Confronting Digital Strategic Orientation and Digital Technologies on MSMEs' Performance in Emerging Countries
Bibliographic record
Abstract
ABSTRACT This study tests the different effects of digital strategic orientation and digital technologies on the performance of micro, small and medium‐sized enterprises (MSMEs) in Peru. It also explores the mediating effect of innovation on the relationship between these variables and MSME performance. Small businesses still struggle to seize the potential benefits of digitalization, creating a need for further empirical evidence on this matter. To test our hypotheses, we apply a structural equation model to a statistically significant sample of 345 MSMEs, analyzing direct, indirect and total effects. The analysis is conducted through a theoretical lens combining the technology–organization–environment (TOE) framework and the technology acceptance model (TAM). The results show that although digital strategic orientation and innovation have a direct effect on MSMEs' performance, digital technologies do not have a direct effect; rather, the effect is channelled through the mediating role of innovation. What is more, the total effect of digital strategic orientation is enhanced by the mediating influence of innovation, whereas the effect of digital technologies becomes significant when mediated by innovation. However, the total effect of the latter is smaller compared to the former. Applying the TOE–TAM framework, we find evidence that helps to understand why many small businesses fail to reap the benefits of digital adoption. Organizational transformation is necessary to fully embrace digitalization; the use of digital technologies is not by itself sufficient.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".