Hitotsubashi University Repository

TitleKnowledge Retention and New Product Development

Performance

Author(s) Aoshima, Yaichi

CitationHitotsubashi journal of commerce and management,

31(1): 13-58

Issue Date 1996-10

Type Departmental Bulletin Paper

Text Version publisher

URL http://doi.org/10.15057/5463

Right

Hitotsubashi Joumal of Commerce and Management 3 1 ( 1996) pp. 1 3-58. C The Hitotsubashi Academy

KNOWLEDGE RETENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE

YAICHI AOSHIMA

Abstract

Drawing on examples from the Japanese automobile industry, this study investigated how

differences in the ability to retain product-related knowledge across product generations affect

product development performance. Two sets of analyses were conducted on this issue, based on data obtained from 229 key

project members in 25 new product development projects. First, we investigated how knowl-

edge retention infiuences perfornrance within well-established component development, which

we called local performance. We found that, in general, dependence on archival-based mecha-

nisms, such as documents, reports and computerized tools, rather than on individual-based

mechanisms, tended to be associated with higher local perforrnance.

Next, we analyzed our data set at the project level. Data suggested that retention of

individual experience bases and communication with previous project members have positive

impact on several performance indicators at the entire project level. In particular, we found that

these individual-based retention mechanisms affected improvement of system performance

derived from the complex interactions among diffierent engineering and functional domains.

However, data also suggested that retention of prior experience tended to cause problems when

projects have to introduce new market concepts.

l . In trod uction

Large manufacturing companies often have a range of product lines, and successively

introduce new products over time. To adapt to changing customer needs, they may replace

existing products at regular intervals and add new product lines. In most cases, these new

products are not "completely" new for a company, both in terms of technologies and market

concepts. A technology developed for one product may subsequently be used in a range of

products (Cusumano, 1991; Meyer and Utterback, 1993; Meyer and Roberts, 1988; Nobeoka,

1993; Nobeoka and Cusumano, 1992, 1994; Sanderson, 1991; Uzumeri and Sanderson, 1995).

Knowledge about existing customers can also serve as a useful basis for interpreting current

customer needs and translating them into technical parameters and physical products (Chris-

tensen and Rosenbloom, 1995). Successful new product development, therefore, at least partially may depend on the

ability to understand technical and market knowledge embodied in existing products; and

14 HITOTSUBASHI JOURNAL OF COMMERCE AND MANAGEMENT [october

adapt this knowledge to support new product development (Iansiti, 1 995; Iansiti and Clark,

1993). In addition, due to intensive competitive pressures, a fast product development cycle

has also become a critical source of competitiveness in many industries (Clark and Fujimoto,

1991; Nobeoka, 1993; Nobeoka and Cusumano, 1994; Sheriff, 1988). Under such circum-stances, retaining and quickly utilizing knowledge across generations of projects, and leaming

from past development activities, may become particularly important both for avoiding

redundant problem solving and for finding new solutions to problems in new product develo pment.

Few studies to date, however, have systematically dealt with this issue of knowledge

retention and utilization. Most existing studies have tended to treat each new project as

independent, and implicitly assume that each new product is the outcome of a self-contained

and distinct problem-solving process (Kofman et. al., 1 993). For example, various researchers

have examined a wide range of factors for successful new product development, such as

communication processes (Allen, 1970, 1977; Ancona and Caldwell, 1992), teams' com-positional characteristics (Ancona and Caldwell, 1 992; Katz and Allen, 1982), team structures

and leadership (Clark and Fujimoto, 1 991; Henderson and Cockbum, 1 994; Imai, Nonaka and

Takeuchi, 1985; Larson and Gobeli, 1988), and design of development processes (Clark and

Fujimoto, 1991; Eisenhardt and Tabrizi, 1995; Iansiti, 1992). However, researchers have paid

little attention to organizational and technological linkages across generations of projects.

Although some recent studies explicitly deal with issues cutting across different projects (e.g.,

Cusumano, 1991; Cusumano and Selby, 1995; Iansiti, 1995 a, b; Meyer and Utterback, 1993;

Nobeoka, 1993; Uzumeri and Sanderson, 1995), they are either case-based studies or limited

to specific elements of knowledge transfer, such as particular components and design concepts.

Broad-based empirical investigations exploring the impact of knowledge retention on organi-

zational performance are rare. Compared to continuous improvement activities at the plant

level (e,g., Kaizen, TQC), improvement of product development process over time has received less direct attention in academic research. As a result, we have little systematic

understanding of the effects of managing multiple generations of products.

This paper addresses the issue of the transfer and retention of knowledge as an essential

element in product improvement. Drawing on examples from the Japanese automobile industry, this study investigates how differences in the ability to retain product-related

knowledge across multiple generations of products affect performance in developing new

products.

The automobile industry is an especially suitable setting for this study because automobile

manufacturers continuously introduce new families of products while upgrading existing ones.

Nobeoka ( 1993), for example, showed that, during the period between 1980 and 1991, 2 10 new

automobile products were introduced worldwide, nearly 70% of which were intended to replace existing models. Such characteristics of the automobile industry - successive intro-

duction and replacement of multiple products - provide us with a favorable setting for this

study because improvement of product performance through learning from past development

actrvitres and knowledge retention across generations of projects may be crucial to competitive

advantage in rapidly changing markets where multiple new products are repeatedly introduced

(Cusumano and Selby, 1995; Iansiti, 1995 b, d; Nobeoka, 1993, Wheelwright and Clark, 1992).

While the focus of this study on Japanese companies makes it difficult to generalize, it also

eliminates the potential bias of a country effect, one of the strong performance predictors in

15 1996] KNOWLEDGE RETENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE

several existing studies (e.g., Clark and Fujimoto, 1991; Iansiti, 1992; Nobeoka, 1993;

Womack et. al., 1990). By exploring differences within Japanese projects, this paper can

extract explanatory factors independent of the country effect.

2. Conceptual Foundations and Hypotheses

This section provides conceptual foundations of the hypothetical relationships between

knowledge retention and product development performance. Below, we begin by discussing

that different performance attributes involved in new product development activities may call

for different types of knowledge retention.

We then discuss several mechanisms to retain knowledge across product generations. This

is important since an empirical part of this paper focuses projects' dependence on particular

retention mechanisms as an indicator for knowledge retention. We close this section by making

the specific･hypotheses to be tested in the subsequent sections.

2-1. Performance Attributes and Knowledge Types

In the conceptual scheme used in this paper, overall performance of new product development consists of two factors, Iocal performance and system performance, as indicated

in Figure I below. Local performance arises only from the local region of product or of product develop-

ment process, and corresponding development efforts within particular technical and function-

al areas (Iansiti, 1995b; Henderson and Cockburn, 1995; Ulrich, 1995). For example, the

aerodynamic performance of automobiles, as indicated by an air drag co-efficient (Cd), is

almost solely determined by the exterior body shape developed by exterior body designers.

On the other hand, system performance characteristics arise from many related elements

of a product or a product development process, and their interaction. It is thus the outcome of

interactive activities among people in different functional and disciplinary areas. For example,

NVH (noise-vibration-harshness) is a critical performance metric for automobiles. While

NVH can be individually ascribed to particular technological elements, such as material

technologies used in tires and bodies, engine systems, body shapes, and suspension systems, it

also comes from the complex set of interactions between these elements.

We define local performance as the portion of overall performance reducible to particular

technological and functional elements, and system performance as the portion attributed to

FIGURE l. LOCAL PERFORMANCE AND SYSTEM PERFORMANCE

16 HITOTSUBASHI JOUl~NAL OF COMMERCE AND MANAGEMENT [October

interactive effects among these elements. The local and system distinction is also applicable to

non-technical performance. For example, development lead time may be shortened either by

compressing the lead time of each technical and functional activity (local performance) or by

facilitating overlaps among them through appropriate adjustments (system performance)

Similarly, in some cases, superior engine technology or effective advertising may become a

primary driver for automobile sales in the market-place (local performance); in other cases, an

appropriate combination among a product concept, component performance, and manufactur-

ing quality may become critical for sales performance (system performance).

This distinction between local and system performance becomes important when examin-

ing the impact of knowledge retention on product development performance since projects

may have to retain different types of knowledge to improve different performance attributes.

Achieving high local performance may require specialized or domain-specific functional

knowledge, often based on fundamental scientific understanding. No chassis engineer in

automobile development, for example, would join a company without a mechanical engineer-

ing background (though engineers of suspension control systems may require electronic

backgrounds). While based on fundamental scientific knowledge, development of actual

component systems requires a more substantial engineering knowhow that goes beyond what

is learned from university education. Such knowhow may be gradually accumulated within

companies through long-standing development experiences. Current component system devel-

opment should benefit from such historically accumulated knowhow. We thus conjecture that

differences in local performance, at least partially, depend upon how engineers effectively

retain and utilize specialized or domain-specific engineering know-how obtained from prior

development activities.

On the other hand, system performance may primarily depend on knowledge that goes

beyond functional and technical boundaries, which we call integrative knowledge. Develop-

ment of new "system" products invariably calls for knowledge to integrate potentially

fragmented and specialized knowledge to apply specific contexts. In the case of automobile

development, for example, body design must be integrated with suspension system design to

minimize the noise level and to improve body strength; product design must be integrated into

process design to achieve smooth ramp-up and high manufacturability; and the whole product

design must be integrated into user contexts to satisfy user needs. All these require knowledge

to integrate different functional domains.

Some recent studies have realized the importance of such integrative knowledge in the

development of complex system products, and have proposed normative mechanisms appropri-

ate for cross-functional and inter-disciplinary coordination, such as co-located cross-

functional teams (Imai et. al., 1985), the heavyweight project manager system (Clark and

Fujimoto, 1991; Wheelwright and Clark, 1992), and project organizations (Allen and Haupt-

man, 1987, Allen, 1987). However, it seem that these structural solutions are easy to imitate

(Kusunoki et. al., 1995; Henderson, 199). Thus, we doubt that they become sustainable

sources of difference in new product development. In our view, a capability for cross-

functional integration is, rather, a historical product (Fujimoto, 1994), and effective retention

of integrative knowledge is of fundamental importance to form a project's ability to solve

cross-functional problems, which may have a particular impact on the system portion of new

product development performance.

The above discussion can be summarized as the following propositions:

19961 KN0wL㎜G喧㎜丁酬丁10NA㎜N・wPROD㏄・DEv肌oPM酬丁冊RF㎝MANcE　　　　17

Proposition1：E価㏄tive　retention　of　domain　spec脆c　md　fu1lctiona1knowledge　across

generations　of　projects　is　positively　associated　with　the1ocal　po11：ion　of　new　product

development　performance・

・・・…iti…：・脆・ti・…t…i…fi・t…そti・・㎞・・1・d・…m・・・…「・tionsof・「oj㏄ts

lsposltlvelyassoclatedwththesystempo竹mofnewpmductdevelopmentperfomance

2－1．Pos附veo・Neg州▼eEπ㏄値ofKmwledgeRetemti㎝

　　　　Contra町to　tllese　propositions，innovation　studies　have　tended　to　discuss　a　negative　e価ect

．f㎞・w1・dg…t・・ti・・㎝im…ti・・p・・f・㎜・・㏄（・・9・，Al1㎝・・dM・・q・i・・1964；L・㎝・・d－

Ba計on，1992；Dougherty，1992；Henderson　and　Clark，1990）．A　common　claim　in　tllese．t．di。。i・th・tpd・・t・・㎞・1．9i・・1㎞・w1・dg・i・・ft・…t・pP1i・・b1・i・・・…1・it・・ti・・

characterized　by　innovation；rather，it　becomes　an　obstacle　for　bringing　in　new　ideas（e．g．，

Anderson　and　Tushman，1990；Hendemon　and　Clark，1990；Christensen　and　Bower，1994）。

Emph・・i・i・gth・…ti・i・・d・・p・・t・fth・p・・t㎞・wl・dg・，・・dth・i…it・b1…d・・t・m・ti・

mtureof㎞owledgeretrieval，studiesofi㎜ovationtendtoaddresstheissuesofhow　tobreak・・・・…m・ti…t・…f・・i叩・・・・…g・1・・・…t・…k…1ed・e・｝nco甲・a「ison・P「ocesses

・f・・1・t1㎎㎞・wl・dg…t・・tm，・pPll・・t1・・，・・dt・…f・・h・・・・…1・・d11ttl・・tt・・tm

　　　　Organizatiom11eaming　literature　has　also　somew1lat　contradictory　conclusions　on　tlle

e脆ct　of　knowledge　retention　on　organizationa1performance，0n　the　one　hand，it　has　been

…ξ・・t・・t・・t・m・1・・・・・・…中・・・・…弓b！i・dd・・i・ig・make「stonewis・ectsof

envlmnm㎝ts　and　thereby　compromlse　an　orgamzatlon’s　e価ect1v㎝ess（March，1972，Nystrom

and　Starbuck，1984；Walsh　and　Fahey，1986）．Memo町ret㎝tion　facilitates　a　si㎎le－loop

learning（ArgyHs　and　Schon，1972）and，thus，enhances　the　existing　mutines　that　may　not　be

apP・ophat・fo・thenewsitu・ti㎝s・　　　　0n　the　other　hand，successful　organizations　embed　their　adaptation　activities　as　organi－

zational　routines．Such　routines，often　reHected　ill　the　standard　operating　Procedures・Pro－

9・・m・，・t・bl・・。mm・i・・ti…h・m・1・，・・d・・g・・i・・ti・ml・tm・t・・…f・m・・dti・・lp・・t・f

organizational　memories．Since　organizational　memories　stored　as　such　routines　are　automat－

i・・11y・・td…d，・・g・・i・・ti・・・・・…d・・・…t・・・…i・t・dwith・・…h・・d・・p・・im・・t・ti㎝

and　thus　increase　task　e冊ciency（March　and　Sim㎝，1959；Ne1son　and　Winter，1982；

Thompson，1967）．　　　　This　contradiction　is　pa㎡ia1ly　explainable　by　considering　d冊erences　in　organizations’

environmental　characteristics．Memory　retenti㎝may　increase　organizati㎝al　perfomance。・lywh㎝・・g・・i・・ti・・・…f・・i・g・t・b1…d㏄れ・i・…i・・㎜・・t・th・t・・11f・…p・titi・・

problem　so1ving．Routines　in　tlle　form　of　standard　operating　procedures　md　programs　can

m。。t・価・・ti・・lyf・・i1it・t…g・・i・・ti…1m・mb…’1・・mi・gf・・…hp・・b1・m・・1・i・g・P・・pl・

wh・・mph・・i・・dp・・iti・…p㏄t・。f㎞・wl・dg…t・・ti・・m・ypHm・・ily1・・k・t・・g・・i・・ti㎝・

f・・i・g・・1・ti・・ly・t・bl・㎝・i・㎝m・・t・．0・th・・th・・h・・d，th…wh…d・・・・…d・・g・ti・・

・・p・・t・・fp・・t㎞・wl・dg・mightp・y・tt・・ti・・t…g・・i・・ti…f・・i・g・…1・・d・・…t・i・

situations．This　is　why　studies　of　innovation　tend　to　emphasize　problems　associated　with

㎞owledge　ret㎝tion．　　　　　The　above　discussion　leads　to　t1le　fo1lowing　proposition：

HITOTSUBASHI JOURNAL OF COMMl3RCE AND MANAGEMENT

Proposition 3: The relationship between knowledge retention and product development

performance is moderated by the degree of task newness involved in new product development activities.

However, other researchers have suggested that prior experiences are important, and even

help firms adapt to new environments (Cusumano, 1991; Cusumano and Selby, 1995; Neustadt

and May, 1986; Huber, 1991; Walsh and Ungson, 1991). Recent theoretical argument in the

area of design studies also tend to assume that any design work is based on past experiences

and accepted tradition, and that past knowledge becomes critical, even to non-routine and

creative design work (Gero, 1990; Oxman, 1990). As an empirical study, Iansiti (1995 a, b, d)

found that system integrators' past experiences in developing the same type of product are

positively correlated with development efficiency and technical performance.

Furthermore, different types of task newness may differentially moderate the relationship

between knowledge retention and product development performance. While some studies found that technological discontinuity substantially changes market dominance, from incum-

bents to new entrants (e.g., Henderson and Clark, 1990; Tushman and Anderson, 1986; Suarez

and Utterback, 1991), others claimed that a change in the customer base and associated

changes in product functionality pose a more serious threat to incumbents than technological

change (Christensen and Rosenbloom, 1995; Christensen and Bower, 1994; Iansiti and Khanna, 1994). We shall take into account these factors in our data analyses.

2-3. Retention Mechanisms and Product Development Performance

One of the critical problems involved in the empirical research dealing with knowledge

retention is that it is not an easy task to measure the amount of retained knowledge, especially

when considering less observable integrative knowledge. One way to overcome this problem

may be to focus on several possible mechanisms for knowledge retention and specify boundary

conditions as capabilities to facilitate knowledge retention across generations of projects. They

are, for example:

1 . the transfer of project members

2. communication with people who have substantial experiences in past development pro jects

3 . the involvement by organizational units that coordinate development activities across

generations.

4, the use of documents and reports describing past problematic and successful practices

5, the use of design standards, design tools and standard design/test procedures

6. the use of computerized information systems, such as CAD and CAE

If any of these mechanisms prove to be more appropriate to retain integrative knowledge

than domain specific knowledge, we can use a projects' dependence on such mechanisms as an

indicator of the retention of integrative knowledge. To do so, however, we need to understand

a difference of the nature between integrative knowledge and domain-specific one.

One of the reason why researchers have paid significant attention to knowledge cutting

across different specialized domains, integrative knowledge, is that such knowledge tends to be

less articulable, thus, it may form the foundation of firm-specific capabilities.

There are a couple of reasons why integrative knowledge tends to less articulable. First,

1996] ICNOWLEDGE RETENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE 19

there is no established languages to communicate integrative knowledge. Domain-specific and

scientific knowledge is often supported by particular disciplinary areas with well-established

languages for teaching and communication. We have social mechanisms for accumulating

disciplinary knowledge, such as professional communities and educational and research

institutions. On the other hand, there is no universal language to communicate integrative

knowledge. There is no social support for its accumulation. In particular, since integrative

knowledge involves knowledge to translate different languages between different thought

worlds, it tends to be difficult to articulate.

Second, integrative knowledge tends to be context-specific: it is knowledge of the

particular circumstances of time and place. It may also be embedded in specific personal

relationships (Badaracco, 1991; Spender, 1994). Thus, it may be difficult to express it in the

fonn of facts and propositions. For example, in the case of automobile development, while the

best vehicle styling to solely maximize aerodynamic performance can be theoretically deter-

mined and generalizable, appropriate linkages between the styling, the body structure, the

engine shapes, and the suspension types may be different among vehicle types with different

sizes, platforms, and customer bases. The most direct way to transfer and retain less-articulable and context-specific integrative

knowledge may be to transfer to or retain individuals with first-hand experience in appropriate

decision settings. For example, Cohen (1991) pointed out that the concept of procedural

memory proposed by Anderson (1983), which refers to methodological knowledge in use as

opposed to facts and propositions, may be better transferred by means of personnel rotation.

When knowledge is embedded in specific relationships between people, it might be

required to make a group of people active for long time. For example, Wilson and Hlavacek

(1984) found that firms which benefited from technologies created in past projects kept

knowledge alive by the active presence of a core group of people.

However, there are obvious limitations in completely depending on a particular individual

or group: when people leave, knowledge disappears. If integrative knowledge can be shared

with, and transferred to, other people and groups, firms can more effectively leverage that

knowledge. In this respect, Nonaka ( 1 994) suggested that tacit knowledge embedded in

individuals can be transferred among individuals by having shared and common direct experiences. He called this type of knowledge transfer (conversion) "socialization." Thus, if

companies can create a chain of overlapped common experiences among people, tacit knowl-

edge can be retained for a long time.

Direct face-to-face interactions may also help individuals share integrative knowledge.

Although fully embedded knowledge may not be directly expressed by words, direct interac-

tions lead to gradual understanding of contextual factors behind artifacts, providing better

ways of knowledge retention than documents or blueprints.

Accordingly, we hypothesize that integrative knowledge is most effectively retained

through individual-based retention facilities, such as the direct transfer of individuals and a

group of people, shared experiences among individuals, and intensive face-to-face interactrons

among individuals. On the other hand, domain-specific and functional knowledge tends to be more articulable

and generalizable. Therefore, retention of such knowledge will most benefit from the use of

archival mechanisms, such as documentation, standardization, and computerized systems

In accordance with the above discussion, Propositions I , 2, and 3 can be rewritten to the

20 HITOTSUBASHI JOURNAL OF COMMERCE AND MANAGEMENT [October

following hypotheses.

Hypothesis I : The use of archival-based knowledge retention mechanisms, reflected in

documents, reports, standards, and computer-aided design systems, will be associated

with high local performance in new product development.

Hypothesis 2 : The use of individual-based knowledge retention mechanisms, reflected in

continuity of project members across generations of projects and communication among

project members in successive generations of projects, will be associated with high system

performance in new product development.

Hypothesis 3 : The relationship between the degree of projects' dependence on knowledge

retention mechanisms and product development performance will be moderated by task

newness, reflected on either or both technical and market newness.

3. Research Methods

This study used a cross-sectional questionnaire survey to address the research questions.

In common with most previous studies, the focal unit is an individual project, but the level of

analysis (Rousseau, 1985) is the inter-project level. Therefore, the questionnaire has a

particular emphasis on the transfer of product-related knowledge from past development activities, focusing on linkages between present and past development activities.

We distributed a questionnaire instrument between March and May 1995 to key members

of projects at seven major Japanese automobile manufacturers. In distributing the question-

naires, we asked a contact person at each company to select recent new product development

projects that satisfy the following two conditions. First, projects should be responsible for

"major" new product development. The meaning of "major" is fairly clear among Japanese

companies since they divide product development projects into "minor model change" projects, "full model change" projects, and "new model development" projects based on the

common criteria. The latter two types are categorized as major new product development

projects to which the Ministry of Transport imposes additional testing requirements not

applicable to minor model changes. The second condition is that projects should develop new

models that replace existing models, that is, "full model change" projects.

The number of projects we requested varied from company to company depending on its

size. We asked for a total of 29 projects and received data on 25 projects. Ten key members of

each project were asked to respond. Those ten key members include a project manager, vehicle

test engineers, Iayout engineers, body design engineers, chassis design engineers, exterior/

interior designers, engine design engineers, electronic component design engineers, marketing

planners, and production engineers. We tailored the questionnaire according to the needs of

different team members to account for the uniqueness of their tasks.

While we obtained all 10 responses from 17 projects, there is some missing data for the

remaining eight projects, since we were unable to obtain responses from some project core

members. As a result, the sample comprises 229 core members.

1996] KNOWLEDGE RETENTION AND NEW PRODUCT DEvaLOPMENT PERFORMANCE 21

We also conducted in-depth interviews with project managers and other core-members at

14 projects. Of the 14 projects, 10 projects participated in the questionnaire research as well.

Therefore, we were able to use qualitative information obtained from in-depth interviews to

interpret the survey results as well as to design the questionnaire instrument.

3. I Research Design

To examine the hypotheses discussed above, we conducted two sets of analyses. First, we

focused on development activities within each component development area, such as body

design, engine design, and chassis design, to explore the impact of knowledge retention on local

performance. We thus empirically regarded local performance as performance of particular

component development attributed only to activities within each component design group.

Next, we analyzed data at the project level. As already mentioned, performance of an

entire project can be infiuenced both by each functional or component development activity

and interaction between them. Therefore, comparison of the results between component level

analyses and project level ones presumably identifies differences between capabilities to

improve system performance and those to improve local performance. The comparison of results obtained from different samples, however, has limitations.

Therefore, we also attempted to compare between factors affecting system performance and

those affecting local performance within the same project-level sample. Finally, we examined

the moderating effects of market and technological newness on the relationships between

experience-based retention and project performance as a partial test for Hypothesis 3.

4. Knowledge Retention and Local Performance

4. I Sample

Out of the 229 entire sample, we focused on only component design engineers to examine

local performance. Although our data sets comprises 1 18 component design engineers, the final

sub-sample analyzed included only 83 engineers because of missing values for some explanato-

ry variables. All these 83 members were key project members, representing five different

engineering or design areas, exterior/interior design, chassis design, body design, engine

design, and electronics component design

4. 2 Performance Measurement

In the questionnaire, we asked respondents to assess perforrnance derived only from

design activities within their engineering areas, as opposed to the performance of overall

product development projects. Using 5-point Likert scales, they rated their satisfaction in

development cost performance, component cost performance, adherence to schedules, manu-

facturability of component systems, novelty of component systems, and technical performance

of component systems. Table I below shows summary statistics for these performance

indicators.

22mTOTSuEASH1JOu皿NAL　OF　COMMI…RCE＾ND　MANAGEMENT　　・ 【Ootobor

TA肌E1．　DEscR1PTIvE　STATIsTlcs　FOR　PERF0RMANcE　INDIcAT0Rs

N＝83 Mem　　S．D．　Min．　M田■．

Component　cos－Pe㎡ormヨ皿06

DewIopm6nt　oost　perfomanoe

Adhor611ce　to　sch6dul0

Manufo伽rability　of　compon㎝t　systems

Novoltyofoomp㎝㎝tsyslemsT㏄hllicaI　perromla皿ce　of　compomellt　systems

3，33

3，04

3，13

3，14

3，21

3．74

1，04

0，09

1，01

0，70

1，01

0．74

1，OO

一．o0

1．O0

1，O0

1．O0

2．OO

5．O0

5，00

5，00

5．O0

5，00

5．OO

　　　　　　　　　　　5－poin1L此帥Sca］es，fmm　l　E　llot　s田tisf田c－oη，to5三ve町satisf刮ctoη．

4．31≡：叩1amatory　V8血阯es

　　　　Table2below　illdicates　descriptive　statistics　for　explanatory　variables　considered　in　the

・・b・・q…t…1y・…E・・h・・plm・t・・y・・由bl・・・・…p・・d・t・・…fth・・i・㎞・wl・dg・

「etentionmechチnisms：d…m・・t…d・…れ・…子・d子・d・…叩・t・・一・id・d…t・m…i・㏄t

trms此r　of　prqject　members，face－to－face　commumcatlon，and　mvo1vemellt　of　mdependent

orga1lizational　units．Below，we　brieHy　explain　how　we　constmcted　t1lese　measures．

σ舵ヴル・”リ・1〃・伽沁㎜∫・1）㏄・㎜θ〃M・〃印・舳，・〃∫伽伽必

　　　　Ou「oセse「vation「evealsthatJ・・・・・・…t？m・bi1…m・子・i・・…t・・t・・・・…。・㎜g・t・

to　store　pr1or　knowledge　The趾st　type　descnbes　standard1zed　knowhow　such　as　techmca1

standards，standard　desigll　procedures，and　standard　test　met110ds，which　engineers　must

follow．Non－standardized　knowhow　or　lessons　obtained耐om　past　activities　were　retained

thmgh　several　other　foma1and　infomal　repo血s　and　memos　such　as　the　test　repoれs，the

kllow110w　documellts，alld　the　user－claim　repo11＝s，t11e　problem－11andling　document，and　t1le

COmmuniCatiOn　memOS．　　　　A1tlloug1l　the　boundary　betweell　standards　and　noll－standardized　knowhow　is　not　clearly

　　　　　　　　　TAB岨2．　DEscR1PTlvE　STATIsTlcs　F0R　Ex肌ANAT0RY　VARlA肌Es

N＝80 Mean　S．D．　Mi皿．　Max．

Ref61－ence　to　doo11mellts與皿d　reporユs

Impo肘a皿㏄ofst㎝dards

US60f　COmputer　Si血阯OtiOn

Compu倣一b㏄ed　d6sign祀telltioll

Dir㏄t　oreation　of　p耐s　pm酊om　by　CAD／CAM

I皿tm－f㎜Cti0皿al　COmmu血Cati0皿

Cross－fmction』comm皿micatio皿

Comm皿nicatio皿witll　ot116r　proj㏄t　m6mbers

Com㎞cati㎝舳prlvio岬ojlctmemb1耐Re1ativepoweroff㎜oti㎝alma㎜ge胴

R61atiwpoworofl㎝g・templ㎝mi㎎9mps9る　of　P1－evi0皿s　p1－0ject　me血1bers

　3，78

　3，92

　3，22

　3，63

　3．48

193，2

　47，0

　22，7

　21．O

－O．24

－1I55

　0．18

O．91

0，99

1，25

0，90

1．32

72，9

19，7

18，4

18．4

1，23

1，56

0．14

1，00

1．O0

1．O0

2，00

1．O0

12．0

4．0

2．5

2．5

－3，OO

－4，00

0，OO

5，00

5，O0

5．O0

5，00

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240．0

110，8

94，3

67．1

2，00

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0．75

1996] KNOWLEDGE RETENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE 23

defined, we tried to separate them in the qu.estionnaire. First, we asked respondents to rate how

frequently they referred to documents and reports that described design solutions and

problems identified in the past development activities on a 5-point Likert Scale, from I = not

refer at all, to 5 = refer very frequently (mean = 3.78, s. d. = 0.91). Second, respondents

rated the importance of standards in designing components during the project on a 5-point

Likert scale, from I = not important at all, to 5 = very important (mean = 3.78, s. d. =

0.99). Standards here include design standards, standard testing procedures, standard design

procedures, and sfandarej design tdols.

Use of Computer-aided Systems We requested respondents to rate the importance of computer-aided systems within six

different areas: CAE simulation (vehicle performance), CAE simulation (structural analysis),

CAD/CAM with direct creation of parts programs, sharing of design information among engineers by CAD/CAE, standardized parts database, and reuse and edit of past design

information stored in CAD/CAE. Respondents rated the importance of each of these

according to a 5-point Likert scale. Some of these six variables are conceptually distinct. For example, the first two variables

together indicate the use of CAE simulation tools; the last two variables indicate the use of

computer-stored past information. Along with this conceptual distinction, a principal compo-

nent analysis enabled us to group these variables into three indicators. The first is the use of

computer simulation consisting of CAE simulation for structural analysis and CAE simulation

for vehicle performance (alpha = 0.83). The second indicates the computer-based past design

retention that consists of the standardized database and the reuse and edit of past design

information (alpha = 0.78).] Third, we preserved a variable of CAD/CAM with direct

creation of parts programs as a separate variable.

Continuity of Engineers Across P?ioduct Generations Each respondent provided the total number of engineers in his or her area involved in the

project. They were then asked for the number of these engineers who also had been responsible

for the previous generation of a project. Based on these numbers, we calculated the percentage

of engineers having experience in the previous project generation. Because of confidentiality

issues, some respondents did not provide us with these numbers. We obtained data only from

90 out of 1 1 8 respondents, which 'significantly decreased our sample size. The average

percentag~ of engineers having experience in the previous projects as estimated by the

engineers themselves was 18% (s.d. = 0.14).

Communication Respondents estimated how often, on average during projects, they communicated with

nine types of individuals indicated by the 3 x 3 matrix' in Table 3 below.2

Respondents rated the frequency of communication on 6-point scales, with I = two to

l The second factor obtained from the principal component analysis consisted of these two variables as well as

the design information sharing variable. However, the computer-based past design retention is conceptually different from design information sharing. Therefore, we excluded the information sharing variable, and then

averaged seores for the remaining two variables. 2 Although our speeific concern is the impact on local performance of communication with individuals who

previously developed the same component systems, we also considered other types of communication, since the

24HITOTSUEASH110URNAL　OF　COMME皿CE＾ND　MANAGEMI…NT ［o吃to㎞r

TABLE3．　TYPEs0F　C0MMUNlcATI0N　RBP0RTED

（Ineasur6d　in　app正oxhnate　days　per　year，N　＝　80）

Communication with the same project another project the previous

Project Members generation of the

belonging to . pro ject

the same engineering area (1) (2) (3)

Mean: 193.2 Mean: I13.l Mean: 84.83 S.D.: 72.9 S.D.: 91,l S.D.: 94, 1 1

different engineering (4) (5) (6)

areas within the product Mean: 58.7 Mean: 17.2 Mean: 18.0 engineering department S.D.: 29.8 S.D.: 19.2 S.D.: 15.8

different functional (7) (8) (9)

departments Mean: 36.0 Mean: 15.1 Mean: 15.1 and suppliers S.D.: 19,l S.D.: 17.9 S.D.: 16.5

　　　　　　　　　The　di価erent　fullctio皿a］d6paれments　sllown　in　tlle　－hird　row　i皿c］ude　production，

　　　　　　　　　m町k6ti皿g／pmdlIct　p1a㎜1ing，蘭1os，quality　imsumnce，purchasing，cost　momgem6nt，

　　　　　　　　　1ong－term　planni1lg　groups，aIld　supPliers．

three　days　per　year　or　less，2＝once　a　month，3＝two　or　three　days　a　month，4＝on㏄a

week，5＝two　or　tl1ree　days　a　week，and6＝every　day．Based　on　a240－day　working　year，

eachscorewastmnsfomedtothcmmberofdaysinthefollowingway：1＝2．5days；2＝12days；3＝30days；4＝52days；5＝120days；and，6＝240days．Then，we　calculated　scoresfor　the　above　nine　types　of　communication，if　required，by　averaging　tlle　number　of　days　for

communication　with　appmpriate　individuals．The　means　and　standard　deviations　are　s110wn　in

the　table3above．

　　　　To　id㎝tify　an　under1ing　pattem，we　subjected　these　nine　indicators　to　a　principal

components　analysis．Four　factors　emerged．Based　on　tllis　amlysis，we　collstmcted　four

measures　for　d冊erent　types　of　communication　by　averaging　coπespondil1g　communication

scores・These　are　intra－fmctional　and　withill－Project　communication，cmss－functional　and

within－project　communication，inter－project　communication，and　commullication　wit1l　the

previous　proj㏄t　members（cmss－9㎝emtiona1communication）．3

07goπ乞αゴoηo1∫ψ〃ε肌θ

　　　　Respondellts　rated　the　inHuences　of　functional　mamgers，1ong－term　teclmology　plaming

groups，and　project　ma1lagers　in　technology　selection　decision－m早king，on5－point　Likeれ

sca1es，from1＝not　involved　at　a11to5：played　a　very　impo11＝ant　role．Based　on　these

answers，we　constmcted　indicators　of　t1le　relative　inHuence　between　project　mamgers，

functional　managers，and　long－tem　plaming　groups，by　subtmcting　scores　for　project

managers’mHuences　fmm　scores　for　t11e　otller　two1㎡1uences　As　a　result，we　obtamed　two

indicators　for　relative　innuences：olle　indicates　the　in肋ence　of　a・functioml　manager　re1ative

to　that　of　a　project　ma㎜ger；the　other　refers　to　the　inOuence　of　a　lo㎎一tem　t㏄㎞o1ogy

existing　studies　deal　with　both　imtm－md　in16r－functiom1commuIlication　wit11people　within／ouふde　proj㏄t

b㎝mdaHes　asi岬舳ntp6rfomo㏄ep祀diot㎝（e－g一，杣㎝，et．刎．，1979；Anoon“皿d　C副1dw61止1992a）．　，How6ver，the㏄m6砥u燗am　mt　completeIy　i皿depend6nt，since　some　proj㏄t　moml〕e冊mi曲t　be　tmmfeπ6d

fmm　the　p祀vious　gen町ation　of　pmj㏄ts，o－11洲o　worked　on　multiple　projects　simuItaneo㎜1y．In　suoh　c砥es，

。om㎜ni・・ti㎝withi・th・pmj・・ti・m・・干・…1・・・・…1榊dwithi・脈Pmj㏄・㎝di皿t・・一g・…舳mlOOmmuniCati011．

1996] KNOWLEDGE IU~TENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE 25

planning group relative to that of a project manager.

Control Variables In addition to the above explanatory variables, we considered five control variables that

presumably have strong infiuences on component development performance. These control

variables are summarized as follows.

Bubble economy: respondents involved in the projects that introduced new models

during 1991 were coded as 1: O otherwise

Micromini Car: respondents who worked on micromini car development were coded as 1; O otherwise

Design Newness: the percentage of change in the component from the existing

design

Engineering area: a dummy variable indicating respondents' engmeenng areas

Company: a dummy variable indicating respondents' companies

4. 4 Results and Discussions

Table 4 below shows the correlations among performance variables and explanatory

variables.

Results in Table 4 appears to support our proposition that local performance is positively

associated with archival-based knowledge retention capability.4 For example, component cost

performance was positively correlated with the reference to documents and reports (r = .33,

p < .O1), the use of standards (r = .25, p < .05), and computer-based design retention (r =

. 3 1 , p < .Ol). Development cost performance has a positive association with the reference to

documents and reports (r = 0.27, p < 0.05). Technical performance was positively related

.44, p < .O1), and the use of CAE with the reference to documents and reports (r =

simulation (r = .32, p < .O1). On the other hand, organization-based and individual-based mechanisms tended not to be

associated with performance indicators. First, none of the communication-related variables

was significantly associated with performance. Second, among the organizational influence

variables, the functional manager's relative power against a project manager had a positive

association only with component cost performance (r = .22, p < .05). Third, the percentage

of engineers who worked on the previous project was found to be positively related with

technical performance (r = .25, p < .05), but has no association with any efficiency-related

4 Among the explanatory variables, the reference to documents and reports, and the use of standards, are highly

correlated (r = 0.58, p < 0.01). As we see in later analyses, this high correlation seems to cause problems in

parameter estimates for some of the fitted regression models. However, we preserved these two as separate because

we are interested in how differently knowledge retention in standardized forms affects performance from that in

non- standardized fonns.

[October HITOTSuEASm　JOURNAL　OF　COMMERCE　AND M＾NAGEMENT

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26

1996] , KNOWIJ3DGE RETENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE 27

performance indicator. ' Table 5 below shows the results of , fitted regression models for each performance

dimension. Model I includes only control variables for each performance indicator. When sets

of dummy variables indicating engineering areas and firms were not significantly associated

with performance, we excluded them in the subsequent models.5 Model 2 includes variables

related to archival-based retention mechanisms in addition to control variables; ' Model 3

includes variables for organizational capabilities. Model 4 for each performance indicator

shows the full fitted regression model. However, we found that the observed high correlation

between the reference to documents and reports, and the use of standards (r = 0.58), seemed

to cause some problems in parameter estimates, so we excluded either of these variables in turn

from Model 5 and Model 6, respectively. Results from the regression analyses are mostly consistent with those from the correlation

analyses. First, as shown in the full models (Model 4), data suggest that the more frequently

engineers referred to documents and reports, the higher performance they reported, in general.

Specifically, this variable was positively associated with component cost performance (p <

.05), development cost performance (p < .O1), manufacturability (p < .O1), and technical

perforrnance (p < .Ol). This implies that reference to documents and reports to learn from

past component development practices has a broad impact on local performance dimensions,

both in terms of development efficiency and technical performance, as hypothesized

Contrary to our hypothesis, the full regression models show that the use of standards is

negatively related to development cost performance, manufacturability, and technical per-

formance. However, all these negative relationships were no longer significant after excluding

the use of documents and reports as shown in Model 5, indicating a problem of multi-collinearity.6

Second, computer-based design retention was positively associated with component cost

performance at the 5% significance level. Since a high score for this variable also indicates the

high degree of reuse of previously-designed parts, this result is understandable. However,

computer-based design retention was not significantly related to any other performance indicators. Although it was consistently positively associated with efficiency-related perform-

ance indicators, the relationships were not statistically significant.7

Third, the use of computef simulation tools was significantly associated with technology-

5 we conducted thc increment-to-R-square test to examine the impact of sets of dummy variables. When either the firm or area dummy variables together did not significantly increase values of R-square (5% Ievel), we

excluded them in the subsequent regression models. 6 The signs of regression coefficients were, however, still negative. It might be that there is some real negative

influence from the use of standards on performance. Problems of dependence on technical standards generally arise when engineers use outdated technical standards and take it for granted. New products were introduced after

1993 in 21 out of the 25 projects in our sample. This means that most projects developed new products after the

record-breaking economic boom in the late 1980s. As we mentioned in the previous section, engineers had to significantly change the way to develop component systems to adapt to much more price-conscious customers in the 1990s. For example, engineers were required to dramatically reduce component costs and the nunrber of parts.

In such circumstances, companies had to revise many existing technical standards that had tended to put too much

quality on component systems by sacrificing cost performance (Fujimoto, 1994), as several interviewees pointed

out. Thus too much reliance on existing technical standards during this period might lead to low performance,

particularly in efficiency-related performance dimensions, at least, in the engineer's subjective evaluation.

7 In the additional analysis which excluded exterior/interior designers from the sample, we found that the

CAD/CAM variable was significantly associated with manufacturabi]ity.

28 HITOTSuEASHI mURNAL　OF COMMI…RCE AND MANAGEMl≡NT 【Ootobe正

TA肌E5． REsULTs0F　T朋FITTED　REG㎜ssI0N　ANALYsEsP0R　LOcAL　PERFORMANcE　INDIcAT0Rs

Compo06皿t　Cost

　岬‘om皿㏄

DowloPmoot　Co筍t　1，or‘o而日田皿‘＝ε

　　　　　　　　　　　　　　　　　　　11IIIII∀VV1阯眺E・・m叫　　　一0．ψ榊刈．50榊一0．47榊伽1榊仙7榊一〇．48榊

Miom　C町　　　　　　　　0，16　0．24榊0．15‘　O．19舳0．19｝O．21

D6sign　Ncw皿偉蝿　　　　　　　　　O．07　　　0．08　　－0，06　　　0．02　　－0，03　　　0－03

Fim　　　　　　　　NotSig．EllgimeH－1g　A祀a　　　　　Sig．　　Im，　　I皿o．　　Im．　　I皿c．　　Illo．

Roforo皿c6to　Dooumc11ts　　　　　　　　O．20壮　　　　　　　O．28一ヰ　　　　　　　0．19‡

U舵of　D6sig11St旦皿dards　　　　　　　　－O．09　　　　　　　　－O．16　　－O，02

ComplIt6一一b舶ed　D巴筥igI1R巴to11ti011　0．17‡　　　　　O．21抽　0．21柚　O．1畠‡

Di祀ct　P舳昌Pmgrom　by　CAD／CAM0．16　　　　　006　0－11　0－06

U舵of　CAE　S㎞阯1ation　　　　　　　O．02　　　　　　0．05　－O．01　　0．02

Rc1帥i冊Power＝FM　　　　　　　　　　　　O．10　0I08　0，09　0．07

Rc1舳c　Power＝L㎝壇一tem　Plm　　　　－O．07－O．02　－O－07　－O，04

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D閑ig皿Now血醐　　　　　　刈一31榊一〇．26壮一〇．28拙一〇。22‡

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Re1砒ive　Power＝Loilg－term　Pla日　　　　　　　　　　O－09　　　015

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　　　　　　＝Cmss－F山mtio皿al　　　　　　　　　O．OO　　O．02

　　　　　　Cro鵬一Gellcmtlo皿　　　　　　　　　0．03　－O．04

　　　　　　・I皿t6r＿Projcct　　　　　　　　　　　　　　0，14　　　0，14

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　　V　　　VI

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　　IIlIIIIVVVI－OI31‡一一〇、36榊一〇．26榊一〇．34榊一〇．27‡一一0．27甘

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㌔＜ ．1，柚pく ．05，榊p＜．O1

1996】　　　　　KN0wu≡DGl≡㎜…丁酬丁10N　AND　M…w　PRoI〕ucT　D旧v肌oPMl…NT　PERF0RMANcE　　　　　　　29

TA肌E5．　REsULTs0F　THE　F1TTED　REG㎜…ss10N　ANALYsEs　　　F0R　LocAL　PERF0RMANcE　IND1cAT0Rs（continued）

丁畠o1111ic田1

NowltyT㏄h㎞cal

I，01＝fO『口1田11C巴

　　　　　　　　　　　　　　　　　　　IIl1IIIVVVI　IIlI1IIVVVI旧11bblo　I…ε01iomy　　　　　　0－16　　0．19ヰ　O．20‡　O．19‡　0．19‡　O．18　　－O．09　－O．03　　0．08　－O－05　　0，04　　0．01

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Co皿munic田ti011＝F凹mti011a1　　　　　　　　－0．22壮一0．25榊刈．25柚刈．25柚　　　　　　　　　　刈．17　－O．19ヰ　ー0．22柵刈．18‡

　　　　　　＝Cmss．Functi011al　　　　　　　　　O．00　　0，05　　0，05　　0，05　　　　　　　　　　　　0，04　　0，12　　0，11　　0，10

　　　　　　Cross．Gemmtio皿　　　　　　　　　0，10　　0，07　　0，06　　0，07　　　　　　　　　　　0．13　－0，11　　0．06　－O．06

　　　　　　：1皿蛇・一Pmj舳　　　　　0，17　0．30壮〇一30林O．30柚　　　　一〇・10　0・080・04　0・09

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Adju5t巴d　R一判11趾e　　　　　－〇一04　刈．01　　0．11榊　O．13帥　O．14榊　0．15軸　　0」2‡　O・25榊一〇・12＃　O・32榊〇一17＃　O・29蝸‘

㌻〈．1，柚p〈．05，榊p〈。Ol

related　perfommce：novelty　ofcomp㎝ent　systems　and　compone皿t　technicalperfomance．

However，it　was　negatively　associated　with　development　cost　performance．This　result　may

suggest　that　tlle　use　of　CAE　too1s　resu1ts　in　higher　t㏄hnica1performan㏄at　the　cost　of

development　e術ciellcy，In　this　respect，several　engineers　poi1lted　out　that，whi1e　CAE　tools

signincantly　impmved　qua1ity　of　the　first　design　prototype，it　llad　Ilot　yet　achieved　projected

development　e冊ciency　improvement，partia11y　because　CAE　tools　tel1d　to　make　engineers

spendtoomuchtimefomarginal　perfo㎜an㏄improvement・Contrary　to　archiva1mecllanisms　and　computerized　systems，organization－based　and

individual－based㎞owledge　ret㎝tion　mechanisms　did　mt　show　any　positive　impact㎝

perfomance　indicators：some　were　actualiy　found　to　havea　negativeimpact．

Amollg　the　communication－related　variables，communication　with　the　previous　project

membershadnosign岨cantassociationwithanyperfo㎜anceindicator．SurpHsing1y，commu一nication　with　engineers　in　the　same　engineering　area　was　negative1y　associated　w仙technical－

related　perfomance　such　as　novelty　of　comp㎝ent　systems　and　technical　perfomance　of

components．。

　　　　011the　other　hand，communication　with　the　other　prqj㏄t　members　was　positive1y

associated　with　performance　in　novelty　of　component　systems（p＜0．05）．This　may　indicate

l・・1甲・i・・…1・・…乎・1l・戸…h・i・．…t・・h干・1・・i・・1i亨…p・com㎜nic干tin・with

mdlv1duals　outslde　thelr　pm］ects　There　ls　anothe正mterpretat1on　Smce　tec1mo1og1ca11y－new

　8This　m劃y　i皿dicate　t］le　inhe祀nt　pf0b］ems　ill　communioation　studies＝lhe　mo正e　pmblems　o㏄叫the　more

f正明11e皿t1y6皿gi鵬crs　h別ve　to　commullicate　with　t11e　other　engi皿e帥to　solve　pmb16ms一

HITOTSUBASHI JOURNAL OF COMMERCE AND MANAGEMENT

components are typically developed for multiple projects, newness of component systems

might require engineers to communicate with ･ other project members to adjust component development activities across projects (Nobeoka, 1 993). '

Organizational influence variables also had no positive impact on any performance

indicators. Although correlation analyses showed that a stronger influence by functional

m4nagers than project managers is associated with better component cost performance, this

association was no longer significant in the regression analysis.

Involvement of long-term planning groups had a significant negative impact on techno-

logy-related performance such as novelty of component systems _(p < 0.01) and technical

performance (p < 0.01). Since long-term technology planning groups often play a critical role

in facilitating carry-over of existing component systems, it is understandable why their

involvement may lead to less novel component systems. Finally, continuity of engineers in successive generations of projects had no significant

association with any performance indicators. This result implies that knowledge retention

through people may not be critical to improve local performance.

In summary, results in this section partially supported Hypothesis I . We found that

dependence on documents and reports for knowledge retention had a broad positive impact on

local performance; dependence on computer-stored prior design information improved prod-

uct cost performance; and use of computer simulation tools was associated with higher technical perfonnance of component systems. On the other hand, organization-based and

individual-based mechanisms for knowledge retention had either no association or negative

associations with performance indicators.

These results imply that investment in formalizing and articulating knowledge may be

critical to improve performance within well-defined component system development areas.

5. Knowledge Retention and New hloduct Development Performance at the Project Level

5-1 Sample

The project level analyses below include data obtained from 229 respondents at 25 new

product development projects. Some project-level data was obtained directly from question-

naires specifically designed for project managers; other data was constructed by aggregating project members' responses, as'described below. Because of 2 1 missing responses, we could not

includes all 25 projects in our analyses. We excluded the three projects from the analyses which

lacked responses from the project managers, resulting in a usable sample of 22 projects.

5-2 Analysis Strategy

Because of the small sample size, we could not utilize fully multivariate techniques to

specify the relationships between project performance and knowledge retention capabilities.

Instead, we take the following steps in the subsequent analyses.

First, we explore the bivari,ate relationships between project perforn}ance and perform-

ance predictors, In the analyses, ' we consider both overall performance aid system perform-

1996] KNOWLEDCE ' RBTENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE '3 1

ance. System performance is statistically separated from overall performance as desc,ribed

below. One of the objectives of this correlation analysis is to explore whether there is any

difference between factors affecting overall performance and system performance. The corre-

lation analysis also identifies important control variables which should be considered to specify

the relationships between project performance and knowledge retention capabilities.

Additionally, we examine the fitted regression models to further confirm results of the

correlation analyses. Since we have only 22 sample projects, these models include only selected

control variables and indicators for knowledge retention capabilities.

Finally, we examine Hypotheses 3, which refers to an interaction effect between ex-

perience-based knowledge retention capability and task newness. We fit regression models

including interaction terms between a technical or a market newness indicator and individual-

based knowledge retention capability indicators, and examine how newness indicators moder-

ate the relationships between individual-based retention capabilities and product development

performance.

5-3 Performance Measurement

Selected project members rated each of the following seven project performances in a 5

-point Likert scale, from I = not satisfactory to 5 = very satisfactory. They also rated this

performance relative to the previous generation of projects in a 5-point Likert scale, from I =

the same level or worse than the previous project (model) to 5 = much better than the

TABLE 6. SUMMARY STATISTICS FOR PERFORMANCE INDICATORS RATED BY SELECTED PROJECT MEMBERS

Pcrformance Satisf action

Perf ormance

Improvement from the Previous Projects

Indicators Descri ption

Mean S.D. Mean S.D.

Product cost

performance

Rated by project managers, Iayout

engineers, and marketing planners 3.21 O. 8 1 3.18 l .03

Development cost perfonnance

Rated by Project managers 3.02 1.01 3,25 1.41

Adherence to schedule

Rated by project managers, Iayout

engineers, and marketing planners 3.15 0.61 2.83 o.69

Manuf acturability Rated by project managers and

production engineers 3.39 0.81 3.23 0.97

Match to customer need

Rated by project managers, Iayout

engineers, and marketing planners 3.54 0.69 3.34 0.77

Technical Novelty Rated by project managers. 3.46 1.14 3.23 1.38

Technical

Performance

Rated by projeet managers with respect to 15 technical performance items *

3.97 o.37 3.78 o.s9

* The 15 items are: space utility, comfortability, nois,~vibration-harshness, driving stability, acceleration,

braking performance, engine performance, handling response, safety, painting quality, body strength, exterior/

interior styling, aerodynamics, and vehicle weight.

32 HITOTSUBASHI JOURNAL OF COMMERCE AND MANACEMENT [october

previous project (model) . In the questionnaire, we clearly requested them to rate overal/ project

performance, as opposed to performance of activities within each engineering area. Scores

obtained from multiple respondents were averaged for each project to construct project level

performance measures. Performance ratings encompass seven areas: product cost perfonn-

ance, development cost performance, adherence to schedule, manufacturability, technical

performance, technical novelty, and degree of match to customer needs. Table 6 shows

summary statistics for these performance indicators. In the following analyses, we call a set of

indicators shown in the third column of this table, which relate to the current project only, as

performance satisfaction , and another set of indicators in the fourth column, which compare

perfonnance to the previous project, as performance improvement.

In addition, we considered market performance, measured by the ratio of realized average

monthly sales volume to the targeted volume announced at the time of introduction. We calculated sales achievement ratios only for the first year of model introduction so as to

maintain data comparability across sample projects. The mean score of this indicator across

sample projects was 1.01 (s,d. = 0.36).

54. Decomposition of Overall Performance

To separate system performance from local performance, we regressed each of the six

indicators for overall performance (product cost performance, development cost performance,

adherence to schedule, manufacturability, technical novelty, and technical performance) on

corresponding local performance indicators. For example, overall product cost performance

was regressed on component cost performances, as rated by component engineers representing

body, chassis, electronics component, engine, and exterior/interior design areas. Indicators for

local performance were the same as used in the analyses in the section 3. Since two

market-related performance indicators, "degree of match to customer needs" and "sales achievement," have no corresponding local performance indicators, we did not make a system

and local distinction for these two.

From the fitted regression models, we used the sets of residuals as indicators for system

performance. We thus conceptualize system performance as the portion of overall perform-

ance that cannot be explained by performance reducible to the outcome of activities within

each engineering and functional area. Since residuals capture all the variance not explainable

by selected local performance variables, our system performance indicators may include more

than the exact system performance. However, they reflect system performance more accurate-

ly than do original performance indicators. Thus, the comparison between factors affecting

original overall performance indicators and those affecting residuals enables us to identify fac-

tors that have a stronger association with system performance than with local performance.9

9 Appendix 2 shows the results of regression models. The results mdicate that efficiency-related perfomrance is

strongly related to performance within body engineering, implying that the body design may be a critical path in

automobile development. Among these overall performance indicators, product cost performance and technica] novelty were most explained by local performance. This implies that these two performance indicators havc fewer system performance characteristics.

19961 KNOWLEDGE Rl~TENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE

TABLE 7. DESCRIPTIVE STATISTICS FOR EXPLANATORY VARIABLES

33

N = 22 Mean S D Mm Max Experience-based retention

Cross-generational communication

Reference to documents and reports

Use of standards and computer-stored

inf ormation

Use of computer simu]ation

Involvement of long-term planning groups

Involvement of super-project managers

O,OO

15.7

3,73

3.76

l.OO

7.02

0.22

0.3 1

- 1.59

5.06

3.43

3 . 20

1.78

29.3 l

4.28

4.37

3.53 0.41 2.65 4.21 2.06 O. 79 1 .OO 3 .46

3.05 1.40 1.00 5.00

5-5. Explanatory Variables

Table 7 below indicates descriptive statistics for a set of explanatory variables examined

in the subsequent analyses. Below, we briefly explain each of these variables.

Experience-Based Knowledge Retention We considered four indicators for experience-based knowledge retention capability. These

four indicators are particularly related to integrative knowledge retention as explained below.

First, we took a percentage of integrators transferred from the previous generation of

projects to indicate experience-based knowledge retention capability (mean = 0.59. s,d. =

0.29),ro We regarded project managers, vehicle layout engineers, and vehicle test engineers as

such integrators in automobile development. Direct transfer of these individuals from the

previous generation of projects thus indicates a project's ability to capture integrative knowl-

edge embodied in the past product.

Second, we considered a percentage of project core-members responsible for the previous

generations of a project to be the second indicator (mean = 0.34, s.d. = 0,19),. Integrative

knowledge may be stored in these people as collective memories (Badaracco, 1991; March,

1988; Huber, 1991; Spender, 1994; Walsh and Ungson, 1991). Third, degree of common past experiences at the project level was considered. To measure

it, we asked respondents whether or not they had worked with the other project core-members

in anypast major development project. Based on this information, we made a 10 by 10 matrix

that demonstrated the combination of project core-members who had worked for the same

project before. Since 10 project core-members were included in each sample project, the

maximum number of combinations was 45. We divided the observed number of combinations

of people with common experiences by 45, which gave us an appropriate indicator for degree

of common past experiences at the project level (mean = 0.61, s.d. = O. 14)

Fourth, we considered how much project members had expected to be assigned to the

focal project before their actual appointment. The idea here is that people who have a high

expectation of assignment to a particular project may store usable information for that project

in advance, and th~t transfer of such people will be associated with retention of useful prior

ro As explained in the previous section, only when projeet members spent an average of a minimum of 30% of

their time for six months in the previous project did we count them as previous project members.

HITOTSUBASHI JOURNAL OF COMMERCE AND MANAGEMENT

knowledge. Respondents wdre asked to rate how much they had expected the appointment to

the focal project on a 5-point scale, from I = O% sure, 2 = 25% Sure, 3 = 50% sure, and 4

= 75% sure to 5 = 100% sure. Obtained percentages were averaged for each project (mean = 0.51, s.d. ~ 0.17).

We subjected the above indicators to a prinpipal component analysis to identify an underlying pattern. One factor emerged (eigenvalue = 2, 38).ll We thus used the frst factor as

a composite measure for experience-based knowledge retention capability.

Communication-Based Retention

We examined the frequency of project members' cross-functional communication with

members in the previous generation of projects as an indicator for the communication-based

retention capability. Since we are interested in the retention of integrative knowledge, we

distinguished this from communication with the previous project members within the same

engineering areas. Respondents rated frequency of communication with previous generations

of project members outside their engineering areas on a 6-point scale. Then, we converted each

point to an estimate of the number of days, as explained in the previous section. Scores

obtained from these project members were averaged to form project level measures.

Archival and Computer-Aided Mechanisms for Knowledge Retention We examined five indicators for archival-based knowledge retention capability: ( I ) the

reference to documents and reports to learn from the past; (2) the use of standards; (3) the

reuse or editing of computer-stored information (including parts database); (4) the use of

computerized simulation tools (CAE); and (6) the creation of direct parts programs by CAD/

CAM. Component engineers, vehicle test engineers, vehicle layout engineers, and production

engineers rated these five indicators on a 5-point Likert scale. Project managers rated the

importance of these archival and computer-based systems for several design and testing

activities on behalf of the entire project. For each of the above five indicators, scores obtained

from these project members were averaged to construct project level measures.

Using a principal component analysis, these indicators yielded two factors. The first three

indicators, all of which are directly related to knowledge retention, seemed to be clustered: the

use of documents and reports, the use of standards, and the use of computer-stored informa-

tion. However, a factor loading for the use of documents and reports was less than the 0.7

cut-off line, and it is also conceptually distinct from the other standard-based retention

mechanisms, so we kept it as a separate variable. We averaged scores for the use of standards

and the use of computer-stored information to measure the standard-based retention capability

(mean = 3.76, s. d. = 0.31, alpha = 0.69).

While the use of computer simulation was clearly loaded on the second factor, the use of

CAD/CAM was almost equally loaded on two factors. We kept only the use of computer simulation as a separate variable for the later analyses to indicate degree of the use of computer

simulatioh.

ll Factor loadings are O.82 for the integrators' experience variable, 0.79 for the core-members' experience, 0.74

for the common experience, and 0.75 for the expectation for assignment.

19961　　　　　　KN0wL嘔DG旧

TABLE8、

Rl≡TENTlON AND　N旧W　PRODUCT Dl…Vl…LOPM1…NT P1…RP0RMANcI…　　　　　　　　35

SUMMARY　STATIsT1cs0F　INDIcAT0Rs　F0R　TEcHNIcAL　C0NTENT，SUPPLIER　INv0LvEMENT，AND　Ec0N0M1c　C0NDITl0Ns

Indic田tors Description Mem　　　　S．D．

1≡‘ubble　EconOIny A　dummy柵dable　that　indicates　pmj㏄t　e皿ded

befo1＝e1992

Micro　C町 A　dlmmy　vari田ble　th＾t　indicates　proj㏄ts町e

miCm　C趾pmj㏄t．

New　Parts　Rotio Per㏄nt田geof血ew1ydosigmdp劃rts（i皿

血umbcrofpais）一

0，69　　　　　　　0．17

New　Plal＝folrm　Ratio Peroent副ge　of　mw　desigll　in　u皿de正一佃oor

panels　alld　suspension　systems（ob帖i皿ed

fmmthequeStiOnnai祀S〕

O．43　　　　　　　0．27

New　Platfo㎜Mio2 Newness　of　platform　d6sigIl　based　on

Nobeok田’s　cliss胴cation　scheme（Nobcok田，

1993）

New　　　　　O］d（code－　1）　　（code＝0）

9　　　　　　　16

Agsembly　Propriet田町P囲rts Pe1℃ent田ge　of　p邑rts　developed6ntire1y　by

asscmbly　make耐（see　C1趾k　a皿d　Flユjimoto，

1991for　the　de血血ition）

O．09　　　　　　　0．23

Black　Box　P割r的 Pa竹s　whose　b田sic　e皿gineering　is　done　by　ca正

皿田kefs　a皿d　whos6detailed　engi鵬ehng　is

doIle　by　pa灯s　suppliers（see　C］ark　and

Fujimoto，1991）

O．45　　　　　　　0．25

SupPlier］旦ngi皿eehllg　Parts Parユs　d6vcloped　entiroly　by　l〕arts　s11pP］i6耐

（sc6，C1趾k　ond　Fujimoto，1991）

O．一6　　　　　　　0．15

〃ソoル2㎜θ〃ψ〃此μ〃ゐ〃0rgoπ‘z〃jo〃σ1σ〃施

　　　　There　are　severa1independent　orgallizational　units　tllat　coordinate　new　product　develop－

ment　activities　across　generations　ofprojects．In　the　questiomaire，we　asked　project　managers

about　the　degree　of　inf1uence　of　t11e　following　organizational　units　or　individua1s：the

long－tem　tec㎞ology　plami㎎group，the1ong－tem　pmductplami㎎group，the　design　for

manufactuh㎎group，the1ong－tem1ayoutp1劃minggmp，andtheseniomanagers1ocatedabove　individual　project　mamge正s（we　cal1t11is　super－Project　manager・）・Proj㏄t　managers

rated　the　degree　of　in肋ence　of　these　groups　or　individuals　across　a　mnge　of　deve1opment

activities　and　d㏄ision　makillg，on　a5－point　Lik帥scale．

　　　　Aprincipa1componentana1ysisyie1dedtwofactors．Thefourindicato㎎wereclustered．We　thus　averaged　scores　for　these　four㎞dicators　to　generate　a　measure　of　the　degree　of

involvement　by1㎝g－tem　p1ami㎎gmups（mean＝2．06，s．d．＝O．79，alpha：O．70）。Wekept　an　indicator　for　invo1vement　by　s叩er－pmject　managers　as　a　separate　variable．

τεc伽たα1Co〃ε〃，∫〃〃1’α〃リo1リε胴2〃，oπ∂0肋〃Po∬必1εCo〃701吻〃o〃ε∫

　　　　Fina1ly，Table8sllows　the　other　variab1es　we　considered　in　t1le　analyses，wllich　inc1ude

teclmica1content，the　degree　of　supplier　invo1vement，and　economic　conditions。

36HlTOTSUEASHl，OuRNAL　OP COMMERCl…＾ND　MANAG1≡MENT 工Octobcr

5－4．Ros111ts　amd　I〕is61Issi0皿s

Coπε肋fo〃ルψ∫2∫

　　　　Tab1es　g　to12be1ow　show　results　of　correlation　analyses　betweel1sets　of　exp1anatory

variab1es　andindicators　forovemllperfomancesatisfactionandperfomanceimpmvement．

Table11and12speci丘cauyshowresu1tsfo正systemperfoman㏄thatwestatisticallysepamted

　　　TA肌E　g　C0R㎜…LATI0Ns　BETw朋N　ExPLANAT0RY　VARIA肌Es　AND1NDIcAT0Rs　　　　　　　　　　　　　　　　　　　　F0R　OvEMLL　PERFORMANcE　SATIsFAcTI0N

Exped㎝㏄一B鵬dRet6ntioIl

X－Gemratio皿＾1

Commu皿ioation

L㎝g－Te㎜PlamingGr㎝ps

Super－PI＝oject

Man田gersStalld趾ds＆ComputerStored　I㎡omati㎝

Dooumcnts　andRcports

Computer　Simu1田tion

（CAE　Tools）

＃P＜．10 榊p＜．05

Pmd皿ct　Development　Adhe－

Cost　Per・　Cost　Per一　祀nc6to

‘o㎜mce　iom田㏄e　Schedule

O，11　　　　　0，35　　　　　0．41ホ‡

一〇．09　　　　－O．17　　　　　0．04

一0．32　　　　－O．13　　　　－0．07

0，27　　　　－O．03　　　　　0．06

O．14　　　　－O．11　　　　－O．08

O．19　　　　　0，01　　　　　0．13

O．19　　　　　0，28　　　　　0．38ホ

‡オ‡I〕＜、01

M㎜ufac－t皿rability

Match　to　T㏄lmic田1T㏄hnical

C1ユstomer　Novelty　　Perfor・

　N㏄ds　　　　　　　　maI1㏄

一0，08　　　　　0．47申＾　　　0．16

O．15　　　　　0．47‡‡　　　0．47“ヰ

O．08　　　　－O．45‡ホ　　　0．23

一0，08　　　　　0，35　　　　　0．07

O．35‡　　一〇．07　　　　　0．43ヰ＾

O，26　　　　　0，10　　　　　0．22

O．50‡‡　一0，05　　　　　0，29

　S創6s

Achiev6－

　monO．06　　　　－O．12

O．23　　　　－O，15

O．29　　　　－O．32

一〇．05　　　　　0．07

一〇．04　　　　－O．13

一〇．02　　　　　0．07

0．60‡‡‡一〇．06

TABLE10． C0RRELATI0Ns　B1…TwE酬Ex肌ANAT0RY　VAR1A肌Es　AND　lNDlcAT0Rs　　　　　　FOR　OvEMLL　PERFORMANcE　IMPROvEMENT

Exped㎝㏄一Bas記Rete皿ti011

X－Gene祀tiomlCommunication

L㎝g－T6m　P1田mi㎎Groups

Super－P1－ojeot

M田皿age耐

Standards＆ComputerSto祀d　Infomati㎝

Documents　andR6po■s

Computcr　Simulation

（CAE　Tools）

‡pく、lO 榊p＜．05

　Pmd㏄一Dev61opm㎝t　Adher㎝㏄Monufact1■ra－Motoh　to

　　　Cost　　　　Cost　　to　Sohedu1e　　bimy　　Custom6r

P6rfoma皿㏄Pe㎡oma㏄e　　　　　　　　　　Needs0，17　　　　　　0．36‡　　　　　O，44‡‡

一〇、01　　　　　　0，01　　　　　　0，19

一〇．30　　　　　－O．24　　　　　－O，55‡‡‡

O，14　　　　　－0，02　　　　　　0．29

一〇．05　　　　　　0．04　　　　　－O．06

O．09　　　　　　0，14　　　　　　0．15

O．22　　　　　　0．37‡　　　　　O．04

榊‡p＜．Ol

Tech皿ical　T㏄hnical

Nove1ly　Perfom㎝㏄

0，05　　　　　　0，13　　　　　　0．01

0，21　　　　　　0，22　　　　　　0．26

一0．11　　　　－0，08　　　　　　0．31

O．10　　　　　　0，16　　　　　　0，02

一〇．14　　　　　　0．39ホ　　　　　O，30

0，01　　　　　　0，11　　　　　　0．17

O．28　　　　　－0，20　　　　　　0．26

O．47榊

O．56榊‡

一0．09

一〇．20

0，17

O．08

O．22

1996］

TABLE11．

KNOWu≡DGE R1…TENTlON AND　N1；W　PRODUCT　DEV肌0PMl…NT PERF0RMANcE　　　　　　　　37

C0RR肌ATI0Ns　BETwEEN　ExPLANAT0RY　VARIABLEs　AND　INDIcAT0Rs　　　　　　　　　　　　　　　　FOR∫γ∫胞㎜μψ7㎜0〃Cθ

Experience－BasedRetentioIl

X－Generationa1

Communicatioll

L㎝g－T6m　PlmningGf0ups

SupeI＝一P1＝oject

Mmagc祀St田nd肛ds＆Comp汕erStored　Infomati㎝

Docllments　and

Repo竹s

Comput6f　Simulati011

　（CAE　Tools〕

‡p＜．lO州P〈一05

　Product　　　Cost

P6『fo『nlance

O．17

D6velopmellt

　　　Cost

Pe㎡0m副皿㏄

　　　0．50榊

Adher㎝㏄　Mamfactu祀一to　Schedule　　　　　bility

O．35‡

0．17　　　　　　－O．09　　　　　　　0．03

O．14　　　　　　－0．17　　　　　　－O．08

O．33　　　　　　－O．08　　　　　　－0．04

O．22　　　　　　－O，06　　　　　　－0．08

0，09　　　　　　　　0，03　　　　　　　　0．27

0，31　　　　　　　　0，13　　　　　　　　0．47＾｝

榊ホpく．01

Technical　　　T㏄hnica1

Novelty　Perfom副n㏄

O，08　　　　　　　　0，32　　　　　　－O．01

0，16　　　　　　　0．60‡‡‡　　　　0．18

一〇．26　　　　　　　　0，22　　　　　　　　0．一0

O．09　　　　　　　　0．18　　　　　　－O．07

0，08　　　　　　　　0．44北‡　　　　一〇．09

0．37｝　　　　　　0．24　　　　　　－0，16

O．48‡‡　　　　　O．20　　　　　　　　0．33

TABLE12． C0R㎜；LATI0Ns　BETwEEN　ExPLANATORY　VAR1A肌Es　AND　INDIcATORs　　　　　　　　　　FOR∫ツ∫犯㎜ρθψr〃一0”C2f〃！ρ70ソθ〃！ε〃’

E叩eH㎝ce・B副sed　Retention

X－Gcner舳onalCommunication

Lo㎎一Tem　Plan㎡㎎

Groups

Super－PmjectMamgers

Stalld肛ds＆ComputerSto肥d　Infom田ti㎝

D㏄um6皿ts　and

Repo廿s

Computer　Simulatio11

　（CAE　Too］s）

　Prod1ユct

　　　Cost

Pe他m田nce

O．56ヰ榊

Developm6皿t　　　Cost

Perfommce　　　　0．48榊

Adher㎝㏄　Manufoctura－to　Schedule　　　　　bility

0．37オ

O．54＃‡ヰ　　　　O．08ホ　　　　　　O．19

O．09　　　　　　　　0．OO　　　　　　－O．52‡北

O，07　　　　　　－0．21　　　　　　－O．01

一〇．04　　　　　　　　0．05　　　　　　－0．17

一0，06　　　　　　　　0，09　　　　　　　　0，08

O．26　　　　　　　　0．32　　　　　　－O．11

TechIlical　　　Teclmic刎

Novelty　Perfoma皿ce

0，25　　　　　　　0．OO　　　　　　　O，39ヰ

O．28　　　　　　　　0，28　　　　　　　　0．52＾＾‡

一〇．30　　　　　　　　0．44‡非　　　　　0．12

一〇．12　　　　　　　　0．OO　　　　　　－O．08

一〇．17　　　　　　　　0．09ホ　　　　　　O，24

O，02　　　　　　　　0，31　　　　　　　　0，06

O．29　　　　　　　　0．39北　　　　　　0．11

‡p＜．10榊p＜．05舳p〈．Ol

f。。ml。・・lp・・f・・m・…t・i・di・・t・p・・f・m・・…h・…t・・i・ti・・d・d・・df・・mi・t・…ti・・s

among　individual　engineering　domains．　　　　R。。。lt．h。。。i．di。。t．th・tb・th・・p・h・…一b…d・・t㎝tio…　p・bi1ity・・d・・…一

9。・。・・ti…1・・mm・i…i㎝…畑ti・・1y・・1・t・dt・・・・…lp・・f・m・・・・…i・b1…tth・

P・句・・tl…1．Sp・・i丘・・lly，・・p・・i・…一b…d・・t・・ti・・w・・p・・iti・・1y・・…i・t・dwithtwo

P。・f・m・・・・…i・f・・ti㎝…i・bl・・一・dh・・・…t…h・d・1・（・＝・41・P＜・05）・・dm・t・hto

。。。t．m。。・。・d・（・一．47，P＜．05）一，・・dth…p・・f・m・…imp・…m・・t…i・b1・・一

38 HITOTSUEASHI JOURNAL OF COMMERCE AND MANAGEMENT [october

adherence to schedule (r = .44, p < .05), development cost performance (r = .36, p < . 1),

and technical performance (r = .47, p < .05). This suggests that retention of experience

affects broad performance dimensions ranging from development process efficiency and

customer satisfaction to technical performance at the project level.

Cross-generational communication was positively related to performance satisfaction in

technical novelty (r = .47, p < .05) and in match to customer needs (r = .47, p < .05), and

technical performance improvement (r = ,56, p < .Ol). This implies that cross-functional

communication with the previous project members may be an important source both for technological and market knowledge. However, cross-generational communication has no association with any efficiency-related performance.

Tables I I and 1 2 also show several positive correlations between experience-based

retention and cross-generational communication and system performance indicators. In partic-

ular, we found that they have broader relationships with improvement of system performance.

Specifically, the experience-based retention variable was positively associated with improve-

ment of product cost performance (r = .56, p < .Ol), development cost performance (r =

.48, p < .05), adherence to schedule (r = .37, p < , l), and technical performance (r = .39,

p < . 1); cross-generational communication was associated with improvement of product cost

performance (r = .54, p < .O1), development cost performance (r = .38, p < .1), and technical performance (r = .52, p < .O1). These results are consistent with our expectation

that the retention of integrative knowledge has a particular contribution to improvement of

system performance derived from complex interactions among different functional domains.

Compared to the impact of individual-based retention capabilities, the impact of archival-

based retention on product development perfonnance seems to be limited. For example, results

here show that the reference to documents and reports has no significant association with any

performance indicator, except for its modest relationship with the system performance indicator on manufacturability. This suggests that, while retention of articulated knowledge

has a significant impact on local performance, it may not be related to system performance at

the project level.

The impact of knowledge retention through standardized information, such as technical

standards and CAD/CAE for design and parts reuse, seemed to be limited as well. It was only

positively associated with both performance satisfaction and performance improvement in

technical novelty (r = .43, p < .05), and moderately related to satisfaction in manufactura-

bility (r = .35, p < . 1). A positive association with manufacturability may reflect recent

significant efforts that Japanese automobile producers have made to formalize knowledge

about manufacturable designs, In addition, since reuse of existing parts designs generally

increased the reliability of component systems, it may lead to fewer problems in manufactur-

ing. The result may also imply that knowledge about a design-manufacturing interface might

be more articulable than we expected. On the other hand, the positive relationship between

knowledge retention through standardized information and performance on technical novelty

seems to suggest that efficient design reuse for mature parts of the product design enabled

projects to focus on new technical solutions in less mature partsl2

The use of computer simulation was positively associated with several overall perform-

12 Most of our sample projects, which came after the Bubble economy period, built from a realization of the

wasteful development styles of this period of opulence. Eighteen out of the 22 sample projects introduced new

1996］ KNOWu…DGl…R1…TENTION　AND柵W　mODuCT　D嗜VI…LOPM酬T　PI…RFORMANCE39

TA肌E13．　SUMMARY　REsULTs0F　T肥C0RRELATI0N　ANALYsEs　F0R　OvEMLL　　　　AND　SYsTEM　P1…RF0RMANcE　SATIsFAcTI0N　AND　SALEs　AcHIEvEM酬丁

Experience-B ased Retention X-Genetional Communication Long-Team Planning Group Standards & Computer-Ttored Information Documents and Reports Com puter simulation (CAE Too]s)

Overall System Overall System Overall System Overall System Overall System Overall System

Product Cost

Development Cost

** * (-)

Schedule ** *

* **

Manufactura-bility

*

* ** **

Tech. Novelty

** *** ** ** Tech_ Performance

*** Match to Customer

** ** Sales Achievement

‡p〈．1， ”p＜．05， ‡舳p＜．01

TABLE14． SUMMARY　REsULTs0F　THE　C0RRELATl0N　ANALYsEs　F0R　OvEMLL　　　　　AND　SYsTEM　PERF0RMANcE1MPR0vEMENT

Experience-Based Retention X-Genetional Communication Long-Team Planning Group Standards & Computer-Ttored Information Documents and Reports Com puter simulation (CAE Tools)

Overall System Overal] System Overall System Overall System Overall System Overall System

Product Cost *** *** Development Cost

* ** *

* (-) * * (-) *

Schedule ** *

Manufactura-bility

Tech. Novelty *

*

Tech. Perforrnance

** * ** ***

Match to Customer *

㍉〈．1，‡‡pく．05， 榊㍉＜．Ol

products舳er1993，which　implies　that　most　sample　pmj㏄ts　teIlded　to　b600st　comcio皿s．If　th6se　cost　comci011s

proj㏄ts　had　to　add　i－mo柵tive　f㎝turos　to　th6ir　pmdmcts，th6y　pmb田bly　would　llavo　to　com膵n鯛t6for　th6

舶sooiated　additio皿d　oost　by　rel1sing　o■isting　d6sig珂s　for　othor　p耐s．The田bove　res皿1t　may　iIldicate　this6脆ct一

40 HITOTSUBASHI JOURNAL OF COMMERCE AND MANAGEMENT [October

ance indicators, especially those for technical-related performance. For example, it was related

to perfonnance satisfaction in manufacturability (r = .50, p < .05) and technical perform-

ance (r = .60, p < .O1). Engineers we interviewed also pointed out that use of CAE simulation has a particular contribution to technical performance and product reliability or

quality, not to development efficiency.

However, data suggest that the use of computer simulation only has a moderate relation-

ship with improvement in development cost performance (r = 0.37, p < . 1). In addition,

despite its significant relationship with overall technical performance satisfaction, Table 12

shows that the use of computer simulation is not significantly related to a system performance

indicator on technical performance. This implies that the use of computer simulation tends to

affect local technical performance more than system performance.

The involvement of long-term planning groups was negatively related to some perform-

ance indicators. Especially, this had a significant negative impact on performance improve-

ment in adherence to schedule (r = -.55, p < .O1). This may simply indicate that long-term

planning groups do not work properly from a project member's point of view. Since the long-term planning groups play critical role in coordination among different projects as well as

across generations, the strong involvement of these groups may indicate that projects needed

to adjust development activities with other related projects, which might cause problems in

adherence to the schedule (Nobeoka and Cusumano, 1994; Nobeoka, 1993, 1995).

The result may also indicate a potential confiict between the autonomy of individual

projects and inter-project coordination by the long-term planning groups (Clark, Fujimoto,

and Aoshima, 1991). The long-term planning groups usually impose several constraints on

individual project activities. For example, in our sample of projects, their involvement had a

strong negative correlation with the new parts ratio (r = -.67, p < .Ol), implying that it

prevented engineers from designing new parts from scratch. As a result, they may have tended

to ascribe low project performance to the long-term planing groups.

Tables 13 and 14 below highlight differences among factors affecting overall performance

and those affecting only system performance. These tables show clearly that experience-based

retention and cross-generational communication, in particular, have positive associations with

indicators for improvement of system performance. On the other hand, archival-based retention and computer simulation tended not to be associated with those indicators.

The tables also seem to indicate that experience-based retention and cross-generational

communication are related more to system performance than overall performance indicators,

although this difference for performance satisfaction indicators is not as clear as for perform-

ance improvement indicators.

Regression Analyses

To further examine the results from the above correlation analyses, we fitted the

regression models including selected control variables and indicators for archival-based and

individual-based knowledge retention capabilities. We excluded other explanatory variables

because of the small sample size. Appendix 3 and 4 shows results of regression analyses.13

13 For each performance indicator, Model I includes only control variables. We selected these control variables

by considering both conceptual reasoning and resu]ts of correlation analyses with performance indicators. Au the Model 2s include controi variab]es and indicators for the standard-based retention capability, the use of documents

and reports, and the use of computer simulatron. Model 3s include controt variables and individuat-based retention

1996] ICNOWLEDGE Rl3TENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE 41

TABLE 15. A SUMMARY TABLE FOR THE RESULTS OF REGRESSION ANALYSES FOR RELATIONSHIPS BETWEEN KNOWLEDGE RETENTION CAPABILITIES

AND performance satisfaction

Standard-based retention Computer simulation Experience-based retention X-generational communication

Product cost performance

Development cost performance *

Adherence to schedule

*** Manufacturability

Technical novelty * **

Technical perf ormance

*** Match to customer needs

** *

Achievement of sales target

*p<.1, **p<.05, ***p<.O1

Results from Models 4s in Appendix 3 for the standard-based retention and computer simulation.

Results from Models 5s in Appendix 3 for the experience-based retention; Model 6s for the crossgenerational

communication

TABLE 16. A SUMMARY TABLE FOR THE RESULTS OF REGRESSION ANALYSES FOR RELATIONSHIPS BETWEEN KNOWLEDGE RETENTION CAPABILITIES

AND performance improvement Standard-based retention Com puter srmulation Experience-based retention X-generational communication

Product cost performance

Development cost performance

* **

Adherence to schedule

**

Manufacturability *

Teehnical novelty

Technical performance

** *** Match to customer needs

*p<.1, **p<.05, ***p<.Ol Results from Mode]s 4s in Appendix 4 for the standard-based retention and computer simulation.

Results from Models 5s in Appendix 4 for the experience-based retention; Model 6s for the crossgenerational

communication

capability indicators. Model 4s include all these explanatory variables except for the use of documents and reports

which showed no significant relationship with any performance indicator in Model 2s. Model 5s and 6s exclude either the experience-based capability or the cross-generational communication indicator to avoid multi-collinearity,

which seemed to be caused by a high correlation between these two indicators (r = 0.51, p < .O1).

HITOTSUBASHI JOURNAL OF COMMERCE AND MANAGEMENT

Tables 1 5 and 1 6 below summarize the results shown in Appendix 3 and 4. Results for the

standard-based retention and the use of computer simulation come from Model 4s. Results for

the experience-based retention and cross-generational communication are obtained from

Model 5s and 6s, respectively, to eliminate problems of multi-collinearity.

These results generally supported the results of the correlation analyses, and indicated

even stronger relationships between experience-based retention and overall performance indicators. Especially, an experience-based retention variable was significantly associated with

development process efficiency. For example, the full regression models show that experience-

based retention is related to performance satisfaction both on development cost and on

adherence to schedule, at the I % significance level. It was also related to performance

improvement in development cost and in adherence to schedule at the 5% Ievel.

The finding that experience-based retention capability tends to be positively associated

with development process performance may indicate that critical experiences retained from the

past development activities is related to knowhow or knowledge to effectively manage the

development process by the mutual adjustment of working relationships.

In contrast, the cross-generational communication variable was specifically related to

technical- and market-related performance indicators, such as satisfaction on technical novelty

and improvement in technical performance, and satisfaction on the match to customer needs,

but not to efficiency-related performance indicators,14 Similar to results of the correlation

analyses, this result indicates that cross-functional communication with the previous project

members is an effective way to acquire technological and market knowledge.

The results are also consistent with the correlation results for retention capabilities

indicated by archives, standards and computerized systems. For example, the standard-based

retention variable had only a moderate relationship with satisfaction in technical novelty, as

indicated in Model 4 (p < . 1). The use of computer simulation was strongly related only to

satisfaction in technical performance (at the I % Ievel). It had moderate relationships with

improvement in development cost performance and in manufacturability (at the 10% Ievel).

In summary, the above correlation and regression analyses seem to support our hypothe-

ses, at least, for some performance dimensions. Contrary to the results in the previous section

regarding local performance, the above analyses generally indicate that individual-based

knowledge retention capabilities are required to improve product development perforrnance at

the project level. Particularly, we find that their impact is stronger, or broader, on system and

improvement performance rather than on static and local performance. On the other hand, we

found that archival-mechanisms for knowledge retention tended not to have a substantial

influence on product development performance at the project level.

Moderating Effects by Task Characteristics on Relationships Between Project Performance and

Individual-Based Retention Capability

Hypothesis 3 suggests that task newness may have moderating effect on the relationship

between experience-based retention capability and product development performance. To examine this possibility, we fitted regression models including interaction terms between

~' h fact, Model 4s show that cross-generational communication was negatively associated with satisfaction in

devetopment cost performance. However, this negative rdationship is probably due to multi-collinearity since results in Model 6s no longer showed significant negative relationship between cross-generational conununication

and satisfaction in development cost performance, though the sign was negative.

1996］　　　　　　KN0wu弧GE㎜…TENTl0N　AND　NEw　PR0DUcT　DEvEL0PMl…NT　PERF0RM＾NcE　　　　　　　43

illdividua1－based　retention　capability　indicators　and　either　technica1or　m虹ket　llewness　in－

volved　in1lew　product　development，

　　　　New　platform　mtios　we正e　used　to　indicate　tec1mical　newness　invo1ved　in　the　proj㏄t　tasks．

Market　newness　was　iden舳ed　by　consideri㎎pmject　mamgers’self－eva1uations，bmnd　mme

cllanges，and　market　class　changes，as　described　il1Appendix5．

　　　　Appendix6shows　resu1ts　ofregression　analyses　that　examine　t1le　moderating　e脆cts　eitl1er

of　tecl1nical　or　market　newness　on　performance　satisfaction，while　Appendix7s1lows　results

fortheirmodemtinge価ects㎝perfomance　improvement．All　the　Model　lsinclude㎞teraction

te㎜s　for　the　expehence－based正etention　variab1es，while　Mode12s　include　those　for　the

cross－9enerational　communication　vahable．

　　　　Hypothesis3implies　that　we　sllould　expect　negative　signs　on　the　regression　coe冊cients　for

intemcti㎝te㎜s．Indeed，wefo㎜dsign冊cantneg囲tivecoe冊cientsfortheintemcti㎝terms

TA肌E17．　EF冊cTs0F　INTEMcTI0Ns　BETw朋N　THE　INDIvmUAL・BAsED　RETENTI0N　　　　　　　　　　　　　AND　THE　TAsK　CHARAcTERIsTIcs0Nμψr伽σ〃cε∫α1ψα10π

Experience-based retention X X-generational communication X

Market newness Technical newness Market newness Technical newness

Product cost performance

Development cost performance * * (-) * * (-)

Adherence to schedule

Manuf acturability * (-)

Technical novelty

Technical performance * * * (-)

Match to customer needs

Achievement of sales target * (-) * * * (-)

ヰp＜．1，榊p〈．05，榊‡p＜、O1

Astemks血em　that　mtemctlo皿s　between　ret611tlon　mechanlsms　and　t田sk　newmss　havc　slgm血c田mt

mgatiw　imp田cts㎝porfom田n㏄indic担t㎝。

TA肌E18．　EF冊cTs0F　INTEMcT10Ns　BETw朋N　T朋IND1vIDUAL－BAsED　Rヱ丁酬丁10N　　　　　　　　　　　AND　THE　TAsK　CHARAcTERIsT1cs0N1，εψ7㎜o”c2ゴ㎜ρ1oソ色㎜ε〃’・

Experience-based retention X X-generational communication X

Market newness Teehnical newness Market newness Technical newness

Product cost performance * (-)

Development cost performance

Adherence to schedule

Manufacturability

Technical noveity

Technical performance

Match to customer needs

‡p〈．1，榊p〈．05，‡舳P＜一01

Ast6risks　mean　tllat　intemctio瓜betwee11祀tentio皿m㏄hanisms　and　task　mw116ss　h州e　siglliiicmt

negativ6impa眺on　perfomallc6i皿dicato凧

44 HITOTSUBASHI JOURNAL OF COMMERCE AND MANAOEMENT [October

in the regression models. This implies that projects tends to benefit from retention of prior

experience bases when they develop new products based on existing platform designs toward

familiar customers. Especially, the results seem to suggest that market newness is more likely

to moderate relationships between individual-based retention capabilities and product develop-

ment performance than technical newness. Tables 17 and 18 below summarize the results.

As these tables show, we found expected moderating effects by market newness on relationships between experience-based retention and satisfaction in development cost per-

formance (p < .05) and sales achievement (p < .1). This implies that, when projects developed new models targeted to new customer bases, retention of prior individual experi-

ences may negatively affect development efficiency and market performance

We also found that a similar expected moderating effect by market newness on relation-

ships between cross-generational communication and satisfaction in development cost per-

formance (p < .05), in manufacturability (p < . 1), achievement of sales target (p < .O1),

and improvement of product cost performance (p < . I ) . These results suggest that retention

of prior knowledge through face-to-face communication may not be appropriate for projects

developing new products with different target markets from the previous models.

Our variable indicating communication with the previous project members also partially

captures transfer of previous members (as we already explained, the more members are transferred from the previous projects, the more current intra-project communication overlaps

communication with the previous project members). Therefore, these results may generally

indicate that, while retention of embedded knowledge may be particularly important in the

case where there is continuity of customer needs, it creates some problems in adapting to new

market conditions.

On the other hand, technical newness had a significant moderating effect on the relation-

ship between technical performance and cross-generational communication variables (p <

.O1). This suggests that, when projects developed new platform designs, communication with

the previous generations of project members negatively affected technical performance. However, technical newness had no other significant moderating effect.15

These results may indicate that knowledge about linkages to the customer base is more

context-specific than technical integrative knowledge, as some researchers have pointed out (e.

g., Christensen and Rosenbloom, 1995; von Hippel, 1994), and thus tend to become obsolete

when there is a significant change in the customer base. On the other hand, existing technical

knowledge might be more widely applicable in different settings, implying that prior knowledge 16 may be useful even in developing novel technological concepts (Iansiti, 1995b).

15 In fact, close examination in the scatter plot indicates that the observed strong moderating effect by technica]

newness for technical performance was, in fact, strongly influenced by one data point as a outlier. 16 Although we found that technical newness tended not to moderate the impact of experience-based retention

on performance, it may not be appropriate to conclude that retention of experience bases is always important

regardless of technical discontinuity. This is beeause, first, our technical newness indicator merely shows the

newness of the platform design, not of fundamental technological approaches, and, second, because automobile technology is generally "mature". This implies that what is new in this industry may not be sufficiently new to

indicate the degree of technological change that might occur in newer mdustries.

1996] KNOWLEDGE RETENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE 45

6. Implications and Conclusions

6-1. Importance of Explicit Management of Knowledge Retention

While existing literature on management of new product development has identified

coordination and communication across specialized activity areas as critical to development

speed, productivity, and product quality, our findings suggest that such coordination and

communication alone may not be enough to achieve project-level integration for high product

development performance. We showed that the success of projects also hinges upon their

ability to learn from past integrative experiences. These findings imply that instantaneous

structural solutions such as cross-functional teams and heavy-weight project structures may

not be the only answer to improve development performance. Projects may be able to execute

their integration activities most effectively when they deeply understand potential interactions

across different knowledge domains through past development experiences.

However, our results also implied that knowledge retention may not always be desirable.

Especially, we found that prior experience bases seem to prevent projects from successfully

introducing products for new markets or unfamiliar customers. This suggests that managers

have to explicitly manage knowledge flows from previous projects in accordance with the

specific objectives for each new product development project. For example, when projects are

trying to introduce a new product line for new customer groups, companies may want to

isolate those projects organizationally from other projects. In such a case, it might also be

appropriate to form projects with members who do not have too much experience in

developing a particular product line.

6-2. Different retention mechanisms for performance improvement

Our results showed that, while improving local performance may require capabilities to

retain knowledge in articulated forms, such as documentation and computerized CAD files,

improving system performance at the project level may call for the transfer of individual

experience bases. This implies that archival-based and individual-based mechanisms for

knowledge retention are not necessarily substitutes, but, rather, they are complementary.

Companies may greatly benefit from formalization of knowledge within well-established

engineering domains. Especially, we believe that advanced computer-aided design systems will

increasingly capture design know-how once embedded in experts and craftsmen in these

specialized domains. However, as long as a new product is the outcome of complex interac-

tions among different knowledge domains, retention of individual experiences may remain

important. Besides, once knowledge is fully articulated and standardized, it becomes relatively

easy to transfer it across companies, which decreases its competitive value. Therefore, the

increasing articulation and standardization of automobile design knowledge do not necessarily

devalue individual experience bases, but rather, they may increase their value if they have

integrative charaeteristics.

Although both archival-based and individual-based knowledge retention are important,

the relative emphasis between these may differ across industries and different stages of industry

46 HITOTSUBASHI JOURNAL OF COMMERCE AND MANAGEMENT [october

evolution. First, the nature of product architecture may affect the relative importance. When

a product is completely modularized both in terms of the physical design and the design

process, its overall performance may be influenced mostly by the initial architecture or design

of how the individual components work separately as well as together, rather than on how the

components interact as a system.11 In this case, investment in archival and computerized

mechanisms for knowledge retention may become important. On the other hand, when a product architecture is highly integrated, including complex interdependencies between differ-

ent components, improvement of product performance may require more subtle knowledge of

interactions among individual components. In such a case, the retention of individual

experience bases may play a critical role.

Second, the characteristics of user requirements may also influence the relative im-

portance between archival or computer-based and experience-based retention. When the required product functionality is stable and consists of only a few clear dimensions, knowledge

about user-design interfaces is relatively simple, thus, a project can concentrate only on

technical issues. We conjecture that, in such a circumstance, archival and computerized

mechanisms may be important ways to retain knowledge. On the other hand, some products,

such as an automobile, can satisfy customers in a number of ways, such as in styling, acceleration, space utility, and mileage. An appropriate combination of different performance

dimensions is often very subtle, which even customers may not be able to articulate. In such

a case, knowledge to integrate customer needs with physical designs may have to be kept as

tacit and embedded knowledge by individuals.

Although we assume in this paper that automobile development involves substantial

complexity and uncertainty both in the product architecture and user interface, this may

change in the future. For example, our interviews revealed that automobile design is increas-

ingly being modularized to enable more efficient sharing of components across different

models. This may result in more importance of archival and computerized mechanisms for

knowledge retention. On the other hand, some interviewees mentioned that it had become

increasingly difficult to understand user needs. This may indicate that roles of persons who

manage linkages between user needs and product designs will become more critical than before. In any case, managers may need to consider the required level of integration activities

involved in new product development to appropriately invest in different knowledge retention

facilities.

HITOTSUBASHI UNIVERSITY

17 However, even if the interfaces for each component isolate interactions, the system can be highly integrated

when important performance characteristics arise from the physical properties of multiple components. For exanrple, on a computer, a design of the disk drive is totally modularized. However, if it is slow, then the computer as a system exhibits poor performance.

19961 KNOWL1…DG喧m…T1…NTlON　AND　Nl…W　PRODUCT　DEV肌0PM酬丁冊RFORMANCE 47

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1996］ KNOWLEDGE R1…TENT10N　AND　NEW　PRODUCT DEVELOPMENT PERFORMANcE　　　　　　　　　　5一

Appendix1． DEc0MPosITI0N0F　OvEMLL　PERF0RMANcE

　　　　Tosepamtesystemperfoman㏄fmovera1lpmjectperfomance，indicatorsforovemllperfomance　satisfaction　and　performa1l㏄improvement　at　t1le　entire　proj㏄t　leve1were

regressed　on　corresponding　local　performall㏄and　Iocal　performallce　improvement　illdicators．

The　results　from　t1lis　are　shown　below：

REGREssI0N　REsULTs　BETwEEN　OvEMLL　PR0個cT　PERF0RMANcE　　　　　　　　　　　　　　　　　AND　LOcAL　PERFORMANCE

Body　D6sig皿

Ch困sis　Design

Extedor／Intehor　D閨igIl

Electm皿ic　Comp011ellt　Design

E1lgine　Desigll

Deve1opmel1t　Cost

　　Pe正fom田皿㏄

　　I

　0，63榊

一0，44

　0．44‡

一0．21

－O．37‡

　　1I

　O．68ホ榊

一〇．05

－O．09

－0，29

　0．07

Product　Cost

PerfOma㏄e　　I

　O．59舳

一〇．07

－O．04

　0，13

　0．24

　　II

　O．63榊‡

一〇．02

　0，02

　0．31“

　O．09

Ad1lere皿ce　to

　Schedu1e

　　I

　O．54榊　O．13

－O．02

－O．21

　0．18

　　II

　O．45榊

　O．20

－O，14

－O．38竈

一〇．02

d．f．of　residua1s

Adjusted　R－squa祀

　16　　　　　　16　　　　　　16

0．27‡　　　　　O．28‡　　　　　O．38“ヰ

　16　　　　　　16　　　　　　16

0．64‡‡“　　　O．18　　　　　　　0．31‡‡

Body　Desigll

Ch田ssis　D6sign

Extedorハntedor　Dcsigll

Electmnio　Compom耐DosigIl

E㎎ineDesign

Mo皿uf㏄伽mbnity

　　I

　O．07

　0．54榊一〇．02

　0．06

－O．27

　　皿

　O．10

　0．23

－0．51榊一0，33

　0．18

Tec］mic刎

NOVe1ty

　I

O．07

0．40舳

O．61‡州

0，18

0．08

I1

0，05

0，20

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56 HITOTSUBASHI JOURNAL OF COMMERCE AND MANAGEMENT [October

Appendix 4: Market Newness

First, project managers were asked to choose the most appropriate descri~tion of their

products from the following three descriptions: "(a) mamly targeted to the exrstmg customer

base;" "(b) targeted both to the existing customer and the new customer base;" and "(c)

mamly targeted to the new customer base " When a proJect manager chose (c), we categorized

his project as "new market"; when he chose (a), we categorized this as an "existing market."

As a result, four projects were categorized as "new market" and five projects as "exrstmg

market " For remammg 1 3 proJects we further classlfied four models as "new market" since

these products were given different brand names from the predecessor models with substantial

price differences. Finally, we classified one product as "new market" since this new model was

clearly positioned in a different market class from the previous model. As a result, seven

projects were classified as "new market," and 1 5 projects as "existing market"

57KNOW岨DGE一岨Tl…NT1ON　AND　NEW　PRODUCT　D1…V肌OPMENT冊RFORMANC正996

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