An Exploratory Study of Determinants and Corrective Measures for Software Piracy and Counterfeiting in the Digital Age
Bibliographic record
Abstract
Software piracy and counterfeiting is a global problem that has resulted in huge economic losses worldwide. This paper proposes a theory-based approach to study the key factors contributing to piracy and counterfeiting issues. We first developed a theoretical model linking the antecedents into the key factors using information acquired from an extended literature review. We then undertook a survey of thirty business professionals representing different industries, functional roles and different levels of work exposure to software usages in Singapore to investigate the issues. Specifically, the objectives of the survey were to: (1) investigate the key issues associated to software piracy and counterfeiting; (2) identify the factors that have contributed to the software piracy and counterfeiting; and (3) draw up a refined list of appropriate measures to counter software piracy and counterfeiting. Through the structural use of Non-Parametric Correlation Test, Chi-Square Test for Independence, Fisher Exact Probability Test and Phi value, our findings showed that the lack of awareness to software usage laws and regulations, the perceived lack of enforcement measures and penalties, and the lack of educational programs catering to the proper usage of software were the key factors contributing to the software piracy and counterfeiting issues. The findings are useful to managers of software companies and policy-makers in reviewing existing software protection policies, laws and regulations, such that any flaws or loopholes can be identified.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".