21COE, University of Tokyo

MMRC Discussion Paper No. 27

MMRC
DISCUSSION PAPER SERIES

MMRC-F-27

The Determinants of Alternative Strategies forNovel Technology Introduction: Findings from 188 Japanese ProductDevelopment Projects
Institute of Innovation Management, Shinshu University（Manufacturing Management Research Center, The University of Tokyo）

Masanori Yasumoto
Graduate School of Economics & Manufacturing Management
Research Center, The University of Tokyo
Takahiro Fujimoto
March 2005

21COE, University of Tokyo

MMRC Discussion Paper No. 27

The Determinants of Alternative Strategies forNovel Technology Introduction: Findings from 188 Japanese Product Development Projects

Institute of Innovation Management, Shinshu University（Manufacturing Management Research Center, The University of Tokyo）
Masanori Yasumoto

Graduate School of Economics & Manufacturing Management
Research Center, The University of Tokyo
Takahiro Fujimoto

March 2005

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Masanori Yasumoto, Takahiro Fujimoto

Abstract
The study considers the determinants of alternative product development strategies for introducing
advanced technologies into new products.
to cross-functional integration.

Past studies have frequently related novel technology introduction

However, the strategies for novel technology introduction could be

distinguished from cross-functional integration in product/process engineering stages.

Novel technology

introduction, which may include advanced technology development, would not necessarily requirecross-functional integration in product/process engineering stages.

We assume alternative productdevelopment strategies for novel technologies introduction: “technology integration” and “separated
technology development.”

We attempt to explore the determinants of each of the product development

strategies for novel technology introduction.

Based on a contingency perspective, we collected and analyzed

questionnaire-based data from 188 successful Japanese product development projects. At first, we confirmedthat cross-functional integration in product/process engineering stages is distinguished from the two novel
technology introduction strategies.

Second, the results also demonstrated that technology development

separation is effective for developing less complex products in relatively volatile markets while technology
integration is apt for relatively complex products in less volatile markets. Firms do not need to choose one of
the alternative strategies for introducing novel technologies, but are required to exploit either of the strategies
according to each product’s product characteristics.

The findings are expected to help firms elaborate not only

platform/multi-project strategies at corporate level but also technology introduction strategies at project level.

Key Words: novel technology introduction, technology integration, separated technology development

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The Determinants of Alternative Strategies for Novel Technology Introduction

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 project success (Clarkand Fujimoto, 1991; Brown and Eisenhardt, 1995). Furthermore, the cross-functional integration and related
practices are assigned the role for exploratory knowledge creation (Benner and Tushman, 2003; Inansiti and
Clark, 1994; Kusunoki et al., 1998).
Product life cycles have dramatically shortened as technologies rapidly change particularly in competitive
hi-tech product markets. The industrial volatility urges firms to develop novel technologies into new products
faster than ever.

Reflecting the industrial volatility, it is frequently emphasized that cross-functional

integration and associated practices for novel technology introduction are regarded as key factors for projectsuccess in high-tech industries.
Product development performances, such as productivity, development speed, and product quality, rely 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 contribute to the performances effectively
integrating novel technologies into new products (e.g., Eisenhardt and Tabrizi, 1995; Gobeli and Foster, 1985;
Gomory, 1989; Iansiti, 1995; 1997; Tatikonda and Rosenthal, 2000; Song and Xie, 2000).
However, novel technology introduction into new products should be distinguished from cross-functional
integration in product/process engineering stages. As is shown in a comparative study between automobile
and supercomputer development projects (Iansiti and Clark, 1994), technology introduction strategies could be
distinguished from cross-functional integration in product/process engineering stages.
Furthermore, recent studies have asserted the power of modularity and associated modularly partitioned
practices in product/process engineering stages, which are mostly found in hi-tech segments such as software,personal computers, network systems, and so on (e.g., Baldwin and Clark, 1997; Cusumano and Yoffie, 1998;
Gawer and Cusumano, 2002; Iansiti and MacCormack, 1997). The studies suggest that firms may refurbish a
portion of a product system by adopting element technologies from the outside of the product developmentgroup, and thereby facilitates the novel technology introduction.

The line of studies also discusses

platform/multi-project strategies drawing on the design concept of “product architecture” (e.g., Gawer andCusumano, 2002; Robertson and Ulrich, 1998).
The above mentioned situation reveals two issues on novel technology introduction strategies at projectlevel. At first, we may cast a doubt if cross-functional integration for novel technology introduction could be
mingled with cross-functional integration for product/process engineering. Second, we pose a question on
how firms should make use of either of novel technology introduction strategies: introducing novel
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Masanori Yasumoto, Takahiro Fujimototechnologies through cross-functional integration or element technology development separation.
Researchers have paid particular attention to the impact of product design attributes on productdevelopment projects. However, antecedents also suggest that market and technological factors other than
product design attributes have impacts on product development projects and platform/multi-project strategies.
We have not sufficiently explored how these factors as well as product design attributes have impacts on novel
technology introduction strategies.
The lack of the knowledge on the determinants of novel technology introduction strategies would hamper
firms from choosing proper product development strategies at project level and shaping effective
platform/multi-project strategies at corporate level.

Making proper use of novel technology introduction

strategies at project level, which are closely interrelated to platform/multi-project strategies, is necessary forfirms particularly in hi-tech industries.
Drawing on the data from successful product development projects of Japanese firms, the study is to
explore how firms make use of novel technology introduction strategies.

Based on a contingency perspective,

the article posits that product development strategies for novel technology introduction may differ according to
product characteristics.
Whereas focusing on project level strategies for novel technology introduction, the attempt would also
contribute to elucidating the impacts of critical factors for effective platform/multi-project strategies for novel
technology introduction (e.g., How platform and derivative projects should play different roles according to thefactors?).

Since the purpose of the article rests on hypothesis generating rather than hypothesis testing, thestudy does not hypothesize any specific causality. We predict nothing but that product development strategies
for novel technology integration may differ according to product characteristics.
The paper outline is as follow.

First, we review antecedents, and thereby propose a generic prediction.

Based on the prediction, we examine the questionnaire-based data from 188 successful Japanese productdevelopment projects.

Following the results, we attempt to draw implications on novel technology

introduction strategies.

Backgrounds
Product Development Strategies for Novel Technology Introduction
Let us review how researchers have characterized effective product development strategies for novel
technology introduction.

From the mid 1980s, drawing on the cases of technology-based productdevelopment projects, which successfully developed advanced technologies into novel products, researchersattempted to explore the effective product development strategies for introducing novel technology into new
4The Determinants of Alternative Strategies for Novel Technology Introduction
products (e.g., Gobeli and Foster, 1985; Gomory, 1989).
In the 1990s, researchers collected product/industry-specific data of successful projects from firms, andexplored effective product development strategies for introducing novel technologies. Based on the data of
about thirty super computer or work station development projects of US and Japanese firms, Iansiti (1995;
1997) examined product development projects, which are accompanied by core technology development.
The study suggested that the “system-focused” approach facilitates the “technology integration” among
related functional groups, which is characterized by overlapping and associated intensive communication
between element technology development and product/process engineering groups.

The system-focused

approach results in shorter development leadtime, and enables more radical technologies than the“element-focused” approach.
The line of studies suggested that communication and overlapping between advanced technology
development and product/process engineering groups are critical for successful commercialization of novel
technologies. Several generic studies made use of large sample data from various industries, and suggested
that cross-functional integration is critical for developing new products with novel technologies (e.g.,
Eisenhardt and Tabrizi, 1995; Song and Montoya-Weiss, 2001; Song and Xie, 2000; Tatikonda and Rosenthal,
2000; Olson et al, 1995).
On the contrary, since the 1990s, product modularity has been highlighted in reference with successful US
firms particularly in IT industries: computer, electronics, and software (e.g., Baldwin and Clark, 1997;
Cusumano and Yoffie, 1998; Gawer and Cusumano, 2002; Iansiti and MacCormack, 1997). Researchers havesuggested that modular design enables manufacturers to decompose complex problem-solving into a set of
localized problem-solving (Baldwin and Clark, 1997).

The decomposability enables firms to separate

advanced component/element technology development from specific product development projects.
Modularity permits the introduction of novel elements/technologies into products with relatively low costand high agility.

The advantage enables firms to drastically improve product performance without tightorganizational coordination (Sanchez and Mahoney, 1996; Ulrich, 1995). Product development based on
modularity seems to obscure the importance of cross-functional integration and associated productdevelopment practices, which were once regarded as one of the critical factors of effective productdevelopment.
Product development capabilities which yield complex/novel products are regarded as the source ofcompetitiveness (Anderson, 1999).

Cross-functional integration for novel technology introduction is effective

in developing these complex/novel products. However, the attention to the novel technology introduction
strategy recently seems to languish. Element technologies may be developed separated from product/process5Masanori Yasumoto, Takahiro Fujimotoengineering, and introduced into products in the form of independent module units.

The research stream

would indicate that there are alternative strategies for introducing novel technologies into products:“technology integration” and “separated technology development.”

Making Proper Use of Product Development Capabilities
In the line of generic studies based on large sample data from various industries, the concept ofcross-functional integration is liable to include both technology integration and cross-functional integration in
product/process engineering stages (e.g., Eisenhardt and Tabrizi, 1995; Song and Montoya-Weiss, 2001; Songand Xie, 2000; Tatikonda and Rosenthal, 2000; Olson et al, 1995). However, industry/product specific studies
demonstrate that novel technology integration is distinguished from cross-functional integration in
product/process engineering stages.
Iansiti and Clark (1994) asserted that technology integration is the effective product development
strategies for technology-based complex products, which are accompanied by high technological uncertainty(e.g., super computers).

The “external integration” is related to uncertain customer needs of consumer productmarkets, and the “internal integration” is for high product complexity (e.g., automobiles). The study makes us
predict that we could distinguish technology introduction strategies from cross-functional integration in
product/process engineering stages.
On the other hand, from the 1990s, researchers have implied that effective (i.e., market success) productdevelopment strategies relevant to novel technology development may differ by product characteristics and/or
industrial dynamism.

As mentioned above, Iansiti (1995; 1997) examined advanced computer development

cases, and suggested the effectiveness of technology integration among related functional groups. The study
demonstrated that the technological uncertainty in advanced core technology development influences the modeof novel technology introduction.
Pisano (1997) examined 23 projects of pharmaceutical development projects, and pointed out that
effective product development process depends upon the uncertainty of manufacturing process engineering.
The study demonstrated that “learn before doing” is suitable for developing conventional chemical products
while “learn by doing” is effective for developing bio-pharmaceuticals.
In the case of conventional chemical product development, separated technology development in advanceof product/process engineering contributes to shortening the development leadtime because the knowledge on
product/process engineering for conventional chemical products is relatively sufficient (relatively low
technological uncertainty).

On the contrary, experimentation in product/process engineering stages

contributes to shortening the development leadtime of bio-pharmaceuticals because knowledge on
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The Determinants of Alternative Strategies for Novel Technology Introduction
product/process engineering is relatively insufficient (relatively high technological uncertainty). The study
would also suggest that technological uncertainty influences product development strategies for novel
technology introduction.
Product complexity as well as technological uncertainty also has impacts on product development projects.
Tight cross-functional integration and associated practices, such as HWPM (Heavy Weight Project Manager)
organization, are effective corresponding to the product complexity of automobiles (Clark and Fujimoto, 1991).
Also in advanced computer development projects, product and/or associated project complexity may have
impacts on product development strategies for novel technology introduction (Iansiti, 1997).

The complexityof the relationships between core technologies and related technologies encourages firms to adopt technology
integration.
Several generic studies have explicitly considered the effects of product characteristics on productdevelopment strategies.

The studies are supposed to contribute to generalizing the results fromproduct-specific case-based studies.

Compared to the case of incremental model change projects, projects fornovel products urge firms to adopt cross-functional integration and overlapping between element technology
development and product/process engineering (Song and Montoya-Weiss, 2001; Song and Xie, 2000; Olson etal, 1995).

Particularly when the products concerned are complex, technological uncertainty is likely toenhance the need for the cross-functional integration (Tatikonda and Rosenthal, 2000).
On the other hand, Eisenhardt and Tabrizi (1995) examined 72 development projects of computer products,
such as personal computers, super computers, work stations, and peripheral products. The study elucidated
that, as a whole, the “experiential approach” based on cross-functional integration contributes to shortening
development leadtime.
The experiential approach is particularly effective in the field of rapidly evolving products.

However, thestudy also suggested that product development projects in relatively stable environments are likely to adopt the“compression approach” based on planning and overlapping.

The line of studies particularly emphasized that

effective product development strategies vary with market variability (Eisenhardt and Martin, 2000).
Cross-functional and associated flexible processes are likely to be adaptive in volatile and thus uncertain
market/industrial environments.
Contrary to the studies, recent studies have suggested that modular product designs and associatedorganizations enable firms to effectively cope with technological changes and/or market variability (e.g.,
Cusumano and Yoffie, 1998; Gawer and Cusumano, 2002; Iansiti and MacCormack, 1997). The capabilities
to cope with technological changes and/or market variability rely on product complexity. If sufficiently
reducing product complexity, firms could refurbish a portion of the product system with novel technologies
7Masanori Yasumoto, Takahiro Fujimoto
according to market variability (Baldwin and Clark, 1997).

Though we find two different suggestions on theimpacts of market uncertainty on novel technology introduction strategies, market uncertainty is expected tohave impacts on the choice of product development strategies.
Reflecting the above findings from past studies, we presume that successful projects make proper use of
product development strategies according to product characteristics: technological uncertainty, productcomplexity, and market uncertainty.
The accumulated knowledge on product innovation management has implied that successful projects
adopt proper product development strategies corresponding to product attributes (Song and Montoya-Weiss,
2001; Song and Xie, 2000; Souder et al., 1998; Tatikonda and Rosenthal, 2000; Yasumoto and Fujimoto, 2005).
The line of studies makes us infer that the mode of novel technology introduction strategies 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.

Empirical Research
Basic Direction
Let us describe the direction of our analysis.

At first, the study examines whether or not firms

distinguish technology introduction strategies from product/process engineering. Second, the study shows
how firms successfully adopt novel technology introduction strategies according to product characteristics.
Because of the variety of products, modern manufacturing firms are required to employ apt productdevelopment strategies for novel technology introduction according to product characteristics and/or industrial
dynamism.

Examining the contingent application of product development strategies for novel technology

introduction would help us understand how firms could successfully introduce novel technologies into the
products.
Though the basic logic of the present contingency analysis is relatively simple, actual data collection andempirical analysis is not easy, partly because of some difficulties in measuring product characteristics,
development strategies, and performances across various industries. After trying various methods, we decided
to use subjective measures as the main yardsticks, and considered that each of the respondents would have abroad perspective in evaluating product development strategies in a better method.
What we measured in this study is the “perceived” characteristics of the product in question and productdevelopment practices. Product development performances were also measured in terms of respondents’
perceptions. This method may have some potential problems in measurement and validity.

8

There is a

The Determinants of Alternative Strategies for Novel Technology Introduction
fundamental trade-off here between accuracy and comparability of data1.
Considering the trade-off, we presumed that objective environments, novel technology introduction
strategies, and performances would be aligned in projects which project leaders themselves regard as successful.
Therefore, focusing on projects deemed to be as successful, we explored the relationship between productcharacteristics and estimated success levels of product development strategies.

Data Collection
We combined clinical field studies and statistical data collection. At first, from 1995 to 1997, we visited
32 development projects of products in various industries, such as apparel, automobiles, construction
equipment, chemical textile and resin, consumer electronics, communication devices, electronic components,
food/beverage, pharmaceuticals, industrial chemicals, industrial machinery, mechanical parts, medical
equipment, office equipment, precision mechanics, software, and toiletries, covering virtually all the
product/industrial categories we intended to study in our questionnaire survey. Combining our knowledge
from both the literature survey and field research, we selected variables and designed the questionnaire. We
then moved on to the questionnaire survey.
We collected data through a questionnaire survey mailed to 700 business units and research laboratories ofJapanese public firms in July 1997. The survey asked 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 todifferent business units or institutes within the same multi-divisional company.

We asked potential

respondents to select a relatively successful project that he or she had direct experience with in recent years,
and to answer the questions consistently about this particular project2.
We received 203 answers (response rate: 29 %) from 145 firms by the end of October, 1997.

The meansof the sales and number of employee of respondent firms were respectively 7.92 billion yen and 12,360
employees3. We checked the non-response bias on firm size (sales)4. No significant difference in firm size
1 A popular method for understanding effective or adaptive strategies is a pair approach: asking the responding firms to give us a pair of projects, a successful
and an unsuccessful one from their point of view, and to evaluate the level of adoption or effectiveness of each routine. If the level is significantly different
between the pairs for a given routine, we could say that it is an effective or adaptive routine. In reality, however, it is rather difficult to get responses about
failed projects from firms.
2 68.85 % of the sample projects consisted of less than 25 members (including part-time members) from planning to release. The mean product sale for the
first year after release was 27.41 billion yen. Projects for novel product categories for the respondent firms accounted 23.98 % of the samples. The meanquantity of related Japanese patents was 27.99. Core project members had on average experienced in respondent firms for 14.21 years. The mean period
after the first model of the product genre was released was 10.13 years, and the mean generation of the product in the product line was 3.14.
3 Firms with less than 5 billion yen constituted more than 50% of the samples, and firms with more than 6,000 employees accounted more than 50% of thesamples.
4 The mean of the sales of potential respondent firms was 6.84 billion yen.

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Masanori Yasumoto, Takahiro Fujimoto
between respondent firms and other potential respondent firms (t = 1.34, p = .18; F = 1.13, p = .27).
The 203 responses were spreading across a variety of products/industries: textile and apparel (n = 18);
food/beverage (n = 15); chemical, pharmaceutical and rubber (n = 43); consumer chemical and toiletry (n = 9);
metal (n = 13); electronics, systems, and software (n = 64); precision mechanics (n = 15); transportation
machines (n = 10). The diffusion of respondents by industries was not significantly different from that ofpotential respondent firms.

Product Development Strategies
We asked respondents 19 variables of product development strategies, and conducted a factor analysis.
The respondents of the questionnaire were asked if each of the following descriptions fits a characteristic of the
product development project in question, compared with other products in general, 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 of more than one (1.00) in Eigen-value, and named the selected factors as
types of development strategies following past studies (Appendix 1). The measures loaded mostly on separate
factors with all factor loadings above .40, which is a common threshold for acceptance.

The factor analysis

model reasonably fitted the data (χ square = 888.495, df = 210, p < .001).
From the original factor analysis, we chose the results of 11 variables concerning element technology
development, function design, product design, prototyping and test, and manufacturing process design5. We
identified three factors related to novel technology introduction and product/process engineering.
We found that the Factor 1 (Eigen-value = 3.96, contribution ratio = .21, α = .82) consisted of variables
such as communication in product/process engineering stages and overlapping between product and processengineering stages. Therefore, the Factor 1 could be named “engineering integration.”

The factor included

cross-functional integration across engineering sections and overlapping between product engineering stages
and pre-manufacturing stages.
Effective overlapping contributes to shortening lead time, and thus raising the accuracy of simulation(Clark and Fujimoto, 1991; Iansiti, 1997).

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 and Fujimoto, 1991; Terwiesch and Meyer, 2002). This isnothing other than cross-functional integration in product/process engineering stages.
5 Since customer involvement, supplier involvement, and development investment amount were supposed to be factors extraneous to the other 16 variables,
we did not include the variables in the factor analysis. We eliminated three factors and variables related to concept integration, front-loading, and leadershipfrom the original result of the factor analysis on 19 variables.

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The Determinants of Alternative Strategies for Novel Technology Introduction
The factor 2, the first factor of the novel technology introduction strategy, was named “technology
integration” (Eigen-value = 1.41, contribution ratio = .07, α = .71) as it was heavily loaded with five variables
related to the search and simulation of element technologies in early stages.

Project members need tocollaborate among technology and product/process development groups to effectively integrate novel
technologies into products (Song and Montoya-Weiss, 2001; Song and Xie, 2000; Tatikonda and Rosenthal,
2000). This is nothing other than the “technology integration” of Iansiti (1997). In super computer productdevelopment, product development projects conduct intensive search and simulation of materials, components,
and product design from early stages.
The factor 3, the second factor of novel technology introduction into products, was heavily loaded with thevariables of the separation of element technology development from product/process engineering.

Therefore,

we may call the factor “separated technology development” (Eigen-value = 1.18, contribution ratio =. 06, α= .60). Firms can reduce technological uncertainty by separating element technology development fromproduct/process engineering.

Separating problem-solving on element technology development fromproduct/process engineering reduces search and simulation for technology integration in product/processengineering, and thus facilitates product/process engineering per se (e.g., Gawer and Cusumano, 2002).

Independent Variables
We employed three measures of product characteristics, and eliminated 15 samples from the original 203
samples because of the defect values in the samples.

At first, as for technological uncertainty, we measured

the necessity of element technology development with a 5-point Likert-scale (1= “not necessary at all” to
5= ”extremely necessary”)6. Technological uncertainty is supposed to arise from novel element technology
development.
As described in the case of advanced computers (Eisenhardt and Tabrizi, 1995; Iansiti, 1997) or
pharmaceutical development (Pisano, 1997), technological uncertainty on advanced/high element technologies
could require the integration of element technology development with product/process engineering (Song andMontoya-Weiss, 2001; Song and Xie, 2000; Tatikonda and Rosenthal, 2000). It is also asserted that, in fastchanging technological environments, firms are required to develop advanced element technologies into
products in short cycles and adopt relatively modularized product development teams (e.g., Cusumano and
Yoffie, 1998; Iansiti and MacCormack, 1997).
6 In our analysis, we did not use the quantity of patents associated with the products concerned (mean=27.89). The quantity of patents was significantly
correlated with the quantity of evaluated product functions (r = .27, p <. 01), quantity of product elements/design drawings (r = .22, p <. 01), and number ofproject members (r = .25, p <. 01) as well as the importance of advanced element technologies (r = 0.19, p <. 01). The number of patents would be related to
product complexity rather than technological uncertainty.

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Masanori Yasumoto, Takahiro Fujimoto
On other hand, we measured the level of product complexity with the quantity of evaluated productfunctions, which were checked in the test process of the projects concerned. We asked respondents to checkthe approximate number on a 5-point logarithm scale (1= “1”, 2 = “10”, 3 = “100”, 4 = “1,000”, 5 = “10,000”).
Kusunoki (1999) considered product complexity in terms of the amount of evaluated product functions,and thereby explicated the cross-functional integration and related routines in Japanese semiconductor firms.
We followed this perspective because of the measurement problem of product complexity.
Product complexity has been conceptualized in terms of the interdependency between product elements.
The level of interdependency between product elements may define the required knowledge and cost forrealizing a new product (e.g., Baldwin and Clark, 1997; Garud and Kamaraswamy, 1995; Langlois andRobertson, 1992).

Product architecture, which is defined in terms of the product design complexity,

determines the proper mode of organizational coordination for a product development project (Sanchez and
Mahoney, 1996; Ulrich, 1995).

Because of the interdependency between product elements, effective

problem-solving in projects for complex product, such as automobile, supercomputer, and so on, requires
overlapping between stages, design-test-build cycles iterations, and related tight coordination between
engineers (Adler, 1995; Clark and Fujimoto, 1991; Iansiti, 1997; Terwiesch and Meyer, 2002)
Thus, a successful manufacturer of a complex hi-tech product, such as supercomputer, may need acoherent cross-functional team headed by a strong project leader (Iansiti, 1995; 1997). On the contrary, a
relatively successful developer of a more modular product, such as a personal computer, software, and so on,
may form a federation of many small module-specific teams that are relatively independent of each other (e.g.,
Cusumano and Yoffie, 1998; Iansiti and MacCormack, 1997).
However, measuring the interdependency is difficult in practice. For the difficulty, product complexity isthought to be measured 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. Logically speaking,adding another component dramatically increases product complexity, since the maximum quantity ofrelationships between components is n (n-1)/2 (n=quantity of component).

Larger amount of productelements would in general cause more interdependent relationships between elements.
Nevertheless, we predicted two difficulties in measuring product complexity with the quantity of productelements.

First, measuring the amount of product elements could not be applicable to process products.

Second, the level of interdependency would not be subject to the quantity of product elements as is the case ofmany of mechanical parts and process products.
Reflecting these problems, we decided to measure product complexity with the quantity of evaluatedproduct functions. The quantity of evaluated product functions in test stages could be measured for both
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The Determinants of Alternative Strategies for Novel Technology Introduction
assembly and process products.

As an objective measure, the variable was presumed to approximate productcomplexity.
Other things being equal, a larger amount of evaluated product functions will be related to several
interdependent elements, and are attained as the results of the synthesis of the interdependent elements.
Therefore, we attempted to estimate product complexity in terms of the quantity of evaluated product functions.
As for market uncertainty, we measured the real number of the standard model change cycle (months)
within the market of the product concerned. In a changing and/or competitive market, faster product releases
could help firms obtain a competitive advantage against competitors (Dater, 1997).
If the state or shift of market needs is difficult to predict, firms do not have sufficient knowledge on thestate or shift of market needs. Some of the past studies suggested that volatile (thus uncertain) markets
encourage firms to develop the products faster than competitors and to adopt more cross-functional routines
than stable markets (e.g., Eisenhardt and Martin, 2000; Eisenhardt and Tabrizi, 1995).
On the contrary, recent studies have suggested that volatile markets urge firms to adopt lesscross-functional product development strategies (e.g., Cusumano and Yoffie, 1998; Iansiti and MacCormack,
1997). A firm may separate element technology development from product/process engineering particularlyin the case that relationships between element technologies are sufficiently standardized across the firm’s
related products. In a volatile market, firms would not be liable to adopt technology integration.

Contexts
In order to examine contextual differences between product types, we measured several context variables
such as target market, product novelty, and project size in the field related to the products. These context
variables would provide fundamental conditions of the sample projects. We considered the effects of twocontext variables, target market and product novelty, in our analysis. Project size7, which we measured with
the number of core project members, was assumed to be substituted by the product complexity variable: thequantity of evaluated product functions.
We presumed that the predictability of customer needs would be fundamentally provided by the distinction
between consumer and industrial products8. We asked respondents to choose the most approximate product7 Product development management 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 to the quantity of evaluated product functions (r = .31 , p < . 01) and amount of productelements (r = .5, p < .01). The number of project members would depend upon product complexity.
8 Although market growth rate was on average similar across the assembly and process product groups, the mean of standard model change cycle of theconsumer product group, 24.97 months, was significantly shorter than that of the industrial product one, 37.59 months (R square = .05, p < .01). The mean of
the number of competing products of the consumer product group, 10.18, was significantly larger than that of the industrial product one, 6.12 (R square = .04, p< .01). These differences would indicate that competition is in general more intensified in consumer product markets than in industrial product ones not only13

Masanori Yasumoto, Takahiro Fujimoto
category in terms of the target market from two categories: consumer products (= 1) or industrial products (= 0).
After the elimination of the samples including defect values from 203 samples, the quantity of consumer
products was 67 (35.64 %) while that of industrial products was 121 (64.36 %).
This simple categorization had the risk of obscuring more specific market characteristics, which might beperceived by project members to have directly impacts on product development strategies. However, wepredicted that the target market could define the fundamental market condition, which could not be attributed toany of the single market uncertainty variables.
Market needs would largely depend on the target customer of the product in question. In development
projects for many of consumer goods, such as apparel, automobiles, consumer electronic appliances, and so on,
customer needs are uncertain and/or equivocal. For example, whereas exterior styling, color, aesthetic design,and feeling are critical factors in automobile development, these ergonomic features are hardly defined inarticulated manners, and changes in tastes are also difficult to predict (Clark and Fujimoto, 1991; Iansiti and
Clark, 1994). As these features may not be attributed to single technologies, novel technology development in
consumer product development would not be separated from product/process engineering.
On the contrary, some studies on industrial products show different market situations.

The target

customers of industrial products in this study included firms, business men, professionals, SOHO, hospitals,
and so on. Even if not clarified at the beginning of product development, requirements on industrial products
would be directly indicated or suggested by customers (von Hippel, 1988).
Also, goal specifications of business/industrial products could be derived from specific functional criteria,such as processing speed, capacity size, and so on, at least as long as the value network is stable (e.g.,
Chrsistensen, 1997). For example, Iansiti (1997) described advancement in a single technological function,
processing speed, as the major goal of super computer development.

Thus, in contrast to the cases ofconsumer product development projects, developing industrial product would be directed to relatively
instrumental and specified features.

As each of these features may be required to be prominent, firms would

employ separated technology development in industrial product development.
We considered product novelty as another context variable. We asked respondents whether or not the
product concerned was completely new to the preceding product genre within the respondent firm (1 = “novel”
or 0 = “conventional”). The quantity of novel products was 46 (24.5%) while that of products in the line ofconventional product genre was 142 (75.5%).
The question was intended to understand the product novelty of the product in question in terms of the
distinction between conventional products following past products and novel products without any preceding
because of market uncertainty but also because of fundamental unpredictability, equivocality, of customer needs.

14

The Determinants of Alternative Strategies for Novel Technology Introduction
product models/lineups. If firms do not have preceding product models/lineups of the product concerned,
firms could hardly reuse the technologies, design, parts, and/or manufacturing process of preceding products.
Product novelty in this sense could have significant impacts on product development strategies (e.g., Songand Xie, 2000; Tatikonda and Rosenthal, 2000).

While a novel product is not necessarily accompanied by

new advanced technologies, advanced technologies are applicable to conventional product lines as is the caseof modular products.

Thus, we collected the data of product novelty apart from technological novelty, which

we measured in terms of the importance of advanced element technologies.

Assembly and Process Product Groups
We conducted the analysis dividing the samples into two groups: “assembly product” and “process
product” groups. Innovation management studies have suggested that product development capabilities aredifferent between assembly products and process products (e.g., Kusunoki et al., 1998; Utterback, 1994).
Kusunoki et al. (1998) asserted that effective product development capabilities are significantly different
between these groups according to the difference in assembly/system and process/material development projectgroups.
However, the distinction between assembly and process products is not as easy as is usually expected.
For instance, products with a small amount of components/ingredients are not always process products, andvisa versa.

We asked the respondents to give the ratio of the engineering-hours in the total productengineering-hours for product and component design in the product/process engineering stages.
We presumed that the ratio of the engineering-hours for product/component design would reflect thefundamental product complexity of the product in question. The fundamental complexity would define theknowledge level on the product structure. Firms of assembly products at least have knowledge that an
assembly product is designed as a set of distinctive components. Accordingly, assembly product development
projects are expected to allocate many of resources to product/component design and related prototyping andtesting.
On the contrary, in many cases, firms of process products would scarcely have sufficient knowledge toarticulate the structure of a process product into a set of physical designs. Thus, process product development
projects would use most of resources for process design and related prototyping/testing. The difference in theknowledge level from the fundamental product complexity could bring about the differences in the productdevelopment strategies between the assembly and process product groups.
Reflecting this difference, we tentatively divided the samples at the ratio of 36 % since the mean of the15

Masanori Yasumoto, Takahiro Fujimoto
ratios is 35.13 %9.

The mean of the assembly product group was 43.06 % while that of the process productgroup was 23.26 %.

The ratio of the assembly product group seemed to be relatively low. The reason will bethat many of resources should be allocated to prototyping and testing even in assembly product development
projects.
Most of the samples from electronics/system/software, precision mechanics, and transportation machineindustries fell into the assembly product group.

Relatively more of the samples from food andchemical/pharmaceutical/rubber industries than from the above mentioned assembly industries were classified
into the process product group.
In order to confirm the differences in statistical structure between these groups, we applied
Brown-Forsythe’s F-test to the ratio of the engineering-hours for product/component design.

The standard

deviation of the assembly and process product groups were respectively 1.98 and 2.59. The result evidenced
the significant difference in the variance between the assembly and process product groups (F = 12.96, p< .0001).

The distinction between the assembly and process product groups could also have significant

meanings also in terms of statistical structure.

Product Development Performance
Much of the literature on product development management has considered project performances in order
to identify effective attributes of product development projects. As the unit of analysis in our study was asingle product development project, we collected the data of six performance variables of the product inquestion: customer satisfaction/total quality, engineering-hours, development leadtime, specific functional
performance, sales/market share, and profit10.
Considering the problem of the comparability of performance between industries, we asked respondents tocheck each of the performance levels on a 5-point Likert-scale (1= “not successful at all” to 5= “highly
successful”). Objective performance measures were not supposed to be appropriate for inter-industrial studies.
Even though we could successfully collect objective performance data, comparing the data across various
industries would be almost impossible.
All the performance scores of the sample projects appeared on average quite high across product types.
Every respondent estimated the selected project as more or less successful in all the measures. Mean scores ofcustomer satisfaction, functional performance, and sales/market share were particularly high across industries:
4.46, 4.40, and 4.24 respectively (Appendix 2). This might indicate that customer satisfaction, functional
9 The ratio of the engineering-hours for product/component design in the total engineering-hours had significant positive correlation with the amount of productelements/design drawings (R square = .30, p< .01).

10 All of the performance variables significantly contributed to sales and profits (p< .01).

16

The Determinants of Alternative Strategies for Novel Technology Introduction
performance, and sales/market share are particularly critical performance measures in successful projects in all
the industries.
In order to check whether or not respondents applied similar criteria of success, we collected the data on
success criteria asking respondents to choose success criteria from five alternatives (multiple-answer).
Respondents chose “compared with products of rivalry firms (n = 148, 37.19%)”, “compared with past
products (n = 109, 27.39 %)”, “compared with past products of the firm concerned (n = 67, 16.83 %)”,
“compared with success criteria within the firm concerned (n = 71, 17.84 %)”, and “others (n = 3, .75 %).”The ratios on the success criteria were not significantly different between the assembly and process productgroups11, which means that respondents applied similar success criteria in both the assembly and process
product groups.
We also checked differences in the mean score and the variance of each performance measures between
the assembly and process product groups (Appendix 2). As suggested in the previous section, assembly andprocess products were different in fundamental product complexity.

The difference would encourage firms topursue different product development performances and adopt different strategies for attaining the different
performances.
However, we could not identify any of significant differences.

This result seemed to suggest that

subjective project performance estimation of project managers, which indicates how project managers put
priorities among performance variables, was similar across both of the product groups. The result made us
infer that examining relationships between performance variables and product development strategies would
not derive significant results from the samples.
We also examined the correlation between product development performances and strategies (Appendix 3).
The data demonstrated that engineering integration was significantly correlated with all of the performancemeasures while other product development strategies but technology integration did not have significant
correlation with all of the performance measures.
The result would mean that, at least for sample Japanese product development projects, most of productdevelopment performances are attributed to engineering integration while technology integration could
contribute to several specific performances: customer satisfaction/total quality, specific functional performance,and profit. These findings indicated that technology integration is more critical for product success thantechnology development separation. We needed to further examine the impacts of product characteristics
and/or industrial dynamism on the choice of novel technology introduction strategies.
11 The results were as the followings: “compared with products of rivalry firms (χ square = .001, p = .98)”, “compared with past products (χ square = .01, p= .92)”, “compared with past products of the firm concerned (χ square = .02, p = .89)”, and “compared with success criteria within the firm concerned (χ square= .61, p = .44).”17

Masanori Yasumoto, Takahiro Fujimoto

Results
We attempted to check the differences in the mean scores of product development strategies, productcharacteristics, and context factors between the assembly and process product groups (Table 1).

We

conducted double sided t-tests on all the variables but the quantity of evaluated product functions.
We applied O’Brien’s F-test to all the variables in order to examine the variance differences in thesevariables. We did not identified significant variance differences in all the variables but the quantity ofevaluated product functions (F = 9.70, p < .01). Nothing but the variance of the quantity of evaluated productfunctions was significantly different between the assembly and process product groups.

Thus, instead ofsimple double sided t-test, Welch's test was applied to the examination of the quantity of evaluated productfunctions.

Table 1 Mean Differences between Assembly and Process Product Groups
Num of Engineering Technology
Sample Integration Integrationassemblys.d.
process
s.d.

118
70

t
Ｆ
total
s.d.

188

0.01
0.88
-0.02
1.07
0.02
1.43
0.00
1.00

-0.23
0.94
0.39
0.76
4.07**
1.76
0.00
1.00

Necessity of
Separated
Element
Technology
Technology
Development
Development

0.16
0.89
-0.26
0.94
-2.80**
0.14
0.01
1.00

3.55
1.02
3.73
0.96
0.95
0.47
3.62
1.29

Quantity of
Model
Evaluated
ChangeProduct
Cycle(mo.)
Functions(a)

2.87
1.02
2.17
0.65
7.40**
9.70**
2.52
0.78

32.34
1.06
31.82
0.89
-0.12
0.89
32.07
27.59

Product
Target Market
Novelty(1=consumer/(1=novel
0=industry)/0=convention(b)al) (b)

0.40
0.49
0.43
0.50
0.50
0.30
0.41
0.49

0.23
0.42
0.24
0.43
0.29
0.10
0.23
0.42

Double-sided t -test and O'Brien's F -Test.
a: Welch's test was applied to the examination of the mean difference.
b: The dummy variables were originally nominal data.
✝p <.10, *p <.05, **p <.01

The results indicated that the means of technology integration (t = 4.07, p <.01) and separated technology
development (t= -2.80, p < .01) are significantly different between the assembly and process product groups.
The mean scores of assembly product development projects were significantly higher than those of process
product cases in separated element technology development, while the mean score of technology integration ofassembly product development projects was less than that of process product development projects.
In the case of assembly product development, engineers could assign a portion of element technology
development to advanced technology/platform development groups.

The task for element technology

development is more likely to be separated from product/process engineering in assembly product development
than in process product development.

These results made us infer that the difference in product development

strategies between the assembly and process product groups would mainly lie in novel technology introduction.
18

The Determinants of Alternative Strategies for Novel Technology Introduction
As for product characteristics, only the mean of the quantity of evaluated product functions of assembly
products was significantly larger than that of process products (t = 7.40, p < .01)12 . As demonstrated above,
the variance was also different between the assembly and process product groups.

These results would imply

that the differences in product development strategies between the assembly and process product groups comefrom the fundamental difference in the level of product complexity.
Following the above analysis, for each of the assembly and process product groups, we used four
multi-regression analysis models from the product characteristics and context variables to the two novel
technology introduction strategies.

Table 2 and 3 shows the results.

Table 2 Results of Multiple-Regression Analysis: Assembly Product Group
Model 1

Technology Integration
Model 2Model 3Model 4

Separated Technology Development
Model 1Model 2Model 3Model 4

Necessity of Element
Technology Development
Quantity of Evaluated
Product Functions

0.32**

0.30**

0.33**

0.28**

0.17*

0.19*

0.16✝

0.18*

0.00

0.01

0.00

0.04

0.16*

0.15✝

0.18*

0.17*

Model Change Cycle

0.04

0.06

0.03

0.04

0.01

-0.04

-0.06

-0.12

-0.10

-0.07

0.03

0.03

0.00

0.02

0.18*

0.19*

-0.03

-0.06

-0.05

0.01

Independent Variables

Necessity of Element
Technology Development*Quantity of Evaluated
Product Functions
Necessity of Element
Technology Development*Model Change Cycle
Model Change Cycle*Quantity of Evaluated
Product Functions
Target Market (Dummy,
0=industry)-0.06

Product Novelty (Dummy,
0=conventional)-0.12

R square
0.11
0.13
F
4.56**
3.32**
n = 118. Standardzed values were used for all the models.
✝p <.10, *p <.05, **p <.01

-0.13

0.21*

-0.13
0.12
2.48*

0.15
2.29*

0.24**

0.13
0.07
2.63*

0.15
3.68**

0.11
0.11
2.19*

0.19
3.17**

12 The quantity of evaluated product functions had positive correlations with the amount of product elements (r = .49, p < .01) and number of project
members (r = .35, p < .01). Accordingly the means of the amount of product elements (F = 182.85, R square= .10, p < .01) and number of project members(F = 21.73, R square = .48, p < .01) were significantly larger in the assembly product group than in the process product group.

19

Masanori Yasumoto, Takahiro Fujimoto

Table 3 Results of Multiple-Regression Analysis: Process Product Group
Independent Variables

Model 1

Technology Integration
Model 2Model 3Model 4

Separated Technology Development
Model 1Model 2Model 3Model 4

0.22✝

Necessity of Element
Technology Development
Quantity of Evaluated
Product Functions

0.20✝

0.19✝

0.22✝

0.23*

-0.28*-0.26✝

-0.3*-0.29*

0.32*

0.30*

0.32*

0.35*

Model Change Cycle

0.22✝

0.33*

0.31*

0.49**-0.31*-0.30*-0.26✝

-0.41*-0.44*-0.42*

-0.01

0.00

0.06

0.18

0.04

0.01

-0.16

-0.43*

-0.23

-0.01

Necessity of Element
Technology Development*Quantity of Evaluated
Product Functions
Necessity of Element
Technology Development*Model Change Cycle
Model Change Cycle*Quantity of Evaluated
Product Functions
Target Market (Dummy,
0=industry)
Product Novelty (Dummy,
0=conventional)

0.21✝

0.23✝

0.18

-0.19✝

-0.27

0.30*

0.30*

0.06

0.13*

-0.11

-0.12

R square
0.16
0.18
F
3.89**
2.56*
n = 70. Standardzed values were used for all the models.
✝p <.10, *p <.05, **p <.01

0.24
3.06**

0.32
3.2**

0.18
4.31**

0.25
3.84**

0.20
2.30*

0.29
2.76**

Model 1 is the baseline model that only includes product characteristics as the main factors. In model 2,
two context variables were added to model 1. The interaction terms for the three product characteristics
variables were added to model 1 in model 3. In model 4, both of the context variables were added to model 3.
In other potential models which focused on examining the effects of each of the interaction terms orcontext variables, the R squares and/or F-values in any models decreased compared to the presented fourmodels.

Each of the interaction terms or context variables rarely had stronger effects in any of the potential

models.

Thus, we decided to consider the presented four models in order to simplify our examination.

We found significant correlations between the quantity of evaluated product functions, model change
cycle and target market, and between the necessity of element technology development and product novelty(Appendix 4 and 5). However, the variation inflation factors (VIF) and condition indexes associated each of
the regression coefficients ranged from 1.03 to 1.08, and the condition indexes associated each of the regression
coefficients were below 2.56. The results suggested no serious problems with collinearity in our analysis.
For each of variables, we paid attention to significant effects common across these models.
interaction terms partly had significant impacts on product development strategies.

The

Nevertheless, it isimportant to note that the main effects of product characteristics remained robust in most of the models evenwhen the interaction terms were included. These interaction terms increased R squares while F-values weredecreased. These interaction terms rarely had strong effects on the product development strategies.
Technology integration was simply explained by the necessity of element technology development,
technological uncertainty, in the assembly product group (p<.01). Also in the process product group, thenecessity of element technology development had significant positive effect on technology integration (in most
20

The Determinants of Alternative Strategies for Novel Technology Introductionof the models, p<.01 or p<.05).
The results would support the past finding on technology-driven product development (e.g., Eisenhardt
and Tabrizi, 1995; Iansiti, 1995; 1997; Song and Montoya-Weiss, 2001; Song and Xie, 2000; Tatikonda andRosenthal, 2000).

The result of the necessity of element technology development would be also consistent

with the finding on process product development (e.g., Pisano, 1997).
In the process product group, the quantity of product functions showed significant negative impact ontechnology integration (p<.05).

The effect of the necessity of element technology development wasmoderated by the quantity of evaluated product functions (p<.05). Technological uncertainty could havenegative impact on technology integration as product complexity increases.
Model change cycle also had significant positive effects on technology integration in the process productgroup (in most of the models, p<.01 or p<.05).

In the process product group, technology integration was

explicated by longer model change cycle. Longer model change cycle, which means lower market uncertainty,
would allow firms to employ technology integration.
Target market scarcely had impacts on technology integration in both of the product group.

Product

novelty had positive effect on technology integration only in the process product group (p<.05). While notapplicable to the assembly product group, the result would be consistent with past studies (e.g., Iansiti, 1997;
Pisano, 1997; Song and Xie, 2000; Tatikonda and Rosenthal, 2000; Olson et al, 1995).
On the other hand, the quantity of evaluated product functions, product complexity, had significant
positive effects on separated technology development in both of the assembly and process product groups(p<.05 or p<.10).

The necessity of element technology development, technological uncertainty, also showedsignificant positive effect on separated element technology development in the assembly product group (p<.05
or p<.10).

Interacting with model change cycle, the necessity of element technology development had

positive impact on separated element technology development in the assembly product group (p<.05).

The

necessity of element technology development slightly explicated separated element technology development in
the process product group (p<.10).
In general, the above mentioned facts would evidence the findings that the knowledge insufficiency fromtechnological uncertainty could be distinguished from the knowledge insufficiency from product complexity(e.g., Tatikonda and Rosenthal, 2000). Thus, in both the assembly and process product groups, the task load
for element technology development in product/process engineering stages could be alleviated by separating
element technology development from product/process engineering.
Particularly when product complexity is sufficiently high, which means that the task load for
product/process engineering is accordingly high, element technology development is to be separated from21

Masanori Yasumoto, Takahiro Fujimotoproduct/process engineering. A project for a complex product could cope with the product complexity byseparating element technology development from product/process engineering.
As for market environments, shorter model change cycle, which scarcely had effects in the assembly
product group, also contributed to separated element technology development in the process product group(p<.05 or p<.10). Market uncertainty would not allow firms to take time for technology integration, but itwould enhance separating element technology development from product/process engineering.

Whileidentified only in the process product group, the results may support the findings and suggestions in previous
studies (e.g., Cusumano and Yoffie, 1998; Gawer and Cusumano, 2002; Iansiti and MacCormack, 1997).
Lastly, we should note that, in both of the assembly and process product groups, separated technology
development was enhanced for industrial product development.

Separated element technology would be

adopted when product functions are restricted within specific business/industrial uses. Product novelty did
not explicate separated technology development in both of the product group.

Discussion
The data helped us reassure that technology integration is distinguished from cross-functional integrationin product/process engineering stages (i.e., engineering integration). In many of the studies, cross-functional
integration has been generalized as the bundle of communication and collaboration for knowledge processing
and creation across functional organizational units (e.g., Brown and Eisenhardt, 1995; Eisenhardt and Tabrizi,
1995; Kusunoki et al., 1998; Olson et al., 1995; Song and Xie, 2000).

The concept of cross-functional

integration includes a broad range of organizational routines for information exchange and collaboration
between functional units.
However, the data here revealed that the concept of cross-functional integration is divided into engineering
integration and technology integration.
product/process engineering.

Novel technology introduction strategy is distinguished fromAt the same time, we identified two strategies for novel technology

introduction: technology integration and separated technology development.
Furthermore, we predicted that, according to product characteristics and/or contexts, firms choose proper
product development strategies for novel technology introduction. Most of the results provided the evidence
that product development strategies for novel technology introduction may vary according to productcharacteristics and/or contextual factors.
At first, we identified that the remarkable differences between the assembly and process product groups
lay in technology integration and separated technology development.

Technology integration was employed

more in the process product group than in the assembly product group.
22

In reverse, separated element

The Determinants of Alternative Strategies for Novel Technology Introduction
technology development was not prominent in the process product group. The differences between the groups
would be attributed to fundamental product complexity: whether or not the product concerned is decomposed
into distinctive elements.
As a whole, in each of the assembly and process product groups, these strategies were reasonably
explicated by technological uncertainty (i.e., necessity of element technology development), productcomplexity (i.e., quantity of evaluated product functions), market uncertainty (i.e., model change cycle), and/or
other contextual factors.

The results were mostly consistent with the findings in previous studies. However,product novelty slightly had the impact on technology integration only in the process product group.
The analysis provided several findings reflecting the difference between assembly and process products.
However, we identified several common determinants of novel technology introduction strategies in both theassembly and process product groups.

Technological uncertainty was the fundamental factor for both of thenovel technology introduction strategies in both of the product groups.
The results may suggest that product complexity as well as technological uncertainty is the critical
determinant of novel technology introduction strategies.

The more products get complex, the more thedevelopment activities could be divided into product/process engineering and element technology development.
Though market uncertainty and other factors may influence novel technology introduction strategies, whether
or not firms could respond to these factors would rely on the level of product complexity.

In the sense,

managing product complexity is not negligible in making a choice of novel technology introduction strategies
as antecedents have suggested (e.g., Baldwin and Clark, 1997). We need to further excavate the relationshipbetween technological, uncertainty, product complexity, and novel technology introduction strategies.
These results demonstrated that firms need to make use of either of technology integration and separated
technology development project by project according to product characteristics and/or contextual factors.
Technology integration, which is accompanied by both of core and related element technology development,
may seem ineffective in the era of market and/or technological volatility.

Nevertheless, employing separated

technology development corresponding to the volatility could result in the excessive application ofstandardized element technologies, and thus would jeopardize firms’ competitiveness.
Standardized element technologies, such as patented technologies and modularized components, would
not contribute to fundamental product innovativeness, which results from organization-specific capabilities(Henderson and Cockburn, 1994). Standardized element technologies could be transferable between firms
and/or imitated by other firms (Anderson, 1999; Baldwin and Clark, 1997; Henderson and Cockburn, 1994;
Kusunoki et al., 1998), and thus would not necessarily secure firms’ competitiveness (Anderson, 1999).
On the other hand, competitors could hardly imitate or acquire the knowledge of complex/novel products23

Masanori Yasumoto, Takahiro Fujimoto(e.g., compact car, precision mechanics, mechanical or electronic parts, fine materials, and so on), which result
from firm-specific capabilities for coping with product novelty/complexity.

Even if the large portion of a

product is composed of modularized/standardized components, a firm which could control theinterdependencies between the components and integrate them would be prominent in the product market(Brusoni and Prencipe, 2001).
Cross-functional integration for novel technology introduction, technology integration, would enable
exploratory knowledge creation, and thus helps firms adapt to complex/novel environments (Benner andTushman, 2003; Iansiti and Clark, 1994; Kusunoki et al., 1998). The novel technology introduction strategy isparticularly critical in order to realize architectural changes of product designs (Chesbrough and Kusunoki,2001).

Technology integration in relation to core technologies/platforms, which contributes to realizing

firm-specific product novelty/complexity, is the indispensable strategy for firms (Gawer and Cusumano, 2002;
Iansiti, 1997).
The data showed that technology integration contributes to product development performances more thanseparated technology development.

Product development performances were more attributed to technology

integration than separated technology development.
However, whereas the result demonstrated the importance of technology integration, firms are required tomake use of separated technology development in complex product development in volatile environments.
Technology integration, which encourages firms to develop novel components in accordance with productdesign, is expected to require more time and cost than separated technology development (Baldwin and Clark,
1997; Garud and Kamaraswamy, 1995; Langlois and Robertson, 1992). Technological changes and volatile
markets do not necessarily allow firms to spend time and cost for technology integration.
Even when attempting to employ separated technology development, firms need to consider both of thenovel technology introduction strategies in order to effectively refurbish core technologies on one hand andapply element technologies to various products on the other hand.

The contrived application of thesestrategies might be particularly required as various element technologies change at uneven paces (Brusoni andPrencipe, 2001).

The above mentioned implications means that the attempt to make use of separated

technology development should be based on contrived multi-project/platform strategies beyond single productdevelopment projects (e.g., Cusumano and Nobeoka, 1998; Funk, 2002; Gawer and Cusumano, 2002).
For instance, Robertson and Ulrich (1998) propose the perspective for platform formation drawing on theconcept of product architecture.

The results here demonstrated that firms need to consider technologicalnovelty, market volatility, and other factors in addition to product complexity. While discussed mostly interms of product design characteristics (e.g., product architecture) multi-project/platform strategies are required
24

The Determinants of Alternative Strategies for Novel Technology Introduction
to reflect and synthesize the impacts of technological novelty, market volatility, and other factors.
As indicated above, these alternative strategies for novel technology introduction largely relies on theseproduct characteristics and contextual factors. In order to make effective use of the two strategies, firms needto devise multi-project/platform strategies which consider product characteristics and contextual factors of eachof the products.

Conclusions
Most of the results supported past findings of product development management studies. We found thatfirms adopt product development strategies for novel technology introduction according to critical productcharacteristics and/or contextual factors. The result means that the contingency framework would be viable
for this line of studies.
In order to make proper use of product development strategies for novel technology introduction, firms
need to contrive coherent product strategies, such as multi-project/platform strategies, beyond single productdevelopment projects (e.g., Cusumano and Nobeoka, 1998; Gawer and Cusumano, 2002).

Without the

product strategies beyond single projects, firms may face problems of over-specification and high product cost
despite high project performances: productivity, speed, and product/technological novelty.
The case of Japanese mobile phone manufacturers might provide the emblematic example.

Whereascommercializing the most advanced technologies in the world, Japanese mobile phone handset manufacturers’
performances are mostly not prominent in the world mobile phone industries.

The reason has been attributed

to the different communication technology specific to Japanese market and the related domestic inter-firm
relationships between manufacturers and service-carriers.
These factors have allowed Japanese manufacturers to commercialize novel technologies in Japanesemarket without sufficient consideration of product lineup strategies and platform management for the world
market (Funk, 2002). As a result, despite the technological novelty, Japanese manufacturers are liable toemploy cross-functional integration for novel technology introduction in the regular handset model
development projects (Yasumoto, 2005). Most of the handsets and components are designed in accordancewith critical novel technologies. In spite of the functional novelty and related product integrity, the problem
of product development strategies would harm the competitiveness of the Japanese firms in terms of cost, speed,
and product variety in the world market.
Refurbishing product/component design in accordance with the emergence of each of novel element
technologies is not effective under the turbulent environments in many of hi-tech industries. Firms need to
pay sufficient attention to both of uneven changes of various element technologies and interdependencies
25

Masanori Yasumoto, Takahiro Fujimoto
between the components (Brusoni and Prencipe, 2001). As suggested in the study, market and/or contextual
factors should be also considered. Furthermore, drawing on the cases of specific industries, we should
explore how the above mentioned implications on novel technology introduction strategies at project level
could contribute to shaping platform/multi-project strategies at corporate level.
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.

Based on a contingency

perspective, the present study attempted to examine the past findings from industry-specific studies within ageneric study context. This type of study is still at an exploratory or preliminary stage.
Hereafter, based on international researches and case studies, we need to contrive product characteristics
and performance indices, derive testable hypotheses, and strengthen the ties between conceptual frameworks
and empirical data. In particular, we need to conduct international researches for the results in the study may
be influenced by Japanese country attributes. Whereas we are still taking the initial steps in this research area,
this line of study seems to deserve further exploration in both content and methodology.

26

The Determinants of Alternative Strategies for Novel Technology Introduction

Appendices
Appendix 1 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

3.74

0.92

0.17

0.54

0.05

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

n =188. Factor loadings were varimax rotated.
The shaded cells indicate those larger than 0.4 or smaller than -0.4 .

Appendix 2 Performance Differences between Assembly and Process Product Groups
Num ofSampleassembly means.d.
process mean
s.d.

118

Product
Development
Leadtime

Specific
Sales/Market
Functional
Share
Performance

Profit

4.42

3.73

3.79

4.36

4.28

0.61

0.76

0.84

0.60

0.77

0.75

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
4.46

0.68
0.45
3.75

0.39
0.15
3.81

1.28
1.51
4.40

-0.98
0.19
4.24

-0.04
0.19
3.93

0.57

0.74

0.83

0.58

0.76

0.76

t
Ｆ
total means.d.

Customer
Product
Satisfaction Development/Total Quality
Cost

Double-sided t -test and O'Brien's F -test.
✝p <.10, *p <.05, **p <.01

27

3.93

Masanori Yasumoto, Takahiro Fujimoto
Appendix 3 Correlation Analysis between Performance Measures and Product Development Strategies
mean

s.d.

Engineering
Integration

Technology
Integration

Separated
Technology
Development

Customer Satisfaction/Total Quality

4.46

0.57

0.16*

0.17*

-0.08

Product Development Cost

3.75

0.74

0.18**

0.07

0.02

Product Development Leadtime

3.81

0.83

0.14*

0.01

0.03

Specific Functional Performance

4.40

0.58

0.12✝

0.14✝

0.01

Sales/Market Share

4.24

0.76

Profitn =188✝p <.10, *p <.05, **p <.01

3.93

0.76

0.25**
0.11✝

-0.04
0.13✝

-0.03
0.03

Appendix 4 Correlation Matrix: Assembly Product Group
mean

s.d.

1

1 Technology Integration

-0.23

1.04

1.00

2

3

4

5

6

7

2 Separated Technology
Development

0.16

0.96

-0.01

1.00

3 Necessity of Element
Technology Development

3.55

1.28

0.31**

0.20*

1.00

4 Quantity of Evaluated
Product Functions

2.87

0.82

0.09

0.11

0.01

1.00

5 Model Change Cycle(mo.)

32.34

30.28

0.06

0.01

0.05

-0.07

1.00

6 Target Market (dummy,
1=consumer/0=industry)

0.41

0.49

0.10

-0.2*

-0.03

0.18*

-0.25**

1.00

7 Product Novelty (Dummy,
1=novel/ 0=conventional)

0.22

0.42

0.12

-0.10

0.14

0.06

-0.01

0.12

1.00

4

5

6

7

n =118
As for product development factors, we used factor score data instead of original product development variables' data.
✝p <.10, *p <.05, **p <.01

Appendix 5 Correlation Matrix: Process Product Group
mean

s.d.

1

1 Technology Integration

0.39

0.83

1.00

2

3

2 Separated Technology
Development

-0.26

1.00

0.01

1.00

3 Necessity of Element
Technology Development

3.73

1.31

0.19

0.24*

1.00

4 Quantity of Evaluated
Product Functions

2.17

0.48

0.11

0.14

0.09

1.00

5 Model Change Cycle(mo.)

31.82

22.30

0.28*

-0.24*

0.02

0.05

1.00

6 Target Market (dummy,
1=consumer/0=industry)

0.49

0.50

0.12

-0.20✝

-0.02

0.27*

-0.21

1.00

7 Product Novelty (Dummy,
1=novel/ 0=conventional)

0.25

0.44

0.16

0.17

0.25*

0.06

0.17✝

-0.21

n =70
As for product development factors, we used factor scores instead of original product development variables' data.
✝p <.10, *p <.05, **p <.01

28

1.00

The Determinants of Alternative Strategies for Novel Technology Introduction

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