The effects of perceived ease of use, usefulness, enjoyment and intention to use online platforms on behavioral intention in online movie watching during the pandemic era
Bibliographic record
Abstract
Pandemic Covid-19 has resulted in disruption in various industry and business sectors. People spend more time at home than they do outside. People who like movies during this time can enjoy the cinema. However, during the covid-19 pandemic, it must be done online to follow strictly regulated restrictions on community activities to avoid the uncontrolled spread of the virus. As a result, streaming platforms with the advancement of internet technology are increasingly playing a role in providing online services for movie fans. This study investigated the effects of perceived ease of use, usefulness, enjoyment, and intention to use online platforms on behavioral intention in online movies during the Covid 19 pandemic. The questionnaires were distributed by sending google form links to respondents who have a streaming platform subscription in Indonesia. As many as 772 questionnaires were filled out completely and could be processed. Data analysis was done by using partial least squares with Smart PLS software. The results have shown that eight proposed hypotheses have been supported in this study. Perceived ease of use positively affects the perceived usefulness, perceived enjoyment, and intention to watch movies online. Furthermore, perceived usefulness affects perceived enjoyment and intention to watch movies online. Perceived enjoyment influences intention to use and behavioral intention. Finally, intention to use online platforms influences behavioral intention. This research contributes in theory to the technology acceptance model and provides film industry practitioners with insight into enhancing customer behavioral intention in the pandemic era.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".