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Record W1734398399 · doi:10.19030/rbis.v4i1.5389

Budget-Related Behavior: Resolving A Portion Of The Performance Puzzle In The Management Accounting System

2000· article· en· W1734398399 on OpenAlexaff
Alfred E. Seaman, Raymond Landry, John Joseph Williams

Bibliographic record

VenueReview of Business Information Systems (RBIS) · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsBrock University
Fundersnot available
KeywordsTask (project management)ContingencyWork (physics)Set (abstract data type)Identification (biology)Computer scienceComponent (thermodynamics)Linkage (software)Contingency managementOperations researchAccountingProcess managementBusinessEngineeringSystems engineeringPsychology

Abstract

fetched live from OpenAlex

An integral component of the management accounting system in large organizations is the budgeting system. Multiple budget-related behaviors (BRBs) characterize what man-agers do in overseeing the work task environment of various departmental units to achieve performance goals. However, just as a four-wheel drive vehicle comes as a com-plete system, not all of its operating components are activated simultaneously under all conditions. In the BRB/performance linkage, uncertainties in the work task environment critically determine which BRBs must be emphasized or de-emphasized for optimal per-formance. This paper uses a structural equation approach to model this uncertainty contingency and ferret-out the appropriate set of significant BRBs. This identification, in turn, helps to resolve part of the performance puzzle.

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)0.013
metaresearch head score (Gemma)0.041
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.041
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0010.008
Scholarly communication0.0060.011
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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

Citations5
Published2000
Admission routes1
Has abstractyes

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