This paper utilises a novel experimental dataset on consumer choice to investigate and benchmark the performance of alternative statistical models under conditions of extreme uncertainty. We compare the results of logistic regression, discriminant analysis, naïve Bayes classifier, neural network, support vector machine (SVM), decision tree, and random forest (RF) to discover that the RF model robustly registers the highest classification accuracy. Variable importance analysis reveals that apart from demographic and situational factors, consumer choice is highly dependent on social network effects and subject to numerous non-linearities, thus making machine learning approaches the best modelling choice.
The issues of privacy and data protection are gaining in prominence, especially against the backdrop of changing citizen preferences and the enforcement of strict legislations such as the EU’s General Data Protection Regulation. Pursuant both article 25 of the Regulation and following good practice, public sector institutions need to apply the principle of Privacy by Design (PbD) to their Information Systems. However, there is limited consensus on how this application is to be carried out. This article aims to fill this gap by constructing an implementation methodology with a particular focus on the e-government domain. This is done by using a design science approach leveraging practical experience and extant literature to design the methodology in accordance to user needs, existing legal requirements, and best practices. The proposed new methodology is applied to a real-life project from Bulgaria’s e-government road-map and evaluated by project stakeholders and experts.
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