Jameson Hayes, University of South Carolina

Edward Malthouse, Northwestern University

Advertising researchers are increasingly using artificial intelligence as a research tool—large language models now serve as survey respondents, moderators, analysts, and predictors. This shift is outpacing academic attention; while practitioners have deployed synthetic respondent platforms and AI-moderated research at scale, concerns about bias, validity, and accuracy remain largely unexamined. This special issue seeks rigorous work evaluating whether and when AI-based methods produce trustworthy advertising research.

The special issue addresses several pressing concerns: synthetic respondents may simulate what people say about ads rather than what ads actually do to them, given that much advertising effect operates through low-attention and implicit processes. The field lacks clear standards for validating these tools, and the gap between commercial deployment and published research is widening. Both quantitative and qualitative approaches, benchmarking studies, and independent evaluations of commercial tools are welcomed.

Appel publié par Journal of Advertising Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• 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), and 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?

Timeline

• July 1, 2027: Submission window opens
• August 1, 2027: Manuscript deadline

This special issue focuses on intercultural relations in contemporary digital environments and unequal global contexts, with particular emphasis on the Global South and non-WEIRD (Western, Educated, Industrialized, Rich, Democratic) populations. The call seeks to expand scholarly understanding by centering research from and about regions often underrepresented in intercultural communication literature.

Appel publié par International Journal of Intercultural Relations. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• April 30, 2027: Submission deadline

Karl Wennberg, Stockholm School of Economics

Jeremy C. Short, University of North Texas

Jeffrey A. Chandler, University of North Texas

Marcus Wolfe, University of North Texas

Sebastian Kruse, RWTH University

Carolin Krieweth, RWTH University

Entrepreneurship research lacks a strong culture of replication despite its importance for building cumulative theory and validating findings. While many influential studies shape theoretical debates and practice, they have rarely been systematically retested to verify their robustness, stability over time, or applicability across different contexts. This special issue seeks to address this gap by encouraging rigorous replication studies of foundational empirical work in entrepreneurship.

The issue welcomes diverse methodological approaches to replication, from exact replications to reanalyses of original data and studies exploring boundary conditions and contextual factors. Rather than merely confirming prior findings, submissions should advance theoretical understanding by clarifying mechanisms, identifying when relationships hold or vary, and contributing to methodological best practices. All work will be evaluated on theoretical contribution and methodological rigor within the journal's standard review process.

Appel publié par Entrepreneurship Theory and Practice. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• The 33 most cited entrepreneurship studies (each exceeding 1,000 citations) identified as priority candidates for replication
• Influential studies within specific BCERC research tracks highlighted as important contributions
• Studies with credible cases for broader influence on entrepreneurship research
• Identifying boundary conditions and contextual contingencies of established relationships
• Clarifying inconsistencies or mixed findings in prior research
• Refining theoretical mechanisms underlying key constructs and relationships
• Contributing to methodological transparency, rigor, and best practices in replication research
• Multi-study designs combining exact replication or reanalysis with empirical generalization or theoretical extension

Timeline

• December 1, 2026: Deadline for short proposals (3,000–5,000 words) for paper development workshop participation
• January 1, 2027: Notifications regarding paper development workshop participation
• February 1, 2027: In-person paper development workshop at University of North Texas
• March 1, 2027: Virtual paper development workshop
• May 1, 2027: Manuscript submission window opens
• August 1, 2027: Manuscript submission deadline

The editors of Economy & Space invite submissions to the journal's Exchanges section that make distinctive and original interventions concerned with method, methodology, and the construction of explanations in economic geography (broadly defined) from a variety of perspectives and vantage points. Relatively short, focused, and 'methods positive' interventions are especially welcome, preferably in the region of 1,500-2,500 words in length.

Appel publié par Environment and Planning A Economy and Space. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Method and methodology in economic geography
• Construction of explanations in economic geography
• Innovative approaches to economic geography research
• Perspectives and vantage points on economic geography

This special issue invites submissions that explore innovative approaches to qualitative research in supply chain management. The initiative aims to advance methodological practices and contribute to novel ways of conducting qualitative investigations in the SCM field.

Appel publié par Journal of Supply Chain Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• September 30, 2026: Deadline for submissions

This call invites scholars to submit registered reports to AMD, a submission format for completed or planned studies where authors present their introduction, methods, measurement information, and analysis plan without results. The evaluation focuses on the significance of the research question, the validity of the analytical approach, methodological quality, and potential contributions to theory and practice.

By separating the evaluation from outcomes, registered reports align with the journal's commitment to publishing studies with null or negligible findings, ensuring that results do not influence editorial decisions.

Appel publié par Academy of Management Discoveries. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Studies with important theoretical or practical implications regardless of results
• Research with rigorous methodological design
• Studies with potential impact on future theorizing and practice/policy

Timeline

• January 1, 2025: Submission window opens
• December 31, 2026: Submission window closes