Theoretical Applications of the MODE Model to Law Enforcement Training and Interventions
Keith L. Zabel,
Kevin L. Zabel,
Michael A. Olson and
Jessica H. Carlson
Industrial and Organizational Psychology, 2016, vol. 9, issue 3, 604-611
Abstract:
As discussed in the focal article, numerous research studies have supported the existence of automatic or implicit racial bias (Ruggs et al., 2016). In this commentary, we argue that examining implicit bias through the perspective of the motivation and opportunity as determinants (MODE) model (see Fazio & Olson, 2014, for a review) offers a framework for industrial–organizational (I-O) psychologists to design and implement strategies that reduce the number of violent interactions between police and communities. The MODE model has been applied to areas such as interpersonal relationships (McNulty, Olson, Meltzer, & Shaffer, 2013), effective treatment of mental disorders (Vasey, Harbaugh, Buffington, Jones, & Fazio, 2012), and crafting of media messages (Ewoldsen, Rhodes, & Fazio, 2015), as well as racial prejudice (Olson & Fazio, 2004). Below, we elaborate on how the I-O-related strategies and interventions described in the focal article can be captured by the components of the MODE model and highlight which interventions may be most efficacious in reducing discriminatory police officer behavior.
Date: 2016
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:cup:inorps:v:9:y:2016:i:03:p:604-611_00
Access Statistics for this article
More articles in Industrial and Organizational Psychology from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().