GENERAL TECHNOLOGICAL CAPABILITIES, PRODUCT MARKET FRAGMENTATION · PDF file ·...

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GENERAL TECHNOLOGICAL CAPABILITIES, PRODUCT MARKET

FRAGMENTATION, AND MARKETS FOR TECHNOLOGY: EVIDENCE

FROM THE SOFTWARE SECURITY INDUSTRY

Alfonso Gambardella Università Bocconi, Milan, Italy

[email protected]

Marco S. Giarratana Universidad Carlos III de Madrid

[email protected]

January 2011

Abstract

The combination of a firm capability (i.e., the ability to generate technologies with many downstream applications) and a market structure condition (i.e., fragmentation of downstream product markets) may encourage licensing. A proposed model identifies conditions that increase the probability of licensing when product markets are fragmented and technologies support general purposes. Evidence consistent with these predictions emerges from a 1993–2001 panel of 87 firms that owned at least one U.S. software security patent between 1976 and 2001. The analysis uncovers some fundamental characteristics of the functioning of external knowledge exploitation; in particular, technology markets thrive when product markets are fragmented and firms have the capability to produce general technologies.

Keywords: Markets for technology, Licensing, Generality, Market Fragmentation, Software

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Introduction

The commercial exploitation of knowledge is a main tenet of knowledge management theory (Argote,

McEvily, and Reagans 2003; Reitzig and Puranam 2009; Teece 1986), for which the exploitation of

external knowledge—particularly so-called markets for technology—is critical. From 1980 to 2003 in

the G8 countries, technology royalty payments and receipts increased annually by an average of

10.7% and reached an annual volume of approximately US$190 billion in 2003 (OECD, 2006). Case

study evidence also has stressed the increasing importance of business models that focus on the

external exploitation of knowledge (Arora and Gambardella 2010; Gans and Stern 2010).

This line of research generally follows a classical Williamsonian framework that hinges in

particular on the interplay between transaction costs and licensing decisions (Arora and Ceccagnoli

2006; Gans, Hsu, and Stern 2002, 2008; Dushnitsky and Klueter 2011). Recent contributions have

established clearer roots in firm based research (Fosfuri 2006; Gambardella, Giuri, and Luzzi 2007;

Lichtenhaler and Lichtenhaler 2009) and shifted the focus from exogenous conditions in which firms

decide to license to firm-based determinants of licensing. The central role of firm strategy and

resources for knowledge exploitation is clearly apparent in the comments of the CEO of Peregrine

Pharmaceuticals (www.peregineinc.com), who acknowledged that the company’s “strategy for

clinical development is designed to maximize the licensing potential of our broad platform

technologies. Our broad patent coverage ... gives us the ability to license and collaborate with many

partners” (Business Wire 2000: 12).

In this context, we adapt a capability-based view (Teece 2007; Winter 2003) to the problem of

the exploitation of external knowledge and argue that an important determinant of licensing is the

firm’s capability to produce general-purpose technologies (Bresnahan and Trajtenberg 1995;

Rosenberg, 1976). We define general-purpose technologies as those that embrace many different

product market applications (e.g., Bresnahan and Gambardella 1998; Maine and Garsney 2006; Von

Hippel 1994). In contrast, a dedicated technology is perfectly suited for the application for which it is

created (i.e., no adaptation effort) but not very useful for other applications.

We develop a simple model to explain why a firm that produces a proprietary technology

might decide to license it, in which firms with a greater ability to produce general technologies are

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more likely to license when the downstream product market is fragmented and composed of distinct

product niches.

An increasing number of firms appear to be betting on their capability to produce such general

technologies (Gambardella and McGahan 2010; Thoma 2009), and many of them link their capability

to strategies that combine technology licensing and internal product development. In a typical trade-

off, the former strategy penalizes the latter, because licensing encourages product market competitors.

Therefore, we consider an environmental condition that makes the ability to produce general

technologies valuable, because it enables firms to enjoy revenues from licensing without penalizing

their product market stakes. Our model intuition is simple: When product markets are fragmented,

licensors can issue licenses to other firms that operate in market niches in which they do not compete

directly, though that scenario requires the licensor to develop general technologies that can support

distant applications.

We test our theory with data from the security software industry (SSI), a relatively recent,

technology-based industry in which innovation plays a major role and that exhibits a clear vertical

distinction among the market for SSI algorithms, the core technology of a SSI products, and markets

for SSI products or services. For example, Hoovers (www.hoovers.com) classifies SSI revenues in

2002 as sales of software products (52.3%), services (30.3%), and revenues from licensing

technological algorithms (17.4%). Security algorithms also exhibit different degrees of generality.

Those that are more specific to particular domains tend to be more effective in their realm but not

applicable to many other domains (The Economist 2007). Moreover, SSI firms often patent their new

software algorithms, which refer to specific technology classes and thus allow for greater precision in

technology proxies. Finally, though SSI started with few submarket niches, it has exhibited niche

proliferation over time.

Our sample of potential technology suppliers consists of 87 firms with at least one SSI patent

with the U.S. Patent and Trademark Office (USPTO) during 1976–2001. Our regressions predict the

hazard that a firm will sell its technology, using a 1993–2001 panel data set. We use proxies for

product market fragmentation and firm capability to determine general technologies, along with

standard controls. Moreover, our analysis relies on patent and licensing data specific to each SSI.

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With these unique data, we find that the probability of licensing increases with product market

fragmentation and the generality of the licensor’s technology, and that the two factors reinforce each

other.

Our article therefore offers two main contributions. First, we contribute to literature on

knowledge management, especially the stream dedicated to external knowledge exploitation (Argote

el al. 2003; Lichtenhaler and Lichtenhaler 2009). Within this realm, we focus on the role of firm-

based determinants. For example, Fosfuri (2006) notes the role of firm market shares in a product

market, Gambardella et al. (2007) consider firm size, and Lichtenthaler, Ernst, and Hoegl (2010)

examine employees’ mental models. We introduce a capability framework (Teece 2007; Winter 2003)

and show that technological capability at the firm level can explain most heterogeneity in licensing

outcomes. The choice of a simple model naturally derives from a setting that analyzes a market with a

demand and supply for a technology. Our model discovers the importance of the joint occurrence of a

general technology and a downstream fragmentation to understand the probability of a licensing

agreement. Following a recent stream in organization studies (Dushnitsky 2010), we think that for

organization scholars a formal approach could constitute a very intuitive tool to capture significant

effects inside a complex environment.

Second, we stress an important link between technological capability and downstream

industry structure. In this respect, our finding replicates a classical theorem of the capability-based

view that highlights the co-evolution of firm capabilities and environmental conditions (Argote et al.

2003; Sorenson 2003). An improved understanding of the relationships between the firm’s capability

to create general technologies and the structure of downstream markets also contributes to literature

on the relationship network among firms that use open innovation approaches (e.g., Chesbrough 2003;

Laursen and Salter 2006) or across broader, more complex industry architectures and firm capabilities

(Jacobides, Knudsen and Augier 2006; Jacobides and Winter 2005).

In the next section, we provide a literature review, followed by our theoretical model. We

then describe the major features of SSI and present our data and empirical evidence. We conclude

with some implications and further research directions.

Theory

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Background

As Teece (1986) establishes, knowledge management studies should involve not only how to capture

value from innovation but also, and more precisely, which conditions make external exploitation (i.e.,

technology licensing) more appropriate than internal exploitation (i.e., technology embedded in final

products). Early economics research focused on the role of transaction costs in shaping technology

markets. In particular, transaction costs encompass the search costs of finding a partner, fear of

opportunism, and lack of valid knowledge protections (Cockburn, MacGarvie, and Mueller 2010). To

solve the transaction cost problem, Arora and Ceccagnoli (2006), Hall and Ziedonis (2001), and Gans

et al. (2002) suggest stronger intellectual propriety rights (IPRs), such that firms should be more likely

to sell their technologies if IPRs are well defined and protect sales of the technology. In contrast, if

IPRs are weak or ill defined, firms can earn rents from technology only by incorporating it into their

own final products (McGahan and Silverman 2006).

Management scholars extend the IPR notion by considering how firm characteristics, such as

market share, size, and human resource strategies, might influence their ability to exploit knowledge

externally (Fosfuri 2006; Gambardella et al. 2007; Lichtenthaler 2007). These contributions reflect a

classical approach of knowledge management theory that highlights two important facets (Argote et

al. 2003): firm characteristics and their fit with some exogenous feature (e.g., IPR context). We base

our theory on a capability view (Winter 2003) that uses firm capabilities to explain heterogeneity in

firm actions and performance. We analyze the interplay between a firm’s capability to generate

knowledge with a particular property, that is, a general technology, and the structure of downstream

markets that may use that technology.

Model

We study an industry in which the product market consists of m submarkets. To operate in a

submarket, a firm must employ a technology. Some firms own one or more technologies that enable

them to operate in one or more submarkets; others do not own any technology, so to operate in a

submarket, they must buy a license. To simplify the model, we assume that if a firm owns the

technology to operate in a submarket, it will not buy a license for it.

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There are n1, n2, …, nm firms in each submarket, such that n ≡ (n1, n2, …, nm) denotes the

corresponding vector. We define d(i) ≡ (di1, di2, …, dij, …dim), i = 1, … m, as the vector of distances in

a product space between a product in submarket i and a product in submarket j = 1, 2, …, m. With

d(i), we attempt to capture the extent to which any factor of one submarket affects the profits of firms

in other submarkets. A natural interpretation of these distances therefore relies on the elasticities of

substitution, such that dij relates to the inverse of the elasticity of substitution between products in two

submarkets. When this elasticity is higher, the competition between products in i and j is stronger. We

also think of dii as related to the inverse of the elasticity of substitution among products in the same

submarket. It is natural that dii ≤ dij, because there should be more intense competition within the same

submarket than across them. We define a more fragmented market as one in which at least one

element dij of d(i) is higher and no element is smaller.

Licensor’s product market profits

We define ( )( ) ( );A

iπΠ ≡ ∑n d n as the profits of a firm that owns technologies to operate in a subset

A of m submarkets, where π(d(i); n) equals the profits in the generic product submarket i. Greater

fragmentation may have two effects: On the one hand, it might increase profits π(d(i); n) because of

the greater differentiation between products in i and those in other submarkets. On the other hand, a

low elasticity of substitution is typically associated with products with different customers or

characteristics, so greater distance may imply fewer synergies among the operations of firms in the

submarkets in which the focal firm operates (e.g., fewer economies of scope in production or

distribution). The net effect depends on the relative strength of the two forces. Using subscripts to

denote the derivatives, we allow πd to indicate a change in π due to increased fragmentation, that is,

in at least one element of d(i).

Because more competitors reduce profits, the notation πn ≤ 0 represents the first derivative of

π when one element of the vector n increases by one unit. Moreover,

( ) ( ) ( ) ( )[ ] ( )' '

1 1 0A A A

π π π−

Π + −Π ≡ Π ≡ + − − ≤∑ ∑nn n n n n ,

where n + 1 means that one element of the vector n increases by one unit; A′ is the set of submarkets

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in which the firm operates when the set of competitors is n + 1; A′ ⊆ A, because as n increases, profits

may become negative and induce exit from some submarkets in which the firms operated before the

new entry; and A – A′ denotes the set of submarkets in A but not in A′. Furthermore, πn ≤ 0, and in the

submarkets in which the firm operates, π(n) ≥ 0, so 0Π ≤n .

Demand for license

A potential licensee identifies a technology, owned by a potential licensor operating in submarket i,

that it can use to sell its products in submarket j. If the licensee buys the technology from this licensor,

its net profits in product submarket j are

( )( ); ; ( ) ( )Bij z p i iπ ε− +d n , (1)

where zi is a measure of the generality of the technology, p(i) is the price of the license, and ε(i) is a

stochastic term. Following our prior discussion, the generality of the technology stems from the

licensor’s capabilities to generate a technology with these characteristics. The other arguments in πB

mirror the structure of the licensor’s profits. Because the licensee operates in j, the relevant vector of

distances is d(j), and n is the vector of competitors in the whole market. As in the previous section,

the effect of fragmentation is ambiguous. On the one hand, a technology originally conceived for a

submarket i should be employed in a more distant submarket, and because technological and

commercial distances correlate, πB declines. On the other hand, the licensee is more distant from the

licensor’s submarket in the product space, which increases πB.

Our key assumption about zi is that 0Bzπ ≥d , which stems naturally from our definition of a

more general technology. Generality makes it less critical that the technology originates in one or

another submarket. As a result, the reduction in πB, which emerged because the technology was

concocted for a more distant product, should be less pronounced if the technology is not dedicated to

the licensor’s submarket.

We make no assumption about the direct effects of a more general technology, such as on

Bzπ . Because a more general technology represents an abstraction from specific applications, it is

probably less effective for any specific application than a technology dedicated only to that

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application. This reasoning further suggests that if the licensee is closer in the product space, a more

general technology has a negative direct impact ( 0Bzπ < ), but the opposite is true if the licensee is

distant ( 0Bzπ > ). Finally, the generality of the technology is exogenous; we assume it is a capability

the firm accumulated in the past.

The probability of buying the technology equals the probability that πB(i) – p(i) – ε(i) ≥ 0Bπ +

ε(0), where the left-hand side of this inequality highlights the origin i of the technology among the

arguments of the πB function in Equation (3), 0Bπ is the profits the potential licensee would derive

from an activity it would conduct if it did not buy the technology, and 0ε is a corresponding

stochastic term. If we assume that ε(i) – ε(0) follows a logistic distribution, the expression for the

probability of buying the technology of the ith licensor is the logit probability:

( )

( )exp ( ) ( )

( )exp (0) exp ( ) ( )

B

B B

i p ii

i p iβ π

ρβπ β π

⎡ ⎤−⎣ ⎦≡⎡ ⎤ ⎡ ⎤+ −⎣ ⎦ ⎣ ⎦

. (2)

The parameter β ≥ 0 controls for the degree of substitution between the two activities. Thus, if β = 0,

ρ = 0.5, whereas if β → ∞ and πB(i) – p(i) > 0Bπ , ρ(i) → 1, but ρ(i) → 0 if πB(i) – p(i) < 0

Bπ .

Licensor’s problem

The licensee approaches the licensor, which issues a price for the license. We assume a take-it-or-

leave-it offer. The licensor chooses the price to maximize its expected profits, taking into account the

following trade-off (Arora and Fosfuri, 2003): If it licenses, the licensor earns revenues equal to the

price of the technology p. Yet by licensing, it also creates a competitor, so its product market profits

become ( +1)Π n . The licensee will not buy the technology if its opportunity cost is higher than the

product market profits earned from the technology, net of the technology price. In this case, the

licensor earns ( )Π n . The maximization problem of the licensor therefore is

( ) [ ] ( )1 ( ) ( 1) ( ) n n np

Max p p xρ ρ ρ− ⋅Π + ⋅ + Π + ≡ Π + ⋅ − , (3)

in which we drop the subscript i, x ≡ – Πn , and ρ is equal to Equation (2). As one could note, we do

not model the bargaining process between the licensor and the licensee. We assume that the licensor

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owns a unique technology offered in monopolistic conditions, and the price of the technology is

bounded by the opportunity cost of the licensee. This means that our theory is perfectly suited in

strong IPRs regimes.

We abstract from the number of licenses offered by the licensor, because we want to focus on

the firm’s strategic decision to license its technologies or—especially in the case of smaller firms and

start-ups—to adopt a licensing business model. The key decision thus becomes whether to issue the

first license. When a technology has been licensed, it becomes hard for the licensor to control its

diffusion; the first license triggers the decision to issue more licenses in the future. In turn, the number

of licenses sold is limited by either demand or licensor scale, such as the resources available to

promote the technology or complete licensing contracts.

Given Equation (2), the first-order condition of Equation (3) must be

1 – ( p – x) ⋅β⋅(1 – ρ) = 0, (4)

where ρp = – βρ(1 – ρ). The second-order condition is satisfied. If we take the derivative of Equation

(4) with respect to p (including ρp) and replace p with its expression from Equation (4), the expression

for the second-order condition simplifies to –β < 0.

Testable implications

In this section, we provide three propositions that we test in our empirical analysis. The first pertains

to the effects of technology generality on the probability of licensing; the second involves the effects

of product market fragmentation on the probability of licensing. Finally, in the third proposition, we

posit that the effects of technological generality and product market fragmentation reinforce each

other. In all cases, the key mechanisms are similar: The change in generality or product market

fragmentation affects the optimal price of licensing because it affects the competitive impact of x, πB,

or both. The change in the price of the technology then influences the probability of licensing, and the

change in generality or fragmentation has a direct effect on the profit of the licensee πB: If the buyer

earns more profit when using the technology, the probability of licensing increases.

Proposition 1. If the potential licensee operates in a submarket distant from that of the licensor, the

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probability of licensing increases with its capability to generate a general technology.

Proof. The differentiation of ρ with respect to z yields ρz = ρp⋅pz + BBzπρ π . Because

( )1B pπρ ρ βρ ρ= − = − , ρz = –βρ⋅(1–ρ)⋅(pz – Bzπ ). To obtain pz, we differentiate Equation (4) with

respect to p and z, such that –βδp + (p – x)β BBzπρ π δd = 0. When we replace (p – x) = [β⋅(1 – ρ)]-1 and

( )1Bπρ βρ ρ= − , we can obtain pz = ρ Bzπ . Then, ρz = β⋅ρ⋅(1 – ρ)2⋅ B

zπ . As we discussed previously,

a more general technology thus increases the profits of the licensee if it operates in a distant product

space, such that Bzπ > 0. QED.

The mechanism underlying this proposition states that if the licensee comes from a distant

submarket, Bzπ > 0 raises the price of licensing, because pz = ρ B

zπ , which reduces the probability of

licensing. However, the direct impact of z on πB more than offsets this effect, as implied by the total

impact of z on the probability of licensing, or ρz = β⋅ρ⋅(1–ρ)2⋅ Bzπ . If the potential licensees are

distributed largely in the same submarkets in which the licensors function, the probability of licensing

a more general technology may be lower, because a general technology is less effective than a

dedicated technology for any specific application. As another way to imagine this situation, we note

that technology-holding firms operate in certain product submarkets, and then new, distant

submarkets emerge, for which other firms have better suited downstream assets. If the technology

owned by firms in existing submarkets is general, firms in emerging submarkets demand it, and a

licensing deal is more likely.

Proposition 2. If fragmentation increases the wedge between the licensee’s profits and its competitive

impact on the licensor, (xd – Bπd ) < 0, the probability of licensing increases with the fragmentation of

the product market.

Proof. We differentiate ρ with respect to any element of d, which yields ρd = ρp⋅pd + BB

πρ πd . As in

the previous proof, we use ( )1B pπρ ρ βρ ρ= − = − . To obtain pd, we differentiate Equation (4) with

respect to p and d, which yields –βδp + [β(1 – ρ)xd + (p – x)β BB

πρ πd ]δd = 0. After replacing (p – x) =

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[β⋅(1 – ρ)]-1, we obtain pd = (1–ρ)⋅xd + ρ Bπd . Then, ρd = –β⋅ρ⋅(1 – ρ)2⋅(xd – Bπd ). QED.

This proposition makes no assumptions about the signs of xd or Bπd . A more fragmented

market may produce a stronger or weaker competitive impact x. Moreover, the licensee may be more

distant in product space, which produces xd < 0. However, a higher d implies that the submarkets are

less competitive, in which case a new competitor may confront a higher basis for profits that it must

erode, which could imply xd > 0. Changes in d also may affect the internal economies of the firm; for

example, the firm may exploit fewer synergies across its operations in various submarkets, which may

move x in any direction. Similarly, a higher d may imply the higher profits πB of the licensee, because

it is more sheltered from competition. Yet the licensed technology is also more distant, which may

have the opposite effect. In summary, whether xd or Bπd are positive or negative, this condition

indicates that the change in the rent dissipation xd should be lower than the change in πB. This point

appears in the first-order condition of Equation (4), which says that a change in πB affects ρ and then

(p – x) in the same direction. Thus, if the product submarket of the licensee becomes more profitable,

the licensor extracts more rents from it through a higher optimal price for the license.

Proposition 3. If Propositions 1 and 2 hold and the complementarity between generality and

fragmentation is strong ( zBπ d is high), a more fragmented market produces a greater increase in the

probability of licensing when a firm is capable of generating a general technology (ρdz ≥ 0).

Proof. We differentiate ρd with respect to z, which yields

ρdz = –β⋅(1–ρ)⋅[ρz⋅(1 – 3ρ)⋅(xd – Bπd ) – ρ⋅(1 – ρ)⋅ zBπ d ].

If we use ρz = β⋅ρ⋅(1 – ρ)2⋅ Bzπ and some algebra, we find that ρdz ≥ 0 iff 3ρ2 – 4ρ + b ≥ 0, where

z 0B

B Bz

bxπ

π π= ≥

⋅ −d

d d. This expression is positive when ρ = 0 or ρ = 1, and its minimum occurs at ρ

= 2/3. When ρ = 2/3, the expression is positive if z13

B B Bz xπ π π≥ ⋅ ⋅ −d d d , which is then a sufficient

condition for ρdz ≥ 0. QED.

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According to this proposition, when Propositions 1 and 2 hold, z 0Bπ ≥d is a necessary

condition for ρdz ≥ 0, and if zBπ d is greater than a positive lower bound, the condition becomes

sufficient. Note the difference between this proposition and Proposition 1: Whereas Proposition 1

refers to the distribution of potential licensees across submarkets, Proposition 3 addresses the change

in the probability of licensing a more general technology when a given submarket grows more distant

in product space (i.e., elasticity of substitution is lower).1

We summarize the key intuition of our theoretical model in Figure 1. With a dedicated

technology, the licensee is willing to purchase, but the licensor is unwilling to supply. With a general

technology, the licensee is less keen to buy, even if the licensor is more willing to sell. With a

homogenous (fragmented) market the licensor is less (more) willing to sell because of competition

threats in the product market. Thus, when markets are fragmented, moving from a dedicated to a

general technology increases the probability of licensing.

[FIGURE 1 ABOUT HERE]

Industry and Patents

Industry

As one of the newest segments of the software industry (Giarratana, 2004), SSI has experienced

unprecedented growth in recent years. Since its initiation at the end of the 1980s, the SSI world

market reached US$8.9 billon by 2001, up from US$6.3 billion in 2000 and US$4.4 billion in 1999

(International Data Corporation 2002).

However, SSI technology is older than the SSI market. Cryptography and encryption studies

in computer science research grew considerably during the early 1970s, especially thanks to military 1 The expected probability that a licensor in submarket i licenses to potential licensees in submarkets j = 1, 2, …,

m, is 1

( ; ) ( ; ) ( )m

jE i j i j f j iρ ρ

== ⋅∑ , where ( )f j i is the share of potential licensees from submarket j that

approach licensor i. Because ( )f j i depends on the distribution of firms that can operate in j without a

proprietary technology (not z or d), ( ; )i

E i jz

ρ∂∂

depends only on whether the weights ( )f j i associated with

ρz(i; j) > 0 are greater than those associated with ρz(i; j) < 0. Similarly, whether ( ; )E i jρ∂∂d

or 2 ( ; )

i

E i jzρ∂

∂ ∂d ≥ 0

depends, respectively, on the weight of the submarkets in which ρd or ρdz ≥ 0.

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investments. It was not until the late 1980s that civilian demand for software security products started

rising. The inception of the industry also coincided with the growing market for personal computers

and the development of the Internet, with its Web-based financial transactions and data transmissions.

In turn, growing commercial demand exerted different requirements that broadened the spectrum and

complexity of the products and services offered.

Moreover, SSI has experienced intense entry and exit patterns throughout its existence, which

likely reflect the minimal set-up costs required to initiate an SSI venture. In addition, the industry has

experienced the intense proliferation of product submarkets, ranging from basic security software,

such as virtual private networks, firewalls, and virus scanning, to advanced security services such as

public key infrastructures, security certification, and penetration testing. In Table 1, we list the major

SSI product niches that have arisen in the past 15 years.

[TABLE 1 ABOUT HERE]

Technology embedded in a software product (i.e., the crypto algorithm) can be defined easily

for sale in a stand-alone contract. It is therefore not surprising that SSI has developed an active market

for technology. As we noted previously, in 2002, software products accounted for more than 50% of

SSI sales; an additional 30% came from services, and the rest derived from licensing algorithms.

Patents in security software

Our theory hinges on the assumption of a monopolistic supply of the technology that results when

IPRs are relatively effective. This approach may seem at odds with studies that reveal ill-defined

software patents and argue software technologies should not be patented (Hall 2003). To align our

theory with our empirical setting, we offer evidence that software security companies do protect their

key technologies (algorithms) with patents though.

The most recent comprehensive study on software patents, by Hall and MacGarvie (2007),

employs an event study methodology to discover that changes in the rules for software patentability

(i.e., the In re Alappat decision) have had significant, albeit small, impacts on firms’ abnormal

returns. When these authors estimate a classical Tobin’s q equation, they find that “software patents

are valued the same by the market as ordinary patents before the 1994/1995 change in software patent

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policy, but they are valued twice as much as ordinary patents following the changes” (Hall and

MacGarvie 2007: 30). Mann and Sager (2007), in their analysis of the importance of patents for

software start-ups (but not specifically software patents) find significant correlations between patents

and firm longevity and the firm’s ability to obtain late-stage financing.

The specialized business press also highlights the increasing importance of patenting

algorithms, especially because reverse engineering is rapidly emerging as a major threat to IPR

management for software (Risk Management 2008). Reverse engineering refers to the process of

examining software to determine its internal algorithms and proprietary know-how, then leveraging

this internal knowledge to produce an equivalent product. Sanjay Tandon, the CEO and founder of an

IT security company that develops corporate security defenses, claims that “developers would never

think of shipping software without ... patent protection. It is critical that we patent our sensitive

algorithms from both competitors and malicious users” (Business Wire 2008: 17)

In terms of our reference industry, the technological core of a software security product is its

crypto-algorithm, which specifies mathematical transformations performed on the data. The speed of

mathematical calculations and security levels represent the two main features of SSI products; the

time required by the encryption and decryption processes depends on the length of the mathematical

algorithms and the power of the computing machines (Giarratana 2004). A good algorithm attempts to

minimize the computer time needed to perform transformation of the data at a specified security level

threshold.

Crypto-algorithms typically are the principal object of a firm patent; SSI patents also usually

report step-by-step encryption and decryption routines, as well as detailed descriptions of the

mathematical procedures for performing the encryption. Algorithms vary in their degree of

technological breadth, which suggests not only that software security firms patent algorithms or

technologies more generally but also that significant variability occurs in the generality of these

technologies and the underlying patents. They therefore provide a good proxy of the firm-level

capability to develop more or less general technologies. For example, USPTO number 7,266,845 is a

software security patent entitled “Maintaining virus detection software.” The first page of its abstract

denotes its specificity: “A method of managing a virus signature database associated with an anti-

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virus application, both of which are resident in a memory of a mobile wireless.” Other patents instead

entail broad technologies that can be used in many applications, such as the Elliptic Curve

Cryptography patent of Certicom (USPTO number 6,141,420), which has 52 different principal

claims and protects an algorithm that needs only 160 computer bits to perform all its procedures (a

standard string needs 1,024 bits). Standard systems are based on integer calculus, but the Certicom

algorithm relies on elliptic curves that can be calculated more easily and faster but provide the same

level of security. Thus, the underlying technology is fundamentally a new method for making the

security check quicker and more thorough, and its potential applications are wider.

We performed interviews with four managers of SSI companies and four inventors of

cryptology algorithms who were not employed in our sample firms. They confirmed most features of

our analysis. In particular, these respondents stated that patents were effective for protecting security

algorithms and that patented algorithms normally provided firms with a two- to three-year lead time

over competitors. Very good algorithms could even persist for more than ten years. Most SSI firms

patented without a well-planned ex-ante strategy for selling the technology. The four managers we

interviewed had been approached by technology buyers who had studied their technology (typically

after seeing it in patent documents) and wanted to use it. Therefore, the bulk of the technology market

in SSI, which includes many small technology suppliers, appears demand driven. This situation

mimics our model structure, in which technology buyers select the technology most suited to their

needs.

Empirical Evidence

Sample and dependent variables

We build our sample using the LECG Corptech Patent database (www.lecg.com), which covers

approximately 80,000 software patents granted by USPTO from 1976 to 2001. We select all patents in

U.S. technological classes 380 (“Cryptology”) and 705, subclasses 50–79 (“Business Processing

Using Cryptography”). A sample of 87 firms own at least one of these patents; they provide the best

proxy of an ideal sample of all the U.S. firms that own an SSI algorithm, the core technology in this

industry. We combine this information with data from the SSI Database, constructed by Giarratana

16

(2004), which includes all SSI licensing deals from the beginning of the industry in 1989 until 2001.

The original data source is the Gale Group’s Infotrac Promt (www.gale.com) database (see also

Pennings and Harianto 1992). Promt offers comprehensive and reliable coverage of companies,

products, markets, alliances, and deals from a vast collection of journals, newsletters, news releases,

and newspapers. We construct our database from firm news announcements in Promt, classified under

standard industrial classification (SIC) code 73726, which corresponds exactly to software security,

and firm events identified as “Licensing Agreements.” We read each text to confirm the technological

nature of the contract. Before 1993, the sample firms did not release licensing contracts, so we restrict

our analysis to 1993–2001 and obtain a panel data set of 694 observations from 87 firms over nine

years. Our 87 sample firms released 72.4% of the technology licenses registered in the SSI Database.

With a hazard model, we estimate the probability of selling a technology in year t, conditional

on owning the technology and not having licensed it at t – 1 (hazard rate). We opt for the piecewise

exponential model specification, which does not make strong assumptions about time dependence

(Blossfeld and Rohwer 2002). The hazard model estimates the probability that a firm issues its first

license at any point in time; after the firm issues its first license, it exits the sample so its subsequent

licenses do not affect the estimation (compared to a panel logit approach that determines the

probability of licensing at any point in time). We prefer hazard models because they provide a stricter

test of our theory. As noted in the theory section, the first license is a signal of the firm’s strategic

decision to adopt a licensing business model, in which the firm uses technology licensing, along with

product development, as its strategy to earn profits.

The shadow dependent variable is LICENSEit, which takes a value of 1 if in year t firm i

announces at least one licensing deal as an algorithm seller and 0 otherwise. Of our 87 firms, 36

signed at least one licensing contract as a seller.

Independent variables

In Table 2, we define all the variables in our analysis, along with the descriptive statistics.


Generality of the technology. There is no general agreement about how to proxy generality.

17

We therefore employ two alternative measures. The first, CLAIM_PATit-1, is a proxy of the annual

average number of claims of the firm in software security patents, lagged by one year. We construct

this variable as the ratio between (1) the total number of claims by the firm in the software security

USPTO classes 380 and 705, subclasses 50–79, lagged by one year, and (2) 1 plus the total number of

patents from which the claims are drawn. Several studies argue that the number of claims provides a

direct measure of scope, because claims are attributions of a potential area of application for the

technology (Hall, Jaffe, and Trajtenberg 2001; Lanjouw and Schankerman 2004; Lerner 1994). Our

confidence in this measure is strengthened because USPTO examiners check the consistency of these

claims.

Our second measure, GENERALITYit-1, is the annual average of patents by the firm in the

generality index developed by Hall et al. (2001). We collect this measure from the NBER patent data

Web site (www.nber.org); it equals 1 minus the Herfindhal index of the forward citations to the patent

from the USPTO three-digit patent classes. If a patent is cited by subsequent patents that belong to a

wide range of fields, the measure is higher, whereas if most citations are concentrated in a few fields,

it will be lower. If forward citations indicate the impact of a patent, a high generality score suggests

that the patent presumably has had widespread impact and influenced subsequent innovations in

various fields.

It is pivotal that these two measures be calculated at the firm level to proxy for the general

capability of a firm to produce, on average, more general technologies. In the robustness checks, we

run the same regressions using alternative measures, such as the maximum and mean plus the

standard deviation, to determine if the effect is truly a firm capability or reflective of the ability to

own one, or a few, very general patents.

Product market fragmentation. The variable FRAGM_MKTt equals the annual share of all

products released in the market sold by firms that sell products in only one of the six SSI product

submarkets in Table 1. We construct this measure from information about all products sold by firms

in SSI, not just those of our 87 firms (SSI Dataset). The rationale for this empirical proxy is that a low

elasticity of substitution across submarkets is typically associated with products with different

features, which implies low economies of scope in the downstream assets. In turn, it is likely

18

associated with specialization advantages at the firm level. If the elasticity of substitution across

submarkets is low, we expect a higher share of products sold by firms that operate in only one

submarket as a proportion of the total number of products. By the same token, a high elasticity of

substitution across submarkets implies products that tend to have similar features and thus higher

economies of scope. The specialization advantages then are less pronounced, and firms are more

likely to operate in different submarkets with products that have similar characteristics. Heterogeneity

exists in our measure; as we show in Table 2, the minimum value of FRAGM_MKTt is 0.019, and the

maximum is 0.259. FRAGM_MKTt proxies the parameter d of our model.

Controls. The firm age, calculated as the different between the current year and the

foundation year (AGEi), provides a proxy for firm experience. To control for firm innovations, we use

both firm-level total R&D expenditures (RDit-1) (million USD) and the stock of patents in software

security (PATENTIit-1). The latter equals the number of patents granted to each firm in the USPTO

classes 380 and 705, subclasses 50–79. Both measures are lagged by one year. We construct the

patent stock from the perpetual inventory formula with a depreciation rate of 0.15, as is typical for

constructing the R&D stock (Lanjouw and Schankerman 2004). We use the total number of software

security patents by the firm in 1993 as the initial stock. We experimented with other depreciation

rates, but the empirical results do not change.

We control for downstream co-specialized assets of the firm, according to the share of live

software trademarks as a proportion of total firm trademarks, multiplied by firm sales

(COSPEC_ASSETSit-1), both lagged by one year. Trademarks are combinations of “words, phrases,

symbols, or designs that identify and distinguish the source of the goods (or services)” (USPTO,

www.uspto.gov). A trademark could protect the name of a product, a brand (colors, fonts), and

advertising campaigns. Trademarks could be live or dead, that is, abandoned, cancelled, or allowed to

expire if they have not been renewed after a fixed period. Therefore, trademarks proxy for the

downstream assets of the firms (e.g., Fosfuri, Giarrantana, and Luzzi 2008; Krasnikov, Mishra, and

Orozco 2009); we proxy for software-specific downstream assets because, following Teece (1986),

we argue that co-specialized assets matter for innovation. For example, the type of buyers or

commercial networks may differ significantly across industries, particularly for software, which

19

follows very special channels. We therefore need a measure that reflects the software-specific

downstream capabilities of the firm as closely as possible.2

We employ three dummies to control for the firm’s core business; they take values of 1 if the

core business is software (SIC code 737, SOFTWAREi), hardware (SIC code 357, HARDWAREi), or

electronics (SIC code 359–370, ELECTRONICSi), and 0 otherwise. We use two geographical dummies

that take a value of 1 if the firm headquarters are located in North America (NORTH AMERICAi) or

Europe (EUi), and 0 otherwise.

All these firm data are drawn from Bureau Van Dijk’s Osiris and LECG Corptech data sets.

Finally, given the hazard function, we estimate time according to a piecewise-constant model, in

which we allow the constant rate to vary within predefined time segments, which we designate as

years.

Estimation results

In Table 3, we provide the results of four hazard models (I–IV). The first two models use

CLAIM_PATit-1 as a measure of technological breadth, whereas the other two use GENERALITYit-1.

Models I and III do not include the interaction term between the measure of technological breadth and

FRAGM_MKTt, whereas Models II and IV do.


We corroborate Proposition 1: Firms with the capability to produce more general technology

are more likely to license it. In all models, the impacts of CLAIM_PATit-1 and GENERALITYit-1 are

statistically significant. Proposition 2 also gains support from the data: As the industry becomes more

fragmented, the probability of a technology holder issuing a license increases. The coefficient of

FRAGM_MKTt, multiplied by CLAIM_PATit-1 or GENERALITYit-1, defines how market fragmentation

attenuates or strengthens the effects of technology generality. Our estimation shows that the

interaction covariate has a significant positive impact on licensing probability, in support of 2 To find software trademarks, we applied a search algorithm (“computer software,” “operating system,” “computer program,” “software algorithm,” “data processing,” or “software application”) to the front page of the trademark, which provides the description of the good or service trademarked in the USPTO database. We validated our measure with the share of firm sales in software compared with total revenues, as published by Software Magazine in the Software 500 List (survey data, www.softwaremag.com). For four sample firms that appear on the Software 500 List, the trademark and revenues shares are very similar: H&P 0.28 versus 0.30; IBM 0.52 versus 0.46; Sun Microsystems 0.32 versus 0.35; and Apple 0.22 versus 0.20 (2000 data).

20

Proposition 3.

To clarify our findings, we report the estimates of the multiplier rate of firm hazard of

licensing, conditional on different values of fragmentation, claims per patents, and the generality

index, in Table 4. A multiplier rate of 1 means that a variable has no effect on the licensing hazard

rate, whereas a rate greater than 1 implies that a particular level of the variable increases the chances.

For example, a multiplier rate of 1.3 indicates that a particular level of a variable increases the chance

of licensing by 30%. Because the hazard model is inherently multiplicative, we cannot discuss the

absolute relation between an independent variable and the licensing probability; instead, we analyze

the partial effects relative to the multiplier rates of Table 4. Using the average firm in the market as a

baseline, we note that the multiplier rate is more than 1 when the fragmentation index is at least 11%.

When the claim per patents variable is greater than 7.1, a fragmentation index of 5% is sufficient to

produce a multiplier rate greater than 1. For the generality index, in the case of 15% fragmentation,

only 10% of the generality index is sufficient to generate a multiplier rate greater than 1, whereas

when generality is 21%, the multiplier rate is already greater than 1 at a 5% level of fragmentation. In

Figure 2, we represent this simulation graphically.

[TABLE 4 AND FIGURE 2 ABOUT HERE]

With regard to the controls, we find that the impact of COSPEC_ASSETSit-1 is insignificant. We

predicted that co-specialized downstream assets would have a negative impact on licensing, because

potential licensors are more productive in commercializing products, which means they have more to

lose from the presence of new competitors. We find no impact though, consistent with Gans et al.’s

(2002) finding that complementary asset ownership does not exert any significant effect on the

probability that start-up innovator firms license a technology. The total R&D of the firm and patent

stock in software security do not have sizable or significant impacts on the hazard rate of licensing;

this result is not surprising, because the hazard rate measures the probability that the firm produces its

first license, whereas R&D and patent stock represent better measures of the scale of licensing.

Fosfuri (2006) obtains the same results in the chemical industry regarding the effect of patents on firm

technological licensing. Finally, age has a sizable and significantly negative impact on the hazard rate,

which suggests that licensing in SSI is a business of young companies.

21

Robustness check: Is it truly a firm capability?

One concern related to our empirical analysis is whether our covariates of interest capture the effect of

a general capability of firms (i.e., production of broad technologies), as the theory predicts, or their

ability to produce one or a few broad patents. In short, have we discovered a firm or a patent effect?

To strengthen our evidence in support of the capability view, we run the same hazard model of Table

3 but substitute the firm average values of CLAIM_PATit-1 and GENERALITY it-1 with three alternative

measures: their medians, their maximum values and the means plus the standard deviation. Indeed, the

median is a measure less affected by outliers compared to the mean, while the other two represent a

direct proxy of the presence of outliers. With these checks, we test if our results are determined by

right-tailed values of the distribution of our core variables. If they do, we cannot confirm whether we

were dealing with a true firm capability effect or with the ability to produce a few star patents.


In Table 5, we report the results of the hazard model. Consistent with our arguments, only the

median values show significant results while right-tail proxies do not exert any effect on the hazard of

licensing. These results support the view that the true heterogeneity among firms occurs at the level of

their average capability to produce general technology and that this heterogeneity decreases toward

the right tail of the variable distribution. In summary, the statistically important feature is the ability to

produce general technologies, not the ability (or luck) to own a few general patents.

As a further robustness check, we tested whether generality matters in the case of product

innovations rather than licensing. The market for SSI products is separated from the market of

technology; while algorithms depend on the ability of one or few researchers to produce clever

mathematical or logical structures, successful releases of new products depend on a more complex set

of factors. In Table 6 we report the results of a hazard model in which the shadow dependent variable

is the probability of releasing a new product a time t. Table 6 shows that the key determinants of new

product releases are the market fragmentation and co-specialized assets. Technological generality

does not matter at all in this case, neither linearly nor interacted with FRAGM_MKTt. This suggests

that technological generality and its interaction with fragmentation were genuinely and selectively

capturing factors associated with the licensing decision, and not a trend produced by the growth of the

22

industry in this period.


Conclusions

This article develops a theoretical model that highlights an important but understudied determinant of

external knowledge exploitation (Argote et al. 2003; Reitzig and Puranam 2009): the combination of a

firm’s technology capability (i.e., to produce general purpose technologies) and the fragmentation of

its downstream product market. We test and validate our model using data from the software security

industry, in which technologies and products provide two important sources of revenues and success.

Our work extends a recent research trajectory that highlights the importance of firm-based

determinants of the propensity for licensing, compared with exogenous conditions such as the IPR

regime (Fosfuri 2006; Gambardella et al. 2007; Lichtenthaler et al. 2010). We jointly demonstrate the

importance of a firm’s technological capability and environmental conditions for explaining

heterogeneity in licensing decisions (Winter 2003).

Understanding the link between a technological capability and a market feature has

remarkable significance for researchers and practitioners. Our argument implies that technology

strategy is a complex construct, in which IPR protection and downstream co-specialized assets are not

the only pillars (Teece 1986). Technology entrepreneurs and managers that aim to use external

exploitation as an important strategy to profit from innovation should realize ex ante the importance

of the type of innovation they produce. Firms with the capability to produce general technologies are

better positioned to sell their technologies in markets. In turn, they can invest in the capability to

attain revenues from licensing, on top of any revenue that they may earn from their products. In

periods in which firms struggle to increase their returns from R&D investments, this option is relevant

for both immediate managerial actions and longer-term strategies. As we noted in the introduction,

quite a few firms bet on this capability currently, and this managerial implication holds particular

importance for start-ups (Dushnitsky 2010). Many start-up firms use licensing to raise initial cash

while waiting for the longer-term returns on their investments to produce products. However,

licensing may affect their opportunities to move into product production if their licenses apply to only

23

one or few product domains in which the licensor and licensee would compete directly. The capability

to develop general technologies implies that firms can license without jeopardizing their ability to

enter distant product markets subsequently. Further investigations could also show whether there is a

relationship between this innovation characteristic and the rate of firm creation and survival.

This discussion in turn raises interesting questions about the level at which this capability

could be nurtured within a firm. We have assumed that it is an exogenously given capability, but in

reality, firms could have some degree of maneuverability with regard to strengthening this capability.

Potential avenues of research therefore might consider human resource management issues for

inventors (Singh and Fleming 2010), such as recruiting (if it is a matter of individual exogenous

capability), contract incentives (if inventors have the option of creating general technologies), and the

organization of labor (team versus individual work).

Our study highlights another novel facet of external knowledge exploitation, related to the

degree of fragmentation in the downstream product market. We thus underscore how a firm’s

positioning in innovative and product spaces becomes central to the formulation of its technology

strategy. A good external knowledge exploitation strategy results in greater coordination across the

different divisions of the firm, including coherence in marketing and technology tactics. This greater

coordination requires a continuous flow of information across different departments at a level higher

than that of each different task. Any friction in this information flow or lack of coordination can be

highly detrimental. For example, when R&D and marketing departments are badly connected,

marketing’s recognition of increasing fragmentation in the downstream market still may not generate

incentives to produce more general technology. This reasoning reflects growing literature on the

different impacts of innovation in markets and industries, as highlighted by McGahan and Silverman

(2006) and Bloom, Schankerman, and Van Reenen (2007). Technology transactions, coupled with

distant product markets, might be the mechanisms underlying the empirical effects identified in these

articles. More generally, our analysis highlights the importance of considering both vertical and

horizontal linkages and rivalry in the growth of industries, and it encourages a better understanding of

how technology markets and product market competition interconnect.

Finally, our article complements a classical transaction cost view (Gans et al. 2002, 2008;

24

Teece 1986), in which the effectiveness of IPR is a pivotal exogenous determinant of licensing. There

could be a relationship between the level of generality of a technological portfolio and the real (or

perceived) rate of protection of technology. Further studies should relate technology generality to the

ability to write well-defined licensing contracts or a firm’s bargaining power in negotiation (Arora

and Ceccagnoli 2004). In this case, a more fine-grained analysis on the type of claims of each patent,

classifying for example claims within a product submarket versus claims across product submarkets,

could add new insights on the true level of generality of the technology.

Of course, our study is not without limitations. First, our evidence is based on one industry.

Therefore, we can test our theory with only one IPRs regime, such as SSI, which is ideal because

patents matter in this context. Studies in different industries could attempt to corroborate our theory

when there is stronger competition among technology suppliers. Second, in our model, both the

accumulation of general competencies and industry fragmentation are exogenous. As a consequence,

we do not treat the potential different cost structures of technology production function (general vs.

single). Therefore, future works could investigate more deeply why licensing is a more probable event

when there are one or few general-purpose technology compared to a bundle of different single-

purpose technologies. Even more generally, in the long run, firms may invest in general competencies

if downstream markets are fragmented; they also may employ marketing investments to change

fragmentation into standardization, or vice versa, according to whether they possess dedicated or

general competencies.

We believe that these and other issues offer new avenues for research into the relationships

among the breadth of firm competencies, industry structure (both vertical and horizontal), and firm

strategies for exploiting external knowledge in technology markets.

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Table 1: Product niches in SSI

Niche Description Authentication–digital signature

Products for the authentication of digital documents with a copyrighted mark

Antivirus Programs that detect and clean viruses from computers Data and hardware protection

Products securing the integrity of sensible data stored in hard drivers

Firewalls A kind of checking door between different networks Utility software Utility software programs that assure protection and execution

of operating systems and applications, giving the possibility to recreate the content of some data packages lost

Network security and management

Network security management packages that guarantee the high performance functioning of different networks

Source: Fosfuri and Giarratana (2007); Giarratana (2004).

Table 2: Descriptive statistics

Description Mean St. Dev. Min Max LICENSEit Dummy = 1 if firm has ≥ 1 license in year t 0.123 0.328 0 1 CLAIM_PATit-1 Total # of claims in PATENT divided by (1 +

PATENT) – average by firm and year 7.017 10.316 0 94.75

GENERALITYit-1 Trajtenberg’s index of generality in PATENT– average by firm and year

0.141 0.182 0 0.85

FRAGM_MKTt Share of software security products sold by

29

firms that sell products in only one of the 6 niches in Table 1

0.884 0.094 0,741 0,981

AGEi Firm age in 1993 14.494 22.199 0 117 RDit-1 R&D expenditures of the firm ($ ml) 226.013 721.060 0 6522 PATENTit-1 Stock of # U.S. software security patents of

the firm (see USPTO classes in text), depreciated at 15%

53.605 166.759 0 1361.758

COSPEC_ASSETSit-1 Firm sales times its share of software trademarks on total trademarks ($ ml)

1024.377 3752.077 0 36451.215

SOFTWAREi Dummy =1 if firm core business is in SIC 737

0.563 0.496 0 1

HARDWAREi Dummy =1 if firm core business is in SIC 357

0.126 0.333 0 1

ELECTRONICSi Dummy =1 if firm core business is in SIC 359-370

0.138 0.345 0 1

NORTH AMERICAi Dummy =1 if firm headquarter is in NA 0.851 0.357 0 1 EUi Dummy =1 if firm core business is in

Europe 0.069 0.254 0 1

30

Table 3: Hazard rates for piecewise exponential model of licensing, 1993–2001

Model I Model II Model III Model IV CLAIM_PATit-1 1.537**

(0.238) 1.048** (0.294)

GENERALITYit-1 8.172** (1.857)

4.52** (0.454)

FRAGM_MKTt 5.780** (0.666)

2.550** (0.258)

4.44** (0.532)

5.63** (0.664)

FRAGM_MKTt* CLAIM_PATit-1

1.631** (0.110)

FRAGM_MKTt* GENERALITYit-1

7.83** (1.12)

AGEi 0.510** (0.101)

0.502** (0.096)

0.493** (0.107)

0.504** (0.102)

RDit-1 1.042 (0.176)

1.052 (0.172)

1.091 (0.192)

1.094 (0.185)

PATENTit-1 0.972 (0.181)

0.964 (0.180)

0.900 (0.158)

0.892 (0.156)

COSPEC_ASSETSit-1 1.144 (0.104)

1.148 (0.100)

1.160 (0.113)

1.153 (0.107)

TIME, CORE SECTOR AND GEOGRAPHICAL DUMMIES

YES YES YES YES

Number of observations

694 694 694 694

Log-likelihood 143.458 144.872 147.100 149.580 Notes: Heteroskedastic-consistent standard errors in parenthesis. All variables are log(1 + x). Betas > 1 imply a positive effect.

** p < 0.05. * p < 0.10.

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Table 4: Simulation with multiplier rates according to Table 3 results.

Claims per Patents Fragmentation

Percentage of products sold by firm mono-niche (not

diversified) over total products 6.8 6.9 7 7.1 7.2 7.3

0.05 0.779 0.868 0.966 1.075 1.197 1.332 0.07 0.870 0.969 1.080 1.203 1.340 1.494 0.09 0.970 1.082 1.207 1.346 1.501 1.675 0.11 1.083 1.209 1.349 1.506 1.682 1.878 0.13 1.208 1.350 1.508 1.686 1.884 2.105 0.15 1.348 1.508 1.686 1.886 2.110 2.361

Generality Index Fragmentation 0.1 0.13 0.15 0.17 0.19 0.21

0.05 0.592 0.686 0.757 0.835 0.921 1.016 0.07 0.671 0.782 0.865 0.957 1.059 1.172 0.09 0.761 0.890 0.989 1.097 1.218 1.353 0.11 0.863 1.014 1.130 1.258 1.401 1.560 0.13 0.979 1.156 1.291 1.442 1.611 1.800 0.15 1.110 1.317 1.476 1.654 1.853 2.077

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Table 5: Piecewise hazard rates for piecewise exponential model of probability of licensing, 1993–2001

Model I Model II Model III

Model IV Model V Model VI

CLAIM_PATit-1 (maximum value)

2.112 1.091

CLAIM_PATit-1 (mean+standard deviation)

2.368 1.456

CLAIM_PATit-1 (median value) 1.046**

(0.293)

GENERALITYit-1 (maximum value) 16.590

16.563

GENERALITYit-1 (mean+standard deviation)

5.271 6.345

GENERALITYit-1 (median value) 4.587**

(0.434) FRAGM_MKTt 7.177*

6.618 3.808 4.105

2.552** (0.258)

3.642 3.857

2.857 3.024

5.634** (0.665)


0.003 0.013

0.002 0.014

1.631** (0.109)


0.001 0.002

0.003 0.005

7.843** (1.103)

AGEi 1.071** 0.032

1.068** 0.034

0.504** (0.093)

1.098** 0.044

1.125* 0.070

0.511** (0.103)

RDit-1 0.395 0.858

0.242 0.501

1.050 (0.177)

0.185 0.410

0.180 0.342

1.083 (0.197)

PATENTit-1 0.809 0.151

0.841 0.163

0.966 (0.183)

0.875 0.179

0.890 0.160

0.894 (0.153)

COSPEC_ASSETSit-1 1.045 0.252

1.022 0.224

1.133 (0.167)

1.015 0.252

0.941 0.191

1.153 (0.108)


YES YES YES YES YES YES

Number of observations

694 694 694 694 694 694

Log-likelihood 148.004 148.452 149.33 146.83 147.504 148.37 ** p < 0.05. * p < 0.10.

Notes: Heteroskedastic-consistent standard errors in parenthesis. Variables are log(1 + x).

33

Table 6: Piecewise Exponential Model for probability of product release, 1993–2001 Model I Model II Model III Model IV CLAIM_PATit-1 1.019

(0.176) 0.871

(1.468)

GENERALITYit-1 0.387 (0.464)

6.711 (13.837)

FRAGM_MKTt 6.690** (0.259)

5.040** (0.260)

6.918** (0.263)

1.735** (0.091)


1.198* (2.340)


3.320 (7.757)

AGEi 0.613** (0.085)

0.614** (0.085)

0.611** (0.083)

0.609** (0.084)

RDit-1 1.185 (0.146)

1.187 (0.149)

1.179 (0.145)

1.157 (0.145)

PATENTit-1 1.089 (0.161)

1.088 (0.167)

1.144 (0.161)

1.174 (0.170)

COSPEC_ASSETSit-1 1.151** (0.074)

1.152** (0.074)

1.149** (0.074)

1.150** (0.074)


YES YES YES YES

# of obs. 664 664 664 664 Log-Lik 165.238 165.240 165.461 165.917

** p < 0.05; * p < 0.10. Hetheroskedastic-consistent standard errors in parenthesis. Variables are log(1+x).

35

Figure 2: Graphical Representation of Simulation in Table 4

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