The Relationship between the Market Value Added of SMEs Listed on AIM Italia and Internal Measures of Value Creation The Role of Corporate Strategic Planning
Bibliographic record
Abstract
Objectives - In saying that measurement of financial performance plays an important role in the capital allocation choices, the aim of this study is to test the relationships between Market Value Added (MVA), stockholders value measures and presence of formal strategic plan.Methodology - The study is among descriptive and correlational researches and using panel data methodology on sample of SMEs listed in AIM Italia. The time under study was from 2010 to 2015. In addition, the hypotheses of the research have been tested using Rahavard Novin software for data collection and SPSS 20.0 for data analysis.Findings - The results indicate that Refined Economic Value Added (REVA) has more correlation with Market Value Added (MVA) than Economic Value Added (EVA); in addition, the results obtained using panel data methodology shows that the use of strategic plans influences the relationship between value performance measures and MVA.Research limits - Data used for this study need to be subjected to more statistical tests in order to establish a more robust validity and reliability. It is necessary to acquire further strengthened data and assume a variety of conditional situations. It is expected that subsequent studies can use larger samples and diversified by sector, a broader geographic base and a multi-faceted analyses.Practical implications - This work offer necessary evidences in order to help capital market participants to make rational decision in investment process.Originality of the study - The originality of this study is the correlation between MVA, financial measures and use of strategic planning for value management.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".