Models for assessing citizen satisfaction with state support measures in the gig economy

In the context of gig economy development and digitalization of social sphere, it is necessary to improve methods and models for assessing citizen satisfaction with measures of state support. To investigate the application of modern mathematical models and artificial intelligence technologies for evaluating the quality of interaction between citizens and the Unified Centralized Platform in the social sector of Russia. The key criteria for assessing user satisfaction were considered when evaluating the effectiveness of interaction with the Unified Digital Platform (UDP) in the Russian social sector providing online access to pension, social, and other public services. These include interface usability, speed of request processing, availability of information, and data security. Examples are given of using CSI and NPS indices for measuring customer loyalty, including regression analysis, natural language processing (NLP), machine learning, and Analytic Hierarchy Process (AHP). Based on proposed models, recommendations have been made aimed at improving the quality of public services by increasing transparency, accessibility, and efficiency of digital platforms.