Effect of Peer Influence on Unauthorized Music Downloading and Sharing: The Moderating Role of Self-Construal
Bibliographic record
Abstract
This research develops an integrative model of music piracy, specifying self-control and differential association as antecedents, peer influence in music downloading/sharing as mediator, and self-construal as moderator. Disentangling peer influence into two forms—informational influence and normative influence—this research examines their differential effects on two aspects of music piracy: unauthorized downloading and unauthorized sharing. The findings suggest that informational influence is the key underlying mechanism through which self-control affects unauthorized downloading, whereas the two forms of peer influence mediate the relationship between differential association and both aspects of music piracy. Furthermore, the relationships among antecedents, mediator, and consequences (i.e., unauthorized downloading and unauthorized sharing) are contingent upon individuals' self-construal. These findings yield important implications and intervention programs (e.g., interpersonal skill training, educational extension programs, and artist-student contact points) that can curb music piracy.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".