Venture capital financing, strategic alliances, and the initial public offerings of Internet startups

Journal of Business Venturing 19 (2004) 721 – 741

Venture capital financing, strategic alliances, and the initial public offerings of Internet startups Sea Jin Chang* School of Business Administration, Korea University, Sungbukku Anamdong, Seoul 136-701, South Korea Received 1 July 2002; received in revised form 1 February 2003; accepted 1 March 2003

Abstract This study examines how Internet startups’ venture capital financing and strategic alliances affect these startups’ ability to acquire the resources necessary for growth. Using the initial public offering (IPO) event as an early-stage measure for Internet startups’ performance and controlling for the IPO market environment, this study found that three factors positively influenced a startup’s time to IPO: the better the reputations of participating venture capital firms and strategic alliance partners were, the more money a startup raised, and the larger was the size of a startup’s network of strategic alliances. D 2003 Elsevier Inc. All rights reserved. Keywords: Venture capital financing; Internet startups; IPO

1. Executive summary Internet technology and the surge of Internet-related business startups have fundamentally impacted the world economy. The Internet allows firms to offer products and services 24 hours a day throughout the world. According to the Securities Data Corporation (SDC) database, $108.2 billion was invested in Internet-related startups during 1995–2000. Since the plunge of the NASDAQ in April 2000, however, the markets’ perception of Internet startups soured. Venture capital funds dried up and many firms that had successful initial public offerings (IPOs) went bankrupt. * Tel.: +82-2-3290-1939; fax: +82-2-922-7220. E-mail address: [email protected] (S.J. Chang). 0883-9026/$ – see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusvent.2003.03.002

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In the aftermath of the Internet bubble, it is easy to discredit all Internet startups. To be sure, capital markets funded many startups that lacked sustainable business models. Yet some startups have done well since their IPOs, and many startups never had IPOs. There have been few systematic studies on what factors contributed to the relative success of Internet startups. This study uses the IPO event to measure several possible success factors. It examines the effects of venture capital financing and strategic alliances networks on startups’ performance. Both venture capital financing and strategic alliances affect a startup’s performance in two important ways. First, they provide a startup with much needed resources such as cash and complementary resources. Second, they provide legitimacy to other resource holders, thus indicating that it is worth investing in or providing resources to a startup. On average, a startup that has such financing and alliances will go to IPO more quickly than will a startup that lacks them. With a sample of Internet startups founded between January 1994 and June 2000 in three broadly defined Internet business areas—e-commerce companies that sell products, ecommerce companies that sell services, and Internet portals—and controlling for the IPO market environment, this study found strong evidence that venture capital financing and strategic alliances significantly affected the IPO rate. We found that early entrants’ rate of going public was more than 12 times higher than the rate of late entrants, which clearly demonstrates that first movers have an advantage in the Internet business. We also found that the reputation of participating venture capital firms in a startup had a strong positive impact on the IPO rate. For instance, startups that were funded by venture capital firms with an average IPO success rate of 30% had an IPO rate that was 2.12 times higher than that of startups that were funded by venture capital firms with an average IPO success rate of 10%. We also found that the reputation of alliance partners and the number of strategic alliances had positive impacts on the IPO rate. One additional strategic alliance increases the IPO rate by 1.17 times. This study’s findings have several practical implications. First, entrepreneurs should get funding from respectable venture capital firms so that they can enjoy the spillover effects of these firms’ reputations. Second, entrepreneurs should develop strategic alliances with prominent firms to access social, technical, and commercial resources that normally require years to accumulate. These alliances also reduce the liability of newness and improve performance.

2. Introduction Internet technology and the surge of Internet-related business startups have fundamentally impacted the world economy. The Internet allows firms to offer products and services 24 hours a day throughout the world (Evans and Wurster, 1999; Hagel and Singer, 1999). According to the SDC database, $108.2 billion was invested in Internet-related startups during 1995–2000. Some Internet startups, such as Yahoo, Amazon, and e-Bay, were very successful, and investors valued them highly: in June 2000, 2 months after the NASDAQ plunged, the market

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values of Yahoo, Amazon, and e-Bay were $77 billion, $16 billion, and $16 billion, respectively. These companies’ founders became extremely rich. Entrepreneurs rushed to start companies, and there was an abundance of venture capital funds to support them. Since the NASDAQ began declining in April 2000, however, the markets’ perception of Internet startups soured. Investors began to realize that many startups were overvalued. Venture capital funds dried up, and many startups that had enjoyed successful IPOs began to face liquidity crunches. Many startups, including Webvan and Pets.com, went bankrupt. This boom and bust cycle resembles what happened in the disk drive industry in the 1970s and 1980s (Sahlman and Stevenson, 1985). Given the rampant speculation in these startups, it is easy to discredit the viability of the entire Internet business sector. Nonetheless, several Internet firms, especially early entrants that had IPOs during the early period of Internet commerce, are performing well even at the end of 2002. Amazon had its first operating profit in the fourth quarter of 2001. e-Bay and Yahoo have been profitable, even though their market valuations are far below what they were in early 2000. Given the enormous amount of money that was invested in these startups, as well as the high mortality rate in this sector, it is important for both academics and practitioners to understand what factors have affected Internet startups’ performance. There have, however, been few systematic studies on what these factors might be. Zacharakis et al. (in press) explored the development of the Internet sector from an environmental ecosystem perspective, but did not examine individual startups’ performance. To our knowledge, this study is the first attempt to use a large-scale database to examine the performance of Internet startups, as reflected by these startups having an IPO. In doing so, it controls for the IPO market environment, which has a significant impact on the IPO event. This study adopts the IPO as an early-stage measure for performance of Internet startups. The IPO has been used as a measure for startup performance since conventional measures for performance, such as profit or sales, are not available for very young firms. (Deeds et al., 1997a; Stuart et al., 1999). Since Internet startups require huge up-front investments in technology and branding, there may be a long lag before conventional financial variables accurately measure their performance. There are also several reasons why the IPO event reflects a startup’s performance early in its life. The IPO transforms a privately held venture into a publicly owned company. Venture capital firms typically wish to take startups public as soon as possible to realize their profits and invest the proceeds in other startups. For entrepreneurs, the IPO is an opportunity to exchange stock for cash and reap personal gains. For a startup, the IPO is an important means for raising capital to ramp up operations. Thus, a firm’s IPO connotes a performance milestone and indicates the firm is ready for further growth. Using the IPO event, this study examines two ways that startups’ venture capital financing and strategic alliances influence their performance. First, they provide resources such as cash and complementary assets to Internet startups. Second, they signal to other resource holders that a startup is worth investing in or providing resources to. Such endorsement provides legitimacy to a startup, which in turn enables the startup to access additional resources. A startup that secures funding from well-regarded venture capital firms and is engaged in

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numerous strategic alliances with prominent customers and suppliers should attract more resources and thus be able to go public earlier than startups that lack these resources. The empirical findings from this study confirm these hypotheses and have important implications for both academicians and practitioners.

3. Theory and hypotheses 3.1. Venture capital financing of Internet startups Creating a new business organization involves considerable uncertainty. Researchers have long noted that startups have higher failure rates than established firms do because they have not yet established effective work roles, relationships with outside suppliers and buyers, and bases of influence, endorsement, and legitimacy (Stinchcombe, 1965; Hannan and Freeman, 1984). Furthermore, startups tend to be small and do not have enough resources to withstand sustained losses. Among organization and entrepreneurship scholars, this vulnerability is referred to as the liability of newness (Stinchcombe, 1965; Baum, 1996). Such uncertainty makes investors, potential employees, suppliers, and buyers hesitant to provide resources to startups. Entrepreneurs try to reduce this uncertainty by gaining legitimacy from well-regarded individuals and organizations. Zimmerman and Zeitz (2002) argued that legitimacy, which connotes a social judgment of acceptance, appropriateness, and desirability, is a resource by itself that enables startups to access other resources needed for survival and growth and helps startups overcome the liability of newness. Although startups can gain legitimacy by conforming passively to the demands and expectations of the existing social structure (DiMaggio and Powell, 1983; Suchman, 1995), they can also do so by acting strategically (Zimmerman and Zeitz, 2002). For instance, startups can choose more favorable environments (Porter, 1980), manipulate their environment by teaming with other successful organizations (Oliver, 1991), and create environments with new norms, values, and models (Aldrich and Fiol, 1994). Several studies have found great variance in startups’ ability to gain access to resources and stable relationships, which in turn leads to differences in these startups’ early performances (Baum, 1996; Fichman and Levinthal, 1991). One important way for startups to act strategically to gain legitimacy is to get endorsed by respectable organizations such as venture capital firms. In startups’ early stages, entrepreneurs rely heavily on venture capital firms for funds, contacts, and managerial advice. Venture capital firms raise funds from investors and invest this money in a startup in exchange for equity. Furthermore, other resource holders can view venture capital firms’ investment as a strong signal of a startup’s quality and future prospects (Spence, 1974; Freeman, 1999; Podolny, 2001; Stuart et al., 1999). Venture capital firms are evaluated on their ability to generate high returns for their investors. Since they take a fraction of the proceeds, they are motivated to generate high performance. Moreover, venture capital firms that have a history of delivering extraordinary returns find it easier to raise funds from investors. Thus, venture capital firms are unlikely to invest in startups that have poor future prospects. In addition,

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since venture capital firms often help startups by providing managerial advice, recruiting senior managers, and arranging alliances with potential customers and suppliers, they increase the chance that these startups become successful. Furthermore, prohibitions on entrepreneurs and venture capital firms from selling all their equity immediately after the IPO provide these parties an incentive to ensure that the firm will remain operationally viable for at least the short term. Thus, endorsement by respectable venture capital firms not only signals the quality of a startup but also serves as a vote of confidence in the startup. By doing so, the endorsing organization’s legitimacy carries over to the recipient, providing it credibility, contact, and support for the entrepreneurs, building a startup’s image, and facilitating the startup’s access to resources.1 Therefore, investors and other potential resource providers pay attention to the identities of venture capital firms to evaluate whether they should support a startup. Deeds et al. (1997b) showed that amount of capital raised by a biotechnology firm’s IPO is positively related to both the firm’s and the industry’s legitimacy at the time of the IPO. Podolny and Stuart (1995) demonstrated that technological inventions were more likely to be adopted when they had been previously adopted by high-status organizations. Stuart et al. (1999) also found that the reputation of investment banks helped startups in the biotechnology industry go to IPO faster and earn greater IPO valuations than did firms that lacked such connections. The signaling and legitimizing role of venture capital firms may be especially important in the Internet industry, which is in its formative years and is subject to great uncertainty (Amit et al., 1998). As has happened with many new industries, great expectations accompanied the beginning of Internet commerce. On-line retailers such as Amazon could enjoy low costs yet offer a wide selection of products. Further, the Internet also made new types of businesses possible. Priceline.com initiated a reverse auction business. e-Bay developed an on-line auction business. Several pundits projected that nimble Internet startups would soon replace old off-line incumbents (Evans and Wurster, 1999). Yet, startups in new industries are especially vulnerable to the liability of newness (Aldrich and Fiol, 1994), even when the industry in question holds considerable promise. Potential investors and other resource holders thus had good reason to pay close attention to the actions of venture capital firms. In this setting, we hypothesize that when venture capital firms with good reputations invest in an Internet startup, the likelihood that a startup will have an IPO will be higher. Hypothesis 1: The higher the reputation of venture capital firms that invest in an Internet startup is, the faster the startup will have an IPO. Venture capital firms also provide financial resources to startups that significantly affect startups’ survival, growth, and strategic options. Boeker (1989) and Churchill and Lewis (1983) noted that lack of financial resources was the most limiting factor for the growth of

1

Zimmerman and Zeitz (2002) labeled such legitimacy as normative legitimacy.

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startups. Davila et al. (2003) argue that startups that receive more funding are able to hire, retain, and pay talented employees, who are critical to startups’ growth and help them go to an IPO more quickly. They found that increases in salaries and the number of employees happened only after startups had received the cash associated with the early financing round. In later rounds of financing, they found the amount of funding was associated with faster increases in personnel, higher average salaries, and lower turnover. Shane and Stuart (2002) found that the cumulative amount of venture capital funding had a strong positive effect on the rate of IPO. We therefore expect that the more funding a startup secures from venture capital firms, the higher its growth rate will be since it can hire and retain talented employees and secure other resources. Because all of our sample firms are in the same industry and are backed by venture capital firms, we believe the total amount a startup raises indicates the startup’s ability to procure and retain more talented employees and other resources. We expect these additional resources will help startups possessing them to have IPOs more quickly. Hypothesis 2: The more money an Internet startup raises from venture capital firms, the faster the startup will have an IPO. 3.2. Strategic alliances of Internet startups Strategic alliances can affect startups’ growth and their likelihood of having an IPO faster by providing both legitimacy and needed resources. Startups can use these alliances to gain legitimacy and overcome the liability of newness (Aldrich and Fiol, 1994; Deeds et al., 1997a,b; Zimmerman and Zeitz, 2002). By having strategic alliances with prominent partners, a startup gains the benefit of these partners’ reputations and thereby improves outside constituencies’ perceptions of itself. Such legitimacy lets the startup access additional resources, which contribute to its growth (Baum et al., 2000; Baum and Oliver, 1991; Gulati, 1998; Miner et al., 1990).2 Stuart et al. (1999) and Stuart (2000) found that technology startups with prominent alliance partners performed better in the biotechnology industry. In industries such as the Internet, where there is a high level of technological and market uncertainty, the impact of aligning with prominent partners to the legitimacy of a startup can be even greater. For instance, an e-commerce firm’s alliances with established and wellregarded firms that provide search engines or security devices reassure potential customers. Alliances with well-established Internet firms such as Amazon or e-Bay or off-line firms such as Pepsico also signal that the startup is trustworthy or is at least worth trying out. Kotha et al. (2001) found that a startup’s own reputation, measured by its media visibility, positively affected its international performance. We therefore hypothesize that the prominence of strategic alliance partners will positively affect the likelihood of having an IPO more quickly.

2

Hoang and Antoncic (2003) review the effects of networks on entrepreneurial startups.

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Hypothesis 3: The higher the reputation of alliances partners of an Internet startup, the more quickly it will have an IPO. Strategic alliances with suppliers, buyers, and other businesses partners bring the complementary resources and capabilities that startups need and facilitate the flow of knowledge among partners, thereby resulting in faster growth and higher performance (Ahuja, 2000; Gulati, 1999; Nohria and Garcia-Pont, 1991; Pisano, 1990; Powell et al., 1996). Several studies have confirmed that strategic alliances improve startups’ performance. Shan et al. (1994) showed that biopharmaceutical startups’ cumulative cooperative ties positively influence their performance as measured by patent outputs. Deeds and Hill (1996) found that strategic alliances among biotechnology startups improve the rate of new product development, although the benefits from alliances decrease as the number of alliance increases. Stuart (1998) showed that startups’ number of technology alliances and their partners’ innovativeness positively affected patent and sales growth rates. Dyer and Singh (1998) showed that firms could generate competitive advantages by accessing social, technical, and managerial resources through forming strategic alliances. Because the Internet has strong network externalities and increasing returns to scale (Arthur, 1990; Shapiro and Varian, 1998), the positive impact of strategic alliances for Internet startups is magnified. For instance, a portal is more successful when it provides a variety of content to customers. The larger the customer base is, the higher the quality of content is. At on-line auction sites such as e-Bay, network economies are more evident. The greater the number of buyers and sellers that participate at an auction site is, the higher the value these actors will derive from this site. Thus, strategic alliances such as marketing agreements, technical agreements, supply agreements, and joint R&D can help Internet startups reach scale quickly. We therefore hypothesize that the larger the size of alliance network of an Internet startup is, the higher its early performance is, as measured by the speed at which it has an IPO. Hypothesis 4: The larger the size of alliance network of an Internet startup is, the more quickly it will have an IPO. 3.3. Environmental conditions To examine the effects of venture capital financing and strategic alliances on the IPO event, we need to control for environmental factors that may also influence the incidence of IPOs. First, we need to control for the general IPO market environment. We expect the likelihood of going to IPO is greater as the IPO market became more bullish. In fact, Fig. 1 shows that IPO returns increased sharply from 1994 to 2000. Our study controls for this factor by including the IPO market index as a time-varying covariate. Population ecologists have long argued that population density influences startups’ performance. Their work has found that increasing population density is initially positively correlated with the founding rate of startups because it provides legitimacy and acceptance. After a certain level, however, population density is negatively associated with the founding

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Fig. 1. IPO market index and the number of Internet IPOs. Note: The IPO market index was calculated by the IPO return, which is defined as the change between the offer price and the closing bid price on the first day of aftermarket trading with a sample of IPOs in the computer and communication industries.

rate due to increased competition in that particular niche (Hannan and Carroll, 1992). In this study, we test the inverted U-shaped relationship between population density and the IPO event by inserting both a monotonic and a quadratic term of intertemporal population density.

4. Research design 4.1. Data and sample This study uses the Venture Economics Database and the Joint Venture/Strategic Alliance Database of the SDC. The former collects data on startup financing from both public and private sources and has information dating back to the early 1970s. We collected information from this database on startup financing, including founding dates, rounds of financing, identities of venture capital firms, amounts raised, and the dates of IPOs for successful startups. The database also has sales figures, but most of this information is missing. We also collected data on the prior investment activities of venture capital firms to measure these firms’ experience in startup financing, as well as their IPO success rates to measure their reputational effects. The sample for this study consists of all Internet startups available in the Venture Economics database, which uses its own classification scheme (Venture Economics Industry Classification: VEIC) to categorize startups. We focused on three broadly defined Internet business segments—e-commerce companies that sell products (VEIC 2811–2829), e-commerce companies that sell services (VEIC 2831–2849), and Internet portals and aggregators

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(VEIC 2851–2869). We did not include firms that are related to Internet businesses but instead supply hardware, software, and other services to e-commerce firms and portals. These suppliers are classified as Internet-related communications/infrastructure companies (VEIC 1551–1569), Internet-related hardware companies such as web servers (VEIC 2142), web design, and software (VEIC 2765–2768), Internet systems software (VEIC 2781–2798), and other Internet services such as data warehousing (VEIC 2871–2879).3 Within the three segments we used, we selected only startups that were founded between January 1, 1994, and June 30, 2000. We used January 1, 1994, as the starting point for our sampling frame since the Internet industry began around this time. Yahoo, Amazon, and e-Bay were founded in March 1994, July 1994, and September 1995, respectively. We closed our sampling frame on June 30, 2000, since financing for Internet startups dropped dramatically after the NASDAQ plunged in April 2000. We initially identified 1213 Internet startups that were founded during January 1994 and June 2000. Among them, 85 firms lacked vital information such as the identities of venture capital firms that provided financing, the total amount raised, and detailed information on their business domains, which is necessary to identify these firms’ market niches. Additionally, 22 firms were acquired before the IPO event and were therefore deleted from the sample. We were left with 1106 Internet startups in our defined business areas during our sampling window. Among these startups, 90 had an IPO by June 2000. Table 1 shows the number of Internet startups founded in each business area and the incidence of IPO events. We then collected data on investment activities of venture capital firms before their investment in Internet startups. Since venture capital firms’ past experience in industries that were not related to the Internet (e.g., biotechnology) might not provide any signaling effects or legitimacy to outside constituencies (Finkle, 1998), we collected data on the prior investment activities of venture capital firms in industries related to Internet, such as computers and communications industries (where the one-digit VEIC code is 1 or 2). We collected information on how many startups a venture capital firm funded in these industries and how many of them had an IPO. We also collected information about the strategic alliance activities of our sample firms using the SDC’s Joint Venture/Strategic Alliances Database. Information on strategic alliances is not readily available. Although some companies list their alliance partners on their own websites, most do not report them voluntarily. Public sources of information such as newspaper articles tend to focus on strategic alliances by large, wellknown firms. The SDC’s Joint Venture/Strategic Alliance Database collects information on alliances based on company announcements, newspaper articles, and document filings. Kale et al. (2002) used the SDC’s Joint Venture/Strategic Alliances Database for their study on alliance capability and firm performance. The SDC database classifies alliance activities into four areas: marketing, technical agreements, R&D, and supply agreements. Alliances commonly involve multiple areas for joint collaboration. We collected 374 cases of strategic alliances by our sample firms from the SDC database that occurred during our sampling window.

3

Previous studies on Internet startups such as Zacharakis et al. (in press) also use the Venture Economics Database. They define the Internet industry more broadly, however, by including Internet hardware, software, and infrastructure businesses.

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Table 1 Sample composition Business area

Founding year

No IPO

IPO

Total

e-Commerce companies selling products

1994 1995 1996 1997 1998 1999 2000 Subtotal 1994 1995 1996 1997 1998 1999 2000 Subtotal 1994 1995 1996 1997 1998 1999 2000 Subtotal

2 10 17 35 60 122 7 253 10 32 36 49 82 213 40 462 14 15 26 31 54 129 32 301 1016

7 11 10 5 6 0 0 39 1 4 12 2 4 1 0 24 5 6 13 1 0 2 0 27 90

9 21 27 40 66 122 7 292 11 36 48 51 86 214 40 486 19 21 39 32 54 131 32 328 1106

e-Commerce companies selling services

Internet portals

Total

4.2. Measurements 4.2.1. Time to IPO We used the time to IPO, measured by months since the date of founding, as a measure for startup performance. Since conventional measures for performance, such as profitability, sales growth, and market share are not readily available for startups, researchers have often used the IPO event as a measure for performance in the early stage of startups (Stuart et al., 1999). The IPO event is important for startups, entrepreneurs, and venture capital firms. Firms that had not yet gone to IPO were entered in the risk set for each period and were right censored at the end of the sampling window. 4.2.2. The reputation of venture capital firms and the total amount raised We captured both the signaling and legitimizing effects of venture capital investment and the financial resources with three measures. The number of prior startup investment by venture capital firms measures the number of startups a venture capital firm invested in the computer and communication industries before their funding of an Internet startup. Since the Internet business is a new niche, we are interested in finding out whether investors and other resource

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holders view a venture capital firm’s prior experience in the computer and communication industries as a signal of the venture capital firm’s ability to select Internet firms that have good prospects. The IPO success rate of venture capital firms measures the ratio of startups that had IPOs out of all the venture capital firm’s prior investments in these industries. For instance, Kleiner, Perkins, Caufield, & Byers had invested in 218 startups in the computer and communications industries by 2000. Seventy-seven of these startups had gone to IPO by 2000, for a 35% IPO success rate. Since several venture capital firms commonly invest in a startup, we average the numbers of prior startup investments and the IPO success rates for all participating venture capital firms in an Internet startup.4 The average IPO success rate of venture capital firms in their prior investment in computer and communications industries is 9%. The total amount raised measures the inflation-adjusted total amount (in millions) invested by venture capital firms since a startup’s founding (Deeds et al., 1997a,b). We expect that the more money a firm raises, the greater is the chance that it would go to IPO. 4.2.3. Strategic alliance activities We also captured both the signaling and legitimizing effects and the complementary resources of strategic alliances. Stuart et al. (1999) argued that the greater the uncertainty about the quality of company is, the larger the impact that the prominence of a firm’s alliance partner has on its performance. They defined prominence as the ‘‘degree to which an organization’s position makes it visible to other actors’’ (p. 328). In this study, we estimated the prominence of alliance partners as the count of articles written about the alliance partners in the Wall Street Journal at the time of the alliance was formed, on the assumption that the media reflects a broad range of stakeholder views and opinions (Chen and Meindl, 1991). Similarly, Kotha et al. (2001) measured an Internet firm’s own reputation through its media visibility by the total number of print articles that appeared about the firm. To measure the amount of complementary resources from strategic alliances, we used alliance count by counting the cumulative number of alliances for each Internet startup and classifying them as marketing agreements, joint R&D, technical agreements, and supply agreements. Since strategic alliances frequently involve agreements for multiple categories, we classified these alliances in all the categories for which they apply. We defined these variables as timevarying covariates and updated them for each quarter. For instance, if a firm founded in January 1995 enters its 25th spell (month) in January 1997, we measured the alliance count by cumulating all strategic alliances up until December 1996. The alliance count remained constant during the same quarter and was then updated for April 1997. 4.2.4. Firm age and business type The Internet industry is characterized by network externalities and positive feedback. Therefore, early entrants can assemble a large dedicated customer base, which gives them competitive advantage against new entrants. The firm age reflects any type of first mover’s 4

It is possible that outside resource holders might infer a startup’s likelihood of success from the most prestigious venture capital firm that invested in a startup. This alternative measure of the IPO success rate, however, generates similar results.

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advantages by interest startups. We measured firm age with two dichotomous variables, founded between 1994–1996 and founded between 1997–1998, by dividing founding years into three periods: 1994–1996, 1997–1998, and 1999–2000.5 Firms founded after 1999 were used as the reference case. Since we classified Internet startups into three areas— e-commerce companies selling products, e-commerce companies selling service, and Internet portals—we included two dichotomous variables for e-commerce companies selling products and portals. Startups selling e-commerce services were defined as the reference case. 4.2.5. IPO market environment and density The possibility of going to IPO is contingent on the general environment of the IPO market. Both entrepreneurs and venture capital firms will be more likely to go public in a bullish market, when a startup’s valuation tends to be higher. We measured the IPO market index by Ritter’s (1984) index of a hot issue market as a time-varying covariate. Ritter measured the degree of a ‘‘hot issue market’’ as the change between the offer price and the closing bid price on the first day of aftermarket trading for which a quotation could be found, with all 1028 IPOs during 1977–1982. We refined Ritter’s measure in three ways: first, we narrowed down the sample to capture the ‘‘hotness’’ of the computer and communication industries only (the same reference group we used to calculate the IPO success rate of venture capital firms) rather than the entire market; second, we used the weighted average of IPO returns by taking the IPO amount as a weight rather than taking a simple average; third, we took the 3-month average for each quarter rather than the monthly moving average since our time-varying covariates were set up on a quarterly basis. There were 819 IPOs in the computer and communication industries during our time study period. Fig. 1 shows that the IPO return was 36.1% in the first quarter of 1994 and peaked in the first quarter of 2000 when it was 390%. It then went down to 129% next quarter, as the NASDAQ plunged in April 2000. The number of Internet IPOs peaked in 1999 and 2000, when the IPO returns in computer and communication industries were at their highest. During the 1994–2000 time frame, the average IPO return in these industries was 92.2%, far higher than what it was during 1987–1993, 28.6%, showing that the time period we used was indeed a hot issue market. We defined density as the number of firms in each market niche at a given point of time. We defined market niche narrowly with the four-digit venture economics industry classification (VEIC). There are 20 four-digit industries within each category of e-commerce companies selling product, e-commerce companies selling service, and Internet portals. We calculated the density for each four-digit VEIC industry each quarter and incorporated it as a time-varying covariate. 4.3. Model In this study, we estimated a model of IPO event of Internet startup companies by a partial likelihood hazard specification (Cox and Oakes, 1984; Kalbfleisch and Prentice, 1980). The 5 We performed sensitivity analyses by using different years for breakup and by using two rather than three periods. The results are consistent with what we report in this paper. The results are available upon request.

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dependent variable in the hazard model is a hazard rate that denotes the likelihood that a firm will go to IPO in each period. Cox’s proportional hazard model estimates the influence of explanatory variables (or covariates) on the hazard of IPO event without specifying a parametric form for the precise time of investment. Instead, it ranks IPO events in terms of their temporal sequence. More specifically, this model presumes that hazard rates can be represented as log-linear functions of the covariates. If h(t; Z,X(t)) is the hazard function for an individual with timeinvariant covariates vector Z and time-varying covariates X(t), the proportional hazard model specifies this hazard as the likelihood that the observed IPO event should have taken place, conditional on the hazards of all startup firms at risk. This formulation leads to the following specification of the likelihood for the ith firm: Li ðtÞ ¼ ho ðtÞexpðli Zi þ bt X ðtÞÞ=ho ðtÞ½Rexp ðli Zi þ bt X ðtÞÞ; jeRt

where ho(t) is the baseline hazard rate at time t; j is an index for startup firms at risk at time t (Rt being the risk set); Zi are independent variables for individual firm i that are constant over time; Xi(t) are the time-varying covariates for firm i; and l and b are coefficients to be estimated. The IPO market index, alliance partner reputation, cumulative alliance count, and density variables (although fixed for the duration of each quarter) are the time-varying covariates used in this study. With this formulation, the model calculates the ratio of the hazards as the conditional probability of an IPO event given all other firms in the same risk set. This model implicitly contains two assumptions. First, it assumes a multiplicative relationship between the underlying hazard rates and the log-linear function of the covariates (the proportionality assumption). Second, it assumes that the effect of the covariates on the hazard function is log-linear. These two assumptions enable the model to leave the baseline hazard unspecified. Since the proportional hazard model does not specify the baseline hazard, there is no bias incurred by misspecifying the stochastic process of the underlying hazard rate. This generality is achieved by assuming the baseline hazard rate is the same for all firms in the risk set. From this assumption, ho(t) cancels out. We can rewrite the likelihood function as: Li ðtÞ ¼ expðli Zi þ bt X ðtÞÞ=½Rexp ðli Zi þ bt X ðtÞÞ; jeRt

The rewritten likelihood function is equivalent to allowing only the conditional probabilities to contribute to the statistical inference. Multiplying these probabilities together for each of the distinct time spells gives the partial likelihood function to be maximized. No information on the precise time of entry is required, providing a partial, rather than full, maximum likelihood estimate. Thus, partial likelihood estimation involves an efficiency loss because the exact investment time is not considered. Nevertheless, the estimates are consistent and asymptotically normally distributed. We can interpret the t values as asymptotically close to

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Mean S.D.

Minimum Maximum

(1) Portals 0.30 0.46 0 (2) e-Commerce companies 0.27 0.44 0 selling products (3) Founded between 0.21 0.41 0 1994 and 1996 (4) Founded between 0.30 0.46 0 1997 and 1998 (5) IPO market index (t) 1.45 0.60 0.12 (6) Density (t) 48.45 33.78 1 (7) Density (t) squared/1000 3.49 4.31 0.00 (8) Number of previous 35.30 50.18 0.00 investments by VCs 0.09 0.09 0.00 (9) IPO success rates by VCs (10) Total amount raised 22.63 30.09 0.01 ($ million) (11) Alliance partner 2.65 22.65 0.00 prominence (t) (12) Alliances count (t) 0.10 0.73 0 (13) Marketing agreements (t) 0.03 0.33 0 (14) Technical agreements (t) 0.01 0.10 0 (15) Joint R&D (t) 0.01 0.01 0 (16) Supply agreements (t) 0.04 0.45 0

1 1 1 1 3.91 120 14.40 399.00 0.67 396.10 357.00 15 7 2 1 11

S.J. Chang / Journal of Business Venturing 19 (2004) 721–741

Table 2 Descriptive statistics

Table 2 (continued ) Correlations (2)

(3)

1.00  .39 .05  .05 .00  .22  .27 .03 .02  .04  .03  .03  .03  .02  .03  .03

1.00  .02 1.00 .09  .33 .06 .33  .19  .02  .22 .00  .02 .06  .01 .07 .10 .14 .03 .01 .12 .13 .11 .14 .05 .13 .07 .14 .10 .10

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11) (12) (13) (14) (15) (16)

1.00  .04  .02  .02  .02 .00 .10 .04  .03  .04  .02  .03  .03

1.00  .04  .03 .04 .11 .22 .15 .07  .02 .12 .10  .06

1.00 .96 .02 .03 .01 .02  .07  .07  .01  .02  .06

1.00 .02 1.00 .03 .68 1.00 .00 .13 .14 1.00 .03 .00 .01 .14 1.00  .06 .02  .01 .03 .08 1.00  .06 .03 .00  .01 .08 .87 1.00  .01 .02 .00  .02 .01 .56 .44 1.00  .02  .02  .03 .13 .03 .63 .41 .40 1.00  .05  .01  .04  .03 .06 .88 .86 .49 .51 1.00

Note: Variables with (t) are time-varying covariates. To generate this statistic, all time-varying covariates are selected at the time of IPO for an IPO event or at the time of censoring for a non-IPO event. N = 1106.

S.J. Chang / Journal of Business Venturing 19 (2004) 721–741

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)

(1)

735

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the full maximum likelihood estimates. (For more detailed information on the assumptions of the model, see Cox and Oakes, 1984.) In the hazard model explained above, each observation is defined as a distinctive time spell until an IPO occurs. There is no left censoring problem in this study since January 1994 was really the beginning of Internet commerce. Right censoring, caused by truncating the observation period at June 2000, is handled by conventional adjustments. Censored observations enter the risk set at each period under observation, but do not contribute to the numerator of the likelihood function.

5. Results Table 2 shows descriptive statistics for the independent variables and Table 3 shows the results from the hazard model. Model 1 is the baseline model with two dichotomous variables noting founding date, two dichotomous variables noting for type of business, and time-varying covariates of the IPO market index and the density variables. The dependent variable is the time (months) until the IPO event. Model 1 shows that firms that were founded between 1994 and 1996 reached the IPO event more quickly compared to firms founded after 1999, suggesting that early entrants in the Internet business were more likely to go to IPO than were late entrants. In fact, early entrants’ rate of going public is 12.63 (e2.54) times higher than that of late entrants. On the other hand, the rate of IPO by startups founded between 1997 and 1998 was not significantly different from that of startups founded since 1999. This result clearly shows the first mover’s advantages in Internet commerce. It also demonstrates that both e-commerce companies selling products and Internet portals were more likely to go to IPO than were e-commerce companies selling services. The IPO market index turned out to be positively significant, suggesting that the more favorable the IPO market environment was, the more likely it was that a startup had an IPO. We also included the density and the squared terms of the density variable to reflect the impact of population density to the likelihood of IPO success. Neither term was significant.6 In Model 2, we added the venture capital financing related variables (i.e., the number of previous startup investments by participating venture capital firms, the IPO success rate for those investments, and total amount raised). The number of previous startup investments by venture capital firms was not significant, but the IPO success rate for these previous investments was significantly positive. When a startup A was funded by venture capital firms with an average IPO success rate of 10% and when a startup B was funded by venture capital firms with an average IPO success rate of 30%, the IPO rate for startup B was 2.12 (2  e0.236) times higher than was that for startup A. This result suggests that public investors took an investment in a startup by a venture capital firm with a good

6

The density and its squared term have a .96 correlation, raising the possibility of multicollinearity. Even if we drop the squared term, the density variable is not significant.

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737

Table 3 Results from the hazard model with the time until IPO of Internet startups Variables Business area Portals e-Commerce companies selling products Startup age Founded between 1994 and 1996 Founded between 1997 and 1998 Environment IPO market index (t) Density (t) Density squared/1000 (t)

(1)

(3)

(4)

0.78 (.33)* 1.41 (.31)***

0.70 (.33)* 1.28 (.31)***

0.66 (.33)* 1.09 (.32)**

0.66 (.33)* 1.13 (.32)***

2.54 (.83)**

2.18 (.84)**

2.09 (.86)**

2.19 (.86)*

0.77 (.70)

0.65 (.70)

0.55 (.70)

0.59 (.70)

0.20 (.05)***  0.001 (.002) 0.00 (.00)

0.19 (.05)***  0.002 (.002) 0.00 (.00)

VC financing Number of previous investments by VCs IPO success rates by VCs Total amount raised Strategic alliances Alliance partner prominence (t) Alliances count (t) Marketing agreements (t) Technical agreements (t) Joint R&D (t) Supply agreements (t)  2 log likelihood Chi-squared (d.f.)

(2)

959.11 43.98 (7)***

0.21 (.06)***  0.003 (.002) 0.00 (.00)

0.21 (.06)***  0.002 (.002) 0.00 (.00)

0.002 (.002)

0.002 (.002)

0.002 (.002)

2.36 (1.18)* 0.01 (.00)**

2.55 (1.16)* 0.004 (.002)*

2.49 (1.17)* 0.004 (.002)y

0.01 (.00)*** 0.17 (.03)***

0.01 (.00)***

942.96 69.83 (10)***

911.01 195.67 (12)***

0.25 (.19) 0.02 (.35) 0.27 (.35) 0.02 (.12) 921.01 144.01 (15)***

Note: Variables with (t) are time varying covariates. Standard deviations are in parentheses. Total of 1,106 spells and 90 events (IPOs). * P < .05. ** P < .01. *** P < .001. y P < .10.

history of previous IPO successes in the computer and communication industries as a signal that the startup was worth investing in. The total amount raised was positively significant with the IPO likelihood, suggesting that the more money a startup could raise, the faster the IPO would occur. In Model 3, we added the prominence of alliance partners and alliance count variables. The prominence of alliance partners is positively associated with the IPO event. One more article on an alliance partner in the Wall Street Journal increases the IPO hazard rate of an Internet startup by 1.01 times (e0.01). The cumulative count of strategic alliances by an Internet startup, which was updated quarterly, is positively associated with the IPO event. Internet

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startups with more strategic alliances were more likely to have an IPO. One more case of strategic alliance increases the IPO hazard rate by 1.18 times (e0.17). Model 4 breaks down the strategic alliances into specific areas of cooperation (i.e., marketing agreements, technical agreements, Joint R&D, and supply agreement). None of these alliance counts by specific areas of cooperation turned significant, possibly because there were high correlations among categories. Table 1 shows that count of marketing agreements correlates highly with the count of supply agreements. Thus, it is possible that alliance counts by areas of cooperation might have weak results because of multicollinearity. In fact, when we drop the marketing agreement variable and reestimate the equation, the supply agreement turns significant, and vice versa.7

6. Discussion and conclusion This study has examined how Internet startups’ venture capital financing and strategic alliances affected their ability to acquire the necessary resources for survival and growth. This study makes several important contributions. First, our results suggest that controlling for the IPO market environment, all four hypothesized factors influenced the speed with which Internet startups had IPOs: the reputations of the venture capital firms from which they raised funds, the amount of money these startups raised, the reputations of strategic alliance partners, and the number of strategic alliances they developed. This study thus provides additional support to the recent findings that endorsements by prominent exchange partners improve startup performance (Baum et al., 2000; Stuart, 2000; Stuart et al., 1999). Second, our study theoretically and empirically separates the effects of signaling and legitimization and the effects of complementary resources of venture capital firms and strategic alliances partners on startups’ initial performances. Previous studies did not clearly distinguish these two effects in their empirical works. By doing so, this study provides strong empirical evidence that by providing legitimacy to startups and helping them access more resources, respectable venture capital firms and prominent alliance partners enable startups to overcome the liability of newness (Zimmerman and Zeitz, 2002). Third, to our knowledge, this study is the first attempt to use a large-scale database to examine the performance of Internet startups, as reflected in these startups having an IPO. A possible reason for the paucity of research on Internet startups is that researchers tend to discredit all Internet startups in the aftermath of the Internet bubble. Acknowledging that many startups without sustainable business models were funded during this period, this study shows that the relative success of startups in the Internet sector could be attributable to essentially the same factors that were found in other sectors—funding by respectable venture capital firms and strategic alliances with prominent partners. This study encourages researchers to conduct further systematic research on the Internet sector.

7

This additional result is available upon request.

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This study has several limitations. We used the IPO event as a measure for performance for Internet startups. We believe this event is a meaningful interim measure of a startup’s performance. This measure is not perfect, however, since 40 Internet startups among 90 firms that had successful IPOs in our sample went bankrupt by the end of 2002. Similar cases of a buoyant stock market for IPOs and the subsequent collapse of startups are well documented, most notably in the hard disk industry (Sahlman and Stevenson, 1985). Bygrave et al. (2000), however, found that although many of these firms went bankrupt or were acquired some hard disk manufacturers such as Seagate and Iomega survived and produced satisfactory returns for investors in the post-IPO market. These authors attributed the success of such firms to a combination of adaptability, ingenuity, and entrepreneurial spirit that allowed the hard disk drive industry as a whole to triumph over shortsighted investors. Further studies should examine which factors have helped Internet startups survive after their IPOs, especially given the adverse economic and stock market conditions since 2000. For instance, researchers may wish to examine various investment activities of Internet startups, e.g., advertisement with the funds they raised from venture capital firms to determine their effectiveness. Future research should also examine how underwriters influence IPO success. Although our study attributes the reputation of venture capital firms itself to having successful IPOs, this causal linkage can be further examined by including the investment bankers in the IPO process. Respectable venture capital firms might help startups secure the endorsements of respectable investment bankers, who might in turn influence the IPO process (Podolny, 1993; Stuart et al., 1999). Lastly, we need to find other sources of information on strategic alliances. Although strategic alliances with other Internet players were important for attracting customers, Internet startups might have benefited more by allying with off-line players. A finer examination of the types of alliance partners and more direct evidence of operational synergies with them may further illustrate the importance of strategic alliances to the performance of Internet startups. For entrepreneurs, this study has two important implications. First, when they found startups, they should get funding from respectable venture capital firms, which provide needed funds and reputational benefits. In addition, they should develop strategic alliances with prominent partners to access social, technical, and commercial resources that normally require years to accumulate. The resources and the legitimacy gained from such relationships reduce startups’ liability of newness and improve their performance. In addition, they let startups build scale relatively quickly; such scale is important in certain sectors, including the Internet. Although startups cannot guarantee long-term success merely by obtaining such resources, especially in volatile new business sectors like Internet commerce, they can nonetheless improve their chances of going to IPO more quickly and let them use the funds they receive to further establish a viable competitive position.

Acknowledgements I appreciate helpful comments and suggestions from John Lafkas, Xavier Martin, Harbir Singh, and two anonymous reviewers. Financial assistance from the Korea Research

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