Biotechnology collaborations: does business model ... - Springer Link

J Manag Gov (2012) 16:377–392 DOI 10.1007/s10997-010-9156-z

Biotechnology collaborations: does business model matter? Raphael Greiner • Siah Hwee Ang

Published online: 5 August 2010 Ó Springer Science+Business Media, LLC. 2010

Abstract This study investigates how a biotechnology firm’s collaboration incidence is affected by the business model it adopts. Specifically, we compare interfirm collaboration conducted by biopharmaceutical firms adopting the hybrid business model with those using the product-focused business model. The analysis based on 1,820 collaborations conducted by 87 dedicated biopharmaceutical firms suggests that firms adopting the hybrid business model generally engage in more collaboration. They also establish a greater proportion of exploration collaboration. These findings have implications for firm’s positioning using business models. Keywords

Business model Collaboration Biotechnology Value chain

1 Introduction While there is no generally accepted definition of business model (Magretta 2002), the common understanding is that a business model provides an integrated description of a firm and the ways it generates revenues (Ghaziani and Ventresca 2005; Schweizer 2005). It also helps define how firms manage their transactions with stakeholders such as customers, partners, investors, and suppliers (Amit and Zott 2001; Zott and Amit 2008). As a result, the concept of business model has consistently been linked to organizational performance, even though empirical studies are scarce. R. Greiner Roche Diagnostics GmbH, Sandhofer Straße 116, 68305 Mannheim, Germany e-mail: [email protected] S. H. Ang (&) Department of Management and International Business, The University of Auckland Business School, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand e-mail: [email protected]

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R. Greiner, S. H. Ang

Collaborations have proliferated in the last two decades. As collaboration allows firms to overcome resource constraints and better focus on developing core competences, it offers significant values for biotechnology firms (e.g. Lechner and Dowling 1999; Roijakkers and Hagedoorn 2006). Both biotech–biotech as well as biotech–pharma collaborations have been found to positively influence the success of a biotechnology venture (e.g. Baum et al. 2000; Roijakkers and Hagedoorn 2006). Given that revenues often come with significant time lag within the biotechnology sector, a firm’s ability to collaborate reflects its attractiveness (Ang 2008) and good (potential) performance (Baum and Silverman 2004). Biotechnology firms are often grouped into four main business models: productfocused, technology platform-focused, hybrid, and fully integrated (Fisken and Rutherford 2002; Nosella et al. 2005; Konde 2009). The product-focused firms focus on discovering new pharmaceutical drugs. They develop these agents to a certain clinical stage with new or already known technologies before out-licensing or selling the intellectual property rights to other pharmaceutical or biotechnology incumbent firms. Technology platform-focused firms develop a set of technologies that are commercialized as products or services for different applications. Hybrid firms also focus on developing new technologies, but they engage in both the commercialization of enabling technologies and in-house drug discovery and development using own technologies. In fully integrated firms, the drugs are discovered and developed in-house right through to commercialization. Since the early 2000s, most technology platform-focused firms have augmented their business model towards the hybrid business model as the hybrid business model allows greater control of the value chain rather than merely providing enabling technologies (Rothman and Kraft 2006). The resource commitments required for the fully integrated business model also lead to its decreasing adoption. Increasingly, we observe greater adoption of the hybrid and product-focused business models, which are the focus of our study. As technology platforms (implicit in hybrid business models) are applied in the early stages of the value chain, they are unlikely to generate as much value as a potential drug in the later stages. Risks involved are also more substantial. Thus, a greater number of collaborations are needed to keep costs and risks manageable. As compared to firms adopting the product-focused model, firms that use the hybrid business model are likely to engage in more exploration collaboration as they seek to leverage on technology platforms. These linkages between business models and collaborations, which have not been examined in empirical research and in particular the biotechnology context, are tested in this study using data on 1,820 collaborations conducted by 87 US and EU-based dedicated biotechnology firms that are active in drug discovery.

2 Background and hypotheses development 2.1 Biotechnology business models Early research on business models tends to focus on a single and exclusive dimension, such as a firm’s transaction structure, alliances, revenue generation or its

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position within an industry’s value chain when describing business models. More recent literature reviews recognized the rather multi-faceted nature of business model. For an extensive literature review refer to the position paper by Onetti et al. (2010). Multiple business models co-exist in the biotechnology industry and no one single business model is dominant for success. Mangematin et al. (2003) categorized biotechnology firms into two main groups. The first group includes R&D-intensive firms, while the second group consists of (usually smaller) firms operating as suppliers of biotechnology-based services and technologies. Firms in the R&D-intensive group commit to big research programmes that produce radical innovations. They are known for adopting the product-focused business model. These firms are in direct competition with big pharmaceutical companies as they also discover and develop medicines. More recently, pharmaceutical companies acquire such firms in order to boost their product pipelines. Firms supplying biotechnology-based products and services operate in niche markets. The need to maintain profitability forces them to concentrate investments in research activities. Such business model is known as the technology platform-focused business model. While Mangematin et al. (2003) posit that firms with low R&D expenditures have little chance to become world leaders, realities seem to suggest otherwise as there have been many successful global service and technology suppliers. Invitrogen and Qiagen, for example, had revenues of US$1.3 billion and US$600 million in 2007, respectively. There are also many firms with huge R&D investments yet offer only services to other firms. These firms typically develop a complex discovery process for drug targets or leads and license the use of the platform or provide service using this platform. This type of firms adopts the technology platformfocused business model. Examples are Celera Genomics, Ciphergen, and Nanogen. Firms that adopt the fully integrated business model engage in all activities from drug development up to approval as well as the commercialization of the products (Bresser et al. 2000, pp. 2–6). The pharmaceutical industry has always been a highly integrated industry where firms discover new potential drugs, develop new leads and manufacture in-house, and finally sell products through their own distribution channels. However, the fully integrated business model is often inefficient due to the challenge of creating value in each part of the value chain, resulting in a revamp of new business models (Schweizer 2005). More recently, some firms have adjusted this model into a virtual vertical model (also known as the orchestrator model). These ventures are office-based, mainly deal with the management of value chain activities that are all outsourced (Fisken and Rutherford 2002; Schweizer 2005). As the activities involved in the fully integrated business model are extensive, few biotechnology firms possess the necessary resources to adopt this model. According to Schweizer (2005), firms adopting the product-focused business model are layer players and focus on at least one specific activity in the new drug development value chain, i.e. early research, late research, early development, late development or marketing. Firms adopting this business model do not necessarily take products through to the market but license them out at a certain stage of development to pharmaceutical or other biotechnology incumbents. The incumbents will then conduct subsequent activities of the value chain, which are usually more

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expensive. The underlying rationale of this licensing strategy is to mitigate the risks and costs of drug development (Fisken and Rutherford 2002). Nevertheless, firms using the product-focused business model still exhibit a high risk-high reward profile and are highly dependent on the capital market, in particular venture capital for financing (Fildes 1990; Cooke 2007) Firms adopting the product-focused business model tend to have shorter time span for investment returns as they are able to license out compounds in the early stages of development. Hence, they are good options for venture capital investments (Cooke 2007). Depending on the licensing strategy (early stage vs. late stage), these firms may possess drug discovery and/or development capabilities. Shire Pharmaceuticals, for example, established development and marketing capabilities for in-licensing products and has later built in-house drug discovery capabilities through merger and acquisition activities. However, most of the firms using the product-focused business model have their own drug discovery capabilities rather than relying on in-licensing products (Fisken and Rutherford 2002). Collaboration, largely with established pharmaceutical firms, has allowed these firms to further grow their businesses (Glick 2008). The hybrid business model largely evolves from the technology platform-focused business model. Firms adopting the technology platform-focused business model provide enabling technologies to support other firms mainly in early and late stage research activities rather than controlling a specific activity of the value chain. Since the year 2000, investors have shown a strong preference for firms that engage in the development of clinical compounds. This entices firms adopting the technology platform-focused business model to augment their approach to the hybrid business model (Rothman and Kraft 2006). Firms adopting the hybrid business model typically grow their businesses with revenues coming from the licensing fees of their technology platforms in the short term and secure long term revenues with drug candidates generated from their own technology platforms (Fisken and Rutherford 2002; Rothman and Kraft 2006). Table 1 summarizes the main differences between product-focused, technology platform-focused and hybrid business models.

2.2 Business model and interfirm collaboration in the biotechnology industry Extant research has highlighted numerous reasons for firms to collaborate. Strategy research suggests that firms collaborate to improve their strategic position (Kogut 1988). Transaction cost theory, on the other hand, argues that firms collaborate when transaction cost is too high for arm’s-length market exchanges but not high enough to mandate vertical integration and markets are at least as efficient as hierarchies in organizing the exchange of goods (Williamson 1975). Collaborations allow firms to acquire externally developed tacit (Inkpen 2000; Harianto and Pennings 1994) or highly specific knowledge (Carayannopoulos and Auster 2010), reduce R&D costs, and increase new product development rate (Kotabe and Swan 1995). They can also be mechanisms that insulate a firm from broader environmental threats (DiMaggio and Powell 1983). Due to these advantages that they

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Table 1 Comparison of the product-focused, technology platform-focused and hybrid business models Product-focused

Technology platform-focused

Hybrid

Risk profile

High

Low-medium

Medium-high

Value creation

High

Low-medium

High

Revenues

Medium-long term

Short term

Short term

Breakeven

Long term

Short-medium term

Medium-long term

Tacit knowledge about technology

Low

High

Moderate

General network profile

Low

High

High

Source Fisken and Rutherford 2002

offer, collaborations are increasingly being utilized in high technology intensive industries (Hagedoorn 1993), particularly in the biotechnology industry (De Carolis 2003; Powell et al. 1996). Biotech–biotech and biotech–pharmaceutical collaborations have been growing steadily in recent years (e.g. Roijakkers and Hagedoorn 2006; Stuart et al. 2007). Biotechnology firms establish collaborations to obtain complementary assets, such as product testing, commercialization, and distribution capabilities, while pharmaceutical companies use these relationships to fill gaps in their research pipelines (Deeds and Hill 1996; Amir-Aslani and Negassi 2006). Research on the performance of biotechnology firms has generally concluded that the degree of interfirm collaboration positively influences research pipelines, innovation and firm performance (Shan et al. 1994; Kotabe and Swan 1995; Deeds and Hill 1996; Baum et al. 2000; Lerner et al. 2003; Rothaermel and Deeds 2004; Xu 2006). Firms using different business models have different value chain constellations. Technology platform providers need to manage a much larger number of partners as compared to product-focused firms (Fisken and Rutherford 2002). This is because a technology platform plays an important role mainly in the early stages of the value chain hence does not bear as much value as a potential drug in the later stages. The transaction volume of a collaboration based on a potential drug might therefore substantially vary from that of a collaboration based on a technology platform. Consequently, firms adopting the hybrid business model need to manage a higher number of customers and partners to maintain their business operation and survival. Moreover, these firms also engage in licensing agreements or collaborations based on their technology platforms. In comparison, firms adopting the product-focused business model tend to concentrate their activities in specific parts of the value chain. As they generally have in-house discovery capabilities, there is less need to collaborate. Thus, it is likely that the incidence of collaboration is higher for firms adopting the hybrid business model than firms using the product-focused business model. Hypothesis 1 Firms that adopt the hybrid business model collaborate more than firms that use the product-focused business model.

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2.3 Business model and exploration/exploitation collaboration The exploration–exploitation framework was first developed by March (1991) and then refined by Levinthal and March (1993). Exploration refers to the discovery of new knowledge that may lead to innovative products, while the precursor of exploitation is the existence of exploitable resources derived from prior exploration activities (March 1991). Firms that engage exclusively in exploration may never appropriate returns from their knowledge. In contrast, firms that engage exclusively in exploitation may suffer from obsolescence (Levinthal and March 1993). Therefore, firms need to find a balance between exploitation and exploration activities to enhance their viability. There is generally an inverted U-shaped relationship between degree of exploration (or exploitation) and organizational performance (e.g. Gupta et al. 2006; Uotila et al. 2009). Biotechnology firms that use a balanced exploration–exploitation strategy in their product development efforts have been found to be more successful with more products in development and on the market (Rothaermel and Deeds 2004). Research has linked the exploration–exploitation framework to collaboration (Koza and Lewin 1998; Hagedoorn and Duysters 2002), and also specifically to collaboration in the biotechnology industry (Rothaermel 2001; Rothaermel and Deeds 2004). Koza and Lewin (1998) argue that collaboration may always be described as having exploration or exploitation objectives. The main activities and partners’ motivations largely determine if a collaboration is exploratory or exploitative. Firms typically establish exploration collaboration to explore new technological opportunities or to discover something new jointly with a partner. Hence, these collaborations inevitably have an R&D component such as the upstream activities of the value chain (basic research and drug discovery) (Rothaermel and Deeds 2004). Exploitation collaborations, on the other hand, enable firms to commercialize the technology or discovery and increase the productivity of employed assets (Koza and Lewin 1998; Hagedoorn and Duysters 2002). The activities involved in this type of collaboration may include clinical trials, regulatory processes, manufacturing, marketing or supply agreements (Rothaermel 2001; Rothaermel and Deeds 2004). Rothaermel (2001) and Rothaermel and Deeds (2004) found empirical support that the new product development path in the biopharmaceutical industry begins with exploration collaborations to discover potential drugs and ends with exploitation collaborations that lead to drugs on the market. As each biotechnology business model covers different activities within the value chain, we assume that the unique value chain constellation is reflected in the types of interfirm collaborations a firm establishes. A firm with core competencies more relevant for the early stages of the value chain will establish more exploration collaborations. The hybrid and product-focused business models are similar to each other as they both focus on drug development. According to Rothaermel and Deeds (2004), the two business models are active in both exploration and exploitation activities along the value chain. Nonetheless, firms adopting the hybrid business model tend to possess a stronger competence in the commercialization of technology platforms which are typically exploration. Hence, they should establish

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a greater proportion of exploration collaborations as compared to firms that adopt the product-focused business model. Hypothesis 2 Firms that adopt the hybrid business model establish a greater proportion of exploration collaborations than firms that use the product-focused business model.

3 Data and methodology 3.1 Sample and data The sample of this study consists of US and EU-based biopharmaceutical drug discovery firms which either adopt a hybrid or product-focused business model. The US and EU-based firms tend to have a longer history that allow us to look at interfirm collaborations over their lifetime. The online database MedTRACK categorizes over 13,700 life science companies into different industries, such as ‘‘Agricultural’’, ‘‘Bioinformatics’’, ‘‘Chemistry’’, etc. We have chosen the subcategory ‘‘Biopharmaceutical Drug Discovery’’ under the main industry heading of ‘‘Enabling Technologies’’. We also utilized the company lists provided in Fisken and Rutherford (2002), Pavlou and Belsey (2005), and Rothman and Kraft (2006). We checked whether the ventures are in line with the OECD definition of biotechnology and a biotechnology venture. We then included only those firms with their own pipeline or those that have stated their intention to develop a pipeline in the near future. A pipeline product is defined as a potential drug in preclinical phase, clinical phase 1–3, pre-approval, or any marketed drug. Based on these selection criteria, 118 dedicated biotechnology firms were included in the sample. Due to missing firm information, the final sample consists of 87 firms. Data were mainly collected from a database developed at the Ernst & Young European Biotechnology Center in Mannheim, Germany, which contains company, collaboration, pipeline and financing details of biotechnology and pharmaceutical firms. Company web sites as well as press releases were also used as additional sources of information. 3.2 Independent variable In this study, we focus on two types of business models, namely hybrid and productfocused. Using information obtained from company websites, we tracked how firms generate revenues from their product offerings. For example, firms which state that they collaboratively research and develop potential drugs by employing their technology platform, or by out-licensing their technology platform indicate the use of a hybrid business model. In comparison, firms that state that they develop new pharmaceutical drugs and clearly do not out-license their technology to collaborative partners were categorized as using a product-focused business model. We discarded cases whereby the information needed to classify business model was ambiguous. Business model is represented by a dummy variable that takes the value

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of 1 for firms that adopt the hybrid business model and 0 for firms using the productfocused business model. 3.3 Dependent variables The database MedTrack provides detailed qualitative information about collaborations including the date, the partners involved, and a detailed synopsis for each collaboration. We counted the total number of interfirm collaborations by each firm, which is the dependent variable for testing Hypothesis 1. Following Rothaermel and Deeds (2004), we coded collaborations that focus on basic research (target identification and target validation) and drug discovery (lead identification and validation) as exploration and collaborations that are targeted towards commercialization (clinical trials, regulatory approval, manufacturing, marketing and sales) as exploitation. The dependent variable for Hypothesis 2 is the ratio of the number of exploration collaborations to the total number of collaborations. 3.4 Control variables We included six control variables. Established firms tend to possess stronger reputation that attracts potential collaboration partners and hence greater collaboration activities. Firm age is measured by the number of years since a firm’s founding to 2007. If a firm was acquired in the past, we used the age as of the acquisition date. Information was obtained from the database MedTrack. A firm’s size increases its capacity to form interfirm collaborations. Larger firms might be more developed and integrated than smaller ones and thus have a more complex network of partnerships. Following previous studies in the biotechnology industry, we used the logarithm of the number of employees in 2007 as the size measurement (Shan et al. 1994; Rothaermel and Deeds 2004; Durand et al. 2008). We obtained information mainly from annual reports. For some of the private firms where we could not find the figures on their websites, we relied on a company survey dataset collected by Ernst & Young in 2007. We included a dummy variable for the firm’s geographic location. Location may influence the number of interfirm relationships as there are more US-based biotechnology firms and there are larger clusters such as the Boston region or Silicon Valley in the US as compared to EU countries. As a result, we expect to see closer networks that may lead to more interfirm collaboration. ‘‘Location’’ was coded 1 for US-based firms and 0 for EU-based firms. Going public can provide legitimacy to the firm (Meyer and Rowan 1977) that may attract potential partners. We therefore controlled for whether a firm is publicly traded (‘‘Listed’’ equals to 1) or not (‘‘Listed’’ equals to 0). On top of providing finance, venture capitalists (VCs) also make networks available to the firms they fund. These networks may have an impact on a firm’s collaboration position (Fried and Hisrich 1995; Baum and Silverman 2004). Hence, we included a dummy variable indicating whether a firm received VC support (coded as 1) or not (coded as 0). This information was retrieved from the MedTrack VC database.

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Finally, we included therapeutic pipeline as a control variable. Firms with a high number of products may have formed a greater number of collaborations to discover and commercialize these products. The stage of development of the products may influence the number of interfirm relationships as early stage products do not necessarily attract as many partners as later stage or marketed products. Hence, we assessed the strength of the pipeline by considering both the number of products and their development stage. Depending on the probability that a drug will attain marketing approval, a pipeline product received lower scores for early stage products and higher scores for later stage products. We named the probability to attain marketing approval for each phase the ‘‘phase success rate’’ and calculated it for each specific phase from the ‘‘phase transition probability’’. The phase transition probability is the likelihood that an investigational drug will proceed from one phase to the next. The phase transition probabilities for products in clinical phases 1–3 were estimated by DiMasi and Grabowski (2007) for biopharmaceuticals and by DiMasi et al. (2003) for small molecule drugs. As the pipelines in our sample contain both biopharmaceuticals and small molecule drugs, we averaged the rates from the two publications and used this average independently from the drug class. These rates are very similar to the estimations published by practitioner-oriented organizations, such as McKinsey (Tyebjee and Hardin 2004) or Evelexa (Kolchinsky 2004, p. 53). Phase success rates for preclinical products and regulatory approval are difficult to estimate so no reliable rate could be found in the literature. However, we used an attraction rate of 50 and 90 percent, respectively. Again, these estimations are in line with those published by McKinsey and Evelexa. We obtained the total number of products, the stage of development for each product, and the product name/brand as of July 2008 from MedTrack. The following stages were defined by MedTrack: ‘‘preclinical’’ (PC), ‘‘clinical phase 1’’ (I), ‘‘clinical phase 2’’ (II), ‘‘clinical phase 3’’ (III), ‘‘pending approval’’ (PA), ‘‘approved’’ (A), ‘‘on the market’’ (M), ‘‘post marketing trials’’ (PM), ‘‘discontinued’’ (D), ‘‘failed’’ (F), and ‘‘no details found (or) at research stage’’ (NA). Data from products with the status D, F, and NA were not collected as these stages do not add value to a firm’s pipeline. The allocated pipeline scores are shown in Table 2. A firm with one launched product, one clinical phase 3 trial, three clinical phase 1 trials, and three pre-clinical products would have received a pipeline score: (1 9 10) ? (1 9 5.97) ? (3 9 2.32) ? (3 9 1.16) = 26.41. This approach to assess a firm’s pipeline is consistent to the one adopted by Maybeck and Bains (2006). Table 3 shows the descriptive statistics and correlation matrix of the variables used in this study. 3.5 Statistical methods As the dependent variable in Hypothesis 1 is the total number count of collaborations established, we used the Poisson regression model for the analysis. The multiple linear regression analysis is used for testing Hypothesis 2 as the dependent variable is the proportion of exploration collaborations, a continuous variable.

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Table 2 Allocated pipeline scores Current phase

PC

I

II

III

PA

A

M

PM

Phase transition probability (%)*

50

77.35

50.25

66.35

90

100

100

100

Phase attrition rate (%)**

50

22.65

49.75

33.65

10

0

0

0

Phase success rate (%)***

11.6

23.2

30

59.7

90

100

100

100

Allocated score

1.16

2.32

3

5.97

9

10

10

10

* Phase transition probability is the likelihood that an investigational drug will proceed in testing from one phase to the next ** Phase attrition rates describe the rate at which investigational drugs fall out of testing in the various clinical phases *** Phase success rate is the probability that a drug will attain marketing approval if it enters the given phase Table 3 Correlation matrix (N = 87) Variable

Mean

SD

(1) Total collaboration

20.92

24.87

(2) Ratio of exploration collaboration

0.71

0.30

(3) Business model (4) Firm age (5) Firm size

(1)

(2)

(3)

(4)

(5)

0.62

0.49

0.32

11.79

5.76

0.45 -0.06

0.05

167.80 265.46

0.65 -0.10

0.16

0.47

0.48

0.11 -0.05

0.01

0.38 0.05

(7) Listed

0.54

0.50

0.46 -0.24

0.04

0.52 0.56

(8) Venture capital

0.75

0.44 -0.15 39.45

(8)

0.46

0.64

26.73

(7)

0.03

(6) Location

(9) Pipeline

(6)

0.04

0.09 -0.02 -0.26 0.08 -0.10 -0.22

0.71 -0.21

0.02

0.37 0.63

0.06

0.31 -0.05

| r | [ 0.182 -p \ 0.10; | r | [ 0.206 -p \ 0.05; | r | [ 0.258 -p \ 0.01; | r | [ 0.365 -p \ 0.001

4 Results The 87 sample firms were founded between 1980 and 2007. The sample comprises of 56 (64 percent) US-based and 31 (36 percent) EU-based firms. The country of origin is well distributed in the sample and does not dramatically differ from the distribution of the entire industry. The average biotechnology firm has 168 employees, is 12 years in age, has a pipeline score of 27, and entered into 21 interfirm collaborations. The US-based firms are on average 13 years in age—about 4 years older than their EU-based counterparts. Following the success generated by the US industry, the EU market emerged later and was particularly triggered by regulatory changes that allow biotechnology firms without profits to be listed (e.g. the LSE in 1992; Neuer Markt in 1997) (Fisken and Rutherford 2002). Firm size is highly identical between US and EU-based firms, with the former having an average of 171 employees and the latter having an average of 162 employees. The hybrid business model is more pre-dominant in both countries—63 percent for US-based firms and 61 percent for EU-based firms. The US has a larger share of

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Biotechnology collaborations: does business model matter? Table 4 Poisson regression results

Variable

387

Model 1

Model 2

Intercept

0.863 (0.125)***

0.348 (0.133)**

Firm age

0.008 (0.006)

0.011 (0.006)*

Firm size

0.698 (0.065)***

0.655 (0.066)***

Location

0.201 (0.062)***

0.128 (0.062)*

Listed

0.601 (0.074)***

0.639 (0.073)***

Venture capital

-0.167 (0.057)**

-0.155 (0.056)**

Pipeline

0.005 (0.001)***

Business model

One-tail tests. p \ 0.10; * p \ 0.05; ** p \ 0.01; *** p \ 0.001

Table 5 Linear regression results

Log likelihood

-538.78

-430.98

LR Chi-square (df)

1252.97 (6)***

1468.56 (7)***

Pseudo R2

0.538

0.630

Number of firms

87

87

Variable

Model 1

Intercept

0.624 (0.135)***

0.545 (0.120)***

Firm age

0.009 (0.007)

0.009 (0.007)

Model 2

Firm size

0.097 (0.085)

0.017 (0.077)

Location

-0.060 (0.072)

-0.062 (0.063)

Listed

-0.204 (0.084)**

-0.173 (0.074)*

Venture capital

0.014 (0.078)

0.038 (0.069)

Pipeline

-0.002 (0.001)*

-0.002 (0.001)

F(df1, df2)

1.80 (6,80)

5.40 (7,79)***

R2

0.119

0.324

Number of firms

87

87

Business model One-tail tests. p \ 0.10; * p \ 0.05; ** p \ 0.01; *** p \ 0.001

0.005 (0.001)*** 0.813 (0.060)***

0.282 (0.058)***

publicly listed firms while the EU has a greater proportion of private firms. The 87 sample firms established 1,820 interfirm collaborations in the 24-year period between 1985 and 2008, of which there are 1,314 (72%) exploration and 506 (28%) exploitation collaborations. Table 4 shows the Poisson regression results for Hypothesis 1. Model 1 presents the baseline model and Model 2 shows the full model. Model 2 suggests that firms adopting the hybrid business model form more collaborations than firms using the product-focused business model (b = 0.813, p \ 0.001). Hypothesis 1 is thus supported. Table 5 shows the results of the test of Hypothesis 2. Models 1 and 2 present the baseline and full model, respectively. The results show that firms using the hybrid business model form greater proportion of exploration collaborations than firms using the product-focused business model (b = 0.282, p \ 0.001), supporting Hypothesis 2.

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5 Conclusion and implications Prior research in the strategy literature has documented the value of interfirm collaboration to biotechnology firms’ competitive advantage. However, little empirical research has linked this to the adoption of different business models. This study empirically investigates the incidence of interfirm collaboration across business models in the biotechnology industry. We examine the impact of a firm’s business model, specifically either the hybrid business model or the product-focused business model on its total number of interfirm collaborations established. We also investigate the engagement in interfirm collaborations along the value chain. To this end, we distinguish between exploration and exploitation interfirm collaborations and their use by firms adopting the two different business models. We found support that collaboration engagement differs across business models and that firms adopting a product-focused business model tend to enter fewer interfirm collaborations. Firms that employ a hybrid business model also enter into a higher proportion of exploration collaborations as compared to firms adopting a product-focused business model. This business model-specific collaboration pattern is derived from the business model’s unique value chain constellation. Specifically, the main focus and increased collaboration engagement of hybrid business model firms lies in collaborations based on early stage activities within the value chain. This collaboration pattern reflects the hybrid business model firm’s strategic approach—growing the business with relatively small revenues coming from the technology platform in a short term and then securing sustainable growth with therapeutic products coming from the pipeline. 5.1 Implications Our findings have implications for executives of life sciences and biopharmaceutical firms particularly in business development for business modelling processes, and for consulting firms, venture capitalists, and other investors for valuation practices and policies. Biotechnology firms are often difficult to value as they neither have any products on the market nor any positive cash flow. Hence, stakeholders conducting valuation processes are looking for tools to value this type of firms. A large body of research has evidenced the benefits of a good network position for firms in general and new ventures in particular. Baum et al. (2000), for example, linked startup upstream or downstream alliances at the time of their founding to high growth rates. Earlier research has also shown that alliance capital is one of the three most important signals that may affect VCs’ assessment of biotech ventures (Baum and Silverman 2004). As it is difficult to value biopharmaceutical ventures, industry practitioners start to use interfirm collaborations as a benchmark to assess young biopharmaceutical ventures. This is particularly important for the valuation of target firms in mergers and acquisitions scenarios and during the due diligence process that VCs perform prior to an investment. Our results in this study imply that hybrid business model firms are generally in a better position during these valuation processes as they collaborate more frequently, which signals their attractiveness

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(Ahuja 2000; Ang 2008). For startup executives, we therefore suggest to design their firm’s business model strategically and to be aware of the advantages of the hybrid business model over the product-focused business model. Interfirm collaboration drives mergers and acquisitions activities in the biopharmaceutical industry (Carayannopoulos and Auster 2010; Haeussler 2007). Therefore, firms with a denser network are more likely to engage in mergers and acquisitions than those that lack such connections. Translating to our study’s context, this suggests that firms using the hybrid business model may be more likely to be acquired by another firm as they collaborate more frequently. This should be taken into consideration by firm executives during the business model design stage and by VCs when undertaking due diligence analysis for potential investments. Today this has particularly become important since mergers and acquisitions are increasingly the favourite exit option for investors as they provide higher returns on investment in a shorter period of time as compared to initial public offerings (Behnke and Hu¨ltenschmidt 2007). The stock market monitors a biopharmaceutical firm’s announcements and particularly responds positively to alliance announcements (Xu 2006; McNamara and Baden-Fuller 2007). Although McNamara and Baden-Fuller (2007) have included non-alliance announcements in their study as well, it implies that a publicly traded firm is valued higher when it announces recently formed partnerships more frequently. Thus, according to our results from Hypothesis 1, hybrid business model firms might be valued higher by the stock market as they are in a better position to collaborate. McNamara and Baden-Fuller (2007) have also found that in the biotechnology industry, the stock market appears to respond differently to announcements pertaining to different activities within the value chain. They were able to demonstrate that these responses peak at the end of both the exploration (before clinical trials) and exploitation (after regulatory approval) activities. Our finding from Hypothesis 2 suggests that firms adopting a hybrid business model are more able to benefit from greater involvement in exploration collaborations. The above discussions suggest that firms adopting the hybrid business model are likely to generate greater alliance capital. Such alliance capital can eventually result in better firm performance and stakeholders’ valuations. Thus, if a firm can overcome resource and capabilities constraints, the hybrid business model is probably the more desirable design in the current biopharmaceutical climate. 5.2 Limitations and suggestions for future research Despite its contribution, this study is not without limitations. Firstly, business models are dynamic and some of our sample firms might have augmented their business model from a technology platform-focused business model towards a hybrid business model in our study period. Another issue that has to be considered when taking into account the dynamism of business models is that the present study is undertaken as the industry faces a time of turbulent change, e.g. the traditional pharmaceutical industry and the biopharmaceutical industry are converging. These may affect our observation of the linkage between business models and collaborations as the collaborations are tracked over time.

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Secondly, future research should focus on the other two business models not covered in this study and also include specialty pharmaceutical firms. These specialty pharmaceutical firms emerged especially due to the emergence of biopharmaceutical ventures and as a result have unique value chain constellations. It is expected that firms adopting this type of business model are the counterparts of biopharmaceutical ventures and that they are primarily active in exploitation of potential drugs and thus will almost always establish greater exploitation collaboration. Thirdly, we have concluded that from an alliance perspective firms adopting the hybrid business model generate more alliance capital. We have however only inferred that alliance capital is beneficial. Linking the possession of alliance capital to actual firm performance and stakeholders’ valuations will deepen our understanding of business models. Acknowledgments The Ernst & Young’s European Biotechnology Center in Mannheim, Germany is gratefully acknowledged for its support of this research.

References Ahuja, G. (2000). The duality of collaboration: Inducements and opportunities in the formation of interfirm linkages. Strategic Management Journal, 21(3), 317–343. Amir-Aslani, A., & Negassi, S. (2006). Is technology integration the solution to biotechnology’s low research and development productivity? Technovation, 26(5–6), 573–582. Amit, R., & Zott, C. (2001). Value creation in E-business. Strategic Management Journal, 22(6–7), 493–520. Ang, S. H. (2008). Competitive intensity and collaboration: Impact on firm growth across technological environments. Strategic Management Journal, 29(10), 1057–1075. Baum, J. A. C., Calabrese, T., & Silverman, B. S. (2000). Don’t go it alone: Alliance network composition and startups’ performance in Canadian biotechnology. Strategic Management Journal, 21(3), 267–294. Baum, J. A. C., & Silverman, B. S. (2004). Picking winners or building them? Alliance, intellectual, and human capital as selection criteria in venture financing and performance of biotechnology startups. Journal of Business Venturing, 19(3), 411–436. Behnke, N., & Hu¨ltenschmidt, N. (2007). New path to profits in biotech: Taking the acquisition exit. Journal of Commercial Biotechnology, 13(2), 78–85. Bresser, R., Heuskel, D., & Nixon, R. (2000). The deconstruction of integrated value chains: Practical and conceptual challenges. In R. Bresser, M. Hitt, R. Nixon, & D. Heuskel (Eds.), Winning strategies in a deconstructing world (pp. 1–50). Chichester: John Wiley & Sons. Carayannopoulos, S., & Auster, E. R. (2010). External knowledge sourcing in biotechnology through acquisition versus alliance: A KBV approach. Research Policy, 39(2), 254–267. Cooke, P. (2007). The financing of biopharmaceutical firms. In P. Cooke (Ed.), Growth cultures: The global bioeconomy and its bioregions (pp. 214–243). New York: Routledge. De Carolis, D. M. (2003). Competencies and imitability in the pharmaceutical industry: An analysis of their relationship with firm performance. Journal of Management, 29(1), 27–50. Deeds, D. L., & Hill, C. W. L. (1996). Strategic alliances and the rate of new product development: An empirical study of entrepreneurial biotechnology firms. Journal of Business Venturing, 11(1), 41–55. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. DiMasi, J. A., & Grabowski, H. G. (2007). The cost of biopharmaceutical R&D: Is biotech different? Managerial and Decision Economics, 28(4–5), 469–479. DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003). The price of innovation: New estimates of drug development costs. Journal of Health Economics, 22(2), 151–185.

123


391

Durand, R., Bruyaka, O., & Mangematin, V. (2008). Do science and money go together? The case of the French biotech industry. Strategic Management Journal, 29(12), 1281–1299. Fildes, R. A. (1990). Strategic challenges in commercializing biotechnology. California Management Review, 32(3), 63–72. Fisken, J., & Rutherford, J. (2002). Business models and investment trends in the biotechnology industry in Europe. Journal of Commercial Biotechnology, 8(3), 191–199. Fried, V. H., & Hisrich, R. D. (1995). The venture capitalist: A relationship investor. California Management Review, 37(2), 101–113. Ghaziani, A., & Ventresca, M. J. (2005). Keywords and cultural change: Frame analysis of business model public talk, 1975–2000. Sociological Forum, 20(4), 523–559. Glick, J. L. (2008). Biotechnology business models work: Evidence from the pharmaceutical marketplace. Journal of Commercial Biotechnology, 14(2), 106–117. Gupta, A. K., Smith, K. G., & Shalley, C. E. (2006). The interplay between exploration and exploitation. Academy of Management Journal, 49(4), 693–706. Haeussler, C. (2007). Proactive versus reactive M&A activities in the biotechnology industry. Journal of High Technology Management Research, 17(2), 109–123. Hagedoorn, J. (1993). Understanding the rationale of strategic technology partnering: Interorganizational modes of cooperation and sectoral differences. Strategic Management Journal, 14(5), 371–385. Hagedoorn, J., & Duysters, G. (2002). Learning in dynamic inter-firm networks: The efficacy of multiple contacts. Organization Studies, 23(4), 525–548. Harianto, F., & Pennings, J. M. (1994). Technological convergence and scope of organizational innovation. Research Policy, 23(3), 293–304. Inkpen, A. C. (2000). Learning through joint ventures: A framework of knowledge acquisition. Journal of Management Studies, 37(7), 1019–1043. Kogut, B. (1988). Joint ventures: Theoretical and empirical perspectives. Strategic Management Journal, 9(4), 319–332. Kolchinsky, P. (2004). The entrepreneur’s guide to a biotech startup. Boston: Evelexa. Konde, V. (2009). Biotechnology business models: An Indian perspective. Journal of Commercial Biotechnology, 15(3), 215–226. Kotabe, M., & Swan, K. S. (1995). The role of strategic alliances in high-technology new product development. Strategic Management Journal, 16(8), 621–636. Koza, M. P., & Lewin, A. Y. (1998). The co-evolution of strategic alliances. Organization Science, 9(3), 255–264. Lechner, C., & Dowling, M. (1999). The evolution of industrial districts and regional networks: The case of the biotechnology region Munich/Martinsried. Journal of Management and Governance, 3(4), 309–338. Lerner, J., Shane, H., & Tsai, A. (2003). Do equity financing cycles matter? Evidence from biotechnology alliances. Journal of Financial Economics, 67(3), 411–446. Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic Management Journal, 14(Winter Special Issue), 95–112. Magretta, J. (2002). Why business models matter. Harvard Business Review, 80(5), 86–92. Mangematin, V., Lemarie´, S., Boissin, J., Catherine, D., Corolleur, F., Coronini, R., et al. (2003). Development of SMEs and heterogeneity of trajectories: The case of biotechnology in France. Research Policy, 32(4), 621–638. March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. Maybeck, V., & Bains, W. (2006). Small company mergers—good for whom? Nature Biotechnology, 24(11), 1343–1348. McNamara, P., & Baden-Fuller, C. (2007). Shareholder returns and the exploration–exploitation dilemma: R&D announcements by biotechnology firms. Research Policy, 36(4), 548–565. Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340–363. Nosella, A., Petroni, G., & Verbano, C. (2005). Characteristics of the Italian biotechnology industry and new business models: The initial results of an empirical study. Technovation, 25(8), 841–855. Onetti, A., Zucchella, A., Jones, M. V., & McDougall-Covin, P. P. (2010). Internationalization, innovation and entrepreneurship: Business models for new technology-based firms. Journal of Management and Governance. doi:10.1007/s10997-010-9154-1.

123

392


Pavlou, A. K., & Belsey, M. J. (2005). The therapeutic antibodies market to 2008. European Journal of Pharmaceutics and Biopharmaceutics, 59(3), 389–396. Powell, W. W., Koput, K. W., & Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology. Administrative Science Quarterly, 41(1), 116–145. Roijakkers, N., & Hagedoorn, J. (2006). Inter-firm R&D partnering in pharmaceutical biotechnology since 1975: Trends, patterns, and networks. Research Policy, 35(3), 431–446. Rothaermel, F. T. (2001). Incumbent’s advantage through exploiting complementary assets via interfirm cooperation. Strategic Management Journal, 22(6–7), 687–699. Rothaermel, F. T., & Deeds, D. L. (2004). Exploration and exploitation alliances in biotechnology: A system of new product development. Strategic Management Journal, 25(3), 201–221. Rothman, H., & Kraft, A. (2006). Downstream and into deep biology: Evolving business models in ‘top tier’ genomics companies. Journal of Commercial Biotechnology, 12(2), 86–97. Schweizer, L. (2005). Concept and evolution of business models. Journal of General Management, 31(2), 37–56. Shan, W., Walker, G., & Kogut, B. (1994). Interfirm cooperation and startup innovation in the biotechnology industry. Strategic Management Journal, 15(5), 387–394. Stuart, T. E., Ozdemir, S. Z., & Ding, W. W. (2007). Vertical alliance networks: The case of universitybiotechnology-pharmaceutical alliance chains. Research Policy, 36(4), 477–498. Tyebjee, T., & Hardin, J. (2004). Biotech-pharma alliances: Strategies, structures and financing. Journal of Commercial Biotechnology, 10(4), 329–339. Uotila, J., Maula, M., Keil, T., & Zahra, S. A. (2009). Exploration, exploitation, and financial performance: Analysis of S&P 500 corporations. Strategic Management Journal, 30(2), 221–231. Williamson, O. E. (1975). Markets and hierarchies: Analysis and antitrust implications. New York: The Free Press. Xu, B. (2006). Market differential evaluations of strategic alliances in the pharmaceutical/biotech industry. Journal of High Technology Management Research, 17(1), 43–52. Zott, C., & Amit, R. (2008). The fit between product market strategy and business model: Implications for firm performance. Strategic Management Journal, 29(1), 1–26.

Author Biographies Raphael Greiner Dipl.-Ing. Biotechnology, MBE (Master of Bioscience Enterprise) holds a current position as a sales manager with Roche Diagnostics based in Mannheim, Germany. He has experience in a variety of functions, including life science R&D, biotech industry analysis and technology transfer. His main interests are in the area of personalized medicine, business development, innovation process as well as marketing and sales strategy. Siah Hwee Ang is Associate Professor of Strategy at the University of Auckland Business School, New Zealand. He earned his Ph.D. from the National University of Singapore Business School, Singapore. His primary research interests are in the areas of corporate strategy, international business strategy, competitive dynamics and technology strategy.

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