Each year billions of dollars are lost due to illegal downloading and copying of intellectual property. Individuals often perceive little or no consequences as a result of digital piracy. Research has shown that perceived consequences could be used to alter an individual's ethical intention to engage in digital piracy (INT). In addition subjective norm (SUN) may also contribute to INT. Therefore, the goal of this study was to determine the factors of perceived consequences and to assess their contribution, as well as the contribution of SUN, to INT. This predictive study developed a quantitative instrument to measure the contribution of the factors of perceived consequences and SUN on INT. In phase one of this study, an anonymous exploratory questionnaire was used to gather a list of perceived consequences. That list was combined with a list of perceived consequences found through an extensive review of the literature and a survey instrument was developed and used in phase two. After data cleaning, a total of 407 responses remained. Exploratory factor analysis incorporating principal component analysis (PCA) identified eight factors of INT: Personal Emotional Consequences (PEC), Freedom Consequences (FRC), Minor Consequences (MIC), Personal Freedom Consequences (PFC), Personal Moral Consequences (PMC), Network Access Consequences (NAC), Self Worth Consequences (SWC), and Industry Financial Consequences (IFC). A model was developed using Ordinal Logistic Regression to determine the contribution of the eight factors of perceived consequences and SUN on INT. PEC, PMC, and IFC as well as SUN were found to be significant contributors to INT. The Mann-Whitney U test determined that INT was the only factor that showed a significant difference for males. Additionally, gender was a significant contributor to FRC, MIC, PFC, PMC, SWC, and IFC. Each of these factors was more significant for females than males. The Kruskal-Wallis test determined that there were no significant differences in the factors of perceived consequences, SUN, and INT based on age or computer usage. Important contributions of this study include the identification of eight perceived consequence factors not previously known as well as the development of a unified predictive model, addressing all forms of digital piracy.