Online ISSN 1347-4456 Print ISSN 1347-4464

ABAS: Annals ofBusiness Administrative
ScienceVol. 3, No. 4, October 2004

Ayako TAKAI

53

Kenichi KUWASHIMA73

The Early Stage Competition in the Japanese Online Securities Industry:
Research Based on Case Studies of Leading Companies

A Note on New Product Project Selection Model:
Empirical Analysis in Japanese Chemical Industry

http://www.gbrc.jp© 2004 Global Business Research Center

ABAS: Annals of Business Administrative ScienceVol. 3, No. 4, October 2004

CHIEF EDITOR

Junjiro Shintaku

University of TokyoEDITORIAL BOARD

Makoto Abe
University of Tokyo
Takahiro Fujimoto University of Tokyo
Makoto Kasuya
University of Tokyo
Hotaka Katahira
University of Tokyo
Nobuo Takahashi University of Tokyo
Technical Editor: Maki Nishida, Global Business Research Center

ABAS: Annals of Business Administrative Science is published quarterly in January,
April, July, and October, by Global Business Research Center, Marunouchi, Chiyoda-ku, Tokyo,
JAPAN. http://www.gbrc.jp

Annals of Business Administrative Science Vol. 3, No. 4 (October 2004)

A Note on New Product Project Selection Model:
Empirical Analysis in Japanese Chemical Industry
Kenichi KUWASHIMA
Graduate School of Business Sciences, University of Tsukuba
E-mail: kuwa@gssm.otsuka.tsukuba.ac.jp

Abstract: By focusing our attention on one particular scoring method which is used
to evaluate R&D projects, this paper seeks to specify empirically the factors that
discriminate successful projects from failed projects in the Japanese chemicalindustry. Our statistical analysis revealed that when projects are evaluated in thisindustry, three factors, marketability, technology, and synergistic potential, tend tobe valued by practitioners approximately in a 3:2:1 ratio. Although the project
evaluations in this research were conducted ex-post, the findings suggest that theresults may also be applicable in the project selection stage. Built on our findings,
we propose a Continuous Improvement Scoring Method (CISM) that contains
continuous improvement cycles, which links ex-ante project selection with ex-post
project evaluation.
Keyword: product development, project selection, chemical industry

1. Introduction

product development project. Another is the

To most firms, the important issue is “how to make“product development process management”: that is,more projects a success under severe resource

the firm focuses on how to manage and lead theconstraints.” Generally, mainly two issues could beselected project to a success. Among these two, thisraised when the firms’ management faces thispaper intends to focus on the first “project

problem.

evaluation” and propose a new evaluation method

One

is

“ex-ante

project

evaluation

selection”: that is, the firm focuses on how to selecta project with high success probability prior tobased on empirical analysis.
Studies on project evaluation date back to the73Kuwashima
1950s. The mainstream of the earliest studies wasand tools have been accumulated in plenty.

something related to project evaluation method (hereNevertheless, eyeing the practice (business) side

we call this “evaluation method studies”). Evaluation

who actually utilize these evaluation models andmethod studies’ concern lay mainly in developing

tools, not much of the fruit of these scholarly

project evaluation criteria and model. Studies began

researches seemed to be enjoyed. For instance, today,from the most simple scoring method, which

it is said that the most popular evaluation method

developed into economic analysis approach, andused in practice is scoring method. However,then to operations research approach, gradually

throughout its half-century history, scoring method

evolving into more complicated method and model

does not provide sufficient accuracy and present(e.g., Asher 1962; Augood, 1973; Disman, 1962;

arbitrariness in practice. Therefore, improvement

Hess, 1962; Mottley & Newton, 1959). Studies on

efforts have been made (e.g., Arikuni, Osawa &evaluation method have been widely popular till the

Murakami, 1997; Murakami, 1992).

early 1970s, however, in the late 1970s, theLeading reasons for efforts of scholarly

mainstream of project evaluation studies shifted toresearches not being applied in practice is: 1)“evaluation process” studies: that is the study on the

Researchers of evaluation studies were mostly

process through which the method is applied to andconcerned in developing precise yet elaborate

evaluated in the actual firms’ project evaluation

evaluation methods, on the other hand paying little

process (here we call this “evaluation processattention on the fact that the method became

studies”). In evaluation process studies, hierarchicalcomplicated and unhandy. 2) From the standpoint ofdecision system and consensus figuration methoddata availability and reasonability, researchers wereapplying Delphi method have been presented (e.g.,

mainly concerned with ex-post evaluation (ex-post

Baker, Souder, Shumway, Maher & Rubenstein,analysis) after the project has ended. On the other

1976; Presley & Liles, 2000; Schimidt & Freeland,

hand, practitioners were only interested in ex-ante

1992; Souder & Mandakovic, 1986).

evaluation of the project sharing little concern in
ex-post analysis, therefore, attached little importanceThus, in the early stages of project evaluation
studies,“evaluation

methodstudies”

in results of scholarly researches based on ex-post

gainedanalysis.

popularity and then, in the late 1970s, the“evaluation process studies” view has been added.

Consequently, this paper will focus on theAlong this process, a lot of extremely elaborate“scoring method” which is most popular in businessmodels and tools have been created to evaluate andtoday in regard of the gap between scholarly studies

select R&D projects. In addition, researches on theand practical businesses. Based on empirical

processes in order to efficiently utilize these models

analyses, we intend to derive “improved scoring74

New Product Project Selection Model
method” assuming practical use. Particularly, exevaluate the ongoing project. For example, it gives

project data from 51 projects of 22 chemicalscores such as 1 to 5 point (five point method) tocompanies in Japan is analyzed empirically to unveil

each item to rate by total score. The most common

the factors which are weighed in deciding projects’

evaluation method is the scoring method which is

success or failure. And in order to utilize (ex-post)

discussed in this paper.

evaluation criteria gained from empirical studies as“Economic analysis approach” is a method“ex-ante evaluation criteria,” this paper proposes awhich value project’s performance by contrast ofprocess which will link ex-post and ex-antecost and profit from economic point of view. In

evaluation model by follow-ups and feedback

particular, methods such as “Net Present Valuemechanisms. We will name the scoring method(NPV) method” which evaluate the project’s value

containing these series of processes as “Continuous

by present discounted value of future cash flow, orImprovement Scoring Method (CISM).”“Internal Rate of Return (IRR) method” which

The framework of this paper is as follows: First

evaluate by internal profit ratio could be named.

in Section 2, we will overview major evaluation

The last “operations research approach” utilizes

methods and examine the characteristics andoperations research method to represent phenomenadifficulties of the scoring method we take up.

which occur in R&D/new product development

Section 3 aims to clarify empirically the evaluation

activities with mathematical models. The method

criteria concerning success/failure of the projects inpredicts the future by alternating involving factors in

the chemical industry by statistical analysis using

a multidimensional and dynamic way to evaluate thedata from the industry. Last in Section 4, we will

project. To name, there are Linear Programming

propose the specific process to apply “ex-post(LP),evaluation criteria” gained from empirical studies toDynamics (SD), and decision tree methods.

“ex-ante project evaluation (project selection).”Dynamic

Programming(DP),

SystemThe above three evaluation methods havehistoricallydevelopedfromdecision

theory2. Three Types of Evaluation Methods

approach to economic analysis approach, and to

2.1. Scoring Method: Brief Description

operations research approach. The accuracies of theGenerally, project evaluation method is divided

evaluations have more or less corresponded to thislargely into three approaches: “decision theoryorder as well, in which operations research approach

approach,” “economic analysis approach,” andis the most refined. However, today, it is said that the“operations research approach” (Baker & Pound,most popular evaluation method used in practice is1964). “Decision theory approach” sets evaluation

the scoring method (decision theory approach). That

items and corresponding decision criteria in order to

is to say, considering the accuracy of the evaluation75

Kuwashimaalone, it is the operations research approach which isproblem is closely related to the benefits of scoring

most effective, on the other hand, operations

method. In other words, scoring method, as

research approach requires detailed and abundant

described before, is based on subjective ratings of

input of information to match the accuracy of

the rater hence requires less elaborate data while

evaluation, which cost a lot. For instance, in suchcapable of including un-numeric factors as strategy

cases as in the downstream stages of pharmaceuticaland technology as ratingproduct research development, where development

consequence, subjectivity of rating always remains

costs thousands of million to billion yen at the sameas an issue. The “rating arbitrariness” issue directly

time uncertainty is high, it should be a reasonablereflects the nature of scoring method as andecision to spend a lot on exhaustive evaluation

evaluation method, therefore fundamental solution toutilizing operations research approach or economic

this problem should not be easily attained.

items. Yet, as aanalysis approach. Nevertheless, not all projects

Meanwhile, the latter “rating accuracy” issue

among any industry require such refined evaluation.

could be given thought for betterment. For we think

In that respect, scoring method cost less as it doesthat one of the largest reasons for the scoring method

not need much information for evaluation, and theto have shown poor rating accuracy is that theprocedure is quite simple. Moreover, scoring method

present scoring method lacked continuous method

could take in qualitative factors in the evaluation

improvement process; that is, a process which

concerning “strategy” and “technology,” which iscompares and verifies ex-ante and ex-post evaluation

hard to introduce in operations research approach or

results and continuously improve itself. As haveeconomic analysis approach. For these benefits, it isbeen illustrated earlier, the background was thatassumed that the scoring method is still very popularscholarly researches did not pay full attention on

in practical use today.

applying the results to ex-ante evaluation in practice;
while practitioners cared much about ex-ante project2.2. Problems and Points to Improve of

evaluation but scarcely eyed on the projectScoring Method

afterwards

Today, the most popular evaluation method practiced

evaluations. The result must have been the fact thatin business is the scoring method, as have beenno efforts were made to build handy in practice anddiscussed. However, whether practitioners arehighly accurate scoring method.

tocompareex-anteandex-postsatisfied with the present scoring method is not a

Thus this paper will attempt to propose acertainty. The most frequently pointed defect of thescoring method in which a continuous improvement

scoring“rating

process is embedded by linking ex-post evaluation

arbitrariness” and “rating accuracy.” The former

model, build upon ex-post data, and ex-ante

methodistheproblemof76New Product Project Selection Modelconsidered as success (go) ex-post.

evaluation using follow-ups and feedbacks. We
theGiven that this study samples projects at varied

evaluation model based on data attained fromcompanies in the chemical industry, the criteriapractical cases we should be able to develop models

attained forthwith should reflect the averagewith higher accuracies.

evaluation criteria in the Japanese chemical industry.

believethatbycontinuously

improving

Or else, this study should unveil the evaluation

3. Empirical Analysis

criteria in which the industry would, say, decide

3.1. Overview of the Analysis

success/failure of a certain project by mutual

The goal of this paper is to propose an “improved

agreement.

scoring method” by applying ex-post evaluation
model attained from empirical analysis to ex-ante

3.2. Method of Analysis

project evaluation through follow-ups and feedback(1) Data and Criteria

cycle. As a first step, we will start by clarifying theThis paper analyzes 51 product development projects“project evaluation criteria” by which the projects’at 22 chemical companies who join Management of

success/failure is decided. Our analysis is based on

Technology Commission of JCII: Japan Chemical

practical ex-post project data gathered from Japanese

Innovation Institute. A questionnaire survey has beenchemical companies.

conducted from December 1999 to January 2000.

Project evaluation in practice is conducted

We received answers from all 22 companies. Valid

before (or in the middle of) the project, where

response was 47, among which 32 were successprojects decided good are called a “go” and projects

projects and 15 were failed projects.

decided not are called a “no-go.” However, in thisempirical analysis, the data available are those of

Table 1. Items of Project Evaluation

already ended projects, therefore we would make the1. amount of sales/market share
2. profit rate
3. man-hour of development/cost
4. development period
5. product cots
6. niche/new market creation
7. product performance and function
8. manufacturing quality (credibility)
9. customer satisfaction/overall quality
10. new technology development
11. new product development
12. development of synergetic technology
13. development of synergetic product14. building new organizational capability inproduct development
15. building future project base (e.g.,
database)following arrangements. On the concerning project,
we asked what evaluation is given ex-post to each
evaluation item such as economic achievement,
quality, and technology, as in ex-ante project
evaluation. At the same time, we asked whether the
project as a whole were to be considered a “success(go)” or “failure (no-go).” This will make clear the“weight of evaluation criteria,” by which each
evaluation item has been valued, in projects
77

Kuwashima
estimate wj statistically by discriminant analysis.

We measured the performances of the projects
with two items. One is the performance of the entire

The model we present here is basically the same

project: If an answer is “success,” it is rated at one,as the model routinely used in scoring method.

on the other hand, if an answer is “failure,” it is rated

Therefore, the evaluation criteria based on weightat zero. The other item is, as shown in Table 1, 15

attained in our analysis could be applied to ex-ante

specific

project evaluation (project selection) as well. In fact,

performances

such

as

economicachievement and quality, by which we measured thewhensuccess

evaluation), scores of each evaluation item (yij) will

rate

by

five-pointscale(1=failure,

5=success) (Table 1).

conducting

project

evaluation(ex-ante

be assigned to this equation, which is multiplied bypreset weights of each item (derived empirically(2) Analysis Frameworkfrom data attained from observed projects) to gain

As the framework of our analysis, we use below

overall score (Zi). According to the value of thismodel of Stahl and Harrel (1983) to bring out thescore, the project’s go or no-go are decided. In the“evaluation criteria” in the sample projects.

following Chapter 4, such procedure of applying
weights (evaluation criteria) attained from empirical

Zi = Σ wj yij(i = 1, 2, …, m; j = 1, 2, …, n)analysis to ex-ante evaluation (project selection) in

Zi: success/failure of the ith project (or total score)yij: the value of factor (item) j which influence thesuccess of the ith project
wj: relative weight of factor (item) j

actual new product development projects are to be
discussed.

3.3. Analysis Results and Validity0≦wj≦1, Σ wj = 1(j = 1, 2, …, n)(1) Result of the Analysis

Here, project “evaluation criteria” corresponds

Commonly, when evaluating a project by scoring

to wj which is the weight of each evaluation item.

method, it is considered that items showing highThis model calculates the overall score (Zi) by thecorrelation must be aggregated. It is likely thatsum of scores of each evaluation item (yij) multiplied

highly correlated items in fact measure identicalby corresponding weights (wj). In this paper, we

matter (construct) by different ways of expression

utilize two kinds of data: ex-post overall evaluation(Cooper, 1981, 1985). We conducted correlation

data of success (go) or failure (no-go), and successanalysis among 15 evaluation items used in ourrates for individual evaluation items. We would treat

study and found certain items showing highthe former data (success or failure Zi) as explainedcorrelation. Therefore, prior to discriminant analysis,

variable and the latter (success rate yij of individual

we conducted factor analysis in order to aggregate

evaluation items) as explanatory variable in order tohighly correlated items. As a result, three factors
78

New Product Project Selection Model
Table 2. Factor Analysis on Evaluation Items(Factor 1)(Factor 2)(Factor 3)

marketability

technology

synergetic potential

Amount of sales/market share

0.799

0.345

0.076

Profit Rate

0.769

0.403

0.155

Man-hour of development/cost

0.775

0.154

0.417

Development period

0.609

0.071

0.136

Product cots

0.655

0.304

0.203

Niche/new market creation

0.680

0.402

0.194

Product performance and function

0.584

0.504

0.324

Manufacturing quality (credibility)

0.398

0.729

0.099

Customer satisfaction/overall quality

0.223

0.838

0.104

New technology development

0.353

0.619

0.293

New product development

0.438

0.494

0.308

Development of synergetic technology

0.171

0.052

0.883

Development of synergetic product

0.311

0.277

0.718

Building new organizational capability inproduct development

0.123

0.375

0.334

Building future project base (e.g., database)

0.114

0.354

0.390

Evaluation items

Note: Factor Loadings are after varimax rotation. Underlines indicate factor loadings over 0.45.

were extracted which eigen values are more than 1

called synergistic potential factor because factor(Table 2).

loadings of items related to technology and productsynergy are high.

In Table 2, we will call factor 1 as marketability

After aggregating evaluation items showing

factor since factor loadings of items related toeconomichigh correlation by above procedure, we treat these

performance as sales and profit rate, customer

three factors as explanatory variables and conductedsatisfaction, and niche creation. As for factor 2, we

discriminant

will call it technology factor since factor loading of

performance (success/failure) of the project as

items related to quality, such as product performance

explained variables. The result is in Table 3.

market

is

high,

for

example,inanalysisusing

aforesaid

total

and manufacturing quality, and items related to

From Table 3, we can see that Wilks’ Lambda istechnical or product novelty are high. Factor 3 issignificant therefore the entire model is statistically79

Kuwashima
This analysis has been conducted as a first step

Table 3. Result of Discriminant Analysis

of successive processes aiming application of

discriminant variable

discriminantcoefficientt valueex-post evaluation model built on empirical analysis

marketability

1.592

8.458**of post project data, to ex-ante project evaluation.

technology

0.960

4.931**

Nevertheless, considering the fact that historically insynergistic potential

0.447

2.311*

invariable

0.615

44.915**F value

Japan, there scarcely existed empirical studies on
project success/failure evaluation criteria in an
individual industry. We think that the result of our37.385**study has a certain point as fact finding.

percent incorrectly
2.1% (1/47)
classified* p<0.05 ** p<0.01
Note: Percent incorrectly classified indicate ratio of
mistakenly predicted failure (success) but success(failure) in practice(2) Validation of the Model
Before ending the empirical analysis, we would liketo examine the validity of the discriminant formulaattained in our analysis. The ultimate verification ofsignificant, and that our formula is quite efficientthe validness of empirically attained model is tosince incorrectly classified rate is low at 2.1%.

substitute samples other than the ones used for

Moreover, looking at the three discriminant variables,

analysis. However, most empirical studies deal withmarketability, technology, and synergistic potential,

data which is hard to gain in addition. Consequently,

we discovered that each variable is significant at 5%

cross-split-half method becomes useful, which is atherefore statistically effective on the decision

hypothetical method of verification (Cooper, 1981).

making of the project’s success/failure. Besides,

Cross-split half method randomly split the sample

comparing the discriminant coefficients for the three

data in two to estimate the discriminant formula withvariables,

half the data and substitute with the other half data to

1.592

for

marketability,

0.960

for

technology, and 0.447 for synergistic potential, we

calculatethediscriminantcan see that among three factors the most weighed is

success/failure.

marketability on deciding success or failure of

conducted by exchanging the halved data. This

sample projects. In fact, the ratio of the three factors’

procedure makes possible to confirm the validness ofweight is approximately 3:2:1.

the

This

discriminant

score

procedure

formulaandiswithout

predict

repeatedlycollecting

additional data.

Fact Finding: In Japanese chemical industry, threefactors,
marketability,
technology,
andsynergistic
potential,areweighedapproximately at 3:2:1 ratio upon project
evaluation.

We checked the validness of the discriminant
formulaattainedinouranalysis

by

using

cross-split-half method. The result was 4.3% (280New Product Project Selection Model
Table 4. Discriminant Results of Cross-Split-Half Method
predicted by cross-split-half method

total

predicted successpredicted failure

actualsuccess32

032

actual failure

2

13

15

34
(incorrectlyclassified case: 2)

13(incorrectlyclassified case: 0)

47(incorrectlyclassified case: 2)

actual data

total

Note: Shaded cells indicate incorrectly classified case.

incorrect classification among 47 projects), which

for practitioners with updated strategies andwas approximately at the equivalent level to the rateevaluations. We would like to call this scoring

gained through the analysis using full sample data

method in which a continuous improvement cycle is(Table 4). By this we can say that the validity of thebuilt in as “Continuous Improvement Scoring

empirically gained discriminant formula has been

Method (CISM).”
In particular, the CISM proposed in this paper

confirmed by cross-split-half method.

takes the following steps. The first step is to prepare4.

Discussion:“Continuous

Proposal

Improvementofa list of items or measures to be considered when

Scoring

conducting project evaluation using scoring method.

Method (CISM)”

With these items and measures we select several or

The model attained by empirical analysis in Chapter

preferably several dozens of terminated projects

3 is based on ex-post project data. Such evaluation(which have been released within six to twelve

criteria (model) built on ex-post data does not reflect

months) and conduct ex-post evaluation (Step 1).

the firms’ present strategies and evaluation criteria

Next, utilizing the evaluated data, we statisticallythus, when used in practice, it is not much effective.

estimate the ex-post evaluation criteria (Step 2). Our

However, if we can link “ex-post evaluation model”

analysis has gone as far as step 2 in this paper.
Then, we use the attained evaluation criteria

built on empirical analysis of post project data andcontinuous(ex-post evaluation model) to conduct ex-post

improvement cycle of “follow-ups” and “feedback,”“variance analysis” (Step 3). That is, deviation

we could possibly build an effective scoring method

between ex-ante and ex-post evaluation concerning“ex-ante

evaluation

model”

by81

Kuwashima
overall evaluation of a project can be decomposed to

ex-post and ex-ante evaluation under the new

deviation between ex-ante and ex-post “evaluation

evaluation system for next term constructing anweight,” and deviation between ex-ante and ex-post

improvement cycle (Step 5).

evaluation scores of “each item.” The former

By repeating these five steps in a cycle, ex-ante

deviation (in evaluation weight) should be madeand ex-post project evaluation become linked. And

clear by comparing the weight system presently usedby continuously repeating such cycle, we could

and the weight system of statistically estimatedactivate the process of theme evaluation hypothesis“ex-post evaluation model.” On the other hand, theverification

latter deviation (in evaluation score of each item)

deviation analysis (variance analysis) thus improveshould be made clear by comparing the ex-antescoring method. As a result we believe that we canevaluation score and the ex-post evaluation scoreenhance the accuracy of ex-ante evaluation.

byongoing

ex-anteandex-postattained in this study. In both cases, if there is asignificant deviation between ex-ante and ex-post

5. Conclusion

results, we shall make close investigation for theThis paper focuses on the scoring method, which is

causes.

one of the methods of product development projects

Now, based on the outcome of above varianceevaluation. We analyzed statistically the evaluation

analysis, we consider improvement of the present

criteria on projects’ success/failure by using data

evaluation system (Step 4). First, we consider

from the chemical industry. Results of the analysis

modification of variables (evaluation items). If ashow that, in Japanese chemical industry, threevariable turns out to be peculiarly unfit for ex-ante

factors, namely marketability, technology, andevaluation, it is an option to eliminate the item fromsynergistic potential,” are weighed at 3:2:1 ratio at

the model. On the contrary, we may add new items if

projects’ success/failure evaluation.

considered more preferable. Next, we consider

The result of the analysis is at any rate ex-post

modification of each item’s weight. We shall add

based on data from already terminated projects. Yet

strategic view in order to revise the model at thethere is a possibility that we could apply it to ex-antesame time referring to scores of empirically analyzed

evaluation (project selection). Based on the results of

discriminant

the analysis, we proposed a new method of

conducted

model.
acrossThese

divisionsimprovements
lead

by

are

research

improvingtheevaluation

methoditselfasmanagement division. When strategic decision“Continuously Improved Scoring Method (CISM),”making is required, top management may have towhich links projects’ ex-post evaluation and ex-anteintervene as necessary.

evaluation to build an ongoing improvement cycle.

Last, we get back to the beginning to conduct

We believe that the proposal we made for improved82

New Product Project Selection Model
process shall in any way fill the existing gap

Disman, S. (1962). Selecting R&D projects for profit.

between academic research and practical businesses

Chemical Engineering, 24(December), 87-90.

at the same time make certain contribution toHess, S. W. (1962). A dynamic programming
approach to R&D budgeting and project selection.

scholarly researches on “evaluation process.”

IRE Transactions on Engineering Management,
References

9(4), 170-179.

Arikuni, T., Osawa, Y., & Murakami, M. (1997).

Mottley, C. M., & Newton, R. D. (1959). The

New Score ho ni yoru fukusu kenkyu tema hyoka

selection of projects for industrial research.

[Multiple research theme evaluation by new score

Operations Research, 7(November-December),method]. R&D Managenment, 7(9), 39-47. (In740-751.
Murakami, M. (1992). Sumitomo Denko (kabu) ni

Japanese)

okeru teiryoteki kenkyu purojekuto no hyoka

Asher, D. T. (1962). A linear programming model fortheallocation

Transactions

of
on

R

and

D

EngineeringIREhoho [Research project evaluation method byManagement,

quantitative approach]. Kenku Gijutsu Keikaku

efforts.

[The Journal of Science Policy and Research andEM20(4), 114-120.

Management], 7(3), 210-223.

Augood, D. R. (1973). A review of R&D evaluation

Presley, A., & Liles, D. (2000). R&D validation

methods. IEEE Transactions on Engineering

planning: A methodology to link technicalManagement, 9(4), 154-157.

validations

Baker, N. R., & Pound, W. (1964). R&D projecttobenefits

measurement.

R&D

Management, 30(1), 55-65.

selection: Where we stand. IEEE Transactions on
Engineering Management, EM-11(4), 124-134.

Schmidt, R. L., & Freeland, J. R. (1992). Recent

Baker, N. R., Souder, W. E., Shumway, C. R., Maher,

progress in modeling R&D project-selection

M. P., & Rubenstein, A. H. (1976). A budget

processes. IEEE Transactions on Engineering

allocation model for large hierarchical R&D

Management, 39(2), 189-201.

organization. Management Science, 23(1), 59-70.

Souder, W. E., & Mandakovic, T. (1986). R&D

Cooper, R. G. (1981). An empirically derived new

project-selection models. Research Management,

product

project

Transactions

on

selection
Engineeringmodel.

29(4), 36-42.

IEEE

Stahl, M. J., & Harrel, A. H. (1983). Identifying

Management,

operative goals by modeling project selection

EM28(3), 54-61.

decisions in research and development. IEEE

Cooper, R. G. (1985). Selecting winning new productprojects: Using the new prod system. Journal ofTransactions

Product Innovation Management, 2, 34-44.

EM-30(4), 223-228.
83

onEngineering

Management,

Kuwashima
Tomita, J., & Kuwashima, K. (2002) Hyoten ho ni

derived project selection model]. Kenkyu Gijutsu

yoru shin seihin kaihatsu purojekuto no hyoka

Kaihatsu [The Journal of Policy and Research

jissho bunseki ni motozuku serekushon moderu no

Management], 17(3-4), 202-211. (In Japanese)

kochiku [A scoring method for evaluating new
product development projects: An empirically〔Received October 3, 2004; accepted October 19, 2004〕

84