IPO

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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S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
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
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
723
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.
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
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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
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
729
(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
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
731
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.
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
733
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
734
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
736
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
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.
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
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
738
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
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.
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
739
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
740
S.J. Chang / Journal of Business Venturing 19 (2004) 721–741
Foundation through the 2000 Faculty Abroad Program is gratefully acknowledged. This
study also benefited from Korea University Business School through the SK Distinguished
Research Award.
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