Over the years, innovations such as community-oriented policing, problem-oriented policing, and crime analysis have helped police agencies control, reduce, and prevent crime and disorder, and improve police and community relations. However, research shows that some officers are resistant to using these innovations in practice. Few studies have examined the causal mechanisms behind innovation resistance. This doctoral dissertation details a study that utilized a mixed method approach to partially test a framework that encompassed two theories to explain officer resistance to innovation: The Model of Consumer Resistance to Innovation from the consumer psychology and marketing fields (Ram & Sheth, 1989), and the Monolithic Model of Police Occupational Culture (Paoline, 2003) from the police culture literature. The study was conducted at a midsize Central Florida police agency. The department recently introduced two new innovations, a community policing activity called the Community Interaction Project and an in-car computer system called Street Smart. The primary goal of the study was to examine officer attitudes toward these innovations using the theoretical framework. An Internet-based survey was disseminated to sworn staff (N = 263). In depth interviews were conducted with a sample of command staff and patrol officers (n = 19). Ordinary least squares multiple linear regression analysis of the survey results revealed that themes from the police occupational culture predicted innovation resistance barriers to the Community Interaction Project. The interviews revealed several additional themes that explain resistance to the Community Interaction Project and Street Smart. The dissertation concludes with a discussion of theoretical and methodological contributions to social science. Policy implications are provided for police practitioners. Limitations and future directions for study are also discussed.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2290 |
| Date | 01 January 2022 |
| Creators | Smith, Justin |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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