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Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines
16 references·3 citing·50 second hop
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References

  • 1979Prospect Theory: An Analysis of Decision under Risk48,459 cited
  • 2005Why Most Published Research Findings Are False10,471 cited
  • 2015Estimating the reproducibility of psychological science8,516 cited
  • 2000A Review and Synthesis of the Measurement Invariance Literature: Suggestions, Practices, and Recommendations for Organizational Research7,981 cited
  • 2006The Influence of a Sense of Time on Human Development2,551 cited
  • 2003Cultural Intelligence1,904 cited
  • 2013P-curve: A key to the file-drawer.1,501 cited
  • 2019Machine behaviour1,359 cited
  • 2018Many Labs 2: Investigating Variation in Replicability Across Samples and Settings944 cited
  • 2023Out of One, Many: Using Language Models to Simulate Human Samples774 cited
  • 2023The ethics of ChatGPT – Exploring the ethical issues of an emerging technology474 cited
  • 2023Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review412 cited
  • 2023Can AI language models replace human participants?399 cited
  • 2010Research into Questionnaire Design: A Summary of the Literature314 cited
  • 2023The Janus Effect of Generative AI: Charting the Path for Responsible Conduct of Scholarly Activities in Information Systems

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292 cited
  • 2023Using LLMs for Market Research185 cited
  • Cited by

    • 2025Ideation with Generative AI—in Consumer Research and Beyond61 cited
    • 2025AI and the advent of the cyborg behavioral scientist20 cited
    • 2025Six Fallacies in Substituting Large Language Models for Human Participants19 cited