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Paper to be presented at the DRUID Summer Conference 2004 on

INDUSTRIAL DYNAMICS, INNOVATION AND DEVELOPMENT

Elsinore, Denmark, June 14-16, 2004

Theme E-F

PARTNERING AMONG BIOTECHNOLOGY COMPANIES: THE ROLE OF INDUCEMENTS AND OPPORTUNITIES IN

EXPLAINING PARTNERING BEHAVIOUR

Tessa van der Valk, Marius Meeus, Ellen Moors, Jan Faber1 and Haifen Hu2

1Department of Innovation and Environmental Sciences of the University of Utrecht, Heidelberglaan 2, 3584 CS Utrecht, the Netherlands (t.vandervalk@geog.uu.nl)

2former project manager of the Biopartner Network

May 1st 2004

This paper deals with the different inducements and opportunities firms are influenced by in their partnering behaviour. These cannot be strictly separated in view of the fact that the resource set of the firm is proposed to affect both. One the one hand, having more resources could positively affect the opportunities for partnering because it makes a firm a more attractive partner to others. On the other hand, however, having more resources could also make a firm less inclined to search for partners indicating a negative effect on the partnering intensity through a negative influence on the inducements for partnering. In this explorative research, the focus was on determining the effects of the availability of R&D resources and financial resources on the partnering intensity of firms active in biotechnology in the Netherlands. The social position of the firm was also taken into account to enable the estimation of interaction effects of this variable with both resource variables. None of the effects proposed in this research could be confirmed due to one control variable, namely the size of the firm as measured by its total number of employees, being dominant.

Keywords: Liability of newness, inducements, opportunities, social capital.

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Introduction Modern biotechnology is a science-based enabling technology with applications in many different sectors. In biotechnology, technological developments evolve rapidly (Bartholomew, 1997), which creates uncertainty concerning which technological fields companies need to focus on. In view of the rapid developments in this sector, it is not possible for companies to maintain all required capabilities in-house. In view of this, partnering is used to make development possible (Buurma et al., 1996; Roijakkers, 2003). Therefore, firms tend to specialize on their core competences and look for appropriate partners when it comes to activities more distant from these core competences, even though being engaged in a partnering process makes a firm dependent on external actors and thus vulnerable. Their choice of partners changes over time, with the evolution of technology (Rosenkopf et al., 1998; Orsenigo et al., 2001). Relevance of the paper During the 1980s, establishing alliances became part of the general business strategy in biotechnology, partly due to legislation developed to stimulate collaborative research, like the Baye-Dole Act introduced in the US (Hagedoorn et al., 2000). This sector accounted for approximately 20 % of all technology alliances (Hagedoorn, 1993). Most studies examining partnering behaviour in biotechnology focus on companies active in the US. Not much is known about the partnering behaviour of Dutch biotechnology companies. Filling this empirical gap is one of the objectives of this research. Next to this, the Dutch biotechnology sector has developed at a relatively slow pace compared to other European countries. When the trend in the number of start-ups in life sciences in Europe is compared to that of the Netherlands, it can be concluded that the Netherlands has lagged behind in establishing start-ups (Ministerie van Economische Zaken, 2003). It is expected that only a few European clusters on biotechnology will remain. It is of crucial importance for the Dutch knowledge economy that one of these clusters is located in the Netherlands (Broersen et al., 2003). In order to increase their chance of survival, start-ups need to overcome the so-called liability of newness (Stuart et al., 1999; Baum et al., 2000), which makes new firms especially vulnerable to environmental selection. In biotechnology, this liability of newness is related to a liability of unconnectedness (Powell et al., 1996). Also, the Netherlands does not have a very strong industrial base in pharmaceuticals (Oosterwijk, 2003) and the amount of venture capital available is relatively low compared to other European countries (Crocker, 2003). In view of the important role these companies play in other regions, it is interesting to see how Dutch biotechnology start-ups active in medical biotechnology cope with this possible relative disadvantage. Finally, partnering in biotechnology is going on right now and therefore these events can be followed while occurring. Aim of the paper In this paper, a tentative explanatory theoretical model for the partnering intensity of companies active in biotechnology is empirically tested. The basis for this theoretical model is provided by the different inducements and opportunities companies have to establish partnerships (Ahuja, 2000). The complexity of this is that some inducements for companies to establish partnerships at the same time make it a less attractive partner. This implies that a specific factor, such as a shortage of knowledge resources, being perceived as an inducement, can increase the probability of partnering, but it can also be perceived as limiting the opportunities to partner (in view of the fact that it makes the focal firm less attractive to other companies). The aim of this paper is to unravel the complex effects of factors that can provide inducements as well as opportunities for partnering on the probability of partnering.

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Sakakibara (2002) also addressed this issue in relation to the participation of firms in Japanese research consortia. In this perspective, the liability of unconnectedness rests on two counteracting factors, namely inducements and opportunities for partnering. Theoretical framework Gulati (1998) defines strategic alliances as ‘voluntary arrangements between firms involving exchange, sharing or codevelopment of products, technologies or services’ (p293). In this paper, partnering also encompasses cooperation between firms and other actors, for instance research institutes.

In biotechnology, relationships between dedicated biotechnology firms and established firms or research institutes are dominant. In the late 1970s through the early 1980s, established firms used partnerships with dedicated biotechnology firms (DBFs) to internalize new biotechnology knowledge. These established firms were said to lack the absorptive capacity to internalize the knowledge generated by research institutes and therefore needed DBFs to translate this knowledge (Pyka et al., 2001). DBFs are still of great importance, in view of the fact that knowledge in biotechnology evolves rapidly and it is therefore too risky if not impossible for established companies to keep up with the developments themselves. DBFs have therefore taken up the role of explorers of new knowledge (Pyka et al., 2001). The main difference in competencies between established firms and dedicated biotechnology start-ups is that the latter lack so-called economic competencies, e.g. competencies related to registration, marketing and distribution (Pyka et al., 2001). Established firms can fill this competence gap and use knowledge provided by DBFs. These two types of firms thus require and gain different assets from cooperating. Liability of Newness The notion of a liability of newness implies that start-ups need to cope with certain disadvantages compared to established firms. Possible disadvantages include not having sufficient resources and great uncertainty in the field about the quality of the organization’s products and services. Stinchcombe (1965) addresses the liability of newness by noting that there is a bias in data concerning organizational failure related to the organizations’ age. New firms have a higher chance of failure than older firms. Factors causing this so-called liability of newness mentioned by Stinchcombe (1965) include: (1) new organizations need to learn their roles, which often demands additional education; (2) The process of getting used to these new roles takes time and effort; (3) new organizations’ lack of contacts makes them reliant on strangers. In return, other organizations are more likely to select a known organization to be their partner instead of a new firm lacking proven qualities; (4) new firms lack a set of stable ties, which decreases the sense of loyalty other organizations experience when dealing with the new firm. The first two aspects mentioned are related to the organization of the start-up, whereas the latter two concern the embeddedness of the start-up in the research environment. Some authors have related the liability of newness to a certain liability of smallness (Stuart et al., 1999; Baum et al., 2000). Baum et al. (2000) consider the liability of newness or smallness when it comes to start-ups in biotechnology. They propose that this originates from the fact that new firms lack a reputation and therefore their reliability as a partner cannot be confirmed. Overcoming this liability of newness thus implies becoming visible to other actors in the field as being a company that could be a trusted partner. Establishing partnerships with other organizations in its turn provides additional resources and contributes to the reputation of the new organization to the rest of the field.

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Prior research by Powell et al. (1996) has shown that in biotechnology, this liability of newness can be explained as a liability of unconnectedness. They found a direct link between the performance of a start-up and its connectedness to other organizations. This liability of unconnectedness could also apply to other sectors or technological fields characterized by rapid technological development. Another study relating the establishment of alliances to performance indicators was by Niosi (2003). In his study, almost 50% of the variation in rapid growth could be contributed to differences in the use of alliances.

The importance of establishing partnerships was also addressed by Stuart et al. (1999). They stated that other organizations’ perceptions of the quality of the new firm to a large extent determine the fact whether investors will be interested in the new firm. The availability of venture capital in its turn is of great importance to the survival of the start-up and the opportunities a start-up has in establishing additional partnerships as the appropriation of venture capital contributes to the overall reputation of the start-up in the technical field (Stuart et al., 1999).

Cooperation in biotechnology cannot be addressed as a temporary phenomenon, but is a general requirement in order to keep pace with technological developments. Pyka and Saviotti (2001) propose that the role dedicated biotechnology firms play within the field changes over time. Initially, they serve as translators between the public research institutes and large diversified firms. In later stages, they obtain the role of explorers. This is explained by the fact that developments in this sector evolve very rapidly and large diversified firms want to ensure a certain readiness to action in selecting the appropriate fields to focus on. The rapid developments in the sector thus make the existence of dedicated biotechnology firms a necessity.

Establishing partnerships is thus essential for survival in biotechnology. It is therefore relevant to address the viability of Dutch biotechnology companies by examining their partnering structures. Accordingly, the performance measure used here, namely the extent to which Dutch biotechnology start-ups are capable of overcoming their liability of unconnectedness, is relevant.

The partnering intensity of dedicated biotechnology firms active in the Netherlands depends on the different inducements and opportunities they are influenced by in determining their strategy. These inducements and opportunities cannot be strictly separated. This issue is more thoroughly addressed in the next section. Inducements versus opportunities From a resource-based-perspective, the establishment of partnerships is explained by resource needs of the firms engaged in these partnerships. These resource needs present inducements for partnering. However, assuming that partnerships are generally beneficial to firms, it is interesting to see that not all firms are engaged in partnering to a similar extent. This is because of another effect acting on the partnering behaviour of firms, namely the effect of the opportunities available to a firm to engage in partnerships. From a structural sociological perspective, these opportunities are related to the social position of the firm within the network (Ahuja, 2000). On the other hand they are also influenced by the resources available to this firm. These resources namely have a profound effect on the attractiveness of the focal firm to other actors in the field. These different aspects are presented in figure 1.

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+

-

+

+

+

+ Inducements for partnering

Opportunities for partnering

Social position of the focal firm

Attractiveness as a partner

Partnering Intensity

Resource set of the focal firm

Figure 1: model of inducements and opportunities affecting the partnering intensity of firms. From figure 1 it can be derived that the role of the resources available to the firm in this respect is dual; they influence the opportunities as well as the inducements for partnering. The effect of these resources on the opportunities for partnering is proposed to be positive, while their effect on inducements for partnering is negative, resulting in opposing hypotheses when it comes to explaining the partnering intensity of firms. It is therefore interesting to determine what effects these different resources have on the partnering in Dutch biotechnology.

Underlying reasons for establishing partnerships include cost reduction, reducing the time-to-market, risk sharing and acquiring access to technology (Hagedoorn, 1990; Tidd et al., 1997). Several more sector-specific aspects influencing partnering behaviour are: the high investments required, and the uncertainty (Eisenhardt et al., 1996; Porter Liebeskind et al., 1996; Schellekens et al., 2001) and complexity (Buurma et al., 1996; Powell et al., 1996) of the development process. In this research, the focus will be on the factors that are expected to be discriminating between firms included in the sample used here. Some of the factors mentioned, like the complexity of the innovation projects and the pressure to reduce the time-to-market, are considered to be of similar importance to all start-ups active in biotechnology and are therefore not taken into account any further. In order to address the counteracting effects the availability of resources is proposed to have on the partnering intensity, the R&D resources and financial resources of the firm are taken into account here. The social position of the firm is an additional aspect influencing the opportunities for partnering (see figure 1) and is also taken into account here. These three aspects focussed on here are addressed more thoroughly in the following sections. R&D resources The extent to which a firm is inclined to establish partnerships aimed at acquiring new technologies is determined by its own technological or R&D resources. On the one hand, having R&D resources decreases the inducements a firm has to engage in partnering, in view of the fact that increased R&D resources makes firms less dependent on other actors in the field in conducting their research and development. This effect was confirmed by Pisano (1990), who studied the pharmaceutical industry. On the other hand, however, having well developed R&D capabilities enhances the absorptive capacity of the firm, resulting in increased returns on cooperation due to increased knowledge transfer and acquirement capabilities (Cohen et al., 1989; Veugelers, 1997; Meeus et al., 2001). Increased internal R&D capabilities provides a firm with a more accurate insight into the usefulness of knowledge

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from the environment (Arora et al., 1990). These effects could provide a stimulus for firms to engage in partnering and is thus also a rationale for the fact that the level of R&D resources acts as an inducement in the partnering process, also for higher levels of R&D resources. A different explanation for a positive effect of the level of R&D resources on the number of partnerships a firm is engaged in concerns the increased attractiveness of a firm with highly developed R&D capabilities compared to a firm with less developed capabilities. This increased attractiveness provides the firm with more opportunities to partner. In prior literature, the number of patents as well as the total internal R&D expenditure have been used to address the R&D resources of a firm

The rationales discussed above lead to the following, conflicting hypotheses: H1: the greater the extent of a firm’s R&D resources, the lower the number of cooperative agreements a firm is engaged in. H2: the greater the extent of a firm’s R&D resources, the higher the number of cooperative agreements a firm is engaged in. Financial resources When it comes to financial resources as a basis for the establishment of partnerships, this can also be explained from both an inducements and opportunities point of view. The availability of financial resources to the firm will determine the extent to which it will be inclined to establish partnerships based on the appropriation of financial resources. To a firm not having sufficient financial resources, this could be an incentive to engage in partnering. On the other hand, not having financial resources has a negative effect on the firm’s attractiveness as a partner and therefore decreases its opportunities for partnering (Ahuja, 2000).

Prior research has confirmed a positive relation between the financial resources of the firm and its partnering intensity (Mitchell et al., 1992; Ahuja, 2000). This finding is explained by the notion that having more financial resources contributes to the attractiveness of the firm to other actors and therefore increases the opportunities the firm has in finding others that are interested in partnering. Firms that are more attractive as partners are more likely to be offered attractive partnering deals (Mitchell et al., 1992). However, a lack of financial resources could also be a stimulus to search for partners to share research and development costs. This stimulus to engage in partnering weakens when the firm obtains its own financial resources and can become more independent. The acquisition of venture capital requires specific attention. On the one hand, obtaining venture capital provides similar advantages as other sources of financial capital, but on the other hand, having acquired venture capital provides the firm with an enhanced reputation. The acquirement of this type of financial resources namely functions as a signal confirming the competences of the firm to other actors in the field (Stuart et al., 1999).

Two conflicting hypotheses can be formulated regarding the effects of the availability of financial resources on the partnering intensity: H3: The greater the extent of a firm’s financial resources, the greater the number of cooperative agreements a firm is engaged in. H4: The greater the extent of a firm’s financial resources, the lower the number of cooperative agreements a firm is engaged in. Another variable that needs to be taken into account when addressing the financial position of the firms included in the population is the total amount of seed and venture capital obtained

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by a firm. Next to the fact that this measure provides some insight into the financial position of the firm, having obtained seed and venture capital also provides a firm with increased attractiveness due to reputation effects. Having obtained venture capital is perceived as a confirmation of the competences of a young firm (Shan et al., 1994). Prior research has therefore shown that support by venture capitalists and having alliance partners are correlated aspects (Stuart et al., 1999). Social position Another group of factors of importance in the partnering behaviour of firms are related to the social position and social capital of the firm. Firstly, the number of linkages formed by a firm is closely related to the firm’s prior network position. The prior network position on the one hand determines the visibility of the firm to other actors in the field, while, on the other hand, it also determines the amount of information available to the firm concerning possible appropriate alliance partners. This increased information flow from the firm to its environment and back decreases the uncertainty related to cooperative agreements. When this information is evaluated as being positive regarding the competences of the potential partner, this would make establishing an agreement an attractive option (Kogut et al., 1993; Shan et al., 1994; Ahuja, 2000). Next to this, the prior network position of a firm is a measure for its expertise in managing alliances and therefore reduces the uncertainty a firm encounters when engaging in such a process (Ahuja, 2000).

When it comes to the formation of alliances by start-ups, the prior network position of the firm is not such a relevant measure. These firms namely have not had the time yet to build up a network of alliances; they are characterized by a liability of unconnectedness (Powell et al., 1996). Of greater importance in this respect is the connectedness of the management team of the firm, as network ties initially exist on the personal level (Sedaitis, 1998; Hite et al., 2001). Prior research had shown that this significantly affects the partnering rate of new firms (Eisenhardt et al., 1996) and therefore provides an appropriate alternative for addressing the social position of a start-up. Next to this, attention also needs to be paid to the use of advisory boards. These advisory boards with well known industry and experts and academics are relatively frequently used by biotechnology firms to obtain access to new partners (Elfring et al., 2002). These boards are used to enhance firms’ opportunities for partnering. H5: The stronger the social position of the firm, the higher the number of cooperative agreements a firm is engaged in.

From the discussion stated above, the following factors explaining the probability of partnering of firms active in Dutch medical biotechnology can be derived: (1) the R&D resources of the focal firm; (2) the financial resources of the focal firm; (3) connectedness of the CEO(s). Of these factors, the R&D resources of the focal firm and the financial resources of the focal firm can act as inducements for establishing partnerships and at the same time affect the firm’s opportunities to engage in partnerships. These different variables discussed above are included in the conceptual model presented in figure 2.

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Partnering intensity

Financial resources

R&D resources

Social position of the firm

+: Eisenhardt et al. (1996); Sedaitis (1998)

+: Cohen et al (1989) V eugelers (1997); Ahuja (2000) -: P isano (1990)

+: Ahuja (2000); M itchell et al. (1996) -: not confirmed

Figure 2: conceptual model of factors affecting the probability of partnering and partnering intensity Figure 2 provides an overview of the diverging results of prior research concerning the effects of R&D and financial resources on the partnering intensity. In this paper, the complex concepts of inducements and opportunities for partnering are unravelled and it is determined whether, in Dutch biotechnology, the R&D resources and financial resources are in fact mainly acting on the inducements or opportunities for partnering. Interaction and non-monotonic effects Next to the effects proposed in the hypotheses discussed above, some interaction effects are also tested. These hypotheses are related to the model presented in figure 1. They deal with the situation where a firm does not have sufficient resources for conducting R&D and this resource need is a primary rationale for engaging in partnering, in this situation, the attractiveness of this firm as a partner, as affected by the available resources, does not significantly contribute to the opportunities for partnering available to the firm. Therefore, in this situation, the social position of the firm is decisive for the extent to which opportunities for partnering are available. It is proposed here that there is an interaction effect between the factors influencing the social position of the firm and the different resource variables. H6: The effect of inducements for partnering resulting from a need for R&D resources on the partnering intensity is moderated by the opportunities for partnering provided by the social position of the firm. H7: The effect inducements for partnering resulting from a need for financial resources on the partnering intensity is moderated by the opportunities for partnering provided by the social position of the firm. Next to the interaction effects discussed above, non-monotonic effects were also controlled for by including the squared terms of the independent variables in the model. When shown to be significant, the coefficients of these squared terms determine whether the relation is U-

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shaped or inverted U-shaped. A positive sign for beta indicated a U-shaped effect, while a negative beta is related to an inverted U-shaped relation between the independent and dependent variable. A U-shaped effect implies that the highest partnering intensity is found at firms with low or high levels of the dependent variable, while a relatively low partnering intensity is found for firms with an average score on the independent variable. An inverted U-shaped effect implies the opposite: for average levels of the independent variable, the partnering intensity will be highest while for both low and high levels of the independent variables the partnering intensity will be relatively low. Methods Sample The Biopartner Network was established in 2000 and is aimed at supporting start-ups in life sciences. The data used here originates from the 2003 Biopartner survey that was distributed among the 126 dedicated biotechnology firms active in the Netherlands. This includes all firms that are located in the Netherlands and have life sciences in their core business. Overall, data on 110 firms was obtained, which implies a response rate of 87% for the total survey. There were however differences in response rate per question. These differences were most prominent for questions related to the partnering behaviour of the firm. The response rate of these questions was about 70% of the total number of dedicated biotechnology companies located in the Netherlands. The survey provides an overview of life sciences activity in the year 2002 (Hu et al., 2003). Measurement of the different variables The Biopartner survey contains data on, for instance, a company’s management, its establishment (e.g. as a spin-off or joint venture), its knowledge base and its most important partners. It also includes data on the perceived competition on the markets a company operates on and the number of patents, licences (in- and out-) and products in the pipeline a company has. The dependent variable ‘partnering intensity’: Data on the number of partnerships firms included in the sample were engaged in was directly available from the survey data. No subdivision was made concerning different types of partnerships. Social position of the firm: In this analysis, the social position of the firm is estimated by two indicators. First of all, the size of the management team was taken into account. Data on this aspect was directly obtained from the survey. Next to this, the use of external advisory boards was also included as a variable. In the survey, a distinction was made between the following categories: (1) supervisory directors; (2) scientific advisory boards and (3) other advisory boards. These three categories were each subdivided into a national and an international category, resulting in six categories overall. Firms could indicate in the survey whether or not they used each type of advisory board. The total number of types of advisory boards used was computed and this was the variable used in the analysis. R&D resources: To measure the R&D resources available to the firm, data on the number of patents held by the firm was used. This data was also obtained from the Biopartner survey. Another indicator used to estimate the R&D resources of the firm was the R&D expenditure of the firm. No distinction could be made between R&D expenditure used internally and invested outside the firm.

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Financial resources: After conducting an explorative analysis using correlation, it was determined that the change in turnover from 2001 to 2002 would be the best measure of financial resources to predict changes in the number of alliances. The correlation of this variable with the dependent variable was the highest and significant. The change in turnover provides a good measure for the firms’ possibilities to invest in additional projects. A decrease in turnover could make firms more reluctant to invest in joint R&D, or it could provide an incentive for the firm to search for partners that can complement their financial assets deficit. In view of that fact that the firms studied in this research are all at most ten years in business (the average age of the firms studied is 3.02 years), the change in turnover from 2001 to 2002 provides a good insight into the financial position of these firms.

Another indicator that was used to measure the financial resources of the firm was the total amount of seed and venture capital obtained by firms. Data on capital obtained in different financing rounds were added and the resulting variable was used in the analysis. Control variables Firm size and age were the control variables used in this explorative research. Firm size was controlled for by using the total number of employees, in view of the fact that it is less useful to use data on sales when it comes to start up biotechnology companies. These mostly do not have any products on the market and therefore do not generate sales. Prior research on high technology firms has shown that firm size, as measured by the total number of employees, can have a negative effect on the total number of alliances (Shan, 1990). The rationale behind this is that large firms will be able to operate relatively independently, while small firms have a greater need for support of partners in conducting their research and development. This rationale is based on the need for resources of small firms (Sakakibara, 2002). Firm age was also included as a control variable. Most dedicated biotechnology firms have been established over the past few years. For instance, over sixty firms have been supported in their establishment by the Biopartner Network since its initiation in 2000 (Hu et al., 2003). This relatively young population of firms is required in view of the objectives of this study, namely examining the liability of unconnectedness. However, there is still some variance in the data concerning the age of the firms included. Firms that have existed for over ten years were therefore excluded from the research, while firm age was used as a control variable for the remaining sample. Analysis The variables included in the research all were available at at least the interval level. Two of the indicators used here, the total amount of R&D expenditure and the change in turnover from 2001 to 2002, were categorized in the survey. In view of the fact that the different categories used were not of the same scope, these variables were recoded using the mean of each category. This provided some difficulties for the variable ‘R&D expenditure’, in view of the fact that two firms had filled out to spend over 5 million euros on R&D and therefore no category mean could be computed. 5 million was used instead of a mean, which underestimates the true R&D expenditure of the firms involved.

For the analysis of the data, two ordinary least squares (OLS) regressions were conducted. The first OLS regression was conducted using the stepwise method in SPSS, while the second was conducted manually, in an iterative way. The listwise method was used for handling the missing values in the data.

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Results In table 1, the descriptive statistics and Pearson correlation coefficients are presented. The pairwise exclusion method was used to deal with missing values in the data. Table 1: Descriptive Statistics

N Minimum Maximum Mean Std.

Deviation

Correlations of independent

variables with total number of

alliances R&D expenditure 82 0,0 5000 538,11 997,027 0,404a

Number of patents 86 0,0 300 6,92 32,693 0,157Change in turnover: 2002 to 2001d 73 -375 2000 306,85 501,703 0,544a

Seed + VC acquirede 97 0,0 4900,00 218,54 778,492 0,608a

Size of the management team 96 0,0 8 2,82 1,515 0,449a

Use of advisory boards 97 0,0 6,00 1,18 1,379 0,293c

total number of alliances 73 0,0 51 5,03 7,123

Valid N (listwise) 50 a correlation is significant at the 0.001 level (2-tailed) b correlation is significant at the 0.01 level (2-tailed) c correlation is significant at the 0.05 level (2-tailed) d The change in turnover is given in euros * 103

e Seed + VC acquired represents the total amount of seed- and venture capital divided by 10.000 (given in euros) As can be derived from table 1, there is a large variance in some of the data. For instance, the average number of alliances firms are engaged in is 5.03, while the standard deviation of this variable is 7.123. The mean number of patents owned by the firms included in the dataset is 6,92, while the minimum value of this variable was 0 and its maximum value was 300. In table 2, the outcome of the stepwise linear regression analysis in SPSS 11.5 is presented. This implies that variables that were excluded during the stepwise procedure in SPSS are not included in this table1. The first model included in table 2 is the final model provided by SPSS. The control variables were entered in the first block of the regression analysis, while the second block contained the linear variables reflecting inducements and opportunities. A third block, containing squared terms of these variables and cross product terms of some of the linear variables, was also added. It can be observed that one of the control variables, namely the total number of employees, is included in the final model, while its coefficient is not significant. In view of this, the model that resulted from the stepwise linear regression was tested again, excluding the total number of employees (model 2 in table 2). This resulted in a lower R2, namely 0,835 compared to 0,843 of the original model.

It can be observed from table 2 that, in both models, the standardized coefficients belonging to the total amount of seed and venture capital and the squared term of this variable are both larger than 1. As these standardized coefficients are based on correlation coefficients, this result cannot be considered reliable. Next to this, model 1 also contains a variable whose

1 For a list of all variables included in the analyses, see appendix I.

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coefficient is not significant (firm size). It is not clear why this variable was not excluded from the model by SPSS.

Table 2: Stepwise linear regression analysis (a)

Independent variables

Indicators Model 1 Model 2

Control variables Firm size 0.117 (0.047)

-

R&D resources R&D expenditure 0.526b (0.001)

0.429b

(0.001) Change in turnover: 2002 to 2001

0.528c

(0.003) 0.544b

(0.003)

Seed + VC acquired -2.088a

(0.000) -1.808a

(0.000)

Financial resources

Seed + VC acquired (sq.) 2.778a

(0.000) 2.549a

(0.000)

Social position - - - Interaction effects Size of the management

team * dturnover -0.711c

(0.001) -0.601c

(0.001) R-square

DF 0.843 48

0.835 52

(a) dependent variable: total number of alliances Standard errors are in parentheses a coefficient is significant at the 0.001 level b coefficient is significant at the 0.01 level c coefficient is significant at the 0.05 level The final model constructed by SPSS contains the variables firm size, total amount of seed and venture capital acquired by the firm and its squared term, the change in turnover from 2001 to 2002, the R&D expenditure and the cross product term of the size of the management team and the change in turnover. This would lead to the confirmation of some of the hypotheses formulated in this paper. However, due to the discussed inconsistencies in this model, the results obtained here cannot be considered reliable.

Next to the inconsistencies observed in the results, the variance in the data could also have a profound impact on these results. By computing Spearman correlation coefficients as well as Kendalls Tau_b coefficients, it was observed that the variance in the data had a significant influence on the outcomes of these correlation coefficients. The Skewness and Kurtosis of the variables were also computed and these values were positive and significantly differing from 0, indicating that the variables were not normally distributed. For instance, the Skewness of the variable ‘number of patents’ was 8,697 and its Kurtosis was 78,459. It was concluded that this distribution of the variables would also hamper obtaining solid results in the regression analysis. To decrease the effect of the extremes in the data on the results obtained, a logistic transformation was applied to all variables (ln(1+x)).

Subsequently, a linear regression analysis was conducted in an iterative way using the enter method in SPSS and manually excluding the variable whose coefficient was least significant. The model obtained by doing this also showed standardized regression coefficients greater than 1. In view of this, a bivariate correlation matrix containing all variables was constructed. The variables significantly correlating with the total number of alliances were ranked according to the size of their correlation coefficient and entered into the regression model one by one. A significance of the regression coefficients of 0.1 was used as a criterion for keeping the newly added variable in the regression equation. From this analysis it was shown that the effect of the variable ‘size of the firm’, which had the highest bivariate

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correlation with the total number of alliances, was very dominant and none of the other variables could be included in the model simultaneously. The regression model containing this variable is shown in table 2. Table 2: Manual linear regression analysis (a)

Model Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta 1 (Constant) ,773 ,174 4,452 ,000 Size of the firm ,333 ,075 ,468 4,463 ,000

DF=72 R2=0.219

a Dependent Variable: Number of alliances In further analysis, no model could be found with a higher explanatory value due to the inclusion of additional variables with significant coefficients. As can be concluded from this explorative study, none of the inducements and opportunities operationalized here had a significant effect on the number of alliances that was not also caused by the size of the firm, as measured by the total number of employees. Discussion and conclusion In this explorative study, the effects of possibly counteracting factors on the partnering intensity of Dutch dedicated biotechnology firms was studied. In view of the dominance of one of the control variables, firm size as measured by the total number of employees, none of the hypotheses formulated earlier in this paper could be confirmed by the data on Dutch dedicated biotechnology firms. The aspects focussed on here, the R&D resources, financial resources and social position of the firm were not distinctive for the partnering intensity of the population of firms studied here. Differences in firm size provided a more accurate explanation for the differences in the dependent variable. This finding makes results obtained in prior research based on analyses not including this aspect of firm size, as measured by the total number of employees, questionable. Further research using additional indicators for a firm’s inducements and opportunities can provide a better insight into the effects of these possibly counteracting factors on the formation of alliances. A path analysis can be conducted on the data used here to see if there are any (probably relatively small) additional effects of the independent variables used here on the dependent variable total number of alliances, that cannot already be fully explained by only taking the size of the firm into account. Furthermore, it can be concluded that the use of the stepwise method in SPSS can lead to inconsistent and unreliable results, and it is therefore preferable to conduct regression analyses manually, in an iterative way. References Ahuja, G. (2000). "the duality of collaboration: inducements and opportunities in the

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Appendix I: variables included in the analyses

Control variables: Size of the firm Firm age R&D resource variables: R&D expenditure (R&D exp.) Number of patents (patent) Financial resource variables: Change in turnover from 2001 to 2002 (dturn.) Amount of seed + venture capital acquired (Seed+VC) Social position variables: Size of the management team (Manag.) Total use of external advisory boards (Ext. adv.) Squared terms of the independent variables: R&D exp. (sq.) patent (sq.) dturn (sq.) Seed+VC (sq.) Manag. (sq) Ext. adv. (sq) Cross product terms R&D exp. * Manag. patent * Manag. dturn. * Manag. Seed+VC * Manag. R&D exp. * Ext. adv. patent * Ext. adv. dturn. * Ext. adv. Seed+VC * Ext. adv.