Why this work is in the frame
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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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it