Optimal budgetary participation and firm performance: empirical evidence
Bibliographic record
Abstract
Purpose This study aims to reexamine the relationship between budgetary participation and firm performance by using a nonlinear model that captures an inverse U-shaped relationship. Design/methodology/approach Adopting a hypothetico-deductive approach, this study analyzes data from a questionnaire-based survey of budget managers in Cameroonian SMEs. Data were collected between January and March 2019, with 200 questionnaires distributed and 80 valid responses retained (a response rate of 40%). We applied multiple regression analysis to test the hypotheses, including interaction and quadratic terms to capture nonlinear effects. We also used partial least squares structural equation modeling to corroborate the consistency of the observed relationships. Findings The results reveal a nonlinear, inverse U-shaped relationship between budgetary participation and firm performance. Within the Cameroonian context, the authors identify an optimal level of budgetary participation: increasing participation from low levels to this optimal level enhances SME performance, while exceeding it leads to a decline in performance. Research limitations/implications This study’s findings are based on a small, culturally homogeneous sample from Cameroon, limiting generalizability. While a nonlinear inverse U-shaped relationship was found, its context-specific nature and reliance on resource constraints mean that future research should use broader, cross-national samples to confirm if this trend is universally applicable. Practical implications SME managers should aim for moderate participation to maximize performance. Excessive participation risks information asymmetry and inefficiency. Originality/value This paper makes two key contributions. First, to the best of the authors’ knowledge, it represents the first study to empirically demonstrate the existence of an optimal level of budgetary participation through modeling the nonlinear relationship between budgetary participation and performance. Second, while extensive research has examined budget participation in large companies in developed countries, this study addresses a notable gap in developing-country contexts by investigating the effects of budgetary participation on SME performance in Cameroon.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".