姝 Academy of Management Review
2012, Vol. 37, No. 3, 396–418.
http://dx.doi.org/10.5465/amr.2010.0403
CATCHING FALLING STARS: A HUMAN
RESOURCE RESPONSE TO SOCIAL CAPITAL’S
DETRIMENTAL EFFECT OF INFORMATION
OVERLOAD ON STAR EMPLOYEES
JAMES B. OLDROYD
SHAD S. MORRIS
The Ohio State University
Because star employees are more visible and productive, they are likely to be sought
out by others and develop an information advantage through their abundant social
capital. However, not all of the information effects of stardom are beneficial. We
theorize that stars’ robust social capital may produce an unintended side effect of
information overload. We highlight the role of human resource management in minimizing the effects of information overload for stars, and we discuss avenues for future
research.
Ernst, Leptein, & Vitt, 2000; Narin & Breitzman, 1995).
As a result of their uniquely valuable human
capital contributions, top performers are often
the most widely recognized employees in a
given organization (Trevor, Hausknecht, & Howard, 2007; Trevor & Nyberg, 2008). Top performers
are also more visible than their peers in internal
and external labor markets (Groysberg, Lee, &
Nanda, 2008), as evidenced by increased interest in hiring practices regarding the highestperforming employees (Gardner, 2005; Lazear,
1986). Research on the professional service industry has demonstrated that top performers
receive particular attention from competing
organizations, which tend to view their achievements as valuable assets ripe for acquisition
(Greenwood, Hinings, & Brown, 1990).
When top performers possess high internal
and external visibility, they are considered to be
stars. Following Groysberg and colleagues
(2008), we define “stars” as employees who (1)
demonstrate superior performance in relation to
others in their respective organizations and (2)
are highly visible in the labor market. These
unique characteristics often endow stars with
what sociologists call a “cumulative advantage”—namely, their productive resources increase at a rate exponentially greater than their
less visible and less valuable peers (Cole &
Cole, 1973; Zuckerman, 1977). For example, stars
in science fields find it easier to acquire the
resources necessary to facilitate research, such
Human resource (HR) scholars have argued
that some employees are more valuable than
others (Becker & Huselid, 2006; Hausknecht,
Rodda, & Howard, 2009; Lepak & Snell, 1999).
Consistent with a resource-based view of the
firm, the highest-performing employees create
disproportionate value, providing a rare but vital opportunity for an organization to increase
its competitive advantage through human capital (Barney, 1991; Barney & Wright, 1998; Lepak &
Snell, 2002). Alternatives such as hiring a
greater number of average performers or enhancing nonhuman assets are not adequate
substitutes for the value created by top performers (Eccles & Crane, 1988; Kelley & Caplan, 1993;
Lepak, Takeuchi, & Snell, 2003; Narin, 1993). For
example, in professional service industries an
organization’s top performers both generate the
bulk of that organization’s business and constitute its core knowledge assets (Eccles & Crane,
1988). Studies of scientists and academic researchers have consistently found that employees at the top of the performance distribution
are many times more valuable than their lowerperforming colleagues (e.g., Cole & Cole, 1973;
We gratefully acknowledge the excellent support of David
Lepak throughout the revision process and thank our anonymous reviewers for their insightful comments and suggestions. James Oldroyd also thanks the generous support of a
Sungkyunkwan University summer research grant in supporting this project. Finally, we thank to Jay Anand, TaiYoung Kim, and Steffanie Wilk for their helpful comments on
this work.
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Oldroyd and Morris
as colleagues seeking collaboration, cadres of
highly capable students, and access to databases (Zuckerman & Merton, 1972). Consequently, they are likely to find themselves embedded in a virtuous cycle: they meet with
increased access to information resources and
opportunities to increase their productivity,
which lead to increased visibility in the labor
market, which, in turn, results in even more resources and opportunities (Allison, Long, &
Krauze, 1982).
From a relational perspective, one by-product
of stardom is the abundance of social capital,
defined here as the structure of relationship networks and information available to an individual (Bourdieu, 1986; Burt, 1992). In this regard, a
higher number of network connections or potential sources of information increase an individual’s social capital (Baker, 1990). Nahapiet and
Ghoshal state that “the central proposition of
social capital theory is that networks of relationships constitute a valuable resource” (1998: 243).
In other words, the value of network ties, as well
as the social capital that comes with them, derives primarily from privileged access to information and opportunities (Burt, 1997). Because
star employees are highly visible in the labor
market and others are likely to seek relationships with them, stars will likely develop exponentially high levels of social capital that, in
turn, perpetuate and reinforce their positions
(Burkhardt & Brass, 1990; Groysberg et al., 2008;
Kang, Morris, & Snell, 2007). For instance,
through a series of studies, Groysberg (2010) has
demonstrated that one of the key factors in stars’
success is not only their unique and uniquely
valuable human capital but also their social
capital—that is, the relationships they possess
with others in the organization.
While an abundance of social capital can positively impact stars’ performance, not all of the
effects of abundant social capital are positive
(Adler & Kwon, 2002). For instance, scholars are
beginning to explore some potential limitations
of social capital due to structural challenges
(Arenas, Díaz-Guilera, & Guimerà, 2001; Burt,
1997; Dodds, Muhamad, & Watts, 2003; Guimerà,
Díaz-Guilera, Vega-Redondo, Cabrales, & Arenas, 2002; Watts, 2004), differential effects of specific network contexts (Cummings & Cross, 2003;
Xiao & Tsui, 2007), links to negative groups
(Lechner, Frankenberger, & Floyd, 2010), limited
control (Buskens & van der Rijt, 2008; Ryall &
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Sorenson, 2007), and overembeddedness (Gargiulo & Benassi, 2000). All of these factors represent some form of structural constraint in which
ties are not as helpful as they might otherwise
be, either because they are redundant or because they offer limited access to novel information. However, while such structural factors limiting the value of social capital are important to
provide a more robust picture of organizational
trials and successes, they do not reflect a serious challenge created by the abundant social
capital amassed by star employees.
We uniquely highlight that because of their
high visibility and performance status, stars are
likely to build up an abundance of ties leading
to nonredundant information flows. But, by the
very nature of their unique positions, stars are
less likely to face structural constraints and are
more likely to amass exponentially high levels
of social capital. As a result, they are much more
susceptible to another form of constraint than
are their average-performing peers: information
overload. In a state of overload, cognitive limitations may constrain the value of a star’s social
capital; if the information load goes unmanaged
for long periods of time, the star may stumble
and, ultimately, fall (Herbig & Kramer, 1994; Van
Gerven, Paas, Tuovinen, & Tabbers, 2003). Thus,
it is important to generate a greater understanding of how and when social capital adversely
affects the performance of both the star employee and the organization in which he or she
is embedded. In so doing we posit that a star is
likely to fall in the absence of specific individual, organizational, and network-wide actions
that help manage the continual increase in both
the information given to and the demands made
of the star. Using a process approach to theory
development, we focus on this important but
previously unexplored boundary condition of
the value of social capital, one that applies principally to star employees.
The conditions under which some stars shine
and others fall have long proven difficult to explain, let alone produce (cf. Groysberg, 2010). We
begin by reviewing the link between social capital and star employees, emphasizing that because of the affiliatory nature of network formation, stars are likely to have exponentially
higher levels of social capital than their less
visible and less highly performing peers. We
then discuss how this abundance of social capital may unintentionally result in information
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overload for star employees. We go on to explore
the implications of a curvilinear theory of social
capital on the performance of star employees
and their organizations, highlighting the boundary conditions of our theory and explicating
when information overload may or may not adversely affect star employees with abundant social capital. We then highlight strategic HR
management responses, targeted at the individual, organizational, and network level, that
might help mitigate the potentially negative
side effects of social capital for stars. Finally,
we conclude by outlining several ways in which
future research can link human resource management, stars, social capital, and information
overload.
STARS’ HUMAN AND SOCIAL CAPITAL
Stars are employees who consistently and
substantially perform better than others in their
organizations and are also highly visible in
their respective labor markets (Groysberg et al.,
2008). Stars are common across industries and
are frequent topics of discussion in knowledgebased industries where organizational value is
largely tied to employees’ individual abilities
and potential for coordination. The work differential between stars and nonstars is vast. For
example, as Groysberg notes:
The phenomenon of stardom— of performers
whose productivity massively outstrips that of
their colleagues—is well documented. One study
found that the top 1 percent of employees in
highly complex jobs outperform average performers by 127 percent. Another reported an eight-toone productivity difference between star computer programmers and average programmers.
The top 1 percent of inventors was found to be
five to ten times as productive as average inventors (2010: 616).
Zucker, Darby, and Armstrong (1998) found,
similarly, that in the biotech industry stars represent only three-quarters of 1 percent of scientists, but they account for 17.3 percent of published articles. Thus, star scientists publish
almost twenty-two times as many articles as
their average colleagues.
The value that stars create for knowledgebased organizations determines both their human and social capital (Groysberg et al., 2008).
The rare, intangible resource of a star’s human
capital is a key source of competitive advantage
July
for his or her organization (Barney & Wright,
1998; Huselid, 1995; Lepak & Snell, 1999). A star’s
firm-specific human capital may include knowledge about how to accomplish complex tasks in
a particular organization, how to develop trust
among a team of employees, and how to create
a sense of commitment to a firm’s success, along
with other vital, valuable information (Barney &
Hansen, 1994; Conner & Prahalad, 1996).
Strategic HR management researchers recognize the importance of stars’ social capital in the
creation of organizational values (e.g., Dess &
Shaw, 2001; Wright, Dunford, & Snell, 2001). Because stars have greater access to information,
as well as access to avenues for sharing it, they
tend to create more value for their organizations
(Dess & Shaw, 2001). For instance, in a study of
R&D project managers, Allen and Katz (1985)
found that star employees are a key source of
technical knowledge. Because of this knowledge, organizational colleagues consulted most
frequently with star employees, and stars spent
significantly more time than their colleagues
conferring with those within and outside of their
own technical specialties. Similarly, Kang et al.
(2007) have argued that top employees create
value for organizations via their social relationships with colleagues across departments and
functions. These relationships provide access to
new information and opportunities that the stars
then use to create greater organizational value.
THE UPWARD SPIRAL OF STARDOM AND
INFORMATION OVERLOAD
The Affiliatory Nature of Social Capital
Recent research in physics (Barabasi & Crandall, 2003), information technology (Ebel,
Mielsch, & Bornholdt, 2002), and biology (Jeong,
Tombor, Albert, Oltvai, & Barabasi, 2000) indicates that network formation often follows an
affiliatory pattern. Rather than forming randomly between actors, associations form by
choice, based on the actors’ preferences and
what they are searching for or hoping to gain
(Newman, 2002). Numerous studies show that affiliatory networks represent trends in social and
work life, in which people tend to gravitate toward a few individuals who are key to achieving their objectives (e.g., Newman & Park, 2003).
The people who are the “recipients”—that is, the
key individuals to whom others gravitate—are
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Oldroyd and Morris
often high-performing and visible actors in
these networks.
Because of affiliation, organizational networks are not composed of randomly generated
connections, Rather, once stars emerge in an
organization, others will tend to gravitate to
them, and these new associations will result in
a virtuous circle that further increases stars’ importance and visibility (Newman, 2002). As a result of their high visibility and strong performance, star employees are likely to be
frequently asked for advice and to have influence over and association with others
(Burkhardt & Brass, 1990). Because this process
is an upward spiral, other employees in the organization are likely to seek out stars once they
have achieved visibility, heightening the upward spiral effect. In other words, when employees seek to build new relationships, they are
most likely to build those relationships with
high-performing, highly visible stars.
Such affiliatory patterns create cumulative
advantages for stars. The stars’ prior success
increases their future endowments of social capital. This is a mechanism in which social capital
and human capital are recursive, with each reinforcing and increasing the other. As other employees form ties with star employees, they, too,
try to access their unique human capital or gain
access to their robust relationship; thus, the
stars’ organizational power and influence increase concurrently (Emerson, 1962).
Using a social network structure approach,
Tichy and Tushman (1979) showed that stars
play a “linking pin” role since they occupy the
core of the organization’s network structure. Additional research has shown that employees
who have the highest organizational recognition have greater access to resources, including
social prestige and knowledge, from which they
might attract others seeking status, information,
or money (Bacharach & Lawler, 1980; Foa & Foa,
1974). For instance, as Allen and Katz (1985)
noted, stars have robust connections and so can
keep up with new developments in their fields,
further increasing their human capital. As a result, stars are frequently embedded in a virtuous
cycle of social capital development, in which
higher social capital (i.e., more associations) increases their appeal, causing more and more
people to gravitate toward them.
Because of the nature of affiliatory tie formation, stars are likely to be connected to many
399
more individuals than average employees; this
connection pattern follows a power-law (Pareto)
distribution of ties. Thus, rather than simply
having a few more ties than average employees,
stars are likely to have exponentially more associations and the concomitant social capital
than average employees. As a consequence,
stardom does not provide a marginal increase in
social capital over that of the average employee
but, rather, an exponential increase.
Underscoring the difference between the marginal increase and the exponential increase in
social capital is the difference between random
and affiliatory networks. This is a vital point
when investigating how social capital affects
information flow to star employees. Figure 1 illustrates this point. In Figure 1 we model four
different networks of 100 people each. Two of
these networks have an average of five ties per
actor, and two have an average of ten ties per
actor. We compare the difference in both types
of network structures for networks formed in a
random versus affiliatory manner. Both the fivetie and ten-tie networks consist of 100 people,
and each of the networks has an equal average
number of ties. However, the distribution of ties
significantly differs between the networks. On
the one hand, in the first network (which has an
average of five ties per actor) and third network
(which has an average of ten ties per actor), the
distributions follow a random pattern of relationship formation. On the other hand, in the
second network (which has five ties) and fourth
network (which has ten ties), the distributions
follow an affiliatory, or preferential, model.1 In
the affiliatory networks the majority of the relationships are linked to a small number of stars,
concentrated in the center of the network. In the
random networks the ties are more evenly dispersed across the network.
Figure 2 quantifies this difference between
the random and affiliatory networks. In this figure we construct a measure of “in-degree centrality” for each actor. In-degree centrality is a
measure of the incoming ties directed toward an
actor. Our results show that in affiliatory networks the most central star employees have
more than two-and-a-half times as many asso-
1
Network theorists also refer to affiliatory networks as
scale-free networks (e.g., Barabasi & Crandall, 2003).
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July
FIGURE 1
Random versus Affiliatory Network Graphs
Random 100-node network with average in-degree centrality of 5
Affiliatory 100-node network with average in-degree centrality of 5
Random 100-node network with average in-degree centrality of 10 Affiliatory 100-node network with average in-degree centrality of 10
FIGURE 2
Centrality in Random and Affiliatory Networks
Comparison of the in-degree centralitya for each node in the
Comparison of the in-degree centralitya for each node in the
random and affiliatory network of 100 actors with an average in- random and affiliatory network of 100 actors with an average indegree centrality of 5 ties. The highest in-degree centrality for
degree centrality of 10 ties. The highest in-degree centrality for
actors in the random network is 10, while the highest in-degree
actors in the random network is 27, while the highest in-degree
centrality for actors in the affiliatory network is 26.
centrality for actors in the affiliatory network is 46.
30
50
45
20
Random
15
Affiliatory
10
0
a
1
8
15
22
29
36
43
50
57
64
71
78
85
92
99
5
40
35
30
25
20
15
10
5
0
Random
Affiliatory
1
9
17
25
33
41
49
57
65
73
81
89
97
25
In-degree centrality is a sum of the ties that are directed toward an actor.
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Oldroyd and Morris
ciations as those most central in random
networks.
Figure 3 takes this analysis even further, demonstrating that the stars’ network ties in affiliatory networks are often exponentially higher
than others’ ties in the same network. For both
the random and affiliatory examples, we have
calculated the percentage of all ties connected
to each actor in the network. In our comparison,
for instance, we found that the top 10 percent of
employees’ in-degree centrality in the random
network accounts for 18 percent of the network’s
in-degree ties. The top 10 percent of actors in the
affiliatory network, however, account for over
40 percent of ties. We see that the magnitude of
this effect intensifies as we narrow our definition of stars to fewer and fewer actors. For instance, the most central actor in a random network receives just 2 percent of the in-degree ties,
whereas in an affiliatory network the individual
occupying that position receives 6 percent of
ties, or three times the number of ties. Because
organizational networks follow an affiliatory
pattern and stars are likely to be key players in
these networks, we posit the following.
Proposition 1: Because of their high
performance and high visibility, star
employees are likely to have exponentially more social capital than average employees.
401
Stars and Information Flow
Recent research in the information sciences
has repeatedly demonstrated that communication networks follow a power-law distribution—
namely, key actors in a given network are likely
to both receive and send more information than
nonkey actors.2 Since stars are likely to have an
abundance of social capital, they have access to
numerous contacts, increasing the average
amount of information they receive (Groysberg
& Lee, 2008; Lechner et al., 2010). As a result,
stars are not only more likely to have more associations than average employees but are
more likely to actively communicate using these
ties. This may be due to their colleagues’ need to
access their human capital, an effect demonstrated through empirical studies of actors with
high human capital. For instance, Burkhardt
and Brass (1990) found that experts are more
likely to be sought out in organizations than
nonexperts.
Furthermore, once a star has obtained information, abundant social capital may enable
2
Baeza-Yates, Boldi, and Castillo (2006) have demonstrated the affiliatory nature of web page links, showing that
regardless of the function used to identify page rank, the
distribution is likely to follow a power-law function indicating that a few key nodes (or sources of information) are
exponentially more likely to be active.
FIGURE 3
Percent of Actors with Ties to Star Employees in Random and Affiliatory Network Conditions
Percent of ties in random and affiliatory networks linked to star
Percent of ties in random and affiliatory networks linked to star
employees in 5 average ties network
employees in 10 average ties network
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.4
Random network
0.3
Affiliatory network
0.4
Random network
0.3
0.2
0.2
0.1
0.1
0
Affiliatory network
0
Top 1% Top 5% Top 10% Top 25%
Top 1% Top 5% Top 10% Top 25%
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Academy of Management Review
him or her to leverage his or her structural position, facilitating the flow of new and valuable
information across structural boundaries or
gaps in the network space. Burt notes that those
with abundant social capital often have a “say
in whose interests are served,” and such an individual will act as an “entrepreneur in the literal sense of the word—a person who adds
value by brokering the connection between others” (1997: 342).3
Here we compare the volume of information
stars are likely to receive with the volume of
information average employees in the affiliatory networks are likely to receive. In this case
we hold flow constant per actor (a conservative
measure), finding that stars (who constitute the
top 1 percent of actors) have nearly nine times
as many ties as average employees. Assuming
that there is an average of five ties present in
the network, that each tie generates the same
information load, and that both incoming and
outgoing information flows emerge from each
tie, we project that stars will receive eighteen
times as much information as average employees. As the number of ties increases, this load
increases in a power-law manner. In addition, it
is likely that attention paid to a star is even
greater, because stars are not only more likely to
have more connections but are also more likely
to be involved in active ties (in the form of requests, questions, etc.). This interactive effect of
combining the number and volume of flow
clearly indicates that stars are likely to receive
and send an abundance of information. Hence,
we posit the following.
Proposition 2: Because of their abundant social capital, star employees
are likely to send and receive exponentially more information than average employees.
3
Several studies demonstrate a strong link between performance and actors who facilitate the connection of other
actors in the network. Some of these advantages include
prestige (Allen & Cohen, 1969), early job promotion for themselves and their subordinates (Burt, 1997; Katz & Tushman,
1983; Podolny & Baron, 1997), higher salaries (Seibert,
Kraimer, & Liden, 2001), and better job performance. Each of
these advantages is distinct to star employees (Brass,
1984, 1985).
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Stars and Information Overload
Individuals process information, according to
Sternberg (1977: 317), by turning information into
intelligence through a process of “components.”
Component processing includes activities such
as coding, inferring, mapping, applying, and responding to information. While individuals vary
in their ability to process information owing to
their cognitive abilities (Ackerman, 1986; Kanfer
& Ackerman, 1989; Locke, 1965), instructional design scholars note that all individuals rely on
short-term or working memory to process information on a day-to-day basis (Baddeley, 1986;
Miller, 1956). Individuals are conscious of and
can monitor only the content of their working
memories. Because information processing requires working memory, information loads that
exceed its capacity may overwhelm an individual’s information processing activities. These
limitations of working memory are widely
known and accepted (Sweller, van Merrienboer,
& Paas, 1998). As stated by Sweller et al.:
Because working memory is most commonly used
to process information in the sense of organizing,
contrasting, comparing, or working on that information in some manner, humans are probably
only able to deal with two or three items of information simultaneously when required to process
rather than merely hold information. . . . it is this
factor that provides a central claim of cognitive
load theory (1998: 252–253).
Much of the research on social capital assumes a linear relationship between information flow and stardom or highly central positions such that the more information an
employee receives, the better his or her performance will be (e.g., Burt, 1992, 1997). Information
processing theory further clarifies our understanding of the potential risks of information
overload on those employees with high levels of
social capital. This theory asserts that individuals benefit from the receipt of information, but
only until they reach a point at which they are
unable to process additional incoming information (O’Reilly, 1980; Tushman & Nadler, 1978).
Beyond this point, additional information becomes a liability (Eppler & Mengis, 2004). In a
state of overload, an individual’s ability to perform rapidly declines (Chewning & Harrell,
1990). In any given context, then, if the amount of
information an individual receives exceeds his
or her information processing ability, the extra
information may harm performance (Boone, van
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Oldroyd and Morris
Olffen, & van Witteloostuijn, 2005; Carpenter &
Fredrickson, 2001; Wadhwa & Kotha, 2006).
While all organizational actors face some risk
of information overload, a star’s robust and constantly increasing social capital places him or
her in a unique position that is likely to lead to
information overload if not carefully managed.
Moreover, a star’s robust social capital not only
likely burdens him or her with extreme levels of
information flow but also places the star in a
position where other employees are likely to
seek advice and expertise and, in so doing,
cause frequent interruptions that compromise
the star’s ability to complete tasks (Rudolph &
Repenning, 2002). Grove (1983: 67), for example,
described the constant request for information
and advice received by managers as “the
plague of managerial work.” Similarly, Perlow
(1999) showed that the frequent coworker interruptions experienced by highly visible software
engineers ultimately led to “a time famine,”
wherein engineers had too many information
requests and could not properly perform
their jobs.
Such frequent information requests require
extra information processing activity and often
necessitate immediate attention. These requests
can also interrupt information processing focused on the task at hand (Cellier & Eyrolle,
1992; Kirmeyer, 1988). In an organizational setting, additional attention and visibility can increase the amount of information requests, de-
403
creasing an employee’s attention and ability to
concentrate on the specific requests themselves
(Oldham, Kulik, & Stepina, 1991; Perlow, 1999).
Moreover, scholars such as Jett and George
(2003) have shown that information technology
has increased the number of interruptions from
information requests, with email and other
forms of electronic communication heightening
the frequency with which people can interact
and interrupt one another at work (e.g., Cutrell,
Czerwinski, & Horvitz, 2001; Speier, Valacich, &
Vessey, 1999).
In line with other studies of network structure
and load (e.g., Watts, Dodds, & Newman, 2002),4
we suggest that even when star employees have
relatively low cognitive costs associated with
processing information for each message, they
are frequently overloaded by the cumulative
burden created by their exponential amounts of
social capital. In Figure 4 we show star employees’ cumulative information burden for an information flow of five and ten messages per network contact. Because of the nature of affiliatory
networks, and assuming only five incoming and
4
Network scholars have demonstrated that affiliatory networks are robust against random breakdowns. However,
they are easy to disrupt with focused attacks (of overload) on
the most highly connected actors. In other words, when stars
are overloaded, their failure to process information can easily disrupt the network (Cohen, Havlin, & ben-Avraham, 2002).
FIGURE 4
Information Load in an Affiliatory Network with Average Centrality of Five Actors at Five and Ten
Messages per Actor
1000
900
800
700
600
Information load 5
messages per tie
500
Information load 10
messages per tie
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200
100
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0
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Academy of Management Review
outgoing communications per tie, we find that
stars carry an information burden of over 450
messages, as compared with 25 messages for
the average employee.
Because exponentially higher levels of social
capital are likely to burden star employees with
information, we posit the following.
Proposition 3: Because of their extreme level of information flow, stars
are likely to experience information
overload.
The Individual Performance Effects of Stars’
Information Overload
A star’s performance is likely to be hindered
by this deluge of information. Information processing studies have clearly demonstrated a
link between effective information processing
and employee performance (Eppler & Mengis,
2004). Scholars exploring information overload
have emphasized that the consequences of information overload not only act as a limit to the
employees’ performance but actually may decrease the overall performance of the individual
experiencing overload (e.g., Jacoby, 1977; Meier,
1963). For instance, Malhotra noted that
although consumers develop mechanisms for
limiting their intake of information, their limited
processing capacity can become cognitively
overloaded if they attempt to process “too much”
information in a limited time, and this can result
in confusion, cognitive strain, and other dysfunctional consequences (1984: 437).
Similarly, in his study of library workers,
Meier (1963) found that overwhelmed individuals had to completely stop the information flow
they received until they could catch up on their
processing tasks. Oskamp (1965) also found that
information improves decision-making ability
up to a certain point, but when the flow of information exceeds that point, additional information diminishes the person’s decision outcomes.
Likewise, Connolly (1977) found that excessive
information leads to a decreased accuracy in
decision making. Schick, Gorden, and Haka
(1990) noted that the burden of information overload leads to confusion, an inability to set priorities, and a deficit in information recall. Overload has also been shown to reduce decision
makers’ ability to identify relevant information
(Hodge & Reid, 1971; Streufert, 1973). Hence, we
posit the following.
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Proposition 4: As star employees experience information overload, their performance is likely to decrease.
The Organizational Performance Effects of
Stars’ Information Overload
Because stars are required to share their
knowledge with others, they are likely to receive
many requests for advice and information. Professional service organizations often identify
stars as “thought leaders” or “knowledge experts”—people others can turn to for help. Stars
are not only identified but often actively put in
contact with others, across business and geographic lines, to ensure visibility and accessibility by peers (Lorsch & Tierney, 2002). They are
likely to be singled out for formal and informal
mentoring responsibilities (Noe, 1988). In fact, as
Phillips-Jones (1983) pointed out, most mentoring
relationships are informal, incited by admiration for the star or by job demands that require a
star’s expertise. Thus, the very mentoring opportunities meant to energize employees can feel
like a punishment for success if the programs
are not designed to consider the potential for
information overload in the case of stars.
Scholars have also argued that organizations
often spend the majority of their efforts providing stretch assignments, “special” projects, and
development programs solely for star players
(Huselid, Beatty, & Becker, 2005). When organizations target these employees, the employees
may become overburdened with responsibilities, and this may cause a decrease in their
ability to share information and mentor others.
When stars experience information overload,
they are likely to become bottlenecks in the organization (Cross & Parker, 2004). In other words,
stars who receive too much information will not
be able to share their expertise with others in
the organization. In this way, information overload may hinder an organization’s ability to leverage a star’s human capital. Consequently,
information overload may not only affect both
the star’s individual performance but also the
organization’s performance on the whole.
Studies of scale-free networks demonstrate
the effect of overload for key network nodes.
These studies note that an “attack [overwhelming the actor] on the most highly connected
nodes of the network” can result in a serious
disruption in network flow (Cohen et al., 2002:
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Oldroyd and Morris
14). The effects of overload for stars ripple
through the system, and a small disruption for a
star “suffices to disrupt the net for all” (Cohen et
al., 2002: 14).
While information flowing to stars may not be
directly proportional to the information they
share with others, it is likely to be correlated. As
a result, the volume of information sent out by
stars can come back to them, adding exponentially to their information load. Figure 5 shows a
comparison of the difference in in-flowing, outflowing, and total flow for actors in random versus affiliatory networks. This highlights that in
affiliatory networks stars are likely to shoulder
the information burden resulting from both incoming and outgoing ties; overloaded stars will
be unable to process and share information.
Limiting stars’ ability to share information
also likely limits their ability to provide advice
and mentoring to others, both of which are necessary elements in fostering human capital
within an organization. For instance, DeLong,
Gabarro, and Lees (2007) found that top performers in professional service firms tend to focus
too much on satisfying clients, to the point of
neglecting their own and others’ personal skill
building. Therefore, we propose the following.
Proposition 5: As stars experience information overload, they are more
405
likely to decrease the amount of valuable information they share with
their peers, stifling organizational
performance.
Next, we draw further distinctions between
star and average employees. Research has
shown that, unlike average workers, star employees have high external visibility, which
means they are likely to be able to leave an
organization if they feel burdened. Trevor (2001)
emphasized this point, noting that star employees’ high performance and visibility lead to high
portability. Spence (1973) argued that when employees make investments in their organizations, they increase productivity and are visible
to others; thus, competitors note “signals” that
these employees possess skills generally applicable across organizations, and such signals invite competitors to attempt to hire these employees (Lazear, 1986), increasing the employees’
likelihood of leaving the organization they initially worked for (Schwab, 1991). In fact, research
suggests that star employees’ turnover rate
is not significantly affected by nationwide unemployment rates, when firms typically engage
in only limited hiring (Lee, Gerhart, Weller, &
Trevor, 2008; Trevor & Nyberg, 2008)—their mobility is robust even during economic uncertainty. The “war for talent” literature shows that
60
In-degree
random
50
Out-degree
random
40
Total degree
random
30
In-degree
free-scale
20
Out-degree
free-scale
10
Total degree
free-scale
99
92
85
78
64
71
57
50
43
36
29
22
8
15
0
1
Centrality
FIGURE 5
In-Degree, Out-Degree, and Total Degree Centrality for Random and Affiliatory Networks
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Academy of Management Review
professional industries typically hire freely from
one another and that highly pursued employees
often have little loyalty to their employers, conceptualizing loyalty instead as a duty to the
larger profession (Gardner, 2005; Greenwood,
Oliver, Sahlin, & Suddaby, 2008). Much literature
focuses on the mobility of these individuals (e.g.,
Groysberg et al., 2008; Marx, Strumsky, & Fleming, 2009).
We argue here that when stars are in a state
of chronic information overload, they may notice
that their performance is suffering and may
seek to remedy the situation. With the difficulty
of single-handedly managing information overload and the relative ease of moving to another
organization, stars are likely to become frustrated and exercise their options to work for
other organizations. Moving can provide them
with the opportunity to access more resources
and face fewer demands on their time and energy.5 Stars experiencing overload and then
“shooting” to competitors is a well-understood
phenomenon. For example, Tom Rath, head of
workplace consulting for Gallup, noted that
companies that require stars to share information “can be perceived as piling on. And that’s
the quickest way to push that person out the
door” (quoted in McGregor, 2010).
When a star employee decides to leave, the
organization suffers from the loss of valuable
human capital, as well as the loss of social
capital. Shaw, Duffy, Johnson, and Lockhart
(2005) found that the loss of both human and
social capital resulting from turnover is particularly detrimental to an organization’s performance; even when a small number of star employees leave an organization, such a loss has a
sharp negative effect on organizational performance. Hence, the extent to which a star’s information load can be effectively managed remains a key strategic concern. As a result, we
posit the following.
Proposition 6: When stars experience
information overload, they are more
likely to leave a given organization,
stifling organizational performance.
July
MANAGING STARDOM AND
INFORMATION LOAD
Prior studies have examined how to preserve
information flow in scale-free networks; these
studies show the importance of focusing on key
actors in the networks. For instance, simulations
of immunization strategies demonstrate that
when a system of random inoculation is utilized,
a network remains contagious “even after immunization of most of its nodes” (Cohen et al.,
2002: 23). However, when key nodes are immunized, even though only a small fraction of actors receive immunization, this strategic inoculation is sufficient to dramatically halt the
spread of infection. Similarly, when HR managers focus on increasing a star’s efficiency and
effectiveness, their efforts are likely to have a
profound effect in managing the side effects of
information overload for the organization as a
whole. We suggest that because networks follow a nonrandom pattern, HR strategies should
follow suit and take a nonrandom approach to
curbing information overload. These strategies
should focus on the key individuals in the organization: the stars. We further suggest that when
such strategies are absent, stars in these organizations are likely to fall.
How can HR managers reduce the information
overload side effects of amassing social capital? To reduce the liabilities of a star’s abundant
social capital, HR managers can use tactics that
increase stars’ individual processing capacity,
concentrate on the organization’s characteristics with respect to information flow, and bolster
the structural foundation of the star’s network
(Eppler & Mengis, 2004). To demonstrate the effect of these practices, we turn to theories of
cognition, HR management, and social networks
to examine (1) individual, (2) organizational, and
(3) structural conditions that may influence the
degree to which stars experience information
overload. We highlight that without active management of the information load, star employees
will tend to become overloaded with information, which will likely lead to either their fall or
their decision to shoot off to other organizations.
Individual Conditions
5
We thank an anonymous reviewer for pointing us to
relevant research demonstrating that star employees have a
higher likelihood of leaving their organization when they
experience information overload.
While information overload is a likely outcome for employees with exponentially high social capital, it is hardly a certainty. Stars have
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Oldroyd and Morris
the power to increase their information processing capacity.6 Instructional design scholars
have demonstrated that individuals’ ability to
process information is restricted by limitations
in short-term memory (Baddeley, 1986), which
allows individuals to process only a few elements before “overloading their capacity and
decreasing the effectiveness of processing” (Kalyuga, Ayres, Chandler, & Sweller, 2003: 23). Daft
and Huber (1987), Sproull and Kiesler (1991),
Whittaker, Swanson, Kucan, and Sidner (1997),
Hansen and Haas (2001), and Kostova and Roth
(2003) have argued that knowledge sharing is
limited by an actor’s inability to act on shared
information and to distinguish between reusable and nonusable information.
This limitation with short-term memory can be
further mitigated by developing long-term memory, which increases information processing by
applying domain-specific knowledge that organizes and categorizes information to aid in decision making. Decision criteria, also known as
schemas, increase an individual’s ability to categorize and prioritize information. Information
that does not fit into existing schemas (Rumelhart, 1975), scripts (Schank & Abelson, 1977),
frames (Minsky, 1975), or categories (Lakoff, 1987)
requires additional effort to process, and may
even require the adaptation of existing linguistic frameworks or the creation of new ones. Once
schemas are created and understood, the information load vastly decreases. Sweller et al.
highlight that
schemas both bring together multiple elements
that can be treated as a single element and allow
us to ignore myriads of irrelevant elements.
Working memory capacity is freed, allowing processes to occur that otherwise would overburden
working memory. Automated schemas both allow
fluid performance on familiar aspects of tasks
and— by freeing working memory capacity—
permit levels of performance on unfamiliar as-
6
One strategy for dealing with information overload is to
simply ignore much of the information one receives. While
ignoring information flow may directly benefit the star by
reducing his or her cognitive burden, it will likely undermine
cooperation and teamwork in the organization. Groysberg,
Lee, and Abrahams note the potential problems of this strategy: “To get the best of your top performers, maintain a
“no-jerks” policy: Stars who don’t play well with others won’t
benefit you in the long run” (2009). Thus, we assume that
encouraging stars to ignore the problem is not a viable
solution for the stars or the organization.
407
pects that otherwise might be quite impossible
(1998: 258).
For example, Morris et al. (2009) found that
when people lack a shared vision or a shared
framework of what is important within the organization, much of the information possessed by
employees is neither transferred nor processed.
To increase stars’ information processing capabilities, then, HR managers should provide them
with the opportunity to have diverse workplace
experiences, which will enable them to quickly
understand subtle nuances of information and
share that understanding with others (Tushman
& Scanlan, 1981). Furthermore, a more robust
exposure to different experiences also increases
stars’ transactive memory, honing their ability
to locate specific information (Wegner, 1986).7
In addition to efforts to increase long-term
memory, organizations can also optimize how
stars allocate their attention capacities to improve information outcomes (Kanfer & Ackerman, 1989). By designating specific times to
check email, voicemail, and texts, organizations
may reduce the cognitive processing load for
stars. These steps standardize processing requirements and reduce interruptions. For instance, star employees may set aside a specific
time to check email each day, ignoring it for the
rest of the day. In this way stars can focus on
information requests and send information
when they have time to fully process the information, replying in an efficient, effective
manner.
In addition to capability, motivation may affect both information processing (LePine,
Colquitt, & Erez, 2000; Sackett, Gruys, & Ellingson, 1998; Witt & Burke, 2002) and knowledge
sharing performance (Szulanski, 2000). This
7
Although schemas may help stars speed decision making, they may not automatically improve decision outcomes.
For instance, Malhotra notes, “When presented with ‘too
much’ information, consumers may become confused, so that
they are unable to effectively and efficiently process the
information, and/or they may adopt some heuristic processing. While consumers may employ heuristics to limit the
intake of information, these heuristics may often involve a
tradeoff between simplifying and optimizing. As the work by
Wright (1975: 62) suggests, ‘simplifying and optimizing are
likely to be antagonistic goals.’ Hence, in the context of
decision making, it is entirely possible for a consumer to
adopt a choice heuristic that may limit cognitive strain but
that may not lead to the ‘best’ or even to a satisfactory
choice” (1984: 438).
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Academy of Management Review
proves to be a serious challenge. As Lorsch and
Tierney note:
Employing stars is necessary but insufficient.
They must also be aligned; that is, they must
behave in ways that move the firm toward its
goals. . . . Unfortunately, such behavior is usually
an unnatural act. This is particularly true in PSFs
[professional service firms], where the professionals’ natural independence is compounded by
the inherently decentralized nature of the work
(2002: 26).
In a network simulation, Tang, Xi, and Ma
(2006) showed that the star actors with the most
connected nodes who were highly motivated to
share information (and not overwhelmed) were
nearly twice as effective in facilitating information flow than were stars with average information-sharing aspirations. Thus, by increasing a
star’s motivation, an organization may induce
him or her to dedicate more attention to information processing (Hwang, Kettinger, & Yi, 2010;
Kanfer & Ackerman, 1989), dramatically changing information flow in the organization.
Organizations can increase motivation by recognizing and rewarding stars for their efforts. A
star’s efforts to effectively process and share
information are often inadequately rewarded.
For example, Perrow (1999) recounted an interview with a star engineer. The engineer was
seen by coworkers as highly visible and productive, and, as a result, many people went to this
engineer for help.
At one point, the engineer approached the software manager and told him that he was having
trouble balancing all the demands for his help
and completing his own deliverables. According
to the engineer, he was told, ‘Do your own work
first, and then, if you want to help others, that is
your choice, but do it on your own time’ (Perrow,
1999: 69).
In this context the star employee was actually
discouraged from sharing information with
other employees, sending a negative message
to employees and decreasing the star’s motivation to help others.
Another way to increase a star’s motivation is
to turn over more control to the star. Scholars
argue that top performers who are included in
making strategic decisions pay more attention
to the information they receive from others (Morris, Alvarez, Barney, & Molloy, 2010). This specifically occurs for knowledge workers in professional service industries. Furthermore,
July
Gargiulo, Ertug, and Galunic (2009) have found
that employees can more effectively process information when they are free from the control of
network ties and the compulsion to volunteer
information. In other words, when stars can
choose whether they respond to employees, they
may be more effective than their peers at processing large amounts of information. This argument is supported by recent research demonstrating that people who feel a diminished
sense of power and control have significantly
impaired information processing and decisionmaking faculties (Smith, Jostmann, Galinsky, &
Van Dijk, 2008). According to Hallowell (2011),
when stars feel as though they have less control
over their networks, their ability to make decisions will likely decrease, as will their ability to
prioritize information, plan, organize, and implement new ideas. As a result, when stars are
required to share more information with others,
rather than being able to choose when they
share information, they have a decreased ability
to cope with high information loads.
In light of these methods for improving star
employees’ long-term memory, information allocation skills, and motivation to share information, we propose the following.
Proposition 7: Increasing a star’s information processing capabilities (increasing working memory, building
efficient attention allocation capabilities, and increasing his or her motivation) will increase his or her ability to
manage information and prevent
overload.
Organizational Conditions
In addition to individual factors that increase
a star’s ability to manage high information
loads, HR professionals can develop organizational processes and systems to help stars ward
off overload. For example, efficient search processes can dramatically reduce the number of
queries sent to stars. When the stars’ colleagues
know where to find information in an organization, they are much less likely to engage in
costly and ineffective search activities (Walsh &
Ungson, 1991). These searches, in which actors
indiscriminately query others for help, are
called “greedy” searches (Huberman & Adamic,
2004). HR activities that focus on facilitating ef-
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Oldroyd and Morris
ficient searches rather than greedy ones will
dramatically reduce the search burden falling
on stars.
However, these efforts are likely to be particularly challenging, since the employees engaged in greedy searches are likely to find such
searches personally efficient (Adamic, Lukose,
Puniyani, & Huberman, 2001). Moreover, in affiliatory networks indiscriminate searches “naturally gravitate towards the high degree nodes”
(Adamic et al., 2001: 5). We do not suggest here
that stars should not be involved in employee
searches; efficient search does not mean that
actors should not engage stars but, rather, that
actors should engage the right stars. Efficient
searches may also cause a reduction in the overall network traffic by “intentionally choosing
high degree nodes” (Adamic et al., 2001: 5) if
these nodes are the right nodes. By increasing
employees’ transactive memory, employers can
increase effective searches, which allows actors
to access information directly and efficiently
(Wegner, 1986). Developing transactive memory
merely requires knowing what all individuals
know across the organization.
Efficient search can also be encouraged by
requiring those searching for information to pay
search costs. For instance, within the World
Bank Group, project leaders have reduced stars’
processing burdens by requiring project teams
and managers from other offices to pay for stars’
time. Thus, the “costs” of processing more information are calculated into the organization and
into the employee’s work schedule (Morris et
al., 2010).
In addition to promoting efficient search, HR
professionals may also employ information filtering mechanisms and information technologies (Bawden, 2001; Edmunds & Morris, 2000),
which can help stars manage their information
burdens. Grant (1996) has argued that processing information for applications requires organizational processes and information systems
that enable an individual to actually use the
information coming to him or her. These systems
codify and simplify information input, capturing
knowledge in a storage system that both preserves the information and shifts the burden of
sharing it from the stars to the information systems themselves. For example, organizations
may capture a star’s valuable information in
brief “lessons learned” or some other sort of
template that allows users to directly access
409
and apply the star’s knowledge in a comprehensive and readily digestible format (Morris &
Oldroyd, 2009). In addition, information systems
can work to eliminate fluctuations in the information flow. For instance, companies can decrease information overload (e.g., Snell, Youndt,
& Wright, 1996) with the use of specific processes
and systems consisting of set routines or guidelines about how information should be received
and disseminated (Hall, 1992; Itami, 1987; Subramaniam & Youndt, 2005; Walsh & Ungson, 1991).
These templates can aid in overcoming the complexities and strains of processing information.
Through information systems and processes,
knowledge often becomes decontextualized and
articulated in databases and other codified systems; this allows employees to more easily understand which information is helpful in which
contexts. In this regard, technology provides employees with an appropriate structural mechanism to receive and share information (Brockbank & Ulrich, 2005; Davenport & Prusak, 1998).
For example, Morris et al. (2009) found that codifying knowledge and embedding it into existing operations allows organizations to capture,
roll out, maintain, promote, and distribute information to others in the organization. In addition
to codifying information, organizations such as
McKinsey and Company have transitioned some
of their star employees to full-time knowledgesharing positions. In these positions the star
employees’ incentives for information processing and sharing are cleanly aligned with their
roles in the organization (Rasiel & Friga, 2002).
HR leaders can further foster trust and meaningful relationships with others within the organization. By doing so they may influence stars’
ability to effectively process information (Groysberg & Lee, 2008). Quality social relations with
other stars can actually decrease information
overload by altering the cognitive processes of
stars. Psychiatrists argue that, aside from individuals’ actual information processing abilities,
feelings of information overload also have a
neurological basis (Hallowell, 2011). When stars
work in environments where trust and respect
for one another proliferate, the deep centers of
the brain send messages through the pleasure
center to the area that assigns resources to the
frontal lobes. Even under extreme forms of information overload, this sense of human connection improves the functioning of top-level executives (Hallowell, 2005). In contrast, those who
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Academy of Management Review
work in physical isolation feel more stress from
information requests (Hallowell, 2005). As a result, we propose the following.
Proposition 8: Organizational processes and systems (effective search,
implementing information technologies, specifying information roles, and
fostering trust) will increase a star’s
ability to prevent information overload.
Structural Conditions
Not only do individual and organizational
conditions influence the likelihood that a star
will experience overload, but HR professionals
may also affect the network’s structural properties to help stars manage their information burdens. One method is to provide support staff to
stars within networks; support staff can monitor
incoming requests and information solicitations, acting as information gatekeepers. Gatekeepers make initial diagnoses with respect to
the urgency or utility of the information and then
decide whether the information should be given
to a star employee (Shumsky & Pinker, 2003). If a
gatekeeper can adequately process and disseminate the information offered or requested, there
is no need to send it on to the star. The gatekeeper can also prioritize information so the star
will know which information to address first.
Gatekeepers further act as quality control mechanisms, ensuring that only valuable information
reaches the star. They can also ensure that potentially harmful or misrepresentative information is not presented to the star. As a result,
network filters may influence the information
load for stars.
Another way to help manage a star’s information burden is by narrowing the breadth of the
star’s networks. Recent work in information science has shown that stars’ ability to manage
information dramatically increases when these
individuals are focused primarily on dense reciprocal interactions. For instance, Adamic and
Adar (2002) found that when employees in an HP
lab were encouraged to interact only with people known to reciprocate knowledge sharing, a
linear, rather than power-law, distribution of information flow emerged. By taking this research
into account, HR professionals can help focus
the information flow of stars to core, reciprocal
communications.
July
Increasing the density of a star’s network may
also play a role in managing the side effects of
his or her information flow. For example, scholars suggest that stars who operate in more
closed networks within a given organization, or
in networks where employees are robustly connected to one another rather than just through
the stars or central nodes, will have reduced
information burdens. Noting that information
quality deteriorates as it moves through only a
few central figures, Baker (1984) argued that
markets with more dense networks, which are
similar to random networks in which everyone
is communicating freely and rapidly, result in
decreased information burdens for the central
few. This improves organizational information
sharing on the whole.
HR professionals may also manage the effects
of information overload on stars by focusing the
benefits of the stars’ social capital on status
outcomes rather than on information outcomes.
In this respect the quality of a star’s information
processing becomes less important to the success of the organization as a whole. For example, Groysberg et al.’s (2008) study of star stock
analysts demonstrated how star employees leveraged their status and shared standard stock
opinions with all contacts, reducing their need
to share unique and customized information.
Standardized information may be highly valued
by such a network; as a result, status may enable stars to increase their ability to overcome
information overload. Hence, we propose the
following.
Proposition 9: Shaping network conditions (increasing network filters, network density, and the value of status)
will increase a star’s ability to prevent
information overload.
IMPLICATIONS
A few key theoretical implications have
emerged from our more comprehensive theory of
information overload and the high levels of social capital for star employees. First, we developed a theory that links star employees and
cognitive constraints, suggesting that information overload is a potential reason why some
stars fall. More specifically, we have argued
that stars are likely to possess exponentially
high levels of social capital, resulting in large
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Oldroyd and Morris
volumes of information flow. Because they have
exponentially higher volumes than their peers,
stars are more likely to be overloaded with information. When stars are in a state of overload,
their decision quality declines and their ability
to share information grinds to a halt, crippling
the performance of both the stars and the organizations in which they are embedded. In other
words, stars’ abundant social capital may, if not
carefully managed, cause them to fall.
Second, even though many studies note the
high mobility of star employees (e.g., Groysberg,
2010), few have explored the intrinsic reasons
why stars leave their organizations. While their
high visibility makes stars easy targets of competitive hiring practices, our theory suggests
that star employees’ turnover may actually be
partially caused by the stars’ desire to avoid
information overload. In other words, instead of
dealing with information overload, stars may
opt to become “shooting stars”—joining forces
with competitors.
Third, the implications for HR professionals
who seek to increase organizational knowledge
sharing may be even more far reaching. For
decades, organizations have sought to increase
social capital by fostering knowledge, coordination, collaboration, and information flow (e.g.,
Davenport & Prusak, 1998; Szulanski, 1996;
Thompson, 1967). The theory we have developed
here places an important caveat on these efforts,
highlighting how, because of stars’ propensity
for overload, HR professionals may be better
served by focusing on alternative strategies. We
suggest that these strategies should shift from
building links to increasing stars’ information
processing capacity, aligning organizational
processes and systems to manage stars’ information processing burdens, using technological
and human gatekeepers to guard stars’ time and
attention, and shaping information networks to
ease stars’ responsibilities. These actions may
more effectively facilitate information flow in
the organizations. Moreover, we suggest a refocusing of managerial attention on stars, who are
the key sources of information in organizations.
Without them, whole organizational knowledge
sharing will likely fail.
Fourth, our theory suggests that individual
stars may benefit by preserving the value of
robust social capital. Efforts to increase longterm memory in the form of schemas, experience, and other strategies can increase stars’
411
ability to process information. In addition, HR
professionals can emphasize aspects of the
stars’ social capital (such as status) that do not
impose a cognitive information processing burden. Because the value of status is not constrained by cognition, stars may be better
served by leveraging their status than by leveraging their information. Finally, stars may move
away from managing structural redundancies
in their network—a point emphasized and investigated previously—and begin to manage
the processes by which they gain information.
Our theoretical model representing these associations is summarized in Figure 6.
Our theory suggests several avenues of future
research. For instance, future work could explore how stars’ efforts to build social capital
may result in different types of social capital,
such as social capital that optimizes status or
social capital that optimizes knowledge flow.8
This research could investigate the performance
effects of firms that differentiate between situations in which status or reputation is paramount.
It could also investigate situations in which information processing is more important, seeking
to deleverage the information flow associated
with their stars’ network position. And it could
explore the synergies or trade-offs that exist between the two types of social capital, along with
their respective advantages.
Future research could also examine specific
HR practices that are tied to hiring employees
with greater information processing capacities.
In addition, researchers could examine practices involving training employees to deal more
effectively with information overload and to
structure work practices so as to reduce information burdens for star employees. In this arti-
8
Prior research has identified an additional limitation to
social networks—namely, the information redundancy that
is primarily due to structural constraints. Specifically, actors
who receive redundant information will yield less value
from their social capital than actors who have more unique
information flows (Burt, 1997; Granovetter, 1995; Uzzi, 1997).
However, since stars’ performance is primarily viewed as
the ability to utilize knowledge (rather than control knowledge), and since stars reside in the center (rather than the
junction) of networks (Berman, Down, & Hill, 2002; Groysberg
et al., 2008), we believe that this type of structural constraint
is less central to our discussion of stars and how they maintain their information advantage. Still, while information
overload differs from our structural constraints, they may
both affect a given star, leading to a type of dual constraint.
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Academy of Management Review
July
FIGURE 6
Theoretical Model
Outcomes
The upward spiral
+
+
Affiliatory
Star employee
Individual
performance
Information
social capital
Turnover
flow
Organizational
+
performance
+
Organizational
Individual
capabilities
processes and
norms
Network
properties
HR management tactics
cle we have discussed general strategies that
organizations can take to help employees manage information overload. We also suggest that
some stars may be better suited than others to
deal with exponentially high levels of social
capital. Future measures could examine the extent to which individuals possess specific capabilities that allow them to deal with high levels
of information flow.
Empirical research may help us understand
specific HR practices and systems that managers can incorporate to reduce the information
burden or increase the information processing
capacity of star employees. For example, Dess
and Shaw (2001) originally proposed a theoretical link between turnover, social capital, and
organizational performance. Shaw and colleagues (2005) later tested and extended that
theory by examining social capital on the turnover-performance relationship among thirtyeight restaurant chains. In a similar vein, this
research could be extended to include specific
HR practices and explanations of how they relate to stars’ ability to deal with uncommonly
elevated levels of social capital.
Further research may also address how the
theory we have delineated can, at the outset,
differentiate star employees from their average
colleagues (e.g., Hausknecht et al., 2009). Social
network analyses could be conducted on entire
employee populations across multiple organizations; these analyses could measure the extent
to which star employees are interconnected. Future network analyses could also consider examining stars’ level of information inflow and
outflow. Psychological measures could then be
used to examine the star employees’ feelings of
being overwhelmed. This can be a difficult task,
since much of the research on work overload
and “time famine” is qualitative (e.g., Berg,
Grant, & Johnson, 2010; Perlow, 1999). For researchers to understand the network and organizational factors involved, future studies
should span organizations to ensure a sample
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Oldroyd and Morris
variance. Then performance measurements
could consider both individual and group performance appraisals and turnover.
Finally, it would be interesting to explore how
the incessant overload of stars impacts various
aspects of their performance. Does overload
cause a decrease generally across all aspects of
performance, or does it limit only certain kinds
of performance? We posit that status will likely
be unaffected by overload, but these differences
could be fruitfully examined in future empirical
research.
This article has several important limitations
and boundary conditions. First, we have focused
on the effects of robust social capital involving
an “average” star employee. However, important differences likely exist in the characteristics of star employees, particularly regarding
their ability to manage information flow. While
we have addressed such differences when discussing the management of stars and information overload, other factors may be beneficial to
the star but harmful to the organization. For
instance, some stars may be highly narcissistic
and unwilling to share information. As a result,
these employees may act as information black
holes, into which vast information is poured
with nothing passed on to colleagues. In these
cases the information burden that the star employee bears is reduced only by reducing the
incoming information processed. In cases such
as this, it is possible to alleviate the side effects
of social capital while heightening the negative
effects on the organization. Future research
could expound upon this important nexus between social capital, information overload, and
actor motivation.
Similarly, some stars may manage excessive
levels of information flow by simply ignoring
them. This loss of information may not be due to
narcissism but, instead, may simply be a result
of a lack of priorities. One factor that allows
employees to become stars is their dedication to
a specific vision or line of work; star employees,
then, may become so absorbed in this work that
they lose track of requests for information or
forget to ask others for information when they
need it. Owing to their singular vision, they may
fail to share information with others along the
way and so may inadvertently ease their own
information burdens.
413
CONCLUSION
In sum, this article contributes to existing discussions of star employees and how organizations manage them by drawing on the social
capital, information processing, and HR management literature. We identify a unique theoretical link between stars, affiliatory network
effects, and information overload, calling attention to practices that might result in a subsequent decline in the job performance of star employees. Exploring these unique associations,
we present a new understanding of how the
information processing constraints of stars may
influence information flow and the stars’
performance.
We also refocus scholarly attention from star
employees’ structural advantages to the potentially burdensome cognitive constraints of social capital, which underscores the situational
mechanism of information flow for star employees. In so doing we attempt to develop a
midlevel theory by using an internal analysis of
system behavior (Coleman, 1990). We explore
how key HR strategies targeted at the individual, organizational, and network structure-wide
levels may determine whether stars experience
overload within and across organizations. Because of the curvilinear relationship between
information overload and performance, stars
must carefully manage the information flow resulting from their social capital, rather than
merely focus on increasing their social capital.
Thus, the development of this new theory of information overload for stars highlights the implicit tension between the stars’ preferred social
structures—the ones that grant information resources—and their ability to utilize these resources for individual and organizational
benefit.
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James B. Oldroyd ([email protected]) is an assistant professor of management
and human resources at the Fisher College of Business, The Ohio State University. He
earned his Ph.D. at the Kellogg School of Management, Northwestern University. His
research focuses on the performance effects of social capital in international organizations, including some of the darker sides of social capital, such as information
overload, brokering negative affect, and social sanctions.
Shad S. Morris ([email protected]) is an assistant professor of management and
human resources at the Fisher College of Business, The Ohio State University. He
earned his Ph.D. at Cornell University. He conducts research in the area of strategic
human resource management, particularly focusing on how international organizations create value through human and social capital.
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