생성AI와 귀납적 기계학습에 기반한 유명인 향수 탐색 사례연구: 성공 및 실패 요인의 도출과 가설의 생성

Exploratory Case Study of Celebrity Perfumes based on Generative AI and Inductive Machine Learning: Generating Hypotheses on the Successes and Failures

This exploratory study investigates the success and failure hypotheses of celebrity fragrances by integrating generative AI and inductive machine learning methods. Initially, 21 cases were manually collected and analyzed to identify explanatory variables. Subsequently, the dataset was expanded to 31 cases through OpenAI’s Deep Research, allowing for more robust variable extraction and automated mapping of case-specific attributes across 13 dimensions, including promotional engagement, product quality, fandom scale, brand continuity, and celebrity-product alignment etc. The resulting decision tree revealed that only those celebrity fragrances that exhibited both high product quality and sustained brand management achieved market success, while the absence of either condition led consistently to failure. These findings illustrate a clear inductive rule set for predicting outcomes in celebrity fragrance ventures. The study highlights the powerful role of AI tools in automating complex case study processes from data collection and hypothesis generation to decision modeling, yet underscores the continued necessity of applying rigorous social science methodologies to validate AI-driven insights. This research contributes to establishing a hybrid methodological framework for empirical case studies in the era of generative AI.