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THE DYNAMICS OF COORDINATION REGIMES:
IMPLICATIONS FOR ORGANIZATIONAL DESIGN
Phanish Puranam
And
Michael G. Jacobides
London Business School University of London
Sainsbury 317, Sussex Place Regent’s Park, London NW1 4SA U.K.
Tel: + 44 (0) 20 7262 5050 Fax: + 44 (0) 20 7724 7875
This Draft – April 2006
We thank Martin Kunc for assistance with simulation models.
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To the extent that contingencies arise, not anticipated in the schedule, coordination requires communication to give notice of deviations from planned or predicted conditions, or to give instructions for changes in activity to adjust to these deviations. We may label coordination based on pre-established schedules coordination by plan, and coordination that involves transmission of new information coordination by feedback. [March and Simon, 1958; p182]
The idea that coordination – the alignment of actions among interdependent
actors (Gulati, Lawrence& Puranam, 2005; Heath & Staudenmayer, 2000) can occur
through two distinct “regimes” - either through ongoing communication and mutual
adjustment or through predefined plans and standards - is hardly novel or exciting in
itself. However it invites intriguing speculations when coupled with the insight that
organizational boundaries often divide activities that are coordinated through plans
and schedules, but enclose activities that require feedback and mutual adjustment to
be coordinated with each other (Thompson, 1967). Arguments that link optimal
organizational forms to the nature of coordination requirements have been the
mainstay of structural contingency theory (Barnard, 1938; March and Simon, 1958;
Lawrence and Lorsch, 1967; Thompson, 1967) and under a broader definition of
coordination requirements, indeed also of transaction cost theorizing (Coase, 1937;
Williamson, 1976; 1985).
These ideas have of late been rejuvenated by the contributions of several
scholars who have used the principles of modularity to understand the internal
organization and boundaries of firms (Baldwin & Clark, 2000; Ethiraj & Levinthal,
2004; Galunic & Eisenhardt, 2001a; Helfat & Eisenhardt, 2000; Langlois, 2001;
Sanchez & Mahoney, 1996; Schilling, 2000). Within this literature, it is often
suggested that modular interfaces that partition complex activity systems into parts
with dense interdependencies within themselves but relatively fewer
interdependencies between them, can also function as organizational boundaries. In
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particular, there are two ways in which this idea is presented in the literature. The first
proposes a link between product and organizational architecture (Baldwin & Clark,
2000; Puranam, 2001; Sanchez & Mahoney, 1996; Schilling & Steensma, 2001),
whereas the second extends this link to ownership (Hoetker, 2002; Schilling &
Steensma, 2001), suggesting that modular production enables market based linkages
between activities.
The proposition that product level modularity can influence modularity in
organization and ownership is a valuable contribution to the study of organizations,
and refocuses our attention towards the role of organizational and ownership
boundaries in demarcating distinct regimes of coordination. While undoubtedly
useful, this line of thinking inherits some of the methodological weaknesses identified
with older structural contingency theorizing, as well as generates some new points of
contention. In our view, there are two critical questions that the literature on
organizational modularity must engage with in order to contribute effectively to our
understanding of organization design.
First, what is the link between organizational and product interfaces? The
carry-over of ideas about technological interfaces to organizational interfaces usually
occurs with a corresponding assumption that interfaces either exist and partition
activity completely, or do not. In contrast, in the tradition of organization design, the
division of labor has not (necessarily) been equated with standardized interfaces and
simplified interactions between the components of labor so divided. For instance,
Lawrence and Lorsch (1967) make clear that task decomposition and the creation of
modules (differentiation) goes hand in hand with mechanisms for coordination that
penetrate the boundaries of the resulting modules (integration). In the context of inter-
firm relationships, a number of authors have commented on the existence of fairly
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thick forms of coordination between as well as within firms – far from being
partitioned off from each other through thin interfaces, there may often be substantial
permeability of firm boundaries (Bradach & Eccles, 1989; Grandori, 1997; Gulati,
Lawrence& Puranam, 2005b).
Second, where do organizational or product interfaces come from? In the
modularity literature, the interfaces that partition activity into modular subsystems are
treated more or less exogenously, with little indication of how they are developed
much as the comparative discussion of coordination by plan and feedback largely fails
to explain where plans and standards come from. This omission makes the modularity
literature vulnerable to a charge of technological determinism (as was structural
contingency theory).
Our goal in this paper is to tackle these issues with a view to assist in a fuller
realization of the potential for ideas about modularity to advance theory on
organization design. We approach this task by first bringing some definitional clarity
to what interfaces mean in organizational contexts, and how product interfaces differ
from organizational interfaces. Second, we propose a dynamic model of how
interfaces are formed, that rests on the concept of systemic knowledge. Systemic
knowledge refers to what is known about the underlying patterns of interdependence
between tasks. As systemic knowledge accumulates, it is possible to define
organizational interfaces (i.e. rely on plans and standards based coordination) that
allow the separation of activity without having to rely on mutual adjustment
(Jacobides and Billinger, 2006). However, systemic knowledge itself accumulates
through mutual adjustment experiences. Therefore, coordination through interfaces
must be preceded by a regime of unstructured coordination involving communication
and mutual adjustment. Further, once adopted an interface-based coordination regime
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actively impedes the return to a unstructured coordination regime as organizations
become less experienced at unstructured coordination. We study the descriptive and
normative implications of this negative feedback process, and draw implications for
how organizational boundaries are formed and dissolve (Jacobides, 2005). Our
analysis thus suggests that rather than simply view plan and feedback as two alternate
regimes of coordination which organizational designers select from, fresh insights are
generated by focusing on the linkages between the two.
Organizational Interfaces: What they are and how they work
In most general terms, an interface describes how elements of a system
interact with each other. When the system is a piece of hardware, such as a PC, the
elements are various components such as motherboard, graphics chip, LCD screen
etc. and interfaces refer to the physical and electronic connections between these
components. When the system in question is a software program, the elements of the
system are various sub-routines and program modules, while the interfaces refer to
sequencing of execution, and information transfer between software modules through
the use of common variables or data. Most relevant to our discussion, when the
system refers to an organization, the elements are various organizational sub-units,
and interfaces describe the nature of communication and shared decision-making
across these sub-units, as embodied in plans, standard operating procedures,
integrating managers and cross-unit teams (Galbraith, 1974; Tushman & Nadler,
1978).
Interfaces are useful concepts to understand the architecture of complexity,
because they demarcate the sub-systems that comprise the system as well as the
manner in which sub-systems link to each other. It is important to note that interfaces
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refer not only to where the partitions in a complex system lie, but also the nature of
those partitions. Two attributes of interfaces are of particular interest to students of
modularity: a) the extent to which interfaces are “thick” or “thin” - which captures
the amount of interaction between elements, b) the extent to which interfaces are
“well-specified” vs. “poorly specified”- the degree of detail with which interactions
between the linked elements of a system can be described before they occur.
To understand the distinction between thickness and specification, consider
standard operating procedures that may allow two units to exchange large volumes of
information through technological systems or engage in complex coordination tasks
but in a very structured, pre-determined format. In contrast, committees or cross-
functional teams allow for a relatively smaller volume of interaction, but of a highly
non-structured nature, which allows for unstructured coordination (Langlois, 2001).
This distinction between the thickness of interfaces and their specification mirrors the
distinction drawn by Daft and Lengel (1986) between information volume and
richness. Daft and Lengel refined earlier views on information processing (Galbraith,
1977; Tushman & Nadler, 1978) to suggest that increasing volume of information
exchange is only useful when problems can be framed in such a way that more
information leads to resolution. However, when problems are unstructured, equivocal
and ambiguous, then it is the richness of information rather than its volume that
matters. Communication media that permit rich information transfer are those that
permit real-time human interactions, such as collocation, video-conferencing and
telephones.
Concepts similar to unstructured coordination have been discussed by other
scholars under names such as integrated problem solving (von Hippel, 1998),
technical dialog (Monteverde, 1995) or qualitative coordination (Langlois, 2001).
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Thus the “thickness” of an interface between any two units may characterize the
amount of information exchange and coordination between two units, but the fact that
the interface is “poorly specified” refers to the large volume of information flow and
coordination that is unstructured. Thus, thickness and specification are distinct but not
orthogonal concepts; interface thickness refers to the total amount of coordination,
whereas (the lack of) interface specification refers to the volume of unstructured
coordination; the latter is a subset of the former.
The distinction between thickness and specification of interfaces suggests an
important distinction between modularity in technology and organization. In designed
artifacts such as hardware and software, regardless of their thickness, interfaces must
always be well-specified in a functioning product. In contrast, both thickness and
specification of organizational interfaces matter, and the benefits of separating
activity within organizations through interfaces depends on both aspects. If anything,
it is possible that the critical parameter may be the degree of specification of
organizational interfaces – the extent to which the interface avoids the need for rich,
unstructured coordination. This is because given the constantly falling costs of
Information Technology (IT), the costs of transmitting large volumes of structured
information are unlikely to prove to be binding constraints in organization design;
however, rich unstructured coordination continues to rely significantly on face-to-face
communication, for which the key constraints remain human information processing
capacities.
We therefore argue that in organizations, as opposed to technological systems,
the key issue is not complete separability and independence of action across sub-
systems but rather the extent to which the nature of interdependence is fully
predictable. Situations of complete independence and the resulting ability to operate
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in a perfectly parallel way are rare in organizations (Thompson, 1967) and even rarer
in terms of the ability of organizational architects to discover such perfect de-
couplings (Ethiraj and Levinthal, 2004). Interdependence between organizational
units is ubiquitous, and may even require ongoing coordination and mutual
adjustment between them (Thompson, 1967) but if such patterns of adjustment are
fully predictable ex ante, then coordination is a “routine” affair (Gulati et al, 2005). 1
Thus, the degree of specification of organizational interfaces is the key theoretical
variable of interest.
Well-specified interfaces work by specifying in advance exactly what each
sub-system that links to another must do. For instance, two interdependent individuals
may coordinate their activities by communicating and through joint decision-making;
alternately a well-specified interface such as a standard operating procedure may
simply specify exactly what each must do individually so that their joint actions are
coordinated. It is in this sense that well-specified interfaces “embed” coordination –
they enable linked systems to act in a coordinated manner without having to explicitly
engage in coordination, by specifying exactly what each sub-system must do in order
for the aggregate system to work effectively (Baldwin & Clark, 2000; Ethiraj &
Puranam, 2004; Sanchez & Mahoney, 1996).
Table 1 provides some examples of well-specified and poorly specified
organizational interfaces. Intermediate degrees of interface specification are also
possible, arising from different weighted combinations of the high and low
specification mechanisms. For instance, one can envisage organizational interfaces
1 We think that Thompson’ s classic treatment of interdependence does not make this point clearly enough, to the detriment of further development of these ideas. Reciprocal interdependence refers to the circular direction of information flows without reference to the intrinsic difficulties involved in coordination. For extended discussions of these points, see Gulati, Lawrence and Puranam, 2005.
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that rely primarily on unstructured, face–to-face coordination, on anonymous,
standardized operating procedures, and some combination of the two. In fact, except
in the most routinized situations, we believe that organizational interfaces are more
often somewhere in the “swollen middle” (Hennart, 1993). This underlines the
important distinction between modularity in products and organizations- interfaces of
intermediate specification may be useful in organizational settings, while
inconceivable in the product domain.
Interface specification vs. Thickness: An example
To crystallize the distinction between interface specification and thickness, we
present an example of a hypothetical organizational modularization problem, using
the commonly used device known as a task structure matrix (Baldwin & Clark, 2000).
The problem is a stylized example of product commercialization, with the various
component tasks and their interdependencies indicated as usual in Table 2a. As is
standard in such representations, an x in row j column k indicates that task j depends
on task k. The representation allows for one-way dependence (a single x below/above
the diagonal) or interdependence (with symmetric x’s below and above the diagonal).
Table 2b shows the same problem after it has been “modularised” into three groups of
tasks- module 1 (product design), module 2(media) and module 3(packaging). An off
diagonal x pertains to a sequencing issue; activity i cannot be performed till activity j
is performed. A set of x’s symmetrically above and below the axes indicates need for
ongoing coordination- “cycling”-activity i depends on j and j depends on i.
There are three interfaces in this representation (between module 1 and 2, 2
and 3 and 1 and 3). The one between module 2 and 3 is the thinnest- there is no
dependence between them at all. The activities in these two modules can in fact
proceed in parallel, require no ongoing coordination, can be measured and rewarded
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separately, can benefit from specialization and can benefit from rapid reconfiguration.
The interface between module 1 and 2 is still relatively thin, though thicker than that
between modules 2 and 3. The interface between modules 1 and 3 are the thickest,
with the most number of dependencies and interdependencies.
However, note that from this representation alone, we cannot say whether the
benefits of modularity are greater at the 1-3 interface or at the 1-2 interface (though
both are certainly lower than at the 2-3 interface). This is because this representation
tells us little about how specified these interfaces are – the extent to which the cross-
module interactions represented by the x’s in Figure 2 can be described in detail
before they occur. For instance, it is clear from Figure 2b that the design of the media
campaign cannot occur before engineering design, prototyping and manufacture; but
what is not clear is the extent of unstructured coordination required between the
media campaign designers ad the engineering team for the former to design an
appropriate campaign that highlights technical features of the product appropriately.
Indeed it is possible that this single x may represent more equivocality and need for
unstructured coordination than the three x’s that appear in the 1-3 interface.
We have consciously chosen an illustration that pertains to organizational
rather than product modularity. As a contrast, one can imagine a similar diagram
showing the sub-modules within a software programme or a piece of hardware; in
such designed artefacts, if they are functioning well, there can be very little doubt
about the nature of interactions. Physical tolerances, information flows, sequencing
must all be fully specified for the artefact to function.
In sum, because of the separation of activity they enable, well-specified (as
opposed to thick or thin) organizational interfaces are the basis for defining
organizational and ownership boundaries. Understanding how such interfaces are
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formed can therefore generate insights on changes in the division of activity within
and between firms.
A dynamic model of organizational interface creation
Our goal in this section is to outline a model that describes the dynamics of
interface formation. Our model represents an organization with two organizational
sub-units. The two units are interdependent, so that their activities must be
coordinated in order to optimize organizational performance. The two sub-units may
be related vertically or horizontally. For instance, these may be design and
manufacturing units, or two product development groups working on sub-systems
within a complex system. We define “systemic knowledge” as the knowledge about
the nature of interdependence between sub-systems that is essential for defining
interfaces between the two units. Such knowledge cannot be assumed; often the
nature of interactions between sub-systems, and indeed of the location of boundaries
between sub-systems may be unknown to an organization designer. Yet over time, as
the personnel in the two units interact, they are likely to build a stock of systemic
knowledge as they begin to understand the true nature of interdependence between
activities in the two units.
We define “unstructured coordination” as a process of mutual adjustment
supported by frequent and bi-directional communication. The hallmark of
unstructured coordination is that it is effective even when systemic knowledge is
imperfect or non-existent. Thus, the individuals in the two sub-units of our
organization may be able to coordinate their activities through a process of trial-and –
error learning even in the absence of detailed knowledge about how their actions are
interdependent. This process of collective trial-and-error learning is what we refer to
as unstructured coordination. While unstructured coordination can take place even in
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the absence of systemic knowledge, it is also a basis for building systemic knowledge.
Indeed such knowledge is more likely to be gathered through unstructured
coordination efforts than through highly structured information exchange;
understanding the full ramifications of changes in one sub-system to another sub-
system is more likely if communication between them is not constrained by
predefined topics and channels of information. We therefore assume that systemic
knowledge tS is a concave increasing function of cumulative experience at
unstructured coordination tV . The concavity builds in diminishing marginal returns,
which is a ubiquitous assumption about knowledge building processes. Thus, the
relationship between systemic knowledge and coordination experience must satisfy
0,0 2
2
<∂∂
>∂∂
t
t
t
t
VS
VS
(1)
While systemic knowledge about the nature of interdependence is being
accumulated through unstructured coordination, over time the organization also
becomes more adept at unstructured coordination itself. Repeated interactions give
rise to trust, interpersonal routines, and common language, which together enhance
the efficacy of mutual adjustment between them. Therefore, over time, an
organization’s competence at unstructured coordination improves. Consistent with
standard learning curve formulations, we model this effect through a reduction in the
costs of unstructured coordination per unit tc as a convex decreasing function of
cumulative experience. The relationship between the per unit costs of coordination
and coordination experience must satisfy:
0,0 2
2
>∂∂
<∂∂
t
t
t
t
Vc
Vc
(2)
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Finally, we assume that the extent of unstructured coordination tU necessary
to manage inter-unit interdependence at any point of time decreases with systemic
knowledge. As the individuals in the two sub-units build systemic knowledge and
gain a better understanding of how their actions are interdependent, they are able to
embed coordination into standards, rules and procedures, instead of relying on
unstructured coordination. We therefore assume that the extent of unstructured
coordination required between the units to manage interdependence reduces as a
convex decreasing function of systemic knowledge; the convexity reflects the
assumption that it is never possible to eliminate entirely the need for unstructured
coordination, because systemic knowledge is ultimately the product of learning by
bounded rational individuals – it is unlikely to be perfect. Further, we allow for a
distinction between the existence of systemic knowledge and its utilization to define
interface standards. We denote by tt Si φ= the systemic knowledge that is used to
define standardized coordination patterns between the sub-units through standards,
operating procedures, rules etc. The parameter � functions as a “switch”- when �=0,
then the organization may possess systemic knowledge but does not utilize it to define
interfaces; when �=1 then this knowledge is utilized. The relationship between the
extent of unstructured coordination and systemic knowledge used to define interfaces
must satisfy:
0,0 2
2
>∂
∂<
∂∂
t
t
t
t
i
Ui
U (3)
The preceding conditions embody the assumptions on which we build our
theoretical arguments. To complete the specification of our model, we also state two
identities. The first equation simply specifies the accumulation function for
coordination experience
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�+=t
tottot dtUVV (4)
The second states that coordination costs tC are equal to the volume of unstructured
coordination multiplied by the cost per unit of unstructured coordination.
ttt cUC .= (5)
The model specification implied by 1-5 captures a fundamental asymmetry between
the two coordination regimes. In an unstructured coordination regime (i.e. no
interfaces) the total costs of coordination must decline over time simply because of
increasing coordination competence resulting from accumulated coordination
experience (2), but the volume of unstructured coordination remains constant. In
contrast, defining interfaces affects both the volume of unstructured coordination (4)
as well as the cost per unit of coordination. Specifically, because interfaces curtail the
volume of coordination at any point in time, they also result in slower accumulation
of coordination experience (4). Investing in interfaces therefore not only reduces the
extent of coordination required at any point in time but also a) slows down the rate at
which per unit coordination costs decline c) as well as the rate at which systemic
knowledge is built.
There are three important implications of these effects. First, the total costs of
coordination may be lower or higher in an interface-based coordination regime than in
an unstructured coordination regime. Relative to an unstructured coordination regime,
introducing interfaces decreases the extent of coordination necessary, but also raises
the per unit cost of coordination. This suggests a boundary condition on when an
interface based coordination regime is preferable to an unstructured coordination
regime (purely on a coordination cost basis- this does not account for the benefits of
interface specification such as gains from specialization or superior monitoring and
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measurement). Put simply, interfaces improve coordination only when the “volume
reduction” effect dominates the “cost increase” effect.
Second, delaying interface definition (i.e. “turning on ��later”� may lead to
higher levels of systemic knowledge. This is because the level of systemic knowledge
at time t will be higher in an unstructured coordination regime than in an interface-
based coordination regime, because of the slower accumulation of coordination
experience in the latter. An interface introduced at time t will therefore be better
specified – have more systemic knowledge to draw on- than one that was introduced
in t0, and from time t onwards, will always have lower levels of unstructured
coordination compared to a system in which the interface was introduced at time t0.
Delayed transition to interface-based coordination may therefore help to control
coordination costs.
Third, coordination competence (as measured by per unit costs of
coordination) is typically likely to be lower in an organization that adopts an interface
based coordination regime. This turns on its head the conventional wisdom that
organizations with high competence at unstructured coordination are less likely to
invest in interface-based coordination. This is a particularly important consideration if
we believe that rarely but surely, exogenous shocks to the system change underlying
patterns of interdependencies and destroy existing systemic knowledge (and therefore
interfaces). Building interfaces impedes the formation of competence at unstructured
coordination, which organizations must fall back on when interfaces are destroyed.
Thus, shocks to systemic knowledge may not only lower coordination performance in
an interface based regime, but may lower performance relative to an unstructured
coordination regime.
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We explore these intuitions about the viability of an interface-based
coordination regime rather than rely on an unstructured coordination regime more
rigorously by simulating the dynamic model specified in equations 1-5. The specific
functional forms used for equations 1-3 are:
tVt eS θ−−= 1 (1’)
tVt ec α−= (2’)
tit eU λ−= (3’)
We rely on exponential forms for all three equations to maintain
comparability. The parameters in these functions affect the second derivatives in
meaningful ways. For instance, the parameters ��and � have meaningful
interpretations as “investments in building systemic knowledge and coordination
competence” respectively. Increasing these parameters increases the rate at which
coordination experience is converted into systemic knowledge and coordination
competence respectively. Since our focus in this study is not on the effectiveness with
which systemic knowledge embedded into interfaces helps to reduce unstructured
coordination, we set �=1 in the simulation experiments described below. Unless
otherwise mentioned, the settings for the simulation runs used to produce graphs are
��������and ��������though in our analysis we verify results for both parameters in the
range [0,1].
The value of adopting interfaces for coordination
If 0.. >∂
∂+
∂∂
tt
tt c
iU
Ui
c then coordination costs ( tt cU . ) may increase through
the adoption of an interface. When is such a condition likely to hold? Both tU and tc
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are always positive and i
Ui
c tt
∂∂
∂∂
, are positive and negative respectively. A simple
intuition is that for interfaces to begin pushing up per unit costs of coordination by
curtailing cumulative coordination experience (i.e. 0>∂
∂i
ct ) some time must elapse,
so that significant differences in coordination experience will result from adopting an
interface. Further, the magnitude of i
U t
∂∂
will decrease over time given the convex
decreasing nature of this function. It therefore seems likely that the “volume
reduction” effect precedes the “per unit cost increase” effect, so that interface
adoption is followed initially by lower coordination costs relative to an unstructured
coordination regime. Subsequently coordination costs may rise.
Figure 1 shows coordination costs tC in an unstructured (�����line 1) and
interface based coordination regime respectively (�����lines 2 and 3). Line 3
represents a higher ���������than line 2 (0.10). As expected, the adoption of an interface –
based coordination regime improves performance initially, but eventually worsens it
with respect to an unstructured coordination regime. Both the initial improvement and
eventual worsening are stronger for higher ���reflecting increases in both the “volume
reduction” and “per unit cost increase” effects with investment in building systemic
knowledge��
There are both descriptive and normative implications of these results. From a
descriptive standpoint, the adoption of an interface-based coordination regime is
likely to result in initial improvements in coordination performance, but eventual
declines. The decline is inevitable because of the increase in per unit coordination
costs (relative to an unstructured coordination regime) that occurs over time. Investing
significantly in extracting systemic knowledge from the lessons provided by
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coordination experience is a double-edged sword, as it results in grater initial
improvements but worsens subsequent coordination performance. From a normative
viewpoint, one would expect that firms with a shorter time horizon and/or high
discount factors are more likely to adopt an interface-based coordination regime, all
else being equal.
Figure 2 shows the impact of higher levels of investment in building
coordination competence (�� on coordination costs in the two regimes. Lines 1 and 3
show unstructured coordination regimes (����, 2 and 4 show interface- based regimes
(�����with a constant ��������. The difference between lines 1 and 2 represent the
gains/losses from interface adoption when ��is low (0.05); the difference between
lines 3 and 4 for when ��is high (0.10). Higher rates of coordination competence
building bring the crossover point (at which the interface-based regime begins to
under-perform the unstructured coordination regime) forward, while also dampening
both the gains and losses from interface adoption. Thus, all other things (such as time
horizons and discount rates) being equal, organizations that can build coordination
competence rapidly are less likely to adopt an interface based coordination regime.
If we thought about both ��and ��as endogenous variables, the preceding
discussion also raises the question whether investments in building systemic
knowledge complement or substitute investments in building coordination
competence. The tentative answer we suggest is that it depends on the time horizon.
In the short run, investments in building systemic knowledge enhance the value of
investments in building coordination competence; in the longer run, such investments
may work at cross purposes to each other, as interfaces (and the systemic knowledge
they rest upon) slow down the rate at which coordination competence is accumulated.
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The timing of interface adoption
Next, we explore the impact of interface adoption on systemic knowledge, and
implications for timing interface adoption. Figure 3 shows the level of systemic
knowledge tS over time, for a low and high value of �������������and for ������Lines 1 and 3
represent systemic knowledge for low and high values of � in an unstructured
coordination regime (i.e. with ����; lines 2 and 4 for low and high values of � in an
interface-based coordination regime (����. This reduction in systemic knowledge in
an interface-based regime suggests that there may be gains from delaying the adoption
of such a regime as the delay translates into enhanced levels of systemic knowledge.
To model delayed adoption of an interface, we introduce a threshold in systemic
knowledge. This modifies the expression for the systemic knowledge used to define
interfaces to tt Si φ= if χ>tS , else 0=ti . Higher threshold values (�� imply later
adoption.
Figure 4 shows coordination costs tC with different threshold levels (0.2,0.4,
0.6). Increasing the threshold in general increases coordination costs relative to a no-
threshold regime (i.e. one in which tt Si φ= at 0t ) up to the time it at which the
threshold is crossed; beyond that point, the higher the threshold, the lower the
coordination costs at every point in time after it . Delayed adopters of interfaces may
therefore enjoy coordination cost advantages over early adopters, after the transition
in coordination regime.
Figure 5 explores the impact of different rates of building coordination
competence on the timing of interface adoption. Lines 1 and 3 are coordination costs
with interface adoption at 0t , 2 and 4 with delayed adoption (������� The effect of
increasing the rate at which coordination competence is built (������������ is to reduce the
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area between the lines for initial and delayed adoption prior to it and increase it after
it . The intuition is simple- before it , increasing coordination competence implies
lower costs of coordination for the same volume of coordination; after it coordination
competence with delayed interface adoption will be higher than with adoption at
0t (because the interface slows down the rate of building coordination competence).
Thus, delayed adopters of interfaces enjoy even larger coordination cost advantages
over early adopters after the transition in coordination regime if both accumulate
coordination competence rapidly.
The impact of shocks to systemic knowledge
To explore the impact of periodic shocks to systemic knowledge- such as
technological or organizational innovations which transform the nature of
interdependence between units- we modify our model specification to allow for
periodic destruction of the value of systemic knowledge gained through coordination
experience. Accordingly, we distinguish between tsV , the stock of coordination
experience relevant to building systemic knowledge, and tcV , the stock of
coordination experience relevant to building coordination competence. At intervals
drawn from a distribution, tsV is depleted by an amount t
sε , whereas tcV is
unaffected. This corresponds to shocks that destroy the systemic knowledge gained
from coordination experience, without damaging the coordination competence that
also arises from coordination experience. Equation 4 is accordingly modified to
dtUVVt
to
ts
ttos
ts ][� −+= ε (4a)
�+=t
totto
ct
c dtUVV (4b)
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Note that when tsε =0, then t
sV is always equal to tcV . We draw t
sε from a
Montecarlo distribution.
In Figure 6, we first plot coordination costs during the time window when
specifying interfaces improves performance relative to an unstructured coordination
regime (���������������. This is shown by the difference between the lines “with
interfaces” and “without interfaces”. The third line in yellow represents the average
coordination costs for 50 runs of the model, with a 15% probability of a shock in each
period that completely destroys the value of systemic knowledge gained through
coordination experience. The impact of the shocks, as we conjectured, is to lower
performance below that of an unstructured coordination regime towards the end of
this time window (see periods 20-40). Put differently, shocks serve to bring forward
the crossover point (at which the interface-based regime begins to under-perform the
unstructured coordination regime).
This result can be understood in terms of the impact of shocks on the twin
consequences of defining interfaces- “volume reduction” and “cost increases”. With
shocks, the specification of interfaces periodically goes to zero, leading to spikes in
the extent of unstructured coordination. This weakens both the coordination volume
reduction effect (as shocks increase the extent of unstructured coordination required)
as well as the unit cost increase effect (since cumulative experience at unstructured
coordination is likely to be higher with shocks). Since the impact of differences in
cumulative coordination experience on the unit costs of coordination are slight
initially, but only build up over time, shocks effectively weaken the volume reduction
effect more strongly than the cost increase effect; the cross over point therefore
“moves” to the left.
22
Figure 7 shows the effect strengthening as the frequency of shocks increases
(from a 10% probability of a shock every period -“I-rare” to 30% - “I-frequent”). As
shocks become more frequent, the performance of the interface based coordination
regime worsens and the crossover point moves to earlier periods. Thus, shocks to
systemic knowledge not only lower coordination performance in an interface-based
regime, but lower performance relative to an unstructured coordination regime. Put
differently, when systemic knowledge is unstable and the underlying patterns of
interdependence are frequently changing, building systemic knowledge and defining
interfaces that are valid for brief intervals may be worse than sticking with an
unstructured coordination regime.
Discussion: Interfaces, Boundaries of Organization & Boundaries of Ownership
Our analysis points to some limits to the usage of interface-based coordination
regimes- the advantages of interfaces relative to unstructured coordination decline
over time, and when systemic knowledge is unstable and subject to frequent revision,
then the advantages of an interface based regime last for an even shorter duration. The
gains from interface-based coordination regimes are also smaller for organizations
that can build coordination competence rapidly. Finally, organizations that delay the
adoption of an interface-based regime are likely to outperform organizations with
unstructured coordination regimes for significant periods of time. Organizational
interfaces have been at the centre of our analysis, because they are the basis on which
the division of activity within and between firms is defined. Rather than simply view
such interfaces as a pre-existing alternative to an unstructured coordination regime,
we have proposed a dynamic model of how the systemic knowledge necessary to
define them builds over time and at the expense of developing competence at
unstructured coordination.
23
Our analysis of the dynamics of the two coordination regimes and their
interactions suggests several implications for organizational form, which we discuss
below. In keeping with standard theoretical treatments about firm boundaries, we
make two assumptions about the difference between intra-and inter-firm contexts:
first, that there is lesser incentive conflict between the two parties interacting through
an interface when both are part of the same firm than when they are not (Williamson,
1985; Williamson, 1991); and second, that costs of unstructured coordination between
the two parties is lower when both are part of the same firm than when they are not
(Kogut & Zander, 1992, 1996). These two assumptions characterize transaction cost
and the knowledge based perspective respectively, and a point of contention between
theorists is the sufficiency of each as an explanation of firm boundaries (Foss, 1996).
However, that is not our focus; we make both assumptions with a view to
understanding how interfaces evolve and confer benefits within the inter-firm as
opposed to the intra-firm context.
Implications for ownership and organizational boundaries
Our analysis suggests an endogenous explanation for why interface-based
coordination regimes are periodically discarded by organizations in favor of
unstructured coordination regimes. Organizations may merge activities separated by
departmental/divisional boundaries, re-integrate into vertical segments they have
exited, and periodically discard existing standards and rules not necessarily because
these become inappropriate due to some exogenous changes to the system, but simply
because their value relative to an unstructured coordination regime inevitably declines
over time. When systemic knowledge is in flux- the pattern of underlying
interdependencies connecting organizational sub-units is changing frequently - this
reduces the period in which interfaces confer coordination cost advantages over
24
unstructured coordination regimes, leading to interfaces being discarded more rapidly.
Note that this is not the same as saying that when systemic knowledge is outdated, the
interface that is based on this knowledge is less valuable - instead we are arguing that
the regime of coordination by interfaces in general becomes less valuable due to the
frequency with which interfaces must be re-defined- even if there were no costs to
creating each interface (as we have assumed).
Our analysis also suggests that the magnitude of the coordination cost
economies from interface-based regimes is diminished in the presence of rapid rates
of coordination competence building. This indicates that the adoption of an interface-
based coordination regime is less likely, the faster an organization can bring about
improvements in coordination competence through building on coordination
experiences. Thus organizational interfaces are more likely to be adopted in
established rather than de novo relationships, because of the greater rate at which
coordination competence can improve with experience in the latter. Similarly, all else
being equal, within firms, coordination competence may develop faster because of
common language, low-powered incentives, propinquity and identification- starting
from similar initial levels (Monteverde, 1995). Thus, we would expect that activities
performed by different organizational units within a firm are more likely to rely on
unstructured coordination, than similar activities coordinated across organizational
boundaries. Interface based coordination regimes should be more widespread between
rather than within firms – the lower levels of standardization of interactions between
units within a firm, compared to similar interactions between firms may in fact be a
sign of efficiency.
There are other important differences between organizational interfaces within
and between firms that we have not directly examined in our model. For instance, the
25
design of interfaces may involve conflicts of interest cannot be ignored in the context
of inter-firm relationships. While we have assumed that having knowledge of the
underlying interaction structure is all that is needed to design an interface, actually,
selecting the appropriate interface may be more like a coordination game- with
multiple equilibria, on which different people may have different preferences. Two
organizational units within a firm may have different preferences on which interface
to select, but agreement will eventually be reached through the intervention of higher
authority, if necessary (Williamson, 1991). By definition, such a final and effective
arbitrator is not available when two independent firms disagree on the interface that
they prefer. The implication is that it is harder to create interfaces between rather than
within firms; but if an interface exists, it is easier to do business with external
organizations.
Our results on the benefits of delaying interface definition till some systemic
knowledge has been built underlines two key elements of our theoretical framework –
first, that an unstructured coordination regime must precede an interface-based
coordination regime, and second that the benefits of interface specification is subject
to diminishing marginal returns. Delaying interface definition allows for the build-up
of systemic knowledge through unstructured coordination, while once an interface is
introduced, the rate at which systemic knowledge is built must diminish (because of
the decrease in the extent of unstructured coordination each period). The
standardization of coordination must not be rushed- reorganization or vertical
disintegration in the aftermath of technological innovations must allow time for
systemic knowledge about new patterns of interdependence to be built.
These results also suggest that in general, using organizational interfaces to
partition activity is a good idea in the longer term only if there are significant gains
26
from the use of interfaces. Our own analysis has only focused on the coordination
costs associated with coordination through interfaces (as opposed to unstructured
coordination), but the potential benefits of using interfaces are well known. The idea
that specialization gains can arise from controlling cognitive diseconomies of scope is
at least as old as Adam Smith’s original description of the pin factory. Because well-
specified interfaces allow focusing cognitive resources on a narrower set of actions
(production only as opposed to production + coordination), they can lead to static and
dynamic improvements in performance. Indeed the formation of well-specified
interfaces can be motivated by the gains from trade that can emerge when less
efficient in-house operations are abandoned and substituted by outside providers
(Jacobides & Hitt, 2005); or the gains from specialization which obtain when firms
focus where they can leverage and develop their competencies (Jacobides & Winter,
2005).2
Well-specified interfaces enhance accountability because they describe
precisely the acceptable outputs from each sub-unit, and lower measurement costs
(Barzel, 1982), enabling the use of sharp incentives that link rewards to outputs
(Zenger & Hesterly, 1997)Jacobides & Billinger; 2006). Since well-specified
interfaces provide clear descriptions of acceptable outputs, they can also result in
lower costs of selecting and finding trading partners, and contracting with them
(Baldwin and Clark; 2003). Well-specified interfaces also enable rapid
reconfiguration – “mix and match”. Individual organizational units (including
suppliers) can be replaced by others, while maintaining the overall pattern of
interdependence between organizational units if the organizational interfaces that
2 It is important to keep in mind that since we are discussing the incentives of an individual firm to invest in an interface, the gains from specialization must be large enough for it to be in the interests of individual firms to gain from interface specification and outsourcing, and not just efficiency enhancing at some global level, unless we assume that selection pressures are indeed highly effective.
27
connect them are well specified. This property lies behind the suitability of the M-
form organization for conglomerate diversification, and of policies for rapid re-
assignment of charters to divisions within companies (Galunic & Eisenhardt, 2001b).3
Interfaces deliver the benefits of rapid reconfiguration even within the inter-firm
setting as they can enable firms to switch trading partners rapidly, as Hoetker shows is
possible in the laptop industry (2002).4
Since the coordination cost advantages of interface-based regimes decline
relative to unstructured coordination regimes in the long run, their adoption is
economically rational only when the gains from interfaces- specialization,
accountability, rapid re-configuration- are relatively large. Put differently,
unstructured coordination regimes may be preferable unless the benefits from
interface specification not only exist but are significant.
Conclusion
We see this paper as making three basic contributions. First, technological
interfaces in products differ in important ways from organizational interfaces; while
the former must be well specified (but may be either thin or thick), the latter need not
be. In fact, from an organizational standpoint, thin interfaces are not necessary for
most of the gains from modularity. Conversely, variations in the degree of interface
specification are important from an organization design perspective, because of gains
3 Localized disruption is the other side of the same coin – in the limit some sub-units can be destroyed and completely replaced with another, with minimal implications for the activities in other units. This is at the heart of the fable of the two watchmakers, Tempus and Hora that Simon entertainingly recounts. Faced with unpredictable disruptions that destroy all interlinked components, the watchmaker who adopts a modular assembly strategy with well-specified interfaces minimizes his maximum rebuild costs. 4 However, it is important to distinguish specificity and specification- a well specified interface, if it is not an industry standard may require significant relationship specific investment and create hold-up hazards. Thus rapid reconfiguration benefits in the inter-firm context are only feasible when interfaces are not only well specified, but open and widely used.
28
from reduced coordination costs and enhanced accountability; these concepts have no
good analogues in product modularity.
Second, the differences between product and organizational modularity
become even more pronounced as we look closer at the process of interface
formation. We have argued systemic knowledge is essential for effective interface
definition. Since unstructured coordination is an important means by which systemic
knowledge is gained, an implication of our arguments is that for novel products, non-
modular organizations may be necessary to develop modular products. Thus, the link
between technological and organizational modularity may be stronger in mature
technological settings rather than in settings with considerable novelty; existing levels
of systemic knowledge may be a powerful moderating variable that helps to explain
when product and organizational modularity go together, as well as when they do not.
Third, our analysis suggests that variations in organizational attributes such as
the rate at which systemic knowledge and coordination competence is built, and
differences in time horizons over which the benefits of interface specification are
evaluated can generate considerable variations in the rates of adoption of
organizational modularity and the degree of interface specification, even for the same
basic technologies. Elaborating on this analytical framework may help us to provide a
sophisticated rebuttal to the critique of technological determinism that often hovers
over the literature on technological and organizational modularity. We have also
sketched out arguments for why ownership may remain integrated despite the
existence of organizational interfaces (because of the need for specific investments to
conform to an interface), and conversely why interfaces may never form despite
ownership already being separated (this can occur if coordination competence is high
29
without a correspondingly high level of systemic knowledge and benefits from
modularity).
From a broader theoretical perspective, our research helps address a seeming
inconsistency in the knowledge-based view of the firm. For instance, Nickerson and
Zenger (2004) recently argued that “the two fundamental arguments within the
literature that support the efficiency of firms relative to markets in knowledge
exchange are fully contradictory. One claims that hierarchies essentially exist to avoid
knowledge transfer, emphasizing the firm’s capacity to exercise authority in directing
others actions. The other view claims that hierarchies exist instead to facilitate
knowledge transfer, emphasizing the firm’s capacity to support the formation of
shared language and identity”. We believe that our discussion shows that far from
being contradictory, these two views are complementary: On the basis of the
evolutionary dynamics we described, extensive knowledge sharing enhances interface
specification which thereafter reduces the need for extensive knowledge sharing over
time. The apparent inconsistency highlighted by Nickerson and Zenger disappears if
we recognize that the two advantages of hierarchies apply at different levels of
systemic knowledge (Nickerson & Zenger, 2004).
We hope that the framework developed in this paper will help to understand
both the benefits and the costs of the two coordination regimes- interface-based and
unstructured - as well as their dynamic properties, and lead to a more balanced,
empirically grounded descriptive theory, as well as more robust prescriptions for
organizational and ownership design.
30
Well-specified Poorly specified
Organizational interfaces
Reliance on Rules & regulations, Formal information systems, Standards
Reliance on cross-unit teams, Committees, Integration managers
Instances Design for Manufacturing processes(Puranam, Singh& Zollo,2006); Financial reporting systems in conglomerates (Markides & Williamson, 1996)
Cross-functional teams in product development (Iansiti, 1998); collocation of engineers in procurement relationships (Dyer, 1996); structural integration in technology acquisitions (Puranam, 2001)
Table 1: Well and poorly specified organizational interfaces
31
Table 2a
Table 2b
Task Structure Matrix
1 2 3 4 5 6 7 8 9 101 Initial engineering design X2 Design packaging X X3 Market research X X4 Manufacture X X X 5 Pick creative team X6 Finalize media capmpaign X X X7 Select packaging material X X 8 Prototyping X X X9 Select ad agency X
10 Design media campaign X X X X
Modularized Task Structure Matrix
1 3 8 4 9 5 10 6 7 21Initial engineering design X X3Market research X X8Prototyping X X X4Manufacture X X X 9Select ad agency X5Pick creative team X
10Design media campaign X X X X6Finalize media capmpaign X X X7Select packaging material X X2Design packaging X X
32
Figure 1: Coordination costs in unstructured and interface-based coordination regimes
Figure 2: Coordination competence and effects of interface adoption
33
Figure 3: Systemic knowledge in unstructured and interface-based coordination regimes
Figure 4: Coordination costs with delayed interface adoption
35
Figure 5: Coordination competence and effects of delayed interface adoption
Figure 6: The impact of shocks to systemic knowledge on coordination costs
Shocks to systemic knowledge
0.0000
0.2000
0.4000
0.6000
0.8000
1.0000
1.2000
0 10 20 30 40 50
Periods
Coo
rdin
atio
n co
sts
Without Interface
With Interface
With interface & shocks
36
Figure 7: Rare vs. Frequent shocks to systemic knowledge
Shocks to Systemic Knowledge
0.0000
0.2000
0.4000
0.6000
0.8000
1.0000
1.2000
0 10 20 30 40
Periods
Coo
rdin
atio
n C
osts
No InterfaceWith InterfaceI-rare shocksI-frequent shocks
37
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