Concurrent engineering performance: Incremental versus radical innovation

ARTICLE IN PRESS Int. J. Production Economics 119 (2009) 136–148

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Concurrent engineering performance: Incremental versus radical innovation Sandra Valle, Daniel Va´zquez-Bustelo  Department of Business Administration, University of Oviedo, Avda. del Cristo, s/n-33071 Oviedo, Spain

a r t i c l e i n f o

abstract

Article history: Received 2 March 2007 Accepted 9 February 2009 Available online 25 February 2009

This article analyzes the link between the use of concurrent engineering (CE) and success in new product development (NPD) under varying conditions of uncertainty and complexity—radical versus incremental innovations. Using linear regression, the results obtained indicate that overlapping activities, inter-functional integration and teamwork positively affect NPD performance in terms of development time and new product superiority in the case of incremental innovations and in terms of development cost in the case of radical innovations. The conclusion is that the use of CE should be contingent to the context or particular conditions which characterize each innovation process and the order of priority given to the objectives pursued. & 2009 Elsevier B.V. All rights reserved.

Keywords: Concurrent engineering Radical innovation Incremental innovation New product development success

1. Introduction Many of the companies competing today in international markets consider new product development (NPD) as an important factor for achieving sustainable competitive advantages. Both researchers and managers are constantly searching for methods and practices that will allow them to improve the organization and management of their NPD processes and boost their effectiveness or success—the average success rate of NPD projects today is approximately 60% (Cooper and Edgett, 2003). The challenge is to achieve excellence in three specific objectives: (1) shorter new product development times, (2) more efficient developments, and (3) superior products. Taking these objectives into account, companies have reorganized their NPD processes and have moved from a sequential path, in which there is minimal interaction amongst the departments involved and the activities required to develop the product are carried out sequentially, towards an integrated path, known as

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E-mail addresses: [email protected] (S. Valle), [email protected] (D. Va´zquez-Bustelo). 0925-5273/$ - see front matter & 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2009.02.002

concurrent engineering (CE), in which the activities overlap and all the departments collaborate from the beginning. This new organizational design has helped companies improve their performance by leading to lower costs, higher quality, major knowledge creation and shorter product development times (Riedel and Pawar, 1991; Rosenblatt and Watson, 1991; Shenas and Derakhshan, 1992; Lawson and Karandikar, 1994; Prasad, 1996; Brookes and Backhouse, 1998; Pawar and Haque, 1998; Barba, 2001; Umemoto et al., 2004), all of which, in turn, has raised their competitive skills. There are many examples illustrating this, to the extent that CE has been considered one of the ‘‘best practices’’ for achieving sustainable competitiveness (Voss et al., 1995). However, more recent research shows that the use of CE does not always lead to positive results and that success in improving innovation capabilities depends on the context in which CE is applied, that is, on the prevailing competitive and technological circumstances. The conclusion is reached that the degree of uncertainty and complexity present in the process of innovation may moderate the impact of NPD characteristics on performance. The matter to be considered is not, therefore, whether CE is a mechanism for improving performance in the

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introduction of new products but, rather, under what circumstances such improvement can be achieved. It seems, however, in spite of many research efforts studying this aspect, that no consensus has been reached and that there are many empirical contradictions. This lack of unanimity is the reason for the present study, the main aim of which is to help determine the circumstances under which the application of CE is effective. With this aim, the impact of this methodology on the results of the NPD process is analyzed in a large sample of Spanish manufacturers, and different innovation scenarios are distinguished according to the degree of uncertainty and complexity involved in the NPD projects. The research is structured as follows. Firstly, the literature is reviewed with regard to the concept of CE and its objectives and basic pillars. Secondly, the empirical contradictions that exist regarding effective application of this path are explained and hypotheses are formulate linking the use of CE to several indicators for success in the NPD process depending on the type of innovation being carried out. Thirdly, the research methodology is explained. Fourthly, the statistical analyses carried out are presented, with the results obtained. Finally, the main contributions are summarized, the conclusions are drawn, some limitations are described and the lines for future research are considered.

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starts when the one before has completely finished, resulting in a total lack of integration and co-ordination between different functional areas and other contributors involved in the process. Each function carries out its work in isolation, with minimum reference to the needs of others. All this translates into continuous retracing of steps in each of the different phases of the project to correct the mistakes made, thereby resulting in very long development times and in additional costs for the design process (Takeuchi and Nonaka, 1986; Cordero, 1991). Similarly, many quality problems arise, basically owing to a lack of communication and understanding between product design, production, and consumers’ needs. As a result, companies resort to a new organizational structure for their NPD processes which, unlike the traditional way, is based on an integrated approach to product development in which everyone involved works in parallel and proper links are established amongst the activities of the different departments. The aim is to avoid continuous setbacks and the other problems that arise with the traditional approach, improving NPD performance. This new practice, named CE, tries to speed up the process, increasing flexibility, adopting a more strategic perspective with more sensitivity to change in the environment, solving problems through teamwork, developing diverse skills, and improving internal communication (Barba, 2001).

2. Theoretical framework 2.3. The basic elements of concurrent engineering 2.1. Definition of concurrent engineering One of the widest known definitions of CE is the one given by the American Institute for Defense Analysis, which considers it to be ‘‘a systematic approach to the integrated, concurrent design of products and related processes, including manufacturing and support. This approach is intended to cause the developers to consider all elements of the product life cycle from conception through disposal, including quality, cost, schedule, and user requirements’’ (Winner et al., 1988, p. 2). Therefore, CE can be seen as ‘‘integrated problem solving’’ (Wheelwright and Clark, 1992), where all activities necessary for the introduction of a new product are considered simultaneously (Shenas and Derakhshan, 1992), so that all factors and questions ‘‘downstream’’ of product development are incorporated into the ‘‘upstream’’ phase of development (Lee, 1992; Hatch and Badinelli, 1999). 2.2. Concurrent engineering versus sequential engineering From the beginning, CE was proposed as a method for dealing with the problems that tend to arise when companies adopt the traditional approach for developing new products. This approach, generally known as ‘‘throwing it over the wall’’, focuses on developing a structured process with clearly-defined and sequential phases, through which the future product is defined, designed, transferred to the manufacturing plant and rolled out to the market (Iansiti, 1995). Each one of these activities only

To achieve the above-mentioned objectives, CE is based on three basic elements (Koufteros et al., 2001): (1) concurrent work-flow, (2) early involvement of all participants and groups contributing to product development, and (3) team work. In other words, CE is the early involvement of a cross-functional team to simultaneously plan product, process and manufacturing activities (Hartley, 1992). 2.3.1. Concurrent work-flow Unlike sequential development, this first basic element stimulates parallel development, either total or partial, of the activities that form part of the NPD process. For example, product design and process planning can be carried out concurrently, process planning can be integrated with production planning, and control or product planning can start long before the concept is finalized. This does not reduce the duration of each activity, but it does decrease the overall development time (De Meyer and Van Hooland, 1990). In addition, such working in parallel allows frequent bilateral exchanges of information amongst the parties, so that activities which traditionally occur much later in the product development process benefit from information generated in much earlier activities (Yassine et al., 1999), thus minimizing errors and unplanned developments. Simultaneous planning of the product, the process and production means that manufacturing issues can be taken into account in final product design. This reduces uncertainty and allows for early detection of problems, avoiding the need for

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time-wasting changes. Being able to identify potential problems early avoids costly delays later (Susman and Dean, 1992). In sum, concurrent work flow generates multiple advantages (Syan and Menon, 1994; Bopana and Chon-Huat, 1997; Portioli-Staudacher and Singh, 1997; Gaalman et al., 1999; Corti and Portioli-Staudacher, 2004). It reduces the need for redesign and rework, reduces development time and offers more chances for smoother production, thus helping to minimize cost and improve quality. Nevertheless, the concurrence of activities must be managed efficiently (Blackburn et al., 1996) and requires a clear process. For example, Nicoletti and Nicolo´ (1998) present a decision support model to decide which activities primarily need to be concurrently scheduled and in what measure. An explicit understanding on how the overlapping it works and how all the different factors are related becomes essential (Haque and Pawar, 2001). 2.3.2. Early involvement of constituents Early involvement by all the groups that contribute to product development—internal (manufacturing, purchasing, marketing,y) and external (suppliers and costumers)— allows the different interested parties to express their opinions and provide their information inputs right from the start of the process. This results in multiple feedbacks, with closer coordination amongst the different process phases and the elimination of information gaps. The early exchange of information amongst all the participants helps reduce imbalance or disparity between the product characteristics and process capabilities and generates less probability of manufacturability problems or of poor adaptability of the product to customer requirements, avoiding the changes and delays that feature largely in the sequential approach (O’Neal, 1993). The end results are shorter development times, better product innovation capabilities, lower coordination costs, improved quality (Fleischer and Liker, 1992; Ulrich et al., 1993; Koufteros et al., 2001) and better ‘‘product integrity’’, that is, internal consistency between product parts, components and functions, and external consistency between performance and consumer expectations (Imai et al., 1985; Takeuchi and Nonaka, 1986; Womack et al., 1990; Clark and Fujimoto, 1990, 1991). In order to facilitate and maximize the benefits of this early exchange of information and to ensure that this is available and accessible in real time for all the participants in the NPD process, CE should be based on the use of new information technologies (Barba, 1993, 2001; Coman, 2000; Ruffles, 2000; Tucker and Hackney, 2000; Ainscough et al., 2003; Portioli-Staudacher et al., 2003). 2.3.3. Teamwork Finally, teamwork is established as the other basic element of CE. This means that participants in the development process are not only involved from the start of the project, openly interacting and exchanging information, as indicated above, but furthermore, they have to work closely together, strengthening one another in working towards common goals. Teamwork should be

understood as a process characterized by common interests, a high degree of transparency, shared risks and multiple synergies (Jassawalla and Sashittal, 1998). CE requires a high degree of mutual inter-dependence, in which the different functions interact, providing mutual feedback and sharing principles based on common goals, thorough visibility of the design parameters for all participants, mutual consideration of all decisions, collaboration to resolve conflicts, on-going improvement and teamwork (Linton et al., 1991; Hauptman and Hirji, 1999). The use of multifunctional teams is one of the most important characteristics of CE and it generates multiple advantages: improved communication and organizational learning (McKee, 1992; Henke et al., 1993), more creative solutions, better decisions, increased commitment (Donnellon, 1993) and improved product innovation and quality. Benefits are also seen in the increase in the technical and inter-personal skills of the team members and in a greater willingness to experiment and make use of any technological synergies arising from the merging of information on the wide variety of skills involved in the different disciplines. Team members thus get to know about other technical fields as well as their own (Tammala et al., 1997).

3. Empirical contradictions and hypotheses Many studies demonstrate that CE can successfully solve the typical problems of traditional NPD, leading to clear improvements in quality and marked reductions in development time and costs (Dean and Susman, 1989; Kusiak and Park, 1990; Clark and Fujimoto, 1991; Millson et al., 1992; Wheelwright and Clark, 1992; Karagozoglu and Brown, 1993; Shenas and Derakhshan, 1994; Durand, 1995; Blackburn et al., 1996; Calantone and Di Benedetto, 2000; Herder and Weijnen, 2000; Barba, 2001; Koufteros et al., 2001). However, some studies show the opposite. For example, regarding the cost of development, authors such as Takeuchi and Nonaka (1986), Uttal (1987), Aitsahlia et al., (1995) and Yassine et al. (1999) conclude that parallel development is less efficient in terms of resource use than sequential development. They consider that CE involves a large cost increase in comparison with the traditional development method. Specifically, Yassine et al. (1999) consider CE to be a clear option but they conclude that it should only be used when reducing development time is a higher priority than reducing cost. As an alternative, they suggest partial overlapping, which may also reduce sequential development time, although to a lesser extent, but with a smaller increase in development cost. In relation to development time, although it has been substantially and consistently demonstrated that concurrence may dramatically reduce product development time, there is no evidence that more concurrence is always better (Cordero, 1991). Although it is certain that to accelerate the time to market a degree of overlapping is preferable to sequential development (Krishnan et al., 1995; Chakravarty, 1995), there is a point at which concurrence has limitations (Hoedemaker et al., 1999)

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and an increase in parallelism might be undesirable. If the necessary communication amongst the concurrent team members does not exist, greater parallelism may make the project longer because there may be delays caused by greater re-working (Haberle et al., 2000). Datar et al., (1997) and Thomke and Fujimoto (2000) also bring to light conflicting findings on upstream planning, a type of functional interaction in which upstream participants anticipate design problems, versus waiting until the problems appear downstream. On the one hand, trying to anticipate problems and take critical decisions in the early stages may cause excessive disturbance and involve the use of imperfect or incomplete information (Dorf, 2000); on the other, waiting until problems appear leads to new versions of the design. Upstream planning has been found to significantly decrease (Cooper and Kleinschmidt, 1994; Hull et al., 1996), significantly increase (Einsenhardt and Tabrizi, 1995) and to have no effect on development time and effort (Datar et al., 1997). Because of all these contradictions, there may have been a tendency to over-exaggerate the benefits of CE while simultaneously playing down its drawbacks and associated risks (Poolton and Barclay, 1998). As a result, an intense debate has arisen about whether CE always produces positive results or whether, depending on the circumstances, it may be inferior to other approaches, including the traditional sequential one. As stated by McDermott and O’Connor (2002), researchers may have been too quick to generalize the utility of multifunctional and integrating practices across very diverse environments. For this reason, some of the research cited and also other recent research tries to answer this question and determine the circumstances or situations that are most suited to effective application of CE. Some studies distinguish between scenarios with a greater or lesser degree of innovation—radical versus incremental innovations, and others, between environments of high or low hostility and turbulence, but in this, too, there are many empirical contradictions. For example, with regard to type of innovation, some authors consider that when companies develop projects characterized by a high level of uncertainty and complexity, i.e. breakthrough or radical projects, advanced CE approaches are necessary, while for companies developing relatively simple products this methodology is unlikely to provide a viable solution (Schilling and Hill, 1998; Wheelwright and Clark, 2000). However, other authors believe that while CE may be appropriate for incremental innovations, it is not suitable for radical ones (Takeuchi and Nonaka, 1986; Handfield, 1994). They believe that when a company is faced with a breakthrough project that introduces radically new technology, the utilization of CE practices may induce a series of hidden costs which make its use inappropriate. The costs of reducing development time may include increased probability of errors, managerial chaos and unexpected inefficiencies that lead to longer development and delivery times (Crawford, 1992; Gaynor, 1993). The concurrent way seems more appropriate for moderate

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levels of innovation, as with incremental NPD projects (Cordero, 1991; Millson et al., 1992), and routine designs where the process characteristics are not critical and are fairly insensitive to design changes (Cantamessa and Villa, 2000). Hoedemaker et al. (1999) also argue that the higher the complexity of the project the higher the limits of CE. On the other hand, with regard to the type of environment, authors such as Koufteros et al. (2001) show that companies operating in fast-changing environments, characterized by uncertainty and ambiguity, adopt higher degrees of CE than those operating in relatively stable environments. These authors believe that, in this type of environment, CE practices allow a better flow of information and facilitate a wider range of solutions, while reducing ambiguity. Integrated action would reduce uncertainty of information, false starts and design rework (Ettlie, 1997). All this makes CE an essential element for companies searching for high performance in environments with fast-changing markets and technologies (Koufteros et al., 2001). However, Terwiesch and Loch (1999) and Bhuiyan et al. (2004) recommend restricting the use of CE to environments with low uncertainty. Their results suggest that compression of the development process through concurrence of activities requires a situation with limited uncertainty, where changes are predictable and can be kept under control, whereas overlapping may cause substantial reprocessing which would have more weight than the time saved by CE (Einsenhardt and Tabrizi, 1995; Ha and Porteus, 1995; Krishnan et al., 1997; Loch and Terwiesch, 1998; Hoedemaker et al., 1999; Terwiesch and Loch, 1999).1 In short, although it seems clear that CE is not a valid or reliable methodology in any possible context or situation, clearly there is confusion about which is the right scenario, both considering the innovation type or the uncertainty, hostility and turbulence of the environment. For this reason, this paper aims to help clarify the circumstances under which the development of a CE approach would be effective for a firm. Specifically, the paper analyses the contingencies related to the innovation type (radical versus incremental), taking into account that information regarding the level of dynamism, hostility and uncertainty of the external environment was not available for the sample at the time this paper was written. Considering the reported above, the following proposition and hypotheses were considered (see Fig. 1): General proposition: CE does not produce positive results in every circumstance or context.

1 Some of the studies mentioned go even further and suggest alternatives to CE depending on the degree of uncertainty present. For example, Einsenhardt and Tabrizi (1995) consider that, in a situation of high market uncertainty, an ‘‘experiential’’ path offers better results than concurrence. Also, Iansiti (1995) proposes, for environments with high uncertainty and turbulence, a ‘‘flexible model’’ that goes further than concurrence. Cantamessa and Villa (2000) propose two alternatives to CE which they call ‘‘first process’’ and ‘‘concurrent R&D’’. Finally, Mileham et al. (2004) present a new option which they describe as an upgraded method of CE and call the ‘‘management by attributes process’’.

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Concurrent work-flow

Early involvement of constituents

H1 H2 H3

Teamwork

NPD performance Development time Development cost Product superiority

CONCURRENT ENGINEERING Research Focus

Type of innovation (Incremental vs. Radical)

External environmental conditions

Fig. 1. Research focus and hypotheses.

Hypothesis 1. The positive effect of CE on NPD time reduction depends on the type of innovation (incremental versus radical) carried out. Hypothesis 2. The positive effect of CE on NPD cost reduction depends on the type of innovation (incremental versus radical) carried out. Hypothesis 3. The positive effect of CE on new product superiority depends on the type of innovation (incremental versus radical) carried out.2 4. Research methodology 4.1. Research design and sample characteristics The information needed to test the above hypotheses was obtained from a survey conducted as part of a wider research project aiming to analyze the main manufacturing strategies and policies of industrial firms in Spain. The target population for the study was made up of the 1,234 manufacturers which in 2003 (the reference date for the study) were located in Spain and employed over 100 workers, according to the Amadeus-SABI database.3 A selection was made of firms in the following industries according to ISIC classification: chemical industry (ISIC 24), fabricated metal products (ISIC 28), machinery and equipment (ISIC 29), office, accounting and computing machinery (ISIC 30), electrical machinery and apparatus (ISIC 31), radio, television and communication equipment and apparatus (ISIC 32), medical, precision and optical instruments, watches and clocks (ISIC 33), motor vehicles, trailers and semi-trailers (ISIC 34), other transport equipment (ISIC 35) and furniture and other manufacturing industries (ISIC 36). These industries were chosen because they are the most usual in most research of this type (Handfield, 1994; Terwiesch and Loch, 1999; 2 The empirical contradictions reported in the literature review led the authors to adopt a cautious position regarding the hypotheses development. 3 Amadeus-SABI is a database containing financial information on over 7 million public and private companies in 38 European countries. It combines data from over 35 information providers. The data concerning Spanish firms is taken from the SABI section.

Hong and Schniederjans, 2000; Koufteros et al., 2001; Portioli-Staudacher et al., 2003). The questionnaire used was designed on the basis of the existing literature and the conclusions obtained from a previous case study. In both the design and the administration of the questionnaire, the techniques highlighted by Fro¨hlich (2002) to improve the response ratio and the rules put forward by Synodinos (2003) were taken into consideration. Before sending out the questionnaire, it was revised by experts in both operations management and survey design. With the aim of checking its validity and improving its design (facilitate readability, reorder questions, reduce size and eliminate ambiguous questionsy), a pre-test was also done on a reduced sample of firms. After making prior telephone contacts, the questionnaire was sent out between January and July 2004, together with a covering letter explaining the purpose of the study, the structure of the questionnaire and the confidentiality statement. The questionnaire section containing the NPD questions were addressed to the NPD manager. In total, 137 questionnaires were received, although it was necessary to eliminate three of these because they were not completed suitably or because they contained clearly contradictory responses. So, after review and analysis of the received results, 134 valid questionnaires were obtained, giving a valid response rate of 10.85%. This response rate can be considered satisfactory taking into account the low response rates in the Spanish context and the scope and length of the survey carried out, which contained a large number of sections and questions to measure contextual variables, production practices, NPD practices, organizational practices, objectives and competitive capabilities, results or performance measurements and classification variables. The most frequent causes of non-response were lack of time for managers, the large number of questionnaires they receive and consideration of some of the information requested as confidential. Using a T-test, the last 25% of respondents were compared to earlier ones and no differences were found in key variables in the analysis at 0.05. Based on the assumption that late respondents are similar to non-respondents (Armstrong and Overton, 1977), nonresponse bias does not appear to be a major problem in this research. In order to evaluate possible bias in the sample of firms analyzed, a Chi-squared test of differences was carried out between observed frequencies (sample) and expected frequencies (population) regarding industry and firm size. In the case of industry, the Chi-squared test was significant at 0.05, indicating that the companies that replied might differ from those that did not in industry representation. This result is not surprising because the section of the questionnaire that was used in this research basically addressed innovative producers. Bearing in mind that the level of product innovation probably differs amongst the selected industries and that the most innovative sectors can be expected to be more likely to reply, it is easy to explain the bias in the classification by industrial codes.

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In the case of firm size, the Chi-squared test (po0.0076) showed that the sample seemed to include more large companies than would be expected for the population. This may seem reasonable because it is often the larger companies that have the resources to make the additional effort to innovate and they would therefore be more prepared to give information on their situation than those that are in a worse position. 4.2. Development of scales and measurement of variables The scales and variables used in the study were developed on the basis of existing theory, the literature review, a previous case study and a formal pre-test with both managers and experts on the subject. Finally, all of them—type of innovation, use of CE, and success of NPD in its different dimensions—were measured using multiitem scales. 4.2.1. Type of innovation In order to measure the type of innovation, that is, whether it is radical or incremental, the following were considered: Radical innovation: This gives rise to really new products for both the company and the market, involving technological revolutions that totally change the name of the competitive game. This type of innovation involves a high level of risk because there is a high degree of complexity in the new product requirements, as yet undefined, as well as a high degree of uncertainty with regard to technology, customers’ needs and competitors’ actions (Song and Montoya-Weiss, 1998). Also, when the company faces this type of innovation, the technology, market and support infrastructure may still be at development stage or non-existent (Lynn et al., 1996). Basically, these are innovations with a high level of complexity and uncertainty, which increase the need for learning, flexibility and adaptability. Incremental innovation: This streamlines and improves certain dimensions of the product design or the production process, allowing them to better meet the needs of specific market segments. This type of innovation arises under conditions of greater certainty. The target market segments are normally known, as are customer needs. Also, the technology required is not usually very different from the company’s conventional business practices and the production processes used are well-understood (Lynn et al., 1998). Such projects are relatively straightforward, are characterized by low technical and market uncertainty, and incremental product changes are based on knowledge, experience and capabilities that already exist in the company (Zirger and Hartley, 1994). It can therefore be considered that what determines whether an innovation is radical or incremental is the degree of innovativeness, complexity and uncertainty (both technological and in the market) involved in it. Here it should be pointed out that, when speaking of ‘‘degrees’’, we imply that a continuum exists for intensity of innovations, so that what we define as a radical and incremental innovation represents both opposite extremes

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of that continuum. As Durand (1992) indicates, it is not acceptable to force innovations into a binary categorization (i.e. revolutionary versus normal change); all radical innovation is not equally radical, all incremental innovation is not just an additional small improvement on what already exists, as there may be some intermediary changes. Bearing this in mind, the variable for type of innovation was measured using a five-point scale of semantic differences comprising four items regarding degree of novelty, degree of technological uncertainty, degree of market uncertainty and degree of project complexity (see Appendix A). 4.2.2. Concurrent engineering In order to measure CE practices, the scale proposed by Koufteros et al. (2002) was synthesized into four items relating to its three basic elements: (1) the parallel and non-sequential development of new products, (2) the early involvement of all the participants in the NPD process and (3) the use of multifunctional teams. The respondents were asked to indicate, on a five-point scale, their level of agreement with the following statements:(a) product designs and production processes are developed simultaneously by a group of employees, (b) various departments or functions (R+D, manufacturing, marketingy) are involved from the beginning in NPD, (c) NPD teams are made up of members of different departments or functions, and (d) the NPD team members work closely together throughout the whole process (see Appendix A). In spite of this simplification that leads to a more narrow scope of measurement, the scale used satisfies the standard requirements for measurement rigour and reinforces the existence of the different CE dimensions originally reported by Koufteros et al. (2002). 4.2.3. Success of the NPD process To measure the performance reached with CE, perceptual data were used with regard to new product development time and cost and new product superiority (in terms of functionality and benefits). As in the previous case, respondents were asked to indicate, on a five-point scale, their degree of agreement with a set of statements. Regarding development time: (a) we have become very skilled at speeding up NPD, and (b) we have shorter NPD times than the competition. Regarding development cost: (a) we are satisfied with the development cost of our new products, (b) in comparison with the competition, we have relatively low NPD costs, and (c) our NPD efficiency allows us to be very competitive. Finally, regarding the superiority of developed new products: (a) we manufacture new products with consistent quality, (b) we manufacture new products with a high level of functionality or benefits, and (c) we develop reliable, long-lasting products (see Appendix A). 4.3. Unidimensionality, reliability and validity In order to guarantee the suitability of the measurement scales used, their psychometric properties (Nunnally, 1978)—unidimensionality, reliability and validity—were analyzed (Table 1).

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Table 1 First-level confirmatory factor analysis: reliability and validity of the different items. Factor

Type of innovation (F1)

Items

Standardized factor loadings (t–value)

Reliability

Discriminant validity

Cronbach Alpha

Composite reliability coefficient

Innova1 Innova2 Innova3 Innova4

0.718 (8.299) 0.782 (10.211) 0.505 (5.289) 0.719 (8.226)

0.779

0.774

IC1 IC2 IC3 IC4

0.693 0.826 0.920 0.892

0.903

0.902

Cost1 Cost2 Cost3

0.587 (7.648) 0.637 (7.147) 0.816 (9.768)

0.724

0.731

Development time (F4)

Time1 Time2

0.792 (10.551) 0.726 (8.366)

0.732

0.730

Product superiority (F5)

Sup1 Sup2 Sup3

0.565 (5.418) 0.925 (10.229) 0.819 (8.511)

0.822

0.814

Concurrent engineering (F2)

Development cost (F3)

(9.220) (11.180) (17.006) (8.992)

Factors

Correlation coefficient (confidence interval)

F1–F2 F1–F3 F1–F4 F1–F5 F2–F3 F2–F4 F2–F5 F3–F4 F3–F5 F4–F5

(0.068–0.472) (0.196–0.620) (0.241–0.625) (0.091–0.475) (0.120–0.516) (0.099–0.523) (0.190–0.570) (0.650–0.982) (0.000–0.512) (0.134–0.532)

Goodness of fit SB w2 (94) ¼ 124.51 (p ¼ 0.000) BBNNFI ¼ 0.948 CFI ¼ 0.959 IFI ¼ 0.961 GFI ¼ 0.888 AGFI ¼ 0.837 SRMR ¼ 0.062 RMSEA ¼ 0.049

So as to study the unidimensionality, that is, whether or not there is a single factor underlying the set of variables that constitute each scale, exploratory factor analyses with Varimax rotation were carried out. The results in all cases showed factor loadings (weight of each variable observed in the factor) of over 0.5 and an accumulated explained variance percentage of over 50%. After exploratory factor analysis, confirmatory factor analysis (CFA) was performed by means of structural equations, using the EQS statistical package. The calculation method used was that of robust maximum likelihood in order to resolve the problem of non-normality of the data. The results of the CFA confirmed the unidimensionality and composition of the scales identified in the previous exploratory factor analyses. In order to analyze reliability, Cronbach’s alpha coefficient and the composite reliability coefficient were calculated. These coefficients reflect the degree of internal consistency of the observed variables, that is to say, to what extent they represent the common latent variable. Cronbach0 s alpha coefficient in all cases was over 0.7, the criterion usually considered to identify strict internal consistency (Nunnally, 1978), exceeding the value of 0.6 recommended in exploratory studies (Hair et al., 1999). In all cases the composite reliability coefficient was over the minimum level of 0.6 recommended by Bagozzi and Yi (1988).

The next step was to analyze the content, convergent and discriminant validity of the measurement scales used. Content validity indicates that the items included in the survey correctly represent the concept to be analyzed. Since the scales were built on the basis of the previous literature (Appendix A) and therefore include items used in scales that had already been validated for measuring similar concepts and assessed by case studies and the questionnaire pre-test, it was considered that each item had the necessary content validity. Convergent validity measures the degree to which the different scales used to measure a latent factor are correlated. A measurement has convergent validity if it converges in the same model as the rest of the measurements that form part of the same concept (Lehmann et al., 1999). Steenkamp and Van Trijp (1991) link the convergent validity of a concept and its corresponding scale of measurement with the significance of the coefficients of the standardized regression factor between the group of explained variables of the scale and their corresponding latent saturation variable. To test convergent validity, the lambda coefficients that measure the relation between the observed and the latent variable were analyzed. All the standardized factor loadings were statistically significant at a 95% confidence level (t41.96, weak condition) and exceed 0.5 (strong condition), with fit

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indexes (BBNNFI, CFI, IFI, GFI, AGFI, SRMR, RMSEA) properly reflecting the goodness of the overall hypothesized model tested. Discriminant validity measures the degree to which the specified latent factors differ even though they are correlated (Hair et al., 1999). Each construct should be sufficiently different from the others to justify its existence (Lehmann et al., 1999). In order to check this validity, the confidence interval of the correlation between each pair of dimensions or scales was calculated. Discriminant validity of the scales was confirmed because none of the confidence intervals contained the value 1 at a 95% confidence level.

5. Analysis and results In order to test each of the hypotheses, linear regression analysis was carried out, considering CE as an independent or explanatory variable in all cases. Before the analysis, the companies in the sample were classified in two large groups depending on the type of innovation carried out—incremental or radical.4 With this aim, an index for the type of innovation was created as an arithmetical mean of the value reached by the four items used to measure this characteristic. Taking as a reference the average value (w) and the standard deviation (Sw) of the index created, the sample was divided into two subsamples: (1) companies which basically carry out incremental innovative processes—87 cases with values at the interval [1, w+0.5Sw), and (2) companies that basically carry out radical innovative processes—47 cases with values between [w+0.5Sw, 5],5 as shown in Fig. 2.

χ–1/2Sx

1

2.664

χ

χ+1/2Sx

3.099

3.534

Incremental innovations n=87

5

Radical innovations n=47

Fig. 2. Sample segmentation criterion and results.

4 Obviously, a single company may carry out different types of innovation. According to Wheelwright and Clark (1992), it is essential for each company to know how to establish the right project mix for the planned horizon, obtaining a strategically balanced project portfolio that is in line with the company’s available resources and capabilities. However, in order to clearly distinguish between radical and incremental innovations and since our unit of analysis is not the project, it was explained in the questionnaire that companies should provide information only for their predominant type of NPD projects. 5 The reason why the sample was divided in this way is that, if only the extremes [1, w0.5Sw] and [w+0.5Sw, 5] are taken into account in the analysis, not only are many observations lost but we would be ignoring the intermediate degrees of innovative intensity. This way, on the one hand we analyze radicality as something completely new and, on the other, incrementality as improvements on something that already exists, while bearing in mind that these improvements may be significant, intermediate or minimal.

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After dividing the sample, the same regression models were replicated for each sub-sample, obtaining the results shown in Table 2 and described below. Firstly, CE is observed to have a positive significant impact (p ¼ 0.029) on reducing new product development time in the sub-sample of companies that basically carry out incremental innovation processes. That is, in companies which face moderate or low levels of novelty, uncertainty and complexity. Conversely, in the sample of companies that carry out radical innovative processes, no significant link was observed amongst the variables studied. This result indicates that the first of the hypotheses proposed in this research can be supported, that is, the positive impact of CE on NPD time reduction depends on the type of innovation being carried out. More specifically, CE leads to reductions in development time when it is used to carry out incremental innovations but there is no significant evidence to sustain this link when applied in radical innovative processes. Secondly, the results show that CE has a positive effect (p ¼ 0.001) on the reduction of NPD costs, but only in companies carrying out radical innovations, that is, only in the sub-sample dealing with high degrees of novelty, complexity and uncertainty in the innovative process. Therefore, the results also sustain the second of the hypotheses under consideration. That is, the positive impact of CE on the reduction of NPD costs depends on the type of innovation being carried out. More specifically, and unlike the previous case, CE leads to reductions in development costs when applied in radical innovations, with no significant links observed amongst these variables for incremental innovations. Finally, the results also show that CE has a positive and significant impact at 99% (p ¼ 0.000) for obtaining superior new products, but only in scenarios with low levels of novelty, complexity and uncertainty, that is, when innovations are incremental. However, this effect is not observed to be significant for the set of companies that carry out radical innovations. The third research hypothesis can therefore also be supported, that is, that the positive impact of CE on new product superiority depends on the type of innovation being carried out. As with development time, CE leads to superior new products when applied in incremental innovations but there is no significant evidence to sustain this link when applied in radical innovations. When discussing these results, it should be considered that the keys to effective CE are, firstly, the constant flow of information that is inevitably generated with this new working methodology and, secondly, achieving optimal inter-functional coordination. Nevertheless, the uncertainty and complexity which characterize radical innovations may make it difficult to achieve the necessary inter-functional coordination. In such complex, uncertain contexts in which changes are frequent and unpredictable, there may be serious deficiencies, such as imperfect communication, limited capacity for processing the complex information, insufficient cooperation or lack of knowledge amongst specialists for dealing with the complex requirements of an inter-functional process of this sort.

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Table 2 Results of regressions for the different sub-samples. Incremental innovations Variables Dependent v.: Development time Concurrent engineering Constant 2

R R2 corrected F statistic F probability N

Dependent v.: Development cost Concurrent engineering Constant R2 R2 corrected F statistic F probability N

Dependent v.: Product superiority Concurrent engineering Constant 2

R R2 corrected F statistic F probability N

Radical innovations

Value

Beta

t-value

Signif.

Value

Beta

t-value

Signif.

0.162 (0.073) 2.432 (0.252) 0.055 0.044 4.961 0.029 87

0.235

2.227

0.029

0.244

1.686

0.199

9.661

0.000

0.167 (0.099) 2.800 (0.366) 0.059 0.039 2.844 0.199 47

7.643

0.000

0.084 (0.066) 2.656 (0.227) 0.019 0.007 1.621 0.206 87

0.137

1.273

0.206

0.457

3.447

0.001

11.694

0.000

0.328 (0.095) 2.094 (0.353) 0.209 0.191 11.884 0.001 47

5.936

0.000

0.256 (0.067) 3.101 (0.230) 0.148 0.138 14.755 0.000 87

0.385

3.841

0.000

0.103

0.692

0.493

13.453

0.000

0.064 (0.092) 3.865 (0.342) 0.011 0.011 0.479 0.493 47

11.296

0.000

The complexity and uncertainty will also probably make it difficult to involve all the associated disciplines, just when they are needed. Likewise, these circumstances can also make more difficult to diagnose problems, because of the lack of experience and the high degree of uncertainty, and to specify all the project details (inputs, outputs, resources, timing, etc.) so that it can be fully understood. The immediate consequences of all these deficiencies are, firstly, repetitions, changes and difficulties for identifying the areas where overlap may exist, thus making it impossible to reduce development times. Secondly, poor cooperation, which may lead to serious contradictions between CE and improved quality because of inconsistency between the internal and external requirements. Although under conditions of radicality it may be more difficult to achieve the necessary inter-functional coordination, thus generating all these difficulties, nevertheless, the constant flow of information involved in CE creates a better environment for promoting new solutions and for preventing surprises from external factors. This means that process costs may be reduced significantly in comparison with a more traditional development approach. By the same reasoning, the characteristics of incremental projects (limited or moderate complexity and

uncertainty) do not prevent proper application of any of the basic pillars of CE. They allow for suitable overlap in the right activities and for an optimal degree of inter-functional coordination. All this leads to better performance times and improved quality, as desired. However, since this type of context with little complexity or uncertainty does not require so much generation or sharing of information as those analyzed above, the constant flow of information provided by CE may not represent such a relevant advantage as to result in significant reductions in process costs. In any case, joint evaluation of all these results supports the general proposition suggested as a starting point, that is, that CE does not offer significant positive results under any circumstances or in any context. They support this argument showing, specifically, that the type of innovation or, in other words, the degree of novelty, uncertainty and complexity in the innovative process, is a variable that has a clear moderating effect on the result achieved using CE methodology. 6. Conclusions, limitations and future research This research studies the link between the use of CE and success in NPD processes with different types of

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innovation, with the aim of helping to identify the most appropriate circumstances for successful adoption of this methodology. On the one hand, the results show that companies adopting CE practices to carry out incremental innovations achieve time reduction in product development and higher product quality. However, companies that adopt this methodology in contexts with a high level of uncertainty, novelty and complexity, that is, for carrying out radical innovations, do not obtain positive results for either of the performance measures indicated (development time and new product superiority). These results are in line with the most supported trend of studies on this topic and reinforce the idea that CE has serious limitations in conditions of extreme uncertainty. In accordance with the arguments sustained in such research, in situations where changes are neither predictable nor can be kept under control, concurrence may generate large problems of communication, integration and rework. These inevitably lead to considerable reprocessing, to a greater probability of errors, to chaos in management and, in short, to inefficiency, which ends up converting the positive results derived from CE into negative results or penalties, rendering its use inappropriate. However, the results also show that companies adopting CE to carry out radical innovations may not be able to reduce development time or obtain superior new products but may be able to reduce product development costs. This result seems to be in line with the other research trend, which points out that the greater flow of information and integration involved in CE is what allows companies to successfully face situations of high uncertainty and complexity. In such situations, CE provides the approach, leadership and resources needed to withstand turbulence in the innovation process. The strong interfunctionality involved in the concurrent path makes it possible to establish closer collaboration between the different stages, reducing wastage in the production process and in design and, therefore, minimizing the probability of designing products that would be costly to manufacture. All these results have an immediate implication for the management of innovative companies: CE is not necessarily a recipe for success. If the objectives are to reduce development time and increase product superiority in contexts of high uncertainty and complexity, that is, in radical innovative processes, it is unadvisable. However, if the top priority is to reduce costs, it would not be advisable in incremental innovative processes. Managers must therefore be cautious when adopting CE and must not fall in the common trap of considering that greater concurrence is always better. In conclusion, this research suggests that the a priori adoption of CE practices may be too risky. Companies should first analyze the characteristics of their innovation process and prioritize their objectives before selecting the most appropriate path to follow. If their main aim is to reduce development time and increase product superiority, they should adopt CE only if they carry out incremental innovations. Companies that aim above all

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to reduce process costs should use CE only when carrying out radical innovations. When, according to the above conclusion, CE is not to be recommended, companies should consider alternative paths—such as those mentioned in previous sections—without forgetting the traditional sequential path. This research study makes several contributions. It offers new insights into the moderating effect of the type of innovation (radical versus incremental) on the link between CE and success in NPD. It also relates this effect with the priority given to the NPD objectives (time, cost and product superiority) pursued. All this is relevant, firstly, because the studies that focus on the effectiveness of CE do not reach a consensus and, secondly, because few of them determine the appropriate innovation context for applying CE taking into account the specific objectives pursued. So, this research study helps to clarify the circumstances under which CE is to be recommended and also strengthens the idea that managers can draw from the contingent theory, i.e. there is no best answer to a particular problem and the appropriateness of managerial decisions is dependent on the prevailing conditions that surround the problem. Despite these contributions, the study is subject to several limitations. First, it uses single-informant reports for the independent and dependent variables. Research based on a single respondent is subject to the possibility that a given respondent may provide a skewed perspective on the business unit under analysis. Although an attempt could be made to solve this problem by obtaining information from more than one source for each unit of analysis, this research work did not consider this possibility, as it would have had negative effects on the response ratio and significantly increased the time and cost of data collection. Although the risk of bias is believed to have been reduced by gathering data from key knowledgeable informants (Akgu¨n and Lynn, 2002), singlerespondent bias can be considered a limitation for this empirical research. Second, the research is based on perceptual data. Despite the extensive use of such retrospective perceptual data in strategy research and, specially, in new product research (Venkatraman and Ramanujan, 1986), the shortcomings associated with subjectivity should not be ruled out. Although firms are frequently reluctant to facilitate objective data, the lack of objective measures to complement the perceptual measures used can be considered a limitation. Third, the response rate is relatively low. Still, we have some reason to believe that the response rate did not jeopardize the representativeness of our sample. Armstrong and Overton’s (1977) test provided some indication of the absence of non-response error, and we had participation from all major sectors and from companies of different sizes. Fourth, the research is based on a cross-sectional design and may not eliminate all the external factors necessary for obtaining industry-specific information (EasterbySmith et al., 1993). The rationale for cross-sectional design was to examine product development across industries rather than product development in a specific industry and to obtain a sample size that would be sufficient for analysis. Fifth, the methodology used considers the

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enterprise (the predominant type for NPD projects) as the unit of analysis. This decision is related to the fact that the data base used comes from a larger study. However, it would be more appropriate to work at project level; therefore this should be considered a structural bias and a weak point of our contribution. Finally, though the validity of the CE scale has been tested, it only takes into account three key dimensions, excluding others that are important such as supplier and consumer involvement in the NPD process. Thus, bearing in mind that CE is probably a clear determining factor for including customers and suppliers in the NPD process, future studies should measure and consider the inclusion of external agents in the analysis. Additionally, the new information technologies have become essential for establishing a suitable link amongst all the agents participating in the NPD process. The use—or absence—of such technologies might affect the results of CE, so further research should be carried out along these lines. It would be of special interest to study to what extent the combined application of several CE-related policies (concurrent work-flow, early involvement of participants, teamwork, customer and supplier inclusion, use of new information technologies, etc.)—or a special combination of any of them—is more effective than applying them individually. Finally, this research focuses on the analysis of technological or market context variables of the NPD process. However, the effectiveness of CE might also be affected by the company’s external environment, i.e. the level of dynamism, complexity and uncertainty. All these aspects should be considered in future research. Work is currently being done on a second survey to be sent out to the same companies that participated in this study in order to obtain data on these variables. Acknowledgments The authors would like to thank the Editor and the anonymous referees for their helpful comments and suggestions. The authors also would like to thank Spanish Ministerio de Ciencia y Tecnologıa (SEJ2006-04753/ECON) in its financial support. A preliminary version of this paper was published as Working Paper No. 229/2005 in Coleccio´n de Documentos de Trabajo de la Fundacio´n de las Cajas de Ahorros (FUNCAS). Appendix A. Questionnaire NEW PRODUCT DEVELOPMENT Please, answer the following questions only if your company is carrying out new product development projects. If so provide information with regard to projects carried out over the last 3 years. (1) State the value that would best describe the characteristics of the predominant innovation projects in your company: Projects that give rise to Projects that give rise to products that are a slight 1 2 3 4 5 radically new products improvement on existing ones Projects that include or are based on known technology (projects with a low level of technological uncertainty)

1 2 3 4 5

Projects that include new technology or processes (projects with a high level of technological uncertainty)

Projects in which customer Projects in which the impact and competitor reactions to the product will have on the market is known (projects 1 2 3 4 5 the new product are unknown with a low level of market (projects with a high level of uncertainty) market uncertainty) Projects with a low degree of Projects with a high degree of 1 2 3 4 5 complexity complexity

(2) State to what extent the following new product development policies are applied: Not at all Product designs and production process are developed simultaneously by a group of employees Various departments or functions (R&D, manufacturing, marketing etc.) are involved from beginning in new product development New product development teams are made up of members of different departments or functions The new product development team members work closely together throughout the whole process

To some extent

To a great extent

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

(3) State to what degree you agree or disagree with the following statements relating to new product development performance: Disagree Agree completely completely Time: We have become very skilled at speeding up new product development We have shorter new product development times than the competition Cost: We are satisfied with the development costs for our new products In comparison with the competition we have relatively low new product development costs Our new product development efficiency allows us to be very competitive Superiority: We produce new products with consistent quality We produce new products with high functionality or benefits We develop reliable and long-lasting products

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

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