1. bookVolume 2017 (2017): Issue 2 (April 2017)
Journal Details
First Published
16 Apr 2015
Publication timeframe
4 times per year
access type Open Access

Cross-Cultural Privacy Prediction

Published Online: 04 Apr 2017
Page range: 113 - 132
Received: 31 Aug 2016
Accepted: 01 Dec 2016
Journal Details
First Published
16 Apr 2015
Publication timeframe
4 times per year

The influence of cultural background on people’s privacy decisions is widely recognized. However, a cross-cultural approach to predicting privacy decisions is still lacking. Our paper presents a first integrated cross-cultural privacy prediction model that merges cultural, demographic, attitudinal and contextual prediction. The model applies supervised machine learning to users’ decisions on the collection of their personal data, collected from a large-scale quantitative study in eight different countries. We find that adding culture-related predictors (i.e. country of residence, language, Hofstede’s cultural dimensions) to demographic, attitudinal and contextual predictors in the model can improve the prediction accuracy. Hofstede’s variables - particularly individualism and indulgence - outperform country and language. We further apply generalized linear mixed-effect regression to explore possible interactions between culture and other predictors. We find indeed that the impact of contextual and attitudinal predictors varies between different cultures. The implications of such models in developing privacy-enabling technologies are discussed.


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