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.

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

Timeline

• March 31, 2027: Full paper submission deadline

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.

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.

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

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 (LLMs) are reshaping the discourse on how autonomous decision systems might operate in complex environments such as supply chains. The motivation for the special issue is to facilitate rigorous research in the transformative potential of agentic technology for Supply Chain Management (SCM). A key objective is to foster collaboration and unlock synergies by merging diverse perspectives from operations and supply chain management, AI, complexity science, and industrial engineering.

An AI agent is characterised as one that has a predefined or emergent goal and uses cognitive tools to achieve its objectives, such as situational awareness, data-driven learning and prediction, decision optimisation, negotiation, and problem-solving with human or other AI agents. Supply chain management scholars have long studied agent-based systems, especially multi-agent systems (MAS) since the 2000s. However, research has stalled as MAS were slow to develop, difficult to code, debug and scale, and were prone to severe coordination problems. Most supply chain agent research was not adopted in the industry.

Recent advances in LLMs have now brought in a new paradigm that might reverse this trend: LLM agents are widely expected to usher in a new era where specialist programming knowledge is no longer required. LLM agents utilize a large language model as their information processing unit for core reasoning, planning, and decision-making. They can also utilize bespoke tools and recall past actions and conversations to generate contextual awareness. Whereas in the past, specialist MAS agents required rigorously defined rules and frameworks to operate, LLM agents begin their operation having already learned from vast amounts of open data. A generic knowledge base enables LLM agents to adapt to complex environments in real-time. This new paradigm makes agents much more flexible and scalable. LLM agents can interact with humans in natural language, coordinate or compete with other LLM agents, use custom tools to perform tasks such as search and optimisation, query documents and databases, and search the web.

Large multi-national corporations like Walmart and Siemens are already experimenting with agentic LLMs to automate their supply chain tasks. Supply chain information system providers, such as SAP, Microsoft, and Google, are actively developing supply chain agent platforms and interoperability initiatives to enable cross-organizational supply chain automation.

Academic agentic supply chain research is in its infancy, with research fragmented across computer science, operations research, and manufacturing engineering. Early research points to its potential for overcoming supply chain inefficiencies through rapid access to data, improved planning, and cross-organisational negotiation, but also warns against agentic AI mirroring human bias, challenges in the verification of output and lack of precise language leading to wrong decisions. As this field continues to evolve, it is crucial for researchers to stay informed about the latest developments, raise awareness of potential challenges, and contribute to the growing body of knowledge on the application of agentic AI in supply chain management.

This Special Issue seeks to integrate multi-disciplinary research from various perspectives on shaping agentic automation in the supply chain. A variety of submissions and perspectives are welcome. Technical solutions, advanced modeling, mixed-methods, rigorous quantitative and qualitative empirical research, experimental and analytical methodologies with practical industry, managerial, and policy implications are welcome.

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

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

This special issue aims to explore the specific financial modelling issues related to Artificial Intelligence (AI), in particular Generative AI and Large Language models (LLMs) modelling and applications in finance. It seeks to offer new insights in consideration of Generative AI/LLM innovation that brings changes to the traditional finance topics such as asset pricing, financial intermediation, financial markets & investment, behavioural finance, banking, accounting, insurance, etc. The special issue particularly encourages work that advances the understanding of how Gen AI and LLM modelling brings forward ethical implications, benefits financial inclusion and/or enhances regulatory and policy changes.

The launch of ChatGPT in November of 2022 and its rapid adoption have demonstrated the promise of Generative AI/LLMs and brought a renewed interest in finance research on artificial intelligence applications. Generative AI has the potential to revolutionize various aspects of finance by enabling better data generation, analysis, decision-making, and risk management. One example of the potential impact is a recent paper on how FinBERT (a finance focused large language model) can improve several existing areas of information processing in finance. However, this is only the beginning, and it is anticipated that more research on modelling and applications using Generative AI is needed.

In the era of increased AI influence, ethical concerns often revolve around the use of big data, decentralized finance, artificial intelligence (AI), and how they affect customer privacy. At the same time, regulatory challenges are equally significant, with AI introducing new methods and risks that may not fit well within existing frameworks. For example, Generative AI can reduce costs associated with predictions and decisions but raises concerns about data privacy and the stifling of human-centred innovation. These challenges beg further research to bring better understanding.

Potential topics

• Asset pricing with Generative AI and LLMs
• Financial intermediation applications of Generative AI and LLMs
• Financial markets and investment strategies using Generative AI and LLMs
• Behavioural finance and AI applications
• Banking and AI applications
• Accounting and AI applications
• Insurance and AI applications
• Ethical implications of Generative AI and LLMs in finance
• Financial inclusion through Generative AI and LLMs
• Regulatory and policy changes driven by AI in finance
• Data generation and analysis using Generative AI
• Risk management with Generative AI and LLMs
• FinBERT and finance-focused language models
• Data privacy concerns in AI-driven finance
• Cost reduction through AI predictions while managing innovation

Timeline

• September 15, 2026: Manuscript deadline