Budget Development and Use in Small‐ and <scp>Medium‐Sized</scp> Enterprises: A Field Investigation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".