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Effect of awareness and notification of body-worn cameras on procedural justice, police legitimacy, cooperation, and compliance: Findings from a randomized controlled trial

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Abstract

Objectives

The study examined the effects of awareness and notification of body-worn cameras (BWCs) on encounter-based citizen perceptions including procedural justice, police lawfulness, compliance with police, and global-level perceptions including police legitimacy, cooperation, and compliance with law.

Methods

A vignette-based cluster randomized controlled trial was conducted. As a cluster, participants were randomly selected and then randomly assigned to one of the four conditions (non-BWC, unawareness of BWC, awareness of BWC, and notification of BWC). Participants in the experimental conditions (i.e., unawareness of BWC, awareness of BWC, and notification of BWC) viewed a video of a real traffic stop in which the officer used a BWC. The data were obtained from a survey about participants’ perceptions. A one-way ANOVA test for bivariate analysis and a hierarchical generalized linear model for multivariate analysis were conducted.

Results

Compared with the control condition, the three experimental conditions significantly improved encounter-based citizen perceptions. Compared with unawareness of BWC, notification of BWC significantly improved the perceptions of procedural justice and police lawfulness, while awareness of BWC significantly improved the perceptions of police lawfulness.

Conclusions

BWC awareness improves citizen encounter-based perceptions. Police should notify citizens of the use of BWCs to increase the effectiveness of BWCs.

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Notes

  1. See also Lum et al., (2019, 2020) and Demir (2021) for the studies that focused on the effect of BWCs on police and citizen behavior such as use of force, citizen complaints, and assault, and police perceptions of BWC.

  2. It is important to note that except for one study (Hamm et al., 2019), these studies did not examine the effect of BWCs on citizen perceptions.

  3. The results of G*Power analysis, a free-to-use software that calculates statistical power (Faul et al., 2007), showed that a sample size of 482, with a power of .80 and a two-tailed test with an α level of .05, would be sufficient to detect a small-sized effect (f = .15) for a one-way ANOVA test with four groups (Cohen, 1988) and a small-sized effect (f = .02) for linear multiple regression with 11 predictors.

  4. Before conducting the OLS regression tests, numerical methods (i.e., the Shapiro–Wilk test for normality and the Breusch-Pagan/Cook-Weisberg test for heteroskedasticity) and/or graphical methods were used for each model to test whether the data satisfied the assumptions of the OLS regression for linearity, normality, homoscedasticity, and collinearity (see Fox, 1991). The assumptions for normality were not met, while the distribution of the data for the outcomes deviated slightly from normality. In addition, except for legitimacy, the assumption for homoscedasticity also was not met for the other outcomes. Linearity was not an issue because all of the independent variables were binary variables (see Fox, 1991). Regarding noncollinearity, variation inflation factors (VIFs) and correlation matrices indicated that the VIF was less than 2 and that Pearson’s correlation coefficient was less than 0.70 (r < 0.40), which suggests that multicollinearity was not a concern. Except for the VIF and the correlation matrix (see Appendix 2), the results are not shown. To address non-normality (although slightly deviated) and heteroskedasticity, OLS values were estimated with cluster-robust standard errors (see Fox, 1991).

  5. HGLM involves units of observation at two different levels. Level 1 characteristics explain within-cluster variation, while level 2 characteristics explain between-cluster variation in the dependent variable (Rabe-Hesketh & Skrondal, 2012).

  6. Students within the same class are more likely to be similar to each other than to students in other classes (see Rabe-Hesketh & Skrondal, 2012, p. 1) because students in the same class are highly likely to have same majors/minors and be exposed to similar knowledge, though their perceptions may differ. Students in some majors are more likely to be exposed to issues related to police and therefore may have opinions that differ from students whose majors do not necessitate exposure to police-related issues.

  7. The results were not statistically significant except for two outcomes: procedural justice and legitimacy (see Table 4). The results suggest that there was no significant difference between the use of HGLM and the OLS model for the other outcomes and that either model was appropriate for those outcomes, while HGLM was appropriate for procedural justice and legitimacy.

  8. Exponentiated (B) values, which are interpreted as odds ratios, are reported in Table 4 for interested readers. However, for clarity, the Exp(B) values were not reported in the text.

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Appendices

Appendix 1. Video script

The following is a transcript of an 82-s BWC recording made during a traffic stop. An officer wearing a BWC pulls over the driver, a college student, for making an illegal U-turn. A passenger also is inside the car.

Police Officer: Hello!

Driver: Good evening officer.

Police Officer: Good Evening, the reason why I’m stopping you is because you made an illegal U-turn back there.

Driver: Yea, I’m sorry I didn’t realize there was a sign there.

Police Officer: That’s okay, you have a driver’s license on you?

Driver: Yea, of course. Yo Hansen, did you take my driver’s license?

Police Officer: What was that ID in there, what was that ID that you just pulled out?

Driver: That’s my student ID.

Police Officer: I thought I saw another ID in there, there wasn’t another ID behind there right?

Driver: No. I have my— I really don’t know where my driver’s license is.

Passenger: Hey it’s in your pocket you had to show it to get in.

Driver: Oh my God yeah.

Police Officer: Did you have another ID in your wallet that I just saw?

Driver: Yea, I did.

Police Officer: Can I see it?

Driver: Of course!

Police Officer: Is it somebody else’s or is it fake?

Driver: Nah, it’s a fake ID.

Police Officer: Fake, fake?

Driver: Yea.

Police Officer: Alright. What about your real one?

Driver: This is my real one.

Police Officer: That’s your real one?

Driver: Yea.

Police Officer: So, you’re a NY person, right?

Driver: Yup.

Police Officer: That’s a pretty cool ID actually.

Driver: Yea, it is cool.

Police Officer: Is this your car?

Driver: No, it’s not.

Police Officer: It’s not? Okay. I’m not gonna worry about the registration-insurance card. How much did you have drink tonight?

Driver: I had nothing to drink.

Police Officer: You had nothing at all?

Driver: Swear to God.

Police Officer: Swear to God? Okay, well, I’m smelling alcohol, are you the designated driver?

Driver: Yes.

Police Officer: Do you mind stepping out just so I know it’s not coming from you?

Driver: Oh, of course.

Police Officer: Yea. Make sure it’s in park, I don’t want it to roll away.

Appendix 2.

Table 6 Collinearity and correlation matrices

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Demir, M. Effect of awareness and notification of body-worn cameras on procedural justice, police legitimacy, cooperation, and compliance: Findings from a randomized controlled trial. J Exp Criminol 19, 311–341 (2023). https://doi.org/10.1007/s11292-021-09487-6

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