Economic value added: a useful tool for SME performance management
Bibliographic record
Abstract
Purpose The aim of this study is to propose a performance measurement and management system (PMMS) for small‐ and medium‐sized enterprises (SMEs), based on an analysis of the connections between these firms' business practices and performance measured by economic value added (EVA). Design/methodology/approach Secondary data from the PDG® database was used on a sample of 108 Canadian manufacturing SMEs over two consecutive years. The primary statistical method used was regression analysis to investigate the influence of diverse business practices on EVA in these firms. Findings This paper shows that EVA can be a useful tool for performance management in SMEs, when used in conjunction with a list of business practices that affect the firm's results. The findings indicate that some business practices have a direct impact on EVA within one year, while others have a deferred influence. The impacts of other practices on EVA were found to be weak or insignificant, an aspect that requires further investigation. Research limitations/implications The main limitation of this study is the lack of generalizability of the findings. However, the sampled SMEs vary widely in terms of their characteristics, which may mitigate the negative impacts of a non‐probabilistic sample. Practical implications This study offers a structured methodology to identify the paths leading to better performance in SMEs, through an improved understanding of their business practices' impacts on EVA. Originality/value To the best of the authors' knowledge, this is the first study that explores the linkage between SME business practices and EVA. When applied in conjunction with a set of business practices, EVA can help managers detect problems and identify sources of improvement.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".