The role of live-streaming commerce in moderating the influence of entrepreneurial marketing and e-commerce adoption on the business performance of fashion SMEs
Bibliographic record
Abstract
Small and Medium Enterprises (MSEs) are one of the people's economic enterprises that have an essential role in advancing the Indonesian economy. Improving the overall, optimal and sustainable performance of SMEs business increases contribution to the economy in provinces in Indonesia, one of which is Bali Province, the province with the highest absorption of SMEs in Indonesia, namely 10 percent of its population. Denpasar City, as the capital of Bali Province, is the center of trade in Bali, especially the relatively even distribution of SMEs in every sub-district in Denpasar City. This research aims to analyze the role of live streaming in moderating the influence of entrepreneurial marketing and e-commerce on the performance of fashion SMEs in Denpasar City. The research involved 134 fashion SMEs in Denpasar City as samples. Data collection was carried out by distributing questionnaires. Then, the data was analyzed using SmartPLS. The test results of live-streaming commerce moderating the influence of e-commerce adoption on business performance show that live-streaming commerce moderates with a quasi-moderation type the influence of e-commerce adoption on business performance. Live streaming commerce moderates the influence between the independent variable e-commerce adoption and the dependent variable business performance and influences business performance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".