Understanding new product project performance Lenny H. Pattikawa Erasmus University Rotterdam, Rotterdam, The Netherlands Ernst Verwaal RSM Erasmus University, Rotterdam, The Netherlands, and Harry R. Commandeur Erasmus University Rotterdam, Rotterdam, The Netherlands Abstract Purpose – The purpose of this paper is to summarize the accumulated body of knowledge on the performance of new product projects and provide directions for further research. Design/methodology/approach – Using a refined classification of antecedents of new product project performance the research results are meta-analyzed in the literature in order to identify the strength and stability of predictor-performance relationships. Findings – The results reveal that 22 variables have a significant relationship with new product project performance, of which only 12 variables have a sizable relationship. In order of importance these factors are the degree of organizational interaction, R&D and marketing interface, general product development proficiency, product advantage, financial/business analysis, technical proficiency, management skill, marketing proficiency, market orientation, technology synergy, project manager competency and launch activities. Of the 34 variables 16 predictors show potential for moderator effects. Research limitations/implications – The validity of the results is constrained by publication bias and heterogeneity of performance measures, and directions for the presentation of data in future empirical publications are provided. Practical implications – This study helps new product project managers in understanding and managing the performance of new product development projects. Originality/value – This paper provides unique insights into the importance of predictors of new product performance at the project level. Furthermore, it identifies which predictor-performance relations are contingent on other factors. Keywords New products, Product development, Performance appraisal, Strategic marketing Paper type Research paper Introduction The last two decades show an increasing number of studies investigating the phenomenon of new product performance. Many studies have been produced from a variety of theoretical perspectives leading to a growing number of variables that are hypothesized to affect new product performance. To name a few, product advantages, market orientation, firm’s synergy, innovativeness, communication and information, cross-functional team, the integration between research and development (R&D) and the marketing department, proficiency of new product development activities, launch activities, etc. Furthermore, a variety of moderator variables have been studied in new product performance research. All these emphasize the need to search for meaningful ways to summarize the empirical findings of this field of research. The current issue and full text archive of this journal is available at www.emeraldinsight.com/0309-0566.htm EJM 40,11/12 1178 Received April 2004 Revised February 2005 and September 2005 European Journal of Marketing Vol. 40 No. 11/12, 2006 pp. 1178-1193 q Emerald Group Publishing Limited 0309-0566 DOI 10.1108/03090560610702768
Transcript
Understanding new productproject performance
Lenny H. PattikawaErasmus University Rotterdam, Rotterdam, The Netherlands
Ernst VerwaalRSM Erasmus University, Rotterdam, The Netherlands, and
Harry R. CommandeurErasmus University Rotterdam, Rotterdam, The Netherlands
AbstractPurpose – The purpose of this paper is to summarize the accumulated body of knowledge on theperformance of new product projects and provide directions for further research.
Design/methodology/approach – Using a refined classification of antecedents of new productproject performance the research results are meta-analyzed in the literature in order to identify thestrength and stability of predictor-performance relationships.
Findings – The results reveal that 22 variables have a significant relationship with new product projectperformance, of which only 12 variables have a sizable relationship. In order of importance these factorsare the degree of organizational interaction, R&D and marketing interface, general product developmentproficiency, product advantage, financial/business analysis, technical proficiency, management skill,marketing proficiency, market orientation, technology synergy, project manager competency and launchactivities. Of the 34 variables 16 predictors show potential for moderator effects.
Research limitations/implications – The validity of the results is constrained by publication biasand heterogeneity of performance measures, and directions for the presentation of data in futureempirical publications are provided.
Practical implications – This study helps new product project managers in understanding andmanaging the performance of new product development projects.
Originality/value – This paper provides unique insights into the importance of predictors of newproduct performance at the project level. Furthermore, it identifies which predictor-performancerelations are contingent on other factors.
Keywords New products, Product development, Performance appraisal, Strategic marketing
Paper type Research paper
IntroductionThe last two decades show an increasing number of studies investigating thephenomenon of new product performance. Many studies have been produced from avariety of theoretical perspectives leading to a growing number of variables that arehypothesized to affect new product performance. To name a few, product advantages,market orientation, firm’s synergy, innovativeness, communication and information,cross-functional team, the integration between research and development (R&D) andthe marketing department, proficiency of new product development activities, launchactivities, etc. Furthermore, a variety of moderator variables have been studied in newproduct performance research. All these emphasize the need to search for meaningfulways to summarize the empirical findings of this field of research.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0309-0566.htm
EJM40,11/12
1178
Received April 2004Revised February 2005and September 2005
European Journal of MarketingVol. 40 No. 11/12, 2006pp. 1178-1193q Emerald Group Publishing Limited0309-0566DOI 10.1108/03090560610702768
The need for a synthesis of research on new product performance has produced twometa-analyses on this subject (Montoya-Weiss and Calantone, 1994; Henard andSzymanski, 2001). Montoya-Weiss and Calantone’s (1994) meta-analysis provides aframework for classifying the numerous variables that have been hypothesized to beassociated with new product performance. However, the meta-analysis performed oneffects sizes was not corrected for artefacts and did not provide a moderator analysis,procedures that can substantially improve the results of a meta-analysis (Hall et al.,1994). The meta-analysis of Henard and Szymanski (2001) fills this gap by includingmore research studies, corrects for artefacts, and performs a moderator analysis.However, due to the limited number of available studies a number of importantweaknesses are still present. First, single correlation coefficients are presented as oneobservation. Thus the number of reported correlations is equal to the number ofobservations. In this manner, one independent sample can present more than onestatistically independent observation. This method will not influence the value of themean effect size of the study; however, if a large number of observations comes fromone single study, the results of statistical tests of significance tests can be influenced(Hunter and Schmidt, 1990, p. 452; Matt and Cook, 1994, p. 509). Second, a very broadconception of new product performance was used, including both organizational leveland project level performance measures. A broad measure of new product performanceincreases the number of observations; however, including different performancemeasures may lead to the so-called apples and oranges problem (Hunter and Schmidt,1990; Hedges and Olkin, 1985; Wolf, 1986). For example, new product projectperformance measures do not take into account many strategic effects of new productdevelopment at the organizational level. A new product may be a failure at the projectlevel but the learning effects from the project may bring forth strategic capabilities atthe firm level which improve overall organizational performance. Thus, organizationallevel and project level performance measures may have differentpredictor-performance relationships and mixing them into one meta-analysis mayproduce ambiguous results.
Our study attempts to synthesize the existing new product performance research atthe project level. First we investigate what variables have been associated in the pastwith new product project performance. Next, we formulate the statistics of thisassociation (in the form of a correlation coefficient) to arrive at the central tendency andthe variance composition, using one average correlation for each study. From this, onecan infer whether a certain association is robust across the studies or whether itexhibits a potential moderator effect. In the latter case, we perform a moderatoranalysis. This study can help both researchers as well as new product projectmanagers in understanding and managing the performance of new productdevelopment projects.
The paper is organized as follows. First, we discuss a framework in classifyingvariables that are hypothesized to affect new product project performance. Second webriefly discuss the methods. Thereafter, results of meta-analysis will be presented,followed by a discussion of the implications of the findings.
Conceptual frameworkThe purpose of the conceptual framework in meta-analysis is to classify the variablesreported in the literature in a meaningful manner. We use the framework of
Montoya-Weiss and Calantone (1994) with some further refinement (see Table I). Theauthors classify the antecedents of new project performance in four main categories:strategy, environment, proficiency of execution of product development activities andorganizational variables. They based their classification on the following: “Newproduct performance is determined by the interaction of the market environment withnew product strategy and development process”. The unit in the organization thatperforms new product development is the new product project. At the project level, theprocess activities are performed, consisting of activities such as predevelopment,marketing, technical and launch activities. Once the new product strategy isformulated (this includes firm’s strategic orientation, product characteristics, andfirm’s resources), it has to be implemented in an effective and efficient way, wherein theorganizational factors play a key role in facilitating the implementation of the newproduct project. This includes factors such as inter-functional coordination, structureand leadership.
Although an organizational category (Sivadas and Dwyer, 2000) is included in theformer framework, it was not stated explicitly. Furthermore, concepts such as“strategic orientation,” “structure” and “leadership” are explicitly stated in Table I,which is not the case in the former framework. Note also that we categorize topmanagement support as organizational factor representing the leadership concept.Montoya-Weiss and Calantone (1994) include this variable as a process category.
MethodFor the purpose of this study empirical studies are selected that include new productperformance at the project level. We made a trade off between similarity of theperformance measures used in the studies and the eligibility of the number of studies.We included studies that measured new product project performance as (perceived)financial or market performance, and the extent to which the new product project hasmet its objectives in financial or market terms. We excluded all organizationalperformance measures and non-financial or non-market performance measures such astime, number of ideas generated and new product development productivity. For moststudies, only subjective performance measures are available. To ensure similarity, weused only subjective measures from studies that provided both objective andsubjective measures.
The selected studies provide correlation coefficients between new product projectperformance and at least one possible determinant. It is not relevant whether newproduct project performance is defined as an independent or dependent variable, ascorrelation coefficients do not assume a specified direction of the relationship betweenthe variables. Most studies, however, have specified new product project performanceas the dependent variable. In most cases, the classification of variables wasaccomplished by the variable description reported by the study. In some cases, theappropriate classification was based on the conceptualization and operationalization ofthe variable. In less than five cases, we modified the correlations in order to reflect theclass construct more appropriately. For example, to measure the degree ofdecentralization, we reverse the sign of the correlation that measures centralization(Lipsey and Wilson, 2001).
In comparing results, the following statistics are taken from each study, namely thecorrelation coefficient between the independent variables and new product project
1. Environment Market potential Measure of market and demand size andgrowth, as well as an indication ofcustomer need level for the product type
Marketcompetitiveness
Intensity of competition in themarketplace in general and/or withrespect to price, quality, service, or thesales force/distribution system
Environmentaluncertainty
The general operating environmentfaced by the firm. It includes risk oruncertainty and the regulatoryenvironment
Producthomogeneity
The degree of product similarity/homogeneity in the market
2. Strategy 2.1 Strategicorientation
Market orientation The organization’s wide generation ofmarket intelligence pertaining to currentand future customer needs,dissemination of the intelligence acrossdepartments, and organization-wideresponsiveness to it
Customerorientation
Customer orientation is the firm’sunderstanding of its target buyers inorder to be able to create superior value.A customer-oriented firm can be definedas a firm with ability and the will toidentify, analyze, understand, andanswer user needs
Competitororientation
The ability and the will to identify,analyze, and respond to competitors’actions. This includes the identificationand construction of competitiveadvantages in terms of quality or specificfunctionalities and enables the firm toposition the new product
Technologyorientation
The capability and will to acquire asubstantial technological backgroundand use it in the development of newproducts. Technology orientation alsomeans that the company can use itstechnical knowledge to build newtechnical solutions to answer and meetnew needs of the users
2.2 Productcharacteristics
Product advantage The customer’s perception of productsuperiority with respect to quality,cost-benefit ratio, or function relative tocompetitors
Product newness tothe firm
The extent to which the firm perceivesthe product and/or product’s technologyas new to the firm
Degree ofradicalness
The degree of technology radicalness ofthe product
(continued )
Table I.Classification and
description of variables
New productproject
performance
1181
Category Subcategory Class Description
Degree ofcustomization
The extent to which the new productserves a specific market in contrast to amass market
Cost of innovation The size of the costs involved in theproduct/innovation development
2.3 Synergy Marketing synergy This factor represents the fit between theneeds of the project and the firm’sresources and skills with respect to thesales force, distribution, advertising,promotion, market research, andcustomer service
Technologysynergy
This represents a measure of the fitbetween the needs of the project and thefirm’s resources and skills with respectto R&D or product development,engineering, and production
Company resources This factor represents the compatibilityof the resource base of the firm with therequirements of the project. It is moregeneral than marketing or technologicalsynergy. For example, it includes capital,manufacturing facilities, and manpowerrequirements
Management skill The extent to which managers possessthe general management skills requiredfor the project
3. Organizational 3.1 Leadership Top managementsupport
This factor refers to top management’scommitment to the project, as well astheir day-to-day involvement,guidance/direction, and control over theproject development
Project managercompetency
The extent that the project leaderpossesses the necessary skills ofmanaging the project
Role of champion The existence of (informal) leadership(champion) who involved actively in theproject
3.2Interfunctionalcoordination
Communication andinformationexchange
This factor refers to the coordination andcooperation within the firm and betweenfirms; for example (1) communication orinformation exchange betweendepartments and external firms, (2)cross-functional participation on projects
Degree ofinteraction
The extent to which the members of theproject are attached to this project. It alsoreflects the degree of how the membersfeel that they are part of the project
(continued )
Table I.
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performance and the reliabilities of all the variables. For the purpose of moderatoranalysis, we use the following data from each study:
. whether they provide reliability measurement for the independent variables; and
. the national setting, with western culture represented by countries such as theUSA, Canada, Europe, Australia, New Zealand, versus Asian countriesrepresenting non-western cultures.
Category Subcategory Class Description
R&D andmarketing interface
Degree ofcommunication/co-operation/informationflow between R&D and marketingdepartment
3.3 Structure Degree ofdecentralization
This measure includes the extent ofcentralization and bureaucratization inthe organization/project
Degree offormalization
The extent to which explicit rules andprocedures govern decision making inthe organization/project
Size The number of employees or sales3.4 Otherorganizationalvariables
Organizationalculture/climate
The extent to which the day to daydecisions are governed withorganization/group’s shared values andnorms
4. Process General productdevelopmentproficiency
Refers to the firm’s general knowledgeand capabilities of product development
Predevelopmentproficiency
Proficiency in initial screening,preliminary market and technicalassessment, detailed market study andmarket research, and preliminarybusiness/financial analysis
Marketingproficiency
Proficiency in marketing research,customer tests of prototypes or samples,test markets/trial selling, serviceadvertising, distribution, and marketlaunch
Technicalproficiency
Proficiency of product development,in-house testing of the product orprototype, trail/pilot production,production start-up, and obtainingnecessary technology
Speed to market Refers to the speed of the productdevelopment process
Proficiency oflaunch activities
Proficiency in launch activities
Financial businessanalysis
This factor reflects the proficiency ofongoing financial and business analysisduring development, prior tocommercialization and full-scale launch Table I.
New productproject
performance
1183
The search for studies was conducted in the following way:. Search by using key words such as “new product”, “new product performance”,
“new product success”, and other key words such as “empirical” and “models” todetect empirical studies.
. Search is conducted for each leading academic journal (since 1986) in whichstudies on new product performance are most likely published.
. Using study references from earlier meta-analysis (Montoya-Weiss andCalantone, 1994; Henard and Szymanski, 2001).
We found a total of 47 studies eligible for meta-analysis. One study (Souder and Song,1997) was removed because outlier analysis indicated an abnormal deviation in theresults. In some instances the same study was used for different publications. Weconsider each sample as only one observation. Two studies are considered as havingtwo observations because they used two samples independently from differentindustries (random assignment for industry) and they provide the correlationcoefficients for each sample. Finally, 43 independent samples could be used asobservations from a total of 47 published studies.
Approximately 25 percent of the studies come from one single journal (Journal ofProduct Innovation Management) and less than 15 percent are articles from the Journalof Marketing Research. This sample of studies does not exhaust all empirical studiesand does not represent a random selection of all the studies that have been conducted.However, this is typical for meta-analyses (Matt and Cook, 1994). In total, 764 variablesare investigated on a relationship with new product project performance involving5,309 firms and more than 8,448 new product projects.
There are 243 variables that could not be categorized in any of the construct classes,leaving 521 variables that are eligible for analysis. Table II presents the direction of therelationship generally hypothesized in the new product performance literature, therange of the correlation value, the number of observations and the confidence intervalof the corrected means.
We follow the meta-analytic method described by Hunter and Schmidt (1990). Thecorrelation coefficient is used as a metric for synthesizing the studies. The mean andthe variance of the effect sizes will be corrected for sample error and errormeasurement in the dependent new product performance variable and in theindependent variables. The reliability measures are infrequently available in studiesand therefore we use the reliability distribution for correcting the correlations.
The variances are split into three components: variance due to sampling error,variance due to reliability variation, and the remaining variance (Hunter and Schmidt,1990). To perform moderator analysis, we apply the 75 percent rule (Hunter andSchmidt, 1990), which suggests that in any given meta-analysis, it is probably the casethat the unknown and uncorrected artefacts account for 25 percent of the variance.Thus, if the real variance estimate is not at least this high, it suggests that there may beno real variance. We also conduct the homogeneity test, which tests the null hypothesisthat there is no real variance in unattenuated correlations. All of the observed varianceis due to variation in reliability and sampling error. This is also a way to test whetherany potential moderator exists. Hunter and Schmidt (1990, p. 440) report that thestatistical power of the 75 percent rule is greater than (or equal to) other methods. Thisparticularly applies if the number of studies and the sample size of each study are
Table II.Descriptive statistics ofthe variables and theconfidence interval of
corrected mean
New productproject
performance
1185
Class
ofconstructs
General
hypothesis
Num
berof
independ
ent
sample
Num
berof
firm
sNum
berof
projects
Minim
umcorrelationvalue
Maxim
umcorrelationvalue
Degreeof
form
alization
þ4
323
431
0.00
0.16
Organizationculture
þ2
152
158
0.08
0.29
Organization/projectsize
a8
1,467
1,838
20.17
0.22
Process
General
NPDprofi
ciency
þ6
536
984
0.02
0.76
Proficiency
ofpredevelopment
activities
þ7
1,094
1,928
20.47
0.71
Proficiency
ofmarket-related
activities
þ11
1,427
2,895
20.30
0.72
Proficiency
oftechnicalactivities
þ10
1,312
2,084
20.35
0.64
Proficiency
oflaun
chactivities
þ9
1,354
2,398
0.08
0.66
Financial
business
analysis
þ4
378
8,008
0.31
0.69
Speedto
market
þ7
456
601
0.00
0.62
Note:a
Generally
theliteraturedoes
nothy
pothesizethisvariabledirectly
tobe
associated
withNPP.T
hisvariableisusually
used
forcontrolv
ariableor
asmoderator
Table II.
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1186
small. These conditions typically apply to the domain of new product performanceresearch.
We perform moderator analysis for variables that exhibit the potential formoderators. This is done by grouping observations in classes of study characteristicsthat are hypothesized to be moderators and subsequently performing a meta-analysis(Rosenthal, 1984). Because not all studies provide the relevant study characteristicssuch as measurement method, number of respondents, and validity testing, we usereliability measurement to represent this moderator[1]. For the same reason, moderatoranalysis at the level of the industry cannot be conducted. The environment moderatoris represented by whether the study is conducted in Asian or western countries.
ResultsThe 521 variables reported in our sample of studies were reduced to 240 variables,which are spread out over 34 classes of variables. Out of these 34 variables classes, 22show significant relations with new product project performance. However, only 12variables show a sizable influence (r . 0:40) which suggests that only a small set ofvariables that are studied by new product performance researchers are able to predictsubstantially new product project performance.
The environmental factors show no sizable relationships with new product projectperformance, and only environmental uncertainty (r ¼ 20:10) and producthomogeneity (r ¼ 20:24) show significant but small effect sizes (see Table III).However, all environmental factors except environmental uncertainty show a largepotential of moderator variables (market potential 84 percent, market competitiveness82 percent, product homogeneity 64 percent). This indicates that more in-depthresearch is needed to examine the possible role of moderators.
The strategy category includes four sizable relationships with new product projectperformance (market orientation, product advantage, technology synergy, andmanagement skill) and four smaller but significant predictor-performancerelationships (competitor orientation, technology orientation, marketing synergy,and company resources). In addition, eight variables show large potential formoderators (market orientation 75 percent, customer orientation 88 percent, competitororientation 82 percent, technology orientation 75 percent, degree of radicalness 66percent, cost of innovation 66 percent, and technology synergy 81 percent).
The organizational category shows three sizable predictor-performancerelationships (project manager competency, degree of organizational interaction andR&D/marketing integration), of which the last two are the largest effect sizes in ourstudy (r ¼ 0:66 and r ¼ 0:59 respectively). Smaller but significant relationships aretop management support, communication and information, organizationalculture/climate. Organizational structure characteristics (decentralization,formalization and size) have no significant relationship with performance.Noticeably, not one organizational factor shows the potential for moderators.
Finally, in the process category, all relationships are significant, except onevariable (see Table III). The effect sizes of the process variables are relativelylarge, including five sizable predictor-performance relationships. The effect size ofspeed to market not significant, however, the effect size is not small (correctedr ¼ 0:39) and shows a large potential for moderators (73 percent). Furthermore,there are no negative correlations reported in the sample. This suggests that there
New productproject
performance
1187
Construct
class
Sample
meana
Samplesize
adjusted
mean
Samplesize
and
reliabilityadjusted
meanb
Total
variance
Variancedu
eto
samplingerror
Variancedu
eto
reliabilityvariation
Rem
aining
variance
%total
variance
Environment
Marketpotential
0.321
0.272
0.329
0.036
0.005
0.001
0.030
84Market
competitiveness
20.058
20.062
20.075
0.032
0.006
0.000
0.026
82Env
ironmental
uncertainty
20.065
20.079
20.096*
0.009
0.009
0.000
0.000
3Produ
cthomogeneity
20.125
20.196
20.237*
0.013
0.004
0.000
0.009
64Strategy
Marketorientation
0.357
0.397
0.480*
0.020
0.004
0.001
0.015
75Customer
orientation
0.301
0.256
0.309
0.031
0.003
0.000
0.027
88Com
petitor
orientation
0.317
0.322
0.389*
0.027
0.004
0.001
0.022
82Techn
ology
orientation
0.249
0.292
0.353*
0.018
0.004
0.001
0.014
75Produ
ctadvantage
0.414
0.453
0.548*
0.020
0.004
0.002
0.014
71Produ
ctnewness
0.046
0.016
0.019
0.014
0.014
0.000
0.000
0Degreeof
radicalness
0.094
0.178
0.216
0.024
0.008
0.000
0.016
66Produ
ctcustom
ization
20.021
20.001
20.002
0.002
0.002
0.000
0.000
0Costof
innovation
20.123
20.164
20.199
0.017
0.007
0.000
0.010
59Marketing
synergy
0.285
0.289
0.349*
0.010
0.006
0.001
0.002
26Techn
ologysynergy
0.282
0.346
0.419*
0.024
0.004
0.001
0.019
81Com
pany
resources
0.156
0.228
0.275*
0.007
0.007
0.000
0.000
0Managem
entskill
0.398
0.411
0.497*
0.004
0.004
0.000
0.000
0Organ
izational
Top
managem
ent
supp
ort
0.212
0.255
0.308*
0.010
0.007
0.000
0.002
24Roleof
cham
pion
0.163
0.129
0.157
0.009
0.009
0.000
0.000
0(continued)
Table III.Central tendency andvariance statistics of theeffect size
may be conditions where increasing the speed of development is less effective,however, under normal conditions this factor is likely to be a substantialdeterminant of new product project performance. In addition, general productdevelopment proficiency (74 percent), predevelopment activities (68 percent),marketing proficiency (34 percent), technical proficiency (54 percent), launchactivities (72 percent) and financial/business analysis (73 percent) show largepotential for moderator effects.
Moderator analysis in meta-analysis is a way to explain variations in effect sizeacross studies. In general, moderator analysis in the meta-analytic literature can begrouped into two categories. First, there is the analysis of differences in methodology.This analysis includes analysis of the measurement method, level of aggregation,model-specific variables, variable definition, study quality, etc. The second category isby grouping the studies into several characteristics based on the existing theories inthe relevant research subject (Assmus et al., 1984). This type of moderator analysisrefers to dividing studies into different groups based on what has been hypothesized inthe literature to have a different effect size in each group, and includes groupingstudies into categories such as type of product, environment differences (such asindustry and national setting), and type of organization. In this study, we performmoderator analysis at the level of the study’s quality and environment. The study’squality can be presented by characteristics such as the measurement method, thestudy’s validity, and the random assignment procedure. Environmental moderatorsinclude cultural differences and industry setting which are often hypothesized tomoderate the relationship between antecedents and new product performance (Hitt andIreland, 1985).
We perform a meta-analysis at the level of the moderator in each variable of thevariables that exhibit the potential for moderators[2]. We found one case in 15 wherethe reliability as moderator can explain some of the remaining variance of thepreliminary meta-analysis. In three of ten cases cultural differences can explain thesum of the remaining variance. Although this result indicates that cultural differencesare an important moderator, we note that all studies that were conducted innon-western countries share one common author. Thus, the author’s personalcharacteristics rather than cultural differences might explain the remaining variation(see also Eagly and Wood, 1994).
DiscussionTable IV provides a summary of the findings of this study. The results show that firmswith a strong market orientation, proficiency in new product development, synergy ofresources and strong inter-functional coordination are most likely to realize high newproduct project performance. The results also show a large potential of moderatoreffects for environmental, strategic and process variables. Environmental factors seemto play a small role in explaining new product project performance and organizationalstructure variables do not have a significant impact on new product projectperformance and do not show the potential for moderator effects. Managers andresearchers may not need to search for structural organizational solutions to improvenew product project performance, but instead should explore new ways to improvetheir strategic orientation, synergy and proficiency in new product development.
The results show a number of differences with previous studies. We found, in contrastto Henard and Szymanski (2001), that top management support and marketingsynergy have stable relationships with new product project performance. Furthermore,market orientation and technology synergy show significant relationships with newproduct project performance in our study, which is not the case in the study of Henard
3. Organizational 3.1 Leadership Top management supportb –Project manager competencyc –Role of champion –
3.2 Interfunctionalcoordination
Communication and informationexchangeb
–
Degree of organizational interaction –R&D and marketing interfacec –
3.3 Structure Degree of decentralization –Degree of formalization –Size –
3.4 Otherorganizationalvariables
organizational culture/climateb –
4. Process General product developmentproficiencyc
Large
Predevelopment activities proficiencyb LargeMarketing proficiencyc SmallTechnical proficiencyc ModerateSpeed to market LargeProficiency of launch activitiesc LargeFinancial business analysisc Large
Notes: a Large . 60 percent, Moderate 40 percent-60 percent, Small 25 percent-40 percent;b Significant driver of new product project performance (p , 0:05); c Significant (p , 0:05) and sizabledriver new product project performance (r . 0:40)
and Szymanski (2001). Similarities of the findings can be found at the processvariables. Of course differences in the definition of the performance measure mayexplain these differences and research on organizational level effects of new productdevelopment may be a fruitful path for future research.
Since we use only published studies, our conclusions may indicate publication biastoward significant findings. A further limitation of this study is that even withinproject performance measures there is considerable heterogeneity across studies. Asnoted in the introduction, this is a trade off between having meaningful observationsand having a clear-cut measure of new product project performance. We chose anapproach where only financial and market measures of new product projectperformance were included. Future studies on new product project performance couldexamine potential differences between measures of new product project performance.Finally, we encourage new product performance researchers to provide full descriptivestatistics and conduct research in a specific environment, or to report results for each ofthe independent samples used. This will help future meta-analysts of new productperformance to identify moderator relationships and to improve their methods.
Notes
1. Moderator analysis is limited by the fact that not all studies provide the information needed.The choice of moderators is based on availability of data, and results should be interpretedwith caution (Hall et al., 1994).
2. We excluded marketing synergy from moderator analysis because the remaining variance isvery close to 25 percent of the total variance and the homogeneity test cannot be rejected.
References
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Further reading
Atuahene-Gima, K. (1996), “Differential potency factors affecting innovation performance inmanufacturing and services firms in Australia”, Journal of Product InnovationManagement, Vol. 13 No. 1, pp. 35-52.
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Souder, W.E. and Jenssen, S.A. (1999), “Management practices influencing new product successand failure. United States and Scandinavia: a cross-cultural comparative study”, Journal ofProduct Innovation Management, Vol. 16 No. 2, pp. 183-203.
Souder, W.E. and Song, X.M. (1998), “Analyses of US and Japanese management processes withnew product success and failure in high and low familiarity markets”, Journal of ProductInnovation Management, Vol. 15 No. 3, pp. 208-23.
About the authorsLenny H. Pattikawa is Research Associate at the Erasmus Research Institute of Management ofthe Erasmus University Rotterdam. Her research interest includes new product and innovationstrategies of the firm.
Ernst Verwaal is Associate Professor of Strategic Management and International Business atRSM Erasmus University and Member of the Erasmus Research Institute of Management(ERIM) of Erasmus University Rotterdam. His research interests include international marketingand entrepreneurial strategies. As a researcher and consultant he published in internationalrefereed academic journals and worked with large companies and SMEs. Ernst Verwaal is thecorresponding author and can be contacted at: [email protected]
Harry R. Commandeur is Full Professor of Industrial Economics and Strategy at the School ofEconomics of Erasmus University Rotterdam and Member of the Erasmus Research Institute ofManagement (ERIM). His research interests include industrial economics and innovation andnetwork strategies. As a researcher and consultant he published in international refereedacademic journals and worked with many international and Dutch companies.
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