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AI–Human Hybrids for Marketing Research: Leveraging Large Language Models (LLMs) as Collaborators

90
Citations
March 1, 2025
Published Date

Research Abstract & Technology Focus

The authors’ central premise is that a research-highlight">human–LLM (large language model) hybrid approach leads to efficiency and effectiveness gains in the research-highlight">marketing research process. In qualitative research, they show that LLMs can assist in both data generation and analysis; LLMs effectively create sample characteristics, generate synthetic respondents, and conduct and moderate in-depth interviews. The AI–research-highlight">human hybrid generates information-rich, coherent data that surpasses research-highlight">human-only data in depth and insightfulness and matches research-highlight">human performance in data analysis tasks of generating themes and summaries. Evidence from expert judges shows that humans and LLMs possess complementary skills; the research-highlight">human–LLM hybrid outperforms its research-highlight">human-only or LLM-only counterpart. For quantitative research, the LLM correctly picks the answer direction and valence, with the quality of synthetic data significantly improving through few-shot learning and retrieval-augmented generation. The authors demonstrate the value of the AI–research-highlight">human hybrid by collaborating with a Fortune 500 food company and replicating a 2019 qualitative and quantitative study using GPT-4. For their empirical investigation, the authors design the system architecture and prompts to create personas, ask questions, and obtain responses from synthetic respondents. They provide road maps for integrating LLMs into qualitative and quantitative research-highlight">marketing research and conclude that LLMs serve as valuable collaborators in the insight generation process.
human hybrids marketing research leveraging large
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