東京大学 COE ものづくり経営研究センター

MMRC Discussion Paper

No. 106

MMRC-F-106

Reconsidering novel technology
introduction strategies: Impacts of technology,
design, and market attributes on 118 Japanese
product development projects
Department of Business Administration, Aoyama Gakuin University
Manufacturing Management Research Center, the University of Tokyo

Masanori Yasumoto
December 2006

21COE, University of Tokyo

MMRC Discussion Paper No. 106

Reconsidering novel technology
introduction strategies:
Impacts of technology, design, and market attributes on
118 Japanese product development projects
Department of Business Administration, Aoyama Gakuin University
Manufacturing Management Research Center, the University ofTokyo
Masanori Yasumoto
December 20061

Yasumoto

Abstract: This study reconsiders product development strategies for the introduction ofadvanced technologies into new products. The exploitation and exploration of novel
technologies in product development are critical issues for manufacturing firms. Yet,thus far, the concept and determinants of novel technology introduction strategies haveoften been blurred. Drawing on 118 successful Japanese assembly productdevelopment projects, this study attempts to elaborate the concept of novel technology
introduction strategies and explores the effects of other determinants on the strategies.
The study finds two alternative novel technology introduction strategies: technology
integration and separated technology development. The results demonstrate thatsuccessful projects exploit either of these strategies according to knowledge regarding
product designs and/or customer/market needs as well as technological uncertainty.
The findings of this study should help firms contrive to develop novel technology
introduction strategies at the project level, as well as multiproject strategies at thebusiness level.
Keywords: technology integration, separated technology development, productattributes

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Reconsidering novel technology introduction strategies

Introduction
Cross-functional integration and associated collaborative practices across different development
stages (e.g., overlapping, preliminary information exchange, and so on) are critical factors for projectsuccess (Brown & Eisenhardt, 1995; Clark & Fujimoto, 1991). Furthermore, researchers have foundcross-functional integration and related practices to be vehicles of exploratory knowledge creation(Benner & Tushman, 2003; Iansiti & Clark, 1994; Kusunoki, Nonaka, & Nagata, 1998).
The interest in technological changes and related industrial dynamism highlights the role ofcross-functional integration for novel technology introduction (e.g., Chesbrough & Kusunoki, 2001;
Eisenhardt & Tabrizi, 1995; Henderson & Clark, 1990; Iansiti, 1997; Tatikonda & Rosenthal, 2000).
Rapid technological changes have dramatically shortened product life cycles in hi-tech industries.
Industrial volatility urges firms to employ novel technologies faster than ever. Reflecting theindustrial volatility, cross-functional integration and associated practices for novel technology
introduction are regarded as key factors for project success in high-tech industries.
Levels of product development performances, such as productivity, development speed, andproduct quality, depend on how firms choose and refine novel technologies so that the technologies
work well together in new products. Cross-functional integration teams for novel technology
introduction are reported to contribute to the performance of product development based on novel
technologies (e.g., Eisenhardt & Tabrizi, 1995; Gobeli & Foster, 1985; Gomory, 1989; Iansiti, 1997;
Song & Xie, 2000; Tatikonda & Rosenthal, 2000).
Yet, the industrial volatility resulting from interfirm modularity and related open interfirm
networks also draws our attention to another novel technology introduction strategy (e.g.,
Chesbrough, 2003; Christensen, Verlinden, & Westerman, 2002). This strategy exploits basic productdesign rules and standardized element technologies that are prepared by developers who are not
involved with specific product development projects. A typical case is the product development basedon product modularity, which is found particularly in digital product segments such as software,personal computers, network systems, and so on (e.g., Baldwin & Clark, 1999; Cusumano & Yoffie,
1998; MacCormack & Verganti, 2003; MacCormack, Verganti, & Iansiti, 2001). In relation to thedesign concept of “product architecture,” these studies suggest that firms may refurbish a portion of a
product system by adopting element technologies from outside the product development group,
thereby fostering novel technology introduction in volatile industries.
At the same time, the surge of globalization encourages firms to prepare technologies apart fromproduct development in order to quickly release a variety of products at a low cost (Tatikonada &3

Yasumoto

Stock, 2003). For instance, wireless handset manufacturers in China make use of the interfirm
modularity of “modular production networks” (Sturgeon, 2002) in which specialized suppliers
provide element technologies (i.e., wireless cores/platforms, components, handset designs, software,etc.) to help handset manufacturers release a new model every 2-3 months. In the mean time, major
global wireless handset manufacturers attempt to adapt to the diversity of the global market byseparating their proprietary core technology development (i.e., handset platform development) from
specific handset model development projects (Funk, 2002).
The above situation reveals two issues pertaining to novel technology introduction strategies atthe project level. First, whether the concept of novel technology introduction strategies is explicit isstill open to doubt. The concept seems rather vague as it includes several interrelated productdevelopment strategies. Second, we may ask what the determinants of each novel technology
introduction strategy are. When novel technologies are introduced, other factors like design andmarket attributes may influence the choice of the apt novel technology introduction strategies.
These issues confuse the relevance between the determinants of novel technology introduction
strategies and the technology introduction strategies themselves. The choice of novel technology
introduction strategies is one of the most critical issues for manufacturing firms (Iansiti, McFarlan, &Westerman, 2003). At the project level, firms in turbulent environments need to make proper use ofnovel technology introduction strategies, which are closely interrelated to platform/multiproject
strategies (e.g., Krishnan & Gupta, 2001; Robertson & Ulrich, 1998; Tatikonda, 1999; Ulrich &Ellison, 1999) and related outsourcing strategies (e.g., Chesbrough, 2003) at the business level. Yet,the lack of knowledge about the determinants of novel technology introduction strategies would
hamper firms from the effective exploitation and exploration of technologies.
Drawing on the data from successful product development projects of Japanese firms, the aim of
this study is to explore how firms utilize novel technology introduction strategies. Based on acontingency perspective, the article posits that product development strategies for novel technology
introduction may differ according to product and market attributes as well as technologicalchange/novelty.
While focusing on project level strategies for novel technology introduction, the attempt of thisstudy also contributes to elucidating the impacts of critical factors of effective platform/multiproject
strategies on novel technology introduction (e.g., how platform and derivative projects should eachplay different roles according to the factors). Since the purpose of the article rests in hypothesis
generating rather than hypothesis testing, the study does not hypothesize any specific causality. All
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Reconsidering novel technology introduction strategies

we predict is that product development strategies for novel technology integration may differ
according to product attributes.
The outline of the paper is as follows. First, we review past researches to streamline theconcepts of product development strategies and their determinants, on the basis of which we propose
generic predictions based on a contingency perspective. Reflecting these predictions, we examine thequestionnaire-based data from 118 successful Japanese assembly product development projects.
Finally, from the results, we attempt to draw implications about novel technology introduction
strategies.

Backgrounds
Product development strategies for novel technology introduction
Since the 1990s, researchers have suggested that effective product development strategies may differ
according to product attributes and/or industrial dynamism (e.g., Eisenhardt & Tabrizi, 1995; Song &Montoya-Weiss, 2001; Song & Xie, 2000; Souder, Sherman, & Davis-Cooper, 1998; Tatikonda &Rosenthal, 2000; Yasumoto & Fujimoto, 2005a). The line of studies revealed that the mode of novel
technology introduction is contingent upon product characteristics and/or industrial dynamism.
Therefore, our next step is to examine how firms employ product development strategies in relation
to novel technology introduction.
First, let us review how researchers have characterized effective product development strategies
for novel technology introduction. From the mid-1980s, drawing on the successful cases oftechnology-based product development projects, researchers have attempted to explore effective
product development strategies for introducing novel technologies into new products (e.g., Gobeli &Foster, 1985; Gomory, 1989).
In the 1990s, researchers collected the product/industry-specific data of successful projects andexplored effective product development strategies for introducing novel technologies. Drawing on
the data of about 30 super computer or workstation development projects of US and Japanese firms,
Iansiti (1997) examined product development projects that were accompanied by core technology
development.
The study suggested that the “system-focused” approach fosters “technology integration” among
related functional groups; this is characterized by overlapping and the associated intensive
communication between element technology development and product/process engineering groups.
Compared to the “element-focused” approach, the system-focused approach results in the5Yasumotodevelopment of more radical technologies in a shorter development lead time. The study
demonstrated that the technological uncertainty in advanced core technology development enhances
the integration for novel technology introduction.
Also, several generic studies drawing on large sample data from various industries suggested
that cross-functional integration is critical for developing new products with novel technologies (e.g.,Song & Montoya-Weiss, 2001; Song & Xie, 2000; Tatikonda & Rosenthal, 2000). The line of studies
suggested that communication and overlapping between advanced technology development andproduct/process engineering groups are critical for the successful commercialization of novel
technologies.
These studies stressed the role of cross-functional integration for novel technology development
and introduction. However, the concept of cross-functional integration often involves confusing
technology integration with cross-functional integration at the product/process engineering stages(e.g., Eisenhardt & Tabrizi, 1995; Olson, Walker, & Ruekert, 1995; Song & Montoya-Weiss, 2001;
Song & Xie, 2000; Takeishi, 2002; Tatikonda & Rosenthal, 2000).
Eisenhardt and Tabrizi (1995) examined 72 development projects involving computer products,
such as personal computers, supercomputers, workstations, and peripheral products, and concluded
that under volatile environments, cross-functional integration contributes to rapid productdevelopment. Yet, the distinction between technology integration and cross-functional integration at
the product/process engineering stages still remains blurred in the study. This problem obscures therequired range of coordination in novel technology introduction.
On the other hand, when examining knowledge partitioning in automobile development projects,
Takeishi (2002) found that when an automobile project includes the development of components
based on new technologies, the fluidity of the boundaries of knowledge calls for overlapping
problem-solving processes even across firm boundaries (e.g., design-in activities). The
exchange/sharing of specific knowledge in the process involves close manufacturer-suppliercollaborations as well as cross-functional integration within manufacturers. In the analysis, novel
technology introduction strategies are, at large, identified with cross-functional integration within andacross firms.
Product development capabilities that yield complex/novel, and thus, inimitable products are
regarded as sources of competitiveness (Anderson, 1999; Pil & Cohen, 2006). Cross-functional
integration is highlighted because of its contribution to developing these complex/novel products.
The interdependency between product elements enhances the problem-solving by means of6

Reconsidering novel technology introduction strategies

overlapping between stages, design-test-build cycle iterations, and the related tight coordination
between engineers (Adler, 1995; Clark & Fujimoto, 1991; Iansiti, 1997; Terwiesch & Meyer, 2002;
Thomke, 1997). Thus, tight cross-functional integration and associated practices are effective for the
product complexity of automobiles (Clark & Fujimoto, 1991).
Yet, the accumulation of industry/product specific studies shows that novel technology
integration is distinguished from cross-functional integration at the product/process engineering
stages. For instance, Iansiti and Clark (1994) asserted that technology integration, which deals withhigh technological uncertainty (e.g., super computers), is an effective product development strategy
for technology-based complex products. In contrast, automobile development calls for “internal
integration”—cross-functional integration at the product/process engineering stages—to deal with the
product complexity, and “external integration” to deal with the uncertain customer/market needs.
Both the cross-functional integrations are vehicles to enhance the coordination between
elements. Nevertheless, it should be noted that technological uncertainty stemming from drastic
technological change is the factor that demands technology integration. This factor requires us todistinguish novel technology introduction strategies from cross-functional integration, which aims at
improving and/or reusing current technologies, at the product/process engineering stages.

Making proper use of product development capabilities
On the contrary, since the 1990s, product modularity and related open intefirm networks seem tohave been increasingly eroding the importance of cross-functional integration and associatedpractices, which were once regarded as one of the most critical factors of effective productdevelopment (Chesbrough, 2003; Christensen, Verlinden, & Westerman, 2002; Gawer & Cusumano,
2002; Sturgeon, 2002). Studies have suggested that modular product designs enable manufacturers to
break down complex problem-solving into a set of localized problem-solving steps (Baldwin & Clark,
1999). Modularity permits the rapid introduction of novel elements/technologies into products at a
relatively low cost.
This advantage enables the drastic improvement of product performance, without tightorganizational coordination (Garud & Kamaraswamy, 1995; Langlois & Robertson, 1992). Thus,
relatively successful developers of a more modular product do not call on cross-functional integration
at the product/process engineering stages; rather, they form a federation of many small
module-specific teams that are relatively independent of each other (Cusumano & Yoffie, 1998;
MacCormack & Verganti, 2003; MacCormack, Verganti, & Iansiti, 2001).
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Yasumoto

These contrasting novel technology introduction strategies indicate that technology introduction
strategies can be divided into two types, “technology integration” and “separated technology
development,” on the basis of the level of product design stability. The difference in novel
technology introduction strategies can be partly attributed to the design attributes of the concerned
product. Product complexity, which arises from the insufficiency of architectural knowledge of therelationships between product elements, has impacts on product development projects. The patterns
of product innovations reflect the architectural stability of the product design (Chesbrough &Kusunoki, 2001; Henderson & Clark, 1990).
Novel technology introductions and related product design changes lead to technology
integration due to the insufficiency of both component and system knowledge (Iansiti, 1997).
Takeishi (2002) further elucidated the integration for novel technology introductions within andbetween firms, examining the manufacturer-supplier relationships in the automobile industry. Whennovel technologies have impacts on the architectural knowledge of product design, firms are liable tocoordinate between elements even across firms to compensate for the insufficiency of architectural
knowledge related to the product system and its components.
Generic studies based on large sample data from various industries have also demonstrated theimpacts of the architectural stability of product designs. When the concerned products are complex,
technological uncertainty is likely to enhance the need for cross-functional integration (Tatikonda &Rosenthal, 2000). Compared to the case of incremental model change projects, projects for novel
products, which include design changes, urge firms to adopt the cross-functional integration andrelated overlap between element technology development and product/process engineering (Olson,
Walker, & Ruekert, 1995; Song & Montoya-Weiss, 2001; Song & Xie, 2000).
Nevertheless, element technologies can be developed so that they are separated fromproduct/process engineering and are introduced into products when the architectural attributes of the
product designs are provided or relatively stable. Firms need to design their products aligned withnovel technology development, unless sufficient architectural knowledge is provided by stable
designs (e.g., Baldwin & Clark, 1999). Without the stability of product designs, firms cannot separate
advanced component/element technology development from specific product development projects
due to the insufficient knowledge of the interrelationships between components.
Even the decomposability of products into relatively independent components, such as modules,
is not secured until sufficient architectural knowledge is provided (Baldwin & Clark, 1999; Ulrich,1995). Facing the interdependencies between components, firms cannot divide product development
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Reconsidering novel technology introduction strategies

Figure 1.

Separated
Yes Technology
Novel
Technology
Introduction
No

Development

Technology
Integration

Patchwork

Engineering
Integration

Hig

Low

Architectural Knowledge of
Product Design (Design Stability)

tasks into distinctive subtasks (von Hippel, 1990). Thus, firms cannot introduce novel technologies
without technology integration when decomposability is not secured due to the lack of stable productdesigns.
On the other hand, the separation of technology development from the product/processengineering stages alleviates heavy task loads in the development of complex products. Stable
designs, such as platforms, provide the architectural knowledge between product elements, and thus,
save the cost and time involved in the integration of these elements (Baldwin & Clark, 1999;
Cusumano & Nobeoka, 1997; Funk, 2002; Tatikonda, 1999). This efficiency increases theorganizational flexibility of product development in response to environmental variety and/or
volatility (Krishnan & Gupta, 2001; Sanchez & Mahoney, 1996).
Following the perspectives in terms of novel technology introduction and product designs, we
can streamline product development strategies (Figure 1).
Product attributes and technological change/novelty and complexity provide the minimum
conditions to adopt either of the novel technology introduction strategies. Yet, ultimately, theadoption of the strategies would depend upon market factors.
Eisenhardt and Tabrizi (1995) elucidated that in the field of rapidly evolving products, thecross-functional integration relevant to the “experiential approach” contributes to shortening thedevelopment of lead time to a greater extent than in the “compression approach” based on planningand overlapping in relatively stable environments. Yet, firms could augment the organizational
9

Yasumotoflexibility to the level of environmental volatility. Firms may also introduce advanced element
technologies into products through modularized technology development, independent of productdevelopment (e.g., Chesbrough, 2003; Cusumano & Yoffie, 1998; Funk, 2002; MacCormack &Verganti, 2003; MacCormack, Verganti, & Iansiti, 2001; Ulrich & Ellison, 1999). In volatileenvironments, novel technology development is separated even from the product development forexisting businesses/markets (Iansiti, McFarlan, & Westerman, 2003).
Organizational flexibility to market variety and/or volatility would depend upon thearchitectural attributes of the product designs. Yet, these conflicting suggestions about industrial
volatility may result from the insufficient attention to the effects of market attributes. The effects ofmarket uncertainty under the concept of industrial volatility are often entangled with the effects oftechnological change/novelty. Many of the studies conducted seem to regard industrial volatility
simply as technological change/novelty and focus on the dynamism of the technologies (e.g.,
Chesbrough & Kusunoki, 2001; Christensen, 1997).
With regard to product development management, Eisenhardt and Tabrizi (1995) examined theimpacts of model change cycles as the variable of industrial volatility, particularly highlighting therelationship of the cycles with technological change/novelty. Nevertheless, at the same time, the lineof studies also stressed that market variability calls for product development capabilities based oncross-functional integration (Eisenhardt & Martin, 2000). As such, model change cycles may reflect
both technological change/novelty and market variability, resulting in the impacts of market factors
on the choice of novel technology introduction strategy remaining blurred.
Technological change/novelty enhances technology integration in coping with technologicaluncertainty only when architectural knowledge is also simultaneously insufficient. In contrast, firms
may exploit various technologies developed elsewhere so long as the concerned product designs arestable. This study sheds light on the impacts of market and product attributes, presuming thatsuccessful projects make proper use of novel technology introduction strategies according to productand market attributes.

Research direction and data collection
Basic direction
Let us describe the direction of our analysis. First, the study attempts to classify novel technology
introduction strategies by sorting them on the basis of cross-functional integration during
product/process engineering. Second, the study examines how successful projects adopt novel
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Reconsidering novel technology introduction strategies

technology introduction strategies according to product and market attributes.
Because of the variety of products, modern manufacturing firms are required to employ novel
technology introduction strategies with regard to product characteristics and/or for industrial
dynamism. Examining the contingent application of the strategies would help us understand how
firms can successfully introduce novel technologies into the concerned products.
Although the basic logic of the present contingency analysis is relatively simple, actual datacollection and empirical analysis is not easy, partly because of some difficulties in measuring productcharacteristics, development strategies, and performances across various industries. After trying
various methods, we decided to use subjective measures as the main yardsticks, and considered thateach of the respondents would have a broad perspective in evaluating product development
strategies.
In this study, we measured the “perceived” characteristics of the product in question and the
product development practices. Levels of product development performance were also measured interms of respondents’ perceptions. This method may have some potential problems with regard tomeasurement and validity. There is a fundamental trade-off here between accuracy and comparabilityof data.1
After considering the trade-off, we presumed that objective environments, novel technology
introduction strategies, and aspects of performance would be aligned in projects that project leaders
themselves regarded as successful. Therefore, focusing on projects deemed as successful, we also
examined the relationship between product characteristics and estimated the success levels of productdevelopment strategies.

Data Collection
We combined clinical field studies and statistical data collection. First, from 1995 to 1997, we visited
32 development projects involving products from various industries, such as the apparel, automobile,
construction equipment, chemical textile and resin, consumer electronics, communication devices,
electronic components, food/beverage, pharmaceuticals, industrial chemical, industrial machinery,mechanical parts, medical equipment, office equipment, precision mechanics, software, and toiletryindustry, covering virtually all the product/industrial categories we intended to study in our
1

A popular method for understanding effective or adaptive strategies is the pair approach. This approach involves asking
the respondent firms to name a pair of projects, a successful and an unsuccessful one from their point of view, and toevaluate the level of adoption or effectiveness of each routine. If the level is significantly different between the pairs for agiven routine, we can say that it is an effective or adaptive routine. In reality, however, it is rather difficult to get
responses about failed projects from firms.

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Yasumotoquestionnaire survey. Combining our knowledge from both the literature survey and field research,
we selected the variables and designed the questionnaire. We then proceeded to the questionnaire
survey.
We collected data in July 1997, through a questionnaire survey mailed to 700 business units andresearch laboratories of Japanese public firms. The survey inquired about product development
projects of commercialized mass-production products.
The unit of analysis was an individual project of product development. Some of the surveys
were sent to different business units or institutes within the same multidivisional company. We asked
the potential respondents to select a relatively successful project that they had direct experience within recent years, and to consistently answer the questions about this particular project.2
We received 203 completed surveys (response rate: 29%) from 145 firms by the end of October
1997. We checked the nonresponse bias with regard to firm size (sales).3 No significant difference infirm size between the respondent firms and other potential respondent firms was found (t = 1.34, p =0.18). The product development of the 203 respondent firms was spread across a variety of
products/industries: textile and apparel, food/beverage, chemical, pharmaceutical and rubber,consumer chemical and toiletry, metal, electronics, systems and software, precision mechanics, andtransportation machines. The diffusion of respondents by industry was not significantly different
from that of potential respondent firms.

Focusing on assembly product development projects
In our analysis, we chose assembly product development project cases in order to examine ourpredictions using more controlled data settings. We attempted to divide the samples into two groups:“assembly product” and “process product” groups.4 Innovation management studies have suggested
that product development capabilities are different between assembly products and process products(e.g., Kusunoki, Nonaka, &, Nagata 1998; Utterback, 1994). Kusunoki et al. asserted that effective
product development capabilities are significantly different between these groups according to thedifference in assembly/system and process/material development project groups.
However, the distinction between assembly and process products is not as clear as is usually
2Core project members had, on an average, worked at the respondent firms for 14.21 years. Projects for novel productcategories accounted for 23.98% of the samples. The mean period after the first model of the product genre was released
was 10.13 years, and the mean generation period of the product in the product line was 3.14 years.
3
The mean of the sales of the potential respondent firms was 6.84 billion yen.
4
See Yasumoto and Fujimoto (2005b) for details of the entire analysis including the process product development groupand other product development strategies and factors.

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Reconsidering novel technology introduction strategies

expected. For instance, products with a small amount of components/ingredients are not always
process products and visa versa. We asked the respondents to provide the ratio of engineering hours
to the total product engineering hours for product and component design in the product/processengineering stages.
We presumed that the ratio of engineering hours to the total product engineering hours for the
product/component design would reflect the fundamental product complexity of the product inquestion. The fundamental complexity would define the knowledge level with regard to the productstructure. Firms of assembly products at least possess the knowledge that an assembly product isdesigned as a set of distinctive components. Accordingly, assembly product development projects are
expected to allocate many of the resources to product/component design and related prototyping andtesting.
On the contrary, in many cases, firms of process products scarcely have sufficient knowledge toarticulate the structure of a process product into a set of physical designs. Thus, process productdevelopment projects use most of the resources for process design and related prototyping/testing.
The difference in knowledge level with regard to the fundamental product complexity could bringabout the critical differences between the assembly and process product groups with regard to the
product development strategies they use.
Reflecting this difference, we tentatively divided the samples on the basis of the ratio of 36%
since the mean of the ratios was 35.13%.5 The mean engineering hours ratio of the assembly productgroup was 43.06% while that of the process product group was 23.26%. The ratio of the assembly
product group seemed to be relatively low. The reason for this is that many of the resources wereallocated to prototyping and testing even in assembly product development projects.
In order to confirm the differences in statistical structure between these groups, we applied
Brown-Forsythe’s F-test to the ratio of engineering hours for product/component design. The
standard deviations of the assembly and process product groups were 1.98 and 2.59, respectively. The
result evidenced the significant difference in the variance between the assembly and process productgroups (F = 12.96, p < 0.0001). The distinction between the assembly and process product groups
could also have significant implications in terms of statistical structure.
After the elimination of the samples that included defect values, we focused on the 118
assembly product group samples for our analysis. The assembly product group included the5

The ratio of engineering hours for product/component design to total engineering hours had a significant positive
correlation with the amount of product elements/design drawings (r = .30, p < 0.01).

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Yasumotofollowing industries/products: computer systems/software (n = 12), consumer electronics (n = 23),
electronic parts (n = 21), industrial machinery (n = 24), mechanical parts (n = 7), precision mechanics(n = 24), and transportation machines (n = 7).

Product development performance
Much of the literature on product development management has considered project performance inorder to identify the effective attributes of product development projects (e.g., Clark & Fujimoto,
1991; Iansiti, 1997). Since the unit of analysis in our study was a single product development project,
we collected the data of six performance variables of the product in question: customer
satisfaction/total quality, engineering hours, development lead time, specific functional performance,
sales/market share, and profit.6
Considering the problem of comparability of performance between industries, we asked
respondents to check each of the performance levels on a 5-point Likert scale (1 = “not successful atall” to 5 = “highly successful”). Objective performance measures are not deemed appropriate forinterindustrial studies. Even though we could successfully collect objective performance data, thecomparison of data across various industries would be almost impossible.
On an average, across all the product types, the performance scores of the sample projects
appeared rather high. With regard to all the measures, every respondent estimated the selected project
as more or less successful. Mean scores of customer satisfaction and functional performance andsales/market share were particularly high in the assembly product group: 4.41, 4.37, and 4.22,
respectively (4.46, 4.40, and 4.24 across all the industries). This might indicate that in all theindustries, customer satisfaction/total quality, functional performance, and sales/market share areparticularly critical performance measures for successful projects.
In order to verify that the respondents applied similar success criteria, we collected data on
success criteria by asking respondents to choose the latter from five alternatives (multiple-choice
format). The success criteria that the respondents chose were “compared with those of the products ofrivalry firms” (n = 148, 37.19%), “past products” (n = 109, 27.39 %), “past products of the concernedfirm” (n = 67, 16.83 %), “success criteria within the concerned firm” (n = 71, 17.84 %), and “others”(n = 3, .75 %). The ratios with regard to the success criteria were not significantly different between
the assembly and process product groups,7 implying that the respondents adopted similar success
6
7

All the performance variables significantly contributed to sales and profits (p < 0.01).
The results were as follows: “compared with products of rivalry firms” (χ square = 0.01, p = 0.98), “compared with past
products” (χ square = 0.01, p = 0.92), “compared with past products of the concerned firm” (χ square = 0.02, p = 0.89),

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Reconsidering novel technology introduction strategies

criteria even across multiple industries.
We also examined the differences in the mean scores and the variance of each performancemeasure between the assembly and process product groups (Appendix 1). In spite of the difference
between the groups, we could not identify any significant differences. This result seemed to suggestthat despite the variety of industries, the subjective project performance estimation, which indicates
how project managers prioritize performance variables, of the project managers, was similar acrossthe sample projects. Thus, it could be concluded that across all the industries, respondents would
provide data in terms of similar performance criteria.

Product development strategies
We questioned the respondents about 19 variables of product development strategies, and conducteda factor analysis. The respondents of the questionnaire were asked if each of the descriptions(mentioned later) fitted a characteristic of the product development project in question; they wereasked to respond by comparing the concerned product to other products in general. Responses weremade using a 5-point Likert-scale (1 = “not successful at all” to 5 = “quite successful”).
After eliminating 15 cases with defect values, we applied factor analysis (principal components
analysis) to 188 samples, selected the factors with an eigenvalue greater than one (1.00), andfollowing past studies, named the selected factors “types of development strategies” (Appendix 2).
The measures loaded mostly on separate factors, and all the factor loadings were above 0.40, which
is a common threshold for acceptance. The factor analysis model fitted the data reasonably well (χsquare = 888.495, df = 210, p < 0.001).
From the original factor analysis, we chose the results of 11 variables concerning element
technology development, function design, product design, prototyping and testing, andmanufacturing process design.8 We identified three factors related to novel technology introduction
and product/process engineering, and used the cases of the assembly product group for our analysis.9
We found that Factor 1 (eigenvalue = 3.96, contribution ratio = 0.21, α = 0.82) consisted ofvariables such as communication at the product/process engineering stages and overlapping between
the product and process engineering stages. Therefore, Factor 1 was named “engineeringand “compared with success criteria within the concerned firm” (χ square = 0.61, p = 0.44).
We eliminated three factors and variables related to concept integration, front-loading, and leadership, from the original
result of the factor analysis on 19 variables.
9
All the strategies are significantly correlated with the collaboration with suppliers (r = 0.24, p < .0.01; r = 0.3, p < 0.01; r= 0.21, p < 0.05). The collaboration with suppliers could work with any of the strategies. These results partly explicate
why novel technology introduction strategies were not distinguished from cross-functional integration—in terms ofsupplier involvement—at the engineering stages (e.g., Takeishi, 2002).
8

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Yasumotointegration.” The factor included cross-functional integration across engineering sections andoverlapping between product engineering stages and premanufacturing stages.
Effective overlapping contributes to shortening lead time, thereby increasing the accuracy of thesimulation (Clark & Fujimoto, 1991). While overlapping between related stages is not necessarily
accompanied by communication between the stages, information exchange is critical for effective
problem-solving in overlapping (Adler, 1995; Clark & Fujimoto, 1991; Terwiesch & Meyer, 2002).
This is nothing more than cross-functional integration at the product/process engineering stages.
Factor 2, the first factor of a novel technology introduction strategy, was named “technology
integration” (eigenvalue = 1.41, contribution ratio = 0.07, α = 0.71) as it was heavily loaded with fivevariables related to the search and simulation of element technologies in the early stages.
Project members need to collaborate among technology and product/process developmentgroups to effectively integrate novel technologies into products (Iansiti, 1997; Tatikonda & Rosenthal,
2000). This is nothing more than “technology integration” (Iansiti, 1997). For instance, in supercomputer product development, from the early stages, product development projects conduct
intensive searches and simulations of materials, components, and product designs.
Factor 3, the second factor of novel technology introduction into products, was heavily loadedwith the variables of separation of element technology development from product/processengineering. Therefore, we called this factor “separated technology development” (eigenvalue = 1.18,
contribution ratio = 0.06, α = 0.60). Studies reported that the separation of novel technology
development from current product development enables firms to respond to drastic technologicalchanges while pursuing effective product development based on current technologies (Iansiti,
McFarlan, & Westerman, 2003). Firms may reduce technological uncertainty in product developmentby the separation of element technology development. Separating problem-solving with regard toelement technology development from product/process engineering diminishes the search andsimulation for technology integration undertaken to accelerate product engineering per se in eachproject (Cusumano & Nobeoka, 1998; Cusumano & Yoffie, 1998; Funk, 2002; MacCormack,
Verganti, & Iansiti, 2001).
The relationships of these strategies with aspects of product development performance further
revealed the necessity of these distinctions. Therefore, we examined the correlations between aspects
of product development performance and strategies (Appendix 3). The analysis demonstrated thatengineering integration was significantly correlated with all the performance measures, with the
exception of customer satisfaction/total quality (p < 0.01 or p < 0.05). At least for the sample
16

Reconsidering novel technology introduction strategies

Japanese product development projects, most of the aspects of product development performance
could be attributed to engineering integration.
Technology integration was significantly related to several specific aspects of performance suchas customer satisfaction/total quality and specific functional performance. Yet, separated technology
development did not show a significant correlation with any of the performance measures. These
differences supported the fact that these product development strategies are mutually distinguishable
also in terms of the aspects of performance.

Analysis
Does market differences matter?
We may presume that the predictability of product development strategies depends upon the
distinction between consumer and industrial products.10 The characteristics of target markets would
depend upon the target customers of the product in question. In the development projects for many
consumer goods such as automobiles, consumer electronic appliances, and so on, customer needs are
uncertain and/or equivocal. For example, while exterior styling, color, aesthetic design, and feelings
are critical factors in automobile development, these ergonomic features can hardly be defined in anarticulate manner; moreover, changes in tastes are also difficult to predict (Clark & Fujimoto, 1991;
Iansiti & Clark, 1994).
On the contrary, the goal specifications of business/industrial products (e.g., products forbusinessmen, professionals, SOHO, hospitals) can be derived from specific functional criteria such asprocessing speed, capacity size, and so on (e.g., Christensen, 1997; Iansiti, 1997). For example,
Iansiti described the advancement in a single technological function—processing capacity—as the
major goal of super computer development. Thus, in contrast to the cases of consumer productdevelopment projects, developing industrial products are focused on relatively instrumental andspecified features that are sometimes directly provided by the customers (von Hippel, 1994).
However, this simple categorization of the target market—whether consumer or industrial—has
the risk of obscuring more specific market characteristics,11 which might be perceived by project10

We asked the respondents to choose the most approximate product category—in terms of the target market—from two
categories: consumer products (= 1) or industrial products (= 0). The number of consumer assembly products was 35(29.66%), while that of industrial assembly products was 83 (70.34%).
11
The mean number of competing products—10.18—in the consumer product group was significantly larger than that in
the industrial product group—6.12 (F = 6.46, p < 0.01). Also, the mean period—21.57 months—of the standard modelchange cycle of the consumer product group was significantly shorter than that of the industrial product group—36.3
months (F = 6.82, p < .0.01). These differences indicate that competition is, in general, more intensified in consumer
product markets than in industrial ones due to the fundamental unpredictability of customer/market needs. In practice, theconsumer product group was significantly correlated with the elusiveness of customer/market needs (r = 0.29, p < 0.01).

17

Yasumotomembers as having direct impacts on product development strategies. Product development strategies
are not adopted according to the dichotomy of markets, which could be fluid according to theattributes of target customers. Even consumer/industrial products (e.g., copiers, personal computers,
software, wireless phones, etc.) are often tailored to industrial/consumer customers.
The fluid boundary between consumer and industrial products made us infer that perceived
market uncertainty rather than market dichotomy has more direct impacts on product development
strategies. The dichotomous characterization of the target markets would blur the factors that havedirect impacts on product development strategies. Thus, we contrived several perceived measures toexplicate product development strategies.

Independent variables
We employed three measures of product characteristics. With regard to technological uncertainty, wemeasured “necessity of element technology development” with a 5-point Likert scale (1 = “notnecessary at all” to 5 = “extremely necessary”). Past studies have mainly focused on the impacts oftechnological change/novelty. Yet, these factors are rarely examined separately from customer/market
volatility (e.g., Christensen, 1997; Cusumano & Yoffie, 1998; Dater, 1997; Eisenhardt & Tabrizi,
1995; MacCormack, Verganti, & Iansiti, 2001).
Further, the intensity of technology development was measured by the quantity of preceding
patents related to the product (e.g., Tatikonda & Rosenthal, 2000). However, the quantity of patents
could be largely related to the complexity of the concerned products;12 this means that in general,
products with more distinctive elements could yield more patents. It should also be noted that asdemonstrated in pharmaceutical industries, patents do not necessarily account for firms’ capabilities(Henderson & Cockburn, 1994). Thus, following Song and Montoya-Weiss (2001), we attempted tomeasure perceived technological uncertainty.
On the other hand, we measured the level of product complexity by using two measures. First,
we attempted to measure the “quantity of evaluated product functions,” which was checked in the test
process of the concerned projects. We asked the respondents to check the approximate number on a
5-point logarithm scale (1 = “1,” 2 = “10,” 3 = “100,” 4 = “1,000,” 5 = “10,000”).13 Product
12

The quantity of patents were significantly related to both the necessity of element technology development (r = 0.19, p <0.05) and technology integration (r = 0.2, p < 0.05). Yet, the quantity of patents was also significantly correlated with thequantity of evaluated product functions (r = 0.27, p < 0.01), the quantity of product elements/design drawings (r = 0.22, p< 0.01), and the number of project members (r = 0.25, p < 0.01). These results showed that the quantity of patents is
related to quantitative product complexity rather than technological uncertainty.
13
The quantity of evaluated product functions had positive correlations with the quantity of product functions required bycustomers (r = 0.26, p < 0.01) and the amount of product elements (r = .49, p < 0.01).

18

Reconsidering novel technology introduction strategies

complexity could be captured in terms of the amount of product elements (Anderson, 1999). An
automobile has product complexity largely because an automobile consists of 20,000 to 30,000 parts.
Other things being equal, a larger amount of evaluated product functions will be related toseveral interdependent elements and are attained as the results of the synthesis of the interdependent
elements. In effect, Kusunoki (1999) considered product complexity in terms of the amount ofevaluated product functions, thereby explicating the cross-functional integration and related routines
in Japanese semiconductor firms.
Nevertheless, we predicted a fundamental difficulty in measuring product complexity with thequantity of product elements. The amount of product elements may be independent of the level ofinterdependencies. Product complexity has been conceptualized in terms of the level ofinterdependencies between product elements, which define the required knowledge, cost, and time
for realizing a new product (e.g., Baldwin & Clark, 1999; Garud & Kamaraswamy, 1995; Langlois &Robertson, 1992).
Thus, we also measured the architectural attributes of product complexity with the level of“decomposability of the concerned products into independent components,” using a 5-point Likert
scale (1 = “extremely low” to 5 = “extremely high”). Product design complexity, which could, forinstance, be characterized by the concept of product architecture, determines the mode oforganizational coordination (Sanchez & Mahoney, 1996; Ulrich, 1995).
Finally, we measured the “elusiveness of customer/market needs” as the variable of market
uncertainty, using a 5-point Likert scale (1 = “not necessary at all” to 5 = “extremely necessary”).14
The impacts of market volatility are often considered in terms of the model change cycle (e.g.,
Eisenhardt & Tabrizi, 1995; MacCormack, Verganti, & Iansiti, 2001). When the model change cycleis short, firms can hardly have sufficient knowledge of the requirements of customers/markets. Yet,such industrial volatility does not necessarily cause market uncertainty.
In volatile markets, faster product releases can help firms obtain a competitive advantage against
competitors (Dater, 1997). Volatile markets in terms of the model change cycle encourage firms to
adopt cross-functional routines to develop products faster than competitors (e.g., Eisenhardt &Martin, 2000; Eisenhardt & Tabrizi, 1995). Nevertheless, volatile markets may also urge firms to
14

Model change cycles had a slight negative correlation with the elusiveness of customer/market needs (r = –0.15, p <0.10). The mean of the supplier involvement of the consumer product group—3.98—was significantly larger than that of
the industrial product group, 3.50 (F = 3.19, p < 0.01). On the contrary, the mean of the customer involvement of theconsumer product group—3.17—was slightly smaller than that of the industrial product group, 3.17 (F = 3.19, p < 0.10).
These results indicate that facing more customer/market needs uncertainty, consumer product development projects areliable to adopt suppliers’ capabilities.

19

Yasumotoseparate element technology development from product/process engineering in order to quickly adopt
novel element technologies for products (e.g., Chesbrough, 2003; Cusumano & Yoffie, 1998;
MacCormack and Verganti, 2003; MacCormack, Verganti, & Iansiti, 2001). These conflicting
findings may arise because model change cycle as a variable of industrial dynamism could reflect
both market and technological volatilities.
A shorter model change cycle may not be an appropriate variable of market uncertainty since themodel change cycle often reflects the velocity of technological change rather than market uncertainty(Yasumoto & Fujimoto, 2005a). The customer/market needs of personal computers are notnecessarily more uncertain than those of automobiles, even though the former’s model change cycle
is much shorter than that of the latter. Automobile firms may face more ambiguous customer/market
needs even when the model change cycle is long (i.e., several years) (Clark & Fujimoto, 1991). Thus,
we decided to adopt a more direct measure of market uncertainty.

Contexts
In order to examine the contextual differences between product types, we measured several context
variables related to the concerned products. These context variables were expected to provide thefundamental conditions of the product development projects. After the examination of thecorrelations between independent and context variables, we decided to use three context
variables—model change cycle, industrial difference, and product novelty—for our analysis. Projectsize,15 which we measured with the number of core project members, was substituted by the productcomplexity variable: the quantity of evaluated product functions.
First, with reference to the past studies reviewed in the previous section, we measured the real
duration of the standard “model change cycle” (months) within the market of the concerned product.
Second, we divided the samples into two industry categories (1 = “electronic” or 0 = “mechanical”)
according to the industrial categories of the products concerned. The difference between these two
industries is important since the impacts of product design attributes are more emphasized in the caseof electronic industries than in the case of mechanical ones (e.g., Baldwin & Clark, 1999).
The number of electronic products was 56 (47.46%), while that of mechanical products was 62(52.54%). Electronic products included computer systems/software, consumer electronics, and15

Studies have suggested that the scale of an organization influences the organizational structure. See Clark and Fujimoto(1991). The number of project members was positively correlated with the quantity of evaluated product functions (r =0.31, p < 0.01) and the amount of product elements (r = 0.5, p < 0.01). The number of project members depends on
product complexity.

20

Reconsidering novel technology introduction strategies

electronic parts. Mechanical products included industrial machinery, mechanical parts, precision
mechanics, and transportation machines. This variable was used as a dummy variable.
Third, we considered “product novelty” as another context variable. This variable was also used
as a dummy variable. We asked respondents whether or not the concerned product was completely
new to the preceding product genre within the respondent firm (1 = “novel” or 0 = “conventional”).
The number of novel products was 27 (22.88%), while that of conventional products was 91(77.12%). Product novelty could have significant impacts on product development strategies (Olson,
Walker, & Ruekert, 1995; Song & Xie, 2000).
The question was intended to capture product novelty in terms of the distinction between
conventional products following past product technologies and novel products without any precedingproduct technologies. If firms do not have preceding product models/lineups of the concerned
product, firms can hardly reuse the knowledge, namely, the marketing, technologies, design, parts,
and/or manufacturing process of the preceding products.

Results
We considered five multiregression analysis models from the product characteristics and context
variables with regard to the two novel technology introduction strategies. Table 1 shows the results(see the correlation matrix between these variables in Appendix 4).
Model 1 was the baseline model that included only product characteristics as the main factors.
Model 2 examined the effects of the model change cycle instead of the elusiveness ofcustomer/market needs, which had a slight negative correlation with model change cycle (r = –0.15,
p < 0.10). In model 3, two context variables were added to model 1. In model 4, the interaction terms
for the three product characteristic variables were added to model 1. In model 5, the context variables
were added to model 4. In the other potential models, either the R squares or F-values decreased
drastically compared to in the presented models, with the exception of model 2. Thus, in order tosimplify our examination, we decided to consider the four presented models 1, 3, 4, and 5.
The variation inflation factors (VIF) and condition indexes associated with each of theregression coefficients ranged from 1.03 to 1.08, and the condition indexes associated with each of
the regression coefficients were below 2.56. The results suggested no serious problems withmulticollinearity in any of our models.
For each of the variables, we paid attention to the significant effects that were common acrossthe models. Yet, it is important to note that the main effects of the product characteristics remained21

Yasumoto

Table 1. Results of multiple regression analysis
Technology Integration
Independent and Context Variables
Necessity of Element Technology Development

Model 1

Separated Technology Development

Model 2Model 3Model 4Model 5Model 1Model 2Model 3Model 4Model 5**0.33 **0.32**0.32**0.36 **0.35

*0.18

✝0.16

*0.20

*0.17

*0.19

0.03

**0.20*0.19*0.18 **0.21*0.19

Decomposability into Independent Components**-0.28 **-0.25 **-0.28 **-0.25 **-0.25

0.10

0.12

0.10

0.10

Elusiveness of Customer/Market Needs✝-0.15

Quantity of Evaluated Product Functions

0.00

-0.01

0.03

0.00

-0.14 ✝-0.16-0.14

Necessity of Element Technology Development*Quantity of Evaluated Product Functions

-0.10

-0.06

0.03

0.01

Necessity of Element Technology Development*Decomposability into Independent Components

0.11

✝0.15

0.05

0.03

Necessity of Element Technology Development*Elusiveness of Customer/Market Needs
Quantity of Evaluated Product
Functions*Decomposability into Independent
Components
Quantity of Evaluated Product Functions*Elusiveness of
Customer/Market Needs

0.09

0.12

0.13

0.11

0.10

0.10

-0.07

-0.05

-0.05

-0.07

-0.05

-0.04

Decomposability into Independent
Components*Elusiveness of Customer/Market Needs

0.00

-0.02

-0.13

-0.11

0.00

Model Change Cycle
Industry Dummy (0 = mechanics, 1 = electronics)
Product Novelty (dummy, 0 = conventional, 1 = novel)

R squareF**-0.23

0.10

0.20**6.050.17*5.05**-0.23 **-0.24 **-0.24

0.06
-0.10
0.13
0.20
**4.58✝-0.16
✝0.15
0.25
0.27
**3.0 **2.96

0.14**5.130.03
0.00**-0.21✝-0.19
0.07
0.16
0.17
0.19
*2.77 **4.74 **2.62 **2.65

notes: n = 118. Standardized values of dependent and independent variables were used for all the models.
† p < .10, * p < .05, ** p < .01

robust in all the models, even when the interaction terms and context variables were included. These
interaction terms and context variables increased the R squares slightly, while reducing the F-values;
further, they rarely showed prominent effects on novel technology introduction strategies. These
results suggested that each of the factors could severally explicate novel technology introduction
strategies.
The necessity of element technology development and technological uncertainty strongly
explained both technology integration (p < 0.01) and separated technology development (p < 0.05).
Yet, separated technology development was explicated by the quantity of evaluated product functions
and quantitative product complexity (p < 0.05 or p < 0.01), rather than technological uncertainty.
The result evidenced that element technology development should be separated fromproduct/process engineering, particularly when quantitative product complexity is relatively high.
Accordingly, the task loads of product/process engineering are high as the projects need to cope witha variety of element technologies. Thus, the task loads for element technology development at the
product/process engineering stages could be alleviated by separating element technology
development from product/process engineering.

22

Reconsidering novel technology introduction strategies

On the other hand, the decomposability of products into independent components, which would
indicate product complexity in terms of architectural stability, did not show any significant effects on
separated technology development, but had significant negative impacts on technology integration (p< 0.01). The result revealed that projects are liable to adopt technology integration when architectural
knowledge, which provides the stability of product designs to secure the independency of eachcomponent, is insufficient.
These results seem to support the finding concerning product architecture such as modularityand platform management. Yet, we should note that decomposability is not necessarily bound tomodularized product development based on a separated core technology/platform development. The
results did not show any significant effects of decomposability on separated technology development.
These results on the decomposability of products into independent components are inaccordance with those on product novelty. Product novelty, which is liable to arise with drastic
design changes, had slightly significant positive effects on technology introduction (p < 0.10);
however, it had significant negative effects on separated technology development (p < 0.01 or p <0.10).
The elusiveness of customer/market needs had significant negative effects on both the strategies(p < 0.01 or p < 0.10). The results revealed that firms are not liable to adopt novel technologies
unless customer/market needs are relatively explicit. In particular, separated technology development
was elucidated by explicit customer/market needs. The strategy of separated technology development
was slightly correlated with customer involvement (r = 0.15, p < 0.10), while technology integration
was not. Accordingly, industrial product development projects, which entail customer involvement
more than consumer development projects do (F = 39.21, p < 0.001), are more liable to adopt
separated technology development as compared to consumer development projects (F = 11.24, p <0.001). However, technology integration did not show such a difference. These results suggested thatfirms employ separated technology development in order to respond to each of the specificrequirements directly designated by customers.
Finally, the model change cycle and industry dummy did not have any effects on either of thenovel technology introduction strategies. These industrial variables per se did not determine thenovel technology introduction strategies. Instead, more specific product attributes, namely, productcomplexity and market uncertainty, are the critical determinants of novel technology introduction
strategies.

23

YasumotoDiscussion
The concept of cross-functional integration included a broad range of organizational routines for
information exchange and collaboration between functional units. In many past studies,cross-functional integration has been generalized as the bundle of communication and collaboration
for knowledge processing across functional organizational units (e.g., Brown & Eisenhardt, 1995;
Clark & Fujimoto, 1991; Eisenhardt & Tabrizi, 1995; Iansiti, 1997; Iansiti & Clark, 1994; Kusunoki,
Nonaka, & Nagata, 1998; Takeishi, 2002).
To further examine cross-functional integration, the analysis began with the demonstration of
the fact that technology integration is distinguished from cross-functional integration at the
product/process engineering stages (i.e., engineering integration). Then, we verified that novel
technology introduction strategies are divided into technology integration and separated technology.
Following the distinctions between product development strategies, the study provided evidence thatsuccessful projects choose either of the novel technology introduction strategies according to productcharacteristics and/or contexts.
The results showed that both the strategies arise from the necessity for novel technologies. Yet,technology integration would be effective in the case where the architectural knowledge of productdesigns is not sufficient to secure the decomposability of the products into independent components.
This novel technology introduction strategy is particularly critical in order to realize drastic
technological changes, which involve architectural changes of product designs (Chesbrough &Kusunoki, 2001; Henderson & Clark, 1990; Iansiti, 1997). Technology integration is suitable forindustrial volatility in the sense of drastic technological change, though seemingly contributing to
respond to both market and technological volatility (Eisenhardt & Martin, 2000; Eisenhardt & Tabrizi,
1995).
Cross-functional integration for novel technology introduction enables exploratory knowledge
creation for the acquisition of novel knowledge, which competitors can hardly imitate or acquire(Benner & Tushman, 2003; Iansiti & Clark, 1994; Kusunoki, Nonaka, & Nagata, 1998). Each of theelement technologies, such as patented technologies and modularized components, does notnecessarily contribute to fundamental product innovativeness, which results from organizational
capabilities (Henderson & Cockburn, 1994). Element technologies such as standardized modules inparticular, could be transferable between firms and/or imitated by other firms, so that thesetechnologies would not necessarily secure firms’ competitiveness (Anderson, 1999; Pil & Cohen,2006).
24

Reconsidering novel technology introduction strategies

Even if a large portion of a product comprises modularized/standardized components, a firm thathas the capabilities to manage the interdependencies between the components and integrate them
(Brusoni & Prencipe, 2001) would be prominent in markets. In practice, the data showed thattechnology integration contributes to some aspects of product development performance, particularly
specific functional performance.
Nevertheless, technology integration, which encourages firms to design products aligned withnovel component development, requires cost and time for each project. Firms can utilize separated
technology development to reduce the task load of each development project (Cusumano & Nobeoka,
1998; Krishnan & Gupta, 2001; MacCormack & Verganti, 2003; Tatikonda, 1999). The results
suggested that this strategy is particularly called upon for the development of quantitatively complex
products, which accordingly include a variety of element technologies changing at uneven paces.
Past studies have also suggested that separated technology development may contribute toshortening product development lead time and would thus be flexible to industrial volatility (e.g.,
Cusumano & Yoffie, 1998; MacCormack, Verganti, & Iansiti, 2001; Thomke, 1997). However, rather
than technological uncertainly and model change cycle, this strategy was explicated by the specificity
of customer/market needs, which, above all, are directly provided by customers (von Hippel, 1994).
These findings allow us to infer that separated technology development could be flexible to a varietyof specific customer/market needs and technologies, rather than to industrial volatility.
The impacts of industrial volatility are often considered in terms of model change cycle (e.g.,
Cusumano & Yoffie, 1998; Eisenhardt & Tabrizi, 1995; MacCormack, Verganti, & Iansiti, 2001). In
volatile industries, faster product releases could help firms obtain a competitive advantage against
competitors (Dater, 1997). Thus, in terms of model change cycles, volatile industries encourage firms
to develop products faster than competitors. Nevertheless, we did not find any significant effects ofmodel change cycle on novel technology introduction strategies. Both the conflicting strategies,
technology introduction and separated technology development, are explicated more appropriately bytechnological uncertainty, quantitative product complexity, product decomposability, and/or market
uncertainty. The results suggest that industrial volatility per se is not as critical as these factors.
While adaptive to industrial volatility with radical technological changes, technology integration
can hardly cope with market and technological varieties/variations. Separated technology
development would complement technology integration. The attempt to utilize separated technology
development, which is adopted in conventional product development projects rather than novel ones,
would be indispensable for responding to the product design varieties following product designs
25Yasumotoverified in preceding projects.
Simply relying on either of the strategies would jeopardize firms’ competitiveness in the age ofinterfirm modularity and related open interfirm networks (e.g., Chesbrough, 2003; Christensen,
Verlinden, & Westerman, 2002; Gawer & Cusumano, 2002; Sturgeon, 2002). Firms need to refurbish
core technologies on one hand, and employ element technologies for various customers/markets on
the other. Any single novel technology introduction strategy at the project level can hardly cope with“variety” or “volatility.” The role of mediating between conflicting requirements is assigned tocontrived product design strategies, for instance, multiproject/platform strategies that are beyond
single product development projects (e.g., Cusumano & Nobeoka, 1998; Funk, 2002; Krishnan &Gupta, 2001; Tatikonda, 1999; Ulrich & Ellison, 1999).

Conclusions
Preceding studies have particularly focused on the impacts of technological change/novelty on
product development, and have emphasized cross-functional integration for novel technology
introduction (e.g., Eisenhardt & Tabrizi, 1995; Iansiti, 1997; Song & Xie, 2000; Tatikonda &Rosenthal, 2000). In the line of studies, the effects of design and market attributes were often blurredand confused with those of technological change/novelty. Yet, the results here indicated thattechnological change/novelty, product design, and customer/market needs, each have different effects
on novel technology introduction strategies.
The architectural attributes of product design could determine the range of technologicalinnovations (Chesbrough & Kusunoki, 2001; Henderson & Clark, 1990). Novel technology
introduction with drastic element technology change may erode the decomposability between productdesigns and elements, and would thus enhance technology integration for problem-solving (Iansiti,
1997; Takeishi, 2002). Yet, novel technology introduction does not necessarily result in technology
integration (Chesbrough, 2003; Iansiti, McFarlan, & Westerman, 2003).
Firms may separate technology development from each of its specific projects and introduce
new element technologies into a portion of the product systems (e.g., Chesbrough, 2003; Cusumano& Yoffie, 1998; MacCormack & Verganti, 2003). The difference between the two strategies mostly
emerges from design attributes and quantitative product complexity and decomposability, which
provide the conditions for novel technology introduction modes. Reflecting the difference, firms
would adopt novel technology introduction strategies according to customer/market attributes.
The results revealed that both novel technology introduction strategies are adaptive to relatively
26

Reconsidering novel technology introduction strategies

explicit customer/market needs. Yet, separated technology development responds to a variety of
specific requirements directly provided by customers; on the other hand, technology integration
focuses on specific aspects of technological performance. Technology integration is the strategy
suitable in the case of strong industrial volatility caused by drastic technological changes. In contrast,
the aim of separated technology development is to respond to the varieties of specificcustomer/market needs and technologies rather than industrial volatility. Separated technology
development is necessary when drastic technological changes through technology integration are notgeared to existing customers/markets (e.g., Iansiti, MacFarlan, & Westerman, 2003).
Simply relying on technology integration is a rather risky approach, even though firms’
competitive advantages rest in their capabilities to integrate various elements. Refurbishing productdesigns and components for each novel technology introduction would not be effective, particularly
where open interfirm networks to foster product development are shaped (Chesbrough, 2003;
Christensen, Verlinden, and Westerman, 2002; Sturgeon, 2002). At the business level, beyond the
project level, firms need to integrate various technologies, paying sufficient attention to both unevenchanges of various elements and interdependencies between components (Brusoni & Prencipe,2001).
In order to make proper use of both novel technology introduction strategies, firms should
devise coherent product development strategies, such as multiproject/platform strategies, at thebusiness level (Cusumano & Nobeoka, 1998; Robertson & Ulrich, 1998; Tatikonda, 1999). Without
product strategies to increase the flexibility at the business level, firms may face the problem of
optimization within single projects, for example, over-specification and high product cost despitehigh project performance (Clark & Fujimoto, 1991; Cusumano & Nobeoka, 1998; Funk, 2002).
The case of Japanese mobile phone manufacturers might provide the emblematic example.
While rapidly commercializing a variety of advanced technologies in the world, Japanese mobile
phone handset manufacturers’ performances are mostly not prominent in the world of mobile phoneindustries. Japanese manufacturers have commercialized novel technologies focusing on the Japanesemarket without sufficient consideration of product lineup strategies and platform management for theworld market (Funk, 2002).
In effect, in most of the regular handset model development projects, Japanese manufacturers
are liable to employ technology integration changing the handset architectural attributes, resulting inthem having difficulties in responding to the variety of elusive customer/market needs in the global
markets (Yasumoto & Fujimoto, 2005b). In spite of the functional novelty and product integrity, the27Yasumotoproblem of product development strategies ruins the competitiveness of Japanese firms in the world
market in terms of cost, speed, and product variety. The implications from this study would spell out
the nature of these problems.
In closing, this study may be regarded as an attempt to bridge the chasm between generic andindustry-specific studies in the field of product development management. The present study
attempted to examine past findings from industry-specific studies within a generic study context.
Hereafter, drawing on the cases of specific industries, we should further excavate how the
implications on novel technology introduction strategies are related to product architectures andplatform/multiproject strategies. At the same time, we also need to conduct international research as
the results in this study may be influenced by the attributes that are specific to Japan. While we arestill taking initial steps in this research area, this line of study seems to deserve further exploration interms of both content and methodology.

* The data used in this study was collected by means of collaborative research with Professor
Takahiro Fujimoto (the University of Tokyo).

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32

Reconsidering novel technology introduction strategies

Appendices
Appendix 1. Performance differences between assembly and process product groups
Number of
Samples
Assembly means.d.
Process mean
s.d.

118

Profit

4.41

3.75

3.81

4.37

4.22

0.62

0.77

0.86

0.61

0.79

0.74

70

4.53

3.80

3.84

4.47

4.18

3.92

0.50

0.70

0.81

0.53

0.73

0.79

188

1.36
1.06
4.45

0.68
0.45
3.77

0.39
0.15
3.82

1.28
1.51
4.41

-0.98
0.19
4.21

-0.04
0.19
3.91

0.57

0.74

0.83

0.58

0.76

0.76

t
Ｆ
Total means.d.

Customer
Product
Product
Specific
Satisfaction
Sales/Market
Development Development Functional/TotalShare
Cost
Lead time
Performance
QualityNote: Double-sided t-test and O'Brien's F-test. † p < .10, * p <.05, ** p <.01

Appendix 2. Factor analysis of product development strategies

Factor 1

Factor 2

Factor 3

mean

s.d.

Engineering
Integration

Technology
Integration

Separated
Technology
Development

Alternative core technologies were compared andanalyzed using prototypes in order to realize theproduct's concept and specifications.

3.56

1.05

0.09

0.73

-0.12

Alternative designs were prototyped and screened
within a spcified search range in order to achieve thetarget product specification and performance.

3.39

0.92

0.11

0.64

0.23

Effective coordination and communication were madebetween advanced element technology development
department and product development department.

3.63

0.97

0.10

0.51

-0.10

Period of core technology development was
overlapped with period of product concept/specification
development.

3.74

0.92

0.17

0.54

0.05

Core technologies were separately developed inadvance of product engineering.

3.66

1.19

0.00

0.46

0.60

The components were developed separetely bycomponent development groups.

3.33

0.96

0.14

-0.11

0.75

Intensive communication was made between members
in element technology development stages.

3.49

0.84

0.11

0.30

0.47

Period of product engineering was overlapped with thatof process engineering.

3.58

0.95

0.54

0.21

0.15

Effective coordination and communication were madewithin product engineering group.

4.01

0.76

0.84

0.03

0.00

Effective coordination and communication were madebetween product engineering department and processengineering/ production technology department.

3.84

0.83

0.76

0.20

-0.04

Intensive communication was made among members
in test/experiment stages.

3.82

0.76

0.65

0.08

0.13

Note: n = 188. Factor loadings were varimax rotated.
The shaded cells indicate values larger than 0.4 or smaller than -0.4.
Eigen Value
Eigen
Value
Product Development Routine Factor 1
Product Development Routine Factor 2
Product Development Routine Factor 3

3.96
1.41
1.18

33

Contribu
tion
Ratio
0.21
0.07
0.06

Cronbachα
0.82
0.71
0.60

3.91

Yasumoto

Appendix 3. Correlation analysis between development strategies and performances
Separated
Technology
Development

Mean

S.D.

Engineering
Integration

Customer
Satisfaction/Total Quality

4.41

0.62

0.10

0.14✝

-0.05

Product Development
Cost

3.75

0.77

0.17*

0.11

-0.03

Product Development
Lead time

3.81

0.86

0.23**

0.08

0.02

Specific Functional
Performance

4.37

0.61

0.15*

0.16*

0.04

Sales/Market Share

4.22
3.91

0.79
0.74

0.26**
0.17*

-0.03
0.10

-0.03
-0.07

ProfitNote: n = 118

Technology
Integration† p < .10, * p < .05, ** p < .01

Appendix 4. Correlation matrix between multiple regression model variables
Mean

S.D.

1

1 Technology Integration

-0.18

0.94

1.00

2 Separated Technology
Development

0.25

0.87

0.04

1.00

3 Necessity of Element
Technology Development

3.62

1.30

**0.32

*0.19

1.00

4 Quantity of Evaluated
Product Functions

2.88

0.83

0.07

0.10

-0.04

1.00

5 Decomposability into
Independent Components

3.16

1.22

**-0.23

✝0.14

0.10

-0.08

1.00

6 Elusiveness of
Customer/Market Needs

2.54

1.25

-0.09

**-0.23

0.10

0.01

-0.09

1.00

7 Model Change Cycle (MO.)

31.78

28.47

0.04

0.04

0.00

-0.10

-0.07

✝-0.151.00

8 Industry (dummy, 1 =electronics/0 = machinery)

0.53

0.50

-0.09

-0.03

0.02

0.07

-0.09

✝0.13

**-0.49

1.00

9 Product Novelty (dummy, 1= novel/0 = conventional)

0.22

0.42

0.08

-0.11

✝0.15

0.08

0.02

0.02

-0.02

0.09

Note: n = 118

2

3† p < .10, * p < .05, ** p < .01

34

4

5

6

7

8

9

1.00