The intermediary role of digital on the transformation of human resource and competitive advantage in women-led enterprises
Bibliographic record
Abstract
This study explores the mediating role of digitalization in the relationship between human resource transformation and competitive advantage in women-led small and medium enterprises. The analysis utilized data from 120 women entrepreneurs across diverse sectors, with the study's methodology incorporating Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that competitive advantage and human resource transformation collectively account for 70.3% of the variance in business performance (R² = 0.703), underscoring their critical Influence. However, the expected mediating effect of digitalization on BP was discovered to be non-significant (P = 0.094), suggesting its role might be more supportive than transformative in this context. Notwithstanding this finding, digitalization remains a pivotal catalyst for operational efficiency and innovation. This study contributes to extant scholarship by drawing upon the Resource-Based View and Dynamic Capabilities Theory, offering novel insights into the strategic importance of digital tools and human capital in women-led Small and Medium Enterprises.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".