Piracy, Price, and Word of Mouth: An Equity Theory Examination of Consumer Digital Piracy Rates—An Abstract

Abstract

Online piracy of digital content including that of music, movies, software, games, and other products continues to be a huge issue for businesses and public policy makers for a number of reasons. First, online piracy takes up significant amounts of Internet bandwidth—24 % globally, and 17.5 % in the USA. Additionally, a study from the Institute for Policy Innovation concluded that global music piracy causes $12.5 billion of economic losses every year, 71,060 lost American jobs, and a loss of $422 million in tax revenues (RIAA 2014). Given the economic and social impact of digital piracy, it is important to study what factors influence the piracy rate and especially whether key marketing mix elements, such as price, affect consumers’ decisions to engage in online piracy. This study uses equity theory as a framework in order to analyze the impact of price-, product-, and purchase-related experiences, as well as word of mouth, on digital piracy rates. Equity theory was created and popularized by Adams (1963) and is especially used in social exchange within organizations. We use equity theory in order to analyze how perceived inputs, such as price and information search, as well as outputs, including product quality and popularity, affect the rate of piracy for copyrighted products. This represents a different perspective compared to studies that focus only on the ethical side of consumers’ decisions and brings attention to product-related factors such as price and product quality. In order to measure the piracy rate for digital content, we downloaded the number of “leechers” (users downloading pirated content at a certain time) for the top 120 most downloaded games from Pirate Bay during a 10-day period. In order to test our model, we also downloaded data regarding the price for each game from Amazon, as well as the quality rating assigned in its reviews from Metacritic. We measured consumer interest in the digital product by using Google Insights and the search index provided for the exact name of the product. We performed a multiple regression procedure using SPSS, with piracy rate on a proxy showing the content from Pirate Bay as the dependent variable, with product price, review rating, search rating, and popularity index as independent variables.