Cooperation, competition, and innovative capability: a panel data of European dedicated biotechnology firms

Technovation 24 (2004) 927–938 www.elsevier.com/locate/technovation

Cooperation, competition, and innovative capability: a panel data of European dedicated biotechnology firms Cristina Quintana-Garcı´a
, Carlos A. Benavides-Velasco Departamento de Economı´a y Administracio´n de Empresas, Facultad de CC Econo´micas y Empresariales, Campus El Ejido, s/n, 29071 Ma´laga, Spain

Abstract Small and medium high-technology firms usually develop upstream and downstream ties in order to perform their new product development process. Many of these alliances are characterized by co-opetition dynamics, that is, partners collaborate and compete simultaneously. Traditionally, competitive and cooperative theory has been analyzed as different research streams. Although scholars and managers have recognized that striking a balance between both strategies (co-opetition) plays a key role in the performance of innovation process, little empirical research shows evidence about this relation. In this paper, firstly, a review of theoretical perspectives of co-opetition is made, and then, we identify alternative strategic behaviors from the combination of competitive and cooperative attitudes. Finally, we show the results from a study of a sample of European dedicated biotechnology firms, where we analyze the effect of co-opetitive strategy on technological diversity and new product development. # 2003 Elsevier Ltd. All rights reserved. Keywords: Co-opetition; Cooperation; Competition; Technological innovation; Product development; Biotechnology industry; Longitudinal study

1. Introduction Industries intensive in technological knowledge usually are motivated to develop alliances with related agents. The need for rapid new product development often precludes internal development of critical technologies, elevating the attractiveness of external technology acquisitions by means of alliances among other methods (Lambe and Spekman, 1997). Many of these alliances are characterized by co-opetition dynamics, that is, partners collaborate and compete simultaneously. During the last decades, competitive and cooperative theory has been analyzed as different research streams. Firstly, competitive advantages are realized either when a firm gains an advantageous position in an industry or when it mobilizes and deploys core competencies (Prahalad and Hamel, 1990) that enable it to offer superior products to customers relative to competitors (Porter, 1980, 1985). The alternative paradigm emphasizes the development of collaborative advantage. From this point of view, the business world
Corresponding author. Tel.: +34-95-213-41-47; fax: +34-95-21312-93 E-mail address: [email protected] (C. Quintana-Garcı´a).

0166-4972/03/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0166-4972(03)00060-9

is composed of a network of interdependent relationships fostered through strategic cooperation agreements with the goal of obtaining mutual benefits (Miles and Snow, 1986; Thorelli, 1986; Yoshino and Rangan, 1995). The strategic collaborations represent institutions of privileged relations among firms and other organizations. These relationships are based on the advantage reciprocity, power association searching the same pre-established target. Given that alliances are typically framed as cooperative, their competitive aspects can often be neglected or even suppressed, and usually, it is unwilling to consider that a firm may be both a competitor and a partner (Khanna et al., 1998). Competitive and cooperative paradigms offer a partial slice of reality. But nowadays, business success requires that firms pursue both strategies simultaneously (Lado et al., 1997). Strategic alliances may help partners to specialize in core business and gain access to certain assets that the firm do not own but are necessary for developing a certain activity. On the other hand, competition is interesting in order to avoid complacence and to keep the creative tension within organizations. So, co-opetition has emerged as suitable strategy in recent days. In fact, scholars and managers have recognized that striking

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a balance between competition and cooperation plays a key role in the performance and survival of enterprises (Jorde and Teece, 1989). Co-opetitors may be critical sources of innovations, organizational learning, complementary products, capabilities and critical resources and lead users (Gulati, 1998; Khanna et al., 1998; Kogut, 1998; Afuah, 2000). But little research has considered that firms can be involved in and benefit from both cooperation and competition simultaneously, and hence both types of relationships need tobeemphasizedatthesametime(Bengtsson,1998:411). We consider that a syncretism between competition and cooperation may foster greater knowledge seeking and capacity to innovate than both strategies pursued separately. In order to contrast this main hypothesis in this paper, first of all, a review of theoretical perspectives of co-opetition is made, and then, we identify alternative types of strategic behavior from the combination of competitive and cooperative attitudes. The aim of our empirical study is to analyze correlation between different strategic behavior and innovative capability. In particular, the hypotheses will be contrasted in the biotechnology industry. We think this industry is of interest due to its several distinctive features. Biotechnology is representative of a high-technology industry where the product-development process (from research activity to commercialization) is very long, research intensive and protracted. It can take from six to nine years to successfully bring a new drug to the market (Powell and Brantley, 1992: 368). This situation demands a particular collection of resources and competencies (finance, knowledge assets, commercial skills) that usually dedicated biotechnology firms (DBFs) do not have completely. For this main reason, biotechnology companies must develop collaboration networks that enable them to source their critical input (patentable scientific knowledge) at minimum sunk cost while overcoming other problems such as uncertainty, appropriability, and intellectual resource immobility. Collaborative relationships help to access, survey and exploit emerging technological opportunities, because interfirm cooperation accelerates the rate of technological innovation and firms can compete more effectively in high-speed learning races. So, DBFs usually keep close ties with universities, venture capitalists and end-users such as chemical, pharmaceutical, energy and agricultural industries, building up upstream and downstream linkages. These partners are complementors for the DBF, but, actually, they also can emerge as competitors. In the downstream side in particular, the end-user companies diversify from their traditional core business to carry out biotechnology activities. Also, universities can develop new drugs directly or cooperate with diversified corporations. Consequently, networks in biotechnology industry are characterized by co-opetition dynamics. Through

information obtained from an international biotechnology database, we will try to test whether through the co-opetition option it is possible to obtain higher innovation performance than pure cooperative or competitive strategies. In particular, a sample of European biotechnology firms will be studied longitudinally over a period of six years (1995–2000). 2. Theoretical perspectives of co-opetition Research on cooperation and competition between horizontal actors has been conducted within different theoretical fields. Interaction between competitors has been studied directly in economic theory with a focus on structure rather than relationships. Competition is described as the direct rivalry that many firms develop due to the structural conditions of the industry (Tirole, 1988; Scherer, 1980). Intense rivalry between many companies is argued to be the most beneficial interaction, and cooperation is considered to hamper effective competitive interaction. In literature on strategic alliances (Kogut, 1998; Yoshino and Rangan, 1995; Gulati, 1998; Gulati et al., 2000), relationships rather than structure are analyzed. The inter-firm cooperation agreement means a strategic option of adjustment to gain access to abilities and knowledge that the firm does not have but are necessary to keep the new product development process. A dyadic and paradoxical relationship may emerge when two firms cooperate in some activities in a strategic alliance context, and at the same time compete with each other in other activities (Bengtsson and Kock, 2000: 412). This phenomenon is called co-opetition. Co-opetition involves two different logics of interaction. On the one hand, there is a hostility due to conflicting interests and, on the other hand, it is necessary to develop trust and mutual commitment to achieve common aims. There are several perspectives that provide a useful theoretical framework for analyzing competition and cooperation jointly; we can stand out (Park and Russo, 1996; Lado et al., 1997:113–117): transaction–cost economics, resource-based view, game theory. 2.1. Transaction–cost economics The rationale for inter-firm cooperation agreements can be positioned within the transaction–cost economics paradigm (Coase, 1937; Williamson, 1985). They can be explained as a form of governance that shares the attributes of markets and internal organizations, attempting to avoid or weaken the hazards of each (Park and Russo, 1996: 877). Cooperation is found in intermediate markets, where problems with both markets and internal organization make the choice of either suboptimal. Particularly, the transaction–cost economics justifies the existence of cooperation to favor the transmission of

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‘tacit knowledge’ among firms. It is difficult to formalize the transmission of tacit knowledge among organizations, and it results unfeasible through market relations. The market mechanisms fail in the transfer of this type of knowledge because, given a potential buyer who is uncertain about the true value, revealing the knowledge to convince the buyer of its worth paradoxically reduces its value since he then would possess it without paying for it (Buckley and Casson, 1976; Madhok, 1997). Thus, certain ways of cooperation among firms, establishing a close relationship among them, can represent efficient ways to access such knowledge. From this approach, strategic alliances are designed to meet the goals both of individual firms and of the collective undertaking, and will be successful when the value of collective outcomes exceeds opportunity cost incurred by participants, and when the distribution of both is fair (Jarillo, 1988). But partners may behave opportunistically to attain their own competitive goals, not the collective ones of the venture. Transaction–cost theory predicts a higher failure rate when the partners are direct competitors. It considers that in this case competitors are both seeking to maximize their learning process. These goals conflict directly, and the venture can prove dysfunctional and eventually fail (Kogut, 1998). Also it is argued that the failure of such cooperation agreements can stem from the risk of uncontrolled information disclosure that is appropriated by one partner (Bresser, 1988: 378). In fact, within this theoretical framework, Park and Russo (1996) confirm their hypothesis about when competitors meet in a joint venture it is significantly more likely to fail. So, transaction–cost economics see co-opetition as a risky business, mainly because protecting key specific know-how from one’s competitors is difficult. The incentives to act opportunistically appear to motivate actions that undermine cooperation agreements. These incentives are intensified by the abilities of competitors to recognize and appropriate key technologies and know-how from partners. 2.2. Resource based-view From Resource based-view, competitive advantage comes from owning unique, valuable, inimitable, nonsubstitutable capabilities that allow the firm to offer its customers better value than competitors (Barney, 1991; Grant, 1991). Fundamentally, two assumptions underpin this approach: (a) firms are heterogeneous with respect to their resource profiles and (b) those resources are not perfectly mobile across firms. Thus, sustained differences in firms’ profits may be attributed to differences in resources. Proponents of dynamic capability approach focus on how asset stocks are accumulated, mobilized, and

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deployed to generate a sustainable competitive advantage (Teece et al., 1997; Makadok, 2001). According to this approach, the strategy of accumulating valuable technology assets is often not enough to support a significant competitive advantage. Companies need dynamic capabilities, that is, the capacity to renew competences so as to achieve congruence with the changing business environment. Competitive advantage requires both the exploitation of existing internal and external firm-capabilities, and developing new ones. The term ‘capabilities’ emphasizes the key role of strategic management in appropriately adapting, integrating, and reconfiguring internal and external organizational skills, resources and functional competences to match the requirements of a changing environment (Teece et al., 1997: 515). The dynamic capability-based perspective provides the base upon which to examine the accumulation of resource stocks through both competition and collaboration. It has been recognized that a firm’s competitive advantage may rest on tacit, inimitable collaborative relationships with and the success of its co-opetitors, the suppliers, customers, complementors and alliance partners with whom it must collaborate and compete. These agents may play a critical role during innovation and they represent an important source of information during the refinement and shepherding of new ideas and their commercialization (Afuah, 2000: 388). Some customers often play a role as lead users and work with their suppliers to discover their needs. Companies often seek co-opetitors to provide complementary assets when these are important for innovation process but difficult to acquire (spillovers, commercial skills, financing, etc). Moreover, cooperation agreements with these agents more than to acquire new knowledge and skills, they are useful to access other capabilities based on intensive exploitation of the existing ones in each firm (Grant and Baden-Fuller, 1995). 2.3. Game theory Game theory allows analysts to study imperfect market situations characterized by small numbers of players, limited information, hidden actions, opportunities for adverse selection, or incomplete contracts. This theoretical perspective has been applied by researchers to study situations in which a cooperative equilibrium appears (or fails to appear) through reciprocal interactions among participants (Nowak et al., 2000: 13). Game theory may be criticized because it emphasizes opportunism as crucial to understanding the structuring and management of interfirm collaborations (Lado et al., 1997: 116). This theory represents another conceptual framework for examining the potential of rent creation through coopetition strategy (Brandenburger and Nalebuff, 1996; Loebecke et al., 1999). The premise of prisoners’ dilemma paradigm is based on the avoidance of costs

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and the pursuit of benefits. In this game, there is an economic pie of size and each player competes for a partition and tries to maximize his or her self-interest. Each player can choose to cooperate with, compete with, or defect from the other player. Several behaviors can occur for the combination of attitudes of agents: unilateral cooperation, mutual cooperation, unilateral defection, mutual defection. Brandenburger and Nalebuff (1996) showed how a firm can use game theory to achieve both positive-sum gains as well as zero-sum benefits by avoiding mutually destructive competition and changing several aspects: the players, the players’ perception of the risk return payoffs associated with the game, the scope of the game, etc. The better way is to find win–win opportunities with competitors because it is very difficult to eliminate them. Although it may be hard to get used to this idea, sometimes the best way to succeed is to let others do well, including the competitors that may represent also complementors. Looking for win–win strategies has several advantages (Brandenburger and Nalebuff, 1995: 59): first, because the approach is relatively unexplored, there is greater potential for finding new opportunities; second, because others are not being forced to give up ground, they may offer less resistance to win–win moves, making them easier to implement; third, because win–win moves don’t force other players to retaliate, the new game is more sustainable; and finally, imitation of a win–win move is beneficial, not harmful. These authors think that game theory encourages managers: (a) to embrace competitive imitation to gain an advantage and (b) to focus on other players’ strategic moves rather than their own strategic positions. 3. Cooperation and competition: alternatives of strategic behavior Strategy researchers have tended to view competition and cooperation as opposite ends of a single continuum. This conceptualization is unfortunate in that it forces researchers and managers to rank strategic alternatives and choose one over the other. As a result of combinations of cooperation and competition behavior, it is possible to distinguish several options within a strategic alliance (Lado et al., 1997:120–124; Bengtsson and Kock, 2000: 415–416): cooperation-dominated relationships, equal relationships (co-opetition) and competition-dominated relationships. 3.1. Cooperative behavior This strategic behavior represents a situation where relationships between partners consist of more cooperation than competition seeking mutual benefits by pooling complementary resources, skills, and capabilities. In this case, the common goals are more

important than one actor’s profit maximization or opportunism. Partners contribute to the total created value in the relationships, and they are satisfied with a smaller share of the profit to maintain the relationship (Bengtsson and Kock, 2000). This does not mean that the benefits are equal for each partner; it is a way of thinking or recognizing that the creative synthesis of knowledge in an alliance creates a total amount of created value. The common benefits of a particular firm are a proportion of this value which, probably, is a function of the relative bargaining power of each firm (Khanna et al., 1998:195). So, collaborative advantage is generated when companies develop a behavior that emphasizes altruism, trust and reciprocity (Kanter, 1994). Trust generates economic rents in several ways (Lado et al., 1997:121): it reduces uncertainty by providing cognitive and moral maps of expectation that guide people as they interact; it serves as a mechanism for social control and reduces the transaction costs that would otherwise be incurred in building governance mechanisms to safeguard against the hazards of partner opportunism. 3.2. Competitive behavior Competition-dominated relationships consist of more competition than cooperation. They reflect a firm’s orientation to achieve a position of superior performance and to generate competitive advantage over other firms by either manipulating the structural parameters of an industry to its advantage (Porter, 1985) or developing difficult to imitate distinctive competencies (Barney, 1991). In the case of a company adopting competitive behavior, the risk of a ‘learning race’ emerges, where it simultaneously looks for a maximum absorption of distinctive competencies from its partner and tries to protect its own core resources and capabilities (Kale et al., 2000). In such a situation, once one firm has learned enough from its partner, it has no incentive to continue in the alliance. This situation of pure private benefits causes firms to race against each other (Khanna et al., 1998:198). From another point of view, competitive strategy behavior can help companies to achieve greater productive efficiency and may generate entrepreneurial rents by promoting the creativity and innovation. This approach, in an alliance scope, has been criticized because (Lado et al., 1997: 119): the rivals tend to structure their relationships as zero-sum games; the competition may encourage firms either to erect barriers around their distinctive competencies and then make the cooperation difficult; and when externalities are present and property rights cannot be efficiently regulated, firms with competitive behavior tend to look for private benefits and such an attitude may culminate in dysfunctional outcomes. So, although this behavior helps to earn tem-

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porary rents, it makes it difficult to maintain a competitive advantage for a long time. 3.3. Co-opetition An equal relationship may be explained by structural conditions within an industry that force companies to act in rivalry relatively to each other, such as social conditions and dependence. The dependence between competitors due to structural conditions can explain why competitors cooperate and compete at the same time. From their empirical study, Bengtsson and Kock (2000) identify two different patterns of division between the two parts of co-opetition. The division is either related to the value chain or to the magnitude of business units. In the first case, the division is based on functional aspects, or what activities the actors perform in the activity chain and the value that they create. In the latter, the cooperation and competition is divided between different business units or product areas indicating that the competitors can compete in certain markets or product areas while they cooperate in others. The first type can be associated to vertical relationships between buyers and sellers. The second one represents horizontal relationships between direct competitors which have not been studied to the same extent. For firms, cooperation with direct competitors involves the trade-off between access to greater resources and the potential for loss of proprietary information or the creation of stronger competitors. In comparing vertical and horizontal relationships, the vertical ones are often built upon a mutual interest to interact, whereas competitors often are forced to interact with each other, giving rise to rivalry and mutual dependence between them (Ring and Van de Ven, 1992; Bengtsson, 1998). Although cooperation is traditionally defined as the conflicting and rivaling relationships between competitors, the literature in strategic alliances has contributed to improve the understanding of competition by pointing out that collaboration among competitors may have many advantages. Moreover, the syncretism between competition and cooperation will foster greater knowledge seeking and development and technological progress than either competition or cooperation pursued separately (Lado et al., 1997: 118). On the one hand, competition may stimulate innovation within the firm, which helps to increase the knowledge and economic, technical and market growth, assuming that property rights are well protected (North, 1990). On the other hand, cooperation among firms including competitors can also stimulate knowledge development and utilization, increase the volume and quality of goods and services, and expanding markets (Jorde and Teece, 1989). So, this implies that a proper equilibrium between cooperation and competition may affect success (consolidation and growth) in a positive way. Competitive collab-

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oration also provides a way of getting close enough to rivals to predict how they will behave when the alliance unravels or runs its course (Hamel et al., 1989: 139). Through this type of tie it is possible to obtain other general advantages from strategic alliance (Bengtsson and Kock, 2000: 414): to complement and enhance each other in different areas such as production, introduction of new products, entry into new markets; reduction of firms cost and risk; creation and transfer of technology and capabilities, etc. So, syncretic behavior (co-opetition) emphasizes the positive-sum, efficiency-enhancing effects of competition and cooperation. Firms that exhibit syncretic rent-seeking behavior will develop flexibility by either holding and striking a variety of strategic options. Although it is important to recognize key limitations of implanting a co-opetition strategy, this may fail to improve a firm’s competitive position when the cost associated with developing the collaborative relationships is higher than future benefits. Such costs result from the need to maintain greater cognitive maps, behavioral routines and organizational resources for enhancing both competitive and cooperative strategy. Problems may also appear for the different absorptive capacity (Cohen and Levinthal, 1990, 2000; Hamel, 1991), a situation that explains why one partner accumulates knowledge assets from the alliance at a slower rate than the other one. Within this theoretical framework, through our empirical study, we want to test the following basic hypothesis: Hypothesis: Co-opetition strategy has a positive effect on capacity to innovate to a greater extent than pure cooperative or competitive strategy. This hypothesis will be decomposed into more specific hypotheses when we explain, in the next epigraph, who are the agents with which biofirms cooperate. 4. Empirical evidence from European biotechnology industry 4.1. Research design 4.1.1. Sample and data selection To test the hypothesis we focus on the biotechnology industry. There are several types of organizations that make up the biotechnology community. The most relevant are (Barley et al., 1992: 320): dedicated biotechnology firms: established primarily to pursue biotechnological research and development in areas of commercial promise; universities: that carry on basic or applied research in biotechnology through either academic department or through centers dedicated to biotechnological research; private or public research institutes: that

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conduct research in one or more areas of biotechnology; diversified corporations: in the chemical, pharmaceutical, energy and agricultural industries that either conduct R&D on biotechnology or that fund research by dedicated biofirms, universities or research institutes; hospitals: involved in clinical trials or the development of new therapeutics and diagnostics based on biotechnological research; and, suppliers or goods: that provide equipment, chemical, and biologicals necessary for RDNA and cell fusion research in bioprocess engineering. In particular, we focus on small and medium dedicated biotechnology firms. Biotechnology shares several characteristics with other science-based industries. Small entrepreneurial firms and venture capitalists have played a major role in each industry’s evolution and established firms lagged behind during industry’s early stages. But biotechnology has been unique in a number of key aspects (Powell and Brantley, 1992: 368): the close ties between universities and commercial firms; founders with virtually no prior management or production experience; and a strong reliance on licensing, partnerships, and various alliances to commercialize the new technology. This phenomenon can be explained because biotechnology itself is a revolutionary technology, and rapid and radical technological innovation within biotechnology threatens to render even current products obsolete within a relatively short time. Therefore, new biotechnology firms (NBFs) and, in general, small and medium-sized dedicated biotechnology firms (DBFs) can sustain a competitive advantage only by continuous innovation that results in valuable and patentable products. So, DBFs must develop collaboration networks that enable them to source their critical input (patentable scientific knowledge) at minimum sunk cost while overcoming other typical problems of this industry such as uncertainty, appropriability, and intellectual resource immobility. Firms in technologically-intensive fields rely on collaborative relationships to access, survey and exploit emerging technological opportunities, because inter-firm cooperation accelerates the rate of new product development and firms can compete more effectively in high-speed learning races (Powell et al., 1996; Takayama et al., 2002). Within these network dynamics, we analyzed if the co-opetition strategy permits a higher levels of competence to innovate than pure competitive and cooperative behavior. The main source of information was Bioscan. The information contained in Bioscan comes from a variety of sources, including direct communication with companies through a questionnaire, newspapers, magazines, journals, annual reports, etc. This database contains information about the international biotechnology community (DBFs, research institutes, diversified corporations, etc). Our aim was to analyze

the evolution of the cooperation dynamics of European dedicated biotechnology firms during the period (1995– 2000). At first, the sample seemed to contain 129 firms. But we did not want to consider DBFs that were subsidiaries of large companies such as pharmaceuticals or chemicals, because they really represent a diversification strategy. We were only interested in small and medium entrepreneurial dedicated biotechnology firms to contrast how network dynamics (co-opetition, competition or cooperation) help to consolidate and develop these companies. Reviewing the database for six years (1995–2000) we also lost some sample elements. So, the final sample include 73 European DBFs. Due to we have a data panel, this means, repeated observations (six years) on the same set of cross-section units (73 firms), the total observations are N¼ 438. 4.1.2. Variables In order to test the basic hypothesis we had to select some indicators of capacity to innovate. At first, the patent number could be accepted. Although it is used frequently, researchers recognize its incapacity to measure the total knowledge and technological production (Zucker et al., 1998), because many innovations are not patented and it does not reflect all representative aspects of the innovative capacity. Thus, we consider as other suitable indicators of innovative performance: the number of products on the market, the number of products in development, process and technological diversity, improvement in time for getting a patent, etc. In our study we selected two dependent variables: the number of different technologies (’technology diversity’) that the companies use, for example, fermentation, cell fusion, tissue culture, recombinant DNA, monoclonal antibodies, etc.; and the number of product lines on the market (’product lines’) such as bioremediation, animal or human vaccines, therapeutic proteins, enzymes, diagnostics based on reagents, etc. Products in development are also an interesting variable, but we had no information about all the companies of the sample. Finally, to select the independent variables we considered that DBFs can develop three basic different types of relationships: competitors, upstream and downstream relationships. The cooperation agreements between DBFs (direct competitors) clearly represent a co-opetition behavior, because they develop some activities (research, development, manufacturing, etc.) in the alliance scope, but compete in other activities. Upstream relationships represent linkages between university and research institutes and dedicated biotechnology firms. Universities and research centers are the principal source of spillovers for the biotechnology industry (mainly basic research), because they can provide access to information about discoveries with potential commercial value made in their own and other university-based labs,

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transmitting complex, tacit knowledge by bench-level collaboration (Zucker et al., 1998: 71). Also, companies can learn from universities by sending employees to participate in their workshops and seminars. But, also, those research centers or universities can compete with DBFs because they may cooperate with competitors or develop other activities that compete directly with the company, for example through spin-offs. Thus, we divided the variable ‘upstream relationship’ in two independent variables: ‘upstream alliances /compete’ and ‘upstream alliances/cooperation only’. The first strategy symbolizes the situation in which DBFs are cooperating with universities and research institutes that also compete; and the second variable represents the situation in which those agents only cooperate but do not compete directly with other activities. Knowledge spillovers related with basic research are important for biofirms, but they also need other types of knowledge to complete the product development process. In this sense, downstream relationships with end-users represent also an important source for product innovations. Evidence indicates that firms can successfully commercialize their innovation by maintaining appropriate relationships with buyers or end-users, and ineffective management of these links has often contributed to the failure of technological innovations (More, 1986; Athaide et al., 1996). In the entire innovation cycle of biotechnology activities, usually large or diversified corporations (pharmaceuticals, chemicals) develop activities related to clinical testing procedures, regulatory processes and commercialization, which cover the largest fraction of these applications. Also, product codevelopment efforts may suggest potential technology applications that sellers (biofirms) were unaware of (More, 1986). In exchange, dedicated biotechnology firms are both a source of information and direct access to leading-edge science for large companies. These large companies can diversify into biotechnology activities, for example, creating subsidiaries. In this case, cooperation between DBFs and large companies represents co-opetition behavior. But DBFs can have collaboration agreements with large companies that only cooperate and do not compete with the same technology in other activities. Thus, we identified two independent variables ‘downstream alliances /compete’ and ‘downstream alliances/cooperating only’. In total, we have five independent variables to test the hypothesis, ‘cooperation with direct competitors’ (CDC), ‘upstream alliances/cooperating only’ (UACO), ‘upstream alliances/compete’ (UPCom), ‘downstream alliances/cooperating only’ (DACO), and ‘downstream alliances/compete’ (DACom). With the information about types of alliances that DBFs develop, we can now decompose the basic hypothesis in more specific others:

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Hypothesis 1: Cooperating with direct competitors has a positive effect on capacity to innovate. Hypothesis 2: Upstream alliances characterized for co-opetition behavior have a positive effect on capacity to innovate to a greater extent than pure cooperative or competitive attitude. Hypothesis 3: Downstream alliances characterized for co-opetition behavior have a positive effect on capacity to innovate to a greater extent than pure cooperative or competitive attitude. We used also two control variables, ‘size’ and ‘age’. A biotech firm’s success might be a positive function of the age (experience) and size as a measure of the strength of the company. The last variable was constructed with the total amount of employees (including PhD and MD). We are working with a panel data. Due to the method of data selection, we used the fixed effects model (Johnson and Dinardo, 1997: 391), so we introduced n1 yr dummy variables (0,1) to introduce the temporal or longitudinal component (w95, w97, w98, w99, w00) and to avoid perfect multicollinearity. 4.1.3. Econometric models For testing the hypotheses, at first, we were considering using the linear regression model that assumes homoskedasticity and normally distributed errors. But the dependent variables (technology diversity and product lines) take integer values that represent the number of events that occur. In these cases the assumptions of linear regression model above enumerated are violated and count models are more appropriate, specially the Poisson model. For the Poisson model, the conditional density of yi given xi is: ProbðYi ¼ yi Þ ¼

e-ki kyi i ;yi ¼ 0;1;2 . . . Yi !

where yi is a non-negative integer valued variable. The more usual specification of ki is the logarithmic–lineal, lnki ¼ b0 xi þ li and the number of expected events in each period is: 0

E ½yi jxi ¼ eb xi þli : Therefore, the models for testing the hypotheses are: Ln E ½product lines ¼ b0 þ b1 CDCit þ b2 UACOit þ b3 UAComit þ b4 DACOit þ b5 DAComit þ b6 AGEit þ b7 SIZEit þ b8 W 95 þ b9 W 97 þ b10 W 98 þ b11 W99 þ b12 W 00 þ lit and Ln E ½technological diversity ¼ b0 þ b1 CDCit þ b2 UACOit þ b3 UAComit þ b4 DACOit þ DAComit þ b6 AGEit þ b7 SIZEit þ b8 W 95 þ b9 b5 W 97 þ b10 W 98 þ b11 W 99 þ b12 W 00 þlit

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To get consistent estimators, we performed the model using the quasi-maximum likelihood (QML) estimator that provides a robust result analogous to the situation in ordinary regression, even if the underlying error distribution is not normally distributed. Also, we set an alternative model. In the case of Poisson model with panel data, it is possible to correct the heteroskedasticity by using the Negative Binomial specification, assuming a gamma distribution for li (Greene, 1999: 809). For this model, we also used the QML estimator to get consistent coefficients. 4.2. Results Table 1 provides the descriptive statistics and correlation matrix for the variables that were used in this study. Table 2 provides the results of the regression analyses using Poisson and negative binomial estimators. In both of them, we used fixed effects to introduce the temporal component (through dummy variables) and the control variables ‘size’ and ‘age’. Tables also show several measures to contrast the statistical significance of the alternative models. Comparing the Poisson model with negative binomial estimator for the dependent variable ‘‘product lines’’, the second model fit a little better due to the fact that log likelihood is higher, and Akaike info, Shwarz and Hannan–Quinn criteria are lower than the Poisson model. Thus, we will discuss the results through the negative binomial estimator. In turn, we used the Poisson model for discussing the variable ‘‘technology diversity’’, because the coefficients and measures of model fit are better. For both, product lines and technological diversity, we can observe that all dummy variables related to temporal aspect of data panel are not statistically significant. This means that the effect of the analyzed variables are constant throughout the selected period of time. With regard to control variables, age, as a measure of experience, has a positive effect on development of product lines but it is not statistically significant for technological diversity. As far as size is concerned it is only significant for technological diversity, but actually, the coefficient is practically zero. There is support for hypothesis 1. The most relevant strategy for product lines is to cooperate with competitors whose coefficient is the highest. This result shows how through collaboration with direct competitors it is important not only to acquire new technical knowledge and skills from the partner, but also to create and access other capabilities based on intensive exploitation of the existing ones in each firm. Collaboration with direct competitors also has a positive impact on technological diversity; partners can be specialized in different new developments of technologies and get a

complementary approach from cooperation agreements. Hypothesis 3 is also supported. With regard to technological diversity, strategic alliances with diversified companies (downstream/compete) is the strategy with the highest positive effect. Small and medium dedicated biotechnology firms are usually very specialized in some technologies and provide leading-edge science to large companies through collaborations. Small firms retain a flexibility and innovativeness which larger firms find difficult to emulate (Sharp, 1999), so diversified corporations cannot keep up with the latest developments without collaborating with small DBFs. But they can provide a wider spectrum of complementary technologies to the new ones and then partners get a multi-disciplinary approach. In turn, ties with large companies that are not diversified into biotechnology have a negative effect on technological diversity although at a low level. The relationship between diversified corporations and DBFs is also positively associated with the number of product lines. This type of collaboration can encourage a higher rate of product development because large companies provide experience and complementary capabilities related to activities such as clinical testing procedures, regulatory processes and commercialization. In turn, the hypothesis 2 is not verified. Upstream relationships with agents (universities and other institutes) that do not compete (e.g. through spin-off) have a positive effect on the development of a higher number of product lines. This impact can be explained because universities provide access to information about discoveries with potential commercial value that biotechnology firms could translate into products on the market. There is not evidence that the variables ‘upstream alliances/compete’ has a significant impact on product lines or technological diversity. 5. Discussion and conclusion The aim of this study was to explore the impact of coopetition strategy on innovative capability. The theoretical framework suggests different effects of this behavior. For example, according to the transaction–cost economics approach, co-opetition is a risky business where the incentives to act opportunistically may undermine cooperation agreement. Competitors, with a high absorptive capacity, may recognize and appropriate key technologies and know-how from partners. In turn, the latter developments of resource based-view and game theory argue that cooperation with competitors may contribute positively to innovation output. On one hand, this type of collaboration may be a key point in the innovation process because as well as acquiring new knowledge and skills, it is possible to access other capabilities based on intensive exploitation of the existing com-

a

Figures in italic are significant at the 0.05 level.

4.60 3.31 1.00 3.24 1.96 0.54 1.11 1.71 0.65 0.53 1.23 0.31 0.22 0.65 0.04 0.60 1.32 0.36 0.83 1.55 0.55 12.97 6.12 0.14 72,79 94,50 0.03 0.17 0.37 0.05 0.17 0.37 0.00 0.17 0.37 0.01 0.17 0.37 0.01 0.17 0.37 0.02

1

1. Product lines 2. Technological diversity 3. Cooperation with direct competitors 4. Upstream/cooperating only 5. Upstream/compete 6. Downstream/cooperating only 7. Downstream/compete 8. Age 9. Size (number of employees) 10. Year 1995 11. Year 1997 12. Year 1998 13. Year 1999 14. Year 2000

S.D.

Mean

Variable

Table 1 Descriptive statistics and Pearson correlation matrix (N¼ 438)a

1.00 0.47 0.12 0.03 0.33 0.55 0.00 0.35 0.06 0.01 0.03 0.03 0.03

2

1.00 0.35 0.20 0.50 0.61 0.03 0.38 0.06 0.01 0.02 0.03 0.03

3

1.00 0.36 0.15 0.14 0.11 0.04 0.01 0.00 0.01 0.01 0.01

4

1.00 0.03 0.11 0.18 0.03 0.06 0.00 0.02 0.03 0.04

5

1.00 0.66 0.00 0.18 0.04 0.00 0.00 0.03 0.03

6

1.00 0.02 0.38 0.04 0.00 0.01 0.02 0.02

7

1.00 0.16 0.17 0.03 0.04 0.11 0.15

8

1.00 0.00 0.00 0.00 0.00 0.00

9

1.00 0.20 0.20 0.20 0.20

10

1.00 0.20 0.20 0.20

11

1.00 0.20 0.20

12

1.00 0.20

13

1.00

14

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C. Quintana-Garcı´a, C.A. Benavides-Velasco / Technovation 24 (2004) 927–938

936

Table 2 Quasi-maximum likehood, fixed effects Poisson count and negative binomial estimatorsa

Independent variables

Poisson count

Negative binomial

Dependent variables

Dependent variables

Product linesb




Constant 0.966 (0.078) CDC: cooperation with direct competitors 0.141


(0.015) UACO: upstream alliances/cooperating only 0.102


(0.017) UACom: upstream alliances/compete 0.063 (0.039) DACO: downstream alliances/cooperating 0.025 (0.021) only DACom: Downstream alliances/compete 0.091


(0.019) Firm age 0.019


(0.004) Firm size 1.28E-05 (0.000) Year 1995 0.031 (0.082) Year 1997 0.073 (0.080) Year 1998 0.056 (0.080) Year 1999 0.078 (0.080) Year 2000 0.064 (0.080) 922.8855 Log likelihood 2.107 Avg. log likelihood 4.273 Akaike info criterion 4.394 Schwarz criterion 4.321 Hannan–Quinn criterion

Technological diversityb Product Linesb




1.005 (0.074) 0.071


(0.016) 0.010 (0.019) 0.068 (0.048) 0.057

(0.021) 0.114


(0.019) 0.008 (0.004) 0.0008

(0.0002) 0.036 (0.079) 0.029 (0.078) 0.067 (0.078) 0.069 (0.078) 0.069 (0.079) 784.5952 1.791 3.641 3.763 3.689






Technological diversityc

0.960 (0.081) 0.145


(0.016) 0.104


(0.018) 0.064 (0.041) 0.026 (0.022)

0.958


(0.074) 0.057


(0.017) 0.013 (0.020) 0.051 (0.036) 0.033 (0.025)

0.091


(0.020) 0.019


(0.004) 2.1E-04 (0.000) 0.033 (0.085) 0.065 (0.084) 0.062 (0.084) 0.084 (0.084) 0.074 (0.084) 919.8413 2.100 4.264 4.394 4.315

0.130


(0.022) 0.004 (0.004) 0.0005 (0.0002) 0.026 (0.078) 0.035 (0.077) 0.059 (0.077) 0.057 (0.078) 0.062 (0.078) 998.2935 2.279 4.617 4.738 4.665

a
b c

P < 0:05;

P < 0:01;


P < 0:001 (two-tailed tests for hypothesized variables). Standard errors are in parentheses These models represent the achieved convergence after four iterations The model represent the achieved convergence after 14 iterations

petencies in each company. On the other hand, game theory suggests that the best way is to find win–win opportunities with competitors and to avoid mutually destructive competition, in order to gain some advantages such as access to complementary resources and to know the partner’s strategic moves. From these last contributions, our study sought to test some hypotheses and demonstrate that a co-opetition strategy encourages better innovation performance than pure cooperative and competitive alternatives. We carried out a longitudinal analysis of a sample of European dedicated biotechnology firms which usually take part in collaboration networks characterized for co-opetition dynamics. Due to the fact that they are normally small or medium size, and they are very specialized in a narrow field of biotechnology, DBFs keep upstream and downstream relationships and ties with direct competitors. These strategic alliances provide them critical input (technology spillovers, financing, marketing and other managerial skills, etc.) for the new product development process. We found some evidence about the positive impact of co-opetition strategy on the capacity to innovate. Cooperation with direct competitors contributes positively and significantly to product lines. This result would confirm the argument of a theoretical framework that sees co-opetition as an appropriate strategy.

Also, collaboration with large companies that diversified into biotechnology has a positive effect on product lines although to a lesser extent than direct ties with competitors. With these agents and universities that do not compete, the DBFs can complete and encourage the chain sequence of industrial R&D (Galhardi, 2000): basic research, precompetitive R&D activities and competitive activity (design and development of new products), and then the commercialization of innovations. Concerning technological diversity, collaboration with large companies that also compete was the strategy found to be most influential. Small and medium biofirms are usually very specialized in leading-edge science, and diversified corporations can provide with complementary technologies to achieve a multi-disciplinary approach. But, in turn, ties with large companies that are not diversified into biotechnology have a negative effect on technological diversity although to a lesser extent. Alliances with direct competitors had a positive effect on technological diversity. These results represent evidence of the biotech firms over a period of time, but they are not generalizable for other industries. Moreover, much of variance in the number of product lines or technological diversity remains unexplained by our regressions. A survey to study the analyzed questions in this paper would be interesting to carry out. With this survey we would try to highlight

C. Quintana-Garcı´a, C.A. Benavides-Velasco / Technovation 24 (2004) 927–938

why different alliances (cooperative, competitive or coopetitive attitudes) have a different impact on innovative capability, and what managerial practices enable that result; this means how firms can structure collaboration to optimally configure the combinations of private and common benefits and thereby positively affect alliance evolution (Kogut, 1998). Also, it would be of interest to extend this type of studies to other industries, and to know what different features explain the greater effectiveness of a specific tie. In this sense, Kale et al. (2000) made one of the few empirical studies that explores, in several industries, the significance of alliance management practices such as managing conflicts integratively and building relational capital, practices that help firms achieve alliance aims that are often believed to be mutually exclusive.

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Cristina Quintana-Garcı´a (Ph.D. in Economics and Management Science) is Associate Professor of Management and Organizational Behavior at the Faculty of Economics and Business Science, Malaga, Spain. She is a member of the research group ‘Technological Innovation and Quality’. She made a research stay at Harvard Business School in 2001. Her research focus on factors that help small and medium high-technology firms (specially biotechnology industry) to perform their innovation processes (e.g. strategic alliances, spatial organization of R&D, etc.). She has published some books and articles and participated in international conferences (Iberoamerican Academy of Management, EURAM, etc.). She is a member of the European Academy of Management (EURAM) and other Spanish scientific association. Carlos A. Benavides-Velasco is Associate Professor of Technological Innovation and Quality Management at Technical Higher School of Industrial Engineer. He has a Ph.D. in Economics and Management Science and Ph.D. Industrial Engineer. Manager of the Research Group ‘Technological Innovation and Quality’. His current research focuses on quality management (EFQM Model, ISO-9000, QFD etc.) and technological innovations (strategic alliances, applications of scientometric techniques to technology monitoring, start-ups, etc.) and he has published many articles and books about these aspects and participated in international conferences. He is Manager of the Evaluation and Teaching Improvement of University of Malaga, responsible of Quality Assurance and Institutional Evaluation Programme in this University. He is a member of the European Academy of Management (EURAM) and other Spanish scientific associations.