Electronic copy available at: http://ssrn.com/abstract=1412424

Research Paper Series

“Growth and Profitability in Small Privately Held Biotech Firms: Preliminary Findings”

Maija Renko

Assistant Professor of Managerial Studies College of Business Administration

University of Illinois at Chicago

Malin Brännback, Alan L. Carsrud, Maija Renko, Ralf Östermark, Jaana Aaltonenand Niklas Kiviluoto

, (May 31, 2009). New Biotechnology, Vol. 25, No. 5, pp. 369-376, June 2009

CBA Paper Series 09-11

Electronic copy available at: http://ssrn.com/abstract=1412424Electronic copy available at: http://ssrn.com/abstract=1412424

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GROWTH AND PROFITABILITY IN SMALL PRIVATELY HELD BIOTECH FIRMS: PRELIMINARY FINDINGS

MALIN BRÄNNBACK

Åbo Akademi University, Department of Business Studies Henriksgatan 7, FIN-20500 Åbo, Finland.

E-mail: Malin.brannback@abo.fi

ALAN CARSRUD

Ryerson University, Loretta Rogers Chair in Entrepreneurship Ted Rogers School of Management, 575 Bay Street, Toronto, Ontario, Canada

E-mail: dralancarsrud@hotmail.com

MAIJA RENKO

Managerial Studies, University of Illinois at Chicago, MC 243, University Hall 2211, 601 South Morgan Street, Chicago IL 60607.

E-mail: maija@uic.edu

RALF ÖSTERMARK

Åbo Akademi University, Department of Business Studies Henriksgatan 7, FIN-20500 Åbo, Finland.

E-mail: Ralf.ostermark@abo.fi

JAANA AALTONEN

Åbo Akademi University, Department of Business Studies, Henriksgatan 7, FIN-20500 Åbo, Finland.

e-mail: Jaana.aaltonen@abo.fi

NIKLAS KIVILUOTO

Åbo Akademi University, Department of Business Studies, Henriksgatan 7, FIN-20500 Åbo, Finland.

e-mail: niklas.kiviluoto@abo.fi

Electronic copy available at: http://ssrn.com/abstract=1412424Electronic copy available at: http://ssrn.com/abstract=1412424

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Abstract This paper reports on preliminary findings on a study of the relationship of growth and profitability among small privately held Finnish Life Science firms. Previous research results concerning growth and profitability are mixed, ranging from strongly positive to a negative relationship. The conventional wisdom states that growth is a prerequisite for profitability. Our results suggest that the reverse is the case. A high profitability - low growth biotech firm is more likely to make the transition to high profitability – high growth than a firm that starts off with low profitability and high growth. Keywords: growth, profitability, life science, biotechnology, Markov chain analysis 1. INTRODUCTION Many have considered biotechnology a high growth industry with enormous profit potential (Pisano, 1997, 2006; DeCarolis & Deeds, 1999; Robbins-Roth, 1999; Wolff, 2001). New product development is extremely expensive and time consuming. While it is highly uncertain that success is the ultimate result of that process, the conventional wisdom in the industry has been that high growth will eventually turn into high profits once the firm succeeds in commercializing their products/services. This assumption was the basis for the initial rather stunning events in the early 1980s (Robbins-Roth, 1999). Genentech went public in October 1980. The stock price was listed at $35 each and went to $89 in 20 minutes, only to close at $70 the very first day. Market value grew at a remarkable rate and it was four years before a product was commercialized on the market. A number of other companies followed suit. While the emergence of modern biotechnology initiated a paradigmatic shift in pharmaceutical research and development (R&D), many seemed to believe that a new business model was also emerging. While these are remarkable accounts of firm growth these are not examples of profitable growth. In fact in most of these studies growth is rarely operationally defined. These are examples of what financial markets demand from firms’ executives – growth imperatives – growth rates in valuation that exceed shareholder expectations, and even at rates, which exceed consensus forecast rates (Christensen & Raynor, 2003). That is, these are valuations based on future expectations, not on actual earnings. The biotechnology industry has for three decades been extremely cyclical, with sudden growth rates followed by steep declines. This cyclical characteristic of the industry are again signs of unrealistic market expectations contrasted by extremely challenging product development processes, where the odds to success are minimal. To date there are still very few profitable biotechnology firms world wide (Pisano, 2006, Brännback & Carsrud, 2008). Hence the biotechnology industry seems to be one where the link between profitability and growth is particularly challenging and fragile. In fact, there are so few profitable firms that one may rightfully ask why it would be interesting at all to study the link between profitability and growth in this sector. The answer is quite simple. It is precisely because success in the biotechnology industry builds on the assumption that growth is a major precursor for

Electronic copy available at: http://ssrn.com/abstract=1412424

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profitability. However, it is not clear to this date whether this assumption is based on substantiated facts or merely the result of investors’ and scientists’ wishful thinking. Almost thirty years after Genentech went public we now know that the business of biotechnology is neither as simple as policy makers or venture capitalists wish it would be, nor as theoretically linear as academic researchers would like to believe (Pisano, 1997, 2006). Moreover, the business of biotechnology is today considered a cluster of interrelated industries, which is often referred to as the life science sector. Businesses are created around knowledge intensive scientific (often basic) research, which is growing because of the dynamic interplay of a wide array of traditional disciplines and newly emerging ones (Renko, 2006). Most studies of the biotechnology industry concern data from publicly traded biotechnology firms because such financial data is most easily available. This would mean that the vast majority of biotechnology firms, which are small and privately held, have been left unstudied. The available research reflects a reality different from that of the vast majority of smaller biotechnology firms. In this article we report on a unique study of the link between growth and profitability among small privately held biotechnology firms. We gained access to a large Finnish database (Voitto+) containing financial data of more than 90,000 Finnish firms. Using a list of 336 Finnish life science firms (www.healthbio.fi) we conducted a study among 90 firms with complete financial data sets for the years 2004-2006. Only three firms in our data set were publicly traded. The analysis shows that it is indeed difficult for firms to make a successful transition from low growth and low profitability to high growth and high profitability. Growth in academic literature and public press is generally considered to be an unconditional indication of business success. However, neither the popular press nor academics can agree on a common definition of growth. However, there is a growth imperative, which rests on the notion that growth is a precursor of profitability. At the same time recent research results show growth may not be a precursor for profitability (Markman & Gartner, 2002; Davidsson et al, 2008). Moreover, it is a well known fact that rapid growth may lead to considerable organizational challenges that can seriously constrain a firm’s ability to generate profits (Aaker & Day, 1986, Gartner, 1997) or that profitable firms may become victims of their own success when failing to grow (Churchill & Mullins, 2001, Christensen & Raynor, 2003). In fact, Markman and Gartner (2002) found no link between rapid growth and profitability. A recent study (Davidsson et al, 2008, Steffens et al, 2009) based on Swedish and Australian data, showed that firms experiencing high growth and low profitability were more likely to become low growth and low profitability firms in subsequent time periods, and less likely to become high growth and high profitable in subsequent time periods. Those firms, which experienced high profitability and low growth, were more likely to make the successful transition into high growth while maintaining high profitability. The Davidsson et al (2008) study used a very broad industry categorization; manufacturing, retailing, wholesale, business services, and other. We followed their recommendation to test their hypotheses in a narrow sample, which in our case is biotechnology or life sciences in Finland. The life science firms are classified in 12 different categories; bioIT, biomaterials, contract research

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organizations, diagnostics, drug development firms, health and nutrition, industrial biotechnology and agriculture, life science suppliers and distributors, life science suppliers and manufacturers, medical device, pharmaceuticals, and service. These 12 categories, while more detailed than the Davidsson et al (2008) study are represented in their study which allows for some degree of comparison with those results. Interestingly our results are surprisingly similar to those of Davidsson et al (2008), but we also find some significant differences. While many will claim that biotechnology is a different business and cannot be compared with other industries our results suggest that this may not be the case after all – at least when analyzing the financial data. Our findings suggest that growth may be particularly painful for small biotech firms and prevent these firms from achieving profitability. This should indeed be of major concern for venture capitalists and other investors. In the next section we will consider the biotechnology sector from the perspective of science and research and the potential limitations on a firm’s ability to grow and ultimately generate profits. We then review growth and firm performance studies in general, in particular those conducted in the biotechnology sector. After this, we report the results of our preliminary study. We conclude the current article with a discussion and suggestions for future research. 2. THE BIOTECHNOLOGY BUSINESS 2.1 The business of S, R, and D One assumption of the early biotechnology industry was that it would lead to significant improvements in drug research and development (R&D) (Pisano, 2006). This may partially explain the initial excessive stock-market expectations that later were hit with considerable disappointment when promises of product commercialization failed or were severely delayed. While the biotechnology industry has been considered high growth industry it has also been an example of a high technology industry. High technology industries are generally seen as knowledge intensive, involving complex R&D activities in developing new innovations. It is a well-known fact that product development in pharmaceuticals is R&D intensive, extremely time consuming and requiring extensive financial resources. When Genentech, Amgen and the like emerged in the early 1980s, pharmaceutical R&D was still conducted within fully integrated pharmaceutical companies – it was closed and sealed innovation (Chesbrough, 2003). The emerging new breed of biotechnology firms were firms often created and managed by a small group of university scientists occupied with small-scale protein production for R&D purposes. The goal was to make the transition into large-scale production for commercial purposes, a task in which only a few have succeeded (Robbins-Roth, 1999, Amdjani et al, 2000, Pisano, 2006). The business logic was that of open innovation (Chesbrough, 2003). Small biotechnology firms would conduct the original research and initial product development, which through license agreements would be further developed and ultimately commercialized by a large pharmaceutical firm. These small private enterprises overtook what had been the market of university laboratories. Some of these firms shared (and still do) laboratory space and equipment

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with universities (Pisano, 2006). In fact, early studies (Zucker & Darby, 1997, Zucker et al, 1998, 2002) show that both university scientists and small biotechnology firms benefited from this close proximity. Nevertheless, these firms set out to commercialize scientific discoveries that were far from validated. They were pre-research (R). They were (S) as in science, about to become (R) research inputs and later if promising to become research outputs, ready to enter development (D). These biotechnology firms were in the business of science. Research universities are by their very nature ‘in the business’ of science. But, as pointed out by Pisano (2006), private firms had entered the business of science without anyone considering what effects that would have on profitability, productivity, and growth. What consequences would that have on business models or revenue models? Moreover, science is not a homogenous concept. The science behind a clean fuel burning car engine or a microchip is far different from the science of human biology. In the two former, there are very few unknowns. But, while our knowledge of human biology is extensive, the unknowns most likely outnumber the knowns; “…decisions must be made in the fog of limited knowledge and experience. Mistakes are common, not because people or firms are incompetent, but because they are constantly dancing on the edge of knowledge.” (Pisano, 2006, p. 12) In other words, considerable uncertainty is connected to the field still to this date Biotechnology remains a business that creates science and tries to capture value from that science. Biotechnology firms are in fact S,R&D firms. For this reason it is even reasonable to assume that the traditional estimation of the R&D process of 12-15 years may, when accounting for the S-factor, increase. This certainly will have consequences for productivity and ultimately profits. Not all biotechnology firms are deeply involved in scientific discovery. Some organizations will have a business model, which resembles much more that of a service firm, e.g. contract research organizations (CRO) with activities are a mixture of development and service. Other biotechnology firms may operate in sectors that do not have to meet the rigorous quality standards required for human use products. In industrial biotechnology or agriculture there may be shortened time to market and cash flow. Thus, we have biotechnology firms with an emphasis of R and firms with an emphasis on D and pure service firms (such as firms offering legal counsel). Hence it is reasonable to assume that the strategic orientation may impact profitability, productivity and growth. 2.2 Different tiers of firms We have noted that there are differences between biotechnology firms with respect to the nature of their activities. Moreover we have noted that there are publicly traded firms and private firms and that most studies have tended to focus on the former leaving the latter largely under researched. Wolff (2001) classifies biotechnology firms in four tiers. The first represents firms that have established records of earnings and have market valuations over US$5billion; these are only a few (Pisano, 2006). The second tier represent firms that have not yet established a meaningful revenue stream, but have begun to sell to the market place, and have a market capitalization of above US$2billion. The third tier consists of firms who have a market value of US$800million or above, but have yet to sell commercially. Firms in this tier have a

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promising near-term pipeline and/or a credible R&D effort for treatment development. Most, if not all of these firms are publicly traded firms with all of the reporting mechanisms that this status entails (Carsrud et al, 2008). The fourth tier consists of firms with market values under US$800million. Carsrud et al (2008) argue there is yet a fifth tier consisting of firms, most of which are privately held, with a market value less than US$25 million. Most of these firms lack a readily identifiable market, have very limited or instable sales revenues, and leadership teams often bereft of management, finance, and strategic planning skills. What they have are scientists. Because these are privately held in the US it has not been possible to obtain financial data to conduct valid research. Yet, there is an urgent need to study these firms to provide a deeper understanding of their challenges and thereby enable the development of effective support mechanisms. 2.3 Different kinds of growth Growth is often in the literature treated as linear proceeding neatly from one stage to the other (Birley & Westhead, 1990). Yet there is evidence, which suggests that firm growth is a multi-stage phenomenon and not linear (Churchill & Lewis, 1983, Kazanjian, 1988). Moreover, firms may choose to pursue different growth strategies; organic or through mergers and acquisitions (M&A). Organic growth appears to be the predominant form of growth for young and small firms (Davidsson et al, 2002, Wiklund & Davidsson, 1999), which require entrepreneurial capabilities and financial strength (Penrose, 1959). While M&A is often seen as an exit strategy (Renko et al, 2009) it is also a valid growth strategy, albeit among more mature firms driven by managerial slack (Penrose, 1959). Among biotechnology firms M&A is often seen as an exit strategy. But, suppose we take the view of M&A as a growth strategy rather than an exit strategy, M&A becomes a way for a small science-based firm in financial distress to ensure continued growth of its knowledge base. M&A becomes a growth strategy resembling that of reincarnation (Brännback et al, 2008, Renko et al 2008). A recent study (Renko et al, 2008) shows that the technological knowledge base alone is not sufficient indicator of a future M&A. The study shows that the higher the market knowledge of a biotechnology firms the more likely it will be involved in M&A. Nevertheless, the same study also shows that there is a strong positive association between technological knowledge and capital investment in the firm. There is a strong positive relationship between technological and market knowledge and the number of innovations licensed out by the firm. Another study (Renko et al, 2009) shows that market knowledge appears to moderate the relationship between superior technology-based innovations and financial performance. Thus, M&A in biotechnology should not be seen as an exit strategy due to failure but as a valid growth strategy ensuring the continued development of critical scientific knowledge. This is completely in line with Penrose’s (1959) conclusion that the growth of a firm’s knowledge base was necessary prerequisite for firm growth. 3. GROWTH AND PROFITABILITY Firm growth and profitability have attracted massive research interest for several decades. In a search of top three entrepreneurship journals, one on economic policy

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and two management journal1 with search words growth, profitability, performance, high technology, and biotechnology, separate and in combinations, and removing all duplicates, 1258 articles were found between 1990 and 2008. It is not surprising to find a large amount of articles as growth and profitability form the very essence of business practice. A firm that does not grow will eventually cease to generate profits and a firm incapable of generating profits will ultimately cease to exist. Moreover, there is a close association between firm growth and success, with the notion that all growth is profitable. Yet, there are numerous examples of unprofitable growth (Churchill & Mullins, 2001). This in turn has created a bias towards studying only successful growth oriented firms (Penrose, 1959, Dess et al, 1997). Despite this massive research interest, results on the relationship between growth and profitability are inconclusive. Some researchers have found very strong and significant relationships and others have found weak or no relationship at all (Shuman & Seeger, 1986; Hart, 1992, Markman & Gartner, 2002, Davidsson et al, 2008). Nevertheless, the overwhelming majority seems to agree that growth is a precursor for profitability and that high-growth markets are more attractive due to the profit potential (Aaker & Day, 1986, Capon et al, 1990). Growing markets offer opportunities but also risks and challenges. Aaker and Day (1986) argued that whether a market could be considered high growth was an insufficient indicator of its attractiveness. The real issue was whether a firm could exploit the opportunities in the market to gain competitive advantage. Barney (1997) argued that a firm’s competitive advantage was determined on the firm’s resources that had to be valuable, rare, inimitable and organized. With respect to biotechnology the first three requirements can be secured through patents and proprietary scientific knowledge. Davidsson et al (2008) interpret organized as the existence of a business model with an efficient revenue model. In other words high growth markets are attractive only if a market presence is profitable. If the research results are inconclusive there is yet another important issue which shows great variance; the measurement of growth and profitability. Studies of growth and profitability show considerable variation in the choice of variables, calculation of growth, subjective or objective measures used, absolute or relative measures used, and varying measurement periods, thus rendering comparisons difficult if not impossible (Delmar, 1997). In some studies growth and performance are treated as synonyms (Birley & Westhead, 1990), but as this study will show; they are not the same. Growth per se is sometimes considered an indication of success irrespective of whether the growth is profitable or not. Nevertheless, success is generally conceptualized in terms of superior financial performance (Renko et al, 2009, Roure & Keeley, 1990). 4. THE STUDY Pisano (2006) accounts for the rather depressing overall economic performance of the biotechnology sector, where profitability is at best at zero if not below. While these results are based on publicly traded US-based firms they also offer further

1 A literature search was conducted of Journal of Business Venturing, Entrepreneurship Theory & Practice, Journal of Small Business Management, Research Policy, Strategic Management Journal, and Journal of Academy of Management.

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justification and legitimacy to investigate the link between profitability and growth in this industry. These results also underscore the necessity to study privately held firms. We report on preliminary findings of a larger study of the relationship between growth and profitability in the biotechnology sector. As pointed out we use the Davidsson et al (2008) study as starting point for testing the same hypotheses in a narrow sample. The relationship between growth and profitability is displayed in a 2x2 matrix where firms are categorized as above or below industry average on each dimension. Thus, four categories are created; Star (high profitability, high growth), Profit (high profitability, low growth), Growth (low profitability, high growth), and Poor (low profitability, low growth) (Figure 1).

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Insert Figure 1 about here

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The tested hypotheses:

H1: firms with high profitability and low growth have a higher probability to reach a state of high growth and high profitability in subsequent periods than are firms that first show high growth and low profitability. H2: firms with high growth and low profitability have a higher probability to reach a state of low profitability and low growth in subsequent periods than firms that first show high profitability and low growth.

The above mentioned study by Davidsson et al (2008) is based on Swedish and Australian data across multiple industries, firm size, and firm age using state transition matrices and standard z-tests. The Swedish data covered the years 1997-2000 while the Australian data covered 1995-1998. The hypotheses were tested over a 1 year transition period as well as longer 2-year to 3-year transitions. Results are surprisingly strong showing that a firm with high profitability and low growth is more likely to reach a state of high growth and high profitability in subsequent periods than are firms that first show high growth and low profitability. Moreover, their results show that firms with high growth and low profitability are more likely to reach a state of low profitability and low growth in subsequent periods than firms that first show high profitability and low growth. Because of unique access to financial data on privately held firms, the Finnish life science cluster; the Finnish HealthBio (www.healthbio.fi) was used as the population frame. We identified a total of 336 firms in 12 different categories (BioIT, biomaterials, diagnostics, medical device, pharmaceuticals, drug development, health & nutrition, services, life science manufacturers, life science distributors, agriculture, and contract research organizations). Firms founded after 1990 with less than 250 employees were included in the study. Firms with more than 250 employees are categorized as large firms within the EU. Moreover the firms had to have uniform accounting standard financial data for the years 2004-2006. The data was sourced

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from Voitto+, the most extensive financial statements database in Finland containing the newest financial statements data and key ratios of over 90,000 companies. Thus, 90 firms met our criteria and were included in the study’s sample. This represents 26.8% of all Finnish biotechnology firms. 4.1 Measures

Davidsson et al (2008) use sales growth as the growth measure and return on assets (ROA) as profitability measure. In this study we used growth in sales as the growth measure. More specifically, we used a relative measure of change in sales turnover, which indicates an increase or decrease in sales in relation to the previous financial year. We used earnings before interest and taxes (EBIT), i.e. relative operating profit, as our profitability measure. EBIT is used as it gives an indication of whether the business model is able to generate revenues – that is, operating profit (Bodie et al, 2004, p. 452). The ratio indicates the result of the company before financial items and is used for indicating the success of a firm’s business activities taking into account differences between lines of business. It is a ratio of results for the operation over sales turnover. We did not use ROA as our sample included service firms, which rarely have any substantial assets. Moreover, EBIT is according to accountants and auditors a more valid measurement of whether revenues are generated from actual business performance. As we also had data on all the firms’ quick ratios and solvency, we could conclude that firms in our sample were not without funds. They clearly had money. The question was whether those financial resources were generated from business operations (profit), capital loans, research grants or venture capital funding streams. Employment growth was not used here because that measure was not sufficiently recorded in our database. Moreover, in Finland it is very difficult to lay off personnel, which means that there is reluctance by firm management to employ new personnel, even when necessary. Rather than exposing themselves to employment legislation, small technology firms often access human resources through collaborative arrangements with universities, non-profit organizations, various types of outsourced consultants, and independent contractors. These practices make the number of employees an unreliable measure for growth. Demographically, the average age of firms in our sample at the beginning of the measurement period 2004-2006 was 7.5 years. Only 8 firms in the sample had been founded after 2000. Hence the demographics of our sample are very different from those of the Davidson, et al. (2008) sample and most other biotech studies.

4.2 Analysis

Davidsson, et al (2008) studied the proportion of firms moving from one stage to another. In this study we use Markov chain analysis, which is a more robust approach to estimate the transition probabilities between the states over consecutive time periods. Mixed Markov Latent Class (MMLC) models make statements about transitions from one point in time to another. The basic nature of the models is

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discrete. Thus, they do not describe the process of change between occasions, as continuous time models do (Langeheine, 1988; Aaltonen & Östermark, 1998). While Markov models have been widely used in social sciences to analyze behavioral panel data (Langeheine, 1988), Markov chain analysis has rarely been applied to accounting information. Its usefulness hinges on the validity of the Markov property for financial information. That is, the description of the present state fully captures all the information that could influence the future evolution of the process (Meyn, 2007). Future states are reached through a probabilistic process, a feature in firms facing domestic/foreign competition, unknown future regulations and crucial world events (e.g. a financial crisis). A Markov chain of order (memory) m - for example, a sequence of yearly financial statements, can be transformed (by a change of variables) into a process having Markov properties. The approach has also been probed intensively in the literature on market efficiency and time series analysis (e.g., Niederhoffer & Osborne, 1966; McQuenn & Thorley, 1991). In business studies, the applications range from market structure analysis and brand switching behavior (e.g., Grover & Srinivasan, 1987; Jain & Rao, 1994) to estimation of loan losses from a portfolio of mortgages (Betancourt, 1999) and the modeling of financial success of listed companies (Aaltonen & Östermark 1998). A finite Markov chain is a discrete-step process that at any step can be in one of a finite number of conditions, or states. If the chain has n possible states, it is said to be an nth-order chain. At each step the chain may change from its state to another, with the particular change being determined probabilistically according to a given set of transition probabilities. Thus, the process moves stepwise but randomly among a finite number of states. By definition an nth-order Markov chain process is determined by a set of n states {S1, S2,..., Sn} and a set of transition probabilities τij, i = 1,2,...,n, j = 1,2,...,n. The process can be in only one state at any time instant. If at time t the process is in state Si, then at time t + 1 it will be in state Sj with probability τij. The transition from time point t to t + 1 can be described in matrix form as shown in Figure 2.

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Insert Figure 2 about here

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Let δik denote the initial proportion of events in state i at time point t, and 1 tt

i | j

represent the probability for an event in state i at time point t, to be in state j at time point t + 1. Then the probability for an event to follow the path Si -> Sj -> Sk, Pijk is achieved by a simple multiplication

. = P 32j |k

21i | j

1ik j i (1)

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Testing for stationarity of the transition probability matrix is essential in Markov chain based random walk tests. If the transition probability matrix is not stationary over time, then the Markov chain will have no predictive power (Tan & Yilmaz, 2001). Given the transition probabilities and the current condition of a firm, we compute the probability of the firm to follow a given path over multiple time periods, e.g. a path of financial success or financial distress. In order to estimate the transformation probabilities, the observed transformation path is first defined for each company. We have four potential outcomes of {profitability (p), growth (g)}: {Low-Low (Poor), Low-High (Growth), High-Low (Profit), High-High (Star)} for a company at time period t and – given a fixed outcome at t - again the same four potential outcomes at time t+1. With three financial periods (04-06) we arrive at 43 = 64 potential paths of {p,g}. For example, a company indicating below median growth and above median profitability in 2004-05 and above median in both dimensions in 2006 was defined to have followed the path Profit-Profit-Star and a company showing below median values in both dimensions all three years was defined to follow the path Poor-Poor-Poor. The transformation probabilities were then estimated based on the frequency distribution of the observed 90 cases over the 64 potential transformation paths applying the Panmark software of Van de Pol, et al (1991). 5. RESULTS

Results of the time homogeneous transitions from 2004 to 2006 are presented in Table 1 and Figure 3. The transition probabilities for the time-homogeneous Markov-model are significant at the 10% level - the transition coefficients are nonzero with a probability of roughly 90%. The obtained significance level is presumably linked to the small sample size, implying a need for further research. The significance level is calculated by the software Panmark using the estimates of transition coefficients and the standard errors and is thus determined by our data. The probability levels for the Likelihood Ratio and Pearson Chi square tests are respectively 0.51 and 0.66, indicating an adequate model. The transition probabilities indicate a clustering in two main categories: {star, profit} and {growth, poor}. The probability for a firm in the former category to remain there is over 70%. A firm in the latter category has a probability of 56-60% to remain there. In fact, a firm focusing on profitability will remain profitable or switch to a star with probability exceeding 80%. A growth-firm has a lower probability to switch into a profit or star than has a poor firm. This evidence suggests that emphasizing growth may be more destructive for a low-profit firm than a more balanced strategy.

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In general, a firm in one category is most likely to remain in ‘its’ category. Should a transition occur we find that our results concur with Davidsson et al (2008). In some instances results are even more pronounced. Profitable firms have nearly a twice as

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high probability than Growth firms to make the transition to a Star firm. A Growth firm in our study has a more than three times higher probability to perform worse and thus becoming a Poor firm. 6. DISCUSSION This explored the relationship between the growth and profitability based on financial data alone. At this point we were not interested in potential explanatory factors, such as strategic orientation, amount of venture capital investments or types of financial instruments exploited by the firm, firm age, etc. These results are preliminary and do not allow for far-reaching generalizations. However, it is obvious that further extensive research is mandated. It is necessary to conduct further research with larger data sets, preferably life science companies across different countries. Moreover, it will be necessary to investigate the impact of explanatory variables that were excluded in this study. In a way this study can be seen as ground zero – a starting point. This study clearly shows that growth and profitability are two distinct concepts and cannot be used as synonyms. The results also underscore the necessity to understand the relationship between firm growth and profitability not only from firm perspective, but also from that of venture capitalists, governments, as well as academic research. Understanding whether profitability drives growth – or vice versa – is important for managerial goal setting as well as for the selection of appropriate dependent variables in research on strategic entrepreneurship. Understanding the relationship is equally important for those in charge of determining support structures for emerging enterprises such as venture capitalist and public policy makers. Especially in fields like biotechnology, where development cycles are long and outcomes uncertain, company growth is often perceived as a positive signal of firm performance in the absence of other indicators, such as profitability. Our empirical results support Davidsson et al (2008). A high profitability - low growth biotech firm is more likely to make the transition to high profitability – high growth than a firm that starts off with low profitability. Also, a biotech venture that demonstrates high growth but low profitability is less likely to become a profitable or “Star”-firm than is a firm that demonstrates both low growth and low profitability. The findings suggest that the process of growth may be a particularly painful one for young biotech firms. Growth may actually prevent such firms from achieving profitability. This should be of concern to venture capitalists. Since our empirical results come from the biotech sector where profitability has typically been suggested to follow growth our results add particularly strong support to the findings of Davidsson et al (2008). Our results also suggest that previous growth is for most firms only a poor guide to future performance. On this evidence, policies based on backing fast growing ventures seem likely to be inefficient at best. A better, albeit a more complex approach, may be an assessment of the internal resources, capabilities and market potential of each company. Interestingly, this can be obtained from financial data as indicated in this study.

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While it is important to analyze financial data in detail, which in this case is secondary data, it will be important to collect survey based primary data. In particular it would be necessary to survey biotech or life science entrepreneur on their perceptions of the relationship of growth and profitability, their experienced challenges. Moreover, it would be necessary to understand how entrepreneurs set goals with respect to growth and profitability, what factors the entrepreneur sees as growth-drivers and profit-drivers. Finally, while Markov chain analysis can estimate the probabilities for transitions from one stage to the other, this does not only impact our understanding of success, but equally well has implications for advancing our understanding of firm failure.

REFERENCES

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Figure 1: Categorization schema of firms by growth and profitability (Davidsson et al, 2008, p. 5)

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Figure 2. A matrix of transition probabilities

S1 S2 ... Sj ... Sn

S1 τ11 τ12 ... τ1j ... τ1n

S2 τ21 τ22 ... τ2j ... τ2n

: : : : :

Si τi1 τi2 ... τij ... τin

: : : : :

Sn τn1 τn2 ... τnj ... τnn

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Table 1. Time homogenous transition matrix (transition probabilities with standard errors in parentheses). Indicator t Indicator t+1 Star Profit Growth Poor Total Star (St.error)

0.519 (0.068)

0.204 (0.055)

0.074 (0.036)

0.204 (0.055)

1.000

Profit 0.298 (0.071)

0.489 (0.076)

0.021 (0.021)

0.191 (0.057)

1.000

Growth 0.179 (0.061)

0.103 (0.049)

0.385 (0.078)

0.333 (0.075)

1.000

Poor 0.200 (0.060)

0.111 (0.047)

0.348 (0.070)

0.370 (0.071)

1.000

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Figure 3: Time homogenous transformation profile (the thicker the lines the higher the probabilities) t t+1

Star Star

Pro fit

Pro fit

Gro wth

Gro wth

Poor Poor