00-029

Copyright © 1999 Morten T. Hansen

Working papers are in draft form. This working paper is distributed for purposes of comment and discussiononly. It may not be reproduced without permission of the copyright holder. Copies of working papers areavailable from the author.

So Many Ties, So LittleTime: A Task ContingencyPerspective on the Value ofSocial Capital inOrganizations

Morten T. HansenJoel M. PodolnyJeffrey Pfeffer

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So Many Ties, So Little Time: A Task Contingency Perspective on

The Value of Social Capital in Organizations

Morten T. Hansen

Harvard University

Joel M. Podolny

Stanford University

Jeffrey Pfeffer

Stanford University

September, 1999

Under Review, Administrative Science Quarterly

Correspondence to: Morten Hansen

Soldiers Field Park

Boston, MA 02163

Phone (617) 495 5590

Fax (617) 496 6568

E-mail: mhansen@hbs.edu

We would like to thank Herminia Ibarra, Rakesh Khurana, and Ezra Zuckerman for helpful comments and JamesSchorr and William Simpson for help with the data analysis.

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ABSTRACT

We used data from 67 new product development teams to show that different tasks require

different network structures to be effective. Results showed that exploratory teams completed their

projects more quickly if they had many strong ties that were non-redundant. In contrast, teams pursuing

tasks that exploited existing expertise took longer to complete if they had this type of network structure,

mainly because ties had to be maintained but were not much needed for the task. However, both types of

teams took longer to complete if they spent time helping others as part of reciprocal arrangements. We

propose that research on structural network theory in organizations needs to be broadened to reflect the

effects of task differences, network costs, and difficulties in getting others to help.

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When organizational scholars first examined the effects of informal social networks in the

workplace, much of the research highlighted the possibility that these networks undermined performance

(e.g., Roy, 1952; Seashore, 1954). This older research was also sensitive to many of the personal costs,

challenges, and normative requirements involved in building, maintaining, and extracting resources from

a personal network. For example, Blau (1964) and Gouldner (1960) argued that an individual’s ability to

maintain a tie and extract resources from another person depends on adherence to a norm of reciprocity

and membership in some relatively well-connected collectivity of which both actors are a part

(Roethlisberger and Dickson, 1939; Homans, 1950; Kapferer, 1969).

In contrast to this older research, more recent organization network research has focused on the

instrumental value of networks, treating informal relations as social capital to be harnessed in pursuit of

goals, such as garnering resources, gaining promotions, and completing tasks (e.g., Burt, 1992; Podolny

and Baron, 1997; Nahapiet and Ghoshal, 1998; Gabbay and Zuckerman, 1998; Hansen, 1999). By

focusing on the instrumental value of social networks, however, recent research has not given much

attention to potential drawbacks of social ties (e.g., Adler and Kwon, 1999; Gabbay and Leenders, 1999).

Individuals and groups in an organization may benefit from receiving knowledge from network contacts,

but their work may also be hampered by time-consuming activities devoted to maintaining ties, helping

others in the network, and trying to get others to provide needed help. These network-related activities

may actually outweigh the benefits derived from network positions in some circumstances and should be

considered in analyzing the effects of social networks on performance-related outcomes. To address this

concern, we analyze both potential benefits and drawbacks of social networks. Our research question is,

under what conditions does an actor’s social capital in an organization enable or hinder the realization of

the actor’s performance-related goals?

We argue that the type of task pursued by the focal actor will determine whether the costs and

difficulties of cultivating and using certain network configurations will outweigh the knowledge benefits

derived from the same network attributes. This approach builds on some recent network research that has

recognized that the value of social networks is contingent on particular circumstances (Burt, 1997;

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Podolny and Baron, 1997; Uzzi, 1997; Walker, Kogut and Shan, 1997; Gabbay and Zuckerman, 1998).

For example, Hansen (1999) showed that the value of strong ties was contingent on whether actors sought

to transfer tacit or explicit knowledge. We advance this research by considering a basic task contingency

variable, which is more encompassing than knowledge type, and by analyzing the effects of a broader set

of network variables than tie strength. Specifically, we draw on March’s (1991) distinction between

exploration and exploitation tasks. Tasks that involve a high degree of exploration, in that they depart

from the existing expertise of the focal organization unit, require different amounts, frequency and type of

help from the network than exploitation tasks, which depart very little from the existing competence base.

Network benefits are likely to vary by these task differences, yielding different returns on a given network

position.

This task contingency view contrasts with a pure structuralist theory of social networks, in which

the focus is almost exclusively on the potential of the pattern of relations to explain individual and

organization-level outcomes (e.g., Brass, 1984; Burt, 1992). In focusing almost exclusively on patterns of

relations, however, the structural perspective has tended to ignore the costs and difficulties of building

and using a network and the different types of tasks involved. If the benefits of a particular pattern of ties

were completely independent of these factors, then the exclusive focus on network structure might be

reasonable, but they are not likely to be independent. While structural network theory provides a valuable

baseline, we seek to broaden the theory by analyzing the task-contingent effects of structural network

variables, including network size, tie strength, and network sparseness (or conversely, density).

We test the task contingency view of network effects with an analysis of network data on 67 new

product development teams situated in a large computers and electronics company. These team-level

network data were aggregated from individual team members’ responses to questions about their

networks within their focal division. To our knowledge, this is one of the first studies capturing team-

level network data from individual responses. The benefit of using these team network data is that we

were able to analyze a clear performance-related goal—i.e., the time it took to complete a project—while

also capturing a team’s network in its focal organization.

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NETWORK STRUCTURE--SOCIAL CAPITAL OR LIABILITY?

Organization network research has invoked the term “social capital” as an overarching concept

for understanding what features of an actor’s social relations are most conducive to the realization of that

actor’s objectives in organizations (see Nahapiet and Ghoshal, 1998; Portes, 1999). In reviewing the

social capital literature, Adler and Kwon (1999) found that many studies have analyzed relative benefits

of various network attributes but few have investigated the costs, risks, and problems associated with

social capital in organizations (for some exceptions, see Brass, Butterfield and Skaggs, 1998; Gabbay and

Leenders, 1999; Leana and Van Buren, 1999). Once costs and other drawbacks of having social network

positions are considered, it is no longer obvious that networks represent a positive social capital. They

may instead be a liability in certain situations.

A dominant trend in social capital studies in organizations has been the analysis of structural

network variables, including network size, network sparseness, and tie strength. For example, scholars

have found that individuals with large and broad networks have more power in the organization (e.g.,

Brass and Burkhardt, 1992; Ibarra and Andrews, 1993). Studies have also focused on the instrumental

benefits of having sparse or non-redundant networks (i.e., there are few ties between contacts in a

person’s network), finding that they lead to faster promotion (Burt, 1992 and 1997) and higher ability to

switch careers (Higgins, 1999). Studying the effects of tie strength, Granovetter (1973) originally showed

that weak ties were instrumental in getting a job, but researchers have since pointed to several values of

strong ties, including transfer of tacit knowledge (Hansen, 1999), exchange of fine-grained information

(Uzzi, 1997) and help in building coalitions (Krackhardt, 1992). Considering all three dimensions, Burt

(1992) argued that the network structure most conducive to the realization of an actor’s objectives is one

that is composed of many strong bridging ties. According to his structural hole theory, sparse networks

are especially beneficial because each contact serves as a bridge to non-redundant sources of information.

Moreover, Burt argued that, controlling for the extent to which a tie is a bridge, strong ties are more

valuable for actors in gaining resources compared with weak ones, because they facilitate both higher

volume and higher quality of information flow.

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In short, a number of organization network scholars in the structuralist network tradition have

highlighted the positive value of social capital in the form of a network composed of many strong and

non-redundant ties. This structuralist thesis provides a valuable baseline in that it is formulated a

relatively high level of abstraction and therefore can be applied equally well across different organization

contexts (e.g., Burt, 1992). The downside to this high level of generality is the neglect of important

features of particular contexts that modify the basic thesis. We consider two related issues in the

organizational context.

First, having direct relations with others typically involves spending time building and

maintaining these relationships, as well as time helping others in the network. Such activities can be

labeled costs (or investments), because they involve discrete time-consuming activities during one time

period that may have a positive return in subsequent periods. Organization network scholars have not

fully considered the effects of such network costs but instead have limited their discussion to a

consideration of the opportunity costs of different network positions. For example, because having direct

ties involves costs, a redundant network tie can be substituted with a tie that yields more non-redundancy,

thus leading to a more optimal network. However, both types of direct ties, whether redundant or not,

involve time devoted to networks, which may be time not spent on completing one’s own work. Once

costs are considered, a network structure composed of many strong ties may not always be valuable.

Second, getting help from network contacts may be a non-trivial issue, especially in sparse

networks. As the works of Coleman (1990), Portes and Sensenbrenner (1993), and Uzzi (1997) suggest,

one actor’s ability to secure resources from another depends on the extent to which the two are part of

some broader common collectivity in which there is some pressure to help others in the collectivity. For

example, Portes and Sensenbrenner used the term “enforceable trust” to refer to a focal actor’s ability to

rely on common third parties to punish that other actor if he or she does not assist the focal actor in the

pursuit of the focal actor’s objectives. By definition, sparse networks have few such third-party ties (i.e.,

there are few ties between an actor’s contacts), leading to difficulties in getting others to help. Because of

this problem, sparse networks may not always be valuable.

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The issues of costs and difficulties raise the question of whether a network structure composed of

many strong and non-redundant ties will hinder the realization of an actor’s performance-related goals in

some circumstances, and hence become a liability. The type of task pursued by an actor informs this

issue.

TASK CONTINGENT EFFECTS OF SOCIAL NETWORKS

Exploration versus exploitation

Depending on the work-related tasks they pursue, focal actors (i.e., individuals and groups) in

organizations rely on various types of knowledge from their network to complete their work. Tasks that

involve a high degree of exploration, in that they depart from the existing expertise of the focal actor and

the focal organization unit, require different types and amounts of knowledge than exploitation tasks,

which depart very little from the existing competence base in an organization unit. Exploration involves

problems that are novel to the focal actor and to other people in the focal organization unit (March, 1991;

Levinthal and March, 1993; Benner and Tushman, 1999). Because the task is novel, a focal actor may

benefit from using network contacts to discuss ideas, exchange views, and brainstorm with them (cf.

Nonaka, 1990; Sutton and Hargadon, 1996). Although the task is novel for network contacts as well, they

may possess analogous experience which they can draw on to provide insight and suggest possible

avenues for identifying viable solutions.

Furthermore, because the task is novel, much of the knowledge involved in exploratory tasks is

likely to be tacit, that is, it is hard to articulate or can only be acquired through experience (Polanyi, 1966;

Nelson and Winter, 1982; Von Hippel, 1988:76, 1994; Hansen, 1999).1 In contrast, much of the

knowledge involved in exploitation tasks is likely not to be tacit (i.e., explicit), because the focal actor has

much of the expertise required and hence is likely to understand the problem, possible solutions, and the

causal mechanisms among the parameters involved in the task. However, actors engaged in exploitation

tasks may still benefit from obtaining knowledge through their network contacts. They may be able to

obtain existing, complementary knowledge that avoids duplication of effort (Teece, 1986). For example, a

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product developer that obtains an existing software code from a network contact may save several months

of work.

Moreover, exploration tasks are unpredictable in that a focal actor cannot easily predict when and

how frequently he or she needs to consult contacts to obtain knowledge. An actor may meet a network

contact once to talk about a novel idea, but they may need to meet again several times because they could

not foresee the development path of the idea. In contrast, in exploitation tasks, the focal actor has a good

understanding of what knowledge is needed to complete the work and therefore knows better when and

how frequently he or she needs to consult contacts to obtain knowledge. In short, actors pursuing

exploration tasks are likely to benefit by obtaining tacit and novel insights from network contacts and by

having immediate and repeated access to network contacts. In contrast, actors pursuing exploitation tasks

do not need to obtain large amounts of knowledge through their network but are likely to derive some

benefits from obtaining existing, explicit knowledge that avoids duplication of effort. These differences

between exploitation and exploration tasks have implications for the value of having many, strong, and

reciprocal ties.

The contingent effects of rich networks

Actors that are engaged in exploratory tasks are likely to benefit from having a network

composed of many, strong and reciprocal relations (hereafter called a rich network). Strong established

relations (i.e., frequent contact and close relations) are beneficial for exploratory tasks, for two reasons.

First, the exchange of tacit knowledge between two individuals is likely to be more effective in strong

than weak ties. Strong ties often mean that the parties to the relationship have established heuristics for

working together and understanding each other’s ways of expressing ideas and thoughts (Uzzi, 1997;

Hansen, 1999). Thus, in a strong tie, even if actors do not know much about the particular knowledge

involved, they can discuss subtle problems and vague approaches with less risk of misunderstanding each

other. Second, because the need for exchanging ideas may occur at unpredictable times in exploration

tasks, actors need quick and easy access to others willing to engage in discussions at a moment’s notice.

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Strong ties afford faster and greater access to such unplanned requests for interactions because contacts

have a greater motivation to assist (Granovetter, 1982; Krackhardt, 1992). In contrast, in exploitation

tasks, the knowledge involved tends to be well-understood, and actors do not need established heuristics

and frequent interactions to exchange it.

In addition, actors pursuing exploratory tasks may benefit from having spent time in the past

helping others. A track record of reciprocal helping activities is likely to create an obligation on the part

of others to assist the focal actor (Gouldner, 1960). This assistance is valuable in exploratory tasks

because of the need for quick and unpredictable access to others to obtain advice and exchange ideas. As

Uzzi (1997) found in his study of embedded ties in the apparel industry, extra effort was voluntarily given

in reciprocated ties. The benefits of having invested in reciprocal helping behaviors are less valuable in

exploitation tasks, however, because there is less need for extra and immediate efforts given by other

people, who only need to spend a few moments to pass on knowledge that is well understood by both

parties.

Actors pursuing exploratory tasks are also likely to benefit from having a relatively large number

of direct ties. Controlling for the redundancy among contacts, large networks afford more advice and

possible solutions to exploratory problems than do small networks, simply because there are more people

with whom the focal actor can brainstorm and exchange views (at some point, however, very large

networks may be counter-productive by creating a problem of assimilating numerous advice, solutions,

and ideas). This positive value of large networks is likely to be less for actors engaged in exploitation

tasks, however, because they do not benefit from considering a range of ideas and possible solutions

afforded by large networks. They already have a good understanding of what knowledge is needed and

are likely to only need a relatively small set of contacts to obtain existing, complementary knowledge that

avoids duplication of effort.

In short, the benefits of having a rich network characterized by many strong ties and extensive

reciprocity are greater for exploratory than for exploitative tasks. However, this network position comes

at a cost. First, strong ties necessarily require greater time and effort to maintain compared with weak ties.

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Frequent interactions involve greater time devoted to maintaining networks, while close ties involve

higher levels of emotional intensity and thus require more energy than do distant relations (cf. Marsden

and Campbell, 1984). Second, an adherence to a norm of reciprocity implies that the focal actor forgoes

the immediate pursuit of his or her own objectives in order to assist others in the pursuit of their

objectives. In a given time period, a focal actor that sets aside time to help another is likely to spend less

time pursuing the completion of his or her own work during that time period.2 Third, having many direct

ties is also more costly than having few, because the relations need to be maintained, even if they are

weak. More time is likely to be spent staying in touch and interacting with others to the extent that an

actor has many contacts. Thus, there are likely to be substantial costs involved a network position of

many strong and reciprocal ties.

In highly exploratory tasks, the benefits of having a rich network are likely to exceed the costs of

this network position, as shown in figure 1. The reason is the difference between the net value of rich

networks (the upper-right quadrant in figure 1) and the problems associated with non-rich networks

(lower-right quadrant in figure 1). A network of few, weak and non-reciprocal ties creates a knowledge

deficit for exploratory tasks, because the actor not only lacks the required expertise at the outset but also

cannot obtain much of it through this type of network. We therefore predict that there is a net positive

value of having rich networks in exploratory tasks:

Hypothesis 1a: In exploration tasks, rich networks (many strong and reciprocal ties) positively

affect the realization of immediate performance-related objectives.

For exploitation tasks, we predict that the costs are likely to outweigh the benefits. The benefits of

having a network of many strong ties and extensive reciprocity are much less than in exploration tasks,

but the costs of rich networks are the same for both types of tasks (compare upper-left with lower-left

quadrants in figure 1):

Hypothesis 1b: In exploitation tasks, rich networks negatively affect the realization of immediate

performance-related objectives.

-------- Insert Figure 1 about here --------

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The contingent effects of network sparseness

In exploratory tasks, actors benefit from tapping into diverse knowledge. Because the task is

novel, there is likely to be more than one viable solution to the problem, implying that there is a benefit in

searching for and considering a broader set of alternatives. A diverse network is likely to be beneficial

because contacts are likely to offer a range of distinct knowledge, ideas, and views (cf. Burt, 1992; Uzzi,

1997). In contrast, in a dense network, contacts know one another and are hence likely to circulate the

same knowledge among themselves. Moreover, a very dense network—one where everybody is

connected--is likely to be closed off from the outside, making it hard for new knowledge, such as new

ideas and different kinds of insights, to penetrate the network. Such networks may therefore be ossified,

steeped in traditional ways of solving tasks and possessing little new knowledge (Uzzi, 1997). Actors

with very dense networks may therefore find it difficult to receive valuable insights from the network to

complete exploratory tasks.

Although exploration tasks are likely to benefit from a sparse network because of diverse

knowledge, such a network position also involves the problem of enforcing a norm of cooperation.

However, the benefits of sparse networks are likely to outweigh the drawbacks when tasks are

exploratory, as shown in figure 2. While an actor in a sparse network benefits from diverse knowledge but

also faces the problem of enforcement (lower-right quadrant in figure 2), an actor in a dense network not

only lacks access to diverse knowledge but also has to maintain costly relations to redundant contacts

(upper-right quadrant in figure 2). The latter situation is therefore likely to be less beneficial than the

former one. Thus, we predict that actors pursuing exploratory tasks derive a net positive value from

increasing sparseness in the network:

Hypothesis 2a: In exploration tasks, network sparseness positively affects the realization of

immediate performance-related objectives.

The discussion so far points to the positive consequence of having sparse networks in exploratory

tasks. There is much less benefit of tapping into diverse knowledge in exploitation tasks, however,

because the task is well understood and can be executed without considering a range of ideas and

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potential solutions that can be obtained through sparse networks. Absent this benefit of sparse networks in

exploitative tasks, the question becomes whether a sparse or dense network returns the most value.

However, both very dense and sparse networks may lead to problems in exploitative task situations, as

shown in figure 2 (compare upper-left with lower-left quadrants). As Uzzi (1997) argued, while there may

initially be some positive returns in shifting from a very dense network to one that is somewhat sparse,

there will at some point be diminishing returns from increasing sparseness. At relatively high levels of

sparseness, the returns to added sparseness may be negative, because it will be more difficult for the focal

actor to obtain knowledge from contacts when there are no third-parties available to enforce the norm that

contacts ought to help the focal actor by providing knowledge (Coleman, 1990; Portes and

Sensenbrenner, 1993). Thus, because the end points of the sparseness dimension creates problems, we

expect that actors pursuing exploitation tasks benefit most by having a medium level of sparseness in their

network:

Hypothesis 2b: In exploitation tasks, there is an inverted U-shaped relationship between network

sparseness and realization of immediate performance-related objectives.

-------- Insert Figure 2 about here --------

By considering costs and problems of network positions relative to the benefits they provide, we

have argued that the net value of networks depend on whether actors pursue exploration or exploitation

tasks. While we predict that exploration tasks attain a net positive value from having a rich and non-

redundant network, this position is likely to have negative consequences for exploitation tasks.

METHODS

Setting

To test our hypotheses we studied 67 new product development teams located in a large,

multidivisional electronics and computer company. These teams represent the focal actors in our

empirical analysis. The company, with annual sales of more than $5 billion, is a highly decentralized firm

engaged in developing, manufacturing, and selling a wide range of electronics and computing products. In

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preparation for systematic data collection on project teams in the firm, we visited 14 divisions and

conducted initial open-ended interviews with more than 50 project engineers and managers. These

interviews helped us better understand the context and develop survey instruments that would allow us to

test our hypotheses about the effects of networks on team performance.

The new product development projects we studied involved products primarily for industrial

markets and typically entailed development of both hardware and software. Projects frequently began

with a product idea and the opening of a budget account. Once an account was open, there were informal

interactions with other departments, such as marketing, and with other people in the division to obtain

advice and exchange ideas. After some investigation and preliminary work, project teams presented

proposals in front of key managers. This was the only formal review meeting for projects in this

company. If the group approved the project and plan, which included a proposed budget and schedule,

the project proceeded.

After approval of the project, obtaining advice and exchanging ideas remained important

activities. Established advice relations enabled team members to obtain knowledge from other product

developers and functional specialists in marketing and manufacturing within the organization (cf.

Henderson and Cockburn, 1994; Eisenhardt and Tabrizi, 1995). However, network activities were not

without drawbacks. Some engineers spent a considerable amount of time working on other projects,

mainly helping solve specific technical problems. One manager whom we interviewed complained that

some of his engineers were too keen on working on interesting technical problems, regardless of whether

they occurred in their own projects. While beneficial for some projects, these activities also made it

harder for some engineers to make good progress on their own projects. In addition, engineers spent

considerable time maintaining their relationships. They frequently interacted informally across teams to

talk about projects, including new problems, opportunities, technical specifications, market demand, and

product updates. While some of these activities were related to specific project work, many of them were

not but nevertheless involved time to stay in touch with others and keep on top of new technology trends.

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Thus, networking activities were not by-products of ongoing work but involved discrete time-consuming

activities.

Data and Methods

To keep the data collection and analysis manageable, we analyzed the within-division networks

for teams located in 25 different divisions, allowing us to test our predictions in 25 different sub-

organizations that share some common characteristics, such as the same top manager and formal reporting

structures. The divisions ranged in size from 100 to 900 people, permitting a focal person to develop

extensive network relations even in the smallest division. Although there were a number of network

contacts spanning divisions (see Hansen, 1999), teams had relatively more contacts within the division.

This is not surprising since there are almost invariably more dense social relations within a boundary than

across that boundary (Deutsch, 1953). Thus, the focal division provided an appropriate membership

boundary for our study (cf. Marsden, 1990).3

We first created a list of all projects that the divisions in our sample undertook during a period of

three years. We limited the study to three years because it was problematic to collect data further back in

time. Because including only successfully completed projects may bias the results, we also included both

canceled projects and projects still in progress.

We administered two surveys: a survey for the project managers of the product development

projects included in the study, and a network survey to all project engineers and project managers

working on these projects. The two surveys were sent out sequentially. The first step was to send the

project survey to the project manager of the project, asking about characteristics of the project, including

starting and ending times. The project managers of 120 projects returned their surveys, yielding a

response rate of 85 percent. The second step was to administer the individual network survey, designed as

a computerized survey on a floppy disk in which respondents answered various questions about contacts

in their network. We submitted a packet of individual network surveys to each project manager, asking

him or her to distribute the surveys to the individual engineers on the team and to complete one him- or

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herself. At this point, 22 project managers decided that their teams should not fill out the network surveys

and consequently did not distribute the individual surveys to the teams. There were no significant

differences between these 22 projects and the remaining 98 projects with respect to project size and

completion time (for completed projects). Among the remaining projects, 510 surveys were distributed,

and 259 (51 percent) were returned.

Because we wanted to have sufficient information on a team’s network relations, we only

included projects with at least a 50 percent response rate for the network survey. Thus, we ended up with

67 projects (with an average network survey response rate of 74 percent). Two of these projects were

cancelled, and 13 were still in progress at time of data collection. As Table 1 shows, there were no

significant differences between the 67 included projects and the 53 excluded projects with respect to

budget size, extent of use of existing software and hardware (existing “ware”), degree of exploitation, and

completion time for completed projects. There was a significant difference in the number of people. The

average number of people is slightly larger in the omitted group (6.37) than in the final sample (5.39),

although the difference is not large.

--------- Insert Table 1 about here ----------

We merged the project data with the individual network data by assigning an engineer’s network

relations to the project on which he or she worked. We only included network ties that existed prior to the

start of the project. We asked respondents to report the duration of each of the ties they reported in the

survey. Following the approach of Burt (1992: 173) and Podolny and Baron (1997), we asked how many

years each of these reported ties had been in existence. Thus, for example, a tie was included in the

project-level measure if an engineer reported that a tie was established in 1994 and the project for which

he or she worked started in 1995.

The potential bias in this approach is that it may exclude some relations that existed prior to a

project’s start but that ceased to exist by the time the respondents filled in the survey. For example, for a

project that started in 1994, an individual may have had a relationship that existed just prior to the project

but that was no longer in existence when the survey was completed in mid-1995. If these relations are

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prevalent, then there should be fewer relations for projects that were started in 1993 (the first year of

observation) than those started later (in 1994), because more ties are likely to have ceased to exist during

this relatively longer time span. The number of reported team ties, however, is fairly similar between

projects that were started in 1993 and 1994 (4.4 and 4.5 advice relations outside the team, respectively,

with no significant difference in t-test of means; t=.13 and p=.89, with equal variance). Thus, we do not

believe that there is much bias in our data.

Dependent and Independent Variables

Project completion time. We focused on project completion time to measure teams’ realization

of immediate performance-related goals. While there were other dimensions of teams’ degree of goal

achievement, completion time was a critical factor because the resulting products had to be marketed in a

timely manner to be competitive in the electronics and computer markets into which they were launched

(cf. Eisenhardt and Tabrizi, 1995). Late project teams were typically seen by managers as being poor

performers, and very late projects were seen as failures because the teams missed important market

opportunities. However, teams could not trade off other performance dimensions, such as product quality

(e.g., few software bugs), for faster completion, because there were quality standards in place to ensure

that marketed products met certain standards. Project completion time was therefore an appropriate

measure of the teams’ immediate performance-related goals.

We measured project completion time as the number of months from the start of concept

development to the time of market introduction for a given project (or time to the end of the study period

or cancellation for ongoing and canceled projects, respectively). We defined starting time as the month

when a dedicated person started working part or full time on the project, which typically coincided with

the time a budget account was opened for the project. We defined the end date as the date on which the

product was released to shipment, which is a formal milestone date in this company because it signifies

that the product is ready to be manufactured and shipped on a regular basis. These definitions turned out

to be very clear and provided few problems in specifying the starting and finishing times.

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Scholars have proposed two other measures of project speed. First, completion time can be

measured as the extent to which the project is finished on schedule (e.g., Ancona and Caldwell, 1992).

The assumption in this measure is that inherent project differences are accounted for by the original

schedule, but also that everybody sets equally ambitious schedules. These conditions were unlikely to be

true in this company, where individual project managers set their own targets. Moreover, it may not be an

objective measure: Cyert and March (1992) proposed that targets such as schedules are often adjusted

according to expectation and experiences and hence become subjective indicators. A second approach to

measuring project speed involves grouping projects by some similarity measure and then assessing a

project's deviation from the mean completion time of the group of similar projects (Eisenhardt and

Tabrizi, 1995). The difficulty with this approach is that it depends on a clear similarity measure, which

was not possible to obtain in this setting. Furthermore, the projects included in this study did not span

several industries but were confined to one 4-digit SIC code. Thus, their inherent differences were not as

large as those in samples where the mean-deviation measure has been used (e.g., Eisenhardt and Tabrizi,

1995). Instead, we used the number of months to complete the project as the dependent variable and then

added project-specific variables to control for differences between the projects.

Network size. To get information on contacts in an individual’s advice network, we asked

respondents, “Looking back over the last year, are there any persons in your division from whom you

regularly sought information and advice to help your project work?” The respondents were asked to enter

the last and first names of the contact and were allowed to name up to five advice relations.4 We asked the

respondent how long each advice relation had been in existence and only included advice ties that existed

prior to the start of the project and that were between a team member and non-team members in the focal

division. The size of a team’s advice network was computed by taking the team’s number of non-

duplicate contacts (outside the team) who were listed as a regular source of advice before the start of the

project (network size).

We relied on several assumptions in aggregating individual relations to team-level networks. Our

approach is illustrated in figure 3 for two individuals, John and Jim, who together constitute a project

19

team. The top half of the figure shows the individual networks (by graph and matrix; network data are

treated as symmetric), and the lower half of the figure depicts the aggregated team network. John has

three network contacts, and he reports one indirect tie between them (between Mary and Tim). Jim also

has three network contacts and reports one indirect tie as well.

The first step in constructing the team-level network data was to divide the ties into those

between team members and those between a team member and outside people. Because John and Jim are

in the team, the tie between them is counted as a within-team tie and is excluded from the team-level

advice network, which only includes ties between a team member and people outside the team. The

second step accounted for duplicate ties. Because both John and Jim have ties to Mary, those ties are

counted as one in the team network.5 The third step was to add up non-duplicate ties. The team illustrated

in the figure is recorded as having direct advice ties to three people (Tim, Laura, and Mary) and thus has

an advice network of three ties.

---------- Insert Figure 3 about here ----------

Network sparseness. We asked the team members whether they thought there was a tie between

each pair of contacts (i.e., indirect ties) they had listed in their egocentric advice network. The

computerized survey prompted the respondent to assess such a tie by generating a list of all possible pairs

in his or her advice network. There are two important assumptions in this approach. First, like Burt (1992)

and Podolny and Baron (1997), we assume that the respondents provided an accurate assessment of

whether there was (or was not) a relationship between two contacts. The second assumption concerns

unknown data among contacts. Because we do not fully know the extent of indirect ties, we relied on the

number of indirect ties that were known in our egocentric network data. In the example in figure 3, we do

not know whether Tim and Laura have a tie between them because neither Jim nor John were asked to

assess whether such a relationship existed (they were only asked to assess indirect ties between contacts

they had named in their own individual network). The area in the figure marked “not known” indicates

the number of ties in the matrix that are not known. We made the assumption that the proportion of

20

indirect ties among the possible number of indirect ties that are unknown equals the proportion of

estimated indirect ties among the possible number of indirect ties that respondents were asked to assess.

We therefore used the maximum number of indirect ties that could have been reported, given the number

of individual responses among team members. This maximum number is given by:

Adjusted maximum number of indirect ties = N*(N-1) / 2 - x*(x-1)/2 - y*(y-1)/2

where N is the number of individuals listed in the team network matrix as depicted in the figure (five

individuals), x is the number of team members in this matrix (John and Jim), and y is the number of

contacts who were named by different respondents; hence, respondents were not asked to assess whether

ties existed between these pairs of contacts--Tim and Laura in figure 1. To arrive at the measure, we

divided the number of reported indirect ties by this maximum score and subtracted this proportion from 1

to measure sparseness, not density (network sparseness):

Network Sparseness = 1- (no. reported indirect ties/adjusted maximum number of indirect ties)

Finally, to test for a curvilinear relationship, we squared the standardized value of this variable

(sparseness-squared).

Tie Strength. To measure the strength of relations of the task-advice ties, we asked about the

closeness and frequency of contact that the respondents had to the named sources of task advice.

Closeness was assessed by a 4-point scale (with anchors, “distant” and “especially close”): “ how close

are you to [name of person in network]?” Frequency was assessed by a 7-point scale: “How often do you

communicate with [name]?”, with anchors “less than once a month” and “several times a day.” We took

the average reported closeness and frequency for the number of advice ties reported by the team (tie

strength).6

Reciprocal helping behavior. Our initial discussions with engineers and managers in the

company suggested that engineers engaged in three somewhat different behaviors to induce social

obligations of reciprocity (Ekeh, 1974). First, some engineers in the company helped others, with the

implicit understanding that they would be seen as good colleagues and be helped by someone some time

in the future. Second, some engineers attempted to take the interest and goals of others into consideration

21

when they asked for support for their own project. For example, some engineers on a project sometimes

considered how their new product development effort could be timed to fit the staffing requirement of

another project. Third, other engineers were somewhat more explicit, helping others but making it

understood that they expected help by the same person sometime in the near future. These behaviors can

be seen as representative of both dyadic reciprocity (i.e., helping another with the expectation that the

particular other will offer help in the future) and generalized reciprocity (i.e., helping another with the

expectation that some one will offer help in the future).

To capture these different behaviors, we asked the respondents to indicate, on a six-point scale

(with anchors of “never” to “very frequently”), how often they engaged in the following behaviors: (1)

“Help others whose support or help you need?”; (2) “think about the interests and goals of others in trying

to obtain their support for your project?”; and (3) “offer help to others with their work if they will help

your project work?” To compute the overall reciprocity behaviors for a team we took the value reported

by a team member for each item and then computed the average value (reciprocal helping). Because the

Cronbach Alpha for this scale was not high (0.62), we also ran models with each of the three dimensions

entered separately.

Degree of project exploitation. We used a four-item scale to capture the extent to which a given

project was similar to the focal division’s existing technical and market expertise and therefore could

exploit that expertise (see Olson , Walker, and Ruekert, 1995). The project manager was asked to answer

four questions on a 7-point scale (1= None of the required expertise, 4 = Half the required expertise, 7 =

All of the required expertise): (1) Prior to this project, how much experience did your team have with the

technologies and technical competencies that the project required? (2) Prior to this project, how much

experience did your team have with the market for which the product was developed? (3) Prior to this

project, how much experience did your division have with the technologies and technical competencies

that the project required? (4) Prior to this project, how much experience did your division have with the

market for which the product was developed? We computed the measure by taking the average of the

scores for the four questions (degree exploitation). The Cronbach Alpha for this scale is 0.85.

22

No project manager reported that the team had substantial expertise but the division had not,

while seven project manager reported that the team had little of the required expertise but the division had

substantial expertise. The similar responses for project- and division-level expertise suggest that division

managers were able to staff the projects with the division’s engineers who had the most relevant expertise

for the project. This confirms our field interviews, where we were told that the most important staffing

criterion was the matching of the engineers’ skills to project requirements. Thus, the four items on the

scale indicate the same underlying construct--the extent to which the focal project task departed from the

division’s existing competence base.7

To test for the contingent effects of exploitation vs. exploration, we interacted the exploitation

variable with tie strength, amount of reciprocal helping, network size, and the network sparseness

variables. The construction of the interaction term including exploitation and the network sparseness

variables were more complicated than a simple two-way interaction term because our hypotheses imply

that three variables need to be interacted (i.e., exploitation, sparseness, and sparseness squared). We chose

to model this as a step function and divided the degree of exploitation into three segments: projects in the

upper third on the exploitation scale—highly exploitative projects (exploitation 3rd); projects in the

middle third on the exploitation scale (exploitation 2nd); and projects in the lower third on the exploitation

scale—highly exploratory projects (exploitation 1st).8 We constructed three dummy variables which took

on a value of 1 if the focal project belonged to the segment, and 0 otherwise. The dummy variables were

then interacted with the two network sparseness variables. The attraction of this approach is that the

results can be easily interpreted for each level of exploitation.

Control variables

Project type. To make the projects comparable, we controlled for several project-specific factors.

We used the log of estimated dollar costs at the start of the project to control for size and scope

differences between the projects (budget). In our field interviews with project managers, we were also

told that estimated costs capture inherent differences in technical complexity among the projects (the

23

more complex the technology, the more engineering hours billed to the project). We used the budget

figure to avoid confounding final costs and the dependent variable. High final costs may reflect long

completion time because of more engineering hours billed to the project. We also created a variable that

measured the extent to which the project team was able to reuse existing software code and hardware

components within its division. The project manager was asked to indicate the percentage of existing

software and hardware that were reused from the focal division (existing ware).

Divisional control. Because the projects were drawn from divisions of varying sizes, we entered

a control variable measuring the sales for the respective divisions (divisional size). Team networks may

operate differently in small than in large organizations, and thus we wanted to control for this possible

source of variation. Data on sales were obtained from a company-specific financial database.

Respondents. To control for the possibility that a team’s number of advice relations is caused by

the number of team members who responded to the survey, we included a measure of the number of team

members who completed the survey (number respondents). We also controlled for differences in

experience among project teams by including the average years of employment in the company among

the team members (team tenure). Finally, we entered a control for the proportion of advice relations

within the team (within-team density), to control for the possibility that a team’s network size is simply a

correlate of the number of relations among the team members. This measure was computed the same way

as we computed a team’s external advice relations, except that we only included ties between team

members. We took the number of reported advice relations among team members, divided by the total

possible number of relations among them (counting asymmetric relations).

Statistical analysis

The statistical analysis of completion time was complicated by the fact that 13 of the 67 projects

were still ongoing at the time of data collection and represented right-censored cases (Tuma and Hannan,

1984). Furthermore, two projects were canceled. To incorporate these cases into the analysis, we

analyzed the time to completion using a hazard rate model. The hazard rate is a measure of the likelihood

24

of a project either completing or terminating at time t, conditional on it not having completed or

terminated before t. The higher the transition rate, the more likely that the project will be completed

faster. The hazard rate model takes the following form:

r(t)j = r(t) j * exp[aXj],

where r(t) j is the completion rate of project j, t is project time in the risk set, and r(t) j* is the completion

rate including the effects of all the control variables in the model. The effects of the independent variables

are specified in the exponential bracket. X is a vector of explanatory variables, and a is vector of

corresponding coefficient estimates.

We used maximum likelihood estimation as implemented in the statistical program TDA

(Blossfeld and Rohwer, 1995). We used the piecewise exponential specification because we did not want

to make any assumption about duration dependence that would require a specific parametric distribution.

To control for duration dependence, the model included four time periods that reflect the time-distribution

of events; the interval marks are 200, 300, 400, 500 and 650 days.9 The transition rate is assumed to be

constant within these periods, and covariates are assumed not to vary across time periods (Blossfeld and

Rohwer, 1995:114).

RESULTS

Descriptive statistics are shown in table 2, and the results of the hazard rate analysis are presented

in table 3. We used standardized values for variables that involve interaction terms in order to avoid high

correlation between the variables (the results reported in table 3 remain the same if non-standardized

values are used). Model 1 in table 3 presents the baseline model with all the control variables and the

main effects for the three main structural network variables we have considered (i.e., network size, tie

strength, and sparseness). The network size variable is positive and significant throughout the models in

table 3. The main effect for tie strength is also significant and positive, but the network sparseness

25

variable is not significant in model 1. Thus, this baseline model shows that teams with many strong ties

took shorter time to complete their projects than teams with few and weak ties. However, as the

subsequent models demonstrate, this baseline does not hold for exploitation projects.

---------- Insert Tables 2 and 3 about here ----------

In models 2 through 7, we entered the variables relating to the richness of the teams’ networks.

The first set of results concerns tie strength. The main effect of tie strength is positive and significant,

while the interaction term including tie strength and exploitation is negative and significant in models 2

through 7. The two effects must be seen together, as follows (from model 7):10

Rate = exp [0.632*tie strength – tie strength*(0.737*exploitation)].

Because the variables are standardized (observed value minus mean, divided by standard deviation), the

relative effects can best be seen through a plot of the results, as shown in figure 4. For exploitation

projects (one standard deviation above the mean on the exploitation scale), the stronger the ties, the lower

the completion rate (i.e., the longer the completion time). For exploration projects (one standard deviation

below the mean on the exploitation scale), the stronger the ties, the faster the completion rate. In short,

these results lend support to hypotheses 1a and 1b, that strong ties are beneficial for exploration projects

but harmful for exploitation projects. We also ran separate models including only one of the two

dimenons of tie strength—frequency and closeness. These results (not reported here) showed that the

magnitude of the negative coefficient for the interaction term was twice as large for frequency than for

closeness. That is, there was a much larger negative impact of having frequent than close ties for

exploitation projects. This was most likely the case because maintaining frequent interactions is likely to

be more time consuming than maintaining close ones.

The results for the interaction term including amount of reciprocal helping and degree of

exploitation is entered in models 3 through 7. This variable is not significant in any of the models, but the

main effect for the amount of reciprocal helping remains significant and negative in all models. The lack

of significant result for the interaction term and the significant estimate for the main variable hold even

26

when we ran separate models for the three sub-items of the reciprocity variable. Teams whose members

engaged in the three reciprocal helping behaviors took longer time to complete their projects.

The variable interacting network size and degree of exploitation is entered in models 4 through 7

in table 3. The effect of the interaction variable is negative and significant throughout the models, while

the main effect for network size remains positive. These two effects must been seen together (from model

7): 11

Rate = exp [0.671*network size – network size*(1.023*exploitation) ].

Figure 5 shows the combined effect of network size and the interaction term for exploitative and

exploratory projects. As the plots reveal, exploratory teams had higher completion rate with increasing

network size, while exploitation teams had lower completion rate with increasing network size. These

results confirm hypotheses 1a and 1b, that large networks are beneficial for exploratory tasks but harmful

for exploitative tasks. These findings must be seen in context of the range of the network size variable

(which ranges from 1 to 17 team-level advice ties). First, because no teams in the sample had zero ties, we

cannot infer that exploitation teams were better off having no ties. Second, because we limited the

maximum number of advice ties to five for each respondent on a team, there is an upper boundary limit

on a teams’ network size. Thus, we cannot infer from the results that exploratory teams with very large

networks were better off.

In short, the results for the network richness variables show that exploratory teams took less time

to complete their projects to the extent that they had many strong ties, while exploitation teams with many

strong ties took longer time to complete their projects. These findings support hypotheses 1a and 1b.

However, both types of project teams took longer to complete their projects to the extent that their

members engaged in reciprocal helping behaviors.

---------- Insert figures 4 and 5 about here ---------

We entered the effects for the network sparseness variables in models 5 through 7. First, in model

5, we added the squared term for the network sparseness variable (but did not interact it with degree of

27

exploitation). When the squared term is entered, the effect for the main sparseness variable becomes

positive and significant, while the effect for the squared term is negative. Thus, there is an inverted U-

shaped relationship between network sparseness and completion rate. Overall, teams completed their

projects more quickly if they moved from a position of low sparseness (i.e., dense network) to one of a

medium level of sparseness, but the completion rate turned negative for high levels of sparseness. Thus,

this overall result reveals that very dense or very sparse networks were harmful for the project teams.

However, this overall result of network sparseness depends on project type. In models 6 and 7,

we entered the network sparseness variables for each of the three segments of exploitation.12 The

curvilinear relationship holds for highly exploitative projects (i.e., projects in the upper third on the

exploitation scale) and medium exploitative projects (i.e., the middle third on the exploitation scale), but

not for exploratory projects (i.e., the lower third on the exploitation scale). Thus, highly exploitative

teams took less time to complete their project if they had a medium-level of network sparseness. The plot

in figure 6 shows that exploitative project teams that went from a very low level of network sparseness

(i.e., high density) to a medium level of sparseness completed their projects more quickly. Beyond a

medium level of network sparseness, however, exploitative project teams took longer to complete. The

turning point is 0.6 standard deviation below the mean level of network sparseness (at this point, teams

had a density of 40% of the maximum possible indirect ties in their egocentric team network). This result

lends support to hypothesis 2a, that there is an inverted U-shaped relationship between network

sparseness and the realization of immediate performance-related goals for exploitation teams.

While the curvilinear relationship between network sparseness and completion time did not hold

for exploratory projects, the effect for the main variable of network sparseness is positive for exploration

teams, as shown in model 7. However, this result is only significant at p < 0.1 and using a one-tailed test

of significance. Because hypothesis 2b states that a high level of network sparseness is more beneficial

for exploratory teams, using a one-tailed test for this directional hypothesis is appropriate. Thus, there is

evidence – albeit weak evidence -- that exploratory teams took less time to complete their projects to the

28

extent that they had sparse networks. This result is also plotted in figure 6. In the discussion, we consider

in greater detail why this particular result is not as strong as some of the others.

--------- Insert figure 6 about here ----------

In summary, the statistical analysis by and large confirmed our hypotheses, with two

qualifications. Our hypothesis about the positive effect of reciprocal helping activity for exploratory

projects were not supported; all teams experienced a negative effect on completion time of engaging in

reciprocal helping activities. In addition, while all other results were significant at the p<0.01 or p<0.05

levels (see model 7 in table 3), the result for the positive effect of network sparseness on completion time

for exploratory projects was only significant at p<0.10 in a one-tailed test.

DISCUSSION

The main finding of this study is that different tasks required different network structures to be

effective. Several network attributes that were valuable for exploratory teams were harmful for

exploitation tasks, and vice versa. Specifically, exploratory project teams in our sample benefited from a

network structure similar to the one laid out in Burt’s (1992) structural hole theory: they took less time to

complete their projects to the extent that they had many strong and non-redundant ties. In contrast,

exploitation teams took less time to finish if they had a network composed of weakly tied contacts that

were moderately interconnected. Thus, the network position that was most beneficial for exploration

teams was a liability for exploitation teams.

Limitations and research issues

Our results are specific to the context we studied. In a high-technology environment in which

speed is important for competition, the time required to build social relationships and to help others can

have negative consequences. In environments in which speed is less critical, the investment of time and

energy in network building may be less problematic. Also, the firm we studied coordinates its

29

development activities informally. By decentralizing so much of its activities to divisions and forgoing

the use of strong, central coordination, the benefits of building efficient networks for completing projects

may be greater.

Nevertheless, our findings are generalizable to some other work contexts, especially to those with

low levels of slack and where network-related activities are simply not a by-product of ongoing work

(Nohria and Gulati, 1996). If individuals and groups experience time constraints, there is likely to be

some tradeoff between spending time doing one’s own work well and allocating time to cultivating

networks and helping others. Because of this tradeoff, there are substantial costs associated with some

network positions, and these costs should affect a range of performance-related goals. For example,

decisions may be delayed or poorly made, production quality may suffer, and the degree of innovation

may be lacking to the extent that individuals and groups do not fully commit time to conduct high-quality

work because they spend time on networks. Subsequent research could apply the basic idea of a tradeoff

between network-time and work-time to investigate network costs in other settings involving a range of

tasks.

Another important issue is the limitation imposed by our unit of analysis. By studying individual

project teams, we were unable to pinpoint whether network positions and activities that prolonged a single

project’s completion had negative consequences for the organization as a whole. Helping others may have

harmed the focal project team but may have provided substantial benefits for other projects, yielding a net

positive effect overall. While this issue can complicate interpretation, it has also been partially addressed

by how we conducted the study. Because we have a representative sample of all projects that this

organization had started during a period of two years, our data set includes many project teams that were

recipients of help from others. Receiving this help should be partially captured by the network variables: a

team’s incoming flow of knowledge through advice-seeking relations is most likely the result of helping

activities carried out by members of other teams. Thus, the benefit of having many advice-seeking

relations (a positive effect for the focal team) is likely to be the result of someone else’s reciprocal

helping behaviors (a negative effect for the focal team). Although our analysis thus partially captured this

30

dual effect, our study did not pinpoint in detail the recipients of specific helping behaviors. We can

therefore not conclude that the demonstrated negative effect of reciprocal helping behaviors on

completion time is offset (or even surpassed) by some positive effect associated with receiving all that

help. Although our result about the negative consequence of reciprocal helping behaviors for task

performance is interesting, more research is needed to understand the various consequences of reciprocal

helping activities for both the focal actor and the aggregate unit.

Finally, because we did not track investments in networks over time, we could not analyze

whether investments made in one period (e.g., helping others and interacting frequently with contacts)

had positive or negative returns in subsequent periods. Theoretically, investments in activities that slow

down current product development efforts might actually enhance completion of subsequent product

development efforts, if people do not need to invest as heavily in the future and if some of the benefits

from these activities are deferred in the form of social capital to be drawn upon in subsequent efforts. This

issue suggests that future research should be more dynamic, considering how network benefits and

investments in the network are allocated differentially across time.

From network structure to contingency theory

An important implication of our study is that the effects of structural network attributes, such as

network size, sparseness, and tie strength, cannot be fully understood without considering the specific

organization context, including costs of networks, difficulties in getting others to help, and different task

requirements. Our results suggest that organization network research would benefit from using the

structuralist approach as a foundation but then proceeding to consider these context-specific issues. The

implication is that network structure theories, such as Burt’s (1992) structural hole theory, Coleman’s

(1990) prediction about the value of connected networks, and Granovetter’s (1973, 1982) weak-tie theory,

need to be modified to reflect the possibility that their predictions only hold in some organization

contexts.

31

An exciting task for organization network researchers is therefore to develop parsimonious

contingency theories about the effects of social networks in organizations. Building on traditional

contingency perspectives on organization design (e.g., Lawrence and Lorsch, 1967; Tushman, 1977),

some recent network research has shown that individual or group network effects are contingent on

particular organization contexts, including whether the task is basic research or product development

(Gabbay and Zuckerman, 1998), the knowledge required is tacit or explicit (Uzzi, 1997; Hansen, 1999),

and managers’ peer groups are large or small (Burt, 1997). Our study contributes to this emerging body of

work in two ways. First, we use March’s (1991) distinction between exploration and exploitation because

it captures a fundamental difference in the nature of work, that is, between work that is inherently novel

(innovation, experimentation, one-time decisions, radical change, etc.) and work that is routine (daily

work, continuous improvement, production efficiency, etc.). Particular work tasks (e.g., basic versus

applied research) can thus be studied at a more abstract level of classification, leading to a more

parsimonious contingency model, because the fundamental distinction between exploration and

exploitation is used instead of many different and highly context-specific task variables.

Second, the distinction between exploration and exploitation complements the distinction

between tacit and explicit knowledge, which has emerged as another important contingency variable for

understanding knowledge flows among people and groups (Zander and Kogut, 1995; Szulanski, 1996;

Uzzi, 1997; Hansen, 1999). While there is likely to be a positive association between the degree of

exploration and the tacitness of knowledge involved, they are not the same underlying dimension. For

example, some exploratory tasks involve innovations that are mainly based on recombination of existing

components, which contain a high level of explicit knowledge (cf. Henderson and Clark, 1990; Nahapiet

and Ghoshal, 1998). Likewise, for exploitation tasks, some routine work involves execution of skills that

are largely based on tacit know-how (cf. Von Hippel, 1988, 1994). While our study highlighted task

differences, future research could profitably combine a task contingency view with a knowledge

contingency approach and study the combined effects of both constructs on outcomes such as task

performance. For example, do exploratory tasks that involve highly explicit knowledge (e.g.,

32

recombination of chunks of existing knowledge) require different network structures compared with

exploratory tasks involving highly tacit knowledge? We speculate that they do. While both tasks are

likely to benefit from a non-redundant network that enables the inflow of diverse knowledge and ideas,

the exploratory-explicit situation may not require strong ties to transfer explicit knowledge. This assertion

needs to be developed further and tested in future research.

Our study also highlights another interesting contingency issue concerning exploratory tasks. We

hypothesized that exploratory tasks benefit from sparse networks. However, our empirical results were

not as strong for this hypothesized effect as for the other results. The reason may be that the positive

benefit of receiving diverse input from sparse networks may be offset by an integration problem.

Disconnected contacts in a sparse network do not have established relationships among one another and

thus are less likely to come together to exchange views, brainstorm, deepen their understanding of a

problem through discussions, and pass on their collective knowledge to a focal actor. Although each non-

redundant contact may have distinct views and knowledge, the contacts are unlikely to engage one

another to develop their collective insight to help the focal actor. Sutton and Hargadon (1996), in their

research on brainstorming among product designers, found that effective brainstorming sessions

involving a team and other designers relied on past and enduring social relationships among the people

involved. Designers knew one another and had established a set of heuristics for relating to one another

(cf. Uzzi, 1997). The existence of a dense network among the designers made it easier, faster, and more

effective to engage in discussions about subtle and vague concepts.

Thus, people and groups that are engaged in exploration tasks may face a tradeoff between having

sparse networks (which enable diverse input) and dense ones (which enable extended brainstorming). For

some exploration tasks, receiving diverse input may be more important than having an extended

brainstorming group, implying that a sparse network is relatively more important. Other exploration tasks

may benefit relatively more from being able to use an interconnected set of people as a forum in which to

brainstorm and gain insight. In our study, we were unable to disentangle the relative values of these

33

benefits for exploratory project teams, but this topic needs to be studied in order to pinpoint the

conditions under which sparse or dense networks are beneficial for exploration tasks.

Our results also point to some other research questions that warrant exploration in subsequent

studies. One of the interesting findings from our analysis is that some exploitation teams had ties that

were too strong and, as a result, their projects were completed more slowly. This raises the question of

why presumably rational individuals overinvest in ties. One answer involves one of the assumptions

implicit in the organization network literature. Much of this literature assumes that the network that is

most conducive to the mobility of the individual is also the network that is most conducive to task

performance (e.g., Burt, 1997; Podolny and Baron, 1997), but this assumption may not hold. For

example, it may be that it is in the individual’s interest to have strong ties because those ties are a basis of

future job opportunities and promotions (e.g., Burt, 1992). This finding means that future studies of the

effects of networks need to consider carefully whether the dependent variable is relevant to the

organization, the individual, or both.

A second possible explanation for overinvestment in building strong ties does not presuppose that

individual and organizational interests are necessarily antithetical. Rather, individuals must make

decisions about network investments under conditions of considerable uncertainty. It is difficult to

forecast what sorts of projects one will work on in the future and what kinds of connections and

information those projects will require. Therefore, even if people are considering the organization’s needs

completely, individuals may still overinvest in social relations, which may be harmful in some projects,

such as exploitation projects. Future research could profitably examine which of these explanations for

network building hold and how individuals decide to make investments in building networks.

Although the older research on networks in organization highlighted the possibility that networks

may undermine performance, this negative view of networks has been largely been replaced by a more

positive view of social network effects in organizations. While organization network research has moved

from a negative to a positive view spanning a period of forty years, there is a dearth of research

34

synthesizing positive and negative effects of social networks in organization. Our task contingency

perspective on network effects in organizations is one approach to this synthesis.

35

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43

Many, strong,reciprocal ties

Few, weak,non-reciprocalties

Networkrichness(direct ties)

Exploitation Exploration

Task Type

Positive:very high knowledge benefits,high network costs

Positive:Some knowledge benefits,low network costs

Negative:Knowledge deficit,low network costs

Negative:Some knowledge benefits,High network costs

Figure 1. Comparing the net value of network richness for exploitation and exploration tasks

44

VeryDense

VerySparse

Networksparseness(indirect ties)

Exploitation Exploration

Task Type

Negative:No diverse knowledge benefit,redundant contacts

Negative:Some knowledge benefits,but enforcement difficulty

Positive:High diverse knowledge benefits,but enforcement difficulty

Negative:Some knowledge benefits,redundant contacts

Figure 2. Comparing the net value of network sparseness for exploitation and exploration tasks

Positive

45

Table 1. Comparing samples.

Excluded Sample Final Sample

N=53 N=67

Mean St.dev. Mean St.dev. T-test (p-level)

Number of people 6.37 3.12 5.39 2.49 1.89*

Budget 6.89 1.19 6.65 0.98 1.39

Existing ware 0.49 0.33 0.41 0.28 1.51

Degree exploitation 4.66 1.43 4.61 1.49 0.19

Completion time + 14.44 5.30 15.04 5.87 0.52

* p < 0.1 +) n = 38 for excluded sample and n=54 for final sample

46

1 1 1

1

1

1 1

1

0 0

0

0

Figure 3. Aggregating individual networks to a team network

John’s Network

Mary

Jim

Tim

John

John

Mary

Laura

Jim

John Jim Mary Tim

John

Jim

Mary

Tim

1 1

11

1

0 0

0

1

0

1

1

John Jim Mary Laura

John

Jim

Mary

Laura

Team Network

Jim

John

Mary

Tim

Laura

11

1

1

1 1

1

1

John Jim Mary Tim

John

Jim

Mary

Tim

1

0

Laura

01Laura

Jim’s Network

Notknown

47

Table 2. Descriptive statistics and correlations

Variable Mean St. dev. Min Max 1 2 3 4 5 6 7 8 9 10 11 12 13 14 151. Existing ware .41 .28 0 .902. Budget 6.65 .98 4.49 9.21 -.373. Tenure mean 13.60 4.85 2.00 25.0 .20 -.144. Product .76 .43 0 1 .26 -.07 .045. Divisional sale (log) 3.74 .93 1.09 4.93 .40 -.16 .12 .206. No. respondents 3.81 1.84 1 10 -.06 .59 -.32 .06 .147. Within-team density .23 .22 0 1.00 .14 -.13 .23 .15 .08 -.308. Exploitation1 0 1.00 -2.24 1.59 .52 -.22 -.01 .41 .27 .10 .269. Network Size1 0 1.00 -1.45 4.03 .03 .29 .09 .17 .10 .44 -.07 .0710. Tie Strength1 0 1.00 -3.78 2.32 -.10 .19 -.23 -.14 .11 .17 -.10 -.07 -.1611. Reciprocity1 0 1.00 -2.24 1.52 .11 .34 -.21 .01 -.04 -.48 -.13 -.05 .44 -.0212. Network sparseness1 0 1.00 -1.91 1.21 -.19 .08 .10 .10 -.28 .06 -.17 -.05 .25 -.21 .0013. Netw. Sparse-squared .98 1.11 0.00 3.63 .03 -.04 -.31 -.07 -.05 -.16 .15 -.01 -.50 .14 -.14 .4514. Strength*Exploit -.06 1.06 -4.83 2.51 .01 .07 .12 .21 .02 .15 .01 .19 .18 -.17 .02 .06 -.1015. Reciprocity*Exploit -.05 .93 -3.17 2.43 .04 .02 -.02 -.11 .05 .12 .27 .02 .15 .02 .22 .13 -.03 -.1916. Netw Size*Exploit .07 .87 -1.83 3.01 -.19 .18 -.05 .02 -.04 -.02 -.11 -.30 -.01 .23 .15 -.28 -.14 .22 -.221 Variables are standardized by subtracting the mean from the value and dividing by the standard deviation.N=67

48

Table 3. Results from Hazard Rate Analysis of Completion Time (N=67)

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7Control variablesPeriod –1 -2.140(1.797) -2.432(1.823) -2.434(1.847) -4.294**(2.099) -5.355**(2.101) -5.580**(2.201) -4.729**(2.044)Period – 2 -.320(1.834) -.654(1.861) -.656(1.875) -2.436(2.099) -3.479*(2.108) -3.677*(2.205) -2.822(2.047)Period –3 .498(1.834) .165(1.856) .164(1.870) -1.615(2.080) -2.513(2.085) -2.687(2.194) -1.830(2.037)Period –4 1.503(1.890) 1.174(1.911) 1.173(1.922) -.568(2.109) -1.405(2.104) -1.557(2.204) -.731(2.0 61)Period- -5 1.452(1.978) 1.125(1.994) 1.124(2.008) -.595(2.183) -1.328(2.184) -1.510(2.281) -.719(2.150)Period- 6 1.991(2.040) 1.743(2.054) 1.741(2.071) .058(2.235) -.819(2.226) -1.078(2.326) -.251(2.193)

Existing ware 2.617***(.816) 2.636***(.815) 2.637***(.824) 2.737***(.823) 3.168***(.834) 2.872***(.980) 2.341***(.858)Budget -.749***(.246) -.706***(.249) -.705***(.252) -.561**(.262) -.656**(.258) -.663**(.278) -.728***(.272)Team tenure -.048(.041) -.032(.042) -.032(.02) -.034(.045) -.025(.044) -.018(.047) -.016(.047)Product .395(.499) .236(.503) .235(.531) .395(.536) .608(.568) .603(.625) .627(.621)Divisional sale (log) -.269(.200) -.261(.199) -.261(.201) -.102 (.210) -.176(.206) -.148(.209) -.119(.210)No. respondents -.255*(.140) -.257*(.138) -.257*(.138) -.274*(.140) -.223*(.134) -.213(.141) -.186(.140)Within-team density -.386(.493) -.381(.485) -.380(.506) -.617(.530) -.726(.535) -.829(.550) -.846(.542)Exploitation1 .131(.192) .213(.207) .214(.213) .112(.237) .213(.248) .391(.565) .095(.485)

Network Richness(direct ties)Advice size1 .691***(.212) .782***(.222) .782***(.230) .867***(.228) .606**(.253) .664**(.277) .671**(.271)Tie strength1 .334*(.194) .326*(.192) .325(.245) .512*(.272 .605**(.291) .642**(.297) .632**(.288)Reciprocity1 -.301*(.177) -.519**(.239) -.519**(.249) -.639**(.267) -.636**(.251) -.632**(.271) -.538**(.251)Tie strength*exploitation -.331*(.187) -.330*(.194) -.628**(.262) -.798***(.286) -.821***(.282) -.737***(.260)Reciprocity*exploitation -.001(.225) .119(.235) .136(.244) .153(.251) .188(.247)Advice size*exploitation -.510**(.248) -.750***(.280) -.895**(.360) -1.023***(.336)

Network Sparseness(indirect ties)Sparseness1 -.069(.155) -.137(.162) -.137(.163) -.052(.168) 2.015**(.847)Sparseness-squared -.583**(.230)

Sparseness*exploit 1st 2.030(1.441) .399†(.290)Sparseness-squared * exploit 1st -.495(.414)

Sparseness *exploit 2nd 1.886*(1.119) 1.261(.961)Sparseness-squared * exploit 2nd -.540*(.319) -.384(.288)

Sparseness *exploit 3rd 2.250*(1.175) 2.380**(1.137)Sparseness-squared * exploit 3rd -.663*(.351) -.726**(.341)

Log-likelihood -356.3 -354.7 -354.7 -352.6 -349.2 -348.7 -349.4Chi square (d.f.) †† 3.2*(1) 3.2 (2) 7.4*(3) 14.2***(4) 15.2*(8) 13.8*(7)1Variables are standardized (see table 2).*p < .10, **p < .05, and ***p < .01 (two-tailed test for variable coefficients) ; †p<.10 (one-tailed test).†† Compared with model 1.

49

Figure 4. Plot of hazard rate for tie strength and degrees of exploitation

Weak tie strength (st.dev from mean) Strong

Note: estimates based on model 7. Formulas were:

Exploitation (+1 st.dev): Rate = exp [0.632*tie strength – tie strength*(0.737*(+1)) ].

Exploration (-1 st.dev): Rate = exp [0.632*tie strength – tie strength*(0.737*(-1)) ].

0

1

2

3

4

5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Mul

tiplie

r of

the

Rat

e

Exploration

Exploitation

50

Figure 5. Plot of hazard rate for network size and degrees of exploitation

Small Network size (st.dev. from mean) Large

Note: estimates based on model 7. Formulas were:

Exploitation (+1 st.dev): Rate = exp [0.671*network size– tie strength*(1.023*(+1)) ].

Exploration (-1 st.dev): Rate = exp [0.671*tie strength – tie strength*(01.023*(-1)) ].

0

2

4

6

8

10

12

14

-2.5 -1.5 -0.5 0.5 1.5 2.5

exploration

exploitation

51

Figure 6. Plot of hazard rate for network sparseness and degrees of exploitation

Network sparseness (st.dev. from mean)*

Note: estimates based on model 7. Formulas were:

Exploitation (upper third on scale): Rate = exp [2.380*sparseness –0.726*sparseness-squared].

Exploration (lower third on scale): Rate = exp [0.399*sparseness].

*) Sparseness variable was rescaled from prior range (-1.91,1.21) to positive range (0, 3.12) in models 6 and 7.

0

1

2

3

4

5

6

7

8

-2.5 -1.5 -0.5 0.5 1.5 2.5

exploration

exploitation

52

1 Although exploration tasks are likely to contain more tacit knowledge than exploitation tasks, we do not

argue that there is a one-to-one relationship between exploration and tacitness of knowledge involved. For

example, many exploitation tasks involve tacit knowledge, including psycho-motor skills such as riding a

bike.

2 It may be in the focal actor’s long-term interest to help others because of other concerns. For example,

even though helping others may hamper a person’s work performance in the short-term, it may be

beneficial to do so for career advancement because he or she is likely to be seen as a helpful colleague.

But this possibility does not mitigate the short-term problem that time spent helping others is time not

spent on one’s own work.

3 A number of team members naturally had relations outside the focal division, including ties to engineers

in other divisions and to peers outside the company. We controlled for the potential bias by including a

variable measuring a team’s number of ties to members of other divisions in the company. Because this

variable did not alter results, we excluded it from the final statistical analysis.

4 We set the limit to five network contacts because of the difficulties of asking for ties between contacts

when many contacts were named.

5 These duplicate ties can be seen as a form of contact redundancy, which may benefit exploratory teams.

We ran separate models including duplicate ties in the network size variable, but these did not alter the

results, and we therefore do not report this additional analysis.

6 Because some scholars have found that these two measures of tie strength represent different dimensions

(e.g., Marsden and Campbell, 1984), we also ran models with frequency and closeness entered separately.

7 We conducted separate analyses using only the two project-level items, but we found no significant

differences in the results and therefore do not report these additional results.

53

8 We chose thirds instead of quartiles to be able to have enough projects in each sub-sample. We

experimented by slightly altering the cut-off values for the boundaries between the thirds, but this did not

change the results. The results are therefore not sensitive to arbitrary boundaries between the three

segments of degree of exploitation.

9 Inspection of the survivor function revealed that the hazard rate is non-monotonic, suggesting that an

exponential model without specific time intervals is not an appropriate functional form for this data set.

10 We use the coefficient estimates from model 7 to assess the magnitude of the effects and plot the

results, because this model is the most specified one and allows for a comparison of the effects for the

various independent variables.

11 The magnitude of the negative coefficient for the interaction term is lower in model 4, meaning that

teams had to be highly exploitative (1.5 standard deviations above the mean value on the exploitation

scale) for the combined effect to turn negative. Regardless of which model is considered, however, results

show that highly exploitative teams with large networks took longer time to complete their projects.

12 We rescaled the standardized network sparseness variable from the original range (-1.91 to 1.21) to a

range with positive values (0 to 3.12), because having only positive values makes it easier to interpret the

combined impact of the main effect and the squared term. Results remained the same before and after

rescaling the variable.