Information systems, strategic ﬂexibility and ﬁrm performance ...Document Transcript
J. Eng. Technol. Manage. 22 (2005) 163–184
Information systems, strategic ﬂexibility and ﬁrm
performance: An empirical investigation
Michael J. Zhang *
Department of Management, Sacred Heart University, 5151 Park Avenue, Fairﬁeld, CT 06825, USA
Available online 18 July 2005
This study investigated the bottom-line impacts of IS support for strategic ﬂexibility. The
performance effects of IS support for two key components of strategic ﬂexibility (product ﬂexibility
and cross-functional coordination) and the moderating effects of unique, complementary knowledge
and information were examined and tested with both survey and archival data. The results showed
that IS support for product ﬂexibility was positively related to sales growth and returns on sales. The
study also found a stronger association between IS support for product ﬂexibility and ROS, and a
positive relationship between IS support for cross-functional coordination and sales growth, when IS
were complemented by unique knowledge and information.
# 2005 Elsevier B.V. All rights reserved.
JEL classiﬁcation: L21
Keywords: Information systems; Strategic ﬂexibility; Competitive advantage; Firm performance
For the past decade, strategic ﬂexibility has been increasingly recognized as a critical
organizational competency that enables ﬁrms to achieve and maintain competitive
advantage and superior performance in today’s dynamic and competitive business
environment (Sanchez, 1995; Hitt et al., 1998). Correspondingly, there has been a growing
research interest in the strategic impacts of the linkage between information systems (IS) and
* Tel.: +1 203 396 8234; fax: +1 203 365 7538.
E-mail address: email@example.com.
0923-4748/$ – see front matter # 2005 Elsevier B.V. All rights reserved.
164 M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184
strategic ﬂexibility (Goldhar and Lei, 1995; Lei et al., 1996; Byrd, 2001; Santhanam and
Hartono, 2003). While many conceptual frameworks, case studies and anecdotes have been
offered to show that ﬁrms can use IS to support the development of strategic ﬂexibility, hence
gaining competitive advantage, it remains unclear whether IS support for strategic ﬂexibility
can actually improve a ﬁrm’s bottom-line performance, due to little prior empirical work on
this issue. Without empirical research assessing the bottom-line performance impacts of IS
support for strategic ﬂexibility, ﬁrms and their managers who are interested in investing IS
for achieving strategic ﬂexibility have little evidence on which to base their IS investments.
In this paper, I seek to address this imbalance by presenting the results from an empirical
study linking IS support for strategic ﬂexibility to ﬁrm performance.
In a broader sense, investigating the relationship between IS support for strategic
ﬂexibility and corporate bottom-line performance contributes to the continuous research
efforts to address a critical, and yet elusive issue of whether IS investments improve
organizational effectiveness (see, for example, Brynjolfsson and Hitt (1998) and Lucas
(1999) for reviews of the empirical studies assessing the performance impacts of IS). In
recent years, a growing number of IS and management researchers has taken the resource-
based view of the ﬁrm in the strategic management literature as a new theoretical lens to
examine the ‘‘productivity paradox’’ regarding the strategic impact of IS (Feeny and Ives,
1990; Clemons and Row, 1991; Mata et al., 1995; Powell and Dent-Micallef, 1997; Lado
and Zhang, 1998; Bharadwaj, 2000; Byrd, 2001; Sambamurthy et al., 2003). One important
insight generated from this line of research is that the crux of competitive advantage from
IS investments may lie in their inﬂuence on value-creating, ﬁrm-speciﬁc and hard-to-copy
resources and capabilities (Feeny and Ives, 1990; Clemons and Row, 1991; Lado and
Zhang, 1998; Bharadwaj, 2000; Byrd, 2001; Sambamurthy et al., 2003). In other words, IS
may enhance a ﬁrm’s bottom-line performance by supporting its efforts to build and exploit
valuable, unique and non-imitable resources and capabilities.
This research attempts to extend the current resource-based research on the strategic
value of IS in several regards. First, since strategic ﬂexibility is widely recognized as a key
organizational capability associated with the long-term success of a ﬁrm (Sanchez, 1995;
Lei et al., 1996; Hitt et al., 1998), assessing the empirical relationship between IS support
for this critical capability and ﬁrm performance provides a test of the resource-based
argument that IS used to create and leverage internal sources of sustainable competitive
advantage are associated with superior ﬁrm performance. Secondly, in view of the
important role of complementary assets (Teece, 1986) in enabling ﬁrms to reap the beneﬁts
from their IS investments (Feeny and Ives, 1990; Clemons and Row, 1991; Powell and
Dent-Micallef, 1997), I incorporate unique, complementary knowledge and information
(ﬁrm-speciﬁc knowledge and information a ﬁrm needs in order to exploit its IS for strategic
ﬂexibility) as a potential moderator in the study and argue that the strength of the
association between IS support for strategic ﬂexibility and ﬁrm performance is likely to
vary across ﬁrms, depending on the existence and distribution of these unique,
complementary resources. While important for ascertaining conditions under which IS can
be used to build ‘‘core’’ or ‘‘distinctive’’ competencies such as strategic ﬂexibility (Miller
and Shamsie, 1996), discerning the moderating effects of unique, complementary
knowledge and information has received little attention in the prior research linking IS
support for critical organizational resources and capabilities to ﬁrm performance. Thirdly,
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184 165
Fig. 1. Research model.
to the extent that IS support for strategic ﬂexibility represents an IS capability or
competency (Bharadwaj, 2000; Sambamurthy et al., 2003), the metrics developed in the
study to measure IS support for strategic ﬂexibility contribute to the development of
continuous measurements of the IS capability, which are deemed as critical to the
advancement of a resource-based theory of IS impacts (Bharadwaj, 2000; Santhanam and
This paper is structured as follows. The next section (1) offers a review of the concept of
strategic ﬂexibility, its competitive value and two of its key contributing capabilities:
product ﬂexibility and cross-functional coordination; (2) elaborates on IS support for these
two capabilities and its performance impacts, and (3) explores the potential moderating
effects of unique, complementary knowledge and information on the relationship between
IS support for product ﬂexibility and ﬁrm performance, as well as the relationship between
IS support for cross-functional coordination and ﬁrm performance. Together, this
discussion provides the conceptual foundation for the research model (Fig. 1) and the
development of the research hypotheses. The third section presents the research
methodology, including the sample and data collection procedure, the operationalization
and measurement of the variables of interest, and the results. The last section of the paper
discusses the implications of the research ﬁndings, the limitations of the study, and some
suggestions for future research and practice.
2. Theory and hypotheses
2.1. Strategic ﬂexibility and competitive advantage
The subject of ﬂexibility has been dealt with extensively in several disciplines (e.g.,
manufacturing management, economics, strategic management, and IT management) and
various conceptualizations of ﬂexibility have been advanced during the past two decades,
reﬂecting a wide range of research interests and theoretical perspectives. There are a
number of excellent reviews of different deﬁnitions and typologies of ﬂexibility, especially
in the manufacturing management literature (e.g., Sethi and Sethi, 1990; Hyun and Ahn,
1992; Gerwin, 1993; Upton, 1994). In keeping with the current strategic perspective of
ﬂexibility (Sanchez, 1995; Hitt et al., 1998), I adopted a broad (strategic) view of ﬂexibility,
166 M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184
referring to a set of organizational abilities to proact or respond quickly to a changing
competitive environment and thereby develop and/or maintain competitive advantage, in
the current study. Indeed, the concept of strategic ﬂexibility has been increasingly
embraced by researchers in ﬁelds such as strategic management, manufacturing
management and IT management, given the growing recognition of the strategic
signiﬁcance of strategic ﬂexibility to ﬁrms competing in today’s changing business
environments (Boynton, 1993; Gerwin, 1993; Upton, 1994; Sanchez, 1995).
Research examining the strategic impact of strategic ﬂexibility has shown that strategic
ﬂexibility contributes to competitive advantage at different organizational levels. At the
tactical or functional level, strategic ﬂexibility is now known to be vital to several value-
creating operational or manufacturing strategies, including mass customization, time-to-
market, operational excellence, lean manufacturing, and stockless inventory (Stalk et al.,
1992; Treacy and Wiersema, 1993; Kotha, 1995; Byrd, 2001). At the business level,
strategic ﬂexibility enables a ﬁrm to avoid the trade-off between low cost and
differentiation and offer high-quality products or services at low costs (Boynton, 1993; Lei
et al., 1996). At the corporate level, since the development and implementation of strategic
ﬂexibility involve constant improvements in the ﬁrm’s organizational processes and
technologies as well as its continuous learning of new organizational knowledge,
capabilities and skills (Hayes and Pisano, 1994; Goldhar and Lei, 1995), strategic
management researchers rooted in the resource-based view of competitive advantage
consider strategic ﬂexibility as a higher-order (dynamic) capability that enables the ﬁrm to
adapt and change over time to maintain its long-term competitiveness (Amit and
Schoemaker, 1993; Collis, 1994; Teece et al., 1997; Eisenhardt and Martin, 2000).
2.2. Two key contributing capabilities of strategic ﬂexibility
While strategic ﬂexibility may entail a number of organizational capabilities and
resources (Volberda, 1997; Hitt et al., 1998), two organizational capabilities (product
ﬂexibility and cross-functional coordination) are most crucial to a ﬁrm’s ability to pursue a
variety of strategic options in response to the demands of changing markets (Zammuto and
O’Connor, 1992; Upton, 1994; Kotha, 1995; Sanchez, 1995; Lei et al., 1996). Denoting the
ability to ‘‘increase the range of products a production system can process and/or reduce the
cost and time required to switch production resources from one product to another’’
(Sanchez, 1995: 143), product ﬂexibility enables ﬁrms to manipulate product variety and
change efﬁciently and rapidly, thus giving them more product strategy options to deal with
environmental uncertainties (Evans, 1991; Gerwin, 1993; Sanchez, 1995). Although other
types of ﬂexibilities (e.g., process ﬂexibility) can also contribute to a ﬁrm’s ability to do things
differently, it is through product ﬂexibility that a ﬁrm can satisfy the changing needs of its
customers. For this reason, product ﬂexibility is often viewed as the most signiﬁcant source of
strategic ﬂexibility (Gerwin, 1993; Sanchez, 1995; Ahmed et al., 1996; Hitt et al., 1998).
Aside from product ﬂexibility, cross-functional coordination has been increasingly
recognized as a key ingredient of strategic ﬂexibility (Zammuto and O’Connor, 1992;
Sanchez, 1995; Ahmed et al., 1996; Lei et al., 1996). In his view of how ﬁrms achieve
strategy ﬂexibility, Sanchez (1995, 1997) notes that the development of strategic ﬂexibility
relies not only on ﬂexibility in various resources capable of creating, producing and
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184 167
marketing products or services, but also ﬂexibility in coordinating the uses of these resources
from different functional areas. Indeed, researches have found that tight cross-functional
coordination within and across ﬁrms promotes smooth acquisition and sharing of critical
information and knowledge that ﬁrms need in order to quickly detect market and product
changes, redesign business processes and workﬂows, and develop new insights and skills
(Vessey, 1992; Boynton, 1993; Goldhar and Lei, 1995; Lei et al., 1996; Bharadwaj, 2000).
Ahmed et al. (1996, p. 565) even argue that, without well coordinated functional
activities, ﬁrms are unlikely to derive competitive advantage from ﬂexibility in that
compartmentalized processes and decision making hinder a ﬁrm’s ability to ‘‘create a
holistic sense of direction and utilize response ﬂexibility to build advantages.’’ In order for
a ﬁrm to beneﬁt from the ﬂexibility advantage, it must blend cross-functional coordination
with ﬂexibility effectively into ‘‘integrated ﬂexibility’’ which integrates different
functional activities into self-contained and highly autonomous units that are allowed
to optimize and change internally. To support their argument, Ahmed et al. (1996) cite the
success of some Japanese manufacturers that use self-control units to achieve higher
quality and shorter throughput times. Other ﬂexibility beneﬁts (e.g., shorter lead times,
better manufacturability of product designs, and more efﬁcient production of small batches
of customize goods) accruing from close coordination of processes and tasks across
functions have been reported in the literature (Zammuto and O’Connor, 1992).
2.3. IS support for strategic ﬂexibility and competitive advantage
It is well acknowledged in the literature that building the capability of strategic
ﬂexibility requires the effective use of other organizational resources, including IS,
organizational culture and structure, product design, and employee skills and experience
(Boynton, 1993; Sanchez, 1995; Upton, 1995; Lei et al., 1996; Hitt et al., 1998; Byrd,
2001). Boynton (1993), for example, notes that ﬁrms need IS to support the rapid
development, collection and dissemination of market, product and process information in
order to effectively respond to quick and unpredictable changes in business conditions. IS
researchers who have examined the strategic role of IS from the dynamic capability view of
the resource-based literature (Teece et al., 1997; Eisenhardt and Martin, 2000) have argued
that IS can contribute to a ﬁrm’s long-term success by serving as a platform for building
dynamic capabilities associated with sustainable competitive advantage, such as strategic
ﬂexibility (Byrd, 2001; Sambamurthy et al., 2003). From this perspective, IS can be linked
to long-term superior performance through their inﬂuence on strategic ﬂexibility. In the
following discussion, I elaborate on IS roles in supporting the development of strategic
ﬂexibility along with their performance implications. Since product ﬂexibility and cross-
functional coordination represent two of the most critical components of strategic
ﬂexibility, I will focus on IS support for these two organizational capabilities.
2.3.1. IS support for product ﬂexibility
Research on the ﬂexibility impact of advanced manufacturing technologies (AMT)
suggests that the computer-aided design (CAD) system, through its support for product
design, engineering, simulation, testing and rapid prototyping, enables a ﬁrm to
signiﬁcantly reduce its costs of creating and evaluating different product designs and
168 M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184
shorten product design cycles (Sanchez, 1995; Lei et al., 1996; Hitt et al., 1998).
Furthermore, ﬂexible manufacturing systems (FMS) using the computer-aided manu-
facturing (CAM) technology can greatly increase the speed of introducing new tools and
dyes as well as integrating previously separated workstations and machining centers into an
interdependent manufacturing system (Clark, 1989; Lei et al., 1996). As a result, IS using
AMT can radically reduce the cost vs. variety and speed vs. variety tradeoffs, leading to
economies of scope — ‘‘the capacity to efﬁciently and quickly produce any of a range of
parts or products within a family’’ (Zammuto and O’Connor, 1992: 702). In other words,
ﬁrms can derive the simultaneous beneﬁts of greater product variety, faster response and
increased productivity from such IS (Chase and Garvin, 1989; Pine, 1993; Hayes and
Pisano, 1994; Goldhar and Lei, 1995). Economies of scale can also be gained from the IS-
derived economies of scope in that the multi-product operations supported by CAD and
CAM eliminate the risk of rendering the investment in a high-volume, single-product plant
obsolete due to changes in market demand (Bakos and Treacy, 1986; Goldhar and Lei,
1995). Because of these operational beneﬁts, IS-based product ﬂexibility has been found
instrumental to the development of mass customization (a widely recognized value-
creating organizational competency) whereby ﬁrms customize a wide variety of products
to customers’ special needs at low costs (Pine et al., 1993; Kotha, 1995; Byrd, 2001).
While research on IS support for product ﬂexibility and mass customization has mostly
focused on the use of IS in manufacturing settings, there is emerging anecdotal evidence
that service ﬁrms can also beneﬁt from using IS to develop product ﬂexibility and become
mass customizers. For example, Boynton et al. (1993) reported an IS (dubbed as the CS90)
designed by Westpac (a South Paciﬁc ﬁnancial service conglomerate) to consolidate its
knowledge and expertise about the processes of developing new ﬁnancial products into a
set of highly ﬂexible software modules. By allowing Westpac to combine different sources
of its knowledge rapidly and efﬁciently, the system enabled the company to handle a
greater variety and range of customer and marketplace needs at low cost and fast speed. In a
more recent study, Sawhney (2001) described how Thomson Financial (a subsidiary of
Thomson Corporation, an electronic information provider) used IS to increase its market
responsiveness and new product offering speed. Thomson Financial accomplished this
through installing a software called ‘‘middleware’’ which allowed the company to
represent legacy IS applications and products as ‘‘objects’’ (modular components) that can
be easily combined and ﬂexibly assembled to create tailored solutions for the customers.
2.3.2. IS support for cross-functional coordination
It is evident in the literature that IS can be used to promote faster, more accurate, more
complete and better-coordinated information and knowledge ﬂows across key business
functions such as marketing, engineering, manufacturing and distribution (Alter, 1996;
Joshi, 1998). Such IS-enhanced information processing capacity allows instant connection,
tapping, combination and recombination of capabilities from different functional activities
to create new skills and insights for rapid and ﬂexible product and service delivery
(Boynton, 1993; Pine et al., 1993; Venkatraman, 1994; Lei et al., 1996; Malone et al.,
1999). Research on IS impact on concurrent engineering (product creation processes that
bring together multiple functions in product design decision making) indicates that IS-
enhanced electronic links among cross-functional, concurrent development teams reduce
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184 169
product development time and costs (Hartley, 1992; Vessey, 1992; Sanchez, 1995; Hull
et al., 1996). For example, Hull et al. (1996) studied the combined effects of IT and two
concurrent engineering practices: early simultaneous inﬂuence (ESI) and in-process design
controls (IDC) on product development performance in a study of 74 Fortune 500
companies. ESI involves the participation of multiple upstream and downstream functions
in the early stages of the product design process, and IDC refer to the use of common
methodologies and protocols among the participants. Hull et al. (1996) found the use of
ﬂexible IT such as CAD and CAM increased the positive effects of ESI and IDC on product
IS support for cross-functional link of rich information has also been found in cross-ﬁrm
collaboration (Lei et al., 1996). Researchers have found IS involving the CAD, computer-
integrated manufacturing (CIM), electronic data interchange (EDI) and Internet technologies
instrumental in establishing ‘‘a quick connect electronic interface’’ (Sanchez, 1995) through
standardizing programming languages, procedural protocols, design documentation and data
structures (Venkatraman and Zaheer, 1990; Reekers and Smithson, 1994; Sanchez, 1995;
Bensaou, 1997). Such an electronic interface integrates buyers, manufacturers and suppliers
into a product creation and production network that offers superior speed, greater variety and
valuable new knowledge in responding to new product opportunities (Pine et al., 1993;
McGill and Slocum, 1994; Sanchez, 1995; Upton and McAfee, 1996; Amit and Zott, 2001).
Ebay, for example, has used the Internet technology to build and enhance virtual customer
communities for product design, feedback and testing (Sambamurthy et al., 2003). It was
reported that Ebay’s customers posted an average of 10,000 messages each week to share
product tips, glitches and change suggestions (Hof, 2001).
Such a virtual value-creating network supported by IS offers a ﬁrm and its trading partners
a competitive advantage from ‘‘quasi-vertical integration’’ that enables the participating
ﬁrms to ‘‘become modular, insertable components in a large system of grouped value-adding
activities (across numerous, specialized ﬁrms)’’ (Lei et al., 1996: p. 510). In other words,
ﬁrms can use the IS-based virtual vertical integration to achieve the beneﬁts of vertical
integration, while also realizing the production economies available to separate, specialized
ﬁrms (Konsynski and McFarlan, 1990; Clemons and Row, 1991). Although competitors with
full vertical integration may potentially match the level of operational integration, it is not as
easy for them to match the production economies and ﬂexibility of independent and
specialized ﬁrms that are connected together by IS (Clemons and Row, 1991).
Hypothesis 1. IS support for product ﬂexibility will be positively related to ﬁrm
Hypothesis 2. IS support for cross-functional coordination will be positively related to
2.4. The moderating roles of unique, complementary organizational resources
While IS can be used to achieve strategic ﬂexibility through their support for product
ﬂexibility and cross-functional coordination, some may argue such IS deployment is
subject to easy imitation because many IS lack characteristics that are unique or difﬁcult to
170 M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184
copy (Mata et al., 1995). However, drawing on the notion of complementary assets —
resources whose presence enhances the values of other resources (Teece, 1986), IS
researchers have recently argued that ﬁrms with certain ﬁrm-speciﬁc, hard-to-copy
resources that complement their IS are in a better position to defend their IS-derived
advantage than those that lack such resources (Feeny and Ives, 1990; Clemons and Row,
1991; Lei et al., 1996; Lado and Zhang, 1998; Bharadwaj, 2000). This argument has
received some empirical support from two recent studies that found IS complemented by
other intangible organizational resources yielded competitive advantage (Powell and Dent-
Micallef, 1997; Bharadwaj, 2000).
It has been increasingly recognized in the literature that the process of implementing IS
for product ﬂexibility and cross-functional coordination requires a number of ﬁrm-speciﬁc,
complementary organizational resources (Lau, 1996; Hitt et al., 1998), among which are
employee knowledge and information resources (Kotha, 1995; Upton, 1995; Lei et al.,
1996). The inﬂuence of unique, complementary knowledge and information on the
performance effects of IS support for product ﬂexibility and cross-functional coordination
is examined next.
2.4.1. The moderating effects of unique, complementary knowledge
It is argued that organizational knowledge plays a pivotal role in enabling ﬁrms to reap
the beneﬁts of IS-based ﬂexibility. Lei et al. (1996) note that the long-term implementation
success of AMT hinges on the richness of a ﬁrm’s tacit knowledge (the insights, heuristics
and experience of the ﬁrm’s employees) applied in the procedures and workﬂows involved
in the use of AMT. In his analysis of ﬂexibility in the manufacturing sector, Upton (1995)
argues that manufacturers with workers adept at carrying out quick changeovers and
responding to the demands of new customers are more likely to create a manufacturing
system that combines IT and employee skills to make ﬂexibility work. Furthermore,
Parthasarthy and Sethi (1992) posit that ﬁrms whose employees possess the skills for
selecting, processing and transmitting complex information quickly would enjoy greater
economic gains from IS-based ﬂexibility.
There is emerging anecdotal evidence supporting the important role of employee
skills in IS support for product ﬂexibility and cross-functional coordination. Kotha’s (1995)
case study of how National Bicycle Industrial Company (NBIC), a Japanese bicycle
manufacturer, developed and implemented mass customization for competitive advantage
revealed that access to highly trained workers and substantial in-house expertise in
engineering and manufacturing played a critical role in NBIC’s ability to develop and
deploy IS to offer a great variety of bicycles at low costs. The study also showed that the
same knowledge resources enabled NBIC to use IS to integrate different functional
activities and establish a close information network with its customers and suppliers.
The competitive advantage derived from blending human expertise with IS is harder to
duplicate because employee skills and knowledge that complement IS for product
ﬂexibility and cross-functional coordination are often unique and contingent on ﬁrm-
speciﬁc organizational routines developed over an extended period of time. In their
resource-based analysis of the competitive value of several IT-related resources, Mata
et al. (1995) concluded that managerial skills in building, implementing and managing IT
are rare among ﬁrms, require long periods of practice and learning, and involve complex
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184 171
social relations. The mass customization experience of NBIC mentioned earlier showed
that the main rivals of NBIC had a hard time trying to imitate its approach to mass
customization because NBIC’s IS that supported its mass customization operation was
built with in-house engineering and manufacturing expertise accumulated over many
years (Kotha, 1995).
2.4.2. The moderating effects of unique, complementary information
While a ﬁrm’s employee skills and knowledge inﬂuence its ability to exploit IS for
product ﬂexibility and cross-functional coordination, the information resources controlled
by the ﬁrm affects the quality of the information fed into and processed by such IS. The role
that information plays in achieving product ﬂexibility has received considerable attention
in the literature. Boynton (1993) posits that rapid and unpredictable market changes require
timely and accurate information about external market and product conditions (e.g.,
current changes in customer needs and preferences, new product sales, and customer
feedbacks). Such information serves as guidance for a ﬁrm’s ﬂexibility efforts (Kotha,
1995; Sanchez, 1995). In his study of NBIC, Kotha (1995) noted that information about the
‘‘innovative’’ users guided the company’s decision to contract or expand product variety.
Pine et al. (1993) further contend that, without current market information, ﬁrms may run
the risk of offering too many product choices. Aside from guiding product variety
decisions, current customer and market information directs ﬁrms’ efforts to dynamically
manage the processes and resources in new product design, production and distribution
(Stalk and Webber, 1993; Sanchez, 1995).
The quality of a ﬁrm’s information resources may also increase the value of IS support
for cross-functional coordination. Although many ﬁrms can use IS to facilitate intra- and
inter-ﬁrm information exchange, ﬁrms with proprietary information are more likely to gain
more beneﬁts from using IS to enhance cross-functional communication. In other words,
the presence of proprietary information may confer value in addition to that provided by IS.
Proprietary information can not only improve a ﬁrm’s decision making (King and Grover,
1991), but also make it difﬁcult for its competitors to reap the same beneﬁts the ﬁrm enjoys
from such IS deployment (Feeny and Ives, 1990).
Hypothesis 3. The interaction between IS support for product ﬂexibility and unique,
complementary knowledge and information will be positively related to ﬁrm performance.
Hypothesis 4. The interaction between IS support for cross-functional coordination and
unique, complementary knowledge and information will be positively related to ﬁrm
3.1. Sample and data collection
I collected the data for this study from two sources. I gathered the data tapping the
independent and moderating variables via a mail survey administered in 1998 and obtained
172 M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184
the data about the performance and control variables from the Research Insight (formerly
known as Compustat) database. The target respondents of the survey were senior IS
executives in leading (Fortune and Forbes) ﬁrms in the U.S. Most of the respondents held
the positions of either vice presidents of IS or chief information ofﬁcers (CIO). I chose the
senior IS executive as the single informant in this study because of his or her familiarity
with both IS and strategic management issues. Several previous studies have found
increasing involvement of senior IS executives in strategic planning and control activities
of ﬁrms (Applegate and Elam, 1992; Stephens et al., 1992; Earl and Feeny, 1994).
Applegate and Elam (1992), for example, found a growing number of CIO reporting
directly to CEO and nearly half of the CIO surveyed were members of the senior
management/strategic policy committee. Moreover, a recent study found the information
offered by key IS executives consistent with the insights obtained from other senior
members of management (Palmer and Markus, 2000). Consequently, IS researchers have
increasingly relied on senior IS executives as single informants in gathering data about
strategic IS issues (Sethi and King, 1994; Karimi et al., 1996; Palmer and Markus, 2000;
Zhu and Kraemer, 2002; Kearns and Lederer, 2003).
I obtain the contact information of the senior IS executives from the Directory of
Top Computer Executives compiled by Applied Computer Research Inc. From this
source, I identiﬁed a sample of 879 ﬁrms that had ﬁnancial data in the Research Insight
database. Before mailing the survey instrument to the target respondents, I pre-tested
and reﬁned the instrument for content validity and item clarity with CIO from ﬁve
Fortune companies headquartered in a mid-western state. One hundred and one
questionnaires were undelivered or returned because the IS executives were no longer
with the companies. Twenty-nine ﬁrms declined to participate in the study in writing,
on the phone, or through e-mail. To boost the response rate, I initiated two follow-up
mailings and one reminder letter after the ﬁrst mailing. Of the 778 ﬁrms that received
the questionnaires, a total of 164 responses were received, out of which 11 responses
were unusable. The effective response rate was thus 20% (153 responses). Although
somewhat low, such a response rate is comparable to those reported in similar studies
using senior IS executives in large ﬁrms (Mahmood and Soon, 1991; Sethi and King,
1994; Powell and Dent-Micallef, 1997; Bryd and Turner, 2001; Kearns and Lederer,
2003). Some salient characteristics of the ﬁrms responding to the survey are proﬁled in
Characteristics of the study sample
Industry group Frequency %
Manufacturing 75 49.0
Transportation and public utilities 13 8.5
Wholesale and retail trade 25 16.4
Service 40 26.1
Average number of employees 35662
Average annual sales (thousand) $7472
Average returns on sales 5.48%
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184 173
To test for potential nonresponse bias, I ﬁrst compared the respondent ﬁrms to the non-
respondent ﬁrms with respect to sales and number of employees. t-test results showed no
signiﬁcant differences between the two groups: sales (t = À1.227, p > 0.22) and number of
employees (t = À1.308, p > 0.19). Following Armstrong and Overton (1977), I conducted
another nonresponse bias check by comparing early with late respondents. t-tests of the
mean differences for each of the three explanatory variables failed to reveal any signiﬁcant
differences: IS support for product ﬂexibility (t = 0.606, p > 0.545), IS support for cross-
functional coordination (t = 0.315, p > 0.753) and unique, complementary knowledge and
information (t = À0.876, p > 0.383). Together, these checks provide some evidence for the
absence of non-response bias in the data.
3.2.1. Independent variables
In this study, IS support for product ﬂexibility is deﬁned as various ways in which a ﬁrm’s
IS support the development of product ﬂexibility and was measured with seven items. I
adopted three of the seven items from Mahmood and Soon (1991) and developed the other
four based on the ideas from Bakos and Treacy (1986), Goldhar and Lei (1995), and Sanchez
(1995). IS support for cross-functional coordination is deﬁned as the extent to which a ﬁrm’s
IS support the efﬁcient and effective coordination of cross-functional activities within the
ﬁrm and with those of its trading partners. I adopted four items from Mahmood and Soon
(1991) and Sethi and King (1994) to measure this construct. For each item measuring the two
independent variables, the respondents were asked to indicate the extent to which their IS had
provided a particular type of support during the previous three years on a ﬁve-point, Likert-
type scale with anchors ranging from ‘‘Very great extent’’ (=5) to ‘‘No extent’’ (=1).
To assess the unidimensionality and discriminant validity of the two scales, I performed
a principal components factor analysis with varimax rotation on the eleven items
measuring the two independent variables. I followed a two-stage rule to assign items into
factors (cf. Nunnally, 1978). First, in deciding whether an item loaded on a factor, I used a
factor loading of 0.40 as the minimum cut-off. Second, in case of cross-loadings, I would
delete an item if the difference between its weights was less than 0.10 across factors. The
factor analysis shown in Table 2 revealed two factors explaining about 59% of the total
variance and corresponding with IS support for product ﬂexibility and IS support for cross-
functional coordination, respectively.
3.2.2. Moderating variable
As previously stated, unique, complementary knowledge and information refer to ﬁrm-
speciﬁc knowledge and information a ﬁrm needs in order to exploit its IS for strategic
ﬂexibility. I measured this variable with two items developed from the ideas of Feeny and
Ives (1990), and Clemons and Row (1991). For each item, the respondents were asked to
indicate the extent to which the use and implementation of their IS required each of two
types of unique organizational resources: (1) ﬁrm-speciﬁc knowledge, skills or experience,
and (2) proprietary databases, on a ﬁve-point, Likert-type scale with anchors ranging from
‘‘Very great extent’’ (=5) to ‘‘No extent’’ (=1). The reliability (Cronbach alpha) of this
scale is 0.57.
174 M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184
Factor analysis of IS support for strategic ﬂexibility
Item description IS support for IS support for
product ﬂexibility Cross-functional
To what extent have your company’s IS provided each of the following support during the past 3 years?
Reduce the cost of tailoring products/services 0.731
to market segments
Reduce the cost of modifying or adding features 0.765
to existing products/services
Make product-line changeover easy 0.689
Improve product/service adaptability 0.656
Allow economies of scale from small production runs 0.490
Reduce the cost of designing new products/services 0.766
Shorten product design cycles 0.774
Reduce costs of coordinating different functional activities 0.669
Reduce costs of coordinating activities with 0.804
those of customers, suppliers or distributors
Provide more effective coordination among 0.746
different functional activities
Provide more effective coordination with 0.818
customers, suppliers or distributors
Eigen Value 3.682 2.756
% of common variance explained 33.48 25.05
Cronbach Alpha 0.86 0.81
3.2.3. Dependent variables
The performance impacts of IS support for product ﬂexibility and IS support for cross-
functional coordination were assessed in terms of proﬁtability and growth. Previous studies
on the performance effects of IS support for strategic ﬂexibility relied heavily on measures
(e.g., reduced product development time and costs) that tend to gauge intermediate
(operational) rather than bottom-line impacts of the IS support. Without assessing how the
IS support affects a ﬁrm’s ﬁnancial and market performance, it remains unclear whether the
operational beneﬁts derived from the IS support would eventually turn into competitive
advantage. For this reason, researchers have increasingly employed ﬁnancial and market
measures to assess the strategic value of IS investments (Kettinger et al., 1994; Brown
et al., 1995; Tam, 1998; Li and Ye, 1999).
In the current study, I used a popular ﬁnancial indicator, return on sales (ROS), to measure
proﬁtability. While other proﬁtability measures such as return on assets (ROA) and return on
equity (ROE) have also been used in previous studies (Brown et al., 1995; Li and Ye, 1999), I
chose ROS over ROA and ROE mainly because ROS is not only closely related to ROA and
ROE, but also less susceptible to variation in accounting procedures (Price and Mueller,
1986; Howell and Sakurai, 1992; Li and Ye, 1999). To measure growth, I employed another
well-established indicator, sales growth, which reﬂects how effective a ﬁrm is in opening up
new markets or expanding in existing markets. Like ROS, sales growth has been frequently
used in prior assessments of the performance impacts of IS (Cron and Sobol, 1983; Weill,
1992). To smooth annual ﬂuctuations and reduce short-term effects, I used a three-year
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184 175
(1996–1998) average for ROS and an average of two sales growth rates (one for the 1996–
1997 period and one for the 1997–1998 period) for sales growth.
3.2.4. Control variables
Since the ﬁrms participating in this study came from a variety of industries, it was
necessary to control, to some degree, the different industry conditions under which the
ﬁrms operated. To control for the industry effects, I ﬁrst used SIC codes to classify the ﬁrms
into four groups: (1) manufacturing; (2) transportation and public utilities; (3) wholesale
and retail trade; and (4) service. Where a ﬁrm operated in more than one industry, I
determined the ﬁrm’s SIC code by identifying the industry from which the ﬁrm received the
largest percentage of sales and the corresponding SIC code. I then created three dummy
variables (each with values of 0 or 1) for the second (transportation and public utilities), the
third (wholesale and retail trade) and the fourth (service) groups of ﬁrms. For each dummy
variable, I assigned a ﬁrm a value of 1 if it belonged to a group.
The fourth control variable was ﬁrm size, which has frequently been used in previous
studies involving ﬁrm performance as a dependent variable (Kivijarvi and Saarinen, 1995;
Tam, 1998; Li and Ye, 1999). Following convention, I used the natural logarithm of the
number of full-time employees to measure ﬁrm size. The ﬁfth control variable was
technological resources. A ﬁrm’s technological resources may inﬂuence its ability to
develop IS for sustainable competitive advantage (Kettinger et al., 1994). While a
preferable measure of technological resources is R&D intensity, the Research Insight data
for R&D intensity were missing for many ﬁrms in the sample. Following Hatten et al.
(1978), Fiegenbaum et al. (1990) and Kettinger et al. (1994), I used an alternative measure,
investment intensity (invested capital to sales), for technological resources.
The next two control variables are related to organizational slack which is indicative of a
ﬁrm’s ability to generate cash ﬂow for reinvestment (Chakravarthy, 1986). Organizational
slack needs to be controlled due to its potential inﬂuence on a ﬁrm’s ﬁnancial performance
as well as the ﬁrm’s ability to invest in and develop IS (Kettinger et al., 1994; Li and Ye,
1999). Following convention (Bourgeois, 1981), I used two traditional ratios (current assets
to current liabilities and debt to equity) to measure organizational slack. The former ratio
measures available organizational slack, while the latter reﬂects potential organizational
To test the main effects of IS support for product ﬂexibility and IS support for cross-
functional coordination as well as the moderating effects of unique, complementary
knowledge and information, I performed two sets of hierarchical regression analyses, using
ROS and sales growth as the dependent variables. In the ﬁrst step of each set of the
analyses, I entered the seven control variables as a set into the regression model. In the
second step, I added the two independent variables and the moderator variable to the
equation. In the third step, I added the two interaction terms to the equation. To avoid
potential multicollinearity among the independent and moderator variables, I used the
factor scores calculated from the factor analysis of the eleven IS support items in the
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184
Means, standard deviations and correlation coefﬁcientsa
Variable Mean S.D. 1 2 3 4 5 6 7 8 9 10 11
1 Return on sales 0.05 0.06
2 Sales growth 0.12 0.23 0.06
3 Industry dummy 1 0.08 0.28 0.01 À0.02
4 Industry dummy 2 0.16 0.37 À0.27 0.07 À0.14
5 Industry dummy 3 0.26 0.44 0.43 0.08 À0.18 À0.26
6 Firm size (log of number of employees) 2.61 1.31 À0.09 0.02 0.10 0.22 À0.27
7 Invested capital/sales 0.75 0.62 0.52 0.06 0.25 À0.29 0.49 À0.25
8 Debt/equity 1.47 5.30 0.01 0.08 0.04 0.06 0.02 À0.04 0.18
9 Current assets/current liabilities 1.89 3.02 0.01 0.01 À0.09 À0.04 0.22 À0.24 À0.01 À0.05
10 IS support for product ﬂexibilityb 0 1 0.22 0.20 À0.03 À0.15 0.13 0.03 0.01 0.06 À0.03
11 IS support for cross-functional coordinationb 0 1 À0.27 À0.11 À0.02 0.20 À0.26 0.17 À0.30 0.12 À0.07 0
12 Unique, complementary knowledge 3.57 0.92 0.07 À0.06 0.12 0.09 À0.01 0.15 À0.08 0.08 0.05 À0.01 0.14
N = 153. Correlations greater than or equal to 0.14 are signiﬁcant at the 0.10 level; r ! 0.16 are signiﬁcant at the 0.05 level; r ! 0.21 are signiﬁcant at the 0.01 level;
r ! 0.26 are signiﬁcant at the 0.001 level; all two-tail tests.
The statistics of these variables are based on their factor scores.
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184 177
regression analyses and mean-centered the moderator variable before entering it into the
Table 3 reports the means, standard deviations and correlations for all the variables. IS
support for product ﬂexibility was positively correlated with ROS (r = 0.22, p < 0.01) and
sales growth (r = 0.20, p < 0.05), while IS support for cross-functional coordination was
negatively correlated with ROS (r = À0.27, p < 0.001). Table 4 displays the results of the
hierarchical regression analyses. Hypothesis 1 states that IS support for product ﬂexibility
will be positively related to ﬁrm performance. Models 2 and 5 reveal that IS support for
product ﬂexibility was signiﬁcantly related to ROS (b = 0.19, p < 0.01) and sales growth
(b = 0.21, p < 0.05) in the expected direction. These results then provide support
for Hypothesis 1. Hypothesis 2 states that IS support for cross-functional coordination
will be positively related to ﬁrm performance. Models 2 and 5 show that IS support for
Results of hierarchical regression analysesa
Variables ROS Sales Growth
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Industry dummy 1 À0.09 À0.12 À0.15+ À0.01 0.01 À0.02
Industry dummy 2 À0.12 À0.09 À0.08 0.10 0.14 0.14
Industry dummy 3 0.19* 0.13 0.12 0.10 0.06 0.05
Firm size (log of number of employees) 0.10 0.07 0.06 0.04 0.04 0.04
Invested capital/sales 0.45*** 0.47*** 0.46*** 0.04 0.03 0.03
Debt/equity À0.07 À0.09 À0.10 0.07 0.08 0.05
Current assets/current liabilities À0.03 À0.03 À0.03 À0.01 0.01 0.02
IS support for product ﬂexibility 0.19** 0.16* 0.21* 0.23**
IS support for cross-functional coordination À0.11 À0.09 À0.12 À0.12
Unique, complementary knowledge 0.14* 0.13+ À0.07 À0.10
IS support for product ﬂexibility Â unique, 0.21** 0.03
complementary knowledge & information
IS support for cross-functional coordination 0.01 0.20*
Â unique, complementary knowledge
R2 0.34 0.40 0.44 0.02 0.08 0.12
DR 2 0.06 0.04 0.06 0.04
F 10.56*** 9.30*** 8.96*** 0.50 1.25 1.58
DF 4.55** 4.79** 2.96* 3.05+
N = 153, standardized regression coefﬁcients are shown.
p < 0.05.
p < 0.01.
p < 0.001.
p < 0.10.
178 M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184
cross-functional coordination had no signiﬁcant association with either ROS or sales growth.
Hence, no support was found for Hypothesis 2. It is worth noting that unique, complementary
knowledge and information were positively related to ROS (b = 0.14, p < 0.05). Therefore,
unique complementary knowledge and information is a quasi-moderator.
Hypothesis 3 states that the interaction between IS support for product ﬂexibility and
unique, complementary knowledge and information will be positively related to ﬁrm
performance. Models 3 and 6 in Table 4 show that the interaction term between IS support
for product ﬂexibility and unique, complementary knowledge and information was
signiﬁcant in predicting ROS in the expected direction (b = 0.21, p < 0.01). However, the
same interaction term was not signiﬁcant in predicting sales growth. Hence, Hypothesis 3
was only partially supported. Finally, Hypothesis 4 predicts that the interaction between IS
support for cross-functional coordination and unique, complementary knowledge and
information will be positively related to ﬁrm performance. Again, the moderation results
provide only partial support for this hypothesis. Speciﬁcally, the interaction term between
IS support for cross-functional coordination and unique, complementary knowledge and
information was signiﬁcant in predicting sales growth as expected (b = 0.20, p < 0.05), but
not signiﬁcant in predicting ROS.
6.1. Overview and research implications of the ﬁndings
The purpose of this research was to investigate the bottom-line impacts of IS support for
strategic ﬂexibility. Testing the main effects of IS support for two key components of
strategic ﬂexibility (product ﬂexibility and cross-functional coordination) on ROS and
sales growth has generated mixed results. Speciﬁcally, the study found signiﬁcant positive
effects of IS support for product ﬂexibility on ROS and sales growth, but no signiﬁcant
effects of IS support for cross-functional coordination on either performance measure.
While conﬁrming the conventional wisdom that ﬁrms may derive economic rents from
using IS to achieve product ﬂexibility (Sanchez, 1995; Hitt et al., 1998; Byrd, 2001), these
ﬁndings suggest that the bottom-line impacts of IS-enabled cross-functional coordination
need further investigation.
There are several possible explanations for the unexpected null effect of IS support for
cross-functional coordination. One possible explanation is the time lag effect. Previous
studies have found the impacts of many IS investments are subject to a time lag of 3–5 years
(Brynjolfsson, 1993; Jurison, 1996). The time lag may be longer for IS used for cross-
functional coordination than IS for product ﬂexibility. It is also possible that the investment
costs for IS support for cross-functional coordination are relatively high (Upton and
McAfee, 1996), thus making it difﬁcult to reap the full beneﬁts of such IS investments in
the short run. Another possibility is that IS support for cross-functional coordination is less
likely to improve a ﬁrm’s bottom-line performance without the support of other
complementary resources. The moderation results from this study seem to provide some
evidence for this explanation. When complemented by unique knowledge and information,
IS support for cross-functional coordination exerted a positive effect on sales growth. In
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184 179
other words, it appears that IS support for cross-functional coordination relies more on
ﬁrm-speciﬁc, complementary resources than IS support for product ﬂexibility in
inﬂuencing ﬁrm performance. Future investigation of the relationship between IS support
for cross-functional coordination and ﬁrm performance may then need to control or
incorporate ﬁrm-speciﬁc, complementary resources that may affect the relationship.
The ﬁndings from this study have several implications for the increasingly popular
resource-based approach to examining the strategic impacts of IS. First, the signiﬁcant main
effects of IS support for product ﬂexibility found here provide some empirical support for the
resource-based argument that the strategic contributions of IS may arise from their ability to
support certain critical ﬁrm-level resources and capabilities linked to sustainable competitive
advantage (Lado and Zhang, 1998; Bharadwaj, 2000; Byrd, 2001; Sambamurthy et al., 2003).
It then appears that future research on the strategic impacts of IS could beneﬁt from
investigating the effects of IS support for different ﬁrm-speciﬁc organizational resources and
capabilities on ﬁrm performance. Aside from strategic ﬂexibility, IS researchers have
recently explored the conceptual linkages between IS and other distinctive organizational
resources and capabilities such as knowledge management, organizational learning, and
superior customer orientation (Bharadwaj, 2000; Alavi and Leidner, 2001; Byrd, 2001).
Additional empirical studies assessing the performance impacts of such linkages would help
advance a resource-based theory of the strategic roles of IS.
Secondly, the signiﬁcant moderating effects of unique, complementary knowledge and
information on the relationship between IS support for product ﬂexibility and ROS and that
between IS support for cross-functional coordination and sales growth lend some support
to another resource-based argument that the potential contributions of IS to ﬁrm
performance rely on the presence of certain unique resources that complement the IS
(Feeny and Ives, 1990; Clemons and Row, 1991; Powell and Dent-Micallef, 1997). Hence,
future resource-based studies on the performance impacts of IS support for different
distinctive organizational resources and capabilities may need to examine the inﬂuence of
other ﬁrm-speciﬁc, complementary resources on such IS support. Besides proprietary
knowledge and information, unique organizational culture and structure, for example, may
affect the development, implementation and exploitation of IS for strategic ﬂexibility
(Upton, 1995; Lei et al., 1996).
Thirdly, the study developed two metrics to measure IS support for strategic ﬂexibility.
Since IS support for strategic ﬂexibility represents an important aspect of the IS capability
or competency (Bharadwaj, 2000; Sambamurthy et al., 2003), the metrics constructed here
contribute to the development of measurements of the IS capability deemed as critical to
the advancement of a resource-based theory of IS impacts (Bharadwaj, 2000; Santhanam
and Hartono, 2003). Unlike previous methods of measuring the IS capability, such as
matched-sample comparisons (Bharadwaj, 2000), the metrics constructed here can be used
for continuous assessment of a ﬁrm’s IS capability (Santhanam and Hartono, 2003).
6.2. Managerial implications
The results of this research have practical implications for IS/IT management for
strategic ﬂexibility. While ﬁrms these days are investing heavily in building and using IS to
increase their ﬂexibility to respond to the rapid changes in today’s business environment
180 M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184
(Upton, 1995), the performance impacts of such IS investments depend on the types of
support IS provide to strategic ﬂexibility and the presence of certain ﬁrm-speciﬁc resources
that complement the IS. Firms are more likely to reap economic beneﬁts (gains in
proﬁtability and sales growth) from IS investments for product ﬂexibility than those for
cross-functional coordination. It is possible that higher sales growth may accrue from IS-
enabled cross-functional coordination. But in order for that to happen, ﬁrms must possess
unique knowledge and information necessary for the implementation of such IS
applications. Moreover, ﬁrms with these resources are in a better position to enjoy higher
proﬁtability from IS support for product ﬂexibility than those without such resources.
Accordingly, developing and utilizing unique knowledge and information that increase the
effectiveness of IS investments for product ﬂexibility and cross-functional coordination are
as important as making such IS investments.
By highlighting some positive effects of IS support for strategic ﬂexibility on ﬁrm
performance, the current study implies a larger role for IS in helping ﬁrms gain competitive
advantage than that suggested by those who question the strategic value of IT (Mata et al.,
1995; Martinsons and Martinsons, 2002). Contrary to the growing skepticism towards
whether IS can be more than a ‘‘strategic necessity,’’ the results from this study suggest that
IS can be a source of competitive advantage and superior economic performance if they are
used to support the development of certain organizational capabilities tied to sustainable
competitive advantage. Accordingly, ﬁrms and their managers should focus more on the
types of support IS provide (Porter, 2001) than IS spending levels and system
characteristics in their evaluations of the potential strategic value of IS.
6.3. Limitations of the study
The ﬁndings in this study need to be interpreted within its limitations. First, in analyzing
and assessing the performance effects of IS support for strategic ﬂexibility, the study
focused on IS support for two key contributing capabilities of strategic ﬂexibility: product
ﬂexibility and cross-functional coordination. It is possible that other organizational
capabilities (e.g., process ﬂexibility and multi-sourcing) may also contribute to strategic
ﬂexibility. Therefore, additional investigation of the performance effects of using IS to
support other contributing capabilities of strategic ﬂexibility would potentially enrich our
understanding of how IS support for strategic ﬂexibility inﬂuences ﬁrm performance.
Second, while the study controlled for a number of industry and organizational factors,
there are other potential performance determinants whose effects were not taken into
account here due to lack of data availability and the small sample size. The exclusion of
those variables might have resulted in overestimating the contribution of IS support for
product ﬂexibility and underestimating the positive effects of IS support for cross-
functional coordination (Berry and Feldman, 1985). Whenever possible, future research
needs to include other environmental and organizational attributes related to ﬁrm
performance in order to provide a more accurate assessment of the performance impacts of
IS support for strategic ﬂexibility.
Third, even though it is argued here that a high level of IS support for product ﬂexibility
or cross-functional coordination would lead to a high degree of strategic ﬂexibility, which
in turn would improve ﬁrm performance, the study only tested the relationships between
M.J. Zhang / J. Eng. Technol. Manage. 22 (2005) 163–184 181
the two types of IS support and ﬁrm performance. Without testing the relationships
between such IS support and strategic ﬂexibility as well as the relationship between
strategic ﬂexibility and ﬁrm performance, it remains unclear whether strategic ﬂexibility is
actually the link between the IS support and ﬁrm performance, as conceptualized in this
study. It is only through addressing this issue empirically in additional research that we can
be more certain of the results found here as well as the mechanism through which IS
support for product ﬂexibility and cross-functional coordination affects ﬁrm performance.
As another limitation, the response rate (20%) for the survey used in this research, while
comparable to those of similar studies, was relatively low and thus limited the
generalizability of the study results. Obtaining high response rates for sensitive
information concerning the strategic use of IS continues to be a challenge for researchers.
The ﬁfth limitation of the study lies in the relatively low reliability of the scale used to
measure the moderating variable. While acceptable for exploratory purposes, this coarse
measure of unique, complementary knowledge and information needs to be reﬁned to
increase its reliability in future studies.
The author would like to thank the editor and the two anonymous referees for their
comments and suggestions that helped improve the article. The research was funded in part
by a research grant from the Research Council of Cleveland State University.
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