Journal of Advertising Research

Machines as Method: The Use of Artificial Intelligence in Advertising Research

Overview

Advertising scholarship has moved quickly to study artificial intelligence as a phenomenon: how consumers respond to AI-generated ads, synthetic endorsers, and algorithmically personalized messages. Far less attention has been paid to a shift that may prove just as consequential: AI is becoming an instrument of advertising research itself. Large language models (LLMs) now sit inside the research process as respondents, moderators, coders, analysts, and predictors. This special issue treats AI not as the object of study but as the method, and asks what advertising researchers, in academia and in industry alike, can and cannot learn through it.

Practice is well ahead of scholarship here. Synthetic respondent platforms (e.g., Simile, Aaru, Evidenza, Synthetic Users, Ditto, Vurvey) have attracted more than a billion dollars in venture funding and count major brands among their clients, while established players such as Qualtrics and NIQ have added synthetic panels to their offerings. AI-moderated interviewing platforms (e.g., Outset, Listen Labs, Strella, Conveo, Voicepanel) are conducting thousands of qualitative consumer interviews at a scale no human research team could match. Predictive creative testing tools (e.g., Zappi, Neurons, Memorable, Dragonfly AI, Kantar Link AI) score attention, emotion, and effectiveness before a single consumer sees an ad. Practitioners are using AI for designing instruments, synthesizing and summarizing qualitative transcripts, analyzing data and identifying patterns, segmenting audiences, and writing the end reports. But there are growing concerns about biases associated with AI skewing outputs, misrepresenting trends, and failing to accurately simulate consumer responses. These tools are being adopted on the strength of vendor validation claims that have rarely been examined independently. The result is a widening gap between what the insights industry does and what published research can say about whether it works.

The field has faced a version of this moment before. When copy testing became central to advertising decision-making, researchers and practitioners together confronted questions of validity, standards, and evidence. The rise of AI-based research methods reopens those questions in a sharper form. The criterion problem returns first: if synthetic respondents are validated only against human panels, and human panels are themselves imperfect predictors of in-market performance, what should count as the benchmark? A second problem is distinctively advertising’s own. LLMs produce fluent, deliberative verbal output, yet a substantial share of advertising effect operates through low-attention processing, implicit memory, and affective response that consumers cannot readily verbalize. Synthetic respondents may therefore simulate what people say about ads rather than what ads do to people, a concern we term the articulate respondent problem. Further, AI-based ad assessment suffers from a holistic processing bias; because computer vision approaches can see everything, it is difficult for assessments to mimic human attention and processing of ads. Whether, when, and for which advertising questions machine-based methods yield trustworthy answers is, at present, largely unknown.

This special issue seeks rigorous empirical, methodological, and conceptual work that closes this gap. We welcome quantitative and qualitative approaches, benchmarking and validation studies, and work conducted jointly by academics and practitioners. Independent evaluations of commercial tools are explicitly encouraged.

Topics of Interest

Submissions may address, but are not limited to, the following areas.

Does It Predict? The Validity of Synthetic Ad Testing

When can LLM-based synthetic respondents stand in for human participants in advertising research, and when do they fail?

• How well do synthetic panels reproduce human results on core advertising outcomes (e.g., attention, recall, attitude toward the ad, purchase intent), and under what boundary conditions?
• Can synthetic respondents replicate established advertising effects (e.g., mere exposure, source credibility, fear appeals), and what does failure to replicate reveal?
• What is the appropriate validation criterion: human panel agreement, or in-market outcomes such as sales and brand lift?
• How much variance compression, homogenization, or demographic distortion do synthetic samples introduce, and how should uncertainty be quantified and reported?
• Given that models are trained on the advertising literature itself, when does an apparent confirmation of theory reflect evidence rather than regurgitation?
• Which advertising responses (e.g., deliberative judgments, verbal attitudes) can LLMs plausibly simulate, and which (e.g., implicit memory, affective response, low-attention processing) remain out of reach?
• How do synthetic responses compare with human data across high- and low-involvement conditions, or across System 1 and System 2 dominant tasks?
• Do synthetic respondents exhibit persuasion knowledge, skepticism, or ad avoidance in ways that mirror or distort human patterns?
• What theoretical frameworks best explain where machine simulation of consumer response breaks down?

Machines That Listen: AI-Moderated Qualitative Research

What happens to qualitative advertising research when the moderator, the coder, or both are machines?

• How does AI moderation compare with skilled human moderation in probing depth, laddering, and the elicitation of meaning?
• Do consumers disclose differently to AI interviewers, particularly for sensitive or socially undesirable topics relevant to advertisers?
• How reliable are LLMs as qualitative analysts relative to human coders, and what is lost or gained in machine-led thematic analysis?
• Does qualitative research at machine scale change what qualitative inquiry is for, or merely how much of it can be done?

AI as Measurement Instrument

How trustworthy are machines as coders and predictors of advertising content and response?

• How valid are AI-predicted attention, emotion, and memorability scores relative to eye tracking, facial coding, and other biometric ground truths, and where do the predictions break down?
• Can LLMs and multimodal models reliably code advertising content at scale (e.g., creativity, emotional tone, brand prominence, message strategy), and how should such measures be validated?
• What can computational reanalysis of large advertising archives (e.g., tracking studies, open-ended verbatims, effectiveness case libraries) reveal that original analyses could not?

Stress Tests and Stand-Ins: Studying What Could Not Be Studied

Can AI extend advertising research into territory that was previously impractical, or impermissible, to study?

• Can adversarial AI populations red-team creative before launch, surfacing misinterpretation, offense, and unintended meanings across segments?
• Under what conditions, if any, are synthetic stand-ins defensible for audiences that are restricted or difficult to research directly (e.g., children, patients, regulated categories)?
• How should the field confront the fidelity paradox: synthetic methods are most attractive precisely where human ground truth is least available?

The Changing Research Pipeline

How is AI reshaping the practice, economics, and integrity of advertising research?

• When creative variants can be generated and scored predictively at scale, what becomes of the pretest as a discrete stage, and of the research function itself?
• How accurate are commercial AI research tools when evaluated independently, and what evaluation frameworks should the field adopt?
• How prevalent is AI contamination of human data (e.g., bots, LLM-assisted respondents), how can it be detected, and what does it mean for the panel infrastructure advertising research depends on?
• What disclosure and reporting standards should journals, firms, and industry bodies require when AI participates in the research process?

References

Brand, J., Israeli, A., & Ngwe, D. (2023). Using LLMs for market research. Harvard Business School Working Paper.

Humphreys, A., & Wang, R. J.-H. (2018). Automated text analysis for consumer research. Journal of Consumer Research, 44(6), 1274–1306.

Korst, J., Puntoni, S., & Toubia, O. (2025, May–June). How gen AI is transforming market research. Harvard Business Review, 103(3).

Sarstedt, M., Adler, S. J., Rau, L., & Schmitt, B. (2024). Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines. Psychology & Marketing, 41(6), 1254–1270.