The Impact of Stocks and Flows of Organizational Knowledge on Firm Performance an Empirical Investigation of the Biotechnology Industry

8/14/2019 The Impact of Stocks and Flows of Organizational Knowledge on Firm Performance an Empirical Investigation of the Biotechnology Industry

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954 D. M. DeCarolis and D. L Deedsthe im portance of firm specific capab ilities, the and Cool, 1989). There are no factor markets resource-based view has focused significant atten- corporate reputations, or dealer loyalty tion on intangible resources which play a critical example. People are endowed with firm-specirole in competitive advantage. In fact, the focus skills and values, which are accumulated throuon intangible resources has led to an extension on the job training and leaming. The idiosyncraof the resource-based view the knowledge-based nature of firm-specific assets makes them nview of the firm . In this perspective, knowledge tradeable. These assets are not only nontradeais the most strategically important of the firm 's but they are also accumulated intemally throuresources (Grant, 1996; Hill and Deeds, 1996). a number of mechanisms over time.The knowledge-based view provides a new lens Asset stocks are accumulated over time through which we may view and understand the choosing appropriate time paths of flows oprimary rationale for a finn's existen ce the ere- a period of time. Corporate reputations, deaation, transfer and application of knowledge loyalties, R&D capabilities are stocks of ass(Dem setz, 1991; Grant, 1996; Nonaka, 1994; which have been accumulated over time. TSpender, 1996). The knowledge-based view "bathtub" metaphor (Dierickx and Cool, 19argues that the heterogeneous knowledge bases illustrates the differences and connectiand capabilities among firms are the main deter- between asset stocks and flow s. At any pointminants of performance differences. This time, the stock of water in a bathtub is indicaapproach to understanding what occurs in the by the level of water in the tub. This stock"black box" of the firm suggests that o rgani- water is the cumulative result of flow s of wazations not only use different knowledge bases into the tub (through the tap) and out of the and capabilities in developing knowledge but also (through a leak). W ith respect to R&D capabhave differential access to extemally generated ties, the amount of water in the tub may represknow ledge. the stock of know-how at a particular pointThe underlying knowledge of firms may be time; current R&D spending is represented conceptualized by both stocks and flows the water flowing into the tub and the wa(Dierickx and Cool, 1989) of know ledge which leaking out illustrates knowledge depreciatcontribute to superior firm performance. Stocks over time. Flows like water coming into aof knowledge are accumulated knowledge assets leaking out of the tub may be adjusted; stowhich are intemal to the firm and flows of knowl- cannot.edge are represented by knowledge streams into An appropriate context for examining stothe firm or various parts of the firm which may and flow s of knowledge and their relationshipbe assimilated and developed into stocks of firm performance would be a dynamic indusknowledge. in terms of knowledge generation, such as As researche rs, how is it possible to capture pharmaceutical industry in general. This industhe construct of organizational knowledg e? This is dependent on the knowledge embedded inarticle addresses that question in the biotechnolresearch departm ents. In the pharmaceutiogy industry. This industry is an interesting arena industry, firm research capabilities may in which to study organizational knowledge and assessed by the number of new drugs the cofirm performance. T he m ost valuable assets of pany has brought to market, its R&D expenbiotechnology companies are their intangible tures, and its past history in terms of finanresearch capab ilities, which represent the potential and technological performance,to develop and deliver new dmgs which , in fact. Although part of the overall pharmaceutical indiffer from the traditional dm g products of the try, there are distinct differences between biotechnestablished pharmaceu tical com panies. These ogy and pharmaceutical companies. Biotechnolresearch capabilities stem from their knowledge firm s typically have no products in the marketplacbases, which need to be continually nurtured only in the pipeline. Further, their research ptjiand developed. are very limited in scope. Most biotechnology fir


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Organizational Knowledge and Firm Perfonnance 9In light of this, assessment of present and as intemally generated flows of knowiedge.

is more uncertain in We then measure stocks of firm knowledin the pharmaceu- through the following variables, which represeis due to the fact an accumulation of knowledge at a moment

the industry is based on highly compiex and time: scientific citations; products in deveiopmeis stiii emerging, uniike and patents. Each of these measures is an attem

of the traditionai to quantify the stock of knowiedge heid by tcompanies (Pisano, 1994). These firm.are generating not oniy new products Thus, in the biotechnoiogy industry, fir

new methods to discover new dmgs and accumuiate their stocks of knowiedge tiirouof medicai instmments and diagnostic severai flow s. In the foiiowing section, we detMuch of their icnowiedge is based not oniy our modei of stocks and flows of organization

and organic chemistry but knowledge in the biotechnoiogy industry and thon such diverse fieids as computer tech- reiationship to firm perfonnance. We then prese

and software deveiopment. Their knowi- our methodoiogy, resuits and a discussiona confluence of discipiines the resuits.

With such an emergence of new knowiedge,on HYPOTHESESto

FLOWS OF KNOWLEDGEand Levinthai, !. i x- J


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56 D. M. DeCarolis and D. L. Deedsa firm's research and deveiopment department, superior resources for ciustered firms (M eieckources of knowiedge are equaiiy criticai i9 85 ). There is then a resource advantage forinnovation. March and Simon (i 95 8) have firm being iocated in a ciuster. Continuthat "b orrow ing" is the cataiyst for reduction of the costs of production due to tnot "invention ." Innovation then, to a avaiiabiiity of supp iies, workers and capitai aextent, is dependen t on a firm's abiiity to the source of unique assets which firms outsiinformation from the ex temai environ- the ciuster are not privy to (Ban ia, Caikins aDaienberg, i99 2; Maarten de Vet and Scott, i99Ciose proximity of organizations with simiiar Saxenian, i99 0).promotes the naturai exchange of ideas In the case of biotechnoiogy firms, the muniough the networks established. Lynn, Reddy cence of the geographic environment is manAram (1996) proposed the term "innovation fested not just in terms of avaiiabie poois munity" to refer to "the organizations directiy imow iedgeabie w orkers, but aiso in the form indirectiy invoived in the commerciaiization access to iocai university researchers, universia new technoiogy". This definition inciudes aii research pro jects, and a ciuster of simiiar firmthe firms and organizations previously included The existence of such entities within a particuidefinitions of organizationai communities and geograph icai iocation is an opportunity for firmany other organization or group invoived in to informaiiy exchange information. Case everciaiizing a given technoiogy. When an dence has pointed to the interdependent reiatiocomm unity is centered in a geographic ships am ong f irms in such cius ters as Siiicthe concentration of successful firms, quaii- Vaiiey, Route 128, and the ceramics productiosuppiiers, skiiied workers, informed investors, compiex in Sassuo io, Itaiy (Porter, i99 0; Saxegenerators and shared resource anan gem ents ian, 1990; Scott, 1989).be partiy responsibie for an increasing pro- Within a geographic ciuster there are ampof industry innovations (Poud er and St. opportun ities for inter-organizationai know ied). The emerging networks within the flows and comm unications. The proximity

munity aiso heip in creating an firms to com petitors, suppiiers, and a quaiifient of creativity and idea exchange iabor pooi increases the flow of know iedge acronian, 1990). a firm 's boundary. Sociai interactions, both foRecent treatments of the importance of iocation maiiy and informaiiy, stimuiate informatinnovation and firm capabiiities have focused exchange about such topics as com petito r's pianthe deveiopm ent of "hot spo ts" defined as deveiopm ents in production technoiogy , anciustered firms within industries recent deveiopm ents within the iocai univ ersitybegin as start-up firms, grow more rapidiy iabs. In teraction among employees of differeother industry pan icipan ts and have simiiar firms and organizations from the same industobiie physicai resource requirements in the located in a geographic ciuster may be faciiitatmn (Pouder and St. John, i99 6) . There are through mem bership in iocai poiiticai anies of such "hot spo ts" around the reiigious organiza tions, invoivement in iocai aoiogy and communications athietic and comm unity groups , residing in ties in San Diego, Califomia, the ceramics same neighborhoods (Yates, 1984) and througry in Coming, New York and Sassuoio, iocai industry events such as trade and prand the computer industry in Austin, Texas, fessionai association meetings (Aim eida aAs a few firms in a singie area start to become Kogut, i99 4; Saxenian, i99 0).a rippie effect occurs in that supp iiers, Empioyee mobiiity among firms is anothfied workers and investors become avaiiabie opportunity for information exchan ge. Evidenthese firms. Consequentiy, simiiar firms and suggests that m anagers and other profession

offs created from parent firms wiii be empioyees wiii seek job s within the same geto the area due to the avaiiabiiity of the graphic area rather tlian move to o ther iocai


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Organizational Knowledge and Firm Perfonnance 9If a particuiar geographic iocation is abie toprovide such a munificent environment with the

benefits suggested above, one outcome shouid beiocaiized knowiedge production. Jaffe, Trajtenbergand Henderson (i993) investigated the extent towhich icnowiedge spiiiovers are geograpiiicaiiyiocaiized by examining the geographic iocation ofpatent citations to that of cited patents. They foundstmng evidence of iocaiization of knowiedge spiiiov-ers on three geographic ieveis-countiy, state andMetropoUtan Statisticai Area (MSA). Aimeida andKogut (i994) examined the reiationship betweengeographic iocation and patent hoiders in thesemiconductor industry. Their fine-grained anaiysisexamined the movement of inventors of major pat-ents from 1974-i994 and found significant intra-r.g/onfl/mobiiity among these inventors paiticuiariyin the Siiicon Vaiiey

Therefore, a firm iocated in a geograpiiic areawith iugh munificence (a hugh concentration ofsimiiar firms, speciaiized supphers, such as researchumvereities, and a iarge pooi of trained iabor) wiiihave access to knowiedge flows winch may beunavaiiabie or difficuit to attain by similar firmswhuch are geograpiiicaiiy isoiated. It is iikeiy thatfirms iocated in geograpiiic hot spots have more

, n_ , . 1 1 J iT 1-- I- 11and frequent access to iaiowiedge flows winch wiiihe accumuiated lntemaiiy and generate supenor per-rormance.Hypo thesis 1: The mun ificence of a firm'sgeograph ic area will have a positive relation-ship with firm performan ce.

AlliancesIn the biotechnoiogy industry, the dmg discoveryand deveiopment process is a compiex and muitidis-cipiinary process requiring new ventures to accessa broad range of iaiowiedge. However, most ofthese firms have iimited capabiiities that are nar-rowiy focused on a few specific applications. Underthese circumstances, bioteciinoiogy firms are forcedto reach ijeyond their ix)undaries to access compie-mentary iaiowiedge (Teece, 1986). In Edition toproviding access to knowiedge for immediate proj-ects, infonnation from these extemai iinkages mayevoive into important sources of new product idea.Consistent with these arguments. Deeds and Hili

ity of the firm. This ieads to our next hypothesiHypothesis 2. The total numb er of strate

'^ with firm performan ce.

Research and development^' suggested by Dienckx and Cooi (1989) tof R&D spending is a flow vanabie th^ ^ , ^ ^ J " ' ^ m stantaneousiy. To achievedesired change m a strategic asset stock such research capabiiities there needs to be a consist

resourceflows-R&Dspending. Greaterto R&D shouid resuit in greater floinfonnation into the fimL T

f ^ ^ of expenditi^es on research adeveiopment has traditionaliy been used as an lncator of innovative activity in many industr(Scherer, i980). Severai studies have iooked at reiationsiiip between R&D spending, productivretums and firm performance (Comanor, 1965; Gix)wski and Vemon, 1990; Graves and Langowi1993; Vemon and Gusen, 1974).

In a iaiowiedge intensive industry, such biotechmoiogy, a significant strategic commitmentR& D IS cnticai to the firm s abmty to deveiop nproducts. Recent studies have used R&D intensj^Qj Qj jy ^ ^ measure of intemai iearm ne, but aas a requirement for extemai ieaming as firms neto deveiop a certain ievei of intemai knowiedge they can understand and appiy extemai knowied(Bieriy and Chakrabarti, 1996; Cohen and Levinthi990). Other studies have tested and found suppfor the reiationsiiip between commitments to R&^^ market vaiue (Hirschey, 1985; Jose, Nichand Stevens, 1986; Lustgarten and Thomadak1987; Morck, Shieifer and Vishny, i988; Mo^nd Yeung, i99i) .

Hypothesis 3: A biotechnology compa ny's RD intensity will have a positive relationship wfirm perfonnance.

STOCKS OF ORGANIZATIONALKNOWLEDGEProducts m development and market value


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58 D. M. DeCarolis and D. L. Deedsandpotentiai investors costs of an issued patent are $i 2,000, which mby firms in be a fairiy insignificant sum to an estabiisThe strength of a pharmaceuticai company iike Merck, but tois considered an important indi- cash strapped startup biotechnoiogy firm, of a com pany's future cash flows. Products expenditure wouid be a significant investmthe pipeiine represent accumuiated stocks of For these reasons in this particuiar industry

ionai Jmowiedge. Therefore, the number believe that patent counts adequateiy capa firm shouid organizationai stocks of knowiedge.a direct reiationship to firm perfonnance. Moreover, research has suggested thebetween patent counts and organizationai inHypothesis 4: The number of new dmgs in vative activity. Basberg (i982) found a b iotechnology company's research pipeline increased commitments to research and deveiwill have a po sitive relationship with firm per- ment donot precede increased patenting but formance. simuitaneous with it. This suggests that the nuber of patents is a better indicator of corpotopursue innovation than the acamount of innovation. Comanor and Schbe considered as representative of (1969) found that simpie patent counts are mof organizationai imowiedge. They are highiy correlated with inputs to deveiopment sof innovative as research personnei than to the rate of nthe product introduction. These studies suggest tof one or severai em pioyees. Patents that simpie patent counts are more ofan indicand per- of a finn's commitment to innovation than quaat many ieveis: region, country, and of innovation. Accordingiy, in the biotechnoias indicators industry, we suggest that increased patenting isin severai empiricai studies indicator of a firm's commitment to innovatand Sen, 1988; Pakes, 1985). Further, Therefore, given the reiative young age ofarewideiy accepted measures by poiicy firms and their cash flow situation, we suggandanaiysts (Van derEerden andSae- that patent counts are an appropriate proxy foin terms of technoiogy strategy and biotechnoiogy finn's stock of codifiabie knoetitive anaiysis. In this study we suggest that edge,of stocks of organizationai Hypothesis 5: The number of patents There is some concem with using simpie patent trolled by a biotechnology company will has a measure of the stock of a firm's a positive relationship with firm performanas being cmde for basicaiiy two reasons. . .. ..* j * - A f Firm citationsdo not in and ofthevaiue of imowiedge. This As stated eariier, the scientific imowiedge bby patent citationsthat is, of a biotechnoiogy firm is a criticai compona finn's patents are cited by of the firm's competitive position. This imoissued patents. Aithough recent edge base resides in the sidiis and imowiedgeto weight the the individuai members of the finn's reseacitations accme with age and this meas- team. One way to capture a firm's stocke is therefore biased towards oid patents. In the imowiedge is in the deveiopment of a measof biotechnology companies, the relative of the imowiedge heid by the key participantsof the firms and their patents when they the organization. Biotechnology in parficular


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Organizational Knowledge and Firm Performance 95's future prospec ts. sample was limited to 218 firms. These firmHowever, attempting to make comparisons of were then contacted by phone with a request foof knowledge across a copy of the prospectus from their IPO. A totto the problem of measurement. One of 106 companies were willing to provideof judging research quality is well known prospectus representing a response rate of 48%the academic comm unitycitation anaiysis. However, two of these companies were exclude

the number of times a from the sample because warrants for sharesor an author is cited as an indication of their parent company were included in the IPOof the work to the field. The more Missing data also forced us to drop 8 firms froa paper, or an individual's body of our sample. Thus, our final sample consisted ois cited tiie more important and hence the 98 firms.the quality of the work. Those of us who To test for potentiai biases in this sample wor are chasing tenure in academia compared the average total assets and averagthe importance citations total iiabiiities of the firms in our sample in 199the tenure process. to the average total assets and liabilities reporteCitation anaiysis has been used to map the by Bunili and Lee (1993) for all 225 publof fields of scientific inquiry (Sm aii biotechnoiogy firms. Our sample averageto estimate the quaiity of the $11,123,000 in total assets, $3,515,000 in totof countries in specific fieids iiabiiities, $11,034,000 in totai revenueand Hock, 1986); to assess the $2,276,000 in revenues from collaborativof academic departments (Waiimark, contract research and $7,034,000 in researand Sedig, 1988) and of scientific and expenditures in 1992. Burriii and Lee (199and Rozek, reported these averages for aii public biotechnoVinkier, 1986). In addition, citation analy- ogy companies firms in 1992 and they werehas recently entered into the discussion of follows: $11,377,000 totai assets, $3,313,(X)0 toVan der Eerden and Saeiens iiabiiities, $20, i 96,000 in totai revenu

the use of citations as indicators $2,440,000 in contract/coUairorative research reand the quaiity of enues and $10,342,000 in total R&D spendinby the Based on these comparisons our sample is similas weli as a tooi to guide competitive to the Burriii and Lee (1993) sample, with tand acquisition targeting and exception of notable differences from the industit is our contention averages on total revenues and total R&D spenthe number of citations a finn's scientists ing. However, these differences are adequateis a proxy for the stock of knowiedge explained by the inciusion of two companiesthe finn's scientific team. Firms the Buniii and Lee sampie which are not in oa higher ievei of citations have iarger stock sampieAmgen and Genetech. They constituthe "outiiers" of the biotechnology firms. SalesAmgen's Epogen and Neupogen and GenentechHypothesis 6: The number of times the pub- Activase accounted for over $750 miiiion in prolished papers of a firm's research team is uct revenues of approximately $3.5 billion in tocited will have a positive relationship with product revenues for the industry. However,firm performance. can be seen in the contract research numbers, tresearch revenues for our sampie and the popiation are comparabie. Overall, we i^elieve whave a fairiy representative sampie of the pubiic. , , iieid biotechnoiogy companies.

and dataof 225 pubiiciy heid


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960 D. M. DeCarolis and D. L Deedsplace and thus , very little sales. Past measures of vaes , 1997). These finding s may limit the inteperformance used for entrepreneurial firms , such prepation and generalizability of our resuits.as retum on assets, sales growth or profit margin Market value is d e fi n e as the totai markare inappropriate because these firms do not have value of the offering firm's equity (19 90 dollarany history of revenues or earnings. Their value at the end of the first day of trading. M arkis contained in intangible assets represented by value at the end of the first day of trading hatheir knowledge and access to know iedge. They b>een used in numerous studies of IPOs (Dow neare years away from any significant revenue and Heink el, 1982; McGu inness, 1993; Rittestream, have very few tangible assets, are sustain- 1984: Titman and Tm eman , 1986). A logarithming significant accounting losses, and require large transformation was used to control for the skewamounts of capital (Burriii and Lee, 1992; Pisano , ness of the distribution.1996). Their most valuabie assets are their knowi- Location. In order to adequateiy operationaiizedge capab ilities, which represent the potential to the muitifaceted na ture of the constm ct of organdeveiop and deiiver state of the art biilion dollar zationai flows of imowiedge accm ing from dm gs. However, the point at wiiich these firms finn's geographic location, we collected eiggo public presents a unique opportunity to meas- measures of scientific and technical activity iure their perfonnance up to that time. the finn's Metropoiitan Statisticai Areas (M SAIssuing an initiai pui>iic offering (IPO) is an Each of the measures is explained b)eiow.important strategic objective for an entrepre- Based on the iocation of the finn's headneud al biotechnoiogy firm . A key factor in a quarters, firms were coded into their MSA baseventure capitaiist's evaluation of a new business on zip code. These locations were then compareas an initiai investment and as an ongoing invest- to the tweive areas identified by Burriii and Lement is the probability that the venture will issue (i 98 6 , 1987, 1988, 1989, 1990, 1991 , 1992an IPO (Guild and Baciiher, 1996; Fiet et al., 1993) as concentrations of biotechnoiogy activit1997). By going public the firm is abie to provide In o rder to capture the variance in the conceninitiai investors an avenue of exit, increase its tration of these tweive areas, the location variablegitimacy, and gain improved access to both is the percentage of the na tion 's totai biotechnoequity and debt capitai (Sutton and Bennedetto, ogy firms iocated in the finn's specific MSA. 1988). These i>enefits are directiy iinked to the "0 " was recorded for firms no t in one of thvaiue the market piaces on the firm. tweive geograpiiicai areas.Going pubiic aiso aiiows the financ iai markets As suggested in an eariier section, beinto make a judgm ent aixjut the vaiue and future iocated in a geographic "hot spot" suggests theam ings potentiai of the firm based upon the there are other institutions and o rganizationfinn's past actions and accom piishments. Accord- wiiich are activeiy woridng and generating imowing to the efficient market iiypothesis, a finn's edge in relevant areas. In the case of biotechnomarket vaiue is assumed to capture aii avaiiabie ogy, these institutions and organizations areievant infonnation about a company, inciuding schoois or departments w ithin un iversities. Tthe potentiai of a finn's knowiedge (Fam a, i9 76 ; capture this activity, we measured four variabieRappaport, 1981). The state of the firm and its the number of m edicai schoois per M SA; thImowiedge base at the time at wiiich it issues an numt)er of biochemistry departmen ts per M SAIPO are the culmination of the actions of the the numb>er of bioengineering departments peentrepren eurs/m anagers of the firm since its MSA and the numijer of microbioiogy deparinception. Therefore, the value placed on a newly ments per MS A. These departments or schoopubiic firm is the m arket's evaluation of the were ciiosen because they are the core disciplinefinn's perfonnance over its lifetime. Firms which from which biotechnoiogy research and produchave made superior decisions and investments deveiopment have been created. Ail of these varwill have greater potential and in tum a higher abies were normaiized for purposes of analysis,market value upon entering the marke t. How ever, Aga in, as stated in an earlier section, huma


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Organizational Knowledge and Firm Perfonnance %of human knowledge capital. These data were of products they had in each of the significcollected from the labor department statistics, stages of the pharmaceutical testing process. NoThis measure was also norm alized. of the firms actually had products on the marFinally, to assess the level of know ledge gener- at the time of their IPO. Therefore, we counating activity occurring in particular ge og r^h ica l the num ber of products the firm had in plocations, we measured the number of grants clinical trials, stage one , stage two , and stawarded by the National Institute of Health (NIH) three trials.per MSA and the total value of grants awarded Patents. From the offering finn's prospectusby the NIH per MSA. These data were gathered count of the total numijer of patents held by tfrom the NIH publication titled Extramural Data firm was obtained. Tiiis includes both pateTrends 1994. granted directly to the firm and patents in whThe normalized variables were then factor ana- the firm is the sole licensee,lyzed. The results of the factor analysis are Citation data. In this study we are usreported in Table 1. The factor analysis produced citation analysis as an indication of the quaa single factor with an eigenvalue of 5.67 that of the scientific personnel of the biotechnoloexplained 70.9% of the variance among the vari- firm. The names of the top scientists employables and all of the variables loaded on the factor by each firm were gathered from the prospecat 0.5 or greater. Based on the strong results of of the firm's initial public offering. Only the factor anaiysis we created a single score to time employees were included in the list in ormeasure the munificence of a geographical area to controi for biases created by firms attemptby averaging the normalized variables. to increase their visibility/legitimacy by hirinNumber of alliances: This is count of the num- long list of scientific advisors or consu ltai)er of active aiiiances the firm has with both Names of aii scientific personnel listed in non-profit and for profit research institutions and prospectus as well as top executives were couniversities at the time of the IPO. This infor- piled. We then used the Science Citation Inmation was gathered from the prospectus. to gather the total number of citations for e

Research and development intensity. R& D scientist in the firm during their career priorintensity is measured as the average percentage the year in wiiich the IPO was issued. Thof total expend itures spent on the R&D process citations were then totaled to create a measduring the last tliree years. The data were gath- of the quality of the scientific team employedered from the IPO prospectus. the bioteciinoiogy firm at the time of its iniNumber of products. In the business section of pubiic offering,each prospectus the company reports the numijerof products under developm ent or which have /-, . i . uiu A .1. 1 * ^ * J f Control variablesreached the market. We created our measure ofthe firm's product pipeline by totaling the number Timing. It has been well documented (Ibix)ttand Jaffee, 1975; Ritter, 1984) that the marfor initiai pubiic offerings experiences periodsTabie 1. Factor anaiysis of iocation measures

Variable

# of Biochemistry Departments# of Bioengineering Departments# of Medical Schools# of Microbiology Departments# of NIH GrantsVaiue of NIH GrantsPercentage of Biotechnoiogy Firms

Factorscore0.880.770.900.930.950.930.73

tialiy higher. Ibbottson and Jaffe (1975) fdocumented the existence of a number of markets' for IPOs during the last 20 years. Thretically, what appears to happen is that invesare periodically over-optimistic about the eamipotential of young growth companies (Rit1984). These so-called 'hot markets' are windoof opportunity which entrepreneurs may useimprove their access to capital by taking adv


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962 D. M. DeC arolis and D. L Deedsbrought to market is significantiy higher thanduring a normai period. In the case of biotechnoi-ogy, the years 1983, 1986, 1991 and 1992 showaii the characteristics of a 'hot market' and haveijeen designated as such by industry anaiysts(Burdii and Lee, 1993). IPOs during the 'hotmarket' years were coded as " 1 " ; ali others werecoded as "0".Total assets. The totai assets of the offeringfirm were used to controi for the influence ofsize on market vaiue. Given that tiiis study isconcemed with the market valuation of imowi-edge stocks and flows, that is intangibie assets,we feit it is important to controi for the impactof the overaii size of the asset base on the marketvaiuation of the firm. Totai asset vaiue was meas-ured pdor to the IPO. These figures were reporfedin the prospectus of each of the initiai pubiicofferings. A iogarithmic transformation was usedto controi the skewness of the distribution.

ANALYSIS AND RESULTSThe data were anaiyzed using ordinary ieastsquares regression. Descdptive statistics of thevadabies are presented in Tabie 2. The averagemarket vaiue of the firms in our sampie was $95miiiion. The average firm had 3.26 products inthe pipeiine and had controi of 3.41 patents.Seventy-five percent of the firms in our sampieissued IPOs dudng 'hot markets'. With respectto iocation, the average firm was iocated in ametropoiitan area with 7.4% of the totai nationaibiotechnoiogy firms. Average research and devei-opment spending was $9.8 miiiion and average

Tabie 2. Descriptive statistics

Log (market value)LocationR&D Intensity# of AlliancesFirm Citations# of Products# of Patents

Mean

4.840.0020.594.81127.603.263.41

Standarddeviation0.360.860.243.46

136.864.144.70

totai assets were $11.2 miiiion. The correlatiomatdx is presented in Table 3, which does nindicate significant problems with multcollineadty.In order to test for the effect of the stock anflows of knowiedge on the market vaiue, wperformed a sedes of regression modeis thresuits of which are depicted in Tabie 4. Modei One, we entered oniy the controi vadabiesize and hot market. This modei was significaand expiained 5i% of the vadance in thregression equation.In Modei Two, we entered the "imowiedgflow" vadabies together with the controi vaabies. The percentage of vadance explained wgreater than the first model. Further, ali thrimowiedge flow vadabies, iocation, R&D intesity and the number of aiiiances are significapredictors with R&D intensity exhibiting the gratest amount of significance, foiiowed by iocatioand then number of aiiiances.In our third modei, we test the effect of th"knowiedge stock" vadabies on market vaiuaiong with the controi vadabies in our bamodei. Compared to Modei Two, there is greater amount of vadance expiained by the stocvadabies as indicated by the increase in the Rover the base modei. Modei Three yieids an Rof 60% compared to the base modei, whiie ModTwo yieids an R2 of 58%. Again, aii three stocvadabies are significant with number of producbeing significant at the 0.00 i ievei foiiowed bfirm citations at the 0.05 ievei. Patents are significant oniy at the 0.10 ievei but the reiationshis inverse to market vaiue.In the final model, ali vadabies are entereand the resuit is the greatest amount of vadancexpiained with an R2 of 63%. In this regressioiocation is the oniy significant flow vadab(0.05). Three stock vadabies remain significawith products in the pipeiine being the mosignificant at the 0.0i ievei, foiiowed by fircitations at the 0.05 ievei. Patents again are sinificant at the 0.10 ievei with an inverse reiatioship.Hypothesis 1 was supported. Location has significant (p < 0.05) positive relationship on biotechnoiogy finn's market vaiue. Specificaiiy,


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Organizational Knowledge and Firm Performance 9Tab le 3. Correlation matrix

1.2.3.4 .5.6.7.8.9.

Log (day)LocationR&D Intensity# of AlliancesFirm Citations# of Products# of PatentsLog (Assets)Hot

11.000.270.410.270.240.360.040.630.36

20.271.000.080.090.050.200.150.140.04

30.410.081.000.330.180.330.080.300.05

40.270.090.331.000.20O.li0.000.170.03

50.240.050.180.201.000.180.070.090.01

60.360.200.330.110.181.000.400.170.02

70.040.150.080.000.070.401.000.070.03

80.630.140.300.170.090.170.071.000.00

90.30.00.00.0O.O0.00.00.01.0

N = 98

Table 4. Regression results Beta coefficients

LocationR&D Intensity# of AlliancesFirm Citations# of Products# of PatentsLog (Assets)HotAdjusted R2F-StatisticSignificance of F

Model #1

0.628****0.363****0.51451.910.0001

Model #20.148**0.185***0.118*

0.532****0.351****0.58628.140.0001

Model #3

0.148**0.282****- 0 .1 3 3 *0.575****0.362****0.60230.050.0001

Model #40.141**0.1110.0940.132**0.219***- ^ . 1 3 3 *0.517****0.355****0.63621.970.0001

N = 98 for all models*p < 0.10; **p < 0.05; ***p < 0.01; ****p < 0.001

Hypotheses 2 suggesting a proposed reiation-ship l)etween number of aiiiances with otherorganizations and perfonnance is not supported.Whiie there was weak supporf in the restdctedModei Two, there is no statisticai support for thereiationship between aiiiances and perfonnance inthe fuii modei.The resuits with respect to the hypothesized posi-tive reiationsiiip between R&D intensity and per-formance are inconsistent across modeis. R&Dintensity is a higiiiy significant predictor of firmperformance (p < 0.01) in the restdcted Modei

In support of Hypothesis 4, the numl>er of products a biotechnoiogy firm has in its pipehas a significant (p < 0.001) positive impacfirm perfonnance.Hypothesis 5 was not supported in any ofmodels. The number of new patents heid by thfirms had a smaii impact on firm perfonnancour sampie. One possibie expianation for the than robust results for our patent measure is patent counts are an ambiguous measure subto firm specific vadations in the propensityfirms to patent given the resource expendi


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964 D. M. DeCarolis and D. L DeedsFinally, the proposed direct relationship loaded on a single factor and this factor expiainebetween firm citations and performance is sup- over 70% of the vadance. Thus, we believe wported (p < 0.05) in all mod els. have developed a valid measure of the knowledgmunificence found in a geographic location. Wthen used this aggregated location measure in th

DISCUSSION OF RESULTS regression models and it is consistently positivand significant (p


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Organizational Knowledge and Firm Perfonnance 9link to perfonnance. Industry analysts stocks versus flow s of know ledge is an interestlist products in deveiopm ent as infor- question. It wouid appear that the market foiloreiied upon to evaiuate biotechnoiogy the bird-in-hand adage and places greater weiBusiness on the intemal stocks of knowledge. Given 1994, 1995). Products in the pipeiine may potentiai uncertainties of the vaiue of the flofact be considered physicai manifestations of particuiariy in an emerging industry such

com pany's stock of accumulated knowledge, biotechnology, this wouid appear to be a iogresuits indicate that products in the pipeiine outcome. The results provide intdguing ina strong predictor of firm perfonnance. cations of the reiative importance of stocks verThe hypothesized reiationsiiip between number flow s, but it aiso indicates the need for additioand firm perfonnance was no t sup- research. A iongitudinai study examining chanThis resuit is not surpdsing for a number in these v adabies and their impact (direct aFirst, the numi^er of firm patents does iagged) on firm performance under vadous indect the quaiity of those pa tents. Second, try conditions needs to i>e undertaken,the expense required for patent fiiings and We b)eiieve the resuits w ith respect to extensive patenting may iDe cost pro- iocation vadabie are a valuabie con tdbu tifor some biotechnoiogy firms. Third, Although location has l>een linked via theory firms are too young to have developed an anecdote to innovation and supedo r performanwhich o ther this study provides objective em pidcai evidemay reiy. Thus, patent citation anaiysis may of a reiationship between munificence of the io. Aitbough they environment and the perfonnance of firms iwiedge, it appears that know iedge intensive industry. Location has i^this industry, simpie patent coun ts are not a viewed, particularly within the Imowiedge-bad predictor of firm perfonnance. view of tiie firm, as an important source of floFirm citations are significant predictors of firm of imow iedge for firms. Pd or research has u(p < 0.05) representing organi- fairiy simpie measures of the munificence of imowiedge in the form of inteiiect and iocai geographical environment (Deeds et iiity. This resuit suggests that the 1997). By ijroadening the measure to inclimowiedge of the finn 's scientific measures of not oniy industry concen tration, cdtic ai to competitive advantage in this aiso the quaiity and size of the research activenvironment. in a given geography and the iocai iabor suppInterestingly, the resuits of our fuii modei sug- we were able to provide fairiy convincing estocks of imowiedge, products in dence of the importance of the iocai environmand oniy one to imowiedge intensive firms,abie representing know iedge flows W hiie our resuits provide strong statisticai s

are important to firm per- port for our conciusion, we must aiso aclmoIn com padng the restdcted Modeis edge that our focus on biotechnoiogy raises quo and Three) and the fuii modei it aiso tions aiwut the generalizabiiity of our stowiedge stocks have a greater beyond this industry. Bioteciinoiogy has seveon firm performance than flows of imowi- unique charactedstics, inciuding a iong prodThe knowiedge stock restdcted model has development and approval cycle, heavy reliahigher R^ then the flow model and the addition upon often arcane basic scientific research the stock vad abies significantiy increases the a very expensive product deveiopment procey power of the modei (Change R^ > However, given these unique charactedstics5). It is interesting that R&D intensity, a flow our sam pie, we stiii believe that our results dab ie, shows no significance in the fuii Modei generaiizabie ijeyond the biotechnoiogy indusPrevious research has typicaiiy found a Basic science appears to be piaying a more s


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966 D. M. DeCarolis and D. L DeedsWhiie the results for our model are strong theinterpretation of our results is limited by thedependent vadabie. The market vaiue of a newlypubiic firm, as we argued previously, is a reflec-tion of the market's evaluation of the firm andits potential to create future cash flows at thetime of the offedng. Some studies on the iongmn perfonnance of IPOs have provided someevidence of significant underperformance by IPOsdue to investor over-optimism (Ritter, 1991;Lougiiran and Ritter, 1995; Rajan and Servaes,1997). However, a recent study concluded thatthere are no statistically significant long mn per-formance differences between IPO firms and firmsof simiiiar size and book-to-market ratios whichhave not issued equity (Brav and Gompers,

1997). These contradictory findings indicate thecomplexity of market vaiue as a dependent vad-abie despite its appropdateness for our study.Given these findings, the generaiizabiiity of ourresuits to the post-IPO perfonnance of these firmsand other pubiic firms may l>e iimited. Whiie itis ciear that the quality of the stock of imowiedgecontroiied by the firm, as weii the imowiedgeb)eing generated in its geographic iocation, isreiated to the vaiue the firm creates up to the

point it issues an IPO, it is unclear whetherthese same relationships wiii hoid in its post-IPOenvironment. This issue is an important areawhich demands further study.Aithough we have found strong empidcai sup-port for our modei it shouid aiso be noted thatthere is stiii a significant amount of vadation yetto be expiained indicating the need furtlierresearch. In particuiar, the roie of geographiciocation in determining firm performance warrantscontinued expioration. There aiso needs to beeffort invested in the deveiopment of additionaimeasures of firm knowiedge in the biotechnoiogyand other industdes. The management of stocksand flows of imowiedge appears to be cdticai tofirm success. Yet, additionai empidcai researchneeds to be compieted to enhance our understand-ing of the reiationship b)etween organizationaiimowiedge and firm perfonnance.

Professor Jeffrey H. Greenhaus for his heipfucomments on earlier drafts of this article.

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The Impact of Stocks and Flows of Organizational Knowledge on Firm Performance an Empirical Investigation of the Biotechnology Industry