Seminars / Conferences

Coming Soon

This research investigates the ability of large language models (LLMs) to generate and evaluate
new product ideas. Across a series of studies, we highlight both the strengths and limitations of LLMs in product innovation. In our first study, we show that LLM-generated ideas achieve higher average quality than human ideas (measured by purchase intent) and are seven times more likely to rank in the top 10%. A second study demonstrates that this creativity advantage is not simply due to the LLM’s superior persuasive skills. Our third and fourth studies reveal a key weakness: AI-supported brainstorming produces ideas that are less novel at the individual level and less diverse at the set level. Finally, in our fifth and sixth studies, we shift focus from idea generation to evaluation, exploring how LLMs can be used to forecast the success of new
opportunities.

Online rental platforms that allow customers to purchase rented goods present a complex pricing
challenge: setting buyout prices that balance immediate sales revenue with future rental income, while accounting for item-specific factors such as condition, popularity, and customer preferences. In this
paper, we develop, estimate, and validate a data-driven framework to inform buyout prices in this setting. Leveraging a Markov Decision Process (MDP), our framework assesses individual item value
based on rental demand, product attrition, and customer purchase likelihood. We use real-world data
from a leading fashion rental company to demonstrate that our methodology significantly improves profitability compared to existing practices and alternative benchmarks. We estimate that the proposed
pricing policy increases earnings by 3.1% over the company’s current practice. Our analysis also shows
that operating a rental-only business model leaves revenue opportunities untapped, underscoring the
strategic value of buyout options in managing inventory and generating additional income.

Coming Soon

Coming Soon