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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".