Digital transformation and competitiveness in Peruvian small business
Bibliographic record
Abstract
Digital transformation has become fundamental to improving the competitiveness of microenterprises worldwide, and Huancayo, Peru is no exception. By adopting digital technologies, microenterprises can improve their operational efficiency, expand their market reach and enhance their abilities to make informed decisions. They have therefore found innovative ways to adapt to changing market conditions, such as incorporating information technologies, online sales, use of social networks and home delivery. Despite this, many microenterprises struggle to survive due to lack of access to financing and adequate government support. This study aimed to analyze how individual, group and organizational factors influence the digital transformation of microenterprises and its impact on their competitiveness. The research was carried out in a sample of 80 multi-sector microenterprises, using a non-probabilistic and cross-sectional research design of a quali-quantitative and explanatory nature, using SEM-PLS. The results of the study indicate a positive relationship between individual, group and organizational factors and digital transformation, as well as with the competitiveness of microenterprises. The coefficients of determination (R2) obtained were 0.8897 and 0.7931 for digital transformation and competitiveness, respectively, indicating a predictive ability in both cases. These findings are of great use to policy makers, business owners and researchers interested in fostering the growth and development of microenterprises in emerging economies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".