Investigation of the Main Factors Influencing Gen Z Users' Willingness to Subscribe/Renew Music Streaming Platforms
Bibliographic record
Abstract
The global music industry is increasingly dominated by streaming services, which now account for 84% of recorded music revenue. For Gen Z, music streaming is a primary mode of consumption, making it vital to understand what drives their subscription decisions. This study investigates the primary factors that influence Gen Z users willingness to subscribe to or renew memberships on music streaming platforms. During the research, this study surveyed a sample of Gen Z users using a Liberty-scale questionnaire measuring these related variables and analyzed the data using Pearson correlation coefficients and multiple regression to test the hypothesized relationships. Results show that perceived ease of use has a strong positive impact on Gen Z users subscription/renewal intentions, whereas neither perceived usefulness nor reliability exert a significant influence. This finding marks a departure from classic TAM expectations, suggesting that in the hedonic context of music streaming, ease of use outweighs functional utility (usefulness) and perceived reliability in driving usage intentions. Theoretically, the findings revise TAM assumptions for entertainment-oriented technologies. Practically, these insights imply that music streaming platforms targeting Gen Z should prioritize user-friendly designs and smooth usability to encourage subscriptions and loyalty, rather than overemphasizing added functionality or trust-building measures that young users may already take for granted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".