Productive opportunities, uncertainty, and science-based ... · portunity, based on novel and often...

Productive opportunities, uncertainty, and science-based firmemergence

Marcela Miozzo & Lori DiVito

Accepted: 23 February 2018# The Author(s) 2018

Abstract We provide greater theoretical precision tothe concept of productive opportunities of Penrose. Weshow firm emergence as a recursive cycle of changingproductive opportunities. We show how those opportu-nities result from the technological base of the firm andare associated with the particular characteristics of thetechnology. We also show how productive opportunitiesrequire the assembly of different internal and externalresources, and therefore partners. We address explicitlyhow the firm and its potential partners perceive uncer-tainty and single out the different mechanisms used bythe firm to address uncertainty—envisioning, pooling,and staging—to secure resources from external partnersand exploit the identified productive opportunities in atimely manner.

Keywords Productive opportunities . Uncertainty .

Science-based firm . Penrose . Partnerships

JEL classification D83 .M13 . O31 . L26

1 Introduction

A key policy concern is to foster entrepreneurialscience-based activity to spur innovation and economicgrowth. Nevertheless, our understanding of how newscience-based firms—research spin-offs from universitydepartments or industrial firms—emerge is still under-developed. These firms must identify a productive op-portunity, based on novel and often disruptive scienceand technology as well as largely tacit knowledgeemerging from the research laboratory (Markman et al.2008), and must frame this emergent knowledge into aviable business opportunity.

Typically emanating from a non-commercial envi-ronment, science-based firms face challenges in devel-oping the necessary resources and competencies and inframing innovations commercially (Mustar et al. 2006;Rothaermel et al. 2007; Siegel et al. 2007; Vohora et al.2004). Existing literature has concentrated on the differ-ent starting resource configurations (Druilhe andGarnsey 2004; Heirman and Clarysse 2004) and com-petencies needed to launch a science-based venturesuccessfully (Rasmussen et al. 2011) and on how theseevolve over time (Miozzo and DiVito 2016). Whilethese contributions offer important insights, we knowrelatively little about how these firms emerge throughidentifying and developing commercially viable pro-ductive opportunities in a context of uncertainty andhow they engage the necessary partners in a timelymanner. We propose that an in-depth investigation intothis emergence process is necessary in order to build amore thorough theoretical framework.

https://doi.org/10.1007/s11187-018-0033-5

M. MiozzoKing’s Business School, King’s College London, Bush House,Strand, London WC2R 2LS, UKe-mail: [email protected]

L. DiVito (*)Amsterdam School of International Business, AmsterdamUniversity of Applied Sciences, Fraijlemaborg, 133 1102CVAmsterdam, The Netherlandse-mail: [email protected]

Small Bus Econ (2020) 54:539–560

/Published online: 27 April 2018

http://crossmark.crossref.org/dialog/?doi=10.1007/s11187-018-0033-5&domain=pdf

To address this question, we build on and extend theconcept of Bproductive opportunities^ developed byPenrose (1959/2009). Penrose’s writings predate thework of evolutionary economists on Schumpeterian in-novation and entrepreneurship as the engine drivingcapitalism (Nelson and Winter 1982; Dosi et al. 1988).As such, we draw on contributions from evolutionaryeconomics to provide greater theoretical precision toPenrose’s concept of productive opportunities. We showfirm emergence as a recursive cycle of changing pro-ductive opportunities. We also show how those oppor-tunities result from the technological base of the firmand are associated with the particular characteristics ofthe technology. We depict how the different opportuni-ties require the assembly and development of differentinternal and external resources, and therefore partners.We address explicitly how the firm and its potentialpartners perceive uncertainty and single out the differentmechanisms used by the firm to address uncertainty—envisioning, pooling, and staging—to secure resourcesfrom external partners and exploit the identified produc-tive opportunities in a timely manner.

Our contribution to the literature is twofold. First, byobserving how new science-based firms identify anddevelop their productive opportunities in a context ofuncertainty, insights into entrepreneurship based on in-novation (Acs et al. 2009) can be gained, which areuseful for understanding the emergence of technologyventures in a dynamic environment in general. Second,by extending Penrose’s concept of Bproductive oppor-tunities,^ we address recent calls for resource-orientedfirm growth studies to build more directly upon Penrose(Lockett et al. 2011; Nason and Wiklund 2015).

We provide next the theoretical framework. Afterthat we describe the research design and data analy-sis. We then present the findings. A discussion andconclusion follow.

2 Theoretical framework

2.1 Productive opportunities and firm emergence

Evolutionary economics and strategic management ofinnovation scholars stress the important role of firms’search process in creating economic opportunities andfinding sources of variety and new combinations ofknowledge (Laursen 2012; Nelson and Winter 1982).For these scholars, knowledge is highly idiosyncratic to

the firm and context-dependent, and does not flow freelybetween organizations. Firms’ search is argued to followestablished technological trajectories (Dosi 1988) and tobe shaped by industry conditions—especially the degreeof technological opportunities, appropriability, and cu-mulativeness of technological knowledge (Breschi et al.2000; Klevorick et al. 1995; Levin et al. 1987).

Technological opportunities arise from scientific ortechnological knowledge and indicate, according toMalerba and Orsenigo (1996, 1997), the likelihood ofinnovation given investment in search. The sources andlevel of technological opportunities differ markedlyamong sectors, because in some sectors opportunitiesarise from scientific breakthroughs in universities,whereas in others from applied R&D or engineeringefforts (Freeman 1982; Rosenberg 1982). Some sectors(such as biopharmaceuticals) have high technologicalopportunities, enabling the easy entry of new entrepre-neurial firms.

For the entrepreneurship literature, in contrast,concern with entrepreneurial or market opportunities(Shane and Venkataraman 2000) has directed theattention of scholars to the early stage of develop-ment of new firms and potential arbitrage profits. Thedebate is polarized between those who suggest thatsuch opportunities are discovered and those whosuggest they are created. One group suggests oppor-tunities exist objectively and that the basis for entre-preneurship activity is unevenly distributed informa-tion across people (Eckhardt and Shane 2003; Shane2000). The second group denies that characteristicsof the opportunity can be discussed meaningfullywithout reference to the entrepreneur (Alvarez andBarney 2007; Alvarez et al. 2013).1

This paper builds on the view that opportunities arecreated over time, which is in line with both the per-spective on technological opportunities from evolution-ary economics and the entrepreneurship Bcreation^ per-spective. These positions have been fruitfully broughttogether by McKelvey (2016), who argues that, in theirsearch processes, firms monitor three types of opportu-nities—technological opportunities, market opportuni-ties, and productive opportunities.We extend this line ofresearch by returning to Penrose’s (1959/2009) work

1 An intermediate position (Sarason et al. 2006), however, holds thatopportunities are subjective but can change considerably during theirdevelopment, gaining greater objectification (Wood and McKinley2010). Others suggest a need for a fundamental re-conceptualizationof the Bopportunity^ concept (Davidsson 2015).

M. Miozzo, L. DiVito540

and attempting to capture the intuition behind Penrose’sconception of productive opportunities, reinterpretingand building on this conception by drawing on contri-butions from evolutionary economics.

We propose here that firm emergence can be regardedas a recursive cycle of changing productive opportuni-ties. We define productive opportunities as the variousproductive possibilities around the firm’s technologicalbases. Penrose argues that the productive activities of afirm are governed by what she calls its Bproductiveopportunity,^ which Bcomprises all of the productivepossibilities that its ‘entrepreneurs’ see and can takeadvantage of^ (p. 28). The conception of the firm as acollection of productive resources and the idea thatresources can provide a variety of different services arekey to understanding the productive opportunities of thefirm. It is the heterogeneity of the productive servicesderived potentially from its resources that gives eachfirm its unique nature. The heterogeneity in the servicesthat can be provided from its material resources enablesthe same resources to be used in different ways and fordifferent aims if the people who work with them havedifferent ideas of how they can be used.

We build on two main insights from Penrose. Thefirst is that the (emergence and) growth of the firm doesnot depend so much on the efficiency with which thefirm is able to organize production as it does on theability to establish one or more Bimpregnable bases^ (orbasic position) from which it can adapt and extend itsoperations in an uncertain world (Penrose 1959/2009, p.121) and that although there are no limits to the fields ofproduction a firm can enter into, there are limits to therate it can enter into new fields of production. Materialand human resources create the subjective productiveopportunity set for each firm. Although there can be arange of objective opportunities open to the firm, theirrecognition is subjective (Druilhe and Garnsey 2004)and depends on access to specialized knowledge. Asfirms grow, they accumulate different resources, includ-ing human andmaterial resources, and the heterogeneityof those resources means that they can be used indifferent ways. We draw on this to explore the need ofthe firm to establish such impregnable bases relying onthe subjective recognition of the entrepreneur.

The second is that resources support different Bpro-duction bases^ or Btechnological bases^ in the firm(Beach type of productive activity that uses machines,processes, skills and raw materials that are complemen-tary and closely associated in the process of

production^) (Penrose 1959/2009, p. 97). This meansthat a move into a new base requires the firm to buildcompetence in a different area of technology. Indeed, thetype of productive opportunities chosen influences theentrepreneurial process and resource requirements, in-cluding partnerships to access the different resourcesrequired to develop and commercialize innovations(Druilhe and Garnsey 2004; Garnsey and Leong2008). Thus, we seek to explore the technological basesavailable to the firm and the resource requirements,including partnerships to develop those bases and accessthe needed resources.

2.2 Productive opportunities and uncertainty

We also start from the position that the emergence offirms (and their search and changing productive op-portunities) unfolds in a context of uncertainty. Un-certainty is a key concept for much of economic anal-ysis, but one which is used in very different ways bydifferent scholars (Lawson 1988). Here we focus ontwo seminal contributions from economics that defineuncertainty as a situation where the totality of possibleoutcomes is unknown and the probabilities associatedwith those that are known are not measurable. The firstcontribution is that of Knight (1921), who was the firstto propose a difference between risk and uncertainty.He argues that risk applies to situations in which boththe set of options and their probability distributionover this set are known. Choices under risk can bemade by standard (optimal) procedure. No such pos-sibility is open to conditions of uncertainty. As arguedby Knight (1921, p. 146), Bin the case of uncertainty ...it is impossible to form a group of instances, becausethe situation dealt with is to a high degree unique.^ ForKnight, uncertainty can be reduced by decreasingthese types of s i tua t ions through grouping(Bconsolidation^) or by selecting people who can bearthe uncertainty (Bspecialization^) (such as science-based entrepreneurs).

The second is that of Keynes (1921/ 1973), who likeKnight regards uncertainty as a situation in which prob-abilities are not numerically determinate, but unlikeKnight does not rely on a binary divide between riskand uncertainty (Lawson 1988). For Keynes, fundamen-tal uncertainty is central to understanding the economyand is not in principle eliminable. Uncertainty is insteadendogenous to the structure of the economy and canvary in degree between different groups and over time

Productive opportunities, uncertainty, and science-based firm emergence 541

(Dow 2014). Keynes claimed that investment is drivenby Banimal spirits,^ and waves of optimistic or pessi-mistic expectations lead to booms and busts. He recom-mended that decision-makers compare courses of actionin terms of Bevidential weight^ or absolute amounts ofrelevant knowledge against Brelevant ignorance.^

We build on two further insights from economics andmanagement studies of uncertainty. The first is that whileuncertainty creates upheaval in the economy, it is a nec-essary condition for entrepreneurial ingenuity and action.Non-orthodox economists, particularly post-Keynesians,argue that economic actors operate in an ever-changingcomplex system and the accompanying uncertaintybrings threats as well as opportunities. Imagination inthe face of uncertainty is a source of novelty. Shackle(1979) and Hay (1980) see the interaction of the entre-preneur/manager’s accumulated experience and his/heringenuity as suggesting various courses of actions (orsets of productive opportunities), each with a pluralityof alternative outcomes and different possibilities (notprobabilities). In this view, entrepreneurial choice is acreative leap of faith. For Loasby (2002), in such acontext, innovating entrepreneurs rely on Bconnectingprinciples^ of association and causation, on contexts ofBincomplete similarity,^ and category-based judgmentsof possibility, which differ between individuals in a firmand firms in an industry (and, moreover, may fail).

A second insight is that uncertainty is a multifacetedconstruct. Beckman et al. (2004) differentiate betweenfirm-specific uncertainty and market uncertainty. Firm-specific uncertainty stems from a variety of sources,including entering a newmarket, acquiring another firm,turnover in top management, and technical uncertainty,which concerns the likelihood of technical success andthe costs associated with that success. Market uncertain-ty is shared across a set of firms and includes competi-tive uncertainty (created when the competitive actionsof a rival influence a firm), demand uncertainty (whichcomes from the general level of demand for anindustry’s products), and input cost uncertainty. In turn,Graffin and Ward (2010) distinguish between technicaluncertainty (the degree to which the capabilities of anactor can be inferred over time based on known perfor-mance dimensions) and performance standard uncer-tainty (which does not relate to the quality of the actor’scapabilities but concerns the standards or yardstickagainst which the actor’s capabilities are to be judgedin order for them to be considered acceptable or desir-able). Performance standard uncertainty is argued to

give rise to equivocal situations and may stem fromtwo sources: (i) if several performance metrics are avail-able, different decision-makers may have conflictinginterpretations of which metric is the appropriate ordesired performance standard and (ii) decision-makersmay prefer ambiguous standards to maintain controlover the assessment process (Graffin and Ward 2010).Although it is not clear whether performance standarduncertainty refers to uncertainty or ambiguity (Ellsberg1961), this contribution suggests that different types ofuncertainty may affect firm emergence.

We thus derive two important insights into the natureand effect of uncertainty on the development of produc-tive opportunities in firm emergence. The first is thatuncertainty is not only created by the emergence of neweconomic structures but can itself be a source of inno-vation and emergence of new economic structures. Thesecond is that uncertainty is multifaceted and needs to beaddressed in a given context of firm emergence by thedifferent organizations involved in decision-making,which may use different types of assessment of theinnovator’s capabilities to provide the resources re-quired to exploit the productive opportunities.

2.3 Extending the concept of productive opportunities:uncertainty and emergence of science-based firms

We propose an extension of Penrose’s concept of pro-ductive opportunities in two ways. First, we suggest thatby giving greater attention to entrepreneurship inscience-based sectors, we can gain deeper insights intothe relation between the development of productiveopportunities and uncertainty. Penrose’s work portraystechnology resources as almost versatile or fungible(Nason and Wiklund 2015). Subsequent research fromevolutionary economists suggests that knowledge isfirm and industry-specific. We think of firm develop-ment and search as resulting from a knowledge base ortechnological base that can expand in new directions.Nevertheless, research demonstrates that leveraging andapplying such technology to additional markets is notsimple. A firm must build complementary assets toserve those markets (Teece 1986; Tripsas 1997) orBdelink^ such technology from existing products andre-link to new ones (Danneels 2002).

Science-based entrepreneurship is itself peculiar,as advances proceed along established technologicaltrajectories and technological opportunities are oftentemporary (Katila and Mang 2003), and those that


identify opportunities need partners to seize them(Almeida et al. 2003). New firms in science-basedsectors face considerable challenges in resolving theuncertainty surrounding the development and produc-tion of an innovation due to the long, complex, andemergent scientific and technological advancementthey are immersed in. This involves much tacit knowl-edge and addressing a unique set of interdependenttechnological and scientific problems (Pisano 2006;von Hippel 1988). Science-based firms are engaged ina unique iterative and inductive process of acquiringand screening information and knowledge, and workat early stages of a technology’s life cycle, whentechnological uncertainty is at its peak (Dosi 1988).Moreover, the uncertainty of science-based entrepre-neurship surrounds not only the focal firm itself butalso the firm’s innovation ecosystem (Adner andKapoor 2010).

These challenges are compounded by the fact thatscience-based firms typically resort to venture capitaland public equity—two funding mechanisms not de-signed for R&D enterprises—as standard accountingmechanisms reflect poorly R&D portfolios (Pisano2006). Thus, they are driven to monetize their intellec-tual property through a corporate partner, but unlikeother high-tech sectors, science-based firms’ knowledgeis not easily tradable; it is complex, involves much tacitknowledge, and is sector-, firm-, and even researcher-specific. Thus, the productive opportunities developedby the firms are in essence the process through whichfirms restructure their productive opportunities (aroundcertain technological bases) in a context of uncertaintyto be an attractive partner for an alliance with anotherorganization.

While innovation collaboration and partnerships havebeen shown to be very important for innovation success(Hagedoorn and Duysters 2002; Mytelka 1990) and es-pecially for entrepreneurial science-based firms (Powellet al. 1996), there are serious obstacles for science-basedfirms to engage the needed partners to exploit the pro-ductive opportunities they identify. Indeed, the short win-dow of opportunity and the different perceptions aboutthe value of the productive opportunity among firms canimpede the process of finding partners to exploit theseopportunities quickly (Katila and Mang 2003). This isbecause tacit knowledge is high in the early stages ofdevelopment of the scientific advances and firm emer-gence, limiting the organizations’ ability to articulate theknowledge of the opportunity to potential partners.

Second, by focusing on the emergence stage ofscience-based firms, we provide further insights intothe relation between the development of productiveopportunities and uncertainty. Firm emergence is char-acterized by great uncertainty regarding the assemblyof resources and the development of new organization-al structures and routines, as firms not only attempt todo somethingnewbut also suffer from lower reputationand legitimacy to access the required external re-sources from partners (Hite and Hesterley 2001). Inthis context, knowledge incompleteness (Dosi andEgidi 1991) precludes decision-making around tradi-tional decision theory (both for the entrepreneurialteam and its partners). In the face of innovation, thereis not just a lack of the necessary information but also alimitation of the problem-solving competence of actorsto frame, recognize or interpret the information andderive a course of action. This is because events be-come partly endogenous to the decision-making pro-cess, with interactions between actions, events, andoutcomes (Dosi and Egidi 1991).

An exploration of the emergence of science-basedfirms can thus offer insights to build on and extendPenrose’s concept of productive opportunities byallowing us to probe further into the relation betweenfirm emergence as the development of productive op-portunities and uncertainty. We draw on this frameworkto address the following questions: how and why doentrepreneurial science-based firms emerge throughidentifying and pursuing their productive opportunitieswhen both their science (and technology) is uncertainand the market for its products or services often does notexist yet? How do they persuade partners to buy in tothese productive opportunities to access the externalrequired resources in a timely manner?

3 Research design

To understand the emergence of a science-based firmas a function of its productive opportunities, we con-ducted a longitudinal, single-case study using processresearch. A process perspective emphasizes the tem-poral sequence and unfolding of events to explainhow something emerges, evolves, or terminates asholistic configurations (Langley et al. 2013). By in-cluding time in our study, we capture the unfoldingand particular process of how a science-based firmBbecomes.^ This is in contrast to variance-based


approaches that ignore time but are able to predictthat A is better than B, yet do not necessarily explainhow or when A occurs (Langley et al. 2013).

From a process perspective, we view the science-basedfirm as engaged in a continuous development of its tech-nological bases and construction of partnerships to accessand develop the resources required in a context of uncer-tainty. We regard organizational phenomena not as anaccomplished event but as an evolving, unfolding process,in which actors make choices interactively and draw onbroader sets of internal and external resources (TsoukasandChia 2002). In this way, science-based firm emergenceand growth, as a function of the constructive identificationand realization of productive opportunities, is anunderexplored phenomenon.

Our temporal period of analysis is not demarcatedby specific beginning and ending events. We are thusnot interested in a performance outcome per se. Wesubscribe to the view held by Langley et al. (2013, p.10) that Ba process perspective would generally viewoutcomes such as organizational performance mea-sured at particular points in time as ephemeral waystations in the ongoing flow of activity … outcomesare probably better understood as inputs that aremade sense of in determining further activity.^ Forour study, the identification and realization of pro-ductive opportunities is at the same time an outcomeof a process and an input for the assembly of re-sources and development of capabilities to pursueand exploit them.

A longitudinal, in-depth single-case study is ap-propriate (Balogun and Johnson 2004), as it allows usto delve deep into the interactions of phenomena incomplex situations (Dubois and Gadde 2002). In-cluding more cases in our research design wouldnot have necessarily increased the explanatory powerof our analysis; in fact, more cases may have reducedthe depth of insight. Our research aim called for theexamination of interdependent variables, or eventsand actions, embedded in time and complex struc-tures. A deeper analysis of a single case with embed-ded sub-cases allowed us to analyze how the produc-tive opportunities change over time and space. Thisapproach enabled us to trace the iterative paths, thetrial and error, of an emerging science-based firm andgave us prolonged and deep interaction with theevents, actions, and outcomes, as they unfolded inthe context of uncertainty. It provided the insight forus to develop more generalized theory on the firm’s

development of productive opportunities around itstechnological bases in a context of uncertainty.

3.1 Case selection and empirical context

We chose to study a biopharmaceutical firm as arevelatory case (Yin 1994), reflecting a real-world,real-time situation. We gained access to the newventure from our prior relation with the founder whenhe was employed at the parent company. He informedus of his intention to start a new firm, and we com-menced data collection in 2013, after his requiredperiod of garden leave.

In line with prior contributions (Rasmussen et al.2011; Vohora et al. 2004), we consider that the firmhas emerged as it has passed an important credibilitythreshold in terms of both adding new team membersbeyond the original founder and gaining early stageinvestment from private sector investors. We follow-ed the venture from its origin to passing this credi-bility threshold, and we continue to follow the ven-ture in real time.

The new venture, BioCure,2 is a spin-off from anestablished biopharmaceutical firm in Manchester, UK.The founders of BioCure, John and Sarah, were priorcolleagues and were both experienced scientists withcomplementary expertise. BioCure was founded withthe purpose of developing and commercializing poten-tial technology underutilized by the parent company.BioCure therefore started with an initial set of scientificand technological knowledge, and possible productiveopportunities, based on the molecular profiling andgrowth of stem cells. The aim of the venture was todevelop and outlicense candidates for novel stem-celltherapeutic drugs in the targeted areas of oncology andregenerative medicine.

3.2 Data collection

Deep probing of single cases requires data collectionfrom multiple sources of data (Yin 1994). Our primarysources of data included semi-structured interviews andcompany documentation. As our case is a new venture,data from historical archival documentation or thirdparty accounts of the firm were limited. We were givenaccess to documentation that BioCure produced or

2 All names are pseudonyms to protect the anonymity of our respon-dents and their collaborators.


received from various actors such as investors, largepharmaceuticals, university research departments, andlawyers. Due to our regularly scheduled interviews, wewere privy to the making of BioCure’s organizationalhistory and captured the evolution longitudinally in realtime. In this sense, our data is a historical record.

We held regularly scheduled semi-structured inter-views approximately every 4 to 6 weeks, totaling 37interviews over a 4-year period (see Table 1). Theseinterviews followed a semi-structured questionnaire,looking back on the past month’s activities and forwardto goals and activities of the coming month. Each inter-view covered the progress of research and developmentactivities and commercial orientation and access andassembly of three specific types of resources: techno-logical development, fundraising, and operations. Theduration of the interview meetings varied between 90and 120 min. Both researchers conducted the interviewsto ensure continuity from one interview to the next,discussion of progress, and agreement on areas of focusin subsequent interview meetings.

The majority of the semi-structured interviews wereheldwith one founder, John; however, in the first year, weheld four interviews with both founders, John and Sarah(after which Sarah left the firm). As we progressed, Johnbecame increasingly candid and used the meetings as away to Bthink out loud.^ Occasionally, he sought ouradvice, for example, on the business proposition or onhow his process compared to others. We were cautious tobe supportive and to provide a sounding board, on theone hand, but on the other not to interfere with the datacollection.3 The founder was unaware of our theoreticalexpectations. He had read our prior work and knew thatwe were interested in the resource acquisition of emerg-ing science-based firms. His candor was rooted in agenuine desire to help us understand science-based firmemergence and more personally in the reflexive practiceof analyzing past events. Interviews were audio recordedand transcribed verbatim.

We also had complete access to all associated compa-ny documentation, including (i) written correspondence

(memos, emails, letters) to and from potential partners,customers, and investors; (ii) contractual agreementsdrafted by lawyers; (iii) drafts and revisions of the busi-ness plan; (iv) presentations to investors and potentialpartners; and (v) financial documents and annual returns.Moreover, we had access to BioCure’s internal projectmanagement system and followed internal messaging onplanning, managing, and communicating about variousbusiness, technology, partner, or fundraising leads.Table 1 provides an overview of the data sources.

The multiple sources of data helped to triangulatethe evidence and establish reliability. Consideringthat our case is a new venture with limited respon-dents, we relied on documentation from potentialpartners and investors to provide additional perspec-tives and to understand the context more fully. Theunit of analysis is not the case itself but rather theproductive opportunities that we identified fromevents and activities (Langley et al. 2013). We vali-dated interpretations of these activities through trian-gulating the data from these different sources.

3.3 Data analysis

Our analytical approach was primarily abductive, aswe continuously iterated between research activitiesand between theoretical and empirical observations(Dubois and Gadde 2002). Although our theoreticalframework provided, deductively, a guideline for ouranalytical constructs, it evolved inductively as we dis-covered emergent themes and insights from our field-work and data (Strauss and Corbin 1997). Based onprior work, we structured our interviews to gather dataabout access and development of three critical re-sources for science-based firms: technological, mana-gerial/operational, and financial resources. This struc-ture provided consistency and focus to our data collec-tion. As we proceeded, we gained a greater empiricalunderstanding of the interactions with potential part-ners and the associated types of uncertainty and re-sponses to such. This empirical understanding helpedus to adjust our theoretical preconceptions, which inturn shaped our subsequent empirical observations.This systematic and iterative process was an essentialpart of our data collection and analysis.

The interview data were transcribed and the tran-scripts and company documentation were coded forevents, activities, and definitions (Gioia et al. 2013).We developed an initial coding template in January

3 Our method is not intended to be action research, which involves fouriterative and repetitive stages, including planning, acting, observing,and reflecting (Lewin 1946). In action research, researchers becomeparticipants (Melrose 2001), anticipating changes and developmentsthat affect activities, learning, and outcomes, and making adjustmentsor using techniques to steer actions and decision-making. Although wecontinue to have regular interviews that encourage reflection, as re-searchers we do not participate in the actions of BioCure’s emergenceand development.


2015, around the time of interview #12.We coded datarelated to technology resources (for example,programs, partners, knowledge, and disease-orientation) and financial resources (such as investors,meetings, and valuations). Each author coded the ini-tial interviews separately, and then we compared anddiscussed the results of our coding, eliminating, col-lapsing, or adding codes as appropriate. As weproceeded with data collection, we discussed theevolving coding template and conducted several itera-tions. Our coding led us to aggregate concepts (such asthose related to resources, technologies, uncertainty,and partnerships), and subsequently we used those asconstructs to identify productive opportunities andguide our analysis (Gioia et al. 2013).

We used various analytical techniques in analyz-ing our data, including (i) development of chronolo-gies of events or temporal sequencing, (ii) develop-ment of narrative summaries, (iii) identification offirst and second order concepts and themes, and (iv)development of key explanatory constructs. We ex-plain each in turn.

As we collected data, we developed chronologiesof events (Langley 1999). Figure 1 provides atimeline of the major events that took place duringdata collection. These chronologies allowed us todevelop temporal brackets that demarcated

qualitative changes in the progression of events.We identified two distinct temporal brackets basedon two different technological bases (or plat-forms)—a mouse model and a human-tissue mod-el—that marked definitional periods of productiveopportunities for BioCure.

Concurrently to the temporal sequencing, wecreated thick narratives that summarized the tempo-ral events. These narratives gave us a more detailedunderstanding of the interrelation of the events andcontext and what type of organizations and actorsand when they were involved (Brown 2006;Pentland 1999; Van de Ven and Poole 2005). Fromthe combined analyses of temporal sequencing andnarratives, and within the two technological bases,we identified six productive opportunities associat-ed with the technologies. We categorized salientcharacteristics of each productive opportunity, in-cluding the time period (an approximate start andend date), the disease orientation, the technical/market rationale, the external partners required toaccess the required resources (financial, scientific,operational), and the outcome. Table 2 summarizesthis information on the productive opportunities.

The analysis of the temporal sequences and nar-ratives illuminated the evolving and changing na-ture of the productive opportunities of the firm

Table 1 Overview of data sources (per year)

2013 2014 2015 2016 2017 Total

Number of interviews 2 10 11 9 5 37 interviews

Number of documents 0 33 57 44 15 149 documents950 pages

Emails, memos, correspondence, agendas 0 10 31 13 5 371 pages

Strategy presentations, business plan 0 6 7 1 1 213 pages

Project proposals, presentations 0 1 1 4 4 164 pages

Fundraising documentation (terms, valuations, applicationsfor investor events)

0 1 7 4 0 55 pages

Financial reports, annual returns, investor updates 0 1 0 8 4 57 pages

Presentations from potential partners 0 0 2 0 0 48 pages

Meeting minutes, action items 0 6 0 0 0 10 pages

Tracking documents for business development, fundraising 0 0 4 1 0 6 pages

Planning, Gantt charts 0 4 0 0 0 4 pages

Mindmaps 0 4 1 0 0 5 pages

Profiles of potential partners 0 0 3 1 0 4 pages

Press articles of BioCure 0 0 1 1 0 2 pages

Scientific publications 0 0 0 0 1 1 page


around its technological bases. We began to inter-pret how the different organizations and actors per-ceived the productive opportunities and how theuncertainty associated with them manifested. Theseinsights were incorporated into our subsequent datacollection and analysis.

Having identified and categorized productive op-portunities, we returned to the coding scheme,making adjustments and continuing to code inter-view transcriptions. We augmented our coding tem-plate with concepts and themes on uncertainty. Asthere were recursive cycles of productive opportu-nity construction, the different sources of uncertain-ty and the mechanisms to address it became moreapparent. As BioCure evolved, the number of or-ganizations and actors involved in productive op-portunity construction and resource acquisition in-creased, and we had access to a growing volume ofdocuments and correspondence. We coded datafrom these sources and added the insights gleanedfrom these to the narrative analysis. Between inter-views #12 and #28, we aggregated iteratively thecoding template, creating second-order themes fromthe first-order coded concepts (see Fig. 2) (Gioiaet al. 2013). This allowed us to progress from thedescriptive surface observations to more abstractprocess theory development.

Finally, we developed key explanatory constructs(or aggregate dimensions) (Corley and Gioia 2004)of access to resources, uncertainty, and mechanismsto address uncertainty, to analyze the developmentof productive opportunities and science-based firmemergence. Although we present our analytical ap-proach as rather consequential, in reality this washardly the case. We cycled through our data severaltimes as we identified emergent patterns andthemes that constituted these constructs and theinterrelation between them.

4 Findings: development of productive opportunitiesand uncertainty

The productive opportunities unfolded subjectively andcontingently based on events, activities, and actions thatcoalesced at particular times. To present our findingsconcisely, we draw on two contrasting productive op-portunities, PO 2.1 and PO 2.3 (see Table 2). These twoproductive opportunities belong to the second

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nce w

ould

be

No

vem

ber 2013

Tw

o p

arts

to t

echnolo

gy

opportu

nity:

i)M

icro bio

logy

ii)

Genetics (based on

mouse p

latform

)

Consid

er

addin

g

mic

ro

bio

logy

serv

ices

Decem

ber 2013

Ste

m c

ell

com

mercia

l

inte

rests

:

i)oncolo

gy

ii)

Regenerative

medic

ine

Jan

uary 2014

First

meeting w

ith

Univ

ersity 1

Technolo

gy opportu

nitie

s

change:

i)N

o 3

rd

founder or

mic

ro b

iolo

gy

ii)

Vie

w g

astr

o in

testinal

(G

I) dis

orders

through presence of

ste

m c

ells

March

2014

GI

tract

ste

m c

ell h

ealth

assessm

ent, ‘from

ste

m t

o

ste

rn’

Meet

with p

riv

ate

investo

r

Identify

new

technolo

gy

opportu

nity –

dia

gnostic

products

:

i)D

ouble

sugar t

est

ii)

Mole

cula

r te

st

Ju

ly 2013

Pitch p

roje

ct

to

Kuw

aiti

research

institu

te

May 2014

Sig

nals

of

obje

ctions f

rom

Univ

ersity 1

Ris

k o

f exposure

Ris

k o

f fa

ilure t

o

rais

e f

undin

g

Ap

ril 2014

Pitch r

evis

ed p

roje

ct

to

Kuw

aiti research

institu

te b

ased o

n

hum

an t

issues

and

hypoth

esis

th

at

leaky

gut

present

in T

ype II

dia

betics.

Meet

with s

everal

dia

gnostic f

irm

s t

o

dis

cuss p

artn

ership

s.

Meet

with s

everal

oth

er scie

ntific

heads o

f G

I groups

at

Univ

ersity 1

Draft heads o

f

term

s fo

r

partn

ership

w

ith

Univ

ersity 1

Ju

ne 2014

Univ

ersity 1 p

ulls

out

of

partn

ership

negotiations

Kuw

aiti proje

ct

approved and

fast-

tracked fo

r

quic

k s

tart

Exchange id

eas

about

dia

gnostic

products

w

ith

priv

ate

in

vesto

r a

nd

Univ

ersity 1

Ph

ase 1 -

Tech

no

lo

gical b

ase o

n

mo

us

e m

od

el p

la

tfo

rm

Ph

ase

2 –

Te

ch

no

lo

gic

al b

as

e o

n h

um

an

tis

su

e p

la

tfo

rm

Ju

ly 2014

Develo

pm

ent

of

busin

ess p

lan

Arabic

identity

–

Arabic

letters

added t

o logo

Sep

tem

ber 2014

Fundrais

ing pitch

giv

en t

o p

ote

ntial

angel in

vesto

rs and

ventu

re capitalists

Approach

incubato

r

No

vem

ber 2014

Attend B

ioE

urope

and

pitch t

o investo

rs

Begin

partn

ership

dis

cussio

n w

ith larg

e

pharm

aceutical

Jan

uary 2015

Follow

up o

n

fundrais

ing le

ads

Large

pharm

aceutical

inte

reste

d if o

ther

fundin

g is r

ais

ed

May 2015

Focus o

n

fundrais

ing

from

angel in

vesto

rs,

netw

orkin

g events

March

2015

Second a

ttem

pt

to c

ollaborate

with U

niv

ersity 1

Operational

–

run t

ests

Univ

esity

1

blo

cks

collaboration

No

vem

ber 2015

Presents

at

Bio

Trin

iity

and o

ther

industr

y events

Generate

s in

terest

and d

evelo

ps

conta

cts

at

large

pharm

aceuticals

Decem

ber 2015

Pitches p

roje

ct

proposals

to t

wo

larg

e

pharm

aceuticals

in

the U

.S.

Sep

tem

ber –

Octo

ber 2015

Pitches t

o large

pharm

aceutical in

Bosto

n

Travels

to

Bio

Japan,

asked t

o

join

Mayo

r of

London’s

trip

Sta

rts

dis

cussio

ns

about

join

t ventu

re

on C

hin

ese

traditio

nal m

edic

ine

Ju

ly 2015

Sta

rts

dis

cussio

n w

ith

Hospital 1 a

bout

lab s

pace a

nd

hum

an t

issue

access

Feb

ru

ary 2016

Reaches a

greem

ent

with H

ospital 1

Angel in

vesto

r

com

mits t

o

investing,

sta

rts

search fo

r a

dditio

nal

investo

rs

May 2016

Pitches o

pen

innovation m

odel

with H

ospital 2

March

2016

Tw

o m

ore

investo

rs com

mit

to investing

Proposes

bio

marker p

roje

ct

for large

pharm

aceutical

Ju

ne 2016

Bio

marker

proje

ct

approved,

but

refe

rs w

ork t

o

prio

r e

mplo

yee

Requeste

d to

propose proje

ct

for H

ospital 2

Sta

rts

proje

ct

with H

ospital 2

on p

eria

nal

fistu

las

Operational

–

runs t

ests

Au

gu

st 2016

Makes

dis

covery w

ith

Hospital 2

Sep

tem

ber 2016

Meets

w

ith

Chin

ese

pharm

aceuticals

in C

hin

a t

o

dis

cuss

collaboration on

Chin

ese

traditio

nal

medic

ine

(pla

nt

based)

PO

1.1

PO

T

1

PO

2.1

PO

2.1

PO

T

1

PO

2.2

PO

2.3

PO

2.4

No

vem

ber 2016

Com

ple

tes 2

nd

round of

seed

financin

g

Pitches w

orkin

g

model fo

r

Crohn’s

Dis

ease

at

Bio

EurpoeDecem

ber 2016

Pitch t

o large

pharm

aceuticals

Crohn’s

peria

nal

fistu

la p

rogram

Gets

bio

psie

s f

rom

Crohn’s

patients

,

runs t

ests

Pitches 2

oth

er

proje

cts

to

large

pharm

aceuticals

March

2017

Technic

al

milesto

ne:

1st

candid

ate

novel

drug t

arget.

Identified m

ast

cell in C

rohn’s

tissue.

Precario

us cash

flow

situation

Subm

itte

d

proposal fo

r

grant

fundin

g

(In

novate

U

K)

Fig.1

Tim

elineof

eventsandactiv

ities


technological base (the human-tissue technology plat-form).4 PO 2.1 is exemplary because it occurred early inthe process of emergence, involved several actors, andintroduced the technical/market rationale that shapedensuing opportunities. PO 2.3 was the productive op-portunity that was realized. By focusing on these twoproductive opportunities, we do not imply that theyhappened in isolation from the other productive oppor-tunities that BioCure was pursuing.

4.1 The gestation of productive opportunities

PO 2.1, which began in December 2013, focused ondeveloping treatments for inflammatory bowel dis-ease (IBD).5 It involved several organizations andactors, but it started with interactions betweenBioCure’s two founders and key scientists at Re-search Institute A at University 1 in London. Itwas pivotal as it enabled the founders to reframethe narrative of the company as they envisionedunlimited access to human tissues. John explained,Bwe could go straight up into a human model. Thereason why that is so compelling and exciting … isthat every patient is a pairwise analysis.^

4 We distinguished two technological bases or platform technologies(see Figure 1). The first, based on mice, enabled the founders to buildon and extend the technology and capabilities that were inherited fromthe founders’ prior employer. The technical rationale for this technol-ogy platform was based on the prior track record of mouse models inoncology supportive care (e.g., gastro-intestinal problems due to tox-icity from chemotherapy). The second, based on human tissues, repre-sented a major change in the technological base and affected profound-ly the formation of the firm. John explained this as Ba game changerbecause that is the preferred tissue [and] it opens up different vistas ofbusiness opportunities.^

5 IBD is a group of inflammatory conditions that affect the digestivetract, mostly the colon and small intestine, and includes Crohn’sdisease and ulcerative colitis.

Table 2 Overview of productive opportunities

Productiveopportunitycharacteristics

Phase 1—mousemodel technologyplatform

Transition—combination

Phase 2—human-tissue technology platform

PO 1.1 PO T1 PO 2.1 PO 2.2 PO 2.3 PO 2.4

Diseaseorientation

Gastro-intestinaldiseases

Diabetes Inflammatory boweldiseases (IBD)

IBD, cancer,biomarkers

Perianal fistulas TraditionalChinesemedicine

Start–end date April to November2013

May 2013 toMay 2014

December 2013 toJune 2014 andMarch to April 2015

July 2014 toJune 2016

June 2016 ongoing October 2015 toDecember 2015

August 2016ongoing

Technicalrationale

Track record ofusing mousemodels

Hypothesis thatdiabetics havecompromisedgut

Advantages of humanmodel (closer topatients)

Advantages ofhuman model(closer topatients)



Marketrationale

GI oncologymarket

Diabetes market Market for GI diseasediagnosis,prevention, andtreatment

GI diseases market GI diseases market GI diseases market

Requiredcriticalresources- Financial- Operational- Scientific

Microbiologyexpertise

FinancingTechnology

validationHuman tissues

Human tissuesClinical knowledge

FinancingTechnology

validation

FinancingTechnology

validation

Financing

Partners Largepharmaceuticals

Diabetes institutein Kuwait

University 1Research Institute A

Institute

Largepharmaceuticals

Hospital 2 Chinesepharmaceutical

University 2

Outcome Abandoned Abandoned Blocked Abandoned Commenced andongoing

Negotiating


BioCure developed significantly its scientific pro-gram during the period of PO 2.1. A flurry ofactivity in the first months defined the scientificprogram in conjunction with Research Institute A’skey scientists. They envisioned PO 2.1 to encom-pass a range of services and products. It was de-scribed in narrative terms, such as Bstem to stern^ orBsausage factory^ to depict the products and servicesoffered at various stages of gastro-intestinal (GI)disease diagnosis,6 prevention, and treatment. Thisincluded personalized medicine, predictive diagno-sis, and stem cell therapies. This full range of prod-ucts and services based on a human tissue

technology platform shaped the pursuit of produc-tive opportunities subsequent to PO 2.1.

To exploit PO 2.1, BioCure sought a partnership withUniversity 1 and its Research Institute A to access criticaltechnological resources, namely human tissues from biop-sies andGI clinical expertise. As the scientific negotiationsprogressed, Research Institute A scientists voiced variousconcerns and requirements. BioCure conceded to all oftheir requirements, which John explained as Bremovingthe obstacles^ and saw the concessions as acceptable for along-term, mutually beneficial partnership.

Once the scientific program was determined throughiterative discussions with key scientists, representativesfrom University 1’s technology transfer office (TTO)became involved in the negotiations. As we explainbelow, the collaboration with University 1 came to ahalt in May 2014 because the TTO had concerns aboutthe viability of BioCure, asking for an equity positionand/or that BioCure raise external financing beforeagreeing to collaborate.

6 Other potential partnerships were also pursued simultaneously torealize a diagnostic product. These partnerships were envisioned asstrategic alliances or joint ventures with small biotech firms. Given thelack of experience and knowledge in developing and producing adiagnostic product, these additional partners were essential to pursuingthe diagnostic element of PO 2.1. But the enthusiasm for these part-nerships and diagnostic product development waned and the partner-ships never materialized.

Assembly of

internal and

external

resources

Mechanisms

to address

uncertainty

Context of

uncertainty

Fundraising from investors /

Funding / Alliances with

pharmaceuticals

Direct and indirect ties to investors

Business proposition, market opportunity

Company valuation

Access to scientific knowledge

/ technological resources

Securing lab space and

equipment, hiring technicians

Prior founder experience / knowledge

Common traditions, shared

experiences/understanding

Complementary knowledge (external)

Location advantages

Contingencies of partnerships

Recruitment – skills required

Assessments from

partners

Feasibility / promise of science, scientific program

Capabilities of founders, management team

Recognition of opportunity

Coordination problems, governance

Problem-solving competence

Creating strategies to

address uncertainty

(envisioning, pooling, staging)

Identifying, anticipating obstacles for ‘buy in’

Establishing credibility, trust, signaling

Environmental scanning and change

(Changing) Narrative around productive

opportunities

Information gathering to assess uncertainty

Consultation with experts, partners

Interpretations of environment, growth expectations

Temporal sequence of

resource acquisition

Outcome of resource acquisition efforts

Order of acquired resources

Type of acquired resources

1st

order concepts 2nd

order themes

Aggregated

(explanatory)

constructs

Fig. 2 Coding template of data structure: analytical concepts, themes, and constructs


Several months later, in February 2015, the op-portunity to collaborate with University 1 throughanother department, the neurogastroenterologygroup, and a large Japanese pharmaceutical firmappeared. In March 2015, BioCure became opera-tional for 2 weeks, hiring temporary technical staffand conducting initial tests, until the TTO againhalted the collaboration. Up to December 2015,John continued to search for other partners (primar-ily large pharmaceuticals) to pursue PO 2.1, basedlargely on the same scientific program.

In contrast, PO 2.3 arose in June 2016 somewhatserendipitously. Whereas in PO 2.1 the sequence ofresource acquisition started with further developmentof the technological resources, in PO 2.3 John placedemphasis on securing access to other resources. In theinterim period between PO 2.1 and 2.3, BioCureengaged in extensive fundraising activities and se-cured initial seed investment of £150K. Simulta-neously, he secured operational resources, rentinglab space at Hospital 1, procuring equipment, andhiring a lab technician. The further development ofthe technological resources followed.

Once BioCure was established at Hospital 1, Johnwas asked to engage in BGrand Round^ presentationsat the adjacent Hospital 2. He expressed his surprise atdiscovering Hospital 2 had important gastro-intestinal(GI) expertise, Bdon’t know why I didn’t know that…Hospital 2 was a GI hospital.^ BioCure was asked tocollaborate on a project on perianal fistulas.7 Initially,the technical rationale to pursue PO 2.3 was weak, butJohn saw it as a way to establish proof of concept,which, as we will explain below, had proven to be anobstacle for persuading large pharmaceuticals to col-laborate on projects. The unmet medical need orBtherapeutic opportunity,^ and a potential market of$2 billion, were also convincing. The collaborationwith Hospital 2 became BioCure’s first collaborativeproject. Shortly after it commenced, the collaborativeteam made the discovery that the cells in anal tissuehad a different composition than cells from intestinaltissue. Although the discovery would not have muchcommercial value for BioCure, it had academic valuefor Hospital 2 scientists and resulted in a scientificpublication.

From the above account of PO 2.1 and PO 2.3, wesee that the temporal sequence of resource assemblyinteracted with the positive or negative outcome ofproductive opportunity realization. In PO 2.1, theemergent firm developed its technological resources(human tissues and various GI expertise) first. Oper-ational resources (lab space, technicians, and clini-cians) were contingent on the development of itstechnological resources. The limited financial re-sources created a perceived lack of credibility forUniversity 1, which BioCure could not eliminatesatisfactorily. In contrast, the development of PO2.3 occurred after the assembly of financial and op-erational resources. BioCure secured financial re-sources based on the scientific program from PO2.1 (albeit slightly modified) and, in turn, thisallowed it to assemble operational resources. Whenthe opportunity to collaborate with Hospital 2 onperianal fistulas arose, BioCure was able to respondquickly and PO 2.3 was realized.

4.2 Uncertainty surrounding productive opportunities

As we followed the different productive opportuni-ties, we began to see that uncertainty was rooted inknowledge incompleteness and problem-solvingcompetence gaps of the emergent firm and its poten-tial partners. We present our evidence on how thisuncertainty manifested and the three mechanisms thatemerge from our analysis—envisioning, pooling, andstaging—that were used to address it.

Uncertainty was apparent and pervasive and wasreflected in the usual request by all of the potentialpartners for better Bproof of concept^ or more Bbaselinedata^ from the technology platform. Many of thesepartners refused to commit resources to the new ventureuntil their requests were met. This led to a Bcatch 22^situation, in which BioCure aimed to enter a collabora-tion with either a university or pharmaceutical firm togenerate data, but the partner requested to see more datafirst before agreeing to collaborate. The founderexpressed frustration regarding these requests for moredata, taking them Bwith a grain of salt.^ John explainedthat large pharmaceutical firms Balways ask for moredata^ and expressed his skepticism about definingcriteria for such further studies and suggested insteadthat it would be more productive to test the pharmaceu-tical firms’ compounds.

7 Perianal fistulas are a complication associated with Crohn’s disease.They are essentially Btunnels^ that develop between the intestine andrectum or anus.


The persistence of this uncertainty hindered the de-velopment of the productive opportunities in a recursivepattern. Leads from trade fairs and networking eventsfor both financial resources and business developmentwere plentiful but resulted in similar requests for base-line data, as evidenced by the following excerpts fromemails from large pharmaceutical firms:

[our] research would like to see more data beforecommitting to investment … we do need supportfrom [our research unit] to go forward with seedfund investment and it is just not there ...The build [discovery platform] is really impres-sive, supported by sustained access to patients, butwe need a few case studies [baseline validation]before continuing discussions.

Uncertainty also manifested in the way partnerorganizations used different assessments to judgeBioCure’s capabilities. In PO 2.1, there were variousgroups from University 1 that were engaged at differ-ent stages and they used varying assessments, whichcontrasts with the simpler assessments in PO 2.3.From our data, we identified three different forms ofassessment—scientific, legal, and reputational – thatwere used (often simultaneously) by the partner orga-nizations. All of these simultaneous and often con-flicting forms of assessment reflected the limitationson the problem-solving competences of potential part-ners to frame and interpret the innovator’s capabili-ties, compromising their buy in. We show evidence ofuse of each form of assessment.

In PO 2.1 and 2.3, the assessment of science capa-bilities and skills and the likelihood of success of thescientific program were scrutinized in meetings andpresentations with scientific peers. The process of sci-entific assessment in the case of PO 2.1 was complex.There were many scientific meetings and brainstorm-ing sessions with different groups of scientists at Uni-versity 1 to Bidentify some central themes.^ The areasof common scientific interests snowballed, as ex-plained by John:

...[At] University 1 there are four or five diseasearea groups that we are looking to engage with.There is inflammatory bowel disease, oncology,diabetes, ageing…, hypermobility syndrome, oe-sophageal reflux [oesophagitis].

John gave several indications of the scientific as-sessment that took place through the tough

questioning and demands. For example, he explained,Bthe IBD guy … was quite abrasive.^ He describedanother scientist as Bthe old school [type], telling me… that [he is] one of the top ten people in [his] field.^

In contrast, the scientific assessment of PO 2.3was quick and simple. John gave a Bone hour GrandRound presentation.^ The number of scientists in-volved in the scientific assessment was limited. Theresponse from scientists at Hospital 2 was Bveryenthusiastic^ and in a follow-up meeting, the founderand the dean of the academic institute agreed todevelop an open innovation collaboration. Shortlythereafter John received a request for a specific pro-ject proposal, which was approved.

We see that in conjunction with the assessment ofscientific capabilities, other forms of assessment wereintroduced, namely legal and reputational. It is difficultto untangle the timing in the use of these forms ofassessment, and in PO 2.1, there is evidence of aniterative and recursive pattern. In PO 2.1, a legal formof assessment was introduced into the scientific discus-sions early on as a two-way confidential disclosureagreement was signed to facilitate the exchange of moredetailed scientific knowledge. Three months after scien-tific discussions started, many questions about legalimplications emerged. Action items and documentationfrom a meeting at the end of March 2014 showed thestart of a broader legal evaluation. An action list showedthe following item: Bwhat is University 1’s policy ontissue ownership?^ indicating concern for potential lia-bility. Another item, Bdoes tissue definition changewhen processed, in particular establishing cryptorganoid cultures?^ implied boundary parameters forpotentially new intellectual property challenges arisingfrom the collaboration.

To address these rising legal considerations, Johnprepared a memo that prioritized the issues, includingintellectual property, tissue ownership, and lab space.He established his credibility by explaining his previ-ous experience in intellectual property and licensingarrangements. At this point, John consulted a law firm,which laterwrote the Bheadsof terms.^ John elaboratedon the boundaries of intellectual property inventorshipand business terms:

The process starts by each party defining ‘back-ground knowledge’ which includes core compe-tences and invention disclosures at the outset ofthe collaboration. Invention disclosures are


submitted to the collaboration’s patent counselthroughout the collaboration. When filing patentapplications, the collaboration’s patent counselwill assess each person’s contribution to the in-vention – regardless of their affiliations – to makea determination on inventorship.

Inventorship rules would determine the businessterms, and he elaborated:

They might have interest in the patent but what-ever deal they have with the university is their deal… If… they get [10%], I don't care. It deals withthe university. If they have a problem with gettingpaid, we say, talk to your university.

The memo outlined BioCure’s position on the issueof tissue ownership. He anticipated Bgenerating a largebank of intestinal organoids and other tissue stem cells^which would be used in the collaboration but also usedseparately by each party freely. Tests would be done onthe university premises. But concerning tissue owner-ship, John was rather indifferent:

I don’t really care if we own them, just as long aswe have restricted use.…We use it for our internalprograms, R&D only, with them, and withpharma. We would not sell the tissues … oncewe get these hundreds of samples and get thisbigger picture, we will probably never look atthose samples anyway.

Furthermore, the cost of tissue ownership was aconcern. This was not specifically mentioned in thememo but John elaborated:

if they own the tissues… then they can pay forstorage. … we don’t want to be responsible forthat. … it’s not an area to ‘go to the mat’ and beugly over, especially if you realize that once yougot the tissues and have that original discovery,you don’t really need them any more.

The issue of tissue ownership was not a trivial matter,and agreeing to have lab space at University 1 was a wayto abate some of the concerns. This became a require-ment for proceeding with the collaboration. The de-mands placed on BioCure during the scientific assess-ment included renting lab space, hiring a clinical nurse,and funding PhDs. This phase of legal assessmentclosed with BioCure requesting a Bmaterial transfer

agreement^ so that they could determine whether theycould Bget stuff done.^ Nevertheless, another form ofassessment was simultaneously at work and impededprogress, namely, reputational assessment.

This form of assessment was most apparent in thecollaboration with University 1 in PO 2.1. The nature ofthe collaboration with University 1 seemed to besurrounded by more uncertainty for all the partners.Once legal assessment began, more actors within Uni-versity 1 became involved in the negotiations, namelybusiness development managers from the TTO. John’sreaction to the first encounter with one of these man-agers was cautiously positive and emphasized the mu-tual benefits of the partnership:

If it is presented as a win-win situation … I thinkthat will fly. If you start hyping that and sayingthese human tissue samples could lead to a curefor this disease, then people will start to say, I wantto be compensated for that. … [It needs to be]very clear that we’re in this game together and thatthere is a mutual benefit.

Nevertheless, the tone of the negotiations changedabruptly when another business development managerentered the negotiations:

… what she did in that meeting was mark herplace in the discussion … It went from an IP[intellectual property] discussion to a general busi-ness discussion. ... we were here to talk about IP,the process of doing this and the licensing terms.All she wanted to talk about was the businessmodel … she said, ‘you guys are the middlemen,you are going to pharma ... We’re the clinicians,you’re the middleman.

John noted the reservations of University 1’s busi-ness development manager and was concerned thatBioCure was being undervalued:

… we are the ones that make the discoveries thattranslate this to the other people. You guys aren’tgoing to do this. You’re not stem cell experts,you’re not biotech people.

Still, University 1’s business development managerwas not fully satisfied and continued to make morerequests, stating that they required B[making] an in-vestment in the company^ and then a request for anequity position instead of an investment because the


manager believed BioCure Bwould be raising moneyon the back ofUniversity 1.^The abrupt turn towards areputational assessment surprised John and Sarah:

We are a young company with no assets. So Isaid what is the exposure?... I suppose the repu-tation. [They] are putting no money in. [It’s a]marginal increase of time to take out a couple ofextra biopsies. We are processing them with acouple of FTEs [full time equivalents] and weare consulting with [them]. So where is the risk?It is primarily reputation. They do not want to beinvolved in anything that is deemed risky. Wecould have an agreement and then we [might notget] the funding and then what would happen? ...it would simply be public knowledge that theyhad a failed engagement.

The reputational scrutiny from University 1 result-ed in greater uncertainty for BioCure. In order forUniversity 1 to discuss internally the possibility ofpursuing the collaboration, the business terms neededto be defined. Yet, there was no agreement on busi-ness terms. Moreover, due to these protracted nego-tiations, John and Sarah began to question the com-petence of University 1 as a potential partner. Prior tothese negotiations, they spoke positively of the pro-fessionalism at University 1, Breally textbook type ofcommunication … I’m very impressed.^ When thediscussions moved to negotiating business terms,their opinion changed:

… when we moved on the heads of terms ... theywere dismissive, like we … low-balled and werereally unreasonable.. I don’t think [University 1]has much experience.

Also, the founders experienced uncertainty andlost confidence in the ability of the head of theResearch Institute A to champion the collaborationinternally:

Our contact had a meeting with the business man-ager and it’s completely gone off the rails ... Lit-erally everything we talked about was shot down… [we] understand that he is not a business per-son. But for him to be so inflexible is a littlepuzzling … I don’t know how much of the actualconversation that happened behind closed doors isaccurately reflected to us.

Having met successfully the scientific assessment,the urgency to remove the concerns associated withreputational assessment was high. Although the foun-ders were keen to Bfigure out a pathway forward withUniversity 1 the business development manager was notswayed:

[our contact] couldn’t imagine a way to go for-ward … [he] tried everything and tried to explainit, but … it sounds like they won’t do anythingwithout [BioCure] getting the funding first.

Negotiations came to halt. BioCure would have toraise funding before the collaboration could take place,but also the new demand from University 1 of owning50% of the company would outstrip any benefit ofcollaborating with University 1 for BioCure.

4.3 Mechanisms to address uncertainty

Our data showed that the founder employed three mech-anisms to address uncertainty in the different productiveopportunities: envisioning, pooling, and staging. Johnemployed these mechanisms strategically at differenttimes or simultaneously.

From the start of the venture, he was continuouslyenvisioning the different future states or scenarios of theemergent firm. In the interviews, there were verbal cuesof these scenarios, such as Bit’s a different story,…,^ Byoureally have to think that through, if …,^ or Bthis is theway it will work,…^ During the pursuit of PO 2.1, thefounder used elaborate scenarios, which provided thebasis for the narrative that persisted through subsequentproductive opportunities. The following illustrates theinitial vision of the collaboration with University 1:

Here is what happens. They give us tissues, wheth-er they are biopsies or resection tissues, andwe takethe tissues and we grow up the stem cells in ourcultures. We can tell them whether the stem cellsare normal or abnormal. Right, if they are abnormalthen we can do whole genome sequencing. Wecould do a number of tests, we can look at doseresponse of drugs, and we could find out what killsspecifically that diseased stem cells and not thehealthy stem cells. Or if we take the tissue, we putit in, and their stem cells are fine, then they don’thave a stem cell disease. [If] they are all bad, thenthe only alternative is stem cell therapy, which isstill very far away. So in the meantime, they get to


learn, is it a stem cell disease, yes or no? If it is astem cell disease, what is the gene defect?We writethem a report, we develop what that report is, a lotof functional studies, we will be doing immunestudies and characterizing them. Then ... what dowe get as BioCure? We get, first, a highly motivat-ed team, technicians, who are going to be givinginformation that is going to be informing clinicaldecisions. That is pretty rare. The second thing isthat as we build our bank we take that asset and wego to pharma and we are looking for drugs that killthose specific human conditions. We have a panelof ten or twelve, however many we have over time,and we can screen their compounds and optimizethese compounds on these tissues. They’ll havecase reports and … we identify drug candidates...

This early envisioning helped the founder developthe narrative he used to get the potential partnersmotivated about the collaboration and to identify theservices of knowledge exchange (e.g., clinical diag-nosis, reports) that BioCure could offer University 1while at the same time advancing BioCure’s researchand development of therapeutic treatments. The foun-der’s envisioning was also very detailed in workingout the terms of intellectual property. He envisionedthe mechanics of inventorship as follows:

… you start off with the collaboration, you recordall the background information, what you knew atthe time you started, including the potential inven-tions, and that sets the base. And then you goforward and you collaborate. As you make inven-tions, we report them to the attorneys and inven-tion disclosures and when it becomes time to writethe patent applications and to identify the inven-tors, the attorneys are the ones who do that. It’s notauthorship, there is no pressure; they look and seewho contributed to it. Clearly there is scope foradding people on because you want to be nice.Once that gets set, the inventors will be allBioCure or all University 1 or both. You try togive them assurances; it’s going to be mostly bothon a lot of this stuff. You’re in a meeting andsomebody picks up on an idea. We know nothingabout the gastrointestinal track; [we] are experts instem cells, so we have advantages there. The mostimportant thing is to get the invention. … the nextstep is the business issues. There are standardbusiness issues for sole or joint IP. The terms ofall that [are decided] in advance and… you never

have to talk about them again. Done. I think thatgave them a lot of reassurance.

The above quotation shows the founder’s envisioningas a deliberate mechanism to address uncertaintythrough helping partners to frame and interpret the in-formation and make a decision to collaborate. For ex-ample, when we questioned the founder about the con-flicts regarding academics’ incentive to publish papers,he envisioned a solution:

We just have a normal disclosure period. Youdon’t actually have to file [patents] until the paperhas been accepted, I think, because technically it issupposed to be confidential when it is beingreviewed, but we may do it earlier. Depends onwhat the invention is.

He developed clear scenarios of the division of laborand stated Byou [the university academics] write thepapers, we write the patents. We are going to be focusedon filing patents, managing IP, managing the business.^

Envisioning was also used with potential investors toaddress uncertainty and establish greater confidence in thelikelihood of the venture’s success. Nevertheless, inves-tors were also concerned about uncertainty arising fromlack of Bproof of concept^ and cited the early stage of thetechnology as the most common obstacle for notinvesting. As BioCure developed, envisioning manifestedin the pitches presented to potential partners and investors,which culminated in a concise and powerful 7-page pre-sentation: Bmy thinking evolved … probably the bestpresentation I’ve ever given.^ Thus, envisioning helpedaddress uncertainty for some partners but not for others,like investors, for which this did not provide sufficientBweight of evidence^ by itself to increase confidence.

In addition to envisioning, the founder engaged inwhat we term pooling, seeking to bundle support forproductive opportunities. The pooling mechanismmanifested in different ways. First, John pooled ad-vice from various experts whose opinion he trusted.In the early period of the firm, he met regularly with atrusted advisor, a board chairman of the parent firm(Bwe have known each other for a long time, havegone through hell and high water together^). Hedescribed him as Bholding his cards close to his chestbut [also] the most honest and fair business man^ heknew. Initially, meetings with the board chairmanallowed John to verify the feasibility of the produc-tive opportunities. In the development of PO 2.1, the


chairman introduced the founders to a consultant forpooling more advice and supporting a potential diag-nostic component of PO 2.1, as well as to diagnosticcompanies that could become potential partners.

Pooling was used to create confidence and maintaininvestors or other potential partners interested in theproductive opportunities. Networking with these poten-tial partners was a key aspect to the success of pooling, inparticular during the fundraising phase. The board chair-man of the parent firm provided introductions to otherventure capitalists but was not transparent about his ownfinancing intention, as John explained:

It might mean that he has no interest other thanbeing a consultant or it might mean that he hasextreme high level of interest and he doesn’t wantto show his hand.

During the search for seed financing (approximately a2-year period between 2014 and 2016), John presentedBioCure’s business proposition at numerous industrytrade shows and venture capitalist events. He continuous-ly pooled information and support from various actors,gaining further network connections, scientific and mar-ket verification, and legitimacy. When John secured around of seed funding in June 2016, his extensive poolingfor support brought the potential investors together andincluded the board chairman as a seed investor. Thepooling mechanism among the investors increased thepositive assessment of the emergent firm’s capabilities.

The pooling mechanism was also used to garnerscientific support and is apparent in PO 2.1. Here,pooling was insufficient by itself as a mechanism toaddress uncertainty. The founders were unable to poolenough support to establish confidence from the busi-ness development manager, despite having the positiveassessment and support of the academics.

Lastly, John engaged in staging, which was a delib-erate phasing of small step progression to address un-certainty. The use of staging was less apparent duringPO 2.1 than in the subsequent POs. However, during thenegotiations of PO 2.1, the founder did employ somestaging or was aware that staging was necessary. Thefollowing quotation exemplifies this:

the relationship is built with lots of communication,a little bit of humor, just to keep things moving.We’ll be down there once a month or less, keeptalking, not be in their face, just stop by and haveshort meetings.

Yet, the data from PO 2.1 provided indications thatperhaps the collaboration was not broken down intosmall enough parts to Bstage^ acceptance. The foundersclaimed that BUniversity 1 were confused about thebusiness structure.^

PO 2.3 is a good example of how John used staging.Proposals or pitches became more focused on particularprojects with clear boundaries and less focused on cre-ating a complex business undertaking together. He con-sciously chose to call the model of collaboration withPO 2.3 Bopen innovation,^ picking up on the Blatesttrend.^ The open innovation model suggested that Hos-pital 2 and BioCure would collaborate on a number ofresearch projects. This was a first step in establishinglegitimacy. The proposal to Hospital 2 differed substan-tially from the complex collaboration that evolvedthrough the discussions with University 1. In the initialproposal, John included a detailed project outline on oneA4 page, with clear boundaries around the researchproject. The staging mechanism allowed the founder toreduce uncertainty and start a project. He described howhe made this transition:

Until recently I have resisted pursuing low valueBstarter^ projects to engage pharmaceutical part-nerships. Instead I have focused on the Ball in^discovery partnership model previously … [but ithas] a higher threshold for eventual engagement.

5 Discussion and conclusion

Our evidence shows that the process of development ofproductive opportunities in the emergence stage of ascience-based firm is not straightforward. The contin-gencies and timing of various events, activities, andactions lead to a myriad of possible productive oppor-tunities (around certain technological bases) for thescience-based firm. However, our data demonstrate thatthe realization of productive opportunities is dependenton the conditions and reduction of uncertainty surround-ing them. In this section, we discuss the process ofproductive opportunity development and realization thatemerged from our data and the implications of our studyfor the extant literature.

The process that we illustrate (Fig. 3) is a snapshot of arecursive cycle of changing productive opportunity devel-opment. Over a period of time, the process repeats with


each cycle of resource assembly and productive opportu-nity development. The process is also confined to the stageof emergence as we argue that emerging science-basedfirms have more difficulties in securing complementaryresources and external partners to realize productive op-portunities because tacit knowledge is particularly high atearly stage of science-based developments.

Although there is a range of possible productive op-portunities open to the science-based firm, depending onits inherited resources (especially technological knowl-edge), there are also limitations due to the need to accessresources from external (network) partners to exploitthem. Each productive opportunity has a high level ofspecificity as the partners define and construct the bound-aries and possibilities of the productive opportunity. Thereis a time-limited nature to these highly specific productiveopportunities, which creates a limited window of oppor-tunity for exploitation. If this window of opportunity ismissed, because uncertainty surrounding the productiveopportunity cannot be sufficiently reduced, then it is un-likely that the productive opportunity will be realized withanother partner constellation. Productive opportunitiesevolve through a process of search, and the changingproductive opportunities chart science-based firms’ possi-bilities and directions of emergence and growth. Althoughit may be possible to go back to an external partner, thetime-limited nature of the opportunity generates a change

in the characteristics of the productive opportunity (dis-ease orientation, technical rationale, market rationale, andresources required). Our data provide a glimpse of thisphenomenon when PO 2.1 was briefly realized with an-other research department at University 1 to test a refinedhypothesis regarding GI diseases.

We show that the iterative cycle in this process lies in theassessment of uncertainty. Uncertainty is both endogenousto innovationanda sourceof firmemergence. It profoundlyaffects the value of the productive opportunities for theinnovating firm and its external partners. Our data revealthat an entrepreneur’s ability to recognize uncertainty andcreate mechanisms to reduce it sufficiently to help its part-ners develop better knowledge for framing and assessingthe firms’ capabilities affects directly the realization ofproductive opportunities. These mechanisms to addressuncertainty influence the problem-solving competence ofactors to interpret information about the productive oppor-tunity and to derive a course of action. The entrepreneurmust also recognize and interpret the different assessments(e.g., scientific, legal, and reputational) used to gauge un-certainty (by the potential partners) and the different mech-anisms (envisioning, pooling, and staging) to address it.This aligns with extant literature that show that symbolicactions of credibility and legitimacy are important in con-texts of high uncertainty where assessment by resourceproviders is difficult (Zott and Huy 2007).

Emergent firm(network)Partners

Assessments

External resourcesInternal resources

inherited/endowed

Assembly of resourcesand capabilities for “impregnable” base

Mechanisms to address uncertainty

UncertaintyLimitation of

problem-solving competences of actors

Productive opportunity

Temporal sequence of

resource acquisition (credibility)

Technological bases

Fig. 3 Process of productive opportunity development


Lastly, we see that the variation in the temporal se-quencing of the emergent science-based firm’s resourceacquisition (in the establishment of an impregnable base)affects how partners view uncertainty. This temporalsequencing of resource acquisition may act as a signalingdevice to potential partners and in effect also acts as amechanism to address uncertainty surrounding produc-tive opportunities (see Miozzo and DiVito 2016). This iscongruent with Dosi and Egidi (1991) who argue thatinnovation is an endogenous mechanism to generateuncertainty and that actor-specific routines reduce envi-ronmental complexity and uncertainty. Our study illumi-nates the variation in the temporal sequencing of resourceacquisition. Nevertheless, future studies would need toinvestigate if this is indeed a signaling mechanism thatreduces uncertainty in the context of productive opportu-nity development and realization.

5.1 Implications

The implications of this study suggest important exten-sions to existing conceptual work. In particular, we buildon and extend two areas of research: (1) resource-basedstudies of science-based firm emergence (Vohora et al.2004; Wright et al. 2004) and (2) entrepreneurship andthe development of productive opportunities (McKelvey2016; Nason and Wiklund 2015).

First, our study contributes to a growing field explor-ing science-based firm emergence from a resource-basedperspective, which examines the resources and capabili-ties of such firms, and the nature of firm growth. Whilethis literature has provided rich insights into the specificresources and capabilities at the early stage of growth ofscience-based firms, two areas have received less atten-tion: the concern with knowledge and development of atechnology base, and a dynamic perspective of how theseare developed (Druilhe and Garnsey 2004; Mustar et al.2006; Wright et al. 2004).

Our study delves into the emergence and developmentof the science-based firm as a function of productiveopportunities around the development of technologicalbases and external partnerships and adopts a dynamicperspective of firm emergence. We show firm emergenceas a process of search and development of evolvingproductive opportunities and the associated constructionof a network of partnerships. We show how differenttechnological bases create the possibility of differentproductive opportunities and influence the entrepreneur-ial process and resource requirements.We probe into how

productive opportunities require the assembly of differentinternal and external resources, and therefore partners,and address explicitly how the firm and its potentialpartners gather information to ascertain and address un-certainty. It is our hope that this study encourages otherscholars to consider further the relationship between firmemergence and uncertainty.

Second, our findings build on and extend the conceptof productive opportunities developed by Penrose (1959/2009), contributing to greater theoretical precision of thisconcept of opportunities and therefore enabling morefruitful design for empirical investigation. By exploringfurther the relation between productive opportunities anduncertainty and conceptualizing the mechanisms that dif-ferent organizations involved in decision-making use toaddress such uncertainty, we define and clarify the con-cept of productive opportunities. We explore how thoseopportunities result from the technological base of thefirm and are associated with the particular characteristicsof the technology. By extending Penrose’s concept ofproductive opportunities, this paper addresses recent callsfor resource-oriented firm growth studies to build moredirectly upon Penrose (Nason and Wiklund 2015).

6 Conclusion

We consider our study an initial step in understanding theinteraction between firm emergence, productive opportu-nities, and uncertainty. We call for further research tounderstand the difficulties in addressing the relation be-tween productive opportunities and uncertainty. It is likelythat when firms pursue opportunities requiring inter-organizational relationships with complex partners, suchas with universities, where academics and administratorshave different forms of assessments of whether the per-ceived capabilities of the emergent firmmeet their requiredstandards, uncertainty is more difficult to overcome (asopposed to relationswith pharmaceutical firms and venturecapitalists). Future research could explore whether factorssuch as past experience of collaborative engagement withother emerging science-based firms canmitigate uncertain-ty in those cases.

Our study offers new conceptual insights into firmemergence and especially into the relation between pro-ductive opportunities and uncertainty. We acknowledgethe challenges to test empirically and extend the analyt-ical framework developed in this study. A major chal-lenge for a large-scale empirical testing is the ability to


operationalize in a large data set the productive oppor-tunities, uncertainty, and the mechanisms used to ad-dress it. Nevertheless, our study offers insights intoproductive opportunity development and realization thatmay be applicable to other firms and industries whereinnovation and technology play an essential role.

Acknowledgments The authors express their sincerest grati-tude and appreciation for the cooperation of the founders andother participants in this study that provided generous accessto their time, processes, and documentation of their entrepre-neurial journey. We are also grateful to our colleagues at theWorkshop on Medical Innovation held at Science Policy Re-search Unit, University of Sussex and to the editors of theSpecial Section for the insightful and constructive commentswe received on earlier versions of the manuscript. This re-search has been supported by the UK Economic and SocialResearch Council [RES 189-25-0227].

Open Access This article is distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestrict-ed use, distribution, and reproduction in any medium, providedyou give appropriate credit to the original author(s) and the source,provide a link to the Creative Commons license, and indicate ifchanges were made.

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