Published in the Journal of the Midwest Association for Information Systems (JMAIS), 2026
Summary:
This study explores how users engage with AI-powered sports betting platforms by identifying behavioral segments through cluster analysis. Rather than focusing on betting outcomes or gambling psychology, the research frames these platforms as consumer-facing decision support systems within the Information Systems domain. Drawing on IS theory, the study examines trust calibration, automation bias, and algorithm aversion to reveal how users interact with AI-driven decision aids. The findings highlight variation in user attitudes toward algorithmic recommendations, offering insights into the design and management of AI-infused digital services.
Key Contributions:
- Applies TwoStep Cluster Analysis to identify distinct user profiles in AI-based sports betting contexts.
- Frames AI-powered betting apps as decision support systems, contributing to IS theory on human-algorithm interaction.
- Explores algorithm aversion, trust calibration, and automation bias in a high-stakes, consumer decision-making environment.
- Offers design recommendations for improving transparency, usability, and trust in AI-driven consumer platforms.
- Demonstrates a cross-disciplinary application of design science principles to a real-world digital service.