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Record W2785108029 · doi:10.2308/jmar-52033

Inequity Aversion, Incentives, and Personal Norms: The Effects on Budget Preparation and Use

2018· article· en· W2785108029 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 Waterloo
Fundersnot available
KeywordsIncentiveHonestyDishonestyBusinessAffect (linguistics)Norm (philosophy)Control (management)MicroeconomicsPublic economicsPublic relationsMarketingEconomicsPsychologySocial psychologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

ABSTRACT We examine two features of control environments expected to affect the honesty of budget submissions by subordinates and their use by managers for planning purposes. First, we predict that subordinates' awareness of incentives available to their managers that they are not eligible to share in, is likely to induce inequity aversion and dishonest budgeting. However, we expect the egocentric bias will make managers insensitive to this increased dishonesty when using budgets for planning purposes. Second, we predict that making subordinates eligible to participate in incentives available to their managers will activate a personal norm of other-regarding behavior resulting in more honest budgeting. Third, we predict that managers whose subordinates are eligible to share in their incentives will recognize factors motivating their subordinates' behavior and, as a result, rely more on their budget submissions for planning purposes. Experimental results confirm all predictions. Implications for practice and research are discussed.

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)0.004
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 score0.086
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.057
GPT teacher head0.413
Teacher spread0.356 · 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