Abstract
Current quantitative research on policy diffusion tends to focus on the citation relationship between policies, while ignoring the nature of policy diffusion, that is, the diffusion of policy targets and instruments contained in policy. As a result, the identified policy path is often a hodgepodge, which contains multiple unrelated policies. It also lacks quantitative and reproducible methods for policy diffusion path analysis. Therefore, this paper proposes a policy target-oriented and citation-based policy diffusion analysis framework. Firstly, we collected relevant policy documents. Secondly, the article identified the policy citation relationships and the policy target patterns embedded in the policy documents. Then, we constructed target-oriented policy citation network and analyzed the patterns and characteristics of policy diffusion. Finally, based on the main path analysis method, we identified the policy diffusion paths of the policy mixes in the multi-target case. A case study of China’s information technology policies was used to demonstrate our method’s reliability.
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References
Agostinis, G. (2019). Regional intergovernmental organizations as catalysts for transnational policy diffusion: The case of UNASUR health. Jcms-Journal of Common Market Studies, 57(5), 1111–1129. https://doi.org/10.1111/jcms.12875
Ahn, Y. S., & Lin, Y. R. (2020). PolicyFlow: Interpreting policy diffusion in context. Acm Transactions on Interactive Intelligent Systems, 10(2), 23. https://doi.org/10.1145/3385729
Bakhtin, P., Saritas, O., Chulok, A., Kuzminov, I., & Timofeev, A. (2017). Trend monitoring for linking science and strategy. Scientometrics, 111(3), 2059–2075. https://doi.org/10.1007/s11192-017-2347-5
Bhatia, A. (2006). Critical discourse analysis of political press conferences. Discourse & Society, 17(2), 173–203. https://doi.org/10.1177/0957926506058057
Boehmke, F. J., Brockway, M., Desmarais, B. A., Harden, J. J., LaCombe, S., Linder, F., & Wallach, H. (2020). SPID: A new database for inferring public policy innovativeness and diffusion networks. Policy Studies Journal, 48(2), 517–545. https://doi.org/10.1111/psj.12357
Bornmann, L., Haunschild, R., & Marx, W. (2016). Policy documents as sources for measuring societal impact: How often is climate change research mentioned in policy-related documents? Scientometrics, 109(3), 1477–1495. https://doi.org/10.1007/s11192-016-2115-y
Boyack, K. W., van Eck, N. J., Colavizza, G., & Waltman, L. (2018). Characterizing in-text citations in scientific articles: A large-scale analysis. Journal of Informetrics, 12(1), 59–73. https://doi.org/10.1016/j.joi.2017.11.005
Braun, D., & Gilardi, F. (2006). Taking ‘Galton’s problem’ seriously—Towards a theory of policy diffusion. Journal of Theoretical Politics, 18(3), 298–322. https://doi.org/10.1177/0951629806064351
Bricker, C., & LaCombe, S. (2020). The ties that bind Us: The influence of perceived state similarity on policy diffusion. Political Research Quarterly. https://doi.org/10.1177/1065912920906611
Butler, D. M., De Vries, C. E., & Solaz, H. (2019). Studying policy diffusion at the individual level: Experiments on nationalistic biases in information seeking. Research & Politics. https://doi.org/10.1177/2053168019891619
Cassi, L., Lahatte, A., Rafols, I., Sautier, P., & de Turckheim, É. (2017). Improving fitness: Mapping research priorities against societal needs on obesity. Journal of Informetrics, 11(4), 1095–1113. https://doi.org/10.1016/j.joi.2017.09.010
Chowdhury, G., & Koya, K. (2017). Information practices for sustainability: Role of iSchools in achieving the UN sustainable development goals ( SDGs). Journal of the Association for Information Science and Technology, 68(9), 2128–2138. https://doi.org/10.1002/asi.23825
Cohen-Vogel, L., Sadler, J., Little, M. H., Merrill, B., & Curran, F. C. (2020). The adoption of public pre-kindergarten among the American states: An event history analysis. Educational Policy. https://doi.org/10.1177/0895904820961002
DeMora, S. L., Collingwood, L., & Ninci, A. (2019). The role of super interest groups in public policy diffusion. Policy and Politics, 47(4), 513–541. https://doi.org/10.1332/030557319x15659214258414
Edwards-Schachter, M., & Wallace, M. L. (2017). ‘Shaken, but not stirred’: Sixty years of defining social innovation. Technological Forecasting and Social Change, 119, 64–79. https://doi.org/10.1016/j.techfore.2017.03.012
Eta, E. A., & Mngo, Z. Y. (2021). Policy diffusion and transfer of the bologna process in Africa’s national, sub-regional and regional contexts. European Educational Research Journal, 20(1), 59–82. https://doi.org/10.1177/1474904120951061
Etemadi, M., Ashtarian, K., Gorji, H. A., & Kangarani, H. M. (2019). Which groups of the poor are supported more by the law? Pro-poor health policy network in Iran. The International Journal of Health Planning and Management, 34(2), e1074–e1086. https://doi.org/10.1002/hpm.2744
Featherston, C. R., & O’Sullivan, E. (2017). Enabling technologies, lifecycle transitions, and industrial systems in technology foresight: Insights from advanced materials FTA. Technological Forecasting and Social Change, 115, 261–277. https://doi.org/10.1016/j.techfore.2016.06.025
Gao, Z., & Tisdell, C. (2004). China’s reformed science and technology system: An overview and assessment. Prometheus, 22(3), 311–331. https://doi.org/10.1080/0810902042000255741
Georgalakis, J. (2020). A disconnected policy network: The UK’s response to the Sierra Leone Ebola epidemic. Social Science & Medicine, 250(10), 112851. https://doi.org/10.1016/j.socscimed.2020.112851
Gilardi, F., & Wasserfallen, F. (2019). The politics of policy diffusion. European Journal of Political Research, 58(4), 1245–1256. https://doi.org/10.1111/1475-6765.12326
Givens, J. W., & Mistur, E. (2021). The sincerest form of flattery: Nationalist emulation during the COVID-19 pandemic. Journal of Chinese Political Science, 26(1), 213–234. https://doi.org/10.1007/s11366-020-09702-7
Goderis, B., & Versteeg, M. (2014). The diffusion of constitutional rights. International Review of Law and Economics, 39, 1–19. https://doi.org/10.1016/j.irle.2014.04.004
Goyal, N. (2021). Policy diffusion through multiple streams: The (non-)adoption of energy conservation building code in India(sic)(sic)(sic)palabras clave. Policy Studies Journal. https://doi.org/10.1111/psj.12415
Graham, E. R., Shipan, C. R., & Volden, C. (2012). The diffusion of policy diffusion research in political science. British Journal of Political Science, 43(3), 673–701. https://doi.org/10.1017/S0007123412000415
Haunschild, R., & Bornmann, L. (2017). How many scientific papers are mentioned in policy-related documents? An empirical investigation using web of science and altmetric data. Scientometrics, 110(3), 1209–1216. https://doi.org/10.1007/s11192-016-2237-2
Hayashi, M., Rothberg, D., & Hayashi, C. R. M. (2010). Scientific knowledge and digital democracy in Brazil: How to assess public health policy debate with applied scientometrics. Scientometrics, 83(3), 825–833. https://doi.org/10.1007/s11192-009-0125-8
Hu, X., Ying, T. Y., Lovelock, B., & Mager, S. (2019). Sustainable water demand management in the hotel sector: A policy network analysis of Singapore. Journal of Sustainable Tourism, 27(11), 1686–1707. https://doi.org/10.1080/09669582.2019.1652621
Huang, C., Su, J., Xie, X., & Li, J. (2014). Basic research is overshadowed by applied research in China: A policy perspective. Scientometrics, 99(3), 689–694. https://doi.org/10.1007/s11192-013-1199-x
Huang, C., Su, J., Xie, X. A., Ye, X. T., Li, Z., Porter, A., & Li, J. A. (2015). A bibliometric study of China’s science and technology policies: 1949–2010. Scientometrics, 102(2), 1521–1539. https://doi.org/10.1007/s11192-014-1406-4
Huang, C., Yang, C., & Su, J. (2018). Policy change analysis based on “policy target–policy instrument” patterns: A case study of China’s nuclear energy policy. Scientometrics, 117(2), 1081–1114. https://doi.org/10.1007/s11192-018-2899-z
Huang, C., Yang, C., & Su, J. (2021). Identifying core policy instruments based on structural holes: A case study of China’s nuclear energy policy. Journal of Informetrics, 15(2), 101145. https://doi.org/10.1016/j.joi.2021.101145
Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks. https://doi.org/10.1016/0378-8733(89)90017-8
Jordana, J., Fernandez, X., Sancho, D., & Welp, Y. (2005). Which internet policy? Assessing regional initiatives in Spain. Information Society, 21(5), 341–351. https://doi.org/10.1080/01972240500253509
Koya, K., & Chowdhury, G. (2020). Cultural heritage information practices and iSchools education for achieving sustainable development. Journal of the Association for Information Science and Technology, 71(6), 696–710. https://doi.org/10.1002/asi.24283
Lasswell, H. D., & Kaplan, A. (2013). Power and society: A framework for political inquiry. Transaction Publishers.
Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97(2), 311–331.
Lehtoranta, S., Nissinen, A., Mattila, T., & Melanen, M. (2011). Industrial symbiosis and the policy instruments of sustainable consumption and production. Journal of Cleaner Production, 19(16), 1865–1875. https://doi.org/10.1016/j.jclepro.2011.04.002
Luo, T., Xue, X. L., Wang, Y. N., Xue, W. R., & Tan, Y. T. (2021). A systematic overview of prefabricated construction policies in China. Journal of Cleaner Production, 280(17), 124371. https://doi.org/10.1016/j.jclepro.2020.124371
Maggetti, M., & Gilardi, F. (2016). Problems (and solutions) in the measurement of policy diffusion mechanisms. Journal of Public Policy, 36(1), 87–107.
Mallinson, D. J. (2021). Who are your neighbors? The role of ideology and decline of geographic proximity in the diffusion of policy innovations. Policy Studies Journal, 49(1), 67–88. https://doi.org/10.1111/psj.12351
McWilliam, W., Brown, R., Eagles, P., & Seasons, M. (2015). Evaluation of planning policy for protecting green infrastructure from loss and degradation due to residential encroachment. Land Use Policy, 47, 459–467. https://doi.org/10.1016/j.landusepol.2015.05.006
Neto, A. B. F. (2021). The diffusion of cultural district laws across US States. Annals of Regional Science. https://doi.org/10.1007/s00168-020-01045-8
Ritter, A., & Lancaster, K. (2013). Measuring research influence on drug policy: A case example of two epidemiological monitoring systems. International Journal of Drug Policy, 24(1), 30–37. https://doi.org/10.1016/j.drugpo.2012.02.005
Roberts, I., Wentz, R., & Edwards, P. (2006). Car manufacturers and global road safety: A word frequency analysis of road safety documents. Injury Prevention, 12(5), 320–322. https://doi.org/10.1136/ip.2006.012849
Saidi, T., Salie, F., & Douglas, T. S. (2017). Towards understanding the drivers of policy change: A case study of infection control policies for multi-drug resistant tuberculosis in South Africa. Health Research Policy and Systems, 15, 41. https://doi.org/10.1186/s12961-017-0203-y
Septiono, W., Kuipers, M. A. G., Ng, N., & Kunst, A. E. (2019). Progress of smoke-free policy adoption at district level in Indonesia: A policy diffusion study. International Journal of Drug Policy, 71, 93–102. https://doi.org/10.1016/j.drugpo.2019.06.015
Sieger, M. H. L., & Rebbe, R. (2020). Variation in states’ implementation of CAPTA’s substance-exposed infants mandates: A policy diffusion analysis. Child Maltreatment, 25(4), 457–467. https://doi.org/10.1177/1077559520922313
Snir, R., & Ravid, G. (2016). Global nanotechnology regulatory governance from a network analysis perspective. Regulation & Governance, 10(4), 314–334. https://doi.org/10.1111/rego.12093
Sun, Y., & Cao, C. (2018). The evolving relations between government agencies of innovation policymaking in emerging economies: A policy network approach and its application to the Chinese case. Research Policy, 47(3), 592–605. https://doi.org/10.1016/j.respol.2018.01.003
Tennis, K. H., & Robinson, R. S. (2020). Where do population policies come from? Copying in African fertility and refugee policies. Population Research and Policy Review, 39(2), 175–205. https://doi.org/10.1007/s11113-019-09530-5
Train, A., & Snow, D. (2019). Cannabis policy diffusion in Ontario and New Brunswick: Coercion, learning, and replication. Canadian Public Administration-Administration Publique Du Canada. https://doi.org/10.1111/capa.12346
Van Dijk, T. A. (1997). Discourse as social interaction. Sage.
Vilkins, S., & Grant, W. J. (2017). Types of evidence cited in Australian government publications. Scientometrics, 113(3), 1681–1695. https://doi.org/10.1007/s11192-017-2544-2
Wang, H. M., Xiong, W., Yang, L. H., Zhu, D. J., & Cheng, Z. (2020). How does public-private collaboration reinvent? A comparative analysis of urban bicycle-sharing policy diffusion in China. Cities, 96(10), 102429. https://doi.org/10.1016/j.cities.2019.102429
Werland, S. (2020). Diffusing sustainable urban mobility planning in the EU. Sustainability, 12(20), 8436. https://doi.org/10.3390/su12208436
Wolkenstein, F., Senninger, R., & Bischof, D. (2020). Party policy diffusion in the European multilevel space: What it is, how it works, and why it matters. Journal of Elections Public Opinion and Parties, 30(3), 339–357. https://doi.org/10.1080/17457289.2019.1666403
Wu, C., Hill, C., & Yan, E. (2017). Disciplinary knowledge diffusion in business research. Journal of Informetrics, 11(2), 655–668. https://doi.org/10.1016/j.joi.2017.04.005
Xiao, Y., Lu, L. Y. Y., Liu, J. S., & Zhou, Z. (2014). Knowledge diffusion path analysis of data quality literature: A main path analysis. Journal of Informetrics, 8(3), 594–605. https://doi.org/10.1016/j.joi.2014.05.001
Yu, J. H., Jennings, E. T., & Butler, J. S. (2020). Lobbying, learning and policy reinvention: An examination of the American States’ drunk driving laws. Journal of Public Policy, 40(2), 259–279. https://doi.org/10.1017/s0143814x18000363
Zhang, Y. L., & Zhu, X. F. (2019). Multiple mechanisms of policy diffusion in China. Public Management Review, 21(4), 495–514. https://doi.org/10.1080/14719037.2018.1497695
Zuin, V., Delaire, C., Peletz, R., Cock-Esteb, A., Khush, R., & Albert, J. (2019). Policy diffusion in the rural sanitation sector: Lessons from community-led total sanitation (CLTS). World Development, 124, 104643. https://doi.org/10.1016/j.worlddev.2019.104643
Acknowledgements
We acknowledge support from the Key Project of the National Natural Science Foundation of China (Grant No. 72134007), Youth Foundation Project of Humanities and Social Sciences in Ministry of Education of China (Grant No. 18YJC870022), and the Fundamental Research Funds for the Central Universities. The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the supporting institutions.
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Yang, C., Huang, C. Target-oriented policy diffusion analysis: a case study of China’s information technology policy. Scientometrics 129, 1347–1376 (2024). https://doi.org/10.1007/s11192-023-04895-z
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DOI: https://doi.org/10.1007/s11192-023-04895-z