Promothesh Chatterjee

About:

I am a Marketing Faculty at the David Eccles School of Business, University of Utah. Previously I was a faculty of Marketing, at University of Kansas. I have a Ph.D. in Marketing from the University of South Carolina and, an MBA (Marketing and Finance) and BS (Math) from India.

My research examines how Generative AI and large language models (LLMs) influence human judgment, preferences, and decision-making. Beyond studying AI, I deploy these same tools methodologically, using LLMs and transformers to generate hypotheses, analyze language at scale, and design studies that would be infeasible through conventional means. Recent highlights include two Lead Articles in the American Psychologist (2024) on machine-assisted hypothesis generation using LLMs and the future of large language models in social science research, and a Lead Article and Editor’s Choice paper in the Journal of Personality and Social Psychology (2023) using embedding-based NLP to examine identity expression at scale. My earlier work in judgment and decision-making provides the foundational behavioral framework that informs my current focus on Generative AI and human behavior.

My research has appeared in leading outlets, including FT50 journals such as the Journal of Marketing Research, Journal of Consumer Research, Production and Operations Management, Journal of the Academy of Marketing Science, and Organizational Behavior and Human Decision Processes, as well as top psychology outlets including the Journal of Personality and Social Psychology, Psychological Science, and American Psychologist. My work has been featured in a variety of media outlets such as MSNBC, TIME, The New York Times, Chicago Tribune etc.

On the teaching side, I currently teach Business Applications of Text Analytics for the MSBA program, covering transformer models, topic models, embeddings, sentiment analysis, and Gen AI tools including prompt engineering and LLM APIs. I served as lead speaker for a faculty-wide workshop on Demystifying Generative AI (2025) and am developing a course on Generative AI for Business Research. My broader teaching portfolio spans Marketing Research, Pricing, Marketing Management, and Judgment & Decision-Making at undergraduate, MBA, and Ph.D. levels.

Promothesh Chatterjee

About:

I am a Marketing Faculty at the David Eccles School of Business, University of Utah. Previously I was a faculty of Marketing, at University of Kansas. I have a Ph.D. in Marketing from the University of South Carolina and, an MBA (Marketing and Finance) and BS (Math) from India.

My research examines how Generative AI and large language models (LLMs) influence human judgment, preferences, and decision-making. Beyond studying AI, I deploy these same tools methodologically, using LLMs and transformers to generate hypotheses, analyze language at scale, and design studies that would be infeasible through conventional means. Recent highlights include two Lead Articles in the American Psychologist (2024) on machine-assisted hypothesis generation using LLMs and the future of large language models in social science research, and a Lead Article and Editor’s Choice paper in the Journal of Personality and Social Psychology (2023) using embedding-based NLP to examine identity expression at scale. My earlier work in judgment and decision-making provides the foundational behavioral framework that informs my current focus on Generative AI and human behavior.

My research has appeared in leading outlets, including FT50 journals such as the Journal of Marketing Research, Journal of Consumer Research, Production and Operations Management, Journal of the Academy of Marketing Science, and Organizational Behavior and Human Decision Processes, as well as top psychology outlets including the Journal of Personality and Social Psychology, Psychological Science, and American Psychologist. My work has been featured in a variety of media outlets such as MSNBC, TIME, The New York Times, Chicago Tribune etc.

On the teaching side, I currently teach Business Applications of Text Analytics for the MSBA program, covering transformer models, topic models, embeddings, sentiment analysis, and Gen AI tools including prompt engineering and LLM APIs. I served as lead speaker for a faculty-wide workshop on Demystifying Generative AI (2025) and am developing a course on Generative AI for Business Research. My broader teaching portfolio spans Marketing Research, Pricing, Marketing Management, and Judgment & Decision-Making at undergraduate, MBA, and Ph.D. levels.