AI-Assisted Bettors: Analyzing AI-Driven Betting Behavior through Cluster Analysis

The use of artificial intelligence (AI) systems in consumer-facing decision-support systems (DSS) such as predictive analytics and automated recommendation platforms is growing in popularity in numerous domains, including sports betting. The degree to which users interact with the output of automated systems, calibrate trust, and exhibit automation bias–consistent behavior is largely unknown. In this study, we investigate the behavioral segments formed by bettors using AI-powered predictive sports betting DSS based on their shifts in confidence, risk-taking behavior, and bankroll management practices. We use survey data from a sample of 200 U.S.-based bettors and SPSS TwoStep Clustering to identify three distinct behavioral profiles: Traditional Bettors, AI-Influenced Confident Bettors, and AI-Adopting Risk-Takers, each with their own unique set of interactions with and through predictive DSS. The findings show that bettors can engage in responsible adoption through strategic bankroll management practices and a tempering of AI trust, while overreliance behaviors can be mitigated or amplified, respectively, by counter or co-aligning with individual differences. Framing betting platforms as real-time, in-the-wild, and consumer-deployed DSS contributes to IS research on algorithmic decision environments, user trust, and human–computer interaction. The results advance IS theory by contributing to the discussion of how cognitive biases, human decision behaviors, and confidence amplification in and through automated systems manifest in such domains. We conclude the paper with implications for responsible DSS design and deployment as well as practical guidelines for user segmentation in predictive analytics DSS environments.