A study on the relationship of e-marketing, e-CRM, and e-loyalty: Evidence from Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The number of website visits is an important issue in the era of industrial revolution 4.0 for the manufacturing industry of Refrigeration and HVAC (heating, ventilation, and air-conditioning) as an effort in obtaining and maintaining customers. Therefore, e-loyalty is needed to improve the number of website visits. Research is done to test the influence given by e-marketing and e-CRM towards e-loyalty of a website owned by one of the Refrigeration and HVAC (RHVAC) companies in Indonesia. data is collected by a simple random sampling method obtained from 170 respondents of website visitors in the RHVAC fair Indonesia 2018. The method used in this research is multiple linear regression with SEM through the help of SmartPLS 3.0 software. The analysis result of this research shows that e-marketing and e-CRM have a positive and significant effect on e-loyalty, both individually and simul-taneously.
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.
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 | 0.009 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it