Pengaruh Predatory Pricing, Sikap Konsumen dan Pemasaran Interaktif terhadap Peningkatan Penjualan UMKM di TikTok Shop (Studi kasus UMKM kuliner di Jakarta Selatan)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh predatory pricing, sikap konsumen, dan pemasaran interaktif terhadap peningkatan penjualan UMKM di TikTok Shop baik secara simultan maupun parsial. Desain penelitian ini menggunakan metode kuantitatif dengan teknik non-probability sampling. Populasi penelitian ini adalah pelaku UMKM kuliner di wilayah Jakarta Selatan yang menggunakan TikTok Shop dengan sampel yang digunakan sebanyak 100 responden dengan menggunakan rumus roscoe. Metode analisis data yang digunakan merupakan analisis regresi linear berganda, uji auto korelasi, uji F (simultan), Uji t (parsial), dan Koefisien Determinasi (Adjusted R Square) dengan bantuan software SPSS 25. Hasil penelitian ini menunjukkan terdapat pengaruh secara simultan antara variabel Predatory Pricing, Sikap Konsumen, dan Pemasaran Interaktif berpengaruh signifikan terhadap Peningkatan Penjualan. Secara parsial, Predatory Pricing dan Sikap Konsumen berpengaruh signifikan terhadap Peningkatan Penjualan. Sementara Pemasaran Interaktif tidak berpengaruh signifikan terhadap Peningkatan Penjualan. Kata Kunci: Predatory Pricing, Sikap Konsumen, Pemasaran Interaktif, Peningkatan Penjualan
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".