Structure-Scope Matching: A Study of the Interrelationship between
Organization Structure and Innovation in the Communications Industry
By
Puay Khoon Toh
Ross School of Business
University of Michigan
701 Tappan Street
Ann Arbor, MI 48109
Tel: (734) 615-8667
E-Mail: [email protected]
Abstract
In this paper, I examine why firms persistently generate different types of resources and
organize their internal activities differently. In the search for antecedents of resource
heterogeneity, prior studies have demonstrated how organization structure influences the type of
resources that a firm creates internally through its innovative process. Thus far, we know less
about why organization structure may not be easily altered, such that resource heterogeneity is
sustained. Using a knowledge-based perspective, I examine how a firm’s structure of innovative
activities simultaneously affects, and is affected by, the scope of application of innovations it
creates. Specifically, in examining two aspects of organization structure – R&D task
specialization and inter-unit integration, I argue and show that both lesser task specialization
within and greater inter-unit integration between a firm’s R&D units lead to an accumulation of
knowledge that induces the units to generate wider-scope innovations. I then demonstrate that as
the units generate more wide-scope innovations, the use of these innovations will in turn reaffirm
the need for low R&D task specialization, and likewise for high inter-unit integration. This twoway interrelationship leads to a structure-scope matching, i.e. generalization (specialization) in
organization structure exhibits a tendency towards a matching with wide-scope (narrow-scope)
innovations. I advance this structure-scope matching as an explanation for the persistence in
resource heterogeneity, and also as a reason for why some firms may have difficulties in
adjusting their organization structures to adapt to changing environments or firm strategies. I
test my propositions in the context of firms from the U.S. communications equipment industry in
the years 1985-2003. This paper also contains a methodological contribution: I also introduce
new measures of scope of application based on a new technique of textual and language structure
analyses that captures the nature of innovations.
2
1.
Overview
Resource heterogeneity is a central concern in the study of firm strategy (Wernerfelt 1984;
Barney 1991). Prior studies typically start with the premise that firms have different resources (Peteraf &
Barney 2003), and proceed to explain why these resources may be difficult to transfer across firms
(Zander & Kogut 1995; Szulanski 1996), or how they may cause some firms to outperform others
(Henderson & Cockburn 1994). Yet, a lingering question remains as to why firms in a given environment
have different resources to begin with (Ahuja & Katila 2004).
To address this puzzle, recent studies focus on firms’ innovative behavior to understand why
firms may be generating different types of resources internally (e.g. Ahuja & Katila 2004). These studies
show that the type of innovation a firm generates depends on the strategies that it adopts, e.g. the way it
searches for new knowledge (Fleming & Sorenson 2004), or on the firm’s characteristics, e.g. the way it
organizes its internal innovative activities (Argyres & Silverman 2004). While this sheds much light on
the resource heterogeneity puzzle, we still lack a comprehensive understanding of the persistence of
different innovative behavior across firms, e.g. it remains unclear why firms are not changing their search
strategies or organization structure so as to homogeneously generate one type of innovation. Yet, it is
crucial to understand this persistence. Heterogeneous resource positions that are easily corrected do not
form the basis of firm performance differentials (Peteraf 1993).
In this paper, I examine why firms may persistently generate different types of innovations, and
in doing so, sustain a scenario of resource heterogeneity across firms. I propose an interrelationship
between organization structure and innovation, and through this, suggest why it may be difficult for a firm
to change the type of innovation it generates. I ask two research questions: (i) ‘how does organization
structure affect the type of innovation that a firm generates?’, and (ii) ‘how does the type of innovation
that a firm generates in turn influence the firm’s organization structure?’. In answering these questions, I
draw on a knowledge based perspective to formulate the mechanisms underlying my propositions. While
‘organization structure’ is a broad construct that encompasses numerous dimensions (see Burns & Stalker
1966), I focus on two that are more likely to be associated with the firm’s knowledge base – R&D task
3
specialization and inter-unit integration (see Pugh et al 1968; Birkinshaw et al 2002). For heterogeneity
in innovations across firms, I focus on the differences in the innovations’ scope of application (see
Klemperer 1990; Matutes et al 1996).
Two sets of hypotheses collectively elucidate my proposed two-way causal relationship between
organization structure and scope of innovation. First, I propose that with low R&D task specialization,
the innovations that an individual R&D unit within a firm generates tend to be of wider scope (and vice
versa). Likewise, the greater the inter-unit integration, the wider will be the unit’s scope of innovation.
Second, I propose that as an R&D unit generates more wide-scope innovations, the use of these
innovations will in turn reaffirm the need for low R&D task specialization (and vice versa). Likewise, the
wider the R&D unit’s scope of innovations, the greater will be its subsequent extent of inter-unit
integration. This two-way interrelationship leads to a stable structure-scope matching, i.e. matching
between the type of organization structure and the scope of innovation.
In short, generalization
(specialization) in structure induces the firm to produce generic (specialized) innovations, and the
effective use of generic (specialized) innovations requires the firm to remain generalized (specialized) in
structure.
Once a firm generates innovations of a particular scope of application, or structures its
innovative activities in a particular way, the structure-scope matching acts as an inherent force that retards
the firm’s abilities to subsequently change either its innovations or its organization structure.
I conduct my empirical analysis using longitudinal data on the ‘Communications Equipment’ and
‘Electronic Components and Accessories’ industries (SIC 366 and 367 respectively). My empirical study
contains a methodological contribution, in the form of new measures for scope of innovation that capture
language structures used to describe the innovations. Recent scholars have highlighted shortcomings of
existing measures of scope based on patents’ technology class assignments or patent citations (e.g. Lerner
1994; Hall & Trajtenberg 2004): that they do not adequately capture the nature of innovations (see e.g.
Allison et al 2003). During unstructured interviews, various patent lawyers and corporate licensing
experts expressed an insight that the nature of patented innovations, in terms of their scope of application,
may be more appropriately reflected in the language structures used in patent texts. As an attempt to
4
circumvent the shortcomings of existing measures, I use a new technique of textual coding to construct
measures of scope based on the language used in patent claims texts.
In the empirical analyses, I adopt a two-stage least square approach to determine the two-way
causal relationships between organization structure and scope of innovation. I use the exogenous shocks
– R&D state tax credit implementations, Northridge earthquake in 1994, and the Telecommunications Act
of 1996 – to predict changes in R&D task specialization, inter-unit integration, and scope of innovation
respectively, with a difference-in-difference approach in each prediction, and examine the effect of these
predicted changes in organization structure (scope of innovation) on scope of innovation (organization
structure). Findings support my hypotheses and confirm the presence of my proposed structure-scope
matching.
The proposed structure-scope matching has many potential contributions, other than constituting
a possible reason for why resource heterogeneity may persist. First, at a broad level, it illustrates an
instance of the generic relationship between structure of activities and the nature of resources, and
demonstrates how they mutually reinforce each other (for related discussions, see Sanchez & Mahoney
1996; Hoetker 2006). The way a firm organizes itself both affects and is affected by what it creates.
Second and relatedly, it stresses that a firm’s decision to be a specialist or generalist (e.g. Hatfield et al
1996; Mezias & Mezias 2000; Siggelkow 2003) can manifest itself in two forms: diversified-or-focused
nature of activities, and generic-or-specialized nature of outputs. While prior studies typically examine
firms’ conscious decisions in either form, e.g. the decision to specialize in activities in order to capture
increasing returns to scale and experience (Adam Smith 1965), few explicitly recognize that the choice in
one form may be influenced by a prior decision in the other. Third, it identifies a potential difficulty that
firms may face when they attempt to change their organization structure. Often, firms may need to adjust
their organization structure to adapt to changing environments (Lawrence & Lorch 1967; Galunic &
Eisenhardt 2001) or improve their search abilities (Siggelkow & Levinthal 2003). Yet, not all firms may
do so, and the structure-scope matching suggests that firms’ abilities to alter their organization structure
may be constrained by the resources that they have previously created.
5
Fourth, it constitutes an
alternative reason for why some firms may not survive radical shifts in their technological environments
(Tushman & Anderson 1986; Tripsas 1997). While a fit between organization structure and innovation as
described in the structure-scope matching may be beneficial to firms in stable environments, such a fit
may hamper the firm’s ability to adapt to subsequent radical changes and in doing so, threaten its
survival. Fifth, the structure-scope matching highlights the endogenous nature of organization structure,
and serves as a caution to future studies examining performance effects of organization structure.
The rest of the paper is structured as follows. Section 2 contains the theoretical development and
propositions in my dissertation. Section 3 describes the design of my empirical study. Section 4 reports
the findings, and section 5 concludes by elaborating on the potential contributions of my dissertation to
studies of organization structure and the innovation literature, as well as to the methods literature.
2.
Theoretical Development and Propositions
2.1.
Effect of Organization Structure on Scope of Innovation
The organization of internal activities is to some extent a decision variable that firms may
manipulate to achieve their strategic objectives. Firms alter their organization structures to accommodate
changing internal characteristics, e.g. expansions in firm size or diversity (Chandler 1962; Starbucks
1965; Burns & Stalker 1966), or to adapt to shifts in external environments, e.g. increases in uncertainty
(Lawrence & Lorsch 1967; Osborn & Hunt 1974). Structural changes are also typically required to
complement other firm strategies (Miller 1986). For instance, firms may adopt low divisionalization in
their organization structures to support strategies of R&D diversification (see Argyres 1996).
Like most other firm decisions, adopting a particular organization structure can impact a firm in
more ways than originally intended. Past researchers have adopted various perspectives in analyzing the
different ways and mechanisms through which organization structure affects firm performance. These
perspectives include agency theory or transaction costs economics, which typically assert that the degree
of centralization shapes incentives for R&D investments (Singh 1986; Hoskisson & Hitt 1988; Argyres &
Silverman 2004). Scholars using a modularity perspective study how the nature of linkages between
6
components within a structure may influence a firm’s flexibility towards changes (Sanchez & Mahoney
1996; Baldwin & Clark 2000; Galunic & Eisnehardt 2001).
Scholars in the product development
literature stress how mechanisms facilitating inter-unit integration within an organization structure may
enhance communication flow and coordination between units (e.g. Ha & Porteus 1995; Nadler &
Tushman 1997).
One perspective that has received less attention in this regard is the knowledge-based view of the
firm. While it is clear that the nature of a firm’s knowledge base affects its innovative outputs (e.g. Pakes
& Griliches 1984; Henderson & Cockburn 1996; Rosenkopf & Almeida 2003), thus far we have few
insights on how knowledge base may in turn be shaped by the way a firm organizes its internal activities.
Prior studies typically treat the firm on the whole as an innovative unit, and often do not recognize that
the firm’s entire knowledge base may not be accessible to each individual unit within the firm. However,
in reality, the portion of the firm’s knowledge base each unit can access may depend on how the units are
structured, and that two firms having identical knowledge bases may yet innovate differently if the
innovative units are structured differently within the two firms.
To use a knowledge-based lens in establishing the impact of organization structure on a firm’s
innovative outputs, I first make an assertion that the way a firm organizes its activities affects the
contextual knowledge that each individual unit possesses. As my objective is to explain why firms
develop different resources, I focus on the organization of units that are involved with resourcegeneration, i.e. innovative (R&D) units. Contextual knowledge refers to the understanding that each
innovative unit has about the firm’s lines of businesses. This involves knowledge about constraints faced
by downstream functions like production and sales when implementing new projects, and includes
understanding the feasibility of implementing new ideas, the nature of the downstream markets, the
specific requirements that clients may have, etc. (for related concepts, see Von Hippel 1988; Mitchell
1992; Ettlie 1995).
As a stylized illustration of the stereotypical ‘ivory tower’ R&D units with little contextual
knowledge, consider the R&D operations within Bell Labs in the early days. The inventions of major
7
technologies like the laser and transistor were done without full appreciation of what they may be used for
and how they may fit in the then-existing applications. This is in contrast with innovative teams for many
application software, e.g. Stata, which accumulate contextual knowledge by corresponding closely with
users and understanding their needs, so as to know the appropriate program upgrades to invest in (for
discussion of customer involvement in innovation processes, see Von Hippel 1988). The notion of
contextual knowledge is also inherent in the choice of product-process integration (see Ettlie 1995),
where a firm decides how closely to integrate its product-design and process-engineering teams so as to
enhance its product team’s appreciation of the contextual constraints faced by the accompanying process
team.
Often, the decision on the internal structure of R&D units involves a tradeoff between depth and
breadth of contextual knowledge that each R&D unit may have. Depth corresponds to the amount of
contextual knowledge that an R&D unit has about a particular line of business.
Breadth may be
conceptualized as the number of lines of businesses about which the R&D unit has contextual knowledge.
Broadly speaking, there are two distinct approaches towards structuring R&D units. The first is to
incorporate greater generalization in structure such that each unit is involved in multiple types of projects
and detached from the downstream activities. This is likely to provide the R&D units with wider breadth
of contextual knowledge, at the expense of in-depth knowledge about any single context. In short, the
individual R&D units ‘know little about many contexts’. Some examples of R&D units approximating
such a structure would be the Bell Labs within AT&T in earlier years and other ‘incubator labs’. The
second approach is to incorporate greater specialization in structure such that the R&D units are more
closely connected to their respective downstream activities. This tends to provide greater depth of
specific contextual knowledge, at the expense of breadth. In other words, the individual R&D units
‘know a lot about few contexts’. Examples of units more likely to adopt this structure are software
development teams that work closely with end-users, product-design teams that are closely integrated
with their supporting process-engineering teams, consumer-product R&D units that communicate closely
with the sales function, etc. Although scholars often preach the benefits of having an ‘ambidextrous’
8
organization structure (O’Reilly & Tushman 2004) and achieving both differentiation with integration
within the firm structure (Lawrence & Lorsch 1967), it is difficult in practice to attain both depth and
breadth of contextual knowledge for each individual unit, and tradeoff is likely to be inevitable in many
instances.
Understanding how this depth-breadth tradeoff induces R&D units to develop different resources
brings us one step closer towards clarifying the link between organization structure and resource
heterogeneity1. Resource heterogeneity may arise when firms internally generate different types of
innovations (Ahuja & Katila 2004). One such difference is the innovations’ scope of application (Lerner
1994). Scope refers to the region of ‘product space’ relevant to the innovation, where ‘product space’
may be perceived as the distance from the point of core application to the point where the innovation has
no applicative relevance (Klemperer 1990; Matutes et al 1996). An extreme example of a wide-scope
innovation is the transistor – a fundamental technology used in virtually every electronic device existing
today. The computer mouse on the other hand, despite its wide-spread use, has a more restricted scope of
application than the transistor. Scope of application is different from scope of search and knowledge
inputs that had generated the innovation (see Argyres & Silverman 2004). The former refers to how
widely the innovative output may be used, whereas the latter refers to the diversity of inputs used to
generate the innovative output. To illustrate this difference, consider the binary code, which was not
invented based on a wide span of knowledge but is used in multiple applications ranging from computer
programming to various forms of data transmissions. Somewhat in contrast, we often observe research
papers drawing on multiple disciplines of studies to establish core arguments, only to contribute towards
advancing a narrow branch of literature.
1
Note that resource heterogeneity here refers to firms developing different types of resources, rather than some
firms developing more resources than others. Indeed, this study is not the first to examine why firms with varying
knowledge base will generate different types of resources. For example, studies have shown that firms with greater
components of science within their knowledge bases are better able to absorb distant knowledge (Cohen & Levinthal
1990) and generate breakthrough inventions (see Henderson & Cockburn 1994; Ahuja & Katila 2004). The
variation in depth-breadth mixes of contextual knowledge represents another way that knowledge bases may differ,
that is thus far under-explored.
9
Variations in scope of application are as considerable a source of heterogeneity, in terms of
economic value (see Lerner 1994; Lanjouw & Schankerman 2004), as variations in other key dimensions
along which innovations may differ, e.g. radical-incremental (Tushman & Anderson 1986), architecturalmodel (Henderson & Clark 1990), product-process (Toh & Ahuja 2006), etc. In the extreme, wide scope
innovations take the form of highly-valuable general-purpose technologies (Bresnahan & Tratjenberg
1995; Hall & Tratjenberg 2004). Through the works of Penrose, we also learn that fungible resources can
spur firm growth through product-market diversification (see Anand & Singh 1997; Silverman 1999).
Wide-scope innovations, by being applicable in many fields of use, are likely to be one form that these
‘fungible’ resources may take. For instance, Honda’s innovations on leading-edge technologies for
internal combustion engines have enabled it to leverage a strong presence across various product markets
like boats, planes, and automobiles.
Looking at how the depth-breadth tradeoff may influence the firm’s scope of innovation is thus a
meaningful way to assess the role of organization structure in creating resource heterogeneity. When
R&D units have greater depth of contextual knowledge, they are likely to have greater abilities at
generating narrow-scope innovations, i.e. focused solutions for problems specific to the individual units.
Likewise, breadth of contextual knowledge improves the ability to generate wide-scope innovations, i.e.
generic solutions applicable in various settings. These abilities come in two forms. First is the individual
R&D unit’s ability to recognize opportunities for innovations.
Opportunities typically arise when
problems and needs are revealed through the course of operations (see ‘problemistic search’ in Cyert &
March 1963). For a problem that is specific to a context, the individual unit often needs to possess indepth knowledge of the particular operation within the relevant line of business, in order to spot the
problem and appreciate its nature. Due to the specificity of the problem to the context, the innovative
solution that the R&D unit creates as a response tends to be of limited applicability in other contexts (i.e.
the innovation is of narrow scope). For a problem that is more widespread, i.e. pertains to multiple lines
of business, having greater breadth of contextual knowledge helps the individual R&D unit recognize the
problem’s commonality and assess the full value of seeking a solution. Being aware of the need to apply
10
the solution in various contexts also guide the design of the solution to be more wide-scope in nature. In
short, depth raises a firm’s awareness of opportunities for narrow-scope innovations, while breadth brings
to light the opportunities for wide-scope innovations.
Second is the individual R&D unit’s ability to successfully find solutions to problems that are
identified. R&D projects are inherently uncertain with no guarantee of successful innovative outputs. Indepth understanding of a context helps the individual unit improves its chances of successfully generating
narrow-scope innovations. As a narrow-scope innovation is often designed to address a context-specific
problem, how viable the innovation is relies on its fit with the particular context. The extent of fit in turn
depends on how well the innovating unit understands the specifics of the context. This parallels the case
of an academic researcher making a marginal contribution towards a particular branch of research: a deep
understanding of the extant literature on a particular topic is often necessary before the researcher can
make a valid contribution that is highly specific to that topic. However, in-depth contextual knowledge is
less helpful towards generating wide-scope innovations, since the fit between any single context and the
innovative solution is less crucial.
In contrast, breadth of contextual knowledge improves the R&D unit’s chances of succeeding in
wide-scope innovations. While generic problems spanning many contexts may be more costly to the
firm, they are also exposed to many contexts through which potential solutions may be found. Solutions
for these wide-spread problems may sometimes be created by incrementally tweaking knowledge learnt
from one context and exporting it to another. R&D units with greater familiarity of multiple contexts are
more likely to be aware of these opportunities. Naturally, this process of pooling knowledge across
contexts lead to wide-scope innovations that can in turn be used in various settings. Also, a successful
creation of an innovation hinges heavily on the experimentation process, i.e. the testing of a new scientific
principle in a particular context (see Pisano 1994; Thomke 1998). Being aware of a variety of contexts
may facilitate the R&D unit in selecting the most appropriate one for the experiments. On the other hand,
when the innovation is designed to solve an idiosyncratic problem in a single context, i.e. narrow-scope,
knowing about multiple contexts does little to improve the R&D unit’s chances at succeeding at
11
generating this innovation. In sum, the depth-breadth tradeoff underlying the structure of R&D units
implies that greater abilities in recognizing and succeeding in one type of innovation are often associated
with lesser abilities at the other. Accordingly, the structure of R&D units within a firm will affect the
scope of their innovations.
A few recent studies have also examined the effect of R&D structure on firm’s scope of
innovation (Argyres & Silverman 2004) and subsequent R&D diversification (Argyres 1996). These
studies have mainly used a transaction costs framework, with incentive-based mechanisms rather than
knowledge-base ones.
The main difference between such incentive-based mechanisms and the
knowledge-based one used in my study is that in the earlier, individual units choose innovation projects
with the objective of minimizing transaction costs, whereas in the latter, individual units’ choices of
innovative projects are determined by the types of knowledge they are endowed with in the first place (for
related discussions, see Conner & Prahalad 1996; Williamson 1999; Madhok 2002).
Looking more closely at what constitutes organization structure provides some hints for how
these two mechanisms may be separated. Starting with the framework of ‘mechanistic’ and ‘organic’
structures as two polaristic structural forms (Burns & Stalker 1966), it is clear that many dimensions of
different structural characteristics connect these two extremes (see Ford & Slocum 1976; Dalton et al
1980; Nayyar & Kazanjian 1993). The myriad of different characteristics include the extents that: tasks
are specialized, procedures are standardized, standardized procedures are formally documented, loci of
authority and control are centralized; different units are integrated, and also spans of vertical and
horizontal control, etc (see Pugh et al 1968; Birkinshaw et al 2002). When examining the incentive-based
effects of organization structure, prior studies have focused on centralization of authority and control (see
Argyres 1996; Argyres & Silverman 2004), which is consistent in spirit with other studies of incentivebased determinants of innovations (e.g. Aghion & Tirole 1994; Rotemberg & Saloner 1994; Rajan et al
2000).
Task specialization and inter-unit integration, on the other hand, more closely capture the
knowledge bases residing within the individual units at the bottom of a hierarchy. Arguably, two firms
with identical sets of highly task-specialized R&D units may experience different degree of centralization,
12
depending on how these units are structured in a hierarchy. Likewise, it is likely that we may observe
collaborations between two separate units in both instances where they are centrally controlled by the
same source higher up in the hierarchy and when they are decentralized in their decision-making
hierarchy.
With increasing specialization in innovative tasks, individual R&D units gain depth of contextual
knowledge at the expense of breadth. Task specialization in this case refers to how focused each R&D
unit is towards particular types of technologies. An R&D unit may be dedicated to and be involved in
projects pertaining specifically to a particular technological nature.
This often involves physically
situating the R&D units next to the downstream activities like production and sales so that the R&D units
can gain deeper understanding of the context in which the technologies are applied. Alternatively, R&D
projects pertaining to various types of technologies may be conducted within fewer R&D units, and each
unit is not dedicated to any particular line of business within the firm. This often involves centrally
locating R&D units, and results in the individual units have less depth but greater breadth of contextual
knowledge. Based on arguments established earlier on the effect of the depth-breadth tradeoff on scope
of innovation, I arrive at the following hypothesis.
Hypothesis 1: The less the current extent of task specialization for each R&D unit within the firm, the
wider will be the scope of the R&D unit’s subsequent innovations.
Inter-unit integration represents another dimension of organization structure that is more closely
related to the underlying knowledge base. As defined in Birkinshaw et al (2002), inter-unit integration
refers to ‘the state of collaboration among units, and the techniques used to achieve this collaboration’.
As a further clarification, the word ‘integration’ here does not refer to the ownership, control or residual
claims as it often does in studies of firm boundaries (e.g. Pisano 1990). How closely integrated the R&D
units are translates into how frequently the separate units collaborate on R&D projects.
Through
communications when collaborating on R&D projects, the units attached to particular lines of businesses
tend to transfer knowledge about their respective contexts to each other. This in essence generates greater
13
breadth of contextual knowledge for each individual R&D unit. Also, all else equal, it is likely that units
that frequently collaborate with other units are less able to devote effort in understanding its direct line of
business. This may result in less depth of contextual knowledge. Again, based on the effects of the
depth-breadth tradeoff established earlier, I arrive at the second hypothesis.
Hypothesis 2: The greater the current extent of inter-unit integration between an R&D unit and other
units within the firm, the wider will be the scope of the R&D unit’s subsequent innovations.
2.2.
Effect of Scope of Innovation on Organization Structure
The two hypotheses above are sufficient to demonstrate how differences in organization structure
contribute towards creating resource heterogeneity across firms. However, they say little about why this
heterogeneity is sustained. If organization structure is indeed easily manipulable by the firm, then there
needs to be a theory explaining why firms choose to organize their activities differently, in order to
establish the role that organization structure plays in resource heterogeneity. Alternatively, one may seek
to understand why organization structure, once determined, may not be easily changed.
I adopt the latter approach in examining how the existing stock of a particular type of innovative
outputs, resulting directly from the firm’s existing organization structure, may subsequently retard a
firm’s ability to change this existing organization structure. A firm’s knowledge base is dynamic in
nature. From the knowledge base, the firm generates innovations. The new knowledge embedded within
these innovations in turn contributes back to the knowledge base and changes its composition in the
process.
For example, as a firm generates more wide-scope innovations, its knowledge base also
accumulates more knowledge components of wide-scope applicability derived from these innovations.
With such a ‘feedback’ effect, a natural follow-up question would be whether this change in
knowledge base subsequently induces the firm to adjust its structure in order to better manage the altered
knowledge base. Some recent studies have argued that firms’ knowledge bases are relevant contingencies
for their organization structures, i.e. firms do adjust their structures in order to best leverage and manage
the types of knowledge they possess (Hedlund 1994; Birkinshaw et a 2002). The emphasis in these
14
studies is on development needs of the knowledge base, i.e. what is required for developing and managing
knowledge, rather than what it takes to create the knowledge in the first place. In other words, given the
particular types of knowledge to start with, firms consider what would be the best way to organize them,
through the structuring of R&D units, to achieve efficient development and use of the knowledge.
Although these studies focus on variants of the tacit-explicit dimension of knowledge (see Winter 1987;
Zander & Kogut 1995), the basic principle is plausibly generalized to other characteristics of the
knowledge base, e.g. scope of application.
If changing the scope of the individual components within a firm’s knowledge base induces the
firm to adjust its structure, then it is important to identify the direction of this effect. Suppose having
wide-scope innovations induces firms to reduce inter-unit collaboration or increase task specialization.
Then the effects proposed in the earlier two hypotheses above may be unstable, and the full magnitudes of
effects of organization structure will probably be overestimated. Alternatively, suppose that wide-scope
innovations induce firms to subsequently increase inter-unit collaboration or reduce task specialization.
Then a ‘spiral’ effect may exist, where the dual relationships between scope and structure would cause
firms to spiral towards a specific scope-structure matching, and the earlier two hypotheses would have
understated the total effects of organization structure in scope of innovation.
I argue that wide-scope innovations are more likely than narrow-scope ones to benefit from
having low R&D task specialization. Consider wide-scope innovations whose developments require joint
involvement of multiple contexts. An example would be technologies that connect two or more products,
like the Bluetooth technologies, where implementing the technologies clearly involve more than one
product group. In these instances, overly specialized R&D units may lead to coordination problems
(Becker & Murphy 1992). Each unit specialized in one context may not understand the complications
and constraints arising in other contexts. Each unit may maximize the value-generation locally, without
factoring in returns to other teams, i.e. each unit may prefer to develop the innovations in particular
manners that may not be optimal for the other teams. For wide-scope innovations whose developments
may be performed independently within one context, excessive task specialization can also be
15
problematic. Overly-specialized units that own and have developed the wide-scope innovations may not
be aware of full value of these innovations, in terms of their applicability in other contexts. Less
specialized R&D units are more effective towards reducing these problems of coordination and underutilization.
Narrow-scope innovations, because they require less inter-unit coordination during the
development phases and are less applicable in other contexts, tend to benefit less from having a low level
of R&D task specialization.
In contrast, R&D units responsible for developing narrow-scope innovations typically require
greater task specialization and closer link with the relevant downstream functions. As mentioned earlier,
narrow-scope innovations are often designed to solve specific, context-dependent problems. Because of
this context-specificity, the development process requires close coordination between R&D units and the
downstream functions.
For example, to implement an innovation that is designed specifically for
improving a particular production process, the R&D unit needs to work closely with the process team.
Personnel in the process team need to be trained regarding the use of the innovation. Also, when
implementing the innovation, the R&D unit often needs to make small tweaks or adjustments to the
innovation ‘on the ground’. Hence, having narrow-scope innovations reaffirms the need to have high
R&D task specialization. Since wide-scope innovations are typically less context-specific, the marginal
benefit of having high R&D task specialization is accordingly less than that of narrow-scope innovations.
Hypothesis 3: The narrower the scope of the R&D unit’s current innovations, the greater will be the
subsequent extent of task specialization for each R&D unit within the firm.
Similarly, having wide-scope innovations is more likely to induce R&D units within the firm to
adopt greater inter-unit integration, relative to having narrow-scope innovations.
For wide-scope
innovations that require joint developments by different R&D units, such joint involvement in essence
forces these different units to communicate more than they otherwise would otherwise.
Frequent
communications increase the recognition of common problems and foster new ideas. As a result, it is
more likely that these different units will subsequently collaborate on other innovative projects. For
16
wide-scope innovations that are developed independently within one unit, the exporting of these
innovations for use in other contexts also increase communications between the different R&D units.
Again, these communications generate new ideas and subsequently increase the instances where the R&D
units collaborate in new projects. On the other hand, narrow-scope innovations often do not require joint
involvement of different R&D units in the development phase, since their application is often restricted to
particular context. This leads to less communications with other R&D units, which accordingly reduce
opportunities for subsequent collaborations.
Hypothesis 4: The wider the scope of the firm’s current innovations, the greater will be the subsequent
extent of inter-unit integration for each R&D within the firm.
It is pertinent at this point to discuss the underlying assumption about firm behavior that is
implicitly made in the last two hypotheses.
As in most studies where the causality between two
constructs runs both ways and at least one construct involves conscious decision-making, one may
question why the ‘feedback’ effect is not accounted for when the decision on one construct is being made.
In this case, one may ask: why does a firm choose to invest in a type of innovation that its current
structure cannot optimally support during subsequent development, such that the firm has to adjust its
structure subsequently? In a world of perfect rationality, one may imagine that the firm would form a
complete set of expectations for all current and future innovations that may arise given the current way it
is structured, their associated values and costs, costs of having to adjust its structure subsequently, etc. In
such a world, we may not see firm structure adjusting to the scope of innovation.
However, if one allows organization structure to be ‘sticky’, then the ‘feedback’ effect is likely to
be observed. For instance, a firm with lower degree of R&D task specialization is able to recognize
opportunities for wider scope innovations (see hypothesis 1).
Development of these wide-scope
innovations may require even lower R&D task specialization than the firm’s current level, but the firm
takes time to adjust its structure subsequently to ‘fit’ these wide-scope innovations. Alternatively, one
could assume a certain degree of bounded rationality of the firm (see Simon 1962; Cyert & March 1963).
17
At the point of investing in innovations, the firm may not be fully aware of the developmental needs
imposed by its current and future innovations it invests in. As the initial organization structure drives the
firm to accumulate more of one type of innovation over time, the organization structure itself becomes
unstable and needs to be adjusted for more efficient management and development of the innovations.
Also, I note here that scope of innovation is not just driven by organization structure alone. When other
factors cause firms to change the scope of innovations they invest in, the mechanisms laid out in
hypotheses 3 and 4 will still work towards driving the firms to change their structures subsequently.
3.
Methods
I test my hypotheses using longitudinal data on the ‘Communications Equipment’ and ‘Electronic
Components and Accessories’ industries (SIC 366 and 367 respectively) for years 1985-2003. This
empirical setting is appropriate for various reasons. First, issues concerning structuring of innovative
activities and innovative outputs are relevant here, as firms in these industries exhibit some of the highest
R&D intensities across all industries (comparable to pharmaceutical firms. See Fransman 2002). Second,
the wide variety and hierarchies of related technologies within these industries provide an ample mix of
innovations with different scope of application2. Third, with the trend towards interconnectedness and
inter-operability between multiple networks in these industries (Fransman 2002), as evident from the
numerous standards bodies set up in recent years (e.g. IEEE, EIA, ITU, etc.), it is likely that R&D units
operating in different technological areas within a firm need to collaborate frequently on R&D projects.
Fourth, firms’ abilities to alter the nature of their technological resources are crucial in this setting, as
2
For instance, for telecommunication applications alone, related technologies include circuit switch and signaling
systems connecting the end-users, data-transmission systems, customer-premise equipments like servers and routers,
communications protocol connecting different networks, network technologies like Ethernet and voice-data
convergence technologies, etc (see Green 2000).
18
there are often substantial threats of substitution arising from multiple competing technologies performing
similar functions3.
3.1
Variable Definition and Operationalization
Innovation’s Scope of Application. This variable reflects how widely each individual innovation
may be applied, and follows closely the concept of coverage over product space (Klemperer 1990;
Matutes et al 1996). Prior studies have mainly used the number of technology classes a patent is assigned
to, or the number of patent claims, as proxies for the innovation’s scope of application (Lerner 1994;
Lanjouw & Schankerman 2004). Unstructured conversations with various patent lawyers and corporate
licensing experts suggest that these measures may not adequately capture the nuances of ‘application
space’ covered by the innovations. For instance, a patent assigned to only one technology class may have
broad applications within the class; while another patent assigned to multiple classes may only cover
narrow and specialized applications within each class. The earlier patent does not necessarily have
narrower scope of application than the latter. Similarly, a patent with only one generic claim constituting
a vital component in many subsequent uses may not have narrower scope of application than another
patent with many incremental claims of insignificant improvements over prior art. Rather, the consensus
among these practitioners is that the nature of underlying technologies may be more accurately observed
through the manner in which the patent texts are drafted.
In an effort to address the above concern, I introduce two new measures of scope of application
that closely relate to the language used in describing the innovations, based on textual coding of claim
texts. Claim texts are appropriate for my purpose as they document the details of novelties embedded
within innovations (Lanjouw & Schankerman 2004). First, Scope1 measures the average number of
independent claims per patent that R&D unit j in firm i applies for in year t. An independent claim states
an unprecedented element of novelty that is not partially described elsewhere in the patent; whereas a
dependent claim represents an extension or elaboration over a previously-described claim in the patent. A
3
For example, different mediums of data transmission compete against each other, like fiber optics light wave
transmission, twisted-pair copper wire and coaxial cables, microwave, radio wave, power lines, satellite
transmissions, etc (see Green 2000).
19
hypothetical example of an independent claim may be ‘a table comprising of three legs’, whereas a
corresponding dependent claim would be ‘a table, as described in claim # (where # refers to a number),
wherein one of the legs has a wheel attachment…’. Accordingly, an independent claim typically contains
greater novelty than a dependent claim (Sakakibara & Branstetter 2001); and the ‘application space’
covered by an independent claim is usually larger. I identify a dependent claim as one that incorporates
the word structure ‘claim #’ in the text. This includes all claims that specify ‘as defined in claim #’, or
‘according to claim #’, or ‘as claimed in claim #’, etc. All other claims are coded as independent claims.
Second, Scope2 captures the types of transition phrase used in claims, specifically, the average
width of transition phrases used in claims within patents that R&D unit j in firm i applies for in year t.
Each independent claim consists of 3 parts: the preamble, followed by the transitional phrase, and then the
body of the claim (see Radak 1995). The preamble sets forth the general technical environment of the
innovation by stating in general terms what the innovation relates to. In the earlier example, the preamble
is ‘A table’. The body describes the detailed composition of the innovation, i.e. the elements of the
innovation and how they are interrelated. This may come in the form of a series of mechanical parts and
how they are put together, or it may be a series of steps in a production or application process. The
transitional phrase connects the preamble to the body of the claim. There are 3 transition phrases used: (i)
words based on the root word ‘comprise’ (e.g. ‘comprising’, ‘comprises’, etc), (ii) words based on the
root word ‘consist’, and (iii) ‘consisting essentially of’. ‘Comprise’ is an open term, and applies to all
innovations that include all elements and more specified in this claim. For example, if the claim is ‘a
table comprising of three legs’, then it applies to all subsequent innovations of tables with three or more
legs. ‘Consist’ is a closed term, and applies to only innovations that contain the exact same elements as
specified in this claim. For example, if the claim is ‘a table consisting of three legs’, then it only applies
to all subsequent innovations of tables with three legs, but not those with four or more legs. ‘Consisting
essentially of’ is a part-open and part-closed term, and applies to all innovations where the vital elements
are the same as those specified in this claim. For example, if the claim is ‘a table consisting essentially of
three legs’, then it applies to all subsequent innovations of tables with three or more legs, if the other
20
incremental legs are not vital to the innovations.
Hence, ‘comprise’ accords the widest scope of
application, followed by ‘consisting essentially of’, and then ‘consist’. Note that firms do not always
have the incentives to use ‘comprise’, as broad claims have lower chances of surviving patent
reexamination processes when their validities are challenged, and it is harder for the patent owner to
establish patent infringement (by competitors) with broad claims. Also, the eventual transition phrase
used is often decided based on a negotiation process between the patent office and the firms, driven by
factors like the nature of the underlying innovations, nature of existing patents, etc.
R&D Task Specialization. This variable refers to the extent that R&D units within the firm are
specialized in their innovative tasks, i.e. how focused they are towards particular areas of technologies. I
introduce two new measures that reflect the overall nature of projects undertaken by each R&D unit
within the firm. First, TaskSpec1 captures the extent that an R&D unit is specialized in particular
technology classes. For each patent applied for by firm i that involved R&D unit j in year t, I note the
main technology class it is assigned to. Then, I calculate a concentration ratio of main technology classes
for all patents applied for by this R&D unit j in year t. To illustrate with an example: suppose in year t,
R&D unit j is involved in two patents, one assigned to technology class A and the other to technology
class B. The value of TaskSpec1 for unit j is then 0.25 + 0.25 = 0.5. Note that if unit j was involved in
three patents, with two assigned to technology class A and the third to technology class B, it would have
been more specialized (in class A) than in the earlier scenario, and the corresponding value of TaskSpec1
would be 0.56.
Second, TaskSpec2 captures the extent that individual inventors within an R&D unit are
specialized in particular classes of technologies. This measure represents a more fine-tuned version of
TaskSpec1, as it captures instances where an R&D unit as a whole may not appear specialized but houses
inventors who are individually specialized. For each individual inventor k involved in patents associated
with R&D unit j in year t, I compile a list of all patents the inventor k is involved in. Then, I calculate a
concentration ratio of main technology classes for all patents involving inventor k in year t. Next, I
calculate the average across all inventors involved in patents associated with R&D unit j in year t. To
21
illustrate with a similar example as before: suppose R&D unit j has two inventors – Kenny and Cartman –
being involved with two patent in year t, one assigned to technology class A and the other to class B.
Kenny is only involved with one patent, whereas Cartman is involved with both patents.
The
concentration scores for Kenny and Cartman are 1 and 0.25 + 0.25 = 0.5 respectively, and the value of
TaskSpec2 is (1 + 0.5)/2 = 0.75. Note that if Kenny and Cartman were involved with only one patent
each, then greater task specialization would have occurred, even though R&D unit j as a whole had the
same patents as before. The measure TaskSpec2 reflects this greater task specialization by taking on a
greater value of (1 + 1)/2 = 1.
Inter-Unit Integration. This variable indicates the extent that different R&D units within the firm
collaborate on R&D projects. I infer the project collaborations from the inventors’ locations. Each patent
filed by R&D unit j in firm i at year t scores a ‘1’ if the inventors involved are in more than one location,
and ‘0’ if the inventors are all from the same location. I then calculate IUinteg as the ratio of ‘1’s in that
unit-year4. To illustrate with an example: suppose R&D unit j was involved with two patent applications
in year t. The first patent involved another unit, whereas the second patent did not. Accordingly, IUinteg
for unit j takes the value of (1 + 0)/2 = 0.5. Note that if both patents individually involved other R&D
units, then greater inter-unit integration would have occurred, and the value of IUinteg would be (1 + 1)/2
= 1.
Control Variables. With growth, firms may both organize their activities differently (Chandler
1962) and have greater slacks allowing them to invest in wide-scope innovations with greater uncertainty
of applicability, as consistent with the Schumpeterian tradition. I control for these potential confounding
effects with the variable Firm Size, which measures the number of employees of the firm. To control for
the possibilities that R&D-intensive firms opt for greater task specialization within their R&D functions
to manage the diverse set of projects, and that these R&D-intensive firms are more inclined towards wide-
4
Other than capturing whether or not the R&D units were collaborating on projects, the construction of this measure
also assigns a weight for the extent of collaboration. Note that this measure does not assume that one patent
corresponds to one R&D project. Rather, it factors in the importance of the R&D project, i.e. a larger scale project
would likely result in more patents, and hence is assigned a greater weight in the measure.
22
scope innovations, I include the variable Total Patents as a proxy for the firm’s R&D intensity. This
measures the firm’s total number of patent application in year t. To some extent, including this variable
mitigates the potential problem that narrow scope as indicated in my measures may reflect a firm’s
strategic separation of a single wide scope innovation into multiple narrow patent filings. To capture any
year-specific effects that may explain changes in scope of innovations as well as organization structure, I
include year dummies in my analyses.
3.2.
Data Sources and Sampling Frame
I form the sample by selecting all firms from two related industries: ‘Communications
Equipment’ (SIC 366) and ‘Electronic Components and Accessories” (SIC 367) in the Compustat
database for years 1985-2003. The CUSIP numbers of these firms are then matched with the assignee
names used by the U.S. Patent and Trademark Office (USPTO), based on the matching file provided by
NBER. Assignee names and the list of all U.S. patents applied for within the sample range are obtained
from the Cassis database. Each assignee is matched to at most one CUSIP number, but multiple assignees
may be matched to a single CUSIP. This occurs when the assignee names represent different divisions
belonging to the same parent company (identified by its CUSIP). The matched sample contains 164 firms
(unique CUSIP) with a total of 222 assignees names.
The unit of analysis is at the level of individual R&D unit within each assignee name. I use
information on the cities in which the firm’s inventors reside, as available from the Cassis database, as
proxies for the firm’s R&D units. To ensure that no duplicates of R&D units are created because of
spelling errors or differences in the way the cities are named across observations, I manually check the
cities names across all 10,005 observations at the assignee-state-city levels, using as reference the list of
U.S. city names available at www.city-data.com. I treat city names within the same state that are based
on the same root words, and that do not show up as separate cities in the reference list, as the same city.
For example, city names of ‘Anaheim Hills’ in California are matched with ‘Anaheim’. Also, city names
with differences in spacing used in the names are merged. For example, names of ‘Sugar Hill’ are treated
as the same city as ‘Sugarhill’. Similarly, I checked for inconsistent acronyms across observations, e.g.
23
‘Fort Lauderdale’ and ‘Ft. Lauderdale’ in Florida. Finally, city names with small differences in spelling
are matched, e.g. ‘Rancho Palos Verdes’ and ‘Racho Palos Verde’ in California. To further enhance the
accuracy of using cities as proxies for R&D units, I drop all observations (R&D unit-year) where only one
patent has been applied for. This removes instances where a unit spuriously exists due to spelling error in
the city name. The remaining sample contains 3449 R&D units across 115 assignees belonging to 102
firms.
Despite the above efforts, the use of cities as proxies of R&D units may still be flawed when
inventors within the same R&D unit live in and commute from different cities with close geographic
proximity. This may lead to over-estimations of R&D task specialization and inter-unit integration. The
alternatives of using the Metropolitan Statistical Areas (MSA) or states as proxies for R&D units, while
they mitigate the above problem, may suffer from over-aggregation, i.e. there may be more than one R&D
unit within an MSA or state for each firm. To ensure that findings are not sensitive to specifications of
R&D units, I subsequently re-classify the inventor locations by MSA, and repeat all analyses (work in
progress).
To generate measures for scope, I first obtain the list of all patents applied for by assignees in the
sample, from the Cassis database. The 115 assignees filed for a total of 70,218 patents during the sample
range. I then create a Java-based language-parser program according to the heuristics described earlier,
and execute this parser on patent texts available electronically from the USPTO. The variables are coded
at the level of individual claims within each patent, and subsequently aggregated at the patent level. Next,
I match the coding to the individual R&D units, based on the location of relevant inventors. Note that an
individual patent is matched to more than one R&D unit when the patent involves inventors residing in
multiple units. Each R&D unit on average was involved in approximately 10 patent applications in a
year, with a standard deviation of around 32 patents and a maximum of 1154 patents per year.
The CUSIP-assignee match used in defining the starting sample may be unstable as firms divest
or acquire new divisions over the years, resulting in inaccuracies when merging the control variables at
the firm level to the R&D units. However, the use of exogenous instruments in my empirical design
24
(described later) largely reduces the impact of these inaccuracies on the empirical tests. Nonetheless, to
further minimize these inaccuracies, I subsequently trace the ownership changes for each of these firms
over the sample range based on information from Who Owns Whom and the Mergers & Acquisitions
database within SDC, and then construct the firm-level control variables using data from the Compustat
database (work in progress).
3.3.
Instruments for Organization Structure and Scope of Innovations
The empirical testing of my hypotheses involves simultaneous equations, i.e. two-way causal
relationships between organization structure and scope of innovation.
I adopt 2-stage least square
estimations with exogenous shocks using difference-in-difference approaches in my first-stage equations
(see Wooldridge 2002). The shocks – R&D state tax credits enactments, Northridge earthquake in
California in 1994, Telecommunications Act of 1996 – serve as instruments for changes in organization
structure (R&D task specialization and inter-unit integration) and scope of innovation respectively. I also
use the extent that firms generate innovations by building on non-patent prior art as an alternative
instrument for scope of innovation. In this subsection, I explain why these instruments are appropriate for
my purpose.
3.3.1.
R&D State Tax Credits5
Following a federal R&D tax credit6 introduced under the U.S Economic Recovery Tax Act of
1981 offering tax relief to firms’ R&D expenses, various states introduced similar state tax credits7 for
firms’ R&D expenses within the states (see Wilson 2006 for schedule of tax credit implementation across
states). These credits were granted in addition to the federal tax credits, with the main objective being to
attract firms’ R&D-related activities to the states.
5
Unless otherwise stated, information is obtained from Wilson D. (2006). "Beggar Thy Neighbor? The In-State,
Out-of-State, and Aggregate Effects of R&D Tax Credits”. Federal Reserve Bank of San Francisco, Working Paper
2005-08.
6
The tax credit equaled to 25% of qualified R&D expenses over a base level, defined as a firm’s average R&D over
the past three years. Qualified R&D expenses include salaries and wages, intermediate or materials expenses, and
the rental costs of certain property and equipment incurred in performing research ‘undertaken to discover
information’ that is ‘technological in nature’ for a new or improved business purpose.
7
Structure of the state tax credits largely resembles that of the federal tax credit, with minor modifications. As of
year 2002, 31 states across U.S. provide R&D tax credits, with effective credit rates similar to or greater than the
federal tax credit.
25
Prior studies have shown that the R&D state tax credits mainly induced firms to relocate their
R&D project investments (Wilson 2006). Upon enactment of the tax credit by a state, firms tend to shift
their R&D projects that would have otherwise been allocated to other states, to the state that had enacted
the tax credit, in order to reduce R&D costs. Thus far, there is no known evidence suggesting that the
state tax credits induced firms to change either their overall R&D investment levels or the types of
projects8. Due to the relocation of projects, units located in the affected states were effectively assigned
more projects with greater mixes of different nature than before, resulting in less task specialization.
These tax credit enactments are arguably exogenous to the individual firms and have no impact on the
nature of R&D projects, which render them appropriate instruments for individual R&D units’ extent of
task specialization.
I use the state tax credit enactments in Oregon (1989) and Pennsylvania (1997) as instruments for
TaskSpec1 and TaskSpec2 respectively. First, I use a time-dummy to capture observations occurring up
to five years after the enactments for each of the state tax credits. For example StateTax1989 takes the
value of ‘1’ for observations occurring after the year 1989, up to year 1994, and ‘0’ otherwise. I then
create dummy variables that capture whether the observation resides in the affected state. For example,
State_1989 takes the value of ‘1’ for R&D units located in Oregon, and ‘0’ otherwise. Finally, I interact
the two sets of dummy variables to form Statetax_DinD1989 and Statetax_DinD1997 which capture the
difference in the effects of each tax credit enactment on R&D units’ task specialization, between affected
and non-affected R&D units.
3.3.2.
The Northridge Earthquake, California 19949
8
Through interviews with officers at R&D-performing firms, OTA (1995) concludes that while the tax and financial
directors were aware of the R&D tax credits and their relevance to firm value, the tax credits have little influence on
the nature of the R&D activities themselves. This is also consistent with other studies demonstrating that R&D
subsidies tend to go to scientists’ wages without accordingly increasing R&D expenses due to inelastic supply of
scientists (e.g. Goolsbee 1998).
9
Unless otherwise stated, information is obtained from the “The Northridge earthquake: extent of damage and
federal response”, Hearing before the Committee on Public Works and Transportation, House of Representatives,
One Hundred Third Congress, Second Session in 1994.
26
A major earthquake struck the heavily populated suburban area of Los Angeles (L.A.) in the San
Fernando Valley at Northridge, California, at 4.31 a.m. on January 17, 1994. Labeled as a ‘Direct Hit on
a Modern American City’ by the U.S. Geological Survey (USGS), it was the first earthquake to strike
under an urban area since the 1933 Long Beach earthquake (Southern California Earthquake Data
Center). The devastation caused by the Northridge earthquake was enormous and widespread10, with
severe impact on the state’s inhabitants11, transportation system12, and businesses13.
In terms of
economic costs, the Northridge earthquake was the most expensive disaster in U.S. history prior to the
21st century (Tierney 1997; Bolin & Stanford 1998; DIS), with total damages estimated at $44 billion
(OES 1997).
The severe damages were likely to have reduced the extent of inter-unit collaboration within
firms with R&D units situated in Southern California during the earthquake, due to physical damages at
the R&D sites, disruptions to employees’ personal lives, dysfunction of the supply networks and delivery
systems, disruptions of communication systems, etc. at the affected units. While this effect was likely
temporary, the short-term reduction in collaboration between R&D units nonetheless constitutes a
convenient exogenous change for IUinteg. I use a dummy variable EarthQ to capture observations
10
Over 3,600 aftershocks were recorded. There were pockets of severe damages in distant locations, like Sherman
Oaks, Santa Monica, etc., and also ground failures at distances up to 90 kilometers from the epicenter (USGS). Gaslines exploded into fires as a result of the earthquake, and thousands of landslides occurred over an area of 10,000
square kilometers, precipitating a subsequent outbreak of valley fever (USGS).
11
The damaged area was as large as Cleveland, and the three counties directly affected by the earthquake together
accounted for 40 percent of state’s population (OES 1997). 57 people were killed (Bolin & Stanford 1998), and
9,158 were seriously injured. 40,000 people were left without natural gas, 700,000 without water, and over one
million people without power. 25,000 people were initially displaced, and over 21,000 homes were tagged
uninhabitable, with 55,000 others damaged. 40,000 other structures were damaged including 28 hospitals and 200
schools. Maintenance and repairs required was estimated to take as long as one year after the earthquake.
12
According to DIS, a specialist firm in earthquake protection, 11 major roads into L.A. were closed, at least 9
bridges collapsed, and six major freeways buckled, out of which four were serving the Los Angeles area. Because
of the high-volume traffic served by these freeways, Los Angeles County Metropolitan Transportation Authority
(MTA) estimated that the earthquake disrupted 689,000 trips per day. The impact of damage to these few critical
routes was felt throughout southern California, and indeed, the Nation.
13
Countless businesses closed and workers idled, and it was estimated that approximately 23 percent of total losses
resulting from the Northridge earthquake were attributable to business interruptions (Gordon et al 1995). About 57
percent of businesses in L.A. and Santa Monica suffered some degree of physical damage in the earthquake (Tierney
1997). The average dollar loss was $156,273 per firm, with the highest reported being $14 million. More than 50
percent of businesses had their electricity or water disrupted. Nearly 60 percent of businesses reported that their
employees were unable to get to work for some time. Nearly 40 percent reported reduction in customer traffic as a
result of the earthquake, and nearly 25 percent had problems delivering goods and services.
27
occurring within one year following the earthquake in 1994, i.e. EarthQ takes the value of ‘1’ for
observations in 1994, and ‘0’ otherwise. Next, I create a dummy variable CA_Unit to reflect R&D units,
not located in Southern California14, more likely to have collaborated with units in Southern California.
CA_Unit takes the value of ‘1’ if a non-Southern Californian unit belongs to a firm that has a unit located
in Southern California within five years (1989-1993) prior to the earthquake, and ‘0’ otherwise. I then
multiply the two dummy variables to form EarthQ_DinD, which captures the difference in the effects of
the earthquake on inter-unit integration, between affected and non-affected R&D units.
The Telecommunications Act 199615
3.3.3.
The Telecommunications Act of 1996 represents a major attempt at large-scale restructuring of
the U.S. telecommunications sector. Being pro-competition in nature, the Act was meant to introduce
competition in the local telephony markets previously monopolized by the incumbent local exchange
carriers (ILECs), while allowing the ILECs to enter long distance service and other related-product
markets16.
While many questioned the effectiveness of the Act in increasing competition in local
telephony (see Fransman 2002; Couper et al 2003; Economides 2004), the Act nonetheless caused
substantial changes in the telecommunications industry. First, there was an increase in involvements of
firms in different markets, even if they were not in the manner envisioned in the Act. It was unambiguous
that the ILECs were eventually successful in entering the long distance service market (Economides
2004)17, and in return, the long distance service providers were entering local telephony via acquisitions18.
14
The Southern California region is defined with the Tehachapi Mountain range as its northern boundary. This
includes the following counties: Los Angeles, Orange, San Diego, San Bernardino, Riverside, Ventura, Imperial,
Santa Barbara, and Kern. I manually trace each Californian city in the sample to determine its corresponding
county, based on information available from www.wikipedia.org.
15
Unless otherwise stated, information is obtained from Economides, N (1998a). "The Telecommunications Act of
1996 and Its Impact". New York University, Center for Law and Business, Working Paper No. 99-003.
16
To facilitate entry in local exchange markets, the Act mandated ILECs’ leasing of unbundled network elements to
new entrants ‘at cost’, and also their sale of services to competitors at ‘wholesale prices’.
17
By 2003, the ILECs were approved to provide long distance services in all states. Also, the Act corresponded
with a time of rapid development in internet telephony, and cable companies were actively entering the local
exchange markets by introducing internet telephony as a substitute for the traditional local loops based on copper
wire lines. As a response to these threats, the ILECs themselves started offering long distance internet telephony
services as well (e.g. AT&T in 1998).
18
The long distance service providers initially attempted to enter the local exchange markets as new entrants via
leasing of the ILEC’s existing network infrastructure. However, when the leasing procedures were substantially
28
Second, the Act helped foster the telecommunications markets’ emphasis on interconnectedness between
technologies.
When firms entered new markets with different technologies, they created massive
requirements for interconnection and interoperability of the separate networks, and also standardization of
communication protocols between networks (Economides 2004)19.
Through these effects, the Act likely constituted a push towards investments in wide-scope
innovations. The telecommunication carriers and network firms themselves, like AT&T and the ILECs,
performed little R&D. Rather, it was the upstream firms in the Communication Equipment and related
industries that were generating (and selling to downstream firms) most of the necessary technologies
(Fransman 2002). The Act, by inducing downstream firms to be involved in more markets with different
applications than before, increased the demand for wider-scope innovations that span multiple
applications. For example, the ILECs, who were restricted in entering other markets prior to the Act,
could subsequently use the wide-scope innovations in more applications spanning different markets post
the Act.
Also, because of the increased interconnectedness and standardization, there were less
technological obstacles for generating wide-scope innovations whose uses span different networks and
applications.
I use a dummy variable, TeleComAct, to capture observations occurring after the year in which
the Act was enacted (1996). Next, I separate the sample into two subsets that are arguably affected
differently by the Act.
While the sample firms are primarily involved in the ‘Communications
Equipment’ industry, some R&D units within each of these firms may be involved in technologies that
are non-related to communications equipment. The Act is likely to affect these units less than other units
dedicated to communications equipment technologies.
To define these two subsets, I regroup the
USPTO’s technology classes of the units’ patents into 36 technology subcategories, based on NBER’s
delayed by lack of agreement over leasing terms and the resulting long-drawn law suits, the long distance service
providers reacted by merging with the ILECs for rapid entries into the local exchange markets (Fransman 2002).
19
Section 251(c)(2) mandates interconnection between local and long distance networks, to facilitate entry in local
exchange markets. Also, the Act, by promoting integration of markets, spurred a near-euphoric optimism within the
financial markets in the stock valuations of telecommunication firms (Fransman 2002). Share prices rose steadily
for at least four years following the Act, and the financial markets were ready to reward firms that were able to
progress on this trend of interconnectedness and integration of different networks and technologies.
29
categorization scheme. Of these subcategories, I identify 10 as being less relevant to communications
equipment: (i) Agricultural, Food, Textile, (ii) Organic Compounds, (iii) Surgery & Medical Instruments,
(iv) Biotechnology Drugs and Med, (v) Motors & Engines + Parts, (vi) Transportation Mechanicals, (vii)
Amusement Devices (not otherwise classified under Communications), (viii) Apparel & Textile, (ix)
Furniture, House Fixtures, and (x) Receptacles (not otherwise classified under Communications). I then
create a dummy variable, TeleCom, taking the value of ‘0’ for units involved in these ten categories, and
‘1’ otherwise. Finally, I multiply the two dummy variables to form TeleComActDinD, which captures
the incremental effect on R&D units affected by the Act, relative to other units.
3.3.4.
Nature of Technologies
As an alternative instrument for scope of application, I measure the extent that prior art used by
R&D units to generate innovations is science-based (see Cockburn & Henderson 1998; Ahuja & Katila
2004). This is based on the notion that science-based innovations tend to be more ‘basic’ in nature, and
have greater applicability (i.e. wider scope), relative to innovations that are built based on applied
technologies. I construct the variable Non-Patent Cite, which measures the average proportion of nonpatent-based citations made by patents that an R&D unit is involved in during a particular year.
3.4.
Econometrics Issues and Model Specification
Two econometrics issues – simultaneity and unobserved heterogeneity – pose challenges of
endogeneity in the design of my empirical tests.
First, the two-way causal relationships between
organization structure and scope of innovation imply that both constructs are endogenously and
simultaneously determined by each other, such that an isolated OLS representation of any single
prediction will lead to biased estimates of coefficients (see Berndt 1991; Wooldridge 2002)20. Second, it
20
As an illustration, consider the following inter-relationships between scope of innovation (Scope) and
organization structure (Structure). (1) Scopei = β0 + β1Structurei + µi. (2) Structurei = γ0 + γ1Scopei + εi. Where
E(µi) = 0, E(µi2) = σ2, E(εi) = 0, and both β1 and γ1 are non-zero. Substituting equation (1) into (2), we arrive at:
Structurei = (1- β1γ1)-1[γ0 + γ 1β 0 + γ1µi + εi]. Hence, E(Structurei) = (1- β1γ1)-1(γ0 + γ1β0), and Structurei E(Structurei) = (1- β1γ1)-1[γ1µi + εi]. Accordingly, Cov (Structurei, µi) = E{ [Structurei - E(Structurei)][µi – E(µi)] }
= E{ [(1- β1γ1)-1(γ1µi + εi)][ µi] } = (1- β1γ1)-1[ γ1σ2 + E(µiεi) ] ≠ 0 since σ2 > 0. The non-zero covariance between
organization structure and the error term in the estimation violates the assumption of OLS and leads to biased
estimates of coefficients in equations (1), and similarly for equation (2).
30
is possible that both constructs may be jointly affected by some other unobserved factors. For instance, a
firm may have capable scientists who are both able to create wide-scope innovations as well as to
collaborate effectively with other scientists working in distantly related technological areas.
Alternatively, the firm’s strategy or incentive system may be designed to discourage both wide-scope
innovations and collaborations between R&D units.
For each of the main variables (organization
structure and scope of innovation), these unobserved heterogeneities may cause non-random assignments
of observations to different levels of the variable, and because the researcher cannot observe the
counterfactuals (e.g. what would be the scope of innovation for a task-specialized unit, had it not been
task-specialized), the resultant empirical estimates are accordingly biased (Holland 1986)21.
To address the two abovementioned issues, I use two-stage least square estimations for each
prediction, with the exogenous shocks as instruments in the first-stage equations based on a difference-indifference approach described in an earlier section (see Wooldridge 2002). To examine the effect of
organization structure on scope of innovation, the following estimations are used.
Stage 1:
TaskSpecijt
= β0 + β1StateTax(Year)ijt
β2+H+1Controlsit + εijt
+
β2StateTax_DinD(Year)ijt
+
∑βhState(Year)ijt +
IUintegijt
= γ0 + γ1EarthQijt + γ2EarthQ_DinDijt + γ3CA_Unitijt + γ4Controlsit + µijt
Stage 2:
Scopeijt
= δ0 + δ1TaskSpecijt-1^ + δ2IUintegijt-1^ + δ3Controlsit + ξijt
TaskSpecijt-1^ and IUintegijt-1^ refer to the 1-year lagged predicted values of TaskSpecijt and IUintegijt from
their respective first-stage equations. Controlsit refers to the control variables as described in an earlier
21
Consider the effect of R&D task specialization on the scope of innovation. For simplicity, suppose task
specialization takes two levels – high and low, and each level leads to a particular scope of innovation. The effect of
task specialization refers conceptually to the average difference between scope of innovation under high
specialization (Yh) and under low specialization (Yl), across all firms. Yet, in an empirical study, each firm is only
associated with a single level of task specialization at any one point, and the researcher cannot observe what its
scope of innovation would have been if it had adopted a different level of task specialization at that same point. In
an estimation equation, the coefficient of task specialization captures (Yh|i=h) – (Yl|i=l), where (Yh|i=h) and (Yl|i=l)
refer to the average scope among firms with high and low levels of task specialization respectively. A problem
arises when (Yh) ≠ (Yh|i=h), and (Yl) ≠ (Yl|i=l), because some other unobserved factors are selecting the firms into
their respective groups (i=h or i=l). This non-random assignment of firms to either levels of task specialization
constitutes the source of biases arising from unobserved heterogeneity.
31
section. To examine the effect of scope of innovation on organization structure, the following estimations
are used.
Stage 1:
Scopeijt
= β0 + β1TeleComActijt + β2TeleComijt + β3TeleComActDinDijt +εijt
Stage 2:
TaskSpecijt
= γ0 + γ1Scopeijt-1^ + γ2Controlsit + µijt
IUintegijt
= δ0 + δ1Scopeijt-1^ + δ2Controlsit + ξijt
Scopeijt-1^ refers to the 1-year lagged predicted values of Scopeit from the first-stage equation. As the
instruments in the first-stage equations are arguably unrelated to the dependent variables in the second
stage, the above two-stage least square estimations avoid the issue of simultaneity. The exogenous nature
of the first-stage instruments results in random assignments of observations into different levels of the
main variables (TaskSpec, IUinteg, and Scope), such that the observed levels of these main variables are
likely not to correlate with any unobserved factors, thereby minimizing the unobserved heterogeneity
issue. The difference-in-difference approach used in the first-stage equations allows for a more focused
identification of the exogenous shocks’ effects, separate from other unobserved events occurring in the
time frame that may be driving both the changes in organization structure and scope of innovation.
4.
Findings
Table 1 contains the descriptive statistics for the main variables.
contains about 3 independent claims in any given year on average.
For Scope1, each patent
The average Scope2 is 2.94,
suggesting that most claims in the sample use the transition phrase with the widest scope of application.
However, the sample contains all three types of transition phrases, and also includes R&D units that only
have the narrowest or the broadest claims in their patents, as evident from the range of Scope2 spanning
from 1 to 3. Comparing the means of TaskSpec1 (0.5) and TaskSpec2 (0.8), the former reflects less task
specialization for the average R&D unit than the latter, even though the definition of R&D unit is the
same across the two measures. This is not surprising, as some units in the sample may not be specialized
32
in particular kinds of technologies, but may house inventors that are individually specialized in particular
technologies. The mean of IUinteg is 0.77, suggesting that the majority of innovative projects managed
by the R&D units in the sample involve collaborations with other units.
The central premise in my propositions is that firms experience a tendency towards a structurescope matching. As an initial validity check of this claim, I examine scatter plots of innovations’ scope of
application against organization structure (Figures 1-6), with least-square fitted lines to demonstrate the
general trends. For each year in my sample range, I calculate the average across R&D units of the
Scope1, Scope2, TaskSpec1 and TaskSpec2, and IUinteg. In Figures 1 to 3, I plot the average Scope1 over
the three measures of organization structure respectively. Similarly, in Figures 4 to 6, I plot the average
Scope2 against the three measures of organization structure respectively. The scatter plots demonstrate
patterns that are consistent with my hypotheses. Figures 1 and 2 show that the more specialized the R&D
units are in particular kind of technologies, the lower the average number of independent claims per
patent, i.e. the narrower the innovations’ scope of application. Figure 3 shows that greater inter-unit
integration is associated with higher number of independent claims per patent, i.e. innovations of wider
scope of application. Figures 4 and 5 provide no clear evidence of associations between width of
transition phrases and the extent of R&D task specialization. However, a closer look at the two figures
reveal that this is likely due to the ‘outlier’ year on the top right corner of the figure, i.e. where both width
of transition phrases and extent of task specialization are high. Cleaning of outliers in the data may
subsequently reveal clearer trends here (work in progress). Finally, Figure 6 shows that greater inter-unit
integration tends to be associated with wider transition phrases, i.e. wider scope of application. These
associations provide an assuring starting point for my analyses.
Table 2 reports the results of first-stage regressions for the two dimensions of organization
structure – R&D task specialization and inter-unit integration. Models 1-2 include instrumental variables
based on the R&D state tax credit implementation in 1989 (Oregon), with TaskSpec1 as the dependent
variable, without and with year dummies respectively. Similarly, models 3-4 use instruments based on
the tax credit implementation in 1997 (Pennsylvania), with TaskSpec2 as the dependent variable. Finally,
33
models 5-6 use earthquake-related instruments as regressors, with IUinteg as the dependent variable, with
varying control variables. The variable Statetax1989 is non-significant across columns 1-2, suggesting
that R&D units across all states do not exhibit any significant changes in R&D task specialization within
the years 1990-1995 as compared to the other years. However, Statetax_DinD1989 is significantly
negative under both specifications, suggesting that in the five years following the implementation of R&D
state tax credit in Oregon, R&D units in Oregon exhibit a reduction in R&D task specialization, relative
to other units across US. In column 3, Statetax1997 is significantly negative, suggesting that units across
all states exhibit a reduction in task specialization during the years 1998-2002. This controls for other
system-wide effects that are unrelated to the tax credit implementation in Pennsylvania. However, the
variable Statetax_DinD1997 is also significantly negative in columns 3-4, showing that the tax credit
implementation induces the inventors within the affected R&D units to be less specialized, relative to
inventors within R&D units not affected by the tax credit. The significantly positive coefficient EarthQ
in column 5 shows that on average, units across all states demonstrate an increase in inter-unit
collaborations in the year 1994. However, the significantly negative coefficients of EarthQ_DinD in
columns 5-6 suggest that controlling for other system-wide changes, units that would have collaborated
with those affected by the earthquake significantly reduced their inter-unit collaborations, relative to units
that would not have experienced such collaborations regardless of the earthquake. Overall results here
thus justify the use of R&D state tax credit implementations and the Northridge earthquake as instruments
for R&D task specialization and inter-unit integration respectively. I use models 2, 4 and 6 to predict
TaskSpec1, TaskSpec2, and IUinteg respectively.
Table 3 reports findings from second-stage regressions for scope of innovation. The dependent
variables in models 1-5 and 6-10 are Scope1 and Scope2 respectively.
In models 1-3, the main
independent variables are the 1-year lagged predicted TaskSpec1, TaskSpec2 and IUinteg respectively,
based on the predictions from the previous first-stage regressions. Models 4-5 include both predictions of
task specialization and inter-unit integration simultaneously. Models 6-10 are similarly structured. The
lagged predicted TaskSpec1 and TaskSpec2 are significantly negative in all models (except in model 4
34
and 10, where the coefficients are negative and significant at 10% level), providing strong evidence that
the greater the extent of R&D task specialization, the narrower will be the R&D unit’s subsequent scope
of innovation. This supports Hypothesis 1. Likewise, the lagged predicted IUinteg is significantly
positive in all models, which strongly suggests that the greater the extent of inter-unit integration, the
wider will be the R&D unit’s subsequent scope of innovation. This supports Hypothesis 2.
Table 4 presents results for the first stage regressions for innovations’ scope of application, based
on instruments related to the Telecommunications Act of 1996, as well as the instrument of Non-Patent
Cite.
The dependent variables in models 1-4 and 5-8 are Scope1 and Scope2 respectively. I examine
each specification with and without controls for year-specific effects. TeleComAct serves as a base-case
control for system-wide effects that are unrelated to the Act per se. The coefficients for TeleComAct are
insignificant across models 1-2 and 5-6, suggesting that R&D units on the whole do not exhibit any
changes in their scope of innovation before and after 1996. However, TeleComActDinD is significantly
positive in all specifications (models 1-2 and 5-6), suggesting that relative to other units less affected by
the Act, R&D units that are more focused on telecommunications technologies demonstrate an increase in
their scope of innovation subsequent to the Act. Likewise, in models 3-4 and 7-8 with Non-Patent Cite as
alternative instruments, the coefficients of Non-Patent Cite are significantly positive. Based on these
results, I infer that both the Act and Non-Patent Cite serve as relevant instruments for R&D units’ scope
of innovation.
I use only the models with year dummies, i.e. models 2 (instrument: the Act), 4
(instrument: Non-Patent Cite), 6 (instrument: the Act), and 8 (instrument: Non-Patent Cite) to predict
Scope1A, Scope1B, Scope2A, and Scope2B respectively.
Table 5 presents results of the second-stage regressions for R&D task specialization.
The
dependent variables in models 1-4 and 5-8 are TaskSpec1 and TaskSpec2 respectively. Each of the
models 1-4 include a 1-year lagged predicted scope variable from before. Models 5-8 are similarly
structured. For the dependent variable TaskSpec1, the lagged Scope1A (model 1) and Scope2A (model 3),
predicted with instruments related to the Act, are both not significant, and hence do not provide evidence
supporting the effect of scope of innovation on task specialization. However, when the two scope
35
variables are predicted with Non-Patent Cite as in Scope1B (model 2) and Scope2B (model 4), the
coefficients of these lagged predicted scope variables are significantly negative. This provides evidence
that the greater the scope of innovation, the less will be the subsequent extent of task specialization in the
R&D units. In models 5-8 with TaskSpec2 as the dependent variables, all four lagged predicted scope
variables (using both the Act and Non-Patent Cite as instruments) are significantly negative, once again
providing strong support for the negative causal relationship between scope of innovation and subsequent
extent of task specialization. These results lend support to Hypothesis 3.
Table 6 reports results of second-stage regressions for inter-unit integration. Models 1-4 are
structured in a similar fashion as in Table 5. All four 1-year lagged predicted scope variables (using both
the Act and Non-Patent Cite as instruments) are significantly positive, providing strong support that the
wider the scope of innovation, the more will be the R&D unit’s subsequent extent of inter-unit
integration. This confirms Hypothesis 4.
Altogether, the findings presented thus far lend strong support to my proposition of the structurescope matching. Specifically, other than the fact that non-task-specialized (highly task specialized) and
highly-integrated (non-integrated) units are typically associated with wide-scope (narrow-scope)
innovations, evidence here strongly suggests the presence of two-way causal relationships between
organization structure and scope of innovation that mutually reinforces each other.
5.
Conclusion and Contributions
My dissertation contributes towards advancing studies of organization structure and innovation.
By situating in the intersection of the two literatures, I delineate the role that a firm’s knowledge base
plays in relating the two literatures. A firm’s organization structure and innovations both contribute
towards changing and are in turn affected by its knowledge base. Through the proposed matching
between organization structure and innovation, I stress the need to consider the bidirectional effects when
determining causality between the two constructs.
36
The proposed structure-scope matching constitutes an alternative reason for why resources may
remain heterogeneous across firms, and therefore contributes to the Resource Based View (Wernerfelt
1984; Rumelt 1991; Barney 1991). Existing studies that attribute resource heterogeneity to differences in
strategies adopted or firm characteristics, while informative, are often incomplete, as they do not explain
why strategies or firm characteristics may differ across firms in the first place. The two-way effects in
my proposed structure-scope matching potentially overcome this shortcoming, by providing a reason for
why the heterogeneities across both organization structure and innovation may remain stable. Resource
heterogeneity may occur when, due to exogenous reasons, some firms are induced to structure their
internal activities differently than others and as a result generate different types of innovation. This
heterogeneity is subsequently maintained because of the mutual reinforcement between the existing type
of organization structure and scope of innovation.
The proposed structure-scope matching also contributes to the literature on organization structure,
by highlighting potential difficulties that firms may face when they attempt to shape their performance
outcomes by altering their organization structures. Researchers have stressed the need for firms to adjust
their structures to complement their strategies (Chandler 1962; Miller 1986), adapt to changing
environments (Galunic & Eisenhardt 2001), or improve their search abilities (Siggelkow & Levinthal
2003). In fact, recent researchers suggest that even without changes in firm environments, it may be
beneficial for firms to alter their organization structures, so as to avoid learning traps (Siggelkow &
Levinthal 2005). The structure-scope matching proposed here acts as a counteracting force hindering
firms from freely changing their structures. This partially accounts for why some firms remain unable to
adapt to changing environments by adjusting their structures, even if they may be aware of the potential
benefits of doing so.
Given the structure-scope matching, a natural extension would be to examine its performance
effect. In establishing the mechanisms in my proposed effects, I highlighted potential complementarities
between organization structure and innovation that arise when the firm puts the innovations to use, i.e. a
firm that matches its organization structure with its innovations may potentially generate greater value
37
from its innovations than a firm that does not. This speaks to an existing tension in the strategy literature,
where some researchers suggest that generic resources are potentially more valuable (see. Penrose 1959;
Lerner 1994; Silverman 1999; Anand & Singh 1997; Hall & Trajtenberg 2004), while others stress that
resources with specialized applications tend to be superior solutions to problems (e.g. Porter 1985;
Montgomery & Wernerfelt 1988; Becker & Murphy 1992; Hatfield et al 1996; Siggelkow 2003). What
the structure-scope matching suggests here is that whether generic or specialized resources create more
value for a firm may depend on the characteristics (in this case, organization structure) of the firm
utilizing the resources. More interestingly, such complementarities between organization structure and
innovation may come with a cost – the rigidity towards subsequent changes. This leaves open the
prospect for future research to explore the contingencies under which a structure-scope matching will
enhance or hamper the firm’s performance.
Finally, my dissertation contains a methodological contribution. My new measures of scope of
application of innovations based on textual coding of patent claims potentially captures the nature of the
underlying innovations more precisely than other existing measures.
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44
Tables and Figures
Table 1: Descriptive Statistics for Main Variables
Variable
Scope1
Scope2
TaskSpec1
TaskSpec2
IUinteg
Firm Size
Total Patents
Mean
3.39
2.94
0.50
0.80
0.77
45.47
9.78
Std. Dev.
1.63
0.20
0.28
0.21
0.29
47.84
32.57
Min
Max
1
1
0.04
0.09
0
0.12
2
27
3
1
1
1
150
1154
Figures 1-6: Scatter Plots of Scope of Application with Organization Structure
„ Average across observations for each year
Fig1: Scope1 – TaskSpec1
Fig2: Scope1 – TaskSpec2
Fig3: Scope1 – IUinteg
Fig4: Scope2 – TaskSpec1
Fig5: Scope2 – TaskSpec2
Fig6: Scope2 – IUinteg
45
Table 2: 1st Stage Regressions for R&D Task Specialization & Inter-Unit Integration
(1)
statetax1989
(2)
TaskSpec1
-0.005
0.008
(0.77)
(0.37)
statetax1997
statetax_DinD1989
-0.112*
(2.46)
(3)
(4)
TaskSpec2
-0.009*
(2.09)
0.010
(0.60)
-0.117**
(2.89)
-0.085*
(2.11)
(6)
IUinteg
-0.107*
(2.36)
statetax_DinD1997
EarthQ
CA_Unit
EarthQ_DinD
state1986
(5)
0.170**
0.175**
(3.05)
(3.15)
state1987
-0.098**
-0.098**
(14.63)
(14.59)
state1988
-0.152*
-0.152*
(2.53)
(2.54)
state1989
-0.096**
-0.101**
(4.98)
(5.22)
state1990
0.012
0.013
(1.36)
(1.44)
state1991
0.009
0.010
(0.68)
(0.75)
state1993
0.135**
0.130**
(3.55)
(3.43)
state1994
0.074**
0.074**
(3.53)
(3.57)
state1996
0.098**
0.096**
(4.93)
(4.78)
state1997
0.041*
0.038*
(2.40)
(2.14)
state1998
-0.081**
-0.080**
(4.59)
(4.52)
state1999
0.066
0.067
(1.48)
(1.49)
state2000
0.217**
0.222**
(4.07)
(4.16)
state2001
-0.083**
-0.083**
(9.98)
(10.01)
Firm Size
-0.001**
-0.001**
(21.36)
(20.89)
Total Patents
-0.002**
-0.002**
(25.00)
(24.80)
Constant
0.606**
0.601**
(109.99)
(33.61)
Year Control
No
Yes
Observations
10892
10892
R-squared
0.13
0.14
Absolute value of t statistics in parentheses
* significant at 5%; ** significant at 1%
-0.000
(0.00)
-0.008
(1.52)
-0.114*
(2.32)
0.009
(0.60)
0.051**
(7.14)
-0.046**
(4.21)
-0.051
(1.65)
-0.097**
(5.68)
0.077**
(4.69)
0.059**
(3.90)
0.018
(1.23)
-0.048
(1.32)
0.012
(0.27)
-0.007
(1.00)
0.000
(0.36)
-0.000
(0.61)
0.802**
(170.48)
No
10892
0.02
0.010
(0.22)
-0.006
(1.11)
-0.103*
(2.11)
0.005
(0.35)
0.049**
(6.84)
-0.045**
(4.14)
-0.061*
(1.97)
-0.097**
(5.74)
0.068**
(4.16)
0.035*
(2.29)
0.019
(1.35)
-0.036
(0.99)
0.022
(0.51)
-0.009
(1.26)
0.000
(1.55)
0.000
(0.06)
0.840**
(57.71)
Yes
10892
0.03
0.141**
(5.49)
0.012
(1.89)
-0.050*
(2.17)
-0.000
(0.01)
0.006
(0.93)
-0.048*
(2.08)
0.672**
(36.13)
Yes
11339
0.01
0.001**
(15.89)
-0.001**
(7.35)
0.778**
(27.05)
Yes
10892
0.04
Table 3: 2nd Stage Regressions for Innovations’ Scope of Application
(1)
Predicted TaskSpec1t-1
(2)
(3)
Scope1
-1.047**
(3.86)
Predicted TaskSpec2t-1
(4)
(5)
-0.432
(1.67)
1.195**
(3.80)
-0.001*
0.001
(2.06)
(1.74)
Total Patents
-0.001
0.001*
(1.19)
(2.25)
Constant
3.930**
5.853**
(26.23)
(16.01)
Observations
7602
7602
R-squared
0.00
0.01
Absolute value of t statistics in parentheses
* significant at 5%; ** significant at 1%
-2.801**
(6.18)
0.925**
(2.92)
0.893*
(2.46)
Firm Size
0.002**
(3.83)
2.432**
(9.89)
7652
0.00
(7)
(8)
Scope2
-0.217**
(7.59)
-3.147**
(6.84)
Predicted IUintegt-1
(6)
0.001
(1.04)
2.885**
(7.87)
7652
0.00
0.001**
(3.13)
4.886**
(10.46)
7652
0.01
(9)
(10)
-0.166**
(5.95)
-0.223**
(4.58)
-0.000
(0.45)
-0.000**
(4.69)
3.053**
(193.41)
7602
0.01
0.000**
(5.55)
0.000
(1.42)
3.112**
(80.43)
7602
0.01
0.237**
(6.97)
0.121**
(3.09)
-0.087
(1.77)
0.228**
(6.66)
0.000**
(4.17)
2.757**
(103.67)
7652
0.01
-0.000*
(2.11)
2.931**
(74.11)
7652
0.01
0.000**
(3.95)
2.833**
(55.99)
7652
0.01
Table 4: 1st Stage Regressions for Innovations’ Scope of Application
(1)
(2)
(3)
Scope1
TeleComAct
-0.028
-0.110
(0.20)
(0.46)
TeleCom
-0.037
-0.040
(0.37)
(0.40)
TeleComActDinD 0.386** 0.397**
(2.63)
(2.71)
Non-Patent Cite
1.518**
(12.84)
Firm Size
0.000
-0.000
0.000
(0.21)
(0.13)
(0.27)
Total Patents
0.001** 0.001** 0.001*
(2.91)
(2.61)
(2.15)
Year Control
No
Yes
No
Constant
3.225** 2.751** 3.185**
(32.01)
(19.09)
(124.82)
Observations
10892
10892
10892
R-squared
0.01
0.02
0.02
Absolute value of t statistics in parentheses
* significant at 5%; ** significant at 1%
(4)
1.456**
(12.39)
0.000
(0.34)
0.001
(1.38)
Yes
2.840**
(17.72)
10892
0.04
47
(5)
(6)
-0.013
(0.82)
-0.006
(0.55)
0.043**
(2.67)
0.041
(1.54)
-0.006
(0.52)
0.043**
(2.65)
0.000**
(5.72)
0.000*
(2.18)
No
2.922**
(261.21)
10892
0.01
(7)
Scope2
0.000**
(5.30)
0.000*
(2.07)
Yes
2.897**
(180.53)
10892
0.02
0.044**
(3.33)
0.000**
(5.39)
0.000
(1.70)
No
2.926**
(1028.11)
10892
0.00
(8)
0.039**
(2.99)
0.000**
(5.27)
0.000
(1.23)
Yes
2.971**
(165.32)
10892
0.02
Table 5: 2nd Stage Regressions for R&D Task Specialization
(1)
Predicted
Scope1At-1
(2)
(3)
TaskSpec1
(4)
(5)
0.001
(0.07)
-0.043**
(4.48)
Predicted
Scope2At-1
-0.047
(0.33)
Firm Size
-0.001** -0.001**
(17.10)
(17.35)
Total Patents
-0.002** -0.002**
(23.05)
(22.56)
Constant
0.503**
0.650**
(12.75)
(19.98)
Observations
7602
7602
R-squared
0.10
0.10
Absolute value of t statistics in
parentheses
* significant at 5%; ** significant at 1%
-0.001**
(15.63)
-0.002**
(23.02)
0.643
(1.53)
7602
0.10
-0.363**
(3.23)
-0.366*
(2.56)
-0.001**
(14.83)
-0.002**
(22.70)
1.578**
(3.77)
7602
0.10
0.000*
(1.99)
-0.000
(1.43)
0.970**
(31.44)
7602
0.00
Table 6: 2nd Stage Regressions for Inter-Unit Integration
(1)
(2)
(3)
IUinteg
(4)
0.079**
(6.54)
0.065**
(6.50)
Predicted Scope2At-1
0.779**
(5.26)
Predicted Scope2Bt-1
Firm Size
(8)
-0.050**
(6.68)
Predicted
Scope2Bt-1
Predicted Scope1Bt-1
(7)
TaskSpec2
-0.047**
(5.15)
Predicted
Scope1Bt-1
Predicted Scope1At-1
(6)
0.001**
0.001**
(14.94)
(15.01)
Total Patents
-0.001** -0.001**
(8.65)
(8.43)
Constant
0.481**
0.529**
(11.82)
(15.74)
Observations
7602
7602
R-squared
0.04
0.04
Absolute value of t statistics in parentheses
* significant at 5%; ** significant at 1%
0.001**
(11.46)
-0.001**
(8.36)
-1.537**
(3.54)
7602
0.04
48
0.890**
(6.02)
0.001**
(11.25)
-0.001**
(8.45)
-1.860**
(4.30)
7602
0.04
0.000
(1.85)
-0.000
(1.43)
0.980**
(38.53)
7602
0.01
0.000**
(3.28)
-0.000
(1.83)
1.874**
(5.70)
7602
0.00
-0.428**
(3.82)
0.000**
(3.49)
-0.000
(1.77)
2.065**
(6.29)
7602
0.00