{"title":"How does policy information shape its adoption? A citation analysis of large-scale energy policies in China","authors":"Leilei Liu , Zhichao Ba , Lei Pei","doi":"10.1016/j.joi.2024.101589","DOIUrl":null,"url":null,"abstract":"<div><p>Understanding the antecedents of policy adoption is essential for facilitating policy diffusion and designing follow-up policies. Previous research on drivers of policy adoption primarily focused on local attributes and government interactions, often neglecting the influence of the policy information itself. This study systematically investigates how policy information (policy design, topics, and attributes) shapes its adoption. Drawing on the Elaboration Likelihood Model (ELM), we developed a framework to explain how such policy information embedded in policy documents influences policy adoption through central and peripheral routes. The adoption performance of each policy is quantified based on a novel policy citation approach. An empirical analysis of large-scale energy policies in China demonstrates that differentiated policy designs and topics exert heterogeneous effects on the intensity and speed of policy adoption. Moreover, their impact on subsequent policy adoptions is more pronounced than on first-time policy adoptions. Policy attributes such as institutional collaboration, reasonable timing agendas, and referencing high-impact policies positively influence policy adoption performance. Additionally, the validity level of a policy positively moderates the relationship between content information and adoption performance. Our research provides practical implications for policymakers to strategically craft appropriate policy-making and targeted promotion strategies for effective policy diffusion.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101589"},"PeriodicalIF":3.4000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Informetrics","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1751157724001019","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding the antecedents of policy adoption is essential for facilitating policy diffusion and designing follow-up policies. Previous research on drivers of policy adoption primarily focused on local attributes and government interactions, often neglecting the influence of the policy information itself. This study systematically investigates how policy information (policy design, topics, and attributes) shapes its adoption. Drawing on the Elaboration Likelihood Model (ELM), we developed a framework to explain how such policy information embedded in policy documents influences policy adoption through central and peripheral routes. The adoption performance of each policy is quantified based on a novel policy citation approach. An empirical analysis of large-scale energy policies in China demonstrates that differentiated policy designs and topics exert heterogeneous effects on the intensity and speed of policy adoption. Moreover, their impact on subsequent policy adoptions is more pronounced than on first-time policy adoptions. Policy attributes such as institutional collaboration, reasonable timing agendas, and referencing high-impact policies positively influence policy adoption performance. Additionally, the validity level of a policy positively moderates the relationship between content information and adoption performance. Our research provides practical implications for policymakers to strategically craft appropriate policy-making and targeted promotion strategies for effective policy diffusion.
期刊介绍:
Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.