Competitive analysis and new venture performance: understanding the impact of strategic...
Publication: Entrepreneurship: Theory and Practice
Date: Sunday, September 22 2002
Understanding the competition is important for success in today's marketplace (Chen, 1996; Youngblood, 1998). This is especially true among new ventures (companies that have been in existence for eight years or less) (McDougall et al., 1994; Zahra & Bogner, 2000). To survive and achieve
A growing body of research within the resource-based view (Penrose, 1959; Barney, 1991; Priem & Butler, 2001) and the dynamic capability perspective (Teece, 1991) suggests that firm-specific variables influence a firm's competitive advantage and its performance (Peteraf, 1993; Rumelt, 1991). Firm-specific variables include any assets, resources, capabilities, and idiosyncratic systems and processes (Barney, 1991). While historical factors might contribute to the accumulation of these resources, they are often the result of strategic decisions (Fahey, 1999). An understanding of the firm's competitive landscape and the intensity of the competition often directs the development and subsequent use of these resources and capabilities (Grant, 1998). Effective CA, therefore, can be especially useful in guiding managers' thinking about the types of assets and resources to develop, the capabilities to be deployed, and the strategic objectives to be emphasized (Fahey, 1999; Oster, 1994).
Understanding and predicting competitors' actions is a complex and challenging process. Competitors do not always behave in rational or comprehensible ways, reflecting internal political realities within companies (Zajac & Bazerman, 1991). These factors complicate the process of CA and make it difficult to forecast rivals' actions. With so much uncertainty, the CA staff has to rely on conjectures and develop different scenarios of potential actions and possible payoffs from these actions. This introduces an element of uncertainty, if not randomness, into CA.
Still, because the external environment poses uncertainty for new ventures, managers have to scan and interpret environmental changes to maintain their firms' viability and performance (Elenkov, 1997). CA, which is a subset of the environmental scanning process, helps new ventures define their opportunities and threats, develop strategies that differentiate them from rivals, and avoid competition with resource-rich competitors (Bell & McNamara, 1991). CA also enables managers to identify current and future rivals and compare their own resources and capabilities to those of their rivals (Porter, 1985). This process can assist managers to maintain an effective fit with their environment and increase their firms' performance (Stone & Brush, 1996). However, limited time and the bounded rationality of managers make the payoff from CA uncertain.
An effective CA system can also help managers identify areas where the firm can differentiate from its rivals and explore ways to make it difficult for the competition to imitate products and strategic moves. This process can decrease competitors' ability to discern the foundation of the company's market success. Effective CA, therefore, increases causal ambiguity (Reed & DeFillippi, 1990) and protects the firm's competitive advantage. Causal ambiguity is magnified by competitors' inability to decipher managers' cognitive styles and decision-making processes. This failure is reinforced by competitors' inability to comprehend the foundations of the firm's strategic choices as social complexity stems from the fact that information about the company's operations lies within their human capital. Social complexity also arises from the cultural norms that exist in a firm. These "taken-for-granted" norms make it difficult for rivals to discern a firm's sources of advantage. Thus, although CA gives managers information about rivals' thinking patterns, organizational cultures and structures, systems and processes, managers' interpretations of and conjectures about the competition will shape their firm's strategy. Managers develop strategies consistent with their companys' values, history and culture. This process can increase the social complexity competitors may experience as they try to analyze or predict a company's future moves (Zahra & Chapels, 1993).
Past CA research has focused on established companies (Ghoshal & Westney, 1991; Subramanian & Ishak, 1998), giving little attention to new ventures. Also, research on the CA practices of new ventures has been limited to discussions of the development and effectiveness of formal business plans. Thus, the way in which new venture executives collect and interpret competitive data is not clear (Stone & Brush, 1996). To date, little research has examined the links between CA and new venture performance (Mohan-Neill, 1995). An exception is the study by Peters and Brush (1996), which concluded that CA was important to new venture success. This informative study, however, was limited to examining new ventures' use of marketing information, rather than CA activities per se. Other CA research has been descriptive and normative in nature (Chen, 1996), relying mostly on case studies of established firms (e.g., Lenz & Engledow, 1988) or data from CA professionals (Prescott & Smith, 1987), which limits its generalizability to new ventures.
Past research offers little guidance on the characteristics that can maximize the payoff from CA systems. While there is evidence that some ventures primarily use informal sources to gather data about their competitors (Peters & Brush, 1996; Stone & Brush, 1996), the contribution of informal versus formal CA systems to venture performance is not well documented. Similarly, some writers continue to tout the importance of a comprehensive CA system for covering a wide range of competitors and conducting a thorough assessment of their capabilities and strategies. Comprehensive CA, however, can be costly and time consuming, especially for those ventures that may not have abundant resources. Accordingly, the venture's ability to develop innovative strategies may be reduced (Stone & Brush, 1996). Finally, user-oriented CA can improve financial performance by making the data accessible to managers in the right format and in a timely fashion (Fuld, 1985). While it is logical to expect this orientation to be positively associated with company performance, there is no compelling evidence to support this proposition, especially among new ventures.
The literature further suggests that the associations between CA characteristics and new venture performance might vary from one venture to another, depending on the characteristics of the venture (Peters & Brush, 1996). Although several venture-related variables (e.g., age and size) may affect these associations, a venture's ownership type (hereafter origin) is important. Origin refers to whether the venture is corporate-sponsored or created by an independent entrepreneur. Ventures with different origins may differ significantly in their missions, resources, and capabilities (Zahra & Bogner, 2000), leading us to conclude that corporate and independent venture managers will use different CA systems. This suggests that certain CA characteristics should be more strongly associated with performance in one type of venture than in the other. This study, therefore, proposes that origin type moderates the associations between the characteristics of CA and performance.
Managers should also consider the uncertainty of their firms' external environment when designing CA systems (Huber, 1991). Different environmental conditions may favor particular CA systems, possibly strengthening the associations between these characteristics and new venture performance. Consequently, if new ventures need to design their CA systems to maximize performance, then the financial payoff from CA characteristics in different environments should be documented.
This study examines the moderating effect of strategic uncertainty on the relationship between CA system characteristics and new venture performance. Strategic uncertainty is defined as managers' subjective assessment of the uncertainty of their firms' external environments, weighted by managers' evaluation of the importance of relevant environmental sectors to the success of their firms' strategy (Elenkov, 1997). Uncertainty refers to the difference between the amount of information required to perform a task and the amount of information available (Boyd, Dess, & Rasheed, 1993; Sharfman & Dean, 1991). The benefits of CA would likely increase when strategic uncertainty is high. CA provides timely and rich data (Daft & Lengel, 1986) that can promote discussions of the venture's markets and competitive strategies, which can strongly improve venture performance.
The remainder of this article consists of three sections. The next section covers the major characteristics of CA and their associations with new venture performance and then discusses the potentially moderating effects of venture origin and strategic uncertainty on these associations. The article then introduces a study that empirically tests the hypotheses and summarizes the results. The conclusion provides a discussion of the findings and their implications for managerial practice and future research.
Theoretical Background and Hypotheses
Research suggests that firm-specific variables, such as the quality of a new venture's management, resources, and strategies, can affect performance (Tsai, MacMillan, & Low, 1991; Barney, 1991; Peteraf, 1993; Priem & Butler, 2001; Rumelt, 1991). Performance also depends on the match a venture achieves between its competitive strategies and its external environment. Achieving this match usually requires the venture to understand its industry's boundaries and competitive dynamics (Grant, 1998) as it seeks to develop the skills, capabilities, and competencies required to achieve a strong and profitable market position. Whereas industry structure can determine the basis and intensity of the competition (Grant, 1998) and a firm's financial performance (Sandberg, 1986), firm-specific resources, and capabilities also influence its ability to achieve market success and attain superior performance.
CA System Characteristics and New Venture Performance
CA can significantly impact performance because competitive information helps the new venture develop a competitive advantage. Researchers, therefore, have attempted to identify the CA characteristics that influence the firm's performance (Prescott & Smith, 1987). Three characteristics appear to be especially important: formality, comprehensiveness, and user orientation.
Formality. Data about competitors can be collected formally and informally (Daft, Sormunen, & Parks, 1988; Porter, 1980). Given new ventures' limited resources, some managers may primarily use informal means to collect competitive data (Stone & Brush, 1996), such as personal sources of marketing data. These sources may include information about rivals' strategies, sales, market shares, and products (Peters & Brush, 1996). Talking to customers, suppliers, consultants, venture capitalists, and bankers can provide diverse competitive data informally and efficiently.
The effectiveness of informal CA activities, however, may be limited. Informal CA is a non-systematic process, which can lead venture managers to emphasize particular sources (e.g., bankers) and types (e.g., financial) of information to the exclusion of others (e.g., marketing strength). Such selective attention is not conducive to developing a complete profile of the competition. Informal CA may also ignore important characteristics of the phenomena being observed, such as timing or frequency (Bluedorn et al., 1994). When informal CA prevails, managers may focus on "short-term issues, and fail to share information with their peers" (Yasai-Ardekani & Nystrom, 1996). Also, data from informal CA may not be carefully analyzed (Peters & Brush, 1996) and, therefore, might not improve company performance.
The limitations of informal CA can be overcome when a more formal system is employed--one that is officially sanctioned and supported by management. A formal system exists when a unit or group (Sutton, 1988) is given specific responsibility for conducting CA activities. Formality, for example, can reduce the information redundancies and gaps resulting from informal CA (Yasai-Ardekani & Nystrom, 1996). It can also increase the payoff from CA by enhancing the legitimacy of the process with internal and external stakeholders, such as bankers (Prescott & Bhardwaj, 1995). Legitimacy gives the CA staff access to executives, which can increase CA funding. Formality also enables CA personnel to interact with the strategic planning staff and influence the new venture's strategy by infusing competitive intelligence into the strategy-making process. The literature also suggests that the payoff from formal strategic planning is higher than from informal or ad-hoc planning. Finally, formality promotes professionalism among the CA staff (Ghoshal & Westney, 1991) and improves their competence in collecting, analyzing, and interpreting data. Therefore,
H1: Formal CA is positively associated with new venture performance.
Comprehensiveness. As a characteristic of CA, comprehensiveness refers to the thoroughness of the process and the breadth and depth of the variables considered and analyzed. Comprehensive CA covers two distinct domains within the venture's environment; namely, its industry/competitive environment and the larger macro-environment that exists outside the firm's competitive arena. As such, ventures can consider a wide range of data related to their current competitors, as well as conduct an interindustry analysis to evaluate the competition in adjacent industries or foreign markets. Comprehensive intra- and interindustry analyses keep venture managers abreast of emerging opportunities and threats that can dramatically impact their firms' ability to succeed.
However, comprehensive intra- and interindustry CA can be costly and time consuming and may even divert managers' attention away from the day-to-day issues facing their ventures. It can also cause information overload or paralysis by analysis that reduces the managers' ability to take advantage of emerging opportunities (Zajac & Bazerman, 1991). Information overload can also pressure managers to focus on data they perceive as important or that supports their beliefs or agendas (Rice & Hamilton, 1979). This, of course, may limit the payoff from comprehensive CA.
A comprehensive CA system has many advantages that can enrich new venture performance (Youngblood, 1998). Smaller firms that undertake comprehensive planning report higher levels of performance than those firms that do not carry out comprehensive planning (Rue & Ibrahim, 1998). Comprehensive CA allows a venture to evaluate its major industry and examine adjacent industries from which entrants may emerge. This analysis can also give venture managers insights into potential shifts in industry structures and boundaries (Porter, 1980). Comprehensive CA typically considers current and potential competitors' functions, assets, capabilities, and operations (Sutton, 1988); examines rivals' missions, goals, and strategies; evaluates competitors' tangible and intangible resources and skills (Grant, 1998); and predicts rivals' moves or countermoves (Chen, 1996). Using various sources of information, the CA staff can thoroughly examine these issues and identify opportunities and threats in the environment, as well as improve venture performance (Daft, Sormunen, & Parks, 1988). Comprehensive CA can clarify competitive conditions and guide the selection and execution of the venture's strategic choices, which, therefore, improves performance. It can also improve venture performance by alerting managers to forthcoming industry changes (Yasai-Ardekani & Nystrom, 1996).
As the above discussion suggests, comprehensive CA can be conducted at the intra-and interindustry levels. Comprehensiveness at both environmental levels is expected to lead to superior performance. Comprehensive intra- and interindustry CA allows the venture to consider major environmental changes within its industry and examine adjacent industries from which new entrants may emerge. Intraindustry comprehensiveness can improve the firm's awareness of competitive forces, opportunities, and threats (Porter, 1980). Interindustry CA may allow the firm to react to changes in technologies or the regulatory environment, as well as trends in the social and cultural arenas. Comprehensive intra- and interindustry analyses, therefore, can lead to improved performance, as follows:
H2a: Comprehensive intraindustry CA is positively associated with new venture performance. H2b: Comprehensive interindustry CA is positively associated with new venture performance.
User Orientation. User orientation becomes evident when the information needs of the executives drive the design, function, operation, output, and distribution of the CA results (Barndt, 1994). Yet, user orientation has some potential drawbacks if carried to an extreme. Excessive concern with the users' immediate needs can lead to the exclusion of contradictory pieces of data that challenge the users' beliefs or preferences. When this occurs, outlier observations that signal emerging trends might be discarded (Zahra & Chapels, 1993). This can lead to "group think" among managers, which can undermine a firm's ability to develop and execute an effective strategy (Zajac & Bazerman, 1991). Recognizing users' divergent needs for competitive data in CA reports, however, can minimize this risk.
User-oriented CA systems are guided by the type of information managers need, not by the type of data that the CA group can gather (Gilad, 1989). This orientation is achieved by collecting and quickly disseminating relevant data about the industry and competition, which would improve venture managers' ability to cope with uncertainty. The timely dissemination of CA data also puts competitive information in the hands of venture managers and, in turn, can promote insightful discussions of industry and competitive trends. This can increase CA contributions to new venture performance (Bernhardt, 1994).
CA systems that do not provide executives with the type and amount of information they need are virtually useless. Ghoshal and Westney (1991, p. 19) observe that in the organizations they studied "a majority of both the [CA] clients and the analysts perceived a significant gap between what was needed by the organization and what was currently being delivered by the company's competitor analysis system." Thus, although CA systems can collect all kinds of data, unless the information is compiled, analyzed, packaged, and disseminated in a user-friendly way, the contribution of the system to company performance is compromised. These observations suggest the following hypothesis:
H3: A user-oriented CA system is positively associated with new venture performance.
Venture Origin and the Effective CA--New Venture Performance Relationship. The above hypotheses suggest that an effective CA system would have four characteristics: formality, inter- and intraindustry comprehensiveness, and user orientation. While the optimal CA system would have all four characteristics in place, this article attempts to illustrate that each dimension individually contributes to the effectiveness of a venture's CA practices. The payoff from CA (i.e., its effect on new venture performance) will be limited if one or more of these three dimensions is absent. If this argument is correct, then the effectiveness of a CA system can be conceptualized as follows (1)
CA effectiveness = formality + inter/intra-industry comprehensiveness + user orientation
The anticipated return from an effective CA system is expected to vary by venture type and level of strategic uncertainty. In this study, venture origin is used to refer to whether the new venture is independently owned or is a part of a corporation. We expect new venture origin to moderate the relationship between an effective CA system and new venture performance. Three factors reinforce the need to examine this moderator effect. First, corporate and independent ventures frequently compete head to head in the same markets. How corporate and independent ventures collect and use data about their competition to increase performance should, therefore, be explored. Second, corporate and independent ventures may differ in their financial performance (Zahra, 1996b). Understanding the differences between corporate and independent ventures' CA efforts may clarify the factors that impact performance among different types of ventures. Third, one shortcoming of past research is the lack of attention devoted to the potential differences in the collection and analysis of CA data of corporate and independent ventures. This study, therefore, sets the stage for future fine-grained analyses of the associations between CA characteristics and performance among different new venture types.
The association between the formality of CA and new venture performance is expected to be higher among corporate than independent ventures. In an independent venture, the manager (owner) is usually responsible for conducting CA activities and acting upon their results. The entrepreneur often uses his or her social network to gather and interpret competitive data (Bell & McNamara, 1991). This informal CA is less costly and does not require specialized expertise (Mohan-Neill, 1995). Since the entrepreneur (owner) is usually the center of the strategy making and CA processes in the independent venture (Bell & McNamara, 1991), formality is not particularly important for gaining legitimacy for the CA system. While some independent venture owners might somewhat formalize their CA activities to meet the scrutiny of financial backers (Stone & Brush, 1996), they may continue to rely on informal sources of competitive data.
An effective CA system, however, is expected to be more strongly associated with performance among corporate ventures. These ventures expand the operations of their parent companies into new industries or enhance their learning about new markets and technologies (Block & MacMillan, 1993). Therefore, corporate parents usually set specific goals for their ventures, provide them with resources, and monitor their progress. Indeed, because many corporate ventures are established to explore opportunities in industries unfamiliar to their sponsors (Burgelman & Sayles, 1986), CA can provide the information necessary for the sponsors' planning and control efforts. An effective CA system can also enhance the legitimacy of the operations of corporate ventures with their sponsors. Legitimacy can improve new ventures' access to the resources needed to achieve high performance. This argument supports Stone and Brush's (1996) observation that planning efforts (which often include some CA) are more important for firms seeking external legitimacy.
The comprehensiveness of an effective CA system is proposed to be more beneficial for corporate than for independent ventures. Corporate ventures usually target broad market segments (Hofer & Sandberg, 1987), which have diverse customers and different competitors. Success in these markets requires attention to a larger set of environmental variables than more narrowly defined markets where the identity and skills of rivals are better known (Grant, 1998). Given that independent ventures typically pursue more narrowly focused markets, they may not conduct comprehensive CA (Oster, 1994). For these independent ventures, comprehensive analyses of multiple market segments and multiple competitors would be overkill (Prescott & Smith, 1987). Corporate ventures are also more likely to have greater resources and skills, which enables them to conduct more comprehensive CA than independent ventures. Similarly, the ability of corporate ventures to respond effectively to the needs of their diverse stakeholders improves when they conduct comprehensive CA. Independent ventures, conversely, may not have the capacity and skills necessary to perform such comprehensive CA because the owners are usually responsible for identifying and analyzing the competition. Broad CA may not be the best use of the independent venture managers' time and may drain the ventures' limited resources, which can lower performance. Therefore,
H4: The association between an effective CA system and new venture performance will be higher among corporate than independent ventures.
Strategic Uncertainty and the Effective CA--New Venture Performance Relationship. Strategic uncertainty is also expected to moderate the associations between CA system characteristics and new venture performance. Even though no prior empirical research has explored the relationship between strategic uncertainty and CA, studies examining a firm's scanning activities and performance highlight the potentially moderating role of strategic uncertainty (Daft, Sormunen, & Parks, 1988; Yasai-Ardekani & Nystrom, 1996). The literature also suggests that strategic planning systems are conducive to higher venture performance in more uncertain environments, even though the evidence is far from conclusive in this regard (Schwenk & Shrader, 1993). Indeed, some new ventures may use CA to limit the ill effects of strategic uncertainty in their environments. In order to maximize the benefits of CA, the ventures' CA systems should match the level of strategic uncertainty they face (Priem, Rasheed, & Kotulic, 1995).
Two limitations of past research should be noted. First, most past studies did not focus on CA efforts per se; rather, they examined environmental scanning or other aspects of strategic planning activities (Priem, Rasheed, & Kotulic, 1995). It is unclear, therefore, whether or not they apply to CA. Second, past studies have also overlooked new ventures, which are especially vulnerable to strategic uncertainty and may lack the resources needed to conduct effective CA.
Strategic uncertainty defines the perceived lack of information about key dimensions of the environment that determine company performance. When strategic uncertainty is high, new venture managers may feel unsure about the potential success of their new ventures' operations. Highly uncertain environments require careful analysis and planning. Consequently, the payoff from formal CA will be greater under conditions of higher than lower strategic uncertainty. This is consistent with Daft, Sormunen, and Parks (1988) and Miller and Friesen (1983) who found stronger correlations between the frequency of environmental scanning and environmental uncertainty in more successful than in less successful firms. While these two studies did not specifically examine CA efforts, they indicate that formal, comprehensive, and user-oriented CA may be more strongly associated with company performance as strategic uncertainty rises.
CA formality ensures managers' attentiveness to collecting data about their external environment and competition and helps offset the ill effects of strategic uncertainty. CA data can promote discussions about the competitive arena and, therefore, prepare managers to cope with the sources of strategic uncertainty (Daft, Bettenhausen, & Tyler, 1993). Thus, when strategic uncertainty is high, the payoff from formal CA will be higher as formal CA can improve new ventures' understanding of their industries (Miller & Chen, 1996).
New ventures competing in highly uncertain environments can also benefit from undertaking comprehensive CA to determine their domains, understand their rivals, identify new niches, and choose the appropriate timing of different competitive initiatives (Smith, Grimm, & Gannon, 1992). As the study by Miller and Chen (1996, p. 432) suggests, when strategic uncertainty is high, a firm must employ multiple competitive tactics to counter the challenges and unforeseen contingencies that might arise within their own or adjacent industries. Comprehensive intra- and inter-CA also helps managers identify and implement effective competitive strategies in uncertain environments, which should improve new venture performance.
The association between the user orientation and new venture performance is also expected to be stronger when strategic uncertainty is high. While important for success in any condition, user orientation is expected to have a greater impact on performance when strategic uncertainty is high. Appropriate data, along with seasoned interpretations made available to the venture's executives at the right time and in the right format, are most useful to executives in mapping effective strategic choices (Fuld, 1985). Rapid dissemination of information can also help managers stay ahead of changing market conditions and expedite the venture's strategic changes. Speedy strategic decision making is conducive to superior performance when strategic uncertainty is high (Eisenhardt, 1989), as it allows the venture to pursue promising opportunities. The user orientation of a new venture's CA system is expected to provide greater payoffs when strategic uncertainty is nigh. Therefore,
H5: The association between an effective CA and new venture
performance will be higher when perceived strategic
uncertainty is high.
METHODS
Sample and Data. A mail survey was conducted in 1994 to collect data from new ventures competing in multiple industries. Names of the ventures were gathered from the directories of companies published by three southern states in 1991 and 1992; 913 manufacturing ventures, eight years or younger, were identified. Two mailings, conducted one month apart, were used to enhance the response rate. Of the 913 questionnaires mailed, 71 were undeliverable, a factor that might bias the results. Of the remaining 842 ventures, 228 completed the questionnaires--reflecting a response rate of 27.1 percent.
The survey targeted the chief executive officers (CEOs) or highest-ranking managers. These executives were believed to be the most knowledgeable individuals about the overall operations and CA activities of the ventures (Brush, 1992; Fuld, 1985). Further, given that senior executives were the major customers of CA results (Sutton, 1988), understanding their views was important. Respondents held senior positions in their ventures: 71 percent were CEOs, presidents, or owners, and the remaining 29 percent were vice presidents.
Given the nature of the research methodology used, common method and same source bias were potential problems. To remedy these issues, we used Harmon's (1967) "single factor" test. Accordingly, we entered all the survey items into an orthogonal factor analysis with a varimax rotation. This analysis produced nine factors, each of which had an eigenvalue of greater than 1.0. The fact that nine factors resulted, instead of having all items load on a single significant factor, suggests that common variance is not a serious issue.
Still, several steps were taken to test for potential biases, including the use of multiple respondents to test for the presence of same source bias and correlating the performance data collected from the respondents with secondary data sources. The questionnaire was sent to a second senior manager in each of the 228 responding ventures. Fifty-three of these managers returned completed responses that were then matched with replies from the first respondents, as done in prior research (Brush & Vander Werf, 1992; Chandler & Hanks, 1993). Simple correlations indicated significant inter-rater reliabilities on CA activities (r = .73, p < .001). Thus, the views of the senior executives were largely shared by the second respondent within the firm, reducing concerns about source bias. Data collected from the first respondents (n = 228) were used in the analyses. Next, secondary data were also correlated with the survey-based measures of the ventures' performance, age, and size. Comparing primary and secondary data is widely used in the literature to establish the validity of survey-based measures (e.g., Brush & Vander Werf, 1992; Chandler & Hanks, 1993, McDougall & Robinson, 1990; Priem, Rasheed, & Kotulic, 1995; Zahra, 1996a, b, c). Given that the correlations between the survey and secondary sources were significant, concern over common method variance was somewhat alleviated. The Appendix reports the steps taken to validate the survey measures.
We established the absence of response bias by finding no significant differences between responding and nonresponding ventures in age (t = .31, p < .64) and number of full-time employees (t = 1.02, p < .29). The [X.sup.2] test also compared responding and non-responding new ventures by origin (corporate vs. independent), but this test was insignificant (p = .41). T-tests also indicated responding firms to the first and second mailings were not significantly different in their age (t = 1.02, p < .23), employees (t = 1.13, p < .21), or sales growth (t = .44, p < .71). These analyses supported the sample's representation of its population.
Measures. Data were collected on CA system characteristics, new venture performance, origin, strategic uncertainty, and statistical controls. Below, the study's measures are described.
1. CA System Characteristics. Responses to 18 survey items were used to measure the characteristics of the ventures' CA systems. The items for CA formality (Brush, 1992; Fuld, 1985; Gilad, 1989; Youngblood, 1998), intra- and interindustry comprehensiveness (Sutton, 1988; Vella & McGongale, 1988), and user orientation (Barndt, 1994; Fuld, 1985) were based on the literature. These 18 items, drawn from a diverse set of studies, were subjected to an exploratory factor analysis as shown in Table 1 to identify different CA dimensions. This analysis was used primarily as a data reduction technique. An orthogonal factor analysis, with varimax rotation, yielded four significant factors (eigen-values > 1.0). The four factors had [alpha] coefficients between .72 and .61 and, together, explained 73.81 percent of the variance. To construct the four factors, scores of the relevant items (those with absolute loadings of .50 or more) were summed. Simple average scores were used in subsequent analyses.
The first two factors reflected different aspects of comprehensive CA. The first factor, which included items covering the variety of areas examined in CA efforts, was named intraindustry comprehensiveness. The second factor focused on potential competitors outside the ventures' main industry and was named interindustry analysis. The third factor represented CA formality, as evidenced by the financial and political support these activities received from the ventures' senior managers. The fourth and final factor captured the user orientation of CA as indicated by the perceived usefulness of the reports generated from this activity.
2. New Venture Performance. Company performance is a complex and multidimensional construct that is difficult to measure (Brush & VanderWerf, 1992; Chandler & Hanks, 1993, 1994; Cooper, 1993; Robinson, 1999). The use of multiple indicators to gauge firm performance, therefore, is recommended (Sandberg, 1986). The four indicators of performance used in this study included both growth and profitability because tradeoffs might exist between these measures (Tsai, MacMillan, & Low, 1991). Both objective and subjective performance indicators were used. The two objective performance criteria were market share growth (Tsai, MacMillan, & Low, 1991) and return on equity (McDougall & Robinson, 1990; Sandberg, 1986).
The market share growth measure has been used in several past studies (e.g., Chandler & Hanks, 1993; Tsai, MacMillan, & Low, 1991; Zahra & Bogner, 2000). It indicated the venture's ability to capture and build a strong market share, which is a key means of achieving profitability. This measure considers industry definition, overcoming a weakness in past researchers' use of sales growth as an indicator of new firm performance. Tsai and colleagues (1991) suggest that this variable "may be the best measure of new venture performance available" (p. 14). However, because market share is defined as the company's sales divided by industry sales, this may make market share growth data hard to interpret because of the constantly changing industry boundaries and the differences in the definitions of companies' industries.
Return on equity has also been widely used in past research as a measure of a venture's profitability (e.g., McDougall & Robinson, 1990; Sandberg, 1986; Zahra & Bogner, 2000). It indicated the effectiveness of the venture's management in generating returns on shareholders' funds. The use of return on equity, however, has been challenged (Chandler & Hanks, 1994) because it is affected by the owners' salaries and other cost items that are frequently not disclosed by the venture.
Gathering objective data for the measure of new ventures' financial performance is a challenging task (Bantel, 1998). Some owners are unwilling to share this information with outsiders (Dess & Robinson, 1984). Even when business owners are willing to share data with researchers, the accuracy of their figures cannot always be established. This has led some researchers (e.g., Chandler & Hanks, 1993) to call for the use of "categorical measures" that objectively capture new ventures' performance. Categorical measures enable managers to quickly and accurately provide the data needed, while still retaining confidentiality. In this study, however, we measured market share growth and return on equity using one item each. Respondents were asked to fill in a series of blanks, as done in some prior studies (Zahra, 1996b, c; Zahra & Bogner, 2000). The use of this format was necessitated by space limitations of the survey. To validate these figures, we collected corroborating data from secondary sources. As the Appendix reports, the correlations between the survey and secondary data were strong and positive, suggesting that the survey data captured new venture performance. Still, future researchers would benefit from using categorical measures, such as those proposed by Chandler and Hanks (1993) and Sandberg (1986).
Subjective Performance Index. The use of subjective data to capture senior managers' evaluations of a company's performance, though widely used (e.g., Chrisman & Leslie, 1989; Covin, Slevin & Covin, 1990; Dess & Robinson, 1984), has been the source of persistent controversy (e.g., Brush & VanderWerf, 1992; Chandler & Hanks, 1993; Smith, Gannon & Sapienza, 1989). Subjective performance evaluations are useful when objective data are not readily available (Dess & Robinson, 1984). Subjective measures also provide some insights into managers' perceptions of (Bantel, 1998) and satisfaction with (Covin, Slevin, & Covin, 1990) their companies' performance. Objective performance measures do not always offer such insights. Still, subjective performance measures are sometimes inaccurate and unreliable. Managers' personality and aspiration levels may affect these subjective evaluations. Also, the use of subjective performance data entails considerable tradeoffs, as illustrated by Chandler and Hanks' (1993) finding that a measure of "satisfaction with performance" had good internal consistency, acceptable inter-rater reliability, but inadequate external validity (p. 405). These authors, therefore, appropriately warn against the erroneous and inconclusive findings researchers might generate by using this measure.
Despite the above noted limitations, the use of subjective and objective performance measures are useful in achieving triangulation (Smith, Gannon, & Sapienza, 1989) because both objective and subjective performance measures have their own limitations. Consequently, we used a seven-item measure that reflected managers' satisfaction with their ventures' performance.
We extracted the seven performance areas from prior studies on new venture performance (e.g., Bantel, 1998; Brush & VanderWerf, 1992; Robinson, 1999) and developed one item for each. We measured satisfaction with (a) return on investments (Chandler & Hanks, 1993; Covin & Slevin, 1990; Covin, Slevin & Covin, 1990; Sapienza, 1992); (b) return on equity (Covin & Slevin, 1990; Covin, Slevin & Covin, 1990; Sandberg, 1986; Stuart & Abetti, 1987); (c) return on assets (Chandler & Hanks, 1993; Dess & Robinson, 1984); (d) sales growth (Bantel, 1998; Birley & Westhead, 1994; Brush & VanderWerf, 1992; Cooper, Woo, & Dunkelberg, 1989; Dess & Robinson, 1984; Sapienza, 1992); (e) net profit margin (Chandler & Hanks, 1993; Covin, Slevin, & Covin, 1990; Zahra, 1996b); (f) growth in number of employees (Birley & Westhead, 1994; Brush & VanderWerf, 1992); and (g) market share growth (Covin & Slevin, 1990; Covin, Slevin, & Covin, 1990; Sapienza, 1992).
An orthogonal factor analysis with a varimax rotation of the seven performance items yielded two significant factors. As shown in Table 2, the first factor had four items (return on investment, return on equity, return on assets, and net profit margins) and was named "profit satisfaction". The second factor had three items (growth in sales, growth in employees, and growth in market share) and was named "growth satisfaction." The two factors explained 71.26 percent of the variance. To construct the two factors, the scores of the relevant items (those with absolute loadings of .50 or more) were summed and average scores were used in subsequent analyses. The Appendix presents evidence of inter-rater reliability, internal consistency, and validity of the two subjective performance indices.
3. Venture Origin. Ventures created by individual entrepreneurs were classified as independent (n = 141). Ventures owned by established corporations were classified as corporate (n = 87). Using dummy coding, corporate ventures were set to 1.
4. Strategic Uncertainty. Measuring the firm's external environment has been a subject of debate (e.g., Boyd, Dess, & Rasheed, 1993; Dess & Beard, 1984; Duncan, 1972; Sharfman & Dean, 1991). Whereas Dess and Beard (1984) and Duncan (1972) focused on complexity and dynamism, concluding that they were separate dimensions, others (e.g., Hitt & Ireland, 1984) argued that these variables were components of a one-dimensional construct: environmental uncertainty. While disagreements continue about the best way to measure the environment, researchers have been consistent in measuring strategic uncertainty (e.g., Daft, Sormunen, & Parks, 1988; Elenkov, 1997; Sawyerr, 1993). We, therefore, used Daft and colleagues' (1988) approach.
Daft and colleagues (1988) propose that it is the interaction of dynamism and complexity (rather than the mere existence of one of these two conditions) that is likely to cause managers to experience uncertainty about their firms' environment. An environment can be complex but predictable, or dynamic and relatively easy to assess the direction of this change. A complex and dynamic environment, however, substantially increases managers' sense of strategic uncertainty (Bluedorn et al., 1994). The interaction of dynamism and complexity makes it difficult for managers to estimate the magnitude, speed, direction, and impact of the environmental forces (Boyd & Fulk, 1996). The study used Daft and colleagues' (1988) four-step approach, as follows:
(a) Executives evaluated the importance of six major sectors of their ventures' external environment for financial success (5 = Of Crucial Importance vs. 1 = Of Little or No Importance). This evaluation was necessary because managers were expected to focus their CA activities on those sectors they believed to be more important for their ventures' survival and success (Boyd & Fulk, 1996; Daft, Sormunen, & Parks, 1988). Strategic uncertainty covered six sectors: competitive, consumer, technological, regulatory, economic, and socio-cultural sectors. This step generated an "importance" score.
(b) Executives also rated the amount of change (dynamism) in each sector, using a 5-point scale (5 = Very high rate of change vs. 1 = Very low rate of change). Environments characterized by high rates of change usually increase executives' uncertainty (Daft, Sormunen, & Parks, 1988). This step generated a "change" score.
(c) Executives reported their perceptions of the complexity of each environmental sector, using a 5-point scale (5 = Complex vs. 1 = Simple). Environmental complexity might introduce confusion about cause-effect relationships and increase strategic uncertainty. This step yielded a "complexity" score.
(d) For each environmental sector, the importance score was multiplied by the product of change and complexity scores. As noted earlier, neither the rate of change nor the complexity of the firm's environment alone will compel managers to actively analyze their environments (Boyd & Fulk, 1996). As such, the following formula was used to calculate strategic uncertainty (Daft, Sormunen, & Parks, 1988; Elenkov, 1997; Sawyerr, 1993):
Strategic Uncertainty = Importance (Change x Complexity)
The resultant scores for the six sectors were then summed, and the total was divided by 6 (the number of sectors). The simple average score on the strategic uncertainty index ([alpha] = .72) was used in the analysis.
5. Statistical Control Variables. Three statistical control variables were also used, as follows:
(a) Company age was measured by the number of years a venture had been in existence. Age was included because older firms may use formal CA more than younger companies (Mohan-Neill, 1995). Even though we studied companies eight years and younger, controlling for company age was necessary for several reasons. Company age was found to be related to product diversity (Shan, 1990), which would increase the complexity of a firm's environment. Bantel (1998) has also found that start-up firms (five years or younger) competed differently from adolescent companies (six to eight years). Bantel further found company age to be associated with the structural formalization of the firm's operations, which can influence CA activities. Company age also affected a venture's resources and, as a result, may influence the support of CA.
(b) Company size was measured by the natural log of a venture's total number of employees. The size of a new venture was entered as a control variable because larger ventures might formalize their CA efforts more than smaller companies (Yasai-Ardekani & Nystrom, 1996).
(c) Industry was also used as a control variable because ventures in different industries might vary significantly in their CA practices (Sutton, 1988). To control for industry effects, the average score for the relevant industry (defined at the four-digit SIC categories) was subtracted from a company score for the study's variables, except for the dichotomous venture origin variable.
ANALYSIS AND RESULTS
Table 3 presents the means and standard deviations for the study's variables. The intercorrelations among the variables suggested that multicolinearity was not a serious problem in the database.
CA Characteristics and Venture Performance (HI through H3). Multiple regression analysis was used to test H1 through H3. Separate regression analyses were conducted for the independent and corporate ventures subgroups. The results appear in Table 4. Six of the eight regressions were significant (at p < .05 or better). In support of H1, CA formality was positively associated with new venture performance in five of the eight regressions; the exceptions were profit satisfaction under independent ventures (which was not significant); growth satisfaction under independent ventures (which was negative; p < .01), and market share growth under independent ventures (which was negative; p < .05).
The results also support H2a, as intraindustry CA comprehensiveness was positively and significantly associated with performance in five of the eight regressions at p < .05 or better. The exceptions were insignificant and under independent ventures: profit satisfaction, growth satisfaction, and market share growth. Comprehensive interindustry analysis (H2b) was also positively and significantly associated with new venture performance measures in five regressions. It lacked significance in the case of independent ventures when profit satisfaction, growth satisfaction, and return on equity were used. User orientation was positively and significantly associated with new venture performance in six of the eight regressions, supporting H3. Table 4 shows that the exceptions were in the case of independent ventures with profit and growth satisfaction.
The Moderating Effect of Venture Origin on the CA-Performance Relationship (H4). Moderated regression analysis was used to test H4, following the three-step procedure suggested by Baron and Kenny (1986). In the first step, the "restricted model" was tested by regressing each control and independent variable on the four measures of venture performance. In the second step, a "full model" was constructed, and the analysis was rerun after adding four interaction terms to the variables already entered in the first step. Interaction terms were created by multiplying venture origin (corporate = 1) by each of the four CA measures.
Table 5 presents the results for the restricted and full models. The existence of significant interactions was determined by comparing the [R.sup.2]s of both models. The interactions of the venture's origin and effective CA were positive and significant in all four full models. These results were consistent with H4a, indicating that formality was more strongly associated with the performance of corporate ventures than independent ventures. All four interactions of formality/origin, as well as all four intraindustry comprehensiveness/origin interactions were significant. Interindustry analysis/origin interactions were significant in only two cases: profit satisfaction and market share growth, which partially supports H4. Three of the four venture origin/user orientation interactions were positive and significant; the exception being the interaction for profit satisfaction.
The results for the full models show that adding the venture origin interaction terms to each of the four restricted models significantly improved the adjusted [R.sup.2]s at p < .05 with profit satisfaction, at p < .01 with return on equity, and at p < .001 with both growth satisfaction and market share growth. These additions significantly increased the adjusted [R.sup.2]s in a range from 6 (profit satisfaction) to 13 percent (growth satisfaction).
Further evidence in support of the moderating relationship between venture origin and performance is provided in Table 4. The subgroup analyses presented in Table 4 show that the relationship between CA practices and performance varied by venture type. Specifically, the four characteristics of CA were significantly and positively associated with the four measures of performance when corporate ventures were considered. However, when the independent ventures were used in the regression equations, CA characteristics were weakly and inconsistently associated with performance. Neither the profit nor growth satisfaction regressions were significant. When return on equity and market share growth were the dependent variables used in the regressions for independent ventures, the regressions were significant at the p < .05 level. The pattern of the betas of the four CA characteristics, however, was not as stable as was found when just corporate ventures were included in the analysis. In the case of return on equity, the betas for for-reality, intraindustry CA, and user orientation where significant and positive (all at the p < .05 level). When market share growth was examined, interindustry CA (p < .05) and user orientation (p < .01 level) were positive and significant, while formality was significantly and negatively associated with market share growth (p < .05 level).
Strategic Uncertainty as a Moderator of the CA-Performance Relationship (H5). Moderated regression analysis also examined the effect of strategic uncertainty on the CA--new venture performance relationship. In the restricted model, each performance measure was regressed on the control variables, and the measures of CA and strategic uncertainty. In the full model, four interaction terms were added to the restricted model. Interaction terms were created by multiplying the ventures' strategic uncertainty score with each of the four measures of CA characteristics.
Table 6 presents the results from these analyses. The regressions for the four full models were significant: two at p < .01 and two at p < .05. Adding the four interaction terms to each of the four restricted models significantly improved the adjusted [R.sup.2] of the full models, at p < .001 in three cases (profit satisfaction, growth satisfaction, and return on equity) and at p < .05 in one case (market share growth). The increases in the adjusted [R.sup.2]s ranged from 8 percent (market share growth) to 11 percent (return on equity). Twelve of the 16 strategic uncertainty/CA interactions were significant at p < .05 or better. The exceptions were the following insignificant interaction terms with strategic uncertainty: interindustry analysis (under profit satisfaction), formality (under growth satisfaction), user orientation (under growth satisfaction), and user orientation (under market share growth). These results supported H6.
DISCUSSION
CA provides important information for the strategic decision-making process (Grant, 1998; Oster, 1994; Youngblood, 1998). Little empirical evidence, however, exists on the associations between the characteristics of CA and new venture performance (Chen, 1996). This study, therefore, has explored the relationships between particular CA characteristics and new venture performance, and has sought to clarify the moderating effect of a new venture's origin and of strategic uncertainty on these relationships.
CA Characteristics and New Venture Performance (H1 through H3). The results support H1, showing that CA formality is positively associated with new venture performance. CA formality can create a sense of accountability among the venture's competitive analysts and can ensure that managers provide adequate resources for this activity (Sutton, 1988; Youngblood, 1998). While the importance of formal, in-house CA activities is evident, formalizing CA activities can result in higher new venture performance. While the study's cross-sectional sample limits our ability to make causal attributions, the finding is nevertheless noteworthy as some venture managers may not support or use formal CA systems (Peters & Brush, 1996). While the value of informal CA cannot be disputed (Yasai-Ardekani & Nystrom, 1996), the results highlight the importance of formalizing these activities.
The results also support the hypothesized positive associations between the intra- and interindustry comprehensiveness of the venture's CA system and performance (H2). Comprehensive intraindustry CA can help decision makers identify and profile various competitors, and collect and analyze data on different aspects of competitors' operations. This information can assist venture managers in avoiding blind spots from their selective attention to particular rivals (Zahra & Chapels, 1993) or prevent them from overemphasizing certain aspects of their competitors' operations.
Comprehensive interindustry analysis is also positively associated with new venture performance (H2b). New ventures that examine potential competitors from adjacent industries are better positioned to achieve higher performance. The payoff from interindustry analysis, however, is not always as strong as intraindustry comprehensiveness. While interindustry CA efforts may warn managers of pending entry, these efforts may not have an immediate impact on performance.
User orientation is also positively associated with new venture performance measures, which supports H3. This orientation facilitates the effective and timely dissemination of CA information and ensures that managers use that information in mapping the venture's strategic options (Bernhardt, 1994; Ghoshal & Westney, 1991). User orientation also makes CA more responsive to the needs of different decisionmakers within the venture (Youngblood, 1998), improving performance.
The Moderating Effect of Venture Origin (H4). The results also clarify the nature of the relationship between CA characteristics and new venture performance. The subgroup analyses presented in Table 4 show that the relationship between CA characteristics and new venture performance varied significantly by venture types. According to the results presented here, corporate ventures more strongly benefit from formal, comprehensive, and user-oriented CA systems than independent ventures. When subjective measures of performance were used, the CA systems of independent ventures were not positively associated with performance. Only when using objective measures was CA significantly and positively associated with performance in the independent ventures.
Consistent with H4, the CA formality/venture origin interactions (Table 5) were positive and significant under all four new venture performance measures, suggesting that corporate ventures may profit more from formalizing their CA systems than independent ventures. These results support the proposition that the differences between corporate and independent ventures (e.g., the goals of the executive team) may influence both the nature of and the benefits derived from CA activities. Comprehensiveness is also more strongly related to new venture performance among corporate ventures than independent ventures, as all four origin/intraindustry comprehensiveness interactions were positive and significant. These results may reflect the corporate ventures' tendency to define their markets broadly (Hofer & Sandberg, 1987), which would justify their need for a more comprehensive CA system. Comprehensive CA of multiple competitors and segments may be overkill for independent ventures, which usually focus narrowly on niches or smaller target markets. Although comprehensiveness contributes to the performance of corporate ventures, it may have the opposite effect among independent ventures. As with excessive formality, comprehensive CA may reduce the independent entrepreneurs' degrees of freedom and stifle their creativity. The results on interindustry CA also partially support H4 as two of the four interaction terms with venture origin were positive and significant (profit satisfaction and market share growth). Corporate ventures might benefit from conducting comprehensive interindustry analyses because these ventures usually define their markets more broadly than independent ventures.
The Moderating Effect of Strategic Uncertainty (H5). The results from Table 6 show that venture managers should consider the level of strategic uncertainty in their environment when designing CA systems. In particular, as strategic uncertainty increases, comprehensive intra- and interindustry CA contributes more consistently and strongly to increased venture performance than either formality or user orientation. New venture success is significantly influenced by the venture's ability to understand its competitors and the arena in which it competes. The value of a comprehensive CA system increases in highly uncertain environments as new ventures struggle to carve out their competitive niche in complex and dynamic markets. The method by which this competitive data is collected (i.e., formal vs. informal) and distributed (i.e., user orientation) appears less important. Broadly and deeply aimed CA practices appear to be an important way for venture managers to understand and react to the ever-changing competitive landscape their ventures face. Conversely, for those new ventures competing in more certain environments, comprehensive CA would be less important to for success.
Limitations. The preceding observations should be tempered with caution because of the study's limitations. The study does not examine all potentially important dimensions of an effective CA system. The use of a cross-sectional sample also precludes the identification of causal links between CA and new venture performance. It is unclear, therefore, whether CA system characteristics cause improved performance or vice versa. Longitudinal designs are needed to clarify this relationship. Although evidence of significant inter-rater reliability and the use of Harmon's (1967) "single factor" tests enhance the confidence in the data, the use of the same source is one of the study's shortcomings. Alternative primary and secondary sources of CA data should be explored in future research. Getting responses from multiple users of CA data might reduce the potential biases of individual users. Although this study attempted to control for same source and common method biases, future researchers could improve upon our methodology by having different respondents answer different sections of the survey, or by altering the order of the questions given to different respondents. Further, new venture performance is a function of many factors outside the scope of this study. Similarly, effective CA practices might simply be an offshoot of other beneficial organizational practices that are positively associated with new venture performance (such as managerial expertise or financial resources). The independent effects of CA practices on new venture performance, therefore, are an important area for future inquiry. The results might also suffer from survivor bias, a common problem in entrepreneurship research (Cooper, 1993). Finally, the study did not examine the effects of CA on the nonfinancial indicators of new Ventures performance, such as innovation and product quality, possibly understating the overall contributions of CA to performance.
Managerial Implications. For corporate venture managers, there is a need to develop and conduct formal and comprehensive CA to complement any informal CA practices. Having a formal CA system does not preclude conducting and using informal CA as formal and informal CA activities often complement one another. Given the results, there is some merit to formalizing a CA system by assigning the staff needed to conduct the analyses, providing them with financial resources, recognizing them within the venture's formal organizational structure, clarifying their mission, and evaluating their progress. This may be especially true for corporate ventures. In some cases, however, formalizing CA systems might have a negative impact on an independent venture's performance. Perhaps the costs of these systems outweigh their benefit for independent ventures. Effective CA systems must also collect and distribute information that can be readily used by both corporate and independent ventures' decisionmakers. When designing new venture CA systems, managers and entrepreneurs also need to consider their external environments because the results show that certain characteristics of CA are more positively and significantly associated with higher performance as environmental uncertainty increases. Managers should consider their external environment and use this information in designing and evaluating CA systems. Failure to achieve this match can reduce the contributions of the CA staff to the venture's performance.
Interindustry CA is also important for corporate ventures, but less so for independent ventures. This characteristic of CA helps corporate ventures learn from other firms or erect barriers to deter market entry. Clearly, corporate venture managers need to examine potential rivals from other industries. Independent ventures' narrower market scope might diminish the contributions of interindustry CA to performance. Implications for Future Research. In addition to the research avenues already noted above, further empirical research in this area is necessary. Presently, little is known about how new ventures use CA systems to study their competition or how these systems are used in strategic planning. Future research can fill this gap in the literature by exploring the associations between CA system characteristics and new venture performance within different environments. Even though this study has offered some insights into this issue, the use of alternative measures of the study's key constructs would be useful.
A related issue for future study is how corporate and independent ventures differ in their collection and use of CA data. Do independent venture owners rely more heavily on personal sources of CA data than corporate venture managers? Do corporate ventures use more formal CA systems than independent ventures? If significant differences between corporate and independent ventures' data collection sources and methods exist, what effect do these differences have on their strategic choices, performance and survival?
Some new ventures may rely on informal CA because of their limited resources and the convenience of collecting informal data. Researchers should identify the conditions that lead some ventures to use informal versus formal CA systems and how these analyses might shape the ventures' strategies. Future studies also need to document the benefits and limitations of formal and informal CA systems and, if they coexist, how venture managers integrate the findings of these systems. Researchers should also examine the dysfunctional impact of formal and comprehensive CA systems. Can these systems cause information overload that slows down the firm's strategic responses to environmental changes?
Measuring new venture performance is a controversial issue (Robinson, 1999). Future researchers should explore multiple financial and nonfinancial measures of new venture performance. Given the problems associated with gathering accurate, reliable, and valid performance data, researchers should be encouraged to follow the insightful suggestions of Brush and VanderWerf (1992), Chandler and Hanks (1993), and Sandberg (1986). Future research should also document the effect of CA on new venture survival.
Similarly, the role outsiders play in new ventures' CA activities is not well documented. For example, little is known about the contributions of venture capitalists to the CA efforts of independent ventures. Do venture capitalists provide competitive intelligence data to the independent ventures? If so, what types of information do they provide? Do independent venture managers view the information obtained from venture capitalists as a substitute for internal, formal CA efforts? Are independent venture managers who receive CA data from venture capitalists less inclined to conduct thorough and comprehensive CA? These questions should be explored in future research.
CA is an area of growing interest among new venture managers, entrepreneurs, and scholars. Yet, to date, little large-scale empirical research has been conducted on the CA practices of new ventures. We hope this study will stimulate further research on the effect of CA on new ventures' strategic choices, survival, and performance. Such studies would greatly help develop and test theories of the determinants of the survival and success of new ventures.
Appendix
New venture performance was measured Using objective and subjective criteria, as follows:
1. Objective Measures. Market share growth and return on equity were the study's two objective measures. To validate the figures provided by managers, data were collected from a subset of companies using annual reports and trade publications. Secondary and survey data were significantly correlated: market share growth (r = .64, n = 53, p < .001) and return on equity (r = .61, n = 51, p < .001). These figures supported the validity of the survey-based measures.
2. Subjective Measures. As reported in the text, seven survey items were also used to assess executives' satisfaction with performance. Executives rated their satisfaction with each item, weighted by that item's importance. The rationale for the use of subjective performance measures was presented in the text. For each of the seven items, two evaluations were used. The first indicated the importance of each item ("importance" score). The second indicated the extent of top managers' satisfaction with the venture's performance of each item ("satisfaction" score). Importance scores were then multiplied by their corresponding satisfaction scores. Factor analyses yielded two significant factors: satisfaction with profitability (Profit Satisfaction) and satisfaction with growth (Growth Satisfaction). Measured by simple rs, inter-rater agreement on profit and growth was .74 and .77 (p < .001), respectively. The items were as follows:
The subjective performance measures were designed to gauge both the growth and profitability aspects of performance. Factor analysis uncovered these two dimensions. Further, the simple correlations of the subjective and objective performance measures were significant (all at p < .001, average r = .62), reaffirming these measures' criterion-related validity.
Table 1
Factor Analysis of CA Characteristics
Items #1 #2 #3 #4
Are comprehensive. .86 .37 -.28 .06
Cover small and large competitors. .77 .29 .25 -.13
Cover competitors' major resources .71 .33 .27 .12
and capabilities.
Cover competitors' strengths and .70 .38 .05 .13
weaknesses.
Cover competitors' strategy. .62 .24 .18 .13
Cover competitors' operations. .58 .27 .16 .18
Cover domestic and foreign .31 .77 .27 .29
competitors.
Cover competitors in other .21 .74 .26 .31
industries.
Examine competitive trends in .19 .65 .23 .22
other industries.
Are usually limited to the -.08 -.53 .14 -.07
company's primary operations
(reverse scored).
Are conducted informally -.17 -.27 -.81 .09
(reverse scored).
Are performed continuously. .30 .25 .78 .15
Are supported by our company's .29 .26 .69 .21
senior executives (or owners).
Are well-supported financially .21 .28 .59 .17
by the company's senior executives.
Generate reports and analyses that .23 .27 .12 .74
match executives' information needs.
Are evaluated frequently to ensure .26 .23 .21 .63
they match informational
needs of managers.
Produce reports that are understandable .27 .25 .09 .61
and relatively easy to use.
Are user-unfriendly (reverse scored). .19 .27 .11 -.51
Eigenvalue. 3.71 2.95 2.66 2.01
variance explained. 24.17 19.22 17.33 13.09
Table 2
Factor Analysis of Subjective New Venture Performance Measures
Profit Growth
Item Satisfaction Satisfaction
Return on investment (ROI) .79 -.12
Return on equity (ROE) .71 .27
Return on assets (ROA) .67 .24
Net profit margins (NPM) .57 .17
Growth in sales .36 .67
Growth in the number of employees -.22 .61
Market share growth .26 .53
Eigenvalue 2.18 1.07
% of variance explained 43.19 28.07
Cronbach [alpha] .78 .71
Table 3
Means, Standard Deviations, and Intercorrelations *
Variables Mean s.d. 1 2
1 Formality 2.73 1.84
2 Intraindustry comprehensiveness 2.81 1.96 .21
3 Interindustry comprehensiveness 3.08 1.77 .21 .29
4 User orientation 3.01 1.81 .16 .19
5 Strategic uncertainty 35.08 22.51 .19 .18
6 Age 3.61 2.91 .08 .12
7 Size (log) 1.44 1.65 .13 .11
8 Profit satisfaction 3.07 1.81 .23 .23
9 Growth satisfaction 3.13 1.62 .26 .26
10 Return on equity 11.09 16.11 .24 .19
11 Market share growth 14.61 11.07 .27 .26
Variables 3 4 5 6
1 Formality
2 Intraindustry comprehensiveness
3 Interindustry comprehensiveness
4 User orientation .24
5 Strategic uncertainty .31 .30
6 Age .19 -.11 .16
7 Size (log) .14 -.03 -.08 .09
8 Profit satisfaction .09 .19 -.11 .10
9 Growth satisfaction .17 .21 -.19 -.06
10 Return on equity .21 .26 -.08 .04
11 Market share growth .18 .22 -.19 .11
Variables 7 8 9 10
1 Formality
2 Intraindustry comprehensiveness
3 Interindustry comprehensiveness
4 User orientation
5 Strategic uncertainty
6 Age
7 Size (log)
8 Profit satisfaction -.08
9 Growth satisfaction .03 .29
10 Return on equity .04 .62 .46
11 Market share growth .02 .51 .69 .50
* For n = 228, r must be .13 to be significant at p < .05.
Table 4
Relationship between Competitive Analysis Characteristics and New
Venture Performance
Profit Growth
Measure Satisfaction Satisfaction
Venture Origin CV IV CV IV
Intercept .68 1.11 * .23 -.51 *
Age -.13 .20 * -.07 -.02
Size -.01 .05 * -.20 * .03
Formality .27 * -.09 * .19 * -.33 *
Intraindustry .17 * .03 .23 * .09
comprehensiveness
Interindustry .23 * .05 .19 * -.02
comprehensiveness
User orientation -.30 ** .11 .23 * .09
Strategic uncertainty .02 .01 -.13 -.01
Adjusted R-squared .17 .11 .22 .10
F-values 2.41 * 1.40 3.71 *** 1.76
Return on Market Share
Measure Equity Growth
Venture Origin CV IV CV IV
Intercept -.53 ** .13 .43 .13
Age .11 .02 .07 .01
Size -.08 -.02 .05 .03
Formality .39 ** .17 * .23 * -.19 *
Intraindustry .31 ** .21 * .24 * -.05
comprehensiveness
Interindustry .26 * .14 .31 ** .21 *
comprehensiveness
User orientation .47 *** .21 * .42 ** .31 **
Strategic uncertainty -.02 -.08 .04 -.09
Adjusted R-squared .21 .13 .26 .15
F-values 2.69 * 2.09 * 3.98 *** 2.01 *
* p < .05 ** p < .01 *** p < .001
Table 5
Venture Origin as a Moderator of the Relationship between CA
Characteristics and New Venture Performance
Profit Satisfaction
Model Base Rest. (1) Full
Intercept 1.45 * 2.71 * 3.01 *
Age .09 .04 .02
Size -.11 -.08 -.05
Formality .33 * .24 * .19 *
Comprehensiveness--CI .27 * .31 ** .27 *
Cross-industry analysis--CII .21 * .23 * .26 *
User Orientation--U .38 ** .34 ** .31 *
Strategic Uncertainty--SU .04 .02
Origin (corporate=1) .07 .11
Origin * F .33 *
Origin * CI .37 **
Origin * CII .41 **
Origin * U .13
F-value 2.91 ** 4.17 *** 6.81 ***
[R.sup.2] (adjusted) .23 .26 .32
Change in [R.sup.2] .06 .13 .11
F-value (for change in 2.08 * 4.76 *** 3.05 **
[R.sup.2])
Growth Satisfaction
Model Base Rest. Full
Intercept 1.09 1.41 1.66
Age -.09 -.03 -0.02
Size .07 .01 .06
Formality .26 * .19 * .23 *
Comprehensiveness--CI .33 ** .26 * .24 *
Cross-industry analysis--CII .20 * .21 * .19 *
User Orientation--U .21 * .27 * .23 *
Strategic Uncertainty--SU -.05 -0.01
Origin (corporate=1) .19 * .26 *
Origin * F .28 *
Origin * CI .31 *
Origin * CII .02
Origin * U .47 **
F-value 2.07 * 2.91 ** 4.09 ***
[R.sup.2] (adjusted) .18 .23 .36
Change in [R.sup.2] .09
F-value (for change in 2.60 *
[R.sup.2])
Return on Equity
Model Base Rest. Full
Intercept .47 0.26 0.49
Age .09 0.03 0.02
Size .08 .03 .05
Formality .08 .09 .11
Comprehensiveness--CI .23 * .21 * .22 **
Cross-industry analysis--CII .04 .13 .16
User Orientation--U .28 * .19 * .24 *
Strategic Uncertainty--SU -.06 -.02
Origin (corporate=1) -.08 -.13
Origin * F .18 *
Origin * CI .30 **
Origin * CII -.13
Origin * U .25 *
F-value 2.37 ** 2.96 ** 6.13 ***
[R.sup.2] (adjusted) .15 .18 .29
Change in [R.sup.2]
F-value (for change in [R.sup.2])
Market Share Growth
Model Base Rest. Full
Intercept 1.83 * 2.03 * 2.19 *
Age 0.08 0.05 0.04
Size .06 .03 .09
Formality .30 * .19 * .20 *
Comprehensiveness--CI .25 * .22 * .27 *
Cross-industry analysis--CII .34 ** .29 * .31 *
User Orientation--U .41 ** .35 ** .35 **
Strategic Uncertainty--SU -.02 -.01
Origin (corporate= 1) .13 .08
Origin * F .27 *
Origin * CI .22 *
Origin * CII .29 *
Origin * U .47 ***
F-value 2.14 ** 3.61 *** 5.87 ***
[R.sup.2] (adjusted) .17 .20 .29
Change in [R.sup.2]
F-value (for change in
[R.sup.2])
(1) Rest. = Restricted regression model. *p <.05***p < .001
Table 6
Strategic Uncertainty as a Moderator of the Relationship between CA
Characteristics and New Venture Performance
Profit Satisfaction
Measure Restricted Full
Intercept 1.93 2.61 *
Age .04 .07
Size -.03 -.02
Formality-F .26 ** .21 *
Intra-Industry Comprehensiveness--CI .39 ** .31 **
Inter-Industry Comprehensiveness--CII .07 .04
User Orientation--U .36 ** .32 *
Strategic Uncertainty--SU .05 .06
SU * F .23 *
SU * CI .43 **
SU * CII .11
SU * U .49 **
F-value 5.01 *** 7.54 ***
[R.sup.2] (adjusted) .24 .33
Change in [R.sup.2] .09 .10
F-value due to change in [R.sup.2] 3.13 ** 2.83 **
Growth Satisfaction
Measure Restricted Full
Intercept 1.21 1.43
Age -.03 -.07
Size .02 .05
Formality-F .17 * .21 *
Intra-Industry Comprehensiveness--CI .22 * .18 *
Inter-Industry Comprehensiveness--CII .26 * .21 *
User Orientation--U .31 ** .33 **
Strategic Uncertainty-SU -.09 -.09
SU * F .15
SU * CI .21 *
SU * CII .54 ***
SU * U .13
F-value 4.09*** 5.11 ***
[R.sup.2] (adjusted) .19 .29
Change in [R.sup.2] .11 .08
F-value due to change in [R.sup.2] 2.62** 1.94 *
Return on Equity
Measure Restricted Full
Intercept .34 .45
Age .07 .07
Size .07 .09
Formality-F .21 * .29 **
Intra-Industry Comprehensiveness--CI .29 * .37 **
Inter-Industry Comprehensiveness--CII .23 * .44 **
User Orientation--U .41 ** .56 ***
Strategic Uncertainty--SU -.02 -.03 ***
SU * F .19 *
SU * CI .31 **
SU * CII .44 **
SU * U .61 ***
F-value 3.71 ** 5.73 ***
[R.sup.2] (adjusted) .15 .26
Change in [R.sup.2]
F-value due to change in [R.sup.2] Market Share Growth
Measure Restricted Full
Intercept 2.37* 2.01
Age .0 .06
Size .05 .07
Formality-F .17 * .19 *
Intra-Industry Comprehensiveness--CI .23 * .25 *
Inter-Industry Comprehensiveness--CII .06 .01
User Orientation--U .38 ** .41 **
Strategic Uncenainty--SU -.09 -.06
SU * F .21 *
SU * CI .31 *
SU * CII .34 *
SU * U .07
F-value 2.81 ** 4.92 ***
[R.sup.2] (adjusted) .17 .25
Change in [R.sup.2]
F-value due to change in [R.sup.2]
*p<.05 **p<.01 ***p<.001
How satisfied are you with
How important is this the company's achievement
goal for your company of this goal
Very Very
Unimportant Important Dissatisfied Satisfied
Return on 1 2 3 4 5 1 2 3 4 5
investment
Return on 1 2 3 4 5 1 2 3 4 5
equity
Net profit 1 2 3 4 5 1 2 3 4 5
margin
Return on 1 2 3 4 5 1 2 3 4 5
assets
Sales growth 1 2 3 4 5 1 2 3 4 5
Market share 1 2 3 4 5 1 2 3 4 5
growth
Growth in 1 2 3 4 5 1 2 3 4 5
the number 1 2 3 4 5 1 2 3 4 5
of employees
* An earlier version of this paper was presented at the Academy of Management, Entrepreneurship Division.
(1.) We are grateful to an anonymous reviewer for suggesting this idea.
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Shaker A. Zahra is a Professor of Management at Georgia State University.
Donald O. Neuhaum is an Assistant Professor of Management at the University of Central Florida.
Galal M. El-Hagrassey is a professor at Kuwait University.
This research is supported by a grant to the first author from the College of Business Administration at Georgia State University. We acknowledge with appreciation the supportive comments of John Butler, Marie S. Mitchell, Paul Sweeney, Patricia H. Zahra, and two anonymous reviewers.
In addition, make sure to read these articles:
- Competitive intelligence at small enterprises.