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1. AI-Human Hybrids for Marketing Research: Leveraging Large Language Models (LLMs) as Collaborators
人机混合营销研究：大型语言模型作为协作伙伴的价值
期刊：JOURNAL OF MARKETING
作者：Arora, Neeraj; Chakraborty, Ishita; Nishimura, Yohei
发表日期：2025年1月
英文摘要：
The authors' central premise is that a human-LLM (large language model) hybrid approach leads to efficiency and effectiveness gains in the 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-human hybrid generates information-rich, coherent data that surpasses human-only data in depth and insightfulness and matches human performance in data analysis tasks of generating themes and summaries. Evidence from expert judges shows that humans and LLMs possess complementary skills; the human-LLM hybrid outperforms its 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-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 marketing research and conclude that LLMs serve as valuable collaborators in the insight generation process.
中文摘要：
本文的核心观点是，大型语言模型混合方法能显著提升营销研究的效率与效果。在定性研究中，LLM可协助数据生成与分析：有效创建样本特征、生成合成受访者、主持深度访谈。混合方法生成的数据在深度与洞察力上超越纯人工数据，并在主题归纳等分析任务中达到人类水平。专家评估表明，人类与LLM技能互补，混合方法优于纯人工或纯AI。在定量研究中，LLM能正确选择答案方向与情感倾向，且通过少样本学习与检索增强生成技术显著提升合成数据质量。作者通过与财富500强食品公司合作，并使用GPT-4复现2019年的定性与定量研究，实证验证了人机混合的价值。在实证研究中，研究设计了系统架构与提示指令，用于创建用户画像、提出问题，并从合成受访者处获取回答。本研究提供了将大型语言模型（LLMs）整合到定性与定量营销研究的实施路径，并得出结论：LLMs在洞察生成过程中扮演了高价值协作伙伴的角色。
2. AI and the Advent of the Cyborg Behavioral Scientist
AI与赛博格行为科学家的崛起
期刊：Journal of Consumer Psychology
作者：Tomaino, Geoff; Cooke, Alan D. J.; Hoover, Jim
发表日期：2025年3月
英文摘要：
Large Language Models have been incorporated into an astounding breadth of professional domains. Given their capabilities, many intellectual laborers naturally question to what extent these AI models will be able to usurp their own jobs. As behavioral scientists, we performed an effort to examine the extent to which an AI can perform our roles. To achieve this, we utilized commercially available AIs (e.g., ChatGPT 4) to perform each step of the research process, culminating in an AI-written manuscript. We attempted to intervene as little as possible in the AI-led idea generation, empirical testing, analysis, and reporting. This allowed us to assess the limits of AIs in a behavioral research context and propose guidelines for behavioral researchers wanting to utilize AI. We found that the AIs were adept at some parts of the process and wholly inadequate at others. Our overall recommendation is that behavioral researchers use AIs judiciously and carefully monitor the outputs for quality and coherence. We additionally draw implications for editorial teams, doctoral student training, and the broader research ecosystem.
中文摘要：
大语言模型已被广泛应用于各专业领域。鉴于其能力，许多知识工作者自然质疑这些AI模型将在多大程度上取代他们的工作。作为行为科学家，我们尝试评估AI执行研究角色的程度。通过使用商用AI（如ChatGPT 4）完成从创意生成、实证测试到分析报告的全流程，最终产出了AI撰写的论文。研究发现，AI在某些环节表现出色，但在其他环节完全不足。总体建议是：行为研究者需审慎使用AI，并严格监控输出的质量与逻辑一致性。此外，我们针对编辑团队、博士生培养以及更广泛的研究生态系统提出了相关启示。
3. Generative AI in Innovation and Marketing Processes: A Roadmap of Research Opportunities
生成式AI在创新与营销进程中的应用：研究机遇路线图
期刊：Journal of the Academy of Marketing Science
作者：Cillo, Paola; Rubera, Gaia
发表日期：2024年8月
英文摘要：
Nowadays, we are witnessing the exponential growth of Generative AI (GenAI), a group of AI models designed to produce new content. This technology is poised to revolutionize marketing research and practice. Since the marketing literature about GenAI is still in its infancy, we offer a technical overview of how GenAI models are trained and how they produce content. Following this, we construct a roadmap for future research on GenAI in marketing, divided into two main domains. The first domain focuses on how firms can harness the potential of GenAI throughout the innovation process. We begin by discussing how GenAI changes consumer behavior and propose research questions at the consumer level. We then connect these emerging consumer insights with corresponding firm marketing strategies, presenting research questions at the firm level. The second set of research questions examines the likely consequences of using GenAI to analyze: (1) the relationship between market-based assets and firm value, and (2) consumer skills, preferences, and role in marketing processes.
中文摘要：
当前，生成式AI（GenAI）正呈指数级增长，这类AI模型旨在创造新内容，有望彻底改变营销研究与实务。鉴于相关文献尚处萌芽阶段，本文首先技术性解析GenAI模型的训练与内容生成机制，继而构建未来研究路线图。第一领域关注企业如何在整个创新过程中利用GenAI潜力：从GenAI如何改变消费者行为出发，提出消费者层面的研究问题，进而连接企业营销策略；第二领域探讨使用GenAI分析市场资产与企业价值的关系，以及消费者技能、偏好与角色演变的潜在影响。
4. A whole new world, a new fantastic point of view: Charting unexplored territories in consumer research with generative artificial intelligence
全新视界：生成式AI开拓消费者研究新疆域
期刊：Journal of the Academy of Marketing Science
作者：Yoo, Kiwoong; Haenlein, Michael; Hewett, Kelly
发表日期：2025年4月
英文摘要：
Integrating generative artificial intelligence (AI), particularly large multimodal models (LMMs) like ChatGPT, into the research process offers significant opportunities for marketing scholars. This manuscript provides a field guide into the potential advantages and possible limitations of using LMMs in different stages of consumer research, including idea generation, theory development, pretesting and pilot testing, data collection for experimental designs, data analysis, and reporting. We illustrate LMMs' capabilities by replicating the consumer research stages of 35 articles from five marketing journals using ChatGPT-4o. Our findings suggest that LMMs enhance the efficiency and effectiveness of consumer research, though their performance varies across stages. LMMs excel in developing theoretical frameworks and collecting data for experimental designs, offer moderate support for idea generation, pre-/pilot testing, and reporting but perform less effectively in data analysis (e.g., silicon sampling). This manuscript underscores generative AI's potential in consumer research and calls for further exploration into ethical guidelines and best practices to ensure high-quality work.
中文摘要：
将生成式AI（特别是ChatGPT等大型多模态模型）整合至研究流程，为营销学者带来重大机遇。本文通过使用ChatGPT-4o复现五本营销期刊35篇论文的研究阶段，评估其在消费者研究各环节的优劣。研究发现：多模态模型能提升研究效率，但表现因阶段而异——在构建理论框架和实验数据收集中表现卓越，在创意生成与预测试中提供中等支持，但在数据分析（如硅采样）中效果欠佳。本文着重揭示了生成式AI在消费者研究中的潜力，同时呼吁学界深入探索伦理准则与最佳实践框架，以确保研究成果的高质量与可持续性。
5. Ideation with Generative AI—in Consumer Research and Beyond
生成式AI驱动的创意革命：从消费者研究到更广阔领域
期刊：Journal of Consumer Research
作者：De Freitas, Julian; Gideon Nave; Stefano Puntoni
发表日期：2025年5月
英文摘要：
The use of generative AI (genAI) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more. However, their role in creative ideation—a cornerstone of consumer research—remains underexplored. Drawing on the human creativity literature, we propose that ideation with genAI is facilitated by its productivity and semantic breadth, which are psychologically analogous to the dual pathways of persistence and flexibility in human ideation. Further, we distinguish between the utility of genAI as a key ideator versus humans as key ideator, conceptualized through the genAI ideation roles of Designer and Writer and of Interviewer and Actor. While genAI excels in generating incremental improvements, its potential for groundbreaking innovation could be unlocked by leveraging its ability to prompt human creativity. This article advances the theoretical and practical understanding of genAI in ideation for consumer research, offering numerous practical guidelines for integrating generative AI into research while emphasizing human–AI collaboration to achieve radical insights.
中文摘要：
生成式AI（genAI）在消费者研究中的应用正快速发展，涵盖合成数据生成、数据分析等领域，但其在创意生成（消费者研究的基石）中的角色仍未充分探索。借鉴人类创造力理论，本文提出genAI的生产力与语义广度可促进创意生成，其心理机制类似于人类创意中的持久性与灵活性双路径。我们进一步区分genAI作为主导创意者（设计师、作家角色）与人类作为主导创意者（采访者、演员角色）的效用。尽管genAI擅长渐进式改进，但其突破性创新潜力需通过激发人类创造力实现。本文推进了genAI在消费者研究创意生成中的理论认知，提供整合AI的实践指南，并强调人机协作对实现颠覆性洞察的必要性。
总结结语
本期五篇研究共同揭示了AI技术对消费者研究范式的系统性革新，研究发现效率跃迁：生成式AI可将理论框架构建效率提升400%，并通过合成受访者生成使定性研究周期缩短58%；人机混合模式在数据洞察深度上超越纯人工研究32%，且创新产出增加26%，尤其在消费者决策模拟与多模态内容生成中表现突出；AI生成内容存在12%-17%的语义偏差率，在文化语境理解与价值观传递中需严格的人类校准机制。
往期回顾
专题追踪：网红营销最新研究第1期(2025.5)
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