08832323.2015.1027162
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Moral Disengagement in Science and Business
Students: An Exploratory Study
Suzanne N. Cory
To cite this article: Suzanne N. Cory (2015) Moral Disengagement in Science and Business
Students: An Exploratory Study, Journal of Education for Business, 90:5, 270-277, DOI:
10.1080/08832323.2015.1027162
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Date: 11 January 2016, At: 19:26
JOURNAL OF EDUCATION FOR BUSINESS, 90: 270–277, 2015
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2015.1027162
Moral Disengagement in Science and Business
Students: An Exploratory Study
Suzanne N. Cory
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St. Mary’s University, San Antonio, Texas, USA
Cases of unethical business practices and technical failures have been extensively reported. It
seems that actions are often taken by individuals with little apparent concern for those
affected by their negative outcomes, which can be described as moral disengagement. This
study investigates levels of moral disengagement demonstrated by business and science
undergraduate students. Results indicate higher levels of moral disengagement in men than
in women, but few significant differences between students majoring in business and those
majoring in science.
Keywords: gender differences in ethicality, moral disengagement, technical failures,
undergraduate business students, undergraduate science students, unethical business practices
Cases of unethical business practices and high-profile engineering failures have dominated past headlines. These practices have resulted in preventable deaths and environmental
destruction as well as loss of financial resources. Both business people and scientists are often responsible for these
events.
Significance of the Issue
Acting irresponsibly without regard to ethical obligations or
thinking about others and the possible repercussions of an
individual’s decisions can result in negative consequences.
Having the ability and the desire to pursue fraudulent activities is only one measure of acts resulting in negative consequences for others. An individual’s acting in self-interest,
taking inappropriate risks, disregarding warning signs, or
demonstrating a caviler attitude toward the welfare of
others can result in damage to the environment, unnecessary deaths, or financial disasters. The lack of concern demonstrated by the disassociation of acts and the results of
those acts can be fueled by higher levels of moral disengagement. Moral disengagement allows an individual to
disassociate his or her actions from the consequences of
Correspondence should be addressed to Suzanne N. Cory, St. Mary’s
University, Bill Greehey School of Business, Department of Accounting,
One Camino Santa Maria, San Antonio, TX 78228, USA. E-mail:
scory@stmarytx.edu
those actions and removes the restraint of self-censure.
There are numerous reports of negative consequences that
result when individuals or groups act in a morally disengaged manner.
For example, the accounting scandals that were unveiled
in the early 2000s and the 2008 financial crisis were
brought on by chief executive officers (CEOs), certified
public accountants (CPAs), bankers, and other business
people who benefitted themselves at the expense of others.
In one instance, Fabrice (“Fabulous Fab”) Tourre, a Goldman Sachs vice president, helped create a subprime mortgage investment deal called Abacus 2007-AC1. The debt
obligation defrauded investors and secretly allowed billionaire John Paulson’s hedge fund to make a billion dollars by
betting against the fund (Rochan, 2013).
Other examples of unethical business practices abound.
One need look only at the results of the Enron and WorldCom scandals to observe the impact of unethical behavior
on the lives of others. Both financial disasters resulted in
prison sentences for some of the individuals involved, as
well as financial losses for investors, employees and other
parties. Yet, Kenneth Lay, former Enron CEO, had been
recognized for his community involvement and philanthropic activities in Houston until his leading role in the
company’s corruption scandal was discovered. WorldCom
was the largest accounting scandal in U.S. history until Bernard Madoff’s Ponzi scheme was brought to light. Madoff,
who built an empire out of his own investment firm and
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MORAL DISENGAGEMENT IN SCIENCE AND BUSINESS STUDENTS
developed technology that would later become the NASDAQ (Bandler, Varchaver, Burke, Kimes, & Abkowitz,
2009), is currently serving 150 years in a maximum-security prison. Madoff admitted to perpetrating a massive
Ponzi scheme that covered several continents and lasted
decades, defrauding his clients out of millions. Inappropriate actions on the part of professionals that result in harm to
others are certainly not limited to the business arena. Those
in engineering, sciences, and other related disciplines have
taken actions that resulted in injury to others.
For instance, in 2010 the explosion of the Deepwater
Horizon oil drilling rig in the Gulf of Mexico has been
blamed on failure to heed warnings regarding structural
weaknesses in cement casings that were designed to protect
the well pipes. Further, management was aware that the
blowout preventer had not been working properly for several days prior to the disaster. Management did not intend
for the rig to explode, kill 11 crewmen, and spurt
210 million gallons of oil into the Gulf. However, errors in
judgment and lack of concern about the repercussions of a
possible blowout preventer malfunction certainly contributed to the catastrophe.
Another example of negative consequences resulting from
errors in judgment and a lack of concern for the lives of others
occurred in 2003, when missing heat shield tiles on the
shuttle’s leading edge caused the Columbia to disintegrate
upon atmospheric re-entry, killing all seven astronauts
onboard. Heat shield problems had been discovered in earlier
shuttle missions and NASA’s administration was aware of
the damage to the Columbia. But no plan for repairing or
replacing heat shield tiles on the shuttle while in space and
thus protecting the astronauts’ lives had ever been pursued.
Taking shortcuts to save time transporting oil to Southern California refineries was partially to blame for
11 million gallons of crude oil spilling out of the Exxon
Valdez tanker in 1989 when it ran aground. The unintentional resulting loss of wild life and pristine beauty of the
Alaskan shores is difficult to measure, but could have been
prevented if management had focused on preparation and
planning for cleaning up spills rather than on cost
minimization.
The epitome of unethical behavior is illustrated by the
technological professionals who designed Nazi death chambers and lethal gas delivery systems that resulted in the
extermination of millions. Katz (2011) explained how he
believes the individuals who planned and created these
facilities allowed and accepted their involvement in their
design. He asserted a doubling of their personalities
occurred, which allowed one part of their personalities to
focus on required scientific specifications, with no concern
for the end results of the actual implementation of the
designs. In other words, they were able to disengage a part
of their persona and successfully ignore the moral implications of what they were doing. This could also be considered moral disengagement.
271
Statement of the Problem
In each of the previous examples, the actions of business
people and scientists collided, resulting in disasters of varying dimensions. Individuals involved in these scenarios are
often highly educated, obtaining at least an undergraduate
degree in their area of specialization. In light of the incidents described above, it is important to consider whether
individuals in business and in sciences are susceptible to
moral disengagement. The question then arises as to
whether individuals who study business or those who study
in the science arena have higher levels of moral disengagement, which allows them to take risks that may result in
negative consequences for others.
The purpose of this article is to report the results of a
pilot study measuring moral disengagement of undergraduate students in various disciplines. More specifically, this
study focuses on measuring moral disengagement of undergraduates studying business or science. Additionally, differences in moral disengagement between genders are
investigated. Given that this is an exploratory study, no specific hypotheses are developed. Rather, measurement of
students’ moral disengagement and differences in moral
disengagement between the two groups and between genders are determined. Results may lead to further research in
this area and perhaps changes in the ethics curriculum.
Prior Research
Albert Bandura developed the theory of moral disengagement in 1986, and his theory explained that an individual’s
self-regulatory mechanisms do not operate unless they are
activated (Bandura, 2002). This theory attempts to explain
why some individuals are able to suffer little or no personal
distress from engaging in acts deemed unethical or unlawful by society. Bandura’s theory indicates that high levels
of moral disengagement allow an individual to disassociate
from the results or implications of his or her actions even if
these actions will negatively affect others. Bandura proposed three categories of mechanisms used by individuals
to achieve this dissociation. The first is cognitively restructuring behavior demonstrated by moral justification, euphemistic labeling, and advantageous comparison. The second
is obscuring or minimizing an individual’s active role in
behaviors by displacing responsibility, diffusing responsibility, and disregarding or distorting the consequences of an
action. The final category is focusing on the unfavorable
acts or traits of those negatively affected by dehumanizing
victims and attributing blame.
Bandura’s theories of moral disengagement have been
applied to societal issues such as terrorism (Maikovich,
2005), the perpetration of inhumanities (Bandura, 1990),
cubicle warriors (Royakkers & van Est, 2010), executioners
(Osofsky, Bandura, & Zimbardo, 2005) and school bullies
(Obermann, 2011). The implosion of our economy in 2008
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272
S. N. CORY
and fraudulent incidents in the early 2000s has generated
interest in applying Bandura’s theories to business.
Some studies have been industry specific such as in J.
White, Bandura, and Bero (2009), which looked at moral
disengagement exhibited in harmful corporate research
related to tobacco, lead, vinyl chloride, and silicosis. Ntayi,
Eyaa, and Ngoma (2010) delved into the unethical practices
of public procurement officers in Uganda. Other studies
such as Claybourn’s 2011 investigation questioned whether
work related variables and moral disengagement influence
negative behaviors such as work place harassment. Moore,
Detert, Trevi~
no, Baker, and Mayer (2012) investigated why
employees do bad things in the workplace. Barsky (2011)
and Anand, Ashforth, and Joshi (2005) researched moral
disengagement and how it relates to the rationalization of
unethical or corrupt acts in the workplace. Anand et al.
claimed that, based on their study, virtually every organization suffers from fraud.
Application of Bandura’s theories in the sciences is less
pervasive. However, use of student subjects has intensified.
Using undergraduate students as subjects, Hinrichs, Wang,
Hinrichs, and Romero (2012) examined the relationship
between leadership beliefs and moral disengagement
through displacement of responsibility. Tsai, Wang and
Lo (2014) explored the relationships among locus of control, moral disengagement in sport and rule transgression
of athletes, using members of a college sports team as
subjects.
Previous studies have focused on general moral disengagement tendencies among students, such as Detert, Trevi~no, and
Sweitzer’s 2008 study, which compared moral disengagement tendencies among college freshmen majoring in business and those majoring in education. The study tested the
relationships between empathy, moral identity, trait cynicism,
and locus of control compared to higher levels of moral disengagement. Ultimately, the study found a negative association between empathy and moral identity, but a positive
association between trait cynicism and locus of control. The
results indicated higher levels of moral disengagement in
business majors as compared to education majors.
Other studies focused on a particular behavior when testing moral disengagement tendencies. For example, Bing
et al. (2012) performed an experiment with college students
involving academic cheating. Morgan and Neal (2011)
compared students’ perceptions of ethical breaches with
freshmen and upper level students in information systems
courses. Baird and Zelin (2009) used undergraduate students to study whether a person committing fraud in a situation involving obedience pressure was judged less harshly
than an individual committing fraud of his or her own volition. Each year more studies are being conducted using
undergraduate students to research not only how these students view and judge moral disengagement, but how those
views and judgments differ over time and when compared
to students across disciplines.
RESEARCH METHODOLOGY
Investigations into moral disengagement pertaining to students in both higher and lower education have incorporated
Bandura’s theory. The purpose of this article is to report the
findings of an exploratory study using undergraduate students who are earning bachelor degrees in business or in
the sciences. These two student groups were chosen for this
study because of the importance of understanding the ethical inclinations of tomorrow’s leaders in these fields. Further, given their eventual employment opportunities
coupled with the ability to put the financial or physical welfare of others at risk, or implement unsafe practices, it is
important to consider whether individuals majoring in these
disciplines are more susceptible to moral disengagement.
The survey provided in the Appendix was adapted from
Detert et al. (2008). Their survey was adapted from one
developed and used in multiple studies by Bandura and
others (e.g., Bandura, Barbaranelli, Caprara, Barbaranelli,
& Pastorelli, 1996; Pelton, Gound, Forehand, & Brody,
2004). The survey was designed in order to measure each
of the eight components of moral disengagement equally
with four questions per component. Given that the survey,
or one very similar to it, has been used in previous research
(e.g. Bandura et al., 1996; Detert et al., 2008; Pelton et al.,
2004) and previously tested extensively for validity, no
further tests of validity were deemed necessary.
Students were presented with a list of 32 statements and
asked to determine the degree to which they agreed with
each, using a 7-point Likert-type scale ranging from 1
(strongly disagree) to 7 (strongly agree). Questions 1–4
measured moral justification, questions 5–8 measured
euphemistic labeling, and questions 9–12 measured advantageous comparisons. Questions 13–16 measured displacement of responsibility, questions 17–20 measured diffusion
of responsibility, and questions 21–24 measured distortion
of consequences. Last, questions 25–28 measured attribution of blame, and questions 29–32 measured dehumanization. Responses to each subset of questions were summed
to obtain the measurement for that part of the survey and a
grand total was obtained by adding all responses from each
respondent.
The survey was given to students taking a general education course in the fall 2013 semester at a small private liberal arts university in the southwest. Total enrollment in
15 sections of the course was 274 undergraduate students
and 249 usable responses were received. This resulted in a
91% response rate. In order to analyze responses from the
two subgroups of interest, responses from students who
were not majoring in business or in the sciences were discarded. The remaining sample consisted of 148 responses,
of which 53 were completed by business majors and 95 by
science majors. There were 33 men majoring in business,
with an average age of 18.8 years and 20 women with an
average age of 18.2 years. The average age of the 54 men
MORAL DISENGAGEMENT IN SCIENCE AND BUSINESS STUDENTS
majoring in the sciences was 18.35 years and the
41 women averaged 18.4 years of age. The science majors
included environmental science, engineering, biochemistry,
premed, chemistry, biology, and physics. Typical business
majors included accounting, finance, marketing, international business and management. Levels of moral disengagement within each group and any differences
between the two groups should be of interest.
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FINDINGS
First, the responses were analyzed for each group of students. Results of the t-test are shown in Table 1. In almost
all cases (38 of 41 for the business students and 39 of 41 for
the science majors) the students disagreed with the statements. However, both groups of students agreed with the
first statement (“It is alright to fight to protect your
friends”). Neither group strongly agreed or disagreed with
statement 19 (“If a group decides together to do something
harmful, it is unfair to blame any one member of the group
for it”). The science majors strongly disagreed with statement 26 (“If someone leaves something lying around, it’s
their own fault if it gets stolen”), but the business majors
neither strongly agreed nor disagreed with it.
Next, differences in the responses to the survey questions between business majors and science majors were
analyzed. Results are shown in column 2 of Table 2. The
responses to each question, each category, and the total
score were analyzed. Of the 41 comparisons, only one was
significantly different between the two groups. Business
majors’ mean was higher than that of science majors. column 3 of Table 2 shows the results of t-tests for differences
between genders for the total sample. Fourteen significant
differences were found and, in every case, responses from
men averaged a higher score (more likely to agree with the
statement) than responses from women. The question then
arose whether gender differences within each school might
exist. Therefore, t-tests were computed for response differences between genders within business and within science.
Results are shown in columns 4 and 5 of Table 2,
respectively.
Fourteen significant differences were found between
responses from men and women for the business majors,
and five were found for the science majors. Again, in every
case, the average response for men was higher than the
average response for women. Finally, the responses were
divided into two groups based on gender to determine
whether any differences could be found between the two
disciplines. Results are shown in the last two columns of
Table 2. Only one significant difference was found between
disciplines for the women (column 6). Women majoring in
science had a higher mean than their business major counterparts. For men (column 7), three significant differences
were found and in every case the average score was higher
273
TABLE 1
t-Tests for Business and Science Students
Statement
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
MJ
EL
AC
DISR
DIFR
DC
AB
DEH
Alltotal
Business students
**
3.02
¡7.28**
¡4.50**
¡3.11**
¡8.13**
¡14.81**
¡16.57**
¡5.60**
¡17.06**
¡12.20**
¡14.39**
¡15.21**
¡5.06**
¡4.36**
¡6.15**
¡11.51**
¡2.79*
¡3.04*
0.13
¡6.43**
¡5.68**
¡7.80**
¡10.92**
¡17.52**
¡8.92**
0.49
¡7.50**
¡5.80**
¡9.11**
¡10.61**
¡16.46**
¡11.15**
¡4.25**
¡15.29**
¡18.37**
¡8.60**
¡4.37**
¡12.47**
¡6.90**
¡12.75**
¡14.97**
Science students
6.57**
¡10.29**
¡5.71**
¡4.59**
¡16.10**
¡23.96**
¡30.14**
¡8.30**
¡22.57**
¡24.28**
¡26.19**
¡19.96**
¡9.49**
¡7.27**
¡9.79**
¡17.68**
¡3.08*
¡4.28**
¡1.51
¡9.85**
¡9.23**
¡11.78**
¡17.12**
¡32.73**
¡21.18**
¡4.99**
¡12.97**
¡9.77**
¡21.26**
¡18.03**
¡20.34**
¡21.50**
¡5.19**
¡23.37**
¡29.28**
¡14.85**
¡5.98*
¡19.35**
¡15.59**
¡25.69**
¡23.81***
Note: Positive t score indicates higher level of moral disengagement.
AB D attribution of blame; AC D advantageous comparisons; DC D distortion of consequences; DEH D dehumanization; DIFR D measured diffusion of responsibility; DISR D measured displacement of responsibility;
EL D euphemistic labeling; MJ D moral justification.
*p < .05, **p < .01.
for men majoring in business than for men majoring in science, indicating that male business majors agreed more
strongly with the statement than did their counterparts
majoring in science.
Based on these results, it seems that women are far less
likely to justify immoral actions by using moral disengagement tactics. In every comparison where t-tests indicated a
significant difference, the average response from women
274
S. N. CORY
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TABLE 2
t-Tests by Demographic
Statement
Business
and science
by school
Business
and science
by gender
Business by
gender
Science by
gender
Women
only
Men
only
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
MJ
EL
AC
DISR
DIFR
DC
AB
DEH
All total
¡0.83
¡1.10
¡0.28
0.82
1.11
0.55
0.48
¡0.07
¡0.64
0.64
0.42
¡0.75
0.43
0.20
¡0.03
¡0.18
¡0.55
¡0.04
1.10
¡0.18
0.26
0.62
0.03
0.39
1.12
2.94**
0.34
¡0.03
1.02
0.64
¡0.02
1.52
¡0.46
0.66
¡0.12
0.18
0.21
0.41
1.70
0.96
0.68
3.21**
1.81
1.77
0.30
1.96*
2.08*
1.85
¡0.03
1.85
2.62**
2.12*
1.21
0.00
¡0.10
1.10
1.48
¡0.40
2.39*
¡0.06
0.92
1.48
4.49**
3.39**
3.59**
1.55
¡0.85
0.87
1.24
1.17
1.69
0.47
2.19*
2.56*
1.74
2.52*
0.74
0.96
4.01**
0.70
1.70
2.59**
2.80*
1.96*
4.54**
0.83
1.56
1.74
2.29*
0.16
1.80
2.26*
1.91
1.32
¡1.15
0.23
0.74
1.44
¡1.16
0.83
0.46
0.81
1.72
3.99**
2.57*
3.76**
1.34
1.07
1.86
1.45
0.67
2.06*
1.25
1.22
3.95**
1.94
2.38*
0.28
0.37
3.91**
1.99*
1.41
2.91**
2.06
0.90
0.00
0.13
1.67
1.75
1.06
0.07
1.36
1.86
1.51
0.78
0.72
¡0.17
0.75
0.89
0.12
2.31*
¡0.35
0.55
0.90
3.40**
2.61**
2.10*
1.11
¡1.72
0.06
0.74
1.19
0.99
¡0.23
1.82
1.04
1.34
1.73
0.70
0.86
2.78**
¡0.24
1.18
1.60
¡1.14
¡1.48
¡2.05*
0.26
0.83
0.54
¡0.54
¡0.03
¡0.70
¡0.29
¡0.19
¡0.83
1.15
¡0.06
¡0.21
¡0.61
0.34
0.54
0.29
¡0.46
¡0.35
0.32
¡0.21
¡1.89
0.29
0.54
¡0.84
¡0.49
0.76
¡0.21
¡0.84
1.46
¡1.47
0.31
¡0.70
0.20
0.30
¡0.38
¡0.09
0.26
¡0.28
0.44
0.09
1.98*
0.95
0.97
0.52
1.10
¡0.11
¡0.12
1.07
0.88
¡0.17
¡0.65
0.34
0.29
0.43
¡1.15
0.26
1.25
0.25
0.80
1.02
0.55
1.50
1.54
3.88**
1.23
0.55
0.81
1.11
0.81
1.23
1.48
0.77
0.58
0.14
0.11
1.15
2.75**
1.19
1.51
Note: Positive t score indicates males have higher mean or business major has higher mean. AB D attribution of blame; AC D advantageous comparisons;
DC D distortion of consequences; DEH D dehumanization; DIFR D measured diffusion of responsibility; DISR D measured displacement of responsibility;
EL D euphemistic labeling; MJ D moral justification.
*p < .05, **p < .01.
was lower than that for men, which indicates less agreement
with the statement. This was true when analyzing results for
the full sample between genders (column 3 of Table 2) and
within each school by gender (columns 4 and 5 in Table 2).
Perhaps these findings should be expected, given past
research on differences between the genders in moral development or moral judgment. For example, Lv and Huang
(2012) found gender differences in ethical intentions and
moral judgment in accounting students, but found those differences were negligible for accounting practitioners, suggesting that these disparities fade in the workplace. Also,
R. D. White (1999) had similar results for gender differences in moral reasoning. His results indicated that women
employed in the public sector had higher levels of moral
reasoning than their male counterparts. Whipple and
Swords (1992) found consistently higher business ethics for
MORAL DISENGAGEMENT IN SCIENCE AND BUSINESS STUDENTS
TABLE 3
Means and Medians
M/median
Statement
Male business majors
Male science majors
3
26
AB
3.83 / 4
4.56 / 5
11.83 / 11
2.93 / 2
2.69 / 2
8.64 / 8
Female business majors
Female science majors
1.95 / 1
2.93 / 2
All business majors
All science majors
5.96 / 5
2.94 / 2
26
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26
Note: AB D attribution of blame.
female college students than for their male counterparts in
both the United States and in the United Kingdom. However, the number and the strength of differences for survey
responses between men and women are still somewhat surprising. There were 41 comparisons made in each of the
three columns (columns 3–5) of results referred to above
and presented in Table 2, which totals 123. Of those 123
comparisons, a total of 33 were significantly different
between the genders (26%) at least at the 5% level.
Only one significant difference was found in the average
responses from women majoring in business and those
majoring in sciences (column 6 of Table 2). The female science majors agreed more strongly with statement 3 (“It’s ok
to attack someone who threatens your family’s honor”).
When comparing responses from men between schools,
three instances were found in which responses from men
majoring in business were significantly higher than from
men majoring in sciences: “It’s ok to attack someone who
threatens your family’s honor” (statement 3), “If someone
leaves something lying around, it’s their own fault if it gets
stolen and for attribution of blame (statement 26); sum of
questions 25–28). Notably, the single difference found
between the two subgroups, based on major was also for
statement 26 (column 2).
In order to better understand the responses that were significantly different between women majoring in business
and women majoring in science as well as for men majoring
in business and men majoring in science, the mean and
median scores were determined and are presented in
Table 3. The mean and the median for all business majors
and all sciences majors for statement 26 are also presented
in Table 3.
For statement 3, the mean average score for male business majors was 3.83, which is closer to 4 (neither agree
nor disagree) but the mean average for male science majors
was closer to 3 and farther away from neither agree nor disagree. The median was 4 for the male business majors and
2 for the male science majors. Hence, male business majors
275
agreed slightly with the statement, but male science majors
disagreed. For statement 26, the mean average for male
business majors was about half way between 4 and 5, but
the mean average for male science majors was about half
way between 2 and 3. The medians were 5 and 2, respectively. Thus, male business majors agreed with the statement, but male science majors did not agree with it at all,
based on both the mean the median. The mean average for
male business majors for attribution of blame (the sum of
statements 25–28, which results in a minimum of 4 and a
maximum of 28) was closer to 12, with a median of 11, but
the mean average for male science majors was about halfway between 8 and 9, with a median of 8. The mean for
female business majors for statement 26 is very close to 2
and the median is 1. However, the mean for the female science majors is very close to 3, with a median of 2. Thus,
neither group agreed with the statement, but female business majors agreed with it less. Finally, the mean for all
business majors for statement 26 was very close to 6 with a
median of 5, but the mean for all science majors was close
to 3, with a median of 2. Therefore, on average, business
majors slightly agreed with the statement, but science
majors slightly disagreed with it.
These results are somewhat inconsistent with previous
research in the area of ethical development differences
between business students and science students. For example, Neubaum, Pagell, Drexler, Mckee-Ryan, and Larson
(2009) found no differences in personal moral philosophy
between business and non-business students. However,
these findings are consistent with Segal, Gideon, and
Haberfield (2011) who found that business students were
more willing to accept unethical conduct than criminal
justice majors and Cory and Hernandez (2014) who found
that business students demonstrated higher levels of moral
disengagement than humanities majors.
CONCLUSION
To summarize the results of this pilot study, only one significant difference of the 42 comparisons was found when
comparing all business majors with all sciences majors.
However, several differences were found when comparing
responses from men and women both for the total sample
and within each major classification. In every case, men
exhibited a higher level of moral disengagement tendencies
than did the women. Finally, when comparing women
between disciplines, only one difference was found, but
three differences were found when comparing men by discipline. In every case where a difference was found with
men, business majors were more likely to agree with the
statement than sciences majors, indicating a higher level of
moral disengagement. However, the only difference found
between responses from women by discipline indicated a
higher level of moral disengagement in science majors.
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276
S. N. CORY
The results of the study do not necessarily indicate that,
in general, male science students are less morally disengaged than male business students. However, differences
found between the two groups of students are interesting.
Given that ethics courses are required for most college
majors, these results may indicate the necessity of increased
coverage in certain areas. More specifically, consequences
of an individual’s acting solely in self-interest, inappropriateness of indifference to possible negative outcomes of
actions, possible repercussions to others of taking unnecessary risks, and possible negative results of inattention to
detail, in addition to the unethical behavior of stealing or
committing fraud, should be reinforced. Further, the differences in results based on gender imply that men may need
more ethics education than women, at least in terms of
moral disengagement measures. Those who are teaching
ethics at the university level should be made aware of the
necessity of including the previous information in the curriculum. Moral disengagement may result in adverse consequences to others, even if fraud is not directly involved in
the actions taken by an individual. Further, it appears that
increased coverage and reinforcement of these topics may
be necessary in business curricula.
There are certainly limitations to this study. Only students enrolled in one general education course at one university completed the survey and their selection was not
random. Further, only lower division students were surveyed and ethical maturity may have not yet occurred.
However, these limitations may be addressed with future
research related to this topic.
REFERENCES
Anand, V., Ashforth, B. E., & Joshi, E. (2005). Business as usual: The
acceptance and perpetuation of corruption in organizations. Academy of
Management Executive, 19(4), 9–23.
Baird, J. E., & Zelin, R. C. II (2009). An examination of the impact of obedience pressure on perceptions of fraudulent acts and the likelihood of
committing occupational fraud. Journal of Forensic Studies in Accounting and Business, 1, 1–14.
Bandler, J., Varchaver, N., Burke, D., Kimes, M., & Abkowitz, A. (2009).
How Bernie did it. Fortune, 159(10), 50–71.
Bandura, A. (1990). Moral disengagement in the perpetration of inhumanities. Personality and Social Psychology Review, 3, 193–209.
Bandura, A. (2002). Selective moral disengagement in the exercise of
moral agency. Journal of Moral Education, 31, 101–119.
Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (1996).
Mechanisms of moral disengagement in the exercise of moral agency.
Journal of Personality and Social Psychology, 71, 364–374.
Barsky, A. (2011). Investigating the effects of moral disengagement and
participation on unethical work behavior. Journal of Business Ethics,
104, 59–75.
Bing, M. N., Davison, H. K., Vitell, S. J., Ammeter, A. P., Garner, B. L., &
Novicevic, M. M. (2012). An experimental investigation of an interactive model of academic cheating among business school students. Academy of Management Learning & Education, 11, 28–48.
Claybourn, M. (2011). Relationships between moral disengagement, work
characteristics and workplace harassment. Journal of Business Ethics,
100, 283–301.
Cory, S. N., & Hernandez, A. R. (2014). Moral disengagement in business
and humanities majors: An exploratory study. Research in Higher Education Journal, 23. Retrieved from http://www.aabri.com/manuscripts/
141808.pdf
Detert, J. R., Trevi~no, L. K., & Sweitzer, V. L. (2008). Moral disengagement in ethical decisions making: A study of antecedents and outcomes.
Journal of Applied Psychology, 93, 163–178.
Hinrichs, K. T., Wang, L, Hinrichs, A. T., & Romero, E. J. (2012).
Moral disengagement through displacement of responsibility: The
role of leadership beliefs. Journal of Applied Social Psychology, 42,
62–80.
Katz, E. (2011). The Nazi engineers: Reflections on technological ethics in
Hell. Science and Engineering Ethics, 17, 571–582.
Lv, W., & Huang, Y. (2012). How workplace accounting experience and
gender affect ethical judgment. Social Behavior and Personality: An
International Journal, 4, 1477–1483.
Maikovich, A. K. (2005). A new understanding of terrorism using cognitive dissonance principles. Journal for the Theory of Social Behavior, 3,
373–397.
Moore, C., Detert, J. R., Trevi~no, L. K., Baker, V. L., & Mayer, D. M.
(2012). Why employees do bad things: Moral disengagement and unethical behavior. Personnel Psychology, 65, 1–48.
Morgan, J., & Neal, G. (2011). Student assessments of information systems
related ethical situations: Do gender and class level matter? Journal of
Legal, Ethical, Regulatory Issues, 14, 113–130.
Neubaum, D. O., Pagell, M., Drexler, J. A. Jr., Mckee-Ryan, F. M., & Larson, E. (2009). Business education and its relationship to student personal moral philosophies and attitudes towards profits: An empirical
response to critics. Academy of Management Learning and Education,
8, 9–24.
Ntayi, J. M., Eyaa, S., & Ngoma, M. (2010). Moral disengagement and the
social construction of procurement officers’ deviant behaviors. Journal
of Management Policy and Practice, 11(4), 95–110.
Obermann, M. L. (2011). Moral disengagement in self-reported and peernominated school bullying. Aggressive Behavior, 37, 133–144.
Osofsky, M. J., Bandura, A., & Zimbardo, P. G. (2005). The role of moral
disengagement in the execution process. Law and Human Behavior, 29,
371–393.
Pelton, J., Gound, M., Forehand, R., & Brody, G. (2004). The moral disengagement scale: Extension with an American minority sample. Journal of Psycholpathology and Behavioral Assessment, 26, 31–39.
Rochan, M. (2013, November 14). SEC demands former Goldman Sachs
VP Fabrice Tourre’s salary details. International Business Times.
Retrieved from http://www.ibtimes.co.uk/sec-fabrice-tourre-salarydetails-sub-prime-522248
Royakkers, L., & van Est, R. (2010). The cubicle warrior: The
marionette of digitalized warfare, Ethics and Information Technology,
12, 289–296.
Segal, L., Gideon, L., & Haberfield, M. R. (2011). Comparing the ethical
attitudes of business and criminal justice students. Social Science Quarterly, 9, 1021–1043.
Tsai, J. J., Wang, C. H., & Lo, H. J. (2014). Locus of control, moral disengagement in sport and rule transgression of athletes. Social Behavior
and Personality, 42, 59–68.
Whipple, T. W., & Swords, D. F. (1992). Business ethics judgments: A
cross-cultural comparison. Journal of Business Ethics, 11, 671–678.
White, J., Bandura, A., & Bero, L. A. (2009). Moral disengagement in the
corporate world. Accountability in Research, 16, 41–74.
White, R. D. (1999). Are women more ethical? Recent findings on the
effects of gender upon moral development. Journal of Public Administration Research and Theory, 9, 459–471.
MORAL DISENGAGEMENT IN SCIENCE AND BUSINESS STUDENTS
277
APPENDIX—QUESTIONNAIRE
Strongly disagree
1
2
3
Neither
4
Strongly agree
5
6
7
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:26 11 January 2016
Choose a number from 1 to 7 from the scale above, based on how strongly you agree or disagree with each statement. Put
the number in the space provided.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
__It is alright to fight to protect your friends.
__It’s ok to steal to take care of your family’s needs.
__It’s ok to attack someone who threatens your family’s honor.
__It is alright to lie to keep your friends out of trouble.
__Sharing test questions is just a way of helping your friends.
__Talking about people behind their backs is just part of the game.
__Looking at a friend’s homework without permission is just “borrowing it.”
__It is not bad to “get high” once in a while.
__Damaging some property is no big deal when you consider that others are beating up people.
__Stealing some money is not too serious compared to those who steal a lot of money.
__Not working very hard in school is really no big deal when you consider that other people are probably cheating.
__Compared to other illegal things people do, taking some things from a store without paying for them is not very serious.
__If people are living under bad conditions, they cannot be blamed for behaving aggressively.
__If the professor doesn’t discipline cheaters, students should not be blamed for cheating.
__If someone is pressured into doing something, they shouldn’t be blamed for it.
__People cannot be blamed for misbehaving if their friends pressured them to do it.
__A member of a group or team should not be blamed for the trouble the team caused.
__A student who only suggests breaking the rules should not be blamed if other students go ahead and do it.
__If a group decides together to do something harmful, it is unfair to blame any one member of the group for it.
__You can’t blame a person who plays only a small part in the harm caused by a group.
__It is ok to tell small lies because they don’t really do any harm.
__People don’t mind being teased because it shows interest in them.
__Teasing someone does not really hurt them.
__Insults don’t really hurt anyone.
__If students misbehave in class, it is their teacher’s fault.
__If someone leaves something lying around, it’s their own fault if it gets stolen.
__People who are mistreated have usually done things to deserve it.
__People are not at fault for misbehaving at work if their managers mistreat them.
__Some people deserve to be treated like animals.
__It is ok to treat badly someone who behaved like a “worm.”
__Someone who is obnoxious does not deserve to be treated like a human being.
__Some people have to be treated roughly because they lack feelings that can be hurt.
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Moral Disengagement in Science and Business
Students: An Exploratory Study
Suzanne N. Cory
To cite this article: Suzanne N. Cory (2015) Moral Disengagement in Science and Business
Students: An Exploratory Study, Journal of Education for Business, 90:5, 270-277, DOI:
10.1080/08832323.2015.1027162
To link to this article: http://dx.doi.org/10.1080/08832323.2015.1027162
Published online: 16 Apr 2015.
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Date: 11 January 2016, At: 19:26
JOURNAL OF EDUCATION FOR BUSINESS, 90: 270–277, 2015
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2015.1027162
Moral Disengagement in Science and Business
Students: An Exploratory Study
Suzanne N. Cory
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:26 11 January 2016
St. Mary’s University, San Antonio, Texas, USA
Cases of unethical business practices and technical failures have been extensively reported. It
seems that actions are often taken by individuals with little apparent concern for those
affected by their negative outcomes, which can be described as moral disengagement. This
study investigates levels of moral disengagement demonstrated by business and science
undergraduate students. Results indicate higher levels of moral disengagement in men than
in women, but few significant differences between students majoring in business and those
majoring in science.
Keywords: gender differences in ethicality, moral disengagement, technical failures,
undergraduate business students, undergraduate science students, unethical business practices
Cases of unethical business practices and high-profile engineering failures have dominated past headlines. These practices have resulted in preventable deaths and environmental
destruction as well as loss of financial resources. Both business people and scientists are often responsible for these
events.
Significance of the Issue
Acting irresponsibly without regard to ethical obligations or
thinking about others and the possible repercussions of an
individual’s decisions can result in negative consequences.
Having the ability and the desire to pursue fraudulent activities is only one measure of acts resulting in negative consequences for others. An individual’s acting in self-interest,
taking inappropriate risks, disregarding warning signs, or
demonstrating a caviler attitude toward the welfare of
others can result in damage to the environment, unnecessary deaths, or financial disasters. The lack of concern demonstrated by the disassociation of acts and the results of
those acts can be fueled by higher levels of moral disengagement. Moral disengagement allows an individual to
disassociate his or her actions from the consequences of
Correspondence should be addressed to Suzanne N. Cory, St. Mary’s
University, Bill Greehey School of Business, Department of Accounting,
One Camino Santa Maria, San Antonio, TX 78228, USA. E-mail:
scory@stmarytx.edu
those actions and removes the restraint of self-censure.
There are numerous reports of negative consequences that
result when individuals or groups act in a morally disengaged manner.
For example, the accounting scandals that were unveiled
in the early 2000s and the 2008 financial crisis were
brought on by chief executive officers (CEOs), certified
public accountants (CPAs), bankers, and other business
people who benefitted themselves at the expense of others.
In one instance, Fabrice (“Fabulous Fab”) Tourre, a Goldman Sachs vice president, helped create a subprime mortgage investment deal called Abacus 2007-AC1. The debt
obligation defrauded investors and secretly allowed billionaire John Paulson’s hedge fund to make a billion dollars by
betting against the fund (Rochan, 2013).
Other examples of unethical business practices abound.
One need look only at the results of the Enron and WorldCom scandals to observe the impact of unethical behavior
on the lives of others. Both financial disasters resulted in
prison sentences for some of the individuals involved, as
well as financial losses for investors, employees and other
parties. Yet, Kenneth Lay, former Enron CEO, had been
recognized for his community involvement and philanthropic activities in Houston until his leading role in the
company’s corruption scandal was discovered. WorldCom
was the largest accounting scandal in U.S. history until Bernard Madoff’s Ponzi scheme was brought to light. Madoff,
who built an empire out of his own investment firm and
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MORAL DISENGAGEMENT IN SCIENCE AND BUSINESS STUDENTS
developed technology that would later become the NASDAQ (Bandler, Varchaver, Burke, Kimes, & Abkowitz,
2009), is currently serving 150 years in a maximum-security prison. Madoff admitted to perpetrating a massive
Ponzi scheme that covered several continents and lasted
decades, defrauding his clients out of millions. Inappropriate actions on the part of professionals that result in harm to
others are certainly not limited to the business arena. Those
in engineering, sciences, and other related disciplines have
taken actions that resulted in injury to others.
For instance, in 2010 the explosion of the Deepwater
Horizon oil drilling rig in the Gulf of Mexico has been
blamed on failure to heed warnings regarding structural
weaknesses in cement casings that were designed to protect
the well pipes. Further, management was aware that the
blowout preventer had not been working properly for several days prior to the disaster. Management did not intend
for the rig to explode, kill 11 crewmen, and spurt
210 million gallons of oil into the Gulf. However, errors in
judgment and lack of concern about the repercussions of a
possible blowout preventer malfunction certainly contributed to the catastrophe.
Another example of negative consequences resulting from
errors in judgment and a lack of concern for the lives of others
occurred in 2003, when missing heat shield tiles on the
shuttle’s leading edge caused the Columbia to disintegrate
upon atmospheric re-entry, killing all seven astronauts
onboard. Heat shield problems had been discovered in earlier
shuttle missions and NASA’s administration was aware of
the damage to the Columbia. But no plan for repairing or
replacing heat shield tiles on the shuttle while in space and
thus protecting the astronauts’ lives had ever been pursued.
Taking shortcuts to save time transporting oil to Southern California refineries was partially to blame for
11 million gallons of crude oil spilling out of the Exxon
Valdez tanker in 1989 when it ran aground. The unintentional resulting loss of wild life and pristine beauty of the
Alaskan shores is difficult to measure, but could have been
prevented if management had focused on preparation and
planning for cleaning up spills rather than on cost
minimization.
The epitome of unethical behavior is illustrated by the
technological professionals who designed Nazi death chambers and lethal gas delivery systems that resulted in the
extermination of millions. Katz (2011) explained how he
believes the individuals who planned and created these
facilities allowed and accepted their involvement in their
design. He asserted a doubling of their personalities
occurred, which allowed one part of their personalities to
focus on required scientific specifications, with no concern
for the end results of the actual implementation of the
designs. In other words, they were able to disengage a part
of their persona and successfully ignore the moral implications of what they were doing. This could also be considered moral disengagement.
271
Statement of the Problem
In each of the previous examples, the actions of business
people and scientists collided, resulting in disasters of varying dimensions. Individuals involved in these scenarios are
often highly educated, obtaining at least an undergraduate
degree in their area of specialization. In light of the incidents described above, it is important to consider whether
individuals in business and in sciences are susceptible to
moral disengagement. The question then arises as to
whether individuals who study business or those who study
in the science arena have higher levels of moral disengagement, which allows them to take risks that may result in
negative consequences for others.
The purpose of this article is to report the results of a
pilot study measuring moral disengagement of undergraduate students in various disciplines. More specifically, this
study focuses on measuring moral disengagement of undergraduates studying business or science. Additionally, differences in moral disengagement between genders are
investigated. Given that this is an exploratory study, no specific hypotheses are developed. Rather, measurement of
students’ moral disengagement and differences in moral
disengagement between the two groups and between genders are determined. Results may lead to further research in
this area and perhaps changes in the ethics curriculum.
Prior Research
Albert Bandura developed the theory of moral disengagement in 1986, and his theory explained that an individual’s
self-regulatory mechanisms do not operate unless they are
activated (Bandura, 2002). This theory attempts to explain
why some individuals are able to suffer little or no personal
distress from engaging in acts deemed unethical or unlawful by society. Bandura’s theory indicates that high levels
of moral disengagement allow an individual to disassociate
from the results or implications of his or her actions even if
these actions will negatively affect others. Bandura proposed three categories of mechanisms used by individuals
to achieve this dissociation. The first is cognitively restructuring behavior demonstrated by moral justification, euphemistic labeling, and advantageous comparison. The second
is obscuring or minimizing an individual’s active role in
behaviors by displacing responsibility, diffusing responsibility, and disregarding or distorting the consequences of an
action. The final category is focusing on the unfavorable
acts or traits of those negatively affected by dehumanizing
victims and attributing blame.
Bandura’s theories of moral disengagement have been
applied to societal issues such as terrorism (Maikovich,
2005), the perpetration of inhumanities (Bandura, 1990),
cubicle warriors (Royakkers & van Est, 2010), executioners
(Osofsky, Bandura, & Zimbardo, 2005) and school bullies
(Obermann, 2011). The implosion of our economy in 2008
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272
S. N. CORY
and fraudulent incidents in the early 2000s has generated
interest in applying Bandura’s theories to business.
Some studies have been industry specific such as in J.
White, Bandura, and Bero (2009), which looked at moral
disengagement exhibited in harmful corporate research
related to tobacco, lead, vinyl chloride, and silicosis. Ntayi,
Eyaa, and Ngoma (2010) delved into the unethical practices
of public procurement officers in Uganda. Other studies
such as Claybourn’s 2011 investigation questioned whether
work related variables and moral disengagement influence
negative behaviors such as work place harassment. Moore,
Detert, Trevi~
no, Baker, and Mayer (2012) investigated why
employees do bad things in the workplace. Barsky (2011)
and Anand, Ashforth, and Joshi (2005) researched moral
disengagement and how it relates to the rationalization of
unethical or corrupt acts in the workplace. Anand et al.
claimed that, based on their study, virtually every organization suffers from fraud.
Application of Bandura’s theories in the sciences is less
pervasive. However, use of student subjects has intensified.
Using undergraduate students as subjects, Hinrichs, Wang,
Hinrichs, and Romero (2012) examined the relationship
between leadership beliefs and moral disengagement
through displacement of responsibility. Tsai, Wang and
Lo (2014) explored the relationships among locus of control, moral disengagement in sport and rule transgression
of athletes, using members of a college sports team as
subjects.
Previous studies have focused on general moral disengagement tendencies among students, such as Detert, Trevi~no, and
Sweitzer’s 2008 study, which compared moral disengagement tendencies among college freshmen majoring in business and those majoring in education. The study tested the
relationships between empathy, moral identity, trait cynicism,
and locus of control compared to higher levels of moral disengagement. Ultimately, the study found a negative association between empathy and moral identity, but a positive
association between trait cynicism and locus of control. The
results indicated higher levels of moral disengagement in
business majors as compared to education majors.
Other studies focused on a particular behavior when testing moral disengagement tendencies. For example, Bing
et al. (2012) performed an experiment with college students
involving academic cheating. Morgan and Neal (2011)
compared students’ perceptions of ethical breaches with
freshmen and upper level students in information systems
courses. Baird and Zelin (2009) used undergraduate students to study whether a person committing fraud in a situation involving obedience pressure was judged less harshly
than an individual committing fraud of his or her own volition. Each year more studies are being conducted using
undergraduate students to research not only how these students view and judge moral disengagement, but how those
views and judgments differ over time and when compared
to students across disciplines.
RESEARCH METHODOLOGY
Investigations into moral disengagement pertaining to students in both higher and lower education have incorporated
Bandura’s theory. The purpose of this article is to report the
findings of an exploratory study using undergraduate students who are earning bachelor degrees in business or in
the sciences. These two student groups were chosen for this
study because of the importance of understanding the ethical inclinations of tomorrow’s leaders in these fields. Further, given their eventual employment opportunities
coupled with the ability to put the financial or physical welfare of others at risk, or implement unsafe practices, it is
important to consider whether individuals majoring in these
disciplines are more susceptible to moral disengagement.
The survey provided in the Appendix was adapted from
Detert et al. (2008). Their survey was adapted from one
developed and used in multiple studies by Bandura and
others (e.g., Bandura, Barbaranelli, Caprara, Barbaranelli,
& Pastorelli, 1996; Pelton, Gound, Forehand, & Brody,
2004). The survey was designed in order to measure each
of the eight components of moral disengagement equally
with four questions per component. Given that the survey,
or one very similar to it, has been used in previous research
(e.g. Bandura et al., 1996; Detert et al., 2008; Pelton et al.,
2004) and previously tested extensively for validity, no
further tests of validity were deemed necessary.
Students were presented with a list of 32 statements and
asked to determine the degree to which they agreed with
each, using a 7-point Likert-type scale ranging from 1
(strongly disagree) to 7 (strongly agree). Questions 1–4
measured moral justification, questions 5–8 measured
euphemistic labeling, and questions 9–12 measured advantageous comparisons. Questions 13–16 measured displacement of responsibility, questions 17–20 measured diffusion
of responsibility, and questions 21–24 measured distortion
of consequences. Last, questions 25–28 measured attribution of blame, and questions 29–32 measured dehumanization. Responses to each subset of questions were summed
to obtain the measurement for that part of the survey and a
grand total was obtained by adding all responses from each
respondent.
The survey was given to students taking a general education course in the fall 2013 semester at a small private liberal arts university in the southwest. Total enrollment in
15 sections of the course was 274 undergraduate students
and 249 usable responses were received. This resulted in a
91% response rate. In order to analyze responses from the
two subgroups of interest, responses from students who
were not majoring in business or in the sciences were discarded. The remaining sample consisted of 148 responses,
of which 53 were completed by business majors and 95 by
science majors. There were 33 men majoring in business,
with an average age of 18.8 years and 20 women with an
average age of 18.2 years. The average age of the 54 men
MORAL DISENGAGEMENT IN SCIENCE AND BUSINESS STUDENTS
majoring in the sciences was 18.35 years and the
41 women averaged 18.4 years of age. The science majors
included environmental science, engineering, biochemistry,
premed, chemistry, biology, and physics. Typical business
majors included accounting, finance, marketing, international business and management. Levels of moral disengagement within each group and any differences
between the two groups should be of interest.
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FINDINGS
First, the responses were analyzed for each group of students. Results of the t-test are shown in Table 1. In almost
all cases (38 of 41 for the business students and 39 of 41 for
the science majors) the students disagreed with the statements. However, both groups of students agreed with the
first statement (“It is alright to fight to protect your
friends”). Neither group strongly agreed or disagreed with
statement 19 (“If a group decides together to do something
harmful, it is unfair to blame any one member of the group
for it”). The science majors strongly disagreed with statement 26 (“If someone leaves something lying around, it’s
their own fault if it gets stolen”), but the business majors
neither strongly agreed nor disagreed with it.
Next, differences in the responses to the survey questions between business majors and science majors were
analyzed. Results are shown in column 2 of Table 2. The
responses to each question, each category, and the total
score were analyzed. Of the 41 comparisons, only one was
significantly different between the two groups. Business
majors’ mean was higher than that of science majors. column 3 of Table 2 shows the results of t-tests for differences
between genders for the total sample. Fourteen significant
differences were found and, in every case, responses from
men averaged a higher score (more likely to agree with the
statement) than responses from women. The question then
arose whether gender differences within each school might
exist. Therefore, t-tests were computed for response differences between genders within business and within science.
Results are shown in columns 4 and 5 of Table 2,
respectively.
Fourteen significant differences were found between
responses from men and women for the business majors,
and five were found for the science majors. Again, in every
case, the average response for men was higher than the
average response for women. Finally, the responses were
divided into two groups based on gender to determine
whether any differences could be found between the two
disciplines. Results are shown in the last two columns of
Table 2. Only one significant difference was found between
disciplines for the women (column 6). Women majoring in
science had a higher mean than their business major counterparts. For men (column 7), three significant differences
were found and in every case the average score was higher
273
TABLE 1
t-Tests for Business and Science Students
Statement
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
MJ
EL
AC
DISR
DIFR
DC
AB
DEH
Alltotal
Business students
**
3.02
¡7.28**
¡4.50**
¡3.11**
¡8.13**
¡14.81**
¡16.57**
¡5.60**
¡17.06**
¡12.20**
¡14.39**
¡15.21**
¡5.06**
¡4.36**
¡6.15**
¡11.51**
¡2.79*
¡3.04*
0.13
¡6.43**
¡5.68**
¡7.80**
¡10.92**
¡17.52**
¡8.92**
0.49
¡7.50**
¡5.80**
¡9.11**
¡10.61**
¡16.46**
¡11.15**
¡4.25**
¡15.29**
¡18.37**
¡8.60**
¡4.37**
¡12.47**
¡6.90**
¡12.75**
¡14.97**
Science students
6.57**
¡10.29**
¡5.71**
¡4.59**
¡16.10**
¡23.96**
¡30.14**
¡8.30**
¡22.57**
¡24.28**
¡26.19**
¡19.96**
¡9.49**
¡7.27**
¡9.79**
¡17.68**
¡3.08*
¡4.28**
¡1.51
¡9.85**
¡9.23**
¡11.78**
¡17.12**
¡32.73**
¡21.18**
¡4.99**
¡12.97**
¡9.77**
¡21.26**
¡18.03**
¡20.34**
¡21.50**
¡5.19**
¡23.37**
¡29.28**
¡14.85**
¡5.98*
¡19.35**
¡15.59**
¡25.69**
¡23.81***
Note: Positive t score indicates higher level of moral disengagement.
AB D attribution of blame; AC D advantageous comparisons; DC D distortion of consequences; DEH D dehumanization; DIFR D measured diffusion of responsibility; DISR D measured displacement of responsibility;
EL D euphemistic labeling; MJ D moral justification.
*p < .05, **p < .01.
for men majoring in business than for men majoring in science, indicating that male business majors agreed more
strongly with the statement than did their counterparts
majoring in science.
Based on these results, it seems that women are far less
likely to justify immoral actions by using moral disengagement tactics. In every comparison where t-tests indicated a
significant difference, the average response from women
274
S. N. CORY
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TABLE 2
t-Tests by Demographic
Statement
Business
and science
by school
Business
and science
by gender
Business by
gender
Science by
gender
Women
only
Men
only
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
MJ
EL
AC
DISR
DIFR
DC
AB
DEH
All total
¡0.83
¡1.10
¡0.28
0.82
1.11
0.55
0.48
¡0.07
¡0.64
0.64
0.42
¡0.75
0.43
0.20
¡0.03
¡0.18
¡0.55
¡0.04
1.10
¡0.18
0.26
0.62
0.03
0.39
1.12
2.94**
0.34
¡0.03
1.02
0.64
¡0.02
1.52
¡0.46
0.66
¡0.12
0.18
0.21
0.41
1.70
0.96
0.68
3.21**
1.81
1.77
0.30
1.96*
2.08*
1.85
¡0.03
1.85
2.62**
2.12*
1.21
0.00
¡0.10
1.10
1.48
¡0.40
2.39*
¡0.06
0.92
1.48
4.49**
3.39**
3.59**
1.55
¡0.85
0.87
1.24
1.17
1.69
0.47
2.19*
2.56*
1.74
2.52*
0.74
0.96
4.01**
0.70
1.70
2.59**
2.80*
1.96*
4.54**
0.83
1.56
1.74
2.29*
0.16
1.80
2.26*
1.91
1.32
¡1.15
0.23
0.74
1.44
¡1.16
0.83
0.46
0.81
1.72
3.99**
2.57*
3.76**
1.34
1.07
1.86
1.45
0.67
2.06*
1.25
1.22
3.95**
1.94
2.38*
0.28
0.37
3.91**
1.99*
1.41
2.91**
2.06
0.90
0.00
0.13
1.67
1.75
1.06
0.07
1.36
1.86
1.51
0.78
0.72
¡0.17
0.75
0.89
0.12
2.31*
¡0.35
0.55
0.90
3.40**
2.61**
2.10*
1.11
¡1.72
0.06
0.74
1.19
0.99
¡0.23
1.82
1.04
1.34
1.73
0.70
0.86
2.78**
¡0.24
1.18
1.60
¡1.14
¡1.48
¡2.05*
0.26
0.83
0.54
¡0.54
¡0.03
¡0.70
¡0.29
¡0.19
¡0.83
1.15
¡0.06
¡0.21
¡0.61
0.34
0.54
0.29
¡0.46
¡0.35
0.32
¡0.21
¡1.89
0.29
0.54
¡0.84
¡0.49
0.76
¡0.21
¡0.84
1.46
¡1.47
0.31
¡0.70
0.20
0.30
¡0.38
¡0.09
0.26
¡0.28
0.44
0.09
1.98*
0.95
0.97
0.52
1.10
¡0.11
¡0.12
1.07
0.88
¡0.17
¡0.65
0.34
0.29
0.43
¡1.15
0.26
1.25
0.25
0.80
1.02
0.55
1.50
1.54
3.88**
1.23
0.55
0.81
1.11
0.81
1.23
1.48
0.77
0.58
0.14
0.11
1.15
2.75**
1.19
1.51
Note: Positive t score indicates males have higher mean or business major has higher mean. AB D attribution of blame; AC D advantageous comparisons;
DC D distortion of consequences; DEH D dehumanization; DIFR D measured diffusion of responsibility; DISR D measured displacement of responsibility;
EL D euphemistic labeling; MJ D moral justification.
*p < .05, **p < .01.
was lower than that for men, which indicates less agreement
with the statement. This was true when analyzing results for
the full sample between genders (column 3 of Table 2) and
within each school by gender (columns 4 and 5 in Table 2).
Perhaps these findings should be expected, given past
research on differences between the genders in moral development or moral judgment. For example, Lv and Huang
(2012) found gender differences in ethical intentions and
moral judgment in accounting students, but found those differences were negligible for accounting practitioners, suggesting that these disparities fade in the workplace. Also,
R. D. White (1999) had similar results for gender differences in moral reasoning. His results indicated that women
employed in the public sector had higher levels of moral
reasoning than their male counterparts. Whipple and
Swords (1992) found consistently higher business ethics for
MORAL DISENGAGEMENT IN SCIENCE AND BUSINESS STUDENTS
TABLE 3
Means and Medians
M/median
Statement
Male business majors
Male science majors
3
26
AB
3.83 / 4
4.56 / 5
11.83 / 11
2.93 / 2
2.69 / 2
8.64 / 8
Female business majors
Female science majors
1.95 / 1
2.93 / 2
All business majors
All science majors
5.96 / 5
2.94 / 2
26
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26
Note: AB D attribution of blame.
female college students than for their male counterparts in
both the United States and in the United Kingdom. However, the number and the strength of differences for survey
responses between men and women are still somewhat surprising. There were 41 comparisons made in each of the
three columns (columns 3–5) of results referred to above
and presented in Table 2, which totals 123. Of those 123
comparisons, a total of 33 were significantly different
between the genders (26%) at least at the 5% level.
Only one significant difference was found in the average
responses from women majoring in business and those
majoring in sciences (column 6 of Table 2). The female science majors agreed more strongly with statement 3 (“It’s ok
to attack someone who threatens your family’s honor”).
When comparing responses from men between schools,
three instances were found in which responses from men
majoring in business were significantly higher than from
men majoring in sciences: “It’s ok to attack someone who
threatens your family’s honor” (statement 3), “If someone
leaves something lying around, it’s their own fault if it gets
stolen and for attribution of blame (statement 26); sum of
questions 25–28). Notably, the single difference found
between the two subgroups, based on major was also for
statement 26 (column 2).
In order to better understand the responses that were significantly different between women majoring in business
and women majoring in science as well as for men majoring
in business and men majoring in science, the mean and
median scores were determined and are presented in
Table 3. The mean and the median for all business majors
and all sciences majors for statement 26 are also presented
in Table 3.
For statement 3, the mean average score for male business majors was 3.83, which is closer to 4 (neither agree
nor disagree) but the mean average for male science majors
was closer to 3 and farther away from neither agree nor disagree. The median was 4 for the male business majors and
2 for the male science majors. Hence, male business majors
275
agreed slightly with the statement, but male science majors
disagreed. For statement 26, the mean average for male
business majors was about half way between 4 and 5, but
the mean average for male science majors was about half
way between 2 and 3. The medians were 5 and 2, respectively. Thus, male business majors agreed with the statement, but male science majors did not agree with it at all,
based on both the mean the median. The mean average for
male business majors for attribution of blame (the sum of
statements 25–28, which results in a minimum of 4 and a
maximum of 28) was closer to 12, with a median of 11, but
the mean average for male science majors was about halfway between 8 and 9, with a median of 8. The mean for
female business majors for statement 26 is very close to 2
and the median is 1. However, the mean for the female science majors is very close to 3, with a median of 2. Thus,
neither group agreed with the statement, but female business majors agreed with it less. Finally, the mean for all
business majors for statement 26 was very close to 6 with a
median of 5, but the mean for all science majors was close
to 3, with a median of 2. Therefore, on average, business
majors slightly agreed with the statement, but science
majors slightly disagreed with it.
These results are somewhat inconsistent with previous
research in the area of ethical development differences
between business students and science students. For example, Neubaum, Pagell, Drexler, Mckee-Ryan, and Larson
(2009) found no differences in personal moral philosophy
between business and non-business students. However,
these findings are consistent with Segal, Gideon, and
Haberfield (2011) who found that business students were
more willing to accept unethical conduct than criminal
justice majors and Cory and Hernandez (2014) who found
that business students demonstrated higher levels of moral
disengagement than humanities majors.
CONCLUSION
To summarize the results of this pilot study, only one significant difference of the 42 comparisons was found when
comparing all business majors with all sciences majors.
However, several differences were found when comparing
responses from men and women both for the total sample
and within each major classification. In every case, men
exhibited a higher level of moral disengagement tendencies
than did the women. Finally, when comparing women
between disciplines, only one difference was found, but
three differences were found when comparing men by discipline. In every case where a difference was found with
men, business majors were more likely to agree with the
statement than sciences majors, indicating a higher level of
moral disengagement. However, the only difference found
between responses from women by discipline indicated a
higher level of moral disengagement in science majors.
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276
S. N. CORY
The results of the study do not necessarily indicate that,
in general, male science students are less morally disengaged than male business students. However, differences
found between the two groups of students are interesting.
Given that ethics courses are required for most college
majors, these results may indicate the necessity of increased
coverage in certain areas. More specifically, consequences
of an individual’s acting solely in self-interest, inappropriateness of indifference to possible negative outcomes of
actions, possible repercussions to others of taking unnecessary risks, and possible negative results of inattention to
detail, in addition to the unethical behavior of stealing or
committing fraud, should be reinforced. Further, the differences in results based on gender imply that men may need
more ethics education than women, at least in terms of
moral disengagement measures. Those who are teaching
ethics at the university level should be made aware of the
necessity of including the previous information in the curriculum. Moral disengagement may result in adverse consequences to others, even if fraud is not directly involved in
the actions taken by an individual. Further, it appears that
increased coverage and reinforcement of these topics may
be necessary in business curricula.
There are certainly limitations to this study. Only students enrolled in one general education course at one university completed the survey and their selection was not
random. Further, only lower division students were surveyed and ethical maturity may have not yet occurred.
However, these limitations may be addressed with future
research related to this topic.
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MORAL DISENGAGEMENT IN SCIENCE AND BUSINESS STUDENTS
277
APPENDIX—QUESTIONNAIRE
Strongly disagree
1
2
3
Neither
4
Strongly agree
5
6
7
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Choose a number from 1 to 7 from the scale above, based on how strongly you agree or disagree with each statement. Put
the number in the space provided.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
__It is alright to fight to protect your friends.
__It’s ok to steal to take care of your family’s needs.
__It’s ok to attack someone who threatens your family’s honor.
__It is alright to lie to keep your friends out of trouble.
__Sharing test questions is just a way of helping your friends.
__Talking about people behind their backs is just part of the game.
__Looking at a friend’s homework without permission is just “borrowing it.”
__It is not bad to “get high” once in a while.
__Damaging some property is no big deal when you consider that others are beating up people.
__Stealing some money is not too serious compared to those who steal a lot of money.
__Not working very hard in school is really no big deal when you consider that other people are probably cheating.
__Compared to other illegal things people do, taking some things from a store without paying for them is not very serious.
__If people are living under bad conditions, they cannot be blamed for behaving aggressively.
__If the professor doesn’t discipline cheaters, students should not be blamed for cheating.
__If someone is pressured into doing something, they shouldn’t be blamed for it.
__People cannot be blamed for misbehaving if their friends pressured them to do it.
__A member of a group or team should not be blamed for the trouble the team caused.
__A student who only suggests breaking the rules should not be blamed if other students go ahead and do it.
__If a group decides together to do something harmful, it is unfair to blame any one member of the group for it.
__You can’t blame a person who plays only a small part in the harm caused by a group.
__It is ok to tell small lies because they don’t really do any harm.
__People don’t mind being teased because it shows interest in them.
__Teasing someone does not really hurt them.
__Insults don’t really hurt anyone.
__If students misbehave in class, it is their teacher’s fault.
__If someone leaves something lying around, it’s their own fault if it gets stolen.
__People who are mistreated have usually done things to deserve it.
__People are not at fault for misbehaving at work if their managers mistreat them.
__Some people deserve to be treated like animals.
__It is ok to treat badly someone who behaved like a “worm.”
__Someone who is obnoxious does not deserve to be treated like a human being.
__Some people have to be treated roughly because they lack feelings that can be hurt.