James M. Leonhardt: Exploring brand personality with large language models
Title
Exploring brand personality with large language models
Mentor
Department
Biosketch
James M. Leonhardt, Ph.D., is the Phil and Jennifer Satre Professor of Marketing at the Βι¶ΉΣ³» College of Business. He is affiliated with the Ozmen Institute for Global Studies and was a visiting scholar at the SGH Warsaw School of Economics and Adolfo Ibáñez University. Leonhardt completed his formal education at the University of California, Berkeley (B.A.) and the Paul Merage School of Business at the University of California, Irvine (Ph.D.). He studied at the Max Planck Institute for Human Development and holds teaching marketing analytics and digital marketing education certificates from the Academy of Marketing Science. Leonhardt actively supervises undergraduate and graduate independent study projects and was a volunteer instructor for the Osher Lifelong Learning Institute at the University of California, Davis, and the Βι¶ΉΣ³». In recognition of his teaching and mentorship, Leonhardt was honored with the Willem Houwink Memorial Award for Teaching Excellence (2020) and the College of Business Excellence in Teaching Award (2019). He was also nominated by the College of Business for the University’s Outstanding Undergraduate Research Faculty Mentor Award.
Project overview
This research project investigates the application of Large Language Models (LLMs) to infer brand personality from brand narratives. A PREP student will contribute to a larger study examining the congruence between human- versus LLM-derived brand personality assessments. The project provides a foundational understanding of LLMs, their application in marketing research, and the principles of quantitative and qualitative data analysis within a computational social science framework. Desirable qualifications include an interest in personality psychology, experimentation, the intersection of AI and human interaction, and a willingness to learn basic programming and statistical concepts. Prior familiarity with Python and LLM APIs is beneficial but not required.
Pack Research Experience Program information and application