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Key research themes
1. How can imperfect compliance influence IT-mediated crowd guidance strategies and what mechanisms improve effectiveness under low compliance?
This theme investigates how varying levels of compliance among individuals impact the success of crowd guidance efforts mediated by IT systems. It addresses the challenges of directing crowds through signage, apps, or audio instructions when not all individuals adhere to guidance, and explores strategies and simulations that deal explicitly with imperfect compliance. Understanding this is crucial for designing resilient crowd management systems capable of alleviating congestion and improving safety in dense environments such as transit hubs.
Key finding: Through simulation of pedestrian flow in a metro-inspired scenario, the study found that only a small fraction of the crowd needs to comply with rerouting instructions to significantly reduce travel times. Furthermore,... Read more
Key finding: Using a social force model augmented with autonomous robots that can exert attractive social forces on pedestrians, the study demonstrated that influencing crowd movement through algorithmically controlled agents can improve... Read more
Key finding: Interviews with professional crowd managers revealed dissatisfaction with existing technological support, particularly in handling imperfect compliance and dynamic crowd behavior. Managers expressed the need for enhanced... Read more
2. What mechanisms and frameworks enable effective problem-solving and data quality assurance through IT-mediated crowdsourcing despite limitations in crowd size and expertise?
This research theme focuses on understanding how crowdsourcing can be optimized to achieve high-quality problem solving and data validation when faced with constraints such as small crowd sizes, heterogeneity in contributor skill levels, and the intrinsic difficulty of tasks. It emphasizes computational models, quality of information frameworks, and consensus algorithms aimed at balancing the trade-offs between the size, skill, and motivation of participants, with practical applications including citizen science and innovation contests.
Key finding: By modeling crowdsourced problem-solving as a search optimization process using genetic algorithms and NK models, the study revealed that parallel search processes and iterative problem-solving phases can compensate for... Read more
Key finding: Introducing an incremental Bayesian consensus model, the research showed that reliable consensus labels can be achieved with very small crowd sizes (3–5 users), even in complex tasks involving 22-class species identification... Read more
3. How do digital technologies and IT-mediated platforms shape crowd identity formation and social interactions within crowdsourcing ecosystems?
This theme examines the socio-political and identity-related dimensions of IT-mediated crowds, exploring how digital platforms and algorithmic mediation influence collective identity creation, social behavior, and the political mobilization of crowds. It highlights the interplay between human actors and non-human actors (algorithms), and how the structuring of digital interactions impacts participant engagement, value co-creation, and social dynamics within crowdsourcing initiatives.
Key finding: By applying identity theory, the study conceptualizes how participants in creative crowdsourcing initiatives accrue identity value (personal, extended, social) which drives engagement and contribution quality. It offers... Read more
Key finding: The paper theorizes how AI-based algorithms on Social Web platforms actively co-produce social interactions and reshape modes of collective action by curating content, constructing social categories, and integrating... Read more
Key finding: Through empirical Bluetooth sensing in urban public spaces, the research visualized digital presence patterns, revealing how spatial properties and synchronization influences emergent collective digital-physical interactions.... Read more