Technological innovation has significantly transformed the field of statistics, not only in data analysis but also in data collection. Traditional methods based on direct observation have evolved into hybrid approaches that combine passively collected data (e.g., from GPS or accelerometers) with active user input through digital interfaces. This evolution has led to Smart Surveys—next-generation tools that leverage smart devices, such as smartphones and wearables, to collect data actively (via questionnaires or images) and passively (via embedded sensors). Smart Surveys offer strategic value in industrial contexts by enabling real-time data collection on worker behavior, environments, and operational conditions. However, the heterogeneity of such data poses challenges in management, integration, and quality assurance. This study proposes a modular system architecture incorporating gamification elements to enhance user participation, particularly among hard-to-reach worker segments, such as mobile or shift workers. By leveraging motivational strategies and interactive feedback mechanisms, the system seeks to foster greater engagement while addressing critical data security and privacy concerns within industrial Internet of Things (IoT) environments.

Evaluation of service quality in public systems represents a critical challenge in the context of increasing demographic pressure and economic uncertainty. Pension systems, such as the Italian system managed by INPS, can be interpreted as complex service infrastructures whose quality depends on financial sustainability, efficiency, and accessibility. This paper proposes an integrated statistical framework combining nonparametric multivariate analysis and stochastic forecasting to assess the quality and sustainability of pension services. The empirical analysis is based on official data from INPS and ISTAT and is conducted across Italian macro‐regions (North, Center, South), capturing territorial heterogeneity. The methodology integrates Spearman rank correlation, variable selection based on collinearity thresholds, and ARIMA time series models, providing a unified analytical pipeline. Digitalization is considered an institutional context that may affect the interpretation of empirical findings; however, it is not explicitly incorporated into the model due to the lack of consistent historical indicators. The results reveal strong dependence relationships among key variables, significant territorial disparities, and increasing uncertainty in future pension dynamics, particularly in Southern Italy. The proposed framework provides a robust and flexible tool for evaluating public service quality and offers relevant insights for policy‐making.