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Innovation via Inter-firm Collaboration: The Role of Partner Organizational Design

Sarath Balachandran* London Business School

John Eklund*

University of Southern California

Draft: 10 January, 2020

* Authors contributed equally and are listed alphabetically

Abstract Firms increasingly leverage external knowledge. Accessing this knowledge within partner firms may be challenging due to partners’ structures. Yet, we have a limited understanding of how firms’ innovation outcomes may be influenced by partners’ organizational structures. We investigate this in the context of corporate venture capital relationships between established firms and startups in the pharmaceutical industry. Using the theoretical lens of knowledge search and access costs, we argue that centralization of Research and Development (R&D) of the established firm is associated with lower search costs associated with startups finding the relevant knowledge. However, decentralization of R&D is associated with lower costs in accessing knowledge due to the more effective usage of higher powered incentives to share information. On average, we find search costs outweigh access costs. Thus, R&D centralization of the established firm is associated with superior startup innovation outcomes. We find that search costs increase with the diversity of the established firm’s knowledge making centralization of R&D even more beneficial for startups. Finally, we find centralized R&D to be most beneficial when the level of market overlap between the firms is moderate due to a trade-off between startups’ absorptive capacities and competitive pressures between the firms limiting knowledge sharing.

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INTRODUCTION

The knowledge-based view of the firm conceptualizes the firm as a means for integrating

the knowledge within its employees (Grant, 1996). This theory highlights that, beyond the amount

and quality of knowledge that a firm possesses, knowledge accessibility and the ability to

recombine knowledge across the organization are critical in helping firms to achieve their specific

goals. How a firm is structured can play a key role in determining how accessible its knowledge

is across the organization and how it is recombined (Puranam, Alexy, & Reitzig, 2014).

Specifically, prior research has demonstrated that organizational structure shapes how knowledge

is recombined to produce innovation (Argyres & Silverman, 2004; Arora, Belenzon, & Rios, 2014;

Henderson & Cockburn, 1994; Lavie, Stettner, & Tushman, 2010). Yet, a great deal of firms’

innovation activity now occurs in collaboration with other organizations with a significant

proportion of the knowledge that firms use not being located within their own boundaries but those

of their external partners (Ahuja, 2000; Dyer & Singh, 1998; Lavie, 2007). The knowledge the

firm is seeking to leverage is therefore embedded in an organizational structure that is not its own.

However, we currently have a limited understanding of how a firm’s ability to employ this

knowledge and thereby to innovate is affected by the organizational structure of its partners.

Despite there being extensive bodies of research examining how a firm’s innovation outcomes are

shaped by its organizational structure and its external relationships, these literatures remain largely

disconnected from each other. This is a significant gap given the growing proportion of firms’

innovation activities that are carried out in partnership with other organizations, and the

demonstrated importance of organizational structure in shaping knowledge creation, renewal, and

flows. Heterogeneity in partners’ structures can thus drive heterogeneity in how firms perform

with respect to their associated innovation activities.

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We study this issue through examining how a firm’s access to its partners’ knowledge, and

consequently its innovation outcomes are shaped by its partners’ organization design. Specifically,

we examine whether a partner’s Research and Development (R&D) unit is centralized within a

single unit or decentralized into multiple, more independent units (Galbraith, 1977; Lawrence &

Lorsch, 1967). We focus on the structure of R&D as this is the key unit which will provide a

partner firm with the knowledge required to undertake its associated innovation activities

(DeSanctis, Glass, & Ensing, 2002). We identify a key trade-off related to R&D decentralization

from a partner’s perspective. Decentralization increases search costs and thus limits access to the

locus of the partner’s knowledge most useful to the focal firm. However, decentralization is also

associated with the more effective use of higher powered incentives and less bureaucracy, resulting

in a firm facing lower hurdles to effectively leveraging a partner’s knowledge.

Which of these aspects of the trade-off are most salient depends on the type of search

process the firm faces in the partnership. We argue that firms benefit from partners having

centralized R&D structures when the knowledge they need is wide ranging and likely to be

dispersed across many different parts of this organization. In these circumstances, the centralized

R&D structure makes it easier to pinpoint where the knowledge or expertise most relevant to the

firm is situated in the partner organization (Singh, Hansen, & Podolny, 2010). Furthermore, a

centralized R&D structure also means the firm can more easily build ties to the specific parts of

the firm where the knowledge they need is situated. However, having partners with decentralized

R&D structures is more likely to be advantageous when the knowledge the firm is seeking to

access is highly focused, and localized to some specific part of the partner organization. Under

these conditions, the additional bureaucracy of a centralized R&D structure is likely to be more of

a hindrance since the focal firm simply needs to interact with a limited part of its partner

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organization. For decentralized R&D, the employees in the relevant parts of the partner

organization are likely to be more incentivized to engage with the focal firm as higher powered

incentives are more likely to be effective (Zenger & Hesterly, 1997). As a result the performance

of the focal firm is also likely to be monitored more closely, meaning that the focal firm is more

likely to be able to gain effective access to the knowledge it needs.

We examine these issues in the context of startups’ relationships with established firms

that arise from corporate venture capital (CVC) investments, i.e. minority equity investments in

the startup by the established firm. On the part of the established firm these relationships are a

window into novel emerging technologies and on the part of the startup a gateway to important

resources to fuel their innovation processes (Dushnitsky, 2012; Dushnitsky & Lenox, 2006; Katila,

Rosenberger, & Eisenhardt, 2008). Our sample consists of corporate VC investments by large

pharmaceutical firms in life science startups from 1995 to 2012. To identify the impact of R&D

decentralization, we draw on R&D reorganizations in the established pharmaceutical firms. We

then examine how the benefits associated with these relationships for startups change as a

consequence of these structural changes. Specifically, we examine how these R&D reorganizations

impact the conversion of technologies into products (Alvarez‐Garrido & Dushnitsky, 2016;

Balachandran, 2018). Technology scholars have highlighted the importance of, and lack of

scholarly attention to, the steps in the innovation process that follow invention (Kapoor & Klueter,

2015). Transforming their technology into a prototype product or application can serve as a signal

of validation to potential customers, investors and acquirers (Hsu & Ziedonis, 2013). In the life

sciences, this step consists of turning a patented molecule into a prototype drug that has approval

to enter human clinical trials (Petrova, 2014).

We find that, on average, these CVC relationships are more helpful to startups in advancing

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their technologies into drugs when the established firms have centralized R&D structures.

However, the benefits of R&D centralization are most pronounced when the established firm’s

knowledge base is more diverse. When the diversity of the established firm’s knowledge base is

low, we find that a decentralized R&D structure facilitates greater benefits for the startup. Hence,

at low levels of knowledge diversity, the benefits of the more effective usage of incentives and

reduced levels of bureaucracy within the established firm outweigh the increased search costs for

startups. We also find that the benefits of R&D centralization are also enhanced when there is a

moderate degree of overlap in the markets being targeted by the established firm and the startup.

At low levels of market overlap, entrepreneurial firms do not have the absorptive capacity to

benefit from the greater access to the established firm’s knowledge base when its R&D is

centralized. At high levels of market overlap, competitive pressures between the established firm

and startup overwhelm the relationship and the established firm’s managers are less prone to

sharing knowledge that would benefit the startup. We also find that these competitive pressures

restrict the knowledge related benefits of these relationships for startups at higher levels of market

overlap when the established firms have decentralized R&D structures.

This study helps to bring together the literatures pertaining to intra-organization design and

inter-firm relationships within an innovation context. Research on each of these topics has

developed significantly over the past decade but they have remained detached from each other.

Research on inter-firm relationships has largely characterized the firms involved as monoliths, and

has thus far not delved into questions relating to how the exchanges that arise in these partnerships

may be shaped by the structures of the firms involved (Lumineau & Oliveira, 2018) Similarly,

research on organization structure has primarily been internally focused, and has not considered

how a firm may be affected by the structures of other organizations it collaborates with. This study

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demonstrates that organization structure can be an important contingency to the exchanges that

arise in inter-firm relationships.

These findings also represent an important addition to research on the value of CVC

relationships for startups. The literature in this domain has been equivocal about whether these

relationships are beneficial to startups, with recent findings suggesting that the principal

impediment to startups’ access to valuable resources is navigating the complex organizations

within which those resources are embedded (Alvarez‐Garrido & Dushnitsky, 2016; Balachandran,

2018; Pahnke, Katila, & Eisenhardt, 2015). We add to this research by examining how startups’

access to valuable resources is related to the organization structure of the established firm, and by

identifying conditions under which different types of structures are most valuable.

THEORY

Scholars have documented the multitude of challenges established firms face to remain

innovative. The need for efficiency mandates a division of labor and corresponding allocation of

resources leading to focus and specialization in different parts of the organization (Puranam et al.,

2014). This localized allocation of responsibilities allows for higher powered incentives and more

effective monitoring, but can conversely lead to a balkanization of the firm, and the creation of

impermeable walls within the firm that restrict intra-organizational knowledge flows (Argyres &

Silverman, 2004; Karim & Kaul, 2015).

Historically, the dominant paradigm was of established firms innovating through internal

R&D units, thus organization design choices played an important role in determining their

innovation outcomes (Kay, 1988). However, more recently established firms have also become

increasingly reliant on knowledge beyond their boundaries in order to innovate (Chesbrough,

2012). Activities that were once exclusively the purview of large firms are now decomposable in

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ways that enable the participation of smaller firms and entrepreneurial ventures that are nimbler

and more specialized. Consequently, firms of all sizes are having to look beyond their boundaries

to access the portfolio of resources they need to innovate and enhance their competitiveness (Alexy

& Dahlander, 2013; Laursen & Salter, 2006). This has led to the rapid proliferation of inter-firm

partnerships involving different types of firms and a multitude of collaborative structures (Gawer

& Cusumano, 2014). Research on these types of collaborations has employed a range of theoretical

perspectives to generate important insights into questions relating to firms’ choice of partners, the

governance of these partnerships, and their performance implications.

However, research on how innovation is shaped by firms’ organization structures, and the

research on how it is shaped by their external partnerships have remained largely separated from

one another. On the one hand, research on organization structure has largely been internally

focused, focusing on how a firm’s innovation outcomes are influenced by internal structure. On

the other hand, research on inter-firm relationships has typically made an important simplification

- characterizing the organizations involved in these relationships as monoliths or unitary actors

(Ghosh & Rosenkopf, 2014; Rogan & Sorenson, 2014). As a consequence, we have had little

research that considers how the external partnerships of firms are influenced by the way these

firms organize internally. This is an important issue to examine as firms rely increasingly on

collaborative structures to drive their innovation activities.

This study contributes towards addressing this gap by examining this issue in the context

of a specific type of inter-firm partnership that has become increasingly prevalent – startups’

relationships with incumbent firms that arise from corporate venture capital (CVC) investments.

In the following sub-sections, we will summarize the relevant literatures and integrate concepts

from CVC and organization design research to develop predictions on how the structure of

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established firms shapes the value startups get from these partnerships.

Established Firms and Corporate Venture Capital1

Corporate venture capital investments now represent the most prominent and widespread

form of partnering between established firms and startups (Paik & Woo, 2017). The involvement

of established firms in venture capital has grown spectacularly in the past decade, both the number

of investments and the volume of capital invested by these firms have more than trebled between

2011 and 2018 (CB Insights, 2019).

For established firms, the relationships with startups that arise from CVC investments

represent a window into novel, emerging technologies (Dushnitsky, 2012; Dushnitsky & Lenox,

2006). Prior literature has suggested that established firms undertake CVC investments for both

financial and strategic reasons (Dushnitsky & Lenox, 2005a, b). With respect to financial rationale,

established firms may simply invest in entrepreneurial firms to boost their profitability through

making high return investments (e.g., Siegel, Siegel, & MacMillan, 1988). However, more recent

studies have highlighted the strategic importance of CVC investments to established firms in that

such investments can provide them with access to insights on new technologies (e.g., Dushnitsky

& Lenox, 2005a; Dushnitsky & Shaver, 2009). For example, Intel Capital invests between $300

and $500 M a year to provide insights into a wide variety of technologies beyond semiconductors

such as healthcare and artificial intelligence.2

Prior studies have also illustrated that both industry and internal firm-specific conditions

shape the propensity of established firms to leverage CVC investments. Focusing on external

factors, if the intellectual property regime of an industry is tight, established firms are more likely

1 Note, we describe CVC investors as established or incumbent firms throughout this paper. The firms in which these firms invest are described as startups or entrepreneurial firms. 2 https://www.zdnet.com/article/intel-capital-announces-117m-in-new-investments-in-14-startups/

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to invest in entrepreneurial firms through CVC (Dushnitsky & Lenox, 2005a; Dushnitsky &

Shaver, 2009). Further, those industries in which there is a significant pace of technological

change, incumbent firms are more likely to invest in CVC (Sahaym, Steensma, & Barden, 2010).

Focusing on internal factors, incumbent firms are more likely to invest using CVC if they

have the internal absorptive capacity to take advantage of the technologies associated with the

entrepreneurial firm in which they invest (Dushnitsky & Lenox, 2005a). As a result, incumbent

firms use CVC investments to supplement their own internal research and development (R&D)

(Sahaym et al., 2010) often with such investments providing real options into new technological

areas (Ceccagnoli, Higgins, & Kang, 2018). However, there is a risk that CVC investments may

be seen as competing with internal R&D projects with internal R&D managers dismissing CVC-

related investments as compared to equivalent internal R&D projects because of issues such as

“Not Invented Here” syndrome (Katz & Allen, 1982). Thus, it appears that established firm CVC

investments in entrepreneurial firms aim to strike a balance between being a complement and a

substitute for internal R&D (e.g., Cassiman & Valentini, 2016).

Startups’ Access to Established Firm Resources

For startups, these relationships with established firms are thought to be of value as a

channel for access to important resources such as knowledge and complementary assets

(Dushnitsky, 2012; Pahnke et al., 2015). Research has however documented substantial

heterogeneity in whether these benefits are realized in practice. Alvarez‐Garrido and Dushnitsky

(2016) compare the effect of CVC investors to conventional VCs on entrepreneurial firms’ rate of

innovation in the biotech industry. They find that the effect of having CVC investment on the rate

of innovation of startups is positive, arguing that these relationships allow startups to tap into

valuable complementary assets. Kim and Park (2017) similarly report a positive relationship

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between CVC investment and the rate of patenting, though only if the startup receives CVC

investment in the first three years of its life. On the other hand, Pahnke et al. (2015) find that the

effect of having a CVC investor on the rate at which entrepreneurial firms innovate is either

insignificant or negative.

While there is broad agreement that established firms control resources that could

potentially be of value to startups, the debate has centered on whether startups can in practice

access them via these relationships. The primary impediment to resource access for startups relates

to the challenge of navigating the organizations within which these resources are embedded. The

established firms in these relationships are typically large organizations with complex hierarchies

and decision processes. For startups, identifying the location of the resources most useful to them

within this firm and finding an effective way to leverage them are far from trivial tasks. As Pahnke

et al. (2015) surmise, “Helpful resources exist within corporations, but dispersed authority,

complex and slow organizational processes, and internal conflicts… complicate ventures’ access

to these resources”. However, the way these issues manifest themselves, and the challenges the

startup faces, are also likely to be shaped in substantial degree by the organizational structure of

the established firm. This is an issue that the research in this domain has thus far not considered,

and forms the focus of this study.

We will focus on a specific innovation outcome that scholars identified as being of critical

importance to startups – the transformation of an invention, i.e. a technological or scientific

discovery, into a product or application prototype for development. This step consists of adapting

a technology to a particular product or process environment, and it bridges the gap between

research and development. Iansiti and West (1999) demonstrate the critical role this step plays in

determining the firms’ success in a number of high technology areas. They also show that the

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challenges associated with transforming high quality research into high quality products or

processes can be subtle and difficult (Iansiti, 1995). This step often necessitates the confluence of

different types of knowledge and expertise, including expertise of the technology itself but also of

the market and the norms of the industry (Kapoor & Klueter, 2015).

This step is likely to be a particularly important one for startups, since transforming their

technology into a prototype product or application can serve as a signal of validation to potential

customers, investors and acquirers (Hsu & Ziedonis, 2013). Recent research suggests that access

to the established firm’s expertise in navigating this step is among the primary sources of value for

startups from corporate VC relationships (Alvarez‐Garrido & Dushnitsky, 2016; Balachandran,

2018). Established firms in high technology industries typically have wide-ranging experience in

managing this step (Iansiti & West, 1997). These firms have extensive research labs and large

product pipelines meaning that the routines associated with driving discoveries into the

development stage are likely to be well developed. An established firm can therefore facilitate this

transition of technology to product prototype by leveraging its relevant prior experiences.

The internal design of the established firm will shape how the startup can locate and access

the knowledge and capabilities embedded within the established firm (Argyres & Silverman, 2004;

Grant, 1996; Zhang, Baden-Fuller, & Mangematin, 2007). Further, the design will also shape

managerial incentives within the established firm to facilitate the innovation outcomes within the

entrepreneurial firm (Puranam et al., 2014; Zenger & Hesterly, 1997).

R&D Decentralization in the Established Firm and Startup Innovation

In examining the established firm’s organization design and its impact on startup outcomes

in these relationships, we focus on the design element of whether a firm’s R&D unit is centralized

or decentralized. We examine the design of R&D because this is the organizational unit within

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which the knowledge and expertise that are pertinent to the startup are housed. Prior work also

suggests that this is the unit through which startups liaise with the established firm (Dushnitsky &

Lenox, 2005a; Grimpe & Kaiser, 2010). As a result, how R&D is structured within a firm is likely

to influence how effectively the entrepreneurial firm can access the resources it requires.

We focus specifically on the key design feature of whether R&D is centralized or

decentralized (e.g., DeSanctis et al., 2002). R&D decentralization relates to dividing R&D into

separate units focusing on, for example, different scientific or product domains (Kay, 1988). For

the purposes of our theoretical argumentation we will focus upon decentralization, recognizing

that centralization represents the direct opposite. We describe decentralization as akin to horizontal

dis-integration of R&D. The decentralized R&D units may be located within business units or be

separate corporate research units reporting to different heads (e.g., Argyres & Silverman, 2004).

We distinguish between a single centralized R&D unit and multiple, decentralized units

through allocation of decision rights (e.g., Jensen & Meckling, 1992). Managers leading a

centralized R&D unit have decision rights across the complete portfolio of firms’ inventions and

hierarchical authority over the parts of the organization working on these inventions with, for

example, the ability to readily shift resources between different R&D projects. In contrast,

managers leading decentralized R&D units only have decision rights for the relevant sub-portfolio

of inventions and hierarchical authority over those associated parts of the organization creating

and developing those inventions and can shift resources between projects within their sub-

portfolios but not across different units. Centralized and decentralized R&D obviously represent

two extremes. Firms may have elements of R&D that have centralized and decentralized features,

resulting in hybrid structures (Argyres & Silverman, 2004). However, as in prior work, we focus

on the dichotomy of centralized versus decentralized R&D and empirically control for other design

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features (e.g., Argyres & Silverman, 2004; Arora et al., 2014).

The principal trade-off identified in the literature between centralized and decentralized

R&D structures relate to knowledge flows on the one hand, and incentives on the other.

Centralization of R&D is associated with greater integration of the firm’s disparate knowledge

resources and with greater intra-organizational knowledge flows (Argyres & Silverman, 2004).

This occurs in part because managers from different areas are more likely to come in contact with

each other in such a structure, but also because these structures instigate less competition between

different parts of the firm and thus facilitate knowledge sharing (Karim & Kaul, 2015). However,

centralized structures are also more likely to be hierarchical, and to be associated with greater

bureaucracy and coordination challenges (Pahnke et al., 2015).

On the other hand, decentralized structures are associated with higher levels of managerial

motivation and effort, since these structures facilitate closer monitoring of managerial performance

and a more finely defined set of incentives (Holmstrom & Milgrom, 1994; Zenger & Hesterly,

1997). Conversely, these structures can often lead to sparse connectedness between different parts

of the firm, leading to the firm’s knowledge resources becoming siloed (Argyres, 1996; Nickerson

& Zenger, 2004). Within a centralized unit the sole hierarchical authority with all-encompassing

decision rights can leverage their hierarchical power to encourage knowledge sharing.

In moving its technology or invention to a prototype (or the development stage of

innovation), startups are typically still trying to understand how a technology works, as well as

how it may interact with other technologies and any other constraints on its design coming from

various external actors such as users and regulators. Overcoming the challenges associated with

this step typically requires bringing together expertise on a range of different areas. For instance,

in the context of the life sciences, while at the conclusion of the discovery stage a startup may

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understand its patented molecule well, turning this into a prototype of a drug that can be tested on

humans necessitates that they address issues like dosage, formulation, toxicology, mode of

delivery, interaction effects with other treatments, regulatory precedent etc. Established firms have

a great deal of experience managing the challenges associated with this stage of innovation.

However, getting access to this type of expertise is by no means automatic for startups in these

relationships. There are primarily two types of organizational impediments they face at this stage.

First, there is the challenge in identifying where in the established firm the appropriate

expertise to help the startup solve the specific challenge they are facing is located, namely the

search costs in finding the relevant information. Established firms can be complex to navigate for

startups due to the variety of divisions, functions and personnel within them. Such firms have a

wide variety of knowledge and it can be challenging to identify where in the established firm the

relevant knowledge required to convert an invention into a prototype resides. Consequently, it can

be a nontrivial task to pinpoint the appropriate source of expertise in relation to a particular issue,

and to develop connections with the employees in the firm with that expertise (Singh et al., 2010).

Second, effectively leveraging that expertise by persuading the relevant managers to

commit some of their time and energy towards helping the startup provide a further challenge. We

articulate this challenge as the access costs associated in accessing the relevant knowledge

required. Managers within the established firm may not be willing to provide the relevant expertise

to the startup. This is because helping portfolio startups is typically not a part of their regular job

descriptions. Consequently, these individuals may de-prioritize the task of working with the startup

if the organizational environment enables them to do so.

We argue that how R&D is structured within the established firm can influence both search

and access costs for startups. First, with respect to the startup finding the relevant knowledge

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within the established firm, the search costs will be lower in centralized R&D structures than in

decentralized R&D structures. In centralized R&D, intra-organizational knowledge flows are

enhanced and managers are more likely to be aware of where the relevant knowledge exists in their

organizations (Argyres & Silverman, 2004). For example, this is achieved through cross-R&D fora

or databases or through common senior management enabling specific connections. Under these

conditions, it is likely to be easier for entrepreneurs to develop their networks in this organization,

and for those networks to enable them to connect with resources of value to them.

On the contrary, decentralized R&D structures facilitate the more effective use of higher-

powered incentives (Zenger & Hesterly, 1997). These incentives are likely to enable startups to

more effectively access the knowledge they require once they have identified where in the

organization this knowledge lies thereby lowering access costs. Further, decentralized R&D

structures enable closer monitoring of managers, making it more likely that startups can elicit the

cooperation of the employees who have been tasked with helping them. These structures are also

less bureaucratic, and decision processes within them are likely to be more streamlined, meaning

that organizational impediments to startups obtaining the required knowledge will be lower than

for firms with centralized R&D units (Pahnke et al., 2015).

We argue that if the relevant knowledge cannot be identified then the lack of incentives to

share this information is irrelevant. Ultimately, negotiation to access the required expertise within

a centralized R&D structure will slow down access but the startup should eventually be able to

access the required expertise. Thus, unless a very narrow, specific set of knowledge is required

that is located within a defined R&D unit, we argue that, on average, established firms with

centralized R&D structures are likely to be better suited to providing the knowledge resources to

startups enabling them to progress more inventions into development as the reduced search costs

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will more than overcome higher access costs:

H1: Startups will drive fewer inventions into development if their partners have decentralized R&D structures as compared to centralized R&D structures. We now examine how this primary relationship is influenced by factors that shift the

startup’s search and access costs. Specifically, we focus upon the knowledge diversity of the

established firm and the degree of market overlap between the established and startup firms.

Established Firms’ Knowledge Diversity and Startup Search Costs

The search costs for startups associated with navigating the established firm and finding

the relevant knowledge to enable their inventions to progress into development are likely to

increase as the diversity of knowledge within the established firm increases. The wider the

diversity of knowledge, the more difficult it is likely to be for startups to locate the expertise that

it needs and to subsequently develop ties to the individuals who possess that expertise. Established

firms with centralized R&D structures are likely to present lower search costs given the greater

levels of connectedness within the firm in these structures and increased intra-organizational

knowledge flows. Consequently, we expect that the previously hypothesized benefits of having a

corporate investor with a centralized R&D structure are likely to be more pronounced when the

diversity of knowledge within the established firm is higher because search costs are higher and

centralization of R&D helps to limit the increase in these search costs.

On the other hand, with decentralized R&D units, managers tend to be specifically

allocated to invention projects associated with a specific R&D unit and are less likely to be working

across multiple R&D units. Thus, such managers will probably not be aware of the knowledge that

exists in their broader organization thereby increasing search costs for startups with the increase

being steeper for firms with a more diverse array of knowledge.

Thus, we argue increased diversity of knowledge within an established firm is associated

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with higher search costs for the startup. At low levels of knowledge diversity, search costs are

lower and outweighed by access costs. Thus, R&D decentralization is more favorable due to the

lower access costs associated with the more effective usage of higher powered incentives

encouraging knowledge access. However, at high levels of knowledge diversity search costs are

higher and outweigh access costs. In this case, R&D centralization is more favorable as it helps to

limit the increase in these search costs due to its associated increased intra-organizational

knowledge flows and more inter-connected design. Thus, we argue that the knowledge diversity

of the established firm negatively moderates the primary relationship outlined by Hypothesis 1:

H2: The negative relationship between the number of inventions startups drive into development, and corporate investors’ decentralization of R&D will be more pronounced when the corporate investor has a more diverse knowledge base.

Market Overlap between the Firms and Startup Access Costs

As well as collaborators the startup and established firms are competitors (Katila et al.,

2008). Cunningham, Ederer, and Ma (2019) show that established pharmaceutical firms frequently

acquire control of the emerging technologies of startups and then fail to invest in developing them

further, particularly when those technologies are a threat to the established firms’ existing market

positions. Given established firms CVC investments are often in startups whose technologies may

threaten their carefully cultivated market positions, competition is also likely to be an important

consideration in this context. Interestingly, there has been little prior consideration about how

competition between firms may shape collaboration dynamics in these relationships.

The extent to which the established firm perceives the startup as a competitive threat will

be related to the degree to which it is targeting the same markets as the established firm. This in

turn may influence its R&D managers’ willingness to share resources and expertise with the

startup. Namely, the degree of perceived competitive threat will influence a startup’s access costs.

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We argue that the way the established firm internalizes and responds to this competitive

threat is also likely to be contingent on its organizational structure. As previously outlined,

decentralization of R&D is associated with the more effective usage of higher powered incentives.

This means that managers within decentralized R&D units will be more sensitive to the

competitive threat posed by startups. The incentives of these managers are closely tied to the

performance of their own R&D units. The nature of CVC relationships is such that, though the

established firm owns a typically small equity stake in the startup, it very rarely has any claim on

the technology of the startup. Hence, a startup’s progression of its technology program is unlikely

to have a direct benefit to the managers in the established firm as compared to the equivalent

progression of an internally created invention. The perceived threat is greater if the startup’s

technology is one that is targeting a market segment that is also of interest to the decentralized

R&D unit. In contrast, for centralized R&D units with their lower powered incentives, managers

will perceive startup’s inventions in similar market segments as less of a threat as they simply have

less to lose. This means that the access costs will increase more steeply for firms with decentralized

R&D than those for firms with centralized R&D as market overlap increases.

Thus, as the degree of market overlap between the startup and established firm increases,

the access costs for firms with decentralized R&D will increase more than those for firms with

centralized R&D. Assuming search costs remain fixed, the increased access costs will mean that

as market overlap increases, startups will be more able to access the resources they need from

firms with centralized R&D. Thus we argue that market overlap between the startup and

established firm negatively moderates the primary relationship outlined by Hypothesis 1:

H3: The negative relationship between the number of inventions startups drive into development, and corporate investors’ decentralization of R&D will be more pronounced when the startup and the corporate investor have higher market overlap.

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METHODS

Research Context and Sample

The context for this study is the US life sciences industry between 1995 and 2012. This

industry provides a suitable context for this study for three key reasons. First, a clear demarcation

between invention and development can be made (e.g., Henderson & Cockburn, 1994; Henderson

& Cockburn, 1996; Hess & Rothaermel, 2011; Kapoor & Klueter, 2015). This enables the

determination of distinct measures of invention and development. Second, there is heterogeneity

in the organization designs of the firms within this industry driven by a variety of factors such as

merger and acquisition activity. Finally, this industry has a rich history of corporate venture capital

funding (e.g., Dushnitsky & Lenox, 2005a; Dushnitsky & Shaver, 2009).

We leveraged multiple commercially available data sources as well as undertook

interviews with a variety of industry informants. We obtained venture capital data from Venture

Xpert, which Kaplan and Lerner (2016) report has the widest coverage of funding events of any

commercially available venture capital database. In line with multiple studies within the strategic

management domain, invention measures are created using patent data (e.g., Fleming & Sorenson,

2004; Karim & Kaul, 2015). Patent data is obtained from the European Patent Office (EPO)

Worldwide Patent Statistical (PatStat) database (e.g., Conti, Gambardella, & Mariani, 2013) and

USPTO’s patentsview database. These databases provide good coverage across multiple

jurisdictions (Kang & Tarasconi, 2016). With its well-defined industry-wide milestones (e.g.,

Chandy, Hopstaken, Narasimhan, & Prabhu, 2006; Kapoor & Klueter, 2015), the progression of

drug candidates through clinical trials provides a means with which to compare firms’

development outcomes. Such development data is collected from the Pharmaprojects database

(e.g., Chandy et al., 2006; Kapoor & Klueter, 2015). Organizational structural data is hand-

20

collected from company 10-K, 20-F, DEF14A SEC filings and annual reports. Financial data is

obtained from Compustat.

Using this data, we focus on the CVC investments of 20 leading US pharmaceutical

companies in 407 small entrepreneurial firms. Focusing on larger pharmaceutical firms that are

responsible for the majority of innovation within the industry is common within the strategic

management literature (e.g., Anand, Oriani, & Vassolo, 2010; Gunther McGrath & Nerkar, 2004;

Kapoor & Klueter, 2015). Compared to a universal sample of listed pharmaceutical firms from

Compustat, the sample dataset of US pharmaceutical companies is significantly larger and more

profitable on average. As multiple incumbent pharmaceutical firms can invest in the

entrepreneurial firms and each incumbent can have multiple investments in an entrepreneurial

firm, we ultimately examine 496 CVC-entrepreneurial firm investment dyads over the period 1995

to 2012. Following prior literature, we did not include investments made by firms that have no

strategic connection to the life sciences such as financial institutions (Dushnitsky & Lenox, 2006).

This primary archival analysis is supplemented with 43 interviews with mid- and senior-

level executives in strategy and R&D roles from 19 of the 20 sample firms. The interviews were

semi-structured and lasted between 30 and 90 minutes. The focus of these interviews was to

evaluate the validity of the organization design measures, to determine how different clinical phase

transitions map to the hypotheses and to discuss how these large incumbent firms manage their

external R&D relationships. In addition, we conducted an additional 25 interviews with other

industry informants that included founders/managers of startups that had received venture capital

from incumbent firms, as well the employees of incumbent firms responsible for making and

managing these investments (i.e. investment managers), and independent (i.e. non-corporate) VC

investors who have co-invested with corporate investors. These interviews focused on the types of

21

exchanges that could arise between incumbent firm and startup personnel post-investment, the

organizational antecedents of these, and how they influenced the startups’ innovation decisions.

Empirical Design and Estimation

The formation of relationships between established firms and startups is the result of a

complex two-sided matching process, i.e. they are not randomly assigned. The startups that receive

investment from a particular established firm may therefore be distinct from others in systematic

ways that are also related to their innovation outcomes. Further, it is possible that the results we

observe are driven by the inherent differences between firms with decentralized and centralized

Research and Development (R&D) that we are unable to observe that lead to the relationships with

startup firms being more or less successful with respect to progressing inventions into clinical

development. This restricts our ability to make strong causal claims in this study.

We undertake a variety of strategies to try and limit the biases associated with non- random

selection of entrepreneurial partners and firms’ internal organization designs. First, the unit of

analysis for this study is an established firm – startup dyad. We then develop a panel dataset that

examines each established firm – startup dyad on an annual basis between 1995 and 2012 using a

variety of variables. The first year for each dyad is the year the relationship is formed, i.e. the year

the corporate VC investment is made. We then track this dyad every subsequent year until the

startup either exits, i.e. is acquired or lists its shares on the public markets, or ceases to exist

(Kaplan & Lerner, 2016). Since our data on startup dissolution is likely to be incomplete, we

assume a startup has ceased operations if it does not exit or raise new capital for three years

continuously. This panel structure has the significant advantage of enabling us to leverage

established-firm – startup dyad fixed effects in all our estimates. Thus our analyses control for any

unobserved aspects of the established-firm – startup relationship that remain constant over time.

22

Thus, the effects that we observe relate to changes in firms’ R&D structure and their impact on the

progression of startups’ drug candidates from pre-clinical to phase 1 clinical trials.

To identify our effects of interest, we rely on re-organizations within the established firms

that cause them to shift from centralized to decentralized R&D structure, or vice versa. About a

fifth of the dyads in our sample experience a change in firms’ R&D structure over the time period

of the sample. Given the established pharmaceutical firms in our sample are typically large

multinational organizations, major structural changes in their R&D functions are unlikely to have

any direct relationship with their corporate venturing activity. Simultaneity bias is unlikely in this

situation as the performance of firms’ CVC investments will have a limited impact on incumbent

firms’ organization design choices as they represent a small proportion of incumbent firms’ drug

development portfolios with candidates being sourced through other channels such as internal

invention, licensing and alliances with other large incumbent firms. Hence, these changes to

structure are unlikely to be endogenous to the performance of these startups through channels that

are unrelated to the startup’s relationship with the established firm itself.

However, it is possible that the reorganizations in the established firms, and the

performance of the startup are both correlated to broader environmental factors. We take a number

of steps to limit the scope of these issues. We include year fixed effects in all our models to account

for macro and industry level factors that could influence both our variables of interest. We also

control for other forms of heterogeneity in incumbent firms’ drug development portfolios through

the use of therapeutic area fixed effects based on Pharmaprojects therapeutic area codes. This

enables us to control for differences in the compositions of incumbent firms’ clinical development

portfolios based on therapeutic class. In addition, we control for a wide variety of variables that

could be correlated with incumbent firms’ organization designs and the progression of CVC-

23

invested startups inventions from pre-clinical to phase 1 clinical trials. Further details of these

control variables are provided below. All models are estimated with OLS regressions.

Measures

Dependent variable. To characterize the entrepreneurial firm’s propensity to drive

discoveries into development, we use a count of the number of new drugs belonging to the

entrepreneurial firm that enter phase 1 of clinical trials. To enter clinical trials in the US, a

prototype drug needs to receive FDA Investigational New Drug (IND) approval, which can be

immensely challenging (Dessain & Fishman, 2017). This is a measure that has been used in prior

studies pertaining to this industry (e.g. Hess & Rothaermel, 2011; Kapoor & Klueter, 2015). We

use the log of 1 + the number of new drugs that enter phase 1 clinical trials in the 3 years subsequent

to the focal year associated with the incumbent firm-entrepreneurial dyad (new drugs).

Independent variables. R&D decentralization: This measure is developed using top

management team (TMT) data available from company 10-K/20-F/DEF 14A SEC filings and

Annual Reports. The use of TMT data to develop high-level organizational structural measures is

relatively common within the strategic management literature (e.g., Albert, 2018; Girod &

Whittington, 2015; Guadalupe, Li, & Wulf, 2014). Such high-level design measures may be

limited in that for firms with the same high-level structure there are design differences below this

high level. For example, a centralized R&D unit may be geographically dispersed and

decentralized R&D units may be co-located and have integrative sub-units designed to share

information. However, our theoretical argumentation is made at this higher organizational level

and thus these measures are appropriate to test our hypotheses. Further, for 19 out of the 20 sample

firms the managers interviewed confirmed that the TMT structure provides an accurate reflection

of their firms’ overall structures, specifically how R&D is designed.

24

A database of 7,459 executive and extended executive team roles for the sample of 20 firms

over the period 1995-2012 is developed. Coding of roles and R&D decentralization are undertaken

by one of the authors through careful review of the management roles in each organization and

further validated through review of organizational descriptions from companies’ filings (e.g.

CEO’s letter to shareholders). To evaluate decentralization of R&D, it was determined whether

firms’ R&D or Research (in the case of functionally separate R&D) is organized into a single unit

or multiple units. For diversified firms which operate beyond pharmaceuticals, R&D units that

pertain to pharmaceuticals were focused upon and R&D units dedicated to areas such as consumer

products were excluded in order to control for the level of diversification. Using this approach, the

variable R&D decentralization is defined as a binary variable set to 1 if there are multiple R&D

groups reporting to separate heads within the TMT covering different pharmaceutical domains or

to leads of business units and 0 if the firm has a single integrated pharmaceutical R&D group.

It is recognized that although the variable R&D Decentralization is binary, firms may have

“hybrid” R&D organizations (e.g., Argyres & Silverman, 2004; Arora et al., 2014). In order to

partly control for this heterogeneity in design, we develop two structural control variables: R&D

functional differentiation and corporate decentralization outlined in the control variables section.

portfolio diversity: In order to evaluate the diversity of relevant knowledge in the

established firm, we develop a measure of the diversification of a firm’s drug development

portfolio across therapeutic classes. To create this measure, we estimate the sum of the squared

proportions of drug candidates in each therapeutic class in the portfolio across all phases from pre-

clinical to phase 3 in a focal year. This Herfindahl measure is then subtracted from 1 to develop a

measure that is higher when the diversity of a firm’s drug development portfolio is higher.

Market overlap: In order to evaluate the level of market overlap between the firms, we

25

focus on the therapeutic areas for which the two firms are developing drugs, and draw on a measure

of overlap developed by Bar and Leiponen (2012). The measure is defined as follows

(𝑀𝑀𝑀𝑀)𝑖𝑖𝑖𝑖 = �min {𝑝𝑝𝑖𝑖𝑖𝑖

𝑛𝑛

𝑖𝑖=1

,𝑝𝑝𝑖𝑖𝑖𝑖}

where MOij is the market overlap between firm i and firm j, and pik is the proportion of firm i’s

drugs that are in therapeutic area k. This is a continuous measure with a range of 0 to1, which is

essentially a measure of the extent to which the established firm and the startup are focused on

developing drugs in the same therapeutic areas. A value of 0 indicates that the firms are targeting

distinct therapeutic areas, whereas a value of 1 indicates perfect overlap in the therapeutic areas.

Control Variables. We develop four categories of control variables which could be related

to an entrepreneurial firm’s innovation outcomes as well as the organization design of the

incumbent firm and its existing knowledge base.

First, we control for other structural parameters associated with an established, incumbent

firm’s organization design. These other structural parameters are likely to be correlated with R&D

Decentralization and could impact how effectively a CVC invested entrepreneurial firm performs

with respect to its innovation outcomes through, for example, influencing resource flows to the

entrepreneurial firm. Corporate decentralization represents whether a firm is more functionally

aligned or more divisionally aligned. This variable is estimated using the composition of firms’

TMTs (excluding CEO). We divide the number of business unit leads by the total size of the top

management team. The greater the value of this variable, the more decentralized a firm (Albert,

2018). R&D functional differentiation represents whether the established firms’ research and

development units are integrated across both functions -research and development or are separated

into individual research and development units. This is developed using companies’ TMT

compositions and set to 0 if R&D is functionally integrated under a single Head or 1 if it is

26

functionally disintegrated into separate research and development units with separate heads.

Corporate development is a dummy set to 1 if the established, incumbent firm has a business

development manager role within the top management team in the relevant year.

Second, we control for a variety of aspects associated with the drug development portfolio

of the established firm. Incumbent firms’ structures may be a reflection of their drug development

portfolios. For example, firms with larger portfolios may tend to decentralize R&D. Also, the

composition of firms’ development portfolios may shape how effectively the entrepreneurial firms

in which they invest through their CVC arms perform with respect to their innovation outcomes.

For example, firms with larger portfolios of drugs under development may have a larger set of

experiences to share with the entrepreneurial firms in which they invest which may shape how

effectively these entrepreneurial firms innovate. Preclinical portfolio is the number of drug

candidates that the incumbent firm has in its pre-clinical portfolio. Portfolio123 is the total number

of drug candidates that the incumbent firm has in its drug development portfolio in clinical phases

1, 2 and 3. External is the proportion of drug candidates in the incumbent firm’s portfolio that is

externally sourced. NME is the proportion of drug candidates that are new molecular entities

(Petrova, 2014). Preclinical diversity is the equivalent measure to portfolio diversity for the drug

candidates in a firm’s pre-clinical portfolio. Novelty measures the degree of novelty of drug-

candidates within an incumbent firm’s portfolio. Novelty in drug candidates at the firm-level often

arises in the form of new mechanisms of action, and the use of new materials within a given

therapeutic domain (e.g., Agarwal, Sanseau, & Cardon, 2013; Klueter, 2013; Petrova, 2014;

Swinney & Anthony, 2011). We draw on these features of innovation within the pharmaceutical

industry to develop a measure for a drug candidate’s degree of novelty. The variable, novelty, takes

on the value of 0, 1 or 2. If both the mechanism of action and the origin of material in the broad

27

therapeutic domain (e.g., oncology, diabetes, cardiovascular) are new to the firm, the value is set

at 2. If one of these is new it is set as 1, and if neither are new it is set to 0. We then measure the

average novelty value between 0 and 2 for all the drug-candidates across all phases within an

established firm’s drug development pipeline on an annual basis. Competition measures the degree

of competition incumbent firms face across their development portfolios. This variable is measured

through the sum of squared “market shares” (by drug-candidate count) of drug-candidates within

all development phases per therapeutic class weighted by proportion of overall portfolio subtracted

from 1. Higher values signify that incumbent firms operate in more competitive therapeutic classes

which may provide a greater impetus for them to develop the drug candidates associated with the

firms through which they invest. patent stock measures the discounted total quantity of patent

families (measured in thousands) filed by focal firm (Arora et al., 2014). Firms with a large stock

of patents may choose not to invest as much effort into their relationships with entrepreneurial

firms associated with CVC partnerships. Originality (Hall, Jaffe, & Trajtenberg, 2001) is measured

using the International Patent Classification (IPC) 4-digit technical classifications of the citations

made by a focal patent. Measures of originality produced by the OECD are utilized (Squicciarini,

Dernis, & Criscuolo, 2013). The larger the value, the more original a patent is as it draws from a

broader array of technologies. The maximum originality patent in a family is assigned as the

originality for that family. These values are then used to estimate an average originality per patent

family for each incumbent firm-year. Established firms that generate more original patents may

limit their support of entrepreneurial firms as they perceive there is less incremental benefit.

Third, we control for a variety of characteristics related to the established firms that can be

associated with their organization structures as well as their ability to support CVC-funded

entrepreneurial firms in their innovation efforts. R&D intensity measures the annual spend on R&D

28

by incumbent firms as a proportion of their annual revenues. Size measures the natural log of the

annual sales of each established firm. SGA measures the natural log of a firm’s selling, general and

administrative (SG&A) expenses. New ceo is a dummy variable set to 1 if a new CEO was

appointed in a specific firm-year. Performance measures the annual return on assets of the firm

(Richard, Devinney, Yip, & Johnson, 2009). SBU reflects the total number of operating segments

that established firms report in their financial statements in their annual reporting documents.

Finally, we control for a variety of factors associated with the innovation capability of the

entrepreneurial firms in which the incumbent firms make their investments. Cumulative patents

measures the cumulative patent count of the entrepreneurial firms since their foundations. Forward

cites represents the total number of citations to an entrepreneurial firms’ patents in the 10 years

following the granting of the respective patents. Finally, the extent to which the established firm

can influence the startup may be affected by the number of other VCs who also invest in the same

period. Hence, we control for the number of investors who invest in the startup in the focal year.

RESULTS

The summary statistics for the dataset that we used to test our hypotheses are illustrated in

Table 1. On average the entrepreneurial firms associated with each CVC dyad move 0.12 drug

candidates into Phase 1 clinical trials per year. On average, the incumbent firms have 56.5 drug

candidates in their pre-clinical portfolios. This highlights that each CVC investment represents a

very small proportion of a firm’s drug development portfolio. As suggested by Hypothesis 1 the

correlation between R&D Decentralization and new drugs is negative and significant (p=0.00). On

average, firms with centralized R&D help entrepreneurial firms progress 0.13 drug candidates into

phase 1 clinical trials per year and those with decentralized R&D progress 0.06 drug candidates

(difference is statistically significant, p=0.00, t=3.2). new drugs is also positively correlated with

29

market overlap (p=0.00) and negatively correlated with portfolio diversity (p=0.00).

Table 2 illustrates the key analyses used to test our hypotheses. Model 1 provides support

for hypothesis 1 in which we argue that decentralization of R&D is associated with entrepreneurial

firms progressing fewer drug candidates into phase 1 clinical trials. The effect size is such that

0.11 fewer drug candidates (0.34 standard deviations) move into phase 1 trials for firms that have

decentralized R&D as compared to firms with centralized R&D.

Consistent with Hypothesis 2, we observe that there is a negative interaction between

portfolio diversity and R&D Decentralization in Models 3 (p=0.09) and 5 (p=0.04). This suggests

that when the incumbent firm has a broader knowledge base that the negative impact of R&D

decentralization is enhanced. For the lowest decile of portfolio diversity, we observe that R&D

Decentralization is associated with 0.51 (1.25 standard deviations) more drug candidates going

into phase 1 clinical trials per year within the entrepreneurial firm. However, at the highest decile

of portfolio diversity, we observe that R&D Decentralization is associated with 0.14 (0.44 standard

deviations) fewer drug candidates going into phase 1 clinical trials per year within the

entrepreneurial firm. Figure 1 graphically illustrates this interaction.

However, we see no support for Hypothesis 3 in Models 4 and 5 with the interaction term

between R&D Decentralization and market overlap not being statistically significant.

Additional Analyses

Focusing on Hypothesis 3, it is possible that there is another consideration that our initial

theory development did not consider. If market overlap is low, then the entrepreneurial firm may

have limited absorptive capacity to take advantage of the knowledge provided by the established

firm (Cohen & Levinthal, 1990). As the therapeutic classes in which the established firm has

expertise are so distant from those of the entrepreneurial firm, the scientists within the

30

entrepreneurial firm may find it more challenging to leverage this knowledge. For example, an

entrepreneurial firm that has deep expertise in cardiovascular drugs focused on a specific

mechanism may struggle to leverage expertise from the incumbent in areas, such as, oncology.

This implies that there may be a curvilinear relationship between market overlap and new

drugs. Namely, at low levels of market overlap there is a positive relationship between this variable

and new drugs. Increasing overlap increases the entrepreneurial firm’s ability to leverage the

knowledge within the focal firm as they have increasing familiarity with the knowledge within the

established firm and are more able to use this knowledge to progress their drug candidates into

development. However, as the market overlap increases the marginal benefit provided by the

established firm decreases as it is providing less incremental knowledge. Eventually, at high levels

of market overlap there is negative relationship between this variable and new drugs and

potentially the technologies developed by the entrepreneurial firm could be seen to be competing

with the internally developed, similar technologies leading to the established firm not effectively

collaborating with the entrepreneurial firm i.e. access costs increase. This argumentation suggests

that new drugs and market overlap should exhibit an “inverted U-shape” relationship.

This curvilinear relationship between market overlap and new drugs is illustrated in Model

1 in Table 3. The coefficient for market overlap is statistically significant (p=0.00) and positive,

with the coefficient for market overlap x market overlap being statistically significant (p=0.00)

and negative. This curvilinear relationship is maintained in Model 2 in which we include the

interaction term R&D decentralization x portfolio diversity. Finally, Model 3 accounts for an

interaction between R&D decentralization and market overlap. The relationship between new

drugs and market overlap is illustrated in Figure 2 for firms with centralized and decentralized

R&D. We observe the expected curvilinear relationship for firms with centralized R&D, however

31

for decentralized R&D we simply observe a decline in new drugs as market overlap increases.

For firms with centralized R&D, the rich intra-organizational knowledge flows and the

readier accessibility of a firm’s knowledge base (i.e. lower search costs), we observe a curvilinear

relationship. However, for firms with decentralized R&D, it appears that as market overlap

between the entrepreneurial and established firm increases that the competition between internal

drug development projects and the CVC related drug development projects increases which may

result in vital resources being taken from the CVC related project i.e. access costs dominate and

increase as overlap increases. Due to the reduced access to the firm with decentralized R&D’s

extended knowledge base resulting in higher search costs, we do not observe the initial increase in

new drugs with market overlap that we observe for firms with centralized R&D as startups start to

be able to increasingly leverage the established firm’s knowledge. This is because of lower search

and access costs and the increasing ability of the startup to use this knowledge in firms with

centralized R&D. However, as market overlap increases, access costs increase and the marginal

knowledge benefits decline leading to fewer inventions moving into development.

This suggests a potential “dark side”, from the startup’s perspective, to the established

firm’s managers having high-powered incentives. Managers within R&D units may be concerned

that the CVC-invested firm’s innovation projects may be substitutes (as opposed to complements)

to internal innovation. At low levels of market overlap or when the established firm has a lower

portfolio diversity then this is less of an issue as the CVC firm will be seen more as complement.

Managers are thus incentivized to ensure that these entrepreneurial firms are successful (i.e. search

and access costs are lower) and this is magnified in firms with decentralized R&D. However, at

high levels of market overlap and portfolio diversity, the CVC-invested firm may be seen as more

of a substitute resulting in resources potentially being diverted away from the entrepreneurial firm

32

reducing the number of drug candidates moving into development (i.e. search and access costs are

higher), with the impact being enhanced for firms with decentralized R&D.

DISCUSSION AND CONCLUSION

Summary of Key Findings

Incumbent firms are increasingly relying on external sources of innovation (e.g.,

Chesbrough, 2012; Chesbrough, 2006). However, in leveraging external sources established firms

will generally need to facilitate the activities of the external innovation organization, or

entrepreneurial firm, through providing important resources such as knowledge. An established

firm’s knowledge is embedded within a specific organizational structure. However, we currently

have a limited understanding of how an entrepreneurial firm’s ability to employ the knowledge

associated with its established partner and thereby to innovate is affected by the latter’s structure.

This is a significant gap given the growing proportion of firms’ innovation activities that are

carried out in partnership with other organizations, and the demonstrated importance of

organizational structure in shaping knowledge creation and flows within an organization.

In this paper, we examine how the organizational structure of established firms’ R&D units

can influence the innovation outcomes of the entrepreneurial firms they partner with through CVC

investments. We find that decentralization of R&D of the established firm is associated with

entrepreneurial firms progressing a lower number of inventions into development. The lower levels

of connectedness between the different parts of the firm in these structures leads to higher search

costs for the startup in its efforts to locate and access valuable resources within the established

firm when these firms have decentralized R&D units. These increased search costs outweigh lower

access costs within decentralized R&D units associated with startups actually utilizing the required

resources within the established firm due to the more effective usage of higher powered incentives.

33

Further, the benefits of centralization of R&D are further enhanced when search costs are

elevated by the established firm having a more diverse array of knowledge. However, when the

diversity of the established firm’s knowledge is low, we find that a decentralized structure actually

facilitates the entrepreneurial firm in progressing inventions into development. At low levels of

knowledge diversity, search and access costs are lower for firms with decentralized R&D units.

The resources required are highly localized within the established firm thereby lowering search

costs and the more effective usage of higher powered incentives and reduced bureaucracy lower

access costs. We also find that the benefits of R&D centralization are greatest when established

firms have a moderate degree of market overlap with the startup. At low levels of market overlap,

entrepreneurial firms do not have the absorptive capacity to benefit from the richer knowledge

flows associated with established firms that have centralized R&D units. At high levels of market

overlap, competitive forces between the startup and established firms become more pronounced

and the levels of knowledge exchange between the firms declines. However, in decentralized R&D

units with their increased search costs, as market overlap increases, the benefits to the startup

decline linearly due to an increase in access costs arising from increasing competition.

Contributions

This study provides three key contributions to the extant strategic management literature.

First, this study helps to bring together the literatures on organizational structure and inter-firm

relationships. These research streams have developed largely independently. Research on inter-

firm relationships has largely characterized the firms involved as monoliths, and not really

examined how exchanges that arise in these partnerships may be shaped by the structures of the

firms involved (Lumineau & Oliveira, 2018). Similarly, research on organizational structure has

primarily been internally focused with little consideration of its potential impact on collaboration

34

partners. This study illustrates how internal organization design features in one firm can influence

the performance of its partner with respect to the partner’s innovation. This raises an important

point for collaborative partnerships of all types, in that firms need to consider how the internal

structures of their partners will shape their ability to access the resources they are looking for.

Second, this study makes a theoretical contribution by highlighting that entrepreneurial

firms in which established firms invest face two significant trade-offs. First, consistent with recent

studies (Eklund, 2018), R&D decentralization of the established firm is associated with less

effective access to the established firm’s knowledge base but comes with the benefit of a simpler

structure with less bureaucracy, and closer monitoring of managers within the established firm

who support the entrepreneurial firm. The advantage of R&D decentralization is particularly

pertinent when the established firm has limited diversity within its knowledge base and search

costs for the entrepreneurial firm are lower. Second, if the level of market overlap between the

established and entrepreneurial firm is low, then the entrepreneurial firm’s limited absorptive

capacity with respect to the knowledge the established firm possesses will restrict the benefits of

partnering with an established firm with respect to innovation. On the other hand, if the level of

market overlap is high, the potential for the established firm’s managers to perceive the startup as

a competitive threat becomes greater, which may limit knowledge sharing. Managers in

decentralized R&D structures will be more sensitive to these competitive dynamics, since they are

subject to higher powered incentives that are closely tied to their own unit’s performance.

Based on these trade-offs, entrepreneurial firms will benefit most from established firms

with centralized R&D as compared to those with decentralized R&D when the degree of

knowledge overlap is moderate and the established firm has a higher knowledge diversity. In

contrast, R&D decentralization of the established firm is more favorable for a startup when market

35

overlap with and knowledge diversity of the established firm are both low. In this case there is

limited competition between the startup and established firm reducing access costs and the required

knowledge resources are likely to be highly localized and easier to find reducing search costs.

These insights therefore highlight that in order to fully understand how entrepreneurial

firms can collaborate effectively with established firms, it is critical to understand the accessibility

of knowledge within the established firm both in terms of the entrepreneurial firm’s ability to

locate and leverage this knowledge effectively (search costs), as well as the established firm’s

motivation to share the knowledge (access costs). Thus we help to add to the extant literature that

examines how structure and knowledge flows are related (e.g., Foss, Lyngsie, & Zahra, 2013;

Grigoriou & Rothaermel, 2017; Kim & Anand, 2018; Sorenson, Rivkin, & Fleming, 2006).

Third, these findings also contribute to the growing literature on the impact of CVC funding

on startups’ performances. Prior studies have had conflicting findings. Recent work suggests that

startups’ performances are contingent on access to valuable resources which is dependent on

navigating the complex organizations within which resources are embedded (Alvarez‐Garrido &

Dushnitsky, 2016; Balachandran, 2018; Pahnke et al., 2015). We add to this research by examining

how startups’ access to resources is related to the organizational structure of the established firm,

and by identifying conditions under which different types of structures are most valuable.

Limitations

As with any empirical study this study has a number of limitations that could serve as

avenues for future studies. First, while our empirical specification focuses on changes in

organizational structure of R&D within entrepreneurial-established firm dyads and controls for

many sources of heterogeneity, we cannot make absolute causal claims regarding the relationship

between R&D decentralization of the established firm and entrepreneurial firms’ innovation

36

outcomes. It is challenging to identify natural experiments in which an exogenous shock leads to

established firms changing their organizational structures as this is such a critical managerial

decision. Potentially, laboratory experiments will enable us to address internal validity concerns

(Keum & See, 2017). Second, we have focused on a specific industry in which CVC is highly

prevalent. There is an opportunity to extend this work into other domains to determine whether the

findings we present in this paper are specific to the pharmaceutical industry or can be extended to

other industries. Finally, we have focused on constructs of a firm’s knowledge base determined by

therapeutic area. This is a coarse measure of a firm’s knowledge as each therapeutic area will have

multiple technical domains (e.g. discrete aspects of protein chemistry, understanding of specific

aspects of ion channels). Further, we have no direct examination of the actual flow of knowledge

from the established firm to the entrepreneurial firm, rather we examine this mechanism indirectly.

Future qualitative work could help to provide greater insight to more precise routes through which

knowledge flows from the established firm to the entrepreneurial firm occurs.

Conclusion

Despite these and other limitations, this study provides keen insights into how the internal

organization design of established firms can impact the innovation effectiveness of the

entrepreneurial firms in which they invest. It appears that R&D centralization, through facilitating

greater access to an established firm’s knowledge, enhances an entrepreneurial firm’s innovation

outcomes when the established firm has a diverse knowledge base that has moderate overlap with

that of the entrepreneurial firm. In the end, entrepreneurial firms should take care when selecting

their partners by ensuring that they are appropriately designed to facilitate knowledge access and

have wide interests that only moderately overlap with their own.

37

TABLES AND FIGURES

Table 1: Summary statistics (N = 2497)

MEAN SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 1. new drugs 0.118 0.328 1.00

2. R&D decentralization 0.129 0.336 -0.06 1.00

3. portfolio diversity 0.804 0.127 -0.06 0.15 1.00

4. market overlap 0.125 0.192 0.38 0.05 -0.02 1.00

5. corporate decentralization 0.292 0.209 -0.01 0.07 0.65 0.03 1.00

6. corporate development 0.253 0.435 -0.03 0.11 -0.06 0.05 -0.01 1.00

7. R&D functional differentiation 0.067 0.250 0.10 -0.10 -0.39 0.01 -0.21 -0.02 1.00

8. preclinical portfolio 56.51 29.93 -0.04 0.24 0.66 -0.01 0.57 0.10 -0.17 1.00

9. portfolio123 83.50 48.51 -0.04 0.25 0.58 0.10 0.59 0.12 -0.23 0.75 1.00

10. external 0.445 0.139 0.11 -0.00 -0.25 0.01 -0.06 -0.03 0.27 -0.23 -0.12 1.00

11. nme 0.808 0.117 0.02 -0.10 -0.09 0.05 -0.01 0.17 0.12 0.13 0.16 -0.08 1.00

12. preclinical diversity 0.796 0.132 -0.08 0.14 0.91 -0.03 0.52 -0.09 -0.46 0.60 0.50 -0.45 -0.18 1.00

13. novelty 0.847 0.128 0.10 -0.07 -0.55 -0.02 -0.49 -0.10 0.19 -0.61 -0.66 0.14 -0.20 -0.47 1.00

14. competition 0.935 0.028 0.04 -0.30 -0.54 -0.08 -0.54 -0.14 0.20 -0.82 -0.93 0.18 -0.07 -0.49 0.59 1.00

15. patent stock 2.823 1.567 -0.02 -0.02 0.60 0.09 0.53 -0.13 -0.24 0.50 0.72 -0.14 0.14 0.51 -0.61 -0.61 1.00

16. originality 0.563 0.116 0.05 -0.00 0.03 -0.08 0.02 -0.03 0.18 0.12 -0.14 0.03 0.24 0.02 0.16 0.10 -0.15 1.00

17. sbu 2.556 1.108 -0.05 -0.02 0.50 0.01 0.40 -0.08 -0.05 0.18 0.24 -0.10 -0.15 0.41 -0.40 -0.21 0.57 -0.10 1.00

18. R&D intensity 0.160 0.066 0.07 -0.08 -0.52 0.12 -0.31 0.11 0.17 -0.27 -0.17 0.14 0.26 -0.51 0.27 0.14 -0.24 0.04 -0.37 1.00

19. new ceo 0.113 0.317 -0.07 -0.02 0.09 -0.01 -0.03 0.00 -0.07 0.12 0.11 -0.04 0.04 0.08 -0.12 -0.09 0.09 -0.07 -0.01 -0.02 1.00

20. size 10.17 0.902 -0.12 0.13 0.79 -0.00 0.51 -0.12 -0.38 0.57 0.62 -0.26 0.08 0.73 -0.75 -0.52 0.71 -0.11 0.54 -0.51 0.11 1.00

21. sga 9.429 0.830 -0.11 0.13 0.79 0.00 0.52 -0.12 -0.40 0.57 0.64 -0.24 0.07 0.74 -0.73 -0.54 0.74 -0.12 0.53 -0.48 0.10 0.98 1.00

22. performance 0.127 0.059 -0.02 -0.05 0.17 -0.09 0.08 -0.24 -0.21 -0.02 -0.08 -0.06 -0.48 0.24 0.08 0.11 -0.13 -0.21 -0.11 -0.51 -0.12 0.11 0.09 1.00

23. cumulative patents 12.20 48.13 -0.00 -0.02 0.04 0.00 0.00 0.00 -0.03 -0.02 -0.03 0.02 -0.01 0.03 -0.03 0.04 0.01 -0.01 0.03 -0.04 0.01 0.06 0.06 0.03 1.00

24. number of investors 5.882 2.633 0.00 -0.08 0.30 -0.04 0.28 -0.22 -0.40 -0.05 0.16 0.03 -0.39 0.32 -0.08 -0.08 0.35 -0.21 0.30 -0.26 -0.02 0.38 0.44 0.34 0.05 1.00

25. forward cites 14.99 104.19 -0.02 0.05 0.06 -0.06 0.05 -0.06 -0.01 0.03 -0.07 0.06 -0.13 0.07 0.03 0.03 -0.07 0.07 0.03 -0.08 0.03 0.01 0.02 0.06 0.29 0.07 1.00

38

Table 2: OLS regressions used to test Hypotheses 1 to 3 DV = new drugs 1 2 3 4 5 (H1) R&D Decentralization -0.116** 0.942 -0.0495 1.654+ (0.0220) (0.589) (0.0381) (0.798) portfolio diversity -0.179 -0.121 -0.152 -0.0564 (0.342) (0.304) (0.360) (0.293) Market overlap 0.0737 0.0760 0.135 0.146 (0.247) (0.250) (0.272) (0.275) (H2) R&D Decentralization x portfolio diversity -1.227+ -1.966* (0.677) (0.897) (H3) R&D Decentralization x market overlap -0.433 -0.485 (0.336) (0.337) corporate decentralization 0.0481 -0.0239 -0.0192 -0.0279 -0.0209 (0.0576) (0.0501) (0.0490) (0.0466) (0.0448) corporate development -0.0208 -0.0364 -0.0390 -0.0330 -0.0368 (0.0239) (0.0232) (0.0236) (0.0229) (0.0234) R&D functional differentiation 0.0208 0.0896** 0.0914** 0.0957** 0.0994** (0.0327) (0.0280) (0.0271) (0.0274) (0.0261) preclinical portfolio 0.0007 0.0003 0.0003 0.0005 0.0005 (0.0005) (0.0005) (0.0005) (0.0005) (0.0005) portfolio123 0.0006 0.0014* 0.0012* 0.0016** 0.0013* (0.0007) (0.0005) (0.0005) (0.0005) (0.0005) external -0.0008 -0.0394 -0.0339 -0.0330 -0.0234 (0.0688) (0.0843) (0.0834) (0.0835) (0.0822) nme 0.0598 -0.0440 -0.0362 -0.0439 -0.0315 (0.107) (0.100) (0.104) (0.0959) (0.103) preclinical diversity -0.218 -0.339 -0.331+ -0.348 -0.338+ (0.131) (0.199) (0.184) (0.205) (0.183) novelty 0.288 0.463** 0.420* 0.472** 0.404* (0.169) (0.146) (0.148) (0.149) (0.153) competition 0.519 1.175+ 1.274* 1.289* 1.462* (0.753) (0.575) (0.528) (0.599) (0.519) patent stock -0.0129 -0.0419+ -0.0406* -0.0416+ -0.0394+ (0.0189) (0.0204) (0.0192) (0.0218) (0.0202) originality -0.260+ -0.323* -0.328* -0.333* -0.342** (0.131) (0.123) (0.122) (0.120) (0.119) sbu -0.0096 -0.0069 -0.0064 -0.0064 -0.0055 (0.0010) (0.0093) (0.0089) (0.0088) (0.0080) R&D intensity -0.0206 -0.142 -0.166 -0.124 -0.160+ (0.110) (0.0847) (0.0981) (0.0740) (0.0901) new ceo -0.0066 -0.0098 -0.0057 -0.0166 -0.0108 (0.0138) (0.0131) (0.0142) (0.0179) (0.0181) size 0.0394 -0.0426 -0.0456 -0.0554 -0.0617 (0.0616) (0.0434) (0.0442) (0.0360) (0.0366) sga -0.0290 0.0856+ 0.0945* 0.0728 0.0855+ (0.0620) (0.0426) (0.0440) (0.0422) (0.0430) performance -0.129 -0.453** -0.504** -0.410** -0.487** (0.243) (0.137) (0.151) (0.133) (0.146) cumulative patents -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 (0.00001) (0.0001) (0.0001) (0.0001) (0.0001) number of investors -0.0004 -0.0053 -0.0075 -0.0064 -0.0010+ (0.0060) (0.0058) (0.0058) (0.0056) (0.0051) forward cites 0.0000 -0.0000 -0.0000 -0.0000 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Year fixed effects Y Y Y Y Y Entrepreneurial-established firm dyad fixed effects Y Y Y Y Y Established firm therapeutic area fixed effects Y Y Y Y Y Entrepreneurial firm therapeutic area fixed effects Y Y Y Y Y N 2497 2497 2497 2497 2497 R2 0.372 0.240 0.242 0.249 0.252

Standard errors values in parentheses: p-values + < 0.1, * <0.05, ** <0.01. Errors clustered at incumbent firm level.

39

Table 3: OLS regressions used to examine alternative model specifications

DV = new drugs 1 2 3 R&D decentralization -0.111** 1.054+ 1.165 (0.021) (0.554) (0.674) market overlap 1.245** 1.262** 1.435** (0.298) (0.297) (0.279) portfolio diversity -0.250 -0.187 -0.230 (0.363) (0.319) (0.295) market overlap x market overlap -1.952** -1.976** -2.239** (0.382) (0.380) (0.343) R&D decentralization x portfolio diversity -1.351* -1.381+ (0.638) (0.756) R&D decentralization x market overlap -1.523** (0.254) R&D decentralization x market overlap x market 2.107* overlap (0.773) Controls Y Y Y Year fixed effects Y Y Y Entrepreneurial-established firm dyad fixed effects Y Y Y Established firm therapeutic area fixed effects Y Y Y Entrepreneurial firm therapeutic area fixed effects Y Y Y N 2497 2497 2497 r2 0.252 0.253 0.268

Standard errors in parentheses: + p < 0.1, * p < 0.05, ** p < 0.01. Errors clustered at incumbent firm level. Figure 1: Estimated effect of portfolio diversity on new drugs for firms with centralized R&D (0) and decentralized R&D (1)

0.2

.4.6

new

dru

gs

0 1R&D Decentralization

Low Portfolio Diversity High Portfolio Diversity

40

Figure 2: Estimated effect of market overlap on new drugs for firms with centralized R&D (0) and decentralized R&D (1)

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