A research and education initiative at the MIT
Sloan School of Management
Competition between Blended Traditional and
Virtual Sellers
Paper 241
July 2008
T. Randolph Beard
Gary Madden
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please visit our website at http://digital.mit.edu
or contact the Center directly at digital@mit.edu
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Competition between Blended Traditional and
Virtual Sellers*†
T. Randolph Beard
Department of Economics
Auburn University
Auburn, AL 36849
USA
Gary Madden
Department of Economics
Curtin University of Technology
Perth, WA 6845
Australia
8 June 2008
Abstract
Competition in many electronic markets involves rivalry between ‘Bricks and Clicks’ firms, which
operate both traditional and on-line vendors, and entrants who rely completely on Internet sales. In
many cases, the goods being offered are identical-only the channels by which they are distributed differ.
This structure is understood as reflecting temporal experimentation by firms operating in an
environment of incomplete information about consumer preferences and costs. Both demand
characteristics and production costs can be expected to differ between traditional and virtual
distribution networks. Further, the blended channel seller faces the issue of cannibalization of demand
upon virtual market entry. This article analyses post-entry performance of a unique sample of
Australian virtual entrants who face incumbent blended channel competitors. The fate of these entrants
depends not just on their abilities to discern relevant parameters of the underlying environment, but
also on incumbent response to entry. The incumbents, in turn, can limit entrant penetration by their
virtual operations, but such operations involve loss of sales in the Bricks and Mortar market segment.
We seek to identify and characterize those features of product markets and cost conditions consistent
with survival of virtual entrants as a long-run equilibrium phenomenon.
JEL Classification: L86, D21, L1, O33
Keywords: E-Commerce, Technology adoption, Retail industries
*
Comments welcome, beardtr@auburn.edu; g.madden@.curtin.edu.au.
† The MIT Center for Digital Business and the Columbia Institute for Tele-Information provided
support during the time this paper was revised. Helpful comments were provided by participants in
seminars at Columbia University and Curtin University of Technology. We are very grateful to Warren
Kimble and Aaron Morey for excellent research assistance. The authors are responsible for all
remaining errors.
1. Introduction
The OECD considers Internet users, hosts and access price, and secure servers accurately
reflect the state of Member Country e-commerce readiness. In particular, in 2004-05
Australian firms’ personal computer and Internet penetration levels are 89% and 77%,
respectively (EIU, 2007). Furthermore, during 2005-06, 20.9% of Australian firms
received orders on-line, while 37.3% placed Internet orders. This asymmetric activity is
thought to reflect that placement of on-line orders only requires access to a computer with
an Internet connection, whereas receiving orders necessitates an established Web
presence with technical support (Australian Bureau of Statistics; ABS, 2006). Importantly,
the ABS (2006) identifies that placement of orders via the Internet (or Web) increases
with firms’ employment, viz., 33% for firms with 0-4 employees compared to 67% for
firms with more than 200 employees placed on-line orders. The pattern for receiving
orders on-line is similar, viz., 19% for firms with 0-4 employees and 28% for firms with
more than 20 employees. Also, Internet revenue from on-line orders grew from $40
billion in 2004-05 to $57 billion in 2005-06. This 2005-06 Internet revenue is
approximately 3% of goods and service, and total business revenue. Of the 21% of
Australian firms that received Internet orders in 2005-06, 64% generated 5% or more of
their income from Internet sales (OECD, 2006). This concentration of e-commerce is
larger firms is rationalised as being caused by smaller firms having a lack of strategic
awareness and technical knowledge, mistrust of technology, and high establishment costs
(DBCDE, 2002; Dunt and Harper, 2002; OECD, 2004).
Competition in many of these electronic markets involves rivalry between ‘Bricks and
Clicks’ firms, which operate as both traditional and on-line vendors, and entrants that rely
completely on Internet selling. Often the goods being offered for sale are identical, and
only the channels by which they are distributed differ. This structure is suggestive of
temporal experimentation by firms operating in an environment with incomplete
information about consumer preferences and costs. Both demand characteristics and
production costs can be expected to differ between traditional and virtual distribution
networks. Further, blended channel sellers face an issue of demand cannibalization on
virtual market entry. Additionally, while the above reports clearly indicate that small and
large firms exhibit substantially different tendencies to embrace the new technology,
adoption rates also differ by product category (ABS, 2006). These patterns suggest
several related questions: How do virtual firms differ in their adoption patterns? In what
type of environments is post on-line market entry performance by virtual and established
firms likely to succeed? What can be said about the relationship between post-entry
performance and the reasons for entry?
Much of the empirical industrial organization literature on market turbulence (firm entry,
exit and survival) concerns the analysis of firm population cohort data (Fotopoulos and
Louri 2000, Mahmood 2000, Segarra and Callejón 2002, Disney et al. 2003). A unifying
theme of this literature is the entry equation,
E j = απ *j − γ B j + e j
where E is the number of firms entering market j , and is explained by π * the profits
expected to occur in market j and costs that follow the entry decision B and e is a
random error. Overviews of this literature by Geroski (1995) and Caves (1998) suggest
that entry is relatively easy, but survival is not. The most palpable consequence of entry
is exit. As most entry attempts ultimately fail, and as most entrants take five to ten years
before they are able to compete on a par with incumbents, incumbents find costly
attempts to deter entry unprofitable. Furthermore, the role entry plays in shaping industry
structure is bound up with the proposition that entry is often a vehicle for introducing
innovation, particularly in early phases of industry evolution (Geroski 1995: 436). At
some point in new market development, consumer preferences become reasonably well
formed and coalesce around a subset of products. At this stage, competitive rivalry shifts
from competition between product designs to competition based on prices and costs of a
particular design (Geroski 1995: 437). In an electronic (on-line or virtual) market, generic
entry is often via an alternative marketing channel (virtual channel) that sells goods with
the same characteristics available in the brick-and-mortar (B&M) market. In these
circumstances, virtual entrants often face severe cost disadvantage. However, exogenous
shifts in costs or demand can undermine such entry barriers.
Rather than turbulence per se, Audretsch (1995) and Reid and Smith (2000) focus on the
growth rates of surviving firms. Audretsch argues that survivor employment growth rates
(defined by employment in 1986 divided by firm employment in 1976) are systematically
greater in highly innovative industries relative to those in less innovative industries.
Additionally, Reid and Smith consider small firm entrant growth rates (for employment
growth, rate of return, and productivity) are higher than those for large firms. Both
studies use what may be termed ‘objective’ measures of performance (Reid and Smith,
2000). The present study seeks to contribute to this research program by considering
performance metrics that might reasonably be considered by optimising firms in the
context of competition between blended traditional and virtual sellers. In particular, the
approach is based on the notion that performance evaluation is only ‘fair’ when criteria
are closely aligned with economic agents’ objectives. That is, for example, productivity
measures typically employ output in the performance calculations. However, a
representative firm rarely, if ever, explicitly maximises output.
This study considers post-entry profit, revenue and cost performance for a unique sample
of Australian small firm virtual entrants that face incumbent blended channel
competitors. 1 The fate of these entrants depends not just on their abilities to discern
relevant parameters of the underlying environment, but also on incumbent response to
entry. The incumbents, in turn, can limit entrant penetration by their virtual operations,
but such operations involve loss of sales in the B&M market segment. The study seeks to
identify and characterize those features of product markets and cost conditions
(consistent with survival of virtual entrants as a long-run equilibrium phenomenon) using
metrics that reflect reasons cited in the literature for entering on-line markets, viz., cost
1
The ABS (2002, p.1) defines small business as employing less than 20 persons and medium business as
employing 20 or more people, but less than 200 people.
2
reduction, market experimentation, quality of service, and react to or pre-empt on-line
market entry. Within this context, this study is interested in whether the performance of
surviving entrants varies systematically by industry, type of firm and motivation for online market entry.
The paper is structured as follows. Section 2 lists the reasons for on-line market entry, by
both (traditional) incumbent and virtual sellers, identified by the literature. In Section 3
descriptive information concerning sample data is provided, and variables in the
empirical analysis are defined. The multivariate probit model used for econometric
estimation is specified in Section 4, while estimation results are reported in Section 5. A
final section suggests some modelling extensions.
2. Factors Associated with On-line Market Entry
The motivations for entry into on-line markets are sourced from a review of the
literatures concerned with: firm survival and growth; new technology adoption;
information and communications technology (ICT) adoption; and e-commerce adoption,
survival and performance, are summarised in Table 1. Additionally, the studies reviewed
are listed in Table 2. Also contained in Table 2 are variables deemed important in
explaining firm behaviour. While variables listed therein may be relevant in many
studies, a variable such as firm size is only associated with Audretsch (1995) because of
chronological precedence. In this selective review only DeYoung (2005) and VilasecaRequena et al. (2007) explicitly model post-entry performance. Vilaseca-Requena et al.
limit their consideration to sales performance, whilst DeYoung consider many banking
sector specific intermediate (e.g., loan rates and labour expense) and final (return on
assets and return on book equity) performance indicators. Variables listed in Table 2 are
either descriptive (firm or industry) or performance determinants. Firm descriptive
variables include: firm size and age, industry, multiple plants and entry order.
Performance related variables are classified in Table 1 by: cost or efficiency
(employment); market experimentation (innovation, extension of geographic market,
product variety, globalization and product mix); quality of service (consumer readiness);
or strategic (entry order, competitive pressure; strategic orientation).
3
Table 1. On-line Market Entry Motivation
Blended Traditional Seller
Virtual Seller
Costs
Reduce cost of providing service, e.g., via scale
effects, and smaller sunk costs to expand customer
reach
Lower operational costs and smaller sunk entry
costs (consistent with small-firm entry)
Experimentation
Examine potential for expansion via an on-line
channel. Fail at minimal sunk cost
Small scale on-line market entry to test market
size. Fail at minimal sunk cost
Quality of Service
Augment B&M sales via ancillary information or
sales channel. Not cannibalize store sales
Support on-line sales via product information.
Core feature of on-line channel
Strategy
Respond or pre-empt rival on-line market entry
React to B&M profit signals
The relative importance of these motivations depends on a particular firm, and on the
state of the industry within which the firm operates. In modelling, it is assumed that
different market environments (B&M versus on-line) entail differential cost structures,
and therefore entry into an e-market environment can imply substantial operating cost
savings. Accordingly, operating cost reduction is often considered an important incentive
for B&M firms to make e-market infrastructure investment (Dinlersoz and Pereira, 2007).
However, it is also probable that B&M and e-market environments face different market
structures, demand curves and customer segments. For example, competition is likely to
be stronger in an e-market environment, and the corresponding price elasticity of demand
higher. To compensate for this, the (potential) size and reach of the e-market needs to be
greater. As a result, firms enter a virtual market to reach a wider customer base and gain a
larger demand pool. However, to do so, firms must improve their efficiency and reduce
prices relative to those ruling in the B&M market.
4
Table 2. Firm Technology Adoption, and Market Entry and Survival Variables
Author (Year Published)
Activity Analysed
Independent Variable Introduced
Audretsch (1995)
Survival, growth
Industry
Firm size
Small firm
Organization
Innovation
Battisti and Stoneman (2005)
New technology adoption
Firm age
Firm employment
Years since new market entry
Lucchetti and Sterlacchini (2004)
ICT adoption
More than one plant
Zhu et al. (2003)
E-business adoption
Consumer readiness
Competitive pressure
Vilaseca-Requena et al. (2007)
E-commerce adoption, on-line sales
Strategic orientation
Dinlersoz and Pereira (2007)
E-commerce adoption
Jeon et al. (2006)
E-commerce adoption
Extend geographic reach
Product variety
Government support
Globalization
Nikolaeva (2007)
E-commerce survival
Entry order
Single sales channel
Product characteristics
DeYoung (2005)
E-commerce performance
Product mix
3. Data and Variables
A profile of Australian small and medium enterprise (SME) electronic commerce (ecommerce) capability and activity is obtained via an August 2004 telephone survey. 2 The
sampling frame consists of an exogenously stratified sample by firm size of 1,001 SMEs.
For inclusion in the sample, a firm must conduct e-commerce via a Web site and employ
less than 200 persons. The state and territory capital cities of Melbourne (201), Sydney
(201), Brisbane (101), Adelaide (100), Perth (99), Canberra (50), Darwin (50) and Hobart
(50) are sampled. Regional centres surveyed include Albury-Wodonga (50), Townsville
(50) and Newcastle (49). 3 The most senior person managing daily e-commerce operations
and planning is questioned. 4 Information concerning firm and respondent characteristics,
reasons for the firm entering e-markets and the initial investment outlay are sought. The
2
For the purpose of the study e-commerce is defined as sale of goods and services, where an order is
placed by the buyer or price and terms of the sale are negotiated over an Internet, extranet, EDI network,
electronic mail, or other online system. Payment need not be made on-line.
3
Numbers of surveyed firms are in parentheses.
4
Contacts are screened to ensure they comply. A repeat telephone call is made if this person is unavailable.
5
success of e-market operations and expectations about market prospects are also sought.
Firm characteristics include geographic (head office location and the number of offices
by location), employment (full-time equivalent employees), industry classification, Web
site capability, e-market sales proportion, and recent trading experience, i.e., revenue,
costs and profit in rates of change. Respondent age, educational qualifications and title
are also sought. Finally, information concerning reasons for entering or delaying entry to
e-commerce markets, initial investment, training and customer response is sought.
Table 2 provides a profile of sampled firms from the 17 ANZSIC single-digit divisions.
The distribution of firms is similar to that reported by the ABS for businesses with a Web
presence at 2004 (ABS, 2005, Table 2.1). These data, which include all firms, shows a
larger proportion of firms in Manufacturing and Property and Business Services and less
in Accommodation, Cafes and Restaurants than sample data. Service industries
(Accommodation, Cafes and Restaurants; Property and Business Services; Personal and
Other Services; and Cultural and Recreational Services) comprised 42.7% of sampled
firms. Retail Trade has the highest industry representation (17.7%). Most firms
commence operation post-1995 (40%), and employ fewer than 6 persons (62.4%).
Further, the distribution by full-time equivalent employees is similar to that reported by
the ABS SME population distribution at 2001 (ABS, 2002, Table 3.4). Most firms
operate a single office, store or plant (72.4%). Of the 276 firms with more than one office,
47.8% of auxiliary offices are located within the local metropolitan area or region, while
only 6.9% of offices are located overseas.
6
Table 3. Sample Firm Characteristics
Sample (%)
ABS (%)
ANZSIC single-digit division
Retail trade
Accommodation, cafes and restaurants
Property and business services
Personal and other services
Manufacturing
Transport and storage
Cultural and recreational services
Finance and insurance
Wholesale trade
Construction
Other
17.7
16.8
10.5
9.2
6.7
6.6
6.2
5.6
5.1
4.7
10.9
14.2
4.1
24.0
7.4
12.5
4.5
6.2
2.5
9.1
9.4
6.1
5.0
3.1
6.7
17.2
28.0
40.0
-
57.7
29.7
11.8
0.8
63.9
29.3
6.2
0.6
72.4
21.9
3.8
1.9
-
47.8
34.8
39.1
6.9
-
Commenced operation
Prior to 1955
1955–1964
1965–1974
1975–1984
1985–1994
1995–2004
Full-time equivalent employees
1–4
5–19
20–99
100–199
Offices, stores or plants
1
2–5
6–10
> 10
Auxiliary office location
Local metropolitan area or region
Intrastate
Interstate
Overseas
Number
132
96
108
19
Note. A dash indicates data is not available. Auxiliary office location applies to firms with
more than one office. Source. ABS (2005) Table 2.1, businesses with Web presence. ABS
(2002) Table 3.4, employer size group by industry division
7
Table 4 and Table 5, respectively, present means, standard deviations and definitions for
the dependent and independent variables used in the regression tests based on a crosssection survey of Australian SMEs. Answers to survey questions are generally coded as
real, binary (0, 1) or categorical (1, 2, 3 …) variables. Categorical responses are
transformed into binary (dummy) variables, e.g., profit is transformed from a Likert scale
response: substantially lower; lower; same; higher; substantially higher to equal unity if
the response is higher or substantially higher, and zero otherwise. In Table 4 the
dependent variable to be analysed are PROFIT, REVENUE and COST. These variables
are typically considered by economists as the ultimate objectives of agents’ optimisation.
Steinfield et al. (2002) argue that the integration of B&M location with on-line commerce
is based on the search for synergistic opportunity. That is, aligning goals across physical
and virtual channels suggests that those involved realize that the ‘parent’ firm benefits
from sales stemming from either channel. Higher revenues can arise from geographic and
product market extension, thus adding revenue streams otherwise not feasible from
physical outlets. Synergistic benefits also arise from lower costs (savings may occur
through improved labour productivity, and reduced inventory, advertising and
distribution costs). Either source of benefits may flow through to PROFIT.
Table 4. Dependent Variable Definitions
Variable
Definition
Mean
Std Dev.
PROFIT
= 1, if profit increased with on-line market entry; = 0, otherwise
0.41
0.49
REVENUE
= 1, if revenue increased with on-line market entry; = 0, otherwise
0.46
0.49
COST
= 1, if total costs increased with on-line market entry; = 0, otherwise
0.15
0.35
Note. All dependent variables are coded as binary (0, 1).
The independent variables contained in Table 5 are classified as: describing the firm or
environment (industry classification), commitment to on-line market entry (initial and
intended Web site investment) and reason for on-line market entry. These entry reasons
are designed by align with the motivations identified by the literature as reported in Table
1. That is, EFFICIENCY indicates whether the firm entered on-line markets with an
intention of reducing operational costs. The variable EXPAND (=1, if the firm intended
to increase sales from geographic and product market extension) and NEWGOOD (=1, if
the firm introduced a new good via the virtual sales channel) are intended to encompass
market experimentation by the firm, whereas the quality of service aspect of firm
behaviour is captured by the CUSTOMER and SUPPLIER variables. CUSTOMER (=1,
if on-line entry is a response to customer requests for this facility) and SUPPLIER (=1, if
suppliers required the firm to conform, e.g., to on-line procurement processes) are
intended to distinguish demand and supply dimensions of quality of service. Finally, the
variable ANTICIPATE (=1, if the firms’ entry into the on-line channel is a reaction to
intended or actual entry by rivals) reflects the state of and reaction to the competitive
environment in which the firm is situated.
8
Table 5. Independent Variable Definitions
Variable
Definition
Mean
Std Dev.
Firm
BLENDED
= 1, if B&M prior to on-line market entry; = 0, otherwise
0.89
0.31
ESTAB*
Years entered on-line market
3.85
3.00
MIX
= 1, if firm sells single good on-line; = 0, otherwise
0.31
0.46
SMALL
= 1, if firm employs less than 20 persons; = 0, otherwise
0.87
0.33
LOCATION
= 1, if head office in Sydney or Melbourne; = 0, otherwise
0.40
0.49
STORES
= 1, if more than one store; = 0, otherwise
0.27
0.44
BUSINESS
= 1, if Business Oriented Services; = 0, otherwise
0.21
0.40
SUPPORT
= 1, if Support Service to Industry; = 0, otherwise
0.09
0.29
PUBLIC
= 1, if Public Goods; = 0, otherwise
0.11
0.32
PRIMARY
= 1, if Primary Industry; = 0, otherwise
0.13
0.34
RETAIL
= 1, if Retail; = 0, otherwise
0.17
0.38
= 1, if initial Web site investment exceeded $20,000; = 0, otherwise
0.11
0.31
0.94
0.23
EFFICIENCY = 1, if firm entered on-line market to reduce costs, = 0, otherwise
0.35
0.48
EXPAND
= 1, if firm entered on-line market to increase sales, = 0, otherwise
0.58
0.49
NEWGOOD
= 1, if firm entered on-line market to introduce a new product; = 0, otherwise
0.40
0.49
CUSTOMER = 1, if firm entered on-line market in response to customers; = 0, otherwise
0.44
0.49
SUPPLIER
= 1, if firm entered on-line market in response to suppliers, = 0, otherwise
0.23
0.42
ANTICIPATE = 1, if firm entered online market in response to rival entry; = 0, otherwise
0.42
0.49
Industry
Web site
INITIAL
INTENTION = 1, if more Web site investment proposed; = 0, otherwise
Entry reason
Note. ESTAB is a continuous variable measured in years. All other independent variables are coded as binary (0, 1).
Table 6 depicts PROFIT, REVENUE and COST response frequencies obtained from
sampling. The responses concern performance by category since the firm entered an online market. The firms’ activity falls into the mutually exclusive and exhaustive
categories of either having increased, remained steady (unchanged) or decreased. The
9
reported frequencies suggest entry into on-line markets is associated with substantial
PROFIT (41.5%), REVENUE (46.8%) and COST (22.4%) improvements. However, cost
increases are reported by 15.2% of sampled firms.
Table 6. Firm PROFIT, REVENUE and COST Performance Frequencies
Observations
Percent
Increase
Steady
Decrease
415
558
28
41.5
55.7
2.8
REVENUE
Increase
Steady
Decrease
468
511
22
46.8
51.0
2.2
Increase
Steady
Decrease
152
625
224
15.2
62.4
22.4
Total
1001
100.0
PROFIT
COST
Based on sample data, the joint and marginal frequency firm performance distributions
are compiled in Table 7. Improved performance for REVENUE (0.47) is higher than that
for PROFIT (0.42) and COST (0.15). While these impacts appear large they are
combinational in nature, viz., no firms report PROFIT- and COST-only effects.
Table 7. Joint and Marginal Firm Performance Probabilities
Joint
Marginal
(PROFIT)
PROFIT only
REVENUE only
COST only
PROFIT and REVENUE only
PROFIT and COST only
REVENUE and COST only
PROFIT, REVENUE and COST
None
0.05
0.00
0.00
0.33
0.00
0.10
0.04
0.48
0.05
Total
1.00
Marginal
(REVENUE)
Marginal
(COST)
0.00
0.00
0.33
0.00
0.33
0.04
0.10
0.04
0.00
0.10
0.04
0.42
0.47
0.15
While the marginal probabilities are useful, the conditional firm performance
probabilities reported in Table 8 show how the performance of PROFIT, REVENUE and
COST are related. For instance, among firms whose REVENUE increases, 70% report an
associated increase in PROFIT and 22% report an increase in COST, which is much
higher than the corresponding unconditional probabilities of 41% and 15%, respectively.
Interestingly, no firm reporting increased PROFIT records a COST increase, whereas
10
80% acknowledge increased REVENUE. Among firms that jointly record PROFIT and
REVENUE increases only 12% report an increase in COST. Finally, of firms that report
both REVENUE and COST increases, only 40% acknowledge increases in PROFIT from
entry.
Table 8. Sample Firm Performance Probabilities
Profit Increase
Revenue Increase
Cost Increase
P (⋅)
0.41
0.46
0.15
P (⋅ COST = 1)
0.00
0.67
1.00
P (⋅ PROFIT = 1)
1.00
0.80
0.00
P (⋅ REVENUE = 1)
0.70
1.00
0.22
P (⋅ REVENUE = 1, PROFIT = 1)
1.00
1.00
0.12
P (⋅ REVENUE = 1, COST = 1)
0.40
1.00
1.00
P (⋅ PROFIT = 1, COST = 1)
1.00
0.00
1.00
4. Multivariate Probit Model
Let I1o denote the underlying latent response associated with the jth type of on-line firm
performance response, for j = 1,..., J , and I j is the binary response performance
outcome associated with the same type. Using the indicator function, I j = 1 if there is an
acknowledged performance of the jth type, and I j = 0 otherwise. Accordingly, the
multivariate probit model is specified as a linear combination of deterministic and
stochastic components as follows:
I1o = x ' β1 + ε1 , with I1 = 1 for I1o > 0; = 0, otherwise
I 2o = x ' β 2 + ε 2 , with I 2 = 1 for I 2o > 0; = 0, otherwise
M
M
o
o
I J = x ' β J + ε J , with I J = 1 for I J > 0; = 0, otherwise
(1)
where x = (1, x1 ,..., x p ) ' is a vector of p covariates which do not differ by performance
category and β j = ( β j 0 , β j1 ,..., β jp ) ' is a corresponding vector of parameters to be
estimated. The stochastic component ε j , consists of unobserved factors that explain the
marginal probability of firm performance in category j . Each ε j is drawn from a J variate Normal distribution with zero conditional mean and variance normalised to unity
(for parameter identification), where ε ~ N (0, Σ), and the covariance matrix Σ is given
by:
11
⎡ 1
⎢ρ
Σ = ⎢ 21
⎢ M
⎢
⎣ ρ J1
ρ12 L ρ1J ⎤
1 L ρ 2J ⎥⎥
M
ρJ 2
M ⎥
⎥
1 ⎦
O
L
.
(2)
The off-diagonal elements ρ sj represent unobserved correlations between the stochastic
component of the sth and the jth performance category. Because of covariance symmetry
ρ sj = ρ js . From the multivariable probit model formulation, the univariate marginal
performance probability by category is:
Pr( I j = 1 x j ) = Φ1 ( x 'j β j )
for j = 1,..., J
(3)
where Φ1 (⋅) is the standard Normal distribution function. Moreover, the bivariate joint
probabilities are given by:
Pr( I i = 1, I j = 1 xi , x j ) = Φ 2 ( xi' βi , x 'j β j ; ρij )
Pr( I i = 1, I j = 0 x i , x j ) = Φ 2 ( xi' β i , − x 'j β j ; − ρij ), and
(4)
Pr( I i = 0, I j = 0 x i , x j ) = Φ 2 (− x β i , − x β j ; ρij )
'
i
'
j
where i, j = Π, R, C; i ≠ j and Φ 2 ( z1 , z2 ; γ 12 ) is the cumulative distribution function of
standard bivariate Normal distribution with γ 12 the correlation coefficient of the two
univariate random elements z1 and z2 . The trivariate joint probabilities
(with i, j , k = Π, R, C ; i ≠ j , i ≠ k , j ≠ k ) ) are:
Pr( I i = 1, I j = 1, I k = 1 x i , x j , xk ) = Φ 3 ( xi' β i , x 'j β j , xk' β k ; ρij , ρik , ρ jk ),
Pr( I i = 1, I j = 1, I k = 0 xi , x j , xk ) = Φ 3 ( xi' βi , x 'j β j , − xk' β k ; ρij , − ρik , − ρ jk ),
Pr( I i = 1, I j = 0, I k = 0 x i , x j , xk ) = Φ 3 ( xi' βi , − x 'j β j , − xk' β k ; − ρij , − ρik , ρ jk ), and
(5)
Pr( I i = 0, I j = 0, I k = 0 x i , x j , xk ) = Φ 3 (− xi' βi , − x 'j β j , − xk' β k ; ρij , ρik , ρ jk )
where Φ 3 ( z1 , z2 , z3 ; γ 12 , γ 13γ 23 ) is the cumulative distribution function of standard
trivariate Normal distribution with γ st the correlation coefficient of two of the three
univariate random elements zs and zt ( s, t = 1, 2,3; s ≠ t ) .
The structure of the corresponding conditional probabilities is;
12
Pr( I i = 1 I j = 1, I k = 1; xi , x j , xk ) =
Φ 3 ( xi' βi , x 'j β j , xk' β k ; ρij , ρik , ρ jk )
Pr( I i = 1 I j = 0, I k = 0; xi , x j , xk ) =
Pr( I i = 0, I j = 0 I k = 1; xi , x j , xk ) =
Pr( I i = 1 I j = 1; xi , x j ) =
Φ 2 ( xi' βi , x 'j β j ; ρij )
,
Φ 3 ( xi' β i , − x 'j β j , − xk' β k ; − ρij , − ρik , ρ jk )
Φ 2 (− x 'j β j , − xk' β k ; ρ jk )
Φ 3 (− xi' β i , − x 'j β j , xk' β k ; ρij , − ρik , − ρ jk )
Φ 2 ( xi' β i , x 'j β j ; ρij )
Φ1 ( x 'j β j )
Φ1 ( xk' β k )
,
(6)
, and
.
Given an i.i.d . sample of J firms and conditional on firm heterogeneity, the
multivariable probit model is estimated by maximising the log-likelihood function:
N
1
1
1
Log ( L) = ∑∑∑∑ hs (i, j , k ) log(Prob( I Π = i, I R = j , I C = k xΠs , xRs , xCs )),
s =1 i = 0 j = 0 k = 0
where
⎧1 if firm s chooses ( I Π = i, I R = j , I C = k )
hs (i, j , k ) = ⎨
⎩0 otherwise.
5. Estimation
The multivariate probit model is not previously employed in modelling firm survival or
performance. Importantly, the probit specification with potentially non-zero off-diagonal
elements in Σ allows for correlations across the disturbances of the latent equations,
which embody unobserved firm characteristics. While the univariate model is a special
case, the multivariate specification allows firms’ cost performance (for example) to aid in
predicting the performance probability of the profit and revenue categories. Estimation is
conducted via LIMDEP version 9.0 by simulated maximum likelihood using a smooth
recursive (GHK) simulator to evaluate the multivariate Normal probabilities. 5 The
simulated maximum likelihood estimator is asymptotically consistent as the number of
observations and draws tend to infinity. 6 Within this framework the variances of the
disturbances are normalised to unity. In the context of the sample data, variation in firms’
on-line performance probability may differ with firm size. In univariate probit models
heteroskedasticity cause parameter estimates to be inconsistent (Davidson and
MacKinnon, 1984). The Lagrange multiplier test for heteroskedasticity is applied to the
5
For details of the algorithm see Train (2003, p. 126-37).
Cappellari and Jenkins (2003) argue that if the number of draws is greater than the square root of the
sample size the parameter estimates are robust to different initial seed values.
6
13
individual equations (estimated by univariate probit regression) that comprise the
multivariate probit system. In all three tests the null hypothesis is rejected.
The marginal means in the multivariate probit model are the univariate probabilities that
the three variables equal unity. LIMDEP analyses the conditional mean
E[ y 1 y 2 = 1, y 3 = 1] = Prob[y1 = 1, y2 = 1, y3 = 1] / Prob[y2 , y3 ] = P1...M / P2...M = E1 .
Greene (2008) constructs the derivatives of this function with x the arguments in the
probability functions, and γ m such that zm = x 'γ m = β m' xm :
∂E1
M⎛
1 ∂P2...M
= ∑m ⎜
.
∂x
⎝ P2...M ∂zm
⎞
M ⎛
1 ∂P2...M
.
⎟γ 1 − E1 ∑ m = 2 ⎜
⎠
⎝ P2...M ∂zm
⎞
⎟γ m .
⎠
The standard errors for the marginal effects are not computed directly but via the
bootstrapped delta approximation (see Greene, 2008).
The estimated correlations coefficients ρ sj between the firms’ profit, revenue and cost
performance categories, reported in Table 9, are statistically significant. The correlations
between PROFIT and REVENUE, and PROFIT and COST equation disturbances are
positive suggesting, for example, that unobservable factors which increase the probability
higher PROFIT also increases the probability of higher REVENUE. The negative
correlation between PROFIT and COST is intuitively reasonable. Namely, unobservable
factors that increase the probability of higher COST also reduce the probability of higher
PROFIT. Furthermore, the likelihood ratio test for the independence between the
disturbances is rejected, implying correlated binary performance reactions between
PROFIT, REVENUE and COST.
Turning to the impact of the explanatory variables several distinct patterns emerge. For
variable describing firm attributes, a positive impact on PROFIT performance is higher
the longer the firm is established, and for small firms and firms with more than a single
location. The mirror impacts on COST performance for these categories suggest the
potential source of improvement in PROFIT performance. Firms’ COST is also less
likely to increase for blended firms (had pre-entry B&M presence) and when selling
several goods or located outside the major metropolitan areas (of Sydney and Melbourne).
Conversely, REVENUE increases are more likely to occur for small short-lived virtual
firms selling a single good outside major metropolitan areas. Improved performance, for
all categories with exception of REVENUE for intended investment, is reported for the
Web site investment variables. Finally, EFFICIENCY (cost reduction), EXPANSION
(increase geographic market coverage), NEWGOOD (introduce a new product),
CUSTOMER (respond to customer request) motivations significantly impact on postentry on-line market performance. The estimated impacts have plausible impacts on
PROFIT, REVENUE and COST (with the exception of PROFIT for CUSTOMER, where
14
the net impact might be negative). SUPLIER negatively impacts on COST only, whereas
ANTICIPATE (anticipating rival entry) impacts on PROFIT through COST.
Table 9. Multivariate Model Estimates
Variable
Firm
BLENDED
ESTAB
MIX
SMALL
LOCATION
STORES
Industry
BUSINESS
SUPPORT
PUBLIC
PRIMARY
RETAIL
Web site
INITIAL
INTENTION
Entry reason
EFFICIENCY
EXPAND
NEWGOOD
CUSTOMER
SUPPLIER
ANTICIPATE
Profit
Coefficient Marginal Effect
Revenue
Coefficient Marginal Effect
Coefficient
Cost
Marginal Effect
-0.158
(0.159)
-0.028
(0.014)
-0.162
(0.103)
-0.016
(0.123)
-0.016
(0.087)
0.111
(0.107)
0.028
(0.028)
0.027**
(0.007)
0.002
(0.009)
0.065**
(0.011)
0.010
(0.008)
0.030**
(0.006)
-0.113
(0.164)
0.015
(0.018)
-0.128
(0.105)
-0.024
(0.137)
-0.087
(0.094)
0.016
(0.114)
-0.225**
(0.068)
-0.156**
(0.036)
-0.228**
(0.068)
-0.278**
(0.066)
-0.127**
(0.037)
-0.031**
(0.006)
-0.116
(0.204)
-0.058
(0.023)
-0.116
(0.134)
-0.119
(0.169)
-0.058
(0.113)
0.047
(0.135)
-0.037**
(0.001)
-0.021**
(0.001)
-0.036**
(0.001)
-0.045**
(0.001)
-0.012**
(0.001)
-0.016**
(0.001)
-0.087
(0.133)
-0.082
(0.172)
-0.0181
(0.158)
-0.055
(0.144)
-0.079
(0.128)
0.060*
(0.032)
0.001
0.008
0.042**
(0.014)
0.042**
(0.020)
-0.026**
(0.001)
-0.134
(0.143)
-0.021
(0.182)
-0.013
(0.167)
-0.070
(0.151)
-0.021
(0.142)
-0.175**
(0.051)
-0.087**
(0.030)
-0.108**
(0.027)
-0.172**
(0.043)
-0.025
(0.017)
-0.112
(0.173)
-0.028
(0.202)
-0.056
(0.198)
-0.097
(0.203)
0.008
(0.166)
-0.030**
(0.001)
-0.011**
(0.000)
-0.019**
(0.000)
-0.026**
(0.000)
0.000
(0.001)
0.067
(0.133)
0.012
(0.145)
0.020**
(0.002)
0.128**
(0.036)
0.024
(0.145)
-0.054
(0.158)
0.014**
(0.013)
-0.365*
(0.079)
-0.011
(0.179)
-0.195
(0.196)
-0.003**
(0.000)
-0.064**
(0.001)
0.157
(0.098)
0.336
(0.115)
0.161
(0.097)
0.066
(0.096)
0.096
(0.104)
0.149
(0.095)
0.012**
0.005
0.080**
(0.013)
0.019**
0.003
-0.0146**
(0.007)
0.003
(0.005)
0.037**
(0.008)
0.062
(0.106)
0.196
(0.121)
0.091
(0.104)
0.041
(0.105)
0.054
(0.117)
0.029
(0.103)
0.101**
(0.048)
0.129*
(0.076)
0.088**
(0.042)
0.109**
(0.038)
0.023
(0.020)
-0.018
(0.019)
-0.001
(0.128)
0.034
(0.135)
0.026
(0.123)
0.025
(0.124)
-0.017
(0.140)
-0.058
(0.124)
0.002*
(0.001)
0.010**
(0.002)
0.006**
(0.001)
0.008**
(0.000)
-0.007**
(0.001)
-0.013**
(0.000)
Correlation coefficients
ρΠR
ρΠC
ρ RC
0.711 (0.007)
-0.265 (0.001)
0.490 (0.005)
Note. Standard errors are in parentheses. * is significant at 10% level. ** indicates significant at 5% level.
15
Another advantage of the multivariate over the univariate probit model is that the more
general specification allows evaluation of the joint and conditional marginal effects
taking into account the correlation across PROFIT, REVENUE and COST performance
categories. Given the high and significant correlation coefficients, it is probable that some
of the marginal effects predicted by the multivariate probit model are substantially
different from those predicted by the univariate probit model (see the Appendix Table).
To illustrate any differences, the univariate and the multivariate probit probability are
estimated, with the correlation coefficients restricted to zero in the latter case. These
marginal effects and selected marginal effects for the PROFIT, REVENUE and COST
categories are reported in Table 10 through Table 12, respectively. Comparison of the
unconditional univariate and constrained multivariate probit marginal effects reported in
Table 10 through Table 12 suggest close correspondence in the reported results.
Specifically, no statistically significant marginal effect in the univariate specification is
insignificant in any multivariate probit specification. Also, and not surprisingly as the
cross-equations correlations play no role in either specifications estimation, there is no
change in sign for any marginal effect.
The marginal probabilities chosen for comparison are P(⋅ = 1 R = 1, C = 1) , P(⋅ = 1 R = 1)
and P(⋅ = 1 C = 1) . The conditional probabilities P(⋅ = 1 R = 1, C = 1) are reported in Table
9 and automatically provided by LIMDEP. The conditional probabilities P(⋅ = 1 R = 1)
and P(⋅ = 1 C = 1) are obtained from separate bivariate probit estimations. The intention is
to compare the signs, magnitudes and statistical significance of the estimated marginal
effects on selected conditional and unconditional specifications. In particular, Table 10
reports the marginal effects on selected profit unconditional and conditional probabilities.
The most general conditional probability specification, P(Π = 1 R = 1, C = 1) , estimates
are most similar to those for P(Π = 1 R = 0, C = 0) . Additionally, the attributed lower
profit performance to BLENDED, MIX and LOCATION vanish. Also, the positive
impact of CUSTOMER is reversed. Importantly, the results from the bivariate probit
specifications also appear unreliable. The impacts on REVENUE or COST of the ‘Web
Site’ and ‘Entry Reason’ variables are no longer apparent. Examination of Table 11
(REVENUE) and Table 12 (COST) provide qualitatively similar results. That is, the more
general (trivariate) probit estimations report more significant marginal impacts compared
to the univariate and bivariate specifications. Finally, while the signs for the marginal
impacts of the arguments are similar in the trivariate specifications (the signs change only
four times on in the PROFIT and twice in the REVENUE equations, respectively,
between P(⋅ = 1 R = 1, C = 1) and P(⋅ = 1 R = 0, C = 0) , there is substantial variation in
their magnitudes.
16
Table 10. Selected PROFIT Probability Marginal Effects
Variable
P(Π = 1)
P(Π = 1 R = 0,C = 0)
P(Π = 1 R = 1,C = 1)
P(Π = 1 R = 1)
P(Π = 1 C = 1)
-0.227**
(0.054)
0.007
(0.005)
-0.063*
(0.035)
0.001
(0.045)
-0.232
(0.032)
0.055
(0.038)
-0.127**
(0.004)
-0.016**
(0.001)
-0.009**
(0.002)
0.0003**
(0.0002)
-0.008**
(0.001)
0.024**
(0.001)
0.028
(0.028)
0.027**
(0.007)
0.002
(0.009)
0.065**
(0.011)
0.010
(0.008)
0.030**
(0.006)
-0.148**
(0.061)
-0.002
(0.007)
-0.010
(0.038)
-0.035
(0.052)
0.011
(0.032)
0.018
(0.043)
-0.197**
(0.049)
0.005
(0.005)
-0.068**
(0.031)
-0.005
(0.038)
-0.023
(0.028)
0.044
(0.033)
-0.106**
(0.045)
-0.147**
0.054
-0.914*
(0.052)
-0.078
(0.051)
-0.118**
(0.046)
-0.038**
(0.003)
-0.084**
0.001
-0.060**
(0.001)
-0.023**
(0.002)
-0.040**
(0.001)
0.060*
(0.032)
0.001
0.008
0.042**
(0.014)
0.042**
(0.020)
-0.026**
(0.001)
-0.005
(0.054)
-0.129*
0.071
-0.079
(0.062)
0.002
(0.051)
-0.072
(0.046)
-0.122**
(0.039)
-0.156**
0.054
-0.108**
(0.047)
-0.109**
(0.047)
-0.115**
(0.043)
0.066
(0.052)
-0.009
(0.053)
0.039**
(0.001)
0.014**
(0.002)
0.020**
(0.002)
0.128**
(0.036)
0.057
(0.047)
0.011
(0.056)
0.058
(0.044)
-0.048
(0.044)
0.057
0.037
0.182**
(0.038)
0.047
0.036
0.010
(0.036)
0.039
(0.041)
0.045
(0.036)
0.021**
0.001
0.090**
(0.004)
-0.004**
0.001
0.003**
(0.001)
0.013
(0.005)
0.016**
(0.001)
0.012**
0.005
0.080**
(0.013)
0.019**
0.003
-0.015**
(0.007)
0.003
(0.005)
0.037**
(0.008)
0.042
0.038
0.091**
0.045
0.001
0.037
0.005
(0.035)
0.008
(0.039)
0.053
(0.036)
0.045
0.030
0.165**
0.035
0.049
0.030
0.014
(0.030)
0.028
(0.035)
0.025
(0.030)
Firm
BLENDED
ESTAB
MIX
SMALL
LOCATION
STORES
Industry
BUSINESS
SUPPORT
PUBLIC
PRIMARY
RETAIL
Web site
INITIAL
INTENTION
Entry reason
EFFICIENCY
EXPAND
NEWGOOD
CUSTOMER
SUPPLIER
ANTICIPATE
Note. Standard errors are in parentheses. * indicates significant at 10% level. ** indicates significant at 5%
level.
17
Table 11. Selected REVENUE Probability Marginal Effects
Variable
P(R = 1)
P(R = 1 Π = 0,C = 0)
P(R = 1 Π = 1,C = 1)
P(R = 1 Π = 1)
P(R = 1 C = 1)
-0.171**
(0.054)
0.012**
(0.005)
-0.091**
(0.036)
0.048
(0.045)
-0.048
(0.033)
0.052
(0.039)
-0.014
(0.018)
0.027**
(0.002)
-0.021**
(0.002)
0.106**
(0.002)
0.003
(0.002)
0.024**
(0.002)
-0.225**
(0.068)
-0.156**
(0.036)
-0.228**
(0.068)
-0.278**
(0.066)
-0.127**
(0.037)
-0.031**
(0.006)
-0.001
(0.042)
0.006
(0.005)
-0.035
(0.027)
0.040
(0.038)
-0.026
(0.023)
0.016
(0.031)
-0.053*
(0.031)
0.007**
(0.003)
-0.014
(0.019)
0.039*
(0.021)
-0.016
(0.017)
0.023
(0.019)
-0.156**
(0.046)
-0.101*
(0.059)
-0.063
(0.055)
-0.120**
(0.052)
-0.098**
(0.049)
-0.047**
(0.008)
-0.035**
(0.004)
-0.002
(0.005)
-0.020**
(0.007)
-0.030**
(0.005)
-0.175**
(0.051)
-0.087**
(0.030)
-0.108**
(0.027)
-0.172**
(0.043)
-0.025
(0.017)
-0.067*
(0.039)
0.024
(0.050)
0.017
(0.043)
-0.054
(0.036)
-0.005
(0.032)
-0.017
(0.025)
-0.004
(0.032)
0.021
(0.030)
0.008
(0.029)
-0.020
(0.026)
0.049
(0.052)
-0.026
(0.054)
0.011**
(0.005)
0.064
(0.007)
0.014**
(0.013)
-0.365*
(0.079)
-0.012
(0.033)
-0.017
(0.042)
0.020
(0.027)
0.042
(0.027)
0.040
(0.037)
0.181**
(0.039)
0.064**
(0.037)
0.014
(0.037)
0.050
(0.041)
0.005
(0.036)
0.002**
(0.000)
0.030*
(0.016)
0.005**
(0.001)
-0.013
(0.002)
0.011**
(0.000)
-0.016**
(0.002)
0.101**
(0.048)
0.129*
(0.076)
0.088**
(0.042)
0.109**
(0.038)
0.023
(0.020)
-0.018
(0.019)
-0.006
(0.027)
0.035
(0.032)
0.029
(0.026)
0.003
(0.025)
0.018
(0.027)
-0.027
(0.025)
0.024
(0.019)
0.072**
(0.024)
0.017
(0.019)
-0.004
(0.019)
0.030
(0.022)
0.014
(0.020)
Firm
BLENDED
ESTAB
MIX
SMALL
LOCATION
STORES
Industry
BUSINESS
SUPPORT
PUBLIC
PRIMARY
RETAIL
Web site
INITIAL
INTENTION
Entry reason
EFFICIENCY
EXPAND
NEWGOOD
CUSTOMER
SUPPLIER
ANTICIPATE
Note. Standard errors are in parentheses. * indicates significant at 10% level. ** indicates significant at 5%
level.
18
Table 12. Selected COST Probability Marginal Effects
Variable
P(C = 1)
P(C = 1 Π = 0, R = 0)
P(C = 1 Π = 1, R = 1)
P(C = 1 Π = 1)
P(C = 1 R = 1)
-0.057
(0.046)
-0.002
(0.004)
-0.053**
(0.024)
-0.018
(0.034)
-0.013
(0.023)
-0.002
(0.027)
-0.026**
(0.004)
-0.011**
(0.001)
-0.023**
(0.002)
-0.011**
(0.001)
-0.007**
(0.001)
-0.004**
(0.001)
-0.037**
(0.001)
-0.021**
(0.001)
-0.036**
(0.001)
-0.045**
(0.001)
-0.012**
(0.001)
-0.016**
(0.001)
-0.054*
(0.031)
-0.001
(0.003)
-0.042**
(0.021)
-0.012
(0.028)
-0.011
(0.017)
0.004
(0.021)
-0.019
(0.070)
-0.010
(0.008)
-0.059
(0.047)
-0.071
(0.059)
0.002
(0.040)
-0.021
(0.046)
-0.090**
(0.026)
-0.068**
(0.032)
-0.075**
(0.029)
-0.100**
(0.026)
-0.038
(0.030)
-0.025**
(0.002)
-0.013**
(0.001)
-0.015**
(0.001)
-0.023**
(0.001)
0.003**
(0.002)
-0.030**
(0.001)
-0.011**
(0.000)
-0.019**
(0.000)
-0.026**
(0.000)
0.000
(0.001)
-0.080**
(0.027)
-0.070**
(0.031)
-0.070**
(0.029)
-0.097**
(0.030)
-0.040
(0.025)
-0.124**
(0.060)
-0.099
(0.073)
-0.142*
(0.073)
-0.166**
(0.070)
-0.040
(0.058)
0.012
(0.039)
-0.144**
(0.051)
0.004**
(0.001)
-0.033**
(0.002)
-0.003**
(0.000)
-0.064**
(0.001)
0.014
(0.028)
-0.087**
(0.031)
-0.003
(0.066)
-0.175**
(0.070)
-0.002
(0.026)
0.039
(0.028)
0.030
(0.027)
0.021
(0.026)
-0.108
(0.028)
-0.034
(0.025)
0.011**
(0.002)
0.032**
(0.004)
0.016**
(0.002)
0.011**
(0.001)
0.002**
(0.001)
-0.001
(0.001)
0.002*
(0.001)
0.010**
(0.002)
0.006**
(0.001)
0.008**
(0.000)
-0.007**
(0.001)
-0.013**
(0.000)
0.001
(0.020)
0.044**
(0.020)
0.026
(0.018)
0.015
(0.018)
-0.005
(0.021)
-0.022
(0.019)
-0.033
(0.046)
-0.026
(0.047)
0.017
(0.044)
0.029
(0.045)
-0.043
(0.049)
-0.044
(0.046)
Firm
BLENDED
ESTAB
MIX
SMALL
LOCATION
STORES
Industry
BUSINESS
SUPPORT
PUBLIC
PRIMARY
RETAIL
Web site
INITIAL
INTENTION
Entry reason
EFFICIENCY
EXPAND
NEWGOOD
CUSTOMER
SUPPLIER
ANTICIPATE
Note. Standard errors are in parentheses. * indicates significant at 10% level. ** indicates significant at 5%
level.
19
6. Conclusions
During the past decade there is considerable research examining firm post-entry survival
and performance. These studies primarily focused, presumably due to the data available,
on survival. When performance per se is addressed the indicators of ‘success’ are
typically some measure output, employment, or their combination productivity. A
problem with this approach is that economic theory does not suggest that firms usually
pursue these goals in an optimising sense. Theory suggests that firms in conducting their
operations maximise profit and revenues or, via a dual program, minimise costs.
Accordingly, the modelling approach employed in this study is based on the premise that
firms enter on-line markets with a view to pursuing these goals. In particular, this study
addresses the questions: How do virtual firms differ in their on-line market adoption
patterns? In what type of environments is post on-line market entry performance by
virtual and established firms likely to succeed? What can be said about the relationship
between post-entry performance and the reasons for entry?
The short answer to these questions (in the context of small Australian firms) is that the
reasons for entry matter for performance, and by type of performance measure. In
particular, entry that seeks to reduce firm cost, expand geographic market coverage or
introduce new goods, while naturally increasing costs, is associated with improved
revenue and profit outcomes. Entry that is a response to customer request or supplier
requirement is ambiguous in outcome. Responding to customers will increase revenue but
may reduce profit. Whereas, acceding to suppliers can reduce the probability of enhanced
cost-based productivity performance. Finally, strategic entry can improve profit via costs,
but not revenue. Another study finding is methodologically orientated, viz., the
econometric estimations clearly indicate that restrictive single or multiple equation
models can provide misleading indications of the marginal impact of arguments on both
conditional and unconditional probabilities of firm success factors from entry.
A limitation of the analysis is that only the mapping from the reasons for entry on postentry success is considered. A more thorough analysis would consider the impact on
‘intermediate variables’ during the traverse to these outcomes. In particular, a more
thoughtful analysis might consider potential impacts on employment, prices, and the
source of cost improvement, e.g., whether via advertising, inventory or distribution cost
reductions. The analysis might also have addressed firms’ initial Web site capability,
whether on-line market performance cannibalised B&M store sales, and the empirical
magnitude and pattern of market expansion.
20
Appendix Table 1. Univariate Probit Model Estimates
Variable
Firm
BLENDED
ESTAB
MIX
SMALL
LOCATION
STORES
Industry
BUSINESS
SUPPORT
PUBLIC
PRIMARY
RETAIL
Web site
INITIAL
INTENTION
Entry reason
EFFICIENCY
EXPAND
NEWGOOD
CUSTOMER
SUPPLIER
ANTICIPATE
Coefficient
Profit
Marginal Effect
Revenue
Coefficient Marginal Effect
Coefficient
Cost
Marginal Effect
-0.576
(0.142)
0.018
(0.014)
-0.164
(0.093)
-0.004
(0.115)
-0.060
(0.084)
0.140
(0.097)
-0.227**
(0.054)
0.007
(0.005)
-0.063*
(0.035)
0.001
(0.045)
-0.232
(0.032)
0.055
(0.038)
-0.433
(0.140)
0.030
(0.014)
-0.230
(0.092)
0.122
(0.115)
-0.120
(0.083)
0.130
(0.097)
-0.171**
(0.054)
0.012**
(0.005)
-0.091**
(0.036)
0.048
(0.045)
-0.048
(0.033)
0.052
(0.039)
-0.222
(0.167)
-0.009
(0.017)
-0.233
(0.114)
-0.074
(0.138)
-0.053
(0.100)
0.008
(0.116)
-0.057
(0.046)
-0.002
(0.004)
-0.053**
(0.024)
-0.018
(0.034)
-0.013
(0.023)
-0.002
(0.027)
-0.281
(0.122)
-0.401
(0.158)
-0.242
(0.144)
-0.204
(0.138)
-0.314
(0.128)
-0.106**
(0.045)
-0.147**
0.054
-0.914*
(0.052)
-0.078
(0.051)
-0.118**
(0.046)
-0.401
(0.122)
-0.260
(0.155)
-0.159
(0.142)
-0.308
(0.137)
-0.249
(0.128)
-0.156**
(0.046)
-0.101*
(0.059)
-0.063
(0.055)
-0.120**
(0.052)
-0.098**
(0.049)
-0.436
(0.147)
-0.333
(0.183)
-0.369
(0.170)
-0.518
(0.173)
-0.171
(0.144)
-0.090**
(0.026)
-0.068**
(0.032)
-0.075**
(0.029)
-0.100**
(0.026)
-0.038
(0.030)
0.168
(0.131)
-0.023
(0.136)
0.066
(0.052)
-0.009
(0.053)
0.124
(0.132)
-0.064
(0.136)
0.049
(0.052)
-0.026
(0.054)
0.050
(0.157)
-0.502
(0.155)
0.012
(0.039)
-0.144**
(0.051)
0.147
(0.093)
0.478
(0.103)
0.121
(0.093)
0.025
(0.093)
0.101
(0.104)
0.115
(0.092)
0.057
0.037
0.182**
(0.038)
0.047
0.036
0.010
(0.036)
0.039
(0.041)
0.045
(0.036)
0.101
(0.093)
0.463
(0.101)
0.162
(0.092)
0.035
(0.092)
0.125
(0.103)
0.012
(0.092)
0.040
(0.037)
0.181**
(0.039)
0.064**
(0.037)
0.014
(0.037)
0.050
(0.041)
0.005
(0.036)
-0.026
(0.112)
0.166
(0.123)
0.126
(0.110)
0.087
(0.110)
-0.046
(0.124)
-0.148
(0.110)
-0.002
(0.026)
0.039
(0.028)
0.030
(0.027)
0.021
(0.026)
-0.108
(0.028)
-0.034
(0.025)
Note. Standard errors are in parentheses. * indicates significant at 10% level. ** indicates significant at 5%
level.
21
References
ABS (2002), Small Business in Australia, Catalogue 1321.0, Australian Bureau of
Statistics, Canberra
ABS (2004), Business Use of Information Technology, 2002-03, Catalogue 8129.0,
Australian Bureau of Statistics, Canberra
ABS (2005), Business Use of Information Technology, 2004-05, Catalogue 8129.0,
Australian Bureau of Statistics, Canberra
ABS (2006), Business Use of Information Technology, 2005-06, Catalogue 8129.0,
Australian Bureau of Statistics, Canberra
Audretsch, D. (1995), ‘Innovation, Growth and Survival’, International Journal of
Industrial Organization, 13, 441-57
Battisti, G. and Stoneman, P. (2005), ‘The Intra-Firm Diffusion of New Process
Technologies’, International Journal of Industrial Organisation, 23, 1-22
Cappellari, L. and Jenkins, S. (2003), ‘Multivariate Probit Regression using Simulated
Maximum Likelihood’, Strata Journal, 3, 278-94
Caves, R. (1998), ‘Industrial Organization and New Findings on the Turnover and
Mobility of Firms’, Journal of Economic Literature, 36, 1947-82
Davidson, R. and MacKinnon, J. (1984), ‘Convenient Specification Test for Logit and
Probit’, Journal of Econometrics, 25, 241-62
Dinlersoz, E. and Pereira, P. (2007), ‘On the Diffusion of Electronic Commerce’,
International Journal of Industrial Organization, 25, 541-7
Disney, R., Haskel, J. and Heden, Y. (2003), ‘Entry, Exit and Establishment Survival in
UK Manufacturing’, Journal of Industrial Economics, 51, 91-112
DBCDE (2002), Forging and Managing Online Collaboration: The ITOL Experience,
Department of Broadband, Communications and the Digital Economy, Canberra
Dunt, E. and Harper, I. (2002), ‘E-Commerce and the Australian Economy’, Economic
Record, 78, 327-42
DeYoung, R. (2005), ‘The Performance of Internet-Based Models: Evidence from the
Banking Industry’, Journal f Business, 78, 893-947.
EIU (2007), Country Commerce 2007, Economist Intelligence Unit, New York
Fotopoulos, G. and Louri, H. (2000), ‘Location and Survival of New Entry’, Small
Business Economics, 14, 311-21
Geroski, P. (1995), ‘What Do We Know About Entry?’, International Journal of
Industrialization Organization, 13, 421-40
Greene, W. (2008), Econometric Analysis, 6th edition, Prentice Hall, New Jersey.
Jeon, B.N., Han, K.S. and Lee, M.J. (2006), ‘Determining Factors for the Adoption of EBusiness: The Case of SMEs in Korea’, Applied Economics, 38, 1905-16
Luchetti, R. and Sterlacchini, A. (2004), ‘The Adoption of ICT among SMEs: Evidence
from an Italian Survey’, Small Business Economics, 23, 151-68
Mahmood, T. (2000), ‘Survival of Newly Founded Businesses: A Log-Logistic Model
Approach’, Small Business Economics, 14, 223-37
Nikolaeva, R. (2007), ‘The Dynamic Nature of Survival Determinants in E-Commerce’,
Journal of the Academy of Marketing Science, 35, 560-71
OECD (2004), OECD Information Technology Outlook, Organisation for Economic
Cooperation and Development, Paris
22
Reid, G. and Smith, J. (2000), ‘What Makes a New Business Start-up Successful?’, Small
Business Economics, 14, 165-82
Segarra, A. and Callejón, M. (2002), ‘New Firms’ Survival and Market Turbulence: New
Evidence from Spain’, Review of Industrial Organization, 20, 1-14
Steinfield, C., Adelaar, T. and Lai, Y. (2002), ‘Integrating Brick and Mortar Locations
with E-Commerce: Understanding Synergy Opportunities’, Proceedings of the
Hawai’i International Conference on System Sciences, January 7-10, Big Island,
Hawaii
Train, K. (2003), Discrete Choice Models with Simulation, Cambridge University Press,
Cambridge
Vilaseca-Requena, J., Torrent-Sellens, J., Meseguer-Artola, A. and Rodríguez-Ardura, I.
(2007), ‘An Integrated Model of the Adoption and Extent of E-Commerce in Firms’,
International Advances in Economic Research, 13, 222-41
Zhu, K., Kraemer, K. and Xu, S. (2003) ‘Electronic Business Adoption by European
Firms: A Cross-Country Assessment of the Facilitators and Inhibitors, European
Journal of Information Systems, 12, 251-68
23