An important post-9/11 objective has been to connect law enforcement agencies so they can share information that is routinely collected by police. This low-level information, gathered from sources such as traffic tickets, calls for service, incident reports and field contacts, is not widely shared but might account for as much as 97% of the data held in police records systems. U.S. policy and law assume that access to this information advances crime control and counterterrorism efforts. The scarcity of functioning systems has limited research opportunities to test this assumption or offer guidance to police leaders considering investments in information sharing. However, this study had access to FINDER, a Florida system that shares low-level data among 121 police agencies. The user-level value of FINDER was empirically examined using Goodhue's (1995) Task-Technology Fit framework. Objective system data from 1,352 users, user-reported "successes," and a survey of 402 active users helped define parameters of user-level success. Of the users surveyed, 68% reported arrests or case clearances, 71% reported improved performance, and 82% reported improved efficiency attributed to FINDER. Regression models identified system use, task-fit, and user characteristic measures that predicted changes in users' individual performance. A key finding was that FINDER affirmed the importance of sharing low-level police data, and successful outcomes were related to its ease of use and access to user-specified datasets. Also, users employed a variety of information-seeking techniques that were related to their task assignments. Improved understanding of user-defined success and system use techniques can inform the design and functionality of information sharing systems. Further, this study contributes to addressing the critical requirement for developing information sharing system metrics.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-1891 |
| Date | 01 January 2006 |
| Creators | Scott, Jr Ernest |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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