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Record W1720712816 · doi:10.1506/rq40-ur50-5crl-yu8a

Avoiding Accounting Fixation: Determinants of Cognitive Adaptation to Differences in Accounting Method*

2005· article· en· W1720712816 on OpenAlexvenueno aff
David T. Dearman, Michael D. Shields

Bibliographic record

VenueContemporary Accounting Research · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsnot available
Fundersnot available
KeywordsActivity-based costingManagement accountingDebiasingCost accountingCognitionAccountingAccounting methodAdaptation (eye)Accounting information systemTarget costingComputer sciencePsychologyEconomicsSocial psychology

Abstract

fetched live from OpenAlex

Abstract Much research over the last 30 years has provided evidence that individuals display accounting fixation; that is, their cognitive process does not appropriately adapt to cross‐sectional or temporal differences in an accounting method. This paper presents the results of a quasi‐experimental test of the hypothesis that cognitive adaptation to a change in accounting method is an ordinal interactive function of three person characteristics: relevant accounting knowledge, general problem‐solving ability, and intrinsic motivation to appropriately engage in the decision task. Based on a product‐pricing decision task in which participants are provided with product costs reported by two generally employed product‐costing methods (activity‐based costing [ABC] and volume‐based costing), the results show that the majority of participants did not change their cognitive behavior when there was a change in the costing method. Further, those participants who did adapt to the change in accounting method, and thus avoided accounting fixation, did so by debiasing costs reported by volume‐based costing but not by ABC. Finally, these adapters generally exhibited high values for all three of the person characteristics compared with those who did not adapt.

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)0.013
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.005
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 head0.143
GPT teacher head0.386
Teacher spread0.243 · 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

Citations16
Published2005
Admission routes1
Has abstractyes

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