Information systems, strategic flexibility and firm performance: An empirical investigation

J. Eng. Technol. Manage. 22 (2005) 163–184 www.elsevier.com/locate/jengtecman

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

Abstract 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

1. Introduction 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 protected]. 0923-4748/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jengtecman.2005.06.003

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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 resourcebased 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,

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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 Hartono, 2003). 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,

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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 valuecreating operational or manufacturing strategies, including mass customization, time-tomarket, 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

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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 crossfunctional 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

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shorten product design cycles (Sanchez, 1995; Lei et al., 1996; Hitt et al., 1998). Furthermore, flexible manufacturing systems (FMS) using the computer-aided manufacturing (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 ISderived 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 valuecreating 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 ISenhanced electronic links among cross-functional, concurrent development teams reduce

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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 development performance. 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, computerintegrated 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 performance. Hypothesis 2. IS support for cross-functional coordination will be positively related to firm performance. 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

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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 DentMicallef, 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 firmspecific 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

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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 performance.

3. Methods 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

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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 Table 1. Table 1 Characteristics of the study sample Industry group

Frequency

%

Manufacturing Transportation and public utilities Wholesale and retail trade Service

75 13 25 40

49.0 8.5 16.4 26.1

Average number of employees Average annual sales (thousand) Average returns on sales

35662 $7472 5.48%

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To test for potential nonresponse bias, I first compared the respondent firms to the nonrespondent firms with respect to sales and number of employees. t-test results showed no significant differences between the two groups: sales (t = 1.227, p > 0.22) and number of employees (t = 1.308, 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 crossfunctional coordination (t = 0.315, p > 0.753) and unique, complementary knowledge and information (t = 0.876, p > 0.383). Together, these checks provide some evidence for the absence of non-response bias in the data. 3.2. Measures 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, Likerttype 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 crossfunctional coordination, respectively. 3.2.2. Moderating variable As previously stated, unique, complementary knowledge and information refer to firmspecific 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.

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Table 2 Factor analysis of IS support for strategic flexibility Item description

IS support for product flexibility

IS support for Cross-functional coordination

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 % of common variance explained Cronbach Alpha

3.682 33.48 0.86

2.756 25.05 0.81

3.2.3. Dependent variables The performance impacts of IS support for product flexibility and IS support for crossfunctional 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

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(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 slack.

4. Analyses To test the main effects of IS support for product flexibility and IS support for crossfunctional 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

176

1 2 3 4 5 6 7 8 9 10 11 12 a

Variable

Mean

S.D.

Return on sales Sales growth Industry dummy 1 Industry dummy 2 Industry dummy 3 Firm size (log of number of employees) Invested capital/sales Debt/equity Current assets/current liabilities IS support for product flexibilityb IS support for cross-functional coordinationb Unique, complementary knowledge and information

0.05 0.12 0.08 0.16 0.26 2.61 0.75 1.47 1.89 0 0 3.57

0.06 0.23 0.28 0.37 0.44 1.31 0.62 5.30 3.02 1 1 0.92

1

2 0.06 0.01 0.27 0.43 0.09 0.52 0.01 0.01 0.22 0.27 0.07

3

0.02 0.07 0.08 0.02 0.06 0.08 0.01 0.20 0.11 0.06

4

0.14 0.18 0.10 0.25 0.04 0.09 0.03 0.02 0.12

5

0.26 0.22 0.29 0.06 0.04 0.15 0.20 0.09

6

0.27 0.49 0.02 0.22 0.13 0.26 0.01

7

0.25 0.04 0.24 0.03 0.17 0.15

8

0.18 0.01 0.01 0.30 0.08

9

0.05 0.06 0.12 0.08

10

0.03 0.07 0.05

0 0.01

11

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. b The statistics of these variables are based on their factor scores.

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Table 3 Means, standard deviations and correlation coefficientsa

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regression analyses and mean-centered the moderator variable before entering it into the regression models.

5. Results 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 = 0.27, 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

Table 4 Results of hierarchical regression analysesa Variables

Industry dummy 1 Industry dummy 2 Industry dummy 3 Firm size (log of number of employees) Invested capital/sales Debt/equity Current assets/current liabilities

ROS

Sales Growth

Model 1

Model 2

Model 3

0.09 0.12 0.19* 0.10 0.45*** 0.07 0.03

0.12 0.09 0.13 0.07 0.47*** 0.09 0.03

0.15+ 0.08 0.12 0.06 0.46*** 0.10 0.03

0.19** 0.11 0.14*

0.16* 0.09 0.13+

IS support for product flexibility IS support for cross-functional coordination Unique, complementary knowledge and information IS support for product flexibility  unique, complementary knowledge & information IS support for cross-functional coordination  unique, complementary knowledge and information R2 DR 2 F DF a * ** *** +

0.34 10.56***

0.40 0.06 9.30*** 4.55**

N = 153, standardized regression coefficients are shown. p < 0.05. p < 0.01. p < 0.001. p < 0.10.

Model 4 Model 5 Model 6 0.01 0.10 0.10 0.04 0.04 0.07 0.01

0.01 0.14 0.06 0.04 0.03 0.08 0.01

0.02 0.14 0.05 0.04 0.03 0.05 0.02

0.21* 0.12 0.07

0.23** 0.12 0.10

0.21**

0.03

0.01

0.20*

0.44 0.04 8.96*** 4.79**

0.02 0.50

0.08 0.06 1.25 2.96*

0.12 0.04 1.58 3.05+

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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. Discussion 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 crossfunctional 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

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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

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(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 ISenabled 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 crossfunctional 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

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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.

Acknowledgements 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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