在消费者和营销研究中使用大型语言模型生成硅样本:挑战、机遇和指导原则

Marko Sarstedt, Susanne J. Adler, Lea Rau, Bernd Schmitt
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引用次数: 0

摘要

消费者研究人员是否应该使用硅样本和基于大型语言模型(如 GPT)的人工生成数据来模仿人类受访者的行为?在本文中,我们回顾了比较硅样本和人类样本结果模式的最新研究,发现不同领域的结果差异很大。基于这些结果,我们提出了在消费者和营销研究中使用硅样本的具体建议。我们认为,硅样本在研究过程的上游环节(如定性预试和试点研究)中大有可为,在这些环节中,研究人员可以收集外部信息,为后续设计选择提供保障。我们还为在主要研究中使用硅样本提供了批判性评估和建议。最后,我们讨论了使用硅样本的伦理问题,并提出了未来的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines
Should consumer researchers employ silicon samples and artificially generated data based on large language models, such as GPT, to mimic human respondents' behavior? In this paper, we review recent research that has compared result patterns from silicon and human samples, finding that results vary considerably across different domains. Based on these results, we present specific recommendations for silicon sample use in consumer and marketing research. We argue that silicon samples hold particular promise in upstream parts of the research process such as qualitative pretesting and pilot studies, where researchers collect external information to safeguard follow-up design choices. We also provide a critical assessment and recommendations for using silicon samples in main studies. Finally, we discuss ethical issues of silicon sample use and present future research avenues.
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