Within Industry Diversification and Performance– An S-shaped Hypothesis
Niron Hashai
The Hebrew University1
This study predicts an S-shaped relationship between within industry diversification and
performance in single business firms. Initial increases in product scope impose complexities
of shifting from a single product focus to simultaneously managing more than one product
category, hence hampering performance. Once firms develop the capability to offset the costs
of such complexities, synergies between related product categories lead to performance
increase. Yet, extensive within industry diversification gives rise to coordination costs
increase and reduces performance once again. Furthermore, greater pace of within industry
diversification increases the costs and moderates the benefits of within industry
diversification, while higher levels of intangible technological assets intensify both the costs
and benefits of within industry diversification.
Key words: within-industry diversification, inter-industry diversification firm performance, Sshape.
1
Please do not cite without permission.
1
The phenomenon of inter-industry diversification and its performance implications is one of
the most researched topics is strategic management (Hoskisson & Hitt, 1990; Palich, Cardinal
& Miller, 2000). Yet, this stream of research is mainly engaged with firms diversifying into
products outside their core industry while devoting only little attention to the expansion of a
firm's product range within its core industry. In direct analogy to inter-industry diversification
a potentially important strategic question is how does within industry diversification affect
firm performance. This line of research may offer valuable explanations to performance
heterogeneity among single business firms that are present in a deviating number of product
categories within their core industry (Li & Greenwood, 2004).
Recent studies indeed show that increasingly, single business firms are diversifying the
product scope of their business activities within their core industry in the pursuit of
competitive advantage (Li & Greenwood, 2004; Stern & Henderson, 2004; Tanriverdi & Lee,
2008). Check Point Software Technologies (CHKP), a leader in the Internet Security industry,
nicely demonstrates within industry diversification. While operating in a single industry
Check Point offers very different product categories including: security gateways (known as
Firewalls), security management applications (such as Virtual Private Networkers, known as
VPNs) and end point security (securing the end user station). Yet, very little is known about
the performance implications of this type of diversification. It is likely to assume that within
industry product scope expansion comes with a set of attendant costs and benefits that, if
incompletely conceptualized, can lead to different inferences about its net performance
effects.
The current study theorizes and empirically investigates the performance implications of
within industry diversification. We synthesize prior research on within industry- and interdiversification to hypothesize and empirically demonstrate an S-shaped relationship between
within industry diversification and performance. This S-curve association indicates that at
first performance declines with increasing within industry diversification, this is followed by
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a positive relationship between increasing within industry diversification and firm
performance, which then declines again at extensive levels of within industry diversification.
Our integrative theoretical framework brings together the benefits and costs of within industry
diversification, the benefits and costs associated with its pace, and technology-based benefits
and costs to unravel the performance implications of within industry diversification across
time. Greater pace of within industry diversification is shown to increase its costs and reduce
its benefits. Higher R&D investments, on the other hand, arguably lead to the creation of
more complex technological knowledge that increases the costs of managing multiple product
categories at initial stages of within industry diversification, increases value creation and
appropriation effects at moderate levels of within industry diversification, but also leads to
greater coordination and control costs at extensive levels of within industry diversification.
The reminder of this study is organized as follows. In the next section we build on the extant
literature regarding the relationship between inter industry diversification and performance
and the literature concerning within industry diversification to present a theoretical
framework that predicts an S-shape pattern in the within industry diversification-performance
relationship of single business firms. We then describe our data and methods and the results.
The final section discusses the results and draws conclusions and managerial implications.
BACKGROUND AND THEORETICAL FRAMEWORK
The relationship between inter industry diversification and performance
Studies on the relationship between inter-industry diversification and firm performance
substantially differ in their definitions of inter industry diversification (where either the level
or type of diversification is studied), in their definitions of performance (where accounting
and market-based measures are interchangeably used) as well as in their predictions regarding
the relationships between diversification and performance. Yet, overall the rich stream of
studies on the relationship between inter industry diversification and firm performance has
mostly led to the received view that moderate levels of diversification lead to higher
performance consequences than limited or extensive diversification, thus supporting an
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inverted U-shaped relationship between inter industry diversification and performance
(Palich, et al., 2000).
In the inter industry diversification literature, there is a consensus that the primary benefits to
inter industry diversification are the exploitation of market imperfections (Teece, 1982).
Following this logic, a variety of studies have highlighted several notable benefits of product
diversification. Among these benefits are the economies of scope that are derived from the
ability to share a number of indivisible resources across businesses, and allow the exploitation
of operating and managerial synergies that can provide cost savings (Teece, 1980; Amit &
Livant, 1988). In addition, firms that are diversifying into, so called, "related" industries
(Rumelt, 1974, 1982) may further enjoy learning curve efficiencies, technology diffusion and
restricted access to factor markets (Barney, 1997). Another benefit of inter industry
diversification is the exploitation of the increased market share and profitability conferred by
diversification to sustain price cuts that drive existing and potential competitors out of the
market. Market power may further allow diversified firms to sustain loss through crosssubsidization and favorable reciprocal buying and selling arrangements between diversified
firms operating both in factor and product markets (Saloner, 1987). Internal capital market
efficiencies are yet another benefit of inter industry diversification where the ability to attract
funds from both external and internal sources may lead to efficiencies that are unavailable to
single business firms (Gertner, Scharfstein & Stein, 1994). Inter industry diversification may
further result in a better allocation of internal resources due to information advantages
possessed by the head office (Shleifer & Vishny, 1991). Other benefits of inter industry
diversification include the ability to exploit excess non-tradable firm specific assets such as
technology or brand names (Markides, 1992), tax and financial benefits (Berger & Ofek,
1995) as well as risk reduction that reduces the cost of capital and increases debt capacity
(Shleifer & Vishny, 1992).
On the other hand inter industry diversification is not costless. The control and coordination
complexities of managing disparate and dissimilar businesses and conflicting "dominant
logics" (Prahalad & Bettis, 1986) increase the costs of diversification, especially where
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extensive diversification is exercised (Grant, Jammine & Thomas, 1988; Markides, 1992). In
fact, such coordination complexities do not only refer to unrelated businesses, as most
commonly assumed. The costs of exercising related diversification can blunt its benefits due
to inefficiencies in intra-firm exchange resulting from coordination and integration demands
as well as agency costs that tax internal governance modes (Hill, Hitt & Hoskisson, 1992;
Jones & Hill, 1988; Nayyar, 1992). Recently, Zhou (2011) has shown that such costs are
more likely to outweigh the benefits of diversification the more complex are a firms business
operations and the more complex are their resulting interfaces. Zhou (2011) argues that since
related products have more interdependencies than unrelated products and since they make
mutual demands on similar inputs the costs of related diversification may often outweigh its
benefits. Overall, the benefits and costs of inter industry diversification suggest that there
are limits to the positive effects of diversification on firm performance.
The relationship between within-industry diversification and performance
While diversification studies often relate to samples of large, well-diversified firms (Geringer
et al., 1989; Hitt et al., 1997), many small to medium sized single business firms often choose
to expand their product scope within their core industry of operation. Within industry
diversification is a natural way to accelerate a firm's growth and may be considered as a more
refined way of looking on related diversification (Li & Greenwood, 2004; Tanriverdi & Lee,
2008) and hence is likely to share the same costs and benefits that arguably occur in this type
of diversification.
Extant studies on the relationship between within industry diversification and performance
provide mixed results. Li and Greenwood (2004) did not find a significant relationship
between within industry diversification and returns on assets (ROA) but did find a positive
relationship between diversification into related "market niches" (i.e. product categories) and
ROA. The fact that such niches may share inputs and customers is a prominent explanation
for this finding. Take for instance Check Point that may offer its different product categories
to the same customers and may further provide customer support for these different categories
5
with the same pool of technological personal. Stern and Henderson (2004) find that the degree
of within industry diversification is not so much associated with firm survival, as is the effect
of competition intensity, reflected by the number of new products that direct competitors
introduce to the market. In another study of the software industry Tanriverdi and Lee (2008)
show that some types of within industry diversification (platform scope) is negatively
associated with market share while other types (product market scope) is positively associated
with sales growth. Pursuing both types of within industry diversification arguably leads to
complementarities and positively affects both market share and sales growth. In the case of
Check Point, for instance, greater "platform scope" (i.e. a greater variety of product
categories) may lead to network externalities by increasing the quality and reducing the costs
of applications. This makes the products of Check Point more attractive to a greater variety of
customers. In turn, Check Point becomes able to leverage its expanded customer base for
garnering new insights on customer preferences and needs and hence to further expand its
platform scope. This virtuous circle arguably leads to positive performance outcomes. Finally,
Kor and Leblebici (2005) report negative performance effects of within industry
diversification of law firms when such firms try to leverage their human resources.
These conflicting results regarding the relationship between within industry diversification
and firm performance could be an outcome of incomplete theorization about the full range of
benefits and costs, and about the changes in these benefits and costs over the time it takes a
firm to fully implement a within industry diversification strategy. We therefore develop a
theoretical framework accounting for the benefits and costs of within industry diversification
and their change across different stages of within industry diversification.
Benefits related to within industry diversification
Within industry diversification enables a single business firm to realize economies of scale
and scope by exploiting synergies between different product categories (Kor & Leblebici,
2005; Li & Greenwood, 2004; Stern & Henderson, 2004; Tanriverdi & Lee, 2008). It helps to
reduce fluctuations in revenue by spreading its investment risks over different product
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categories. It helps to increase the firm's net revenues by strengthening a firm's market power
over its competitors, suppliers, distributors, and customers (Li & Greenwood, 2004).
The initial impetus to a firm's within industry diversification often comes from the
opportunity to exploit market imperfections in the use of indivisible intangible assets
(Penrose, 1959; Teece, 1980). Thus, single business firms can gain above-normal returns by
exploiting their firm-specific assets, especially intangible ones, in multiple market niches (Li
& Greenwood, 2004). Within industry diversification further allows using firms' excess
assets, such as specific knowledge on technologies, the firm's customer base or its experience
with existing products, to penetrate additional product categories (Stern & Henderson, 2004;
Tanriverdi & Lee, 2008). The tendency to expand the firm's product scope intensifies in
hypercompetitive environments where the need to move from one product category to
alternative ones is often a competitive necessity (D'Aveni, 1994).
Within industry diversification may also have an effect on organizational learning. The
introduction of multiple products can help enhancing the firm's technological knowledge base,
capabilities, and competitiveness through experiential learning and intra-firm knowledge
diffusion (Stern & Henderson, 2004). In addition, each specific product market has its own
unique resource endowments and specific advantages, which might not be available in
existing product categories. Such specific advantages can motivate a firm to diversify to
explore these advantages and augment its competitiveness in both its existing and new
product markets.
Costs related to within industry diversification.
When diversifying within their industry a single business firm's managers contend with many
challenges related to a new operation, such as purchasing and installing facilities, staffing, and
establishing internal management systems and external business networks. These challenges
are likely to be even more acute for small firms with scarce managerial resources (Penrose,
1959) that do not always have the management capabilities or the appropriate organizational
structures to manage the increased diversity. They can put a business operation in a new
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product category at a disadvantageous position, compared to an established business, and can
lead to higher costs as a within industry diversifying firm may not be able to conduct business
activities as effectively as it did with a narrower product scope. Diversifying into a new
product category increases the likelihood of making mistakes in various business decisions.
Challenges can be experienced in any new product category, but there are difficulties specific
to new businesses that are too close to the firm's original business. These are mostly
concentrated around the imperfect replication of the firm's activities in existing product
categories as well as cannibalization of existing products by new ones and are therefore
expected to become pronounced in the case of within industry diversification.
Furthermore, there are specific complexities involved in managing more than one product
category due to the need of efficiently transferring knowledge between units involved in
different but yet highly related product categories and the need to deal with multiple tasks
simultaneously. Such complexities may overwhelm the firm management and impose high
learning costs to achieve efficient replication of activities in new product categories and
effective joint management of different product categories (Stern & Henderson, 2004; Rivkin,
2000). We contend that the transition from being a "single product focused" firm to a "multi
product focused" firm is a fundamental shift that is potentially more challenging than the shift
from being a "multi product focused" firm to a "more" multi-focused one. A large volume of
research emphasizes that organizations have limited (or bounded) rationality and cognitive
scope (March, 1978; Levinthal & March, 1993; Simon, 1991). They cannot react or adapt
optimally to complex situations. Bounded rationality can limit the capability of managers to
move from a single product focus to a multi product focus. This is largely due to the fact that
managerial time and efforts have to be efficiently spread between the multiple simultaneous
tasks required for managing different product categories. The transition from a single product
focus to a multi product focus is likely to be involved with "set up" costs where managers that
are used to focus their managerial time, attention and efforts on dealing with a single product
category have to develop very different set of routines and capabilities for effectively splitting
their time, attention and efforts between multiple products. The shift from a "single product
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focused" firm to a "multiple product focused" firm is therefore likely to be associated with
inefficient allocation of managers' time and efforts between the tasks demanded by multiple
product categories.
In fact, in the case of inter-industry diversification decentralized organizational structures
such as the M-form divisional structure (Bartlett & Ghoshal, 1993; Hoskisson, Harrison, &
Dubofsky, 1991) may reduce the overall organizational complexity of managing multiple
businesses by a better allocation of resources and decision-making and reduced coordination
costs (Williamson, 1981; Bower, 1986). These organizational structures thus support the shift
from a single business to a multi business firm, where semi-autonomous divisions take charge
of separate businesses. In the case of within industry diversification of single business firms
such supporting decentralized organizational structures are less likely to exist, especially
where small and young firms are considered.
However, the costs associated with the transition into a multi-product focused firm are likely
to decrease as a firm builds reputation and legitimacy in the new product categories to which
it enters, and as its management gradually learns how to effectively manage the operations of
multiple product categories. The likelihood of making competitiveness impairing mistakes
and the costs associated with the handling multiple product categories within a given industry
are likely to attenuate with experience, in a learning-by-doing process (Vermeulen &
Barkema, 2002). Yet, such cost reduction is likely to be accompanied by transaction and
coordination cost increase (Jones & Hill, 1988) with the degree of within industry
diversification. As the number of internal transactions increases with the extent of within
industry diversification, coordination and governance costs can rise rapidly to a point at which
the governance costs exceed any integration benefits. Such coordination costs are likely to
intensify for single business firms that often lack the adequate organizational structures to
support such coordination. They are further likely to intensify for relatively small firms with
scarce managerial resources and become even more acute where intensive transfer of complex
knowledge is required (Kogut & Zander, 1992; Zhou, 2011).
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It follows that coordination and governance costs often rise in extensive levels of intra
industry diversification. This mainly results from the need to effectively coordinate complex
inter-product category linkages between indivisible intangible inputs (such as technological
knowledge) as the firm becomes more diversified within its core industry. The
interdependencies between product categories belonging to the same industry and the
competing demands they make on the firm's input base are likely to result in high costs (Zhou,
2011). The complexity of coordinating operations across multiple product categories may
therefore lead to diseconomies in managing larger and larger operations that significantly
increase information-processing demands on a firm's managers and administrative systems.
Firm performance across within industry diversification stages
Given the benefits of within industry diversification and the costs associated with managing
operations across product categories, we can now specify how these benefits and costs vary
across the stages of a firm's within industry diversification process. In Figure 1 we show that
the integration of benefits and costs along this process is expected to result in a nonlinear Sshaped relationship between within industry diversification and performance.
[Insert Figure 1 about here]
The total benefits from within industry diversification, are expected to linearly increase, up to
a point of diminishing returns. Naturally, not all firms will encounter the same linear increase,
as the extent of benefits is closely related to a firm's possession of excess valuable, rare and
inimitable intangible assets (Farjoun, 1994; Markides & Willimason, 1994; Robins &
Wiersema, 1995). The costs of within industry diversification are linked to the shift to similar
but yet distinct product categories, to the shift from a single- to multi- product focus and to
the coordination of multiple related product categories. The extent of these costs changes as
the firm diversifies, thus having a fundamental impact on the net performance outcomes of
within industry diversification.
The total costs of entering similar product categories decrease over time with learning and
with improvements in reputation and legitimacy. Likewise, the costs associated with the shift
from a single product focus to a multiple product focus are likely to be reduced over time as a
10
result of managerial learning how to effectively allocate managerial resources to multiple
product categories simultaneously.
In contrast, total coordination and governance costs
accelerate with the addition of new product categories. The interplay between these benefits
and costs of within industry diversification results in the S-shaped curve portrayed in Figure
1. This curve identifies three distinct stages in the relationship between within industry
diversification and performance.
At the initial stage of within industry diversification (stage 1), a firm encounters the costs of
imperfect replication and cannibalization when entering new product categories and the costs
of ineffectively managing multiple product categories which both lead to reduced
performance. Given that firms are likely to be at early stages of within industry diversification
when they are still young and small (Stern & Henderson, 2004; Tanriverdi & Lee, 2008) and
likely lack the adequate organizational structures to support the shift from single- to multiplefocus firms these cost increase further. Since at relatively low levels of within industry
diversification revenues are still low, these costs are expected to outweigh the benefits of
within industry diversification, thereby extending the time until net positive performance
outcomes of within industry diversification are realized. With the increase in within industry
diversification, experiential learning how to efficiently establish new product categories and
how to effectively manage multiple product categories reduces the costs associated with
initial within industry diversification. At the same time, growing within industry
diversification enables asset advantages to be exploited across a greater spread of product
categories, which occurs alongside the development of new capabilities in new product
markets. The result is stage 2, in which an increasing product scope is associated with an
increase in a firm's performance. Although the costs related to managing multiple product
categories are being reduced during stage 2, the second set of costs we depict, those for
governance and coordination, begin to rise. As a firm's network of new product categories
becomes more extensive, and as the firm has operations in more and more related product
categories within its core industry, governance and coordination costs escalate to the point
where costs can again surpass the benefits of within industry diversification, and firm
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performance declines, marking stage 3. Putting the above arguments together, we hypothesize
an S-shaped relationship between the extent of a firm's within industry diversification and
performance
Hypothesis 1. The relationship between within industry diversification and firm
performance is nonlinear, with the slope negative at low levels of within industry
diversification, positive at medium levels of within industry diversification, and
negative at high levels of within industry diversification.
Interaction effects of pace and technological knowledge with within industry diversification
Although our theoretical framework should hold for all firms, the slopes in the
different stages of the S-shaped relationship outlined in Figure 1 will likely vary across firms.
The forces contributing to the net influence of within industry diversification can vary in their
magnitude with firm specific characteristics. For example, the pace of product category
expansion may well influence the performance outcomes of a within industry diversification
strategy. The pace of adding new product categories cannot be rushed. It requires managerial
attention, administrative effort and time. It follows that when the firm decides to expand its
product scope in a short time span, it will have to allocate more administrative resources for
this task. In cases where the firm enters new product categories quickly, time compression
diseconomies (Dierickx & Cool, 1989) will arise due to diminishing returns to managerial
efforts. Sudden increases in managerial investments, required for entering new product
categories, are likely to be accompanied with convex adjustment costs—i.e., the costs of
expansion increase disproportionally to the benefits when the rate of expansion is accelerated
(Knott, Bryce & Posen, 2003). Hence, adding new product categories within a short period of
time will likely be more costly than a gradual increase in product scope over a longer period
of time. Greater pace of within industry diversifications is therefore likely to increase the
managerial costs associated with the shift to a multi product focused firm as discussed above.
It is further expected to increase the governance and coordination costs of managing multiple
product categories that are penetrated within a short time span. A particularly high product
expansion pace will further limit the firm’s managerial capacity to successfully identify
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complementarities and synergies, and create efficient interfaces between the resources
allocated to different product categories.
This reasoning is consistent with the literature indicating that expanding quickly may
adversely affect firm performance. According to Vermeulen and Barkema (2002),
organizations are overwhelmed in situations where a significant increase in complexity occurs
within a short time span, and thus they may find it difficult to capture the benefits of
expansion. As the firm speeds up the expansion into new product categories in a short period
of time, the contribution of new products to performance diminishes. By contrast, time
compression diseconomies are less likely to arise when the product categories are added in a
gradual way because the organization will be better able to handle the associated complexities
(Vermeulen & Barkema, 2002). Taken together, the above arguments suggest that a greater
pace of product scope expansion is likely to intensify the costs of within industry
diversification and negatively moderate its benefits. We accordingly hypothesize that:
Hypothesis 2. Greater pace of within industry diversification will make the slope of
the within industry diversification-performance relationship more negative at low
levels of within industry diversification, less positive at medium levels of within
industry diversification, and more negative at high levels of within industry
diversification.
Another important dimension that can affect the benefits of a within industry diversification is
a firm's intangible assets and particularly its technological knowledge. Extant literature
emphasizes the role of intangible assets, such as technological knowledge, in affecting both
the benefits and costs of firms. The development of intangible technological assets requires
substantial investments in capital, time, and human resources (Dierickx & Cool, 1989). An
intangible technological asset's value is not likely to depreciate significantly when it is applied
in different markets (Morck & Yeung, 1998). Given the considerable costs of developing
technological assets, the efficiency of- and returns to- their exploitation is greater when their
scope of use is greater (Teece, 1986). Hence, one way to exploit intangible technological
13
assets to their full value is to deploy them in a broad range of "related" markets, such as
through within industry diversification. Consequently, firms with technological intangible
assets should be able to generate abnormal higher benefits from their within industry
diversification through scale and scope economies and through the exploitation of market
imperfections in the trade of intangible technological assets (Teece, 1980). We expect within
industry diversification to generate more value the more substantial the intangible
technological assets.
On the other hand, higher levels of intangible technological assets also imply greater
difficulty of technological knowledge transfer between the firm operations responsible for
different product categories (Fang, Wade, Delios, & Beamish, 2007; Rivkin, 2000; Zhou,
2011). This greater difficulty derives from the fact that technological knowledge is often
complex and hence hard to codify and to teach (Kogut & Zander, 1992; Martin & Salomon,
2003; Teece, 1977). A higher level of intangible technological assets is hence likely to
increase the burden on the managers of firms that are expanding their product scope. It is
further expected to intensify the costs of shifting to a multi product focused firm as well as the
governance and coordination costs encountered in within industry diversification. Taken
together we hypothesize that a higher level of intangible technological assets will have both
positive and negative performance implications where within industry diversification is
concerned in proportion to the relative magnitude of the costs and benefits of such
diversification:
Hypothesis 3. A higher level of intangible technological assets will make the slope of
the within industry diversification-performance relationship more negative at low
levels of within industry diversification, more positive at medium levels of within
industry diversification, and more negative at high levels of within industry
diversification.
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DATA AND METHODS
Sample
To test our hypotheses regarding the relationship between within industry diversification and
performance we need longitudinal data on the within industry diversification of firms which
are mostly operating in a single core industry. Data regarding within industry diversification
is not readily available in traditionally used datasets, such as COMPUSTAT, where mostly
large, mature and substantially diversified firms are included (Stern & Henderson, 2004).
Further, we are looking for a sample of single business firms that are actively engaged in
product scope expansion (and not stagnant firms with a given set of product categories for
multiple years) and that obtain measureable intangible technological assets. The most natural
group of firms that stratifies all these criteria is high technology small and medium enterprises
(SMEs). This type of firms is further fairly homogenic in terms of the strategic motivations
for making product category expansions and as such has a good likelihood for finding a
systematic relationship between within industry diversification and performance2. Indeed,
within industry diversification has been often observed in high technology SMEs (Stern &
Henderson, 2004; Tanriverdi & Lee, 2008). On the one hand such firms have to penetrate new
product categories in order to sustain growth. On the other hand, the high costs and
uncertainty involved in new technology development, lead such firms to expand their product
scope within a fairly limited technological domain. We have therefore constructed a novel
dataset containing specific data on the product scope expansion of a sample of Israeli single
business high technology SMEs. The focus on high technology SMEs is important for our
analysis since the dynamic and intensive within industry diversification in this sector
enhances the meaningfulness, reliability, and variance of the relationships we wish to test.
Israel is an appropriate setting for this type of sample because of the high number of Israeli
based high technology, single business SMEs. Israel is ranked first in the world in the number
of per capita high technology start up initiatives (Bosma & Levie, 2009). The contribution of
2
See Verbeke, Li and Goerzen (2009) for a parallel discussion regarding the geographicdiversification-performance relationship.
15
the high technology sector, which is mostly composed of small and medium sized firms, to
the total industrial exports of Israel is above 51% (Central Bureau of Statistics, 2010).
Our hypotheses were tested on a sample of randomly selected high technology private and
public firms. The sample was derived from the full list of Israel-based high-tech firms
constructed by the Dolev and Abramovitz Ltd consulting firm for the year 2007. The Dolev
and Abramovitz dataset is well recognized as a comprehensive resource for this sector in
Israel. The dataset included 400 high technology firms that have reached the stage where they
sell their products and represents the vast majority of high technology industries.
Data on sales, number of employees, firm age and attracted investments were
collected from the Dolev and Abramovitz dataset and the Israel Venture Capital (IVC) dataset.
Dolev and Abramovitz Ltd is a private company collecting and publishing annual information
on Israeli high-tech firms. The IVC dataset is another comprehensive source for Israeli hightech industries3, including: enterprise software, electronics, telecommunication and capital
equipment, storage and data centers, medical devices, internet, chip design and homeland
security. This allows us to test predictions on firms operating in a single core industry by
examining a sample of firms from several industries. Since single industry samples are
restricted in range (Johns, 1991) it is more difficult to identify significant relationships in such
samples than in multi industry samples (Li & Greenwood, 2004). Using annual financial
reports and prospectuses, data on fixed assets were further collected. These data are readily
available for public firms. Access was granted to key figures in the financial reports of private
firms, which represent 72 percent of the sample. Finally, we also collected patent data
available from the United States Patent and Trademark Office (USPTO).
Data on the product categories of the sampled firms were collected from press announcements
in Lexis Nexis Academic and archives of leading Israeli financial newspapers such as
TheMarker and Globes. These archival sources were used to identify announcements on
existing and new product categories. Where deemed necessary within industry diversification
3
As such, formal publications of the Israeli Central Bureau of Statistics concerning the high-technology industries
in Israel are based on data from this source.
16
data was supplemented and verified via web based sources or by contacting the senior
management of the firms themselves. These sources provided nearly complete information on
the within industry diversification of the sampled firms, making it possible to develop a
relatively complete, longitudinal profile of the sample's within industry diversification
activities. After screening firms with incomplete data, we had a sample of 147 Israeli high
technology firms engaged in within industry diversification in the 2000-2007 period, with a
total of 895 firm-year observations. Basic T-test comparisons between the 147 participating
firms and the 253 non-participating firms did not show evidence of any non-response bias in
terms of the averages of firm sales, number of employees, firm age, firm valuation and
industrial classification.
The number of new product category entries in which the sampled firms were involved
(within their core industry) averaged 6.65 with a standard deviation of 13.21. This range in
within industry diversification activity indicates that our sample captures firms with varying
levels of within industry diversification, as required to test our hypotheses.
Variables
Following a large volume of research (e.g. Stuart, 2000; Tanriverdi and Lee, 2008),
our main firm performance measure is the annual sales growth of each firm4. Although
operationalizations that rely on profitability are robust performance measures in large
established corporations (Goerzen & Beamish, 2005), they may not be appropriate measures
for young and small high technology firms such as the firms in our sample (see descriptive
statistics in Table 1). Since such firms direct many of their resources to new product
development (Hart, 1995; Lee, Lee & Pennings, 2001), they reach profitability only at a later
stage of their lifecycle. The use of sales growth also enables us to avoid the slower adjustment
of profitability measures of performance (Stuart, 2000). This specification of the dependent
variable yields unbiased and efficient estimates under the linearity, homoscedasticity and
independence assumptions of Ordinary Least Squares (OLS) regression (Stuart, 2000).
4
We also use the firm's overall market share in its core industry in our robustness tests.
17
For within industry diversification, we developed a count measure of a firm's number of
product categories in each year (within the firm's core industry). By "product category" we
specifically refer to products that differ in their technological specifications and design,
following the approach taken by Katila and Ahuja (2002) and others. The determination of
what constitutes a new product category (rather than a new model of an existing product
category) was conducted based on the guidelines of a panel of five high technology industry
experts. These industry experts have first examined all product categories and other product
announcement independently (based on their experience, the data in the press announcement
and web based information) and have then discussed their classifications together (and with
one of the authors). In cases where the industry experts were not in agreement in their
individual classifications (less than 10% of the cases) they have reached mutual agreement as
to what constitutes a new product category and what is a new model of an existing product, in
the joint discussions. In some few cases the firms' senior management was contacted in
request for clarifying information on their different product categories. Overall, about 3000
press announcement of new and existing product categories were examined for the sampled
firms.
While this type of "count" measure has the drawback of not recognizing the size distribution
of specific product categories, prior research has indicated that count measures are "among
the most basic features of corporate portfolios" (Robins & Wiersema, 2003) and represent
"pure diversification" (Kumar, 2009; Robins & Wiersema, 2003; Voss, Sirdeshmukh & Voss,
2008). The critical point here is that weighted diversification measures (such as Herfindahl or
Entropy) fail to distinguish between a firm's entry into new product categories and expansion
within existing product categories. A product category count measure therefore provides an
accurate picture of the expansion of firms into new product categories as required for testing
the predictions of our hypotheses.
Our measure of within industry diversification pace was the annual rate of increase in the
number of new product categories of a given firm. Our measure of intangible technological
assets represents the firm's R&D intensity. Caves (1996:7-8), noted that R&D intensity (the
18
ratio of R&D expenditures to sales) is the most robust measure of intangible technological
assets. This measure of intangible technological assets is well-accepted in the literature
(Delios & Beamish, 1999; Morck & Young, 1991).
We included controls for several variables that are expected to affect firm performance.
First, by including one-year lagged measures of sales (Ln_Sales) we control for heterogeneity
ascribed to a firm’s past sales (Stuart, 2000). We further control for firm size that may impact
a firm’s performance (Ahuja, Lampert & Tandon, 2008). Firm size is measured as the
logarithm of the number of employees. A logarithmic transformation of this measure is used
to reduce skewness. To control for the effects of the firm’s tangible resources, our model also
includes a measure of fixed assets. Another factor that may affect performance is investments
in the firm. We have therefore controlled for the total investments (in $ US Million) that were
made in each firm by private investors, venture capital funds, corporate venture capital and
acquisitions or through public offerings. Since investments were heavily skewed, we use a
logarithmic transformation. We have further controlled for geographic diversification that
makes competing demands on the managerial time and efforts of high technology firms
(Delios & Beamish, 1999). The geographic diversification of firms is operationalized as an
entropy measure of their sales across regions. Following Kim, Hwang and Burgers (1993),
Hitt, Hoskisson and Kim (1997) and many others, this sales entropy measure reflects the
extent of geographic diversification across six foreign regions: North America, South and
Central America, the European Union, rest of Europe, Asia, and rest of world.
Some of our arguments regarding the ability of managers to handle the transition from a
single product focused firm to a multiple product focused firm and to coordinate multiple
product categories may be influenced by prior experience of the firm's top management. We
have therefore created a dummy variable coded for "1" for firms that had, in a given year,
members of their top management with prior managerial experience in other firms, and "0"
otherwise. The latter data were mostly obtained from web sites and from the firms
themselves. We had fixed effects for each firm's core industry. The distribution of the firms
19
core industries is as follows: Capital Equipment (23%), Medical Devices (21%),
Telecommunications (17%), Enterprise Software (11%), Storage and Data Centers (6%),
Home Networking and Homeland Security (5%), Multimedia and Broadcasting (4%) as well
as Other sectors including: Cellular, Chip Design, Internet, and Electronics (13%). Table 1
presents descriptive statistics and a correlation matrix of all the variables in our sample. The
table reflects the fact that the firms in our sample are relatively small to medium sized, young
and technology intensive. Appendix Table 1 presents detailed description of all measures.
Modeling Procedures
We examined the performance implications of within industry diversification using the firmyear unit of analysis. Besides a lagged measure of sales, all independent variables and
controls (denoted by the covariate matrix xi,t) are lagged by one year relative to the dependent
variables. This also allows us to facilitate causal inference. We also centered the variables on
their means to minimize their colinearity.
We have further used Two Stage Least Squares (2SLS) within-firm fixed effects regression
models. The use of the two stages research design stems from the potential endogeneity
between performance and diversification moves (Miller, 2006). In other words it is not clear
whether a within industry diversification move is driven from performance or whether
performance is driven by within industry diversification. While lagged independent variables
somewhat mitigate such possible endogeneity, it is still important to rule it out.
2SLS regressions (Jaccard & Wan, 1996; Kmenta, 1986; Wooldridge, 2002) enable to test the
relationships between two endogenous variables by using two stages where in the first stage
one of the endogenous variables is estimated based on all other independent variables and
then this estimation is used to predict the other endogenous variable. In the current study,
following the reasoning of our hypotheses, the first stage variable predicts within industry
diversification, which is then tested in the second stage against the firm's sales growth.
The 2SLS technique enables to account for the correlation in the disturbance term
across equations to produce more efficient estimates. A crucial condition for such estimation
20
is the inclusion of an instrumental variable (IV) which is correlated with the second stage
dependent variable only through its correlation with the first stage one. The IV used for within
industry diversification is Alliance function. This variable measures the dispersion of existing
alliances across R&D, production, marketing and customer support activities. Alliance
function should be positively correlated with within industry diversification due to greater
learning and resource complementarily potential (Dyer & Singh, 1998; Gulati, 1999; Kale, et
al., 2002; Lavie & Rosenkopf, 2006). It is thus likely to allow firms to expand their product
scope by building on other firms' resources to overcome their resource constraints in
expanding into additional product categories. On the other hand, Alliance function should not
necessarily have a direct effect (other than its effect on within industry diversification) on
sales growth. Alliance function is significantly correlated with within industry diversification
but not with sales growth (see Table 1) and hence meets the criteria of being an IV.
Within firm-fixed effects models allow us to test for intra-firm variance in within industry
diversification and sales growth (rather than inter-firm variation) while controlling for
unmeasured firm specific effects on these measures. The analysis of within-firm variation in
specific years seems to be the most appropriate to test our predictions, since the reasoning
underlying our hypotheses pertain to the impact of an increase in the firm's product scope
expansion on its performance.
Within firm fixed effects models further enable us to control for the impact of unmeasured
firm specific effects which are not changing over time, such as firm age or industry specific
effects (as industry is fixed per firm). These models further allow controlling for year specific
effects on firms' within industry diversification (e.g. the burst of the "dot.com" bubble in
2001-2002).
RESULTS
We report the results of the second stage regressions in Table 2. Sales growth is the dependent
variable for the ten models. Model 1 is the baseline model that includes only the IV measure,
the control variables and the measures of within industry diversification pace and firm
21
intangible technological assets. R&D intensity and pace have a significant positive impact on
sales growth. Most other control variables have positive influences on firm performance,
except for tangible resources which does not have a significant impact on sales growth.
We test Hypothesis 1 using models 2, 3, and 4, in which we build the test of the S-shaped
relationship by adding the linear term of within industry diversification in model 2, its
squared term in model 3, and its cubic term in model 4. We conducted Wald tests on the
significance of the inclusion of each additional variable. As shown in the Wald chi-square
statistics, the inclusion of the quadratic and cubic terms significantly improves model fit.
Hypothesis 1 is strongly supported: firm performance (in terms of sales growth) is negatively
related to the linear term of within industry diversification, positively related to the square
term of within industry diversification, and then negatively related to the cubic term of within
industry diversification. Hypothesis 2 predicts that the pace of within industry diversification
will have a negative effect on the relationship between within industry diversification and
firm performance at all levels. Models 5, 6 and 7 test Hypothesis 2 by entering the interaction
of within industry diversification and its pace. The interaction between within industry
diversification and its pace is negatively signed and significant in all models (at either the 5%
or 1% significance levels). A Wald test confirms that the inclusion of the interaction terms
significantly improves model fit. Hypothesis 2 is therefore also supported. Finally, Hypothesis
3 predicts that intangible technological assets will have a linear, positively moderating impact
on the relationship between within industry diversification and firm performance at
intermediate levels, but a negative impact at low and high levels of within industry
diversification. Models 8, 9 and 10 test Hypothesis 3 by entering the interaction of within
industry diversification and R&D intensity. The interaction between within industry
diversification and R&D intensity is negatively signed and significant in models 5 and 7, and
positively signed in model 6. The Wald test for these models confirms that the inclusion of
the interaction terms significantly improves model fit. Hypothesis 3 is therefore also
supported. It is important to note that the "main effect" between within industry
22
diversification and performance remained robust in all the models when the interaction terms
are included.
An analysis of the inflection points in the S-shape relationship revealed Table 2, indicates that
the first inflection points occurs approximately at three product categories (average inflection
point of models 2-10 is 2.99). This implies that, on average, the firms in our sample begun to
witness positive performance implications in terms of sales growth only after they have been
active in three product categories. The second inflection point (average of models 3-10)
occurs at 6.25 product categories. Given that the average number of products for the firms in
our sample is 6.65 this result implies that many of the firms in our sample are beginning to
witness the negative performance implications of within industry diversification.
Robustness Tests
We conducted several robustness tests. We increased the lag structure to 2 years and
3 years and obtained consistent results, although the variance explained (the value of R2)
becomes smaller as sales growth lag increases and significance levels of the explanatory
variables and the interactions typically reduce to the 5% level. We have further used firm
value as an alternative performance measure. Firm value also captures performance
irrespectively of firm profitability and reflects the fact that small and young firms (such as the
firms in our sample) often have their evaluations derived from capitalization of future cash
flows. Each firm's value in a given year was determined according to respective investments
that were made in that firm and their resulting ownership percentages ("after the money"
valuation). Investments were either made by private investors, venture capital funds,
corporate venture capital, acquisitions or through public offerings. Since firm values were
heavily skewed, we performed a logarithmic transformation in order to reduce skewness
values. The tests of the relationships between within industry diversification and firm value
(available upon request) remain very similar to those reported in Table 2 both with respect to
the main effects and with respect to the interaction effects.
23
Following Tanriverdi and Lee (2008) we have also used market share growth (as reported by
the firms in their various financial reports) as an alternative performance measure. The Sshape relationship was retained, but at a typical significance level of 5% for the main effects.
We have replaced R&D intensity with the number of patents applied in each year (and
granted at some point in the future) as an alternative measure for technological assets. The
interaction effects have remained at similar significance levels and in the same direction. We
have further controlled for the fact that 14% of the firms in our sample had some activity
outside their core industry5 by adding a dummy indicating whether a firm is operating in more
than a single industry. Results have not changes. Likewise, adding a dummy indicating
whether a firm is private or public (72% of the firms in the sample are private) did not had an
effect on the observed S-shape relationship.
DISCUSSION AND CONCLUSIONS
This paper examines the nature of the relationship between within industry diversification and
firm performance at different stages of within industry diversification, across firms with
different pace of within industry diversification and different levels of intangible
technological assets. It is found that within industry diversification had a nonlinear
relationship with performance. At high and low levels of within industry diversification, the
extent of within industry diversification is negatively associated with firm performance, while
at moderate levels of within industry diversification, greater product diversity is accompanied
by higher performance. This S-shaped relationship between within industry diversification
and seems to be at odds with the inverted U-shaped curve as well as with diminishing returns
curves observed in research on the relationship between inter-industry diversification and
performance (Palich, et al. 2000). Unlike inter-industry diversification studies the current
study has the advantage that it looks on relatively fine grained diversification levels. In interindustry diversification strategies limited levels of diversification are unobserved (as they are
all compounded under the single business classification), hence masking strategically
5
These firms can be viewed as dominant business unit firms (Rumelt, 1974).
24
important cost and benefit generating factors. Our results are consistent with the extant
literature on the geographic diversification-performance relationship. This literature adopts
the view that limited levels of geographic diversification decrease performance, moderate
levels of geographic diversification increase performance and extensive geographic
diversification decreases performance once again, hence supporting an S-Shaped relationship
(Contracotr, Kundu & Hsu, 2003; Lu & Beamish, 2004). Similarly to the case of within
industry diversification, this stream of literature is also able to observe limited levels of
diversification (e.g. internationalization into a single foreign country). We contend that the
ability to observe more fine grained levels of diversification is important as it enables the
researcher to observe relationships that are unobserved otherwise.
We further show that a firm's pace of diversification and its level of technological intangible
assets are two important moderators for the within industry diversification-performance
relationship. The pace of within industry diversification has been shown to intensify the costs
of within industry diversification while negatively moderating its benefits. This view is
consistent with previous findings from the international strategy literature (Vermeulen &
Barkema, 2002) regarding the negative effects of a high pace of expansion on performance. It
is further consistent with the contention that the firms in our sample lack the organizational
structures and/or the managerial experience to more effectively manage a high pace of within
industry diversification. As such they need to conduct such diversification more slowly in
order to limit a sharp increase in its cost consequences.
A firm's investments in technological assets provide evidence that intangible assets augment
the benefits of product category expansion, but also intensify its costs due to the complexity
they infer (Zhou, 2011). Importantly, the robustness of the main effects of the within industry
diversification variables to the inclusion of these moderating effects reinforces our
identification of two main costs drivers of within industry diversification: 1) at limited levels
of within industry diversification the fundamental shift to from a single product focus to a
multi product focus hampers performance; 2) at extensive levels of within industry
25
diversification the high costs of coordinating highly related products and operations hampers
performance once again.
The identification of the shift from a single product focus to a multi product focus and the
observation that coordination and governance complexities may arise already at extensive
levels of within industry diversification are, to the best of our knowledge, novel contributions
to strategy literature. First, the allegedly highly distinct routines and capabilities required to
deal with "one" relative to "many" product categories are brought to the forefront. Second, it
is evident that coordination costs may not only arise in highly unrelated diversifiers as extant
literature often argues (Bergh & Lawless, 1998; Hill & Hoskisson, 1987; Hitt et al., 1997;
Markides, 1992, 1995), or for related inter-industry diversifiers as been recently argued
(Zhou, 2011) but can also be prominent at extensive levels of intra industry diversification.
These two cost factors are important theoretically, as they give scholars a better understanding
of the factors that may shape performance differences between single business firms, and are
also important practically, because managers in within industry diversifying firms must be
aware of their existence. The fact that the magnitude of these cost factors intensifies in cases
where the pace of within industry diversification is high and in cases where the firm builds
heavily on intangible technological assets makes them even more important. To continue with
this line of inquiry, researchers should begin to explore how the configuration of within
industry diversification in terms of the choice of modes, the sequence of product categories or
markets chosen for expansion (e.g. in terms of their competitiveness level, see Stern &
Henderson, 2004), and organizational structure may moderate the factors underlying the
identified S-curve relationship and influence its slopes and inflection points.
Further, by studying within industry diversification our research shows the value of not using
pure archival data. Frequently, researchers select samples using large, publicly traded firms.
This practice creates a bias as it excludes smaller and private firms that are important drivers
of the world's economy. This study offers practical guidance to managers in small within
industry diversifying firms. Although care should be taken in interpreting the slopes, heights,
and inflection points identified in this study, our findings do suggest that managers need to
26
take a long-term view of within industry diversification. At initial stages, there might not be
immediate positive performance implications for within industry diversification. During this
stage, declining performance need not halt product category expansion efforts, provided
management devotes attention to rectifying the initial disadvantages of not being experienced
in simultaneously managing multiple product categories to permit the intrinsic benefits of
within industry diversification to arise and improve firm performance. As well as being
resolute during early stages of product expansion, managers need to be aware of the potential
downside of excessive within industry diversification and to be proactive in the design and
implementation of within industry diversification strategies by optimizing the configuration of
their firm activity to keep the scope of within industry diversification activities at an optimal
level. Alternatively and perhaps more importantly, management can extend the peak of
performance encountered in stage 2 of the within industry diversification-performance
relationship and move the threshold of within industry diversification to a higher level. One
means to do so would be to optimize the pace of within industry diversification and avoid
rushed diversification. Another way would be proactively develop capabilities for managing
technological complexity (Hitt et al., 1997), for instance by managing the time and pace of
new products development in such a way that the benefits of high levels of technological
assets exceed their resulting costs. For firms in stage 3 of the within industry diversification
and performance relationship, managers should learn to adjust organizational structures and
systems to handle the coordination problems we identified. As learning tends to be
incremental (Levinthal & March, 1993), this trend will likely continue in a cyclical fashion;
decreases in performance will be associated with new complexities at higher levels of product
expansion, and then increases in performance will occur as management learns how to
manage the new complexities (Hitt, Hoskisson & Ireland, 1994).
The most notable limitation of this study is that we derived our empirical results from a
sample of Israeli high technology SMEs, thus raising the concern that the findings might be
specific to the chosen sample. We have further not controlled for the effect of the external
competitive environment on the within industry diversification-performance relationship
27
(Henderson & Stern, 2004; Li & Greenwood, 2004). Furthermore, future research should
examine the effects of internal organizational moderators, such as a firm's organizational
design and its staffing (Bartlett & Ghoshal, 1993; Hoskisson, Harrison, & Dubofsky, 1991)
for the implementation of a within industry diversification strategy, on the relationship
between within industry diversification and performance to explain a higher proportion of
firm performance.
To conclude, in developing a comprehensive stage model of the relationship between within
industry diversification and performance, our study not only makes an important contribution
to the understating of an under-researched diversification dimension, but also allows a more
fine grained differentiation between single business firms as means to explain their
performance heterogeneity. Our analyses demonstrate that the relationship between within
industry diversification and performance varies with the stage of within industry
diversification and that this level of diversification for single business firms has a differential
effect on such firms' performance, given their level of within industry diversification, and its
interaction with diversification pace and with the firm's level of intangible technological
assets. Research in this area should give equal attention to the costs and the benefits of
product scope expansion, at both the early and late stages of this process. Our empirical
findings illustrate that the relationship between within industry diversification and
performance is dynamic. This demonstration requires the emerging body of literature on
within industry diversification to go beyond simple, linear explanations. In fact, our
observation regarding the costs of coordinating a large number of product categories in the
same core industry, leads us to speculate that such costs may be an additional factor leading
firms to diversify across industries. This type of motivation for inter-industry diversification
has not been tested before. It may be that the inter-industry diversification is driven by the
desire to reduce the connectedness of operations by establishing separate organizational
routines and structures that are responsible for the firm's operations in different industries.
Such diversification can be as a reaction to the decreased performance in the firm's core
industry resulting from the shift from increasing performance to decreasing one, as per the
28
proposed S-shape pattern. This motivation coupled with the question how firms
simultaneously expand across and within industries, are therefore important subject for future
research.
29
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34
Figure 1 – The within industry diversification - performance relationship
Performance
Stage 1
Stage 2
Stage 3
Within Industry
Diversification
35
Table 1 – Descriptive Statistics and Pearson Correlations (N=895)
Variable
1. Ln_Sales
2. Within Industry Diversification
3. Pace (of Within Industry Diversification)
4. R&D intensity
5. Firm size
6. Tangible resources (in $ Millions)
7. Total investments,(in $ Millions)
8. Patents
9. Geographic diversification
10. Firm Age
11. Prior Experience
12. Alliance function
Mean
(Std. Deviation)
17.21
(5.19)
6.65
(13.21)
2.12
(1.18)
0.25
(0.13)
30.24
(80.12)
42.57
(59.09)
21.07
(16.32)
10.12
(16.21)
0.82
(0.35)
5.67
(5.02)
0.43
(0.21)
0.51
(0.19)
1
2
3
4
5
6
8
7
9
10
11
1
0.087*
1
0.029
-0.021
1
0.079*
0.090*
0.031
1
0.314***
0.243**
0.036
0.173**
1
0.145**
0.088*
-0.013
0.127**
0.247***
1
0.324***
0.125**
0.192***
0.012
0.151**
0.018
1
0.197**
0.121*
-0.047
0.393***
0.021
-0.096*
0.089*
1
0.097**
0.173**
0.008
0.215***
0.158**
0.079*
0.072*
0.012
0.235***
0.123**
0.016
0.136**
-0.165**
0.036
0.098*
0.021
0.127**
1
0.143**
0.093*
0.074*
0.045
0.137**
0.025
0.035
0.017
0.183**
0.101*
1
0.074*
0.252***
0.028
0.049
0.002
-0.075*
0.087*
0.185**
0.166**
0.089*
0. 129*
Notes: *** statistically significant at 0.1%; ** statistically significant at 1%; * statistically significant at 5%.
36
1
Table 2 –Second stage regression models for the relationships between within industry
diversification and firm performance 2000-2007 (N=895)
Effects on Sales Growth
Intercept
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
-0.034*
-0.021*
-0.017*
-0.026*
-0.029
-0.028*
-0.027*
-0.032*
-0.031*
-0.034*
Within Industry Diversification
-1.908*** -1.902*** -1.915** -1.914*** -1.929*** -1.919** -1.912*** -1.916*** -1.919***
Within Industry Diversification Squared
0.328** 0.314** 0.320** 0.329** 0.315** 0.318** 0.315** 0.320**
Within Industry Diversification Cubed
-0.003** -0.004** -0.005** -0.004** -0.005** -0.004** -0.004**
Pace X Within Industry Diversification
-0.058** -0.054** -0.021*
Pace X Within Industry Diversification Squared
-0.035*
-0.031* -0.059**
-0.026* -0.061** -0.046** -0.037* -0.068**
Pace X Within Industry Diversification Cubed
-0.021*
R&D intensity X Within Industry Diversification
-0.034*
-0.039*
-0.042*
-0.253** -0.315*
-0.332*
R&D intensity X Within Industry Diversification
Squared
0.287** 0.254**
R&D intensity X Within Industry Diversification
Cubed
-0.195**
Pace
0.137*
0.141*
0.116*
0.126*
0.118*
0.114*
R&D intensity
0.423**
0.417** 0.428** 0.433** 0.427** 0.421** 0.422**
0.419*
0.422*
0.414*
Ln_Sales
0.523**
0.512** 0.552** 0.537** 0.579** 0.523** 0.592** 0.450** 0.436** 0.475**
Firm Size
0.217*
0.242*
0.186*
0.163*
0.147*
0.141*
0.143*
0.145*
0.147*
0.156*
Tangible resources
0.354
0.260
0.365
0.351
0.358
0.367*
0.384*
0.363*
0.350
0.279
Total investments
0.174**
0.126
0.137
0.145*
0.159**
0.155*
0.159*
0.123
0.169*
0.145*
Geographic diversification
0.013*
0.025** 0.053**
0.016*
0.072** 0.062** 0.047** 0.039** 0.027** 0.034**
Prior Experience
0.115*
0.153*
0.126*
0.144*
0.162*
0.173*
0.121*
0.139*
0.145*
0.119*
Year
+
+
+
+
+
+
+
+
+
+
Industry
+
+
+
+
+
+
+
+
+
+
0.125
0.144
0.153
0.172
0.126
0.134
0.159
0.169
0.175
0.189
Adjusted R 2
Wald X2
Wald test X2
94.22**
0.129*
0.119*
0.125*
0.120*
105.61** 112.41** 113.20** 97.11** 103.64** 115.44** 126.12** 133.97** 142.48**
11.81**
7.12*
1.4
6.92*
Notes: *** statistically significant at 0.1%,** statistically significant at 1%, * statistically
significant at 5%.
37
13.21**
5.34*
12.33**
Appendix Table 1 – Description of Variables and Measures
Variable name
Variable description
Sales growth
For each firm i at year t, sales growth is measured using the following
logarithmic power function: ln(Salesi,t+1)=αln(Salesi,t)+π’xi,t+ei,t;
Within industry diversification
Number of product categories of firm i in year t (in core industry)
Pace
(number of product categoriesi,t– number of product categoriesi,t-1) / number
of product categoriesi,t-1
R&D intensity
The ratio of R&D expenditures to sales in year t
Firm size
Ln (LAN) of Sales (in Million $US) at the end of year t
Tangible resources
Firm i's fixed assets in year t (in Million $US)
Total investments
Ln (LAN) of total investments (in Million $US) made up to a given year t
Patents
Number of patents applied at year t (granted patents only)
Geographic diversification
Sales dispersion across different regions. The entropy measure is defined
as: where in each year t Pj is the proportion of sales attributed to region j
(out of total sales) and ln(1/ Pj) is the weight given to each region.
Firm age
Age of firm i
Prior experience
A dummy measure where "1" indicates that members of firm i's top
management have prior experience in other firms and "0" otherwise
Alliance function
The dispersion of existing alliances across different value chain activities a
given year t. The entropy measure is defined as: where Pj is the proportion
of alliances of in value chain j (out of total existing alliances) and ln(1/Pj) is
the weight given to each value chain activity.
38