Human–AI Delegation in Social Science Codifiable Tasks vs. Tacit Knowledge, Theoretical Originality, and Field Judgment In an age where everyone is talking about AI, social science research finds itself at a delicate crossroads. Recently, Yongjun Zhang (2026) from Stony Brook University raised a provocative question in his latest paper, "Vibe Researching as Wolf Coming": what parts of your research can only you do, and what can the machines never replace? Is it running tediou

Generative AI democratization in consumer research: broader participation brings new voices into the data ecosystem. Today, a consumer researcher beginning a new project may start not with a database, but with a generative AI system. Within seconds, a structured framework emerges. Literature streams are synthesized, research questions are articulated, and potential hypotheses are outlined. The efficiency is undeniable. Yet Huang and Rust argue that efficiency is not the centr

How GenAI Transforms Qualitative Data Analysis Generative Artificial Intelligence (GenAI) is reshaping qualitative consumer culture research by introducing new efficiencies while raising epistemological tradeoffs across theoretical, embodied, empirical, and historical dimensions. In other words, when consumer culture researchers upload interview transcripts, fieldnotes, or archival materials into large language models (LLMs), what changes is not merely research efficiency. Ep

The rapid development of generative artificial intelligence is redefining how consumer and marketing researches are conducted. When a model can replicate the consumer research processes of 35 top-tier journal articles within minutes, are we still engaging in “human-led science”? A study by Yoo, Haenlein, and Hewett, published in the Journal of the Academy of Marketing Science, addresses precisely this question: Is AI merely transforming research tools, or is it reshaping rese

The contrast between human interpretive work and computational code reflects the tension between qualitative meaning-making and generative AI’s promise of efficiency Generative AI is increasingly used in qualitative research, yet its promise of efficiency raises fundamental questions about interpretation, meaning-making, and the role of the researcher. This article is based on the academic presentation "Interpreting Qualitative Data with AI: Pitfalls and Potential" by Duc Ngu

Economics in the Age of AI Agents Over the past two years, the way economists conduct research has undergone a profound structural transformation. In his 2025 NBER Working Paper AI Agents for Economic Research, Anton Korinek argues that the research community is moving from simple conversational systems such as ChatGPT to a new paradigm centered on autonomous AI agents. These systems integrate text generation with planning, memory, and tool use, enabling them to execute multi

AI creativity compared to human imagination: while large language models generate patterns based on training data, human creativity draws from unique experiences and emotions. Setting the Stage: AI and Creativity Large language models (LLMs) are advancing at a remarkable speed. From writing stories and poetry to brainstorming ideas, their presence in creative work is undeniable. But here lies a fundamental question: can these models genuinely support collective creativity, or

Generative AI in Social Science Generative Artificial Intelligence (Generative AI) is profoundly transforming the way social science research is conducted. It can write, code, translate, assist with literature searches, and even generate materials for experiments (Berg, 2023; Dabić et al., 2023; Lin, 2023; Lund & Wang, 2023; Porsdam Mann, Earp, Møller, et al., 2023). Yet, discussions about its ethical implications in academia largely remain at the “principles checklist” level

Artificial intelligence (AI) is transforming the landscape of academic research at an astonishing speed. From idea generation to data production, from writing to journal publishing, AI is infiltrating every link of the academic ecosystem (Van Quaquebeke et al., 2025). In this article published in The Leadership Quarterly, Van Quaquebeke, Tonidandel, and Banks point out that we are already in an era where AI deeply participates in the production of knowledge. The real question

Introduction Generative AI is profoundly reshaping consumer research, enhancing creative ideation, supporting qualitative analysis, and redefining the field’s trajectory. Three empirical studies and one review from the Journal of Consumer Research, Oxford Academic, offer complementary frameworks and practical guidance, revealing the vast potential and pitfalls of human–AI collaboration. Creative Ideation: The Double-Edged Sword of LLMs As a cornerstone of consumer research, c

As we bid farewell to 2024, we reflect on a remarkable year of innovation and achievement. Our journey began with the launch of several...