Understanding Competencies in Platform-Based Product ...

J PROD INNOV MANAG 2012;29(3):452–472 © 2012 Product Development & Management Association DOI: 10.1111/j.1540-5885.2012.00917.x

Understanding Competencies in Platform-Based Product Development: Antecedents and Outcomes* Kah-Hin Chai, Qi Wang, Michael Song, Johannes I. M. Halman, and Aarnout C. Brombacher

While previous studies have shed light on the benefits and antecedents of platform-based product development, there is still much to be learned on the subject, particularly through an empirical approach. Based on the literature, this study proposes the concept of product platform competency and identifies four antecedents related to the development process, knowledge sharing, and the organization of development. The study hypothesizes that such competency directly affects the performance of platform-based product development. To test these hypotheses, a large-scale survey was conducted in the United States with 242 firms. The study found empirical evidence to support the existence of the concept of product platform competency, which comprises the reusability of subsystems, the compatibility of subsystem interfaces, and the extensibility of platform-based products. The results show that a formalized development process, knowledge sharing across platform-based products, continuity of platform-based product development teams, and the existence of a champion in platform-based product development will significantly enhance product platform competency. More interestingly, the results show that factors which have a significant effect on platform development cost are statistically different from those that have a significant effect on platform development time. For example, while formalized product development process and continuity of development team play a very significant role in shortening development time, they are less crucial in reducing development costs. In contrast, the presence of a product champion is a significant factor in achieving cost efficiency, but it has relatively lower impact on development time. Knowledge sharing was found to affect both development time and cost. In sum, this study makes three main contributions to the existing literature. The first contribution is the empirical-tested concept of product platform competence based on key elements suggested by Robertson and Ulrich. The second contribution is the development of constructs specifically for platform development based on previous studies. Relating to this is the third contribution: the development and validation of measurement items related to key drivers to platform development, the three aspects of platform development competence, and the performance measurements. These measurement items can be used by practitioners as guidelines to identify areas for improvement as well as the level of capability in platform development.

Introduction

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n increasingly popular new product development strategy is the use of a platform-based approach to create a hopefully successful product family. In contrast to the previous practice of designing one product at a time, many companies are adopting the platform approach to develop and produce product families with the aim of increasing variety, improving customer satisfaction, shortening lead time, Address correspondence to: Michael Song, University of MissouriKansas City, Henry W. Bloch School of Management, 5100 Rockhill Road, Kansas City, Missouri 64110-2499. Email: [email protected]. Tel: 816235-5841. * The authors wish to thank C. Anthony Di Benedetto and two JPIM reviewers for their constructive comments in the previous two versions. This research was funded in part by the National University of Singapore, Eindhoven University of Technology, University of Twente, and China Natural Science Foundation (Award #: 70528002).

and reducing costs (Simpson, Siddique, and Jiao, 2006). This approach has been widely advocated in the literature (e.g., Jones, 2003; Krishnan and Gupta, 2001; Meyer and Lehnerd, 1997; Meyer, Tertzakian, and Utterback, 1997; Veenstra, Halman, and Voordijk, 2006) as a way of creating desirable variety at a cost acceptable to consumers. While the benefits of modular and platform-based product development are well known (Mikkola and Gassmann, 2003), research on what leads to platform success remains limited (Halman, Hofer, and Vuuren, 2003; Jones, 2003). According to Meyer (1997, p. 17), “robust product platforms do not appear by accident.” As shown in Hauser’s (2001) five-year study at one high technology firm, if the platform approach is not applied properly, it does not increase profitability. His study reveals that, interestingly, the use of methods such as quality function deployment and robust design may affect the effectiveness of platform development negatively. Krishnan

PLATFORM-BASED PRODUCT DEVELOPMENT

BIOGRAPHICAL SKETCHES Dr. Kah-Hin Chai is an assistant professor at the Industrial and Systems Engineering Department, National University of Singapore. He received his Ph.D. degree from Cambridge University (2000) in the area of manufacturing management. His work experience includes management consulting and semiconductor manufacturing in Singapore and Malaysia. His current research interests are new product development, service innovation, and knowledge management. His work appears in journals such as Journal of Product Innovation Management, IEEE Transactions on Engineering Management, Journal of Service Research, European Journal of Operational Research, and International Journal of Service Industry Management. Dr. Qi Wang, based in Shanghai, is an assistant to CEO of Arvato Services (China), which is a Bertelsmann Company and specializes in Customer Relationship Management and Loyalty Program Consulting, Customer Interaction Center, Logistic Service, and Finance Service. He received his joint Ph.D. from National University of Singapore and Eindhoven University of Technology in 2010, and two Bachelor’s Degrees (Hons), in electrical engineering and industrial engineering, from Shanghai Jiao Tong University in 2000. Before his Ph.D. study, he worked for Lucent Technologies (China, 2000–2003) and focused on new product development processes. Dr. Michael Song holds the Charles N. Kimball, MRI/Missouri Endowed Chair in Management of Technology and Innovation and is Executive Director of the Institute for Entrepreneurship and Innovation at the University of Missouri-Kansas City. He also serves as a contract professor of innovation management at the Harbin Institute of Technology in China. He received an M.S. from Cornell University and an M.B.A. and Ph.D. from the University of Virginia. Since 2007, Dr. Song has been ranked as World’s #1 Innovation Management Scholar by Journal of Product Innovation Management (both in 2007 and 2012), one of the top 20 technology management scholars by research and development management in 2006, one of the most prolific researchers in technology innovation management field by the International Association of Technology Management in 2004 and in 2009, and among the “Most-Cited Scientists in Economics and Business” over a 10-year period by Essential Science Indicators. He received the 2005 Excellence in Research Award from the American Marketing Association. He is an associate editor of six academic journals and serves on editorial board of several top academic journals. He has published over 90 papers in academic journals including Management Science, Strategic Management Journal, Academy of Management Journal, Journal of Marketing Research, Marketing Science, Journal of Marketing, Journal of the Academy of Marketing Science, Journal of International Business Studies, Journal of Operations Management, JPIM, and others. Dr. Johannes I. M. Halman is a professor at the University of Twente in the Department of Construction Management and Engineering. He earned an M.Sc. in construction engineering from Delft University of Technology in the Netherlands; an M.B.A. (cum laude) from Rotterdam School of Management at Erasmus University in the Netherlands; and a Ph.D. in technology management from Eindhoven University of Technology in the Netherlands. His research interests are in the field of innovation management with primary focus on program and project management of innovation processes, new product platform development, and high-tech start-ups. He specializes in the area of risk management. He has advised international firms, such as Philips Electronics and Unilever, on the implementation of risk management strategies within their innovation processes. Dr. Aarnout C. Brombacher is a professor in “business process design” at the faculty Industrial Design of Eindhoven University of Technology,

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and Gupta (2001) also report that high design costs and low product quality can occur when using the platform approach. Despite its popularity in industry, only a handful of research has shed light on how to adopt and implement platform product development in organizations. Cusumano and Gawer (2002) argue that the success of platform leaders depends on how well they manage the scope of innovation to undertake, the degree of modularity and interfaces of the product, the relationship with external partners, and the organization structure and culture. Meyer, Anzani, and Walsh (2005) reveal IBM’s experience of using dedicated and separate development teams on subsystem and product development, with program managers acting as “glue” between these two divisions. At a more engineering design level, De Weck (2006) demonstrates how operation research techniques can be used to determine the ratio between number of platforms and derivative products, given a set of market and manufacturing conditions. In a similar vein, De Weck and Suh (2006) propose a design process to cope with uncertain variant demand and specification changes. To employ the product platform effectively and to achieve the desired performance, one needs to know the critical organizational factors and practices that underpin successful platform-based product development. According to Mills, Platts, Bourne, and Richards (2002) and Kleinschmidt, Brentani, and Salomo (2007), one sustainable way to improve performance is to improve the underlying competency to achieve a competitive advantage. Therefore, it may be fruitful to view platform-based product development from a competency-based perspective. Specifically, this research is directed at building a holistic framework for managing platform-based product development from a platform competency perspective. The aim of this study is twofold: first, to identify and understand what constitutes product platform competency and to examine the impact of such competency on platform performance; and, second, to identify the underlying factors that enhance product platform competency.

where he is responsible for research and education in the fields of quality information flows and customer perceived quality in highly innovative product design and development processes. He obtained his B.Sc., M.Sc. (cum laude) in electrical engineering, and Ph.D. in engineering science at Twente University of Technology. Aarnout Brombacher has experience in industrial quality and reliability improvement projects and the development of quality and reliability analysis methods and tools. He has authored and coauthored over 80 papers on these subjects and has written a book with the title “Reliability by Design.” In 2010 he was appointed dean of the Department Industrial Design of Eindhoven University.

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The paper is organized in the following manner. It begins by building the theoretical foundation of the study. Next, hypotheses based on existing literature are presented. Subsequently, the research method is described, and the findings from a sample of 242 firms in the United States are reviewed. The paper concludes with a discussion of the results and the contributions made by the study.

Theoretical Background Competency-Based Theory and Firm Competitive Advantage The concept of competency has its origins in Selznick’s (1957) sociological analysis, in which he refers to what is better in one organization than other organizations (Eriksen and Mikkelsen, 1996). More specifically, it can be seen as the capability of structuring and using resources for productive purposes in a way that potentially delivers a competitive advantage (Christensen, 1996; Grant, 2005). Such capability may be rooted in routines (Nelson and Winter, 1982) and constrained by the thought worlds of the organization members (Dougherty, 1992). Competence can also be described as how well a firm performs its necessary activities, which can be further categorized into different organizational levels, such as a firm’s corporate core competencies as well as its business unit competencies (Mills and Platts, 2003; Mills et al., 2002). For instance, Prahalad and Hamel (1990) examined the “core competencies” used to generate new business at a corporate level, whereas Liedtka’s research focused on the competencies at the business unit level, which were less obvious to competitors or customers but key in enhancing the value and exploitation of the business units’ competencies (Liedtka, 1999; Mills and Platts, 2003). The competency concept can also be extended to lower levels in an organization, such as the group and individual levels (Eraut, 1994; Mills and Platts, 2003; Mills et al., 2002). Not surprisingly, competency theory has been widely applied in different branches of management (Mills et al., 2002). Lado and Wilson (1994) explored the potential of human resource systems from a competency-based perspective, to enhance the understanding of strategic human resources management. Vickery, Droge, and Markland (1993) applied a competency-based perspective to manufacturing and concluded that production competency has a strong effect on business performance. Further, the competency-based perspective can also be applied to product development (Foss and Harmsen, 1996), seeing a firm’s ability to enhance its offerings by building products at lower costs and more speedily than competitors as

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a vital competence (Autio, Sapienza, and Almeida, 2000; Prahalad and Hamel, 1990). Indeed, as advocated by Foss and Harmsen (1996), a more precise picture of product development, including the underlying causes of longlived profitability differences, can be achieved by discussing the empirical results of success factors in the context of a competency-based perspective. Therefore, viewing platform-based product development from a competency-based perspective has the potential to provide a better understanding of what leads to success in platform-based product development.

Hypotheses Development Product Platform Competency Various definitions have been proposed in the platform development literature (Muffatto and Roveda, 2000). This study follows Meyer and Lehnerd (1997), who define product platform as “a set of subsystems and interfaces developed to form a common structure from which a stream of derivative products can be efficiently created” (p. 39). New products can be derived through addition, exclusion, or substitution of one or more modules (Farrell and Simpson, 2003; Ulrich, 1995) to address a related set of market applications. Such a practice often results in families of products that share similar subsystems. According to Lado and Wilson (1994), firm competencies reflect the specific capabilities that enable the firm to develop and implement value-enhancing strategies. Therefore, product platform competency can be defined as the specific capabilities based on the platform that allow products to be developed more efficiently and produced more economically. Such capabilities in platformbased product development are closely associated with the underlying architectures and designs that are the basis of products (Meyer and Utterback, 1993). The next three sections describe the three proposed key elements of product platform competence, that is, (1) extensibility of platform-based products; (2) reusability of subsystems; and (3) compatibility of subsystem interfaces, and the hypotheses on their effects on platform development performance. A survey-based method allows the study to measure these key elements independently as well as the relationships between them, in particular how the extensibility of a platform (i.e., number of products developed from a platform) is dependent on the reusability of subsystems and the compatibility of subsystems interfaces. Extensibility of platform-based products. To maintain a viable product platform, firms need to achieve econo-



Da

Derivative Products

Value-Added Subsystems

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Common interface between S1A-S1D and S2 Subsystem 2

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Interface between S2 and S3 Subsystem 3

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Interface between S3 and S4 Subsystem 4

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Figure 1. Platform Subsystems and Interfaces (Adapted from Meyer and Lehnerd, 2004)

mies of both scale and scope in product development while maintaining desirable product performance (Garud and Kumaraswamy, 1995; Kim, Wong, and Eng, 2005). While it is not likely that firms can use one single platform to meet the needs of all markets (De Weck, 2006), firms that only produce a few derivative products from their platforms need to introduce costly platforms at a more rapid rate (Jones, 2003). Therefore, knowing how to produce derivative products could be critically important as it reflects one aspect of effective product development. This is consistent with Barney, Wright, and Ketchen (2001), who viewed the abilities to be alert to changes and able to change quickly at a lower cost than others as a source of competitive advantage. Therefore, the extensibility of platform-based products can be seen as one of the main elements of product platform competency. Indeed, one of the goals companies wish to achieve using product platforms is that new products can be derived with short lead times at a low cost. Given the high costs incurred in initial platform designs compared with the development of a single product, firms want to leverage their platforms as much as possible by producing follow-up products at minimum extra cost. When implemented properly, such an approach can result in lower

average development costs and cycle times as compared with the nonplatform approach. Reusability of subsystems of platform-based products. The second key element is reusability of subsystems, which refers to using identical product features or subsystems in a group of derivative products (Kima and Chhajed, 2001). According to Ulrich (1995), from an architecture perspective, a product is composed of two major elements: physical components and interfaces. Components are frequently the functional modules in a modular product and can be viewed as subsystems. Therefore, a platform-based product can be considered as a technical architecture composed of subsystems and interfaces. The technical subsystems embody specific functions, while the subsystems interfaces of platform-based products provide the connections between the subsystems. As shown in Figure 1, subsystems 2, 3, and 4 are reused across derivative products from a common platform. Such reusability of subsystems across multiple products makes it possible for firms to create derivative products with minimum incremental effort when offering similar features to reflect customer preferences (Kima and Chhajed, 2001).

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In practice, many companies try to reuse as many existing building blocks and off-the-shelf components as possible. From an engineering perspective, this practice can provide greater reliability than developing new modules as the existing modules are generally known and predictable. From a product development perspective, the reusability of subsystems of platform-based products allows a firm to build product families rapidly and efficiently (Tabrizi and Walleigh, 1997), and this provides opportunities to leverage current product technology and functionality into new markets (Meyer and DeTore, 2001), as well as providing cost-effective variety (Krishnan and Gupta, 2001; Lee and Tang, 1997; Sanderson and Uzumeri, 1997). It has been observed that one of Nokia’s advantages in product development efficiency is having a large number of common subsystems across their product lines because this enables them to offer more models than their competitors within a given period (Funk, 2003). Hence, the reusability of subsystems of platform-based products can help firms achieve economies of scale. Compared with conventional product development, reusable platform subsystems enable product platforms to offer a number of derivative products effectively and rapidly, which increases a platform’s ability to extend the product scope, in other words, the extensibility of the platform. Thus, it can be proposed that: H1: The level of subsystem reusability in platform-based products is positively associated with the extensibility of platform-based products.

Compatibility of subsystem interfaces in platformbased products. The connections between subsystems, known as the interfaces, enable the integration and deintegration of product subsystems and provide the properties that allow subsystems to interact and correlate to perform full-product functions (Chen and Liu, 2005; Meyer and Lehnerd, 1997). One key feature of platformbased product design is that it must allow for change not just for a single product but rather for a series of derivative variants (Rothwell and Gardiner, 1990). An effective architecture is created when the interfaces between functional components are standardized and specified so as to allow the substitution of a range of subsystems without requiring changes in the designs of other subsystems (Garud and Kumaraswamy, 1995; Sanchez and Mahoney, 1996). According to Funk (2003), the compatibility of Nokia’s GSM900 product interfaces with their cdmaOne phones contributed greatly to Nokia’s success. As a consequence, Nokia was able to introduce more new

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phones than their competitors in a shorter period, and at a lower cost, an advantage given the number of different languages and other permutations required for the different markets (Funk, 2003). That is to say, if the platformbased product interfaces are designed with a high level of compatibility, firms can easily derive more products based on the same platform. Hence, the compatibility of the interfaces of platform-based products enables firms to obtain economies of scope for their product platforms—providing greater product variety and more potential applications. Accordingly, the platform’s ability to extend the scope of its derivative products is enhanced. Therefore: H2: The extent of the compatibility of subsystem interfaces of platform-based products is positively associated with the extensibility of platform-based products.

Platform Performance in Terms of Development Cost and Time Given that the goal of platform development is to develop a series of products, the performance measurement of a product platform should reflect the stream of products based on a common architecture rather than an individual product. Of the three common aspects of performance measures (technical, market, and financial), Kim et al. (2005) argue that technical performance is the most appropriate measurement because financial and market performances are dependent on the former. According to Meyer and Utterback (1993), higher levels of core capability will lead to higher levels of performance. In the context of platform-based product development, product platform competency represents the specific capability that enables the platform to develop products more efficiently and economically. The greater the number of products that can be derived from a platform, the lower the average associated development cost and time. Indeed, the power of product platforms is their ability to accommodate new component technologies and variations because this allows firms to create derivative products at minimum incremental cost and time (Meyer and Lehnerd, 1997). Such product variety from a single platform enables economies of scope, which ultimately increase a firms’ product family performance (Kim et al., 2005). Therefore, the following hypotheses are put forward: H3a: The extensibility of platform-based products is positively associated with platform cost efficiency. H3b: The extensibility of platform-based products is positively associated with platform cycle time efficiency.


Antecedents of Product Platform Competency This section presents the four antecedents of product platform competence related to the development process, knowledge sharing, and the organization of development. These antecedents are based on Robertson and Ulrich (1998), who see a product platform as a collection of assets underpinned by common components, processes, knowledge, and people. According to the competence perspective, a firm’s capability and competence are rooted in its routines or practices (Dosi, Nelson, and Winter, 2000). Therefore, the way knowledge, process, and people are organized can be regarded as the antecedents to platform competence, of which common components form a part.

Formalized Product Development Process as an Antecedent of Platform Competency To create a truly effective platform, platform designers have to be concerned with all aspects of the product realization process, such as working closely with business strategy managers and interacting intensively with supply chain professionals as well as with manufacturing engineers (Sanchez, 2004). Hence, it is not surprising to find that “successful new growth platform companies like UPS, P&G, Medtronic, and Inverness had all systematically defined the processes of new growth platform creation and the roles of the various participants” (Laurie, Doz, and Sheer, 2006, p. 88). Compared with traditional product development, platform-based product development is complex, involving a great range of activities to be performed by many developers with different technical skills because of the need to accommodate future derivative products from the very beginning. According to Kleinschmidt et al. (2007), when the level of complexity surrounding the process (i.e., the number and diversity of projects) and the scope of information required increase, a formalized development process is key in ensuring that every element has been identified and taken into consideration. A formalized platform-based product development process tends to be well documented and closely followed by design engineers. In addition, it also allows regular monitoring and easy tracking of the development progress. Consequently, a formalized process for platformbased product development will help to instill discipline among engineers in ensuring the reusability of their design, which dictates the overall success of platformbased product development. If the same development process is followed, even if different individuals are


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involved, engineers are less likely to develop radically different products because of the path-dependency effect, thus maintaining compatibility between the various subsystems. The pressure to reduce development time and the requirement to follow standardized development procedures will result in engineers trying to reuse existing subsystems whenever possible, even when faced with new product requirements. This is because these existing subsystems will have been proven in previous derivative products, and using such proven subsystems will help engineers to meet tight development deadlines. The above arguments can be summarized in the following hypotheses: H4a: A formalized platform-based product development process has a positive influence on the reusability of subsystems of platform-based products. H4b: A formalized platform-based product development process has a positive influence on the compatibility of subsystem interfaces of platform-based products. H4c: A formalized platform-based product development process has a positive influence on the extensibility of platform-based products.

In addition, by reusing established product and design concepts through adopting a formalized development process, firms face lower uncertainty and confusion that would otherwise tend to extend development cycle time (Koufteros, Vonderembse, and Jayaram, 2005). Thus, one can further hypothesize that: H4d: A formalized platform-based product development process is positively associated with the platform cycle time efficiency.

Knowledge Sharing as an Antecedent to Platform Competency In platform-based product development, companies are essentially trying to leverage their recognized resources, knowledge, and skills across a series of products based on one platform, with new products being adaptations, refinements, and enhancements of existing products built on established platforms (Brentani, 2001). Perhaps more than any other form of resource, knowledge is crucial in new product development, especially when viewed from a knowledge management perspective (Madhavan and Grover, 1998). Therefore, effective knowledge management is likely to be crucial to the success of platformbased product development. For instance, when

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knowledge on the requirements of customers from different regions and the objectives of the product design are well shared and disseminated, product developers are more likely to ensure that their design can accommodate such diverse and potential changes, thus increasing the reusability of their designs. In addition, when there is a high level of knowledge sharing and dissemination, developers will be more aware of each other’s design and are thus more likely to design subsystems which are compatible. Finally, when knowledge is well disseminated, product developers become more aware of what the platform is designed for and thus can try to maximize the full potential of the platform. To sum up the above discussion, this study propose that: H5a: Knowledge sharing across platform-based products has a positive influence on the reusability of subsystems of platform-based products H5b: Knowledge sharing across platform-based products has a positive influence on the compatibility of subsystem interfaces of platform-based products. H5c: Knowledge sharing across platform-based products has a positive influence on the extensibility of platform-based products.

Continuity of the Development Team as an Antecedent to Product Platform Competency Studies on traditional product development have shown that team stability improves the success rate of product development projects (Cooper and Kleinschmidt, 1993a, 1993b; Kahn, Barczak, and Moss, 2006). In the context of platform-based product development, the concept of team stability needs to be viewed from a multiproduct perspective, as the goal of platform development is to develop a series of successful products rather than a single product. Meyer and Dalal (2002) have shown that the “carryforward” of development team managers and key technical contributors bring greater reuse in nonassemble platform products. There are several ways in which team continuity will positively affect platform development competence. First, a policy of team continuity will mean that team members know that they will be responsible for future derivative products, in addition to the current product. This will give them an incentive to ensure reusability of their design for future products, compared with team members who are only concerned with a single derivative product. Second, continuity in the development team helps to establish a shared mental model, that is, the unconscious assumptions about the way the world works,

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along with a shared common language and shared organization memory (Madhavan and Grover, 1998). As a result, team members are likely to have the same problem-solving approach and orientation that results in greater design compatibility. Third, team continuity will likely result in members having a greater awareness of the potential of the platform and also a greater sense of ownership of the overall platform success. Given these aspects, the full potential of the platform is more likely to be realized through the development of derivative products by a stable team. Based on these arguments, the following hypotheses are proposed: H6a: Continuity in a platform-based product development team will have a positive influence on the reusability of subsystems of platform-based products. H6b: Continuity in a platform-based product development team will have a positive influence on the compatibility of subsystem interfaces of platform-based products. H6c: Continuity in a platform-based product development team has a positive influence on the extensibility of platform-based products.

Moreover, as Akgun and Lynn (2002) have argued, changes to team members inevitably lead to knowledge loss, and new members need time to fill gaps, resulting in longer product development times. Also, when team members work alongside each other over a period of time, they may become familiar with one another and improve communication and the sharing of so-called “tacit knowledge,” which can reduce problem-solving cycles and enable them to more rapidly complete their work (Akgun and Lynn, 2002; Muffatto and Roveda, 2000). Therefore, one can further hypothesize that: H6d: Continuity in a platform-based product development team is positively associated with platform cycle time efficiency.

Existence of a Champion as an Antecedent to Platform Competency A champion has been defined as someone who “takes an inordinate interest in seeing that a particular process or product is fully developed and marketed” (Rosenau, Griffin, Anscheutz, and Castellion, 1996, p. 519). According to Markham (1998, p. 491), central to the concept of a champion is the perception that champions “achieve distinctiveness by accepting risk, vigorously


supporting or advocating the project, helping the project through critical times, overcoming opposition, or leading coalitions.” In the context of platform-based product development, the ultimate goal of leveraging a product platform is to maximize long-term profits for the firm. However, adopting a platform approach may involve a considerable investment in both time and money, with a prolonged period required to design a platform whose benefits will only be realized once future products are derived (Krishnan and Gupta, 2001). Hence, the evaluation of platform product development performance requires a longer horizon than typical single product development (Meyer and Dalal, 2002; Siddique, 2006). In terms of design complexity and the length of the time horizon, there will be more challenges in platform-based product development than in single product development. According to Thieme, Song, and Shin (2003), champions with high-level technical and management skills will be able to help a team take appropriate actions and overcome obstacles. In this sense, a platform champion who is particularly effective in maintaining impetus during platform-based product development and in overcoming the difficulties encountered along the way should be extremely valuable and may help platform-based development teams achieve continuity in developing appropriate technologies and in addressing specific technical difficulties. Meyer and Dalal (2002) have shown that the continuity of management and engineering leadership increases greater reuse in nonassemble platform products. In platform-based product development, a champion is likely to be someone who knows how to utilize the platform power to its maximum and has great influence on other team members. By constantly emphasizing that the goal of the platform is overall family success, rather than a single product, champions help to instill discipline and to ensure that team members design subsystems and modules that can be reused in the future. Given the complexity involved and priority differences, team members are likely to have conflicting views on how to design a subsystem. In such situations, the presence of a champion is likely to help resolve conflicts between designers and to maintain harmony by emphasizing the overall goal of platform design. Such a peacemaking role will help to overcome conflicts, which in turn will lead to better product compatibility. Finally, because of the overarching importance of the platform development, the champion needs to ensure that the return on the huge investment is maximized, which requires pushing product developers to derive as many products as possible from the same platform. Summarizing these arguments, it can be proposed that:


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H7a: The presence of a champion in platform-based product development will have a positive influence on the reusability of subsystems of platform-based products. H7b: The presence of a champion in platform-based product development will have a positive influence on the compatibility of subsystem interfaces of platformbased products. H7c: The presence of a champion in platform-based product development will have a positive influence on the extensibility of platform-based products.

Moreover, the champion’s experience in solving problems with existing products provides information on how to deal with similar problems with successive products. Without his guidance, team members may go down to the wrong path of development, which leads to higher development cost. Therefore, one can further hypothesize the following: H7d: The presence of a champion in platform-based product development will be positively associated with platform cost efficiency.

Figure 2 shows the research model and the hypotheses.

Questionnaire Design and Implementation To test the hypotheses, a questionnaire largely based on existing literature was developed (see Appendix I). When no suitable measurements could be found, new ones were developed. A preliminary draft of the questionnaire was sent to a panel of academics and practitioners to check for ease of use and to correct interpretation of the measurement items. These reviews helped in refining a number of items. The revised questionnaire was then submitted to seven experienced research and development (R&D) managers and engineers based in Singapore to check for clarity and appropriateness. Based on their feedback, some items were removed, and others were modified. Prior to the large-scale survey, a final pretest was conducted with four executives from a software company and from an automotive company in the United States. The executives were asked to complete the survey and raise any issues of concern. By this stage, the pretesting resulted in only minor refinements to a few of the measurement items. The questionnaire was then finalized using the results of pretests. As will be shown later in the statistical analysis, the measures developed demonstrate satisfactory quality and represent

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Process • Formalized platformbased product development process

H4d H4c H4a H4b

Platform Performance

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Knowledge sharing across platform-based products

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Product Platform Competency a. Reusability of subsystems of platform-based products

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c. Extensibility: Number of product derivatives b. Compatibility of subsystem interfaces of platform-based products

Organization • Continuity of platformbased product development team

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• Relative platform cost efficiency

• Relative platform cycle time efficiency

H6a H6b H6c H6d H7b

• Existence of a champion in platform-based product development

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Figure 2. Research Model and Hypotheses

a contribution to research and practice. This contribution will be elaborated in the Limitations and Contributions section.

Data Collection The data were collected using mail surveys. The sampling frame consisted of 1,000 randomly selected U.S. firms drawn from all nonservice firms listed in the World Business Directory in the following industries: Industrial Machinery and Equipment; Electronic Parts and Equipment; Communications Equipment; Software Products; Computers and Peripheral Equipment; Motor Vehicles and Motor Vehicle Parts; Chemicals and Allied Products; Medical, Dental and Hospital Equipment. One of the authors sent an initial letter to all 1,000 firms seeking their agreement to participate and assessing their suitability using three questions (Likert scale 1–7) Q1: product platform is a known concept and applied within our company, Q2: Our product platform designs enable us to accommodate several generations of products, which are regarded as one product family, and Q3: Our product platform designs are drawn to accommodate future generations of products, which will be regarded as one product family. The cutoff for inclusion in the data is 5 or above (where 4 indicates neutral). Of the 1,000 firms, 387 firms responded and provided a contact person to participate in

the survey. Sixty-seven companies declined to participate or did not have product platforms. Sixty-three letters were returned because of invalid contact people or addresses. The remaining 483 companies did not respond. In administering the final survey, the authors followed the total design method for survey research (Dillman, 1978). The first mailing packet included a personalized letter, the questionnaire, a priority-prepaid envelope with an individually typed return address label, and a list of research reports make available to participants of the survey. The package was sent by priority mail to the 387 firms that agreed to participate plus the 483 nonresponding firms. The letter asked the contact person to select a representative product platform currently in use in the company from which several products had been derived and commercialized. The contact person was also asked to distribute the questionnaire to the R&D manager, or the person in the company responsible for leading the development of the selected platform. To increase the response rate, four follow-up mailings to the companies were sent. One week after the initial mailing, a follow-up letter was sent. Two weeks later, a second package with the same content as the first package was sent to all the companies, which had failed to respond. After two additional reminders, the researchers received completed questionnaires from 256 firms, a response rate of 29.4% (256/870).


Result and Analysis After examining the data, it was found that there were 14 companies with only one product resulting from the selected platform, which did not meet the two products from the platform criterion for the study. Consequently, these 14 samples were removed, and the usable data set thus dropped to 242 companies for further analysis. Before carrying out an exploratory factor analysis (EFA) for these multi-item scales, the steps suggested by followed Langerak, Hultink, and Robben (2004) and Barczak, Sultan, and Hultink (2007) were carried out to compute the inter-item correlations and item-to-total correlations for each item, taking one scale at a time. Three items for which these correlations were not significant (p < .01) were eliminated. Next, EFA using varimax rotation and an eigenvalue of 1 as the cutoff value was conducted to determine item loadings and to eliminate items that do not clearly load onto any factors. The factor analysis resulted in the expected nine factors, and two more items were subsequently dropped because they failed to load clearly. Thus, all together, five were trimmed from the initial 42 items. The final results, after the trimming exercise, showed a unidimensionality for each scale with eigenvalues greater than 1 and all items loading onto their respective factors at greater than .60. To assess the reliability, and the convergent and discriminant validities of the measurement model, the measures were subject to a further purification process as recommended by Churchill (1979) and Gerbing and Anderson (1988). For the multiple item scales, a confirmatory factor analysis (CFA) was performed using the maximum likelihood estimates in LISREL 8.7 (Joreskog and Sorbom, 1993). The covariance matrix of the items was used as the input. A single measurement model encompassing all the elements of the model could not be used as it would violate the condition that there must be at least five respondents for each estimated parameter (Hair, Anderson, Tatham, and Black, 1998). Instead, two measurement models were evaluated prior to the structural model to purify the scales and to prevent misspecification in the measurement tools (Pillai, Schriesheim, and Williams, 1999). Following the recommendations of Ayers, Dahlstrom, and Skinner (1997), the measures were divided into two subsets of theoretically related variables. One CFA was performed for the dependent and the mediating variables, and another was performed for the independent variables. During the CFAs, attention was paid to each construct and trimmed items that were loaded on multiple constructs, had low item-to-construct loadings, or had


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low qualitative accuracy for the measurement (especially for construct compatibility). After the deletion of the problematic items, CFA was carried out again. If the indices still fell short, the process was repeated. The CFAs were iterated until the items with the largest standard residuals were successfully removed and the statistics of overall model fit were satisfactory. The results of the measurement models are given in Table 1. The results shown in Table 1 indicate that the models provide a good fit according to the fit measures discussed above. The overall fit indices (Bentler and Bonett, 1980) range from .91 to .96. The loadings of all the measurement item onto their respective constructs are highly significant (p < .001). In addition, as shown in Table 1, individual item reliability, composite reliability, and the average variance extracted (AVE) were all calculated (Fornell and Larcker, 1981). Cronbach’s alphas ranged from .72 to .87. All scores were above the recommended threshold of .7 (de Vaus, 2002), which is an indication that the measures were internally reliable (Nunnally, 1978). The composite reliability of each scale and measurement model was between .75 and .90, and so exceeded the .70 threshold for acceptable reliability recommended by Fornell and Larcker (1981). The AVE results ranged from .50 to .74, similarly exceeding the .50 threshold recommended by Fornell and Larcker (1981). This is an indication that the variance due to measurement error is smaller than the variance captured by the construct. Collectively, the AVEs, the composite reliabilities, and the Cronbach’s alpha values suggest that the measurement scales for each of the constructs have a high convergent validity (Bagozzi, Yi, and Phillips, 1991; Hair et al., 1998). Discriminant validity was examined by calculating the confidence intervals around the estimated inter-factor correlations (Anderson and Gerbing, 1988). Because none of the confidence intervals of the construct correlations contained the value of unity (p < .01), it can be concluded that the constructs possess discriminant validity. Discriminant validity can alternatively be verified by examining the square roots of the constructs’ AVE scores (Fornell and Larcker, 1981). All the AVE square roots were greater than the correlation levels of the constructs, which implies that each construct shares a larger variance with its own measures than with other measures. Following Bagozzi et al. (1991), the discriminant validity across the scales was conducted by twice estimating two-factor models for each possible pair of scales and computing differences in chi-square values for each set of constructs: first, by constraining the correlation between the latent variables to unity, and second, by removing this con-

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Table 1. Confirmatory Factor Analysis Resultsa Construct/items

Factor Loadings

t-value

Composite Reliabilityb

AVEc

Cronbach’s Alpha

Chi-square = 148.63; df = 48 c2/df = 3.10; RMSEA = .093; SRMR = .051; NFI = .94; CFI = .95; GFI = .91

Measurement model 1 (dependent and mediating variables)

Reusability of subsystems of platform-based products REU1 .88 16.09* REU3 .75 12.89* REU5 .78 13.55* Compatibility of subsystem interfaces of platform-based products COM2 .76 12.88* COM5 .86 15.40* COM6 .63 10.19* Platform cost efficiency COST1 .91 17.58* COST2 .85 15.81* COST3 .83 15.27* Platform cycle time efficiency TIME1 .95 18.32* TIME2 .76 13.50* TIME3 .84 15.42*

.85

.65

.82

.80

.57

.76

.90

.74

.87

.89

.73

.83

Chi-square = 125.73; df = 48 c2/df = 2.62; RMSEA = .082 SRMR = .051; NFI = .93 CFI = .95;GFI = .92

Measurement Model 2 (independent variables)

Formalized platform-based product development process FOR1 .79 14.09* FOR2 .97 18.84* FOR3 .71 12.31* Continuity of platform-based product development team CON1 .93 17.73* CON2 .78 13.71* CON4 .80 14.39* Knowledge sharing across a series of platform-based products DES1 .75 11.44* DES3 .79 12.19* DES5 .56 8.41* Existence of a champion in platform-based product development CHA2 .87 15.59* CHA4 .93 17.14* CHA5 .54 8.74*

Model Fit

.87

.70

.84

.88

.70

.84

.75

.50

.72

.83

.63

.78

a

Single-item scales are not reported. Internal composite reliability (ICR) is calculated according to Fornell and Larcker (1981) and should be greater than .7. ICR = (Slyi)2/[(Slyi)2 + SVar(⑀i)], where l is the loading of each item. c Average variance extracted (AVE) score is calculated according to Fornell and Larcker (1981) and should be greater than .5. AVE = S(lyi)2/[S(lyi)2 + SVar(⑀i)], where l is the loading of each item. * p < .001 (two-tailed). df, degrees of freedom; RMSEA, root mean square error of approximation; SRMR, standardized root mean residual; NFI, normed fit index; CFI, confirmatory factor analysis; GFI, goodness-of-fit index. b

straint. If the results of the chi-square difference tests show the chi-square value of the unconstrained model to be significantly lower, then discriminant validity is present (Segars, 1997). This condition was satisfied (p < .05) for all the constructs, thus demonstrating discriminant validity.

Next, the structural model is estimated using structural equation modeling by means of LISREL 8.7 (Joreskog and Sorbom, 1993). Three variables were controlled for in the analysis: sales, number of employees in the R&D division, and the R&D expenditure ratio. The descriptive statistics and intercorrelations are reported in Table 2.

Correlation is significant at the .01 level (two-tailed). Correlation is significant at the .05 level (two-tailed). S.D., standard deviation; R&D, research and development. b

a

1 3 3 3 3 3 3 3 3 1 1 1

5.31 12.38 14.12 15.47 15.05 13.66 12.12 14.01 13.49 3.09 2.02 10.14

3.23 4.30 4.04 4.27 5.09 4.18 4.57 3.62 4.27 .90 1.04 2.90

.548a .581a .488a .257a .472a .442a .530a .496a .518a –.025 –.361a

.549a .337a .055 .312a .492a .339a .503a .420a –.052 –.199a

.459a .227a .310a .479a .474a .486a .405a –.015 –.275a

–.015 .278a .273a .299a .226a .298a .067 .380a .063 .371a .082 –.006 –.089 –.192a –.134b

.121 .266a .288a .604a .099 –.106

.315a .387a .162b .068 –.271a

.283a .428a -.069 –.187a

.444a -.123 –.262a

.007 –.123

.054


Extensibility of platform Reusability of subsystem Compatibility of interfaces Cost Time Formalized process Continuity of team Knowledge sharing Product champion Employees R&D spending Last year sales

Extensibility Reusability Compatibility of Platform of Subsystem of Interfaces Items Means S.D.

Table 2. Descriptive Statistics and Intercorrelationsa

Cost

Time

Formalized Continuity Knowledge Product R&D Process of Team Sharing Champion Employees Spending


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The structural relationships among the constructs in the research model (i.e., Figure 2) were examined. The structural model fit indices for the overall model were chisquare/degrees of freedom (df) = 3.222; root mean square error of approximation = .097; standardized root mean residual = .040; normed fit index = .96; confirmatory factor analysis = .97; and goodness-of-fit index = .96, which are all well above the acceptability thresholds (Cuttance, 1987; Kelloway, 1998; Steiger, 1990). This is an indication of a good fit between the data and the research model. Table 3 presents the estimated path coefficients, with their corresponding t-values and significance levels, for each path that can be used to test the hypotheses. As Table 3 indicates, all the hypotheses were confirmed except H7c, where the hypothesized relationships were not found to be significant. Most of the other paths are significant at the a = .01 level, except H4b, H5a, H6d, and H7d, which were found to be significant at the a = .05 level. Thus, most of the hypotheses in the model have strong empirical support. The analysis on the total effects (Table 4) reveals that while the factors affecting platform development time and cost are similar, their degree of influence is different. For example, a formalized product development process plays a very significant role in shortening development time, but it is not a crucial factor in reducing development cost. In contrast, the presence of a product champion is very significant for cost efficiency, but it has relatively lower impact on development time. To examine whether the difference in influence is statistically significant or not, the differences between each pair of total effects on time and cost were compared using chi-square difference. Two corresponding total effects were constrained to be equal in a new model and we computed its chi-square. The critical value of chi-square difference test is Dc2/Ddf >3.84 at the .05 level. In this study, Ddf (the difference of df) between the new constrained model and the original model is 1; therefore, if Dc2 (the difference of chi-square) between the new constrained model and the original model without the total-effect-equality constraint is larger than 3.84, the difference between the pair of total effects is statistically significant. The analysis shows that the influence of the three factors, that is, formalized platform-based development process (chi-square difference = 18.16), continuity of development team (chi-square difference = 23.60), and the existence of a champion (chi-square difference = 25.08) on development time and cost are statistically different at .05 level. The influence of other factors on cycle time and development cost is not statistically different.

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Table 3. Results from Path Model Analyses Hypothesis H1 H2 H3a H3b H4a H4b H4c H4d H5a H5b H5c H6a H6b H6c H6d H7a H7b H7c H7d

Path from

Path to

Path Coefficient

t-value

Standard Errors

Conclusion

Reusability of subsystem Compatibility of interfaces Extensibility of platform Extensibility of platform Formalized process Formalized process Formalized process Formalized process Knowledge sharing Knowledge sharing Knowledge sharing Continuity of team Continuity of team Continuity of team Continuity of team Product champion Product champion Product champion Product champion

Extensibility of platform Extensibility of platform Cost Time Reusability of subsystem Compatibility of interfaces Extensibility of platform Time Reusability of subsystem Compatibility of interfaces Extensibility of platform Reusability of subsystem Compatibility of interfaces Extensibility of platform Time Reusability of subsystem Compatibility of interfaces Extensibility of platform Cost

.10 .10 .55 .40 .16 .12 .13 .44 .13 .31 .17 .30 .24 .09 .16 .31 .25 .05 .14

2.95** 2.72** 5.11** 2.65** 2.88** 2.40* 3.66** 4.49** 1.99* 5.18** 4.39** 5.67** 4.96** 3.03** 2.06* 5.26** 4.73** 1.46 2.07*

.03 .04 .11 .15 .06 .05 .04 .10 .07 .06 .04 .05 .05 .03 .08 .06 .05 .04 .07

Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Not supported Supported

c2 = 64.45; d.f. = 20; c2/d.f. = 3.222; root mean square error of approximation = .097; standardized root mean residual = .040; normed fit index = .96; confirmatory factor analysis = .97; goodness-of-fit index = .96 * level of significance = .05; ** level of significance = .01. d.f., degrees of freedom.

Impact of Product Platform Competency on Platform Performance

Discussion Product Platform Competency As shown in Table 3, the paths coefficient between subsystems reusability and product platform extensibility, and between compatibility of subsystem interface and platform extensibility, indicate a significant (p < .01) positive relationship, which confirms H1 and H2. That is, extensibility of platform depends very much on subsystem reusability and interface compatibility.

As discussed above, of the three elements of product platform competency, reusability of subsystems and compatibility of subsystem interfaces are regarded as the antecedents of extensibility of platform-based products in this study. Hence, extensibility of platform-based products has a major role in product platform competency, and, accordingly, it was hypothesized that it would have a direct influence on platform cost efficiency (H3a) and

Table 4. Results of LISREL Maximum Likelihood Estimation: Total Effects of Determinant Factors on Product Platform Performancea Unstandardized Total Effects Factor Reusability of subsystems of platform-based products Compatibility of subsystem interfaces of platform-based products Extensibility of platform-based products Formalized platform-based product development process Knowledge sharing dissemination across a series of platform-based products Continuity of platform-based product development team Existence of a champion in platform-based product development

Standardized Total Effects

Platform Cost Efficiency

Platform Cycle Time Efficiency

Platform Cost Efficiency

Platform Cycle Time Efficiency

.06*(2.56) .06*(2.40) .55**(5.11) .09**(3.35) .12**(3.80)

.04*(1.97) .04(1.90) .40**(2.65) .50**(5.24) .09*(2.40)

.06* .05* .37** .09** .10**

.03* .03 .23** .41** .06*

.08**(3.62) .20**(2.95)

.22**(3.12) .04*(2.03)

.09** .20**

.20** .04*

a t-values (in parentheses) greater than 1.96 are significant at the 95% confidence level using a two-tailed test. * p < .05; **p < .01.


platform cycle time efficiency (H3b). Table 3 shows the path loading between product platform extensibility, and platform cost efficiency is significant (p < .01), which supports H3a. Likewise, it was found that product platform extensibility is positively associated with platform cycle time efficiency (again, p < .01), which is in line with H3b. These results indicate significant relationships between product platform extensibility and both platform cost efficiency and platform cycle time efficiency.

Antecedents of Product Platform Competency In this study, the effects of four antecedents, that is, formalized platform-based product development process (H4a); design knowledge dissemination across platformbased products (H5a); continuity of platform-based product development team (H6a); and existence of a champion in platform-based product development (H7a) on the reusability of subsystems, were examined. The data provide empirical support for all four hypotheses in that all the relationships are positive and significant (see Table 3). In other words, to increase the reusability of subsystems of platform-based products, one could strengthen the formalized development process, improve knowledge dissemination, enhance the continuity of the development team, and enhance the role of a product champion in platformbased product development activities. Second, turning to the direct effects of these four antecedents, that is, formalized platform-based product development process (H4b); design knowledge dissemination across platform-based products (H5b); continuity of platform-based product development team (H6b); and existence of a champion in platform-based product development (H7b), on the compatibility of subsystem interfaces, it is found that all of these path coefficients were positive and significant (p < .01) These results emphasize the importance of formalizing the platform-based product development process, disseminating design knowledge across platform-based products, and maintaining the development team’s continuity from one product to another in enhancing the compatibility of subsystem interfaces of platform-based products. Third, the four management practices considered were also hypothesized to have a positive impact on the extensibility of platform-based products. The results shown in Table 3 indicate that H4c (p < .01), H5c (p < .01), and H6c (p < .01) were strongly supported. This means that the extensibility of platform-based products is likely to be improved by having a formalized platform-based product development process, disseminating design knowledge


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across platform-based products, and ensuring continuity in the platform-based product development team, all of which may also be seen as its antecedents. However, no significant link was found between the existence of a champion in platform-based product development and the extensibility of platform-based products (H7c). Contrary to the expectations, increasing the involvement of a champion in platform-based product development does not have a strong effect on the degree of product platform extensibility. One possible reason for this nonsignificant result could be that champions are usually concerned with strategic matters such as product strategy and the overall success of a platform, rather than the nitty-gritty of product development. The direct influence of champions on platform extensibility is therefore minimal. Instead, the effect on champions on platform extensibility is largely through the constant encouragement to reuse subsystems (as shown in H7a) and to ensure interface compatibility (as shown in H7b). However, the extensibility of platform-based products is supposed to be an important determinant of platform cost efficiency, as was confirmed in H3a. It was also hypothesized that the existence of a champion is another important factor with a positive impact on platform cost efficiency (H7d). The data confirm this hypothesis, as shown in Table 3, as the existence of a champion has a significant (p < .05) positive relationship with platform cost efficiency. This finding suggests that the existence of a product champion in platform-based product development has a significant influence on cost savings in successive products. In addition, it was also argued that not only the extensibility of platform-based products (H3b) but also a formalized platform-based product development process (H4d), and continuity in the platform-based product development team (H6d), would have clear positive effects on platform cycle time efficiency. The results shown in Table 3 provide empirical support for both H4d and H6d, as both a formalized platform-based product development process and continuity in the platformbased product development team show significant positive relationships with platform cycle time efficiency (t = 4.49, p < .01 and t = 2.06, p < .01). In other words, a formalized development process and stability in the development team appear to enhance platform performance in terms of cycle time efficiency. This may enable firms to spend less time than their competitors in developing successive products based on an existing platform, leading to both cost and time savings. Results of this study are consistent with previous studies on single product development. Although Katz (1982) asserts that team instability may be helpful in

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challenging and improving existing methods and accumulated knowledge because new team members may bring fresh ideas and approaches, Kessler and Chakrabarti (1999) found that team instability had a significant negative influence on development cycle time based on their study of 75 new product development projects. The latter conclusion is further supported by Akgun and Lynn’s (2002) empirical study, where a significant positive relationship was found between team stability and speed to market. In the setting of platform-based product development, this study provides empirical support on the need for continuity in platform-based development teams. One explanation for this result, advanced by Fredrickson and Iaquinto (1989), is that high levels of continuity make decision processes more efficient. Given the close similarity of many of the functions of derivative products from a common platform, design activities based on a common platform are likely to be more efficient than designing a similar number of products that have no common basis. Given this advantage, it is not surprising that continuity within a platform-based product development team improves development time efficiency, without compromising design quality. In addition, using a formal process has shown to increase success in product development (Cooper and Kleinschmidt, 1985). Griffin (1997) explicitly argues that a formal process may reduce cycle time and demonstrates that the reduction may be greatest for complex products. Because platform-based product development involves more resources, more people, and more products with more functions than single product development, it can be regarded as a particular form of complex development work. Therefore, the findings of this study provide empirical evidence that supports Griffin’s (1997) view and confirm the importance of a formalized process in the context of platform-based product development. This is because such an approach not only provides the development activities with a sense of structure and sequence; it can also be efficiently reapplied for follow-on products derived from the same platform. This approach is especially useful in organizing the management of interactions and interfaces and in avoiding ambiguities in what to work on and when (Griffin, 1997; Salomo, Weise, and Gemunden, 2007; Tatikonda and Montoya-Weiss, 2001). This study also reveals that factors that lead to better development cost are statistically different from those which lead to shorter development time. As shown in Table 4, platform development cost is most affected by the presence of a champion, with the other three factors (formalized development process, knowledge sharing, and team continuity) contributing equally but only half of

K.-H. CHAI ET AL.

that of champion. The influence of a champion on development cost is found to be significantly different from that on development time. In contrast, development time is most significantly influenced by formalizing product development process as well as continuity of development team. The influence of these two factors, formalized product development process and continuity of development team, on development time is statistically different from their influence on development cost. Knowledge sharing was found to affect both development time and cost, with no significant difference. Taken together, these results imply that while in general the more of these practices (i.e., knowledge sharing, champion, formalized process, and continuity of development team), the better platform development would be; when faced with resource constrains, firms can prioritize which practice to focus on, depending on the desired outcome (cost or time). For instance, firms developing products for which the reduction of development cost is maybe more important than the shortening of the cycle time should make the presence of a champion a priority. It should be more important than ensuring team continuity and formalized development process. With reference to fast-movingconsuming electronic goods such as digital cameras and third generation mobile phones, by contrast, managers need to place the priority on team continuity and formalized development process over the presence of a champion. These findings allow managers to focus on the most effective practice, adapted to the desired improvement, reducing the guesswork to a minimum and thereby saving precious time and effort.

Limitations and Contributions This study has several limitations that should be considered when interpreting the findings, and which offer opportunities for future research in platform-based product development. First, Robertson and Ulrich’s (1998) framework adopted in this study does not give adequate consideration to market factors. Products derived from a common platform are essentially targeting a related set of segmented markets, where heterogeneous customers can choose products close to their preferences (Farrell and Simpson, 2003; Meyer and Lehnerd, 1997). Platforms need to serve a range of products, and over a longer period than with single product development, and it can be difficult for companies to forecast customers’ needs accurately in the initial stages of development due to the rapidly changing nature of those needs. Therefore, future


investigations should include market variables such as clarity of targeted market segments and predevelopment platform-based product positioning. Second, the sample survey in this study is made up of companies in the United States, which constrains any generalization of the results to other countries. In addition, the study focuses on the application of product platforms in industries such as computer and electronic products, automobiles, and medical equipment. However, the concepts and principles of platforms can also have a role in service industries, such as insurance companies (Meyer and DeTore, 2001), media networks, studio entertainment, Internet, and direct marketing (McGrath, 2001). It would be very interesting to see if the findings of this study can be extended to service sectors. Third, the survey is this study used a Likert scale to measure the variables as companies are not likely to give actual figures because of confidentiality reason. The need to collect data from different industries also warrants a survey approach. Therefore, the response is subject to respondents’ estimation error. Future research may consider collecting data from multiple respondents to improve the data reliability. Fourth, this study does not consider the possible effects of product complexity. It is possible that product complexity moderates the influence of the antecedents on platform performance. For instance, it may be harder to derive products from a platform for power tools, compared with aircrafts. Future studies should pay greater attention to the influence that product and technology complexity has on platform development. Finally, this research is focused on products from a single platform. To sustain long-term success, platforms must be continuously renewed (Meyer and Lehnerd, 1997), as the benefits of leveraging an existing platform for more products will eventually be surpassed by the benefits of redesigning a platform (Jones, 2003). Hence, developing and revitalizing product platforms is essential to a company’s success (Meyer, 1997). Given this situation, when and how to replace an existing platform, could be an exciting research topic.

Main Contributions Notwithstanding the limitations, through a large-scale cross-industry survey, this study lends empirical support to the idea of product platform competency. The results show that having a formalized development process, disseminating design knowledge across platform-based products, ensuring continuity in a platform-based product development team, and having a champion in platform-


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based product development all significantly enhance product platform competency. This study makes three main contributions to the existing literature. The first contribution is the empiricaltested concept of product platform competence based on key elements suggested by Robertson and Ulrich (1998). Although earlier work has advanced the understanding of the importance of product variety (e.g., Kim et al., 2005) and the rate of introducing derivative products (e.g., Jones, 2003), there has been no clear definition of product platform competency or formal empirical testing of its effect in platform-based product development. The model in this study, which links management practices, platform competence, and platform development outcome, contributes to a better understanding of what leads to success in platform-based product development. In addition, while the success drivers identified are similar to those in traditional product development, the holistic analysis (Table 4) reveals a novel observation that factors which lead to lower development cost are statistically different from those that lead to shorter development time. Specifically, a formalized product development process and team continuity play very significant roles in reducing development time, but they are not as important in lowering development costs as having a product champion and effective knowledge sharing. This novel finding allows managers to prioritize their areas of improvement, depending on their performance priority on either development time or cost. Until now, studies in platform development have not differentiated the impact of various management actions on development time and cost. The second contribution is the development of constructs specifically for platform development based on previous studies and the authors’ understanding. This study adapted the “platform performance metrics” proposed by Meyer et al. (1997) and included all derivative products in the unit of analysis. Previous studies measured success either on the single project (product) level or at the program (firm) level (Johne and Snelson, 1988); little research has measured performance at the product platform level. Some studies directly borrowed items from previous research and ignored the unique context of platform-based product development when measuring the performance of a product family (e.g., Kim et al., 2005; Tatikonda, 1999). Additionally, whereas previous researchers only considered relative performance within a single organization, this study included performance relative to external competitors, which might be a more comprehensive approach. These performance measures, validated in this study, should help researchers to conduct more surveys in platform development, which so far has

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largely been done in the form of case studies. Relating to this is the third contribution: the development and validation of measurement items related to key drivers to platform development, the three aspects of platform development competence, and the performance measurements. These measurement items can be used by practitioners as guidelines to identify areas for improvement as well as the level of capability in platform development.

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Appendix I. Questionnaire Design Stage I The statement “product platform” refers to a set of common components, modules, or parts from which a stream of derivative products can be efficiently created and launched. To assess their agreement to participate and assessing their suitability, three questions (Likert scale 1–7) were asked:

•

that were used in previous products from this platform (adapted from Rothenberger, Dooley, Kulkami, and Nada, 2003) After the new product design is completed, we realize high commonality with the functional modules that were used in previous products from this platform* (adapted from Rothenberger et al., 2003) We usually follow a design strategy where common functional modules are used in several products derived from this platform (developed in this study) We reuse a large number of similar functional modules in different products derived from this common platform* (adapted from Worren, Moore, and Cardona, 2002) We try our best to reuse “off the shelf” functional modules in different products from this platform wherever possible (adapted from Hofer and Halman, 2005)

Compatibility of subsystem interfaces of platform-based products

• • • •

There is a high degree of standardized system layout in our product architecture from this common platform (developed in this study) There is a high degree of common interfaces among different products derived from this common platform* (developed in this study) It is very difficult to make changes in modules without redesigning other parts in the existing products from this common platformR * (adapted from Worren et al., 2002) It is quite easy to add new functional modules without changing other parts to develop new derivative products from this common platform* (adapted from Worren et al., 2002) The interfaces of our existing product architecture from this platform are compatible with many different functional modules (adapted from Souder and Song, 1997) The interfaces of our existing product architecture from this platform are not suitable for future derivative productsR (adapted from Souder and Song, 1997)

Q1: Product platform is a known concept and applied within our company Q2. Our product platform designs enable us to accommodate several generations of products, which are regarded as one product family Q3. Our product platform designs are drawn to accommodate future generations of products, which will be regarded as one product family.

•

Stage II: Measurement Items

•

Please answer the following questions based on your product platform. For each statement, please indicate the extent to which you agree or disagree with the statement (Anchor: 0 = strongly disagree; 7 = strongly agree) Reusability of subsystems of platform-based products

Continuity of Platform-based Product Development Team

•

After the new product requirements are defined, we realize high commonality with the functional modules

•

Extensibility of Platform-based Products

•

Number of products that have been derived and commercialized from this platform (adapted from De Weck, 2006) From inception through launch, the same team was accountable for a series of products derived from this platform (adapted from Cooper and Kleinschmidt, 1993a)


• • •

From the beginning to the end, the same team was responsible for several products derived from this platform (adapted from Cooper and Kleinschmidt, 1993a) Department managers who were on the team remained on it from one product to another based on this common platform* (adapted from Akgun and Lynn, 2002) Team members who were on the team remained on it from one product to another based on this common platform (adapted from Akgun and Lynn, 2002)

Formalized Platform-based Product Development Process

• • • • • •

We have a well-defined development process for creating products from this platform (adapted from Rothenberger et al., 2003) There is a well-written document of the process that guides our engineers in developing products from this platform (adapted from Griffin, 1997) We regularly check the development progress of our products derived from this platform (developed in this study) We monitor the development progress of our products derived from this platform using standard procedures* (developed in this study) We do an exceptionally good job in keeping track of the development progress of derivative products from this platform* (adapted from Rothenberger et al., 2003) Our engineers closely follow a process in developing products from this platform* (adapted from Rothenberger et al., 2003)

Design knowledge dissemination across a series of platform-based products

• • • •

The level of knowledge shared and disseminated is high among our different product development teams from this common platform (adapted from Song, Berends, van der Bij, and Weggeman, 2007) There are a lot of informal “hall talks” concerning our technology development tactics or strategies among our different product development teams from this common platform* (adapted from Song et al., 2007) Data on technology development are disseminated at all levels among our different product development teams from this common platform (adapted from Song et al., 2007) Our different product development teams from this common platform periodically circulate documents (e.g., reports, newsletters) that provide new information and/or knowledge* (adapted from Song et al., 2007)


• •

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We do not communicate information internally about successful technology development across all the products derived from this common platformR (adapted from Song et al., 2007) We freely communicate information internally about unsuccessful technology development across all the products derived from this common platform* (adapted from Song et al., 2007)

Existence of a champion in platform-based product development

• • • • •

There was a champion who showed tenacity in overcoming obstacles in our product development from this platform* (adapted from Howell, Shea, and Higgins, 2005) There was a champion who continued to be involved with the design until it was implemented in our product development from this platform (adapted from Howell et al., 2005) There was a champion who overcame barriers to the design in our product development from this platform* (adapted from Howell et al., 2005) There was a champion who persisted in the face of adversity in our product development from this platform (adapted from Howell et al., 2005) There was a champion who stuck with the objectives despite experiencing negative outcomes in our product development from this platform (adapted from Howell et al., 2005)

Platform cost efficiency

• • •

Compared to our competitors, the average R&D costs of all the products from this platform were much higher than our competitor’s costsR (adapted from Song and Parry, 1997) Compared to our original projected costs, the average R&D costs of all the products from this platform were much less than our projected costs (adapted from Song and Parry, 1997) Compared to the R&D cost of the first product from this platform, the average R&D costs of the follow-up derivative products from this platform were much less than the first product (Definition: The follow-up derivative products refer to all the products that have been derived and commercialized from this platform excluding the first product) (adapted from Meyer, Tertzakian, and Utterback, 1997)

Platform cycle time efficiency

•

Compared to our competitors, the average development cycle time of all the products from this platform

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• •


was much longer than our competitor’s cycle timeR (adapted from Song and Parry, 1997) Compared to our original planned cycle time, the average development cycle time of all the products from this platform was far shorter than our planned cycle time (adapted from Song and Parry, 1997) Compared to the development cycle time of the first product from this platform, the average development cycle time of the follow-up derivative products from

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this platform was far shorter than the first product (Definition: The follow-up derivative products refer to all the products that have been derived and commercialized from this platform excluding the first product.) (adapted from Meyer et al., 1997) Notes: R reversed coded; * item deleted during factor analysis.

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