supply chain integration and shareholder value: evidence from consortium based industry exchanges

Supply chain integration and shareholder value: Evidence

from consortium based industry exchanges

Sabyasachi Mitra 1, Vinod Singhal *

College of Management, Georgia Institute of Technology, Atlanta, GA 30332, United States

Received 28 April 2006; received in revised form 6 May 2007; accepted 29 May 2007

Available online 2 June 2007

www.elsevier.com/locate/jom

Journal of Operations Management 26 (2008) 96–114

Abstract

Recent trade and academic literature point to the importance of supply chain integration among partners as a key determinant of

value creation. This paper analyzes the shareholder value effects of setting up industry exchanges, a prominent mechanism used to

achieve supply chain integration. Shareholder value effects are estimated by measuring the stock market reaction (abnormal returns)

associated with announcements to form or join industry exchanges. We find that abnormal returns from participation in industry

exchanges are positive but only marginally significant in the whole sample of 144 firms in 18 exchanges formed during 2000–2001.

In the sub-sample of 88 exchange founders who were part of the original announcements to form the exchange, the abnormal market

reaction is about 1% and significant. We also find that firms with greater bargaining power and higher process efficiency benefit

more from participation in industry exchanges.

# 2007 Elsevier B.V. All rights reserved.

Keywords: Industry exchange; Supply chain integration; Electronic commerce; Event study

1. Introduction

Recent practitioner and academic literature

(Magretta, 1998; Sahin and Robinson, 2005; Watson

and Zheng, 2005; Kulp et al., 2004; Frohlich, 2002;

Frohlich and Westbrook, 2001; Cua et al., 2001),

emphasize the role of supply chain integration among

partners as an important determinant of value creation.

Higher levels of product variety, global marketplaces,

shorter product life cycles, and the demand for better

customer service have significantly increased the need

for integration with supply chain partners. At the same

* Corresponding author. Tel.: +1 404 894 4908;

fax: +1 404 894 6030.

E-mail addresses: [email protected] (S. Mitra),

[email protected] (V. Singhal).1 Tel.: +1 404 894 4365; fax: +1 404 894 6030.

0272-6963/$ – see front matter # 2007 Elsevier B.V. All rights reserved.

doi:10.1016/j.jom.2007.05.002

time, firms are outsourcing more activities and devel-

oping long-term relationships with a stable set of partners

who perform critical functions such as component

design, manufacture, assembly, and distribution (Parker

and Anderson, 2002). Recent technological innovations

have also facilitated the use of the Internet to support

inter-firm business processes. Consequently, the domi-

nant belief is that the most successful companies are

‘‘those that have carefully linked their internal processes

to external suppliers and customers in unique supply

chains’’ (Frohlich and Westbrook, 2001, pp. 185).

To facilitate the integration of supply chains among

many firms within an industry, the last few years have

witnessed the creation of several consortium based,

business-to-business (B2B), industry-specific electronic

exchanges (we henceforth refer to them as industry

exchanges). Unlike public exchanges that are third party

operated and owned, industry exchanges are set up by

the trading partners themselves, with typically one or

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http://dx.doi.org/10.1016/j.jom.2007.05.002

S. Mitra, V. Singhal / Journal of Operations Management 26 (2008) 96–114 97

Table 1

Consortium based industry exchanges (2000–2001)

Exchange details Exchange details

Industry: airline; exchange: AirNewCo; date: 27 April 2000;

set-up by: buyers; selected founders: American, Air France,

British Airways, Continental, Delta and United Airlines;

status: not operating

Industry: metal; exchange: Metal Spectrum; date: 2 May 2000;

set-up by: sellers; selected founders: Alcoa, Allegheny

Technologies, Kaiser Aluminum, North American Stainless, Olin,

Reynolds Aluminum, Thyssen, Vincent Metal; status: operating

Industry: airline; exchange: AeroXchange; date: 24 August 2000;

set-up by: buyers; selected founders: America West, Northwest,

Air Canada, All Nippon, Cathay Pacific, FedEx, Japan Airlines,

Lufthansa, KLM, Scandinavian, Singapore, Air New Zealand,

Austrian; status: operating

Industry: mining; exchange: Quadrem; date: 15 May 2000;

set-up by: buyers; selected founders: Alcan, Phelps Dodge,

Newmont, Alcoa, Noranda, Inco, Barrick Gold, Anglo

American, DeBeers, Broken Hill, WMC, Comp. V. Rio Doce,

Comp. N.D. Cobre, Rio Tinto; status: operating

Industry: automobile; exchange: Covisint; date: 25 February 2000;

set-up by: buyers; selected founders: GM, Ford, Daimler-Chrysler;

status: operating

Industry: paper; exchange: Forest Express; date: 23 March 2000;

set-up by: sellers; selected founders: International Paper,

Georgia-Pacific, Weyerhaeuser, Mead; status: operating

Industry: chemical; exchange: Elemica; date: 17 May 2000; set-up by:

sellers; selected founders: DuPont, Dow, Rohm and Haas, Rhodia,

Uniroyal, Cabot, Celanese, PolyOne; status: operating

Industry: plastics; exchange: Omnexus; date: 5 April 2000;

set-up by: sellers; selected founders: Dupont, Dow, Celanese,

M.A. Hanna Company, Geon; status: operating (acquired by

SpecialChem)

Industry: defense; exchange: ExoStar; date: 28 March 2000; set-up by:

buyers; selected founders: B AE Systems, Boeing, Lockheed Martin,

Raytheon; status: operating

Industry: railroad; exchange: Rail Marketplace; date:

17 January 2001; set-up by: buyers; selected founders:

Burlington Northern Santa Fe, Canadian National, Canadian

Pacific, Norfolk Southern, Union Pacific; status: operating

Industry: electronics; exchange: e2Open; date: 7 June 2000; set-up by:

buyers and sellers; selected founders: Hitachi, IBM, LG Electronics,

Matsushita, Nortel Networks, Seagate, Solectron, Toshiba; status:

operating

Industry: real estate; exchange: Home Builders Ex.; date:

5 May 2000; set-up by: buyers; selected founders: Centex

Corp., D.R. Horton, Kaufman & Broad Home Corp.,

Lennar, Pulte Corp.; status: not operating

Industry: electronics; exchange: eHitex; date: 1 May 2000; set-up by:

buyers and sellers; selected founders: Hewlett-Packard, Compaq

Computer Corp., Gateway, AMD, Solectron, Agilent, Canon,

Hitachi, NEC, Quantum, Samsung, SCI, Tatung, Western Digital;

status: operating (changed name to converge)

Industry: retail; exchange: Global Net Ex.; date:

28 February 2000; set-up by: buyers; selected founders:

Sears, Roebuck, Carrefour; status: operating (merged

with WW Ret. Exch.)

Industry: healthcare; exchange: Global Health Ex.; date: 29 March

2000; set-up by: sellers; selected founders: Johnson &

Johnson, GE Medical Systems, Baxter International, Abbott

Labs, Medtronic; status: operating

Industry: retail; exchange: Worldwide Retail Ex.; date:

31 March 2000; set-up by: buyers; selected founders:

Several including Albertson, Best Buy, CVS, JC Penney,

Kmart, Rite Aid, Radio Shack, Safeway, Target, Toys R US,

Walgreen, Winn Dixie; status: operating

Industry: healthcare; exchange: Health Nexis; date: 18 April 2000;

set-up by: sellers; selected founders: AmeriSource Health, Cardinal

Health, Fisher Scientific, McKesson HBOC; status: operating

(merged with Global Health Exch.)

Industry: rubber; exchange: Rubber Network; date: 17 April

2000; set-up by: buyers; selected founders: Goodyear Tire

& Rubber, Continental AG, Cooper Tire & Rubber, Groupe

Michelin, Pirelli SpA, Sumitomo Rubber; status: operating

two dominant exchanges in each industry. Further,

unlike other Internet-based business models based on

the matching and aggregation of buyers and sellers, the

primary purpose of industry exchanges is to facilitate

the integration of supply chain related business

processes among existing trading partners (Chris-

tiaanse, 2005). Table 1 summarizes the prominent

industry exchanges set-up in the US during 2000–2001,

the primary years when such industry exchanges were

established in various industries.

As a mechanism to achieve supply chain integration,

industry exchanges have many advantages. The

exchange founders listed in Table 1 demonstrate that

industry exchanges are usually set-up either by a few

prominent buyers (e.g. the Covisint exchange in the

auto industry) or a few prominent sellers (e.g. the

Healthcare Exchange for hospital supplies). Conse-

quently, they have instant buy-in from the major

industry players and have the potential to achieve

critical volume of transactions rapidly. Further, the

exchange makes it less expensive to participate, as

proprietary one-to-one links between partners are

replaced with a single, less expensive connection to

the industry exchange (Christiaanse, 2005). The

S. Mitra, V. Singhal / Journal of Operations Management 26 (2008) 96–11498

industry exchange also extends the benefits of integra-

tion to smaller partners who need just a web browser to

access the functionality of the exchange to track orders,

submit invoices, and accept payments.

This paper addresses two key issues in the context of

industry exchanges. First, do mechanisms such as

industry exchanges that facilitate supply chain integra-

tion create value for participants? For the reasons

mentioned above, the trade literature initially greeted

the establishment of such exchanges with high

expectations (Anonymous, 2000). However, it is not

clear whether a shared infrastructure such as an industry

exchange can benefit individual participants (Carr,

2003) and what the magnitude of this benefit is for the

average firm. To answer this question, we use event

study methods (Brown and Warner, 1985) to estimate

the stock market reaction (abnormal returns) to the

announcement by firms to form or join industry

exchanges. Abnormal returns are the difference

between the actual change in stock price in response

to an announcement and a benchmark to adjust for the

overall market-wide influences. The efficient market

hypothesis asserts that stock prices will respond rapidly

to the information contained in public announcements,

and that the market’s response would include a

capitalization of future benefits and costs associated

with the firm’s participation in the exchange. Kothari

and Warner (2006) indicate that the number of

published event studies exceeds 500, and continues to

grow. Table 4 in Hendricks and Singhal (2003) describe

event studies in various functional areas. Recent event

studies that have examined supply chain management

issues and technology investments related to supply

chain management include Hendricks et al. (2007),

Hendricks and Singhal (2003), Im et al. (2001),

Subramani and Walden (2001), and Chatterjee et al.

(2002).

Table 2

Alternative supply chain integration mechanisms

Integration mechanism Description Functiona

EDI, XML Technologies to integrate business

partner systems

Focus on

Limited s

activities

Extranet Browser based access to business

partner systems

Focus on

activities

Private marketplace Firm owned online market to buy

or sell products

Auctions

for quotat

Industry exchange Consortia based online exchange to

integrate business partner systems

Focus on

as non-tra

Neutral exchange Third party online market to buy or

sell products

Multiple a

for comm

The second issue that we examine is whether all

participants benefit equally from participating in

industry exchanges. According to Sahin and Robinson

(2005, pp. 580), even though the benefits of supply

chain integration are well recognized, ‘‘the source,

potential magnitude, and the allocation of improve-

ments across channel members are not clear.’’ Cachon

and Fisher (2000) also note that the operational benefits

of increased coordination vary considerably across

research studies, ranging from 0% to 35% of total costs.

Similarly, there is also growing recognition among

information systems (IS) researchers that firms may not

benefit equally from similar technology investments

(Lee et al., 1999). We combine the extant literature on

bargaining power (Coff, 1999), joint ventures (Yan and

Gray, 1994), complementarity theory (Milgrom and

Roberts, 1990) and resource dependency theory

(Pfeffer, 1982) to identify firm specific factors that

can explain value creation for specific participants. We

empirically test whether these factors affect the value

that participants derive from participation.

The rest of the paper is organized as follows. Section

2 describes industry exchanges in more detail, and

summarizes related literature. Section 3 presents our

theoretical framework and hypotheses. Section 4

describes the data set and methodology. Section 5

discusses the results. Section 6 concludes the paper.

2. Industry exchanges and related literature

2.1. Consortium based industry exchanges

Table 2 summarizes electronic mechanisms (includ-

ing industry exchanges) that facilitate the integration of

supply chain partners within an industry (Mahadevan,

2003; Gunasekaran and Ngai, 2004). Mahadevan (2003)

classifies these mechanisms into three primary cate-

lity Marketplace characteristics

integrated transactions.

upport for non-transactional

Interaction with existing

partners

non-transactional collaborative Interaction with existing

partners

and reverse auctions. Request

ion, bid support

Set-up by single buyer or

single seller

integrated transactions as well

nsactional activities

Set-up by limited number of

buyers and/or sellers. Participation

is initially restricted

uctions and reverse auctions

odity products

Many buyers and sellers. Open

membership in the exchange


gories. Collaborative technologies such as electronic

data interchange (EDI) and extensible markup language

(XML) focus on connecting business partner information

systems to enable integrated transactions, while proprie-

tary Extranets provide browser based access to business

partner systems for collaborative work. Quasi-market

mechanisms, such as buyer and supplier-centric private

markets, as well as the consortia based industry

exchanges examined in this paper, extend the function-

ality of collaborative technologies and provide a quasi-

marketplace with a limited set of participants. They are

initiated by one (private) or multiple (consortium based)

buyers or sellers, who enroll other participants, host and

monitor the marketplace, and govern market behavior

(Mahadevan, 2003). Neutral market mechanisms such as

hubs, vertical exchanges and public exchanges are set up

by neutral third parties and provide a forum where a large

number of buyers and sellers meet for the procurement of

standardized and commodity items. Due to low barriers

of entry, numerous neutral marketplaces were established

during the Internet boom years. Mahadevan (2003)

suggests that neutral marketplaces are viable only when

the industry is highly fragmented with no dominant

players on either side of the market.

In this paper, we focus on consortium-based industry

exchanges as a mechanism for supply chain integration

for several reasons. First, announcements about the

formation of industry exchanges contain a detailed list

of all member firms that collaborate to establish the

exchange. Firms that join the exchange at a later time

also announce their participation through outlets such as

PR Newswire or Business Wire. This allows us to

precisely identify the announcement date that is critical

in event study analysis (Brown and Warner, 1985). In

contrast, announcements to establish third-party owned,

neutral exchanges do not usually include a compre-

hensive list of participating firms. Furthermore, firms do

not usually announce their participation in neutral

exchanges and they may participate for a short period of

time to meet specific needs. Firms also do not

consistently announce their adoption of collaborative

technologies such as EDI and XML, or the establish-

ment of private websites. Without public announce-

ments or precise announcement dates one cannot

measure the stock market reaction. Second, unlike

neutral market mechanisms, industry exchanges facil-

itate linkages with existing trading partners, and

abnormal returns primarily capture integration benefits

rather than the benefits of an expanded marketplace.

Third, by focusing on a single mechanism, we simplify

the analysis and reduce the impact of unobserved

heterogeneity between different mechanisms.

Industry exchanges support interaction and colla-

boration at various stages of the product life-cycle

between supply chain partners. Typical functionalities

provided by the industry exchange can be subdivided

into three broad categories. Integrated Transaction

Support includes electronic ordering and order tracking,

electronic invoice and payment processing, and

electronic data interchange (EDI). Collaboration Sup-

port provides tools for collaborative design and new

product introduction, collaborative planning, forecast-

ing and replenishment (CFPR), just-in-time (JIT),

Kanban and other inventory management practices.

Ancillary Services include support for transportation

and delivery management, integration of third party

logistics providers, and specialized services such as

Sarbanes-Oxley (SOX) compliance management.

Many of the industry exchanges listed in Table 1 are

set-up by major buyers (e.g. Covisint, AeroXchange and

Exostar) for online procurement of raw materials, parts

and other inputs from several sellers. A few industry

exchanges are set-up by large sellers and distributors

(e.g. Global Health Exchange and Metal Spectrum) for

online selling of products to many buyers. The two

exchanges in the electronics industry (e2Open and

eHitex) include both buyers and sellers among the

founding members. Further, a few industries have two

competing exchanges founded by competitor firms that

choose alternative paths. However, one of the compet-

ing exchanges may cease operations in the long run (e.g.

AirNewCo in the airline industry), or merge operations

with another (e.g. Global Net Exchange and World

Wide Retail Exchange; HealthNexis and Global Health

Exchange). It is also interesting to note that in spite of

the downturn in the Internet economy, industry

exchanges have had remarkable survival rates. Of the

18 industry exchanges listed in Table 1, only 2 have

ceased operations at this time. However, the stock

market did not have this information at the time of the

announcement, and we do not exclude these two

industry exchanges from our analysis.

2.2. Literature review and contributions

The theoretical arguments for closely integrating

operations between suppliers, manufacturers and

customers are well recognized in the operations

management literature (Lee and Clark, 1997; Watson

and Zheng, 2005). Real time information flows back-

wards through the integrated supply chain, while

products flow forward in a just-in-time manner

(Frohlich, 2002). This reduces the bullwhip effect,

inventory, lead times, and order delays, and increases


customer satisfaction and profitability (Sahin and

Robinson, 2005; Kulp et al., 2004; Lee and Clark,

1997).

Several recent case studies examine the antecedents

and consequences of supply chain integration in various

industries. The issues examined in this literature include

the impact of information technology (IT) on supply

chain integration and operational performance (Tatsio-

poulos et al., 2002; McAfee, 2002), the enablers and

inhibitors of supply chain integration (Pagell, 2004),

and the role of the supply chain manager in achieving

integration across firm boundaries (Parker and Ander-

son, 2002). While case studies provide a rich under-

standing of the phenomenon, their results are often

difficult to generalize.

Several empirical studies also evaluate the impact of

supply chain integration on various measures of firm

performance (Narasimhan and Kim, 2002; Christensen

et al., 2005). On the supplier side, Petersen et al. (2005)

finds that increased coordination results in better design

and financial performance. Likewise, on the customer

side, Vickery et al. (2003) find beneficial impacts of

supply chain integration on customer service metrics.

Kulp et al. (2004) also document the beneficial impact

of information integration efforts such as collaborative

planning and vendor managed inventory. Further,

Frohlich and Westbrook (2001) report higher perfor-

mance improvements for firms that have the widest

‘‘arcs of integration.’’ Johnson and Whang (2002)

provide a recent overview of the literature on supply

chain integration. We contribute to this literature in two

ways that are outlined below.

First, to capture the multiple and complex dimen-

sions of supply chain integration, most empirical studies

have relied on survey data and self-reported measures of

performance (Narasimhan and Kim, 2002; Petersen

et al., 2005; Frohlich and Westbrook, 2001; Christensen

et al., 2005). While such measures have drawbacks

(Bollen and Paxton, 1998; Ketokivi and Schroeder,

2004), this research stream has been critical in

establishing a relationship between supply chain

integration and firm performance. However, we are

unaware of research that has explicitly evaluated the

impact of supply chain integration on stock market

based measures of performance. This gap in the

literature is significant, given the centrality of the topic

to operations management and the importance of stock

performance to managers and investors.

Second, empirical research on supply chain integra-

tion has investigated the importance of the linkage

between a single firm and its trading partners. The

industry exchange context that we investigate here is

unique because it provides an infrastructure for supply

chain integration that is shared by many firms

(including competitors) within an industry. While such

a shared infrastructure has many benefits (Christiaanse,

2005), it also raises two new questions regarding the

appropriation of benefits by participants. Can individual

firms benefit from a shared resource and are the benefits

from participation shared equally among supply chain

partners? We answer these questions by evaluating

complementary factors that affect the value that

individual firms derive from participation.

There is some empirical research on the impact of

B2B e-commerce in the literature. Chen and Siems

(2001) estimate the abnormal returns to vendors and

technology providers from business-to-business e-

marketplace announcements but do not focus on

industry exchanges and their participants. Subramani

and Walden (2001) estimate the abnormal returns from

e-commerce announcements, but do not specifically

analyze the impact of industry exchanges.

3. Theoretical development and hypotheses

The hypotheses that we develop are stated in terms of

stock market reaction (abnormal returns) to announce-

ments by firms to form or join industry exchanges.

There are three reasons why we believe that any positive

benefits from exchange participation will translate into

positive abnormal returns on the day of the announce-

ment. First, the efficient market hypothesis asserts that

stock prices will respond rapidly to the information

contained in public announcements, and that the

market’s response would include the capitalization of

future costs and benefits associated with the firm’s

participation in the exchange (Brown and Warner,

1985). There is significant evidence in the finance

literature that supports the efficient market hypothesis

(Basu, 1977; Malkiel, 2003). Thus, any expected net

benefits from participation (such as lower transaction

and infrastructure costs) should translate to positive

abnormal returns on the announcement date.

Second, as we describe later in the paper, industry

exchanges facilitate collaboration and tighter linkages

with suppliers and partners, and consequently have the

potential to reduce inventory. The operations manage-

ment literature highlights the beneficial impact of

tighter linkages and lower inventory on financial

performance (Fullerton et al., 2003; Huson and Nanda,

1995). For example, based on a study of 52 Japanese

automotive firms, Lieberman and Demeester (1999)

find that inventory reductions lead to productivity gains.

Roumiantsev and Netessine (2005) analyze panel data


for a sample of over 700 firms and find that responsive

inventory management is associated with higher earn-

ings. Thus, any expected benefits of lower inventory and

tighter linkages from industry exchange participation

should also translate to positive abnormal returns on the

day of the announcement.

Finally, other research has demonstrated a link

between supply chain related events and stock market

performance. Hendricks and Singhal (2003) document

evidence of significantly negative abnormal returns for

announcements related to supply chain disruptions,

while Hendricks et al. (2007) find marginally positive

abnormal returns associated with enterprise resource

planning (ERP) and supply chain management (SCM)

system implementations. Chen et al. (2005) find lower

stock returns for firms that operate with higher than

normal levels of inventory. Francis et al. (1996) find

significantly negative abnormal returns associated with

Table 3

Benefits of industry exchanges

Dimensionsa Description

Basic Trade Processes

Search The information gathering and par

evaluation process

Price discovery The process of negotiating or disc

a purchase or sale price

Logistics The process of coordinating the ac

delivery of goods and services

Payment and settlement The process to define the payment

and ensure the settlement of invoi

Authentication Process to authenticate parties, ens

non-repudiation and monitor comp

Trade Content Processes

Communications and computing Communication and computing

infrastructure that connect all part

Product representation Processes to specify product attrib

to buyers and sellers

Legitimation Processes to validate trading agree

and define the rules of engagemen

Influence structures Processes to enforce obligations an

penalties for non-compliance

Dispute resolution Processes for resolving disputes an

specifying decision rights in the ev

of a dispute

Support Processes

Collaboration support Processes to aid non-transactional

collaboration between trading part

Ancillary services Processes to link with third parties

provide support services

a Dimensions of basic trade and trade content processes are based on Ka

inventory write-offs. Overall, the evidence in the

literature points to significant stock market valuation

effects of supply chain related events. In the next few

sections, we focus on describing the beneficial impact

of industry exchange participation on the firm, and we

rely on the above reasoning to provide the link to

announcement date abnormal returns.

3.1. Value creation through the exchange

Kambil and Van Neck (1998) provide a framework

that summarizes the transactional benefits of industry

exchanges. They identify 10 distinct processes that

operate in an exchange relationship. Their framework,

adapted in Table 3, recognizes that basic trade processes

(such as search, price discovery, logistics, settlement

and authentication) reside within a larger context of

support processes (termed trade content processes) that

Industry exchange characteristics

tner Searchable electronic product catalogs; limited set of

exchange participants

overing Support for the RFQ process; electronic auctions and

reverse auctions

tual Electronic ordering and order tracking; integration with

back-end systems

terms

ces

Electronic contracts and pre-negotiated prices;

matching of invoices and purchase orders; electronic

delivery of invoices and payments

ure

liance

Digital certificate technology; workflow definitions and

enforcement; transaction audit trails

ies

Shared infrastructure and economies of scale; browser

access for smaller participants

utes Industry exchanges can promote industry-wide

standardization of product descriptions, product

numbers and product specifications

ment

t

Industry exchanges can play a role in standardizing

the rules of engagement

d Industry exchanges may play a role, but dominant

players may be reluctant to relinquish control

d

ent

Industry exchanges can maintain audit trails to

increase transparency of transactions and aid in

dispute resolution

ners

Industry exchanges can provide processes and

technology to collaboratively manage inventories,

design products and manage projects

that Industry exchanges can link trading partners with third

party logistic providers, financial institutions, law firms

and others who provide needed ancillary services

mbil and Van Neck (1998).


provide rules of engagement, product representation,

dispute resolution, and other mechanisms to enforce

obligations. By integrating the back-end systems of the

trading partners, industry exchanges have a direct

impact on the basic trade processes, and play a

supporting role for the trade content processes.

In addition to the transactional benefits described in

Kambil and Van Neck (1998), Table 3 also describes

two other support processes that are facilitated by

industry exchanges. First, industry exchanges facilitate

collaboration between multiple companies ‘‘to manage

inventories, design products and manage projects more

effectively.’’ (Christiaanse, 2005, pp. 96). The role of IT

in facilitating inter-organizational collaboration is well

recognized in the operations management literature

(Vickery et al., 2003; Kulp et al., 2004; Sahin and

Robinson, 2005). As observed by Johnson and Whang

(2002, pp. 420), ‘‘while e-commerce and e-procurement

have captured most of the business press headlines over

the past 5 years, the promise of e-collaboration may be

far greater.’’ Second, industry exchanges link partici-

pants to third parties that provide needed ancillary

services such as financial institutions, transportation

and delivery services, law firms, and systems integra-

tors.

While few would argue that industry exchanges

could potentially reduce transaction costs and facilitate

collaboration, there are also several reasons why the

stock market may not react positively to participation.

First, it is not clear whether benefits are sustainable in

the long term for the average participant. The resource

based view (RBV) of the firm argues that a resource

must be valuable, inimitable and scarce for it to be the

basis of sustained competitive advantage (Peteraf,

1993). If membership is open to all, then there is no

competitive advantage to any single firm and benefits

from lower transaction costs are ultimately passed on to

customers through lower prices. Second, industry

exchanges require many firms within an industry

(including competitors) to work together and agree

on standards, rules and functionality, a phenomenon

that is complicated to achieve in practice (Christiaanse,

2005). This threatens the long-term viability of the

industry exchange. Third, conformance with industry

exchange standards can reduce flexibility available to

the firm to define the best solution.

However, several factors mitigate the negative effects

described above. Unlike third party owned public

exchanges, participation is initially restricted to current

trading partners of the exchange founders. For example,

when the Covisint exchange was established in the auto

industry, participation was initially restricted to the major

suppliers of GM, Ford, Daimler-Chrysler and Nissan.

Consequently, exchange participation can provide

advantages at the expense of others initially excluded

from the exchange. Further, exchange founders have a

vested interest in ensuring the survival of the exchange

and providing transaction volume to sustain the

exchange, thereby increasing its long-term viability.

Accordingly, we posit the following hypothesis.

Hypothesis 1. The stock market reaction to announce-

ments by firms to form or join industry exchanges will

be positive.

3.2. The complementarity view of exchange

participation

Our remaining hypotheses examine additional

factors that affect the value obtained by individual

firms from participating in industry exchanges. In

deriving the hypotheses, we have relied on the notion of

complementarity that has wide acceptance in the

management literature (Powell and Dent-Micallef,

1997). Complementarity theory is based on the notion

that the marginal value of having more of one factor

increases by having more of another complementary

factor (Milgrom and Roberts, 1990). The operations

management literature also recognizes that the benefits

associated with supply chain integration are not equally

allocated among supply chain partners (Sahin and

Robinson, 2005). In the context of exchange participa-

tion, the complementarity view argues that comple-

mentary, firm-specific factors are necessary to take

advantage of the shared infrastructure.

In deriving the firm-specific complementary factors

that are important for obtaining benefits from industry

exchange participation, we examine two relevant

literature streams. First, the literature on joint ventures

has long recognized the impact of bargaining power on

the appropriation of benefits from a collaborative effort

(Inkpen and Beamish, 1997; Yan and Gray, 1994). The

basic logic is that a participant’s bargaining power has a

direct impact on the structure of management control in

a joint venture, which, in turn, affects the extent to

which specific participants achieve their strategic

objectives. Second, the IS literature recognizes the

importance of business process efficiency as an

important complementary factor that enables firms to

obtain differential benefits from IT systems (Barua

et al., 1996). The basic argument in this literature is that

efficient business processes are a pre-requisite for

effective IT implementations. Consequently, we derive

our hypotheses regarding the impact of these two


complementary factors (bargaining power and process

efficiency) on the benefits that a firm derives from

exchange participation.

3.3. Bargaining power and benefits from exchange

participation

Bargaining power refers to the ability to win

concessions from the other parties involved in a

negotiation and favorably affect the outcome (Dwyer

and Walker, 1981). The joint venture literature identifies

two sources of bargaining power: context based and

resource based (Yan and Gray, 2001; Coff, 1999).

Context based sources of bargaining power derive from

the willingness of the participant to walk away from the

deal, such as when the participant has other alternatives

or when the deal is not as strategically important to the

participant. Resource based sources of bargaining

power derive from the critical resources that the

participant brings to the table, such as money, expertise

and technology. Along similar lines, resource depen-

dency theory (Frooman, 1999; Pfeffer, 1982) provides a

compelling argument that organizations must ‘‘attend to

the demands of those in its environment that provide it

resources important and necessary for its continued

survival.’’ (Pfeffer, 1982, p. 193).

Based on the existing literature, larger firms have

greater context based sources of bargaining power (Yan

and Gray, 2001; Coff, 1999) because, in the absence of

industry exchanges, they often have private online

marketplaces and proprietary solutions for their partners.

For example, before the Covisint industry exchange was

established, the major auto manufacturers like GM and

Ford had their own private exchanges that were

subsequently merged into the Covisint exchange. Thus,

larger firms are less dependent on consortium based

solutions and can walk away from the deal if their

requirements are not met. Larger firms also have greater

resource based sources of bargaining power (Yan and

Gray, 2001; Coff, 1999) because they bring more

resources, technical know-how and expertise to support

the industry exchange, especially during the initial

formative years. In addition, a critical factor influencing

the survival of the industry exchange is transaction

volume, and larger participants typically sell or buy more

through the exchange. Consequently, resource depen-

dency theory (Pfeffer, 1982) would predict that influence

strategies such as threats of withdrawal (Frooman, 1999)

provide larger firms significant control over the workings

of the exchange. Thus, while there are other sources of

bargaining power such as proprietary technology,

knowledge and patents, we argue that firm size is an

important source of bargaining power that allows firms to

derive greater benefits from exchange participation. This

forms the basis of the following hypothesis.



be positively associated with firm size.

We measure firm size through the annual revenue of

the firm. Other possible measures of firm size include

total assets and number of employees, which generally

have strong positive correlation with sales. Annual

revenue is a better measure because it is relatively

insensitive to the capital/labor mix of inputs used by the

firm.

3.4. Process efficiency and benefits from

participation

It is well accepted in the information systems and

operations management literature that successful IT

implementations require process changes to take

advantage of the technology. For example, Barua

et al. (1996) argue that complementary process and

organizational factors increase the value of re-engineer-

ing projects. Case studies by Lee and Clark (1997)

describe how electronic marketplaces combine invest-

ments in on-line trading with radical process redesign.

Bresnahan and Greenstein (2001) discuss the role of

‘‘co-invention,’’ where technology based innovation in

organizations is accompanied by workers reinventing

underlying work processes to take advantage of the new

technology. In summary, while inter-organizational

systems such as industry exchanges provide the

opportunity to exchange information and integrate

supply chains, benefits from such systems are obtained

only if the necessary process changes have been made to

take advantage of the technology.

We hypothesize that process efficiency is an

important complementary asset that affects the benefits

that a firm derives from industry exchange participation.

There are three reasons that support this viewpoint.

First, industry exchanges are one of a sequence of

technological innovations (e.g. EDI, extranets, web

services and RFID) that firms combine with process

innovations (e.g. collaborative planning, continuous

replenishment, and vendor managed inventory) to build

a superior business process capability that reduces cost

and inventory. Thus, firms that have higher process

efficiency are likely to have the necessary experience

and track record in reengineering their processes and

effectively integrating technology. Second, Schmenner

and Swink’s (1998) theory of bottlenecks postulates that


the flow in a supply chain is restricted by its most

constrained resource. Without streamlined internal

logistical processes in receiving, shipping, production

logistics and warehouse management, there would be

little value to superior external coordination through the

industry exchange. Third, the many-to-many connec-

tivity enabled by industry exchanges reduces switching

costs for buyers and sellers. This increases competi-

tiveness in the industry, benefits more efficient firms,

and acts to the detriment of the weaker players (Porter,

2001).

While process efficiency is a broad concept, we

define two dimensions of process efficiency based on

the major benefits that firms can derive from participa-

tion in industry exchanges—inventory efficiency and

cost efficiency. Inventory efficiency reflects the degree

to which a firm’s supply chain processes are streamlined

and integrated with those of its trading partners. Cost

efficiency reflects the ability of the firm to produce its

goods and services at the lowest possible cost. The trade

literature on industry exchanges also argues that a

reduction in inventory and costs are the major benefits

that firms can achieve through participation in industry

exchanges (Magretta, 1998; Anonymous, 2000). This

forms the basis of the following two hypotheses.



be positively associated with inventory efficiency.



be positively associated with cost efficiency.

We measure inventory efficiency through the industry

adjusted inventory turns (Total Cost/Total Inventory) of

the firm, calculated by subtracting the median industry

inventory turn (four digit SIC code match) from the

inventory turn of each firm in our sample. Inventory

turnover has been extensively used in the literature as a

measure of supply chain performance (McKone et al.,

2001; Swamidass et al., 1999; Lee et al., 1999). Low

inventory can indicate better demand management, better

links with customers, and the use of advanced supply

chain technology (Magretta, 1998). Total cost includes

the cost of goods sold (COGS) and selling, general and

administrative expenses (SGA), but excludes extra-

ordinary items and taxes. We measure cost efficiency

through the industry adjusted operating margin (1 – Total

Cost/Sales) of the firm. As before, we subtract the median

industry operating margin (four digit SIC code match)

from the operating margin of each firm in our sample, to

adjust for industry differences.

4. Data set and methodology

4.1. The data set

To identify industry exchanges and their participants

we exhaustively searched the Wall Street Journal for

announcements of industry exchanges established

during the years 2000–2001. Following the establish-

ment of the Covisint exchange in February 2000, the

next few months witnessed several similar industry

exchanges formed in quick succession in various other

industries, shown in Table 1. As noted by other research,

it is not uncommon for technology adoption to occur in

waves (Carow et al., 2004). Thus, the years 2000–2001

were the primary years when such industry exchanges

were established, and we restricted the analysis to these

two years to keep the search tractable and efficient. The

search string used was ‘‘((b2b or b-to-b or business-to-

business or online or Internet or net or web or

electronic$) and (supply$ or exchange$ or purchase$

or market$ or trading or portal$)).’’ From the several

hundred articles that matched our search string, we

found the names of 18 industry exchanges that are

shown in Table 1. To the best of our knowledge, Table 1

includes all industry exchanges formed during these two

years that were reported in the Wall Street Journal.

The names of these exchanges were then used to

search the Wall Street Journal again to identify articles

and announcements related to these exchanges. After

excluding consulting firms, technology providers,

financial partners, private firms, and firms not listed

on the NYSE, AMEX, and NASDAQ exchanges, we

identified a total of 149 firms that participated in these

18 exchanges and have stock price information

available from the Center for Research on Security

Prices (CRSP) at the University of Chicago. There were

two firms that participated in more than one exchange.

They were counted twice with different event dates for

each exchange.

The Wall Street Journal sometimes reports events a

day after the public announcement is made by firms. To

accurately determine the announcement dates, we

searched the Dow Jones Newswire, the Business Wire,

and PR NewsWire databases with the firm and exchange

names as keywords. These sources are often the first

outlet for reporting firm specific announcements. We

compared these and the Wall Street Journal dates, and

used the earliest date as the announcement date. In cases

where the announcement was made after 4 p.m. EST

(closing time for markets) or during the weekend or a

public holiday, we used the next trading day as the

announcement date. Finally, for each of the 149 sample


firms, we searched a database of major business

publications (Factiva from Dow Jones Corporation) for

a month preceding the formal announcement date, to

identify any prior reporting of the event. Based on this

search, we modified the announcement dates for two

exchanges. We excluded four firms from our sample

because their date of joining the exchange could not be

accurately determined. An additional firm was elimi-

nated because it did not have sufficient trading data to

calculate the abnormal returns. The remaining 144

companies formed our sample. This careful sample

selection procedure allowed us to pinpoint the announce-

ment date with greater accuracy and focus on a one day

announcement period, in contrast to multi-day announce-

ment periods used by many previous event studies. A 1-

day announcement period enables us to more accurately

measure the magnitude and statistical significance of the

stock market reaction to the announcement.

Table 4 describes the sample of 144 exchange

participants. Panel A shows that the sample is diverse

both in terms of firm size and profitability. Based on

year 2000 data, the annual revenue varied between $11

million to $180 billion. Operating margins (unadjusted)

varied from �19% to 52%. Panel B shows the

distribution of firms across the 18 industry exchanges

Table 4

Sample description

(Panel A) Descriptive statistics for the sample of 144 participants in 18 ex

Revenue OIB

Units $Million $M

Minimum 11 �1

Maximum 180,557 37,

Average 19,264 258

Median 10,164 104

S.D. 28,666 515

(Panel B) Distribution of 144 participants across 18 industry exchangesb

Exchange name Industry No. of firms Type Exch

Aero Xchange Airline 4 BH, S Glob

Air NewCo Airline 6 BH, S Hom

Covisint Auto 12 BH, M Meta

E2open Electronics 8 BH, M Heal

EHitex Electronics 12 BH, M Omn

Elmica Chemical 8 SH, M Quad

Exostar Defense 4 BH, M Rail

Forest Express Paper 6 SH, M Rubb

Global Health Health Products 20 SH, M Wor

a OIBD, operating income before depreciation; OM, operating margin; R

Inventory. All numbers are based on year 2000 data.b BH, buyer set up hub; SH, seller set-up hub; M, manufacturing, S, service.

buyers or sellers, respectively. Two of the exchanges were set up by both buye

or sellers among exchange founders. Firms in the exchange were classified

industry exchange were either all manufacturing or all service firms.

in the sample indicating the number of firms from each

industry exchange that are in our sample, whether the

exchange is set-up by the buyers or sellers, and how

many of the firms included in our sample are

manufacturing and service firms.

4.2. Calculating abnormal returns

We use the event study methodology to estimate the

changes in stock price (the abnormal return) attributable

to the announcement. Abnormal returns are calculated

using both the market model and mean adjusted model

as described by Brown and Warner (1985).

The market model posits a linear relationship between

the return on a stock and the return on the market portfolio

over a given time period. This relationship is expressed

as: rit = ai + birmt + eit, where rit is the return of stock i on

day t, rmt the return of the market portfolio on day t, ai the

intercept of the relationship for stock i, bi the slope of the

relationship for stock i, and eit is the error term for stock i

on day 0. The term (birmt) is the return to stock i on day t

that can be attributed to market wide movements, while

eit is the unexplained part of the return that captures the

effect of firm specific events on day t. For each firm, we

estimate ai and bi using ordinary least squares (OLS)

changesa

D INVT OM

illion Number %

43 1.4 �19

342 124 52

6 11 13.6

7 6.8 10.9

0 13.9 10.2

ange name Industry No. of firms Type

al Net Retail 3 BH, S

e Builder Real estate 5 BH, M

l Spectrum Metal 10 SH, M

th Nexis Health 5 SH, S

exus Plastic 5 SH, M

rem Mining 12 BH, M

Market Railroad 6 BH, S

er Network Rubber 2 BH, M

ld Wide Retail Exchange Retail 16 BH, S

evenue, annual sales; INVT, inventory turns defined as Total Costs/

BH and SH indicate whether the founders of the industry exchange are

rs and sellers and were classified based whether there were more buyers

as service (S) or manufacturing (M). In our sample, firms within each


regression over an estimation period of 200 trading days,

with the equally weighted CRSP index as a proxy for the

market portfolio. Each firm’s estimation period (EstP)

ends 10 trading days prior to the announcement date. A

minimum of 40 return observations in the estimation

period is required for the estimation procedure. The

abnormal return (Ait) for stock i on day t from the market

model is: Ait ¼ rit � ai � birmt, where rit is the actual

return on stock i on day t. The rationale here is the normal

return for the stock can be predicted by the market model

parameters.

In contrast to the market model, the mean adjusted

model calculates the abnormal return by subtracting the

mean return of the stock over the estimation period from

its event day return. As before, each firm’s estimation

period ends 10 trading days prior to the announcement

date. A minimum of 40 return observations in the

estimation period is required for the estimation

procedure. The abnormal return (Ait) for stock i on

day t from the mean adjusted model is: Ait ¼ rit � ri,

where ri is the mean daily return for stock i during the

estimation period. The rationale here is that the normal

return for stock i on day t can be predicted by the mean

daily return during the estimation period.

To pool observations across time, for each firm in our

sample, we translate calendar time to event time as

follows: The announcement date is day 0 in event time,

the next trading date is day 1, and trading day preceding

the announcement day is day �1, and so on. We focus

on the abnormal returns on day 0 to estimate the stock

market reaction to industry exchange announcements,

but calculate day�1 returns also to check for leakage of

information. The daily mean abnormal return (At) is

calculated as: At ¼PNt

i¼1 Ait=Nt, where Nt is the number

of firms in the sample for which Ait values are available

on day t.

Under the null hypothesis (that announcements have

no impact on market value), the central limit theorem is

used to argue that the term At is distributed normal with

a mean of zero and a variance given by the sample

variance of At over the estimation period. The following

test statistic (TSt) is well specified even with event date

clustering (Brown and Warner, 1985):

TSt ¼AtffiffiffiffiffiffiffiffiffiSA2

t

q ; where SA2t

¼ 1

ðDEstP � 1ÞX

t2EstP

ðAt � ¯AÞ2; ¯A

¼ 1

DEstP

Xt2EstP

At;

and DEstP = number of days in trading period (200 in

our case).

4.3. The cross-sectional regression models

We use the following two regression models to test

Hypotheses 2–4:

� M
odel 1: AR-Mean = b0 + bS(SALES) + bI(INVT)
+ bOM(OM) + other control variables.

� M
odel 2: AR-Market = b0 + bS(SALES) + bI(INVT)
+ bOM(OM) + other control variables.

where AR-Mean is the mean adjusted model day 0

abnormal return (%); AR-Market the market model day

0 abnormal return (%); SALES the annual sales for year

2000 ($billions); INVT the industry adjusted inventory

turn (Total Cost/Inventory) for year 2000; OM is the

industry adjusted operating margin ([Sales � Total

Cost]/Sales) for year 2000 (%).

Although it is common to use the market model in

estimating abnormal returns and in the cross-sectional

analyses of abnormal returns, we also use the mean

adjusted model returns to account for any potential bias

introduced through the use of firm performance metrics

(such as operating margin and inventory turns) as

predictor variables in our analyses. The potential bias

here is that stock returns may be normally larger for

firms with better operating performance. The mean

adjusted model accounts for such biases by subtracting

the mean daily return of the stock during the prior 200

trading days from the event day return. Thus, the

abnormal return calculated in this manner is the return

that is above what is normal for that firm.

In addition, we evaluated two models that incorpo-

rate a control variable (FOUNDER) to indicate whether

the firm is a founder of the industry exchange or not.

Since exchange founders have greater control over the

workings of the exchange, they may benefit more from

participation. In our sample, the exchange founders

were significantly larger than non-founders. The mean

annual revenue for founders and non-founders are $25

billion and $10 billion, respectively. The t-test for the

difference in mean is significant at the 1% level. Thus,

to avoid potential multi-collinearity problems, we do

not include the SALES variable in models that include

the FOUNDER variable. Specifically, we consider the

following two additional models:

� M
odel 3: AR-Mean = b0 + bI(INVT) + bOM(OM)
+ bF(FOUNDER) + other control variables.

� M
odel 4: AR-Market = b0 + bI(INVT) + bOM(OM)
+ bF(FOUNDER) + other control variables.


where FOUNDER = 1 if the firm was part of the

original announcement to form the exchange, 0

otherwise.

4.4. Control variables

We use two other control variables in our analyses.

First, industry exchanges can be classified as buyer-

centric or seller-centric based on whether they were

initiated by dominant buyers or sellers in the industry.

This is consistent with the buyer and supplier integration

dimensions in Frohlich and Westbrook (2001). We define

BUYHUB = 1 if the exchange was buyer-centric and

BUYHUB = 0 if the exchange was seller-centric. Most

participants in a buyer-centric (seller-centric) hub are

buyers (sellers). Prior research indicates that there may be

differences between the benefits obtained by buyers and

sellers in the business-to-business context (Lee et al.,

1999). Second, we classify the firms in the industry into

two groups (manufacturing and service) based on

whether they manufacture a physical product (e.g.

automobile, electronics, mining and chemical) or provide

a service (e.g. airline, retailing and distribution). We

define IND = 1 for manufacturing firms and IND = 0 for

service firms.

4.5. Methodological issues

Within each exchange, the announcement dates for

individual firms are the same for all founders of the

exchange. Further, many of the participants in an

exchange belong to the same or related industries. In

Table 5

Abnormal returns for the full sample of 144 exchange participants

Day �1

(Panel A) Abnormal returns on the day before the announcement (day �1

(abnormal returns are estimated using the market model)

Mean abnormal return �0.17%

T-statistics �0.48

Median abnormal return �0.19%

Wilcoxon signed-rank test Z-statistic �0.66

% Abnormal returns positive 46.52%

Binomial sign test Z-statistic �0.75

(Panel B) Abnormal returns on the day before the announcement (day �1

(abnormal returns are estimated using the mean adjusted model)



Median abnormal return �0.30%



Binomial sign test Z-statistic �0.25

**, * indicate significantly different from zero at the 5% and 10% levels,

such situations, ordinary least squares (OLS) estimates

of the regression parameters do not adjust for the

heteroskedasticity and cross-sectional correlation pre-

sent in the regression residuals. The error term

covariance matrix is not diagonal, much less a scalar

identity. Karafiath (1994) uses Monte Carlo techniques

to compare the performance of several estimators in the

presence of event clustering. The overall conclusion is

that there is no apparent advantage in taking into

account cross-sectional correlations in this regression

framework, but incorporating a priori information about

heteroskedasticity provides some gain.

We account for heteroskedasticity by using weighted

least squares (WLS) regression to estimate the

parameters of our models. We use the Groupwise

Heteroskedasticity procedure as described in Greene

(2003). We assume that the error term variance is equal

within each group (firms within the same industry

exchange) and differs across groups. The variance of the

error term within each group is estimated using the

group specific sub-vectors of OLS residuals, and the

reciprocal of the variance is assigned as the weight for

each firm within the group. For comparison purposes,

we report both the OLS and WLS estimates of the

parameters.

5. Results

5.1. Abnormal returns

For the full sample of 144 exchange participants,

Table 5 presents the abnormal returns from the market

Day 0 Day �1 and 0

), the day of the announcement (day 0) and day �1 and 0 together

0.41% 0.24%

1.18 0.50

0.20% 0.39%

1.94* 1.43

55.56% 53.47%

1.25 0.75

), the day of the announcement (day 0) and day �1 and 0 together

0.65% 0.42%

1.77* 0.81

0.56% 0.30%

2.55** 1.67*

54.17% 53.47%

0.91 0.75

respectively, based on a two-tailed test.


model (Panel A) and mean adjusted model (Panel B) on

the announcement date (day 0) and the day before the

announcement (day �1). The results show that day 0

abnormal returns are positive. The day 0 mean abnormal

returns are positive, and are 0.41% for the market model

(not significant at the 10% level based on a two-tailed

test, p-value = 0.24) and 0.65% for mean adjusted

model (significant at the 10% level based on a two-

tailed test, p-value = 0.08). The median day 0 abnormal

returns are also positive, and are 0.20% in the market

model (significant at the 10% level, p-value = 0.06) and

0.56% in the mean adjusted model (significant at the 5%

level, p-value = .01). In both models nearly 55% of the

day 0 abnormal returns are positive. However, the

binomial sign tests are not significant at the 10% level

for both models, indicating that the percent positive

abnormal returns are not significantly different from

50%. The stock market reaction on the day before the

announcement (day �1) is not statistically significant

on all reported measures, suggesting that there is no

leakage of information prior to the announcement day.

Overall, the results in Table 5 indicate marginally

significant positive stock market reaction to announce-

ments by firms to form or join industry exchanges in the

overall sample.

Exchange participants in our sample can be divided

into two groups—exchange founders and others who

join the exchange later. Typically, exchange founders

have an equity stake in the exchange and share in the

profits made through transaction fees. Further, industry

exchanges have the support of the dominant players in

the industry, raising entry barriers for new platform

Table 6

Abnormal returns for the sample of 88 exchange founders

Day �1

(Panel A) Abnormal returns on the day before the announcement (day �1

(abnormal returns are estimated using the market model)



Median abnormal return 0.01%

Wilcoxon signed-rank test Z-statistic 0.25


Binomial sign test Z-statistic 0.25

(Panel B) Abnormal returns on the day before the announcement (day �1

(abnormal returns are estimated using the mean adjusted model)



Median abnormal return 0.07%



Binomial sign test Z-statistic 0.75

***, **, * indicate significantly different from zero at the 1%, 5%, and 10

providers who intend to provide similar services. This

enables the exchange to charge transaction fees and

generate profits that it distributes to its equity

participants. Thus, we separately examine the stock

market reaction to announcements by industry

exchange founders.

In our sample of 144 participants, 88 participants can

be considered to be founders who were part of the

original announcement to set-up the exchange. Table 6

reports the abnormal returns for the market model

(Panel A) and mean adjusted model (Panel B) for the

sub-sample of 88 founders. The mean (median) day 0

abnormal return is 0.88% (0.61%) for the market model,

with the mean significantly different from zero at the

10% level ( p-value = 0.08) and the median significantly

different from zero at the 1% level ( p-value = 0.003).

The mean (median) day 0 abnormal return is 1.13%

(0.81%) for the mean adjusted model, with the mean

significantly different from zero at the 5% level ( p-

value = 0.04) and the median significantly different

from zero at the 1% level ( p-value = 0.001). The

binomial sign test is significant at the 1% level for both

models, indicating that the percentage of positive

abnormal returns is significantly greater than 50% ( p-

value � 0.01). The results indicate a significant positive

market reaction for the 88 exchange founders.

5.2. Results from the cross-sectional regression

Even though we observe significant differences in

abnormal returns between exchange founders and

others who join the exchange later, it is important to

Day 0 Days �1 and 0

), the day of the announcement (day 0) and days �1 and 0 together

0.88% 0.84%

1.78* 1.20

0.61% 0.98%

2.96*** 2.75***

62.50% 62.50%

2.96*** 2.75***

), the day of the announcement (day 0) and days �1 and 0 together

1.13% 1.05%

2.09** 1.38

0.81% 0.74%

3.33*** 2.58***

61.36% 61.36%

2.58*** 2.58***

% levels, respectively, based on a two-tailed test.


note that these differences can result from differences

in size and process efficiency between the two groups

of firms, rather than from founding membership in the

exchange. To explore this further, we next describe the

cross-sectional regression analyses to test our

hypotheses on size and process efficiency factors that

can influence the magnitude of abnormal returns. We

obtained the data to compute the independent

variables from Compustat. For nine firms in our

original sample, Compustat did not have the data we

required. We obtained data for five of these firms from

their annual reports and 10-K filings. We could not

locate any information for two of the remaining firms

because they had ceased to exist or merged with no

10-K information available. Two additional firms did

not report their inventory values separately. Two more

firms had firm level data available, but we could not

compute industry medians because Compustat did not

have at least two other firms in the same SIC code.

These six firms were excluded from the regression

runs. We also excluded another firm from the analysis

that had a jDFFITj greater than 1.0 (Kutner et al.,

1996).

Table 7

Cross-sectional regression analysis for Models 1–4 with full sample

t-Values are in parenthesis. ***, **, and * indicate significantly different from

test. Model 1: AR-Mean = b0 + bS(SALES) + bI(INVT) + bOM(OM) + bBH

bI(INVT) + bOM(OM) + bBHBUYHUB + bINDIND. Model 3: AR-Mean =

DIND. Model 4: AR-Market = b0 + bI(INVT) + bOM(OM) + bF(FOUNDER

expressed as percentages. IND, BUYHUB and Founder are 0/1 indicator var

account for the heteroskedasticity in the regression residuals using the gro

centered by subtracting the mean of the sample from the sales for each firm

Table 7 reports the cross-sectional regression results

based on 137 observations. Results are reported for when

the dependent variable is the abnormal return from the

mean adjusted model (Model 1) and from the market

model (Model 2). Results are also reported for Models 3

and 4 that include the FOUNDER variable. Both the

WLS and OLS results are shown in the table. The results

are consistent across models and methodologies.

Overall the models are highly significant with F-

values greater than 2.5 for most models, an indication

that the models are significant at the 1% level or better.

R2 values are about 10%, which are comparable to those

observed in previous studies on cross-sectional regres-

sion models that attempt to explain abnormal return

behavior.

As predicted, the estimated coefficient of firm size is

positive and statistically significant at the 10% level or

higher based on a two-tailed test. The stock market

reaction to announcements to form or join industry

exchanges is higher for larger firms in the sample,

reflecting the bargaining power of larger firms to capture

more of the benefits. The estimated coefficient for

inventory efficiency as measured by industry-adjusted

zero at the 1%, 5%, and 10% levels, respectively, based on a two-tailed

BUYHUB + bINDIND. Model 2: AR-Market = b0 + bS(SALES) + -

b0 + bI(INVT) + bOM(OM) + bF(FOUNDER) + bBHBUYHUB + bIN-

) + bBHBUYHUB + bINDIND. AR-Mean, AR-Market and OM are

iables. INVT is a number or ratio. Sales is in $billion. WLS estimates

up-wise heteroskedasticity model in Greene (2003). Sales are mean

. Maximum variance inflation factor (VIF) for all models was 1.23.


inventory turns is positive and statistically significant at

the 5% level or better in all regressions. The results

indicate that firms with higher inventory efficiency obtain

higher abnormal returns from industry exchange

participation.

The parameter estimates for cost efficiency, as

measured by industry adjusted operating margin (OM),

are positive in all regressions reported. However, the

estimates in only three out of the eight regressions are

statistically significant at the 10% level. These results

provide weak support for Hypothesis 4.

To explore the sensitivity of our results to possible

outliers, we repeat our analyses by excluding six cases

with jDFFITj> 2ffiffiffiffiffiffiffiffip=n

pwhere p is the number of

variables including the intercept and n is the sample size

(see Kutner et al., 1996). The results for the OLS

regressions for Models 1–4 are presented in Table 8

under TRIMMED SAMPLE 1 (the results for the WLS

regressions are similar and hence not reported here).

The parameter estimates are consistent with the results

without trimming. Note however that the significance

levels of the parameter estimates are stronger in the

Table 8

Cross-sectional regression analysis using trimmed samples


test. Trimmed sample 1 excludes six influential data points with jDFFITj> 2

Trimmed sample 2 further excludes 17 firms that reported supply chain prob

Model 1: AR-Mean = b0 + bS(SALES) + bI(INVT) + bOM(OM) + bBHB

bI(INVT) + bOM(OM) + bBHBUYHUB + bINDIND. Model 3: AR-Mean =

DIND. Model 4: AR-Market = b0 + bI(INVT) + bOM(OM) + bF(FOUNDER

the tables. AR-Mean and OM are expressed as percentages. Ind, BUYHUB an

is in $billion. Sales are mean centered by subtracting the mean of the sam

models with trimming when compared to the models

without trimming. In particular, the parameter estimates

of operating margin (OM), which measures cost

efficiency, are highly significant and positive in all

four models. The results in Table 8 provide stronger

support for all the hypotheses. Also note that the R2

values under TRIMMED SAMPLE 1 are higher when

compared to the non-trimmed sample in Table 7.

A possible issue regarding the use of inventory turns as

a measure of supply chain efficiency is that low inventory

may lead to part shortages, work stoppages and

backorders. However, recent research shows that

advanced supply chain technologies allow a firm to

achieve both low inventory and low stock-out rates at the

same time and not necessarily at the expense of each

other (Lee et al., 1999). Nevertheless, to explore this

issue, we trimmed the sample further by excluding all

firms that made any announcement in the year prior to the

announcement of joining the exchange regarding part

shortages, inventory problems, shipping delays, and

other similar problems related to low inventory. We used

a broad search string to search publications in the Factiva


�ffiffiffiffiffiffiffiffiffiffip=N

pwhere p is the number of parameters and N is the sample size.

lems related to parts shortages or difficulty meeting customer demand.

UYHUB + bINDIND. Model 2: AR-Market = b0 + bS(SALES) +

b0 + bI(INVT) + bOM(OM) + bF(FOUNDER) + bBHBUYHUB + bIN-

) + bBHBUYHUB + bINDIND. Only OLS estimates are reported in

d Founder are 0/1 indicator variables. INVT is a number or ratio. Sales

ple from the sales for each firm. The maximum VIF is 1.18.


database for each firm in the sample, and then read the

articles to identify firms that had such problems. We

specifically focused on problems related to the shortage

of components and inability to meet customer demand.

An additional 17 firms were excluded through this

analysis. The results are reported in Table 8 under

TRIMMED SAMPLE 2. The coefficient estimates for the

SALES, INVTand OM variables are significant at the 5%

level or higher and provide strong support for our

hypotheses.

To evaluate the relative importance of each predictor

variable (process efficiency and sales), we performed

hierarchical regressions utilizing Trimmed Sample 2 for

Models 1 and 2. The results are shown in Table 9. Model

1 utilizes mean adjusted market returns, while Model 2

utilizes market model returns as the dependent variable.

The control variables (BUYHUB and IND) were

entered first, followed by the process efficiency

variables (OM and INVT), and finally the firm size

variable (Sales). The process efficiency variables

increased the R2 by approximately 8%, while the firm

size variable further increased the R2 by 4%.

A possible concern regarding the regression analysis

is that larger firms can have greater process efficiency,

resulting in high correlation between the size (Sales)

Table 9

Hierarchical regression analysis using trimmed sample


test. Model 1A: AR-Mean = b0 + bBHBUYHUB + bINDIND (control varia

bI(INVT) + bOM(OM) (control variables and process efficiency variab

bI(INVT) + bOM(OM) + + bS(SALES). Model 2A: AR-Market = b0 + bBH

ket = b0 + bBHBUYHUB + bINDIND + bI(INVT) + bOM(OM) (control varia

b0 + bBHBUYHUB + bINDIND + bI(INVT) + bOM(OM) + + bS(SALES). O

expressed as percentages. IND, BUYHUB and Founder are 0/1 indicator var

centered by subtracting a constant (the mean of the sample) from the sales

and process efficiency (OM and INVT) variables. We

address this in our analysis in three ways. First, the

Pearson correlation coefficient between the Sales and

the OM and INVT variables in our sample was very low

(0.01 and 0.06, respectively), perhaps due to the

industry adjustments that we perform on the variables.

Second, the maximum variance inflation factor (VIF)

for all models we evaluated was 1.23, well below the

recommended cut-off of 10 (Kutner et al., 1996),

indicating that multi-collinearity was not a significant

problem in the analysis. Third, as described above, we

performed hierarchical regressions to determine the

relative importance of the size and process efficiency

variables, and observed significant increases in R2 for

each.

6. Summary and discussion

6.1. Summary of results

This paper analyzes the shareholder value effects of

setting up industry exchanges, a prominent mechanism

used to achieve supply chain integration in practice. It

utilizes event study methodologies to investigate the

value created by such exchanges for its participants by


bles only). Model 1B: AR-Mean = b0 + bBHBUYHUB + bINDIND +

les only). Model 1C: AR-Mean = b0 + bBHBUYHUB + bINDInd +

BUYHUB + bINDInd (control variables only). Model 2B: AR-Mar-

bles and process efficiency variables only). Model 2C: AR-Market =

nly OLS estimates are reported in the tables. AR-Mean and OM are

iables. INVT is a number or ratio. Sales is in $billion. Sales are mean

value for each firm. The maximum VIF is 1.17.


measuring the stock market reaction (or abnormal

returns) associated with announcements to form or join

industry exchanges. For the whole sample of 144 firms,

we find that abnormal returns from participation in

industry exchanges are positive but marginally sig-

nificant. In the sub-sample of 88 firms (exchange

founders) who were part of the original announcements

to form the exchange, the abnormal market reaction

based on the market (mean adjusted) model is 0.88%

(1.13%) and statistically significant. Cross-sectional

regression analysis reveals that abnormal returns from

exchange participation are more positive for larger firms

and for firms that have higher cost and inventory

efficiencies.

6.2. Discussion of results and implications

There are four broad implications of our results.

First, supply chain integration is an important issue

that has been extensively studied in the operations

management literature. The general consensus, pri-

marily based on surveys and perceptual measures of

performance, is that integration leads to improved

performance (Narasimhan and Kim, 2002; Christensen

et al., 2005; Frohlich and Westbrook, 2001). Con-

sortium based, industry exchanges facilitate integra-

tion among existing trading partners and the valuation

benefits (abnormal returns) primarily capture the

benefits of integration, rather than those that arise

from an expanded marketplace. Thus, our results

provide additional evidence on the linkage between

supply chain integration and performance by demon-

strating that the stock market reacts positively to

integration activities among supply chain partners.

Consistent with other research (Hendricks and Singhal,

2003; Chen et al., 2005), we document that the stock

market does value supply chain related activities, and

managers must be proactive in communicating such

activities to the market.

Second, our results show that the abnormal return

from industry exchange participation is contingent upon

the existing capabilities of the firm. Specifically, we find

that firms that have better process efficiency (higher

cost efficiency and better inventory performance)

benefit more from participation. The arguments here

are similar to that in the strategy literature on the role of

absorptive capacity in obtaining the benefits from

technology acquisitions (Zahra and George, 2002). That

is, firms that have higher process efficiency have the

necessary knowledge, expertise and track record to take

advantage of integration technologies such as industry

exchanges. Thus, future research in operations manage-

ment that investigates the link between supply chain

integration and firm performance should take into

account the role of process efficiency in obtaining

benefits. Further, managers must build such process

capabilities before implementing technology to facil-

itate integration.

Third, higher benefits to larger firms indicate the

need to develop proper safeguards and incentives for

the smaller participants of the exchange. Many

smaller participants are concerned that larger firms

will use the flexibility provided by the exchange to

squeeze margins and increase competition (Luening,

2000). Supply chain integration mechanisms such as

industry exchanges provide significant cost and

efficiency advantages for the industry as a whole.

However, the network externality benefits are reduced if

many are reluctant to participate, highlighting the need

for safeguards and incentives for the smaller firms.

Finally, our results have implications for the

current debate in the IT literature on whether shared

infrastructures such as industry exchanges level the

playing field by reducing infrastructure investments and

providing a common platform for inter-firm collabora-

tion. Carr (2003) argues that IT is a commodity resource

that raises productivity for all, and provides competitive

advantage to none. This view would argue that smaller

firms benefit more from industry exchanges because

they lack the resources to implement proprietary

integration mechanisms on their own, while larger

firms can implement private solutions for their partners.

The commodity view of IT also posits a ‘‘regression to

the mean’’ perspective where less efficient firms stand

to gain the most from industry exchanges, while more

efficient firms lose the advantage they have from

superior processes. Our findings indicate that the stock

market thinks otherwise; that existing advantages such

as size and process efficiency play an important role in

determining the abnormal market reaction from

participation announcements.

6.3. Limitations and future research

The primary limitations of this study stem from two

sources: the event study methodology used to measure

value creation through the exchange and the proxies

developed from accounting data to represent variables

such as bargaining power and process efficiency.

Abnormal returns associated with announcements to

form or join industry exchanges measure the value

created through the exchange under assumptions of

market efficiency. While efficient market assumptions

have dominated the literature in Finance and Econom-


ics, there is some evidence of market inefficiency

(Shleifer and Vishny, 2003). Thus, while there are many

benefits of the event study approach, evaluation of

benefits and costs through longitudinal accounting data

can reveal long-term impact on the participants. The

challenge, of course, would be to isolate the impact of

exchange participation from other confounding factors

that can also affect quarterly or annually reported

accounting data.

We have used proxies based on accounting data to

measure constructs such as bargaining power and

process efficiency. While accounting data is audited,

objective and readily available, it is also difficult to

measure complex constructs through such secondary

data sources. The typical benefits of industry exchanges

described in the trade literature, such as a reduction in

IT costs, rationalization of the supplier base, better

vendor information, reduced cycle times, and improved

on-time performance can only be documented through

primary data collection approaches. An approach that

combines primary data collection from industry

exchanges and secondary market data analysis is a

viable future research direction.

We believe that qualitative and grounded approaches

would also be valuable in developing theories that

explain benefits and success factors of industry

exchanges. Case studies of successful and unsuccessful

exchanges can also increase our understanding of the

internal workings of an exchange and provide specifics.

Industry-wide improvements in performance and

changes in industry structure due to the exchange are

other interesting research issues that can be explored

through such research.

Finally, comparisons of specific mechanisms of

supply-chain integration (e.g. third party, private,

consortium based, global, etc.) are also worth exploring

through qualitative, quantitative and empirical

approaches. As such mechanisms become more

common, the most effective mechanism to deliver

value is an important and practical research issue.

Acknowledgements

We are grateful to the associate editor and reviewers

for their many helpful comments and suggestions.

References

Anonymous, 2000. B2B Savings Could Cut Sticker 3.6%, Study Says,

Automotive News 78 (5876), 46.

Barua, A., Lee, C.H., Whinston, A.B., 1996. The calculus of reen-

gineering. Information Systems Research 7 (4), 409–428.

Basu, S., 1977. Investment performance of common stocks in relation

to their price-earnings ratios: a test of the efficient market hypoth-

esis. The Journal of Finance 32 (3), 663–682.

Bollen, K.A., Paxton, P., 1998. Detection and determinants of bias in

subjective measures. American Sociological Review 63 (3), 465–

478.

Bresnahan, T., Greenstein, S., 2001. Technical progress and co-

invention in computing and in the uses of computers. Brookings

Papers on Economic Activity: Special Issue on Microeconomics,

pp. 1–77.

Brown, S.J., Warner, J.B., 1985. Using daily stock returns: the case of

event studies. Journal of Financial Economics 14 (1), 3–31.

Cachon, G., Fisher, M., 2000. Supply chain inventory management

and the value of shared information. Management Science 46 (8),

1032–1048.

Carr, N.G., 2003. IT doesn’t matter. Harvard Business Review 81, 5–

12.

Carow, K., Heron, R., Saxton, T., 2004. Do early birds get the returns?

An Empirical investigation of early-mover advantages in

acquisitions. Strategic Management Journal 25 (6), 563–585.

Chatterjee, D., Pacini, C., Sambamurthy, V., 2002. The shareholder

wealth and trading volume effects of information technology

infrastructure investments. Journal of Management Information

Systems 19 (2), 7–42.

Chen, A.H., Siems, T.F., 2001. B2B e-marketplace announcements

and shareholder wealth. Economic and Financial Review, First

Quarter, pp. 12–22.

Chen, H., Frank, M., Wu, O., 2005. The JIT revolution: what actually

happened in the inventories of American companies between 1981

and 2000? Management Science 51, 1015–1031.

Christensen, W.J., Germain, R., Birou, L., 2005. Build-to-order and

just-in-time as predictors of applied supply chain knowledge and

market performance. Journal of Operations Management 23, 470–

481.

Christiaanse, E., 2005. Performance benefits through integration hubs.

Communications of the ACM 48 (4), 95–100.

Coff, R.W., 1999. When competitive advantage doesn’t lead to

performance: the resource-based view and stakeholder bargaining

power. Organization Science 10 (2), 119–132.

Cua, K.O., McKone, K.E., Schroeder, R.G., 2001. Relationship

between implementation of TQM, JIT, and TPM and manufactur-

ing performance. Journal of Operations Management 19, 675–694.

Dwyer, F.R., Walker, C.C., 1981. Bargaining in an asymmetrical

power structure. Journal of Marketing 45 (1), 104–115.

Francis, J., Hanna, J.D., Vincent, L., 1996. Cause and effects of

discretionary asset write-offs. Journal of Accounting Research

34 (Supplement), 117–134.

Frohlich, M.T., 2002. e-Integration in the supply chain: barriers and

performance. Decision Sciences 33 (4), 537–547.

Frohlich, M.T., Westbrook, R., 2001. Arcs of integration: an interna-

tional study of supply chain strategies. Journal of Operations

Management 19, 180–200.

Frooman, J., 1999. Stakeholder influence strategies. Academy of

Management Review 24 (2), 191–205.

Fullerton, R.R., McWatter, C.S., Fawson, C., 2003. An examination of

the relationship between JIT and financial performance. Journal of

Operations Management 21, 383–404.

Greene, W.H., 2003. Econometric Analysis, 5th ed. MacMillian, New

York, NY.

Gunasekaran, A., Ngai, E.W.T., 2004. Information systems in supply

chain integration and management. European Journal of Opera-

tional Research 159, 269–295.


Hendricks, K.B., Singhal, V.R., 2003. The effect of supply chain

glitches on shareholder value. Journal of Operations Management

21 (5), 501–522.

Hendricks, K.B., Singhal, V.R., Stratman, J.K., 2007. The impact of

enterprise systems on corporate performance: a study of ERP,

SCM and CRM system implementations. Journal of Operations


Huson, M., Nanda, D., 1995. The impact of just-in-time manufactur-

ing on firm performance in the US. Journal of Operations Manage-

ment 12, 297–310.

Im, K.S., Dow, K.E., Grover, V., 2001. Research report: a reexamina-

tion of IT investment and the market value of the firm—an event

study approach. Information Systems Research 12 (1), 103–117.

Inkpen, A., Beamish, P.W., 1997. Knowledge, bargaining power and

instability of international joint ventures. Academy of Manage-

ment Review 22 (1), 177–202.

Johnson, M.E., Whang, S., 2002. E-Business and supply chain man-

agement: an overview and framework. Production and Operations

Management 11 (4), 413–423.

Kambil, A., Van Neck, E., 1998. Reengineering the Dutch flower

auctions: a framework for analyzing exchange organizations.

Information Systems Research 9 (1), 1–19.

Karafiath, I., 1994. On the efficiency of least squares regression with

security abnormal returns as the dependent variable. Journal of

Financial and Quantitative Analysis 29 (2), 279–300.

Ketokivi, M.A., Schroeder, R.G., 2004. Perceptual measures of per-

ceptions: fact of fiction? Journal of Operations Management 22

(2), 247–264.

Kothari, S.P., Warner, J.B., 2006. Econometrics of event studies. In:

Espen Eckbo (Eds.), Handbook of Empirical Corporate Finance.

Elsevier/North Holland.

Kulp, S.C., Lee, H.L., Ofek, E., 2004. Manufacturer benefits from

information integration with retail customers. Management

Science 50 (4), 431–444.

Kutner, M.H., Natschtschiem, C.J., Wasserman, W., Neter, J., 1996.

Applied Statistical Linear Models, 4th ed. McGraw Hill/Irwin.

Lee, H.G., Clark, T.H., 1997. Market process reengineering through

electronic market systems: opportunities and challenges. Journal

of Management Information Systems 13 (3), 113–136.

Lee, H.G., Clark, T.H., Tam, K.T., 1999. Can EDI benefit adopters?

Information System Research 10 (2), 186–195.

Lieberman, M.B., Demeester, L., 1999. Inventory reduction and

productivity growth. Linkages in the Japanese automotive indus-

tries. Management Science 45, 466–485.

Luening, E., 2000. Regulation of B2B market sparks debate, CNet

News, March 6, 2000.

Magretta, J., 1998. The power of virtual integration: an interview with

Dell computer’s Michael Dell. Harvard Business Review 76 (2),

72–85.

Mahadevan, B., 2003. Making sense of emerging market structures in

B2B e-commerce. California Management Review 46 (1), 86–100.

Malkiel, B., 2003. The efficient market hypothesis and it’s critics.

Journal of Economic Perspectives 17 (1), 59–82.

McAfee, A., 2002. The impact of enterprise information technology

adoption on operational performance: an empirical investigation.

Production and Operations Management 11 (1), 33–53.

McKone, K.E., Schroeder, R.G., Cua, K.O., 2001. The impact of total

productive maintenance practices on manufacturing performance.

Journal of Operations Management 19, 39–58.

Milgrom, P., Roberts, J., 1990. The economics of modern manufac-

turing: technology, strategy and organization. American Eco-

nomic Review 80 (3), 511–528.

Narasimhan, R., Kim, S.W., 2002. Effect of supply chain integration

on the relationship between diversification and performance:

evidence from Japanese and Korean firms. Journal of Operations


Pagell, M., 2004. Understanding the factors that enable and inhibit the

integration of operations, purchasing and logistics. Journal of

Operations Management 22, 459–487.

Parker, G.G., Anderson, E.G., 2002. From buyer to integrator: the

transformation of the supply chain manager in the vertically

disintegrating firm. Production and Operations Management 11

(1), 75–91.

Petersen, K.J., Handfield, R.B., Ragatz, G.L., 2005. Supplier integra-

tion into new product development: coordinating, product, process

and supply chain design. Journal of Operations Management 23,

371–388.

Peteraf, M.A., 1993. The cornerstones of competitive advantage: a

resourcebasedview. StrategicManagementJournal14(3),179–191.

Pfeffer, J., 1982. Organizations and Organization Theory. Pitman

Publishing, Marshfield, MA.

Porter, M., 2001. Strategy and the Internet. Harvard Business Review

79, 63–78.

Powell, T.C., Dent-Micallef, A., 1997. Information technology as

competitive advantage: the role of human, business and technol-

ogy resources. Strategic Management Journal 18 (5), 375–405.

Roumiantsev, S., Netessine, S., 2005. Should inventory policy be lean

or responsive? Evidence for US Public companies. Working Paper.

The Wharton School, University of Pennsylvania, PA.

Sahin, F., Robinson, E.P., 2005. Information sharing and coordination

in make-to-order supply chains. Journal of Operations Manage-

ment 23 (6), 579–598.

Schmenner, R.W., Swink, M., 1998. On theory in operations manage-

ment. Journal of Operations Management 17, 97–113.

Shleifer, A., Vishny, R.W., 2003. Stock market driven acquisitions.

Journal of Financial Economics 70, 295–311.

Subramani, M., Walden, E.A., 2001. The impact of e-commerce

announcements on the market value of firms. Information Systems

Research 12 (2), 135–154.

Swamidass, P.M., Nair, S.S., Mistry, S.I., 1999. The use of a neural

factory to investigate the effect of product line width on manu-

facturing. Management Science 45 (11), 1524–1538.

Tatsiopoulos, I.P., Ponis, S.T., Hadzillias, E.A., Panayiotou, N.A.,

2002. Realization of the virtual enterprise paradigm in the clothing

industry through e-business technology. Production and Opera-

tions Management 11 (4).

Vickery, S.K., Jayaram, J., Droge, C., Calantone, R., 2003. The effects

of an integrative supply chain strategy on customer service and

financial performance: an analysis of direct versus indirect rela-

tionships. Journal of Operations Management 21, 523–539.

Watson, N., Zheng, Y., 2005. Decentralized serial supply chains

subject to order delays and information distortion: exploiting

real-time sales data. Manufacturing & Service Operations Man-

agement 7 (2), 152–168.

Yan, A., Gray, B., 1994. Bargaining power, management control and

performance in United States-China joint ventures: a compara-

tive case study. Academy of Management Journal 37 (6), 1478–

1512.

Yan, A., Gray, B., 2001. Antecedents and effects of parent control in

international joint ventures. Journal of Management Studies 38

(3), 393–416.

Zahra, S.A., George, G., 2002. Absorptive capacity: a review, recon-

ceptualization, and extension. Academy of Management Review

27 (2), 185–203.

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