THE DOT COM EFFECT REVISITED: AN EVENT STUDY OF THE
VALUE PROPOSITIONS OF EC INITIATIVES ON THE MARKET
VALUE OF FIRMS
Mani R. Subramani
Assistant Professor in Information and Decision Sciences
msubramani@csom.umn.edu
Eric A. Walden
Doctoral Candidate in Information and Decision Sciences
ewalden@csom.umn.edu
Carlson School of Management
University of Minnesota
Minneapolis, MN 55455
Last Revised: October 2, 2001
Abstract: The value drivers of electronic commerce are not well understood. Based on
pioneering work in the field of information technology value, we propose and empirically
test a general theory of electronic commerce value. Specifically, we propose that the
important factor to consider is the creation of intangible, electronic commerce technology
complementing assets. We use the event study methodology to test the hypothesis that
organizations that are most easily able to create and deploy these intangible assets will
derive the greatest return for investors. Not only do we use the short run event study
common to the information systems literature, but we also apply new techniques for long
run event study analysis hitherto unused in information systems research. Our results
show support for the theory of the primacy of intangible assets in determining returns to
electronic commerce initiatives. We further supplement our analysis by evaluating
returns through the lens of electronic commerce business models. The results of this
analysis suggest that consideration of intangible assets explains return to electronic
commerce initiatives better than business model considerations.
Keywords: Intangiable assets, electronic commerce, event study, financial
methodologies, business value of information technology
Acknowledgements: The authors would like to thank Gordon Davis, Tom Holmes, and George John for
their comments, Kelly Slaughter for his coding expertise, and the participants of the University of
Minnesota IDSC workshop. All omissions, errors, and blunders remain the sole responsibility of the
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"Bottom Line: Nobody Really Knows" {|Neuborne, 2000 #217, page 98|}
INTRODUCTION
The turn of the century bore witness to an entirely new type of information
technology (IT) enabled way of doing business—electronic commerce (EC). The
Internet, the purview of a few research scientists and enterprising college students,
became accessible to the public at large. Hardware, such as inexpensive personal
computers and fast modems, combined with software, such as Mosaic and Netscape, to
allow worldwide access to the existing network infrastructure. Joined with location
independent transaction systems, such as credit cards, and established parcel delivery
systems EC was an inevitable and logical extension to traditional commerce. Now with
the number of internet users approaching a billion <ref> and the number of web pages
well beyond that number <refs>, EC is clearly an established way of doing business.
However, the value propositions of EC are far from understood. Considerable upfront IT investments are required to be a viable player in the current EC environment.
The Gartner Group estimates that firms pursuing EC initiaitives spend $1 million in the
first five months, and $20 million “for a place in cyberspace that sets them apart from the
competition” {|Diederich, 1999 #230|}. Moreover, these costs are projected to increase
considerably over time {|Satterthwaite, 1999 #231|}. Enough dotcom firms failed in
2001 to prompt Fortune Magazine to add a feature entitled Deathwatch1, which puts the
number of firm failures in through September 2001 at 303 {|Schlosser, 2001 #232|}. The
quintessential Internet firm, Amazon, has lost billions and has not yet shown a profit,
while CDNow and Pets.com have both folded. However, other foremost Internet firms
like Yahoo, eBay and AOL have consistently posted profits.
We propose that the primary driver of EC value is not the considerable investments in
IT or the business model used, but the ability of the organization to create and deploy
intangiable assets to complement the IT. We investigate this using capital market
reactions to these different EC initiatives as a measure of business value created. Not
only do we examine these reactions through the common short run event study {|Dos
Santos, 1992 #176; Im, 2001 #184; Subramani, 2001 #185; Chatterjee, 2001 #209|}, but
we also add a long run event study analysis as is becoming accepted in finance research
{|MacKinlay, 1997 #175; Cowan, 2001 #187; Fama, 1998 #194|}. In combining both a
short run and a long run analysis, we hope to shed additional light on the important issue
of how EC creates value.
The paper is organized as follows. In the next section, we review the literature on EC
value and the case for using capital market reactions to measure it. Then we discuss the
theoretical foundations underlying number important EC initiative characteristics. We
then describe the event study methodology for both short run and long run event studies.
Following that we discuss the data and collection methodology, after which, we present
our findings. We close by discussing the findings, recognizing opportunities for future
research and drawing conclusions.
1
See also http://www.downside.com/deathwatch.html.
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LITERATURE REVIEW
Measuring value creation through IT has historically been a difficult task. While the
work of a number of researchers has established that IT does create business value,
{|Brynjolfsson, 1998 #154|}, actual quantification of IT benefits remain difficult. Prior
approaches to measure returns from IT and complementary investments have used a
variety of accounting data, such as return on assets {|Barua, 1995 #172|}, cost savings
{|Mukhopadhyay, 1995 #173|} or return on investment {|Hitt, 1996 #174|} to understand
the value of these investments. While they have certainly expanded our understanding
these accounting-based measures present special difficulties in the measure of EC
business value. First, they tend to be insensitive to the strategic nature of IT investments
that often create benefits to firms in the form of flexibility and expanded operating
choices in future periods {|Benaroch, 1999 #183|}. Moreover, as these benefits often
accrue over time periods as long as eight years {|Brynjolfsson, 1998 #154|}, for which
EC data is not yet available.
The use of forward-looking measures is suggested as one way to overcome these
deficiencies {|Brynjolfsson, 1997 #215; Bharadwaj, 1999 #182|}. Consistent with this
view, we examine the impact of individual firms' EC initiatives on the expected stream of
future benefits by focusing on capital markets’ reactions to EC initiatives. The abnormal
returns generated by an EC initiative are the consensus estimate of a large number of
informed investors of the expected stream of future benefit streams associated with the
initiative. In an efficient capital market, investors are assumed to collectively recognize
future benefit streams accruing from initiatives and reflect them in the stock price of the
firm. If EC activities of firms are expected to enhance future cash flows, the capital
market would respond favorably to unanticipated EC announcements, resulting in an
increase stock price. The event study methodology {|Fama, 1969 #162; Brown, 1985
#160|} is designed specifically to take advantage of this aspect of financial markets,
making it a very useful tool for management researchers to examine consensus estimates
of the future benefits streams attributable to organizational initiatives {|McWilliams,
1997 #159|}. This methodology is well accepted and has been used to study the effect on
the economic value of firm actions such as IT investments {|Dos Santos, 1992 #176; Im,
2001 #184|}, appointment of new chief information officers (CIOs) {|Chatterjee, 2001
#209|} corporate acquisitions {|Chatterjee, 1986 #177|}. More importantly, for our
purposes the event study has been used to examine EC initiatives in several fields such as
information systems (IS) {|Subramani, 2001 #185|}, economics {|Chen, 2001 #208|} and
finance {|Cooper, 2001 #216|}.
To the existent literature on EC value, we add a number of innovations. First and
foremost, we develop theory to explain the value drivers of EC. Specifically we propose
and test the idea that EC value is driven by intangiable, It complementing assets. We test
this by examining the asset stucture inharent in several stratgic choices including
customer type, firm type, production type, governace structure and innovativeness.
Second, we examine EC initiatives based on the atomic business model used. Business
models are an aspect of EC that has garnered a great deal of theoretical attention {|Aksen,
2000 #206; Timmers, 1998 #207; Weill, 2001 #205|}, but has had very little empirical
examination. What empirical analysis has been done has been limited to descriptive case
studies {|Weill, 2001 #205|}, but to the best of our knowledge no research has yet to
measure the value derived from different EC business models.
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We also apply a methodology new to the IS literature—the long run event study—in
addition to the well known short run event study. While the event study methodology,
and this paper, assume efficient capital markets, we acknowledge that markets can be
wrong. Market efficiency means that all of the information available about a firm is
quickly incorporated into the price of the firm’s stock. Hence, the traditional short run
event study measures the abnormal change in a stock price over a very short window—
typically two or three days. However, in the initial phases of a new commerce method
markets may not have access to high-quality information about that method because it
has not yet been generated. In such a situation, the short run evaluations might be
lacking. However, in the long run as investors gain more information about how the firm
pursues the initiative, how customers react to the initiative, how competitors respond to
the initiative and how overall perceptions of EC change the may very well revise their
initial opinions. Given the new information gained over the long run investors may
rebalance their portfolios thereby changing the level of abnormal returns. To the best of
our knowledge a long run event study methodology has not yet been applied in the IS
literature. By doing so, we not only offer greater insight into our own domain of
question, but add (or perhaps subtract) validity to other short run event studies in the IS
literature.
THEORY DEVELOPMENT
While the tangible costs of IT can be quite large—Amazon.com’s 10-K lists an eye
popping $187,345,000 in computer equipment and software—they are only the “tip of the
iceberg” {|Brynjolfsson, 2000 #218; Brynjolfsson, 1997 #215|}. Research carried out by
Brynjolfsson and others suggest that to make computing technology productive requires
sweeping organizational change {|Brynjolfsson, 2000 #218; Brynjolfsson, 1997 #215;
Bresnahan, 1998 #155|}. The true gains in productivity stem not from the IT itself, but
form the considerable investments in intangible, complementary assets such as new
business processes, better information sharing and new distribution channels. The ratio
of intangiable assets to IT assets has been estimated to be as high as 19:1 {|Brynjolfsson,
1997 #215; Brynjolfsson, 2000 #218|}, which means that Amazon.com may have over
$3.5 billion worth of intagiable assets complementing its IT.
David {|David, 1990 #219|} explains how this intangible, complementary investment
combines with technology to produce value in a historical examination of electrical
power. Steam powered shops were organized with a large boiler powering a steam
turbine at one end. The turbine turned a large shaft which ran across the shop floor
providing, through a system of gears and smaller shafts, motive power to equipment.
Thus, the equipment had to be arranged according to power needs, with the machines
requiring more power positioned closer to the shaft. Initially shops replaced the steam
turbine with an electrically powered turbine, producing some minor cost savings because
electrical plants were more efficient at generating power than individual boilers.
However, the more important productivity gains came when shops were restructured to
take advantage of the characteristics of electricity. Rather than using a central shaft
electricity could be brought throughout the shop floor providing equal power at any point.
This allowed firms to organize machines by process order rather than power needs.
A similar illustration can be drawn using EC technologies. One of the first major
groups of commercial users of the internet were universities. Originally the application
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procedure involved the student sending a letter requesting application materials, the
admissions department then mailed the application materials, which the student
completed and mailed back to the university. With the advent of the internet, many
admissions offices would accept email requests for materials, saving one mailing.
However, the same technology would pay off to a much greater degree if the whole
process were streamlined so that applicants filled out their application online and the
computer system automatically distributed it to the appropriate persons2. This would
require significant organizational change, redefining (or maybe eliminating) the role of
the admissions department. It is likely that the organizational restructuring costs would
be much greater than the IT costs in theis scenerio.
Our central argument is firms that are more able to make the investments necessary to
develop intangible, complementary assets will be more successful. We start with the
hypothesis that EC initiatives, in general, generate positive abnormal returns. EC
initiatives undertaken by firms reflect the active engagement of firms to build resources
and capabilities for the new medium {|Peteraf, 1993 #104|}. These initiaitves are
expected to position the firms advantageously to exploit opportunities created by the
growth in electronic commerce, thus creating benefits to the firm in future periods.
Consistent with the original spirit of the event study methodology {|Fama, 1969 #162|},
announcements of EC initiatives convey information to markets of the willingness of an
innovative, forward-looking, profit-oriented management team to make the investments
in both IT and the intangible, complementary assets necessary to address growing online
markets.
These arguments suggest that firms announcing EC initiatives are likely to realize
significant strategic and operational advantages in the future. If so, investors should react
positively to EC announcements, creating a positive abnormal stock market return - a
risk-adjusted return in excess of the average stock market return - around the date of the
EC announcement by firms. This leads to the hypothesis that EC initiatives are associated
with enhanced benefits streams in the future and consequently enhanced market
valuation. We describe our overall hypothesis H1.
H1: The abnormal returns attributable to announcements of EC initiatives
are positive
FIRM TYPE AND MARKET VALUE
Firms undertaking EC initiatives fall into two basic categories. The first category is
the pure play net firm, like Amazon or eBay. These firms maintain no physical presence
in the traditional sense operating in space rather than place {|Weill, 2001 #205|}. The
other category we term click-and-mortar, although the popular vernacular seems to now
be brick-and-click. These are traditional types of firms with physical presence that also
operate in a non-physical EC channel.
Those firms which are best able to make the investments in organizational changes
needed to complement the EC focused IT should generate significant profits in the future.
The goal of a firm pursuing an EC initiative is to create intangible assets to complement
IT. There are three basic starting points for the creation of these assets. One is that a
2
The University of Texas at Austin has made a move in this direction, having an online application form.
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firm may start with no intangible, complementary assets. By virtue of the fact that EC is
a new phenomena, all net firms start from this point and have to build all of their assets
from the ground up.
Another staring point is that a firm has intangible assets in place that are actually
detrimental to the creation of EC complementing, intangible assets. The resources
created by firms to compete in conventional markets, in some cases, may be ill suited or
even constraining in changing environments, a phenomenon described as the incumbents'
curse {|Chandy, 2000 #233|} or the late mover advantage {|Shankar, 1998 #234|}. That
certain components of the resource stock of the firm developed in one environment may
turn into serious limitations in another parallels the observation that core rigidities often
have their roots in core competencies {|Leonard-Barton, 1992 #235|}. There has been a
variety of literature which has suggested that the internet changes everything, and hence
completely new forms of organization must be developed to address EC {|Downes, 1998
#220|}. Net firms have significant advantages that make them particularly suited to the
current EC environment. These environments are characterized by considerable volatility
and are described as a parallel universe requiring radically different organizational
strategies and managerial mindsets. Net firms tend to be technology-driven and have
significant capabilities related to Internet technologies. Evidence suggests that they are
characterized by entrepreneurial cultures and have the ability make rapid changes to their
strategies to leverage and align with changes to the fluid technological and market
environments {|Yoffie, 1999 #88|}.
The final possibility is that a firm starts with some intangible assets that are already
complementary to EC technologies. These firms already have assets in place and thus
have less cost to complete the development of the proper complementary assets. We
believe that click-and-mortar firms fit this description because they already have
significnat IT complementing assets in place. Trust and reputation are two such
intangible assets, which are certainly complementary with EC, usually possesed in
greater degree by click-and-mortar firms. Moreover, click-and-mortar firms are not
entirely ignorant of EC technologies. These firms have had MIS department and CIOs
for decades. Many firms transformed their processes through reengineering in the
1990’s, and many click-and-mortar firms are exemplars of effective IT use. Not only is it
a mistake to assume that such firms start with negative EC complementing assets, but it
should be recognized that they actually have many such assets already in place.
Furthermore, we should consider the likely possibility that those firms making the
transition from brick-and-mortar were exactly the firms that already had a significant
advantage in intangible, IT complementary assets. That is to say that the firms who were
truly at a disadvantage for building those complementary assets choose not to move into
the internet arena. Merrill Lynch is a good example of a firm that was slow to move into
the internet arena because of its disadvantage in being able to build the intangible, IT
complementary assets needed. Weill and Vitale give a good example of this in their
introductory chapter {|Weill, 2001 #205|}.
Another tremendous intangiable asset possessed by click-and-mortar firms is
reasonable cost of capital. Click-and-mortar firms have the collateral, reputation and
cash flow to secure financing at reasonable rates. The ability to secure capital is
especially important in the EC context where all storefronts are global. There are
tremendous costs of computing capital and software, and even greater costs of building
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the complementary, intangible assets to make the computers and software productive.
Net firms, in contrast, are often financed by venture capital which may claim as much as
50% of the firm in exchange for capital. Net initial public offerings (IPOs) were
notorious for the amount left on the table. For example, theglobe.com raise $28 million
on its IPO price of $4.50 per share, but the price of the stock on the close first day of
trading was $31.75, making the value of the IPO shares $197 million {|Tully, 1999
#222|}. Thus, to raise $28 million, theglobe.com had to sell almost $200 million worth of
equity. And this trend was widespread during the timeframe of data, no less than $62
billion was left on the table in 1999 and 2000 {|Tully, 2001 #223|}. Clearly if a firm like
Ford or Coca-Cola wanted to raise $28 million, they would have to sell only $28 million
in equity. Clearly, this is a tremendous advantage of click-and-mortar firms over net
firms.
One argument in favor of net firms is that, by virtue of having no complementary,
intangible assets they can more easily implement the changes necessary to develop such
assets. While that is certainly true, we argue that the set of firms that choose to pursue
brick-and-mortar operations in 1999 was a group that had almost the right structures in
place and a head start in building intangible, complementary assets. Thus, while the net
firms had an easy road to travel, it was a long road. The brick-and-mortar firms had a
harder road to travel, but it is much shorter, and it was not that much harder. Because,
brick-and-mortar firms have a significant head start in building IT complementary,
intangible assets, we conjecture that investors will recognize this and reward them with
abnormal returns. We explore the complex issue by examining the two subgroups in the
data.
H2: The abnormal returns attributable to EC announcements of
conventional firms are different from the abnormal returns attributable to
EC announcements of net firms.
CUSTOMER TYPE
An important distinction in the popular press and the literature is between different
customer types, specifically the differences between business-to-business (B2B) and
business-to-consumer (B2C) EC {|Chen, 2001 #208; Subramani, 2001 #185; Kauffman,
2001 #72|}. The tantalizing promise of B2C EC derives from the possibility of the large
and rapidly growing population of web users being a market for goods and services. The
concept that customers would order goods online to be delivered directly to their doors,
bypassing traditional intermediaries such as retail outlets is epitomized by highly
publicized firms such as Amazon.com. B2B on the other hand, taps a market estimated
in to be worth trillions of dollars {|Junnarkar, 1999 #225|} and derives from a small
number of firms making purchases on a massive scale. Following the theory laid out
above, we conjecture that the firms which are able to develop IT complementary,
intangible assets at the lowest cost will generate the most value.
Firms have been using EC technologies to facilitate transactions in the form of
electronic data interchange (EDI) for several decades prior to the advent of consumer
transactions via the internet {|Swatman, 1992 #92; Emmelhainz, 1993 #90; Riggins, 1999
#71|}. With this long timeframe of both theory and practice of EC for B2B transactions
comes a great deal of knowledge about what sorts of intangible assets are most
complementary to the EC technologies and how to most efficiently implement the
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changes needed to build those assets. Thus, even for firms new to the B2B marketplace
the know-how is available and the asset building should be relatively straightforward.
B2C, in contrast, is an entirely new way of using EC technologies. Even firms with
the commitment to building the necessary complementary assets have no easy access to
the know-how about what those assets might be or how to build them. Thus, B2C firms
face a relatively more difficult task of first understanding what must be done, then doing
it. Furthermore, the B2C marketplace is characterized by masses of arms-length
transactions making it very difficult to determine how customers perceive the firm. This
contrasts with B2B marketplaces, where the smaller number of more important customers
leads to closer relationships.
One very large area where B2C is lacking is in transaction processing. B2C is
primarily driven by credit card transactions, which charge a considerable clearing fee3.
B2B firms have adopted a variety of lest costly transaction clearing processes. Shipping
costs are another area in which the B2C players lack cost efficient complementary assets.
Trust is another intangible asset, which is lacking in B2C markets, though organizations
such as eTrust (www.etrust.com) are helping to address these issues. Simply stated for
small transactions the costs to the customer of resolving improper or defective orders are
often greater than the value of the transaction. With large B2B transactions the relative
cost of legal or other action is much smaller.
In sum, the infrastructure, processes and organizational forms constituting the
intangible, IT complementing assets are much less developed for the B2C marketplace
than for the B2B marketplace. To inform the debate on the payoffs to firms from B2B
and B2C EC, we propose to explore the relative benefits to firms from such initiatives
with the following hypothesis:
H3: The abnormal returns attributable to B2B EC announcements are
different from the abnormal returns attributable to B2C EC
announcements.
TYPE OF PRODUCT AND MARKET VALUE
Several authors make a distinction between products which are digital and those that
are tangible {|Negroponte, 1995 #196; Shapiro, 1999 #197; Subramani, 2001 #185|}.
Tangible products are those such as books, toys and automobiles which are composed of
atoms. Digital products on the other hand are those composed of bits {|Negroponte, 1995
#196|}. One of the defining characteristics of a digital product is that is has virtually zero
marginal cost of production{|Shapiro, 1999 #197|}. While it is not immediately obvious
what effect marginal cost should have on business value, it does seem reasonable to argue
that products composed of bits would naturally lend themselves to EC {|Subramani, 2001
#185|}. However, this viewpoint is based solely on a consideration of the IT assets of an
EC firm. Considering the intangible, complementary assets this may not be the case.
The mass distribution of digital products has only been possible for a few years, and
for the most part has been limited to distribution on inexpensive media such as disk or
CD ROM. EC technologies allow for rapid, media free distribution. However, because
of the newness of this capability, there is very little supporting infrastructure for selling
3
Typically Visa and Master Card charge merchants 2% of the transaction, plus a batch processing fee. The
minimum charge is $0.25, which makes small transactions cost prohibitive.
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digital products. This would include legal rules, deterrent technologies and transaction
systems.
As the case of Napster demonstrates, there is very little legal president for addressing
pure digital distribution. Certainly, we must consider the ruling against Napster as an
intangible, IT complementing asset of firms like Sony or BMG. In general, enforcement
efforts have focused on individuals selling bootlegged products or firms violating copy
write and licensing, but have ignored free sharing of digital products {|Gopal, 2002
#226|}. Thus, the legal environment is not conducive to making profit from selling
digital products via the internet4.
Further, firms have been reluctant to install preventive controls such as passwords and
encryption on digital products because of the trouble it causes legitimate customers
{|Gopal, 2002 #226|}. Also, the actual tools and standards for such preventive controls
are not well established. Real Juke Box (www.real.com) offers individual users the
choice of encrypting the music that they record, so they may only be used by the original
user. However, without an agreed upon encryption standard this makes such encrypted
music unusable by other legitimate players on the users machine.
As we touched upon in the B2B case, the transaction systems available online are
effectively limited to credit cards—though some firms like PayPal (www.paypal.com) are
attempting to address this problem. If the marginal cost of digital products is close to
zero, then in competition the price of digital products should be close to zero {|Hurley,
1999 #227; Shapiro, 1999 #197|}. However, credit cards are very inefficient for charges
close to zero. This helps explain why so many digital products are free. A perfect
example is games.com, which offers a number of arcade games like Asteroids for
interactive play. Millions of people have demonstrated a willingness to pay a quarter to
play Asteroids, but games.com can not charge a quarter, because the minimum credit card
charge is a quarter and there is a batch processing fee. Charging a quarter would actually
cost games.com money and thus, they provide the games for free.
In sum, while the EC technologies for distribution of digital products are easily
accessible, the intangible, complementary assets are very costly to develop because of the
current state of the EC environment. To begin to address these issues, we hypothesize
that:
H4: The abnormal returns attributable to EC announcements involving
tangible goods are different from the abnormal returns attributable to EC
announcements involving digital goods.
GOVERNANCE STRUCTURE
EC initiatives my be pursued unilaterally or in tandem with another firm. We define
three different types of potential alliance partners{|Chen, 2001 #208|}—traditional,
computer and pure play internet. Following the theory developed above, we conjecture
that shareholder wealth effects will accrue to the governance structures which allow for
the least costly building of intangible, IT complementing assets. In general, the
4
The reader may argue that one of the most successful companies of our time, Microsoft, has made billions
of dollars from the sale of digital products. We would point out that such products are not sold via the
internet and are almost always bundled with some sort of hardware when sold to individuals. Only where
license enforcement is relatively easy—in sales to businesses—does Microsoft sell products in free form.
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coordination required to develop assets between multiple firms would be a barrier to the
creation of those assets. To the degree that those intangible assets were specific or
proprietary we would expect to find a number of well document difficulties arising from
joint asset development {|Williamson, 1985 #121|}. Furthermore, joint ownership can
lead to a number of incentive difficulties that make it difficult to develop the optimal
level of intangible assets {|Grossman, 1986 #13; Hart, 1988 #5; Bakos, 1993 #3|}. There
is some preliminary empirical evidence to support the idea that having greater numbers of
partners reduces the shareholder return to EC initiatives {|Subramani, 2000 #186|}.To
begin to address this question, we hypothesize that:
H5: The abnormal returns attributable to EC initiatives undertaken
unilaterally are different from the abnormal returns attributable to EC
initiatives undertaken with alliance partners.
INNOVATIVENESS OF INITIATIVE
One factor that has repeatedly been demonstrated to generate positive abnormal
returns in IT event studies is the level of innovation of the announcement {|Chatterjee,
2001 #209; Dos Santos, 1992 #176; Im, 2001 #184|}. Initiatives can be either
executional/extensional or transformational. Executional/extensional initiatives are those
in which the firm is streamlining or augmenting an existing process or product.
Transformational initiatives are those in which the firm is developing a new business
process or new product type. In terms of intangible, IT complementary assets this is a
complex issue worthy of its own paper. We will touch briefly on an argument for
applying the theory of complementary assets.
It would seem that executional/extensional initiatives would be better able to leverage
the assets already in place. However, the literature has consistently found that
transformational initiatives generate significantly greater returns than
executional/extensional initiatives {|Chatterjee, 2001 #209; Dos Santos, 1992 #176; Im,
2001 #184|}. This may be the case because transformational initiatives move the firm
into an entirely new space without excluding the old space. Furthermore, if the marginal
returns to complementary asset building investments are diminishing, then for the same
level of investment the firm can build greater complementary assets in the new space than
in the old space where some assets are already in place. Roughly speaking, a firm is face
with the choice of working very hard to add a small amount of complementary assets to
its existing IT, or working very little to add a great deal of complementary assets to an
entirely different set of IT. This argument depends on the nature of the returns to asset
building investment, which is an empirical question beyond the scope of this work.
However, such a conjecture does help explain the stylized facts of prior event studies in
the IT literature. To begin to address this question, we hypothesize that:
H6: The abnormal returns attributable to transformational EC initiatives
are different from the abnormal returns attributable to
executional/executional EC initiatives.
BUSINESS MODELS
Another popular way of looking at EC initiative is through the lens of the business
model {|Aksen, 2000 #206; Timmers, 1998 #207; Weill, 2001 #205|}. While there is no
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universally agreed upon definition, a business model describes how a firm does its
business. There are a variety of ways to separate out different business models, but we
choose to follow Weill and Vitale’s framework {|Weill, 2001 #205|}. This is a
particularly appealing framework because it focuses on atomic business models of EC
firms. An initiative is composed of some combination of atomic business models. The
eight business models examined are described in Table 1.
We present the business model analysis as an exploratory, alternative or complement
to our other examination. Thus, we propose that:
H7: The abnormal returns attributable to different EC business models
are different from one another.
OWNERSHIP
In addition to business models Weill and Vitale propose several different classes of
ownership, which will give rise to profits. Namely they propose that a firm may own the
relationship with the customer, the customer data and the transaction. The authors
propose that different business models will, in general, have ownership of different sets
of these three items and that the ownership of these items will lead to profits. Without
specifying the relative magnitudes the authors posit that ownership of more aspects will
lead to greater profits. We examine this claim through the hypothesis that:
H8: The abnormal returns attributable to each ownership type is positive.
METHODOLOGY
Event study methodology draws on the efficient market hypothesis that capital
markets are efficient mechanisms to process information available on firms {|Fama, 1969
#162|}. The logic underlying the hypothesis is the belief that investors in capital markets
process publicly available information on firm activities to assess the impact of firm
activities, not just on current performance but also the performance of the firm in future
periods. When additional information becomes publicly available on firm activities that
might affect a firm’s present and future earnings, the stock price changes relatively
rapidly to reflect the current assessment of the value of the firm. The strength of the
method lies in the fact that it captures the overall assessment by a large number of
investors of the discounted value of current and future firm performance attributable to
individual events which is reflected in the stock price and the market value of the firm
(see {|McWilliams, 1997 #159|} for a detailed review).
Though financial markets may be efficient information processors, even an efficient
processor is limited by the availability of information. EC is a new phenomena, and even
now it is not clear exactly what form successful EC companies will take. In evaluating
the value of EC events the market is limited by the fact that there is little information
available for decision making. Thus, short run event studies with windows of a one to
three days may not result in accurate measures of capital markets true valuations for EC
initiatives. Moreover, all of the information about an event is not likely to be available in
one announcement. Over time, more information about the success or failure will
become available to the market, and expectations about the event will be adjusted. For
example, a firm may announce the release of a new EC technology, like Amazon.com’s
one click shopping patent. However, the entire value cannot be known until customers
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adopt (or fail to adopt) the technology, as other firms develop competing technologies,
and as courts choose how to enforce intellectual property rights involved in the
technology. As information about these factors becomes available, investors will revise
their expectations, and reward firms with good events with additional abnormal return,
while punishing those with bad events with negative abnormal returns.
For the EC context in particular, markets will also learn meta-information about the
probably success of an industry, over time. Thus, investors are likely to revise estimates
of EC value across the board over time, as it becomes more clear what the likely
industrial profits will be. This effect has been documented by several IS researchers, who
have found that the market reaction to identical events has changed over time {|Im, 2001
#184|}. Anecdotally, we would expect that the market’s expectation of EC has declined
over time, as it is widely believed that the market was in a bubble. Thus, we might
expect to find negative abnormal returns for all EC firms. However, these returns should
be significantly less negative for firms having good events.
Recently, the methodology has enjoyed great success in the IS literature. One of the
earliest works examined the impacts of innovative verses incremental investments in IT,
and finds that financial markets react positively only to innovative announcements {|Dos
Santos, 1992 #176|}. This finding has been expanded using the original data and over
100 new data points {|Im, 2001 #184|}. The new data suggest that not only do markets
react, by they react more in the later part of the data set, and they react more for smaller
firms. IS researchers have also used event studies to understand the impacts of EC
announcements on firm value. Subramani and Walden have found that capital markets
react positively to EC announcements in general, but that the reactions are greater for
announcements focused on physical products than on those focuses on digital products,
and greater for those announcements focused on B2C initiative than on B2B initiatives
{|Subramani, 2001 #185|}. The authors further explore the value drivers of the B2B
announcements and find that market reaction is only significant for internet firms
undertaking B2B initiatives {|Subramani, 2000 #186|}. While the event study
methodology has been widely used by IS researchers none have yet taken advantage of
the relatively new innovations in financial econometrics to look at the long run impacts of
events. <<NEED TO ADD OTHER STUDY FROM MISQ>>
The event study methodology provides management researchers a powerful technique
to explore the strength of the link between managerial actions and the creation of value
for the firm {|McWilliams, 1997 #159|}. This methodology is well accepted and has been
used in a variety of management research to study the effect on the economic value of
firm actions such as IT investments {|Dos Santos, 1992 #176|}, corporate acquisitions
{|Chatterjee, 1986 #177|}, CEO succession {|Davidson, 1993 #178|}, joint venture
formations {|Koh, 1991 #181|}, celebrity endorsements {|Agrawal, 1995 #179|} and new
product introductions {|Chaney, 1991 #180|}.
DATA
Following Subramani and Walden, we define the event as a public announcement of a
firm’s EC initiative in the media. We collected the data from a full text search of
company announcements related to EC in the period between January 1, 1999 to
December 31, 1999, using two leading news sources: PR Newswire, and Business Wire.
Based on an examination of several candidate announcements, we used the online search
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features of Lexis/Nexis to search for announcements containing the words launch or
announce within the same sentence as the words online or commerce5, and .com. The
search yielded 2170 potential announcements.
CODING
Coding involved a full text examination of the announcements. To consider an
announcement as an event it had to be a major initiative focused on new EC product
offerings or new EC processes implementation. Thus, we excluded several categories of
announcements from consideration. We excluded announcements of customer
acquisition as not being a major new initiative. Rather we feel, customer acquisition,
while important, is a day-to-day business activity, that all firms do on a regular basis. We
code such an announcement as an event if the method of customer acquisition was a new
EC initiative (e.g. firm xyz signs up first online customer). We also code the
announcement as an event for the customer if the product was used for a new EC
initiative (e.g. firm xyz acquires firm abc as a new customer for xyz’s online procurement
auctions). Another excluded category is minor promotions. EC firms often announce
give-aways or other devices to attract customers. These sorts of promotions are not
coded. We also do not code earnings announcements as events, though firms frequently
issue a press release every quarter detailing the company financials. We do not code
website redesign, unless the redesign is part of some larger initiative. Furthermore, we
do not code mergers and acquisitions as events. Though these may be important EC
initiatives, the value of EC is hopelessly entangled with the value of the merger process.
Out of the 2170 potential announcements 881 were coded as announcements of EC
initiatives, while the others were earnings announcements, management change
announcements, merger announcements, announcements of promotions, or
announcements of things other than actual EC initiatives. After removing those firms
with not enough data for analysis the total came to 616. Following Subramani and
Walden {|Subramani, 2001 #185|} we removed firms with an average share price of less
than one dollar (14 firms) and firms with an average trading volume of less than 50,000
shares per day (101 firms), leaving 501 initiatives for examination.
FIRM TYPE
The first coding task involves separating firms into internet-only businesses and clickand-mortar businesses. We follow we followed the classification system devised for the
Dow Jones Internet Index that considers net firms to be those deriving more than 50% of
revenue from Internet activities {|Subramani, 2001 #185|}. Of the 501 announcements
145 were coded as involving internet-only firms and 356 were coded as involving clickand-mortar firms.
The coding of announcements by type was based on detailed analysis of the full text of
the announcement. The announcements were coded as falling into three categories.
5
We observed considerable variation in the wording of announcements related to electronic commerce.
Using the word “commerce” captured the most common variants: EC, e commerce, and electronic
commerce.
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TYPE OF VALUE ADDING ACTIVITY
Other than firm type, which is a characteristic of a firm, the rest of our hypothesis
revolve around the nature of the activity undertaken by a firm. Thus, the unit of analysis
is the initiative. This allows us to draw general conclusions that can be applied to firms
regardless of their other characteristics. The focus on initiatives requires us to look only
at the characteristics of the initiative independent of the characteristics of the firm. To
this end we define the EC value adding activity. This allows us to focus on the EC part
of the announcement of a firm that has a variety of value adding activities. For example,
Yahoo sells advertising to industrial clients. These agreements are sold by a sales force
that negotiates with clients, in the same way vendors in souks, bazaars, and flea markets
have been doing it for thousands of years. At the same time Yahoo provides search and
content aggregation value adding services to end consumers in an EC mode. Thus, the
initiatives undertaken by Yahoo that will be of interest to us, are the ones focusing on the
EC part of the firm. We would not be interested in announcements of the acquisition by
Yahoo of a new industrial client (unless that acquisition was done electronically). We
will be interested in two questions about the EC value adding activity detailed in an
announcement—for whom does the initiative add value and how is the value produced.
We turn now to the latter issue.
PRODUCTION TYPE
One of the main arguments about EC, and the new economy in general, concerns the
means of production of products. Many arguments are advanced that suggests that
producing digital products has zero marginal cost, which is a large departure from
traditional economic analysis {|Bakos, 1991 #100; Choi, 1997 #200; Kauffman, 2001
#72; Shapiro, 1999 #197|}. We use this ideal of digital production being zero marginal
cost production, for the coding of announcements as digital or tangible.
Specifically, an announcement was coded as being digital if the amount of EC value
added could be increased significantly without significant increases in costs. Conversely,
if an increase in EC value added entailed a similar increase in costs the announcement
was considered as tangible. For example, initiatives where the goods or services were
made available online for use or downloaded for use as involving digital products. For
instance, announcements of firms offering products or services such as rock concerts on
demand, online trading, signup for telecom services and purchase of insurance services
were coded as involving digital products. Similarly, announcements by firms of online
forums for exchange or trade were coded as involving digital products. Announcements
of online availability of products such as sports merchandize or books were classified as
involving tangible goods. Of the 501 events, 214 events were coded as involving
tangible goods and 287 as involving digital goods.
CUSTOMER TYPE
To develop the coding scheme for the orientation of the announcement we use a
modified version of the coding scheme of {|Subramani, 2001 #185|}. Their coding
scheme was based on the revenue model of the firms involved in the announcement.
Announcements were coded as B2B if the revenue source of the associated firm was
primarily from institutional customers. An announcement was coded as B2C if the
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revenue source was primarily from end consumers. This scheme is based on the firm in
question, whereas our focus is on the initiative. Thus, we use the concept of EC value
added rather than revenue.
We consider a firm to make a B2B announcement if the EC value added is primarily
value for another firm. In contrast a firm is considered to be making a B2C
announcement when the EC value added is value added for an end consumer6. When
there were multiple firms mentioned in announcements, the decision about whether the
event was B2B or B2C was made for each firm involved. For example, the
announcement "Reel.com Opens Virtual Video Store Front on America Online" was
coded as B2B for AOL and as B2C for Reel.com. The logic was that the EC value added
by AOL was the one-time fee paid by Reel.com as well as periodic payments based on
traffic channeled to Reel.com. The EC value added by Reel.com is from video sales to
individuals channeled to the company website. On the other hand the announcement
"Digital River Adds Kmart Corporation to Growing Network of Online Software
Dealers" was coded as B2C for Kmart and as B2B for Digital River as Digital River
would receive periodic consolidated payments based on individual purchases by
customers at Kmart's online site. On the other hand, Kmart was delivering EC value
added for the end consumers shopping it’s site. Instances where the revenue
arrangements were unclear were dropped. Of the 501 announcements, 162 were coded as
B2B and 339 coded as B2C.
GOVERNANCE TYPE
Coding for unilateral vs. alliance involved an examination of the text of the
announcement. An announcement was coded as unilateral if it involved only one firm, or
if it involved tow firms, but one firm was simply implementing the other firm’s
technology. The second condition was added because frequently software firms will
announce new customer acquisition in announcements that read, firm ABC has been
added to firm XYZ’s list of clients implementing software PQR. While we consider this
an EC initiative for ABC, we do not consider this an alliance.
Alliances were coded by the type of partner {|Chen, 2001 #208|}. If the alliance was
with an Internet firm it was coded as pure-play. If the alliance was with a large and wellrecognized computer industry leader (e.g. IBM, Microsoft, SAP) then the alliance was
coded as computer. If the alliance was with a click-and-mortar or brick-and-mortar firm
other than a computer industry firm, it was coded as traditional. This classification
allows for not only new economy EC firms and old economy traditional firms, but also
for middle age information economy firms in the computer industry.
INNOVATIVENESS
Following prior literature we coded initiatives as either being innovative or noninnovative {|Im, 2001 #184; Dos Santos, 1992 #176|}. innovative announcements are
those where new technology is being developed or applied, while non- innovative
6
Note, we do not address the question of how much of the value gets to the other firm or the end consumer,
we are only concerned that the EC value added would primarily benefit one or the other types of entities.
How the value is split is a topic for another day, and well beyond the scope of this work.
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initiatives are marketing announcements, follow-up announcements about a technology,
or announcements of use of well established technologies.
ATOMIC BUSINESS MODEL
There are a whole range of business models for electronic commerce being put
forward by a variety researchers {|Aksen, 2000 #206; Timmers, 1998 #207; Weill, 2001
#205|}. We adopt the categorization developed by Weill and Vitale. This categorization
looks at atomic business models as building blocks for EC initiatives. The authors
develop eight atomic business models that can be combined in various ways to develop a
much larger number of EC initiatives. The advantage of focusing on the atomic models
is that they are stable building blocks over time and that there are a small number of them
to consider. Weill and Vitale liken these atomic business models to atoms, while the full
scale business model of a firm is likened to a chemical compound. Thus, each atomic
business model can be combined with other such models to accurately represent the
overall business model of a given firm. These eight atomic business models are listed in
Table 1. As these are atomic models, and our analysis focuses on initiatives, each
initiative may contain multiple atomic business models.
Table 1: Atomic Business Models of Electronic Commerce from Weill and Vitale (2001)
ATOMIC BUSINESS MODEL
DESCRIPTION OF MODEL
Content Provider
Provides content via intermediaries.
Direct to Customer
Provides products directly to consumers.
Full Service Provider
Provides a full range of services (aggregation)
directly and via complemetors. Focus on owning
customer relation.
Intermediary
Brings together buyers and sellers by concentrating
information.
Shared Infrastructure
Brings together multiple competitors to cooperate by
sharing common IT infrastructure.
Value Net Integrator
Coordinates activities across the value net by
gathering, synthesizing, and distributing
information.
Virtual Community
Creates and facilitates an online community of
people with a common interest.
Whole of Enterprise
Provides a firm-wide single point of contact,
consolidating all services provided by a large multiunit organization
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OWNERSHIP OF VALUE ADDING CUSTOMER ASSETS
In addition to the atomic business models Weill and Vitale propose that firms may
own different value adding customer assets. The three assets that they propose are the
customer relationship, the customer data and the customer transaction. While, they
classify ownership of each of the assets as being a function of the chosen business model,
we seek further empirical validation and code ownership separately from the business
model type. For a given initiative, a firm may own none of these assets, some of these
assets, or all of the assets.
DATA DEMOGRAPHICS
Table 2, below, details the number of initiatives coded into each of the subcategories.
Those marked with an “*” sum to the total number of announcements. The other
categories sum to greater than the total number of initiatives, because an initiative may
entail more than one sub-category. For example, an initiative can entail a firm owning
both the customer data and the customer relation.
For the short run part of the study we required firms to have 251 trading days prior to
the event plus one day beyond the event. For the long run part of the study we required
firms to have one trading day prior to the event and to still be trading one year after the
event. For the long run we also winsorise the returns as described below.
The final column looks at the long run returns with only firms who undertook a single
initiative in the one year time period. If a firm undertook two initiatives during 1999,
then we have no way of separating the abnormal returns to the different events. This is a
particular problem in long run event studies. One way to deal with it is to look only at
the first event {|Mitchell, 2000 #192|}, but because it is relatively prevalent in our sample
we choose to remove all firms with multiple events and present both the full sample and
the truncated sample results.
Note that while there is a great deal of overlap the long run and short run datasets are
not subsets of one another. Thus, we create a final dataset which contains only the
unique initiatives for which we can evaluate both the short run and the long run returns.
This is called the compatible unique dataset.
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Table 2: Number of announcements coded in each category
*
*
*
*
Short Run
Variable
(n=501)
Content Provider
23
Direct to Customer
329
Full Service Provider
15
Intermediary
124
Shared Infrastructure
39
Value Net Integrator
1
Virtual Community
21
Whole of Enterprise
2
Relation
443
Data
437
Transaction
308
Net
145
Click-and-Mortar
356
B2B
162
B2C
339
Digital
287
Tangible
214
Unilateral
275
Pure-play Ally
167
Computer Ally
20
Traditional Ally
57
Executional / Extensional
375
Transformational
126
Long Run
(n=601)
31
366
18
176
45
2
26
4
528
524
346
243
358
191
410
356
244
315
191
27
84
452
149
Long Run
Unique
(n=244)
11
168
6
50
17
1
9
2
215
218
170
66
178
88
156
113
131
147
66
7
28
181
11
Compatible
Unique
(n=192)
8
136
5
35
13
1
7
1
168
171
138
31
161
72
120
84
108
120
52
7
17
144
48
PRICES VOLUMES AND RETURNS
Table 3: Demographics for Datasets
Short Run
Variable
Before1
Event
After1
ABRET
R2
Price
Volume
17
N
Mean
Std Dev Minimum Maximum
501
0.6%
9.7%
-74.4%
131.8%
501
2.0%
13.5%
-37.8%
169.1%
501
1.4%
14.5%
-87.1%
138.1%
451
-10.2%
84.0%
-105.4%
591.1%
501
0.124
0.135
0.000
0.810
501
$29.24
$23.82
$1.15
$118.56
501 2618117 5684251
50340
28083361
DRAFT VERSION
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Long Run
Variable
Before1
Event
After1
ABRET
R2
Price
Volume
N
Mean
Std Dev Minimum Maximum
601
0.6%
8.5%
-74.4%
131.8%
601
1.6%
11.8%
-37.8%
169.1%
447
1.4%
14.3%
-87.1%
138.1%
601
-27.0%
67.0%
-105.4%
294.8%
601
0.097
0.133
0.000
0.810
601
$27.56
$21.80
$1.15
$107.66
601 2243990 5252828
50340
28083361
Variable
Before1
Event
After1
ABRET
R2
Price
Volume
N
Mean
Std Dev Minimum Maximum
244
1.0%
11.1%
-29.0%
131.8%
244
2.6%
15.5%
-33.5%
169.1%
192
2.5%
17.2%
-39.8%
138.1%
244
-13.3%
80.3%
-105.4%
294.8%
244
0.067
0.102
0.000
0.549
244
$21.48
$17.37
$1.15
$107.66
244
663240 1109213
50340
10683254
Variable
Before1
Event
After1
ABRET
R2
Price
Volume
N
Mean
Std Dev Minimum Maximum
192
1.3%
12.6%
-29.0%
131.8%
192
3.3%
17.5%
-33.5%
169.1%
192
2.5%
17.2%
-39.8%
138.1%
192
-3.9%
78.8%
-100.8%
285.0%
192
0.085
0.107
0.000
0.549
192
$22.16
$18.25
$1.15
$107.66
192
708080 1221720
50340
10683254
Long Run Unique
Compatible Unique
DATA ANALYSIS
SHORT RUN EVENT STUDY
The methodology for the short run event study is relatively well documented in the IS
literature {|Dos Santos, 1992 #176; Im, 2001 #184; McWilliams, 1997 #159; Subramani,
2001 #185; Chatterjee, 2001 #209|}. A brief description follows. First we estimate a
market model for each individual firm using 250 trading days of returns regressed against
a market index7. We then calculate abnormal returns (ARs) for each day of a three day
window (the day before the announcement, the day of the announcement, and the day
after the announcement) as:
ARshort  Rs ,t  ˆ s  ˆ s Rm,t .
(1)
The term in parenthesis is the expected return on the stock where α and β are estimates
from the day 251 to day 2 before the event. The R terms are returns with the subscript t

7

In this case we use the S&P 500, which is the standard index. Other studies have found very little
difference between the NASDAQ, the Dow Industrials and the S&P 500 {|Subramani, 1999 #70;
Subramani, 2001 #185|}.
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denoting time, the subscript s refers to a specific stock and the subscript m refers to the
market return. The cumulative abnormal return (CAR) is the summation of abnormal
returns. The value of interest is the CAR for the three day event window starting on the
day before the event and ending on the day after the event {|Rajgopal, 2000 #229;
Chatterjee, 2001 #209|}. Some authors use only a two day window consisting of the day
before and the day after {|Dos Santos, 1992 #176; Im, 2001 #184|}. We use this value
because when the announcements are made late in the day, or after market close, the
market may not be fully able to adjust in the same day. We also look one day before to
capture any pre announcement drift {|McWilliams, 1997 #159|}.
TEST STATISTICS
There are two candidate test statistics to analyze the significance of CAR. The first
and simplest is to use the standard formula to estimate the mean and standard deviations
of all the CARs in the sample. This yields
1 N
CAR   CARs ,
(2)
N s 1
and
2
1 N
var( CAR) 
CARs  CAR .
(3)

N  1 s 1
The test statistic is
CAR
(4)
t
~ t( , df  N 1) .
var( CAR)
However, as CAR is the sum of several independent ARs, and the distribution of those
ARs can be estimated from the prediction window, the variance of CAR can be estimate
from the variance of AR. In this case the variance is:
1 N 1
var (CAR) 
(5)
  var( ARs ,t ) ,
N  1 s 1 t  1
where,
 

2
 

Rm, t  R m  
1
2

 ,
var( ARs , t )  S s 1   P
(6)
  P
2 

Rm ,i  R m 
 

 
i 1
 






and


2
1 P
ARshort, s  ARshort .
(7)

P  1 s 1
In this case P is the number of days used to predict the market model in (1) or 250.
Thus, to calculate the variance, we find the variance of the abnormal return in the
prediction interval, then use (6) to calculate the standard error of prediction. This takes
into account not only the variance of abnormal returns, but also the variance of the
predicted value of the market model. In other words, (6) recognizes that α and β are
estimates of the true parameter values and thus the predicted return has variance arising
S s2 
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not only from the random nature of returns, but also from the inaccuracy of using
estimates rather than true parameters for calculation of AR.
The variance calculation in (5) is thus preferred to the variance calculation in (3),
because the calculation in (3) ignores the additional information available in the
prediction interval. More simply stated, we have 253 observations of AR for each event.
The variance in (5) uses all 253 observations while (3) essentially drops 250 of them.
Furthermore, (5) takes into account the prediction accuracy of the model used to estimate
returns while (3) does not. In the limit, (3) and (5) should converge, but in finite samples
(5) is preferred because it uses all of the information and uses it properly. Note also that
(3) is the form calculated by regression analysis, thus applying regression to analyze the
differences will result in an improper test statistic.
LONG RUN EVENT STUDY
To analyze long run market reactions to events requires application of new techniques
from financial economics specifically designed and tested to evaluate long run returns
given the many stylized facts about stock price performance {|Cowan, 2001 #187;
Barber, 1997 #189; Kothari, 1997 #188|}. These techniques make use of the buy-andhold abnormal return (BHAR) in contrast to the use of cumulative abnormal return
(CAR) used in short run event studies (see {|McWilliams, 1997 #159; Subramani, 2001
#185|} for a detailed explanation of CAR). An abnormal return for firm i at time t is
defined as:
ARit  Rit  ERit  .
(8)
CAR then is
T
CAR   ARit .
(9)
t 1
In contrast, BHAR is
BHAR  1  Rit   1  ERit  .
T
T
t 1
t 1
(10)
CAR then is simply the day by day sum of abnormal returns and does not represent the
actual return over the holding period. BHAR on the other hand is actual return over the
entire holding period minus the expected return over the holding period. To illustrate,
consider a firm that has an expected return of zero over a two day period. On the first
day after the event the stock price increases by 100%, and on the second day the stock
price decreases by 50%. In this case the CAR is 50%, but the BHAR is 0%. Thus BHAR
is the better measure because the stock price is identical at the end of the two day period.
Barber and Lyon document this discrepancy between CAR and BHAR in the general
population of stocks, showing that CAR is generally higher than BHAR at low levels of
return {|Barber, 1997 #189|}. This can be particularly problematic for EC research as
firms can experience returns as high as 900% in one day{|Subramani, 2000 #190|}, which
means that no drop in stock price could result in negative CAR for at least two trading
weeks.
The discrepancy between CAR and BHAR is smaller at shorter time periods. In fact,
the two measures are identical for a one period return, and thus CAR is a good measure
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for typical short run studies. However, the difference becomes more pronounced as the
number of periods under consideration increases. This, combined with the fact that the
problem is greater for more volatile stocks, means that BHAR is a much more
appropriate measure for a long run study of EC value.
ISSUES IN LONG RUN EVENT STUDIES
One major concern in using BHAR rather than CAR is that BHARs are more skewed
than CARs. It is well known that stock returns are skewed {|Cowan, 2001 #187|}.
Conversely, by the central limit theorem, the returns of a portfolio approach a normal
distribution as more firms are added. Subtracting the skewed firm return from the
(approximately) normal portfolio return yields a skewed distribution, which makes
classical t or z tests mis-specified. In fact, even non-parametric tests may be misspecified8. The impact of this skewness bias declines with sample size, but increases with
the length of the return horizon.
A final, important source of potential measurement difficulty is in the construction of
the test statistic. In general, the test statistic is the estimated mean of the returns divided
by the estimated standard error of that mean. The standard error is typically calculated
assuming that observations are independent, so that the variance of each observation can
be summed. However, this is not likely to be a good assumption, as returns are cross
sectionally correlated in time {|Fama, 1998 #194; Brown, 1985 #160; Mitchell, 2000
#192; Cowan, 2001 #187|}. By assuming independence when returns are actually
correlated, the estimated variance is too small, leading to over rejection of the null
hypothesis. Research has found that “[i]t is common for t statistics to fall from over 6.0
to less than 1.5 after accounting for cross-correlations {|Mitchell, 2000 #192|} p 291.”
We expect that this will be a particularly important factor in our work, because the
market sentiment, regarding EC firms, seemed to vary considerably from month to
month.
METHODOLOGICAL CORRECTIONS
The skewness bias can be corrected for in two ways. The first is winsorization.
Winsorization corrects for skewness by limiting how far from the mean an extreme
observation is allowed to be. Observations beyond some threshold are set equal to the
threshold, effectively weighting them less than observations below the threshold (see
{|Cowan, 2001 #187|} for a detailed description). This mitigates the impact of the
skewness so that test statistics converge more quickly to hypothesized distributions.
The second method to control for skewness bias is to choose a reference portfolio to be
a single stock {|Barber, 1997 #189|}. This controls for skewness because the reference
stock is also drawn from a skewed distribution. Subtracting the event firms’ skewed
return from a reference firm’s similarly skewed return produces a symmetrical
8
A typical test is to count the number of positive and negative returns with the idea that if there is no effect
then the positive and negative returns will be spilt 50-50 (see for example {|Karpoff, 1994 #191;
Subramani, 2000 #190|}). If the distributions are skewed then it is not the case that a zero mean would lead
to a 50-50 split on positive and negative returns, because the 50-50 split requires the median and mean to
be identical.
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distribution, which converges more quickly to normal, and which reduces bias toward
over rejection in either tail.
These ideas are illustrated in Figure 1. Panel A shows a hypothetical, skewed
distribution of observed returns. Panel B represents the abnormal return, where the
expected return is based on a reference portfolio. Because, the reference portfolio is the
average of several stock’s returns it is assumed to be normally distributed by the central
limit theorem. The abnormal returns in this case will be the difference between a normal
and a skewed variable, and will thus be skewed as the panel shows. The final panel, C,
illustrates the reference firm abnormal return. In this case, the abnormal return is
calculated as the difference of two (identical) skewed distributions. This results in a
symmetrical distribution. Note that in all three cases the mean of the distribution is zero.
Figure 1: Distributions of Returns and Hypothetic Abnormal Returns
Event Firm - Reference Firm
Mean = 0
Mean = 0
-10%
Frequency
Frequency
Event Firm - Portfolio
Mean = 0
Frequency
Event Firm Returns
-5%
0%
5%
10%
Observed Return
Panel A: Event firm hypothetic
return distribution
-10%
-5%
0%
5%
10%
-10%
-5%
0%
5%
10%
Observed Abnormal Return
Observed Abnormal Return
Panel B: Abnormal returns as
observed returns minus
reference portfolio with normal
distribution
Panel C: Abnormal returns as
observed returns minus
reference firm return.
Reference firm comes from
same distribution as event firm
Barber and Lyon {|Barber, 1997 #189|} find that using a single stock as the reference
portfolio produces good results. However, they only examine samples of size 200.
Cowan and Sargent {|Cowan, 2001 #187|} compare the winsorization procedure to the
single firm procedure and find that the single firm procedure is very sensitive to number
of events in the sample. While they do find good results in samples of size 200, they find
relatively poor performance in samples of size 1000, while they find that winsorization
produces good results regardless of sample size. Because we use samples of different
sizes, we choose to apply winsorization to correct for skewness, and follow Cowan and
Sargent by removing observations beyond +/- three standard deviations.
RESULTS
SHORT RUN RETURNS
The results of the initiative characteristics (see Figure 2) are consistent with the theory
that greater returns will accrue to those firms most easily able to build intangible, IT
complementing investments. The absolute return to B2B is larger than the return to B2C,
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and only the B2B return is significant. Production type follows a similar theme with
returns to tangible production being significant, and greater than the non-significant
returns to digital production. Returns to click-and-mortar firms about twice as great as
returns to pure-play net firms, and the net firm return is non-significant while the clickand-mortar return is significant. Unilateral initiatives produced positive and significant
returns, while none of the alliance choices produced significant results, and both pureplay and computer alliances actually produced negative returns. Lastly, transformational
initiatives produced positive and significant results, while extensional/executional
initiatives produced smaller non-significant results. This accords well with prior
research, which has almost unanimously found transformational initiatives to be an
important factor in IT deployment.
Figure 2: Short Run Returns to EC Initiatives (CAR for three day window)
4.0%
3.0%
2.5%
3.0%
1.6%
2.0%
1.0%
3.0%
2.3%
0.8%
0.9%
0.8%
0.7%
0.8%
0.0%
-1.0%
-0.5%
-1.0%
EXECUT
TRANSFOR
TRAD_AL
C_AL
PP_AL
UNI
MORTAR
NET
TANGIBLE
DIGITAL
B2B
B2C
-2.0%
The returns to business models and customer asset ownership show interesting results
as well (see Figure 3). Each of the ownership types lead to positive and significant return
as put forward by Weill and Vitale {|Weill, 2001 #205|}. However the only business
models which result in significant returns are the direct to customer model and the
content provider model. The content provider model results in a negative 5.2% return.
Full details of the analysis for both the business model and initiative characteristics are
presented in Table 4.
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Figure 3: Short run Return to Business Models (CAR of three day window)
3.7%
3.2%
2.5%
2.7%
2.3%
1.6% 1.7%
0.2%
-2.0%
-2.9%
TRANS
DATA
REL
WOE
VC
VNI
SI
INTER
FSP
D2C
-5.2%
CP
5.0%
4.0%
3.0%
2.0%
1.0%
0.0%
-1.0%
-2.0%
-3.0%
-4.0%
-5.0%
-6.0%
Table 4: Impacts of Initiative Characteristics for Short Run Data
Whole Sample (n = 501)
-1
0
1
Mean
Return
0.6%
2.0%
1.4%
Std
0.3%
0.4%
0.5%
P-Val
Customer Type
B2C
(n = 339)
0.061*
0.000***
0.012**
-1
0
1
Mean
Return
0.3%
1.5%
0.8%
-1
0
1
Mean
Return
1.1%
2.9%
2.5%
Std
0.3%
0.5%
0.6%
P-Val
0.359
0.003***
0.176
Business Models
CP
(n = 23)
-1
0
1
Mean
Return
-0.9%
-2.3%
-5.2%
Std
P-Val
1.5%
2.1%
2.6%
0.543
0.285
0.052*
B2B
(n = 162)
Std
0.6%
0.9%
1.1%
P-Val
0.073*
0.001***
0.023**
Production Type
D2C
(n = 329)
-1
0
1
Mean
Return
1.3%
3.0%
2.5%
Std
P-Val
-1
Mean
Return
-0.9%
1.7%
0
2.7%
1
3.2%
-1
0
Mean
Return
-0.6%
0.7%
FSP
(n = 15)
Std
-1
0
1
Mean
Return
0.1%
1.1%
0.7%
0.609
-1
Mean
Return
1.2%
0.4%
0.004***
2.4%
0.273
0
3.0%
0.6%
0.000***
2.9%
0.293
1
2.3%
0.7%
0.002***
Std
P-Val
0.7%
1.1%
0.429
0.488
0.3%
0.5%
0.6%
P-Val
DIGITAL
(n = 287)
0.000***
0.000***
0.000***
TANGIBLE
(n = 214)
Std
P-Val
0.4%
0.6%
0.8%
0.821
0.070*
0.382
Std
P-Val
Firm Type
INTER
(n = 124)
24
NET
(n = 145)
-1
0
Mean
Return
0.3%
1.6%
Std
P-Val
0.6%
0.9%
0.626
0.075*
DRAFT VERSION
1
0.2%
-1
0
1
Mean
Return
0.2%
1.5%
2.3%
SI
(n = 39)
available online at http://www.ericwalden.net
1.3%
0.857
Std
P-Val
1.0%
1.4%
1.7%
0.832
0.271
0.184
1
0.8%
-1
0
1
Mean
Return
0.7%
2.1%
1.6%
MORTAR
(n = 356)
1.1%
0.453
Std
P-Val
0.4%
0.5%
0.6%
0.052*
0.000***
0.011**
Governance Type
VNI
(n = 1)
-1
0
1
Mean
Return
0.6%
0.4%
-2.9%
-1
0
1
Mean
Return
-0.7%
-1.1%
-2.0%
-1
0
1
Mean
Return
1.9%
2.8%
3.7%
VC
(n = 21)
WOE
(n = 2)
Std
5.4%
7.7%
9.4%
P-Val
UNI
(n = 275)
NA
NA
NA
Std
P-Val
1.3%
1.8%
2.2%
0.610
0.539
0.386
Std
P-Val
1.6%
2.3%
2.8%
0.452
0.443
0.417
-1
0
1
Mean
Return
1.0%
3.2%
3.0%
-1
0
1
Mean
Return
-0.5%
0.1%
-1.0%
-1
0
1
Mean
Return
1.1%
-1.0%
-0.5%
-1
0
1
Mean
Return
1.1%
0.9%
0.9%
PP_AL
(n = 167)
C_AL
(n = 20)
Std
0.5%
0.6%
0.8%
P-Val
0.036**
0.000***
0.000***
Std
P-Val
0.5%
0.7%
0.8%
0.338
0.873
0.218
Std
P-Val
1.3%
1.9%
2.3%
0.411
0.623
0.817
Std
P-Val
0.8%
1.1%
1.3%
0.173
0.440
0.499
Ownership
REL
(n = 443)
-1
0
1
Mean
Return
0.6%
2.1%
1.6%
-1
0
1
Mean
Return
0.6%
2.2%
1.7%
Std
0.3%
0.5%
0.6%
P-Val
0.066*
0.000***
0.005***
TRAD_AL
(n = 57)
Innovativeness
DATA
(n = 437)
Std
0.3%
0.5%
0.6%
P-Val
TRANSFOR
(n = 126)
0.088*
-1
0.000***
0
0.004***
1
Mean
Return
0.3%
2.5%
3.0%
Std
0.6%
0.8%
1.0%
P-Val
0.639
0.003***
0.004***
TRANS
(n = 308)
Mean
Std
P-Val
EXECUT
Mean
Std
P-Val
Return
(n = 375)
Return
-1
1.3% 0.4% 0.000***
-1
0.7% 0.4% 0.061*
0
3.2% 0.5% 0.000***
0
1.8% 0.5% 0.001***
1
2.7% 0.6% 0.000***
1
0.8% 0.6% 0.198
* represents significance at the 10% level, ** at the 5% level and *** at the 1% level.
All tests are two tailed.
While all of the initiative characteristics are mutually exclusive neither the business
models nor the customer asset ownership structures are exclusive. A single initiative can
consist of multiple business models and multiple ownership of customer assets. In
general, firm initiatives used one business model, and occasionally two, with the
maximum being four. However, initiatives often entailed multiple customer asset
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ownerships, and ownership of all three customer assets was not uncommon. This leads to
a problem in analyzing each model in isolation as the returns attributable to an initiative
are not properly divided among the business models used in the initiative. For example,
an initiative in which the firm owns all three customer assets would inappropriately
attribute the entire return to each of the assets. At the extreme, if all initiatives resulted in
ownership of all customer assets then each asset would appear to have the overall mean
return, which in this case, would be a positive and significant return. To address this issue
and properly partition the return among the non exclusive models and asset ownerships,
we regress the return from each initiative against all of the business model and customer
asset ownership dummy variables. This resulted in the coefficients estimated in Table 5.
Table 5: Short Run Joint Analysis of Business Model Impacts (Dependant Variable CAR Day +1)
F
1.150
Intercept
CP
D2C
FSP
INTER
SI
VNI
VC
WOE
REL
DATA
TRANS
P-val
0.320
Return
-2.9%
-2.8%
3.8%
2.1%
1.9%
3.6%
0.0%
-0.8%
2.9%
-0.1%
0.0%
1.9%
R2
0.025
Std
3.4%
4.0%
2.6%
3.8%
2.5%
3.2%
14.7%
3.6%
10.3%
2.3%
2.3%
1.8%
Adjusted R2
0.003
P-Val
0.398
0.485
0.142
0.577
0.434
0.266
0.997
0.834
0.781
0.983
0.986
0.303
These results seem to indicate that there are no significant effects of either business
models or customer asset ownerships. Not only are none of the coefficients significant,
but the F test indicates that we do not have a regression. We must note at this point that,
while regression will properly estimate the coefficients, it will mis-estimate the standard
errors as discussed above. However, even accepting slightly mis-specified standard error
the joint analysis does not show much promise or offer additional insight.
LONG RUN RETURNS
The time period in question, 1999-2000, represents a quite unpleasant time to invest in
EC related stocks. In all cases the one year returns to EC initiatives were negative and
significant. Overall the 601 initiatives in the long run data set averaged a –27% BHAR.
This makes interpretation difficult, as the goal is to find initiatives that resulted in less
negative returns. However, judging based on this criteria, we find that the results still
support the theory laid out above. Figure 4 illustrates this. B2B one year BHAR were
less negative than B2B BHAR, and were not significant at the 1% level. Click-andmortar firms experienced less of a drop than pure-play net firms. Unilateral action
resulted in less negative BHAR than any type of alliance and Transformational initiatives
were less negative than executional/extensional initiatives. The only difference between
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the relative values of short and long run returns was that in the long run there was
virtually no difference between digital and tangible initiatives.
Figure 4: Long Run BHAR
EXECUT
TRANSFOR
TRAD_AL
C_AL
PP_AL
UNI
MORTAR
NET
TANGIBLE
DIGITAL
B2B
B2C
0.0%
-5.0%
-10.0%
-15.0%
-14.4%
-20.0%
-16.4%
-17.7%
-20.1%
-25.0%
-30.0%
-27.2%-26.7%
-30.1%
-35.0%
-31.7%-30.8%
-32.9%
-40.0%
-40.5%
-45.0%
-42.7%
-50.0%
The results for the business model analysis were also similar in the short and long run.
As illustrates, content providers did relatively worse than other business models and
ownership of the transaction resulted in less absolute negative BHAR. The numeric
results are presented in .
Figure 5: Long Run BHAR for Business Models
0.0%
-10.0%
-20.0%
-18.5%
-19.4%
-22.1%
-29.4%
-30.0%
-40.0%
-50.0%
-60.0%
-22.8%
-26.5%-24.9%
-34.7%
-25.1%
-47.3%
-70.0%
-80.0%
-90.0%
-86.4%
27
TRANS
DATA
REL
WOE
VC
VNI
SI
INTER
FSP
D2C
CP
-100.0%
DRAFT VERSION
available online at http://www.ericwalden.net
Table 6: Long Run BHAR
Whole Sample (n = 601)
CP (n = 31)
D2C (n = 366)
FSP (n = 18)
INTER (n = 176)
SI (n = 45)
VNI (n = 2)
VC (n = 26)
WOE (n = 4)
Mean
Return
-27.0%
-47.3%
-22.1%
-18.5%
-29.4%
-19.4%
-86.4%
-25.1%
-34.7%
2.7%
10.5%
3.5%
21.2%
5.0%
11.1%
11.9%
14.8%
31.0%
0.000***
0.000***
0.000***
0.396
0.000***
0.088*
0.087*
0.103
0.344
Ownership
REL (n = 528)
DATA (n = 524)
TRANS (n = 346)
-26.5%
-24.9%
-22.8%
2.9%
2.9%
3.6%
0.000***
0.000***
0.000***
Customer Type
B2C (n = 410)
B2B (n = 191)
-32.9%
-14.4%
2.9%
5.9%
0.000***
0.016**
Production Type
DIGITAL (n = 356)
TANGIBLE (n = 244)
-27.2%
-26.7%
3.6%
4.2%
0.000***
0.000***
Firm Type
NET (n = 243)
MORTAR (n = 358)
-42.7%
-16.4%
4.2%
3.5%
0.000***
0.000***
PP_AL (n = 191)
C_AL (n = 27)
TRAD_AL (n = 84)
-20.1%
-31.7%
-30.8%
-40.5%
4.1%
4.4%
10.9%
5.6%
0.000***
0.000***
0.009***
0.000***
TRANSFOR (n = 149)
EXECUT (n = 452)
-17.7%
-30.1%
6.0%
3.0%
0.004***
0.000***
Business Models
Governance Type UNI (n = 315)
Innovativeness
Std
P-Val
To consider the impacts of business models, controlling for the non-exclusive nature
of the coding, we regress the BHAR against the eight business models and the three
ownership structures. These results are reported in Table 7. They seem to indicate that
over the long run, the direct to customer business model produced significantly greater
BHAR returns than having no business model. Likewise the intermediary model and the
shared infrastructure model produce marginally greater BHAR than having no model at
all. Ownership of the various customer assets produced no significant BHAR over
having no ownership. Further, given that the sum of the models is less than the value of
the model with the highest score, it seems unlikely that even owning all of the assets
would result in a significant return over owning none of the assets. These results should
be interpreted with caution as the F statistic fails to reject the null that all coefficients are
simultaneously different from zero.
Table 7: Long Run Joint Analysis of Business Model Impacts (Dependant Variable BHAR)
F
1.359
P-val
0.188
Return
28
R2
Adjusted R2
0.025
0.007
Std
P-Val
DRAFT VERSION
Intercept
CP
D2C
FSP
INTER
SI
VNI
VC
WOE
REL
DATA
TRANS
available online at http://www.ericwalden.net
-57.3%
4.0%
24.7%
9.0%
17.4%
25.3%
-36.6%
17.7%
6.9%
-6.2%
11.7%
4.0%
14.2%
16.0%
10.5%
16.1%
10.4%
13.5%
48.4%
15.1%
34.5%
9.5%
9.5%
7.4%
0.000***
0.802
0.019**
0.574
0.094*
0.061*
0.450
0.242
0.842
0.515
0.219
0.584
LONG RUN UNIQUE
One potential problem with the examination of the long run BHAR is that firms
pursuing multiple initiatives may have overlapping return periods. For example, if a firm
starts an initiative in March of 1999 the starts another initiative in June of 1999, there will
be a period of overlap from June 1999 to March 2000. The return in this period will be
attributed to both initiatives, and thus double counted. This is similar to the problem of
non-exclusitivity of the business models. Unfortunately, there is no way to separate the
returns and properly attribute them. To account for this, we remove all of our sample
firms pursuing more than one initiative in 1999. The results of the initiative
characteristics are presented visually in Figure 6.
The results are somewhat different from those of the previous analyses, but still show
a similar pattern. In the long run unique sub sample the BHAR for firms undertaking
B2B initiatives were positive though not significantly so, while B2C was significantly
negative. The relative value of digital and tangible initiatives switch so that digital
initiatives are not significantly different from zero, while tangible initiatives are
significantly negative. Unilateral actions are not significantly different from zero, while
both pure-play and traditional alliances are significantly negative. However, computer
alliances generated positive, albeit non-significant, BHAR. Transformational initiatives
were not significantly different than zero, while executional/extensional initiatives we
significantly negative.
29
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Figure 6: Long Run Unique BHAR
20.0%
10.4%
10.0%
1.5%
0.0%
-10.0%
-7.0% -4.6%
-9.9%
-20.0%
-15.6%
-16.2%
-30.0% -26.7%
-40.0%
-6.8%
-21.4%
-30.4%
EXECUT
TRANSFOR
TRAD_AL
C_AL
PP_AL
UNI
MORTAR
NET
TANGIBLE
B2B
DIGITAL
-44.2%
B2C
-50.0%
The results of the business model analysis are presented in Figure 7. They show the
same basic pattern as the previous two data sets. One exception is that ownership of the
data produced less negative BHAR than ownership of the transaction. Full statistics are
displayed in .
Figure 7: Long Run Unique BHAR
10.0%
1.4%
0.0%
-10.0%
-20.0%
-30.0%
-1.1%
-6.2%
-12.3%
-20.5%
-14.3%-11.2%-12.9%
-20.1%
-20.2%
-40.0%
-50.0%
-60.0%
-70.0%
-80.0%
TRANS
DATA
REL
WOE
VC
VNI
SI
INTER
FSP
D2C
CP
-74.4%
Table 8: Long Run Unique BHAR
Business Models
Whole Sample (n = 244)
CP (n = 11)
D2C (n = 168)
FSP (n = 6)
30
Mean
Return
-13.3%
-20.5%
Std
P-Val
5.1%
24.7%
0.010**
0.426
-12.3%
1.4%
6.0%
59.9%
0.042**
0.983
DRAFT VERSION
available online at http://www.ericwalden.net
INTER (n = 50)
-20.2%
11.9%
0.096*
Ownership
SI (n = 17)
VNI (n = 1)
VC (n = 9)
WOE (n = 2)
REL (n = 215)
-6.2%
-74.4%
-1.1%
-20.1%
-14.3%
23.3%
NA
28.3%
71.8%
5.4%
0.793
NA
0.971
0.826
0.009***
Customer Type
DATA (n = 218)
TRANS (n = 170)
B2C (n = 156)
-11.2%
-12.9%
-26.7%
5.5%
5.9%
5.1%
0.043**
0.031**
0.000***
Production Type
B2B (n = 88)
DIGITAL (n = 113)
10.4%
-9.9%
10.6%
8.2%
0.326
0.227
Firm Type
TANGIBLE (n = 131)
NET (n = 66)
-16.2%
-30.4%
6.5%
11.6%
0.014**
0.011**
Governance Type
MORTAR (n = 178)
UNI (n = 147)
-7.0%
-4.6%
5.5%
7.1%
0.209
0.519
PP_AL (n = 66)
-21.4%
9.2%
0.023**
C_AL (n = 7)
TRAD_AL (n = 28)
TRANSFOR (n = 63)
1.5%
-44.2%
-6.8%
31.2%
9.8%
10.1%
0.964
0.000***
0.505
EXECUT (n = 181)
-15.6%
6.0%
0.010***
Innovativeness
Again we regress BHAR against the business model characteristics to examine the
joint impact. This is presented in Table 9. In this case no variables attain significance,
and the F statistics fails to reject the joint hypothesis that all coefficients are zero.
Table 9: Long Run Unique Joint Analysis of Business Model Impacts (Dependant Variable BHAR)
F
0.373
Intercept
CP
D2C
FSP
INTER
SI
VNI
VC
WOE
REL
DATA
TRANS
31
P-val
0.965
Return
-11.9%
-7.0%
-5.0%
15.3%
-16.0%
0.2%
-73.5%
3.6%
-12.7%
-16.4%
27.4%
-6.4%
R2
Adjusted R2
0.017
-0.029
Std
31.6%
34.4%
23.8%
33.9%
23.0%
27.5%
85.4%
34.0%
62.7%
18.4%
20.0%
15.8%
P-Val
0.707
0.840
0.836
0.653
0.487
0.995
0.390
0.917
0.839
0.374
0.172
0.683
DRAFT VERSION
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COMPARABLE DATA SETS
Thus, far we have looked at BHAR separately from CAR. We construct a final data
set which contains data for firms that have both the 250 trading days prior to the
announcement need for CAR analysis and the year of trading days post announcement
need for BHAR analysis. We eliminate the non-unique firms from the sample and
present the final results in Table 10. The results produce an interesting contrast to the
short and long run. B2B is superior in the long run, but B2C is superior in the short run,
though not significantly so. Digital initiatives are superior in the long run but tangible
initiatives are superior in the short run. Click-and-mortar firms are superior to pure play
net firms in both the long and short run. Unilateral initiatives seem to produce more
value for shareholders than initiatives with alliance partners. Transformational initiatives
produce more value in the long run, but executional/extensional initiatives produce
greater return in the short run, although the short run difference is not significant.
In terms of business models the direct to customer model seemed to produce better
than average returns in both the long and short run. The full service provider model
produced great short run returns, but performed particularly badly in the long run.
Ownership of the transaction seemed to produce good returns in both the long and short
run.
Table 10: Comparable BHAR and CAR
BHAR
Business
Models
Whole Sample (n = 192)
CP (n = 8)
D2C (n = 136)
FSP (n = 5)
INTER (n = 35)
SI (n = 13)
VNI (n = 1)
VC (n = 7)
WOE (n = 1)
Ownership
REL (n = 168)
DATA (n = 171)
TRANS (n = 138)
Customer
Type
Production
Type
Firm Type
B2C (n = 120)
B2B (n = 72)
32
CAR
[-1,+1]
-3.9% 5.7% 0.497
2.5%
0.5% 30.0% 0.988
-2.5%
-0.8% 6.7% 0.900
4.4%
-57.3% 14.6% 0.017**
10.3%
-17.7% 12.4% 0.164
-0.1%
14.0% 28.3% 0.629
0.0%
-74.4% NA
NA
-2.9%
-37.6% 18.4% 0.087*
-4.9%
51.7% NA
NA
2.6%
-4.2%
-0.4%
-0.7%
Std
6.0%
6.1%
6.6%
P-Val
0.489
0.949
0.915
-17.5% 5.5% 0.002***
18.8% 11.7% 0.113
9.3%
7.1%
Std
P-Val
0.8%
3.7%
0.9%
5.7%
1.9%
2.7%
NA
4.3%
NA
0.002***
0.520
0.000***
0.145
0.970
0.999
NA
0.293
NA
2.9%
2.7%
4.1%
0.8%
0.8%
0.9%
0.001***
0.001***
0.000***
2.8%
2.0%
1.0%
1.2%
0.007***
0.110
DIGITAL (n = 84)
TANGIBLE (n = 108)
-1.0%
-6.1%
0.911
0.393
0.2%
4.2%
1.3%
1.0%
0.854
0.000***
NET (n = 31)
MORTAR (n = 161)
-5.5% 18.4% 0.766
-3.5% 5.8% 0.542
-1.0%
3.2%
2.7%
0.8%
0.701
0.000***
DRAFT VERSION
available online at http://www.ericwalden.net
Governance UNI (n = 120)
Type
PP_AL (n = 52)
C_AL (n = 7)
TRAD_AL (n = 17)
Innovative- TRANSFOR (n = 48)
ness
EXECUT (n = 144)
3.1% 7.9% 0.692
-13.7% 9.1% 0.136
1.5% 31.2% 0.964
-28.2% 13.6% 0.054*
4.2%
-0.6%
-0.1%
0.3%
1.0%
1.4%
4.1%
2.5%
0.000***
0.677
0.973
0.919
11.0% 12.0% 0.366
-8.8% 6.4% 0.171
2.1%
2.6%
1.6%
0.9%
0.193
0.005***
In order to examine the relation between short and long run returns we regress the
short run return against the long run return and a constant. If the returns are related we
would expect a positive and significant coefficient. This is presented in Table 11. The Fstatistic suggest that this does not produce a regression, and neither coefficient is
significant.
Table 11: Relation between BHAR and CAR (Dependant variable is BHAR)
F-stat
1.193
P-val
0.276
R2
0.006
R2 Adjusted
0.001
Intercept
CAR[-1,+1]
Coefficient
-0.030
-0.363
Std
0.057
0.332
P-val
0.606
0.276
DISCUSSION
INITIATIVE CHARACTERISTICS
The analysis is, in general, supportive of the theory that intangible, IT complementing
investments are necessary to successfully create value via EC activities. We postulate
that EC initiatives which leverage a firm’s existing intangible assets or a firm’s ability to
build the necessary assets will result in higher return. We find that, in the short run, all of
our predictions hold suggesting that investors also believe this. In the long run we find
that click-and-mortar firms produce greater return than pure-play net firms, that B2B
initiatives produce greater returns than B2C firms, and that unilateral initiatives, in
general, produce greater return. This is consistent with our discussions and expectations
concerning intangible, IT complementing assets. However, the returns to digital and
tangible production in the long run were quite similar.
BUSINESS MODELS
Relative to business models, the analysis suggested that the direct to customer model
was a reliable model. There was also some evidence that ownership of the transaction
was a preferred customer asset. However, when we accounted for the double counting of
returns introduced by the non-exclusitivity of the business models and ownership
structures, we consistently failed to find any evidence that the overall impact of both
business models and customer assets was significantly different from zero. Thus, we are
33
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lead to presume that neither business model nor customer asset ownership influences the
return to EC initiatives.
This does not mean that the atomic business models examined are not useful, and in
fact, the creators of the models make no claim that any model is better than any other.
Rather the purpose of the models is to help firms conceptualize their business and to
point out the issues specific to them. In terms of our theory, this would suggest that
while different models may require different complementary assets, there is no systematic
difference in the overall level of complementary assets required. It seems somewhat
comforting to acknowledge that initiatives of many different types can be successful if
they are carried out properly. The lack of results in the area of customer asset ownership
is no less satisfying for the theory of this paper. It simply suggests that the intangible, IT
complementing assets are the important assets to own rather than the customer assets.
FUTURE RESEARCH
Based on prior literature, we have postulated that EC initiatives create value through
the formation and application of intangible, IT complementing assets. A logical
extension to this line of theory development is to identify those intangible assets. Once
identified theory should be developed to explain the conditions and contributions of those
assets, and empirical validation performed to measure validate that theory. We offer
three suggestions of possible candidates.
First, we suggest that standardization is an asset that complements IT. The advantage
of IT is that it can reliable process large amounts of structured data. If the elements of
data are standardized that will give them the structure necessary to be efficiently
processed by IT. This is not to say that all processes in a firm must be standardized, be
cause the environment may be quite non-standard. However, standardizing those
processes which can reasonably be standardized should help IT perform more efficiently
and free up firm resources to deal with non-standard issues.
The second IT complementing asset is an organizational learning focus. By this we
mean that an organization puts forth considerable effort not only to perform its functions,
but also to position itself for the future through continual learning. This complements IT,
because IT lacks inertia. Once in place IT faithfully performs the same tasks. Without an
organizational impetus to drive IT changes there will be no changes and the environment
will shift, rendering the fixed IT obsolete.
A third IT complementing asset is the technical expertise of the user. We see this
clearly in EC, where there were few viable transactions until a large portion of the
population became familiar with the use of the hardware and software necessary to access
the internet.
CONCLUSIONS
We have proposed that the ability to deploy and create intangible, IT complementing
assets is necessary to the creation of business value in the EC context. We examine this
hypothesis using the event study methodology in two ways. First we apply the traditional
short run methodology, then we apply a newer long run methodology. In both cases we
find reasonable evidence that firms which can more easily deploy the relevant intangible
assets produce greater return for their owners. We compare this to the return generated
by firms based on business model and customer asset ownership, and find no compelling
34
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evidence that business model or customer asset ownership determines return on EC
investments. We note that this does not mean business models do not have academic
value, but rather that different business models may be successful if the proper intangible
assets are in place.
This work contributes to the literature in a variety of ways. First it puts forward the
theory that intangible IT complementing assets are an important determinant of EC
success. A second contribution lies in introducing the long run event study methodology
into the Is literature. Short run event studies have recently become popular, but as our
analysis shows it is not clear that the long run returns are well predicted by short run
returns. This occurs simply because more information comes available in the long about
the intangible assets being put into place to support the IT. A third contribution is the
first empirical examination of the value created by EC business models. We find that the
form of the business model is not a great determinant of value, but rather the
implementation of the business model is important. We echo Michael Porter’s recent
commentaries that suggest the strategy supporting EC is more important than either the
for of the business model or the technology involved {|Porter, 2001 #228|}.
REFERENCES
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