Financial and Strategic Factors Associated with the Profitability and Growth of SME in Portugal
Bibliographic record
Abstract
Drawing on literature from finance and strategic management, eight likely factors associated with the profitability and growth of unquoted, small and medium-sized enterprises (SMEs) are identified and evaluated. These factors are: leverage, liquidity, education, industry performance, low cost, differentiation, product focus and customer focus. The sample comprises 134 unquoted SMEs aged five years or more, operating in different sectors throughout the main districts of Portugal. Data are collected through face-to-face interviews and these are supplemented with secondary sources. Twenty-one independent variables are identified and LISREL is used to produce measurement equations relating the variables to factors. Hypotheses concerning the factors’ impact on profit and growth are tested through structural equation modelling using LISREL. The results show that low debt, effective liquidity management, operation in a profitable sector, differentiation, the avoidance of low cost and customer focus favour SMEs’ profitability. For high growth, although effective liquidity management and differentiation remain as key factors, they are joined by a product focus. These results carry a number of important implications for SME strategy most notably that there may not be one set of strategies that maximise both profitability and growth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".