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Title
Author(s)
Citation
Issue Date
Type
Knowledge Retention and New Product Development
Performance
Aoshima, Yaichi
Hitotsubashi journal of commerce and management,
31(1): 13-58
1996-10
Departmental Bulletin Paper
Text Version publisher
URL
http://hdl.handle.net/10086/5463
Right
Hitotsubashi University Repository
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 knowledge retention infiuences perfornrance within well-established component development, which
we called local performance. We found that, in general, dependence on archival-based mechanisms, 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 circumstances, 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' compositional 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 introduction 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
1996]
KNOWLEDGE RETENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE
15
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 development 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 manufacturing quality may become critical for sales performance (system performance).
This distinction between local and system performance becomes important when examining 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 engineering 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 development 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. Development 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 appropriate for cross-functional and inter-disciplinary coordination, such as co-located crossfunctional teams (Imai et. al., 1985), the heavyweight project manager system (Clark and
Fujimoto, 1991; Wheelwright and Clark, 1992), and project organizations (Allen and Hauptman, 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 crossfunctional 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
bents to new entrants (e.g., Henderson
and Utterback, 1991), others claimed
changes in product functionality pose a
substantially changes market dominance, from incumand Clark, 1990; Tushman and Anderson, 1986; Suarez
that a change in the customer base and associated
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 determined 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 knowledge 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 interactions 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 questionnaires, 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, manufacturability of component systems, novelty of component systems, and technical performance
of component systems. Table I below shows summary statistics for these performance
indicators.
22
mTOTSuEASH1JOu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
3，33
1，04
1，OO
DewIopm6nt oost perfomanoe
3，04
0，09
一．o0
5，00
Adhor611ce to sch6dul0
3，13
1，01
1．O0
5，00
Manufo伽rability of compon㎝t systems
3，14
0，70
1，O0
5．O0
Novoltyofoomp㎝㎝tsyslems
3，21
1，01
1．O0
5，00
T㏄hllicaI perromla皿ce of compomellt systems
3．74
0．74
2．OO
5．OO
5．O0
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
3，78
O．91
1，00
5，00
Impo肘a皿㏄ofst㎝dards
3，92
0，99
1．O0
5，O0
US60f COmputer Si血阯OtiOn
3，22
1，25
1．O0
5．O0
Compu倣一b㏄ed d6sign祀telltioll
3，63
0，90
2，00
5，00
Dir㏄t oreation of p耐s pm酊om by CAD／CAM
3．48
I皿tm−f㎜Cti0皿al COmmu血Cati0皿
193，2
1．32
1．O0
5．00
72，9
12．0
240．0
Cross−fmction』comm皿micatio皿
47，0
19，7
4．0
110，8
Comm皿nicatio皿witll ot116r proj㏄t m6mbers
22，7
18，4
2．5
94，3
Com㎞cati㎝舳prlvio岬ojlctmemb1耐
21．O
18．4
2．5
67．1
Re1ativepoweroff㎜oti㎝alma㎜ge胴
R61atiwpoworofl㎝g・templ㎝mi㎎9mps
−O．24
1，23
−3，OO
2，00
−1I55
1，56
−4，00
9る of P1−evi0皿s p1−0ject me血1bers
0．18
0．14
0，OO
3，O0
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 component 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
24
［o吃to㎞r
HITOTSUEASH110URNAL OF COMME皿CE＾ND MANAGEMI…NT
TABLE3． TYPEs0F C0MMUNlcATI0N RBP0RTED
（Ineasur6d in app正oxhnate days per year，N ＝ 80）
Communication with
Project Members
the same project
another project
belonging to .
the same engineering area
the previous
generation of the
pro ject
(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
(4)
(5)
(6)
Mean: 58.7
Mean: 17.2
Mean: 18.0
S.D.: 29.8
S.D.: 19.2
S.D.: 15.8
(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
different engineering
areas within the product
engineering department
different functional
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＝12
days；3＝30days；4＝52days；5＝120days；and，6＝240days．Then，we calculated scores
for 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ml
OOmmuniCati011．
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
with the reference to documents and reports (r = .44, p < .O1), and the use of CAE
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.
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[October
M＾NAGEMENT
HITOTSuEASm JOURNAL OF COMMERCE AND
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 performance. 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 multicollinearity.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 performance 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
TA肌E5．
【Ootobe正
REsULTs0F T朋FITTED REG㎜ssI0N ANALYsEs
P0R LOcAL PERFORMANcE INDIcAT0Rs
Compo06皿t Cost
DowloPmoot Co筍t
岬‘om皿㏄
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
I1IIIIIVVVI
−0．30材刈．35榊一0．29榊一〇．33榊｛．2フ榊一〇．27軸
Miom C町 0，16 0．24榊0．15‘ O．19舳0．19｝O．21
O．26 0，13 0，15 0，12 0，12 0．I2
D6sign Ncw皿偉蝿 O．07 0．08 −0，06 0．02 −0，03 0−03
−0．I1 −0．10 −0．11 −O．14 −O．24 −O．23‡
Fim NotSig．
Not Sig．
EllgimeH−1g A祀a Sig． Im， I皿o． Im． I皿c． Illo．
Not Sig。
0．45榊 0．24柚
0．43榊
Roforo皿c6to Dooumc11ts O．20壮 O．28一ヰ 0．19‡
一0．39榊一〇．17
一〇．39榊
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‡
O．15
Di祀ct P舳昌Pmgrom by CAD／CAM0．16 006 0−11 0−06
0．01
U舵of CAE S㎞阯1ation O．02 0．05 −O．01 0．02
0．15
刈一02
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O．04
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−O．18‡
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68 69 69
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0．41榊O．46榊O．38榔O．47榊0．44榊O．47｝帥
0．12ヰ
0．27榊0．09‡
0−23榊0．11‡ O．15芯
Mmufocto岨刷ity
Adhe祀皿c6to
Solleduk
1 II I11 1V
V VI
Eubble Economy −O．06 −O−01 0．01 −O．04
O．OO O．02
Mic正o Cπ O．41 0，04 0，08 0．02
0，02 0．08
D閑ig皿Now血醐 刈一31榊一〇．26壮一〇．28拙一〇。22‡
−O．26，ヰ ーO．22
IIlIIIIVVVI
−OI31‡一一〇、36榊一〇．26榊一〇．34榊一〇．27‡一一0．27甘
O．＝一9串一 〇．09 0，12 0，11 0，11 0，16
0，06 0，11 0．O0 0．05 −O．03 0．06
Not Sig−
Fim Not Siε．
E皿gimorillg A爬a Not Sig．
Not Sig一
Use o『Desig皿St田皿d田rd5 −O−14 −O．1喀
0．40｝ヰ O．21‡
O．34榊
O．10
Re‘or61100to DoouIne皿t5 0，17 0．19
一〇．36軸一〇．15
一0．32｝
刈一08
Computer−bas6d Dosi筥11Roto皿tio皿 0−05 0．07
0，08
O．02
O．O］
O．04
Di■㏄t P耐s Pmgr㎜by CAD／CAM−O，02 −O．01
0，02
0，05
0．i2
O．06
0，12
U舵o『CAE Si皿1凹1ation 一一〇I01 0．02
0，01
0．04
0．11
0，08
0，05
Ro1巴tiw Pow航：FM 0，01 0IOO
0，02
Re1砒ive Power＝Loilg−term Pla日 O−09 015
0．13
Com皿111Ilic巴tio皿・Flmctlona1 −O．09 −O．11
−O．12
一〇．03
O．06
刈．01
−0．01
0．12
刈．11
＝Cmss−F山mtio皿al O．OO O．02
0，02
0，01
Cro鵬一Gellcmtlo皿 0．03 −O．04
0，05
0−02
0．09
−0．04
−O．Oヨ
刈．01
−O．03
ーO．03
−O．01
−O．02
O．06
0．03
−O．18
0．03
−O．01
0．01
刈．02
0．02
0，03
0．Ol
0．09
−O．03
・I皿t6r＿Projcct 0，14 0，14
0，13
O．17
−OI21オ
刈．16
−O．19
−0．16
％of P祀vio唖Mombe耐 一〇．03 0．10
0．11
0．03
¶．15
−O，18
｛．15
−0．18
0．11
d．rof−esid山田1s
69 75
68 68
69 69
69
75 68
68 69 69
Adj㎜ted R一明u肛e
−0，01 0．Ol
0．02 −O．02
−O．03 −O．02
0．13紳
0．14榊O．05
0．I2‡ 0，03 0．05
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
T㏄h㎞cal
Nowlty
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
Miom C趾 O．23 −0．05 −O．14 −O．12榊一0−12柚刈．13 0，24 0−04 −O．06 −0．06 刈．06 −O．Ol
D齪ig皿N・㎜。・・ O．13 0．l10−00 0．l10・12 0・11 刈・07 0・OO¶・30−0・04仙5刈・03
Fim NotSig− NotSig・
E㎎㎞・・d㎎ル・・ NotSig− N・tSig・
M鵬皿o．t．D。。。m㎝t． 0．08 −O．04 0，02 0．55榊 O．50榊 O−38榊
U・。。fD齪卿S伽由｛ 0，05 0．l1O．09 −0・22｝ 一〇・24＃0・02
C㎝叩t肚一b冊ωD㏄i即R・t・皿ti㎝O．03 −0−09−O．09伽8 −0・06 ■0・07−O・05■O・15
1）iI㏄t P舳s Pmg正a皿by CAD／CAM−0−05 0，09 0，08 0．11 −O．13 −O，09 −0−02 −O．07
UsE0f CAE Si皿汕刮ti011 0，13 0．27帥 O．27＃ O．30＃ O．32榊 O．38I蛛O．35伸一〇。38帖＝
Ro1田t∼e Powr＝FM 0，07 0，04 0，04 0，05 0．19‡ O．11 0，16 0．09
R．1．tiΨ。p．w。。：L㎝g一。。mpl。皿 刈．2g榊一〇．35榊一〇。34榊刈。34榊 一〇．28榊一〇一25榊一〇．31榊一〇．27榊
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
％of Provio㎜Mombe晒 O．05 0．OS O．08 0，07 0，13 0，14 0，18 0．12
d．f of I＝csidods 69 75 68 68 69 69 69 75 68 68 69 69
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 technology-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 product 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 questionnaires 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 performance 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 correlation 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 experience-based knowledge retention capability and task newness. We fit regression models
including interaction terms between a technical or a market newness indicator and individualbased knowledge retention capability indicators, and examine how newness indicators moderate 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
Descri ption
Indicators
Product cost
performance
Rated by project managers, Iayout
engineers, and marketing planners
Mean
S.D.
Mean
S.D.
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
Rated by project managers, Iayout
engineers, and marketing planners
3.15
0.61
2.83
o.69
Rated by project managers and
production engineers
3.39
0.81
3.23
0.97
Match to customer
need
Technical Novelty
Rated by project managers, Iayout
engineers, and marketing planners
3.54
0.69
3.34
0.77
3.46
1.14
3.23
1.38
Technical
Rated by projeet managers with
respect to 15 technical performance
3.97
o.37
3.78
o.s9
schedule
Manuf acturability
Performance
Rated by project managers.
items *
* 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 perfonnance, 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 performance 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 accurately 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.
33
DESCRIPTIVE STATISTICS FOR EXPLANATORY VARIABLES
N = 22
Mean S D Mm Max
Experience-based retention
O,OO
l.OO
- 1.59
Cross-generational communication
15.7
7.02
5.06
29.3 l
Reference to documents and reports
3,73
0.22
3.43
4.28
Use of standards and computer-stored
3.76
0.3 1
3 . 20
4.37
1.78
inf ormation
Use of computer simu]ation
3.53 0.41 2.65 4.21
Involvement of long-term planning groups
2.06 O. 79 1 .OO 3 .46
Involvement of super-project managers
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 information. 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 Rl≡TENTlON
TABLE8、
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
1≡‘ubble EconOIny
A dummy柵dable that indicates pmj㏄t e皿ded
Mem S．D．
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
New Plal＝folrm Ratio
Per㏄nt田geof血ew1ydosigmdp劃rts（i皿
血umbcrofpais）一
0，69 0．17
Peroent副ge of mw desigll in u皿de正一佃oor
O．43 0．27
panels alld suspension systems（ob帖i皿ed
fmmthequeStiOnnai祀S〕
New Platfo㎜Mio2
Agsembly Propriet田町P囲rts
Newness of platform d6sigIl based on
New O］d
Nobeok田’s cliss胴cation scheme（Nobcok田，
（code− 1） （code＝0）
1993）
9 16
Pe1℃ent田ge of p邑rts developed6ntire1y by
O．09 0．23
asscmbly make耐（see C1趾k a皿d Flユjimoto，
1991for the de血血ition）
Black Box P割r的
Pa竹s whose b田sic e皿gineering is done by ca正
O．45 0．25
皿田kefs a皿d whos6detailed engi鵬ehng is
doIle by pa灯s suppliers（see C］ark and
Fujimoto，1991）
SupPlier］旦ngi皿eehllg Parts
Parユs d6vcloped entiroly by l〕arts s11pP］i6耐
O．一6 0．15
（sc6，C1趾k ond Fujimoto，1991）
〃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，andtheseniomanagers1ocated
above 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）。We
kept 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。
36
工Octobcr
HlTOTSUEASHl，OuRNAL OP COMMERCl…＾ND MANAG1≡MENT
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
Pmd皿ct Development Adhe−
Cost Per・ Cost Per一 祀nc6to
M㎜ufac− Match to T㏄lmic田1T㏄hnical
t皿rability
‘o㎜mce iom田㏄e Schedule
Exped㎝㏄一B鵬d
O，11 0，35 0．41ホ‡
S創6s
C1ユstomer Novelty Perfor・
Achiev6−
N㏄ds maI1㏄
mon
一0，08 0．47申＾ 0．16
O．06 −O．12
Ret6ntioIl
X−Gemratio皿＾1
一〇．09 −O．17 0．04
O．15 0．47‡‡ 0．47“ヰ
O．23 −O，15
一0．32 −O．13 −0．07
O．08 −O．45‡ホ 0．23
O．29 −O．32
Super−PI＝oject
0，27 −O．03 0．06
一0，08 0，35 0．07
Man田gers
Stalld趾ds＆Computer
Commu皿ioation
L㎝g−Te㎜Plaming
Gr㎝ps
一〇．05 0．07
O．14 −O．11 −O．08
O．35‡ 一〇．07 0．43ヰ＾
一〇．04 −O．13
Stored I㎡omati㎝
Dooumcnts and
Rcports
O．19 0，01 0．13
O，26 0，10 0．22
一〇．02 0．07
Computer Simu1田tion
O．19 0，28 0．38ホ
O．50‡‡ 一0，05 0，29
0．60‡‡‡一〇．06
（CAE Tools）
＃P＜．10 榊p＜．05
‡オ‡I〕＜、01
TABLE10． C0RRELATI0Ns B1…TwE酬Ex肌ANAT0RY VAR1A肌Es AND lNDlcAT0Rs
FOR OvEMLL PERFORMANcE IMPROvEMENT
Pmd㏄一Dev61opm㎝t Adher㎝㏄Monufact1■ra−Motoh to
Cost Cost to Sohedu1e bimy Custom6r
Tech皿ical T㏄hnical
Nove1ly Perfom㎝㏄
P6rfoma皿㏄Pe㎡oma㏄e Needs
Exped㎝㏄一Bas記
0，17 0．36‡ O，44‡‡
0，05 0，13 0．01
O．47榊
0，21 0，22 0．26
O．56榊‡
Rete皿ti011
X−Gene祀tioml
一〇、01 0，01 0，19
L㎝g−T6m P1田mi㎎
一〇．30 −O．24 −O，55‡‡‡
Communication
一0．11 −0，08 0．31
一0．09
Groups
Super−P1−ojeot
O，14 −0，02 0．29
O．10 0，16 0，02
一〇．20
M田皿age耐
Standards＆Computer
Sto祀d Infomati㎝
Documents and
一〇．05 0．04 −O．06
一〇．14 0．39ホ O，30
0，17
O．09 0，14 0．15
0，01 0，11 0．17
O．08
O．22 0．37‡ O．04
O．28 −0，20 0．26
O．22
R6po■s
Computcr Simulation
（CAE Tools）
‡pく、lO 榊p＜．05 榊‡p＜．Ol
1996］
KNOWu≡DGE
R1…TENTlON
AND N1；W PRODUCT DEV肌0PMl…NT PERF0RMANcE 37
TABLE11． C0RR肌ATI0Ns BETwEEN ExPLANAT0RY VARIABLEs AND INDIcAT0Rs
FOR∫γ∫胞㎜μψ7㎜0〃Cθ
Product
D6velopmellt
Adher㎝㏄ Mamfactu祀一
Cost
Cost
to Schedule bility
P6『fo『nlance
Pe㎡0m副皿㏄
O．17
Experience−Based
0．50榊
O．35‡
Technical T㏄hnica1
Novelty Perfom副n㏄
O，08 0，32 −O．01
RetentioIl
0．17 −O．09 0．03
0，16 0．60‡‡‡ 0．18
L㎝g−T6m Plmning
Gf0ups
O．14 −0．17 −O．08
一〇．26 0，22 0．一0
SupeI＝一P1＝oject
O．33 −O．08 −0．04
O．09 0．18 −O．07
Mmagc祀
St田nd肛ds＆Comp汕er
O．22 −O，06 −0．08
0，08 0．44北‡ 一〇．09
0，09 0，03 0．27
0．37｝ 0．24 −0，16
0，31 0，13 0．47＾｝
O．48‡‡ O．20 0．33
X−Generationa1
Communicatioll
Stored Infomati㎝
Docllments and
Repo竹s
Comput6f Simulati011
（CAE Tools〕
‡p＜．lO州P〈一05
榊ホpく．01
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
Prod1ユct
Developm6皿t
Adher㎝㏄ Manufoctura−
Cost
Cost
to Schedule bility
Pe他m田nce
Perfommce
O．56ヰ榊 0．48榊
0．37オ
TechIlical Teclmic刎
Novelty Perfoma皿ce
0，25 0．OO O，39ヰ
Retention
X−Gcner舳onal
O．54＃‡ヰ O．08ホ O．19
O．28 0，28 0．52＾＾‡
Communication
Lo㎎一Tem Plan㎡㎎
O．09 0．OO −O．52‡北
一〇．30 0．44‡非 0．12
O，07 −0．21 −O．01
一〇．12 0．OO −O．08
Groups
Super−Pmject
Mamgers
Stalld肛ds＆Computer
一〇．04 0．05 −0．17
一〇．17 0．09ホ O，24
Sto肥d Infom田ti㎝
D㏄um6皿ts and
一0，06 0，09 0，08
Repo廿s
Computer Simulatio11
O．26 0．32 −O．11
O，02 0，31 0，06
O．29 0．39北 0．11
（CAE Too］s）
‡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 particular, 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 archivalbased 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 manufacturability (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 manufacturing. 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 perform12 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
39
KNOWu…DGl…R1…TENTION AND柵W mODuCT D嗜VI…LOPM酬T PI…RFORMANCE
1996］
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
*
*
**
**
***
* (-)
* * (-)
*
Schedule
*
Development Cost
Manufactura-bility
**
*
Tech. Perforrnance
** ***
*
Match to Customer
*
*
Tech. Novelty
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 performance indicators. Especially, this had a significant negative impact on performance improvement 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 performance 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
41
ICNOWLEDGE Rl3TENTION AND NEW PRODUCT DEVELOPMENT PERFORMANCE
1996]
TABLE 15. A SUMMARY TABLE FOR THE RESULTS OF REGRESSION ANALYSES
FOR RELATIONSHIPS BETWEEN KNOWLEDGE RETENTION CAPABILITIES
AND performance satisfaction
Computer simulation
Standard-based retention
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 hypotheses, 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
X-generational communication X
Experience-based retention 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
Market newness
Product cost performance
Teehnical newness
X-generational communication X
Market newness
Technical newness
* (-)
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 development 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 experiences may negatively affect development efficiency and market performance
We also found that a similar expected moderating effect by market newness on relationships between cross-generational communication and satisfaction in development cost performance (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
may be useful even in developing novel technological concepts (Iansiti, 1995b). 16
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 interactions 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 increasingly 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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KNOWLEDGE
1996］
R1…TENT10N AND NEW PRODUCT
Appendix1．
DEVELOPMENT
PERFORMANcE 5一
DEc0MPosITI0N0F OvEMLL PERF0RMANcE
Tosepamtesystemperfoman㏄fmovera1lpmjectperfomance，indicatorsforovemll
perfomance 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
Deve1opmel1t Cost
Pe正fom田皿㏄
Product Cost
PerfOma㏄e
Ad1lere皿ce to
Schedu1e
I
1I
I
II
I
II
Body D6sig皿
0，63榊
O．68ホ榊
O．59舳
O．63榊‡
O．54榊
O．45榊
Ch困sis Design
一0，44
一〇．05
一〇．07
一〇．02
O．13
O．20
Extedor／Intehor D閨igIl
0．44‡
−O．09
−O．04
0，02
−O．02
−O，14
Electm皿ic Comp011ellt Design
一0．21
−0，29
0，13
0．31“
−O．21
−O．38竈
E1lgine Desigll
−O．37‡
0．07
0．24
O．09
0．18
一〇．02
d．f．of residua1s
16 16 16
16 16 16
Adjusted R−squa祀
0．27‡ O．28‡ O．38“ヰ
0．64‡‡“ O．18 0．31‡‡
Mo皿uf㏄伽mbnity
Tec］mic刎
I
皿
I
Body Desigll
O．07
O．10
O．07
Ch田ssis D6sign
0．54榊
0．23
0．40舳
Extedorハntedor Dcsigll
一〇．02
−0．51榊
O．61‡州
Electmnio Compom耐DosigIl
0．06
一0，33
E㎎ineDesign
−O．27
0．18
T㏄1mioa1
I1
NOVe1ty
P6㎡oma皿㏄
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0，05
O．53舳
O．25
0，20
O．40｝
0．47“
0．54ホ榊
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O．36
0，18
O．22
0，04
0．10
0．08
0．28＾
0．19
−O．15
d．f．ofI＝osidu坦1s
16 16 16
16 16 16
Adjl1sted R一刈11町6
0．27‡ O．05 0．39‡‡
0．56‡‡‡ O，34｝‡ O．20
I＝P6rfom㎜㏄Satisfacti㎝トP6rio㎜a㏄6㎞pm6me皿1
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HlTOTSu皿ASH1 JOURNA1−OF COMMERCE AND MANAGEM嘔NT
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KNOWLEDGE RET酬丁10N AM〕M…W PRODUCT DE珊LOPM1…NT冊RFORMANC1≡
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HlTOTSUEASH1JOURNAL OF COMMERCl…AND MANAG1…M唱NT
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KNOWu…DGE R旧Tl…NT10N AND冊W PRODuCT D臣V肌0PM酬丁肥RFORMANCE
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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"
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