The role of AI and big data in increasing the success of e-commerce assistance and its implications on the quality of MSME capacity development models with disabilitie
Bibliographic record
Abstract
The aim of this research is to analyze the influence of the role of AI and big data in increasing the success of e-commerce assistance and its implications for the quality of the capacity development model for MSMEs with disabilities in Serang City, Semarang City and Banjarmasin City, Indonesia. The sample in this study was 184 respondents consisting of 3 (three) regions including Serang City, Semarang City and Banjarmasin City, Indonesia. Sampling technique using technique random sampling. Data collected through questionnaires was then analyzed using SEM-PLS. The results of research and data analysis show that: The role of AI directly has a positive and significant effect on increasing the success of e-commerce assistance; Big Data directly has a positive and significant effect on increasing the success of e-commerce assistance; The role of AI directly has a positive and significant influence on the Quality of the MSME Capacity Development Model; Government Big Data directly has a positive and significant effect on the Quality of the MSME Capacity Development Model; Increasing the Success of E-Commerce Assistance directly has a positive and significant effect on the Quality of the Capacity Development Model for MSMEs with Disabilities in Serang City, Semarang City and Banjarmasin City, Indonesia. Increasing the Success of E-Commerce Assistance is able to mediate indirectly on the Role of AI and Big Data on the Quality of the Capacity Development Model for MSMEs with Disabilities in Serang City, Semarang City and Banjarmasin City, Indonesia.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".