Empirical study of Indonesian SMEs sales performance in digital era: The role of quality service and digital marketing
Bibliographic record
Abstract
This research aims to analyze the relationship between digital marketing on quality service, digital marketing on sales performance, quality service on sales performance, and digital marketing on Sales performance through quality service. The research methodology is a quantitative method and divided into research design and research subjects, data collection methods, and analysis methods. The study is conducted on 125 small and medium (SMEs) in Banten, Indonesia in the digital region. The study uses primary data based on the results of distributing online questionnaires to 125 managers of SMEs in Banten who were selected by simple random sampling. The questionnaire was designed online, and each question/statement item was given five answer options, namely: strongly agree (SS) score 5, agree (S) score 4, neutral / doubt (N) score 3, disagree (TS) score 2, and strongly disagree (STS) score 1. The method for processing data is by using PLS and using SmartPLS version 3.0 software. Based on data analysis by SmartPLS, digital marketing has a significant effect on quality service, digital marketing has a significant effect on sales performance, quality service has a significant effect on sales performance, and digital marketing significantly affects sales performance through quality service in the digital era.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".