Pengaruh Promosi dan Distribusi Terhadap Kepuasan Pelanggan Pada PT Tiga Serangkai Internasional Cabang Bandung
Bibliographic record
Abstract
The objectives of the research are to know the responses about promotion, distribution, customer satisfaction, and customer trust, also to analyze the influence of promotion and distribution to customer satisfaction and its implication on customer trust. The population in this study is PT. Tiga Serangkai International in the reater Bandung Region. The research method used in this research is descriptive and verification research method with a sample size of 90 respondents, data collection by interview using a questionnaire, observation and literature. Sampling technique using the "Disproportionate Starfied Random Sampling" method. Data analysis method used is path analysis. The results showed that promotion and distribution simultaneously influence customer satisfaction, partially promotion and distribution have a significant effect on customer satisfaction. Promotion gives an influence on customer satisfaction by 16%, while distribution gives an effect of 75.9%. The contribution of promotion and distribution variables to customer satisfaction is 73.7%, the remaining 26.3% is the contribution of the variables not included in the study
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".