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J PROD INNOV MANAG 2004;21:199–214 r 2004 Product Development & Management Association

Product Development Time Performance: Investigating the Effect of Interactions between Drivers Roberto Filippini, Luigi Salmaso, and Paolo Tessarolo

Rapid and punctual new product development (NPD) has become a top priority in many organizations as competitors rush to commercialize emerging technologies and to satisfy customer needs. Despite the importance of this issue, conceptual models or systematic testing of specific drivers that could improve time performances in NPD are few and far between. There is, however, a lack of extensive empirical research into whether ‘‘interactions’’ between different drivers affect time performances. This article aims to investigate whether drivers can interact and can influence time performances with a ‘‘synergistic’’ effect. A survey was carried out in order to study the effects of two-way driver interactions on ‘‘launch on time’’ and ‘‘launch against an accelerated schedule.’’ Three groups of drivers within the development-process, organizational-mechanisms, and strategic-capabilities were considered. As this is an exploratory study, two-way interactions between drivers of different groups were analyzed in order to detect which drivers had a synergistic effect on time performances. The study was based on a sample of 85 manufacturing firms producing mainly industrial goods. The NPD program within each company was considered, i.e., the new products developed and launched in the last three years. The statistical approach used is suitable for exploratory surveys. In the first phase, the G-correlation test was used to verify the effects of single drivers in order to help interpret the results regarding two-way driver interactions. In the second phase, regression models with two-way driver interaction were performed with both linear and logistic regression in order to discover which significant models had a significant driver interaction. The resulting 13 models showed that interactions played an important role in determining time performances. The following are some of the most interesting results, as they have managerial implications. The NP Strategic Guide (clear definition and communication of new product goals) interacts with and enhances the influence of other drivers, such as predevelopment tasks, project manager use, and supplier and customer involvement. Technological and up-front staff capabilities create important interactions with product definition and with customer involvement, which avoids development delays. Furthermore, the authors of this study discovered that the adoption of an overlapping approach without a high level of interfunctional team use may not be time efficient. Thus, if a firm has to work to a

Address correspondence to Roberto Filippini, Department of Management and Engineering, University of Padova, Str. S. Nicola, 3, 36100 Vicenza, Italy. E-mail: roberto.fi[email protected]. The authors would like to thank the editor and two anonymous reviewers for helpful comments on a previous version of this article.

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J PROD INNOV MANAG 2004;21:199–214

R. FILIPPINI, L. SALMASO, AND P. TESSAROLO

tight development schedule, it should seek and should integrate any possible synergistic effects between team use and overlapping development phases. The insights into interactions provide useful information that can be used when setting priorities and can help to attain higher performances by adopting a combination of selected drivers. In particular, the best practices, which many studies have highlighted, do influence time performances that depend mainly on the so-called strategic-capabilities drivers. These latter variables, unlike practices and activities, require a complex learning process. The path toward improvements within the development-process requires both long periods of time and an integrated view of the process; hence, improvements cannot be achieved by simply applying common practices. Therefore, analysis of interactions within the NPD field looks promising and requires further study.

Introduction and Background

A

s global competitive pressure increases, many companies are trying to shorten their product development time (Griffin, 1993). Indeed, in a time competition environment, a firm will be most successful if its development times are shorter and if its products generated faster than its competitors’ (Meyer and Utterback, 1995). Thus, rapid development of new products quickly has become a top priority in many organizations as competitors rush to commercialize emerging technologies and to satisfy fragmenting customer needs (Labahn et al., 1996).

BIOGRAPHICAL SKETCHES Roberto Filippini is professor of management in the Department of Management and Engineering at the University of Padova. His research interests are in the area of operations management and new product development. His articles have been published in various journals, including Journal of Operations Management, International Journal of Production Research, International Journal of Operations & Production Management, International Journal of Production Economics, and International Journal of Technology Management. Luigi Salmaso is associate professor of statistics in the Department of Management and Engineering at the University of Padova. He received his Ph.D. in statistics from the University of Padova. His current research topics include multivariate analysis, design of experiments, quality control, nonlinear regression, and nonparametric statistics. His articles have been published in various journals, including Journal of Applied Statistical Science, Journal of Applied Stochastic Models in Business and Industry, International Journal of Nonlinear Modeling in Science and Engineering, Statistical Methods and Applications, and Metron. Paolo Tessarolo is Ph.D. candidate in the Department of Management and Engineering at the University of Padova and is visiting faculty in the Department of Management at Arizona State University. He earned his cum laude degree in management and engineering at the University of Padova. His research interests include interfunctional cooperation and acceleration of new product development.

Faster, accelerated new product development (NPD) makes it possible to incorporate new technologies into the new products more thoroughly in order to improve profitability and to achieve innovation success (Karagozoglu and Brown, 1993; Droge et al., 2000). The importance of keeping to time schedules in order to fulfill customer requirements also largely is recognized (Blackburn, 1991; Cooper and Kleinschmidt, 1994). Despite the importance of this issue, conceptual models or systematic testing of specific factors that could improve time performances in NPD are few and far between (Kessler and Chakrabarti, 1996; Ragatz et al., 1997). Some recent studies have begun to fill this gap and have produced results that are very interesting from a managerial point of view (Zirger and Hartley, 1994, 1996; Griffin, 1997a, 1997b; Kessler and Chakrabarti, 1999). Kessler and Chakrabarti (1996) developed a conceptual model and proposed two groups of factors that influence product innovation speed. The first concerns strategic orientation, such as goal clarity, product concept development, top-management support, coordination and communication—both within the project team and across departments—and the use of external sources. The second group of factors concerns organizational capabilities that include project leader strength, team representatives, as well as team member experience and process organization, to name but a few. In an empirical study (Kessler and Chakrabarti, 1999), the same authors found that clear time-goals, longer tenure among team members, and parallel development all could increase speed, whereas design for manufacturability, frequent product testing, and computer-aided design (CAD) systems decreased speed. Moreover, they also found that some factors speed up radical innovation (e.g., concept clarity) and slow down incremental innovation.

TIME PERFORMANCE—INTERACTIONS BETWEEN DRIVERS

Empirical studies have identified several factors that appear to reduce NPD time. Griffin (1997a) summarized previous results and distinguished different types of variables. Structural and strategic factors include reduced product complexity, strategic intent, minimization of technical difficulty, and increase of codevelopment with suppliers. Other types of factors are related to the development-process: factors such as providing clear project objectives, increasing the frequency of milestones, overlapping process steps, increasing the customer information available in the initial phases of the process, and setting up cross functional teams early in the process. Some studies (Cooper and Kleinschmidt, 1994; McDonough, 1993) found that both schedule adherence and time efficiency were influenced by a number of variables, such as interfunctional team and project manager use, top-management support, clear goals, and up-front activities. Droge et al. (2000) studied the ability to minimize the new product development timing and its introduction in the automotive supplier industry. The unit of analysis was an individual firm or a strategic business unit (SBU). The drivers considered were measured within each firm/SBU, and the extent of usage was taken into account using categorical scales (from ‘‘extremely low use of item’’ to ‘‘extremely high use of item’’). In this way, they found which factors, often present at different levels in different firms, influenced the new product development timing within the companies studied. These factors were standardization, supplier partnership, concurrent engineering, and cross-functional teams. From the firm’s point of view, it is useful to know which drivers are correlated to time performances. However, firms do have limited resources and cannot change everything at once. Thus, they require guidance on how to set priorities for improvement mechanisms (Griffin, 1998). In other words, a firm has to understand whether one of the drivers is more important than another when they are considered jointly using a multivariate approach (Griffin, 1998; Kessler and Chakrabarti, 1999). Furthermore, it would be very useful for companies to be able to understand whether drivers can ‘‘interact’’ and whether this could influence time performances. Interaction can be defined as ‘‘a measure of how much the effect of one variable upon another is determined by the values of one or more other variables’’ (Gove, 1986). This approach may help firms to determine which drivers should be combined in order to have an


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impact on time performances. For example, two drivers may have no noticeable effect on time performance in themselves, even when they are considered in a multivariate setting. However, the way they interact may have an effect on the performance considered. Thus, firms should implement these drivers and foster their interaction.

Context of the Study and Research Questions Only a few studies have highlighted that the effect one driver has on time performances may depend on another driver. For example, Clark and Fujimoto (1991) found the synergistic effect of the project leaders and the organizational structure of the projects on NPD speed. Griffin (1997b) highlighted that the team’s impact on cycle time depended on product newness, while Trygg (1993) said that the key ingredient in successful concurrent engineering was teamwork. Furthermore, it was emphasized that interfunctional teams may have a greater influence on performance if they are able to communicate with customers or if they make product decisions using a concurrent approach (Cordero, 1991). There is, however, a lack of extensive empirical research on whether the interactions between different drivers affect time performances. This article aims to investigate whether drivers can interact and can influence time performances with a ‘‘synergistic’’ effect. As this survey is exploratory in nature, only first-order interactions between drivers (i.e., two-way interactions) and their effect on time performances in a multivariate setting are considered, explicitly including the interaction term in the models. Results from models with interaction provide more complete information on the relationship between time performances and drivers in comparison to the univariate analysis, which can aid the interpretation of the multivariate analyses with interaction. Then, a set of hypotheses will be proposed regarding the influence two-way driver interactions have on time performances, as well as a set of univariate hypotheses. Mechanical and electronic manufacturing companies were considered in this study. The unit of analysis was the NPD program, i.e., the new products developed and launched in the last three years by the firm or by the SBU considered. The firm’s respondent initially was asked to identify and to list the completed projects within the NPD program, taking into

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account three categories of new products: (1) new products for new markets or segments; (2) new products that replace existing products; and (3) important improvements or extensive revisions to existing products. Then the respondent was asked to calculate the percentage of those projects that, for example, were launched on time or used an interfunctional team. Thus, the unit of analysis was identical across both the dependent variables (time performances) and the independent variables (drivers).

Time Performance Time performance has been defined in different ways in literature. One dimension concerns the cycle time, which elapses between the start of the developmentprocess and market launch (Clark and Fujimoto, 1991; Griffin, 1993, 1997a; Kessler and Chakrabarti, 1999; Labahn et al., 1996). Griffin (1997b) investigated the length of the development time and found that it increased with greater product complexity and newness. On account of this, it is often difficult to compare the cycle times of various projects with different complexity and newness within a company or between different firms. Thus, research tends to focus on perceptual and relative measures of speed, for example asking the respondent to compare innovation speed with that of competitors (Cooper and Kleinschmidt, 1994; Droge et al., 2000) or asking them to refer to similar past projects within their company, considering the acceleration of the development or, in other words, the launch against an accelerated schedule (Kessler and Chakrabarti, 1999; Millson et al., 1992; Nijssen et al., 1995). Another dimension of time performance is the extent to which a project meets an assigned schedule (i.e., ‘‘on-time performance’’ or ‘‘staying on schedule’’ or ‘‘launched on time’’) (Cooper and Kleinschmidt, 1994; Kessler and Chakrabarti, 1999; McDonough and Barczak, 1991). In some empirical studies, this dimension has been found to be correlated to the acceleration of the development (Cooper and Kleinschmidt, 1994; Kessler and Chakrabarti, 1999). In the present study, two time-performance dimensions were considered, and the respondent was asked to consider the percentage of projects over the last three years that had been (1) launched on time; and (2) launched against an accelerated schedule. As will be seen later, the two dimensions were found to be significantly correlated, and the resulting scale has been called time performance (TP).


Drivers and Their Effects on Time Performance As discussed above, many factors have been identified as time performance drivers. These factors can be grouped in a range of areas. For example, Kessler and Chakrabarti (1999) considered strategic orientation and organizational capability groups, while Griffin (1997b) grouped drivers into project strategy, process characteristics, and team structure. Three groups of drivers were considered in this article: developmentprocess drivers, organizational-mechanism drivers, and strategic-capability drivers. Development-process drivers. These refer to the formal execution of the various development-process phases and to the extent they overlap. This set of drivers has been investigated in many studies. As far as development task overlapping is concerned, a number of studies have agreed that parallel processing has a positive influence on time performances (Clark and Fujimoto, 1991; Cordero, 1991; Millson et al., 1992; Murmann, 1994; Wheelwright and Clark 1992). Several studies also have hypothesized a positive relationship between formal execution of the process phases and time performances (Cooper and Kleinschmidt, 1994; Griffin, 1993; Kessler and Chakrabarti, 1996), especially when the products are complex (Griffin, 1997b). Generally, an NPD process includes the following main phases (Clark and Fujimoto, 1991; Cooper 1993; Crawford, 1987; Dolan 1993; Wheelwright and Clark, 1992): idea generation, concept definition (where the original idea is translated in terms of the attributes and functions perceived by customers), product concept test (to identify important attributes and to improve the product concept), preliminary design, detailed design, prototyping and early modifications made during the pilot or trial production phase before market launch. Some of the phases must be done (e.g., detailed design), while others could be done (e.g., concept definition and test and preliminary design). These latter tasks are activities that could be eliminated in order to accelerate the NPD process (Nijssen et al., 1995). Nevertheless, subsequent development tasks could be speeded up by doing these activities because a lot of problems already have been identified and perhaps even have been solved (Cooper and Kleinschmidt, 1994; Kessler and Chakrabarti, 1996). For example, Gupta and Wilemon (1990) found that poor initial definition of the new product was the number-one reason for development delays. Thus, it seems time efficient to discuss design solutions earlier and to provide product modifications before manufacturing activities begin (Meyer and Utterback, 1995; Murmann, 1994). In this first group, the following drivers were considered: product definition, predesign


and early modification, and use of an overlapping approach among phases. This study proposes the following univariate hypothesis (identified by the subscript ‘‘U’’): H1U: Formal execution of each process phase (product definition, predesign and early modification) and an overlapping approach are correlated positively with time performance.

Organizational-mechanism drivers. These refer to the communication and coordination mechanisms adopted during development both internally (i.e., project manager and team use) and externally (i.e., coordination with suppliers and customers). A number of studies have found significant links between the use of such drivers and time performances. Project manager use can ensure better time performances in the development-process (Clark and Fujimoto, 1991; Wheelwright and Clark, 1992). The interfunctional team use often has been seen as an important time-performance driver (Clark and Fujimoto, 1991; Droge et al., 2000; Griffin, 1997a; Kessler and Chakrabarti, 1996; Kruglianskas and Thamhain, 2000; Thamhain, 1990). Early involvement of suppliers may reduce the development time, thus avoiding delays (Gupta and Souder, 1998; Hartley et al., 1997; Murmann, 1994; Ragatz et al., 1997). It is not easy for companies to carry out codesign efficiently with their suppliers, but the practice is becoming more and more common in many sectors (Trygg, 1993). Customer involvement enables a firm to translate customer requirements both into product definition and into product design, eliminating the need to spend time and money on subsequent, expensive modifications and reworking (Cooper and Kleinschmidt, 1994; Gupta and Souder, 1998; Karagozoglu and Brown, 1993). Customer involvement is necessary particularly when products are customized or semi-customized. Thus, the following drivers are considered in the organizationalmechanism group: project manager use, team use, supplier involvement, and customer involvement. The following univariate hypothesis is proposed: H2U: The use of each organizational-mechanism is correlated positively with time performance.

As the aim of the study was to discover interactions between drivers, it was wondered whether a driver within the development-process group would interact with one from the organizational-mechanism group in order to exert additional influence on time performance. In literature, it is not common to find results about these types of interactions. One example is the multifunctional team, which favors the implementation of an overlapping approach, thus emphasizing its positive effect on time performance (Trygg, 1993). As


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the development-process is embedded in an organizational environment, it performs better when the organizational-mechanisms work with it and support the execution of the development tasks. Thus, this hypothesis about two-way interaction effects (identified by the subscript ‘‘I’’) is proposed: H1I: Interactions between each organizational-mechanism driver and each development-process driver positively affect time performance.

Strategic capability drivers. New product development has been recognized as one of the most complex and difficult processes a company must manage (Clark and Fujimoto, 1991). This process is characterized by uncertainty, requires problem-solving cycles, and involves the creation and use of new knowledge (Brown and Eisenhardt, 1995; Murmann, 1994). The need for speed enhances this complexity. Several studies have recognized that strategic or dynamic capabilities, on both the technological and the market side, are crucial for the development of successful new products (Helfat and Raubitschek, 2000; Iansiti and Clark, 1994; Teece et al., 1997). Furthermore, the ability of top management to define and to communicate clear project objectives to everyone involved was perceived as being the most important of several drivers that have an effect on development-time reduction (Karagozoglu and Brown, 1993; Murmann, 1994). A company that possesses and develops these capabilities will be able to reduce uncertainty, thus speeding up the development activities (Cooper and Kleinschmidt, 1994; Schoonhoven et al., 1990). However, as these capabilities are not merely project specific, but usually are company specific, in the medium or long term they are the cornerstone of sustainable product success in all the different projects a company is running (Meyer and Utterback, 1995). Thus, new product performances not only depend on best practices and well-formalized processes but also on the capabilities available. This study considers the following strategic-capabilities: technological capabilities, market and customer understanding capabilities (up-front capabilities), and top-management capabilities to support and to guide development (new product strategic guide). The following univariate hypothesis is proposed: H3U: Technological capabilities, up-front capabilities, and new product strategic guide are correlated positively with time performance.

Regarding interactions, a few studies have found that strategic-capability variables can play a key role in supporting and in sustaining product development (Iansiti and Clark, 1994). Using a dynamic capability approach (Teece et al., 1997), these drivers are assumed to function as enabling factors that speed up

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the development-process. In other words, the development-process phases are expected to be more time efficient if they are sustained by high strategic-capabilities. Thus, the following hypothesis is proposed regarding the effects of two-way interactions: H2I: Interactions between each strategic-capability driver and each development-process driver positively affect time performance.

On the other hand, these capabilities may also interact with organizational-mechanisms. Project managers and teams play a key role in determining time performance if supported by a high level of strategic-capabilities. Involvement of external subjects like customers and suppliers, even though important for product success, is a complex and time-consuming activities. These external links can be organized in a more time-efficient way when accompanied and guided by strong strategic-capabilities. Thus, the following hypothesis regarding the effects of twoway interactions is proposed: H3I: Interactions between each strategic-capability driver and each organizational-mechanism driver positively affect time performance.

Methodology Data Collection and Sample Data for this empirical research were gathered from Italian manufacturing firms producing mainly industrial goods, working in the mechanical and electronic sectors [standard industry classification (SIC) codes 35 and 36]. Companies located in the north of Italy, the most industrialized area in the country, were considered. The reference population (405 companies) consisted of firms with an NPD department of more than 100 and less than 1,000 employees and with a revenue of more than 10 million Euro per year. A preliminary research questionnaire was tested on some companies to ensure the correct formulation and understanding of the questions. The resulting questionnaire was mailed to the senior executive of the NPD department, accompanied by a letter detailing the purpose of the research, the structure of the questionnaire, and the unit of analysis. The addresses of the firms were taken from Dun & Bradstreet’s Business-to-Business database. The respondents were asked to consider the NPD program, including all the new products (i.e., new products for new markets or


segments, new products that replace existing products, important improvements or extensive revisions to existing products) developed and launched in the last three years by their firm. Assistance was provided to ensure that the information gathered was both complete and correct. The sample was made up of 85 firms, 59 from SIC 35 (machinery manufacturing) and 26 from SIC 36 (electrical, electronic machinery, equipment, and supplies). The mean number of employees for the sample was 250 [standard deviation (SD) 171, range: 100– 937]. Mean sales were 37.5 million Euro (SD 17.5, range: 11–198). The products made by the sample firms are shown in Table 1.

Performance Measures Respondents were asked to refer to two relative measures of time performance: ‘‘launch on time’’ and ‘‘launch against an accelerated schedule.’’ Launch on time measures whether the development-process keeps to the time schedule, and launch against an accelerated schedule measures whether new products are being developed faster than similar past projects within the company—in other words projects with similar levels of complexity and newness (as defined in Griffin, 1997b). Considering the products developed and launched over the past three years (i.e., its NPD program), each respondent was asked to consider the percentage of products launched on time and the percentage of products launched against an accelerated schedule using two items from 1 to 5, where 5 represents 100 percent (or about 100 percent) of the projects Table 1. Sample Composition Metalworking Machine Tools Automotive Industry Components Components for Electrical and Wiring Systems Pumps and Gas Compressors Packaging Systems Machines for Textile Production and Manufacturing Electromechanical Systems and Devices Electronic Components Air Conditioning Components Industrial Woodworking Machines Fluid Flow-Control Valves Agricultural Equipment and Machines Lifting Systems and Devices Mechanical Components Other

11 9 8 7 7 7 7 6 4 4 4 3 3 2 3

Total

85


launched on time or against an accelerated schedule; 4 represents about 75 percent; 3 about 50 percent; 2 about 25 percent; and 1 when almost no projects had been on time or had been accelerated. When analyzed, the two time-performance-related items had a high r correlation coefficient (r5.71; p5.000). The resulting scale was called time performance (TP). Note that Cronbach’s a and r-correlation coefficient assume the same value for a two-item scale.

Driver Measures The following measures for each of the three groups of drivers. Development-process drivers. This usually includes several tasks. In this study, three critical phases were considered that are not strictly necessary to develop a new product: product definition, predesign, and early modifications. The use of an overlapping approach also was considered in the development-process. Considering the products developed and launched by each firm in the last three years, respondents were asked to consider the percentage of projects in which the various tasks were implemented, using a 1 to 5 scale (55about 100 percent of the projects y, 35about 50 percent of the projects y, 15almost no projects). (1) Product Definition: Two items were considered— formal product concept definition before the design phase; and concept test to check whether the concept suits customer needs. The two items have a high correlation coefficient (r5.57, p5.000); (2) Predesign: This has two items—formal definition of preliminary design, where the product concept is translated into technical specifications, target performances, architecture, and component choices; and preliminary design approval. The scale has a high correlation coefficient (r5.86; p5.000); (3) Early Modifications: This construct refers to the product modifications made during the pilot or trial production phase, before the market launch. (4) Overlapping Approach: This refers to the interrelationship among tasks and in particular to a development-process performed in a parallel, rather than sequential, way. Organizational-mechanism drivers. All these measures were operationalized using categorical answers (five-point scales) that considered the percentage of


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projects within the NPD program in which these practices were used extensively (55about 100 percent of the projects y, 35about 50 percent of the projects y, 15almost no projects).

(1) Project Manager Use: This refers to the formal adoption of such a role during new product development; (2) Team Use: This item measures the percentage of projects in which a multifunctional team oversaw and managed the development; (3) Customer Involvement: This is a two-item scale— formalized involvement of customers during development in order to align technical specs with customer needs; and e-connection with the customers involved in the design in order to facilitate communications with them. The scale has a high correlation coefficient (r5.72; p5.000). (4) Supplier Involvement: This is a two-item scale— early involvement of suppliers, which means the involvement of main suppliers from the beginning of the development; and e-connection with the suppliers involved in the design in order to facilitate cooperation during development. The scale has a high correlation coefficient (r5.64; p5.001).

Strategic-capabilities drivers. These measures are organized following three main concepts. (1) New Product Strategic Guide: This is a three-item scale—(a) clear definition of new product objectives; (b) clear communication of the role of the new products in influencing company objectives; and (c) good and well-recognized strategy to guide the development of the new products. Each respondent again was asked to consider the products developed and launched over the last three years and to determine the percentage of projects in which there were clear definition of objectives, clear communication, and well-recognized strategy, respectively. Each item was measured using a 1-to-5 scale (55about 100 percent of the projects y, 35about 50 percent of the projects y, 15almost no projects). This scale has high internal reliability (a5.73) and high correlation coefficients (rab5.70, pab5.000; rbc5.81, pbc5.000; rbc5.68, pbc5.001). (2) Up-Front Capabilities: This is a multi-item scale. Using a 1-to-5 point scale (15extremely low; 55extremely high), respondents were asked to indicate (a) the capability of development staff

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to understand clearly the different aspects of market and competitive evolution early on; and (b) the capability to have a clear, in-depth understanding of customer needs early on. The scale has a high correlation coefficient (r5.83; p5.000). (3) Technological Capabilities: This is a multi-item scale (15extremely low; 55extremely high) that includes (a) the level of development staff capabilities regarding product-technology innovation compared to those of competitors; and (b) the level of capabilities regarding process-technology innovation compared to those of competitors. The scale has a high correlation coefficient (r5.74; p5.000). Table 2 shows basic statistics for the drivers.

Method of Analysis The main aim of this article is to investigate two-way interactions between drivers. In order to obtain reliable results, statistical analysis was performed in two sequential steps. First step: nonparametric G-correlation test. In the first step of the statistical analysis, the Goodman-

Kruskall G-correlation coefficient between each driver and the time performance was calculated, according to the H1U, H2U, and H3U hypotheses. This coefficient detects every possible kind of correlation (in terms of a monotonic relationship)—not only linear (Sheskin, 1997)—that could occur between the variables considered. Indeed, when dealing with categorical variables, as in this study’s case, linear correlation coefficients may not detect all possible existing correlations. The Goodman-Kruskall G coefficient can vary within the interval [ 1; þ 1]; a monotonic relationship was considered to be effective when the nonparametric hypothesis testing on a G coefficient other than zero showed a one-tailed p-value less than 5 percent. This test was calculated by using a permutation method, which is a powerful, reliable approach to perform exact statistical analysis (Metha and Patel, 2000; Pesarin, 2001). The G-correlation method also helped interpret the regression models with two-way driver interactions performed in the second step of analysis.

Second step: multivariate regression models with two-way driver interaction. According to the aim of the article, the second step sought to detect significant two-way driver interaction effects, taking into account the H1I, H2I, and H3I hypotheses. This step was the core of this study’s statistical analysis, and it was carried out using two different multivariate approaches. First,

Table 2. Mean, Standard Deviation, G-Correlation Coefficient (One-Tailed p-value) Driver DevelopmentProcess

1 Product Definition 2 Predesign

1.4 1.2

3 Early Modifications 3.4

1.4

4 Overlapping Approach

3.3

1.0

3.6

.29 (.011) 1.2 .27 (.019) 1.3 .23 (.014) 1.0 .01 (.464)

3.7 2.6

9 NP Strategic Guide

3.5

1.0

3.6

0.9

3.5

0.8

2.4

.51 (.000) .28 (.010) .27 (.025)

1.6

7 Customer Involvement 8 Supplier Involvement

10 Up-Front Capabilities 11 Technological Capabilities po.05 po.01 po.001

1

2.6 3.7

Organizational- 5 Project-Manager Mechanisms Use 6 Team Use

StrategicCapabilities

Mean SD

.44 (.000) .31 (.003) .19 (.049)

2

3

4

5

6

7

8

9

10

.39 (.000) .19 .35 (.070) (.007) .39 (.000) .61 (.000) .10 (.155) .01 (.457)

.03 (.398) .49 (.000) .20 (.031) .08 (.232)

.12 (.216) .21 (.084) .11 (.196) .17 (.096)

.35 (.008) .13 (.112) .12 (.146)

.46 (.000) .34 (.000) .20 (.031)

.24 (.013) .36 (.001) .04 (.362)

.45 (.000) .14 (.141) .29 (.018)

.19 .39 (.050) (.000) .17 .30 (.085) (.021) .08 .23 (.255) (.041)

.21 (.037) .01 (.463)

.31 (.001) .26 .16 (.002) (.049) .28 .09 (.003) (.147) .06 .05 (.287) (.332)

.43 (.000) .31 .39 (.002) (.000)



multivariate linear regressions were performed with twoway driver interaction, adopting a significant a-level of at least 5 percent both for the model F-test and for the t-tests on parameters. Each parameter estimate represents the individual contribution of each driver or interaction to the performance when the values of other variables are held fixed. The statistically significant increase also was tested for in the R2 of each model with two-way driver interaction over the model including only the single driver effects using the Chow test (Chow, 1960, 1983). Second, ordinal logistic regressions also were performed with two-way driver interaction, which are suitable particularly for categorical variables, in order to verify and to confirm linear regression results and to discover any additional relationship that sometimes

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may not be linear. However, Tables 4–6 show that in all cases logistic regressions confirmed the results obtained with linear regressions at a lower p-value for parameter estimates. this analysis was carried out for both approaches using SAS 8.2 (SAS Institute, 1999). For each logistic regression, the following statistical tools were used to interpret the output (Agresti, 1984, 1990; Agresti and Finlay, 1986): (1) The model p-value [likelihood ratio (LR) test]: If less than 5 percent, then the model fits the data; (2) The parameter estimates: A positive parameter estimate indicates an inverse association between each single driver or interaction and the time performance, while a negative parameter estimate

Table 3. G-Correlation Coefficient between Time Performance and Each Driver (One-Tailed p-Value) Driver

G Coefficient

p-Value

Development-Process

Product Definition Predesign Early Modifications Overlapping Approach

.13 .06 .17 .25

.108 .283 .060 .025

Organizational-Mechanisms

Project-Manager Use Team Use Customer Involvement Supplier Involvement

.05 .22 .09 .00

.351 .039 .168 .495

Strategic-Capabilities

.23 .18 .22

New Product Strategic Guide Up-Front Capabilities Technological Capabilities

.008 .041 .024

po.05 po.01

Table 4. Significant Two-Way Driver Interactions (Development-Process–Organizational-Mechanisms) Linear Regression Single Drivers and Interaction Term

p-Value (F-Test)

Parameter Estimate

Model 1 Overlapping Approach+ Team Use+ Overlapping Approach Team Use

.009

0.95 0.76 0.33

Model 2 Predesign Supplier Involvement Predesign Supplier Involvement

.000

Model 3 Early Modifications Customer Involvement Early Modifications Customer Involvement

.013

+

Positively correlated with TP at univariate level (see Table 3).

po.05 po.01

Logistic Regression R2

p-Value (LR Test)

.132

.010

1.50 2.61 0.71

.238

.000

0.40 0.75 0.25

.124

.027

Parameter Estimate 1.08 1.11 0.40 2.05 3.38 0.93

0.46 0.85 0.27

208



Table 5. Significant Two-Way Driver Interactions (Development-Process–Strategic-Capabilities) Linear Regression

Logistic Regression

p-Value (F-Test)

Parameter Estimate

R2

Model 4 New Product Strategic Guide+ Product Definition New Product Strategic Guide Product Definition

.001

0.04 1.23 0.25

.186

.001

0.08 1.34 0.28

Model 5 Technological Capabilities+ Product Definition Technological Capabilities Product Definition

.001

0.83 2.16 0.55

.177

.000

0.81 2.36 0.60

Model 6 Up-Front Capabilities+ Product Definition Up-Front Capabilities Product Definition

.008

0.19 1.33 0.29

.134

.010

0.32 1.38 0.30

Model 7 New Product Strategic Guide+ Predesign New Product Strategic Guide Predesign

.005

0.96 1.32 0.39

.146

.003

1.02 1.55 0.42

Model 8 New Product Strategic Guide+ Early Modifications New Product Strategic Guide Early Modifications

.003

0.41 0.78 0.27

.158

.008

0.38 0.81 0.26

Single Drivers and Interaction Term

+

p-Value (LR Test)

Parameter Estimate


po.05 po.01 po.001

indicates a direct association between each single driver or interaction and the time performance. In the case of direct (or inverse) association, an increase in the categories of an independent variable indicates an increase (or a decrease) in the time performance categories; (3) A chi-square test for each parameter estimate: If p-value is less than 5 percent, then the independent variable is associated significantly with time performance. A final check for the goodness of fit of all models by performing residual analysis and regression diagnostics also was done. In every case a high goodness of fit was obtained for every performed model. In a multivariate setting, a regression model with two variables and their interaction can generally be expressed as: y ¼ f ðx1 ; x2 ; x1 x2 ; eÞ;

where y is the dependent variable (time performance), f is the link function (linear or logistic), x1 and x2 are drivers, and x1x2 is the interaction between them, obtained by considering the product of x1 and x2; e is a random variable corresponding to the error term in the model. In the case of linear regression, the model becomes y ¼ a þ bx1 þ gx2 þ dx1 x2 þ e: The model emphasizes the role of the two-way driver interaction (represented by the parameter d) when both drivers x1 and x2 were present in the model. Indeed, a multivariate regression model that does not include the interaction parameter d is unable to investigate the effect of a two-way driver interaction. It is worth noting that different statistical approaches could be taken into consideration in order to detect two-way interactions between drivers (Agresti, 1990). The approach presented here is very



209

Table 6. Significant Two-Way Driver Interactions (Organizational-Mechanisms–Strategic-Capabilities) Linear Regression

Logistic Regression

p-Value (F-Test)

Parameter Estimate

R2

Model 9 New Product Strategic Guide+ Supplier Involvement New Product Strategic Guide Supplier Involvement

.003

0.43 1.90 0.46

.154

.011

0.34 1.62 0.41

Model 10 New Product Strategic Guide+ Customer Involvement New Product Strategic Guide Customer Involvement

.005

0.40 1.30 0.37

.147

.009

0.35 1.23 0.35

Model 11 New Product Strategic Guide+ Project-Manager Use New Product Strategic Guide Project Manager Use

.002

0.56 1.21 0.33

.161

.001

0.73 1.43 0.38

Model 12 Technological Capabilities+ Project-Manager Use Technological Capabilities Project Manager Use

.077

0.57 1.09 0.31

.081

.036

0.54 1.16 0.32

Model 13 Technological Capabilities+ Customer Involvement Technological Capabilities Customer Involvement

.046

0.71 1.28 0.42

.093

.045

0.66 1.29 0.41

Single Drivers and Interaction Term

+

p-Value (LR Test)

Parameter Estimate


po.05 po.01

sensitive since it performs all the possible two-way models with interaction according to the hypotheses H1I, H2I, and H3I (Agresti and Finlay, 1986; Agresti, 1990; Gunst and Mason, 1991; Montgomery, 1997). This procedure enables us to obtain reliably significant results from an observational survey with a limited sample size and to look for every possible significant two-way driver interaction by considering all the possible models y ¼ f ðx1 ; x2 ; x1 x2 ; eÞ. In order to find significant associations, the statistical analysis was focused mainly on hypothesis testing with F- or ttests in linear regression and LR tests or chi-square tests in logistic regression rather than on descriptive coefficients such as R2 in linear regression or oddsratios in logistic regression. Nevertheless, the corresponding R2 coefficients also are given for linear regression models. All the possible models with twoway driver interaction were performed, and the value of R2 for each significant model should be quite low. This is because the whole variability in time performance was divided among all the significant models,

in line with this study’s main aim, which is not to construct a powerful global model to explain the whole variability in time performance but is to detect every possible statistically significant influence two-way driver interactions can have on time performance.

Results and Discussion The first step of this investigation is to carry out the nonparametric correlation analysis between each driver and time performance (see Table 3). As far as the drivers within the development-process group are concerned (H1U), we find that overlapping approach is correlated strongly to time performance. This is in line with the results of previous studies (Cordero, 1991; Murmann, 1994). The initial phases (i.e., product definition and predesign) before the product engineering are not correlated with time performance. This result highlights the controversial influence of the initial

210


phases on TP (Nijssen et al., 1995). Early modifications are correlated weakly to TP, showing that managing product modifications before the production phase could improve punctuality and speed. Among organizational-mechanism drivers (H2U), team use is found to influence TP significantly. This result is in line with those of previous studies (Crawford, 1992; Trygg, 1993; McDonough, 2000). The other variables within this group (project-manager use, customer involvement, supplier involvement) are not correlated to the dependent variable, despite the results of some previous studies on the influence of these variables on time performances (Brown and Eisenhardt, 1995; Cooper and Kleinschmidt, 1994; Karagozoglu and Brown, 1993; Kessler and Chakrabarti, 1999). All three drivers within the strategic-capabilities group are correlated with TP (H3U). The new product strategic guide—which includes a clear definition of new product objectives, a clear communication of the NP role, and the NP recognized strategy to guide the development—is correlated strongly with TP. Thus, the importance of a strategic guide is confirmed, as highlighted by other studies (Karagozoglu and Brown, 1993; Murmann, 1994). Similarly, both up-front and technological capabilities have a significant influence on TP, as highlighted in previous studies (Gupta and Souder, 1998; Gupta and Wilemon, 1990). This first phase of analysis shows that a competencebased perspective seems to be appropriate particularly in the NPD field when analyzing time performances. Furthermore, overlapping approach and team use would seem to be crucial for speeding up NPD. The second step of the study aims to identify the existing two-way interactions by using both linear and logistic regression. In a multivariate setting, each twoway driver interaction could be considered a new separate latent variable produced jointly by the two drivers in the model. Among the range of relationships with time performance that may take place in a two-way driver interaction model, a single driver may not be significant by itself, but it can produce a statistically significant interaction with the other driver. Moreover, the interaction parameter reveals an effect that is independent of the effects of the other drivers in the model. For example, a nonsignificant driver and a driver with an inverse relationship with time performance may give rise to a direct association with TP in the interaction. In this case the nonsignificant driver exerts more influence over the production of a significant interaction effect than the other driver (Montgomery, 1997).


Two-Way Interactions between Organizationalmechanism and Development-Process Drivers (H1I) H1I states that two-way interactions between each driver within the organizational-mechanism group and each driver within the development-process group positively affect time performance. Three different significant models with significant interactions are found (models 1–3, see Table 4). All the models have a statistically significant increase in the R2 with respect to the corresponding models without interaction (see Table 7 in the Appendix). Model 1 shows an interaction between overlapping approach and team use. These drivers, as seen previously, are correlated with TP at univariate level. However, in this model they are not significant as individual parameters: Their effects are transferred completely to the interaction term, whose parameter is positive. This means that the influence of each driver, which is always positive, depends entirely on the other’s value; thus, the two variables support one another. In other words, in order to improve TP, considering at the same time these two drivers and their interaction, it is discovered that it might not be time efficient to adopt only an overlapping approach without also having a high level of interfunctional team use and vice versa. Therefore, if a firm has to work to a tight development schedule, it should seek and should integrate any possible synergistic effects between team use and overlapping development phases. Model 2 highlights interaction between predesign and supplier involvement, none of which is correlated with TP, while the interaction and the single drivers are significant in the model, with a positive and a negative influence on TP, respectively. Predesign is time consuming, because it entails the definition of the product architecture. Furthermore, involvement of suppliers is recognized as a complex and difficult task. Thus, each of the two drivers, as shown in model 2, has a negative influence on TP. However, the model suggests a significant and positive interaction between these drivers, and this interaction helps reduce the negative effect due to the single drivers. Predesign, even if it is time consuming, makes it possible to define at an early stage the product architecture and the interfaces between modules and components, some of which are developed by suppliers. Thus, a firm that performs the predesign and involves suppliers could reduce the negative effect on TP of these activities, because suppliers could develop components or


modules early on within clear boundaries and according to precise information stemming from the predesign choices. Customer involvement interacts with early modifications to the product (model 3). This means that product revisions carried out before production launch may shorten the development time and may ensure punctuality when customer needs are considered in that phase. This approach in the developmentprocess avoids modifications after product delivery, which may delay the full commercialization of the products.

Two-Way Interactions between Strategic-Capabilities and Development-Process Drivers (H2I) The H2I hypothesis states that two-way interactions between each driver within the strategic-capability group and each driver of the development-process group positively affect time performance. Five significant different models with significant interaction are found (models 4–8; see Table 5). All the models have a statistically significant increase in R2 with respect to the corresponding models without interaction (see Table 7 in the Appendix). The first three models (4, 5, and 6) give an interesting insight into the way that product definition determines time performance. While this driver is not correlated to TP at a univariate level, in a multivariate setting it interacts with each of the three drivers within the strategic-capability group. In other words, product concept development and its testing, which are complex tasks and are often lengthy activities that sometimes take months, enable other development phases to be shortened but only when the interacting drivers (i.e., new product strategic guide, technological and up-front capabilities, respectively) take high values. Furthermore, the individual strategic-capability drivers, correlated at a univariate level with the TP, are not significant as individual parameters: This means that, in a multivariate setting, the positive effect of strategic capability is transferred to the interaction factor. Thus, it could be said that although product definition has a negative association with TP as an individual parameter in these models, interaction with strategic-capabilities drivers does have a positive influence on TP, which is greater when each of these drivers has a high value. Thus, a firm that possesses extensive strategic-capabilities can exploit the positive effects of implementing a product-defini-


211

tion phase to speed up the subsequent development activities. However, if strategic-capabilities are low, product definition may cause delays in the development. Model 7 provides similar information to the results obtained in model 4. Also, in this case extensive strategic-capabilities enable the positive effects of the predesign phase to speed up the development. However, if strategic-capabilities are low, predesign may cause delays in the development. In conclusion, a company with high strategic-capabilities may carry out the predevelopment tasks (product definition and predesign) without negatively influencing TP. When strategic-capabilities are low, those tasks nearly always will lengthen development time. Lastly, model 8 shows that early modifications to the product before the launch, which is not correlated with TP, has a positive impact as it interacts with the new product strategic guide. Indeed, only the interaction is significant in this model: The new product strategic guide enables all the product revisions required to reach the product goals, including TP targets, to be defined promptly.

Two-way Interactions between StrategicCapabilities and Organizational-Mechanism Drivers (H3I) H3I states that two-way interactions between each driver within the strategic-capability group and each driver in the organizational-mechanism group positively affect time performance. Five different significant models with significant interaction are found (models 9–13; see Table 6). All the models have a statistically significant increase in the R2 with respect to the corresponding models without interaction (see Table 7 in the Appendix). In this case too, the new product strategic guide has an important role because it interacts with three out of the four drivers included in the organizational-mechanism group. Although uncorrelated with TP at a univariate level, supplier involvement, customer involvement, and project-manager use in particular do have a positive effect on the dependent variable when they interact with the new product strategic guide and when the new product strategic guide is strong (models 9–11). The involvement of external organizations and subjects during the development (i.e., suppliers and customers) entails complex relationships and

212



managing a lot of information exchanges. In such a situation, this involvement may lead to misunderstandings and delays if the definition of the goals is not clear or shared. Indeed, models 9–10 show that supplier involvement and customer involvement do make a negative contribution as individual parameters. However, these negative effects can be counteracted by a high new product strategic guide, due to the greater positive influence of the interaction on TP. Looking inside the development organization, project-manager use, which is uncorrelated with TP at univariate level, is not time efficient in itself. Models 11 and 12 give further insights into this matter: Projectmanager use, as an individual parameter, affects TP negatively, while it has a positive influence when it interacts with the new product strategic guide or technological capabilities of the staff members. Despite widespread use of project managers and the belief that they are needed to manage development, a firm should sustain and should accompany this role with a strong strategic guide and substantial technology capabilities in order to achieve high time performances. As previously seen in model 10, customer involvement may shorten development time and may ensure punctuality when objectives are clear and recognized. Furthermore, it interacts with technological capabilities (model 13). In other words, the availability of strong technological capabilities enables customer needs to be translated quickly into technical specifications for the new products.

main development phases have a twofold effect on TP. First, they directly influence TP because they help to reduce the level of uncertainty during development and to send clear signals to everyone involved in the process, thus avoiding delays and errors (Karagozoglu and Brown, 1993). Second, they interact and enhance the influence of other drivers, such as predevelopment tasks, project-manager use, and supplier and customer involvement. Thus, they could be considered successful ingredients for rapid, punctual product development.

Concluding Comments and Implications for Managers The results of this study suggest that time performance in new product development is linked to a complex set of factors—accelerating drivers, which directly affect TP, and interactions between drivers, which have crucial effects on TP. These findings, summarized following, offer new insights both for academics and for practitioners. Of course, the following conclusions and comments reflect the associative and exploratory nature of the study.

In many cases, product success is determined both by rapid development and by a high level of product quality capabilities. Nowadays, it is recognized that activities such as predevelopment and customer and supplier involvement, which require time, can favor product-quality capability (Clark and Fujimoto, 1991). However we found that these activities, if sustained by a strong new product strategic guide and a high level of capabilities, also may speed up development and may ensure punctuality. Thus, managers should foster the improvement of strategiccapabilities within the company.

A Strategic Guide Can Be an Essential Synergistic Ingredient for TP

Team Use and Overlapping Approach Are Associated Strongly with TP

Clear definition and communication of new product goals as well as a recognized strategy that guides the

These are two important drivers for time performances, as recognized in literature (Cordero, 1991;

Strong Capabilities May Favor High TP Development staff with strong technological and upfront capabilities not only provide high performing products and fulfill customer requirements (Brown and Eisenhardt, 1995) but also are important for achieving high TP. These factors alone have an effect and also give rise to important interactions with the initial phase of the process (product definition) and with customer involvement. In-depth knowledge of product technologies and market needs seems to contribute significantly to overcome the difficulties of defining products in accordance with customer needs and helps avoid delays in product development.

Predevelopment Phases and Customer and Supplier Involvement Can Play an Important Role in Determining TP


Murmann, 1994) and confirmed in this study. Furthermore, an interaction effect was found between the two drivers. Thus, a company could obtain time advantages by setting up interfunctional teams to manage an overlapping development-process. The overlapping approach requires information exchanges and decision-making in uncertain conditions. The interfunctional team is an organizational structure that is suited particularly to performing these tasks. Once companies recognize the importance of interactions between drivers, they gather suitable information on whether the effect of one practice is reinforced by the presence of another. These insights provide useful information that can be used when setting priorities and can help to attain higher performances by adopting a combination of selected drivers. This study found that the ‘‘best practices’’ many studies highlighted are associated with TP. However, such association is influenced by the presence of the socalled strategic-capability drivers. These latter variables, unlike practices and activities, require a complex learning process. A path to improvement in the development-process requires both long periods of time and an integrated view of the process; thus, it cannot be obtained by simply applying common practices. Consequently, analysis of interactions within the NPD field looks promising and requires further study. This research could be extended and improved in several ways—for example, by gathering data from other countries and by considering other types of industries or companies of other sizes. Another possible extension could be to perform global models, including many drivers belonging to different groups and the corresponding interaction terms, considering in particular the significant interactions found in this research. This further extension should enable emphasis to be placed on the importance of some single drivers and/or interactions in influencing time performance. Furthermore, on the basis of the significant two-way models found in this study, more complex interaction effects, such as second- or third-order interactions, could be studied on the basis of a larger sample size.

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Appendix. Comparison between Two-Way Models with and without Interaction Table 7. Significant Increases in R2 : p-Value of Chow Test

Model

R2 (Model with Interaction)

R2 (Model without Interaction)

p-Value of Chow Test

Development-Process – Organizational-Mechanisms

1 2 3

.132 .238 .124

.088 .018 .053

.044 .000 .013

Development-Process – Strategic-Capabilities

4 5 6 7 8

.186 .177 .134 .146 .158

.147 .055 .090 .088 .113

.049 .001 .045 .021 .041

9 10 11 12 13

.154 .147 .161 .081 .093

.091 .090 .093 .033 .046

.015 .022 .011 .043 .041

Organizational-Mechanisms – Strategic-Capabilities po.05 po.01