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Strategic Management Journal Strat. Mgmt. J., 22: 615–640 (2001) DOI: 10.1002/smj.181

INTERNAL CAPABILITIES, EXTERNAL NETWORKS, AND PERFORMANCE: A STUDY ON TECHNOLOGYBASED VENTURES CHOONWOO LEE1, KYUNGMOOK LEE2 and JOHANNES M. PENNINGS3* 1

College of Business Administration, Chungbuk National University, Chungbuk, Korea 2 College of Business Administration, Seoul National University, Seoul, Korea 3 The Wharton School, University of Pennsylvania, Philadelphia, U.S.A.

This study examined the influence of internal capabilities and external networks on firm performance by using data from 137 Korean technological start-up companies. Internal capabilities were operationalized by entrepreneurial orientation, technological capabilities, and financial resources invested during the development period. External networks were captured by partnership- and sponsorship-based linkages. Partnership-based linkages were measured by strategic alliances with other enterprises and venture capitalists, collaboration with universities or research institutes, and participation in venture associations. Sponsorship-based linkages consisted of financial and nonfinancial support from commercial banks and the Korean government. Sales growth indicated the start-up’s performance. Regression results showed that the three indicators of internal capabilities are important predictors of a start-up’s performance. Among external networks, only the linkages to venture capital companies predicted the start-up’s performance. Several interaction terms between internal capabilities and partnership-based linkages have a statistically significant influence on performance. Sponsorship-based linkages do not have individual effects on performance but linkage with financial institutions has a multiplicative effect with technological capabilities and financial resources invested on a startup’s performance. Implications and directions for future research were discussed. Copyright  2001 John Wiley & Sons, Ltd.

INTRODUCTION This study explores the relationship between entrepreneurial strategies and the performance of technological start-up companies. That relationship has neither been a focus of serious conceptual discussion nor tested with robust methodology and data. Among various entrepreneurial strategies, this paper deals with the performance implications of strategies for accumulating internal capabilities and for networking with external resource holders. Schumpeter (1934) has informed us about new Key words: entrepreneurship; capabilities; social capital; social networks; start-up companies; innovation *Correspondence to: Johannes M. Pennings, The Wharton School, University of Pennsylvania, 2000 Steinberg Hall– Dietrich Hall, Philadelphia, PA 19104, U.S.A.

Copyright  2001 John Wiley & Sons, Ltd.

business ventures and their entrepreneurs as the prime movers in modern economic development. They foster technological innovations of industries (Tushman and Anderson, 1986), create new jobs, and generate new wealth for society (Kao, 1995). However, we do not know much about value-creating entrepreneurial strategies, since previous studies have explored the value-creating process of a new venture without exposing its entrepreneurial strategies. In this paper, we attempt to uncover the role of entrepreneurial strategies—especially strategies in building internal capabilities and in developing external networks—in the value creation process. To account for the variation in wealth creation among entrepreneurial firms, we invoked two guiding firmlevel theories: the resource-based view of the firm and social capital theory. First, the resource-based view of the firm

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(hereafter RBV) emphasizes firm idiosyncratic resources (e.g., Barney, 1991; Penrose, 1959; Wernerfelt, 1984), especially resources that reside within organizations. RBV regards the firm as a bundle of resources and suggests that their attributes significantly affect the firm’s competitive advantage and, by implication, its performance (Barney, 1986, 1991; Penrose, 1959; Peteraf, 1993; Wernerfelt, 1984). Most conspicuous among these resources are those that are valuable, scarce, imperfectly tradable, and hard to imitate (Barney, 1991; Dierickx and Cool, 1989; Peteraf, 1993; Reed and DeFllippi, 1990). RBV suggests that start-ups pursue entrepreneurial strategies that focus on the accumulation of intangible resources for survival or growth. Second, social capital theory suggests that a firm’s external networks form a major contributor to its performance (Leenders and Gabbay, 1999). Organizations transact with suppliers and other partners in order to acquire external resources to produce products/services at competitive prices, adjusted for quality such that they can attract and retain customers (Burt, 1992; Pennings and Lee, 1999; Pennings, Lee, and Witteloostuijn, 1998; Uzzi, 1996). Their ability to mobilize extramural resources, attract customers, and identify entrepreneurial opportunities is conditional on external networks, since social relations mediate economic transactions and confer organizational legitimacy (Granovetter, 1985). Social capital theory implies that start-ups should pursue strategies focusing on the development of valuable networks with external resource holders in order to succeed. These two perspectives have divergent concerns with the roots of value creation, with RBV stressing the internally accumulated resources or capabilities while social capital theory underscores its relational characteristics with external entities. The two theories ought to be synthesized, since start-ups should develop firm-specific assets while obtaining complementary external resources through their social networks. Drawing on the two perspectives, this paper examines the joint influence of internal capabilities and external contacts on the performance of Korean technological start-up companies. We used data from 137 startups that are involved in computer software, electric and electronic, and biotechnological products. This study pushes the envelope of research on creative destruction by high-tech start-ups by integrating RBV and social capital theory. The Copyright  2001 John Wiley & Sons, Ltd.

current body of knowledge on wealth creation in start-up companies is still in its infancy. Theoretically, this study can further test the empirical validity of RBV and social capital theory regarding competitive advantage. The work on social capital has been enriched by an impressive program of empirical data collection but awaits further theoretical fine tuning. Although several studies (e.g., Pennings et al., 1998) attempted to integrate those two theoretical domains to explain organizational performance, we believe that none have done so in the context of start-up firms. Practically speaking, this study provides managerial implications to entrepreneurs in technologybased industries that face a great deal of uncertainty about what kind of entrepreneurial strategies should be pursued. For example, we can make suggestions regarding the kinds of internal capabilities or external contacts that the entrepreneur should develop to create more value. In the next section, we review extant research and propose hypotheses that link internal capabilities and social capital with a technological startup’s performance. In the following section, we describe the sample, measurement, and analytic techniques to test the hypotheses. We then present results of statistical analysis. Finally, we discuss implications for entrepreneurial strategies and provide future research direction.

THEORY AND HYPOTHESES Internal capabilities and organizational performance The RBV literature suggests that idiosyncratic internal resources define a durable competitive advantage. Which resources stand out in shaping performance of technological start-up companies? Several previous studies in the entrepreneurship fields have assumed that the start-up firm is an extension of the founder (e.g., Bru¨derl, Preisendo¨rfer, and Ziegler, 1992, 1998; Bru¨derl and Preisendo¨rfer, 2000; Chandler and Jansen, 1992; Van de Ven, Hudson, and Schroder, 1984), and discussed the characteristics of the entrepreneurs as predictors of the performance of small enterprise. Yet, they have not provided substantiated evidence on the relationships between founders’ characteristics and venture performance (e.g., Low and MacMillan, 1988; Stuart and Abetti, 1990). A major exception is the study by Bru¨derl Strat. Mgmt. J., 22: 615–640 (2001)

Internal Capabilities, External Networks, and Start-up’s Performance and Preisendo¨rfer (2000), who included seven characteristics such as gender and selfemployment experience, but found only the founder’s management experience to predict whether a start-up was a slow or a ‘fast-flyer.’ In this paper, we do not consider the founder’s characteristics as firm-level resources but individual-level resources, and thus treated them as control variables. Notwithstanding the important role of the founder, several papers have emphasized the attributes of the venture’s top management team (founding team) such as size, level of joint work experience, and member heterogeneity in functional backgrounds (e.g., Eisenhardt and Schoonhoven, 1990; Roure and Maidique, 1986). Still other works on a technological start-up’s success and failure have sought to cover characteristics of the start-up as a whole. They include founding strategy (Romanelli, 1989; Bru¨derl and Preisendo¨rfer, 2000), technical innovation within the core technology (Boeker, 1989; Bru¨derl and Preisendo¨rfer, 2000), and level of capital infusion after founding (Schoonhoven, Eisenhardt, and Lyman, 1990). This paper examines several attributes of the start-up as a whole while controlling for founder’s attributes. The RBV-inspired entrepreneurship literature combined with exploratory interviews with top executives of several Korean technological startups suggests three important internal capabilities that significantly influence start-up performance. They include entrepreneurial orientation, technological capabilities, and financial resources invested during its development period. Entrepreneurial orientation Entrepreneurs usually create and run their venture to develop a market niche with new products/services or to substitute established players with better quality, cheaper price, etc. These processes/activities are identified with the process of creative destruction and defined as entrepreneurship (Schumpeter, 1934, 1947). The concept of entrepreneurship has been extended from individual level to organizational level, which is called entrepreneurial orientation (Covin and Slevin, 1991; Lumpkin and Dess, 1996). The term entrepreneurial orientation (EO hereafter) captures the organizational processes, methods, and styles used to implement the start-up’s foundCopyright  2001 John Wiley & Sons, Ltd.

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ing strategy (Lumpkin and Dess, 1996; Miller, 1983). EO is the entrepreneurship defined at the firm level rather than at the individual level. Conceptually, we distinguished three dimensions of EO (innovativeness, risk-taking propensity, and proactiveness), as suggested by Miller (1983) and adopted or extended by several other studies (e.g., Covin and Slevin, 1989; Lumpkin and Dess, 1996). Though the positive relation between EO and a start-up’s performance has been suggested by several studies (e.g., Covin and Slevin, 1991; Lumpkin and Dess, 1996), the strong theoretical frame has not been combined with effective empirical work. For instance, most studies used subjective measures of EO and organizational performance (e.g., Wiklund, 1999; Zahra, 1991), and thus the results were very likely to be inflated by common-method bias. This study explains the relationship by using RBV and will test it with objective data. Entrepreneurship literature suggested that EO constitutes one of the most critical resources for venture performance (e.g., Covin and Slevin, 1991; Lumpkin and Dess, 1996). EO can be regarded as organizational resources that provide sustainable competitive advantages (Covin and Miles, 1999; Lado and Wilson, 1994; Zahra, Nielson, and Bogner, 1999), since EO is embedded in organizational routines (Knight, 1997; Lumpkin and Dess, 1996), is intangible, and is dispersed among organization members (cf. Mosakowski, 1998). Firms cannot buy a high level of EO from the market and should invest a great deal of time to cultivate such culture and thus EO can be a source of sustainable competitive advantage. Now we turn to three dimensions of EO. Innovativeness reflects a firm’s propensity to engage in new idea generation, experimentation, and R&D activities resulting in new products and processes (Lumpkin and Dess, 1996). Creative destruction calls for entrepreneurs to suspend current paradigms and to think of new ways of doing things (Kao, 1995; Schumpeter, 1934, 1947). Without innovation, young organizations would have to rely on traditional ways of doing business; traditional products/services, traditional distribution channels, etc. Head-to-head competition with established players is bound to result in failure due to resource shortcomings, scale diseconomies, and questionable reputation. As a result, start-ups should differentiate themselves Strat. Mgmt. J., 22: 615–640 (2001)

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from incumbents by introducing product, process, or marketing innovations. Competitors cannot easily mimic innovativeness of a start-up company, since it depends on quality and quantity of R& D personnel and the complex social relationships among these research scientists. The importance of a start-up’s innovation strategy is amply demonstrated in a very prominent study of German entrepreneurship (Bru¨derl and Preisendo¨rfer, 2000). In a high-quality, large sample of Bavarian start-ups these authors discovered that innovation is the single most important firm predictor of firm growth, reaching a coefficient that is thrice the standard error and dwarfs the effect of many other strategic and founder attributes. These authors performed a logistic regression with the binary outcome being the start-up as slow vs. fast growing. Innovation remained a powerful predictor of growth, even as additional variables were added to the stepwise equation. Firms with an entrepreneurial orientation typically display risk-taking behavior, illustrated by large resource commitments to high-risk and high-return business. Any innovation involves considerable uncertainty before it is ready to be commercialized (Nelson and Winter, 1982). The risk-taking propensity of a firm can be inferred from its willingness to incur large resource commitments to uncertain and novel business (Lumpkin and Dess, 1996; Miller, 1983). The risk-taking propensity is not easily established since it depends on foresight regarding new business or risk-taking propensity of the entrepreneur and co-founders (Mosakowski, 1998). Finally, proactiveness refers to a firm’s approach to market opportunities through active market research and first mover actions such as introduction of new products/services ahead of competitors (Lumpkin and Dess, 1996). Proactiveness is a crucial organizational process, since it entails a forward-looking perspective. Being a pioneer by anticipating and pursuing new opportunities and participating in emerging markets is a hallmark of entrepreneurship. Proactive startups tend to become first movers by forging a new market segment or by replacing established companies with new products/services (Christensen, 1997). In fact, incumbents often have a mindset bolstered by incentives that neglect emergent markets. In other words, organizational members, especially mid-level managers Copyright  2001 John Wiley & Sons, Ltd.

at established companies, tend to resist the abandonment of existing businesses and redirection of the firm to emergent markets, since strategic innovation means the loss of their power, erosion of their human capital, and the end of their careers (Garud, Nayyar, and Shapira, 1997). By exploiting asymmetries in the market place, proactive business ventures can capture unusually high returns and get a head start in establishing brand recognition. These arguments lead to the following hypothesis: Hypothesis 1: The level of entrepreneurial orientation is positively associated with a technological start-up’s performance. Technological capabilities In RBV, technological capabilities define the roots of a firm’s sustainable competitive advantage, since the capabilities comprise patents protected by law, technological knowledge, and production skills that are valuable and difficult to imitate by competitors. Such capabilities are obviously even more central in high-technology firms in general (e.g., Bettis and Hitt, 1995; Henderson and Clark, 1990; Tushman and Anderson, 1986) and technological start-ups in particular (Chandler and Hanks, 1994b; Dollinger, 1995; Shrader and Simon, 1997). They comprise technological knowledge, trade secrets, know-how generated by R&D and other technology-specific intellectual capital (Dollinger, 1995). Intellectual property protected by patent laws confers value creation by allowing new ventures to solely commercialize the toils of their new product development efforts, seize market opportunities, and differentiate themselves from incumbents. Not all capabilities can be shielded by patent laws, most notably those that defy codification. Technological capabilities that are not protected by patent laws are vulnerable to imitation and replication by competitors and thus weaken a firm’s appropriability regime, which refers to a firm’s ability to capture quasi rents generated by the capabilities (Teece, 1987). Further appropriability can be incurred on start-ups when those capabilities are embodied in technicians and researchers. When they leave the firm and establish their own start-ups or are recruited by competitors, the firm can no longer create value from their capabilities. However, skills that are comStrat. Mgmt. J., 22: 615–640 (2001)

Internal Capabilities, External Networks, and Start-up’s Performance plex and tacit are hard to be copied because they remain largely embedded in the routines and practices of the firm (Kogut and Zander, 1995; Winter, 1987). Therefore, tacit capabilities enjoy a tight appropriability regime, under which an innovator is almost assured of translating its innovation into market value for some period of time (Teece, 1987). Among tacit skills is a firm’s quality control capability. Since quality control requires complex organizational arrangements, it constitutes a major competitive advantage for new business ventures that cannot be readily alienated (Winter, 1987). Some people can argue that the quality control capability is not tacit anymore, since manuals for quality control and guidelines for acquiring international TQM requirements such as ISO standards articulate how to control quality. Though several firms invested a great deal of resources to benchmark other firms with a high level of quality control capability such as Motorola and Toyota and refer to those manuals for enhancing their product/service quality, they failed to reach the quality level of Motorola and Toyota. This failure suggests that some parts of quality control capabilities are still tacit and thus can be a source of sustainable competitive advantage. These arguments lead to the following hypothesis: Hypothesis 2: Technological capabilities represented by the number of patents and quality control capabilities of a technological start-up company are positively associated with the start-up’s performance. Financial resources invested during development period During their formative years, start-ups invest much of their available financial capital in product and market development. However, they usually run short of financial resources for technology development, marketing research, and advertising, because they lack the liquid assets or credit lines of more established companies. Since they typically have no history of transactions with financial institutions and moreover are seen as extremely risky, they must pay a premium for external resources obtained from banks, suppliers, and other firms. Compared with established firms, start-ups are charged higher interest rates by financial institutions, pay higher prices with harsher Copyright  2001 John Wiley & Sons, Ltd.

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credit terms for supplies and parts, and are forced to provide higher long-term remuneration to attract competent employees. In short, during their early years, start-ups with inadequate financial resources face a critical disadvantage before they evolve into a full-fledged company (Dollinger, 1995; Schoonhoven, Eisenhardt, and Lyman, 1990; Shrader and Simon, 1997). Young firms well endowed with financial capital during their development period enjoy many advantages and thus can perform better. They can exploit a resource-rich market niche, which requires a great deal of initial investment for a venture to enter. It is because those firms have financial resources to develop products, advertise, and recruit valuable human capital. In contrast, young firms short on financial capital during their development period cannot exploit a resource-rich market niche, but are likely to be pushed to inferior regions of the resource space due to the lack of initial financial capital. Other things being equal, start-ups that exploit the resource-rich market niche are likely to perform better than startups that enter the inferior and small market niche. Supporting the argument, Roberts and Hauptman (1987) showed that ‘under-financed’ biomedical firms pursuing significant technological breakthrough endured lower performance. In the RBV literature, financial resources are not considered to provide sustainable competitive advantage, since such resources are neither rare, imitable, nor tradable. However, financial resources invested during the development period can be a source of sustainable competitive advantage, since a start-up that invested disproportionately more financial resources during the period is likely to accumulate a larger stock of strategic assets than peer ventures that lacked financial resources during the development period (Dierickx and Cool, 1989). These arguments lead to the following hypothesis: Hypothesis 3: Financial resources invested during the development period are positively associated with a technological start-up’s performance. External networks and organizational performance Organizations, whether established or start-ups, cover only part of their value chain and depend Strat. Mgmt. J., 22: 615–640 (2001)

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critically on their environment (Pfeffer and Salancik, 1978). Firms are truncated in their resource endowment, outsource certain parts of the value chain, and transact with other economic actors having complementary assets. External contacts perform a very important role in the procurement of those assets and the identification of entrepreneurial opportunities, since economic actions are embedded within larger interorganizational networks (Burt, 1992; Granovetter, 1985). It is therefore surprising that the most prominent study on entrepreneurship, carried out on a sample of new ventures in Bavaria, did not consider the role of the start-up’s social capital in accounting for new venture growth. Networks are vital to the discovery of opportunities, to the testing of ideas, and to garner resources for the formation of the new organization (Aldrich and Zimmer, 1986). Potential partners are often very reluctant to put their reputation, capital, or other resources at risk in a start-up, whose financial prospects, if not its longevity, are uncertain. ‘Embedded’ ties with partners, which can be defined as ‘ties that are reinforced by mutual feelings of attachment, reciprocity, and trust’ (Uzzi, 1996), can enhance support for a start-up by the commitment of their resources. As networks provide information benefits, a focal firm with higher level of social capital is better positioned to find entrepreneurial opportunities. Other firms having ties with the focal firm provide information regarding new technological and market opportunities, and solicit collaboration in exploiting new entrepreneurial opportunities. These firms also make referrals on behalf of the focal firm to third parties that are in search of strategic alliances to exploit or explore new entrepreneurial opportunities. Contacts are also conducive to the mobilization of external resources from third parties since those very contacts signal positive assessments regarding the start-up’s future prospects (Dacin and Oliver, 1997; Hitt et al., 2000; Stuart, Hoang, and Hybels, 1999). Dollinger (1985) provided ample evidence that successful entrepreneurs were particularly active in networking with business people and regulators. Hansen (1995) likewise found that entrepreneurial networks are positively associated with organizational growth. Social capital captures the beneficial effect of social networks on organizational performance (e.g., Pennings et al., 1998). Corporate social Copyright  2001 John Wiley & Sons, Ltd.

capital can be defined as ‘the set of resources, tangible or virtual, that accrue to a corporate player through the player’s social relationships, facilitating the attainment of goals’ (Gabbay and Leenders, 1999: 3). Most prior studies investigated the concept, attributes, and function of social capital, but have not articulated its nature in the context of start-ups and their value creation. In this paper, we make an important distinction between ‘partnership-based linkages’ and ‘sponsorship-based linkages’ in order to conceptualize the social capital of high-technology ventures. Partnership-based linkages are cooperative, bilateral relationships in which partners give and take resources and maintain long-term ties. Sponsorship-based linkages are unilateral relationships as the sponsor commits unilateral support to a business venture without receiving explicit rewards. The most important criterion in differentiating partnership-based vs. sponsorship-based is whether the relationship involves explicit bilateral exchanges of resources. For instance, we treated the linkages to venture capitalists as partnershipbased since venture capital firms receive explicit benefit in the form of stock ownership as compensation of their financial and managerial involvement in start-ups. Partnership-based linkages Partnership-based linkages with external actors can be defined as cooperative bilateral relationships with environmental constituents. Our literature review and interviews with top executives of technological start-up companies in Korea suggest that four kinds of partnership-based linkages are crucial in promoting start-up performance.1 They comprise ties to (1) other enterprises, (2) venture capitalists, (3) universities and research institutes, and (4) venture associations. Strategic alliances with other enterprises engender long-term relationships with suppliers, customers, and other firms with complementary resources. Strategic alliances assist technological start-up companies to perform better in two ways. First, allies can directly provide information, 1

We interviewed top executives and upper echelon managers of 50 technological start-up companies. Initially we selected 128 firms, which have been reported as successful technological start-ups in newspapers and business journals. Though we contacted all 128 firms, we were able to interview only 50 firms. We did not include those 50 firms in statistical analysis. Strat. Mgmt. J., 22: 615–640 (2001)

Internal Capabilities, External Networks, and Start-up’s Performance knowledge, and complementary resources to the start-ups (Cohen and Levinthal, 1990; Hagedoorn, 1993; Hamel, Doz, and Prahalad, 1989; Teece, 1987). Technological start-ups as less resourcerich firms generally seek technical, managerial, and financial resources through alliances as emerging market firms seek those resources through alliances with developed market firms (Hitt et al., 2000). Second, strategic alliances can help start-ups in procuring resources from third parties through the signaling role of the alliance (Baum and Oliver, 1991; Stuart et al., 1999). Strategic alliances with established companies signal to third parties that the start-ups stand a good chance of success. The enhanced legitimacy helps the start-up to dispel hesitation among resource holders who are concerned with the chance of harvesting their investments in the start-ups. Equity investment of venture capitalists into a new venture not only provides financial resources and management know-how to the venture but also engenders legitimacy. Since participating venture capital firms have an incentive to make the venture succeed, they provide management related know-how to the start-ups by having a seat on the board, create access to professionals, and initiate the change of top management team when the start-ups fall short of the expectation of the venture capitalists (Gorman and Sahlman, 1990; Fried and Hisrich, 1995; Sapienza, Manigart, and Vermeir, 1996). The active involvement of venture capitalists in start-ups where the capitalists invested holds also in Korea.2 Their involvement will further alleviate the hesitancy of potential suppliers, buyers, investors, and employees when considering their commitment to the start-up. Their equity participation signals to others that the new venture enjoys favorable prospects. The legitimacy and reduction of inferred uncertainty enable it to procure external resources on better terms (Podolny, 1993; Stuart et al., 1999). The collaboration with universities and research institutes provides a means of 2

We interviewed 10 venture capitalists who were associated with large Korean venture capital companies. The interviews indicated that venture capitalists are actively involved in the management of technological start-ups and provide managerial know-how to them. Interviews with CEOs of successful startups corroborated the active involvement of venture capitalists, when they invested equity in the start-up.


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developing technological knowledge, which the new venture could not have accomplished singlehandedly (Bower, 1993; Industrial Research Institute, 1995; Santoro and Gopalakrishnan, 2000). Universities and research institutes also provide consulting assistance to a new venture and opportunities for continuing education for professional employees (Reams, 1986; Saxenian, 1994). In the long run, the collaboration enables new ventures to recruit high-caliber researchers. Personal ties between the start-up and university personnel are also conducive to the migration of human capital to the new venture (Powell, Koput, and SmithDoerr, 1996). Graduate students who participate in venture-related projects become familiar with its technology and are likely to move to the venture. As expected, interviews with the founders of successful technology-based Korean startups indicate that its founders actively exploited ties with universities and research institutes for both new technology development and recruitment. Participation in venture associations and supplementing these ties by forging an informal network has the effect of further accumulating social capital. Founders can obtain managerial knowhow about the management of start-ups, intelligence about new market trends and opportunities, and the discovery of valuable partners through the informal network (Birley, 1985; Pennings and Harianto, 1992). A founder’s network provides access to information that he/she could process alone. Information mediated by such trustworthy relations is more credible and interpretable, due to the identity of actors and the intensity of their social ties (Uzzi, 1996). As the network also becomes a channel of referrals through which the founder’s interests are represented in a positive light, the network helps firms identify and develop new entrepreneurial opportunities (cf. Burt, 1992). Furthermore, the network provides access to professionals such as lawyers, accountants, and venture capitalists. In sum, partnership-based linkages of a technological start-up company facilitate the firm to garner complementary external resources, to dispose the output with better terms, and to identify and develop new entrepreneurial opportunities. These arguments lead to the following hypothesis: Hypothesis 4: Partnership-based linkages of a technological start-up company (specifically to Strat. Mgmt. J., 22: 615–640 (2001)

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other firms, venture capital companies, universities/research institutes, and venture associations) are positively associated with the start-up’s performance. Sponsorship-based linkages Sponsorship-based linkages represent unilateral relationships in that the outside party furnishes support without a quid pro quo. Newly created firms often receive major support from governmental agencies and other organizations and, if they do, they enjoy a competitive advantage (Flynn, 1993; Stuart et al., 1999). What we argued earlier, about the benefits of partnershipbased linkages, applies here with even greater force. Shrouded by the start-up’s financial uncertainty, a sponsor’s visibility and prominence go a long way in shielding those ventures against adverse selection. Such social capital increases availability of external resources and provides opportunity for organizational growth. Reducing the potentially harmful effects that are common during the vulnerable early stage of a start-up (Stinchcombe, 1965), such networks protect the new ventures from environmental threats (Miner, Amburgey, and Stearns, 1990). Young organizations can acquire critical support, free of charge or on better terms (Starr and MacMillan, 1990). This sponsorship also enhances their legitimacy and prestige (Baum and Oliver, 1991; Podolny, 1993; Rao, 1994; Stuart et al., 1999). The enhanced legitimacy and prestige enable the emerging firm to procure scarce and critical resources from the external environment. In the Bru¨derl and Preisendo¨rfer (2000) study, ‘state support,’ however, failed to have any effect on start-up performance when firm and environmental variables were added to the regression model. In the Korean context, the national government has taken pains to generate a richer and more nurturing environment conducive to birth and survival of technology-based ventures. The Korean government leveraged its influence over the private sector through its pressure to subscribe to the Korean ideals of becoming a high-technology society. The government itself nominated several technology-based ventures as ‘promising’ ones and provided them with R&D funding. Such endorsement also bolsters their legitimacy. Several commercial banks also took the initiative to annually nominate promising small enterprises. Copyright  2001 John Wiley & Sons, Ltd.

When selected by a Korean financial institution, the new venture obtains access to loans at below market rates. Such endorsement likewise bolsters a start-up’s legitimacy. These arguments lead to the following hypothesis: Hypothesis 5: Sponsorship-based linkages of a technological start-up company (i.e., with commercial banks and government agencies) are positively associated with the start-up’s performance. Interactions Hypotheses 4 and 5 suggest that internal capabilities and external networks influence a technological startup’s performance independently. Internal capabilities point to skills for the transformation of inputs into outputs, while corporate social capital pertains to availability of channels for securing inputs and disposing of outputs and to the possibility of identifying and developing more rewarding opportunities (Burt, 1992; Pennings et al., 1998). Internal capabilities help firms accumulate social capital, as potential partners are more willing to collaborate with the firms having a higher level of internal capabilities. Similarly, social capital helps firms accumulate internal capabilities as social capital provides access to information, technology, and, at times, human and financial capital that are needed for the accumulation of internal capabilities (Burt, 1992). Furthermore, internal capabilities and social capital are complementary in creating value. Thompson (1967: 19) suggested the complementarity of internal capabilities and social capital as follows. … organizational rationality involves three major component activities: (1) input activities, (2) technological activities, and (3) output activities. Since these are interdependent, organizational rationality requires that they be appropriately geared to one another. The inputs acquired must be within the scope of the technology, and it must be within the capacity of the organization to dispose of the technological production.

The value of internal capabilities to a firm is contingent on its social capital (cf. Burt, 1997). To create more value from internal capabilities at hand, firms should mobilize complementary external resources in which to apply the capabilities and have to dispose of produced outputs. Strat. Mgmt. J., 22: 615–640 (2001)

Internal Capabilities, External Networks, and Start-up’s Performance Organizations with more social capital receive higher returns to their internal capabilities because they are positioned to identify and develop more rewarding opportunities (Burt, 1992), to acquire complementary external resources (Teece, 1987), and to dispose their technological production with better terms. Likewise, when organizations have less social capital, their internal capabilities are bound to generate fewer rents and the market to value them to be much lower. Similarly, the value of social capital to a firm is contingent on its internal capabilities. Internal capabilities help firms better use the complementary external resources that can be obtained on the basis of their social capital. Inputs acquired through social capital and a firm’s ability to dispose outputs are less useful without internal capabilities, since the firm cannot efficiently transform the inputs into the outputs. Additionally, a higher level of internal capabilities and thus higher level of absorptive capacity help firms learn more from their networks and create more value from the opportunities provided by their networks. Lacking internal capabilities, firms experience difficulty in generating value from their social capital. In the context of new business ventures, internal capabilities are not enough for new ventures to enjoy a superior performance, since they are very likely to be deficient of complementary resources. In order for new ventures to fully extract value from their internal capabilities, they should have external networks through which they can mobilize complementary external resources and identify more rewarding opportunities. To extract more value from its social capital, the firm should have internal capabilities. Specifically, entrepreneurial orientation will help startups to create more value when they have external ties that provide scarce and valuable complementary resources to them and lucrative economic opportunities. Also some knowledge and physical or nonphysical resources mobilized through external networks aid in further leveraging its technological capabilities (Teece, 1987). Similarly, extramural resources accessed though social networks complement capabilities and reputation built through financial investment during the development stage to create more wealth. In sum, internal capabilities and social capital are complementary in creating value for start-ups. These arguments lead to the following hypothesis: Copyright  2001 John Wiley & Sons, Ltd.

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Hypothesis 6: Internal capabilities and external networks of technological start-up companies have a positive interaction effect on the start-up’s performance.

METHODS Sample An ideal sample for this study would have been drawn from the total population of technological start-up companies in Korea. However, since we could not easily draw a random sample from such a sampling frame, we sampled firms from those that were enrolled as new business ventures in the Korean Small and Medium Business Administration (KSMBA). If enrolled as a new business venture, the organization enjoyed various governmental benefits such as favorable tax breaks and subsidies. Any firm with fewer than 300 employees could apply. But certification was conditional upon any of four criteria: (1) firms with more than 10 percent equity in the hands of venture capital firms, (2) firms that invested more than 5 percent of sales in R&D, (3) firms enjoying a favorable appropriability regime through patent or copyright application, and (4) firms with governmentally supported R&D projects. To avoid the contamination of results by unobserved heterogeneity (compare Bru¨derl and Preisendo¨rfer, 2000), we sampled technological startup companies from those enrolled in the KSMBA by employing two additional criteria: (1) industrial scope and (2) independent start-ups rather than corporate spin-offs. To select technologyintensive firms, we sampled firms in computer software, biotechnology, and electric or electronic products. We chose technological start-ups as our sample, since they are considered as more important in creating new wealth for the society than low-tech start-ups and they can better manifest the influence of internal capabilities and social capital on organizational performance. At the end of 1998, 2043 firms were enrolled as business ventures in the KSMBA. On the basis of these criteria, 1012 firms were entered into our sample. Data collection The survey questionnaire was our major data collection tool. We designed an instrument that Strat. Mgmt. J., 22: 615–640 (2001)

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could effectively gather data on all relevant variables. To create the questionnaire, we conducted personal interviews with CEOs of Korean technological start-ups, and carefully examined the entrepreneurship literature. The questionnaire performs the actual interrogation function in a mail survey and therefore warrants considerable attention. Our design and administration of the questionnaire involved the ‘total design method’ of Dilman (1978). He employed ‘social exchange theory’ to develop multiple avenues for encouraging high response rates. We implemented most of his suggestions. To generate measurement items germane to performance, we reviewed the RBV and social capital literature, coupled with an inventory of organizational characteristics among 128 successful Korean business ventures by performing a content analysis of business magazines, newspapers, and brochures issued by Korean technological startups. We interviewed top executives and upperechelon managers of 50 firms to calibrate and triangulate the measurement tools. To assess the face validity of the measurement items, we employed business school faculty members and doctoral students as expert judges. After several iterations of item editing and refinement, we conducted pretest interviews with three entrepreneurs and two venture capitalists in order to identify any problems with question wording and questionnaire layout. These interviews, which ranged from 90 to 120 minutes, yielded many useful suggestions that strengthened the content and concurrent validity of the research tools. Furthermore, in December 1998, we pretested the questionnaire on 11 firms. The final version of our questionnaire was sent to the CEO or founders in March 1999. To collect 1999 sales volume data, we used the KSMBA web site in April 2000. We also used telephone interviews with the CEOs of questionnaire respondents, when the web site did not provide sales volume data for the responding firms. We targeted entrepreneurs and CEOs of the firm first and, if unsuccessful, sought to ascertain the cooperation of members of the founding team. These individuals were chosen because of their extensive knowledge of their firm’s organizational characteristics. Considering smallness and newness of our sampled firms, they were very likely to have exact information (Castrogiovanni, 1991; Chandler and Hanks, 1994a). It should also be Copyright  2001 John Wiley & Sons, Ltd.

emphasized that we solicited objective information rather than beliefs and attitudes. Indeed, our respondents are treated as ‘informants’ rather than as carriers of attitudes and opinions. The key informant method has been commonly used in organizational research when secondary archival data were unavailable (Hansen and Wernerfelt, 1989; Pugh et al., 1969). Following Dilman (1978), we took great pains to optimize the response set: we (1) made phone calls to executives of our sample firms for explaining the research projects and soliciting cooperation, (2) sent a letter convincingly explaining the objectives of this research project and requesting cooperation, and (3) mailed a second wave of surveys to nonrespondents. Respondents (or ‘informants’) We sent the questionnaire to all of the 1012 firms. Of these firms, 88 questionnaires were returned because of ambiguous address or moving schedule. Of the 924 firms receiving questionnaires, 175 firms (19% response rate) responded to the questionnaire. Follow-up phone calls were made to alleviate incomplete data. To reduce unobserved heterogeneity, we deleted 22 firms that were founded by large Korean conglomerates or firms founded before 1988. We also deleted 13 responding firms due to missing information and three responding firms whose CEOs participated in interviews for developing the questionnaire. The final tally showed 137 firms (about 14.8% usable responses). This response rate is not great but similar to the 15–24 percent range reported in similar studies (e.g., Provan and Skinner, 1989) and was satisfactory considering this study’s requirement for senior executive’s direct involvement. Ninety-seven firms revealed that their top executive responded to the questionnaire, while the remaining 40 firms suggested that some top-echelon manager filled out the questionnaire. Possible nonresponse bias was examined by comparing the sales volumes of survey respondents with all of the nonrespondents. We collected the sales volume of years 1997 and 1998 of all the nonresponding firms from the KSMBA web site and compared them with those of the respondents. A one-way analysis of variance showed that 1997 sales volume (F-value = 2.048) and 1998 sales volume (F-value = 0.236) between respondents and nonrespondents are statistically Strat. Mgmt. J., 22: 615–640 (2001)

Internal Capabilities, External Networks, and Start-up’s Performance insignificant at p < 0.05. Threats to reliability and countermeasures Using questionnaires for collecting firm-level data has potential weaknesses. To check the reliability and validity of our data, we undertook additional steps to validate our data. First, we asked objective (not opinions and attitudes) information whenever possible, since using subjective information gathered through a single method such as a questionnaire is very likely to produce common method bias and thus inflate the coefficients. The severity of the bias is much higher when the questions require subjective judgment of the respondents. To avoid this common method bias created by using subjective measures, we tried to collect ‘objective’ measures whenever possible. Second, we checked the reliability of our data by mailing identical questionnaires to alternate senior executives from a random subset (N = 78) of the responding firms. We selected 78 cases rather than all responding firms to economize research expenses. The random subsampling was employed by Parkhe (1993) and Zahra (1996). Thirty-nine executives returned these independently completed questionnaires. All of the correlations of matched variables between the two raters were within the range of 0.98 and 1.00. We also compared initial responses with secondary responses for 29 items, which we used for this study from the questionnaire. Among 1031 answers (29 items ⫻ 39 firms), responses on only nine items from four firms were different from initial responses (accuracy rate = 99.1%). The comparisons indicated strong interrater reliability. Third, we triangulated reported data with secondary data (cf. Zahra, Ireland, and Hitt, 2000). Since the information on most variables of interest in this study was not available from published sources, independent corroboration of all questionnaire items is not possible. Therefore we corroborated our data with selected variables that are publicly available. We collected the archival record of sales volume in years 1997 and 1998 of respondents from the KSMBA web site and compared the secondary data with data reported in the questionnaire. The correlations of sales volume between survey response and archival data from the KSMBA web site were 0.95 for year 1997 and 0.98 for year 1998. The high level of correlations indicated the reliability of the Copyright  2001 John Wiley & Sons, Ltd.

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selected variable and may also reflect favorably on the likely accuracy of other reported data. Finally, we used scale reordering suggested by Salancik and Pfeffer (1977). The scale reordering seeks to reduce the sequencing effects of consistency by arranging the items on a self-report questionnaire so that measures of the dependent variables follow, rather than precede, those of the independent variables. We adopted this method by placing the organizational performance after the internal capabilities and social capital variables. Measurement of internal capabilities We captured internal capabilities by three variables: entrepreneurial orientation, technological capabilities, and financial resources invested. Entrepreneurial orientation Following suggestions by Miller (1983), Covin and Slevin (1991), and Stevenson and Jarillo (1990), we measured entrepreneurial orientation by three dimensions: innovativeness, risk-taking propensity, and proactiveness. First, following the suggestion of Lumpkin and Dess (1996), Miller and Friesen (1982), and Hage (1980), we measured innovativeness by two indices: (1) the number of R&D employees in 1997 and (2) the number of products/services that created a new market niche, penetrated established markets successfully, or significantly substituted imports from foreign countries in 1998. Those products/services are very likely to result from innovative efforts that occurred before 1998. To create a single measure for the innovativeness, we standardized two indicators by using mean and standard deviation of the corresponding indicator and added the two standardized scores. Second, based on Miller’s (1983) approach to EO, we measured risk-taking propensity at the firm level by two indices: (1) the number of risky R&D projects in 1997 and (2) expenditure on the risky R&D projects in 1997. The heavy resource commitment on risky projects is consonant with the definition of risk-taking propensity. We asked respondents to treat a project for commercializing brand new technology or product concepts into a product as a risky R&D project. To create a single measure for the risk-taking propensity, we standardized two indicators by using mean and standard deviStrat. Mgmt. J., 22: 615–640 (2001)

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ation of the corresponding indicator and added the two standardized scores. Third, proactiveness was measured by two indices: (1) the number of first mover pursuing projects based on an idea suggested by Miller (1983) and Naman and Slevin (1993), (2) first mover pursuing project expenditure in 1997. We asked respondents to treat a project for pursuing the first one of the international, domestic, or industrial market as first mover pursuing project. And to create a single measure for the proactiveness, we standardized two indicators by using mean and standard deviation of the corresponding indicator and added the two standardized scores. We additionally standardized the three dimensions of EO by using mean and standard deviation of the corresponding dimension. Reliability and factor analysis indicated that indices for each dimension of EO can be aggregated (Cronbach’s alpha for innovativeness, risk-taking and proactiveness is 0.6073, 0.6193 and 0.6092, respectively) and that the three dimensions of EO can be aggregated to produce one factor (eigenvalue = 1.765, Cronbach’s alpha = 0.6329). To create a single composite indicator for entrepreneurial orientation, we multiplied each standardized score by corresponding factor loading and added them up. Technological capabilities We measured technological capabilities by three indices: (1) the number of technologies that were internally developed, including the number of patents obtained or submitted until the end of 1997, (2) the number of utility models and designs3 that were registered to the Korean Patents Administration at the end of 1997, and (3) the number of foreign and domestic quality assurance marks acquired until the end of 1997. Since the average age of our sample firms is 4.18 years and acquiring a patent usually takes 3 or more years in Korea, we could not use the number of patents only. We standardized each index by using the mean and standard deviation of the corresponding indicator. Since reliability test and

3

The Korean Patents Administration grants utility models and design patents to any person who has invented any new and nonobvious ornamental design and structure for an article of manufacture that can be used for practical purpose in industries. This is equivalent to design patents in the United States.


factor analysis indicate that three standardized items can be aggregated to produce one factor (Cronbach’s alpha = 0.6339, eigenvalue = 1.791), we multiplied each standardized score by corresponding factor loading and added them up. Financial resources invested Schoonhoven et al. (1990) measured financial resources invested with monthly average of total costs and expenses accrued after organizational founding. We measured the variable by the amount of total R&D investment, advertising expenditure, and market research investment in 1997. The logic is that organizational performance largely depends on the amount and appropriate timing of financial resources invested during the development period. Measurement of external linkages Partnership-based linkages We measured partnership-based linkages by four indices. The first one pertains to the number of other firms with which a focal firm has a strategic alliance for marketing or technology development. The second is the number of venture capital firms that invested equity in the focal firm. The third is the number of collaborating R&D projects and technology exchange programs with universities or research institutes. The last index entails the number of entrepreneurial associations in which the entrepreneur of the start-up actively participated. Sponsorship-based linkages We measured sponsorship-based linkages by two indices. The first one reflects sponsorship from commercial banks and consists of two indices: (1) the frequency with which financial institutions named the focal firm as a ‘promising small enterprise’, and (2) the number of financial services firms from which the focal firm received a loan with a below market rate in 1997. Since reliability test and factor analysis indicate that standardized scores of the two indices can be aggregated to produce one factor (Cronbach’s alpha = 0.6704, eigenvalue = 1.400), we multiplied each standardized score by corresponding factor loading and added them up. Strat. Mgmt. J., 22: 615–640 (2001)

Internal Capabilities, External Networks, and Start-up’s Performance The second indicator reflects sponsorship from the Korean governmental agencies and comprises also two indices: (1) the frequency with which Korean central or local governmental agencies named the focal firm as a ‘promising small enterprise,’ and (2) the number of governmentsponsored research projects that were carried out in the focal firm, alone or with other organizations during 1997. Since reliability test and factor analysis indicate that standardized scores of the two indices can be aggregated to produce one factor (Cronbach’s alpha = 0.8719, eigenvalue = 1.774), we multiplied each standardized score by corresponding factor loading and added them up. To verify the naming of a focal firm as a ‘promising small enterprise’ designated by government agencies and by financial institutions, we randomly sampled 10 firms from the respondents. We visited those firms and asked them to show us documents issued by government agencies or by financial institutions. The numbers they reported to the questionnaire were correct. We could not check the list of designated firms published by each government agency and financial institution, since they did not publish such data. Measurement of organizational performance How can we measure the performance of new business ventures? Profitability, such as return on investment, may not be an appropriate performance indicator for new business ventures, because many of them are still in the stage of product development (Hart, 1995). Return on investment or on capital is often a useless indicator as many start-ups have failed before generating any profit. In electric commerce, a well-known start-up like Amazon.com has yet to produce a single penny in profit. Yahoo! Korea still awaits its first non-negative earning. Abnormal return and market valuation from the stock market are not useful for this study, since most of our sample firms are not enrolled in the stock market. Sales growth rate, such as sales volume of years 1998 and 1999 divided by sales volume of years 1996 and 1997, cannot be used in the current setting, since many of our sample firms did not generate sales until 1997. Nor could we use the speed of shipping the first product for revenues following founding (Schoonhoven et al., 1990), organizational growth (Eisenhardt and Schoonhoven, 1990), or survival (Bru¨derl et al., Copyright  2001 John Wiley & Sons, Ltd.

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1992, 1998), since we did not have firm-level data from the date of founding. More in line with German traditions, the important study by Bru¨derl and Preisendo¨rfer (2000) did not focus so much on financial measures of performance, but on exponential growth in employment. After interviewing top managers and considering prior studies on venture performance, we measure it by sales growth—the sales volume of years 1998 and 1999 subtracted by the sales volume of years 1996 and 1997. Thus we assumed that our independent variables have their influence on a start-up’s performance during the subsequent 2 years. Control variables We controlled for several variables, which fall outside the purview of our theory, yet might affect organizational performance. These control variables include firm size, firm age, environmental munificence, and the entrepreneur’s industryspecific human capital at the end of 1997. We controlled for firm size, which is measured as the number of full-time employees, since the number of employees can be highly correlated with sales growth. We controlled for organizational age, which is the number of years elapsed after founding until 1997, since it would predict performance as the ‘liability of newness’ arguments suggest (Stinchcombe, 1965). Prior studies suggested that environmental munificence is highly conducive to entrepreneurial success (Castrogiovanni, 1991; Chandler and Hanks, 1994a; Schoonhoven et al., 1990). Whenever the environment shows largesse and opportunities are more abundant, entrepreneurs stand to perform better. We controlled for environmental munificence by two indices: average growth rate of market in which the focal firm competed during 1997 and the number of competing firms (or industry ‘density’) in 1997. The significance of such controls surfaced also prominently in the Bavarian study on start-ups (Bru¨derl and Preisendo¨rfer, 2000). Previous research also suggested that start-up companies are extensions of their entrepreneurs. His/her previously acquired knowhow affects organizational performance. Since an entrepreneur’s industry-specific experience is a very important predictor of venture performance (Bru¨derl et al., 1992, 1998; Helfat, 1999), we controlled for the length of the entrepreneur’s Strat. Mgmt. J., 22: 615–640 (2001)

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career in the industry. Analysis As was already mentioned, we adopted a lagged dependent variable model to examine the relationship between the firm’s capabilities/social capital and organizational performance. Since we do not know a priori whether the lag period was varied by firm and/or industry, we used the identical fixed lag period. We lagged the effect of independent variables by 2 years. In other words, independent variables in this study were either ‘stock’ or ‘flow’ indicators before the end of 1997 and our dependent variable is sales growth in years 1998 and 1999. We selected the length of lagging on the basis of interviews with top executives and consideration of product life cycle of our sample firms. According to the response to our questionnaire, the average length of product life cycle of our sample firms is 42 months on average. It is very reasonable to assume that the internal capabilities and social capital in 1997 affect sales growth in years 1998 and 1999. The lagged dependent variable model would be a more rigorous test of the effects of firm characteristics on firm performance (Mosakowski, 1993). We employed ordinary least squares (OLS) regression to analyze the data. In order to test the additive effects of internal capabilities, external networks, and the interaction between internal capabilities and external linkages, we ran various models for each set of dependent variables. The first model with only control variables is a benchmark against which to test the effects of internal capability on organizational performance. The second model has both control variables and internal capabilities in order to test positive global effects of internal capability in comparison to the first model. The third adds external networks to the second model. To test interaction effect of internal capabilities and external linkages on organizational performance, we produced 18 interaction terms (three internal capability variables ⫻ six external linkage variables), and added each interaction term to Model 3. We did not dump all interaction variables in a single model, for two reasons. First, correlations between interaction terms are too high, and thus putting them in one model can produce multicollinearity problems. Second, our sample size is not large enough to allow the operation. Copyright  2001 John Wiley & Sons, Ltd.

RESULTS Table 1 provides the means, standard deviations, and correlations of all variables. Several positive and statistically significant correlations between internal capability indicators and social capital indicators suggest that internal capabilities can help the development of social capital and vice versa. Also notable are positive and statistically significant correlations among social capital indicators. Global tests Tables 2 and 3 report the results of various regression models explaining sales growth. We conducted a series of global tests comparing successive models by using incremental F-tests, as shown at the bottom of Table 2. The first global test indicates that Model 2, which includes internal capabilities as well as control variables, explains sales growth significantly better than Model 1 (p < 0.01). Also, the second global test indicates that Model 3, which includes social capital indicators, explains the dependent variables significantly better than Model 2 (p < 0.10). The addition of social capital indicators did not substantially change the main effect of internal capability indicators on the start-up’s sales growth. The addition of each interaction term to Model 3 produces differing results depending on the selected interaction term. In Table 3, we reported regression models only when incremental F-tests indicate that the models can predict sales growth significantly better (p < 0.10) than Model 3 in Table 2. In Table 3, we included all control variables used in Model 3 but did not report the intercept and the effects of control variables, which are not substantially different from those in Model 3. These global tests indicate that we have to consider internal capabilities, external linkages, and their interaction terms together to better understand the performance of technological start-ups. Internal capabilities We can test each of the hypotheses on the basis of Model 3 and the models with interaction term. Hypothesis 1 predicts that entrepreneurial orientation is positively associated with a technological start-up’s performance. EO has a positive and Strat. Mgmt. J., 22: 615–640 (2001)

Descriptive statistics and correlation matrix (N = 137)

Variables 1. Sales growth∗ 2. Entrepreneurial orientation of the firm 3. Technological capabilities 4. Financial resource∗ 5. Linkages to other enterprise 6. Linkages to venture capital 7. Linkages to universities 8. Linkages to venture networks 9. Linkages to financial institutions 10. Linkages to government 11. Organizational size 12. Organizational age 13. Market growth rate 14. Number of competitors 15. Entrepreneur’s experience 16. Computer/software industry 17. Biotech industry

Mean

S.D.

1

32.715 0.00

77.115 0.657

0.47

0.00 1.577 2.350 0.504 1.810 1.190

0.917 3.958 2.342 1.138 1.784 2.724

0.00

0.7550

0.00 30.584 4.182 82.917 9.891 13.975 0.562 0.146

2

0.45 0.55 0.76 0.42 0.02 0.03 0.62 0.37 0.11 0.22 −0.01 −0.02 0.15

0.29

3

4

5

6

7

0.42 0.08 0.07 0.38 0.67 0.24 0.07 0.03 −0.01

0.07 0.31 0.13

0.19 0.05

0.12

0.28

0.17

0.24

0.20

0.18

8

9

10

11

12

13

14

15

16

0.07

0.6049 0.18 0.25 0.33 0.25 0.25 0.34 0.30 0.12 0.38 43.885 0.53 0.34 0.44 0.76 0.08 0.64 −0.03 −0.01 0.30 0.29 2.784 0.18 0.16 0.34 0.29 0.09 0.26 0.02 −0.02 0.28 0.30 0.43 272.066 0.07 0.24 0.05 0.04 −0.13 −0.03 0.17 −0.05 −0.01 −0.09 −0.04 −0.04 17.940 0.06 −0.02 0.03 0.16 0.10 0.13 −0.05 0.09 0.08 0.12 0.13 0.08 0.01 6.586 0.06 0.27 0.30 0.13 0.12 0.07 0.18 0.13 0.13 0.17 0.18 0.30 0.23 0.17 0.498 0.09 −0.05 −0.12 0.12 0.16 0.04 −0.20 −0.05 −0.03 −0.01 0.14 −0.01 −0.04 0.14 −0.03 0.354 −0.12 −0.10 −0.04 −0.11 −0.15 −0.01 0.06 −0.01 −0.05 −0.08 −0.08 0.03 −0.04 −0.10 −0.05 −0.47

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Note: p < 0.05 if | r | > 0.17 ∗ Measured in 100 million Korean won (US $ 1 was about 1120 Korean won in May 2000).

Internal Capabilities, External Networks, and Start-up’s Performance


Table 1.

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Table 2. Results of OLS models: sales growth in 1998 and 1999 (N = 137) Variables

Model 1

Model 2

Model 3

Intercept

17.851 (17.260) 0.975∗∗∗ (0.147) −1.129 (2.360) 0.026 (0.022) −0.033 (0.327) −0.544 (0.953) −3.416 (13.157) −17.102 (18.304)

34.689∗∗∗ (13.022) −0.178 (0.158) −0.976 (1.722) 0.003 (0.016) −0.184 (0.242) −0.915 (0.713) 5.758 (9.915) −3.951 (13.465) 15.230∗ (8.262) 16.133∗∗ (7.806) 14.503∗∗∗ (1.747)

0.257 −

0.605 38.898∗∗∗

27.886∗ (14.334) −0.160 (0.194) −0.673 (1.738) 0.001 (0.017) −0.192 (0.240) −0.584 (0.717) 7.919 (9.932) −9.549 (13.280) 13.776∗ (8.126) 16.687∗∗ (8.236) 12.126∗∗∗ (1.922) −2.337 (2.006) 14.641∗∗∗ (5.347) 0.554 (2.759) 0.711 (1.559) −3.695 (5.976) −5.563 (5.864) 0.623 1.996∗

Organizational size Organizational age Market growth rate Number of competitors Entrepreneur’s experience Computer/software industry (dummy) Biotech industry (dummy) Entrepreneurial orientation Technological capabilities Financial resource Linkage to other enterprise Linkage to venture capital Linkage to universities Linkage to venture networks Linkage to financial institutions Linkage to government Adj. R2 Incremental F-test Note: Standard errors are in parentheses. ∗ p < 0.10; ∗∗p < 0.05; ∗∗∗p < 0.01 (two-tailed test)

marginally statistically significant effect on sales growth in Models 2 and 3 (p < 0.10). These results provide weak support for Hypothesis 1. Hypothesis 2 suggests that technological capabilities of a start-up are positively associated with organizational performance. As predicted, the variable has a positive and statistically significant effect on sales growth in Models 2 and 3 (p < 0.05). These results provide strong support for Hypothesis 2. Hypothesis 3 predicts that a technological start-up’s financial resources invested during its development period are positively associated with its performance. Supporting the hypothesis, the financial resources have a positive Copyright  2001 John Wiley & Sons, Ltd.

and statistically significant coefficient in Models 2 and 3 (p < 0.01). External networks Hypothesis 4 suggests that partnership-based linkages (with other enterprises, venture capital companies, universities and research institutes, and venture networks) are positively associated with organizational performance. Ties with other enterprises, venture associations, and universities are not significantly associated with sales growth in Model 3. Only the linkage to venture capital companies has a positive and statistically signifiStrat. Mgmt. J., 22: 615–640 (2001)

Results of OLS models with interactions (N = 137)

Variables

Model 4

Model 5

Model 6

Model 7

Model 8

Entrepreneurial orientation

7.542 (8.426) 15.844∗ (8.099) 8.561∗∗∗ (2.437) −2.587 (1.973) 10.907∗ (5.592) 0.278 (2.713) 0.743 (1.532) −4.209 (5.875) −7.251 (5.807) 14.619∗∗ (6.322)

17.937∗∗ (7.650) −4.219 (9.092) 8.912∗∗∗ (1.943) −2.162 (1.874) 7.224 (5.282) 0.898 (2.578) 0.880 (1.457) −6.295 (5.613) −10.147∗ (5.579)

15.995∗∗ (7.901) 13.209 (8.056) 7.683∗∗∗ (2.376) −1.970 (1.947) 2.800 (6.504) 1.053 (2.676) 0.766 (1.510) −8.657 (6.016) −11.531∗ (6.014)

4.079 (9.974) 14.854∗ (8.252) 11.384∗∗∗ (1.960) −2.137 (1.997) 11.652∗∗ (5.608) 0.504 (2.739) 0.928 (1.554) −5.938 (6.086) −5.052 (5.830)

17.547∗∗ (7.929) −8.419 (11.237) 11.267∗∗∗ (1.873) −1.257 (1.967) 9.970∗ (5.365) 0.337 (2.662) 0.866 (1.505) −8.391 (5.953) −3.039 (5.713)

Technological capabilities Financial resource Linkage to other enterprise Linkage to venture capital Linkage to universities Linkage to venture networks Linkage to financial institutions Linkage to government Entrepreneurial orientation ⫻ Linkage to venture capital Technological capabilities ⫻ Linkage to venture capital Financial resource ⫻ Linkage to venture capital

0.636 5.347∗∗

0.671 18.589∗∗∗

Note: The coefficients and standard errors of control variables are not reported in this table. Standard errors are in parentheses. ∗ p < 0.10; ∗∗p < 0.05; ∗∗∗p < 0.01 (two-tailed test)

4.236∗∗∗ (1.409)

0.647 9.041∗∗∗

7.218∗ (4.350)

0.628 2.735∗

8.768∗∗∗ (2.776)

0.649 9.980∗∗∗

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Entrepreneurial orientation ⫻ Linkage to universities Technological capabilities ⫻ Linkage to universities Financial Resource ⫻ Linkage to universities Technological capabilities ⫻ Linkage to venture network Financial Resource ⫻ Linkage to venture network Technological capabilities ⫻ Linkage to financial institutions Financial resource ⫻ Linkage to financial institutions Adj. R2 Incremental F-test

16.476∗∗∗ (3.821)

Internal Capabilities, External Networks, and Start-up’s Performance


Table 3.

632

(continued)

Variables

Model 9

Model 10

Model 11

Model 12

Model 13

Entrepreneurial orientation

14.801∗ (7.817) 12.930 (7.998) 5.729∗∗ (2.678) −1.961 (1.932) 8.530 (5.463) −2.671 (2.826) 0.805 (1.499) −6.781 (5.820) −3.456 (5.673)

14.731∗ (7.956) 5.863 (9.111) 11.868∗∗∗ (1.882) −1.215 (2.014) 17.321∗∗∗ (5.335) −0.076 (2.709) 0.904 (1.527) −2.783 (5.856) −9.746 (5.967)

11.603 (8.157) 14.995∗ (8.227) 11.659∗∗∗ (1.925) −2.059 (1.998) 12.499∗∗ (5.446) −0.136 (2.765) −0.158 (1.626) −3.310 (5.931) −6.123 (5.825)

15.497∗ (7.977) 9.792 (8.501) 11.512∗∗∗ (1.895) −1.942 (1.969) 12.892∗∗ (5.275) −0.748 (2.747) 0.876 (1.526) −2.869 (5.854) −8.379 (5.842)

14.848∗ (8.061) 17.805∗∗ (8.171) 9.879∗∗∗ (2.243) −2.640 (1.990) 10.782∗ (5.672) 0.149 (2.738) 1.067 (1.554) −10.455∗ (6.913) −1.642 (6.163)

Technological capabilities Financial resource Linkage to other enterprise Linkage to venture capital Linkage to universities Linkage to venture networks Linkage to financial institutions Linkage to government Entrepreneurial orientation ⫻ Linkage to venture capital Technological capabilities ⫻ Linkage to venture capital Financial resource ⫻ Linkage to venture capital Entrepreneurial orientation ⫻ Linkage to universities Technological capabilities ⫻ Linkage to universities Financial Resource ⫻ Linkage to universities Technological capabilities ⫻ Linkage to venture network Financial resource ⫻ Linkage to venture network

Strat. Mgmt. J., 22: 615–640 (2001)

Technological capabilities ⫻ Linkage to financial institutions Financial resource ⫻ Linkage to financial institutions Adj. R2 Incremental F-test

2.515∗∗∗ (0.762)

0.652 10.885∗∗∗

11.826∗∗ (4.654)

0.639 6.458∗∗

Note: The coefficients and standard errors of control variables are not reported in this table. Standard errors are in parentheses. ∗ p < 0.10; ∗∗p < 0.05; ∗∗∗ p < 0.01 (two-tailed test)

3.269∗ (1.890)

0.629 2.990∗

20.015∗∗ (7.888) 0.639 6.438∗∗

4.224∗ (2.238) 0.631 3.564∗



Table 3.

Internal Capabilities, External Networks, and Start-up’s Performance cant effect on sales growth. These results do not provide support for Hypothesis 4 in general, but indicate that contribution of partnership-based linkages to a start-up’s performance depends on the types of the linkages. Hypothesis 5 predicts that sponsorship-based linkages are positively associated with organizational performance. Contrary to the hypothesis, both linkages to financial institutions and linkages to government agencies do not have any statistically significant effect on sales growth. These results do not provide any support for Hypothesis 5. Interaction terms Hypothesis 6 suggests that internal capabilities and external networks have a positive interaction effect on a technological start-up’s performance. The effects of interaction terms are mixed in general. Out of 18 possible interaction terms, we found 10 positive and statistically significant coefficients, and eight statistically nonsignificant coefficients. We should compare the regression results in Table 3 with Model 3 in Table 2 to comprehend the interaction effects. Several social capital indicators (linkages to universities/research institutes, linkages to venture networks, and linkages to financial institutions) that do not have any main effect on sales growth have a statistically significant and positive interaction effect when they are multiplied with internal capability indicators. Among four types of partnership-based linkages, linkages to venture capital and to universities have statistically significant interactive effects with all three indicators of internal capabilities on the start-up’s performance. Only the coefficient of the interaction between EO and linkages to university is marginally statistically significant (p < 0.10). As predicted, all of the interaction effects that are statistically significant are positive. Linkages to venture network have a positive and statistically significant interaction effect with technological capabilities on the startup’s sales growth. The coefficient of the interaction between linkages to venture network with financial resources is positive but marginally statistically significant (p < 0.10). Linkages to other enterprises through strategic alliances do not have statistically significant interaction effects with indicators of internal capabilities on the start-up’s sales growth. Copyright  2001 John Wiley & Sons, Ltd.

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Among sponsorship-based linkages, linkages to government do not have any statistically significant interaction effect with indicators of internal capabilities on the start-up’s sales growth. Linkages to financial institutions have positive interactive effects with technological capabilities (p < 0.05) and financial resources invested during the development period (p < 0.10). Also notable is that the statistically significant effect of EO in Model 3 disappeared when its interaction with social capital indicators is included in Models 4 and 7. Similarly, the statistically significant effect of technological capabilities in Model 3 disappeared when its interaction with social capital indicators is included in Models 5, 8, 10, and 12. This is somewhat to be expected because of the collinearity problems presented by interaction effects with main effects. By contrast, the main effect of financial resources did not disappear even when its interaction with social capital indicators is included. The results provide partial support for Hypothesis 6.

DISCUSSION AND CONCLUSIONS This study investigated the effects of the internal capabilities and external network on the performance of technological start-ups. We treated internal capabilities and social capital as outcomes from entrepreneurial strategies. Results indicated that technological capabilities and financial resources invested during the development period are positively associated with the start-up’s performance. Entrepreneurial orientation has a positive and marginally statistically significant effect on performance. Among social capital indicators, the only statistically significant predictor of performance is the linkage to venture capital companies. More importantly, this study suggests that internal capabilities and social capital interactively influence the start-up’s performance. Our results provide weak support for the effect of EO on the start-up’s performance, though entrepreneurship literature has emphasized the importance of EO for a start-up’s success. The high level of innovativeness, risk-taking, and proactiveness in 1 year may not significantly raise sales growth during the following 2 years, when new product shipment requires more than 2 years after starting entrepreneurial commitment. Our study suggests that it may take longer than 2 Strat. Mgmt. J., 22: 615–640 (2001)

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years for EO to enhance performance significantly. Note also that Bru¨derl and Preisendo¨rfer (2000) discovered that only 4 percent of the firms in their sample were ‘fast-flyers.’ The bulk of new ventures are satisfied with minimal growth. This study confirmed the importance of technological capabilities and financial capital invested during a venture’s development period. Obviously, technological capabilities are a very critical success factor for technological start-ups. Financial resources invested during the development period enable start-ups to exploit rich resource spaces. This study revealed weak main effects of social capital variables, but provided several strong interaction effects of internal capabilities and partnership-based linkages on a start-up’s performance. Linkages to other enterprises through strategic alliances do not have any main effects or interaction effects with internal capabilities. Private equity placement by established large firms had been extremely rare in Korea, as was likewise witnessed in countries like Sweden and Germany. Most of a start-up’s strategic alliances are with other small firms, and thus the ties do not provide a great deal of resources or reputation. By contrast, linkages to venture capital companies have very strong main effects and interaction effects with internal capabilities. Venture capital companies that invested equity in a start-up have a very strong incentive to help the venture succeed. Such involvement furnishes unequivocal signals to others who contemplate their involvement with a new venture. Financial resources and management skills provided by venture capital firms also enable the start-ups to create more wealth from their internal capabilities. For instance, startups can utilize their technological capabilities to develop more marketable products with the help of venture capital firms. Linkages to universities do not have main effects on the start-up’s performance but have a positive and statistically significant interaction effect with the three indicators of internal capabilities. The results suggest that, absent of internal capabilities, such ties do not contribute to the start-up’s performance. Only start-ups with internal capabilities can effectively absorb knowledge and technologies that are cooperatively developed with universities and research institutes (cf. Cohen and Levinthal, 1990; Hitt et al., 2000), and are better positioned to hire graduate students Copyright  2001 John Wiley & Sons, Ltd.

and researchers through the ties. Similarly, results regarding linkages to venture networks show no main effects but reveal positive and statistically significant interaction effects with technological capabilities and financial resources invested. Only start-ups with internal capabilities can take advantage of entrepreneurial opportunities and information garnered through venture networks (Hansen, 1995; Uzzi, 1996). All sponsorship-based linkages do not have main effects and only weak interaction effects on performance. Linkages to financial institutions have weak interaction effects with technological capabilities and financial resources invested in the development period on technological start-up’s performance. Linkages to government have neither statistically significant main effects nor interaction effects with internal capability indicators. Governmental agencies and financial institutions usually do not have a strong incentive to evaluate business ventures accurately when they engage in nominating ‘promising’ start-ups, since they do not commit substantial resources to the nominated companies. The lack of strong incentives makes such nominations a weak and even an undesirable signal to third parties who might be drawn to the start-up. Earlier we observed that the Bavarian entrepreneurship study (Bru¨derl and Preisendo¨rfer, 2000) did not show strong ‘state support’ effects on start-up growth either. We believe that this study can significantly contribute to our knowledge of entrepreneurial wealth creation. First, we explored the importance of internal capabilities and social networks in the wealth creation of technological start-up companies. We combined RBV and social capital theory that have been considered central in the explanation of firm performance. The RBV has stressed a firm’s unique proprietary assets, especially intangible assets such as patents and employee skills (Hall, 1993). The notion of social capital that emerged out of theories on social networks shifted the attention away from the RBV-inspired question of ‘what you know’ to ‘who you know,’ by taking stock of a firm’s linkages with external suppliers, financial institutions, clients, and other actors (Jarillo, 1988; Pennings et al., 1998; Starr and MacMillan, 1990; Zhao and Aram, 1995). The results regarding technological capabilities and financial resources invested during the development period are consistent with the RBV of the firm. This study Strat. Mgmt. J., 22: 615–640 (2001)

Internal Capabilities, External Networks, and Start-up’s Performance provides only weak support for social capital theory. Only the linkage to venture capital companies has a consistently significant effect on a start-up’s performance. The results suggest that the commitment of networking partners to the start-up may be as important as the prominence of the partners, which is suggested by Podolny (1993) and Stuart et al. (1999). Second, this study suggests that RBV and social capital theory need to be simultaneously considered and integrated to better account for entrepreneurial wealth creation. To our knowledge, this study is the first attempt to integrate those two perspectives to explain wealth creation among a set of start-up companies. Most prior studies on entrepreneurship and strategy did not investigate the impact of internal capabilities and social capital on performance in an integral way. Those studies tried to show either the importance of external networks (e.g., Hansen, 1995; Knoke, 1999; Leenders and Gabbay, 1999; Starr and MacMillan, 1990; Uzzi, 1996) or the stock of internal resources (e.g., Chandler and Hanks, 1994a; Grant, 1991; Prahalad and Hamel, 1990; Shrader and Simon, 1997). Our results, however, indicate that internal capabilities are more conducive to wealth creation when their creators/owners can identify and develop new entrepreneurial opportunities and garner complementary external resources through social capital. Furthermore, our results suggest that social capital is only valuable when a firm is endowed with internal capabilities. This study has several methodological weaknesses. First, our sample size is not large enough to allow us to analyze the effects of all interaction terms in one equation. Additionally, our response rate is not that great, even though we followed Dilman’s (1978) data-collecting procedure to increase response rate. Since we collected most of our data from a single questionnaire, the results can be subject to common method bias. Second, we used data from Korean technological start-up companies to test our hypotheses. As a result, generalizability of the results to other country settings or to nontechnological start-ups is questionable and awaits further research in other kinds of settings. Large Korean conglomerates, called chaebols, and government intervention have dominated the Korean economy. Chaebols have been reluctant to pursue win–win strategy with start-ups, and government agencies and financial Copyright  2001 John Wiley & Sons, Ltd.

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institutions lost their credibility in nominating promising start-ups. Third, informal networks based on East Asian culture, such as school ties, geographical ties, and kinship ties, could be more important to organizational growth than formal interorganizational ties in Korea (Bae and Lee, 2000), since the country scores very high on cultural dimensions such as collectivism and Confucian dynamism (cf. Hofstede and Bond, 1988). Fourth, this study did not use fine-grained measures of external networks, which capture the quality and intensity of collaboration. In order for start-up companies to create more value from their social networks, they should have highquality ties with partners having complementary resources. The crudeness of our measures of social capital may cause the very weak support for social capital theory. Finally we should notice the limited size of our sample, which yielded only 137 usable cases. Notice, for example, that the Bru¨derl and Preisendo¨rfer (2000) high-quality sample was almost 2000 in size, and covered a large range of covariates of firm founding, including industry characteristics, firms’ attributes and an elaborate profile of their founders. Note also that their sample was purged of phony firms, for example those that were registered but did not commence operations or had never realized a sale. We should replicate the Bavarian study, but now with the inclusion of social capital variables so that we can more fully assess the interactive effects of firm resources and social capital, and this over a wider range of industries—both low- and hightech. The weaknesses provide directions for future research. First, that research could investigate wider aspects and ramifications of internal capabilities and social capital. Other classes of intangible assets, such as trade secrets or unique types of human capital whose migration is limited or restricted, might figure prominently in the success of start-ups. That program of research also needs to consider informal interorganizational relationships or social networks like the entrepreneur’s and founding team’s personal connections, such as bamboo networks and guanxi ties (Dubini and Aldrich, 1991; Ostggard and Birley, 1994). These latter networks are likely to be more ‘embedded’ (Uzzi, 1996) and thus significantly influence organizational performance. Also we need to investigate whether performance is conditional Strat. Mgmt. J., 22: 615–640 (2001)

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upon pairs or combinations of firm assets. If a bundle of resources are co-specialized with other firm resources, it is likely to produce more wealth for the firm (Conner, 1991; Milgrom and Roberts, 1992; Mosakowski, 1993). Second, future research ought to examine conditions under which the interaction effects of internal capabilities and corporate social capital are most prominent. As social capital theory implies, the difficulty in evaluating firm outputs, and the firm itself, may increase the strength of interaction effects. The performance due to an interaction between internal capabilities and social capital will also depend critically on the conditions in a firm’s competitive and general environment (Foss, 1997). Results might show the relative size of interaction effects among different industries and sectors by using samples from diverse industries. Third, future research could investigate the dynamic relationship among internal capabilities, social capital, and a start-up’s performance by using longitudinal data. For example, new avenues of research can explore whether entrepreneurial orientation has an impact on technological capabilities, which in turn affects future sales growth. Similarly, new research could examine whether external networks are conducive to the accumulation of internal capabilities, which in turn facilitates the crafting of new ties with powerful or valuable external partners. Longitudinally, that research could uncover the cursive and recursive effects of a start-up’s performance. Wealth creation, whether at the venture level or at the level of creative destruction in the industry, is a process that ultimately requires the triggers, catalysts, and outcomes of that process. Hopefully, this study will further induce that line of research. Finally, our line of research can be connected with the emergence of new organizational forms that are geared to the creation process of new firms: incubators (Hansen et al., 2000; Pennings and Powell, 2000). ‘Facilitators’, such as CMGI and Safeguard Scientifics, as well as corporate incubators like those at Ericsson, provide a range of resources for start-ups, including financial as well as intellectual assets, but they are also instrumental in providing networks. Indeed it is this tendency to combine firm resources and external networks that renders our research implications prescriptive for the design of incubators. Research Copyright  2001 John Wiley & Sons, Ltd.

could show whether incubator success in fostering new businesses is conditional upon the optimum combination of these two sets of factors. Since this study explores the performance contribution of internal capabilities and social capital, which have been accumulated through entrepreneurial strategies in the past, this research can provide several managerial implications for entrepreneurs of technological start-ups. Entrepreneurs usually have very limited time and resources and thus should manage them fastidiously. First, entrepreneurs need to run the startups with innovativeness, risk-taking, and proactiveness as suggested by the entrepreneurship literature. They also should invest resources and time to develop intangible assets, such as technological capabilities and corporate culture. Additionally, entrepreneurs should invest more efforts in mobilizing financial resources during the development period such that the start-up can exploit a more lucrative resource space. Second, entrepreneurs should encourage external partners to commit their resources to the startup. Our results indicated that sponsorship-based linkages, where the partners do not have a significant stake in the start-up, hardly contribute to a start-up’s performance. Strategic partners that committed their resources to the start-up are likely to put forth more effort to make the start-up succeed, and the ties with the committed others strongly signals the positive prospect of the startup to third parties. Usually, entrepreneurs are very reluctant to provide stock of the start-up to potential partners since they might be perturbed about loss of management control and dilution of their share. This study suggests that entrepreneurs need to view the exchange of their stocks with partners’ commitment as a win–win game. Third, in order to create value and economic wealth, entrepreneurs should accumulate internal capabilities and develop external networks simultaneously. When an entrepreneur puts most of his effort into accumulating internal capabilities, the start-up will fail to mobilize complementary external resources and plans not to collect the rents from his innovation. Similarly start-ups of which the entrepreneur expends most of his/her energy to develop external networks cannot succeed in the long run, since the network partners are reluctant to put their proprietary assets repeatedly at risk (Chung, Singh, and Lee, 2000; Gouldner, 1960). Strat. Mgmt. J., 22: 615–640 (2001)

Internal Capabilities, External Networks, and Start-up’s Performance ACKNOWLEDGEMENTS We appreciate the comments and helpful suggestions from two anonymous reviewers and the guest editors as well as Josef Bru¨derl, Yoo Keun Shin, Byung-do Kim, Jay Barney, Yonghak Kim, and Benjamin Powell for their helpful comments on the earlier stages of the writing of this manuscript. Financial support from the Korea Research Foundation in 1999 is gratefully acknowledged.

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