The influence of social media marketing on customer loyalty through perceived usefulness of streaming technology, perceived enjoyment, and brand loyalty
Bibliographic record
Abstract
Social media has become a forum for discussion formed with the rapid development of internet technology. Using the internet makes getting various information according to your needs more leisurely. This research aims to determine the influence of social media marketing on customer loyalty in the use of streaming application technology as a form of technology acceptance model. Data collection was carried out in 2020 -2023 with 1294 respondents spread across several provinces in Indonesia. The data obtained were analyzed using SEM-PLS. The data processing results to answer the research hypothesis showed that social media marketing as a product promotion tool positively impacted increasing perceived usefulness, enjoyment, and brand trust. The perceived usefulness obtained influences increasing brand trust but does not impact increasing perceived enjoyment. Streaming application technology with the growing feeling of being entertained and accessing online movies to get happiness as a form of perceived enjoyment affects brand trust by getting the benefit that the product used meets expectations and never disappoints. Perceived enjoyment and brand trust increase customer loyalty by increasing the number of active users who recommend online films to other people and staying positive about streaming technology on social media. The practical contribution of the research is optimizing social media as a company marketing tool for the film business to promote newly released films in the community of social media users. Theoretical contribution by enriching consumer behavior theory and technology acceptance models in the application of streaming technology in online film access.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".