Understanding Determinants of Firm Performance: An Analysis of Non-Financial Factors
Bibliographic record
Abstract
This study examines the influence of non-financial factors on the performance of 303 Italian small and medium-sized enterprises over the period 2012-2021. The analysis employs a panel data model with random effects. Performance indicators (ROA, ROE, EBIT, EBITDA and CFOR) are regressed on the variables of interest (firm age, gender of the direct manager, ownership structure and religiosity). Control variables include firm-specific financial data; sectoral dummy variables are used to account for industry effects. Ownership structure has a significant positive impact on firm performance, especially in terms of ROA and EBIT. Firm age shows a negative relationship with EBIT. The gender of the direct manager exhibits a marginal impact on ROA, while religiosity does not significantly influence any of the performance indicators. For managers, these findings highlight the importance of carefully balancing ownership structure to optimize performance while avoiding the risks associated with excessive concentration. Furthermore, while certain non-financial factors like firm age and manager gender may influence performance, their impact appears context-dependent and may not be as significant as previously thought. This study provides empirical evidence on the impact of non-financial factors on firm performance in the context of Italian SMEs. The results underscore the complexity of these relationships and the need for further research to explore the contextual factors that may moderate their impact.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".