Bibliographic record
Abstract
Purpose The purpose of this paper is to shed light on the nature of intellectual capital in small to medium‐sized enterprises (SMEs) and how it is linked to strategy and performance. Design/methodology/approach Using structural equations, a multivariate model is presented where multiple relations are tested between different components of intellectual capital and performance. The model is tested first on a unique sample of 267 SMEs and second on two subsamples where SMEs are grouped according to their strategic profile. Findings Findings confirm that SMEs that adopt different strategies organize their intellectual capital in a particular and adapted way. When an attempt is made to link intellectual capital components to performance, it is noticed that the latter is strategy specific, just as the variables that influence performance. Prospectors dominate defenders on most intellectual capital components. Research limitations/implications Use of secondary data may provide less precise results that could make an incentive to conduct other studies with specific determinants of intellectual capital and try to make clear definition and measurement of this concept and its components. Practical implications Even if the results have an exploratory nature, they confirm that SMEs organize and develop their intellectual capital in conjunction with their needs and strategic profile, revealing their heterogeneity. This has implications on the ability to generalize specific behaviors to all SMEs, and could prevent government from developing public policies that are supposed to fit all SMEs. Originality/value Most research on intellectual in capital SMEs is conducted on specific sectors linked to activities requiring high levels of knowledge or technology. But these results concern a small proportion of SMEs. This study expands those analyses to a much broader variety of sectors, revealing some links between specific components and performance taking into account strategic orientation. This is the first study on manufacturing SMEs that considers various non‐technological sectors and strategic profiles.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".