Performance Management for Growth: A Framework Based on EVA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Some of the constructs in the field of performance management are intuitive or not empirically validated. This study provides a data-driven framework for measuring and improving the performance through synchronized strategies. The ultimate goal was to provide support for increasing business performance. Empirical research materializes in an exploratory case study and a statistical analysis with econometric models. The case study revealed that a company can improve its performance, even in periods of growth, being characterized by consistent investments. The statistical analysis, performed on a restricted sample of companies, confirmed the results that were provided by the case study. The measurement of performance was made by capitalizing on financial and non-financial data precisely to intensify the interest for corporate sustainability. The obtained results, contrary to previous research that showed that economic value added (EVA) is negatively influenced by the increase in invested capital, open up new research perspectives to find out whether, at the industry level, performance appraisal that is based on EVA stimulates the development of a business’s economic capital. The research has a double utility: scientific (by providing an overview of the state of the art in the field of performance management) and practical (by providing a reference model for measuring and monitoring performance).
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it