Advances in technology continue to revolutionize policing in important ways. Three key advancements that are being used today are body-worn cameras, license plate readers, and gunshot detection syste
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Strategic policing philosophy and the acquisition
of technology: findings from a nationally
representative survey of law enforcement
Joshua A. Hendrix, Travis Taniguchi, Kevin J. Strom, Brian Aagaard & Nicole
Johnson
To cite this article: Joshua A. Hendrix, Travis Taniguchi, Kevin J. Strom, Brian Aagaard & Nicole
Johnson (2019) Strategic policing philosophy and the acquisition of technology: findings from a
nationally representative survey of law enforcement, Policing and Society, 29:6, 727-743, DOI:
10.1080/10439463.2017.1322966
To link to this article: https://doi.org/10.1080/10439463.2017.1322966
Published online: 07 May 2017.Submit your article to this journal Article views: 778View related articles View Crossmark dataCiting articles: 2 View citing articles Strategic policing philosophy and the acquisition of technology:
findings from a nationally representative survey of law
enforcement
Joshua A. Hendrix, Travis Taniguchi, Kevin J. Strom, Brian Aagaard and Nicole Johnson
Policing, Security, and Investigative Science, Center for Justice, Safety, and Resilience, RTI International, Research
Triangle Park, NC, USA
ABSTRACTPolice departments that emphasise certain strategic models (e.g.
community-oriented policing, problem-oriented policing) may adopt
specific types of technology to better achieve their core missions. A
contrasting theory is that police agencies do not invest strategically in
technology; rather, they adopt technology in a‘black box ’without a
larger plan for how a particular technology fits within the agency’ s
guiding philosophy or operational goals. Despite the importance of this
discourse, very little research has been conducted to address these
claims. Using survey data from a large and nationally representative
sample of police agencies in the United States ( N= 749), we examine
whether strategic police goals are associated with technology use for six
core technologies (crime mapping, social media, data mining software,
car cameras, license plate readers (LPRs), and body-worn cameras
(BWCs)). Nationally, across the sample of all US law enforcement
agencies, we find little relationship between strategic goals and
technology. Agency size, rather than policing philosophy was a more
important determinant of technology use. However, stronger
relationships between strategy and technology emerged when the
analysis was limited to a subsample of larger agencies (250 or more
sworn officers). Specifically, community and hot spot policing strategies
were positively associated with the use of geographic information
system technology, social media, and LPRs. Agencies who emphasised
hot spot policing were also more likely to have used BWCs. Implications
of these findings are discussed. ARTICLE HISTORYReceived 29 September 2016
Accepted 20 April 2017
KEYWORDSLaw enforcement;
technology; policing strategy
Advancements in computers and communication tools over the last several decades have made
numerous technologies available to law enforcement that were virtually unheard of even a few
decades ago. Many departments are implementing technology to increase efficiency and improve
outcomes, especially in times of diminished resources and enhanced public scrutiny of law enforce-
ment tactics. Despite the accelerating use of technology in policing and speculative connections
between technology and policing tactics, it is not well understood how technology is selected by
police agencies. It is also unclear as to whether the acquisition of technology among law enforcement
is calculated and rationally driven or based on a constellation of ad hoc factors that do not necessarily
relate to the fit between an agency ’s goals and the technology it implements. Importantly, police
agencies vary in philosophy, culture, management strategies, and goals (Weiss 1997), yet there is
limited information on the extent to which the devotion to particular strategic models is linked to
the use of particular technological devices.
© 2017 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Joshua A. Hendrix [email protected]
POLICING AND SOCIETY
2019, VOL. 29, NO. 6, 727 –743
https://doi.org/10.1080/10439463.2017.1322966 Given that technology can have a dramatic impact on how policing is done, how successful com-
munity relations are, and to what extent public safety is ensured, it is imperative that police execu-
tives, elected and appointed officials, and policy-makers have sound empirical evidence regarding
the factors that affect technology acquisition. We drew upon data from a nationally representative
survey of police agencies ( N= 749) to address the following research question: To what extent are
different technological advancements associated with policing strategies that are designed and
implemented to control crime? Our analysis focused on six technologies: crime mapping, social
media, data mining software, car cameras, license plate readers (LPRs), and body-worn cameras
(BWCs).
Theoretical framework
Technological advances in recent years have changed the nature of policing so significantly that
many methods from just a decade ago have become antiquated and incompatible with current tech-
nology (Goodison et al.2015 ). Some of these advances include location-monitoring devices for the
tracking of high-rate offenders, predictive analytics and crime mapping software for the deployment
of officers into crime hotspots, crime scene technology that enhances the collection and processing
of evidence, and interoperable Web-based and other communication devices that facilitate connec-
tions between police and the communities they serve. Although these advances have enhanced
police capabilities (Danziger and Kraemer 1985, Roth et al.2000 , Ioimo and Aronson 2004, Roman
et al .2008 ), it is not clear that they have made police more effective (Sherman and Eck 2002, Lum
2010 ). For example, despite advances in DNA technology and computer databases for handling for-
ensic data, clearance rates for violent and property crime have remained relatively stable since the
mid-1990s (Federal Bureau of Investigation 1996,2011 ). Regardless, it is indisputable that technologi-
cal innovations have created seismic shifts in law enforcement tactics and can have a major effect on
what police do on a day-to-day basis (Harris 2007). It is therefore important to understand key factors
that influence the adoption of police technology. Generally speaking, the processes by which law enforcement technology is acquired are not well
understood. One commonly invoked theoretical perspective for understanding technology acqui-
sition within organisations is the diffusion of innovations model, which classifies adopters of technol-
ogy as innovators, early adopters, early majority, late majority, and laggards (Rogers 1962). Although
this taxonomy has intuitive appeal, it is limited in its ability to describe how technologies are acquired
by police departments. For example, the categories are not mutually exclusive in practice. Police
agencies do not easily fit into one subgroup when considering a specific type of technology, let alone
across different types of technology. An agency could be considered both an early adopter and a
laggard when it comes to geographic information systems (GIS) technology if mapping is done at
an aggregate level but without incident-based geocoding. Additionally, the same agency may be
a laggard in regard to LPR usage but an innovator in the use of BWCs. Thus, a more comprehensive
conceptual framework is needed to understand whether strategic models are related to technology
acquisition. The existing literature on organisational choice provides a useful starting point and an overarching
theoretical framework for the present study, as it describes four perspectives for understanding how
organisations identify and achieve agency goals. The rationalperspective posits that organisations
behave rationally by identifying official goals, designing strategies to accomplish those goals, and
then implementing technology that supports the strategies they have designed (Cyert and March
1963 , Simon 1997). The contingency perspective suggests that organisations operate in particular
environments and key decisions depend on external factors and events (Lawrence and Lorsch
1967 ). The institutional perspective argues that organisations have their own interests as well –sur-
vival, status and prestige, maximising resources, and protection from threats (Scott 2008). Finally, the
entropic perspective depicts organisations more as anarchies than as well-oiled machines, often iden-
tifying solutions before they have a specific problem demanding to be solved (Cohen et al.1972 ).
728 J. A. HENDRIX ET AL. According to this perspective, organisational options (such as technology) are frequently just lying
around waiting for an opportunity to be adopted.The rational and entropic perspectives are most relevant for the present study, as they are diame-
trically opposed in the ways they view the process by which technology is adopted. Whereas the
rational perspective would anticipate connections between strategy and technology, the entropic
perspective would assume that decisions regarding technology are made ad hoc, independently
of strategic goals. Our study tested these competing viewpoints. Below, we describe prominent poli-
cing strategy models, summarise what is known regarding factors that predict technology acqui-
sition, and anticipate connections between strategic models and technology according to rational
and entropic perspectives.
Background and literature review
Police strategy
Significant debate exists among practitioners and researchers regarding the labels used to identify
policing strategy models (Moore and Trojanowicz 1988, Weisburd and Braga 2006). This discourse
has suggested the existence of at least eight strategic models that include professional, community,
problem-oriented, intelligence-led, hot spot, offender targeting, predictive, and broken windows/
zero-tolerance policing. Although strategies are not mutually exclusive, each one emphasises differ-
ent activities or objectives (e.g. improving police-community relations), which in turn might lead to an
emphasis on different types of technology (e.g. intelligent use of social media). The professional policing model emphasises rigid hierarchical organisational structures, limits the
use of discretion, and prioritises efficient response times. Community policingpromotes organis-
ational strategies, including the systematic use of partnerships and problem-solving techniques, to
proactively address the immediate conditions that give rise to public safety issues. The problem-
oriented policing model subjects discrete pieces of police business to microscopic examination in
hopes that what is learned about each problem will facilitate discovery of more effective strategies
for dealing with it. Intelligence-led policing is a business model and managerial philosophy in which
data analysis and crime intelligence are pivotal to an objective decision-making framework that facili-
tates crime and problem reduction, disruption, and prevention through strategic management and
enforcement strategies that target serious offenders. The hot spot policingmodel prioritises the
identification and targeting of specific locations that generate the most calls for police service. Offen-
der targeting policing emphasises the importance of identifying and prioritising repeat offenders. Pre-
dictive policing uses predictive analytics and other techniques to pinpoint specific geographic
locations most susceptible to crime. Finally, broken windowsorzero-tolerance policing is based on
Wilson and Kelling ’s(1982 ) seminal article suggesting that targeting minor forms of social and phys-
ical disorder will reduce more serious crime.
Technology acquisition
As noted, the knowledge base pertaining to factors that influence police departments to acquire
and implement technology is underdeveloped. Som e evidence suggests that the size and location
of an agency affects the number and types of technology it acquires (Mamalian et al.1999 ,
Chamard 2002,2003 ,2006 ). The impact of other agency- or conte xtual-level factors on technology
acquisition is less understood. According to Schuck ( 2015), the adoption of technology can be
understood as a complex interact ion between characteristics of the technology, organisational
culture, and features of the larger social-structu ral environment. Using data from the Law Enforce-
ment Management and Administrative Statistics, Schuck examined factors that could explain why
agencies adopt dash and mobile cameras, including characteristics of the technology (design, func-
tionality, and congruency with agency goals), organisational traits (hierarchical structure,
POLICING AND SOCIETY 729 formalisation, spatial differentiation), characteristics of the community (income and demographic
composition), and features of the local political environment. While the strongest predictor of
mobile camera adoption in large agencies was local c rime, organisational size and spatial differen-
tiation (sprawl) were positively associated with mobile camera adoption in smaller and medium-
sized agencies. A study by Weisburd and Lum ( 2005) found that the adoption of some types of technology, such
as computerised crime mapping, was related to the ‘cosmopolitanness’of the agency. That is, early
adopters had a tendency to employ officers with more sophisticated levels of knowledge of research
related to crime mapping, GIS, and hot spot policing. Skogan and Hartnett ( 2005) found that technol-
ogy adoption and use in law enforcement are largely independent of one another and motivated by
separate processes. Whereas the most important predictors of adoption were whether the agency
was involved in ‘cosmopolitan networks ’, experience levels for using enforcement databases, and
the degree of human capital in the organisation, key factors predicting usage were organisational
resources and experience levels for using the system. Leong and Chan ( 2014) found that number
of employees, the extent to which the department used digital information, and number of hours
of training spent on new recruits were the key predictors of the adoption of web-based mapping.
Finally, Randol ( 2014) reported that vertical height (i.e. number of vertical ranks and other aspects
of complexity in the organisation ’s command structure), functional differentiation (i.e. number of
specialised units with personnel dedicated to their functions), formalisation (i.e. number of written
policies and procedures), and community policing were all associated with the likelihood of adopting
crime analysis technology. Because of the limited research on technology a cquisition specifically, we also look to other
areas of change experienced by law enforcement agencies. Some research has assessed the influ-
ence of organisational and contextual factors of police departments on adoption of new policing
strategies (Katz 2001,Morabito2008, Darroch and Mazerolle 2013). Examining whether insti-
tutional processes, including mimesis (i.e. emula tion of other agencies), publication, and profes-
sionalisation influence the adoption of intelligence-led policing, Carter ( 2016)foundthat
institutional pressures, along with perceived crime control benefits had a positive effect on the
adoption of intelligence-led policing. Similarly, Giblin ( 2006) examined the role of mimesis, exter-
nal funding, and national accreditation as institu tional pressures on the formation of crime analy-
sis units, finding that the most likely predictors of agencies forming a crime analysis unit were
agency size and accreditation. Willis et al.(2007) explored agency adoption of COMPSTAT
through the lens of the rational versus institution al organisational perspectives, and found that
institutional pressures to ‘appear progressive and successful ’influenced agencies to adopt COMP-
STAT more so than technical considerations. Ot her scholars have also examined community and
macro-contextual influences on police strateg y implementation and organisational change
(Bayerl et al.2013 , Darroch and Mazerolle 2015).
Theoretical connections between strategy and technology
Despite the frequency of discussions within the criminal justice arena pertaining to policing models
and their impact on law enforcement activities (Moore and Trojanowicz 1988, Weisburd and Braga
2006 ), our review finds virtually no research that speaks to associations between different orien-
tations towards common policing strategies and the acquisition of technology. This research ques-
tion is important, given that it could provide context to previous studies suggesting that the
increasing use of innovative policing technology has not necessarily led to more effective outcomes
(Lum 2010). Specifically, if agencies are adopting technology without a strategic understanding of
how that technology integrates within their overarching goals, acquisitions may be limited in their
impact on key agency outcomes. As noted, relationships between technology and strategy can be
anticipated by viewing them through the lens of the rational and entropic organisational
perspectives.
730 J. A. HENDRIX ET AL. The rational lens
The rational organisational perspective views organisations as calculating entities that make strategic
decisions to complement the global goals of the organisation. This viewpoint anticipates that police
departments will implement technologies that directly support and facilitate their respective over-
arching strategic missions. Although we were unable to identify past research that has directly
tested this assertion, it is logical to believe that an agency’s devotion to a particular model would
influence the types of technology it uses for achieving certain outcomes. Technology could make
a new strategy possible, provide a new tool for an existing strategy, or allow for a combination of
both scenarios. For instance, the 911 system has been described as a major force that has shaped
and reinforced reactive policing (Sparrow et al.1990 , Skogan and Frydl 2004).
Drawing from the technology literature and using the rational organisational lens, we anticipate
several connections between police strategy and technology. First, we expect relationships between
crime mapping software and intelligence-led, hot spot, predictive, and community policing. The soft-
ware ’s ability to identify geographic locations where crimes cluster and the potential for this infor-
mation to inform crime reduction and prevention (Sherman and Weisburd 1995, Mazerolle et al.
1997 , Braga et al.1999 , Mamalian et al.1999 , Braga and Bond 2008, Braga et al.2012 ) lend themselves
to both intelligence-led and hot spot policing models. The use of advanced mapping techniques,
such as risk terrain modelling, may help departments to anticipate where future crimes will occur
and to inform deployment decisions (Caplan et al.2011 ); therefore, this technology may also be com-
patible with the predictive policing model. Likewise, crime mapping has been described as central for
community policing, as it allows departments to produce maps that can be accessed by citizens to
increase their awareness of local threats to public safety (Dunworth et al.2001 , Randol 2014).
Second, we hypothesise connections between social media and community, predictive, and intel-
ligence-led policing models. Although rarely examined empirically, the available information
suggests that social media is perceived to enhance departments ’abilities to interact positively
with the community (Burger 2013); thus, we see connections with the community policing model.
With a vested interest in community outreach, departments can use social media to post information
about suspects, crime prevention, or other public safety issues, and active social media use can huma-
nise officers and ultimately enhance police-community trust (Stevens 2010). Recent surveys also find
social media to be helpful for investigating crimes and for anticipating future crimes, which lends
itself to intelligence-led and predictive policing models. In a 2013 survey of agencies by the Inter-
national Association of Chiefs of Police (IACP), 80% of the sample reported that social media was a
valuable investigative tool and helped them to solve crimes (IACP 2014). A 2014 survey by LexisNexis
revealed that 67% of respondents perceived social media to be an effective investigative tool and
platform for anticipating future crimes (LexisNexis 2014).
Third, we anticipate relationships between data mining and intelligence-led and predictive poli-
cing. Intelligence-led policing emphasises the use of data to facilitate crime reduction and preven-
tion. Similarly, the predictive policing strategy is based on the logic that future crimes can be
better anticipated, responded to, or prevented using intelligence collected from a variety of data
sources. Since the 9/11 terrorist attacks, law enforcement agencies have been under pressure to
become more data-driven in their daily operations. These data can take numerous forms; for
example, they may derive from the agency ’s records management system, census databases,
mobile resources (e.g. smartphones), LPRs, or social media. Data mining technology was designed
to address needs related to handling prolific quantities of data from diverse sources (Fayyad and
Uthurusamy 2002). Analysts may use data mining software to mine text data, visualise crime net-
works, identify possible suspects, or recognise crime patterns and characteristics associated with
them to guide the deployment of officers (Chau et al.2002, Hauck et al.2002 , Pearsall 2010). The
potential for data mining software to uncover underlying causes of crime trends and patterns that
can then inform the allocation of police resources as a crime prevention strategy is therefore consist-
ent with the basic premises of intelligence-led and predictive policing.
POLICING AND SOCIETY 731 Fourth, we expect to find relationships between the use of car cameras and community policing.
Although the diffusion of dash cameras throughout American law enforcement was initially a conse-
quence of increased attention on drinking and driving in the 1980s, allegations of racial profiling
against the police, and demands for greater officer safety (Westphal 2004), research has documented
the potential for car cameras to improve community relations. For example, in 2004, the IACP studied
the use of in-car camera systems among 47 state police agencies that had received funding under the
In-Car Camera Incentive Program in the late 1990s. Results indicated that officers perceived numer-
ous benefits of the in-car camera systems, including improved community perceptions, agency
accountability, and enhanced professionalism. Interviews with patrol officers suggested that in-car
cameras also augmented officer safety and facilitated more harmonious relationships with the com-
munity, as the presence of a camera can de-escalate confrontational situations when citizens are
informed of being recorded (IACP 2004).
Fifth, we anticipate a relationship between LPRs and intelligence-led and offender targeting
models of policing. Automatic LPRs are high-speed cameras paired with character recognition soft-
ware that can document thousands of license plates per minute while also recording the date, time,
and geographic location of every scan. Some police departments, such as those in New York and
Sacramento, have reported increases in arrests or reductions in reports for auto thefts as a function
of implementing LPRs (see Hsu 2014). Additionally, 68% of agencies from a study by Roberts and
Casanova ( 2012) reported that LPRs had enabled them to increase stolen vehicle recoveries, and
55% reported that auto-theft-related arrests had increased.
1The use of LPRs for vehicle recovery
and other purposes seems most compatible with the intelligence-led and offender targeting
models of policing, given that it is a form of innovative data collection that can help to prevent or
resolve problems in the community and also to target high-risk offenders. Finally, we may expect a relationship between the use of BWCs and the community policing
model. Multiple studies have indicated that the use of BWCs can help to reduce the number of com-
plaints filed against local police departments, which may ultimately enhance police trust of the com-
munity they serve. For example, the Rialto, California, police department found that shifts where
BWCs were not deployed had more than twice as many use-of-force incidents as shifts which used
them, and complaints against the police had decreased from 24 complaints filed during the 12
months before the study to 3 during the 12 months of the study (Barak et al.2014 ). In a 2015
study of the Phoenix, Arizona, police department ’s use of BWCs, complaints dropped sharply
(23%) among the BWC group, compared with a 10.6% increase in the comparison group (Katz
et al .2015 ). Given the national attention on BWCs, it is possible that willingness to adopt BWCs is
a latent reflection of a department ’s intentions to keep up with expectations for modern policing
(Byrne and Marx 2011). Therefore, BWCs may also be associated with more progressive and strategic
models such as hot spot, problem-oriented, predictive, or intelligence-led policing.
The entropic lens
In contrast to the rational perspective, the entropi c perspective does not anticipate clear or consist-
ent connections between police s trategy and technology. Specifically, this perspective suggests
that law enforcement agencies adopt technology a ccording to trivial or external factors that do
not relate systematically to the agency ’s central or long-term goals. Indeed, some research has
suggested that law enforcement agencies select, implement, and integrate technology indepen-
dent of existing empirical evide nce or concern for how these sys tems affect departmental oper-
ations, strategic decisions, or crime outcomes. Byrne and Marx ( 2011) argue that empirical
research that documents the effectiveness of a gi ven technology typically plays a minor role in
the decisions to adopt or continue using that tec hnology. In essence, it is argued that law enforce-
ment adopts technology as a ‘black box ’(Weisburd and Neyroud 2011). Therefore, notwithstanding
the connections between strategy and technology anticipated when viewing these domains
through the rational lens, the entropic perspecti ve expects few or inconsistent relationships
732 J. A. HENDRIX ET AL. between strategy and technologybecause it does not perceive law enforcement agencies to make
decisions about technology in a tactical and deliberate way.
Agency size
To assess the extent to which associations exist between technology use and belief in particular stra-
tegic models, we drew upon survey data from a nationally representative sample of state and local
law enforcement agencies, including appropriate proportions of small, medium, and large agencies.
However, research has indicated that the use of technology, policing activities, and other agency
characteristics (e.g. style of leadership) may vary significantly by agency size (Mamalian et al.1999 ,
Chamard 2002,2003 , Lum et al.2010 , Schuck 2015). Larger agencies tend to have more slack
resources to invest in new technologies, and they tend to have greater diversity of job functions
and more specialised units that require the adoption of more technologies (Nunn 2001, Mastrofski
et al .2003 , Skogan and Hartnett 2005, Randol 2012). Larger agencies with more specialised job func-
tions may be more ‘in the know ’of the newest research, practices, and technologies available to
inform agency goals (Weisburd and Lum 2005). Because the vast majority of agencies in the
United States have fewer than 250 full-time sworn officers, analytical models that have been adjusted
to represent the overall population of police departments will provide information reflective of small
agencies and will say little about larger departments. Therefore, in addition to the primary analysis of
the nationally representative sample, we also assessed the relationship between strategy and tech-
nology for a subset of large agencies.
Methods
The study was conducted collaboratively by the P olicing, Security, and Investigative Science
Program at RTI International and the PERF. To a ssess our key research question, we developed a
questionnaire and administered it to a nationa lly representative sample of law enforcement
agencies. The content of the survey was develop ed with input from an expert panel, which was
convened in June 2013 in Washington, DC. The panel consisted of nine criminal justice pro-
fessionals and civilians who had expertise worki ng in law enforcement and experience selecting
and implementing technology in police agencies. Once the survey instrument was finalised, the
panel reviewed and provided recommendations to ensure that the team was able to meet the pro-
ject ’s goals and objectives.
The sampling frame for the survey was developed using the 2012 National Directory of Law Enfor-
cement Administrators (NDLEA), an electronic list obtained from the National Public Safety Infor-
mation Bureau (NPSIB). The 2012 NDLEA is composed of contact information for 15,847 law
enforcement agencies in the United States. On the basis of a power analysis, our initial goal was
to obtain a minimum of 949 surveys. Assuming a 74% completion rate, this would have required a
sample of 1283 law enforcement agencies. To ensure adequate representation from each type of
agency in the survey responses, the sample included all tribal ( n= 69) and state agencies ( n= 49).
2
The remaining desired sample count was stratified to ensure adequate representation across
census regions (Northeast, Midwest, South, and West) and further stratified by agency type and
agency size (1 –99, 100 –249, 250 –499, and more than 500 sworn officers).
The required sample size, after subtraction of the tribal and state agencies, was evenly dispersed
across the 32 cells created by the cross-tabulation of region/type by size. It was clear that low cell
counts would affect agencies with more than 250 sworn officers. As a result, all agencies with 250
or more sworn officers were included in the sample ( n= 360). The remaining count ( n= 707) was dis-
tributed across the other 16 cells of the cross-tabulation (as sizes 250 –499 and 500+ were removed).
Using this process, we needed to randomly select 45 agencies within each remaining cell. Some cells
within the 100 –249 size range had fewer than 45 agencies and were fully sampled. A random selec-
tion of agencies was then generated within each stratum.
POLICING AND SOCIETY 733 Data collection
Survey respondents were contacted and prompted via nonresponse follow-up through multiple
mailings and phone calls. The survey was initially distributed in February 2014, followed by two
reminder letters sent three and six weeks after the initial survey distribution. Next, a mailed notifi-
cationletterfromtheprojectofficerwassentinApril 2014. To address nonresponse, an email was
sent to nonresponding PERF (general) members in May 2014, followed by a mailed reminder letter
in May 2014 to all nonresponding agencies. In an effort to boost response rates among small
agencies, an additional mailed reminder letter was sent with a targeted explanation of the impor-
tance of the project and its relevance to small age ncies. After the mailed survey prompts, we con-
ducted two waves of reminder phone calls to the 350 nonresponding agencies in June and July
2014. At the conclusion of the data collection period, we had obtained a response rate of 60.5%
( N =776).
An analysis of the final sample showed some differences with the sampling frame derived from
the2012NDLEA.Forinstance,thefinalsampleund errepresents agencies from the Northeast and
Midwest and overrepresents agencies from the W est. Additionally, the sample underrepresents
police departments but overrepresents county/sheriff ’s offices, tribal agencies, and state/
highway agencies. A higher proportion of the s ample is composed of agencies with at least 100
full-time sworn officers. To adjust these propo rtions so that they resemble proportions from the
2012 NDLEA, we used a procedure known as raking ratio estimation. Raking assigns a weight
value to each survey respondent so that marginal totals of the a djusted weights on specified
characteristics are in line with the corresponding totals for the population. A major advantage of
raking is its ability to produce respondent wei ghts that are based on multiple control totals
(Kalton 1983, Battaglia et al.2004 ).
Twenty-seven agencies were dropped from ana lysis because they answered only a few ques-
tions at the beginning of the survey. An assessment of these agencies ’key characteristics as they
relate to size, type, and region did not indicate any systematic bias and thus there was no reason
to believe that dropping these cases altered the results. Missing data on individual items through-
out the survey were minimal. On average, 2.8% and 4.2% of respondents had missing data on
items related to policing strategies and policing activities, respectively.
3Multiple imputation
was used to estimate a set of plausible values for missing data and to replace missing values
with the combined results (Little and Rubin 2002). A series of five imputations was used to
predict missing values; the resulting estimates reflected statistically valid inferences with
adjusted standard errors that account for the uncertainty that derives from missing values
(Allison 2002).
Measures
Dependent variables
This study has six dependent variables, one for each of the six types of technology: crime mapping,
social media, data mining, car cameras, LPRs, and BWCs. Respondents were asked to indicate whether
they had used each technology in the past two years. Responses were coded 0 if the agency had not
used the technology in the past two years and 1 if it had.
Independent variables
The key independent variables were eight survey items that asked respondents to indicate how
important various policing strategies were for supporting the agency ’s core mission on a scale of 1
(not important at all) to 5 (highest importance). The policing strategies derived from the expert
panel and include professional, community, problem-oriented, zero-tolerance, hot spot, offender tar-
geting, intelligence-led, and predictive policing.
734 J. A. HENDRIX ET AL. Control variables
Our analytical models predict the odds of technology use with agency strategy, controlling for region,
agency type, and agency size. Initially, our models included controls for agency annual budget and
local crime rates; however, concerns related to collinearity led us to drop these items from the
models. There were few instances in which these items were statistically significant. Four regions
of the United States were represented by three dummy variables: Northeast, South, and Midwest
as designated by the US Census. The West region was assigned as the reference category. In the
NDLEA 2012, agency type was originally composed of four values: police department/city sheriff’s
offices (municipal), county police/sheriff ’s offices, state police or highway patrol, and tribal police
departments. Because of small cell sizes for tribal and state police/highway patrol agencies, we
recoded agency type into a single dummy variable comparing municipal police departments to all
others. The number of sworn officers was recoded into an interval item with seven categories for
the full sample, based on the number of full-time sworn officers reported (0 –4; 5 –9; 10– 24; 25 –49;
50– 99; 100 –249; 250+). For the subsample of large agencies, the sworn officer variable was
recoded to represent three levels (250 –499; 500 –999; 1000+ full-time sworn officers).
Analytical approach
Our primary models predict the odds of technology use in the past two years for six types of tech-
nology by strategy and other characteristics using logistic regression. We also predict the odds of
technology use among a subsample of large law enforcement agencies (i.e. agencies with 250 or
more full-time sworn officers) ( n= 302).
Results
Table 2 provides descriptive statistics for key variables used in the analysis. As shown, car cameras are
the most commonly used technology; about 70% of the sample reported use in the past two years.
Similarly, about 68% of respondents reported that they had used social media for public communi-
cation in the past two years. About a third of the sample had used BWCs or GIS technology, whereas
smaller proportions of the sample had used LPRs or data mining tools. Turning to policing strategies,
professional policing had the highest average score (4.79 on the 5-point scale), whereas zero-toler-
ance policing had the lowest average score (3.29). The South made up the largest geographic cat-
egory, whereas the least number of agencies were from the West. Most responding agencies are
municipal, and 70% of agencies have fewer than 25 officers. Table 3 presents results from logistic regression models predicting technology use in the past two
years for each of the six technologies. As shown, there are few statistically significant relationships
between strategies and technology. Only three exceptions can be identified. First, the odds of
social media use are 2.73 times higher with each one-unit increase on the community policing
measure. An assessment of predicted probabilities, holding all other variables at their means,
suggests that only 8% of agencies who marked community policing as ‘not important at all’have
used social media in the past two years, compared to 87% of agencies who marked it as ‘highest
importance ’. Alternatively, the odds of social media use are about 60% lower for every one-unit
increase on the zero-tolerance policing measure. Predicted probabilities show that 96% of agencies
who marked zero-tolerance policing as ‘not important at all’had used social media in the past two
years, compared with only 45% of those who marked it as highest importance. Finally, the odds of
LPR use are about 67% higher for each unit increase on the predictive policing measure. Agency size was a more consistent predictor of technology use than strategy, as it was statistically
significant and positive in direction for four of six technologies (GIS, data mining software, social
media, and LPRs). Regional effects were scarce and inconsistent. The odds of use of data mining soft-
ware were lower for Northeast agencies than for Western agencies, whereas Northeast agencies were
POLICING AND SOCIETY 735 Table 1.Descriptive statistics for sample, sampling frame, and weighted sample.
Sample
( N = 776) M 2012 Directory
( n = 15,847) MFinal weighted sample
(N = 776) M(n )
Region Northeast 0.150.20 0.20 (155)
Midwest 0.210.33 0.33 (256)
South 0.360.35 0.35 (272)
West 0.280.12 0.12 (93)
Agency type Municipal 0.480.79 0.79 (613)
County/sheriff ’s offices 0.40 0.190.19 (147)
Tribal 0.060.01 0.01 (8)
State or highway 0.060.01 0.01 (8)
Sworn officers
0–4 0.020.20 0.20 (155)
5– 9 0.040.22 0.22 (171)
10 –24 0.090.28 0.28 (217)
25 –49 0.050.13 0.13 (101)
50 –99 0.050.08 0.08 (62)
100 –249 0.320.05 0.05 (39)
250+ 0.430.04 0.04 (31)
Table 2.Descriptive statistics for key variables (weighted) ( N= 749).
Mean SD
Used technology in past two years Car cameras 0.700.46
Social media for public communication 0.680.47
BWCs 0.330.47
GIS 0.310.46
LPR 0.200.40
Data mining tools for massive databases 0.100.30
Policing strategies Professional policing 4.790.52
Community policing 4.400.71
Problem-oriented policing 4.300.75
Intelligence-led policing 3.910.92
Hot spot policing 3.740.96
Offender targeting policing 3.810.97
Predictive policing 3.541.01
Zero-tolerance policing 3.291.03
Control variables Geographic region Northeast 0.200.40
Midwest 0.330.47
South 0.350.47
West 0.120.32
Agency type
Municipal 0.790.41
County/sheriff’ s offices 0.190.40
Tribal 0.010.10
State or highway 0.010.06
Agency size (number of full-time sworn officers) 0–4 0.200.40
5– 9 0.220.41
10 –24 0.280.45
25 –49 0.130.34
50 –99 0.080.27
100 –249 0.050.22
250+ 0.040.20
736 J. A. HENDRIX ET AL. more than five times more likely to have used social media. Midwestern agencies were considerably
more likely than Western agencies to have used car cameras in the past two years. 4
Table 4presents results for a subsample of agencies with 250 or more full-time sworn officers.
Agencies that embrace community policing, hot spot policing, and offender targeting policing are
more likely to have used GIS technology, whereas agencies who emphasise predictive policing are
less likely. Municipal agencies are also several times more likely to have used GIS than other types
of agencies. No statistically significant effects were identified for data mining software or BWCs.
The odds of social media use are twice as high with every incremental increase in the importance
of community policing and hot spot policing. Similar results are found for LPRs; community policing
and hot spot policing each positively predict the odds of LPR use. The odds of car camera use are
considerably higher in the Midwest and South relative to the West, although strategy appears to
be unimportant. Interestingly, agency size is not statistically significant for any of the technologies.
Considering it was the most reliable predictor of technology for the national sample, there
appears to be a threshold after which size is no longer important for technology acquisition. The
finding that size matters more for technology acquisition in smaller agencies is consistent with
some past studies (e.g. Schuck 2015).
Discussion
Policing and technology are increasingly intertwined, yet unanswered questions remain on how
police departments select technology. One of the key questions this study sought to address is
whether law enforcement agencies adopt technologies based on the strategic, organisational frame-
work they align with or if technologies are selected in a manner that is independent of larger agency
goals. When looking at the national sample of all agencies, our findings show little evidence that
agencies adopt technology based on strategic goals, as we found no statistically significant relation-
ships between technology and strategy for GIS, data mining software, car cameras, or BWCs.
A negative association was identified between zero-tolerance policing and social media use; while
important, these findings are not surprising given that this style of policing is rooted in tactics that
emphasise non-discretionary, heavy enforcement of both crime and public disorder. Zero-tolerance
is not typically associated with strategies that emphasise extensive uses of technology to target
resources in time or space or to solve particular problems. We also found a positive relationship
between predictive policing and the use of LPRs, which although was unanticipated, has reasonable
Table 3. Logistic regression predicting technology use in the past two years ( N= 749).
GIS Data mining Social media LPR Car camera BWC
Strategies Professional 0.74 (0.25) 1.23 (0.32) 0.62 (0.34) 1.14 (0.37) 0.84 (0.34) 0.74 (0.27)
Community 1.06 (0.35) 0.98 (0.28) 2.73* (1.17) 1.38 (0.43) 0.69 (0.26) 0.69 (0.26)
Problem-oriented 1.35 (0.39) 0.51 (0.17) 0.85 (0.35) 0.58 (0.21) 1.27 (0.46) 0.75 (0.28)
Zero-tolerance 1.07 (0.23) 0.79 (0.13) 0.40** (0.12) 0.77 (0.15) 1.05 (0.21) 1.00 (0.22)
Hot spot 0.62 (0.17) 1.17 (0.25) 1.01 (0.29) 1.07 (0.38) 0.64 (0.20) 0.88 (0.24)
Offender targeting 0.95 (0.22) 1.41 (0.30) 1.71 (0.51) 0.78 (0.21) 1.22 (0.32) 0.76 (0.18)
Intelligence-led 1.40 (0.35) 1.15 (0.33) 0.50 (0.19) 1.11 (0.31) 1.31 (0.55) 1.34 (0.41)
Predictive 1.02 (0.26) 1.28 (.30) 1.75 (0.57) 1.67* (0.31) 0.87 (0.36) 1.13 (0.33)
Sworn officers 1.87*** (0.29) 1.98*** (0.15) 1.49** (0.20) 2.16*** (0.26) 1.03 (0.12) 0.87 (0.11)
Region Midwest 0.76 (0.43) 0.36 (0.20) 0.74 (0.49) 0.57 (0.39) 7.19** (5.03) 0.60 (0.34)
South 0.72 (0.48) 0.88 (0.55) 0.87 (0.58) 0.71 (0.45) 2.55 (1.40) 1.31 (0.74)
Northeast 0.71 (0.46) 0.26* (0.17) 5.61* (4.07) 0.79 (0.45) 0.46 (0.28) 0.57 (0.36)
Type Municipal 0.45* (0.17) 0.21*** (0.08) 0.95 (0.38) 0.94 (0.36) 0.97 (0.42) 0.96 (0.36)
F 2.73*** 14.69*** 2.95*** 6.76*** 2.43** 0.94
Pseudo- R
2 0.25 0.300.220.29 0.180.09
Note: Coefficients are odds ratios, standard errors in parentheses. *<.05; **<.01; ***<.001.
POLICING AND SOCIETY 737 conceptual tie. LPRs produce an extensive amount of data that could feasibly be used to predict
future patterns including crime prone locations, offender travel pathways, and other issues related
to public safety. A closer examination of the benefits and limitations of the use of LPR data for pre-
dictive policing is needed to better understand this connection.By default, the overall lack of relationships between technology and strategy lends more support
to the entropic perspective, or the notion that technology is adopted independently of an agency ’s
strategic goals. These findings may suggest that other agency- or contextual-level factors identified in
past research may be more important for determining the types of technology police departments
implement. It may be the case that agency characteristics such as their involvement in cosmopolitan
networks, institutional pressures to appear progressive or as keeping up with other agencies, or staff
experience levels handling various types of technology exert a more direct effect on the agency ’s
decision to seek out technologies (Skogan and Hartnett 2005, Willis et al.2007 , Leong and Chan
2014 ).
Perhaps not surprisingly, the relationship between strategy and technology was contingent on
agency size. For large agencies, we found stronger connections between the policing strategies
and technologies implemented. In some instances, technologies have clear implications with
regard to strategy implementation. For example, as predicted, agencies that emphasise community
policing and hot spot policing are more likely to have used GIS. Crime mapping technology is useful
for identifying where crimes cluster, a core tenet of hot spot policing, and for informing the public
regarding where crimes occur, which is compatible with underlying goals of community policing.
Contrary to what we hypothesised, the odds of GIS use were lower among agencies who emphasised
predictive policing. The meaning of this relationship is unclear, especially given that GIS is essential
for predictive analytical techniques. It may be that predictive analytic methods are seen as indepen-
dent of GIS technology. Although predictive modelling is not necessarilydependent on GIS, predictive
policing methods have tended to focus on geography and, therefore, should be strongly related to
GIS. Additionally, while not hypothesised, the finding that hot spot policing and social media use are
interconnected among larger agencies is important. It is possible that agencies who emphasise hot
spot policing perceive value in social media for gathering intelligence regarding the occurrences of
crime in the community. Social media may also be seen as an important method of identifying short-
term crime or disorder hotspots (e.g. flash mobs or party locations). We also did not predict that community or hot spot policing would be positively associated with
LPR use. Considering that community and hot spot policing are associated with several technologies
Table 4. Logistic regression predicting technology use in the past two years (large agencies) ( n= 302).
GIS Data mining Social media LPR Car camera BWC
Strategies Professional 0.77 (0.28) 1.06 (0.22) 0.76 (0.33) 0.90 (0.24) 1.42 (0.29) 0.88 (0.18)
Community 3.79*** (1.50) 1.51 (0.41) 2.13* (0.78) 2.30** (0.73) 0.80 (0.24) 1.50 (0.40)
Problem-oriented 0.52 (0.21) 0.88 (0.23) 0.59 (0.18) 0.73 (0.19) 0.83 (0.24) 0.87 (0.21)
Zero-tolerance 0.62 (0.16) 0.84 (0.14) 0.73 (0.15) 0.83 (0.15) 0.78 (0.19) 0.77 (0.13)
Hot spot 2.40** (0.76) 1.16 (0.27) 2.17** (0.66) 2.52*** (0.63) 0.78 (0.19) 1.77* (0.44)
Offender targeting 2.43* (1.00) 1.09 (0.29) 0.90 (0.33) 0.48* (0.14) 1.39 (0.31) 1.04 (0.21)
Intelligence-led 1.91 (0.85) 1.62 (0.49) 1.44 (0.74) 1.56 (0.52) 0.82 (0.24) 0.83 (0.21)
Predictive 0.38* (0.17) 0.81 (0.17) 0.58 (0.22) 0.80 (0.17) 1.41 0.29 1.07 (0.21)
Sworn officers 1.81 (0.77) 1.12 (0.25) 0.84 (0.27) 1.60 (0.42) 1.33 (0.37) 1.03 (0.26)
Region Midwest 3.31 (3.06) 1.65 (0.87) 1.84 (1.41) 2.69 1.72) 9.67** (6.80) 0.79 (0.41)
South 2.42 (1.51) 1.23 (0.53) 2.65 (1.38) 2.01 (0.87) 3.16** (1.33) 1.54 (0.64)
Northeast 0.67 (0.55) 1.79 (1.04) 0.72 (0.56) 3.13 (1.84) 0.28* (0.14) 0.20 (0.18)
Type Municipal 4.85** (2.55) 1.33 (0.42) 2.15 (1.07) 1.60 (0.59) 0.71 (0.29) 1.35 (0.49)
Model F 3.43*** 1.23 2.03* 2.22** 3.62*** 1.41
Pseudo- R
2 0.49 0.09 0.35 0.250.260.12
Note: Coefficients are odds ratios, standard errors in parentheses. *<.05; **<.01; ***<.001.
738 J. A. HENDRIX ET AL. when the analysis is limited to large agencies, it may be that large agencies that emphasise these
strategies are more technologically savvy, forward-thinking, and‘in the know’when it comes to tech-
nology (Weisburd and Lum 2005). Some scholars have suggested that the adoption of emerging
technologies and adherence to innovative policing strategies are ways that agency leaders can dis-
tinguish themselves professionally by showing their willingness to adopt state-of-the-art methods in
place of standing methods of practice (Byrne and Marx 2011).
Implications
The evidence suggests only a limited coupling between strategy and technology. Closer alignment
between technology implementation choices and prioritised philosophies is mainly limited to
larger agencies and analytically based technologies and policing strategies. The lack of a more wide-
spread relationship between strategy and technology suggests that technology adoption is not typi-
cally driven by careful consideration of the agency ’s larger strategy or future direction. This raises
questions about how technology can and should be used to support broader policing planning
and mission. Some seemingly foundational relationships between technology and strategy (e.g.
hot spot policing and the use of GIS) were not found. It is unclear how agencies are operationalising
these strategies without the use of these technologies. We found more evidence that strategy was linked to technology among large law enforcement
agencies. Perhaps some characteristics of larger agencies make them more likely to pair technology
with strategy. For instance, perhaps larger organisations need to have more complex structures and
technologies to support key functions (Pugh and Hickson 1976). Larger agencies also tend to have
staff in place whose roles are at least partially focused on identifying and selecting technologies
and setting the strategic mission of the agency. In addition, larger agencies may have more staff
and resources to develop clearly articulated strategic plans from which technology choices can be
rooted. Whatever the case, future research in this area should be focused on disentangling the relation-
ship between agency size, personnel capabilities and capacity, technology, and policing strategy. This
work should be guided by past research on police innovation and change, as framing these questions
in different organisational change perspectives may provide further insight into why police depart-
ments adopt technology. Developing a better understanding of the barriers for small and mid-size
agencies should also be prioritised. This research may lead to actionable information that small
agencies can implement to better take advantage of technologies to support strategy. For
example, if the main barrier for these agencies is the lack of qualified personnel, then joint powers
or regional associations may be developed to thoughtfully implement technologies that are consist-
ent with the aims of member organisations. Weak, or non-existent, relationships between strategy and technology adoption may represent a
major disadvantage for agencies. The lack of relationships between strategy and technology may be
due to the absence of coordinated planning by law enforcement agencies. Without proper longer
range planning around strategic direction and how agencies acquire and implement technologies,
the acquisition, implementation, maintenance, and evaluability of police technologies will be nega-
tively affected. Rather, we argue that technology use within an agency can be maximised if individual
technology decisions are made within a larger framework and in concert with other technology
decisions. For instance, an agency planning to purchase BWCs should first determine if the techno-
logical foundational is in place. For agencies, this means that planning is essential to maximise the utility of a technology in sup-
porting core strategies. During the acquisition process, agencies would be required to properly vet
technology for usefulness. Similarly, agencies should ensure fidelity to the technology implemen-
tation and maintenance process for maximum returns. Finally, planning is essential for an agency
to collect key metrics of technology use and to evaluate how new technologies impact agency out-
comes and strategy. The ineffective evaluation of past technology acquisition limits an agency ’s
POLICING AND SOCIETY 739 ability to learn from past efforts. Building an evaluation mindset into an agency’s regular practices is
critical if an agency wants to create successful technology implementation process.
Limitations
Some limitations of this analysis must be acknowledged. First, the dependent variable is a binary
measure of use in the past two years. This measure does not consider the span of adoption (e.g.
LPRs on 2% of cars or on 80% of cars), nor does it consider the quality of adoption (e.g. deep inte-
gration of LPRs with records management and other systems versus an LPR system that only collects
data that must be manually reviewed on a case-by-case basis). Our inability to measure these poten-
tially salient factors makes it more difficult to speculate on how strategy affects technology. Second, the agency surveys were filled out by a single point of contact in each department.
Whereas some questions should be largely unaffected by this –questions about the use of a tech-
nology, for example, should be relatively consistent from person to person within the agency –
others may not be so consistent: questions about agency perspective and orientation towards poli-
cing philosophy may have distinct differences between people within the same department. There is
little information about how perceptions of policing strategy vary within the department. Further
research is needed to learn if there are differences across ranks and if these differences manifest
themselves in orientation towards policing strategy. Finally, we make no claim on the causal relationship between strategy and technology. Although
we found social media and community policing to be related, this does not indicate that the orien-
tation towards community policing caused the adoption of social media. It may be that certain tech-
nologies are seen as representative of progressive law enforcement agencies and certain strategies
are seen as similarly progressive. The causal relationship between strategy and technology would be
better studied with longitudinal research.
Conclusion
Technology is now essential to police operations and will continue to accelerate in its use and reach
within agencies. Implemented properly, technology can be a key resource in carrying out police
activities and larger strategy. The results of this study, however, suggest that police technology
and strategy are not strongly coupled in US law enforcement agencies. However, there were some
relationships between technology and strategy (e.g. the use of social media and community policing)
that make sense conceptually and practically. Nevertheless, other strong links between technology
and agency strategy that appeared conceptually sound (e.g. use of GIS and hot spot policing
among smaller agencies) did not manifest. In general, the relationships between technology and
strategy were more consistent among larger agencies. Even among these, however, there were
inconsistencies with theoretical perspectives. Taken together, these results suggest that, while technology implementation is being
implemented in policing at an increasingly rapid pace, there needs to be greater emphasis on evi-
dence-based, informed decision-making about new and existing technology. Furthermore, agencies
must come together to develop agency-specific and also shared visions for how certain technologies
can help them achieve their goals. Similar to new staff, technologies should be viewed as potential
assets to agencies but to maximise their impact decisions need to be made that specify how they will
be used, by whom, and what will be accomplished by their use.
Notes
1. Results from a randomized controlled experiment in Mesa, Arizona, conducted by the Police Executive Research Forum (PERF) indicated no relationship between the number of scanned license plates and vehicle theft rates
740 J. A. HENDRIX ET AL. (Tayloret al.2012 ). Similarly, others find that the use of LPRs does not have an appreciable effect on reducing auto
thefts (Lum et al.2010 ).
2. Hawaii does not have a state police agency.
3. To account for missing data for the remaining sample ( N= 749), we first performed tests to ensure that the
missing data were missing at random. Logistic regression models were used to predict the odds of having a
missing value on each of our dependent variables by key agency characteristics (region, size, type). Results did
not indicate that specific agency characteristics were associated the odds of having a missing value on various
technologies.
4. This effect may be directly due to differences in political climate and differences in funding received, and indirectly related to prevalence of alcohol-impaired driving (Jewett et al.2015 , Schuck 2015).
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by National Institute of Justice [2012-MU-CX-0043].
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