Driving a More Prosperous Future

The GenAI Future of Consumer Research
Journal of Consumer Research

We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapses occurs when GenAI outputs lose human sensibilities. Data and model challenges arise as democratization enhances data representation but embeds real-world biases; the average trap, caused by next-token prediction algorithms, leads to generic outputs that lack individuality; and model collapse occurs when GenAI increasingly learns from its own outputs, amplifying machine bias and diverging from human behavior. To address these challenges, researchers can leverage democratization to study marginalized consumers and prioritize human-centered research over purely data-driven methods. The average trap can be mitigated by fine-tuning models with task-specific and marginalized consumption data while engineering responses for uniqueness. Preventing model collapse requires integrating human-machine hybrid data and applying theories of mind to realign AI with human-centric consumption. Finally, we outline three future research directions: preserving data distribution tails to support consumption democratization, countering the average trap in next-token prediction, and reversing the trajectory from democratization to model collapse.

Ming-Hui Huang, Distinguished University Professor, National Taiwan University
Roland T. Rust, Distinguished Professor and David Bruce Smith Chair in Marketing, University of Maryland

• AI
• Fairness and Equity
• Feelings Economy
• Personalization