Explaining Entry Decisions and Crowdedness
in Business-to-Business Electronic Commerce Markets
Dr David Croson, Dr Michael Jacobides, Dr Amy Nguyen
Centre for the Network Economy
CNE WP05/2002
Explaining Entry Decisions and Crowdedness
in Business-to-Business Electronic Commerce Markets
David C. Croson
Assistant Professor of Operations and Information Management
Senior Fellow, Financial Institutions Center
The Wharton School of the University of Pennsylvania
Michael G. Jacobides
Assistant Professor of Strategic & International Management
London Business School
Amy T. Nguyen
Associate, Financial Institutions Group
AT Kearney, Inc.
Version 2.1: October 31, 2001
Working Paper for the Centre for the Network Economy
London Business School
This document represents work in progress. Please do not cite or quote without permission.
Comments are appreciated. Correspondence can be sent to David Croson
[crosond@wharton.upenn.edu].
2
Abstract:
Billions of dollars of venture capital were committed in 1998-2000 to fund new entrants in
business-to-business (B-to-B) electronic marketplaces. B-to-B marketplaces offer
compelling value propositions (reducing both frictional and relational transactions costs,
aggregating buyer power, etc.) and exhibit economies of scale, network externalities and
winner-take-all effects, which suggest that industrial concentration should eventually be quite
high. We show empirically, however, using a detailed survey data set collected in 2000 from
over 300 business-to-business exchanges, that markets with larger potential revenues attract
significantly more than proportional amounts of entrants’ attention, unambiguously
predicting severe future shakeout. In particular, many of these new entrants are clustered in a
small number of large, high-profile markets (such as chemicals and auto parts). We examine
several competing theoretical explanations to assess compatibility with the observed data:
economic analysis of market structure, under conditions of competition, oligopoly, and
increasing return to scale; analysis of option value of early presence in markets requiring
uncertain capabilities; and finally an agency explanation, with entrepreneurs seeking markets
where fundability is assured, even at the expense of future profitability, which turns out to be
largely supported.
3
I.
Motivation
One of the most prominent types of new players in the e-conomy has undoubtedly
been business-to-business (B-to-B) firms. With $5.4 billion of venture capital committed in
2000 (Morgan Stanley Dean Witter, 2000; Goldman Sachs, 2000) to their founding and
expansion, they have risen to the forefront of business attention. Each of these entrants
perceived that there were rents to be had through establishing a B-to-B electronic commerce
presence in their chosen markets. With often purely hypothetical business plans, B-to-Bs tried
to formulate various revenue models to capture a sizable part of the efficiencies they would
bring by joining together supply and demand in a novel way. Yet while both the popular
business press and online sources were replete with B-to-B success stories (and, of late,
populated with stories of abject failure as well), we still lack a consistent explanation of the
motivation of potential entrants to these markets – and hence the drivers of the resulting
industry structure, the predicted intensity of rivalry in these markets and ultimately the
competitive prospects of the firms who chose to enter.
Focusing on prospective B-to-B entrants’ motivations poses some interesting puzzles.
Business-to-business e-commerce markets have been broadly characterized as large,
potentially profitable markets with low barriers to entry encouraging multiple entrants, but
with network externalities and economies of scale encouraging consolidation. Assuming
these characterizations are correct, existing theory predicts unambiguously that in these types
of markets, only the very few “best” firms will survive and grow to dominate the market,
thanks to network externalities and the corresponding increasing returns to scale (David,
1985; 1992; Varian and Shapiro, 2000). Anecdotal evidence shows, however, an
astoundingly persistent number of new entrants – far more than these markets can support,
even if all were to peacefully coexist without price rivalry destroying industry profitability.
What makes this empirical problem of explaining the entry patterns in B-to-B markets
even harder is that we lack a consistent theory that would explain de novo entry in new
industries. While entry has been studied for existing markets (e.g., Shapiro and Khemani,
1987; Carree and Thurik, 1996; Amel and Nellie, 1997) we do not have any body of theory
that can help us explain the rate and dynamics of entry in new industries.1
Note that the analyses of firm “turnover” and the evolution of industry structures along industry life cycles
(Klepper, 1996; Utterback and Suarez, 1993), as well as discussion of “entry through diversification” (e.g.,
Teece et al., 1994) bear on drivers of the initial entry decision only tangentially for the case of new markets.
1
4
In this paper, we try to take the first step towards filling this empirical and theoretical
gap by generating a number of internally consistent theories of entry into B-to-B markets and
performing preliminary empirical tests of these theories’ plausibility using a dataset captured
at the height of the Internet bubble. Our dataset collects information on B-to-B entrants’
perceptions of requirements for success and allows calculation of the relative crowdedness of
various B-to-B marketplaces. We thus (a) offer alternative models to explain the
“crowdedness” of particular B-to-B markets induced by various entry criteria, thus providing
some theory of entry into new markets, and (b) examine the influence of perceived drivers of
success on such entrants, emphasizing the relative roles of firm capabilities and market
structure. We conclude with an opportunity for a natural experiment on the determinants of
exit in overcrowded markets.
II.
Defining the Drivers of Entry into and Crowdedness of B-to-B Markets
Our first major set of questions is: “What affects the decision to enter any particular
market? Is it true that firms in general try to avoid populated markets – or are such markets
actively sought out, and why?” We explore three different theoretical justifications,
documenting the specific predictions of each on the “crowdedness” (defined as the number of
entrants for a given dollar amount of market size) of these new B-to-B markets.
II-1:
Entry under Perfect Competition
Economic theory predicts entry based on market-structure characteristics and the
opportunity for profitable operations. In perfectly competitive markets, the absolute size of
the market does not matter other than to determine how many entrants will arrive; each firm
that enters will operate at the point of minimum long-run average cost, earning zero
economic profits. (See Mankiw and Whinston, 1986, for an analysis of the strategic entry
decision under price competition but costly entry.) The number of firms in the market is
uniquely determined by the market size divided by this minimum efficient scale. Given that
the number of entrants thus increases linearly in market size, the resulting hypothesis on
crowdedness is thus that
H1a: We expect no relationship between crowdedness and market size; and
Such attempts to use industry history to predict industry evolution, as with all backwards-looking explanatory
models, miss revolutionary changes in industry structure – as business-to-business e-commerce is claimed to be
by its proponents.
5
H1b: Crowdedness should be decreasing in the size of barriers to entry/minimum efficient
scale.
II-2:
Entry under Oligopoly
In markets with oligopolistic competition (wherein profit-maximizing firms decide
whether to enter or not taking into account the actions of other firms in their own strategic
decisions), economic theory predicts both that prices and margins fall with the entry of more
firms, and that the number of active firms should rise less than proportionately with market
size (formally, in an increasing and concave relationship). Given that fewer entrants enter the
market than in perfect competition, this result, predicted by many theories of oligopoly (see
Waterson, 1984; Tirole, 1985 for summaries) and validated empirically by Bresnahan and
Reiss (1991) and Campbell and Hopenhayn (1999), suggests that under oligopoly,
H2a: Crowdedness should be negatively related to market size.
II-3:
Entry under Network Externalities or Increasing Returns to Scale
Structural market characteristics other than size may play a significant role in the
incentives for entry. We may expect B-to-B markets to display strong network externalities
(i.e., increases in any individual buyer’s valuation for the exchange’s service given the
presence of other buyers), increasing returns to scale (i.e., decreasing average costs of
service provision as quantity increases) or demand-based economies of size (i.e., factors
inducing buyers to choosing the largest among a group of competitors), markets with a few
players will be significantly more profitable than markets with many. As there will
eventually be only one (or a very few) dominating firms, new entrants would be foolish to try
to invade relatively densely populated industries, preferring to attack industries that are less
fragmented. We would thus expect to see a strong negative correlation between existing
crowdedness and the marginal entrant’s desire to get in the market. Furthermore, we would
expect that the absolute size of the market will not be particularly important; beyond a certain
minimum size, getting 100% of the market will always be worth the costs of entry. These
two basic properties of profitability under network externality or increasing/demand-based
returns to scale suggest both that
H3a: Crowdedness should be unrelated to absolute market size;
H3b: Crowdedness should be decreasing in barriers to entry; and
H3c: Entry should be strongly negatively related to the existing amount of players.
6
II-4:
Entry into Nascent B-to-B Markets as Real Options
The second theoretical perspective on B-to-B entry motivations comes from real
options theory (Dixit and Pindyck 1994, 1995; Amram and Kulatilaka, 1999). Under the
real-options theory, early investment in these nascent markets permits a range of future
strategies not available to late entrants. The total volume expected to be transacted in
business-to-business marketplaces is large -- covering hundreds of billions of dollars of
transactions per year in the USA alone, and trillions worldwide (Morgan Stanley Dean
Witter, 2000; Lucking-Reiley and Spulber, 2001). Many proposed B-to-B marketplaces offer
compelling value propositions to their clients if they cover a sufficiently large fraction of the
market’s buyers and sellers -- reducing frictional and relational transactions costs (Lee and
Clark, 1997), driving total acquisition prices down both through search and reverse-auction
capabilities (Segev and Gebauer, 2001) and intermediating trades to match buyers and
suppliers according to specific attributes (Lucking-Reiley and Spulber, 2001).
Given a proper match between their value propositions and revenue models (Amit and
Zott, 2001), we would expect such value-creators in large markets to be able to overcome
their fixed costs of entry. Finally, there are large economies of scale created by liquidity
concerns, large development costs and required advertising expenditure to build critical mass
of buyers and sellers (Narayandas, 1999; Sculley and Woods, 2000). Given these economies
of scale, we would expect high industrial concentration to appear in the long run, with
commensurately attractive returns on equity.
The extraordinarily large potential value of dominating a large, profitable market is
substantially larger (possibly by one or more orders of magnitude) than the actual
expenditures required to enter these markets with some (not necessarily complete) set of
technological capabilities. Given that the market is new, the critical capabilities that will
form the cornerstone of the IT-enabled dominant design, as in McKenney et al. (1994) are yet
unknown. It is thus unsurprising that many entrants would want to “take a shot” at being
dominant — exchanging their modest entry costs for a lottery-like gamble that returns 0 most
of the time, but which offers a low probability of a large payoff.2
2
Note that for this option value to be real, substantial uncertainty about drivers of future profitability is needed - for example, the required competences for long-term success being unknown at the time of entry. Each player
knows that some player(s) will make extraordinary profits, but no player knows whether she will. Nor does any
player know initially that she is not the one, and thereby faces a high opportunity cost of not staying in the
market. This leads to rent-seeking entry behavior (Tullock, 1989) and suggests that too many firms will enter
compared to the social optimum (Mankiw and Whinston 1986). This places a premium on firms learning that
they are not the ones who possess the critical capabilities, and swiftly exiting the business which will just
7
Consider, for example, the following highly stylized model of a winner-take-all
market in which only a minimal investment is required to enter, but increasing levels of
investment improve the chances of success. (Compare this simple rent-seeking entry model
to the psychological model of entry motivation proposed by Kahneman and Lavallo, 1993, or
the rent-seeking model of political influence developed by Tullock, 1989). Let the expected
total market size (in the sense of the amount of transactions expected to be conducted therein)
be s. There is some unknown critical capability which, if possessed, results in complete
market dominance. c is the indivisible unit of cost required to enter the market, or to extend
the capabilities of an entry already made. All firms invest some multiple of c in their entry
attempts; a given firm may invest c, 2c, 3c or any other positive multiple. Each investment of
c creates one capability, any of which might be the critical capability required to dominate the
market. The probability that a given firm i wins dominance of the market, as a function of its
investment is thus equal to firm i’s share of the total costs devoted to entry in the market:
pi 
ci
n
c
j 1
j
A one-time random event (the exogenous determination of the required capabilities) occurs
(or its result becomes publicly known) after all potential entrants have had the opportunity to
sink their entry costs. After this random event occurs, the entrant who holds the winning
“ticket” – that is, who has made the investment in the critical capability -- will reap positive
return on sales , making profits per period of s; their discount rate of  = (1/1+r), where r is
the per-period cost of capital, makes this profit stream worth s/(1-. We ignore the
possibility that multiple players will have the critical capability and thus model a “winnertake-all” or “superstar” environment (Rosen, 1981; Frank and Cook, 1995; Varian and
Shapiro, 2000).
A risk-neutral individual firm, which seeks to maximize expected profit through
entry, will thus choose its ci to maximize expected profit, net of investment:
continue to be unprofitable (see Jovanovic, 1982, which accounts for “infant mortality” among firms in highrisk industries: Inefficient firms learn of their lack of capabilities only through experience in the market and exit
when they are sufficiently certain that they will not be profitable at any point in the future. For uncensored
examples of company mortality, along with accounts of the death throes of unsuccessful e-commerce entrants,
see www.f—edcompany.com.)
8
Max
ci
s
 ci
(1   )  c j
j
yielding an optimal choice for ci of
ci* 
s
 cj
(1   ) j i
2
for each of the n firms who enter. Given n identical firms, the optimal choice of ci for each
firm simplifies to
ci* 
s
 (n  1)ci*
(1   )
2
and thus n 
s
c (1   )
*
i
1
The total cost sunk by all entrants in this rent-seeking game is
ns
 ( n  n )c
(1   )
nc 
2
2
*
i
*
i
which exceeds the profit achieved by the winner whenever the number of entrants exceeds 1.
Taking the derivative of the number n of entrants with respect to s, the market size, gives us
n

 *
 0,
s ci (1   )
Clearly, n is increasing in s: the number of entrants is larger in a market of larger
absolute size. If
n
 1, n is increasing more slowly than s as s increases and thus
s
crowdedness will be decreasing in market size. Conversely, if
n
 1, n is increasing faster
s
than s as s increases, and thus crowdedness is increasing in market size. This occurs when 
is relatively large, c is relatively small, and  is high.
Let us compare the mathematical conditions under which crowdedness is increasing
in market size with the state of the B-to-B market in late 1999. First, for most B-to-B ecommerce markets, the return on sales (revenues less variable costs, as a fraction of revenues)
approaches 100%, as variable costs of serving an additional $1 of transaction volume are
negligible once the exchange capabilities are built. Second, the required investment c to
9
establish a minimal presence (perhaps only a business plan) is relatively small. Third,
crowdedness should increase in market size when  is high – that is, when the opportunity
cost of capital, r, is low. One could argue that this characterizes the overall economic climate
of late 1999-early 2000, when traditional stocks were reaching all-time highs (lowering their
expected returns), bond yields were at all-time lows, and a flood of venture capital chased a
relatively small number of high-quality investment opportunities. Given the limited
appreciation potential of public-market securities and the abundance of private equity, the
opportunity cost of capital was arguably indeed quite low – despite venture capitalists’ return
goals in excess of 20% per annum.
This simple “winner-take-all lottery” thus indicates that we expect:
(a) The number of firms that enter a market will be increasing in market size;
(b) Crowdedness will be increasing in market size, under economic conditions
experienced in 1999-2000;
(c) Crowdedness will be increasing in the expected return on sales after the uncertainty is
resolved; and
(d) Crowdedness will be increasing as the opportunity cost of capital decreases.
Although not all of these theoretical propositions can be tested with our dataset, we can test
H4a: There will be a significant positive relationship between absolute market size and
number of entrants; and
H4b: Crowdedness (the density with which firms populate a new market) should be
increasing in market size.
II-5:
B-to-B Entry and Inefficient Capital Inflows – the Agency View
An alternate theory of entry into heavily venture-backed, high-profile industries
involves managerial preferences for forms of success other than long-term shareholder value.
We note that the standard compensation for a B-to-B CEO was heavily weighted towards
stock and option-based compensation in lieu of a market salary, rather than the traditional
balance of cash, noncash benefits, pension contributions and a stock purchase plan. He or she
thus stands to capture a substantial portion of the upside from a successful IPO but little of
the downside of squandered venture capital provided from outside the firm (cf. Jensen and
Meckling, 1976 for the analogous story with corporate debt and equity holders’ desire to take
10
on overly risky projects_. If an entrepreneur’s true motive (either inborn or induced by such
a high-powered incentive scheme) is to generate a windfall capital gain on low-cost shares
through an IPO, rather than stick around for long-term sustainable extraordinary return on
equity, he or she may prefer to maximize the probability of such a successful IPO, rather than
maximize the expected return on capital. While we will emphasize the distortion of the entry
decision, we should note in fairness that the agency costs of this distortion may be sharply
limited simply because the actions undertaken by the entrepreneur to maximize the IPO
probability may not diverge significantly from those desired either by the venture capitalist or
by the fund’s underlying investors [Jacobides and Croson, 2001].
This theory would imply that entrepreneurs want to enter markets of proven
“fundability” or “IPO-ability” rather than ones that offer profits from operation. In this
respect, the selection of the market to enter is similar to Sutton’s Law, named after noted
bank robber Willie Sutton: “Why do I rob banks? Because that’s where the money is.” This
selection mechanism contrasts sharply to capital-allocation theories of entry based on market
attractiveness (e.g., Porter, 1980), where the goal is to commit capital to markets that will
deliver sustainably extraordinary returns on equity – effectively going to where the money
can be earned, not where it happens to be now.3
Sutton’s law indicates that markets that are receiving high levels of venture funding
even before the entrant in question makes its bid would be the most attractive to target. This
funding level may be high for two levels: first, because entry may be expensive on an
absolute level (representing a barrier to entry, or at least a high minimum investment
required); second, because many entrants have already come in at a given entry cost. The
agency theory explanation would thus predict:
H5a: There will be a positive relationship between the size of the market and the rate of
entry;
H5b: There will be a positive relationship between the crowdedness of the market and the
rate of entry;
H5c: There will be a positive relationship between the amount of venture funding committed
to the market and the rate of entry; and
3
Hockey great Wayne Gretzky is heavily quoted by would-be B-to-B entrepreneurs for support via two
aphorisms. First, “Some people skate to where the puck is. I skate to where the puck is going to be.” Only
slightly less popular is his quote “You miss 100% of the shots you never take.”
11
H5d: There will be a positive relationship between barriers to entry in a market and its
crowdedness.
III. Extension: Is Entry Driven by Resources or Market Attributes?
Having considered the possible drivers of any firm to enter, and having examined the
econometric analysis at the industry level, we next look at the perennial strategic question of
“What determines success?” From the mix of firms that could enter, what determines who
will succeed? Is success mostly an issue of market positioning, of resources, or of
capabilities?
Our survey examined the relative importance of design (capability) and market-based
features. We asked that companies rate how much these features relatively impact the success
of their business by allocating a fixed percentage across the categories offered. The other
questions in the first section of the survey ask the respondents for data regarding these
features.
To get at this important question, we asked the respondents of our survey to identify
what matters most in this new B-to-B environment. We can thus analyze the primary
perceived performance drivers of online marketplaces – those which affect the entry
decisions and strategy selection of B-to-B entrepreneurs – classified by two characteristics:
target market features and design and execution features. Target market features are
characteristics of the industry or target market that the online marketplace serves, ranging
from market size to market inefficiencies. Given that these are characteristic of the market
served, they form commonly attractive (or unattractive) features to all potential entrants
regardless of idiosyncratic capabilities. Design and execution features include the online
marketplace’s business model and assets (people, proprietary technology, relationships, etc.)
– which essentially capture the importance of firm–specific factors of success (i.e., the critical
resources anticipated to be deployed in pursuit of the operating strategy of these businesses).
IV. Description of Dataset
To test these hypotheses and explore the motivations of entry in B-to-B e-commerce
marketplaces, we assembled an extensive list of online B-to-B intermediaries, created
through direct search of sites as well as by poring through Internet and industry trade
journals, major newspapers, financial and industry analyst reports, and research from major
technology and Internet research and consulting firms such as Forrester Research, Gartner
12
Group and NetMarketMakers (www.nmm.com). Following individual examination of each
candidate’s site, 313 could be classified as online marketplaces — online B-to-B
intermediaries that facilitate the matching of many buyers with many sellers. The estimates
of other research organizations who track the creation of online marketplaces suggest that this
number captures nearly all B-to-B intermediaries who had begun operations as of December
1999. A survey composed of twenty questions on performance drivers and measurements
was sent (via mail and e-mail) to executive and senior managers of all the identified online Bto-B marketplaces for which contact information was available — 292 in total. The survey
could be completed online (via FlashBase) as well as on paper. We promised (and delivered)
an aggregate-level summary of the survey results (responses to all survey questions) to all
survey participants, attached as Appendix A. Although only aggregate-level results were
released to maintain confidentiality, sector information was provided only where the number
of sites in that sector exceeded two to prevent deducing opponents’ information in sparselypopulated markets.
Completed surveys (paper-based and online) were received over a span of
approximately 8 weeks between December 1999 and February 2000. 81 survey responses
were received during this period — a response rate of 28%. Although the responses clearly
do not form a comprehensive view of the population of online B-to-B intermediaries, we
believe they comprise the largest data set of online B-to-B intermediaries gathered to date.
V.
Quantitative Results: Presentation & Statistical Analysis of Survey Data
While the full results of our survey cannot be presented in this brief paper, a few
overall descriptive statistics should be noted. First, in terms of the overall division of
subjective importance of performance drivers, the sample divided nearly evenly (51.9%
favoring design-and-execution features, and 48.1% favoring market characteristics). Most
respondents, however, ascribed a higher role to specific market characteristics than they did
to specific design or capability-based advantages, as shown in Tables 1 and 2.
13
Table 1: Importance of Design and Execution Features
Question Asked: How much do the following design and execution features impact the success of your
business? Please allocate a percentage across the features; the total allocation should equal 100%, but you can
allocate 0 points to one or more features.
Average % allocated
Feature Cited
16.9
Critical mass of users
11.9
11.8
11.2
9.7
9.7
6.2
5.5
Domain expertise of management, senior staff
Brand awareness in target market
Quality of management execution
Revenue model
Content
Proprietary technology
Provision of offline services (order fulfillment, logistics, etc.)
Table 2: Specific High-Importance Metrics
Question Asked: “This metric would be assigned a rank of high importance on a low-medium-high scale.”
%
72.7%
72.7%
66.2%
54.5%
46.8%
45.5%
39.0%
39.0%
36.4%
35.1%
31.2%
26.0%
26.0%
9.1%
Specific High-Importance Metric
Number of transactions
Total revenues
Number of registered users
Number of repeat transactions
Average transaction size
Number of sales leads converted to transactions
Number of unique visitors
Number of inquiries/sales leads
Revenue impact or cost savings generated for users
Operating profits
Return visits per registered user
Number of page views
Customer satisfaction
Number of hits
As shown in Table 2, the metrics identified as “high importance” by the most
respondents were total revenues (73%), number of transactions (73%), number of registered
users (66%) and number of repeat transactions (55%). The most commonly tracked
performance metrics from the list presented were the number of registered users (91%), total
revenues (85%), number of transactions (84%) and average transaction size (73%).
Furthermore, within the design-and-execution-based advantages noted for their
contribution to success, the items that managers cited had more to do with improving the
perception of the firm by outsiders, rather than the exploitation of specific managerial skills –
14
which also points to the dominance of factors external to the firm.4 Note the dramatic lack
of profit-based measures from metrics tracked (or assigned importance) by B-to-B managers,
supporting the widely-held attitude that “profits matter less than revenues” in early stages of
e-commerce ventures.
Of particular interest to our “crowdedness” question is the average percentage weight
allocated by market characteristic that affects the success in entering a new market. In Table
3, we see that “market size” (the most heavily weighted criterion) was rated twice as
important as “presence of other online B-to-B intermediaries in the same market” (the
seventh-most heavily weighted criterion, exceeding only “technology penetration in the
market” in perceived importance). Furthermore, the prominence of “degree of market
fragmentation” indicates — since fragmentation is the opposite of concentration — that these
new entrants were actively seeking markets with many “brick-and-mortar” rivals and were
not particularly concerned (either positively or negatively) with rivalry in the online market.
Table 3: Impact of Target Market Features on Success
Question Asked: How much do the following target market features impact the success of your business?
Please allocate a percentage across the features; the total allocation should equal 100%, but you can allocate 0
points to one or more features. (N=79)
Average % allocated
17.0%
16.1%
14.9%
14.5%
12.0%
9.0%
8.7%
8.0%
Feature Cited
Market size
Degree of market fragmentation
Procurement inefficiencies
Information inefficiencies
Distribution inefficiencies
Degree of product specialization
Presence of other online B-to-B intermediaries in the same market
Technology penetration (EDI/VAN, e-commerce capabilities of incumbents)
Finally, B-to-B entrants seem to have targeted only very large markets. 50% of the
online marketplaces in our sample are targeting markets in excess of $100 billion in sales.
Only 20% of the online marketplaces are targeting markets with $15 billion or less in sales,
and only 2% targeting markets with less than $1 billion in sales. While it is possible that this
effect is a remnant of the venture-capital funding decision – that is, that the economies of
scale in B-to-B are perceived to be so important that no small market will be funded, thus
censoring the sample of smaller markets -- evidence corroborates our statistical findings in
pointing to significant agency problems and mis-allocation of investment capital.
4
This extreme external focus may be exacerbated by the competitive necessities of an environment
characterized by increasing returns to scale and/or network externalities, as noted above.
15
VI. Basic Results of Hypothesis Testing
In the following correlational analyses, the crowdedness metric comes from the
original diligence performed on the B-to-B marketplace market -- that is, crowdedness and
number of entrants in a market were measured unconditionally on the decision to complete
our survey. The other two metrics (market size and minimum efficient scale) come directly
from our survey results. Five simple correlational analyses suffice to distinguish among the
theories of competition, oligopoly, winner-take-all and agency, as seen in Table 4.5
Table 4: Basic Correlational Testing of B-to-B Entrant Dataset
Dependent Variable
Independent
Variable
Sign
Significance at 5% Level
Crowdedness
Crowdedness
Market Size (total)
BTE/MES


Yes
No
Crowdedness
# entrants
# of entrants
Market size (total)
Market size (e-only)



No
No
BTE/MES

# entrants
5
No
Yes
Note that the test of statistical significance for a simple correlation is equivalent to the overall significance of
the single-variable regression Y=a+bX+e, where Y and X are the variables to be correlated – that is, rejecting
the “all slopes null” hypothesis. All five regressions passed the associated F-test for significance at the 10%
level; two (Crowdedness on market size and Number of entrants on BTE) passed at the 1% and 5% level,
respectively.
16
Table 5: Summary of Hypothesis Durability under Correlational Testing
Hypothesis
Framework ?
Comments
H1a: Crowdedness independent of market size
H1b: Crowdedness decreasing in BTE/MES
Competition
Competition
N
N
Two-sided test
Crowdedness increases in BTE/MES
H2a: Crowdedness decreasing in market size
H3a: Crowdedness independent of market size
H3b: Crowdedness decreasing in BTE/MES
H3c: Crowdedness decreasing in # entrants
H4a: # entrants increasing in market size
Oligopoly
Inc Returns
Inc Returns
Inc Returns
Winner-take-all
N
N
N
N
(Y)
One-sided test of H1a is rejected
Same as H1a
Same as H2a
Empirical contradiction of Share Lemma
(Monotonicity – compare H3b)
H4b:
H4c:
H4d:
H5a:
H5b:
H5c:
H5d:
Winner-take-all
Winner-take-all
Winner-take-all
Agency
Agency
Agency
Agency
Y
N
N
(Y)
Y
Y
(Y)
(Convexity – compare H3a)
(Contrast H4c)
(Contrast H4d)
Same as H3a
Same as H3b
(Contrast H3c)
(Contrast H3d)
Crowdedness increasing in market size
# entrants decreasing in BTE/MES
Crowdedness decreasing in BTE/MES
# entrants increasing in market size
Crowdedness increasing in market size
# entrants increasing in BTE/MES
Crowdedness increasing in BTE/MES
Legend: “N” means hypothesis is rejected by correlation test at the 5% level of significance
“Y” means hypothesis is supported by correlation test at the 5% level of significance
“(Y)” means hypothesis is supported by correlation test at less than a 5% level of significance
From Table 5, we make the following observations:
(a) The hypothesis that rational competitive behavior (either in perfect competition or in
oligopoly) explains entry patterns in B-to-B e-commerce is soundly rejected;
(b) Although the “Winner-take-all” model explains the effects on crowdedness of market
size, this model cannot be the only explanation of crowdedness, as it fails to predict
correctly the effects of barriers to entry/minimum efficient size; and
(c) The agency model alone predicts the signs of all five tests.
We thus tentatively conclude that agency issues, and in particular the asymmetry
between gains and losses for the entrepreneurs, is a necessary driving explanatory force for
B-to-B entry decisions over the 1999-2000 period. In addition, the “winner-take-all” model,
combined with the agency model, is compatible with the observed data.
Our analysis above, despite its direct relevance to our hypotheses, is admittedly an
industry-level analysis of average propensity to enter a market, rather than a firm-level
analysis of the actual entry decision process. To answer the question “On the margin, how
does market size affect a firm’s propensity to enter?”, we would need to formulate and
estimate a basic reduced-form model of the relationship between market size and the
17
entrant’s decision to enter. Our dependent variable would be a measure of “crowdedness”
(number of entrants per dollar of market transaction volume), whose relationship with market
size should be negative, nonexistent or positive, depending on whether we accept the
competitive/oligopoly analysis, real-option theory or agency cost/fundability approach,
respectively. As a proxy for barriers to entry, we could use the minimum scale perceived by
entrants to be required to break even. We would thus need to estimate the following equation
for each market i:
Crowdednessi = (# of Entrants in Market i)/(Market Size of Market i) =  +  (Market
Size)i+  (Barriers To Entry)i+  (Size)i*(BTE)i + I

Given that the hypotheses from the above theories predict different signs of the
coefficients, we could use this regression to discuss the relative validity of the theories in
explaining the observed empirical regularities in B-to-B e-commerce. Such a market-level
analysis would, however, make the assumption that all entrants within the market were
homogeneous in their capabilities and probabilities of success – which eliminates much of the
explanatory power of managerial theories of entry. Unfortunately, data scarcity makes
industry-by-industry regression analysis problematic using firm-level data. Even had all 292
identified e-marketplaces provided us with extensive data, the 57 distinct markets they
occupy would imply that this regression would be overspecified for all but the 13 markets
with the largest numbers of entrants (agriculture, automotive, chemicals, food, commercial
printing, electronic components, energy, finance, healthcare, industrial supplies, small
business services, telecommunications and transportation).
VII.
Conclusion
Our paper features empirical evidence from one of the most visible, and perhaps
meteoric new segments: B-to-B intermediaries. We have also attempted to contribute to the
theoretical understanding of entry in new markets, examining the real drivers of entry
decision, and focusing on considerations of economically inefficient yet privately lucrative
motives tied to the characteristic B-to-B entry structure of venture-capital financing, highpowered incentives and uncertainty in required capabilities for success. While our empirical
analysis is admittedly an early stage, the evidence so far is that some heretofore unexplored
factors are important determinants of the B-to-B entry decision. In particular, crowdedness
seems to be driven by winner-take-all and agency effects, rather than profit-maximizing entry
18
intent. It appears, from our empirical investigation of data collected during the 1999-2000
peak of the B-to-B intermediaries’ market, that entry in B-to-B markets has been largely
driven by the visibility of the market and the potential for extraordinary financial returns via
IPO, rather than a cold-blooded tradeoff of the required entry costs vs. the expected “prize”
for successful implementation.
While these entry decisions may be privately economically rational for their decisionmakers (in this case, entrepreneurs) they do not improve the prospects for returns on invested
equity – and, in some cases, virtually guarantee investor losses until the number of firms
equilibrates to that supportable by the market. Extraordinarily high crowdedness, especially
caused by reasons other than high expected profitability, predicts a severe shakeout once
uncertainty about required capabilities is resolved. Note that scarcity of further venture
capital places a distinct cap on entry, but does not necessarily affect the severity of the
shakeout (or the incentive to take desperate measures to maximize the probability of IPO)
among those already in. We therefore speculate that the future profitability of segments
characterized by entry-induced crowdedness will be even lower than what is currently
anticipated by financial analysts. To be examined in future research is a guide for
managerial action in new markets with significant network externalities and a taxonomy of
policy and strategy implications for the evolution of this new market now that the entry wave
has abated.
19
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