OEM new product development practices: the case of the automotive industry.
As companies seek ways to reduce costs, speed time-to-market and improve product quality, supplier performance plays a critical role in maintaining the competitiveness of value chains (Fitzgerald 1997). In the auto industry, over 70 percent of a product's total value is created by suppliers (Leenders, Fearon, Flynn and Johnson 2002). Supplier on-time performance determines the ability of the original equipment manufacturer (OEM) to implement engineering changes while also quickly ramping up the production of resulting new products (Hartley, Zirger and Kamath 1997). The quality of an OEM's final product can only be as good as the quality of the inputs they receive from suppliers (Forker 1997). OEMs rely on suppliers to achieve major reductions in product cost (Afuah 2003).
Many researchers have found that new product development (NPD) processes are improved as a result of collaborative relationships between OEMs and their suppliers (Gold 1987; Cusumano and Takeishi 1991; Mabert, Muth and Schemenner 1992). Cusumano and Takeishi (1991) argue that involving suppliers in product development allows a firm to reduce its workload, to focus on its core activities and to capitalize on the supplier's competence. Early supplier involvement increases OEM perceptions of supplier performance by leveraging the supplier's knowledge early in the design process to reduce costs (e.g. Hartley et al. 1997) and lead times (Birou and Fawcett 1994). Similarly, Wynstra, Van Weele and Weggeman (2001) emphasize the great potential that supplier involvement brings to product development.
Concurrent engineering is the early involvement of a cross-functional team to plan products, processes and manufacturing activities simultaneously (Izuchukwu 1996; Ponticel 1996; Koufteros, Vonderembse and Doll 2002). Both Womack, Jones and Ross (1991) and Cusumano and Takeishi (1991) suggest that working concurrently with suppliers may shorten product development time, improve product quality and reduce product costs.
Most literature in NPD in the auto industry has emphasized practices for improving firm performance (Clark 1989; Brown and Eisenhardt 1995; Koufteros, Vonderembse and Doll 2001). The operations literature differentiates between firm performance and supplier performance and has identified factors that predict supplier performance (Forker 1997; Bozarth, Handfield and Das 1998; Shin and Collier 2000). However, within the auto industry, only a few product development studies (Birou and Fawcett 1994; Fitzgerald 1997; Hartley et al. 1997) focus on analyzing and improving OEM perceptions of supplier performance.
This research explores the relationship between supplier performance and OEM product development practices. The researchers developed and validated a measure of OEM perceptions of supplier performance. This study shows that OEM perceptions of suppliers performance is due not only to supplier performance per se; OEM practices directly impact OEM perceptions of supplier performance.
LITERATURE REVIEW AND THEORY DEVELOPMENT
The supplier literature has focused on both firm performance and supplier performance in operations and in NPD (see Table I). The literature can be classified into four categories or quadrants: Q1 (impacts on firm performance in operations), Q2 (impacts on supplier performance in operations), Q3 (impacts on firm performance in NPD) and Q4 (impacts on supplier performance in NPD). Table I summarizes the major studies in each category.
The first quadrant (Q1) has been the focus of many researchers. This body of research involves supplier-related practices and performance in the operations area. Carr and Pearson (1999) present empirical results of a study testing the relationship between supplier involvement and both strategic purchasing and firm performance. Research in this quadrant indicates that supplier involvement and supplier performance have a direct impact not only on NPD performance but also on firm performance.
The second quadrant (Q2) emphasizes the importance of supplier performance in the operations area. Shin and Collier (2000) present a model that shows a positive relationship between supplier involvement and supplier performance. Practitioners and researchers in quality management, supply chain management, and other philosophies and principles affecting quality conclude that a firm's output (i.e., performance) can be only as good as the quality of its inputs (Forker 1997). Because the inputs of manufacturing processes are provided by suppliers, improving supplier performance establishes a starting point for improving the quality of their outputs.
Practitioners have a real interest in measuring supplier performance because it has been proved to have a direct impact on manufacturer performance (Choi and Hartley 1996). Previous research has attempted to measure supplier performance using a multidimensional first-order model (Shin and Collier 2000; Choon Tan et al. 2002). However, Krause et al. (2001) find that, in a search for substantive relationships, supplier performance literature in operations has largely overlooked methodological issues such as measurement.
The third quadrant (Q3) depicts the literature related to NPD and firm or project performance. In this field of research, the unit of analysis is typically the firm or an NPD project, and the research focus is NPD practices that enhance firm or project performance rather than supplier performance (Clark and Fujimoto 1989; Brown and Eisenhardt 1995; Koufteros et al. 2001). Therefore, these studies do not predict OEM perceptions of supplier performance. However, they do show that supplier involvement in NPD can have a major impact on improving project time (Clark and Fujimoto 1989; Clark and Fujimoto 1991), enhancing product quality, and reducing product development costs (Ragatz et al. 1997). Maylor (1997) argues that improvement of NPD projects should be measured in terms of time, cost, quality or a combination of these three. These improvements in product development performance help the firm maintain or improve its competitive position (Koufteros et al. 2002).
Quadrant four (Q4) depicts the literature on supplier performance in NPD rather than operations. As more product design work is shifted to suppliers, supplier performance is increasingly critical to project success and overall OEM competitive performance. Birou and Fawcett (1994) report that supplier involvement in NPD is critical to product development success. They find that supplier quality, on-time delivery and cost are the three most important criteria for supplier selection in both the United States and Germany.
A multiple response survey done by Fitzgerald (1997) indicates that 76 percent of engineers in the automotive industry consider supplier quality to be the most important criterion for evaluating supplier performance. Supplier cost was a distant second (28 percent). The same study revealed that dissatisfaction with suppliers with respect to on-time delivery was also a key concern.
Despite significant research in this quadrant, it does not reflect an understanding that supplier performance is, in part, an outcome of the practices followed by OEMs in their NPD processes. If the OEMs are not satisfied with their suppliers' performance in NPD, their own practices may be to blame. The researchers present a model that explores the relationship among three variables: OEM practices that involve suppliers in design, concurrent engineering practices of the OEM, and OEM assessments of supplier performance. To test this model, a measurement model for OEM perceptions of supplier performance is necessary. No proper validation of this measure was found in the literature, as with the other two variables in the proposed model (supplier involvement and concurrent engineering). The current research proposes in Figure 1 that OEM perceptions of supplier performance represent a single second-order factor with three first-order factors: supplier quality, supplier on-time delivery and supplier cost (Birou and Fawcett 1994). Statistically, second-order models are warranted when one variable (such as supplier performance) is measured by other subvariables (e.g., supplier on-time delivery, supplier quality and supplier cost).
[FIGURE 1 OMITTED]
The rationale for this measurement model results from the fact that supplier performance in product development is an overall assessment and the component factors are interrelated. For example, if supplier quality is high, the cost of poor quality will be low and the rework will be minimized; therefore, supplier costs will be reduced. At the same time, as rework and quality inspection will be minimized, on-time delivery will be high. Therefore, the researchers propose:
H1: Supplier performance is a second-order model with three first-order constructs (supplier on-time delivery, supplier quality and supplier cost).
The relationships among supplier involvement, concurrent engineering and supplier performance in NPD are depicted in Figure 2. The current research posits that supplier performance is, in large part, a function of OEM practices. OEM practices in concurrent engineering and supplier involvement directly influences OEM perceptions of supplier performance. Supplier involvement also enhances OEM perceptions of supplier performance indirectly by enhancing the concurrency of OEM development activities.
NPD literature associates supplier quality, cost and on-time delivery performance with early supplier involvement with the OEM (Birou and Fawcett 1994; Hartley et al. 1997). Considering the supplier's manufacturing constraints during design may reduce the number of problems that arise during production trials or start-ups. Thus, the next hypothesis suggest:
H2: In NPD projects, higher supplier involvement results in higher OEM perceptions of supplier performance.
Hartley et al. (1997) also suggested that involving suppliers earlier in a product development project increases the suppliers' perceived contributions to concurrent OEM engineering processes. Suppliers can have critical influences on the design-engineering group, especially when product components involve new or unfamiliar technologies (Swink 1998). Suppliers help concurrent engineering teams speed up the product development cycle and offer valuable insights on the design of the new product (Carr and Pearson 1999). Therefore, the researchers present the following hypothesis:
H3: In NPD projects, the higher the supplier involvement, the greater the concurrency with OEM development activities.
According to Carr and Pearson (1999), suppliers are critical team members assisting with initial product design suggestions, technology and resource contributions, and quality assurance considerations. Womack et al. (1991) agree that working concurrently with manufacturers in the auto industry helps suppliers reduce product development time and improve product quality. Cusumano and Takeishi (1991) also found a positive relationship between suppliers working concurrently with manufacturers and suppliers' ability to reduce their product cost. Value engineering can reduce 15-70 percent of part costs without sacrificing quality. Heizer and Render (1999) indicate that, for every dollar spent on value engineering, $10 to $25 in savings can be realized. Concurrent engineering teams finally determine which suppliers will be participating in the NPD teams and therefore, concurrent engineering has a positive impact on OEM perceptions of supplier performance. Thus, the next hypothesis posits that:
H4: In NPD teams, the more concurrent the OEM engineering work, the higher the OEM perceptions of supplier performance.
The items used to measure supplier involvement and concurrent engineering were developed and tested for reliability and validity by Koufteros (1995). Therefore, the researchers performed a pilot study for supplier performance constructs only. Specifically, these constructs consisted of supplier on-time delivery, supplier quality and supplier cost. The pilot study conducted with 33 U.S. respondents achieved several objectives: purification, reliability, convergent and discriminant validity, as well as predictive validity. Survey items were deleted, modified and added as necessary. The final items are presented in Appendix A. A large-scale study was conducted after the pilot study.
A survey instrument on NPD was conducted in the United States and Germany. The unit of analysis was the project team. A professional engineering association provided a mailing list consisting of engineers with the job title of Program Manager, Program Director, Project Manager, Director of Engineering, Engineering Team Leader, Manager of Product Development, Engineering Manager, Vice-President of Engineering, Director of Research & Development, Chief Project Engineer or Director of Product Development. The respondents were asked to identify a recently completed project in which they were responsible and to answer the survey questions with respect to the project team working on that project. A respondent bias analysis was performed for both U.S. and German samples.
[FIGURE 2 OMITTED]
The large-scale survey was mailed to 2,912 product development professionals in the U.S. auto industry and 975 product development professionals in the German auto industry. Each sample consisted of individuals from auto manufacturers (OEM) and first-tier auto suppliers. Surveys were mailed twice in both the United States and Germany, with three weeks separating each mailing.
The U.S. survey was in English, and the German survey was in German. A native German speaker with a master's degree in business who works in the automotive industry translated the English survey into German. An American graduate student, who used to live in Germany, translated back to English. Revisions were made as necessary. Finally, a professor of German literature checked the translation.
Of the 2,912 surveys mailed in the United States, 296 responses were collected for a 10.2 percent response rate. Of the 975 surveys mailed in Germany, 145 responses were received, providing a 14.8 percent response rate. The total response rate was 441, with 35 unusable responses because of incomplete questionnaires or industries that were not part of the desired sample, such as R & D firms or heavy-truck suppliers. Therefore, 406 responses were usable for a combined response rate of 10.4 percent. The final sample consists of 267 responses from the United States and 139 responses from Germany.
Criteria for Fit Indicators
The researchers used a combination of several fit indexes for model testing, as proposed in the literature. Analyses were based on the following criteria used to evaluate the fit between the hypothesized model and the sample data--(1) the ratio of [chi square] to degrees of freedom (Netemeyer, Bentler, Bagozzi, Cudeck, Cote, Lehmann, McDonald, Heath, Irwin and Ambler 2001), which should be in the range of 3-1; (2) the nonnormed fit index (NNFI) (Bentler and Bonett 1980), which should be above 0.9; (3) the comparative fit index--(CFI) (Bentler 1980), which should be above 0.9 and decreases as the model is refined and (4) the root mean square error of approximation (RMSEA), which should be approximately 0.5 (Hu and Bentler 1995; Byrne 1998). The target coefficient index (the ratio of the [chi square] of the first-order model to the [chi square] of the higher-order model) was used to provide evidence of the existence of a higher-order construct (Marsh and Hocevar 1985). This index reflects the extent to which the higher-order factor model accounts for covariation among the first-order factors. The target coefficient can be interpreted as the percent of variation in the first-order factors that can be explained by the second-order construct (Doll and Ragu-Nathan 1995).
Discriminant validity was assessed by conducting a [chi square] test for each pair of constructs (Bagozzi and Phillips 1992). This test was done by obtaining the difference in [chi square] between a fixed correlation of 1 between the constructs and a freed correlation between the same constructs. The average variance extracted (AVE) was a second method used for testing discriminant validity (Fornell and Larker 1981). Reliability estimation was left as a final analytic tool because, in the absence of a valid construct, reliability might not be relevant (Koufteros 1999). Finally, Chronbach's [alpha] was used as well as an AVE value for assessing reliability.
Developing a Measurement Model for Supplier Performance
In order to avoid possible interactions between the measurement and the structural model, as proposed by Gerbing and Anderson (1988), the researchers tested the measurement model first and the structural model second. Theoretical and empirical research studies using supplier performance in NPD typically assume that supplier performance is a single first-order construct (Shin and Collier 2000). To avoid potential pitfalls of this assumption, the researchers tested the hypothesis of a second-order factor with three first-order factors; comparing the results with the alternative hypothesis (and the typical assumption) that supplier performance is a single first-order factor.
To ensure that the measurement model for supplier performance could be cross-validated, the 406 respondents were divided into randomly selected groups of approximately 50 percent. Using the first random sample (213 cases), LISREL was used in a model-generating mode. The researchers started with five items for each of the factors--supplier on-time delivery, supplier quality and supplier cost (see Appendix A). Afterwards, the measurement model was modified. The modification indexes were examined along with the factor loadings. Additionally, the logic and theoretical support for deleting items was analyzed. When appropriate, one item at a time was deleted. Otherwise, the items remained in the model. Modifications continued until an acceptable model fit was obtained.
Second, the second random sample of 193 cases was used to cross-validate the model. Cross-validation provides assurance that the results obtained are not sample specific and enhances confidence that results can be generalized to the referent population (Doll and Ragu-Nathan 1995). If the model is determined acceptable, then the related hypothesis could be accepted. Finally, a comparison was made between first-order and second-order measurement models of supplier performance. The best-fitting model was used to continue testing the substantial hypotheses depicted in Figure 2.
Methods for Testing Hypotheses
As stated by Shin and Collier (2000), "structural equation models decompose the empirical correlation or covariance among the variables to estimate the path coefficients." In order to provide a good causal model, the researchers provide first a good measurement model, and then present the final structural equation model for showing the relationships among the variables under study (supplier involvement, concurrent engineering and supplier performance).
Criteria delineated in Developing a Measurement Model for Supplier Performance of this paper were also used for model testing. The structural equation model was developed using all respondent cases from Germany and United States (n = 406). Pairs of items for the constructs supplier involvement and concurrent engineering were used to decrease error terms and to focus on the relationships themselves (Bagozzi and Heatherton 1994). Similarly partially aggregated models are used widely in research in education and psychology and related areas.
Results of this study include the supplier performance measurement model, a measurement analysis of all factors and a test of the hypothesized relationships in the model of OEM NPD practices that drive supplier performance.
Supplier Performance Measurement Model
The first step in data analysis included the development of an instrument for measuring supplier performance using a random sample of 213 cases. The initial indicators were 2.87 [chi square] to degrees of freedom ratio, 0.91 CFI, 0.89 NNFI and 0.096 for RMSEA.
The measurement model modification procedure was repeated six times until an acceptable model fit was reached. The required [chi square] to degrees of freedom ratio was less than 3, and data yielded a ratio of 1.94. CFI (0.97), NNFI (0.96) and GFI (0.95) are above 0.90, and all t statistics for the indicator items were greater than 9.93 (significant at p < 0.001), which indicated that an adequate fit of the data was achieved for the measurement portion of the model. The factors with their corresponding factor loadings (above 0.6) are presented in the standardized solution of the measurement model (Figure 3).
The second random sample of 193 cases was used to cross-validate the measurement model. The standardized results in Figure 4 show an adequate fit of the data was achieved for cross-validation of the measurement model for supplier performance in NPD. The [chi square] to degrees of freedom ratio=0.866, the CFI=1.00, NNFI=1.01 and GFI=0.98. All factor loadings are over 0.6, and the t statistics for indicator items were greater than 8.33 (significant at p < 0.001).
For comparison, the researchers examined the typical practice of measuring supplier performance as a single first-order factor. All nine items were used as indicators of a single first-order supplier performance factor. The model did not show an acceptable fit. The [chi square] to degrees of freedom ratio=11.15, the CFI=0.73, NNFI=0.64 and GFI=0.76, and RMSEA=0.219. Although the factor loadings are acceptable, the subjective fit indexes do not indicate acceptable model-data fit. The researchers therefore reject the alternative hypothesis that supplier performance is a single first-order factor. Instead, the hypothesis that supplier performance is a second-order model with three first-order variables (supplier on-time delivery, supplier quality and supplier cost) was accepted.
Based on the entire sample (n=406), Table II provides descriptive data for each of the first-order factors for supplier performance, along with the correlation matrix, reliability and AVE (on the diagonal), and the difference in [chi square] and the p-value of the test of discriminant validity (d.f. = 1) for each pair of variables. Using Chronbach's [alpha], the reliabilities of each factor are 0.80 or higher. Second, discriminant validity was verified by a [chi square] test (Liu 2003). All pairs of constructs demonstrated discriminant validity at p < 0.001. Finally, the estimates of AVE are higher than 0.77, which exceeded the 0.50 critical value, providing further evidence of reliability.
In evaluating discriminant validity using AVE and squared correlations, the researchers noticed that the highest squared correlation for the supplier performance factors was between supplier on-time delivery and supplier quality (0.31). This value was significantly lower than their respective AVEs (0.81 and 0.79).
[FIGURE 3 OMITTED]
As shown in Table II, the researchers also note that the reliabilities of each factor yielded values of 0.80 for supplier performance factors, 0.85 for supplier involvement and 0.86 for concurrent engineering. All these reliability estimates are within acceptable levels. Second, discriminant validity was demonstrated by the differences in [chi square] (p < 0.001). Finally, all estimates of AVE exceeded the 0.50 critical value, providing further evidence of reliability. In evaluating discriminant validity between the supplier performance factors and the other two constructs (supplier involvement and concurrent engineering) using AVE and squared correlations, the researchers noticed that the highest squared correlation was between supplier cost and supplier involvement at 0.16. This value was significantly lower than their respective AVEs (0.77 and 0.82). Therefore, the data support the discriminant validity of the five factors in Table II.
[FIGURE 4 OMITTED]
Supplier cost has higher correlations with supplier involvement (0.399) and concurrent engineering (0.328) than supplier quality and supplier on-time delivery. Using a test of the difference between correlations, the researchers examined whether these differences were significant. The tests indicated that, at the [alpha]=0.05 level of significance, there were no differences in correlations between OEM practices (supplier involvement and concurrent engineering) and the three first-order supplier performance factors.
OEM NPD Practices That Drive Supplier Performance
All 406 cases were used to develop the substantive structural equation model relating OEM supplier involvement and concurrent engineering with OEM perceptions of supplier performance in NPD (Hypotheses H2-H4). The standardized solution of the model is presented in Figure 5.
[FIGURE 5 OMITTED]
The [chi square] to degrees of freedom ratio is 3. CFI (0.96), NNFI (0.95) and GFI (0.96) are above 0.9, which means that an adequate fit of the data was achieved for the structural equation model. The t-values for the path coefficients between each pair of factors are greater than 4.3. Therefore, these paths in the model are positive and significant at p < 0.05. These results support the hypotheses that the higher the supplier involvement, the higher the supplier performance (H2); the higher the supplier involvement, the higher the concurrent engineering (H3) and the higher the concurrent engineering, the higher the supplier performance (H4).
OEM outsourcing decisions are based on their perceptions of supplier performance. When OEMs involve suppliers in NPD processes, suppliers have greater opportunities to improve OEMs' perceptions of their performance. As a result, they are in a better position to get the next contract and participate in product innovations. The participation of the supplier earlier in the OEM NPD process led to higher supplier performance, which, in turn, benefits the whole supply chain. The research findings suggest that OEMs should involve suppliers earlier in design and manufacturing to ensure improved performance. Supply chain management suggests that suppliers, customers and manufacturers work collaboratively rather than as individual companies. Therefore, involving suppliers earlier in the development process decreases uncertainty on the suppliers' side and improves performance, while simultaneously increasing the quality of products and communication within the supply chain.
OEM practices in concurrent engineering and supplier involvement have significant influences on OEM perceptions of supplier performance. The main implication of these findings is that the evaluation of supplier performance must be conducted integratively along the entire supply chain. Unless OEMs improve their NPD practices, their perceptions of supplier performance will not improve. OEM practices are, in part, responsible for supplier performance. The research findings support the new common practice of helping suppliers to improve their processes by using OEM resources, such as engineers and even information technology capabilities. Firms are realizing that investing in suppliers has a positive impact on final product quality and concurrent engineering practices.
Another important implication of this study is that supplier involvement in NPD drives OEMs to move towards concurrent engineering. Supplier queries require evaluation from both a design and a manufacturing perspective. Thus, supplier involvement requires a cross-functional and integrated response to queries about design or manufacturing changes. Concurrent engineering teams should be required to respond to suppliers adequately. These two OEM practices, in turn, improve OEM perceptions of supplier performance.
This research provides empirical evidence that supplier performance in NPD is a second-order construct with three first-order factors (supplier quality, supplier cost and supplier on-time performance). The nine-item measurement instrument developed in this study appears to fit adequately the data collected; moreover, it was cross-validated with a second set of data. As stated by Doll and Xia (1994), researchers should be cautious in using instruments that have not been cross-validated in confirmatory studies.
The empirical results in this study support an integrative approach to examining relationships between supplier involvement, concurrent engineering and supplier performance. The implications for managers were discussed carefully in the previous section. Increased supplier involvement improves both the concurrent engineering practices of the OEMs and their perceptions of supplier performance. Also, better OEM concurrent engineering practices improve directly their perceptions of supplier performance. In a serious effort to improve both supply chain performance and concurrent engineering practices, suppliers should be involved early in NPD processes.
This study presents data from Germany and the United States but does not include other countries that have strong automotive traditions, such as Japan and Mexico. Additionally, the current results from empirical data can only be applied to the automotive industry. The researchers therefore suggest the replication of this study to other countries' automotive sectors and other industries to generalize conclusions.
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TABLE A.1. MEASUREMENT ITEMS Aggregate Items Items Description Supplier involvement (Sup_Inv) SI12 SI1 Our suppliers develop component parts for us SI2 Our suppliers develop whole subassemblies for us SI34 SI3 Our suppliers do the product engineering of component parts for us SI4 Our suppliers are involved in the early stages of product development SI56 SI5 We ask our suppliers for their input on the design of component parts SI6 We make use of suppliers for their input on the design of component parts Concurrent engineering (Con_Eng) CE12 CE1 Much of process design is done concurrently with product design CE2 Product development group members represent a variety of disciplines CE35 CE3 Various disciplines are involved in product development from the early stages CE5 Manufacturing engineers are involved from the early stages of product development CE67 CE6 Product and process designs are developed concurrently by a group of employees from various disciplines CE7 Product development group members share information Supplier performance (Sup_Perf) Supplier on time performance Our suppliers On_time SO2 Deliver the parts they design on time SO4 Meet engineering change deadlines on time SO5 Meet our product development schedules on time Supplier quality performance Our suppliers Quality SQ1 Provide high-quality parts SQ2 Design high-quality products SQ3 Meet our quality specification Supplier cost performance Our suppliers Cost SC2 Help reduce our overall cost SC3 Improve their cost performance SC5 Design parts that reduce our manufacturing cost
Gioconda Quesada is an assistant professor of management information systems and decision sciences in the Department of Marketing and Supply Chain Management in the School of Business and Economics at the College of Charleston, in Charleston, South Carolina.
Ahmad Syamil is an assistant professor in the Department of Computer and Information Technology in the College of Business at Arkansas State University in State University, Arkansas.
William J. Doll is a professor of management in the College of Business Administration at the University of Toledo in Toledo, Ohio.
Table 1 LITERATURE REVIEW SUMMARY Area/performance Firm Performance Supplier Performance Operations Q1 Q2 Bechtel and Patterson (1997), Bozarth et al. (1998), Blancero and Ellram (1997), Choi and Hartley Buvik and John (2000), Carr (1996), Choon Tan, and Pearson (1999), Choi and Lyman and Wisner Hartley (1996), Ellram (2002), (2002), Forker (1997), Ellram, Zsidisin and Siferd Kolay (1993), Krause, (2002), Frohlich and Westbrook Pagell and Curkovic (2001), Tan and Lyman (2002) (2001), Shin and Collier (2000) NPD Q3 Q4 Brown and Eisenhardt (1995), Birou and Fawcett Clark and Fujimoto (1989), (1994), Cusumano and Clark and Fujimoto (1991), Takeishi (1991), Hartley et al. (1997), Fitzgerald (1997), Izuchukwu (1996), Koufteros Hartley et al. (1997) (1995), Koufteros et al. (2001), Maylor (1997), Moffat (1998), Ponticel (1996), Primo and Amundson (2002), Ragatz, Handfield and Scannell (1997), Swink (1998), Wynstra et al. (2001) Table II MEASUREMENT ANALYSIS TABLE Supplier On-Time Delivery Supplier Quality Supplier Cost Supplier on-time [0.84] (a) delivery 0.8056 (b) Supplier quality 0.554 [0.83] (a) [chi square] = 0.7880 (b) 274.35 (c) p-value < 0.001 Supplier cost 0.439 0.412 [0.80] (a) [chi square] = [chi square] = 0.7680 (b) 306.64 (c) 328.40 (c) p-value < 0.001 p-value < 0.001 Supplier 0.223 0.263 0.399 involvement [chi square] = [chi square] = [chi square] = 522.40 (c) 552.77 (c) 355.09 (c) p-value < 0.001 p-value < 0.001 p-value < 0.001 Concurrent 0.237 0.243 0.328 engineering [chi square] = [chi square] = [chi square] = 473.04 (c) 472.77 (c) 364.78 (c) p-value < 0.001 p-value < 0.001 p-value < 0.001 Mean (std. dev) 3.28 3.64 3.22 (0.754) (0.686) (0.731) Supplier Involvement Concurrent Engineering Supplier on-time delivery Supplier quality Supplier cost Supplier [0.85] (a) involvement 0.8208 (b) Concurrent 0.353 [0.84] (a) engineering [chi square] = 0.8008 (b) 448.01 (c) p-value < 0.001 Mean (std. dev) 3.20 3.58 (0.904) (0.741) Note: (a) Reliabilities (Cronbach's [alpha]) are on the diagonal. (b) AVE, average variance extracted. (c) Difference in [chi square] (fixed and free correlation) along with p-value.
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|Author:||Quesada, Gioconda; Syamil, Ahmad; Doll, William J.|
|Publication:||Journal of Supply Chain Management|
|Date:||Jun 22, 2006|
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