Note
Author(s) affiliated with: Tom K. Casady - City of Lincoln, NE;
Ian Cottingham - University of Nebraska-Lincoln; Juan Paulo Ramirez - University of Nebraska; Ashok Samal - University of Nebraska-Lincoln; Alan J. Tomkins - University of Nebraska; Kevin Farrell - University of Nebraska-Lincoln; Joseph A. Hamm - University of Nebraska; David I. Rosenbaum - University of Nebraska-Lincoln; Nancy Shank - University of Nebraska.
Résumé
This report summarizes a project that developed, implemented, and evaluated a GIS-enabled application that dynamically identifies the location of persons of interest, such as gang members, sex offenders, parolees, and so on. The application, called P3i, is designed for use by law enforcement officers. P3i pushes the location data to officers’ smartphones, tablets, and MDT (mobile display terminal)/laptops.
A randomized evaluation was conducted with the Lincoln (Nebraska) Police Department (LPD). Officers (N = 90) were randomly assigned to one of five GPS-enabled devices or a no-P3i, control condition. Over a six-month period, 75 treatment officers were compared to the 15 control officers on a variety of productivity measures as well as to officers’ prior year’s performance. Measures included citation arrests, warrant arrests, and information reports. We also collected self-report data from surveys asking officers about the duration, frequency, and intensity of their use of the technology.
Analyses provide some evidence that officers who used the P3i application on GPS-enabled devices were more productive than controls and more productive than they had been during the prior year. Follow-up analyses suggested a variety of individual difference factors (e.g., high performance officers in 2011, low performance officers in 2010, males) also were correlated with increases in productivity.
In focus group discussions with a subset of the officers in the study, the officers expressed great enthusiasm for the P3i application and their use of new mobile technologies in general. The officers had many suggestions for improvements and provided insights into how they specifically used their devices and P3i.
A cost-benefit analysis suggested that implementation of the device results in a savings of around $800 per officer, assuming a five-year device life and 7% interest rate. Additional assumptions considering different economic and technical scenarios were also developed and reported, but the results remained basically positive except for iPads, only have positive net benefits when there are no usage costs, while other devices have positive net benefits in almost all cases.
In order to better understand officer adoption of P3i as well as their use of smartphones and tablets for work purposes, the project also examined what might account for the use of technology. Consistent with other studies, we found performance expectancy, a belief that the application would aid the participant in the performance of his or her duties, had the primary, significant effect on P3i usage.
Thus, this study supports the position that if technology is made available, and officers have a reasonable expectation that it will help them in their work, they will utilize the technology. Further, if that technology actually can help them in their efforts, it is likely that the increase in productivity can be measured and captured. Our project also shows, however, that it may be difficult to find sensitive measures that will capture increases in productivity. One problem that can be anticipated is that outcomes are multi-determined, and thus it can be difficult to find measures that will be sensitive to positive impacts because of the multiple causes for the effects in which we are interested.
Contenu
Introduction. --
The Lincoln, Nebraska, LBS, Law Enforcement Application Project. --
Goal #1: Creating the LBS Application. --
Goal #2: Evaluation of the LBS Technology: An Experimental Field Trial. --
Participants. --
Random Assignment Check. --
Measures. --
Quantitative Results: Law Enforcement Outcomes. --
Comparisons, 2010 versus 2011. --
Qualitative Methods and Results. --
Focus Groups. --
Interviews. --
Cost-Benefit Analysis. --
Alternative Scenarios. --
Goal #3: Determine the Best Social Science Model for Explaining LBS Technology Adoption. --
Methods. --
Results. --
Model Testing. --
Discussion.