THE SUCCESS AND FAILURE OF DOTCOMS:
A MULTI-METHOD SURVIVAL ANALYSIS
Robert J. Kauffman
Co-Director, MIS Research Center
Professor and Chair
rkauffman@csom.umn.edu
Bin Wang
Doctoral Program
bwang@csom.umn.edu
Information and Decision Sciences
Carlson School of Management
University of Minnesota
Minneapolis, MN 55455
______________________________________________________________________________
ABSTRACT: Using a multi-method survival analysis, we explore the drivers behind DotCom
success and failure. Specifically, we test the impact of industry-, firm- and e-commerce-specific
factors on a DotCom’s sustainable competitive advantage.
KEYWORDS: DotCom firms, econometric analysis, electronic commerce, empirical research,
strategic morphing, survival analysis.
______________________________________________________________________________
ACKNOWLEDGEMENTS: The authors thank Kemal Altinkemer and Kaushal Chari, cochairs of the Fall 2001 INFORMS Conference on Information Systems and Technology, and the
anonymous reviewers of an earlier version of this manuscript for helpful comments. We also
thank Tim Miller, President, Webmergers.com, for access to data and project sponsorship.
______________________________________________________________________________
INTRODUCTION
The number of DotCom firms exploded during the last six years, as pure-play Internet
startups emerged and existing companies migrated some of their operations onto the Internet to
take advantage of the new channel for business. We define DotCom firms as those that do
business via the Internet only. Among the DotComs that are publicly traded or have obtained
investments of more than $1 million, more than 500 have ceased their operations since January
2000 (Internetweek, 2001). Still, many of the remaining DotComs are struggling to sustain
themselves in a marketplace with fiercer competition coming from both online and offline
competitors, and more reluctant investors.
Why are so many DotComs failing? With this extended abstract, we begin to report on an
ongoing industry-sponsored research project that aims to investigate the efficacy of Internet
business models in electronic commerce. In the present research, we will address the following
three exploratory research questions:
q
q
q
First, can we build an explanatory model with which we can predict the timing of the
failure time and survival rates of specific DotCom firms, going beyond what we know of
the general backdrop of the initial fallout of business-to-consumer (B2C) and then
business-to-business (B2B) firms?
Second, what are the factors -- both industry- and firm-specific -- that relate to the
success and failure of B2C and B2B businesses? What are their relative strengths in
determining firm survival?
Third, are some companies, either because of the industry to which they belong or
because of the specific business models that they employ, more likely to survive on the
Internet?
LITERATURE REVIEW
In formulating the theoretical background for our research, we review previous research on
the success and failure of firms. We also discuss some specific issues that relate to the survival
of electronic commerce firms from the current literature in IS.
Business Failure
Using the technique of survival analyses, Audretsch and Mahmood (Audretsch, 1991, 1995;
Audretsch and Mahmood, 1991, 1995) find that new business success relates to both industryand firm-specific characteristics.
First, new establishments are more likely to survive in industries characterized by an
entrepreneurial regime, where new entrants to the market have advantages with technological
innovation over the incumbents (Audretsch, 1991; Audretsch and Mahmood, 1995). On the
other hand, in an industry that has a routinized technological regime, where incumbents possess
advantages as innovators, it is more difficult for new startups to survive.
Second, firm-specific characteristics such as startup size and ownership structure can also
influence the survivability of a business. Firms with a large startup size are more likely to
survive. Audretsch and Mahmood (1995) also find that new independent firms tend to have
higher hazard rates (i.e., greater likelihoods of failing over time, Kalbfleisch and Prentice, 1980)
than the new branches of existing firms.
Similarly, Agarwal and Gort (1996) find that survival rates for new startups are industry
specific, but in terms of the development stage of the market. Their results suggest new firms
endure higher hazard rates when a market in its early stages.
Hensler, Rutherford and Springer (1997) analyze survival time for initial public offerings
(IPOs) of stock. They find that factors such as firm size, the age of the firm at the offering, the
initial return on investment in the stock issue, the number of IPOs co-occurring in the market,
and the percentage of the firm owned by insiders can enhance the survival time for IPO firms. In
contrast, a higher average price level in the stock market at the time of IPO and a higher number
of risk characteristics associated with the firm lead to reduced survival time. Hensler et al. also
find an industry effect in their results, with IPOs in the computer and data, wholesale, restaurant,
and airline industries having a shorter survival time and firms in the optical or pharmaceutical
industries enjoying a longer survival time.
Honjo (2000) conducts two studies on the failure of new manufacturing firms in Japan from
1986 to 1994 -- one based on the age of the firm and the other one on the calendar time. He
finds, while financial capital and firm size are both significant predictors of business failure
when they are incorporated into the model independently, only financial capital is significant
when they are added into the model at the same time. Honjo concludes that previous research
that finds significant effects associated with firm size, in effect, may be capturing the impact of
the financial capital. His results also indicate that firms founded just before a crash or market
bubble or just after a market crash are more likely to fail. In the analysis based on calendar time,
Honjo find a positive relationship between age and failure, and a negative relationship between
age-squared and business failure. In addition, his results indicate that a higher entry rate and
higher geographical concentration in an industry lead to a higher hazard rate for firms.
Even though the above research indicates that firms with large startup size and large postentry size are more likely to survive, there are also contradictory findings. For example, Das and
Srinivasan (1997) find that firms with a larger startup size are more likely to exit. Ghemawat
and Nalebuff (1985) analyze an oligopoly scenario in a declining industry and show that large
firms are more likely to exit. Lieberman (1990) empirically tests this relationship. Even though
he does not find any evidence of a higher exit rate for large firms, Lieberman’s results indicate
that, in declining industries, large firms are more likely to close individual plants.
Accounting and finance researchers have used various accounting ratios and funds flow
components to predict business bankruptcy. Among these, there are models based on the
discounted cash flow analysis in finance. This analysis argues that a firm’s value equals the sum
of its future net cash flows discounted at its cost of capital. Using techniques such as multiple
discriminant analyses, and probit and logit models, Gentry, Newbold and Whitford (1985a,
1985b, 1987) identified twelve cash flow components and found three variables—dividends,
investment and receivables—relate significantly to business bankruptcy. However, there are also
contradictory findings. Casey and Bartczak (1985) and Gombola et al. (1987) found that cash
flow from operations did not significantly relate to corporate bankruptcy. In a recent study,
Mossman et al. (1998) discovered the predictive accuracy of the cash flow model is better during
the last two to three years before bankruptcy.
Based on our review of the literature on business failure, we identify the following factors as
predictors of the survival rate of a firm:
q Industry-specific characteristics. These include the rate of new firm entry, industry
growth as measured by price-cost margins, and the technological regime of the industry;
and,
q Firm-specific characteristics. These include financial capital, cash flow components,
startup size, post-entry firm size, founding time, the percentage of the firm owned by
insiders, and whether the firm is a new startup DotCom or the online version of an
existing business.
Assessment of Internet Business Models
In addition, there are a number of recent articles in the IS research literature that help us
identify relevant theories to formulate the factors associated with survival and casualty among
Internet firms. First, we review literature on network externalities. Second, we discuss literature
on e-commerce related to different business models on the Internet.
Network externalities refer to the phenomenon where the utility a user of a technology
obtains increases as the number of users of the same technology increases (Katz and Shapiro,
1986, 1994). Because of network externalities, a potential adopter is more likely to acquire the
technology when the network of users is large. In the empirical testing of network externalities,
researchers use the current installed base or expected installed base to examine the effect of
network size on adoption (Dybvig and Spatt, 1983). In the current study, network externalities
that reflect whether a DotCom firm has achieved a critical mass in its market can make the
difference between success and failure for an Internet firm.
Both business professionals and academic IS researchers observe the performance
differences across different business models on the Internet. Various authors point out that
certain companies are more likely to succeed on the Internet. Examples are companies that
deliver “killer applications” (Downes and Mui, 1998), those that provide solutions or offer
services that relate to emerging technological standards (Burnham, 1999), and those that create
or recreate new sources of digital value (Mougayar, 1998). Recent empirical results on the
failure of many group-buying firms corroborate some of these perspectives (especially Cook,
2001, and Sandoval and Kawamoto, 2001). Even though they appeared to be appealing initially
(Rugullies, 2000), the inherent flaws in the design of the group-buying market microstructure,
such as targeting at price sensitive customers and operating in a market that has low barrier to
entry, made profitability and long-term survival a mission for many group-buying firms at which
they could never succeed (Kauffman and Wang, 2001).
Preliminary Conceptual Model
Overall, our survey of the literature leads us to the following preliminary conceptual model.
(See Figure 1.) The +’s and –‘s on the various arcs in the model indicate the hypothesized
effects. The model emphasizes clusters of factors that are general to business failure, both in
terms of industry- and firm-specific drivers, as well as e-commerce-specific aspects, that have
only begun to reveal themselves during the last year. We also consider a set of control variables.
The initial conceptual model is subject to change and refinement.
The industry-specific characteristics include the rate of new firm entry, industry growth as
measured by price-cost margins, and the technological regime of the industry. Firm-specific
characteristics include financial capital, cash flow components, startup size, post-entry firm size,
founding time, insider ownership and whether the company is an independent startup or the
online versions of existing companies. We also include two other e-commerce-specific
characteristics in the model - a critical mass of customers and the business model. In addition,
we also include a set of control variables in our model to eliminate the impact of the
macroeconomy.
DATA COLLECTION AND METHODOLOGY
We now turn to a discussion of data collection and empirical analysis methodologies that we
intend to apply to obtain results and insights on the efficacy of DotCom business models in
electronic commerce.
Data Collection
This research is formulated as an empirical design, and is dependent upon our successfully
obtaining and extending data that are available to us in a proprietary industry data set. The data
will be in panel form, extending from early 1998 to the present, with quarterly intervals for the
observations.
The dependent variables are either binary, so that we can represent the survival and
continued operation or the failure and default of a firm, or categorical, so that we can represent
survival, and different degrees of failure, including merger or acquisition by another firm, a
filing for Chapter 11 with real plans to reorganize, or the total shutdown of a firm. Arrayed with
this dependent variable information will be additional variables that describe the three primary
clusters of drivers that we described in the prior section, as well as a set of appropriate control
variables.
Figure 1. Preliminary Conceptual Model for DotCom Survival in E-Commerce
Industry-specific characteristics
Rate of new firm entry
Industry growth
+
Technical regime
+
Financial capital
Survival
Cash flow components
Startup size
Post -entry firm size
Founding time
Insider ownership
New startup
+
+
Control variables
Firm-specific characteristics
Critical mass
Business model
E-commerce-specific
characteristics
Methods for Empirical Analysis
We will use multiple methods for our analysis of DotCom survival and failure. First, we will
perform a semiparametric survival analysis using a proportional hazards model. Second, we also
will use nonparametric techniques to estimate the survival functions for subgroups and compare
the results with those of our semiparametric analysis.
Semiparametric Analysis. We will use the Cox (1972, 1975) proportional hazards model to
analyze the DotCom survival data we obtain. Similar to Honjo (2000), we will estimate both a
hazard model based on firm age and a hazard model based on the calendar time. The primary
aspects that calendar time survival analysis can shed light on the general economic backdrop to
DotCom firm performance. The firm age data will be more revealing of firm-level issues that
are either amplified or suppressed by firm time-in-operation.
In the analysis of firm survival, we can observe the failure time of a firm, or its continued
operation. Since we can only observe what is happening with DotCom firms up to the present,
and some will continue to operate, our data set will be right-censored. Thus, a firm in our sample
will still be at risk at time t if its survival time or the censored time is greater than or equal to t.
The Cox model assumes the following functional form for the hazard function:
h (t , X , β ) = h0 (t ) e Xβ
(1)
In Equation 1, h0 (t) is the unknown baseline hazard function and ß is a vector of parameters
to be estimated. This expression enables us to capture the baseline hazard rate as a result of the
age of the firm via h0 (t), and the impact of other factors that vary across firms through their
vector of time-varying covariates, X, via the estimated parameters, ß.
Based on this hazard function, the corresponding survival function is given by:
S (t , x , β ) = [ S 0 ( t )] exp( xβ )
(2)
In Equation 2, S0 (t) ∈ {0,1} is the baseline survival function. This expression represents the
likelihood that a firm will continue to be in existence at time t, in view of the baseline rate of
survival among observed firms and other economy, firm and e-commerce-related characteristics
that also vary over time, given that it has been in operation continuously in prior periods.
For a firm i that is still at risk at time t i, its likelihood of failure at time t i compared to other
firms that are at risk at time t i is given by
h( t i , xi , β )
e xi β
Li ( β ) =
=
(3)
∑ h(ti , x j , β ) ∑ e x j β
j∈ R ( t i )
j∈ R ( t i )
In Equation 3, R(t i) is the set of all firms that are still at risk at time t i. For a data set that contains
n firms, the partial likelihood is given by:
ci


n
 exi β 
Lp ( β) = ∏ 
(4)
xj β 
i =1  ∑ e

 j∈R (ti ) 
In Equation 4, ci is 1 if the observation is not censored and 0 if the observation is censored.
Using this partial likelihood function, the parameters can be estimated without specifying the
baseline hazard function.
Following Honjo (2000), we further develop a multiplicative hazards model based on
calendar time. Instead of comparing firms at the same age, we now compare them at the same
~
calendar time. The function, h0 ( ~
t ) , now denotes the baseline hazard function based on calendar
time. It allows us to incorporate the impact of the macroeconomy into the baseline hazard
function.
Nonparametric Analysis. As an alternative method, we will report on the results of a
nonparametric analysis, for which we will use the Kaplan-Meier estimator to estimate the
survival function (Hosmer and Lemeshow, 1999). The Kaplan-Meier estimator is defined as:
n − di
Sˆ (t ) = ∏ i
(5)
ni
ti ≤ t
where ni is the number of firms that are still at risk at time t i and di is the number of firms that
actually failed at time t i. The Kaplan-Meier estimator provides a reading on the likelihood of
survival at time t based on the survival history of all firms. Using results from the
semiparametric analysis, we can stratify the data set into different subgroups and calculate the
Kaplan-Meier estimator for each group. We can then use standard statistical tests to compare the
differences among the groups and compare the results with our regression results from the
proportional hazards models.
EXPECTED RESULTS, THREATS TO VALIDITY AND TIMELINE
We expect to obtain a reading on industry- and firm-specific characteristics, and on various
aspects of e-commerce-specific variables to be significant predictors of the survival and the
failure of DotCom firms. Our firm age-based and calendar time-based hazard functions allow us
to cross-validate our results using multiple methods. We will further compare these results with
those from the nonparametric data analysis. This “triangulation process” with the data should
give us a preliminary and exploratory reading on the manner in which the various classes of
variables have tended to drive the outcomes in the marketplace with respect to the survival and
failure of DotCom firms.
There are a number of threats to validity and challenges to making this work successful that
we face at the outset. They include the difficulty of collecting data from multiple sources, the
possible confounding effect of the slowing down economy, the influence of venture capital
considerations, and the generalization of our results beyond the period in which the Internet
stocks were irrationally over-valued, and then followed by the failure of many Internet firms.
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