The impact of information technology quality on electronic customer satisfaction in movie industry
Bibliographic record
Abstract
The use of social media becomes a common habit in today's community and routinely used to interact with the community member.Also, many companies used social media to create a social media community concerning the products and services provided to strengthen the company's brand.This study surveys as many as 231 respondents and data analysis uses the PLS method utilizing smart PLS software.The result reveals that the use of information technology that is getting faster with high-speed data accessibility enhances the intensity of interaction between the community member with the path coefficient value of 0.605.Furthermore, the use of information technology with high-speed data accessibility also increase the satisfaction of movie trailer viewers with the path coefficient value of 0.392 since it can provide excitement and entertainment.Besides, the increased use of information technology provides higher satisfaction to the audience.The results also show that the presence of a social media community could provide satisfaction for movie trailer viewers with a coefficient of 0.332.The availability of films in the community provides excellent interactive communication between users.This research has focused only on the use of information technology in the respondents who watch movie trailers and is limited to a region of East Java province, Indonesia.Further research is required to be performed, which focuses on different types of social media and context and needs to analyze the comment of the film viewer in order to provide a better benefit on the latest films and for the entertainment company.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".