Willy Bolander, Cinthia B. Satornino, Douglas E. Hughes, & Gerald R. Ferris
Social Networks Within Sales
Organizations: Their Development
and Importance for
Although the study of salesperson performance traditionally has focused on salespeople’s activities and relationships
with customers, scholars recently have proposed that salespeople’s intraorganizational relationships and activities
also play a vital role in driving sales performance. Using data from 286 salespeoplein a unique social network analysis,
the authors explore the effects of salespeople’s intraorganizational relationships on objective salesperson performance
as well as the role of political skill in developing intraorganizational relationships. The results indicate that two types of
social network characteristics (i.e., relational centrality and positional centrality) contribute substantially to salesperson
performance. Moreover, salespeople’s political skill is shown to be an antecedent to relational centrality but, surprisingly,
not positional centrality. This ﬁnding demonstrates that researchers should not assume that all centralities represent
similar underlying network characteristics. In light of these results, the authors discuss several implications for both
managers and researchers as well as directions for further research.
Keywords: social network development, network centralities, sales performance, political skill, intraorganizational
For decades, marketing researchers have attempted to
understand the determinants of salesperson performance.
In addition to considering various salesperson charac-
teristics, this effort has focused largely on customer-directed
behaviors (e.g., Brown and Peterson 1994; Szymanski 1988).
Overall, this research activity has been somewhat limited in
terms of scholars’ability to explain variance in sales per-
formance (Plouffe and Barclay 2007). In response, researchers
have acknowledged the idea that salesperson performance (and
that of frontline employees, more generally) may also be
largely determined by the actions salespeople take to maneuver
and inﬂuence those within their own organizations (e.g.,
Gonzalez, Claro, and Palmatier 2014; Ustuner and Godes
Not surprisingly, researchers in marketing have begun
thinking about these intraﬁrm relationships using theories of
social exchange, social capital, and social networks (e.g.,
Ustuner and Iacobucci 2012). Initial empirical ventures into
this area have discussed the development of new measures
(e.g., Plouffe and Gr´
egoire 2011) and the application of
inﬂuence behaviors to an internal audience (e.g., Plouffe and
Barclay 2007). Of particular interest is an increasing body of
work that highlights the importance of social networks in
quantifying internal dynamics for sales organizations (e.g.,
Ahearne et al. 2012; Gonzalez et al. 2014; Ustuner and
A social network is a complex pattern of interpersonal
social ties whereby the presence of a tie between parties
serves as a conduit for information and resource ﬂow
(Balkundi and Harrison 2006; Wasserman and Faust 1994).
According to Lin (1999), there are four ways that investments
into these types of social ties function as resources that can
produce performance beneﬁts for salespeople. First, the
information ﬂowing through social ties can provide what Burt
(2010) terms a “vision advantage,”which enables people to
learn of nonobvious opportunities. Second, social ties exert
inﬂuence on decision makers who shape the opportunities
and constraints present in the network. For example, certain
network connections may offer a salesperson the ability to
provide a customer with terms, prices, or information that
would otherwise be unavailable. Third, social ties can gen-
erate “social credentials”that function as assurance that a
person has resources beyond his or her own personal
resources to use for the beneﬁt of others, including customers.
Fourth, social ties reinforce identity and recognition, which
furthers a person’s perception that (s)he has access to, and is
deserving of, important performance-enhancing resources.
Willy Bolander is Assistant Professor of Marketing, College of Business,
Florida State University (e-mail: email@example.com). Cinthia B.
Satornino (corresponding author) is Assistant Professor of Marketing,
D’Amore-McKim School of Business, Northeastern University (e-mail:
firstname.lastname@example.org). Douglas E. Hughes is Associate Professor of Mar-
keting, Eli Broad College of Business, Michigan State University (e-mail:
email@example.com). Gerald R. Ferris is Francis Eppes Professor of
Management, College of Business, Florida State University (e-mail:
firstname.lastname@example.org). All authors contributed equally to this article.
Christian Homburg served as area editor for this article.
©2015, American Marketing Association Journal of Marketing
ISSN: 0022-2429 (print) Ahead of Print
1547-7185 (electronic) DOI: 10.1509/jm.14.04441
The salesperson’s ability to create value by leveraging
these internal resources and harnessing ﬁrm capabilities to
address customer problems and needs can be an important
differentiator in a crowded marketplace. Moreover, increasing
complexities in the selling environment (e.g., longer sales
cycles, technology changes, shifting customer demands; see
Jones et al. 2005; Schmitz and Ganesan 2014) underscore the
importance of better understanding internal inﬂuences on
sales organization effectiveness. As buyers become larger,
more sophisticated, and more powerful amid a large array of
competitive offerings, the risk of commoditization has become
increasingly relevant to the seller, giving rise to a more
integrated sales approach that requires effort and cooperation
from various members within an organization (Hughes, Le
Bon, and Malshe 2012; Plouffe and Barclay 2007). This
demands a look “inward”at salesperson connectivity to add
to the vast research focusing on the external relationship
between salesperson and customer.
However, few studies provide normative direction to
marketing or sales managers on acquiring and leveraging
advantageous network positions. Our understanding of the
complex interplay between salespeople’sspeciﬁc interpersonal
skills, attained social positions within a ﬁrm’s network, and
actual sales performance is limited, and insights for sales
managers and marketing scholars remain scarce (Ryals and
Humphries 2007). Given the link between access to resources
and job performance (e.g., Seibert, Kraimer, and Liden 2001),
social network perspectives provide potent insights into internal
organizational dynamics and their impact on individual-level
outcomes (e.g., Ahearne et al. 2012; Gonzalez et al. 2014).
We therefore aim to advance the ﬁeld’s understanding of
internal drivers of sales performance—speciﬁcally, those that
relate to intraﬁrm social networks—and, in the process,
generate actionable implications for practitioners and suggest
new avenues of exploration for researchers. To that end, we
turn to our colleagues “down the hall”in management, who
have been dealing with issues of intraﬁrm relationships for
a long time, to provide useful insights that shed light on
variables affecting relationship development in organizations.
Of speciﬁc interest to our research is the concept of political
skill (Ferris et al. 2007) because it has strong ties to inter-
personal network development, in terms of social capital
theory, and a decades-long history of effectively predicting
important work outcomes such as job performance (see Ferris
et al. 2012). However, this construct has yet to be examined
in the marketing and/or sales literature.
Political skill is a multidimensional construct (i.e., com-
posed of interpersonal inﬂuence, social astuteness, networking
ability, and apparent sincerity; Ferris et al. 2007), whose
dimensions are distinct yet moderately related. Political skill
ultimately refers to a person’s ability to understand the social
dynamics of an organizational setting and apply that knowl-
edge to inﬂuence others to enhance personal or organizational
goals (Ferris, Davidson, and Perrew´
e 2005). Recent empirical
work has demonstrated its consistently strong relationships
with job performance (e.g., Munyon et al. 2015). This effect
exists, at least in part, because of political skill’scompre-
hensive nature and its expected relationship with internal social
networks (Wei, Chiang, and Wu 2012); politically skilled
people are expected to hold desirable social network positions
(Munyon et al. 2015). Despite this intuitive link, the role of
network positions as the mechanism by which political skill
affects job performance remains largely unstudied. Examining
internal dynamics as a function of political skill promises to
yield signiﬁcant and unique insights into the relationship
between intraﬁrm actions and salesperson performance.
The purpose of the present study, then, is twofold. First,
the fundamental premise of the article is that internal social
network structures, and the implied social capital that occu-
pying advantageous network positions provides, affect not
only internal outcomes such as performance evaluations and
job satisfaction for salespeople but also salespeople’s ability to
generate actual sales. We suggest two ways in which the social
structure can enhance sales performance: through relational
centrality or positional centrality. We expect both of these
desirable network positions (described in detail subsequently),
along with their unique beneﬁts, to enhance salesperson
performance. Although social network analysis has spurred a
great deal of interest in the marketing and sales literature, to
date relatively few empirical marketing studies have used
network analysis (e.g., Ahearne et al. 2012; Gonzalez et al.
2014; Ustuner and Iacobucci 2012). Some of these studies
have examined either positional or relational network char-
acteristics, but rarely do they assess both. However, we expect
each position to provide the salesperson with different types of
valuable resources (reputational vs. informational resources;
for a visual depiction, see Figure 1), each with a distinct wayof
inﬂuencing salesperson performance.
Second, the present study adds to this emerging body of
work by exploring an important antecedent of network
characteristics, a noted research gap in the sales and marketing
literature (Flaherty et al. 2012). Speciﬁcally, we test for the
effects of political skill, working through our two network
attributes, on objective salesperson performance. In so doing,
we deepen our contribution to this emerging stream of re-
search by introducing political skill as a potential antecedent
to these two important network positions that subsequently
affect salesperson performance. This is perhaps particularly
noteworthy given that political skill, along with the dimen-
sions that compose it, represents a managerially actionable
variable; understanding what political skill can and cannot
enhance within the intraorganizational network will help
deﬁne the speciﬁc skills managers can emphasize to help
individual salespeople acquire desirable network positions.
Furthermore, although a litany of salesperson traits and
characteristics could be reasoned to potentially affect social
network development, Ferris et al. (2007) argue that political
skill plays a key mediating role between individual charac-
teristics and work outcomes. Indeed, recent research has
demonstrated empirically that political skill mediates the
relationship between personality traits (e.g., extroversion,
active inﬂuence orientation) and important work outcomes
(Ferris et al. 2007; Liu et al. 2007). In other words, political
skill seems to be the variable through which these various
traits take effect, making it an ideal variable for producing a
model that is both complete and parsimonious.
There are several important questions to be addressed
through this process: Are both network attributes (i.e., relational
2 / Journal of Marketing, Ahead of Print
centrality and positional centrality) important contributors to
sales performance? Is political skill equally useful in con-
tributing to each network position? Is the effect of political skill
completely or partially mediated by these network variables?
How much of the variance in sales performance does each
centrality explain? Finally, will the inclusion of intraﬁrm
network characteristics help us explain additional variance in
salesperson performance beyond the 10%–20% range histor-
ically found in customer-directed sales research (Plouffe and
Using a unique data set that combines survey data, network
scores, and objective sales performance in a social network
analysis, our results show that both network characteristics
signiﬁcantly contribute to salesperson performance and that
modeling these variables enables us to explain 26.6% of
the variance in salespeople’s objective sales performance—
exceeding the historical range detailed by Plouffe and Barclay
(2007). This ﬁnding provides strong supporting evidence for the
suggestion that intraorganizational factors can contribute greatly
to sales performance (Ryals and Humphries 2007). Our results
also show that political skill positively inﬂuences salespeople’s
development of relational centrality, and because it takes time
to develop this social network position, this relationship is
strengthened by salespeople’s tenure in the organization.
We also ﬁnd that the interaction between political skill and
organizational tenure has an impact on sales performance
beyond that of the network characteristics.
Surprisingly, however, political skill does not affect
salespeople’s development of positional centrality, and
tenure with the organization does not serve to activate this
relationship. In a post hoc analysis probing this unexpected
outcome, we ﬁnd that positional centrality is affected by
salespeople’s personality (i.e., extroversion), which we ini-
tially included as a control variable. However, the impact of
extroversion on positional centrality, while signiﬁcant, is
small in magnitude, indicating that more work is needed to
uncover the antecedents of positional centrality. Importantly,
our ﬁndings regarding positional centrality emphasize the
need for social network studies to examine multiple types of
network attributes, as their antecedents and consequences
should not be assumed to be equivalent. Altogether, these
ﬁndings have important managerial implications, which we
explicate as the article develops.
In the following sections, we review the research on
political skill and relational and positional network attributes
through the lens of structuralist social capital theory. Next, we
discuss the conceptual model (see Figure 2), present the
theoretical underpinnings, and develop the hypotheses. We
then test the model and discuss the results. Finally, we outline
implications for theory and practice and offer suggestions for
Theoretical Foundations and
Social Capital and Network Attributes
Social capital theory (Lin 1986, 1999) stems from classical
capital theory (Lin 1999), in which capital is the investment
of resources into a marketplace with expected returns. The
focus of social capital is on social resources and investment
into the social marketplace and the returns generated from
such investment in terms of helping people fulﬁll instru-
mental needs. The theory connects macro-level (structural)
characteristics of the social fabric with micro-level (indi-
vidual) actions, capturing the complex interplay between the
structural and individual levels of social interactions. Social
capital theory asserts that the social structure generates value
through access to resources such as knowledge and authority,
which are embedded within the web of social ties. These
Contrasting Relational and Positional Network Centralities
• Salesperson A is connected to other individuals who are
themselves well connected.
• His/her connections are influentially connected.
• He/she possesses “reputational resources” derived from
access to powerful others.
• Salesperson B is connected to other individuals who are not
• His/her connections are not connected, except through
• He/she possesses “informational resources” derived from
access to unique information.
Social Networks Within Sales Organizations / 3
resources can be transmitted to a person who, in turn, can
apply the resources to a complex problem (Burt 1997). The
structure of the network determines the opportunities and
constraints in accessing the embedded resources (e.g.,
Hughes, Le Bon, and Rapp 2013). These opportunities and
constraints drive individual action (Lin 1999).
Measuring social capital requires considering three ele-
ments (Lin 1999): (1) the number of people willing or
obligated to help, (2) the strength of those relationships, and
(3) the resources that the obligated people possess. From
these elements, social capital research can be grouped into
two general approaches: the social resource perspective,
which is focused on the resources possessed by contacts
within the network, and the structuralist perspective, which
accounts for the number of ties and strength of relationships.
Scholars working from the social resource perspective
of social capital are focused on analyzing the resources
embedded in a network through the measurement of indi-
vidual characteristics of social contacts (e.g., income, repu-
tation, education) and how they are mobilized in the network.
In other words, they measure the quantity of resources
possessed by obliged contacts—the third element of social
capital (Lin 1999). Conversely, structuralist scholars focus on
the pattern of social ties and the resulting network topography
to assess opportunities and constraints. The fundamental
premise of this perspective is that the structure provides
competitive advantage to people who occupy strategic
positions in the network. Therefore, structuralists address the
ﬁrst two elements of social capital: the number of people
willing to help a person and the strength of the relationship
with them (Lin 1999).
The structuralist and social resource perspectives are
intuitively intertwined, and research within each perspective
serves to inform and enhance the other. One of the advantages
of the present work is the combination of both individual
data (political skill, tenure, extroversion, and gender) with
structural network data. However, our focus is not on
measuring individual social resources. Rather, we focus our
examination on the impact of structural characteristics on
performance, aligning ourselves with and contributing to the
body of work employing the structuralist lens (e.g., Borgatti
and Foster 2003; Brass and Halgin 2012; Brass and
Krackhardt 1999; Burt 1992). In summary, social capital
theory suggests two key propositions relevant to the present
research: (1) access to internal resources increases the
effectiveness of selling efforts (as “instrumental actions”; Lin
1999) and (2) social position determines the constraints on a
salesperson’s access to resources. Therefore, individual
social capital, the value of a person’s social ties, can be
understood by examining the social network characteristics
of people within the network (Burt 2000).
To examine these network structures, one or more
appropriate centrality measures must be identiﬁed. This is
not a simple process, as there are multiple centrality measures
that may be of interest when examining network positions.
For example, previous research has used degree, closeness,
and betweenness centralities (Freeman 1977), as well as
eigenvector and beta centralities (Bonacich 1972, 1987), each
capturing a different characteristic of a given person’s net-
work position. Degree centrality, the number of ties a person
has, is arguably the most well-known centrality. However,
some controversy remains regarding whether it provides
sufﬁcient information about a person’s position in relation to
the entire network, particularly given that the structure of the
network is not needed to calculate it (Borgatti 2015).
Closeness centrality is the sum of the distances between a
focal person and all other people in the network, and it
measures the time it takes for information to ﬂow to or from
the focal person. Clearly, this metric is important in under-
standing information ﬂow but does not tie directly to our
focus on explaining variance in salesperson performance.
Finally, beta centrality is, under most common conditions,
equivalent to eigenvector centrality or degree centrality,
making the measure potentially redundant.
Salesperson Political Skill
• Interpersonal influence
• Social astuteness
• Networking ability
• Apparent sincerity
Time 1: Independent Variables Time 2: Objective Outcome
4 / Journal of Marketing, Ahead of Print
The remaining centralities—relational centrality (oper-
ationalized as eigenvector centrality) and positional centrality
(operationalized as betweenness centrality)—represent two
of the most advantageous and, importantly, nonredundant
strategic network positions uncovered by the extant network
research. These centralities have been shown to correlate with
power, status, reputation, control, and access to information
(e.g., Bonacich 1972; Burt 1992; Freeman 1977). What
remains underexplored are the drivers of these advantageous
positions and the distinctness and magnitude of their impact
on performance outcomes. Therefore, in assessing individual
social capital, these two advantageous network positions bear
Relational centrality is deﬁned as organizational status
that is derived from social ties to powerful others (Bonacich
and Lloyd 2004); the more inﬂuential a salesperson’s con-
nections are within the network, the more inﬂuential that
salesperson is. Status is an intangible resource with subjective
value, suggesting that it is a complex social resource that is
difﬁcult to quantify (Binning and Huo 2012). However, a
salesperson’s network position serves as a signal of organi-
zational status (Bonacich and Lloyd 2004), which has been
linked to such beneﬁts as “greater access to desirable things”
(Henrich and Gil-White 2001, p. 166) and, importantly, career
success (Seibert, Kraimer, and Liden 2001). A salesperson
with high levels of relational centrality possesses reputational
resources derived from his or her access to inﬂuential others as
well as the ability to inﬂuence those inﬂuential others in the
decision-making process and receive feedback from high-
status others. Therefore, we expect relational centrality to
facilitate the manifestation of three beneﬁts of strategic net-
work positions: social inﬂuence, social credentials, and the
reinforcement of identity and entitlement to resources.
Whereas relational centrality is a measure of organiza-
tional status, positional centrality is a measure of access to
information made available by social ties to unconnected
others (Burt 2000). This deﬁnition suggests that salespeople
with high positional centrality have control over informa-
tion ﬂow and access to unique resources and information.
Access to unique information is particularly relevant in
examining variance in salesperson performance. Although
many salespeople have information regarding best practices,
we propose that those with positional centrality possess
informational resources derived from unique sources. These
resources enable them to develop more novel strategies than
their less connected peers on the basis of the unique com-
bination of distinct information (Taylor and Greve 2006). We
therefore expect positional centrality to facilitate the mani-
festation of the fourth beneﬁt of strategic network positions:
informational beneﬁts. Understanding the impact of these
distinct network-based endowments on salesperson perfor-
mance extends traditional performance models beyond
customer-directed behavior to include intraﬁrm behavior.
It also warrants mentioning that social networks are
dynamic in that social relations continuously form and dis-
solve (Holme and Saram¨
aki 2013). Social relations take time
to initiate, cultivate, and maintain, particularly when exam-
ining the transfer of complex knowledge (Singh 2005). We
therefore examine the interplay between time (in the form of
tenure at the organization) and the development of social
networks, providing insight into the dynamic nature of social
networks and their impact on performance. Finally, to ensure
managerially actionable implications, we aim to uncover
speciﬁc skills managers can emphasize to help people acquire
and exploit these advantageous network positions. To that
end, in the next section, we continue to extend the social
capital–based view of salesperson performance by examining
the role of political skill in securing advantageous network
Social Capital: The Role of Political Skill
At its core, social capital theory focuses on people’s ability to
capture value from the social context within which they
operate. The value-capture process shifts between the indi-
vidual (micro) and the social structure (macro) levels, in
which a given social structure represents opportunities and
constraints, and a person chooses actions taken within the
social structure on the basis of these opportunities and
constraints. Capturing value in the social network requires
investment of social resources (time and effort) to connect
and maintain ties with social contacts. Although some people
may seem to fall into advantageous positions by chance or by
virtue of static personality traits (Brancaleone and Gountas
2007; Goldsmith, Clark, and Goldsmith 2006), we assert that,
for most people, securing advantageous positions in social
networks requires certain skills—speciﬁcally, political skills.
Organizations are, in essence, political entities (Mintzberg
1983), so it follows that political skills are critical to suc-
cessfully navigate the social fabric of the organization.
Political skill is a multidimensional construct (i.e., composed
of interpersonal inﬂuence, social astuteness, networking
ability, and apparent sincerity; Ferris et al. 2007) that describes
aperson’s ability to understand others in an organizational
setting and apply that knowledge to inﬂuence others in pursuit
of personal or organizational goals (Ferris, Davidson, and
The skills captured by the political skill inventory
explicate how people read, inﬂuence, and alter their social
environment (Ferris et al. 2007). For example, people with
higher levels of social astuteness are able to better read and
translate the interactions with and between others, suggesting
that they perceive the social network more accurately. The
ability to read informal networks more accurately has been
linked with increased perceived power in an organization
(Brass and Krackhardt 1999). Similarly, the interpersonal
inﬂuence dimension is a reﬂection of a person’s adaptability
in social situations to further one’s own goals. Networking
ability suggests that people are adept at identifying important,
resource-laden contacts in the organization and making
connections with them. They know how to make alliances,
build friendships, and position themselves to take advantage
of the resources their contacts control (Ferris et al. 2007).
Finally, apparent sincerity is a measure of the perception of
the person by others, which facilitates the transfer of resources
because people with high levels of apparent sincerity are
perceived as genuine and authentic, making them desirable
to others as social ties.
Social Networks Within Sales Organizations / 5
In summary, political skill’s four dimensions read like a
list of core competencies for successful salespeople in vir-
tually any industry. However, although they seem obvious as
important skills for salespeople to possess in terms of cus-
tomer interactions and relationships, their importance in
terms of (1) the development of intraorganizational network
positions and (2) the resulting impact on objective per-
formance is far less obvious.
It should be noted that Plouffe and Barclay (2007) and
Plouffe and Gr´
egoire (2011) investigate a similar, but nar-
rower, construct they call “intraorganizational employee
navigation”(IEN) in examining salesperson effectiveness.
However, their relatively new, and therefore less-tested,
construct is not as comprehensive as political skill. Indeed,
Plouffe and Gr´
egoire even include two of the dimensions of
political skill (i.e., social astuteness and networking ability)
but omit two dimensions (apparent sincerity and interpersonal
inﬂuence) in their scale-development study. They attempt to
demonstrate that their IEN scale would show some correlation
with the political skill dimensions but that the constructs
would be able to demonstrate their discriminant validity. They
conclude that an IEN correlation of .57 with networking
ability and .34 with social astuteness is sufﬁcient evidence
to support this point. So, although we see value in future
exploration of the IEN construct, political skill is our focal
antecedent because it is better established, more compre-
hensive, and thoroughly vetted across multiple disciplines.
Furthermore, its function as a mediator of the relationship
between various individual variables and resulting per-
formance beneﬁts makes it an optimal variable to include to
achieve the joint goals of completeness and parsimony (Ferris
et al. 2007; Liu et al. 2007). As a result, we view political skill
as a productive addition to the sales and marketing literature,
especially in light of the changing and critical role of the
salesperson as knowledge broker (Verbeke et al. 2011) and
internal resource consolidator, as referenced previously.
Next, we use social capital theory to guide the devel-
opment of a series of hypotheses. We then test the hypotheses
and present the results. Finally, we discuss the implications
for both scholars and practitioners.
Political skill and relational network centrality. Politi-
cally skilled salespeople identify and leverage power, and
they do so through their astuteness in adjusting their behavior
to the perceived demands of different people and varied sit-
uations. Their behavior is perceived as authentic, genuine, and
sincere and thus inspires trust and conﬁdence from others in
their organization. Furthermore, because of their own sense of
efﬁcacy, politically skilled salespeople reﬂect a calm sense of
self-conﬁdence that attracts others (e.g., Ferris et al. 2007, 2012).
Relational centrality is status that is derived from pos-
sessing social ties to powerful or inﬂuential others, whereby
salespeople gain greater status and reputation through the
status and inﬂuence of their connections. Politically skilled
salespeople are particularly adept at identifying inﬂuential
others and developing connections, relationships, and alli-
ances with those inﬂuential others through their social
perceptiveness, adaptability, and networking ability, in addi-
tion to their sincere and genuine interpersonal style. In par-
ticular, social astuteness should lead salespeople to identify
inﬂuential others early on and use their political skills to
cultivate relationships with them. Thus, politically skilled
salespeople should be effective at securing positions of rela-
: Political skill has a direct and positive impact on relational
Political skill and positional network centrality. Fur-
thermore, because politically skilled salespeople are socially
astute and recognize opportunity, they also recognize struc-
tural holes in an organization’s informal network (Burt 2001).
Structural holes separate people and groups from one another
in an organizational network (Burt 2000). Unconnected
groups and people are potential sources of unique information.
These politically skilled salespeople have the capacity to
effectively bridge existing structural holes through inter-
personal inﬂuence to translate opportunity into effectiveness
through opportunity capitalization (e.g., McAllister et al.
2015). Politically skilled salespeople are effective at rela-
tionship development not only in terms of forming relation-
ships between themselves and others but also in terms of
developing, fostering, and brokering relationships among
disconnected others (e.g., Ferris et al. 2012). Recognizing the
potential in ﬁlling a structural hole, politically skilled sales-
people are expected to serve as a bridge more than their less
politically skilled peers. Therefore, politically skilled sales-
people should be more adept at securing positional centrality
by deliberately bridging the links between unconnected others.
: Political skill has a direct and positive impact on positional
Political skill ·organizational tenure interaction. Though
often included in organizational studies as a control, or-
ganizational tenure is known to inﬂuence performance and
other organizational outcomes, such as core-task behavior;
citizenship behaviors; counterproductive behaviors; and,
importantly, in-role performance (e.g., Ng and Feldman 2010).
Although we control for other potential outcomes of tenure
on performance, we deliberately examine the interaction
between political skill and tenure, proposing that one of the
mechanisms by which tenure affects in-role performance is that
longer tenure, combined with political skill, facilitates the
development of network ties and, subsequently, advantageous
network positions. Therefore, rather than treating tenure as a
control variable, we examine it as the dynamic component in
the acquisition of advantageous social network positions.
It takes time to develop social ties, even for politically
skilled salespeople (e.g., Brass and Krackhardt 2012).
Although we expect political skill to facilitate the develop-
ment of relational and positional network centralities, we do
not expect this relationship to appear overnight. A politically
skilled salesperson still needs time to initiate contact with
others and develop these newfound relationships into
something of value. In short, relationship development—and
therefore, social network development—takes time (e.g.,
Dwyer, Schurr, and Oh 1987; M¨
oller and Halinen 1999). As a
6 / Journal of Marketing, Ahead of Print
result, we expect that, after controlling for the effects of
tenure on the network positions and performance variables, a
salesperson’s tenure within an organization will strengthen the
relationship between political skill and relational and positional
network centrality. Therefore,
: There is a positive and signiﬁcant interaction between
political skill and organizational tenure on relational
centrality, such that as organizational tenure increases, the
effect of political skill on relational centrality increases.
: There is a positive and signiﬁcant interaction between
organizational tenure and political skill on positional
centrality, such that as organizational tenure increases, the
effect of political skill on positional centrality increases.
Relational and positional centrality and sales
performance. Salespeople who have very active and far-
reaching networks wield signiﬁcant inﬂuence (Brass and
Krackhardt 1999). The informal social network therefore
represents a source of power that is tied to either reputational
resources (in the case of relational centrality) or access to
informational resources (in the case of positional centrality;
Brass and Krackhardt 1999). Relational centrality, as a mea-
sure of inﬂuence through the status of a salesperson’ssocial
ties, is a measure of reputation in the social network (Bonacich
and Lloyd 2004). The following excerpt from Cialdini (1989,
p. 45) illustrates the impact of connections to well-connected
or powerful others:
At the height of his wealth and success, the ﬁnancier Baron
de Rothschild was petitioned for a loan by an acquaintance.
Reputedly, the great man replied, I won’t give you one
myself; but I will walk arm-in-arm with you across the ﬂoor
of the Stock Exchange, and you soon shall have willing
lenders to spare.
Connections to important others offer dual beneﬁts
as both a signal of inﬂuence to others and an indication
of personal access to resources. Mehra et al. (2006) assert
that connections are an excellent indicator of access to
information. French and Raven (1959) note that expertise is
a source of power because those who possess expertise
are often sought out by others (a phenomenon known as
“preferential attachment”). Thus, we can assume that highly
connected people in an organizational network (1) have some
measure of expertise beyond the average salesperson and
(2) are relatively easily identiﬁable in sales organizations (in
which performance and hierarchy are communicated regu-
larly, such as when celebrating breaking personal records,
monthly sales e-mails, etc.). Individual salespeople often
require expertise and cooperation from others in the organi-
zation to adequately meet customers’needs.
Therefore, well-connected contacts are expected to have
higher levels of expertise that salespeople can draw on to
better meet customer needs and enhance their performance.
Moreover, being perceived to have a prominent friend in an
organization boosts a person’sownreputationasagood
performer,whichinturninﬂuences the actions of others,
making them more willing to connect or share their
expertise. Therefore, the ties to inﬂuential others increase a
salesperson’s ability to make things happen in the organi-
zation (Mehra et al. 2006).
In addition, the ties to inﬂuential others have been shown
to increase perceived self-efﬁcacy through feedback (Bandura
1982). In addition to required job skills, perceived self-
efﬁcacy is needed to successfully complete challenging
tasks such as selling (Bandura 1982, 1986). Previous research
has shown that an antecedent of self-efﬁcacy is vicarious
experience (Bandura 1986), suggesting that salespeople learn
and develop self-efﬁcacy by modeling others’behavior. If
salespeople are connected to inﬂuential others, they are able
to learn how to be inﬂuential themselves (Bandura 1982),
which improves their ability to complete tasks such as
successful prospecting and closing sales, thereby enhancing
their performance. Therefore,
: Relational centrality has a positive, signiﬁcant effect on
Individual interaction with a variety of outside inﬂuences
has been linked to positive outcomes in organizational set-
tings because interactions with outside inﬂuences allow for
cross-fertilization of ideas (Perry-Smith and Shalley 2003).
Positional centrality is a measure of individual opportunity
for cross-fertilization of ideas, techniques, and approaches to
sales-related problem solving. Bridging structural holes in the
organization provides salespeople access to unique knowl-
edge resources (Burt 2000). Speciﬁcally, although people on
opposite sides of a structural hole are aware of each other,
they operate in different information ﬂows that are insulated
by the structural hole (Burt 2000), which ensures that in-
formation from these sources is nonredundant and additive.
The lack of redundancy increases both the volume and the
uniqueness of the information available to salespeople who
bridge structural holes (Burt 2002) and enables the bridging
salespeople to combine the unique information into more
creative solutions to complex problems (Perry-Smith and
Shalley 2003), helping them deliver more value to customers
and thus boost sales performance.
Positional centrality is indicative of the advantages a
salesperson acquires from bridging the links between
unconnected others, which suggests both control over
information ﬂow and access to unique resources. Access to
unique information is particularly relevant in examining
variance in salesperson performance because it serves
as novel input for generating more innovative strategies,
which in turn enables the salesperson to think beyond those
strategies generated through the use of commonly shared
knowledge. Moreover, new information exerts informa-
tional inﬂuence (Forsyth 1990) that prompts people to
reinterpret or rethink key aspects of a challenging issue
or problem and facilitates the use of more varied strategies
and the creation of more innovative solutions to complex
problems. It stands to reason, then, that if a salesperson has
high positional centrality, (s)he will also have access to
more unique knowledge resources embedded in the network
that can be used to develop more novel and effective
sales techniques and more compelling solutions to customer
problems and needs. Therefore,
: Positional centrality has a positive, signiﬁcant effect on
Social Networks Within Sales Organizations / 7
In summary, we suggest that salespeople’s performance
is determined in part by their ability to evaluate organization
dynamics, develop relationships that place them in prominent
social network positions and in contact with inﬂuential
others, and leverage the resources afforded by their achieved
network positions into elevated job performance (Ferris et al.
2007, 2012). We also suggest that these objectives take
time to play out. This provides a distinct view of the process
by which salespeople acquire and convert internal resources
into performance outcomes. Although previous research has
found that political skill predicts both employee and manager
performance (for a meta-analysis, see Munyon et al. 2015),
research to date has not explicitly investigated the per-
formance of politically skilled salespeople. In the next sec-
tion, we describe the procedure for testing the proposed
hypotheses and subsequently discuss the results.
Sample and Data Collection Procedure
Data were collected from the outside sales force of a U.S.-
based company that sells high-end items directly to consumers
(business-to-consumer). The context is similar to a sales force
selling customized home technology packages (e.g., security
systems, home automation)to owners of upscale homes in and
around major metropolitan areas (e.g., Dallas, New York, San
Francisco). Within these metro areas, there are no territory
restrictions, as is common among direct sales organizations.
Salespeople at this ﬁrm are paid solely through commissions
based on their individual sales performance. However, given
the degree of customization required for each individual sale,
these salespeople require strong intraorganizational networks
to ensure that customer solutions are implemented correctly
and in a timely fashion. Accordingly, they strive to build
valuable relationships with others in the company to exchange
information, bolster their reputation within the organization
by connecting with powerful peers, and so on. In this way, we
expect intraorganizational networks to be important drivers of
The survey was announced at the company’s annual sales
conference, where all employees were told about the de-
velopment and intent of the project. Furthermore, they were
afforded the opportunity to meet a member of the research
team and were able to see ﬁrsthand that the project had the full
support of the company’s top leadership. There were several
beneﬁts from working within a single ﬁrm for this project.
First, individual nodes can have signiﬁcant impact on overall
network measures (Wasserman and Faust 1994). Given that
networks are, by deﬁnition, patterns of interactions, deﬁning
the boundary of the network is of critical importance in
distinguishing relevant nodes from irrelevant ones. For
example, misspeciﬁed network boundaries could result in the
exclusion of critical nodes from the analysis, or conversely,
the inclusion of irrelevant nodes. Therefore, such errors in
boundary speciﬁcation can result in inaccurate calculations of
network measures. For the sake of network analysis, working
within a single ﬁrm provides a clear network boundary (e.g.,
Ahearne, Lam, and Kraus 2014).
Second, and relatedly, working within a single company
makes it easier to work with top leaders to ensure that
employees are fully engaged in the survey. Because a strong
response rate is critical for an accurate representation of the
network, high engagement from employees is absolutely
essential (Wasserman and Faust 1994). Third, focusing our
study on one company enables us to control for a variety of
external factors such as organizational culture, company size,
industry competitiveness, and so on, which can confound the
results of multiﬁrm studies.
The participating company employed a total of 397
salespeople, all of whom were asked to respond to the survey;
completed surveys were received from 286 salespeople
(40% female). Response rates for social network analysis are
calculated as nodal and as relational response rates. Rela-
tional response rates account for the fact that, for nondirected
networks such as the one in this study, information regarding
the social ties can be measured from either one or both
members of the dyad (Knoke and Yang 2008). Speciﬁcally,
for a complete, nondirected network of N actors with no alter
reports from M actors, the response rate for a particular
relation is calculated as follows:
=100%when M =0orM =1
N·100%when 1 <M<N
=0%when M =N
N=N!/[2! ·(N -2)!] and N! is the product of all the
positive integers from 1 to N. The number of possible ties in
the sales network, calculated as [n ·(n -1)] for an undirected
network (Wasserman and Faust 1994), is 157,212. With
286 responses (72% of total salespeople), we can account
for 92% of dyads (Knoke and Yang 2008). Therefore, our
data represent a relatively complete network for the focal
sales organization. Furthermore, the relationships captured
in this network are not limited to ties between members of
the sales force. Indeed, there were 253 nonsalespeople
named in the survey (i.e., nearly the same as the number
of responding salespeople), representing 763 unique dyads
within the social network. Our network measures therefore
represent a robust view of this organization’s intraorganiza-
tional relationships—both within and beyond the sales
Measures and Operationalization
Political skill. To assess salesperson political skill, we
adapted the Political Skill Inventory (PSI; Ferris et al. 2005).
The resulting adapted scale comprised 12 items, with 3 items
for each dimension of political skill: social astuteness, inter-
personal inﬂuence, networking ability, and apparent sincerity.
We retained items from the trimming process because we
believed them to best represent the core meaning of each
political skill dimension. To ensure that this was the case, we
consulted a developer of the PSI about all trimming decisions.
Table 1 provides the speciﬁc items adapted from the PSI
(Ferris et al. 2005).
Social network reporting. Rather than relying on self-
reported measures of relational and positional centrality,
which can be confounded by social desirability bias and other
8 / Journal of Marketing, Ahead of Print
pitfalls, we calculated the measures from the network graph.
Speciﬁcally, we employed a free-recall method rather than
the roster method for constructing the networks. The roster
method was not feasible given that the ﬁrm distributed the
survey (so we did not know in advance the list of salespeople
to whom the survey was distributed), the number of sales-
people at the ﬁrm, and the desire to capture all ties to inﬂuential
people, whether salespeople or support staff. In the free-recall
method, respondents were asked to list as many people as they
could (with a required minimum of two) who were inﬂu-
ential in their work lives. We developed the network graph
from these lists and calculated our centrality variables for
each person. In other words, our relational and positional
centrality measures were calculated from data provided
by the entire sales force rather than any single individual
report. We next discuss our relational and positional cen-
Relational centrality calculation. We operationalize re-
lational centrality using eigenvector centrality. Eigenvector
centralityis an objective measure of a person’scentralityinthe
social network, adjusted for the connectivity of their contacts.
Unlike a degree centrality measure, which simply counts the
number of direct ties to an actor, eigenvector centrality
accounts for the fact that not all connections are equal;
connections who are themselves well connected are more
inﬂuential than less connected contacts. Given that our
objective isto measure organizational status on the basis of the
status of a salesperson’s connections, the adjusted measure
provides a more accurate depiction of an actor’sstatusinthe
social network (e.g., Treadway et al. 2013). Eigenvector
centrality is proportional to the sum of the centralities of the
nodes connected to a focal node i and can be calculated as
follows for all i 2I:
i=an individual actor in the network;
(i) =an eigenvector centrality measure for individual i;
=the degree centrality of actor j, which is deﬁned as the
number of edges incident on node j;
=1 if actor i is linked to actor j in the network or, con-
versely, 0 if the two actors are unconnected; and
l=a constant representing the number of actors j that are
linked to actor i.
Positional centrality calculation. We operationalize posi-
tional centrality using betweenness centrality. The degree to
which a salesperson serves as a bridge to connect two oth-
erwise unconnected nodes on a geodesic path can be assessed
using betweenness centrality (Hanneman and Riddle 2005;
Wasserman and Faust 1994). Therefore, it can be considered
resources embedded in the network (Brass and Halgin 2012).
Betweenness centrality at the individual level, C
(i), is com-
puted as follows for all i 2I:
i=an individual actor in the network;
=the total number of geodesic paths between individuals j
and k in network matrix A, for all 1 £j<k£n;
jk =the total number of geodesic paths between individuals
j and k in network matrix Athat contain individual i, for
all 1 £j<k£n and i 2I: i „j and i „k; and
(i) =a betweenness centrality measure for individual i.
Organizational tenure. We operationalized salesperson
tenure with the organization as the number of days with the
ﬁrm from the date of hire and captured this number through
Sales performance. The participating company’s sales-
people work solely in large metro areas (e.g., Dallas, New
York, San Francisco) and are not limited to an assigned
territory. As a result, there are no concerns regarding dif-
ferences in territory potential or performance expectations
making raw “units sold”the ideal measure for individual
sales performance. Individual sales performance data for
the two months following the measurement of the social
network structures were pulled from ﬁrm records. Temporal
separation serves to address endogeneity as a result of
Political Skill Items
Abbreviated Political Skill Inventory
(Signiﬁcant at p<.001)
SA I am particularly good at sensing the
motivations and hidden agendas
SA I understand people very well. .903
SA I have good intuition or “savvy”about
how to present myself to others.
II I am able to communicate easily and
effectively with others.
II I am good at getting people to like me. .879
II It is easy for me to develop a good
rapport with most people.
NA I am good at building relationships
with inﬂuential people at work.
NA I am good at using my connections
and networking to make things
happen at work.
NA I spend a lot of time at work
developing connections with others.
AS I try to show a genuine interest in
AS It is important that people believe I am
sincere in what I say and do.
AS When communicating with others,
I try to be genuine in what I say
Notes: We adapted this table from Ferris et al. (2005). SA =social
astuteness; II =interpersonal inﬂuence; NA =networking
ability; AS =apparent sincerity. All items were measured using
a seven-point Likert-type scale (1 =“strongly disagree,”and
Social Networks Within Sales Organizations / 9
simultaneity. By separating the performance dependent
measure from the calculation of network positions, we
establish temporal precedence, permitting us to make causal
inferences regarding the results (Hui et al. 2013). We looked
at monthly “units sold”numbers (a company-speciﬁctrans-
formation of sales dollars where each unit represents approx-
imately $417 in sales) to compute an average monthly
Controls. To avoid taxing respondents, we included a
short form measure of extroversion as a covariate using items
from Gosling, Rentfrow, and Swann (2003). We included it
because of its relevance to salespeople’s behaviors, attitudes,
and performance (see Furnham and Fudge 2008). Speciﬁ-
cally, we reason that extroversion is likely to play a role in
salespeople’s network-building behaviors and competencies.
Gender also was collected as a single-item survey question
under the premise that a person’s gender may affect network-
building behaviors and success as well as the resulting sales
performance (Ibarra 1993). Table 2 lists the descriptive
statistics for all study variables.
The present work employs multiple methods to test the
hypothesized model. First, we employ social network anal-
ysis (using UCINET) to calculate relational and positional
centralities for the respondents. UCINET is a leading social
network analysis package designed by Borgatti, Everett, and
Freeman (2002) and is commonly used for social network
analysis across disciplines. Next, we employed partial least
squares structural equation modeling (PLS-SEM) to test the
structural model (Ringle, Wende, and Will 2005).
We selected PLS-SEM as the appropriate method for four
reasons. First, PLS is focused on predictive analysis. Spe-
ciﬁcally, the objective of PLS-SEM is to maximize the var-
iance of the endogenous variables explained by the exogenous
variables (Hair et al. 2014). The predictive focus is appropriate
to meet the objectives of the current study. Second, PLS does
not require meeting the assumptions of normality for the data
distributions (Hair et al. 2012). Network data distributions tend
to be skewed and/or leptokurtic. Therefore, PLS-SEM is an
appropriate method because results are not adversely affected
by the nature of the data. Third, PLS is ideal for the estimation
of complex models, particularly for higher-order or hier-
archical component models (Hair et al. 2014; Lohmoller
1989). Although prior literature has established the multi-
dimensional nature of political skill, many studies that include
the construct do not model its hierarchical nature, but PLS-
SEM permits the estimation of the hierarchical, second-order
reﬂective-formative construct to provide additional informa-
tion regarding the dimensions of political skill. Finally, PLS is
preferred for testing interactions because it does not inﬂate
measurement error (Chin, Marcolin, and Newsted 2003).
It is important to note that the results for covariance-based
structural equation modeling (CB-SEM) procedures (e.g.,
AMOS, LISREL) and PLS-SEM analysis do not usually
differ signiﬁcantly; PLS-SEM results serve as good (and
conservative) proxies for CB-SEM results (Hair et al. 2014).
Although there are limitations to PLS-SEM (e.g., results tend
to overestimate the item loadings [lambdas] and underestimate
path coefﬁcients and R-squares [structural relationships]), CB-
SEM also has limitations (e.g., results tend to overestimate
structural relationship and underestimate lambdas, sug-
gesting that PLS-SEM actually offers a conservative test of
the hypotheses). It is generally understood that PLS-SEM’s
weaknesses are CB-SEM’s strengths and vice versa (Hair et al.
2012), and given the nature of our data (nonnormal) and model
(which includes a reﬂective-formative higher-order con-
struct), we believe PLS-SEM to be the appropriate meth-
odological approach. It is important to note that concerns
over data normality and higher-order, complex constructs
are commonly cited as justiﬁcation for using PLS in top
marketing journals (e.g., Ernst, Hoyer, and Rubsaamen
2010; Hennig-Thurau, Houston, and Heitjans 2009).
For each construct, we assessed the reliability and con-
vergent and discriminant validity of the measures. The results
indicated that composite reliabilities were greater than .80
and all items load on their respective constructs. The square
Correlations, Means, Standard Deviations, Construct Reliabilities, R-Squares, and Square Roots of AVE
123 4 567891011
1. Political skill .608
2. Relational centrality .127 —
3. Positional centrality .099 .325 —
4. Sales performance .040 .418 .271 —
5. Tenure -.127 .281 -.087 .330 —
6. Extroversion .418 -.033 .148 -.026 -.226 .921
7. Gender .133 -.235 .071 -.244 -.456 .179 —
8. PS—Apparent sincerity .607 .041 .083 -.121 -.176 .116 .247 .821
9. PS—Interpersonal inﬂuence .794 .095 .032 .068 -.038 .430 -.044 .283 .839
10. PS—Networking ability .787 .173 .131 .125 -.154 .354 .125 .342 .494 .863
11. PS—Social astuteness .703 .034 .037 -.008 -.008 .265 .100 .289 .457 .344 .826
M 5.925 5.351 .524 79.026 9.241 5.327 1.401 6.430 6.112 5.380 5.779
SD .567 4.554 1.044 51.674 8.994 1.476 .491 .613 .677 1.051 .791
CR .873 ————.918 —.861 .877 .898 .865
—.153 .034 .266 ———————
Notes: CR 5construct reliability. The square roots of the AVEs for each construct appear in boldface on the diagonal of the correlation matrix.
10 / Journal of Marketing, Ahead of Print
root of the average variance extracted (AVE) for each con-
struct exceeds the correlation with other constructs in the
model, indicating discriminant validity (Fornell and Larcker
1981). Given that political skill is a multidimensional con-
struct and each dimension represents a unique aspect of it
(Ferris et al. 2005, 2007), we used a second-order, reﬂective-
formative construct to model political skill (Hair et al. 2014).
The average AVE for each dimension of political skill also
exceeded the squared correlation with the other constructs in
the model, indicating that each dimension of political skill
exhibits discriminant validity.
In addition, for ease of interpretation (Echambadi and
Hess 2007), we mean-centered all indicators before calcu-
lating multiplicative terms. Table 2 displays the correlations
and AVEs (for political skill, as a hierarchical component
model, we report the average AVE for each dimension). As a
ﬁnal step, we evaluate the predictive relevance of the model
value (Geisser 1974; Stone 1974)
and the blindfolding procedure in SmartPLS (Hair et al.
2014). When the model demonstrates predictive relevance,
this indicates that it accurately predicts the items in reﬂective
measurement models of multi-item and single-item endog-
enous constructs. Q
values for relational and positional
centrality and sales performance were considerably above
zero, indicating that the model has predictive relevance for
the endogenous latent variable.
Overall, the results of the structural model test support the
proposed model while offering some surprising insights.
Table 3 lists the direct effect of political skill on relational and
positional centrality (H
, respectively), the interaction
effects of tenure on the political skill–social network rela-
), and the direct effects of relational (H
) centrality on performance. We discuss the
speciﬁc results of the analysis next.
Both relational (H
) and positional (H
) centrality directly
and positively affect performance (b=.262, p=.000; b=
.209, p=.000, respectively). Although the direct effect of
political skill on positional centrality is nonsigniﬁcant ( p>
is unsupported), the results support the hypothesis
that increased political skill results in a stronger relational
network position (H
is supported); thus, political skill sig-
niﬁcantly and directly affects salesperson relational centrality
(b=.189, p=.002). As we hypothesized, organizational
tenure moderates the relationship between political skill and
relational centrality (b=.169, p=.027; H
counter to our expectation, it does not moderate the politi-
cal skill–positional centrality relationship (p>.05; H
In the ﬁnal stage of analysis, we test the mediating role
of social network centralities in the political skill–
salesperson performance relationship. We test the mediation
by using the Preacher and Hayes (2004) method to deter-
mine the signiﬁcance of the indirect effect of the political
skill–tenure interaction on salesperson performance through
network centrality. The method uses bootstrapping tech-
niques, which make no assumptions about variable dis-
tribution (Hair et al. 2014). Moreover, the Preacher and
Hayes method has more power statistically than the Sobel
test commonly used to test the signiﬁcance of indirect
effects (Hair et al. 2014).
Given that the political skill to positional centrality path
coefﬁcient was not signiﬁcant, thereby indicating no medi-
ation through positional centrality, we only tested the medi-
ation effects on relational centrality. The preceding stages
tested paths from the interaction to the proposed mediator, the
mediator to the dependent variable, and the independent
variable to the dependent variable in the presence of the
mediator. To complete the mediation analysis, we analyzed
the remaining path, the path from the independent variable
to the dependent variable in the absence of the mediator. The
path is signiﬁcant (b=.113, p<.05, one-tailed) when the
mediator is not included in the model. Although research has
shown that this is not a necessary condition in cases of full
mediation (Iacobucci, Saldanha, and Deng 2007), the presence
of a direct effect makes interpretation of the mediation easier
(Hair et al. 2014). The magnitude of the indirect effect
=.058) and the variance
Total Effects on Performance
Political Skill Relational Centrality Positional Centrality Sales Performance
Political skill (PS) .189** n.s. .058*
PS ·Tenure .169* n.s. .062*
Relational centrality .262***
Positional centrality .209***
Tenure .217*** n.s. .284***
Extroversion n.s. .117* n.s.
Gender -.154** n.s. -.128*
PS—Apparent sincerity .272*** .051** n.s. .016*
PS—Interpersonal inﬂuence .375*** .071** n.s. .022*
PS—Networking ability .400*** .075** n.s. .023*
PS—Social astuteness .316*** .059** n.s. .018*
Notes: One-tailed tests of signiﬁcance. n.s. =not signiﬁcant.
Social Networks Within Sales Organizations / 11
accounted for by the mediation is more than 20% but less than
80%, indicating a partial and signiﬁcant mediation.
Control variables yielded signiﬁcant effects on perfor-
mance and salesperson network characteristics. Speciﬁcally,
both gender and tenure had signiﬁcant effects on relational
position (b=-.154, p=.002; b=.217, p=.001, respectively),
and tenure also had a signiﬁcant and direct effect on per-
formance (b=.237, p=.000). Notably, extroversion was
the only signiﬁcant predictor of positional centrality (b=
.117, p=.011). In the next section, we explore the impli-
cations of the results and consider the insights they provide.
This study applied social network analysis to a unique data set
that combines survey responses of salespeople’s network
positions with objective salesperson performance data to
address two important gaps in our current understanding of
the determinants of salesperson performance. First, we address
whether salespeople’s intraorganizational social networks are
useful in predicting objective sales performance. Second, we
explore a possible antecedent of salespeople’s intraorganiza-
tional social network development—speciﬁcally, we inves-
tigate the role of salespeople’s political skill. We next address
each of these key issues in more detail.
What Relationship Exists Between
Intraorganizational Social Networks and
We began our introduction by noting that most of the lit-
erature on the determinants of salesperson performance in
marketing focuses primarily on customer-directed behaviors.
Like Plouffe and Barclay (2007), we note that this research
has been able to explain only 10%–20% of the variance in
sales performance and assert that examining intraorganizational
factors may enhance our ability to explain additional variance
in sales performance.
Our results suggest that salespeople’s intraorganiza-
tional networks, operationalized as relational and positional
network centrality, signiﬁcantly affect salespeople’sper-
formance. Moreover, our modeling of these network vari-
ables is capable of explaining 26.6% of the variance in sales
performance—enhancing and extending prior models of
customer-directed salesperson behaviors. These ﬁndings are
consistent with some conceptual suggestions that rela-
tionships and behaviors within a salesperson’s organization
may be even more important for determining performance
than those outside their organization (e.g., Ryals and
Humphries 2007). Our study now provides empirical sup-
port for this discussion. Given that the academic literature
has focused on customer-directed behaviors for decades, it
seems that researchers may have mistakenly neglected the
important role of intraﬁrm factors in the process.
What Are Some Antecedents of Intraorganizational
Social Network Development?
Given that we have shown that positional and relational
network centrality are both signiﬁcant drivers of actual
salesperson performance, it is important to discuss how these
network types are developed. As we expected, we found
salespeople’s political skill to demonstrate a positive rela-
tionship with relational network centrality. However, unex-
pectedly, political skill showed no relationship with positional
centrality. In other words, these salesperson skills and
behaviors affect relational centrality but not positional cen-
trality. These results draw attention to the important notion
that, contrary to the common assumption that different
social network metrics are merely alternative (and therefore
substitutable) operationalizations of a common underlying
network characteristic, different network positions (i.e.,
centralities) have distinct antecedents and may offer unique
beneﬁts and effects on performance. Moreover, the surprising
results regarding positional centrality support the claim that
little is known about the antecedents of network positions
and the process by which social networks of various types
Diving deeper, we note that an interesting ﬁnding of this
study is that extroversion is the only signiﬁcant predictor of
positional centrality. In retrospect, this makes some sense.
Political skill represents a deliberate, strategic set of com-
petences and behaviors (Ferris et al. 2007). As such, sales-
people with high levels of political skill are able to choose
to engage in actions that enable them to be effective at
identifying and connecting with high-status others. In other
words, it is obvious to politically skilled salespeople that
making high-status connections represents a good strategy
for approaching intraorganizational relationships and boosting
performance. However, it is not as obvious to them that
bridging disconnected parties within the organization is
also a viable way to improve their social capital and,
therefore, their sales performance.
Behaviors driven by being highly extroverted, in contrast,
are not strategic, deliberate behaviors used to gain extrinsic
rewards. Instead, they are intrinsically motivated because
these people receive personal satisfaction from social engage-
ment with others (Clark and Watson 1999). In other words,
highly extroverted people build connections with others because
of who they are—not because they are trying to gain a pro-
fessional advantage. As a result, we suspect that these people
somewhat accidentally stumble into desirable bridging network
positions because they are intrinsically motivated to build
relationships with the various people they encounter in their
work (regardless of whether they judge someone to be a high-
status, strategically desirable connection). This raises the
following questions: Are there other skill- or behavior-based
variables that could be useful in explaining variance in
positional centrality? If political skill does not explain posi-
tional centrality, then what does? We discuss these questions in
the “Limitations and Future Research Directions”subsection.
Network links are conducive to transferring complex knowl-
edge that is not easily codiﬁed (Singh 2005). These networks
provide a sustainable competitive advantage to members of
the speciﬁc social network; the beneﬁts of membership within
the network can be leveraged to accomplish various goals and
12 / Journal of Marketing, Ahead of Print
outcomes (Burt 2001; Lin 1999). Therefore, salespeople’s
network positions, and the connectivity they represent, are
inexorably linked to their performance outcomes. So what
are sales managers to do with this information? First,
managers should take notice of the value of intraorganizational
networks. As boundary spanners, salespeople are encouraged
to spend a great deal of time outside the ofﬁce focused on
establishing and nurturing external relationships (Fu,Bolander,
and Jones 2009). Obviously, this is a fundamental and essential
part of the sales job, but managers should also ensure that
salespeople are allocating adequate time and energy to navi-
gating and building relationships within their own organizations
(Plouffe and Barclay 2007). This could be accomplished
by coordinating company-wide events, including inter-
departmental interactions during new salesperson orientation,
or assigning salespeople to mentors in different departments
or districts to encourage them to develop a variety of valuable
connections. In addition, related activities could be built
into the performance management system. In any case, effort
should be taken to prevent salespeople from focusing 100%
of their attention on parties outside the organization while
inadvertently neglecting the social resources available to them
Second, and relatedly, this research identiﬁes a set of
skills salespeople can learn that are useful in driving the
development of relational network centrality—namely, the
components of political skill (i.e., interpersonal inﬂuence,
social astuteness, networking ability, and apparent sincerity).
To help salespeople develop these types of skills, managers
should emphasize knowledge other than that related to (product
or ﬁrm) content. Speciﬁcally, managers should include political
skill as part of a comprehensive, noncompetitive, and sup-
portive new-hire training program (Ferris, Davidson, and
e 2005). As part of such training, examples and exer-
cises that have internal as well as external foci could be helpful
in contextualizing learning and skill development and in
raising awareness of the importance of internal social net-
works. Because training in interpersonal skills is challenging
to execute in traditional lecture-/presentation-based training
programs, experiential training techniques should be explored
(Bolander, Bonney, and Satornino 2014; Ferris, Davidson, and
Third, because extroversion, rather than political skill,
inﬂuences positional centrality, it is important for managers
to consider that until additional antecedents to positional cen-
trality are uncovered, they may need to consider the different
personality types in recruiting and hiring practices and when
setting their expectations of salespeople regarding their intra-
organizational relationships. Moreover, they may also consider
exploring interventions that might encourage and enable the less
extroverted salesperson to forge these useful connections that
occur naturally with his or her extroverted counterparts.
Limitations and Future Research Directions
As with all studies, the current research should be interpreted
in light of a few limitations. First, we collected all data from
a single ﬁrm. Although we believe that this provided some
important characteristics that are required for network analysis
(e.g., bounded network, access to the complete network),
further research is warranted to explore how these relation-
ships hold up across multiple ﬁrms, industries, and so on. Are
ﬁrms operating in business-to-business contexts more—or
less—affected by network variables? Similarly, do ﬁrms
selling complex, high-tech products have an easier or more
difﬁcult time building their intraorganizational networks?
Research across a variety of ﬁrms and industries can explore
Second, although we use a lagged sales performance
outcome, the present study primarily utilizes cross-sectional
data, which limit our ability to draw causal inferences be-
tween political skill and relational network centrality or
between extroversion and positional network centrality. In
other words, we cannot empirically eliminate the possibility
that, for example, it is actually the network position that leads
to political skill. However, even in the absence of concrete,
empirical support for the order of variables in our hypothesized
model, there is still substantial conceptual grounding for the
order we present. Consider that political skill is a construct
of social effectiveness while network position indicates the
salesperson’s placement within an intraorganizational struc-
ture. It seems far more plausible that a person secures and
maintains a position within a network on the basis of social
effectiveness than that (s)he develops strong interpersonal
skills because of some chance placement within the net-
work. In other words, if the causality were reversed, how did
the salesperson acquire the network position in the ﬁrst
place? Because a network position is not something a sales-
person can be “dropped into,”it makes sense that the causality
must ﬂow from political skill (i.e., effectiveness) to network
position (i.e., outcome).
Nevertheless, although we believe our proposed ante-
cedents are justiﬁable on theoretical grounds, longitudinal
network development studies should be undertaken to obtain a
cleaner view of how, exactly, employees develop their net-
works over time. Some recent research using longitudinal
growth modeling techniques has suggested a method of
analysis that could be useful in this pursuit (Ahearne et al.
2010; Boichuk et al. 2014; Fu et al. 2010). Perhaps the
largest barrier to such work would be the challenge of
collecting network data across occasions.
Third, the ﬁnding that relational centrality seems to be
driven by political skill whereas positional centrality is not
suggests an avenue for exciting further research in differ-
entiating network metrics and uncovering the drivers of net-
work development and maintenance. Speciﬁcally, if political
skill does not inﬂuence positional centrality, what about other
skill- or behavior-based variables such as intraorganizational
navigation (e.g., Plouffe and Gr´
egoire 2011) and emotional
intelligence (e.g., Kidwell et al. 2011)? Future researchers
should try to identify something other than a ﬁxed personality
trait (i.e., which managers would perhaps best account for as a
hiring decision) that can drive positional, as well as relational,
centrality and other advantageous network positions. For
managers, it would be useful to identify something actionable
and trainable to help salespeople develop positional centrality
as an important network position that can serve as a com-
Social Networks Within Sales Organizations / 13
Fourth and ﬁnally, although we have shown that political
skill is a powerful driver of relational network centrality for
salespeople, it is important for further research to consider
other issues for which political skill could prove useful. One
increasingly popular topic comes to mind: interdepartmental
interfaces. Future studies should consider the role of political
skill in improving effectiveness at a wide variety of departmental
boundaries, including those marketing holds with research and
development, ﬁnance, and so on (e.g., Ernst, Hoyer, and
Rubsaamen 2010; Hughes, Le Bon, and Malshe 2012).
Ahearne, Michael, Son K. Lam, Babak Hayati, and Florian Kraus
(2012), “Intrafunctional Competitive Intelligence and Sales
Performance: A Social Network Perspective,”Journal of Mar-
keting, 77 (September), 37–56.
———,———, and Florian Kraus (2014), “Performance Impact
of Middle Managers’Adaptive Strategy Implementation: The
Role of Social Capital,”Strategic Management Journal, 35 (1),
———,———, John E. Mathieu, and Willy Bolander (2010),
“Why Are Some Salespeople Better at Adapting to Organiza-
tional Change?”Journal of Marketing, 74 (May), 65–79.
Balkundi, Prasad and David A. Harrison (2006), “Ties, Leaders, and
Time in Teams: Strong Inference About Network Structure’s
Effects on Team Viability and Performance,”Academy of
Management Journal, 49 (1), 49–68.
Bandura, Albert (1982), “Self-Efﬁcacy Mechanism in Human
Agency,”American Psychologist, 37, 122–47.
——— (1986), Social Foundations of Thought and Action: A Social
Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall.
Binning, Kevin R. and Yuen J. Huo (2012), “Understanding Status
as a Social Resource,”in Handbook of Social Resource Theory,
ornblom and Ali Kazemi, eds. New York: Springer,
Boichuk, Jeffrey P., Willy Bolander, Zachary R. Hall, Michael
Ahearne, William J. Zahn, and Melissa Nieves (2014), “Learned
Helplessness Among Newly Hired Salespeople and the Inﬂuence
of Leadership,”Journal of Marketing, 78 (January), 95–111.
Bolander, William, Leff Bonney, and Cinthia Satornino (2014),
“Sales Education Efﬁcacy Examining the Relationship Between
Sales Education and Sales Success,”Journal of Marketing
Education, 36 (2), 169–81.
Bonacich, Phillip (1972), “Factoring and Weighting Approaches to
Status Scores and Clique Identiﬁcation,”Journal of Mathe-
matical Sociology, 2 (1), 113–20.
——— (1987), “Power and Centrality: A Family of Measures,”
American Journal of Sociology, 92 (5), 1170–82.
——— and Paulette Lloyd (2004), “Calculating Status with Neg-
ative Relations,”Social Networks, 26 (4), 331–38.
Borgatti, Stephen P. (2015), “Centrality,”presented at LINKS
Center Summer Workshop Advanced Track, University of
Kentucky, (June 3), Lexington, KY.
——— and Pacey C. Foster (2003), “The Network Paradigm in
Organizational Research: A Review and Typology,”Journal of
Management, 29 (6), 991–1013.
———, Martin G. Everett, and Linton C. Freeman (2002),
“UCINET for Windows: Software for Social Network Analysis.”
Brancaleone, Vito and John Gountas (2007), “Personality Char-
acteristics of Market Mavens,”in Advances in Consumer
Research, Vol. 34, Gavan Fitzsimons and Vicki Morwitz, eds.
Duluth, MN: Association for Consumer Research, 522–27.
Brass, Daniel J. and Daniel S. Halgin (2012), “Social Networks:
The Structure of Relationships,”in Personal Relationships:
The Effect on Employee Attitudes, Behavior, and Well-Being,
Lillian T. Eby and Tammy Allen, eds. New York: Routledge,
——— and David Krackhardt (1999), “The Social Capital of
Twenty-First Century Leaders,”in Out-of-the-Box Leadership:
Transforming the Twenty-First-Century Army and Other Top-
Performing Organizations, J.G. Hunt, G.E. Dodge, and L.
Wong, eds. Greenwich, CT: JAI Press, 179–94.
——— and ——— (2012), “Power, Politics, and Social Networks
in Organizations,”in Politics in Organizations: Theory and
Research Considerations, G.R. Ferris and D.C. Treadway, eds.
New York: Routledge/Taylor and Francis, 355–75.
Brown, Steven P. and Robert A. Peterson (1994), “The Effect of
Effort on Sales Performance and Job Satisfaction,”Journal of
Marketing, 58 (April), 70–80.
Burt, Ronald S. (1992), Structural Holes: The Social Structure of
Competition. Cambridge, MA: Harvard University Press.
——— (1997), “The Contingent Value of Social Capital,”
Administrative Science Quarterly, 42 (2), 339–65.
——— (2000), “The Network Structure of Social Capital,”
Research in Organizational Behavior, 22, 345–423.
——— (2001), “Structural Holes Versus Network Closure as Social
Capital,”in Social Capital: Theory and Research, R. Burt, ed.
New York: Aldine de Gruyter, 31–56.
——— (2002), “The Social Capital of Structural Holes,”in The New
Economic Sociology: Developments in an Emerging Field,
Mauro F. Guill´
en, Randall Collins, Paula England, and Marshall
Meyer, eds. New York: Russell Sage Foundation, 148–90.
——— (2010), Neighbor Networks: Competitive Advantage Local
and Personal. Oxford, UK: Oxford University Press.
Chin, Wynne W., Barbara L. Marcolin, and Peter R. Newsted
(2003), “A Partial Least Squares Latent Variable Modeling
Approach for Measuring Interaction Effects: Results from a
Monte Carlo Simulation Study and an Electronic-Mail Emotion/
Adoption Study,”Information Systems Research, 14 (2),
Cialdini, Robert B. (1989), “Indirect Tactics of Image Management:
Beyond Basking,”in Impression Management in the Organi-
zation, Robert A. Giacalone and Paul Rosenfeld, eds. Hillsdale,
NJ: Lawrence Erlbaum Associates, 45–56.
Clark, Lee Anna and David Watson (1999), “Temperament: A New
Paradigm for Trait Psychology,”in Handbook of Personality:
Theory and Research, 2nd ed., L.A. Pervin and O.P. John, eds.
New York: Guilford Press, 102–38.
Dwyer, F. Robert, Paul H. Schurr, and Sejo Oh (1987), “Developing
Buyer-Seller Relationships,”Journal of Marketing, 51 (April),
Echambadi, Raj and James D. Hess (2007), “Mean-Centering Does
Not Alleviate Collinearity Problems in Moderated Multiple
Regression Models,”Marketing Science, 26 (3), 438–45.
Ernst, Holger, Wayne D. Hoyer, and Carsten Rubsaamen (2010),
“Sales, Marketing, and Research-and-Development Cooperation
Across New Product Development Stages: Implications for
Success,”Journal of Marketing, 74 (September), 80–92.
Ferris, Gerald R., Sherry L. Davidson, and Pamela L. Perrew´
(2005), Political Skill at Work: Impact on Work Effectiveness.
Mountain View, CA: Davies-Black.
14 / Journal of Marketing, Ahead of Print
———, Darren C. Treadway, Robyn L. Brouer, and Timothy P.
Munyon (2012), “Political Skill in the Organizational Sciences,”
in Politics in Organizations: Theory and Research Implications,
G.R. Ferris and D.C. Treadway, eds. New York: Routledge/
Taylor and Francis, 487–528.
———,———, Robert W. Kolodinsky, Wayne A. Hochwarter,
Charles J. Kacmar, Ceasar Douglas, et al. (2005), “Development
and Validation of the Political Skill Inventory,”Journal of
Management, 31 (1), 126–52.
———,———, Pamela L. Perrew´
e, Robyn Brouer, Ceasar
Douglas, and Sean Lux (2007), “Political Skill in Organi-
zations,”Journal of Management, 33 (3), 290–320.
Flaherty, Karen, Son K. Lam, Nick Lee, Jay Prakash Mulki, and
Andrea L. Dixon (2012), “Social Network Theory and the Sales
Manager Role: Engineering the Right Relationship Flows,”
Journal of Personal Selling & Sales Management, 32 (1),
Fornell, Claes and David F. Larcker (1981), “Evaluating Structural
Equation Models with Unobservable Variables and Measure-
ment Error,”Journal of Marketing Research, 18 (February),
Forsyth, Donelson R. (1990), Group Dynamics, 2nd ed. Belmont,
Freeman, Linton C. (1977), “A Set of Measures of Centrality Based
on Betweenness,”Sociometry, 40 (1), 35–41.
French, John R.P. and Bertram H. Raven (1959), “The Bases of
Social Power,”in Studies of Social Power, Dorwin Cartwright,
ed. Ann Arbor, MI: Institute for Social Research.
Fu, Frank Q., Willy Bolander, and Eli Jones (2009), “Managing the
Drivers of Organizational Commitment and Salesperson Effort: An
Application of Meyer and Allen’s Three-Component Model,”
Journal of Marketing Theory and Practice,17(4),335–50.
———, Keith A. Richards, Douglas E. Hughes, and Eli Jones
(2010), “Motivating Salespeople to Sell New Products: The
Relative Inﬂuence of Attitudes, Subjective Norms, and Self-
Efﬁcacy,”Journal of Marketing, 74 (November), 61–76.
Furnham, Adrian and Carl Fudge (2008), “The Five Factor Model of
Personality and Sales Performance,”Journal of Individual
Differences, 29 (1), 11–16.
Geisser, Seymour (1974), “A Predictive Approach to the Random
Effects Model,”Biometrika, 61 (1), 101–07.
Goldsmith, Ronald E., Ronald A. Clark, and Elizabeth B. Goldsmith
(2006), “Extending the Psychological Proﬁle of Market
Mavenism,”Journal of Consumer Behaviour, 5 (5), 411–19.
Gonzalez, Gabriel R., Danny P. Claro, and Robert W. Palmatier
(2014), “Synergistic Effects of Relationship Managers’Social
Networks on Sales Performance,”Journal of Marketing,
78 (January), 76–94.
Gosling, Samuel D., Peter J. Rentfrow, and William B. Swann Jr.
(2003), “A Very Brief Measure of the Big-Five Personality
Domains,”Journal of Research in Personality,37(6),504–28.
Hair, Joseph F., Jr., G. Tomas M. Hult, Christian Ringle, and Marko
Sarstedt (2014), A Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage
———, Marko Sarstedt, Christian M. Ringle, and Jeannette A.
Mena (2012), “An Assessment of the Use of Partial Least
Squares Structural Equation Modeling in Marketing Research,”
Journal of the Academy of Marketing Science, 40 (3), 414–33.
Hanneman, Robert A. and Mark Riddle (2005), Introduction to
Social Network Methods. Riverside: University of California
Hennig-Thurau, Thorsten, Mark B. Houston, and Torsten Heitjans
(2009), “Conceptualizing and Measuring the Monetary Value of
Brand Extensions: The Case of Motion Pictures,”Journal of
Marketing, 73 (November), 167–83.
Henrich, Joseph and Francisco J. Gil-White (2001), “The Evolution
of Prestige: Freely Conferred Deference as a Mechanism for
Enhancing the Beneﬁts of Cultural Transmission,”Evolution and
Human Behavior, 22 (3), 165–96.
Holme, Petter and Jari Saram¨
aki (2013), Temporal Networks.
Heidelberg, Germany: Springer.
Hughes, Douglas E., Jo¨
el Le Bon, and Avinash Malshe (2012),
“The Marketing–Sales Interface at the Interface: Creating
Market-Based Capabilities Through Organizational Syn-
ergy,”Journal of Personal Selling & Sales Management,
———,———, and Adam Rapp (2013), “Gaining and Leverag-
ing Customer-Based Competitive Intelligence: The Pivotal
Role of Social Capital and Salesperson Adaptive Selling
Skills,”Journal of the Academy of Marketing Science, 41 (1),
Hui, Sam K., J. Jeffrey Inman, Yanliu Huang, and Jacob Suher
(2013), “The Effect of In-Store Travel Distance on Unplanned
Spending: Applications to Mobile Promotion Strategies,”
Journal of Marketing, 77 (March), 1–16.
Iacobucci, Dawn, Neela Saldanha, and Xiaoyan Deng (2007), “A
Meditation on Mediation: Evidence that Structural Equations
Models Perform Better than Regressions,”Journal of Consumer
Psychology, 17 (2), 139–53.
Ibarra, Hermina (1993), “Personal Networks of Women and
Minorities in Management: A Conceptual Framework,”Acad-
emy of Management Journal, 18 (1), 56–87.
Jones, Eli, Steven B. Brown, Andris Zoltners, and Barton A. Weitz
(2005), “The Changing Environment of Selling and Sales
Management,”Journal of Personal Selling & Sales Manage-
ment, 25 (2), 105–11.
Kidwell, Blair, David M. Hardesty, Brian R. Murtha, and Shibin
Sheng (2011), “Emotional Intelligence in Marketing Exchanges,”
Journal of Marketing, 75 (January), 78–95.
Knoke, David and Song Yang (2008), Social Network Analysis.
Thousand Oaks, CA: Sage Publications.
Lin, Nan (1986), “Conceptualizing Social Support,”in Social
Support, Life Events and Depression, N. Lin, A. Dean, and
W. Ensel, eds. New York: Academic Press.
——— (1999), “Building a Network Theory of Social Capital,”
Connections, 22 (1), 28–51.
Liu, Yongmei, Gerald R. Ferris, Robert Zinko, Pamela L. Perrew´
Bart Weitz, and Jun Xu (2007), “Dispositional Antecedents and
Outcomes of Political Skill in Organizations: A Four-Study
Investigation with Convergence,”Journal of Vocational
Behavior, 71 (1), 146–65.
Lohmoller, Jan-Bernd (1989), Latent Variable Path Modeling with
Partial Least Squares. Heidelberg: Physica-Verlag.
McAllister, Charn P., B. Parker Ellen III, Pamela L. Perrew´
R. Ferris, and Daniel J. Hirsch (2015), “Checkmate: Using
Political Skill to Recognize and Capitalize on Opportunities in
the ‘Game’of Organizational Life,”Business Horizons, 58 (1),
Mehra, Ajay, Andrea L. Dixon, Daniel J. Brass, and Bruce Rob-
ertson (2006), “The Social Network Ties of Group Leaders:
Implications for Group Performance and Leader Reputation,”
Organization Science, 17 (1), 64–79.
Mintzberg, Henry (1983), Power In and Around Organizations.
Englewood Cliffs, NJ: Prentice-Hall.
oller, Kristian K. and Aino Halinen (1999), “Business Rela-
tionships and Networks: Managerial Challenge of Network Era,”
Industrial Marketing Management, 28 (5), 413–27.
Social Networks Within Sales Organizations / 15
Munyon, Timothy P., James K. Summers, Katina W. Thompson,
and Gerald R. Ferris (2015), “Political Skill and Work Outcomes:
A Theoretical Extension, Meta-Analytic Investigation, and
Agenda for the Future,”Personnel Psychology, 68 (1), 143–84.
Ng, Thomas W.H. and Daniel C. Feldman (2010), “Organizational
Tenure and Job Performance,”Journal of Management, 36 (5),
Perry-Smith, Jill E. and Christina E. Shalley (2003), “The Social
Side of Creativity: A Static and Dynamic Social Network Per-
spective,”Academy of Management Review, 28 (1), 89–106.
Plouffe, Christopher R. and Donald W. Barclay (2007), “Sales-
person Navigation: The Intra-Organizational Dimension of the
Sales Role,”Industrial Marketing Management, 36 (4), 528–39.
——— and Yany Gr´
egoire (2011), “Intraorganizational Employee
Navigation and Socially Derived Outcomes: Conceptualization,
Validation, and Effects on Overall Performance,”Personnel
Psychology, 64 (3), 693–738.
Preacher, Kristopher J. and Andrew F. Hayes (2004), “SPSS and
SAS Procedures for Estimating Indirect Effects in Simple
Mediation Models,”Behavior Research Methods, Instruments,
& Computers, 36 (4), 717–31.
Ringle, Christian M., Sven Wende, and Alexander Will (2005),
“SmartPLS 2.0 (M3),”(accessed August 31, 2015), [available at
Ryals, Lynette J. and Andrew S. Humphries (2007), “Managing Key
Business-to-Business Relationships,”Journal of Service Research,
9 (4), 312–26.
Schmitz, Christian and Shankar Ganesan (2014), “Managing
Customer and Organizational Complexity in Sales Organi-
zations,”Journal of Marketing, 78 (November), 59–77.
Seibert, Scott E., Maria L. Kraimer, and Robert C. Liden (2001), “A
Social Capital Theory of Career Success,”Academy of Man-
agement Journal, 44 (2), 219–37.
Singh, Jasjit (2005), “Collaborative Networks as Determinants of
Knowledge Diffusion Patterns,”Management Science, 51 (5),
Stone, Mervyn (1974), “Cross-Validatory Choice and Assessment
of Statistical Predictions,”Journal of the Royal Statistical
Society. Series A (General), 36 (2), 111–47.
Szymanski, David M. (1988), “Determinants of Selling Effective-
ness: The Importance of Declarative Knowledge to the Personal
Selling Concept,”Journal of Marketing, 52 (January), 64–77.
Taylor, Alva and Henrich R. Greve (2006), “Superman or the
Fantastic Four? Knowledge Combination and Experience in
Innovative Teams,”Academy of Management Journal, 49 (4),
Treadway, Darren, Jacob W. Breland, Lisa M. Williams, Jeewon
Cho, Jun Yang, and Gerald R. Ferris (2013), “Social Inﬂuence
and Interpersonal Power in Organizations,”Journal of Man-
agement, 39 (6), 1529–53.
Ustuner, Tuba and David Godes (2006), “Better Sales Networks,”
Harvard Business Review, 84 (7/8), 102–12.
——— and Dawn Iacobucci (2012), “Does Intraorganizational
Network Embeddedness Improve Salespeople’s Effectiveness?
A Task Contingency Perspective,”Journal of Personal Selling &
Sales Management, 32 (2), 187–205.
Verbeke, Willem, Bart Dietz, and Ernst Verwaal (2011), “Drivers
of Sales Performance: A Contemporary Meta-Analysis. Have
Salespeople Become Knowledge Brokers?”Journal of the
Academy of Marketing Science, 39 (3), 407–28.
Wasserman, Stanley and Katherine Faust (1994), Social Network
Analysis:Methods and Applications. Cambridge, UK: Cambridge
Wei, Li-Qun, Flora F.T. Chiang, and Long Zeng Wu (2012),
“Developing and Utilizing Network Resources: Roles of Political
Skill,”Journal of Management Studies,49(2),381–402.
16 / Journal of Marketing, Ahead of Print