One expectation among criminal justice administrators and practitioners is that advances in information technology (IT) have the potential to improve the efficiency and productivity of criminal justic
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One expectation among criminal justice administrators and practitioners is that advances in information technology (IT) have the potential to improve the efficiency and productivity of criminal justice agencies. Some have even argued that the major crime drop in America over the past couple decades was, at least in part, due to advances in technology. For this assignment, you must find two research articles outside of the course readings that address this issue. That is, you must find two articles from academic journals that say something concrete, based on research, about whether or not advancements in technology seem to have improved the functioning of the criminal justice system. You must then write a short paper (3-4 pages DOUBLE SPACED) that describes the findings of these studies. Note that the articles do not have to agree in their findings. Note also that it is not necessary for you to understand all parts of the articles. Some parts may be quite advanced statistically, but you do not necessarily need to discuss those parts.
Be sure to thoroughly address each of the components listed above. You must write with correct grammar and punctuation, and at a level suitable for a graduate course. Failure to do so will result in a loss of points. A sample outline is provided below. Note that the sources for this paper MUST be academic journal articles – not books, websites, or government reports. Refer to assignment 3 for instructions for finding journal articles and for instructions on citations and references. You must use the citation and referencing instructions or you will lose points.
I. introduction (approx. 1-2 paragraphs)
A. brief introduction to the issue
B. purpose of the paper
II. body (approx. 2 pages)
Your description of the articles can be organized either according to article (e.g. first describe article 1 and then article 2), or they can be organized according to the general findings (e.g. first describe the findings in support of technology in criminal justice, then describe the findings arguing that the use of technology does not reduce crime).
III. conclusion (approx. 1-2 paragraphs)
A. summary of what you found
B. questions for future research
IV. bibliographic references page (1 separate page – not included in the 3-4 page total)
One expectation among criminal justice administrators and practitioners is that advances in information technology (IT) have the potential to improve the efficiency and productivity of criminal justic
The Benefits and Costs of Information Technology Innovations: An Empirical Assessment of a Local Government Agency Author(s): Mary Maureen Brown Source: Public Performance & Management Review , Jun., 2001 , Vol. 24, No. 4 (Jun., 2001), pp. 351-366 Published by: Taylor & Francis, Ltd. Stable URL: https://www.jstor.org/stable/3381224 REFERENCES Linked references are available on JSTOR for this article: https://www.jstor.org/stable/3381224?seq=1&cid=pdf- reference#references_tab_contents You may need to log in to JSTOR to access the linked references. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected] Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at https://about.jstor.org/terms Taylor & Francis, Ltd. is collaborating with JSTOR to digitize, preserve and extend access to Public Performance & Management Review This content downloaded from 220.127.116.11 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms THE BENEFITS AND COSTS OF INFORMATION TECHNOLOGY INNOVATIONS An Empirical Assessment of a Local Government Agency MARY MAUREEN BROWN University of North Carolina at Charlotte Ongoing advances in information systems and communication technologies pur- portedly allow organizations to achieve greater levels of productivity, efficiency, and service delivery. Information technology (ITyf F D Q V W L P X O D W H S U R G X F W L Y L W W K H J D L Q V R f which are often passed on to customers through improved service delivery. Through the use of technology, task cycle times can also be reduced, eliminating steps in a work process. But, these benefits do not come without substantial investments in a variety of resources. The following study illustrates one agency’s attempt to leverage technology to improve responsiveness and stimulate productivity. Relying on a pretest-posttest research design, the findings provide insight into the outcomes achieved from a large-scale IT initiative at a major metropolitan police department. Preliminary find- ings from this study demonstrate that significant achievements can be acquired. But, the study also draws attention to the many intangible resource investments that may be required to achieve success. This article draws special attention to the extent to which public agencies are prepared for both the tangible and intangible costs that result from technology innovations. Background: Community Policing and IT Police organizations are attempting to incorporate many of the management find- ings of the last 25 years. Many organizations are attempting to shift from a reactive approach to policing to a proactive, prevention-oriented approach. Police are no longer viewed as reactionary figures whose primary responsibilities are to respond to calls for service, quell the tension of immediate situations, and file reports. Today’s paradigm requires officers to own neighborhood problems, broker resources, and facilitate prob- lem resolution. At a fundamental level, community problem-oriented policing Public Performance & Management Review, Vol. 24 No. 4, June 2001 351-366 C 2001 Sage Publications, Inc. 351 This content downloaded from 18.104.22.168 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms 352 PPMR / June 2001 (CPOPyf L Q Y R O Y H V G H Y H O R S L Q J S D U W Q H U V K L S V Z L W K V W D N H K R O G H U V D Q G V H U Y L F H S U R Y L G H U V W o resolve crime-related problems (Goldstein, 1990yf 7 K H & 3 2 3 S D U D G L J P L V U R R W H G L n partnership, problem solving, and communication. Although the effectiveness of the community policing paradigm hinges on several critical success factors, good infor- mation resources and good communication techniques are two of the most important predictors of success. Partnership relationships are more productive when CPOP offi- cers can relate up-to-the-minute information on crimes, resource availability, and progress toward problem resolution to stakeholders and service providers on demand. In testimony before the House Judiciary Committee’s Subcommittee on Crime and Criminal Justice, David Boyd (1995yf G L U H F W R U R I W K H 2 I I L F H R I 6 F L H Q F H D Q G 7 H F K Q R O R J y at the U.S. Department of Justice, called attention to the important role that IT and knowledge play in combating crime: If, through technology, the level of crime could be reduced by a mere 1 percent, it could mean 250 fewer murders; 1,000 fewer rapes; 1,000 fewer assaults; 128,000 fewer bur- glaries, larcenies, and robberies; 14,000 fewer victims of crime burdening the health care system; and $700 million less in economic cost. The question is how to increase the effec- tiveness of law enforcement to realize these benefits. (p. 1yf By some accounts, roughly 92yb R I D Q R I I L F H U V W L P H L V V S H Q W D F T X L U L Q J F R D O H V F L Q J , or distributing information in one form or another. All aspects of CPOP rely on quality information. Information that is accurate, verifiable, timely, organized, meaningful, useful, cost-effective, and readily available provides the foundation from which prob- lem identification, program intervention, problem resolution, and program evaluation are achieved. In the course of their work, officers often rely on data from a variety of sources (Goldstein, 1990; Sparrow, 1988yf . Quality information can provide police executives with a valuable resource for ana- lyzing unstructured problems and developing strategic plans. Quality information can provide command staff members with an immediate resource for developing and implementing tactical and operational decisions. Additionally, quality information can provide field level officers with the ability to integrate problem-solving strategies and communicate them to citizens for partnership development and ultimate problem resolution. Through its ability to store, forward, retrieve, and distribute organizational information, IT provides a critical support structure for the community policing para- digm. Police organizations that successfully deploy IT innovations stand to gain much from their efforts. A sound information base and its associated technology tools can facilitate productivity, efficiency, and service delivery (Davis et al., 1992; Lowery, 1998yf . In recognition of the impact that information and its associated technologies can have on operational outcomes, over the past 8 years, more than $6 billion in grant funds has been distributed to roughly 11,300 law enforcement agencies to adopt technology to advance community policing and problem solving (U.S. Department of Justice, 1999yf 7 K H ‘ H S D U W P H Q W R I – X V W L F H V S U R J U D P & 2 3 6 0 2 5 ( & R P P X Q L W 2 U L H Q W H d Policing Services Making Officer Redeployment Effectiveyf K D V H Q D E O H G O D Z H Q I R U F H – ment agencies to replace aging legacy systems and incorporate new technologies that can promote efficiency and effectiveness. Subsequently, in 1998, Congress passed his- This content downloaded from 22.214.171.124 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms Brown / INFORMATION TECHNOLOGY INNOVATIONS 353 toric legislation authorizing the largest justice assistance program in history. Public Law 105-251 authorized the expenditure of $250 million in each of the next 5 years to promote the integration of justice system technology. According to the legislation, criminal records, arrest records, mug shots, fingerprints, and other relevant data are located in separate, unconnected data systems. “The entire criminal justice system needs immediate access to comprehensive, reliable, and timely data on individuals, incidents, and cases” (Volunteers for Children Act, 1998, p. 1yf 7 R G D W H F L W L ] H Q V , elected officials, and criminal justice professionals all hope to obtain significant crime prevention benefits from these large investments in IT. According to a study of 365 public and private sector IT executive managers, one third of all IT projects were canceled before completion (The Standish Group, 1995yf . Moreover, only 16yb R I W K H S U R M H F W V Z H U H V X F F H V V I X O O F R P S O H W H G R Q W L P H D Q G R Q E X G – get. Over 50yb R I W K H S U R M H F W V H [ F H H G H G W K H L U R U L J L Q D O F R V W H V W L P D W H V E D O P R V W b, and roughly one third of the projects experienced schedule delays of 200yb W R b. The Standish Group estimated that American companies and government agencies spent $81 billion on canceled IT projects in 1995 alone. In another study, Cats-Baril and Thompson (1995, p. 563yf F O D L P H G W K D W b of all IT projects are scrapped before completion; 80yb R I W K R V H W K D W D U H F R P S O H W H G I L Q L V K E H K L Q G V F K H G X O H R Y H U E X G J H W D Q d with lower functionality than originally anticipated. Over the past 10 years, reports from the U.S. General Accounting Office (1996, 1997a, 1997byf K D Y H F R Q W L Q X D O O y called attention to the high failure rate of IT initiatives at the federal level. Moreover, recent studies have also illustrated many of the difficulties state and local governments experience in their IT efforts (Anthes, 1996; Kavanaugh, 1997ayf . As noted by Sparrow (1993yf , 7 F D Q D G Y D Q F H S R O L F L Q J H I I R U W V % X W S R R U O G H V L J Q H d and badly managed systems can frustrate managerial purposes, enshrine old values, emphasize outdated and inappropri- ate performance measures, give power to the wrong people, perpetuate old ways of doing business, create false or misleading public expectations, destroy partnerships, and impose crippling restrictions on new styles of operation. (Sparrow, 1993, p. 2yf Sparrow went on to say that advancing community policing requires police organiza- tions to reexamine the types of data they collect and the manner in which information flows throughout their departments. This study reveals the outcomes field officers received from the implementation of IT. The Charlotte-Mecklenburg Police Department (CMPDyf U H F H L Y H G V X E V W D Q W L D l funding from the COPS MORE effort. In total, the CMPD was allocated roughly $11 million in federal funds and approximately $8 million in local funds to support its IT initiative. In 1997, the City of Charlotte adopted the Charlotte-Mecklenburg Police Department Master Information System Plan. The plan called for the development of the Knowledge-Based Community Oriented Problem Solving System, which involved three distinct stages. Stage 1 consisted of an in-depth needs analysis and pro- vided a framework for the pretest data collection effort. Stage 2 involved implement- ing much of the system architecture (laptops, servers, networks, etc.yf D Q G V H Y H U D O V R I W – ware applications (e-mail programs, word processors, presentation software, etc.yf . Stage 3 involves upgrading many of the CMPD’s most frequently used data sets (e.g., This content downloaded from 126.96.36.199 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms 354 PPMR / June 2001 arrests, incidents, problem solvingyf $ V R I Q R Z 6 W D J H K D V Q R W E H H Q F R P S O H W H G 7 K e data collection effort for assessing the impacts of Stage 3 will take place in late 2001. Hence, the results below are based on the pretest and first posttest observation, which took place after the completion of Stage 2. Stage 1: Needs Assessment As mentioned, Stage 1 involved a needs assessment to determine the extent to which field-level officers had access to timely, relevant data that advanced their work efforts. The primary research questions focused on the extent to which community crime information (ayf Z D V D Y D L O D E O H W R S D W U R O R I I L F H U V E f was of sufficient quality, (cyf was shared among police department units, (dyf I D F L O L W D W H G M R E H I I H F W L Y H Q H V V D Q G H f facilitated an understanding of community problems. Two data collection efforts occurred to test the degree to which information was of sufficient quality, readily available, shared openly, improved job performance, and facilitated an understanding of community problems. First, a survey was distributed to all patrol officers. The survey tapped issues pertaining to data collection, distribution, and usage. Of the 720 patrol officers who received surveys, 365 responded, a response rate of 51yb . Second, 10 focus group sessions were held involving 112 officers and civilians to help identify the information needs of patrol officers. Table 1 lists the survey and focus group items employed in Stage 1 of the study. SURVEY FINDINGS As illustrated in Table 2, for the most part, crime-related information was not readily available to patrol officers. Only 26yb R I W K R V H V X U Y H H G L Q G L F D W H G W K D W W K H y were often informed of the felonies that occur in their districts. Thirty-five percent related that they were often informed of misdemeanor occurrences, and 38yb F O D L P H d that they often had knowledge of suspects operating within their response districts. Eighty-nine percent of the officers claimed that they were not often informed of the investigative status of criminal cases that occur within their jurisdictions. Only 25yb indicated that data are accessible in a timely manner, yet roughly half the sample iden- tified that the information collected is often accurate and relevant to their job needs (46yb D Q G b, respectivelyyf . In addition, the officers were highly critical of the sufficiency of the information captured (see Table 3yf 2 Q O D S S U R [ L P D W H O b of the sample indicated that felony and misdemeanor information was completely sufficient for their needs. Thirty-four percent of the officers indicated that the information captured on calls for service from the community was sufficient. Roughly 70yb R I W K H R I I L F H U V I H O W W K D W L Q I R U P D W L R Q R Q W K e availability of various community services and ethnic and cultural needs was not sufficient. As discussed above, although the officers were quite negative in terms of the avail- ability and sufficiency of various crime-related information, they were especially dis- paraging regarding the extent to which information was shared among departmental units. As shown in Table 4, although 38yb J D Y H W K H L U G H S D U W P H Q W V K L J K P D U N V I R U V K D U – This content downloaded from 188.8.131.52 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms Brown / INFORMATION TECHNOLOGY INNOVATIONS 355 Table 1. Survey Items Item Scale Survey items How well informed are you on the various felonies that occur within your patrol district? 1 to 7 (not informed to very informedyf How well informed are you on the various misdemeanors that occur within your patrol district? Do you receive information as to suspects operating within your district? I to 7 (never to very oftenyf Do you receive information on victims within your district? Do you receive feedback on reports taken by you, but not assigned to you for follow, as to: Case status Suspect arrested Suspects prosecuted Property recovered Do you find the information gathered from completed offense and supplement reports to be: Accurate Accessible in a timely manner Relevant to job needs How sufficient is the information you receive pertaining to: I to 7 (completely Felony crimes committed insufficient to completely Misdemeanor crimes committed sufficientyf Calls for service in the area Community services available Ethnicity/cultural needs Awareness as to community problems How relevant is the following information to performing your job effectively: 1 to 7 (very irrelevant Felony crimes committed to very relevantyf Misdemeanor crimes committed Calls for service in the area Community services available Ethnicity/cultural needs Awareness as to community problems Focus group and elite interview items What kinds of facts and data do patrol officers need to advance community policing? What are the current barriers to access necessary information? How would you describe overall communications between units and between officers and supervisors? ing information on the presence of suspects operating within their districts, only roughly 10yb R I W K H V D P S O H L G H Q W L I L H G W K D W L Q I R U P D W L R Q S H U W D L Q L Q J W R F D V H V W D W X V , whether suspects had been arrested or prosecuted, whether stolen property had been recovered, or victims is often shared. In short, the survey indicated that the vast major- ity of the officers felt uninformed of felony, misdemeanor, and suspect information. This content downloaded from 184.108.40.206 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms 356 PPMR / June 2001 Table 2. Availability of Information to Patrol Officers Survey Item Never Sometimes Often M SD Patrol officers are informed of felonies that occur in district (n = 362yf b 38yb b 1.89 0.78 Patrol officers are informed of misdemeanors that occur in district (n = 364yf b 36yb b 2.06 0.80 Patrol officers receive suspect information (n = 357yf b 36yb b 2.11 0.80 Patrol officers receive case status information (n = 339yf b 20yb b 1.41 0.68 Crime data collected is accurate (n = 327yf b 42yb b 2.34 0.68 Crime data is accessible in a timely manner (n = 330yf b 47yb b 1.96 0.73 Crime data is relevant to job needs (n =331yf b 47yb b 2.26 0.69 Note. Scales collapsed for reporting purposes. Table 3. Sufficiency of Information Available to Patrol Officers Perceptions of Sufficiency of Completely Occasionally Completely Information Available On Insufficient Sufficient Sufficient M SD Felony crimes committed (n = 360yf b 43yb b 1.97 0.76 Misdemeanor crimes committed (n = 360yf b 43yb b 1.86 0.74 Calls for police service (n = 358yf b 38yb b 2.06 0.78 Community services available (n = 358yf b 38yb b 1.90 0.78 Community ethnic and cultural needs (n = 357yf b 35yb b 1.85 0.80 Note. Scales collapsed for reporting purposes. Moreover, the officers claimed that they received little feedback on case status or on residents who had been victimized. Despite the dismal portrayal regarding the availability, sufficiency, and sharing of information, for the most part, officers did appear to believe that information was rele- vant to performing their jobs effectively. When asked about the relevance of different data items to job effectiveness, approximately 60yb L Q G L F D W H G W K D W L Q I R U P D W L R Q S H U W D L Q – ing to felonies, misdemeanors, and calls for police service is very relevant to job effec- tiveness (see Table 5yf 0 R U H R Y H U U R X J K O b of the officers perceived that informa- tion on comnmunity services, community problems, and ethnic and cultural needs was at the very least occasionally relevant, if not very relevant, to job effectiveness. To evaluate more formally the correspondence between the presence of sufficient information and the extent of its influence on job effectiveness, two measures of asso- ciation were obtained. The two statistics, gamma and tau-b, provide evidence of the strength and direction of association between the two variables. The statistics provide a capsule assessment of the degree to which sufficient information influences job effectiveness. Essentially, the primary question was, Do officers perceive greater degrees of job effectiveness when information is available? In other words, in situa- tions where specific information is available, how relevant is it to achieving job effec- tiveness? Many times, officers are asked to speculate on the extent to which informa- tion would improve job performance. The analysis examines those situations where This content downloaded from 220.127.116.11 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms Brown / INFORMATION TECHNOLOGY INNOVATIONS 357 Table 4. Extent to Which Departmental Units Share Information Information Type Never Sometimes Often M SD Case status (n = 339yf b 20yb b 1.42 0.68 Suspects operating in district (n = 357yf b 36yb b 2.10 0.80 Suspects arrested (n = 338yf b 19yb b 1.41 0.67 Suspects prosecuted (n = 338yf b 17yb b 1.30 0.57 Victims within district (n = 356yf b 29yb b 1.57 0.73 Property recovered (n = 338yf b 18yb b 1.29 0.56 Note. Scales collapsed for reporting purposes. Table 5. Relevance of Infonnation for Improving Job Effectiveness Perceptions of How Information Very Occasionally Very Improves Job Effectiveness Irrelevant Relevant Relevant M SD Felony crimes committed (n = 354yf b 35yb b 2.53 0.61 Misdemeanor crimes committed (n = 360yf b 36yb b 2.47 0.65 Calls for police service (n = 356yf b 37yb b 2.49 0.64 Community services available (n = 353yf b 38yb b 2.37 0.70 Community ethnic and cultural needs (n = 354yf b 35yb b 2.25 0.77 Note. Scales collapsed for reporting purposes. information is available and the extent to which the information is relevant to job performance. As portrayed in Table 6, the gamma statistics ranged from a low of .23 to a high of .39, suggesting a weak to moderate relationship between items pertaining to crime-related information and perceived improvements in job effectiveness. Each of the relationships is statistically significant beyond conventional levels of acceptance. Although suggesting a somewhat weaker relationship between the variables, the tau-b statistics support the associations and again are significant beyond conventional lev- els. Thus, according to this sample, sufficient crime-related information (e.g., felonies, misdemeanors, calls for service, community services, and ethnic and cultural needsyf does appear to be associated with officer job effectiveness. The study also examined the association between the presence of specific crime information and officer awareness of community problems (see Table 7yf 7 K H J D P P a and tau-b statistics showed a moderate to strong relationship between information availability and perceived awareness of community problems. All gamma and tau-b statistics were significant beyond conventional levels of acceptance. As related in Table 7, the availability of felony, misdemeanor, suspect, case status, service call, and victim information is strongly associated with officers’ awareness of community prob- lems (gamma scores were .62, .62, .41, .54, .60, and .48, respectivelyyf 0 R U H R Y H U a moderately strong association was also observed between awareness of community problems and the timeliness and accuracy of available information (gamma scores were .37 and .28, respectivelyyf . This content downloaded from 18.104.22.168 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms 358 PPMR / June 2001 Table 6. Officer Ratings of the Extent to Which Sufficient Information Influences Job Effectiveness Survey Item Gamma Significance Tau-b Significance Felony crimes committed (n = 354yf 0 Misdemeanor crimes committed (n = 355yf 0 Calls for police service (n = 354yf 0 Community services available (n = 351yf 0 Community ethnic and cultural needs (n = 352yf 0 Table 7. Cross-Tabulation Results of Factors That Influence Perceived Awareness of Community Problems Survey Item Gamma Significance Tau-b Significance Felony information availability (n = 357yf 0 Misdemeanor information availability (n = 357yf 0 Infonnation on calls for service (n = 355yf 0 Infonnation on suspects (n = 353yf 0 Infonnation on case status (n = 335yf 0 Infonnation on victims (n = 352yf 0 Accuracy of report information (n = 324yf 0 Timely accessibility of infonnation (n = 327yf 0 In sum, the statistical findings indicated that crime-related information was neither readily available nor of sufficient quality for patrol needs. Further, the study revealed that the availability and timeliness of critical information pertaining to felonies, mis- demeanors, suspects, case status, and victimization all influenced the officers’ job effectiveness. Additionally, these critical information variables were also tied to the officers’ perceptions pertaining to awareness of community problems. According to the findings discussed above, information plays a pivotal role in perceptions of job effectiveness and meeting community needs. The following section explores the results of the Delphi focus group data-collection effort. DELPHI FOCUS GROUP FIDINGS The overall purpose of the Delphi group exercises was to elicit detailed information requirements. The objective was to assess the information needs of the officers, super- visors, and special units of the CMPD. In addition, the participants were asked to iden- tify the existing barriers that stymied the effective use of information in performing their duties and responsibilities. Ten focus groups were selected from several levels and divisions of the CMPD. They included representatives from each of the major units of the CMPD. Participants were chosen by the appropriate deputy chiefs with the assistance of their respective units. The focus groups ranged in size from 9 to 18 people; total group attendance was 1 12. This content downloaded from 22.214.171.124 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms Brown / INFORMATION TECHNOLOGY INNOVATIONS 359 The groups were asked three broad questions: * What kinds of facts and data do you need to do your job better? * What are the current barriers to your access to necessary information? * How would you describe overall communications between units and between officers and supervisors? Each group session included four rounds of activity. The first three rounds con- sisted of the participants responding to the three questions above in a round-robin dis- cussion format. Each response was numbered and written on a flip chart by a CMPD cofacilitator who was recruited from the group. Responses were posted on the wall so that each participant could refer to the total group responses. The fourth round was a written vote by each participant, identifying the five most important responses, in rank order, to each of the three questions. Each group saw only its own responses, not those of earlier groups. The groups overwhelmingly agreed that there was a need to improve accessibility to departmental data. They felt that arrest records, suspect profiles, field interviews, and related data must be centralized, indexed, and capable of search queries from both district offices and the field. Most groups also felt a great need for more computer equipment. Among all the focus groups, a pattern of responses emerged as to the rea- sons why information is difficult to locate and use. The most frequently identified bar- riers were the lack of adequate training on computer equipment, fragmentation among the databases and information sources, and the lack of information sharing across unit lines and from one level of the organization to another. Finally, the focus group participants were asked to assess the CMPD’s overall departmental communications. Their responses identified the need for better ways to coordinate investigations, better communications among departmental units and with other police agencies, and electronic bulletin boards to send information quickly and efficiently. Consequently, in 1997, the Charlotte City Council approved a master plan that authorized the establishment of an information infrastructure to advance commu- nity-oriented problem solving. The assumptions that guided the subsequent efforts were fourfold. First, IT development efforts must be geared toward improving the community’s policing efforts. The IT initiatives would specifically target the needs of community policing and the officers in the neighborhoods. The underlying assump- tion was that if the problem-solving officers’ needs were met, the technology would also provide the data necessary to serve as a management information system. Next, all efforts should be designed to deliver as much data as possible to the officers in the field. In essence, the police vehicle is the office, and all efforts should support delivering sys- tems and data to the vehicle. Third, officers would no longer be just “report takers.” They would own the problems that occurred in their neighborhoods. As such, officers in the field were expected to conduct preliminary investigations. Moreover, unlike under the old policing paradigm, officers would receive updates and feedback on case status and efforts underway to resolve neighborhood-based problems. And, finally, all system development efforts should be focused on empowering the community mem- bers to become active participants in community problem-solving efforts. It is impor- This content downloaded from 126.96.36.199 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms 360 PPMR / June 2001 tant to note that these four assumptions had a tremendous impact on the manner in which systems were both designed and deployed. For example, networks were devel- oped to maximize file transfer rates with district offices, and databases were designed to coach officers through the investigative process. Stage 2: Deployment of IT Architecture and Routine Software Applications The passage of the information system plan and the acquisition of substantial fund- ing (approximately $20 millionyf O H G W R W K H O D X Q F K R I D Q X P E H U R I W H F K Q R O R J L Q L W L D – tives. Stage 2 involved deploying a wide area network, a local area network, 19 servers, 2,000 laptop computers, and a host of software applications. Table 8 provides an over- view of the computer capabilities that field officers received in Stage 2. After an aver- age of 1 year of experience with the laptop computers, officers were surveyed to deter- mine the effects that information and technology had on the problem-solving efforts. The research setting had the advantage of controlling for several intervening vari- ables that might otherwise have obscured findings. The police officers used identical information, hardware, and software. Moreover, each police officer received the same computer training and was required to pass the same competency examination before a uniform laptop computer was issued. The laptops were issued as standard equipment, and each police officer was required to use the technology in his or her daily opera- tions; other computer technology was not made available to them. Additionally, the laptops provided access to the same databases and the same software applications. Thus, all members of the sample had identical hardware, software, data, training, and experience. To assess the extent to which the technology deployment efforts to date have improved performance and stimulated problem-solving efforts, two data collection efforts were conducted in March 2000. In the first part of the analysis, officers were simply asked to rate the new computers and system support for facilitating community policing. In the second part of the analysis, the extent of the benefits received to date based on the pretest-posttest analysis was examined. Because computer technology is believed to have its greatest perceived effects shortly after its introduction, the group was surveyed when they had had the technology long enough to master it and the tech- nology was sufficiently novel to demonstrate recognizable impacts. STAGE 2 FINDINGS, PART 1: POST-COMPUTER IMPLEMENTATION ATTITUDINAL PERCEPTIONS In the first part of the analysis, patrol officers were asked to rate the extent to which the laptops improved police operations and system operations. The respondents were asked to compare the current laptop technology with the previous, mainframe-based mobile data terminals on a number of criteria (see Table 9yf 7 K R V H U H V S R Q G H Q W V Z K o had not used the old technology were asked to refrain from answering these compari- son questions. Seventy-one percent (n = 21 1yf R I W K H J U R X S L Q G L F D W H G W K D W W K H O D S W R S V S U R Y L G H G S U R – ductivity improvements by a factor of 3 or more (see Table 10yf 2 Q O b of the sam- This content downloaded from 188.8.131.52 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms Brown / INFORMATION TECHNOLOGY INNOVATIONS 361 Table 8. Computer Capabilities Provided to Officers in Stage 2 Hardware deployed Individual laptops to 1,500 officers Six hundred fifty desktop computers Local area network Wide area network Wireless CDPD network Software applications Computer-aided dispatch E-mail Microsoft Office suite Tax and water records Police master name index Calls for service database Departmental announcements Note. CDPD = cellular digital packet data. Table 9. Additional Survey Items Used in Stage 2 By a factor of -3 to +3, compared to the old mobile data terminals, the new laptops Improve productivity Improve efficiency Save time Enhance call response Enhance problem solving Improve communications On a 13-point scale, ranging from F to A+, rate the new system based on Ease of use System performance System support System reliability System availability Response time ple (n = 57yf L Q G L F D W H G H L W K H U Q R L P S U R Y H P H Q W R U D U H G X F W L R Q L Q S U R G X F W L Y L W O H Y H O V ( I I L – ciency gains were similar to those witnessed in the performance arena. Seventy-three percent of the group indicated gains by a factor of 3 or more, and 65yb Q f claimed high productivity improvements in communications. Although still positive, the officers were less sanguine regarding enhancements in calls for service and problem-solving capabilities. As identified in Table 10, only 54yb (n = 163yf R I W K H V D P S O H S H U F H L Y H G D W K U H H I R O G L P S U R Y H P H Q W L Q F D O O U H V S R Q V H b (n = 71yf L Q G L F D W H G Q R L P S U R Y H P H Q W D Q G b (n = 25yf L Q G L F D W H G U H G X F W L R Q V L Q S U R E O H P – solving productivity. Forty-six percent (n = 237yf R I W K H V D P S O H S H U F H L Y H G W K U H H I R O G R r more gains, and 34yb Q f witnessed either no gains or reductions in problem- solving capabilities. This content downloaded from 184.108.40.206 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms 362 PPMR / June 2001 Table 10. Ratings on Improvements in Police Operations Factor Compared With the Old Mobile Data Terminal System, the New Laptops -3 -2 -J 0 +1 +2 +3 Improve productivity (n = 301yf b 1yb b 11yb b 9yb b Improve efficiency (n = 303yf b 1yb b 8yb b 8yb b Save time (n = 300yf b 2yb b 8yb b 8yb b Enhance call response (n = 300yf b 0yb b 24yb b 9yb b Enhance problem solving (n = 300yf b 1yb b 24yb b 12yb b Improve communications (n = 302yf b 0yb b 15yb b 7yb b Overall, the respondents gave system support and operations fairly high marks. When asked about the laptops’ ease of use, system performance, system support, sys- tem reliability, system availability, and response time, the average respondent gave the system an academic grade of approximately C+. Few respondents (n = 20, 38, 30, 52, 38, and 39, respectivelyyf J U D G H G W K H R S H U D W L R Q V L Q W K H ‘ R U ) F D W H J R U D Q G I D L U Q X P E H U s (n = 234, 187, 179, 152, 180, and 201, respectivelyyf J U D G H G R S H U D W L R Q V L Q W K H $ R U B category. STAGE 2 FINDINGS, PART 2: PRE- AND POST-COMPUTER IMPLEMENTATION COMPARISON As mentioned, Stage 1 involved the collection of a baseline survey from which future IT efforts could be measured. The findings below are based on a pretest-posttest comparison. The initial survey (the pretestyf Z D V F R O O H F W H G L Q O D W H 7 K H S U H W H V t group consisted of roughly 364 field-level officers and achieved a response rate of 51yb 7 K H V H F R Q G V X U Y H W K H S R V W W H V W f was collected approximately 1 year after the implementation of Stage 2 and was answered by approximately 330 field-level offi- cers, a response rate of 46yb * L Y H Q W K H U D W H R I S H U V R Q Q H O W X U Q R Y H U D Q G U H D V V L J Q P H Q W D t the CMPD (less than 5yb D Q Q X D O O f, the pretest and posttest groups did not differ in important demographics such as sex, race, age, educational level, or length of time as a police officer. The two groups were compared on improvements in the availability of departmental information, neighborhood-based information, and case feedback. As related in Table 12, in comparing the pretest and posttest groups, the posttest group demonstrated greater information benefits. When asked about information from the offense report, the posttest group rated information accessibility, accuracy, and timeliness higher than the pretest group. On a scale of 1 to 7, the pretest group means ranged from 3.5 to 4.0. The posttest group means ranged from 4.5 to 4.7. The differ- ence between the two groups was statistically significant for each of the models (p < .01 for eachyf 0 R U H R Y H U W K H S R V W W H V W J U R X S D O V R J D Y H U H O H Y D Q F H D Q G F R P S O H W H Q H V V R f the information higher marks than the pretest group (Ms = 4.35 and 4.0yf $ Q G D J D L Q , each of these models was statistically significant (p < .01 for eachyf . When asked about feedback information received on the cases to which they responded, officers were less positive. As shown in Table 13, the pretest and posttest groups did not differ in any demonstrable way in terms of their perceptions of feedback This content downloaded from 220.127.116.11 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms Brown / INFORMATION TECHNOLOGY INNOVATIONS 363 Table 11. Ratings of System Operations Item A B C D F Ease of use (n = 332yf b 43yb b 4yb b System performance (n = 332yf b 45yb b 11yb b System support (n = 331yf b 37yb b 8yb b System reliability (n = 332yf b 37yb b 13yb b System availability (n = 332yf b 43yb b 10yb b Response time (n = 331yf b 42yb b 9yb b Table 12. Mean Comparison of Pretest and Posttest Groups on Evaluation of Offense Information Item (evaluation of information collected on offense reportsyf 3 U H W H V W 0 H D Q 3 R V W W H V W 0 H D Q 6 L J Q L I L F D Q F e Easily accessible 3.52 (n = 330yf Q f .00 Accurate 4.11 (n = 327yf Q f .00 Accessible in a timely manner 3.66 (n = 330yf Q f .00 Relevant to problem-solving needs 4.00 (n = 331yf Q f .00 Complete 4.00 (n = 327yf Q f .00 received on case status, suspect identification, suspect arrest, suspect prosecution, or property recovery. The means for these models ranged from a low of 2.4 to a high of 2.7 (none achieved statistical significanceyf . In terms of neighborhood problem-solving information, the pre- and posttest groups differed on some items but were fairly similar on others (see Table 14yf 2 n information pertaining to calls for service, availability of community services, and involvement of residents in problem-solving activities, the posttest group rated the information availability higher than the pretest group. Each of these models was statis- tically significant (p > .01, .03, and .01, respectivelyyf D Q G W K H P H D Q V U D Q J H G I R U P 4 to 3.74. The difference between the two groups on awareness of problem-solving activities was only marginally significant (Ms = 3.35 and 3.53, p> .07yf + R Z H Y H U W K e two groups did not differ at all in their awareness of resident concerns (Ms = 3.48 and 3.53, respectivelyyf . Findings and Conclusion Although the initial findings appear favorable, it should be noted that the achieve- ments that have occurred to date did not occur easily. Transitioning from a minimalist approach to technology (a single mainframe, 200 dumb terminals, and 6 support staff membersyf W R D V H U Y H U F O L H Q W R S H U D W L R Q U H T X L U L Q J V X S S R U W V W D I I P H P E H U s demanded a tremendous amount of resources in time, energy, and capital. Whereas the annual IT operating budget for the CMPD more than tripled from less than $1.8 mil- This content downloaded from 18.104.22.168 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms 364 PPMR / June 2001 Table 13. Mean Comparison of Pretest and Posttest Groups on Feedback Received From Reports Item (evaluation of feedback received onyf 3 U H W H V W 0 H D Q 3 R V W W H V W 0 H D Q 6 L J Q L I L F D Q F e Case status 2.73 (n = 339yf Q f .55 Suspect identification 2.74 (n = 337yf Q f .73 Suspect arrest 2.69 (n = 338yf Q f .36 Suspect prosecuted 2.51 (n = 338yf Q f .39 Property recovered 2.51 (n = 338yf Q f .39 Table 14. Mean Comparison of Pretest and Posttest Groups on Availability of Neighborhood Information Item (evaluation of availability of neighborhood informationyf 3 U H W H V W 0 H D Q 3 R V W W H V W 0 H D Q 6 L J Q L I L F D Q F e Calls for service 3.74 (n = 338yf Q f .00 Available community services 3.55 (n = 358yf Q f .03 Residents’ perceptions of pressing problems 3.48 (n = 358yf Q f .58 Awareness of problem-solving efforts 3.35 (n = 358yf Q f .07 Involvement of residents in problem- solving activities 3.14 (n = 358yf Q f .01 lion to nearly $6 million, support requirements increased by a factor of 10 (from 200 dumb terminals to 2,000 client server devicesyf . Similar to most technology innovations, many of the project’s subtasks experienced cost overruns and schedule delays. The project suffered from the difficulties associ- ated with anticipating the extent and breadth of the demands the effort would require. The cost overruns resulted primarily from shifting user requirements, which led to pro- ject expansion. Furthermore, the project experienced equipment malfunctions and incompatibilities as well as shortages in technical expertise and high turnover. Given these limitations, the project was forced to take on an incremental, iterative deploy- ment process, meaning that the project had to rely on a very short, iterative, incremen- tal planning cycle that placed other sections of the organization under high stress. The ramifications of the project scope changes, equipment incompatibilities, and short- ages of qualified technical personnel that necessitated the incremental project approach rippled through the organization in interesting ways. The rippling effects demanded heightened levels of internal capacity to sustain the project. For example, technical personnel shortages and turnover rates required the human resources section of the agency to assume the burden of continuous recruitment and hiring. Changes in scope required ongoing, intensive user involvement, and con- tract changes placed heavy burdens on the contracting and procurement section of the agency. Because of the rippling nature of the ongoing horizontal commitment require- ments top-level leadership engagement was vital to the project. Substantial coordina- tion, communication, and leadership capacities were required to sustain the initiative. This content downloaded from 22.214.171.124 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms Brown / INFORMATION TECHNOLOGY INNOVATIONS 365 With more than $20 million allocated for new project development and an annual operating budget of approximately $6 million, much has been achieved to date. A very sophisticated computer network has been built linking every remote site and every mobile police vehicle to CMPD headquarters. In sum, the analysis indicates that the efforts of Stage 2 provided substantial benefits to the officers in the field. Productivity and efficiency gains received from the technology of greater than or equal to a factor of 3 were perceived by the majority of officers. Moreover, the majority of the officers were pleased with the system’s operations. In terms of the pretest and posttest survey, officers noted that the new system did in fact provide them with many more capabili- ties that did enhance their efforts. Yet, no improvements were made on case feedback or perceptions of residential problems. In total, the results indicate that greater improvements are required before the IT efforts can be seen as truly successful. For the most part, information, although more available than before, remains difficult to obtain for most officers. At no time did the availability of offense information, case feedback, or neighborhood information receive average ratings much above four on a scale of one to seven. Hence, the ratings for information availability, although significantly improved over those of the past, remain unacceptably low. However, given that Stage 3 involves upgrading many of the officers’ most frequently used data sets (e.g., arrests, incidents, and problem solvingyf L W L V Q R W V X U S U L V L Q J W K D W K L J K U D Q N L Q J D Q G V L J Q L I L F D Q W L P S U R Y H P H Q W V D F U R V V D O l variables were not noted. If anything, given the tasks that remain, the progress to date is impressive. It should be noted that the generalizability of these findings is somewhat suspect. The study focused on only one agency’s attempt to leverage IT to address productivity and performance needs. Nonetheless, the findings are noteworthy. Recent studies have called attention to the difficulties associated with acquiring benefits from IT invest- ments (Anthes, 1996; Davis et al., 1992; Flowers, 1996yf 3 U H O L P L Q D U I L Q G L Q J V I U R m this study demonstrate that significant achievements can be acquired. But, the study also draws attention to the many intangible resource investments that may be required to achieve success. Coordination, communication, and leadership commitment requirements placed heavy burdens on the organization. The extent to which public agencies are prepared for both the tangible and intangible costs that result from tech- nology innovations is an important finding worth noting. References Anthes, G. (1996yf , 5 6 S U R M H F W I D L O X U H V F R V W W D [ S D H U V E L O O L R Q D Q Q X D O O & R P S X W H U Z R U O G f, 1-30. Boyd, D. (1995, May 17yf 7 H V W L P R Q E H I R U H W K H 6 X E F R P P L W W H H R Q & U L P H D Q G & U L P L Q D O – X V W L F H R I W K H – X G L – ciary Committee, U.S. House of Representatives. Cats-Baril, W. L., & Thompson, R. L. (1996yf , Q I R U P D W L R Q W H F K Q R O R J D Q G P D Q D J H P H Q W % X U U 5 L G J H , / : Irwin. Davis, G. B., Lee, A. L., Nickles, K. R., Chatterjee, S., Hartung, R., & Wu, Y. (1992yf 6 2 6 ‘ L D J Q R V L V R I D n information system failure. Information and Management, 23, 293-318. Flowers, S. (1996yf 6 R I W Z D U H I D L O X U H 0 D Q D J H P H Q W I D L O X U H D P D ] L Q J V W R U L H V D Q G F D X W L R Q D U W D O H V 1 H Z < R U N : John Wiley. Goldstein, H. (1990yf 3 U R E O H P R U L H Q W H G S R O L F L Q J 1 H Z < R U N 0 F * U D Z + L O O . Lowery, D. (1998yf , 6 2 $ F H U W L I L F D W L R Q E D V H G W H F K Q R O R J I R U U H L Q Y H Q W L Q J W K H I H G H U D O J R Y H U Q P H Q W . Public Productivity & Management Review, 22, 232-250. This content downloaded from 126.96.36.199 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms 366 PPMR / June 2001 Kavanaugh, J. (1997a, Julyyf & K L O G V X S S R U W V V W H P L Q W U R X E O H * R Y H U Q P H Q W 7 H F K Q R O R J . Kavanaugh, J. (1997b, Marchyf / $ 3 ‘ W H F K Q R O R J R I I L F H S U R S R V H G * R Y H U Q P H Q W 7 H F K Q R O R J . Sparrow, M. K. (1988, Novemberyf , P S O H P H Q W L Q J F R P P X Q L W S R O L F L Q J 3 H U V S H F W L Y H V R Q 3 R O L F L Q J 1 & J 114217yf : D V K L Q J W R Q ‘ & 8 6 ‘ H S D U W P H Q W R I – X V W L F H . Sparrow, M. K. (1993yf , Q I R U Q D W L R Q V V W H P V D Q G W K H G H Y H O R S P H Q W R I S R O L F L Q J 3 H U V S H F W L Y H V R Q 3 R O L F L Q J 1 & J 139306yf : D V K L Q J W R Q ‘ & 8 6 ‘ H S D U W P H Q W R I – X V W L F H . The Standish Group. (1995yf & K D R V > 2 Q O L Q H @ : H V W < D U P R X W K 0 $ $ X W K R U $ Y D L O D E O H K W W S – Z Z Z . standishgroup.com/visitor/chaos.htm U.S. Department of Justice. (1999yf & X U U H Q W S U H V V U H O H D V H V > 2 Q O L Q H @ : D V K L Q J W R Q ‘ & $ X W K R U $ Y D L O D E O H : http://www.usdoj.gov/cops/news_info/press-rleases/default.htm U.S. General Accounting Office. (1996yf , Q I R U P D W L R Q W H F K Q R O R J L Q Y H V W P H Q W $ J H Q F L H V F D Q L P S U R Y H S H U I R U – mance, reduce costs, and minimize risks (GAO/OIMC-96-64yf : D V K L Q J W R Q ‘ & 8 6 * R Y H P Q P H Q t Printing Office. U.S. General Accounting Office. (1997ayf + L J K U L V N D U H D V $ F W L R Q V Q H H G H G W R V R O Y H S U H V V L Q J P D Q D J H P H Q t problems (GAO/AIMD/GGD-97-60yf : D V K L Q J W R Q ‘ & 8 6 * R Y H P P H Q W 3 U L Q W L Q J 2 I I L F H . U.S. General Accounting Office. (1997byf 0 D Q D J L Q J W H F K Q R O R J % H V W S U D F W L F H V F D Q L P S U R Y H S H U I R U P D Q F e and produce results (GAO/T-AIMD-97-38yf : D V K L Q J W R Q ‘ & 8 6 * R Y H P P H Q W 3 U L Q W L Q J 2 I I L F H . Volunteers for Children Act, Pub. L. No. 105-251, 42 U.S.C. ? 14601 Stat. 1871 (1998yf . Weikart, L., & Carlson, P. (1998yf , P S O H P H Q W L Q J F R P S X W H U V V W H P V L Q W K H I D F H R I I L V F D O V F D U F L W $ P R G H O I R r technologically frustrated managers. Public Productivity & Management Review, 21, 284-292. Mary Maureen Brown, D.RA. (University of Georgiayf L V D Q D V V R F L D W H S U R I H V V R U R I L Q I R U – mation technology and public administration at the University of North Carolina at Charlotte. Her teaching and research interests concentrate on the use of information- based technologiesfor reengineering operations to improve service delivery throughout all levels of government. Her service activities focus on the design, development, and implementation of advanced technologies to enhance organizational operations. In her most recent work with the U.S. Department ofJustice, Brown is responsiblefordesigning and implementing over $20 million in technological solutions at the Charlotte-Mecklenburg Police Department to enhance community policing initiatives. Brown’s research publica- tions in the area of technological innovations have appeared in Public Administration Review, the Journal of Public Administration Research and Theory, Administration & Society, Social Sciences Computer Review, and State and Local Government Review. Contact: [email protected] This content downloaded from 188.8.131.52 on Sat, 19 Nov 2022 02:08:22 UTC All use subject to https://about.jstor.org/terms
One expectation among criminal justice administrators and practitioners is that advances in information technology (IT) have the potential to improve the efficiency and productivity of criminal justic
DATE DOWNLOADED: Fri Nov 18 21:08:06 2022 SOURCE: Content Downloaded from HeinOnline Citations: Bluebook 21st ed. Cynthia Lum, Christopher S. Koper & James Willis, Understanding the Limits of Technology’s Impact on Police Effectiveness, 20 POLICE Q. 135 (2017). ALWD 7th ed. Cynthia Lum, Christopher S. Koper & James Willis, Understanding the Limits of Technology’s Impact on Police Effectiveness, 20 Police Q. 135 (2017). APA 7th ed. Lum, C., Koper, C. S., & Willis, J. (2017). Understanding the limits of technology’s impact on police effectiveness. Police Quarterly, 20(2), 135-163. Chicago 17th ed. Cynthia Lum; Christopher S. Koper; James Willis, “Understanding the Limits of Technology’s Impact on Police Effectiveness,” Police Quarterly 20, no. 2 (June 2017): 135-163 McGill Guide 9th ed. Cynthia Lum, Christopher S. Koper & James Willis, “Understanding the Limits of Technology’s Impact on Police Effectiveness” (2017) 20:2 Police Q 135. AGLC 4th ed. Cynthia Lum, Christopher S. Koper and James Willis, ‘Understanding the Limits of Technology’s Impact on Police Effectiveness’ (2017) 20(2) Police Quarterly 135 MLA 9th ed. Lum, Cynthia, et al. “Understanding the Limits of Technology’s Impact on Police Effectiveness.” Police Quarterly, vol. 20, no. 2, June 2017, pp. 135-163. HeinOnline. OSCOLA 4th ed. Cynthia Lum, Christopher S. Koper & James Willis, ‘Understanding the Limits of Technology’s Impact on Police Effectiveness’ (2017) 20 Police Q 135 — Your use of this HeinOnline PDF indicates your acceptance of HeinOnline’s Terms and Conditions of the license agreement available at https://heinonline.org/HOL/License — The search text of this PDF is generated from uncorrected OCR text. — To obtain permission to use this article beyond the scope of your license, please use: Copyright Information Article Police Quarterly 2017, Vol. 20(2) 135-163 Understanding the @ The Author(s) 2016 Reprints and permissions: Li m its of Technology’s sagepub.com/journalsPermissions.navDOl: 10.1 177/1098611II16667279 Im pact on Police journals.sagepub.com/home/pqx Effectiveness OSAGE Cynthia Lum’, Christopher S. Koper’, and James Willis’ Abstract Technology has become a major source of expenditure and innovation in law enforcement and is assumed to hold great potential for enhancing police work. But does technology achieve these expectations? The current state of research on technology in policing is unclear about the links between technologies and outcomes such as work efficiencies, effectiveness in crime control, or improved police-com- munity relationships. In this article, we present findings from a mixed-methods, multiagency study that examines factors that may mediate the connection between technology adoption and outcome effectiveness in policing. We find that police view technology through technological and organizational frames determined by trad- itional and reactive policing approaches. These frames may limit technology’s poten- tial in the current reform era and cause unintended consequences. Keywords technology, policing, effectiveness, technological frames, police outcomes Introduction Technology has become a major source of expenditure and innovation in law enforcement in the last four decades and is often assumed to hold great potential for enhancing the ability of police to do their work. At the most basic level, ‘Department of Criminology, Law and Society, George Mason University, Fairfax, VA, USA Corresponding Author: Cynthia Lum, Department of Criminology, Law and Society, George Mason University, 4400 University Drive, MS 6D 12, Fairfax, VA 22030, USA. Email: [email protected].edu technology is seen as a means to increase an organization’s technical efficiency, defined as maximizing outputs using the lowest cost, time, and resources possible (see Rutgers & van der Meer, 2010; see also McGowan, 1984a). For example, police adopted license plate readers to detect stolen vehicles because the readers could enable them to automatically scan hundreds of vehicle license plates in minutes, as opposed to manually entering selected plates into computer data- bases one-by-one. Computerized records management systems replaced the hand reporting, shelving, and analysis of paper police reports so that information could be more easily searched, retrieved, and analyzed. Police cars and radios were purchased so officers could react to citizen 9-1-1 calls more quickly. However, aside from technology making the police more technically efficient, technology is also thought to improve the outcome effectiveness of the police. Although arguably a complementary component to efficiency (see Hatry, 1978, 2014), outcome effectiveness is distinguished from efficiency in the public admin- istration literature by its emphasis on achieving specific outcomes (see, e.g., Goodman & Penning, 1977; Quinn & Rohrbaugh, 1981; Rutgers & van der Meer, 2010). In policing, for example, technology is believed to improve the ability of police to identify and monitor offenders or bolster evidence collection to resolve cases. Information technologies help facilitate the identification of hot places and people to target them for crime prevention with the goal of reducing crime and recidivism. Agencies have also acquired new social media technologies to enhance communication between themselves and citizens to strengthen that relationship. One important question for policing that we explore in this article is whether technology helps police to be more effective in achieving outcomes or whether it primarily increases organizational efficiencies (Lum, 2010; Maguire, 2014). Of course, efficiency and effectiveness are not zero-sum trade-offs; scholars have long discussed the interaction between efficiency, effectiveness, and cost- effectiveness (see, e.g., Hatry, 1978; McGowan, 1984a, 1984b). Hatry (1978, 2014) in particular has discussed the need to track performance in terms of outcome efficiency rather than output efficiency (see Hatry, 2014, p. 21), an idea which combines notions of technical efficiency and outcome effectiveness in performance measurement. Nonetheless, questions about the effectiveness and efficiency of police technology are far from settled in empirical research. Some research as well as technical and vendor assessments assert that technologies can make policing processes faster and easier, as illustrated by the aforementioned license plate reader example. At the same time, research and police practice reveal that using technology can sometimes reduce efficiency and may not help achieve outcomes such as the prevention or reduction of crime or the improve- ment of citizen trust, confidence, and satisfaction with the police. The difficulty in linking technological advances in policing with outcomes such as crime prevention, improved community relations, or accountability may have several causes. Independently, technology does not create outcomes I136 Police Quarterk 20(2) in policing. Rather, technology outcomes depend on the way that officers, civilians, and analysts use technology to achieve outcomes. Organizational subcultures, systems, leadership, and officer behavior and cultures might also distort and impede the intended uses (and outcomes) of technologies. Resource limitations, legal concerns, and technical problems can also impede technology’s full potential. Technology can therefore have unintended consequences, under- mining an agency’s broader objectives or the specific goals they have for adopted technologies. Thus, whether technology adoption and use lead to specific outcomes that technology purports to produce is an important question law enforcement agen- cies face. Not only is more evaluative evidence needed to inform these choices, but we also need more contextual and qualitative knowledge about why tech- nologies may or may not be linked to their promised outcomes. In this study, we explore technology effectiveness by presenting findings from a mixed- methods (interviews, focus groups, and surveys), multiagency technology analysis that examines various factors which may mediate the links between technology adoption and outcome effectiveness in policing. We primarily focus on two major and related technologies in policing: information technolo- gies and crime analytic systems. Technological Frames and Their Impact on Outcomes Most research on policing technology focuses on the technical efficiencies that it yields, which is often the motivation behind technological adoption (Allen & Karanasios, 2011). Information technologies (e.g., computer-aided dispatch [CAD], records management, mobile computer terminals [MCTs], etc.) are cases in point. These technologies have the greatest potential to impact policing, especially in speeding up processes (Boudreau & Robey, 2005; Chan, 2001, 2003; Ericson & Haggerty, 1997; Harris, 2007; Manning, 1992b; Mastrofski & Willis, 2010; Rosenbaum, Graziano, Stephens, & Schuck, 2011). For example, Colton (1980) dis- covered that CAD systems can reduce police response times. Groff and McEwen (2008) found that information technology (IT) can enhance reporting speed and accuracy as well as the ease with which officers can identify suspects, vehicles, or places of interest. Agrawal, Rao, and Sanders (2003) compared MCTs with radios and telephones, arguing that MCTs increased communications and saved time (see also Brown, 2001; Ericson & Haggerty, 1997; loimo & Aronson, 2004). However, research also shows that technology can reduce agency efficiency and produce unintended and negative consequences (Chan, Brereton, Legosz, & Doran, 2001; Colton, 1980). Case studies have provided uncertain results as to whether new information and other technologies (such as license plate readers, surveillance cameras, and forensics technologies) improve case clearances or reduce crime, even though they may improve other productivity measures (see, e.g., Danziger & Kraemer, 1985; loimo & Aronson, 2003; Koper, Taylor, & Lum et al. 137 Woods, 2013; La Vigne, Lowry, Markman, & Dwyer, 2011; Lum, Hibdon, Cave, Koper, & Merola, 2011; Nunn, 1993, 1994; Roman Reid, Reid, et al., 2008; Zaworski, 2004). Technologists and organizational theorists hypothesize possible reasons for this inconsistency between technology and intended outcomes. In particular, Orlikowski and Gash (1994) theorize that technological frames mediate the impact of technology on outcomes. Similar to organizational frames or schema (see also Bandura, 1986; Goffman, 1974), and akin to sensemaking (see Weick, 1995), Orlikowski and Gash define technological frames as invol- ving “the assumptions, expectations, and knowledge [members of an organiza- tion] use to understand technology in organizations. This includes not only the nature and role of the technology itself, but the specific conditions, applica- tions, and consequences of that technology in particular contexts” (1994, p. 178; see also Orlikowski, 2000). Such frames reflect the members’ experiences, values, objectives, and roles within an organization, as well as the organiza- tion’s history of technology use. In turn, these frames can shape technology uses and products in an organization and, therefore, the outcomes associated with those technologies (see also Boudreau & Robey, 2005; Orlikowski, 1992; Robey, Boudreau, & Rose, 2000). Technological and organizational frames may also vary across members and units of a police service. Such incongruence (Orlikowski & Gash, 1994, p. 180) can result in conflicts about the develop- ment, use, and meaning of technologies in a police organization, as well as different outcomes of technology (see Rocheleau, 1993, for further discussion). For example, a police chief may view a new information system as increasing efficiency and accountability. However, patrol officers and detectives may see the same innovation as threatening their discretion or autonomy or making their daily work more difficult and time-consuming (e.g., see Chan et al., 2001; Harris, 2007; Manning, 1992a). Manning’s (1977, 1992a, 1992b, 2008) ethnographic work illuminates these frames for policing. Manning argues that technology’s interpretation and use is shaped by policing’s structures and cultures (rather than vice versa), which have been stable and dominant for decades despite the introduction of many techno- logical advances. In particular, he identifies the reactive nature of policing as a strong cultural aspect, which structures policing and “confers sanctity upon traditional strategies and tactics” (2008, p. 251). This reactive nature of policing, characterized and fostered by an incident-based, response-oriented, and proce- dures-dominated approach, is often referred to as the standard model of policing (see Kelling & Moore, 1988; Weisburd & Eck, 2004). Manning and others (see Harris, 2007; Sanders & Henderson, 2013; Sanders, Weston, & Schott, 2015) suggest that the technological and organizational frames that are nurtured by the standard model filter technology adoption. In turn, this filtering process influences the way technology is used and therefore, the outcomes achieved with technology. I138 Police Quarterk 20(2) A related argument was put forth by loimo and Aronson (2004), invoking Goodhue’s (1988, 1995) task-technology-fit theory. They emphasize how technol- ogy is fit to policing’s existing frames. Officers likely fit technology use and expect- ations to their daily tasks, which are much more focused on reaction and arrest. They argue that if a particular technology is not viewed by officers as related to daily tasks, or if officers are asked to use technologies in ways they believe are unrelated to their work, then less effective outputs and outcomes result. These observations about the influence of technological and organizational frames are important in today’s policing context. Adopting new technologies such as information and records management systems, body-worn cameras, license plate readers, analytic tools, or forensics technologies might produce benefits in administrative efficiency, accuracy, and timeliness of crime data, response to calls, and detection and apprehension capabilities. Yet, these changes may not be sufficient to produce substantial improvements in police performance without congruent technological frames and practices that promote technology use in strategic ways. Garicano and Heaton’s (2010) national study, for instance, found no direct relationship between IT systems and police per- formance (as measured by reductions in crime and improvements in case clear- ances) at the agency level (see also Brown, 2014). However, they did find that greater use of IT was linked to improved performance when complemented with other organizational changes including Compstat (a strategic management and accountability system) (Willis, Mastrofski, & Weisburd, 2007). Similarly, some police leaders, scholars, and reformers may see technology as a means to facilitate innovations (e.g., problem/community-oriented policing; hot spots policing, and third-party policing) that can reduce crime or improve citizen trust, rather than just as a means to react to crime or increase arrests and detections. However, these expectations might be overly optimistic if these inno- vations are not part of daily police work or are inconsistent with the techno- logical frames of officers, detectives, or supervisors. Hence, the potential benefits of technological innovations may not be realized if, due to organizational and technological frames, officers throughout the organization-including line-level personnel-do not fully capitalize on the aspects of technology that enable them to do things that could make them more effective (i.e., proactive, preventive, targeted, or problem-oriented policing). Indeed, this may help to explain why case studies have yielded mixed findings with respect to the effects of IT on proactive policing, community policing, and problem solving (Agrawal et al., 2003; Brown, 2001; Brown & Brudney, 2004; Chan et al., 2001; Colvin, 2001; loimo & Aronson, 2003, 2004; Nunn, 2001; Nunn & Quinet, 2002). Our study explores technology’s interplay with police discretion, efficiency, and effectiveness that may help unveil these technological frames, and in doing so explain the general disconnect between technological advancement and improved outcomes in policing. First, we examine how police use information and analytic technologies and how they shape officers’ discretionary activities Lum et al. 139 in the field. In particular, we consider whether police across various units and ranks tend to view and use technology in ways that reinforce traditional policing styles or whether they do so in ways that might best promote more strategic and prevention-oriented styles of policing. Second, we investigate officers’ views on whether and in what ways technology makes them more-or perhaps less productive in their work. And, finally, we consider the relationship between these views, their links to policing outcomes, and the role of techno- logical frames in mediating those linkages. These deeper organizational issues have been discussed by police and organ- izational scholars (e.g., Brown & Brudney, 2003; Chan, 2001; Ericson & Haggerty, 1997; Manning, 1992a; Mastrofski & Willis, 2010; Orlikowski, 2000), but empirical studies of these issues in policing (and in the public sector more broadly-see discussion in Brown, 2014) have been limited in number and scope. Our study extends this line of research by examining these issues with extensive qualitative and quantitative data from two agencies. As described later, these agencies provide sharp contrasts in their technological sophistication and management style, which helps us to illuminate how technology frames, uses, and outcomes can vary across organizational contexts. Our examination of these issues in a contemporary time frame (our fieldwork was conducted over the course of 2011-2013) also updates a generally older literature. Unveiling Technological Frames Using a Mixed-Methods Case Study Design We investigate these issues drawing upon results from a large multiagency, multimethod study that examined various behavioral, social, and organizational aspects of implementing police technologies. Using agency-wide surveys of all sworn officers as well as targeted focus groups and interviews from two large police agencies, we examined how officers throughout these organizations thought about technology and perceived its effects. We were particularly inter- ested in assessing to what extent technology appeared to be delivering important outcomes (such as crime prevention, improved community relations, police accountability, etc.) and to what extent officers shared similar perspectives or interpretive frames on how technology influenced their everyday discretion. Did they conceive of it primarily in terms of efficiency or effectiveness? Was technol- ogy being adapted to the traditional police culture’s reliance on knowledge acquired through experience to respond to individual incidents or was it being used in new or innovative ways? Did personnel share similar perspectives on technology’s role and function across different assignments and ranks? And to what extent did perspectives on technology and experiences with technology overlap or vary across organizational contexts? To explore these questions, we examined IT systems as well as related crime analytic technologies. IT systems are central to core policing functions and have I40 Police Quarterk 20(2) high potential to transform police work. IT systems include an array of data systems and their supporting hardware and software used for storing, managing, retrieving, sharing, and analyzing information. These systems have become more advanced in recent years and include integrated CAD and records management, mobile computing technology, and global positioning systems (including automatic vehicle locators). Related to these systems are analytic technologies, which include computerized mapping, advanced crime analysis, analytic soft- ware, and analysts themselves. These also have great potential for enhancing police effectiveness, especially in targeted patrols, problem-oriented policing, and investigative work (Boba-Santos, 2014; Ratcliffe, 2008; Taylor & Boba, 2011). By examining how officers in these agencies perceived and reported using these technologies-and how those perceptions and uses varied within and across agencies-we sought to reveal technological frames that might influence the links between technology and police outcomes. Study Sites We selected two case study sites-“Avalon Police Department (PD)” and “Greenvale Police Department (PD)” 2-to carry out our analysis. The selection of Avalon and Greenvale PDs was purposeful as each agency had very different histories with information and crime analytic technologies, which we hypothe- sized might reveal different technological frames. Avalon PD serves a diverse (two-thirds White and one-third multiethnic mix) and affluent, low-crime, urban-suburban county with a population of more than 1 million and slightly under 1,500 sworn officers. This agency recently implemented a new automated IT system in 2010 to upgrade from a paper-based, manual reporting system. Officers now have the ability to file reports from the field for the first time in the agency’s history, and they have in-field access to a wider variety of data on crime reports, citizen contacts, and other information. For Avalon PD’s leadership, a primary motive for establishing the new IT system-in addition to greater effi- ciency in report writing was to improve the accuracy and timeliness of the agency’s crime statistics and analysis, increase connectivity with the agency’s previously disjointed information systems, and transition to the state’s inci- dent-based reporting system for uniform crime reporting. New analytic tools were also associated with this technology that enhanced the agency’s crime ana- lysis capabilities and gave managers the ability to monitor agency performance. This recent technological change was turbulent for the agency despite concerted efforts to train and prepare officers for the new system. New reporting require- ments, difficulty with wireless and remote access, and the perceived cumbersome nature of the technical interface itself were chief among officers’ complaints. Greenvale PD is an urban law enforcement agency with more than 1,500 sworn officers serving a densely populated city with between 500,000 and 1 million persons (60% White, 30% African American, and 10% other Lum et al. 141 ethnicities) with relatively higher crime rates. Unlike Avalon PD, Greenvale PD converted from a paper-based IT system to an automated report writing and management system in the late 1990s and also had a well-developed and advanced crime analysis system, which was developed in 2002. The command staff in Greenvale PD emphasized the use and integration of advanced crime analysis in the agency’s operational decisions. At the time the research team visited, the crime analysis unit had already developed its own systems to find, systematize, collate, manage, and analyze data. The crime analysis unit was also highly integrated into the agency’s problem-solving specialized units as well as its investigative units. Greenvale PD, therefore, provided an opportunity to exam- ine how IT and crime analysis were received and used at both the managerial and line levels in an agency with more advanced technological and analytic capabil- ities and a greater managerial emphasis on the use of data-driven decision- making. Methods and Data We conducted in-depth case studies in these agencies, employing a convergent parallel design with embedded mixed-methods elements (see Creswell, 2014; Creswell & Plano-Clark, 2011). These included targeted interviews and focus groups and agency-wide surveys, conducted in concert, and used together for interpretive analysis. This organizational case study approach is a bedrock approach for understanding the relationship of technologies and organizations more generally (see, e.g., Boudreau & Robey, 2005; Robey et al., 2000; Strauss & Corbin, 1990). Conducting in-depth case studies in a small number of sites enabled the research team to develop a detailed and nuanced understanding of the technological capabilities of the agencies studied, as well as their organiza- tional structure, culture, history, and external environment (key contextual fac- tors that shape the uses and impacts of technology). Participants for our targeted interviews and focus groups were selected by the police agency based on our specific requests for representatives from various units, assignments, shifts, geographic areas, and ranks and officer availability. They included patrol officers, detectives, officers in specialized units, first- and second-line supervisors, command staff, crime analysts, research and planning staff, and other administrative and support personnel. Because of the varied shift schedules of officers, we relied on each police agency to facilitate participant availability. In the case where more than one individual was interviewed at the same time, we ensured that individuals were not from different ranks, and interviewers took pains to ensure each member of the focus group had an oppor- tunity to answer questions. The interviews also took place on multiple days, so that members of different shifts would be represented. Almost all the interviews and focus groups, which generally lasted 1 to 1.5 hours, were conducted by two or more members of the research team who both 142 Police Quarterk 20(2) Table I. Number of Participants in Interviews and Focus Groups for Each Site and Proportions Within Types of Personnel. Avalon PD Greenvale PD Agency (n= 100) (n= 141) Officers and detectives 62 (62%) 57 (40%) First- or second-line supervisors 7 (7%) 27 (19%) Command staff 18 (18%) 28 (20%) Civilians (analysts, records, and IT unit) 14 (14%) 29 (21%) Note. PD = police department; IT = information technology. audiorecorded the sessions and took notes. Field notes were drafted for each of these contacts (shortly after the interaction), and they were reviewed and edited by each researcher who participated based on listening to the recordings and confirming information from their written notes. In total, we interviewed 100 individuals from Avalon and 141 individuals from Greenvale (see Table 1). In both agencies, we believed that we reached a level of saturation (Charmaz, 2006) toward the end of our interviews, at which point new insights became scarce. The interviews used a semistructured instrument that was developed for this project and grounded in the literature on policing and technology. Of particular interest to this study were questions focused on discretion, technical efficiency, and outcome effectiveness. For example, with regard to how police used tech- nology and whether it affected their daily discretion and decision-making, we asked officers to describe how they used information and crime analytic tech- nologies, and how technology and crime analysis changed (if at all) the way they respond to calls for service, carry out proactive activities, or engage with the community. To gauge the impact that technology had on technical efficiency and work productivity, we asked officers to describe ways in which technology allowed them to do their work faster, with greater ease, or less effort. We also asked officers whether they had specific requirements that they had to fulfill with the use of technology and whether technologies presented burdens to their work. Finally, with regard to outcome effectiveness, we asked officers whether and how information technologies and crime analysis helped them to reduce, deter, prevent, or detect crime. We also asked officers to talk about other outcomes (such as improved interactions with citizens) that resulted from their use of technology. Our approach was anchored in grounded theory (Strauss & Corbin, 1990), where we first examined and coded material to find commonalities and themes. Given the richness and intricacies of our interviews, we chose to take a manual approach, rather than use computer software to analyze our findings. Specifically, we built content through deep analysis, reading, and discussion of Lum et al. 143 the interviews. This allowed revelations to emerge, which ultimately helped to illuminate technological frames in the police agency. We developed our conclu- sions through an iterative process to strengthen their reliability and validity. One member of the research team took the lead on drafting conclusions in regard to our key themes. The other authors then reviewed these drafts to pro- vide further elaboration and identify additional issues for discussion or potential points of disagreement. This approach allowed us to assess convergence and divergence of participant views across units, ranks, and agencies. In addition to our targeted interviews and focus groups, our convergent design included agency-wide surveys administered to all sworn staff in both agencies by e-mail to assess technology uses and perceptions, with a focus on information and analytic technologies. These were conducted concurrently and independently of the above qualitative interviews and focus groups, and we chose not to analyze the survey results until the qualitative work was completed. Once both sets of data were collected and analyzed, we then brought these sources together to compare and contrast using descriptive and interpretive ana- lysis. The survey data are used here descriptively to provide an interpretive context for our qualitative findings. Although we do discuss some notable dif- ferences in the survey patterns across agencies, particularly as they relate to our qualitative findings, we did not use the survey data to formally test hypotheses about differences across or within agencies. Rather, using the survey data in this study allowed us to come to a more complete understanding of the uses and perceived impacts of technology in these agencies. Similar to the interviews, the survey questions used for this analysis examined the uses of technology for discretionary activities and the perceived impacts of technology on efficiency and effectiveness. To probe technology’s impact on officer discretion, patrol officers were asked how often they used technology for different types of activities (e.g., to “locate suspects, wanted persons, and other persons of interest” or to “collect and search for information during a field interview”) on a 5-point scale: never, rarely, sometimes, often, and very often. We also asked supervisors about their use of technology in managerial tasks (e.g., to “monitor the daily activities of officers, detectives, or supervisors who work for you” or to “identify crime trends and problems in your area of responsibility”) using the same 5-point scale. A final set of items on discretion and decision- making asked all respondents to indicate their level of agreement or disagree- ment (on a 4-point scale of strongly agree, agree, disagree, and strongly disagree) with a few additional items reflecting the role of technology in making decisions about discretionary activities (e.g., “when making decisions about crime prob- lems, I tend to rely more on my own experience than using information tech- nologies to help me to engage in proactive, self-initiated activities”). Questions related to technology’s impact on efficiency included those that asked respondents whether IT systems were easy to use, helpful, and made them more or less productive. Questions related to technology’s impact on I44 Police Quarterk 20(2) effectiveness asked about whether an agency’s technology systems helped them in addressing crime-related issues and assisting citizens. The survey also had an additional effectiveness item that asked patrol officers only about whether IT increased their capacity to prevent crime when not answering calls. The effi- ciency and effectiveness items were based on the 4-point agreement scale described earlier. Requested participation was voluntary and anonymous. 3 In Avalon PD, 40% of sworn personnel (529 of 1,327) answered the survey while in Greenvale PD, 42% of officers (674 of 1,616) responded. With some exceptions, differences between the respondent characteristics and those of the agencies overall tended not to be large (even if statistically significant). For example, in Avalon PD, line- level officers accounted for 77% of survey respondents and 83% of all officers, first-line supervisors accounted for 11% of survey respondents and 5% of all officers, and higher level managers accounted for 12% of both survey respondents and all officers. In Greenvale PD, line-level officers accounted for 80% of survey respondents and 84% of all officer, 4 first-line supervisors accounted for 13% of respondents and 10% of all officers, and higher level supervisors and commanders accounted for 8% of respondents and 6% of all officers. Findings Technology and Discretion The most common uses of both information and crime analytic technologies in Agencies 1 and 2 were for retrieving information when reacting or responding to a call for service, situation, stopped individual, or a criminal investigation. To officers and detectives, these activities define their everyday work and are viewed as central to policing. Data from mobile computer systems, databases, and crime analysis were also seen as valuable in increasing officer safety and reducing uncertainty about what to expect when attending to a call. This finding aligns with loimo and Aronson’s (2004) task-technology-fit and Manning’s empirical findings discussed earlier. For example, interviewees stated that technology allows them to determine the domestic violence history of a location before responding or lets them run a quick criminal history check on persons they are questioning in the field. Mostly, information was obtained from information technologies to find wrongdoing or warning signs of problems within existing situations to which officers had responded, which could then shape their subse- quent decisions. One officer in Avalon PD stated, “If you make a traffic stop for speeding and see that someone has been arrested four times for drugs, you will pay a lot of attention.” Some officers also suggested that having such informa- tion helped guide their discretion about not pursuing an arrest: “If you are going to cut somebody a break,” one officer pointed out, “you are more informed on their background [because of the information technology].” Lum et al. 145 Our interviews are aligned with our survey results with regard to technology and discretion. Although differences existed between the agencies (discussed later), Table 2 shows that patrol officers in both agencies were most likely to use technology to check criminal or location histories when responding to calls, collect and search for information during a field interview, or locate vehicles and individuals of interest in an investigation. In contrast, the most proactive, preventive, and community-oriented potential uses of technology-that is, determining how to respond to crime problems, determining where to patrol between calls, and providing general information to citizens (not related to a Table 2. Survey Results for Technology, Discretion, and Decision-Making Among Patrol Officers. Question: To what extent do you use information technology and analytic systems to do the following (Often/Very Avalon Greenvale Often, Sometimes, or Never/Rarely) PD (%) PD (%) Check the history of a specific location or persons before responding to a call for service Collect and search for information during a field interview Locate suspects, wanted persons, and other persons of interest Locate vehicles of interest Determine how to respond to a crime problem Determine where to patrol when not answering a call for service Provide information to citizens that is not related to a specific call or emergencies Often/Very Often Sometimes Never/Rarely Note. PD= police department. The sample size varies for each item listed above and for each agency. The sample size range is 267-268 for Avalon PD and 153-157 for Greenvale PD. 69 27 4 52 35 13 46 43 II 34 46 20 18 39 43 I5 35 51 II 37 52 146 Police Quarterk 20(2) particular call) were among the least frequent uses of technology reported by officers from both agencies. Thus, technology was less often used to proactively identify or address prob- lems during an officer’s noncommitted time (see Kelling, Pate, Dieckman, & Brown, 1974) and more often used in response to experiential and visual cues during their reactive response to calls. Officers in both agencies frequently mentioned that there was little systematic guidance or encouragement for using technology during their noncommitted time. Consequently, more pro- active technology uses appeared dependent on the will and skill of individual officers. For example, an officer in Greenvale PD mentioned that while he checked the crime analysis unit’s website at the beginning of his shift to guide his patrol work, he did not believe this to be the norm with other officers. One of his colleagues remarked that officer activities during noncommitted time were often guided by the “flavor of the week” as opposed to systematic analysis. A patrol officer in Avalon PD said that officers do not regularly per- form such proactive activities unless they get bored or are personally motivated. In general, technology’s influence on the noncommitted, discretionary time of those with whom we spoke appeared very modest. We found similar patterns in our discussions with detectives. In both agencies (and especially in Greenvale PD), detectives most heavily used and valued crime analysis for tracking leads in cases they were investigating (in both agencies, crime analysts conducted geographic analyses to support patrol operations but also supported detectives with analyses of cases, patterns, persons, and groups). One exception was provided by detectives in Greenvale PD, who stated that sometimes they would use information on a crime series to anticipate future events or to determine patterns within a modus operandi. However, the use of crime analysis for proactive decision-making that might help to prevent crime was rare. Yet, substantial differences also existed between the agencies in the agency- wide surveys, as noted in Table 2. Greenvale PD officers reported being consid- erably more likely than their counterparts in Avalon PD to use information technologies and crime analysis often or very often to determine how to respond to a crime problem (48% vs. 18%, respectively), determine where to proactively patrol (50% vs. 15%), and provide general information to citizens (29% vs. 11%). Not only did Greenvale PD officers use technology more often for pro- active uses compared with Avalon PD but they were also more likely to use technology in more traditional capacities as well, such as locating individuals and vehicles of interest. This finding is reinforced by additional survey items in Table 3, which show that patrol officers in Avalon PD were more likely than officers in Greenvale PD to rely on their own experience, rather than on infor- mation technologies to make decisions (87% agreement vs. 60%, respectively). On the other hand, officers in Greenvale PD were more likely than Avalon PD officers to view those technologies as useful to their self-initiated activities (82% agreement vs. 52%). Lum et al. 147 Table 3. Impact of Technology on Proactive Discretion Among Patrol Officers. Avalon PD Greenvale PD average (% agree) average (% agree) When making decisions about 3.16 (87%) 2.70 (60%) crime problems, I tend to rely more on my own experience than using information technologies Information technologies help 2.48 (52%) 2.95 (82%) me to engage in proactive, self-initiated activities Note. PD = police department. The sample size for Avalon PD is 255 and for Greenvale PD is 147. Some of these differences may reflect longer exposure to advanced informa- tion systems in Greenvale PD as well as better functioning systems in that agency (see Discussion and Conclusion section). However, managerial approaches, agency leadership, and culture also appeared to play a significant role in shaping the technological frames and behaviors of line-level officers. For instance, although both agencies used crime analysis to identify crime hot spots and pat- terns, Greenvale PD commanders placed a greater emphasis on using crime analysis to guide operational decisions. This managerial emphasis was apparent in our fieldwork as well as in our survey results. Supervisors and commanders from Greenvale PD who answered our survey reported being much more likely than those in Avalon PD to use information and analytic technologies often or very often to identify crime trends and problems (63% vs. 48%, respectively), determine what to do about crime trends and problems (51% vs. 34%), focus the activities of personnel on specific problematic locations (58% vs. 48%), and share information with community and business leaders (41% to 24%). In contrast, managers in Avalon PD would often or very often use technology for more traditional purposes and administrative tasks, such as monitoring the activities of officers (57% in Avalon PD to 43% in Greenvale PD) and identify- ing problem behaviors among subordinates (40% in Avalon PD to 26% in Greenvale PD). Discretion and the Contradictions of Efficiency Officers in both agencies judged the value that technology brings to discretion through the technological frame of efficiency. By efficiency, we specifically mean technical efficiency as described by the maximizing of outputs with the fewest resources or costs as possible. Officers often verbalized these outputs as gaining 148 Police Quarterk 20(2) information fasteror with greater ease. However, it should be noted that officers did not necessarily discuss efficiency in terms of maximizing outputs against minimizing costs and resources, as would be described in the performance man- agement literature, above.5 Rather, they focused on (most often) how quickly technology helps them achieve a task, with the greatest ease of effort. For exam- ple, mobile computer terminals (MCTs) were positively viewed as providing officers with information quickly (as opposed to calling in to a dispatcher and waiting for information to be returned), thereby giving them greater ability to retrieve information in the field than in the past, when they would have to call dispatch or conduct a manual search of files at the records management division. Greenvale PD detectives were especially supportive of crime analysis because of such efficiencies that analysts brought to their work. One detective remarked that crime analysis was “very useful, and frees us to do actual detective work- knock on doors, interview people, and talk to folks.” Another also added that “information that would have taken a whole team in homicide to collect over several weeks can take a couple of guys a few days now.” One commander remarked that technology “allowed for more data connectivity” and that “cer- tain things are now done faster, such as DNA or fingerprinting.” Regional databases and the Internet were also seen by Avalon PD detectives as facilitating their investigations. Because efficiency is the lens (or frame) through which officers perceive and use technology, problems with efficiency have significant ramifications for how tech- nology influences discretion. In some cases, these problems reduce or constrain discretion and discretionary time (similarly found by Chan, 2001). This point was emphasized by officers from Avalon PD, which, as noted, had just undergone a major change from a paper-based system. Many complained that the new auto- mated system increased the time required to write reports, distracted them from other duties, and reduced their job satisfaction. These changes were compounded by new reporting codes that officers had to learn for incident-based reporting and by connectivity problems with the new system that sometimes delayed their ability to file reports or caused them to lose their work. As one officer summarized: “Bottom line is that it is taking us longer to do our job and document what we do-whether we are tech savvy or not.” Sometimes technology use challenged efficiency so much that officers avoided using it altogether. For example, at the beginning of the transition to the new system, Avalon PD experienced a significant drop in traffic citations, which in the past were used as proactive activities conducted in-between calls for service. Some officers stated that writing traffic citations “wasn’t worth it” given the burdens of the computerized report writing system. One commander, reflect- ing about this move, stated that it “may sound good at the strategic level, but on the ground it may be more difficult … Paper is part of a larger system; when you go from paper to digital, it disrupts that system” (as illustrated by Ericson & Haggerty, 1997). Lum et al. 149 Officers in Greenvale PD, on the other hand, were much more positive about their technology systems and the ability of those systems to enhance productiv- ity. Hence, the impact of the technological frame of efficiency on discretion may diminish over time (or be replaced by alternative frames (i.e., outcome effective- ness and proactivity) depending on how long an agency has been using infor- mation technologies, how well those systems function, and the degree to which they are user-friendly). Figure 1 shows the differences between patrol officers in Agencies 1 and 2 on a series of efficiency-related survey questions. Large majo- rities of respondents in the survey for Greenvale PD agreed or strongly agreed that technology was easy to use (74%) and improved productivity (86%), com- pared with only 27% and 38%, respectively, in Avalon PD. Avalon PD officers, in contrast, overwhelmingly reported that the use of technology creates extra work (86% vs. 49% in Greenvale PD), and they were less likely to be satisfied with the quality of information in their systems (49% were satisfied vs. 76% in Greenvale PD). These differences in perceptions of the efficiency gains of tech- nology seemed to be another factor contributing to the differences in technology use described earlier. Officers in Avalon PD had difficulties adapting to and using their technology systems, which in turn reduced their time for proactive prevention-oriented work and also seemed to worsen their perceptions of the utility of using technology for these purposes. Perceptions of how technology impacted efficiency and work productivity (and further, discretion) also varied across ranks and units within each agency, producing the incongruencies mentioned by Orlikowski and Gash (1994). Higher ranking commanders, in general, tended to view technology and analysis more favorably from the standpoint of efficiency and productivity than did unranked officers and detectives and even first-line supervisors, who felt Technology is easy to use I Technology leaves me satisfied with quality of info I Technology creates extra work for me Technology improves my productivity 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Agency 1 I Agency 2 Figure 1. Percentage of patrol officers who agreed or strongly agreed about the efficiency aspects of technology. The sample size range is 267-268 for Avalon PD and 136-152 for Greenvale PD. I50 Police Quarterly 20(2) more burdened by reporting requirements and were somewhat suspicious of management using technology or analysis as a mechanism for monitoring them. This division was primarily the case in Avalon PD, where 74% of second-line supervisors and higher level command staff reported in the survey that technology made them more productive, in contrast to 42% of line-level staff and first-line supervisors. Higher ranking officers in Avalon PD also found reports easier to read and share with other shift supervisors as a result of the new system, and many were able to more quickly account for completed reports. Incongruence was also found between detective units and patrol. Detectives were much more likely to value crime analysis than their patrol counterparts and view information systems as useful to their daily work. They were able to articu- late why they valued crime analysis and provide examples of using that resource. In contrast, patrol officers questioned the analysts’ role in policing and either did not understand the purpose of analysis or saw it as unhelpful. Differences in the views of detectives and patrol officers were most pronounced in Avalon PD, where 50% of detectives agreed that technology made them more productive in comparison to 38% of patrol officers. Detectives and patrol officers in Greenvale PD reported fewer and smaller differences in their views in the survey and were much more positive than their Avalon PD counterparts (80% and 86% agreeing, respectively). Overall, views about technology and efficiency were much more consistent across ranks and assignments in Greenvale PD. This consistency suggests that perception gaps between units and ranks may become less pronounced as an agency becomes more accustomed to its IT and crime analytic systems and when agency leadership places a more consistent emphasis on the strategic use of these technologies. How Frames Affect Perceptions of Effectiveness The technological and organizational frames discussed earlier affect not only discretionary behavior (which in turn impacts outcomes) but also influence def- initions and expectations about outcomes themselves. For example, the term effectiveness was most often defined by officers (and used interchangeably) to mean efficiency or the ability to respond to crime and to quickly identify sus- pects, victims, witnesses, and other aspects of crimes to resolve cases. Less often did officers define effectiveness in terms of their ability to achieve specific out- comes of interest to the police department, such as preventing crime or improv- ing their relationship with community members. Some evidence of how technological frames impact perceptions of effective- ness can be seen in our agency-wide survey results. As shown in Figure 2, when asked about whether information and analytic technology made them more effective, patrol officers were most likely to agree that it made them more effective in “identifying and locating suspects, wanted persons, and other persons of interest” (75% agreed or strongly agreed in Avalon PD and 94% Lum et al. I51I ID and locate people Preventing crime Problem-solve Interacting w/citizens Helping victims 0% 20% 40% 60% 80% 100% Agency 1 * Agency 2 Figure 2. Percentage of patrol officers who agreed or strongly agreed that technology could help them with proactive or community-oriented tasks. The sample size range is 266-268 for Avalon PD and 137-150 for Greenvale PD. Note. PD= police department. agreed or strongly agreed in Greenvale PD). Officers in both agencies were considerably less likely to agree that technology enhanced their ability to reduce crime when not responding to calls for service (47% and 69%), to under- stand and respond to crime problems (56% and 86%), to interact and commu- nicate with citizens (30% and 55%), and to provide assistance to crime victims (37% and 78%). This pattern was also apparent for detectives in both agencies (results not shown). Across the board, officers in Greenvale PD were much more likely to believe that technology made them more effective in these regard, which would again seem to reflect their higher levels of comfort and experience in using their IT systems, as well as Greenvale PD’s greater overall emphasis on technol- ogy and data-driven decision-making. However, our qualitative work suggested that these survey differences may not have reflected large differences between the agencies in strategic, preventive uses of technology at the street level. Our interviews were especially insightful in probing the effectiveness issue. Detectives saw crime analytic technology as effective because it helped them catch offenders and close cases, while patrol officers discussed being able to quickly run information on stopped individuals to see if they were wanted. Officers and detectives were much less likely, on the other hand, to associate technology with improving crime control effectiveness or crime prevention effectiveness related to proactive policing. Although crime analytic, information-sharing, and records management technologies have been central to the development of strategies that target patrol and investigative activities to proactively reduce and prevent crime, sworn personnel at all ranks who were interviewed were much less likely to view technology as making the police more effective in these ways-except insofar as these enhanced their ability 152 Police Quarterly 20(2) to screen people when making proactive stops. When discussing effectiveness, officers in both agencies tended to emphasize anecdotal examples of identifying and apprehending suspects by searching for and piecing together information from various police and nonpolice databases that they could access in the field. Similarly, analysts from both agencies asserted that information technologies could assist with closing individual cases. However, they were more cautious when asked about the causal connection between the use of these technologies and clearance rate trends. Most individuals who discussed how technology contributed to crime reduc- tion strategies in patrol were high-level commanders or civilian crime analysts. An exception was officers and supervisors of a specialized unit within Greenvale PD, who were responsible for proactive policing tactics. They directly and con- sistently interacted with the crime analysis unit and found crime analysis tech- nology to be valuable to their work in a very different way than the detectives did. However, these officers were also trained in conducting problem-solving and using crime analysis. They also defined good police work differently than other officers in their agency, focusing more on an officer’s ability to be proactive and prevent crime. In other words, the organizational frames and technological through which this unit received technology differed from frames of other offi- cers, allowing them to view the effectiveness of technologies in different ways. One officer’s statement in that unit reflects this atypical state of mind: “[Using crime analysis to solve problems] is really the way of the future. It is not about getting more people or resources, but we have to be smarter and use what we have in a more intelligent way.” Aside from that unit, the lack of connection between technology and out- comes such as crime prevention, reductions in calls for service, or even improve- ments in community-oriented outcomes (i.e., community trust and satisfaction) was a common theme in our interviews and focus groups. When we asked offi- cers how they used their mobile terminals, for example, they rarely mentioned problem-solving strategies such as using them to select specific areas to pro- actively patrol or to study crime problems in hot spots. This trend was true among officers in Greenvale PD as well as those in Avalon PD, despite the survey ratings discussed earlier (see the higher scores for Greenvale PD officers in Figure 2). Indeed, our discussions suggested that survey respondents may have largely interpreted the questions about preventing crime and problem sol- ving through the lens of using technology to investigate people and incidents. Likewise, when we asked analysts in Greenvale PD about the requests they receive from officers and detectives, they reported that a majority relate to searches for vehicles, names, criminal histories, witnesses, arrest information, or partial descriptors (e.g., scars, nicknames, and tattoos) to help solve cases. They acknowledged that it is less often the case that officers asked them about hot spots, trends, patterns, or how to use analysis proactively (to prevent crime). In a related study, Koper, Lum and Hibdon (2015) found that even when officers Lum et al. 153 were directed to proactively patrol hot spots, they still primarily used technology for enforcement and surveillance, rather than problem solving or prevention. At the command level in both agencies, there was a much better understand- ing of how crime analytic and information technologies could help agencies identify places and people for purposes of proactive problem solving or crime prevention; yet, some also expressed skepticism. When the team asked one com- mander in Avalon PD how optimistic he was about officers on the street under- standing the value of crime analysis for crime prevention and using it toward those goals, he stated, “[T]he number one barrier to this approach were people’s attitudes, especially those who fight the system or think that [the new records management system] is garbage.” The same high-level official was unsure whether information technologies were connected with crime reduction or case clearances. Similarly, an unranked officer remarked, “There is no substitute to ‘good police work’ (emphasis added) and a certain amount of being in the right place at the right time.” Our discussions also sometimes revealed officers’ skepticism about the links between technology, analysis, and crime prevention, including a lack of explanation for why they were being directed to certain areas for preventative patrol. Patrol officers in Greenvale PD, for instance, pointed out that sometimes crime analysis forced them to “fight dots with cops.” They gave the example of field interview reports. Officers said that once hot spots are established through crime analysis, they are required to go to the hot spots and conduct field interview reports, with little understanding as to why. One officer stated, “Crime analysis just tells us what we already know.” Another stated, “We know where crimes are we don’t need a computer to tell us or Compstat meetings to tell us where they are.” Yet another remarked that using crime analysis to guide their patrol efforts “wasn’t worth the effort” but also acknowledged that they do not know the results of their efforts, nor do they get feedback. In addition to the impact that the efficiency frame had on perceptions of technology outcomes, we also found that officers held views about how technol- ogy can reduce outcome effectiveness. Some officers associated technology with increasing officer distraction, reducing officer situational awareness, or reducing the amount of time doing what they viewed as real police work. One officer remarked as follows: Ever since I have been on, you learned your area, you knew roads, road names, etcetera. Now we have GPS in the car. Before you had to be more aware of exactly where you are. Now, I think that officers have to almost force themselves to have to think “this computer is not here.” Another officer stated, “Officer safety issues with so much technology is bad” and “that it “reduces situational awareness.” He remarked that he has had “people sneak up on me while I’m typing.” We also often heard that some I54 Police Quarterk 20(2) officers were too reliant on technology and lacked skills in interacting with people in ways that might build good community relations and intelligence. Indeed, in the Koper et al. (2015) study mentioned above, greater use of tech- nology for enforcement or surveillance was actually associated with weaker crime prevention effects. This might be due to the unintended effects of technol- ogy that these officers are describing. In sum, officers interpret effectiveness not in reference to their agency’s or even their district’s effectiveness in reducing crime but in their individual effectiveness in carrying out their duties as defined by their general orders and their perception of their roles as officers. Discussion and Conclusion Police reformers and leaders place big hopes on technology to improve efficiency in police organizations and also effectiveness in a variety of outcomes. However, our examination of how technology impacts police effectiveness suggests com- plex linkages between the acquisition, implementation, and uses of technology, and desired outcomes like crime prevention, case clearances, or better police- community relations. Both organizational and technological frames mediate the relationship between the adoption, implementation, and use of technology, and outcomes sought. Specifically, the reactive standard model of policing that con- tinues to dominate law enforcement practice creates strong organizational and technological frames, which powerfully mediate the effects of technology on discretion, efficiency, and effectiveness. These frames influence what police think technology should be used for, and they also contribute to officers equat- ing efficiency with effectiveness (and not in the way that Hatry, 2014 envisioned). These perspectives can thereby limit the potential of technological use in recent reforms such as community policing, problem solving, intelligence-led policing, or other innovations. In the agencies we studied, technology was not used sys- tematically throughout the organization to identify crime patterns, to learn how to best respond to crime problems (see Willis et al., 2007), or to facilitate other innovations. In particular, police officer views on technology are strongly shaped by the value they place on technical efficiency, which is a dominant technological frame. Efficiency is such a powerful frame that other important effects such as crime reduction, proactivity, and outcome effectiveness are swallowed into the frame and its meaning. When technologies are introduced that are not regarded as efficient or do not contribute to what officers believe to be their primary tasks, then those technologies are resisted, even if their use may make officers more effective. This resistance is especially acute among those who are disproportion- ately affected by its implementation, evidencing incongruence mentioned by Orlikowski and Gash. This explains why commanders, supervisors, and detect- ives who used records management and report writing systems less (particularly in Avalon PD) were more positive about technology’s cost benefits than patrol Lum et al. I55 officers who had to struggle with laborious data entry processes. Incongruence of technological frames across ranks or units within an agency can also impede a strong connection between technology and outcome effectiveness and lead to cynicism and frustration among lower ranks. However, comparing Avalon PD with Greenvale PD, such incongruence can be moderated by management practices, agency culture, and other contextual factors. We discovered large differences between our two agencies on the impact of these frames attributed to ease of use/familiarity with technology, to what degree management used and advocated for technology, and other aspects of the organization that facilitated technology use. Indeed, officers in Greenvale PD were more likely to see the value of technology in accomplishing valued out- comes. This was especially true of specialist units who had direct experience with how technology could be used for proactive policing and crime prevention. Further, comparing Avalon and Greenvale PDs illustrates how desired effects from technology (such as improving clearance rates and reducing crime) may take considerable time to materialize, if they do at all, as agencies adapt to new technologies and refine their designs, operations, and uses over time. All of these factors may contribute to the absence of a clear and consistent relationship between technological advances and improved performance in poli- cing. Of course, generalizing from our findings should be done cautiously, as they are based on the study of two police agencies with experiences that may be different from those of many other agencies. Further, our surveys and interviews assessed agency personnel’s experiences with and perceptions of technology; as such, they help to illuminate the dynamics of technological change in police agencies but do not provide a basis for causal inferences. Nonetheless, we specu- late on the implications of the patterns revealed here, patterns that echo themes found in other theoretical and empirical work, as they might be instructive to those seeking to use technology to impact change (see, e.g., Chan et al., 2001; Garicano & Heaton, 2010; Manning, 1992a, 1992b). For example, our findings are provocative because a great deal of research now suggests that the standard policing model that so strongly shapes policing frames is not as effective in reducing, preventing, or even clearing crime than models which are proactive, problem-oriented, targeted, and place-based (e.g., Lum, Koper, & Telep, 2011; National Research Council, 2004; Sherman & Eck, 2002; Weisburd & Eck, 2004). However, despite the interest of some police chiefs and scholars in advancing a more proactive and problem-oriented policing model, police are still very much focused on reacting to individual incidents, leading officers to judge the immediate gains and losses of technological change on efficiency in the context of their position within the agency and their perceived roles and responsibilities. This is why introducing technological innovations such as crime analysis and information technologies may not produce expected returns for new policing paradigms that incorporate these approaches (i.e., prob- lem solving, evidence-based policing, and community policing) unless officers see I156 Police Quarterk 20(2) these alternative approaches as “real police work”. Hence, strengthening these frames and cultures through training about proactive and evidence-based stra- tegies and the development of training, organizational infrastructures, and reward systems for the use of technology in support of these strategies would be important adjustments that could help agencies receive and use tech- nology in more innovative ways and reap greater benefits from technological advancements. Although our discussion has focused on information and analytic technolo- gies, we can also see these same difficulties playing out with other technologies in the law enforcement arena. Body-worn cameras, for example, are touted as a technology that will improve police accountability and police-community rela- tionships. However, police often view them as a way to protect themselves from the community (as their insurance against frivolous or false complaints, for example. Or, officers may view body cameras as restricting their discretion, per- haps causing them to shy away from proactive activities or to become more legalistic and rigid in their decision-making. In other words, how police officers perceive and use body-worn cameras may be quite different from the commu- nity’s intended objectives (e.g., reducing implicit bias or increasing accountabil- ity) because of the technological frames by which they are filtered. From a policy and practice perspective, adjusting those frames (e.g., through training, technical support, and organizational incentives) becomes important to adjusting the outcomes that agency leaders or citizens want from technology. Consequently, developing more insight about frames may be very helpful to police in planning for and implementing technological changes. In particular, anticipating outcomes by understanding the filters through which technology is implemented can focus a commander’s attention on adjusting those filters (or frames), rather than on simply implementing technology and hoping for an innovative outcome. In conclusion, technological adoption can not only be a challenging and continuous process, but one that is connected to many other aspects of policing, including daily routines and deployments, job satisfaction, interaction with the community, internal relationships, and crime control outcomes. Consequently, technological changes may not bring about easy and substantial improvements in police performance without significant planning and effort to adjust techno- logical and organizational frames. In turn, managing technological change in policing is closely connected to managing other organizational reforms that attempt to adjust those frames, such as improving professionalism and changing accountability mechanisms. Strategizing about technology application is thus essential and should involve careful consideration of the specific ways in which new and existing technologies can be designed, deployed, and used at all levels of the organization to meet goals for improving efficiency, effectiveness, and agency management. Further attention to these issues may help police to more fully realize the potential benefits of technology for policing. Lum et al. I57 Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors disclosed receipt of the following financial support for the research, author- ship, and/or publication of this article: This work was supported by the National Institute of Justice, U.S. Department of Justice (grant number 2010-MU-MU-0019). Notes 1. The results presented here are taken from a larger study that involved four agencies and examined several information, crime analytic, communications, sensory/surveillance, and forensics technologies (Koper, Lum, Willis, Woods, & Hibdon, 2015). We focus here on two sites where the authors conducted all fieldwork and where we obtained substantially higher response rates to the officer survey dis- cussed later. We also limit our discussion to information and analytic technologies, which we emphasized more heavily across the study sites (particularly in the officer survey). The larger study also examined a number of additional themes (e.g., the interactions of technology with organizational structures and internal accountability and management systems) that are discussed elsewhere (references to be added after peer review). 2. Pseudonyms are used to protect the identity of the agencies and their members, per human subjects and confidentiality agreements with those agencies. 3. Acting on behalf of the research team, the command staff of each agency sent an e- mail to all sworn staff that provided background on the project, explained the purpose of the survey, and provided a link to the survey site. We conducted the survey over several weeks in each agency, sending out periodic reminder e-mails through the agency’s command staff. In the Avalon PD, we supplemented this approach with hard copy distribution of the survey at randomly selected roll calls to boost an initially low response rate. 4. In Greenvale PD, patrol officers tended to be underrepresented among survey respondents, while detectives had very high response rates. 5. This important point was raised by one reviewer of this article. References Agrawal, M., Rao, H., & Sanders, G. L. (2003). Impact of mobile computing terminals in police work. Journal of Organizational Computing & Electronic Commerce, 13(2), 73-89. Allen, D., & Karanasios, S. (2011). Critical factors and patterns in the innovation pro- cess. Policing: A Journal of Policy and Practice, 5(1), 87-97. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Boba-Santos, R. (2014). The effectiveness of crime analysis for crime reduction: Cure or diagnosis? Journal of Contemporary Criminal Justice, 30(2), 147-168. 158 Police Quarterk 20(2) Boudreau, M. C., & Robey, D. (2005). Enacting integrated information technology: A human agency perspective. Organization Science, 16(1), 3-18. Brown, M. M. (2001). The benefits and costs of information technology innovations: An empirical assessment of a local government agency. Public Performance and Management Review, 24(4), 351-366. Brown, M. M. (2014). Revisiting the IT productivity paradox. American Review of Public Administration, 45(5), 565-583. Brown, M. M., & Brudney, J. L. (2003). Learning organizations in the public sector? A study of police agencies employing information and technology to advance knowledge. Public Administration Review, 63(1), 30-43. Brown, M. M., & Brudney, J. L. (2004). Achieving advanced electronic government services: Opposing environmental constraints. Public Performance and Management Review, 28(1), 96-113. Chan, J. (2001). Technological game: How information technology is transforming police practice. Criminal Justice: The International Journal of Policy and Practice, 1, 139-159. Chan, J. (2003). Police and new technologies. In T. Newburn (Ed.), Handbook of policing (pp. 655-679). Cullompton, Devon, England: Willan Publishing. Chan, J., Brereton, D., Legosz, M., & Doran, S. (2001). E-policing: The impact of infor- mation technology on police practices. Brisbane, Australia: Queensland Criminal Justice Commission. Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. London, England: Sage. Colton, K. (1980). Police computer technology: The case of the San Diego computer- aided dispatch system. Public Productivity Review, 4(1), 21-42. Colvin, C. (2001). Evaluation of innovative technology: Implications for the community policing roles of law enforcement officers. San Francisco, CA: Psychology Department, San Francisco State University. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Thousand Oaks, CA: Sage. Creswell, J. W., & Plano-Clark, V. L. (2011). Designing and conducting mixed methods research (2nd ed.). Thousand Oaks, CA: Sage. Danziger, J. N., & Kraemer, K. L. (1985). Computerized data-based systems and prod- uctivity among professional workers: The case of detectives. Public Administration Review, 45(1), 196-209. Ericson, R. V., & Haggerty, K. D. (1997). Policing the risk society. Toronto, Canada: University of Toronto Press. Garicano, L., & Heaton, P. (2010). Information technology, organization, and product- ivity in the public sector: Evidence from police departments. Journal of Labor Economics, 28(1), 167-201. Goffman, E. (1974). Frame analysis: An essay on the organization of experience. London, England: Harper and Row. Goodhue, D. L. (1988). IS attitudes: Toward theoretical and definitional clarity. Data Base, 19(3-4), 6-15. Goodhue, D. L. (1995). Understanding user evaluations of information systems. Management Science, 41(12), 1827-1844. Lum et al I59 Goodman, P., & Pennings, J. (Eds.). (1977). New perspectives on organizational effective- ness. San Francisco, CA: Jossey-Bass. Groff, E., & McEwen, T. (2008). Identifying and measuring the effects of information technologies on law enforcement agencies: The making officer redeployment effective program. Institute for Law and Justice Report. Washington, DC: Office of Community Oriented Policing Services, U.S. Department of Justice. Harris, C. J. (2007). The police and soft technology: How information technology contrib- utes to police decision making. In J. Byrne & D. Rebovich (Eds.), The new technology of crime, law and social control (pp. 153-183). New York, NY: Criminal Justice Press. Hatry, H. (1978). The status of productivity measurement in the public sector. Public Administration Review, 38(1), 28-33. Hatry, H. (2014). Transforming performance measurement for the 21st century. Washington, DC: The Urban Institute. Ioimo, R. E., & Aronson, J. E. (2003). The benefits of police field mobile computing realized by non-patrol sections of a police department. International Journal of Police Science and Management, 5(3), 195-206. Ioimo, R. E., & Aronson, J. E. (2004). Police field mobile computing: Applying the theory of task-technology fit. Police Quarterly, 7(4), 403-428. Kelling, G. L., & Moore, M. H. (1988). The evolving strategy of policing. Perspectives on Policing, No. 4. Washington, DC: U.S. Department of Justice, National Institute of Justice. Kelling, G. L., Pate, T., Dieckman, D., & Brown, C. E. (1974). The Kansas City preventative patrol experiment: A summary report. Washington, DC: The Police Foundation. Koper, C. S., Taylor, B. G., & Woods, D. J. (2013). A randomized test of initial and residual deterrence from directed patrols and use of license plate readers at crime hot spots. Journal of Experimental Criminology, 9(2), 213-244. Koper, C. S., Lum, C., Willis, J., Woods, D., & Hibdon, J. (2015). Realizing the Potential of Technology in Policing: A Multisite Study of the Social, Organizational, and Behavioral Aspects of Implementing Police Technologies. Washington, DC: National Institute of Justice. Koper, C. S., Lum, C., & Hibdon, J. (2015). The uses and impacts of mobile computing technology in hot spots policing. Evaluation Review, 39(6), 587-624. La Vigne, N., Lowry, S., Markman, J., & Dwyer, A. (2011). Evaluating the use of public surveillance cameras for crime control and prevention. Washington, DC: Urban Institute. Lum, C. (2010). Technology and mythology of progress in American law enforcement. Science Progress, February 11, 2010. Retrieved from http://www.scienceprogress.org/ 2010/02/police-technology/ Lum, C., Koper, C. S., & Telep, C. (2011). The evidence-based policing matrix. Journal of Experimental Criminology, 7(1), 3-26. Lum, C., Hibdon, J., Cave, B., Koper, C. S., & Merola, L. (2011). License plate reader (LPR) police patrols in crime hot spots: An experimental evaluation in two adjacent jurisdictions. Journal of Experimental Criminology, 7(4), 321-345. Maguire, E. (2014). Police organizations and the iron cage of rationality. In M. Reisig & R. Kane (Eds.), The Oxford handbook of police and policing (pp. 68-98). New York, NY: Oxford University Press. I160 Police Quarterk 20(2) Manning, P. (1977). Police work. Prospect Heights, IL: Waveland Press. Manning, P. (1992a). Technological dramas and the police: Statement and counterstate- ment in organizational analysis. Criminology, 30(3), 327-346. Manning, P. (1992b). Information technologies and the police. Crime and Justice, 15, 349-398. Manning, P. (2008). The technology of policing. New York: New York University Press. Mastrofski, S. D., & Willis, J. J. (2010). Police organization continuity and change: Into the twenty-first century. Crime and Justice, 39(1), 55-144. McGowan, R. (1984a). Improving efficiency in public management: The torment of Sisyphus. Public Productivity Review, 8(2), 162-178. McGowan, R. (1984b). Strategies for productivity improvement in local government. Public Productivity Review, 8(4), 314-331. National Research Council. (2004). Fairness and effectiveness in Policing: The evidence. Washington, DC: The National Academies Press. Nunn, S. (1993). Computers in the cop car: Impact of the mobile digital terminal tech- nology on motor vehicle theft clearance and recovery rates in a Texas City. Evaluation Review, 17(2), 182-203. Nunn, S. (1994). How capital technologies affect municipal service outcomes: The case of police mobile digital terminals and stolen vehicle recoveries. Journal of Policy Analysis and Management, 13(3), 539-559. Nunn, S. (2001). Police information technology: Assessing the effects of computerization on urban police functions. Public Administration Review, 61(2), 221-234. Nunn, S., & Quinet, K. (2002). Evaluating the effects of information technology on problem-oriented-policing: If it doesn’t fit, must we quit? Evaluation Review, 26(1), 81-108. Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technol- ogy in organizations. Organization Science, 3(3), 398-427. Orlikowski, W. J. (2000). Using technology and constituting structures: A practical lens for studying technology in organizations. Organization Science, 11(4), 404-428. Orlikowski, W. J., & Gash, D. C. (1994). Technological frames: Making sense of infor- mation technology in organizations. ACM Transactions on Information Systems, 12(2), 174-207. Quinn, R., & Rohrbaugh, J. (1981). A competing values approach to organizational effectiveness. Public Productivity Review, 5(2), 122-140. Ratcliffe, J. (2008). Intelligence-led policing. Cullompton, Devon, England: Willan publishing. Robey, D., Boudreau, M. C., & Rose, G. M. (2000). Information technology and organ- izational learning: A review and assessment of research. Accounting, Management Information Technology, 10(2), 125-155. Rocheleau, B. (1993). Evaluating public sector information systems: Satisfaction versus impact. Evaluation and Program Planning, 16(2), 119-129. Roman, J. K., Reid, S., Reid, J., Chalfin, A., Adams, W., & Knight, C. (2008). The DNA field experiment: Cost-effectiveness analysis of the use of DNA in the investigation of high-volume crimes. Washington, DC: The Urban Institute. Rosenbaum, D. P., Graziano, L. M., Stephens, C. D., & Schuck, A. M. (2011). Understanding community policing and legitimacy-seeking behavior in virtual reality: A national study of municipal police websites. Police Quarterly, 14(1), 25-47. Lum et al 161 Rutgers, M. R., & van der Meer, H. (2010). The origins and restriction of efficiency in public administration: Regaining efficiency as the core value of public administration. Administration & Society, 42, 755-779. Sanders, C. B., & Henderson, S. (2013). Police ’empires’ and information technologies: Uncovering material and organisational barriers to information sharing in Canadian Police Services. Policing and Society, 23(2), 243-260. Sanders, C. B., Weston, C., & Schott, N. (2015). Police innovations, ‘secret squirrels’ and accountability: Empirically studying intelligence-led policing in Canada. British Journal of Criminology, 55(4), 711-729. Sherman, L. W., & Eck, J. E. (2002). Policing for crime prevention. In L. W. Sherman, D. P. Farrington, B. C. Welsh & D. L. MacKenzie (Eds.), Evidence-based crime prevention (pp. 295-329). New York, NY: Routledge. Strauss, A. L., & Corbin, J. (1990). Basics of qualitative research: Grounded theory pro- cedures and techniques. Newbury Park, CA: Sage. Taylor, B., & Boba, R. (2011). The integration of crime analysis into patrol work: A guidebook. Washington, DC: U.S. Department of Justice. Weick, K. E. (1995). Sensemaking in organizations. Thousand Oaks, CA: Sage. Weisburd, D., & Eck, J. E. (2004). What can the police do to reduce crime, disorder, and fear? The Annals of the American Academy of Political and Social Science, 593(1), 42-65. Willis, J., Mastrofski, S., & Weisburd, D. (2007). Making sense of COMPSTAT: A theory-based analysis of organizational change in three police departments. Law and Society Review, 41(1), 147-187. Zaworski, M. J. (2004). Assessing an automated, information sharing technology in the post “9-11” era -Do local law enforcement officers think it meets their needs? (Doctoral Dissertation). Florida International University, Miami, FL. Author Biographies Cynthia Lum is an associate professor in the Department of Criminology, Law and Society and director of the Center for Evidence-Based Crime Policy at George Mason University. Her research has included evaluations of policing interventions and police technology, understanding the translation and receptiv- ity of research in evidence-based policing, examining place-based determinates of street-level police decision-making, and assessing security efforts of federal agen- cies. She received a PhD in criminology and criminal justice from the University of Maryland in 2003 and is a former police officer and detective. Christopher S. Koper is an associate professor in the Department of Criminology, Law and Society at George Mason University and the Principal Fellow of George Mason’s Center for Evidence-Based Crime Policy. Dr. Koper holds a PhD in criminology and criminal justice from the University of Maryland and has worked previously for the Police Executive Research Forum, the University of Pennsylvania, the Urban Institute, the RAND 162 Police Quarterk 20(2) Lum et al. 163 Corporation, the Police Foundation, and other organizations. His current research in policing focuses on the effects of police technology, police crime control strategies (particularly place-based and firearms-focused strategies), and the institutionalization of evidence-based practices in policing. James Willis is an associate professor in the Department of Criminology, Law and Society at George Mason University. His current research examines the relationship between the craft and science of policing and the effects of technol- ogy on police organizational structures and practices. He earned his PhD in sociology from Yale University in 2000.
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