Behavioral Data and Decision Making

Section chair: Alexey Gorgadze, Kristina Kanunnikova

Contact details: sbas@itmo.ru

Section language: English

This section focuses on the analysis of consumer behavior and decision‑making processes grounded in behavioral economics, the Theory of Planned Behavior (TPB), bounded rationality, and related conceptual frameworks. Particular attention is given to empirical studies based on surveys, experiments, and qualitative methods, as well as to practices of behavioral design and nudge interventions in business and public contexts. The section is suitable for researchers examining motivations, barriers, and cognitive biases in socio‑economic environments.

Consumer behavior and decision-making studies

Section chair: Ivan Burkov, Polina Shalygina

Contact details: sbas@itmo.ru

Section language: English

This section focuses on the study of user behavior in digital products through the analysis of digital trace data and technology adoption models. Contributions are expected to examine how user experience (UX) and value perception are formed in digital environments, how product design influences decision-making and user commitment, and how large-scale data analytics can be used to predict behavioral patterns. The section welcomes empirical research employing quantitative and mixed-method approaches, including text mining, log data analysis, A/B testing, and interface behavior analytics.

Digital Traces and User Experience

Section chair: Vsevolod Bogodist, Alexandra Sabantseva

Contact details: sbas@itmo.ru

Section language: Russian

The session «Application of AI Methods in Consumer Behavior Analysis» serves as a research platform where artificial intelligence methods are examined as tools for generating meaningful insights into consumer preferences, motivations, and decision-making in order to support strategic business decisions.
The session will feature empirical studies demonstrating how algorithmic analysis of behavioral data enhances the understanding of demand, pricing, and customer needs, thereby providing a scientifically grounded foundation for managerial practices in marketing and sales.

Application of AI Methods in Consumer Behavior Analysis

Section leaders: Alexey Gorgadze

Contact details: sbas@itmo.ru

Section language: English

This section focuses on the role of social and organizational networks in shaping economic behavior, innovation processes, and strategic outcomes. It welcomes empirical and methodological contributions applying social network analysis (SNA), graph theory, and computational approaches to examine inter-organizational ties, knowledge diffusion, collaboration networks, board interlocks, startup ecosystems, and digital communities. Particular attention is given to how network structures influence performance, resilience, governance, and decision-making within firms and across ecosystems. The section encourages studies using quantitative network metrics (centrality, modularity, structural holes), longitudinal network data, and mixed-method designs combining network analytics with qualitative insights.

Networks, Social Structures and Organizational Dynamics

2025

FTMI ITMO

CONTACT

Ksenia Klimshina

Head of the Center for Research Organization Faculty of Technological Management and Innovations