Research Paper

Knowledge Development Approaches and Breakthrough

Innovations in Technology-Based New Firms* Dzidziso Samuel Kamuriwo, Charles Baden-Fuller, and Jing Zhang

Compared to large established firms, technology-based new firms (TBNF) seem well placed to produce break- through innovations although questions remain as to their adeptness at subsequent exploitation. Building on the innovation and strategy literatures, the study identifies two different knowledge-development approaches or modes (business models) in TBNFs—internal versus external—and examines their relation to breakthrough innovation and subsequent progression of the product to market. The internal mode assembles knowledge inside the firm to generate its innovations, whereas the external mode relies heavily on alliances to develop and assemble knowl- edge among firms embedded in a creative network. The study uses a unique panel dataset of 69 UK new biotech- nology firms over an 11-year period to explore this issue empirically. The findings show that the external knowledge-development mode is associated with more breakthrough innovations and a faster movement of innova- tions to market. The externally focused mode is not impeded by its relative lack of internal knowledge; it uses partners to access, assemble, and develop a wide scope of knowledge in a flexible manner. In addition, partners provide deep domain expertise to undertake the requisite deep-dives. In contrast, the internal mode has the huge challenge of assembling knowledge resources internally and suffers from a quicker onset of path dependence that impedes the generation of breakthroughs. This study provides a choice of business models (internal or external) that is associated with different breakthrough and speed to market performance outcomes. Going forward, policy makers and managers seeking breakthrough innovations, and speedy progression of the innovations to market should consider the potential resource efficiency of the external mode and the vital role played by collabora- tions—small firm versus large firm and private versus public entities.

Practitioner Points

� In organizing for breakthrough innovation, firms are faced with a choice between external or internal

knowledge-development approaches

� The external mode is better at achieving break- throughs as well as speedily progressing products

under development to market

� The advantages of the external mode are resource efficiency and flexible access to knowledge of

partners

� The internal mode’s performance is hampered by its need for substantial resources and the relative lack of

flexibility

Introduction

A s noted by Colombo, Rossi-Lamastra,

Stephan, and van Krogh (2015), large, estab-

lished incumbent firms confront severe

obstacles when engaging in the development of break-

through innovations (Christensen, 1997; Henderson,

1993). This means smaller and newer firms are expected

to fill the gap. There is a particular group of small firms

that hold special interest—technology-based new firms

(TBNFs)—that are found in many locations, including

Address correspondence to: Dzidziso Samuel Kamuriwo, Cass Busi- ness School, City, University of London, 108 Bunhill Row, EC1Y 8TZ, London. E-mail: d.s.kamuriwo@city.ac.uk. Tel: 1442070408689.

*We thank the editor, associate editor and reviewers of this special issue for their encouragement and insights that helped improve our work. We are grateful for financial support from the EPSRC grant EP/ E037208/1 on Financial and Organizational Innovation in Biotechnol- ogy and the EPSRC grant EP/K039695/1 on Building Better Business Models. We also thank Tong Guan and Sungu Ahn for assistance in data collection. A previous version of this paper without the enriched data set and better theorizing was published in the Academy of Man- agement Proceedings (2009), doi: 10.5465/AMBPP.2009.44256471. We benefited from helpful and extensive discussions with colleagues at Cass Business School and Sussex University (SPRU). We particularly thank Elena Novelli, Hans Frankort, Simone Santoni, Santi Furnari, Michael Hopkins, Vincent Mangematin, Paul Nightingale, Davide Rav- asi, Joanne J. Zhang, and the late John Pool. The views expressed in this paper, errors, and omissions are our own. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

J PROD INNOV MANAG 2017;34(4):492–508 VC 2017 The Authors Journal of Product Innovation Management published by Wiley Periodicals, Inc. on behalf of Product Development & Management Association DOI: 10.1111/jpim.12393

Silicon Valley and Cambridge, USA, and Cambridge and

London, UK.

TBNFs should be well placed to produce breakthrough

innovations because they are relatively more creative and

free from the inertia and myopia of their larger counter-

parts (e.g., Schneider and Veugelers, 2010). Yet, organiz-

ing for breakthrough in dynamic markets, such as in

biotechnology, is challenging for TBNFs because it

requires deep domain expertise to undertake deep-dives

and to assemble and develop a wide scope of knowl-

edge—usually from disparate sources (e.g., Kaplan and

Vakili, 2015; Phene, Fladmoe-Lindquist, and Marsh,

2006). To handle all the necessary development required

for breakthrough, TBNFs look to re-combinations of inter-

nally and externally sourced knowledge (e.g., Carayanno-

poulos, 2009; Powell, Koput, and Smith-Doerr, 1996).

Even though all TBNFS use alliances to source

external knowledge, what is not clearly understood is

whether in organizing for breakthrough TBNFs should

focus their creativity more toward internal or external

knowledge. The second question that is unclear is

which organizing approach, given the resource mobili-

zation challenge that underpins each organizing

approach, allows TBNFs that successfully produce

breakthrough innovations to also progress products

faster toward the market. This question arises from the

tension highlighted by March (1991), that successful

exploration in generating breakthrough innovations

may be far removed from successful exploitation, i.e.,

bringing products to market quickly.

Grant (1996) and Grant and Baden-Fuller (2004) note

that there are two fundamentally different “knowledge-

development approaches or modes” among firms that are

involved in alliances. These modes differ in terms of

their loci of creativity and the way knowledge is assem-

bled and, hence, are likely to yield quite different knowl-

edge outcomes. For the case of TBNFs, the first

organizational approach that we label “internal mode”

involves efficient knowledge integration by organizing

multidisciplinary R&D capabilities within the boundaries

of the firm under hierarchical control, deemed to save

coordination costs and limit expropriation concerns (e.g.,

Cassiman, Di Guardo, and Valentini, 2005; Chesbrough

and Teece, 1996; Grant, 1996; Spithoven, Frantzen, and

Clarysse, 2010). Despite its small size and relative new-

ness, a TBNF can successfully adopt this mode focusing

on internal R&D, supplemented where necessary by

external knowledge typically gained in alliances.

In contrast, the other less-explored “external mode”

avoids large commitments to in-house R&D and in-

house laboratories. Instead, the firm engages external

partners for almost all development work, including

both the assembly of knowledge and its development,

and it uses creative organizational arrangements to

mitigate the anticipated high transaction costs and risks

of loss of control (e.g., Grant and Baden-Fuller, 2004;

Lorenzoni and Baden-Fuller, 1995). The external

mode, typically labeled virtual or hollow, requires a

rich network of partners that hold and develop the nec-

essary knowledge. The existing literature on innova-

tion has tended to down-play the importance of this

second approach––citing issues of opportunism and

coordination costs (see for instance Cassiman et al.,

2005; Chesbrough and Teece, 1996)—yet for the

TBNFs seeking nonstandard outcomes such as speedy

breakthrough innovation, the knowledge access

approach may be very attractive because of the lower

commitment, greater flexibility, and the possibility of

accessing knowledge over a wider domain.

BIOGRAPHICAL SKETCHES

Dr. Dzidziso Samuel Kamuriwo is a senior lecturer in strategy at

Cass Business School, City, University of London. His research focus

is knowledge and innovation strategies using alliances and acquisi-

tions. His other research is on board-level knowledge and network

strategies and how they relate to important business outcomes includ-

ing innovation and economic performance. His research has been pub-

lished in leading innovation and management, journals including,

among others—Research Policy and Leadership Quarterly. Dr.

Kamuriwo holds an MBA and PhD in Management from City Univer-

sity of London, Cass Business School.

Dr. Charles Baden-Fuller is the Centenary Professor of Strategy and

leader of the Strategy Group at Cass Business School, City, University

of London. He is famous for his strategy insights into the management

of mature firms, his work on networked organisations and the manage-

ment of young high technology firms. More recently, he has become

one of the leading global thinkers on the topic of Business Models:

what they are, how they work, how they can be improved, and how

they can be deployed in an increasingly digitalized world. This work is

supported by substantial research grants and a team of academics

based at Cass Business School, Sussex University, LSE, CREATE-

Glasgow, Grenoble EM, and the Wharton School. Charles is a fellow

of the Strategic Management Society and fellow of the Academy of

Social Sciences.

Dr. Jing Zhang has been an assistant professor in management,

Strome College of Business, Old Dominion University since 2012.

Her research spans entrepreneurship, technological innovation, and

knowledge management. She has published more than 20 refereed

publications in journals such as Entrepreneurship: Theory and Prac-

tice, Journal of Business Venturing, Journal of Management, Journal

of Management Studies, Journal of World Business, Strategic Entre-

preneurship Journal, and Research Policy. Before coming to ODU,

she gained her PhD from National University of Singapore, and

worked at Iowa State University (US), Lancaster University (UK), and

City University London (UK) as well as with Lenovo Co. (China). Dr.

Zhang is a member of AIB, AOM, and IACMR.

KNOWLEDGE DEVELOPMENT APPROACHES J PROD INNOV MANAG 2017;34(4):492–508

493

In the theory development section, hypotheses

are formally developed relating the two modes of

organizing knowledge to breakthrough innovation and

subsequent exploitation in product development. Using

the knowledge-development and innovation literatures,

the paper looks at how each mode influences the way

in which key prerequisites for breakthrough innova-

tion, i.e., the assembly of distant and diverse knowl-

edge and the undertaking of deep-dives (e.g., Kaplan

and Vakili, 2015; Phene et al., 2006), are organized.

Even though TBNFs are generally considered rela-

tively flexible (e.g., Schneider and Veugelers, 2010),

the relatively quicker onset of path dependency in the

internally focused mode as the TBNF grows presents

problems that may limit the possibilities of break-

through innovation and fast product development (e.g.,

Cohen and Levinthal, 1990; Ghemawat and del Sol,

1998; McGrath and MacMillan, 2000; Padgett and

Powell, 2003; Puranam, Singh, and Zollo, 2003; Roth-

aermel and Deeds, 2006). The paper suggests that the

external development mode will be more successful at

breakthrough innovation and the related product devel-

opment because the richer network arrangement is

associated with flexibility and “option-like” character-

istics, which allow the TBNF to probe the unexpected

without the extensive commitments and path depen-

dency associated with the internal development mode

(McGrath and MacMillan, 2000).

The setting of new drug development in biotechnol-

ogy is used to explore the validity of our theorizing.

The unique dataset includes 69 UK biotechnology

firms’ knowledge-development modes and product

innovation over an 11-year period from 1995 to 2005.

The empirical work supports the theoretical proposi-

tions. The findings are that after controlling for impor-

tant environmental factors, particularly funding levels,

the externally focused development mode is associated

with superior breakthrough outcomes and related prod-

uct development.

It is revealing to selectively compare two quite

well-known firms in the database: Arrow (a firm with

an internal knowledge mode) and Alizyme (a firm

with an external knowledge mode), both of which

were carefully interviewed by the first author. Arrow

was founded around 1998 and raised approximately

£40 million by the end of 2005. It engaged in ten alli- ance partnerships, seven of which took place in the

first two years of its life. Adopting an internal knowl-

edge-development mode, Arrow integrated alliance

partners’ knowledge into its own laboratories. Its

achievements by the end of 2005 include two U.S.

patents, one project passing Phase I clinical trials, and

several others in the pipeline. However, none of

Arrow’s patents can be considered a “breakthrough.”

In contrast, Alizyme, founded in 1997, raised £59 mil- lion by the end of 2005 and never built a lab and did

not employ “wet bench” scientists but engaged in 18

alliances. Alizyme won six U.S. patents (from work

that was subcontracted to partner firms), and one of

these patents was a “breakthrough” (a potential block-

buster anti-obesity drug). Additionally, Alizyme had

projects passing Phase I clinical trials faster than

Arrow. Founded at similar times, with similar levels of

funding, Arrow (internal mode) was less productive

compared to Alizyme, as Arrow, in particular, did not

achieve any breakthrough patents. 1

This study extends the strategy perspective of inno-

vation management by identifying how two different

knowledge-development approaches that can be

adopted by TBNFs influence breakthrough innovation

and subsequent product development.

Theoretical Framework

The topic of this special issue is breakthrough innova-

tions, defined as high-impact innovations (Conti, Gam-

bardella, and Mariani, 2014; Kaplan and Vakili, 2015;

Phene et al., 2006) or products “that create entirely

new markets or radically change existing ones”

(Colombo et al., 2015). It is typically argued that pro-

ducing breakthrough innovation involves re-

combination of distant and diverse knowledge bases

(e.g., Cassiman et al., 2005; Kaplan and Vakili, 2015)

and the ability to explore complex issues with a deep-

dive into a specific arena (e.g., Padgett and Powell,

2003; Weisberg, 1999). Below, the features of external

and internal knowledge-development modes are first

articulated, particularly how the modes organize the

assembly and subsequent development of knowledge

for breakthrough innovation. Then, the hypotheses on

how the knowledge-development modes relate to

breakthrough innovation and product development rate

will be presented.

External Development Mode

The externally focused knowledge-development mode

is associated with more virtual organizations (Ches-

brough and Teece, 1996), network organizations (e.g.,

1 Neither firm exists today—their patent portfolios have been absorbed into other

firms.

494 J PROD INNOV MANAG 2017;34(4):492–508

D. S. KAMURIWO ET AL.

Miles and Snow, 1986) or modular organizations (San-

chez and Mahoney, 1996). The knowledge base of the

firm lies mainly outside its boundaries, with the found-

ers and top management team holding a complemen-

tary base of expertise. In the case of TBNFs, their

teams typically include star scientists with managerial

experience drawn from within the biopharmaceutical

industry (e.g., Powell and Sandholtz, 2012). The exter-

nal mode firm, through its founders and managers,

plays an integrative role in a network of partners orga-

nized to assemble and develop knowledge. The focal

firm role involves raising money, helping with the

design of studies in collaboration with partners (but

not having wet bench scientists), in-licensing of IP for

further development, developing the network by

recruiting partners (e.g., providing leadership that

includes getting buy-in on the innovation concepts

from potential partners, making use of industry con-

tacts and experience in recruiting partners) and manag-

ing the network (i.e., providing an overall governance

structure and coordination to facilitate knowledge shar-

ing and development and, finally, project managing

the product development process from stage to stage),

(Kamuriwo and Baden-Fuller, 2016).

Why should partner firms cooperate and engage in

such knowledge assembly? Why should they engage in

the deep-dives required for innovation and give up the

intellectual property (IP) rights to the final product?

Partner firms are specialist firms that provide either

specialist or complex services that are usually for only

a segment of the value chain. For example, the biotech

value chain is lengthy and has distinct product devel-

opment stages. Partners are recruited to provide spe-

cialist services, such as chemistry services providers or

specialist testing laboratories. Other partner firms pro-

vide more-complex services, which may cover a dis-

tinct value chain stage (Kamuriwo and Baden-Fuller,

2016). The motivation to participate goes far beyond

cash or future option payments. Some partners are

motivated by being part of a much more valuable net-

work of firms in which the focal firm can add value

by communicating and transferring knowledge gained

across the network (e.g., Nambisan and Sawhney,

2011; Kamuriwo and Baden-Fuller, 2016). Large

incumbents have knowledge already assembled for

another purpose—where the knowledge is not being

fully utilized or is quite complementary to that of the

focal firm. Thus, large incumbent firms may perceive

partnering with TBNFs as a highly profitable use of

already assembled knowledge as well as an opportu-

nity to learn new things by participating with others

that have different perspectives on challenging issues

(Lipparini, Lorenzoni, and Ferriani, 2014; Lorenzoni

and Baden-Fuller, 1995).

The process by which the focal firm assembles

knowledge is quite different from its internally focused

counterparts. It assimilates, or “accommodates” in the

terminology used by Todorova and Durisin (2007),

knowledge through external mechanisms with no pri-

mary intention to integrate external knowledge inter-

nally (e.g., Grant and Baden-Fuller, 2004). These

processes involve network development that avails rel-

evant diverse knowledge to the focal firm for product

development (e.g., Chakma, Calcagno, Behbahani, and

Mojtahedian, 2009; Nambisan and Sawhney, 2011).

In making the above points, we recognize that many

have criticized the capacity of virtual arrangements to

achieve success in any dimension. The criticisms relate

to many issues, such as interfirm transaction costs and

risk of loss of control (e.g., Chesbrough and Teece,

1996), opportunistic risk and other transaction costs asso-

ciated with arm’s-length contracting (Hagedoorn and

Narula, 1996), entrepreneurs underestimating the diffi-

culty of running the external mode (Gulati and Kletter,

2005), and externally oriented firms being overtaken by

their competitors (Askenazy, Thesmar, and Thoenig,

2006). These concerns may be overestimated because

the well-organized externally oriented firm develops

defense mechanisms to minimize such risks. These

mechanisms may include owning the specific intellectual

property rights (IPR) on the innovations they purchase

and ensuring that the large firms gain from the transac-

tion to leave them satisfied (e.g., Katila, Rosenberger,

and Eisenhardt, 2008). To fully appreciate the external

mode, it is valuable to explicate the alternative approach.

Internal Development Mode

The internal development mode is typically associated

with large incumbents and, in this context, with

smaller and newer firms that closely follow the struc-

tures and processes of large firms. It involves assem-

bling, exploring, and exploiting knowledge within the

boundaries of the firm to ensure efficient management

of dense transactional knowledge flows and cross-

domain linkages normally associated with deep-dives

required in breakthrough innovation (e.g., Brusoni and

Prencipe, 2011; Padgett and Powell, 2003). It also

involves developing routines for evaluating, acquiring,

and exploiting external knowledge that fills knowledge

gaps—a process called absorptive capacity (e.g.,

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495

Cohen and Levinthal, 1990). The basic premise of an

internally focused mode is that the firm develops deep

domain expertise and engages in the necessary deep-

dives and knowledge integration activities that produce

breakthroughs (cf. Pammolli, Magazzini, and Ricca-

boni, 2011; Weisberg, 1999). In this context, the firm

needs a set of “higher-level organizing principles” and

a conducive social context to support its knowledge

management activities (Grant, 1996; Kogut and Zan-

der, 1996). These principles outline what has to be

done and, importantly, smooth away internal impedi-

ments (such as haggling and turf wars) through fiat-

based mechanisms (e.g., Conner and Prahalad, 1996).

The principles that underlie these outcomes are a

strong commitment to assembling diverse knowledge

internally (perhaps with alliances), exploring that

knowledge (with deep-dives) and then exploiting that

knowledge (e.g., Weisberg, 1999). A firm requires

deep domain knowledge to understand the foundational

assumptions of knowledge domains, and this knowl-

edge includes knowledge of weaknesses that may need

to be overcome to achieve breakthrough innovations

(Taylor and Greve, 2006).

However, there is an important additional issue that

arises where knowledge covers a wide terrain and where

that knowledge within these different spheres evolves at

different speeds. In such cases, the firm has to make

choices about which knowledge to assemble and where to

take the deep-dives, and these choices may give rise to

path dependency, shutting out some possibilities but

increasing others. This onset of path dependency (focus)

has potential negative effects for achieving breakthrough

innovation because it tends to move the firm forward

based on the refinement of relatively well-known paths

(Sørensen and Stuart, 2000) or on the basis of making use

of well-known heuristics (Chase and Simon, 1973), all of

which reduces the likelihood of using “outside the box

approaches.” In addition, the small TBNF following this

internal mode can be seriously challenged to assemble the

necessary knowledge to innovate—it may take time and

money to assemble the knowledge and build the relevant

processes (e.g., Powell et al., 1996; Sapienza, Autio,

George, and Zahra, 2006). Only those TBNFs that wisely

choose their knowledge terrain can be successful.

Knowledge Development Modes and Breakthrough

Innovations

As noted above, the internal mode of knowledge-devel-

opment encourages dense transactional knowledge flows

and cross-domain linkages that aid breakthrough inno-

vation (e.g., Brusoni and Prencipe, 2011; Padgett and

Powell, 2003). However, the internally focused knowl-

edge-development mode commits early to a technology

approach and limits future search breadth. This commit-

ment leads to path dependency, which is reflected in

the subsequent development of knowledge-sharing pro-

cesses, routines, shared communication codes, and cul-

ture (Cohen and Levinthal, 1990). All of these factors

make the internally focused business model relatively

less flexible (Leonard-Barton, 1992; Zahra and George,

2002). Put another way, the internal mode is likely to

be associated with a narrow scope, fixed purpose, and

limited search that makes the firm’s knowledge base

“sticky” and its direction difficult to reverse. Thus,

although the internal mode may create breakthrough

innovation in its chosen path, it has a relatively limited

capacity to achieve the necessary variation to support a

number of breakthrough innovations, especially in fast-

moving markets.

In contrast, the externally focused knowledge

development mode has more flexibility than the inter-

nal mode because, for example, the firm’s value chain

activities or its components can be more easily disen-

gaged or recombined in different ways to react to dif-

ferent opportunities (Schilling and Steensma, 2001).

This means that the external mode can generate more

options that allow the search to range over a far

wider area (Grant, 1996; McGrath and MacMillan,

2000) and is less likely to suffer from the onset of

core rigidities than the internal mode (Leonard-Bar-

ton, 1992). Therefore, firms with the external mode

will retain a greater willingness and ability to act on

new knowledge. With the external mode, it is not the

firm but the partner who provides the majority of the

resources (including the absorptive capacity; Cohen

and Levinthal, 1990). Thus, although the focal firm

shows a relative lack of commitment, the amount of

resources in the whole system may be higher than

that in the internal mode, giving rise to more break-

through innovations (Lipparini et al., 2014; Lorenzoni

and Baden-Fuller, 1995).

As noted above, it is not sufficient to merely note

the possibility of combinations—the firm has to

engage in deep-dives to make the combinations hap-

pen. Here, the externally organized firm is challenged

because the deep-dives often have to be undertaken by

partners. However, this is possible. The history of

ARM, the UK microchip design company that has

won more than 90% of the world’s mobile phone mar-

ket through external knowledge development, is an

496 J PROD INNOV MANAG 2017;34(4):492–508

D. S. KAMURIWO ET AL.

illustration of just these points. It has used its partners

to search over a wide domain, and it has used its part-

ners to deep-dive and make the new combinations

work. O’Keeffe and Williamson (2002) and subsequent

shareholder presentations and annual reports document

some of the processes adopted by ARM, as well as

how the firm achieved a series of breakthrough

innovations.

H1: Other things being equal, the externally focused knowledge development mode will achieve more breakthrough innovations than the internally focused mode.

Knowledge Development Modes and Product

Development

When considering the organizational arrangements that

favor breakthrough innovations, it is also necessary to con-

sider the issue of timely and prompt exploitation. Society

as a whole, and investors in particular, are impatient. It

was March (1991) who pointed out that tensions between

effective exploration and timely exploitation can often

hinder firm success; and it was Teece (1986) who empha-

sized that firms that engage in the greatest breakthrough

innovation are challenged to exploit those benefits in a

timely manner, citing examples of 19th century innovators

such as Eli Whitney dying in penury before he could reap

the benefits of his highly successful cotton-gin invention.

Carayannopoulos (2009), among others, explains that the

chances of successful exploitation of breakthrough innova-

tions are strongly influenced by whether innovation chal-

lenges the industry assumptions about architecture and

modularity, with far greater chances of success for those

that are not challenging the status quo on many dimen-

sions. The context here, like so many, is one where break-

throughs are radical but do not necessarily require changes

in the industry’s architecture. Regulatory constraints force

all drug development firms, no matter how groundbreak-

ing the drug, to yield to clinical testing and use established

channels of distribution. Yet, there is still a gap between

the breakthrough innovation (typically represented by a

successful patent) and ultimate success—which can only

be bridged if the firm puts its innovation into a product

that can be launched in the market. In the context of this

paper, it is therefore possible that one mode of knowledge

development may favor developing an innovation but be

paradoxically less effective at putting innovations on the

market.

The two modes of organizing—external and inter-

nal knowledge development—have different implica-

tions for how (scarce) resources are allocated toward

progressing products under development to market

(e.g., Conti et al., 2014). The internal mode is associ-

ated with a large firm mentality and relatively slow

internal knowledge sharing. Typically attributed to

functional silos, these barriers keep the R&D laborato-

ries separated from business development units (Tsai,

2002).

In contrast, a TBNF that adopts the external model

is likely to move faster, because the external mode is

based on quick and flexible access to relevant distant

or diverse external knowledge in specialized partners

or large established organizations. The functional silo

issue is also much less evident in the external mode.

Moreover, while having diverse domains in-house may

be good for creative tension, it may also create serious

problems such as divergent objectives, domains com-

peting for organizational resources, and high coordina-

tion costs across domains (Lerner and Merges, 1998).

The strategic logic of the externally focused knowl-

edge development mode also allows for quick access to

new resources or new knowledge, particularly in fast-

paced environments (Kamuriwo and Baden-Fuller,

2016; Volberda, 1996). Just as the internally focused

knowledge development mode is associated with earlier

commitment, the external mode offers flexibility that is

associated with keeping choices open and thus affords

strategic flexibility or speed to change course, a style

that is associated with flexible options (McGrath, 1999;

McGrath and MacMillan, 2000, esp. chapter 11).

Hence, adopting a virtual business model, firms with

the external mode become more reactive and sensitive

to customer needs, and they will achieve faster product

innovation compared to firms with the internal mode.

H2: Other things being equal, for firms that have achieved breakthrough innovations, the externally focused knowledge development mode will be associated with a higher product development rate than the internally focused mode.

Methodology

Research Setting, Data, and Sample

The biotechnology sector is associated with break-

through innovations (Lazonick and Tulum, 2011).

Novelty is the objective of all drug development R&D

KNOWLEDGE DEVELOPMENT APPROACHES J PROD INNOV MANAG 2017;34(4):492–508

497

programs because new molecules (drugs) cannot be

patented, tested, and then licensed unless they can be

shown to be novel to both patent examiners and regu-

lators. However, not all drugs are breakthrough

drugs—many merely fill gaps.

This study focuses on TBNFs located in the UK,

where biotech is thriving and the two knowledge-

development modes are prevalent. The UK leads

Europe in biotech development and holds a significant

place in the world (Owen-Smith, Riccaboni, Pammolli,

and Powell, 2002). In the UK, there is plentiful data

on TBNFs because of public disclosure laws (unlike

the United States, where it is very difficult to obtain

data on TBNFs that are not publicly listed because pri-

vate firms are not required to disclose their financial

and organizational characteristics). Despite its pecu-

liarities, this context is economically and socially

important in its own right, and it has features that are

highly relevant to other contexts—it takes knowledge

from well-established domains and develops new

knowledge by a process of search, deep-dives, and

recombination that produces results that clearly change

the world.

All UK biotechnology firms founded between 1995

and 2005 were identified. The data were multi-sourced

from directories of the UK Government DTI Bioscience

unit; the United States Patent and Trademark Office

(USPTO); the British Venture Capital Association;

regional life sciences networking groups; and commer-

cial databases such as BIO Century, VentureXpert, and

FAME that are verified with the UK companies’ regis-

tration office database at Companies House. In line

with past research (Rothaermel and Deeds, 2004), this

study focused on those companies that are involved in

the research, development, and commercialization of

human therapeutics that are placed in the human body

(in vivo) and not those that are used outside the human

body (in vitro therapeutics). This means the study

excluded companies that were involved in other bio-

technology application segments such as specialist prod-

uct or service suppliers, diagnostics, tissue engineering,

drug delivery, medical engineering, and agro-based bio-

technology companies.

From this initial set of 120 companies, each firm’s

history was traced and we collected data for each year

of observation from founding until 2005 or when the

firm either ceased to exist or was acquired, whichever

came first. Those firms that are subsidiaries of estab-

lished firms or of foreign origin were excluded. After

dropping companies because of lack of data, the final

sample consisted of 69 firms.

Measures

Dependent variables. The first dependent variable measures the number of the firm’s patents that turned

out to be a breakthrough innovation in the three-year

window by year t. It takes two steps to calculate this variable. First, the potential influence or importance of

the firm’s inventions in year t was measured. It is widely agreed in the literature that potential influence

can be measured by the net forward cites of a firm’s patents (e.g., Kaplan and Vakili, 2015; Phene et al.,

2006; Trajtenberg, 1990). This was measured by first

identifying a firm’s original patents at year t and then computing for each patent the total number of forward

citations (excluding self-citations) in the ten-year win-

dow from the date of application. Because the data-

base ended in 2005, all patents had an equal ten-year

window of observations.

In the second step, the total number of break-

through innovations a firm has achieved in the three-

year window by year t were identified and counted. Following previous writers (e.g., Kaplan and Vakili,

2015; Phene et al., 2006), the original patents of the

whole dataset were sorted according to their number

of net forward citations received and coded as a break-

through if the patents in year t received the top 5% net forward citations. In robustness checks, breakthrough

innovations for firms receiving the top 10% of all cita-

tions were computed in a similar way. Finally, the

total number of breakthrough innovations in a three-

year window were counted. Thus, the breakthrough

innovation for any firm in year 2000, for instance, is

the sum of the number of breakthrough innovations in

the years between 1998 and 2000. The year window

method is commonly adopted by prior studies that use

patents to measure firm innovation outcomes, as it

attenuates any annual fluctuations and thus captures a

firm’s patenting propensity more accurately (e.g.,

Ahuja, 2000; Rothaermel and Deeds, 2004; Zhang and

Baden-Fuller, 2010). In our robustness checks, a four-

year window was used.

The second dependent variable, product develop- ment rate, is a time-dated hazard rate for the firm’s first product development milestone, phase I clinical

trials. The hazard rate incorporates two pieces of infor-

mation: first, whether the firm reached the product

development milestone in year t, and second, the rate of development (i.e., the time in months it takes from the founding of the firm to its first product develop-

ment milestone of clinical phase I). When a biotech-

nology firm’s products in development have

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D. S. KAMURIWO ET AL.

successfully entered phase I clinical trials, it signals

that the firm has effectively managed to generate and

apply knowledge, a point that is widely acknowledged

in this field (e.g., Rothaermel and Deeds, 2004). Many

firms in this sector do not have commercial products

for a long time, and this measure is thus appropriate

as a measure of a key (albeit interim) milestone suc-

cess (e.g., Rothaermel and Deeds, 2004). In addition,

since the focus was on whether and when the firm is

able to achieve the product development milestone for

those firms that achieve breakthrough innovations, the

sample was limited to those firms that have obtained

at least one breakthrough innovation during the

observed years and excluded firms without any break-

through innovations.

Independent variables: Knowledge development mode. The firms’ knowledge development mode was classified as either internally or externally focused. It

is well documented that TBNFs in biotechnology often

adopt an external mode of knowledge management

(see, for instance, Luukkonen, 2005, on vertical disin-

tegration in young Finnish biotech firms; Mangematin

et al., 2003, on French biotech firms’ knowledge tra-

jectories; and Haagen, Haussler, Harhoff, Murray, and

Rudolph, 2007, on the knowledge structures of young

UK and German biotech firms). Following the logic of

the argument, for each firm in year t, its knowledge management mode was classified as “internally

focused” and set the value as “1” if the firm has a lab-

oratory and “wet bench” scientists to cover upstream

activities of the pharmaceutical value chain. 2

On the

other end, the externally focused mode is coded as

“0,” which applies to virtual firms that do not have

R&D facilities of their own, i.e., that have no “wet

bench” scientists and rely on outsourcing for their

R&D knowledge development activities (e.g., Cavalla,

Flack, and Jennings, 1997; Chakma et al., 2009).

The data on whether a company had a laboratory

was obtained from detailed financial reports of the

firms (from Companies House), filing documents from

the stock exchange (for those firms listed on the stock

exchange), press releases, and industry and company

websites. While the coding was largely based on

objective data, to ensure the reliability of the coding,

five follow-up interviews were first conducted with the

founders of firms to verify the nature of their knowl-

edge approach and to confirm that the coding approach

made sense. Then, two of the co-authors coded the

variables separately, and the inter-rater reliability was

0.93—well above the conventional cut-off point

(Cohen and Cohen, 1983). Finally, all three authors

verified and agreed on the coding. As a result of the

coding, 56 of the 69 firms were consistent in their

knowledge approach throughout their lifetimes, and 13

switched. Of these 13, only 1 switched twice (external

to internal and then back to external). The remaining

firms switched only once, and the most common

change was for a firm to start with the external mode,

then to move to the internal mode.

Below, a few examples are provided to illustrate

how firm knowledge-development modes were classi-

fied. The first, Arrow Therapeutics Limited, was one

of the UK’s premier small biotechnology firms and

employed an internally focused knowledge develop-

ment model. Arrow developed a fully kitted laboratory

with core research capabilities in virology, microbiol-

ogy, and chemistry under the firm hierarchy. At its

peak, Arrow had 60 multidisciplinary R&D employees

out of a total of approximately 80. Arrow’s internal

research capabilities were the primary driver of its

innovation development strategy. In addition, Arrow

had complementary alliances with academic and public

research units, specialist providers, and large pharma-

ceutical companies. The second example is of a virtual

and thus externally focused knowledge development

model typified by Evolutec plc, a UK-based 1998

spin-off from a public research institution. Although

Evolutec managed to raise funds comparable to Arrow,

it had only approximately 11 employees, most of

whom were considered administrative staff. Evolutec

had no R&D facilities or any in-house “wet bench”

scientists. Employees classified as R&D staff were

project managers involved in coordinating the develop-

ment of projects in their partner network. Another

externally focused firm, Alizyme, mentioned earlier,

had 19 employees, including 14 scientists, but (con-

firmed by interview) none of these scientists were

involved as “wet bench” scientists, as there was no

laboratory. The scientists’ main duties included design-

ing R&D studies in conjunction with partner scientists

and monitoring R&D work undertaken by partners.

2 The progression of drug discovery and development stages in biotechnology is

well documented (e.g., Giovannetti and Morrison, 2000; Smith, 2004; Rothaermel

and Deeds, 2004). The first stage is target identification, which largely involves

molecular biology and genomics. The next stage is lead discovery, which is an

iterative process involving chemistry and biochemistry and then a lead optimiza-

tion process that involves technologies such as high throughput screening and

combinatorial chemistry. This stage is followed by pharmaceutical development,

which involves a preclinical phase and three clinical phases of development that

look at the toxicological characteristics and involve pharmacology, chemical

development, pharmaceutics, volunteer studies, etc. Although this listing is not

meant to be exhaustive, it underlies the fact that the process is multidisciplinary,

sequential, and systemic, i.e., there are feedback loops between the distinct

activities.

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499

Controls. Past research has highlighted the impor- tance of several control variables that may impact suc-

cessful firm innovation. Except where indicated, all

controls were time varying.

The most important control is money raised by the

firm, which we expect to have a positive influence on

innovation outcomes. The variable funds measures the cumulative amount of funds raised since the firm’s

founding by year t, measured in £ sterling. This mea- sure indicates the amount of funds the firm is able to

commit to R&D programs both inside and outside the

firm’s boundaries, and it signals capacity to innovate

(e.g., Sørensen and Stuart, 2000). The natural loga-

rithm of funds was used in data analysis due to the

skewed distribution of the variable.

VC backing is included to capture whether a firm was financed by venture capital firms (VCs) in any

particular year, expecting this control to be positively

related to innovation outcomes (e.g., Rothaermel and

Deeds, 2004). Approximately 60% of our sample had

such backing. VCs, as specialist investors attempting

to achieve consistently high returns, can have a sub-

stantial influence on a TBNF’s knowledge building tra-

jectory and strategy and can encourage focus and

effective outcomes through the execution of a fast

growth strategy (Haagen et al., 2007).

A binary variable, listing, was included that takes the value 1 if a firm was listed on a public stock

exchange in year t. Public exchanges are a real alterna- tive for funding small firms that may not want to go the

VC route (e.g., Rothaermel and Deeds, 2004). Only a

small number of sample firms achieved listing during

the time of our research. Public listing could positively

influence a firm’s attitude toward breakthrough innova-

tion (see, for instance, Owen and Hopkins, 2016).

The total number of employees was used as a proxy for firm size. Past researchers in the biotech field

make the point that larger firms are more likely to be

effective at innovation. Although firm size is often

measured in revenues or market share, most biotech

firms do not have significant revenue streams in their

early stages, thus making the measure inappropriate in

this sector (Shan, Walker, and Kogut, 1994). We use

the natural logarithm of employees in data analysis

because of the skewed distribution of the variable.

The number of alliances the firm is involved in at year t are used as a control. Alliances are a key source of resources for the focal firm, and many researchers

argue that alliances improve innovative performance

(e.g., Baum, Calabrese, and Silverman, 2000).

A time invariant dummy, technology type, was included to indicate whether a firm’s technology is

based on small molecule technology or any other type,

such as large molecule biologics. This measure was

used to control for the fact that different technologies

may have different levels of difficulty that may affect

innovative outcomes.

Finally, therapeutic categories were included, mea- sured by the number of customer segments in which

the company has active research projects. The informa-

tion is reported in Bio Century (e.g., Hoang and

Rothaermel, 2010). The measure denotes a firm’s par-

ticipation in different product-market applications. A

biotech firm can be involved in multiproduct innovation

projects whereby each project is classified in terms of a

major therapeutic area that is perhaps more likely to

produce breakthrough innovations (but subject to a

given level of funding, size, and other control factors)

but is less likely to produce a good product-develop-

ment rate. A given level of the TBNF’s resources is

spread over more projects (e.g., Hoang and Rothaermel,

2010). Table 1 summarizes the variables and their

measurements.

Estimation Procedure

The dataset is an unbalanced 11-year panel consisting

of 370 firm-year observations for the 69 firms in our

sample. As noted before, only 13 of the 69 firms have

ever changed the knowledge development mode during

the 11 years, among which 12 firms only changed

once. Therefore, random-effects models were used in

the data analysis. This is because the aim is not to

understand why a firm might switch its knowledge

mode but rather what are the effects of the mode.

Fixed-effects models should be applied only when the

research seeks to analyze the impact of variables that

vary over time (Baltagi, 2008).

To test H1, negative binomial regressions were

modeled because the dependent variable is the count

of breakthrough innovations, and its distribution is

skewed (Phene et al., 2006). To test H2, where the

dependent variable combines the probability of and the

time to an event, i.e., the first key product develop-

ment milestone, Cox propositional hazard models were

employed to perform the event history analysis (e.g.,

Cox, 1972). The results of Cox models can be inter-

preted as follows: “a positive (negative) coefficient

sign would indicate a greater (lower) hazard of the

focal event occurring. Hence, the variable of interest

500 J PROD INNOV MANAG 2017;34(4):492–508

D. S. KAMURIWO ET AL.

leads to a faster (slower) occurrence of the focal event.

Higher (lower) hazard rates, in turn, suggest a larger

(smaller) number of such events within a given time

period” (Hoang and Rothaermel, 2010, p. 745).

Results

Descriptive Analysis

Table 2 provides the descriptive statistics and the cor-

relations between variables. To check for potential

problems with collinearity, the variance inflation fac-

tors (VIFs) were estimated, and they were well below

the recommended limit of 10 (e.g., Stevens, 1992).

The relationships between these variables will be

examined more thoroughly in the regression model

analysis that follows.

The average age of the sample firms was approxi-

mately 5 years, and they had raised an average of

approximately £13 million. Just over 50% of these firms had the internally focused knowledge develop-

ment mode, and most obtained funding from VCs. On

average, all sample firms sourced knowledge from

approximately seven external partners, participated in

two therapeutic categories, employed 25 employees,

and were granted approximately three to four patents.

Table 1. Measurement of the Variables

Variable Measurement

Time varies

for each firm

Breakthrough invention Number of breakthrough patents in 3-year window by year t. Breakthrough patents are defined as the top 5% patents ranked by total net patent citations each patent received in

a 10-year window from application date. Robustness checks use the top 2% and 10%

definitions and a 4-year window.

Yes

Product development rate Dummy variable that takes a value of 1 if and when a firm first announces a product is in

clinical phase I trials

Yes

Time (month) The number of months when a product reaches the clinical phase I trials Yes

Knowledge development

mode

Dummy that takes a value of 1 for internal mode, that is, if the firm has a laboratory and

employs “wet bench scientists” and zero for the external mode

Rarely a

Listing Dummy variable that takes a value of 1 if and when the firm is publicly listed Rarely b

VC backing Dummy variable that takes a value of 1 if and when the firm is venture capital backed and

zero otherwise

Rarely b

Ln (funds) Cumulative funds raised by firm in any given year Yes

Ln (employees) Total number of employees in firm Yes

Alliances Number of alliances announced by firm in year Yes

Therapeutic categories Number of therapeutic categories the firm is reported to engage in as reported by Bio

Century

No

Technology type Dummy variable that takes a value of 1 if the firm R&D program involves small molecules

and zero otherwise

No

a 56 of the 69 firms were consistent in their knowledge approach throughout their lifetimes, and 13 switched. Of these 13, only 1 switched twice

(external to internal and then back to external). The remaining firms switched only once. b A firm’s listing or VC backing status will take the value “0” until it went public or received VC funds. After that, the status will take the value “1.”

Table 2. Descriptive Statistics and Bivariate Correlation Matrix

Mean S.D. Min Max 1 2 3 4 5 6 7 8 9 10

1 Breakthrough innovation 0.19 0.39 0 1 1.00

2 Knowledge develop. mode 0.61 0.49 0 1 0.15 1.00

3 Product development rate 0.23 0.42 0 1 0.18 0.09 1.00

4 Time (months) 47.93 27.95 1 129 0.27 0.24 0.49 1.00

5 Listing 0.08 0.27 0 1 0.32 20.04 0.35 0.31 1.00

6 VC backing 0.63 0.48 0 1 0.00 0.12 0.01 0.12 20.14 1.00

7 Ln (funds) 1.54 1.78 23.00 4.45 0.34 0.50 0.48 0.62 0.26 0.24 1.00

8 Ln (employees) 2.34 1.25 0 4.70 0.29 0.71 0.37 0.53 0.14 0.11 0.80 1.00

9 Alliances 1.32 2.47 0 41 20.09 20.01 20.09 20.09 0.03 0.03 20.01 0.04 1.00

10 Therapeutic categories 1.95 0.99 1 5 20.11 20.10 0.06 0.04 0.09 20.01 0.15 0.04 0.13 1.00

11 Technology type 0.66 0.47 0 1 0.04 0.05 0.04 0.05 0.14 0.12 0.22 0.14 0.07 0.13

p < .05 when coefficient is larger than 0.10.

KNOWLEDGE DEVELOPMENT APPROACHES J PROD INNOV MANAG 2017;34(4):492–508

501

These results are comparable to those obtained in other

management studies in the biotech sector (Mangematin

et al., 2003; Rothaermel and Deeds, 2004).

Hypotheses tests. Tables 3 and 4 report the results of testing H1 and H2, respectively. H1 predicts that

the external knowledge mode is associated with more

breakthrough innovation. A negative binomial regres-

sion is employed. Model 1 in Table 3 is the baseline

model with all control variables, and the variable

knowledge development mode is introduced in model 2. The results in model 2 show that the firms with the

externally focused knowledge-development mode are

associated with more breakthrough innovation outputs

(b 5 20.784, p < .10). The effect size of the knowl- edge development mode was calculated. The result

suggests that the internal mode is associated with

45.7% as many breakthrough innovations as the exter-

nal mode, i.e., is 54.3% less productive, holding all

other variables in the model constant. Overall, H1 is

supported.

H2 suggests that, compared to those with the inter-

nally focused knowledge development mode, firms

with the external mode will be associated with a

higher rate of achieving the milestone that the first

product goes under development in phase I clinical tri-

als; and the sample is restricted to those firms that

achieve a breakthrough innovation. To test this

hypothesis, Cox propositional hazards models were

applied. Model 2 in Table 4 shows that the firms with

the internal mode are significantly and negatively asso-

ciated with achieving the product development mile-

stone (b 5 23.854, p < .05). Again, the effect size of the knowledge-development mode was checked. Using

a 95% confidence interval, the lower bound hazard

ratio is 0.129 for the variable, which suggests that

adopting the externally focused knowledge-develop-

ment mode will increase the probability of achieving

the product innovation milestone and that a firm that

has not yet achieved a milestone by a certain time has

at least 7.7 times (51/0.129) more chance to achieve a

milestone at the next point in time compared to firms

with an internally focused mode. All results lend sup-

port to H2.

Robustness checks. A number of robustness tests on H1 were conducted. First, the effect of eliminating

ln(funds) in the data analysis was examined, since this

control variable is highly correlated with another con-

trol variable ln(employees) (b 5 0.80). Second, a four-

Table 3. Effects of the Knowledge-Development Modes on the Number of Breakthrough Innovation (H1)

Breakthrough (5% and 3-year window)

Breakthrough

(5% and 4-year

window)

Breakthrough

(10% and 3-year

window)

Model 1 Model 2 Model 3 Model 4 Model 5

Listing 1.097** 0.937 1.062* 1.135*** 0.500

(0.487) (0.555) (0.448) (0.352) (0.307)

VC backing 0.959** 1.034** 0.804 1.075*** 20.143

(0.480) (0.474) (0.406)* (0.361) (0.270)

Ln (funds) 0.938*** 0.828*** 0.824*** 0.299

(0.226) (0.222) (0.196) (0.174)

Ln (employees) 0.237 0.533*** 1.149** 0.520*** 0.577**

(0.170) (0.206) (0.202) (0.193) (0.225)

Alliances 20.138 20.156 20.200* 20.189** 20.204**

(0.085) (0.089) (0.096) (0.087) (0.090)

Therapeutic categories 20.294 20.346 20.301 20.316 20.192

(0.173) (0.188) (0.178) (0.161) (0.112)

Technology type 0.389 0.353 0.269 0.418 0.109

(0.354) (0.356) (0.317) (0.298) (0.267)

Knowledge development mode 20.784* 21.147** 20.596* 20.553*

(external50; internal51) (0.459) (0.484) (0.369) (0.308)

Year dummies Included Included Included Included Included

Constant 216.083 216.928 218.277 213.212 214.487

N 373 373 391 373 373

Log pseudo likelihood 2114.69 2113.68 2131.35 2142.47 2208.11

Pseudo R 2

0.3558 0.3614 0.3022 0.3491 0.2103

Coefficients are reported, and standard errors are in parentheses.

*** p < .01, ** p < .05, * p < .1.

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D. S. KAMURIWO ET AL.

year window was adopted to replace the three-year window

when counting the number of breakthrough innovations.

Third, the top 5% of patent citations were substituted by

10% when defining a breakthrough innovation. The effect

of the knowledge mode variable remained, as shown in

Table 3 Models 3–5 respectively. Hence, the result on the

H1 test is robust to a satisfactory level.

A number of robustness tests on H2 were also con-

ducted. For similar reasons as above, ln(fund) in the model

was first excluded. Then, the top 5% of patent citations

were substituted by 10% when defining breakthrough

innovation. The effect of the knowledge mode variable

remained, as shown in Table 4 Models 3–4. Hence, the

result on the H2 test is robust to a satisfactory level.

Additional tests related to the hypotheses were con-

ducted. First, using Cox models, the external mode was

tested for its association with a higher rate of break-

through innovation. The hypothesis is supported, but only

for the first breakthrough innovation (b 5 21.44, p < .05). The finding is consistent with and adds to the original

findings in testing H1. Second, H2 was tested using Probit

models instead of Cox models. Again, the result remained

(b 5 21.92, p < .05). This suggests that the external mode will give firms a higher chance of reaching the

milestone of product innovation, which is consistent with

and adds to the original finding in testing H2.

Endogeneity is a potential concern in this study

because the firm’s choice of knowledge development

mode may be influenced by some variables that also

affect firm innovation outcomes. Most of the obvious

variables that may cause endogeneity were controlled

for, such as firm funds, employee size, number of alli-

ance partners, and type of technologies. The potential

endogeneity issue caused by missing variables is further

minimized with a panel data set (Semykina and Wool-

dridge, 2010). The knowledge development mode repre-

sents a managerial attitude to knowledge development

that occurs before founding and is not often changed.

The interviews carried out as part of this study sug-

gested that such changes occur when a firm hits a crisis

and needs a fundamental change of direction. These

issues are touched on in more detail in the next section.

Discussion

In answer to the call that authors should study the

coordination mechanisms, models, and approaches that

Table 4. Effects of the Knowledge-Development Modes on the Hazard Rate of Reaching the First Product Devel-

opment Milestone (H2)

Breakthrough (5%) Breakthrough (10%)

Model 1 Model 2 Model 3 Model 4

Listing 1.152 0.0639 20.655 0.700

(1.275) (1.454) (1.321) (1.389)

VC backing 0.699 0.685 21.037 1.630

(1.563) (1.965) (2.303) (1.506)

Ln (funds) 20.882 21.681* 21.786**

(0.765) (0.953) (0.890)

Ln (employees) 0.0705 2.057 0.993 1.910

(0.451) (1.318) (0.827) (1.228)

Alliances 20.121 20.123 20.191 20.113

(0.369) (0.368) (0.371) (0.364)

Therapeutic categories 20.737 20.656 21.269 20.406

(0.848) (1.002) (1.437) (0.702)

Technology type 1.192 1.449 0.712 1.181

(1.235) (1.749) (1.396) (1.561)

Knowledge development mode 23.854** 22.916* 23.550**

(external50; internal51) (1.808) (1.536) (1.800)

N 71 71 76 77

chi-square 4.747 11.20 8.303 13.60

df 7 8 7 8

Log likelihood 223.03 219.81 221.54 222.12

Pseudo R 2

0.0934 0.220 0.162 0.235

Coefficients are reported, and standard errors are in parentheses.

** p < .05, * p < .1.

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503

are most effective at producing breakthrough innova-

tions, this study identified two different firm knowl-

edge development modes used by technology-based

new firms (TBNFs): internally versus externally

focused knowledge development. These two modes are

organized differently to meet the prerequisites for

achieving a breakthrough, i.e., assembling distant and

diverse knowledge and undertaking deep-dives (Kaplan

and Vakili, 2015; Phene et al., 2006). The approaches

differ in loci of creativity, how they organize knowl-

edge assembly and subsequent development, and how

they deal with path dependence. For the internal

mode, the locus of creativity is inside the firm, and

knowledge assembly often requires moving knowledge

from outside the firm to inside and developing it inter-

nally. This mode is associated with a strong onset of

path-dependence. In contrast, with the external mode,

the locus of creativity is in the network, and knowl-

edge assembly does not require knowledge transfer—

to the contrary, almost all development is by partners.

This frees the firm from strong path-dependence ten-

dencies. And in terms of resources, the external mode

is far more flexible, because it utilizes the resources of

others rather than assembling its own.

The two modes of knowledge development, as

explained in the paper, are essentially mutually exclu-

sive when applied to small firms. The setting of

TBNFs in the highly creative UK biotechnology drug

industry is used to show how these two development

modes mapped onto the likelihood of breakthrough

innovations and subsequent product development.

Even though the total population of TBNFs is small in

this setting, there appears to be strong evidence that

the external mode was associated with more break-

through innovations and speedier development of prod-

ucts to market. The statistical analysis suggests that

firms with an external mode are more productive in

achieving breakthrough innovations compared to firms

with an internal mode. In addition, adopting the exter-

nal mode will speed up the process of achieving the

subsequent product innovation milestone.

For the internal mode, the creative tension neces-

sary for breakthrough innovation rests primarily on in-

house multi-disciplinary R&D teams with gaps being

filled through alliances. However, the relatively

quicker onset of path dependence is tempered some-

what with the ability of the internal mode to achieve

the requisite variation necessary for greater break-

through productivity, at least relative to the external

mode, which on balance proves to be more adept

because of its flexible access to knowledge.

Additionally, the difficulty of assembling multi-

disciplinary in-house capabilities and building alliances

simultaneously proves to be a relatively greater chal-

lenge that impedes progress for an internal mode firm

(e.g., Sapienza et al., 2006).

The results also show that the external mode is not

necessarily impeded by its inability to undertake deep-

dives—an obvious advantage the internal mode has.

By playing an integrative role in a network of partners

that provide specialist services limited to specific parts

of a value chain, the external mode is able to tap into

and access external knowledge. Sufficient motivation

for partners can be mustered, ranging from financial,

i.e., providing services for a fee, to strategic, where

partners view their involvement as an option on devel-

oping new technology that may be of future value.

The external mode as a focal firm plays a key integral

role that is responsible for the vision, building, devel-

oping, and managing of the network. Additionally,

partners find value in the ability of the focal firm to

share knowledge from across the network or as a prof-

itable use of knowledge that they already have but is

not fully utilized.

Theoretical Implications

This study offers important theoretical implications

regarding the understanding of how TBNFs organize for

breakthrough innovation. First, as emphasized in the spe-

cial issue call, the innovation literature generally consid-

ers start-ups as the natural engines of breakthroughs

because of their creativity and lack of organizational

inertia. This study shows that the loci of creativity in

start-ups is actually varied and is linked to how the firm

is organized. The loci of creativity can either be firmly

within the firm or in its network, depending on the orga-

nizing approach. Moreover, the approaches have differ-

ent underlying assumptions and models, particularly the

learning approach in the alliances formed (e.g., Grant

and Baden-Fuller, 2004; Hoang and Rothaermel, 2005,

2010). More specifically, each mode is associated with a

different approach by which firms can identify distant

knowledge, organize subsequent deep-dives into the

assembled knowledge, and arrange the subsequent codi-

fication and exploitation of that knowledge to yield

potentially breakthrough results (e.g., Brusoni, Prencipe,

and Pavitt, 2001; Padgett and Powell, 2003; Powell

et al., 1996; Prencipe, 1997; Rothaermel and Deeds,

2004).

504 J PROD INNOV MANAG 2017;34(4):492–508

D. S. KAMURIWO ET AL.

This study extends innovation literature that has

hitherto focused on highlighting specific ways in

which firms involve external actors in developing

their knowledge to foster innovations (e.g., Ches-

brough, 2003, 2006). For example, Fey and Birkin-

shaw (2005) examine R&D collaborations generally,

whereas Grimpe and Kaiser (2010) and Laursen and

Salter (2006) look specifically at the role of in-

licensing and acquisition of R&D services. This

paper’s contribution is uniquely about the different

ways in which networks are used by start-ups. In the

internal mode, networks are used to either spur inter-

nal efforts and to fill gaps, whereas in the external

mode, networks are used for both exploration and

exploitation. There is a parallel between the external

development mode and the way individual scientists

(or small groups of scientists) increasingly use col-

laborations embedded in networks. Stephan (2012)

notes that investigators that have a greater external

focus can move away from safe, easily fundable

projects to less easily fundable ones but with uncer-

tain but potentially path-breaking outcomes. Whilst it

may be difficult to infer organizational principles dis-

cussed at the level of the firm to those for individuals

or small groups of investigators, both can benefit

from the same principles.

Second, there is need to re-evaluate the traditional

literature concerning the connection between absorp-

tive capacity and innovation. Absorptive capacity is

normally associated with the firm’s ability to form

effective alliances, absorb knowledge from outside the

firm, and subsequently produce innovations internally

(e.g., Cohen and Levinthal, 1990; Kaplan and Vakili,

2015; Phene et al., 2006). This work suggests that a

TBNF can take an external approach and can utilize

the search capabilities and absorptive capacity of part-

ners when undertaking fundamental research and that

it is partners that can undertake the deep-dives associ-

ated with breakthrough (Weisberg, 1999). External

development approaches do not contradict traditional

notions—they reflect how they may be stretched to

emphasize different innovation outcomes. This is a

topic ripe for further research.

Third, this study suggests that choosing between

different knowledge-development modes is, in effect, a

choice between different risk paths and approaches to

option building (McGrath and MacMillan, 2000).

External knowledge modes seem to be less shackled

by path dependence and use their inherent flexibility

to source knowledge more widely (e.g., Schilling and

Steensma, 2001) and at the same time use partners to

achieve necessary deep-dives. This means that innova-

tion outcomes for the external mode are more likely to

be breakthrough and reach the market more speedily.

Seeing these two paths as a choice between mean-

enhancing innovations versus those that encourage

variance-enhancing outcomes (in particular break-

throughs) also goes some way towards answering the

questions raised by Carayannopoulos and Auster (2010)

and Taylor and Greve (2006). Also, hints are given of

how traditional concerns of agency costs in the external

mode may arise from opportunism or lack of control

(Chesbrough and Teece, 1996) can be overcome. Care-

ful strategies to mitigate these risks include disclosure

strategies (Katila et al., 2008) or the right incentives

(Owen-Smith et al., 2002; Powell et al., 1996).

Fourth, this study contributes to the emerging litera-

ture on business models that labels different activity

systems and governance arrangements between the firm

and external actors as different business models (see,

for instance, Amit and Zott, 2001; Zott and Amit,

2010). Past empirical work has only looked at the effect

of different business model arrangements on profitabil-

ity outcomes, whereas this study extends the work to

breakthrough innovation. Moreover, this investigation

suggests that these business model types can be tied up

to different sets of managerial processes, in line with

emergent thinking about business models as cognitive

types (see, for instance, Baden-Fuller and Haefliger,

2013; Baden-Fuller and Morgan, 2010).

Managerial implications. This study sheds light on a current challenge of the biotechnology industry, with

important implications for policy makers and managers

about the value of small firm–large firm (and public–

private) collaborations that are central in the networks

of the externally organized firms. Recent reviews have

noted that there has been a relative dearth of break-

throughs in biotech for the level of investment that has

been poured into the sector (Owen and Hopkins, 2016;

Pisano, 2006). Pisano, in particular, highlights the

challenges faced by conventional organizations. This

research suggests that the external knowledge mode,

as an organizational arrangement, may be a way for-

ward. It achieves a different (and arguably better) bal-

ance between the tension of searching widely for

external and distant knowledge (Kaplan and Vakili,

2015) and the capacity to undertake deep-dives (Weis-

berg, 1999), two prerequisites that have been

highlighted in the innovation literature as necessary

for achieving breakthroughs.

KNOWLEDGE DEVELOPMENT APPROACHES J PROD INNOV MANAG 2017;34(4):492–508

505

This research also hints that the external mode may

also be resource efficient. This suggests that managers

of TBNFs should seriously consider the external mode

if they value speedy product development that exploits

breakthrough innovations. However, something that

was not investigated in this paper is that the long-term

effectiveness of the external mode depends on the

availability of external partners. The interviews in this

study suggested that government-sponsored laborato-

ries that operate either within universities or are

located within big charities, such as Welcome Trust,

are important potential partners. This means that pub-

lic policy has a potentially important role in fostering

external knowledge development, with potentially

important social benefits. To be more certain, a wider

research project is needed that looks at innovation per-

formance across both large and small firms and incor-

porates the role of public laboratories in multiple

contexts to fully understand the determinants of break-

through innovations and the barriers to their exploita-

tion (Narin, Kimberly, and Olivastro, 1997).

Limitations and Future Research

One of the major limitations of this study is that the

antecedents to the knowledge development mode

choices were not explored, which is constrained by the

scope of our study. Indeed interviews in this study

suggest that the choice of the modes reflect a differ-

ence in orientation of founders, but this leaves open

the question of how these orientations come about.

Future studies will need to model the firms’ choice of

knowledge development modes directly. As mentioned

before, endogeneity is a potential concern because the

firm’s choice of knowledge development mode might

be influenced by variables that also affect firm innova-

tion outcomes. Endogeneity concerns were minimized

by controlling for most of the obvious variables that

may cause endogeneity and by using a panel data set.

A future study on the antecedents to the knowledge

development mode choice is the only way to truly

unpack the endogeneity concern and obtain more-

robust conclusions.

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