The Relative Advantage of Marketing over Technological...

The Relative Advantage of Marketingover Technological Capabilities inInfluencing New Product Performance:The Moderating Role of CountryInstitutionsMartin Eisend, Heiner Evanschitzky, and Roger J. Calantone

ABSTRACTMarketing and technological capabilities are major drivers of new product performance. Prior research has suggestedthat marketing capabilities outperform technological capabilities. This study shows that the relative advantage ofmarketing over technological capabilities for new product performance depends on the institutional context in a country.Meta-analytic data of 341 effect sizes of the relationship between capabilities and new product performance taken from50 articles with 57 independent samples and collected in 17 different countries reveal new contingencies to the capabilitiesframework. Although in general, marketing capabilities have a stronger influence than technological capabilities on newproduct performance, this effect is moderated by institutional context factors. The relative advantage decreases and evenreverses with increasing growth rates; it further decreases with increasingly stronger rules of law in a country; and itincreases in societies that put emphasis on self-expression values over survival values. These findings contribute toresearch on the utility of different capabilities, inform the institution-based view of firms in international marketing, andprovide implications for international marketing managers.

Keywords: capabilities, institutions, new product success, meta-analysis

Capabilities, the organizational processes by whicha firm develops and transforms resources intovalue offerings for the market, are major sources

for companies to achieve competitive advantage andsuperior performance (Day 1994; Morgan, Kaleka, andKatsikeas 2004). Companies deploy different kinds ofcapabilities to increase their performance. Recent re-search indicates that, in general, marketing capabilitieshave a stronger impact on performance than other maincapabilities such as research and development or oper-ations capabilities (e.g., Krasnikov and Jayachandran

2008). However, prior research has been mainly con-ducted in the specific cultural context of Western anddeveloped countries. The findings, therefore, raise thequestion of whether the advantage of marketing capa-bilities over other capabilities persists in other culturalcontexts.

Variation in the relative advantages of different capa-bilities across cultures has been suggested by researcherswho claim that the utility of capabilities depends on themarket environment and that institutions in markets are

Martin Eisend is Professor of Marketing, European University Viadrina(e-mail: [email protected]). Heiner Evanschitzky is Professor ofMarketing, Aston University (e-mail: [email protected]).Roger J. Calantone is Eli Broad Chaired University Professor of Business,Michigan State University (e-mail: [email protected]). Seigyoung Auhserved as associate editor for this article.

Journal of International Marketing©2016, American Marketing AssociationVol. 24, No. 1, 2016, pp. 41–56DOI: 10.1509/jim.15.0068ISSN 1069-0031X (print) 1547-7215 (electronic)

The Relative Advantage of Marketing over Technological Capabilities 41

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http://dx.doi.org/10.1509/jim.15.0068

likely to shape the effects of capabilities on firm perfor-mance (Eisenhardt and Martin 2000; Meyer and Sinani2009). Institutions—that is, formal rules and informalrestraints that structure political, economic, and socialinteractions within a market, society, or country (North1991)—regulate human activity, set standards for ac-ceptable behavior, and influence beliefs and emotions(Scott 2001). As a result, the institutional context in acountry can shape how companies operate and how theyperform (Peng, Wang, and Jiang 2008; Williams andMartinez 2012), and the influence of capabilities on firmperformance can depend on the institutional context(Boso et al. 2013; Wu 2013).

In this study, we investigate the relative advantages ofdifferent capabilities and how they depend on the insti-tutional context. We focus on new product performanceas a company’s outcome and on the two capabilities thathave been identified as major drivers of new productperformance (Henard and Szymanski 2001; Moormanand Slotegraaf 1999): marketing and technological capa-bilities. Prior meta-analyses have shown that, in gen-eral, marketing capabilities have a stronger influenceon new product success than technological capabilities(Evanschitzky et al. 2012; Henard and Szymanski 2001).Furthermore, it has been shown that organizations with amarket culture show the highest new product perfor-mance, whereas hierarchy-type organizations show thelowest performance, and that this effect is moderated by avalue congruency between organizational and nationalculture (Eisend, Evanschitzky, and Gilliland 2015). Priorresearch has further shown that the effect of a singlecapability such as marketing capability varies acrosscountries and that differences in socioeconomic, legisla-tive, and cultural institutions among these countriesmoderate the influence of the capability on performance(Wu 2013). Thus, in the current study, we look at therelative advantage of marketing over technological ca-pabilities as drivers of new product performance andinvestigate whether and how country institutions mod-erate the relative advantages of these two core capabil-ities; that is, we investigate how the relative advantage ofmarketing capabilities over technological capabilities isattenuated or even reversed by country institutions.

For this purpose, we theoretically combine research thathas looked at the relative influence of capabilities (e.g.,Krasnikov and Jayachandran 2008) with research thathas looked at how the institutional context moderates theinfluence of a specific capability on important outcomes(Wu 2013). The conceptual background of our studyrefers to the capabilities framework and the institutional

view. Our empirical approach is based on ameta-analysison the capability–new product performance relationship,and we investigate the moderating influence of importantinstitutions in different countries. We select data from aprior meta-analysis on drivers of new product success(Evanschitzky et al. 2012) and update this data set. Ourdata set differs from the prior meta-analysis not just interms of the additional and newer data but also becausewe have coded and analyzed further moderator variables(e.g., rule of law) that the prior meta-analysis did notconsider. A similar methodological approach has beenused for a meta-analysis that has selected a different set ofantecedents of new product success, namely, antecedentsrelated to organizational culture (Eisend, Evanschitzky,and Gilliland 2015).

Because each empirical study in the meta-analysis was setin a particular national context at a specific point in time,the combined analysis of these studies allows us to drawmeta-analytical insights on the moderating role of countryinstitutions that would hardly be possible by conductingprimary research in a small set of countries at one point intime. The necessity of this work is also amplified by the keyrole of innovation in the renewal of the revenue-producingelements of the firm.

The contribution of this study to the international mar-keting literature is twofold. First, the study findingscontribute to the capabilities framework by illuminatingthe differential effect of two important capabilities asdrivers of new product success across different countries(e.g., Kaleka 2011; Krasnikov and Jayachandran 2008;Vorhies and Morgan 2005). International marketingscholars have employed the capabilities framework tostudy the origins of competitive advantage in globalmarkets, but they have usually focused on either mar-keting or other capabilities while neglecting the relativeadvantages of somemajor capabilities (e.g., Ju et al. 2013;Kaufman andRoesch 2012; Kemper, Engelen, and Brettel2011). By applying an institution-based perspective oncapabilities and testing the moderating effect of institu-tional context, this study identifies conditions underwhich marketing capability is a superior or inferior driverof new product performance, compared with techno-logical capability.

Second, our study contributes to the institution-basedview of the firm and the related stream of research ininternational marketing on country-specific influences onfirm performance (e.g., Chang, Bai, and Li 2015; Dwyer,Mesak, and Hsu 2005). The institutional view suggeststhat the market environment shapes which strategies

42 Journal of International Marketing

firms employ, what kind of choices they make, and howthey perform. However, the existing international mar-keting literature has offered limited insights into howinstitutions explain the advantages of different kinds ofcapabilities. Drawing on established typologies of insti-tutional subsystems (North 1990; Scott 2001), we detailhow different institutions explain the relative advantagesof different capabilities. These insights provide importantimplications for marketing practitioners, in particular asrelated to new product development activities of inter-national and global firms. The findings of this study in-dicate in which institutional settings these firms shouldfocus on either marketing capabilities, technological ca-pabilities, or both.

MARKETING CAPABILITIES, TECHNOLOGICALCAPABILITIES, AND NEW PRODUCTPERFORMANCE

Capabilities enable a firm to deploy firm resources and toperform value-creating tasks more effectively, both ofwhich are key determinants of a firm’s competitive advan-tage and, thus, performance (Day 1994; Harmancioglu,Droge, and Calantone 2009; Teece, Pisano, and Shuen1997). Two main capabilities have been identified asdrivers of new product success (Henard and Szymanski2001; Moorman and Slotegraaf 1999): marketing ca-pability and technological capability. New productsuccess or performance relates to either technologicalperformance of a new product (e.g., superior productquality) or financial or market-based performance (asmeasured by, e.g., market share or return on investment).Both marketing and technological capabilities can in-crease new product performance because these capabil-ities help a firm adapt to the evolving requirements ofcustomers and markets, which is needed to develop andintroduce new products successfully.

Marketing capability represents a firm’s ability to un-derstand and predict customers’ needs better than itscompetitors (Krasnikov and Jayachandran 2008); in thecontext of new product development, it is the capabilitywith which a firm conducts marketing activities andlaunches a product, such as initial screening, preliminarymarket assessment, detailed market study, customer testsof products, and market launch (e.g., Verona 1999). Thestronger a company’s marketing capability, the morelikely the company will successfully introduce products.

Technological capability refers to a firm’s ability to de-velop and apply technology to produce new products

more effectively and efficiently than its competitors; inparticular, it relates to a firm’s capability in using andapplying technology in a new product initiative, such aspreliminary technical assessment, prototype development,pilot production, and production start-up (e.g., Yalcinkaya,Calantone, and Griffith 2007). The stronger a company’stechnological capability, the more likely the company willsuccessfully introduce products.

Previous research has suggested that capabilities tend tobe positively related to performance (Krasnikov andJayachandran 2008). However, capabilities per se do notguarantee competitive advantage; they confer advantageonly if they are applied in a way that delivers superiorvalue to customers over competitors in dynamic markets(Eisenhardt and Martin 2000). Capabilities can eventurn into rigidities, which can have a negative influenceon performance (Leonard-Barton 1992). For instance,technology-driven core capabilities can lead to path de-pendencies if a firm fails to adapt to market developmentsand continues to stick with an outdated technology. Theextant literature has tried to explain the variance in therelationship between capabilities and performance byapplying different types of capabilities that are assumed tohave relative advantages over each other (Barney 1991).

Following the assumptions of the capabilities perspec-tive (Teece, Pisano, and Shuen 1997), Krasnikov andJayachandran (2008) argue that the impact of differenttypes of capabilities on performance depends on two char-acteristics of the knowledge that drives these capabilities:imitability and mobility. The more difficult it is to copycapabilities (imitability), the greater the capabilities’ im-pact on performance. The more difficult it is to obtaincapabilities on the market (mobility), the greater thecapabilities’ impact on performance. These authors fur-ther argue that marketing capabilities, compared withtechnological capabilities, are less susceptible to imitationbecause of the tacit knowledge involved, are less likely tobe observed from the market, and are almost immobile.As a result, marketing capabilities lead to better perfor-mance than technological capabilities.

The relative advantages of capabilities might be specific tothe institutional context of a country, because variationsin institutional settings are related to imitability andmobility of particular capabilities and thus to the influ-ence of different capabilities. For instance, the imitabilityand mobility of technological capabilities depends onformal rules and laws for licensing and patents, a con-dition that varies across countries. In the next section, werefer to the institution-based view of the firm and theorize


contingencies under which either marketing or tech-nological capability is a superior driver of new productperformance.

THE MODERATING ROLE OF INSTITUTIONS

An institution is defined and characterized as a set offormal rules and informal restraints that structure po-litical, economic, and social interactions in order toperpetuate order and safety within a market or society(North 1991). Institutions regulate human activity, setstandards for acceptable behavior, and influence beliefsand emotions (Scott 2001). The institutional context canshape how companies operate and how they perform(Peng, Wang, and Jiang 2008; Williams and Martinez2012), and the influence of capabilities on firm perfor-mance can depend on the institutional context (Boso et al.2013; Wu 2013).

The institutional view has emerged as a useful paradigmfor explaining strategies and competitive advantages offirms in different countrymarkets (Peng,Wang, and Jiang2008). For example, Peng (2003) has shown that as soonas market-supporting institutions develop in markets,firms rely less on networks and personal relations andmore on contracts, strategies, and core capabilities. Thus,the influence of different capabilities depends on the in-stitutional context in different markets.

Building on institutional theory by North (1991) andScott (2001), Burgess and Steenkamp (2006) distinguishbetween three distinct but related institutional subsystemsthat provide structure to society: socioeconomic, cultural,

and regulative systems. The authors further suggest keycomponents to capture each of the institutional sub-systems, which we incorporate into our research.

The socioeconomic system and its development can becaptured by the gross domestic product (GDP) growthrate for each economy (Berry, Guillen, and Zhou 2010).National regulative systems have been captured usinginformation on rule of law that measures the respect forthe institutions that govern interactions of citizens and thestate (Kaufmann, Kraay, andMastruzzi 2010; Steenkampand Geyskens 2006). Cultural systems are measured bycultural indices for different countries: we refer to Inglehart’scultural values for the cultural system (Steenkamp andGeyskens 2012). We examine how these institutionsmoderate the relationship between capability and newproduct success.

Figure 1 outlines the research approach of this study. Itindicates the relative influence of two types of capabilitieson new product performance as the main relationshipthat is moderated by different institutions. In the fol-lowing section, we derive the moderating hypothesis foreach of the suggested institutional subsystems.

Socioeconomic System: GDP Growth Rate

The socioeconomic system refers to conditions and dy-namics caused by economic changes and developments in asociety. A core component is the rate at which the economydevelops, usually measured by the annual GDP growth rate.

Krasnikov and Jayachandran (2008) argue for the com-petitive disadvantage of technological capabilities in

Figure 1. Conceptual Framework

Capability type:Marketing versus technological

capability

Socioeconomic system (H1)

Regulative system (H2)

Cultural system (H3a, H3b)

New product performance


rapidly developing markets due to a higher degree ofimitability, as well as the idea that mobility applies mainlyto markets with low growth rates, in which licensing oftechnologies is more common. Technological knowledgeis more likely to be codified (e.g., by patents) and is morelikely to use standard procedures (e.g., total qualitymanagement, international standards) than marketingknowledge in rapidly developing markets. Althoughpatents and licensing are mechanisms to protect intel-lectual property, they require the licensing company todisclose critical information about the technology,thereby enabling competitors to use this information fortheir own technology developments. The disclosure ofsuch information makes technology more susceptible toimitation than marketing knowledge, which is mainlybased on tacit knowledge (Simonin 1999). In rapidlydeveloping markets, in which licensing of technologiesis less common, lower imitability of technological capa-bilities increases their competitive advantage.

Furthermore, Wu (2013) shows that the influence ofmarketing capabilities is stronger in markets with lowgrowth rates thanmarkets with high growth rates becausefirms with strong and sophisticated marketing capabil-ities can address consumers’ diverse preferences andunique needs in thesemarkets better. Inmarkets with highgrowth rates, most customers have low purchasing powerand prefer affordable products that offer basic func-tionality and are marketed as such over products withnovel features and premium prices that are due to lowimitability and mobility of marketing capabilities. As aresult, marketing capabilities are less important for suc-cessfully addressing customers in these markets (Song,Nason, and Di Benedetto 2008). A set of studies in themarketing logistics global effects cluster has pointedout a long relationship between the physical distributionleverage of a country’s infrastructure and the ability ofthat country to progress, utilize all of their productioncapabilities, and distribute food efficiently to create morejobs for those who decide to seek economic prosperity inurban areas. Thus, marketing capabilities, when put intothe dynamics of a rapidly developing economic situation,provide not only strategic slack to firms but also a latenteconomic platform for progress, variety in consumption,and less expensive food (Bowersox and Calantone 1998;Bowersox, Calantone, and Rodriques 2003).

As a result, marketing capabilities, compared withtechnological capabilities, are the main drivers of newproduct success in markets with low growth rates, butthis advantage becomes smaller for rapidly developingmarkets.

H1: The relative advantage of marketing over tech-nological capabilities related to new productperformance decreases with increasing growthrates in a country.

Regulative System: Rule of Law

The regulative system in a country involves the capacity toestablish formal rules and to inspect society members’conformity to these rules (Scott 2001); it defines rightsand obligations in exchange relationships in markets (Wu2013). Rule of law is an essential component of theregulative system (North 1990) and addresses the degreeto which the behavior of organizations is guided byformal, transparent, and legal rules and to what extentindividuals and organizations have confidence in andabide by these rules (Kaufmann, Kraay, and Mastruzzi2010; Steenkamp and Geyskens 2006).

Rule of law facilitates the development of technology-based innovations (Oxley and Yeung 2001). Techno-logical knowledge is likely to be more imitable andmobile than marketing knowledge because technologicalknowledge is codified and better accessible than mar-keting knowledge, which is often based on tacit knowl-edge (Simonin 1999). Countries with strong regulativesystems can support the control and restriction ofimitability and mobility of these capabilities, for example,through patents that provide the inventor firm withprotection from copying. Therefore, technological ca-pabilities’ influence on new product success should bestronger in countries with strong rules of law.

On the other hand, a weak regulative system in a countrymakes market transactions costly and uncertain. Wu(2013) has argued that in such situations, marketingcapabilities work as a substitute for strong regulationbecause they help firms reduce transaction costs anduncertainty in exchange relationships. An important facetof the marketing capability as defined earlier in thisarticle is the gathering of market information, for ex-ample, information about the financial situation ofcustomers and suppliers as well as the building of du-rable relationships with customers that discourageopportunistic behavior (Day 1994). In countries withstrong legislative systems, in contrast, contracts arestable and boundaries exist for acceptable behavior(Steenkamp and Geyskens 2006). Firms can easily ac-quire information about customers and business part-ners through intermediaries. Thus, the role of marketingcapabilities in reducing transaction costs and uncer-tainty becomes less important.


H2: The relative advantage of marketing over tech-nological capabilities related to new product per-formance decreases with increasing rule of law.

Cultural System: Inglehart’s Cultural Values

The cultural system represents the culturally supportedvalues of a society that guide individual and firm be-havior. Prominent approaches to measuring culturalsystems in international marketing are Hofstede’s (2001)work and theGlobe project (House et al. 2004). Theworkby Inglehart (1997) has been introduced as a fruitfulapproach in distinguishing between cultural orientationsof markets (Steenkamp and Geyskens 2012). Other thanHofstede’s work and the Globe project, Ingelhart’smeasures of culture allow for variations and develop-ments over time, and they are therefore more appropriatefor the data in our study that vary across countries andtime, because they take into consideration the culturalchanges within countries.

Inglehart (1997) suggests that socioeconomic develop-ment of a society leads to cultural changes. He proposestwo major and separate dimensions that capture thesechanges and the differences across societies: the dimen-sion of traditional values moving toward secular-rationalvalues and the dimension of survival values moving to-ward self-expression values.

Traditional Values Moving Toward Secular-RationalValues. The dimension of traditional values moving to-ward secular-rational values reflects the bureaucratiza-tion, centralization, rationalization, and secularization ofsocieties that occurs typically in the industrializationphase of societies. Secular-rational values support clearrules and authorities, rational organizations, and a hier-archical governance structure (Steenkamp and Geyskens2012). Such rules and authorities are useful for exploita-tion of technological knowledge because a strong gov-ernance structure can control and restrict imitability andmobility of technological capabilities.

Marketing capabilities, on the other hand, suffer undertoo much bureaucracy, centralization, and rationaliza-tion. Organizations with a bureaucratic governance andhierarchical leadership are the ones considered leastsuccessful in creating successful innovations (Desphandeand Farley 2004). Such cultural orientations in organiza-tions are strongly linked to cultural institutions in a countryand reveal similar effects on firm performance (Eisend,Evanschitzky, and Gilliland 2015). We therefore expectthat when moving from traditional toward secular-rational

values across countries, the relative advantage of marketingover technological capabilities decreases.

H3a: The relative advantage of marketing over tech-nological capabilities related to new productperformance decreases the more a countryemphasizes secular-rational values.

Survival Values Moving Toward Self-Expression Values.The dimension of survival values moving toward self-expression values reflects the emphasis on individualautonomy and self-expression that typically occurs in thephase of postindustrialization. Societies that value auton-omy and self-expression give consumers a sense of humanautonomy and creativity (Inglehart andWelzel 2005).Withthis autonomy and creativity, consumers develop diverseneeds and unique preferences. Firms need to develop strongmarketing capabilities to identify these small segments ofconsumers with diversified preferences and to quickly re-spond to them, to develop products that satisfy the par-ticular needs of these segments, and to target these segmentsin an efficient way and ahead of competitors (Wu 2013).For example, the development of identity-related meaningsof brands is more important for a society that values au-tonomy and self-expression, such as the United States, thanfor a societywhere survival values aremore important, suchas Russia (Strizhakova and Coulter 2008).

Self-expression values can be addressed by technologicalcapabilities, too. For example, companies such as Appleprovide superior technology that helps customers to ex-press their identity. However, as described before, be-cause technological capabilities are likely to be moreimitable and mobile than marketing capabilities, andbecause marketing measures are easier to adapt tochanging market environments than technology,marketing capabilities are more apt to address the di-versity of needs of consumers and to communicateidentity-related meanings to consumers in countriesthat put emphasis on self-expression values.

H3b: The relative advantage of marketing over tech-nological capabilities related to new productperformance increases the more a countryemphasizes self-expression values.

RESEARCH DESIGNData Collection and Coding

For this study, we use data from a previous meta-analysison predictors of new product performance (Evanschitzky


et al. 2012) and extend the data set by adding morerecent studies. Evanschitzky and colleagues performed acomprehensive meta-analysis of 33 antecedents of newproduct success that provided a total of 2,618 effectsizes. We selected effect sizes that refer to effects ofmarketing and technological capabilities (as definedpreviously) on new product success. In particular, weselected all effects sizes from the categories marketingproficiency, launch proficiency, and technological pro-ficiency. In total, 42 articles with 48 independent sam-ples reported on 325 effects of capabilities on newproduct success.

Because the data set included studies that were publishedup to 2011, we also searched for studies published sincethen by using keyword searches in electronic databases(e.g., EBSCO) and on Google Scholar (using keywordssuch as “technological capability,” “technological profi-ciency,”“marketing capability,” in combinationwith either“new product success” or “new product performance”).We found eight more articles with nine independentsamples. The final data set contains 50 studies with57 independent samples that provide 341 effect sizes.1

Table 1 describes the coding and operationalization of theinfluencing variables. The data for the institutional sys-tem variables were annual data per country (except fordata on cultural systems), and we assigned the values toeach study according to publication year.We subtracted 3from each publication year because three years is theaverage time it takes from data collection until publi-cation of a study. For example, if the study was con-ducted in the United States and was published in 2006,we used indicators for institutional system variables ofthe United States from 2003. The data for culturalsystems by Inglehart are updated approximately everyfive years, and we assigned the value scores that wereclosest to the data collection year of each individualstudy.

As control variables, we refer to the taxonomy of mod-erators provided and detailed by Evanschitzky et al.(2012) in their meta-analysis of new product success. Onthe basis of previous meta-analyses (e.g., Henard andSzymanski 2001; Montoya-Weiss and Calantone 1994;Pattikawa, Verwaal, and Commandeur 2006) and theirexperiences during coding, those authors apply sevenmoderator variables: data source (senior manager vs.project manager data), data type (subjective vs. objectiveperformance data), product type (services vs. goods), re-gion (North America/Europe vs. Asia), scale type (single-item vs. multi-item performance measure), technology

(low-technology vs. high-technology markets), and timing(short-term vs. long-term performance data). We hadto exclude the variable for region because it is highlycorrelated with some of the institutional system vari-ables. We add performance type as another controlvariable, because the studies in our meta-analysis differin whether they investigate technological performanceor market-based and financial performance. The dis-tinction between these types of performance variablesfollows the coding of outcome measures in a previousmeta-analysis (Krasnikov and Jayachandran 2008),and it captures the distinction made in research oncapabilities and their varying role in terms of marketoutcomes or efficient integration and use of resources(e.g., Eisenhardt and Martin 2000). We refrain fromadding further control variables such as more detailedfirm characteristics (e.g., firm size, particular industry)because the information is not readily available in allprimary studies.

Two coders independently coded the variables accordingto instructions in a coding sheet. Coding conformity wasachieved in 95% of the cases. The few differences wereresolved through discussion.

Meta-Analytic Procedures

The effect size metric selected for the meta-analysis isthe correlation coefficient; higher values of the co-efficient indicate a stronger effect of capabilities on newproduct success. All effect sizes were adjusted for un-reliability following common guidelines for meta-analysis (e.g., Lipsey and Wilson 2001). For the analysis,the reliability-corrected correlations were transformedinto Fisher z-transformed values according to the commonprocedure in the literature (e.g., Krasnikov and Jayachandran2008).

To test for the effects of the suggested variables, we modelthe transformed coefficients as a linear function of theinfluencing variables. Our data set comprises multipleeffect sizes, that is, more than one effect size per study.Furthermore, we have influencing variables varying atthe effect size level, that is, variables that differ within astudy; and influencing variables at the study level, thatis, variables that differ only between studies. We use avariance-known hierarchical linear model (HLM) es-timation procedure as it is commonly applied in theliterature and highly recommended as the preferredprocedure to account for nested data and within-studyerror correlation between estimates (Bijmolt and Pieters2001).


Table1.

Variables

Used

intheMeta-Analysis

Variable

Description

andOperation

alization

Source

forCod

ing/DataSo

urce

Cod

ingan

dDataDescription

MainVariables

Cap

ability

type

aCap

tureswhether

thecapa

bilityrefers

tomar-

keting

capa

bilitiesor

techno

logical

capa

bilities.

Krasnikov

andJaya

chan

dran

(200

8)0=marketing

capa

bility(143

estimates)

1=techno

logicalcapa

bility(198

estimates)

Socioecono

micsystem

Cap

turesthemacroecon

omic

cond

itions

and

dyna

micscaused

byecon

omic

chan

gein

asocietyan

dismeasuredby

thean

nual

percentage

grow

thrate

ofGDP.

World

Ban

k,W

orld

Develop

mentIndicators

(http://w

ww.w

orldba

nk.org/);CIA

World

Factbo

ok(https://www.cia.gov

)

Con

tinu

ousva

riab

le(M

:3.86,

min:.08

,max

:12

.70)

Regulativesystem

Cap

turesthecapa

city

toestablishform

alrules

andto

inspectsociety

mem

bers’conformityto

theserulesan

dismeasuredby

ruleof

lawfrom

Kau

fman

nan

dcolleag

ues’go

vernan

ceindi-

cators

(Kau

fman

n,Kraay

,an

dM

astruzzi

2010

).Itmeasurestheperception

sof

theex-

tent

towhich

agents

have

confidence

inan

dab

ideby

therulesof

society,

andin

particular,

thequ

alityof

contract

enforcem

ent,prop

erty

righ

ts,the

police,an

dthecourts,a

swella

sthe

likelihoo

dof

crim

ean

dviolence.

World

Ban

k,W

orldwideGov

erna

nceIndica-

tors

(http://info.worldba

nk.org/gov

erna

nce/

wgi/in

dex.asp)

Con

tinu

ousva

riab

le(M

:1.27

,min:−.89,

max

:1.90

)

Culturalsystem

Cap

turestheculturally

supp

ortedva

lues

ofasocietythat

guideindividu

alan

dfirm

be-

havior

andismeasuredby

Inglehart’s(199

7)cu

lturaltheo

rythat

isgrou

nded

inmacro-

processesof

mod

ernizatio

nan

dindu

strialization

andap

pliestw

odimension

s:(1)Traditio

nalv

s.secular-ratio

nalv

aluesdimension

reflectsthe

contrastbetw

eensocietiesin

which

religionis

very

impo

rtan

tan

dthosein

which

itisno

t.(2)

The

tran

sitio

nfrom

indu

strial

topo

stindu

strial

societiesbrings

apo

larizatio

nbetw

eensurvival

andself-expression

values.

WorldValueSu

rvey

(http://w

ww.

worldva

luessurvey.org)

Con

tinu

ousva

riab

les:

Tradition

alvs.secular-ration

alva

lues

(M:.17,

min:−1.55

,max

:1.96

)Su

rvival

vs.self-exp

ressionva

lues

(M:.96,

min:−1.25

,max

:1.99

)

Con

trol

Variables

Datasource

Cap

tureswhether

theda

taarega

thered

from

projectman

agersor

from

senior

man

agers.

Henardan

dSzym

anski(200

1)0=senior

man

ager

data

(34samples)

1=projectman

ager

data

(23samples)


http://www.worldbank.org/

https://www.cia.gov

http://info.worldbank.org/governance/wgi/index.asp

http://info.worldbank.org/governance/wgi/index.asp

http://www.worldvaluessurvey.org

http://www.worldvaluessurvey.org

Table1.

Continued

Variable

Description

andOperation

alization

Source

forCod

ing/DataSo

urce

Cod

ingan

dDataDescription

Datatype

Cap

tureswhether

performan

cewas

operation-

alized

throug

hob

jectiveda

tafrom

compa

nyrecords(e.g.,return

oninvestment,sales,

profit)or

throug

hsubjective

data

from

man

agers’assessments.

Henardan

dSzym

anski(200

1)0=ob

jectiveperforman

ceda

ta(3

samples)

1=subjective

performan

ceda

ta(54samples)

Performan

cetype

aCap

tureswhether

theperforman

cemeasure

refers

tofina

ncial/m

arket-ba

sedperforman

ceor

totechno

logicalperforman

ce.

Eva

nschitzkyet

al.(201

2);Krasnikov

and

Jaya

chan

dran

(200

8)0=fina

ncial/m

arket-ba

sedperforman

ce(217

estimates)

1=techno

logicalperforman

cemeasures

(124

estimates)

Prod

ucttype

Cap

tureswhether

prod

ucts

areservices

(e.g.,

bank

ing)

orgo

ods(e.g.,food

).Henardan

dSzym

anski(200

1)0=services

(6samples)

1=go

ods(51samples)

Scaletype

Cap

tureswhether

performan

cewas

measured

bysing

leitem

ormulti-item

scales.

Henardan

dSzym

anski(200

1)0=sing

le-item

measure

(18samples)

1=multi-item

measure

(39samples)

Techn

olog

yCap

tureswhether

prod

uctsaretrad

edon

high

-techno

logy

markets(e.g.,electron

icsindu

stry)

orlow-techn

olog

ymarkets(e.g.,food

sector).

Henardan

dSzym

anski(200

1)0=low-techn

olog

ymarket(9

samples)

1=high

-techn

olog

ymarket(48samples)

Tim

ing

Cap

tureswhether

performan

cewas

measured

closer

towhentheprod

uctwas

introd

uced

(<36

mon

ths)

oraftermoretimeha

delap

sed

sinceintrod

uction

(>36

mon

ths).

Henardan

dSzym

anski(200

1)0=short-term

performan

ceda

ta(44samples)

1=long

-term

performan

ceda

ta(13samples)

a Variables

withda

tavaryingwithinstud

ies;that

is,theywerecollected

attheeffect-sizeestimatelevel(withatotalo

f341

estimates).The

remaining

variab

lesareba

sedon

data

varyingat

thestud

ylevel(betw

eenstud

ies);

that

is,they

werecollected

atthelevelof

independ

entsamples

(with57

samples).


We specify the estimated model as follows:

Zij = b0j + b1jX1ij + b2jX2ij + eij,(1a)

b0j = g00 + �n

1bnkUkj + u0j,(1b)

b1j = g10 + �m

1bmkUkj, and

b2j = g20,

where Zij is the ith effect size reported within the jthsample, and b1j and b2j describe parameter estimates forboth influencing variables X1j and X2j that vary at thestudy level (i.e., within studies), where X1ij = 1 if thecorrelation is between technological capabilities and newproduct performance and 0 if the correlation is betweenmarketing capabilities and new product performance,and X2ij = 1 if the correlation is between capabilities andtechnological performance and 0 if the correlation is be-tween capabilities and financial/market-based performance.

Whereas Equation 1a describes the impact of differentcapability types and performance types, which varywithin astudy, Equation 1b describes the effect of study character-istics Uk on the intercept and slopes in the level 1 equationwhere gn0 represents the fixed effects in the intercept andslopes b0j andb1j anduj is the study-level residual error term.The study characteristics are coded as follows:

U01j = socioeconomic system: GDP growth rate,

U02j = regulative system: rule of law,U03j = cultural system 1: traditional versus secular-

rational values,U04j = cultural system 2: survival versus self-expression

values,U05j = data source: senior manager versus project

manager data,U06j = data type: objective versus subjective per-

formance data,U07j = product type: services versus goods,U08j = scale type: single-item versus multi-item

performance measure,U09j = technology: low-technology versus high-

technology markets, andU10j = timing: short-term versus long-term perfor-

mance data.

We include all study characteristics Ukj as predictors for theintercept b0j, and we include U01j to U04j as predictors of theslope b1j to measure cross-level interactions as suggested inour hypotheses. To ensure that this model specification issound, we compare different model specifications with the

suggestedmodel empirically (e.g., an intercept-onlymodel, amodel with random effects in the intercept, a model withrandom effects in the intercept and slopes). To verify themodels, we use the deviance statistics. The change in de-viance ΔD follows a chi-square distribution and can be usedto test for model fit improvements.Whenwe compare the fitof the models, the suggested model is supported.

Collinearity is a common problem both in meta-regressionsand in cross-national studies that apply nation-based mea-sures such as cultural indicators (e.g., Steensma et al. 2000).Because there is no direct diagnostic for multicollinearity inHLM, we performed a weighted regression analysis andchecked the collinearity statistic. Because we found highvariance inflation factor values (>5) for rule of law andsurvival/self-expression values, we ran separate regressionmodels in which we excluded either rule of law or survival/self-expressionvalues, similar to theprocedure inothermeta-analytic studies that deal with national moderators (e.g.,Samaha, Beck, and Palmatier 2014). Because the results ofbothmodels resemble the findings of themodel that includesall predictors, we report the findings of the model with allpredictors. We run HLM 7 to estimate our model.

RESULTS

The average of 341 correlations indicates a significantpositive relationship between capability and new productsuccess (r = .26, p < .001). The range of the individualcorrelations varies from −.49 to .60 and is consideredheterogeneous, as indicated by the homogeneity test (Q =5,548.23, p < .001). The relevant parameter estimates ofthe HLM for the hypothesis tests are presented in Table 2.

The positive effect of capabilities is once more supportedby the significant intercept in the model (b = .52, SE = .24,t = 2.19, p = .03). We find support for three cross-levelinteractions. To facilitate interpretation, the interactioneffects are plotted in Figure 2.

Socioeconomic system moderates the influence of capa-bility type on new product performance (b = .04, SE = .01,t = 2.93, p = .004). The first diagram in Figure 2 shows theinteraction effect: the advantage of marketing capabilitiescomparedwith technological capabilities for new productsuccess decreases with increasing growth rate in a country;the relative advantages of both capabilities even reverses incountries with high growth rates. This finding supports H1.

Regulative system moderates the influence of capa-bility type on new product performance (b = .28, SE = .07,


t = 3.77, p < .001). The interaction effect is depicted in thesecond diagram in Figure 2: the relative advantage ofmarketing capabilities, compared with technological ca-pabilities, decreases with increasing rule of law. Thesefindings are in line with H2.

Although cultural system 1 (traditional vs. secular-rationalvalues) shows a nonsignificant moderation effect (t = .57,p = .57), cultural system 2 (survival vs. self-expressionvalues) moderates the relationship between capability typeand new product performance (b = −.11, SE = .03, t = 3.49,p < .001). The third diagram in Figure 2 shows the in-teraction effect: the relative advantage of marketing overtechnological capabilities increases the more a societyemphasizes self-expression values over survival values.These findings reject H3a but support H3b.

We do not find an effect on the intercept b0j for any of thecontrol variables except for performance type: the effect

on technological performance is weaker than on financial/market-based performance (b = −.06, SE = .01, t = 4.84,p < .001), which shows that capabilities are more in-fluential on market outcomes than efficient integrationand use of resources (e.g., Eisenhardt and Martin 2000).Furthermore, we find a main effect for capability type,indicating that marketing capabilities (b = −.05, SE = .02,t = 2.59, p = .01) have stronger effects than technologicalcapabilities, supporting the strong effect of marketingcapabilities that has been found in prior research(Krasnikov and Jayachandran 2008).

DISCUSSION AND IMPLICATIONS

This study shows that the relative advantage of marketingover technological capabilities on new product perfor-mance depends on the institutional context in a country.Meta-analytic data of 341 effect sizes of the relationship

Table 2. Variance in the Capability–New Product Performance Relationship: HLM Estimates

Predictor Variables Hypothesis b (SE) t

Effect-Size Level (Within Studies)Capability type: marketing vs. technological capability (X1) −.05 (.02) 2.59**Performance type: financial/market-based vs. technologicalperformance (X2)

−.06 (.01) 4.84***

Study Level (Between Studies)Intercept .52 (.24) 2.19*Socioeconomic system: GDP growth rate (U01) −.01 (.03) .46Regulative system: rule of law (U02) −.12 (.11) 1.08Cultural system 1: traditional vs. secular-rational values (U03) .01 (.05) .26Cultural system 2: survival vs. self-expression values (U04) .01 (.07) .18Data source: senior manager vs. project manager data (U05) .09 (.09) 1.07Data type: objective vs. subjective performance data (U06) −.01 (.17) .02Product type: services vs. goods (U07) −.12 (.15) .82Scale type: single-item vs.multi-item performancemeasure (U08) −.01 (.09) .12Technology: low-technology vs. high-technology markets (U09) −.07 (.10) .72Timing: short-term vs. long-term performance data (U10) .07 (.10) .69

Cross-Level InteractionsCapability type × Economic system (X1 × U01) H1 .04 (.01) 2.93**Capability type × Regulative system (X1 × U02) H2 .28 (.07) 3.77***Capability type × Cultural system 1 (X1 × U03) H3a .01 (.02) .57Capability type × Cultural system 2 (X1 × U04) H3b −.11 (.03) 3.49***

Model FitΔD 119.97***

*p < .05 (two-tailed tests).**p < .01 (two-tailed tests).***p < .001 (two-tailed tests).Notes: ΔD refers to the change in deviance between the unconditional model (intercept-only model without influencing variables) and the conditional model (model withinfluencing variables) and follows a chi-square distribution.


between capabilities and new product performance showthat marketing capabilities have a stronger influence thantechnological capabilities on new product performancebut that this effect is moderated by institutions: the relativeadvantage decreases and even reverses with increasinggrowth rates; it further decreases with increasinglystronger rules of law in a country; and it increases insocieties that put emphasis on self-expression valuesover survival values.

Theoretical Implications

The study findings contribute to the capabilities frame-work by investigating the differential effect of two im-portant capabilities as drivers of new product success andfirm performance (e.g., Kaleka 2011; Krasnikov andJayachandran 2008; Vorhies and Morgan 2005). Pre-vious findings have suggested that, in general, marketingcapabilities are more influential than other capabilities(Krasnikov and Jayachandran 2008; Vorhies and Morgan2005). However, most research has been done in thesame cultural context, namely, in Western societies. Atthe same time, international marketing scholars who haveemployed the capabilities framework to study the originsof competitive advantage in global markets have lookedat different cultural contexts but usually focused on theeffects of either marketing or other capabilities (e.g., Juet al. 2013; Kaufman andRoesch 2012; Kemper, Engelen,and Brettel 2011). Adding to these research streams, thecurrent study shows that the relative influence of differentcapabilities depends on institutions, too. That is, a singlecapability can have different effects across countries andinstitutions, and these effects can alter the advantage ofone capability over another. By applying an institution-based perspective on capabilities and testing the moder-ating effect of institutional context, this study identifiesconditions under which either of two major capabilitiesrelated to new product development is a superior or in-ferior driver of performance. Thus, the study introducesnew contingencies to the capabilities framework. Therationales explaining the contingent effects of institutionsare based on the source of relative advantages of capa-bilities related to new product success: mobility andimitability. Both can change depending on the context,which changes the relative advantages, too.

This empirical insight of our research supports the claimsby other authors who have pointed out that prior researchin marketing has mainly focused onWestern societies anddeveloped markets that substantially differ from othersocieties and markets in the world and have providedconditions that challenge the assumptions of theories

Figure 2. Cross-Level Interactions

Annual GDP Growth (Mean-Centered)

Marketing capabilities

Technological capabilites

Eff

ect

Size

(Z

)

.41

.37

.34

.30

.26–2.25 –.86 .52 1.91 3.29

(1)

Rule of Law (Mean-Centered)



Eff

ect

Size

(Z

)

.56

.44

.32

.20

.07–1.59 –1.07 –.56 –.04 .48

(2)

.52

Eff

ect

Size

(Z

)

.44

.37

.30

.23–2.01 –1.26 –.51 .23 .98



Survival vs. Self-Expression Values (Mean-Centered)

(3)


developed inWestern economies (Burgess and Steenkamp2006). For example, the insight that marketing capa-bilities contribute more to the competitive advantage offirms than technological capabilities, because techno-logical capabilities have a higher degree of imitability andmobility than marketing capabilities, applies primarily toWestern markets, in which licensing is more common andthus knowledge about technological capabilities is moreeasily accessible. Our study supports the idea that inother markets, the relative advantage disappears oreven reverses.

Such insights not only support the body of scholarly workon capabilities that has made conceptual suggestions thatthe utility of capabilities and their effect on performancedepends on institutions in markets (Eisenhardt andMartin 2000) but also adds to the institution-based viewof firms by developing an institution-contingent per-spective on capabilities. The institutional view suggeststhat the market environment shapes which strategiesfirms employ, what kind of choices they make, and howthey perform. However, the related international mar-keting literature has focused on country-specific in-fluences on firm performance (e.g., Chang, Bai, and Li2015; Dwyer, Mesak, and Hsu 2005) but has offeredlimited insights into how exactly country institutionsexplain the relative advantages of different kinds ofcapabilities.

Managerial Implications

Marketing-related and technology-related capabilities arethe core capabilities and major drivers of new productperformance (Henard and Szymanski 2001; Moormanand Slotegraaf 1999). Generally, the two capabilities havesynergistic effects, which is why firms should always try todevelop both capabilities (Song et al. 2005). Most firms,however, have a clear advantage in one or the othercapability. Our findings provide important implicationsfor such firms.

First, our findings show which country markets aremore apt for successful new product introductionsdepending on the superior capability of a firm. Forexample, a company that excels in marketing capabil-ities is best advised to focus on countries with lowgrowth rates (e.g., the Netherlands, Australia), orcountries with low rules of law (e.g., Taiwan). Acompany that excels in technological capabilities shouldselect countries with high growth rates (e.g., India) andconsumers who focus on survival over self-expressionvalues (e.g., China).

These findings are obviously important for interna-tional and global firms that develop products fordifferent markets: they call for a strategic adaptationover standardization where the organizational unitswithin a firm that are responsible for different marketsneed to focus on developing capabilities with varyingemphasis. Furthermore, the evolutionary path ofstandardization→customization→personalization is am-plified both in speed and breadth of industries thatbenefit as “hard”marketing (new product development;new product launch, physical distribution, and productpackaging) gives way to branding, high degrees of pro-motions, and proliferation of multicompetitor categories(Rodriguez, Bowersox, and Calantone 2005).

LIMITATIONS AND FUTURE RESEARCH

One major limitation of meta-analysis is that it is con-strained to main effects analysis and does not providemeaningful ways to integrate interaction effects. Priorresearch has addressed how companies develop multiplecapabilities simultaneously and how these capabilitiescan either have complementary effects and help enhanceperformance (Moorman and Slotegraaf 1999) or becounterproductive because of opposing objectives(Grewal and Slotegraaf 2007). Simultaneous effects arecontingent, and thus we can assume that in some envi-ronments, the interaction of multiple capabilities enhancesperformance, and in other environments, single and fo-cused capabilities might be more helpful (e.g., Song et al.2005). Further research that goes beyond meta-analyticdata is needed to address this issue in more detail.

The definitions of marketing and technological capabil-ities vary from study to study, some interpreting them as astrategic orientation, others as an organizational culture,others as a set of practices, and so on. We have chosen avery narrow definition that is in linewithmeasures used inprior research that captures the idea of both capabilities asrelated to new product success. Such definition is practicalbecause it allows us to identify a sufficient number ofstudies for our meta-analysis that have investigated therelationship between both capabilities and new productperformance. At the same time, the definition clearlyinfluences the findings, and a different or broader defi-nition might have brought different results. Also, thefocus on new product success as a performance measuremight narrow the idea of firm performance, which is usuallythe major focus in capabilities research. However, a recentmeta-analytic study has shown that new product successas a performance measure of firms is strongly related to


other performance measures of a company, with a corre-lation of .75 (Eisend, Evanschitzky, and Gilliland 2015).

Another issue that has to be considered when interpretingthe results of this meta-analysis refers to the underlyingheterogeneity and conditional nature of the meta-analyticeffects. The relative impacts of marketing and techno-logical capabilities depend on the company itself and thecompany’s position within the market. In this vein, ourmeta-analysis, like any other meta-analysis, provides ageneralization of the direction and strength of the dif-ferential relative impacts of marketing and technologicalcapabilities, rather than an analysis of single companies.As the field evolves, updated analyses on the capability–new product performance link should be undertaken indue course. One could think of including additionalinfluencing factors of the capability–new product perfor-mance link, such as competitiveness of industry or theposition of the company in the market. Analyzing theseadditional factors would result in a more complete modelexplaining new product performance, an endeavor worthpursuing.

NOTE

1. The list of studies is available from the authors uponrequest.

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