How Firm Strategies Influence the Architecture of Transaction

Transcrição

How Firm Strategies
Influence the Architecture of
Transaction Networks
Jianxi Luo
Daniel E. Whitney
Carliss Y. Baldwin
Christopher L. Magee
Working Paper
11-076
Copyright © 2011 by Jianxi Luo, Daniel E. Whitney, Carliss Y. Baldwin, and Christopher L. Magee
Working papers are in draft form. This working paper is distributed for purposes of comment and
discussion only. It may not be reproduced without permission of the copyright holder. Copies of working
papers are available from the author.
How Firm Strategies Influence the Architecture of Transaction
Networks
Revision and Resubmission to Industrial and Corporate Change
Jianxi Luo*
Massachusetts Institute of Technology
[email protected]
Daniel E. Whitney
[email protected]
Carliss Y. Baldwin
Harvard Business School
[email protected]
Christopher L. Magee
[email protected]
* Corresponding author
January 27, 2011
How Firm Strategies Influence the Architecture of Transaction Networks
Abstract
In the context of business ecosystems, hierarchy is an architectural property that refers to
the degree to which transactions proceed in a single direction, from “upstream” to
“downstream.” It is often assumed that a unidirectional flow of goods in a value chain implies a
corresponding hierarchy in the transaction networks of firms participating in the chain. However,
this is an untested hypothesis: in fact, little is known about whether hierarchy varies across
transaction networks, and, if so, what causes such variation. In this study, we apply
network-based methods to define and measure the degree of hierarchy in interfirm transaction
networks in two industry sectors in Japan: automotive and electronics. Our empirical results
show that the electronics sector exhibits a much lower degree of hierarchy than the automotive
sector due to the existence of numerous interfirm transaction cycles. Transaction cycles in turn
can arise when a subset of firms adopt the strategy of vertically permeable boundaries. Such
firms are vertically integrated in the sense of participating in multiple stages of the value chains,
but their internal upstream units also sell into and downstream units buy from intermediate
markets. Our comparative analysis suggests that firms elect the strategy of vertically permeable
boundaries when they face low transaction costs and high rates of product innovation, but at the
same time believe there are knowledge complementarities between different stages of the value
chain. Vertically permeable boundaries allow such firms to take advantage of cross-division
knowledge complementarities while maintaining the competitiveness of upstream units through
their participation in intermediate markets.
Keywords: industry architecture; transaction networks; firm boundaries; vertical integration;
knowledge complementarities; innovation; hierarchy; modularity
THE ARCHITECTURE OF TRANSACTION NETWORKS
1 Introduction
Transactions are the most basic form of interfirm relationship and a fundamental unit of
economic analysis (Commons, 1943; Williamson, 1981). Inspired by the seminal work of Coase
(1937) and Williamson (1975, 1981, 1985), over the last three decades, a great deal of scholarly
work has focused on transactions. Transaction cost economics (Coase, 1937; Williamson, 1975;
1981; 1985), knowledge-based theories of the firm (Grant, 1996; Teece et al., 1997), and
modularity (Baldwin and Clark, 2000; Baldwin, 2008) have been used as theoretical lenses to
examine the characteristics of transactions and related strategies such as “make or buy.”
Most research on transactions has focused on single firms or industries or bilateral
relationships between customers and suppliers (Nishiguchi, 1994; Argyres, 1996; Argyres and
Liebeskind, 1999; Helper et al., 2000; Hoetker, 2006; Parmigiani, 2007; Nagaoka et al., 2008;
Novak and Stern, 2009). Yet many technical systems have multi-step value chains, while others
give rise to complementary firms producing different parts of a large system. Thus a growing
body of research has been conducted at the level of a sector in which firms collectively innovate,
design, and produce a coherent set of system products (Dosi, 1988; Langlois and Robertson,
1992; Malerba and Orsenigo, 1993; 1996; 1997; Teece et al., 1994; Baldwin and Clark, 2000;
Brusoni and Prencipe, 2001; Malerba, 2002; Jacobides, 2005; Dalziel, 2007; Nakano and White,
2007; Adner and Kapoor, 2010). Increasing attention has also been paid to industry architecture
and dynamics (Baldwin and Clark, 2000; Jacobides, 2005; Jacobides and Billinger, 2006;
Stefano et al., 2009). However, despite this prior work, we still know very little about how
transaction patterns vary across sectors or what factors influence the strategic choices of firms in
such sectors.
A business ecosystem is a group of firms linked by transactions and complementarities
that together provide complex products and related services to end users (Iansiti and Levien,
2004; Adner and Kapoor, 2010). Recently, business ecosystems have emerged as an important
form of economic organization, in many cases replacing the vertically integrated corporations
studied by Chandler (1962, 1977) and Williamson (1975, 1985). Industrial firms participate in
ecosystems through transactions and, by their individual strategic choices, collectively form
transaction networks. In this paper, we explore the architecture of transaction networks in order
to shed light on firms’ strategies in different business ecosystems. (In what follows, we will use
the terms “sector” and “ecosystem” interchangeably.)
1
In addition to the above-cited work on transactions, there is a growing literature aimed at
understanding interfirm network structures in general and their effects on firm performance
(Gulati, 1995, 1998; Sorenson and Stuart, 2001; Rosenkopf and Schilling, 2007; Schilling and
Phelps, 2007). Various kinds of network ties between firms, such as alliances (Gulati, 1995,
1998; Stuart, 1998; Rosenkopf and Schilling, 2007), social and financial ties (Sorenson and
Stuart, 2001) and skill relatedness (Neffke and Henning, 2010) have been studied. However, the
methods used in these studies cannot be directly applied to transaction network analysis, because
the links in alliance and social networks are non-directed. In contrast, transactions form directed
links: goods flow from Firm A to Firm B. Networks with directed links, such as transaction
networks, require different methods of analysis, which is one of the contributions of this paper.
To begin the work of exploring transaction networks, this paper addresses four questions:
1) Do the hierarchies of interfirm transaction networks vary across different ecosystems?
In the context of business ecosystems, “hierarchy” is an architectural property that refers
to the degree to which transactions proceed in a single direction, from “upstream” to
“downstream.” In prior work Nakano and White (2007) conjectured that a unidirectional
flow of goods in a production process implies a corresponding hierarchy in transactions
between firms, but their data was limited to a single ecosystem. In this paper, we apply a
new network-based metric developed by Luo and Magee (2011) to transaction data from
the Japanese automotive and electronics sectors. The empirical results show that the pure
hierarchy hypothesis does not always hold: specifically, the electronics sector is only
partially hierarchical due to the wide presence of interfirm transaction cycles. This part of
the research contributes to the industry architecture literature on a macro level by
constructing a measure of hierarchy that can be applied to any interfirm transaction
network and by showing that different networks do indeed display different degrees of
hierarchy.
2) What practices explain the differences in hierarchy across these two ecosystems?
To explain the variation in hierarchy across ecosystems, we investigated the theoretical
determinants of hierarchy in a transaction network. We show that violations of pure
hierarchy arise only if a subset of firms participate in multiple stages of their value chains,
and their downstream internal units buy from and upstream internal units sell to
intermediate markets. Such firms have vertically permeable boundaries in the sense
2
defined by Jacobides and Billinger (2006). A significant number of the largest electronics
firms have adopted the strategy of vertically permeable boundaries. In contrast, the
largest automotive firms in Japan concurrently source components from internal and
external suppliers (Parmigiani, 2007), but their internal component divisions do not sell
to external customers.
3) How do firms’ strategies influence hierarchy in different ecosystems?
To better understand these differences in strategy and behavior, we conducted interviews
with managers at key firms in the two sectors. We found that the electronics firms with
vertically permeable boundaries faced short product life cycles and low transaction costs,
creating pressures towards vertical disintegration. However, the firms’ managers also
believed that there were significant knowledge complementarities across stages of the
electronics value chains, which caused them to want to retain in-house component
divisions (and related capabilities). The internal component divisions in turn honed their
capabilities by competing with more specialized suppliers in intermediate markets.
4) What environmental factors explain the differences in the largest firm’s strategies in each
ecosystem?
The contrasting strategies of the largest firms in the automotive and electronic sectors in
turn arose because the two groups of firms face different environmental conditions. As
indicated, electronics firms face short product life cycles, low levels of asset specificity,
and low transaction costs, whereas automotive firms face relatively long product life
cycles, high levels of asset specificity and high transaction costs. These differences,
which arise from differences in the modularity of the underlying products and processes,
change the relative benefits and costs associated with the strategy of vertically permeable
boundaries. Differences in modularity in turn can be traced back to the basic physics of
automobiles vs. electronic products and processes.
This article proceeds as follows. Section 2 reviews the relevant literature. Sections 3 and
4 introduce our measurement methods and describe our empirical results. Section 5 explains how
the observed differences in the two transaction networks can be traced back to differences in the
participating firms’ strategies and underlying technological constraints. Section 6 concludes by
discussing this paper’s contributions, limitations of the analysis, and future research directions.
3
2 Literature Review: Hierarchy in Industry Architecture
We are interested in measuring variations in industry architecture across different
ecosystems, and explaining this variation in terms of firms’ strategic choices and technological
constraints. In this section, we briefly review three strands of related literature involving: (1)
industry architecture, (2) hierarchy, and (3) the influence of technology on industry structure.
2.1 Industry Architecture
Prior research has investigated industry structures in terms of horizontal concentration
versus diversification within a single layer or industry (Penrose, 1959; Chandler, 1962; Teece,
1982; Davis and Duhaime, 1992), and vertical integration and disintegration (Baldwin and Clark,
2000; Sturgeon, 2002; Nagaoka et al., 2008; Helfat and Campo-Rembado, 2009). Single industry
studies often investigate horizontal divisions of labor, revenue, or assets by firm or region. Many
are based on theories as to how and why firms differ in terms of their resources and capabilities
(Penrose, 1959; Wernerfelt, 1984; Barney, 1991; Teece et al., 1997). Studies of vertical
integration/disintegration in turn investigate the boundaries of the firm, the division of activities
between customer and supplier, and the nature of contracts. These analyses are often based on
transaction cost economics (Williamson, 1975; 1981; 1985) or knowledge-based theories of the
firm (Grant, 1996; Teece et al., 1997).
Most prior work has not addressed the possibility that numerous complementary goods
will be combined into larger systems supplied by many different firms. That is, many end-user
products are not designed and produced within a single industry with stable boundaries, but by a
network of firms connected by transactions in intermediate markets whose boundaries co-evolve
over time (Brandenburger and Nalebuff, 1996; Langlois and Robertson, 1992; Jacobides, 2005).
Such networks, made up of firms in different sub-industries that supply complementary goods,
are sometimes called “sectors” (Malerba, 2002). More recently, the term “ecosystem” has come
into widespread use (Iansiti and Levien, 2004; Evans, Hagiu and Schmalensee, 2006; and Adner
and Kapoor, 2010). To avoid confusion, we consider a group of firms making similar products
(for example, firms making engines or semiconductor chips) to form an “industry.” We call a
collection of industries linked by transactions a “sector” or “ecosystem,” using these last two
terms interchangeably. For example, the automobile sector (or ecosystem) includes firms making
whole automobiles, plus those making sub-systems such as engines or interiors, components
4
such as pistons or seats, and materials such as glass, plastic, and steel. Transactions serve to
coordinate activities across firm boundaries within a sector (Christensen and Rosenbloom, 1995;
Jacobides and Winter, 2005).
“Industry architecture” has been defined by Jacobides et al. (2006) as a somewhat stable
but evolving set of relationships used to organize a given set of production and innovation
processes.1 These relationships set the patterns through which labor and assets are divided in a
sector between different types of industry participants, and the associated set of “rules and roles”
that guide their behavior in the short and intermediate run. The necessary level of analysis for
understanding industry architectures is the sectoral network of firms, comprising a group of
related industries, rather than a supplier/customer pair or a single industry.
An increasing number of studies at the sector level have explored how the internal
boundaries of industries change, how intermediate markets emerge (Langlois and Robertson,
1992; Jacobides, 2005; 2006; Jacobides et al., 2006), and how sectors differ in terms of their
technological bases, innovation patterns, and economic performance (Nelson and Winter, 1982;
Dosi, 1988; Malerba and Orsenigo, 1993, 1996; 1997; Castellacci, 2007). However, our
understanding is limited in terms of how industry architectures vary across sectors, and how
technological constraints and/or strategic choices contribute to such variation. In particular,
quantitative empirical descriptions of overall industry architectures are still lacking.
2.2 Hierarchy
As indicated, hierarchy is an architectural property that refers to extent to which
transactions proceed in a single direction, from “upstream” to “downstream.”2 At the sector
level, existing studies often observe or suggest hierarchical industry architectures (Coase, 1937;
Malerba, 2002; Dalziel, 2007; Nakano and White, 2007) in which production processes are
organized into sequential stages (Coase, 1937; Abernathy et al, 1983; White, 2002a; Jacobides,
2005). Firms that perform higher level tasks depend upon firms that perform lower level tasks
(Dalziel, 2007), a form of sequential interdependence (Thompson, 1967). According to Harrison
1
Industry architecture generally focuses on firms and the transactional relationships between them. In contrast,
classic supply chain analysis focuses on inventories or plants and shipments between them. Clearly the two views
are complementary.
2
This is different from the meaning of “hierarchy” in organization theory, where it refers to a command-and-control
type of decision structure (e.g., Williamson, 1975). The difference between a “flow”, i.e., transaction, hierarchy and
a “containment”, i.e., organizational, hierarchy are discussed in the next section.
5
White (2002b:87), “production markets” show “persistent directionality in continuing flows of
intermediate goods” in which “only a niche within an industry establishes you in a line of
business.” In contrast, such hierarchy does not exist in a pure market, such as a stock market,
where traders can both buy from and sell to one another.3
Nakano and White (2007) offer empirical evidence that the network of firms connected
by supplier-customer relationships in the Tokyo industrial district exhibits a strict hierarchical
architecture. Based on this evidence, they further hypothesized that hierarchy is a general
property of production markets because firms in such markets tend to become entrenched in their
positions and roles as buyers and sellers over time. In contrast, firms in financial markets
constantly change roles, so that an enduring institutionalized role structure does not exist
(Nakano and White, 2007).
Nakano and White’s analysis neglected the role of firm strategy, however. For reasons of
strategy, some firms might not adhere to fixed positions and roles: in dynamic markets, such
commitments might be disadvantageous in the long run. For example, Foxconn, the largest
Taiwanese original design manufacturer (ODM) of personal computers, supplies finished
computers directly to personal computer makers, such as Dell and Apple, but it also produces
and sells many connecters, cables, and printed circuit boards to other suppliers upstream in the
personal computer manufacturing sector. Foxconn plays multiple (and changing) roles, and as a
result, its vertical position in the value chains is ambiguous. If a sector includes many firms like
Foxconn, the transaction flows in the sectoral network may not be purely hierarchical, but
instead partially hierarchical or even non-hierarchical.
If hierarchy in a transaction network can exist along a continuum, then the appropriate
question to ask is not whether a transaction network is hierarchical or not, but is it more or less
hierarchical than others. Answering this question requires new methods of analysis. A general
approach to quantifying hierarchy in networks has been recently developed in related work (Luo
and Magee, 2011). In section 3, we introduce this metric and in section 4 we use it to measure
the variable degree of hierarchy in two interfirm transaction networks.
3
In Williamson (1975)'s Markets and Hierarchy, the market is assumed to be a non-hierarchical space, and it
represents the opposite to hierarchy as a means of organizing economic activity. Following Williamson, the term
“hierarchy” is often used to mean relationships within enterprises or firms where command and control replace
market transactions.
6
2.3 The Influence of Technology on Industry Structure
The extent to which a given transaction network is hierarchical is the collective result of
firms’ strategic choices within the constraints of the technologies they use. Technology has been
considered as a factor influencing the structure of alliance networks (Rosenkopf and Schilling,
2007) and technological relatedness networks (Neffke and Henning, 2008). Christensen and
Rosenbloom (1995) and Murmann and Frenken (2006) have also suggested that hierarchical
supply relationships between firms may mirror the hierarchical structure of their products
(comprising of systems, subsystems, components, and so on). In addition, Paprzycki (2005),
based upon several descriptive cases in the Japanese electronics sector in the 1990s, argues that
the previously hierarchical supplier-customer relationships collapsed to some degree, when more
modular and open product architectures emerged. He reasoned that firms at the time had to
actively search outside their well-aligned hierarchical value chains in order to access external
resources or capabilities required by new architectures (Penrose, 1959; Wernerfelt, 1984; Teece
et al., 1997). Thus technological changes toward more modular architectures can cause
intermediate markets to emerge. Formerly vertically integrated firms might then find that their
internal divisions are no longer competitive in certain stages of the value chain (Jacobides, 2005).
Their choice is then to exit from such stages or increase the competitiveness of their internal
divisions. The end result of this process could be a set of purely focused firms or a set of
vertically integrated firms with permeable boundaries.
It is often suggested that a higher level of modularity in a technical system is associated
with a higher level of vertical disintegration. Industry studies have addressed this issue in two
ways (Colfer and Baldwin, 2010). First, some scholars have asked, do industries vertically
disintegrate when modular product architecture becomes the dominant design (Suarez and
Utterback, 1995; Utterback, 1996)? Conversely, does an industry vertically integrate when an
integral dominant design emerges? Transaction cost economics predicts such “mirroring”
between product architecture and transactions (Williamson, 1981; Henderson and Clark, 1990;
Baldwin, 2008), but firms’ strategies generally go beyond simple transaction cost logic.
Thus Helfat (1997) has suggested that vertically integrated firms can take advantage of
knowledge complementarities in building long-term dynamic capabilities (Teece and Pisano,
1994). She supported her argument by documenting that oil companies with complementary
knowledge and physical assets undertook more R&D on synthetic coal conversion in response to
7
rising oil prices during the 1970s. In a dynamic model of alternating modular and integral
architectures (Fine, 1998; Fine and Whitney, 1999), Helfat and Campo-Rembado (2009) further
argued that even when modular architectures are advantageous, forward-looking firms still might
choose to remain vertically integrated in order to maintain their capabilities over the technology
cycle. Thus vertically integrated firms would co-exist with vertically specialized firms if the
former perceived that the future value of their dynamic capabilities outweighed the short-term
disadvantages of vertical integration.
Numerous empirical studies have attempted to correlate modularity with vertical
integration/disintegration, in industries including stereo systems (Langlois and Robertson, 1992),
computers (Baldwin and Clark, 2000; Hoetker, 2006), banking products and services (Jacobides,
2005), and bicycles (Galvin and Morkel, 2001; Fixson and Park, 2008). Overall, the results have
been mixed. On balance it appears that modularity is a necessary but not sufficient condition to
explain vertical disintegration in an industry or sector (Colfer and Baldwin, 2010).
In section 5, we will argue that differences in the modularity of products and processes
lead firms to select different strategies with respect to vertically permeable boundaries. These
differences in turn explain why the two transaction networks we studied differ in terms of their
empirically measured degrees of hierarchy. In the next two sections, we explain how we measure
hierarchy and describe our empirical results.
3 Measuring Hierarchy in Industry Architecture
A hierarchy is a generic structure, in which levels are asymmetrically ranked according to
a specific type of relation (Ahl and Allen, 1996; Luo and Magee, 2011). A flow hierarchy arises
when there is directional movement through a series of stages. For example, if firm B purchases
a good from firm A, processes and adds value to the good and then sells the new good to firm C,
the goods flow from A to B to C in a sequence of value-adding stages. The order of stages
essentially determines the direction of the flows of goods. Such flows are fundamental to the
organization of the economic system, and (at a high level of aggregation) are reflected in national
input-output tables (Leontief, 1951).4
4
Flow hierarchies differ from organizational hierarchies. Specifically, an organizational hierarchy is containment
hierarchy, also known as a “nested hierarchy” (Ahl and Allen, 1996; Simon, 1962; Christensen and Rosenbloom,
1995; Murmann and Frenken, 2006) or a “hierarchy of inclusion” (Wilson, 1969). In a containment hierarchy, lower
8
Interfirm transaction networks can be represented as directed graphs in which the nodes
are firms and the links are transactions. Such networks include cycles and sequences (Thompson,
1967). A pure flow hierarchy has only sequences, and all flows are unidirectional, as shown in
the example networks A, B and C in Figure 1. Thus a pure flow hierarchy generalizes
Thompson’s notion of “sequential interdependence” (Thompson, 1967, p. 54). Cycles violate the
principle of strict hierarchy because flows can come back to their origin. Networks D, E, F in
Figure 1 embed cycles to different extents. The firms in such networks display some degree of
“reciprocal interdependence” either direct or indirect (Thompson, 1967, p.55).
1
1
3
2
4
5
6
1
3
2
7
4
5
A)
6
3
2
7
4
5
B)
1
7
C)
2
1
6
2
3
5
3
1
3
2
4
6
8
4
5
6
7
7
9
D)
E)
5
4
F)
Figure 1 Examples of different flow hierarchies
Directionality is the fundamental principle of a flow hierarchy. It follows that we can
measure the degree of hierarchy in a network by capturing to what extent it contains or does not
contain cycles. Our hierarchy metric (h) is calculated as the percentage of links that are not
included in any cycle:
m
∑e
i
h=
i =1
(1)
m
where m is the number of links in the network and ei=0 if link i is in a cycle and 1
levels lie within or are aggregated into upper levels, and upper levels contain lower levels. Complex products like
airplanes are often viewed as containment hierarchies, because they are made up of subsystems, which contain
sxmaller components and parts (Tushman and Murmann, 1998).
9
otherwise. In general, this metric is advantageous in its clarity and ease of computation in
comparison to other potential metrics. It has wide applicability in other network systems, such as
organizations, teams, and products.5
In some applications, it is useful to weight the links by, for example, the volume of flows.
However, in this paper we focus on unweighted networks because our empirical data includes
incomplete information about weights of all the links we draw. In addition, this metric only
counts whether a link is involved in any cycle, but does not take into account the length of cycles.
Completely tracing cycle sizes is computationally difficult when networks are large and adds
little insight.
We tested the flow hierarchy metric on the simple networks shown in Figure 1. The
results are given in Table 1. Networks A, B, and C, in which all flows proceed in one general
direction and no cycle exists, are purely hierarchical, thus h=1. Networks D and E are partially
hierarchical and their degrees of hierarchy are 0.71 and 0.40 respectively. Network F is a pure
cycle, thus h=0.
Table 1 Degrees of hierarchy in the example networks in Figure 1
Networks in Figure 1
A
B
C
D
E
F
Hierarchy Degree (h)
1
1
1
0.71
0.40
0
Architectural Regime
Purely
Hierarchical
Purely
Hierarchical
Purely
Hierarchical
Partially
Hierarchical
Partially
Hierarchical
Purely
Cyclic
Table 1 shows that the flow hierarchy metric potentially characterizes interfirm
transaction networks into three canonical architectures:
(1) Purely hierarchical (single-directional transaction flow), h=1;
(2) Purely cyclic (every firm is involved in cycles), h=0;
(3) Partially hierarchical (sequence and cycle are combined), 0<h<1.
In the next section, we apply the hierarchy metric, as well as network visualization tools,
to transaction data from two industry sectors in Japan, and show that one sector is substantially
more hierarchical than the other.
5
For more details on this metric, including the algorithm to calculate it for large-scale networks, comparisons with
other metrics, and its applications to other kinds of networks, see Luo (2010) and Luo and Magee (2011).
10
4 Data and Empirical Results
4.1 Data
We extracted supplier-customer transactional relationship data from the series data books
“The Structure of the Japanese Auto Part Industry” and “The Structure of the Japanese
Electronics Industry,” which are based on regular surveys by Dodwell Marketing Consultants.
The company directories in these two data books provide information on the major customers
and suppliers for each firm. Such information makes it possible to extract “who-supplies-whom”
type of connections between firms,6 and to build multi-tier sectoral transaction networks. The
data books are only available in hard copy and had to be manually entered into an electronic
database. We used the data books published in 1983, 1993, and 2001, but we believe the data
actually represents the situation approximately two to three years before the publishing year,
because the publications were refreshed every two to three years. Table 2 lists the largest firms
included in our data by revenue.
Largest 10 Firms in
Electronics Network
Largest 10 Firms in
Automotive Network
Table 2 List of the largest firms included in the network data
Company
Year Ending
Toyota Motor
Nissan Motor
Honda Motor
Mitsubishi Motors
Mazda Motor
Isuzu Motors
Suzuki Motor
Fuji Heavy Industries
Daihatsu Motor
Hino Motors
Hitachi
Matsushita Electric Industrial*
Toshiba
Sony
NEC
Fujitsu
Mitsubishi Electric
Canon
Sanyo Electric
Sharp
June 1993
March 1993
March 1993
March 1993
March 1993
October 1993
March 1993
March 1993
March 1993
March 1993
March 1992
March 1992
March 1992
March 1992
March 1992
March 1992
March 1992
December 1991
November 1991
March 1992
Revenue
(Billion Yen)
9,031
3,897
2,695
2,615
2,191
1,199
1,053
873
785
632
7,766
7,450
4,722
3,915
3,744
3,422
3,343
1,869
1,616
1,555
* Matsushita Electric Industrial was renamed to Panasonic Corporation in 2008
6
We do not have the detail on the specifics of individual transactions.
11
For each sector in a specific year, we constructed a directed network in which nodes are
firms and links are supplier-customer transactional relationships. The transactions indicated are
compensated transactions of physical products, excluding services and intellectual property.
Basic network statistics, including Number of Firms (n), Number of Transactional
Relationships (m), and Average Degree7 (k=m/n), are shown in Table 3. The automotive
transaction networks contain more nodes and links and have a higher average degree than the
electronics transaction network in 1993.
Table 3 Network descriptive statistics
Network Attributes
Japanese Automotive Sector
Japanese
Electronics
Sector
Year
1983
1993
2001
1993
Number of Firms (n)
356
679
627
227
Number of Transactional
Relationships (m)
1480
2437
2175
648
Average Degree (k=m/n)
4.157
3.589
3.469
2.855
Existing studies of interfirm alliance networks have shown skewed degree distributions,
that is, a small number of firms have a large number of alliance partners. It is conjectured that
firms with larger size, higher prestige, and stronger architectural control8 tend to attract a greater
number of alliances than smaller and less prestigious firms (Stuart, 1998; Rosenkopf and
Schilling, 2007). Similarly, our interfirm transaction data (see Table 4) indicate that the largest
firms (according to Table 2) and the firms that control product architectures have the highest
numbers of suppliers in both the automotive and electronics networks.
7
In graph theory, the degree of a node means the number of nodes connected to it. In a directed network, there are
two types of degrees applying to a single node: in-degree (number of nodes connected to it) and out-degree (number
of nodes it connects to). Average in-degree and out-degree of a network are equal.
8
Architectural control refers to “the ability of a firm(s) to define specifications for both the individual subsystems
of a product as well as the integration of these subsystems to form the end product.” (Rosenkopf and Schilling,
2007)
12
Table 4 The most connected firms in automotive and electronics interfirm transaction networks in 1993
Top 10 firms with the highest number of suppliers
Electronics Sector
Automotive Sector
Company
Mitsubishi Motors
Nissan Motor
Honda Motor
Toyota Motor
Mazda Motor
Isuzu Motors
Fuji Heavy industries
Suzuki Motor
Daihatsu Motor
Hino Motors
Hitachi
Toshiba
NEC
Sony
Fujitsu
Mitsubishi Electric
Matsushita Electric Industrial
Sharp
Sanyo Electric
Victor Company of Japan
Number of
Suppliers
226
176
169
166
157
135
127
125
99
98
52
40
38
36
34
33
30
23
15
15
Top 10 firms with the highest number of customers*
Number of
Customers
0
0
0
0
0
0
0
0
0
0
17
26
18
3
12
7
27
3
3
1
Company
Du Pont Japan
Fuji Bellows
Bando Chemical Industries
Sumitomo Electric Ind.
Hashimoto Forming Ind.
Tokai Rubber Industries
Meiwa Industry
Nishikawa Rubber
Nisshinbo Industries
Ogihara
Toshiba
NEC
Hitachi
Alps Electric
TDK
Fujitsu
Yokogawa-Hewlett-Packard
Nippon Motorola
Intel Japan k.k.
Number of
Suppliers
0
0
0
4
3
3
2
0
0
0
30
40
38
52
1
1
34
5
1
0
Number of
Customers
18
17
15
12
11
11
11
11
11
11
27
26
18
17
15
13
12
10
10
10
* “Customers” are within sector and do not include end-users.
However, there is a notable difference between two sectors in that the automotive
assemblers have no customers within the sector while some of the largest electronics firms (such
as Matsushita Electric, Toshiba, NEC, Hitachi, and Fujitsu) have the highest numbers of
customers in the sector. In other words, the largest purchasers and suppliers of electronics
components are the same firms. Meanwhile, in the automotive sector, the firms with the largest
number of suppliers normally have no customers within the sector, and vice versa.
With these basic facts in hand, we now analyze the two transaction networks using
standard network tools plus our hierarchy metric. In sub-sections below, we present graphical
visualizations, matrix visualizations, hierarchy calculations, and an analysis of embedded cycles
for the two transaction networks.
4.2 Graphical Visualization
We used Netdraw, a leading social network visualization software (Borgatti, 2002), to
create graphical pictures of the interfirm transaction networks in the automotive and electronics
sectors in a comparable year (1993). The visualizations are shown in Figure 2. They allow us to
13
see that the automotive network has more nodes and links and that both networks contain a
number of “hubs” (nodes with many links). But the diagrams do not reveal the presence of cycles
or hierarchy in the two networks.
A) Automotive Sector
B) Electronics Sector
Figure 2 Japanese interfirm transaction networks in 1993
4.3 Matrix Visualization
Matrices are better than graphs at revealing flow hierarchies in networks. In design
theory, the Design Structure Matrix (Steward, 1981; Eppinger et al., 1994) is used to examine the
dependencies between design elements in a square matrix. These elements are often design tasks,
design parameters, or the actual components in a designed or engineered system, such as
software (MacCormack et al., 2006) and automotive brake systems (Eppinger et al., 1994).
In contrast to the design processes that are the focus of most DSM research, interfirm
transaction networks are not designed centrally: instead they are self-organizing systems that
emerge from numerous, decentralized “make or buy” decisions by the constituent firms. A
square matrix can still be used to examine the overall pattern of economic interdependencies
between firms, but calling it Design Structure Matrix is inaccurate. Instead, we call it
Dependency Structure Matrix (DSM).
Figure 3 shows DSM representations for the automotive and electronics interfirm
transaction networks in Japan in 1993. The elements on both axes are firms listed in the same
order, and the dots represent dependencies. If firm j is a customer of firm i, i.e., firm j depends
on firm i for its supplies, we put a dot in the cell (i, j) of the matrix. For example, in the
automotive DSM, dot (359, 524) indicates that Nippon Denso (firm 524) is a customer of Arai
14
Seisakusho (firm 359). In the electronics DSM, dot (147, 124) indicates that Omron (firm 124) is
a customer of Matsushita Electric Industrial (firm 147–now renamed Panasonic).
0
50
100 150 200
250
300 350 400
450
500 550 600 650
0
0
50
100
150
200
0
Arai Seisakusho
50
Matsushita Electric
Industrial (Panasonic)
Partially
Hierarchical
Hierarchical
100
150
50
200
250
100
300
350
Omron
400
150
450
500
550
Nippon Denso
200
600
650
A) Automotive Sector
B) Electronics Sector
Figure 3 Dependency Structure Matrices for Japanese interfirm transaction networks in 1993.
The small boxes drawn inside the DSMs encapsulate strong components (Wassserman and Faust, 1994), in which all nodes are
on cycles with each other. The automotive sector DSM shows there is only one strong component in the automotive network, and
its size is (3 nodes, 3 links). In contrast, in the electronics network, there are four strong components, and their sizes are (84
nodes, 254 links), (3 nodes, 4 links), (2 nodes, 2 links), and (2 nodes, 2 links), respectively. The dots in a row indicate how many
suppliers the firm in that row has. In DSM A, the dense bottom rows are the large car manufacturers like Toyota and Nissan. No
such dominance appears in B.
In the DSMs in Figure 3, firms are ordered according to their visibilities,9 thus firms on
connected cycles will be grouped together (MacCormack et al., 2010). In the automotive DSM,
almost all dots are below the main diagonal, indicating that this network is extremely
hierarchical. In contrast, the electronics DSM has many dots above the diagonal, indicating that
many firms participate in transaction cycles. Furthermore, most of these cycles are intertwined
together in one strongly connected component that encapsulates 84 nodes (37% of the firms) and
254 links (39% of the transactional relationships). Firms in the large component buy from and
sell to each other in complex patterns.
In general, the comparison of the two DSMs in Figure 3 reveals significant differences
between the two sectors in terms of their hierarchical degree. We now use the Luo and Magee
(2011) metric, described above, to quantify the differences.
9
“Visibility” is the count of all the direct and indirect dependencies a node possesses with other nodes.
15
4.4 Hierarchy Measurement
We computed the hierarchy metric, h, for the interfirm networks of the Japanese
electronics production sector in 1993 and the Japanese automotive production sector in 1983,
1993, and 2001. Table 5 repeats the descriptive statistics of Table 3 (for convenience) and
calculates the degree of hierarchy (h) for each network. The comparison of these two sectors in
one year (1993) shows that the electronics production sector (h=0.5957) is quantitatively much
less hierarchical than the automotive production sector (h=0.9988), due to the presence of many
transaction cycles. Furthermore, the degree of hierarchy in the automotive sector in Japan did not
change much over time and remained high from the early 1980s to the early 2000s.
Table 5 Empirical Measurement Results
Network Attributes
Japanese Automotive Sector
Japanese Electronics
Sector
complete remove 10
network largest firms
Year
1983
1993
2001
1993
1993
Number of Firms (n)
356
679
627
227
216
Number of Transactional
Relationships (m)
1480
2437
2175
648
221
Average Degree (k=m/n)
4.157
3.589
3.469
2.855
1.023
Number of Firms in Cycles (nc)
4
3
2
91
13
Number of Links in Cycles (mc)
4
3
2
262
14
Degree of Hierarchy (h=1- mc/ /m)
0.9973
0.9988
0.9991
0.5957
0.9367
many*
7 two-node
cycles
Cycle Tracking
2 two-node 1 three-node 1 two-node
cycles
cycles
cycle
* In the 1993 electronics sector network, there were 51 two-node cycles, 12 three-node cycles, 92 four-node cycles, 107
five-node cycles, 598 six-node cycles, and many larger cycles. There were four strong components in which all nodes
are on cycles with each other, and their sizes are (84 nodes, 254 links), (3 nodes, 4 links), (2 nodes, 2 links), and (2
nodes, 2 links), respectively. These four strong components are boxed in the electronics sector DSM in Figure 3.
In the automotive sector, only one or two small cycles are found in the network in any
year. These are shown in Figure 4.
16
A) The only two cycles found in 1983
Arakawa Auto Body
(Araco)
• major products: car assembly (trucks, buses, specialized vehicles, etc) 46%,
interior parts 28%, other auto parts 6%, other items 20%
• this company changed its name to “Araco” in the early 1990s
This link disappeared after 1983
Toyota Auto Body
Fuji Kiko
• major products: car assembly 84% (passenger cars 45%, commercial vehicles
31%, trucks 8%), auto parts, etc. 16%
• major products: steering columns, safety belts, transmission parts, pedals,
engine parts, reclining seat equipment, suspension system parts, etc. 99%,
agricultural machinery parts, other items 1%
This link disappeared after 1983
Ikeda Bussan
• major products: seating 78.3%, interior accessories 14.4%, chemical products
6%, other items 1.3%
B) The only cycle found in 1993
Araco
• major products: vehicle assembly (trucks, buses, specialty vehicles, etc) 50%,
seating, door trims, and roof linings 50%
• customers: Toyota 98.4%, Daihatsu motor 0.2%, Toyota Auto Body 0.1%
Toyota Auto Body
• major products: car assembly 84% (passenger cars 45%, commercial vehicles
31%, trucks 8%), auto parts, etc. 16%
• customers: Toyota Motor, Toyota Tsusho, Gifu Auto Body Industry
this link disappeared after 1993
Gifu Auto Body Industry
• major products: bodies for trucks, specialty vehicles 64%, pressed auto parts
(seat adjuster, radiator baffles, door trims) 31%, others, 5%
• customers: Toyota Motor 90%, Takashimaya Nippatsu Kogyo 3%, Toyota
Shatai 1%, Dahatsu Motor 1%, Araco
Toyota Auto Body Co Ltd (TA) acquired the vehicle manufacturing and sales business of Araco Corp (AR), a
manufacturer of automotive seat cover, and a unit of Toyota Motor Corp (TM) – announced on October 1st, 2004
Toyota Auto Body Co Ltd (TA) acquired the remaining 89.09% interest of Gifu Auto Body Co Ltd, a manufacturer
of automobile and truck bodies – announced on October 1st, 2007
C) The only cycle found in 2001
Delta Kogyo K.K.
• major products: seating 68%, shift levers 11%, sun visors 3%, ashtrays 1%, door
parts 1%
In 1983 and 1993 data, this link did not exist
In 1983, this link did not exist
Toyo Seat
• major products: seating for automobiles 95.4%, exhaust pipes 0.9%, others 3.7%
Figure 4 All the cycles found in the automotive interfirm transaction networks in 1983, 1993, and 2001
17
The transactional links in these cycles are of only minor volume10, that is, they are “weak
ties” in the network analysis term (Granovetter, 1973). These cycles also did not last long. For
instance, in the 2000s, Toyota Auto Body acquired the other two firms (Araco and Gifu Auto
Body Industry) involved in the only cycle found in the 1993 network, so this cycle no longer
exists. In brief, the automotive transaction network is very hierarchical, and thus is clear which
firms are “upstream” and which are “downstream.”
In contrast, approximately 40% of the interfirm transactional relationships in the
electronics sector in 1993 were involved in cycles. In the electronics network, there were 51
two-node cycles, 12 three-node cycles, and many larger cycles. Figure 5 presents two examples
extracted from the data.
A) An example of 2-node cycle
PC, Server, Systems
Shindengen Electric Mfg
Fujitsu
Components, Power Units
B) An example of 3-node cycle
PCB
Assemblies
(Panasonic)
CMK
Components
Matsushita Electric Works
Materials for boards
Figure 5 Example cycles found in the electronics interfirm transaction network
Note: Fujitsu owned a 7.2% share of Shindengen Electric Mfg in 1992. Matsushita Electric Industrial owned a 32.5% share of
Matsushita Electric Works, and Matsushita Electric Works owned a 3.6% share of CMK in 1992. Information on what was
transacted comes from our interviewees at Fujitsu and Panasonic, respectively, based on their knowledge of the firms’ business in
the early 1990s.
Fujitsu purchased components and power units from Shindengen Electric Manufacturing
for the integration of its personal computer, server and system products, and then also supplied
such products to Shindengen Electric. Matsushita Electric Industrial (now Panasonic) sold
10
The Dodwell data for the auto part industry has some but incomplete information about the portion of
procurement from each of the major suppliers that a customer firm lists. Fortunately, we can find such information
for the firms involved in these four cycles in the automotive networks, but not for all the firms.
18
components to Matsushita Electric Works, which sold materials for making electronic boards to
CMK. CMK in turn was a supplier of PCB (printed circuit board) assemblies to Matsushita
Electric Industrial.
We found that most of the cycles in the electronics sector included at least one large
diversified firm, such as Panasonic or Fujitsu. These large firms apparently play a major role in
forming cycles of varied sizes. We tested this hypothesis by removing the largest 10 firms by
revenue (see Table 2) from the network. In the new network without the largest 10 firms, only 14
out of 221 links were on cycles (i.e. h=0.9367), while only 13 of 216 firms participated in cycles.
This indicates that most of the cycles in the electronics sector were bridged by a small number of
large firms.
In brief, the electronics interfirm transaction network is only partially hierarchical. Inside
the strongly connected component of the network (the large central block in Figure 3B), it is not
clear which firms are “upstream” and which are “downstream.” This transaction network thus
provides a counterexample to Nakano and White’s (2007) hypothesis that hierarchy is a general
property of production networks. In the next section, we will analyze the strategies and
technological constraints of the largest automotive and electronics firms, to see how these may
have affected the formation of transaction cycles.
5 Understanding Hierarchy in Industry Architecture
As stated above, industry architecture is not designed centrally but emerges from the
interaction of individual firms’ strategies and behaviors, subject to their social and technical
environments. In this section, we conduct a theoretical analysis to understand how transaction
cycles result from firms’ strategic choices. Our analysis is based on the basic properties of
networks augmented by information from interviews conducted in 2009 with managers at three
firms in the automotive sector (Denso, Sumitomo Light Metal, and Panasonic Automotive
Systems), and six firms in the electronics sector (Fujitsu, Panasonic, Casio-Hitachi, Seiko-Epson,
Sony, and Takashima Sangyo). In particular, the interviews revealed significant differences
between the two sets of firms in terms of their strategies, organizational capabilities, product
architectures and innovation dynamics.11
11
Details about the interviewees and the firms are reported in Luo (2010).
19
5.1 Transaction Cycle and Vertically Permeable Boundary
We first explore how transaction cycles emerge from firms’ strategies in a generic
self-organizing transaction network. On the one hand, if all firms in a sector are vertically
integrated and do not buy or sell goods in intermediate markets, there will be no (intermediate)
transactions, and perforce no cycles. This is the classic Chandlerian model of vertical integration
(Chandler, 1962; 1977). Alternatively, if all firms are purely focused on a single stage of the
value chain, then the interfirm transaction flows will mirror the uni-directional flow of goods
through the stages of production. There will be many transactions, but no cycles. Finally, firms
may practice “concurrent sourcing,” obtaining components from both internal units and external
suppliers (Parmigiani, 2007). However, if such firms only purchase and do not sell components
to other firms in their sector, then again, there can be no cycles.
Cycles are possible only if some firms are present in multiple stages of the value chain
and simultaneously downstream internal units buy components from and upstream internal units
sell components to other firms in the sector. This is the case for Foxconn (discussed above),
Fujitsu and Panasonic (shown in Figure 5).
One of our interviewees described a cycle of transactions at a large electronics
manufacturer in Japan.12 This firm (Firm A) makes package substrates, chipsets, and whole
systems. Internally, its substrate unit transfers its products to the chipset unit which in turn
transfers products to the systems unit. But the substrate unit also sells products to Firm B, a
specialized chipset maker, while the systems unit purchases chipsets from that firm. Thus Firm A
has a vertically permeable boundary as described by Jacobides and Billinger (2006). It allows
goods to flow from division to division within the firm, but at the same time, internal divisions
are allowed, encouraged, or required to buy from and sell to external suppliers and customers. In
the case described by our interviewee, a transaction cycle is formed between Firms A and B as
shown in Figure 6. Firm A has a vertically permeable boundary, while Firm B, which is present
in only one stage of the value chain, does not.
12
The firms’ names have been disguised at the request of the interviewee.
20
Firm A
Systems
Chipsets
Firm B
Subsystems
Subsystems
Package
Substrates
Components
Market
Transaction
Internal
Transfer
Firm
Intermediate Products / Processes
Figure 6 Vertically permeable boundary and interfirm transaction cycle.
Figure 7 shows another way in which vertically permeable boundaries can give rise to
interfirm transaction cycles. Here Firms C and D have internal divisions that participate in
upstream and downstream stages of different value chains within the same sector. For example,
Firm C might make PCB (Printed Circuit Board) (a subsystem) and TV sets (a system), while
Firm D makes flat panel displays (a subsystem) and computers (a system). In this stylized
example, there are no product flows between the subsystem and system divisions within each
firm, but both firms are present in different stages of technologically related value chains
computers and TVs). (We might call such firms “vertically diversified.”) If Firm C sells PCB to
Firm D and purchases flat panel displays from it, then a transaction cycle exists between the two
firms. Both firms are present in multiple stages of their value chains and both buy from and sell
into intermediate markets, hence, by our definition, both have vertically permeable boundaries.13
13
Interfirm transaction cycles can also arise across sectors if some firms adopt a strategy of unrelated
diversification. The incidence of cross-sector cycles depends on the prevalence of business groups made up of
technologically unrelated units in the economy. Investigating such patterns is an interesting topic for future research.
21
Computers
Television Sets
Firm C
Firm D
System 2
Subsystem s for 1
Market
Transaction
System 1
PCB
Flat Panel
Display
Firm
Subsystems for 2
Intermediate Products / Processes
Figure 7 Vertically permeable boundaries and interfirm transaction cycle: a different example.
Jacobides and Billinger (2006) described how a European clothing manufacturer, facing
increased competition, made its vertical boundaries permeable. In adopting this strategy, the firm
sought to: (1) improve transparency and the ease of monitoring performance of its business units;
(2) gain operational efficiencies; (3) improve resource allocation; (4) facilitate innovation; and (5)
nurture dynamic capabilities. However, this paper focused on a single firm, thus did not consider
the potential impact of vertically permeable boundaries on the surrounding transaction network.
Our analysis of the Japanese electronics sector shows that when a few large firms create
vertically permeable boundaries, it becomes possible for transaction cycles to emerge, and
hierarchy may be reduced as a result.
Any interfirm transaction cycle must include at least one firm whose vertical boundary is
permeable in both directions: that is, a firm whose downstream units buy and upstream units sell
components in intermediate markets. Our data reveal that the largest electronics firms satisfy this
condition, whereas the largest automotive manufacturers do not (Whitney, 2007; MacDuffie,
2008). Although some of the largest automotive manufacturers (e.g. Toyota, Nissan)
concurrently source components from internal units and external suppliers, such firms almost
never sell components made by their upstream units to external customers. (See Table 4.)
What explains this difference in the strategies of the largest firms in the two sectors? On
first glance, one possible explanation for vertically permeable boundaries might be the keiretsu
business culture in Japan. A keiretsu is a group of companies with long-time interlocking
22
business relationships and shareholdings. For example, Denso Corporation is a long-time
keiretsu member of Toyota. Indeed many external suppliers are keiretsu members of the largest
assemblers in both sectors, and the presence of keiretsu may be related to the practice of
concurrent sourcing, which is found in both sectors. However, interfirm transaction cycles only
took place widely among keiretsu firms in the electronics sector, not in the automotive sector.
Thus, the keiretsu business culture, which is present in both sectors, cannot explain their
different degrees of hierarchy.
Below we offer an explanation that rests on well-documented technological differences
between the automotive and electronics sectors and perceived knowledge complementarities
within the electronic sector.
5.2 The Impact of Modularity
While our two sectors are culturally similar, they differ substantially in the technological
dimensions of their products and processes. In particular, firms in the automotive and electronics
sectors have strategically embraced different degrees of modularity in their major products
(Fujimoto, 2007; MacDuffie, 2008). In two survey papers, Fujimoto (2007) and Schilling and
Phelps (2007) asked experts for their subjective evaluations of modularity versus integrality of
different industrial goods. In both studies, experts agreed that most electronic and electrical
products are far more modular than automobiles.
When products and processes are modular, it is relatively easy to decompose and
recombine components (Simon, 1962; Whitney, 1996; Baldwin and Clark, 2000; Schilling,
2000). In contrast, integral products exhibit many strong functional or physical
interdependencies between components, hence are hard to break apart (Simon, 1962). Modularity
in turn has been shown to affect both transaction costs and the rate of innovation in technical
systems (Baldwin and Clark, 2000; Sturgeon, 2002; Baldwin, 2008; Colfer and Baldwin, 2010).
Integrality of the Automotive Sector. As indicated, contemporary automobiles are
integral products in which components and subsystems are highly interdependent. Takeishi and
Fujimoto (2001) observed that, in the Japanese automotive industry, the functions assigned to
individual parts of an automobile have become increasingly complex, and the need for structural
or functional coordination has increased commensurately. MacDuffie (2008) argued that the
integrality of contemporary automobiles is due to a number of systemic requirements, including
23
energy efficiency, emissions, noise, vibration, safety, stability, driving feel, design, and cost, that
must be met in order to attract consumers and satisfy regulators.
Interdependencies in automotive designs demand a specific tailored response by the
supplier that designs the component and its interfaces. In general major automotive components,
such as piston rings and mufflers, cannot be designed without detailed knowledge of the products
in which they will be used and the other components with which they interact. In the automotive
sector, component designs are specific to the systems in which the components are used.
Such component specificity, also called “synergistic specificity” by Schilling (2000),
gives rise to Williamsonian asset specificity in the following way (Williamson, 1975; 1981). In
order to guarantee that components will function in a given system, product designs must be
specifically tailored. As a result, contracts between firms are relational and “hand-in-glove.”
Both suppliers and customers must invest in skills, assets and resources (including knowledge)
that are valuable only in the context of their specific relationship (Asanuma, 1989; Baker et al.,
2002). Indeed the automotive suppliers we interviewed confirmed that they had to tailor their
product designs and production processes to the particular requirements of their customers, and
invest in deep, ongoing relationships. Such relationships have continuously improved quality,
cost and delivery in the automotive sector (Sako, 1992; Nishiguchi, 1994; Helper et al., 2000).
Consistent with Parmigiani’s (2007) concept of concurrent sourcing, automotive
manufacturers often procure intermediate products from external suppliers, in order to
benchmark or discipline internal units, or for other reasons (Fine and Whitney, 1999). However,
they rarely sell from their own vertically integrated component divisions to other automakers (or
even the suppliers of other automakers). External sales are not particularly attractive for two
reasons. First the need to design components specifically for customers’ systems means that
external sales do not increase economies of scale or lower manufacturing costs for component
divisions. Second the fact that external customers are also competitors raises concerns about
excessive vulnerability, particularly the potential leakage of intellectual property—in both
directions (Henkel and Baldwin, 2010).
In a nutshell, system integrality leads to component and asset specificity in the
automotive sector. Such specificity reduces the scale benefits and increases the transaction costs
of having upstream internal units sell into intermediate markets. For these reasons, the major
automotive firms have boundaries that are permeable in one direction only. They source
24
components from external suppliers, but they do not sell components to external customers. In
the absence of vertically permeable firm boundaries, interfirm transaction cycles do not emerge.
This contrasts with the practice of major electronics firms, discussed next.
Modularity of the Electronics Sector. In contrast to automobiles, since the 1980s,
electronics products, e.g., computers, communications, and consumer electronics, have been
designed as modular systems with largely standardized components (Whitney, 1996; Baldwin
and Clark, 2000; MacDuffie, 2008). Modularity in turn has implications for both transaction
costs and rates of innovation in this sector, which affect the benefits and costs of vertically
permeable boundaries.
First, with respect to transaction costs, because of their standardized interfaces, the
behavior of electronic components does not change when they are used in different systems, as
long as some design rules are obeyed. Thus in contrast to automobiles, the design and production
of many electronic components, such as memory chips and batteries, can be conducted without
detailed knowledge of the products in which they are used (Whitney, 1996). This low level of
component specificity results in low asset specificity between suppliers and customers
(Williamson, 1975; 1981). Arm’s-length contracts and/or spot-market transactions are
economical in this setting and this in turn allows intermediate markets to form. There is no need
for the long-term, hand-in-glove relational contracts that are common in the automotive sector.
Second, the modularity of electronics allows independent or unsynchronized
development activities, which in turn lead to high rates of innovation in modules (Henderson and
Clark, 1990; Teece, 1996; Baldwin and Clark, 2000). In this respect, Koh and Magee (2008)
showed that information technologies achieved much higher rates of performance increase than
energy technologies over the past 100 years. High rates of innovation in modules result in short
product life cycles for both components and systems that combine components in new ways
(Klepper, 1997; Tushman and Murmann, 1998). Indeed all of the major electronics
manufacturers that we interviewed identified “short product life cycles” as the major challenge
facing their business. (In contrast, our interviewees in the automotive sector viewed
technological innovations as rare and their sector as slow-paced.)
Short product life cycles create volatile demand—what customers want this year is not
the same as last year. Such conditions favor specialized component suppliers with larger
production scales and higher product development speeds than may be found in the internal
25
component divisions of vertically integrated firms. Thus, as our interview results indicate, large
electronics firms, which were formerly vertically integrated, now procure components from
external suppliers (including the component divisions of other vertically integrated firms) if the
latter can offer better performance, price, and quality than their internal divisions. Paprzycki
(2005) also observed that since the 1990s, Japanese electronics firms have increasingly
outsourced components from independent suppliers or the component divisions of competitors.
At the same time, the internal component divisions of such firms strive to sell
components to external customers in order to increase scale economies, use capacity more
effectively, and benchmark their efficiency and product quality to industry standards. For
instance, a large vertically integrated electronics firm that we interviewed operates a so-called
Industrial Marketing Group at corporate headquarters, which focuses on promoting component
product sales in intermediate markets. Such efforts are not observed in the automotive sector.
Thus the vertical boundaries of the largest Japanese electronics firms are permeable in
both directions. Such firms not only procure components from external suppliers but
simultaneously sell components through the intermediate industrial markets. As shown in the
previous section, such firms account for the vast majority of cycles in the sector’s transaction
network: removing the ten largest firms increases the network’s degree of hierarchy (h) from
below 60% to over 93%.
If scale economies are crucial and transaction costs are low in the electronics sector, why
do the largest electronics firms not move further in the direction of vertical disintegration? In
other words, why do they continue to maintain internal component divisions? The rationale
offered by our interviewees was essentially that knowledge complementarities across vertical
stages of the value chains gave their firms dynamic capabilities, enabling them to create new
systems products and respond rapidly to changing market conditions (Teece and Pisano, 1994;
Teece et. al., 1997). The capabilities cited involved the ability to engage in architectural
innovation (in addition to modular innovation) in order to generate a variety of new and
distinctive system products (Whitney, 1996). That is, the largest electronics firms continually
strive to create differentiated system products including PCs, cameras, mobile devices, and TVs,
for end-user markets. Historically, the Japanese electronics sector as a whole has grown based
upon its success in utilizing pre-existing component technologies to create successful system
products, such as radios and Walkmans (Nakayama et al., 1999).
26
According to our interviewees, knowledge of diverse component-level technologies is an
important resource for the rapid development of architecturally novel systems. Maintaining and
managing such knowledge resources in house allows the firms to share and deploy these
resources more effectively than if they had to be procured outside the firm. This strategic choice
is supported by the fact that in electronics, component-level knowledge is generic and coherent
(Teece et al., 1994). The same basic knowledge of electronics and electrical engineering can be
used across computing, communications, and consumer electronics applications. Neffke and
Henning (2010) show empirically that firms are much more likely to diversify into skill-related
industries than into skill-unrelated industries. In this respect, one can view the presence of
diversified system groups and their component divisions in large electronics firms as a form of
skill-related diversification.
One of our interviewees, a senior manager at a large electronics firm, confirmed that their
pursuit of systems innovations was a partial explanation of his firm’s preference for partial
vertical integration coupled with vertically permeable boundaries, as opposed to the simpler
strategy of full vertical disintegration. Another interviewee claimed that his firm’s core
competence was the capability to leverage component-level resources and knowledge to create
diverse system products for end-user markets. At the same time, the component divisions
maintained their own capabilities and realized scale economies by competing in intermediate
markets via vertically permeable boundaries.
In a nutshell, facing short product life cycles, low transaction costs, and low asset
specificity, but convinced of knowledge complementarities between system and component
divisions, a number of large Japanese electronics firms have adopted a strategy of partial vertical
integration with vertically permeable boundaries. According to several interviewees at these
firms, a robust stream of innovative system products is made possible through the firms’ control
of complementary assets, knowledge and capabilities with respect to components. Vertically
permeable boundaries in turn increase the viability of the component divisions by exposing them
to competition. This strategic choice by a relatively small number of large firms in the
electronics sector has led to the emergence of cycles and a lower degree of hierarchy in this
sector’s interfirm transaction network than in the automotive sector’s network.
27
5.3 Physical Limits to Modularity
As explained in the previous section, different degrees of modularity in automotive and
electronic products affect asset specificity, transaction costs, and the rate of innovation in the two
sectors. But modularity is also to some extent a strategic choice (Baldwin and Clark, 2000;
Henkel and Baldwin, 2010). Thus why are the technologies of one sector more modular than the
other?
Whitney (1996) suggests that strategic choices of modularity are subject to physical
limits. Specifically, in an automobile, significant energy is processed, and significant power is
involved in the functioning and interaction of components. Indeed high power is needed to fulfill
the automobile’s basic function—to transport humans and goods of a given mass at high speeds.
High-powered systems, like automobiles, incur difficult-to-anticipate side effects, such as heat
and vibration, which can only be addressed through interdependent, iterative design processes.
In contrast, electronics products, such as computers, phones and other devices, process
information via low-powered signals. Lower power causes less severe and less frequent side
effects, hence the decomposition of low-powered systems into modular components is relatively
easy (Simon, 1962). Furthermore, many forms of information, such as interfaces, can be
standardized. Standardized interfaces in turn support information hiding between different parts
of a system, which is a pre-requisite of modularity (Parnas, 1972; Baldwin and Clark, 2000).
From these arguments, it follows that strategic initiatives to pursue modularity and
standardization may face inherently more difficult technical challenges in the automotive sector
than in the electronics sector (Whitney, 1996; Takeishi and Fujimoto, 2001; MacDuffie, 2008).
Thus we can trace a line from the technological properties of artifacts (high or low power), to
product architecture (integral or modular) to firms’ sourcing strategies (concurrent sourcing or
vertically permeable boundaries) to the structure of interfirm transaction networks (purely or
partially hierarchical). However, firms’ beliefs and perceptions enter this chain of logic as well—
at the point where the firms formulate their strategies.
While low transactions costs and short product life cycles are consequences of
modularity, and higher transaction costs and longer life cycles are consequences of integrality,
how best to prosper in these environments is open to debate. Our analysis reveals and our
interviews confirm that the major Japanese electronics firms have by and large rejected purely
focused strategies in favor of partial vertical integration combined with vertically permeable
28
boundaries. Their espoused theory is that knowledge complementarities between component and
systems designs allow them to generate a stream of novel systems products, while vertically
permeable boundaries allow their component division to realize economies of scale and stay
close to the state of the art.
In the automotive sector, our analysis reveals and our interviews confirm that the major
automakers have rejected both pure vertical integration and vertically permeable boundaries in
favor of concurrent sourcing and long-term relational contracts with external suppliers. Their
espoused theory is that such relationships support component specificity and have continuously
improved quality, cost and delivery in the sector. They did not explicitly consider vertically
permeable boundaries as a potential strategy, possibly because the scale benefits are low and
potential hazards in terms of intellectual property leakage are too high.
6 Conclusion
In this research, we have empirically measured the variation in hierarchy in the industry
architectures of the automotive and electronics sectors in Japan, and explained this variation in
terms of product modularity and firm strategy. This research hopefully points the way to new
approaches to analyzing industry architectures and understanding the strategic choices of firms
participating in business ecosystems.
Business ecosystems, that is, groups of firms linked by transactions and
complementarities that jointly provide complex products to end users, are an increasingly
important form of economic organization. At the same time, transactions are the most basic form
of interfirm relationship. However, as indicated, comparatively little is known about how
individual transactions between firms in business ecosystems become aggregated into interfirm
transaction networks.
This paper has sought to address this gap, and in the process has made four contributions
to the literature on strategy and industry architecture. First, we defined “flow hierarchy,” showed
how the concept applied to transactions, and applied a metric that can be used to measure the
hierarchy of transaction networks. Second, we used standard network tools in conjunction with
the hierarchy metric to analyze transaction data from the Japanese automotive and electronic
sectors and showed that they were significantly different. Third, we traced the observed
differences in transaction network structure to differences in the largest firms’ strategies with
29
respect to vertical integration, concurrent sourcing and vertically permeable boundaries. Fourth,
we showed how the firms’ strategic choices were conditioned by the modularity of their products
and influenced by their managers’ beliefs about how best to nurture and maintain the firms’
dynamic capabilities.
Like all studies, this one has significant limitations. First, because of the difficulty of
obtaining good transactions data, we are able to analyze only two sectors in the same country in
close years. If data for other sectors with different technological bases, cultures (e.g. American,
European, Chinese), and in different stages of evolution were collected, our empirical results and
our theoretical reasoning would be better tested. However, it is difficult to collect and compile
sector-wide data on transactions: many firms are unwilling to share information on their
suppliers and customers. We hope that as interest in industry architecture, ecosystems and
transaction networks rises, new data sources will become available.
A second limitation of this study pertains to our understanding of firms’ strategies. We
interviewed a relatively small number of managers in the two sectors. In terms of positions, they
were well-placed to understand their firms’ strategies, and their accounts were consistent.
However, they may have been expressing personal views and not representing their own firm’s
strategic intent. It is also possible that the firms’ espoused strategies were simply ex post
justifications of their path-dependent histories.
Related to the problem of path-dependence, the network structures we observed may
have been transient, non-equilibrium patterns. This is more likely to be a problem in the
electronics industry, for which we have only a snapshot year (1993), than in the automotive
industry, where we have three observations spanning almost twenty years (1983-2001). We
relied on the literature (Paprzicki, 2005) and our interviews (conducted in 2009) to show that
interfirm transaction cycles still exist in this sector. However, the Japanese electronics sector
may become more hierarchical as it continues to evolve and mature. For example, some large
Japanese electronics firms seem to be loosening their ties with internal component divisions and
forming joint ventures, such as Renesas,14 to focus on component technologies. If this trend
toward vertical disintegration persists, the Japanese electronics interfirm transaction network will
14
Renesas Technology Corporation was spun off from Hitachi (55%) and Mitsubishi (45%) Electric in 2003, and
aimed to make the research, development, and production of semiconductors more economic and efficient (Renesas
News Release, April 1, 2003). In 2009, NEC electronics and Renesas agreed to merge and establish the world’s third
largest semiconductor firm (Renesas News Release, April 27, 2009).
30
become increasingly hierarchical in the future.
This paper opens up several avenues of potential future research. First and foremost is the
opportunity to seek out new data sources which permit the study of interfirm transaction
networks across diverse sectors and over time. In this regard, we think it is especially fruitful to
link formal network metrics and tools with qualitative research on strategic reasoning within
firms. Simple theories based on technology alone would not have predicted that vertically
permeable boundaries are an important dimension of strategy for large Japanese electronics firms
nor that transaction cycles would be prominent feature of that sector’s transaction network.
Interviews with key managers were needed to elucidate plausible reasons for these empirically
documented patterns.
In conclusion, we hope this paper may be seen as an invitation to further explore
transaction patterns in business ecosystems in order to gain a deeper understanding of the
linkages between technology and innovation, firm strategies and capabilities, and industry
architecture and evolution.
31
Acknowledgments
We thank Michael Jacobides, Joel Moses, Oliver de Weck, Joel Sussman, Daniel Roos, Margaret
Dalziel, William Mitchell, Lee Branstetter, and Susan Helper, whose insights helped us refine
this research, as well as seminar participants at Carnegie Mellon University and at the Academy
of Management professional development workshop on Innovation, Firm, and Ecosystem. The
authors also thank the International Motor Vehicle Program and the MIT-Portugal Program at
MIT for financial support. We particularly thank Takahiro Fujimoto, Daniel Heller, Masanori
Yasumoto, and other researchers at the Manufacturing Management Research Center at the
University of Tokyo for generous support and help with the field work and data collection in
Japan. Finally, the authors thank the executives and managers of the investigated firms for their
time, patience, valuable input, and the spirit of knowledge sharing that has allowed a deep
understanding of their strategies and the related context and rationales. The authors alone are
responsible for any errors and oversights.
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38

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