Machine Learning Algorithms for Big Data Applications With Policy Implementation

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Author(s): Jianzu Wu (School of Management, Lanzhou University, China)and Kunxin Zhang (School of Management, Lanzhou University, China)
Copyright: 2022
Volume: 34
Issue: 3
Pages: 13
Source title: Journal of Organizational and End User Computing (JOEUC)
Editor(s)-in-Chief: Sangbing (Jason) Tsai (International Engineering and Technology Institute (IETI), Hong Kong)and Wei Liu (Qingdao University, China)
DOI: 10.4018/JOEUC.287570

Keywords: Computer Science & IT / End-User Computing / Engineering Science Reference / Human-Computer Interaction

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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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Machine Learning Algorithms for Big Data Applications With Policy Implementation

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