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Firm-specific resources and wealth creation in high-technology ventures: evidence from newly public biotechnology firms.

Given the tremendous amount of entrepreneurial activity in the United States and its importance to the national economy in terms of growth and employment, it is no wonder that so much discussion and empirical research is devoted to understanding the performance of new ventures. The determinants of new venture success have been associated with industry structure (Porter, 1980), amount of

capital initially invested (Chaganti, DeCarolis, & Deeds, 1995), and motivation of the founder (Chaganti et al., 1995). Recent reviews of research in the field of entrepreneurship have called for the field to focus on the development of causal models of new venture performance (Bull, 1995; Cooper, 1995; Low & MacMillan, 1988; VanderWerf, 1989).

In addition to examining the determinants of new venture success, there is also some debate concerning what constitutes success in small firms. Accurate and appropriate measurement of performance is critical to the validity and value of entrepreneurship research. In recent years several authors have examined the definitions of performance used in entrepreneurship research (Brush & VanderWerf, 1992; Kunkel & Hofer, 1991; Murphy, Trailer, & Hill, 1996). The results of these studies have indicated that there is little agreement among researchers on the definitions of successful performance for new ventures and the variables used to measure performance. In fact, the most recent study concludes that "most of the research did not justify the performance dimensions included" (Murphy et al., 1996). This situation poses a significant impediment to the advancement of the study of entrepreneurship.

In this paper we address both of these issues. First of all, we argue for the use of a relatively new measure of success for entrepreneurial firms called Market Value Added. We suggest that this measure is superior to other measures of new venture performance because it focuses on the wealth created by the firm. Second, we propose a theoretical model of the determinants of success in entrepreneurial firms based on the resource-based perspective of firm behavior. The model developed suggests that wealth creation is a function of firm-specific capabilities. Finally, we test our model with both absolute and relative Market Value Added as dependent variables on a sample of 89 biotechnology firms.


In order to justify a performance measure for entrepreneurial firms, we look to the definition of entrepreneurship itself. Entrepreneurship has been defined as the dynamic process of creating incremental wealth (Ronstadt, 1984); as the creation of an innovative economic organization for the purpose of gain or growth under conditions of risk and uncertainty (Dollinger, 1994); as profiting from bearing risk and uncertainty (Knight, 1921); and as the endowing of existing resources with new wealth-producing capacity (Drucker, 1985). The common ground of these definitions lies in the goal of wealth creation. While entrepreneurs may create organizations for reasons other than economic gain, such as personal challenge or lifestyle choices, from an economic perspective, the goal of entrepreneurship remains the creation of wealth through innovative activity. Therefore, a measure of the performance of an entrepreneurial firm should attempt to quantify the amount of wealth created by the endeavor.

Accounting Measures

Several measures of new venture success are available to the researcher. The standard accounting measures of firm performance have been shown to be only weakly correlated to firm value (Rappaport, 1981; Stewart, 1991). In the case of firms in the developmental stage, these measures are almost meaningless due to large accounting losses stemming from heavy expenditures on R&D and organization building. This is particularly true in R&D-intensive industries such as biotechnology, because under standard accounting practices R&D and organization building are treated as expenses, rather than investments in the development of critical assets. Under these circumstances, it is apparent that accounting-based measures of performance fail to accurately reflect the amount of shareholder wealth being created by a firm.

Growth Measures

Measures of growth, in particular sales growth or employee growth, also have been used extensively in the entrepreneurship literature (Murphy et al., 1996). However, while growth may be an important goal for an entrepreneurial firm, it does not take into account profitability or returns to shareholders and is independent of the risk of the company. In fact, recent analysis has found a negative correlation between sales growth and the performance measures of earnings per share, return on equity, and return on investment (Murphy et al., 1996). If the measure of an entrepreneurial endeavor is the creation of new wealth, growth in sales or employees is at best a weak proxy.

Subjective Measures

Subjective assessments of firm performance by the owner or manager of new ventures are yet another category of performance measures available. However, Sapienza, Smith, and Gannon (1988) found no correlation between subjective measures of performance and sales growth or return on sales. Since these measures are multiple measures, their comparability depends upon how the index was weighted (Cooper, 1995). Moreover, the variation in the level of expectations among entrepreneurs makes comparisons across studies difficult (Cooper, 1995). Therefore, while potentially attractive for assessing performance of very young start-ups, subjective measures of new venture performance are very problematic.

Market-Based Measures

Market-based measures of success are also widely used to gauge performance. In the case of newly public firms the standard measure has been the market value of the firm (Downes & Henekel, 1987; Ritter, 1984; Titman & Trueman, 1986; Trueman, 1986). Market value has several significant advantages as a measure of wealth creation. First, it represents the present value of the firm's future earnings potential (Rappaport, 1981; Fama, 1976). Thus, a company's ability to sustain long-term competitive advantage is reflected in its market value. Second, market value is an objective measure of firm performance that captures all available relevant information about a company including the value of the firm's intangible assets. Third, market value is the mechanism used by stockholders to assess managers' actions (Fama, 1976).

However, as a measure of new venture success, market value fails to account for the amount of capital invested in the firm. This is a significant drawback. Consider the following example. Organization A has invested $50 million in its operations and has a market value of $100 million. Similarly, Organization B also has a market value of $100 million but a total investment of $1 million. If we based new venture success strictly on market value, then Organization A and Organization B would both be equally successful. However, entrepreneurs, investors, and venture capitalists would all clearly consider the latter firm to be more successful than the former. If we are attempting to measure the success of an entrepreneurial firm this situation is simply unacceptable.

Tobin's Q

Tobin's Q, which is the ratio of the market value of the firm (debt + equity) to the current replacement costs of the firm's physical assets (property, plant, equipment, and inventories), has been used by financial economists as a measure of a firm's growth opportunities (Tobin, 1969; Lindberg & Ross, 1981). However, in the case of a research-intensive start-up very little of the firm's investment will be in tangible property, but rather in expensed items such as R&D. Therefore, Tobin's Q poses the same problem as market-based measures because it is limited to tangible assets and therefore underestimates the level of investment in the venture.

Market Value Added

An alternative method of measuring firm performance has been developed by Stewart (1991) - Market Value Added (MVA). MVA is an attempt to overcome the weaknesses of both accounting and market-based measures. The basis for MVA begins with the concept of free cash flow (FCF) first developed by Modigliani and Miller (1958). FCF is essentially cash from operations that is available to lenders or shareholders. MVA is an attempt to estimate the FCF generated by a company by adjusting the distortions created in the accounting system. Stewart developed the system by examining the underlying cash implications of bookkeeping adjustments to earnings, such as goodwill amortization, deferred taxes, LIFO Reserve, and R&D expense. While these adjustments are considered expenses under General Accepted Accounting Principles, these items bias a true economic valuation of the firm based upon the FCF Model (Stewart, 1991).

MVA emphasizes the amount of net or actual wealth that has been created by the organization by considering contributed capital in the evaluation of new venture performance. If a firm's market value falls below the amount invested in the firm, then shareholder wealth has not been created. Wealth is created only when the value of the firm exceeds the amount of capital that has been invested in the firm. Therefore, to measure the absolute amount of wealth created by a firm at a given point in time requires the total value of the capital contributed to the firm be netted out from the market value of the firm's equity. Equation I presents the calculation.

[MVA.sub.t] = [MV.sub.t] - [C.sub.t] (1)

Where: [MVA.sub.t] = Market Value Added at time t

[MV.sub.t] = Market Value of the Firm at time t

[C.sub.t] = Value of the Capital Invested in the Firm at time t

However, investors, while interested in the absolute amount of wealth created by a firm, are generally equally concerned about the return on their investment. Therefore, a measure that captures the amount of wealth created relative to the total investment would also be an appropriate measure of new venture performance. This concept is captured by dividing MVA by the capital invested in the firm. This measure is referred to as the Relative Market Value Added (RMVA).

In this paper, MVA and RMVA are measured at the end of the first day of public trading of the firm's equity. MVA represents the cumulative measure of the stock market's assessment at a particular time of the net present value of all of a company's past and planned capital projects (Stewart, 1991). RMVA is a measure of the return the firm has earned on the capital provided by its investors. The value of the capital invested in the firm is the company's total assets at the time the measure is employed less non-interest-bearing current liabilities plus certain equity equivalent accounting reserves (bad debt, LIFO, goodwill amortization, R&D, unusual losses). In the case of high-technology ventures, the critical addition is the inclusion of depreciated R&D and unusual losses. MVA and RMVA measure how successful the company has been up to that time at investing capital and how successful the market believes it is likely to be in the future. Essentially, MVA and RMVA measure how much entrepreneurial profit(1) the firm has created (MVA) and the rate of wealth creation (RMVA).

MVA has generated a significant amount of interest in the corporate community and in the fields of finance and economics (Armitage & Jog, 1996; Burkette & Hedley, 1997; Greene, Stark, & Thomas, 1996; Grant, 1996; O'Hanlon & Peasnell, 1996; Lee, 1996; Lehn & Makhija, 1996). A survey by the Manufacturer's Alliance found that more than 30% of senior executives who responded had adopted the use of MVA (Christinat, 1996). A recent study of 241 large U.S. companies between the years 1987 and 1993 by Lehn and Makhija (1996) has found that MVA is significantly positively correlated with stock price performance and a Herfindahl index measuring corporate focus and significantly negatively correlated with CEO turnover. In fact, while MVA was significantly related to CEO turnover and corporate focus, traditional accounting measures of ROA and ROE were not related to CEO turnover and ROS and ROE were not related to corporate focus. These results led the authors to conclude that "MVA are effective performance measures that contain information about the quality of strategic decisions and serve as signals of strategic change" (Lehn & Makhija, 1996, p. 37).

MVA is limited by the information contained within the companies' accounting statements. The major limitation this presents is that the timing of investments into the company cannot be tracked. Therefore, no consideration is given to the time value of money. Under these circumstances MVA is likely to favor older companies. Therefore, when using MVA one must control for the age of the firm.

Newly public firms present a unique opportunity to apply MVA/RMVA and to assess the impact of the strategies followed by the entrepreneurial managers on new venture performance. In new ventures, MVA/RMVA can be used as a market-based measure of how much wealth has been created by the firm and its return on invested capital during the period from inception to going public. Of course, if a new venture succeeds in issuing an IPO it has achieved a certain level of success. Yet, the performance of all newly public firms is far from equal. For example, in our sample of 89 new public biotechnology firms, the amount of shareholder wealth created as measured by MVA varied from a low of $1.6 million to a high of $318 million and RMVA varied from 0.1 to 13.9. The spread in the amount of wealth created in this sample leads to our research issue, which is, what accounts for the differences in the amount of wealth created by new ventures? The next section develops a model of entrepreneurial wealth creation based on intrinsic firm capabilities.


The resource-based view of the firm (Penrose, 1959; Barney, 1991) proposes that a firm's ability to create wealth is largely determined by its unique capabilities. Firm success or failure is not entirely dependent upon industry structure, but rather a function of the resources and capabilities controlled by the firm, deployed by managers, and developed and extended by the organization (Schendel, 1994). A basic premise in this theory is that those firm capabilities that are rare, inimitable, and difficult to trade form the basis for sustainable competitive advantage (Barney, 1991). Subsequent researchers have highlighted the importance of intangible resources such as knowledge and scientific capabilities to competitive advantage (Deeds, DeCarolis, & Coombs, 1997; Henderson & Cockburn, 1994; Kogut & Zander, 1992; Petraff, 1993). These capabilities are usually difficult to observe, quantify, and measure, making the study of organizational capabilities difficult.

However, firms 'going public' provide researchers with a unique opportunity to study the relationship between the performance of the entrepreneurial firm and organizational capabilities. The implications of the resource-based view is that new venture performance will be dependent upon the ability of the venture to develop resources and capabilities that are rare, inimitable, and difficult to trade. During the process of going public the firm provides detailed information about the key personnel, firm strategies, and resource endowments.

Within the context of the biotechnology industry new ventures face a hostile environment in which numerous new firms, as well as a cadre of large well-financed pharmaceutical companies, race to develop new drugs or diagnostics. In most cases these firms are years away from any significant revenue stream, have very few tangible assets, are sustaining significant accounting losses, and are desperate for capital (Burill & Lee, 1992). In essence, most of these firms have little more than the talent and skills of the individual members of the firms. Thus, their research capabilities are their only valuable assets, as these capabilities represent the potential to develop and deliver state-of-the-art billion-dollar drugs. This leads to our basic premise that in the biotechnology industry the quality of the firm's scientific and research capabilities is a critical determinant of the wealth created by the firm. The following sections outline a model using specific variables that capture these scientific and research capabilities in biotechnology firms.

Geographic Location and Wealth Creation

Knowledge creation in the form of process and product innovations do not occur in the isolated confines of a firm's research and development department. External sources of knowledge are critical to innovation. This is evidenced not only at the national level, as in the case of Japan (Mansfield, 1988; Rosenberg & Steinmuller, 1988), but also at the industry level, as illustrated in the cases of computers (Brock, 1975), aluminum (Peck, 1962), and semiconductors (Saxenian, 1990). In fact, March and Simon (1958) have suggested that "borrowing" is the catalyst for innovation, not "invention." Innovation then, to a large extent, is dependent on a firm's ability to absorb information from the external environment (Cohen & Levinthal, 1990). Consequently, the physical location of a firm may serve to enhance innovation through communication flows. Close proximity of organizations with similar interests promotes the natural exchange of ideas through both formal and informal networks established among the organizations.

The idea that location matters to competitive advantage is not new (Marshall, 1920) and is receiving renewed attention (Almeida & Kogut, 1994; Jaffe, Trajtenberg & Henderson, 1993; Saxenian, 1990; Krugman, 1991). Marshall (1920) describes how, throughout history, economic activity was clustered in areas rich in the 'atmosphere' of ideas. Krugman (1991) discusses three factors that foster the concentration of industries in particular geographic locations. The first two reasons are economic in nature: (a) the pooling of demands for specialized labor and (b) the development of specialized intermediate goods industries. The last reason he cites for geographic proximity of industries is based on knowledge spillovers.

Therefore, a firm located in a geographic area with a high concentration of similar firms will have access to information, personnel, and support structures that are unavailable to firms that are geographically isolated. This increased access to resources will enhance the firm's ability to create wealth.

H1: The concentration of biotechnology firms located in a firm's geographic area will have a positive relationship with the wealth created by the firm.

However, the relationship between geographic concentration and performance may not be as simple as the proposed linear relationship. Organizational ecology argues that at low and moderate levels of density the legitimation dynamic predominates, improving performance, but at high levels of density the competitive dynamic predominates and performance will be decreased (Hannan & Freeman, 1989). Thus the rate of failures decreases until it reaches an inflection point at which the rate begins to increase. The converse occurs for founding rates. This model has been empirically tested on organizational populations such as museums (Blau, 1991), newspapers (Carroll & Hannan, 1989), breweries (Carroll & Wade, 1991), and several other industries. Therefore, the relationship between wealth creation and geographic concentration is likely to be an inverse U-shaped relationship with wealth creation increasing initially until some point at which the increasing competition for resources decreases a firm's ability to create wealth.

H1A: There will be an inverted U-shaped relationship between the concentration of biotechnology firms located in a firm's geographic region and the wealth created by the firm.

Citation Analysis and Wealth Creation

The quality of a firm's scientific team is critical to the product development process. However, attempting to make comparisons of scientific teams across firms leads to the question of how to measure the quality of scientific research. A widely accepted method of assessing research quality in the academic community is citation analysis. Citation analysis uses the number of times a paper or an author is cited as an indication of the importance of the work to the field. Those of us who have chased or are chasing tenure in academia are quite familiar with the importance citations are given during the tenure process.

Citation analysis has been used to map the development of fields of scientific inquiry (Small & Griffith, 1974); to estimate the quality of the scientific capabilities of countries in specific fields (Heeley, Rothman, & Hock, 1986); to assess the performance of academic departments (Wallmark, McQueen, & Sedig, 1988); and as the basis for the assessment of scientific and technical research programs (Narin & Rozek, 1988; Vinkler, 1986). Therefore, it is our contention that the number of citations a firm's scientists have is an indication of the quality of a firm's scientific capabilities, which is key to wealth creation in biotechnology ventures.

H2: The total number of times the works of a firm's top scientists have been cited will have a positive relationship with the wealth created by the firm.

Patents and Wealth Creation

Patents have been associated with innovation and performance at many levels: region, country, company. Patents are considered indicators of important technology positions and innovative activity (Ashton & Sen, 1988) and can be considered inputs in the new product development process (Mansfield, 1977; Pakes, 1985). A firm's patent stock is an indication of the size of a firm's stock of intellectual property. Therefore, a firm's patent library is an indication of the firm's research productivity and should be positively related to the amount of wealth created by the firm.

H3: The number of patents controlled by a biotechnology company will have a direct positive relationship with the wealth created by the firm.

The Rate of New Product Development and Wealth Creation

In industries populated by entrepreneurial high-technology firms a primary determinant of enterprise success is the rate at which the firm develops new products (Stalk & Hout, 1990). The ability to rapidly develop new products and bring them to market is important in order to gain early cash flows for greater financial independence, gain external visibility and legitimacy as soon as possible, gain early market share, and increase the likelihood of survival (Schoonhoven, Eisenhardt, & Lyman, 1990). Moreover, the faster a firm is at developing new products and bringing them to market, the more likely it is to capture first-mover advantages. This is certainly true in industries such as pharmaceuticals where the relative effectiveness of patent protection leads to patent races in which a "winner takes all" scenario exists (Gilbert & Newbery, 1982; Tirole, 1988). But even in industries where patent protection is weak, the advantages of being first, in terms of market pre-emption, reputation effects, experience curve effects, and the like, can still be of major importance (Lieberman & Montgomery, 1988). Given the nature of the biotechnology industry, the rapid development of new products is sure to be a critical factor in the creation of shareholder wealth.

H4: The rate at which a biotechnology company develops new products will have a positive relationship with the wealth created by the firm.

Relative Research and Development Expenditures and Wealth Creation

The intensity of a firm's expenditures on research and development has traditionally been used as an indicator of innovative activity in many industries (Scherer, 1980). Several studies have looked at the relationship between R&D spending and productivity returns (Comanor, 1965; Grabowski & Vernon, 1990; Graves & Langowitz, 1993; Vernon & Gusen, 1974), and several studies have linked R&D expenditures to increases in market value. Therefore, where R&D productivity is as critical to success as it is in the biotechnology industry, the relative level of R&D expenditures is an indication of the intangible scientific assets of the firm and a predictor of the probability of the firm successfully completing the R&D process. Therefore, those firms that pursue a strategy of investing a higher proportion of their resources in R&D will create significantly more shareholder wealth.

H5: A biotechnology company's relative R&D expenditures will have a positive relationship with the wealth created by the firm.


The Sample and Data

The 225 publicly held biotechnology companies in 1993 provides the population of firms for this investigation (Burrill & Lee, 1993). The sample from this population was limited to firms that went public between 1982 and 1993. Thus, the initial sample was limited to 218 firms. These firms were then contacted by phone with a request for a copy of the prospectus from their IPO. A total of 106 companies were willing to provide a prospectus, representing a response rate of 48%. However, 15 of these companies were excluded from the sample due to missing data and two were excluded because warrants for shares in their parent company were included in the IPO. Thus, our final sample consisted of 89 firms.

To test for potential biases in this sample we compared the average total assets and average total liabilities of the firms in our sample in 1992 to the average total assets and liabilities reported by Burill and Lee (1993) for all 225 public firms. Our sample averaged $10,845,000 in total assets and $3,678,000 in total liabilities. Burill and Lee (1993) reported the average total assets and total liabilities of the 225 public biotechnology firms in 1992 as $11,377,000 and $3,313,000, respectively. In addition, the percentage of non-pharmaceutical health care companies in our sample was 15% and the industry-wide percentage, as reported by Burill and Lee (1993) was 17%. based on these comparisons and the size of our sample, we believe we have a fairly representative sample of the publicly held biotechnology companies.

The data used in our analysis were gathered from (1) the prospectus for each of the initial public offerings by the firms in our sample, (2) Ernst and Young's industry annual reports on the biotechnology industry, and (3) the CRSP data tapes.

Dependent Variables

Market value added. Market value added is the difference between the company's market value and the capital employed by the company. In this study, MVA is measured at the end of the first day of public trading of the firm's equity. MVA is the difference between two figures - an approximation of the fair market value of all the company's debt and equity capitalization and the capital employed by the company.

The market value is the actual market value of the company's common equity plus the book value of preferred stock, minority interests, long-term non-interest-bearing liabilities, all interest-bearing liabilities, and the present value of all non-capitalized leases. The capital employed by the company is essentially the company's assets less non-interest-bearing current liabilities plus certain equity equivalent accounting reserves (bad debt, LIFO, goodwill amortization, R&D, unusual losses). In the case of biotechnology companies, the important adjustments are the addition of the accumulated deficit during the start-up phase, which we considered an unusual loss, and depreciated R&D. Investment in R&D is depreciated at a rate of 20% per year. These are both added into total capital because in an economic sense they represent investments by the organization and therefore should be considered part of the capital employed by the firm.

The market value of the firm's common equity was gathered from the CRSP data tapes at the end of the first day of trading. The accounting information was gathered from the most current financial statements included in the prospectus for the initial public offering.

Relative market value added. In order to measure the return on the capital invested in these biotechnology firms, we divided a firm's MVA by the total invested capital.

Independent Variables

Location. based on the location of the firm's headquarters at the time of its IPO, firms were coded into geographic territories based on zip code and MSA (Metropolitan Statistical Areas). These locations were then compared to the eight areas identified by Burill and Lee as concentrations of biotechnology activity. In order to capture the variance in the concentration of these eight areas, the location variable is the percentage of the nation's total biotechnology firms located in the firm's specific MSA at the time of the firm's IPO. A "0" was recorded for firms not in one of the eight geographical areas.

Citation data. In this study we are using citation analysis as an indication of the quality of the scientific personnel of the biotechnology firm. The names of the top scientists employed by each firm were gathered from the prospectus of the firm's initial public offering. Only full-time employees were included in the list in order to control for biases created by firms attempting to increase their visibility/legitimacy by hiring a long list of scientific advisors or consultants. Names of all scientific personnel listed in the prospectus as well as top executives were compiled. We then used the Science Citation Index to gather the total number of citations for each scientist in the firm during his/her career. These citations were then totaled to create a measure of the quality of the scientific team employed by the biotechnology firm.

Patents. From the offering firm's prospectus, a count of the total number of patents held by that firm was obtained. This includes both patents granted directly to the firm and patents in which the firm is the sole licensee.

Rate of product development. In the business section of each prospectus the companies report the number of products under development or that have reached the market. Only products that had reached the pre-clinical stage of development or beyond were included. Multiple applications of the same product were counted as a single product. The total number of products was then divided by the age of the firm to create a measure of the firm's rate of new product development.

Relative R&D intensity. R&D intensity was measured as total R&D expenditures reported by the firm divided by the total expenditures of the firm. The traditional measure of R&D intensity has been R&D as a percentage of sales, but given their early stage of development, most of these companies have little or no revenue, therefore dividing through by total expenditures was the logical choice to measure the firm's focus on R&D. However, to be consistent with past research (West, 1992; Zahra, 1996) a three-year average of the R&D intensity was used as the measure.

Control Variables

Age. MVA is a cumulative measure of the wealth created by the firm. Therefore, the age of the firm in years was entered into the model as a control variable.

Number of employees. To control for any possible effects of size on a firm's ability to generate wealth we entered the total number of employees into the model as a control. The number of employees was chosen as the control for size because total assets is being used in the calculation of the dependent variable.

Statistical Modeling Technique

You will recall that we have competing hypotheses 1 and 1A. Hypothesis 1 predicts an increasing linear relationship between geographic concentration and performance. Hypothesis 1A predicts an inverted U-shaped relationship with performance initially increasing with concentration and then decreasing after a certain level. We used a quadratic model to test these hypotheses. This has the following form:

Market Value Added = 1 + [b.sub.1] [multiplied by] concentration + [b.sub.2] [multiplied by] [concentration.sup.2] + [b.sub.3] [multiplied by] [Z.sub.1] + [b.sub.4] [multiplied by] [Z.sub.2] + . . . . . [b.sub.n] [multiplied by] [Z.sub.n] + e

If [b.sub.1] is positive and [b.sub.2] is negative, and both coefficients are statistically significant, there is evidence for an inverted U-shaped relationship. However, if b2 is positive and statistically significant and b3 is insignificant, then the relationship between concentration and performance matches that of hypothesis 1.


The data were analyzed using ordinary least squares regression. Descriptive statistics of the variables are presented in Table 1. The average MVA of the firms in our sample was $65.7 million. The RMVA for the average firm in our sample was 3.25, indicating that the average firm had created an amount of wealth equal to over three times the amount of capital invested in the firm. The average firm developed 0.65 products per year and had control of 3.35 patents. With respect to location, the average firm was located in a metropolitan area with 7.4% of the total national biotechnology firms. The average firm spent 60% of its money on R&D. The research of the key scientists of the average firm had been cited 126 times. The average firm was 5.5 years old and employed 66 people. The correlation matrix is presented in Table 2.
Table 1

Descriptive Statistics

Variable                               Mean       Standard Deviation

Market value added                65,688,602       48,556,612
Relative market value added                3.25             3.05
Location                                   7.37             5.31
Firm citations                           125.64           134.81
Patents                                    3.35             4.88
Rate of new product development            0.649            0.804
R&D intensity                              0.598            0.229
Age                                        5.52             4.099
# Employees                               66.52            72.76

Table 3 presents the results of the regression analysis with MVA as the dependent variable. Table 4 present the results of the regression analysis with RMVA as the dependent variable. Model 1, in both tables, tests the simple linear relation between concentration and performance. Model 2 tests the inverted U-shaped relationship developed in hypothesis 1A. The adjusted [R.sup.2] for Model I with MVA as the dependent variable is 0.334, and 0.124 with RMVA as the dependent variable. The F-statistics are 7.30 and 2.87, respectively, which are significant beyond the 0.01 level. These results indicate that the model is explaining a significant amount of the variation in MVA and RMVA and that the model is a good fit for the data. The adjusted [R.sup.2] for Model 2 with MVA as the dependent variable is 0.352, and 0.119 with RMVA as the dependent variable. The F-statistics are 6.8 and 2.57, respectively, which are significant at the 0.05 level. This indicates that the models are a good fit, but are not a significant improvement over Model 1 in either case.

The geographic concentration variable was significantly positively related to MVA and RMVA at the 0.05 level in Model 1. In Model 2 the location variable was positive and significantly related to MVA at the 0.09 level and not significantly related to RMVA, and the square of the location concentration was negative and insignificant with both dependent variables. These results provide support for hypothesis 1 and no support for hypothesis 1A. Firms that are located in a cluster of firms appear to create significantly more entrepreneurial profit. At the geographic concentration levels in our sample there appears to be no significant decrease in wealth creation due to increasing competition at higher levels of density. Since Model 1 is the parsimonious model and the correlation between the two concentration variables is so high, Model I will provide the basis for the rest of the discussion of our results.

The firm citation measure was significantly positively related to MVA at the 0.01 level. However, this measure was not significantly related to RMVA. These results provide mixed support for hypothesis 2. A strong research team, as measured by the citation of their previous works, significantly improves the absolute performance of the new venture performance, but this increase in absolute performance does not translate to an increase in the relative performance of the firm.

Table 3

Regression Results with Market Value Added as the Dependent

                                       Model 1           Model 2

Variable                           Beta   Sig. T      Beta   Sig. T

Location                           .175    0.03       .139    0.09
Location squared                                    -0.051    0.54
Firm citations                     .390    0.01       .409    0.01
Patents                            .021    0.22       .031    0.27
Rate of new product development    .151    0.05       .147    0.05
R&D intensity                      .369    0.01       .345    0.01
Age                                .056    0.38       .074    0.41
# Employees                        .302    0.01       .313    0.01
Adjusted [R.sup.2]                0.334              0.352
F-Statistic                         7.3    0.0001    6.8      0.0001

n = 89

The number of patents controlled by the firm was not significantly related to MVA or RMVA. No support was found for hypothesis 3.

The rate of new product development was positively related to MVA and RMVA at the 0.05 level. These results provide support for hypothesis 4. The rate of new product development appears to be significantly positively related to firm performance.

The measure of relative R&D intensity was significantly positively related to MVA and RMVA at the 0.01 level. These results provide strong support for hypothesis 5. Pursuing a strategy of intense R&D investments significantly improves new venture performance.
Table 4

Regression Results with Relative Market Value Added as the Dependent

Model 1 Model 2

Variable                           Beta   Sig. T     Beta   Sig. T

Location                           .199    0.05     -.044    0.89
Location squared                                     .054    0.46
Firm citations                    -.01     0.92    -0.03     0.97
Patents                           -.08     0.39     -.08     0.45
Rate of new product development    .229    0.03      .244    0.03
R&D intensity                      .292    0.01      .293    0.01
Age                               -.167    0.10     -.179    0.08
# Employees                       -.240    0.02     -.229    0.03
Adjusted [R.sup.2]                0.124              .119
F-Statistic                       2.87     0.01     2.57     0.015

n = 89


As stated earlier, the goals of this study were to introduce and justify the use of MVA and RMVA as measures of new venture performance and to test the relationship between firm-specific capabilities and wealth creation in new ventures. In particular, we suggested that the wealth created by an entrepreneurial firm is accurately captured when contributed capital is considered in the calculation of firm performance. The inclusion of contributed capital in the equation creates a more realistic representation of a high-technology ventures achievement. How the firm creates wealth was developed in our model of firm-specific capabilities.

The results provide strong evidence for the underlying premise that investment in the development of critical skills and capabilities is an important determinant of new venture performance. Four of our six hypotheses received support at the 0.05 level or better for MVA and three of the six hypotheses were supported at the 0.05 level for RMVA. We were able to explain over a third of the variation in the absolute amount of wealth created by the firms in our sample. In addition, the congruency between our results on R&D intensity using MVA/RMVA and previous research on the relationship between R&D intensity and firm performance (Grabowski & Vernon, 1990; Graves & Langowitz, 1993) provides some additional evidence of the validity of MVA/RMVA as performance measures.

There has been significant theoretical and anecdotal discussion of the link between a firm's location and its performance. Our results provide empirical support for the assumption that firms that are located close to other firms in the same industry will enjoy benefits from their proximity. While our results fail to provide any insight as to which of the specific mechanisms outlined by Marshall (1920) are applicable, it seems apparent that firms located in a cluster of similar firms create significantly more shareholder wealth than those located outside a cluster. In addition, no support was found for the position that higher levels of geographic concentration create competition for resources and decreases wealth creation. This may be due to the nature of the biotechnology industry, which does not directly compete in the customer market, or it may be that the industry has failed to reach the levels of concentration necessary to impact a firm's ability to create wealth. These results are not inconsistent with the findings of researchers in organizational ecology, where more support has been found for the relation between foundings and density than failures and density (Carroll & Wade, 1991; Singh & Lumsden, 1990).

We suggest that this performance difference due to location may be explained as follows. Within each of the eight biotechnology clusters, there exist not only biotechnology firms but also major non-profit research institutions. In many ways these institutions resemble the specialized intermediate goods industries that Krugman (1991) addresses. The universities and non-profit research institutions provide basic scientific research upon which biotechnology firms build, experienced research and technical personnel, and specialized technical expertise unavailable elsewhere. In addition, these organizations attract skilled personnel to the geographic area, which helps create and sustain a superior labor pool. Trying to discern the separate impact of each of these on our results is difficult, but it is very clear that the market places value on a firm being within a cluster of similar firms. These results lend credence to the idea that choice of geographic location is an important strategic decision that should be given careful consideration by entrepreneurs.

Perhaps even more significant is the magnitude of the difference geographic location makes in a firm's ability to create wealth. based on our results, a firm that operates in Silicon Valley would create $27 million more in shareholder wealth than a firm isolated geographically, and would increase its return on invested capital by 1.7 times, all other things being held equal. Given that the average amount of wealth created in our sample is $65.6 million and 3.25 time invested capital, this is a very significant increase in a firm' s performance. The strength of our results lend credence to the ideas that geographic location plays an important role in the success of an entrepreneurial firm and that choice of geographic location is an important strategic decision that should be given careful consideration by entrepreneurs.

The results for our citation measure indicates that the development of superior capabilities or talents critical to the firm's scientific and research endeavors dramatically improves a firm's absolute ability to create wealth. A one standard deviation improvement (134 citations) in the level of citations increases the amount of wealth created by the firm by over $13.8 million. These results indicate that in the biotechnology industry the superior scientific capabilities of a firm's research team create significant shareholder wealth. However, the lack of results for RMVA appears to indicate that this factor does not improve the returns on invested capital. The offsetting costs of hiring more frequently cited scientists and supporting their research agenda appears to offset any increase in the returns to shareholders. Thus, the hiring of quality personnel based on accepted measures, such as citations in the scientific community, will improve the firm's absolute ability to create value, but is unlikely to improve the overall percentage returns to shareholders.

The number of patents held by these firms had a negligible impact on the ability of the firms in our sample to create value. One possible explanation for the less-than-robust results for our patent measure is that patent counts are an ambiguous measure subject to firm-specific variations in the propensity of firms to patent, given the resource expenditure required by the patent process (Mansfield, 1977; Pakes, 1985). Second, simple patent counts may fail to capture the value of a firm's patent library due to the large variation that exists in the value of individual patents. Future research needs to examine the potential ways of controlling for this variation by weighting patents based on their citations or some other appropriate reflection of the patent's value. In sum, there is probably significantly more 'noise' in the patent variable than in our other measures.

The rate of new product development is clearly critical to the creation of shareholder wealth in the biotechnology industry. A one standard deviation increase in the rate of new product development (0.8 new products/yr) would lead to an increase of over $9 million in wealth and a 0.64 increase in RMVA. Clearly, the rate of new product development is critical to new venture performance, as argued by Stalk and Hout (1990).

Finally, our results for relative R&D intensity indicate that a strategy of narrowly focusing on R&D during the development stage leads to significant increases in shareholder wealth. Simply shifting one percentage point of spending from other expenditures to R&D spending increases the wealth created by the company by over $540,000 and RMVA by 0.037. These results are in line with previous research on the effect of R&D spending on market value (Pakes, 1985; Jaffe, 1986).

Our results provide evidence to support the capabilities position in the current debate between the industry and capabilities schools within the field of entrepreneurship. Except for patents, all of our measures of firm-specific scientific capabilities were significantly positively related to MVA, location, R&D intensity, and the rate of new product development were all significantly positively related to RMVA. In addition, the magnitude of the coefficients indicates significant performance improvements in firms that develop firm-specific capabilities. A firm that located in Silicon Valley and increased by one standard deviation the number of citations, rate of new product development, and intensity of its R&D investment (134 citations, 0.8 products per year, and 23% more spending on R&D) could increase the amount of wealth created by over $62.7 million and RMVA by over 3. Considering the size and the average MVA and RMVA (65.7 and 3.25) of these firms, this provides very strong evidence that the development of firm-specific capabilities are an important aspect of the wealth creation process.

While our results provide strong statistical support for our conclusion, we must also acknowledge that our focus on biotechnology raises questions about the generalizability of our study beyond this industry. Biotechnology has several unique characteristics, including a long product development and approval cycle, heavy reliance upon often arcane basic scientific research, and a very expensive product development process. However, increasing reliance on basic scientific research in numerous industries has been documented by Dasgupta and David (1987). Given these characteristics, we still believe that our results are generalizable beyond the biotechnology industry. What our results indicate is that focusing on the development of key capabilities, such as scientific capabilities, dramatically improves the performance of new ventures. While key capabilities may differ between industries, there is no reason to believe that the biotechnology industry is uniquely dependent on firm-specific capabilities. Clearly, this research needs to be extended beyond the boundaries of the biotechnology industries to other high-technology ventures such as software, electronics, etc.

While we have found strong empirical support for our model it should also be noted that there is still a significant amount of variation in firm performance that remains unexplained. Obviously, there remain other variables of potential interest that demand further study, including the effects of CEO and management team background, personal characteristics and remuneration, and numerous other areas. Overall, the determinants of the performance of new ventures is ripe for further research and is a critical issue for entrepreneurship research.

In addition, further research using MVA and RMVA as performance measures for other industries and across industries examining the impact of industry structure on new venture performance is needed. Given the definitions of entrepreneurship and Schumpeter's definition of entrepreneurial profit, MVA appears to be particularly appropriate for the study of new ventures.

Finally, important implications for entrepreneurs follow from our results. First, location is clearly an important and often-overlooked strategic decision. It is clear from our results that the right geographic location can provide a high-tech venture with advantages in access to talented people, information, and specialized suppliers. Entrepreneurs need to consider more than simply the tax and legal environments and lifestyle advantages in their choice of location. Second, new venture wealth creation may be enhanced by hiring people whose talents have been recognized by others in their field. This study has shown that objective indicators such as citations provide a good measure of the individual capabilities of potential employees. Past performance, particularly well-documented performance, appears to be a strong predictor of the future performance of research talent. Third, during the development stage, rapidly developing new products and focusing and investing in R&D enhance firm performance. Developingstage companies cannot afford to waste precious resources on overhead expenditures such as administration, plant, equipment, etc. Entrepreneurs in development-stage companies need to be single minded in their pursuit of the development of new products. Finally, this empirical study has provided strong evidence suggesting that the development of rare, valuable, and inimitable capabilities that are valuable within the context of the market in which the firm operates significantly improves firm performance.

1. Schumpeter defined entrepreneurial profit as the "expression of the value of what the entrepreneur contributes to production in exactly the same sense that wages are the value expression of what the worker 'produces'. . . . It attaches to the creation of new things, to the realization of the future value system" (1936, pp. 153-154).


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David L. Deeds is Assistant Professor of Entrepreneurship at The Weatherhead School of Management at Case Western Reserve University.

Dona DeCarolis is Assistant Professor of Strategic Management at Drexel University.

Joseph E. Coombs is Assistant Professor of Entrepreneurship at James Madison University.
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Author:Deeds, David L.; DeCarolis, Dona; Coombs, Joseph E.
Publication:Entrepreneurship: Theory and Practice
Date:Mar 22, 1998
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