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Record W4383737605 · doi:10.1111/1911-3846.12886

Asymmetric adjustment of control

2023· article· en· W4383737605 on OpenAlexvenueno aff
Victor van Pelt

Bibliographic record

VenueContemporary Accounting Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
FundersUniversiteit van TilburgUniversiteit van AmsterdamGeorgia Institute of Technology
KeywordsControl (management)PsychologySocial psychologyReinforcementTest (biology)Principal–agent problemPrincipal (computer security)Self-controlEconomicsComputer scienceManagementComputer security

Abstract

fetched live from OpenAlex

Abstract This study examines how principals adjust their control over agents based on their prior controlling experience. According to standard economic theory, principals should be equally willing to decrease their control as they are to increase it. However, I use psychological theory to predict that prior experience with exercising tight control reinforces a principal's belief that agents are self‐interested and that they should be controlled. In contrast, I predict that the reinforcement of the belief that agents are socially interested and should not be controlled is weaker for principals who have prior experience with exercising loose control. I test my prediction using an experiment that exposes principals to either an increase or a decrease in the economic costs of control. The results support the predictions by exhibiting an asymmetric adjustment pattern. The data also show theory‐consistent conditions under which the asymmetry in principals' control adjustments diminishes. Overall, my study suggests that prolonged experience with exercising high levels of control over agents may cause principals to hold on to their control disproportionally.

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)0.001
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.159
GPT teacher head0.440
Teacher spread0.280 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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