Revisiting Gender Differences: What We Know and What Lies Ahead

Abstract

Efforts to identify and understand gender differences have a long history that has sparked lively debate and generated much public interest. Although understanding gender differences is pivotal to consumer researchers and marketers, investigations into this issue by such individuals have been few in number, often weak in theory, and rather limited in progress made. This paper strives to reinvigorate such inquiry. We begin by describing four major theories of gender differences (socio-cultural, evolutionary, hormone-brain, and the selectivity hypothesis) and then assess relevant research from 2000-2013 in marketing, psychology, and biomedicine. From this, five conclusions emerge: Males are more self-oriented, while females are more other-oriented; females are more cautious responders; females are more responsive to negative data; males process data more selectively and females more comprehensively; and females are more sensitive to differentiating conditions and factors. We conclude by identifying several areas of opportunity for advancing our understanding of gender differences.

Full text

Research Review
Revisiting gender differences: What we know and what lies ahead☆
Joan Meyers-Levy ⁎, Barbara Loken
Carlson School of Management, University of Minnesota, Minneapolis, MN 55455, USA
Received 4 April 2014; received in revised form 8 June 2014; accepted 10 June 2014
Available online 18 June 2014
Abstract
Efforts to identify and understand gender differences have a long history that has sparked lively debate and generated much public interest.
Although understanding gender differences is pivotal to consumer researchers and marketers, investigations into this issue by such individuals have
been few in number, often weak in theory, and rather limited in progress made. This paper strives to reinvigorate such inquiry. We begin by
describing four major theories of gender differences (socio-cultural, evolutionary, hormone-brain, and the selectivity hypothesis) and then assess
relevant research from 2000 to 2013 in marketing, psychology, and biomedicine. From this, five conclusions emerge: Males are more self-oriented,
while females are more other-oriented; females are more cautious responders; females are more responsive to negative data; males process data
more selectively and females more comprehensively; and females are more sensitive to differentiating conditions and factors. We conclude by
identifying several areas of opportunity for advancing our understanding of gender differences.
© 2014 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.
Keywords: Gender differences; Sex differences; Information processing
Contents
Introduction ............................................................... 130
Theories of gender differences ...................................................... 131
Socio-cultural theories ........................................................ 131
Evolutionary theory ......................................................... 132
Hormonal exposure and the brain .................................................. 133
The selectivity hypothesis ...................................................... 133
Domains revealing evidence of gender differences ........................................... 134
Ethics and morality ......................................................... 134
Trust ................................................................. 135
Communion-alignedemotions:anxiety,worry,fear,andsadness ................................... 135
Emotion regulation and inhibition .................................................. 136
Sensitivity to nonverbal cues ..................................................... 136
Parental styles ............................................................ 137
Responses to promotional activity .................................................. 137
☆Our sincere thanks to Amy West, Librarian for Data Services, Economics, Psychology and the Institutes of Advanced Study and Child Development, University
of Minnesota, for her assistance in conducting all literature searches. We also thank Nick Olson and Yajin Wang, doctoral students at the University of Minnesota, for
their assistance in collecting articles and preparing this manuscript.
⁎Corresponding author.
E-mail address: jmeyers@umn.edu (J. Meyers-Levy).
http://dx.doi.org/10.1016/j.jcps.2014.06.003
1057-7408/© 2014 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.
Available online at www.sciencedirect.com
ScienceDirect
Journal of Consumer Psychology 25, 1 (2015) 129 –149

Shopping behavior .......................................................... 138
Internet usage and search behavior ................................................ 138
Online shopping .......................................................... 138
The impact of shopping with friends ............................................... 139
Simplifying decisions through intuition based heuristics ..................................... 139
Customer loyalty ......................................................... 139
Product symbolism ........................................................ 139
Sound symbolism ......................................................... 140
Competitiveness, risk, and confidence ................................................ 140
Power ................................................................. 140
Self-construal ............................................................. 141
Agency-aligned emotions: anger and hostility ............................................ 142
Sexual activities ............................................................ 143
Conclusions ............................................................... 143
Opportunities ............................................................... 144
Contribution statement .......................................................... 145
References ................................................................ 145
Introduction
It's common knowledge that companies market their
products differently to males and females. They might position
a convenience meal to working moms rather than dads,
develop luxury brand relationships online for men but employ
more personal messages for women, or develop child-targeted
advertising that focuses on different benefits for girls versus
boys. Although many factors such as expertise or interest
differentiate the genders (e.g. men may have more interest in
automotive goods and women in home furnishings), the study
of gender extends beyond such obvious differences, attempting
to understand more fundamental gender differences in, say,
processing, attention, or skills, and uncover how and when
they affect behavior. Even though male–female differences are
often small and between-gender variance is frequently smaller
than that observed within each gender, gender differences
that recur and the factors that qualify them are not only
intriguing but also frequently consequential. Gaining insight
into gender differences is important for researchers in both
psychology and marketing. For consumer psychologists,
understanding how males and females differ in their cognitive
processing styles, affective responses, and reactions to marketing
stimuli is essential for anticipating their product choices and
preferences. And such knowledge can be highly informative for
marketing practice where gender is a common building block of
the customer portfolio.
Research in psychology has produced a sizable body of
findings on gender differences as well as rich theoretical
discussion on key debates (e.g., Eagly & Wood, 2013). In
consumer psychology and marketing, the study of gender
differences has been less programmatic and robust. Although
here scattered gender studies exist, in general gender has been
treated as an interesting moderating variable and less as a
subject of theoretical inquiry. Given the importance of gender
differences across disciplines and their downstream implications
for companies, more systematic theory-based research is needed
in consumer psychology.
This article provides an overview of the main theoretical
approaches to the study of gender and reviews recent empirical
evidence of gender differences in both psychology and
marketing, with an emphasis on consumer psychology. In the
sections that follow, we first describe three theoretical
approaches that encapsulate much of the current thinking
about gender differences: the (a) socio-cultural, (b) evolution-
ary, and (c) hormone and brain science approaches. A fourth
theoretical perspective, which originated in consumer research
and was developed by the first author and a colleague, is also
described, namely the selectivity hypothesis. Most empirical
findings of gender differences can be explained by more than
one of these perspectives. Further, all approaches to gender
study now acknowledge the role of both biological (nature)
factors (e.g., physical differences, evolved traits, hormonal
influences) and socio-cultural (nurture) factors (e.g., social and
cultural role learning, stereotyping, the role of media and
marketing messages). Although the terms “sex”versus “gender”
tend to be used more in the biological versus social–psycholog-
ical literatures respectively, we use these terms interchangeably.
After reviewing the literature in areas where gender differences
are reliably observed, we offer our conclusions and identify
opportunities for advancing extant knowledge.
Our literature search included six academic journals from
the Business Source Premier database (JCR,JM,JCP,JMR,JA,
MktgSci) for the years 2000–2013, with gender-related terms
appearing in titles or article abstracts. Psychology (PsychInfo)
and health (PubMed) databases were also searched, but due
to their size, searches were restricted to meta-analyses and
reviews. We reduced the abundant publications that resulted by
assigning priority to experimental research and using our
judgment to compile a fairly comprehensive and representative
array of topics on gender differences that are of relevance to
consumer psychology.
130 J. Meyers-Levy, B. Loken / Journal of Consumer Psychology 25, 1 (2015) 129–149

Theories of gender differences
This section outlines three major and interrelated theories
that purport to explain the origins of gender differences. In our
view, these theories are more complementary than competing
accounts, for they attempt to explain the emergence of gender
differences through alternative lenses (i.e., social–psychological,
anthropological–evolutionary, and medical science) that simply
emphasize different aspects of the development process. The first,
social–cultural theory, holds that differences in the genders'
inherent physical capacities (e.g., size, strength, child-bearing
capability) prompted males and females to adopt different roles,
and this in turn gave rise to congenial cultural beliefs and
orientations (i.e., agency and communion) that have perpetuated
over time. A second, evolutionary theory, informs the former
theory by identifying adaptive programs that our early ancestors
developed in response to their environmental challenges. This
theory then explains why and shows how these programs evolved
and manifest themselves today in people's behavior. The third
theory of gender differences adds to both of the others by shedding
light on the differing hormonal makeup and brain processes of
the genders. By so doing, it provides evidence that enhances the
plausibility of the other two theories, and it offers convergent
evidence of fundamental gender differences in agency and
communion. Finally, we also discuss a fourth theory, the selectivity
hypothesis, which is silent about the origins of gender differences,
but provides an account of gender differences in information
processing. This theory contributes to the others by deepening our
understanding of gender variation in a particular domain and
identifying important boundary conditions.
Socio-cultural theories
One theoretical approach to gender differences proposes that
gender differences arise from social, cultural, psychological,
and other environmental forces. Like other gender theories,
socio-cultural theories acknowledge the roles of both biological
and learned influences. We focus here on one prominent theory
of this type, the biosocial constructionist model by Wood and
Eagly (2012). According to this theory, two factors determine
gender differences: physical differences between genders and
socio-cultural influences. The key physical differences include
women's abilities to bear and nurse children and men's greater
size, speed, and strength, which the authors argue have historically
created task efficiency differences leading to division of labor.
Childbearing and nursing of infants increased women's ability to
perform home-based activities (e.g., cooking, caring for the home)
and, given their time and energy investment in these activities,
reduced their flexibility regarding activities outside of the home.
Physical strength and size increased men's ability to obtain
resources (e.g., hunt large animals), clear land for farming, and
fight in wars. Power differences between the sexes emerged later
in more complex societies as new economically productive roles
such as accumulation of resources came about—a role dominated
more by men than women. Variations that occurred between
societies arose from developing novel solutions to local environ-
mental factors (e.g. weather, natural resources).
The gender division of labor is important because it contributes
to the formation of cultural beliefs. Cultural beliefs, or gender
roles, are shared beliefs that members of a culture hold about men
and women. They are formed in myriad ways. Socialization of
boys and girls occurs by imitation of others (e.g., role modeling of
parents' and peers' behavior) and through learning by reinforce-
ment (e.g., punishing “weak”emotions in boys). Throughout
development and into adult life, these beliefs promote ease of
categorization by gender. For example, if women are observed to
care for children, then women are believed correspondingly to be
nurturing, kind, and possess other communal traits like emotional
intelligence. If men are observed in strength-intensive tasks, they
are believed to be assertive and dominant and have skills in
leadership, math, and mechanics. These positive stereotypes of
communion and agency allow women and men to take pride in
their gender roles and are sometimes used to justify continuation
of these divisions.
An important function of gender roles or cultural beliefs about
men and women is to guide behavior. Societal expectations
influence behavior through social rewards and punishments for
conforming or not conforming to roles and may create gender
differences that otherwise might not have occurred. For example,
female leaders are evaluated more negatively than male leaders,
and even more so when they exhibit agentic traits like dominance,
directness, confidence, or anger (cf. Koenig, Eagly, Mitchell, &
Ristikari, 2011). Men are punished for pursuing female occupa-
tions (e.g., ballet) or for communal traits such as agreeable-
ness or being a “nice guy”(Judge, Livingston, & Hurst,
2012). Gender roles create pressures to conform and become
internalized as gender identities, such that even when others are
not present, people behave consistently with an internalized
self-image.
Gender roles and beliefs are pervasive, can be activated with
subtle priming cues, and their effects on individuals' responses
depend on the context. Expectations about male and female skills
can enhance or impair performance on gender-typical or atypical
tasks. As Wood and Eagly (2012) note, activation of a strong
(versus weak) gender stereotype is found to impair performance of
social sensitivity in males and math and leadership performance in
women. However, the reverse also can occur, demonstrating the
importance of the social and psychological context. Illustrating
this, sometimes priming gender-atypical stereotypes can enhance
performance (e.g., women on math tests), and career experiences
in gender-atypical fields can immunize women from stereotype
threats.
Gender roles can be used by men and women to self-regulate
their behavior. The emotions experienced by men and women
can serve as feedback and reinforce behavioral change in more
gender-typical ways. As a result, males and females with strong
(versus weak) gender identities experience higher self-esteem
and positive affect when they conform to gender standards
(Witt & Wood, 2010). Both genders also prefer brands with
personalities that match their own gender identity (Grohmann,
2009). Because of their communal tendencies, women may be
particularly sensitive to environmental cues, making them more
likely than men to modify their behavior in context-appropriate
ways (Wood & Eagly, 2012).
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The socio-cultural perspective also proposes that gender
roles and behaviors should change across cultures and time.
Cultures with more versus less gender equality exhibit weaker
communal–agentic stereotypes (Glick & Fiske, 2001) and smaller
gender differences in domains such as preferences for mates with
gender-typical attributes, scores on math tests, and sexual activity.
Self-reported measures of gender-typical attributes show fewer
cross-cultural effects.
Across time, gender roles and behaviors have changed,
particularly for women (Wood & Eagly, 2012). While communal–
agentic roles remain, the stereotype for women has broadened to
accommodate an increased focus on careers and greater accept-
ability of agentic traits like assertiveness. The male stereotype
also has changed, such as men's increased responsiveness to
social influence, but lack of acceptance of most feminine
attributes in males has remained fixed. Changes in these social
role beliefs mirror those seen in society, such as an increase of
women in male-dominated occupations, a slower increase of men
in female-dominated occupations, and decreased support by both
sexes for gender inequality (for a review, see Wood & Eagly,
2012).
Evolutionary theory
Evolutionary psychology offers a second perspective on the
origins of gender differences. It focuses on the impact of human
biology, namely, the evolved mechanisms that humans devel-
oped to adaptively address environmental challenges faced by
their ancestors. The central premise is that natural selection
spawned a human brain designed with assorted programs, each
specialized to solve a recurring problem faced by our hunter–
gatherer ancestors (Tooby & Cosmides, 2005). These problems
included finding a mate and producing offspring, rearing and
protecting children, and navigating during hunting or gathering.
Because early males and females often had different concerns as
they confronted these problems, the evolved programs frequently
differed by gender. Evolutionary researchers seek to identify
these programs and the histories that spawned them to explain
how and why males and females today exhibit the particular
behaviors they do. Similar to other perspectives, the evolutionary
view acknowledges that factors beyond biology (e.g., culture) can
also affect human development (Kenrick & Luce, 2000).
Most gender research by evolutionary theorists focuses on
the programs that early males and females developed to solve
mating-related problems. Research has confirmed several basic
premises about the genders' mating and sexual activity, such
as males' versus females' desired number of sexual partners
and the characteristics each gender desires in choosing mates
(Smiler, 2011). Also, supporting the logic that females have
more at risk in mate selection and mating (i.e., a possible
pregnancy), findings show that males typically profess love
first in relationships, doing so to motivate sex and to offer a
signal of willingness to commit (Ackerman, Griskevicius, & Li,
2011). And because males benefit from intimidating mating
rivals, research affirms that males smile less than females,
particularly during their most reproductively active years.
Indeed, higher levels of testosterone (more common among
males) inhibit smiling and may lead males to exhibit dominance
in the less sanguine right hemisphere (Ellis, 2006).
Mating theory also has inspired work that concerns consumer
relevant issues. For example, research has linked: the relative
abundance of males to females in a community to males' decreased
desire to save and increased incurrence of debt for immediate
(e.g., mate-attracting) purchases—presumably behaviors aimed at
beating out male rivals (Griskevicius et al., 2012); males' (but not
females') conspicuous spending to the desire to attract short-term
mates (Sundie et al., 2011); recessionary sales of beauty products
to females' efforts at boosting their attractiveness so as to attract a
mate with resources (Hill, Rodeheffer, Griskevicius, Durante, &
White, 2012); females' ovulation to their increased choice of sexy
versus conservative attire aimed at outdoing female competitors
(Durante, Griskevicius, Hill, Perilloux, & Li, 2011); males'
(females') use of creativity to attract a short- or a long-term (only a
long-term) mate (Griskevicius, Cialdini, & Kenrick, 2006); and
activation of a mating mindset to males' (but not females')
increased use of creativity-correlated relational processing that
aids males in making sense of distally related brand extensions
(Monga & Gürhan-Canli, 2012).
Evolutionary researchers also suggest that mating concerns
may explain some well-established gender differences, namely
males' greater aggressiveness and proclivity toward risk taking.
Based on arguments by Fischer and Mosquera (2001) and Ellis
et al. (2012), such behaviors were more functional and fitness-
enhancing for males than females in their evolutionary past.
Aggression and risk taking promote not just males' physical
competencies but their social status, which is the core of males'
(but not females') self-esteem. These behaviors elevate males'
social status by increasing their control over valued resources
and/or facilitating admittance into status elevating cliques. In
turn, the latter promotes two pivotal male goals: limiting male
competition, and gaining access to more sex and reproduction
opportunities (owing to females' preference for mates with more
resources). Supporting elements of this logic, Griskevicius et al.
(2009) found that activating status motives increased males'
(but not females') direct aggression. Moreover, Li, Kenrick,
Griskevicius, and Neuberg (2012) observed that activating
mating motives lowered loss aversion and increased gain
seeking among males (but not females), suggesting that mating
goals may increase males' risk taking.
Evolutionary researchers also have explored gender differ-
ences thought to stem from non-mating problems. Some propose
that certain female superiorities evolved from females' compar-
atively greater responsibility for child-rearing. Child care requires
superior speed and accuracy at recognizing others' facially
communicated emotions, and accordingly females display such
an advantage at identifying positive and negative emotions, with
the advantage stronger for negative emotions that may signal
survival jeopardy (Hampson, van Anders, & Mullin, 2006). Also,
delay gratification—inhibiting satisfaction of one's own needs in
favor of satisfying others' needs, should be adaptive for child care
givers, and a meta-analysis found a female advantage in this area
(Silverman, 2003a).
Finally, evidence supports the theory that gender differences in
navigational strategies evolved because of early males' (females')
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role as hunters (gatherers; Silverman & Choi, 2005). Data find
that males typically use an orientation strategy that entails
constantly maintaining a sense of one's position in relation to
global markers like the sun or Euclidean (e.g., east, west) cues.
This makes sense because this strategy should be advantageous
for male hunters, who navigated large unfamiliar spatial areas and
often followed circuitous routes before eventually finding their
way home. Females typically use a landmark-based navigational
strategy that entails learning local visual markers along one's
route and the relationships between markers. This strategy, which
involves creating detailed mental maps of smaller areas that
previously have been observed, was more adaptive for female
gatherers, who may have tended to small children and therefore
needed to recall hiding places or escape routes in the event of
danger.
Hormonal exposure and the brain
Growing research has indicated that biological factors
contribute to gender differences in behavior and cognition. Pre-,
neo, and postnatal exposure to gonadal hormones can influence
brain development permanently and thus the propensities people
display (i.e., Hines, 2004). Conventional wisdom suggests that
androgens and estrogens are male- and female-gonadal hormones,
respectively, but in truth, both genders are exposed to these
hormones to some degree. Hormone exposure can produce
assorted and complex gender differences only during critical
periods of development. Research shows that testosterone (T), an
androgen typically present at higher levels in males than females,
plays a major role in producing gender differences. Higher T levels
generally promote more male-typical development, an influence
that is both graded and linear. Estrogens, present at higher levels in
females than males, do not femininize development, suggesting
that femininization represents the default.
Ethical considerations preclude manipulation of human
exposure to gonadal hormones. Thus, much of our knowledge
of hormonal influences comes from indirect approaches, such as
comparing control group data with that obtained from individuals
with disorders that produce gender-atypical hormone exposure or
children whose mothers took androgenic progestins during
pregnancy. Yet, newer approaches now exist, including examin-
ing normal population variability in hormone exposure, where
hormone levels are assessed from umbilical cord blood, maternal
serum, or amniotic fluid (Cohen-Bendahan, van de Beek, &
Berenbaum, 2005).
The most convincing evidence that prenatal hormone
exposure contributes to gender differences comes from studies
of children's play (e.g., play with dolls or trucks, or rough and
tumble play). For example, Pasterski et al. (2005) found that girls
with congenital adrenal hyperplasia (CAH), a disorder that
produces elevated androgen levels, displayed more male-typical
toy choices compared to their unaffected sisters. But as is
commonly observed, boys with and without CAH did not differ.
Hines, Golombok, Rust, Johnston, and Golding (2002) observed
comparable outcomes, but here normal children's T levels were
assessed from pregnant mothers' blood samples. Auyeung et al.
(2009) also found parallel outcomes using fetal T levels from
amniotic fluid, but here T levels and male-typical play were
positively related for both genders.
Meta-analyses have shown gender differences favoring
males on some specific cognitive abilities, such as mental
rotations, spatial perception, math problem solving, and math
word problems. Differences also exist and favor females for
verbal fluency, vocabulary, math calculations, and perceptual
or processing speed (Hines, 2004; Roivainen, 2011). However,
findings are mixed or weaker concerning T levels and these as
well as other gender-related characteristics. Several studies
suggest that prenatal T influences gender identity and sexual
orientation (Hines, 2006). Some research also finds that females'
over-production of androgens due to CAH leads to greater
aggression (Mathews, Fane,Conway, Brook, & Hines, 2009)and
benefits in both spatial and visual spatial cognition (Mueller et al.,
2008). Similar benefits emerged when T levels were assessed using
amniotic fluid (Cohen-Bendahan et al., 2005). Still, outcomes are
sometimes mixed (Manson, 2008), particularly concerning aggres-
sion and visual spatial performance and for female sex-typed
emotions such as tenderness (Mathews et al., 2009) and empathy
(Hines, 2010), where some studies indicate that heightened T
levels undermine such emotions.
Substantial work also has examined how the genders' brain
hemispheres operate. Investigating this and the brain's functional
networks, Tian, Wang, Yan, and He (2011) found that males
(females) tend to be more locally efficient in their right (left)
hemisphere networks. Strong evidence also indicates that males'
hemispheres are more lateralized (i.e., functionally specialized)
than those of females. For example, when decoding faces and
expressions, males display strong right hemispheric dominance,
whereas females exhibit more bilateral processing (Bourne,
2005). A study noted by Cohen-Bendahan et al. (2005) found
indirect evidence that prenatal T levels were related to indicators
of lateralization.
Studies using neuroimaging and other techniques add to this,
showing that gender differences also exist in the connectivity
between brain areas (Gong, He, & Evans, 2011). Verma and
colleagues investigated the pathways connecting different areas
of the brain (Graff, 2013) and found that males' brains displayed
greater neural connectivity from front to back and within a single
hemisphere, a pattern likely to benefit males' performance on
tasks requiring both astute perception and coordinated action. In
contrast, females displayed greater connectivity between the two
brain hemispheres, seemingly advantageous for females when
cognitive tasks require bilateral or interhemispheric processing,
as often occurs during multitasking. This latter finding corre-
sponds with work showing a positive correlation for females but
not males between the bulbosity of the corpus callosum—the
major tract connecting the two hemispheres that is larger in
females—and performance on several complex neuropsycholog-
ical tasks.
The selectivity hypothesis
The selectivity hypothesis provides a unique perspective of
gender differences in two major ways: it is a “homegrown”theory,
conceived and developed by scholars of consumer research, and it
133J. Meyers-Levy, B. Loken / Journal of Consumer Psychology 25, 1 (2015) 129–149

makes no specific claim about the origins of gender differences.
Instead, this mid-level theory notes linkages that suggest how its
major tenets are compatible with agentic versus communal sex
roles and the socio-cultural perspective of gender differences
(Meyers-Levy, 1989), the hormone exposure and brain operation
perspective (Meyers-Levy, 1994),andtosomeextenteven
the evolutionary view concerning how natural selection led to
modern humans' faculties, behaviors, and gender differences
(Meyers-Levy & Sternthal, 1991).
The selectivity hypothesis posits that the genders employ
different strategies and have different thresholds for processing
information (Meyers-Levy, 1989; Meyers-Levy & Maheswaran,
1991; Meyers-Levy & Sternthal, 1991). More specifically, it
proposes that, compared to males, females tend to process
incoming data more comprehensively, and they possess a lower
threshold at which they apprehend information. This renders
females more likely to detect, elaborate more extensively, and use
relatively less accessible and more distally relevant information
when forming assessments. In contrast, males are more selective
data processors and, relative to females, rely more on less
effortful heuristics. These heuristics frequently involve relying on
cues that are highly salient, relevant to the self (versus others),
singular in number or theme, or cues that activate well developed,
easily accessible notions or preconceptions.
This theory not only accounts for many outcomes derived
from its logic (e.g., Laroche, Saad, Cleveland, & Browne,
2000; Meyers-Levy & Zhu, 2010; Richard, Chebat, Yang, &
Putrevu, 2010), but also appears to accommodate a broad range
of other findings, including unpredicted, applied, and non-
theoretically grounded observations. To exemplify, the selec-
tivity hypothesis seems to explain why, compared to males,
females more accurately detect and interpret subtle nonverbal
cues (e.g., body language, paralanguage; Rosip & Hall, 2004),
scan more data (i.e., perform more eye fixations), producing a
recognition advantage (Heisz, Pottruff, & Shore, 2013), engage in
more patient-focused behaviors as health-care providers (e.g., give
longer consultations and more patient feedback; Street, 2002),
screen and process more problem-free loans as loan officers (Beck,
Behr, & Guettler, 2013), and employ a more employee-attuned
(versus task-focused) supervisory style (Doughty & Leddick,
2007).
Two often overlooked aspects of the theory merit mention.
First, the theory implies that gender differences are condi-
tional and will not always occur. Rather, because gender
differences stem from females' relatively lower threshold for
detecting and using target data, gender differences should
obtain only when access to such data is above females' but
below males' threshold. Thus, when data are either blatant or
exceptionally obscure, differences in the genders' use of it
are likely to be absent. Second, these gender differences in
comprehensiveness and the use of heuristics are not value-
laden. While more thorough processing of data might seem
to be an advantage for females, it also can be less efficient,
foster resource depletion that can lead to negative down-
stream consequences (e.g., in consumer contexts containing
vacuous, hyperbolic, and misleading data), and produce
psychological costs (e.g., prompt anxiety or indecision).
Thus, neither males' nor females' approach should be construed
as representing a normative ideal.
Domains revealing evidence of gender differences
Gender differences have been observed in many and diverse
domains. In the sections that follow we loosely organize these
domains into two sequential categories: those that are more
informative about females' communal proclivity and those that
do the same concerning males' agentic proclivity. To foreshadow
notable themes that arise from the vast array of findings, the
following five propositions recur across domains: (a) males are
more self-oriented and females more other-oriented, (b) females
are more cautious and avoidance focused while males are more
risk-taking and assertive, (c) females are more responsive than
males to negative stimuli in their environment, (d) males are more
selective in their intake and processing of data, whereas females
are more comprehensive, and (e) females are more sensitive to
environmental cues and differentiating factors, whereas males'
responses are more consistent across contexts.
Ethics and morality
Meta-analyses and literature reviews on gender differences in
moral judgment find rather limited support for the notion that
women are more moral or ethical than men (Jaffee & Hyde, 2000;
Walker, 2006). This is exemplified in the research on forgiveness.
While one meta-analysis found that women are more forgiving
than men (Miller, Worthington, & McDaniel, 2008), another that
excluded self-report measures and included dissertation research
found no relation to gender (Fehr, Gelfand, & Nag, 2010). While
forgiveness is correlated with female-stereotypic traits, such as
empathy and relationship commitment, it is negatively related to
others, such as rumination and severity of victimization (Fehr et
al., 2010). Forgiveness and other ethics behaviors may be a
complex interplay of affect and cognitions that reflect combina-
tions of female- and male-stereotypic traits.
Given such inconclusive findings, calls have been made for
research to focus on more specific psychological processes that
influence morality and moral development (Walker, 2006).
Moral sensitivity is a specific morality-based construct that
measures awareness of how one's actions affect others, including
an understanding of the cause–consequence chain of events and
the use of empathy and perspective-takingskills. A meta-analysis
found that women exhibited more of this trait than men (You,
Maeda, & Bebeau, 2011). Women also tend to report greater
interest and engagement in environmentally-conscious actions
(Zelezny, Chua, & Aldrich, 2000), which are associated with
ethics concerns, but gender differences are absent in skepticism
toward “green”ad appeals (do Paço & Reis, 2012). Honesty and
telling lies have been examined from a behavioral economics
perspective of weighing costs and gains of lying to self and
others. Men were found to be more likely than women to lie to
obtain a monetary benefit for the self (Dreber & Johannesson,
2008; Erat & Gneezy, 2012), whereas women were more willing
to lie when the lying benefited a person but harmed no one
financially (Erat & Gneezy, 2012).
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Females' ethics related responses also may be more context-
sensitive than males'. Women were found to lie less often than
men for personal gain when the payoff was small (about one
dollar; Dreber & Johannesson, 2008; Erat & Gneezy, 2012), but
gender differences disappeared when the monetary benefit was
larger (about $10; Childs, 2012). Childs (2012) argues that men
may respond quite uniformly to opportunity costs of lying that
emphasize personal gain, but women may weigh the relative
costs of altruism and personal gain. Women seem to favor
greater altruism in exchange relationships (Gneezy, Niederle,
& Rustichini, 2003; Gneezy & Rustichini, 2004), but when
stakes are higher, they consider personal gain outcomes.
Gender differences in response to charitable appeals have been
observed in marketing contexts. Brunel and Nelson (2000) found
that women preferred a cancer-prevention charity appeal that
focused on helping others, whereas men preferred a utilitarian
appeal that focused on helping oneself and one's in-group. These
researchers also observed that women scored higher than men
on the trait “world view,”which contrasts caring moral views
over justice concerns. Parallel differences were reported in a
meta-analysis by Jaffee and Hyde (2000).Otherworkby
Kemp, Kennett-Hensel, and Kees (2013) found that women
were persuaded more and intended to donate more money than
men when a charity appeal generated sympathy rather than
pride. Men, however, had greater intentions to give when the
appeal generated pride rather than sympathy.
In evaluating corporate moral transgressions, women react
more negatively than men. Not only were they more outraged
than men about unethical corporate behaviors, but also their
outrage was found to increase boycotting of the corporation
(Lindenmeier, Schleer, & Pricl, 2012). Women also were more
likely than men to blame the company in a product harm case
(Laufer & Gillespie, 2004). While women felt moreempathy than
men for the victims, their attributions of blame reflected their
feelings of being personally vulnerableifasimilarsituation
befell them. In contrast, men's attributions were based on their
assessment of the corporation relative to their personal beliefs
about fairness and justice (i.e., a moral-equity norm).
Trust
Prior research on trust found that women are both more
trusting than men (Feingold, 1994) and more likely to be trusted
by others, perhaps due to their greater tendency toward social
affiliation (Beck et al., 2013; Buchan, Croson, & Solnick,
2008; Kosfeld, Heinrichs, Zak,Fischbacher,&Fehr,2005).
Interestingly, however, gender differences in trust reverse
when evaluated in the context of e-commerce and online
games. In these contexts that often involve short, anonymous
interactions, men tend to be more trusting (Midha, 2012)and
aredeemedmoretrustworthythanwomen(Lee & Schumann,
2009). Women's lack of trust in online relationships is related
to their greater concern about online privacy (Midha, 2012).
Women are more concerned about misuse of online informa-
tion (Garbarino & Strahilevitz, 2004), are more likely than men
to read privacy notices, and favor enacting laws that protect
confidentiality (Midha, 2012). These concerns are abated for
women but not for men when a website is recommended by a
friend (Garbarino & Strahilevitz, 2004). Brain imaging (fMRI)
data show that when engaging in online trust relationships, a
greater number of brain areas are activated for women than men
(Riedl, Hubert, & Kenning, 2010). This finding concurs with the
idea that women may process data more extensively when
assessing the trustworthiness of online relationships.
Males' greater trust also applies to gaming contexts that
involve monetary exchange. For example, when playing an
investment game with an anonymous male or female partner,
men were more likely than women to trust their partner, and they
gave more money to female than male players (Lee & Schumann,
2009). Buchan et al. (2008) studied behavior in an investment
game where participants' only option to increase personal wealth
was to send money to another player. Here, trusting the other
player to respond in kind was a means of achieving personal gain.
Men, more than women, trusted the other player, and they did so
because they expected more in return. The authors argued that
this indicates males' greater focus on instrumentality. This game
also allowed participants to return money to the sender, a more
communal response not associated with monetary gain. This
response was used more often by women.
Communion-aligned emotions: anxiety, worry, fear, and sadness
Women are more likely than men to express more feelings
of anxiety, worry, fear (McLean & Anderson, 2009; Robichaud,
Dugas, & Conway, 2003) and sadness (Fischer, Rodriguez
Mosquera, van Vianen, & Manstead, 2004). They report greater
chronic stress and minor daily stressors, rate their life events as
more negative and less controllable (Matud, 2004), and report
more somatic symptoms and psychological distress (McLean &
Anderson, 2009; Toufexis, Myers, & Davis, 2006). Men's lower
reporting of anxiety emerges even when the genders' physical
responses are held constant. For example, Stoyanova and
Hope (2012) found that when asked to approach a tarantula,
women reported more anxiety and avoidance than men, but
men underreported their anxiety relative to their physiological
responses.
Explanations abound for such findings. Some argue that
women perceive events as more stressful than men (Laufer &
Gillespie, 2004), while others suggest that women are exposed
to higher levels of stressors than men and therefore experience
more stress (Day & Livingstone, 2003; Stoyanova & Hope, 2012).
Theoretical explanations include ones that are socio-cultural—that
parents reward girls but punish boys for expressing negative
emotions like fear and sadness (Garside & Klimes-Dougan, 2002),
evolutionary—that women's care and protection of offspring
contribute to greater anxiety about threatening situations, and
hormonal—that hormonal fluctuations in women increase anxiety
(Toufexis et al., 2006). Socio-cultural theories link the subjective
experiences of emotion and control to power differences in men
and women. If a negative event is appraised as within one's
control, the resulting emotion is likely to be anger, implicating
power and invulnerability. But if the event is beyond one's
control, sadness or fear is more likely with an appraisal showing
powerlessness and vulnerability (Robichaud et al., 2003). Using
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data from 37 countries, women reported feeling more powerless
emotions than men (e.g., sadness and fear; Fischer et al., 2004),
and this gender difference was abated in non-Western countries
where emotional suppression of feelings is less often discouraged
in males.
Women and men also process information differently when
in a negative mood. Women were found to use more detailed
processing and did so more when they were in a sad mood
(Martin, 2003). In contrast, men used a distraction strategy to
repair a sad mood. Women also engage in more rumination,
which can increase depression and anxiety (Nolen-Hoeksema,
2012).
Emotion regulation and inhibition
Emotion regulation is the ability to inhibit or modulate one's
thoughts, emotions, and behavior in response to an emotionally-
charged situation. When regulating emotions, one might suppress
inappropriate behavior or continue to focus one's attention on
task despite an emotional trigger. Research has examined how
women and men differ in their coping styles, ability to inhibit
emotion, and ability to regulate emotions when performing
cognitive tasks.
Gender differences are found in the methods used for coping
with negative emotions, often following communal and agentic
roles. A meta-analysis found that women more frequently than
men used coping strategies that involved verbalizations to seek
emotional support, rumination, and positive self-talk (Tamres,
Janicki, & Helgeson, 2002). Women also more often sought
social support as a coping mechanism and countered negative
emotions with positive ones (Day & Livingstone, 2003; Matud,
2004). Because women perceive stressors as more severe than
men, they may expend more effort in response to potential threats
by using these active coping strategies (Tamres et al., 2002). In
contrast, men have been found to engage in more emotional
suppression, and more rational and detachment coping (Matud,
2004).
Brain imagery studies too find that women engage in more
emotion-focused coping than men in response to a negative
emotion triggered by an unpleasant olfactory stimulus (Koch et
al., 2007) or unpleasant photos (McRae, Ochsner, Mauss,
Gabrieli, & Gross, 2008). For example, when asked to cognitively
reappraise emotionally-charged photos to make them appear
less negative (e.g., view them from a different perspective),
both genders were effective in down-regulating their negative
emotional responses, but they used different brain activity in
the process (McRae et al., 2008). The authors speculated that
men engage in more automatic and less deliberate processes
than women (i.e., males' brains decreased activity of areas
associated with emotional regulation), whereas women gener-
ate positive affect as a strategy for down-regulating their
negative affect (i.e., females' brains increased activity of areas
associated with reward).
Overall, regulation of emotion appears to be more effortful
after gender-typical than atypical emotions. A literature review
by Glenberg, Webster, Mouilso, Havas, and Lindeman (2009)
reported that sentence comprehension was affected by the type
of emotion generated by a reading. When women read about a
sad (versus angry) event, they subsequently slowed their
processing of a happy event. When men read about an angry
(versus sad) event, they subsequently slowed their processing
of a happy event. Other common findings on male aggression
suggest that men may have more difficulty than women in
regulating their anger and arousal in non-violent ways. A
meta-analysis by Knight, Guthrie, Page, and Fabes (2002)
found that at low levels of arousal, the genders were equally
effective in curbing aggressive behaviors, but at high arousal
levels, men were more aggressive.
Meta-analyses have shown that across various ages, women
and girls more easily delay gratification and resist temptation
than do men and boys (Silverman, 2003a,b). Evolutionary theory
proposes that early reproductive success for women depended on
their ability to inhibit non-optimal sexual and social behaviors,
and success in child-rearing depended on their ability to satisfy
their children's needs over their own (Bjorklund & Kipp, 1996).
Sensitivity to nonverbal cues
Women display superiority to men in reading nonverbal cues
(Hall & Matsumoto, 2004; Rosip & Hall, 2004) and accurately
inferring thoughts and feelings of others (Klein & Hodges, 2001).
Such observations are often attributed to females' greater
empathic responses and are consistent with both socio-cultural
perspectives (i.e., women are socialized to decode emotions) and
an evolutionary one (i.e., empathic responses ensure the survival
of children).
In particular, research shows that women are more accurate
than men in decoding photographs of eyes as showing playfulness,
comfort, irritability, or boredom, and such results held across 10
countries (Kirkland, Peterson, Baker, Miller, & Pulos, 2013).
Females' advantage in correctly decoding facial emotions occurs
for both positive and negative emotions (Hampson et al., 2006),
stimuli of extremely short durations (Hall & Matsumoto, 2004),
among children (McClure, 2000), and it extends to memory for
new faces. For example, women were more likely than men to
attend to detailed facial information when encoding male and
female faces, which improved subsequent recognition (Heisz et al.,
2013). They also are found to engage in greater bilateral
processing than do men in such contexts, which may increase
their access to processing mechanisms in both hemispheres, reflect
a sex difference in the efficiency of inter-hemispheric information
transfer, or simply reflect the different cognitive processing styles
of the genders (Bourne, 2005).
Gender differences are more pronounced in the decoding of
negative versus positive facial emotions (Hampson et al.,
2006), and women show more selective responses to subtleties
in negative facial expressions. For example, women showed
more and different brain activity for angry versus fearful facial
expressions. That men did not was attributed to women's
greater attention to ambiguous emotional cues (McClure et al.,
2004). Women also may be more sensitive to gradations in
negative cues. A study found that while both genders reacted
strongly to pictures of extreme anger or disgust, females were
more sensitive to lower levels of these emotions (Montagne,
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Kessels, Frigerio, de Haan, & Perrett, 2005), and men were
less sensitive to negative stimuli at lower levels of salience or
intensity (Li, Yuan, & Lin, 2008). Evidence of gender differences
in adults' but not adolescents' brain activity in emotion decoding
contexts suggests that a developmental change may spawn such
differences (McClure et al., 2004).
Women also show more reactivity to negative stimuli
in contexts other than facial decoding. Women show greater
sensitivity than men to aversive pictures (Hampson et al., 2006;
Wrase et al., 2003), with women's brain activity more focused on
pain processing centers (Wrase et al., 2003).Womenalsoshow
lower tolerance than men for pain, experience pain as more intense,
and better discriminate painful stimuli than men (Vallerand &
Polomano, 2000). In marketing contexts, women have been
found to be less persuaded than men by negative ad messages
and are more likely to vocalize negative thoughts in response to
negatively-framed messages (Putrevu, 2010).
Parental styles
Consistent with the socio-cultural perspective, parenting
style influences boys' and girls' learning of sex-typed attitudes.
A meta-analysis by Tenenbaum and Leaper (2002) found that
parents' attitudes toward gender roles were related to their
children's attitudes toward gender-related work, themselves,
and others. Yet, parents' attitudes have only weak links to their
children's gender-related interests or developmental behaviors.
Further, mothers and fathers interact with and influence
their children differently. Studies that have examined whether
parents treat their sons and daughters in sex-typed ways found
only limited support. Differences that did emerge tended to
follow communal and agentic roles, with mothers encouraging
more two-way communication and using both more supportive
and more negative speech, and fathers establishing norms and
standards for children to follow and using more directive and
informative speech (Hsieh, Chiu, & Lin, 2006; Tenenbaum &
Leaper, 2002). Children were observed to talk more to mothers
than to fathers, and after a communication breakdown with a
parent, engaged in more elaboration with mothers than fathers
(Lanvers, 2004). A brand preference study of Taiwanese children
(Hsieh et al., 2006) found that mothers were more likely to
influence their children's brand attitudes by encouraging them to
express their opinions and communicate openly. In contrast,
fathers tended to influence their children's brand attitudes by
encouraging obedience and social harmony.
The frequent use of correlational designs and self-report
measures limits the research on parenting (Lanvers, 2004). An
exception is a longitudinal study that observed mothers' and
fathers' responses to certain emotions as their children played a
game. Among preschool children (age 4), only fathers attended
more to daughters' than sons' submissive emotions (e.g., sadness),
and parental attention at this age predicted submissive behavior
two years later. Among early school age children (age 6), fathers
attended more to sons' than daughters' disharmonious emotions
(e.g., anger), and such emotions predicted later conduct disorders
(Chaplin, Cole, & Zahn-Waxler, 2005).
Responses to promotional activity
Research has investigated when and how the genders differ
in responding to promotional materials. For example, based on
speculation that males (females) engage in greater item-
specific (relational) elaboration, Putrevu (2004) found that
males (females) responded more favorably to ads that were
simple (complex), focused on attributes (the product category),
and included relatively antagonistic comparative ad claims
(claims that emphasized product-nature harmony). Other work
indicates that because females are apt to think more deeply
than males about attention-attracting stimuli like comparative
ads, such ads can produce opposing outcomes on the genders'
attitudes. Specifically, Chang (2007) found that while expo-
sure to a more attention-getting comparative versus noncom-
parative ad increased males' ad involvement and thus their
attitudes toward the target brand, females' attitudes were less
favorable to a comparative ad as it prompted them to ponder
the ad more deeply and infer that it aimed to manipulate
consumers. Further, consistent with the selectivity hypothesis
contention that females (versus males) are more sensitive to
detailed information, Berney-Reddish and Areni (2006) found
that only females were less accepting of ad claims containing
qualifying subtle details, such as hedge (e.g., “probably,”
“may”) and pledge (e.g., “definitely,”“
absolutely”) words.
Other studies have uncovered additional subtle moderators that
can produce gender differences. Along such lines, Meyers-Levy
and Zhu (2010) found that the genders differ in the meanings they
infer and use from background esthetic elements in ads, like
music or graphic art. Music can convey two alternative meanings:
referential meaning, which is more resource-demanding to
discern, relays descriptive ideas spawned by the music, while
less demanding embodied meaning is purely hedonic, consisting
of feelings elicited by the music's structural properties (e.g., its
energy level). Based on this, Meyers-Levy and Zhu proposed
that: (a) females are likely to infer both, not just one of these
meanings, (b) the genders will differ in when they infer a given
meaning, and (c) these meanings may be inferred not only from
music, but also from other esthetic elements used in promotions.
Based on the selectivity hypothesis' notion that females
process data more comprehensively—including harder to
extract data, whereas males tend to selectively process single
cues (Meyers-Levy, 1989; Meyers-Levy & Sternthal, 1991),
the authors reasoned that which meaning(s) the genders infer
and use to form perceptions of the advertised product should
vary depending on their Need for Cognition (NFC) level.
When NFC is high (low), males should discern and use only the
more onerous (easier) to infer referential (embodied) meaning.
But because females process data more fully, they should discern
and use both meanings, regardless of their NFC level. These
predictions concerning the genders' product perceptions were
upheld and also found to apply to other esthetic ad elements like
graphic art.
Fisher and Dubé (2005) found that the genders react
differently to ads that relay alternative types of emotions. Because
sex roles dictate that males should exhibit agency and do so
especially when same-sex individuals are present, these researchers
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reasoned that unlike females, males' responses to emotional ads
should vary depending on both the agency of the emotion evoked
and whether same- or opposite-sex persons are present. Results
supported these predictions across multiple ad stimuli and contexts.
Males (but not females) rated ads as less pleasant and viewed them
less favorably when the ads invoked low-agentic emotions
(e.g., anxiety or tenderness) and were viewed with another
male. In contrast, males' responses were unaffected when they
viewed such ads privately or when ads with high-agentic
emotions (e.g., anger or joy) were viewed either alone or in
another male's presence. Yet, as predicted, females' responses
were stable irrespective of the ad emotion type and social
context.
Finally, Noseworthy, Cotte, and Lee (2011) proposed that
females' relative superiority at visuo-spatial elaboration—noticing
new objects added to a visual display and seeing commonalities
among them (e.g., Voyer, Postma, Brake, & Imperato-McGinley,
2007)—might influence how the genders interpret promotions
with visual ads for multiple products. Prior research has shown
that displays with competing (i.e., same product category) versus
unrelated (i.e., diverse product category) ads encourage relational
elaboration that highlights product commonalities. Given this, the
researchers posited that when presented with an array of visual ads
where one contains an extreme visual incongruity, females'
visuo-spatial elaboration should enable them alone to make sense
of and hence favorably evaluate the incongruent product, but only
in a competing (not an unrelated) ad context. Nevertheless, it was
further reasoned that this female advantage might produce a
resource constraint that could impair females' (yet not males')
processing of verbal data (e.g., ad claim recognition). Three
studies supported these predictions. Compared to males, females
correctly categorized, relationally processed, and more favorably
evaluated extremely incongruent products when they appeared in
a competing but not an unrelated ad context. However, this
advantage came at a cost, as females more poorly recognized
specific ad claims for the products when ads appeared in a
competing versus unrelated ad context.
Shopping behavior
Internet usage and search behavior
Research suggests that despite their similar usage rates,
females are less intense users of the Internet (Ono & Zavodny,
2003). Paralleling the agentic–communal distinction, males use
the Internet more to explore personal interests, such as seeking
entertainment or investment data (Hupfer & Detlor, 2006;
Weiser, 2000), while females use it more for social purposes
(i.e., emailing others; Weiser, 2000). Differences also exist in
self-reported skill. Females perceive themselves as less skilled
than males in Internet usage (Hargittai & Shafer, 2006), view
the Internet as harder to understand (Dittmar, Long, & Meek,
2004), and feel less in control and effective at searching for data
(Ford, Miller, & Moss, 2001).
Still, whether the genders truly differ in Internet search skill
is not an open and shut question. When Hargittai and Shafer
(2006) assessed adults on assorted and reasonably challenging
online search tasks, gender differences in performance were
absent. Null effects also were observed by Hupfer and Detlor
(2006) on self-reported search behaviors. Yet, illuminating gender
differences in search was reported in two children's studies.
Results of both studies are quite consistent with the selectivity
hypothesis, which contends that males are less thorough processors
than females. Large, Beheshti, and Rahman (2002) found that
when 6th graders searched online for data about a sport of their
choice, boys entered fewer words in their search queries than girls,
employed more one-word searches, spent less time viewing
individual pages, and jumped pages at a higher rate per minute.
Another study by Roy, Taylor, and Chi (2003) found that while 8th
grade boys and girls both gained relevant knowledge when
searching online for a school project, boys' gains were larger.
Three specific behaviors explained why: (a) boys conducted more
unique search queries, but girls thoughtfully examined more
uncovered documents, (b) boys did more scrolling and quick
scanning of their search results, and (c) boys' search queries
produced higher quality results, apparently because boys hastily
scanned material with more topic relevant data. Hence, in this
study, girls' more thorough and detailed perusal of their search
results offered no advantage compared to the knowledge boys
gleaned by hurriedly scanning their more pertinent search output.
Notably, females' propensity to spend more time than males
examining website content also has been observed among adults.
Danaher, Mullarkey, and Essegaier (2006) found this in a panel
study that assessed the duration of visits to the top 50 websites
frequented by members whose ages were quite representative of
the overall population.
Online shopping
Whereas females more greatly enjoy and outnumber males
in shopping at traditional offline venues, males view online
shopping more favorably (Van Slyke, Comunale, & Belanger,
2002) and engage in it twice as much as females do (Kwak,
Fox, & Zinkhan, 2002). Females perceive online purchasing as
less emotionally satisfying and practical than do males, and
they are less trusting of it (Rodgers & Harris, 2003).
Gender differences in shopping motivations and perceptions
of e-commerce shed light on this situation. For brick and mortar
venues, Kotzé, North, Stols, and Venter (2012) found that
females outscored males on almost all motivations, including
shopping to browse, for bargains, to socialize, to exercise, and
for sensory stimulation. Qualitative analysis of online shopping
motivations by Dittmar et al. (2004) revealed many gender
similarities (e.g., on convenience and making price compari-
sons). But while females more greatly appreciated the control
offered by online shopping (i.e., can visit only sites of interest
and thus not waste money), females viewed online shopping as
impersonal, less involving (i.e., less sensory buzz and less
bargain hunting thrills), and lacking in social sensory
experience (i.e., just viewing a screen and clicking buttons).
The researchers' survey reinforced these themes. Whereas for
males, buying consumer goods involved mostly functional
concerns (i.e., economics, efficiency, acquiring information),
females emphasized the emotional and social–experiential
elements of shopping and reported more identity-related concerns
(i.e., buying to move closer to the ideal self). Interestingly,
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however, Wang, Baker, Wagner, and Wakefield (2007) found
that the perceived social limitations of internet shopping may be
alleviated by the use of avatars, virtual, humanlike and lifelike
characters. The social cues of avatars increased internet users'
pleasure and arousal, with women versus men showing a stronger
impact of arousal on hedonic value.
The genders' responses to aspects of product websites are
concordant with the selectivity hypothesis view that as more
comprehensive processors, females prefer data displays that are
more dense, complete, and reliable. Richard, Chebat, Yang, and
Putrevu (2010) found that males responded more favorably to
well-organized websites, but they were less inclined to explore
informational (e.g., informative, resourceful) websites. In con-
trast, females' website involvement grew when its content was
more comprehensive and actionable (e.g., complete, accurate,
and up-to-date). Similarly, Phillip and Suri (2004) found that
females responded more favorably than males to promotional
emails that offered links to additional sources of information.
Females also displayed higher liking for such emails that enabled
forwarding them to a friend, were sent only to a circumscribed
audience (e.g., only interested persons), or contained a coupon.
Findings from a field study by Laroche et al. (2000) also
conform with the selectivity hypothesis, revealing that females'
search process during Christmas shopping was far more
complex than males'. Females undertook more general and
specific searches, purchased more gifts, made more shopping
trips, and began shopping earlier than males. In contrast, males
simplified their search task by using heuristic cues, like sales
clerk recommendations or price.
The impact of shopping with friends
Kurt, Inman, and Argo (2011) reasoned that males' agentic
and females' communal values might affect their motivations
and spending when shopping with friends versus alone. Agency
emphasizes self-confidence, competence, and mastery, while
community accentuates maintaining social connections and
interpersonal harmony. Thus, these researchers anticipated that
due to their agentic concerns, males in social (versus private)
shopping contexts would pursue self-bolstering heavy spending
activities aimed at acquiring the admiration and respect of
friends. Yet females' communal desire to just be part of the
group would discourage such spending that could cast females
in the spotlight. Results confirmed these outcomes, showing
that males spent more money when they shopped with friends
versus solo, while females' spending was constant across
contexts.
Simplifying decisions through intuition based heuristics
A corollary to the selectivity hypothesis view that females
are more comprehensive processors than males is that males
should be more likely than females to simplify decisions by
using intuition or salient cue-implied heuristics. Several
findings support this deduction. For example, when selecting
national lottery numbers to play, males more frequently
invoked the “gambler's fallacy”—the intuition that an event is
less likely to reoccur if it occurred recently (Suetens & Tyran,
2012). Hence, unlike females, they were less likely to select
lotto ticket numbers that were winners in the previous week. Males
also rely more than females on the intuition that marketers use
more eye catching colors to draw attention to good buys. Only
males perceived price discounts as greater when ads featured them
inredratherthanblack(Puccinelli, Chandrashekaran, Grewal, &
Suri, 2013).
Still, care must be taken in interpreting findings concerning
the use of heuristics. Without knowing how consumers actually
use a particular cue, one cannot be certain whether its use
signifies reliance on a simple heuristic or the inferring of more
thoughtful diagnostic deductions implied by the cue. To explain,
Shao, Baker, and Wagner (2004) found that females' service
quality expectations and intentions to use a banker were higher
when the banker dressed more professionally. While males'
showed the same pattern, the effect was weaker than for females.
Such findings could be viewed as contradicting the selectivity
hypothesis prediction, indicating that females, not males, may be
more likely to rely on heuristics implied by a salient cue (i.e., the
heuristic that the attire cue implies that the banker should deliver
professional quality service). But an equally viable interpretation is
that the findings actually support the selectivity hypothesis view
that females process data more comprehensively and thus more
thoughtfully interpret subtle yet diagnostic cues. That is, in the
absence of any valid indicator of service quality, females may have
drawn the reasonable inference that the banker's professional attire
suggests that (s)he takes pride in his/her work and hence is diligent
to customers' needs. As this example underscores, whether a cue is
central (i.e., diagnostic of substantive issues) or peripheral (i.e., a
heuristic cue) depends on how people actually use the cue (Petty,
Cacioppo, & Schumann, 1983).
Customer loyalty
Gender differences also can affect customer loyalty. Noble,
Griffith, and Adjei (2006) found that males' loyalty to local
merchants was motivated by convenience and information
attainment, but females' was driven by desire for browsing,
assortment, uniqueness and social interaction opportunity.
Further, loyalty programs that incorporate alternative features
are likely to strengthen relationships with male versus female
customers. Drawing on evolutionary theory, Melnyk and van
Osselaer (2012) posited that males should respond more
positively to features that signal power and status (i.e., signs
that boost one's standing against rival males), whereas females
who emphasize personal relationships should place greater
value on features that highlight one's idiosyncratic preferences
so long as privacy concerns are respected. Four studies upheld
these deductions: Males favored loyalty programs that magnified
status when such status was salient to others, while females
favored programs that highlighted personalization that was not
publically visible.
Product symbolism
Consumers often buy products for their symbolic benefits
(e.g., bolstering self-esteem or status), yet research indicates
that the genders differ in the value they assign to such benefits.
Overall, females display higher levels of brand sensitivity and
brand consciousness (Beaudoin & Lachance, 2006; Workman
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& Lee, 2013) and regard luxury brands more favorably
(Stokburger-Sauer & Teichmann, 2013), while males demon-
strate more materialistic values (Segal & Podoshen, 2012) and
conspicuous product consumption (Segal & Podoshen, 2012).
Reflecting the antecedents of these differences, Hays (2013)
found gender differences in preferences for power versus status
that seem to parallel the agentic–communion distinction.
Specifically males exhibited a preference for power, which is
frequently acquired agentically by controlling resources and
thus gaining mastery over others. In contrast, females preferred
status, which is acquired passively via interpersonal commu-
nality as it is volitionally bestowed on individuals based on
admiration or respect. Work by Wang and Griskevicius (2014)
illustrates females' penchant for accomplishing objectives in a
more passive manner. They found that females may use the
luxury goods they wear in social contexts to tacitly signal to
competitors that their partners are loyal to them.
Sound symbolism
Sound symbolism research indicates that the sounds compris-
ing a brand name can themselves convey meanings (Klink, 2000;
Yorkston & Menon, 2004), with brand names that use front versus
back vowel sounds imparting different meanings. Front vowel
sounds occur when the highest position of the tongue is toward the
front of the mouth (i.e., in English, the sounds of iand e), whereas
the tongue is in the back when generating back vowel sounds
(i.e., the sounds of oand u). Research has shown that front, not
back vowel sounds connote feminine characteristics like smaller,
lighter, milder, weaker, softer, prettier, and friendlier. Given this
sound–gender relationship, Klink (2009) demonstrated that when
given pairs of brand names for products that differed only in their
front versus back vowel sounds (i.e., Giva vs. Gova), females
more frequently selected the names with front vowels, while
males chose those with back vowels. Also, consistent with their
greater responsiveness to stimuli in most modalities, females
were more sensitive than males to the vowel sounds of brand
names.
Competitiveness, risk, and confidence
Three commonly observed gender differences are that females
respond more negatively to competition than males, are more risk
averse, and are less confident of their performance (Croson &
Gneezy, 2009). Nevertheless, a good deal of research reveals a
more complex picture. For example, Gneezy et al. (2003) found
that males outperformed females in a mixed-gender maze-solving
competitive task. However, while both genders performed
better under competitive than noncompetitive conditions, only
females' performance was sensitive to the gender of their
competitors. That is, they performed considerably better when
they competed against an all-female versus a mixed-gender
group, whereas males' performance was relatively constant
irrespective of competitors' gender. These findings suggest
that females' responses to competition are more malleable, as
they are sensitive to the particulars of the situation.
Corroborating this view, Small, Gelfand, Babcock, and
Gettman (2007) examined the genders' responses to a competitive
bargaining situation in which individuals could negotiate their
payment. When this situation was framed as a negotiation
opportunity—a frame that is intimidating to low power individuals
such as females, males bargained for a higher payment than did
females. But when the framing was less intimidating—an
opportunity to “ask for more,”gender differences disappeared.
Similarly, Amanatullah and Morris (2010) found that females' but
not males' behavior was sensitive to the particulars in a job
salary competitive negotiation situation. When females advo-
cated for themselves, they anticipated a backlash due to others'
communal expectations of them (i.e., concern for others) and
used fewer competing tactics, resulting in a lower salary than that
negotiated by males. But when females advocated for others,
which eliminated backlash concerns, females' tactics and out-
comes were comparable to males'.
Research also finds that males take more risks than females
(e.g., Charness & Gneezy, 2012; Ertac & Gurdal, 2012), with
variation in risk taking linked to different neural activity
patterns for men and women (Lee, Chan, Leung, Fox, & Gao,
2009). Supporting this, He, Inman, and Mittal (2008) found
that, overall, males took more risk than females when making
financial decisions, and males' but not females' risk seeking in
selecting investments increased when they felt they were more
skilled in investing. However, interestingly, females' typical
aversion toward risk depended on the particulars of the situation.
They not only became more risk seeking but also accepted as
much risk as males did when they perceived their investing skills
as higher and they could limit their risk by purchasing investment
insurance.
Research also confirms that females exhibit less confidence
than males (Croson & Gneezy, 2009), yet this too seems to
depend on the particular situation. For example, Nekby, Thoursie,
and Vahtrik (2008) examined how competitors in racing, a
male-dominated sport, reacted to a rule change that permitted
runners to self-select into start groups based on their self-assessed
running time for a race. Across multiple measures, they found that
in this context female runners actually exhibited higher overcon-
fidence than males.
Power
Power connotes the asymmetric control one has over valued
resources in social relations (Rucker, Galinsky, & Dubois, 2012).
People with high versus low power exhibit different psychological
states and behaviors, including their perceptions of events and
influence strategies.
Researchers have long posited that power and gender are
related. For example, females' higher incidence (versus males')
of certain nonverbal (e.g., head nodding) and verbal (e.g., tag
questions) communication is often viewed as evidence of males'
greater power (e.g., Helweg-Larsen, Cunningham, Carrico, &
Pergram, 2004). Some contend that this power difference reflects
the assignment of higher power social roles to males (Carli,
1999). Others suggest that high (low) power fosters a male
agentic (female communal) orientation that emphasizes assertion
and expansion of the self (fostering and maintaining social
relations and harmony; Rucker et al., 2012). Regardless of what
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explains the relationship, many differences observed in the power
research parallel those seen in the gender literature. Hence, here,
we shall characterize certain effects involving power and then
outline their conceptual counterparts in the gender literature.
Research indicates that people high versus low in power
assume a more self- versus other-oriented perspective, which
lessens sensitivity to others' views and how one assigns priorities
(Rucker et al., 2012). Parallel effects obtain in gender research. In
the domain of empathy, which clearly taps sensitivity to others'
perspective, research finds that females are more empathetic than
are males and more accurate in inferring others' feelings (Klein &
Hodges, 2001). A meta-analysis also finds that females versus
males are more likely to resolve conflict via compromise (Holt &
DeVore, 2005). In assigning priorities, resource allocation
studies commonly find that males favor providing gains to the
self, whereas females favor equity-based allocations that benefit
others and the self (Fehr-Duda, De Gennaro, & Schubert, 2006).
Similarly, Barone and Roy (2010) found that among frequent
patrons of a store, males favored exclusive promotional deals
that benefited few besides the self, whereas females preferred
inclusive deals that benefited many others as well as the self.
Examining this in a different context, Winterich, Mittal, and Ross
(2009) found that when people's moral identity was important,
those adopting a feminine gender identity (i.e., predominantly
females) increased their charitable donations to out-groups
(i.e., groups not associated with the self), but those with a
masculine gender identity (i.e., predominantly males) heightened
donations to in-groups (i.e., groups associated with the self). And
in a study by Dommer and Swaminathan (2013) concerning the
endowment effect—the tendency to inflate the value assigned to
one's possessions, findings showed that exposure to social threat
strengthened the endowment effect among both genders for
in-group goods, but the effect entirely disappeared among males
(but not females) for out-group goods.
As implied by research concerning psychological distance,
people high versus low in power are found to think more
abstractly (Rucker et al., 2012). Although we found no work
exploring gender differences in abstract versus concrete
thinking, research implies that differences are likely. To
explain, self-construal research indicates that males (females)
typically adopt an independent (interdependent) self-view,
where an independent (interdependent) self-view means that
the self is perceived as separate from (integrated with) others
(e.g., Lin & Raghubir, 2005). Supporting the premise that
males (females) are likely to engage in more abstract (concrete)
thinking, Spassova and Lee (2013) found that people with a
salient independent (interdependent) self-view construed
actions in a more abstract (concrete) manner.
Higher power has been found to trigger three other propensi-
ties: taking action or behaving assertively, exhibiting optimism,
and feeling greater confidence (Rucker et al., 2012). Analogously,
gender research finds that males versus females exhibit these
same propensities. Regarding assertive behavior, findings show
that males behave more aggressively than females (Card, Stucky,
Sawalani, & Little, 2008; Knight et al., 2002). Further, the
genders view assertive gestures like fist making differently. For
males, a fist expresses increased hope for power and elicits
positive judgments of a target who acts assertively, but for females
it prompts decreased hope for power and negative judgments of
such a target (Schubert, 2004).
Gender differences favoring males also exist in optimism
and positive thought, particularly about the self. Exemplifying
this, when buying durable goods, males were less likely than
females to perceive that a product will fail, making them less
likely to buy an extended warranty (Chen, Kalra, & Sun, 2009).
Males also displayed a stronger optimism bias than did females
about their likelihood of being happily married or divorcing
(Lin & Raghubir, 2005). While, overall, the genders do not vary in
reports of happiness and subjective well-being (Diener, Suh,
Lucas, & Smith, 1999), Roothman, Kirsten, and Wissing (2003)
found that males scored higher than females on established scales
that gauged their frequency of positive cognitions or positive
self-statements, their sense of self-worth and adequacy as a person,
and their physical being (e.g., health, body, and physical skills).
Further, when investigating gender differences in regulatory focus,
where a promotion- (prevention-) focus appears to signal greater
(reduced) optimism by implicating heightened attentiveness to
positive (negative) outcomes, McKay-Nesbitt, Bhatnagar, and
Smith (2013) found that males were more promotion-focused,
suggesting that they are more optimistic.
Finally, studies show that males express greater confidence
than do females in assorted domains and irrespective of their
competence. For example, meta-analyses on self-estimates of
general intelligence and mathematical/logical, spatial, and verbal
abilities revealed that except for verbal ability, males consistently
reported higher self-estimates than females (Syzmanowicz &
Furnham, 2011).Similaroutcomeswereobtainedonseveral
esteem-related facets of the self, such as personal self, self-
satisfaction, athletic self, and physical appearance (Gentile et al.,
2009).
Self-construal
People form perceptions of themselves on many different
dimensions. One concerns their gender identity, that is, the
degree to which one defines the self in a masculine agentic
manner characterized by an emphasis on being autonomous,
assertive, and instrumental, or in a feminine communal manner
that emphasizes fostering social harmony and being sensitive to
others and the situation. Although an individual's sex and gender
identity are isomorphic, most males adopt a masculine agentic
identity and most females a feminine communal identity.
Interestingly, recent research indicates that males (but not females)
strongly view same- versus other-gender-typical traits as essential
to their gender identity. Hence, only males must earn their gender
identity by simultaneously demonstrating same-gender traits and
stamping out opposite-gender ones (Bosson & Michniewicz,
2013).
People's gender identity has important consequences on
their behavior. Because certain products are strongly associated
with a particular gender (e.g., meat is associated with maleness,
Rozin, Hormes, Faith, & Wansink, 2012), people whose gender
identity corresponds with such products may consume more
of them. Further, heightening the salience of people's gender
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identity also can influence their responses to products or items
of this type, although sometimes counter-productive outcomes
occur. Consider two studies that exemplify this point. McShane,
Bradlow, and Berger (2012) found that the sight of males driving
male-oriented vehicles (e.g., pickup trucks) increased purchases
of new vehicles of this type (i.e., male-oriented ones) more
among male than female consumers. Yet, Puntoni, Sweldens, and
Tavassoli (2011) found that females exposed to stimuli that did
versus did not make their gender identity salient contributed
lower donations to a female focused (i.e., ovarian) cancer charity.
This counterproductive outcome occurred because heightening
the salience of females' gender identity triggered a defense
mechanism, which actually lowered females' perceived risk of
acquiring a deadly disease that affects only women (i.e., ovarian
cancer).
Burgeoning work on self-construal focuses on a different
dimension of people's self-view: People vary in whether they
view themselves as independent, meaning that the self is an
autonomous unique entity that is individualistic in its pursuits,
or it is interdependent—fundamentally connected with others
and the environment (e.g., Aaker & Lee, 2001). Considerable
evidence indicates that males are typically independent and
females are interdependent in their self-construal. For example,
when given positive and negative items that corresponded
with these two self-construals, males defined themselves as
higher in independence and females viewed themselves as
higher in interdependence (Guimond, Chatard, Martinot, Crisp,
& Redersdorff, 2006). Further, Wang, Bristol, Mowen, and
Chakraborty (2000) found that males (females) were more
persuaded by adappeals that relayed separateness and differences
from others (connectedness and alignment with others).
Because gender corresponds with self-construal, some studies
have used self-construal theory to predict gender differences. For
example, Kwang, Crockett, Sanchez, and Swann (2013) posited
that being in a romantic relationship should contribute to both
sexes' self-esteem, but it should do so for different reasons.
The intimate emotional connection with another in a relationship
should contribute to females' self-worth by satisfying their
interdependent needs. However, for males who value indepen-
dence and being distinctive, such emotional connections should
not be relevant; instead, a utilitarian benefit of being in a
relationship should enhance males' self-worth, for being in a
relationship can signify elevated social distinctiveness or status,
and thus bolster males' self-esteem. Studies supported these
deductions.
Refinements of self-construal theory have led scholars to
distinguish between two types of interdependence, with the
genders favoring different types. Females favor relational
interdependence by forming dyadic relationships with individual
entities (e.g., being a friend of Mary). However, males satisfy
their belongingness needs via collective-interdependence,where
their connection involves membership in a larger collective
(e.g., being a Cubs fan). Accordingly, Maddux and Brewer
(2005) foundthatwhethermalestrustedapersoninanonline
game depended on whether they shared a group membership
(i.e., the person was a student at their own versus a different
university), but females trusted people who shared either a
direct connection (i.e., the person was a student at their own
university) or an indirect relationship connection (i.e., the person
was a student at a different university that an acquaintance also
attended). Similar outcomes emerged in the types of commercial
entities that garnered the genders' loyalty. Melnyk, van Osselaer,
and Bijmolt (2009) found that males were more loyal to
multi-person entities or companies, but females were more loyal
to individual service providers.
The correspondence between individual's self-construal and
their gender suggests that many other outcomes on which
independents versus interdependents have been found to differ
are also apt to show gender differences. The propositions offered
next underscore this parallel by identifying and linking outcomes
for which self-construal differences have been reported with
gender studies that show similar outcomes: Compared to their
counterparts (interdependents and females), independents and
males are less likely to conform in response to social pressure
(see Eagly & Chrvala, 1986; Torelli, 2006), show less sensitivity
to the context in which stimuli appear (see Kühnen, Hannover, &
Schubert, 2001; Noseworthy et al., 2011), display less sensitivity
to the mental perspective of another person (see Wu & Keysar,
2007; You et al., 2011), attend more to generalities versus
specifics (e.g., traits vs. exemplars; see Ng & Houston, 2006;
Roalf, Lowery, & Turetsky, 2006), adopt a promotion (versus
prevention) regulatory focus (see Aaker & Lee, 2001; McKay-
Nesbitt et al., 2013), and exhibit greater impulsiveness (see Cross,
Copping, & Campbell, 2011; Zhang & Shrum, 2009).
Agency-aligned emotions: anger and hostility
Anger is experienced about equally by both genders (Kring,
2000), a finding consistent across cultures (Fischer et al., 2004).
However, the expression of anger differs by gender. Anger
tends to trigger direct forms of aggression, such as irritability,
conduct disorders, confrontation and violence, more commonly
for men and boys than for women and girls (Anderson &
Bushman, 2002; Baxendale, Cross, & Johnston, 2012; Berkout,
Young, & Gross, 2011). In contrast, females use more indirect
aggression, such as excluding others, and use direct aggression
only when resources are extremely scarce (Griskevicius et al.,
2009).
Different theories can explain males' greater involvement in
crime and violence that is precipitated by anger. Social role
theorists contend that gender differences in poverty, parenting
styles, and coping styles in response to stress contribute to such
differences (Bennett, Farrington, & Huesmann, 2005; Berkout
et al., 2011). For example, females may be more apt to learn
verbal “scripts”to use in response to anger and they may also
benefit from greater interhemispheric communication (Bennett
et al., 2005). Evolutionary theory suggests that aggression is an
expression of males' desire for status and dominance as this
facilitates acquiring mates (Griskevicius et al., 2009). Biological
theories posit that such gender differences may stem from
hormonal variation, such as males' higher level of testosterone, a
hormone that has been linked to feelings of power, anger,
dominance, and aggression (Peterson & Harmon-Jones, 2012).
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Sexual activities
Clear gender differences exist in cognitions, attitudes, and
behaviors related to sexual activity. Men exhibit a stronger
sex drive than women (Baumeister, Catanese, & Vohs, 2001;
Petersen & Hyde, 2010), as evidenced by more frequent
thoughts about sex, desiring sex more often, desiring more
sexual partners, masturbation frequency, more initiation of sex,
greater likelihood of engaging in casual sex, more permissive
attitudes toward casual sex, greater use of pornography, and
more interest in a broader variety of sexual activities. No
studies report more desire for sexual activity among women
than men (Baumeister et al., 2001).
Both biological and socio-cultural theories argue that men
have a greater desire for casual sex and women have a greater
desire for long-term commitment. Evolutionary theory (e.g., Buss
& Schmitt, 1993) proposes that because women tend to invest
more time and energy in producing offspring, they are more
selective and likely to seek a partner who will commit to a
long-term relationship. In contrast, men produce more offspring
by engaging in sexual activity with many women, fostering a
drive for more and varied sex. And at some threshold level,
hormones, like higher levels of testosterone, can increase the sex
drives of both men and women (cf. Baumeister et al., 2001). A
socio-cultural view (e.g., Wood & Eagly, 2012)proposesthatthe
male role is associated with power and control of resources, and
due to their more dominant status, men may be more likely to
expect women to satisfy their physical needs. Women's prefer-
ences for committed relationships may stem from their historical
dependence on men to secure resources and their greater desire
for interpersonal communication. Cultural components such as
sexualized images in media also may reinforce gender power and
role differences.
Based on a meta-analysis and large data sets, Petersen and
Hyde (2010) found that while men have more permissive sexual
attitudes and more varied experiences, gender differences are
smaller in cultures that have more gender equity. Others
(e.g., Wood & Eagly, 2012) contend that context based gender
differences may exist. For example, differing cognitive processing
styles may contribute to gender effects in reporting incidence of
sexual partners. Men tend to estimate the number of partners and
then round up, while women try to recall each partner and, due to
occasional forgetting of some, tend to undercount (Baumeister et
al., 2001). Note that females' use of a more detailed internal search
is consistent with the selectivity hypothesis.
An emerging body of literature on sexuality suggests that the
female sex drive is more malleable than males' in response
to socio-cultural and contextual variables. Baumeister (2000)
reviewed three types of evidence supporting this thesis. First,
individual women more than individual men vary in their
sexual behavior across time. For example, women are better
than men at adapting to more or less sexual activity, and they
also tend to develop more permissive attitudes toward sexual
activity over their lifetimes. Second, females' versus males'
sexuality responses show larger effects to socio-cultural
variables like education, religion, political ideology, or peer
influence. Third, the relationship between sexual attitudes and
sexual behaviors is lower for women than for men. For
example, attitudes toward virginity or approval of extramarital
sex have weaker links to behavior for women than for men.
Further, women report higher sexual incidence when told that
their responses are private (versus public) and when told that
lying can be detected (Alexander & Fisher, 2003). Baumeister
(2000) proposes that women may need to adapt to the needs of
men who have greater control of resources. Such malleability
is consistent too with the selectivity hypothesis in that women
may be more likely than men to review contextual information
and weigh that in addition to their physical arousal. Women
also have the ability to have intercourse at times when they
lack sexual desire, whereas men generally do not. In contrast to
women's varied responses, research suggests that men's responses
are determined more consistently by physiological arousal.
A meta-analysis that examined how the genders respond to
sexually explicit content showed that women responded more
negatively than men (Allen et al., 2007). Dahl, Sengupta, and
Vohs (2009) also observed more negative responses from women
to sexually explicit advertising. But they were able to reduce these
negative responses by priming women with data about a man's
commitment to a woman. In contrast, men's responses did not
vary with such a prime or even when the sexually explicit content
was paired with thoughts of a disloyal partner.
Baumeister and Vohs (2004) proposed a sexual economics
theory of gender differences in sexual activity. They contend that
the price of sex will vary with supply and demand, competition,
and other marketplace factors. Given their greater desire for
sexual activity, men are the “buyers”and female sexuality is the
valued product. Supportive evidence for this comes from research
on prostitution, courtship rituals (e.g., gift-giving), and female
competition (e.g., sexual appeal relative to other women). The
researchers concluded that in heterosexual relationships, female
sexuality has high exchange value, whereas male sexuality has
little or none.
Conclusions
The research discussed in this review implies several
important conclusions. First, it reinforces the agentic–communal
gender role distinction by indicating that males generally
emphasize instrumentality and independence, whereas females
value inclusiveness and interdependence. A smattering of the
many examples includes the following. Compared to females,
males are likely to favor promotions that benefit the self (versus
others), spend more money to elevate their status when they shop
with others, favor more efficient online shopping, and use
detachment to cope with negative emotions. In contrast, females
more than males favor equity-based resource allocations that
benefit both self and others, indicate greater awareness of how
their actions affect others, are more responsive to message
appeals that focus on helping others (not just people in their own
in-group), prefer the social- and sensory-rich atmosphere of
traditional shopping, favor loyalty programs that are person-
alized yet not visible to others, and use social support to cope
with negative emotions. Notably, many other examples of such
differences also appear in the power and self-construal literatures,
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where these constructs are claimed to be defined by variation in
agency/independence versus communality/interdependence and
assorted outcomes observed on these constructs show parallel
gender differences.
A second conclusion from the research is that compared to
males, females temper or exhibit more cautiousness and
avoidance in their behavior, whereas men show more risk
seeking, assertiveness and directness. To exemplify, females
show more caution in economic transactions and competitive
situations, are less trusting in e-commerce and investment
contexts, are more concerned about their privacy, report lower
incidence of sexual activity when told that their responses are
public (versus private), and express anger indirectly (versus
directly). In contrast, males are more likely to be risk takers in
economic transactions, engage in more casual sex, use more
directive speech, express anger directly with aggression, and
respond impulsively.
A third conclusion is that females, perhaps owing to their
greater cautiousness, display greater sensitivity and responsiveness
than do males to stimuli that could have negative implications.
Thus, in addition to avoiding negative consequences associated
with risk and fraud, females are better able than males to resist
temptations, delay gratification, and regulate their anger. In
addition, females react more strongly or unfavorably to negative
images, negative messages, corporate transgressions, incidences of
product harm, and stimuli that evoke pain. Further, they also
express negative emotions, such as fear, sadness, anxiety, worry,
and depression, more than males do.
A fourth conclusion that emerges from this review is that
females tend to be more inclusive or comprehensive than males in
detecting and using data. Evidence appears in search, communi-
cation, and assessment contexts. Compared to males, females
show better detection, use, and memory for subtle facial cues,
enter more words in online queries, spend more time viewing
online search results, perform more general and specific in-store
searches, draw more inferences or ruminate about data concerning
others or professionally pertinent issues (e.g., patient health care,
loan applications, supervisory relationships), detect and form
perceptions based on multiple (versus a single) meanings of
esthetic stimuli, and engage in more extensive thought in stressful
situations. In contrast, males fixate on less data, engage in less
elaborative talk with their children, pursue more simplified or
directed search when shopping by relying on salesperson
recommendations or price data, and rely more on heuristics or
intuitions when rendering assessments. Two observations that
may help explain the preceding differences are females' greater
connectivity between their brain hemispheres and their greater
use of bilateral processing. Such properties may enable females to
access and integrate qualitatively different representations of the
same information from their two brain hemispheres or to access
more target-related data stored in different hemispheres.
A final conclusion indicated by this review is that females
display more nuanced or differentiated responses than do males
to subtle and discriminating contextual cues. For example, only
among females is their competitive performance sensitive to
their competitors' sex, the framing of the situation, and the
person (i.e., self or other) who benefits from their performance,
their trust sensitive to the type of out-group the target person
belongs to, and their persuasion sensitive to subtle claim wording
(e.g., hedge or pledge words). Females are also more responsive
than males to subtle variation in negative facial expressions, and
their sexual activity is more varied over a lifetime and as a
function of factors like education, religion, and peers.
Finally, many and possibly all of these conclusions may be
explained by the three origin-focused theories of gender differences
(the socio-cultural, evolutionary, and hormone-brain accounts), as
discussed in various sections of our gender literature review.
Likewise, the selectivity hypothesis can account for the conclu-
sions. Indeed, it is noteworthy that the final two conclusions
follow directly from that theory's tenets concerning females'
fuller processing and synthesis of a larger array of relevant data.
Opportunities
How can our understanding of gender differences be furthered
and grown? One challenge is to develop a larger and more
encompassing theory capable of integrating the many individual
gender difference findings. Along such lines, connections may
exist between each gender's cognitive processing approach and
their temperament. For example, could females' greater expres-
sion of anxiety, worry, fear, and sadness emerge as a consequence
of their more comprehensive processing? If females consider their
environment and related contexts more fully than males by, say,
elaborating on each constituent event, imagining the alternative
ways in which their actions might play out, ruminating about both
the upsides and downsides of potential outcomes, such cognition
might exert a toll on their feelings (i.e., increase the incidence
of negative emotions) and prompt females to be more wary of
risk and competition as well as experience deflated confidence
concerning their prospects. Likewise it could be that males'
increased propensity to exhibit direct anger, greater aggres-
sion, and weaker resistance to temptations are consequences of
their more selective processing. Research needs to explore such
possible connections. In addition, theory is needed that not only
sheds light on the full spectrum of potential benefits and costs of
each gender's manner of processing or response pattern, but also
anticipates when which type of these outcomes (i.e., benefits or
costs) will occur.
A second avenue for making progress—one of particular
benefit to consumer research—would be to deepen our under-
standing of the cognitive mechanisms that underlie the genders'
responses. This might entail developing new mid-range gender
theories that, similar to the selectivity hypothesis, shed light on
important aspects of the genders' cognitive processing. Examples
of questions in need of answers are: how do males and females
identify which pieces of data will be most influential in shaping
their responses, how do they resolve conflicting implications
suggested by multiple yet equally accessible and compelling
pieces of data, and how do they prioritize the importance they
assign to explicit claims versus the inferences they make from
such claims?
A third question that must be answered entails distinguishing
between and anticipating when data will serve simply as a detail
and when it will serve as a heuristic cue. Specifically, when will
144 J. Meyers-Levy, B. Loken / Journal of Consumer Psychology 25, 1 (2015) 129–149

information that is often viewed as tangential and of scant
diagnosticity for the target issue (e.g., the color of store signage or
a service provider's manner of dress) operate as a detail cue that
females, as more comprehensive processors, are more likely to
detect and incorporate in their assessments, and when will it
operate as a single, salient, easily processed heuristic cue that
males are more likely to employ to simplify assessment-making?
A fourth opportunity for advancement involves identifying
critical factors that can qualify whether gender differences will
be observed or alter the nature of their direction. Insufficient
identification or explication of such factors can mask gender
differences in meta-analyses, which typically are regarded as
the most powerful tests of such differences, and this may
explain why multiple meta-analyses may arrive at different
conclusions. For example, although abundant studies find that
females are more moral or ethical than males, a meta-analysis
produced an inconclusive verdict (Jaffee & Hyde, 2000). It is
possible that an important qualifying factor ignored by the
meta-analysis was whether the studies assessed participants'
self-reported responses to a list of options provided about a self-
generated or hypothetical ethical situation, or studies instead
assessed participants' actual behavior to an experienced ethical
situation responded to in real-time.
A final area that requires more investigation involves
conducting studies that provide more direct evidence for the
five conclusions identified in the preceding section. Although
considerable extant research seems to support these deductions,
for all but the first conclusion, few studies have been designed
expressly to test the focal premises. Systematic, theoretical, and
programmatic research that focuses squarely on understanding
gender differences is especially sparse in the consumer literature,
even though this topic seems to be of crucial importance to both
consumer researchers and marketers whose interest centers on
consumers and their behavior. Further, consumer research
itself could be advanced if researchers investigated when
gender differences emerge in various consumption-related do-
mains, such as products (e.g., instructions or claims on packaging,
selection of brand names and symbols), price (e.g., reliance on
price-quality inferences, consumers' derivation of reference
prices), promotion (e.g., the use of color to influence emotions or
motivations, brand positioning, social media practices), and place
(e.g., website design, in-store experiential activities).
We hope that this article and the opportunities identified
will serve as a catalyst for researchers, particularly consumer
researchers, to study gender. Although considerable progress
has been made in understanding how consumers' gender affects
their assessments and other consumption-related behaviors, many
compelling questions still remain to be tackled and provide every
reason to believe that inquiry into this topic will continue to be a
fascinating, fruitful, and relevant area of study.
Contribution statement
To date, researchers of consumer psychology have devoted
limited theoretical attention to gender differences, even though
such differences would seem to be central to understanding
consumer behavior. Moreover, no review of the research on
gender differences has been published in the consumer literature.
This paper aims to address this gap and propel further research in
this area. It discusses the major theories that have been offered
concerning the ontogeny of gender differences, and it reviews the
past 14 years of research published on gender differences in the
areas of marketing, psychology, and biomedicine. Based on a
synthesis of this literature, we propose five major conclusions
concerning gender differences that emerge from the work
reviewed, and identify several areas of opportunity that offer
important and fruitful avenues for advancing our understanding
of gender differences.
References
Aaker, J. L., & Lee, A. Y. (2001). “I”seek pleasures and “we”avoid pains: The
role of self‐regulatory goals in information processing and persuasion.
Journal of Consumer Research,28(1), 33–49.
Ackerman, J. M., Griskevicius, V., & Li, N. P. (2011). Let's get serious:
Communicating commitment in romantic relationships. Journal of
Personality and Social Psychology,100(6), 1079–1094.
Alexander, M. G., & Fisher, T. D. (2003). Truth and consequences: Using the
bogus pipeline to examine sex differences in self‐reported sexuality.
Journal of Sex Research,40(1), 27–35.
Allen, M., Emmers-Sommer, T. M., D'Alessio, D., Timmerman, L., Hanzal, A.,
& Korus, J. (2007). The connection between the physiological and
psychological reactions to sexually explicit materials: A literature summary
using meta-analysis. Communication Monographs,74(4), 541–560.
Amanatullah, E. T., & Morris, M. W. (2010). Negotiating gender roles: Gender
differences in assertive negotiating are mediated by women's fear of
backlash and attenuated when negotiating on behalf of others. Journal of
Personality and Social Psychology,98(2), 256–267.
Anderson, C. A., & Bushman, B. J. (2002). Human aggression. Annual Review
of Psychology,53(1), 27–51.
Auyeung, B., Baron-Cohen, S., Ashwin, E., Knickmeyer, R., Taylor, K.,
Hackett, G., et al. (2009). Fetal testosterone predicts sexually differentiated
childhood behavior in girls and in boys. Psychological Science,20(2),
144–148.
Barone, M. J., & Roy, T. (2010). Does exclusivity always pay off? Exclusive
price promotions and consumer response. Journal of Marketing,74(2),
121–132.
Baumeister, R. F. (2000). Gender differences in erotic plasticity: The female sex
drive as socially flexible and responsive. Psychological Bulletin,126(3),
347–374.
Baumeister, R. F., Catanese, K. R., & Vohs, K. D. (2001). Is there a gender
difference in strength of sex drive? Theoretical views, conceptual
distinctions, and a review of relevant evidence. Personality and Social
Psychology Review,5(3), 242–273.
Baumeister, R. F., & Vohs, K. D. (2004). Sexual economics: Sex as female
resource for social exchange in heterosexual interactions. Personality and
Social Psychology Review,8(4), 339–363.
Baxendale, S., Cross, D., & Johnston, R. (2012). A review of the evidence on
the relationship between gender and adolescents' involvement in violent
behavior. Aggression and Violent Behavior,17(4), 297–310.
Beaudoin, P., & Lachance, M. J. (2006). Determinants of adolescents' brand
sensitivity to clothing. Family and Consumer Sciences Research Journal,
34(4), 312–331.
Beck, T., Behr, P., & Guettler, A. (2013). Gender and banking: Are women
better loan officers? Review of Finance,17(4), 1279–1321.
Bennett, S., Farrington, D. P., & Huesmann, L. R. (2005). Explaining gender
differences in crime and violence: The importance of social cognitive skills.
Aggression and Violent Behavior,10(3), 263–288.
Berkout, O. V., Young, J. N., & Gross, A. M. (2011). Mean girls and bad boys:
Recent research on gender differences in conduct disorder. Aggression and
Violent Behavior,16(6), 503–511.
145J. Meyers-Levy, B. Loken / Journal of Consumer Psychology 25, 1 (2015) 129–149

Berney-Reddish, I. A., & Areni, C. S. (2006). Sex differences in responses to
probability markers in advertising claims. Journal of Advertising,35(2), 7–16.
Bjorklund, D. F., & Kipp, K. (1996). Parental investment theory and gender
differences in the evolution of inhibition mechanisms. Psychological
Bulletin,120(2), 163–188.
Bosson, J. K., & Michniewicz, K. S. (2013). Gender dichotomization at the
level of ingroup identity: What it is, and why men use it more than women.
Journal of Personality and Social Psychology,105(3), 425–442.
Bourne, V. J. (2005). Lateralised processing of positive facial emotion: Sex
differences in strength of hemispheric dominance. Neuropsychologia,43(6),
953–956.
Brunel, F. F., & Nelson, M. R. (2000). Explaining gendered responses to “help-
self”and “help-others”charity ad appeals: The mediating role of world-
views. Journal of Advertising,29(3), 15–28.
Buchan, N. R., Croson, R. T., & Solnick, S. (2008). Trust and gender: An
examination of behavior and beliefs in the Investment Game. Journal of
Economic Behavior & Organization,68(3), 466–476.
Buss, D. M., & Schmitt, D. P. (1993). Sexual strategies theory: An evolutionary
perspective on human mating. Psychological Review,100(2), 204–232.
Card, N. A., Stucky, B. D., Sawalani, G. M., & Little, T. D. (2008). Direct and
indirect aggression during childhood and adolescence: A meta‐analytic
review of gender differences, intercorrelations, and relations to maladjustment.
Child Development,79(5), 1185–1229.
Carli, L. L. (1999). Gender, interpersonal power, and social influence. Journal
of Social Issues,55(1), 81–99.
Chang, C. (2007). The relative effectiveness of comparative and noncompar-
ative advertising: Evidence for gender differences in information-processing
strategies. Journal of Advertising,36(1), 21–35.
Chaplin, T. M., Cole, P. M., & Zahn-Waxler, C. (2005). Parental socialization
of emotion expression: Gender differences and relations to child adjustment.
Emotion,5(1), 80–88.
Charness, G., & Gneezy, U. (2012). Strong evidence for gender differences in
risk taking. Journal of Economic Behavior & Organization,83(1), 50–58.
Chen, T., Kalra, A., & Sun, B. (2009). Why do consumers buy extended service
contracts? Journal of C onsumer Research,36(4), 611–623.
Childs, J. (2012). Gender differences in lying. Economics Letters,114(2),
147–149.
Cohen-Bendahan, C. C., van de Beek, C., & Berenbaum, S. A. (2005). Prenatal
sex hormone effects on child and adult sex-typed behavior: Methods and
findings. Neuroscience & Biobehavioral Reviews,29(2), 353–384.
Croson, R., & Gneezy, U. (2009). Gender differences in preferences. Journal of
Economic Literature,47(2), 448–474.
Cross, C. P., Copping, L. T., & Campbell, A. (2011). Sex differences in
impulsivity: A meta-analysis. Psychological Bulletin,137(1), 97–130.
Dahl, D. W., Sengupta, J., & Vohs, K. D. (2009). Sex in advertising: Gender
differences and the role of relationship commitment. Journal of Consumer
Research,36(2), 215–231.
Danaher, P. J., Mullarkey, G. W., & Essegaier, S. (2006). Factors affecting web
site visit duration: A cross-domain analysis. Journal of Marketing Research,
43(2), 182–194.
Day, A. L., & Livingstone, H. A. (2003). Gender differences in perceptions of
stressors and utilization of social support among university students.
Canadian Journal of Behavioural Science,35(2), 73–83.
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-
being: Three decades of progress. Psychological Bulletin,125(2), 276–302.
Dittmar, H., Long, K., & Meek, R. (2004). Buying on the Internet: Gender
differences in on-line and conventional buying motivations. Sex Roles,
50(5–6), 423–444.
do Paço, A. M. F., & Reis, R. (2012). Factors affecting skepticism toward green
advertising. Journal of Advertising,41(4), 147–155.
Dommer, S. L., & Swaminathan, V. (2013). Explaining the endowment effect
through ownership: The role of identity, gender, and self-threat. Journal of
Consumer Research,39(5), 1034–1050.
Doughty, E. A., & Leddick, G. R. (2007). Gender differences in the supervisory
relationship. Journal of Professional Counseling: Practice, Theory, &
Research,35(2), 17–30.
Dreber, A., & Johannesson, M. (2008). Gender differences in deception.
Economics Letters,99(1), 197–199.
Durante, K. M., Griskevicius, V., Hill, S. E., Perilloux, C., & Li, N. P. (2011).
Ovulation, female competition, and product choice: Hormonal influences on
consumer behavior. Journal of Consumer Research,37(6), 921–934.
Eagly, A. H., & Chrvala, C. (1986). Sex differences in conformity: Status and
gender role interpretations. Psychology of Women Quarterly,10(3),
203–220.
Eagly, A. H., & Wood, W. (2013). The nature–nurture debates 25 years of
challenges in understanding the psychology of gender. Perspectives on
Psychological Science,8(3), 340–357.
Ellis, L. (2006). Gender differences in smiling: An evolutionary
neuroandrogenic theory. Physiology & Behavior,88(4), 303–308.
Ellis, B. J., Del Giudice, M., Dishion, T. J., Figueredo, A. J., Gray, P.,
Griskevicius, V., et al. (2012). The evolutionary basis of risky adolescent
behavior: Implications for science, policy, and practice. Developmental
Psychology,48(3), 598–632.
Erat, S., & Gneezy, U. (2012). White lies. Management Science,58(4),
723–733.
Ertac, S., & Gurdal, M. Y. (2012). Deciding to decide: Gender, leadership and
risk-taking in groups. Journal of Economic Behavior & Organization,
83(1), 24–30.
Fehr, R., Gelfand, M. J., & Nag, M. (2010). The road to forgiveness: A meta-
analytic synthesis of its situational dispositional correlates. Psychological
Bulletin,136(5), 894–914.
Fehr-Duda, H., De Gennaro, M., & Schubert, R. (2006). Gender, financial risk,
and probability weights. Theory and Decision,60(2–3), 283–313.
Feingold, A. (1994). Gender differences in variability in intellectual abilities: A
cross-cultural perspective. Sex Roles,30(1–2), 81–92.
Fischer, A. H., & Mosquera, P. M. R. (2001). What concerns men? Women or
other men?: A critical appraisal of the evolutionary theory of sex differences
in aggression. Psychology, Evolution & Gender,3(1), 5–25.
Fischer, A. H., Rodriguez Mosquera, P. M., van Vianen, A. E., & Manstead, A.
S. (2004). Gender and culture differences in emotion. Emotion,4(1), 87–94.
Fisher, R. J., & Dubé, L. (2005). Gender differences in responses to emotional
advertising: A social desirability perspective. Journal of Consumer Research,
31(4), 850–858.
Ford, N., Miller, D., & Moss, N. (2001). The role of individual differences in
Internet searching: An empirical study. Journal of the American Society for
Information Science and Technology,52(12), 1049–1066.
Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived
risk of buying online and the effects of receiving a site recommendation.
Journal of Business Research,57(7), 768–775.
Garside, R. B., & Klimes-Dougan, B. (2002). Socialization of discrete negative
emotions: Gender differences and links with psychological distress. Sex
Roles,47(3–4), 115–128.
Gentile, B., Grabe, S., Dolan-Pascoe, B., Twenge, J. M., Wells, B. E., &
Maitino, A. (2009). Gender differences in domain-specific self-esteem: A
meta-analysis. Review of General Psychology,13(1), 34–45.
Glenberg, A. M., Webster, B. J., Mouilso, E., Havas, D., & Lindeman, L. M.
(2009). Gender, emotion, and the embodiment of language comprehension.
Emotion Review,1(2), 151–161.
Glick, P., & Fiske, S. T. (2001). An ambivalent alliance: Hostile and benevolent
sexism as complementary justifications for gender inequality. American
Psychologist,56(2), 109–118.
Gneezy, U., Niederle, M., & Rustichini, A. (2003). Performance in competitive
environments: Gender differences. The Quarterly Journal of Economics,
118(3), 1049–1074.
Gneezy, U., & Rustichini, A. (2004). Gender differences in competition at a
young age. American Economic Review,94(2), 337–381.
Gong, G., He, Y., & Evans, A. C. (2011). Brain connectivity gender makes a
difference. The Neuroscientist,17(5), 575–591.
Graff, S. (2013). Brain connectivity study reveals striking differences
between men and women, Penn Medicine. Retrieved February 5, 2014,
from http://www.uphs.upenn.edu/news/News_Releases/2013/12/verma/
Griskevicius, V., Cialdini, R. B., & Kenrick, D. T. (2006). Peacocks, Picasso,
and parental investment: The effects of romantic motives on creativity.
Journal of Personality and Social Psychology,91(1), 63–76.
Griskevicius, V., Tybur, J. M., Ackerman, J. M., Delton, A. W., Robertson, T.
E., & White, A. E. (2012). The financial consequences of too many men:
146 J. Meyers-Levy, B. Loken / Journal of Consumer Psychology 25, 1 (2015) 129–149

Sex ratio effects on saving, borrowing, and spending. Journal of Personality
and Social Psychology,102(1), 69–80.
Griskevicius, V., Tybur, J. M., Gangestad, S. W., Perea, E. F., Shapiro, J. R., &
Kenrick, D. T. (2009). Aggress to impress: Hostility as an evolved context-
dependent strategy. Journal of Personality and Social Psychology,96(5),
980–994.
Grohmann, B. (2009). Gender dimensions of brand personality. Journal of
Marketing Research,46(1), 105–119.
Guimond, S., Chatard, A., Martinot, D., Crisp, R. J., & Redersdorff, S. (2006).
Social comparison, self-stereotyping, and gender differences in self-construals.
Journal of Personality and Social Psychology,90(2), 221–242.
Hall, J. A., & Matsumoto, D. (2004). Gender differences in judgments of
multiple emotions from facial expressions. Emotion,4(2), 201–206.
Hampson, E., van Anders, S. M., & Mullin, L. I. (2006). A female advantage in
the recognition of emotional facial expressions: Test of an evolutionary
hypothesis. Evolution and Human Behavior,27(6), 401–416.
Hargittai, E., & Shafer, S. (2006). Differences in actual and perceived online
skills: The role of gender. Social Science Quarterly,87(2), 432–448.
Hays, N. A. (2013). Fear and loving in social hierarchy: Sex differences in
preferences for power versus status. Journal of Experimental Social
Psychology,49(6), 1130–1136.
He, X., Inman, J. J., & Mittal, V. (2008). Gender jeopardy in financial risk
taking. Journal of Marketing Research,45(4), 414–424.
Heisz, J. J., Pottruff, M. M., & Shore, D. I. (2013). Females scan more than
males: A potential mechanism for sex differences in recognition memory.
Psychological Science,24(7), 1157–1163.
Helweg-Larsen, M., Cunningham, S. J., Carrico, A., & Pergram, A. M. (2004).
To nod or not to nod: An observational study of nonverbal communication
and status in female and male college students. Psychology of Women
Quarterly,28(4), 358–361.
Hill,S.E.,Rodeheffer,C.D.,Griskevicius,V.,Durante,K.,&White,A.E.(2012).
Boosting beauty in an economic decline: Mating, spending, and the lipstick
effect. Journal of Personality and Social Psychology,103(2), 275–291.
Hines, M. (2004). Brain gender. Oxford: Oxford University Press.
Hines, M. (2006). Prenatal testosterone and gender-related behaviour. European
Journal of Endocrinology,155(1), 115–121.
Hines, M. (2010). Sex-related variation in human behavior and the brain.
Trends in Cognitive Sciences,14(10), 448–456.
Hines, M., Golombok, S., Rust, J., Johnston, K. J., & Golding, J. (2002).
Testosterone during pregnancy and gender role behavior of preschool children:
A longitudinal, population study. Child Development,73(6), 1678–1687.
Holt, J. L., & DeVore, C. J. (2005). Culture, gender, organizational role, and
styles of conflict resolution: A meta-analysis. International Journal of
Intercultural Relations,29(2), 165–196.
Hsieh, Y. C., Chiu, H. C., & Lin, C. C. (2006). Family communication and
parental influence on children's brand attitudes. Journal of Business
Research,59(10), 1079–1086.
Hupfer, M. E., & Detlor, B. (2006). Gender and web information seeking: A
self‐concept orientation model. Journal of the American Society for
Information Science and Technology,57(8), 1105–1115.
Jaffee, S., & Hyde, J. S. (2000). Gender differences in moral orientation: A
meta-analysis. Psychological Bulletin,126(5), 703–726.
Judge, T. A., Livingston, B. A., & Hurst, C. (2012). Do nice guys—and gals—
really finish last? The joint effects of sex and agreeableness on income.
Journal of Personality and Social Psychology,102(2), 390–407.
Kemp, E., Kennett-Hensel, P. A., & Kees, J. (2013). Pulling on the heartstrings:
Examining the effects of emotions and gender in persuasive appeals.
Journal of Advertising,42(1), 69–79.
Kenrick, D. T., & Luce, C. L. (2000). An evolutionary life-history model of
gender differences and similarities. In T. Eckes, & H. M. Trautner (Eds.),
The developmental social psychology of gender (pp. 35–63). Mahwah, NJ:
Erlbaum.
Kirkland, R. A., Peterson, E., Baker, C. A., Miller, S., & Pulos, S. (2013). Meta-
analysis reveals adult female superiority in“reading the mind in the eyes
test”.North American Journal of Psychology,15(1), 121–146.
Klein, K. J., & Hodges, S. D. (2001). Gender differences, motivation, and
empathic accuracy: When it pays to understand. Personality and Social
Psychology Bulletin,27(6), 720–730.
Klink, R. R. (2000). Creating brand names with meaning: The use of sound
symbolism. Marketing Letters,11(1), 5–20.
Klink, R. R. (2009). Gender differences in new brand name response.
Marketing Letters,20(3), 313–326.
Knight, G. P., Guthrie, I. K., Page, M. C., & Fabes, R. A. (2002). Emotional
arousal and gender differences in aggression: A meta‐analysis. Aggressive
Behavior,28(5), 366–393.
Koch, K., Pauly, K., Kellermann, T., Seiferth, N. Y., Reske, M., Backes, V.,
et al. (2007). Gender differences in the cognitive control of emotion: An
fMRI study. Neuropsychologia,45(12), 2744–2754.
Koenig, A. M., Eagly, A. H., Mitchell, A. A., & Ristikari, T. (2011). Are leader
stereotypes masculine? A meta-analysis of three research paradigms.
Psychological Bulletin,137(4), 616–642.
Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005).
Oxytocin increases trust in humans. Nature,435(7042), 673–676.
Kotzé, T., North, E., Stols, M., & Venter, L. (2012). Gender differences in
sources of shopping enjoyment. International Journal of Consumer Studies,
36(4), 416–424.
Kring, A. M. (2000). Gender and anger. In A. H. Fischer (Ed.), Gender and
emotions (pp. 211–231). Cambridge University Press.
Kühnen, U., Hannover, B., & Schubert, B. (2001). The semantic–procedural
interface model of the self: The role of self-knowledge for context-
dependent versus context-independent modes of thinking. Journal of
Personality and Social Psychology,80(3), 397–409.
Kurt, D., Inman, J. J., & Argo, J. J. (2011). The influence of friends on
consumer spending: The role of agency–communion orientation and self-
monitoring. Journal of Marketing Research,48(4), 741–754.
Kwak, H., Fox, R. J., & Zinkhan, G. M. (2002). What products can be
successfully promoted and sold via the internet? Journal of Advertising
Research,42(1), 23–38.
Kwang, T., Crockett, E. E., Sanchez, D. T., & Swann, W. B. (2013). Men seek
social standing, women seek companionship: Sex differences in deriving
self-worth from relationships. Psychological Science,24(7), 1142–1150.
Lanvers, U. (2004). Gender in discourse behaviour in parent–child dyads: A
literature review. Child: Care, Health and Development,30(5), 481–493.
Large, A., Beheshti, J., & Rahman, T. (2002). Gender differences in collaborative
web searching behavior: An elementary school study. Information Processing
& Management,38(3), 427–443.
Laroche, M., Saad, G., Cleveland, M., & Browne, E. (2000). Gender differences
in information search strategies for a Christmas gift. Journal of Consumer
Marketing,17(6), 500–522.
Laufer, D., & Gillespie, K. (2004). Differences in consumer attributions of
blame between men and women: The role of perceived vulnerability and
empathic concern. Psychology & Marketing,21(2), 141–157.
Lee, T. M., Chan, C. C., Leung, A. W., Fox, P. T., & Gao, J. H. (2009). Sex-
related differences in neural activity during risk taking: An fMRI study.
Cerebral Cortex,19(6), 1303–1312.
Lee, E. J., & Schumann, D. W. (2009). Proposing and testing the contextual
gender influence theory: An examination of gender influence types on trust
of computer agents. Journal of Consumer Psychology,19(3), 440–450.
Li, Y. J., Kenrick, D. T., Griskevicius, V., & Neuberg, S. L. (2012). Economic
decision biases and fundamental motivations: How mating and self-
protection alter loss aversion. Journal of Personality and Social
Psychology,102(3), 550–561.
Li, H., Yuan, J., & Lin, C. (2008). The neural mechanism underlying the female
advantage in identifying negative emotions: An event-related potential
study. NeuroImage,40(4), 1921–1929.
Lin, Y. C., & Raghubir, P. (2005). Gender differences in unrealistic optimism
about marriage and divorce: Are men more optimistic and women more
realistic? Personality and Social Psychology Bulletin,31(2), 198–207.
Lindenmeier, J., Schleer, C., & Pricl, D. (2012). Consumer outrage: Emotional
reactions to unethical corporate behavior. Journal of Business Research,
65(9), 1364–1373.
Maddux, W. W., & Brewer, M. B. (2005). Gender differences in the relational
and collective bases for trust. Group Processes & Intergroup Relations,
8(2), 159–171.
Manson, J. E. (2008). Prenatal exposure to sex steroid hormones and
behavioral/cognitive outcomes. Metabolism,57(2), S16–S21.
147J. Meyers-Levy, B. Loken / Journal of Consumer Psychology 25, 1 (2015) 129–149

Martin, B. A. (2003). The influence of gender on mood effects in advertising.
Psychology & Marketing,20(3), 249–273.
Mathews, G. A., Fane, B. A., Conway, G. S., Brook, C. G., & Hines, M. (2009).
Personality and congenital adrenal hyperplasia: Possible effects of prenatal
androgen exposure. Hormones and Behavior,55(2), 285–291.
Matud, M. P. (2004). Gender differences in stress and coping styles. Personality
and Individual Differences,37(7), 1401–1415.
McClure, E. B. (2000). A meta-analytic review of sex differences in facial
expression processing and their development in infants, children, and
adolescents. Psychological Bulletin,126(3), 424–453.
McClure, E. B., Monk, C. S., Nelson, E. E., Zarahn, E., Leibenluft, E., Bilder,
R. M., et al. (2004). A developmental examination of gender differences in
brain engagement during evaluation of threat. Biological Psychiatry,
55(11), 1047–1055.
McKay-Nesbitt, J., Bhatnagar, N., & Smith, M. C. (2013). Regulatory fit effects
of gender and marketing message content. Journal of Business Research,
66(11), 2245–2251.
McLean, C. P., & Anderson, E. R. (2009). Brave men and timid women? A
review of the gender differences in fear and anxiety. Clinical Psychology
Review,29(6), 496–505.
McRae, K., Ochsner, K. N., Mauss, I. B., Gabrieli, J. J., & Gross, J. J. (2008).
Gender differences in emotion regulation: An fMRI study of cognitive
reappraisal. Group Processes & Intergroup Relations,11(2), 143–162.
McShane, B. B., Bradlow, E. T., & Berger, J. (2012). Visual influence and
social groups. Journal of Marketing Research,49(6), 854–871.
Melnyk, V., & van Osselaer, S. M. (2012). Make me special: Gender
differences in consumers' responses to loyalty programs. Marketing Letters,
23(3), 545–559.
Melnyk, V., van Osselaer, S. M., & Bijmolt, T. H. (2009). Are women more
loyal customers than men? Gender differences in loyalty to firms and
individual service providers. Journal of Marketing,73(4), 82–96.
Meyers-Levy, J. (1989). Gender differences in information processing: A
selectivity interpretation. In P. Cafferata, & A. Tybout (Eds.), Cognitive and
affective responses to advertising (pp. 219–260). Lexington, MA:
Lexington Books.
Meyers-Levy, J. (1994). Gender differences in cortical organization: Social and
biochemical antecedents and advertising consequences. In E. M. Clark, T.
C. Brock, & D. W. Stewart (Eds.), Attention, attitude, and affect in response
to advertising (pp. 107–122). Hillsdale, NJ: Erlbaum.
Meyers-Levy, J., & Maheswaran, D. (1991). Exploring differences in males' and
females' processing strategies. Journal of Consumer Research,18(1), 63–70.
Meyers-Levy, J., & Sternthal, B. (1991). Gender differences in the use of
message cues and judgments. Journal of Marketing Research,27(1),
84–96.
Meyers-Levy, J., & Zhu, R. (2010). Gender differences in the meanings
consumers infer from music and other aesthetic stimuli. Journal of
Consumer Psychology,20(4), 495–507.
Midha, V. (2012). Impact of consumer empowerment on online trust: An
examination across genders. Decision Support Systems,54(1), 198–205.
Miller, A. J., Worthington, E. L., Jr., & McDaniel, M. A. (2008). Gender and
forgiveness: A meta-analytic review and research agenda. Journal of Social
and Clinical Psychology,27(8), 843–876.
Monga, A. B., & Gürhan-Canli, Z. (2012). The influence of mating mind-sets
on brand extension evaluation. Journal of Marketing Research,49(4),
581–593.
Montagne, B., Kessels, R. P., Frigerio, E., de Haan, E. H., & Perrett, D. I.
(2005). Sex differences in the perception of affective facial expressions: Do
men really lack emotional sensitivity? Cognitive Processing,6(2), 136–141.
Mueller, S. C., Temple, V., Oh, E., VanRyzin, C., Williams, A., Cornwell, B.,
et al. (2008). Early androgen exposure modulates spatial cognition in
congenital adrenal hyperplasia (CAH). Psychoneuroendocrinology,33(7),
973–980.
Nekby, L., Thoursie, P. S., & Vahtrik, L. (2008). Gender and self-selection into
a competitive environment: Are women more overconfident than men?
Economics Letters,100(3), 405–407.
Ng, S., & Houston, M. J. (2006). Exemplars or beliefs? The impact of self‐view
on the nature and relative influence of brand associations. Journal of
Consumer Research,32(4), 519–529.
Noble, S. M., Griffith, D. A., & Adjei, M. T. (2006). Drivers of local merchant
loyalty: Understanding the influence of gender and shopping motives.
Journal of Retailing,82(3), 177–188.
Nolen-Hoeksema, S. (2012). Emotion regulation and psychopathology: The
role of gender. Annual Review of Clinical Psychology,8, 161–187.
Noseworthy, T. J., Cotte, J., & Lee, S. H. M. (2011). The effects of ad context
and gender on the identification of visually incongruent products. Journal of
Consumer Research,38(2), 358–375.
Ono, H., & Zavodny, M. (2003). Gender and the Internet. Social Science
Quarterly,84(1), 111–121.
Pasterski, V. L., Geffner, M. E., Brain, C., Hindmarsh, P., Brook, C., & Hines,
M. (2005). Prenatal hormones and postnatal socialization by parents as
determinants of male‐typical toy play in girls with congenital adrenal
hyperplasia. Child Development,76(1), 264–278.
Petersen, J. L., & Hyde, J. S. (2010).A meta-analytic review of research on gender
differences in sexuality, 1993–2007. Psychological Bulletin,136(1), 21–38.
Peterson, C. K., & Harmon-Jones, E. (2012). Anger and testosterone: Evidence
that situationally-induced anger relates to situationally-induced testosterone.
Emotion,12(5), 899–902.
Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral
routes to advertising effectiveness: The moderating role of involvement.
Journal of Consumer Research,10(2), 135–147.
Phillip, M. V., & Suri, R. (2004). Impact of gender differences on the evaluation
of promotional emails. Journal of Advertising Research,44(4), 360–368.
Puccinelli, N. M., Chandrashekaran, R., Grewal, D., & Suri, R. (2013). Are men
seduced by red? The effect of red versus black prices on price perceptions.
Journal of Retailing,89(2), 115–126.
Puntoni, S., Sweldens, S., & Tavassoli, N. T. (2011). Gender identity salience
and perceived vulnerability to breast cancer. Journal of Marketing
Research,48(3), 413–424.
Putrevu, S. (2004). Communicating with the sexes: Male and female responses
to print advertisements. Journal of Advertising,33(3), 51–62.
Putrevu, S. (2010). An examination of consumer responses toward attribute-
and goal-framed messages. Journal of Advertising,39(3), 5–24.
Richard, M. O., Chebat, J. C., Yang, Z., & Putrevu, S. (2010). A proposed
model of online consumer behavior: Assessing the role of gender. Journal
of Business Research,63(9), 926–934.
Riedl, R., Hubert, M., & Kenning, P. (2010). Are there neural gender
differences in online trust? An fMRI study on the perceived trustworthiness
of eBay offers. MIS Quarterly,34(2), 397–428.
Roalf, D., Lowery, N., & Turetsky, B. I. (2006). Behavioral and physiological
findings of gender differences in global–local visual processing. Brain and
Cognition,60(1), 32–42.
Robichaud, M., Dugas, M. J., & Conway, M. (2003). Gender differences in
worry and associated cognitive-behavioral variables. Journal of Anxiety
Disorders,17(5), 501–516.
Rodgers, S., & Harris, M. A. (2003). Gender and e-commerce: An exploratory
study. Journal of Advertising Research,43(3), 322–329.
Roivainen, E. (2011). Gender differences in processing speed: A review of
recent research. Learning and Individual Differences,21(2), 145–149.
Roothman, B., Kirsten, D. K., & Wissing, M. P. (2003). Gender differences in
aspects of psychological well-being. South African Journal of Psychology,
33(4), 212–218.
Rosip, J. C., & Hall, J. A. (2004). Knowledge of nonverbal cues, gender, and
nonverbal decoding accuracy. Journal of Nonverbal Behavior,28(4),
267–286.
Roy, M., Taylor, R., & Chi, M. T. (2003). Searching for information on-line and
off-line: Gender differences among middle school students. Journal of
Educational Computing Research,29(2), 229–252.
Rozin, P., Hormes, J. M., Faith, M. S., & Wansink, B. (2012). Is meat male? A
quantitative multimethod framework to establish metaphoric relationships.
Journal of Consumer Research,39(3), 629–643.
Rucker, D. D., Galinsky, A. D., & Dubois, D. (2012). Power and consumer
behavior: How power shapes who and what consumers value. Journal of
Consumer Psychology,22(3), 352–368.
Schubert, T. W. (2004). The power in your hand: Gender differences in bodily
feedback from making a fist. Personality and Social Psychology Bulletin,
30(6), 757–769.
148 J. Meyers-Levy, B. Loken / Journal of Consumer Psychology 25, 1 (2015) 129–149

Segal, B., & Podoshen, J. S. (2012). An examination of materialism,
conspicuous consumption and gender differences. International Journal of
Consumer Studies,37(2), 189–198.
Shao, C. Y., Baker, J. A., & Wagner, J. (2004). The effects of appropriateness
of service contact personnel dress on customer expectations of service
quality and purchase intention: The moderating influences of involvement
and gender. Journal of Business Research,57(10), 1164–1176.
Silverman, I. W. (2003a). Gender differences in delay of gratification: A meta-
analysis. Sex Roles,49(9–10), 451–463.
Silverman, I. W. (2003b). Gender differences in resistance to temptation:
Theories and evidence. Developmental Review,23(2), 219–259.
Silverman, I. W., & Choi, J. (2005). Locating places. In D. M. Buss (Ed.), The
handbook of evolutionary psychology (pp. 177–199). Hoboken, NJ: John
Wiley & Sons, Inc.
Small, D. A., Gelfand, M., Babcock, L., & Gettman, H. (2007). Who goes to the
bargaining table? The influence of gender and framing on the initiation of
negotiation. Journal of Personality and Social Psychology,93(4), 600–613.
Smiler, A. P. (2011). Sexual strategies theory: Built for the short term or the
long term? Sex Roles,64(9–10), 603–612.
Spassova, G., & Lee, A. Y. (2013). Looking into the future: A match between self-
view and temporal distance. Journal of Consumer Research,40(1), 159–171.
Stokburger-Sauer, N. E., & Teichmann, K. (2013). Is luxury just a female
thing? The role of gender in luxury brand consumption. Journal of Business
Research,66(7), 889–896.
Stoyanova, M., & Hope, D. A. (2012). Gender, gender roles, and anxiety:
Perceived confirmability of self report, behavioral avoidance, and physiolog-
ical reactivity. Journal of Anxiety Disorders,26(1), 206–214.
Street, R. L., Jr. (2002). Gender differences in health care provider–patient
communication: Are they due to style, stereotypes, or accommodation?
Patient Education and Counseling,48(3), 201–206.
Suetens, S., & Tyran, J. R. (2012). The gambler's fallacy and gender. Journal of
Economic Behavior & Organization,83(1), 118–124.
Sundie, J. M., Kenrick, D. T., Griskevicius, V., Tybur, J. M., Vohs, K. D., &
Beal, D. J. (2011). Peacocks, Porsches, and Thorstein Veblen: Conspicuous
consumption as a sexual signaling system. Journal of Personality and
Social Psychology,100(4), 664–680.
Syzmanowicz, A., & Furnham, A. (2011). Gender differences in self-estimates
of general, mathematical, spatial and verbal intelligence: Four meta
analyses. Learning and Individual Differences,21(5), 493–504.
Tamres, L. K., Janicki, D., & Helgeson, V. S. (2002). Sex differences in coping
behavior: A meta-analytic review and an examination of relative coping.
Personality and Social Psychology Review,6(1), 2–30.
Tenenbaum, H. R., & Leaper, C. (2002). Are parents' gender schemas related to
their children's gender-related cognitions? A meta-analysis. Developmental
Psychology,38(4), 615–630.
Tian, L., Wang, J., Yan, C., & He, Y. (2011). Hemisphere- and gender-related
differences in small-world brain networks: A resting-state functional MRI
study. NeuroImage,54(1), 191–202.
Tooby, J., & Cosmides, L. (2005). Conceptual foundations of evolutionary
psychology. In D. M. Buss (Ed.), The handbook of evolutionary psychology
(pp. 5–67). Hoboken, NJ: John Wiley & Sons, Inc.
Torelli, C. J. (2006). Individuality or conformity? The effect of independent and
interdependent self-concepts on public judgments. Journal of Consumer
Psychology,16(3), 240–248.
Toufexis, D. J., Myers, K. M., & Davis, M. (2006). The effect of gonadal
hormones and gender on anxiety and emotional learning. Hormones and
Behavior,50(4), 539–549.
Vallerand, A. H., & Polomano, R. C. (2000). The relationship of gender to pain.
Pain Management Nursing,1(3), 8–15.
Van Slyke, C., Comunale, C. L., & Belanger, F. (2002). Gender differences in
perceptions of web-based shopping. Communications of the ACM,45(8),
82–86.
Voyer, D., Postma, A., Brake, B., & Imperato-McGinley, J. (2007). Gender
differences in object location memory: A meta-analysis. Psychonomic
Bulletin & Review,14(1), 23–38.
Walker,L.J.(2006).Gender and morality. In M. Killen, & J. Smetana
(Eds.), Handbook of moral psychology (pp. 93–115). Mahwah, NJ:
Erlbaum.
Wang, L. C., Baker, J., Wagner, J. A., & Wakefield, K. (2007). Can a retail web
site be social? Journal of Marketing,71(3), 143–157.
Wang, C. L., Bristol, T., Mowen, J. C., & Chakraborty, G. (2000). Alternative
modes of self-construal: Dimensions of connectedness–separateness and
advertising appeals to the cultural and gender-specific self. Journal of
Consumer Psychology,9(2), 107–115.
Wang, Y., & Griskevicius, V. (2014). Conspicuous consumption, relationships,
and rivals: Women's luxury products as signals to other women. Journal of
Consumer Research,40(5), 834–854.
Weiser, E. B. (2000). Gender differences in Internet use patterns and Internet
application preferences: A two-sample comparison. CyberPsychology and
Behavior,3(2), 167–178.
Winterich, K. P., Mittal, V., & Ross, W. T., Jr. (2009). Donation behavior
toward in‐groups and out‐groups: The role of gender and moral identity.
Journal of Consumer Research,36(2), 199–214.
Witt, M. G., & Wood, W. (2010). Self-regulation of gendered behavior in
everyday life. Sex Roles,62, 635–646.
Wood, W., & Eagly, A. H. (2012). Biosocial construction of sex differences and
similarities in behavior. In M. P. Zanna, & J. M. Olso (Eds.), Advances in
experimental social psychology. San Diego, CA: Academic Press.
Workman, J. E., & Lee, S. H. (2013). Relationships among consumer vanity,
gender, brand sensitivity, brand consciousness and private self‐
consciousness. International Journal of Consumer Studies,37(2),
206–213.
Wrase, J., Klein, S., Gruesser, S. M., Hermann, D., Flor, H., Mann, K., et al.
(2003). Gender differences in the processing of standardized emotional
visual stimuli in humans: A functional magnetic resonance imaging study.
Neuroscience Letters,348(1), 41–45.
Wu, Shali, & Keysar, Boaz (2007). The effect of culture on perspective taking.
Psychological Science,17(7), 600–606.
Yorkston, E., & Menon, G. (2004). A sound idea: Phonetic effects of brand
names on consumer judgments. Journal of Consumer Research,31(1),
43–51.
You, D., Maeda, Y., & Bebeau, M. J. (2011). Gender differences in moral
sensitivity: A meta-analysis. Ethics and Behavior,21(4), 263–282.
Zelezny, L. C., Chua, P. P., & Aldrich, C. (2000). New ways of thinking about
environmentalism: Elaborating on gender differences in environmentalism.
Journal of Social Issues,56(3), 443–457.
Zhang, Y., & Shrum, L. J. (2009). The influence of self‐construal on impulsive
consumption. Journal of Consumer Research,35(5), 838–850.
149J. Meyers-Levy, B. Loken / Journal of Consumer Psychology 25, 1 (2015) 129–149