Market Myopia and Firm Specific Risk: Reexamining the Financial
Value of Information Technology Decisions
Anitesh Barua*
Deepa Mani**
*University of Texas at Austin
** Indian School of Business
January 2012
Abstract: Firm-level studies of the financial impacts of Information Technology (IT) events have often
focused on announcement period returns based on the capital asset pricing model (CAPM). This approach
may have two sets of distinct but related limitations for many classes of IT events. First, the use of
announcement period assumes the market is efficient in its assimilation and pricing of all information
about the event. However, a firm not be aware of the organizational changes required for success of the IT
event, or may not have the incentive to disclose such information for competitive reasons. Either way, we
expect many types of IT events to be characterized by low information disclosure, which, along with
investor biases, is likely to impede efficient pricing of the IT event by financial markets. Second, event
studies in Information Systems (IS) largely rely on CAPM, which considers only systematic risks in the
pricing of expected returns on IT assets, and assumes that idiosyncratic or firm-specific risks are
eliminated through efficient diversification. Yet one of the foundations of the IS discipline is the notion
that IT matters, largely because firms have different capabilities to develop, deploy and manage IT
resources to create value. Thus there is a disconnect between a basic theoretical tenet of the IS field and
the methodology deployed to assess the value of IT events. We develop a framework involving the
maturity of the IT event and the scope of complementary changes to assess the extent of information
disclosure and idiosyncratic risk, which, in turn, indicate the suitability of different methodologies to
assess financial value of the IT event. We empirically illustrate our approach for the case of large scale IT
and IT-enabled outsourcing, and conclude with implications for future IS research.
1.
Introduction
One stream of firm-level studies of Information Technology (IT) impacts has deployed market-based
measures of performance developed from capital market theories (Im et al. 2001; Melville et al. 2004).
Such market based models assess the impact of IT “events” on shareholder value by estimating the
difference between stock market returns conditional on the event and the unconditional expected returns
over the announcement period. This approach assumes that the market efficiently prices the IT event
during the announcement period itself. Further, these studies are based on the capital asset pricing model
(CAPM), which considers only systematic risks stemming from the macro environment of a firm, but not
firm specific or idiosyncratic risks (Bettis 1983; Chatterjee et al. 1998). We posit that depending on the
nature of the IT event, there may be serious issues involving the assumption of efficient pricing during the
announcement period as well as the exclusion of firm specific risk. Therefore we seek to highlight these
potential pitfalls and develop a framework to assess the suitability of various market based assessment
approaches for different categories of IT events.
The use of change in shareholder wealth associated with the event as a measure of value of the
underlying technology investment is appealing for various reasons. Stock prices directly measure
shareholder value, reflect several relevant aspects of firm performance such as growth and profitability,
and can be easily obtained for all publicly traded firms (Lubatkin and Shrieves 1986). Thus event studies
have become an important part of the literature on the business value of IT. They have been used to assess
the financial value of investments in innovative IT applications (Dos Santos et al. 1993), Enterprise
Resource Planning (ERP) (Ranganathan and Brown 2006), electronic commerce (Subramani and Walden
2001) and CIO appointments (Chatterjee et al. 2001).
We draw on the fields of finance and strategy to argue that market based approaches using
announcement period returns should be used with caution for a large class of IT events. The
announcement period returns method is based on the efficient markets hypothesis (EMH), which posits
that at any given time, the stock price of a firm reflects all publicly available information, and, in turn, the
collective belief of investors regarding future prospects of the firm. However, for many types of IT
events, the market may not have enough information to be efficient during the announcement period.
While it may appear that greater disclosure by a firm of its IT capabilities will help reduce its cost of
capital and increase its valuation, research in IS (e.g., Clemons 1991; Mata et al. 1995) finds that the
extent to which IT confers competitive advantage is a function of its heterogeneity and imperfect
mobility. This implies that information disclosure will likely engender competitive parity to an extent not
justified by improvement in investor forecasts. This is especially pertinent to information on
complementary investments in governance structures or work processes that determine heterogeneity in
returns from IT investments (e.g., Brynjolfsson et al. 2002). For example, if a firm has conducted a
comprehensive, ex ante cost-benefit analysis of a proposed investment in IT and business processes, a
detailed disclosure of such information may have a negative impact in that competitors may act on such
information to the detriment of the firm making the announcement.
IT events may also involve substantial uncertainty in the realization of benefits. Any downward
revision to the ex ante benefits communicated in a detailed announcement will result in an adverse
reaction from financial markets, thus providing a disincentive for elaborate market disclosure. As a result,
announcements of IT investments typically entail the nature and broad objective of the investment,
vendors involved, and a qualitative statement of anticipated benefits. Generally there is no mention of
risks, options, managerial capabilities and investments in processes and human capital that may be needed
to realize payoffs from the IT investment. Yet another scenario involves a firm undertaking an IT
initiative without being aware of the complementary changes required for the successful assimilation of
the technology applications (Barua et al. 1996; Brynjolfsson et al. 1996; Zhu 2004; Tanriverdi 2005). In
such cases, there will be no disclosure of the critical non-IT facets of the event, making it difficult for the
market to assess the likelihood of success within the announcement period.
A twin conundrum that limits the applicability of the market models involves their dependence on
the CAPM, which incorporates only systematic risks in the pricing of expected returns on assets (Fama
and MacBeth 1973), and which assumes that unsystematic, firm specific risks are eliminated through full
diversification 1. However, theory and research in the field of IS are based on the premise that firms differ
in their ability to assimilate technology into their work processes, and that such differential capability is
an important source of competitive advantage (Armstrong and Sambamurthy 1999; Bharadwaj 2000;
Bhatt and Grover 2005). This argument, often invoked in response to Carr’s (2003) controversial thesis
that “IT doesn’t matter”, underscores the point that even though an IT application or innovation may be
easily available to most firms today, not all of them will be able to take advantage of such an opportunity
due to firm specific differences, engendering significant variance in firm performance.
There is also some empirical evidence that IT risk, which is idiosyncratic to the firm, explains
variance in firm returns. The IT business value and productivity literature reports abnormally high
estimates of returns to IT investments 2. Further, Dewan et al. (2007) estimate about 30% of the gross
return on IT investment may be explained by the premium associated with IT risk. They also find that IT
risk is a significant contributor to firm risk. Thus there is both theoretical and empirical justification as to
why firm specific differences (and hence, idiosyncratic risk) matter, and why deployment of
methodologies based on CAPM may not be appropriate for many IT events.
Despite such theory and evidence, to test the hypothesis that IT investments or decisions matter,
IS researchers have often used methodologies for inferring information from stock prices that assume the
alternative hypothesis. Thus, while on the one hand, IS theories have touted the idiosyncratic nature of
how IT creates value as well as the risk of implementing IT successfully, market value based empirical
studies have used variants of the capital asset pricing model that automatically assume that “IT (or any
other idiosyncratic factor) doesn’t matter.”
We propose a framework to qualitatively assess the market efficiency and idiosyncratic risk of an
IT event which is consistent with the foundation of IS research that individual firm capabilities vary, and
1
Systematic risk or the firm’s beta is defined as the ratio of covariance between the returns to the security and those
to the market portfolio to variance in returns to the market portfolio. Unsystematic or firm-specific risk is defined as
the variance in a security's returns that cannot be explained by movements in the market portfolio.
2
Brynjolfsson and Hitt (1996) find that the average marginal product of IT capital in their sample of firms was 81%
while that of non-IT capital was 6.3%. Brynjolfsson et al. (2002) find that an increase of $1 in IT capital stock is
associated with an increase of $10–15 in the market valuation of firms. Similarly, Anderson et al. (2003) report IT
valuation multiples ranging from 26 to 62.
which offers a resolution to the twin challenges posed by the assumptions of efficient markets and
idiosyncratic risk. The framework incorporates the maturity and the organizational scope of the IT event.
Maturity is the extent to which the technical and organizational nuances of a technology event are wellunderstood and documented (Weiss and Dale 1998). The scope of an IT event comprises complementary
changes that are required for the success of the event. For example, ERP implementations will require
major changes in business processes, decision rights and incentives to be effective (Jarrar et al. 2000).
Our theoretical framework enables us to assess the incompleteness of information disclosure and
idiosyncratic risk characterizing an IT event, thereby providing guidance in selecting an appropriate
methodology to assess the value of the event. We illustrate the use of the framework with an analysis of
the financial value of large scale outsourcing of IT and IT-enabled business functions. We find that the
financial market prices these outsourcing announcements inefficiently, resulting in long-term abnormal
returns following the implementation of the outsourcing contract and a reversal of some results
established in prior research. Further, we find that firm specific factors that influence returns to the
outsourcing initiative are a significant contributor to the idiosyncratic risk of the firm. Together, the
results emphasize the need to shift from market models to those that include firm characteristics in pricing
IT events that are low in maturity and high in the scope of organizational change that they engender.
The balance of this article is organized as follows. In section 2, we delineate the theoretical
concerns in the use of announcement period returns to assess the financial value of IT events. Section 3
introduces our framework involving the maturity and scope of an IT event to assess the suitability of
different methods to estimate the financial value of the event. Section 4 applies our framework to large
scale outsourcing initiatives to provide empirical evidence of market myopia and the contribution of firm
attributes, which influence long-term returns to the IT event, to the idiosyncratic risk of the firm. Section
6 enumerates implications for research and practice, and section 7 concludes.
2. Challenges to the Use of Short-term Market Performance Measures
Event studies are an important part of the literature in management and economics. Their usefulness
primarily stems from the assumption that the magnitude of abnormal returns following an organizational
event provides an estimate of the impact of the event on shareholder value creation, which, according to
many schools of thought, is the most important objective of a publicly traded firm. Several studies in IS
(see Table 1) have used announcement period returns to provide evidence of value creation or destruction
by strategic decisions in a firm. In doing so, these studies implicitly assume (i) efficient pricing of the
decision by financial markets, and (ii) diversification of idiosyncratic risk. However, while prior research
in IS has assumed market efficiency and largely estimated the magnitude and direction of announcement
returns, event studies have been used in Finance to test market efficiency:
“…Systematically nonzero abnormal security returns that persist after a particular type of corporate
event are inconsistent with market efficiency. Accordingly, event studies focusing on long-horizons
following an event can provide key evidence on market efficiency…” (Kothari and Warner 2004):
Tests of market efficiency involve estimation of abnormal returns over longer horizons, typically twelve
months or more. Interest in the finance literature in long-horizon studies was spurred by accumulation of
evidence inconsistent with the efficient markets hypothesis and the identification of risk factors other than
market risk that explained variance in firm returns. Yet, there is little research in IS that examines whether
the response of the financial market to IT choices or events is slow, incomplete or even biased. Table 1
summarizes selected articles identified in our review of IS journals3 from 2000 to 2008. These studies
involve a range of technology decisions from internal IT investments to externalization of IT assets to
appointments to IT leadership positions. All these studies assume market efficiency and consider event
periods ranging from one to ten days. They do not analyze long-term returns following the
implementation or announcement of the IT event.
-----------------------------Insert Table 1 about here
-----------------------------3
We searched for keywords such as “event study”, “financial value”, “market value”, “stock market returns”,
“shareholder value”, “information systems” etc. in literature databases such as Business Source Premier (EBSCO),
JSTOR, and Science Direct. We identified twenty three papers that were published in major IS journals such as MIS
Quarterly, Information Systems Research, Management Science and Journal of Management Information Systems.
Twenty two of the sample papers were published between 2001-2008; the exception was DosSantos et al. (1993).
2.1 Efficiency of Market pricing
The assumption of market efficiency is critical to inferences drawn from short-horizon event studies.
However, we argue that this assumption is not particularly suited to the evaluation of complex IT
decisions. Research in finance (e.g., Eberhart et al. 2003) distinguishes between various types of
information that financial markets respond to - scaled measures of past performance such as book to
market ratios, long-term investments such as R&D, and managerial decisions such as stock repurchases.
The finance literature also distinguishes between tangible and intangible information. This distinction is
described in the finance literature as the difference between explicit measures of past performance such as
sales or cash flow information that can be observed from the firm’s accounting statements and the
orthogonal component of information about future performance, which is unrelated to past accounting
performance (Daniel and Titman 2006). Daniel and Titman (2006) demonstrate that there is no
discernible relation between a firm’s future stock returns and tangible performance information. The
returns are related to realizations of intangible information. Thus, markets are imprecise in interpreting
intangible performance information. Although the finance literature posits a relation between intangible
information and long-term abnormal returns, the nebulous definition of intangible information in terms of
what it is not limits the ability of the field to explicitly define sources of intangible information At best,
Daniel and Titman (2006) conjecture intangible information is related to firms’ growth opportunities.
We suggest that large and complex IT decisions are akin to long-term investments. Further, even
though IT events are represented by their direct costs when expensed in accounting statements, we argue
that the benefits of the IT decision reflect intangible information on future cash flows. This is because
many decisions such as governance choices for IT outsourcing or management processes to assimilate
new technologies that impact returns to the IT investment are rarely announced formally, engendering
important acquisition costs for such information. Further, even if this information were disclosed,
financial markets may incur important learning costs in interpreting and in turn, pricing the information.
Information acquisition and learning costs incurred in pricing IT events lead to multiple cognitive
biases that are well documented in the finance literature (Oler et al. 2008). For instance, the prior
hypothesis bias describes the proclivity of investors to make choices based on prior beliefs regarding
relationships between investment variables despite evidence to the contrary (Levine 1971; Pruitt 1961).
This behavior often leads investors to severe and systematic errors. Similarly, the representativeness bias
(Tversky and Kahneman, 1974) reflects the inability of decision makers to accurately assess the
representativeness of available information so that the true value of the information in making predictions
is unrecognized. The effects may be severe for complex, ambiguous information events that are difficult
to interpret (Oler et al. 2008; Daniel and Titman 2006). For example, Zuckerman (2004) finds that both
volume and volatility of stocks with “incoherent” market identities are higher because such stocks are
more likely to be subject to differences in the interpretive models used to understand material
information. Similarly, Zajac and Westphal (2004) find that the efficiency of market reactions to certain
corporate practices is contingent on the degree of institutionalization of the practice. They find that stock
market reactions to stock repurchase plan announcements dramatically changed from negative in the late
seventies, when repurchase plans were largely seen as an indicator of lack of attractive investment
prospects for the firms, to positive in the mid eighties, when they were viewed as a means of preventing
managers from expending free cash flow on “empire-building projects”.
The above biases may be particularly salient to IT investments. For example, investment in ERP
systems may result in a lukewarm initial reaction from the market based on several prior failures in ERP
implementation or the initial dip in firm productivity due to lack of familiarity with resultant new systems
and/or processes. Similarly, since there is mixed evidence regarding operational gains from business
process outsourcing (BPO) (Mani et al. 2010), the market may not react significantly to a large BPO
announcement. The market may wait for more definitive signals to price the event or correct its price as
firms realize major benefits from such investments.
2.2 Idiosyncratic risk
Another assumption that is integral to short horizon studies is that of diversification of firm specific risk.
We first discuss why this assumption has been challenged in finance and strategy, and then proceed to
discuss the implications for the IS discipline. Chatterjee et al. (1999) point out that “investors are not as
diversified” as assumed by asset pricing models used to calculate expected returns unconditional on the
event. They propose that the concept of risk premium is multivariate comprising macroeconomic, tactical,
strategic, and normative risks, and it is dynamic, involving an ongoing interplay between elements of the
firm's activities and market forces. Bettis (1983) argues that the incorporation of unsystematic risk in
asset pricing models is especially important since the very idiosyncratic risk that the equity markets do
not reward lies at the heart of strategic management.
In addition to these theoretical arguments, empirical studies also contend that markets care about
more than just systematic risk. For instance, Chan, Hamao and Lakonishok (1991) find that earnings and
cash flow yield, size, and book to market ratio are significantly correlated with expected returns in the
Japanese market. These variables proxy for risks not otherwise accounted for by beta. Bhandari (1988)
concludes that “a measure of risk different from beta is required” in evaluating investment performance
and estimating the cost of capital of firms. Other studies which find idiosyncratic risks to be important
predictors of stock returns include Basu (1983), Brown, Harlow and Tinic (1993), and Merton (1987).
The most prominent empirical challenge to the predictive validity of systematic risk comes from
Fama and French (1992, 1996). They examine several variables such as size, book to market ratio,
leverage and dividend yield, and find that the cross-sectional variation in expected returns of assets is
explained by two characteristics – size and book-to-market. Beta, the traditional measure of systematic
risk, explains insignificant cross-sectional variation in expected returns once size is taken into account.
Consequently, they propose a factor model comprising three risk factors – market risk, as measured by
MKT, is long in the market portfolio and short in the risk-free asset, growth risk, as measured by SMB, is
long in a portfolio of small capitalization stocks and short in large capitalization stocks, and value risk, as
measured by HML, is long in high book-to-market stocks and short in low book-to-market stocks. The
Fama and French (1993) three factor model has emerged as the dominant choice of model for estimation
of expected returns in the finance literature. More recently, Daniel and Titman (1997) argue that the
superior returns to the factors described by Fama and French (1993) are not compensation for factor risk;
rather, firms in similar factor portfolios have similar firm characteristics. These characteristics are a
component of undiversifiable firm risk, and explain abnormal returns.
These arguments are especially pertinent to the pricing of IT events. Heterogeneity in
performance of IT investments is often attributed to firm level characteristics including organizational
structures, processes and other capabilities that complement the IT investment (Armstrong and
Sambamurthy 1999; Brynjolfsson et al. 1996). These firm attributes are an important predictor of
volatility in cash flows from the IT investment and hence, an important component of IT risk.
Further, the risk associated with large IT decisions, due to the latter’s broad scope and salience to
a firm’s strategy and operations, is likely to affect firm risk. The initial failure of Nestle’s ERP project had
such an adverse firm-wide impact that an HSBC securities analyst downgraded the stock (Worthen 2002).
Similarly, Nike’s operational problems, resulting from the implementation of its supply chain
management system, is said to have caused the firm’s stock price to decline by 20% in 2000 (Koch 2004).
Dewan et al. (2001) find that (a) about 30% of the gross return on IT investment corresponds to the risk
premium associated with IT risk, and (b) IT risk is an important contributor to firm risk. Together, the
results suggest that the pricing of IT events must shift from models that consider market risk alone to
include idiosyncratic firm characteristics and risk. In the next section, we address these issues and offer a
theoretical resolution for the use of performance measures in market value based IS research.
3. Reconciliation of Market Performance Measures with Research in Information Systems
We present a theoretical framework to classify IT events in terms of the maturity of the event and the
scope of organizational changes required to successfully assimilate the event. We argue that such
classification helps us choose the right methods to price or asses the financial value of the event.
IT maturity has been studied in terms of the S-shaped adoption curve, which captures how the
total number of adopters increases over time. Weiss and Dale (1998) argue that IT risks decrease with
longevity. Mature technologies are “easy to use and possess the comforting attributes of consistent
performance and predictable life-cycle costs” (Weiss and Dale 1998). A technology’s longevity confers
advantages of customer trust and a history of incremental improvements that have resulted in very stable
products and services. Organizational learning that accompanies mature technologies also contributes to
lower risks of implementation (Boudreau and Rose 2000). When a technology is immature, successful
implementation may entail multiple risks such as knowledge of critical success factors or adoption by
business partners. While some firms may manage such uncertainty well, others may face significant
challenges. If a firm is aware of such challenges, it may not have the incentive to disclose information on
such challenges or its potential response to contingencies that may arise. While the maturity of the
technology itself may be more of a systematic risk factor, the idiosyncratic risk arises from the variance in
the ability of the firm to assimilate a relatively new and unproven technology. Thus with a less mature
technology, there is lower information disclosure and higher idiosyncratic risk.
The scope or the extent of the effort required to manage the IT event also determines the level of
idiosyncratic risk and information disclosure. Barua et al. (1996), Brynjolfsson et al. (1996), Zhu (2004)
and Tanriverdi (2005) have argued that successful IT investments need complementary organizational
changes that involve strategies, business processes, decision rules, and authority. Some IT decisions such
as the adoption of a new supply chain system require major technology and business process changes, not
only within the focal firm, but also in partner organizations (Barua et al. 2004).These changes required for
IT events, if not properly managed, can lead to significant organizational turmoil, even “jeopardizing the
core operations of the implementing organization” (Hong and Kim 2002). The larger the scope of an IT
event, the larger is its potential impact on the performance of the firm. As outlined earlier, the firm may
not have the incentive to disclose information on such risk at the time of the announcement for
competitive reasons. Thus, the greater the investment in complementary organizational capital required to
manage an IT initiative, the higher is the idiosyncratic risk, and lower is the information disclosure.
Together, the maturity and the organizational scope of the IT event determine the ex ante
uncertainty in cash flows from the IT investment and, in turn, the market efficiency in valuing the
investment (Figure 1). We discuss four cases below to illustrate our framework:
Scope of the IT event 
Figure 1: IT Event Attributes and Market Efficiency
Highest idiosyncratic risk
Lowest information disclosure
Lowest market efficiency
E.g., Large-scale
outsourcing of IT services
and business processes
High idiosyncratic risk
Low information disclosure
Low market efficiency
E.g., Enterprise Resource
Planning
Medium idiosyncratic risk
Medium information disclosure
Medium market efficiency
E.g., Cloud computing
Lowest idiosyncratic risk
High information disclosure
High market efficiency
E.g., Electronic Data
Interchange
Maturity of the IT event 
Electronic Data Interchange (EDI) is characterized by high maturity owing to its existence and
widespread adoption over nearly three decades (http://www.edi-guide.com/edi-history.htm).
Moreover, being a document exchange standard, the adoption of EDI may not require significant
complementary changes in the firm and may be easily replicable across firms. Thus, as shown in Figure 1,
EDI and other document related standards belong to the category of high maturity-low scope events. As a
consequence, this category of IT announcements is likely to be priced efficiently by capital markets.
Cloud computing, which is defined as “Internet-based computing [with] shared servers
[providing] resources, software, and data to computers and other devices on demand, as with
the electricity grid” (http://en.wikipedia.org/wiki/Cloud_computing), is a relatively new mode of
technology delivery, which is rapidly evolving along technical and organizational dimensions. Although
technical challenges such as data security and performance guarantees are gradually getting resolved,
organizations are still trying to understand how their IT infrastructure strategies can leverage cloud
computing effectively to extract benefits such as flexibility, scalability, and increased availability of their
infrastructure, and reduced implementation and maintenance costs (Rittinghouse and Ransome 2010). As
cloud computing evolves, organizations may leverage it to reduce time to market and empower mobility
in the future which will require major changes in business processes or even organizational strategies.
However, today, since it mainly affects the technology infrastructure choices of a firm, it requires little
complementary investments relative to certain other IT events discussed in this section. In spite of the low
scope of organizational changes, some market inefficiency is still expected in pricing cloud computing
investments due to the differential ability of firms to assimilate this evolving technology.
In its modern form, ERP has been in existence since the early nineties (Hong and Kim 2002).
Many academic and industry studies have uncovered best practices and critical success factors for ERP
implementations. To that extent, the decision to implement an ERP system today can be considered a
mature event. However, it is well established that ERP success is contingent on several complementary
organizational changes, including assimilation of new processes and work systems, end user training, top
management support, and other change management procedures. The business press is replete with
examples of high-performing firms (e.g., Nestle, Hershey, etc.) which experienced major snafus in their
ERP implementations because of their inability to make some of these complementary changes (Worthen
2002; Katz 2001). Thus, in spite of high maturity of the event, ERP implementations may be associated
with high idiosyncratic risk, low information disclosure, and in turn, high market inefficiency.
To provide some prima facie empirical evidence that markets may significantly underreact to
ERP events, we consider two published studies by Ranganathan and Brown (2006) and Hayes et al.
(2001), which found announcement period returns of 1.47% and 0.19% respectively. In line with the
efficient markets hypothesis, we interpret the increase in the market value of a firm during the
announcement period as the net present value (NPV) of the ERP project. We use the ERP cost/revenue
ratio from documented case studies to create a proxy for ERP costs 4, and calculate the implied ERP cost
for the Ranganathan and Brown (2006) and Hayes et al. (2001) studies as shown in Table 2.
4
In 1999, Hershey spent $115 million on an ERP system (Katz 2001), while Nestle U.S.A., the $8.1 billion
subsidiary of Nestle, invested $210 million on an ERP project to overhaul its existing IT systems (Worthen 2002).
---------------------------------Insert Table 2 about here
----------------------------------
If markets are efficient, then the NPVs in Table 2 represent the true value of ERP
implementations in the sample. However, are these NPVs large enough for rational managers to invest in
large, complex, and transformative initiatives like ERP? One survey found that 80% of ERP projects
experience cost overruns that range between 40 and 85%, while another found an average cost overrun of
178% (http://www.snartak.com/erp.htm). It would take a cost overrun of 40.2% (the minimum reported
overrun in the above survey) and just 6.2% in the cases of Ranganathan and Brown (2006) and Hayes et
al. (2001) respectively to drive the corresponding NPVs to zero. At the highest reported overrun of 178%,
the corresponding NPVs would be -439M and -$316M respectively. Given the well-documented risks of
cost overruns as well as major delays and other organizational disruption, senior management would not
have approved projects with such high costs and relatively low benefits. Assuming firms made rational
investment decisions, we can infer that the market under-reacted to these ERP events during the
announcement period, and that only long-term returns can capture the impact of such an investment.
Large scale outsourcing of IT and IT enabled business functions, the focal case for the balance of
the paper, is a relatively new phenomenon (for example, a majority of our sample of 100 largest
outsourcing engagements occurred after 2000) compared to, say, ERP or EDI. Firms engaging in large,
complex outsourcing deals are still experimenting with appropriate governance mechanisms, reflected in
the misfit between governance choices and the nature of the outsourced task in a significant percentage of
outsourcing engagements (e.g., Mani et al. 2010, Susarla et al. 2009). Further, large-scale outsourcing
engagements entail significant effort in contract design and management, and communication and
coordination between the client and vendor, all of which necessitate important complementary
investments in processes, control structures, and technologies (Bapna et al, 2010). Given the low maturity
Using the ratio of ERP investment to revenues, we obtain 2.90 for Hershey (revenue = $3.97 billion in 1999), and
2.59 for Nestle U.S.A. as the upfront cost of ERP systems as a percentage of revenue. We use (2.895 + 2.59)/2 =
2.74 as the average ERP/Revenue ratio. We assume 10% of the upfront cost as annual maintenance cost and 8% as
the cost of capital.
and expansive scope of large scale outsourcing initiatives, we expect the latter to be characterized by high
idiosyncratic risk, low information disclosure, and lowest market efficiency in our framework.
These four technology decisions are representative of classes of IT investments that differ in
maturity and scope of organizational change that they induce. The key implications of this framework are
that IT events with lower maturity and higher scope are expected to be associated with lower information
disclosure, higher idiosyncratic risk and higher market inefficiency relative to events involving higher
maturity and lower scope. The assessment of the financial value of these events should use long-term
abnormal returns and incorporate firm specific factors. In the next section, we use the context of large
scale outsourcing initiatives to illustrate the importance of choosing market based measures that are
consistent with the maturity and scope of IT decisions.
4. Market Myopia and Idiosyncratic Risk in the Context of Outsourcing: An Illustrative Example
4.1
Theory and Hypotheses Development
To illustrate the use of our framework and assessment of the financial value of large, complex IT events,
we collected and analyzed data on the 100 largest outsourcing initiatives implemented between 1996 and
2005. At the end of 2008, the outsourcing of IT and IT-enabled business processes constituted over 50
percent of the average firm’s technology budget (Gottfredson et al. 2005). Further, outsourcing has
transitioned from a cost saving tool for transaction intensive functions such as payroll to include the
externalization of end-to-end business functions such as product development or financial management
that directly impact firm competitiveness. This shift in the nature of outsourcing is evidenced in the
growing value of outsourcing contracts – Gartner reported that in 2007, the average outsourcing contract
value was $204 million while the total contract value for that year was $30 billion.
However, despite their expanded reach and impact, researchers and practitioners have highlighted
the high failure rate of emergent large scale outsourcing initiatives5, emphasizing that organizations
5
70% of the respondents in a 2005 survey by Deloitte Consulting expressed significant dissatisfaction with their outsourcing
projects. Similarly, a survey conducted by Bain Consulting found that although 82% of large firms in North America engage in
remain largely unprepared to manage the transformation brought about by these initiatives. As a
consequence, although the cost of a large outsourcing decision is tangible because it is announced and
expensed, its benefits are uncertain and reflect “intangible” information about future cash flows. The
uncertainty in future cash flows and allied unpredictability in lifecycle costs emphasizes the low levels of
maturity of emergent, broad outsourcing initiatives in the firm.
Further, the management of large-scale, end-to-end services outsourcing poses a set of challenges
that are relatively new and still evolving. Prior research (e.g., Mani et al. 2010; Gopal et al. 2003) shows
that the performance of such outsourcing initiatives is contingent on choice of appropriate governance
mechanisms, including contractual structures and relationship management processes. In particular, the
outsourcing firm must design governance mechanisms that are aligned with the uncertainty and
complexity of the outsourcing engagement to realize performance gains. Simpler and more stable tasks
such as payroll or benefits management may readily scale to the provider’s existing capabilities and
performance expectations may be more easily specifiable. In this case, the ownership and control of the
outsourced process may be transferred to the provider through an arms length contract that provides the
right incentives to meet service expectations. On the other hand, more complex and dynamic tasks such as
financial planning or new product development are characterized by significantly higher transaction costs.
The provider must be incentivized to make relationship specific investments that add strategic value to the
outsourcing firm and often needs to coordinate extensively with various constituents in the outsourcing
firm through the lifecycle of the contract. The need to discern these unique management challenges and
invest managerial time and effort in solutions in large outsourcing initiatives reflects the relatively greater
scope of these initiatives. Figure 2 below reflects the transition in outsourcing from the periphery to the
core of the firm and the allied shift in maturity and organizational scope of the outsourcing initiative.
Figure 2: The Shift in Maturity and Scope of Outsourcing Initiatives
Scope of the IT Event
Emergent, large
scale, end-to-end
outsourcing
BPO, almost half of the respondents sayinitiatives
their outsourcing programs fall short of expectations. According to SAP INFO
Solutions, four out of five BPO contracts inked today will need to be renegotiated within two years. Further, 20% of all such
contracts will collapse (SMR Intelligence 2006).
Early transactional
outsourcing
initiatives
The relatively lower maturity and expanded scope of large scale outsourcing events render them
an ideal context to study whether markets are efficient or myopic in their evaluation of complex IT
decisions. In order to efficiently price the outsourcing event, financial markets must be able to distinguish
between different types of outsourcing initiatives in terms of various task and relational attributes, and
their associated management challenges. The outsourcing firm itself may not recognize the above
challenges and solutions, and even if it does, it may not have the incentive to disclose such information
for competitive reasons. Thus, financial markets must incur information acquisition costs to efficiently
price the outsourcing event. In addition to costs the market must expend on acquiring information on firm
and task attributes that may impact the outcome of outsourcing, the interpretation of such information
involves important information learning costs. When information is inexpensive, or when informed
traders get precise information, then the market price will reveal most of the informed traders’
information (Grossman and Stiglitz 1980). However, prices cannot perfectly reflect costly information,
“since if it did, those who spent resources to obtain it would receive no compensation” (Grossman and
Stiglitz 1980). The acquisition and learning costs associated with information on the outsourcing event
render arbitrage costly. Thus, the assumptions that all markets are always in equilibrium and always
perfectly arbitraged are inconsistent with the nature of the event. We expect that information asymmetries
introduced by acquisition and learning costs will be persistent and take a long time to be corrected by
arbitrage forces. This engenders abnormal performance over a long horizon following the outsourcing
event so that the price response to this IT event refutes the principle of efficient markets:
Hypothesis 1 – Financial markets will be myopic rather than efficient in incorporating information on
large-scale outsourcing of IT and IT enabled business functions. This results in significant long-term
abnormal returns following the implementation of the outsourcing initiative.
The risk of outsourcing a modular and simple business function like payroll is relatively low and
unlikely to affect firm level risk. However, large scale outsourcing such as the externalization of end-toend functions such as supply chain management or new product development may carry significant risk
for the entire firm, for any failure is likely to cause major disruptions to its operations.
Outsourcing risk starts with the very decision to externalize IT services or one or more business
function(s). Given that firms differ considerably in their work processes and routines, the outcomes of
externalization of end-to-end functions may have high variance. For example, a firm may have modular
processes while another may have a tightly coupled value chain; if both firms decide to outsource a given
business function, the former is likely to extract more value from the arrangement, everything else being
equal. Similarly, many firms are less prepared than others to handle the management of the outsourcing
relationship as well as ensuing transformations in the management of their value chain.
A second contributor to outsourcing risk stems from the differential ability of firms in selecting
and executing the right governance mechanism. Transaction Cost Economics (TCE) focuses on relational
uncertainty, and provides guidance on the choice of governance structures to mitigate hazards in
exchange relationships. For example, Mani et al. (2009) posit that simple outsourcing tasks should be
governed by arms length relationships and well-defined performance criteria, while more complex
engagements may require elaborate relational governance mechanisms. Misfit between the ideal and
actual governance choices lowers operational performance and client satisfaction (Mani et al. 2010).
These above challenges are further exacerbated by factors such as the uncertainty in the firm’s
business environment (which necessitates frequent changes in the outsourced task(s)) and the level of
coordination required with the service providers (Barua et al. 2008). Mani et al. (2011) demonstrate that
more complex outsourcing tasks require significant attention to joint action with the vendor and
investment in communication technologies. Similarly, experience in outsourcing management and prior
association with vendor(s) are expected to help clients realize favorable outcomes from the outsourcing
engagement (Mani et al. 2009). In other words, various firm attributes make it easier or more difficult for
some firms to succeed in their outsourcing endeavors than others. Given the scale and impact of the IT
event, these attributes that determine abnormal returns to the outsourcing initiatives are an important
component of idiosyncratic or firm specific risk. Thus we posit:
Hypothesis 2 –Firm characteristics explain variance in the cross section of abnormal returns as well as
idiosyncratic risk in large-scale outsourcing of IT and IT enabled business functions.
4.2
Data
Our data comprises of the hundred largest outsourcing initiatives implemented between 1996 and 2005.
The largest outsourcing contracts have important advantages over a similar random sample. First, the
firm-level economic impact of outsourcing is more likely to be detected when the contract value is large.
The average lifetime contract value in our sample is $922 million. The aggregate contract value of $83
billion represents approximately 18 percent of the total outsourcing contract value for the sample period.
Second, our focus on large deals reduces the probability of confounding events; firms are less likely to
sign as large contracts immediately prior or subsequent to the outsourcing agreement. Our approach to
sample selection follows prior research in finance (e.g., Healy, Palepu and Ruback 1992) that examines
the performance impact of managerial decisions such as mergers or acquisitions.
Information on the hundred largest outsourcing initiatives and their governing contracts is
obtained from International Data Corporation’s (IDC) services contracts database. IDC tracks outsourcing
contracts signed around the world with the database comprising nearly 21,000 service contracts. This data
dates back to 1996, and is the primary input to this study. We use Lexis-Nexis and the Dow Jones News
Retrieval Service to verify and supplement IDC information on announcement and signing dates. We use
the Center for Research on Security Prices (CRSP) files to compute abnormal stock returns, and the
Compustat Basic and Research files to assess firm characteristics, develop operating performance
measures, and estimate insider trading activity. Our final sample comprises the 100 largest outsourcing
contracts that satisfy two requirements. First, the firm must be publicly traded on a major United States
stock exchange. Second, information on the contract used to govern the outsourcing initiative must be
available. Our final sample of 100 contracts includes 66 firms.
Table 3 describes the characteristics of our sample. Panel A lists the distribution of our sample
across SIC codes and types of outsourcing initiatives while Panel B describes certain characteristics of the
sample outsourcing firms and contracts. Outsourcing initiatives in our sample are classified as one of
Information Systems (IS) Outsourcing, Business Process Outsourcing (BPO) or Processing Services, and
Application, Network and Desktop Management. In the case of IS outsourcing services, the service
provider takes ownership of and responsibility for managing all or large part of a client’s IS infrastructure
and operations, often involving customized, one-to-one engagements. If only the network and desktop
environment are outsourced, the spending is classified as network and desktop management services.
Likewise, if only the application environment is outsourced, the spending is classified as applications
outsourcing. Network management services involve the outsourcing of the operations of a specific
segment or entire network communication system of a company. Desktop management captures contracts
for which several desktop services are outsourced to the same provider. BPO involves outsourcing
business processes or functional areas (such as logistics or HR), with performance metrics tied to new
business opportunities, revenue generation, customer satisfaction and business transformation.
-----------------------------Insert Table 3 about here
------------------------------
4.3
Empirical Analyses and Results
First, we test the market efficiency hypothesis (Hypothesis 1) by examining the significance of long-term
abnormal returns following the outsourcing event. We also compare the magnitude and direction of longterm abnormal returns with that of announcement period returns to establish market myopia. Finally, we
test Hypothesis 2 by analyzing the impact of various attributes that are idiosyncratic to the outsourced
task environment on long-term abnormal returns and idiosyncratic risk of the firm.
Long-term Abnormal Returns: As a first test of market efficiency, we report the three year buy and hold
abnormal returns (BHAR) for all sample outsourcing firms following the implementation of the
outsourcing contract. We use the characteristic based matching approach, also known as the event-time
portfolio approach to calculate long-term abnormal returns. Mitchell and Stafford (2000) describe event
time BHAR as “the average multi-year return from a strategy of investing in all firms that complete an
event and selling at the end of a pre-specified holding period versus a comparable strategy using
otherwise similar nonevent firms”. Thus, the BHAR for stock i over holding period T is:
BHARi ,T = BHRi ,T − BHRm ,T
,
(1)
where BHRi,T is the buy-and-hold return of the sample firm and BHRm,T is the buy-and-hold return of the
matching control firm over the same period. Here, the buy-and-hold return for holding period T beginning
time a through time b is:
b
BHRi ,T = [∏ (1 + rit ) − 1]
t =a
,
(2)
where rit is the return for firm i in month t; in this study, period a is the month after the contract effective
month and period b is the earlier of the firm’s delisting date or the end of the three year period following
the contract effective date.
Following Barber and Lyon (1997), we consider an industry-, size- and book-to-market matched
sample as a benchmark of returns post implementation of the outsourcing contract. We begin with a group
of firms in the same two-digit SIC code as the sample that do not engage in a strategically significant
outsourcing initiative as of the beginning of the contract effective year. From this initial screen, a matched
firm is defined as the firm that has the lowest absolute value of the joint difference in size (equity
capitalization) and book-to-market ratio (equity capitalization divided by book value of equity).
As noted earlier, prior research (e.g., Mani et al.2010) finds that the contractual risks – the risk of
not choosing the right contract to overcome incentive conflict and facilitate cooperation between
participant firms – and procedural risks – the risk of not establishing the right relationship management
processes and information exchange mechanisms to overcome cognitive conflict and facilitate
coordination between the client and the provider – increase with uncertainty and complexity of the
outsourcing initiative, thereby, adversely impacting potential gains from outsourcing. The greater the
uncertainty and complexity of the outsourcing initiative, the more difficult it is for participant firms to
anticipate contingencies in the relational and task environments and design appropriate contractual and
informational responses to these contingencies. Therefore, we expect that the magnitude and direction of
the BHAR too will vary by the uncertainty and complexity of the outsourcing initiative.
We draw on prior research to identify a set of firm, process and relational attributes that may
influence such uncertainty and complexity and in turn, the transaction costs and operational and financial
gains from outsourcing. These attributes include uncertainty in the business requirements of the client
(UNCER), coordination requirements of the outsourced task (COORDN), prior association between the
client and the provider (PRIOR), and experience of the outsourcing firm in managing similar outsourcing
initiatives (EXP). The operationalization of these variables is described in Table 4.
-----------------------------Insert Table 4 about here
------------------------------
Panel A of Table 5 reports the mean three year BHAR from outsourcing for the lowest and
highest 30 percent of firms ordered by UNCER, INTER, PRIOR and EXP. In line with the greater
transaction costs of managing complex, uncertain relationships, we find that outsourcing initiatives in the
highest 30 percent of UNCER and INTER and the lowest 30 percent of PRIOR and EXP were
characterized by negative BHAR. In contrast, outsourcing initiatives in the lowest 30 percent of UNCER
and INTER and the highest 30 percent of PRIOR and EXP were characterized by positive BHAR. The
“difference” column reports the difference in returns between the two sub-samples. Panel B reports
equivalent results for the sample of firms for which all three years’ return data is available - the returns
reported in Panel A underestimate those in Panel B.
As a robustness check, we also estimated BHAR by contract type. These results are reported in
Panel C of Table 5. Outsourcing contracts may be classified as fixed price or variable price contracts.
Prior research (e.g., Crocker and Reynolds 1993; Gopal et al.2003; Mani et al. 2010) finds that given the
tradeoff between ex ante contract design costs of more complete fixed price contracts and ex post
contractual inefficiencies and coordination costs of more incomplete variable price contracts, highpowered incentives and high renegotiation costs of fixed price contracts, the latter are an optimal choice
in the case of relatively simple, stable outsourcing initiatives while variable price contracts are often
chosen to govern more complex, dynamic outsourcing initiatives. Thus, the contract type is a signal of the
complexity of the outsourcing initiative. Consistent with the results in Panel A, we find that the sample of
fixed price contracts was associated with large positive abnormal returns, while that of variable price
contracts experienced large negative abnormal returns.
Thus, the results in Table 5 confirm Hypothesis 1 – markets are unable to efficiently price large
scale outsourcing events, low in maturity and expansive in their scope, resulting in long-term abnormal
returns following the event. In particular, at the announcement of the event, the market is unable to
distinguish between different types of outsourcing initiatives in terms of the management challenges
posed by different firm, task and relational factors and the consequent performance impacts.
-----------------------------Insert Table 5 about here
------------------------------
Announcement Period Returns: As further evidence of market myopia, we report announcement period
returns and wealth effects for our sample of initiatives in Table 6. Prior research in IS on the financial
value of outsourcing initiatives (see Table 1) has almost exclusively focused on announcement period
returns. Consistent with this stream of research, we estimate daily abnormal returns for the firms as:
, where
day t while
are firm specific abnormal returns. Here,
denotes the daily returns for firm i on
are the predicted daily returns. Following prior research on strategic alliances (e.g.
McGahan and Villalonga 2003), we estimate the following market model:
, where
denotes the corresponding daily returns to the value weighted S&P 500. An
estimation period of 150 days [-170,-21] prior to the announcement date is used to estimate the market
model. Significance of the returns is based on the market model standardized residual method with
Scholes-Williams (1977) betas. The estimates from this model are then used to predict daily returns for
each firm i over three event windows 6 – a 3 day period [-1,+1], a 12 day period [-10, +1] and a 20 day
period [-10,+9] surrounding the announcement of the outsourcing initiative.
Table 6 report results of the above event analyses for the lowest and highest 30 percent of firms
ordered by UNCER, COORDN, PRIOR and EXP. Returns are positive and significant for initiatives
characterized by high coordination complexity. The magnitude and direction of returns are consistent with
the findings of prior research reported in Table 1. It is likely that the market views complex initiatives as
characterized by strong strategic intent, and hence, rewards them. Similarly, the markets penalize prior
association and reward firms for choosing a new vendor. Interestingly, both these outcomes are reversed
in the estimation of long-term abnormal returns. The results are robust to estimation by contract type. The
insignificance of announcement period returns and reversal of the direction of returns for various
categories of outsourcing initiatives provide evidence of market myopia in support of Hypothesis 1.
-----------------------------Insert Table 6 about here
------------------------------
Idiosyncratic Risk and Returns: In order to test Hypothesis 2, we analyze whether (a) characteristics of
the IT event contribute to the long-term BHAR of the firm, and (b) characteristics of the IT event are an
important component of idiosyncratic firm risk. In this case, we analyze whether attributes of the
outsourced task environment – UNCER, COORDN, PRIOR and EXP – influence the BHAR following
the implementation of the outsourcing contract and the idiosyncratic risk of the outsourcing firm.
6
Prior research in IS has focused almost exclusively on the 3 day window. However, given the scale of the
initiative, we expect information leakage in the market, and report returns for longer event windows as well.
Table 7 presents the results of our analyses. Model I estimates the influence of the firm-, processand relationship-level attributes on the three year BHAR following the implementation of the outsourcing
contract while controlling for self-selection into the outsourcing decision and choice of outsourcing
contract 7. Firm and time effects are also controlled for. Robust standard errors clustered by firm are
reported in parentheses. We find that uncertainty in the outsourcing firm’s business environment,
coordination requirements and prior association with the vendor impact returns to the outsourcing
initiative. The results suggest that complex outsourcing initiatives, characterized by changing business
requirements and higher levels of coordination, are correlated with dissipation of efficiency gains from
outsourcing. This is likely because of greater risks of cost overruns, opportunism, and coordination
failures in these environments. There are “severe limits to what can be achieved through contracting”
(Banerjee and Duflo 2002) to mitigate these risks, and outsourcing firms must often invest in appropriate
relationship management procedures to counteract the problems created by the limitations of contracting.
Yet, empirical research (Mani et al. 2009) on outsourcing governance finds that the lack of organizational
awareness or preparedness for such investments results in efficiency losses from outsourcing. Our results
are consistent with this outcome. Further, they suggest that efficiency losses in complex outsourcing
environments are exacerbated by lack of prior association between the outsourcing firm and the vendor.
More importantly, our results emphasize that idiosyncratic characteristics of the firm that impact
its ability to manage an IT event are important determinants of the financial value created by the IT event.
Our measure of buy-and-hold abnormal returns controls for industry, value and growth risks. Our results
suggest that in addition to these risks, idiosyncratic characteristics of the outsourcing firm, relationship
and task environment also influence returns to the initiative.
Model II analyzes whether the characteristics of the outsourced task environment that impact
returns to the outsourcing initiative are an important component of the idiosyncratic risk of the firm. We
measure the idiosyncratic risk for each of the outsourcing firms as the annualized standard deviation of
residuals from a market model regression based on daily returns (Bali et al. 2005, Ali et al. 2003, Malkiel
and Xu 2006). We find that uncertainty in the firm’s business environment positively contribute to the
idiosyncratic volatilities of the firm. The results in Table 7 also suggest unobserved firm attributes that
influence the choice of a contract are associated with long-term returns and idiosyncratic risk of the firm.
Overall, our results confirm that firm capabilities to manage IT events are important determinants
of returns to the event. Further, our findings suggest that these capabilities are an important element of
idiosyncratic firm risk. Thus, the use of asset pricing models such as CAPM that assume diversification of
idiosyncratic risk and only consider non-diversifiable market risk in pricing IT events is inconsistent with
the very nature of IT events. Abnormal returns to IT events may be estimated after controlling for market
risk and other risks such as value or growth risks; however, the association between these abnormal
returns and firm characteristics must be understood to accurately price the IT event.
-----------------------------Insert Table 7 about here
------------------------------
5.
Implications for Future Research
The above theoretical arguments and empirical evidence have important implications for what measures
of financial value should be used to price IT events in consonance with the basic tenet of IS research firms differ in their ability to manage and extract value from IT investments and such differences are a
key source of competitive advantage. In this context, we identify below research issues that require
attention while testing hypotheses pertaining to the financial value of IT management decisions.
Time horizon for calculation of returns to the IT investment: Our study emphasizes the need to consider
the maturity of the underlying technology and the scope of complementary investments in organizational
structures and process in valuing the IT event. These attributes provide guidance regarding the expected
level of information disclosure and hence the magnitude of information acquisition and learning costs
incurred by financial markets in valuing the IT event, and in turn, the efficiency of the market in valuing
the IT decision. As already noted, in many studies published in the field of information systems and
strategy during the past decade, researchers have used announcement period returns to assess the financial
value of strategic technology investments. In doing so, these studies ignore important information
acquisition and learning costs associated with information on these IT events (Hypothesis 1) and assume
the market model as a predictor of returns unconditional on the event (Hypothesis 2). This introduces
dissonance between hypotheses and their empirical formulation and testing (Chatterjee et al. 1999).
Further, IT events are often the outcome of a series of related events, their announcement being one of
them. To this extent, announcement period returns may underestimate the full magnitude of abnormal
returns related to the IT event.
The above problems are minimized by extending the time horizon for calculation of abnormal
returns. Long event windows also introduce bias by increasing the likelihood of capturing extraneous and
related events in the event window. Related events are often common to the sample experiencing the
strategic information event and hence, may be captured by systematic controls in the returns model. The
influence of extraneous events may be reduced by choosing a window length that represents an
“acceptable tradeoff between estimating reasonably stable coefficients and capturing the impact of
extraneous events” (Lubatkin and Shrieves 1986). Window lengths of three to five years have been shown
to achieve this objective. They reduce the likelihood of confounding events since firms are unlikely to
engage in equally economically significant initiatives immediately prior or subsequent to the strategic
information event. Hence, the sample size is largely unaffected in these window lengths. In summary, the
properties of long-term abnormal returns render them an appropriate measure of price response that is
consistent with the nature of strategic information events. However, issues of expected return modeling,
aggregation of security specific returns and the calibration of their statistical significance are critically
important in long horizon event studies (Kothari and Warner 2004), and must be addressed.
Model of predicted returns: As argued in Hypothesis 2, CAPM based measures of risk and return are
inappropriate in determining predicted returns unconditional on information events with low maturity
and/or high scope. We have demonstrated in the context of outsourcing that incorporating firm specific
factors such as experience and prior association and task characteristics such as coordination requirements
can help better explain the variance in abnormal returns. Our prescription of including firm and task
specific factors in IT value research also relates to sample stratification issues. Studies in finance largely
analyze uncategorized samples of events that are assumed to be drawn from a homogeneous population
(Lubatkin and Shrieves 1986). However, emergent research in economics suggests that identifying
discrete event characteristics and comparing performance outcomes associated with each characteristic
may reveal important patterns in the market’s price response. For instance, in our sample of the 100
largest outsourcing initiatives implemented between 1996 and 2005, we find no significant long-term
abnormal returns. However, the mean three year buy-and-hold abnormal return for firms engaged in
complex outsourcing initiatives governed by variable price contracts and characterized by dynamic
process requirements, high coordination costs, lack of prior cooperative association with the provider, and
limited experience is -21.2 percent. The equivalent return for relatively simple outsourcing initiatives
governed by fixed price contracts is 17.5 percent. Similarly, Gompers et al. (2003) construct a
“Governance Index” to proxy for the level of shareholder rights at about 1500 large firms during the
1990s. An investment strategy that bought firms in the lowest decile of the index (strongest rights) and
sold firms in the highest decile of the index (weakest rights) earned abnormal returns of 8.5 percent per
year during the sample period. These studies demonstrate how theoretical stratification of the sample can
reveal important patterns in returns to information events.
A unique strength of the field of IS is its ability to ascribe meaning to performance differences
between organizations and the persistence of such differences. Thus, the field is best positioned to
identify theoretically distinct groups within a sample of firms experiencing the event to identify what
drives price responses to the event. For instance, prior research in strategy suggests that heterogeneity in
performance across joint ventures may be explained by various factors - limitations posed by partners’
size, age and prior performance, the compatibility between partners, resource complementarity, partners’
inability to transfer resources to the venture, or the cumulative experience of the firm in managing similar
ventures. The assumption that a sample of joint ventures is drawn from a homogenous population results
in the reporting of returns that fail to reflect these regularities in returns to joint venture activity. Thus,
moving forward, it is important that research in IS leverage its theoretical knowledge of strategic
information events to identify drivers of abnormal returns.
Implications for Practice: Our framework has implications for asset managers who seek investment
opportunities based on major IT events while avoiding the pitfalls. Given the lack of information
disclosure regarding firm specific competence and risks involved in managing an IT event with low
maturity and/or high scope of complementary changes, asset managers must incur the cost of learning
about a firm’s basic ability to manage or mitigate the risks associated with the IT event and to extract
maximum value through effective governance and the inculcation of a culture of business innovation.
Traditional asset pricing models have assumed efficiency of capital markets, and largely focused on the
management of beta or market risk in portfolios. However, our results underscore the inefficiency of
markets in pricing certain managerial decisions that reflect intangible information on future cash flows.
This, in turn, points to the opportunity in shifting the focus of asset management from beta to the quality
of management decisions that impact firms’ competitiveness and strategic value.
Our study also suggests that since markets under-react to limited information disclosure, senior IT
managers and business executives should carefully weigh the benefits of more detailed disclosure (when
the firm is aware of the management challenges as well as its strengths in managing such complexity)
against potential opportunity costs in the form of its specific skills and expertise becoming common
knowledge to the advantage of its competitors.
7. Conclusion
The assessment of the financial or market value of large IT events is an important avenue for IS research.
It may be the most convincing means to demonstrate the value of IT to C-level executives, who are
concerned about shareholder value. However, such research must deal with many challenges, stemming
primarily from the high cost of information acquisition and learning that are necessary for capital markets
to price an IT event efficiently. We developed a framework involving the maturity and the scope of
complementary changes associated with an IT event to assess the expected level of market efficiency in
pricing the IT event. We argued that for three out of four possible classes of IT events in the framework,
we expect to witness low levels of information disclosure, high idiosyncratic risk and low market
efficiency. A direct implication of our study is that research on market value of IT events will generally
involve significant under-reaction from the market during the announcement phase, and that long-term
abnormal returns are likely to capture the true effects of major IT decisions.
We also underscore the major limitation of market models such as CAPM that assume away firm
specific idiosyncratic risks. We argue and demonstrate that this assumption is contrary to the central
notion of the IS field that the capability to manage and create value from IT differs widely across firms.
Thus our prescription, based on both theory and empirical evidence, is to incorporate factors other than
market risk that are specific to the ability of a firm to manage the IT event as well as characteristics of the
IT event. Such an approach will help uncover the contribution of a firm’s IT management capabilities to
market value as a direct response to the assertion of “IT Doesn’t Matter.”
We have also outlined the implications of our study to practice and asset managers. By investing
in information gathering regarding firm capabilities and learning about the challenges of managing the
information event, they will be able to better predict if the delayed reaction of the market will be positive
or negative based on firm and task characteristics. Given the increasing share of IT events in the overall
spend of the firm, understanding the nuances of market reactions to important IT events will help the asset
manager in maximizing returns.
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Table 1: Selected Studies that Examine the Financial Value of IT Investments (Adapted from Roztocki and Weistroffer 2009,
Ranganathan and Brown 2006)
Reference
Type of IT Investments
Long-term focus?
Event Window
Dewan
(2007)
and
Ren
e-Commerce initiatives
No
(-1, +1), (-10, +10)
Meng
(2007)
and
Lee
Value of IT investments to
China versus the United
States
Transformative, informate
or automate
No
(0, +2)
No
(-1, +1)
Exploratory
versus
exploitative investments
No
(-1, +1)
ERP Investments
No
(-1,0), (0,+1),
(-1,+1), (0, +2),
(-2, +2)
Infrastructure
versus
Application Investments
No
(-1,0), (0,+1),
(-1,+1), (-2, +2)
Hayes et al. (2001)
ERP
No
(0, +1)
Im et al.(2001)
IT investments between
1981-1996
No
(-1,0)
Dehning
(2003)
et
al.
Hunter (2003)
Ranganathan
Brown (2003)
Chatterjee
(2002)
et
and
al.
Magnitude of Abnormal
Returns
(-1, +1): CAR of 0.15%,
2.94%, and -1.29% in 1996,
1998 and 2000 respectively
(-10, +10): CAR of 10.89%,
and -4.49% in 1998 and 2000
respectively
China: 1.08%
USA: insignificant
Firms
investing
in
transformational IT: 1.51%
Firms in industries with
transform industry IT strategic
role: 1.41%
Firms making exploitative
investments: -0.68%
Firms making exploratory
investments: -1.11%
Overall returns of 1.47% in (0,
+1) event period. Significant
gains were found for all of the
expanded event windows.
Overall returns of 1.99% in (0,
+1) event window.
Infrastructure: 2.01%
Application: 0.84%
Overall mean standardized
abnormal returns of 0.19%
Small firms: 0.25%
In subsample from 1991-1996,
IT investments: 0.16%
Financial firms: 0.27%
Small firms: 0.63%
Mid-sized firms: -0.22%
Subramani
and
Walden (2001)
Dos Santos et al.
(1993)
Florin et al. (2005)
e-Commerce initiatives
No
(-5, +5), (-10, +10)
Innovative IT investments
No
(-1,0)
(-5, +5) event period: 7.5%
(-10, +10) event period: 16.2%
1.03%
IT/
IS outsourcing
announcements and the
moderating
effect
of
organizational
restructuring
Yes
(-30, -1), (0, +1),
(+2, +250)
(0, +1): Significant CAR of
1.9%
Hunton, Reck and
Hayes (1999)
Farag and Krishnan
(2003)
IS
outsourcing
announcements
IS
outsourcing
announcements
categorized by project
type and industry
No
(-1, +1)
No
10 day and 5 day
windows
Agrawal,
Kishore
and Rao (2006)
e-Business
outsourcing
announcements sorted by
strategic intent, project
execution swiftness and
task complexity
No
(-1, +1)
Hayes, Hunton and
Reck (2001)
IS
outsourcing
announcements
No
(0, +1)
Restructuring changes after
the announcement moderate
the relationship between the
short-term
effect
of
announcements and long-term
abnormal returns so that the
latter become negative when
followed by restructuring
efforts
resulting
from
outsourcing
Significant returns of 0.35%
on day +1.
Overall CAR of 1.72% over a
10 day time window.
For strategic projects,
CAR over 5 day window:
3.34%
CAR over 10 day window:
7.56%
For cost cutting projects,
CAR over 10 day window: 2.07%. 5 day window results
were not significant.
Strategic Intent:
Commercial
exploitation:
CAR of 2.05%
Other intent: CAR of -1.75%
Execution swiftness:
Regular execution: CAR of 1.73%
Swift execution: CAR of 3.3%
Task complexity:
Low: CAR of -2.28%
High: CAR of 2.08%
CAR of 0.124% in the two
day event window. Day 0
abnormal return of 0.02%;Day
1 abnormal return of 0.33%
Table 2: Returns from ERP investments
Average
revenue of
sample (R)
Ranganathan &
Brown (2006)
Hayes et al.
(2001)
Announcement
period return from
ERP
announcement (r)
Implied NPV of
ERP
initiative
NPV = M*r
Calculated
upfront ERP
cost
C=.0274*R
$8.3B
Average
market
capitalization
of
sample
(M)
$8.7B
1.47%
$128M
$227.4M
Present
value
of
maintenance cost for five
years M = .1*C each
year discounted at 8%
cost of capital
$91M
$4.8B
$6B
0.19%
$11.4M
$131.5M
$52.5M
Table 3: Sample Characteristics of Outsourcing Announcements, 1996 – 2005
Panel A reports the distribution of sample firms across primary SIC codes, and Panel B reports various characteristics of the
sample outsourcing contracts and user firms. All accounting data are obtained one year prior to the contract effective year.
Further, such data are adjusted by the CPI to reflect 2005 dollars.
Panel A: Distribution of sample firms across primary SIC codes
IS Outsourcing
BPO and Processing
Services
SIC
Sector
Number
0
Agriculture, Forestry, and
Fishing
Mining and Construction
Manufacturing
Manufacturing
Transportation,
Communications,
Electric,
Gas, and Sanitary Services
Wholesale and Retail Trade
Finance, Insurance, and Real
Estate
Lodging and Entertainment
Services
Public Administration
All Sectors
1
2
3
4
5
6
7
8
9
Application,
Network
and
Desktop
Management
Number % of all
deals
0
0%
All Deals
0
% of all
deals
100%
Number
0
% of all
deals
0%
0
% of all
deals
0%
Number
0
10
16
10
0%
56%
55%
53%
0
5
8
4
0%
28%
28%
21%
1
3
5
5
100%
17%
17%
26%
1
18
29
19
100%
100%
100%
100%
3
10
60%
43%
2
8
40%
35%
0
5
0%
22%
5
23
100%
100%
4
0
0
80%
0%
0%
0
0
0
0%
0%
0%
1
0
0
20%
0%
0%
5
0
0
100%
100%
100%
53
53%
27
27%
20
20%
100
100%
Panel B: Sample Characteristics of Outsourcing Initiatives
Outsourcing Firm Characteristics
Market Value of Equity ($M)
Market to Book Ratio
Tobin’s Q
Contract Characteristics
Contract Value – All Deals ($M)
Contract Value – IS Outsourcing ($M)
Contract Value – BPO and Processing Services ($M)
Contract Value – Application, Network and
Management ($M)
Contract Length (months)
Desktop
N
100
100
100
Mean
37,331
3.27
0.18
Median
23,802
2.71
0.09
100
53
27
20
922
1,100
703
747
563
645
517
425
100
93
84
Table 4: Measurement of Variables
Variable
Description
Measure
Transactional Attributes
TYPE
Type of outsourcing Ordinal variable in decreasing order of maturity of the
initiative
outsourcing market is one of:
1. Information systems outsourcing
2. Application, network and desktop management
3. Business process outsourcing and processing services
Relational Attributes
CONTRACT
Incompleteness
of Fixed price: fixed payment per billing cycle or per
outsourcing contract
transaction per billing cycle
Variable price: payment based on variable factors such as
time and materials used during the billing cycle or
improvements against key performance indicators or any
combination of these factors
COORDN
Anticipated
Anticipated interdependence based on the strategic rationale
coordination
for outsourcing the given business function. The
Source
IDC
IDC
IDC
TRUST
CONTINUITY
Firm Attributes
UNCERTAINTY
STRIMP
EXP
SIZE
BTM
PRIOR_PERF
requirements of the outsourcing literature points to eight rationales that cover
outsourced function
the spectrum of outsourcing logics:
1. Reduction of costs
2. Improve management focus on core competences
3. Access to competitive capabilities not available inhouse
4. Growth strategy
5. Speed to market
6. Access to new markets
7. Proximity to customers
8. Business transformation
The rationales were assessed from the IDC description of
the outsourcing initiative and the public announcement of
the initiative.
Mutual trust inferred We infer trust based on the bid type, which is one of IDC
based
on
prior competitive, incumbent or sole sourced. Competitive
cooperative association bidding suggests the absence of prior association between
between the firms
the firms. Incumbent bidding implies that the outsourcing
firm has an existing relationship with the provider. A solesourced contract means that the provider is the only
provider of the outsourced function. The outsourcing firm
may enter into sole-source negotiations with an incumbent
in which case the bid type is recorded as incumbent.
Expectation
of Length of the contract in months
IDC
continuity
of
the
outsourcing relationship
Uncertainty in business
requirements of the
outsourcing firm
Strategic importance of
the
outsourcing
initiative
Outsourcing experience
of the firm
Market value of equity
of the outsourcing firm
Book to market ratio of
the outsourcing firm
Prior
financial
performance
Variance in the outsourcing firm’s return on assets (RoA) Compustat
over the three years prior to the contract effective year. RoA
is defined as the ratio of operating income to total assets.
Ratio of contract value to operating expenses.
IDC,
Operating expenses is defined as the sum of cost of goods Compustat
sold, sales and administrative expenses, and depreciation
and amortization expenses
Market value of equity, defined as the product of the Compustat
number of shares outstanding and market price
Ratio of book value of equity to market value of equity of Compustat
the outsourcing firm
Buy and hold returns for the three year period preceding CRSP
implementation of the outsourcing contract
Table 5: Long-term Buy-And-Hold Abnormal Returns following Outsourcing Implementations
Panel A reports buy-and-hold returns for the sample firms, and buy-and-hold abnormal returns (BHAR) for the sample firms
relative to control firms, for the three year period following the date the outsourcing contract was signed. A control firm is
defined as a firm in the same 2 digit SIC code as the outsourcing firm that has the lowest absolute value of the joint difference in
size (equity capitalization) and book-to-market ratio (equity capitalization divided by book value of equity). Panel B reports
BHAR for sample firms where return data is available for all three years.
Panel C reports the mean three year BHAR from outsourcing for the lowest and highest 30 percent of environmental uncertainty,
anticipated coordination requirements, prior cooperative association between the outsourcing firm and vendor, and outsourcing
experience. The “difference” column reports the difference in returns between the two sub-samples. Panel D reports equivalent
results for the sample of firms for which all three years’ return data is available.
Panel A: Long-term BHAR – Full Sample
Lowest 30 percent
(a)
UNCERTAINTY
0.34***
COORDN
0.39***
Highest 30 percent
(b)
-0.56***
-0.51***
Difference
(a-b)
0.90***
0.90***
PRIOR
-0.15***
0.22***
-0.37**
EXP
-0.23***
0.15***
-0.38**
Panel B: Long-term BHAR – Three Year Sample
UNCERTAINTY
0.30***
-0.69***
0.99***
COORDN
0.33***
-0.65***
0.98***
PRIOR
-0.24***
0.31***
-0.55**
EXP
-0.33***
0.21***
-0.54**
Panel C: Three-Year Buy-and-Hold Abnormal Returns (%) – Full Sample
Raw Return (in percent)
BHAR (in percent) – Ind/ Size/ BTM Adjusted
All Contracts
Fixed Price
Variable Price
All Contracts
Fixed Price
Variable Price
32.21***
48.41***
21.51***
-6.15
17.45**
-21.17*
Panel D: Three-Year Buy-and-Hold Abnormal Returns (%) – Three Year Sample
Raw Return (in percent)
BHAR (in percent) – Ind/ Size/ BTM Adjusted
All Contracts
Fixed Price
Variable Price
All Contracts
Fixed Price
Variable Price
27.50***
49.08***
12.52*
-9.78
23.62**
-32.68**
*p<0.10, **p<0.05, ***p<0.01
Table 6: Announcement Period Returns following Outsourcing Announcements
Panel A reports announcement period returns for the 20 day event period (-10, 9) while Panel B reports returns for the 12 day
event period (-10, 1).
Panel A: Abnormal returns for the event period (-10, 9)
Lowest 30 percent
Highest 30 percent UNCERTAINTY
2.01%
1.55%
COORDN
0.87%
2.98%**
PRIOR
-0.03%
-0.64%
EXP
-0.47%
2.45%
Panel B: Abnormal returns for the event period (-10, 1)
UNCERTAINTY
0.41%
-0.40%
COORDN
-0.36%
1.84%*
PRIOR
2.01%*
-1.61%*
EXP
2.43%
0.57%
Table 7: Model of buy-and-hold abnormal returns (BHAR) and Idiosyncratic Risk
Model I tests the influence of attributes of the outsourcing firm, relationship and task environment on the three year BHAR
following the implementation of the outsourcing contract. Model II tests the influence of these attributes on the idiosyncratic
volatility of the outsourcing firm. The latter is estimated as the annualized standard deviation of residuals from a market model
regression based on daily returns (Bali et al. 2005, Ali et al. 2003, Malkiel and Xu 2006). Gains in income efficiency, selfselection into the outsourcing and contracting decisions, and firm and time effects are controlled for in both models. Robust
standard errors clustered by firm are reported in parentheses.
Model I
Model II
(BHAR)
(Idiosyncratic
Volatility)
0.149
(0.114)
TYPE
-0.001
UNCERTAINTY
-0.283*
(0.141)
0.250
(0.315)
0.186
(0.131)
-0.483**
(0.230)
-0.002
(0.101)
0.199**
STRIMP
EXP
COORDN
CONTINUITY
PRIOR
0.485***
(0.172)
0.214
(0.348)
-0.243
(0.168)
-0.107
(0.142)
-0.153
(0.123)
-0.077
PRIOR FIN_PERF
IMR1 – Outsourcing
IMR2 – Contract
Constant
R-square
*p<0.10, **p<0.05, ***p<0.01
(0.095)
-0.091
(0.119)
0.379
(0.248)
-0.285*
(0.144)
0.099
(0.110)
0.39
(0.166)
-0.145
(0.149)
-0.512*
(0.280)
0.319*
(0.178)
-0.130
(0.145)
0.36