Strategic Management Journal
Strat. Mgmt. J., 32: 624–639 (2011)
Published online EarlyView in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.889
Received 13 January 2009; Final revision received 17 August 2010
SYNERGY, COORDINATION COSTS,
AND DIVERSIFICATION CHOICES
YUE MAGGIE ZHOU*
Robert H. Smith School of Business, University of Maryland, College Park, Maryland,
U.S.A.
Sharing common inputs across business lines can potentially generate synergy that justifies
related diversification. The pursuit of such synergy through diversification is, however, fundamentally driven by the indivisibility of inputs between firms. Following Penrose’s insight, I
argue that to realize this synergy, a firm needs to actively manage the interdependencies between
different business lines, which, in turn, increases its coordination costs. The coordination costs
may increase faster than synergy and set a limit to related diversification. This is particularly
salient when the firm’s existing business lines already have complex interdependencies among
them. I test these arguments on a dataset of U.S. equipment manufacturers for the period 1993
to 2003. The results show that a firm is more likely to diversify into a new business when its
existing business lines can potentially share more inputs with the new business; however, the
firm is less likely to diversify into any new business when its existing business lines are complex.
Importantly, the firm’s likelihood of diversifying into a new business decreases more with the
complexity in the firm’s existing business lines if they share more inputs with the new business.
These results suggest that increasing coordination costs counterbalance the potential synergistic
benefits associated with related diversification. Copyright  2010 John Wiley & Sons, Ltd.
INTRODUCTION
Diversification is of central concern to strategy
scholars as it is a critical engine for firm growth.
Most researchers have focused on the comparative
synergistic benefits of related diversification versus
unrelated diversification (e.g., Montgomery and
Wernerfelt, 1988; Rumelt, 1974).1 This synergistic
Keywords: related diversification; coordination costs;
complexity; modularity; firm scope; integration
∗
Correspondence to: Yue Maggie Zhou, Robert H. Smith School
of Business, University of Maryland, 3345 Van Munching Hall,
College Park, MD 20742-1815, U.S.A.
E-mail: [email protected]
1
This paper follows the literature that considers related and
unrelated diversification as a matter of degree, which is continuously variable (Chatterjee and Wernerfelt, 1991; Montgomery
and Wernerfelt, 1988).
Copyright  2010 John Wiley & Sons, Ltd.
view prescribes a continuous path for diversification: a firm will start with the most related industries, expand through progressively less related
industries, and stop when potential synergy diminishes to zero. Yet the limits of related diversification are understudied. Why do firms sometimes
diversify into a business with less synergy but
avoid businesses with greater synergy? Why do
firms in the same primary industry differ systematically in their diversification choices, such as in
the degree of relatedness between a new business
and their existing businesses?2
2
In addition to exogenous market opportunities, the existing
literature provides several explanations for unrelated diversification. Portfolio theory argues that firms pursue unrelated diversification to reduce business risk when there are bankruptcy
costs (Amit and Livnat, 1988) or transaction costs that prevent
shareholders from fully diversifying their investments in capital
Synergy, Coordination Costs, and Diversification Choices
This paper examines whether the pursuit of synergy itself explains limits to related diversification
and therefore the choice for unrelated diversification. It argues that to realize the potential synergy,
a firm must actively manage the interdependencies between new and existing businesses, which
results in coordination costs. Net synergy may
decline not because of exogenous opportunity constraints but because of the rising costs of coordinating interdependencies across an increasing array
of related business lines. Therefore, while diminishing synergistic benefits limit diversification in
general, increasing coordination costs moderate the
impact of synergy on the choice of industries and
set a limit to related diversification.
Synergy as a driver for related diversification
and coordination costs as a limit to firm scope are
both well established in the literature. However,
the conjuncture that related diversification may be
more costly to coordinate than unrelated diversification has been proposed by only a few scholars
(Hill, Hitt, and Hoskisson, 1992; Jones and Hill,
1988; Nayyar, 1992). This paper extends this work
in two ways. First, building on the theory of the
firm, the paper operationalizes a mechanism that
causes both synergy and coordination costs to rise
with related diversification: input sharing between
business lines. The pursuit of synergy through
input sharing within a firm is fundamentally driven
by the indivisibility of these inputs between firms:
if these inputs were divisible, firms could share
them through contracting (Teece, 1980). At the
same time, indivisibility creates coordination costs
within diversified firms. Therefore, the potential
for input sharing between a firm’s existing businesses and a new business can both attract and
deter entry into the new business, depending on the
synergy vis-à-vis the attendant coordination costs.
Second, building on the recent modularity literature, the paper specifies a contingency under which
marginal coordination costs surpass marginal synergistic benefits: corporate-level complexity in the
firm’s existing business lines, or the extent of
their interdependence. Complexity increases the
markets (Amit and Wernerfelt, 1990). Agency theory proposes
that managers pursue unrelated diversification for their private
benefits (Amihud and Lev, 1981; Jensen, 1986). Institutional theory suggests that firms often follow each other into new markets,
related or unrelated, through mimetic processes to conform to
their social environment. This paper proposes a theory for limits
to related diversification that is independent of these alternative
explanations.
Copyright  2010 John Wiley & Sons, Ltd.
625
prevailing coordination demand. Complexity also
exacerbates the coordination problem associated
with input sharing: it increases the amount of existing interdependencies that must be adjusted when a
new business imposes its own requirements on the
same pool of inputs that are shared across business
lines. Therefore, firms with greater complexity in
the mix of their existing business lines are more
likely to see marginal coordination costs surpass
marginal synergistic benefits and face tighter constraints on the degree of input sharing they pursue
by diversification.
These arguments are tested using information
on business activities of U.S. equipment manufacturers from 1993 to 2003. Potential synergy
is operationalized using overlap in input requirements between a firm’s existing business lines
and a target new business. Business lines requiring similar measurable inputs not only have the
potential for scope economy but also can utilize common forms of less measurable technical
and managerial knowledge that a firm already
possesses, both of which lead to potential synergy (Robins and Wiersema, 1995). Coordination
costs are proxied using complexity in input-output
flows among the firm’s existing business lines. The
results show that a firm is more likely to diversify into a new business when its existing business
lines can potentially share more inputs with the
new business; however, the firm is less likely to
diversify into any new business when its existing business lines are complex. Importantly, the
firm’s likelihood of diversifying into a new business decreases more with the complexity in the
firm’s existing business lines if they share more
inputs with the new business. These results suggest
that increasing coordination costs counterbalance
the potential synergistic benefits associated with
related diversification.
The proposition that coordination costs may offset economies of scope for diversified firms is forwarded in a recent study. Rawley (2010) shows
that coordination costs and organization rigidity
negatively impact the productivity of taxicab companies diversifying into the limousine business.
While Rawley and this paper both focus on the
coordination costs of diversification that seeks
scope economy, the two papers differ in both theoretical mechanisms and managerial implications.
First, the two papers propose different mechanisms for an increase in coordination costs: Rawley
focuses on organizational rigidity, while I focus
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
626
Y. M. Zhou
on complexity. Complexity can be viewed both as
a cause of coordination costs that is independent
of rigidity, or as a source of rigidity (Kaplan and
Henderson, 2005). By focusing on complexity, I
highlight a coordination problem rooted in interdependence. Second, the two papers imply different
consequences of coordination costs. Rawley studies firm performance (productivity) after diversification, while I study the firm decision of whether
and where to diversify. Therefore, the two papers
treat the diversification decision differently: Rawley as exogenously driven by market opportunities, while I examine whether firms endogenously
make their diversification decisions based on market opportunities, perceived potential synergy, and
coordination costs.
In addition to the diversification literature, this
study relates to recent literature on limits to hierarchies relative to markets (e.g., Nickerson and
Zenger, 2008). It emphasizes the comparison of
transaction and integration costs at the firm level
rather than the transaction level. It incorporates
organizational costs into rational-choice models of
firm scope, and emphasizes the cost of a boundedly
rational decision-making process that influences
firm choice and hence a firm’s reaction to external
opportunities (Amit and Schoemaker, 1993; Radner, 1996; Simon, 1955). In addition, the argument
that complexity in a firm’s business lines affects its
future diversification choices implies that diversification and integration choices are both substitutive
and path-dependent. Efforts to reduce coordination
within existing activity systems through standardization and outsourcing will free up coordination
capacity for other activities, such as horizontal
diversification.
COORDINATION COSTS
OF DIVERSIFICATION
According to Teece (1980: 224): ‘Diversification
can represent a mechanism for capturing integration economies associated with the simultaneous
supply of inputs common to a number of production processes geared to distinct final product
markets.’ These inputs are often indivisible and
difficult to share between firms (Penrose, 1959).
For example, sharing common raw materials and
machinery between multiple products gives rise to
scope economy. But if these materials and machinery are specialized or require special knowledge
Copyright  2010 John Wiley & Sons, Ltd.
to transform or operate, sharing them between
different producers subjects the firms to holdup
and haggling over the synergistic surplus (Teece,
1980). These transaction hazards justify integration; instead of sharing inputs with others, a firm
leverages scope economy by diversifying into business lines that share similar inputs with its existing
businesses.
However, diversification merely shifts transaction costs into the boundary of the firm, albeit
in a slightly different form—coordination costs.
Sharing common inputs creates interdependencies
between business lines. It requires joint designing,
joint scheduling, and mutual adjustments, as well
as setting transfer prices and designing incentive
schemes for cooperation. These interdependencies
challenge three elements of coordination: communication, information processing, and joint decision
making (Marschak and Radner, 1972). Interdependent business lines must engage in ongoing
communication to understand the factors affecting
each other’s decisions and to track the decisions
that are made, particularly when multiple equilibria exist (Arrow, 1974; Becker and Murphy, 1992).
The high number of interactions between decisions also increases demand for information processing (Simon, 1955). Because of the increased
workload of communication and information processing, there are more opportunities for decision
errors (Levinthal, 1997; Sutherland, 1980).
Even though at the transaction level the costs of
managing interdependent activities within an integrated firm may be less than between two separate
firms (Williamson, 1975), at the firm level such
costs rise dramatically as the firm’s total coordination demand approaches its coordination capacity
(Simon, 1947). Therefore, to understand marginal
coordination costs, we need to investigate all the
integrated activities that influence a firm’s prevailing coordination demand. For example, car manufacturing demands a large number of supporting
activities, such as manufacturing flat glass, internal combustion engines, motors and generators,
power transmission equipment, radio and communication equipment, lights, and fabricated metal
products. Many of these components require subcomponents and provide support to each other: fabricated metal products are often manufactured by
machine tooling, and some fabricated metal products are used to make machine tools themselves.
Indeed, the overall production process is basically
a complex flow chart with input-output transfers
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
Synergy, Coordination Costs, and Diversification Choices
between multiple business segments. Changing an
activity in one segment is likely to cause a ripple
effect through the rest of the operation. The chart
becomes even more complex when the car company shares components and processes between
cars and other products such as trucks, motorcycles, or home appliances.
To reduce transaction costs between firms, firm
boundaries should be located such that interdependencies between integrated activities and outsourced activities are weak (Baldwin, 2008).3 But
for a particular firm, what determines whether an
interdependent relationship is too weak to be integrated or too strong to be left out? Why do some
automakers produce aircrafts and others do not?
The answer lies in a firm’s prevailing coordination demand: the greater it is, the less coordination
capacity will be spared for a new activity, and the
greater will be the marginal coordination cost if
the new activity is integrated.
Interdependence can be treated as an inherent
relationship between activities, dictated by nature
rather than chosen by the firm, but the firm has
a choice of whether to exploit a particular relationship between two activities (Lenox, Rockart,
and Lewin, 2006; Siggelkow and Rivkin, 2005;
Yayavaram and Ahuja, 2008). In other words, there
is the exogenous potential for interdependencies
between activities, but a firm can affect interdependencies within its boundary by choice of activities. The firm can integrate and cospecialize two
activities that are potentially interdependent, or it
can integrate one activity and standardize and outsource the other. By standardizing its requirements
and becoming independent of the excluded activity, the firm lowers the overall complexity and
coordination demand, leaving more coordination
capacity for new activities. For example, if an
automaker standardizes and outsources steel and
iron processing, it will be able to spend more
managerial resources coordinating input sharing
between cars and aircrafts.4 Therefore:
3
The modularity literature broadens the notion of transaction
costs to include not only the transaction hazards among opportunistic agents, but also the “mundane” costs of defining, describing, measuring, adjusting, searching, and compensating for the
transfer of material, information, and energy among agents with
congruent interests (Baldwin, 2008).
4
In addition to lower coordination costs, standardization and
outsourcing allows firms to benefit from scale and learning
economies on the side of the suppliers (Jacobides and Hitt,
2005), and gives firms greater flexibility in selecting suppliers
based on capabilities (Hoetker, 2005).
Copyright  2010 John Wiley & Sons, Ltd.
627
Hypothesis 1: A firm is less likely to diversify
into a new business when its existing business
lines are more complex.
COORDINATION COSTS
OF RELATED DIVERSIFICATION
While coordination costs pose a challenge to diversification in general, they are greater for firms
pursuing more related diversification. More input
sharing between a firm’s existing business lines
and a new business adds more interdependencies.
Coordination costs go up more than linearly with
the number of business lines; they increase with
the amount of interdependencies among them. For
example, the number of communication channels
among K interdependent business lines can be as
high as K × (K − 1)/2 if each business line needs
to talk to other K − 1 lines for mutual adjustments.
Therefore, coordination costs increase with firm
scope at a greater rate for more related than for less
related diversification. Depending on how quickly
coordination costs and synergy increase relative to
each other, the difference in marginal coordination
costs between more and less related diversification may become greater than the difference in
marginal synergistic benefits between the two. In
that case, diversification into a less related business becomes more attractive than a more related
business.
In order to save coordination costs, a firm can
diversify into a highly related business but not
integrate it with existing business lines. Unfortunately, to realize the potential synergy that justifies
diversification in the first place, the firm needs
to actively manage and align the interdependent
activities. The development of the IBM personal
computer (PC) is a case in point. In the early
1980s, IBM ventured into the PC business. At
first, it treated the new business as being separate from its core mainframe business. However,
as the PC business grew, IBM faced the multiple
challenges of protecting the mainframe segment,
achieving greater economies of scope between the
mainframe and PC segments to outperform competitors in each market, and keeping the PC business up to the technological frontier. To protect
the mainframe segment, IBM manufactured most
of the components in-house and offered limited
support for plug-compatible competitors and thirdparty peripheral vendors. To achieve economies
Strat. Mgmt. J., 32: 624–639 (2011)
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628
Y. M. Zhou
of scope, IBM emphasized backward compatibility of the two segments in terms of components
and design. The requirement of backward compatibility, however, imposed technical and productive constraints on the PC segment. ‘Forcing
the new division to coordinate with the existing imposed costs on the new, and these costs
contributed to the new division’s decline,’ and
‘the IBM PC division died slowly in the stranglehold of cooperating with the rest of IBM’
(Bresnahan, Greenstein, and Henderson, 2008:
24 and 31).
In sum, related diversification is more costly to
coordinate than unrelated diversification. It also
exacerbates the problem of coordinating a complex
portfolio more than unrelated diversification. The
more inputs that are shared between the new and
old business lines, the more existing relationships
need to be adjusted (i.e., the greater the ‘ripple’
effect). Therefore:
Hypothesis 2: A firm’s likelihood of diversifying
into a new business decreases more with the
complexity in the firm’s existing business lines
if they share more inputs with the new business.
EMPIRICAL RESEARCH DESIGN
The hypotheses were tested using information on
the business activities of U.S. equipment manufacturers in Standard Industrial Classification (SIC)
codes 34–38 and their diversifying entries into
all manufacturing industries (SIC 20–39) between
1993 and 2003. The level of analysis is firm
year for each four-digit SIC target industry. I
estimated the probability that a particular firm
diversifies into a particular target industry in a
particular year. Firms in SIC 34–38 produce
fabricated metal products, industrial machinery
and equipment, electrical and electronic equipment, transportation equipment, and instruments
and related products. This empirical setting is especially suitable because equipment manufacturing
entails multiple stages and requires large quantities of intermediate inputs, which provides large
variation in scope and complexity across firms
in the same primary industry. According to data
provided by the Bureau of Economic Analysis
(BEA), equipment manufacturers produce about
$1.6 trillion of output in terms of shipment value,
Copyright  2010 John Wiley & Sons, Ltd.
or 30 percent of the output produced by all manufacturing firms in the United States.
Data and variables
To test the hypotheses, data are needed on firms’
existing business activities (and the relationships
between them), potential sets of new businesses
(and their relationships with existing business
lines), and firms’ actual diversification choices.
The dependent variable, ENT RYij kt , is whether
firm i diversified from its primary industry j into
industry k in year t. Any industry k in which firm
i did not operate in the previous year (t − 1) was
treated as a potential target destination industry in
the current year (t). Among the target industries,
those that firm i did operate in year t were entries
and coded as 1.
Information about firms’ existing portfolios and
their actual diversification choices was drawn from
the Directory of Corporate Affiliations (DCA) provided by LexisNexis. DCA describes up to 30
segments (four-digit SIC) for each business unit
within firms that have more than 300 employees
and $10 million in revenue (LexisNexis, 2005).
The dataset is compiled from information reported
by the companies, as well as from annual reports
and business publications in the LexisNexis
database. In addition, each company was contacted directly for information verification. The
DCA dataset for 1993–2003 contains 1,599 publicly traded and diversified U.S. companies whose
primary industries are in SIC 34–38.
Financial information was extracted from Compustat. The datasets were matched by parent company names, first through a software program and
then through manual checks. Ambiguous matches
were further verified via company Web sites. A
total of 1,147 (72%) diversified companies in SIC
34–38 were matched. Lagged values were used
for all explanatory variables. After 182 firms were
dropped due to missing values, 965 firms remained
in the sample.
There are 459 four-digit SIC manufacturing
industries. Industry growth and concentration
information was available for 429 of them from
the U.S. Bureau of Census. Each of these 429
industries that a firm does not operate in the previous year is a potential target for entry. The final
sample contains 1,970,256 firm-year target industry observations. Entry occurred in 808 (0.04%)
of them.
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
Synergy, Coordination Costs, and Diversification Choices
Data from the BEA Input-Output (IO) ‘Use’
Tables were used to construct measures for the
independent variables. The Tables contain the
value of pair-wise commodity flows among IO
industries, which can be converted to commodity flows among SIC industries through an IO-SIC
concordance. They are updated every five years.
Because BEA changed the IO industry codes in
1997, the 1992 Tables were used to ensure comparability. Except for the code change, coefficients
in the Tables are fairly stable over time (Fan and
Lang, 2000; Hortacsu and Syverson, 2009). Even
though the same IO Tables were used for all years,
firms in the sample have different values for complexity from year to year as their business portfolios change.
COMP LEXI T Yit−1 is the percentage of segment pairs in firm i’s portfolio in year t − 1 that
supplied significant inputs to one another. The flow
of inputs between two industries is defined as significant if they, on average, contribute more than
one percent of the inputs to one another. Prior studies of segment relationships use either one percent
(Lemelin, 1982) or five percent (Matsusaka, 1993;
Schoar, 2002; Villalonga, 2004) as the threshold. I
used one percent in the main analyses and five percent in the robustness checks; results were similar.
The complexity measure is similar to a ‘density’ measure if the firm’s business portfolio were
to be viewed as a network of activities supplying inputs to one another. It corresponds to the
theoretical definition of complexity in the modularity literature (Baldwin and Clark, 2000; Levinthal,
1997). Similarly, Burton and Obel (1980) use a
computer-simulated IO table to measure technological complexity.
I NP U T SI MI LARI T Yj k, 1992 is the degree to
which firm i’s primary industry, j , shares inputs
with a target industry, k. I followed Fan and
Lang (2000), who calculate the correlation coefficients across industry input structures between
the amounts of intermediate inputs that two industries directly require per dollar of their respective
output. A large correlation coefficient suggests a
significant overlap in inputs required by the two
industries. Such a measure has been used in prior
studies of diversification (Fan and Lang, 2000;
Lemelin, 1982; Matsusaka, 1993; Schoar, 2002;
Villalonga, 2004). It does not depend on the hierarchical scheme of the SIC system, but uses resource,
input, and activity similarity between industries to
Copyright  2010 John Wiley & Sons, Ltd.
629
proxy relatedness and synergy potential (St. John
and Harrison, 1999).5
Control variables are included for firm, target
industry, and firm-industry relatedness characteristics. Firm-level variables included size, age, R&D
and capital intensity, firm scope (number of segments), geographic dispersion, past performance,
and financial slack, among others. Target industry variables included growth, concentration, and
R&D and capital intensity. Firm-industry relatedness variables capture the applicability of firmspecific resources to the target industry. They
included the absolute difference between the firm’s
R&D (capital) intensity and the target industry’s
R&D (capital) intensity. Finally, growth and concentration in the firm’s primary industry were
included to control for the opportunity cost of
diversification.
Table 1 provides descriptive statistics of the
sample. At the firm level, COMPLEXITY has a
mean value of 49 percent (meaning that 49% of
the segment pairs supply significant input to one
another) and a standard error of 35 percent. The
average INPUT SIMILARITY correlation coefficient between a firm’s primary and target industries is 0.28. An average firm in the sample has
1,500 (exp(0.4) × 1, 000) employees, and operates
in 7.39 segments and 2.8 (exp(1.05)) countries.
Table 2 summarizes the pair-wise correlation
coefficients between the key variables.
Model specification
A logit model was used to estimate the probability
that a firm diversifies into a particular industry:
E[ENT RYij kt = 1] = β0 + β1
× COMP LEXI T Yi,t−1
+ β2 × COMP LEXI T Yi,t−1
× I NP U T SI MI LARI T Yj k,1992
5
It is worth noting that both the INPUT SIMILARITY and
COMPLEXITY measures are constructed from the IO ‘Use’
Tables, albeit in different ways. INPUT SIMILARITY is the
correlation coefficient across industry input structures between
the amounts of intermediate inputs that the primary and target
industries directly require per dollar of their respective output.
It reflects how ‘similar’ they are in their requirements of inputs
from other industries. In contrast, COMPLEXITY is calculated
for the set of industries that resemble the firm’s existing business
segments. It is a proxy for the ‘density’ of vertical relationships
among these segments.
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
Copyright  2010 John Wiley & Sons, Ltd.
Average R&D expenditure/sales for all firms in the
target industry
|b − d |
Annual growth in shipment value in the target
industry
Herfindahl index of concentration in the target
industry
Average CAPEX/sales for all firms in the target
industry
|a − c |
Segment pairs that supply more than one percent of
input to one other, as percentage of total number
of segment pairs
Correlation in productive inputs between the primary
and target industries
Log (thousand employees)
Number of segments (four-digit SIC)
Log (number of countries)
Log (number of years since establishment)
EBIT/total assets
Cash/total assets
CAPEX/sales
R&D expenditure/sales
Annual growth in shipment value in the primary
industry
Herfindahl index of concentration in the primary
industry3
Definition
2
1
N = 4, 683 firm-year observations.
N = 1, 970, 256 firm-year target industry observations.
3
The Herfindahl index is calculated by the Bureau of Census for each manufacturing industry based on shipment values.
Absolute difference in R&D intensity between the
firm and the target industry
Absolute difference in capital intensity between the
firm and the target industry
R&D intensity of the target industry (d)
Capital intensity of the target industry (c)
Concentration in the target industry
Target industry variables2
Growth rate in the target industry
Concentration in the primary industry
Firm size
Firm scope
Geographic dispersion
Firm age
Past performance
Financial slack
Capital intensity (a)
R&D intensity (b)
Growth rate in the primary industry
INPUT SIMILARITY
Firm variables1
COMPLEXITY
Variables
Table 1. Descriptive statistics
0.75
0.07
0.15
0.07
726.00
1.10
0.11
0.21
0.05
655.00
0.10
497.00
603.00
0.02
1.83
9.19
1.06
0.78
0.14
0.14
0.04
0.07
0.16
0.21
0.35
sd
0.40
7.39
1.03
3.51
0.09
0.14
0.05
0.07
0.08
0.28
0.49
mean
3.37
0.34
−0.01
0.00
0.68
0.68
2999.00
0.38
0.00
0.00
1.00
−0.28
2717.00
6.61
105.00
4.51
5.14
0.54
0.49
0.21
0.36
0.63
−4.83
2.00
0.00
0.00
−0.51
0.00
−1E-3
0.00
−0.32
1.00
1.00
1.00
Max
1E-4
0.00
min
630
Y. M. Zhou
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
Copyright  2010 John Wiley & Sons, Ltd.
Growth rate in the primary industry
Growth rate in the target industry
Concentration in the primary industry
Concentration in the target industry
Capital intensity of the firm
Capital intensity of the target industry
Absolute difference in capital intensity between
the firm and the target industry
R&D intensity of the firm
R&D of the target industry
Absolute difference in R&D intensity between
the firm and the target industry
(cont’d)
ENTRY (1,0)
COMPLEXITY
INPUT SIMILARITY
COMPLEXITY INPUT SIMILARITY
Firm size
Firm scope
Geographic dispersion
Firm age
Past performance
Financial slack
Growth rate in the primary industry
Growth rate in the target industry
Concentration in the primary industry
Concentration in the target industry
Capital intensity of the firm
Capital intensity of the target industry
Absolute difference in capital intensity between
the firm and the target industry
R&D intensity of the firm
R&D of the target industry
Absolute difference in R&D intensity between
the firm and the target industry
Correlation matrix
p > 0.1 for |r| > 0.01.
(18)
(19)
(20)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
Table 2.
0.09
−0.01
0.13
1.00
0.12
−0.02
−0.00
0.17
0.05
0.10
1.00
−0.00
0.01
0.00
−0.12
−0.04
−0.00
−0.11
−0.06
0.05
−0.01
(13)
0.02
0.22
0.00
1.00
0.61
0.00
−0.02
−0.00
0.03
0.04
−0.01
−0.00
0.07
−0.10
−0.05
0.04
0.05
−0.02
(3)
1.00
0.00
−0.07
0.01
0.08
0.00
(12)
0.20
−0.00
0.15
−0.01
0.01
−0.00
(11)
1.00
−0.04
0.61
0.11
−0.20
0.01
−0.20
−0.03
0.20
0.22
0.01
−0.03
−0.00
0.06
−0.00
0.10
(2)
1.00
−0.01
0.02
0.00
0.03
0.03
0.02
0.01
0.01
−0.01
−0.00
0.01
0.00
−0.01
−0.00
0.01
−0.00
(1)
−0.00
0.08
0.01
1.00
−0.00
0.07
0.00
(14)
0.12
0.16
0.10
1.00
0.06
−0.13
0.06
−0.10
0.01
0.12
0.14
0.05
0.08
−0.02
0.07
0.07
0.08
(4)
0.25
0.00
0.01
1.00
0.01
0.02
(15)
−0.21
−0.00
−0.10
1.00
0.62
0.46
0.34
0.36
−0.25
−0.01
−0.01
0.22
0.00
0.19
0.01
−0.08
(5)
−0.02
0.56
0.18
1.00
0.34
(16)
−0.19
−0.01
−0.11
1.00
0.48
0.35
0.14
−0.24
−0.08
0.00
0.20
0.00
0.00
−0.00
−0.09
(6)
0.14
0.20
0.70
1.00
(17)
−0.02
−0.01
0.01
1.00
0.27
0.22
−0.10
0.04
0.01
0.05
0.00
0.13
0.00
0.00
(7)
1.00
0.01
0.21
(18)
−0.35
−0.00
−0.19
1.00
0.18
−0.31
−0.14
0.02
0.06
0.00
−0.03
0.01
−0.13
(8)
1.00
0.21
(19)
−0.44
−0.01
−0.05
1.00
−0.06
0.05
0.06
−0.02
0.00
0.12
0.02
−0.00
(9)
1.00
(20)
0.47
0.01
0.11
1.00
0.09
−0.05
−0.06
−0.00
0.11
−0.02
0.07
(10)
Synergy, Coordination Costs, and Diversification Choices
631
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
632
Y. M. Zhou
+ β3 × I NP U T SI MI LARI T Yj k,1992
+ Ij,t−1 + Ik,t−1
+ i,t−1
(1),
where Ij,t−1 and Ik,t−1 are characteristics of primary
industry j and target industry k, respectively, and
i,t−1 is a vector of characteristics of firm i, in
year t − 1.
Since there are large quantities of nonentries,
the standard logit model may underestimate the
probability of entry, and the coefficients may be
biased. A rare-event logit model (RELogit) was
implemented to correct for the potential biases
(Tomz, King, and Zeng, 1999). To account for
potential autocorrelation in a firm’s diversification
decisions, standard errors were clustered at the firm
level.
The model takes the firm’s existing portfolio
as given; therefore, complexity is treated as predetermined. A few steps were taken to address
endogeneity. First, to reduce potential simultaneity, lag values were used for all the explanatory
variables. Second, informed by prior studies, a rich
set of control variables were added to account for
factors that might confound the empirical results
for the independent variables. Third, industry dummies were included to partly account for unobserved industry factors. Finally, a firm’s entry
choices across target industries may not be independent of each other: The firm compares the relative attractiveness of each target industry based on
the relative industry characteristics, along with a
firm-specific object function that is not observable
to econometricians. Therefore, a conditional logit
model was run with firm fixed effects (McFadden, 1974). The fixed-effects model significantly
reduced the sample size but generated similar
results.
RESULTS
Table 3 presents the estimated impact of input similarity and complexity on diversification choices.
Column (1) starts with the logit model with only
the control variables and INPUT SIMILARITY.
Columns (2) to (3) add COMPLEXITY and its
interaction with INPUT SIMILARITY. Column (4)
presents a full RELogit model.
Looking across models in Table 3, the coefficient to INPUT SIMILARITY suggests that the
Copyright  2010 John Wiley & Sons, Ltd.
more the primary and target industries share inputs,
the greater the probability of entry, which supports the synergistic view that firms rank order
their diversification choices based on synergy to
minimize the opportunity cost of diversification
(Levinthal and Wu, 2010; Montgomery and Wernerfelt, 1988).
Coefficients to the control variables are also consistent and in the direction of expectation, with
a few exceptions. At the firm level, both capital
and R&D intensity of the firm are negatively associated with diversifying entry, suggesting that in
these industries, capital- and R&D-intensive firms
are more likely to expand within their existing
segments than to diversify. At the industry level,
while growth and concentration in the target industry have the expected impact on entry, growth
and concentration in the primary industry have
insignificant effects. The insignificance is partly
due to the industry dummies. It is also consistent
with prior conclusions (e.g., Silverman, 1999) that
the characteristics of the primary industry are not
as significant as the characteristics of the firm and
the target industry.
Column (2) of Table 3 introduces COMPLEXITY. As suggested by Hypothesis 1, COMPLEXITY
is negatively associated with diversifying entry.
Introducing COMPLEXITY also significantly
improves the fit of the logit model, as evidenced
by the log-likelihood ratio test (χ 2 (1) = 55.86,
p < 0.0001).
Column (3) introduces the interaction term
between INPUT SIMILARITY and COMPLEXITY.
The coefficient is significantly negative, suggesting
that input similarity magnifies the negative relationship between complexity and the likelihood of
diversifying entry. Thus Hypothesis 2 is supported:
a firm’s likelihood of diversifying into a new business decreases more with complexity if the new
business shares more inputs with the existing business lines. Column (4) uses the RELogit model to
address potential bias that may result from using
the standard logit model; results are similar.6
To quantify the impact of COMPLEXITY on
the estimated probability of entry, a risk ratio
6
Since the marginal effect of interaction terms in logit models may not be read directly from the coefficients, I plotted
the estimated probability of entry against selective values of
INPUT SIMILARITY and COMPLEXITY. It shows that across
all deciles, INPUT SIMILARITY is positively associated with
entry. COMPLEXITY both shifts and rotates the probability
curve downward, as Hypotheses 1 and 2 suggest.
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
Copyright  2010 John Wiley & Sons, Ltd.
Capital intensity of the firm
Concentration in the target industry
Concentration in the primary industry
Growth rate in the target industry
Growth rate in the primary industry
Financial slack
Past performance
Firm age
Geographic dispersion
Firm scope2
Firm scope
Firm size
INPUT SIMILARITY
COMPLEXITY ∗ INPUT SIMILARITY (H2)
COMPLEXITY (H1)
4.122∗∗∗
[0.152]
0.502∗∗∗
[0.036]
0.080∗∗∗
[0.009]
−0.001∗∗∗
[0.000]
−0.083∗
[0.046]
0.082
[0.055]
1.502∗∗
[0.604]
−0.899∗
[0.488]
−0.036
[0.288]
2.236∗∗∗
[0.324]
8E-5
[8E-5]
−0.001∗∗∗
[0.000]
−5.968∗∗∗
[1.410]
(1)
Logit
Impact of input similarity and complexity on diversification choices
DV= ENTRY (1,0)
Table 3.
−0.818∗
[0.433]
−1.292∗
[0.727]
4.521∗∗∗
[0.276]
0.500∗∗∗
[0.036]
0.066∗∗∗
[0.009]
−0.001∗∗∗
[0.000]
0.014
[0.048]
0.068
[0.054]
1.431∗∗
[0.606]
−0.694
[0.483]
0.169
[0.291]
2.234∗∗∗
[0.324]
8E-5
[8E-5]
−0.001∗∗∗
[0.000]
−6.383∗∗∗
[1.417]
−1.505∗∗∗
[0.207]
4.114∗∗∗
[0.152]
0.500∗∗∗
[0.036]
0.066∗∗∗
[0.009]
−0.001∗∗∗
[0.000]
0.013
[0.048]
0.071
[0.054]
1.441∗∗
[0.607]
−0.69
[0.483]
0.171
[0.291]
2.230∗∗∗
[0.324]
7E-5
[8E-5]
−0.001∗∗∗
[0.000]
−6.451∗∗∗
[1.416]
(3)
Logit
(2)
Logit
−0.812∗∗
[0.373]
−1.292∗∗
[0.512]
4.519∗∗∗
[0.222]
0.500∗∗∗
[0.093]
0.066∗∗∗
[0.019]
−0.001∗∗∗
[0.000]
0.013
[0.126]
0.065
[0.128]
1.415
[0.878]
−0.673
[0.850]
0.171
[0.434]
2.234∗∗∗
[0.343]
8E-5
[2E-4]
−0.001∗∗∗
[0.000]
−6.335∗∗
[2.476]
(4)
RELogit
Synergy, Coordination Costs, and Diversification Choices
633
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
Copyright  2010 John Wiley & Sons, Ltd.
[0.051]
−10.502∗∗∗
[0.283]
[0.051]
−11.010∗∗∗
[0.282]
Yes
1,970,256
−5680.44
χ 2 (1) vs. model 1 = 55.86∗∗∗
[0.288]
−4.490∗∗∗
[1.315]
2.581∗∗∗
[0.367]
−0.136∗∗∗
[0.287]
−5.710∗∗∗
[1.352]
2.554∗∗∗
[0.367]
−0.149∗∗∗
Yes
1,970,256
−5708.37
2.452∗∗∗
[0.828]
−0.785∗∗∗
2.478∗∗∗
[0.827]
−0.774∗∗∗
Yes
1,970,256
−5678.87
χ 2 (1) vs. model 2 = 3.15∗
[0.051]
−10.723∗∗∗
[0.311]
[0.288]
−4.520∗∗∗
[1.316]
2.602∗∗∗
[0.367]
−0.138∗∗∗
2.470∗∗∗
[0.828]
−0.788∗∗∗
Yes
1,970,256
n.a.
n.a.
[0.073]
−10.695∗∗∗
[0.554]
[0.382]
−4.452∗
[2.567]
2.604∗∗∗
[0.454]
−0.136∗
2.472∗∗∗
[0.880]
−0.779∗∗
∗ ∗∗ ∗∗∗
, ,
significant at the 10%, 5%, and 1% levels for two-tailed tests. Robust standard errors clustered at the firm level in brackets.
Note: Logit and rare-event logit (RELogit) models of diversification choices of U.S. equipment manufacturers from 1994 to 2003. All explanatory variables are lagged by a year.
Industry dummies (two-digit SIC)
Observations
Log-likelihood
Log-likelihood ratio test
Constant
Absolute difference in R&D intensity between
the firm and the target industry
R&D of the target industry
R&D intensity of the firm
Absolute difference in capital intensity between
the firm and the target industry
Capital intensity of the target industry
634
Y. M. Zhou
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
Synergy, Coordination Costs, and Diversification Choices
is calculated after running the RELogit model in
Column (4). The results (not presented here) show
that increasing COMPLEXITY from its mean value
to one standard deviation above the mean (keeping
all other variables at their mean values) reduces the
probability of entry by 24 percent.
Alternative motivations for diversification
Table 4 examines alternative motivations for diversification choices. Column (1) adds a leverage
ratio (Amit and Livnat, 1988). Portfolio theory predicts that a higher leverage ratio implies greater
risk and will be correlated with more diversification, especially unrelated diversification. On the
other hand, agency theory predicts that higher
leverage implies a higher level of supervision
and governance by lenders, which leads to less
diversification, especially unrelated diversification.
Column (2) adopts a corporate governance index
to measure agency problems (Gompers, Joy, and
Metrick, 2003). This index is only available for a
subset of firms. A higher value for the index indicates less agency problems that will, in turn, lead
to less diversification, especially unrelated diversification. More consistent with the agency theory
than portfolio theory, the results show that firms
that are more ‘disciplined’ by external investors
(i.e., firms that are more indebted and firms that
have better corporate governance) are less likely to
diversify in general, but prefer more related industries if they do diversify.
Column (3) of Table 4 controls for mimetic market entry by adding the number of large competitors (those in the top quartile for size) from the
firm’s primary industry that had diversified into
the target industry by the end of the previous year
(Haveman, 1993). Using return on assets instead of
size to identify successful competitors generates a
similar outcome. The results show that consistent
with prior research, firms do tend to follow large
and successful firms when diversifying into other
industries, especially less related industries.
Column (4) expands the concept of relatedness
to include both horizontal and vertical relatedness.
Vertical relatedness is measured using the percentage of inputs the primary and target industries
request of each other. The result shows that vertical relatedness is positively associated with the
probability of entry, consistent with both assetspecificity and market-power explanations for vertical integration. Similar to horizontal relatedness,
Copyright  2010 John Wiley & Sons, Ltd.
635
the positive impact of vertical relatedness is dampened by COMPLEXITY.
After controlling for these alternative motivations for diversification, the main results for
COMPLEXITY and its interaction with INPUT
SIMILARITY remain qualitatively the same.
DISCUSSION AND CONCLUSION
This paper investigates the joint effect of synergy and coordination costs on a firm’s diversification choices. Diversification patterns of U.S.
equipment manufacturers between 1993 and 2003
show that the similarity of inputs (a source of
synergy) between a firm’s business lines and a
target business increases the probability that the
firm diversifies into the target business, but the
firm is less likely to diversify into any new business when it has greater complexity (a source
of coordination costs) in its existing business
lines. Importantly, the potential for input sharing magnifies the negative impact of complexity
on entry. These results confirm that coordination costs are an important explanation for limits to related diversification (relative to unrelated
diversification) that is independent of existing
explanations such as risk pooling, agency, and
imitation.
Coordination costs of diversification apply to
both internal growth and external expansion. For
example, while mergers and acquisitions (M&A)
theories emphasize synergy as a major source of
value creation and growth, empirical studies show
zero to negative value added for shareholders.
Coordination-driven integration cost is a major
reason (Coff, 1999; Shaver, 2006). In untabulated
models, I accounted for entry modes using M&A
and alliance data collected from the SDC Platinum
database. The main results are qualitatively the
same for organic growth and M&A.
The empirical analyses in this paper are based
on the sharing of physical inputs, but the theory
is generalizable to other types of inputs. It might
be argued that some inputs, such as reputation,
are easier to transfer than others, such as heavy
machinery. However, coordination is not about a
one-time transfer of inputs or resources to a second user; rather, it is an ongoing process of making
joint decisions and investments to generate, maintain, and exploit synergy among multiple users of
the indivisible inputs or indivisible firm-specific
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
636
Y. M. Zhou
Table 4. Alternative motivations for diversification choices
COMPLEXITY (H1)
COMPLEXITY∗ INPUT SIMILARITY (H2)
INPUT SIMILARITY
Leverage ratio
∗
INPUT SIMILARITY
(1)
(2)
(3)
(4)
Risk
Agency
Imitation
Vertical
relatedness
−0.810∗∗
[0.337]
−1.314∗∗∗
[0.510]
4.606∗∗∗
[0.223]
−0.076∗∗∗
[0.012]
0.202∗
[0.106]
Corporate governance index
∗
INPUT SIMILARITY
−0.379
[0.718]
−2.792∗∗
[1.227]
3.185∗∗
[0.998]
−0.100∗
[0.060]
0.198∗∗
[0.099]
Number of large competitors having diversified from
the primary industry into the target industry
∗
−0.823∗∗
[0.358]
−1.252∗∗
[0.511]
4.451∗∗∗
[0.223]
1.416∗∗∗
[0.157]
−1.356∗∗∗
[0.219]
INPUT SIMILARITY
Vertical relatedness
∗
COMPLEXITY
Observations
1,970,256
566,641
−0.860∗
[0.379]
−1.075∗∗
[0.566]
4.348∗∗∗
[0.235]
1,970,256
7.773∗∗∗
[2.834]
−10.268∗
[6.251]
1,970,256
∗ ∗∗ ∗∗∗
, ,
significant at the 10%, 5%, and 1% levels for two-tailed tests. Robust standard errors clustered at the firm level in brackets.
Note: Rare-event logit models of diversification choices of U.S. equipment manufacturers from 1994 to 2003. All explanatory
variables are lagged by a year. All models include the control variables in Table 3, a constant term and primary industry (two-digit
SIC) dummies.
resources that the inputs need to be combined with
to generate value. In that sense, even reputation
is sticky and difficult to share across products or
services without the risk of contamination (Greenwood et al., 2005). Knowledge is another input
that has a non-exhaustive feature. But successful knowledge sharing depends on the ability of
business units to combine their individual knowledge base (Henderson and Cockburn, 1994), which
requires active collaboration (Argyres, 1996). The
more extensive the interdependencies among different elements of knowledge, the more costly the
coordination effort (Ethiraj and Levinthal, 2004).
My untabulated results of robustness checks based
on patent data (described below) support this
argument.
In addition to the diversification literature, this
study contributes to existing theories of firm scope,
which explain ‘why there were firms but not how
the functions which are performed by firms are
Copyright  2010 John Wiley & Sons, Ltd.
divided up among them,’ (Coase, 1993: 73). After
more than three decades of extensive research, we
understand fairly well how contracting costs set
limits to markets, yet how organizational costs
increase with firm scale and scope is still puzzling (Holmstrom and Tirole, 1989). To identify
the sources of organizational costs that set limits to firms, this paper starts with the fundamental problem that gives rise to contracting costs
and the existence of firms: the indivisibility of
inputs or resources. While diversification and integration avoid contracting costs, they create interdependence and coordination costs within firms.
Even though at the transaction level coordination costs under a common hierarchy may be
lower than contracting costs between two independent firms (Williamson, 1975), at the firm
level coordination costs may rise dramatically
with multiple interdependencies as the firm’s total
coordination demand approaches its coordination
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
Synergy, Coordination Costs, and Diversification Choices
capacity (Simon, 1947), setting a limit to firm
scope.
The paper has three managerial implications.
First, it suggests that, in making diversification
choices, a firm needs to balance the potential
synergy with the associated coordination costs and
evaluate in particular the impact of complexity.
All else equal, a firm’s performance will suffer if
it diversifies into a highly related industry when
its existing business lines are already complex, or
if it diversifies into unrelated industries when its
existing business lines are not so complex (thereby
forgoing synergy and saving little in coordination
costs).
Second, the paper suggests that because a firm’s
overall coordination capacity is limited, its scope
choices may be substitutive: a firm may not
expand into all markets where it can apply excess
resources, since doing so will impose coordination burden on the company. Likewise, standardizing components and outsourcing existing activities
along the vertical value chain may free up coordination capacity for horizontal diversification.
Therefore, in making integration and diversification decisions, the firm needs to be aware of
the potential constraints in terms of coordination
capacity these decisions will impose on its future
integration and diversification choices.
Third, the observed heterogeneity across firms
in their scope of integration and diversification
suggests that firm-specific organizational capabilities may offset some limitations of coordination
costs. Firms may obtain such organizational capabilities through acquisition of managerial expertise
(Capron, Dussauge, and Mitchell, 1998), development of knowledge and routines (Nelson and
Winter, 1982), and/or adaptation of organizational
structure (Hill et al., 1992).
This study has two limitations. First, it treats
complexity as predetermined and analyzes its
impact on future diversification choices. It does not
further investigate why some firms choose to integrate complex productive activities when others
standardize and outsource them. The literature provides ample explanations for why some firms are
more integrated than others. A firm may integrate
to leverage its core competencies into adjacent
value chain activities (Leiblein and Miller, 2003),
to facilitate coordination at the activity level (Monteverde, 1995; Williamson, 1975), or to accommodate differential positioning strategies for its
products (Argyres and Bigelow, 2007). The current
Copyright  2010 John Wiley & Sons, Ltd.
637
study does not argue against these justifications for
integration. Rather, it points out that there is an
opportunity cost of coordination that may lead to
less horizontally related diversification. How firms
endogenously choose complexity and the degree
of related diversification is left for future study.
Second, the measures for the independent variables are not entirely at the firm level. COMPLEXITY is a firm-specific measure of industry average
inputs (the density of input-output flows between a
firm-specific portfolio of segments, assuming these
segments supply inputs to one another that equal
the industry average). This variable has a great deal
of variation since firms in the same primary industry have different portfolios and hence different
values for complexity. Still, segments within a firm
may choose to share and supply inputs more or less
than the industry average. Unfortunately, the use
of industry-level measures is still prevalent in the
diversification literature, where detailed information on individual segments within firms is difficult
to obtain. Measures of interindustry relationships
have been used in a wide range of prior studies to
proxy intersegment relationships within firms or to
predict the direction of diversification/integration.
The less than fine-grained measures result in
measurement errors. Given my empirical context,
these measurement errors are not likely to be correlated with the dependent variable. Therefore,
they result in an attenuation bias that would likely
make the results more conservative (Cameron and
Trivedi, 1998). Nevertheless, to mitigate potential
bias caused by unobserved firm heterogeneity, I
clustered robust standard errors at the firm level
to allow for heteroskedasticity. I also estimated a
conditional logit model (McFadden, 1974). As an
additional robustness check, I constructed alternative measures using data about the 195,150
patents granted to the sample firms over the
sample period. I followed prior studies of diversification based on technological resources (Silverman, 1999) and studies of knowledge complexity (Fleming and Sorenson, 2001; Yayavaram and
Ahuja, 2008) to construct firm-level measures of
INPUT SIMILARITY and COMPLEXITY. Regressions using these alternative measures generated
similar results.
In conclusion, this study provides theoretical
arguments and empirical evidence that coordination costs arising from managing complex interdependencies between business lines set a limit
Strat. Mgmt. J., 32: 624–639 (2011)
DOI: 10.1002/smj
638
Y. M. Zhou
to related diversification. It highlights the tradeoff between synergy and coordination costs that
imposes constraints on firms’ diversification
choices. Notwithstanding the limitations, this work
serves as a starting point for further empirical
exploration of firm-level coordination costs.
ACKNOWLEDGEMENTS
This paper is based on my dissertation. For their
invaluable guidance, I am indebted to Gautam
Ahuja, Scott Page, Jagadeesh Sivadasan, Jan Svejnar, and especially Sendil Ethiraj. I gratefully
acknowledge the thoughtful suggestions provided
by Wilbur Chung, Brent Goldfarb, Gabriel Natividad, Hart Posen, Brian Wu, Minyuan Zhao, Editor Will Mitchell, and two reviewers. I appreciate
the questions and comments raised by seminar
participants at Duke, INSEAD, LBS, Maryland,
Michigan, UCLA, UIUC, UNC and USC. Minyuan
Zhao helped with matching data between DCA and
Compustat. All errors remain mine.
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