An Empirical Investigation of Factors Determining Actual Usage of Entertainment Streaming Apps in India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rise of internet and penetration of smartphones have made digital content accessible though Entertainment Streaming Apps (ESA). With the flexibility of time and place, ESA platforms are changing the dynamics of entertainment consumption. The current study explored the determinants of actual usage of ESA using the theory of planned behavior, flow theory and factors affecting entertainment related technology adoption including engagement, content, entertainment value, convenience value and monetary value. Data is collected through an online survey from 215 Indian ESA users and the proposed framework is empirically tested using partial least squares structural equation modeling (PLS-SEM). The findings of the study contribute to the growing body of literature on streaming apps adoption and usage by expanding the understanding of the factors that explain its usage behavior.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it