The effect of digital marketing on the management of relationships with university students in times of Covid-19
Bibliographic record
Abstract
The present study's main objective was to analyze and determine the impact of digital marketing on the management of relationships with university students in times of Covid-19. The study was conducted from a quantitative approach, with a non-experimental transactional correlational transactional research design. A questionnaire was applied to 400 students aged 18 to 37 years belonging to the Continental University of the city of Huancayo in Peru. The analysis of the results was developed through a data structure and tabulation model with the SmartPLS3 program and it was obtained that Content Marketing has a significant influence on the operational management of customer relationships (p<0.05), as well as on the analytical and collaborative management of customer relationships. As for Social Media Marketing, it was identified that it has a significant influence on operational customer relationship management (p<0.05), as well as on analytical and collaborative customer relationship management, due to the fact that the digital media used by Continental University are attractive to parents and families. It is concluded that Digital Marketing has a great impact on the management of relationships with students of the Continental University in the city of Huancayo in Peru.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".