Modeling public perception and political trust in mandatory conditions: the case of digital public services

Contemporary academic literature has focused on technology adaptation and innovation in politics and public administration. At present, there is a large number of studies focusing on actively developing phenomenon as public digital services. Despite the diverse range of theoretical approaches, there is no established methodological tradition of modelling perception of digital services. In addition, many studies are based on the premise that the user’s actions are voluntary. We assume that in the absence of voluntariness, the classical modelling form of decision-making – ‘yes or no’, ‘to act or not to act’ – loses much of its meaning. Mandatory environment gives rise to a number of important consequences, some of which require new modelling solutions.

To fill this gap, the author presents in this paper a computational model of the formation of attitudes towards public digital services under the mandatory conditions. The reason behind the choice of this area is the magnitude of the coercive power of the state and its ability to apply it to the widest range of social groups.

The main properties of agents in the model are trust in government and digital skills. Public services differ in technical requirements and, most importantly, in sensitivity – the scope of users’ rights and obligations that the service affects, and the volume of personal data it requires. The model shows how these variables interact under the conditions of voluntary and mandatory service use.

Computational experiments demonstrated that mandatory conditions give rise to a polarization of users’ attitudes when the service provided is sensitive. The polarization is intensifying when dissatisfaction with the associated risks affects the level of trust in government. Involuntariness also serves as a ‘catalyst’ for the attitude formation processes: in its presence, both the scale of changes and the leverage of other factors (sensitivity in the first place) increase.

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