Towards a better understanding of intellectual capital in Mexican SMEs
Bibliographic record
Abstract
Purpose – The purpose of this paper is to understand the characteristics of intellectual capital (IC) in Mexican small and medium enterprises (SMEs). Due to the shift from traditional factors of production to knowledge-based economy, an understanding of the role of IC has become crucial for SMEs to develop a competitive advantage. Design/methodology/approach – This study takes an in depth look at the three components of IC: human, organizational, and external capital. In order to do so, a quantitative study on 445 SMEs was conducted based on data collected through an online survey. A structural equation model is proposed that is a fit with the reality of Mexican SMEs. Regional differences are highlighted by means of multigroup analysis. Findings – The results suggest that the features of human and organizational capital are consistent with previous studies on SMEs in emerging economies. However, external capital shows some distinctive characteristics unique to Mexican context. Practical implications – Implications for managers and policymakers are discussed, whereby an adaptation of programs and policies are required to fit the Mexican context at the national and regional levels. Originality/value – To the best of the authors knowledge, this is the first study that observes the components of IC in Mexican SMEs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".