Celebrity endorsement in social media contexts: understanding the role of advertising credibility, brand credibility, and brand satisfaction
Bibliographic record
Abstract
Online tutoring is a new phenomenon in Indonesia that is quite popular at the moment, especially after the pandemic. The emergence of online tutoring for schoolchildren provides families with an alternative option for educating their children; additionally, online learning becomes an effective alternative because it does not pose a health risk during a pandemic. Online tutoring marketing optimizes marketing strategies through digital content using celebrity endorsements. The purpose of this study was to examine the effects of a comprehensive new model of using celebrity endorsement based on its dimensions and its effect on brand satisfaction, advertising credibility, brand credibility, and repurchase intention. The data collection technique in this study was quantitative and involved distributing questionnaires to 175 respondents in the city of Jakarta. This study uses a structural equation model (SEM) with the SmartPLS tool. The results obtained are able to identify and measure the impact of celebrity endorsement in digital marketing strategies that have an impact on repurchase intention. The results of the study explain that all pathways proved to have a positive effect, unless expertise on repurchase intention has a negative effect. There are 6 hypotheses accepted and 8 hypotheses rejected. The biggest influence on repurchase intention is advertising credibility. In terms of the total indirect effect, only attractiveness and trustworthiness have a significant total effect on repurchase intention, while expertise does not have a significant total effect. The implications of this research can be used as a basis for determining a comprehensive strategy for online tutoring companies to retain their customers after the pandemic.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".