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Bad Apples, Bad Cases, and Bad Barrels: Meta-Analytic Evidence About Sources of Unethical Decisions at Work


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As corporate scandals proliferate, practitioners and researchers alike need a cumulative, quantitative understanding of the antecedents associated with unethical decisions in organizations. In this meta-analysis, the authors draw from over 30 years of research and multiple literatures to examine individual ("bad apple"), moral issue ("bad case"), and organizational environment ("bad barrel") antecedents of unethical choice. Findings provide empirical support for several foundational theories and paint a clearer picture of relationships characterized by mixed results. Structural equation modeling revealed the complexity (multidetermined nature) of unethical choice, as well as a need for research that simultaneously examines different sets of antecedents. Moderator analyses unexpectedly uncovered better prediction of unethical behavior than of intention for several variables. This suggests a need to more strongly consider a new "ethical impulse" perspective in addition to the traditional "ethical calculus" perspective. Results serve as a data-based foundation and guide for future theoretical and empirical development in the domain of behavioral ethics.
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Bad Apples, Bad Cases, and Bad Barrels: Meta-Analytic Evidence About
Sources of Unethical Decisions at Work
Jennifer J. Kish-Gephart, David A. Harrison, and Linda Klebe Trevin˜o
Pennsylvania State University, University Park Campus
As corporate scandals proliferate, practitioners and researchers alike need a cumulative, quantitative
understanding of the antecedents associated with unethical decisions in organizations. In this meta-
analysis, the authors draw from over 30 years of research and multiple literatures to examine individual
(“bad apple”), moral issue (“bad case”), and organizational environment (“bad barrel”) antecedents of
unethical choice. Findings provide empirical support for several foundational theories and paint a clearer
picture of relationships characterized by mixed results. Structural equation modeling revealed the
complexity (multidetermined nature) of unethical choice, as well as a need for research that simulta-
neously examines different sets of antecedents. Moderator analyses unexpectedly uncovered better
prediction of unethical behavior than of intention for several variables. This suggests a need to more
strongly consider a new “ethical impulse” perspective in addition to the traditional “ethical calculus”
perspective. Results serve as a data-based foundation and guide for future theoretical and empirical
development in the domain of behavioral ethics.
Keywords: unethical behavior, intuition, decision making, intention
For over 30 years, researchers have attempted to determine why
individuals behave unethically in the workplace. Once viewed as
the province of philosophers—a “‘Sunday school’ subject not
worthy of serious investigation”—behavioral ethics has become a
legitimate and necessary field of social scientific inquiry (Trevin˜o,
1986, p. 601). Indeed, as ethical scandals have garnered attention
across multiple sectors of society (e.g., business, government,
sports, religion, education), research examining the determinants
of individual-level unethical choices at work has grown dramati-
cally (for reviews, see O’Fallon & Butterfield, 2005; Tenbrunsel &
Smith-Crowe, 2008; Trevin˜o, Weaver, & Reynolds, 2006). Be-
tween 1996 and 2005, over 170 investigations were published
(O’Fallon & Butterfield, 2005). Yet, despite this increased atten-
tion, much remains to be understood about how and under what
circumstances individuals make unethical choices. Recent qualita-
tive reviews of the behavioral ethics literature (O’Fallon & But-
terfield, 2005; Tenbrunsel & Smith-Crowe, 2008) noted that stud-
ies have produced inconsistent findings for many proposed
antecedents of unethical choices. These authors have called for
quantitative summaries to “derive statistically valid conclusions”
about the proposed antecedents (O’Fallon & Butterfield, 2005, p.
405; see also Robertson, 1993).
In this paper, we attempt to provide a clearer empirical and
theoretical picture of what we know (and don’t know) about
multiple sources of influence on unethical behavior at work. We
begin by using two meta-analytic techniques to summarize evi-
dence of the influence of individual characteristics (cognitive
moral development, locus of control, Machiavellianism, moral
philosophy, and demographics), moral issue characteristics (i.e.,
moral intensity; T. M. Jones, 1991), and organizational environ-
ment characteristics (ethical climate, ethical culture, and codes of
conduct) on unethical choices. We then investigate the potential
moderating effect of using intention as a proxy for behavior—a
practice commonly employed by behavioral ethics researchers (see
O’Fallon & Butterfield, 2005; Weber, 1992; Weber & Gillespie,
1998). Last, using structural equation modeling, we investigate the
unique explanatory power of each of our proposed antecedents on
unethical intention and unethical behavior. Through these analy-
ses, we not only aim to elucidate the relationships among popular
predictors (e.g., ethical climate and ethical culture) but also seek to
reveal the complexity of unethical decision making. We then use
our results to present a potential road map for future research as
well as implications for organizational practice.
Definitional Framework for the Criterion: Unethical
Behavior, Intention, and Choice
Rest’s (1986) four-stage model of ethical decision making has
guided the majority of research and narrative reviews of research
findings within behavioral ethics (see T. M. Jones, 1991; Loe,
Ferrell, & Mansfield, 2000; O’Fallon & Butterfield, 2005; Trevin˜o
et al., 2006). According to this model, individuals pass through
Jennifer J. Kish-Gephart, David A. Harrison, and Linda Klebe Trevin˜o,
Department of Management and Organization, Pennsylvania State Univer-
sity, University Park Campus.
We extend a special thanks to Joe Youn and the Ethics Resource Center
for assistance in acquiring data for this project and to Penn State’s Arthur
W. Page Center for its financial assistance and support. We also extend our
thanks to Aimee Hamilton for her assistance with coding as well as to Dan
Chiaburu, Jim Detert, Gary Weaver, and the members of the ORG seminar
at Penn State for their feedback on earlier drafts of this article.
Correspondence concerning this article should be addressed to Jennifer
J. Kish-Gephart, Department of Management and Organization, 439A
Business Building, Pennsylvania State University, University Park, PA
16801. E-mail:
Journal of Applied Psychology © 2010 American Psychological Association
2010, Vol. 95, No. 1, 1–31 0021-9010/10/$12.00 DOI: 10.1037/a0017103
several stages during the process of making an ethical or unethical
decision. The process eventually leads to the stage of moral inten-
tion (when one commits to a particular action) and ends with moral
action or behavior (when one carries out the intended behavior).
In this paper, we follow Rest (1986) in organizing our analysis and
defining our focal dependent variables. Unethical intention is
defined as the expression of one’s willingness or commitment to
engage in an unethical behavior. Unethical behavior is defined as
any organizational member action that violates widely accepted
(societal) moral norms. The latter definition is consistent with
recent behavioral ethics literature (Kaptein, 2008; Trevin˜o et al.,
2006). According to Trevin˜o et al., “behavioral ethics refers to
individual behavior that is subject to or judged according to
generally accepted moral norms of behavior....Within this body
of work...researchers have focused specifically on unethical
behaviors, such as lying, cheating and stealing” (2006, p. 952).
Therefore, employee behaviors such as theft, sabotage, lying to
customers, and misrepresentation in financial reports are included
in our definition. Other negative workplace behaviors, such as
lateness, are not included because they do not necessarily violate
widely accepted moral norms.
We also distinguish unethical behavior from two related con-
cepts: workplace deviance and illegal behavior. First, unethical
behavior is not a synonym for workplace deviance or counterpro-
ductive work behavior (Sackett, Berry, Wiemann, & Laczo, 2006).
These latter behaviors are defined as violating organizational
norms (Bennett & Robinson, 2003) rather than widely accepted
societal norms. It is possible for a behavior to violate widely
accepted societal norms while remaining normative in the organi-
zation (e.g., lying to customers). However, some less serious forms
of workplace deviance (e.g., gossiping, working slowly) that vio-
late organizational norms may not violate widely accepted societal
norms (Dalal, 2005; Robinson & Bennett, 1995). Thus, despite
some overlap, all forms of counterproductive or deviant work
behavior do not fall under the unethical behavior definition. In this
meta-analysis, we include only serious forms, such as production
deviance (Robinson & Bennett, 1995), as described further in the
Method section.
Additionally, some unethical behaviors overlap with illegal be-
haviors. This relationship between ethics and the law can be
represented as a Venn diagram (Trevin˜o & Nelson, 2007), wherein
the overlapping area of the two circles represents behaviors that
are both illegal and unethical. For example, stealing is considered
to be unethical because it breaches widely accepted societal norms.
It is also illegal. However, the two circles do not overlap com-
pletely. Some of the many unethical behaviors that are widely
prohibited in corporate codes of conduct (Ethics Resource Center,
2007; e.g., conflicts of interest such as giving or receiving large
gifts to influence business relationships) are often not illegal.
Nevertheless, because of widespread agreement that they are
wrong, these behaviors are defined as unethical behavior.
The distinction between unethical intention and unethical be-
havior is important because it influences both theory and method-
ology in the behavioral ethics literature. A common assumption
based on Rest’s (1986) ethical decision-making model is that
intention precedes behavior and, thus, can be substituted for be-
havior when the latter is unavailable for study (Ajzen, 1991;
Fishbein & Ajzen, 1975). Accordingly, and following established
convention in the behavioral ethics literature (e.g., Borkowski &
Ugras, 1998; Martin & Cullen, 2006; Whitley, 1998; Whitley,
Nelson, & Jones, 1999), we begin by treating unethical intention
and unethical behavior as one overarching construct, hereafter
referred to as unethical choice (see Figure 1). We then separate
studies of unethical intention and unethical behavior to investigate
the potential moderating effects of using unethical intention versus
unethical behavior as a criterion in this realm of research.
Hypothesis Development: Sets of Antecedents
In this section, we present our hypotheses for unethical choice
(unethical intention and unethical behavior) based on three main
categories of antecedents. The antecedents included in our study—
and thus, the categories used herein—were drawn from existing
work, as with all meta-analyses. On the basis of our exhaustive
search of the literature (explained in detail below), we identified
three main types of antecedents that can be classified as charac-
teristics of the individual (“bad apples”), the ethical issue itself
(“bad cases”), or the organizational environment (“bad barrels”).
First, according to the bad apples argument, unethical behavior at
work is the result of “a few unsavory individuals” (Trevin˜o &
Youngblood, 1990, p. 378). Thus, we begin this section by pro-
posing the effects of individual differences (characteristics of
potentially bad apples) on unethical choices at work (including
cognitive moral development, moral philosophy, Machiavellian-
ism, locus of control, job satisfaction, and demographic variables).
Second, we consider how aspects or circumstances of a particular
ethical dilemma being faced (such as closeness to the victim or
anticipated harm; T. M. Jones, 1991) may provoke or prevent
unethical choices. We refer to these characteristics as “bad cases”
because we believe the term case, with its context-sensitive con-
notation, aptly conveys the idea that moral issue characteristics
vary by the specific circumstances being faced at the time (along
with the symbolic meaning of case as a smaller, more proximal
container than barrels for individual apples). Cases subsume fea-
tures of specific moral dilemmas that exist within organizations
and that are experienced by individual employees. Third and last,
we hypothesize how unethical choices may reflect Trevin˜o and
Youngblood’s (1990) “bad barrels,” or characteristics of one’s
more general organizational environment (ethical climate, ethical
culture, and codes of conduct).
Individual Characteristics
Cognitive moral development. Tested and developed for
over 20 years in developmental psychology before being intro-
duced to the organizational literature (e.g., Trevin˜o, 1986), the
theory of cognitive moral development (CMD; Kohlberg, 1969)
explains how individuals advance from childhood to adulthood in
the complexity and elaboration of their thinking about why actions
are morally right or wrong (Rest, 1986). Rather than focusing on
the final decisions themselves, the theory emphasizes the individ-
Although Rest (1986) originally used the term moral motivation to
describe this component, it has been equated in several reviews (and
likewise many empirical studies) with “moral intention” (e.g., T. M. Jones,
1991; Loe et al., 2000; O’Fallon & Butterfield, 2005; Trevin˜ o et al., 2006).
The terms are conceptually similar in meaning and relate to an individual’s
readiness or willingness to engage in a particular action.
ual’s reasoning process, particularly the justifications individuals
provide for their thinking in ethical dilemma situations. Levels of
CMD exist on a hierarchy of five stages. As individuals develop,
they advance from stage to stage in sequence. Although CMD is
considered to be generally stable in adults, one’s CMD can con-
tinue to advance in adulthood with training interventions and other
opportunities to practice moral reasoning (cf. Trevin˜o, 1992a).
At the highest level of CMD (“principled,” or Stage 5), indi-
viduals cognitively process ethical dilemmas by using sophisti-
cated reasoning. In making ethical judgments, they rely upon
ethical principles of justice and rights and consider societal good.
However, most adults operate at the “conventional” level of CMD,
meaning that their judgments about what is right are influenced by
the expectations of peers and significant others (Stage 3) or by
policies and rules including the law (Stage 4; for a review, see
Trevin˜o & Weaver, 2003). When thinking about what is right and
wrong, individuals with the lowest level of CMD invoke consid-
erations such as obedience and avoiding punishment (Stage 1) or
acting in their own self-interest (Stage 2). CMD is thought to guide
behavior for cognitive consistency reasons. For someone who is
capable of reasoning in sophisticated ways about an ethical issue,
acting in a way that reflects lower level thinking is uncomfortable
because of the cognitive tension that is created.
The Defining Issues Test (Rest, 1986) is the most widely used
measure of CMD. Individuals respond to a series of hypothetical
ethical dilemmas by rating and ranking the importance of various
types of considerations that could be taken into account in deciding
what is the right thing to do. Many empirical studies have reported
a negative relationship between CMD and unethical choices, in-
cluding behaviors in the workplace (see Blasi, 1980; for reviews,
see Trevin˜o, 1992a, and Trevin˜ o et al., 2006). Therefore, we
hypothesized a negative relationship between CMD and unethical
Hypothesis 1: CMD is negatively related to unethical choices,
as (a) unethical intention and (b) unethical behavior, in orga-
Moral philosophies—idealism and relativism. Moral phi-
losophies are derived from normative philosophical theories (For-
syth, 1980). Measures of moral philosophy capture individuals’
stated beliefs or personal preferences for particular normative
frameworks. Among the many moral philosophies available, For-
syth (1980) proposed that most people can be classified along two
separate continua: (a) idealism, one’s concern for the welfare of
others, and (b) relativism, one’s emphasis on moral principles
being situationally determined rather than universal. Highly ideal-
istic individuals believe that one can always avoid harming others
when faced with an ethical dilemma, but nonidealists believe that
“harm is sometimes...necessary to produce good” (Forsyth,
1992, p. 462). Individuals who are low on relativism believe that
every situation is governed by a common moral principle, but
highly relativistic individuals believe that situations differ and that
one must weigh the circumstances when making decisions (i.e., no
moral principle can govern every situation). Although the content
of these philosophies overlaps somewhat with CMD (e.g., some-
one low on relativism may believe that justice is an important
ethical principle), moral philosophies focus more on individually
• Behavior
• Cognitive moral
• Idealism
• Relativism
• Machiavellianism
• Locus of control
• Job satisfaction
• Egoistic ethical climate
• Benevolent ethical climate
• Principled ethical climate
• Ethical culture
• Code of conduct
• Code enforcement
Concentration of effect
• Magnitude of consequences
• Probability of effect
• Social consensus
• Temporal immediacy
• General moral intensity
•Education level
• Behavior
• Cognitive moral
• Idealism
• Relativism
• Machiavellianism
• Locus of control
• Job satisfaction
• Egoistic ethical climate
• Benevolent ethical climate
• Principled ethical climate
• Ethical culture
• Code of conduct
• Code enforcement
Concentration of effect
• Magnitude of consequences
• Probability of effect
• Social consensus
• Temporal immediacy
• General moral intensity
•Education level
Figure 1. Meta-analytic framework for antecedents of unethical choices in the workplace.
preferred ways of thinking. In contrast, CMD offers a develop-
mental approach to the moral reasoning process, with higher stages
representing more mature and cognitively complex ways of think-
ing. Also, CMD is arrayed along a single progression of stages
(i.e., a single continuum), but Forsyth’s concepts represent con-
structs on two separate, distinct continua. Past research suggests
that relativism and idealism do not correlate to a high degree with
CMD (e.g., Forsyth, 1980).
Although Forsyth (1992) did not propose direct links between
moral philosophy and ethical choice, some investigations have
shown that, compared to relativists, idealists are more likely to
judge unethical actions critically (e.g., Barnett, Bass, & Brown,
1994; Forsyth, 1985). In this way, idealism may be negatively
related to unethical choices, because idealists are more concerned
about harming others (Henle, Giacalone, & Jurkiewicz, 2005).
Relativism, on the other hand, may be positively related to uneth-
ical choices, because these choices are easier to rationalize for
relativists, who lack strict moral guidelines.
Hypothesis 2: A moral philosophy of idealism is negatively
related to unethical choices, as (a) unethical intention and (b)
unethical behavior, in organizations.
Hypothesis 3: A moral philosophy of relativism is positively
related to unethical choices, as (a) unethical intention and (b)
unethical behavior, in organizations.
Machiavellianism. Drawing from the political writings of
Niccolo Machiavelli (Gilbert, 1971), Christie and Geis (1970)
introduced the Machiavellianism personality construct. Individuals
high in Machiavellianism, which is generally “synonymous with
amoral action, sharp dealing, hidden agendas, and unethical ex-
cess” (Nelson & Gilbertson, 1991, p. 633), tend to use interper-
sonal relationships opportunistically and deceive others for per-
sonal gain (Christie & Geis, 1970). In contrast to CMD and moral
philosophies, Machiavellianism represents a more traditional per-
sonality trait. Yet, it also has a clear moral component that should
be linked to the incidence of unethical choices in an organizational
environment. In fact, some experiments have found Machiavel-
lianism conducive to decisions to pay kickbacks in a marketing
simulation (Hegarty & Sims, 1978, 1979).
Hypothesis 4: Machiavellianism is positively related to un-
ethical choices, as (a) unethical intention and (b) unethical
behavior, in organizations.
Locus of control. Another personality construct, locus of
control (Rotter, 1966), represents a single continuum that captures
the beliefs of individuals about whether the outcomes of their
actions are contingent on what they do or on the machinations of
outside forces. Internals attribute life’s events to their own abilities
or efforts. Externals attribute life’s events to some external source,
such as fate, luck, or powerful others.
Unlike the constructs described above, locus of control does not
have ostensibly moral or ethical content, so its relationship to
unethical behavior is less obvious. Trevin˜o (1986), however, pro-
posed that internals would be less likely to engage in unethical
choices. Her theory posits that, because internals see outcomes as
contingent on their own actions, they are more likely to recognize
their personal responsibility for those outcomes. Following this
logic, internals faced with pressure or opportunity to act unethi-
cally will be more likely to see that such an action will bring about
potentially negative outcomes (i.e., harm to others), and thus they
will be more likely to avoid it. On the other hand, externals may be
more likely to act unethically because they can more easily offload
blame onto someone or something else (powerful others, chance,
or uncontrollable circumstances). Although the findings in the
literature remain “somewhat mixed” (O’Fallon & Butterfield,
2005, p. 392), several empirical studies support the above theoret-
ical argument (e.g., G. E. Jones, 1992; Trevin˜o & Youngblood,
1990). For this reason, we hypothesized that external locus of
control would be related to increased unethical choices.
Hypothesis 5: External locus of control is positively related to
unethical choices, as (a) unethical intention and (b) unethical
behavior, in organizations.
Job satisfaction. Although job satisfaction is not an individ-
ual difference per se, we have included it in the individual char-
acteristics section of our explanatory framework for two reasons.
First, job satisfaction is at least partially dispositional (i.e., Arvey,
Bouchard, Segal, & Abraham, 1989; Staw, Bell, & Clausen, 1986).
Second, as a positive or negative evaluation of one’s job (Weiss &
Cropanzano, 1996), job satisfaction occurs at the individual level.
Indeed, it is this personal evaluation that suggests that job satis-
faction may be related to unethical choices. For example, Adams’s
(1965) equity theory suggests that dissatisfied individuals seek to
balance perceived imbalances of their outcome/input ratios relative
to the ratios of others. These avenues can include unethical actions
designed to “even the score” (e.g., stealing company property).
Similarly, empirical results support a negative relationship be-
tween job satisfaction and workplace deviance (i.e., Dalal, 2005;
Judge, Scott, & Illies, 2006). These ideas and data led us to
hypothesize that higher job satisfaction would reduce the incidence
of unethical choices.
Hypothesis 6: Job satisfaction is negatively related to uneth-
ical choices, as (a) unethical intention and (b) unethical be-
havior, in organizations.
Demographics. Demographic variables (e.g., gender, age,
education) are among the most widely studied individual-level
factors in behavioral ethics (O’Fallon & Butterfield, 2005). De-
spite this attention, empirical results have often been inconsistent
(Tenbrunsel & Smith-Crowe, 2008) and theoretical explanations
have been limited. In this section, we present hypotheses for
gender, age, and education level. However, we consider these
hypotheses to be tentative because theory and empirical evidence
suggesting a particular direction of effect are generally weaker for
these antecedents than for other variables included in the meta-
Gender. Business ethics researchers have long been interested
in the effects of gender on ethical decision making (e.g., Ambrose
& Schminke, 1999; see McCabe, Ingram, & Dato-on, 2006). For
example, Gilligan (1977) argued strongly that females and males
reason differently about ethical issues and suggested that females
are more likely to make judgments based upon care for others. In
this way, females should be more ethical because they will be more
concerned about and refrain from any action that would harm other
people. Despite over 30 years of research, however, empirical
results on gender differences in ethical decision making are mixed
(Tenbrunsel & Smith-Crowe, 2008). Meta-analyses by Borkowski
and Ugras (1998) and Franke, Crown, and Spake (1997) found that
female business students report more ethical attitudes and make
more ethical judgments than do male business students. When
focusing on unethical choices specifically, some studies have
found that women behave more ethically than men (e.g., Latham &
Perlow, 1996) and others have reported nonsignificant (e.g., He-
garty & Sims, 1978, 1979) or trivial differences (see Thoma &
Rest, 1986, for a review). Although the relationship remains un-
clear, the weight of empirical evidence and theory led us to predict
that women would be less likely than men to make unethical
Hypothesis 7: Gender (0 female, 1 male) is positively
related to unethical choices, as (a) unethical intention and (b)
unethical behavior, in organizations.
Age. Research on age and unethical choice has produced in-
consistent results (O’Fallon & Butterfield, 2005; Tenbrunsel &
Smith-Crowe, 2008), including positive (i.e., Henle et al., 2005),
negative (i.e., Lasson & Bass, 1997), and nonsignificant (i.e.,
Singhapakdi, 1999) relationships. However, the fact that age (at
least through young adulthood) has been empirically linked with
CMD (Kelley, Ferrell, & Skinner, 1990; Kohlberg, 1969; Trevin˜o,
1992a) suggests that older individuals—perhaps through instruc-
tive life experiences—may operate at higher levels of moral rea-
soning (Trevin˜o, 1992a; Trevin˜ o & Weaver, 2003). Age has also
been associated with lower Machiavellianism (Hunt & Chonko,
1984). Further, the negative relationship between age and criminal
behavior is well known in the criminology literature: Violent and
nonviolent crime peak during the teenage years and then decline
steadily during the adult years (Farrington, 1986). Thus, we pro-
posed that age would be associated with fewer unethical choices.
Hypothesis 8: Age is negatively related to unethical choices,
as (a) unethical intention and (b) unethical behavior, in orga-
Education level. Theorists have suggested, in arguments sim-
ilar to those for age, that individuals with higher education levels
may encounter more “teachable” ethical dilemmas and, thus, less
likelihood of unethical choices (Tenbrunsel & Smith-Crowe,
2008). Indeed, CMD research has found that years of formal
education remains the staunchest covariate of CMD, with some
correlations reported as high as r.50 (Rest, 1986). Although the
theoretical link is not entirely clear, researchers have speculated
that higher education supports general cognitive and social devel-
opment, including a feeling of greater personal responsibility for
individual outcomes (see Thoma & Rest, 1986, for a review). It is
also possible that adults with higher education have been exposed
to ethics training that more explicitly targets moral judgment (e.g.,
Dellaportas, 2006). Therefore, we hypothesized that a higher level
of education would tend to reduce unethical choices. However,
some of the most vivid, public examples of unethical behavior in
business have been committed by those with advanced degrees,
and this has led many to question the relationship between higher
education (especially business education) and ethical conduct (e.g.,
Pfeffer, 2003). The lack of clear or compelling data sets an ideal
stage for applying meta-analysis.
Hypothesis 9: Education level is negatively related to uneth-
ical choices, as (a) unethical intention and (b) unethical be-
havior, in organizations.
Moral Issue Characteristics
Complementing person-based factors, characteristics of the eth-
ical dilemma being faced by the person have also been proposed to
affect unethical choices. According to T. M. Jones (1991), re-
searchers need to move beyond a focus on persons or organiza-
tional features to aspects of the moral issue being broached—that
is, to consider the moral intensity of the situation.
Moral intensity. T. M. Jones (1991) formulated an issue-
based approach to ethical decision making and proposed that the
moral intensity of a particular ethical issue comprises six distinct
elements. These include (a) magnitude of consequences, the total
harm that could befall victims of an unethical choice; (b) social
consensus, the degree of peer agreement that the action is wrong;
(c) probability of effect, the likelihood that the action will result in
harm; (d) temporal immediacy, the length of time before harmful
consequences of the act are realized; (e) proximity, the social,
psychological, cultural, and physical nearness to the victim of the
act; and (f) concentration of effect, the “inverse function of the
number of people affected by an act of given magnitude” (T. M.
Jones, 1991, p. 377). According to the theory, as any one element
of these situational features increases, the overall moral intensity
of the situation increases proportionally.
T. M. Jones (1991) proposed that moral intensity is likely to
reduce the incidence of unethical choices, in part by increasing
attributions of responsibility to oneself for the choice’s likely
consequences to others. Thus, when one considers an unethical
issue, such as dumping toxic waste into a river, the possibility of
substantial harm and nearness to the victims should increase moral
intensity and thereby decrease one’s intention to dump the waste
and the likelihood of actually doing so. Although Jones’s hypoth-
eses have received some empirical support in vignette-based stud-
ies (e.g., May & Pauli, 2002; Nill & Schibrowsky, 2005; Paolillo
& Vitell, 2002), questions remain regarding the unique effects of
the specific intensity dimensions (see O’Fallon & Butterfield,
2005). Thus, we included each of the six issue-based elements of
moral intensity (along with a general measure of moral intensity)
in our hypotheses and analyses and predicted that each element
would reduce the likelihood of unethical choices. Because no
existing studies in the workplace have connected these dimensions
to unethical behavior, our hypotheses are directed only at unethical
Hypotheses 10a–g: The moral intensity of an issue—
including (a) concentration of effect, (b) magnitude of con-
sequences, (c) probability of effect, (d) proximity, (e) social
consensus, (f) temporal immediacy, and (g) general moral
intensity—is negatively related to unethical choices (as inten-
tions) in organizations.
Organizational Environment Characteristics
In this section, we examine what might be considered more
distal (than moral issue features) elements in an individual’s en-
vironment. These broader constructs capture shared beliefs, norms,
and formalized procedures and rules for governing workplace
behavior. We specifically hypothesized about the effects of ethical
climate, ethical culture, and the existence and enforcement of a
code of ethics on unethical choices.
Ethical climate and culture. Behavioral ethics researchers
have often described an organization’s environment in terms of
perceived ethical climate (Victor & Cullen, 1988) or perceived
ethical culture (Trevin˜o, 1986, 1990). Both constructs refer to
ethics-relevant features of the organizational environment, and
they appeared in the behavioral ethics literature at about the same
time (Trevin˜o, 1986; Victor & Cullen, 1988). However, they were
initially developed for different purposes, and their definitions
diverge. We broach each one in turn, developing separate hypoth-
eses for each construct’s influence on unethical choice. Then, we
review their distinctions.
Ethical climate. Ethical climate (see Martin & Cullen, 2006,
for a recent review)
can be conceptualized as a type of organiza-
tional work climate (Reichers & Schneider, 1990). However,
rather than a single climate dimension, ethical climate was con-
ceptualized as “a group of prescriptive climates reflecting the
organizational procedures, policies, and practices with moral con-
sequences” (Martin & Cullen, 2006, p. 177). This multidimen-
sional ethical climate construct was introduced by Victor and
Cullen (1987, 1988) to measure and differentiate among multiple
dimensions of shared employee beliefs that “arise when members
believe that certain forms of ethical reasoning or behavior are
expected standards or norms for decision-making within the firm”
(Martin & Cullen, 2006, p. 177). Victor and Cullen asserted that
employee perceptions of ethical climate could be mapped onto two
independent dimensions: (a) ethical criteria, including egoism,
benevolence, and principled, and (b) locus of analysis, including
individual, local, and cosmopolitan. Although the latter dimension
was based on sociology, the former dimension was drawn from
philosophy and “the basic criteria used in moral reasoning, i.e.,
maximizing self-interest, maximizing joint interests, or adherence
to principle, respectively” (Victor & Cullen, 1988, p. 104). By
combining these dimensions, Victor and Cullen (1987, 1988)
proposed the existence of nine types of ethical climates; they also
created the Ethical Climate Questionnaire to measure perceptions
of these ethical climates. However, empirical studies that include
factor analyses (e.g., Cullen, Victor, & Bronson, 1993; Victor &
Cullen, 1988) have generally found support for the uniqueness and
importance of only a subset of the proposed nine ethical climates.
Common factors derived in empirical research include an egoism-
based dimension, a benevolence-based dimension, and multiple
principle-based dimensions (termed independence, rules, and laws
and code to coincide with the locus of analysis described above;
see Martin & Cullen, 2006).
In our meta-analysis, we focused on three key dimensions that
parallel the three proposed ethical criteria (i.e., egoism, benevo-
lence, and principle). Following previous research (i.e., Barnett &
Vaicys, 2000; Watley, 2002; Wimbush, Shepard, & Markham,
1997), we focused on one principle-based dimension that com-
bines rules with the laws and code categories into a climate
referred to as “principled.” A sample Ethical Climate Question-
naire (Victor & Cullen, 1988) item for principled climate is “It is
important to follow strictly the organization’s rules and proce-
dures.” Instrumental and caring climates correspond to the ethical
criteria of egoism and benevolence, respectively, so we used the
latter labels. A sample egoism item is “In this organization, people
protect their own interests above other considerations.” A sample
item measuring a benevolent climate is “People in this organiza-
tion are actively concerned about the customer’s and public’s
interest.” Finally, we excluded Victor and Cullen’s independence
dimension from our analysis because individuals in this environ-
ment are presumed to “do as they see fit” (Trevin˜o, Butterfield, &
McCabe, 1998, p. 450). Incidences of unethical behavior would
then represent personal inclinations rather than ethical climate.
Ethical climates represent beliefs about “what constitutes right
behavior” in an organization and, thus, provide behavioral guid-
ance for employees (Martin & Cullen, 2006, p. 177). With an
egoistic climate, for example, employees perceive that the organi-
zational environment emphasizes self-interest (Victor & Cullen,
1988) and encourages decision making based on personal instru-
mentality. Therefore, the normative push for individuals in such a
climate is to make self-interested choices without considering the
social consequences of their actions (Martin & Cullen, 2006).
Indeed, research shows a positive relationship between egoistic
climates and unethical choices (e.g., Barnett & Vaicys, 2000;
Peterson, 2002b). Therefore, we hypothesized the following:
Hypothesis 11: Egoistic ethical climates in organizations are
positively related to unethical choices as (a) unethical inten-
tion and (b) unethical behavior.
In a benevolent ethical climate, individuals see that what is best for
employees, customers, and the community is important in the orga-
nization (Victor & Cullen, 1988). That is, there is a (shared)
perception that nurturance or care for others is valued by the
organization and is an important part of the firm’s social fabric. In
a principled organizational climate, decisions are perceived to be
based on formal guidelines, such as laws and explicit policies
regarding appropriate behavior (Victor & Cullen, 1988). Decisions
are considered ethical when they comply with those governing
rules (Barnett & Vaicys, 2000). Accordingly, through a focus on
concern for others (benevolent climate) or emphasis on rule-
abiding behavior (principled climate), both the benevolent and
principled climates are likely to encourage fewer unethical choices
(Wimbush & Shepard, 1994).
Hypothesis 12: Benevolent climates in organizations are neg-
atively related to unethical choices as (a) unethical intention
and (b) unethical behavior.
Hypothesis 13: Principled climates in organizations are neg-
atively related to unethical choices as (a) unethical intention
and (b) unethical behavior.
Martin and Cullen (2006) used meta-analytic techniques to explore the
relationship between ethical climate dimensions and “dysfunctional behavior.”
However, we take this relationship a step further by (a) adding studies not
included in their meta-analysis, (b) comparing ethical climate with ethical
culture, and (c) separating the intention and behavior results in a section below.
Ethical culture. Trevin˜o (1986) proposed that because most
employees are at the conventional level of CMD and are therefore
susceptible to external influence, their behavior should be influ-
enced by the guidance provided by an organization’s ethical cul-
ture (Trevin˜o, 1990). Trevin˜ o et al. (1998) later differentiated
ethical culture, with its narrower focus on formal and informal
organizational systems aimed at behavioral control, from ethical
climate, with its broader focus on perceived organizational values.
The ethical culture construct was conceptualized as representing a
more singular perception of the organization’s systems, proce-
dures, and practices for guiding and supporting ethical behavior.
These ethical culture systems communicate behavioral and ac-
countability expectations. That is, ethical culture includes specific
organizational elements such as executive ethical leadership (e.g.,
“The top managers of this organization represent high ethical
standards”; M. E. Brown, Trevin˜o, & Harrison, 2005) and reward
or disciplinary policies (e.g., “Management disciplines unethical
behavior”; Trevin˜o et al., 1998). The combination contributes to
employees’ beliefs about the patterns of ethical and unethical
conduct that the organization supports or discourages. If ethical
culture systems such as leadership, norms, and reward policies
encourage the achievement of bottom-line goals only, with no
attention to ethical concerns, the culture is more likely to support
unethical conduct.
Hypothesis 14: The strength of ethical cultures in organiza-
tions is negatively related to unethical choices as (a) unethical
intention and (b) unethical behavior.
Theoretical elaboration of the ethical culture construct (Trevin˜o,
1990) was based upon the assumption that an organization with a
strong ethical culture sends clear and targeted messages to em-
ployees about behavioral expectations via multiple organizational
mechanisms. The explicit goal was to propose a relationship be-
tween employees’ perceptions of ethical culture and employee
ethical behavior (Trevin˜o, 1990). In contrast, ethical climate was
originally developed with the idea that it should differentiate
between organizations and that its dimensions should be associated
with employee attitudes such as organizational commitment
(Cullen et al., 1993). Others (e.g., Wimbush & Shepard, 1994)
further developed the connection with behavior.
We do not explicitly hypothesize here about the relative predic-
tiveness or discriminant validity of ethical climate versus ethical
culture, as few studies have examined their simultaneous relation-
ship with unethical choice (e.g., Trevin˜o et al., 1998). Instead, we
see our meta-analysis as part of the sorting out that needs to occur
in this literature. That is, the cumulative effect sizes involving
these two sets of constructs will answer questions about whether
one contributes more or less than the other to unethical choice or
whether these constructs overlap in their influence.
Codes of conduct. Codes of ethical conduct in work organi-
zations extend as far back as 1913 when J. C. Penney introduced
a set of guidelines for proper employee behavior (Trevin˜o &
Weaver, 2003). Today, codes have become routine in workplaces
across the business, government, and nonprofit sectors (Ethics
Resource Center, 2007; Pater & Van Gils, 2003), though it is
unclear whether their existence actually discourages unethical be-
havior (Helin & Sandstrom, 2007; McCabe, Trevin˜o, & Butter-
field, 1996). The received wisdom would argue for a code’s ability
to reduce unethical behavior by heightening issue salience and
clarifying appropriate and inappropriate behaviors (Somers, 2001;
Trevin˜o & Brown, 2004). Although several studies have reported
a negative relationship between the existence of a code and un-
ethical choices (i.e., Hegarty & Sims, 1979; Izraeli, 1988; McCabe
et al., 1996; Okpara, 2003; Peterson, 2002a; Trevin˜o et al., 1998),
others have noted no significant effect (e.g., Brief, Dukerich,
Brown, & Brett, 1996; Cleek & Leonard, 1998). Despite these
mixed results, the balance of existing evidence supports the idea
that an organizational code of conduct reduces the incidence of
unethical choices.
Hypothesis 15: Existence of a code of conduct is negatively
related to unethical choices, as (a) unethical intention and (b)
unethical behavior, in organizations.
Related to the issue of code existence is the extent to which an
organization enforces its code (McCabe et al., 1996; Trevin˜o &
Weaver, 2001). According to McCabe et al., an effective code
“must be more than ‘window dressing’” (1996, p. 464). One way
for the organization to convey this to employees is to discipline
rule violators in a visible manner (Trevin˜o, 1992b). In this way, a
code of conduct is perhaps a primary or salient component of
ethical culture. Although few empirical studies have investigated
the impact of code enforcement on unethical choices, several of
these studies supported a negative relationship (i.e., McCabe et al.,
1996; Paolillo & Vitell, 2002; Trevin˜o & Weaver, 2001). Thus, we
expected that code enforcement would reduce unethical choices
(e.g., McCabe et al., 1996; Trevin˜o & Weaver, 2001).
Hypothesis 16: Enforcement of a code of conduct is nega-
tively related to unethical choices, as (a) unethical intention
and (b) unethical behavior, in organizations.
Further exploration of inputs.
Other moderators. In addition to investigating the potential
moderating effects of intention and behavior, we looked for any
differences that might exist in the following methodological mod-
erators: type of sample (student vs. employee), publication source
(peer-reviewed journal vs. unpublished), and research strategy
(field study vs. lab experiment). These moderators were chosen
because of their widespread use and potential to impact empirical
results in the behavioral ethics literature. However, we do not have
a theoretical basis to present any a priori hypotheses for these
variables and thus report only the results below.
Comparative strength of effects. If the data are available, one
of the advantages of meta-analyses is that they can evaluate the
unique or incremental power of hypothesized antecedents in a
cumulative way. To that end, we collected and aggregated every
pairwise correlation possible among our focal antecedents (which
totaled 105 additional meta-analytic relationships). Although we
scoured the literature for studies that included correlations between
the individual, moral issue, and organizational environment char-
acteristics, very few studies included variables from more than one
category of antecedents (this is apparent from the lack of overlap-
ping elements in the middle columns of Table 1). Therefore, we
were able to conduct this examination of comparative strength
only within each of our three groupings of independent constructs:
individual, moral issue, and organizational environment character-
Table 1
Summary of Studies Included in the Meta-Analysis
Study Year Sample
Independent variable
Moral issue
Abdolmohammadi & Sultan 2002 S CMD Behavior
Adams et al. 2001 NS CD EX Behavior
Aquino 1998 S BNV Behavior
Aquino & Douglas
2003 NS DEMO Behavior
Ashkanasy et al. 2006 S DEMO Behavior
Baker et al.
2006 NS CUL Behavior
Baldwin et al. 1996 NS CMD Behavior
2002 S CMD Intention
Banerjee et al. 1996 NS DEMO Intention
Barnett 2001 S MOC Intention
Barnett & Vaicys
2000 NS EGO, BNV, PRC Intention
Barnett & Valentine
2004 NS DEMO MOC, PRX, SC, TI Intention
Bass et al.
1999 NS IDL, REL, MACH, LOC Intention
Bay & Greenberg 2001 S DEMO Behavior
Betz et al. 1989 S DEMO Intention
2000 S CMD, MACH, LOC,
Bhal & Debnath
2006 NS IDL, REL Intention
Brandon 2003 S CMD Intention
Brief et al. 1996 NS CD EX Behavior
Brown & Weathington
2008 S JS Behavior
Bruk-Lee & Spector 2006 NS DEMO Behavior
2005 NS EGO Intention
Burnfield et al.
2005 NS JS Behavior
Chen & Spector
1992 NS JS Behavior
Cherry & Fraedrich
2000 NS LOC Intention
Cherry et al.
2003 NS DEMO Intention
Cleek & Leonard 1998 S CD EX Intention
Coate & Frey 2000 S DEMO Intention
Cohen et al. 1998 S DEMO Intention
Cohen et al.
2007 NS CMD, DEMO Intention
Cole 1996 NS CMD Intention
Detert et al.
2008 S LOC, DEMO Intention
Doty et al. 2005 S DEMO Intention
Douglas & Wier
2000 NS IDL, REL Intention
Eastman et al. 1996 NS DEMO Intention
Ethics Resource Center
2000 NS CD EX, CD ENF Behavior
Ethics Resource Center
2003 NS CD EX, CD ENF Behavior
Ethics Resource Center
2005 NS CD EX, CD ENF Behavior
Flannery & May
2000 NS MOC EGO Intention
Fox & Spector
1999 NS LOC, JS Behavior
Fox et al.
2007 NS JS Behavior
2000 NS BENV, PRC Intention
Goles et al. 2006 NS COE, MOC, POE, PRX,
Green et al. 2000 NS DEMO Intention
2002 NS CMD Behavior
Gruys & Sackett
2003 NS DEMO Intention
Gul et al. 2003 NS CMD, DEMO Intention
Harrell & Hartnagel 1976 S MACH Behavior
Harrington 1996 NS CD EX Intention
2006 NS EGO, BNV, PRC, CUL Intention
Henle et al.
2005 NS DEMO Behavior
Honeycutt et al. 1995 NS JS Behavior
Hudson & Miller 2005 S DEMO Intention
Izraeli 1988 NS CD EX Behavior
Jackson 2000 NS DEMO Behavior
1992 S CMD, REL, DEMO Intention
Jones & Kavanagh
1996 S MACH, LOC Intention
Kamp & Brooks
1991 S, NS JS CUL Behavior
Kish-Gephart et al.
2007 S IDL, REL, MACH,
Table 1 (continued)
Study Year Sample
Independent variable
Moral issue
Kish-Gephart et al.
2008 NS IDL, REL, LOC, JS,
Kwok et al.
2005 NS JS, DEMO Behavior
2002 S, NS DEMO Intention
Lane 1995 S DEMO Intention
Lasson & Bass
1997 S CMD, DEMO Both
Latham & Perlow 1996 NS DEMO Behavior
2004 S COE, MOC, POE, PRX,
Liao et al.
2004 NS DEMO Behavior
Libby & Agnello 2000 S DEMO Intention
Libby et al. 2005 S DEMO Intention
2000 S DEMO Behavior
Magers 1996 NS CD EX Behavior
Malinowski & Berger 1996 S DEMO Intention
Marta et al. 2004 NS DEMO Intention
Marta et al.
2001 NS IDL, REL CUL Intention
Mastranglo & Jolton
2001 S DEMO Behavior
May & Pauli
2002 S COE, SC, GEN Intention
McCabe et al.
1996 NS CD EX, CD ENF Behavior
McMahon 2000 S CMD, DEMO Behavior
2004 NS MOC EGO Intention
Mount et al.
2006 NS JS Behavior
Nill & Schibrowsky 2005 S DEMO CUL Intention
Okpara 2003 NS CD EX Behavior
Paolillo & Vitell
2002 NS JS GEN CUL, CD ENF Intention
Papadakis et al. 2005 NS DEMO Behavior
Pater & Van Gils 2003 NS CD EX, CD ENF Intention
Perlow & Latham
1993 NS LOC, DEMO Behavior
2002a NS DEMO Intention
2002b NS EGO, BNV, PRC,
2004 NS DEMO Intention
Ponemon 1992 NS CMD Behavior
Radtke 2000 NS DEMO Intention
1996 NS DEMO Behavior
Richmond 2001 S CMD, MACH, DEMO Intention
Robin et al. 1996 S CMD Intention
Roman & Munuera
2005 NS JS, DEMO Behavior
Ross & Robertson
2003 NS MACH, DEMO CUL Intention
Rottig & Koufteros
2007 S CD EX Intention
Sackett et al.
2006 NS DEMO Behavior
Schwepker 1999 NS CMD Intention
Seale et al. 1998 NS DEMO Behavior
2004 NS MOC, PRX, SC, TI Intention
Sims 1999 S DEMO Intention
2002 NS JS Intention
Singhapakdi 1999 NS DEMO Intention
2004 S IDL, REL, DEMO Intention
Singhapakdi et al. 1996 NS COE, MOC, POE, PRX,
Smith & Rogers 2000 S DEMO Intention
Somers 2001 NS CD EX Behavior
Spector et al.
2006 S JS Behavior
Stanga & Turpen 1991 S DEMO Intention
Street & Street
2006 S MACH, LOC, DEMO Behavior
Tang & Chen
2008 S DEMO Intention
Tang et al.
2007 NS MACH, DEMO Intention
Trevino et al.
1998 NS JS EGO, PRC, CUL Behavior
Trevino & Youngblood
1990 S CMD, LOC Behavior
Tsalikis & Ortiz-Buonafina 1990 S DEMO Intention
Tyson 1992 S, NS DEMO Intention
Uddin & Gillett 2002 NS CMD Intention
(table continues)
istics. In addition, because no frameworks or formulations exist
that make sharp predictions about relative or unique effects, we do
not offer hypotheses about differential impacts.
Compilation and Coding of Original Studies
We used many sources of original studies to acquire effect sizes for
this paper. First, we searched online databases including ABI/Inform,
ERIC, Nursing Abstracts, PsycINFO, PubMed, and Sociological Ab-
stracts. Initially, we searched on the dependent construct using a
variety of general keywords and phrases including (un)ethical behav-
ior, moral behavior, moral action, (un)ethical intention, moral inten-
tion, (un)ethical conduct, moral conduct, deviance,counterproductive
work behavior, dysfunctional behavior, antisocial behavior, maladap-
tive behavior, and organizational misconduct. We also searched by
specific types of unethical behaviors, such as theft, sabotage, piracy,
cheating, lying, dishonesty, misrepresentation, aggression, kickback,
bribery, and workplace violence, and by variations of each indepen-
dent variable chosen for inclusion in our study (e.g., code of ethics,
corporate code,code of conduct). Second, we used Google Scholar,
dissertation abstracts, e-mail requests sent through professional list-
servs, and elicitations at professional conferences to locate unpub-
lished documents. Third, we obtained effect sizes from organizational
questionnaire data collected for the 2000, 2003, and 2005 National
Business Ethics Surveys (Ethics Resource Center, 2000, 2003, 2005).
Fourth, we manually searched references from previously published
review papers (e.g., Berry, Ones, & Sackett, 2007; Borkowski &
Ugras, 1998; Ford & Richardson, 1994; Loe et al., 2000; O’Fallon &
Butterfield, 2005; Trevin˜o et al., 2006). Last, we included effects from
data collected in our own recent research, which involved vignette-
driven and survey data from separate samples of current business
students and working alumni from a large, public, northeastern uni-
Our search procedures produced over 6,000 “hits” for viable
papers in various scholarly literatures. Upon further investigation,
these were reduced to 200 empirical studies. However, we found
that over one third of the studies that fit our inclusion parameters
did not report effect sizes or that effect sizes could not be calcu-
lated from the information provided in the original article. We
contacted and followed up with the authors of each study by e-mail
and included a detailed description of the information that was
needed; this process reduced the rate of unusable studies some-
what. Consequently, the final number of independent samples in
this meta-analysis was 136. These samples comprised a total of
43,914 people aggregated across all relationships (note that this
number excludes those studies classified as clear outliers; see
below). Table 1 provides a listing of all included studies.
These relationships included only antecedent–unethical choice
pairs for which at least k3 independent effect sizes were
available. For example, few behavioral ethics researchers have
examined the relationship between affect and unethical choice.
Thus, affect was not included as an independent variable.
Our selection process ultimately led to specific elements of the
antecedent framework argued in the hypotheses above and presented
in Figure 1. Studies were classified by independent variable on the
basis of the scale used to measure the proposed antecedent of interest.
Most of these proposed antecedents in this meta-analysis were mea-
sured by one or two widely accepted scales, including CMD (mea-
sured by the Pscore of the Defining Issues Test; Rest, 1986; Social
Reflection Measure; Gibbs, Basinger, & Fuller, 1992), moral philos-
ophy (Ethics Position Questionnaire; Forsyth, 1980), Machiavellian-
ism (Mach IV; Christie & Geis, 1970), locus of control (Levenson,
1981; Rotter, 1966), and ethical climate (Ethical Climate Question-
naire; Victor & Cullen, 1988). For the other antecedents, we used
widely accepted definitions for job satisfaction (Weiss & Cropanzano,
1996), moral intensity (T. M. Jones, 1991), and ethical culture
(Trevin˜o, 1990; Trevin˜o et al., 1998). For example, we used T. M.
Jones’s (1991) definitions for the six dimensions of moral intensity to
Table 1 (continued)
Study Year Sample
Independent variable
Moral issue
Valentine & Rittenburg
2007 NS DEMO Intention
2001 NS EGO, BNV, PRC Behavior
Vitell et al.
2003 NS IDL, REL GEN CUL Intention
1993 NS DEMO Behavior
2002 NS IDL, REL EGO, BNV, PRC Intention
Watley & May
2004 NS PRX Intention
Werner et al. 1989 NS DEMO Behavior
Wimbush et al.
1997 NS DEMO EGO, BNV, PRC Intention
Wyld et al. 1994 S CMD Intention
Zagenczyk et al.
2008 NS MACH, DEMO Behavior
Note. This table reflects the studies that remained after an outlier analysis was performed. Certain studies may include more than one independent data
Sstudent; NS nonstudent.
CMD cognitive moral development; DEMO demographic variable(s); IDL idealism; REL relativism;
MACH Machiavellianism; LOC locus of control; JS job satisfaction.
MOC magnitude of consequences; PRX proximity; SC social
consensus; TI temporal immediacy; COE concentration of effect; POE probability of effect; GEN general moral intensity.
BNV benevolent
ethical climate; EGO egoistic ethical climate; PRC principled ethical climate; CUL culture; CD EX code existence; CD ENF code
Study reported at least one or more reliability estimates.
Intention and behavior were measured in separate independent samples.
categorize studies by moral intensity. Likewise, ethical culture was
classified on the basis of Trevin˜o’s conceptualization of ethical culture
as a “multidimensional interplay among various ‘formal’ and ‘infor-
mal’ systems of behavioral control that are capable of promoting
either ethical or unethical behavior” (Trevin˜o et al., 1998, p. 452).
Finally, code existence or code enforcement was captured if a study,
respectively, measured the presence of a firm-level, publicly dissem-
inated, statement of proper employee behavior or the extent to which
employees perceived that individuals were being held responsible for
code compliance.
To partially address issues of publication bias (or the “file drawer”
problem), we categorized studies for source (as either published in
peer-reviewed journals or unpublished manuscripts) and then exam-
ined possible determinants of variation in effect size. Studies were
also categorized by type of sample (student or nonstudent) and re-
search strategy (field study or lab experiment; McGrath, 1982). Stu-
dent samples were made up of any combination of undergraduate or
graduate students; and any sample that included both employees and
students was coded as a “student” sample. For research strategy,
studies were categorized as either a field study or a lab experiment on
the basis of use of a real or contrived setting, respectively (McGrath,
1982). Interrater agreement on these categorizations was 100, 100,
and 95%, respectively. As evident in Tables 5, 6, and 7 (see below),
the vast majority of included effect sizes were based on in situ surveys
of working adults (field study). This was especially true for investi-
gations focusing on moral intensity or the environmental predictors.
Inclusion of Dependent Variables
Our dependent variables are unethical intention and unethical be-
havior (together comprising unethical choice). Retained studies were
coded using the conceptual definitions of these variables. Intention is
an expression of one’s willingness or plan to engage in a course of
action, and behavior is that action itself. Effect sizes were included
only if the measurement of the construct itself was consistent with our
definition. For example, studies were coded as intention if the partic-
ipants were asked what they “would do” or the “likelihood” that they
would engage in a specific action given a specific situation. However,
studies were excluded if what was labeled as “intention” was instead
measured by asking participants what they “should” do (a moral or
normative judgment rather than a behavioral intention; Harrison,
1995). For behavior, studies were included if unethical behavior was
measured with self-reports of personal behavior, social reports of
observed coworker behavior (for organizational environment anteced-
ents), and behavior based on archival records.
Agreement among independent raters on the intention versus be-
havior code for each effect size was 97%; disagreements on the
remaining 3% were resolved through discussion between raters. In
performing these codings, we discovered that there is virtually no
overlap in included papers that examined intentions and behavior in
the same study or for the same unethical actions (see Table 1).
Therefore, the effects listed for intentions and the effects listed for
behavior come from separate studies, and we could not provide a
strong estimate of the intention–behavior connection or contrast those
studies that might have used a two-wave, intention–behavior versus
behavior–intention, design. This is a clear weakness in the literature
that needs empirical attention.
We also limited original studies on the basis of their setting. In
particular, because we are interested in unethical choices in the
workplace, we selected studies in which the dependent variable
was collected in relation to a work organization context (either real
or simulated). An organization can be defined as a formalized
collective of individuals in pursuit of mutual, specific goals (Scott,
2003). This criterion meant that we did not include, for example,
incidences of academic cheating of college students or software
piracy outside of the work organization.
Finally, it is important to note that, despite some conceptual and
empirical overlap, counterproductive work behavior (CWB) and
workplace deviance are not synonymous with unethical behavior
and our meta-analysis does not replicate those in the CWB area
(Dalal, 2005; see Trevin˜o et al., 2006). Whereas the definition of
unethical behavior is rooted in societal norms, workplace deviance
is based more on organizational norms that may or may not
coincide with the broader strictures (Robinson & Bennett, 1995;
Trevin˜o et al., 2006). Therefore, our meta-analysis includes only
those CWBs that are violations of both organizational and societal
norms. We included distinct types of workplace deviance such as
sabotage, theft, lying, and workplace aggression. Likewise, fol-
lowing Robinson and Bennett’s (1995) typology, we included
serious general forms of interpersonal deviance (i.e., “personal
aggression”) and organizational deviance (i.e., “property devi-
ance”) that are commonly considered unethical behaviors. In the
instances where minor CWB was broken down into interpersonal-
directed or organizational-directed actions (Bennett & Robinson,
2000), we included the latter only. This was done because the
majority of the interpersonal-directed CWBs reflect less serious,
political deviance that is based on organizational norms (e.g.,
showing favoritism or gossiping about coworkers). We did not
include any general measures of CWB that spanned all four
dimensions of Robinson and Bennett’s (1995) typology.
Meta-Analytic Techniques
To comprehensively test our hypotheses, we used two meta-
analytic procedures for creating and cumulating correlations: the fixed
effects (Hunter & Schmidt, 2004) and random effects (Erez, Bloom,
& Wells, 1996) models. Unlike the random effects model, the fixed
effects model starts with an assumption of homogeneity among indi-
vidual studies and the existence of one true population correlation.
Although this is a widely used approach in management research, the
broad variety of the populations, methods, and measures that make up
our target domain of research suggests the existence of a set or family
of correlations rather than a single correlation. The random effects
model has the ability to explicitly account for (potential) between-
study or between-population differences (Erez et al., 1996).
Fixed effects estimates. Following Hunter and Schmidt (2004),
we converted reported statistics (such as chi-squares, ttests, or p
values) into zero-order correlations from each study. We then used
these effect sizes to calculate an uncorrected weighted average cor-
relation, which is reported in our tables. Confidence intervals were
constructed around the uncorrected weighted average correlations to
see if the effect was likely under the null hypothesis of no relationship.
Effect sizes from individual studies were adjusted for measurement
error in the independent and/or dependent variables to obtain cor-
rected population estimates. If reliability estimates were unavailable
from the original article, an average reliability was imputed from
studies that did report the reliabilities of equivalent independent or
dependent variables. Following current practice (e.g., Balkundi &
Harrison, 2006), calculated effect sizes were permitted to be used only
once for each relationship we hypothesized. For example, some
studies reported three effect sizes for the relationship between gender
and three vignette measures of unethical behavior, all using the same
sample. If we had included all three effect sizes, we would have
overstated the total sample size, made the sampling error appear too
small, and given that particular investigation too large a weight in
determining the average correlation. Therefore, when more than one
effect size in a relationship used the same sample, a single, composite
correlation was calculated (Hunter & Schmidt, 2004).
To gauge the likelihood that there is more than one population
of effects under examination, we calculated Qstatistics for the
heterogeneity of observed effect sizes (Hedges & Olkin, 1985). In
the same vein, we calculated the percentage of that heterogeneity
in effect sizes that might be attributable to artifacts (unreliability
and sampling error). We also calculated sample-adjusted meta-
analytic deviancy statistics from Arthur, Bennett, and Huffcutt
(2001) to account for and remove the influence of outliers in the
distribution of effect sizes in the primary studies. Outliers were
identified and removed if they were |3.48| standard errors away
from the estimated population values, an indication that they were
not part of the “family” of correlations under investigation.
Random effects estimates. The estimated rho in the fixed
effects model is the same (asymptotically) as the mean rho (over all
subpopulations) in the random effects model. However, the
population-level variation around rho is presumed larger in random
effects models, essentially due to the idea that there is a set or family
of rho’s. Such models allow for explicit, formula-based estimates of
the systematic variation in subpopulation correlations, referred to as
(Erez et al., 1996). Therefore, our tables show confidence intervals
and estimates of this systematic variation for the random effects
model, which were obtained by fitting hierarchical linear statistical
equations using HLM 6 (Raudenbush, Bryk, Cheong, & Congdon,
2004). We used the same models to test the impact of methodological
(source, sample, and research strategy) and substantive moderators
(intention vs. behavior as criterion).
Individual Characteristics and Unethical Choices:
Hypotheses 1–9
Table 2 presents our meta-analytic findings for the set of indi-
vidual (primarily dispositional) characteristics proposed in Hy-
potheses 19. Hypothesis 1 was supported, as the confidence
interval around the meta-analytic effect does not cover zero. CMD
was negatively related to unethical choices, with an average cor-
rected correlation of ␳⫽⫺.164 (ksamples 22, npersons
In Hypothesis 2, we predicted that holding an idealistic moral
philosophy would be negatively related to unethical choices. This
hypothesis was supported (␳⫽⫺.209, k10, n2,619).
Individuals with an internal, accessible belief prohibiting harming
others were less likely to form unethical choices. In addition,
consistent with our prediction for Hypothesis 3, a relativistic moral
philosophy was positively related to unethical choice (␳⫽.197,
k12, n2,924), suggesting that a cognizant belief in flexible
moral strictures (i.e., high relativism) enhances the likelihood of
unethical conduct.
We proposed in Hypothesis 4 that Machiavellianism (Christie &
Geis, 1970) would positively influence unethical choices. In line
with that proposition, the average corrected correlation was ␳⫽
.267 (k11, n2,290). Consistent with Hypothesis 5, external
locus of control—those who believe the environment primarily
determines their experiences—was also positively related to un-
ethical choices (␳⫽.134, k11, n2,683). Likewise, Hypoth-
esis 6 was supported. Higher job satisfaction was related to a lower
likelihood of unethical choices (␳⫽⫺.242, k20, n3,913).
We also tested relationships involving three demographic vari-
ables. In Hypothesis 7, we expected a greater frequency of uneth-
ical choices for men than for women. Our expectation was sup-
ported but with a weak correlation (␳⫽.098, k60, n21,927).
Hypothesis 8 was also supported: Age was negatively related to
unethical choices but again weakly so (␳⫽⫺.088, k35, n
15,939). Last, the evidence did not support Hypothesis 9. We did
not find a reliable inverse link between education level and uneth-
ical choices (␳⫽.019, k22, n12,626); the confidence
interval for this relationship included zero.
Moral Issue Characteristics and Unethical Choices:
Hypotheses 10a–10g
As described above, moral intensity (T. M. Jones, 1991) consists
of six distinct dimensions. The results presented in Table 3 support
Hypotheses 10a through 10f regarding these dimensions as com-
ponents of moral intensity. Concentration of effect (␳⫽⫺.338,
k6, n1,495), magnitude of consequences (␳⫽⫺.362, k
10, n2,444), probability of effect (␳⫽⫺.419, k4, n
1,151), proximity (␳⫽⫺.225, k7, n1,930), social consensus
(␳⫽⫺.338, k8, n1,960), and temporal immediacy (␳⫽
.306, k7, n1,771) are all moderately and negatively linked
to unethical choice. It is of interest, following T. M. Jones’s (1991)
theory (and our Hypothesis 10g), that a general or aggregate
configuration of moral intensity has an extremely strong link to
unethical choice (␳⫽⫺.746, k5, n1,341).
Organizational Environment Characteristics and
Unethical Choices: Hypotheses 11–16
The last set of main effect predictions involved elements of the
workplace environment. We predicted relationships between un-
ethical choice and each of the three types of ethical climate as well
as between unethical choice and ethical culture. Hypothesis 11
forwarded that strength of an egoistic climate would increase the
likelihood of unethical choices. The results in Table 4 support this
hypothesis (␳⫽.121, k12, n2,662), but the effect is
somewhat weak. Overall, a climate that emphasizes self-interest
slightly fosters the incidence of unethical behavior in the work-
place. Hypotheses 12–13 predicted inverse relationships of the
strength of benevolent and principled ethical climates with uneth-
ical choice. Consistent with these predictions, moderate, negative
correlations were observed (␳⫽⫺.272, k9, n2,206; ␳⫽
.299, k10, n2,295; respectively). In Hypothesis 14, we
predicted that a stronger ethical culture would be inversely related
to fewer unethical choices. This idea was affirmed, with a robust,
negative correlation (␳⫽⫺.329, k12, n2,969).
Our final two propositions about direct effects dealt with the
proposed impacts of the existence and enforcement of organizational
codes of conduct. As presented in Table 4, Hypothesis 15 was not
confirmed. The existence of a code of conduct had a trivial correlation
with unethical choice (␳⫽⫺.038, k19, n10,414); and its
confidence interval included zero. On the other hand, consistent with
Hypothesis 16, a strong, negative link was found between code
enforcement and unethical choice (␳⫽⫺.479, k7, n6,092).
Moderator Analyses
Design differences. After examining the direct effects of each
of the determinants on unethical choice, we separated the effects in
the original studies by their use of different samples and methods,
as well as their use of intention or behavior as a criterion (see the
latter results in Tables 2–4). As described above, we coded for
three types of methodological moderators including publication
source (published vs. unpublished), sample type (student vs. non-
student), and research strategy (field study vs. lab experiment). To
examine the potential impact of these methodological moderators,
we first separated the effect size by the moderator for each
determinant–unethical choice pair (Hunter & Schmidt, 2004). In
the cases where fewer than two independent variables fit a partic-
ular moderator category, no results could be calculated. This
occurred, for example, with moral philosophy and the organiza-
tional variables that almost exclusively utilized the field survey
The results (see Tables 5, 6, and 7) revealed no clear systematic
pattern across the variables. Moving beyond pairwise observation
to determine potential systematic unique effects of these modera-
tors, we next conducted an analysis using weighted regression
(Erez et al., 1996; Hedges & Olkin, 1985; Steel & Kammeyer-
Table 2
Meta-Analytic Estimates of the Effect of Individual Influences on Unethical Choice: Hypotheses 1–9
Individual influences kNMean rVar. r
95% CI
(fixed effects) Est. Var.
95% CI
(random effects)
(random effects) T
Cognitive moral development
(Hypothesis 1) 22 3,109 .133 .014 [.183, .083] .164 .012 [.321, .137] .230 .031 68.68
Unethical intention 12 1,837 .137 .016 [.334, .066] .173 .016
Unethical behavior 10 1,272 .127 .011 [.193, .061] .152 .006
Moral philosophy: Idealism
(Hypothesis 2) 10 2,619 .176 .010 [.238, .114] .209 .008 [.237, .083] .162 .013 37.90
Unethical intention 10 2,619 .176 .010 [.238, .114] .209 .008
Unethical behavior
Moral philosophy: Relativism
(Hypothesis 3) 12 2,924 .168 .003 [.135, .200] .197 .000 [.163, .248] .209 .003 19.31
Unethical intention 12 2,924 .168 .003 [.135, .200] .197 .000
Unethical behavior
(Hypothesis 4) 11 2,290 .219 .012 [.154, .284] .267 .015 [.208, .394] .314 .026 50.60
Unethical intention 7 1,744 .222 .010 [.149, .296] .272 .012
Unethical behavior 4 546 .209 .020 [.071, .346] .250 .033
Locus of control
(internal 0;
Hypothesis 5) 11 2,683 .112 .011 [.050, .174] .134 .011 [.091, .276] .188 .017 43.65
Unethical intention 7 1,970 .072 .006 [.012, .131] .085 .010
Unethical behavior 4 713 .219 .008 [.132, .346] .250 .033
Job satisfaction
(Hypothesis 6) 20 3,913 .209 .027 [.281, .138] .242 .029 [.300, .115] .212 .041 139.77
Unethical intention 5 733 .052 .019 [.172, .069] .059 .016
Unethical behavior 15 3,180 .228 .021 [.301, .155] .278 .023
Gender (females 0,
males 1;
Hypothesis 7) 60 21,927 .090 .008 [.067, .113] .098 .007 [.077, .145] .112 .012 210.54
Unethical intention 43 16,564 .090 .007 [.066, .114] .097 .005
Unethical behavior 17 5,350 .087 .013 [.033, .141] .096 .012
(Hypothesis 8) 35 15,939 .081 .006 [.108, .054] .088 .005 [.146, .057] .102 .011 134.71
Unethical intention 18 10,905 .075 .004 [.105, .044] .080 .003
Unethical behavior 17 5,034 .095 .011 [.144, .045] .108 .009
Education level
(Hypothesis 9) 22 12,626 .017 .004 [.009, .042] .019 .002 [.049, .022] .014 .004 56.35
Unethical intention 15 10,005 .021 .003 [.008, .050] .024 .002
Unethical behavior 7 2,621 .002 .004 [.050, .047] .001 .002
Note. CI confidence interval.
Empty cells represent relationships wherein less than two studies met the inclusion requirements.
Chi-square tests from the random effects analysis
were larger but gave the same indication of between-study variance as the Q statistic.
Although job satisfaction has dispositional components, we are
not conceptualizing it as being solely a dispositional variable; it is an individual rather than situational or organizational attribute, so we have included it
in the current table for ease of presentation.
Mueller, 2002). Each uncorrected correlation was transformed into
a Fisher z,and the standard error was used as the weighting factor
in a random effects framework (Erez et al., 1996; LePine, Erez, &
Johnson, 2002), in which we regressed the transformed effect sizes
onto all three methodological moderators simultaneously. With 46
possibilities for significant determinants of the variation in effect
sizes, these design details were found to be important in only five
inconsistent cases, which is close to the nominal alpha level of 5%.
Hence, there was no compelling evidence that samples and meth-
ods were creating variation in effect sizes for influences on uneth-
ical choice.
Comparative strength of effects on intention and behavior.
The substantive moderator we thought might be important deals
with the potential differences in effects on stated intentions to
engage in unethical behavior versus (reports of) those behaviors
themselves. Hence, when possible, correlation strengths were
compared for the same antecedent’s effect on each of these two
forms of unethical choice. The two moral philosophies (ideal-
ism, relativism) and the dimensions of moral intensity could not
be included in this intention versus behavior analysis due to the
lack of studies that measured behavior. For the remaining
variables, our analysis revealed that the correlations with in-
tention and behavior were consistently in the same direction.
This suggested that using intention as a proxy for behavior is
not likely to influence the direction of correlations. Moreover,
the results showed that many of the variables correlated more
highly with behavior than with intention (refer to Tables 2–4).
For example, of the individual differences, both locus of control
.250) and job satisfaction (
.278) followed this pattern. Similarly, with the exception of
code existence, nearly all of the organizational determinants
correlated more highly with behavior than with intention. This
includes egoistic climate (
.221), benevolent
climate (
⫽⫺.403), principled climate (
⫽⫺.517), ethical culture (
and code enforcement (
To determine whether these differences between intention and
behavior were significant, we conducted the kind of moderator
analysis described above, using weighted regression in an HLM
framework. This analysis revealed that the difference between
intention and behavior was significant ( p.05) for half (5 of 10)
of the individual and organizational variables. Furthermore, when-
ever this difference was significant, the correlation with behavior
was always greater than with intention. Implications of this pattern
are drawn out in the Discussion.
Unique Effects of Predictors
A final type of analysis was afforded by our meta-analytic
cumulation, one that allows greater insight into the unique, simul-
taneous contributions of the proposed antecedents of unethical
choices. Viswesvaran and Ones (1995) forwarded the use of meta-
analytically derived matrices for structural equation models, and
that type of modeling has been reported many times in applied
psychology (e.g., Bhaskar-Shrinivas, Harrison, Shaffer, & Luk,
2005). Following this approach, we were able to construct matrices
of meta-analytic correlations among all of the proposed anteced-
ents but only within each of their sets of proposed determinants:
nine individual characteristics (45 interpredictor correlations), six
moral issue dimensions (30 interpredictor correlations), and six
features of organizational environments (30 interpredictor
Table 3
Meta-Analytic Estimates of the Effect of Moral Issue Influences on Unethical Choice: Hypotheses 10a–10g
Moral issue influence kNMean rVar. r
95% CI
(fixed effects) Est. Var.
95% CI
(random effects)
(random effects) T
Concentration of effect 6 1,495 .277 .022 [.396, .159] .338 .029 [.503, .120] .337 .067 50.91
Unethical intention 6 1,495 .277 .022 [.396, .159] .338 .029
Unethical behavior
Magnitude of consequences 10 2,444 .325 .016 [.403, .247] .362 .017 [.489, .239] .369 .047 53.45
Unethical intention 10 2,444 .325 .016 [.403, .247] .362 .017
Unethical behavior
Probability of effect 4 1,151 .378 .008 [.465, .291] .419 .006 [.577, .323] .451 .023 11.08
Unethical intention 4 1,151 .378 .008 [.465, .291] .419 .006
Unethical behavior
Proximity 7 1,930 .202 .012 [.284, .119] .225 .013 [.374, .128] .251 .026 33.62
Unethical intention 7 1,930 .202 .012 [.284, .119] .225 .013
Unethical behavior
Social consensus 8 1,960 .290 .011 [.361, .219] .338 .013 [.468, .250] .352 .027 33.04
Unethical intention 8 1,960 .290 .011 [.361, .219] .338 .013
Unethical behavior
Temporal immediacy 7 1,771 .274 .017 [.370, .179] .306 .020 [.428, .141] .291 .046 41.53
Unethical intention 7 1,771 .274 .017 [.370, .179] .306 .020
Unethical behavior
General moral intensity 5 1,341 .610 .013 [.708, .511] .746 .025 [.847, .504] .681 .151 35.87
Unethical intention 5 1,341 .610 .013 [.708, .511] .746 .025
Unethical behavior
Note. CI confidence interval.
Empty cells represent relationships wherein fewer than two studies met the inclusion requirements.
Chi-square tests from the random effects analysis
were larger but gave the same indication of between-study variation as did the Qstatistic.
Very few original studies have examined variables
in more than one of these sets at the same time (see Table 1). This
led us to create five meta-analytic structural models. Respectively,
each one gauged the simultaneous—and therefore unique—
influences of (a) individual characteristics on unethical intention,
(b) individual characteristics on unethical behavior, (c) moral issue
dimensions on unethical intention, (d) organizational environment
features on unethical intention, and (e) organizational environment
features on unethical behavior. Results of these tests are shown in
the path models in Figure 2. Sample sizes for these models were
the harmonic mean of the sample sizes across all cells of the
meta-analytic correlation matrix.
Individual characteristics. Referring to Panel A of Fig-
ure 2, we see that all of the individual-level psychological
predictors, but none of the demographic variables, made a
unique contribution to unethical intention or unethical behavior.
All of those contributions were in the direction consistent with
our original hypotheses. That is, once the psychological vari-
ables are accounted for, the demographic variables are incon-
sequential. For unethical intention, CMD, Machiavellianism,
and moral philosophy, but not external locus of control, were
simultaneously important predictors. For unethical behavior in
particular, all of the psychological variables were significant contrib-
utors (except for moral philosophies because too few original studies
examined their relationship with unethical behavior and thus, they
could not be included in the model).
Notably, only two correlations of the 45 between any of the
individual predictors in our meta-analytic matrix of correlations were
␳⬎|.40|: Machiavellianism with external locus of control (␳⫽.43),
and Machiavellianism with relativism (␳⫽.45). This suggests that,
despite some conceptual overlap, the individual constructs are largely
distinct. Furthermore, the demographic variables had little overlap
with the psychological predictors (␳⬍|.15|) and should not be
regarded as surrogates for them in future research.
Moral issue dimensions. When entered simultaneously,
four of the six moral intensity dimensions significantly helped
explain unethical intention (see Figure 2, Panel B). Magnitude
of consequences and temporal immediacy were nonsignificant,
and at first blush it might be possible to conclude they are not
important. However, the intercorrelations among these and two
other moral issue dimensions (probability of effect and concen-
tration of effect) ranged from ␳⫽.57 to ␳⫽.82. Such an
intercorrelation pattern suggests the potential for one underly-
ing “expected valence of harm,” or EV factor. We explore some
of the possibilities for this tight cluster of dimensions in the
Discussion. In contrast, social consensus and proximity were
correlated under ␳⫽.40 with all the other dimensions, perhaps
undergirding their unique impacts.
Organizational environment features. For the predicted an-
tecedents in an individual’s workplace environment, five of the six
variables had simultaneous and significant unique impacts on
either unethical intention or unethical behavior (see Figure 2, Panel
C). Strength of benevolent climate, principled climate, and code
enforcement explained significant variance in unethical intention.
Matrices of meta-analytic correlations among the predictors are avail-
able from the authors upon request.
Table 4
Meta-Analytic Estimates of the Effect of Organizational Environment Influences on Unethical Choice: Hypotheses 11–16
Organizational environment
influence kNMean rVar. r
95% CI
(fixed effects) Est. Var.
95% CI
(random effects)
(random effects) T
Ethical climate: Egoistic
(Hypothesis 11) 12 2,662 .100 .013 [.035, .164] .121 .012 [.043, .231] .139 .020 49.51
Unethical intention 7 1,887 .069 .007 [.008, .130] .083 .004
Unethical behavior 5 775 .174 .020 [.049, .300] .221 .018
Ethical climate: Benevolent
(Hypothesis 12) 9 2,206 .235 .008 [.295, .175] .272 .008 [.375, .197] .290 .016 26.48
Unethical intention 5 1,484 .197 .005 [.261, .132] .226 .004
Unethical behavior 4 542 .340 .002 [.381, .298] .403 .000
Ethical climate: Principled
(Hypothesis 13) 10 2,295 .251 .014 [.323, .178] .299 .015 [.428, .208] .314 .031 45.37
Unethical intention 5 1,520 .192 .010 [.280, .104] .228 .011
Unethical behavior 5 775 .365 .000 [.384, .347] .440 .000
(Hypothesis 14) 12 2,969 .270 .031 [.369, .170] .329 .034 [.537, .243] .424 .089 115.59
Unethical intention 6 1,753 .153 .006 [.213, .093] .188 .003
Unethical behavior 6 1,216 .438 .019 [.549, .327] .517 .015
Code of conduct: Existence
(Hypothesis 15) 19 10,414 .031 .007 [.069, .008] .038 .007 [.123, .018] .071 .010 94.18
Unethical intention 6 1,517 .022 .010 [.102, .058] .024 .007
Unethical behavior 13 8,897 .032 .007 [.077, .013] .041 .007
Code of conduct: Enforcement
(Hypothesis 16) 7 6,092 .334 .003 [.378, .290] .459 .010 [.494, .269] .409 .029 47.94
Unethical intention 2 288 .124 .002 [.189, .058] .138 .000
Unethical behavior 5 5,804 .344 .001 [.375, .313] .484 .002
Note. CI confidence interval.
Empty cells represent relationships wherein less than two studies met the inclusion requirements.
Chi-square tests from the random effects analysis
were larger but gave the same indication of between-study variation as did the Qstatistic.
Table 5
Methodological Moderators: Individual Characteristics
Individual characteristic kNMean rVar. r95% CI Est. Var.
Cognitive moral development
Published 14 1,420 .199 .014 [.359, .137] .247 .008
Unpublished 8 1,689 .077 .007 [.137, .018] .093 .005
Student 11 1,886 .087 .008 [.140, .034] .106 .005
Nonstudent 11 1,223 .204 .015 [.277, .131] .246 .011
Field study 15 2,249 .135 .015 [.197, .074] .169 .014
Lab experiment 7 860 .128 .013 [.211, .044] .152 .007
Moral philosophy: Idealism
Published 6 1,970 .201 .006 [.321, .139] .229 .005
Unpublished 4 649 .101 .015 [.219, .018] .125 .010
Student 3 358 .082 .005 [.158, .006] .101 .006
Nonstudent 7 2,261 .191 .009 [.262, .120] .219 .009
Field study 10 2,619 .176 .010 [.238, .114] .209 .008
Lab experiment
Moral philosophy: Relativism
Published 6 1,970 .171 .002 [.200, .204] .200 .000
Unpublished 6 954 .159 .007 [.093, .225] .189 .002
Student 5 663 .192 .004 [.136, .249] .239 .000
Nonstudent 7 2,261 .160 .003 [.120, .200] .185 .001
Field study 12 2,924 .168 .003 [.135, .200] .197 .000
Lab experiment
Published 6 1,385 .167 .004 [.187, .217] .202 .000
Unpublished 5 905 .299 .014 [.194, .404] .377 .021
Student 8 1,280 .295 .007 [.236, .355] .376 .005
Nonstudent 3 1,010 .122 .002 [.076, .168] .146 .000
Field study 9 2,051 .221 .012 [.148, .293] .269 .016
Lab experiment 2 239 .205 .010 [.064, .345] .249 .004
Locus of control (internal 0)
Published 8 2,137 .109 .012 [.019, .186] .129 .013
Unpublished 3 546 .168 .000 [.148, .189] .215 .000
Student 7 1,231 .166 .010 [.092, .240] .221 .006
Nonstudent 4 1,452 .083 .008 [.004, .170] .094 .007
Field study 9 2,434 .101 .007 [.048, .153] .120 .005
Lab experiment 2 249 .320 .006 [.213, .428] .396 .000
Job satisfaction
Published 15 3,259 .237 .022 [.471, .162] .275 .023
Unpublished 5 654 .069 .027 [.212, .073] .081 .025
Student 4 491 .212 .028 [.377, .047] .247 .025
Nonstudent 16 3,455 .173 .022 [.246, .099] .268 .031
Field study 20 3,913 .209 .027 [.281, .138] .242 .029
Lab experiment
Gender (females 0, males 1)
Published 48 13,398 .090 .012 [.031, .120] .098 .010
Unpublished 12 8,529 .090 .003 [.060, .121] .098 .002
Student 32 7,971 .124 .012 [.085, .163] .136 .010
Nonstudent 28 13,956 .071 .005 [.045, .096] .076 .003
Field study 57 21,272 .088 .008 [.066, .111] .096 .006
Lab experiment 3 655 .144 .017 [.004, .292] .152 .014
Published 27 8,013 .097 .009 [.212, .062] .108 .007
Unpublished 8 7,926 .065 .004 [.108, .022] .070 .003
Student 13 3,469 .084 .014 [.147, .020] .094 .011
Nonstudent 22 12,470 .080 .005 [.108, .052] .087 .004
Field study 34 15,484 .082 .007 [.109, .054] .089 .006
Lab experiment 1 455 .050
Education level
Published 17 5,580 .004 .006 [.068, .040] .005 .003
Unpublished 5 7,046 .026 .002 [.010, .063] .029 .001
Student 9 2,518 .015 .005 [.059, .030] .014 .001
Nonstudent 13 10,108 .024 .003 [.006, .054] .027 .002
Field study 22 12,626 .017 .004 [.009, .042] .019 .002
Lab experiment
Note. CI confidence interval.
Empty cells represent relationships wherein no studies were available.
Although job satisfaction has dispositional components, we are not concep-
tualizing it as being solely a dispositional variable; it is an individual rather than situational or organizational attribute, so we have included it in the current
table for ease of presentation.
The same variables, along with strength of egoistic climate and
code existence, remained significant predictors in the unethical
behavior model. However, despite its strong independent effect,
ethical culture did not account for unique variance in either un-
ethical intention or unethical behavior beyond these other predic-
tors. This result likely stemmed from the high correlation of ethical
culture with several other predictors, including egoistic climate
(␳⫽⫺.51), benevolent climate (␳⫽.69), principled climate (␳⫽
.72), and code enforcement (␳⫽.60). None of the other predictors
were correlated more than ␳⫽|.45| with each other, except
benevolent and principled climates (␳⫽.61).
By cumulating knowledge across diverse literature sources, this
meta-analysis provides a comprehensive quantitative summary of the
individual (bad apples), moral issue (bad cases), and organizational
environment (bad barrels) antecedents of unethical choices in the
workplace. Our empirical synopsis and examination not only helps
shed light on 30 years of research that has been described as “decid-
edly mixed” in many areas (Tenbrunsel & Smith-Crowe, 2008, p.
579) but also provides a lens into potential future research opportu-
nities. First, our findings provide support for several long-standing
theories (e.g., T. M. Jones, 1991; Trevin˜o, 1986; Victor & Cullen,
1987) as well as a definitive direction for several relationships
plagued by inconsistent prior findings. Second, our findings reveal a
high degree of underlying complexity in unethical choices. That is,
such choices cannot be explained by one or two dominant anteced-
ents. Rather, they are multidetermined, with substrates spread widely,
even within the distinct realms of individual, moral issue, and orga-
nizational environment characteristics. In that regard, it is time for
behavioral ethics researchers to empirically integrate these multiple
sets of predictors (studying bad apples, cases, and barrels simulta-
neously) to fully understand this complicated phenomenon. Third,
when relationships with intention and behavior are investigated sepa-
rately, the results ostensibly call into question what has been the tradi-
tional, deliberative approach to ethical decision making (e.g., Rest, 1986)
and instead suggest what might be a more “impulsive” formulation.
Table 6
Methodological Moderators: Moral Issue Characteristics
Moral issue characteristic kNMean rVar. r95% CI Est. Var.
Concentration of effect
Published 4 1,159 .285 .007 [.452, .205] .346 .007
Unpublished 2 336 .241 .067 [.600, .118] .298 .095
Student 4 896 .189 .014 [.305, .073] .230 .015
Nonstudent 2 599 .403 .003 [.474, .333] .485 .000
Magnitude of consequences
Published 6 1,866 .338 .007 [.472, .270] .366 .007
Unpublished 4 578 .279 .039 [.473, .086] .286 .045
Student 4 975 .321 .012 [.427, .215] .338 .011
Nonstudent 6 1,469 .326 .018 [.432, .219] .353 .022
Probability of effect
Published 3 970 .343 .002 [.381, .294] .381 .000
Unpublished 1 181 .534 —
Student 2 552 .325 .003 [.396, .255] .360 .000
Nonstudent 2 599 .418 .006 [.524, .312] .466 .004
Published 5 1,603 .182 .006 [.295, .113] .206 .005
Unpublished 2 327 .280 .026 [.505, .055] .297 .033
Student 2 552 .241 .000 [.259, .224] .281 .000
Nonstudent 5 1,378 .182 .014 [.286, .077] .200 .015
Social consensus
Published 5 1,478 .278 .006 [.431, .209] .325 .007
Unpublished 3 482 .317 .019 [.474, .161] .367 .025
Student 4 896 .309 .002 [.352, .265] .374 .000
Nonstudent 4 1,064 .270 .015 [.391, .148] .305 .021
Temporal immediacy
Published 4 1,289 .247 .007 [.388, .164] .278 .007
Unpublished 3 482 .334 .030 [.530, .137] .366 .038
Student 3 707 .304 .006 [.390, .218] .350 .004
Nonstudent 4 1,064 .248 .020 [.386, .111] .273 .024
General moral intensity
Published 4 1,160 .605 .014 [.723, .487] .744 .030
Unpublished 1 181 .639
Student 2 299 .403 .001 [.452, .355] .472 .000
Nonstudent 3 1,042 .669 .000 [.684, .653] .837 .002
Note. CI confidence interval. All studies that measured moral intensity used a field study research strategy. Therefore, no moderator for strategy was
reported in this table.
Individual Characteristics: Who Are the Bad Apples?
Our results reveal that individuals who obey authority figures’
unethical directives or act merely to avoid punishment (i.e., are
lower in CMD; Kohlberg, 1969), who manipulate others to orches-
trate their own personal gain (i.e., are Machiavellian), who fail to
see the connection between their actions and outcomes (i.e., have
an external locus of control), or who believe that ethical choices
are driven by circumstance (i.e., hold a relativistic moral philoso-
phy) are more likely to make unethical choices at work. These
findings support several foundational theories within behavioral
ethics (e.g., Forsyth, 1980; Trevin˜o, 1986), including the notion
that bad apples contribute to unethical behavior in organizations
(Trevin˜o & Youngblood, 1990).
Despite the prima facie similarity of these individual determi-
nants, none of them serve as substitutes for the others; all are
implicated in mutually predicting unethical choices. Still, an in-
teresting common theme among these dispositional determinants is
the apparent importance of self-gain, self-preservation, or self-
interest (Johns, 1999). For example, Machiavellians and those
lower in CMD (who make more unethical choices) are “looking
out for number one” and those high in internal locus of control
(who make fewer unethical choices) are likely to have a greater
concern about consequences for others. Some of the covariation of
what might be self-interest across constructs may help to explain
the moderate correlations ( p|.40|) between Machiavellianism
and locus of control and between Machiavellianism and relativism.
Furthermore, though job satisfaction is not a dispositional trait per
se, its effects also suggest a self-focus. Those who have a negative
assessment of their job may be more focused on their dissatisfac-
tion or on retaliation for feeling badly than on the cost of their
Table 7
Methodological Moderators: Organizational Environment Characteristics
Organizational environment
characteristic kNMean rVar. r95% CI Est. Var.
Ethical climate: Egoistic
Published 8 1,867 .107 .012 [.008, .181] .133 .010
Unpublished 4 795 .082 .016 [.043, .208] .095 .016
Nonstudent 12 2,662 .100 .013 [.035, .164] .121 .012
Field study 12 2,662 .100 .013 [.035, .164] .121 .012
Lab experiment
Ethical climate: Benevolent
Published 6 1,321 .218 .010 [.380, .139] .253 .010
Unpublished 3 705 .267 .005 [.344, .191] .306 .002
Student 1 80 .336
Nonstudent 8 1,946 .231 .008 [.294, .167] .268 .008
Field study 8 1,946 .231 .008 [.294, .167] .268 .008
Lab experiment 1 80 .336
Ethical climate: Principled
Published 7 1,590 .248 .015 [.474, .156] .297 .019
Unpublished 3 705 .256 .009 [.366, .146] .303 .007
Nonstudent 10 2,295 .251 .014 [.323, .178] .299 .015
Field study 10 2,295 .251 .014 [.323, .178] .299 .015
Lab experiment
Ethical culture
Published 10 2,580 .262 .032 [.570, .150] .323 .037
Unpublished 2 389 .321 .018 [.506, .135] .367 .015
Student 2 339 .392 .049 [.700, .085] .457 .065
Nonstudent 10 2,630 .254 .026 [.355, .153] .312 .028
Field study 12 2,969 .270 .031 [.369, .170] .329 .034
Lab experiment
Code of conduct: Existence
Published 15 9,024 .030 .006 [.138, .011] .038 .006
Unpublished 4 1,390 .037 .013 [.151, .076] .039 .013
Student 2 722 .034 .001 [.008, .077] .038 .000
Nonstudent 17 9,692 .036 .007 [.077, .005] .045 .007
Field study 19 10,414 .031 .007 [.069, .008] .038 .007
Lab experiment
Code of conduct: Enforcement
Published 6 5,944 .336 .003 [.585, .290] .467 .009
Unpublished 1 148 .232
Nonstudent 7 6,092 .334 .003 [.378, .290] .459 .010
Field study 7 6,092 .334 .003 [.378, .290] .459 .010
Lab experiment
Note. CI confidence interval.
Empty cells represent relationships wherein no studies were available.
Cognitive Moral
Locus of Control
Mach iavellianism
Job Satisfaction
Education Level
-.18*; -.13*
-.01; .13*
.11*; .12 *
-.03; -.21 *
-.04; -.05
.02; .06
.02; .01
Effects of Individual Characteristics
Idea lism
Relativism .14*
Concentration of
Magnitude of
Con seque nces
Proba bility of
Tem po ra l
Immed iacy
Effects of Moral Issue Characteristics
Ethica l Culture
Egoistic Ethical
Ethica l Climate
Principled Ethical
Code Existence
Enf orcem ent
.04; .09*
-.17*; -.07 *
-.19*; -.21*
-.08*; -.33 *
.06; .10*
.13; -.08
Effects of Organizational Environment
Figure 2. Simultaneous unique effects of proposed antecedents on unethical intention and behavior. First entry
is effect on unethical intention; second entry (after semicolon) is effect on unethical behavior.
actions to the organization or to others within it. These findings
provide an interesting avenue for future research: To what extent
is self-interest the key driver behind bad apples at work? (See
Cropanzano, Stein, & Goldman, 2007; Moore & Lowenstein,
Although demographics are among the most frequently investi-
gated groups of variables in behavioral ethics (see O’Fallon &
Butterfield, 2005), our meta-analytic results suggest either weak or
null relationships between age, gender, and education level and
unethical choices. Indeed, when evaluated side by side in the same
regression models with individual psychological (and generally
dispositional) characteristics, demographics add nothing to the
explanation of either unethical intention or unethical behavior.
These results have several implications for the current thinking
about identifying those likely to be bad apples via demographic
First, the findings counter theories that males and females differ
markedly in how they puzzle through ethical dilemmas (e.g.,
Gilligan, 1977) or that social expectations lead to systematic,
gender-specific responses (e.g., Eagly, 1987) to ethical dilemmas
by actors in the workplace. Second, the findings do not support the
idea that older individuals consistently behave more ethically than
younger individuals because of their past experience dealing with
ethical dilemmas (Tenbrunsel & Smith-Crowe, 2008). Third, the
findings related to education challenge the common myth that
work organizations can rely on educated adults to do the right
thing without further ethical guidance (Trevin˜o & Brown, 2004).
They also suggest an important but barbed question: Is higher
education abdicating its responsibility to advance the ethical de-
velopment of students? This is especially unsettling for academics,
given the well-documented, momentous, and continuing failures of
ethical decision making in so many organizations, coupled with
research demonstrating that moral reasoning can be advanced
within individuals through carefully designed training (Thoma &
Rest, 1986).
From a practical perspective, then, can organizations keep from
picking bad apples? Our results suggest they can, but demographic
strategies are not likely to be useful. Instead, by developing selec-
tion tests based on the individual differences included in this study,
organizations may be able to avoid hiring new employees who are
more likely to behave unethically at work. Indeed, to avoid ethics-
related cues, locus of control (Rotter, 1966) may be an unobtrusive
and fruitful measure as part of a selection battery. Another useful
area for future research may be to map the overlaps of these
individual differences with the constructs tapped by widely used
integrity tests (Ones, Viswesvaran, & Schmidt, 1993). Can varia-
tions on integrity or conscientiousness (or other elements of the
five-factor personality model) add to the explanation of unethical
choice, or are the more narrow constructs we have studied likely to
be the most proximal and potent inputs? Finally, given that integ-
rity tests are most often used with lower level employees, are the
individual differences studied here more useful with managerial
employees or are they equally predictive across employee popu-
Moral Issue Characteristics: What Are the Bad Cases?
In terms of issues themselves, or the “case” parameters of the
decisions that individuals are faced with making, high-intensity
moral issues are, according to T. M. Jones, “more likely to catch
the attention of the moral decision maker and be recognized as
having consequences for others” (1991, p. 381). Those conse-
quences will also be more likely to increase one’s effort in moral
reasoning (Weber, 1990). We found, consistent with this line of
thought, that when an ethical dilemma is perceived as a “good
case” (in the positive end of the spectrum for any of the six
separate moral intensity dimensions), an employee is less likely to
form an unethical intention (i.e., the ethical alternative is more
likely to be chosen). The opposite is true for choice situations that
are “bad cases,” wherein unethical alternatives are more easily
Our meta-analytic results for correlations among those di-
mensions revealed that four of the six (magnitude of conse-
quences, concentration of effect, probability of effect, and
temporal immediacy) were seen by study participants as being
highly interrelated (.57 ⬍␳⬍.82), perhaps forming a cluster
of what defines a good or bad case and leaving the other
dimensions (social consensus, proximity) as relatively indepen-
dent. Consistent with recent research (e.g., McMahon & Har-
vey, 2007), our results yield a tentative but certainly viable
notion that T. M. Jones’s (1991) moral intensity may comprise
three, rather than six, dimensions.
Magnitude of consequences, concentration of effect, proba-
bility of effect, and temporal immediacy are all arguably asso-
ciated with aspects of the potentially risky consequences to the
victim. For example, probability of effect refers to the likeli-
hood that harm will occur; temporal immediacy refers to the
passage of time before that harm will occur; magnitude of
consequences refers to the actual amount of harm that will
occur; and concentration of effect refers to the relative amount
of harm in relation to the number of victims potentially harmed.
In contrast, social consensus and proximity are not directly
related to the harm itself but to agreement or psychological
similarity. Thus, it is not surprising that the four dimensions
directly related to consequences to the victim are overlapping
and likely form one dimension related to the amount of “ex-
pected harm” (McMahon & Harvey, 2006).
Although these findings are based on studies of unethical inten-
tion alone (as we were unable to locate any investigations that
measured moral intensity and actual unethical behavior), they
suggest that organizations may be able to reduce unethical behav-
ior in the workplace by “sharpening the edges” of ethical dilem-
mas. That is, if means exist to highlight moral intensity features in
organizational decision making (just as means exist to highlight
financial features, such as information about financial risks and
expected profits), unethical choices might be more frequently
suppressed. For example, unethical behavior may be reduced if
employees learn to associate potential unethical behavior with
severe, well-defined harm (magnitude of consequences) to a fa-
miliar or recognizable victim similar to the actor (proximity).
Likewise, organizations may be able to prevent unethical behavior
by making behavioral norms (creating strong social consensus)
more prominent and clearly defined. Future research should con-
sider how and to what extent moral issues can be made salient to
employees (e.g., via personal contact with potential victims; Grant
et al., 2007) and whether doing so affects unethical choices.
Organizational Environment Characteristics: Where
Are the Bad Barrels?
Our findings suggest that organizations create bad and good
social environments (“barrels”) that can influence individual-level
unethical choices. We found, in support of Victor and Cullen
(1988), that firms promoting an “everyone for himself” atmo-
sphere (egoistic climates) are more likely to encourage unethical
choices. However, the reverse relationship is found where there is
a climate that focuses employees’ attention on the well-being of
multiple stakeholders, such as employees, customers, and the
community (benevolent climate), or on following rules that protect
the company and others (principled climate). Likewise, a strong
ethical culture that clearly communicates the range of acceptable
and unacceptable behavior (e.g., through leader role-modeling,
rewards systems, and informal norms) is associated with fewer
unethical decisions in the workplace (Trevin˜o, 1990).
Our findings also revealed, however, that ethical culture is
highly related to the three ethical climates (|.50| ⬍␳⬍|.72|) and
to ethical code enforcement (␳⫽.60). In addition, when consid-
ered as a predictor simultaneously with the ethical climate dimen-
sions, ethical culture did not explain any unique variance in un-
ethical intention or behavior. These findings suggest that future
research into the relationship between the broad organizational
environment and employee unethical choice will benefit from
focusing on the three ethical climate dimensions. Further, our
results suggest that organizations interested in gauging how em-
ployees perceive their broad ethical environments should assess
the three climate dimensions. Given the statistical overlap between
ethical culture and ethical climate, future research on ethical cul-
ture will need to demonstrate conceptually and empirically
whether and how it can have a unique impact on unethical choice.
It is possible that perceptions of ethical culture may be a source of
employees’ broad ethical climate perceptions because the ethical
culture measure taps perceptions of specific organizational sys-
tems and practices (e.g., leadership, reward systems) and their
ethics-related messages. For example, performance management
systems that reward individual bottom-line achievement (no matter
how it is achieved) and that fail to discipline self-serving behavior
are likely to give rise to perceptions of a highly egoistic climate. If
so, measures that tap these specific organizational systems may
provide managers with information about levers they can use for
influencing perceptions of the ethical climate.
Codes of conduct have received much attention, but they have
produced mixed results in the literature (O’Fallon & Butterfield,
2005). Our meta-analysis revealed that the mere existence of a
code of conduct has no detectable impact on unethical choices,
despite the considerable amount of statistical power that comes
from doing a meta-analytic summary. One likely explanation is
that codes of conduct have become so ubiquitous that they have
lost their potency. They have become ground rather than figure.
Another possibility is that codes are often little more than a facade.
Enron’s code, for example, contained more than 60 pages of
prescriptions, but the board explicitly voted to override the code
when it approved Andrew Fastow’s (Enron’s CFO) financial mal-
feasance (Stevens, Steensma, Harrison, & Cochran, 2005). This
does not mean, however, that codes of conduct are unnecessary in
organizations. Rather, as our results revealed, a properly enforced
code of conduct can have a powerful influence on unethical
choices (McCabe et al., 1996; Trevin˜o, 1992b). Therefore, future
research should move away from examining whether or not a code
of conduct exists and focus instead on how codes can be effec-
tively enforced to shape behavior.
Separating Intention and Behavior: Evidence of a Less
Deliberative Approach?
As noted earlier, Rest’s (1986) four-stage model provides the
backdrop for much of the behavioral ethics literature (see T. M.
Jones, 1991; Loe et al., 2000; O’Fallon & Butterfield, 2005;
Tenbrunsel & Smith-Crowe, 2008; Trevin˜o et al., 2006). This
model suggests, consistent with the well-supported theories of
reasoned action and the theory of planned behavior (e.g., Ajzen,
1991; Fishbein & Ajzen, 1975), that intention precedes behavior in
succession and should therefore be more strongly connected to
similarly specific perceptions, beliefs, and attitudes (Harrison,
Newman, & Roth, 2006). Furthermore, barring strong constraints
or disruptions by the environment, or large time or measurement
discrepancies between intention and behavior, the two should be
robustly associated. Following these implicit assumptions, uneth-
ical intention has often been substituted for unethical behavior in
behavioral ethics research (e.g., Borkowski & Ugras, 1998; Chang,
1998; Martin & Cullen, 2006; Whitley, 1998; Whitley et al., 1999;
see O’Fallon & Butterfield, 2005; Weber, 1992; Weber &
Gillespie, 1998). It remains unclear, however, to what extent this
proxy logic is appropriate. Behavioral ethics investigations rarely
include both intention and behavior in the same investigation. Our
comprehensive search found only two studies that measured un-
ethical intention and behavior within the same sample. This fact
underscores a strong and immediate need to do so in future studies.
Although we did not posit formal a priori hypotheses about the
difference in effect sizes involving intention and behavior, we set
out to investigate the potential moderating effects of measuring
unethical choice as intention versus behavior. Our results yielded
important insights. First, all of the correlations with intention and
behavior shared the same sign or direction of effect, suggesting
initial support for the use of intention as a proxy for behavior.
Second, despite the consistency in direction of effects, many
antecedents were correlated more strongly with unethical behavior
than with unethical intention. A subsequent moderator analysis
using a random effects weighted regression (Erez et al., 1996;
Hedges & Olkin, 1985) revealed significant differences ( p.05)
between these intention and behavior correlations for five of the 10
antecedents that allowed a statistical comparison. In every case of
demonstrably different results, the correlation with behavior was
stronger than with intention. Given Rest’s (1986) model, these
results were unexpected. The unidirectional flow of the four-stage
model (from awareness to judgment to intention to behavior)
suggests that antecedent effects should be stronger with intention
than with behavior (see Ajzen, 1991, and Fishbein & Ajzen, 1975).
There are several possible explanations for this correlation pat-
tern. One statistics-based explanation is that greater variance exists
in the behavior measures than the intention measures. Although the
dependent variables were assessed with a broad variety of instru-
ments (and are therefore not directly comparable in terms of their
variance), we saw no evidence of this across original studies.
Further, it is likely that proscriptions on unethical behavior would
be stronger than those on intention and thus would create more
restriction of range (Sutton, 1998). Similarly running counter to
our findings, intention measures should have been more predict-
able, as they tended to have higher reliability estimates than did
behavior measures (average ␣⫽.92 vs. .85).
A potential theoretical explanation is consistent with an emerg-
ing conceptual approach to ethical decision making. We refer to it
as the ethical impulse perspective. Several scholars have recently
theorized that individuals respond to ethically charged situations in
ways that are more automatic than deliberative (Chugh, Bazerman,
& Banaji, 2005; Haidt, 2001; Moore & Lowenstein, 2004; Reyn-
olds, 2006; Sonenshein, 2007; Sunstein, 2005). This ethical im-
pulse perspective contrasts with the step-by-step, controlled cog-
nitive processing evoked in the traditional ethical calculus
perspective (e.g., Chang, 1998; Lewicki, 1983; Rest, 1986;
Trevin˜o, 1986). Reynolds (2006), for example, argued that indi-
viduals use prototypes to match ethical situations to actions. When
a prototype is available (e.g., based on a similar situation one has
experienced previously), the behavior linked with the prototype is
more unthinking or mechanical and emanates from “reflexive
judgment.” According to this logic, employees would default to a
more automatic type of processing unless something in the situa-
tion, such as novelty (i.e., absence of a prototype; Reynolds, 2006),
triggers more controlled processing (Haidt, 2001; Moore &
Lowenstein, 2004).
Although the lack of prior work examining intention and be-
havior limits our interpretation of these results, substantial evi-
dence exists within the broader psychological literature that “we
are often not aware of our own mental processes or of what is
guiding our daily moods, thoughts, and behavior” (Chartrand &
Bargh, 2002, p. 14). For example, Bargh and colleagues found that
contextual cues can trigger certain types of behavior (including
antisocial behavior such as rudeness, hostility, and aggression)
without conscious awareness (through preconscious automaticity;
e.g., Bargh, 2006; Bargh & Ferguson, 2000; Bargh, Gollwitzer,
Lee-Chai, Barndollar, & Trotschel, 2001; Dijksterhuis, Chartrand,
& Aarts, 2007). In addition, automatic behavior can be primed via
a consciously chosen goal that then continues to influence behav-
ior unintentionally and without the individual’s awareness (i.e.,
goal-dependent automaticity; Bargh, 1989; Bargh & Chartrand,
1999, 2000).
This literature stream demonstrates the complexity of labeling any
behavior as either automatic or controlled (see Moors & de Houwer,
2007, for a review) and thus has important implication for future
behavioral ethics research. In particular, according to Bargh (1989, p.
8), “all automaticity is conditional”: that is, the individual features of
pure automaticity (i.e., unintentional, involuntary, effortless, autono-
mous, and nonconscious) exist only in degrees. In this way, the
“gradual view” (see Moors & de Houwer, 2007) suggests that uneth-
ical behavior is not purely automatic or controlled but rather exhibits
greater and lesser amounts of the various features of automaticity. For
instance, lying to a supervisor may reflect preconscious automaticity
(e.g., primed by contextual cues outside of conscious awareness),
whereas embezzlement and other white-collar crimes may require a
consciously chosen goal but perhaps little cognitive effort after the
fact (i.e., goal-dependent automaticity). Accordingly, future research
should “investigate each automaticity feature separately and deter-
mine the degree to which it is present” (Moors & de Houwer, 2007,
p. 11; see also Bargh & Chartrand, 2000) across types and situations
of unethical behavior. This will also help elucidate the relationship
between the ethical calculus and ethical impulse perspectives. That is,
when are certain unethical behaviors likely to be more automatic than
Furthermore, to the extent that certain unethical behaviors are
more automatic, future research should examine how individuals
might engage in impulse control. For example, Shmueli and Mu-
raven (2007) found that undergraduates were more likely to cheat
after completing an earlier activity that required high amounts of
self-control (e.g., avoid typing “e” or thinking of a white bear).
This is consistent with the conception of ego depletion described
by Baumeister and colleagues (see Baumeister, Muraven, & Tice,
2000; Baumeister & Vohs, 2004; Baumeister, Vohs, & Tice,
2007): Self-control is a limited resource that can be reduced or
partially exhausted in the short term after it is used. Therefore,
certain characteristics of an organizational or task environment
may “test” an employee’s ability or motivation to engage in highly
calculative ethical processing. Similarly, Hofmann, Friese, and
Strack (2009) theorized that some individuals (e.g., those high on
trait self-control) appear to be better at impulse control than others,
suggesting that individual differences related to self-regulation
may contribute to researchers’ understanding of how to prevent the
more impulsive types of unethical behavior.
Given our recommendations to study the calculative and impul-
sive pathways simultaneously, behavioral ethics researchers will
likely need to consider alternative research methodologies. For
example, the widespread use of vignettes and scenarios (Weber,
1992; Weber & Gillespie, 1998), although useful, may in some
cases inadvertently prompt deliberation that takes participants out
of the realm of more impulsive types of unethical decisions. An
alternative option with great potential is the use of laboratory
experimentation (McGrath, 1982). Prior research on automatic
behavior provides well-developed laboratory-based paradigms that
can be adapted for research on unethical behavior (see Bargh &
Chartrand, 2000, and Fazio & Olson, 2003, for thorough reviews).
For example, behavioral ethics researchers may be able to prime
participants with a nonconscious goal that is characteristic of one
of the three ethical climates (e.g., benevolent, egoistic, or princi-
pled climate; Victor & Cullen, 1988). Subjects could then be given
the opportunity to cheat or lie to benefit themselves (e.g., Bargh et
al., 2001). Another, albeit more ambitious alternative lies in the
use of brain imaging technologies, such as the functional MRI, that
offer the opportunity to more directly study nonconscious cogni-
tive processing in morally charged situations (e.g., Greene, Som-
merville, Nystrom, Darley, & Cohen, 2001; Robertson et al.,
Implications for Deviance Research
Given the partial overlap between workplace deviance and eth-
ical decision making (Robinson & Bennett, 1995; Trevin˜o et al.,
2006), this meta-analysis of the latter suggests important implica-
tions for research directed at explaining the causes of the former.
First, after a thorough search of both literatures, we found little
intersection between the antecedents studied by behavioral ethics
and deviance investigators. For instance, in comparison to devi-
ance (e.g., Cohen-Charash & Mueller, 2007; George, 1989; Pelled
& Xin, 1999), affect is largely unstudied and is certainly under-
studied in the behavioral ethics domain (Gaudine & Thorne, 2001;
Trevin˜o et al., 2006). Likewise, few narrow personality or cogni-
tive style antecedents studied in behavioral ethics (such as CMD or
locus of control) are investigated in the deviance literature. Our
meta-analytic results also suggest the potential benefit of using
broad normative environment variables as complementary expla-
nations. Second, an ethical impulse perspective suggests that un-
ethical behavior may be more intuitive than intentional. Although
workplace deviance is often assumed to be more intentional (e.g.,
Spector & Fox, 2002), future research should consider that partic-
ular antecedents may differentially influence deviance depending
on the behavior’s more deliberative or intuitive characteristics
(e.g., Lee & Allen, 2002).
Limitations and Further Research Opportunities
Our meta-analytic findings are tempered by the lack of primary
data for certain relationships. For example, we set a lower bound
of at least three studies (k3) for antecedent–unethical relation-
ships. However, in our behavior versus intention moderator com-
parisons, the number of studies of moral intensity and code en-
forcement as antecedents dipped below the k3 bound. Thus, we
were unable to strongly test the differential sensitivity of intention
versus behavior for these constructs, and we had to temper our
conclusions accordingly. Although the lack of appropriate studies
(k) clearly limits our conclusions, it also recommends areas for
future research. For instance, there is a pressing need for research
that examines the influence of moral issue characteristics on eth-
ical and unethical behavior. Certain dimensions of moral intensity,
such as proximity to potential victims, may be more influential in
“real” behavior situations than with contrived scenarios or vi-
gnettes (e.g., Grant et al., 2007). In addition, although we found a
pattern of higher behavior correlations with some predictors, it is
possible that issue-related variables (i.e., moral intensity) will
make the harm of a particular ethical decision more salient to an
individual and thereby affect intention (through controlled deci-
sion making) more than behavior.
Another implication of low kin some cells of our meta-analytic
correlation matrix was that we were unable to compare the relative
effect of all antecedents on each other or to provide a comprehen-
sive test of certain foundational theories. First, as shown in Table
1, few studies measured multiple variables across predictor sets
(individual, moral issue, and organizational environment), so we
were able to test only the comparative effects of antecedents within
the three major categories. Second, most of the studies we re-
viewed for this meta-analysis investigated only direct relationships
between a proposed determinant and unethical choices. Thus, we
were able to support only specific elements of foundational theo-
ries and provide partial rather than comprehensive tests. For ex-
ample, Trevin˜o (1986) proposed an explicitly interactionist model
that uses environmental and dispositional variables to moderate the
CMD–unethical behavior relationship. Our results address the
CMD–unethical behavior main effect, but only a few studies
included more than one component of the interactionist model
(Ashkanasy, Windsor, & Trevin˜o, 2006; Greenberg, 2002; Trevin˜o
& Youngblood, 1990). Consequently, there is a need for broader
band research that investigates more complex configurations of
individual, moral issue, and organizational environment variables.
Given the complexity we uncovered even in main effects, such
research is likely to turn up additional nuances.
Finally, the distinction between intention and behavior as a
criterion, although important, did not account for all of the varia-
tion in effect sizes. After we split the sets of estimates, there was
still significant variation in correlations for all of the predicted
relationships. As we noted in the Results, this variation was not
attributable to methodological factors. Future investigations
should explore both the contextual or organizational conditions
that might moderate individual difference effects and the mix of
individual differences in a sample that might buffer or strengthen
environment effects. In other words, these results further argue for
studies that incorporate the interactionist perspective of apples in
specific cases or barrels and study their conjunctive or combina-
torial influences on unethical choices in organizations.
Despite increasing practitioner and academic interest in ethical
decision making, many questions have remained about the funda-
mental drivers of unethical decisions. Are bad apples to blame for
unethical ethical decisions; who are they? What are the character-
istics of bad cases and bad barrels that might spoil the bunch? To
help answer these questions, this meta-analysis compiled 136
studies from multiple literatures to test portions of extant theories
and prominent variables within the behavioral ethics domain. We
examined not only the potential impact of the widespread use of
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... These findings are in line with theories suggesting close relationships between unethical judgements, intentions, and behaviors (Ajzen, 1991;Rest, 1986). Given that unethical judgements, intentions, and behaviors are empirically and theoretically closely linked, we follow past work (e.g., Kish-Gephart et al., 2010) and treat them as one overarching construct. For this overarching construct, we will use the umbrella term unethical choice in the following. ...
... 2 During the preregistration, we had hoped this meta-analysis could provide some insights into the processes underlying the gender difference in unethical choice. Specifically, we wanted to explore whether the processes are rather automatic or rather controlled by testing outcome type (i.e., unethical judgement, intention, and behavior) as moderator (for a similar approach see: Kish-Gephart et al., 2010). To this end, we included an explorative research question in our preregistration: Is the gender difference a function of unethical judgement, unethical intention, and unethical behavior? ...
... Our finding that women showed less unethical choice in negotiations falls in line with the broader literature on gender differences in ethics. For example, results from prior meta-analyses reported small but significant gender differences suggesting that women as compared to men have a stronger moral identity (Kennedy et al., 2017), behave more honestly in experimental tasks (Gerlach et al., 2019), seem to be more susceptible for communal motives in teams (not letting down the team partners; Weber & Hertel, 2007), have a higher moral sensitivity (You et al., 2011), judge specific hypothetical business practices as more unethical (Franke et al., 1997), and show less unethical intentions and behaviors at work (Kish-Gephart et al., 2010). ...
Based on role congruity theory, this preregistered meta-analysis examines whether women negotiate less unethically than men. We predicted that moderators related to the person (negotiation experience) and the negotiation context (e.g., advocacy, cultural gender-role inequality) influence the proposed gender difference. We conducted a Bayesian three-level meta-analysis to test our predictions on a sample of 116 effect sizes from 70 samples (overall N = 14,028, including employees, MBA students, undergraduate students). As predicted, women negotiated less unethically than men (Hedges' g = 0.25). The gender difference held for unethical judgements (Hedges' g = 0.29), unethical intentions (Hedges' g = 0.21), and unethical behaviors (Hedges' g = 0.17). The gender difference decreased when parties negotiated for others as compared to for themselves, when parties strategically used positive affect, and tended to decrease when parties were experienced as compared to inexperienced negotiators. We discuss implications for theory and research.
... This research makes some contributions. Much of the literature on unethical behavior conceptualized unethical behavior as primarily self-benefiting (Kish-Gephart et al., 2010;Tacke et al., 2022). However, employees sometimes engage in unethical behavior to benefit their organization (Umphress & Bingham, 2011;Umphress et al., 2010). ...
... However, activities that support unethical behavior involve risk, uncertainty and even failure along the way to success. Most people are risk averse and do not want to engage in unethical behavior (Kish-Gephart et al., 2010;Xu, Wang, & Zhu, 2019). This study demonstrates that supervisor's sincerely care and benevolence for employees can make it very difficult to reject such a claim of unethical pro-organizational behavior. ...