Citation bias in measuring knowledge flow: Evidence from the web of science at the discipline level |
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| Institution: | 1. School of Information Management, Nanjing University, Nanjing 210032, China;2. Department of Information Management, Peking University, Beijing 100871, China;1. School of Information Management, Nanjing University, Nanjing, China;2. Centre for R&D Monitoring (ECOOM) and Department MSI, KU Leuven, Belgium;3. Faculty of Social Sciences, University of Antwerp, Belgium;4. School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China;1. Warsaw University of Technology, Faculty of Physics, ul. Koszykowa 75, Warsaw 00-662, Poland;2. Deakin University, School of IT, Geelong, VIC 3220, Australia;3. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, Warsaw 01-447, Poland;1. School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China;2. Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia;3. Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia;4. STEM, University of South Australia, Adelaide, SA 5001, Australia;5. College of Computer and Information Science, Southwest University, Chongqing 400715, China;6. Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3353, Australia;1. School of Information Management, Nanjing University, Nanjing, Jiangsu Province, 210093, China;2. Institute for Chinese Social Sciences Research and Assessment, Nanjing University, Nanjing, Jiangsu Province, 210093, China;3. School of Journalism and Communication, Hunan University, Changsha, Hunan Province, 410082, China |
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| Abstract: | Knowledge flow between scientific disciplines has commonly been measured based on citation data. Previous studies using citing relationships have mostly considered direct citations but have paid little attention to indirect citations (IDC) to indicate how knowledge diffusion from one discipline to another via one or more intermediaries. In this study, we measured knowledge flow between disciplines from two perspectives: direct citations (DC) and discipline potential energy (DPE), which is proposed to combine both direct and indirect citations. Data were collected from the Web of Science (WoS) database. Findings include: (1) DPE overshadows previous measures by considering not only direct citations but also indirect citations between disciplines which was usually ignored in previous measures, and revealed that the knowledge contribution of some disciplines had been underestimated by previous measures, such as Physics and Engineering. (2) The proportion of IDC contribution is close to that of direct knowledge contribution when the discipline scale is removed, which suggests that it is essential to consider IDC to distinguish the knowledge relationship (net-outflow/inflow) between disciplines. (3) Both measurements show that Biology & Biochemistry has always been the top discipline with the highest net outflow of knowledge, which is inconsistent with the history of science that Mathematics, Physics and Chemistry would be the highest net outflow disciplines. The results show that even considering IDC does not fully reveal the knowledge contribution and academic influence of disciplines. This paper also analyzes the potential reasons for citation bias in revealing the contribution of disciplinary knowledge from a citation perspective. Therefore, caution should be taken in the use of citations as a primary measure of knowledge flow. |
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