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Using Users: When Does External Knowledge Enhance Corporate Product Innovation?

Prior research on corporate innovation highlights the importance of accessing external knowledge from other firms and universities. However, survey evidence indicates that product users are perhaps the most important source of external knowledge. We build on existing theory to identify the conditions under which user knowledge contributes to corporate innovation and when the benefits will be greatest. Using a panel dataset of medical device companies and their collaborative efforts with innovative physicians, we find evidence that inventive collaborations with users enhance corporate product innovation, and that the benefits are greatest in new technology areas and in the generation of radical innovations. Keywords: innovation strategy, knowledge sourcing, open innovation, health care strategy, intellectual property strategy, R&D management

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Introduction

Collaboration between medical device firms and the physicians that use their products is

economically significant and controversial. For example, according to the U.S. inspector general,

four leading orthopedic device makers spent $800 million on physician consultants between

2002 and 2006.1 Firms claim that physicians are critical sources of knowledge and feedback that

enable the development of innovations, while regulators and policymakers worry that these

arrangements create conflicts of interest where physicians are compensated in return for using or

recommending a particular medical device. These concerns led to a Department of Justice

investigation of the U.S. orthopedic industry in 20052 and spurred new rules governing

transparency of these relationships in the 2010 Affordable Care Act.3 We examine this important

phenomenon through the lens of the academic literature on knowledge management and

innovation, with special emphasis on user innovation. In doing so, we explore not only whether

these collaborations with physicians increase innovative outcomes for medical device firms, but

also the conditions under which the benefits are the greatest. These collaborations are just one

example of the broad set of strategies firms employ to source external knowledge.

A considerable amount of academic research has examined how firms manage

innovation, focusing on both internal and external sources of new ideas (Arora and Gambardella

1990; Grant 1996a, 1996b; Chesbrough 2003; Karim and Mitchell 2004; Cassiman and

Veugelers 2006; Phene et al. 2006; Bercovitz and Feldman 2007; Sampson 2007). Due to the

limitations of developing new knowledge internally (Thompson 1965; Nelson and Winter 1982;

Levitt and March 1988; Christensen and Bower 1996), accessing and integrating external

knowledge is paramount (Cohen and Levinthal 1994; Rosenkopf and Almeida 2003; Laursen and

Salter 2006). While most prior literature focuses on extramural knowledge from other firms and

universities, and has found evidence that sourcing this knowledge is beneficial to the focal firm’s

innovative performance, Cohen, Nelson, and Walsh (2002) identify customers as the most

important source of information for suggesting new projects. A substantial literature on user

innovation (von Hippel 1988; Riggs and Von Hippel 1994; Lilien et al. 2002) explains why this

1 Testimony of Gregory Demske, Assistant Inspector General for Legal Affairs, February 27th, 2008 https://oig.hhs.gov/testimony/docs/.../demske_testimony022708.pdf (Last accessed January 11th, 2013.) 2 For a summary of this investigation and its impact, see Healy and Peterson (2009). 3 For a summary of these provisions from the American Medical Association, please see: http://www.ama-assn.org/resources/doc/cme/sunshine-provisions-sullivan.pdf. (Last accessed February 26th, 2013.)

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variety of external knowledge is unique and potentially valuable. However, few studies have

estimated the impact of external knowledge from product users on corporate innovation, an

important gap in the extant literature. Moreover, we lack theory and systematic empirical

evidence about the conditions under which sourcing external knowledge from users will be most

beneficial for a firm.

To address these gaps in the literature, we integrate theoretical insights from the literature

on knowledge management and innovation to generate predictions about how and when

knowledge from product users increases firms’ innovative performance. We specify conditions

under which external knowledge from users may be especially beneficial, establishing important

contingencies to guide future work on sources of innovation.

To test our predictions, we examine the impact of U.S. medical device firms’ inventive

collaborations with product users on firms’ innovative performance, in the form of new products.

The key product users, physicians, often have valuable and unique knowledge that can help

medical device firms develop new products. As summarized by Marybeth Thorsgaard, a

spokeswoman for Medtronic, ‘[t]he products we develop and manufacture cannot be invented by

trying a new formula in a lab like in the pharma industry. They must be designed and produced

in close collaboration with the men and women who will use them: the world's most highly-

skilled and innovative doctors and surgeons.’4 Despite the contentious debate over the benefits

and risks of relationships between medical device firms and physicians, we have little evidence

on the value of these collaborations.

We utilize a new dataset covering an unbalanced panel of 128 publicly owned medical-

device firms in the United States from 1985 to 1997. We examine the effect of prior

collaborations with physicians, in the form of co-invented patents, on the number of products

approved by the U.S. Food and Drug Administration (FDA), a proxy for innovations. Our results

demonstrate that firm collaboration with physicians is associated with an increase in firm

innovation, as expected. A one-standard-deviation increase in firm-physician collaborations is

associated with 17 percent more innovations. In addition, we find that the benefit of

collaboration depends on the maturity of the technology area; collaborations in new technology

areas are associated with performance benefits, while those in established technology areas are

4 Moore, Janet. “Medical Device Payments to Doctors Draw Scrutiny.” Minneapolis Star Tribune, September 8th, 2008.

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not. Further, we find evidence that collaborations with physicians are especially valuable for

developing radical innovations. Our results are robust to additional analyses that account for

potential endogeneity in the timing and level of firm-physician collaborations and potential

complementarities between physician and non-physician inventions, factors that are typically not

addressed in the literature in this domain. In the next section, we review the relevant prior theory

and develop three testable hypotheses. We then introduce our dataset and empirical context and

conclude with a discussion of the implications of this research.

Theory and Hypotheses

Sources of knowledge for innovation

While firms certainly benefit from an established base of internally developed knowledge, this

same knowledge and the existing organizational practices within the firm can inhibit product

innovation (Nelson and Winter 1982; Anderson and Tushman 1990). Scholars have concluded

that an organization’s prior experience may constrain internal development of substantial novel

inventions and innovations (Nelson and Winter 1982), corporate bureaucracy (Thompson 1965),

competency traps (Levitt and March 1988), and existing customer preferences (Christensen and

Bower 1996). As a consequence, established firms may struggle to identify and develop new

ideas internally (Henderson 1993; Dushnitsky and Lenox 2005).

Increasingly, scholars and practitioners are documenting that valuable knowledge may

reside outside of the firm (Cohen and Levinthal 1990), and that accessing and integrating this

knowledge is critical to firms’ innovative performance (Rosenkopf and Almeida 2003).

Innovations, especially the most novel and important innovations such as drug-eluting stents or a

bone cement to treat spinal fractures, are formed from the recombination of diverse knowledge

(Fleming 2001; Rosenkopf and Nerkar 2001), which often requires new knowledge from outside

the firm. How firms access extramural ideas and combine knowledge across organizational

boundaries, whether drawing from regional networks, other firms, or universities, has been the

subject of a substantial recent literature (Mowery 1983; Saxenian 1990; Mowery et al. 1996;

Powell et al. 1996; Almeida and Kogut 1999; Stuart 2000; Ahuja and Katila 2001; Cohen,

Nelson and Walsh 2002; Grant and Baden-Fuller 2004).

This literature has been rightfully influential on studies on corporate innovation, but there

are opportunities to extend this work. Most importantly, this literature has not fully incorporated

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product users as an important and unique source of external knowledge, despite considerable

evidence that users generate valuable knowledge (von Hippel 1988; Lilien et al. 2002; von

Hippel 2005). Below, we attempt to synthesize key ideas advanced by user innovation scholars

that are directly relevant to the conditions under which external knowledge is most beneficial for

corporate innovative performance. We first review selected prior literature that documents the

extent and importance of innovation from professional users and hobbyists in order to draw out

the factors that make knowledge generated by users valuable and distinct from corporate

knowledge. Based on this theory, we discuss important contingencies that influence the value of

user knowledge to corporations, the key contribution of this paper. Then we describe the new

product development process in the medical device industry, highlighting the role of physician

innovation with a brief case study.

Users and the product development process: prior evidence

Early scholarly work on innovation generally assumed that producers would generate

innovations and benefit from commercializing them (Schumpeter 1934). Eric von Hippel (1976,

1986, 1988) and co-authors identified that users, individuals or firms that benefit from using a

product, could also be an important source of innovation. Their subsequent studies have

confirmed that user innovation is widespread and significant (von Hippel 1988, 1998). This

stream of research has found that 20–80 percent of important inventions across a wide variety of

industries were generated by users (von Hippel 1988). Chatterji and Fabrizio (2012) find that

patented corporate inventions that include contributions from users have different attributes, in

terms of quality and breadth, than other corporate patented inventions, implying that unique and

valuable knowledge resides with product users. In addition, survey evidence indicates that

customers often provide important insights for new R&D projects, and contribute substantially to

the completion of existing R&D projects (Cohen, Nelson and Walsh 2002).

Why can users generate ideas that are so distinctive and valuable? Prior work indicates

that this phenomenon arises because communities of users have different motivations and

knowledge than incumbent firms (von Hippel, 1986; Riggs and Von Hippel, 1994; von Hippel,

and Krogh 2003; Luthje et al., 2005; Shah, 2006; Gächter et al., 2010). Users are motivated by

trying to meet unmet needs they have identified through experience and enhancing their

reputation in the community of users (Shah 2006). For example, users are more likely to focus on

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improving product functionality for themselves and others in the community of users rather than

on selecting projects for commercial viability (von Hippel, 2005). These communities can also

encourage the development of prototypes, provide feedback on early inventions, and facilitate

the wider adoption of a new product (Hienerth and Lettl, 2011). Incumbent manufacturers, on the

other hand, are more likely to invest in innovations that can quickly be brought to the mass

market. Thus, distinct motivations drive substantial differences between the inventions generated

by users and those generated by established firms.

Product users also possess knowledge that is fundamentally different from the knowledge

developed by researchers within firms. Users experience a product’s functions and limitations

firsthand. These experiences may uncover problems that manufacturers did not anticipate and

may also suggest potential solutions or improvements that are relevant to other users. Indeed,

Cohen et al. (2002) found that customers are the most important source of information

suggesting new projects, more so than a firm’s own manufacturing operations.

While the standalone value of user innovations has been well explored, we know far less

about the contribution of user knowledge to corporate invention and innovation. Select papers

have documented the contributions of product users to corporate innovation process (von Hippel

et al. 1999; Jeppesen and Frederiksen 2006) and suggested that firms can create strategies to

encourage user contributions to corporate innovation (Jeppesen and Molin 2003). These papers

lay a foundation for future work by explaining what motivates individuals to participate in firm-

sponsored user communities (Jeppesen and Frederiksen, 2006) and providing practical insights

for managers looking to identify promising users (von Hippel et al., 1999). Other papers explore

how the work of users can benefit corporations more indirectly. For example, Baldwin et al.

(2006) argue that users often initiate production processes that require little capital and have high

variable costs, setting the stage for entry by incumbent manufacturers later on in the technology

life cycle. However, there is no systematic evidence regarding the impact of user contributions

on corporate innovation outcomes or when collaborations with users are most beneficial for

firms.

Furthermore, despite the suggestion that users are sources of valuable knowledge for

corporations, an influential literature suggests that relying on the typical customer’s experience

will actually inhibit innovation (Hamel and Prahalad 1991). Rather than being constrained by

‘tyranny of the served market’ (Hamel and Prahalad, 1991:83), this research argues that

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managers seeking to develop innovations must nudge their customers toward the new products,

not just cater to existing customer preferences. This argument is consistent with Christensen

(1997), who argues that firms that rely on existing customers are more likely to develop

sustaining innovations than disruptive innovations. This literature reinforces the notion that

understanding ‘when’ to engage users is paramount.

These two views of the value of working with product users can be reconciled somewhat

by consulting studies that emphasize the type of user that is valuable to firm innovation. The

diverse user innovation literature has explored both professional users, who innovate in their

main occupation (Riggs and von Hippel, 1994; Jeppesen and Frederiksen, 2006; Chatterji and

Fabrizio, 2012), and avid hobbyists (e.g. Dahlin et al. 2004). Even within these groups, scholars

have tried to identify lead users, who experience needs ahead of the rest of the population, are

most likely to benefit from innovation, and are most likely to generate innovations (von Hippel

1986; Urban and von Hippel 1988; Luthje and Herstatt 2004; Lettl et al. 2006; Hoffman et al.

2010). These lead users are also the least likely to be constrained by ‘functional fixedness’

(Lilien et al. 2002) that inhibits creative problem solving. The users in our empirical setting are

all professionals and share many characteristics with the lead users described in prior research,

because we examine inventive collaborations between practicing physicians and medical device

companies.

There is limited empirical evidence about the role that professional users play in the

medical device industry. Lettl et al. (2006) describe four case studies where ‘inventive users’

make significant contributions to radical innovations in the medical equipment industry. These

authors find evidence that users develop valuable networks to broadly disseminate their

innovations, a boon for established manufacturers. Their findings also suggest that professional

users, specifically practicing physicians, are in a stronger position to develop valuable

innovations, relative to the typical hobbyist in another industry setting. These findings are

consistent with Chatterji et al. (2008), who demonstrate that 20 percent of patented inventions in

the medical device industry come from practicing physicians, the key product users in this

industry. Chatterji and Fabrizio (2012) find that these patents are broader and more significant in

terms of the pattern and number of citations received than non-user patents. While these papers

imply that practicing physicians have important ideas, no systematic evidence on their

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contribution to corporate innovation is yet available, nor have possible contingencies been

considered.

There are a variety of mechanisms by which users can contribute to the firm innovative

performance. First, in their role as customers, users can provide insights about what features they

desire and the most effective sales and marketing strategies. Firms that incorporate these insights

into their product development plans may develop more (or different) products than they would

have otherwise. Second, influential lead users can play a special role in ‘certifying’ products and

recommending them to others, potentially leading to more sales of existing products but not

necessarily the development of innovations. Finally, users can engage with companies to co-

develop inventions. Because these inventions reflect users’ valuable and distinct knowledge and

insight, they are more likely to be robust to the remaining hurdles in the product development

cycle, leading to innovations that the firm would not otherwise have developed. While this list of

user contributions is neither exhaustive nor mutually exclusive, we focus this paper on

establishing the impact of upstream, invention-focused collaborations between firms and product

users. These inventive collaborations are much more likely to involve valuable knowledge

transfer from physicians to firms, as opposed to collaborations focused on marketing and sales.

In adopting this narrow lens, we likely understate the full value of users to the corporate

innovation process. However, the benefit of this approach is that we can examine the impact of

identifiable user knowledge contributions on firm innovative outcomes.

Taken together, our argument is that user knowledge, like other knowledge external to the

firm, can enhance the ability of firms to generate innovations. The ideas conceived by product

users spring from particular motivations, experience, and knowledge sets that are difficult for

firms to replicate. A firm’s own innovations are conditioned by its own accumulated experience

with research, development, and existing products. Attempts to bring users ‘in-house’ would

likely destroy the value of their contributions because it would undermine the users’

distinctiveness. Therefore, in order to exploit potentially valuable user knowledge, firms manage

these collaborations across the firm boundary, akin to alliances and corporate venture capital

investments. When firms and product users collaborate on inventions, they can conceive and

develop valuable and novel ideas, which are commercialized as innovations. Thus, we propose:

H1: Inventive collaborations with product users will increase corporate innovative performance.

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When is user knowledge most beneficial?

The issues raised in prior work underscore that important contingencies dictate the value of user-

generated knowledge to corporate innovation (Baldwin, Hienerth and Von Hippel 2006; Hienerth

and Lettl 2011). We move beyond the prior literature to identify the conditions under which

sourcing external knowledge from users will be most beneficial. Given that organizing

innovative activity across the firm boundary increases the difficulty of coordination,

communications, and knowledge integration (Grant 1996b), it is critical to examine when the

benefits of accessing external knowledge are more likely to exceed these costs. We focus on two

instances where we expect user knowledge to be beneficial: (1) in the early phases of technology

development, and (2) in the process of developing radical innovations. In both cases, we argue

that users are likely to possess knowledge that facilitates corporate innovation and that is difficult

for firms to replicate.

New technology areas

According to the prior literature on industry and product life cycles,5 in the early period of the

cycle, knowledge is distributed unequally. This ‘era of ferment’ (Anderson and Tushman

1990:604) or ‘entrepreneurial regime’ (Winter 1984:295) is marked by investigations of various

ideas and new entrants as the industry seeks to converge around a particular standard. In this

period, the knowledge required to develop new ideas is typically not embedded in existing

routines (Agarwal and Gort 2002), and it is not yet clear which characteristics are most important

to product users (Dosi 1982).

Building on this work, we propose that external knowledge is most valuable at the

beginning of the product life cycle, when a technology area is new. Because new technology

areas are characterized by greater uncertainty about ideal product attributes and consumer

preferences, users can represent isolated pockets of valuable knowledge. In this stage, product

users might have insights about what will become the most salient product characteristics,

helping the firm to understand the nature of consumer demand and potential customer

5 Some studies refer to ‘product life cycle’ while others use the term ‘industry life cycle.’ Our focus is on differences in the impact of user collaborations across the life cycle phases of the medical device sector, and we use the term ‘product life cycle,’ consistent with Klepper (1996).

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preferences, and to estimate the market size. At the earliest stages of the industry and product life

cycle, it will also be the most difficult for firms to replicate outside knowledge in-house (Shah

and Tripsas 2007; Tripsas 2008). Therefore, in the early stages, users have knowledge that is

useful in the innovation process that is not available either within the firm or from other sources.

This is consistent with existing evidence that user inventions in the medical device sector tend to

occur earlier in the product life cycle (Chatterji and Fabrizio, 2012).

As the cycle progresses, a dominant design emerges (Utterback and Abernathy 1975;

Anderson and Tushman 1990), firms and customers develop a stronger and more uniform sense

of the market, and knowledge diffuses across the industry. Next, in the ‘retention’ stage

(Anderson and Tushman 1990), firms develop complementary assets to support

commercialization and new inventions become less frequent. During these later stages, we

expect external knowledge to become comparatively less valuable to corporate innovation

because knowledge is widely diffused, more standardized and codified, and more easily

replicable.

As product characteristics become standardized and widely adopted, and reviews of

existing products diffuse knowledge of consumer reactions to product features, firms comes to

understand the market, existing technology, and customers’ desires. In these later stages, either

firms will have developed the knowledge internally, through experience and research, or such

knowledge will be accessible through patents, trade publications, and other means. Thus, we

expect that user knowledge will be less beneficial for firm innovation in older technology areas.

H2: Inventive collaborations with product users will increase corporate innovative performance

more in newer technology areas than in older technology areas.

Development of radical innovations

Firms are less likely to possess the knowledge required to generate radical innovations, as

opposed to incremental innovations, and thus user knowledge will likely be more beneficial in

the former case. We will use the terms ‘radical’ and ‘incremental’ according to the definitions

provided by Henderson and Clark (1990:9): ‘Incremental innovation introduces relatively minor

changes to the existing product, exploits the potential of the established design, and often

reinforces the dominance of established firms,’ and ‘Radical innovation in contrast is based on a

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different set of engineering and scientific principles and often opens up whole new markets and

potential applications.’

Innovation based on the recombination of established, familiar knowledge components is

less likely to be a significant breakthrough (Henderson 1995; Fleming 2001; Rosenkopf and

Nerkar 2001). By drawing on areas of established expertise within the firm, firm researchers

exploit the learning from prior research activities (both successes and failures) and develop

innovations that are technologically proximate to prior innovations (Helfat 1994; Stuart and

Podolny 1996). The incremental innovation process exploits and reinforces the accumulated

knowledge within the firm and fits within the firm’s established organizational routines.

At the other end of the spectrum, innovations that require new recombinations of diverse

knowledge components are more likely to be either breakthroughs or failures (Cyert and March

1963; Fleming 2001; Katila and Ahuja 2002). These novel recombinations may require different

internal processes and incentive structures that diverge from the firm’s established practices.

Such organizational changes are difficult for an established firm and may be inconsistent with

other firm activities (Henderson and Clark 1990). Most importantly for our case, novel

recombinations require access to diverse and divergent knowledge (Nelson and Winter 1982;

March 1991), which may not exist within the firm (Cohen and Levinthal 1990).

It is important to note that if we only observe ‘successful’ innovations—those that reach

at least some lower threshold of expected value such that firms pursue their development—we

will not observe the failures that result from new combinations of diverse knowledge. Instead, if

the line of reasoning above holds, we will observe that radical innovations are associated with

combinations of distinct sets of knowledge and that incremental improvements are associated

with combinations of established, local knowledge. Since users’ knowledge is distinct from what

the firm can develop internally, we predict that incorporating knowledge from product users is

more beneficial for the production of radical innovations than incremental innovations.

At first glance, this prediction may appear to contrast with prior literature that has

suggested that user innovators most often generate incremental innovations (Luethje et al., 2005;

von Hippel, 2005), but our argument is not inconsistent with these findings. First, inventive

physicians, similar to professional users in other settings, possess characteristics that make their

contributions particularly useful for generating radical innovations (Lettl et al., 2006). Second,

our arguments are about when collaborating with users will benefit corporate innovation most

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significantly. If user-inventors working with firms are mostly contributing incremental ideas, the

firm likely has the same overlapping knowledge, so collaboration will not provide a substantial

increase to corporate innovative performance. However, in the (perhaps small fraction of) cases

when the users provide the kind of insights that leads to radical innovations, the firm would not

have had access to this valuable and unique knowledge without the user. In these cases, the

impact on corporate innovation will be substantial. Therefore, regardless of whether the typical

user innovation is incremental or radical in nature, we argue that the contribution of user

knowledge to firm innovation is larger for radical innovation.

H3: Inventive collaborations with product users will increase corporate innovative performance

more with respect to radical innovations than incremental innovations.

Empirical context: collaborations between physicians and medical device firms

The medical device industry is R&D-intensive and characterized by several well-established

incumbent firms and thousands of small ventures that manufacture a wide variety of medical

devices, instruments, and diagnostics across numerous medical specialties.6 While similar to the

pharmaceutical industry in some ways, a key difference is that the product cycles are generally

much shorter in the medical device industry.7 Furthermore, intellectual property rights to

inventions in the medical device industry are especially important; according to a recent survey

(Cohen et al., 2002), the importance of patents for securing rent appropriation is even greater in

medical devices than in pharmaceuticals.

This industry is well suited for studies about innovation since the intermediate knowledge

relevant for the innovation process is clearly defined and identifiable using records of patented

inventions. Although patents do not capture all knowledge used in innovation development, the

high degree of appropriability via patents in the medical device industry strongly supports our

use of patents to create measures of knowledge and collaboration. Finally, the data on FDA-

approved medical devices provides a reliable record that is a reasonable approximation of

innovation outcomes.

6 Advanced Medical Technological Association, AdvaMed Website, (http://www.advamed.org/MemberPortal/About/Industry/). Last accessed June 5, 2007. 7 The Food and Drug Administration Website, (http://www.fda.gov/cdrh/ocd/mdii.html). Last accessed June 5, 2007.

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Most importantly, the medical device industry provides an ideal setting in which to study

users’ contributions to industrial innovation. First, there is a well-identified set of users

(physicians) who are part of a professional community of practice. Users in this industry are

highly trained, participate interactively in user communities, and develop experience with

products through use. These attributes create an environment in which users are keenly aware of

existing problems, possess the knowledge to generate potential solutions, and have insights into

future market needs that, taken together, can support the generation of potentially valuable

inventions and innovations.8

Innovation in the medical device industry involves interaction between physicians and

device companies at all stages of development (Gelijns and Rosenberg 1994). From product

conception to clinical testing to dissemination, medical device companies devise strategies to tap

the knowledge of their most important customers: practicing physicians (Chatterji et al. 2008). In

this paper, we focus exclusively on physician-firm inventive collaboration at the earlier stages of

product research and development, and not on product dissemination, marketing, endorsement,

or other activities.

There are two general scenarios through which physicians and companies collaborate on

inventions (Carlin 2004). In the first scenario, a physician or team of physicians will patent a

new invention that generates interest from a medical device company. If both parties agree, a

license or a transfer of patent rights from inventor to company can be arranged. In a famous

example of this case, Dr. Thomas Fogarty, a prolific medical device inventor, licensed the patent

for his revolutionary balloon catheter to Edwards Life Sciences (White 2006). This example has

parallels to previous academic work on ‘star scientists’ (Zucker and Darby 1996). While these

arrangements are quite common in the industry, they do not always represent collaborative

interactions between physicians and companies, because the physician may have developed the

idea independently before ever engaging the firm.

Under the second scenario, the physician inventor will consult or co-develop an idea with a

medical device company, resulting in a patented invention (Carlin 2004). According to our

discussions with industry experts, these collaborations can be connected to a longer term 8 It is important to note that these conditions are not specific to the medical device industry. Other studies, including Riggs and von Hippel (1994) and Shah and Tripsas (2007), have demonstrated the value of user inventions in industries as diverse as juvenile products, sports equipment, and scientific instruments.

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consulting agreement whereby a contracted physician automatically assigns any resulting

intellectual property to the firm. In these cases, any resulting patents will list the physician as an

inventor but will be assigned to the medical device company. Co-invention without a long term

consulting agreement is also common, though these arrangements will typically have a narrower

scope of work and concrete deliverables. Industry experts agree that the appearance of a

physician on a company patent has significant ramifications and would thus not be observed

without substantial contributions from the individual in the development of the idea. It is this

type of inventive collaboration, where the company is listed as the assignee and a physician

appears as an inventor on the patent, that we examine in our paper. This allows us to

systematically track inventive collaborations with observable data and provides a measure that is

closest to the phenomenon of sourcing external knowledge described in the prior literature.

The case of stent development at ACS

To illuminate the context further, we offer a brief example of a fruitful physician-firm

collaboration involving Dr. Richard Stack, a renowned cardiologist, and Advanced

Cardiovascular Systems/Guidant on the bio-absorbable stent.9 Stents are medical devices used to

prop open blocked arteries, often during percutaneous coronary intervention. In contrast to open-

heart surgery, where a large incision is made through the patient’s breastbone, stents offer a

minimally invasive alternative procedure by allowing the device to be threaded through a small

incision in the femoral artery and navigated to the blocked vessel.

Dr. Stack was the founder of the interventional cardiology department at Duke University

and invented an early version of the bio-absorbable stent in 1981. At the time, there were two

competing stent designs, the Sigwart self-expanding stent and the Palmaz balloon expandable

stent. Stack’s design was arguably ahead of its time. It was made of a polymer that eventually

dissolved into carbon dioxide and water after effectively propping open the affected artery. This

feature would prove to be crucial years later, but Stack’s initial stent was not as strong and

malleable as the metal alternatives. Johnson & Johnson used Palmaz’s design to develop its

blockbuster bare metal stent in 1994, and later generations of stents added a drug-eluting coating

intended to reduce the relatively high rate of re-blockage of the artery.

9 This section is based on a March 2010 interview with Dr. Stack and information from secondary sources.

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During this period, Stack collaborated with a California-based company, Advanced

Cardiovascular Systems (ACS), to further develop his bio-absorbable stent. (He later left Duke

as a professor emeritus to found a string of successful medical device companies.) As part of this

collaboration, Stack worked with ACS researchers to develop patented inventions that list both

Stack and ACS employees as inventors.10 Stack noted that ACS funded some of his earlier

research through Duke and that physician-firm collaborations could be initiated by either side.

On one hand, physicians may generate an idea and ‘shop’ it around to medical device companies

or venture capital firms. On the other hand, medical device firms send representatives to medical

research conferences and are often well aware of promising research in academic institutions.

Moreover, company sales representatives often build strong relationships with doctors and can

easily list the most innovative doctors in their region. Collaborations could originate from the

efforts of a firm, a physician, or even an intermediary at a medical device incubator.

Stack possessed years of clinical experience in cardiology and a deep understanding of

the unmet need—namely, to provide a minimally invasive solution without leaving a foreign

object inside the patient. He was motivated by his conviction that using existing stents was akin

to placing a cast on a broken arm, but not removing the cast when the bone healed. In addition,

he had seen that leaving a foreign object inside the body led to increased risks of clotting,

generating considerable controversy over the safety of bare metal and drug-eluting stents.

Stack’s unique knowledge and motivation to address an unmet need allowed him to

envision and build a potential solution: the bio-absorbable stent. In addition, the foresight that he

accumulated through clinical experience led to later innovations in stent delivery. As a leader in

the cardiology community, he knew that a stent that performed just as well or better than those

currently on the market, but reduced the risk of clotting (by removing the foreign object from the

body), would be popular with physicians, patients, and hospital administrators. In the subsequent

section, we describe a large dataset that will help us understand if collaborations like this benefit

medical device firms on average, and when these benefits are most apparent.

Data and Methods

The goal of our empirical analysis is to estimate the impact of firm inventive collaborations with

physicians on new product innovation at the firm level. Specifically, we explore the variation in

10 ACS was later acquired by Eli Lilly, spun out as Guidant in 1994, and sold to Abbott as part of the Boston Scientific acquisition of Guidant in 2006.

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levels of firm co-inventions with physicians over time for a given firm and compare the

associated change in new product approvals. The null hypothesis is that such collaborations with

physicians will not substantially influence innovative outcomes of the firm, which would be the

case if co-inventions with physicians only served as a means to incentivize physicians to use or

promote the firm’s products (as often alleged by industry critics concerned about conflicts of

interest), or if user involvement in the innovation process was a hindrance to innovation, as

claimed in some of the literature reviewed above. For Hypotheses 2 and 3, the null hypothesis is

that the impact of firms’ collaborations with physicians on innovative performance does not vary

by the age of the technology area or the radicalness of the innovation. This is a viable alternative

if one believes that users contribute only incremental ideas, relating primarily to established

technologies, or contribute equally across vintages of technologies.

The ideal experiment to test our predictions would involve randomly assigning firms to

collaborate with inventive physicians in randomly selected years, and observing the change in

innovative performance in the subsequent period, relative to before the collaboration. Obviously,

such an experiment is not possible. Because firms may choose whether and when to work with

physicians, and physicians can select which firms to work with, collaborations are not random. A

pooled OLS analysis would therefore provide correlations reflecting both the patterns of

selection into collaboration and the effects of collaboration. As described below, we use panel

data so that we can compare the innovative performance of each firm over time, and examine

how innovative performance changes as the firm collaborates with physicians (or does not).

However, the collaboration itself is a choice of the firm. This creates an empirical challenge:

firms will elect to collaborate with physicians when it is beneficial for them to do so, and will

collaborate more when they benefit more from doing so. In other words: collaboration with

physicians is endogenous. We discuss the various dimensions of this challenge, mapping to

specific sources of endogeneity, and provide analyses to address it in the ‘Empirical Challenges’

section below. First, however, we discuss our baseline empirical model.

As described in detail below, we make use of patent data and also compile FDA data on

products approved through the 510(k) and PMA processes. Following Hausman et al. (1984) and

Griliches (1990), we employ a production function model to estimate the elasticity of innovation

(output) to knowledge inputs including R&D investment, accumulated knowledge stock, and

firm employment. To test our hypotheses, we also include physician collaborations, i.e., the

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count of patents co-invented with a physician, as a separate knowledge input. Our maintained

assumption is that after controlling for these knowledge inputs and characteristics of the firm, as

well as a firm-specific fixed effect,11 there are no unobserved, time-varying factors that drive

innovative performance that are also correlated with physician collaborations. As in Jaffe (1989),

the Cobb-Douglas production function is as follows:

where Ii is the innovative output of firm i, DrPats is the count of patents co-invented with a

physician, R&D is the firm’s current expenditures on research and development, Knowl.Stock is

the firm’s accumulated stock of knowledge, and Employ is the number of employees of the firm,

a measure of firm size. Taking the natural log of both sides yields the equation to be estimated:

We estimate this model using Poisson quasi-maximum likelihood estimation. The

primary hypothesis, that collaboration with physicians increases innovative performance, would

be supported if is positive and statistically significantly different than zero. To test the

prediction that collaboration with physicians in newer technology areas is more beneficial for

firm innovation (Hypothesis 2), we categorize each physician co-invention according to the age

of the technology class to which the patent is assigned, as described below. To test the prediction

that physician collaboration is more useful for development of radical, rather than incremental,

innovations, we separate the FDA-approved innovations ( ) into those requiring PMA approval

(radical technologies) and those approved through the 510(k) process (incremental technologies),

as described in detail below. We estimate the model separately for these two outcome measures.

Sample

We constructed a large, unbalanced panel of public medical device companies in the United

States with data from 1985 to 1997. The sample includes all public firms in the primary medical

device Standard Industrial Classification (SIC) code that were granted at least 10 patents

between 1980 and 2002. This approach purposefully excludes large conglomerates and firms that

11 A Hausman test rejected the appropriateness of the Random Effects model at the 1% level.

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are primarily pharmaceutical firms in order to focus on medical device firms.12 This method also

intentionally excludes firms with only rare patented inventions in order to narrow our study to

firms that are pursuing an innovation-based strategy. In practice, because patenting is so critical

to appropriability in this industry and innovations drive revenue, all firms patent heavily. Firms

with very few patents tend to be small and private, and therefore would not be in the sample of

public firms in any case.

In order to avoid right truncation associated with not observing patents that have been

applied for but not yet granted, we only include patents through 1997 in the analysis. Likewise,

our identification of physician inventors extends only back to 1980; in order to include controls

for knowledge and product stock, based on the prior five years, we cannot use data before 1985

in the analysis. The resulting dataset includes 128 firms and 803 firm-year observations. The

dataset includes information on the firms’ FDA-approved products from the Center for Devices

and Radiological Health (CDRH), granted patents from the Hall et al. (2001) dataset, and firm-

year-level data from the Standard & Poor’s Compustat database. In order to identify

collaborations with physicians, we relied on the American Medical Association (AMA)

Masterfile data, which includes biographical information for all licensed physicians in the U.S.

We constructed our dataset as follows. For the firms meeting the criteria described above,

we identified all successful U.S. medical device patents applied for between 1980 and 1997

using the NBER patent data (Hall et al. 2001). We used that data to identify all inventors on each

patent, including inventors’ first, middle, and last names and their locations by cities and states.

The AMA Physician Masterfile contains the name, demographics, address history, practice type,

and medical school information for all licensed U.S. physicians. Using this data, we can identify

which inventors in our sample of medical device patents were physicians.

The matching process involved several steps. We initially identified any physicians with

the same first and last name and state location as an inventor in our sample. To do this, we relied

on the physicians’ historic and current locations in the AMA data and the inventors’ addresses in

the patent data, matching the timing of the patent application to the years of licensure at each 12 Including conglomerate firms would necessitate using firm-level data on number of employees and R&D expenditures. Assuming that a significant portion of employees and R&D spending was allocated to activities not related to the medical device industry, including these firms would bias the coefficients on these control variables down and inflate the error terms for the focused medical device firms. Depending on the correlation between physician collaboration and the significance of non-medical device industry activity, this could bias the coefficient on physician collaborations either up or down. In order to avoid these problems, we limit the empirical analysis for firms whose primarily industry is medical devices.

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address. After identifying potential matches, we examined them more closely to assure a

legitimate match. For each record, if there was a middle name or initial available from both the

patent data and the AMA data, we either confirm that these records are complete matches or

eliminate them from our list. When one or both of the middle initial observations were not

present, we cross-checked records by city location in both files. In those instances where

observations did not have middle name data and did not match on city, we explored these records

more closely to assess whether it was a legitimate match, and eliminated any that we could not

confirm. There are 5437 unique physician-inventors on the co-invented patents of medical device

firms in our sample. Note that the physician co-inventors represent a diversity of employment

roles. For example, 38 percent were in group practice, 24 percent in solo practice, and 5 percent

in medical schools. The physician co-inventors also represent a diverse set of specialties.

Physicians in various surgery specialties were well represented, collectively forming the largest

contingent in the data (about 15%).

Measures

Innovations. Many studies of innovation rely on counts of patents or citation-weighted patents

because they lack information on actual product introductions. We are fortunate to have

systematic and reliable data on innovations. We constructed a firm-year count of the number of

FDA-approvals (NumInnov) based on the CDRH data. To date the innovations, we used the year

that the application was received by the FDA. Our base measure is the aggregated count of

product approvals via the 510(k) and PMA process, including all classes of products. We also

considered separately the count of 510(k) and PMA product approvals to test Hypothesis 3.

Because our empirical approach relies on identifying radical innovations, it is important to

briefly summarize how regulators classify medical devices. To bring a new medical device to

market, the FDA’s Center for Devices and Radiological Health (CRDH) must approve an

application.13 New medical device products are reviewed and approved through one of two

processes: pre-market notification and pre-market approval. Approval via pre-market

notification, commonly referred to as the 510(k) process, requires demonstration of ‘substantial

equivalence’ to a device currently on the market, called a predicate device. This is intended for

less risky devices that are similar to devices proven safe based on a history of sales and use.

13 Although it is technically correct to say that the application—rather than the product itself—has been approved, we will refer to product approvals throughout the paper for simplicity.

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Devices approved through the 510(k) process are thus intended to be incremental and are often

modifications to existing products. The approval process is simplified and often takes less than

three months (Singh 2007). As a result, products on the 510(k) track can sometimes be brought

to market in as little as a year from conception (Lawyer et al. 2007). The total investment

required ranges between $10 million and $20 million.14

The most novel devices, for which no equivalent device exists, undergo the much more

rigorous pre-market approval (PMA) process, which involves animal testing and human clinical

trials (Singh 2007). Recent research has found the average review time for a PMA application in

1998–2005 was approximately 409 days, and significantly longer for orthopedic devices (Singh

2007). The total investment required to complete the PMA process ranges from $30 million to

$100 million.15

In 2006, the FDA granted 39 PMAs and 3210 510(k)s, a ratio of PMAs to 510(k)s roughly

similar to approvals over the last several years (Lawyer et al. 2007). A recent study by the U.S.

Government Accountability Office found that between 2003 and 2007, the 510(k) process had a

90 percent approval rate and the PMA process had a 78 percent approval rate.16

Physician co-invention. Our primary independent variables of interest are firm-physician

inventive collaborations in the current and prior year, which we proxy for with the count of

patents assigned to the firm that are co-invented with a physician in a firm-year observation,

where the year is the application year (DrPatst and DrPatst-1).17 If at least one inventor on a

firm’s patent is a physician, we counted this as a physician co-invented patent. In practice, 83

percent of these physician co-invented patents also include non-physician inventors, presumably

company employees. One potential concern is that we are missing collaborations between firms

and university-based physicians if the university, rather than the partner firm, is listed as the

assignee on such co-inventions. However, we found that only 7 percent of all physician-invented

medical device patents were invented by physicians at medical schools. An examination of these

14 “Drug Eluting Stents: A Paradigm Shift in the Medical Device Industry,” Stanford Graduate School of Business Case-OIT-50, 02/13/06, Lyn Denend and Stefanos Zenios. 15 “Drug Eluting Stents: A Paradigm Shift in the Medical Device Industry,” Stanford Graduate School of Business Case-OIT-50, 02/13/06, Lyn Denend and Stefanos Zenios. 16 “Medical Devices: FDA Should Take Steps to Ensure That High-Risk Device Types Are Approved through the Most Stringent Premarket Review Process.” U.S. Government Accountability Office, January 2009. 17 We experimented with models including up to five years of lagged physician co-inventions. Results indicate that it is co-inventions in the most recent two years that have an effect on firms’ innovative outcomes, a reasonable result given that product cycles in the industry can be as short as 18 months.

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patents demonstrated that, while many are assigned to universities, many are also assigned to

medical device firms. Therefore, while this is a limitation of our measure, we do not feel that it is

a major concern for interpreting our results.

Age of technology area. To measure the degree to which an area of technology is more

established or more uncertain, we used data from the USPTO to determine the age of the

technology areas in which the firm was inventing. The USPTO evaluates the classification

system used for patents on a quarterly basis. When a new technology emerges, patents are

initially allocated to existing classes. When the new technology area becomes sufficiently

significant, the USPTO recognizes it with a separate class. Pre-existing patents that belong in the

new class are re-assigned to reflect the new classification scheme. The establishment date of a

technology area therefore may post-date the application date (and even the grant date) of patents

within that area. We used establishment dates at the primary and subclass level to determine the

technology-class age of each patent as equal to the application year of the patent less the

establishment date of the class to which it is currently assigned. Since patents are re-classified as

technology classes are established, this class age may be negative. Then, for each physician-firm

co-invented patent, we categorized the patent as belonging to a nascent (<0), new (0–5 years

old), or established (5+ years old) technology class, and aggregated nascent, new, and

established class co-inventions separately into three measures of firm-physician collaboration.

These are approximately equal partitions of the distribution of technology class age: roughly 30

percent of the physician co-invented patents are in each of three categories (nascent, new, and

established).18 Importantly, physician co-inventions were well distributed across the technology

class ages (see summary statistics in Table 1).

Controls

Control variables include the firm’s R&D expenditures in millions of dollars (R&D) and the firm

size, measured by the number of employees in thousands (Employ). We expect that more R&D

expenditures will lead to more inventive output and more new product approvals. We included a

control for the firm’s accumulated knowledge stock (Knowl.Stock), measured with the

18 The distribution is very similar if we consider all patents in the sample (including those without physician inventors).

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depreciated stock of patents over the prior five-year period using a discount rate of 20 percent.19

We also included a control for the firm’s accumulated product-market knowledge

(ProductStock), measured analogously with the depreciated stock of FDA-approved products

over the prior five-year period. These measures control for time-varying heterogeneity in firms’

knowledge bases.

Methods

The dependent variable (NumInnovs) takes on non-negative integer values. OLS estimation

would provide inefficient and potentially biased estimates. We adopted a Poisson quasi-

maximum likelihood estimation procedure to account for the discrete nature of these outcome

variables. This estimation procedure is similar to the familiar Poisson model, but does not

depend on the assumption of constant dispersion (i.e., variance equal to the mean) present in the

negative binomial model (Wooldridge 1999). This method also allows for conditional firm fixed

effects, to control for unobserved heterogeneity across firms, and the calculation of robust

standard errors with clustered correlation structures to generate appropriate test statistics. The

estimation method provides an estimator that is consistent under very general assumptions and

standard errors that are robust to arbitrary patterns of correlation.

In general, we included the natural logs of the explanatory variables with a one-year lag.

Thus, we are estimating the relationship between, for example, last year’s R&D expenditures and

this year’s product approvals outcomes. This approach is consistent with prior literature (Jaffe

1989; Dushnitsky and Lenox 2005). Since we do not have strong priors regarding when the

benefits of user collaborations will be evident in the firm’s inventive outcomes, and

conversations with industry professionals indicate that patents and FDA applications can often

occur simultaneously, we included the concurrent and one-year-lagged values.

Analysis and Results

Sample summary statistics are presented in Tables 1–3. There is considerable variation across

variables in the model. Physician co-invention was common for the sample firms, representing

21 percent of firm patents, but the degree of co-invention differed considerably across firms. One 19 To calculate the stock of patents and products, we used the formula , where X is the annual count of patents or products, t is the current year, and r is the discount rate. In order to avoid overlap with the variables reflecting current and lagged physician co-invented patents and other firm patents, we calculate the knowledge stock to capture lagged years t-2 through t-5.

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third of the firms (41 of the 128) had no patents with physician inventors during our sample

period, while 13 percent of the firms had 1 such patent and one firm had 204 such patents.20

Table 2 reports the correlations across the dependent and independent variables. In several cases,

there was substantial correlation among explanatory variables. We examined the variance

inflation factors for the variables included in each of our models and found that they did not

exceed standard acceptable limits. Table 3 provides summary statistics for selected companies in

the sample in order to provide additional insight into the data. Medtronic, a well-known medical

device company, is one of the largest firms in our sample. This firm co-invented 15 percent of its

patents with physicians. Some smaller firms, such as Biomagnetic Tech., developed more of

their patented inventions with physicians; others, such as Luther Medical Products, developed

fewer patented inventions with physicians. It is important to note that the identification in our

empirical analysis does not depend on the substantial differences across firms—these differences

are conditioned out in the fixed-effect estimation. Instead, our estimation makes use of within-

firm variation over time to identify the effect of physician collaborations on a firm’s production

of innovations.

Impact of physician collaborations

Results of estimates testing our hypotheses are presented in Tables 4–7. Note that using a

Poisson quasi-maximum likelihood fixed-effects model necessitates dropping firms with one

observation (i.e., one year of data, which is the case for 19 firms) or with all zero outcomes (i.e.,

the 26 firms with no new FDA-approved products during our sample period, 8 of which also had

only one year of data). The analysis is therefore based on 727 observations for 91 firms. We

report robust standard errors, clustered by firm. The results demonstrate that physician

collaboration was associated with an increase in firms’ innovative performances.

Results of a Poisson model without firm-level fixed effects are reported in column 1 for

comparison. Based on the results in column 2 of Table 4, concurrent and one-year-lagged

physician collaborations were associated with an increase in innovations, though the current year

had a larger (though not statistically different) impact.21 Holding every other variable at its mean,

20 The mean number of physician patents for firms that have any patents is 11 during the sample period. If we replicate our analysis for the 595 observations of the 68 firms in our sample that we observe co-inventing with a physician, the results of the estimations are nearly identical to those reported in the paper (in several cases, the significance or magnitude of the estimated coefficient on physician co-invention is slightly larger). 21 The estimated coefficient on the one-year-lagged collaboration measure is significant at the 8% level.

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an increase from the mean (1.26) to the mean-plus-one standard deviation (4.86) physician co-

inventions was associated with an increase of 0.70 in the number of approvals (based on Model 2

in Table 4), an increase of 17 percent from the mean number of product approvals.22 This

provides evidence consistent with Hypothesis 1. It is also interesting to note that while the

control for firms’ patent stock was not a significant predictor of innovations, the firms’ stock of

prior innovations (ProductStock) is a significant predictor of innovations.

One might be concerned that the presence of a physician co-invented patent reflects a

particularly high-productivity year for the firm, and that it is general inventiveness in the year,

and not the collaboration with the physician, that generates this result. Since the firm fixed

effects capture only average inventiveness of the firm, and the recent patent stock reflects the

accumulated knowledge stock, this would not be captured in the controls. To explore this, we re-

estimated the model including also the number of patents applied for by the firm in the given

year that did not include a physician inventor. Results, reported in column 3 of Table 4, show

that when we included this control, the estimated coefficient on physician co-invented patents

remains positive and significant and decreases in magnitude only slightly. Because existing

literature establishes that physician-invented patents are more important on average than other

firm patents (Chatterji and Fabrizio 2012), we also experimented with including a control for the

firm’s internally developed ‘important’ patents, matched in terms of forward citations to the

physician co-invented patents.23 Results in column 4 confirm that even controlling for important

patents, firms’ innovative outcomes increased with physician co-invention. Therefore, it appears

that physician co-inventions reflect something distinct from overall firm inventiveness in a year.

To test Hypothesis 2, that inventive collaborations with physicians in newer technology

areas will be more beneficial for firm innovative performance, we categorized each physician co-

invention into categories of nascent, new, and established technologies, as described above, and

counted the co-inventions for each firm-year separately by category. The results of the

estimations including these more disaggregated measures of collaboration are reported in Table

5. Consistent with our prediction, physician co-inventions in newer technology classes were

positively related to firm innovative output, while those in more established technology areas

22 The calculation of the implied changes in the dependent variable are calculated using the Clarify software (King et al., 2000; Tomz et al., 2003). 23 To identify these important internal firm patents, we identify the firm patent without a physician inventor that is closest to each physician co-invented patent in terms of the number of citations received within a five-year window.

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were not. We categorized the firm’s own (non-physician) patents the same way, according to the

age of the technology class, and estimated the impact on innovation for comparison. As reported

in column 2, the firm’s internally developed patents were most predictive of innovations when

they were in established technology areas. This contrasting pattern between user knowledge

reflected in co-invented patents and the internal knowledge reflected in the firm’s own patents

underscores the distinctiveness of physician and firm knowledge.

The final hypothesis predicts that physician collaboration will be more beneficial for

radical, rather than incremental, innovation. To test this hypothesis, we separated the FDA-

approved innovations into those requiring PMA review (radical innovations) and those approved

through the 510(k) process (incremental innovations). We estimated the impact of physician co-

invention on each type of innovative outcome. Results are reported in Table 6. First, note that the

sample in these estimations differed from the sample used in the previous analyses. Because we

included firm-level fixed effects, firms that never generated an innovation of a particular type

(radical or incremental) were excluded from the estimation for that outcome. This constraint was

not very severe for the incremental innovations—87 of the 91 firms generated at least one

incremental innovation during our sample period—but was more limiting for the radical

innovations because only 20 firms generated at least one radical innovation. To the extent that

ever generating a radical innovation reflects a particular strategy of the firm, this limitation is

actually desirable for our analysis; we would not want to include firms with a strategy of only

generating incremental improvements of existing products in such an analysis.

Even with the small number of observations, the results are striking. As reported in

column 1, physician co-inventions are associated with a much larger impact on the number of

radical innovations produced, relative to the impact on all innovations reported above. The

estimated coefficient suggests that an increase from the mean to the mean plus one standard

deviation in physician co-inventions was associated with an increase of 0.20 PMAs, which is a

286 percent increase from the mean number of PMAs. In column 2 we also include the measures

of the firm’s own internal patents. These are not predictive of radical innovations, consistent with

the expectation that radical innovation requires knowledge diversity and that internal

development may be inhibited by established organizational routines. For incremental

innovations, the pattern is very different. The impact of physician co-inventions was smaller in

magnitude (the coefficient is statistically significantly different from that in column 1) and the

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firms’ own patents were predictive of innovative outcomes. This is consistent with the

expectation that internal knowledge and routines will be better suited to develop incremental

innovation.

Given that these results are based on different samples of firms, it is possible that firms

that generate radical innovations benefit more from user collaborations, regardless of innovation

type, than do firms that generate incremental innovations. To explore this potential alternative

interpretation, we re-estimated the regression models for incremental innovations using the

sample of firms that have introduced a radical innovation at least once during the sample period.

We found that the coefficients on physician collaborations were comparable to those reported for

the sample of all firms generating incremental innovations. Thus, the conclusion that the benefit

of physician collaborations is greater for the production of radical innovations stands.

Empirical challenges

As noted above, the choice and opportunity to engage in co-invention with physicians is

potentially endogenous to the inventive performance outcomes studied here. Including firm-level

fixed effects helps alleviate this potential source of bias to the extent that the drivers of

endogeneity are constant across the time period studied. For example, if ‘better’ firms attract

physicians interested in engaging in collaborative research, and if this is a permanent

characteristic of the firm, then exploiting changes over time for each firm avoids bias associated

with endogeneity. In addition, to the extent that firm-physician co-inventions represent ideas that

physicians bring to firms, the timing of such inventions is not likely to be endogenous to the

innovative activities of the firm. This scenario appears to be the most common, according to our

discussions with physicians. However, it is also likely that medical device companies seek out

consulting physicians to participate in firm R&D activities. If the drivers of collaboration change

over time in a way that is correlated with our outcome measures and not accounted for in our

estimation, it can result in biased estimates.

Broadly, endogeneity threatens our empirical strategy in three ways: (1) the endogenous

choice of the level of collaboration with doctors, (2) the endogenous choice of the timing of

collaboration with doctors, and (3) the potential complementarity of doctors and non-doctor

inventions. We discuss the implications of each of these challenges, and then describe three

analyses that address these issues.

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First, as with any firm activity, the firm controls the degree to which it collaborates with

physicians. Firms realizing a larger net benefit from this activity are expected to engage in more

of it. The additional (beyond the average) benefit realized by high-benefit firms, but not reflected

in the coefficient on collaboration in the regression model, is part of the error term, which as a

consequence is correlated with the level of collaboration. Our model includes firm-level fixed

effects, which will capture the time-invariant portion of this firm-specific part of the error term

(i.e., if marginal benefits are heterogeneous, but constant over time). However, to the extent that

firms that benefit more from collaboration subsequently increase collaboration, the collaboration

term will be correlated with the error term. This is the classic case of endogeneity, which creates

possible bias in the coefficients and plagues many empirical studies in management.

Second, the firm selects both the level and the timing of collaboration with doctors. This

is problematic because firms may elect to collaborate with physicians in a year when it will be

particularly beneficial, perhaps when they are in the midst of developing significant new devices.

As in the first case, this results in more collaboration when it is more beneficial, leading to

correlation between the collaboration variable and the error term.

Finally, the empirical model is complicated by potential complementarity between the

firm’s own (non-physician) research activities and physician collaboration. In our data, it is true

that the level of physician co-inventions and non-physician patents are positively correlated over

time within a given firm, indicating that firms increase (or decrease) the two activities together.

If the two are complements, then firms would benefit more from one activity when they were

doing more of the other activity. If this were the case, then the additional benefit from doctor

collaboration not captured by the term in the model would be correlated with the level of non-

physician patenting, which could create a spurious positive coefficient on non-physician patents.

Likewise, if firms have heterogeneous benefits from non-physician patents (i.e., the elasticity of

innovation outcomes with respect to non-physician firm patents varies across firms), and the

benefits are positively correlated with the level of collaboration, then the traditional regression

model could result in a spurious positive coefficient on physician collaboration.

We pursue three additional analyses to address these problems. First, we examined the

possibility of reverse causality (the possibility that innovative outcomes causes collaboration)

using a test for Granger causality. Consistent with the results in the paper, the current and lagged

physician co-inventions are significant predictors (i.e., ‘Granger cause’) of current innovation

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performance, even when we control for lagged counts of innovation. However, the lagged and

current counts of innovation do not predict current physician co-inventions, when controlling for

lagged physician co-inventions. Innovation therefore does not appear to cause physician

collaboration. We also tested whether the one-year leading value of innovation was predictive of

doctor collaboration in the current year, and found that it was not.

Second, we estimate a random coefficients model, which allows for firm-specific

coefficients on the physician collaborations and non-physician patents variables.24 The results of

this model indicate that the elasticity of innovations with respect to doctor collaborations (i.e.,

the benefit from collaboration) and non-doctor patents (i.e., benefit from own-firm inventions)

both vary significantly across firms. To the extent that the heterogeneous benefits from

collaboration are constant over the time period of our study for each firm, this model eliminates

the correlation between the collaboration variable and the error term described in the first case

above. In addition, allowing for the heterogeneous benefits for both external and internal

knowledge eliminates the possibility that complementarity across the two activities is causing

spurious positive coefficients, as in case three above.

Allowing for this more flexible model, the results support the baseline results reported in

the paper: the average effect of doctor collaborations on innovation is significant and positive,

and the magnitude of the coefficient is nearly identical to the main results above (results are

reported in Appendix Table 1). We also test directly whether physician collaboration and non-

physician inventions exhibit complementarity (in the sense that the benefits from one activity are

positively correlated with the level of the other). For both activities, the correlation was not

significant, suggesting no complementarity. This is consistent with Knott (2008), who examined

the complementarity of internal R&D and the pool of external R&D, and found no evidence of

complementarity, using the same methodological approach. This further suggests that the results

reported in the paper are not biased by complementarity, alleviating the third concern above. The

shortcoming of this approach is that the firm-specific elasticities are constrained to be fixed over

time. Thus, this method cannot address the potential endogeneity of timing of collaboration.

The third additional analysis is a two-stage least-squares instrumental variables analysis.

We instrument for the number of doctor collaborations in each firm-year with the exogenous

24 This type of model is adopted by Knott (2008) in her investigation of the complementarity of internal and external knowledge sources to account for the same challenges outlined above. We follow that study in our implementation of the random coefficients model.

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conditions that reflect the firm’s opportunities to collaborate with doctors. The instruments we

use are (1) the total number of physicians in the firm’s MSA in that year (from the ‘Physician

Characteristics and Distribution in the U.S.,’ American Medical Association, 1986–1997), which

indicates the local availability of physicians to collaborate with; (2) the number of other medical

device firms (in our sample) in the focal firm’s MSA, which reflects the amount of competition

for collaboration with those physicians; and (3) the ratio of number of doctors in the MSA to the

number of medical device firms in the MSA, reflecting both the presence of physicians and the

reality that any firm is in competition with other firms for the physician collaborators.

Our focus on regional (i.e., within-MSA) conditions is justified because the majority of

collaborations between medical device firms in our sample and physicians are local. A random

sample of 50 of the physician co-inventions in our sample supports this: of the 50 patents

assigned to companies and with a physician inventor, 73 percent included collaboration with a

‘local’ physician (i.e., the physician-inventor listed an address in the same or an adjacent city to

the location of the company assignee).25

We use these three time-varying instruments to predict the number of collaborations with

physicians for each firm-year. These variables satisfy the requirements to be valid instruments.

First, they impact the firm’s innovative outcomes only through the collaborations between the

firm and the physicians. Because the market for medical devices is multinational, not local, the

number of local physicians does not reflect the market size for the medical device firm, and so

the number of local physicians does not induce innovation via demand. The impact of local

physicians on firm innovation outcomes is reasonably assumed to pertain only to collaborative

interactions between the firm and physicians. Second, the number of physicians in an MSA is not

responsive to the (time-varying) desires of medical device firms to work with physicians in the

short term. Medical licenses are provided at the state level with considerable time and effort,

limiting the responsiveness of physician relocation to short-term medical device company needs.

Third, these variables are predictive of the firm-year number of doctor collaborations. The F-test

for joint significance of the instruments in the first stage indicates significance at the 5 percent

level. The t-tests for significance of the coefficient on each instrument suggests that most of the

25 The dominant form of ‘non-local’ collaboration among this sample was a company located in Minnesota collaborating with a physician in California, although there were many cases of Minnesota-based companies collaborating with local physicians, and even one case of a California-based company collaborating with a physician in Minnesota.

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predictive power is coming from the ratio of local physicians to local medical device firms. The

test for underidentification rejects the null of rank=K-1 (underidentified) at the 3 percent level.

The first-stage results are reported in the first column of Table 7.

The results of the second stage of the instrumental variables analysis, reported in column

2 of Table 7, are consistent with the results from the estimation reported in the paper.26 In

particular, once we instrument for doctor collaborations, predicted collaborations cause an

increase in the number of FDA-approved innovation outcomes for the firm. The instrumental

variables analysis is particularly powerful because it solves all three of the empirical challenges

listed above.27

Discussion

This research makes several important contributions to the existing literature on managing

innovation and sourcing extramural knowledge. First, our results support the notion that users

should be included in a broader conception of ‘open innovation’ (Chesbrough 2003, 2006),

whereby firms take advantage of a variety of extramural sources of knowledge, including other

firms and universities, to create innovations. Our work brings together the significant literatures

on external knowledge sourcing and user innovation, and assesses the impact of inventive

collaborations to tap this unique repository of knowledge on firms’ output of innovations. By

establishing product users alongside other firms and universities as sources of valuable

knowledge, we lay the foundation for future work that will incorporate a more complete model

of the innovation ecosystem that shapes the development of new ideas.

Furthermore, we build on the prior work on external knowledge sourcing by detailing

conditions under which this knowledge is most useful. Prior work has generally found that

external knowledge enhances corporate innovation, but it seldom explains when it is most

valuable. We found that the benefits derived from inventive collaborations with product users

vary negatively with the age of the technology area. This result makes sense if we consider that

26 We are missing data on the number of physicians by MSA for 13 firm-year observations, reflecting missing data in the source documents for various years for 4 MSAs. These observations are dropped from the instrumental variables analysis. 27 One limitation of this analysis is that it depends on the geographic diversity of the sample of firms. In the analysis presented, there is enough variation for the instruments to be predictive. However, in the subsample of firms that generate radical innovations (i.e., PMA approved product innovations), there is not enough variation in the limited number of firms, and the first stage is not highly predictive. Therefore, while we have estimated an instrumental variables model for the incremental innovation outcome, and the results are consistent with those reported in the paper, we are unable to use these instruments to estimate a model for the radical innovation outcome.

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internal knowledge becomes more valuable as knowledge is more standardized and widely

diffused, because the accumulated knowledge within the firm is more appropriate for generating

innovations and it is easier to imitate new ideas. In the extreme case where a technology area is

brand new, established internal knowledge is a poor guide for developing innovations and

replicating new knowledge internally is difficult.

We also find that inventive collaborations with users are most beneficial in the

development of radical innovations. Once again, the result hinges on the distinctness of

physician knowledge relative to the knowledge base of the firm. Prior work has emphasized that

the diversity of knowledge is particularly important for the development of radical innovations.

One potential source of diverse knowledge is from outside of the firm, specifically product users.

Our paper aims to draw a connection between unique user insights, diverse knowledge, and

radical innovation. Our results are particularly important because they contrast with the notion

that customer involvement in innovation is a hindrance, at best focused on incremental

innovation and at worst impairing the innovative performance of the firm.

These results also have potentially important implications for practice. First, by

introducing a contingency perspective in assessing the value of collaborations with product

users, managers are advised to build frameworks to strategize about the costs and benefits of

working with product users. Our study finds that these collaborations are most beneficial in new

technical areas and in the pursuit of radical innovations. Managers will be able to estimate the

organizational and financial costs of collaboration and should consider any reputational risks that

might arise from conflicts of interest and regulatory sanction. Weighing these potential benefits

and costs against one another should help managers decide whether or not to move forward with

a particular collaboration with a physician inventor.

This paper should also inform the ongoing policy debate over potential conflicts of

interest in physician-firm collaborations. The medical device industry has been under intense

scrutiny in recent years, exemplified by the 2005 Department of Justice investigation in

orthopedics and the new rules regarding disclosure in the Patient Protection and Affordable Care

Act in 2010. This paper quantifies the potential benefits of physician-firm collaborations, which

should ideally be weighed against conflict of interest and other concerns. The outcomes of

interest in this paper, especially PMA approved medical devices, can have a tremendous impact

Using Users

32

on patient care, health, and survival. Our results indicate that physician-firm collaborations have

a significant impact on the introduction of these important new products.

Theoretical implications

In light of these new theoretical predictions and empirical results, this paper can inform future

research in several ways. First, by specifically outlining the contingencies where users are most

valuable to corporate innovation, we build on the findings of Baldwin et al. (2006), Hienerth and

Lettl (2011), and Lilien et al. (2002). Our work moves the literature one step closer to a more

integrated theory of innovation that includes traditional producer firms and user inventors. Future

research is encouraged to consider the role of product users alongside other established sources

of external knowledge, such as alliances, new employees, and corporate venture capital

investments.

Second, our research is also relevant to the work on the knowledge-based view (KBV) of

the firm. The KBV argues that firms, as opposed to markets, are the most effective governance

mechanism to encourage ‘knowledge formation,’ knowledge integration, and innovation

(Nickerson and Zenger 2004; Macher 2006). The process of drawing from knowledge sources

across organizational boundaries to generate innovations is viewed as especially challenging due

to lack of communication, shared identity, and aligned incentives across this chasm. If

‘organizations are conceptualized as superior settings for the transfer and integration of

knowledge between individuals’ (Gray and Meister 2004), then why do markets and other forms

of governance exist? As KBV scholars concede, there must be instances where the advantages of

market-based governance dominate the benefits of internal organization. As pointed out by

Cohen and Levinthal (1990) and a significant body of follow-on work, knowledge outside the

boundaries of the firm can be especially valuable in creating innovations and can best be

obtained through other forms of governance, including alliances, corporate venture investments,

participation in networks, and collaborations. However, each of these strategies entails costs, and

prior work has not been clear on when to seek external knowledge as opposed to looking inward.

Our work informs this gap by considering when the benefits of seeking external knowledge

are likely to outweigh the costs. This adds a useful contingency to the KBV and better integrates

this work with other theories of the firm and competitive advantage. Taken together, we hope

Using Users

33

this contribution will facilitate more integrated theoretical development and empirical work that

can differentiate between competing predictions in the management of innovation literature.

Limitations

There are important limitations to our work. First, FDA approval is not the only measure of

innovative performance. Future research could consider whether collaboration with users leads to

increased sales or profits related to a particular product. Such an effect, if demonstrated, could be

due to both improvements in the quality of innovations and the potential exploitation of

physicians as a marketing mechanism, regardless of the innovation quality.

In addition, further research should continue to delve deeper into which kinds of users are

most valuable and when in the product development process to engage with them. As mentioned

above, other scholars have documented that listening to existing customers can also inhibit

innovation (Hamel and Prahalad, 1991; Christensen, 1997). It seems clear that there are

important distinctions between the typical customer, the avid hobbyist, and the professional user.

For example, some prior work has documented that the typical innovation by hobbyists is

incremental (Luthje et al. 2005). Other work has suggested that lead users, a small fraction of all

users, are most capable of developing truly radical innovations (Lilien et al., 2002).

There is likely considerable heterogeneity even within these various categories

(professional user, hobbyist, etc.) in terms of who qualifies as a lead user (von Hippel, 1986;

Urban and von Hippel, 1988). We do not explicitly identify lead users in our study. It is

reasonable to assume that physicians who patent with medical device firms (including all of the

users in our sample) could be an important set of lead users, but there are likely many other lead

users who do not patent and likewise many users who do not offer particularly valuable insights

to firms. Further research could identify lead users in this industry and demonstrate the

differential benefits of collaborations with these individuals as opposed to generic users.

It is also important to note that there are several stages in the medical device product

creation, including discovery, development, and dissemination (Chatterji et al., 2008). Our study

purposefully examined collaboration in the early stages, focusing on inventive collaborations,

and so we are likely understating the impact of physicians on innovation overall. We chose this

more conservative approach to avoid misclassifying pure marketing and sales relationships as

collaborations to develop innovations.

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Moreover, future work should consider whether particular firms, due to unique capabilities

and market positioning, benefit more from working with product users than their competitors.

The results of the random coefficients model presented above suggest that there is substantial

heterogeneity across firms in the benefits gained through user collaborations. Research in this

spirit ought to consider the selection process by which firms choose to work with users in the

first place and develop new theory to explain differential benefits at the firm level. This research

trajectory will benefit from close integration with the work on absorptive capacity (Cohen and

Levinthal, 1990), which posits that firms have heterogeneous abilities to identify and integrate

external knowledge more generally. What makes the medical device industry an especially

interesting empirical context is that firms can neither easily forward integrate into treating

patients directly (becoming ‘users’) nor employ physicians full time without drastically reducing

their experience as users. This tension makes capabilities related to external knowledge sourcing

even more important in this industry.

Next, it will be important to test these ideas beyond the medical device industry, where

users are likely to be especially important. For example, it would be interesting to test for

differences across industries in the degree to which firms can benefit from collaboration with

users. For example, these patterns may differ between ‘Business to Business’ and ‘Business to

Consumer’ settings. In addition, while the medical device industry is a high-tech industry with

professional product users, the contingencies introduced in this paper need to be tested with

different kinds of users in different industry environments. In any setting where users share

characteristics with physicians, such as in the case of professional users of scientific instruments

or software, we would expect our propositions to be applicable.

There could be potential issues with our measure of firm-physician collaborations. We

cannot measure the underlying degree of collaboration or effort expended in collaborating with

physicians, nor do we have project-level data. The co-inventing measure that we do have

represents successful collaborations with physicians, at least to the degree that a patentable

invention resulted. We control for the level of successful invention by the firm using the firm’s

own patented inventions, and therefore the coefficient on physician collaboration indicates the

benefits of involving a physician in successful research projects, above and beyond the firm’s

own level of success. This concern is also addressed with the instrumental variables analysis, the

results of which are consistent with our primary analysis.

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Conclusion

Corporations rely on internal and external knowledge to develop new products. The insights of

product users, along with contributions from university research and other firms, can be valuable

to corporate innovation. By introducing contingencies on this value, we hope to encourage

further research on when sourcing knowledge outside of the firm is most beneficial.

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Table 1: Summary statistics (N=727 firm-year observations) Mean Std. dev. Min Max #Innovations 4.22 7.88 0 70 #Radical innovations 0.07 0.32 0 3 #Incremental innovations 4.12 7.81 0 70 Doctor collaborations 1.26 3.60 0 40 Lagged doctor collaborations

1.16 3.48 0 40

Doctor collaborations – nascent tech class

0.38 1.41 0 19

Doctor collaborations – new tech class

0.47 2.11 0 33

Doctor collaborations – established tech class

0.41 1.21 0 11

Non-doc firm patents 4.90 11.95 0 135 Lagged non-doc firm patents

4.40 10.65 0 135

Non-doc firm patents – nascent tech class

1.22 3.90 0 43

Non-doc firm patents – new tech class

1.65 6.05 0 81

Non-doc firm patents – established tech class

2.03 5.34 0 59

Knowledge stock 7.63 18.18 0 182.78 Product stock 10.16 18.27 0 177.76 Lagged employees (‘000) 2.23 7.71 0.01 65.90 Lagged R&D (M) 16.58 43.16 0.02 345.00

Table 2: Correlations (N=727) 1 2 3 4 5 6 7 8 9 10 1 #Innovations 2 #Radical innovations 0.17 3 #Incremental innovations 0.99 0.13 4 Doctor collaborations 0.43 0.25 0.43 5 Lagged doctor collaborations 0.40 0.23 0.40 0.87 6 Non-doc firm patents 0.56 0.38 0.55 0.75 0.75

7 Lagged non-doc firm patents 0.56 0.35 0.55 0.71 0.76 0.93 8 Knowledge stock 0.53 0.33 0.52 0.65 0.73 0.86 0.91 9 Product stock 0.77 0.13 0.76 0.42 0.43 0.56 0.59 0.62 10 Lagged employees (‘000) 0.71 0.13 0.71 0.27 0.26 0.40 0.42 0.45 0.70 11 Lagged R&D (M) 0.66 0.26 0.66 0.41 0.40 0.58 0.60 0.62 0.76 0.84

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Table 3: Statistics for select sample companies

Avg. annual # employees

(‘000)

Avg. annual # patents

Avg. annual # FDA-

approved products

Avg. annual # doctor patents

Avg. annual % patents w/

doctor

Medtronic 8.92 63.46 15.46 11.23 15.81% Stryker 2.80 4.31 6.92 1.00 17.79% Biomagnetic Tech 0.10 1.67 0.56 0.44 25% Luther Medical Prods. 0.04 2 0.92 0.08 9% Table 4: Firm innovations as a function of current and lagged physician collaborations (1) (2) (3) (4) ln_DrPats 0.218 0.247 0.210 0.218 (0.069)** (0.084)** (0.080)** (0.086)* ln_DrPats_lag 0.005 0.122 0.073 0.102 (0.083) (0.069) (0.079) (0.078) ln_NonDocPats 0.222 (0.088)* ln_NonDocPats_lag 0.024 (0.087) ln_ImportantPats 0.102 (0.076) ln_ImportantPats_lag 0.020 (0.070) ln_Know.Stock_lag 0.013 -0.074 -0.122 -0.090 (0.056) (0.092) (0.099) (0.097) ln_ProductStock_lag 0.611 0.218 0.218 0.212 (0.071)** (0.062)** (0.066)** (0.061)** ln_emp_lag 0.123 0.369 0.320 0.342 (0.055)* (0.155)* (0.151)* (0.148)* ln_rd_lag 0.032 0.180 0.138 0.164 (0.045) (0.133) (0.128) (0.135) Constant -0.036 (0.205) Log likelihood -1728 -1268 -1257 -1265 Observations 727 727 727 727 Number of firm FEs 91 91 91 Robust standard errors in parentheses. All specifications include firm fixed effects and year indicator variables. * significant at 5%; ** significant at 1% Results demonstrate that the number of innovations generated by the firm increases with physician collaborations.

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Table 5: Number of innovations as a function of physician collaborations in new, young, and old Technology classes (1) (2) ln_DrPat_NascentTech 0.175 (0.084)* ln_DrPats_NascentTech_lag 0.042 (0.092) ln_DrPat_NewTech 0.077 (0.076) ln_DrPats_NewTech_lag 0.212 (0.073)** ln_DrPat_EstablishedTech 0.083 (0.104) ln_DrPats_EstablishedTech_lag -0.120 (0.067) ln_NonDoc_NascentTech 0.084 (0.075) ln_NonDoc_NascentTech_lag 0.085 (0.068) ln_NonDoc_NewTech 0.072 (0.082) ln_NonDoc_NewTech_lag 0.081 (0.086) ln_NonDoc_EstablishedTech 0.205 (0.086)* ln_NonDoc_EstablishedTech_lag -0.011 (0.069) ln_Know.Stock_lag -0.077 -0.155 (0.102) (0.109) ln_ProductStock_lag 0.281 0.281 (0.075)** (0.063)** ln_emp_lag 0.352 0.314 (0.163)* (0.155)* ln_rd_lag 0.140 0.114 (0.145) (0.132) Log likelihood -1266 -1260 Observations 727 727 Number of firms 91 91 Robust standard errors in parentheses. All specifications include firm fixed effects and year indicator variables. * significant at 5%; ** significant at 1% Results demonstrate that the number of innovations generated by the firm increases with physician collaborations in nascent and new technology areas and with the firm’s own patents in established technology areas.

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Table 6: Impact of physician collaborations on novel and incremental firm innovation Novel Innovations

(PMAs) Incremental Innovations

(510(k)s) (1) (2) (3) (4) ln_DrPats 0.788 0.966 0.248 0.207 (0.198)** (0.229)** (0.088)** (0.087)* ln_DrPats_lag 0.302 0.405 0.119 0.075 (0.326) (0.340) (0.076) (0.083) ln_NonDocPats -0.305 0.159 (0.268) (0.078)* ln_NonDocPats_lag -0.220 0.050 (0.307) (0.091) ln_Know.Stock_lag 0.979 1.023 -0.099 -0.150 (0.444)* (0.409)* (0.093) (0.102) ln_ProductStock_lag -0.723 -0.715 0.230 0.233 (0.340)* (0.358)* (0.060)** (0.064)** ln_emp_lag 0.226 0.342 0.368 0.312 (0.812) (0.821) (0.163)* (0.161) ln_rd_lag -0.158 -0.068 0.202 0.163 (0.390) (0.417) (0.134) (0.130) Log likelihood -69 -69 -1255 -1244 Observations 198 198 702 702 Number of firms 20 20 87 87 Robust standard errors in parentheses. All specifications include firm fixed effects and year indicator variables. * significant at 5%; ** significant at 1% Results demonstrate that the number of radical innovations generated by the firm increases with physician collaborations more than the number of incremental product innovations.

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Table 7: Instrumental variables analysis: Firm innovations as a function of physician collaborations (1) (2) First stage,

DV: ln_DrPats Second stage,

DV: Firm Innovations ln_DrPats 8.667 (4.059)* ln_Know.Stock_lag 0.043 -0.155 (0.041) (0.490) ln_ProductStock_lag 0.152 0.405 (0.040)** (0.741) ln_emp_lag 0.040 0.625 (0.053) (0.624) ln_rd_lag 0.031 0.293 (0.042) (0.489) DocsPerFirm (‘00s) 0.007 (0.003)** ln_Docs in MSA-Year -0.885 (0.732) # Firms in MSA-Year 0.064 (0.037) Constant 7.315 -0.501 (6.216) (3.136) Observations 714 714 Number of firms 91 91 R-squared 0.10 0.60 Standard errors in parentheses; * significant at 5%; ** significant at 1% F-test for joint significance of instruments in first-stage regression has p-value of 5%. Test for identification rejects null (under-identified) at 3% level.

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Appendix Table 1: Poisson Random Coefficients Model Dependent variable: Number of innovations in firm-year (1)

Coef. mean (2)

Coef. std. dev. Random coefficients: ln_DrPats 0.213 0.232 (0.035)** (0.024)** ln_NonDrPats 0.213 0.486 (0.031)** (0.026)** Non-random coefficients: ln_Know.Stock_lag -0.077* (0.034) ln_ProductStock_lag 0.311 (0.040)** ln_emp_lag 0.212 (0.029)** ln_rd_lag 0.107 (0.025)** Constant 0.352 0.756 (0.135)** (0.040)** Observations 698 Number of firms 88 Log likelihood -1522 The Poisson Random Coefficients Model estimates multiple parameters for each of the variables (ln_drPats and ln_NonDrPats): a common coefficient for the sample and a firm-specific coefficient for each firm. The common coefficients are reported in column 1 and the standard deviation of the coefficient, based on the variation across the firm-specific coefficients, are reported in column 2. Column 1 reports the coefficient estimates and the standard errors in parentheses. Column 2 reports the estimated standard deviation of the coefficients (across firms) and the standard errors in parentheses. * significant at 5%; ** significant at 1%