A holistic approach to new product development: new insights.

INTRODUCTION

A participative approach to new product development (NPD) has been advocated in the literature since the mid-1980s (e.g., Takeuchi and Nonaka 1986; Day 1994; Eppinger 2001; O'Marah 2002). Nonetheless, the integration of NPD remains one of the top challenges facing managers of U.S. manufacturing firms (Ettlie 1995, 1997; Repenning, Goncalves and Black 2001). A recent panel discussion among leading researchers and executives indicates many companies continue to lack internal integration across their functional areas and with the external members of their supply chains, in spite of everything that has been written over the years (Harvard Business Review 2003).

For example, many U.S. manufacturers seem to cling to an outdated mind-set toward NPD that overemphasizes short-term cost-based tactics that drive the various functional departments in conflicting directions instead of toward strategic agility (Munro and Noori 1988; Hayes and Pisano 1994; Roth 1996). This lack of a participative methodology concerning NPD runs counter to the philosophy of Wernerfelt (1984), who defines a resource as anything that could be thought of as a strength of a given firm. He contends that one category of valuable resources includes those that lead to a first-mover advantage. That is, this type of resource translates into a competitive barrier that enables the firm to realize higher returns.

Ongoing integrated NPD leads to long-term viable advantage in three ways: making products available before competitors, incorporating the latest technology into these products, and expending fewer resources in providing them than the competition (Suri 1998). This is particularly relevant under the conditions of uncertainty and fierce competition facing many manufacturers today (Hendricks and Singhal 1997; Kulatilaka and Perotti 1998; Kengpol and O'Brien 2001). Thus, it can be argued that managers who intentionally nurture a participative/integrated NPD process are generating an intangible resource (Hall 1992; Lado, Boyd and Wright 1992) that provides sustainable competitive advantage through the consistent creation of goods and services that fulfill customer needs.

The research reported here attempts to provide answers to three important questions. First, does the participation of what this paper labels "nontraditional" parties (internal: manufacturing, purchasing, logistics; external: suppliers, customers) in NPD actually benefit U.S. manufacturing firms over the long haul? Second, to what extent are these parties being involved, in general? Finally, do the topperforming manufacturing firms overall display a different pattern of activity regarding their participation?

Concurrent Engineering--An Update

Concurrent engineering (CE) is a participative approach to NPD that stresses integration. CE originally focused on encouraging cooperation between design and process engineering simply to accelerate NPD. Today it has evolved into a concept that promotes building competitive advantage through the inclusion of members from every functional area--as well as suppliers and customers--on a formal development team with a documented process and an established leader (Blackburn, Hoedemaker and Van Wassenhove 1996; Griffin 1997; Dowlatshahi 1998; Handfield, Ragatz, Petersen and Monczka 1999; Swink 1999; Atuahene-Gima and Evangelista 2000). The immediate results are that team members have access to each other throughout development, information processing is more intense, capabilities and requirements are better understood, and conflicts and disagreements are uncovered and resolved earlier. In the long term, CE fosters organizational learning that often provides a foundation for future strategic competitive advantage (Wheelwright and Clark 1992; Corso and Pavesi 2000).

Even the most astute managers today find it difficult to evaluate the potential of new business ideas--their attractiveness to potential customers, the realistic price that may be obtained and whether they can be made available profitably (Kim and Mauborgne 2000). Yet, despite the wealth of information generated concerning participative NPD, it is evident that practice still seldom follows theory: many firms have not adopted CE, or have only partially implemented it (Adler et al. 1996; Ettlie 1997; Suri 1998; Repenning et al. 2001). Part of the reason is that U.S. businesses have historically concentrated on short-term returns when allocating resources, a system that hinders sustained investment in intangible assets such as product development (Porter 1992).

It is possible that participative approaches to NPD such as CE are not being implemented because they are perceived as introducing additional complexity into the process, which results in higher product and development costs, inferior product performance and longer development time in the short run (Hartley, Zirger and Kamath 1997). However, other literature (e.g., Coughlan and Wood 1991; Xie, Song and Stringfellow 1998; Minderhoud 1999; O'Marah 2002) indicates that participative NPD processes can result in lower costs, better quality and shorter time to market in the short term as well as being competitively advantageous in the long term.

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More research into the actual benefits of a truly participative NPD process is needed. Most studies in the area have focused on individual projects and their short-term outcomes, when the long-term consequences--what these projects provide strategically, as opposed to their immediately observable results--are a more viable measure of success (Dowlatshahi 1998; Schilling and Hill 1998; Ulrich and Pearson 1998; Balbontin, Yazdani, Cooper and Souder 2000; Wynstra, Weele and Weggemann 2001; Gerwin and Barrowman 2002). Engineering, R & D, marketing and finance are the internal departments most often included as members of NPD teams. This is rightfully so and well supported by the literature (e.g., O'Marah 2002). The supply chain management approach to business incorporates NPD as a critical component and provides support for a CE approach where additional participation from parties such as manufacturing, purchasing, logistics, suppliers and customers is endorsed.

The research presented here utilizes the business literature to develop a theory-based model hypothesizing a priori the influence that including these parties early in NPD may have on manufacturing efficiency, manufacturing agility, delivery service and overall organizational performance over a three-year period. It investigates whether their participation is beneficial, the extent to which they are currently being involved and areas for improvement by management.

THE ROLE OF NPD IN SUPPLY CHAIN MANAGEMENT

The current prominent approach to business administration is supply chain management (SCM). Bechtel and Jayaram (1997) contend that research devoted to new product development must consider SCM issues if it is to provide a comprehensive analysis of the elements of the NPD process. A common view is that SCM is simply another name for logistics. Cooper, Lambert and Pagh (1997) contend SCM is much more than logistics. In conjunction with the Global Supply Chain Forum, they offered the following definition in a later piece: "Supply Chain Management is the integration of key business processes from end user through original suppliers that provides products, services and information that add value for customers and other stakeholders" (Lambert, Cooper and Pagh 1998). They listed product development and commercialization and returns among the principal SCM processes requiring integration.

Figure 1 is based on their work and that of Marien (1998), Mabert and Venkataramanan (1998) and Hahn, Duplaga and Hartley (2000). Competition today must be regarded as supply chain versus supply chain, with the ultimate objective of producing and supplying products and services that out perform existing solutions (Blackwell and Blackwell 1999), which includes promoting environmental sustainability. Whereas traditionally the supply chain was considered to terminate with delivery to the customer, Figure 1 depicts a simplified supply chain flow that includes returns and recyclables. This is warranted due to increasing worldwide public concern and governmental regulation concerning environmental issues, which has led to the growth of concepts such as green product development, design for disassembly, remanufacturable products and reverse logistics (Clendenin 1997; Carter and Carter 1998; Marien 1998; Kim and Mauborgne 2000; Toktay, Wein and Zenios 2000; Chen 2001). Ensuring the proper transfer, storage and disposal or recycling of products, components and packaging during NPD can help to build strategic competitive advantage in today's environment (Tate and Ellram 2003).

NPD, while a process in itself, is also an important component of the all-encompassing SCM process. Any product will be less than completely financially successful if its design is not "supply chain friendly" (Anderson and Lee 2001). The NPD process must contribute significantly to the ability of the supply chain to meet the needs of customers. This entails the reliable delivery of customized products, services and information that meet the current needs of individual targeted markets. It also entails doing this efficiently, which means habitually emphasizing overall supply chain optimization at the expense of the optimization of some of its parts (Troyer 1997; Keegan 1999; Chaudhry and Schnieper 1999).

Bovet and Sheffi (1998) maintained that early consideration of all supply chain variables in business decision making is critical to meeting the needs of customers cost-efficiently. Ettlie (1997) contended that genuine integrated product design includes the various disciplines and functions that span a life cycle of new products and services, extending through recycling or disposal of the product's remnants. Repenning et al. (2001) asserted that problems discovered late in the product development process often result in the need for extra resources and deviation from standard development procedure. This in turn leads to an inefficient firefighting mode of development across the organization that can significantly hurt its capacity to create valuable products.

In summary, efficient SCM requires an integrated CE-type approach to NPD and thus a management commitment and willingness to change and adapt to the new global marketplace (Coughlan and Wood 1991; Jassawalla and Sashittal 2000). This means that multiple partners, including manufacturing, purchasing, inbound and outbound logistics, suppliers and customers, must meaningfully participate in the NPD process (Mabert and Venkataramanan 1998; Vokurka and Lummus 2000) in addition to the traditional participants such as marketing, engineering, and so forth. The next section develops a causal model with accompanying hypotheses to test this theory.

MODEL AND HYPOTHESIS DEVELOPMENT

True cross-functional integration occurs at the working level (Wheelwright and Clark 1992). However, management must first adopt the appropriate mind-set and behavior before this can take place (Suri 1998). This means reengineering the traditional sequential approach to NPD to take a holistic approach, where a multidisciplinary team works together from start to finish (Takeuchi and Nonaka 1986). The SCM philosophy indicates that a NPD project is complete only when a product is being manufactured and distributed that is fully supply chain compatible. That is, when the chain is profitably producing products and accompanying services that people actually want. This often entails opening membership to the NPD team early to parties, both internal and external to the firm, that have traditionally been excluded. A conceptual path model depicting the relationships proposed is shown in Figure 2.

Manufacturing's role in NPD

Integrated product development internally (IPDI) is defined here as the participation of the manufacturing, purchasing and logistics functions throughout the NPD process. Manufacturing personnel's involvement is necessary to ensure that product design and process development occur concurrently and that there is a balance between what may be marketable and what is technically feasible (Swink 1999). This facilitates manufacturability taking its place as an integral design criterion by enabling these members to contribute at the very earliest stages of concept proposal and evaluation, as well as in the first testing and assessment of prototypes. Their expertise can be very important to the selection and implementation of the most beneficial product, process and quality assurance technologies, as well as to critical make-or-buy decisions.

Purchasing's Role in NPD

Empirical research demonstrates that purchasing effectiveness has a positive effect on production and logistics activities, customer service and firm performance (Tracey 1998; Das and Narasimhan 2000; Tracey and Vonderembse 2000). A well-managed purchasing function is important strategically because it can help improve the firm's long-term competitive position, particularly if its personnel have the opportunity to participate early in NPD (Burt and Soukup 1985; Reck and Long 1988; Rozemeijer, van Weele and Weggeman 2003). Initial involvement in NPD enables them to interact with other areas (e.g., engineering, manufacturing and logistics) and mold activities such as supplier selection, development and evaluation to the needs of the project (Milligan 2000; Wynstra et al. 2001). They can also contribute to make-or-buy decisions as well as propose design alterations that reduce material, inventory holding, transportation and production costs; improve product quality; reduce time-to-market; and facilitate disassembly and recycling (Carter and Carter 1998; Dowlatshahi 1998). Furthermore, they often play key roles in their firms' efforts to gain access to needed technology through the development of strategic alliances with suppliers (Schilling and Hill 1998).

Logistics' Role in NPD

Major logistics activities include inventory management, materials handling, packaging, order processing, warehousing, demand forecasting, transportation and returns (Stock and Lambert 2001). Inbound logistics--the movement and storage of materials for production--and outbound logistics--the movement and storage of finished goods to the customer--are critical components of value/supply chain management (Porter and Millar 1985; Mabert and Venkataramanan 1998). As the ultimate goal of the NPD process is to bring to fruition a supply chain friendly product, a strong case exists for including logisticians on these teams.

Simultaneously managing assets and customer service well remain at the foundation of good business. Firms must provide a physical flow of materials and goods that satisfies their customers as well as Wall Street (Bradley, Suwinski, Thomas and Thomas 2001). This means that sound decision making concerning tradeoffs is a continuing part of the NPD process. For example, the choice of a particular production process that lowers manufacturing and supplier costs may at the same time result in a loss of flexibility or excessive inventory carrying costs that diminish the total competitiveness of the chain. Logisticians frequently possess the knowledge and overall perspective required to provide sound input regarding decisions like these (Sheffi 1990; Langley and Holcomb 1992; Lambert and Burduroglu 2000).

Logisticians can also contribute to the NPD process by ensuring that the basic logistics activities contributing to delivery of the proposed product, servicing and its ultimate return transpire efficiently (LaLonde 1999; Wagner and Frankel 2000; Giblin 2001). The importance of logisticians' participation in NPD is amplified several times over if the company plans to engage in international trade. A seemingly well designed product loses much of its value if it cannot flow freely from point to point throughout the world because export/import regulations regarding labeling, packaging and documentation have not been met (Zuckerman 1999).

Early involvement of logisticians in NPD also enables them to present the pros and cons of various product options with respect to cost-effective methods of achieving appropriate service levels. Packaging is one area where this is particularly relevant. The marketing of products today includes customizing the packaging: the shipping container; labels; bar codes; and materials handling, storage and hazardous material information (Gilmore and Pine 1997). These elements of packaging are very important to the logistics function, as well as to purchasing, which is usually involved in the selection and procurement of the necessary packing materials. The aforementioned trend toward environmentally friendly global supply chains is elevating packaging to an increasingly vital component of SCM in general and of logistics in particular, which provides further support for including logisticians on NPD teams.

Another trend that supports the initial inclusion of logisticians in NPD is the growth of the postponement philosophy (Billington and Amaral 1999; Brown, Lee and Petrakian 2000; Johnson and Anderson 2000). This technique has been found to be effective in reducing supply chain costs while simultaneously improving customer service, and it is blurring the distinction between manufacturing/assembly and outbound logistics. Prerequisite to the successful implementation of a postponement format, however, is the careful design of products and processes to support it, and logisticians as main players must be early participants.

Note that the depiction of SCM in Figure 1 includes After Market Service as an important component. Dennis and Kambil (2003) pointed out that many companies (e.g., General Motors, Hewlett-Packard, Epson and Gillette) consider their initial product offering largely to be the impetus for after-sale services and parts transactions, which account for a larger portion of their profits. Building a leading-edge service management program clearly complicates a firm's supply chain, due in part to the logistical concerns of increased parts inventories and locations, as well as critical decisions regarding transportation. They contended that an essential component of SCM for these firms is strategically designing products for serviceability. Again, logisticians as major players should have a meaningful role in the process.

In summary, including people from the logistics function early in the NPD process may be strategically beneficial due to their ability to contribute to decision making regarding a variety of tradeoff scenarios, to ensure that the proper logistical support is provided for the ultimate product, to enable the implementation of the postponement methodology, and to provide efficient aftermarket service.

Suppliers' Role in NPD

An argument against including additional personnel early in NPD is that their presence adds to project complexity. However, it has also been shown that this complexity can be managed and may actually be advantageous, perhaps even leading ultimately to better financial and market performance (Xie et al. 1998; Swink 1999). This study proposes that IPDI--including manufacturing, purchasing and logistics in NPD--strategically benefits the company. One way it does so is by facilitating the inclusion of suppliers and customers.

A key doctrine of SCM is synchronizing processes internally and with all supply chain partners to satisfy customers by reducing the duplication of competencies and tasks (Westbrook 1999). The participation of suppliers in NPD is strategically important because they have a significant impact on cost, quality, technology application and time-to-market (Handfield et al. 1999; Tan 2001). Supplier involvement (SUPI) can lead to better supplier performance, improved manufacturing, and product and process advancements that in turn enhance customer satisfaction and firm performance (Narasimhan and Jayaram 1998; Vonderembse and Tracey 1999; Shin, Collier and Wilson 2000; Kannan and Tan 2002).

The integration of supplier and company development capabilities and operations is the main source of competitive advantage resulting from suppliers' participation in NPD (Twigg 1998; Nellore 2001). Enabling suppliers to submit ideas based on their technical expertise introduces additional possibilities for product and process design and better integration between the two (Swink 1999). Suppliers can also be an important source of market intelligence. In other words, suppliers often have the capacity to contribute both product ideas and the means to realize them (Harmsen, Grunert and Bove 2000). This in turn can simultaneously bring about a reduction in production costs (manufacturing efficiency), provide the ultimate customers with a wider variety of desired products (manufacturing agility) and facilitate excellent delivery service (Dowlatshahi 1998; McGinnis and Vallopra 1999a, 1999b; Narasimhan and Das 1999; Tracey and Vonderembse 2000).

Customers' Role in NPD

The fundamental goal of SCM is to consistently execute processes in a manner that provides superior value to customers (Sabath and Frentzel 1997). This entails offering a unique mix of product and service attributes that enables the chain to consistently differentiate itself from competitors (Kaplan and Norton 2000). Customer requirements frequently change, even during the course of a product's development (Ulrich and Ellison 1999). Obtaining information early, directly and continually from potential buyers is crucial to NPD. Companies need to know the types of product features and product support customers actually value during development (Goffin and New 2001). This can be accomplished by including them on product development teams, which permits an ongoing appreciation of customer needs as well as opportunities to make product and process alterations that improve sales potential (Iansiti and MacCormack 1997; Bhattacharya, Krishnan and Mahajan 1998). Accordingly, customer involvement on development teams (CUSI) should lead to more suitable processes and lower costs (manufacturing efficiency), product and process integration that permits flexibility (manufacturing agility) and fast, reliable delivery service (Roth 1996; Narasimhan and Jayaram 1998).

HYPOTHESES

Balbontin et al. (2000), Gerwin and Barrowman (2002), Harmsen et al. (2000) and Wynstra et al. (2001) indicated that empirical studies regarding NPD to this point have generally focused on individual projects and shortterm results utilizing development time as the only criterion for success. They maintained that development time is not the sole factor to achieve commercial success; that long-term and intangible goals must be considered. They suggested the actual contribution of NPD to company success is difficult to measure because its impact on organizational performance is not direct. Rather, it is moderated by a number of interrelated objectives and competencies. They asserted that NPD success should be measured by the capacity of the process to contribute to new products that are differentiated in the eyes of customers, and that achieving this success requires a long-term course of action.

NPD is a process in itself that is an important component of the overall SCM process, as noted previously. McGinnis and Vallopra (1999b) found process improvement to be a source of competitive advantage through lower cost, meaningful differentiation or both. This study expands their findings by utilizing four variables/constructs (manufacturing efficiency, manufacturing agility, delivery service and organizational performance) to aid in examining the influence of including nontraditional functions more in the NPD process.

The first of the variables is labeled manufacturing efficiency (ME). Evidence suggests that manufacturing cost reduction--production costs per unit, rework costs, work-in-process inventory cost and the ability to establish and meet production schedules--subsumes quality, productivity gains and dependability as performance indicators (Narasimhan and Jayaram 1998; Lieberman and Demeester 1999; Narasimhan and Das 1999; Swink 1999). In other words, reducing these four types of manufacturing costs entails achieving an overall high level of efficiency in production: not merely doing things inexpensively, but also doing the right things well. Manufacturing costs reduction thus may be considered an appropriate performance indicator of overall manufacturing efficiency.

Hypothesis 1 -- Impact on Manufacturing Efficiency (ME)

Figure 2 depicts IPDI having direct positive effects on SUPI and CUSI. These proposed paths are based on another fundamental SCM tenet: successful supply chain integration requires each organization to coordinate its internal activities effectively first (Bowersox, Closs and Stank 1999; Anderson and Lee 2001). Mariotti (1999) stated that potential supply chain partners often must sense trust and information sharing among the firm's functional units before they will join in the collaboration. Support for this sequence is also found in the product development literature. Twigg (1998) and Handfield et al. (1999) contend that supplier integration in NPD represents a major change that must be agreed to beforehand by multiple areas within the organization. Adjustment of internal attitudes and procedures must occur prior to the inclusion of suppliers for it to be successful. Along the same line, Wynstra et al. (2001) found that the further the organization has evolved in terms of cross-functional/process-level thinking, the smoother the implementation of integrated NPD.

Cohen and Levinthal (1990) asserted that firms with high levels of absorptive capacity--the ability to recognize the value of information and apply it to commercial ends--will be most successful. A firm's overall level of absorptive capacity depends on the individual absorptive capacities of the personnel who are positioned at the interface between internal subunits or at the interface of the firm and its environment. They stated that manufacturing personnel fit this criterion. Purchasing and logistics personnel also fit because they serve in an important boundary, spanning and coordinating roles both inside and outside the firm (Cooper and Ellram 1993). These personnel have ties with all of the departments within the organization and are the main direct points of contact with suppliers and customers. Allowing manufacturing, purchasing and logistics people to participate early on in NPD should enable them to build their knowledge bases (i.e., their absorptive capacity) and, just as importantly, to communicate to other departments the advantages of including suppliers and customers. Their involvement also facilitates the firm presenting an image of internal cohesion that will encourage these external parties to become involved on NPD teams.

Product design determines a large majority of the ultimate cost of manufacturing (Ulrich and Pearson 1998). Accordingly, the most evolved version of CE is an enhanced model of collaborative product development that includes all of the company's functional areas, its suppliers and its customers (Hammer 2001). Support for IPDI, SUPI and CUSI having positive direct effects on manufacturing efficiency is presented throughout this paper. IPDI is also expected to have positive indirect effects on ME through its facilitating of SUPI and CUSI.

H1: The inclusion of manufacturing, purchasing and logistics personnel (IPDI), suppliers (SUPI) and customers (CUSI) from the early stages of product development will each have a significant positive total effect on manufacturing efficiency (ME).

Hypothesis 2 -- Impact on Manufacturing Agility (MA)

In the world of business, efficiency is essential but not enough (Wheelwright and Clark 1992). The supply chain must also satisfy constantly changing customer demands for unique value and distinctive performance. This entails achieving manufacturing agility (MA): product variety, customized features and timely new product introductions (Roth 1996; Ettlie 1997). Bona fide MA is influenced greatly by the abilities of both the upstream (suppliers, purchasing and inbound logistics) and downstream (distribution/outbound logistics) functions to respond cost-effectively to demand volatility and changes in the product mix (Narasimhan and Das 1999; Lockamy, Beal and Smith 2000). For example, Caterpillar Corp. attributes much of its success to the close relationships it has developed with dealers, which enables the company to gain insight into individual markets that translates into alterations to products and services to better meet customer needs (Fites 1996). Support for IPDI, SUPI and CUSI having positive direct benefits for MA was presented previously. IPDI is also expected to have positive indirect impacts on MA through its facilitating of SUPI and CUSI.

H2: The inclusion of manufacturing, purchasing and logistics personnel (IPDI), suppliers (SUPI) and customers (CUSI) from the early stages of product development will each have a significant positive total effect on manufacturing agility (MA).

Hypothesis 3 -- Impact on Delivery Service (DS)

Meaningful differentiation for competitive advantage encompasses the customization of products and excellent delivery service (DS): reliable order processing times, frequent and dependable delivery of complete orders and accurate projected delivery dates and responses to customer inquiries regarding the status of orders (Holcomb 1994; Tracey 1998). IPDI is predicted to have a direct positive impact on DS. Dependencies among the components of product design and process design are administered more effectively when both upstream and downstream internal functions are given an elevated voice in NPD, which enables them to perform their responsibilities more efficiently (Swink 1999).

Supplier and customer participation on NPD teams are also each expected to have a direct positive influence on DS (Dowlatshahi 1998; Narasimhan and Jayaram 1998; Handfield et al. 1999). When suppliers are involved initially in product and process design and management, they are intimately familiar with the standards that have been established and are better able to coordinate their operations with the overall supply chain. This in turn enables the chain to be more reliable in making and supporting promises to customers. Early involvement of customers permits them to communicate which services attributes would be of value and allows adjustments to be implemented during development. Manufacturing efficiency and manufacturing agility also each are considered to have a direct positive influence on DS (Roth 1996; Tracey, Vonderembse and Lim 1999). Therefore, several paths in Figure 2 support IPDI, SUPI and CUSI having overall beneficial impacts on DS.

H3: The inclusion of manufacturing, purchasing and logistics personnel (IPDI), suppliers (SUPI) and customers (CUSI) from the early stages of product development will each have a significant positive total effect on delivery service (DS).

Hypothesis 4 -- Impact on Organizational Performance (OP)

Finally, the competitive advantage ultimately to be gained through the inclusion of nontraditional functions in NPD is captured by the organizational performance (OP) construct: sales growth, return on assets, market share gain and overall competitive position over a three-year period. The ME, MA and DS constructs are each expected to have a direct positive effect on OP (Hayes and Pisano 1994; Roth 1996; Ettlie 1998; Keegan 1999; Swink 1999; Rutner and Langley 2000). Accordingly, several paths in Figure 2 support IPDI, SUPI and CUSI each having an overall beneficial impact on OP.

H4: The inclusion of manufacturing, purchasing and logistics personnel (IPDI), suppliers (SUPI) and customers (CUSI) from the early stages of product development will each have a significant positive total effect on organizational performance (OP).

METHODOLOGY

Instrument Validation

Testing the causal model proposed in Figure 2, and hence the four hypotheses, first required the verification of reliable and valid measures for seven constructs: integrated product development--internal: manufacturing, logistics and purchasing (IPDI); integrated product development--supplier involvement (SUPI); integrated product development--customer involvement (CUSI); manufacturing efficiency (ME); manufacturing agility (MA); delivery service (DS); and organizational performance (OP). The instrument was administered to managers at manufacturing firms with titles such as president, general manager, director or plant/facility manager. It was critical the questions could be answered in an insightful manner by these types of executive.

An extensive review of the business literature facilitated theory development, helped define the constructs and revealed useful measures developed in previous studies (e.g., Bowersox, Daugherty, Droge, Rogers and Wardlow 1989; Byrne and Markham 1991; Holcomb 1994; Novack, Rinehart and Langley 1994; Roth 1996; Tracey et al. 1999; Vonderembse and Tracey 1999). In some cases, items established for one discipline were adapted for use in another. Previously validated perceptual measures were employed regarding organizational performance: sales growth, return on assets, market share gain and overall competitive position (McKee, Varadarajan and Pride 1989; Davis and Schul 1993; Narver and Slater 1995). While more objective measures of performance would have been preferable, they are often difficult to obtain, and perceived measures have been determined to be valid substitutes (Venkatraman and Ramanujan 1986; Ward, Leong and Boyer 1994). See the Appendix for the entire list of the items generated.

A five-point Likert scale (1 = Strongly Disagree, 2 = Inclined to Disagree, 3 = Neither Agree or Disagree, 4 = Inclined to Agree, 5 = Strongly Agree) was used, with the N/A = Not Relevant/Don't Know response also available for each item. The survey directions instructed the respondent to base answers on the firm or division over the previous three-year period, which should alleviate the limitations of previous empirical work in the area that focused on individual projects and short-term results. The survey was mailed to 2,000 subscribers of the publication Industry Week from across the United States who characterized themselves as higher-level executives at manufacturing firms. Six were returned as undeliverable. A total of 249 responses was received, a response rate of 12.5 percent. However, 69 were unusable because no manufacturing was performed at the location, company policy did not allow it or, unfortunately, some persons at smaller manufacturing firms do not believe that SCM is relevant to their situation. A summary of the results was offered as an incentive, but due to budgetary constraints, no second mailing or other type of reminder was utilized.

The 180 usable responses were subjected to analysis of variance (ANOVA) utilizing Tukey pairwise comparisons across every item on the survey to control for firm size as indicated by the number of employees at the respondent's location (< 100, n = 91; 100 to 500, n = 59; 501 to 1000, n = 11; > 1000, n = 18; 1 missing), type of manufacturing operation (job shop, n = 67; flexible manufacturing system, n = 25; batch processing, n = 24; high volume: discrete, n = 11; continuous flow, n = 9; assembly line, n = 9; custom projects, n = 9; 3 missing) and industry classification (fabricated metal, n = 86; electronics, n = 37; machinery, n = 32; other, n = 21; 4 missing). No significant differences ([alpha] = 0.05) in the means for any item were found. This lack of significant differences across size, type of operation and industry classification provides some support for the generalizability of the findings, at least in regard to U.S. manufacturing firms operating in these industries. The average breakdown in sales for the responding firms was 72 percent industrial and 28 percent commercial.

The 180 responses were then utilized to refine the scales for the constructs with more than one item by employing confirmatory factor analysis (CFA) methods suggested by Bollen and Lennox (1991); Hair, Anderson, Tatham and Black (1995); Chau (1997); Garver and Mentzer (1999) and Mentzer, Flint and Kent (1999). The CFA process--utilizing the standardized residuals and modification indices provided by LISREL[R] to purify the scales by testing for unidimensionality--was first performed independently with each latent variable, for example, the three items IPDI1 to IPDI3. No replacement of missing values was employed here or at any other phase of the study.

The analysis of the potential items to measure manufacturing efficiency found ME1--production costs per unit of finished product--clearly to be the most robust of the four, which reduced this scale to one item. It is possible that rework costs, WIP inventory cost and the costs associated with not establishing and meeting production schedules are captured in ME1 in the same way manufacturing cost subsumes quality, productivity gains and dependability as performance indicators, as mentioned earlier. While it seems legitimate intuitively that production costs per unit taps all costs, quality, productivity and dependability elements, future research should examine developing a true multi-item scale to measure manufacturing efficiency. The same holds for the supplier and customer involvement constructs, which in this exploratory study were designed as singleitem scales.

The CFA process was then repeated with all possible combinations of the four remaining constructs with more than one item. Finally, an overall measurement model was constructed including all four constructs. The items retained for each construct are listed in the Appendix, along with the deleted items, the substantive reasons for dropping them and the construct reliability and variance extracted measures for the constructs with multi-item scales. All are greater than the recommended minimum values of 0.70 and 0.50, respectively. The three goodness-of-fit indices recommended by Garver and Mentzer (1999)--the root mean squared approximation of error (RMSEA) = 0.047, the comparative fit index (CFI) = 0.95 and the nonnormed fit index (NNFI) = 0.94--indicate a very good final overall measurement fit. Table 1 provides the standardized loadings, errors and t-values for the items retained in the multi-item scales. Each t-value is well over 2.00, indicating each item is a statistically significant indicator at [alpha] = 0.05, and all but four of the loadings are [greater than or equal to] 0.70, which is the recommended value for acceptable item reliability.

Correlation Results

Though it does not prove causation, correlation can serve as a predictor of causation (Sekaran 2000). Table II was constructed to get a feel for the associations among the seven constructs constituting the model. The means and standard deviations given for SUPI, CUSI and ME are those for their single indicators. The means and standard deviations given for the remaining constructs are for the items found to be statistically significant indicators. For example, for Integrated Product Development: Internal, the mean (3.01) and standard deviation (1.18) are based on the mean rating assigned by the respondents to IPDI1, IPDI2 and IPDI3. The most noticeable observation from Table II is that each of the six remaining constructs (IPDI, SUPI, ME, MA, DS: [alpha] = 0.01, and CUSI: [alpha] = 0.05) is significantly correlated with the Organizational Performance construct. In addition, eight of 14 correlations among these six are significant at [alpha] = 0.01 or 0.05. This indicates that many of the constructs are statistically related, which validates the possibility of finding meaningful causal relationships during the next phase of the research--path analysis.

Path Analysis Results

To test the significance of the proposed paths and the hypothesized total effects, the causal model in Figure 2 was submitted to LISREL, using as indicators those items found to be statistically significant during instrument development. It was not possible to utilize fully specified causal model analysis to test relationships due to three constructs (SUPI, CUSI and ME) having single indicators. However, the utilization of composite measures and classical path analysis within the LISREL package is appropriate for testing the statistical significance and strengths of a priori specified structural relations (Mentzer et al. 1999). Therefore, the mean ratings assigned to the items found to be statistically significant were employed as the composite measure indicator for the IPDI, MA, DS and OP constructs.

[FIGURE 3 OMITTED]

LISREL is a powerful tool for testing causal models because it enables the simultaneous evaluation of the individual paths constituting the model and the complete model's goodness of fit (Hair et al. 1995). A path with a computed t-value greater than the absolute value of 2.00 is considered significant at [alpha] = 0.005 within the context of the model. The program generates fit indices that appraise the entire model's ability to predict the actual data. It also provides total (direct + indirect) effect t-values that are interpreted in the same manner as individual path t-values. They assist the researcher in appraising the comprehensive effect of one variable on another within the context of the model. The program also provides statistics that indicate the need to consider reversing the direction of a proposed path and/or of adding paths, if a theoretical basis supports doing so.

Initially, the 16 direct paths depicted in Figure 2 were included in the analysis. Those that were found to be insignificant at [alpha] = 0.05 (i.e., a t-value < | 2.00 |; see Table III) were dropped one at a time (Byrne 1989). The results are shown in Figure 3. The three fit indices in this case indicate a very good final overall causal model fit: RMSEA = 0.020, CFI = 1.00, NNFI = 1.00.

RESULTS OF HYPOTHESIS TESTING 

Table IV provides the completely standardized total effect t-values and LISREL coefficients generated from Figure 3. Hypothesis H1 is not completely supported due to the direct paths (and thus total effects) from SUPI and CUSI to ME not being significant. Thus, the inclusion of suppliers and customers does not seem to have a significant total effect on manufacturing efficiency. These and the other individual paths found to be insignificant during the analysis of the model are discussed below.

IPDI does have a significant positive total effect on ME due entirely to its significant direct impact. In this instance the path t-value (t = 2.71) and coefficient ([gamma] = 0.20) and the total effect t-value and coefficient are identical. Within the context of this model, involving manufacturing, purchasing and logistics personnel from the early stages of NPD has an advantageous effect on manufacturing efficiency via their direct participation. Their involvement seems to help make possible product and process integration that ultimately reduces production costs per unit.

Hypothesis H2 is supported entirely, even though the direct path from IPDI to MA is not significant. The participation of manufacturing, purchasing and logistics personnel, suppliers and customers from the early stages of NPD all have positive significant total effects on the firm's level of manufacturing agility. The inclusion of the internal parties apparently impels firms to include their suppliers and customers as well. The LISREL coefficients next to the direct paths in Figure 3 indicate that those from IPDI to SUPI and CUSI, 0.39 and 0.48 respectively, are the most robust in the model. It is fair to state the participation of these internal functions in NPD seems to lead to firms being better able and more willing to have suppliers and customers represented early on product development teams.

The direct paths from SUPI (t = 2.06, [gamma] = 0.15) and CUSI (t = 2.59, [gamma] = 0.19) to MA are significant. Consequently, the participation of manufacturing, purchasing and logistics personnel (total effect t = 3.41, coefficient = 0.15), suppliers and customers appears to enhance the firm's ability to produce an advantageous range of products, features and new introductions. In the case of the internal functions, this apparently results more from their boundary-spanning relationships with suppliers and customers than from their direct contributions to manufacturing agility. This does not mean their direct input is not important; it does signify that a good portion of their positive impact is the consequence of their unique capacity to work with suppliers and customers.

Hypothesis H3 is not entirely supported. The direct path from SUPI to DS (t = 2.92, [gamma] = 0.21) is significant, indicating the inclusion of suppliers enables the chain to be more effective in executing its downstream activities: providing dependable processing times, delivery, projected delivery dates and order status information. This is interesting given that effect of SUPI on manufacturing efficiency was found not to be significant during the testing of H1. Perhaps the effect of including suppliers early in NPD goes beyond simply enhancing manufacturing efficiency to benefiting the performance of the supply chain robustly on a broader scale.

Manufacturing, purchasing and logistics personnel's early participation in NPD has a significant total effect on delivery service (t = 3.20, coefficient = 0.12). This occurs externally through their support for the participation of suppliers and internally through their direct contribution to improvements in manufacturing efficiency, which each in turn have a positive impact on DS. The direct effect of IPDI on DS is not statistically significant, which should not be interpreted as it not being important. Rather, the main influence of internal personnel is generated from their relationships with suppliers and direct beneficial impact on manufacturing efficiency.

Early customer involvement in NPD does not seem to have a meaningful direct or total effect on DS. In fact, its correlation coefficient from Table II (r = -0.04) and path t-value from Table III (t = -1.60), while not significant, are negative numbers. A possible explanation is that when customers are given the opportunity they will initially ask for the maximum concerning delivery service, and time and effort must be expended to reach cost-effective solutions (Ross 1996). The direct path from MA to DS was also found not to be significant. Conceivably the influence of manufacturing agility on an organization is more comprehensive than simply positively influencing delivery service (Roth 1996). Hence the significant (t-value = 5.18) effect of MA on OP in terms of sales growth, return on assets, market share gain and overall competitive position.

Finally, H4 is supported completely. The direct paths from manufacturing efficiency, manufacturing agility and delivery service to OP are all significant, as are the total effects of IPDI, SUPI and CUSI on organizational performance. Manufacturing, purchasing and logistics personnel's early participation in NPD has a significant total effect on organizational performance (t = 3.90, coefficient = 0.12) via several paths. Suppliers' participation in NPD has a significant total effect on organizational performance (t = 2.81, coefficient = 0.09) due to paths through manufacturing agility and delivery service. Customers' participation in NPD has a significant total effect on organizational performance (t = 2.32, coefficient = 0.06) solely via the path through manufacturing agility. This implies that the involvement of customers provides useful information to the firm during the development process concerning the products and features they would value, but offers little helpful input on the processes needed to produce and deliver them efficiently.

The total effect LISREL coefficients indicate that the nontraditional internal (i.e., manufacturing, purchasing, logistics) functions' joint effect on OP is one and one-third stronger than the effect of supplier involvement (0.12 to 0.09) and twice as strong as the effect of customer involvement (0.12 to 0.06). The main point is that given the rigor of the methodology employed, utilizing the perceptions of 180 manufacturing managers from across the United States, the validity of H4 cannot be rejected (Bollen 1989). Thus it is fair to conclude that including these nontraditional functions from the early stages of product development is a means to strategic advantage and improved organizational performance.

DISCUSSION

Of course, firm performance is affected by many issues other than which parties are or are not included in NPD. Table V was constructed specifically to learn more about the behavior of the top-performing manufacturing firms regarding the involvement of these nontraditional parties in NPD. It places the 180 firms from the study into low, mid and high performing groups based on the mean response to the composite measure of organizational performance over the previous three years. Fifty-three firms with a mean response equal to or greater than 4.0 (that is, they were inclined to agree or strongly agree that the firm performed satisfactorily in these areas) were placed in the high performance group. Eighty-two firms with a mean response within the 2.50 to 3.75 interval inclusive (they generally perceived the firm performed about average in these categories) were placed in the mid performing group. Forty-five firms with a mean response of 2.25 or less (they were inclined to disagree or strongly disagree that the firm performed acceptably in these categories) were placed in the low performing group. The mean score for each composite construct and its individual items (where applicable) was calculated across the three groups. ANOVA utilizing Tukey pairwise comparisons was employed to determine if significant differences at [alpha] = 0.01 or 0.05 existed among them.

It was previously mentioned that initially every item on the survey was subjected to ANOVA with Tukey pairwise comparisons across the number of employees at the respondent's location, type of manufacturing operation and industry classification. Consequently, the findings that follow concerning the conduct of high performance manufacturers in general are true regardless of firm/division size, industry or type of manufacturing operation.

The top performers distinguish themselves from the low performers at [alpha] = 0.01 in involving the manufacturing, purchasing and logistics functions from the early stages of product development. The most obvious area in which this occurs is logistics. A significant difference at a 99 percent level of confidence was found between these two groups in their response to item IPDI3 (see the Appendix). Nonetheless, substantial opportunity for increased participation exists even for the high performers. Their mean response of 2.64 indicates they generally disagree that they involve logistics in NPD, with the mean response of the low performers (1.71) at nearly a full point less. The high performers generally agree they are involving manufacturing and purchasing (items IPDI1 and IPDI2), but there is room for improvement across all of the groups in these areas as well. The involvement of manufacturing received the greatest mean response across the three groups, indicating the most progress has been made as far as involving this function.

All three groups tended to disagree with the statement that they have supplier representatives on their product design teams (item SUPI), so there is obviously opportunity for greater supplier participation. There are no statistically significant differences across the groups, but the mean response of the high performance group (2.84) is noticeably greater than those of the low and mid performers. The mean responses pertaining to customer involvement (CUSI) are higher in general than those for SUPI, indicating that many manufacturers are including their customers more than their suppliers in NPD. There are no significant differences across the groups regarding this item as well, again with the mean response of the high performance group (3.64) being noticeably larger than those of the low and mid performers.

The high organizational performance group reported ratings superior to the low performance group at [alpha] = 0.01 across the manufacturing efficiency, manufacturing agility and delivery service variables. This may be partly attributable to greater participation of the manufacturing, purchasing and particularly the logistics functions early in the new product development process. However, there are prospects for meaningful improvement in the participation of all the nontraditional parties for most of the companies. This collectively may help to alleviate the weakest performance area across the manufacturing efficiency, agility and delivery service variables--range of new product introductions (item MA3). Here even the high performers generally neither agree nor disagree that they are above their industry average. This is particularly troubling in that new product introductions have been empirically verified as core drivers of increased asset growth and profit rate (Bayus, Erickson and Jacobson 2003).

IMPLICATIONS FOR MANAGERS

Barney (1991) asserts that a valuable resource is one that enables the firm to formulate and execute strategies that lead to competitive advantage by enhancing its effectiveness and efficiency. An exceptional new product development process should certainly be considered a valuable intangible resource, particularly within the context of supply chain management. Outstanding new product development relies on a participative approach that includes all of the functions relevant to the life of the product, from the initial idea through the ultimate disposal of the product's remains (Ettlie 1997). Nonetheless, Westbrook (1999) stated that many companies remain unwilling or unable to synchronize their internal processes, much less collaborate with their supply chain partners. His contention--at least in regard to new product development--is verified here, and this is unfortunate given the findings of the study.

Many managers continue to pay far less attention to intangible assets that can have an enormous influence on firm performance in the long run than to tangible investments, for example, equipment. A main reason they may do so is that the benefits of organizational capabilities, such as an ability to innovate or to respond to varying customer needs, are difficult to measure (Ulrich and Smallwood 2004). This research provides evidence that greater participation from parties traditionally excluded from the early stages of new product development leads to better organizational performance over the long term. First, the combined beneficial impact of including the firm's manufacturing, purchasing and logistics functions is significant in regard to increasing the involvement of suppliers and customers, manufacturing efficiency, manufacturing agility, delivery service and organizational performance. Second, the total effect of having supplier representatives on product design teams is positive in relation to manufacturing agility, delivery service and overall performance. Finally, the involvement of customers is advantageous to manufacturing agility and overall performance. Furthermore, the research demonstrates that the higher performing manufacturing firms--regardless of size, type of operation or industry--are including these functions to a greater extent.

This research reveals that despite the wealth of information that has been provided on the subject over the years, a majority of firms still have many opportunities for increasing the meaningful participation of the manufacturing, purchasing and logistics functions as well as suppliers and customers in NPD. Perhaps the key is expending the energy necessary to include them. An integrated approach to new product development requires managers to be willing to challenge long-standing procedures and the entrenched cultural and political structures within the organization. It also requires managers to see beyond short-term setbacks and do what is best for the firm in the long run. Involving these nontraditional parties in product development may add complexity to the process, and extra efforts may be needed to identify and resolve it. However, this research substantiates the idea that promoting and managing their participation is clearly worth the effort due to the ultimate positive impact on the firm's bottomline. The obvious place to begin is by inviting the firm's internal manufacturing, purchasing and logistics functions to be included along with the customary marketing, R & D and engineering departments.

AREAS REQUIRING FURTHER RESEARCH

This exploratory empirical study provides support at the strategic or macro level for why parties traditionally left out of new product development should be included to a greater extent. It confirms that their participation in general indeed has long-term benefits for a manufacturing organization. It also validates the impression that practice still does not actually follow theory, in that their involvement remains low at many firms. It does not address how this may be successfully implemented at the supply chain, organizational, tactical or operational levels, but it provides an impetus for additional research and pragmatic change in the area.

Much work remains to determine the best procedures to create, coordinate and manage truly integrated new product development processes under varying circumstances. Many gaps and questions in the research exist. For example, which suppliers or customers bring the most to the process and thus should be included? How might the proposed relationships with external parties be managed to minimize competitive, legal and financial concerns? How can management overcome entrenched cultural and political norms that have traditionally limited participation to a select set of parties? How can management motivate internal people to become involved when they may perceive their participation as simply more work added to their already busy schedules?

Future studies should also collect more extensive new data to confirm, refine and expand upon the construct measures, particularly those pertaining to supplier involvement, customer involvement and manufacturing efficiency. This would enable verification of the structural model results utilizing a fully specified causal model. This more extensive data should include additional industries beyond fabricated metal, electronics and machinery. Another valuable extension would be to develop measures and incorporate the involvement of traditional parties (R & D, marketing, the various engineering disciplines, etc.) in the model to examine the comparative effect of each. The data set used here was limited to manufacturing firms in the United States. Including manufacturing operations located outside of the USA would permit testing the generalizability of the results on an international basis.

How far a firm has evolved toward a cross-functional, system/process oriented, supply/value chain management type of organization may alter the suitability of the model concerning specific applications. If reliable and valid measures of this "evolution" construct could be developed, then it could be incorporated into future research as a moderating variable.

Appendix

RESULTS OF SEM: MEASUREMENT MODEL
(RMSEA = 0.047, CFI = 0.95, NNFI = 0.94)

Integrated Product Development: Internal (construct reliability 0.78,
  variance extracted 0.51)
IPDI1  We involve manufacturing from the early stages of product
         development.
IPDI2  We involve purchasing from the early stages of product
         development.
IPDI3  We involve logistics from the early stages of product
         development.

Integrated Product Development: Suppliers (single item)
SUPI   We have representatives from our suppliers on our product design
         teams.

Integrated Product Development: Customers (single item)
CUSI   We involve our customers from the early stages of product
         development.

Manufacturing Efficiency (single item)
ME1    Production costs per unit of finished product have declined.
*ME2   Production rework costs have declined. (Tapped in ME1)
*ME3   Work-in-process inventories have decreased. (Tapped in ME1)
*ME4   We establish and meet production schedules. (Tapped in ME1)

Manufacturing Agility (construct reliability 0.78, variance extracted
  0.55)
MA1    Our range of product variety is above the industry average.
MA2    Our range of product features is above the industry average.
MA3    Our range of new product introductions is above the industry
         average.
*MA4   Customer demand determines our production lot sizes. (Deals with
         lot size, not the products)

Delivery Service (construct reliability 0.90, variance extracted 0.60)
DS1    Our customers are pleased with our frequency of delivery.
DS2    Our customers are satisfied with our level of completeness for
         routine shipments.
DS3    Orders submitted to us are delivered on time, as defined by the
         customer.
DS4    We supply accurate projected delivery dates.
DS5    We offer customers a reliable order processing time.
DS6    We respond with accurate information to a customer inquiry
         concerning an order.
*DS7   We work with each customer to develop a delivery schedule that is
         acceptable. (Tapped in DS1)
*Deleted items (substantive reason for deletion in parentheses).

Organizational Performance (construct reliability 0.86, variance
  extracted 0.62)
OP1    Our growth in sales is satisfactory.
OP2    Our return on assets is acceptable.
OP3    Our market share gain is acceptable.
OP4    We are satisfied with our overall competitive position.
*OP5   Our customer retention rate is excellent. (Tapped in OP2 and OP4)
*OP6   We generate new business through customer referrals. (Tapped in
         OP1 and OP3)

Figure 1 A DEPICTION OF SUPPLY CHAIN MANAGEMENT

                     Inbound
Sourcing             Logistics           Manufacturing

Supplier Selection   Transportation      Process Selection,
  and Management     Receiving           Development and
Design, Develop      Warehousing         Maintenance
  and Produce Raw    Materials           Design, Develop
Materials, Parts,    Inventory Control   nd Produce End
Subassemblies        Materials           Products
                     Handling            WIP Inventory
                                         Control

Outbound            Customer       After Market
Logistics           Demand         Service

Packaging           Quality        Product and
Transportation      Price          Packing Disposal
Warehousing         Variety        Spares Inventory
Finished Goods      Delivery       Control
Inventory Control   Order/Billing  Repair
Order Fulfillment   Information    Returns
                                   Recycling

Table 1 COMPLETELY STANDARDIZED LOADINGS AND ERRORS (LISREL MEASUREMENT
MODEL)

Item   IPDI  MA    DS    OP    Error  t-value (a)

IPDI1  0.70                    0.54   - (b)
IPDI2  0.87                    0.32    7.44
IPDI3  0.61                    0.49    7.05
MA1          0.68              0.54   - (b)
MA2          0.82              0.32    8.06
MA3          0.72              0.49    7.77
DS1                0.83        0.31   - (b)
DS2                0.68        0.54    9.82
DS3                0.82        0.32   12.86
DS4                0.81        0.35   12.50
DS5                0.79        0.39   11.96
DS6                0.70        0.51   10.32
OP1                      0.85  0.31   - (b)
OP2                      0.66  0.54    9.63
OP3                      0.88  0.32   13.85
OP4                      0.73  0.35   10.99

(a) A t-value of 2.00 is required for an item to be considered a
statistically significant indicator at [alpha] = 0.05.
(b) T-value not available because value was set to 1 for scaling
purposes.

Table II CORRELATIONS, MEANS ([mu]) AND STANDARD DEVIATIONS ([sigma])
FOR ALL CONSTRUCTS

Construct ([mu], [sigma])         IPDI     SUPI     CUSI

Integrated Product Development:
  Internal (3.01, 1.18)           --
Integrated Product Development:
  Suppliers (2.56, 1.12)          0.391**
Integrated Product Development.:
  Customers (3.28, 1.52)          0.484**  0.143
Manufacturing Efficiency
  (3.61, 1.09)                    0.199**  0.141     0.140
Manufacturing Agility
  (3.13, 1.24)                    0.210**  0.179*    0.213**
Delivery Service (3.97, 0.71)     0.141    0.234**  -0.041
Organizational Performance
  (3.15, 1.01)                    0.205**  0.173**   0.180*

Construct ([mu], [sigma])         ME       MA       DS
Integrated Product Development:
  Internal (3.01, 1.18)
Integrated Product Development:
  Suppliers (2.56, 1.12)
Integrated Product Development.:
  Customers (3.28, 1.52)
Manufacturing Efficiency
  (3.61, 1.09)
Manufacturing Agility
  (3.13, 1.24)                    0.135
Delivery Service (3.97, 0.71)     0.196**
Organizational Performance
  (3.15, 1.01)                    0.310**  0.366**  0.241**

** Correlation is significant at the 0.01 level.
* Correlation is significant at the 0.05 level.

Table III PATHS DROPPED FROM INITIAL PATH ANALYSIS MODEL

Path                   LISREL t-value

IPDI [right arrow] DS      1.22
IPDI [right arrow] MA      1.01
SUPI [right arrow] ME      0.98
CUSI [right arrow] ME      0.74
CUSI [right arrow] DS     -1.60
MA [right arrow] DS       -0.91

A t-value of |2.00| is required for a path to be considered
statistically significant at [alpha] = 0.05.

Table IV T-VALUES AND COMPLETELY STANDARDIZED TOTAL EFFECTS FROM THE
FINAL PATH ANALYSIS MODEL

               Integrated      Integrated
               Product         Product         Manufacturing
Variable/On    Development:    Development:    Efficiency
               Suppliers       Customers

Integrated
Product        t-value = 5.67  t-value = 7.37  t-value = 2.71
               coefficient =   coefficient =   coefficient =
Development:       0.39            0.48           0.20
Internal
Integrated
Product            --              --             --
Development:
Suppliers
Integrated
Product            --              --             --
Development:
Customers
Manufacturing
Efficiency         --              --             --
Manufacturing
Agility            --              --             --
Delivery
Service            --              --             --


Variable/On    Manufacturing   Delivery Service  Organizational
               Agility                           Performance
Integrated
Product        t-value = 3.41  t-value = 3.20    t-value = 3.90
               coefficient =   coefficient =     coefficient =
Development:       0.15            0.12            0.12
Internal       t-value = 2.06  t-value = 2.92    t-value = 2.81
Integrated     coefficient =   coefficient =     coefficient =
Product            0.15            0.21            0.09
Development:
Suppliers
Integrated     t-value = 2.59                    t-value = 2.32
Product        coefficient =       --            coefficient =
Development:                       0.19            --
Customers
Manufacturing                  t-value = 2.30    t-value = 3.85
Efficiency         --          coefficient =     coefficient =
                                   0.17              0.26
Manufacturing                                    t-value = 5.18
Agility            --              --            coefficient =
                                                     0.16
Delivery                                         t-value = 3.03
Service            --              --            coefficient =
                                                     0.20

A t-value of |200| is required for a total effect to be considered
statistically significant at [alpha] = 0.05. The corresponding
coefficients provide an indication of the relative magnitude of the
effect of each construct on the others within the context of the model.

Table V PATHS DROPPED FROM INITIAL PATH ANALYSIS MODEL

Construct        Low Performance  Mid Performance   High Performance
Item             n = 45           n = 82            n = 53

IPDI             2.61             3.07              3.26 (a)
IPDI1            3.13             3.52              3.58
IPDI2            2.98             3.39              3.57
IPDI3            1.71             2.30              2.64 (a)
SUPI             2.43             2.44              2.84
CUSI             2.98             3.21              3.64
ME               3.16             3.57              4.06 (a)
MA               2.53             3.17 (d)          3.57 (a)
MA1              2.96             3.51              3.83 (a)
MA2              2.82             3.35              3.87 (a)
MA3              1.80             2.66 (c)          3.00 (a)
DS               3.72             3.98              4.16 (a)
DS1              3.75             3.97              4.34 (a)
DS2              3.84             4.12              4.19 (b)
DS3              3.47             3.89              4.04 (a)
DS4              3.42             3.74              3.98 (a)
DS5              3.82             3.98              4.15
DS6              3.98             4.11              4.21

(a) A significant difference exists between the high and low performance
groups ([alpha] = 0.01).
(b) A significant difference exists between the high and low performance
groups ([alpha] = 0.05).
(c) A significant difference exists between the medium and low
performance groups ([alpha] = 0.01).
(d) A significant difference exists between the medium and low
performance groups ([alpha] = 0.05).

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AUTHOR

Michael Tracey is an associate professor of production and operations management in the College of Business and Economics at the University of Idaho in Moscow, Idaho.