A study on the relationship of e-marketing, e-CRM, and e-loyalty: Evidence from Indonesia
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.
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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 | 0.002 | 0.003 |
| 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".