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Record W4308137573 · doi:10.1177/09731741221129350

Experimental Evidence on Group-based Attendance Bonuses in Team Production

2022· article· en· W4308137573 on OpenAlexaboutno aff
Theresa Thompson Chaudhry, Zunia Saif Tirmazee, Umair Ayaz

Bibliographic record

VenueJournal of South Asian Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAttendanceIncentiveProduction (economics)Quarter (Canadian coin)Demographic economicsLabour economicsTest (biology)Factory (object-oriented programming)EconomicsBusinessOperations managementMarketingMicroeconomicsComputer scienceEconomic growth

Abstract

fetched live from OpenAlex

We test the effectiveness of a group-based attendance bonus in a field experiment in a factory in Pakistan, where workers manufacture electric fans in stages using batch-production methods. We find that the group-based attendance bonus increased by more than a quarter the average number of days that the team’s attendance target was met. This effect was larger for junior and mid-level workers as compared to senior workers. We find that the bonus incentivized better coordination among workers, especially in the latter part of the month, rather than through higher average attendance. Our experiment’s results suggest that temporary incentive programmes may help workers in the transition period to new ways of organizing production and may prove to be a valuable tool for change management. Group-based bonuses offer an alternative to individual or tournament-based incentives based on one’s own or relative performance, which may have deleterious effects on intrinsic motivation and pro-social behaviour.

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)0.007
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.061
GPT teacher head0.330
Teacher spread0.269 · 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 designBench or experimental
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

Citations0
Published2022
Admission routes1
Has abstractyes

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