Inter-firm R&D networks and firms’ technological knowledge base: a co-evolutionary

perspective

Abstract

This paper investigates how a firm’s technological knowledge base co-evolves with firm’s

position within the network of R&D alliances. In particular, we argue that the more a firm

moves towards the core of the R&D network the more it will develop a generalist

technological knowledge base; furthermore, the more a firm develops a generalist

technological knowledge base the more it will move towards the core of the network. To test

the existence of this self-reinforcing dynamics, we analyze a large panel data set describing

patent and financial variables for all the firms involved in all the R&D alliances registered in

the US between 1984 and 2002. Based on a simultaneous equation model, the data strongly

support our argument and hypotheses.

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Introduction

For several decades now, the complex relationship linking an organization to its

environment has been a chief topic of investigation in organization theory (Emery and Trist

1965; Andrews 1980; Blau & Schoenherr 1971; Burns & Stalker 1961; Grinyer & Yasai-

Ardekani 1981; Hofer & Schendel 1978; Lawrence & Lorsch 1967; Prescott 1986; Pugh et al

1969; Thompson 1967). Over the years, multiple lines of theoretical and empirical research

have emphasized different aspects of the organization-environment interface. While early

works were primarily concerned with the role of environmental uncertainty (Duncan 1972a,

1972b; Lawrence & Lorsch, 1967), later research expanded this notion by suggesting that the

external environment encompasses a wider range of relevant dimensions (Aldrich 1979, Dess

and Beard 1984). Accordingly, a voluminous body of literature has studied how organizations

are affected by environmental characteristics such as munificence (Castrogiovanni 1991),

dynamism (Li and Simerly 1998), complexity (Child 1972, Pennings 1975, Tung 1979),

consensus (Aldrich 1979), connectedness (Pfeffer and Salancick 1978), density and

legitimation (Hannan and Freeman 1989). Similarly, many have investigated how

organizations purposefully seek to position themselves where environmental conditions are

more conducive to their success (e.g., Child 1972, Porter 1980, Hrebiniak and Joyce 1985).

Whether their analytical focus was on the role of the environment in affecting

organizations or, conversely, on the role of firms in navigating the environment, thus far the

predominant approach among studies of the organization-environment interface has been to

treat the environment as exogenous to the organization. However, "environments affect

organizations through the process of making available or withholding resources” (Aldrich

1979, p. 61), which for the most part are controlled by other organizations (Pfeffer and

Salancick 1978). As a consequence, one chief way in which firms seek to secure access to the

resources residing in their environment is by developing a network of exchange relationships

3

with other organizations. Contrary to macro characteristics of the environment, such as

density or dynamism, some of the characteristics of these networks can hardly be regarded as

exogenous to the organization. In particular, to the extent that an organization pursues its best

interests in choosing who to include in its network, many of these choices will reflect

characteristics of the organization itself.

While the importance of investigating the role of endogenous dynamics between an

organization and its environment has been recently emphasized (McKelvey 1997, Calori et al.

1997, Koza and Lewin 1998), research on the subject is still in its infancy. The goal of the

present paper is to cover part of this gap in the literature by examining how the technological

knowledge base of an organization co-evolves with the network of research and development

alliances it maintains with other organizations. More specifically, we will argue and show

that there exists a mutually reinforcing dynamics between the degree of generality of a firm’s

technological knowledge base and the “coreness” of its position in the economy-wide inter-

organizational R&D alliance network. That is, the more a firm develops technological

knowledge that has applications spanning across technological sectors the more it will move

towards the core of the network; and, the more a firm moves towards the core of the network

the more it will develop technological knowledge that is applicable across multiple

technological sectors.

There are three principal reasons to focus on this particular aspect of the organization-

environment relationship. First, the linkage between a firm’s technological knowledge base

and its R&D network promises to offer a fertile empirical ground for the exploration of

endogenous dynamics in the organization-environment interface. On the one hand, the

influence of inter-organizational R&D networks on the development of firms’ technological

knowledge bases has been widely established (e.g., Powell et al. 1996; Hagedoorn and

Schankenraad 1994; Shan et al. 1994, Walker et al. 1997; Stuart 2000; Ahuja 2000). On the

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other hand, evidence also suggests that in selecting their R&D alliance partners, firms will

take into serious consideration the potential complementarities existing between their own

and other firms’ technological knowledge bases (Grant and Baden-Fuller 2004, Lavie and

Rosenkopf 2006, Sampson 2007). Second, technological knowledge is a crucial source of

competitive advantage in the knowledge-based economy (Romer 1990). Consistent with this

notion, an interesting stream of research has recently emerged that characterizes

organizations based on their technological knowledge base (e.g., Brusoni et al. 2001, Katila

and Ahuja 2002, Nerkar and Paruchuri 2005). Therefore, by focusing on the technological

knowledge base of organizations, we aim to shed new light on an organization-level construct

that is substantively relevant for contemporary students of organization. Similarly, and third,

understanding under which conditions firms generate general purpose technologies or,

conversely, specialized ones, is essential to explain how productivity shifts and economic

growth occur, an issue with huge implications for the material and welfare conditions of our

societies (Breshnan and Trajtenberg 1995, Helpman 1998). Quite unexplainably, thus far

organizational scholars have failed to contribute to this important debate; hopefully, this

study will provide a useful starting point for students of organization to engage more actively

in the productivity debate.

The paper proceeds as follows. We start by clarifying the notions of network coreness

and technological knowledge base, after which we develop our theory and hypotheses.

Subsequently, we describe the data, its operalization, and the statistical methods. We

conclude the paper by discussing the results and implications of our analysis.

Core-periphery structures in R&D collaboration networks

Inter-organizational R&D collaboration represents a major mechanism by which firms

absorb technological knowledge from their environment (e.g., Powell et al. 1996). Compared

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to hierarchy or market transactions, R&D alliances often provide a superior means to learn

externally generated technological knowledge since much of that knowledge is tacit and

organizationally embedded (Kogut 1988). In line with this view, Ahuja (2000, p. 448) argued

that alliances “serve as sources of resources and information” and demonstrated a positive

link between the extent of a firm’s alliance activity and firm patenting or innovation.

Similarly, Baum et al. (2000) showed that biotech start-ups were more innovative when they

had many alliances, suggesting that alliances contribute to a firm’s knowledge base. While

most extant research has analyzed R&D alliances at the dyadic level, recently increasing

attention has been paid to the whole network of R&D relationships within which a firm is

embedded, both within and across industries (Powell et al. 2005, Goerzen and Beamish

2005). In the present study, we build on this line of research in order to explore how a firm’s

position within the core-periphery structure of the R&D network affects the firm’s trajectory

of technological development.

Core/periphery structures are characterized by a cohesive clique of densely

interconnected core actors surrounded by a fringe of weakly connected peripheral actors

(Borgatti and Everett 1999: 375). Figure 1 visually illustrates an ideal-typical core/periphery

structure. The cloud of densely connected dark nodes at the center of the network represents

the network core. Peripheral members, conversely, are tied to the core and to each other

mostly through indirect connections. Sociologists have long argued that such core/periphery

macro structures exert a deep influence on social actors’ behavior by shaping both their

access to information and resources and their inducements (e.g., Mintz and Schwartz 1981,

Barsky 1999, Cummings and Cross 2003). However, the possibility that core-periphery

structures may surface and play a role in the R&D alliance system has never been

investigated. To explore this possibility, we analyze in this paper the entire network of R&D

relationships formed in the United States between 1985 and 2002. Hence, our R&D network

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includes a wide variety of inter-firm alliances spanning across all existing economic and

technological sectors. In particular, our analysis focuses on the notion that the actors that

occupy core positions in a network are proximate not only to each other but to all actors in

the network; by contrast, the actors on the outskirts of the network are no more than

moderately close to the core and, furthermore, they are far away from the vast majority of

other peripheral actors. For that reason, the actors occupying the core of the network “play

the key coordinating roles…, whereas the periphery is occupied by actors with less

integrative importance.” In the context of our study, this notion is reflected in the fact that the

closer is a firm to the core of the R&D network, the more closely it is connected to firms

across technological and economic sectors. Accordingly, ampler opportunities to exploit

complementarities among these sectors are likely to accrue to core firms because, for ideas to

travel between mutually unconnected peripheral firms, these ideas must necessarily pass

through the core of the network.

As we will argue in the next sections, the extent to which a firm is in the core of the R&D

network has important implications for the technological knowledge base the firm will be

able and willing to develop. Furthermore, the technological knowledge base a firm develops

influences whether the firm will be able and willing to move towards to core of the network.

Before we explicate in more detail our theory and hypotheses, we now turn to describing the

construct of technological knowledge base.

Generality of firms’ technological knowledge base

Firms’ success increasingly depends on continuously improving their productivity, either

through internally developed innovations or through the integration of innovations developed

by other organizations. Accordingly, "knowledge-creating companies" and "learning

organizations" are widely celebrated for their ability to generate and integrate both internal

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and external technological knowledge (Nonaka and Takeuchi 1995; Simonin 1997; Leonard-

Barton 1995). By the same token, firm-level differences in developing and managing

technological knowledge are argued to influence strategic outputs such as market

diversification (Kim and Kogut 1996), technological innovation (Katila and Ahuja 2002;

Ahuja 2000; Nerkar and Parachuri 2005), new product introduction (Nerkar and Roberts

2004), and firm boundaries (Brusoni et al. 2001). Furthermore, prior research has shown that

firm’s technological knowledge is important to organizational adaptation in a technologically

dynamic environment (Ahuja and Katila 2001, Rosenkopf and Nerkar 2001).

Organizations adapt to their environments through innovations, which for the most part

stem from local search and recombination of familiar knowledge (Lewin et al. 1999). Hence

firms generate new technological knowledge in a path dependent fashion, and their current

technological knowledge base demarcates the space of technological search and opportunities

salient to the firm (Stuart and Podolny 1996; Ahuja 2000b). Various aspects of a firm’s

technological knowledge have been analyzed based on the stock of patents the firm produced

over time (e.g., Katila and Ahuja 2002, Rosenkopf and Nerkar 2001), its R&D expenditures

(e.g., Helfat 1994) or its human resources (e.g., Chang 1996). Scholars have characterized the

technological knowledge base of a firm in terms of its internal heterogeneity (Pavitt 1997),

breadth and depth (Prencipe 2000), and complexity (Singh 1997). Furthermore, Yayavaram

and Ahuja (2004) conceptualized a firm’s technological knowledge base as a network of

knowledge elements, where the technological knowledge of a firm is embodied both in the

elements themselves and in the combinative relationships between these elements. Also,

firms’ technological knowledge base has been specified by its distance from competitors

(Stuart and Podolny 1996; Ahuja 2000b) or a technology cluster (Jaffe 1989). In sum, the

significantly increased importance of technological knowledge as an economic and

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organizational asset for the firm has led many scholars to characterizing firms in terms of

their technological knowledge base.

In this study, we characterize the technological knowledge base of a firm along the

generality-specialism dimension. In particular, we distinguish firms based on the extent to

which the technological knowledge they develop finds applications across few or, conversely,

many technological sectors. Accordingly, in our conceptualization an extreme form of

technological generalist is a firm developing technological knowledge applicable across

virtually all sectors of the economy. Historically, this happened in the case of firms

developing ideal-typical general purpose technologies such as the electric dynamo, the steam

engine, or the computer (Breshnan and Trajtenberg 1995, David 1990, Rosenberg and

Trajtenberg 2004). By contrast, an extreme technological specialist in our conceptualization

is a firm that develops technological knowledge with applications in one technological sector

only. In actuality, of course, most firms fall somewhere in between these two extreme cases.

Our hypothesis is that the generality of a firm’s technological knowledge base co-evolves

with firm’s degree of “coreness” within the broader inter-organizational network of research

and development relationships. Namely, we submit that the closer is a firm to the core of the

R&D network the more it will have the capabilities and incentives to build a generalist

technological knowledge base; furthermore, the more a firm has built a generalist

technological knowledge base the more it will have the capabilities and incentives to move

towards the core of the R&D network.

The co-evolution of technological generality and network coreness

While the use of R&D alliances is evident and increasing (Morris and Hergert 1987;

Mowery 1988), their performance varies widely and failures abound (Bleeke and Ernst 1993;

Kogut 1989). Given the importance of inter-organizational learning and technological

knowledge sharing to outcomes from R&D alliances, characteristics of the technological

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knowledge bases of alliance partners are a crucial factor in determining the value generated

through an alliance. Extant research, for example, has shown that the degree of similarity

between the technological knowledge bases of alliance partners has a strong influence on

whether and how well firms learn from each other (Mowery et al.1996; Lane and Lubatkin

1998; Ahuja 2000). Similarly, Baum et al. (2000) found that biotech firms that partnered with

organizations having different kinds of relevant knowledge, such as pharmaceutical firms,

universities, and government labs, were more successful after their initial public offerings

than firms engaging in alliances with only single types of partners.

More generally, the added value of an R&D alliance depends to a considerable extent on

the complementarities existing among partners’ technological knowledge bases (Sampson

2007). As said, a generalist technological knowledge base is one featuring complementarities

across a wide spectrum of technological sectors. Therefore, we expect a firm characterized by

a generalist technological base to be perceived as a potentially valuable R&D partner by

firms from multiple and possibly diverse technological sectors. By the same token, a firm

with a general technological knowledge base should find it particularly attractive to establish

R&D partnerships across technological sectors so as to be able to exploit more fully the wide

applicability of its technological knowledge. Because, as we argued, being in the core implies

being directly exposed to a wider array of technological knowledge bases than being in the

periphery, we submit that the more a firm is characterized by a general technological

knowledge base the more it is attractive for, and the more it is attracted to, firms that occupy

a core position in the R&D network.

By contrast, a specialist technological knowledge base is one that generates applications

only in one or very few technological domains. Therefore, a firm characterized by a specialist

technological knowledge base ought to be regarded as a potentially attractive R&D partner by

firms from a more limited set of technological sectors than a technological generalist.

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Similarly, a firm with a specialist technological knowledge base has no incentive to develop

R&D partnerships outside its field of application because no added value can be anticipated

in return of the costs of those alliances. Therefore, the more a firm has developed a specialist

technological knowledge base the more we expect it to be attractive for, and attracted to,

firms occupying a peripheral position in the network. Takes together, these arguments lead us

to our first hypothesis:

Hypothesis 1: The more generalist is the technological knowledge base of a firm the more

the firm will move towards the core of the network; conversely, the more specialized is the

technological knowledge base a firm the more the firm will move towards the periphery of the

network.

We believe that the causal relationship between the generality of a firm’s

technological knowledge base and its degree of coreness in the network runs in the opposite

direction too. There are two reasons for our conjecture. First, firms strive to shape their

supply of technology so as to meet demand and maximize their return on investment (Scherer

1965). When firms make investment decisions concerning the development of their

technological knowledge base, the structure of incentives they face is likely to vary

depending on their degree of coreness in the network. In particular, we expect firms in the

core to have a strong incentive to pursue general purpose technologies because they are

directly exposed to a wide array of potential complementarities, and hence potential R&D

partners, across multiple technological sectors. Conversely, the choices of technology

investment made by firms in the periphery are driven by the fact that their complementarities

with potential R&D partners, and hence their opportunities for R&D collaborations, are

confined within a limited domain of technological application. Therefore, we expect firms in

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the periphery to more inclined towards specializing, rather than generalizing, the scope of

applicability of their technological knowledge base in order to more fully rip the

complementarities between them and their potential R&D partners.

Second, as said, firms absorb technological knowledge from their environment and

R&D inter-organizational collaboration networks are a prime source of learning and

innovation (Powell et al. 1996). Because firms in the core are exposed to more numerous and

more distant technological sectors than firms in the periphery, we can expect the former to

absorb from the environment a wider base of technological knowledge than the latter.

Therefore, firms in the core should develop greater capabilities and environmental resources

to build a generalist technological knowledge base than do firms located in the periphery of

the network. By the same token, exploiting the complementarities inherent in a specialist

technological knowledge base requires mastery of the domain and a deep knowledge of the

specificities of the technological and economic sector wherein potential R&D partners

operate. Because firms in the periphery are surrounded by a more focused network of R&D

collaborators, such deep knowledge is more easily generated in peripheral than in core

network positions. Taken together, these arguments lead us to our second and last hypothesis:

Hypothesis 2: The closer is a firm to the core of the network the more it will develop

a generalist technological knowledge base; conversely, the closer is a firm to the periphery of

the network the more it will generate a specialist technological knowledge base.

To test these hypotheses, we use a large data set on US-based R&D alliances. In the next

section, we turn to describing the data.

Data

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The population for this study was all R&D alliances registered with the US Department of

Justice under the NCRA between 1984 and 2005. Thus our data is similar to that reported in

the CORE database in that both are drawn from filings reported in the Federal Register.

However, the unlike the CORE database, we have obtained and tracked membership in the

various research joint ventures (RJV) at the firm and arrangement level. However, each filing

is made by a distinct organizational entity and does not conform to a common standard for

naming its members. This leads to an inconsistency in naming of organizational members

across Federal Register filings. Therefore, we limit our analysis to publicly traded firms in the

US, for which we have identified a unique standard identifier: the CUSIP number, used in the

Standard and Poors, Compustat database. Limiting the sample to only publicly traded firms

which joined or formed an RJV in the period 1984-2005 gives a final sample of 762 firms for

a total of 4941 firm-year observations, with the average firm being in 6.5 consortia over the

observation period.

Measures

We hypothesized a self-reinforcing dynamic between the extent to which a firm occupies a

core position within the inter-organizational R&D network and the generality of the firm’s

technological knowledge base. This hypothesis therefore entails two dependent variables,

which we measure as follows.

Coreness: The degree of coreness of a firm within the R&D network is measured following

Borgatti and Everett (1999). The authors propose a continuous model in which each node is

assigned a measure of coreness depending on how far they are to the core of the network. In a

Euclidean representation, their model corresponds to distance from the centroid of a single

point cloud. Our network data consist of continuous values representing the strength

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relationships, as reflected by the number of R&D alliances between pairs of firms at any

point in time. In this situation, Borgatti and Everett (1999) suggest using the following model

to define the coreness of each firm in the network:

Xij=cicj

where X is a vector of nonnegative values indicating the degree of coreness of each node.

Thus, the pattern matrix has (i) large values for pairs of nodes that are both high in coreness,

(ii) middling values for pairs of nodes in which one is high in coreness and the other is not,

and (iii) low values for pairs of nodes that are both peripheral. Thus, the model is consistent

with the interpretation that the strength of a relationship between two firms is a function of

the closeness of each to the center. It may be worthwhile noting that this is the same situation

found in factor analysis, where the correlations among a set of variables are postulated to be a

function of the correlation of each to the latent factor (Nunnally 1978), and in consensus

analysis (Romney et al., 1986), where agreements among pairs of takers of a knowledge test

is seen as a function of the knowledge possessed by each one. Thus, when the continuous

model fits a given dataset, it provides an extremely parsimonious model of all pairwise

interactions (Borgatti and Everett 1999). We therefore use this formulation of the core-

periphery model to estimate coreness empirically on our R&D data.

Technological generality. To measure how generalist is the technological knowledge base of

a firm we looked collected patent and patent citation data for each firm in our population.

Following Trajtenberg, Jaffe and Henderson (1997), and leaving time subscripts aside for the

sake of simplicity, we first measured the generality of each patent granted to any firm as:

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Pi = 1 - ∑=

n

jijs

1

2

where sij indicates the percentage of citations received by patent i that belongs to patent class

j, out of ni patent classes. Therefore, if patent i is cited by subsequent patents that belong to a

wide range of technological fields, the measure will be high, whereas if most citations are

concentrated in a few fields, the measure of generality will be close to zero. A high generality

score suggest that a patent had a widespread impact influencing subsequent innovations in a

variety of fields. Then, the generality of firm k’s technological knowledge base is calculated

as the average generality across all patents granted to k:

Gk = ∑=

z

i

i

ZP

1

In our analysis, we control for the following variables, which we obtained from Standard &

Poor’s Research Insight database:

Total assets. The sum total of all assets of each firm

Employees. The total number of employees in each firm

Intangibles. This variable represents the intangible assets included in a company’s balance

sheet. Assets covered in this valuation include, but are not limited to copyrights, patents,

licenses, trademarks, trade names, and goodwill.

Advertising costs. We assess the extent to which firms incur advertising costs as one

indicator of complementary assets.

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Firm performance. As there is no single best indicator of firm performance, we included

separate measures of firm’s:

- Sales

- Return on investment

- Return on equity

- Return on sales.

Econometric specification

As said, our goal is to test the hypothesis of a two way causal relationship between the degree

of coreness of a firm within the R&D network and the generality of the firm’s technological

knowledge base. From an econometrics standpoint, therefore, we need to model and

parametrize an endogenous process between these two variables. Our hypotheses are

corroborated to the extent that (i) there is an endogenous component linking coreness and

generality; furthermore, net of this endogenous component, (ii) coreness positively influences

technological generality and (iii) technological generality positively influences coreness. The

most straightforward econometric approach to test our hypotheses is a simultaneous

equations model of the following kind:

y it = a1 + b1x it + i1r 1it + c1z it + d1ki + f1ht + e1it

x it = a2 + b2y it + i2r 2it + c2z it + d2ki + f2ht + e2it

where e1it and e2it are assumed to be independently and identically distributed disturbances

within each equation, although possibly correlated across equations; ht represents unit-

invariant time-varying factors; ki models firm-level fixed effects (i.e., dummies); y it and x it

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measure firm’s coreness and firm’s technological generality, respectively; z it is a set of

control variables common to both equations; r 1it is an instrumental variable for x it; and, r 2it is

an instrumental variable for y it.

All variables on the right-hand side are assumed to be exogenous with the exception

of x it in the first equation and of y it in the second. Therefore, this model assumes y it and x it to

be endogenously related. To the extent that the used instruments are valid, this model

provides an efficient and unbiased estimator in the presence of endogeneity (Wooldridge

2006). As a robustness check, we used both 2-stage and 3-stage least square estimation for

our test, obtaining identical results. Further robustness checks were performed by lagging the

independent variables; again, results were perfectly consistent with those reported here.

Results

To test the presence of endogeneity between y it and x it, we performed a Hausman

test (1978) and compared the model estimates with and without the endogeneity assumption

(Wooldridge 2006). According to the test, the null hypothesis that the two variables are

exogenously related must be rejected. Hence, it can be concluded that the degree of coreness

of a firm and the generality of its technological knowledge base are endogenously related.

Table 1 reports a correlation matrix and descriptive statistics of our variables of interest.

Table 2 reports the results of the simultaneous equations model. Equation 1 specifies

Coreness as a dependent variable while, Generality as an endogenous variable, and a dummy

variable representing service firms according to the NAICS classification as an instrument for

Generality. Equation 2, by contrast, specifies Generality as a dependent variable, Coreness as

an endogenous variable, and the average number of claims per patent made by a firm as an

instrument for Coreness. Sales, R&D costs, Advertising costs, and Total assets are treated as

exogenous independent variables in both Equation 1 and Equation 2. Furthermore, in both

17

equations we modeled a non-linear and possibly non-monotonic time trend by introducing the

variables Time and Time squared; firm-level fixed effects are modeled away by means of

firm dummies. All continuous variables are logged in both equations.

------------------------------TABLE 1 AROUND HERE----------------------------

Starting with Equation 1, there turns out to be a humped time trend of Coreness; that

is, the average degree of coreness of the firms in our population declines in the initial phase

of our observation period, but then starts to increase again. Not surprisingly, the more a firm

spends on R&D the more it is close to the core of the R&D network. However, firms that

spend more on advertising and that have larger sales tend to be closer to the periphery. As

predicted by our hypothesis, the more a firm has a technological knowledge base with wide

applications across technological sectors the more the firm is close to the core of the network.

It is important to stress again that the positive effect of Generality on Coreness is net of the

endogenous component inherent in the relationship between these two variables.

Turning to Equation 2, there appears to be no time trend in the extent to which firms

generate generalist or specialist technological knowledge. The larger is the amount of assets

owned by a firm the more the firm tends to develop a generalist technological knowledge

base. By contrast, firms that spend more on advertising and firms that achieve larger sales

tend to develop less general knowledge. Interestingly, R&D investments appear to have no

effect on the generality of a firms’ technological knowledge base. Lastly, and most

importantly, the more a firm is in the core the more it develops technological knowledge with

broad applications across technological sectors; again, we would like to stress that this effect

is net of endogenous dynamics. Hence, the estimates in Equation 2 provides support for our

hypothesis 2.

--------------------TABLE 2 AROUND HERE-----------------------

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Conclusions

The paper investigated the relationship between the coreness of a firm within the R&D

alliance network and the generality of the firm’s technological knowledge base. It was argued

and showed that the more a firm moves towards the core of the network the more it tends to

build a technological knowledge base with wide applicability across technological and

economic sectors. Furthermore, the more a firm develops a generalist technological

knowledge base the more it tends to move towards the core of the network. Therefore, there

is a self-reinforcing endogenous dynamics between a firm positioning in the core of the R&D

network and the generality of its technological knowledge base.

This finding has notable theoretical implications. The complex relationship linking an

organization to its environment has always been a chief topic of investigation in organization

theory (Emery and Trist, 1965; Andrews 1980; Blau & Schoenherr 1971; Burns & Stalker

1961; Grinyer & Yasai-Ardekani 1981; Hofer & Schendel,1978; Lawrence & Lorsch 1969;

Prescott 1986; Pugh et al. 1969; Thompson 1967). While so far the predominant approach has

been to treat the environment as exogenous to the organization, the importance of

investigating the role of endogenous dynamics between an organization and its environment

has been recently acknowledged (McKelvey 1997, Calori et al. 1997, Koza and Lewin 1998).

By examining how the technological knowledge base of an organization co-evolves with the

network of research and development alliances it maintains with other organizations, the

present paper represents one of the very first attempts to systematically study the role and

relevance of one such dynamics.

The present study offers interesting results also from a substantive standpoint. The

question of how inter-organizational networks evolve over time is an important one, and

numerous scholars have begun to analyze it in recent years (e.g., Koza and Lewin 1998,

Powell et al. 2005). We identified and investigated the role of an organizational variable – the

19

generality of a firm’s technological knowledge base, and we showed that it plays a crucial

role in shaping the evolution of one of the most widely studied forms of inter-organizational

networks – the one formed by R&D alliances among firms. Our study also suggests that the

co-evolution of the R&D network and of firms’ technological knowledge base tends to yield

a well-defined core-periphery network structure, and that firms choose their technology

strategy based o their position within that structure. Indeed, the very existence of such core-

periphery structure appears to be a non-trivial finding in its own right, which we are

investigating in a companion paper.

Lastly, this study makes a substantive contribution to the important debate on the

determinants of general purpose technologies. Understanding under which conditions firms

generate general purpose technologies or, conversely, specialized ones, is essential to explain

how economy-wide productivity shifts occur, an issue with huge implications for the material

and welfare conditions of our societies (Breshnan and Trajtenberg 1995; Helpman 1998).

Quite unexplainably, thus far organizational scholars have failed to contribute to this

important debate. This study showed that the extent to which firms are willing and able to

develop general or specialist technological knowledge depends on their position within the

economy-wide network of R&D alliances. We hope this study will provide a useful starting

point for students of organization to engage more actively in the productivity debate.

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FIGURES

Figure 1. A core-periphery structure. (Source: Cattani and Ferriani 2008)

30

TABLES

Table 1. Correlation matrix Mean Std. Dev. Coreness 0.028 0.043 1.0000 Generality 0.35 0.269 0.1130 1.0000 R&D costs 459.64 899.92 0.3977 -0.1161 1.0000 Sales 9660.33 18389.29 0.2905 -0.0962 0.8125 1.0000 Adv Costs 281.93 496.17 0.0846 -0.1755 0.6406 0.7485 1.0000 Total Assets

13127.23 35452.59 0.1732 -0.0839 0.6038 0.7685 0.4767 1.0000

Time 9.60 4.35 -0.1404

-0.6136 0.1020 0.0475 0.1113 0.0774 1.0000

Table 2. 3-Stage Least Squares Regression

Equation 1 Dependent Variable: Coreness Observations: 4941 Endogenous Variable: Generality R-Sq: 0.168 Chi-Sq: 1297.6 Variable Coefficient Z value P value Constant -4.80*** -20.26 0.000 Generality 0.52*** 6.78 0.000 Sales -0.13*** 0.03 0.000 R&D costs 0.61*** 19.97 0.000 Adv. Costs -0.18*** -8.02 0.000 Total assets -0.03 -1.04 0.298 IV (dummy) -1.82*** -16.69 0.000 Time -0.17*** -3.23 0.001 Time-squared 0.01*** 3.64 0.000 Generality 4941 0.126 794.6

Equation 2

Dependent Variable: Generality Observations: 4941 Endogenous Variable: Coreness R-Sq: 0.126 Chi-Sq: 794.6 Constant 0.15 0.64 0.522 Coreness 0.17*** 4.65 0.000 Sales -0.06** -2.51 0.012 R&D costs -0.01 -0.30 0.761 Adv. Costs -0.12*** -7.31 0.000 Total assets 0.07*** 3.71 0.000 IV (dummy) 0.11*** 16.85 0.000 Time -3e-3 -0.10 0.922 Time-squared -9e-4 -0.42 0.675