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Record W2808538281 · doi:10.2308/jmar-52152

I Know Something You Don't Know: The Effect of Relative Performance Information and Individual Performance Incentives on Knowledge Sharing

2018· article· en· W2808538281 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management Accounting Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of WaterlooWilfrid Laurier University
Fundersnot available
KeywordsIncentiveKnowledge sharingAffect (linguistics)ProductivityBusinessKnowledge managementPsychologyMarketingMicroeconomicsEconomicsComputer science

Abstract

fetched live from OpenAlex

ABSTRACT When employees share knowledge with their colleagues, the efficiency of the colleagues' performance improves, which positively affects their productivity. However, employees can engage in counterproductive behavior by choosing not to share knowledge (passive behavior) or by choosing to share inaccurate knowledge with their colleagues (active behavior). In this study, we examine how providing relative performance information (RPI) and rewarding individuals with performance-based incentives can jointly affect individuals' choices to engage in counterproductive knowledge sharing behavior. Using an experiment, we identify an interactive effect of RPI and individual incentives, such that participants engage in counterproductive knowledge sharing behavior most frequently when they receive RPI and are assigned individual performance-based incentives. We also observe that RPI increases the frequency of both active and passive counterproductive knowledge sharing behavior.

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.

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)0.014
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.003
Open science0.0010.001
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 head0.055
GPT teacher head0.392
Teacher spread0.337 · 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