JOURNAL ARTICLE

Community‐oriented policing (COP): An empirical study of its effectiveness on fear of crime.

  • Published In: Social Science Quarterly (Wiley-Blackwell), 2023, v. 104, n. 5. P. 988 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lee, Jae‐Seung; Lee, Heeuk D.; Zhao, Jihong Solomon 3 of 3

Abstract

Background: Community‐oriented policing (COP) has been a central tenet in policing for the past three decades. Accordingly, studies have examined its effectiveness in reducing citizen's fear of crime, one of the legitimate objectives of COP. However, the results of studies are somewhat unclear about the effectiveness. Objectives: his study attempts to disclose how COP program affects citizens' fear of crime using two waves of survey data collected from citizen participants of the COP program in Houston, Texas. Methods: Structural equation modeling is employed for analysis in this study. Results: The findings of this study reveal that COP contributed to reducing fear of crime. In particular, the crime prevention‐related information learned at the COP program increased the participants' sense of collective efficacy, which shows a positive impact on reducing fear of crime. Conclusions: This study suggest that providing crime prevention information at COP program is important to strengthen volunteer's informal social control, which contribute to reducing their fear of crime. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social Science Quarterly (Wiley-Blackwell). 2023/09, Vol. 104, Issue 5, p988
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:0038-4941
  • DOI:10.1111/ssqu.13282
  • Accession Number:171875744
  • Copyright Statement:Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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