MSME digitalization: How are social capital factors in encouraging the use of digital applications?
Bibliographic record
Abstract
This research explores the impact of social capital on the adoption of digital applications within Micro, Small, and Medium Enterprises (MSMEs). In the current competitive environment, embracing digitalization is essential for improving the competitiveness of MSMEs; however, challenges such as weak social networks can impede technology uptake. Utilizing a quantitative approach, the study distributed questionnaires to 160 MSME participants, with data analysed through path analysis using SmartPLS. The findings indicate that social capital—which includes relationships, networks, and trust among business stakeholders—plays a critical role in facilitating access to and adoption of digital applications. Results suggest that MSMEs with strong social networks are more adept at integrating digital technologies, fostering innovation, and improving overall business performance. The study recommends initiatives to enhance collaboration and strengthen social networks among MSMEs to support their digitalization efforts. Ultimately, this research deepens the understanding of social capital's influence on MSME digitalization and offers practical strategies for stakeholders to increase the utilization of digital applications in this sector.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".