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Record W3021023727 · doi:10.1287/mnsc.2018.3193

Complementarity of Performance Pay and Task Allocation

2019· article· en· W3021023727 on OpenAlexaff
Bryan Hong, Lorenz Kueng, Mu-Jeung Yang

Bibliographic record

VenueManagement Science · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsWestern University
Fundersnot available
KeywordsComplementarity (molecular biology)DecentralizationEndogeneityIncentiveMicroeconomicsEconomicsExploitOrganizational performancePublic economicsManagement control systemBusinessIndustrial organizationControl (management)MarketingEconometricsComputer science

Abstract

fetched live from OpenAlex

Complementarity between performance pay and other organizational design elements has been argued to be one potential explanation for stark differences in the observed productivity gains from performance pay adoption. Using detailed data on internal organization for a nationally representative sample of firms, we empirically test for the existence of complementarity between performance pay incentives and decentralization of decision-making authority for tasks. To address endogeneity concerns, we exploit regional variation in income tax progressivity as an instrument for the adoption of performance pay. We find systematic evidence of complementarity between performance pay and decentralization of decision making from principals to employees. However, adopting performance pay also leads to centralization of decision-making authority from nonmanagerial to managerial employees. The findings suggest that performance pay adoption leads to a concentration of decision-making control at the managerial employee level, as opposed to a general movement toward more decentralization throughout the organization. This paper was accepted by Bruno Cassiman, business strategy.

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)0.000
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 score0.085
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.204
Teacher spread0.192 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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