Consumer loyalty of Indonesia e-commerce SMEs: The role of social media marketing and customer satisfaction
Bibliographic record
Abstract
The purpose of this study is to analyze the influence of social media marketing on e-commerce customer satisfaction and loyalty. This study provides insight into the importance of consumer loyalty in the e-commerce industry. The approach used in this study uses quantitative methods through surveys. This study uses a sample of 222 respondents of e-commerce customers. The study uses an online questionnaire through Google Docs. The questionnaire given contains structured questions that are limited by screening, profiling questions and questions related to research variables that affect e-commerce consumer loyalty. The distribution of the questionnaires was carried out by posting on social media groups and direct messages to respondents in accordance with the research requirements. Data analysis of this research, using SEM model using SmartPLS 3.0 software. The results of this study indicate that social media marketing had a significant effect on e-commerce consumer satisfaction, social media marketing had a significant effect on e-commerce consumer loyalty and satisfaction had a significant effect on e-commerce consumer loyalty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".