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Record W4393867171 · doi:10.1002/job.2789

The brothers are watching: The peer monitoring mechanism of rivalry in reducing cheating behavior at work

2024· article· en· W4393867171 on OpenAlexaff
Ruo Mo, Meena Andiappan

Bibliographic record

VenueJournal of Organizational Behavior · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsMcMaster University
FundersIrish Research Council for the Humanities and Social Sciences
KeywordsRivalryCheatingCompetition (biology)PsychologySocial psychologyPerspective (graphical)PerceptionInterpersonal communicationSocial exchange theoryEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Summary The consensus of the literature has suggested that competition prompts individuals to act unethically. However, prior research that explored the ethical implication of competition has largely ignored the role of contexts within its examination—studying competition generally independent of other social relationships/actors. Drawing on the relational perspective of competition (i.e., rivalry), we propose that broader social relationships in which rivalry relationships are embedded will give rise to alternative psychological processes that can account for a curbing effect of rivalry on workplace cheating behavior. Results from one pre‐registered experiment and three surveys on working professionals suggest that exposure to rivalry leads to a heightened perception of peer monitoring, which in turn is associated with lower cheating behavior at work. We further find that the effect of rivalry on the perception of peer monitoring is stronger (weaker) when employees' leader–member exchange (LMX) relationship with their supervisor is lower (higher), highlighting the significance of other social actors in the rivalry process. This research complements the literature with a balanced perspective regarding the ethical implication of competition and contributes to the theory building of rivalry by providing an interpersonal lens to the psychology of rivalry.

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)0.006
metaresearch head score (Gemma)0.005
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 score0.016
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.143
GPT teacher head0.408
Teacher spread0.265 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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