Matching strategic resources with strategy and industry structure.

Bacon, Calvin M., Jr. ; Hofer, Charles W.

ABSTRACT

Although new ventures have provided many benefits to society, the failure rate for new ventures has been extremely high. Entrepreneurship theorists have suggested that strategic resources are a possible determinant of new venture success and that strategic resources should be matched to business strategy and environmental situations. However, few empirical studies have considered strategic resources. Therefore, this exploratory study empirically examined the influence of resources on new venture performance in the presence of certain strategic and industry structure conditions. This study found a significant relationship between distinctive competencies and new venture performance when matched to low-cost strategies and between intellectual resources and new venture performance for firms in industries in the shakeout stage. T h e s e results imply that entrepreneurs should match their strategic resources to the situation. New ventures adopting a low-cost approach should develop administration competencies in order to establish a competitive advantage over rivals. It is possible that adding better and more administrative personnel could pay for itself in a higher return to stockholders.

Furthermore, when new ventures are in industries in the shakeout stage, intellectual property such as patents and copyrights become more important. This effect may be particularly important in high-growth industries considered in this study. Possessing intellectual property may enable firms to differentiate their goods and services from their competition or may provide a means of limiting direct competition.

INTRODUCTION

Research on the determinants of new venture performance has focused on three possible factors: the entrepreneur, industry structure, and strategy. Of these, studies have identified strategy and industry structure, and the interaction between strategy and industry structure as important determinants of new venture success.

Other than strategy, industry structure, and the entrepreneur, resources have also been identified as a possible determinant of new venture performance. Resources are instrumental in developing new products and services in existing markets and in preparing ventures for entry into new markets (Azzone, Bertele, & Rangone, 1995; Brush, Greene, & Hart, 2001). Resources are particularly important in turbulent environments in which long-term competitiveness depends upon the venture's ability to identify, develop, and exploit critical resources (Azzone, et al., 1995). Additionally, resources such as knowledge and technical skills are directly linked to technical entrepreneurship and innovation (Klavans, 1994) that are vital to high-growth ventures.

Resource-based theory proposes that resources determine a firm's strategic advantages and firm performance (Wernerfelt, 1984; Mahoney & Pandian, 1992; Barney, 1991). In this view, the concept of resources includes attributes of firms that affect the formulation and implementation of strategy (Barney, 1991; 2001). There is a progression of value creation, starting with Generic Resources, and passing through Capabilities, Distinctive Competencies, Strategic Resources, and finally to Strategic Advantage (Brush, et al., 2001). Because resources are critical to the firm's success, the type, amount, and timing of resources accumulated are vital.

Resource-based theory rests on three major assumptions. The first major assumption is that firms seek to earn above-average returns (Wernerfelt, 1984; Conner, 1991; Robinson, 1998). Viewing firms as seekers of above-average returns is important because it suggests behaviors which might be different if firms were seeking average returns. For example, firms seeking average returns may be willing to adopt strategies that imitate an average firm while firms seeking above-average returns are forced to consider innovative strategies that may involve more risks.

The second major assumption within resource-based theory is that resources are asymmetrically distributed across competing firms (Conner, 1991; Schulze, 1994; Barney, 2001). The heterogeneity of resources may be caused or maintained by several mechanisms. For example, asymmetries may arise from resource mobility barriers due to asset specificity (Williamson, 1985); cognitive effects (Fiol, 1991); or social complexity (Barney, 1991). Also, isolating mechanisms that may develop due to time compression diseconomies of imitation or due to regulatory requirements such as patent law, copyright law, or trademark law (Conner, 1991). Resource-based theory proposes that at least one of these mechanisms acts to prevent the equal distribution of resources within and between industries.

The third major assumption is that differences in resources lead to differences in product characteristics or service characteristics that result in variation in firm performance (Conner, 1991; Schulze, 1994; Barney, 2001). Because these differences in product characteristics lead to strategic advantages, they have been classified into two categories: characteristics that allow a firm to sell a differentiated product at a higher price, and characteristics that permit a firm to make an undifferentiated product at a lower cost (Schumpeter, 1950; Porter, 1991; Peteraf, 1993).

According to resource-based theory, above-average returns occur as firms create value through the accumulation and deployment of resources (Conner, 1991). Determining new combinations of resources is an innovative activity which requires entrepreneurial vision (Schumpeter, 1950). Because the combinations of resources must be unique, some researchers have suggested it may be impossible to generalize ways to systematically create strategic advantages (Dierickx & Cool, 1989; King & Zeithaml, 2001). However, other researchers believe that there may be general configurations of resources that influence the survival and growth of firms (Hall, 1993; Miller & Shamsie, 1995).

Strategic management theory generally proposes that the return of above-average profits comes from strategic advantages. These advantages are the result of two mechanisms: product differentiation or low cost (Schumpeter, 1950; Porter, 1980; Conner, 1991). That is, firms may either create products that are superior to others and thereby command a relatively high price while maintaining moderate costs, or they may provide common products at reasonable prices that are relatively inexpensive to make.

This research suggests a relationship between new venture performance and resources. Because previous studies have confirmed the importance of strategy and industry structure in determining new venture performance, strategy and industry structure are included in the general model used in this study. Therefore, the general model for this research include new venture performance (NVP), industry structure (IS), strategy (S), and resources (R) as given by:

NVP = (S, IS, R)

While the relationship may be simple and direct, there are also other possibilities. First, it is possible that resources explain the same variation as do industry structure and strategy. Therefore, this study examines resources in the presence of strategy and industry structure. Second, the influence of resources may be through an interaction with strategy or industry structure. Third, resources may not influence new venture performance at all.

DEVELOPMENT OF HYPOTHESES

Generic Strategy and Distinctive Competencies Prior Findings

Chandler and Hanks (1994) found an interaction effect on new venture performance between generic strategy and distinctive competencies. They noted that new ventures adopting differentiation strategies and possessing competencies in service areas performed better than other firms. This effect was particularly evident when the firms' capabilities allowed them to provide customer service training and to implement process technologies.

In addition, the results in Chandler and Hanks (1994) indicate that low-cost strategies were more successful when matched with the appropriate resource-based capabilities. This was most apparent in new ventures for which there were few customers. The authors suggested that firms with dedicated products going to few customers were able to lower marketing costs and product development costs to create low-cost advantages. In keeping with prior findings, this research expected to find that low-cost strategies will be more successful when matched with product competencies or administration competencies. Furthermore, this study expected to find firms with strategies dealing with differentiation to be superior when new ventures have competencies in research and development or in marketing. Generic Strategy and Distinctive Competencies Hypotheses

1a: Ventures adopting low-cost strategies and possessing production
competencies will perform better than other ventures.

1b: Ventures adopting low-cost strategies and possessing administration
competencies will perform better than other ventures.

1c: Ventures adopting differentiation strategies and possessing R&D
competencies will perform better than other ventures.

1d: Ventures adopting differentiation strategies and possessing
marketing competencies will perform better than other ventures.

Stage of the Industry Life Cycle and Intellectual Resources Prior Findings

In their study of Hollywood film studios from 1936 to 1965, Miller and Shamsie (1996) found a relationship between industry structure and strategic resources. The research showed that ventures with knowledge-based resources performed better than other firms in the late stages of the industry life cycle. According to Miller and Shamsie (1996), firms in late stages of the industry life cycle face uncertainties that knowledge-based resources handle best due to their inherent flexibility. This research expected to confirm Miller and Shamsie's (1996) findings. It was expected that ventures entering late stages of the industry life cycle will perform better than other firms when they possess intellectual resources.

Stage of the Industry Life Cycle and Intellectual Resources Hypotheses

2a: Ventures entering late stages of the industry life cycle and owning
patents will perform better than other ventures.

2b: Ventures entering late stages of the industry life cycle and owning
trademarks will perform better than other ventures.

2c: Ventures entering late stages of the industry life cycle and owning
copyrights will perform better than other ventures.

OPERATIONALIZING THE VARIABLES

Strategic Resources

This investigation concentrated on two types of intangible resources: intellectual resources and distinctive competencies. Intellectual resources include number of copyrights, number of patents, and number of trademarks. Distinctive competencies include: research and development, production/operations, marketing/sales/distribution, and administration. Strategic resources were operationalized from previous studies of intangible resources. In specific, patent count and patent citation count categories were from studies by Wright (1994), and Finkle (1996). Cooperative agreement categories were adapted from McGee (1994), Eisenhardt and Schoonhoven (1996), Miller and Shamsie (1995; 1996), and Mosakowski (1991). Trademarks and copyrights as predictors of performance were not found in the strategic resources literature. This research extended prior studies by including these measures of intangible resources. Competencies were adapted from Rangone (1999).

Intellectual Resources

Indicators of patents and patent applications were found in the S-1 documents. The self-disclosed IPO data were considered to be the best data for measuring patents. Trademarks were operationalized as registered or unregistered trademarks self-disclosed in the initial public offering statement. The prospectus documents were chosen as the best source of data because companies have a high level of responsibility in reporting the information. Copyrights were operationalized from the Library of Congress database. In comparing the results of the database search with the initial public offering statements, it was noticed that some companies do not choose to report copyright information in the prospectus statements. Apparently the writers of the documents did not see copyrights as information that is material to the prospective investor. Therefore, this research used the Library of Congress information.

Distinctive Competencies

Distinctive competence was operationalized by evaluating the relative strength of the functional areas of the firm. Four functional areas were assessed: research and development; production/operations; marketing/sales/distribution; and administration. Each functional area was assigned a value of strong, average, or weak, based on the company's ability compared to the competition (referred to as strategic competence on the classification grid).

When firms had more than one strong functional area, the internal capabilities rankings were used to determine the firm's distinctive competence. Therefore, when the firm has more than one strong functional area relative to the competition, the distinctive competence was defined as the strong functional area which has the highest internal capabilities ranking.

Generic Strategy

Generic strategy was adopted from the model proposed by Porter (1987). There are two possible generic competitive advantages: low cost and differentiation. Companies were classified as low cost if their prospectus said the company was "price competitive." It was inferred that low prices were associated with low cost. Companies were classified as differentiation if the prospectus indicated that the firm provided unique products or services.

Stage of the Industry Life Cycle

This study used a seven-stage model of the industry life cycle (Robinson, 1998; Robinson & McDougall, 1998). The seven stages were: startup, growth, shakeout, maturity, saturation, decline, and rejuvenation. Each stage in the industry life cycle was operationalized with certain indicators: industry growth rate, long-term trend in the industry growth rate, and gross national product.

New Venture Performance

Shareholder value created was a ratio based on changes in common stock prices and dividends of ventures. It was calculated by the following equation (Robinson & McDougall, 1998):

SVC = (SP4 - SP1) + D2-4) / SP1

where SVC represents shareholder value created, SP4 was the stock price at the end of year four, SP1 was the stock price at the end of year one, and D2-4 was the cumulative dividends for years two through four. Stock prices were adjusted for stock dividends and stock splits.

METHOD

The Selection of the Industries to be Studied

In addition to the three criteria listed above, one additional criterion was used to select the sample for this study. The sample had to account for the degree to which the industries involved had contributed to job growth. This criterion was added because one of the major contributions of new ventures to society is job growth. And, since job growth is closely associated with rapid growth in total demand, the first step in the selection process was to identify high-growth industries in the U.S. economy.

An analysis of the 100 fastest-growing firms in the U.S. (Fortune, 1996) helped to identify the highest-growth industries. Of the 100 fastest-growing firms, 38 were in the High-Technology Sector, 12 were in the Health Care Sector, and 7 were in the Energy Sector. This suggested that many high-growth industries would be in these sectors of the economy.

A list of industries with at least 10 new ventures conducting initial public offerings during 1987 through 1993 is presented in Table 1 (Inc. Magazine, 1987-1993). The list shows that the most frequently occurring industrial codes were 7372 (prepackaged software), 2834 (pharmaceutical preparations), and 6712 (bank holding companies). Furthermore, an analysis of the types of new ventures that went public indicates that 12 of the 17 most frequently occurring new venture industry codes were in the high-technology or health care sectors. This evidence supported the idea that high-technology and health care sectors were growing rapidly.

To further investigate which industries should be included in this study, the Standard Industrial Classification Codes were classified as Health Care Sector and High-Technology Sector for the initial public offerings between 1987 and 1993 as given in Table 2. The Pharmaceutical, Biotechnology, and Medical Equipment industries comprised almost 6 percent of the 2,371 initial public offerings during that period. The Computer, Semiconductor, and Software industries contributed another 6 percent.

High-technology and health care sectors each had more initial public offerings than any other sector in the U.S. from 1987 to 1993 and the industries within the high-technology and health care sectors had more initial public offerings than any other industry except for banking. Due to the high rates of reorganization and because no banking firm was listed on the top 100 fastest-growing firms, the banking industry was not included in the sample for this study.

Venture Selection

Because this research dealt with the performance of new ventures, three criteria were required. First, the age of the venture was eight years or less (Biggadike, 1979; Weiss, 1981). Second, ventures were independent startups rather than corporate ventures (Hofer & Sandberg, 1987). Third, the ventures were involved in the creation of goods and services rather than serving as types of financial instruments.

An initial search found 303 firms qualified for this study. Twenty five firms were randomly selected from each of the six industries in the study. The three industries of the health care sector each contribute 25 firms for a total of 75 firms from health care and the three industries of the high-technology sector each contribute 25 firms for a total of 75 firms from high-technology. The six industries combine for a total sample of 150 firms. The ventures in the sample were blocked to ensure equal representation in all 6 industries.

Data Collection

There were four major reasons for selecting Securities and Exchange Commission initial public offering prospectus filings as a source of new venture data. First, these filings must meet Securities and Exchange Commission content standards, and therefore they offer a reliable source of new venture data (Marino, Castaldi, & Dollinger, 1990). Second, the standardized form used in initial public offering filings facilitated the use of content analysis. Third, Securities and Exchange Commission filings were considered more comprehensive than proposals submitted to venture capitalists. Finally, because of Securities and Exchange Commission oversight, the filings were thought to provide more objective information than questionnaires that may be subject to rationalization of actions by self-reporting entrepreneurs (McGee, 1994).

High-growth new ventures for this study were taken from the list of all initial public offerings between 1987 and 1993 as given by Investment Dealer's Digest (1987-1994). This time period was selected for three reasons. First, 1987 through 1993 was a time of high levels of activity for high-growth ventures. Therefore, there was a relatively large number of new ventures with public information available. Second, one of the industries in the study, software, was first identified as a separate industry by the standard industrial classification system in 1987. Consequently, it was decided that all data should begin in 1987 to facilitate intra-industry comparisons. Third, due to need to operationale new venture performance over a three year period, 1993 was the latest initial public offering date in which performance data were available. N e w venture performance data included stock prices, dividends, stock splits, and number of employees. Stock prices were gathered from the Daily Stock Report (1987-1996). Dividends, stock splits, and number of employees were gathered from Moody's Investment Services (1995-1997a, b, c). Copyright data were gathered from the Library of Congress Information System.

Because data came from initial public offering registration statements, the researcher had performed content analysis to code the data collection forms. To check coding accuracy, the study included an inter-rater reliability procedure. In all, the researcher included 29 data points on 150 observations for a total of 4,350 individual pieces of data. Due to the magnitude of the data collection effort, it was deemed impractical to conduct an inter-rater reliability check on the full sample. Instead, this study adopted the procedure discussed in Kachigan (1986) whereby a sub-sample of 18 ventures were used to infer the reliability for the full sample. A chi-square test was used on the 18 observations to check the reliability. Every measure except stage of industry life cycle indicate positive inter-rater reliability (p<.0001 in all cases).

Sample Description

The average performance of ventures included in this study as measured by shareholder value created was -0.42751. In other words, on the average, shareholders lost 42.7 percent of their investment (adjusted for the NASDAQ average return during the period of the study). The range of shareholder value created was from -1.6255 to 3.7296. So some shareholders lost over 100 percent of their investment (after adjusting for the market return) while some shareholders almost quadrupled their money in three years. The average age was 5.3 years, the range was from 1 to 8 years, and the mode was 6 years. Ventures in this sample had about 5 to 6 years of history before going public.

Statistical Methods

Data in this study were extremely non-normal (SAS Institute, 1988). A normal probability plot of the residuals using the final sample clearly indicated non-normality, and the W statistic for normality in the SAS procedure was 0.944576 with a corresponding p-value of 0.001. So, it was extremely unlikely that the residuals were normally distributed. Because the data were very non-normal, it was decided that the analysis should proceed with SAS nonparametric statistics. Specifically, the research used the Kruskall-Wallis analysis of variance procedure for testing the equality of medians from three or more samples. This non-parametric procedure was used because non-parametric statistics were more robust than parametric statistics when parametric assumptions are violated (Gibbons, 1985; Daniel, 1990).

Results

The results for the possible interaction between generic strategy and strategic resources are given in Table 3. These results generally support the research hypothesis that new venture performance varies based on the possible interaction between generic strategy and distinctive competencies. In particular, new ventures adopting low-cost generic strategies perform better when they possess administration distinctive competencies.

The results for the possible interaction between intellectual resources and stage of the industry life cycle are presented in Table 3. These results support the research hypothesis that new venture performance varies based on a possible interaction between stage of the industry life cycle and intellectual resources. Specifically, this study shows that new ventures owning patents or owning copyrights perform better than other new ventures when they are entering late stages of the industry life cycle.

Discussion

Findings from this study have implications for entrepreneurship practice. For example, these results may be helpful in the decision-making process for venture capitalists, investment bankers, angel investors, or other new venture investors. In addition, these findings may be instructive to entrepreneurs in their understanding of elements necessary in the enterprise-creation process.

Theorists have long speculated that performance is related to strategic resources, but few empirical studies have been conducted to confirm or disconfirm the relationship. This research provides much-needed evidence to add to the discussion of resource-based theory as well as to the study of entrepreneurship. Furthermore, this study identifies multiple measures of strategic resources that may be used in future studies of new venture performance. In particular, this work indicates the importance of intangible resources which may provide a link between resource-based theory and knowledge-based theory in future research.

This study has shown that the current model of new venture performance should be revised to include strategic resources in certain situations. Prior studies have shown the importance of intellectual resources (Chatterjee & Wernerfelt, 1991) and distinctive competencies (Chandler & Hanks, 1994; Markides & Williamson, 1996). However, none of the previous studies considered strategic resources and the other variables in the research model, namely strategy and industry structure, in the same study.

It is suggested that future research help overcome the limitations of this study and extend the scope of this research based on the findings here. One suggestion for future research design is to incorporate methods of normalizing data, so parametric statistics may be utilized. As entrepreneurship research advances, studies should begin testing causal models. In addition, this research could be extended through development of a broader, more representative sample to make the conclusions more generalizable. Further, extending the research sample by additional time periods may provide for longitudinal testing, and future entrepreneurship research may benefit from using alternative forms of data-gathering techniques such a direct observation, interviews, or surveys.

There are several theoretical extensions of this study which may help to explain the overall effect of new venture performance. The most pressing is probably the testing of strategy, industry structure, strategic resources, and entrepreneurial team in the same model. Other theoretical extensions might include the influence, if any, of organizational learning, of employee knowledge, skills, and abilities, or of rates of technology development on new venture performance.

CONCLUSIONS

These results imply that entrepreneurs should match their strategic resources to the situation. New ventures adopting a low-cost approach should develop administration competencies in order to establish a competitive advantage over rivals. It is possible that adding better and more administrative personnel could pay for itself by a higher return to stockholders.

Furthermore, when new ventures are in industries in the shakeout stage, intellectual property such as patents and copyrights become more important. They may be particularly important in high-growth industries such as explored in this study. Possessing intellectual property may enable firms to differentiate their goods and services from their competition or may provide a means of limiting direct competition.

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Charles W. Hofer, University of Georgia

Table 1: Frequency of New Venture Industry Codes of Initial Public
Offerings for 1987 through 1993

Industry Code Name SIC Code Frequency

Pharmaceutical Preparations 2834 57
Prepackaged Software 7372 57
Bank Holding Companies 6712 44
Surgical & Medical Instruments 3841 35
Biological Products Except Diagnostic 2836 34
Semiconductor & Related Products 3674 34
Computer Peripheral Equipment 3577 25
Electromedical Equipment 3845 24
Eating Places 5812 24
Fire, Marine, & Casualty Insurance 6331 24
Telephone & Telegraph Apparatus 6331 24
Surgical Appliances & Supplies 3661 20
Diagnostic Substances 2835 16
Electronic Computers 3571 16
Radiotelephone Communications 4812 14
Electronic Components 3679 13
Health & Allied Services Nec 8099 13

Table 2: Initial Public Offerings Between 1987 and 1993 by Sector

Sector Industry SIC Name

Health Care Pharmaceutical Pharmaceutical Preparation
Health Care Biotechnology Biological Products
Health Care Medical Eqmt. Surgical & Medical Instr.
Health Care Medical Eqmt. Electromedical Equipment
Total Health Care
High Technology Computers Electronic Computers
High Technology Computers Computer Storage Devices
High Technology Computers Computer Terminals
High Technology Computer Computer Peripheral Eqmt.
High Technology Semiconductor Semiconductors
High Technology Software Computer Programming
High Technology Software Prepackaged Software
High Technology Software Computer Integrated Syst
Total High Technology
Total New Ventures

Sector SIC Count

Health Care 2834 60
Health Care 2836 34
Health Care 3841 35
Health Care 3845 24
Total Health Care 153
High Technology 3571 34
High Technology 3572 7
High Technology 3575 4
High Technology 3577 29
High Technology 3674 36
High Technology 7371 9
High Technology 7372 63
High Technology 7373 11
Total High Technology 193
Total New Ventures 346

Table 3: Summary of Results

H Measures p

1a Production Competencies, Low Cost > Others, 0.4102
 Low Cost
1b Administration Competencies, Low Cost > Others, 0.0222
 Low Cost
1c R&D Competencies, Differentiation > Others, 0.7493
 Differentiation
1d Marketing Competencies, Differentiation > Others, 0.1043
 Differentiation
2a Patents, Shakeout > No Patents, Shakeout 0.0364
2b Trademarks, Shakeout > No Trademarks, Shakeout 0.7287
2c Copyrights, Shakeout > No Copyrights, Shakeout 0.0078