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Record W3126913354 · doi:10.1111/1911-3846.12672

Authority, Monitoring, and Incentives in Hierarchies*†

2021· article· en· W3126913354 on OpenAlexvenueno aff
Christian Hofmann, Raffi Indjejikian

Bibliographic record

VenueContemporary Accounting Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
FundersCollege of Engineering, Michigan State UniversityDeutsche ForschungsgemeinschaftMichigan State University
KeywordsIncentiveDelegationPrincipal (computer security)Control (management)Compensation (psychology)Quality (philosophy)AnalogyBusinessPrincipal–agent problemManagement control systemMicroeconomicsEconomicsPublic economicsFinanceManagementComputer scienceCorporate governanceComputer security

Abstract

fetched live from OpenAlex

ABSTRACT We study three elements of management control: incentive compensation, performance monitoring, and delegation of authority to managers to contract with lower‐level employees. Using a principal‐agent model, we highlight important direct and indirect interactions between and among these endogenous control elements, themes often emphasized in the economics and accounting literatures using the analogy of a three‐legged stool. We identify circumstances in which control elements are complements or substitutes and exhibit a coherent pattern of practices observed together. For instance, contrary to typical predictions that quality monitoring complements steep effort incentives, we find that when contracting authority adjusts easily to changes in firm circumstances, then both incentive pay and contracting authority substitute for monitoring quality, while incentive pay complements contracting authority. Overall, our findings suggest a number of empirical implications and generally inform a growing literature that documents the presence or absence of complementarities among management control elements.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
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 head0.063
GPT teacher head0.323
Teacher spread0.260 · 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.

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

Citations13
Published2021
Admission routes1
Has abstractyes

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