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Record W3087793429 · doi:10.1111/1911-3838.12231

Budget Development and Use in Small‐ and <scp>Medium‐Sized</scp> Enterprises: A Field Investigation

2020· article· en· W3087793429 on OpenAlexaffvenue
Howard M. Armitage, Dorian Lane, Alan Webb

Bibliographic record

VenueAccounting Perspectives · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsControl (management)BusinessManagement control systemField (mathematics)Field researchProcess (computing)Operating budgetMarketingElement (criminal law)Knowledge managementProcess managementComputer scienceEconomicsManagementFinancePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT We examine the process by which operating budgets are developed and how they are used for control, evaluation, and reward purposes in small‐ and medium‐sized enterprises (SMEs). SMEs (i.e., fewer than 500 employees) represent the dominant organizational form in North America but surprisingly little research has examined how these companies develop and use management controls. Our study focuses on a key element of the management control system, operating budgets, because prior research on SMEs indicates this as an important and commonly used control tool in such companies. Prior research on budgeting practices, while extensive, has almost exclusively examined larger companies. We conduct in‐depth field interviews at 12 participating SMEs to address four theory‐based research questions intended to provide insights regarding the development and use of budgets by SMEs. Our first question examines how budgets are developed, top‐down versus collaborative. Our second, third, and fourth research questions examine, respectively, whether budgets are used tightly or loosely for results control, performance evaluation, and reward purposes. As a first step in providing a deeper understanding of budget development and use in SMEs, our results have implications for practice, theory development, and management accounting education.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.017
GPT teacher head0.200
Teacher spread0.183 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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