Machine Learning Algorithms for Big Data Applications With Policy Implementation
This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature,...
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| Published in | Journal of organizational and end user computing Vol. 34; no. 3; pp. 1 - 13 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hershey
IGI Global
01.05.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1546-2234 1546-5012 |
| DOI | 10.4018/JOEUC.287570 |
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| Abstract | This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed us to provide snapshots of different research topics and variation across covariates but also let us track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation. |
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| AbstractList | This article examines the policy implementation literature using a text mining technique known as a structural topic model (STM) to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed the authors to provide snapshots of different research topics and variation across covariates but also let them track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation. This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed us to provide snapshots of different research topics and variation across covariates but also let us track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation. |
| Audience | Trade |
| Author | Zhang, Kunxin Wu, Jianzu |
| AuthorAffiliation | School of Management, Lanzhou University, China |
| AuthorAffiliation_xml | – name: School of Management, Lanzhou University, China |
| Author_xml | – sequence: 1 givenname: Jianzu surname: Wu fullname: Wu, Jianzu organization: School of Management, Lanzhou University, China – sequence: 2 givenname: Kunxin surname: Zhang fullname: Zhang, Kunxin organization: School of Management, Lanzhou University, China |
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| Cites_doi | 10.1093/oxfordjournals.jpart.a003544 10.1111/j.1467-9299.2006.00625.x 10.1007/s11077-015-9217-x 10.1111/j.1540-6210.2012.02623.x 10.1016/j.jenvman.2013.06.038 10.2307/2149628 10.1214/07-AOAS114 10.1093/oxfordjournals.jpart.a024270 10.1111/1468-2370.00066 10.1177/009539977500600404 10.1111/j.1541-1338.1987.tb00031.x 10.1177/106591298603900210 10.1017/S0143814X00001975 10.1016/j.jbusres.2019.01.053 10.17763/haer.47.2.v870r1v16786270x 10.1007/s10551-015-2622-4 10.1093/pan/mpu019 10.2139/ssrn.2499725 10.1017/S0143814X00003846 10.1007/s11077-016-9259-8 10.1017/S0143814X00006486 10.1504/IJPP.2015.070550 10.1111/j.1541-0072.1980.tb01266.x 10.3102/01623737009002171 10.1111/padm.12570 10.1111/ajps.12103 10.1111/j.1540-6210.2010.02229.x 10.1111/j.1541-0072.2000.tb02013.x 10.1093/scipol/scs128 |
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| SubjectTerms | Algorithm Algorithms Big Data Bureaucrats Data mining Data warehousing/data mining Forecasts and trends Generations Machine learning Market trend/market analysis Public policy Researchers |
| Title | Machine Learning Algorithms for Big Data Applications With Policy Implementation |
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