This paper is explorative in nature. Based on an empirical analysis of two different industrial settings (life sciences, LS;
information and communication technologies, ICT), it investigates network growth and firm growth in networks. We find a
remarkable correspondence between a fewfundamental findings of the ‘old’ stochastic approach to the analysis of firm internal
growth, and empirically observed patterns of firm external growth through collaborative agreements.We show that scale-free
behavior in real-world industrial networks can be accounted for by a general and parsimonious model, originally developed by
Herbert Simon in 1955, based on entry and proportional growth. However, relevant departures from the stochastic benchmark
are revealed that cannot be ascribed to the effect of mergers and acquisitions (M&As) and growth autocorrelation. Moreover,
different regimes of growth are found to be at work in the life sciences for originators versus developers of new business
opportunities, reflecting the fact that growth is driven by specialization and division of labor in the processes of generation
and attraction/development of technological opportunities.
Division of innovative labor and R&D collaborative
contractual relationships are recognized as increasingly
important economic phenomena (see Arrow,
1983; Arora et al., 2001). In particular, networks of
contractual relationships among firms specialized in
research and exploration (originators) and firms focused
on development, production, and commercialization
(developers) are ever-widening organizational
forms, especially in high-tech, knowledge-intensive
fields (see Orsenigo et al., 2001).
In the last 10 years, several studies have shown that
network structure and positions in networks influence firm performance and growth (Powell et al., 1996,
1999) and ultimately, market structure (Mc Lean and
Padgett, 1997; Pammolli and Riccaboni, 2002). Moreover,
most of the literature agrees that networks have
to be analyzed as a distinct organizational solution for
the access to outside knowledge sources, the coordination
of heterogeneous learning processes by agents
endowed by different skills, competencies, access to
innovation, and assets (Pavitt, 2000).
A different stream of literature focuses on dyadic
relationships within and between organizations
and trade-offs defined at that level (Arrow, 1974;
Williamson,1991): particularly the trade-off between
economies from specialization exploited through task
partitioning and the transaction costs involved in
transferring knowledge and technological information
through arm’s length contracts (see Pisano, 1990;
Teece, 1988, 1998; Arora et al., 2001). In general, despite a growing consensus on networks
as a distinct organization form and on their importance
in innovation, learning, and evolution (see Freeman,
1991), economic models of division of (innovative)
labor tend to focus on either standing-alone organizations
or dyadic contractual relationships. Correspondingly,
the literature on firm growth considers firms as
fixed, elementary, and independent unit of analysis to
focus on the dynamic properties of a set of quantities
associated with them (e.g. sales, employees, innovative
outcome, value added). As a consequence, firms’
structure, interactions, collaborations and transformations
can hardly be accommodated in the usual dynamical
system theoretical models.
Against this background, we aim at moving a step
forward in the analysis of processes of growth in networks.
We consider the links between originators and
developers as instances of firm external growth. On the
one side, originators discover new technological opportunities
and establish contractual relationships that
generate income and give access to relevant assets. On
the other side, developers rely on collaborations with
originators to get access to outside knowledge sources
and capture new technological opportunities.
We represent size and growth in terms of firms’
connectivity (k) as measured by the number of collaborations
established, seen as independent business opportunities
of size unity arising over time (see Ijiri and
Simon, 1977). Our empirical investigations on growth
in networks in the life sciences (LS) and information
and communication technologies (ICT) offer a neat
picture, revealing the existence of scaling phenomena,
with the firm connectivity distribution being well described
by a power law of the form N = αK
−γ , where
N is the number of firms with more than K connections.
This result, which is stunningly equivalent to well
known empirical regularities on processes of growth
in several domains of both natural and social sciences
(see Simon, 1955; Albert and Barabási, 2001; Fujita
et al., 1999), suggests that an apparently general phenomenon
ought to be central in any modeling effort
of firms, regions and networks growth.
Along this way, we focus on the mechanisms behind
the dynamic properties of growing networks
and on firm growth in networks, unraveling striking
analogies between processes of internal growth and
processes of external growth through collaborative
agreements.
We do show that the scale-free behavior detected in
networks of innovators can be accounted for by a very
general and simple model, which is rooted in the ‘old’
stochastic approach to the analysis of firm growth.
While growing, networks in LS and ICT selforganize
into scale-free structures shaped by entry of
new firms and by proportional growth of the connectivity
of individual firms, with remarkable departures
from a regime of universal random growth.
In addition, departures from the Pareto distribution
in the life sciences sector cannot be ascribed
to ‘traditional’ explanatory variables (growth autocorrelation,
mergers and acquisitions). In LS, different
regimes of specialization and growth are found to be
at work for originators versus developers, reflecting
differences in the processes of generation versus absorption/
development of technological opportunities.
In particular, the population of originators is characterized
by a regime of proportional growth which
corresponds to a ‘popularity is attractive’ mechanism
(see also Zucker et al., 1998), while for developers
this mechanism is attenuated by an additional random
component.
While our preliminary results cannot be fully explained
given the present status of our knowledge,
they are highly coherent with an interpretation of firm
growth and networking activities which is rooted in a
competence-based view of organizational growth and
division of labor (Penrose, 1995; Richardson, 1972;
Nelson and Winter, 1982; Dosi, 2000).
The empirical findings presented in this paper—as
well as in Orsenigo et al. (2001) and in Pammolli
and Riccaboni (2002)—show that processes of network
growth are sustained by dynamic complementarities
between patterns of specialization in knowledge
production (originators) and processes of diversification
of in-house capabilities by large multi-product,
multi-technological companies (developers, see also
Granstrand et al., 1997; Pavitt, 2000).
Our analysis points to some basic principles behind
the growth of firms in technological networks, providing
a simple benchmark for future investigations. In
addition, one important feature of our work is related
to the fact that, since we are dealing with the dynamics
of a set of links, we can exploit the duality of
the overall system, extracting topological information
which can be used to uncover the underlying causal
data generating mechanisms. That is to say, the study of firm growth in systems of division of labor is important
also because it enables plausible restrictions
on the acceptable classes of conditional predictive distributions
and on the dynamics of the processes which
generated them, contributing to a better understanding
of firm growth in general.
In this paper, we have shown how an extension
of stochastic explanations of internal firm size and
growth fits a whole range of empirical findings. The
scale-free structures that are in place in the two industrial
networks that we have investigated can be considered
as the outcome of a fairly general ‘popularity
is attractive’ principle, which seems to sustain also the
growth of systems of division of labor and of firms
acting in them.
Being very general, mechanisms sustaining external
growth in networks do not seem to differ from the
ones that sustain firm internal growth. This result is
suggestive of the existence of organizational principles
that are general in nature, and map on both the internal
structure of firms and the structure of markets and
networks.
Moreover, we have shown how the dual nature of
networks can convey information on topological properties
of industries and roles/positions of firms within
them (to begin with, the distinction between originators
and developers), which can be used to understand
some fundamental structures, mechanisms, and generative
processes behind the growth of firms and industries,
in the direction of building parsimonious and, at
the same time, realistic, representations.
At present, our analysis has some obvious limitations.
First, apart from information on firms’ age and
on the distinction between originators and developers,
we did not take into account any node-specific
attribute. Second, we have considered links of size
unity, without addressing the properties of weighted
networks and interactions strength. Third, the relational
propensities of different nodes stay unchanged
in our model. Finally, we do not dwell on decay
and exit processes with the exception of mergers and
acquisitions.
These shortcomings notwithstanding, this paper
provides a benchmark in the analysis of firm growth
in networks. Despite its limitations, it gives a parsimonious
and general framework to ‘reverse engineering’
and compare the growth of networks in different industries,
as we attempt to make our models more
realistic.
Some of the current limitations of our analysis could
be overcome, in the future, based on a higher availability
of data on real systems and, in particular, of detailed
topological and economic information on real-world
networks. While at present, such data are relatively
rare, the increasing interest in industrial networks is
leading to the development of suitable data sets, offering
further guidance for modeling and interpreting
the growth of these complex and important economic
systems.
