An examination of the influence of industry structure on eight alternative measures of new venture performance for high potential independent new ventures

AN EXAMINATION OF THE INFLUENCE OF INDUSTRY STRUCTURE ON EIGHT ALTERNATIVE MEASURES OF NEW VENTURE PERFORMANCE FOR HIGH POTENTIAL INDEPENDENT NEW VENTURES KENNETH CHARLES ROBINSON Kennesaw State University

This study examined the influence of the structure of new ventures’ entered industries on eight alternative measures of new venture performance for 199 high potential independent new ventures. Each of the 199 entrepreneurial ventures had undertaken an initial public offering (IPO) within the first 6 years of the venture’s founding date and were free of corporate sponsorship or prior corporate parentage. Specifically, this research examined the influence of: (1) stage of the life cycle; (2) industry concentration; (3) entry barriers; and (4) product differentiation on eight alternative measures of new venture performance. The eight measures of new venture performance examined in this research consisted of: (1) change in sales; (2) sales level; (3) net profit; (4) earnings before interest and taxes; (5) return on sales; (6) return on assets; (7) return on invested capital; and (8) return on equity. Most prior research examining the influence of industry structure on new venture performance has: (1) utilized only one or two measures of new venture performance as indicators of the venture’s overall

EXECUTIVE SUMMARY

Address correspondence to Kenneth Charles Robinson, Department of Management and Entrepreneurship, Coles College of Business, Kennesaw State University, Kennesaw, GA, 30144. This research was funded in part by the Center for Entrepreneurial Leadership Inc. and the Ewing Marion Kauffman Foundation. Journal of Business Venturing 14, 165–187  1998 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010

0883-9026/99/$–see front matter PII S0883-9026(97)00083-9

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effectiveness and efficiency; (2) often failed to provide theoretical justification for the measure(s) of new venture performance or industry structure examined; and (3) utilized data derived from questionnaires and/or the PIMS data base of corporate-sponsored new ventures. In addition, prior industry structure studies examining independent new ventures have often utilized relatively small sample sizes. This study sought to advance the progress in the field of entrepreneurship with regard to understanding the influence of the structure of new ventures’ entered industries on new venture performance by: (1) examining eight alternative measures of new venture performance; (2) providing theoretical justification for the measures of new venture performance and industry structure examined; and (3) utilizing the largest nonquestionnaire data base of independent new ventures developed to date. This research found that the stage of the life cycle of the venture’s entered industry was the most important determinant of new venture performance among the four industry structural elements examined. Stage of the life cycle had a statistically significant relationship, at a 0.05 level, with the majority of the new venture performance measures examined in this research. In addition, ventures entering industries in the introductory stage of the life cycle achieved the highest levels of venture performance, particularly when compared with those ventures that entered industries in the mature stage of the life cycle. However, this study did not find a statistically significant relationship between stage of the life cycle and change in sales. This suggests that there is a trade-off between profitability and sales growth, and that new ventures that undertake an IPO have a stronger focus on achieving profitable operations rather than sales growth during the initial years after their IPO. This may be due to pressures placed on the new ventures to achieve profitability by the external credit market. Conversely, this research found that: (1) industry concentration; (2) entry barriers; and (3) product differentiation did not have statistically significant relationships, at a 0.10 level, with any of the eight alternative measures of new venture performance examined in this research. However, this research did find that over 90% of the new ventures entered industries characterized by: (1) a low degree of industry concentration and (2) a high degree of product differentiation. The relative absence of new venture entry into industries characterized by: (1) high degrees of concentration and (2) low degrees of product differentiation provides support for prior theory, which suggests that successful entry into such industry environments may be substantially more difficult. In sum, the results of this research suggest that high potential independent new ventures that undertake an IPO should enter industries in the introductory stage of the life cycle. In addition, the results of this research suggest that industries characterized by: (1) relatively low degrees of industry concentration and (2) highly heterogenous products may be necessary but not sufficient conditions for successful entry by high potential independent new ventures seeking to raise equity capital through an IPO.  1998 Elsevier Science Inc.

INTRODUCTION There is substantial evidence that the formation and growth of new ventures is responsible for the vast majority of the job creation and growth in the U.S. economy (Birley 1986; Birch 1987; Kirchhoff 1991). Furthermore, the importance of new venture formation and growth to the health and vitality of the U.S. economy is expected to become even more critical, particularly with the growing obsolescence and continued downsizing of many of the large U.S. corporations (Merrifield 1993). Regrettably, new ventures also have a high failure rate. Thus, research that identifies factors that influence alternative measures of new venture performance is important not only to successful entrepreneurial endeavors and management practice, but also to the vitality of the U.S. economy. Prior studies on the determinants of new venture performance have utilized differing measures of (1) new venture performance (Brush and VanderWerf 1992; Cooper 1993; Murphy, Trailer, and Hill 1993) and (2) industry structure (Kunkel 1991). In addi-

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tion, such studies have often failed to provide theoretical justification for the particular measure(s) of new venture performance and industry structure selected for examination. These differing approaches utilized in such studies have produced different and frequently conflicting results as to the relative influence of various industry structural elements on alternative measures of new venture economic performance. Thus, it is difficult to assemble a robust set of findings that can be used for practitioner guidance. This research attempted to overcome some of the limitations of prior studies on the determinants of new venture performance. Thus, this research examined: (1) various measures of new venture performance, which prior theory and research suggest are important indicators of venture effectiveness and efficiency; and (2) various measures of industry structure that prior theory and research suggest are important structural characteristics of industries.

MEASURES OF NEW VENTURE PERFORMANCE There have been a number of studies that have outlined the importance of using multiple measures of economic performance (e.g., Kirchhoff 1977; Venkatraman and Ramanujam 1987). However, the majority of prior entrepreneurship studies have utilized only one or two measure(s) of venture performance, while often failing to provide justification for the measure(s) selected. Cooper (1993) noted that the diversity of performance measures that have been utilized in prior research makes comparisons across studies difficult. Cooper stated, “We also need to understand more fully the effects of different performance measures and whether the factors that enhance performance vary according to the measure used” (1993, p. 251). The selection of measure(s) of organizational effectiveness and efficiency with which to compare the members of the sample is an issue of critical importance. Etzioni (1960) distinguished between the: (1) goal model of organizational effectiveness, which utilizes the goals and objectives of the organization as the criteria; and (2) system model, which requires the researcher to determine the criteria that reflect factors upon which organizational survival depends. With regard to the goal model, prior studies have found: (1) net profit level; (2) sales growth; (3) sales level/market share; (4) return on investment; (5) return on assets; and (6) return on equity to be the most important goals and objectives of owners and senior managers of business enterprises (Bourgeois 1980; Chandler and Hanks 1993; VanderWerf 1994). The system model, as utilized in strategic management and entrepreneurship research, has typically involved assigning some measure of business economic performance as the criterion of organizational effectiveness. Bourgeois (1980) noted that this is particularly true for studies of publicly held corporations “who have a legal obligation to provide economic utility to their stockholders” (p. 235). Bourgeois further stated: While the classical microeconomic theory of the firm holds profitability as the criterion function, the now-classic works of Baumol (1967) and Penrose (1959) explicate the emergence of growth as the economic goal of firms run by ‘professional’ management—that is publicly held firms in which ownership and management are divorced [italics added] (1980, p. 235).

In addition, Venkataraman and Ramanujam (1987) noted that financial performance is the dimension of business performance that reflects “the fulfillment of the eco-

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nomic goals of the firm” (p. 803), and includes such indicators as sales growth, profitability, return on investment (total assets or net assets), return on sales, and return on equity. Murphy, Trailer, and Hill (1993) provided a summary of the measures of economic performance used in 51 prior entrepreneurship studies that explicitly used firm performance as a dependent variable. After consolidating for equivalent measures in the Murphy et al. study, the most frequently utilized measures of new venture performance are: (1) change in sales; (2) return on sales; (3) return on equity; (4) sales level; (5) return on investment; (6) net profit level; and (7) return on assets. These are also among the most commonly used measures of performance in strategic management studies (Hofer 1983). In short, the preponderance of prior theory and research provide a strong rationale for the use of multiple indicators of organizational effectiveness. Thus, this study utilized multiple measures of economic performance, which prior theory and research suggest are appropriate indicators of an organization’s effectiveness: (1) change in sales (sales growth); (2) sales level; (3) net profit level; (4) earnings before interest and taxes (EBIT); (5) return on sales (ROS); (6) return on total assets (ROA); (7) return on invested capital, i.e., return on net assets (ROIC); and (8) return on equity (ROE). In addition, the use of these eight performance measures facilitate comparisons with both prior and future studies on the influence of industry structure on alternative measures of new venture performance. Although there exists some mathematical relationships among these eight alternative measures of new venture economic performance, they are not necessarily interchangeable proxies for one another. For example, Murphy et al. (1993) found that only 35% of the correlations among 19 measures of new venture performance had statistically significant positive relationships. Furthermore, they noted that many of these statistically significant positive correlations were not substantive. The eight measures of new venture performance examined in this study reflect different, although somewhat related in some instances, facets of organizational effectiveness and efficiency. Table 1 provides additional rationale for the selection of the eight measures of performance examined in this study.

MEASURES OF INDUSTRY STRUCTURE The structure-conduct-performance model of industrial organization (IO) developed by Mason (1939) and Bain (1959) proposed that industry structural variables are key determinants of economic performance. Bain (1959, p. 28) stated that the primary structural distinctions of industries are: (1) the degree of seller concentration; (2) the extent of product differentiation; and (3) the condition of entry (entry barriers) to an industry. Bain (1959) also suggested that “The ‘trend of demand’ for industry output—whether it is secularly growing, declining, or remaining more or less stable . . . might offer added explanations of observed differences in market conduct and performance” (p. 265). Caves (1972) supported Bain’s theory regarding the primary structural characteristics of industries, and stated that the most important elements of industry structure are: “(1) seller concentration; (2) product differentiation; (3) barriers to the entry of new firms; and (4) growth rate of market demand” (p. 16). Kunkel (1991) reviewed major theoretical and empirical works in the fields of industrial organization, strategic management, and entrepreneurship to determine the relative importance of 50 industry structural variables. Kunkel devised a weighting scheme

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TABLE 1 Additional Rationale for the Selection of the Eight Measures of New Venture Performance Examined in This Study Variable

Additional Rationale for Selection/Importance of Measures

Sales change

Sales growth is necessary for developing new ventures to fund future operations; Indicative of increasing customer acceptance of venture’s product/service offerings

Sales level

Sales level indicates the venture’s success in its market transactions; Superior to market share for this sample as pioneer ventures (e.g., Apple, Lotus) could be quite successful in increasing sales but experience declines in market share due to entry by followers

Net income

Profit contributes directly to venture’s ability to fund investment; May be related to the caliber of investment opportunities within the venture’s areas of specialization; Also a component of bond rating variables which influences borrowing ability

EBIT

A superior measure of current profitability to net income, which is not influenced by: (1) alternative uses of debt or equity to finance operations; or (2) income tax reductions in any given year due to net losses in prior years that are carried over

ROS

Indicates management’s ability to: (1) operate venture with sufficient success to recover the cost of goods/services and operating expenses; and (2) leave a margin of adequate compensation to the common shareholders for putting their capital at risk (Helfert 1994)

ROA

Indicates management’s effectiveness in employing the assets entrusted to them and does not depend on the alternative uses of debt versus equity to fund such assets

ROIC

Indicates management’s effectiveness in employing the total capitalization, i.e., net assets (equity 1 long-term debt 5 total assets 2 current liabilities) available to the venture

ROE

Indicates management’s effectiveness in generating a return on the funds invested by the common shareholders, to whom management is ultimately responsible and accountable

for these 50 variables based on the relative importance which prior theory and research assigned to these variables. Based on this review, Kunkel determined that the most important industry structural variables are: (1) life cycle stage (which is related to growth rate); (2) industry concentration; (3) entry barriers; and (4) product differentiation. Thus, this research examined the influence of: (1) stage of the life cycle; (2) industry concentration; (3) entry barriers; and (4) degree of product differentiation on eight alternative measures of new venture economic performance due to the importance assigned to these industry structural variables by prior theory and research. The results of some of the key studies examining the influence of these four industry structural variables on measures of new venture performance are discussed below.

INFLUENCE OF INDUSTRY STRUCTURE ON NEW VENTURE PERFORMANCE Stage of the Life Cycle Hofer (1975) theorized that stage of the life cycle is the most important contingency variable affecting firm performance. Subsequently, there has been a large number of studies that have examined the influence of stage of the life cycle, both as a contingency variable and as a main effect variable, on measures of firm performance. Spence (1981) explained the potential advantages accruing to ventures entering industries early in the life cycle:

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TABLE 2 Prior Entrepreneurship Research Findings Regarding Stage of the Life Cycle Studies

Operationalization

Findings

Biggadike (1979)

Four-stage PIMS life cycle model

Sandberg (1986)

Six-stage Hofer life cycle model

Ventures entering industries in the introductory stage achieved higher relative market share than those entering growth stage; Ventures entering moderately growing industries (6–20%) achieved superior ROI Ventures entering industries in the development or growth stage generated higher returns to investors than those ventures entering later stages Ventures entering industries in mature stage achieved lower levels of market share Ventures entering industries in mature stage achieved superior “initial quantified success“ Ventures entering the growth stage were more successful on performance index than those entering introductory stage; No difference found in Sales Ventures entering mature stage achieved higher ROE than those ventures entering developmental stage Stage of life cycle unimportant

MacMillan and Day (1987) Stuart and Abetti (1987) Covin and Slevin (1990)

Four-stage PIMS life cycle model Likert scale questionnaire Four-stage PIMS life cycle model

Kunkel (1991)

Six-stage Hofer life cycle model Five-stage life cycle model Four-stage PIMS life cycle model

McCann (1991) Tsai, MacMillan, and Low (1991)

Ventures in early stages achieve best market share gains; No difference found in ROI

The learning curve creates entry barriers and protection from competition by conferring cost advantages on early entrants and those who achieve large market shares. These cost advantages are not permanent. But with moderately rapid declines in unit costs, they have significant impacts on market shares and profitability (p. 68).

New ventures entering industries in the introductory stage may also realize the benefits of establishing: (1) product standards; (2) a reputation in the marketplace; (3) higher customer awareness; (4) switching costs; (5) control of scarce resources; and (6) control of distribution channels (Lieberman and Montgomery 1988). In addition, industries in the early stages of development and growth provide an opportunity for new ventures to capture the new demand in markets that have relatively little likelihood of retaliation by established incumbents. There have been a number of research studies in the field of entrepreneurship that have examined the influence of stage of the industry life cycle on measures of new venture performance. Table 2 provides a summary of key new venture stage of the life cycle studies. (Note: studies examining the stage of the venture development, e.g., Kazanjian and Drazin 1990, are not examined in this study). As shown in Table 2, a variety of approaches has been utilized to operationalize stage of the life cycle. Nonetheless, seven of these eight studies found the stage of the life cycle in the venture’s entered industry is an important determinant of subsequent new venture performance. In sum, both prior theoretical developments and empirical research in the field of entrepreneurship suggest that the stage of the life cycle is an important determinant of new venture performance. Thus, it is hypothesized that: H1: New ventures will differ in economic performance based on the stage of the life cycle in the venture’s entered industry.

Although the majority of the prior research has found differences in new venture performance attributable to the stage of the life cycle in the venture’s entered industry,

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TABLE 3 Prior Entrepreneurship Research Findings Regarding Industry Concentration Studies

Operationalization

Findings

Biggadike (1979)

Categorization based on fourfirm concentration ratio

Kunkel (1991) Tsai, MacMillan, and Low (1991)

Categorization based on fourfirm concentration ratio Three-firm concentration ratio

Ventures entering industries with low concentration achieved higher ROI and ROS than those ventures entering highly concentrated industries; No difference found for Relative Market Share Industry concentration does not affect ROE

McDougall, Robinson, and DeNisi (1992)

Four-firm concentration ratio

Ventures in highly concentrated industries achieve best market share gains; No difference found in ROI Ventures entering highly concentrated industries experience slower market share growth; No difference found in ROI

there is some disagreement as to which stage of the life cycle is associated with superior new venture performance. In addition, studies that utilized alternative measures of new venture performance found that the stage of the life cycle had divergent impacts on differing performance measures, as shown in Table 2. For example, five of the eight studies indicate that new ventures entering industries early in the life cycle achieved superior performance on at least one measure of new venture performance. Conversely, two of the eight studies found that ventures were more successful when entering industries in the mature stage of the life cycle. Nonetheless, prior theory and the majority of prior research suggests that new ventures that enter industries in the introductory stage of the life cycle achieve superior levels of venture performance. Thus, it is hypothesized that: H1a: New ventures entering industries during the introductory stage of the life cycle will achieve higher levels of economic performance than ventures entering in other stages of the life cycle.

Industry Concentration Industry concentration is theorized to be the most important element of industry structure in the field of industrial organization. However, McGee (1988) pointed out that IO research has produced conflicting results regarding the influence of industry concentration on performance. Hofer (1975) and Porter (1980) theorized that industry concentration is an important industry structural element in the field of strategic management. However, the majority of the prior research in strategic management has failed to find a statistically significant relationship between industry concentration and firm performance (e.g., Marshall and Buzzell 1990; Ravenscraft 1983; Yip 1982). There have been relatively few entrepreneurship studies examining the influence of industry concentration on new venture performance. Table 3 provides a summary of some of the key studies. These studies have produced conflicting evidence regarding the influence of industry concentration on measures of new venture performance. These differing results may be attributable to the differing measures of new venture performance utilized in such

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studies. Thus, three of these four studies examined the influence of industry concentration on the venture’s relative market share. One study found no statistically significant relationship, one found a positive relationship, and one found a negative relationship. In addition, three of these four studies found that industry concentration does not affect profitability-based measures of new venture performance. In short, prior theory suggests that industry concentration is an important element of industry structure that should influence firm performance. However, the vast majority of the strategic management and entrepreneurship research has failed to find a statistically significant relationship between industry concentration and firm profitability. In addition, prior entrepreneurship research has produced conflicting evidence regarding the relationship between industry concentration and relative market share. Due to the mass of evidence in strategic management and entrepreneurship suggesting that industry concentration does not influence firm profitability, it is hypothesized that: H2: There will be no differences in new venture economic performance based on the degree of concentration in the venture’s entered industry.

Entry Barriers Entry barriers are theorized to be an additional important structural characteristic of industries in the field of industrial organization. In particular, prior theory and research suggests that product differentiation is the strongest source of entry barriers. Conversely, research on the influence of other measures of entry barriers, such as economies of scale and absolute cost barriers, suggests that these measures are substantially less robust in their relationship with performance (Bain 1959). Shepherd (1975) argued that entry barriers are a secondary characteristic of market structure. Prior strategic management studies have produced conflicting evidence regarding the influence of entry barriers on firm performance. Thus, Harrigan (1981) found high entry barriers were associated with higher firm profitability. Conversely, Marshall and Buzzell (1990) found that high entry barriers were associated with lower firm profitability. Two other primary studies (Harrigan 1983; Yip 1982) examined the influence of entry barriers on the likelihood of entry, which is not the focus of this study. Six key research studies in the field of entrepreneurship that have examined the influence of entry barriers on measures of new venture performance are summarized in Table 4. Three of these six studies examined the influence of entry barriers that rose after the venture’s entry into the industry on subsequent new venture performance. However, this study examined the influence of entry barriers at the time of the venture’s entry into the industry on subsequent new venture performance. Thus, three of these six studies are not relevant for this research. All three of the remaining studies provided evidence that entry barriers at the time of a venture’s entry into the industry do not influence most subsequent measures of new venture performance (Feeser 1987; Kunkel 1991; McDougall 1987). In sum, prior theory and research suggests that entry barriers, exclusive of product differentiation, are a relatively unimportant determinant of new venture performance. Thus, it is hypothesized that:

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TABLE 4 Prior Entrepreneurship Research Findings Regarding Entry Barriers Studies

Operationalization

Findings New ventures generated higher returns for investors when barriers to entry rose after such ventures entered the industry Ventures achieved higher ROI when no major competitors had entered within past 5 years Entry barriers do not influence sales growth

McDougall (1987)

Categorization based on structural barriers and threat of retaliation by incumbents Categorization based on whether significant competitors had entered market within past 5 years Categorization based on questionnaire to founders Composite variable of five subvariables

Stuart and Abetti (1990)

Categorization based on Likert scale questionnaire

Kunkel (1991)

Categorization based composite variable of five subvariables

Sandberg (1986)

Miller and Camp (1985) Feeser (1987)

Entry barriers do not influence market share or market growth; Lower entry barriers associated with higher ROI Subsequent barriers to entry were associated with better composite performance measure Entry barriers do not influence ROE

H3: There will be no differences in new venture performance based on the height of entry barriers in the venture’s entered industry.

Product Differentiation Product differentiation is theorized to be the second most important structural characteristic of industries in the field of IO (Bain 1959; Caves 1972). Prior theory in the fields of IO and strategic management suggests that firms occupying industries characterized by homogenous products compete primarily on the basis of price, with resulting lower relative profit margins (Caves 1972; Porter 1980). Nonetheless, prior IO research has produced conflicting evidence with regard to the relationship between the degree of product differentiation in the industry and performance, as discussed by McGee (1988). The influence of the degree of product differentiation in an industry on measures of firm performance has received relatively little attention in the field of strategic management. Prior research in strategic management has examined the degree of product differentiation, i.e., relative quality or differentiation, as a competitive strategy variable. However, this study focused on structural characteristics of industries as potential determinants of new venture performance rather than on individual firm-level competitive strategies and tactics. Thus, these competitive strategy studies are not reviewed here. Two primary studies in the field of entrepreneurship have examined the influence of the degree of product differentiation in a venture’s entered industry on measures of new venture performance. Sandberg (1986) found that ventures entering industries characterized by a high degree of product differentiation achieved superior levels of new venture performance. By contrast, Kunkel (1991) did not find a statistically significant relationship between the degree of product differentiation in the venture’s entered industry and new venture performance. However, Kunkel did find that approximately 90% of the ventures examined in his study entered industries characterized by a high degree of product differentiation, which suggests that industries with high degrees of product differentiation are more attractive. Based on prior theory and research that

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suggests that the degree of product differentiation is an important industry structural characteristic, it is hypothesized that: H4: There will be differences in new venture performance based on the degree of product differentiation present in the venture’s entered industry.

Based on prior theory and research, it is also expected that new ventures will be more successful when entering industries with high degrees of product differentiation. Thus, it is hypothesized that: H4a: New ventures entering industries characterized by a high degree of product differentiation will achieve superior levels of new venture economic performance.

METHODS Research Design and Sample Selection This research utilized a longitudinal research design for new ventures that entered various industries. The primary reasons for the use of this type of design were to determine: (1) the influence of the structure of the venture’s entered industry on the subsequent performance of new ventures; and (2) the generalizability of this study’s findings for new ventures undertaking an initial public offering (IPO). The data base selected for this study consisted of firms that had undertaken an IPO between 1980 and 1987. The criteria, which were used to screen for inclusion into this study’s final sample, included: (1) independence from corporate sponsorship or prior corporate parentage; (2) under 6 years old at the time of the IPO; (3) still operating under the founding management team; and (4) involved in the creation of goods or services (mutual firms and holding companies excluded). The final data base of 199 ventures competed in seven of the ten industry sectors defined the Standard Industrial Classification code. The seven industry sectors (SIC codes) and number of ventures within each industry sector were: mining (1041–1499) 3; manufacturing (2011–3999) 117; transportation, communications, and public utilities (4011–4971) 12; wholesale trade (5012–5199) 4; retail trade (5211–5999) 7; finance and real estate (5141–6799) 9; and services (7011–8999) 47. It should be noted that some prior research studies have classified those SIC codes between 4011–6799 as “services.” However, this study followed the classification of industry sectors defined by the SIC code, which is also consistent with the major industrial sector classifications utilized by the Census Bureau. In sum, 71 SIC codes were represented by 199 ventures, yet ventures entering the manufacturing sector of the economy comprised 59% of the final sample. Within the manufacturing sector of the economy, there were 31 four-digit SIC codes represented. However, 57 of the 117 ventures entering the manufacturing sector competed in the “357” three-digit SIC code classification, which represents computer hardware. Representative companies include success stories such as Sun Microsystems, Compaq, and Seagate as well as (eventual) market failures such as Osborne Communications, Pinetree Computer, and Visual Technology. In addition, 13 ventures entered the biotechnology segment and 12 ventures entered the medical instruments segment within the manufacturing sector. An additional 24% of the ventures entered the services sector, with 24 of these

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TABLE 5 Characteristics of New Venture Sample Used in the Study Characteristic Revenues for fiscal year prior to IPO Net income for fiscal year prior to IPO Total assets for quarter prior to IPO Total equity for quarter prior to IPO Proceeds of IPO to venture Age of venture at time of IPO

Lower Quartile

Median

Mean

Upper Quartile

297,866

3,878,778

10,865,231

13,507,000

2796,289

15,000

32,718

901,195

1,325,530

5,539,000

11,150,898

15,460,600

287,855

1,853,000

4,850,724

7,649,000

3,870,000

6,900,000

12,572,189

15,897,000

29 months

40 months

40 months

54 months

47 ventures entering the “737” three-digit SIC code, which represents business services (computer software and design). The ventures included in the final data base are not representative of all new ventures due to the availability of equity capital. However, this sample does offer some comparability to other samples of firms undertaking IPOs. In particular, the average total assets (mean) prior to the quarter in which the venture went public was $11,151,000, which is very similar to comparable averages of $11,123,000 and $11,377,000 for studies conducted by Deeds, Decarolis, and Coombs (1997) and Burrill and Lee (1993). It should also be noted that the ventures included in the final sample were not a homogenous set of firms with regard to pre-IPO characteristics such as revenues, net income, total assets, and total equity. Thus, the amount of proceeds from the IPO that went directly to the venture (after fees and equity to shareholders) also exhibited substantial variation. The characteristics of this study’s sample with regard to the aforementioned variables are shown in Table 5.

Data Sources The COMPUSTAT data base served as the primary source for gathering financial information for assessing all eight measures of new venture performance, and also provided information necessary for classifying the entry barriers variable. The COMPUSTAT data base is the largest available data base on publicly held companies in the U.S., with over 200 variables on more than 15,000 companies covering the last 20 years. Unlike the PIMS data base, the COMPUSTAT data base allows the researcher to identify company specific data, which is derived from the public filings of such companies. Four-digit SIC codes were used in gathering industry information. Market growth rates were obtained from Industry Norms and Key Business Ratios compiled by Dun and Bradstreet Credit Services. The IPO prospectuses submitted to the Securities and Exchange Commission (SEC) between 1980 and 1987 provided information, exclusive of market growth rates, necessary for the classification of stage of the life cycle. IPO prospectuses were also utilized to obtain information necessary for classifying the product differentiation variable. Marino, Castaldi, and Dollinger (1989) noted that IPO prospectuses offer a rich source of data on the strategies and competitive environments/ industry structures of new ventures. They further comment on the reliability of such data, “Due to reporting requirements, SEC scrutiny, and sanctions for falsification, the

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data sources discussed here [IPO prospectuses] can largely be relied on” (p. 59). Industry concentration ratios for each venture’s entered industry were obtained from Census of Manufactures Concentration Ratios in Manufacturing, which is published by the U.S. Bureau of Census.

Operationalization of Economic Performance Variables This study examined the influence of industry structural elements on eight measures of new venture performance: (1) sales level; (2) change in sales; (3) net profit level; (4) earnings before interest and taxes; (5) return on sales; (6) return on assets; (7) return on invested capital; and (8) return on equity. The average of the first 3 complete fiscal years after a venture’s IPO was used for each of these eight measures. The use of 3-year averages is common in prior studies on the determinants of new venture performance (e.g., Kunkel 1991; Sandberg 1986). In addition, the use of 3-year averages smoothes out the yearly fluctuations, which are likely to be quite extreme with this sample of new ventures, while also providing measures which are more long-term in nature.

Operationalization of Industry Structural Variables Prior research in the fields of industrial organization economics, strategic management, and entrepreneurship have utilized somewhat divergent operationalizations of the four industry structural elements examined in this research, as shown in Tables 2, 3, and 4. To provide consistency with prior research in the field of entrepreneurship, this study utilized operationalizations of these four industry structural elements, which have been the most frequently utilized in prior entrepreneurship studies.

Stage of the Life Cycle The approach used to operationalize the stage of the life cycle variable was based on the four-stage PIMS life cycle classification utilizing the following criteria (Biggadike 1979): 1. Introductory: primary demand just starting, many potential users unfamiliar with product; 2. Growth: real growth 10% or more, technology and/or competitive structure still changing; 3. Maturity: potential users familiar with products, technology and competitive structure still changing; and 4. Decline: products viewed as commodities, weaker competitors exiting (p. 117).

Industry Concentration The approach used to operationalize industry concentration was based on the categorizations developed by Biggadike (1979). Industry concentration was classified as high if the four-firm concentration ratio was greater than or equal to 75%. Industry concentration was classified as moderate if the four-firm concentration ratio was between 55% and 75%. Industry concentration was classified as low if the four-firm concentration ratio was less than or equal to 55%.

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The four-firm concentration ratio is only available for SIC codes, which consist of manufacturers, i.e., those SIC codes between 2000 and 3999. Therefore, the influence of industry concentration in the venture’s entered industry can only be tested for 117 of the 199 ventures included in this study’s sample.

Entry Barriers The approach used to operationalize entry barriers was based on combining five subvariables into a composite score to measure the height of entry barriers: (1) industry concentration; (2) economies of scales; (3) product differentiation; (4) capital intensity; and (5) market growth rate (Kunkel 1991; McDougall 1987). These are also the five measures of entry barriers theorized to be important in IO. Because the scales of these five variables are not the same, the raw scores can not simply be added to attain a composite score. Each of the five variables was standardized to have a mean of zero and a standard deviation of one. Since the rate of growth of total market demand inversely affects height of entry barriers, the standardized score for this subvariable was multiplied by negative one. Thus, higher scores on each of the subvariables contribute to higher entry barriers. Classification of the composite height of entry barrier into the three categories was based on the standardized Z-score: (1) high if the Z-score was greater than or equal to 0.15; (2) average if the Z-score was between 20.15 and 0.15; and (3) low if the Z-score was less than or equal to 20.15. Data required to calculate the height of entry barriers composite score is only available for manufacturing firms, represented by SIC code between 2000 and 3999. Therefore, the influence of entry barriers in the venture’s entered industry can only be tested for 117 of the 199 ventures in this sample.

Product Differentiation The approach used to operationalize product differentiation followed the approach utilized by Sandberg (1986) and Kunkel (1991). More specifically, the degree of product differentiation in each venture’s entered industry was classified as high, moderate, or low based on the four attributes identified by Scherer (1970): (1) evidence of multidimensionality of products; (2) the rate of change in the technology of products; (3) differences in quality of products; and (4) image differences in products. Evidence that consumers prefer one venture’s products over those of competitors with equality of prices was also used as an indication of the degree of product differentiation.

Interrater Reliability The classifications of the stage of the life cycle and product differentiation variables were based, in part, on information contained in the IPO prospectus of each venture. Consistent with other studies that have utilized content analysis (e.g., Kunkel 1991; Sandberg 1986), interrater reliability was used to check these two classifications. The independent rater is an assistant professor at a major university who is very familiar with the classifications utilized in this research. Chi-square tests revealed a probability of less than 0.001 that the initial agreement level could have resulted from random chance. Moreover, all classifications were agreed

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TABLE 6 Characteristics of Independent New Venture Sample Used in the Study Industry Structural Element

Classifications

Class Composition Membership Percentages

Stage of life cycle

Introductory Growth Maturity Decline High Moderate Low High Moderate Low High Moderate Low

9.05 51.26 30.15 9.55 6.84 1.71 91.45 18.80 47.86 33.33 90.45 6.53 3.02

Degree of industry concentration

Height of entry barriers

Degree of product differentiation

on after the raters discussed the classifications on which they initially differed. In 94% of the cases, these differences occurred because one rater had overlooked a key piece of data relevant to the classification.

Class Compositions for Industry Structural Variables The class compositions for this study’s industry structural variables are shown in Table 6. As shown in Table 6, the operationalizations of (1) stage of the life cycle and (2) entry barriers produced reasonable differentiation among the industry environments entered by new ventures. Conversely, this study found that over 90% of the ventures examined entered industries characterized by: (1) a low degree of industry concentration and (2) a high degree of product differentiation. As previously mentioned, this study included ventures that entered seven different sectors of the economy. Statistical tests were utilized to determine if differences in any of the eight measures of new venture performance could be attributable to interindustry variations in performance. This study found that none of the eight measures of new venture performance examined in this study had statistically significant differences attributable to the sector of the economy that a new venture entered. This study also found that the use of four-digit industry adjusted performance measures had negligible impact on the levels of statistical significance with regard to the influence of industry structure on new venture performance. Thus, this study reports the results for each venture’s unadjusted measures of new venture performance, which is also the approach utilized in the prior new venture research discussed above.

Data Analysis Techniques This study utilized nonparametric statistical data analysis techniques to examine the influence of industry structure on alternative measures of new venture performance. Nonparametric techniques were utilized as the assumptions of normality and equal variances underlying the theoretical development of analogous parametric statistical techniques were substantially violated by the data utilized in this research. This study utilized the nonparametric Kruskall-Wallis analysis of variance proce-

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TABLE 7 Nonparametric Analysis of Variance Results (p-Values) for Industry Structural Variables

Performance Variables Sales level Change in sales Net profit Earnings before interest and taxes Return on sales Return on assets Return on invested capital Return on equity

Stage of Life Cycle p-Values (n 5 199)

Industry Concentration p-Values (n 5 117)

Entry Barriers p-Values (n 5 117)

Product Differentiation p-Values (n 5 199)

0.0015 0.2607 0.0072 0.0241 0.0433 0.0149 0.0077 0.2976

0.9594 0.5310 0.3430 0.3313 0.6549 0.8314 0.8720 0.7706

0.3900 0.1690 0.8452 0.8719 0.5237 0.8890 0.9098 0.7569

0.3498 0.9403 0.4760 0.5207 0.4414 0.7339 0.6654 0.1458

dure for testing the equality of medians from three or more samples. A nonparametric alternative of Fisher’s least-significant difference multiple comparison procedure was utilized when the Kruskall-Wallis procedure produced statistically significant results. Similarly, the nonparametric Mann-Whitney-Wilcoxon pairwise comparison procedure was utilized when Fisher’s procedure produced statistically significant results.

RESULTS The results of the nonparametric Kruskall-Wallis analysis of variance tests for the primary industry structural hypotheses are shown in Table 7 for the eight measures of new venture economic performance. The results shown in Table 7 are in the format of p-values, which denote the level of statistical significance found for each of the tests. Gibbons (1985) and Daniel (1990) recommended reporting p-values so that the readers can draw their own conclusions regarding the results. In addition, Dunn (1964), Gibbons (1985), and Neave and Worthington (1988) recommend utilizing p-values of 0.15 to 0.25 to denote statistically significant results when testing for overall comparisons involving three or more classes. Neave and Worthington (1988) state, “As a general rule, the higher the value of k [the number of classes] the larger the value of that should be used” (p. 257). Dunn further states (1964, p. 248): On the general subject of a, I believe that in making multiple tests and comparisons, one might tend to use a value of a considerably larger than the traditional .05. The advantage of using the overall level rather than making p tests each at a .05 level, say, lies in being able to communicate one’s results better with an overall level. And so it seems that there is usually no reason to choose the level so high that substantial differences become exceedingly difficult to establish.

Nonetheless, this research conservatively utilized a p-value of 0.10 to denote statistically significant results for the tests, involving overall comparisons among three or more classes.

Results of Stage of the Life Cycle Hypotheses The nonparametric ANOVA results shown in Table 7 provide support for the hypothesis that new ventures would differ in economic performance based on the stage of the

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TABLE 8 Nonparametric Pairwise Comparison Results (p-Values) for Stage of Life Cycle Variable Performance Variables Sales level Change in sales Net profit Earnings before interest and taxes Return on sales Return on assets Return on invested capital Return on equity

Introductory . Growtha p-Values

Introductory . Maturitya p-Values

Introductory . Declinea p-Values

0.0492 0.9540 0.0249

0.0004 0.5369 0.0004

0.0138 0.2184 0.0164

0.0285 0.0833 0.0127

0.0020 0.0135 0.0023

0.0334 0.0730 0.1073

0.0140 0.2619

0.0007 0.0934

0.0442 0.4011

a Denotes the significance level for hypothesis that ventures in introductory stage achieve higher levels of new venture performance.

life cycle in the venture’s entered industry, at a 0.05 level, for six of the eight measures of new venture economic performance for the 199 ventures included in the analysis. In addition, the other two measures of new venture economic performance approached the upper limit of statistical significance of 0.25 recommended by Dunn (1964) and others for overall comparisons involving three or more classes. The results shown in Table 8 provide support for the hypothesis that new ventures entering industries in the introductory stage of the life cycle would achieve superior new venture performance, particularly when compared to those which entered the maturity stage. Finally, all of the results were in the hypothesized direction, although some were not statistically significant at a 0.05 or a 0.10 level.

Results of Industry Concentration Hypothesis The nonparametric ANOVA results shown in Table 7 provide support for the hypothesis that there would be no differences in new venture performance based on the level of concentration in the new venture’s entered industry, at a 0.10 level of significance. The industry concentration analysis involved those 117 ventures in the manufacturing sector for which concentration ratios are available.

Results of the Entry Barrier Hypothesis The nonparametric ANOVA results shown in Table 7 provide support for the hypothesis of no differences in new venture performance based on the height of entry barriers in the venture’s entered industry, at a 0.10 level of significance. As was the case with the industry concentration analysis, information necessary for the classification of the new ventures’ entered industry into entry barrier categories is only available for the manufacturing sector, which consisted of 117 ventures.

Results of the Product Differentiation Hypothesis The nonparametric ANOVA results shown in Table 7 do not support the hypothesis of differences in new venture performance based on the degree of product differentia-

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tion in the venture’s entered industry, at a 0.10 level of significance, for the 199 ventures included in the analysis.

DISCUSSION This study sought to determine the influence of industry structural elements on alternative measures of new venture economic performance. In particular, this study heeded calls by Cooper (1993) and others for examining the influence of industry characteristics on differing performance measures for independent new ventures. As noted above, a large number of prior studies have examined the influence of industry structure on only one or two measures of new venture performance for samples consisting of corporate ventures utilizing the PIMS data base. Finally, this study examined those measures of industry structure and new venture performance that prior theory and research suggest are important.

Influence of Stage of the Life Cycle on Alternative Measures of New Venture Performance The results of this study provide strong support for prior theory and research, which suggests that the stage of the life cycle in the venture’s entered industry is a strong determinant of subsequent new venture performance. In addition, this research found that new ventures that entered industries in the introductory stage of the life cycle achieve superior levels of new venture performance, particularly when compared with those ventures that entered industries in the maturity stage. These findings suggest that developing industries offer new ventures greater opportunities for establishing a viable position in the market with relatively little likelihood of retaliation by established incumbents. A surprising finding was that there was not a statistically significant relationship between stage of the life cycle and sales growth, which is contrary to both prior theory and research. However, it is understood that new ventures experience trade-offs between growth and profitability objectives. Thus, this finding may be a result of the sample utilized in this study. As noted by Kazanjian and Drazin (1990, p. 140), new ventures go through different stages of development. After the commercialization stage, ventures enter the growth stage and experience pressures to attain profitability, which must be carefully balanced against future growth. Donaldson (1991) provides further support of the pressures on new ventures to attain profitability: “Thus by initially responding to product market forces and the priorities of growth and diversification, the company becomes more dependent on the external capital market, and must then reemphasize ROI and shareholder benefit as the price of that dependency” (p. 125). The vast majority of the new ventures in this study’s sample are just entering the growth stage of venture development, and all of the ventures are subject to the demands of the external capital market for attaining profitability. Thus, sales growth after the IPO could be a secondary objective of such ventures.

Influence of Industry Concentration on Alternative Measures of New Venture Performance As hypothesized, the level of industry concentration in the venture’s entered industry did not have a statistically significant relationship with any of the eight alternative mea-

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sures of new venture performance examined in this study. However, caution should be utilized when interpreting this result. This study found that over 90% of the new ventures entered industries characterized by low degrees of industry concentration, which is consistent with the findings of Kunkel (1991). Thus, entering industries with low degrees of concentration could be a necessary but not sufficient condition for achieving successful entry. Partial support for this explanation is provided by Dean and Meyer (1996), who found that new venture formations are inversely related to the four-firm industry concentration ratio. In addition, this study’s sample of high potential new ventures that had undertaken an IPO is a special subset of all new ventures. Thus, the sample could contain a bias for ventures entering industries with low concentration, which prior theory and research suggests are more attractive environments for entry, particularly with regard to obtaining equity capital (Siegel, Siegel, and MacMillan 1993).

Influence of Entry Barriers on Alternative Measures of New Venture Performance As hypothesized, the height of entry barriers prior to the venture’s entry into their industry did not have a statistically significant relationship (at a 0.15 level) with any of the eight measures of new venture performance. This finding is consistent with prior entrepreneurship research on the influence of entry barriers at the time of the venture’s entry into the industry on subsequent new venture performance. There are three explanations for the lack of statistically significant relationships among measures of new venture performance and the height of entry barriers. First, the ventures examined in this study were able to raise relatively large amounts of equity capital by undertaking an IPO. The infusion of equity capital offered these ventures an opportunity to build more efficient plants, while also providing reserves for shortterm losses. Second, Dean and Meyer (1996) found that high industry growth, which is a characteristic of the majority of the industry environments in this sample, eliminates barriers to entry. Finally, Yip (1982) found that technological change, which is also characteristic of the majority of this study’s industry environments, destroys barriers to entry. Entry barriers had the strongest relationship was with the change in sales variable (p 5 0.17), with none of the remaining performance measures achieving a level of significance below 0.39. The moderate relationship between entry barriers and the change in sales variable could be due to the entered industries having less established firms with more room for sales growth for the entering firms. In addition, early entrants in these industries could have erected subsequent barriers to entry.

Influence of Product Differentiation on Alternative Measures of New Venture Performance Contrary to the expectations of this study, the degree of product differentiation in a venture’s entered industry did not have a statistically significant relationship (at a 0.10 level) with any of the eight measures of new venture performance. The strongest relationship was with the return on equity variable (p 5 0.15), with none of the remaining measures achieving a level of significance below 0.35. The moderate relationship be-

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tween product differentiation and ROE could be due to those ventures which entered industries characterized by commodity like products requiring more equity capital to fund future investments in fixed assets. Such ventures would require highly efficient operations in to compete with established competitors on the basis on price, while also achieving lower relative profit levels (e.g., ventures entering the mining sector). This study found that over 90% of the new ventures entered industries characterized by high degrees of product differentiation, which is consistent with the findings of Kunkel (1991) who also examined ventures undertaking an IPO. Thus, both samples are a special subset of all new ventures. As such, entering industries with high degrees of product differentiation could be a necessary but not sufficient condition for achieving successful entry. Partial support for this explanation is provided by Harrigan (1981) and Yip (1982), who found that high degrees of product differentiation induces entry.

Limitations This study’s sample of high potential independent new ventures is not typical of all new ventures due to their access to relatively large amounts of equity capital raised through an IPO. Conversely, these independent ventures did not have access to capital resources of a parent company, which differentiates them from corporate ventures contained in the PIMS data base. Although this study’s sample did share some commonalities with other samples of ventures that had undertaken an IPO, (e.g., Burrill and Lee 1993; Deeds et al. 1997; Kunkel 1991), the above results are not necessarily generalizable for all new ventures. A second potential limitation is that this study did not control for the amount of resources either prior to or immediately after the IPO. However, Shrader and Simon (1997) did not find a relationship among venture resources and venture performance, and suggested that “the leverage of existing resources may be more important than the possession of any given resource” (p. 63). Finally, this study utilized operationalizations of industry structural variables that have been the most frequently utilized in prior entrepreneurship research in order to provide consistency with prior studies and provide comparability with such research. However, the operationalizations of industry concentration and product differentiation failed to provide reasonable differentiation among the industry environments entered by the new ventures examined in this research, as discussed above. The operationalization of industry concentration developed by Biggadike (1979) in his study of corporate ventures utilizing the PIMS data base is probably inappropriate for this and Kunkel’s (1991) study of independent ventures. Support for this proposition is provided by Marshall and Buzzell (1990), who note that the PIMS data base is a biased sample that is overrepresented by firms occupying industries with high concentration levels. In addition, operationalizing the degree of product differentiation utilizing advertising intensity may be more appropriate than the method adopted by Sandberg (1986) and Kunkel.

Managerial Implications This research has important implications for practitioners and investors. In particular, this research indicates that high potential new ventures undertaking IPOs achieve higher levels of profitability and sales when entering industries in the introductory stage of the life cycle, thus offering investors greater potential returns. Such ventures typically

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face weaker competitive pressures, have the opportunity provided by the infusion of equity capital to establish an important presence in the marketplace, and have the potential to erect subsequent barriers to entry. The results of this study also provide support for the relative attractiveness of industry environments characterized by (1) low degrees of industry concentration and (2) high degrees of product differentiation, which are also among the criteria utilized by venture capitalists for funding decisions when examining the attractiveness of the market (Siegel et al. 1993). Although over 90% of the ventures entered these two types of industry environment, there were minimal performance differences attributable to ventures entering these environments. Thus, the presence of low concentration and high differentiation in the industry environment may be necessary but not sufficient conditions for achieving higher returns for those ventures (and their investors) seeking to undertake an IPO. The result of this study also suggest that entry barriers may be relatively less important in attractive industry environments that are characterized by: (1) relatively high growth rates in the early stages of the industry life cycle; (2) low degrees of industry concentration; and (3) high degrees of product differentiation. These factors and the presence of technological change mitigate the influence and importance of entry barriers by offering new ventures the ability to pursue heterogenous strategies. Finally, this research found that industry structural variables have differential impacts on alternative measures of new venture performance, and that a trade-off between profitability and sales growth may exist for ventures undertaking IPOs. Although the pressures of the external capital market may require a focus on profitability for new publicly held ventures, such ventures need to assess which performance variables are the most important indicators of effectiveness that can lead to long-term success in the marketplace. In sum, new ventures must be cognizant of the opportunities and threats provided the external environment of the industry in which they enter, and formulate effective strategies that enable the venture to capitalize on the available opportunities and minimize the inherent threats in their environment.

Directions for Future Research This study sought to advance the literature on the influence of industry structure on alternative measures of new venture performance by: (1) providing theoretical justification for the measures of industry structure and new venture performance selected for examination; and (2) examining the influence of industry structural elements on eight alternative measures of new venture performance for independent new ventures. Several areas of future research have already been suggested. In particular, future research should seek to develop more “fine-grained” measures of industry concentration and product differentiation that provide more discrimination among differing industry environments. This research found that the most commonly utilized operationalizations of these measures in prior entrepreneurship research failed to provide reasonable differentiation among the industry environments entered by the ventures examined in this study. It appears that the “cutpoints” utilized by Biggadike for industry concentration may be inappropriate for studies that do not examine the corporate-sponsored ventures in the PIMS data base. Future research should examine alternative classifications/opera-

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tionalizations of the industry concentration ratio to see if this study’s findings are corroborated or refuted. In addition, the operationalization of product differentiation utilized in this study failed to provide reasonable differentiation. Future research should examine the usefulness of alternative operationalizations of the product differentiation variable, such as (1) the industry advertising intensity ratio or (2) categorizations based on the enduser of the product (industrial or consumer), as suggested by Caves (1972). Future research should also attempt to cross-validate these results on other samples of new ventures such as corporate-sponsored ventures and independent ventures, which do not undertake an IPO. Finally, future research should examine the influence of new venture strategy, and the joint influence of new venture strategy and industry structure on multiple measures of new venture performance. Prior studies have determined that the joint influence of strategy and industry structure is a stronger determinant of new venture performance than either of these factors in isolation (Kunkel 1991; McDougall 1987; Sandberg 1986). However, most of these studies have only utilized only one or two measure(s) of new venture performance. Thus, it would be useful to determine if such factors have a differential impact on alternative measures of new venture performance.

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