Determinants of Consumer Intention to Pirate Digital Products
Bibliographic record
Abstract
Digital products, such as software, music, videos, books, and pictures, are vulnerable to digital piracy. The lossescaused by pirated digital products have been increasing over years in Indonesia. Long histories of intellectualproperty rights protection are unable to suppress the piracy behavior. Several studies have been conducted toexamine factors affecting consumer intention to pirate digital products. However, a systematic study onconsumer intention to commit digital piracy in Indonesia is still limited. The present study aims to address theunder-research issue.The current study is a modified replication of Yoon’s (2011) research. Self-administered questionnaires weredistributed to 218 students at several universities in Daerah Istimewa Yogyakarta (DIY), Indonesia. Ten researchhypotheses were tested using multiple regression analyses. The results indicate that three of the ten hypotheseswere not supported, while one hypothesis could not be examined due to its failure to pass a reliability test.Attitude towards digital piracy positively affects consumer intention to commit digital piracy, while moralobligation is a negative predictor of the dependent variable. Subjective norms and perceived behavioral controlwere found to have insignificant impacts on intention to pirate digital products.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".