Networks, Diversity and Performance

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RESEARCH PAPER SERIES

GRADUATE SCHOOL OF BUSINESS

STANFORD UNIVERSITY

Research Paper No. 1585

Networks, Diversity, and Performance:

The Social Capital of Corporate R&D Units

Ray Reagans

Ezra W. Zuckerman

October 1999


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Data for this research were collected with the support of NSF grant ISI-8304340, Division of Industrial Science and

Technological Innovation, Bernard P. Cohen, Principal Investigator. We are in debt to Professor Cohen, Lisa Troyer,

and Shaul Gabbay for help with obtaining and facilitating use of the data. We have also benefited from the comments

of Linda Argote, Ronald Burt, Kathleen Eisenhardt, Paul Goodman, and Barbara Lawrence. We alone are responsible

for any remaining mistakes.

Research Paper No. 1585



Ray E. Reagans

Ezra W. Zuckerman

October, 1999


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Abstract

Paralleling debates in the social capital literature, existing theory is of two minds

regarding the performance implications of demographic diversity. One view sees

diversity as problematic for organizational teams because of the strains that plague

relationships across social divides. According to the second view, diverse teams are

enriched through linkages between individuals with different skills, resources, and

perspectives. This debate is usefully framed in terms of a teams social network: do

teams achieve a higher performance when the network among members connects diverse

individuals or when ties are localized among members of the same demographic

category? In this study, unique data on the social networks, organizational tenure, and

performance of 223 corporate R&D units are analyzed to address this question. We find

that, controlling for its tenure distribution, a team is less productive when its network

remains concentrated among members of neighboring cohorts. In addition, we find that

teams with more dense patterns of communication are more productive than units with

more sparse structures.


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Introduction

Do organizational teams achieve higher performance when their membership is diverse

or when members are homogeneous? According to one line of thinking, diversity is

problematic because it introduces social divisions that hinder effective teamwork. In his

classic statement on organizational demography, Pfeffer (1983) illustrates this view with

the example of tensions between members of different organizational cohorts. He argues

that informal social networks and a sense of shared identity take root among individuals

who enter the organization at the same time. This leads to an increased capacity for

intracohort communication but a potential for strain in intercohort relations.

Homogeneous groups are thus expected to perform at a higher level because such groups

coordinate their actions more easily than do diverse teams (cf., McCain et al., 1983;

OReilly et al., 1989; Zenger and Lawrence 1992).

A second approach to the question of diversity argues that a heterogeneous

membership improves a teams performance. Proponents of this view also invoke

organizational tenure to illustrate their thinking. For example, Ancona and Caldwell

(1992, p. 355) write that teams that draw their members from various cohorts achieve

higher performance because Members who have entered the organization at different

times know a different set of people and often have different technical skills and different

perspectives on the organizations history. That is, since greater diversity entails

relationships among people with different sets of contacts, skills, information, and

experiences, diverse teams enjoy an enhanced capacity for creative problem solving. By

contrast, while homogeneous groups may in fact be more harmonious, the performance of

such teams is limited by the relative redundancy of members perspectives, information,


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and resources (e.g., Ancona and Caldwell 1992; Bantel and Jackson 1989; Pelled et al.,

1999).1

These opposing views on the value of diversity have parallels in the burgeoning

literature on social capital. While work falling under this heading is quite diverse (see

e.g., Portes 1998; Adler and Kwon 1999; Gabbay and Leenders 1999), most relevant to

debates on organizational diversity are the two rival conceptions of social capital that

emerge from social network theory. The first view emphasizes the benefits that arise

when social networks are characterized by closure (Coleman 1988; 1990); i.e., when

relations are embedded in a dense web of third-party connections (cf., Granovetter1

985).

Such closure is thought to foster identification with the group (Portes and Sensenbrenner

1993) and a level of mutual trust that facilitates exchange (Coleman 1988). A second

approach, advocated by Burt (1992; cf., 1982), understands social capital as value derived

from structural holes or the absence of social closure. Burt argues that an actor who

bridges disconnected social circles enjoys better access to information (cf., Granovetter

1973); such an actor also profits when she brokers between a pair of rivals or

disconnected exchange partners, thereby controlling the terms of trade (cf., Simmel 1955;

Pfeffer and Salancik1978; Cook et al., 1983).

It is evident that, aside from such issues of brokerage and control2, the network

structures discussed as providing social capital underlie the two views on diversity. In

particular, those who stress the drawbacks of diversity do so precisely because

heterogeneity hinders the emergence of the dense social networks that lead members to

identify with one another and thereby facilitate mutual coordination. Indeed, just as

discussions of social closure tend to focus on the collective trust found in cohesive, ethnic


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same degree of tenure diversity. That is, controlling for the diversity of a group, we may

ask whether the groups performance is degraded or enhanced by an increase in contact

across demographic categories.

We believe that such a direct examination of networks across demographic

categories represents an important step in advancing the diversity-performance debate. In

the present study, we undertake such an analysis using a unique data set of corporate

R&D units. The survey of 223 R&D units we study includes detailed information on the

communication networks among members of each unit. In addition, as the data set also

contains information on organizational tenure, unit performance, and other relevant

factors, it frees us from simply positing that a particular communication pattern mediates

the affect of tenure diversity on performance and enables us to take a direct look at how

relations within and across cohorts affects a teams performance.

We proceed as follows. First, we describe the setting for our research. Next, we

present the hypotheses that we test in our analysis. In the subsequent sections, we

describe the methods used and then present our results. To preview our main findings,

we observe that R&D units that have a high frequency of cross-cohort ties are more

productive than those whose networks are concentrated among members of neighboring

cohorts. This result supports the view that teams perform at a higher level when they

bring into contact individuals who have a variety of backgrounds and experiences. In

addition, we find that teams with a higher density of interaction are more productive, a

result which confirms the importance of social closure. Finally, our analysis indicates

that demographic composition of the teams has no direct impact on performance; rather,

the diversity effects we find are apparent only when seen as a property of the teams


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informal network structure. We conclude with a discussion of implications of these

results for the study of diversity and performance.

The Setting: Corporate R&D Units

A consistent theme sounded by studies of corporate R&D over the past thirty years

highlights the importance of informal communication networks as a critical means by

which scientists keep up with technological and scientific developments as well as

organizational directives (e.g., Allen 1977; Katz and Tushman 1979; Katz 1982; Zenger

and Lawrence1

992; Hansen1

999). Furthermore, while much research focuses on the

communication links that bind R&D units to one another, within-unit interaction has been

shown to be important as well (Allen and Cohen 1969; Tushman 1977). Thus, to the

degree that the network patterns posited by the opposing perspectives on diversity have

greater effects on performance in contexts where networks are more salient, such effects

should be evident in a study of corporate R&D units.

The survey data we use in our analysis are uniquely suited to shed light on the

issues at hand. The survey of 223 R&D units in 29 corporations was administered in

1985-1986. The sample was designed to gain a broad coverage of industries and tasks

rather than a representative sample of all R&D units (see Cohen and Zhou 1991; Shenhav

1991). It included a wide variety of R&D units, ranging from teams focused on basic

research to those working on more applied projects such as product and process

development and improvement. Unit leaders or managers were asked a series of

questions regarding their unit, including its productivity. Unit members were asked a

wide variety of questions on their work and how it relates to that of other members.


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Most relevant to the issues at hand, the data include information on the members

tenure in the organization as well as their level of contact with fellow unit members.

Organizational tenure is a useful demographic variable in this context because its

meaning is largely invariant across organizations and subunits and because it allows us to

link our research with a large number of previous studies on the correlates of diversity, in

which organizational tenure has figured quite prominently (e.g., Ancona and Caldwell

1992; OReilly et al., 1989; Pfeffer1983; Wagner et al., 1983; Zenger and Lawrence

1992).3

Our network analysis focuses on the information generated by asking each

scientist to indicate how frequently he communicates with each colleague (0 = never,1

= less than once a month, 2 = 1 to 3 times a month, 3 = 1 to 3 times a week, 4 =

daily). Together with data on organizational tenure, these network data afford a rare

opportunity to assess directly whether team performance is enhanced or degraded by ties

that cross demographic boundaries.

Hypotheses

In the analysis that follows, we focus on a teams network homogeneity, the extent to

which within-team networks link members of proximate cohorts rather than members of

distant cohorts. Note that the demographic diversity of a team necessarily conditions the

level of network homogeneity it displays: at the limit, when all members belong to the

same demographic category, team networks will be completely homogeneous as well.

However, following Lawrence (1997), we posit that the network structure of a team is not

reducible to its demographic composition. For instance, two teams with the same tenure

distribution may differ quite substantially in the extent to which members of different


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cohorts interact with one another. Thus, our strategy is to examine the impact on

performance of a teams network while controlling for its demographic composition. In

addition, our measure of network homogeneity is conditioned on a scientists opportunity

for interacting with members of distant cohorts, which varies by her tenure and the tenure

distribution of the group. Thus, we examine the impact of network homogeneity

independent of a teams demographic composition.

In particular, we aim to assess which of the two theories discussed above best

explains the relationship between network homogeneity and performance. According to

the first view, teams are most effective when they are characterized by dense,

homogeneous networks (e.g., Pfeffer1983; McCain et al., 1983; OReilly et al., 1989;

Zenger and Lawrence 1992). By contrast, coordination among members of different

demographic categories should be more difficult, thereby lowering team performance.

Following scholars who understand social capital as a property emergent from closed,

community-like networks (e.g., Coleman 1988, 1990; Portes and Sensenbrenner1993),

this perspective expects the relatively inharmonious relations typical of a diverse task

group to limit its effectiveness. That is,

H1a: The greater the network homogeneity of a team, the higher its performance.

The second view emphasizes the benefits of ties that cross social boundaries,

arguing that encounters among people with differing skills, information, and outlooks,

enhance a teams capacity for creative action (e.g., Ancona and Caldwell 1992; Bantel

and Jackson 1989; Pelled et al., 1999; March 1991, pg. 74-81). Consistent with those

who see social capital as a matter of bridging disconnected social circles (e.g., Burt


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1992), this approach expects a teams performance to improve when its network includes

numerous links between members of different cohorts. That is, while such relations may

produce greater tension, they enhance the teams capacity for creative problem solving to

the extent that such a team tends to be more productive than teams with lower network

diversity. Thus, this perspective entails that:

H1b: The greater the network homogeneity of a team, the lower its performance.

The assumption in the foregoing discussion is that a groups demography has no

direct impact on a groups performance. Rather, any effect of demography is mediated

through the teams network structure. Indeed, it is difficult to understand how the

diversity of a team could affect its performance without supposing that diverse teams

differ from those that are more homogeneous in how team members relate to one another.

It is possible, however, that such mediation occurs not through changes in the level of

interaction among team members but through other social processes. For example,

following social categorization theory (cf., Tajfel 1981; Turner1987), one variant of the

perspective that sees diversity as problematic expects individuals to attribute positive

characteristics to members of their own demographic category and negative traits to other

categories. As a result, greater diversity may heighten social tensions via an increase in

the ratio of negative to positive attributions but without changing interaction patterns

(e.g., Ely, 1994; Pelled 1997; Riordan and Shore 1997). Similarly, Ancona and Caldwell

(1992) suggest that diversity may impede performance not by lowering social cohesion

but by degrading the teams capacity for such group processes such as goal setting.

Alternatively, it may be that the benefits of diversity arise not through the development of

relationships among individuals with different perspectives but by creatively resolving


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conflict between such actors in a team context (Eisenhardt et al., 1997). That is, whether

diversity promotes or erodes team performance, such an effect may not occur through its

social network.

Unfortunately, the data set we use does not allow us to measure social

categorization or group processes. However, to the extent that such processes affect

performance independent of changes in a teams social network, a relationship between

demographic diversity and performance should be observed independent of the network

structure of the team. Thus, in parallel with the two hypotheses presented regarding the

effect of network homogeneity, we submit two contradictory hypotheses regarding the

impact of demographic diversity on performance. That is,

H2a: The greater the demographic diversity of a team, the lower its performance.

H2b: The greater the demographic diversity of a team, the higher its

performance.

Method

Analytic Framework

We test these hypotheses with a set of regression analyses, which assess the effects on

team productivity of the diversity variables and other factors discussed below. Since our

data set has a nested structure in that multiple units within the same firm are considered,

we estimate the models as fixed-effects regression analyses whereby a dummy variable

for each firm is included in each model. Coefficients in these equations reflect how a

covariate affects within-firm variation in performance (see, e.g., Hannan and Young

1977).


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Measures

Dependent Variable: Team Productivity We construct our measure of team performance

from answers to the following question, asked of unit managers regarding the eleven

items listed in table 1:

Consider each of the following written products and/or prototypes that could have

resulted from the work of this unit during the last three years. How many of each

has this unit produced? For each product, choose one of the listed alternativesand enter its number in the space provided.

Note that, since this question asks managers only about the amount of work a unit has

produced in various areas,4

it can speak only to the groupsproductivity but not other

aspects of performance. In an effort to discern a single measure of productivity, we

perform a least-squares regression factor analysis (see Jobson 1992, ch.9) on the

correlation matrix among the eleven items. Overall, three factors explain 44% of the

common variance and the first factor explains 24% of this variance. Table 1 gives

summary statistics for the eleven items and their loadings on this first factor. As we can

see, this factor represents a basic productivity dimension in that it reflects the extent to

which a unit has generated many papers, proposals, patents, and reports. While all eleven

items load positively on this factor, those that have low factor loadings such as books and

computer programs are relevant only to a small number of units. In the analysis that

follows, we treat the first factor score as our dependent variable, keeping in mind that,

while this measure appears to capture key aspects of team productivity, there may be

other dimensions of team performance that are not reflected in this variable.

Table 1 About Here


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Main Independent Variables

Tenure Diversity We measure diversity in organizational tenure with the Gini coefficient

of mean difference (CMD; Kendall and Stuart, 1977; pg. 48):5

The CMD differs from the more familiar Gini index in that the latter divides the

CMD by twice the group mean. The typical motivation for scaling a measure of

inequality such as the Gini index--or, its near equivalent, the coefficient of variation-- by

a function of the mean is to tap the intuition that, holding constant the dispersion on some

resource, an increase in the level on that resource lowers the degree of felt inequality

(Allison 1977, p. 867). For instance, as the average level of income in a population

increases, absolute differences in income become less important. However, while this

rationale for scaling by the mean makes sense for inequality, it is not clear whether it

applies to the case of diversity measure. For instance, with respect to organizational

tenure, this would imply that work groups with higher mean tenure are less diverse than

those with lower mean tenureindependent of the dispersion in tenure. It is possible that

differences in tenure become less salient as average tenure increases; however, it is

equally reasonable to think that tenure is less salient when most team members are recent

arrivals. Thus, rather than assume a particular relationship between mean tenure and the

experience of diversity, we consider the CMD and mean tenure as separate variables so

that we may disentangle empirically the effects of each.Note that similar issues apply to

the coefficient of variation.6

=

I

ji

J

ij kk

jkik

kNN

ttCMD

)1(

|| where tikand tjkindicate to the individual i and jstenure in the organization, and Nkis the number of

individuals in R&D unit k.


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Network Measures We are primarily concerned with how a units productivity varies by

its level of network homogeneitythe extent to which interaction is concentrated among

members of neighboring cohorts or is spread out to include contact among members of

distant cohorts.7

Clearly, the overall level of interaction among members of a teami.e.,

the density of the teams networkestablishes a baseline for network homogeneity and

thus should be included as a control variable. Thus, we derive two measures from the

based on the networks of communication frequency generated from the question cited

above.8

Density First, network density is the average level of communication between any two

members of unit k,

Density varies from 0 (no relations between team members) to 1 (maximum strength

relations between all team members).

Note that, beyond its role as a control variable for network homogeneity, the

effect of density on performance is of interest because previous research suggests that

this association should be positive. Indeed, as emphasized above, the perspective that

sees diversity as problematic understands this negative effect to be mediated by a decline

in group cohesion. This view is supported by a long line of research that generally finds

a positive effect of cohesion on performance (see Evans and Dion 1991 for review).

Thus, while our results on network homogeneity speak directly to the specific question of

)1(

)max(/

=

kk

I

ji

J

ij

ijkijk

kNN

zz

Density

where zijkis the tie from team member i to teammember j, max(zik) is the largest of is relation

to anyone, and Nkis the number of members in

unit k.


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whether cross-boundary ties enhance or degrade group performance, the effect of density

has implications for more general questions regarding the types of network structures that

facilitate or hinder success.

Network Homogeneity To measure network homogeneity9, we begin with a measure of

the similarity in organizational tenure between two individuals i and j in unit k, ijk, that

is conditioned on scientist is position in the units tenure distribution:

Next, we measure the strength of relationship between actors, pijkas a proportion of the

total volume of contact in which i engages:

=

I

ji

ijkijkijk zzp /

Using these terms, network homogeneity is a density measure where relations are

weighted by how proximate ego and alter are in their number of years of service in the

organization:

)1(

*

=

kk

I

ji

ijk

J

ij

ijk

kNN

p

NH

The higher a teams network homogeneity, the more concentrated is interaction among

scientists of similar years of service in the corporation; a low score, by contrast, indicates

that the team has achieved a high level of network diversity.10

=I

ji

ijkik

ijkik

ijk

dd

dd

)max(

)max(

where dij is the absolute distance in years of

tenure in the organization and dmaxk is the

maximum distance between actor i and anyother actor in unit k.


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Control Variables

Task The R&D units under study vary in the type of research they perform. It is

particularly important to control for the type of unit because the productivity measure we

use may vary in salience across such types. Following Cohen et al., 1986, we classify the

units as devoted to basic research (17% of all units), product development (42%), product

improvement (6%), process improvement (24%), and research targeted toward fixing a

specific problem with a product or process (11%). Units are classified in one of these

categories based on the mean response by unit members to a question that asked them to

choose which of these types best describes their unit (Cohen et al.,1

986).

Competition Prior research has indicated that competition in the surrounding market

affects team performance (Ancona and Caldwell, 1992). We measure the

competitiveness of the market context with the managers or unit leaders response to the

follow question:

What is the competitive pressure in this product area?

The manager or unit leader could respond to the question on a four-point scale ranging

from Not keen (uncontested market available) to Prohibitive (any sales increase

highly contested).

Size Finally, team size represents an important control variable. The units vary

considerably in size, ranging from one team, which contains three members to one that

contains thirty-four scientists. Since the dependent variable is a function of the volume

of work produced by group members, it should be significantly related to size. In


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addition, since the density of a groups networkas well as its degree of network

diversity-- is generally a negative function of its size, it is important to include size as a

control in our analysis.

Results

Table 2 contains summary statistics and table 3 produces a correlation matrix for the

covariates in the analysis. Several relationships in the latter table are noteworthy. First,

we see confirmation of the importance of size as a covariate: large teams have

significantly less dense and less diverse networks than do small teams. Second, note the

insignificant correlation between tenure diversity and network homogeneity (r=.-07).

This reflects the construction of the network homogeneity measure in that it is

conditioned on each actors opportunity for interaction with scientists of distant cohorts.11

Tables 2 & 3 About Here

Table 4 presents the fixed-effects regression results in stepwise fashion. In the

first column, we enter the covariates that capture key control factors-- the size of the

team, the type of research performed by the unit, and the competitive intensity

experienced by the unit. As suggested above, since the dependent variable essentially

measures the volume of output of a unit, the positive coefficient on team size should not

be interpreted as a meaningful effect. However, team size is an important control

variable for that very reason. Research type is measured by a series of dummy variables

for four of the five types with units focused on process improvement treated as a

reference category. We see that units that are oriented towards fixing particular problems

and those engaged in improving an existing product score lower on the productivity


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measure than do other units. These results make sense in that the elements that form the

core of this measurethe number of many papers, proposals, patents, and reportsare

likely to be less relevant for units that are not engaged in the development of a new

product or process or in basic research. Finally, the finding that units operating in more

competitive environments are more productive corresponds to that found by other

researchers (Ancona and Caldwell, 1992). It may be that competitive intensity

concentrates the attention of researchers; it is also possible that more organizational

resources are directed towards efforts that face greater competition.

Tables 4 About Here

The second model introduces the measures of demographic compositionthe

mean tenure of the group and the degree of diversity in tenure. We see that teams that are

more senior in their membership achieve a higher degree of productivity, which suggests

the importance of experience for performance. However, we see no effect for tenure

diversity. Diversity in firm tenure appears neither to enhance nor to degrade team

performance.

The third model adds density as a covariate. We see a significant positive

relationship, which indicates that teams that average more frequent contact among their

members achieve higher productivity. This result lends support to those who understand

social capital as a function of a groups capacity for collective action (e.g., Coleman

1988; Portes and Sensenbrenner1993). Better communication links among members of a

group enable its members to achieve a greater degree of coordination and hence a level of

performance that is unattainable by teams that are less well-connected.


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However, the final model in table 4 suggests support for the second view of social

capital as it has been applied to thinking on diversity. In particular, we see that teams

that experience more extensive links among members of different cohorts achieve a

higher level of productivity than teams with high network homogeneity. That is, as

suggested by those who see value in diversity and as reflected in hypothesis 1b, the

formation of links across demographic boundaries-- and the different sets of information,

experiences, and outlooks that such boundaries divideenriches the research process and

promotes greater productivity. By contrast, this result indicates a lack of support for

hypothesis1

a: it does not appear that increased levels of intra-cohort contact improve

team performance. Finally, it is noteworthy that we find a negative effect for network

homogeneity but no effect for tenure diversity. This confirms our strategy of exploring

the impact of the network processes that underlie theories of the diversity-performance

relationship. A direct examination of the frequency of interaction across organizational

cohorts sheds light on this relationship in a way that would have been unattainable had

we merely focused on measures of demographic composition.

Summary and Discussion

We regard the preceding results as an important first step in gaining a better

understanding of the social processes that link the demographic composition of teams and

their performance. As argued above, existing theory on this relationship may be usefully

classified in terms of two views on how social structure affects a teams capacity for

effective actioni.e., its social capital. In particular, corresponding to those who see

social capital as emerging from the dense networks emblematic of close-knit


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communities, one view on diversity worries that demographic diversity introduces

potential bases for social cleavage, which prevent such cohesion from developing. We

have found support for such a view of social capitalbut not as it applies to diversity. In

particular, we find that R&D units that have more dense networks of interaction achieve a

higher level of productivity than do those with sparse networks. However, we find that

demographic diversityper se has no effect on productivity and that, in fact, teams that

display greater levels of contact within cohorts are less productive than teams where ties

link members of distant cohorts. The latter results reflects the orientation of a second

view on diversity, corresponding to a second perspective on social capital, which

emphasizes the importance of interchange among individuals with a wide range of skills,

information, and experiences, for maximizing a groups capacity for creativity and

effective action.

That we find some support for two very different perspectives on social capital

should not be surprising: both views capture important elements of what it takes for a

task group to achieve success in reaching its goals. A team that does not develop the

network connections among their members that enables it to coordinate effectively

clearly faces an uphill battle. However, when such networks remain concentrated among

homogeneous sets of individuals, the team fails to generate the creative tensions that can

come only from interaction among different individuals.

We hope that our results set the stage for future research on the performance of

organizational teams, which may vary both in their demographic composition and

network structure. In particular, a consideration of three important limitations in the

current study suggests directions that future studies might direct their focus. First, the


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patterns we find pertain to firm tenure but may not be as relevant for other demographic

variables. In particular, while firm tenure is often a salient characteristic because of the

way it relates to hiring practices and organizational seniority systems (Pfeffer1983), it is

likely that the relationship between diversity, networks, and performance is somewhat

different in the context of such societally-salient traits as gender and race. For instance,

while the benefits of bridging members of distant cohorts appears to outweigh the social

strains that such relations often entail, it may be that the social strains involved in

transgender or interracial ties are more difficult to overcome.

Second, the findings presented here may be limited to R&D teams. As discussed

above, research and development is an area where having communication links with

others who are engaged in similar or related research is critical for achieving success. As

such, it may be that achieving a high degree of network diversity may be less important

in other types of organizational teams.12

Thus, only with similar analyses of other

settings may we build a general theory of the relevant processes.

Finally, while we find that network diversity increases productivity, it may be that

extensive inter-category links have negative implications for other outcomes. In

particular, those who see diversity as problematic stress the conflict that arises from the

introduction of social divisions into a group (e.g., Pelled et al., 1999). Thus, greater

network homogeneity may help reduce the level of conflict among members and thereby

improve its performance on outcomes other than productivity. As Williams and OReilly

(1997, p. 98) emphasize, the key question is whether the enrichment in information and

skill than derives from diversity outweighs the negative consequences that result from

possible increases in conflict. Following the strategy we have proposed and adopted


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here, we suggest that our understanding of the social mechanisms involved in such

processes must begin with a focus on the social networks that do or not occur within and

across demographic categories.


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Endnotes

1 It is worth noting that, while Pfeffer has been taken as asserting that diversity lowers performance, he has

also suggested that diversity promotes learning. For instance, he describes the benefits of employee

turnover in terms of the different perspective that new blood often brings to an organization (Pfeffer1983,

pp. 325-330; cf., Staw 1980). He also counsels managers to defy the homophily principle by selecting

assistants from different cohorts, thereby improving the managers access to parts of the organization to

which he might otherwise be cut off (Pfeffer1985, pp. 75-76)2 The seeming tension between brokerage and social closure is actually illusory (see Gabbay and

Zuckerman 1998, p.195). By contrast, the information benefits associated with boundary-spanning ties do

indeed come at the cost of a loss of social cohesion, and vice versa (Granovetter1973).3 While a focus on firm tenure has clear advantages, a more complete account of the relationship between

networks, diversity, and performance would involve a consideration of other demographic characteristics

and particularly those that are relevant to major social divisions, such as race and gender. Unfortunately,

the R&D units under study are too homogeneous with respect to these variables to lend themselves to

useful study. Thus, as we emphasize in the discussion below, future research would do well to analyze

populations in which such demographic characteristics, and their interaction with networks and

performance, may be examined.4 In particular, managers were asked to whether the unit had produced 0, 1, 2-5, or more than 5 of each of

the eleven items.5 In the original formulation, the denominator is N2. We use N(N-1) here as the latter is the true number of

dyads in a group.6 In analyses not presented here, we find no difference in our results when the CMD is replaced by the

standard deviation of firm tenurethe numerator of the coefficient of variation.7 It should be noted that our measurement of demographic diversity and network homogeneity capture

cohorts in a rather crude fashion. In particular, the cohort concept is properly considered not as a function

of calendar time but of social time; it groups together individuals who have moved through social structure

in parallel (e.g., Ryder1965; Elder1974). In the present context, identifying such cohorts would require

isolating those tenure ranges that are socially similar. Unfortunately, while an effort was made to follow

the method of Burt (1991) in isolating such cohorts through the network of inter-year relations, the data

under study do not lend themselves to such a strategy. In particular, each firm included in the study

provides insufficient network and tenure data to render reliable cohort categories.8

Results based on friendship networks generated substantially the same results as those we report for thecommunication networks.9 One might think that, rather than introduce an additional variable, hypotheses 1a and 1b may be tested by

examining for interaction effects between diversity and density. That is, if relations across cohorts lower

(raise) performance then there should be a negative (positive) interaction effect between density and

diversity. However, while such an interaction effect is broadly consistent with the hypotheses, it suffers

from the ecological fallacy (Robinson 1950). For instance, while a positive interaction effect would imply

that more dense networks increased the diversity effect, the relations responsible for this effect could be

intercohort or intracohort ties. Thus, in order to distinguish between these possibilities, one must directly

analyze the pattern of ties on either side of demographic boundaries.10 Non-response poses a potential problem for the measurement of tenure diversity, density, and network

homogeneity because these variables require information on all members of a team. In particular, each

non-response produces N-1 missing relations in a network. Fortunately, the response rate for the present

study was excellent: we have full information on 83.8% of all possible network relations, and at least one

response on another14.6%. Following Gabbay and Zuckerman (1997, p.201), we conducted a series of

sensitivity analyses to assess the reliability of the results presented below in the face of different

assumptions about pattern of missing response both to the network items and firm tenure. We also

compared the results using raw network data with networks that were scaled using a loglinear model (see

Gabbay and Zuckerman 1997, p. 200). We found our results to be highly robust to all such transformations

of the data. Details of these tests are available from the authors.11 Alternative measures that do not control for this opportunity set correlate very highly with

tenurediversity, as one would expect.


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12 The inclusion of several different types of R&D in the present data set might seem to lend itself to an

analysis of how the effects for network density and network homogeneity vary by task type. Some tasks

are more dependent upon the exchange of information and others are more dependent upon diverse

information. We added slope adjustments for network density and network homogeneity for each task

type. None of the adjustments were significant. Indeed, it would seem that, when considered in light of the

full range of possible work tasks, that all types of R&D involve roughly similar issues.


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Item Mean SD

Positions Papers. 1.5 1.2

Project proposals. 2.0 1.1

Published scientific/ technical articles. 1.4 1.3

Patents or patent applications. 1.5 1.2

Books (including editorship). .13 .47

Algorithms, blueprints, drawings, etc. 1.4 1.4

Reports which remained within the unit. 2.0 1.3

Reports which circulated outside the unit. 2.5 .93

Experimental prototypes of devices, instruments, components of devices, etc. 1.4 1.3

Experimental materials, e.g., fibers glass, plastics, metals, drugs, chemicals, etc. 1.2 1.3

Prototype computer programs. 1.1 1.3

Table 1: Team Performance


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Table 2: Descriptive Statistics

Va r i a b l e

P e r f o r ma n c eB a s i c Re s e a r c hA p p l i e d Re s e a r c hP r o d u c t De v e l o p me n tP r o d u c t I mp r o v e me n t

P r o c e s s I mp r o v e me n tMa r k e t Co mp e t i t i o nA v e r a g e T e a m T e n u r eT e a m Di v e r s i t yT e a m Si z eNe t w o r k De n s i t yNe t wo r k Ho mo g e n e i t y

Mean

0.17.11.42.06

.242.1

10. 68.2

10. 2.541.9

S D

.86

.37

.3149.23

.431.14.93.84.9.181.7

Mi n

-2.00000

00

1.913

.180

Ma x

1. 51111

14

24. 918341

9. 4


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1 Performance

2 Basic Research

3 Applied Research

4 Product Development5 Product Improvement

6 Process Improvement

7 Market Competition

8 Average Team Tenure

9 Team Diversity

10 Team Size

11 Network Density

12 Network Homogeneity

These are zero-order correlations. * = p < .10, ** = p < .05, *** = p < .001.

Table 3: Correlations

1

1

-.03

-.07

.004-.09

.12*

.23

.16**

.07

.33***

.001

-.37***

2

1

-.15**

-.38***-.11*

-.26***

-.09

-.08

-.13**

-.14**

.16**

.09

3

1

-.30***-.09

-.20**

-.04

-.02

.02

.05

-.09

-.09

4

1-.21**

-.49***

.07

.03

.04

-.005

.03

.08

5

1

-.14**

.02

.07

.12*

-.09

-.09

.05

6

1

.02

-.002

-.02

-.13**

-.05

-.14**

7

1

.02

.07

.10

-.05

-.09

8

1

.72***

.02

-.08

-.06

9

1

-.01

.004

.07

10

1

-.51***

-.63***


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Predictors

Constant

Basic Research

Applied Research

Product Development

Product Improvement

Market Competition

Team Size

Team DiversityAverage Team Tenure

Network Density

Network Homogeneity

Model Fit

N

R-squaredAdj R-squared

I

-.55

-.06

-.38**

.02

-.28

.10**

.04**

211

.41

.29

II

-.88

-.04

-.40**

.02

-.20

.09**

.04**

-.001.03*

211

.42

.30

III

-1.6

-.02

-.34*

.04

-.09

.10**

.06***

-.004.04**

.88**

211

.44

.31

Table 4: Networks and Performance

These are ordinary least squares for variables predicting team performance. The units are clustered

within firms and models are estimated using the AREG procedure in STATA. AREG controls for

mean differences across firms, for predictors and the dependent variable.

* = p < .10, ** = p< .05, *** = p < .001

IV

-.97

-.01

-.33*

.07

-.24

.10**

.03*

-.001.03

.84**

-.14**

211

.47

.35

Documents

Networks, Diversity and Performance