It's Time to Let Go: Stopping Criteria Recommendations in Content-rich Domains
Matej Scerba, Ladislav Peška · 2025
When shopping for new products, people typically tend to adopt maximizer or satisficer behavior patterns.While satisficers stop searching as soon as they find a suitable product, maximizers seek the best option among all available choices.Even though most people normally behave as satisficers, they tend to adopt maximizer patterns in high-stakes decisions.In this work, we argue that contemporary e-commerce solutions are well-suited to supporting satisficers, but they often lack features that assist maximizers.Out of this missing functionality, cut-off alerts, i.e., reassurances that the user has already covered all/most of the potentially relevant options, have not yet been explored in related research.To address this gap, we leverage the observation that high-stakes decisions often occur in content-rich domains.Building on this, we propose an enhanced human-computer interaction model incorporating contextual explanations and cut-off alerts.The proposed functionality was evaluated in a user study where the stopping criteria interface variant substantially outperformed the unseen statistics, which resembles the interfaces commonly available on e-commerce.