Predictors of Patrol Officer Interest in Cybercrime Training and Investigation in Selected United States Police Departments

Abstract

Cybercrime has created substantial challenges for law enforcement, particularly at the local level. Most scholars and police administrators believe that patrol officers need to become more effective first responders to cybercrime calls. The evidence illustrates, however, that many patrol officers are neither adequately prepared nor strongly interested in taking an active role in addressing cybercrime at the local level. This study, therefore, examined the factors that predicted patrol officer interest in cybercrime training and investigations in two southeastern U.S. cities. The study specifically examined the relationship between demographics, cybercrime exposure, computer training, computer proficiency, Internet and cybercrime perceptions, and views on policing cybercrime with officer interest in cybercrime investigation training and conducting cybercrime investigations in the future. Officer views on policing cybercrime, particularly whether they valued cybercrime investigations and believed that cybercrime would dramatically change policing, along with their computer skills, were the strongest predictors of interest in cybercrime efforts. Officers who had received previous computer training were less interested in additional training and conducting investigations. These findings support the argument that more command and departmental meetings focusing on the value of investigating these types of crime need to be held in order to increase officer interest.

Introduction

Over the last three decades, the use of technology to facilitate crimes in both the physical and cyber worlds have increased.^1–4 This problem has created substantial challenges for law enforcement, particularly at the state and local level.^4–13 Local law enforcement agencies are the primary investigative units that are contacted in the event of a suspected criminal incident and should respond to calls regardless of the nature of the event.^4,5,11 Since officers respond based on availability, they should have a rudimentary understanding of many forms of crime because of the likelihood that they will act as first responders to diverse calls.

The increasing prevalence of cybercrime means that officers should have the ability to effectively manage cybercrime cases by securing the scene, preserving evidence, and interviewing possible witnesses.^4,5,9,11,12 Errors in this early phase of an investigation can have serious consequences for later evidentiary analysis and successful prosecution.^4,11,13,14 Unfortunately, most patrol officers do not have the proper training to investigate computer-related offenses.^{4,6,8,12,15,16} As Hinduja¹⁵ found in his investigation of Michigan police agencies, the training needed was for their “front-line officers, so that they are equipped with the basic skill set essential for the proper management of interrogations, crime scenes, computer evidence, and the attendant paper work involved”.¹⁵^(p352)

Studies that explore law enforcement perceptions of cybercrime typically do so by acquiring information from law enforcement administrators.^4,15,17 A few scholars have examined these issues from the officers' perspectives.^18,19 Recent research has found that officers are cognizant of the risks posed by cybercriminals and perceive some cybercrimes to be as severe as street crimes.¹⁸ Officers, however, would prefer not to work directly with the general public to assist in cybercrime investigations.¹⁹ Instead, they feel it more valuable to educate the public and increase legal sanctions in lieu of direct investigative responsibilities. In fact, they believe that local agencies do not have the necessary resources and that state and federal law enforcement should be primarily responsible for the investigation of cybercrime.^2,4,12,19 Local patrol officers have simply not shown the same interest as administrators and scholars in improving patrol officer capabilities to respond to cybercrime calls.^4,11

There is little research on which factors are associated with local officers' interest in receiving cybercrime investigation training and conducting future cybercrime investigations.⁴ Using the literature on officer attitudes toward cybercrime in general, along with research examining the general public's interest in technology, we would expect that certain major factors, such as demographics, cybercrime exposure, training, computer proficiency, and perceptions of cybercrime and how the police are responding to it, would influence whether a patrol officer would be interested in this type of training and investigation.

The general literature on interest in technology would suggest that young, college educated, male officers would be more interested in cybercrime training and investigations, as these groups are the most likely to regularly interact with and own various technological devices.^20,21 Exposure to cybercrime, both indirectly via the news and directly by responding to a cybercrime call, should provide officers more insight on the dangers of cybercrime as well as how the local police department could handle these cases more effectively by receiving additional training and being better prepared to conduct future investigations.^{6,8,17–19,22} Previous computer and cybercrime investigation training could also increase officer interest, as they would feel better prepared to handle these difficult cases. Some officers, however, might feel that past training was sufficient and further training would be redundant and unnecessary.¹⁹ Officers with higher levels of computer skills, regardless of whether they have received previous training, should feel more equipped to receive advanced forms of training and to conduct investigations themselves.^2,4,11,14,19

Finally, previous research has well established that officers who are most interested in getting involved in new police initiatives are those who have accepted both the importance of the problem and the methods for addressing it.²³ Thus, officers who perceive cybercrime to be a larger problem than their fellow officers and view local law enforcement as having an active role in its investigation should be more interested in training and conducting cybercrime investigations. For example, officers who focus more on the positives of the Internet rather than its negatives might be more interested in learning how to use these resources better. Similarly, those officers who perceive cybercrimes to be unique, frequent, serious, and important in the eyes of the citizens should be more willing to accept their role as first responders, desire additional training, and participate in these types of investigations in order to protect the citizens they serve.^12,18,19

Officers who support local investigations of cybercrimes, including responding to cybercrime calls by patrol instead of specialized computer crime units, should also logically be more interested in training and personal involvement in investigations, as they believe that patrol officers at the local level should be more engaged.¹² In addition, clear managerial and collegial support may increase officer willingness to respond to cybercrime calls for service, because they feel such efforts are valued.^{4,6,7,15,18,19} Similarly, the officers' perceptions of cybercrime investigations may affect how they want to invest themselves in calls for service.^5,16 Finally, patrol officers who believe that cybercrime will continue to challenge local law enforcement should be more interested in staying at the forefront of their professions by demonstrating interest in both training and participation in future investigations of a problem that will affect first responders for the foreseeable future.

Due to the dearth of research on how local law enforcement is responding to the threat of cybercrime, it is imperative that researchers determine which factors influence local officers' interest in cybercrime efforts. Such information would enable law enforcement administrators to more efficiently identify these officers in a time of scarce resources while also being able to make policy changes that could influence officer interest. Thus, this study sought to identify the factors that predicted officer interest in cybercrime training and investigations in a sample of patrol officers from two southeastern U.S. cities.

Methods

Participants

The data were collected from patrol officers in the two southeastern U.S. cities of Charlotte, North Carolina, and Savannah, Georgia. Although located in the same region, these cities differ based on their populations, racial composition, and the size and scope of their respective police departments. Charlotte has more than two million residents in the combined statistical area and is predominately White (55 percent). Savannah has only 134,669 residents and is largely Black (57 percent).²⁴ The Charlotte−Mecklenburg police department (CMPD) had more than 1,400 patrol officers; the Savannah−Chatham Metropolitan police department (SCMPD) had just less than 400. The majority of officers in both departments were male (85 percent) and White (78.3 percent CMPD; 59.4 percent SCMPD). The CMPD also operates a specialized cybercrime task force. Similar to most jurisdictions, data were not readily available regarding the number of cybercrimes that occurred in these jurisdictions to allow comparisons.

A survey instrument was created using questions adapted from studies on computer crime awareness among the police^4,8,12,15 and general public.²² Paper surveys were provided to the SCMPD command staff at a weekly meeting in the Spring of 2008 and then distributed to the precincts for all patrol officers ranked Sergeant and below to complete. The command staff forwarded 144 completed surveys back to the research team. The CMPD utilized an electronic survey instrument hosted on an internal departmental website. An electronic data file consisting of 124 completed instruments was sent back to the researchers.

The overall officer demographics were similar to police across the United States (see Appendix Table A1).^25,26 For example, the overwhelming majority of respondents were male (87 percent) and White (84 percent). Half the sample had more than 10 years of policing experience. Forty percent had completed a 4-year degree.

Dependent measures

Officers were asked to gauge their interest in receiving cybercrime investigation training and conducting future cybercrime investigations with the following two items: (a) I am interested in receiving cybercrime investigation training; and (b) I am interested in conducting cybercrime investigations in the future (1=strongly disagree to 5=strongly agree).

Independent measures

Independent variables were categorized into six blocks based on the literature: (a) demographics; (b) cybercrime exposure; (c) computer training; (d) computer proficiency; (e) Internet and cybercrime perceptions; and (f) views on policing cybercrime. The survey items along with their descriptive statistics are presented in the Appendix Table A1.

Block 1: demographics

The demographic predictors examined were sex, race, age, years of total policing experience, highest level of education obtained, and the city in which they served.

Block 2: cybercrime exposure

The following indirect and direct measures were included: whether they had read or heard cybercrime news stories; whether they had discussed computer crime recently with anyone; and when was the last time they had responded to a computer crime case.

Block 3: computer training

Officers were asked whether they had received any general computer training within the previous year and the number of hours of previous cybercrime investigation training.

Block 4: computer proficiency

The following measures were included: how many hours they spent online for any reason; whether they had been able to use the Internet on a case; and self-reported computer-skill level.

Block 5: Internet and cybercrime perceptions

Officers were asked to assess whether the Internet's negatives outweigh its positives in general and for policing specifically. In addition, officers were asked their perceptions regarding the uniqueness, frequency, and seriousness of cybercrime. The frequency scale was created by averaging their responses to how frequently (1=never to 5=very frequently) they perceived seven computer crimes occurred: copyright infringement such as software and media piracy; credit card fraud; electronic theft of money from accounts; online harassment; identity theft; pedophilia on the Internet; and malicious software infection (α=0.93). The seriousness scale was created by averaging their responses regarding how serious (1=least serious to 5=most serious) the seven cybercrimes just listed were based on the “financial and emotional harm to victims, and their threat to life, liberty, and personal property” (α=0.85). Finally, they were asked whether they believed that citizens in their community understood the risks of computer crime and whether most computer crimes go unreported to law enforcement.

Block 6: views on policing cybercrime

The final block of predictors relate to officers' views on policing cybercrime, including (a) who should be responding to computer crime calls at the local level; (b) whether cybercrime is being taken seriously enough by law enforcement and upper management; (c) fellow officer support for computer crime investigations; (d) value of investigating cybercrime; and (e) whether cybercrime will dramatically change police work.

Analytical procedures

We examined the predictors of officer interest in cybercrime investigation training and investigations by running hierarchical ordinary least-squares models using pairwise deletion. Blocks of predictors were entered into the models in order from most distant (i.e., demographics, past exposure and training, etc.) to the most proximate predictors (i.e., computer proficiency and currently held perceptions). Variance Inflation Factors and tolerance levels indicated that multicollinearity was not an issue for any model.

Results

More than half of the patrol officers were interested in receiving cybercrime investigation training (57.7 percent); 27.7 percent were indifferent. A smaller proportion of respondents expressed an interest in conducting cybercrime investigations (39.5 percent); 33.5 percent had no opinion (Table 1).

Table 1.

Interest in Cybercrime Investigation Training and Conducting Future Cybercrime Investigations Among Patrol Officers in Two Southeastern Cities

	Mean	Std. dev.	Min.	Max.	SD (percent)	D (percent)	Neither (percent)	A (percent)	SA (percent)
Cybercrime investigation training	3.58	1.01	1	5	3.2	11.5	27.7	39.5	18.2
Future cybercrime investigations	3.17	1.11	1	5	7.6	19.5	33.5	27.1	12.4

SD, strongly disagree; SA, strongly agree.

In the models predicting interest in cybercrime investigation training (Table 2), the following blocks significantly improved the models: demographics (ΔF=6.3); exposure (ΔF=2.8); computer proficiency (ΔF=7.5); and views on policing cybercrimes (ΔF=6.4). Specifically, officers who were classified as other races, older, and those in the SCMPD were consistently more likely to want additional training (models 1–6). Although block 2 significantly influenced the overall model, none of the three cybercrime exposure measures were significant (models 2–6). Although block 3 did not significantly influence the model, officers who had received previous general computer training were less interested in future training (models 3–6). Model 4 indicated that all three computer proficiency measures were significant. When controlling for all measures, however, computer skills was the only remaining significant proficiency measure (model 6).

Table 2.

Hierarchical Ordinary Least Squares Models Predicting Officer Interest in Cyber Investigation Training (N=253)

	Zero-order	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Block 1: demographics
Sex	0.023	0.046	0.019	0.018	0.051	0.051	0.022
Race	0.120^*	0.058	0.073	0.067	0.049	0.050	0.016
Other race	0.132^**	0.135^**	0.142^**	0.142^**	0.123^**	0.128^**	0.081
Age	0.185^***	0.264^***	0.275^***	0.278^***	0.295^****	0.266^***	0.215^***
Years policing	0.007	−0.110	−0.145	−0.166^*	−0.162^*	−0.139	−0.093
Education	−0.083	0.053	0.043	0.044	0.007	0.016	0.020
Site (Savannah=1)	0.313^****	0.291^****	0.248^****	0.248^****	0.246^****	0.224^***	0.202^***
Block 2: cybercrime exposure
News	0.098	—	0.057	0.050	0.010	0.005	−0.026
Discussion	0.177^***	—	0.108	0.101	0.049	0.058	0.081
Cybercase	0.164^***	—	0.085	0.079	0.050	0.050	0.018
Block 3: training
Computer training	−0.157^**	—	—	−0.121^**	−0.117^**	−0.104^*	−0.122^**
Cyber investigation training	0.000	—	—	0.050	0.015	0.020	−0.025
Block 4: computer proficiency
Hours online	0.236^****	—	—	—	0.137^**	0.121^*	0.069
Internet case	0.168^***	—	—	—	0.138^**	0.112^*	0.094
Computer skill	0.224^****	—	—	—	0.151^**	0.164^***	0.136^**
Block 5: perceptions cybercrime
Internet positives	−0.028	—	—	—	—	0.054	0.064
Internet/impact LE	−0.165^***	—	—	—	—	−0.118^*	−0.146^**
Uniqueness	−0.056	—	—	—	—	−0.030	0.011
Frequency	0.061	—	—	—	—	−0.066	−0.062
Seriousness	0.188^***	—	—	—	—	0.111	0.052
Citizens' risk	−0.029	—	—	—	—	0.060	0.083
Unreported	0.141^**	—	—	—	—	0.070	−0.018
Block 6: policing cybercrime
Federal/state level	−0.194^***	—	—	—	—	—	0.036
Local level	0.076	—	—	—	—	—	−0.012
CCU	−0.101	—	—	—	—	—	−0.125^**
LE seriousness	0.085	—	—	—	—	—	0.107^*
Upper management	−0.017	—	—	—	—	—	0.087
Violent crime recognition	0.054	—	—	—	—	—	0.077
Traditional crime recognition	0.060	—	—	—	—	—	0.134^**
Drains valuable resources	−0.261^****	—	—	—	—	—	−0.124^**
Computer stakeouts	0.436^****	—	—	—	—	—	0.264^****
Dramatically change	0.311^****	—	—	—	—	—	0.208^****
	R ²	0.161	0.190	0.206	0.278	0.305	0.469
	ΔR²	0.161	0.030	0.016	0.072	0.026	0.164
	ΔF	60.348^****	20.801^**	20.250	70.482^****	10.178	60.411^****

Standardized coefficients are presented: ^*p<0.1; ^**p<0.05; ^***p<0.01; ^****p<0.001.

LE, law enforcement; CCU, computer crime units.

Model 5 illustrated that their perceptions of cybercrime did not affect their interest in training. Officers who believed that the Internet negatively impacts law enforcement, however, were more likely to not want training (model 6). Finally, five measures related to their views on policing cybercrime were significant (model 6). Officers who believed that cybercrime calls should go directly to a cybercrime unit were less likely to want training. Officers who saw the importance of cybercrime investigations were more likely to want additional training. In fact, support for computer stakeouts (β=0.264) had the largest influence in this model. Social support for cyber investigations did not greatly influence their interest, except that officers who thought that catching traditional criminals would be rewarded more were actually more likely to want training. Finally, officers who thought that cybercrime would dramatically change policing were more interested in receiving investigation training. In all, the six blocks accounted for 46.9 percent of why these officers were interested in cybercrime investigation training.

In the models predicting officer interest in conducting future cybercrime investigations (Table 3), five of the blocks significantly improved the models. The final model explained 42 percent of the variation (model 6). Demographics provided little insight; older officers, however, were once again more interested (model 1). Although both cybercrime case experience and having had a recent discussion regarding cybercrime were correlated with interest in future investigations, these indicators were not significant in later models when controlling for other factors (models 2–6). Similar to interest in training, officers who had received past general computer training were less interested in conducting cybercrime investigations. Having been trained in cybercrime investigations did not significantly influence their interest in actually conducting them (models 3–6). All three computer proficiency measures were significant (model 4).

Table 3.

Hierarchical OLS Models Predicting Officer Interest in Participating in Future Cybercrime Investigations (N=251)

	Zero-order	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Block 1: demographics
Sex	−0.048	−0.025	−0.048	−0.048	−0.007	−0.011	−0.033
Race	0.067	0.038	0.050	0.045	0.022	0.020	0.000
Other race	0.093	0.094	0.102	0.096	0.071	0.060	0.020
Age	0.170^*	0.209^**	0.219^**	0.235^**	0.255^**	0.227^**	0.178^**
Years of policing	0.059	−0.058	−0.096	−0.128	−0.118	−0.101	−0.050
Education	−0.070	−0.008	−0.019	−0.029	−0.076	−0.067	−0.064
Site (Savannah=1)	0.158^**	0.128^***	0.084	0.089	0.084	0.085	0.076
Block 2: cybercrime exposure
News	0.070	—	0.055	0.041	−0.007	−0.016	−0.041
Discussion	0.185^*	—	0.146^**	0.126^***	0.063	0.041	0.062
Cybercase	0.129^*	—	0.057	0.055	0.021	0.035	−0.005
Block 3: training
Computer training	−0.168^*	—	—	−0.144^**	−0.137^**	−0.116^***	−0.138^**
Cyber investigation training	0.083	—	—	0.117^***	0.070	0.095	0.041
Block 4: computer proficiency
Hours online	0.258^****	—	—	—	0.157^**	0.137^**	0.083
Internet case	0.206^****	—	—	—	0.148^**	0.143^**	0.128^**
Computer skill	0.299^****	—	—	—	0.221^****	0.233^****	0.215^****
Block 5: perceptions cybercrime
Internet positives	−0.004	—	—	—	—	0.032	0.045
Internet/impact LE	−0.085	—	—	—	—	−0.020	−0.052
Uniqueness	−0.087	—	—	—	—	−0.095	−0.064
Frequency	0.136^**	—	—	—	—	0.015	0.003
Seriousness	0.187^*	—	—	—	—	0.109	0.064
Citizens' risk	−0.006	—	—	—	—	0.085	0.121^**
Unreported	0.156^**	—	—	—	—	0.073	−0.018
Block 6: policing cybercrime
Federal/state level	−0.191^*	—	—	—	—	—	0.032
Local level	0.103	—	—	—	—	—	−0.005
CCU	−0.069	—	—	—	—	—	−0.079
LE not serious	0.149^**	—	—	—	—	—	0.151^**
Upper management	−0.016	—	—	—	—	—	0.092
Violent crime recognition	0.113^***	—	—	—	—	—	0.148^**
Traditional crime recognition	0.067	—	—	—	—	—	0.131^**
Drains valuable resources	−0.234^****	—	—	—	—	—	−0.126^**
Computer stakeouts	0.386^****	—	—	—	—	—	0.221^****
Dramatically change	0.271^****	—	—	—	—	—	0.177^*
	R ²	0.065	0.100	0.130	0.243	0.274	0.420
	ΔR²	0.065	0.034	0.030	0.113	0.031	0.146
	ΔF	20.318^**	20.904^**	30.961^**	110.165^****	10.339	50.202^****

Standardized coefficients are presented; ^*p<0.01; ^**p<0.05; ^***p<0.1; ^****p<0.001.

Although a few cybercrime perceptions were correlated with interest in conducting investigations, they were not significant in the models (model 5–6). Instead, officers who believed that citizens understood the risk of cybercrime were more likely to want to get involved (model 6). Finally, model 6 illustrated that the block of indicators examining their views on policing cybercrime had the greatest impact (ΔR²=0.15). Officers who felt that law enforcement was not taking cybercrime seriously enough were more interested in getting involved themselves. Both social support measures were significant, albeit in an unexpected direction. Officers who believed in the importance of computer stakeout investigations and resources were more interested. Finally, believing that cybercrime will dramatically change policing significantly increased interest.

Discussion

Local police agencies will serve as first responders in the investigation of cybercrimes, though it is not clear whether line officers desire or are prepared to serve in this capacity.^4,6,8,19 We found moderate interest among these patrol officers in receiving cybercrime investigation training (57.7 percent) and conducting future investigations (40 percent). Interestingly, many officers expressed no opinion on these two items, indicating that their views were malleable.

This study found that the general profile of officers who were more interested in cybercrime investigation and training were older, had not received previous general computer training, had higher self-reported computer skills than their colleagues, believed that cybercrime investigations were valuable, and predicted that cybercrime will dramatically change policing. Police administrators should not make assumptions regarding officer interest based on the demographic correlates found in the general public. In addition, knowing about an officer's exposure to cybercrime, either indirectly or directly, is not as important as knowing his/her computer skill level and views on policing cybercrime.

Officers with greater computer skills report an increased interest in both cybercrime training and investigation. These officers may be more comfortable with technology and may find the abuse of it to be inherently more interesting than others. Officers who received previous computer training, however, were less interested in both cybercrime training and investigations. It is possible that these officers took computer training courses in the past to address knowledge gaps and currently have no interest in further training. It is also possible that these previous courses were not interesting to the officers and that they assumed their previous experiences would be indicative of future courses. Officers who had received previous cybercrime investigative training were not more or less interested. Police administrators should, therefore, not assume that officers who have had previous computer and/or cybercrime investigation training are better candidates.

These findings indicate that officer interest is most significantly influenced by their currently held views on the police response to cybercrime. Specifically, knowing whether an officer values cybercrime investigations and realizes that cybercrime will dramatically alter policing will provide insight into their interest in cybercrime training and investigations. In addition, officers who believe that law enforcement is not doing enough are those who are more likely to want to get involved. Thus, management should assess these predictors in addition to the officers' computer skill levels before spending limited training resources.

These overall findings indicate that discussing cybercrime in command and rank-and-file meetings could increase officer willingness to engage for several reasons. First, many officers had no opinion about their current and future roles in addressing cybercrime, and their perceptions can be shaped. In addition, these meetings could allow officers to gain more knowledge and appreciation for these types of crime and the value of cybercrime investigations. Such discussions would demonstrate agency commitment to discuss and respond to these cases, which may filter down to line officers and foster a culture supportive of cybercrime training and investigations.^4,15

As with any study, there are limitations that need to be noted. Although both departments have been frequently studied in the police literature,^27–29 the limited nature of this sample requires further investigation with a larger sample of law enforcement agencies in order to expand the study's generalizability. Our study also experienced low response rates similar to previous studies on cybercrime due to the salience of the topic to patrol officers and the online delivery of the CMPD surveys.^2,17,30 The demographics of the sample, however, closely resemble that of other police departments.²⁵ If a volunteer bias existed in the data, fewer officers would have responded “no opinion” on survey items previously examined.^18,19

In addition, the use of forced choice responses limits the ability to understand officers' interest in cybercrime training and investigations in their own words. As a consequence, qualitative research utilizing open-ended interview questions and focus groups could provide greater exposition on the reasons that officers are interested in participating in cybercrime training and investigations. This research could identify weaknesses in training sessions that decrease officer interest in further training and investigations. This line of research could also examine the social reinforcements surrounding cybercrime investigations and the role that it has in influencing officer participation. Finally, further insight into why officers believe that cybercrime investigations are important and how cybercrime will alter policing would be most beneficial.

Footnotes

Acknowledgments

This project was partially supported by a Research and Scholarship internal grant received from Armstrong Atlantic State University. The authors would like to thank both agencies for allowing access to their officers.

Author Disclosure Statement

No competing financial interests exist for the authors.

Appendix

Appendix Table A1.

Measures and Descriptive Statistics ^a

Variables	Mean	SD	Min.	Max.	n
Dependent variables
(1) Interest in receiving cybercrime investigation training
(I am interested in receiving cybercrime investigation training)	3.58	1.01	1	5	253
(2) Interest in conducting future cybercrime investigations
(I am interested in conducting cybercrime investigations in the future)	3.17	1.11	1	5	251
Block 1: demographics
(1) Sex (1=Female)	0.13	0.34	0	1	260
(2) Black (1=Black)	0.16	0.37	0	1	253
(3) Other race (1=Race other than Black or White)	0.09	0.29	0	1	253
(4) Age	36.88	8.69	22	63	263
(5) Years of total policing experience
(0=none; 1=less than 1 year; 2=1–2; 3=3–5; 4=6–9; 5=10–14; 6=≥15)	4.18	1.62	1	6	263
(6) Highest level of formal education
(0=high school/GED; 1=some college/Associate's degree; 2=4-year college degree; 3=some graduate/law school work; 4=graduate or law degree)	1.52	0.94	0	4	262
(7) City (1=Savannah)	0.53	0.50	0	1	263
Block 2: cybercrime exposure
(1) Read/heard on news
(I have read or heard stories on cybercrime in the news; 0=no or unsure; 1=yes)	0.80	0.40	0	1	251
(2) Discussion
(Have you discussed cybercrime or computer crime recently with anyone? 1=yes)	0.19	0.39	0	1	261
(3) Previous cybercrime case
(When is the last time that you responded to a computer crime (cybercrime) case? 0=never; 1=over year ago; 2=within last year; 3=within last several months; 4=within last several weeks)	0.76	1.16	0	4	257
Block 3: computer training
(1) General computer training
(How many hours of general computer training (e.g., Microsoft word, Internet, programming) have you received within the last year? (0=no training; 1=training)	0.28	0.45	0	1	257
(2) How many hours of computer crime investigation training have you received?
(0=no training; 1=8 hours or less; 2=more than 8 hours)	0.17	0.51	0	2	263
Block 4: computer proficiency
(1) Hours online
(In an average day, how many hours do you spend on-line for any reason? (0=less than 1 hour; 1=1–2; 2=3–4; 3=5–6; 4=6 or more))	0.84	0.99	0	4	263
(2) Use Internet for jobs
(I have personally been able to use the Internet to find important information that is relevant to a case or to help me perform my job; 0=no or unsure; 1=yes)	0.67	0.47	0	1	253
(3) Skill level with computers
(0=I am afraid of computers and don't use them unless I absolutely have to; 1=I can surf the ‘net use common software, but not fix my own computer; 2=I can use a variety of software and fix some computer problems I have; 3=I can use Linux, most software, and fix most computer problems I have)	1.56	0.65	0	3	263
Block 5: perceptions of Internet and cybercrime
(1) Internet negatives
(In general, I believe that the Internet's negatives outweigh its positives)	2.35	0.97	1	5	252
(2) Internet impact on law enforcement
(The Internet has caused more problems for law enforcement than it has helped)	2.53	0.96	1	5	253
(3) Uniqueness of cybercrime
(Cybercrime is mostly traditional crimes that use a computer)	3.18	0.85	1	5	251
(4) Frequency of cybercrime
(Average responses to how frequently (1=never to 5=very frequently) they perceived seven computer crimes occurred: copyright infringement such as software and media piracy; credit card fraud; electronic theft of money from accounts; online harassment; identity theft; pedophilia on the Internet; and viruses and malicious software infection; α=0.93)	3.81	0.93	1.14	5	253
(5) Seriousness of cybercrime
(Averaging responses regarding how serious (1=least serious to 5=most serious) the seven cybercrimes just listed were based on the “financial and emotional harm to victims, and their threat to life, liberty, and personal property; α=0.85”)	4.15	0.61	1.57	5	254
(6) Citizens understand risk
(Citizens in our community understand the risk of computer crime)	2.32	0.91	1	5	252
(7) Unreported to law enforcement
(Most computer crimes often go unreported to law enforcement)	3.66	0.80	1	5	250
Block 6: views on policing cybercrime
Responsibility for responding to local computer crimes
(1) Federal/state law enforcement
(Controlling computer crime in the local area is the primary responsibility of the federal and state governments)	3.03	0.85	1	5	251
(2) Local law enforcement
(Controlling computer crime in the local area is the primary responsibility of the local police)	2.72	0.90	1	5	253
(3) Computer crime units (CCU)
(Most cybercrime reports/calls should be responded to directly by a computer crime unit)	3.87	0.85	1	5	252
Seriousness of police response
(4) Not serious enough
(Computer crime is not taken seriously enough by law enforcement)	2.97	0.83	1	5	252
(5) Upper management	2.93	0.61	1	5	253
(Average to two items: upper management in our department treats cybercrime serious enough; and upper management in our department is taking the proper steps to address cybercrime; α=0.70)
Social support
(6) Recognition of violent crime investigations
(Police officers who investigate violent crimes should be given more recognition)	3.08	0.95	1	5	253
(7) Recognition of catching traditional criminals
(A police officer would be given more recognition for catching a traditional criminal compared with a computer criminal)	3.17	0.90	1	5	252
Value of cybercrime investigations
(8) Drains valuable resources
(Investigating cybercrime drains valuable police resources that should be spent investigating other crimes)	2.34	0.92	1	5	252
(9) Value of computer stakeouts
(Conducting a stakeout on the computer is just as important as a traditional stakeout)	3.42	0.92	1	5	252
Impact on policing
(10) Dramatically change policing
(Cybercrime will dramatically change police work)	3.56	0.82	2	5	254

Options were 1=SD to 5=SA unless noted otherwise.

GED, general education development test; SD, strongly disagree; SA, strongly agree.

References

Holt

, Blevins

. Examining sex work from the client's perspective: assessing johns using online data. Deviant Behavior, 2007; 28:333–354.

Burns

, Whitworth

, Thompson

. Assessing law enforcement preparedness to address Internet fraud. Journal of Criminal Justice, 2004; 32:477–493.

Brenner

. 2008. Cyberthreats: the emerging fault lines of the nation state. New York: Oxford University Press.

Stambaugh

, Beaupre

, Icove

et al. 2001. Electronic crime needs assessment for state and local law enforcement. Washington, DC: National Institute of Justice.

Goodman

. Why the police don't care about computer crime. Harvard Journal of Law and Technology, 1997; 10:465–494.

Hinduja

. Computer crime investigations in the United States: leveraging knowledge from the past to address the future. International Journal of Cyber Criminology, 2007; 1:1–26.

McQuade

. Technology-enabled crime, policing and security. Journal of Technology Studies, 2006; 32:32–42.

Senjo

. An analysis of computer-related crime: comparing police officer perceptions with empirical data. Security Journal, 2004; 17:55–71.

Taylor

, Fritsch

, Liederbach

et al. 2010. Digital crime and digital terrorism, 2nd. Upper Saddle River, NJ: Pearson Prentice Hall.

10.

Wall

. 2001. Cybercrimes and the Internet. Wall

. Crime and the Internet. New York: Routledge, 1–17.

11.

National Institute of Justice. 2008. Electronic crime scene investigation: a guide for first responders, 2nd. Washington, DC: National Institute of Justice.

12.

Holt

, Bossler

, Fitzgerald

. 2010. Examining state and local law enforcement perceptions of computer crime. Holt

. Crime on-line: correlates, causes, and context. Raleigh, NC: Carolina Academic, 221–246.

13.

Smith

, Grabosky

, Urbas

. 2003. Cyber criminals on trial. Port Melbourne: Cambridge.

14.

Ferraro

, Casey

. 2005. Investigating child exploitation and pornography: The Internet, law, and forensic science. San Diego: Elsevier.

15.

Hinduja

. Perceptions of local and state law enforcement concerning the role of computer crime investigative teams. Policing: An International Journal of Police Strategies and Management, 2004; 27:341–357.

16.

Swire

. No cop on the beat: underenforcement in e-commerce and cybercrime. Journal of Telecommunications and High Technology Law, 2009; 7:107–126.

17.

Marcum

, Higgins

, Freiburger

et al. Policing possession of child pornography online: investigating the training and resources dedicated to the investigation of cybercrime. International Journal of Police Science and Management, 2010; 12:516–525.

18.

Holt

, Bossler

. Police perceptions of computer crimes in two southeastern cities: an examination from the viewpoint of patrol officers. American Journal of Criminal Justice[Epub ahead of print]10.1007/s12103-011-9131-5.

19.

Bossler

, Holt

. Patrol officers' perceived role in responding to cybercrime. Policing: International Journal of Strategies and Management, 2012; 35:165–181.

20.

Lenhart

, Purcell

, Smith

et al. 2010. Social media and young adults. Pew Internet and American life project. www.pewinternet.org/Reports/2010/Social-Media-and-Young-Adults.aspx. 2012 January 15.

21.

Zickuhr

. 2011. Generations online in 2010. Pew Internet and American life project. www.pewinternet.org/Reports/2010/Generations-2010/Overview.aspx. 2012 January 15.

22.

Furnell

. 2002. Cybercrime: vandalizing the information society. Boston: Addison-Wesley.

23.

Skogan

, Hartnett

. 1997. Community policing, chicago style. New York: Oxford University Press.

24.

US Census Bureau. 2009. Annual estimates of the population of metropolitan and micropolitan statistical areas: April 1, 2000 to July 1, 2008. www.census.gov/popest/metro/tables/2008/CBSA-EST2008–01.csv. 2011 May 1.

25.

Law Enforcement Management and Administration Statistics (LEMAS). 2007. Local police departments, 2007, NCJ 231174. www.bjs.ojp.usdoj.gov.index.cfm?ty=pbdetail&iid=1750. 2011 April 24.

26.

Bureau of Labor Statistics. 2009. Current population survey, 2008. Washington, DC: Bureau of Labor Statistics.

27.

Alpert

, Macdonald

, Dunham

. Police suspicion and discretionary decision making during citizen stops. Criminology, 2005; 43:407–434.

28.

Lord

. An impact of community policing: reported stressors, social support, and strain among police officers in a changing police department. Journal of Criminal Justice, 1996; 24:503–525.

29.

Rutherford

, Blevins

, Lord

. An evaluation of the effects of a street crime unit on citizens' fear of crime. Professional Issues in Criminal Justice, 2009; 3:21–36.

30.

Dillman

. 2007. Mail and Internet surveys: a tailored design method. Hoboken, NJ: John Wiley.