Journal of Operations Management 17 Ž1999. 411–428

Manufacturing technology and strategy formulation: keys to enhancing competitiveness and improving performance
Michael Tracey a , Mark A. Vonderembse a b,)

, Jeen-Su Lim

b

Purdue UniÕersity, West Lafayette, Indiana 47907, USA b UniÕersity of Toledo, Toledo, OH 43606-3390, USA Received 1 July 1997; accepted 23 July 1998

Abstract Porter wPorter, M.E., 1996. What is strategy? Harvard Business Review 74 Ž6., 61–78.x claims that a proper link between strategy and manufacturing operations is a key to developing sustainable competitive advantage. To be successful in this globally competitive, rapidly changing environment, organizations must formulate strategic plans that are consistent with their investment in and use of manufacturing technology. This study proposes that organizations that invest in advanced manufacturing technology and develop mechanisms for manufacturing managers to participate in strategy formulation will have improved competitive capabilities and better performance than firms that do not. Using the result from a large-sample survey, this study develops valid and reliable measures of advanced manufacturing technology and manufacturing managers’ participation in strategy formulation as well as the competitive capabilities of a firm. Linear structural equation analysis ŽLISREL. results show that the relationships between a firm’s practices in these two areas and its competitive capabilities are found to be statistically significant and positive. Also, high levels of these competitive capabilities lead to high levels of performance as measured by customer satisfaction and marketing performance. q 1999 Elsevier Science B.V. All rights reserved.
Keywords: Empirical research; Operations strategy; Measurement and methodology; Technology management

1. Introduction Expanding global competition, rapidly changing markets and technology, and increasing complexity and uncertainty are creating a new competitive environment ŽManufacturing Studies Board, 1986; Bayus, 1994.. These changes are causing manufacturing firms to carefully examine a shift from industrial systems driven by efficiency and enabled by hard-automation to post-industrial systems where success
Corresponding author. Tel.: q1-419-530-4319; fax: q1-419530-8497; e-mail: mvonder@uoft02.utoledo.edu
)

depends on quick response to customer demands for customized, high quality products ŽSkinner, 1969, 1986; Hayes et al., 1988; Doll and Vonderembse, 1991; Goldhar et al., 1991; McCutcheon et al., 1994; Roth, 1996.. In the post-industrial environment, high quality and reliability, timely delivery, enhanced customer service, rapid new product introduction, flexible systems, and efficient capital deployment, not cost reduction, are the primary sources of competitive advantage ŽSkinner, 1986.. In the industrial era, firms focused on manufacturing a narrow range of products and sustaining efficient mass-production operations through productiv-

0272-6963r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. PII: S 0 2 7 2 - 6 9 6 3 Ž 9 8 . 0 0 0 4 5 - X

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ity improvement programs ŽHuber, 1984; Skinner, 1985.. The connection between manufacturing and corporate success was rarely more than achieving high efficiency and low costs ŽSkinner, 1969.. In the post-industrial environment, successful strategies often hinge on an organization’s ability to anticipate markets and to develop production systems that quickly design, produce, and deliver high-value products that meet specific customer needs ŽHall, 1992; Lado et al., 1992; Porter, 1992; Vonderembse et al., 1997.. Success depends on close and careful linkages between a firm’s manufacturing strategy and its overall strategy. These linkages help to guide decisions about how manufacturing technologies are applied, which competitive capabilities are achieved and, ultimately, how well firms perform ŽSkinner, 1969; Porter, 1996.. The design of manufacturing systems should focus on developing competitive capabilities that satisfy customer needs and improve performance ŽWard et al., 1994.. As manufacturing systems evolve from industrial to post-industrial, these capabilities change, i.e, response time emerges as an important dimension of competition ŽBlackburn, 1991.; the emphasis that customers place on capabilities change, i.e., product quality becomes more important than product cost ŽVonderembse et al., 1995.; and the ways organizations achieve these capabilities change, i.e., there is a transition from economies of scale to economies of scope ŽGoldhar and Jelinek, 1983; Hayes and Pisano, 1994.. To cope with the changing environment, customer needs, and competitive factors, organizations should Ž1. develop policies and practices that enable manufacturing managers to participate in strategy formulation and Ž2. allow these linkages to guide their investments in and use of manufacturing technologies. Porter Ž1996. claims that a proper link between strategy and operations is a key to developing sustainable competitive advantage. Skinner Ž1969. states that manufacturing is the missing link in corporate strategy. Upton Ž1994. contends that firms must match their manufacturing systems capabilities with their strategic competitive priority in order to be successful. Ward et al. Ž1994. found that high performing firms have a stronger commitment to longterm investment in manufacturing capabilities than low performing firms. Case studies by Meredith and

McTavish Ž1992. describe the global marketing benefits that can be achieved from the strategic deployment of advanced manufacturing technology. Large-sample empirical studies that measure a firm’s level of advanced manufacturing technology, manufacturing manager’s participation in strategy formulation, competitive capabilities Že.g., ability to offer a broad product line and dependable delivery., and overall firm performance are not available. As a result, we know little about whether or under what circumstance the levels of these two practices improve a firm’s competitive capabilities and performance ŽRoth and Miller, 1992.. The purpose of this study is to investigate the research questions: do firms with a high level of advanced manufacturing technology and with a high level of manufacturing manager’s participation in strategy formulation have high levels of competitive capabilities and do firms with high levels of competitive capabilities have greater customer satisfaction and improved performance? To test these research questions, a large-sample, organizational-level study has been completed. Valid and reliable measures of advanced manufacturing technology, manufacturing manager’s participation in strategy formulation, and competitive capabilities are developed from these data. Scales developed by Swamidass and Newell Ž1987. and Ward et al. Ž1994. are starting points for developing the instrument to measure manufacturing manager’s participation in strategy formulation. An existing instrument is used to measure the firm’s level of performance ŽVenkatraman and Ramanujan, 1986; McKee et al., 1989; Davis and Schul, 1993; Heskett et al., 1994; Narver and Slater, 1995.. Valid and reliable instruments are the foundation for research and are essential for testing structural relationships. LISREL is used to test the structural model that addresses these research questions. 2. Manufacturing technology and strategy In 1969, Wickham Skinner wrote,‘‘ A company’s manufacturing function typically is either a competitive weapon or a corporate millstone. It is seldom neutral.’’ More recent writings promote the strategic importance of creating and maintaining an appropriate base of manufacturing assets to achieve the com-

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petitive capabilities that insure long-term success ŽHofer and Schendel, 1978; Montanari, 1978; Hansen and Wernerfelt, 1989; Cohen and Levinthal, 1990; Collis, 1991; Barney, 1991; Carlsson, 1992; Hall, 1992; Boynton, 1993.. Research studies by Swamidass and Newell Ž1987. and Ward et al. Ž1994. provide further support for these claims. Swamidass and Newell Ž1987. surveyed 35 firms from the Pacific Northwest. They found that firms with high levels of manufacturing managers participation in strategic decision-making had higher performance as measured by growth in sales, return on total assets, and return on sales. Ward et al. Ž1994. examined 60 firms across five industries all operating in the state of Ohio. The study showed that firms with high levels of manufacturing managers involvement in strategy development, investment in specific manufacturing capabilities, and worker participation also had high performance as measured by market share and sales. In addition, longitudinal case studies by Meredith and Vineyard Ž1993. found that the lower the firm’s performance, the lesser the role of manufacturing managers in strategic decision making. This finding is similar to the other studies, but the causal direction is reversed. These regional, small-sample studies do not measure a firm’s competitive capabilities nor examine the impact of advanced manufacturing tech-

nology and manufacturing manager’s participation in strategy formulation on these capabilities. The proposed model, Fig. 1, illustrates that organizations which invest in Advanced Manufacturing Technology ŽAMT. and have manufacturing managers who participate in strategy formulation ŽMMP. will have enhanced Competitive Capabilities ŽCC. and improved Levels of Performance ŽLOP. ŽDay, 1994.. This study defines these variables and describes the relationships shown in Fig. 1. It uses a nationwide, large-sample survey to develop appropriate measures for the variables and to test the hypotheses. 2.1. AdÕanced manufacturing technology AMT is the application of computer-enhanced, applied science to a firm’s production system. AMT is a resource that enables a firm to efficiently produce multiple products across the same asset base, thereby achieving economies of scope ŽGoldhar and Jelinek, 1983.. Investments in AMT such as computer-aided design and computer numerical controls provide resources that enable a firm to respond to rapid market change and adapt to shorter product life cycles by designing and producing high-quality, custom designed products ŽDoll and Vonderembse, 1987; Roth and Miller, 1992; Handfield and Pagell, 1995..

Fig. 1. Linking technology and strategy to create competitive capabilities and improve performance.

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These resources enable the firms to develop an effective mix of platform and derivative products to meet specific customer requirements ŽWheelwright and Sasser, 1989; Wheelwright and Clark, 1992., and allows management to develop quick response strategies to compete in the global marketplace ŽMeredith and McTavish, 1992.. These technologies encourage firms to implement cross-functional teams that reduce time and costs as well as improve quality in both product design and manufacturing. In delivery, AMT leads to higher order fill rates because the system can adjust rapidly to changing customer needs ŽSlack, 1987.. Order cycle time is cut and more frequent deliveries can be made because the manufacturing system has shorter production runs. Computer-based manufacturing systems also create an environment that permits more accurate and timely shipment information ŽRoth, 1996.. Hypothesis 1: A firm’s level of Advanced Manufacturing Technology ŽAMT. has a positive effect on its Competitive Capabilities ŽCC..

To successfully implement advanced technology, an organization must allow manufacturing’s evolving competencies to be a driving force in strategy formulation ŽHarrison, 1990; Parthasarthy and Sethi, 1992; Ettlie and Penner-Hahn, 1994.. Porter Ž1996. describes the relationship between strategy and operational effectiveness as fundamental to competitive advantage and even more important to sustain that advantage. This involvement increases top management awareness of manufacturing’s important role in reaching organizational goals and encourages upperlevel support for technological innovations that span the organization’s value chain. Manufacturing managers involvement in strategic decision-making can help to shape how an organization employs its manufacturing systems to gain competitive advantage. This results in the co-alignment of manufacturing system design and the organization’s strategy. Hypothesis 2: A firm’s level of Manufacturing Manager’s Participation in Strategy Formulation ŽMMP. has a positive effect on its Competitive Capabilities ŽCC.. 2.3. CompetitiÕe capabilities

2.2. Manufacturing manager’s participation in strategy formulation MMP measures the extent to which manufacturing plant managers are involved in organizationallevel strategy development. Involving manufacturing managers in strategy formulation enables firms to develop organizational-level strategies that coordinate marketing, engineering, information systems, and other functional areas with manufacturing ŽUpton, 1994; Porter, 1996.. This allows the organization to generate a steady stream of product and process innovations that improve a firm’s competitive capabilities and enhance the firm’s competitive position ŽSkinner, 1969; Wheelwright and Clark, 1992; Wheelwright and Sasser, 1989; Vonderembse et al., 1997.. It also helps to create a shared learning environment that increases the rate of information exchange and provides opportunities to eliminate waste, reduce waiting time, and implement innovations ŽSusman and Chase, 1986; Zuboff, 1988; Susman, 1990; Weick, 1990..

CC are the attributes of an organization that attract customers; they are potential points of differentiation between an organization and its competitors. They are not directly controllable by management but are outcomes of critical management decisions. Innis and LaLonde Ž1994. and Koufteros Ž1995. define a set of CC that describes an organization’s capacity to satisfy customers including price offered, product quality, product line breadth, order fill rate, order cycle time, order and shipment information, and frequency of delivery. An organization’s underlying cost structure must be low enough to offer a price that is comparable to the competition, or the products offered must be higher in value than the competition so a premium price can be commanded. Product quality and product line breadth Žvariety. must meet or exceed customer expectations. The organization should have high order fill rates, short order cycle times, accurate order and shipping information, and frequent deliveries. These capabilities should enable firms to achieve high levels of customer satisfaction and market performance.

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2.3.1. Price offered Price charged received the highest ranking among the 32 customer service attributes included in Innis and LaLonde’s Ž1994. study. A manufacturer’s ability to offer competitive prices andror command premium prices is influenced by the costs it incurs across the supply chain as well as the level of accompanying service it is able to offer ŽBresticker, 1992; Davis, 1993.. Price affects both profits and market share. The price and value trade-off is one of the key determinants of customer satisfaction ŽBergman, 1995.. 2.3.2. Quality of products Quality has become a key competitive issue in the global marketplace, both domestically and internationally ŽGarvin, 1988; Flynn et al., 1994; Anderson et al., 1995.. Quality is defined as fitness for use and includes product performance, reliability, and durability. Quality is influenced by product design, manufacturing performance, incoming quality from suppliers, and delivery performance ŽNovack et al., 1992.. Quality can affect the number of units sold, and it is a key element of value-to-customer. 2.3.3. Product line breadth Customers expect availability of various products and features that satisfy their individual requirements ŽMeredith et al., 1994.. AMT enables the ongoing production of customized products at reasonable expense ŽGoldhar and Jelinek, 1983; Ramamurthy and King, 1992.. Product line breadth influences both value and market share. The more precisely a product fits a customer need, the more value the customer will assign to it. As the product line breadth expands, more customers are able to find a product that meets their needs and sales should increase. 2.3.4. Order fill rate This is the percent of orders that are filled on-time ŽHolcomb, 1994.. Providing a large number of product offerings and achieving a high order fill rate requires a manufacturing system that can react quickly to changing customer demand ŽDavis and Gibson, 1993.. When orders are filled completely and correctly the first time, operating costs decline and customers are not dissatisfied.

2.3.5. Order cycle time The order cycle is defined by Lambert and Stock Ž1993. Žpp. 116. as ‘‘the total elapsed time from the initiation of the order by the customer until delivery to the customer.’’ Lowering cycle time is a primary issue in the current business environment for manufacturers of industrial and consumer products ŽStark, 1989; Goldhar and Lei, 1991; LaLonde and Powers, 1993; Holcomb, 1994.. Shortening the time it takes to bring a product from concept to production to market requires a manufacturing system that can respond quickly ŽBockerstette and Shell, 1993; McCutcheon et al., 1994.. Rapid response to orders reduces operating costs and enables customers to enjoy the product’s benefits immediately ŽStalk and Hout, 1990; Blackburn, 1991.. 2.3.6. Orderr shipment information Innis and LaLonde Ž1994. found that customers want meaningful information when they place an order, e.g., product availability, projected shipping date, and projected delivery date. The ability to gather and transmit accurate data to customers is dependent on the level of real-time, computer-based, manufacturing flexibility present in the firm. 2.3.7. Frequency of deliÕery In the 1980s, customers began to recognize the actual cost of carrying inventory and started to push it back toward the manufacturer ŽCoyle et al., 1992.. Today, customers as a matter of practice expect more frequent shipments and there is a strong tendency toward the reduction of incoming shipment sizes ŽVonderembse et al., 1995.. The capacity to fulfil this service request while incurring reasonable expense is highly dependent on the flexibility of the organization’s manufacturing system. Hypothesis 3: A firm’s level of Competitive Capabilities ŽCC. has a positive effect on its Level of Performance ŽLOP..

2.4. LeÕel of performance LOP includes items that measure customer satisfaction and market performance. Customer satisfac-

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tion is measured by the value customers perceive in the product, customer retention rates, and customer referrals. Market performance is measured by sales gains and market share growth ŽVenkatraman and Ramanujan, 1986; McKee et al., 1989; Davis and Schul, 1993; Heskett et al., 1994; Narver and Slater, 1995.. The items used to measure LOP are listed in Appendix A. Five-point Likert scales are used for each question. Responses range from 1 s Unacceptable to 5 s Superior. The response X s Not RelevantrDo Not Know was made available.

3. Scale development: methods and pilot study results To develop valid and reliable scales to measure AMT, MMP, and CC, procedures suggested by Churchill Ž1979., Flynn et al. Ž1990., and Gerbing and Anderson Ž1984. were followed. An extensive literature review facilitated theory development and item generation, helped to define the domain of the constructs, and uncovered useful measures employed in previous studies. A pre-test was completed to enhance content validity. A pilot study was executed utilizing respondents similar to the target respondents. These steps were taken to insure content validity, reliability, and parsimony as well as construct and predictive validity of the instruments.

3.1. Item generation In developing measures of AMT, a decision was made to ask general questions about the use of real-time process controls and computer-based production technology rather than to ask questions about specific technologies such as robotics or flexible manufacturing systems. A list of specific technologies may miss some that are critical in one industry but not in another. Questions about specific technologies are sometimes misunderstood and often do not capture how effectively and extensively these technologies are used. Swamidass and Newell Ž1987. and Ward et al. Ž1994. describe items to measure MMP. Some of their items have been modified and

others have been eliminated to increase the likelihood of achieving unidimensionality in this construct. Items that measure CC are drawn from Cooper et al. Ž1992., Holcomb Ž1994., and Koufteros Ž1995.. Five-point Likert scales are used for all questions. Responses range from 1 s Strongly Disagree to 5 s Strongly Agree. The response X s Not RelevantrDo Not Know was also made available. The list of items and definitions for each construct was presented to six executives from six manufacturing firms. They were given several days to examine the model ŽFig. 1. as well as information regarding the types of executives who would be the target respondents. They were asked to review the questionnaire and to comment on the language and clarity of each question as well as the overall format of the instrument. They were encouraged to provide suggestions for additional items if they perceived that the items offered did not cover the intended domain of the variable, or to drop items they felt were redundant or inappropriate. Their input was gained through interactive, structured interviews and was helpful in improving the instrument with respect to its wording, clarity, and relevance. The resulting instrument was pre-tested. In the pre-test, inputs were received from two leading consultants in the area of manufacturing and eight academic experts in the disciplines of operations management, marketing, logistics, and industrial engineering. Each expert was mailed a copy of the instrument organized by construct with a definition for each dimension. They were asked to evaluate each item on the scale: keep, modify, or drop. They were also asked to suggest additional items if they felt that part of a construct was not adequately covered ŽGatewood and Field, 1994..

3.2. Pilot study A pilot study was conducted using respondents similar to the target respondents. The instrument was sent to 520 managers in manufacturing firms including: General ManagersrPresidents, OperationsrManufacturing Managers, FacilityrPlant Managers, and MaterialsrPurchasing Managers. Subjects were systematically selected from a mailing list pur-

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chased from American Business Lists w , a division of Dunn and Bradstreet. Fifty usable responses were received from the pilot study mailing. The responses from the pilot study were used to explore the instrument with several objectives in mind: purification, reliability, parsimony, as well as construct and predictive validity. As described by Churchill Ž1979., the instruments were purified by examining the corrected-item total correlations ŽCITC.. Items with CITC less than 0.5 were dropped. The item inter-correlation matrices provided by SPSS w were also used to drop items if they did not strongly contribute to Cronbach’s alpha ŽCronbach, 1951. for the dimension under consideration ŽFlynn et al., 1995.. Some items which did not contribute strongly to alpha, but whose content was considered important to the research, were designated for modification. Items related to a specific construct Že.g., AMT. were also submitted as a group to exploratory factor analysis to assess their internalrconvergent validity. Maximum likelihood was chosen as the extraction procedure and the varimax method was utilized for factor rotation. Items which did not load at 0.60 or above were generally eliminated at this stage. Dillion

and Goldstein Ž1984. Žpp. 69., however, point out the researcher needs to consider an item’s importance to the research objective as well as its ‘loading’ during factor interpretation. Accordingly, some items which had a weak factor loading were designated for modification during this initial phase of analysis. Cronbach’s alpha Ž1951. was calculated for the retained items only to ensure that the items carried forward were internally consistent. In the next step, the externalrdiscriminant validity of each construct was appraised by submitting the items remaining for the entire construct Že.g., CC. to exploratory factor analysis to uncover significant cross-loadings. The sample size of 50 observations was just large enough to justify factor analysis at the pilot study stage ŽHair et al., 1995: pp. 373., so the Kaiser–Meyer–Olkin ŽKMO. measure of sampling adequacy was calculated for each construct using SPSS w . This method helps to determine if it is appropriate to employ factor analysis ŽKaiser, 1970.. In each case, factor analysis was appropriate. Where significant cross-loadings were discovered, items were either dropped or modified. In some instances, an item or items were added to strengthen the measurement of a specific dimension.

Table 1 AMT and MMP Items, CITCs, and reliabilities after purification Ž n s 474. Item AdÕanced manufacturing technology AMT1: We have incorporated real-time process control into our production systems AMT2: We utilize production technology that is among the most flexible in our industry AMT3: We apply computer-enhanced technology to improve the flexibility of manufacturing AMT4a : We reorganize our facilities as necessary to increase our manufacturing flexibility Manufacturing manager’s participation in strategy formulation MMP1: The input of manufacturing plant managers is an integral part of the strategy formation process MMP2: Manufacturing plant managers are involved in decisions related to strategies for company growth MMP3: Manufacturing plant managers have a good under-standing as to how companyrdivisional strategy is formed a CITC

Cronbach’s a retained items

0.599 0.666 0.596 0.481

a s 0.7727

0.585 0.625 0.545

a s 0.7538

Item dropped.

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The reliability of the remaining items comprising each dimension was examined using Cronbach’s alpha Ž1951.. Finally, predictive validity was assessed by correlating composite measures of the constructs.

The instruments used in the large-sample survey for AMT and MMP are in Table 1 and the instruments for CC are in Table 2. Detailed results from the pilot study are available from the authors.

Table 2 Competitive capabilities ŽCC. items, CITCs, and reliabilities after purification Ž n s 474. Item Price offered PR1: We offer competitive prices PR2: We are able to compete based on our prices PR3: We are able to offer prices as low or lower than our competitors PR5a : We guarantee our prices PR4 a : We are able to sell our products at prices that are above average Quality of products QP2: We are able to compete based on quality QP3: We offer products that are highly reliable QP4: We offer products that are very durable QP5: We offer high quality products to our customers QP1a : We offer products that function according to customer needs Product line breadth PLB1: We respond well to changing customer preferences regarding products PLB2: We respond well to changing customer preferences regarding accompanying services PLB3: We alter our product offerings to meet client needs PLB4: We respond well to customer demand for "new" features PLB5a : We offer the products and services our customers want Order fill rate FR1: Our frequency of customer backorders is low FR2: Our customers are satisfied with our level of completeness for routine shipments FR3: We deliver the assortment of products ordered FR4 a : We deliver the desired quantities of products Order cycle time OCT3: Orders submitted to us are delivered on-time, as defined by the customer OCT4: We provide on-time delivery of customer orders OCT2 a : The time from our receipt of an order to possession of the shipment by that customer is acceptable to our clients OCT1a : We offer customers a reliable order processing time Orderr shipment information OSI1: We supply accurate projected shipping dates OSI2: We supply accurate projected delivery dates OSI3 a : We supply clients with accurate information regarding product availability OSI4 a : We respond with accurate information to a customer inquiry concerning an order Frequency of deliÕery FD1: Our customers are pleased with the frequency of our delivery FD2: We can alter our delivery schedule per each customer’s requirements FD3: We are flexible in developing delivery schedules FD4: We work with each customer to develop a delivery schedule that is acceptable a CITC 0.532 0.585 0.458 0.273 y0.080

a : retained items a s 0.7899

0.641 0.761 0.695 0.772 0.578

a s 0.8588

0.730 0.665 0.660 0.676 0.561

a s 0.8425

0.577 0.666 0.575 0.430

a s 0.7456

0.832 0.825 0.753 0.637

a s 0.9192

0.801 0.784 0.655 0.631

a s 0.9057

0.587 0.584 0.614 0.631

a s 0.7916

Items dropped.

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4. Scale development: large-sample methods and results The instruments developed in the pilot study along with items measuring LOP were mailed to 3333 executives on the American Business Lists w . These executives represent 3333 different organizations from four SIC codes: a25—Furniture and Fixtures; a34—Fabricated Metal Products; a35—Industrial and Commercial Machinery; and a36,—Electronic and Other Electrical Equipment. The organizations have from 50 to 1000 employees. These SIC codes were selected because they contain many discrete part manufacturers. A cover letter, signed by the Executive Vice President of the National Association of Purchasing Management, was included to encourage participation. A follow up letter and questionnaire was sent to those who do not return the initial questionnaire after a five week waiting period. Fourteen packets were returned as undeliverable. Of the responses received, 58 were appraised as being unsuitable for the large-scale analysis. Most of the rejected questionnaires were due to a lack of manufacturing at the respondent’s location, or to an insufficiently completed survey. A total of 474 responses were appraised as suitable for the large-scale analysis giving an effective response rate of 14.5% w474 % Ž3333 y 14 y 58.x. Nearly all of the respondents were manufacturing managers at the director level or above. Approximately, 75% of the firms had 500 or fewer employees at the respondent’s location, and the types of manufacturing operations were approximately uniformly distributed across the spectrum from continuous flow to job shop.

The 474 acceptable responses from the large-scale survey were used to further refine the instrument using the same criteria as in the pilot test. The methods were the same as those used in the pilot test except that single dimension factor analysis was not done. 4.1. Scale deÕelopment: adÕanced manufacturing technology (AMT) and manufacturing manager’s participation in strategy formulation (MMP) Table 1 displays the results of purification using the CITCs and Cronbach’s alpha Ž1951. for AMT and MMP. One item was dropped from the AMT because the CITC for item AMT4 was less than 0.5. All three items for MMP were retained. Table 3 displays the results of submitting the six remaining items to factor analysis to determine if the instruments have construct validity. The KMO measure of 0.77 indicates that factor analysis was appropriate. MMP3 was retained even though its loading was slightly below 0.60 because it was judged to be important to the research. Loadings below 0.40 are not reported. 4.2. Scale deÕelopment: competitiÕe capabilities (CC) Table 2 displays the results of purification using the CITCs and Cronbach’s alpha Ž1951. for the CC factors. Two items were dropped from the Price Offered ŽPR. dimension and one item was dropped from the Order Fill Rate ŽFR. because their CITCs were less than 0.5. One item was dropped from Quality of Products ŽQP. and another from Product

Table 3 AMT and MMP—factors, loadings, and reliabilities after factor analysis Ž n s 474. Kaiser–Meyer–Olkin measure of sampling adequacys 0.77 Item Advanced manufacturing technology factor Ž1st. 0.7611 0.6761 0.6497 0.7847 0.6822 0.5888 1.52 Manufacturing manager’s participation in strategy formulation factor Ž2nd.

a for retained items

APT2 APT1 APT3 MMP2 MMP1 MMP3 Eigen values

a s 0.7727

a s 0.7538

1.56

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Line Breadth ŽPLB. because they did not strongly contribute to Cronbach’s alpha Ž1951. in their respective construct and there were four other items in those constructs. Two items were dropped from both Order Cycle Time ŽOCT. and OrderrShipment Information ŽOSI. dimensions because Cronbach’s alpha Ž1951. increased substantially for each factor. The four Frequency of Delivery ŽFD. dimension items were retained for factor analysis. Table 4 displays the results of submitting the 22 items remaining to factor analysis to assess the construct validity for CC. Loadings below 0.40 are not reported. The KMO measure of 0.93 indicates that factor analysis was appropriate. Four factors were retained and placed on the suggested final instrument. Price Offered ŽPR., Quality of Products ŽQP., and Product Line Breadth ŽPLB. emerged as distinct factors ranking fourth, second, and third, respectively, in variance explained. During the pilot study analysis, the items for Order Fill Rate ŽFR., Order

Cycle Time ŽOCT., and Frequency of Delivery ŽFD. loaded on a single factor. In the factor analysis for the large-sample data set, items from these factors as well as OrderrShipment Information ŽOSI. once again loaded together. This larger dimension was labeled the Delivery Capability ŽDC. factor. It explained the largest amount of variance for the CC construct. There is support in the literature for re-conceptualizing these four dimensions as a single factor. Goldhar et al. Ž1991., Hall Ž1992., Lado et al. Ž1992., and Porter Ž1992. contend that customer satisfaction is contingent on an efficient, flexible delivery system. The managers surveyed perceive order fill rates, order cycle times, order and shipment information, and frequency of delivery as facets of a single competitive capability, i.e., DC. Two of the DC items measure customer satisfaction ŽFR2. or customer pleasure ŽFD1. rather than capabilities. This is a minor problem. In future stud-

Table 4 Competitive capabilities ŽCC. —factors, loadings, and reliabilities after factor analysis Ž n s 474. Kaiser–Meyer–Olkin measure of sampling adequacys 0.93 Item PR2 PR1 PR3 QP4 QP3 QP5 QP2 PLB1 PLB2 PLB4 PLB3 OSI2 OCT3 OSI1 OCT4 FD1 FR2 FR1 FD4 FD3 a FR3 a FD2 a Eigen values a Price offered factor Ž4th. 0.8288 0.7054 0.6436

Quality of products factor Ž2nd.

Product line breadth factor Ž3rd.

Delivery capability factor Ž1st.

a for retained items

a s 0.7899
0.8215 0.7934 0.7141 0.6233 0.7760 0.6708 0.6651 0.6543 0.8559 0.8549 0.8299 0.8194 0.7272 0.6789 0.6654 0.6536 0.5291 0.5197 0.5036 4.95

a s 0.8588

a s 0.8425

a s 0.9292

1.75

2.48

2.27

Items dropped.

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ies, it may be appropriate to reword these items to reflect capabilities rather than customer satisfaction. 4.3. LeÕel of performance (LOP) The instrument used to measure LOP are listed in Appendix A along with a final list of items for all the constructs. The six LOP items loaded on a single factor, had factor loadings of 0.635 or higher, and had a reliability of 0.89. 4.4. PredictiÕe Õalidity Composite measures of the AMT and MMP as well as CC, and LOP constructs were then submitted to SPSS w to determine the Pearson product-moment correlation coefficients Ž r .. The correlation coefficient for the combination of AMT and MMP with CC is 0.51, and it is 0.48 for CC with LOP. These coefficients are significant at a s 0.01. This indicates that the constructs are statistically related which validates the possibility of causal relationships. 4.5. Generalizability across industries To demonstrate the generalizability of these scales, Cronbach’s alpha Ž1951. was calculated for the in-

dustries with a sufficient number of responses: fabricated metal Ž184 responses., electronics Ž111., and machinery Ž61.. For all instruments, AMT, MMP, CC, and LOP, there is no significant difference in reliability across these industries.

5. Large-sample results: LISREL analysis and structural modeling Fig. 2 is a restatement of the model shown in Fig. 1 which displays the relationships to be tested with structural equation modeling. In structural equation modeling, it is preferable to have several indicators of a construct as opposed to a single indicator ŽHair et al., 1995.. The items retained from scale development are utilized as the observable indicators of the exogenous latent variables, AMT and MMP. The composite scores for the Price Offered ŽPR., Quality of Products ŽQP., Product Line Breadth ŽPLB., and Delivery Capability ŽDC. factors are shown as the observable indicators of the endogenous latent variable, CC. The composite measures were calculated by summing the individual scores for each item in a dimension and then dividing by the number of items. For example, the responses to PR1, PR2, and PR3 were summed and then divided by three to determine

Fig. 2. The impact of technology and strategy on competitive capabilities and performance.

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M. Tracey et al.r Journal of Operations Management 17 (1999) 411–428 Table 5 Summary of LISREL generated results ŽFigs. 2 and 3. Relationship t-value Significant LISREL coefficient 0.423 0.342 0.803

the composite measure PR. The responses to the six items listed in Appendix A were used as the observable indicators of the endogenous latent variable, LOP. LISREL is a vigorous method for testing causal models with both observable and latent variables as it is capable of simultaneously evaluating the measurement and causal components of complex models. LISREL consequently is becoming preferred to correlation, regression, or path analysis by researchers for testing causal models ŽDillion and Goldstein, 1984.. The goodness-of-fit index ŽGFI. is used to evaluate the appropriateness of the models tested. It is relatively robust against departures from normality and appraises all of the model’s parameters—including measurement items, directional relationships, and error terms—at the same time. GFI provides a measure ranging from 0 to 1. GFI will be close to 1 if a ‘good’ model to data fit is detected ŽDillion and Goldstein, 1984.. The statistical distribution of the GFI measure is unknown, so there is no absolute standard with which to compare them ŽJoreskog and ¨ Sorbom, 1989.. ¨ All of the 474 responses were submitted to LISREL to evaluate the model in Fig. 2. The GFI of 0.951 indicates a good model to data fit. The goodness-of-fit index adjusted for degrees of freedom ŽAGFI. was 0.929, which is also good. The computed t-values, which evaluate the statistical significance of the indicators Žmeasurement portion of LISREL., ranged from 7.951 to 15.196. These are well above the minimum acceptable t-value of 2.00 Žat a s 0.05.. The top portion of Table 5 displays a summary of the data related to testing the hypothesized relationships shown in Fig. 2. The computed t-values judge the statistical significance of each theorized relationship, and they are well above the minimum acceptable value of 2.00. LISREL coefficients give an indication of the relative strength of each relationship Žat a s 0.05.. Hypotheses 1 and 2 are both supported because a significant positive relationship is shown between AMT and CC and between MMP and CC. Firms that invest heavily in computer-enhanced, real-time process technology achieve higher levels of competitive capabilities than firms with lower levels of invest-

Test of hypotheses 1, 2, and 3 ŽFig. 2. AMT ™CC 5.309 Yes MMP™CC 4.382 Yes CC ™ LOP 7.262 Yes

Impact of AMT and MMP on the dimensions of CC ŽFig. 3. AMT ™PR 4.158 Yes 0.331 AMT ™QP 1.636 No 0.120 AMT ™PLB 3.843 Yes 0.288 6.618 Yes 0.529 AMT ™DC MMP™PR 0.611 No 0.047 MMP™QP 4.570 Yes 0.362 MMP™PLB 3.141 Yes 0.236 MMP™DC 0.249 No 0.017

ment. Firms that involve manufacturing facility managers in the strategy formulation process also achieve higher levels of competitive capabilities than firms with lower levels of involvement. Because these relationships were tested simultaneously using LISREL both impacts are significant. Hypothesis 3 is also supported because a positive relationship is demonstrated between CC and LOP. Firms with high levels of competitive capabilities— specifically the ability to control pricing, achieve high product quality, offer product line breadth, and have dependable delivery,—are high performing as measured by customer satisfaction and market performance. Taken in conjunction with the acceptance of Hypotheses 1 and 2, it may be appropriate to claim that AMT and MMP positively affect organizational performance through their impact on competitive capabilities.

6. Implications for management: improving competitive capabilities While it is certainly useful to understand that AMT and MMP may have a positive impact on a firm’s competitiveness and performance, it seems important to know which factors of CC are impacted by AMT and MMP. To examine these relationships at the factor level, the model in Fig. 3 was submitted to LISREL. The entire group of 474 suitable re-

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Fig. 3. Examining relationships between both AMT and MMP and the dimensions of competitive capabilities.

sponses were again utilized. The scales developed for AMT and MMP were again utilized as the observable indicators of the exogenous latent variables. The scales developed for Price Offered ŽPR., Quality of Products ŽQP., Product Line Breadth ŽPLB., and Delivery Capability ŽDC. are employed as indicators of the individual dimensions of CC. The GFI of 0.935 indicates a clearly acceptable model to data fit. The AGFI is 0.915, which is also good. The computed t-values for the indicators Žmeasurement portion of LISREL. ranged from 7.948 to 17.437. The bottom portion of Table 5 displays a summary of the data generated by LISREL related to

the testing of the hypothesized relationships between the constructs. The impact of AMT on PR, PLB, and DC are significant with t-values at 3.843 or higher. Investing in computer-enhanced, real-time manufacturing technology appears to give organizations better cost management capabilities so they can become price setters and provide greater flexibility so they can deliver a wider variety of products and deliver them quickly, accurately, and on-time. The impact of AMT on QP is not statistically significant at a s 0.05. This may be attributable to the fact that quality is more a function of management practices, processes,

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and procedures than equipment capabilities. In other words, good management practices including a strong organizational-level focus on quality may enable firms to achieve high quality even if their equipment has low levels of automation and is inflexible. This discussion should not preclude organizations from investing in new facilities and equipment where new process technology is essential for high product quality or new equipment is needed to hold tighter tolerances. The impact of MMP on QP and on PRB are both significant with t-values of 4.570 and 3.141, respectively. Involving manufacturing plant managers in strategy formulation may bring quality issues to the forefront so that managers from all functions recognize the importance of achieving quality objectives. Quality as defined externally by the customer is multi-faceted; it is not the sole responsibility of manufacturing. To achieve this externally defined goal, a coordinated, cross-functional effort is required. To achieve product line breadth, it is essential to involve manufacturing managers in strategy formulation because product line breadth can only be attained through the coordinated efforts of marketing, engineering, manufacturing, and suppliers.

7. Discussion and conclusion The primary purpose of this study is to investigate the research questions: do firms with a high level of advanced manufacturing technology and with a high level of manufacturing manager’s participation in strategy formulation have high levels of competitive capabilities and do firms with high levels of competitive capabilities have greater customer satisfaction and improved performance. In the process of addressing these questions, valid and reliable instruments were developed to measure AMT, MMP, and CC. Four factors emerged as representative of a firm’s CC: price offered, quality of products, product line breadth, and delivery capabilities. The research design included a rigorous literature review and structured interviews with practitioners and academic experts. Great care was taken during item generation, pre-testing, and pilot testing to ensure content validity. The instruments are unidimensional with strong evidence of convergent, discriminant, and predictive

validity. The instruments have high reliability for all industries in the sample which lends support to the claim that the instruments are generalizable across industries. Analysis of a large-sample, organizational-level survey of manufacturing firms from across the US was used to develop the instruments and examine the research questions. The results of structural equation model testing clearly indicate that there is a positive relationship between advanced manufacturing technologies and competitive capabilities and between manufacturing manager’s participation in strategy formulation and competitive capabilities. The study also confirmed the notion that firms with high levels of competitive capabilities achieve high levels of performance as measured by customer satisfaction and market performance. This is the first large-scale study to investigate these research questions. It provides support for the claims of Skinner Ž1969. and others that manufacturing should be an integral part of corporate strategy formulation and that investments in advanced manufacturing technology should be guided by organizational-level strategy. Linking strategy and technology may be a critical determinant of organizational performance. When the impacts of AMT and MMP are examined on the four factors of CC, five of the eight relationships are significant. AMT has its largest impact on Delivery Capabilities ŽDC., and it also has a significant impact on Price Offered ŽPR. and Product Line Breadth ŽPLB.. Both DC and PLB are enhanced by the creation of manufacturing systems that can produce a wide variety of products. This finding could indicate that the computer-based automation and real-time process control achieved by AMT may enhance manufacturing flexibility. In addition, MMP has a significant impact on PLB. So, manufacturing managers who successfully install advanced manufacturing technologies and participate in high-level strategy formulation may increase product flexibility. This finding is in contrast to early critics of advanced manufacturing technology ŽJaikumar, 1986. who maintained that manufacturing managers in the US applied this technology with a mass production mindset and achieved only lower labor costs. It would appear that an improving understanding of the technology and increasing involvement in strat-

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egy formulation have helped manufacturing managers to implement this technology successfully. Future research should include a second independent large-scale survey and employ confirmatory methods to verify and fine-tune the scales. It should evaluate the items for trait and method variance using LISREL and test hypotheses with alternative measurements scales and a between subject design. Flexibility is an essential component of research. A clear definition of manufacturing flexibility as well as valid and reliable scales would be important contributions. With good measures of flexibility, studies of its impact on competitive capabilities and organizational performance could be completed. Roth Ž1996., Weick Ž1990., and others maintain utilizing advanced manufacturing technology to its full potential entails allowing the operators full involvement in managing the system. What is the role of a participative management style in the design, implementation, and execution of advanced manufacturing technology? Does the benefits of manufacturing manager’s participation in strategy development extend to the participation of shop floor employees in planning and executing continuous improvement on the shop floor?

A.2. Manufacturing manager’s participation in strategy formulation (MMP) -The input of manufacturing plant managers is an integral part of the strategy formation process ŽMMP1.. -Manufacturing plant managers are involved in decisions related to strategies for company growth ŽMMP2.. -Manufacturing plant managers have a good understanding as to how companyrdivisional strategy is formed ŽMMP3.. A.3. CompetitiÕe capabilities A.3.1. Price offered (PR) -We offer competitive prices ŽPR1.. -We are able to compete based on our prices ŽPR2.. -We are able to offer prices as low or lower than our competitors ŽPR3.. A.3.2. Quality of products (QP) -We are able to compete based on quality ŽQP2.. -We offer products that are highly reliable ŽQP3.. -We offer products that are very durable ŽQP4.. -We offer high quality products to our customers ŽQP5.. A.3.3. Product line breadth (PLB) -We respond well to changing customer preferences regarding products ŽPLB1.. -We respond well to changing customer preferences regarding accompanying services ŽPLB2.. -We alter our product offerings to meet client needs ŽPLB3.. -We respond well to customer demand for ‘new’ features ŽPLB4.. A.3.4. DeliÕery capability (DC) -Our frequency of customer backorders is low ŽFR1.. -Our customers are satisfied with our level of completeness for routine shipments ŽFR2.. -Orders submitted to us are delivered on-time, as defined by the customer ŽOCT3.. -We provide on-time delivery of customer orders ŽOCT4..

Acknowledgements The authors wish to thank the Information Systems and Operations Management Department’s Academic Challenge Grant Committee at The University of Toledo for its generous support of this research.

Appendix A. Final List of Items for AMT, MMP, CC, and LOP A.1. Automated manufacturing technology (AMT) -We have incorporated real-time process control into our production systems ŽAMT1.. -We utilize production technology that is among the most flexible in our industry ŽAMT2.. -We apply computer-enhanced technology to improve the flexibility of manufacturing ŽAMT3..

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-We supply accurate projected shipping dates ŽOSI1.. -We supply accurate projected delivery dates ŽOSI2.. -Our customers are pleased with the frequency of our delivery ŽFD1.. -We work with each customer to develop a delivery schedule that is acceptable ŽFD4.. A.4. LeÕel of performance (LOP) -Customers perceiving they receive their money’s worth when they purchase our products. -Customer retention rate. -Generating new business through customer referrals. -Sales growth position. -Market share gain. -Overall competitive position.

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