Journal of Measurement and Evaluation
JME, Vol. 1, No. 1, 2026, pp.123-143.
Print ISSN: 3135-4661; Online ISSN: 3135-467X
Journal homepage: https://www.jmeacta.com
DOI：Https://doi.org/10.64058/JME.26.1.07
How Policies Influence Academia: Evolution and Influencing Factors Based on Knowledge Flow
Siluo Yang, Tianxiu Chen, Longfei Li, Xiaojuan Liu, Yi Liang
Abstract: Existing studies rarely examine, at a fine-grained level, the specific effects generated when policy texts are cited by academic papers. This study aims to provide decision support for policymakers seeking to enhance policy influence and policy-to-knowledge translation efficiency, while offering academia a new perspective for understanding the diffusion of policy knowledge and promoting synergy between policy communication and knowledge innovation. Focusing on the academic dissemination of policy within policy–academia interactions, this study takes artificial intelligence (AI) policies and the academic papers citing them as samples. More than 4,000 sentence-level citation chains containing policy knowledge elements were extracted. Using citation analysis, the thematic evolution, diffusion characteristics, and influencing factors of differences in policy knowledge flow were examined from the perspective of knowledge diffusion. In thematic terms, policy topics such as AI education, AI ethics, and AI justice receive concentrated scholarly attention and citations, and thus constitute key channels through which policy affects academia. Analysis of differences in knowledge flow shows that the dissemination power of policy knowledge elements follows a long-tail distribution: a small number of policy elements exert substantial influence, whereas most have only limited impact. The major drivers of differences in knowledge flow include policy topic and the administrative level of the issuing body, while institutional co-authorship and the time lag of secondary citation also show potential associations. Temporally, policies generally trigger scholarly responses in the short term (usually within three years), although some also exhibit delayed resurgence in knowledge flow, often in connection with national strategies or industrial development. It is therefore recommended that future policy design emphasize thematic focus and inter-organizational coordination, innovate policy activation mechanisms, and renew policy communication approaches.
Keywords: citation analysis; policy text; knowledge diffusion; knowledge evolution; knowledge flow; influencing factors
Author Biographies: Siluo Yang, Ph.D., Professor, and Ph.D. Supervisor. Research interests: bibliometrics and science evaluation. Tianxiu Chen, Master's student. Research interests: scientometrics and science evaluation. Longfei Li, Doctoral student. Research interests: scientometrics and science evaluation. Xiaojuan Liu, Ph.D., Professor, and Ph.D. Supervisor. Research interests: informetric and science evaluation. Yi Liang (translator), Ph.D., Associate Professor, currently at Hebei GEO University. His research interests include low-carbon energy management, machine learning and intelligent algorithms, and big data analytics in logistics. E-mail: lianglouis@126.com.
Received: 30 Jan 2026 / Revised: 09 Feb 2026 / Accepted: 21 Feb 2026 / Published online: 30 Apr 2026 / Print published: 30 May 2026.
This article is an English translation of the following Chinese article: Yang, S. L., Chen, T. X., Li, L. F., et al. (2025). How Policy Texts Influence Academia: An Analysis of Evolution and Influencing Factors Based on Knowledge Flow. Information Studies: Theory & Application, 48(12), 24-33. https://doi.org/10.16353/j.cnki.1000-7490.2025.12.003
