Role change of design engineers in product development

Journal of Operations Management 24 (2005) 63–79 www.elsevier.com/locate/jom

Role change of design engineers in product development Paul Hong a, Mark A. Vonderembse a,*, William J. Doll a, Abraham Y. Nahm b a

The University of Toledo, College of Business Administration, 2801 W. Bancroft St., Toledo, OH 43606, USA b University of Wisconsin, Eau Claire, USA Received 10 October 2002; received in revised form 1 November 2004; accepted 1 March 2005 Available online 10 May 2005

Abstract Product development is recognized as cross-functional, knowledge-intensive work that has become increasingly important in the fast-paced, globally competitive environment. Researchers and practicing managers contend that design engineers may play an important role in product development efforts. However, their effect on the product development process is not well understood and the extent of their impact on product development performance has not been adequately accessed. This research defines the changing role of design engineers, and it discusses their impact on setting clear project targets and sharing knowledge about customers. The study investigates the impact of these variables on product development productivity. Data collected from 205 manufacturing firms were used to create valid and reliable instruments to assess role change of design engineers, clarity of project targets, shared knowledge about customers and product development productivity. Results from structural equation modeling indicate that as the role of design engineers expands the clarity of project targets increase. This increase impacts the extent of shared knowledge about customers. Increases in the clarity of project targets and shared knowledge about customers appear to enhance product development productivity. # 2005 Elsevier B.V. All rights reserved. Keywords: Role change; Design engineers; Marketing/manufacturing interface; Measurement/methodology; Product development; Teams

1. Introduction Product development is knowledge intensive work that creates successful new products by linking upstream activities such as research and development, marketing and product conceptualization with downstream activities such as manufacturing system design, * Corresponding author. Tel.: +1 419 530 4319; fax: +1 419 530 7744. E-mail address: [email protected] (M.A. Vonderembse).

operations and supplier chain management (Brown and Eisenhardt, 1995; Clark and Fujimoto, 1991; Clark and Wheelwright, 1993; Ettlie, 1995; Cooper, 1999; Peterson et al., 2003). Successful product development requires the integration of these activities to create a team-oriented environment that facilitates information exchange and shared decision-making (Davenport and Jarvenpaa, 1996; Khurana and Rosenthal, 1998; Koufteros et al., 2001, 2002). Product development design engineers help the team transform the concepts generated by customer contact, market study and research and development

0272-6963/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jom.2005.03.002

64

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

into shapes, characteristics and functions that meet customer price and performance expectations (Ettlie and Stoll, 1990; Sandstro¨m and Toivanen, 2002). Positioned at this significant junction between abstract ideas and viable products, design engineers have the opportunity to coordinate important aspects of the product development process including concept development, planning, system level design, detail design, testing and refinement and ramp-up (Twigg, 1998; Ulrich and Eppinger, 1995). Design engineers also support the product development team to clarify targets and to receive, analyze and disseminate knowledge among team members. These activities are necessary for successful cross-functional product development. For design engineers to participate effectively in this new environment, they should possess an expanded set of capabilities. Beyond rigorous technical skills, they should be effective team players and communicators who can participate in cross-functional decisionmaking and problem solving (Jassawalla and Sashittal, 2000; Lam, 1996; Sandstro¨m and Toivanen, 2002). As project targets are clarified, efforts of the design team should become more focused and efficient. Khurana and Rosenthal (1998) emphasize the importance of knowledge sharing in the fuzzy front-end of product development. Hoopes and Postrel (1999) and Paashuis (1998) indicate that more knowledge sharing may facilitate greater product development productivity. With shared knowledge about customer wants, it is easier for groups to reach consensus because they have a common understanding of product design issues and expected outcomes. This study develops and discusses a conceptual framework that describes the relationships among the role change of design engineers, the clarity of project targets, shared knowledge about customers and product development productivity. The study develops valid and reliable measures for each variable using approximately half of the data collected from the Society of Automotive Engineers. The remainder of the sample is used to test the relationships among these variables.

2. Role changes in integrated product development An expanded role for design engineers is driven by the evolution of product development from a

sequential, functional and loosely linked process to a concurrent, cross-functional and integrated process (Brown and Eisenhardt, 1995; Koufteros et al., 2001). In the traditional approach, functional groups work independently to develop and evaluate a list of alternatives, and each functional area has a narrowly defined responsibility. This kind of specialized and limited communication is not conducive for coordination and collaboration, and it may hide critical problems. As these problems (e.g., design defects, cost and quality) surface late in process, the cost and time to resolve them may be excessive (Ettlie and Stoll, 1990). Integrated product development is a cross-functional approach that seeks customer inputs, comprehends organizational capabilities, understands regulatory, technical and competitive threats and opportunities, and considers a broadly defined set of stakeholders. Product development teams seek senior management’s guidance regarding a project’s strategic fit (Wheelwright and Clark, 1992). They assess suppliers’ capabilities (Clark and Wheelwright, 1993) and know regulatory requirements (Toffel, 2003). They recognize the impacts of a project on business results (i.e., stockholders) (Narver and Slater, 1990), but they understand that customer satisfaction is a primary consideration (Paashuis, 1998). The team guides work activities by identifying and pursing specific project’s targets that lead to product development. In this environment, the crossfunctional team engages in interdependent and rich interactions in intra- and inter-organizational networks (Tidd, 1995; Sanchez, 1996; Harmsen et al., 2000). As a firm shifts toward integrated product development, the functions (R&D, marketing, engineering and manufacturing) operate in a collaborative manner, barriers to communication are removed, knowledge is properly transferred and complex problems are resolved in a timely manner. As a consequence, product development teams move towards a globally optimized design with multiple performance measures (e.g., time, quality, cost and delivery) through a shared understanding of the product and process. They identify sets of project targets and communicate the extent of shared knowledge on customers, suppliers, products, competitors and capabilities (Hong et al., 2004b). Such upfront planning is essential in integrated

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

product development (Ancona and Caldwell, 1992; Khurana and Rosenthal, 1998; Koufteros et al., 2002). Product development success demands continuing technical, operating, financial and customer inputs (Athaide et al., 1996; Karlsson et al., 1998; MacCormack and Verganti, 2003). Functional specialists are expected to work beyond their traditional boundaries and to provide ideas and feedback that lead to overall project success. In this context, design engineers are positioned to help the team bridge the gap between product ideas and the realities of production (Bucciarelli, 1994; Cordero et al., 1998; Ettlie, 1995; Sobek et al., 1999). 2.1. A research model: role changes of design engineers in integrated product development Clark and Fujimoto (1991) emphasize the importance of providing a vision and goals for product development. These efforts help to clarify project targets, break down organizational barriers and enable product development teams to function effectively. Going further, Clark and Fujimoto state (1991, p 9), ‘‘many of the critical problems in developing a new car – integrating engineering and manufacturing, establishing links between technical choices and customer requirements and establishing effective leadership – show up in the development of most ‘fabricated-assembled’ products.’’ Design engineers understand these problems, and they can play an important role in working cross-functionally to address them (Bucciarelli, 1994; Cordero et al., 1998; Ettlie, 1995; Sobek et al., 1999). Their position in the product development process implies that they interact with marketing managers and understand customer expectations, which should help the team to provide substantive, purposeful direction for product development. They are also in a position to comprehend the requirements of manufacturing including the implication of design decision on quality and cost. As a result, the responsibility of design engineers in product development may increase. The traditional role of design engineers in product development was limited to technical design decisions. As product development efforts use crossfunctional teams and knowledge sharing, design engineers are required to do more and to know more. In this new environment, these professionals provide a

65

link from unstructured market and R&D generated concepts to working products that meet cost and performance targets. As effective team players, the role of design engineering changes from working participant to one that communicates ideas and helps the team to coordinate activities (Chung and Whitefield, 1999). Fig. 1 indicates the impact of role change of design engineers on clarity of project targets and shared knowledge about customers. As engineers become more involved in product development activities, information on project targets is more effectively communicated and better understood and knowledge about customer expectations is more widely shared. Clarity of project targets is the extent to which project goals are communicated and understood (Clark and Wheelwright, 1993; Gupta et al., 1992; Khurana and Rosenthal, 1998). Effective project targets are based on realistic customer requirements (Day, 1990; Gale, 1994; Rosenau, 1989) and good understanding of competitive situations and technical risks (Clark and Wheelwright, 1993). Shared knowledge about customers is the level of understanding that is common to all product development team members (Hong et al., 2004a; Hoopes and Postrel, 1999). It is a valuable resource that helps firms achieve competitive advantage by improving decision-making (Nonaka and Takeuchi, 1995). As targets are clarified and knowledge is shared, product development teams focus on tasks that satisfy customer wants and meet organizational goals. This eliminates unneeded activities and improves product development productivity. Table 1 defines these variables and provides appropriate references. 2.1.1. Role change of design engineers Role change of design engineers (RCDE) is the degree of change in technical and interpersonal skills needed for cross-functional product development. The role of design engineers should expand because they have access to important design information and a good understanding of manufacturing limitations (Ettlie, 1995; Twigg, 1998). As team interaction increases, their work takes on key behavioral components (e.g., the ability to communicate, work together and resolve conflicts) (Redmond et al., 1993; Vliert et al., 1999). As integrated product development is implemented, design engineers tackle a wide array

66

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

Fig. 1. Role changes of design engineers in integrated product development (research framework).

of other functional issues that may include marketing and manufacturing (Cordero et al., 1998). As a crossfunctional team reviews project status with respect to customer needs, cost, quality and time, design engineers face new sets of responsibilities. The work redesign literature is useful in understanding the role change of design engineers (Hackman

and Oldham, 1980). While their research does not investigate design engineers specifically, it does examine the job characteristics, behavioral changes and work relationships in groups/teams. The results indicate that work redesign may create the need to change skills requirements (e.g., qualifications, training), the nature of work (e.g., job enlargement, job

Table 1 List of constructs Constructs

Definitions

Literature

Role change of design engineers (RCDE)

The degree of change in technical and interpersonal skills needed for cross-functional product development.

Clarity of project targets (CLARITY)

The extent to which project goals are communicated and understood.

Shared knowledge about customers (SKCUST)

The level of understanding of current and future customer needs that is shared by product development team members. The team’s efficiency in creating new products (e.g., allocating resources rationally and effectively).

Cordero et al., 1998; Ettlie, 1995; Hackman and Oldham, 1980; Spreitzer, 1996; Tatikonda and Rosenthal, 2000 Clark and Wheelwright, 1993; Ettlie and Stoll, 1990; Khurana and Rosenthal, 1998; Rosenau, 1989 Clark and Wheelwright, 1993; Cordell, 1997; Day, 1990, 1994; Dolan, 1993; Slater and Narver, 1995 Adler, 1995; Ali et al., 1995; Crawford, 1992; Tersine and Hummingbird, 1995

Product development productivity (DEVPROD)

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

complexity) and work relationships (i.e., influence and communication). These, in turn, may improve productivity and enhance employee work experience (Baird et al., 2000; Chung and Whitefield, 1999). As design engineers embrace an expanded role in product development, they may have the opportunity to clarify project targets and share knowledge, which improves decision-making. 2.1.2. Clarity of project targets Clarity of project targets (CLARITY) is the extent to which project goals are communicated and clearly understood. It can be measured by the amount of relevant knowledge that team members grasp. The concept of goal setting is central to current treatments of work motivation (Mcdonough, 2000). It is used for evaluating alternatives among existing and potential projects and deciding what a project is expected to accomplish. CLARITY requires unambiguous definition, rich communication and common understanding of project goals among team members (Gupta et al., 1992; Pahl et al., 1999; Perry and Sanderson, 1998). 2.1.3. Shared knowledge about customers Shared knowledge about customer (SKCUST) is the level of understanding of customer needs that is common to product development team members (Calantone et al., 1995; Griffin and Hauser, 1992; Narver and Slater, 1990). The extent of shared knowledge indicates the level of intellectual work aimed at creating high customer value. Participant in product development should understand the changing needs of customers (Slater and Narver, 1995) and the level of customer satisfaction (Day, 1990; Gale, 1994; Gatignon and Robertson, 1991). 2.1.4. Product development productivity Product development performance has several attributes. This study focuses on product development productivity (DEVPROD), which is the team’s efficiency in creating new products (e.g., allocating resources rationally and effectively). Product development team members with a high level of productivity would get work done quickly and reduce cost and engineering hours (Adler, 1995; Ali et al., 1995; Crawford, 1992; Tersine and Hummingbird, 1995). DEVPROD is measured by overall technical

67

and team performance in terms of efficient use of resources (Ancona and Caldwell, 1992; Cooper, 1999; Cooper and Kleinschmidt, 1995). 2.2. Examining the impact of role change of design engineers The following sections discuss the proposed relationships among RCDE, CLARITY, SKCUST and DEVPROD shown in Fig. 1. 2.2.1. Impact of role change of design engineers on clarity of project targets Product development teams attempt to match engineering and manufacturing capabilities with customer expectations. This requires the unified effort of participants from several functional areas. Design engineers, who work at the intersection of product concept and manufacturing reality, are positioned to help in this effort. They have an important role in setting project targets because they make decisions that impact product costs, manufacturability and quality. They apply various practices such as design for manufacturing (Dean and Susman, 1989; Ulrich and Pearson, 1998), quality function deployment (Lockamy and Khurana, 1995; Prasad, 1998), concurrent engineering (Koufteros et al., 2001; Swink, 1998), structured design methods (Pahl et al., 1999) and engineering design teams (Baird et al., 2000; Reid et al., 2000) to improve product designs. With appropriate contacts and a working knowledge of marketing, research and development and manufacturing, design engineers can have an expanded role on product development teams. As the role of design engineers expands to include participation in cross-functional product development, design engineers see the process in total rather than as a set of segmented actions. With this expanded view, they can effectively communicate ideas among team members, clearly identify project targets and make certain that ideas are understood. As design engineers accept their changing role, they help the team determine its direction, set meaningful performance targets (Sobek et al., 1999) and pursue project goals (e.g., cost and quality targets) in an information-intensive environment (Ettlie and Stoll, 1990).

68

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

Hypothesis 1. As the role change of design engineers expands (RCDE) the clarity of project targets increases (CLARITY). 2.2.2. Impact of role change of design engineers on shared knowledge about customers According to Davenport et al. (1998), knowledge is fuzzy and closely linked to the people who hold it. Building an infrastructure for knowledge management means establishing a set of roles and creating organizational groups where members have the skills to serve as resources for individual projects. Shared knowledge requires collaboration and cooperation among team members. As the role of design engineers expands in scope, they are positioned to exercise their influence and to provide coordination and communication for the team. Their positive role of collaborator and coordinator should enhance the extent of shared knowledge among team members. Hypothesis 2. As the role change of design engineers expands (RCDE) the shared knowledge about customers increases (SKCUST). 2.2.3. Impact of clarity of project targets on shared knowledge about customers Product development is regarded as a knowledge exploitation process, rather than a knowledge exploration process (Fiol, 1996). Knowledge sharing in product development is goal-driven. Excluding firms that focus on basic research, most companies concentrate on applying knowledge to develop marketable new products. The emphasis is often on using existing or embedded knowledge quickly and efficiently to meet project goals (Madhavan and Grover, 1998). Goals stimulate interactions among team members, and goals that are related to customers’ needs can serve as ‘‘rallying points for team members from different functional areas’’ (Swink, 1998). A clear set of goals helps teams to determine the level of knowledge as well as the degree of knowledge sharing about customers. If project targets are clear, the knowledge shared about customer by product development team members should be high. Hypothesis 3. As the clarity of project target increases (CLARITY), the shared knowledge about customers increases (SKCUST).

2.2.4. Impact of clarity of project targets on product development productivity Teams with stretch goals and incentives achieve good performance. Knight et al. (2001) show that team performance is positively affected by goal clarity. Assigned goals provide a sense of direction and purpose, stimulate action and serve as a standard on which performance can be measured and improved (Appelbaum and Hare, 1996). Similarly, customer-focused goals help teams to resolve crossfunctional conflicts and to work together effectively (Swink, 1998). A clear set of goals guide development efforts and increase product development productivity. Hypothesis 4. As the clarity of project targets increases (CLARITY), the level of product development productivity increases (DEVPROD). 2.2.5. Impact of shared knowledge about customers on product development productivity The extent to which team members understand customer expectations early in the product development process is crucial to product development success (Clark and Wheelwright, 1993). Instead of relying on the experience or insight of a particular team member, common experiences may improve information quality and knowledge content (Brown and Eisenhardt, 1995; Dougherty, 1992; Jaworski and Kohli, 1993). Shared knowledge about customers also enhances the team’s ability to meet changing customer needs, cope with the dynamics of how customers make their purchase decisions (Davenport et al., 2001; Holak and Lehmann, 1990), and assess characteristics of target customers (Cooper, 1999; Wheelwright and Clark, 1992). Shared knowledge improves product development efforts and increases productivity. Hypothesis 5. As shared knowledge about customers increases (SKCUST), the level of product development productivity increases (DEVPROD).

3. Research methods This study develops valid and reliable scales to measure the RCDE, CLARITY, SKCUST and DEVPROD, which are essential for testing the model

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

in Fig. 1. The process was based on commonly accepted methods for developing standardized instruments (Churchill, 1979; Nunnally, 1978). An extensive literature review ensured that the research model is grounded in theory. In addition, case studies and structured interviews with product development executives helped to define the domain of the constructs and facilitate item generation. A pre-test and a pilot test were completed to enhance content validity. 3.1. Item generation The following steps were taken to ensure content validity. First, items were gathered from articles published in major journals in the fields of marketing, product development, strategy, teamwork, organizational learning, and knowledge and quality management. These items served as the basis for developing questions to measure the domain of RCDE, CLARITY, SKCUST and DEVPROD. A five-point scale was used—1, strongly disagree and 5, strongly agree. Second, items were grouped by their theoretical construct and presented to 10 product development managers during structured interviews. These managers were engineers with experience on product development teams. For each construct, managers were asked to discuss important questions such as: (1) what did they think about the importance of CLARITY in product development; (2) what aspects of CLARITY were critical; (3) would they be qualified to answer all the questions. Based on their input, items were added, deleted or modified. 3.2. Pre-test Twelve individuals (three CEO’s, four design engineers, one consultant and four design or manufacturing engineers) were selected as participants in a pre-test. Each was involved in a cross-functional product development project. The items were revised based on their input. To assess their understanding, a knowledge ability test was also administered (Kumar et al., 1994). The participants were asked to rate their subject knowledge after answering the questions. On a scale of five (1, not very; 2, a little; 3, somewhat; 4, moderately; 5, very), the mean response was 4.25

69

(standard deviation = 0.71). A mean that is greater than four shows evidence of knowledge ability. 3.3. Pilot test For the pilot test, the Society of Automotive Engineers (SAE) randomly selected 500 managers from its membership. These participants were from four industries (i.e., fabricated metal products, industrial machinery/equipment, electronic/other electrical equipment and transportation equipment). They were primarily from Ohio, Indiana, Illinois, Michigan and Pennsylvania. The companies selected to participate in the pilot study were excluded from the large-scale mailing. Results of the pilot study are available from the authors. The items entering the large-scale survey are in Appendix A. 3.4. Large-scale survey method A list with 2262 entries was obtained from the Society of Automotive Engineers. From the mailing, 205 useable responses were received from SIC Codes 34 (Fabricated Metal Products, 22.9%), 35 (Industrial and Commercial Machinery, 7.3%), 36 (Electronic and Electrical Equipment and Machinery, 17.6%) and 37 (Transportation Equipment, 30.1%). The remainder did not specify a SIC Code. The respondents were: CEO/presidents (2.4%), senior managers (36.1%) and project managers (32.7%). The remainder did not specify a position. The number of employees was: less than 500 (40.0%); 500–999 (15.1%); 1000–4999 (22.4%); 5000–9999 (8.8%); 10,000 or more (12.2%). The remainder did not specify firm size. The majority of respondents were project managers or senior managers with experience in leading crossfunctional product development teams. To check for possible response bias, the SIC codes and firm size for respondents were compared with those in the SAE mailing list. Using chi-square goodness of fit test, the characteristics of the respondents were not significantly different from the corresponding parameters in the SAE mailing list. This implies little difference in characteristics between respondents and non-respondents. The response rate was 9.1%, which is relatively low. This is caused in part by the extensive nature of the survey, which required more than 30 min for the

70

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

busy managers to complete. Griffin (1997) and Koufteros et al. (2001) had similar response rates for product development research. 3.5. Method of analysis To test the measurement and structural models, the responses (n = 205) were split into two groups (n = 100 and 105, respectively). A x2-test of homogeneity between the samples, using SIC code and firm size as criteria, indicated no difference in sample characteristics. Structural equation modeling was used to examine the measurement and structural models because it can examine a series of dependent relationships simultaneously, while providing statistical efficiency (Hair et al., 1995). It was selected because (1) in the measurement model, confirmatory factor analysis affords a stricter interpretation of unidimensionality than traditional methods (Gerbing and Anderson, 1988) and (2) in the structural model, it enables researchers to analyze the relationships among multiple exogenous and endogenous variables and between endogenous variables simultaneously. To assess model fit, the non-normed fit index (NNFI), comparative fit index (CFI), root mean square error of approximation (RMSEA) and x2/degree of freedom (d.f.) are calculated. For NNFI and CFI, values between 0.80 and 0.90 represent a good fit (Bentler, 1990; Segars and Grover, 1993). The recommended maximum value for RMSEA is 0.10 (Chau, 1997; Hair et al., 1995). Heck (1998) states that the x2/degrees of freedom ratio provides a rough estimate of the statistical fit of the model (i.e., the error present) versus the number of parameters estimated (i.e., degrees of freedom). Better models exhibit a low ratio, such as less than two.

4. Results for the measurement model Confirmatory factor analysis was performed by applying LISREL to 100 responses. Initially, the measurement model fit was moderate. Upon examination, several correlated error terms were found. In such situations, there are two common prescriptions: eliminate the most problematic indicators assuming that content validity is not seriously impacted or retain

the items and re-specify the model. In this case, problematic items were eliminated where content validity would be retained, thus favoring parsimony. For RCDE, the error terms for the items ‘‘power of design engineers’’ (D1C) and ‘‘design engineers’ job complexity’’ (D1G) were correlated with ‘‘influence of design engineers’’ (D1D) and ‘‘design engineers’ job enlargement’’ (D1H), respectively, indicating that they share variance and probably measure the same content. After careful examination, ‘‘influence’’ (D1D) and ‘‘job enlargement’’ (D1H) were kept because they captured the definition of the construct sufficiently and ‘‘power’’ (D1C) and ‘‘job complexity’’ (D1G) were deleted. ‘‘Design engineers’ job enrichment’’ (D1I) and ‘‘job satisfaction’’ (D1J) were deleted because their error terms were correlated with each other and each item had a low standardized correlation with the specified construct. This result supports the claim that ‘‘design engineers’ job satisfaction’’ (D1J) is closely related to the level of ‘‘job enrichment’’ (D1I), but the items do not appear to be part of the construct’s domain. The item ‘‘clear definition of customer requirements’’ (BX1) had substantial correlated errors with the other items so it was eliminated. The error terms for ‘‘how customers make purchase decision’’ (A1J) and ‘‘our target customers’’ (A3J) were correlated with ‘‘current customer needs’’ (A2K) and ‘‘customer requirements’’ (A1D), respectively. A1J and A3J were deleted because they were redundant with the other items. The items ‘‘how customer needs were changing’’ (A1A), ‘‘how well we are doing on customer satisfaction ratings’’ (A1H) and ‘‘which customer groups we are targeting’’ (A2G) from the construct shared knowledge about customers, were deleted because they had high error terms and low standardized correlations with the specified construct. ‘‘Allocated personnel realistically’’ (C1A) from product development productivity was deleted for the same reason. The resulting trimmed model (Table 2) has high completely standardized coefficients (loadings). It has a good model-to-data fit (x2 = 148.93/113 degrees of freedom, RMSEA = 0.057, NNFI = 0.95, CFI = 0.96, x2/degree of freedom of 1.32) and no significantly correlated error terms. When the fitted residual matrix was examined, the smallest residual was
0.19 and the largest 0.17, indicating that the model explains the

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79 Table 2 Summary data for indicators and sub-constructs (measurement sample n = 100) t-valuesa

Completely standardized coefficients (loadings) Exogenous indicators D1A 0.68 D1D 0.72 D1E 0.80 D1F 0.68 D1H 0.67

– 6.08 6.55 5.79 5.69

Endogenous indicators BE1 0.78 BG1 0.82 BK1 0.89 BN1 0.84 A1D 0.60 A2D 0.77 A2K 0.85 A3D 0.90 C1D 0.73 C1G 0.63 C1J 0.77 C1L 0.72

– 8.91 9.72 9.12 – 5.90 6.30 6.47 – 5.69 6.75 6.39

The actual indicators that correspond to the coding can be found in Appendix A; x2 = 148.93, 113 degrees of freedom, x2/degree of freedom which equals 1.32, RMSEA = 0.057, NNFI = 0.95, CFI = 0.96. a All t-values significant at a < 0.01 (one-tailed t-test, d.f. = 1).

correlations quite well. Further evidence of convergent validity is provided by the completely standardized coefficients. The coefficients and their associated tvalues indicate statistical significance in relating

71

observed indicators and latent constructs. The standardized coefficients and t-values of the exogenous and endogenous indicators, which use Maximum Likelihood as the method of estimation, are shown in Table 2. They are all statistically significant at p < 0.01 and are large in magnitude. Descriptive statistics, composite reliability, average variance extracted and discriminant validity tests results are given in Table 3. Discriminant validity was assessed in two ways. First, structural equation modeling methodology (Bagozzi et al., 1991) was used to test discriminant validity between pairs of constructs. These tests were run with the correlation between the latent variables fixed at 1.0 and with the correlation between the latent variables freed to assume any value. A x2 difference of 7.879 or higher at d.f. = 1 between the fixed and freed solutions provide evidence of discriminant validity at the significance level of p = 0.001 (Koufteros et al., 1998; Koufteros, 1999). Second, discriminant validity was tested by comparing the average variance extracted with the squared correlation between constructs. Fornell and Larcker (1981) suggest that discriminant validity exists if the items share more common variance with their respective construct than any variance the construct shares with other constructs. Therefore, the average variance extracted for a construct should be substantially higher than the squared correlation between that construct and each of the other constructs. Both methods have demonstrated evidence of discriminant validity.

Table 3 Descriptive statistics, correlations, composite reliability, average variance extracted and discriminant validity tests (structural sample n = 105) Constructs

Mean

Standard deviation

Role change of design engineers

Role change of design engineers (RCDE)

3.73 (5-items)

0.65

0.83a [0.50]b

Clarity of project targets (CLARITY)

3.69 (4-items)

0.87

0.344** (129.11)c

0.90 [0.69]

Shared knowledge about customers (SKCUST)

3.96 (4-items)

0.76

0.212* (156.43)

0.578** (105.62)

0.87 [0.62]

Product development productivity (DEVPROD)

3.55 (4-items)

0.74

0.244* (102.65)

0.575** (54.98)

0.542** (62.79)

a b c * **

Clarity of project targets

Shared knowledge of customers

Product development productivity

0.81 [0.51]

Composite reliabilities are on the diagonal. Average variances extracted are on the diagonal in brackets. x2 differences are indicated in parentheses. All differences in x2 for one degree of freedom are significant at 0.001. Correlation is significant at the 0.05 level (two-tailed t-test, d.f. = 1). Correlation is significant at the 0.01 level (two-tailed t-test, d.f. = 1).

72

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

Appendix A shows the final items for the constructs in italic along with the item mean and standard deviation.

5. Results for the structural model After determining the measurement model, the structural model was tested by applying LISREL to the remaining 105 responses. The results indicate that there was sufficient model-to-data fit (x2 = 147.65/114 degrees of freedom equals 1.30, NNFI = 0.96, CFI = 0.96, RMSEA = 0.053, with the 90% confidence interval for RMSEA being 0.023–0.076). No problems were revealed among the 136 residuals in the fitted residuals matrix (Hu and Bentler, 1995), only one had a value greater than 0.20. Because the structural model has a reasonable model-to-data fit,

the path coefficients can be investigated (Marsch and Hocevar, 1985). Fig. 1 and the ‘‘structural sample (n = 105)’’ column in Table 4 display the path results, which shows that four of the five proposed hypotheses were supported. The first hypothesis predicted that role change of design engineers would be directly related to clarity of project targets. As seen in Fig. 1 and Table 4, the gamma coefficient from RCDE to CLARITY was significant and positive (standardized coefficient = 0.25, t = 2.26, p < 0.05). This indicates that the RCDE affect CLARITY positively. The third hypothesis predicted that the CLARITY would be directly related to the extent of shared knowledge about customers. The beta coefficient from CLARITY to SKCUST was significant and positive (standardized coefficient = 0.67, t = 5.47, p < 0.01). This indicates that the CLARITY positively affects the level of

Table 4 Hypotheses testing, direct/indirect effects and role of firm size Relationships

Direct effects Role change of design engineers; clarity of project targets Role change of design engineers; shared knowledge about customers Clarity of project targets; shared knowledge about customers Clarity of project targets; product development productivity Shared knowledge about customers; product development productivity Indirect effects Role change of design engineers; shared knowledge about customers Role change of design engineers; product development productivity Clarity of project targets; product development productivity a

Hypothesis Structural sample (n = 105)a

Small firms Large firms All firms (<500 employees) (>=500 employees) (n = 205) (n = 82)d (n = 123)d

H1

0.25b (2.26*,c) 0.35 (2.75**)

0.33 (3.09**)

0.33 (4.10**)

H2

0.10 (1.10)

0.16 (1.35)


0.05 (
0.58)

0.04 (0.52)

H3

0.67 (5.47**)

0.54 (4.06**)

0.70 (5.11**)

0.65 (6.80**)

H4

0.48 (3.39**)

0.43 (2.82**)

0.57 (4.47**)

0.50 (5.06**)

H5

0.32 (2.26*)

0.37 (2.41**)

0.24 (1.95*)

0.30 (3.05**)

0.17 (2.12*)

0.19 (2.37**)

0.23 (2.70**)

0.22 (3.60**)

0.21 (2.38**)

0.28 (2.70**)

0.23 (2.75**)

0.24 (3.82**)

0.21 (2.25*)

0.20 (2.23*)

0.17 (1.99*)

0.19 (3.03**)

For the structural sample (n = 105) x2 = 147.65/114 degrees of freedom which equals 1.30, RMSEA = 0.053, NNFI = 0.96, CFI = 0.96, GFI = 0.86. The model explains 54% of the variance (R2) in product development productivity. b Numbers are standardized coefficients. c Numbers in parentheses are t-values. d Analysis by firm size is based on the full sample (n = 205). * Significant at a < 0.05. ** Significant at a < 0.01 (one-tailed t-test, d.f. = 1).

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

SKCUST among project team members. The results further indicate that CLARITY (H4) and SKCUST (H5) are positively related to product development productivity. These relationships have significant beta coefficients: 0.48 and 0.32, with t-statistics of 3.39 ( p < 0.01) and 2.26 ( p < 0.05), respectively. This suggests that product development teams with CLARITY and high SKCUST experience high DEVPROD. The result for Hypothesis 2 (standardized coefficient = 0.10, t = 1.10) was positive, but not statistically significant, which indicates that RCDE may not directly affect the level of SKCUST among project team members. The result may indicate that the design engineers are not the primary persons in the project teams to promote SKCUST among team members. It may also indicate that the impact of RCDE on SKCUST is indirect through CLARITY. To further examine this possibility, the indirect effects are given in Table 4 (see the ‘‘structural sample (n = 105)’’ column). Such relationships were not hypothesized but may provide some insight on relationships among the variables. The results show significant indirect effects from the RCDE to SKCUST via CLARITY (indirect effect = 0.17, t = 2.12, p < 0.05), from the RCDE to DEVPROD (indirect effect = 0.21, t = 2.38, p < 0.01) and from the CLARITY to DEVPROD via SKCUST (indirect effect = 0.21, t = 2.25, p < 0.05). The indirect effects from RCDE to SKCUST and from RCDE to DEVPROD further indicate the importance of RCDE in product development.

6. Implications for management This study indicates that design engineers have a changing role in product development. The new role expects them to work cross-functionally and to be an effective team player and communicator, and it provides them with the potential for greater influence and responsibility. This role enhancement may be facilitated by the design engineer’s position in the product development process, a position that bridges the gap between the ‘‘idealistic’’ world of R&D and product conceptualization and the ‘‘pragmatic’’ world of manufacturing and supplier execution. In this new position, design engineers may require an

73

interdisciplinary perspective and well-developed behavioral skills. As firms implement cross-functional product development, the design engineers’ workload as well as the type of work may increase. In this process, their role expands from traditional technical specialists to include coordinator and knowledge provider. In this role, they need additional skills and education. This study indicates that design engineers, who possess team-building skills and can work effectively in teams, may help the team improve product development productivity. With these additional responsibilities, their jobs take on new dimensions that should be captured in the organization’s measurement and reward systems. In an iterative design process, the expanded role of design engineers may enhance the quality of the team’s work and may increase participation by other team members, who develop a growing understanding of the product, its capabilities and its limitations. As the team move through multiple iterations in product development, the knowledge and capabilities of the other team members may increase. Role development occurs in the form of design engineers growing in influence among team members. Success in product development requires project teams to have well-defined outcomes. A clear vision, sense of purpose and specific project targets are important factors for successful product development. They help to determine the course of action for the product development team, and they become the basis for settling disputes among participants. Gathering customer knowledge is certainly important in product development, but the extent to which that knowledge is shared is an important dimension for improving product development productivity. Product development teams must invest time to ensure that customer knowledge is shared among participants. The impact of knowledge sharing in teams has been discussed in information system research (Nelson and Cooprider, 1996) and outsourcing research (Lee, 2001). In many cases, firm size is an important factor, so understanding the implications of size on this research may be useful. To examine the impact, the structural sample (n = 105) could be divided into large and small firms based on the number of

74

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

employees. However, splitting this sample may result in sample sizes that are too small for meaningful analysis using LISREL. To address this concern, the post hoc examination divides the total sample (n = 205) into small and large firms. The authors recognize the limitations associated with this action but believe that having sufficient sample size justifies the decision. For this post hoc analysis, small firms have less than 500 employees (n = 82) and large firms have 500 employees or more (n = 123). As described earlier, structural modeling is applied to these groups, and the results are shown in the last two columns in Table 4. For small firms, the direct and indirect relationships are similar to the ‘‘structural sample’’ case (with n = 105). The standardized coefficients for the small firms are positive and tend to have the same order of magnitude as the ‘‘structural sample’’ case. For large firms, the standardized coefficients are positive, except for role change of design engineers to shared knowledge about customers. The standardized coefficients for the large firms have, in most cases, the same order of magnitude as the ‘‘structural sample’’ case. While the direct impact of role change of design engineers on share knowledge about customers is not statistically significant in either the small or large firm case, it is interesting to note that it is approaching significance in the case of small firms. With fewer people on the project team, it may be easier for design engineers in small firms to communicate shared knowledge. When small and large firms are compared, there are some differences that should be mentioned. The coefficient for clarity of project targets to shared knowledge about customers and clarity of projects targets to product development productivity, are higher for large firms than small firms. This seems to support the notion that large firms should spend more time and effort to clarify project targets than small firms so that large firms can improve product development productivity. This may be caused by the fact that job roles tend to be more specialized in larger firms, and therefore more people need to be involved in product development. The other difference is shared knowledge about customers to product development productivity. In this case, the coefficient for small firms is higher than the coefficient for larger firms.

Smaller firms may find it easier to share knowledge about customers and may use that knowledge more effectively to improve product development performance.

7. Limitations and future research The research uses a single-method (survey) and a single-informant from each firm, which can cause common method variance and informant bias. In addition, the data collected are perceptual. This can sometimes be problematic, especially in collecting performance data, as managers may be unwilling to admit poor performance. The study attempts to capture role change of design engineers. While an expanding role for design engineers can be inferred from the results, the study does not assess leadership. Additional research could address the single-method and single-informant concerns as well as assess the leadership role of design engineers. The study hypothesizes a direct and positive relationship between role change of design engineers and share knowledge about customers, which is not supported. The study shows an indirect relationship from role change of design engineers through clarity of project targets to shared knowledge about customers. When the sample is divided into small and large firms, neither sub-sample shows a statistically significant direct relationship between role change of design engineers and shared knowledge about customers. However, the difference between the path coefficients for these two groups is large. Future research could investigate this relationship to clarify whether it is direct or indirect, determine if firm size makes a difference and provide compelling theoretical justification. In addition, the dependent variable, product development productivity, is only one outcome of product development performance. Additional outcomes could be measured and examined within the research framework shown in Fig. 1. The sample was split to do measurement and structural modeling separately. Another study that confirms the results of the structural model may be useful. In addition, other contextual variables, e.g., unionization, internationalization, management values and strategy, may have impacts on the hypothesized relationships and thus should be explored.

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

More broadly, as firms attempt to integrate work processes across functional and organizational boundaries, researchers could investigate role change for other specialists such as procurement and outsourcing professionals. This offers opportunities to understand how teams of organizations can work together to improve supply chain performance in product development, manufacturing, physical supply and distribution.

8. Summary and conclusions This study develops a research framework that describes the links among role change of design engineers, clarity of project targets, shared knowledge about customers and product development productivity. Structural model testing implies that as role change of design engineers expands, clarity of project targets improves, which in turn, increases shared knowledge about customers. Clarity and shared knowledge have positive, direct and significant impacts on product development productivity. In addition, the study defines these variables and develops valid and reliable scales to measure them. The final scales, listed in Appendix A, are short and easy to use. Each scale has five or fewer items and the total number of items across all four scales is only 17. The content domains of the sub-dimensions have been adequately covered because rigorous procedures were used during item generation and testing. The factor structure is simple and has high loadings, and the scales have both discriminant and convergent validity. The instruments exceed generally accepted validity and reliability standards for basic research. These instruments provide a foundation to support future research. The study provides important insights regarding the nature of the role change for design engineers. In their expanded role, design engineers have more influence on product development teams because they possess knowledge that facilitates the process. In addition, to more rigorous technical skills to perform their engineering work, the design engineer’s job has enlarged from technical specialist to participating member of a cross-functional team where communication and cooperation are key success factors.

75

According to Moody (2001), ‘‘A crucial factor in achieving quality and speed is the extent to which a company’s manufacturing processes can deliver what the product designers envision.’’ Effective product development requires integration to occur at the conceptual level because product development is knowledge intensive work. Design engineers are in a position to help the team provide this integration. Design engineers have access to and understand the technical data and knowledge that determine the characteristics, features and performance of the product. They work closely with marketing specialists to transform concepts and ideas into products that customer’s want. The design engineer’s position in the product development process allows them to bridge the gap between market conceptualization and the realities of production. Clarity of project targets and shared knowledge about customers mediate the relationship between the role change of design engineers and product development productivity. Clarity of project targets means that project targets are clearly set, understood by all team members and guide development efforts. Design engineers play an important role in setting and communicating these targets. Clarity of project targets provides a framework for sharing knowledge about customers and it drives the organization towards higher product development productivity. Shared knowledge about customers measures the degree to which customer wants and needs are understood by the project team. As shared knowledge about customers expands, organizations can make better decisions faster, which may improve product development productivity. Understanding the expanding role of design engineers on product development teams offers an opportunity to improve firm performance. In an environment where customer demand is constantly changing and market segments are shrinking, product development is the life blood of successful organizations. Product development stimulates demand, and it is a primary determinant of product cost, quality, features and performance. As a result, it is a critical element in the value proposition that influences a customer’s decision to purchase. Research on product development and the role change of design engineers may offer opportunities for firms to built competitive advantage.

76

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

Appendix A. Measurement items entering large-scale survey (final items in italic) Constructs

Measurement items

Mean

S.D.

Role change of design engineers (RCDE)a

D1A qualifications required for design engineers have been upgraded D1B training required for design engineers has been more extensive D1C power of design engineers in product development team has increased D1D influence of design engineers in product development team has increased D1E technical skills required for design engineers have been more rigorous D1F behavioral skills (e.g., team work, inter-communication) required for design engineers have been more important D1G design engineers’ jobs have become more complex D1H design engineers’ jobs have been enlarged D1I design engineers’ jobs have been enriched D1J overall, design engineers feel more satisfied with their work D1A qualifications required for design engineers have been upgraded

3.49 3.45 3.51 3.51 3.58 4.05

0.83 0.90 0.88 0.86 0.83 0.81

4.03 3.92 3.48 3.31 3.49

0.74 0.77 0.90 0.86 0.83

Clarity of project targets (CLARITY)a

BE1 a clear set of project targets guided development efforts BG1 project targets were clearly understood by all team members BK1 project targets were clearly communicated to all team members BN1 project targets were clear BX1 project targets clearly defined customer requirements

3.60 3.60 3.73 3.70 3.77

1.05 1.04 0.92 0.94 1.03

Shared knowledge about customers (SKCUST)a

This product development team shared knowledge about A1A how customer needs were changing A1D customer requirements A1H how well we were doing on customer satisfaction ratings A1J how customers make purchase decisions A2D which features were most valued by target customers A2G which customer groups we were targeting A2K current customer needs A3D what our customers want A3J our target customers

3.90 4.18 3.60 3.28 3.93 3.77 3.93 4.02 3.98

0.82 0.84 0.87 1.08 0.94 1.04 0.94 0.91 0.92

Product development productivity (DEVPROD)a

This product development team C1A allocated personnel realistically C1D was productive C1G used financial resources sensibly C1J used all product development resources rationally C1L used product engineering hours efficiently

3.26 3.97 3.58 3.33 3.32

1.01 0.85 0.90 0.98 0.95

a

Italic font indicates the final items used in structural model testing.

References Adler, P.S., 1995. From project to process management: an empirically based framework for analyzing product development time. Management Science 41 (3), 458–484. Athaide, G.A., Meyers, P.W., Wilemon, D.L., 1996. Seller-buyer interactions during the commercialization of technological process innovations. Journal of Product Innovation Management 13, 406–421. Ali, A., Krapfel Jr., R., LaBahn, D., 1995. Product innovativeness and entry strategy: impact on cycle time and break-even time. Journal of Product Innovation Management 12 (1), 54–69. Ancona, D.G., Caldwell, D.F., 1992. Bridging the boundary: external process and performance in organizational teams. Administrative Science Quarterly 37, 634–665.

Appelbaum, S.H., Hare, A., 1996. Self-efficacy as a mediator of goal setting and performance: some human resource application. Journal of Managerial Psychology 11 (3), 33–47. Bagozzi, R.P., Yi, Y., Phillips, L.W., 1991. Assessing construct validity in organizational research. Administrative Science Quarterly 36, 421–458. Baird, F., Moore, C.J., Jagodzinski, A.P., 2000. An ethnographic study of engineering design teams at Rolls-Royce Aerospace. Design Studies 21, 333–355. Bentler, P.M., 1990. Fit indexes, lagrange multipliers, constraint changes, and incomplete data in structural models. Multivariate Behavioral Research 25, 163–172. Brown, W., Eisenhardt, K.M., 1995. Product development: past research, present findings, and future directions. Academy of Management Review 20, 343–378.

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79 Bucciarelli, L.L., 1994. Design Engineers. MIT Press Cambridge, Massachusetts. Calantone, R.J., Vickery, S.K., Droge, C., 1995. Business performance and strategic new product development activities: an empirical investigation. Journal of Product Innovation Management 12 (3), 214–223. Chau, P.Y.K., 1997. Reexamining a model for evaluating information center success using a structural equation modeling approach. Decision Sciences 28 (2), 309–334. Chung, S., Whitefield, A., 1999. A comparison of the social standing of the design professions in Korea and Australia. Design Studies 20, 381–396. Churchill, G.A., 1979. A paradigm for developing better measures of marketing constructs. Journal of Marketing Research 16, 64– 73. Clark, K.B., Fujimoto, T., 1991. Product Development Performance: Strategy, Organization, and Management in the World Auto Industry. Harvard Business School, Boston, Massachusetts. Clark, K.B., Wheelwright, S.C., 1993. Managing New Product and Process Development: Text and Cases. The Free Press, New York, NY. Cooper, R.G., Kleinschmidt, E.J., 1995. Benchmarking the firm’s critical success factors in new product development. Journal of Product Innovation Management 12, 374–391. Cooper, R.G., 1999. The invisible success factors in product innovation. Journal of Product Innovation Management 16, 115–133. Cordell, V.V., 1997. Consumer knowledge measures as predictors in product evaluation. Psychology and Marketing 14 (3), 241–260. Cordero, R., Farris, G.F., Ditomaso, N., 1998. Technical professionals in cross-functional teams: their quality of work life. Journal of Product Innovation Management 15, 550–563. Crawford, C.M., 1992. The hidden costs of accelerated product development. Journal of Product Innovation Management 9 (3), 188–199. Davenport, T.H., Jarvenpaa, S.L., 1996. Improving knowledge work processes. Sloan Management Review 37 (4), 53–66. Davenport, T., DeLong, D.W., Beers, M.C., 1998. Successful knowledge management projects. Sloan Management Review Winter 43–57. Davenport, T., Haris, J., Kohli, A.K., 2001. How do they know their customers so well? Sloan Management Review Winter 63–73. Day, G.S., 1990. Market Driven Strategy, Processes for Creating Value. The Free Press, New York, NY. Day, G.S., 1994. Continuous learning about markets. California Management Review 36 (Summer), 9–31. Dean Jr., J.W., Susman, G.I., 1989. Organizing for manufacturable design. Harvard Business Review January–February, 28–36. Dolan, R.J., 1993. Managing the New Product Development Process: Cases and Notes. Addison-Wesley, Reading, Mass.. Dougherty, D., 1992. Interpretative barriers to successful product innovation in large firms. Organizational Science 3, 179–202. Ettlie, J.E., Stoll, H.W., 1990. Managing the Design Manufacturing Process. McGraw-Hill, Inc., pp. 85–88. Ettlie, J.E., 1995. Product-process development integration in manufacturing. Management Science 41 (7), 1224–1237.

77

Fiol, C.M., 1996. Squeezing harder doesn’t always work: continuing the search for consistency in innovative research. Academy of Management Review 21 (4), 1012–1021. Fornell, C., Larcker, D.F., 1981. Evaluations structural equations models with unobservable variables and measurement error. Journal of Marketing Research 18, 39–50. Gale, B., 1994. Managing Customer Value. The Free Press, New York, NY. Gatignon, H., Robertson, T.S., 1991. Innovative decision process. In: Robertson, T.S., Kassarijian, H.H. (Eds.), Handbook of Consumer Behavior. Prentice-Hall, Englewood Cliffs, NJ, pp. 316– 348. Gerbing, D.W., Anderson, J.C., 1988. An updated paradigm for scale development incorporating unidimensionality and assessment. Journal of Marketing Research 25, 186–192. Griffin, A., Hauser, J.R., 1992. Patterns of communication between marketing, engineering and manufacturing–a comparison between two new product teams. Management Science 38 (3), 360–373. Griffin, A., 1997. PDMA research on new product development practices: updating trends and benchmarking best practices. Journal of Product Innovation Management 14, 429– 458. Gupta, A.K., Brockhoff, K., Weisenfeld, U., 1992. Making tradeoffs in the new product development process: a German/US comparison. The Journal of Product Innovation Management 9 (1), 11–18. Hackman, J.R., Oldham, G.R., 1980. Work Redesign. Addison– Wesley, Reading, MA. Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 1995. Multivariate Data Analysis, fourth ed. Prentice Hall, Englewood Cliffs, NJ. Harmsen, H., Grunert, K.G., Bove, K., 2000. Company competencies as a network: the role of product development. Journal of Product Innovation Management 17, 194–207. Heck, R.H., 1998. Factor analysis: exploratory and confirmatory approaches. In: Marcoulides, G.A. (Ed.), Modern Methods for Business Research. Lawrence Erlbaum Associates, Mahwah, NH, pp. 177–215. Holak, S.L., Lehmann, D.R., 1990. Purchase intentions and the dimensions of innovation: an exploratory model. Journal of Product Innovation Management 7 (1), 59–73. Hong, P., Doll, W.J., Nahm, A., 2004a. The role of project target clarity in an uncertain project environment. International Journal of Operations and Production Management 24 (12), 1269–1291. Hong, P., Doll, W.J., Nahm, A.Y., Li, X., 2004b. Knowledge sharing in integrated product development. European Journal of Innovation Management 7 (2), 102–112. Hoopes, D.G., Postrel, S., 1999. Shared Knowledge, ‘‘glitches’’, and product development performance. Strategic Management Journal 20, 837–865. Jassawalla, A.R., Sashittal, H.C., 2000. Strategies of effective new product team leaders. California Management Review 42 (2), 34–51. Jaworski, B.J., Kohli, A.K., 1993. Market orientation: antecedents and consequences. Journal of Marketing 57, 53–70.

78

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79

Karlsson, C., Nellore, R., So¨derquist, K., 1998. Black box engineering: redefining the role of product specifications. Journal of Product Innovation Management 15, 534–549. Khurana, A., Rosenthal, S., 1998. Towards holistic ‘‘front ends’’ in new product development. Journal of Product Innovation Management 15, 57–74. Knight, D., Durham, C.C., Locke, E.A., 2001. The relationship of team goals, incentives, and efficacy to strategic risk, tactical implementation, and performance. Academy of Management Journal 44 (2), 326–338. Koufteros, X., Vonderembse, M.A., Doll, W.J., 1998. Developing measures of time-based manufacturing. Journal of Operations Management 16, 21–41. Koufteros, X., 1999. Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling. Journal of Operations Management 17, 467–488. Koufteros, X., Vonderembse, M.A., Doll, W.J., 2001. Concurrent engineering and its consequences. Journal of Operations Management 19, 97–115. Koufteros, X., Vonderembse, M.A., Doll, W.J., 2002. Integrated product development practices and competitive capabilities: the effects of uncertainty, equivocality, and platform strategy. Journal of Operation Management 20, 331–355. Kumar, N., Stern, L.W., Anderson, J.G., 1994. Conducting interorganizational research using key informants. Academy of Management Journal 36 (6), 1633–1651. Lam, A., 1996. Engineers, management and work organization: a comparative analysis of engineers’ work roles in British and Japanese electronic firms. Journal of Management Studies 183– 212. Lee, J.N., 2001. The impact of knowledge sharing, organizational capability and partnership quality on IS outsourcing success. Information and Management 38, 323–335. Lockamy III, A., Khurana, A., 1995. Quality function deployment: total quality management for new product design. International Journal of Quality and Reliability Management 12 (6), 73–84. MacCormack, A., Verganti, R., 2003. Managing the sources of uncertainty: matching process and context in software development. Journal of Product Innovation Management 20, 217–232. Madhavan, R., Grover, R., 1998. From embedded knowledge to embodied knowledge: new product development as knowledge management. Journal of Marketing 62, 1–12. Marsch, H.W., Hocevar, D., 1985. Application of confirmatory factor analysis of the study of self-concept: first and higher order factor models and their invariance across groups. Psychological Bulletin 97 (3), 562–582. Mcdonough, E.F., 2000. Investigation of factors contributing to the success of cross-functional teams. The Journal of Product Innovation Management 17 (3), 221–235. Moody, P.E., 2001. The path to more, better, faster. Sloan Management Review Spring 13. Narver, J.C., Slater, S.F., 1990. The effect of a market orientation on business profitability. Journal of Marketing 54, 20–35. Nelson, K.M., Cooprider, J.G., 1996. The contribution of shared knowledge to IS group performance. MIS Quarterly 409–429. Nonaka, I., Takeuchi, H., 1995. The Knowledge-Creating Company. Oxford University Press, New York, NY.

Nunnally, J.C., 1978. Psychometric Theory. McGraw-Hill Book Company, New York, NY. Paashuis, V., 1998. The Organization of Integrated Product Development. Springer–Verlag London Limited, London, Great Britain. Pahl, G.P., Badke-Schaub, P., Frankengerber, E., 1999. Resume of 12 years interdisciplinary empirical studies of engineering design in Germany. Design Studies 20, 481–494. Perry, M., Sanderson, D., 1998. Coordinating joint design work: the role of communication and artifacts. Design Studies 19, 273– 288. Peterson, K.J., Handfield, R.B., Ragatz, G.L., 2003. A model of supplier integration in new product development. Journal of Product Innovation Management 20, 284–299. Prasad, B., 1998. Review of QFD and related deployment techniques. Journal of Manufacturing Systems 17 (3), 221–234. Redmond, M.R., Mumford, M.D., Teach, R., 1993. Putting creativity to work: effects of leader behavior on subordinate creativity. Organizational Behavior and Human Decisions Process 55, 120–151. Reid, F.J.M., Culverhouse, P.V., Jagodzinski, A.P., Parsons, R., Burningham, C., 2000. The management of electronics engineering design teams: linking tactics to changing conditions. Design Studies 21, 75–97. Rosenau Jr., M., 1989. Schedule emphasis of new product development personnel. Journal of Product Innovation Management 6 (4), 282–288. Sanchez, R., 1996. Strategic product creation: managing new interactions of technology, markets and organizations. European Management Journal 14 (2), 121–138. ¨ m, J., Toivanen, J., 2002. The problem of managing SandstrO product development engineers: can the balanced score card be an answer? International Journal of Production Economics 78, 79–90. Segars, A.H., Grover, V., 1993. Re-examining perceived ease of use and usefulness: a confirmatory factor analysis. MIS Quarterly 17 (4), 517–525. Slater, S.F., Narver, J.C., 1995. Market orientation and the learning organization. Journal of Marketing 59 (3), 63–74. Sobek II, D.K., Ward, A.C., Liker, J.K., 1999. Toyota’s principles of set based concurrent engineering. Sloan Management Review Winter 67–83. Spreitzer, G.M., 1996. Social structural characteristics of psychological empowerment. Academy of Management Journal 39 (2), 483–504. Swink, M.L., 1998. A tutorial on implementing concurrent engineering in new product development programs. Journal of Operations Management 16, 103–116. Tatikonda, M.V., Rosenthal, S.R., 2000. Successful execution of product development projects: balancing firmness and flexibility in the innovation process. Journal of Operations Management 18, 401–425. Tersine, R.J., Hummingbird, E.A., 1995. Lead-time reduction the search for competitive advantage. International Journal of Operations and Production Management 15 (2), 8–18. Tidd, J., 1995. Development of novel products through intraorganizational and interorganizational networks: The case of home

P. Hong et al. / Journal of Operations Management 24 (2005) 63–79 automation. Journal of Product Innovation Management 12, 307–322. Toffel, M.W., 2003. The growing strategic importance of end-of-life product management. California Management Review 45 (3), 102–129. Twigg, D., 1998. Managing product development within a design chain. International Journal of Operations and Production Management 18 (5), 508–524. Ulrich, K.T., Eppinger, S.D., 1995. Product Design and Development. McGraw-Hill, New York NY.

79

Ulrich, K.T., Pearson, S., 1998. Assessing the importance of design through product archaeology. Management Science 44 (3), 352– 369. Vliert, E.V.D., Nauta, A.E., Giebels, E., Janssen, O., 1999. Constructive conflict at work. Journal of Organizational Behavior 20, 475–491. Wheelwright, S., Clark, K.B., 1992. Creating project plan to focus product development. Harvard Business Review March–April, 70–82.