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Information Processing & Management (IPM)

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インパクトファクター:
6.9
出版社:
Elsevier
ISSN:
0306-4573
閲覧:
61453
フォロー:
91

論文募集

Information Processing & Management (IPM) is an academic journal published by Elsevier. (ISSN 0306-4573, impact factor 6.9, CCF B).

This journal is ranked by The Chartered Association of Business Schools' Academic Journal Guide, Australian Business Deans Council, Chinese Academy of Sciences (CAS), China Computer Federation (CCF), BFI (Denmark), Computing Research & Education (CORE) Journal Ranking, The Publication Forum (Finland), Science Citation Index Expanded, Social Sciences Citation Index, Scopus, and SCImago Journal Rank (SJR). Information Processing and Management publishes cutting-edge original research at the intersection of computing and information science concerning theory, methods, or applications in a range of domains, including but not limited to advertising, business, health, information science, information technology marketing, and social computing. The journal aims to serve the interests of primary researchers but also practitioners in furthering knowledge at the intersection of computing and information science by providing an effective forum for the timely dissemination of advanced and topical issues. The journal is especially interested in original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Specifically, the journal is interested in four types of manuscripts, which are: Research manuscripts addressing topics at the intersection of computer and information science. Methods manuscripts focusing on the application of novel methods at the intersection of computer and information science. Review manuscripts assessing, in a critical and in-depth manner, a broad trend at the intersection of computer and information science, providing integration of the prior research, and recommendations for further work in the area. Critical application manuscripts concerning system design research at the intersection of computer and information science.
最終更新：Dou Sun（）

Special Issues

Special Issue on Large Language Models (LLMs) for Tourism and Tourists 投稿締切日: 2026-08-31 Special Note: This is a joint Article Collection of Annals of Tourism Research and Information Processing & Management. Both journals have an interest in how LLMs can be used in the tourism context, assisting tourists, tourism businesses, and tourism destinations. Articles will be published in one of the journals. The Article Collection (entire set) will be posted on the websites of both journals, increasing exposure of the research for the authors. Guest editors: Yang Yang, Associate Editor, Annals of Tourism Research Sara Dolnicar, Editor-in-Chief, Annals of Tourism Research Jim Jansen, Editor-in-Chief, Information Processing & Management Special issue information: Large Language Models (LLMs) and Generative AI (GenAI) are changing how tourists are inspired to travel, how they search for tourism-related information, where they book travel-related services, and how they plan their itineraries. LLMs are changing how tourism businesses assess guest satisfaction, how they determine their brand image, and how they can save money by delegating routine operational tasks to LLMs. LLMs are also changing how tourism destinations monitor consumer sentiment and how they develop marketing campaigns. As LLMs inevitably become more powerful and refined, the number of applications in tourism will skyrocket. Annals of Tourism Research and Information Processing & Management have already published several articles investigating the use of LLMs in tourism or leveraging the power of LLMs as a tool for tourism research, including.: Mikulić, J. (2025). Large language models in tourism research: Support, substitution, or something else?. Annals of Tourism Research, 114, 104003. Ali, F. (2025). Rethinking synthetic data in tourism research: Ethical risks, epistemic shifts, and the RSDU-T framework. Annals of Tourism Research, 114, 104009. Ramrakhiyani, N., Varma, V., Palshikar, G. K., & Pawar, S. (2025). Gauging, enriching and applying geography knowledge in Pre-trained Language Models. Information Processing & Management, 62(1), 103892. Shrestha, R. K., Whitford, M., & Zhang, J. (2025). Tourism and social representations of Sherpas from Nepal. Annals of Tourism Research, 113, 103981. Hsu, C. H., Tan, G., & Stantic, B. (2024). A fine-tuned tourism-specific generative AI concept. Annals of Tourism Research, 104, 103723. Fan, Z., & Chen, C. (2024). CuPe-KG: Cultural perspective–based knowledge graph construction of tourism resources via pretrained language models. Information Processing & Management, 61(3), 103646. This joint article collection builds on this work and aims to identify areas where LLMs can be ethically deployed to assist tourists, tourism businesses, and tourist destinations. Additionally, the joint collection aims to develop purpose-built LLMs for specific challenges. The collection is also open to articles that discuss the implications of increased LLM adoption in the tourism sector. The strength of this joint Article Collection is that expert reviewers from both information technology and tourism are available to assess contributions, thus speeding up focused knowledge development and ensuring that the bodies of literature in tourism and information technology do not develop in parallel, but rather benefit from cross-fertilisation. Possible topics of submissions: Generative AI for Smart Tourism Experiences: Exploring how LLMs can personalize itineraries, provide real-time guidance, and adapt recommendations to tourists’ preferences Conversational Agents in Hospitality: The role of LLM-powered chatbots in hotels, airlines, and travel agencies for customer service and engagement Multilingualism and Accessibility in Tourism: Leveraging LLMs for translation, cross-cultural communication, and inclusion of diverse traveler groups LLMs and Destination Marketing: How generative AI can create compelling narratives, promotional content, and social media engagement for destinations Ethics, Trust, and Transparency in AI-driven Tourism: Addressing concerns of bias, misinformation, and trustworthiness when deploying LLMs in travel contexts Tourism Education and Training with LLMs: Using AI tutors and assistants to support tourism and hospitality management students and professionals Data-Driven Insights from Tourist Interactions: Using LLMs to analyze feedback, reviews, and online conversations for destination management and policy making Cultural Heritage and Storytelling through LLMs: Reimagining museum guides, historical tours, and immersive cultural experiences using generative AI Sustainability and Responsible Tourism with LLMs: AI-enabled nudges and dialogue systems to promote eco-friendly travel behaviors and sustainable tourism development Future of Work in Tourism with Generative AI: How LLMs reshape roles of travel agents, tour guides, and hospitality staff, balancing automation with human expertise Manuscript submission information: Important dates: Submissions Open: 10 September 2025 Submissions Close: 31 August 2026 Submission Guidelines for Authors: Select one of the journals to submit your article For Information Processing & Management: Submit your manuscript by selecting the article type ‘VSI: LLMTourism’ through the online submission system of Information Processing & Management. All the submissions should follow the general author guidelines of Information Processing & Management. For Annals of Tourism Research: Submit your manuscript by selecting the article type ‘VSI: LLMTourism’ through the online submission system of Annals of Tourism Research. All the submissions should follow the general author guidelines of Annals of Tourism Research. Keywords: LLM, Large Language Models, AI, ChatGPT https://www.sciencedirect.com/special-issue/326025/joint-special-issue-call-for-papers-large-language-models-llms-for-tourism-and-tourists
最終更新：Dou Sun（）
Special Issue on Explainable AI and Network Science for Social Systems and Collective Intelligence 投稿締切日: 2027-03-31 Guest editors: Tao Wen, Research Fellow, Alliance Manchester Business School, The University of Manchester, Manchester, UK Email: [email protected] Xinyi Zhou, Assistant Professor, Department of Computer Science, Boise State University, Idaho, USA Email: [email protected] Richard Allmendinger, Professor, Alliance Manchester Business School, The University of Manchester, Manchester, UK Email: [email protected] Kang Hao Cheong, Associate Professor, School of Physical & Mathematical Sciences, Nanyang Technological University, Singapore, Singapore Email: [email protected] Special issue information: As humans increasingly communicate in real time on digital platforms, networked online social systems are reshaping how information spreads, opinions interact, communities form, and collective decisions emerge. Yet research on these processes in advanced network models, such as multilayer networks coupled across multiple social platforms and higher-order networks that capture group interactions and complex communication patterns, remains limited. At the same time, recent advances in artificial intelligence (AI) have provided powerful tools for modelling and analyzing behavior on these platforms. However, many AI-based models still operate as “black boxes”, making it difficult to explain or justify their outputs. This lack of transparency is critical in areas such as public opinion management, understanding the emergence of collective behaviors, and analyzing social influence. With generative AI, recommender engines, and autonomous agents now being deployed at scale, it is urgent to understand how AI technologies interact with network structure and dynamics, and how their interactions influence collective intelligence (CI) and decision-making. This special issue aims to bring together explainable AI and network science to advance the study of social networks, information cascades, and the emergence of CI. We invite research that integrates AI, machine learning, and data-driven methods with rigorous network modelling for networked social systems, including multilayer and higher-order networks. Topics of interest include the use of explainable AI (XAI), graph neural networks (GNNs), large language models (LLMs), causal inference, and optimization algorithms to study information propagation, influence maximization, key user identification, recommender systems, and information source localization. We also welcome studies that apply responsible AI principles to explain and model human collective decision-making and CI, including consensus formation, polarization, cooperation, coordination, and innovation diffusion. Methodologies may include opinion dynamics, game theory, graph learning, reinforcement learning, multi-agent systems, and large-scale data mining. This special issue offers a dedicated venue for interdisciplinary research on information flow, user behavior, and CI in social systems. By combining modern AI methods with strong foundations in network science, it seeks to advance both fundamental understanding and the development of trustworthy and practical solutions for real-world complex social systems. Possible Topics of Submissions: This special issue is interested in, but not limited to, the following topics: Influence and leadership identification in dynamic networks: Identify task-specific influential users over time in dynamic and multiplex networks through explainable methods. Higher-order interactions and collective behavior beyond pairwise edges: How hypergraphs and simplicial complexes affect contagion, cooperation, consensus, and collective decision-making. Reputation assessment and trust formation in human-AI systems: How to assess AI agent reputation and build trust for informed human-AI decisions in noisy, biased, or adversarial settings. AI-driven collective decision-making under information disorder: How groups can make informed decisions under misinformation, manipulation, and LLM-generated or synthetic content. Human-AI collective intelligence in online communities: How collective performance is enhanced or reduced when LLMs participate as autonomous agents on platforms and communities. Knowledge graphs for explainable collective intelligence: Use knowledge graphs to represent and explain collective intelligence by integrating agents, content, contexts, and causal pathways. Graph learning for collective behavior prediction: Forecast diffusion, coordination, and group decision dynamics under noise and uncertainty by graph learning (e.g. temporal and higher-order GNNs). LLM-based social simulation for behavior prediction: Study how LLM-driven agents interact in social systems to explain and predict diffusion, coordination, and collective behaviors. Early-warning signals and risk forecasting in complex social systems: Detect early-warning signals and predict cascading risks in complex social networks using AI and network science. Fairness, inequality, and polarization in AI-mediated social systems: How AI agents redistribute exposure, attention, and power, potentially amplifying inequality and polarization. Collective intelligence mechanism for complex systems: Explore incentives, rules, and governance mechanisms that enable reliable collective intelligence under uncertainty and heterogeneity. Causal inference for platform interventions and policy evaluation: Estimate the causal effects of platform and policy interventions in the presence of feedback loops and partial observability. Manuscript submission information: Submit your manuscript to the Special Issue category (VSI: AINet) through the online submission system of Information Processing & Management. All the submissions should follow the general author guidelines of Information Processing & Management. Keywords: Explainable AI, LLM social simulation, Network science, Complex systems, Decision-making, Collective intelligence https://www.sciencedirect.com/special-issue/332020/explainable-ai-and-network-science-for-social-systems-and-collective-intelligence
最終更新：Dou Sun（）

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