Purchasing pirated software: an initial examination of Chinese consumers
Bibliographic record
Abstract
Purpose To analyze Chinese consumers in purchasing pirated software; to establish and empirically validate a model for analyzing consumers in software piracy; and to help software companies understand the software piracy issue in China and design anti‐piracy strategies. Design/methodology/approach A research model was established by extending a model used by Ang et al. in studying Singaporeans' purchasing pirated CD. A survey was conducted. Hypotheses were tested through stepwise regressions. An exploratory factor analysis was carried out to analyze Chinese consumers' attitude toward software piracy. Findings Four personal and social factors were found important in influencing Chinese consumers' attitude toward software piracy, including value consciousness, normality susceptibility, novelty seeking, and collectivism. Five attitude measures, which were important in influencing consumer purchase intention, were identified as reliability of pirated software, recognized social benefits of piracy, functionality of pirated software, risks of purchasing, and perceived legality of purchasing. An exploratory study identified three attitude attributes. Research limitations/implications As student samples were used, caution needs to be exercised when generalizing findings from this study. Regressions were used to test construct relationships in the model, and the model was not tested as a whole. Practical implications This research provides an in‐depth understanding on Chinese consumers, and the research findings are useful in designing anti‐piracy strategies in China. Originality/value This research is one of the first to examine the Chinese market, which is a focus of piracy problems for the software industries. This research contributes to theory development in developing and testing a model and important constructs, and to industrial practice in providing understanding on Chinese consumers to help design anti‐piracy strategies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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".