|Title:||Untangling the Web of Relationships Between Wealth, Culture, and Global Software Piracy Rates: A Path Model|
|Author(s):||Moores, T. T.|
|Citation:||Moores, Trevor T. (2011). Untangling the Web of Relationships between Wealth, Culture, and Global Software Piracy Rates: A Path Model. International Comparisons of Information Communication Technologies: Advancing Applications: Advancing Applications : 110.|
|Key Related Studies:|
|About the Data|
|Data Description:||The Business Software Alliance (software piracy rates for 114 countries from 2002 to 2007) and the World Bank (gross national income data for 227 countries) gather world economic data on countries' software piracy rate, treating the globe as a single market, with a heavier influence by larger markets.|
|Data Type:||Secondary data|
|Secondary Data Sources:|
|Data Collection Methods:|
|Data Analysis Methods:|
|Cross Country Study?:||Yes|
|Government or policy study?:||No|
|Time Period(s) of Collection:||
This article examines the relationship between Hofstede’s national culture indices (IDV, PSI, MAS, and UAI), economic wealth (GNI), and national software piracy rates (SPR). Although a number of studies have already examined this relationship, the contribution of this article is two-fold. First, we develop a path model that highlights not only the key factors that promote software piracy, but also the inter-relationships between these factors. Second, most studies have used the dataset from the pre-2003 methodology which only accounted for business software and did not take into account local market conditions. Using the latest dataset and a large sample of countries (n=61) we find there is an important triadic relationship between PDI, IDV, and GNI that explains over 80% of the variance in software piracy rates. Implications for combating software piracy are discussed.
Main Results of the Study
This article seeks to explain the differing software piracy rates, especially amongst economically developed countries by examining culture and other correlated factors. Hofstede's four factor cultural model measures PDI (power distance index, the accepted societal the power gap), masculinity-feminity (MAS), uncertainty avoidance index (UAI), and IDV (individual-collectivism) are examined to determine if and how culture influences in for higher software piracy rates. Even taking all into account, low GDP is still the most predictive factor in software piracy, which is significantly and positively correlated with IDV and PDI. However, culture and wealth are interrelated and pose complicated challenges to reducing software piracy rates.
Policy Implications as Stated By Author
To decrease software piracy rates, companies must provide market-sensitive pricing coupled with advertising aimed at high PDI (power distance index, to lessen societal the power gap) and low IDV (individual-collectivism, to increase individuality).
Coverage of Study
|Level of aggregation:||Country|
|Period of material under study:||2002-2007|