This special issue examines the relationship between human reasoning processes and the reasoning capabilities of large language models, focusing on where their approaches align and where they diverge. The collection invites research exploring both the similarities and differences in how humans and language models understand, process, and generate information.

Appel publié par Information Processing & Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• December 31, 2026: Submission deadline

This special issue addresses the challenges of governing content and ensuring safety in generative AI systems, particularly as large language models become increasingly prevalent. The issue seeks research on establishing trustworthy frameworks and mechanisms for managing content while developing safer generative AI technologies.

Appel publié par Information Processing & Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• December 31, 2026: Submission deadline

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

Weimin Li, Shanghai University

Healthcare and education systems increasingly rely on complex, knowledge-intensive web platforms generating large-scale heterogeneous data. While Large Language Models excel at natural language understanding and human-computer interaction, directly applying general-purpose LLMs to these domains faces significant challenges including factual accuracy, lack of domain knowledge representation, and insufficient interpretability needed for sensitive applications.

Knowledge-enhanced LLMs address these limitations by incorporating structured knowledge, Web semantics, and multimodal representations into language models. This approach improves reliability, explainability, and controllability while enabling better integration with existing system components such as data retrieval and information management modules. The special issue seeks high-quality research on theoretical models, methods, frameworks, and applications of knowledge-enhanced LLMs specifically designed for healthcare and education web services.

Appel publié par International Journal of Web Information Systems. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Knowledge-enhanced LLM architectures for Web information systems
• Knowledge graphs, ontologies, and Web semantics for LLM reasoning and generation
• Retrieval-augmented generation for Web-based health and education services
• Web knowledge mining, information extraction, and knowledge integration for LLM-based systems
• Multimodal knowledge representation and fusion for health and learning applications on the Web
• Trustworthy, explainable, and privacy-preserving LLMs for sensitive Web environments
• LLM-enhanced medical information retrieval, health question answering, and decision support
• LLM-enhanced personalized learning, intelligent tutoring, automated assessment, and learning feedback
• Agentic LLMs and AI agents for Web-based health and education service management
• Benchmarks, datasets, evaluation metrics, and real-world applications of knowledge-enhanced LLMs in Web information systems

Timeline

• October 1, 2026: Closing date for abstract submissions
• November 1, 2026: Closing date for manuscript submissions

This special issue invites submissions exploring the applications, implications, and innovations of Large Language Models (LLMs) in the tourism industry and for enhancing tourist experiences. The rapidly evolving landscape of artificial intelligence presents unprecedented opportunities and challenges for tourism stakeholders, from destination management organizations to hospitality providers to individual travelers.

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

Potential topics

• Applications of LLMs in tourism management and operations
• LLMs for personalized tourist experiences and recommendations
• Natural language processing applications in tourism marketing
• LLMs for tourism chatbots and customer service
• Language translation and communication in tourism contexts
• LLMs for travel planning and itinerary generation
• Sentiment analysis and tourist feedback analysis using LLMs
• LLMs for tourism research and data analysis

Timeline

• August 31, 2026: Full paper submission deadline

This special issue focuses on the applications and implications of Large Language Models (LLMs) in the tourism sector. We invite research exploring how LLMs can enhance tourist experiences, improve tourism service delivery, and support both tourism businesses and individual travelers. The issue welcomes empirical studies, theoretical frameworks, case studies, and critical analyses of LLM technologies in tourism contexts.

Appel publié par Information Processing & Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Applications of LLMs in tourism industry
• LLMs for personalized tourist recommendations
• Natural language processing for tourism content
• Chatbots and virtual assistants in tourism
• Tourist information retrieval using LLMs
• Language translation for tourism
• Sentiment analysis of tourist reviews
• LLM-based travel planning and itinerary generation
• Multilingual tourism communication
• AI ethics in tourism applications

Timeline

• August 31, 2026: Submission deadline

This special issue seeks to explore how Large Language Models (LLMs) are transforming methodological approaches in technology management research. We invite submissions that demonstrate innovative applications of LLMs as research tools, methodological enhancers, and theoretical instruments for advancing our understanding of technology management, innovation, and organizational change.

Contributions should address both the opportunities and challenges of incorporating LLMs into research practice, including questions of validity, reliability, ethical considerations, and the development of new theoretical frameworks that account for LLM capabilities and limitations in research contexts.

Appel publié par Technological Forecasting and Social Change. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Applications of LLMs in technology management research
• LLMs for data analysis and pattern recognition in technology studies
• Novel methodological approaches enabled by LLMs
• Theoretical frameworks for understanding LLM capabilities and limitations in research contexts
• LLMs for literature review and synthesis
• LLM-assisted qualitative and quantitative research methods
• Validation and reliability of LLM-based research findings
• Ethical considerations in using LLMs for academic research
• LLMs for forecasting technological trends
• Integration of LLMs with traditional research methodologies

Timeline

• January 1, 2027: Submission deadline

Appel publié par Technovation. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• March 31, 2027: Full paper submission deadline

Steve Yang, Stevens Institute of Technology

Jing Chen, Cardiff University

Aparna Gupta, Rensselaer Polytechnic Institute

Zachary Feinstein, Stevens Institute of Technology

William Knottenbelt, Imperial College

Appel publié par The European Journal of Finance. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Asset pricing models using Generative AI and LLMs
• Financial intermediation applications of AI technologies
• Generative AI applications in financial markets and investment analysis
• Behavioral finance research incorporating LLM insights
• Banking sector applications of Generative AI
• Accounting and auditing with LLM technologies
• Insurance industry applications of AI and LLMs
• Ethical implications of Generative AI in finance
• Financial inclusion benefits through AI/LLM innovations
• Regulatory and policy changes driven by Generative AI
• Data generation and analysis using LLMs in finance
• Risk management with Generative AI and LLMs
• Natural language processing applications in finance
• Information processing improvements through FinBERT and similar models
• Data privacy concerns in AI-driven finance
• Regulatory challenges of Generative AI in financial systems

Timeline

• September 15, 2026: Manuscript deadline

Alexandra Brintrup, University of Cambridge

Thomas Choi, Arizona State University

George Huang, Hong Kong Polytechnic University

Dmitry Ivanov, Berlin School of Economics and Law

Recent advances in agentic Large Language Models have created new opportunities for autonomous decision-making in supply chains. This special issue seeks to advance rigorous research on how these AI agents can transform supply chain management by integrating perspectives from operations, AI, complexity science, and industrial engineering. While multi-agent systems have been studied for years, recent LLM breakthroughs now enable more flexible, scalable, and practical implementations that major corporations and technology providers are already exploring.

The special issue welcomes diverse research methodologies including technical solutions, modeling, empirical studies, and experimental work with practical implications. Topics span from using agentic systems for optimization and forecasting to managing risks, designing interorganizational coordination systems, and addressing technical and governance challenges such as trustworthiness, safety, and performance evaluation.

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

Potential topics

• Discourse on utilising agentic systems for complex scenarios in supply chain management: Risk and disruption management, logistics and supply chain optimisation, transportation routes, inventory management, quality control, demand forecasting, and warehouse planning and location, and cash flow predictions
• Supply network design with agentic technology: Supplier relationship configurations, agentic digital twins to simulate inventory flows, sustainability implications across the supply chain, circular supply chains, supply chain visibility, and supply chain financing
• Interorganisational agentic systems: Effective multi-agent negotiation and coordination, the design of mediative and persuasive agentic systems, preservation of organisational privacy during multi-agent communication
• Hybrid systems: Integration of agentic systems with blockchain, IoT, Omniverse, and traditional multi-agent systems
• Emergence and Complexity: Unintended consequences of agentic deployment at the system scale, governance, trustworthiness and safety, centralised versus decentralised control, human-in-the-loop agentic systems
• Technical challenges: Performance evaluation, efficient task division, ablation analysis and back testing, sensitivity analysis, agentic architectures operating in high uncertainty environments, long-term horizon reasoning, overcoming hallucinations

Timeline

• September 30, 2026: Manuscript deadline