Journal Information
Information Processing & Management (IPM)
http://www.journals.elsevier.com/information-processing-and-management/
Impact Factor:
7.466
Publisher:
Elsevier
ISSN:
0306-4573
Viewed:
26367
Tracked:
76
Call For Papers
Information Processing & Management is devoted to refereed reporting of:

1. Basic and applied research in information science, computer science, cognitive science and related areas that deals with: the generation, representation, organization, storage, retrieval, and use of information; the nature, manifestations, behavior, and effects of information and knowledge; communication and distribution of information and knowledge; and human information behavior.

2. Experimental and advanced processes, related to: information retrieval (IR); digital libraries; knowledge organization and distribution; digitized contents - text, image, sound and multimedia processing; and human-computer interfaces in information systems. Implementations in information retrieval systems and a variety of information systems, networks, and contexts. Related evaluation.

3. Management of information resources, services, systems and networks, and digital libraries. Related studies of the economics of information and the principles of information management.

The aim is to provide an international forum for advanced works and critical analysis in these interdependent and interdisciplinary areas. Invited are original papers and critical reviews of trends reporting on:
• Progress in the theory, principles, and procedures in information processing, particularly involving information retrieval; search engines; knowledge and distributed intelligence; information representation, classification, extraction, filtering and summarization; question answering; information navigation, browsing and visualization; and human-computer interaction in information systems.
• Research on the formal characteristics and properties of information and knowledge and the associated processes of communication among humans and between humans and machines. Includes studies of human information needs, seeking, searching, and use; and bibliometric and infometric studies of the structural and statistical properties of information artifacts.
• Modeling and evaluation of information systems performance, particularly of information retrieval systems, knowledge systems, and digital libraries. Studies of their effectiveness, efficiency, value, or impact.
• Studies in management and economics of information and information systems. Use of information for decision making and problem solving.
• Studies in information policies. Data and issues relevant to information policies on organizational, national, and international levels. Derivation and use of information indicators.
Last updated by Dou Sun in 2022-11-20
Special Issues
Special Issue on From Digital Technologies to Digital Entrepreneurship
Submission Date: 2023-02-28

This special issue calls on scholars in the field of entrepreneurship to take this opportunity to promote the theoretical development of digital entrepreneurship, and deepen the theoretical and empirical research of digital entrepreneurship by adopting various methods, themes, levels, and modes. ------------ Guest editors: ------------ Lead Guest EditorProf. Sang-Bing TsaiSchool of Business, Wuyi University, Fujian, ChinaEmail: sangbing@hotmail.com ------------ Prof. Xusen ChengSchool of Information, Renmin University of China, Beijing, ChinaEmail: xusen.cheng@ruc.edu.cn ------------ Prof. Yanwu YangSchool of Management, Huazhong University of Science and Technology, Wuhan, ChinaEmail: yangyanwu.isec@gmail.com ------------ Prof. Jason XiongWalker College of Business, Appalachian State University, Boone, USAEmail: xiongjj@appstate.edu ------------ Dr. Alex ZarifisUniversity of Cambridge, Cambridge, UK; University of Nicosia, Nicosia, CyprusEmail: alex.e.zarifis@gmail.com ------------
Last updated by Dou Sun in 2022-11-20
Special Issue on Causal Inference in Information Science
Submission Date: 2023-05-31

The objective of setting up this special issue is to bridge the Information Science community with causal inference communities and to showcase how future information scientists establish their research in a causal inference way instead of purely at a correlation level. ------------ Guest editors: ------------ • Yi Bu (buyi@pku.edu.cn), Department of Information Management, Peking University, China. ------------ • Meijun Liu (meijunliu@fudan.edu.cn), Institute for Global Public Policy, Fudan University, China. ------------ • Zaida Chinchilla-Rodríguez (zaida.chinchilla@csic.es), Consejo Superior de Investigaciones Científicas (CSIC), Instituto de Políticas y Bienes Públicos (IPP), Spain. ------------ • Ying Ding (ying.ding@ischool.utexas.edu), School of Information, University of Texas at Austin, U.S.A.
Last updated by Dou Sun in 2022-12-18
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