The dynamics of knowledge stocks and knowledge flows

Industrial and Corporate Change,Volume20,Number2,pp.555–583
doi:10.1093/icc/dtr001
Advance Access published February18,2011
The dynamics of knowledge stocks and knowledge flows:innovation consequences of recruitment and collaboration in biotech
Andreas Al-Laham*,Daniel Tzabbar**and Terry L.Amburgey y
To extend the knowledge-based view of the firm,we examine how managing the dynamic balance that a firm must undertake between applying knowledge stocks and accessing knowledge flows may determine innovativeness.We found that while the effect of scientist’s recruitment and alliances as two sources of knowledge flow decay overtime,high degrees of human and social capital stock reduces the speed of new assets erosion.Failing to account for the interactions
between knowledge stocks and flows,as well as the underlining causalities asso-
ciated with each knowledge source,will result in an incomplete picture of the relationship between knowledge development efforts and innovative success.
We test our assumptions on a longitudinal event history data set of the complete
US biotech population of857firms founded during the period1973–1999.
1.Introduction
A basic premise of the knowledge-based view is that the heterogeneous knowledge bases and capabilities among firms,as embodied in their employees’knowledge and skills and their relationship with other firms,represent the main determinants of performance differences(Teece,1982;Kogut and Zander,1992;Spender,1996). Scholars in this tradition emphasize the dynamic balance between applying *Andreas Al-Laham,Lehrstuhl Strategisches und Internationales Management Schloss,Universita¨t Mannheim,68163Mannheim,Germany.e-mail:al-laham@uni-mannheim.de
**Daniel Tzabbar,LeBow College of Business,Drexel University Philadelphia,PA19104,USA. e-mail:daniel.tzabbar@
y Terry L.Amburgey,Joseph L.Rotman School of Management,University of Toronto,Toronto, ON,M5S3E6,Canada.e-mail:amburgey@rotman.utoronto.ca
ßThe Author2011.Published by Oxford University Press on behalf of Associazione ICC.All rights reserved. at National Library of China on January 12, 2012 / Downloaded from
current knowledge (i.e.,knowledge exploitation)and acquiring new knowledge (i.e.,knowledge exploration)(Kogut and Zander,1992).Dierickx and Cool’s (1989)distinction between knowledge stocks (current knowledge)and knowledge flows (new knowledge acquisition)also provides a useful conceptual lens for exam-ining the performance consequences of dealing with the demand for balance between knowledge stocks and flows.
Recent studies provide considerable empirical support for the hypothesis that both stocks of knowledge,conceptualized as accumulated knowledge that represents part of the firm’s initial endowment,and flows of knowledge that reflect access to external knowledge contribute to innovative performance (i.e.,Jensen and Webster,2009).For example,firms possessing a higher absolute endowment of knowledge stocks than their rivals can favorably position themselves in their industries (Miller and Shamsie,1996;Simon,1999;McEvily and Chakravarthy,2002).The balance between exploiting knowledge stocks and exploring knowledge flows may determine a firm’s innovative success,because success requires not only the application of given knowledge stocks but also the dynamic capability to update and renew that know-ledge stock continuously by accessing knowledge developed elsewhere,whether through hiring or through alliances.
Firms that access external knowledge through alliances and recruitment increase their ability to learn new skills and to overcome competence traps (Rosenkopf and Almeida,2003;Vega-Jurado et al .,2009).For instance,the extent literature on alli-ances commonly agrees that alliances are a mean by which firms acquire knowledge and learn new capabilities (Hamel et al .,1989;Powell et al .,1996).Similarly,there is growing evidence that recruitment is a useful mechanisms to graft competences (Song et al .,2003),learn new knowledge (Tzabbar et al .,2007),overcome compe-tence traps (Rosenkopf and Almeida,2003),and facilitate the development of new technological capabilities (Tzabbar,2009).Interestingly,these two parallel streams of research have tended to examine the impact of knowledge stocks on innovative outcomes independent from the impact of knowledge flows (e.g.,DeCarolis and Deeds,1999;Rothaermel and Deeds,2004).
Such a limited focus on the independent impact of knowledge flow and stock on innovative success draws our attention away from a broader question in strategic innovation:how does the interaction between stocks and flows affects innovative success?Put differently,does the stock of knowledge have the same effect on the rate of innovation,when the firm has high levels of knowledge flows as when it has low levels of knowledge flows?
The first purpose of this study is to fill this void by examining the interaction between knowledge stocks and flows.The accumulation of a stock of knowledge increases a firm’s absorptive capacity (i.e.,the greater the knowledge stock,the greater the knowledge assimilation).However,knowledge decays over time.The effect of the stock of knowledge on innovation should thus depend on an ad-equate flow of new knowledge.Building on the knowledge-based view of the firm,556 A.Al-Laham et al.
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we explore the interdependencies between two types of knowledge flows and stocks:human capital and externally generated knowledge.Prior research relies on the notion that access to external knowledge universally increases the firm’s chance to innovate (Rosenkopf and Almeida,2003).However,still unanswered is the question of the frequency with which firms should access and acquire knowledge.
The second purpose of our research is to examine differences in the moderating effects of knowledge flows.Should all knowledge stocks be refreshed at the same rate?More specifically,should human capital and knowledge acquired through strategic alliances be refreshed at the same rate?We posit that the interdependencies between knowledge stocks and flows may determine a firm’s innovative success because suc-cess requires not only the application of given knowledge stocks but also the dynamic capability to update and renew knowledge continuously.We further argue that high flows of human capital and externally generated knowledge will decrease the rate by which knowledge stocks decay.Thus,we contend that innovation depends on the frequency with which firms refill their existing stocks.
We conceptualize knowledge flow as the time since the firm has accessed a certain knowledge resource.We argue that although frequent access to some knowledge resources facilitates innovation,frequent elicitation of others may impede innovation given the different underling mechanism by which knowledge is transferred through each one of the knowledge sources.Accordingly,we examine how the interaction between knowledge stocks and flows moderates their direct impact on firms’innova-tive success.
To address these two voids in the literature,we investigate the interrelationship between stocks and flows of human capital and research alliance capital among 857dedicated US biotechnology firms founded between 1973and 1999.Because hiring experienced scientists and establishing research alliances reflect different means (and underlying processes)by which firm’s access and graft external knowledge,we argue that these issues deserve particular empirical attention.
Our findings demonstrate that failing to account for the interactions between knowledge stocks and flows results in an incomplete picture of the impact of the knowledge development process on innovative success.By measuring these inter-action effects,we also find that a failure to refresh human capital can offset the positive impact of high levels of human capital stocks.However,frequent engage-ment with new alliance partners results in a decreased impact of alliance capital on innovative success.We argue that the causal mechanisms involved in the acquisition of human capital through the recruitment of scientists and the acquisition of external knowledge through strategic alliances differ in fundamental ways.We explain our findings according to the need to develop and deploy different capabilities over time and,more generally,to strike a balance between the exploration and exploitation of knowledge.Innovation consequences of recruitment and collaboration 557
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2.Literature review
2.1Knowledge and innovation in the biotechnology industry
Innovation traditionally has been conceptualized as a process of accumulation and recombination of knowledge embodied in science and technology (Schumpeter,1939;Kamien and Schwartz,1982).Economists and organization theorists agree that the only way for an organization to sustain innovation is by constantly upgrad-ing its knowledge base (Dosi et al .,1988;Spender,1996;Nightingale,2008).According to the knowledge-based view (Spender and Grant,1996),the dynamic balance between applying knowledge stocks (exploitation)and accessing external knowledge flows (exploration)determines innovative success.Thus,firms must both acquire new knowledge outside their boundaries and build internal learning capabilities to integrate,transform,and apply knowledge to innovative products and services (Kogut and Zander,1992).
Biotechnology provides a salient example of a knowledge-intensive,dynamic set-ting in which patents indicate innovative success;in addition,generating new know-ledge and balancing between exploitation of existing knowledge and exploration of new knowledge constitute important components of any competitive strategy (Lerner,1995;Amburgey et al .,1996;Calabrese et al .,2000).The race for techno-logical innovation motivates firms to build their competitive advantages and cap-abilities to penetrate new technological domains (Powell et al .,1996).Establishing such a competitive advantage requires the accumulation of knowledge stocks in the form of human capital (e.g.,hiring scientific experts)and exclusive access to know-ledge developed by others through research alliances (Teece et al .,1997)for patenting purposes.Biotechnology firms’performance thus depends on their investment in and accumulation of talented and skilled human capital (Zucker et al .,1998;Tzabbar,2007)and their development of collaborative capabilities (Shan et al .,1994;Deeds and Hill,1996).
2.2Knowledge stocks and innovative performance
How do knowledge stocks affect performance?Both the resource-(Wernerfelt,1984;Barney,1991;Mahoney and Pandian,1992)and knowledge-(e.g.Kogut and Zander,1992;Grant,1996;Spender,1996)based views of the firm suggest that firms that acquire higher levels of knowledge stocks are better positioned in terms of their ability to innovate successfully (DeCarolis and Deeds,1999).Original work by Dierickx and Cool (1989)refers to the effects that the level of stocks have on the development of a knowledge-based advantage.That is,firms with a higher initial stock of knowledge,such as R&D know-how,benefit from first-mover advantages compared with firms with low initial knowledge stocks.
The sustainability of early advantages gets further enhanced by asset mass effi-ciencies,because adding increments to an existing asset stock is easier when the firm 558 A.Al-Laham et al.
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already possesses high levels of that stock,a phenomenon labeled “absorptive capacity”(Cohen and Levinthal,1990;Mowery et al .,1996).Furthermore,during path-dependent,trial-and-error learning processes,firms gradually adopt and apply knowledge that leads to favorable outcomes,which increases the efficiency with which they use any particular procedure,typically leading to firm specialization and increasing returns on experience (Levitt and March,1988).
2.3Knowledge flows and innovative performance
What are the consequences of knowledge flows for innovative performance?One possibility is that knowledge flows have no direct effect on innovativeness,because knowledge stocks mediate the relationship.This viewpoint draws on the observation that external flows of knowledge represent learning-based streams of fresh knowledge into the firm,which may be assimilated and developed into stocks of knowledge (Bontis,1999;Vermeulen and Barkema,2001).Consequently,the only effect of knowledge flows would be on the level of knowledge stocks.
However,most scholars working with the knowledge-based view adopt a different stance:knowledge flows have a direct effect on innovative performance,independent of their effects on the level of knowledge stocks.This approach is predicated on an additional characteristic of knowledge,timeliness.A failure to refresh the firm’s knowledge stock therefore leads to progressive obsolescence of that knowledge.As Dierickx and Cool (1989,p.166)point out,all asset stocks “decay”in the absence of adequate “maintenance”expenditures.For example,R&D know-how depreciates over time because of technological obsolescence.The contributions of older know-ledge components become less valuable as time passes.The organizational knowledge base narrows,organizational knowledge becomes obsolete,and the firm itself becomes increasingly simple (Miller,1994).
Incoming flows of external knowledge therefore infuse knowledge and practices into the firm and foster the development of new knowledge,because new knowledge often results from the combination and integration of existing forms of knowledge (Kogut and Zander,1992;Henderson and Cockburn,1994).These streams of fresh knowledge serve as mechanisms that prevent firms from becoming inert—that is,rigid,narrow,and simple—because of the obsolescence of their knowledge base (Leonard-Barton,1992;Levinthal and March,1993;Miller,1993,1994).Streams of new knowledge also help firms develop new capabilities and prevent them from falling into a competency trap (March,1991).This phenomenon reflects the obser-vation that firms sometimes persist in using a set of routines and capabilities that may be far from optimal (Levitt and March,1988).Because firms prefer to learn in areas in which they already have expertise (Levinthal and Myatt,1994;Leonard-Barton,1995),they also tend to confine themselves to a limited set of knowledge domains and have difficulty responding to developments outside these areas.Furthermore,a lack of adequate “maintenance”expenditures can simplify the Innovation consequences of recruitment and collaboration 559
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firm’s knowledge base (Leonard-Barton,1992;Miller,1994;Bierly and Kessler,1999).
2.4Speed of knowledge decay
R&D know-how depreciates over time because of technological obsolescence.The contributions of older knowledge components become less valuable as time passes.Hence,a failure to refresh the firm’s knowledge stocks lead to progressive obsoles-cence of that knowledge (Dierickx and Cool,1989).In this section,we argue that the rate at which knowledge decays depends on both the scope of knowledge that is being transferred and the learning mechanism associated with extracting such knowledge from the knowledge source.
Forming a research alliance provides a means to facilitate inter-organizational learning and gain access to knowledge outside the firm’s boundaries (Powell et al.,1996).The positive influence of research collaborations on innovative success derives from the ability of partners to share technological knowledge,take advantage of scale economies in research,and leverage complementary assets (Teece,1992;Powell et al.,1996).Because they explicitly aim to jointly develop,transfer,and integrate new knowledge,research alliances entail the absorption of some of the partner’s valuable and often tacit capabilities (Gambardella,1995;Gulati,1995).They also contribute to innovative success by providing crucial intellectual input,such as shared access and rights to collective knowledge.Partners’joint efforts produce unique common knowledge that is available to all participants (Khanna et al.,1998).This common knowledge then can offer private benefits for each participant to varying degrees,depending on their relative absorptive capacities.
For private benefits to materialize to patent application,the recipient firm needs to codify the knowledge accumulated.Yet,the complex and specific nature of know-ledge embedded in the technologies and practices of the alliance partner (Nelson and Winter,1982)require that firms take sufficient time to translate complementary know-how into a novel idea.From a knowledge perspective,transferring complex and causally ambiguous knowledge typically requires reconstruction and adaptation processes (Attewell,1992).Time enables partners to strengthen the interpersonal connections between their scientists,which influences how easily they can combine existing and new knowledge (Szulanski,1996;Hansen,1999;Reagans and McEvily,2003).By dedicating such time,firms can better deal with the ambiguity associated with replicating a partner’s capability (Lippman and Rumelt,1982),especially if this knowledge requires modification to fit the new application or context (Winter,1995;Teece et al .,1997).
The private benefits from the recruitment of a scientist,on the other hand,is argued to require less time to result in a patent application (Williamson,1975).Since knowledge becomes increasingly difficult to separate from those who possess it as it becomes more tacit,Dosi (1988)suggested that hiring people is a rich mechanism of 560 A.Al-Laham et al.
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communication that enables a faster rate of knowledge transfer.Among the first to advance this perspective were Baty et al .(1971),who posited that individuals carry with them to subsequent positions knowledge and information accrued in their previous position.For instance,Gabarro (1985)and Filgstein (1993)have found that the initial actions of new hires were in areas where they had functional experi-ence.Their experience in other organizations,thus,determines the specific set of knowledge and resources that individuals bring to new organizations (Gunz and Jalland,1996).The resultant knowledge transfer is restricted to a mobile scientist’s familiarity with a specific area,reducing the transfer related problems associated with the transfer of tacit knowledge (Tzabbar et al.,2007).
Unlike knowledge gained through an R&D alliance,the appropriation of external technical knowledge transferred by hiring a scientist does not require learning by incumbent scientists.This reduces the information and time demands on the organization (Huber,1991).In a study of inventor mobility and search in the US biotechnology industry,Tzabbar et al .(2007)found that much of post-hiring change in a firm’s search behavior can be attributed to the hired scientist’s involvement.As a consequence,a hiring firm draws more heavily on a newly hired employee’s own prior art than on that of her prior employer.They interpret these results as evidence that much of what appears to be exploration at the organizational level is in fact exploitation at the individual level.Given that mobile inventors contribute in their area of expertise it may facilitate faster processing of information within the organization (Williamson,1975).
Rosenkopf and Almeida (2003)were the first to compare alliances and recruit-ment as conduits for knowledge transfer.Specifically they argued and demonstrated that both alliances and the recruitment of scientists influenced the transfer of know-ledge.Yet,while both methods—R&D alliance and recruitment—provide access to external knowledge,both the ease of access to the knowledge and the ability of the focal firm to codify the knowledge attained to patent,may differ.While not including the time dimension in their model,implicit in their argument and findings is that since each involves different underlining processes of knowledge building the dur-ation of the effect of each one of them may vary.
Accordingly,we argue that when R&D alliances are the contributors of knowledge,the firm may require more time to assimilate and codify the knowledge gathered and thus the effect of alliance on innovative success may decay at a slow rate.However,when mobile inventors are the contributors of knowledge,they tend to contribute in their limited area of expertise.This facilitates faster processing of information within the organization (Williamson,1975),and thus the effect of recruitment on innovative success may decay at a faster rate than the effect of R&D alliances.
2.5Conceptual model
We assume that the failure to refresh knowledge stocks with outside knowledge flows reflects the exploitation–exploration dilemma,which has been central to Innovation consequences of recruitment and collaboration 561
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organizational learning theory.Over time,firms must strike a balance between ex-ploration,or the search for new and fresh knowledge,and exploitation of their existing knowledge base or stock (March,1991;Levinthal and March,1993).During exploitation,firms focus on routines as the parts of their knowledge stock that contribute most to their success and filter out those routines that are less suc-cessful for the given task environment (Cyert and March,1963).Through repeated applications of knowledge stock,managers become more settled in their opinions about the causality of success,and routines are more firmly established in the or-ganization (Nelson and Winter,1982).Therefore,knowledge proven less successful in a particular setting will disappear from the organization’s memory.However,as a negative consequence of these self-confirming processes,the search for new oppor-tunities and new routines becomes limited over time (Huber,1991),which hinders the important recombination and reintegration of new and existing knowledge com-ponents into the innovative process (Kogut and Zander,1992).
In terms of stocks and flows,the balancing of exploitation and exploration sug-gests that the interaction between stocks and flows is of fundamental importance.Biotech firms that are unbalanced in terms of their exploration and exploitation (i.e.,that fail to refresh their knowledge stock with flows of new knowledge)therefore should experience a degradation of their ability to generate new intellectual property.Consequently,the patent rate,or the probability of acquiring a new patent per unit time,should decrease.Although maintaining a balance between exploiting existing knowledge stocks and exploring new knowledge—such as through hiring experts or joining research alliances—is important,we do not know the ideal frequency with which firms should access each type of knowledge.
We cite these two types of knowledge stocks as they are central to a firm’s success in a knowledge-intensive such as the biotech industry.Thus,we specifically investi-gate the speed and frequency with which firms should refill their stock of human capital in the form of talented scientists and stock of alliance capital in the form of accumulated alliance experience to maintain the balance between their knowledge exploration and exploitation activities.
In turn,we argue that recruiting new scientists and engaging in research alliances reflect two fundamentally different processes,which means the speeds at which they should be refreshed differ.Human knowledge and skills tend to be embedded in specific task and organizational contexts,which makes it difficult to transfer them across innovative problems.Firms therefore need to refresh their stock of human capital frequently to be able to explore entrepreneurial opportunities beyond their existing knowledge boundaries (Tzabbar,2007).This recruitment requires that the firm grafts expert knowledge onto its own knowledge stocks and obtains an immediate contribution from the expert’s area of expertise (Tzabbar et al .,2007).In contrast,research alliances require significant time and the commitment of human and physical resources for the firm to learn and assimilate its partner’s knowledge (Wathne et al .,1996).Alliances are both costly and require time to 562 A.Al-Laham et al.
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develop.Therefore,frequent research commitments with different firms may disrupt existing learning efforts by firms,as we outline in Figure 1.
3.Hypotheses
3.1Moderating effect of the interaction between human capital stock
and flow
Because human capital is distributed heterogeneously across firms,we must account for it when examining firm-level innovation (Barney,1991).Human capital refers to the aggregation of knowledge,skills,and vigor in highly skilled,talented employees such as research scientists.By transforming input into outputs,they become the main source of value creation (Hatch and Dyer,2004;Zollo and Singh,2004).In turn,a firm’s innovative performance is at least partially a function of the value of its human capital (Hitt et al .,2001),especially for biotechnology firms,whose race to appropriate knowledge has enhanced their reliance on the input of scientists skilled in a wide variety of disciplines (Cockburn et al .,2000;Tzabbar et al .,2008).The strategic management literature founded on the knowledge-based view high-lights the role of human capital in a firm’s ability to build new capabilities,because human capital provides inimitable tacit knowledge (Kogut and Zander,1992;Almeida et al .,2002).As an example,Prahalad and Bettis (1986)posit that individual employees carry beliefs,theories,and propositions about the relationship between
Knowledge Flows
Knowledge Stocks Figure 1.Conceptual frame of reference.
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assets and organizational outcomes and offer particular firms unique advantages in exploiting specific product markets.Similarly,Henderson and Cockburn (1994)find that locally embedded knowledge and skills among a firm’s intellectual human cap-ital may offer a unique innovation competence for the firm.The disciplinary focus of groups of scientists within a firm creates deeply embedded knowledge that is not easily codified and difficult to transfer or imitate.In the same vein,Leonard-Barton (1992)indicates that the tacit knowledge developed by skilled employees over time may develop into a source of innovation.Moreover,over time,local groups within the firm become repositories of deeply embedded routines and thus strategically important capabilities (Teece et al .,1997).Much of the value of human capital therefore is context dependent,because it relates closely to the nature of existing organizational routines.The specificity of the external and internal learning processes necessary for a firm to innovate favors firms that invest in and maintain significant levels of human capital.
However,because human knowledge and skill tend to be embedded in specific task and organizational contexts (Nonaka,1994),task expertise is limited to narrow knowledge domains (Glaser and Chi,1988)and difficult to transfer across innovative problems (Szulanski,2003),which limits the firm’s ability to explore entrepreneurial opportunities beyond its existing knowledge boundaries (Tzabbar,2007).Through frequent communication,incumbent scientists share and integrate their knowledge (Tzabbar et al .,2007),and over time,long-established groups likely become familiar with members’knowledge,which increases their likelihood of being trapped by their prior competencies and internal processes,affecting the team’s commitment to the status quo,its informational diversity,and its attitude toward opportunity seeking (Katz,1982;Finkelstein and Hambrick,1990;Michel and Hambrick,1992).The search for solutions in the neighborhood of the group’s current knowledge thus increase,whereas its ability to explore novel opportunities declines (Pfeffer,1983;Ancona,1989;Ancona and Nadler,1989).
Scholars who adopt a knowledge-based view typically argue that to remain com-petitive and ensure the consistent ability to differentiate,firms must develop their human capital in ways that enhance the supply of information and knowledge avail-able to the firm (Barney,1991).By acquiring and grafting new members who possess knowledge not previously available within the organization,organizations increase their stock of knowledge (Huber,1991).As such,personnel recruitment can expand firms’non-local search capabilities and enable them to search beyond their existing trajectories (Dosi,1988;Miner,1990).
Recruiting new members,particularly in technical or professional occupations,provides new bodies of knowledge and skills,which often represent sources of in-novation.Such transitions create opportunities for new members to apply new competencies,skills,resources,and perspectives to existing innovative problems (Ancona,1989;Wiersema and Karen,1992;Milliken and Martins,1996)that serve as the basis for experimentation (O’Reilly and Flatt,1989;O’Reilly et al .,1989).564 A.Al-Laham et al.
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