Opportunity Costs, Industry Dynamics, and Corporate Diversification: Evidence from the Cardiovascular Medical Device Industry, 1976-2004

Brian Wu

Assistant Professor of Strategy Stephen M. Ross School of Business

University of Michigan 701 Tappan Street

Ann Arbor, MI 48109-1234 734-647-9542 (phone)

734-764-2555 (fax) wux@umich.edu

Forthcoming at Strategic Management Journal

Abstract

This paper examines how demand conditions across alternative markets impact diversification decisions and firm performance by influencing the opportunity costs of deploying non-scale free capabilities. Using data within the cardiovascular medical device industry, this study shows that: (1) firms with a larger stock of pre-entry innovation experience are more likely to diversify; (2) firms in a current market with greater relative demand maturity are more likely to diversify; (3) diversification is associated with a performance decrease in the current market; and (4) diversification is associated with a performance increase at the corporate level. These findings shed new light on the self-selection process of corporate scope, the conceptualization of firm capabilities, and the connection between industry dynamics and resource deployment.

Running Head: Opportunity Costs, Industry Dynamics, and Corporate Diversification

Keywords: opportunity costs, non-scale free capabilities, relative demand maturity, corporate diversification, self-selection, cardiovascular medical device

I wish to thank the Mack Center for Emerging Technologies at the Wharton School for generously supporting this research. I am grateful for insightful comments from Vikas Aggarwal, Gautam Ahuja, Richard Bettis, J. P. Eggers, Sendil Ethiraj, Aseem Kaul, Anne-Marie Knott, Gregory Kruse, Daniel Levinthal, Nicolaj Siggelkow, Harbir Singh, Gordon Walker, James Westphal, Margarethe Wiersema, Sid Winter, Min Zhu, two anonymous reviewers, and seminar audiences at Emory University, Georgetown University, INSEAD, London Business School, Southern Methodist University, UCLA, University of Maryland, University of Michigan, University of Minnesota, University of Northern Carolina at Chapel Hill, University of Toronto, the Academy of Management, the CCC Doctoral Consortium, and the INFORMS Organization Science Dissertation Proposal Competition.

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INTRODUCTION

Corporate diversification decisions and the associated performance effects are a central

concern of the strategy field. Building on Penrose’s (1959) seminal work, the resource-based

perspective recognizes that firms diversify in order to leverage firm-specific resources for which

factor markets are imperfect (Penrose, 1959; Teece, 1982).1 This literature highlights the role

that market relatedness and the fungibility of resources play in the corporate diversification

process (Bettis, 1981; Chang, 1996; Robins & Wiersema, 1995; Rumelt, 1974; Silverman, 1999).

While these studies offer important insights, they generally focus on supply-side considerations

for rare and distinctive resources (Collis & Montgomery, 2005; Helfat & Lieberman, 2002;

Markides & Williamson, 1994). This focus is not surprising given that one of the primary

motivations in introducing the resource-based perspective to the strategy field was to shift

attention from an economics-oriented focus on the product market to a view that conceptualizes

the firm as a bundle of productive resources (Penrose, 1959; Wernerfelt, 1984). This resource-

based view has made significant progress (Barney, 1986; Dierickx & Cool, 1989; Peteraf, 1993;

Wernerfelt, 1984), evolving into a dominant and organizing framework for a large portion of the

literature on corporate diversification (Mahoney & Pandian, 1992). However, due perhaps to the

momentum and success of the resource-based view, the diversification literature along this line

has paid relatively less attention to how diversification efforts are influenced by the alternative

market opportunities in which firms may apply their resources. This imbalance raises the

question of whether new insights into the diversification process can be gained by taking a more

balanced perspective on firm capabilities and their connection to the demand context.

This study addresses this question by examining how capabilities are conceptualized. In

particular, I distinguish between two types of capabilities by taking into account whether they 1 ‘Resources’ and ‘capabilities’ are used interchangeably in this paper.

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have opportunity costs in their use (Levinthal & Wu, 2010). Scale-free capabilities, such as

knowledge and brand name, resemble public goods within firm boundaries (Anand & Singh,

1997; Teece, 1982; Winter & Szulanski, 2001). They are limited by their fungibility, but do not

need to be allocated based on opportunity costs. In contrast, non-scale free capabilities, such as

effective product development teams and managerial attention (Capron, 1999; Helfat &

Eisenhardt, 2004; Teece, 1980), are subject to opportunity costs.

The existence of non-scale free capabilities implies that, by influencing the opportunity

costs of using these capabilities, the demand maturity of the firm’s current market relative to

alternative markets impacts diversification decisions. Corporate diversification can be viewed as

a redeployment process of non-scale free capabilities, which, while consistent with profit-

maximizing decisions at the corporate level, may have a negative impact on performance at the

market level. Using a fine-grained dataset from the cardiovascular medical device industry, and

controlling for corporate governance, market relatedness, and various industry conditions, this

study offers support for four implications of this perspective: (1) firms with a larger stock of

innovation experience before diversification are more likely to diversify; (2) firms in a current

market with greater relative demand maturity compared to the new market are more likely to

diversify; (3) diversification is associated with a performance decrease in the current market; and

(4) diversification is associated with a performance increase at the corporate level.

These findings highlight the importance of jointly considering supply-side and demand-

side heterogeneity to understand firms’ diversification behavior. The diversification literature has

shown that the individual businesses of a diversified firm underperform relative to a set of

comparably focused firms (Lang & Stulz, 1994). While such underperformance may be due to

diversification-induced value-destroying agency behavior (Jensen, 1986), it alternatively may

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result from a tradeoff between corporate-level profit and individual market-level profit when the

firm seeks to diversify based on the considerations of opportunity costs. This argument is

consistent with recent findings which suggest that diversification per se might not destroy value,

since there may be something systematically different about firms that choose to diversify

(Campa & Kedia, 2002; Villalonga, 2004). On the other hand, the findings of this study imply a

selection process that differs from that described by recent corporate finance literature, which

argues that the ex-post low average returns of diversified firms reflect the sorting of firms with

fewer capabilities into diversification events (Gomes & Livdan, 2004). The findings of this study

are consistent with the proposition in the strategy field that firms with more capabilities are more

likely to enter a new field (cf., Helfat, 2003; Helfat & Lieberman, 2002).

The present study extends prior work that examines how diversification activities are

affected by a demand decline in existing markets (Anand, 2004) or the attractiveness of new

markets (Chang, 1996; Montgomery & Hariharan, 1991). This paper focuses on the relative

demand conditions across alternative product markets. In addition, the current findings should be

interpreted as a specific mechanism, not as final conclusions regarding whether diversification

creates or destroys value in general. Indeed, three major alternative theories on diversification –

evolutionary economics, industrial organization economics (IO), and agency theory – consider

the impact of demand change on diversification actions.2 Evolutionary economics suggest that

diversification will not be an effective organizational response to demand change if firm

capabilities are context-specific (Nelson & Winter, 1982); the IO perspective suggests that

consolidation may be a better choice than diversification to cope with demand change (Anand &

Singh, 1997); and agency theory suggests that demand maturity can induce value-destroying

2 For more comprehensive reviews of the diversification literature, see Collis and Montgomery (2005) and Palich, Cardinal, and Miller (2000).

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agency behavior (Jensen, 1986). Within the boundaries set by these theories, I investigate

demand dynamics from the resource perspective, aiming to provide a more complete picture of

diversification in the face of industry dynamics (Anand, 2005; Bettis & Prahalad, 1983; Palich et

al., 2000).

Conducted in a within-industry setting, this study also seeks to make an empirical

contribution by using more refined measurements to complement cross-industry studies

(Argyres, 1996; Helfat, 2003; Silverman, 1999). The empirical setting of the cardiovascular

medical device industry contains multiple product markets mimicking the SIC-based system

(Figure 1). Since diversification in this setting occurs within the same broad industry, firm

capabilities should be more fungible across different product markets compared to diversification

across different broad industries. This fine-grained empirical approach further facilitates the

study of the demand side. Industry classification at the four-digit SIC level may incorporate

many distinct submarkets, each having different demand patterns. Such demand heterogeneity

can be illustrated by the current empirical setting. Although the cardiovascular medical device

industry is primarily within SIC 3841 and 3845, relevant demand conditions for a given

manufacturer are more nuanced than an aggregate four-digit measure provides (Figure 2). Thus it

is important to measure demand dynamics more closely corresponding to actual product market

conditions faced by firms. This more refined empirical approach helps unpack the critical

elements of firm heterogeneity that result in firms being sorted into corporate scope decisions

(Helfat, 2003; Helfat & Lieberman, 2002).

THEORY AND HYPOTHESIS

The distinction between scale free and non-scale free capabilities

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A scale-free capability, such as knowledge, reputation, or brand name, ‘… displays some

of the characteristics of a public good in that it may be shared in many different non-competing

applications without its value in any one application being substantially impaired’ (Teece, 1980:

226). The use of scale-free capabilities is constrained by the breadth of their fungibility. These

capabilities, however, do not need to be allocated based on the opportunity costs of using them in

one product market or another, which would be affected by the demand conditions across these

markets. Indeed, in their study on replication strategies, Winter and Szulanski (2001) identify the

Arrow core, an information-like resource, as a paradigmatic type of scale-free capability. As they

note, ‘… unlike any resource that is rivalrous in use, an information-like resource is infinitely

leverageable … it does not have to be withdrawn from one use to be applied to another’ (Winter

& Szulanski, 2001: 741).

In contrast, non-scale free capabilities, such as managerial attention and effective product

development teams, are capacity constrained. The use of these capabilities in one activity at any

point in time precludes their use in other settings. Compared to the public good nature of scale-

free capabilities, their non-scale free counterparts resemble congestible public goods. As

suggested by Teece (41: 1982): ‘… the transfer of productive expertise requires the transfer of

organizational as well as individual knowledge … if diversification is based on scope economies,

then there will eventually be a problem of congestion associated with accessing the common

input.’ Therefore, while scale-free capabilities can be shared by multiple markets simultaneously

regardless of industry changes, non-scale free capabilities need to be inter-temporally reallocated

in response to changing market contexts (Capron, 1999; Helfat & Eisenhardt, 2004). Moreover,

although the stock of non-scale free capabilities may increase over time, the process of capability

adjustment can lag exogenous demand changes since it takes significant time and effort to

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develop new, rent-generating resources through flows of investments and activities, especially

when strategic factor markets are incomplete and imperfect (Dierickx & Cool, 1989; Helfat &

Eisenhardt, 2004). Therefore, inter-temporal transfer is still necessary, albeit to a lesser degree,

even when firms can develop new non-scale free capabilities (Helfat & Eisenhardt, 2004).

Sharing scale-free capabilities or reallocating non-scale free capabilities are subject to

different kinds of constraints, leading to distinct strategic implications. Winter and Szulanski

(2001) recognize that even sharing the Arrow core, a paradigmatic type of scale-free capability,

within the same organization may be difficult (Szulanski 1996). Thus, they emphasize

exploration in the early stage of the replication process to discover the knowledge template

before sharing scale-free capabilities. Similarly, Zollo and Singh (2004) highlight the importance

of codifying knowledge to enable sharing between acquiring and acquired firms. Redeploying

non-scale free capabilities is also challenging. Reallocation within firm boundaries may be

difficult due to adjustment costs, such as the cost of moving employees between businesses

(Helfat & Eisenhardt, 2004) or arduous relationships between divisions (Szulanski, 1996).

Likewise, reallocation through acquisitions may incur integration costs (Capron, 1999; Puranam

& Srikanth, 2007). To deal with these difficulties, firms need to develop integrative knowledge

(Helfat & Raubitschek, 2000) or integration capabilities (Mitchell & Shaver, 2003; Zollo &

Singh, 2004), both solutions refer to organization capabilities to manage a portfolio of different

businesses on a dynamic basis (Helfat & Eisenhardt, 2004).

Non-scale free capabilities and opportunity costs

The current study examines how the opportunity costs of non-scale free capabilities

impact the diversification process. When facing the decision to participate in multiple markets,

firms consider both the stock of non-scale free capabilities and the relative demand conditions

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across alternative markets, which determine the opportunity costs of deploying non-scale free

capabilities. This argument connects Penrose’s capability-based perspective on diversification

with demand-side considerations, generating implications for both diversification decisions and

the associated performance consequences. Next, I develop a set of four hypotheses that

collectively make a novel contribution to the diversification literature.

On the supply side, I follow the existing literature and establish the baseline hypothesis

that firms are more likely to diversify into the target market when the relatedness between the

firm’s current market and the target market is higher (Anand, 2004; Chang, 1996). Then I

examine the impact of a firm’s pre-entry capabilities on diversifying entry. Following Helfat and

Lieberman (2002), I define a firm’s pre-entry capabilities as the stock of capabilities that a firm

accumulates in the current market before the firm diversifies into a new market. A larger stock of

pre-entry capabilities implies that a firm is more likely to have sufficient capabilities to establish

competitive viability in the new market. Thus, the marginal return from reallocating some of

these capabilities to alternative uses is more likely to be positive for firms with a larger stock of

pre-entry capabilities. Furthermore, as the level of pre-entry capabilities continues to increase,

such capabilities begin to experience diminishing returns for their use in the current market.

Therefore, the opportunity cost of not reallocating non-scale free capabilities increases with the

stock of pre-entry capabilities (Levinthal & Wu, 2010). I therefore expect that

Hypothesis 1a (supply-side decision): Other things being equal, the likelihood of a firm

diversifying into a new market increases with market relatedness.

Hypothesis 1b (supply-side decision): Other things being equal, the likelihood of a firm

diversifying into a new market increases with the stock of its pre-entry capabilities.

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Hypothesis 1b reconnects with Penrose’s (1959) work on the excess capacity of firm

capabilities; however, the emphasis is still on the supply-side. To connect with the demand-side,

I distinguish between the absolute demand in the new market and the relative demand in the

current market compared to the new market. The relative maturity of the current market may

result from the absolute decline of the current market, as in the defense industry after the Cold

War (Anand & Singh, 1997), or from the relatively faster growth of other markets, as in the

hand-held device market vis-à-vis the desktop PC market. A higher relative demand maturity in

the current market increases the opportunity cost of not redeploying non-scale free capabilities to

alternative markets where these capabilities are fungible. Therefore, firms are more likely to

diversify when their current markets become relatively mature. It is important to note that if

capabilities were purely scale-free and did not require allocation based on opportunity costs,

firms would only need to be concerned with whether the absolute demand in the new market is

sufficiently attractive, and their diversification actions would not be affected by relative demand

conditions in the current markets. I thus expect that

Hypothesis 2 (demand-side decision): Other things being equal, the likelihood of a firm

diversifying into a new market increases with the relative demand maturity of its current market

compared to the new market.

The argument in Hypothesis 2 extends Penrose’s (1959) concept of excess capacity of

firm capabilities to a more general context with demand-side considerations and opportunity

costs. Focusing on supply-side dynamics, Penrose highlighted indivisible investments and

continuous learning as the source of excess capacity. I argue that excess capacity of firm

capabilities may also arise in the current market due to demand-side dynamics, in the sense that

the firm would do better by reallocating some capabilities from the current market to the new

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market when relative demand maturity raises the opportunity costs of not doing so. This demand-

side consideration of excess firm capabilities is relevant because it affects not only

diversification decisions, but it also has distinct performance implications.

If excess capacity of non-scale free capabilities results only from supply-side factors,

such as indivisible investments, such capabilities become excess in absolute terms because they

no longer add additional value in the current market. Therefore, allocating away the excess will

not affect firms’ performance in the current market. However, if excess capacity becomes

available in the current market because capabilities have better use in alternative markets due to

opportunity costs, reallocation of some of the capabilities away from the current market to the

new market may be associated with a performance decrease in the current market. In other

words, for the benefit of the current market’s performance, it is always better for the current

market to retain all capabilities, because, by definition, more capabilities imply higher

profitability in a given market. However, from the corporate perspective, total profit will increase

by reallocating some capabilities to a related market and equalizing marginal returns across

different markets. This reallocation process implies a trade-off between corporate-level profit

and market-level profit. It further implies that, while marginal returns from diversification may

be positive, consistent with a profit maximization intention, average returns may decrease

(Montgomery & Wernerfelt, 1988).

It is important to note that the capability reallocation process described is but one

mechanism that may lead to lower average profitability of a diversification strategy. Lower

average profitability also can arise from firms’ entry into new markets that have lower industry-

level profit margins, or from the imperfect fungibility of capabilities when they are applied in

increasingly less related new markets. For example, although Cisco’s entry into the consumer

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segment may be a rational choice at the corporate level, it may decrease Cisco’s average

profitability because the industry-level profit margin in the consumer segment is much lower

than its high-end corporate segment or because its capabilities are less fungible in the new

market. By examining the decrease in average profitability at the corporate level, we cannot

disentangle the impact of reallocating non-scale free capabilities from the two alternative

mechanisms identified above. Therefore, in order to unpack the role of reallocating non-scale

free capabilities, I focus on the performance change in the current market. These two alternative

mechanisms operate through the new market, but do not directly impact the current market. In

addition, I control for corporate governance, market relatedness, and various industry conditions.

In this way, an opportunity cost-based mechanism adds to these existing explanations. Thus, with

respect to the current market, I expect that

Hypothesis 3 (performance effect in the current market): Other things being equal,

diversification is associated with a decrease in the firm’s performance in its current market.

Before I lay out the final hypothesis on corporate-level profits, it is useful to point out the

connection between the argument on diversification performance and the previous two

hypotheses on diversification decisions. Since these arguments hinge on the role of the demand

environment in influencing opportunity costs, such connections highlight a contribution of the

current study. While Hypothesis 1b reconnects with the seminal idea of a stock of firm

capabilities put forth by Penrose and Chandler (Chandler, 1962; Penrose, 1959), it is not an

entirely new concept in the strategy field given the recent work on pre-entry capabilities (e.g.,

Helfat & Lieberman, 2002). This hypothesis becomes novel, however, when considered in

conjunction with hypotheses 2 and 3, because together they suggest that a capability-based

mechanism can explain the observation that diversified firms may have lower performance

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compared to focused firms in individual markets. Corporate finance literature explains such an

observation as a self-selection effect in which firms that are ex ante less capable choose to

diversify (Gomes & Livdan, 2004). Through hypotheses 1 and 2, the current study suggests an

alternative selection process: Firms may be ex ante more capable, but operate in an existing

market characterized by greater relative demand maturity compared to the new market. These

firms then choose to diversify because they are subject to higher opportunity costs. As a

consequence, they need to spread their capabilities, and may, in turn, have lower performance at

the individual market level.

To support the argument regarding the resource reallocation process, I also examine

performance implications at the corporate level. In the within-industry diversification setting

used in this study, resources are more likely to be fungible across different product markets than

across different two-digit SIC industries typical of unrelated diversifications. As such,

diversification is more likely to be associated with a performance increase at the corporate level

if firms rationally allocate their non-scale free capabilities (Maksimovic & Phillips, 2002) to

achieve inter-temporal economies of scope (Helfat & Eisenhardt, 2004). This argument is

consistent with the finding in the strategy literature that firms pursuing related diversifications

tend to outperform those pursuing unrelated ones (Bettis, 1981; Palich et al., 2000; Robins &

Wiersema, 1995; Rumelt, 1974). However, this study does not argue that diversification is profit

maximizing across all settings. Rather, the primary objective of this study is to draw implications

about the link between diversification behavior and the deployment of non-scale free capabilities

from the resource perspective. If supported, these hypotheses would jointly provide evidence for

the non-scale free nature of resources, and introduce opportunity costs and demand context as

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critical factors in understanding patterns of diversification (Helfat & Eisenhardt, 2004; Levinthal

& Wu, 2010).

Hypothesis 4 (performance effect at the corporate level): Other things being equal, in a

within-industry diversification setting, diversification is associated with a performance increase

at the corporate level.

THE CARDIOVASCULAR MEDICAL DEVICE INDUSTRY

The empirical analysis is conducted based on a dataset within the cardiovascular medical

device industry from 1976 to 2004. This industry is a strong candidate for the current study for

the following reasons:

First, the cardiovascular medical device industry can be divided into three main sectors:

interventional cardiology, cardiac rhythm management, and cardiac surgery (Level 1 in Figure

1). Each sector consists of a number of independent horizontal product markets (Level 2 in

Figure 1), which is the focus of the current study. This nested structure allows me to study firms’

diversification activities across different product markets (Level 2 in Figure 1). These

diversification activities differ from adding product variety in marketing, e.g., adding one more

line of pants, because there is little price elasticity across different markets at this level. The

nested structure also provides a measure of market relatedness in that markets within the same

sector are more related to each other than those in different sectors. Further, these different

markets exhibit distinct demand patterns over time (Figure 2), which allows me to better identify

the effects of relative demand conditions across alternative markets.

Second, the process of developing medical devices justifies the use of innovation

experience as a fine-grained measure of capabilities, given the complexity and precision of the

scientific and engineering inputs into medical device development. These innovations are

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perfected and turned into products through a trial-and-error process that can only be developed

through extensive experience in the design and product markets. In addition, medical devices

implanted in the body must adhere to a stringent FDA approval process since the performance of

the device is critical to maintaining patient health. In sum, experience in developing

cardiovascular devices, and subsequently adhering to FDA regulations, represents a critical

capability for competing in this market.

Third, the Medical Device Amendments of 1976 to the Federal Food, Drug, and

Cosmetic Act requires all medical devices to be systematically approved by the FDA. This

dataset not only allows me to compile a complete history of firm innovation and market entry,

but it also provides a rare opportunity to measure experience on novel innovations (see Table 1

for an overview). Pre-Market Approvals (PMA’s) correspond to novel innovations, PMA

Supplements correspond to incremental innovations, and 510K’s correspond to imitations.

Accordingly, experience with novel innovations in this specific context, rather than being a

broad measure of overall experience, can better capture factors such as R&D capabilities and

experience with regulation.

[Insert Figure 1, Figure 2, and Table 1 about here]

EMPIRICAL ANALYSIS

Sample and Data

Product approval data and the respective product categories from 1976-2004 for all firms

in the cardiovascular devices area are obtained from the Center for Devices and Radiological

Health (CDRH) of the FDA. The initial sample ranges from highly complex products, such as

pacemakers, to fairly simple products, like plastic tubing for IVs. Since the analysis focuses on

innovation and the development of complex products, I restrict the sample to Class III product

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categories and Class II categories that contain PMA approvals (Table 1).3 In total, the sample

contains eight product markets, 274 unique firms, and 3,468 FDA approvals (200 PMAs, 1,064

PMA supplements, and 2,204 510Ks). Demand data are obtained from the National Hospital

Discharge Survey (NHDS). Starting from 1964, this database maintains hospital discharge

records from a nationally representative sample of hospitals. Each surgery involving the use of a

certain type of medical device is identified through a unique procedure code, called ICD-9

(International Classification of Diseases). Therefore, I am able to measure demand by counting

the number of surgeries conducted within each market.

To address issues such as ownership changes, name changes, mergers, acquisitions, and

dissolutions, I manually verify firms’ information from sources including annual reports (if

publicly listed), firm websites, SDC Platinum, Thomas' Register, Hoovers, Corptech, as well as

information providers specializing in this industry, such as Medical Device Register: The Official

Directory of Medical Suppliers Resource and Informagen. These sources also provide

information on each company, including total sales, firm age, and whether a firm is public,

foreign, a pharmaceutical firm, or has its primary business activities in the medical device area. I

obtain corporate governance data for public firms from multiple data sources, including Wharton

Research Data Services (WRDS), Compact Disclosure, and the microfiche stored by the Library

of Congress and Thomson Reuters.

Variables

Diversification decisions (Hypotheses 1 and 2)

Dependent variable

3 I include these Class II categories because some of the product classes over time may change from Class III to Class II as these product categories are shown to be less risky. These Class II categories are included to rectify this potential bias.

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Diversifying entry: A firm is at risk to diversify into a given target market after it enters

its first market in the cardiovascular medical device industry. The unit of analysis is thus a firm-

target_market-year. Over time, as the firm diversifies into a given market, this firm-

target_market is dropped from the analysis.4 Therefore, the subject under analysis is firm-

target_market, and the event is diversifying entry. A firm is censored if it ceases to exist due to

closure or acquisition. Overall, there are 274 firms and seven markets over the 29-year time

period (1976-2004), yielding 1,591 firm-target_market subjects and 77 entry events.5

Explanatory variables

Pre-entry capabilities: As discussed in the industry section, I use the cumulative count of

a firm’s internally developed Pre-Market Approvals (PMA) to measure a firm’s pre-entry

capabilities. Since firms must file FDA approvals in order to market their products, this measure

serves as a consistent proxy of a firm’s capabilities to develop new products and deal with FDA

regulations. Following the literature in calculating R&D stock (Henderson & Cockburn, 1994), a

depreciation rate of 15% is used to construct this measure. Results are robust with respect to

alternative depreciation rates.

Market relatedness is constructed based on the nested structure of the industry (Figure 1).

The relatedness between two markets is defined as a dummy variable, which takes the value of

one if these two markets belong to the same broad sector (Level 1 in Figure 1), and zero if not.

To construct the firm-specific relatedness measure, I calculate the weighted average of the

relatedness between the target market and each of the focal firm’s current markets, where the

4 For example, when Medtronic was initially founded to serve the pacemaker market within the cardiovascular medical device industry, it could diversify into seven markets besides pacemakers. Entering new markets reduces the number of potential target markets; entering the catheter market in 1982, for example, left Medtronic with six potential markets. 5 The current analysis focuses on the decision to diversify, but not the choice of entry mode between internal growth and acquisition. Results are robust using only entry through internal growth.

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weight is the focal firm’s cumulative count of product years in a given market during a given

year. The same weighting method is used to construct the following measures based on a

weighted average.

Growth in the new market: I use the growth rate of demand level as the demand measure

because it captures market demand in dynamic terms (Cohen & Levin, 1989). Moreover, a

growth rate is comparable across markets given that the current measure of demand level is a

unit count from different markets. Finally, in order to avoid spurious growth due to year-to-year

random fluctuation, I use a three-year moving average of the yearly growth rate (McGahan &

Silverman, 2001): Let YGt represent the yearly growth rate in year t; then, the three-year moving

average that measures the growth trend in year t, Gt,, is equal to (YGt-1 + YGt + YGt+1)/3.6

Relative demand in the current market(s): I measure the relative demand maturity in the

current market as compared to the new market as the difference in growth rate between the new

and the current market. The growth rate in the current market(s) is calculated as the weighted

average of all markets in which the focal firm currently operates.

Control variables

Age and size are included to control for a firm’s general experience. Age is defined as the

current year minus a firm’s founding year. Size is measured with overall firm sales. Since some

firms are private and the size information I obtain from trade journals is only a categorical

variable, I use size quartiles to measure size. This variable takes a value of one for firms with

sales of 0-1 million dollars, two for 1-10 million dollars, three for 10 million-1 billion dollars,

and four for 1 billion dollars and above.

6 Results are robust using windows of four years and five years. Results are also robust if I shift the window one year backward (assuming the firm is more backward looking) or one year forward (assuming the firm is more forward looking).

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Scope, pharmaceutical firm, primary business in medical device, and foreign firm are

included to control for a firm’s experience beyond the focal product market in the US. Scope is

defined as the number of markets in which the firm currently operates. Pharmaceutical firm is a

dummy variable taking the value of one if the firm is a pharmaceutical firm and zero otherwise.

Primary business in medical device is a dummy variable taking the value of one if the firm has

primary business activities in the medical device area and zero otherwise. Foreign firm is a

dummy variable taking the value of one if the firm is a foreign firm and zero otherwise.

To control for agency behavior, I include public firm, managerial ownership, and ratio of

outsider to insider on the board. Public firm is a dummy variable taking the value of one if the

firm is public and zero if not. Managerial ownership is measured by the fraction of the firm’s

total outstanding shares held by top managers and directors (Himmelberg, Hubbard, & Palia,

1999). Ratio of outsider to insider on the board is measured by the ratio of outside board

directors to inside board directors (Anand, 2004). Since managerial ownership and ratio of

outsider to insider on the board are available only for public firms, I include the interaction

between the public firm dummy and these two corporate variables. Private firms are less likely to

demonstrate agency behavior than public firms, because a private company is more likely to be

operated by the owner. Managerial ownership and ratio of outsider to insider on the board

provide further controls.

Multi-market contact, number of acquisitions in each market, and number of firms in

each market are included to control for multi-market competition and industry consolidation

from the IO perspective. I calculate multi-market contact following Baum and Korn (1996).

Number of acquisitions in each market is the number of acquisition events in each product

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market, controlling for the degree of within-industry consolidation (Anand & Singh, 1997;

Campa & Kedia, 2002; Villalonga, 2004).

Product approvals in each product market represents a set of variables counting both the

flow and the stock of different types of product approvals in each product market (PMAs - novel

innovation, PMA supplements - incremental innovation, and 510Ks - imitation: See Table 1 for a

summary). This set of controls is important because the demand measure, or the realized industry

output, may be affected by both demand-side and supply-side factors (Cohen & Levin, 1989). By

providing complete industry-level data on novel innovation, incremental innovation, and

imitation throughout industry lifecycles, the current setting offers an opportunity to control for

the supply-side aggregate efforts.7 For a given firm in a given year, such a measure is included

for both its target market and its current market(s). When the focal firm currently operates in

more than one market, a weighted average is calculated, using the same weighting method to

construct market relatedness. Summary statistics of these variables are provided in Table 2.

[Insert Table 2 about here]

Performance (Hypothesis 3)

Hypotheses 3 and 4 examine the performance effect of diversification. Below I discuss

the variables for Hypothesis 3, which concerns the market-level analysis. For corporate-level

performance in Hypothesis 4, I conduct an event study analysis, for which I will discuss the

variables and methods together in the results section.

Dependent variable

7 The stock-based control variables tend to be highly correlated in a given market, due to the accumulation of industry innovations or imitations up to the later years of the sample period. To address this issue, these variables are orthogonalized within the target or the current market(s), capturing different effects of aggregate innovation or imitation activities. These variables are included together to ensure completeness in controlling the supply-side factors across all years.

19

New product introduction is defined as the number of FDA approvals a given firm

obtains in a given individual market in which it operates during a given year.8 This variable

captures innovation performance (Bayus, Erickson, & Jacobson, 2003).

Explanatory variable

Diversification is a dummy variable. For a given individual market in which a firm

operates, it takes a value of one for the years after the firm adds one more market and zero for

the years before such a diversification move occurs.9

Control variables

I include a number of controls for different firm and industry characteristics that were

used in the decision analysis. For firm characteristics, I include age, size, public firm, managerial

ownership, ratio of outsider to insider on the board, and total capabilities. For industry

characteristics, I include demand growth in each market, number of acquisitions in each market,

number of firms in each market, and number of approvals in each market.

A few differences in variable inclusion from the decision analysis are worth noting.

Time-invariant variables – pharmaceutical firm, primary business in medical device, foreign firm

– are not included due to the use of a fixed-effects specification (see the methods section). Total

capabilities is defined in the same way as pre-entry capabilities used in the decision analysis. I

8 There are two differences between this measure and the measure for pre-entry capabilities used in the entry analysis. First, following the standard treatment in the resource-based view literature, this measure for new product introduction is an annual flow variable, while the measure for pre-entry capabilities is a stock variable that captures cumulative innovation experience (Dierickx & Cool, 1989; Winter, 1987). Second, and more important, the new product introduction variable includes all types of FDA approvals in order to capture overall product performance. By contrast, the pre-entry capabilities measure, which uses only PMA-type FDA approvals, is meant as a proxy for the core drivers of innovation capabilities in this industry. I also checked an alternative specification where PMA-type FDA approvals are used only for measuring pre-entry capabilities and thus are excluded from the annual count of product introductions. Results are robust. 9 For instance, Firm A starts with Market 1 in year 1, and then diversifies into Market 2 in year 5 and into Market 3 in year 10, and does not diversify again. For Firm A-Market 1, the dummy variable diversification takes the value of 0 from year 1 to year 5 and 1 thereafter. For Firm A-Market 2, the dummy variable diversification takes the value of 0 from year 5 to year 10, and takes the value of 1 afterward. Since Firm A does not diversify further after entering Market 3, the Firm A-Market 3 combination is not included in the analysis because no comparison can be made with regard to performance change for the same firm-market before and after the diversification move.

20

use a different variable name here because the subject of analysis is no longer diversifying entry

as defined in the decision analysis. This variable is included to address the concern that the

decrease in new product introductions for a given firm after diversification in its current

individual market may be merely a consequence of declining firm capabilities. Summary

statistics of these variables are provided in Table 3.

[Insert Table 3 about here] Econometric Methods

The Cox proportional hazards model is used to analyze the diversification of firms into

other product markets (Allison, 1984). The Cox model estimates the hazard of diversifying entry

of firm i into market j in year t using the following functional form:

(1) jtitij YXtth )()(log

where )(thij is the hazard rate that firm i diversifies into market j in year t conditional on that it

has not done so by year t, )(t is an arbitrary baseline hazard function, itX is a vector of firm

characteristics, and jtY is a vector of market characteristics.10 Robust standard errors are used to

account for intra-firm non-independence of observations. All explanatory variables are lagged

two years to account for product development time in this industry. Results are robust to

alternative time lags. Market dummies are included to control for unobservable factors across

different target markets. Results are robust using a rare event logit model.

Hypothesis 3 examines the performance effect of diversification on current market(s) for

those firms that diversified into other markets. Since the dependent variable, or the number of

FDA approvals for a given firm-market-year, is a non-negative integer, a count model is used.

The negative binomial model was used to account for over-dispersion in the current count data.

10 All included variables pass the test for the proportional hazard assumption.

21

Most important, testing this hypothesis requires identifying the performance change for the same

firm in the same individual current market before and after the firm diversifies into the new

market. Driven by this theoretical purpose, an unconditional fixed-effects negative binomial

model is used in which a set of firm-market dummy variables is included in the negative

binomial model to control for all time-invariant firm-market specific effects (Allison &

Waterman, 2002; Cameron & Trivedi, 1998).11 The model takes the following functional form:

(2) ijtijjtitijt ZYXnE )(log

where )( ijtnE is the expected number of FDA approvals that firm i obtains in market j in year t,

itX is a vector of firm characteristics, jtY is a vector of market characteristics, and ijZ is a vector

of firm-market dummy variables. Robust standard errors are used to account for intra-firm non-

independence of observations. All explanatory variables are lagged two years to account for

product development time in this industry. Results are robust to alternative time lags. All models

contain year dummies to control for unobservable changes in the industry over time.

Results

Results on diversification decision (Hypotheses 1 and 2)

The effects of pre-entry capabilities and relative demand on the likelihood of entry are

reported in Table 4. Model 1 is the baseline model with all the control variables and market

relatedness; Model 2 adds pre-entry capabilities; Model 3 is the full model.

[Insert Table 4 about here]

11 This method is chosen over the conditional fixed-effects negative binomial estimator because the latter cannot control for all the time-invariant firm-market specific effects (Allison & Waterman, 2002). In addition, the unconditional fixed-effects negative binomial estimator does not suffer from the “incidental parameters” problem, which tends to make the fixed effects estimator in nonlinear panel data models inconsistent when the length of the panel is small and fixed. However, we need to adjust for potential downward bias in the standard error estimates by multiplying the standard errors by the square root of the ratio of the deviance to its degrees of freedom (Allison & Waterman, 2002).

22

Consistent with Hypothesis 1a, the significantly positive coefficient on market

relatedness indicates that firms are more likely to diversify into related markets. Consistent with

Hypothesis 1b, the significantly positive coefficient on pre-entry capabilities indicates that firms

with superior capabilities are more likely to diversify. Consistent with Hypothesis 2, the

significantly positive coefficient on relative demand in the current market(s) indicates that firms

in an existing market with greater relative demand maturity over the new market are more likely

to diversify. Interestingly, growth in the new market is positive but not significant. This finding

is not unusual since it is generally hard to predict the growth potential of a new market

opportunity, especially at its early stage. Indeed, this result is also found in previous research that

uses the growth rate of the target market as a control variable (Chang, 1996; Montgomery &

Hariharan, 1991).12 In the full model, the economic impact of a standard deviation increase in

pre-entry capabilities is to increase the hazard of diversifying into a new market by about 21.1%.

The economic impact of a standard deviation decrease in relative demand in the current

market(s) is to increase the hazard of diversifying into a new market by about 104.4%.13

With respect to firm-level control variables, medium-sized firms (the second and third

quartile of size) are more likely to diversify into new markets. Scope has a significantly positive

coefficient, which to some extent indicates a firm’s dynamic capabilities to enter a new market

(King & Tucci, 2002). All models consistently show that public firm, pharmaceutical firm, and

primary business in medical device are more likely to diversify into new markets.

12 It is useful to look at an equivalent model specification. In the current model, relative demand = growth rate in the new market growth rate in the current markets. Equivalently, in a linear recombination, I include growth rate in the new market and growth rate in the current markets separately. Their coefficients are 1.408 and -3.108, respectively. The growth rate in the new market is not significant and the growth rate in the current market is significant. 13 Percentage change in hazard rate = 100*(eβ 1).

23

Interestingly, multi-market contact has a significantly positive effect on market entry.

This result supports recent findings showing that in hi-tech industries a higher overlap in product

market coverage between the focal firm and firms in the target market can lead not only to

competition concerns but also can reveal a greater relevance of the focal firm’s capabilities with

respect to the target market (Anand, Mesquita, & Vassolo, 2009; Lee, 2008). Number of firms in

the new market is significantly positive, while the stock of novel innovation, incremental

innovations, and imitations in the new market is significantly negative. This suggests competitive

deterrence arises from the accumulation of innovation or imitation activities, but not necessarily

the number of firms. The flow of imitations in the new market is significantly negative in

affecting market entry, reflecting the concern of expropriation. In contrast, the flow of

incremental innovations in the new market is significantly positive in attracting market entry,

reflecting the existence of a high growth potential that can be harvested with incremental

innovations. Correspondingly, the flow of incremental innovations in the current market(s) is

negative, suggesting that firms are reluctant to shift attention and resources away from industries

that can be further exploited.

To infer resource allocation consistent with the capability perspective, I include proxies

for boundary conditions delineated by alternative theories, including evolutionary theory (market

relatedness), industrial organization theory (multi-market contact, number of firms in each

market, number of acquisitions in each market, and number of approvals in each market), and

agency theory (public firm, managerial ownership, and outsider to insider ratio on the board).

Results are robust to these additional variables.14

14 As an alternative control for evolutionary theory, I use patent data to construct a capability distance variable measuring the distance between the focal firm and the target market (Anand, 2004; Chang, 1996). Results are robust and available upon request.

24

The main model uses internally developed novel experience to measure a firm’s pre-entry

capabilities. Since firms may pursue both internal development and acquisitions, I compare their

relative impacts. Following the same approach for constructing pre-entry capabilities, I construct

another variable, acquired experience, which is the cumulative count of a firm’s acquired Pre-

Market Approvals. I include these two variables separately in Model 4 of Table 4. To compare

their different scores, I standardize these two variables, then test the difference of their

coefficients. Acquired capabilities are not significant, and internally developed capabilities have

a significantly higher score than acquired capabilities.

I also check robustness with respect to the econometric method. The Cox model does not

specify the form of the baseline hazard function. As a robustness check, I estimate a piecewise

exponential hazard rate model where the baseline hazard is constant within predefined time

pieces, but varies across time pieces. Results are similar and are reported in Model 4 of Table 4.

Results for performance at the market level (Hypothesis 3)

[Insert Table 5 about here]

Model 1 of Table 5 presents all the control variables. Model 2 presents the main results.

The fixed-effects model controls for firm-market effects, which allows me to examine the impact

of a diversification action on the same firm’s performance in the same market in an average year.

Consistent with Hypothesis 3, the sign of diversification is negative and significant, indicating

that, controlling for firm characteristics (age, size, public firm, managerial ownership, ratio of

outsider to insider on the board, and total capabilities) and market conditions (demand growth in

each market, number of acquisitions in each market, number of firms in each market, and

number of approvals in each market), the number of FDA approvals obtained by a given firm in

a given individual market in which it operates in a given year decreases after a diversification

move occurs. The economic impact of diversifying into another market leads to a significant

25

decrease of 35.9% in the expected number of FDA approvals in an average firm-current_market-

year.15 This finding regarding the negative impact of an increase in corporate scope on the

current market is consistent with findings by Rawley (2010), Roberts and McEvily (2005), and

Schoar (2002). Adding to this research stream, Hypotheses 1, 2, and 4 show that such a negative

impact at the market level may be consistent with the firm rationally reallocating its scarce

resources from the current market to the new one, thus creating value at the corporate level.

Model 2 also presents the results with respect to the control variables for firm

characteristics. An increase in both size (the third and fourth quartile) and total capabilities have

a significant positive effect. The sign of public firm is negative and significant. Perhaps the firm

starts expanding into other product markets after it goes public, thereby allocating resources

away from the current market. With respect to the control variables for market conditions,

number of firms in the focal market has a significantly positive effect on an individual firm’s new

product introductions. This result is consistent with the argument that there is a positive

correlation between product market competition and innovative output (Cohen & Levin, 1989).

The robustness checks for the performance results are presented in Models 3 and 4 in

Table 5, alongside the main model. In the primary specification (Model 2), the negative binomial

model is used to account for over-dispersion in the current count data (over-dispersion parameter

α = 0.262, p<0.05). Model 3 estimates a fixed-effects Poisson model. Although the Poisson

model may be subject to the over-dispersion problem, the advantage of a fixed-effects Poisson

estimator is that it does not suffer from ‘incidental parameters’ problems, which tend to make the

15 Percent change in the expected number of FDA approvals = 100*(eβ 1).

26

fixed effects estimator in nonlinear panel data models inconsistent when the length of the panel

is small and fixed (Allison & Waterman, 2002; Cameron & Trivedi, 1998).16

The main model uses firm-market fixed effects to control for time-invariant unobserved

firm-market heterogeneity. However, the change in firm capabilities may not be fully captured

by firm-market fixed effects. To address this concern, in the main model, I include the firm-level

variable total capabilities to control for changing firm capabilities, supplementing the firm-

market fixed effects. A related concern is agency behavior. If managers diversify in expectation

of declining capabilities, their objective may be to maintain growth, and ultimately to work to

their personal benefits. As such, I include public firm, managerial ownership, and ratio of

outsider to insider on the board in the main model to control for agency behavior.

In the robustness check, I further address the issue of self-selection using a two-step

analysis. In the first step, I estimate the likelihood that the binary explanatory variable

diversification defined in the performance equation (2) takes the value of one using a probit

model. As the instrumental variable in this estimation, I use the average flow of incremental

innovations across the markets in which the firm does not currently operate. Following Colak

and Whited (2007), I make two assumptions to justify this instrumental variable. First, if there is

a higher flow of incremental innovations in the other markets, the uncertainty associated with

novel innovations has reduced in these markets, but developing incremental innovations remains

a possibility. This may signal a good time to diversify into these markets. As such, this

instrumental variable is relevant in predicting diversification. Second, I assume that the flow of

incremental innovations in the other markets does not directly affect the firm’s performance in

the current market other than through influencing diversification decisions and, correspondingly,

16 Unlike a fixed-effects negative binomial model, the two estimation methods – unconditional maximum likelihood estimation and conditional maximum likelihood estimation – always yield identical estimates for a fixed-effects Poisson model (Cameron & Trivedi, 1998).

27

resource allocation decisions. From the estimation in this first step, I calculate the inverse Mill’s

ratio and then include it in the second step to estimate equation (2) in the main model, with firm-

market fixed effects included.17 Results are robust in this two-step analysis (Model 4 of Table 5).

Results for performance at the corporate level (Hypothesis 4)

To test the change in corporate-level profit in Hypothesis 4, I use an event study to

examine the stock market’s response to diversification moves made by public medical device

firms in the current within-industry diversification setting. The stock market’s response is a

forward-looking metric to assess the marginal return to a firm’s investment decision. There are

two reasons for this choice over Tobin’s q or ROA. First, Tobin’s q or ROA captures average

returns, but not marginal returns, and therefore does not measure the change in corporate-level

profit that the current analysis seeks to measure (Montgomery & Wernerfelt, 1988). Second,

limitations of the current empirical design make it infeasible to follow the chop-shop method in

constructing industry-adjusted Tobin’s q or ROA (Campa & Kedia, 2002; Lang & Stulz, 1994;

Villalonga, 2004), because financial performance is not broken down to the market level.

I use the market model to estimate the cumulative abnormal returns (MacKinlay, 1997).

The market model assumes a linear specification relating the return of stock i in period t to

the market return , or , where is the zero mean disturbance term. I

use the CRSP value weighted index as the market return and estimate the market model

17 In the first step, probit = -2.963 + 0.328* total capabilities - 0.006* demand growth + 0.353*age + 0.423*size_2 + 0.515*size_3 + 0.051*size_4 + 0.989*public firm - 1.770*managerial ownership*public firm - 0.139*outsider-insider ratio*public firm - 0.007*number of acqusitions - 0.002*number of firms + 0.036*novel innovation_stock + 0.183*incremental_innovation_stock - 0.029*imitation_stock + 0.007*novel innovation_flow + 0.016 *incremental_innovation_flow + 0.002*imitation_flow + year + market + 0.104*incremental innovations in other markets. The coefficient for incremental innovations in other markets is significant (p<0.01), supporting the assumption made about this instrumental variable. Following Greene (1995), the two-step analysis takes a linear Taylor series approximation to the nonlinear conditional mean function and thus includes the inverse Mill’s ratio linearly in estimating equation (2). In this sense, the inverse Mill’s ratio can be viewed as a generalized residual. Results are robust with a Poisson specification. Given this caveat, this analysis should be seen as a supplement to the fixed-effects model and detailed control variables.

28

parameters using stock returns from event day -210 to event day -11. Results are robust to using

the S&P 500 index or the CRSP equal weighted index as the market return. The cumulative

abnormal return is , ∑ .

I examine different lengths for the event window ( 1, 1), ( 2, 2),

and ( 5, 5), and use a one-tailed test to examine the null hypothesis that the

cumulative abnormal returns are negative or equal to zero. For acquisition-based diversifications

(9 out of 42 events), the event date is defined as the acquisition announcement date, and for

internal development-based diversifications (33 out of 42 events), the event date is defined as the

date on which the firm received its first FDA approval in the newly-entered market. The

cumulative abnormal return is 3.49% (p=0.091) based on the (-1, 1) window, 5.17% (p=0.012)

based on the (-2, 2) window, and 6.99% (p=0.004) based on the (-5, 5) window, respectively.18

These results, combined with the performance analysis in the current individual market(s),

support the existence of a mechanism where diversification may lead to a tradeoff between the

corporate-level and the market-level performance.

DISCUSSION AND CONCLUSION

This study investigates how relative demand dynamics across alternative markets

influence diversification decisions and their associated performance effects. The existence of

non-scale free capabilities implies rational diversification decisions are based on the opportunity

cost of their use in one domain over another. Firms with stronger capabilities in a more mature

existing market diversify to maximize total profits, but may incur lower performance in current

markets. I find strong support for these arguments based on data within the cardiovascular

medical device industry from 1976 to 2004. 18 Since the result based on the shortest window is marginally significant but becomes stronger as the length of the event window increases, it appears that it takes the stock market some time to fully incorporate the impact of the news.

29

This paper seeks to enhance the explanatory power of the resource-based view by

shedding some new light on the self-selection process of corporate diversification. The resource-

based view argues that more capable firms rationally diversify to create value, but empirically

we observe a diversification discount. This seeming inconsistency undermines the explanatory

power of the resource perspective. The recent literature helps clarify this issue, finding that lower

performance in individual markets does not necessarily mean that diversification per se destroys

value (Campa & Kedia, 2002; Villalonga, 2004). However, the existing model in corporate

finance accounts for the self-selection process by suggesting that less capable firms diversify,

which is still inconsistent with the resource-based view. The current findings suggest an

alternative explanation consistent with the resource-based perspective (Helfat, 2003; Helfat &

Lieberman, 2002). The fine-grained empirical setting supports this alternative by disentangling

supply-side versus demand-side firm heterogeneity often masked by the four-digit SIC

classification typically used in the existing literature.

More generally, this paper highlights opportunity costs as a mechanism connecting firm

capabilities with the relative demand conditions across alternative product markets.

Incorporating demand dynamics adds to the resource-based view on diversification originally put

forward by Penrose (1959) and Chandler (1962). Perhaps due to its historical focus on the

supply-side factors, the resource-based view has not fully conceptualized how the impact of

demand dynamics may be driven by the underlying firm capabilities. Filling this gap helps

develop a deeper understanding of the diversification process rooted in the resource-based view.

In turn, this study will facilitate the contrast and integration different theoretical perspectives to

study corporate scope decisions (Anand, 2005; Bettis & Prahalad, 1983; Palich et al., 2000).

30

The current study aims to contribute to a broader perspective that a more precise

characterization of the nature and properties of firm capabilities can increase our understanding

of the strategy-making process. The present paper conceptualizes the distinction between scale-

free and non-scale free capabilities, which offers a new angle to examine a number of strategic

issues. Depending on the type of their capabilities, firms need to choose different organization

structures to fit with their strategic choices. For example, a centralized organizational structure

better coordinates sharing scale-free capabilities across different divisions, thereby achieving

intra-temporal economies of scope. In contrast, a decentralized and modular organizational

structure may be required to facilitate inter-temporal transfer of non-scale free capabilities in

achieving inter-temporal economies of scope (Helfat & Eisenhardt, 2004). The distinction

between the two types of capabilities also impacts the dynamics of leveraging and developing

capabilities. Scale-free capabilities (e.g., a new patent) can enlarge the opportunity set and their

applicability determines the expansion trajectory, while non-scale free capabilities (e.g., financial

resources and engineers) control the tempo of such expansion efforts (Kaul, 2012). Relatedly,

firms can seek to relax the capacity constraint to their non-scale free capabilities by making their

capabilities more scale-free as opposed to developing new capabilities. For instance, the

codification of tacit knowledge can free the firm from being subject to the limited bandwidth of

the engineering team. This codified knowledge, in the presence of appropriation mechanisms,

can allow the firm to replicate its successes in a greater number of new markets (Winter &

Szulanski, 2001).

Different types of capabilities generate distinctive implications. When decisions are

based on scale-free capabilities, the focus should be on the new market, and, in particular, on

whether capabilities are fungible in the new market or whether the new market is attractive. In

31

contrast, the use of capabilities having opportunity costs implies that portfolio decisions should

be based on the relative market conditions in alternative product markets. Related to this

implication, entry into a new market will not affect performance in the current market when

capabilities are scale-free, but will have a negative effect when capabilities are not scale-free.

This line of analysis can be extended to examine alliances (Aggarwal, 2012). While the scale-

free capabilities (e.g., status) of an industry incumbent can be shared by everyone, thus attracting

more start-ups, the capacity constraints to its non-scale free capabilities (e.g., distribution

channel) also imply that each individual start-up may receive fewer resources and less attention.

Individual start-ups may pay attention to this trade-off when choosing industry incumbents as

alliance partners.

The current findings are also relevant to the technology and innovation management

literature. The results show that when demand growth in a firm’s current market(s) relative to

alternative markets is still sufficiently high, even a very capable firm may choose not to move

into a new market. Arguably, this opportunity costs argument provides a rational explanation for

the observation by Christensen and Bower (1996) of established firms’ reluctance to move into a

new market. Established firms may not lack capabilities or be inert; rather, given the technical

uncertainty in the new market, it may be rational for them to refrain from allocating their

capabilities based on opportunity cost considerations.

While taking a rational perspective, this study suggests a channel of potential decision

biases. The opportunity costs of using firm capabilities imply that average profits may be

sacrificed if firms reallocate resources to maximize total profits. However, firms may be

reluctant to pursue (or tend to exit) activities that reduce overall average profits, even though

profit-maximizing decisions should be based on marginal profits. This decision bias has been

32

demonstrated by recent experimental studies (Shapira & Shaver, 2010). Expanding this line of

research can shed new light on the resource allocation process.

This paper has a number of limitations. First, because I focus on a single industry, these

findings may not be generalizable to other empirical settings. Second, it is necessary to use

innovation performance as a proxy for performance at the market level, because a number of

firms are not publicly traded, and firms do not break out performance data for the level of

markets defined in the current analysis. Third, even at the corporate level, financial information

is available only for public firms. Thus, while the sample has the important virtue of including all

participants, both private and public, in the industry, this sampling strategy restricts the use of

financial measures of performance. Given these limitations, the current study can be placed at

one end of the continuum of market relatedness, where markets are closely related and the

resource-based view may have a bigger impact, while the cross-industry design marks the other

end, where the role of other theories (e.g., agency theory) may become more salient. Thus,

findings based on the current within-industry approach should be viewed as a supplement to the

cross-industry design to facilitate the conversation between the resource-based logic and other

theories.

33

Note: One minor market, Occluder, is dropped from the final analysis due to the lack of demand data.

Figure 1. Industry classification

Figure 2. Distinct demand patterns across markets

Table 1. Innovation classification

Class Safety risk Nature Approval Type

Class I Low Commodity 510K Imitation Class II Intermediate Commodity 510K Imitation

Class III High R&D

PMA Novel innovation

PMA Supplement Incremental innovation

510K Imitation

Level 1

Level 2

Cardiovascular Medical Device

Cardiac Surgery

Bypass Graft Valve

Cardiac Rhythm

Defibrillator Pacemaker

Interventional Cardiology

Stent Catheter Occluder

-0.2

0

0.2

0.4

0.6

0.8

1

1976 1981 1986 1991 1996 2001

Defibillator

Bypass

Stent

Graft

Pacemaker

Heart Valve

Catheter

0

200

400

600

800

1000

1200

1400

1973 1978 1983 1988 1993 1998 2003

Stent

DefibillatorGraft

Bypass

Pacemake

Catheter

Heart Valve

Table 2. Summary of data for diversification decision Mean S.D. Min Max (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31)

(1) Entry 0.01 0.08 0 1 1

(2) Market relatedness 0.2 0.38 0 1 0.01 1

(3) Pre-entry capabilities 0.12 0.48 0 6.33 0.1 0 1

(4) Grow th new market 0.1 0.18 ‐0.15 1 0.01 0.14 0.01 1

(5) Relative demand in current market 0 0.23 ‐0.97 0.97 0.02 0.13 0.03 0.68 1

(6) Age 3.06 0.94 0 5.82 0.02 0.02 0.04 ‐0.01 0 1

(7) Size 1.91 1.13 1 4 0.06 0.02 0.22 0 0.02 0.68 1

(8) Existing scope 1.24 0.7 0 7 0.08 0.04 0.49 0.03 0.06 0.26 0.38 1

(9) Pharmaceutical f irm 0.06 0.23 0 1 0.04 0.01 0.02 0.03 0.02 0.33 0.45 0.17 1

(10) Primary business in medical device 0.65 0.48 0 1 0.01 0 0.06 ‐0.01 0.01 ‐0.42 ‐0.38 0.03 ‐0.33 1

(11) Foreign firm 0.16 0.36 0 1 0.01 ‐0.01 0.07 ‐0.01 ‐0.04 0.15 0.11 ‐0.02 0.1 ‐0.05 1

(12) Public f irm 0.31 0.46 0 1 0.06 0.01 0.2 ‐0.01 0 0.33 0.53 0.31 0.2 ‐0.11 ‐0.17 1

(13) Managerial ow nership*Public f irm 0.05 0.13 0 2.18 0.01 ‐0.01 0.05 ‐0.01 ‐0.01 0 0.07 0 ‐0.05 0.07 ‐0.05 0.52 1

(14) Outsider-insider ratio*Public f irm 0.14 0.36 0 5 0.03 ‐0.01 0.1 0 0.02 0.13 0.21 0.12 0.1 ‐0.08 ‐0.09 0.6 0.43 1

(15) Multi-market contact 0.02 0.05 0 0.17 0.05 0.02 0.19 0.01 0.04 0.24 0.31 0.58 0.12 ‐0.05 ‐0.04 0.25 ‐0.02 0.08 1

(16) Acquisitions in new market 1.02 1.78 0 10 0 ‐0.05 ‐0.02 ‐0.01 ‐0.06 0 ‐0.02 ‐0.06 ‐0.04 0.01 0.01 0 0.02 0 ‐0.03 1

(17) Count of f irms in new market 23.13 19.98 0 82 0.01 ‐0.06 ‐0.02 ‐0.02 0 ‐0.01 ‐0.03 ‐0.07 ‐0.04 0.02 0.02 ‐0.02 0.01 ‐0.01 ‐0.03 0.65 1

(18) Novel innovation: stock in new market 0 1 ‐2.75 2.35 ‐0.02 0.11 ‐0.02 0.03 0.1 0.04 0.01 0.03 ‐0.06 0.03 ‐0.01 0.04 0.04 0.01 0.07 ‐0.06 ‐0.03 1

(19) Incremental innovation: stock in new market 0 1 ‐0.5 3.67 ‐0.01 ‐0.04 ‐0.04 ‐0.14 ‐0.06 0.02 ‐0.01 ‐0.07 ‐0.07 0.02 0.01 0.01 0.03 0 ‐0.04 0.68 0.76 0 1

(20) Imitation: stock in new market 0 1 ‐2.18 3.13 0.01 0.09 ‐0.05 0.09 ‐0.02 ‐0.01 0 ‐0.04 0.06 ‐0.04 ‐0.04 ‐0.04 ‐0.04 ‐0.02 ‐0.01 ‐0.03 0.1 0 0 1

(21) Novel innovation: f low in new market 0.91 1.23 0 6 0 0.07 ‐0.01 0.09 0.13 0.01 0 ‐0.01 ‐0.03 0.01 0.01 0.01 0.02 ‐0.01 0.01 0.16 0.26 0.43 0.17 ‐0.1 1

(22) Novel innovation: f low in new market 5.14 11.45 0 68 0.01 ‐0.04 ‐0.02 ‐0.07 ‐0.03 ‐0.01 ‐0.03 ‐0.06 ‐0.03 0 0.02 ‐0.04 0.01 ‐0.01 ‐0.04 0.39 0.6 0.05 0.48 0.16 0.24 1

(23) Novel innovation: f low in new market 8.2 15.22 0 98 0.01 0.02 ‐0.05 0.11 0.04 ‐0.01 0 ‐0.07 0.02 ‐0.02 ‐0.02 ‐0.03 ‐0.01 ‐0.02 ‐0.04 0.31 0.56 0.09 0.26 0.47 0.28 0.43 1

(24) Acquisitions in current market 1.97 2.25 0 10 ‐0.02 ‐0.03 ‐0.04 0 0.11 0.01 ‐0.02 0 ‐0.03 0.04 ‐0.07 0.01 0.07 0.04 0.02 0.03 ‐0.01 0.15 0.04 ‐0 0.02 0.03 ‐0.11 1

(25) Count of f irms in current market 38.06 26.43 2 82 ‐0.03 ‐0.05 ‐0.07 ‐0.06 0.08 0.05 0.01 0.01 ‐0.07 0.05 ‐0.1 0.04 0.08 0.05 0.02 0.04 0.02 0.33 0.12 ‐0.1 0.09 ‐0.02 ‐0.12 0.73 1

(26) Novel innovation: stock in current market 0 1 ‐1.28 3.5 ‐0.02 0 ‐0.02 ‐0.13 ‐0.1 0.08 0.01 0 ‐0.03 ‐0.01 ‐0.02 0.02 ‐0.03 ‐0.03 ‐0.02 0.07 0.11 0.14 0.22 ‐0.1 0.05 0.03 ‐0.08 0.14 0.28 1

(27) Incremental innovation: stock in current market 0 1 ‐0.68 2.18 ‐0.02 ‐0.03 ‐0.03 ‐0.07 0.14 0 ‐0.01 0 ‐0.07 0.08 ‐0.06 0.04 0.09 0.07 0.02 0.03 0.01 0.33 0.1 ‐0.2 0.09 ‐0.06 ‐0.16 0.66 0.87 0 1

(28) Imitation: stock in current market 0 1 ‐2.96 2.91 ‐0.02 ‐0.04 ‐0.1 0.04 0.04 0.06 0.03 ‐0.02 0.01 ‐0.04 ‐0.12 ‐0.03 ‐0.03 ‐0.05 0 ‐0.05 ‐0.02 0.11 ‐0.08 0.13 0.03 ‐0.02 0.14 0 0.27 0 0 1

(29) Novel innovation: f low in current market 0.84 1.09 0 4 ‐0.01 ‐0.03 ‐0.05 ‐0.11 ‐0.07 0.05 0.01 ‐0.01 0 ‐0.02 ‐0.07 0 ‐0.02 ‐0.03 0 ‐0.02 0.01 0.1 0.06 0.01 ‐0.02 0 ‐0.03 0.16 0.37 0.55 0.16 0.27 1

(30) Novel innovation: f low in current market 9.32 16.42 0 68 ‐0.02 ‐0.02 ‐0.02 0.02 0.14 0 ‐0.02 0.01 ‐0.01 0.02 ‐0.09 ‐0.01 0.03 0.04 0.03 ‐0.04 ‐0.04 0.03 ‐0.03 0.1 ‐0.01 0.11 ‐0.09 0.74 0.56 0.2 0.43 0 0.31 1

(31) Novel innovation: f low in current market 11.25 11.51 0 45 ‐0.03 ‐0.05 ‐0.05 ‐0.01 0.17 0.01 ‐0.01 ‐0.01 ‐0.03 0.03 ‐0.12 0 0.03 0.04 0.02 ‐0.05 ‐0.04 0.16 0 0.03 0.03 ‐0.01 ‐0.09 0.76 0.79 0.22 0.67 0.2 0.39 0.77 1

Table 3. Summary of data for performance analysis Mean S.D. Min Max (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)

(1) New product introduction 1.1 2.6 0 22 1

(2) Diversification 0.6 0.49 0 1 ‐0.03 1

(3) Total capabilities 1.04 1.91 0 10.73 0.19 0.19 1

(4) Growth_rate_3 0.1 0.16 ‐0.15 1 0.03 ‐0.08 ‐0.07 1

(5) Age 3.61 0.98 0 5.1 ‐0.12 0.19 ‐0.07 0.02 1

(6) Size 3.04 1.05 1 4 0.04 0.2 0.34 ‐0.01 0.55 1

(7) Public firm 0.72 0.45 0 1 ‐0.02 0.19 0.23 0.01 0.27 0.48 1

(8) Managerial ownership*Public firm 0.05 0.09 0 0.57 0.01 ‐0.07 ‐0.03 0.06 ‐0.23 ‐0.24 0.36 1

(9) Outsider-insider ratio*Public firm 0.31 0.39 0 4.67 0.02 0.01 0.02 0.05 0.26 0.29 0.5 0.18 1

(10) Acquisitions in new market 1.7 2.32 0 10 0.01 0.16 0.05 ‐0.08 ‐0.07 ‐0.04 0.04 0.04 ‐0.07 1

(11) Count of firms in new market 36.06 23.25 0 82 0.07 0.2 ‐0.01 ‐0.06 ‐0.07 ‐0.07 ‐0.01 0.03 ‐0.11 0.68 1

(12) Novel innovation: stock 0 1 ‐1.62 2.37 0.07 0.12 0.15 0.15 ‐0.06 0.01 ‐0.11 ‐0.12 ‐0.15 ‐0.07 ‐0.03 1

(13) Incremental innovation: stock 0 1 ‐0.69 2.35 0 0.23 0.11 ‐0.21 ‐0.11 ‐0.04 0.03 0.04 ‐0.1 0.72 0.8 0 1

(14) Imitation: stock 0 1 ‐3.18 2.15 0.12 ‐0.16 ‐0.19 0.08 ‐0.01 ‐0.15 ‐0.2 ‐0.03 0.02 ‐0.06 0.06 0 0 1

(15) Novel innovation: flow 1.13 1.31 0 6 0.13 0.03 0 0.05 ‐0.06 ‐0.04 ‐0.07 ‐0.06 ‐0.08 0.1 0.27 0.42 0.09 ‐0.01 1

(16) Novel innovation: flow 10.17 16.6 0 68 0.15 0.08 ‐0.05 ‐0.11 ‐0.06 ‐0.12 ‐0.05 0.03 ‐0.06 0.4 0.58 0.14 0.42 0.07 0.27 1

(17) Novel innovation: flow 18.84 22.74 0 98 0.24 ‐0.11 ‐0.16 0.05 ‐0.06 ‐0.13 ‐0.2 ‐0.09 ‐0.01 0.15 0.35 0.05 0.05 0.53 0.26 0.25 1

Table 4. Results for diversification decision (Dependent variable: Diversifying entry)

(1) (2) (3) (4) (5)

Controls only Capabilities

Full model

Capability types

Piecewise exponential

Market relatedness 1.088** 1.113** 1.056* 1.058* 1.040** (0.410)** (0.412)** (0.414)* (0.413)* (0.403)**

Pre-entry capabilities 0.379** 0.398*** 0.204** 0.410*** (0.124)** (0.114)*** (0.065)** (0.118)***

Growth new market -1.700 -1.644 -1.789 (1.236) (1.250) (1.128)

Relative demand in current market

3.108** 3.028** 3.158** (1.139)** (1.156)** (1.076)**

Acquired experience 0.063 (0.056)

Age -0.260 -0.121 -0.099 -0.008 -0.147 (0.199) (0.206) (0.195) (0.234) (0.211)

Size_2 1.360** 1.313** 1.275* 1.224* 1.307* (0.516)** (0.505)** (0.499)* (0.497)* (0.525)*

Size_3 1.233+ 1.001 1.088+ 0.974 1.125+ (0.641)+ (0.652) (0.622)+ (0.622) (0.664)+

Size_4 1.095 0.601 0.471 0.349 0.623 (0.722) (0.764) (0.765) (0.735) (0.796)

Existing scope 0.441*** 0.257+ 0.244+ 0.235+ 0.236 (0.112)*** (0.143)+ (0.138)+ (0.137)+ (0.145)

Pharmaceutical firm 1.180* 1.359** 1.376** 1.343* 1.410** (0.493)* (0.520)** (0.534)** (0.535)* (0.523)**

Primary business in medical device

1.086** 1.009** 0.961** 0.997** 0.981** (0.386)** (0.375)** (0.366)** (0.363)** (0.376)**

Foreign firm 0.575 0.582 0.612 0.641+ 0.582 (0.361) (0.379) (0.375) (0.379)+ (0.382)

Public firm 1.193* 1.206* 1.195* 1.189* 1.223* (0.531)* (0.522)* (0.507)* (0.497)* (0.530)*

Managerial ownership *public firm

-1.724 -1.806 -1.857 -1.657 -2.017+ (1.233) (1.150) (1.157) (1.180) (1.108)+

Outsider-insider ratio *public firm

0.380 0.365 0.388 0.378 0.412 (0.319) (0.313) (0.310) (0.309) (0.341)

Multi-market contact 4.918* 5.663* 5.480** 5.243* 5.094* (2.375)* (2.331)* (2.109)** (2.155)* (2.283)*

Acquisitions in new market

0.076 0.079 0.125 0.123 0.060 (0.116) (0.111) (0.112) (0.112) (0.104)

Count of firms in new market

0.062*** 0.056** 0.074*** 0.071*** 0.058** (0.018)*** (0.018)** (0.019)*** (0.020)*** (0.020)**

Novel innovation: stock in new market

-0.969** -0.917** -1.033** -1.067** -1.030** (0.341)** (0.331)** (0.333)** (0.328)** (0.352)**

Incremental innovation: stock in new market

-2.209*** -2.152*** -2.453*** -2.434*** -2.219*** (0.446)*** (0.433)*** (0.448)*** (0.458)*** (0.440)***

Imitation: stock in new market

-1.178** -1.197** -1.241** -1.220** -1.249*** (0.369)** (0.368)** (0.377)** (0.389)** (0.375)***

Novel innovation: flow in new market

-0.080 -0.061 -0.085 -0.078 -0.060 (0.119) (0.120) (0.118) (0.116) (0.111)

36

Incremental innovation: flow in new market

0.029* 0.031* 0.029* 0.029* 0.032** (0.012)* (0.013)* (0.013)* (0.013)* (0.012)**

Imitation: flow in new market

-0.068*** -0.065*** -0.074*** -0.074*** -0.068** (0.018)*** (0.017)*** (0.017)*** (0.017)*** (0.021)**

Acquisitions in current market 0.007 0.018 0.026 0.026 0.076 (0.144) (0.135) (0.144) (0.146) (0.160)

Count of firms in current market

-0.012 -0.016 -0.007 -0.011 -0.014 (0.025) (0.024) (0.023) (0.024) (0.023)

Novel innovation: stock in current market

-0.057 -0.011 -0.048 0.002 -0.026 -0.258 -0.254 -0.257 -0.258 -0.225

Incremental innovation: stock in current market

-0.021 0.021 -0.199 -0.108 -0.126 -0.57 -0.564 -0.578 -0.594 -0.554

Imitation: stock in current market

0.013 0.076 0.02 0.057 0.026 -0.269 -0.262 -0.265 -0.261 -0.269

Novel innovation: flow in current market

0.139 0.152 0.256 0.244 0.267 (0.242) (0.226) (0.215) (0.214) (0.222)

Incremental innovation: flow in current market

-0.042+ -0.041+ -0.050* -0.047* -0.050* (0.022)+ (0.022)+ (0.022)* (0.022)* (0.021)*

Imitation: flow in current market

0.032 0.035 0.031 0.028 0.036 (0.034) (0.034) (0.037) (0.038) (0.037)

Market dummies Y Y Y Y Y

Time pieces Y

Number of observations 13424 13424 13424 13424 13424

Number of firm-markets 1591 1591 1591 1591 1591

Number of firms 274 274 274 274 274

Number of events 77 77 77 77 77

Log Likelihood -390.677 -386.373 -380.618 -379.946 -189.595

Chi Square 328.69 425.199 487.561 688.093 5119.434 + significant at 10%; * significant at 5%; ** significant at 1%; *** significant at 0.1%. Two-tailed test. Robust standard errors are in parentheses.

37

Table 5. Results for performance (Dependent variable: No. of FDA approvals in each firm-market-year)

(1) (2) (3) (4) Controls only Full model Poisson Two step

Diversification -0.444** -0.422** -0.487** (0.150)** (0.141)** (0.156)**

Total capabilities 0.142** 0.154** 0.149*** 0.125* (0.053)** (0.052)** (0.040)*** (0.051)*

Demand growth -0.043 -0.074 0.041 0.031 (0.307) (0.298) (0.278) (0.301)

Age 0.330 0.528* 0.571** 0.287 (0.228) (0.208)* (0.181)** (0.214)

Size_2 0.166 0.142 0.023 0.042 (0.262) (0.241) (0.284) (0.241)

Size_3 1.006* 1.009** 0.921* 0.799* (0.417)* (0.385)** (0.397)* (0.394)*

Size_4 1.188* 1.126* 1.091* 0.940+ (0.513)* (0.482)* (0.448)* (0.488)+

Public firm -1.145** -1.226** -1.015* -1.897*** (0.418)** (0.391)** (0.406)* (0.419)***

Managerial ownership*public firm 2.247 2.032 1.792 2.366+ (1.461) (1.473) (1.247) (1.384)+

Outsider-insider ratio*public firm -0.185 -0.203 -0.221** -0.200+ (0.128) (0.135) (0.085)** (0.121)+

Acquisitions -0.012 -0.014 -0.005 -0.022 (0.031) (0.031) (0.025) (0.032)

Count of firms 0.052** 0.051** 0.053*** 0.047** (0.016)** (0.016)** (0.015)*** (0.016)**

Novel innovation: stock -0.216 -0.256 -0.21 -0.329 -0.263 -0.269 -0.257 -0.272

Incremental innovation: stock -0.596+ -0.584+ -0.663* -0.607+ (0.340)+ (0.342)+ (0.324)* (0.339)+

Imitation: stock -0.292 -0.364 -0.398+ -0.354 -0.222 -0.241 (0.229)+ -0.235

Novel innovation: flow 0.039 0.032 0.035 0.042 (0.034) (0.033) (0.031) (0.035)

Incremental innovation: flow 0.002 0.003 0.004 0.002 (0.006) (0.006) (0.006) (0.006)

Imitation: flow -0.005 -0.004 -0.005 -0.003 (0.006) (0.006) (0.005) (0.006)

Selection hazard -0.791** (0.288)**

Market & year dummies Y Y Y Y

Constant -22.421*** -21.956*** -25.130*** -19.773*** (2.082)*** (2.003)*** (1.986)*** (2.213)***

Number of observations 1446 1446 1446 1446

Number of firm-markets 98 98 98 98

Number of firms 46 46 46 46

Log Likelihood -1277.254 -1271.357 -1310.013 -1264.594

Alpha 0.277 0.262 0.254 + significant at 10%; * significant at 5%; ** significant at 1%; *** significant at 0.1%. Two-tailed test. Robust standard errors are in parentheses.

38

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