The role of e-marketing and e-CRM on e-loyalty of Indonesian companies during Covid pandemic and digital era
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 aim of this research is to analyze the effects of e-Marketing, e-CRM and e-Marketing on e-Loyalty and company performance. The study is quantitative with a questionnaire approach. Data processing tools use the SmartPLS 3.3.3 software. The primary data collection method was by distributing online questionnaires through online surveys to 286 managers of non-e-commerce companies during covid pandemic and digital era. The regression test results show the e-Marketing has a significant effect on Company Performance, e-CRM has significant effect on Company Performance, e-Marketing has no significant effect on e-Loyalty, e-CRM has no significant effect on e-Loyalty), e-Loyalty has no significant effect on Company Performance, e-CRM has no significant effect on business sustainability through e-Loyalty. Finally, e-Marketing has no significant effect on Company Performance through e-Loyalty. That means e-marketing and e-CRM have a relationship and influence on e-loyalty both individually and simultaneously.
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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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