and performance: Evidence from a real‐effort experiment

2023· article· en· W4313573806 on OpenAlexfundno aff
Veronica Rattini

Bibliographic record

VenueJournal of Economics & Management Strategy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
FundersUniversità di BolognaYork UniversityUniversity of Pittsburgh
KeywordsAutonomyFlexibility (engineering)Scheduling (production processes)Psychological interventionCognitionLimitingComputer scienceTask (project management)Baseline (sea)PsychologyOperations managementEconomicsEngineeringManagement

Abstract

fetched live from OpenAlex

Abstract Worker flexibility in effort allocation is a crucial factor for productivity and optimal job design. This paper runs a real‐effort experiment that manipulates both the degree and type of autonomy individuals have in scheduling their effort, and it examines the causal effects of these manipulations on final performance. The main findings come from comparing subjects with different levels of cognitive ability. Using individual data on scheduling decisions, I find significant baseline differences in performance and effort‐allocation strategies between high‐ and low‐cognitive ability subjects. Moreover, the experiment shows that high‐ability individuals reach higher performance when they have full scheduling flexibility while limiting any task‐ordering possibility increases the performance of low‐ability individuals. Overall, this paper provides new and robust evidence on the importance of cognitive ability in explaining effort‐allocation decisions, and it identifies job design interventions to increase the performance of high‐ and low‐ability workers.

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 score0.203
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.077
GPT teacher head0.344
Teacher spread0.267 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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