Digital marketing, online trust and online purchase intention of e-commerce customers: Mediating the role of customer relationship management
Bibliographic record
Abstract
In the digitalization era, e-commerce plays a crucial role in the economy, followed by the internet and smartphone technology. Also, it has a positive effect on humankind. Indonesia has reported the highest e-commerce adoption in the world. However, limited previous studies utilize customer relationship management (CRM) as a mediator in influencing online purchase intention. The present study seeks to analyze the mediating role of CRM in the relationship of digital marketing and online trust on the online purchase intention of e-commerce customers in Banda Aceh city, Aceh province, Indonesia. This study involved all the customers of e-commerce companies in Banda Aceh City. The sample was determined by using a rule of thumb. A total of 150 respondents participated and were collected using purposive sampling. The results indicated that Digital Marketing and Online Trust have a significant positive relationship with CRM. Also, Digital Marketing and CRM have a significant relationship with online purchase intention. Unfortunately, Online trust does not significantly affect online consumer purchase intention. In addition, the CRM mediates the relationship of digital marketing and online trust towards the online purchase intention of e-commerce consumers in Banda Aceh, Aceh Province, Indonesia. This study concludes that CRM plays a role as a mediator in the relationship of the studied variables on the online purchase intention of e-commerce consumers. Also, this study has successfully analyzed the factors that influence online purchase intention and proved the role of CRM.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".