httpsdoi.org10.11770886260517710486Journal of Interpe

https://doi.org/10.1177/0886260517710486
Journal of Interpersonal Violence
1 –27
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DOI: 10.1177/0886260517710486
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Article
Social Learning
Theory, Gender, and
Intimate Partner
Violent Victimization:
A Structural Equations
Approach
Ráchael A. Powers, PhD,1 John K. Cochran, PhD,1
Jon Maskaly, PhD,2 and Christine S. Sellers, PhD3
Abstract
The purpose of this study is to examine the applicability of
Akers’s
Social Learning Theory (SLT) to explain intimate partner
violence (IPV)
victimization. In doing so, we draw on the Intergenerational
Transmission

of Violence Theory (IGT) to extend the scope of SLT to the
explanation of
victimization and for a consideration of uniquely gendered
pathways in its
causal structure. Using a structural equation modeling approach
with self-
report data from a sample of college students, the present study
tests the
extent to which SLT can effectively explain and predict IPV
victimization and
the degree, if any, to which the social learning model is gender
invariant.
Although our findings are largely supportive of SLT and, thus,
affirm its
extension to victimization as well as perpetration, the findings
are also
somewhat mixed. More significantly, in line with IGT literature,
we find that
the social learning process is not gender invariant. The
implications of the
latter are discussed.
1University of South Florida, Tampa, USA
2The University of Texas at Dallas, USA
3Texas State University, San Marcos, USA
Corresponding Author:
Ráchael A. Powers, Department of Criminology, University of
South Florida, 4202 E. Fowler
Ave., SOC 107, Tampa, FL 33620, USA.
Email: [email protected]
710486 JIVXXX10.1177/0886260517710486Journal of
Interpersonal ViolencePowers et al.
research-article2017
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2 Journal of Interpersonal Violence 00(0)
Keywords
social learning, intimate partner violence, victimization, gender,
intergenerational
transmission of violence
Introduction
A longstanding theoretical perspective on intimate partner
violence (IPV) is
the Intergenerational Transmission of Violence Theory (IGT;
Straus, Gelles,
& Steinmetz, 1980). This theory argues that experiencing
household violence
(direct victimization) and/or witnessing it (indirect/vicarious
victimization),
particularly during childhood, leads to subsequent IPV, either
perpetration or
victimization. The causal process involved in IGT is most often
attributed to
a learning process (e.g., Alexander, Moore, & Alexander, 1991).
In this way,
IGT shares much in common with Akers’s Social Learning
Theory (SLT; see
Fox, Nobles, & Akers, 2011; Sellers, Cochran, & Branch, 2005;
Sellers,
Cochran, & Winfree, 2003; Wareham, Boots, & Chavez, 2009a,
2009b). For
instance, IGT and SLT stress exposure to influential role
models (parents)
who perpetrate or experience interpersonal violence within the

household
that children witness and later imitate. IGT and SLT also
articulate the impor-
tance of the transmission of beliefs, values, and norms
conducive to IPV.
However, SLT can also explicitly accommodate other common
explanations
for IPV including extrafamilial socialization, gender roles and
violent mascu-
linity, and the role of differential reinforcement.
On the contrary, Akers’s SLT is explicitly a theory of
perpetration, and
makes no claim to account for victimization. Conversely, IGT
can account
for both IPV perpetration and victimization. Given the high
level of concep-
tual and propositional congruity between the theories, it is our
contention that
IGT can offer a theoretical basis for expanding the scope of
SLT to include
victimization as well as perpetration. In addition, some of the
literature on
IGT has suggested that the causal processes are highly
gendered, whereas
SLT is ostensibly gender invariant. Although the results are not
conclusive,
previous research has found that men and women react
differently to violence
in the family of origin, and therefore, a gendered application of
IGT may be
warranted (see Stith et al., 2000). Extending this rationale to
SLT, an other-
wise gender invariant theory, it is possible that its causal
processes do not
operate identically for men and women with regard to IPV

victimization.
To that end, the primary purpose of the present study, and one
of its more
significant contributions to the literature, is to examine the
applicability of
SLT to explain IPV victimization. In so doing, we are able to
provide a
further test of its theoretical scope, which, as a “general” theory
of
Powers et al. 3
behavior, it should be able to accommodate. Moreover, we also
examine the
degree to which SLT as an explanation for IPV victimization is
gender
invariant, another important contribution and purpose of the
present study.
To the extent that SLT cannot account for IPV victimization, or
to the extent
that it is not gender invariant, it may not be as “general” a
theory as some
may claim it to be. Finally, this studycontributes to the
literature testing
SLT by employing a full structural equation model of the theory
with com-
plete representation and strong measurement properties for all
four of the
key SLT constructs while also accounting for the
feedback/reciprocal influ-
ence of IPV victimization.
Intergenerational Transmission of Violence

Experiencing physical violence in childhood has been
associated with a sub-
stantial increase in the odds of IPV perpetration (e.g., Gómez,
2011) and
victimization (e.g., Hamby, Finkelhor, & Turner, 2012). The
most common
explanation for this relationship focuses on learning processes.
Children
learn behavior from their experiences and observations of social
interactions.
These observations are particularly salient when the modelers
are of high
status, such as parents and caregivers (Bandura, 1973).
Therefore, when chil-
dren experience violence or hostile parenting practices, they
learn that vio-
lence is an acceptable means of conflict resolution and will later
model that
behavior in their relationships (Akers & Sellers, 2009).
Although the direct link between childhood exposure to or
experiences of
interparental violence and later life involvement in IPV is
presumably well
established, it is far from conclusive. Recent studies using
prospective meth-
ods or advanced statistical procedures (e.g., propensity score
matching) sug-
gest that this relationship may not be causal; once other adverse
childhood
experiences or selection bias has been taken into account, the
direct impact of
childhood abuse and childhood exposure to domestic violence
on later IPV
perpetration and victimization disappears (Jennings et al., 2014;

Widom,
Czaja, & DuMont, 2015). In a meta-analysis of 39 studies of
IGT conducted
by Stith and colleagues (2000), they suggest that support for the
theory is
weak to moderate. With regard to victimization, they suggest
that child abuse
and witnessing interparental aggression have weak to moderate
effects on
later intimate partner victimization. They point to the need for
more “com-
plex studies” that are able to move beyond the examination of
the direct rela-
tionship between violence in the family of origin and later IPV.
For example,
Messing and colleagues (2012) suggested that posttraumatic
stress disorder
(PTSD) may partially mediate the relationship between some
forms of child-
hood trauma and later adult victimization.
Likewise, several studies that are in line with the processes
outlined in
Akers’s SLT model have been employed to further disentangle
this relation-
ship between childhood experiences and adult IPV. The
majority of these
studies focus on the role of attitudes and beliefs surrounding
violence and
how they shape the risk for later perpetration and victimization.
Several stud-
ies have found that experiences of child abuse are related to

later acceptance
or condoning of violence against women, which may increase
the likelihood
of perpetration or risk of entering into a violent relationship
(e.g., Markowitz,
2001). Indeed, this is often considered a crucial link in the
learning process
between childhood abuse and adult IPV.
Taken together, this research suggests that there is a complex
relationship
between childhood experiences of violence and adult IPV.
However, the
learning processes and mechanisms by which violence is
transferred are not
well understood. Akers’s SLT articulates some of these learning
mechanisms
and processes more explicitly.
Akers’s SLT
SLT (Akers, 1998) proposes that crime and conformity are
learned through
interactions with other people that expose the individual to
definitions and
behaviors, reinforcements, and role models that either favor or
oppose crime.
Depending on the unique configuration of associates with whom
one inter-
acts, as well as the weight of each one’s influence on the
individual, one may
be exposed to attitudes, behaviors, reinforcements, and models
that, on bal-
ance, favor or oppose crime. In brief, SLT predicts that criminal
behavior is
likely to increase as association with criminal individuals

outweighs associa-
tion with noncriminal individuals; when this occurs, rewards for
crime out-
weigh the costs of crime, the number of criminal role models
outweighs the
number of conforming role models, and one’s positive or
neutralizing defini-
tions of crime outweigh one’s own negative definitions of
crime.
SLT posits a processual model whereby differential association
exerts
both a direct effect on criminal behavior and a partially
mediated or indirect
effect via its influence on differential reinforcement, imitation,
and defini-
tions, which likewise exert direct effects on criminal behavior.
Moreover,
Akers (1998) argued that the model incorporates reciprocal and
feedback
effects, in which an increase in criminal behavior also amplifies
association
with others favorable to crime, which then continues one’s
exposure to the
other three elements of SLT.
Akers advanced SLT in a series of statements beginning with
the differen-
tial association-reinforcement theory (Burgess & Akers, 1966)
and culminat-
ing with the social structure-social learning model (Akers,
1998). Few
Powers et al. 5

empirical investigations of the theory were conducted until
Akers himself
published the first test of the full social learning model (Akers,
Krohn, Lanza-
Kaduce, & Radosevich, 1979), which demonstrated remarkable
predictive
accuracy of SLT in accounting for alcohol and drug use among
adolescents.
Tests of SLT flourished thereafter. Most of these studies were
simple tests of
the direct, linear, independent effects of one or more of the four
social learn-
ing variables on a dependent variable, the latter most frequently
a form of
substance use or common delinquency (for a review and meta-
analysis, see
Pratt et al., 2010). Far less common in the body of empirical
research on SLT
are tests of the causal sequencing of the full social learning
model. Akers and
Lee (1996) used structural equation modeling (SEM) to estimate
both causal
and reciprocal/feedback effects of social learning and teenage
smoking (see
also Cochran, Maskaly, Jones, & Sellers, 2017; Krohn, Skinner,
Massey, &
Akers, 1985), confirming (with the exception of the imitation
variable) the
hypothesized social learning effects. Lee, Akers, and Borg
(2004) found sim-
ilar results in their SEM analysis of the Social Structure Social
Learning
(SSSL) model of adolescent alcohol and marijuana use.
Extending the social
learning causal model to physical aggression rather than

substance use,
Cochran and colleagues (2017) demonstrated direct and indirect
effects of all
social learning variables on violence perpetrated against an
intimate partner;
moreover, IPV also exerted reciprocal/feedback effects on the
four social
learning variables.
Criminological theories like SLT are advanced explicitly to
account for
offending behavior. However, these theories in some instances
are also pos-
ited as viable explanations of criminal victimization (see, for
instance,
Schreck, 1999, regarding low self-control; Smith & Jarjoura,
1988, regarding
social disorganization theory; or Zavala & Spohn, 2013,
regarding general
strain theory), in part because of the undeniable overlap
between criminal
offending and victimization (Lauritsen, Sampson, & Laub,
1991). There is at
least some evidence of similar overlap in the victimization and
perpetration
of IPV (Graham-Kevan & Archer, 2003), especially in instances
of what
Johnson (1995) refers to as “common couple violence.” Prior
research pro-
vides some support of SLT as an explanation of IPV
perpetration and less
often, victimization (Cochran et al., 2017; Fox et al., 2011;
Sellers et al.,
2005, 2003; Wareham et al., 2009a, 2009b). Within the context
of SLT, the
likelihood of IPV victimization increases not only as exposure

to a violent
partner increases, but also as exposure to other victims of
violence increases
(differential association), especially when other victims express
attitudes
(neutralizing definitions) that excuse or rationalize the violence
perpetrated
against them (e.g., I was asking for it; he was drunk; that’s how
a man shows
he loves me). SLT acknowledges that these socialization
processes can occur
external to the family, including the cultural acceptance of
violence as a
means of conflict resolution (Krug, Mercy, Dahlberg, & Zwi,
2002) and the
influence of traditional gender roles and violent masculinity.
With regard to
reinforcement, there is research to suggest that tenets of operant
conditioning
and reinforcement may explain the risk of IPV victimization or
the stay–
leave decision of victims. For example, Miller, Lund, and
Weatherly (2012)
applied operant learning principles to the examination of stay–
leave deci-
sions among women in violent relationships. The unpredictable
pattern of
offending behavior and subsequent reconciliation after abusive
episodes pro-
vides partial positive and negative reinforcements. Websdale
(1998) sug-

gested that the balance of reinforcements (e.g., the “honeymoon
phase,”
financial dependence) versus costs (e.g., physical injuries,
emotional trauma,
presence of children) of IPV victimization may at times tip
toward repeated
victimization. The response-cost of IPV may be quite high
(Miller et al.,
2012), and therefore, victimization, and repeated victimization,
becomes
more likely.
Gendered Learning Processes in IPV
IPV is a gendered phenomenon with regard to both perpetration
and victim-
ization (e.g., Johnson, 1995). As a general theory, SLT purports
to account for
the behavior of both men and women. In general, men are far
more likely
than women to be offenders and slightly more likely than
women to be vic-
tims of crime. SLT would explain that men are more likely than
women to
operate in learning environments that are more conducive to
offending and
victimization. However, some research on IPV finds that men
and women are
equally likely to be perpetrators as well as victims of aggression
(Johnson &
Ferraro, 2000). Furthermore, the social learning predictors of
IPV differ by
gender for both perpetration (Sellers et al., 2003) and
victimization (Cochran,
Sellers, Wiesbrock, & Palacios, 2011), but these tests were
restricted to sim-

ple regression-based approaches which do not allow for both
direct and indi-
rect/mediated social learning processes.
What progress has been made to investigate the processual
features of SLT
has largely focused exclusively on offending or deviant
behavior such as sub-
stance use. However, SLT has begun to be found to be a viable
explanation of
victimization as well and less often, of gendered processes in
IPV. Although
research is scant, social learning variables have been associated
with stalking
(Fox et al., 2011) and IPV (Cochran et al., 2011; Sellers et al.,
2003). Sellers
and colleagues (2003) tested both the efficacy of SLT to explain
IPV perpe-
tration and the gender invariance of SLT. They found that SLT
could account
for IPV perpetration but that several of the SLT measures were
not gender
Powers et al. 7
invariant. Similarly, Cochran and colleagues (2011) tested the
efficacy of
Akers’s SLT against self-report data on repetitive intimate
partner victimiza-
tion; they found that for both male and female victims,
repetitive IPV victim-
ization was associated with both differential association and
differential
reinforcement. However, they did not test for gender invariance.

Moreover,
neither the Sellers and colleagues (2003) study nor the Cochran
and col-
leagues (2011) study employed an SEM approach that would
have permitted
them to examine both the direct and indirect effects of SLT
variables on IPV
and the reciprocal effects of IPV on the SLT process.
The moderating effects of gender on IPV perpetration and
victimization
have been more fully articulated and explored in the IGT
literature. Several
studies have found that the conclusions regarding the influence
of experienc-
ing or witnessing violence in the home are contingent on the
gender of the
child or the parental aggressor, which suggests that there are
gendered pro-
cesses in the transmission of violence. Whereas many have
found that both
male and female children are adversely impacted by exposure to
or experi-
ences of violence in the home, some have found that this
relationship holds
only for men (Alexander et al., 1991) or women (Douglas &
Straus, 2006).
Marshall and Rose (1988) found that experiencing child abuse
was correlated
with both IPV perpetration and victimization for men, but only
victimization
for women.
Others have attempted to disentangle this relationship by
focusing on the
gender dynamics of the parental relationship. Jankowski,

Leitenberg,
Henning, and Coffey (1999) suggested that the gender of the
child and the
parental aggressor interact such that vicarious victimization
increased the
likelihood of later IPV perpetration only when the child and
parent were of
the same sex. Likewise, Laporte, Jiang, Pepler, and
Chamberland (2011)
found that although both male and female teens who
experienced child abuse
were more likely to perpetrate IPV, these effects were stronger
for men in
general and strongest for men who experienced abuse by their
fathers.
In sum, SLT provides more causal mechanisms and perhaps
more explic-
itly articulated mechanisms for explaining how experiencing or
witnessing
violence in the home leads to later IPV perpetration. However,
it has rarely
been extended to examine IPV victimization. Furthermore, the
theory
assumes gender invariance. IGT, on the contrary, has been
extended to vic-
timization and provides compelling evidence that these learning
processes
may be gendered. The present study draws from the theoretical
basis of IGT
as well as the empirical literature to examine the utility of SLT;
it tests the
efficacy of SLT to explain IPV victimization; it tests for gender
invariance in
the SLT process; and it examines the direct, indirect, and
reciprocal relation-

ships between SLT constructs and IPV through an SEM analytic
approach.
Method
Data
The data for this study were gathered through a self-
administered survey
of students attending a large urban university in Florida. The
students were
surveyed in graduate and undergraduate classes randomly
selected from
the course offerings of five colleges (Arts and Sciences,
Business
Administration, Education, Engineering, and Fine Arts) during
the first 4
weeks of the spring 1995 semester. Courses were sampled from
each col-
lege in proportion to the enrollments each college contributed to
the uni-
versity’s total enrollment. This sampling strategy targeted a
total of 2,500
students; however, absenteeism on the day of the survey and
enrollments
of students in more than one sampled course produced an
overall response
rate of 73%. The current study is based on those students who
completed
the questionnaire, who report being currently involved in an
intimate rela-
tionship (i.e., married or dating), and who also report having

had at least
one previous serious relationship (n = 1,124). The
sociodemographic pro-
file of the sample was very similar to that of the total
enrollment at the
university. Importantly, these data, unlike most other self-
reported data
collections, were specifically designed to examine the efficacy
of Akers’s
SLT on IPV. Finally, while these self-report data are derived
from a sample
of college students, it is noteworthy that a substantial number of
the
respondents were married or cohabiting, and as we report
below, the prev-
alence and frequency of IPV among the students sampled was
quite
substantial.
Measures
Dependent variable: IPV. The dependent variables used in this
study were
latent constructs developed from a single set of measures of
self-reported
intimate partner violence victimization (IPV-V) by one’s
current partner: a
total scale composed of eight items and a subscale of the five
more serious/
injurious items. All are drawn from the physical aggression
items in Straus’s
(1979) Conflict Tactics Scale (CTS)—The data were collected
prior to the
development of the CTS-II. Specifically, respondents were
asked with regard
to their current marital or dating relationship how many times

their partner
had done any of the following eight acts of IPV: (a) threw
something; (b)
pushed, grabbed, or shoved; (c) slapped; (d) kicked, bit, or hit
with a fist; (e)
hit with something; (f) beat up; (g) threatened with a knife or
gun; and (h)
used a knife or gun. Responses to these items were never, once
or twice, 3 to
5 times, 6 to 10 times, 11 to 20 times, and 21 or more times,
coded from 0 to
Powers et al. 9
7. Identical constructs were also constructed for victimization
by a previous
romantic partner. These were employed as exogenous variables
and provide
a method for assessing the reciprocal/feedback effects of prior
victimization
experience on the social learning process.
Independent variable: Social learning constructs. The
independent variables in
this study are first- or second-order latent constructs
representing each of
Akers’s four social learning concepts: differential association,
imitation, defi-
nitions, and differential reinforcement. We endeavored to
measure the con-
structs using items and scales derived near exactly as they were
measured by
Akers and colleagues (1979), though modified to reflect IPV
rather than ado-

lescent substance use.
Differential association is a second-order latent construct
comprised of a
single-item measure of the respondents’ estimation of the
proportion of their
best friends who had been physically victimized by a romantic
partner (1 =
none or almost none, 2 = less than half, 3 = more than half, and
4 = all or
almost all), and two first-order latent constructs. The first of
these first-order
latent constructs is comprised of four items measuring mother’s,
father’s,
partner’s, and best friend’s attitudes toward IPV. For these
items, respondents
were asked to indicate to what degree each of these significant
others would
approve/disapprove of the use of physical violence against a
partner (1 =
strongly disapprove to 4 = strongly approve). The second of
these two first-
order latent constructs used to constitute differential association
is composed
of five indicators of physical violence used against significant
others.
Specifically, respondents were asked to indicate how often their
mother,
father, siblings, other family members, and best friends were
victims of IPV
(1 = never, 2 = seldom, 3 = usually, and 4 = always).
Imitation is measured by a first-order latent construct
comprising seven
different admired role models the respondent had actually seen
being physi-

cally victimized (i.e., hit, slapped, kicked, or punched) by an
intimate partner
during a disagreement. These admired models included actors
on television
or in movies, mother, father, siblings, other family members,
friends, and
others.
Definitions is another second-order latent construct comprising
a single-
item measure of respondents’ own approval/disapproval of the
use of physi-
cal violence against a partner (1 = strongly disapprove to 4 =
strongly
approve), and three first-order latent constructs. The first of
these three first-
order latent constructs is a two-item measure of respondents’
attitudes favor-
able toward the violation of the law in general and indicated by
the extent to
which respondents agreed/disagreed with the following Likert-
type state-
ments (1 = strongly agree to 5 = strongly disagree): “We all
have a moral duty
to abide by the law” (reverse coded) and “It is okay to break the
law if we do
not agree with it.” The next of these three second-order latent
constructs rep-
resents definitions approving of IPV indicated by three Likert-
type state-
ments (e.g., “It is against the law for a man to use violence

against a woman
even if they are in an intimate relationship”). Finally, the third
of these first-
order latent constructs measures neutralizing definitions and is
composed of
responses to three Likert-type statements (e.g., “Physical
violence is a part of
a normal dating/marital relationship”).
The last social learning construct, differential reinforcement, is
a second-
order latent construct comprised of two first-order constructs
and two single-
item measures. First, respondents reported the actual or
anticipated reaction
of three different sets of significant others (i.e., parents, other
family mem-
bers, and best friends) to the respondent’s physical
victimization by their
partner. Respondents indicated that these significant others
would either 1 =
disapprove and report to the authorities, 2 = disapprove and try
to stop it,
3 = disapprove but do nothing, 4 = neither approve nor
disapprove, and 5 =
approve and encourage it. Second, a single 3-point, ordinal
measure of the
overall balance of reinforcement for partner violence was
included. This item
measured the respondent’s perception of the usual or anticipated
net outcome
from being victimized by their current partner (1 = mostly bad,
2 = about as
much good as bad, and 3 = mostly good). Third, the net
rewards-to-costs of
being physically victimized by their partner was measured by

asking respon-
dents to indicate which, if any, of five social and nonsocial
rewards and seven
social and nonsocial costs they associated with IPV by their
current partner.
An example of a reward was “It showed me my partner really
loved me,” and
an example of a cost was “My friends criticized me.” To
compute the net
rewards-to-costs, the sum of the identified costs was subtracted
from the sum
of the identified rewards; this produced a measure with values
ranging from
−7 (all costs and no rewards) to 5 (no costs and all rewards).
The results from
the full confirmatory factor analysis (CFA) are included in the
appendix.
Analytic Strategy
Considering that the purpose of the current study is to examine
the direct and
indirect effects of various components of the social learning
process on IPV
victimization, the most appropriate analytical technique is SEM.
Following
the two-step process (see Kline, 1998), we first develop and test
a measure-
ment model using CFA. Following the recommendations of
Hoyle and Panter
(1995), we report several fit indices (i.e., χ2, standardized root
mean square
residual [SRMSR], root mean square error of approximation
[RMSEA], and
comparative fit index [CFI]). We follow the recommended
general criterion

Powers et al. 11
values for fit statistics (e.g., Hu & Bentler, 1995). Following
the measure-
ment model, we test the structural model. Our structural models
proceed in
two phases, examining how SLT predicts (a) IPV victimization
by one’s cur-
rent partner and (b) the extent to which the social learning
model of IPV
victimization is gender invariant (measurement models available
upon
request).
Although no variable had more than 15% missing, using the
standard list-
wise deletion procedure would have resulted in approximately
30% attrition.
Markov Chain Monte Carlo (MCMC) simulation was used to
impute 10 data-
sets to fill in missing values. All models were estimated in
MPlus 7.4 using
the weighted least squared Muthen version (WLSMV) estimator
to account
for the limited nature of the indicators (Rhemtulla, Brosseau-
Liard, &
Savalei, 2012).
Results
The baseline social learning model is presented in Figure 1. It
allows for an
examination of both direct and indirect effects of the

theoretically expected
paths between the social learning constructs and IPV
victimization.
Importantly, it also controls for the anticipated feedback or
reciprocal effects
of IPV victimization on the social learning constructs as
expressed by the
effects of IPV victimization by one’s past partner on the social
learning pro-
cess that predicts IPV victimization by one’s current partner.
Overall, the model fit the data (χ2 = 660.94; SRMSR = .0120;
RMSEA =
.0009; CFI = .999). The results are somewhat mixed with regard
to SLT’s
ability to explain intimate partner victimization. First, the
model accounts for
only about 15% of the variance in IPV victimization by one’s
current partner.
While such a value is not uncommon in tests of many micro-
social theories
of criminal/deviant behavior, it is well below the R-square
values typically
observed in tests of Akers’s SLT. Second, we observe
significant direct effects
on victimization by one’s current partner in the theoretically
expected direc-
tion for only two of the four social learning constructs:
differential associa-
tion (b = .13; p < .05) and differential reinforcement (b = .12; p
< .001).
Conversely, we observed a nonsignificant effect for definitions
(b = .04; ns)
and an inverse effect for imitation (b = −.06; p < .05).
Differential association, as expected, also exerted direct effects

on all
three of the other social learning constructs (b = .30; p < .001
on differential
reinforcement; b = .21; p < .001 on definitions; and b = .22; p <
.001 on imita-
tion). These effects support a partially mediated, indirect effect
of differential
association on intimate partner victimization through
differential reinforce-
ment (b = .04; p < .05), a small spurious component to its total
effect via the
definitions construct (b = .008; p < .05); and an unexpected
diminution of its
total effect due to imitation (b = −.01; p < .05) which, as noted
above, is
inversely associated with IPV victimization.
In addition to the social learning constructs, it is worth noting
that IPV
with a past partner (included in the model as a proxy for the
expected recipro-
cal/feedback effects of behavior on the social learning process)
is a signifi-
cant predictor of current partner victimization (b = .16; p <
.001) and all of
the other social learning constructs with the exception of
definitions (b = .17;
p < .001 on differential association; b = .25; p < .001 on
differential reinforce-
ment; b = .04; p < .001 on imitation; and b = −.00; ns on
definitions). The

results suggest that being the victim of IPV perpetrated by a
prior partner
seems to influence a person’s differential association and
differential rein-
forcement. Hence, being the victim of prior IPV also
significantly, though
Figure 1. Baseline social learning model of IPV victimization
by one’s current
partner.
Note. Model fit: χ2(1,015) = 660.94; SRMSR = .0120; RMSEA
= .0009; CFI = .9987. Total
variance explained by model = 15.4%. IPV = intimate partner
violence; SRMSR = standardized
root mean square residual; RMSEA = root mean square error of
approximation; CFI =
comparative fit index.
*p < .05. **p < .01. ***p < .001.
Powers et al. 13
indirectly through differential association and differential
reinforcement,
increases the likelihood of also being the victim of IPV in their
current rela-
tionship (b = .02; p < .05 and b = .03, p < .001, respectively). In
addition,
there are more distal indirect effects of past partner intimate
partner victim-
ization that are transmitted through differential association to
all of the other
social learning constructs onto current partner IPV
victimization. These distal
indirect effects are theoretically consistent for both differential

reinforcement
(b = .006; p < .05) and definitions (b = .001; p < .05), but
theoretically incon-
sistent for imitation (b = −.002; p < .05).
Gendered Models
To determine whether the social learning model operates
differently for
men and women, we conduct a series of tests to examine model
invariance
across gender. This process involves estimating a series of
separate models
for each group with progressively fewer restrictions between the
group
models. While it is desirable to modify the measurement model
and the
structural model simultaneously, this was not possible in the
present study
due to the complex measurement structure of these data (i.e.,
single-item
constructs, first-order latent variables, and second-order latent
variables).
Therefore, we first test for measurement model invariance
between men
and women independently and then turn our attention to the
invariance of
the structural model. Testing model invariance is done by
comparing the
Δχ2 and the ΔCFI between the less restrictive model and the
more restric-
tive model (Byrne, 2010).
The test of invariance in the measurement model looks
specifically at
whether items measure the same construct in the same manner

for members
of both groups—here women and men. Starting with the most
restrictive
model, assuming the items measure the latent traits the same in
both men and
women, we see the model fits the data well (χ2 = 2,400.76;
SRMSR = .0549;
RMSEA = .0547; CFI = .9843). Only the model in which all
parameters were
allowed to freely vary between groups fit the data almost as
well as the fully
constrained model (χ2 = 2,281.93; SRMSR = .0531; RMSEA =
.0515; CFI =
.9863); however, this effect was not a significant improvement
in model fit.
When examining the factor loadings, the results suggest that the
magnitudes
of certain intimate partner victimization experiences vary for
men and
women. Therefore, we elect to use the more parsimonious
measurement
model for two reasons. First, there is no significant
improvement in model fit
based on the change. Second, the substantive meaning of the
measures does
not change between the two groups—rather the magnitudes of a
small num-
ber of factor loadings vary between genders.
Next, we examine the potential for model invariance between
men and
women in the structural component of the model. Again, we

start with the
most restrictive model that assumes the causal process and the
magnitudes of
the effects are the same across gender. This model fits the data
poorly (χ2 =
9,149.20; SRMSR = .0918; RMSEA = .122; CFI = .503), which
suggests
there are likely substantial differences in the social learni ng
processes of inti-
mate partner victimization between men and women. Working
through the
iteration of model constraints, we find the best fitting model to
be the one
where all values are allowed to vary freely between genders (χ2
= 2,768.19;
SRMSR = .0462; RMSEA = .035; CFI = .994). Hence, Akers’s
SLT is not
gender invariant with regard to its ability to predict and explain
intimate part-
ner victimization.
The results of this model are presented in Figure 2. This figure
depicts the
same pooled model estimated previously, but now independently
presenting the
Figure 2. Gendered social learning model of IPV victimization
by one’s current
partner.
Note. Model fit: χ2(1,273) = 2768.19; SRMSR = .0462; RMSEA
= .035; CFI = .994. Total
variance explained by model = 24.5%. IPV = intimate partner
violence; SRMSR = standardized
root mean square residual; RMSEA = root mean square error of
approximation; CFI =
comparative fit index.

*p < .05. **p < .01. ***p < .001.
Powers et al. 15
parameter estimates for men and women. Unlike the results
from the pooled
model, we see varying degrees of support for the social learning
constructs’ abil-
ity to directly explain intimate partner victimization with a
current partner. The
only direct effect of a social learning construct on current
partner victimization
that is significant for both men (b = .15; p < .05) and women (b
= .11; p < .05) is
differential reinforcement. Interestingly enough, there are
different predictors of
intimate partner victimization between the genders. Differential
association,
which tends to be one of the most robust of the social learning
constructs, is
significant for women (b = .17; p < .05), but not for men (b =
.14; ns).
Furthermore, we note theoretically inconsistent effects for
imitation for both
genders. The direct effect of imitation for men is significant and
negative (b =
−.31; p < .05), whereas the effect for women is nonsignificant
(b = −.03; ns).
Additional evidence of gendered social learning effects is seen
in the direct
effect of definitions, which exerts a theoretically expected
effect for women (b
= .06; p < .05) and an inconsistent effect for men (b = −.06; ns).

Although there are inconsistent findings in the direct effects
between the
social learning constructs and intimate partner victimization
with a current part-
ner, there are more theoretically consistent indirect effects of
differential asso-
ciation through the other social learning constructs across
gender. Specifically,
for men, we see the expected indirect effects of differential
association through
differential reinforcement (b = .066; p < .05) and imitation (b =
.078; p < .05),
although the effect through definitions, while significant, is
theoretically unex-
pected (b = −.025; p < .05). For women, we see theoretically
expected indirect
effects of differential association through both differential
reinforcement (b =
.02; p < .05) and definitions (b = .008; p < .05); conversely, we
see a theoreti-
cally unexpected yet significant indirect effect for imitation (b
= −.006; p < .05).
The feedback effect of victimization by a past intimate partner
on victimiza-
tion by one’s current partner is substantially stronger for men (b
= .48; p < .05)
than for women (b = .06; p < .05). Furthermore, we again see
there are more distal
indirect effects of being the victim of prior IPV through
differential association
and then through other social learning constructs. Again, these
patterns are con-
sistent with the prior results. Specifically, the distal indirect
effect through dif-
ferential reinforcement is theoretically anticipated for both men

(b = .01; p < .05)
and women (b = .003; p < .05). However, the nature of this
distal indirect effect
through imitation (bMen = −.01; p < .05; bWomen = −.001; p <
.05) and definitions
(bMen = −.004; p < .05; bWomen = .001; p < .05) are
inconsistent between genders.
Supplementary Analyses
Because our measure of intimate partner victimization includes
several items
that could be deemed less serious or less injurious, we
replicated the baseline
and gendered models present with models in which the latent
variable for IPV
victimization is restricted to the five more serious forms of IPV
(i.e., slapped;
kicked, bit, or hit with a fist; hit with something; beat up;
threatened with a
knife or gun; used a knife or gun). By and large, the findings
from these
supplementary analyses mirror those reported above (results
available upon
request). That is, we observed somewhat mixed support for
SLT’s ability to
effectively predict IPV victimization, and, more importantly, we
also
observed that the social learning process of IPV victimization
is, again, not
gender invariant.

Also, because IGT stresses the unique effects of early childhood
expo-
sure to violence in the family of orientation on subsequent
perpetration
and/or victimization in one’s family of procreation, we also
elected to
test a pure IGT model on IPV victimization (see Wareham et al.,
2009a,
2009b). This model (available upon request) restricted the
components
of the latent variables to only those associated with mother’s
and father’s
use of IPV witnessed by our subjects, mother’s and father’s
supportive
definitions of IPV, role modeling of IPV, and rewards and
punishments
for subject’s IPV on subjects’ definitions of IPV and their IPV
victimiza-
tion. Several findings are especially noteworthy. First, the
model restrict-
ing attention to parental influences on IPV victimization did not
fit the
data nearly as well as the full social learning models presented
herein
(e.g., R2 of .08 for the IGT model vs. .15 for the SLT model).
Second, the
parameter estimates for the effects of these latent constructs
were con-
siderably weaker and less likely to attain statistical significance
than
were their parallel effects from the more complete SLT model.
Notable
among these was the very weak, inverse, and nonsignificant
effect of
witnessing parents’ use and role modeling of IPV in childhood

(b = −.02;
p > .05).
Discussion
The findings are rather mixed with regard to SLT’s ability to
explain and
predict IPV victimization. On one hand, the theory does seem to
work as
expected. There was empirical support for most of the pathways
articulated
in the theory. Likewise, the fully explicated social learning
model fit the data
considerably better than the IGT model assessed in our
supplementary analy-
ses. As a test of the scope of Akers’s SLT, these results do
establish that the
theory can be extended to effectively explain and predict IPV
victimization
as well as perpetration. With regard to whether these
relationships are gender
invariant, in both of the gendered models examined for the
effects of social
learning variables on IPV victimization, we found more support
for fully
Powers et al. 17
gendered pathways for the social learning processes of male and
female IPV
victimization.
These results ultimately underscore the importance of
consideration of

gendered pathways for IPV and challenge traditional
criminological theories
to address how and why processes that lead to perpetration and
victimization
may differ for men and women. For example, Schwartz and Pitts
(1995) inte-
grated feminist theory into routine activities theory (RAT) to
explain how
college campuses are criminogenic for violence against women
perpetrated
by men. By focusing on the motivation for violence among
college men, they
challenge traditional criminological theory while elucidating
specific pro-
cesses that lead to violence against women. For SLT as applied
to IPV in
particular, SLT may benefit from the explicit acknowledgment
of how these
same macro-level cultural values in a patriarchal society
influence individual
learning processes. For example, drawing from DeKeseredy and
Schwartz’s
(1993) modified male peer support theory, differential
association with men
who support values that condone violence against women likely
impart these
values, which leads to an increase in the likelihood of
perpetration. Likewise,
abuse and toxic masculinity is reinforced by these same peer
groups. More
broadly, as men and women both live in a patriarchal society,
these cultural
values excuse or legitimize the use of violence in intimate
partnerships.
Therefore, although SLT is aptly suited to more fully articulate
the learning

processes of IPV victimization, given the unique nature of IPV,
SLT would be
strengthened by the integration of feminist theories that situate
offending and
victimization experiences in a larger cultural context.
Implications for Future Research
These analyses provide new avenues for research in this area.
For example,
in the current study, differential association was only directly
related to vic-
timization for women and imitation was negatively related to
victimization
for men. This finding is at first counterintuitive; however, as
Bandura (1973)
suggested, high status role models may influence the social
learning process
more, and some research on IGT suggests that the gender of the
parental
aggressor matters in that a same-sex parent exerts a more
meaningful influ-
ence on the child’s later adult behavior (e.g., Jankowski et al.,
1999). These
same dynamics may also influence social learning processes for
victimiza-
tion. For example, in the current study, it is not known what
proportion of
their friends who are victims are of the same gender as the
respondent.
Likewise, although our ancillary models isolated more severe
forms of IPV
in terms of the likelihood of injury as the outcome, we were not
able to
explore the severity of violence among the respondents’ family,
friends, and

role models. Therefore, future research should disentangle
which role models
are most salient in shaping risk as well as how the type of
violence experi-
enced in one’s social network shapes IPV experiences for
victims.
Future research should also explore how the processes and
mecha-
nisms of SLT differ among those who co-share the role of
victim and
offender, as the victim–offender overlap with regard to IPV has
been
observed in several studies (e.g., Lauritsen et al., 1991). It is
possible that
some of these processes, particularly those related to
neutralizing defini-
tions, operate differently for those who are in mutually
combative rela-
tionships. It is important to examine not only whether those who
are
victims may also be considered perpetrators, but also the
context of the
perpetration, as some have suggested that women often use
violence in
self-defense or in anticipation of victimization (Allen, Swan, &
Raghavan,
2009).
Another distinction in the type of domestic violence is more
difficult

to disentangle, but equally important. Johnson (1995) suggested
that
although there may be relative gender symmetry with regard to
overall
likelihood of domestic violence, there are different types of
domestic vio-
lence: common couple violence (CCV), intimate terrorism (IT),
violent
resistance (VR), and mutual violent control (MVR) (Johnson &
Ferraro,
2000). These distinctions are important for a number of reasons.
First,
women are more likely than men to be victims of IT, which
often involves
not only physical aggression but also psychological abuse.
Therefore, the
reinforcement process and the definitions that condone or
neutralize these
behaviors may operate differently on the aggregate between men
and
women. Although research has explored these ideas with regard
to perpe-
tration and has found that IT and CCV perpetrators differ in
their attitudes
toward women (see Johnson, 2006; Johnson & Ferraro, 2000),
research
has not thoroughly explored the role of these factors for
victims. Second,
the processes that tap into observed violence or knowledge of
violence
between parents, friends, and role models may differ continge nt
on
whether that violence featured coercive control.
Implications for Policy and Prevention/Intervention Strategies

Differential association (i.e., knowing others who are
victimized, parents’
attitudes toward IPV) predicts differential reinforcement (i.e.,
perceptions
of others’ reactions to IPV, cost/benefit analysis of IPV) for
both men and
women. Likewise, differential reinforcement impacts risk of
victimiza-
tion. Taken together, this suggests that the risk of entering and
remaining
in a violent relationship is shaped the experiences and perceived
reactions
Powers et al. 19
of close family and friends. Research has long recognized the
role of the
attitudes of significant others in shaping risk of perpetration
and the
micro-level (e.g., peer groups) and macro-level (e.g., cultural
norms)
influences on those attitudes, albeit the focus has been
predominately on
violence against women (see Flood & Pease, 2009). In addition,
research
has pointed to the role of positive social support from family
and friends
on victims’ recovery (e.g., Coker et al., 2002) and in the
decision-making
process to leave a violent relationship (Miller et al., 2012). This
suggests
that prevention programs that focus on changing social norms
surround-
ing IPV and explicitly focusing on how informal social

networks can sup-
port healthy relationships and remove barriers to leaving violent
relationships may be effective.
Likewise, the response-cost of staying in abusive relationships
can be
directly addressed through an increase in victim services and
punishments for
offending. Women who are victims of IPV do not lack agency,
rather they
make decisions based on the perceived costs and benefits of
leaving. Choice
and Lamke (1997) suggested that conceptually, these decisions
fall under one
or both domains: “Can I do it?” and “Will I be better off?” The
role of the
criminal justice system is to remove the structural and personal
barriers to
leaving (e.g., fear of retaliation, financial burden) so that
women are able to
leave abusive relationships.
Furthermore, there is psychological variation in the experiences
of IPV
victims, and this has implications for intervention strategies.
For example,
Lerner and Kennedy (2000) found that coping, trauma, and self-
efficacy
play a role in the decision-making processes of female IPV
victims. These
likely interact with learning processes, particularly in terms of
perceptions
of response-cost. This suggests that practitioners should
acknowledge
variation in women’s experiences and tailor intervention
strategies accord-

ingly. Likewise, intervention strategies should focus on not only
removing
tangible barriers to leaving an abusive relationship but also
fostering cop-
ing skills and self-efficacy to empower women who choose to
leave.
Limitations
The mixed results with regard to Akers’s SLT, particularly
those most at
odds with previous tests of the theory, suggest that the present
study may
be hampered by a number of possible limitations. For instance,
both IGT
and Akers’s SLT are processual theories that require
longitudinal data for
proper testing. The present study is restricted to cross-sectional
data, as are
most other tests of SLT. Although the investigation of both
direct and indi-
rect social learning effects through SEM and the inclusion of a
measure of
past partner IPV victimization as a surrogate measure for the
feedback/
reciprocal effect of behavior on the social learning process
mitigate this
limitation somewhat, future research should explore these
processes using
panel data.

Another limitation of these data that needs to be addressed is
the dated
nature of these data; they are more than 20 years old. While the
age of the
data should not, in most cases, be a relevant concern for tests of
a general
theory, there has be to considerable social and technological
changes that
have taken place that have had an influence on both IPV and
social learning
processes. Over this passage of time, there has been
considerable attention in
research, policy, practice, and the media on the issue of IPV,
raising social
awareness about this problem. In turn, this greater awareness
may have
altered persons’ perceptions and attitudes toward IPV in ways
much different
than they were when the college students in these data were
surveyed.
Moreover, social media now play a much more prominent role
in our daily
lives. These social media permit new ways for violent partners
to contact,
surveil, stalk, threaten, and/or terrorize their victims. At the
same time, social
media also provides victims of IPV additional outlets for social
support and
assistance.
Finally, our measures of differential association and differential
reinforce-
ment rely heavily upon respondents’ perceptions on how
significant others
were victims of IPV, their perceptions of their significant
others’ attitudes

toward IPV, and their perceptions on their significant others’
reactions to
respondent’s IPV victimization. Measurements that rely on such
perceptual
measures are prone to projection bias in measurement (see
Rebellon &
Modecki, 2014).
Conclusion
Whereas Akers’s SLT may be a more fully articulated
theoretical frame-
work that allows for a more thorough exploration of the causal
processes in
IPV victimization, these results suggest that these pathways are
highly gen-
dered. The current state of social learning research cannot fully
explain
why these processes are different for men and women. However,
SLT, cou-
pled with insights from the IPV literatures on IGT, cultural
norms surround-
ing gender, and typologies of domestic violence provide
promising avenues
of research to disentangle under what conditions these factors
matter in
shaping risk of intimate partner victimization.
Powers et al. 21
Appendix
CFA Estimates.
Latent Variable/Sublatent Variable (When Applicable)/Item

(Variable Name) Coefficient (SE)
Differential associations
Number of friends experiencing IPV (NOFRPHYV) 0.36
(.03)***
Friends and families definitions 0.59 (.05)***
Mother’s definitions of IPV (momdef) 0.49 (.03)***
Father’s definitions of IPV (daddef) 0.83 (.04)***
Best friend’s definitions of IPV (bfdef) 0.47 (.05)***
Sexual partner’s definitions of IPV (spdef) 0.32 (.04)***
Victimization experiences of friends and family 0.91 (.05)***
Mother victim of physical IPV (damaphyv) 0.76 (.03)***
Father victim of physical IPV (dafaphyv) 0.52 (.03)***
Sibling(s) victim(s) of intimate partner IPV (dasbphyv) 0.56
(.03)***
Another family member victim of physical IPV (daofphyv) 0.55
(.03)***
Best friend victim of intimate partner IPV (dabfphyv) 0.52
(.03)***
Differential reinforcement
Rewards minus costs for IPV victimization (R − C) 0.30
(.03)***
Usual result of IPV victimization (VOUTCOME) 0.62 (.06)***
Reactions to violence 0.52 (.05)***
Parents reactions to IPV victimization (PARREACP) 0.90
(.01)***
Friends reactions to IPV victimization (FRREACP) 0.80

(.01)***
Other relatives’ reactions to IPV victimization (ORREACP)
0.90 (.01)***
Imitation
Ever seen someone you admire. . .
Actor victim of IPV (imacphyv) 0.34 (.04)***
Father victim of IPV (imfaphyv) 0.31 (.04)***
Mother victim of IPV (immaphyv) 0.34 (.04)***
Siblings victim of IPV (imsbphyv) 0.51 (.05)***
Another relative victim of IPV (imorphyv) 0.35 (.04)***
Friends victim of IPV (imfrphyv) 0.42 (.04)***
Another admired person victim of IPV (imotphyv) 0.35 (.04)***
Definitions
Index of participant’s own definitions of IPV (OWNDEF) 0.64
(.05)***
General definitions of law abiding 0.71 (.03)***
We all have a moral duty to abide by the law (lawabid1—
reversed)
0.41 (.05)***
It’s okay to break the law if we do not agree with it (okbrlaw)
0.42 (.02)***
(continued)

Laws controlling violence, even in relationships, should be
obeyed (lawobey)
0.70 (.02)***
Neutralizing definitions 0.71 (.05)***
Physical violence is a normal part of a dating relationship
(violnorm—reverse)
0.50 (.02)***
I believe victims provoke physical violence (victprvk—reverse
coded)
0.49 (.03)***
In dating relationships, physical abuse is never justified
(abusnev) 0.61 (.02)***
Specific definitions 0.74 (.05)***
It’s illegal for a man to use violence against a woman, even in a
relationship (mviolw)
0.80 (.02)***
It’s illegal for a woman to use violence against a man, even in a
relationship (wviolm)
0.85 (.01)***
Past partner victimization

How many of your partners in prior committed
relationships have. . .
Threw, smashed, hit, or kicked something (vppthrew) 0.85
(.01)***
Pushed, grabbed, or shoved you (vpppush) 0.78 (.01)***
Slapped you (vppslap) 0.83 (.01)***
Kicked, bit, or hit you with fist (vppkick) 0.89 (.01)***
Hit or tried to hit you with something (vpphit) 0.90 (.01)***
Beat you up (vppbeat) 0.53 (.02)***
Threatened you with a knife or gun (vppthrgun) 0.35 (.02)***
Used a knife or gun against you (vppgun) 0.28 (.02)***
Current partner victimization
How many times has your current partner. . .
Threw, smashed, hit, or kicked something (vcpthrew) 0.67
(.02)***
Pushed, grabbed, or shoved you (vcppush) 0.70 (.01)***
Slapped you (vcpslap) 0.81 (.01)***
Kicked, bit, or hit you with fist (vcpkick) 0.81 (.01)***
Hit or tried to hit you with something (vcphit) 0.87 (.01)***
Beat you up (vcpbeat) 0.44 (.02)***
Threatened you with a knife or gun (vcpthrgun) 0.51 (.02)***
Used a knife or gun against you (vcpgun) 0.26 (.03)***
Covariance terms
momdef, bfdef 0.07 (.03)*

daddef, bfdef −0.21 (.11)*
Appendix (continued)
(continued)
Powers et al. 23
daddef, spdef −0.19 (.08)*
damaphyv, daofphyv −0.17 (.05)**
damaphyv, dabfphyv −.034 (.06)***
dafaphyv, dabfphyv −.0.17 (.04)***
dasbphyv, daofphyv 0.08 (.03)*
imacphyv, imorphyv 0.78 (.01)***
imfaphyv, immaphyv 0.28 (.03)***
OWNDEF, General Defs. 0.37 (.03)***
OWNDEF, Neutral Defs. 0.41 (.03)***
OWNDEF, Specific Defs. 0.23 (.03)***
General Defs., Neutral Defs. 0.95 (.03)***
General Defs., Specific Defs. 0.68 (.03)***
Neutral Defs., Specif Defs. 0.61 (.03)***
vcpthrew, vcppush 0.19 (.03)***
vcpthrew, vcphit 0.27 (.03)***
vcpslap, vcphit −0.29 (.05)***
vcpslap, vcpbeat 0.23 (.03)***

vcpbeat, vcpthrgun −0.20 (.03)***
vppslap, vppbeat 0.22 (.02)***
vpphit, vppbeat 0.13 (.03)***
vpphit, vppgun 0.06 (.02)***
vppbeat, vppthrgun 0.26 (.02)***
vppbeat, vppthrgun 0.36 (.02)***
vppbeat, vppgun 0.36 (.02)***
vppthrgun, vppgun 0.73 (.01)***
Cur. Part. Vic., Past Part. Vic. 0.30 (.02)***
Note. χ2(1,027) = 698.51; SRMSR = 0.009; RMSEA = 0.004;
CFI = 0.9989. Items in bold
and left justified are latent constructs; those in bold, italicized,
and indented are sublatent
constructs; and right justified items are observed variables. Text
in parentheses are variable
names with reversed = items that were reverse coded. All
covariance terms refer to the
error/disturbance term associated with the variable. CFA =
confirmatory factor analysis;
IPV = intimate partner violence; SRMSR = standardized root
mean square residual; RMSEA =
root mean square error of approximation; CFI = comparative fit
index.
*p < .05. **p < .01. ***p < .001.
Appendix (continued)
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research,

authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research,
authorship, and/or publi-
cation of this article.
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Author Biographies
Ráchael A. Powers, PhD, is an associate professor in the
Department of Criminology
at the University of South Florida. Her research interests
surround violent victimiza-

tion including intimate partner violence, sexual violence, and
hate crime. She has
been published in Justice Quarterly, Journal of Interpersonal
Violence, and Child
Abuse & Neglect, among other outlets.
John K. Cochran is a professor of criminology at the University
of South Florida. He
earned his PhD in sociology at the University of Florida (1987).
He has more than 100
peer-reviewed manuscripts, most of which involve tests of
micro-social theories of
criminal behavior and macro-social theories of crime and crime
control. His current
research interests involve tests of micro-social theories of
criminal behavior. He is
also continuing his work on issues associated with the death
penalty.
Jon Maskaly is an assistant professor in the Criminology
Program at the University
of Texas at Dallas. He received his doctorate from the
University of South Florida.
His research interests are in quantitative applications to test
criminological theory,
communities and crime, and law enforcement. His recent
publications have appeared
in Social Science Research, Crime & Delinquency, and the
American Journal of
Criminal Justice.
Christine S. Sellers is professor and director of the School of
Criminal Justice at
Texas State University. She is coauthor with Ronald Akers and
Wesley Jennings on
Criminological Theories: Introduction, Evaluation, and

Application, now in its sev-
enth edition. Her research interests include criminological
theories and the role of
gender in the explanation of criminal and delinquent behavior.
Running head: PORTFOLIO PROJECT
1
PORTFOLIO PROJECT
2
Portfolio Project
Name
Institution
Introduction
The exponential growth in the amount of information generated,
analyzed, and stored in the banking sector has led to the need
for establishing a dedicated information governance program.
This portfolio project is a proposal for the implementation of
information governance program in a bank. The banking sector
has been collecting vast amount of data which is stored either in
hardcopies or electronic forms. Customer data stored in rational
databases has been experiencing challenges due to lack of
administration which compromises data integrity issues. The
company has been experiencing duplication of customer data
due to lack of administration. The company also lacks policies
that can be used to address the issue of handling customer and
business data. The company is interested in utilizing the social
media in leveraging its marketing power but it lacks the legal
issues and policies needed.

The bank should consider developing and implement the
information governance program which will facilitate numerous
benefits to the business and customers. Information governance
will enable the company to comply with existing laws and avoid
litigation activities. The banks will be in a position to obtain
tangible cost saving and storage utilization since unnecessary
data will be eliminated from the system (Najjar, Alharbi, &
Fasihuddin, 2020). The information governance program should
be able to identify information that is valuable and determine
storage media that will be required in managing and processing
the data. The company should develop and implement effective
information governance program that will promote business
agility as well as profitability. Information governance program
will enable the company to understand the value of collected
data and enable the company to set in place procedures and
processed to access it securely when required (Faria, Macada, &
KUmar, 2013). Thus, the program will enable the company to
turn the generated date into valuable information for the
business since information governance will set procedures and
policies that allow secure access to authorized persons and
ensure compliance with existing regulation.
Information governance will also facilitate cost reduction of
information administration and storage. The program will
enable the company to have few version of information and
develop an automated deletion and archival policy that will save
the company’s data storage and IT Infrastructure (Wingard,
2021). Some of the information many not be essential to the
company yet it is still stored and consuming space. Information
governance will help in identifying and eliminating unnecessary
data to increase storage and reduce cost.
The program will also promote collaboration between the
employees, partners and customers. It will create and manage a
secure environment to increase collaboration. Information
governance will establish policies and procedures that will

leverage the collaboration platform to maximize business value
and minimize risks. This will increase chance of the company
identifying how it can promote its use of social media in
leveraging its marketing power.
As a result, this paper is a proposal from the implementation of
information governance program in the bank. It will discuss the
strategies of implementing the information governance in the
banking sector. It contains a literature review of existing
practices that can be effective in the banking industry. The
strategies discussed will be effective in tracking key metrics
and data management to mitigate cases of duplicating
information. It will also help the company to identify strategies
of cleansing and converting data from the legacy to digital
format as well as determining the applicability of the data in the
business and its stakeholders. The strategy will also ensure
compliance with existing regulations to avoid litigation. The
project will propose strategies of using the social media, cloud
computing, and emails to leverage the marketing power of the
bank. The proposal will help the management in identifying key
metrics needed to track the performance and risk of the
company through the key indicators.
Annotated biography
du Fresne, A. J. (2020). Can Audits Be an Effective Method to
Improve Information Governance Compliance
Objectives? (Doctoral dissertation, The University of Findlay).
The article explores whether an appraise of the firm’s IG
package could be a valuable tool to enhance conformity and
program policy goals. The author addresses the slightest
prerequisites that must be tackled to meet an organization’s
supervisory, legal, and business requirements via procedures
and policies to enhance IG. I will draw valuable data for my
research since the article gives suggestions on ways a company
can use appraising to gauge the effectiveness of an IG program.

Laksamana, P. (2018). Impact of social media marketing on
purchase intention and brand loyalty: Evidence from
Indonesia’s banking industry. International Review of
Management and Marketing, 8(1), 13-18.
The article reviews how social media marketing impacts brand
loyalty and purchasing intention in the banking industry. The
author describes how banks have spun to social media to
provide information, sell, and foster relationships with their
clients. The author investigates whether there is a constructive
connection between purchase intention and social media
marketing. The article will be valuable in my study since it
presents findings on how banks and financial institutions can
exploit social media marketing to increase brand loyalty and
purchase intention.
Manzira, F. M., & Bankole, F. (2018, October). Application of
Social Media Analytics in the banking sector to drive growth
and sustainability: A proposed integrated framework. In 2018
Open Innovations Conference (OI) (pp. 223-233). IEEE.
The article outlines how banks have begun to tap into
progressive predictive and prescriptive analytics to obtain
insights, oversee high expenses of non-compliance and
compliance, including financial risks, thus producing a
considerable effect in business operations. The paper is relevant
to my study since it offers insights into how banking
institutions implement social media analytics to enhance their
IG and business operations.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2020). The
role of information governance in big data analytics-driven
innovation. Information & Management, 57(7), 103361.
The article's authors investigate the interaction of a company’s

“big data analytics abilities (BDACs)” and its IG undertakings
in structuring innovational abilities. This study is relevant to
my research subject since the authors give insights on the
impacts of BDAC on innovative business aptitudes. The
research used ‘self-reported data, which would result in bias,
and the study will be a valuable source of data for my research.
S. Earley. (2016). Metrics-Driven Information Governance. IT
Professional, 18(2), 17–21.
https://doi.org/10.1109/MITP.2016.26
The paper studies the essentiality of IG program metrics. The
authors suggest the crucial metrics for an efficient IG program.
The writer describes the drivers and benefits of an IG program,
including revenue optimization, risk mitigation and cost control.
My work will draw from this article on recommendations on IF
metrics and the benefits of tacking major performance and risk
indicators to accomplish the banking institution IG program.
Serrado, J., Pereira, R. F., da Silva, M. M., & Bianchi, I. S.
(2020). Information security frameworks for assisting GDPR
compliance in the banking industry. Digital Policy, Regulation,
and Governance.
The paper describes the importance of GDPR implementation
that aligns with industries policies and laws, particularly in the
banking sector. The authors argue that data could be perceived
as the critical asset of companies, and data breaches
significantly affect a company's revenues, image, and clients.
The paper will be helpful in my work in that it will expand my
understanding of various practices that could aid the banking
institutions in GDPR applications.
Scott, H. S., Campbell, D., & Gulliver, J. (2021). Regulation of
Governance & Risk Management: The Intersection of Banking
& Technology. Program on International Financial Systems.
The article evaluates the regulatory makeup for risk
management in banking institutions in the US compared to
technology firms. The authors assess the suitable supervisory

compositions for cloud service suppliers to banking institutions
since banks and financial institutions are augmenting their
dependence on cloud computing for their information needs, and
efficient management control can securely ease that transition.
The paper will help further explain the regulatory guidelines or
legal issues that could occur.
Tanwar, M., Duggal, R., & Khatri, S. K. (2015, September).
Unravelling unstructured data: A wealth of information in big
data. In 2015 4th International Conference on Reliability,
Infocom Technologies and Optimization (ICRITO)(Trends and
Future Directions) (pp. 1-6). IEEE.
The authors of this article review the significance of the
breakdown of unstructured data alongside structured data in
businesses to obtain all-inclusive understandings. The authors
outline what big data analytics is and review diverse methods
and tactics to analyze unstructured data. The research is helpful
in my study as it emphasized the need for appropriate and
efficient analytical techniques for knowledge unearthing from
vast quantities of assorted data in unstructured layouts.
Tyagi, A. (2021). Information Governance Program
Implementation and Strategies in Banking Sector. Available at
SSRN 3804620.
The article presents a suggestion of applying an IG program in
the banking industry. The author introduces IG and its
application, implementation approaches, and financial services
and banking sector issues. The author also discusses best
practices for IG system implementation in the banking industry.
The article is relevant to my study because it presents strategies
for managing data and tracking major metrics and proposes
approaches to leverage the marketing influence of cloud
computing, social media, and email technologies.
Wang, H., Ma, S., Dai, H. N., Imran, M., & Wang, T. (2020).
Blockchain-based data privacy management with nudge theory

in open banking. Future Generation Computer Systems, 110,
812-823.
The paper suggests a novel data privacy management outline
centered on blockchain technologies for the financial and
banking sector. The authors contend that the present blockchain
technologies stances some challenges in ultimately meeting the
prerequisites of financial information privacy fortification. To
tackle these presented issues, the author proposes the novel
privacy management model. This paper is relevant to my work
since it expounds on how the banking sector could use
blockchain technologies to maintain data integrity and
authenticity.
Literature review
The significance of having an excellent IG program in banking
is reinforced because numerous researches have indicated a
positive relationship between regulatory compliance and
corporate performance and an efficient IG program. (Manzira,
(2018). According to Tanwar, (2015), the effect of technology
both on products and governance practices has indicated a
positive effect on obtaining a competitive sector. This includes
the use of social media platforms. Social media platforms
generate a vast quantity of big data consisting of structured,
semi-structured, and unstructured data, commonly called
"structural heterogeneity in big data." Only 5% of data obtained
from these platforms is systematized. Information having no
specific schema, format or outline is called unstructured data
and could be any format including word files, audio, video, PDF
files, tweets, emails, and more. Further, the literature indicates
that companies need to adopt blockchain solutions to convert
the said data into actionable and meaningful insights to exploit
and capitalize this unstructured data. (Mikalef, (2020). Social
media big data has valuable information that could make a
massive distinction to the company's profits if mined entirely.
The figure below shows the types of big data that the company

will address. The figure below shows the kinds of big data that
the company will handle.
According to literature by Laksamana, (2018), social media
continues to develop and become part of everyday activities for
clients, and business has incorporated their marketing
movements into social media marketing. Therefore, banks must
use up-to-date technology to engage with clients, including
social media, which can be opened 24/7 on mobile devices.
Subsequently, as a portion of future selling endeavours and one-
on-one relations building, social media sells services and
products and develops brand loyalty. Manzira, (2018) notes that
vast quantities of data produced from social media have
increased the need for social media analytics usage in business
operations. Banking institutions should begin to tap into
progressive predictive and prescriptive analytics to cultivate
insights, optimization of business portfolios, managing
expenses of non-compliance and compliance, services and
products offering, and other practices toward accomplishi ng
maintainable and lucrative growth.
Further literature review shows that states that open banking
introduces both the openings and challenges to banking
institutions across the globe. Because of every nation's diverse
economic growth level and the interludes amongst fiscal
settings maturity, all nations have distinct approaches and
regulations concerning privacy fortification of information in
financial backgrounds, including GDPR. (Wang, (2020).
Blockchain resolutions break the long-standing rules regarding
companies' authority and trust makeups and ways in which
information and records will be generated, overseen, and
utilized. Blockchain offers a robust audit trail, thereby enabling
transactions to be administered and validated almost instantly.
According to Serrado, (2020),” GDPR compliance is not about
technology, it is about how it is used.” Whereas blockchain

might assist the business in meeting some of the GDPR's
prerequisites, including a safety by design, its application might
also stance many compliance problems, such as making sure
data subjects can apply their rights.
A literature review had indicated that data could be perceived as
the primary resource for companies, and data breaches
significantly affect the company's revenues, image, and clients.
According to Tyagi, (2021), companies should apply
information security frameworks intended to aid companies to
evolve into up-to-date domain exercises. Preceding global
research has assumed that the effect of outside monitoring on
banks' risk is indefinite. However, according to Scott, (2021),
“regulations matter for banks risk-taking conditional onboard
attributes: board independence, the board size, and board
diversity. Except for the capital needs, the market discipline
exerted by external monitoring is unable to mitigate the
propensity to higher risk-taking by banking institutions.”
The significance of tracing IG metrics in the shape of key
Indicators and Key Performance has been indicated to have a
positive relationship with the accomplishment of a company’s
IG program in several empirical researches. (S. Earley, (2016).
The literature review shows that the "metric-driven IG program
encourages cross-functional collaboration”, thus making the
program more efficient. The literature review further indicates
that the most significant difficulty for the company will be how
to establish which data to utilize and which to eliminate based
on its worth. However, more companies are concerned about the
consequences that social media data usage could incur to them.
According to Mikalef, (2020),” the company should understand
the data threats and worth so that a distinct strategy could be
derived to manage social media communication to regulate
disclosure to both consequences and brand/customer
forfeitures." The company should identify the business worth of
the data, create accountability for the threat and manage

identity, which forms the foundation for an efficient IG
framework.
References
du Fresne, A. J. (2020). Can Audits Be an Effective Method to
Improve Information Governance Compliance
Objectives? (Doctoral dissertation, The University of Findlay).
Faria, F., Macada, A., & KUmar, K. (2013). Information
Governance in the Banking Industry. 46th Hawaii International
Conference on System Sciences (pp. 4436-4446). Hawaii:
University of Hawaii .
Laksamana, P. (2018). Impact of social media marketing on
purchase intention and brand loyalty: Evidence from
Indonesia’s banking industry. International Review of
Management and Marketing, 8(1), 13-18.
Manzira, F. M., & Bankole, F. (2018, October). Application of
Social Media Analytics in the banking sector to drive growth
and sustainability: A proposed integrated framework. In 2018
Open Innovations Conference (OI) (pp. 223-233). IEEE.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2020). The
role of information governance in big data analytics-driven
innovation. Information & Management, 57(7), 103361.
Najjar, W., Alharbi, S., & Fasihuddin, H. (2020). Challenges of
IT Governance in the Financial Sector: A Study from Saudi
Arabia. TEM jornal 9(4), 1580-1588.
S. Earley. (2016). Metrics-Driven Information Governance. IT
Professional, 18(2), 17–21.
https://doi.org/10.1109/MITP.2016.26
Serrado, J., Pereira, R. F., da Silva, M. M., & Bianchi, I. S.
(2020). Information security frameworks for assisting GDPR
compliance in the banking industry. Digital Policy, Regulation,
and Governance.

Scott, H. S., Campbell, D., & Gulliver, J. (2021). Regulation of
Governance & Risk Management: The Intersection of Banking
& Technology. Program on International Financial Systems.
Tanwar, M., Duggal, R., & Khatri, S. K. (2015, September).
Unravelling unstructured data: A wealth of information in big
data. In 2015 4th International Conference on Reliability,
Infocom Technologies and Optimization (ICRITO)(Trends and
Future Directions) (pp. 1-6). IEEE.
Tyagi, A. (2021). Information Governance Program
Implementation and Strategies in Banking Sector. Available at
SSRN 3804620.
Wang, H., Ma, S., Dai, H. N., Imran, M., & Wang, T. (2020).
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