Paper to be presented at the DRUID Summer Conference 2004 on
INDUSTRIAL DYNAMICS, INNOVATION AND DEVELOPMENT Elsinore, Denmark, June 14-16, 2004
THEME: Networks, Clusters and other inter-firm relations as Vehicles for Knowledge Building and Transfer
TOWARDS A DYNAMIC MODEL OF NETWORKS AND INNOVATION Stine Grodal Stanford University Management Science and Engineering Terman Engineering Building Office 432A 94305 Stanford, California, USA Email:
[email protected] Office phone: (+1) 650 723 3858 May 1st 2004 In the knowledge society innovation plays a key role in determining a firm’s competitive advantage. Simultaneously interorganizational alliances have grown over the last decade. These two factors combined place alliances’ contribution to the innovative capabilities of the firm as an important question within organization studies. Most of the literature within the field has exclusively examined singular causal relationships, which have obscured the recursive dynamics that appear when multiple causal relationships are taken into consideration. Combining evidence from multiple studies suggests a Matthew effect, where dominant firms constantly increase their innovativeness, centrality, and status. In the long run this development might, however, be dysfunctional due to network closure, and limited access for young firms to enter into the network. Keywords: Innovation; alliances; networks; knowledge JEL codes: L14; L22; M10
INTRODUCTION The innovative competence of the firm has traditionally been linked to intraorganizational factors such as R&D investments and gifted inventors (Ruttan 2001; Schumpeter 1955; Shane and Venkataraman 2000). Recent research has, however, provided evidence that interorganizational alliances play a central role in determining a firm’s innovative capability by demonstrating that innovation occurs in networks of organizations across multiple contexts (Burt 1992; Van de Ven et al. 1999). The network perspective, thus, augments the intraorganizational perspective by emphasizing that innovation is not a sole consequence of organizations but also of networks of organizations (Burt 1992; Granovetter 1985). Historically networks have played an important role in the growth and sustenance of the modern economy (Padgett 2001). In the knowledge-based economy, interorganizational networks facilitate the production of complex goods that are difficult to produce in their entirety within a single firm (David and Foray 2003). Examples of complex goods are the production of modern cars, mainframes, and high yielding modern varieties of rice, which all were produced as a collaborative activity across multiple firms (Ruttan 2001). Over the last fifteen years there has been unprecedented growth in the number of interorganizational alliances (David and Foray 2003; Gulati and Gargiulo 1999; Hagedoorn 2002). Prior to 1975 strategic alliances were primarily formed to exploit natural resources. In the last two decades there has been a surge in the number of strategic alliances within the technology-intensive industries (e.g., semiconductors, computers, software, commercial aircraft), focused on joint R&D, product development, and higher levels of knowledge exchange and technology transfer (Mowery, Oxley and
1
Silverman 1996). The theoretical model that predominantly has been applied to the analysis of alliances is the resource-based view of the firm (e.g. Barney 1991; Penrose 1959; Wernerfelt 1984). At the core of the resource-based view is the assumption that firms achieve and sustain a competitive advantage though a heterogeneity of resources located within the firm. Innovation is closely linked to the exchange and recombination of knowledge (Nonaka and Takeuchi, 1995). Traditionally innovation researchers have distinguished between invention and innovation. In this perspective invention is defined as the discovery of something that did not already exist (for example the recombinant DNA technique), whereas innovation is defined as the subsequent recombination of knowledge (including inventions) into commercialized goods (for example applying the recombinant DNA technique to produce human insulin). Newer research dismisses this distinction to describe innovation as an iterative process that simultaneously includes discoveries and recombinations (David and Foray 1995; Gittelman and Kogut 2001; Pavitt Forthcoming; Ruttan 2001). At the core of the new framework is the exchange of knowledge as facilitator of both discoveries and the recombination of knowledge (Hargadon and Sutton 1997; Merton 1995). Interorganizational relationships pose a way for such knowledge exchanges to occur (Powell, Koput and Smith-Doerr 1996a). Transferring knowledge in interorganizational networks poses challenges for our standard notions of commodity exchange in market relationships. Knowledge differs from other commodities, because it has the characteristics of a public good, i.e. multiple consumers can enjoy the good at the same time, because the use of knowledge by one consumer does not prevent its use by others. One of the specific characteristics of the
2
“public good” quality of knowledge is its non-appropriability (Arrow 1962; Callon 2002; Nelson 1959). The problem of appropriability refers to the fact that knowledge can be transferred and still be possessed by its first owner (Arrow 1962; Nelson 1959). A person can for example transfer knowledge of how to make a semiconductor to someone else, and still retain knowledge of the production process. With ordinary commodities, however, you loose the commodity when you transfer it. If you sell the physical semiconductor, you are left with no semiconductor. The non-appropriability of knowledge leads to difficulties in determining the value of knowledge prior to an exchange, because if knowledge is disclosed for the buyer to evaluate its worth, the potential buyer need not to buy it, since he already possesses it (Arrow 1962; Nelson 1959). The appropriability of knowledge, thus, makes it difficult to exchange in standard market relationships. Williamson (1975; 1985; 1996) argues that exchanges are likely to be removed from the market and integrated into the firm when the exchange involves high complexity and uncertainty and when the exchange enables individual opportunism. These circumstances are encountered when knowledge is exchanged (Arrow 1962). Exchanging knowledge involves high complexity, uncertainty and possibilities for individual opportunism due to the appropriability and problems determining the value of knowledge. Within transaction cost theory, the traditional solution to knowledge exchange would be vertical integration (Williamson 1975). Another solution suggested by Nelson (1959) and Arrow (1962), is to make knowledge a free good by giving public institutions (e.g. universities) responsibility for producing it.
3
An alternative to transaction cost theory’s focus on vertical integration is interorganizational strategic alliances (Powell, Koput and Smith-Doerr 1996a; Powell 1990). Interorganizational collaboration eliminates the problem of appropriability in part by subjecting knowledge exchanges to contractual agreements. Empirical research, however, shows that
contractual agreements are not sufficient
to facilitate
interorganizational knowledge exchange. A high degree of trust between the partnering firms is also necessary (Browning, Beyer and Shetler 1995; Kale, Singh and Perlmutter 2000; MacDuffie and Helper 1997). Exchange relationships based on trust conflicts with transaction costs’ central assumption of an “opportunistic agent”, who acts with selfinterest and guile (e.g. Williamson 1985). Network forms of organizing offers an intermediate position between markets and hierarchies. Networks eliminate some of the problems of appropriability, and simultaneously combine some of the incentive structures of markets (alliances can be terminated if they do not operate successfully and new alliances can be formed) with the monitoring capabilities and administrative controls associated with hierarchies (Mowery, Oxley and Silverman 1996). Scholars have, however, pointed to negative consequences of forming alliances, as for example “ties that blind” (firms get so focused on each other that they are not aware of other developments within the industry), wasted energy on coordination/collaboration, limits on freedom of action, and exclusiveness that inhibits the free exchange of knowledge (Glasmeier 1991; Portes and Sensenbrenner 1993). A case study by Glasmeier (1991) suggests that innovation in some instances is further enhanced by vertical integration than by a network structure. This argument would be in line with transaction
4
cost theory's (e.g. Williamson 1975) statement that transaction costs might be reduced when resource exchange take place within a company in contrast to on the market. The network form of organizing, for example, limited the innovative capacity of the Swiss watch industry. In contrast the Japanese watch industry, which was more vertically integrated, adapted quicker to the technological change (Glasmeier 1991). Moreover, there is a costs related to maintaining networks ties (Burt 1992; Uzzi 1997). Abandoning the market in favor of a network of alliances to avoid the appropriability problem might therefore have negative unintended consequences. The knowledge that we have about the relationship between alliances and innovation still has many gaps. One of the important gaps results form the fact that most studies of the relationship between alliances and innovation have focused on singular causal relationships, for example, the impact of alliances on innovation (Ahuja 2000a; Baum, Calabrese and Silverman 2000; George, Zahra and Wood 2002; Godoe 2000; Kraatz 1998; Sarkar, Echambadi and Harrison 2001b; Stuart 2000; Walker, Kogut and Shan 1997), tie diversity on innovation (Baum, Calabrese and Silverman 2000), centrality on innovation (Powell et al. 1999), relationship building on innovation (Vinding 2002), innovation on alliances (Ahuja 2000b; Stuart 1998), and technological crowdedness and alliances (Stuart 1998). Focusing on singular causal relationships obscures larger dynamics that often appear when multiple causal relationships are taken into consideration. In combination singular causal relationships may form positive or negative feedback loops that determine the overall development of the system (Bateson 1972; Luhmann 1995). Feedback loops have
5
been found to determine the development of many biological, psychological and social systems (Bertalanffy 1950; Bertalanffy 1972; Maturana and Varela 1986; Zeleny 1980). In this paper I apply a systems approach to the study of alliance innovativeness. I draw on the causal relationships presented in the existing literature to suggest an allianceinnovation dynamic, which includes multiple causal relationships. Following Edquist (Forthcoming) an innovative system ought to fulfill five criteria to be considered from a system perspective. These five points are: (i) components and (ii) relations among them. In the analysis of alliances and innovation, organizations constitute the components and alliances constitute the relations among them. (iii) The system must constitute a coherent whole. Each organization within an alliance network fulfills the role of knowledge production and resource sharing to achieve a common goal. Additionally (iv) there must be a reason why the system exists, a function that it needs to perform i.e. there must be some activities that the system or the system components perform that make them part of the system. In the study of alliances and innovation the functioning of the system increases the innovativeness of the partnering firms (v) there has to be some way of identifying what is and what is not part of the system. Firms can be considered part of the system if they are connected to other firms through alliances. To my knowledge no one has yet studied the systemic dynamics of networks and innovation. From the existing empirical research I develop a model, which shows a coevolution of innovation and centrality within an alliance network. Feedback loops between alliances, centrality, and innovation promote innovation, centrality, and status for firms that are already central within the network, but might limit access into the
6
networks by small and young firms, who have the most to gain from network participation.
THE DYNAMIC BETWEEN NETWORKS AND INNOVATION
Singular causal relationships Most researchers have examined the relationship between alliances and innovations by analyzing how formalized alliances’ influence the frequency of patenting. The empirical evidence strongly indicates a positive relationship between alliance formation and innovation (1)1, within as diverse industries as chemicals (Ahuja 2000c), biotechnology (Baum, Calabrese and Silverman 2000; George, Zahra and Wood 2002; Powell et al. 1999;
Walker,
Kogut
and
Shan
1997),
telecommunications
(Godoe
2000),
semiconductors (Stuart 2000), colleges (Kraatz 1998), and across industries (Sarkar, Echambadi and Harrison 2001a). This positive relationship also holds when the alliances are formed, not with other firms, but with universities (George, Zahra and Wood 2002). The diversity of the research contexts provides support for the effect being generalizable, even though most research yet has to move beyond high technology. Moreover, all studies use patents as a proxy for innovation except Vinding (2002), Kraatz (1998), and Sarkar, Echambadi and Harrison (2001) who use questionnaires.
1
The numbers after the paragraphs refer to the causal arrows in figure 1 and figure 2.
7
The
Figure 1: Singular causal relationships
alliances
Innovation + (3)
Technological crowding + (1) (6) +
(2) +
+ (4)
+ (5) Experience
Diversity Centrality
Alliance formation
relationship and
between
innovation
moderated
by
characteristics.
Research
is
several has
shown that the network structure (i.e.
whether
a
firms
relationships are characterized
by structural holes verses redundant ties) (Ahuja 2000a), the absorptive capacity (Cohen and Levinthal 1990; Vinding 2002), the characteristics of the knowledge involve in the exchange relationship (Simonin 1999), and the establishment of trust between the partnering firms (Baum, Calabrese and Silverman 2000; Kale, Singh and Perlmutter 2000) play a key role in enhancing the innovative benefit. Traditional control variables like age, size and R&D spending have an increased positive effect on innovation (George, Zahra and Wood 2002). Fewer studies have examined the converse relationship: The impact that innovations have on alliance formation. Ahuja (2000b) demonstrated that a firms stock of patents determined alliance formation in the chemicals industry. Likewise Stuart (1998) showed that alliance formation rates in the semiconductor industry were higher among firms with patents. These findings show that possessing patents facilitates the formation of collaborative relationships (2), perhaps because owning patents increases the firms’ legitimacy. This explanation would be aligned with the views of the new institutionalism (e.g. Powell and DiMaggio 1991), which emphasizes legitimacy as a key factor in determining interfirm relations. Stuart (1998) shows that more prestigious patents lead to
8
more alliances (i.e. they are widely cited), and that there seems to be a linear relationship between patent prestige and alliance formation. Combining these two singular causalities suggest a positive feedback loop between alliances and innovation: Establishment of collaborative ties facilitates innovativeness, and innovativeness further facilitates the establishment of collaborative ties. This dynamic points to a positive feedback loop, where firms that are able to distinguish themselves from other firms either by being involved in collaborative relationships or by possessing patents, become increasingly innovative. This positive feedback loop is, however, only part of the dynamic. Research has shown that centrality within the larger network of alliances also facilitates innovations (3) (Powell et al. 1999). Firms centrally positioned in a network have quicker access to novel information, than do firms in a more peripheral position. Moreover, firms that are centrally located enjoy a high status (Burt 1992; Shrum and Wuthnow 1988). The substance of the ties that firms form also facilitates innovation. Firms can form alliances for different business activities, such as, financing, marketing, manufacturing, supply/distribution or investment. Research within the biotechnology industry shows that diversity in the type of alliance positively affects innovation (4) (Baum, Calabrese and Silverman 2000; Powell et al. 1999). Diverse ties provide firms with access to diverse information, which makes the recombination of information more probable (Ruef 2002). But, diversity exhibits diminishing returns, which suggests that at some point establishing even more diverse ties will minimally benefit the firm (Powell et al. 1999).
9
The firm’s experience managing alliances is another factor that shapes how alliances affect innovation. Network experience has a positive influence on patenting, even though the rate of increase diminishes with increased experience (5). This finding suggest that firms eventually exploit the most obvious opportunities to learn, which makes further learning harder (Powell et al. 1999). In general, the effects of diversity and network experience are smaller than that of centrality, which might be related to the status advantages gained form being centrally located within a network. Stuart (1998) has shown that semi-conductor firms in more technologically crowded niches tend to form more alliances, where technological crowding was measured as the overlap between firm’s patents in a five-year period (6). Apparently the relationship between technological crowding and alliances formation is non-linear. The rate at which firms form alliances decreases as a niche becomes more crowded. There are several possible explanations for why firms in technologically crowded areas form more alliances. First when the technologies of the partnering firms are similar, they should be able to absorb each others knowledge more quickly (Stuart 1998). Second alliances can eliminate redundant R&D investments among otherwise competing firms.
The double loop dynamic A number of recent studies offer evidence that the individual causal relationships examined by most researchers and displayed in figure 1 may be part of a larger recursive system. Powell, Koput, and Smith-Doerr’s (1996a) show that R&D alliances between biotechnology firms facilitate the construction of non-R&D ties (7), and increase a firm’s centrality within the network (8). George, Zahra et al. (2002) further find that firms with
10
university alliances tend to form more alliances than firms that do not have university links. Powell, Koput, and Smith-Doerr (1996a) also show that forming more non-R&D ties lead to an increased tie diversity (9) and increased network experience (10). A diversity of ties also results in firms becoming more central within the network (11). The final element in the dynamic is that centrality within the network facilitates the establishment of R&D collaborations (12) (Powell, Koput and Smith-Doerr 1996a). Coupling the second feedback loop with the findings presented earlier (that diversity, experience, centrality, and alliance formation lead to increased innovativeness, and technological crowdedness and increased innovation lead to alliance formation), an important double feedback loop occurs (depicted on figure 2). The difficulties with combining all these results is that Powell, Koput et al. (1996b) differentiates between collaborative R&D alliances and other alliances, whereas the studies presented in the prior section do not. A conservative integration stresses that the relationship between alliances and innovation at least holds for collaborative R&D alliances, since R&D collaborations are the alliances that are most closely linked to innovative activity. Hence, Figure 2: The double feedback loop
Innovation (13)
(3) (12)
Technological crowding
(6)
Centrality
(8) (1)
(2)
Formation of collaborative R&D
(11)
(7)
Formation of non-R&D ties
(9) (10)
(4)
(5)
Diversity Experience
11
I introduce the positive relationship between collaborative R&D alliances and innovation depicted on figure 2. Expanding on the dynamic with the other results introduces that not only does collaborative R&D alliances facilitate non-R&D alliances which again facilitates diversity and network experience, but both diversity (4) and network experience (5) also facilitates innovation. An important element in the dynamic is that centrality also has a positive impact on innovation (3). One of the implications of these causal relationships is a co-evolution of alliance formation and status. This phenomenon is apparent within the semi-conductor industry where nine of the ten highest prestige firms in the industry appeared among the fifteen firms with the most alliances. These were also among the largest and most innovative within the industry (Stuart 1998). A final causality links innovation to alliance formation through technological crowding (13). That technological crowding facilitates alliance formation has already been shown. It can further be argued that when firms increase their innovativeness, they also increase the technological crowdedness of the technological niche in which they are embedded. This is due to firms tendency to patent within areas, where they are already active (Katila 2003; Rosenkopf and Nerkar 2001; Sørensen and Stuart 2000; Stuart and Podolny 1996). This is a result of a path dependence, which arise both because firms have accumulated knowledge within specific areas, but also because they tend to make R&D investments within areas where they have a prior history of R&D spending (Helfat 1994). Their new patents will often contain the same patent citations as their previous patents, and, thus, be structurally equivalent to the patents they already possess.
12
The combination of results from multiple studies suggests a co-evolution of alliance formation, innovation and centrality, supported by several feedback loops. Not only does the formation of collaborative R&D increase innovation, which again increases the formation of collaborative R&D, but collaborative R&D also leads to increased diversity, experience and centrality within the network, all of which increases innovation. Simultaneously centrality within the network facilitates the formation of collaborative R&D. The multiple causalities of the network are shown in the following example (see figure 3). Two innovative firms (A and B) in a young industry decide to form a research alliance. “A” later decides to form a manufacturing alliance with a third firm (C), whereas B forms a marketing alliance with a fourth firm (D). Due to their alliance A and B become more innovative, and thus more attractive alliance partners for other firms; they therefore form research alliances with firm E and F respectively. After a while A decides that it wants a marketing alliance. Since A partners with B, which has had marketing alliance experience with D, A decides to also form a marketing alliance with
Figure 3: An example of network development
F
Research
Research
A
Research
Research
B
H
Marketing
Production
G
Research
E
C
D
13
D. B goes through the same process when seeking a manufacturing alliance and partners with C. A and B now both benefit from their network experience, tie diversity and centrality within the network to become increasingly innovative. This increases their status, and after a while both form a new research alliance, A with G and B with H, which further increases their centrality, innovativeness and status.
DISCUSSION The centrality dynamic A key implication of the alliance-innovation dynamic is that important relationships between networks and innovation unfold within a temporal framework. If the relationship between innovations and alliances are only considered cross-sectionally the consequences of the systemic dynamics are not revealed. The temporal framework also emphasizes that the causal relationships described in the alliance-innovation dynamic are not instantiated simultaneously, but evolve along side the development of the alliance network. Viewed temporally the implication of the multiple causality is a Matthew effect: “For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken away even that which he hath” (Merton 1968 p. 58). Merton showed that within science high status researchers are attributed more credit than they deserved, whereas low status researchers receive less. This leads to status-enhancing and status-suppression feedback loops exaggerating already existing status differences within the field. The same dynamic appears to occur within alliance networks. Once a firm partakes in the network it will increase its innovativeness and centrality, and gradually become more
14
central within the network, hereby further increasing its innovativeness and prestige. Another process supporting the Matthew effect is that high status organizations will enjoy lower costs than lower-status counterparts. This has been shown within the banking industry, where high-status banks are able to underbid lower-status banks (Podolny 1993). A central implication of the alliance-innovation dynamic is that first movers will be at an advantage, since they establish a central role within the network earlier, and benefit from the double innovation loop. This dynamic is consistent with findings by Blundell, Griffith et al.’s (1999) that firms with a larger market share are more innovative. Once a firm has achieved a central position early in the network’s formation it will most likely retain its position, since centrality both increases its direct access to collaboration partners and its innovativeness, thus making the firm an even more attractive collaboration partner. This dynamic is strengthened by high status firms’ ability to set technical standards within the industry (Podolny and Stuart 1995), hereby further exacerbating the power structure. This network development parallels Albert and Barabási’s (1999) results on the general problem of network structuration and growth. Their computational model includes two premises: (i) networks expand continuously by adding new elements, and (ii) new elements attach preferentially to sites that are already well connected. This model closely parallels some of the central features of the alliance-innovation dynamic. The pattern that arose from the computational model was a self-organizing system, in which most nodes have few ties, and few nodes have many ties following a “power law” distribution. This
15
structure is a likely outcome of the dynamic described between alliance formation and innovation. An important element of the network dynamic is the extent to which the networks become closed over time as firms form alliances primarily with other firms that are already part of the network. The closure of the network is a logical consequence of centralized firms being more prone to form alliances. Empirical research shows that as interorganizational networks emerge alliances shift from forming due to resource dependencies, towards forming based on network participation; i.e. firms partner with other firms that are already part of the network (Gulati and Gargiulo 1999). Firm A is for example linked to firm B, and firm B is linked to firm C. As the network evolves firm A will have a greater likelihood of forming a tie with firm C, than with another firm like C, which is not part of the network. This dynamic might occur due to competition, since forming an alliance with a firm might exclude the focal firm from forming an alliance with the partner’s competitor. These findings support the recursive structures in alliance networks presented in the alliance-innovation dynamic by showing how action and structure are closely intertwined in the formation of interorganizational network structures. It is through alliance formation in accordance to the double loops mechanisms that the structure of the network emerges, both with regards to centrality within the network and with regards to the innovative capabilities of the firm. All firms within the network are, however, not equally prone to partner. Results from the California Wine industry show that actors who occupy high-status positions benefit more from high-status affiliations than actors in low-status positions. High-status actors are therefore more willing and able to invest in high-status affiliations and use them to
16
further their position in the larger status ordering (Benjamin and Podolny 1999). The consequence of these network dynamics is that an elite structure emerges within the network. Central firms form alliances with each other to establish a clique of dominant firms, which are able to control the industry through industry standards and alliances with lower-status firms. Elite clique structures have been shown to exert a large influence within academia (Cole and Cole 1972), the political top in the United States (Moore 1979), in Israel across political orientations (Maman 1997), in the birth of the renaissance state in Florence (Padgett and Ansell 1993) and among Broadway musicals (Uzzi, Spiro and Delis 2002). In these cases central actors were able to sustain their power through dense partnerships to other high-status actors, while controlling the rest of the network through sparser connections. Many of these elite structures have been shown to be very stable and exist over a long period of time (Lifschitz 2003; Podolny 1993; Uzzi, Spiro and Delis 2002). The same process appears to occur in the alliance innovation network.
The long-term effects The alliance-innovation dynamic describes that firms’ partnership choices arise from within the network; a principle that in the short run provides it with an innovative advantage. Another consequence is, however, that the network becomes self-referential and closed over time. The short-term beneficial effects of the alliance-innovation dynamic might in the long run be dysfunctional, because novel information does not enter the network and thus impairs the organizations’ innovative capabilities. Negative consequences of such self-referential behavior are show in a computer simulation by Strang and Macy (2000). The results show that if firms mirror other firms that they
17
perceive to be successful, it can easily lead to the adaptation of unproductive fads, since the success of the firm might be due to random fluctuations (or other causal factors than the ones perceived). Thus, some of the disadvantages of a closed learning network might be that firms start to reproduce inefficient routines or dubious knowledge. Support for the long run negative effect of stable cliques comes from Lifschitz’s (2003) study within the Scottish shipbuilding industry shows that there is a negative relationship between clique age and clique effectiveness, where cliques consist of exchange relationships between subcontractors and customers. Thus the older the clique is the less effective it becomes. Another long term effect is that networks decay and transmute over time (Davies and Koza 2001), and many alliances persist even though they have stagnated or failed to show their usefulness (Inkpen and Ross 2001). This points to a negative effect of network tenure on innovation, which is supported by the finding that an R&D team produces most innovations 1.5 years after founding (Katz and Allen 1982). Podolny (1993) further shows that status positions are both enabling and constraining. Even though high-status firms have a competitive advantage over lower-status firms they have the disadvantage that they cannot move beyond the market associated with their high-status position and enter low-status markets. The high-status centralized position within the network that in the short run leads to increased innovativeness can thus in the long run create limitations to the firms’ innovative capability. A way centralized prestigious firms compensate for the long run innovation barriers is to form alliances with firms outside their technological area. Research shows that firms possessing prestigious patents attract collaboration partners from other technical fields (Stuart 1998). The high status firms might thus be able to further increase their power
18
base by accessing novel and rare information, and thereby sustain their innovativeness through the input of novel information while contributing to an influx of novel information into the alliance network. Another method for centralized firms to maintain their innovativeness is to partner with small young firms, which would be able to provide them with novel information. This network structure would resemble the one produced by Albert and Barabási’s (1999) simulation of network structure and growth; few centralized elite firms, who are densely connected, partner with many younger and smaller firms that are not mutually interlinked.
Entry into the Network The positive effect of networks on innovation is stronger for small and young firms than it is for large and old firms (Baum, Calabrese and Silverman 2000; Sarkar, Echambadi and Harrison 2001b; Shan, Walker and Kogut 1994; Stuart 2000). Small and young firms are, however, seldom located in the center of the network, which leads to the question of which admission ticket firms need to be allowed into the network. There are several possibilities for how firms can gain access into the network. One is that firms demonstrate innovativeness in the form of patentable inventions, which increases the firm’s legitimacy, and facilitate collaborative partnerships (Stuart 1998). The status achieved through innovativeness might, however, not translate directly into status advantages. Within the banking industry Podolny (1993) shows that the quality of the firm's products/services and the status of the firm are loosely coupled. A change in quality is not immediately translated into a change in status.
19
Firms who posses an innovative strategy (Eisenhardt and Schoonhoven 1996) and who are proactive (Sakar, Echambadi and Harrison 2001) also form more alliances. Having a proactive innovative strategy might, thus, help firms gain access into the network. Other results show that participation in technical committees facilitates alliance formation between firms. This is especially true when firm’s have few established alliances (Rosenkopf, Metiu and George 2001). The interpersonal ties and trust that develop through continued personal interaction might enable firms to obtain network access. Moreover, partnering with high status firms is important for young firms trying to enter into the alliance network, since this aids them in forming future relationships (Stuart and Podolny 1999).
Knowledge exchange and industry dynamics Following the evidence provided in this paper one can hypothesize that the increase in interorganizational alliances that has occurred over the last couple of decades (Hagedoorn 2002) might have facilitated a new innovation and power dynamic within industry structures. This power dynamic is driven by the exchange of knowledge in interorganizational networks, which provides an alternative to markets and hierarchies in dealing with the appropriability and indivisibility of knowledge. In contrast to markets where exchange relationships are driven by price the centrality of a firm within the alliance network and the innovative prestige of the firm seem to drive exchange within networks. Alliances and prestige may play a larger role in networks than in market exchanges because they help firms overcome the difficulty of evaluating knowledge. Alliances are also suited to the search of knowledge because such exchanges tend to
20
require more time than exchanges of goods, and involve a higher level of uncertainty (Coleman 1990). Linking the hypothesis brought forward in this paper to studies within population ecology the alliance-innovation dynamic implies that one of the elements that leads to organizational survival is the firm’s ability to achieve the alliance-innovation benefits. This is especially true since the alliance-innovation dynamic not only leads to an increase in innovation, but also to firm growth (Powell, Koput and Smith-Doerr 1996a). The alliance-innovation dynamic, thus, also implies that the innovative and central firms grow at a higher rate than their competitors. Drawing on findings within population ecology, which show the existence of a liability of smallness (e.g. Baum 1996; Hannan et al. 1998; Hannan and Freeman 1977) the alliance-innovation dynamic suggests that firms, which enter the network early and achieve a lead position will be the long term survivors.
Future research The most important implication of the processes described in this paper is the need to carry out empirical research that includes multiple instead of single causalities; since the dynamic aspects of the relationship, between in this case alliances and innovation, is overlooked when considering only single causalities. Since the complexity of the alliance-innovation dynamic might make it difficult to test in its completeness, verifying some of the positive feedback loops separately would be an important area of research; e.g. the central dynamic between formation of collaborative R&D alliances, centrality within the network and increased innovation.
21
Another implication of the alliance-innovation dynamic is the necessity to monitor the evolution of alliance networks over time, and track the development of a core clique of firms. This is important for accessing the power and elite structures that evolve within the network. The other key implication of the temporal dynamic is to look at how innovation is affected by network tenure, and which processes enable the network to retain its innovativeness over time. This is important for mapping how network structure, power, and innovativeness are mutually constituted. Another central future research area is whether there is an upper limit to the beneficial amount of network ties (Powell et al. 1999; Uzzi 1997). Not only may alliance formation lead to network closure, but there is a cost associated with maintaining network ties (Burt 1992), which might make excessive alliance formation non-beneficial. Support for this argument comes from Powell et al.'s (1999) finding that even though network experience has a positive influence on innovation the rate of increase diminishes with increased experience. An important research area would therefore be to examine if there is an upper limit to the alliance-innovation dynamic. Future research could also examine in which environments vertical integration provides an innovative advantage in comparison with network structures. A final point is that most studies of networks and innovation have been limited to the high technology industries (semi-conductor, biotechnology and the chemical industry). This might limit the validity of the model, since industry specific characteristics can influence the dynamic. A key industry difference is that the share of newly established R&D partnerships that are formed within high-tech have risen dramatically over the last forty years in comparison to medium-tech and low-tech industries. In 1998 over 80% of
22
all collaborative R&D partnerships were formed within the high-tech industry (Hagedoorn 2002). Another difference is that many high-tech industries are very young, and the interorganizational dynamics might be different in low-tech. A fruitful area for future research might therefore investigate how the alliance-innovation dynamics play out in low-tech industries.
CONCLUSION
This paper applies a system theoretical approach to the relationship between alliances and innovation. It is shown that the existing focus on singular causal relationships within alliance and network studies is flawed, since the overall dynamic between alliances and innovation only is revealed when multiple causal relationships are taken into consideration. Evidence from several studies is examined to show that when many singular causal relationships are combined and viewed as a system, they constitute a dynamic with several positive feedback loops between alliance formation and innovation. The main consequence is a Matthew effect: firms that are already innovative or centrally placed within the alliance network become increasingly more innovative and more central. These innovation centrality dynamics in turn lead to closure of the alliance networks over time. The result might be the development of an elite, who constantly enhances its powerbase through increased innovation, centrality and status. In the long run this power structure within the network might, however, lead to decreased innovativeness, since new information is not included into the network. This effect can be avoided by firms
23
attracting alliance partners from related industries, and by new firms making their way into the network.
24
REFERENCES
Ahuja, G. 2000a. "Collaboration Networks, Structural Holes, and Innovation: A Longitudianl Study." Administrative Science Quarterly 45:425-455. —. 2000b. "The Duality of Collaboration: Inducements and opportunities in the Formation of Interfirm Linkages." Strategic Management Journal 21:317-343. Ahuja, Gautam. 2000c. "Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study." Administrative Science Quarterly 45:425-455. Arrow, K. J. 1962. "Economic Welfare and the Allocation of Resources for Inventions." in The Rate and Direction of Inventive Activity: Economic and Social Factors, edited by R. R. Nelson. Princeton, N. J.: Princeton University Press. Barabási, Albert-László, and Reka Albert. 1999. "Emergence of Scaling in Random Networks." Science 286:509-512. Barney, J. 1991. "Firm resources and sustained competitive advantage." Journal of Management 17:99-119. Bateson, Gregory. 1972. Steps to an Ecology of Mind: Chandler Publishing Company. Baum, J. A. C. , T. Calabrese, and B. S. Silverman. 2000. "Don't Go It Alone: Alliance Network Composition and Startups' Performance in Canadian Biotechnology." Strategic Management Journal 21:267-294. Baum, Joel. 1996. "Organizational Ecology." Pp. 77-114 in Handbook of Organizational Studies: Sage. Benjamin, B., and J. Podolny. 1999. "Status, quality and social order in the California Wine industry." Administrative Science Quarterly 44:563-589. Bertalanffy, Ludwig Von. 1950. "The Theory of Open Systems in Physics and Biology." Science 111:23-29. —. 1972. "The History and Status of General Systems Theory." The Academy of Management Journal 15:407-426. Blundell, R. , R. Griffith, and J. Van Reenen. 1999. "Market share, market value, and innovation in a panel of British manufacturing firms." The Review of Economic Studies 66:529-555. Browning, L. D., J. M. Beyer, and J. C Shetler. 1995. "Building Cooperation in a Competitive Industry: Sematech and the Semiconductor Industry." Academy of Management Journal 38:113-151. Burt, R. S. 1992. Structural Holes - The Social Structure of Competition. Cambridge, Massachusetts, and London, England: Harvard University Press. Callon, Michel. 2002. "From Science as an Economic Activity to Socioeconomics of Scientific Reseach." in Science Bought and Sold, edited by P. Mirowski and E-M. Sent: University of Chicago Press. Cohen, W. M. , and D. A. Levinthal. 1990. "Absorptive Capacity: A New Perspective on Learning and Innovation." Administrative Science Quarterly 35:128-152. Cole, Jonathan R., and Stephan Cole. 1972. "The Ortega Hypothesis." Science 178:368375. Coleman, James S. 1990. Foundations of Social Theory: The Belknap Press of Harvard University Press.
25
David, P. A., and D. Foray. 1995. "Accessing and Expanding the Science and Technology Knowledge Base." STI Review. —. 2003. "Economic Fundamentals of the Knowledge Society." Policy Futures in Education - An e-journal 1. Davies, P. N. , and M. P. Koza. 2001. "Eating Soup with a Fork: How Informal Social Networks Influence Innovation in High-technology Firms." Strategic Change 10:95-102. Edquist, C. Forthcoming. "National systems of innovation." in The Handbook of Innovation, edited by J. Fagerberg, D. C. Mowery, and R. R. Nelson: Oxford University Press. Eisenhardt, K. M., and C. B. Schoonhoven. 1996. "Resource-based View of Strategic Alliance Formation: Strategic and Social Effects in Entrepreneurial Firms." Organization Science 7:136-150. George, Gerard, Shaker A. Zahra, and D. Robley Wood. 2002. "The Effects of BusinessUniversity Alliances on Innovative Output and Financial Performance: A Study of Publicly Traded Biotechnology Companies." Journal of Business Venturing 17:577-609. Gittelman, M. , and B. Kogut. 2001. "Does Good Science Lead To Valuable Knowledge? - Biotechnology Firms and the Evolutionary Logic of Citation Patterns." Workingpaper. Glasmeier, A. 1991. "Technological Discontinuities and Flexible Production Networks: The Case of Switzerland and the World Watch Industry." Research Policy 20:469-485. Godoe, H. 2000. "Innovation Regimes, R&D and Radical Innovations in Telecommunications." Research Policy 29:1033-1046. Granovetter, Mark. 1985. "Economic action and social structure: The problem of embeddedness." American Journal of Sociology 91:481-510. Gulati, R., and M. Gargiulo. 1999. "Where Do Interorganizational Networks Come from?" American Journal of Sociology:177-231. Hagedoorn, J. 2002. "Inter-firm R&D partnerships: an overview of major trends and patterns since 1960." Research Policy 31:477-492. Hannan, M. T., G. R. Carroll, S. Dobrev, and J. Han. 1998. "Organizational mortality in European and American Automobile manufacturing industries, Part I." European Sociological Review 14:279-302. Hannan, Michael T. , and John Freeman. 1977. "The population ecology of organizations." American Journal of Sociology 82:929-64. Hargadon, A., and R. I. Sutton. 1997. "Technology Brokering and Innovation in a Product Development Firm." Administrative Science Quarterly 42:716-749. Helfat, Constance E. 1994. "Evolutionary Trajectories in Petroleum Firm R&D." Management Science 40:1720-1747. Inkpen, A. C., and J. Ross. 2001. "Why Do Some Strategic Aliances Persist Beyond Their Useful Life." California Management Review 44:132-148. Kale, R. , H. Singh, and H. Perlmutter. 2000. "Learning and Protection of Proprietary Assets in Strategic Alliances: Building Relational Capital." Strategic Management Journal 21:217-237.
26
Katila, R. 2003. "Innovative product portfolios by navigating markets and technology." Working paper. Katz, R., and T.J. Allen. 1982. "Investigating the Not Invented Here Syndrome: A Look at the Performance, Tenure and Communication Patterns of 50 R&D groups." R&D Management 12:7-19. Kraatz, M. S. 1998. "Learning by Association? Interorganizational Networks and Adaptation to Environmental Change." Academy of Management Journal 41:621643. Lifschitz, Eric. 2003. "The Dark Side of Networks: Age, Stability and Effectiveness in Cliques of Organizations." Working paper. Luhmann, Niklas. 1995. Social Systems: Stanford University Press. MacDuffie, J. P. , and S. Helper. 1997. "Creating Lean Suppliers: Diffusing Lean Production Through the Supply Chain." California Management Review 39:118151. Maman, Daniel. 1997. "The Power Lies in the Structure: Economic Policy Forum Networks in Israel." The British Journal of Sociology 48:267-285. Maturana, Humberto, and Francisco Varela. 1986. Kundskabens Træ. Århus: Forlaget Ask. Merton, Robert K. 1968. "The Matthew Effect in Science." Science 159:56-63. —. 1995. "The Thomas Theorem and the Matthew Effect." Social Forces 74:379-422. Moore, Gwen. 1979. "The Structure of a National Elite Network." American Sociological Review 44:673-692. Mowery, D. C. , J. E. Oxley, and B. S. Silverman. 1996. "Strategic Alliances and Interfirm Knowledge Transfer." Strategic Management Journal 17:77-91. Nelson, R. R.. 1959. "The Simple Economics of Basic Scientific Research." Journal of Political Economy 67:297-306. Padgett, J. 2001. "Organizational Genesis, Identity and Control: The Transformation of Banking in Renaissance Florence." in Markets and Networks, edited by J. Ranch: Russell Sage. Padgett, John F., and Christopher Ansell. 1993. "Robust Action and the Rise of the Medici, 1400-1434." American Journal of Sociology 93:1259-1319. Pavitt, K. Forthcoming. "The Process of Innovation." in The Handbook of Innovation, edited by J. Fagerberg, R. R. Nelson, and D. C. Mowery: Oxford University Press. Penrose, E. 1959. The theory of the growth of the firm. Oxford: Blackwell. Podolny, J. 1993. "A status-based model of market competition." American Journal of Sociology 98:829-72. Podolny, Joel M., and Toby Stuart. 1995. "A Role-Based Ecology of Technological Change." American Journal of Sociology 100:1224-1260. Portes, Alejandro , and Julia Sensenbrenner. 1993. "Embeddedness and Immigration: Notes on the Social Determinants of Economic Action." American Journal of Sociology 98:1320-1350. Powell, W. W. , K. W. Koput, and L. Smith-Doerr. 1996a. "Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology." Administrative Science Quarterly 41:116-145.
27
Powell, W. W., K. W. Koput, H. L. Smith, and J. Owen-Smith. 1999. "Network Position and Firm Performance: Organizational Returns to Collaboration in the Biotechnology Industry." Networks In and Around Organizations. Powell, Walter W. 1990. "Neither market nor hierarchy: Network forms of organization." Pp. 295-336 in Research in Organizational Behavior, edited by L. L. Cummings: JAI. Powell, Walter W., and Paul J. DiMaggio (Eds.). 1991. The New Institutionalism in Organizational Analysis. Chicago, IL: University of Chicago Press. Powell, Walter W., K. Koput, and L. Smith-Doerr. 1996b. "Interorganizational Collaboration and the Locus of Innovation." Administrative Science Quarterley 41:116-45. Rosenkopf, L., A. Metiu, and V. P. George. 2001. "From the Bottom Up? Technical Committee Activity and Alliance Formation." Administrative Science Quarterly 46:748-772. Rosenkopf, L., and A. Nerkar. 2001. "Beyond Local Search: Boundary-Spanning, Exploration, and Impact in the Optical Dist Industry." Strategic Management Journal 22:287-306. Ruef, Martin. 2002. "Strong ties, weak ties and islands: structural and cultural predictors of organizational innovation." Industrial and Corporate Change 11:427-449. Ruttan, Vernon W. 2001. Technology, Growth, and Development - An Induced Innovation Perspective. New York Oxford: Oxford University Press. Sakar, K. B. , R. Echambadi, and J. S. Harrison. 2001. "Alliance Entrepreneurship and Firm Market Performance." Strategic Managemetn Journal 21:369-386. Sarkar, K. B. , R. Echambadi, and J. S. Harrison. 2001a. "Alliance Entrepreneurship and Firm Market Performance." Strategic Management Journal 21:369-386. Sarkar, M. B. , R. Echambadi, and J. S. Harrison. 2001b. "Alliance Entrepreneurship and Firm Market Performance." Strategic Management Journal 22:701-711. Schumpeter, J. A. 1955. The Theory of Economic Development - An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Cambridge, Massachusetts: Harvard University Press. Shan, W., G. Walker, and B. Kogut. 1994. "Interfirm Cooperation and Startup Innovation in the Biotechnology Industry." Strategic Management Journal 15:387-394. Shane, S. , and S. Venkataraman. 2000. "The Promise of Entrepreneurship as a Field of Research." Academy of Management Journal 25:217-226. Shrum, Wesley, and Robert Wuthnow. 1988. "Reputational Status of Organizations in Technical Systems." American Journal of Sociology 93:882-912. Simonin, B. L. 1999. "Ambiguity and the Process of Knowledge Transfer in Strategic Alliances." Strategic Management Journal 20:595-623. Sørensen, J. B., and T. E. Stuart. 2000. "Aging, Obsolenscence, and Organizational Innovation." Administrative Science Quarterly 45:81-112. Strang, David, and Michael Macy. 2000. "In Search of Excellence: Fads, Success Stories and Adaptive Evolution." American Journal of Sociology 107:147-82.
28
Stuart, T. E. 2000. "Interorganizational Alliances and the Performance of Firms: A study of Growth and Innovation Rates in a High-technology Industry." Strategic Management Journal 21:791-811. Stuart, T. E. , and J. M. Podolny. 1996. "Local Search and the Evolution of Technological Capabilities." Strategic Management Journal 17:21-38. Stuart, T. E., and J. Podolny. 1999. "Positional consequences of strategic alliances in the semiconductor industry." Research in the sociology of Organizations 16:161-182. Stuart, Toby E. 1998. "Network positions and propensities to collaborate: An investigation of strategic alliance formation in a high-technology industry." Administrative Science Quarterly 43: 668-699. Uzzi, B. 1997. "Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness." Administrative Science Quarterly 42:464-476. Uzzi, Brian, Jarrett Spiro, and Dimitri Delis. 2002. "Emergence: The Origin and Evolution of Career Networks in the Broadway Musical Industry, 1877-1995." Working paper. Van de Ven, A. H., D. E. Polley, R. Garud, and S. Venkataraman. 1999. The Innovative Journey: Oxford University Press. Vinding, Anker Lund. 2002. "Interorganizatoinal Diffusion and Transformation of Knowledge in the Process of Product Innovation." PhD Thesis, Aalborg University. Walker, G., B. Kogut, and W. Shan. 1997. "Social Capital, Structural Holes and the Formation of an Industry Network." Organization Science 8:109-125. Wernerfelt, B. 1984. "A reource-based view of the firm." Stragetic Management Journal 5:171-181. Williamson, O. E. 1975. Markets and Hierarchies: Free Press. Williamson, Oliver E. 1985. The Economic Institutions of Capitalism: Free Press. —. 1996. The Mechanisms of Governance. Oxford, UK: Oxford University Press. Zeleny, Milan. 1980. Autopoiesis, Dissipative Structures, and Spontaneous Social Orders. Boulder, Colorado: Westview Press, Inc for the Ammerican Association for the Advancement of Science.
29
