Moral Choice and Traits 1
Abstract
This study was exploratory and designed to add to the growing body of knowledge in the
field of computer criminal behavior analysis. The study examined certain characteristics
of individuals engaged in computer criminal activity. Introductory psychology students
took part in the study as part of their course requirements for experimental credits (N =
381). The students filled out a battery of self-report questionnaires that measured their
frequency of criminal computer activity (Rogers, 2001), big-five factor traits (Goldberg,
1992), exploitive manipulative behavior (Altemeyer, 1995), and morality (Hladkyj,
2002). It was predicted that individuals engaging in criminal computer activities would
be introverted, antagonistic, undirected, neurotic and be more open to experience than
individuals who do not engage in criminal computer activities. The first hypothesis was
not supported by the findings. It was also hypothesized individuals engaging in criminal
computer activities would be more exploitive and manipulative than individuals who do
not engage in criminal computer activity. The second hypothesis was supported. The
third hypothesis predicted that individuals engaging in criminal computer activities would
have higher hedonistic morality than individuals who do not engage in criminal computer
activity. The third hypothesis was also not supported. Additional exploratory analyses
and the implications for future research are also discussed.
Background
With society’s increasing dependence on technology, we are becoming more vulnerable
to individuals who can exploit this technology. The rise of the Internet is as much a
societal revolution as it is a technical one (Rogers, 2001). The realm of cyberspace
competes with the “real world” for our attention, time and economic resources. The
dependence, both societal and technological, has not been lost on the criminal element of
our society. In the past few years, we have seen dramatic increases in criminal computer
behavior, and the accompanying losses to businesses and private citizens (Power, 2002).
The RCMP has reported that computer crime is one of the fastest growing criminal
categories in Canada (RCMP, 2003). The CSI/FBI’s 2002 computer crime survey
indicated that the business loss due to computer crime was nearly US $500,000 (Power,
2002).
The international community has recognized the danger and negative impact that
computer crime has on the world economy and on the personal lives of its constituents
(e.g., identity theft, fraud etc.). Several countries have passed legislation in an attempt to
deal with the growing crisis. Unfortunately, most of these laws have proven ineffective
in dealing with a criminal activity that transcends international borders, and our out dated
notions of legal jurisdictions (Parker, 1999).
Research to date indicates that criminal computer behavior is on the rise and will
continue to do so for the next few years (Power, 2002). It is important then that we
develop a reasonable understanding of those people who become involved in criminal
computer behavior. Part of this understanding must include personality characteristics,
motivations, and attraction to this new criminal behavior.
Moral Choice and Traits 2
The current study is exploratory and expands on the previous work of Skinner & Fream
(1997) and Rogers (2001). The study examined personality traits as operationalized by
Goldberg’s Big-Five Factor Model (i.e., extraversion, agreeableness, conscientiousness,
neuroticism, and openness to experience), moral decisions, and exploitive manipulative
behavior.
Hypotheses
1. Individuals engaging in criminal computer activities will be introverted,
antagonistic, undirected, neurotic and be more open to experience than individuals
who do not engage in criminal computer activities.
2. Individuals engaging in criminal computer activities will be more exploitive and
manipulative than individuals who do not engage in criminal computer activity.
3. Individuals engaging in criminal computer activities will have a higher rate of
hedonistic morality, and lower rates social and internal morality, than individuals
who do not engage in criminal computer activity.
Method
Participants
The participants consisted of 381 introductory psychology students. Their participations
was voluntary, but did form part of their course requirement for experimental participant
credits. The demographics of the participants can be found in table 1.
Instruments
1. Computer Crime Index (CCI) (Rogers, 2001) – measures the frequency and
prevalence of self-reported criminal computer activity.
2. Big-Five Factor Questionnaire (Goldberg, 1992) – self-report questionnaire
measuring personality traits based on five factors: extraversion (.87),
agreeableness (.90), conscientiousness (.84), neuroticism (.79), and openness to
experience (.79).
3. Exploitive Manipulative Amoral Dishonesty Scale (EMAD) (Altemeyer, 1995) –
self-report scale that measures the degree of exploitive and manipulative behavior
(.88 ).
4. Moral Decision Making Scale (MDKS) (Hladkyj, 2002) – adhoc self-report
questionnaire that measures participants moral decision making across three
subscales: Internal (.73), Social (.59), and Hedonistic (.71).
Moral Choice and Traits 3
Table 1
Responden t Demogr aphics
Gende r
Age
Marit al Status
Ethn ic ity
Male
Female
Mis sing
Total
Percentage
(Frequen cy)
45.1 (172)
54.9 (209)
0.0 (0)
100.0 (381.0)
Less than 18
18
19
20
21-22
23-25
26-30
31-36
37-45
46 or older
Mis sing
Total
0.5 (2)
27.8 (106)
27.6 (105)
13.9 (53)
13.4 (51)
9.7 (37)
4.2 (16)
0.5 (2)
1.8 (7)
0.3 (1)
0.3 (1)
100.0 (381)
Single
In relation no tcohab it ating
Comm on-law
Married
Separated
Divorced
Mis sing
Total
59.3 (226)
Caucas ian
Abo riginal
East Ind ian
Asian
Polynes ian
Middle Eastern
African
Central American
South A merican
Australi an
Mis sing
Total
69.8 (266)
5.5 (21)
4.5 (17)
13.4 (51)
0.3 (1)
0.3 (1)
2.4 (9)
1.3 (5)
0.8 (3)
1.0 (4)
0.8 (3)
100 (381)
32.5 (124)
3.7 (14)
2.4 (9)
0.5 (2)
1.3 (5)
0.3 (1)
100 (381)
Moral Choice and Traits 4
Table 2 Correlations
CCG
EMAD
EXT
AGR
NEU
OPN
CSN
MC-I
MC-S MC-H
CCG
1.00
EMAD
.15
1.00
EXT
.01
-.14
1.00
AGR
-.08
-.34
.42
1.00
NEU
-.05
-.12
.41
.37
1.00
OPN
.05
-.13
.47
.42
.39
1.00
CSN
-.08
-.18
.43
.52
.40
.52
1.00
MC-I
-.14
-.50
.13
.39
.09
.27
.28
1.00
MC-S
-.18
-.27
.03
.22
-.04
.02
.19
.56
1.00
MC-H
-.05
.04
.07
.08
.10
.13
.15
.38
.43
1.00
Note: CCG = Computer Crim e Frequency Grouped, EMAD = Exploitive
Manipul ative Total Score, EX T = Extrave rsion, AGR = Agreeable nes s, NEU
= Neuroticism, OPN = Openne ss to Exp erience, CNS = Conscientiousne ss,
MC-I = Moral Cho ice Internal, MC –S, = Moral Cho ice Social, MC-H =
Moral Cho ice Hedon ism
Results
The hypotheses were tested using multiple one-tailed t-tests (See Table 4). The power of
the tests were found to be .95
Moral Choice and Traits 5
Table 3 Descriptive Statistics
Descriptive
EMAD
MC-I
MC-S
Crimi nal Catego ry
M
SD
n
No Crim Activit y
71.77
24.13
171
Crim Activit y
79.06
24.50
172
Total
75.43
24.06
343
No Crim Activit y
29.02
4.20
188
Crim Activit y
27.82
4.60
192
Total
28.41
4.44
380
No Crim Activit y
26.14
4.50
186
Crim Activit y
24.41
4.85
189
Total
25.27
4.75
375
Note: EMAD = Exp loiti ve Manipulative Total S core, MC-I = Moral Choice Interna l,
MC –S, = Moral Choi ce Social.
Figure 1 Mean Comparisons
Mean Comparisons
80
70
60
50
No Criminal Activity
40
Criminal Activity
30
20
10
0
EMAD
MC-I
MC-S
Moral Choice and Traits 6
Table 4 T-Tests
Independen t Samples T-Test
t
df
95% Conf idenc e Interval
Lower
Upper
EMAD
2.84**
341
2.24
12.35
MC-I
-2.65**
378
-2.09
-.31
MC-S
-3.60**
373
-2.68
-.78
Note: EMAD = Exploiti ve Manipulative Total Score, MC-I = Moral Choi ce Internal,
MC –S = Moral Choice Social.
**p < .001.
Exploration
A logistic regression analysis was conducted using the variables identified as being
significant during the primary hypotheses testing (See Table 5)
Table 5 –Logistic Regression
Logistic Regression: Variables in Equation: Backward Stepwise Wald Procedure
Step 1
Step 2
EMAD
MC-I
MC-S
Constant
B
.01
.02
-.08
.41
SE
.01
.03
.03
1.12
Wald
4.15*
.49
6.85**
.14
Exp (B)
1.01
1.02
.93
1.51
EMAD
MC-S
Constant
.01
-.07
.96
.01
.03
.80
3.75*
7.01**
1.42
1.01
.94
2.60
Note: EMAD = Exploitive Manipulative Total Score, MC-I = Moral Choice Internal,
MC –S = Moral Choice Social.
*p < .05. **p < .001.
Moral Choice and Traits 7
Discussion
The findings indicate that there was no significant difference regarding the Big-Five
Factor traits between individuals who have engaged in criminal computer behavior and
those who have not. This is contrary to what had been hypothesized and more
importantly, contrary to the currently held stereotype of computer criminals (Verton,
2002).
The results indicated that computer criminals are more exploitive and manipulative. This
makes intuitive sense as these characteristics are consistent with criminals in general and
are not unique to computer criminals alone.
The findings did not support the hypothesis that computer criminals would tend to make
their moral decisions based more on hedonism than on internal or social morality. Here
again, the findings contradict previous research and commonly held stereotypes. Moral
decisions based on hedonism is roughly equivalent to being at Kholberg’s preconventional morality stage. Previous studies have indicated that computer criminals tend
to aggregate in the pre-conventional morality stage (Gordon, 2000).
Caution must be taken when interpreting the results as the findings are somewhat
preliminary, the study is exploratory, and two of the instruments used are new (CCI and
MDKS).
Despite the limitations of this study, it is an important step in the direction of developing
a better understanding of computer criminals.
Moral Choice and Traits 8
Selected References
Goldberg, L. (1992). The development of markers for the big-five factor
Psychological Assessment, 4, 26-42
structure.
Hollin, C. (1989). Psychology and crime: An introduction to criminological
psychology. New York: Routledge.
Parker, D. (1998). Fighting computer crime: A new framework for protecting
information. New York: John Wiley & Sons, Inc.
Power, R. (2002). CSI/FBI 2000 computer security survey. Computer Security Institute.
Rogers, M. (1999a). Psychology of hackers: Steps toward a new taxonomy.
Retrieved May 5, 1999 from the World Wide Web:
http://www.infowar.com
Rogers, M. (1999b). Psychology of computer criminals. Paper presented at the annual
Computer Security Institute Conference, St. Louis, Missouri.
Rogers, M. (2001). A social learning theory and moral disengagement analysis of
criminal computer behavior: An exploratory study. Unpublished doctoral
dissertation, University of Manitoba, Canada
Skinner, W., & Fream, A. (1997). A social learning theory analysis of computer crime
among college students. Journal of Research in Crime and Delinquency, 34, 495518.
Spafford, E. (1997). Are hacker break-ins ethical? In, Ermann, M., Williams, M. &
Shauf, M. (Eds.) Computers, ethics, and society. (pp. 77-88). New York: Oxford.