CO-OPETITION AND PERFORMANCE: EVIDENCE FROM

Co-opetitionBIOTECH
II ANNUAL CONFERENCE OF EURAM ON: "INNOVATE RESEARCH IN MANAGEMENT". STOCKHOLM (SWEDEN), MAY 9-11, 2002. Track: "COOPETITION STRATEGY: TOWARDS
A NEW KIND OF INTERFIRM DINAMICS"
CO-OPETITION AND PERFORMANCE: EVIDENCE FROM EUROPEAN BIOTECHNOLOGY INDUSTRY
Authors:
Dra. CRISTINA QUINTANA GARCÍA
Dr. CARLOS A. BENAVIDES VELASCO
University of Malaga (Spain)
Address:
Dra. CRISTINA QUINTANA GARCÍA
Profesora Asociada de Organización de Empresas
Dpto. Economía y Administración de Empresas
Facultad de Ciencias Económicas y Empresariales
Campus El Ejido, s/n
29071 MÁLAGA (SPAIN)
Telephone: (34) 95 213 41 47 Fax: (34) 95 213 12 93
E-mail: cqg@uma.es
Dr. CARLOS A. BENAVIDES VELASCO
Profesor Titular de Organización de Empresas
Dpto. Economía y Administración de Empresas
E.T.S. Ingenieros Industriales
Campus El Ejido, s/n
29071 MÁLAGA (SPAIN)
Telephone: (34) 95 213 28 76 Fax: (34) 95 213 12 93
E-mail: cabv@uma.es
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CO-OPETITION AND PERFORMANCE: EVIDENCE FROM EUROPEAN BIOTECHNOLOGY INDUSTRY
INTRODUCTION
Traditionally, 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, 1985). The alternative paradigm emphasizes the development of collaborative
advantage (Miles and Snow, 1986; Thorelli, 1986; Yoshino and Rangan, 1995). The strategic collaborations represent institutions of privileged relations
among firms and other organizations are based on the advantage reciprocity, power association searching the same pre-established target (Benavides,
1997:113). But nowadays, business success requires that firms pursue both strategies simultaneously (Lado, Boyd and Hanlon, 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. Scholars and managers
have recognized that striking a balance between competition and cooperation (co-opetition) plays a key role in the performance and survival of business
enterprises (Jorde and Teece, 1989). But little research has considered that firms can be involved in and benefit from both strategies simultaneously, and
hence both types of relationships need to be emphasized at the same time (Bengtsson and Kock, 2000:411)
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
performance. Particularly, the hypothesis 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). 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. Specially, in the downstream side, 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 better results than pure
cooperative or competitive strategies. A sample of European biotechnology firms will be studied longitudinally during a period of six years (1995-2000).
For measuring the performance we focus specially on competence to innovate.
THEORETICAL PERSPECTIVES OF CO-OPETITION
Research on cooperation and competition has been conducted within different theoretical fields. Interaction between competitors has been studied
directly in economic theory with a focus on industrial structure rather than relationships (Scherer, 1980; Tirole, 1988). In literature on strategic alliances
(Kogut, 1988; Yoshino and Rangan, 1995; Gulati, 1998; Gulati, Nohria and Zaheer, 2000), relationships rather than structure are analyzed. 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 three perspectives that provide a useful theoretical framework for the development of a syncretic model of competition and
cooperation (Park and Russo, 1996; Lado, Boyd and Hanlon, 1997:113-117): transaction-cost economics, resource-based view, game theory.
Transaction-cost Economics
The rationale for inter-firm cooperation agreements can be positioned within the transaction-cost economics paradigm. 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. So, for example, the transaction-cost economics justifies the existence of cooperation to favor the transmission of "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.
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 share of the home market. These goals conflict directly, and the venture can prove dysfunctional and eventually fail (Kogut,
1988). 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.
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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 the other perspective assume a dynamic process and focus on how asset stocks are accumulated, mobilized, and deployed to generate a
sustainable competitive advantage (Teece, Pisano and Shuen, 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.
The dynamic capability-based perspective provides the base upon which to examine the accumulation of resource stocks through both competition and
collaboration (Lado, Boyd and Hanlon, 1997:115). 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). For example, 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 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).
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. Game theory has been applied by researchers to study situations in which a cooperative
equilibrium appears (or fails to appear) through reciprocal interactions among participants (Nowak, Sigmund and Leibowitz, 2000:13). Game theory may
be criticized because it emphasizes opportunism as crucial to understanding the structuring and management of interfirm collaborations (Lado, Boyd and
Hanlon, 1997:116).
This theory represents another conceptual framework for examining the potential of rent creation through co-opetition strategy (Brandenburger and
Nalebuff, 1996; Loebecke, Van Fenema and Powell, 1999). The premise of prisoners’ dilemma paradigm is based on the avoidance of costs 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.
COOPERATION AND COMPETITION: ALTERNATIVES OF STRATEGIC BEHAVIORS
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, Boyd and Hanlon, 1997:120-124; Bengtsson and Kock,
2000:415-416): cooperation-dominated relationships, equal relationships (co-opetition) and competition-dominated relationships.
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, Gulati and Nohria, 1998).
Collaborative advantage is generated when companies develop a behavior that emphasize altruism, trust and reciprocity (Kanter, 1994). Trust
generates economic rents in several ways (Lado, Boyd and Hanlon, 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.
Competitive behavior
Competition-dominated relationship 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
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"learning race" emerges, where it simultaneously looks for a maximum absorption of distinctive competencies from its partners and tries to protect its own
core resources and capabilities (Kale, Singh and Perlmutter, 2000). In such a situation, once one firm has learned enough from its partner, it has no
incentive to continue in the alliance (Khanna, Gulati and Nohria, 1998:198).
From an other perspective, competitive strategy behavior can help companies to achieve greater productive efficiency and may generate
entrepreneurial rents by promoting the creativity and innovation in products, markets, process and other organizational aspects. This viewpoint, in an
alliance scope, has been criticized because (Lado, Boyd and Hanlon, 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 temporary rents, it make it difficult to maintain a competitive
advantage for a long time.
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.
Although competition 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, economic and market growth, and technological
progress than either competition or cooperation pursued separately (Lado, Boyd and Hanlon, 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 socioeconomic progress by enhancing
knowledge development and utilization, increasing the volume and quality of goods and services, and expanding markets (Jorde and Teece, 1989).
Competitive collaboration 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, Doz and Prahalad, 1989:139). Through this type of tie it is possible to obtain other general advantages from a 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 cost and risk; creation and transfer of technology and capabilities, etc.
These arguments mean that 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 (Lado,
Boyd and Hanlon, 1997:123). 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; Hamel, 1991), a situation that explains a tradeoff between access to
greater resources and the potential for loss of proprietary information or the creation of stronger competitors.
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 decompose in others more specific when we explain, in the next epigraph, who are the agents with which DBFs cooperate and
what are the measures of innovate competence selected.
EMPIRICAL EVIDENCE FROM EUROPEAN BIOTECHNOLOGY INDUSTRY
Research design
Sample and data selection
To test the hypothesis we focus on the biotechnology industry. It is interesting to notice that there are several types of organizations that make up the
biotechnology community. The most relevant are (Barley, Freeman and Hybels, 1992:320): dedicated biotechnology firms, universities, private or public
research institutes, large companies, diversified corporations (in the chemical, pharmaceutical, energy and agricultural industries that either conduct R&D
on biotechnology), hospitals and suppliers or goods. Particularly, we focus on small and medium dedicated biotechnology firms that have played a major
role in each industry's evolution. These companies 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. Inter-firm cooperation accelerates the rate of technological innovation and firms can compete more effectively in high-speed learning races
(Powell, Kogut and Smith-Doerr, 1996).
Within these network dynamics, we analyzed if the co-opetition strategy permits higher levels of success than pure competitive and cooperative
behavior. The main source of information was Bioscan. 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 represented a diversification strategy. We were only interested in small and medium
DBFs 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 dedicated biotechnology firms. But, in
fact, we have a data panel, this mean, repeated observations (six years) on the same set of cross-section units (73 firms), thus the total observations are
N=438.
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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, Darby and Armstrong, 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.
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. 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 (Zucker, Darby and Armstrong, 1998:71). Those research centers or universities can also compete with DBFs because they may
cooperate with competitors or develop other activities that compete directly with the company. Thus, we divided the variable "upstream relationship" in two
independent variables: "upstream/compete" and "upstream/ cooperating 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.
Downstream relationships with end-users represent other important source of spillovers. In the entire innovation cycle, usually large or diversified
corporation (pharmaceuticals, chemicals) develop activities related to clinical testing procedures, regulatory processes and commercialization, which cover
the largest fraction of the applications in biotechnology. 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/compete" and
"downstream/ cooperating only".
In total, we have five independent variables to test the hypotheses. "cooperation with direct competitors", "upstream alliances / cooperating only",
"upstream alliances / compete", "downstream alliances / cooperating only", and "downstream alliances / compete".
With the information about dependent variables and types of alliances that DBFs develop, we can now decompose the basic hypothesis in more
specific others. With regard to the number of product lines, we want to test the following hypotheses:
Hypothesis 1a: Cooperating with direct competitors has a positive effect on development of product lines.
Hypothesis 1b: Upstream alliances characterized for co-opetition behavior have a positive effect on development of product lines to a
greater extent than pure cooperative or competitive attitude.
Hypothesis 1c: Downstream alliances characterized for co-opetition behavior have a positive effect on development of product lines to a
greater extent than pure cooperative or competitive attitude.
Concerning technological diversity used by DBFs, the next hypotheses are established:
Hypothesis 2a: Cooperating with direct competitors has a positive effect on technological diversity.
Hypothesis 2b: Upstream alliances characterized for co-opetition behavior have a positive effect on technological diversity to a greater
extent than pure cooperative or competitive attitude.
Hypothesis 2c: Downstream alliances characterized for co-opetition behavior have a positive effect on technological diversity to a greater
extent than pure cooperative or competitive attitude.
We also used 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 n-1 years dummy variables (0,1) to introduce the temporal or longitudinal component and to avoid perfect multicollinearity.
Econometric models
For testing the basic hypothesis, at first, we were considering using the linear regression model that assume 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:
e-λ i λiyi
Prob (Yi = yi ) =
Yi !
, yi = 0, 1, 2 ...
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where yi is a non-negative integer valued variable. The more usual specification of λi is the logarithmic-lineal,
ln λi = β ´xi + µi
and the number of expected events in each period is:
E [yi  xi] = e β´xi + µi
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 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 µi (Greene, 1999:809). For this model, we also used the quasi-maximum likelihood (QML) estimator to get consistent coefficients.
Results
Tables 1 provides the descriptive statistics and correlation matrix for the variables that were used in this study. And 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.
Variable
Mean
S.D.
1
2
3
4
5
6
7
8
9
10
11
12
1. Product lines
4.60
3.31
1.00
2. Technological diversity
3.24
1.96
0.54
1.00
3. Cooperation with direct competitors
1.11
1.71
0.65
0.47
1.00
4. Upstream alliances / cooperating only
0.53
1.23
0.31
0.12
0.35
1.00
5. Upstream alliances / compete
0.22
0.65
0.04
0.03
0.20
0.36
1.00
6. Downstream alliances / cooperating only
0.60
1.32
0.36
0.33
0.50
0.15
0.03
1.00
7. Downstream alliances / compete
0.83
1.55
0.55
0.55
0.61
0.14
0.09
0.66
1.00
8. Age
12.97
6.12
0.14
0.00
0.03
-0.11
-0.16
0.00
-0.02
1.00
9. Size (numer of employees)
72,79
94,5
0.34
0.35
0.38
-0.04
-0.03
0.18
0.38
0.16
1.00
10. Year 1995
0.17
0.37
-0.05
-0.06
-0.06
-0.01
-0.06
-0.04
-0.04
-0.17
0.00
1.00
11. Year 1997
0.17
0.37
0.00
0.01
0.01
0.00
0.00
0.00
0.00
-0.03
0.00
-0.20
1.00
12. Year 1998
0.17
0.37
0.01
0.03
0.02
0.01
0.02
0.00
0.01
0.04
0.00
-0.20
-0.20
1.00
13. Year 1999
0.17
0.37
0.01
0.03
0.03
0.01
0.03
0.03
0.02
0.11
0.00
-0.20
-0.20
-0.20
1.00
14. Year 2000
0.17
0.37
0.02
0.03
0.03
0.01
0.04
0.03
0.02
0.15
0.00
-0.20
-0.20
-0.20
-0.20
Table 1. Descriptive statistics and Pearson correlation matrix (N=438).
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13
14
1.00
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Independent variables
Poisson Model
Negative Binomial
Dependent variables
Dependent variables
Product linesa
Technological diversitya
Product linesa
Technological diversityb
Constant
0.966***
(0.078)
1.005***
(0.074)
0.960***
(0.081)
0.958***
(0.074)
Cooperation with competitors
0.141***
(0.015)
0.071***
(0.016)
0.145***
(0.016)
0.057***
(0.017)
Upstream alliances / cooperating only
0.102***
(0.017)
0.010
(0.019)
0.104***
(0.018)
0.013
(0.020)
Upstream lliances / compete
-0.063
(0.039)
-0.068
(0.048)
-0.064
(0.041)
-0.051
(0.036)
Downstream alliances / cooperating only
-0.025
(0.021)
-0.057**
(0.021)
-0.026
(0.022)
-0.033
(0.025)
Downstream alliances / compete
0.091***
(0.019)
0.114***
(0.019)
0.091***
(0.020)
0.130***
(0.022)
Age
0.019***
(0.004)
-0.008
(0.004)
0.019***
(0.004)
-0.004
(0.004)
Size
1.28E-05
(0.000)
0.0008**
(0.0002)
2.1E-04
(0.000)
0.0005
(0.0002)
Year 1995
-0.031
(0.082)
-0.036
(0.079)
-0.033
(0.085)
-0.026
(0.078)
Year 1997
-0.073
(0.080)
0.029
(0.078)
-0.065
(0.084)
0.035
(0.077)
Year 1998
-0.056
(0.080)
0.067
(0.078)
-0.062
(0.084)
0.059
(0.077)
Year 1999
-0.078
(0.080)
0.069
(0.078)
-0.084
(0.084)
0.057
(0.078)
Year 2000
-0.064
(0.080)
0.069
(0.079)
-0.074
(0.084)
0.062
(0.078)
Log likelihood
-922.8855
-784.5952
-919.8413
-998.2935
Avg. Log likelihood
-2.107
-1.791
-2.100
-2.279
Akaike info criterion
4.273
3.641
4.264
4.617
Schwarz criterion
4.394
3.763
4.394
4.738
Hannan-Quinn criterion
4.321
3.689
4.315
4.665
a: these models represent the achieved convergence after 4 iterations;
b: the model represent the achieved convergence after 14 iterations
*p <0.05; **p <0.01; ***p <0.001 (two-tailed tests for hypothesized variables)
Standard errors are in parentheses
Table 2. Quasi-maximum likehood, fixed effects Poisson Count and Negative Binomial estimators
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 business behavior of all companies is more and less constant throughout the analyzed 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.
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There is support for hypothesis 1a. The most relevant strategy 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. The relationship between diversified corporations and DBFs is
also positive 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. Thus, there is also support for hypothesis 1c. In turn, the hypothesis 1c 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.
With regard to technological diversity, there is support for hypotheses 2a and 2b. In this case, strategic alliances with diversified companies
(downstream / compete) is the strategy with the highest positive effect on technological diversity. The explanation could be that 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 spectre of complementary technologies
to the new ones and then partners get a multi-disciplinary approach. Collaboration with direct competitors is an other linkage that had a positive impact on
technological diversity; direct competitors can be specialized in different new developments of technologies and get a complementary approach by
cooperation agreements. In turn, ties with large companies that are not diversified into biotechnology have a negative effect on technological diversity
although at a low level.
There is not evidence that the variables "upstream alliances/ compete" has a significant impact on product lines or technological diversity.
DISCUSSION AND CONCLUSION
The aim of this study was to explore the impact of co-opetition strategy on business success, specially on innovative competence. 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.).
We found support for many hypotheses. Cooperation with direct competitors contributes positively and significantly to product lines. This result would
confirm the argument of theoretical framework that sees co-opetition as an appropriate strategy. Also, collaboration with universities and research institutes
that do not compete and with large companies diversified into biotechnology has a positive effect on product lines although to a lesser extent than direct ties
with competitors. Actually, with all these agents DBFs can complete and encourage the chain sequence of industrial R&D (Galahardi, 2000): basic
research, precompetitive R&D activities and competitive activity (design and development of new products), and then the commercialization of
innovations. Concerning second group of hypotheses, collaboration with large companies that also compete was the strategy found to be most influential on
technological diversity. Small and medium biotechnology firms 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, they are not generalizable for other industries. Moreover, much of the
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 why different alliances (cooperative, competitive or co-opetitive
attitudes) have a different impact on performance, and what managerial practices enable that result. 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.
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