THE TWO FACES OF COMPLEXITY:
ORGANIZATIONAL COMPLEXITY, INTERNAL INEFFICIENCIES AND INNOVATION
PERFORMANCE
Julian Birkinshaw
London Business School
Maya Cara
University of Sussex
Suzanne Heywood
McKinsey and Company
Earlier versions were presented at AOM conference, JMS conference, Copenhagen Business School.
Thanks to Sendil Ethiraj, Nicolai Foss, Freek Vermeulen for their valuable comments
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Abstract. In a complex organization, parts interact in non-linear ways creating outcomes that are hard
to predict. Many studies have examined the positive consequences that arise from non-linear
interactions between agents in a system, such as innovation and collaboration. However, it is also
possible that such interactions have negative consequences, because the costs of coordination
compound in a way that makes highly-complex organizations hard to manage. In this paper we
consider these two faces of complexity at the same time, arguing that organizational complexity
facilitates emergent order in the form of innovation outputs, and also disorder in the form of internal
inefficiencies, which in turn dampens the organization’s innovation outputs. Using a new database
that combines internal survey findings for 211 large firms with objective measures of their innovation
outputs, we find strong empirical support for our arguments. Our empirical findings are a useful
complement to the largely modelling-oriented literature on organizational complexity that has
emerged over the last fifteen years.
THE TWO FACES OF COMPLEXITY: ORGANIZATIONAL COMPLEXITY, INTERNAL
INEFFICIENCIES AND INNOVATION PERFORMANCE
An influential perspective in the field of organization studies is the notion of an organization as a
complex system, one in which the parts interact in non-linear ways so that changes in one or two
parameters can “drastically change the behaviour of the whole system” (Anderson, 1999: 217). An
important feature of this perspective is the notion that there are limits to the capacity of the “designer”
of the organization to get it right. Even the best designed system, when it has a large number of parts,
has non-predictable interactions that are likely to have second or third order consequences that cannot
be anticipated.
Researchers have therefore sought to model such systems in order to makes sense of their complex
dynamics. Earlier work using cybernetics and general systems theory has now given way to the
notion of the firm as a Complex Adaptive System (Holland, 1992), where order is an emergent
property of individual interactions at a lower level of aggregation. Over the last twenty years, this
perspective has come to dominate the academic literature on organizational complexity (e.g.
Anderson, 1999; Levinthal, 1997; Rivkin and Siggelkow, 2003).
One important characteristic of complex adaptive systems is the notion that they are “sustained by
importing energy” (Anderson, 1999: 219). By bringing in energy from outside, the organizational
system “sucks orderliness from its environment” (Schrödinger, 1944: 74) and this helps it to evolve
into a state of order. However, in the absence of external energy the system will degenerate
(according to the second law of thermodynamics) to a point of maximum disorder, or entropy. For an
organizational system that is neither fully-open nor fully-closed, we would therefore expect some
combination of these two processes to transpire. Such a system would be, as observed by Boisot and
McKelvey (2010: 428), “subject both to the emergence of order and, according to the second law of
thermodynamics, its erosion […] while some structures temporarily stabilize, others rapidly
disintegrate. To understand organizational phenomena is to understand these opposing processes.”
However, a curious feature of the recent academic literature on organizational complexity is that it
focuses almost exclusively on the positive side of this tension, i.e. on how order emerges from a
complex system. Theory papers seek to understand the conditions and mechanisms that give rise to
self-organization (e.g. Anderson, Meyer, Eisenhardt, Carley and Pettigrew, 1999), and modelling
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papers show how simple rules applied to lower-level agents can lead to unpredictable structures and
outputs at the level of the system as a whole (e.g. Rivkin and Siggelkow, 2003). As McKelvey (2001)
has observed, complexity science has become “order-creation science.”
While this body of literature is highly insightful, our concern is that it rests on an assumption of
openness to external sources of energy that may not be entirely accurate. Of course, all organizations
have some openness to outside influence, but many parts are internally-focused bureaucracies (Adler
and Borys, 1996; Crozier, 1969; Weber, 1948), and the larger the organization, the more insular it
typically becomes (Williamson, 1975). A more accurate perspective would be to say that
organizations are partially open and partially closed (Thompson, 1967).
If this is true, we would expect organizations to show evidence of both emergent order and disorder at
the same time. Anecdotal evidence suggests this is indeed what occurs: large firms often exhibit high
levels of collaboration, innovation and learning (manifestations of emergent order), while also
suffering from bureaucracy insularity, duplication of activities, and inefficient processes
(manifestations of disorder). However, this ‘two faced’ view of complexity has not, as yet, been given
a significant amount of attention in the academic literature.
Our purpose here is to address this gap in our understanding of the consequences of organization
complexity. We assume that organizations are partially open to their external environment, so that
some parts of the organization will tend towards emergent patterns of order while other parts tend
towards disorder. This conceptualization then allows us to develop testable propositions linking the
level of organizational complexity to both positive and negative outcomes for the organization as a
whole.
We provide an exploratory test of our theoretical ideas using a novel body of data on 211 firms which
combines primary questionnaire-based data (to measure aspects of the internal organization) and
secondary data (to measure outcome variables). Consistent with recent literature, organizational
complexity is defined in terms of the variety of elements in the system and the interdependencies
between these elements (Levinthal, 1997, Simon, 1962). We argue that organizational complexity
enables the emergence of positive outcomes in the form of innovation, while at the same time it
creates the risk of negative outcomes in the form of internal inefficiencies. We further suggest that
internal inefficiencies have a negative influence on innovation, thereby dampening the positive
relationship between complexity and innovation. Our findings are consistent with these arguments. A
key insight from the empirical analysis is that very high levels of organizational complexity appear to
be required for both the positive outcomes (innovation) and negative outcomes (internal
inefficiencies) to be observed.
Our research makes three contributions. First, we make the important conceptual point that a
partially-open organizational system is likely to exhibit both emergent order and disorder at the same
time. While this point is well established in complexity science, it has been neglected in the field of
organization research which has instead adopted a mostly “pro-order” perspective. This perspective
allows us to develop a clearer understanding of the nature and origins of internal inefficiencies in
firms. Second, we conduct an empirical analysis using internal firm-level data of a type that previous
studies have not had access to. This complements the recent mostly modelling-based literature, and it
provides some important nuances on the consequences of organizational complexity. Finally, our
research has important managerial implications as well. There has been considerable discussion in the
popular business press about the consequences of complexity in large firms, notably a concern that
such firms are too large to fail but too complex to manage. Our findings show that organizational
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complexity has a net positive relationship with innovation, but it also points to significant differences
between firms in the extent to which they are able to manage the internal inefficiencies that often
accompany high levels of complexity.
BACKGROUND
Organizational complexity has been a phenomenon of interest to management scholars for many
years. It is possible to identify two distinct bodies of research, one focused primarily on the
relationship between structural features of organizations (such as size and formalization) and
organization-level outcomes such as innovation and performance, the other built on insights from
complexity science to show how order emerges from the interaction of the many constituent parts of
an organization.
The original studies of organizational complexity were conducted in the 1970s and 1980s, and they
operationalized complexity in terms of the various dimensions of differentiation (spatial,
occupational, hierarchical, and functional) as well as firm size (Miller and Contay, 1980; Beyer and
Trice, 1979; Hall, 1977; Blau, 1970). These studies were built on the open systems view of
organizations, and their overarching logic was that organizations should be designed to match the
complexity of the environment in which they were operating (Galbraith, 1982; Thompson, 1967).
However, the results of these studies were not entirely conclusive. A meta-analytic review by
Damanpour (1996) revealed many contingency factors affecting the (weak) positive impact of
structural complexity and size on innovation, while a recent study by Larsen, Manning and Pedersen
(2014) summarized the evidence for the relationship between complexity and firm performance as
“ambivalent.”
The second wave of organization-level research began in the mid-1990s. Building on ideas adapted
from the natural sciences, this body of research viewed organizations as complex adaptive systems
(Holland, 1992). Complexity, according to this view, was a function of both the diversity of parts
making up the system and also the nature of interdependencies between those parts (Axelrod and
Cohen, 2000; Levinthal, 1997; Simon, 1962). In such a system, the parts end up interacting in nonlinear ways, so that changes in one or two parameters can have dramatic and unpredictable
consequences for the behaviour of the system as a whole.
This new perspective was a significant improvement over the traditional way of looking at
complexity, because it explicitly accounted for the interdependencies between people and activities in
organizations, and it helped to explain why processes of change in organizations often have somewhat
unpredictable consequences. Some studies applied this perspective to the practical challenges of
managing complex organizations (e.g. Brown and Eisenhardt, 1998; Browning et al, 1995). The vast
majority of studies adopted a modelling approach to their analysis of organizational complexity.
Building on techniques taken from complexity science (Kaufmann, 1993), they used agent-based
modelling techniques to explore the consequences of simple behavioural rules at the individuals level
for organization-level outcomes (Rivkin and Siggelkow, 2003; Levinthal, 1997). For example,
studies examined the role of tight coupling within organizations on their capacity to engage in
exploration and exploitation (Rivkin, 2000; Levinthal and Warglien, 1999), and the consequences of
modularity in organization design for firm adaptation (Ethiraj and Levinthal, 2004).
While our understanding of the causes and consequences of organizational complexity have improved
significantly as a result of these studies, some significant gaps remain. First, there are limits to how
much progress can be made through modelling. While agent-based models have produced many
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useful insights into the way complex organizations work, their insights should be viewed as
hypotheses that require real-world verification, rather than as definitive findings. With a few
exceptions (e.g. Ethiraj, 2007; Zhou, 2013), there have been remarkably few studies based on primary
data seeking to test the arguments developed in this line of work. This is particularly surprising given
that the previous body of research developed during the 1980s was almost entirely empirical in nature.
It would be useful to revisit these older studies in the light of recent theoretical advances to see if
some of the inconclusive findings from prior research (Damanpour, 1996) can be resolved.
Second, an interesting feature of the recent literature is what one might call its “pro-order” bias. One
of the hallmarks of the complex adaptive system perspective is the notion that order is an emergent
property of individual interactions at a lower level of aggregation. This principle has shaped the
modelling techniques used in this area (e.g. Levinthal, 1997) and it has influenced the way complexity
is tackled in practice (e.g. Brown and Eisenhardt, 1998). However, it is open to investigation whether
order necessarily emerges from complexity in a social system. Complexity researchers are aware of
the second law of thermodynamics, which states that in the absence of external energy, a system will
tend towards a state of maximum disorder (entropy). By analogy, this suggests that unless an
organization has some degree of openness to its external environment, it will tend towards internal
disorder, not order.
This point is recognized in the organizational complexity literature. In an overview paper, Anderson
(1999: 223) noted, “self-organization does not occur absent a continual flow of energy into a system.
Yet studies of how managers energize organizations have been divorced from inquiries into how
pattern and structure emerge and evolve.” Much earlier, Pondy and Mitroff (1979: 13) made a similar
argument: “If an open system insulates itself from environmental diversity… eventually its own
internal structure will deteriorate to the point that open system properties can no longer be
maintained.” Thus, whether based on recent advances in complexity science, or on the traditional
open system view (Thompson, 1967), the notion that systems run the risk of deteriorating when closed
off to external inputs is well-established. Strangely, though, there are few studies (either empirical or
model-based) that have sought to understand the potentially deleterious consequences of complexity.
These limitations motivate our current study. Our overall purpose is to shed light on the benefits and
costs of organizational complexity. We frame this study in terms of complexity theory, more
specifically in terms of how complex organizations can generate order and disorder at the same time.
And in our empirical analysis we focus on one particularly important manifestation of emergent order,
namely innovation. Thus, our study addresses a well-known practical issue, whether complexity is
good or bad for innovation, though our hope is that it can also potentially be generalized to a
conversation about the broader consequences of complexity for organizations.
THEORY AND HYPOTHESES
We follow current convention in defining organizational complexity in terms of the number of
elements in the system and the interdependencies between those elements (Levinthal, 1997; Simon,
1962). We view the level of organizational complexity in a firm as largely a design choice. In other
words, those at the top of the firm make choices about such things as the number of product units, the
number of reporting lines, and the use of integrating mechanisms to enable cross-unit coordination, to
help deliver on their objectives (Galbraith, 1995; Tushman and Nadler, 1978; Lawrence and Lorsch,
1967). On the basis that structure follows strategy (Chandler, 1962), these can be viewed as strategic
choices made by top executives to deliberately create a structure that matches the complexity of its
task environment.
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However, complexity theory makes it clear that the capacity of top executives to make the right
design choices is limited. Even the best designed system, when it has a large number of parts, has
non-predictable interactions that are likely to have second or third order consequences that cannot be
anticipated. The nonlinear nature of the system, in other words, creates uncertainty in terms of
emergent patterns of behaviour.
Using the perspective of complex adaptive systems, we would expect to see a pattern of emergent
order following from the initial design choices made by the firm’s top executives. Thus, in our
chosen setting, the top executives in the firm might deliberately provide exposure to multiple stimuli,
and create opportunities for interaction between disparate parts of the firm. This would be expected to
help employees be creative and collaborative, and thereby innovate more effectively (Kogut and
Zander, 1992; Schumpeter, 1934).
However, it is also possible for complexity to have deleterious consequences. In complexity theory
terms, a system without external sources of energy would gradually tend towards maximum disorder
(entropy). In our chosen setting, there are likely to be coordination costs that arise through
complexity, and these potentially interact in non-linear ways to create internal inefficiencies such as
unclear accountabilities, misaligned systems and overlapping responsibilities (cf. Leibenstein, 1969;
Williamson, 1975; Zenger, 1994). Our expectation is that these internal inefficiencies will be greater
the more complex the firm is and will, in turn, will have a negative influence on the overall level of
innovation in the firm because they reduce the capacity and motivation of employees to engage in
innovation activities.
Our core argument, in other words, is that organizational complexity has two faces: its consequences
are both positive and negative. There is an underlying process through which employees are steered
towards desirable patterns of order, which in our setting means innovation outputs. There is also an
underlying process through which coordination costs compound, leading gradually towards disorder,
which manifests itself in the form of internal inefficiencies. These internal inefficiencies reduce the
positive impact of organizational complexity on innovation. Figure 1 provides a graphical summary of
these arguments, and in the paragraphs that follow we develop them in greater detail.
--------------------------------------Insert Figure 1 about here
---------------------------------------The positive consequences of organizational complexity
As noted earlier, organizational complexity is the product of the variety of elements in the system
(e.g. number of lines of business or countries) and the interdependencies between those elements (e.g.
multiple reporting lines or cross-cutting processes). While we might expect variety and
interdependencies to have direct relationships with innovation (and we test for this possibility in our
empirical analysis), our central argument is about how the two interact to enable firm-level
innovation.
Considering the variety of elements first, a well-established line of argument in the innovation
literature is that innovation occurs through the combination of existing and new knowledge
(Schumpeter, 1934). An important attribute for firms seeking to enhance their innovative capabilities
is therefore to increase their access to diverse stimuli, for example through regional networks
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(Almeida and Kogut, 1999; Saxenian, 1990;), academic and government labs (Cohen et al., 2002),
linkages with partner firms (Ahuja and Katila, 2001; Powell et al., 1996; Gulati, 1995), and
relationships with suppliers and customers (Dyer, 1994; Day, 1990; von Hippel, 1988). The
knowledge these relationships give access to is then used by the firm, in combination with its existing
knowledge, to create new outputs. In addition, gaining access to a larger knowledge base may also
enhance a firm’s absorptive capacity, which in turn makes it easier for future opportunities to be
recognized and incorporated (Cohen and Levinthal, 1990).
The existence of variety, in terms of access to new sources of knowledge, is necessary for innovation
to transpire but it is unlikely to be sufficient. New outputs typically involve recombining existing
elements of knowledge into new combinations (Fleming, 2001; Kogut and Zander, 1992; Tushman
and Rosenkopf, 1992; Henderson and Clark, 1990; Schumpeter, 1934), and for these new
combinations to transpire, it is necessary for the firm to develop mechanisms and processes that
enable the disparate parts of the firm to collaborate together (Galbraith, 1995). These
interdependencies manifest themselves as an array of formal and informal linkages between people in
a firm, and to the extent that these are recognized by employees they will enable the necessary levels
of collaboration that make innovation possible (Hansen, 2009; Szulanski, 1995). For instance,
common interfaces such as integration teams in case of mergers, meetings within and between the
R&D units of different business units, and extensive face-to-face communication with customers and
suppliers all enable decision makers to learn about each other’s technology and processes (Gerpott,
1995).
Taken together, these arguments suggest that the level of innovation in a firm is likely to be enhanced
when the variety of elements and the interdependencies between them are both high. In other words,
it is the interaction between these two separate dimensions that is most productive with regard to
innovation. Thus:
Hypothesis 1: The higher the overall level of organizational complexity (in terms of the
variety of elements in the organization and the interdependencies between elements), the
higher the level of innovation in the firm.
The negative consequences of complexity
It is well established that there are costs associated with internal coordination (e.g. Baldwin, 2000;
Larsen et al, 2014; Rawley, 2010; Zhou, 2013). These coordination costs can be thought of as a
subset of the transaction costs (Coase, 1937; Williamson, 1975) incurred in economic exchange
between independent firms: while internal coordination does not require negotiations over price or the
drawing up of legal documents (as these transactional elements have been internalized), it still
requires mutual adjustment between parties, alignment of efforts and monitoring of outputs, all of
which require time and effort for the individuals involved.
How would we expect these coordination costs to manifest themselves in a complex organization? A
defining feature of a complex system is that the parts interact in non-linear ways, resulting in
unpredictable consequences. While this argument is usually applied in a positive way (e.g.
serendipitous connections between individuals enabling innovation), we suggest it can also be applied
in a negative way. In other words, we expect the costs of coordination to rise significantly with the
level of complexity of the system, with potentially deleterious second- or third-order consequences for
how the organization functions. At an intuitive level, this logic can be readily understood. Imagine,
for example, a global firm that has just put in place a new enterprise resource planning (ERP) system,
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and is now seeking to integrate a large multi-country acquisition. Should executives prioritise working
through the implementation of the ERP system, or integrating the acquisition, or both at the same
time? Whichever approach is taken, there are likely to be complex and not entirely foreseeable
challenges in getting all the different parts of the global firm to work smoothly together.
To formalize this discussion, we introduce the concept of internal inefficiencies. Internal
inefficiencies are defined here as dysfunctional aspects of the structures and systems of the
organization that make it challenging for individuals to do their work effectively. Internal
inefficiencies are commonplace in organizations, as shown for example in Bloom and van Reenen’s
(2007) analysis of the differences in qualities of management practices among seemingly-similar
enterprises, and Leibenstein’s (1966) analysis of X-inefficiencies. However, explanations for the
conditions under which they emerge have not been well understood up to now (Syverson, 2011).
We argue that internal inefficiencies arise in firms as a function of the overall level of organizational
complexity, that is, the combination of the variety of elements and the interdependencies between
those elements. As these two variables increase, so do the coordination costs associated with each
one, and these coordination costs then interact and compound in ways that managers struggle to
anticipate or respond to. This explains how internal inefficiencies arise in the first place. It might be
anticipated that these inefficiencies would be gradually resolved, because it is indeed the job of
executives to make their organizations work effectively. However, many organizations suffer from
what Williamson (1975: 127) calls bureaucratic insularity, whereby managers become relatively
closed off to the external environment (recall our earlier arguments about organizations being only
partially-open to sources of energy from the outside). Such organizations become less accountable for
their actions and more concerned about protecting their own interests (Blau and Meyer, 1987; Crozier,
1969). The number and scope of internal procedures also grows, often resulting in ambiguity over
who is responsible for what (Williamson, 1975). Bureaucratic insularity is further compounded by the
bounded rationality of managers (Cyert and March, 1963). Because managers have limited capacity to
attend to the full complexities of the organizations in which they work (Ocasio, 1997; 2012), they are
likely to prioritize issues that are salient and proximate, which typically means the first-order
consequences of problems rather than their second or third order consequences
In sum, we expect that a combination of interacting coordination costs and bureaucratic insularity will
result in internal inefficiencies. Coordination costs cause internal inefficiencies to arise in the first
place, and bureaucratic insularity hampers the ability of executives to resolve those inefficiencies.
Stated more formally:
Hypothesis 2: The higher the overall level of organizational complexity (in terms of the
variety of elements and the interdependencies between them), the greater the internal
inefficiencies in the organization.
What are the consequences of internal inefficiencies for firm innovation? Building on the earlier
arguments, we expect the relationship between these two variables to be negative for two reasons.
First, internal inefficiencies reduce the effectiveness of coordination across the organization.
Managers are likely to be more cautious about whom they share information with, and the ambiguity
over who is responsible for what means that information does not always flow to the appropriate
places. As a result, individuals will feel more constrained in their work, and more prone to stay
within their narrowly-defined silos of responsibility (Adler and Borys, 1996). This will limit the
amount of collaboration between individuals, thereby reducing the likelihood of innovation.
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Second, internal inefficiencies will also have a negative effect on individual motivation. A wellestablished argument in the literature is that bureaucratic insularity (Williamson, 1975) leads to
alienation and disengagement of employees, as they become detached from the outputs of their labour
(Marx, 1927; Durkheim, 1897). A number of studies have provided empirical and theoretical support
for this argument (Balain and Sparrow, 2009; Damanpour, 1996; Thompson, 1965). However, the
nature of innovation is that it requires individuals to be creative and to pursue projects with uncertain
returns, and these types of activities are only likely to transpire when employees are intrinsically
motivated and highly engaged in their work. We therefore expect that internal inefficiencies will limit
both the capacity and motivation of employees to engage in innovative activities, thereby reducing the
overall level of innovative outputs. Thus:
Hypothesis 3: The higher the level of internal inefficiencies in the firm, the lower the
level of innovation in the firm.
Taken together, the three hypotheses suggest that the overall effect of organizational complexity on
firm innovation is hard to predict, in that there are both positive and negative mechanisms at work at
the same time. We do not take a strong position on this issue, primarily because our theoretical
framing around the “two faces” of complexity recognizes that the net effect could end positive or
negative. We therefore view the overall consequences for firm innovation as an empirical question.
We also do not develop a mediation argument here. It would be conceivable that organizational
complexity influences innovation outputs through (the presence or absence of) internal inefficiencies,
but there is nothing in the theoretical arguments developed above to make a strong claim in this
regard. Nonetheless, we remain open to this possibility, and we conduct a careful post hoc analysis to
investigate the possibility of mediation.
METHODOLOGY
To test our arguments linking organizational complexity, internal inefficiencies and firm innovation,
we needed to build a body of data that provided systematic insight into the nature of internal
complexity, which by its nature is not available from public sources. We therefore employed a multistage process to develop and administer a survey instrument to executives in a sample of large
international firms. The findings from this survey were then used, alongside objective firm-level data,
to test our hypotheses.
Sample
We worked with an international advisory company to develop a sample of large firms operating in
multiple industries and multiple countries. Our selection criteria were: (a) activities in more than one
country and/or industry, to ensure their operating environment was at least moderately complex, (b)
industries with significant global competitors, to rule out highly-protected and/or locally-focused
industries, (c) at least 1000 employees, to exclude small and medium sized enterprises that often have
different managerial and organizational characteristics to large firms, and (d) a stock market listing, to
give us access to public-source data.
We sent our survey to 998 firms, and after excluding responses with missing data, and those where the
respondent was more than three levels from the top of the company, we ended up with 211 usable
responses, a response rate of 21%. This compares favourably with typical response rates for
international business surveys (Harzing, 1997). Table 1 provides descriptive data for the sample. We
analysed the differences between respondents and non-respondents on measures of size, industry,
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country of origin, and performance, and no significant differences were uncovered. The survey was
completed in 2006.
----------Table 1---------To generate responses across a meaningful sample of companies, we elected to use key informants
whose answers were deemed to be representative of the firm they were working for (Seidler, 1974).
This approach is widely used in management research and has been found to be reliable (Crampton
and Wagner, 1994). To verify the reliability of this approach, we obtained survey responses from a
second senior executive in the same firm for a third of the sample. The inter-rater reliability across the
pairs of executives within the same firm was r=0.81, well above the suggested cut-off point of r=0.70
(Cohen et al, 2002). Note that in the actual analysis, we used the more senior executive as the key
informant (rather than the average of the ratings from the two executives) because this allowed us to
control for certain individual-level characteristics in our regression models.
The survey focused primarily on the measurement of organizational complexity and internal
inefficiencies (as described below), alongside various other firm-specific and individual-specific
factors that were used as control variables. To mitigate concerns about common-method bias, the
dependent variable, firm innovation outputs, was measured using secondary sources.
Measurement Approach
One of the key challenges in this study was the lack of established survey measures for organizational
complexity and internal inefficiencies. We therefore used a grounded, three-step approach to define
and operationalize these two constructs. First, we conducted interviews with 12 senior executives
working in large firms. For organizational complexity, we asked them about (a) the dimensions of
diversity and variety in the firm, and (b) the various ways they gained coordination across these
dimensions (Levinthal, 1997). For internal inefficiencies, our guiding question (in line with the
definition earlier) was to ask them about the aspects of their internal organization that made it
challenging for them to do their jobs effectively. By asking the questions in a relatively open-ended
way, we were able to group their answers into various categories and to develop an inductive
operationalization of our constructs.
Second, we assembled a group of six experts, a mix of academics, practitioners and consultants, to
review the provisional scales. This process allowed us to refine our chosen measures, and it helped us
to link our inductively-generated constructs back to the academic literature. Third, we pilot-tested our
provisional list of measures with a further 12 executives, allowing us to fine-tune the wording and to
drop those items that did not work.
This process yielded the three main independent variables in our model (variety of elements,
interdependencies between elements, internal inefficiencies; specific wording of questionnaire items
below). A confirmatory factor analysis confirmed a three-factor model that fitted the data reasonably
well: Comparative fit index (CFI) = 0.82, root mean squared error of approximation (RMSEA) =
0.058. All item loadings were as proposed and significant (p < 0.01). The items composing the
constructs and the fit indices of CFA are available from the authors on request.
To measure firm innovation, we followed the well-established practice of using the number of
published patents as an objective indicator of innovative activity (e.g. Scherer, 1984). Patents have
long been recognized as a very rich and potentially fruitful source of data for the study of innovation,
as (1) they are directly related to inventiveness, and are granted only for ‘non obvious’ improvements
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or solutions with discernible utility; (2) they represent an externally validated measure of
technological novelty (Griliches, 1990); (3) they confer property rights upon the assignee and
therefore have economic significance (Scherer and Ross, 1990; Kamien and Schwartz, 1982). Patents
also correlate well with other measures of innovative output, such as new products (Comanor and
Scherer, 1969), innovation and invention counts (Achilladelis et al., 1987), and sales growth (Scherer,
1984). Patents also have their limitations as measures, as some inventions are not patentable, others
are not patented, and the inventions that are patented differ greatly in economic value (Griliches,
1990; Trajtenberg, 1990; Cohen and Levin, 1989). Nonetheless, on balance we decided that patentbased measures of firm-level innovation were superior to other options available for the current study.
Until recently, the NBER database on U.S. patents has been the most reliable source of patents used
by researchers, with detailed information on almost 3 million U.S. patents granted between 1963 and
2006. However, our sample included a large percentage of non-U.S. companies, so the NBER
database was not appropriate. We therefore developed a dataset for the 211 firms in our sample using
the Thomson Innovation database. To our knowledge, this database covers the largest number of
patenting jurisdictions in addition to using the most up-to-date corporate trees. Furthermore, to
account for the fact that firms publish patents under the names of multiple subsidiaries (e.g. Schneider
Electric patents under the names of 11 subsidiaries) and change their names over time (e.g. ‘Motorola
Inc.’ changed its name to ‘Motorola Solutions’ in 2010) we used the corporate tree provided by
Thomson Innovation while complimenting it with the information on restructuring from firms’ 10k
reports in the period from 2006 to 2010.
Measures
Variety of elements is a measure of the scope of activities the firm is involved with. Respondents were
asked the following questions: (1) How many different direct customers you have across all
operations and business units? (2) How many products and services do you supply? (3) How many
different suppliers do you have? (4) How many countries do you operate in? (5) How many industries
do you conduct business in? (6) How many ways of making money – business models – there are in
your organization? (7) How many M&A has the company made in the last 15 years? (8) How many
joint ventures and alliances has the company made in the last 15 years? To ensure that these eight
items were weighted approximately equally, each question had a range of possible answers arranged
on a five-point scale.
Variety of elements is a formative construct, that is, it derives its meaning from the combined
influence of all its constituent items (Bollen, 1989; Mackenzie, Podsakoff and Jarvis, 2005). There is
no reason, for example, to expect a firm with many direct customers to also make a large number of
acquisitions, yet they both increase the overall level of variety. For this reason, it is not appropriate to
calculate a reliability measure, such as Cronbach’s Alpha, in the way one would if dealing with a
reflective construct. Instead, we report on whether the measures are consistent with our theoretical
understanding of these constructs using a Confirmatory Factor Analysis (see earlier discussion).
Interdependencies is the extent to which disparate parts of the firm are linked together to enable
collaboration and effective decision making. Respondents were asked the following questions: (1) To
what degree decisions require input from multiple business units within the company? (2) To what
extent does your organization use matrix structures, which force employees to respond simultaneously
to multiple, potentially conflicting, decision premises? (3) To what extent do senior managers in your
company have multiple reporting lines? (4) To what extent does your company have multiple
dimensions of equal importance at the top management level? (1-disagree completely, 5=agree
11
completely). As above, we conceptualize interdependencies as a formative construct, in that different
firms are likely to use different mechanisms for building interdependencies (Galbraith, 1973), so we
would not expect uniformly high or low scores on these questions.
Organizational complexity is calculated as the product of variety of elements and interdependencies.
Internal Inefficiencies are the dysfunctional aspects of the structures and systems of the organization
that make it challenging for individuals to do their work effectively. Respondents were asked to rate
the following statements: (1) Accountabilities are often shared in the company, so it is frequently
unclear who is responsible for what; (2) There is significant duplication of activities across the
organizations; (3) Target objectives are poorly defined; (4) Financial rewards are not clearly tied to
targets; (5) Management processes are inefficient, unclear and require more information; (6)
Operating processes are inefficient, unclear and require more information; (7) The company is not
very integrated; systems and processes are not interlinked, use different data and run on different
timetables; and (8) The IT systems are ineffective; they are overly complex and do not keep pace with
company development (1-disagree completely, 5=agree completely).
Firm innovation output (the dependent variable) was measured through the patenting frequency of
firms, that is, the number of successful patent applications by a firm in a given year. We measured
innovation output as a lagged variable, i.e. the number of patents applied for in 2007, i.e. one year
after the survey was completed. We also measured the patent applications two, three and four years
after the survey, and the results were broadly consistent across all four periods.
To be more specific, we measured the number of successful patent applications, or granted patents.
The granted patent carries the date of the original application. We use this date to assign a granted
patent to the particular year when it was originally applied for. Some previous researchers claim that
the actual timing of the patented inventions is closer to the application date than to the (subsequent)
grant date (Hall et al. 2001). This is so because inventors have a strong incentive to apply for a patent
as soon as possible following the completion of the innovation, whereas the grant date depends upon
the review process at the Patent Office. Therefore, our procedure permits consistency in the treatment
of all patents and controls for differences in delays that may occur in granting patents after the
application is filed (Trajtenberg, 1990).
Control Variables. We included a number of measures commonly used in the analysis of firm-level
innovation as controls, including annual firm research expenditures in millions of dollars (R&D) and
firm size measured as total number of employees (Size).We would expect that larger in-house
research expenditures would lead to greater patenting output (Henderson and Cockburn, 1996). In
terms of size, most studies have reported a positive effect of size on innovation (Chaney and
Devinney, 1992; Cohen and Levinthal, 1990), while others have shown a negative effect (Mansfield,
1968), or no effect at all (Clark, Chew, & Fujimoto, 1987). We also included the age of the firm (Age)
which is calculated using the founding date available in the Capital IQ database. Similarly to size,
prior research on the effects of age on innovation have been mixed (Sorensen and Stuart, 2000). In
addition to the above, we included environmental variables such as munificence, instability and
regulation that could influence firm innovation and sector dummy variable to account for industry
effects. We also incorporated various individual controls to account for the characteristics of the
respondents which may have influenced how they perceived the complexity of their organizations.
These are the hierarchical level of the respondent (number of layers below CEO), the tenure of the
respondent inside the organization, and function dummy variable to account for differences in the
12
work done by respondents. Lastly, we included the number of industries the firms operated in and
their overall level of international diversification.
Model Specification
To test our hypotheses we used OLS regressions with and without Poisson estimation, because in
most of the models the dependent variable (innovation output) is a count variable taking on discrete
nonnegative integer values, including zero. We applied the following specification of a Poisson
regression model:
Log(Patentsi)= β0+βiXi+ei
where Patentsi is the expected number of patents assigned to firm i, and Xi is a vector of regressors
containing the independent and control variables described above. In our Poisson regression, we also
opted to obtain robust standard errors for the parameter estimates as recommended by Cameron and
Trivedi (2009) to control for mild violation of the distribution assumption that the variance equals the
mean. Furthermore, to allow for a meaningful comparison of the variables measured along different
scales, we standardized some of the control variables before entering them into the regression models.
RESULTS
Table 2 presents the summary statistics and the pair-wise correlation matrix for our measures. Since
the sample includes firms from multiple industries, it is not surprising that there is considerable
variance on all the key variables such as Patents, R&D, Size, and organizational complexity. We see
that the average issued number of patents for the firms in our sample is 386 for 2007. The variables
reflecting the hypothesized effects are not very highly correlated among themselves or with the
control variables.
----------Insert Table 2 about here---------Tables 3 and 4 provide results for our main models using Poisson and OLS regression estimators
(reported with empirical standard errors). The variables reflecting the hypothesized effects were
entered into the regression individually and log-likelihoods are reported where appropriate. In table 3,
innovation output (the number of patents applied for in 2007 and published) is the dependent variable.
In table 4, internal inefficiencies is the dependent variable. Models 1 and 6, in Table 3 and 4
respectively, present the base models in which environmental munificence, environmental instability,
environmental regulation, R&D expenditure, size, age, number of industries, international
diversification, tenure, hierarchical level, sector and respondent function dummies were included as
control variables. In models 2-4 and 7-9, we introduced variety, interdependencies and organizational
complexity to assess those variables' effects on innovation and internal inefficiencies.
----------Insert Tables 3 & 4 about here---------Hypothesis 1 argued for a positive relationship between organizational complexity and innovation
output. This hypothesis is supported: in fact, the individual coefficients for variety of elements and
interdependencies and the interaction between the two (i.e. organizational complexity) are all
significant in model 4. To shed further light on the nature of this relationship, we created a plot
(Figure 2), in which variety of elements and interdependencies are dichotomized as ‘high’ and ‘low’
at one standard deviation above and below the mean. The plot suggests that if interdependencies are
high, then variety of elements has a positive relationship with innovation, but if interdependencies are
low then variety has a slightly negative effect on innovation. The implication is that a threshold level
13
of interdependencies between the variety of elements needs to exist for the positive effect on
innovation to be obtained. Or stated the other way round, a high level of organizational variety only
becomes useful when there are mechanisms in place, within the firm, to bring those elements together.
----------Insert Figure 2 about here---------Hypothesis 2 argued for a positive relationship between organizational complexity and internal
inefficiencies. This hypothesis is supported: the coefficient for organizational complexity is positive
and significant in model 9, though in this case neither the variety of elements nor the
interdependencies coefficients are individually significant.
Hypothesis 3 argued for a negative relationship between internal inefficiencies and innovation output.
This hypothesis is also supported: in model 5 in Table 3, the coefficient is negative and significant.
The effects of the control variables were mostly in line with our expectations and here we discuss only
a few of them. The strong result that older firms patent more is not surprising given that previous
streams of research found that organizational competence may improve over time (March, 1991;
Hannan and Freeman, 1984; Stinchcombe, 1965). Thus, if the passage of time leads to an
accumulation of foundational knowledge, organizational competence will increase with age.
Similarly, we confirm that environmental munificence has a positive influence on firm innovation.
We conducted two post hoc analyses to investigate possible findings that were not directly
hypothesized. First, we investigated a potential curvilinear effect between interdependencies and
innovation. We started by adding a squared term for interdependencies in model 10 (Table 4) and
found that it was significant and positive. We then added the same tern in model 11 (Table 5) and
found that it is significant and negative, suggesting a curvilinear (inverted-U) relationship. However,
when we added internal inefficiencies into the same regression equation (model 12), the squared term
for interdependencies became insignificant. This suggests that the curvilinear relationship between
interdependencies and innovation in model 11 could be spurious, a point we return to in the discussion
section.
----------Insert Table 5 about here----------
Our second post hoc analysis was to explore the possibility that internal inefficiencies partially or
fully mediate the relationship between organizational complexity and innovation. We followed the
three-step approach recommended in the literature (Baron and Kenny, 1986; Kenny, Kashy, & Bolger,
1998; Mackinnon & Dwyer, 1993).
First, it is necessary to establish that the independent variable (organizational complexity) influences
the potential mediator (internal inefficiencies). This is Hypothesis 2 in our theoretical framework, and
it is supported in model 9 of Table 4. The second step is to demonstrate that the independent variable
(organizational complexity) influences the dependent variable (innovation). This is Hypothesis 1 in
our theoretical framework, and it is supported in model 4. Lastly, one must demonstrate that the
mediator (internal inefficiencies) influences the dependent variable (innovation), with the independent
variable (organizational complexity) controlled for. If, in this final step, the effect of organizational
complexity on innovation is still significant when the mediator is in the model, partial mediation is
indicated (Baron & Kenny, 1986). As shown in model 5, the coefficient for internal inefficiencies was
negative and significant, indicating support for Hypothesis 3. Further, with internal inefficiencies in
14
the equation, the coefficient for organizational complexity was still significant and almost 10% bigger
(β= .305, p < .03), suggesting that internal inefficiencies may partially mediate the relationship
between organizational complexity and innovation.
We tested the statistical significance of the mediated effect by applying the Sobel test (Sobel, 1982;
1986), thus obtaining a z-score with marginal significance (z = -1.71, p = .087, one-tailed). We also
conducted the extended Sobel test with the bootstrapping meditation approach (Preacher and Hayes,
2004; Zhao, Lynch and Chen, 2010). The extended Sobel test model is: z-score = ab / sqrt(a2sb2 +
b2sa2 + sa2sb2) , where a and sa are coefficients and standard errors (from the bootstrapping) for the
impact of independent variables on mediators, while b and sb are coefficients and standard errors for
the impact of mediators on the dependent variable.We find that the results from Sobel test with the
bootstrapping meditation are barely significant (z score = -1.64, p = 0.1). In sum, we conclude that
we do not have evidence, in our study, for internal inefficiencies mediating the organization
complexity – innovation relationship.
Robustness tests
We ran several sensitivity tests to check the robustness of the results. One possible concern with our
analysis was that the one-year lag between 2006 (when the questionnaire data was collected) and 2007
(for the innovation output dependent variable) was not appropriate. We therefore collected patent
filing data also for 2006, 2008, 2009 and 2010. For the 2006 data, the results were entirely consistent
with what is reported in tables 3-5; for the years 2008-2010 the results were similar but not identical.
Specifically, using the 2008 and 2009 data provided support for Hypotheses 1 and 3, and the 2010
data provided support for Hypotheses 1. Hypothesis 2 was not tested since its dependent variable is
internal inefficiencies.
We tested the strength of the results by relaxing the assumption that our data fits Poisson’s statistical
distribution. To accomplish this, we estimated OLS and negative binomial models of our main models
4 and 5. The OLS and negative binomial results again exhibited very similar pattern as the original
results indicating that the results of our hypotheses testing are robust. These last results are included in
model 13.
Because the correlations between the three control variables, R&D, Size and Age were somewhat
high and significant, two preventive measures were taken to alleviate for the potential effects of
multicollinearity. First, we further explored our results by excluding some of the control variables
given the high correlations among them, following Ahuja and Katila, 2002. Our results consistently
supported the main findings of the study (findings available from the authors on request). Second,
variance inflation factors were calculated for all the regression models with sales’ growth and value
creation dependent variables and were found to be less than 5, a threshold value that could be
considered an indication of sufficient influence of multicollinearity (Neter et al. 1985). For the final
model (model 8), variance inflation factor was 2.1. From these two tests, therefore, it was concluded
that the degree of multicollinearity was not high enough to cause serious concern over the estimates of
the regression coefficients.
We addressed the threat of unobserved heterogeneity in our findings using two different instrumental
variables, product sale dispersion and competitor size. A risk with our chosen design was that an
unobservable factor was causing some firms to deliberately select a path of greater complexity than
others as a means of enhancing their potential for innovation. An instrumental variable approach helps
to mitigate this risk. Product sale dispersion is an indicator of the firm’s underlying decision to
15
operate in a wide or narrow breadth of markets. Competitor size is a proxy for the overall complexity
of other firms in the same industry, on the basis that executives may opt for greater complexity due to
isomorphic pressures, rather than to enhance their innovative outputs. These measures of product sale
dispersion and competitor size (collected from secondary sources) correlated with the endogenous
decision (i.e. the variety of elements and the interdependencies between elements) but not with the
outcome variable of interest (innovation). In models 14 and 15 we report the instrumental variables
(IV) Poisson models in Stata using program code from Nichols (2007). Standard errors are computed
by nonparametric bootstrap with 400 replications. The results of both instrumental two-step
approaches confirmed our main findings.
DISCUSSION AND CONCLUSIONS
In this paper we used insights from complexity theory, and specifically the notion of a firm as a
complex adaptive system, to develop arguments about the relationships between organizational
complexity, internal inefficiencies, and innovation outputs. These arguments were tested and
supported using a new novel body of data on 211 international firms.
The empirical findings can be summarized as follows. First, organizational complexity
(operationalized as the product of the “variety of elements” the firm is exposed to and the
“interdependencies” between those elements) was found to have a positive relationship with
innovation (operationalized as the lagged number of patents produced by the firm). Second,
organizational complexity was also found to have a positive relationship with internal inefficiencies in
the firm. Third, internal inefficiencies were found to have a negative relationship with firm
innovation. Taken together, the net effect of organizational complexity on firm innovation remained
positive: while internal inefficiencies dampened this effect, they did not cancel it out. Moreover,
internal inefficiencies did not mediate the complexity-innovation relationship in a significant way.
Taken together, these results suggest that there are two faces to organizational complexity at work
inside large firms at the same time: it has both positive and negative qualities.
There are two aspects of this empirical analysis that are worthy of further discussion. First, the
graphical plot of the interaction between variety of elements and interdependencies (Figure 2) shows
clearly that high levels of innovation are achieved only when both variety and interdependencies are
very high. Exposure to variety is therefore a necessary but not sufficient condition for firms to be
innovative, and it is only when there are linkages made between those sources of variety that
innovative outputs are achieved. At the same time, internal inefficiencies in firms also arise under
conditions of high organizational complexity. It is noteworthy that neither the variety of elements in
the firm nor the interdependencies between elements is on its own a significant predictor of internal
inefficiencies – it is only the interaction between the two that is significant. Putting these two findings
together suggests an important insight, namely that the conditions in which innovation is potentially
the highest (i.e. high variety and high interdependencies) are also the conditions in which internal
inefficiencies are most likely to emerge. This suggests, as we discuss below, some very interesting
managerial challenges for firms seeking to become more effective innovators.
Second, we conducted a post hoc analysis in which we included a squared term for the
interdependencies between elements as an independent variable. In model 11 this squared term was
significant, suggesting that interdependencies between elements (which is one important component
of complexity) have a positive effect on innovation outputs up to a certain level, after which their
effect becomes negative. However, once we added in the internal inefficiencies variable (model 12)
this squared term became insignificant, suggesting the inverted-U shaped relationship was spurious.
16
This is a potentially important finding: An inverted-U relationship is, essentially, a way to
acknowledge that there are unmeasurable costs that eventually catch up with and override the benefits
provided by a particular variable. Our findings suggest that internal inefficiencies, arising from
coordination costs and bureaucratic insularity, are an important part of those previously unmeasurable
costs, because when added in to the regression equation the squared term loses significance. Future
research, building on the tradition reported in Damanpour (1996), might present a more consistent and
coherent relationship between complexity and innovation if internal inefficiencies are properly taken
into account.
Contributions to the academic literature
Moving beyond the specific findings, our study contributes to the academic literature in two
significant ways. First, we contribute to the complexity perspective which over the last twenty years
has become an influential way of understanding the internal dynamics of organizations. We framed
our study using the lens of complexity theory, arguing that firms are partially open to the external
environment, and therefore they are likely to exhibit patterns of emergent order (i.e. resulting in
innovation) and also disorder (i.e. resulting in internal inefficiencies) at the same time. In particular,
we applied two sets of arguments from complexity theory. First, we noted that in complex systems
parts interact in non-linear ways, and this was likely to be true both in terms of the benefits and costs
of complexity. This gave rise to our arguments about innovation levels being related to the variety of
elements and the interdependencies between them, and to our arguments about internal inefficiencies
arising when the costs of coordination compound. Second, we observed that complex systems may
veer towards emergent order or disorder depending on how open they are to sources of energy from
the outside. This led to our argument that complexity might have both positive and negative
outcomes, and it also helped to motivate our argument that internal inefficiencies are sustained, in
large part, through bureaucratic insularity, that is, the tendency of actors to turn inward and to
prioritize their own personal interests (Williamson, 1975).
The findings provide validation for our overall argument that a partially-open organizational system is
likely to exhibit both emergent order and disorder at the same time. While this point is well
established in complexity science, it has been neglected in the field of organization research which
has instead adopted a mostly “pro-order” perspective. Our hope is that by highlighting some of the
negative consequences of complexity we have helped to reorient the literature towards a more
balanced view of the effect of complexity on organizational outcomes. This suggests a couple of
interesting directions for future research. One would be to find a way of operationalizing the notion of
“openness” to external sources of energy, as this is implicitly an important factor in shaping the
balance between emergent order and disorder. Another would be to undertake a process study to show
how these opposing forces play out over time in practice. These would both be challenging research
projects, but potentially very valuable as a way of shedding further light on these important
phenomena.
Second, we contribute to the well-established body of literature on organizational complexity.
Consistent with recent convention, we defined complexity in terms of the variety of elements in the
system and the interdependencies between these elements. However, rather than use this definition in
a modelling exercise, we developed a survey-based instrument for measuring it inside firms. This
allowed us to make some useful contributions back to the existing literature. This study is the first, to
our knowledge, to provide an empirical validation of the basic proposition in NK models (Levinthal,
1997), namely that organizational complexity can be usefully conceptualized in terms of the number
of elements in a system and the linkages between those elements. Earlier empirical studies of
17
organizational complexity used conceptually less appealing operationalizations, for example, spatial,
occupational, hierarchical and functional differentiation (Blau, 1970), that failed to take the complex
linkages between elements into account. We hope future empirical studies will build on our approach
here, and test more of the concepts developed through NK modelling techniques.
Also, by developing and measuring the internal inefficiencies that often accrue in complex
organizations, we were able to resolve some of the ambiguities in prior studies and also point to some
interesting future research directions. Damanpour’s (1996) meta-analysis showed that the results of
prior studies of the complexity-innovation relationship were inconclusive, and he argued that the best
way forward was to improve understanding of the contingency factors shaping that relationship. Our
approach suggests a different logic: the effect of complexity does not just “depend” on certain
external or internal contingencies; it is also a function of the management capabilities of the firm, and
more specifically the ability of its executives to avoid the internal inefficiencies that often emerge in
complex organizations.
Again, some interesting future research directions are suggested by this discussion. One would be to
analyse the internal heterogeneity within firms, to clarify which parts of organizations are most
affected by internal inefficiencies. Another would be to examine some of the proposed “solutions” to
excessive complexity that firms have tried, such as using standardized processes or decentralizing
decision making to lower operating levels (Galbraith, 1995), and to assess whether these have a
moderating influence on the negative effect of internal inefficiencies.
Finally and more speculatively, our study hints at an interesting new angle in research on
organizational complexity. In putting our survey together, it was evident that for many people
“complexity” was the difficulties they had in getting their work done on account of the way the firm
was organized. In other words, rather than conceptualizing complexity as an objective feature of the
firm (in terms of the elements of variety and the interdependencies between them), it would be
possible to conceptualize it as an experienced phenomenon. Interestingly, a few studies have
attempted to do this already, by viewing complexity as something that exists in the eye of the
beholder (e.g. Campbell, 1988; Fioretti and Visser, 2006; Wood, 1986). Such an approach would
open up avenues for further research. For example, it would be interesting to examine the difference
between objective and experienced complexity: are some organizations objectively complex, but
sufficiently well-managed that they allow people to work in a streamlined and non-challenging way?
And are there other organizations that create experienced complexity in the eyes of their employees,
even though they are actually doing relatively simple and non-challenging work? Equally, it would
be interesting to understand the conditions that create experienced complexity: is it the immediate
working environment, as shaped by the organization’s leaders, or the external operating environment,
that is more important? These are interesting questions that should be explored carefully in future
research projects.
Limitations
There are some limitations to our study which should be acknowledged. First, our level of analysis
was the firm as a whole, which meant that we examined the relationships between our variables at an
aggregate level and did not explore the underlying mechanisms in any detail. It would be worthwhile
for future research to adopt a more granular approach, so that the specific characteristics of
organizational complexity required to generate innovation could be observed (Siggelkow and
Levinthal, 2003; Ethiraj and Levinthal, 2009). Second, there are limitations to the value of patents for
measuring innovative output. Although patents are reasonably good indicators of innovative output,
18
they are best regarded as intermediate outcomes between acquisitions and value creation. While our
results indicate that organizational complexity provides conditions in which innovation transpires,
they do not allow us to directly measure the value generated by these innovations. Examining the
economic value of innovations would be a natural extension of the work in this study and would
enable a more complete assessment of the contribution of variables of organizational complexity to
development of new knowledge. Third, the inclusion of several industries in our sample had benefits
in terms of the diversity of organizational arrangements we studied, but it created some challenges as
well that were only partially remedied through the use of control variables in our analysis. Future
research might benefit from focusing on one or a smaller group of industries because the relevance
and utility of the patent-based measures of knowledge are likely to be limited to industries in which
patents are themselves meaningful indicators of innovation. For instance, some of the contexts in
which these measures could be applied include information technology, pharmaceuticals, and
telecoms. Finally, our analysis by its construction cannot exactly determine whether there is a clear
causal relationship between the associations we report. In an effort to mitigate concerns of reverse
causality, we adopted lagged specifications, examining the association between this year’s values of
our independent variables and the following years patenting rates. We recognize that a panel dataset
would be preferable for conducting research in this area in future, though panel data that includes
internal firm-level attributes is very difficult to assemble.
Implications for practice
Finally, our study suggests some interesting implications for management practice. The level of
complexity in the organization is, first and foremost, a design choice for the executives at the top, in
that they can decide the variety of products, markets, channels, and so on that they enter, and they can
also decide how many interdependencies to create across these various dimensions of the
organization. However, our study shows that these design choices can also have negative side-effects,
in the form of internal inefficiencies, and these negative consequences are particularly prevalent at
higher levels of complexity. There is no indication in our research that these negative consequences
are inevitable: there is a high degree of variation across our sample in terms of the absolute level of
internal inefficiencies. This therefore points to the important role of the “quality of management”
(Ghoshal and Bartlett, 1994) in creating sufficient complexity to gain high levels of innovation, but
while avoiding the internal inefficiencies that typically go with it.
To conclude, our purpose in this paper was to put forward a new way of conceptualizing the
phenomenon of organizational complexity, by looking at both the benefits it brings as well as the
unintended costs that accompany it. Much remains to be done, and we hope that these ideas and
initial empirical tests will stimulate others to build on our work and to establish the validity and
efficacy of this new perspective.
19
Figure 1. Conceptual Framework
Organizational
Complexity
Innovation
Output
H1 (+)
H2 (+)
H3 (-)
Internal
inefficiencies
Figure 2: Effects of variety on innovation for low and high interdependencies
50
Innovation (Patents)
40
30
20
10
0
Low variety
High variety
Low inter
High inter
20
Table 1: Descriptive data for the sample
Sample Overview (211 firms)
Headquarters
North America
91
Europe
76
Asia
40
Rest of the world
4
Business Sector
Manufacturing
Telecom
IT
Finance
Pharma & Chemicals
Retail
Other
21
59
37
34
32
24
9
16
Employees
<10,000
10,000-50,000
50,000-100,000
>100,000
54
83
36
38
Table 2: Summary Statistics and Correlation table
Variable
1. Innovation output
2. Variety of elements
3. Interdependencies
4. Variety of elements*Interdependencies
5. Environmental munificence
6. Environmental instability
7. Environmental regulation
8. Ln(Age)
9. Ln(R&D)
10. Ln(Size)
11. Number of industries
12. International diversification
13. Tenure
14. Hierarchical level
* p<0.1
Obs Mean SD Min Max
1
2
3
4
5
6
7
8
9
10
11
12 13 14
211 386 958 0.0 3484
1
211 2.9 0.7 1.2 4.9 0.18*
1
211 2.7 0.6 1.3 4.4 0.11 0.03
1
211 9.1 3.6 2.2 19.3 0.15* 0.61* 0.72*
1
211 0.9 0.1 0.6 1.4 0.09 0.03 0.01 0.01
1
211 1.2 0.1 1.1 2.5 -0.05 -0.01 0.01 -0.05 0.02
1
211 3.6 0.8 1.0 5.0 0.01 0.08 0.06 0.10 0.16* -0.15*
1
211 3.8 1.0 1.1 5.4 0.18* 0.23* -0.04 0.15* -0.08 -0.02 0.06
1
211 2.7 3.2 0.0 8.9 0.17* 0.09 -0.19* 0.06 -0.22* -0.12 0.17* 0.12*
1
211 3.3 1.5 1.8 6.6 0.12 0.23* -0.04 0.24* 0.01 0.01 0.01 0.27* 0.37*
1
211 4.2 1.8 2.0 8.0 0.13 0.05 -0.10 -0.02 -0.01 0.03 0.13 0.14 0.27* 0.30*
1
211 0.7 0.6 0.0 2.0 0.00 -0.01 -0.11 0.00 -0.19* -0.07 -0.17* 0.12 0.42* 0.21* 0.11
1
211 8.3 2.6 1.0 19.0 0.10 0.14* -0.07 0.09 0.07 0.04 -0.02 0.06 0.17* 0.10 0.06 0.04
1
211 2.7 1.0 1.0 4.0 0.00 -0.01 -0.11 0.03 -0.19* -0.07 0.03 0.01 -0.05 0.01 -0.03 -0.05 0.08 1
22
Table 3: Poisson regression models with innovation output
DV: Innovation output
Variety of elements
Model 1
Model 2
0.308***
(0.115)
Model 5
0.273**
(0.108)
Interdependencies
0.343**
(0.147)
Organizational complexity
0.305**
(0.151)
Internal inefficiencies
-0.261**
(0.125)
Environmental munificence 2.552*
2.612*
3.019**
2.896*
2.519*
(1.514)
(1.582)
(1.514)
(1.479)
(1.404)
Environmental instability
-0.717
-0.756
-1.027
-1.120
-1.094
(0.777)
(0.740)
(0.762)
(0.789)
(0.790)
Environmental Regulation
-0.101
-0.119
-0.0983
-0.0736
-0.0915
(0.233)
(0.226)
(0.204)
(0.194)
(0.170)
ln(R&D)
0.0516
0.0638
0.0891*
0.0770
0.0566
(0.0495)
(0.0510)
(0.0503)
(0.0504)
(0.0465)
ln(Size)
0.0721
0.0273
-0.0169
-0.0208
-0.00505
(0.110)
(0.110)
(0.102)
(0.107)
(0.104)
ln(Age)
0.511***
0.470***
0.523***
0.501***
0.471***
(0.145)
(0.136)
(0.139)
(0.137)
(0.142)
Number of industries
0.0885
0.0805
0.0599
0.0686
0.0614
(0.136)
(0.132)
(0.131)
(0.126)
(0.117)
International diversification -0.174
-0.154
-0.0314
0.0196
0.0346
(0.381)
(0.406)
(0.415)
(0.387)
(0.385)
Tenure
0.0650
0.0448
0.0474
0.0430
0.0365
(0.0539)
(0.0528)
(0.0547)
(0.0556)
(0.0569)
Hierarchical level
-0.199
-0.190
-0.191
-0.180
-0.186
(0.124)
(0.118)
(0.119)
(0.123)
(0.124)
Constant
-0.196
0.0974
0.143
0.498
0.765
(1.888)
(1.940)
(1.868)
(1.881)
(1.789)
Log pseudolikelihood
-86131.129 -82568.511 -78529.024 -77276.981 -74792.223
Observations
211
211
211
211
211
Sector Dummies
Included
Included
Included
Included
Included
Respondent Function Dummies Included
Included
Included
Included
Included
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
23
Model 3
0.300***
(0.104)
0.415***
(0.139)
Model 4
0.271**
(0.106)
0.332**
(0.137)
0.238*
(0.136)
Table 4: OLS regression models with internal inefficiencies
DV: Internal inefficiencies
Variety of elements
Model 6
Model 7
-0.0287
(0.0791)
Model 10
-0.0188
(0.0737)
Interdependencies
-0.0886
(0.0669)
Interdependencies_sq
0.0966*
(0.0501)
Organizational complexity
0.278***
0.283***
(0.0848)
(0.0853)
Environmental munificence -1.224*
-1.223*
-1.227*
-1.333**
-1.120*
(0.624)
(0.626)
(0.625)
(0.598)
(0.597)
Environmental instability
-0.129
-0.133
-0.125
-0.249
-0.211
(0.430)
(0.436)
(0.432)
(0.416)
(0.410)
Environmental Regulation
-0.0301
-0.0295
-0.0298
-0.0402
-0.0348
(0.0898)
(0.0903)
(0.0910)
(0.0835)
(0.0806)
ln(R&D)
-0.0301
-0.0299
-0.0318
-0.0515*
-0.0441
(0.0274)
(0.0274)
(0.0271)
(0.0269)
(0.0273)
ln(Size)
0.152***
0.155***
0.158***
0.171***
0.159***
(0.0502)
(0.0512)
(0.0512)
(0.0516)
(0.0503)
ln(Age)
-0.169**
-0.167**
-0.169**
-0.196**
-0.181*
(0.0821)
(0.0814)
(0.0812)
(0.0801)
(0.0789)
Number of industries
0.0529
0.0541
0.0569
0.0589
0.0648
(0.0544)
(0.0546)
(0.0550)
(0.0533)
(0.0523)
International diversification -0.0472
-0.0522
-0.0584
-0.0455
-0.0444
(0.130)
(0.131)
(0.131)
(0.128)
(0.126)
Tenure
-0.0410
-0.0401
-0.0416
-0.0438*
-0.0420*
(0.0254)
(0.0252)
(0.0255)
(0.0249)
(0.0250)
Hierarchical level
-0.00149
-0.00139
-0.000310 -0.0106
-0.000434
(0.0697)
(0.0700)
(0.0699)
(0.0689)
(0.0675)
Constant
0.928
0.916
0.884
1.359
0.977
(1.128)
(1.114)
(1.119)
(1.035)
(1.056)
R-squared
0.120
0.121
0.123
0.173
0.187
Observations
211
211
211
211
211
Sector Dummies
Included
Included
Included
Included
Included
Respondent Function Dummies Included
Included
Included
Included
Included
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
24
Model 8
-0.0303
(0.0790)
-0.0469
(0.0675)
Model 9
-0.0189
(0.0742)
-0.0705
(0.0674)
Table 5: Continuation of Poisson regression, Negative binomial and IV Poisson models with
innovation output
DV: Innovation output
Variety of elements
Interdependencies
Interdependencies_sq
Organizational complexity
Poisson
Model 11
0.269***
(0.103)
0.377**
(0.157)
-0.190*
(0.0988)
0.286*
(0.153)
Internal inefficiencies
Environmental munificence
2.475*
(1.505)
Environmental instability
-1.037
(0.750)
Environmental Regulation
-0.0903
(0.192)
ln(R&D)
0.0645
(0.0503)
ln(Size)
-0.00272
(0.107)
ln(Age)
0.467***
(0.135)
Number of industries
0.0405
(0.124)
International diversification 0.0437
(0.378)
Tenure
0.0402
(0.0535)
Hierarchical level
-0.175
(0.124)
Constant
1.202
(1.901)
Log pseudolikelihood
-76106.305
Observations
211
Sector Dummies
Included
Respondent Function Dummies Included
IV Poisson:
product sale
Poisson
Nbreg
dispersion
Model 12 Model 13 Model 14
0.268**
0.299**
0.863*
(0.105)
(0.138)
(0.508)
0.374**
0.387**
0.484**
(0.160)
(0.182)
(0.207)
-0.143
(0.1000)
0.336**
0.406*
0.440*
(0.159)
(0.225)
(0.262)
-0.233**
-0.315**
-0.471**
(0.116)
(0.158)
(0.211)
2.261*
2.991***
0.911
(1.432)
(1.145)
(2.430)
-1.038
-1.269
-1.765**
(0.752)
(1.171)
(0.859)
-0.101
-0.206
-0.271
(0.172)
(0.147)
(0.231)
0.0499
0.124*
0.143
(0.0472)
(0.0676)
(0.107)
0.00852
0.257*
0.219
(0.105)
(0.152)
(0.227)
0.449**
0.110
-0.0901
(0.142)
(0.191)
(0.240)
0.0409
0.0829
0.0585
(0.116)
(0.109)
(0.142)
0.0495
-0.529*
-1.039*
(0.378)
(0.316)
(0.544)
0.0357
-0.0500
-0.119
(0.0551)
(0.0665)
(0.0786)
-0.181
-0.0218
-0.236
(0.124)
(0.148)
(0.369)
1.267
1.510
6.297*
(1.832)
(2.265)
(3.252)
-74151.649 -1143.281
211
211
211
Included
Included
Included
Included
Included
Included
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
25
IV Poisson:
competitor's
size
Model 15
0.783*
(0.461)
0.662**
(0.337)
0.545**
(0.241)
-0.638**
(0.314)
1.528
(2.201)
2.406
(4.011)
0.0364
(0.180)
0.0701
(0.0867)
0.0615
(0.176)
0.405*
(0.246)
0.211*
(0.124)
-0.486
(0.527)
-0.0938
(0.103)
-0.317
(0.219)
0.838
(5.544)
211
Included
Included
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