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Industrial Marketing Management 39 (2010) 317329
Contents lists available at ScienceDirect
Industrial Marketifunctions as key to organizational performance
(Balasubramanian &Bhardwaj, 2004; Ho & Zheng, 2004;
Malhotra & Sharma, 2002;Sawhney & Piper, 2002). Mismatch
between these two functions leadto production inefciency and
customer dissatisfaction, whereas aproper t lead to superior
competitive advantage and sustainableprots (Ho & Tang, 2004).
It is widely accepted even among businessleaders that ability to
integrate such cross-functional expertise isessential for continued
growth and protability (Wind, 2005).
Diversication strategy, in terms of entering into a related
or
1987; Kim, Hwang, & Burgers, 1993) on rm performance.
Hitt,Hoskisson, and Kim (1997) argued that the ability of an
organizationto manage such diversication depends on their
cross-functionalcapabilities and coordination activities. It is
widely accepted thatefcient linkage of various internal functions
within an organizationand interactions among them is crucial to
manage the curvilineareffects of diversication on performance
(Narasimhan & Kim, 2002;Palich et al., 2000).
From the above discussions, it is clear that functional
capabilities
unrelated business and/or entering into a ne
Corresponding author. Tel.: +44 115 846 8122; fax:E-mail
addresses: [email protected]
[email protected] (S.
[email protected] (R. Ramanathan).1 Tel.: +44
115 8466634.2 Tel.: +44 115 846 7764; fax: +44 115 846 6341.
0019-8501/$ see front matter. Crown Copyright
20doi:10.1016/j.indmarman.2008.09.001hat add and create valuef
management sciencearketing and operations
business (Berger & Ofek, 1995; Bettis & Mahajan, 1985)
and,international diversication or geographical diversication in
adifferent market (Fang, Wade, Delios, & Beamish, 2007;
Ghoshal,to customers. There is a growing body oliterature which
stresses the integration of mcontribute towards delivery of goods
aoperations are the two key functionalnd services but marketing
andareas tResource-based view
1. Introduction
Traditionally, marketing and opestudied separately in
managementMarketing focused on creation of coffer customers a
unique value prooperations focused on managementdemand. Porter
(1985) argued that as functions have beenure (Karmakar, 1996).r
demand and how ton. On the other hand,ply to fulll customertional
areas of business
considered to be of crucial importance to an organization's long
termleadership position in its own industry (Hoopes, 1999; Goerzen
&Beamish, 2003; Nachum, 2004; Narasimhan & Kim, 2002).
Strategicmanagement literature has studied extensively the costs
and thebenets of diversication strategy and its effect on
competitiveadvantage for an organization (Chakrabarti, Singh,
&Mahmood, 2007;Palich, Cradinal, & Miller, 2000; Ramanujam
& Varadarajan, 1989).Researchers have particularly focused on
the effect of product/servicediversication which is dened as the
synergy in different lines ofPerformanceEfciency
on its nancial performanc
Crown Copyright 2008 Published by Elsevier Inc. All rights
reserved.The impact of marketing capability, operaon performance: A
resource-based view
Prithwiraj Nath , Subramanian Nachiappan 1, RamakNottingham
University Business School Jubilee Campus, Wollaton Road Nottingham
NG8 1
a b s t r a c ta r t i c l e i n f o
Article history:Received 17 December 2007Received in revised
form 16 June 2008Accepted 1 September 2008Available online 23
October 2008
Keywords:Marketing capabilityOperations
capabilityDiversication
Using resource-based viewof a rm's functional capabiservice and
international divon the rm's relative efcieof 102 UK based
logisticsnancial performance. Thisperformance than a rm focon a
narrow portfolio of proOur ndings provide a neww geographic market
is
+44 115 846 6667.(P. Nath),pan),
08 Published by Elsevier Inc. All rigns capability and
diversication strategy
hnan Ramanathan 2
UK
) of the rm as a theoretical backdrop; we aim to nd out the
relative impacts (namely, marketing and operations) and
diversication strategies (product/ication) on nancial performance.
We hypothesize that this linkage dependsto integrate its
resourcecapabilitiesperformance triad. Using archival datapanies,
we nd marketing capability is the key determinant for superiordy
highlights that a market-driven rm is likely to have better
businessg solely on operational capabilities. Also, rms are better
off when they focusts/services for the clients and concentrate on a
diverse geographical market.spective to model a rm's functional
capabilities and diversication strategyd offer a benchmarking tool
to improve resource allocation decisions.
ng Management(marketing and operations) and diversication
strategies (product/service and international diversication) have
signicant impact on arm's nancial performance. But to our
knowledge, there has been noresearch to integrate all these
constructs and nd out the relativeimpact of each of them on rm
performance. Thus, our rst researchobjective is to understand the
nature of relationship betweenmarketing capability, operations
capability, and diversication strat-egy (product/service and
international) on organization's nancial
hts reserved.
-
318 P. Nath et al. / Industrial Marketing Management 39 (2010)
317329performance. Capabilities are broadly dened as complex bundle
ofskills and accumulated knowledge that enable rms (or
strategicbusiness units SBU) to coordinate activities and make use
of theirassets (Day,1990, p. 38). As a theoretical background of
our study, weuse the resource-based view (RBV) framework to assess
howindividual organization's resources and capabilities affect its
nancialperformance (Wernerfelt, 1984). RBV theory suggests that
eachorganization has a distinctive set of resources and
capabilities, andsome capabilities will have superior impact on
nancial performancethan the others (Song, Benedetto, & Nason,
2007). Such difference inimpact is attributed to the efciency with
which a rm is able toconvert its resources into valuable difcult to
imitate capabilitiesand into nancial performance (Liebermann &
Dhawan, 2005).Efciency is dened as the ratio of a rm's output to
that of its inputand is measured in terms of the maximum feasible
output which canbe obtained with a given set of inputs (Liebermann
& Dhawan, 2005).In this study, we specically study the
relationship in two contexts:high vs. low efcient rms in making
this transformation. Thus, oursecond research objective is to
understand how efciency of a rm toconvert its resources into
nancial outputs moderates the relationshipbetween the functional
capabilities and diversication strategy onoverall business
performance.
We accomplish our research objectives in three stages.
First,following RBV rationale, we model the functional
capabilities(marketing and operations) of a rm in the form of
inputoutputtransformation. This enables us to understand how a rm
is able tooptimally use its function specic resources to achieve
functionspecic objectives. Such identication of sub-optimal
resource usageprovides insights to better resource allocation
decisions. We usesimilar approach to classify rms into high and low
efcient groups asper their overall business performance. Second, we
propose andempirically test how diversication strategy affects rm
performance.Third, we examine how business performance measured
using multi-factor construct in stage 1 affects the relationship
between functionalcapabilities and diversication strategy on rm's
nancialprotability.
We test our conceptual framework using archival nancial data
forUK road based logistics service providers. A logistics rm,
operating inbusiness to business context, has to excel in both
operationscapabilities through superior process knowledge and
marketingcapability through continuous creation of customer value.
Firms inlogistics industry are extremely dependent on the overall
economicgrowth of the country; and the performance of freight
intensiveindustries such as manufacturing, agriculture, and retail.
However,with increase in focus on services dominant industries,
stagnanteconomic growth, increase in fuel cost, and congestion on
the roads,the logistics industry in UK is experiencing stagnation.
The growth infreight transport in UK has been less than the GDP
growth of thecountry (Ofce of National Statistics, 2006). In UK,
the numbers ofroad freight operators have steadily fallen by 15% in
the last decade.Rail and water based transport has steadily
replaced road transport.The cost of moving freight by rail and sea
has decreased over the yearswhereas, the cost of road transport has
increased by a third during thelast decade making it more
challenging for the road transportoperators to compete and sustain
(Department of Transport, 2004).Thus, recession in economy,
spiraling cost of operation, and tighterprot margin has made it
imperative for the logistics companies to re-think about their
value propositions to their customers, diversifythrough expansion
of services offered and geographical coverage.Many logistics
companies are thus going towards consolidation oftheir business
portfolio to achieve greater efciency. Despite thegloomy industry
forecasts, there is a signicant variation in perfor-mance of the
logistics rms. The small and medium logistics rmsexperience a
negative growth in business and very large rms havesignicantly
higher prot than the rms in the other end of the
spectrum (Ofce of National Statistics, 2006). Thus, it becomes
criticalto understand how functional capabilities and long term
diversica-tion strategies of logistics rms affect their business
protability andhow efciency of rms moderates this
inter-relationship.
The rest of the paper is structured as follows. The next
sectiondiscusses our theoretical underpinning of using RBV
framework andthe conceptualization of functional capabilities and
diversication forlogistics rms. Section 3 discusses the data and
the methodology formeasuring resources, capabilities and efciency.
Section 4 presentsthe empirical ndings and Section 5 highlights the
implications of ourresult, limitations of our study and provides
direction for futureresearch.
2. Conceptual framework
This section narrates our conceptual framework developed on
thebasis of resource-based view (RBV) theory. It is organized as
follows.In subsection 2.1, we give a synopsis of RBV theory
explaining the keyconcepts of resources, capabilities and their
linkage to rm perfor-mance. In subsection 2.2, we describe the
principal functionalcapabilities namely marketing and operations.
We also explain therole of diversication and its impact on long
term competitiveadvantage along with the arguments for hypotheses
formulation.We hypothesize that such relationships between
capabilities, diversi-cation and performance is moderated by a rm's
efciency intransforming its nancial resources into protability
outputs.
2.1. Resource-based view (RBV) a synopsis
RBV views a rm as a bundle of resources and
capabilities(Wernerfelt, 1984). Amit and Schoemaker (1993) dene
resource asstocks of available factors that are owned or controlled
by the rm.Resource consist of tangible components like nancial and
physicalassets like property, plant and equipment, and intangible
componentslike human capital, patent, technology knowhow (Grant,
1991; Amit &Schoemaker, 1993). Capability is dened as the
ability of the rm touse its resource to effect a desired end (Amit
& Schoemaker, 1993). Itis like intermediate goods generated by
the rm using organiza-tional processes to provide enhanced
productivity to its resources(Amit & Schoemaker,1993).
Capabilities are invisible assets, tangibleor intangible
organizational processes developed by a rm over aperiod of time
that cannot be easily bought; they must be built(Teece, Pisano,
& Shuen, 1997). RBV argues that rms will havedifferent nature
of resources and varying levels of capabilities. Firms'survival
depends on its ability to create new resources, build on
itscapabilities platform, and make the capabilities more inimitable
toachieve competitive advantage (Day & Wensley, 1988; Peteraf,
1993;Prahalad & Hamel, 1990). Thus, mere possession of superior
resourcescannot achieve competitive advantage for the rm, but how a
rmdeploys its scarce resources, put its capabilities to best use,
invest andcomplement its existing capabilities infrastructure can
bring immo-bility and inimitability to its resource-capability
framework (Peteraf,1993; Song et al., 2007). In marketing
literature, there has beenextensive use of RBV framework to analyze
rm performance (Dutta,Narasimhan, & Surendra, 1999; Liebermann
& Dhawan, 2005), tounderstand the interaction between marketing
and other functionalcapabilities and their effect on performance
(Song et al., 2007; Song,Droge, Hanvanich, & Calantone, 2005;
Song, Nason, & Benedetto,2008), and particularly to understand
inter-organizational relation-ship performance (Palmatier, Dant,
& Grewal, 2007). The resultssuggest that there is a signicant
relationship between capabilitiesand performance. Strategic
management researchers have used RBVto understand the inter-rm
difference in performance (Barney, 1986;Peteraf, 1993; Makadok,
2001). In addition, RBV theory suggests thatheterogeneity in rm
performance is due to ownership of resourcesthat have differential
productivity (Makadok, 2001). Since, a rm's
capability is dened as its ability to deploy resources
(inputs)
-
cap
319P. Nath et al. / Industrial Marketing Management 39 (2010)
317329available to it to achieve the desired objectives (outputs)
(Dutta et al.,1999), so in this study, we use an inputoutput
framework in the formof efciency frontier function to understand
the optimal conversion ofa rm's resources to its objectives.
2.2. Resources, capabilities, diversication and performance
In our conceptual framework, we consider how a rm exploits
itscritical capabilities in marketing and operations; and pursue
adiversication strategy to achieve competitive advantage.
Accordingto RBV, a rm diversies to extend its resources into
newmarkets andbusinesses. Resources and capabilities such as
business knowledge,technological expertise, and international
diversication experienceare transferred between the parent company
and its businesssubsidiaries (Fang et al., 2007; Lu & Beamish,
2001). RBV posits thatas rms diversify within the scope of their
resources and capabilities,they obtain economies of scale through
lower operational costs andleverage superior business efciency
through shared xed assets likecommon production facilities,
distribution channels, or even brandnames (Hitt et al., 1997).
Marketing capability involves integration ofall marketing related
activities of a rm using superior marketknowledge from customers
and competitions. Operations capabilityis the process, technology,
reliability and quality of the overalloperations of the rm.
According to RBV, a coordinated effort by therm tomake these two
capabilities as immovable and inimitable canbring the competitive
edge (Dutta et al., 1999; Liebermann & Dhawan,
Fig. 1. Framework to measure resources2005; Narsimhan, Rajiv,
& Dutta, 2006). Day (1994) suggests thatevery business develops
its own conguration of capabilitiesaccording to the environment,
and it is not possible to enumerateall possible capabilities. So,
in this study, we focus on the principalfunctions of a logistics rm
(namely marketing and operations) andstudy how their functional
capabilities along with diversicationstrategies affect their
business performance. Fig. 1 represents theconceptual framework for
our study.
2.2.1. Marketing capabilityMarketing capability is dened as the
integrative process, inwhich
a rm uses its tangible and intangible resources to
understandcomplex consumer specic needs, achieve product
differentiationrelative to competition, and achieve superior brand
equity (Day, 1994;Dutta et al., 1999; Song, Benedetto et al., 2007;
Song, Droge et al.,2005). A rm develops its marketing capabilities
when it can combineindividual skills and knowledge of its employees
along with theavailable resources (Vorhies & Morgan, 2005). A
rm that spendsmore resources to interact with customers can enhance
their marketsensing abilities (Narsimhan et al., 2006). Such
capabilities, oncebuilt are very difcult to imitate for competing
rms (Day, 1994).Thus, marketing capability is considered to be an
important source toenhance competitive advantage of rms.
The role of being market-driven and its impact on rm
perfor-mance has been an active area of research inmarketing
discipline (Songet al., 2008). Songet al. (2007) suggestmarketing
capability helps a rmto create and retain strong bondwith customers
and channel members.Marketing capability create a strong brand
image that allows rms toproduce superior performance (Ortega &
Villaverde, 2008). Marketingliterature suggests thatrms use
capabilities to transform resources intooutputs based on their
marketing mix strategies and such marketingcapabilities is linked
to their business performance (Vorhies & Morgan,2003, 2005).
Based on the above arguments, we hypothesize:
Hypothesis 1a. The greater is the marketing capability of a rm;
thebetter is its business performance
2.2.2. Moderating effect of rm efciency on marketing
capability-business performance linkage
Extant literature suggests that the impact of marketing
capability ona rm's business performance varies according to a rm's
owncharacteristics (Ortega & Villaverde, 2008; Song, Benedetto
et al.,2007; Song, Droge et al., 2005; Song, Nason et al., 2008).
Song et al.(2007) studied the moderating role of a rm's strategy
based on Milesand Snow framework and found a positive impact of
marketingcapability on nancial performance for rms which can
sustain
abilitiesperformances transformation.customer loyalty through
their unique marketing communication.Ortega and Villaverde (2008)
propose marketing capability has moreimpact on nancial performance
for rms which invest on better assetsto innovate in a dynamic
business environment. Strategic managementliterature suggests that
marketing capability has varied impact onperformancedependingon
theyway inwhich arm can align itselfwithits business environment
(Conant, Mokwa, & Varadarajan, 1990;Desarbo, Benedetto, Song,
& Sinha, et al., 2005; Song et al., 2007).Firms with proactive
market orientation have distinct competencies inmarket
planning,marketing resource allocation and overall control thanrms
who prefer to wait and watch. Thus, innovative rms devotesignicant
resource on its marketing activities whereas, defender rmsfocus
more on cost reduction rather than develop their criticalinnovative
abilities. Market orientation literature suggests that rmswith
superior market orientation frequently outperform their lessmarket
oriented rivals in delivering better customer value (Jaworski
&Kohli, 1993; Kumar, Ganesh, & Echambadi, 1998; Narver
& Slater, 1990).Vorhies andMorgan (2005) emphasize
thatmarketing capability is rmspecic and unique to it. Such
customer value-adding capabilities arenot imitable, replaceable, or
transferable, and thus provide basis for
-
320 P. Nath et al. / Industrial Marketing Management 39 (2010)
317329competitive advantage. Competing rms targeting similar
marketevolve comparable marketing capability but not identical
ones. Firmsare classied as more efcient if they have a superior
resourcecapabilityperformance transformation ability and less
efcient other-wise. Following the RBV rationale, we posit that
marketing capabilitiesof rms differ and unique rm characteristics
like efciency inuenceperformance. Thus, we investigate the
following:
Hypothesis 1b. Marketing capability has a stronger impact on
businessperformance for efcient rms rather than the inefcient
ones.
2.2.3. Operations capabilityOperations capability is dened as
the integration of a complex set
of tasks performed by a rm to enhance its output through the
mostefcient use of its production capabilities, technology, and ow
ofmaterials (Dutta et al., 1999; Hayes, Wheelwright, & Clark,
1988).Manufacturing strategy literature highlights the role of
operationscapability on rm performance (Gonzalez-Benito &
Gonzalez-Benito,2005; Hayes & Pisano, 1996; Roth & Miller,
1990). It argues that a rmcan achieve competitive advantage by
handling an efcient materialow process, careful utilization of
assets; and acquisition anddissemination of superior process
knowledge (Tan, Kannan, &Narasimhan, 2007). Superior operations
capability increase efciencyin the delivery process, reduce cost of
operations and achievecompetitive advantage (Day, 1994). Extant
literature emphasizes therole of an integrative approach in
combining marketing and opera-tions capability; and suggest
operations success is a pre-condition tomarketing success
(Hausmana, Montgomery, & Roth, 2002; Tatikonda&
Montoya-Weiss, 2001). Thus, we hypothesize
Hypothesis 2a. The greater is the operations capability of a rm;
thebetter is its business performance
2.2.4. Moderating effect of rm efciency on operations
capabilitybusiness performance linkage
Extant literature suggests that the impact of operations
capabilityon a rm's business performance varies according to a rm's
owncharacteristics (Ortega & Villaverde, 2008; Song, Benedetto
et al.,2007; Song, Droge et al., 2005). Operations capability is
likely to bemore important for rms which are not cost effective at
this momentand want to reduce their cost of operations, develop
their productionfacilities, improve their value proposition to
their customers, and thusincrease their efciency in running their
business (Song et al., 2008).Operations capability improves
performance of rms which competeswith superior competitors from a
relatively disadvantaged position interms of product and process
development, cost of operations, andinnovative characteristics
(Ortega & Villaverde, 2008). Strategicmanagement literature
suggests that operations capability has variedimpact on performance
depending on they way in which rms alignthemselveswith their
business environment (McDaniel & Kolari,1987;Song et al., 2005;
Wu, Yeniyurt, Kim, & Cavusgil, 2006). Innovatorshave superior
product engineering technology, high R&D budgets, andprioritize
technology as a source of competitive advantage. Followersare more
interested to maintain status-quo, rely less on new product/service
development, do not invest resources to understand andforecast
technological changes. This follows the RBV rationale asoperations
capability is inimitable, immobile and classied as a sourceof
competitive advantage. Cool and Schendel (1988) demonstrates
thatrms in the samemarket segment having similar operations
capabilitydiffer in terms of their nancial performance. Using the
abovearguments, we posit rms which are less efcient in
resourcecapabilityperformance transformation need superior
operationscapabilities, and such capabilities have cumulative
effect on theirbusiness performance. Thus, we suggest the
following:
Hypothesis 2b. Operations capability has a stronger impact on
business
performance for inefcient rms rather than the efcient
ones.2.2.5. Diversication strategies and performanceRamanujam and
Varadarajan (1989) dene diversication as the
entry of a rm into new lines of business activity through
internalbusiness development or acquisition. Strategic management
literaturehas delved extensively onwhy a rm diversies, cost of
diversication,when diversication can improve rm performance and
when it isdetrimental to it (Chakrabarti et al., 2007; Montgomery,
1994;Ramanujam & Varadarajan, 1989). The principal reasons for
diversi-cation are perceived benets associated with greater target
market,utilization of unused productive capacity, risk reduction in
terms ofdiverse portfolio of business, and capability build-up.
Conceptually,diversication should have a positive inuence on rm
performanceas it helps the rms to achieve economies of scale,
greater reach, andleverage its experience in other markets (Rumelt,
1974). However,empirical studies on the role of diversication on rm
performancegive a different result. Montgomery and Wernerfelt
(1988) suggestthat diversication has negative impact on
performance. Diversica-tion often increase the cost of operation,
causes conict in terms ofgreater managerial and organizational
complexities; and inhibitsrms from responding to major external
changes (Chakrabarti et al.,2007; Grant, Jammine, & Thomas,
1988). Researchers have studied theeffect of product/service
diversication (Berger & Ofek, 1995; Bettis &Mahajan, 1985),
and international diversication (Ghoshal, 1987; Kimet al., 1993) on
rm performance. In this study, we focus on the servicediversication
aspect as the context we have chosen is the servicesector. Service
diversication can either be in related or unrelatedcategory. For
example, logistics rms offer a complete supply chainmanagement
solutions coordinating the ow of information andgoods between
suppliers, manufacturers, retailers and customers issaid to pursue
a related service diversication strategy. They offerwarehousing,
distribution, and inventory management solution to theentire supply
chain and act as an integrated partner to the clientorganizations.
On the other hand, logistics rms transportingconsumer goods like
food, clothing diversify into offering specializedinsurance
services, export, import and customs clearance services issaid to
pursue an unrelated service diversication strategy as offeringsuch
diversied services require different skill sets. Similarly,
inter-national diversication can be in related or unrelated
geographicalmarkets depending on the synergy between the principal
and the newmarkets entered by the logistics rms. RBV theory
explains diversi-cation improves performance if the resources
likemarket knowledgetransferred between partners are rare, valuable
and inimitable(Prahalad & Hamel, 1990). Thus related
diversication improves rmperformance through better use of
resources and capabilities, whereasunrelated diversication exceeds
the range of resource utilization,surpasses management capabilities
and proves to be detrimental torm performance (Tallman & Li,
1996). Extant literature suggests thatthere exist a mixed relation
between diversication and rmperformance (both positive and negative
according to context) andthe relationship is not a linear function
but turns out to be U shapedcurvilinear (Datta, Rajagopalan, &
Rasheed, 1991; Geringer, Tallman, &Olsen, 2000; Narasimhan
& Kim, 2002). In this study, we do notattempt to study the
curvilinear impact of diversication as our focusis not to identify
the threshold point in diversication where itsimpact on rm
performance changes from positive to negative or viceversa. Rather,
on the basis of the above arguments on the impact ofdiversication
on long term business performance, we propose:
Hypothesis 3a. Diversication (service and international) has
anegative impact on a rm's business performance
2.2.6. Moderating effect of rm efciency on
diversication-businessperformance linkage
RBV theory assumes a rm to be a source of distributed
knowledge(Tsoukas, 1996). Although, managers assume that such
knowledge trans-
fer is seamless between the parent organization and the
diversication
-
and return on capital employed which directly reects how well
alogistics rm is able to convert its inputs to generate
superiorprotability. Return on assets measures protability of a rm
relativeto its total assets and indicates earnings of a rm
generated from itsassets. Return on capital employedmeasures on
howwell a rm is ableto utilize its capitals to generate revenue. It
indicates the efciency andprotability of a rm's capital investment.
Such choice of measures iswell supported in DEA literature like to
study protability efciency ofFortune 500 companies (Zhu, 2000);
operational efciency of thirdparty logistics providers (Min &
Joo, 2006). Also, such measures arewidely employed by logistics
companies (evident from their annual
321P. Nath et al. / Industrial Marketing Management 39 (2010)
317329partners, but it does not take place always in a real life
world.Diversication literature suggests that rms which are
successful insuch knowledge transfer between parent and partners
are also successfulin their resourcecapabilitiesperformance
transformation. Chatterjeeand Wernerfelt (1991) posits that the
impact of diversication onperformance depends on the resource
(knowledge, technology) proleofrms, andrmswith superior
resourceportfolio are likely tohavebetterdiversication performance.
Song et al. (2005) highlights the role ofmarketing and technology
(operations in our context) capabilities onperformanceandsuggests
thedifferential effectsof such resourcesdependon how a rm transfers
knowledge between itself and its subsidiaries.Fang et al. (2007)
empirically demonstrate that success in internationaldiversication
depends on the rms' capability to transfer knowledge toits
subsidiaries. Thus, we conclude, rmswith greater allocated
resources,lesser cost of operations and superior information
processing power havebetter capability to handle the challenges of
diversication. Based on theabove argument, we propose:
Hypothesis 3b. The negative impact of diversication (service
andinternational) on business performance is less negative for
efcientrms rather than the inefcient ones
3. Methodology
3.1. Description of the data set
We chose the logistics companies in UK specializing in
roadtransport to test our conceptual framework. These companies
havingthe primary UK SIC code as 6024 and provide a wide range of
serviceslike outsourced logistics services for manufacturing and
retailcustomers; operate in sectors like industrial, consumer, and
food;design, implement and handle supply chain solutions; operate
ware-houses and vehicles for their customers. The data is retrieved
fromFAME data base for the year 20052006. This is a database
whichcaptures information from audited nancial statements available
inpublic domain for all listed UK based companies. Initially, we
obtainedtop 200 logistics rms based on their turnover. Out of that,
98companies did not have complete information. So, in our nal
study,we chose 102 logistics rms and used their archival data for
analysis.The logistics services offered by these companies can be
broadlyclassied into freight forwarding (22%), warehousing (12%),
transpor-tation of goods (13%), whereas the majority (53%) offer a
mix of allthese services. These companies cater to awide range of
industries likeautomobile, retail, engineering equipment
manufacturers, construc-tion; and offer specialized services such
as temperature controlledtransportation and a host of supply chain
management services.
3.2. Framework for measuring rm efciency
RBV theory considers a rm uses its resources (inputs) to
generatebusiness performance (outputs) through functional
capabilities (pro-cess transformation). Thehigher is the
transformative powerof therm;the better is the chance to achieve
its nancial objectives. A rm isclassied as efcient if it is able to
maximize its nancial performancewith its given resource
constraints. A rm is classied as inefcient ifthere are other rms in
the industrywho can generate the same level ofoutputs with less of
at least one resource. Relative efciency of rms ismeasured by the
ratio of weighted sum of nancial performancemeasures (outputs) to
the weighted sum of resources used (inputs).
In this study, we use data envelopment analysis DEA
(Charnes,Cooper, & Rhodes, 1978) as a tool to measure this
inputoutputtransformation. DEA framework helps this study in
several ways:(i) identify rms that are efcient and inefcient in
inputoutputtransformation this help in benchmarking of rms (ii)
estimate themaximum output development potential for inefcient rms
relative
to the efcient ones this canmeasurewhere and by howmuch a rmcan
improve. We use DEA in two stages (i) measure efciency of rmsin
terms of their overall resourceperformance transformation
andclassify them into efcient and inefcient groups, (ii) measure
themarketing and operations capability of rms in terms of
theirefciency in transforming marketing and operations resources
(func-tion specic inputs) to marketing and operations objectives
(functionspecic outputs). This is done separately for the efcient
andinefcient group of logistics rms. In the next section, we give a
briefoverview of DEA and then we describe our inputoutput variables
tomeasure rm efciency.
3.2.1. Data envelopment analysis (DEA) an overviewDEA is an
operations research technique to measure relative
efciency of rms (also called decision making units DMUs) thatuse
multiple inputs to produce multiple outputs. DEA identies DMUsthat
produces the largest amounts of outputs by consuming the
leastamounts of inputs. These DMUs are classied as efcient and
belong tothe efciency frontier (Cooper, Seiford, & Tone, 2006).
The concept ofDEA is explained in Fig. 2. Consider a single
inputoutput hypotheticalexample of ve rms which uses a varying
level of marketingexpenditures (input) to generate their marketing
objectives (output).From Fig. 2, we identify that rms B and D use
more resources togenerate less outputs compared to C and E. Thus, B
and D fall below theefciency frontier and are classied as inefcient
rms. On the otherhand, some rms (A, C, and E) maximize their
resource-objectivestransformation, fall on the efcient frontier,
and are classied asefcient rms. There are numerous applications of
DEA in marketingparticularly to study marketing communication
efciency (Luo &Donthu, 2006), marketing productivity (Donthu,
Hershberger, &Osmonbekov, 2005), advertising efciency (Luo
& Donthu, 2001).
3.2.2. Inputs and outputs to measure rm efciencyBusiness
performance is a multi-dimensional construct. We chose
two inputs total assets andworking capital (see Fig. 3). A
logistics rmuses assets like warehouses, trucks, trailers,
containers, as well as landand building to manage critical
inventories, consolidate freightservicing and improves value added
services to their customers. Assetsare used by the rm to generate
cash owand increase its value. It alsouses working capital which is
more like liquid assets to expand andimprove business operations.
Working capital also signies theoperational efciency of a rm in
terms of how it is able to use itscurrent assets like cash, account
receivables, inventories to meet theshort term needs. We chose two
output measures return on assets
Fig. 2. DEA efciency frontier illustration.reports) to measure
their protability. We use input oriented constant
-
reso
322 P. Nath et al. / Industrial Marketing Management 39 (2010)
317329return to scale (CRS) DEA model (Cooper et al., 2006) to
measure theefciency of such transformation (see Appendix for
detailedformulation).
3.3. Measuring marketing capability
Traditionally, marketing literature has always measured
marketingcapability using subjective survey based indicators, such
as knowledgeof competitors, effectiveness of advertisement, and
managing durablecustomer relationships (Song, Benedetto et al.,
2007; Song, Droge et al.,2005). There is a debate in literature
about the accuracy of results whichhave been derived on the basis
of managers' perception. Mezias andStarbuck (2003) concluded that
survey studies based on managerialperception data often yield
erroneous results as managers' perceptionabout their organization
or its environment are often not accurate. So, inthis studywe
decided to use archivalnancial data for our analysis.
Veryfewstudies attempted tomeasuremarketing capabilityusing
secondary,archival data (Dutta et al., 1999; Narsimhan et al.,
2006). As marketingcapability is an integrative process in which a
rm uses its resources toachieve its market related needs of
business (Vorhies & Morgan, 2005),so we use the inputoutput
framework to measure it and archivalnancial data is the bestway to
do it. The marketing goal of a rm is toenhance the value of its
products/services in the minds of current andfuture customers. This
goal is partly reected in increase of sales throughbetter
understanding of consumer needs, and proper positioning totarget
customer groups.We thus use sales as the output measure. Usingsales
as an output for marketing activity is also supported in
literature(Dutta et al., 1999; Kotabe, Srinivasan, & Aulakh,
2002; Slotegraff,Moorman, & Inman, 2003). This goal is achieved
by increasingexpenditure in all marketing related activities, such
as trade promotion,marketing communication, and customer
relationship management. Inthis study, we assume that increasing
sales is the principalmotivation ofrms to engage in building
marketing capability, and consider the costsinvolved to achieve
sales as the marketing resources. We thus use fourinputs as
measures of marketing resources: stock of marketingexpenditure,
intangible resource, relationship expenditure and installedcustomer
base. First,we take the stock ofmarketing expenditurewhich isdened
as the total amount ofmoney spent by a rm in all
itsmarketingrelated activities (Narsimhan et al., 2006). This is
measured by sales,general and administrative expenses (SGA) and is
a proxy for expenseslike on market research and sales effort (Dutta
et al., 1999). A logistics
Fig. 3. Framework to measurerm uses such expenditures to offer
better incentives to its customersand sales team. Second, we take
the intangible resources which reect arm's success in building
relationship and brand equity (Slotegraff et al.,2003). This is
measured by the monetary value of intangible assets asreected in
nancial statements. It is a proxy for a rm's brand equityand other
intellectual property rights like patents, goodwill for which arm
can charge a price premium. In a competitive business to
businessenvironment like logistics, investing in building brand
equity in themarket is extremely important. Third, we include
relationship expen-ditures which are measured by cost of
receivables. It is a proxy forcustomer relationship effort made by
a rm (Dutta et al., 1999) andincludes all claims against cash used
by a rm to build and maintaincustomer relationships. Logistics rms
use such investments to offerbetter trade incentives like higher
credit margin and period to buildcustomer relationships. Fourth, we
use installed customer base as amarketing resource. This is dened
as the stock of sales from previouscustomers (Dutta et al., 1999).
A rm uses its existing base of customersto improve its sales
through cross-selling and up-selling. It is measuredby the growth
in sales revenue (Vorhies & Morgan, 2005). It
indicatesmarketing effectiveness by capturing spillover from
previous sales. Inany industrial setup like logistics, repeat sales
from existing customerbase is quite important.
So, we use the following marketing frontier function:
Sales = f stock of marketing expenditure; intangible
resources;relationship expenditure; installed customer base
1
In the inputoutput classication, marketing capability of a
rmmeasures how close it is to the sales frontier given a set of
resources(see Fig. 4). Thus the closer is the sales value realized
by the rm fromthe sales frontier, the better is its marketing
capability. We use inputoriented constant return to scale (CRS) DEA
model (Cooper et al.,2006) to measure the efciency of such
transformation for both theefcient and the inefcient group of rms.
The DEA efciency scoremeasures marketing capability of each rm. We
also measure relativemarketing capability of each rm dened as
Rel MC i = MC i=Xmi=1
MC i =m !
2
where (Rel_MC)i=relative marketing capability of ith rm
(MC)i=marketing capability of the ith rmm=number of rms in each
group (efcient and inefcient).
3.4. Measuring operations capability
The operations goal of a logistics rm is to deliver the goods to
theright place in the right time at aminimumcost (Novack &
Thomas, 2004).Efciencyof operations functionsof a
logisticsrmthroughall its principalactivities like transportation,
inventory control, warehousing, orderprocessing is driven by its
objective to reduce costwithout compromisingon its quality of
service (Novack, Rinehart, & Langley, 1995). From themarketing
perspective sales maximization is the key performance driver,
urceperformance efciency.whereas from theoperationsperspective
costminimization andefciencywithout compromising on quality is the
key performance driver (Duttaet al., 1999). Marketing involves
customer interface, so the ability of therm to grow its sales is an
indicator of its marketing efciency. On theother hand, operations
function involves production and delivery ofproducts/services, so
the ability of the rm to produce and deliver at aminimum cost
without compromising on quality is an indicator ofoperations
efciency (Piercy, 2007). In this study, we assume costminimization
is the business objective of rms from their operationsfunction.
Extant literature has measured operations capability
usingsubjective, survey based measures like efciency in delivery
process,technology development capabilities, new product/service
developmentcapabilities (Song, Benedetto et al., 2007; Song, Droge
et al., 2005).
-
asu
323P. Nath et al. / Industrial Marketing Management 39 (2010)
317329Logistics studies, on the other hand, has used both soft
(perceptual survey based) and hard measures (archival nancial), as
well asengineering measures like asset management, eet management,
fuelefciency, loading costs, labor costs and storage costs to
measureoperations capabilities of logistics rms (Caplice &
Shef, 1995; Mentzer& Konrad, 1991; Novack & Thomas, 2004).
Excellent discussion ofmeasures used in logistics performance
measurement studies can befound in Chow, Heaver, and Henriksson
(1994). In our study, we focusratheronamoregenericproblemon
functional capabilitiesmeasurement.Following RBV rationale, we use
the inputoutput framework tomeasureoperations capability of a rm.
We use cost of operations as the outputmeasure (Dutta et al., 1999;
Narsimhan et al., 2006). This is dened as allthe costs incurred by
the rm tomanufacture, create and deliver product/service to its
customers. In case of logistics rms, we use cost of sales as aproxy
for cost of operations. This includes all direct and indirect
expensesincurred by the rm like order processing costs, lead
generation costs inorder to boost its sales. We use two inputs to
measure operationsresources: cost of capital and cost of labor.
Logistics industry is capital andlabor intensive. It uses capital
like warehouses, trucks, and qualitymanpower likemanagers,
dispatchers, cargohandlers, drivers to provideservice to its
customers. So, cost of capital is our rst input. This cost
ofcapital is used by the logistics rms to improve on their
businessinfrastructure (like newer eets, delivery depots) and
upgrading theirprocess technology to deliver better service to
their customers. We usetangible assets from the nancial statements
as a proxy for cost of capital(Min& Joo, 2006). Our second
input is cost of laborwhich is dened as thecost of employee'swages
and benets tomaintain superior service (Duttaet al., 1999). This
labor cost includes the cost of recruiting and retaininghigh
quality employees. We use remuneration (salaries and wages)
ofemployees as a proxy for cost of labor (Min & Joo, 2006).
High quality ofmanpowerwith tremendous functional and domain
knowledge is used as
Fig. 4. Framework to mea source of competitive edge by logistics
rms. Use of such archival hardnancialmeasuresare also supported
inproductivity literatureon logistics
Fig. 5. Framework to measurrms (see Abrahamsson & Aronsson,
1999 for a review on how nancialmeasures are used alongwith
engineeringmeasures like delivery quality,transit time, capacity
utilization and transportation cost per unit).
So, we use the following operations frontier function:
Cost of operations = g cost of capital; cost of labor 3
Operations capability is the closeness of the rm to the cost
frontier.We use input oriented constant return to scale (CRS) DEA
model(Cooperet al., 2006) tomeasure theefciencyof such
transformation forboth the efcient and the inefcient group of rms.
The DEA efciencyscore measures operations capability of each rm
(see Fig. 5). We alsomeasure relative operations capability of each
rm dened as
Rel OC i = OC i =Xmi=1
OC i =m !
4
where (Rel_OC)i=relative operations capability of ith rm
(OC)i=operations capability of the ith rmm=number of rms in each
group (efcient and inefcient).
3.5. Measuring performance
Studies on business performance measurement have consideredboth
the nancial measures such as sales, prot margin, return
oninvestments (Song, Benedetto et al., 2007; Song, Droge et al.,
2005)and non-nancial measures like customer orientation,
competitororientation, customer satisfaction, market effectiveness
(Olson, Slater,& Hult, 2005; Vorhies & Morgan, 2005) to
measure rm performance.In this study, we focus on the nancial
measure of performance forlogistics rms. Specically, we consider
protability as a measure of
re marketing capability.logistics rms' business performance. We
use operating prot as anindicator of the rm's protability as it
best reects the efciency of
e operations capability.
-
ditu
tion
ed
324 P. Nath et al. / Industrial Marketing Management 39 (2010)
317329the rm in its resource-output transformation (Min & Joo,
2006). Wealso measure relative performance of each rm dened as
Rel Perf i = Perf i=Xmi=1
Perf i =m !
5
where (Rel_Perf)i=relative performance of ith rm
(Perf)i=performance of the ith rmm=number of rms in each group
(efcient and inefcient).
3.6. Measuring diversication strategies
Diversication (both product/service and
international/geo-graphic) is often measured in strategic
management literature byusing measures like entropy (Palepu, 1985)
or Herndahl Index(Chakrabarti et al., 2007). Application of such
measures requires
Table 1Variables and their measures.
Variables
Marketing capabilityResources Stock of marketing expen
Intangible resourcesRelationship expenditureInstalled customer
base
Objectives Sales
Operations capabilityResources Cost of capital
Cost of laborObjectives Cost of operations
Diversication strategyService diversication Sectoral
concentrationInternational diversication Foreign market
concentra
Business performance Protability
EfciencyInputs Assets
Working capitalOutputs Return on assets
Return on capital employinformation on market share (in terms of
sales value) of the variousproducts/services offered by the rm or
the geographical markets inwhich the rm operates. In our case, such
information on sales guresaccording to service portfolio or
geographical market is not available.So, for service diversication,
we measured the actual number ofsectors like automotive, clothing,
food retail, non-food retail, buildingmaterials inwhich the rm
operates. We collected this information onindividual rm's portfolio
or sectoral concentration from their annualreports and websites.
For international diversication, we use thenumber of foreign
subsidiaries of the rm. The number of globalmarket regions in which
a rm operates is indicated by its number offoreign subsidiaries
(Narasimhan & Kim, 2002). So, a rm operating inmore sectors or
having bigger service portfolio is considered to havegreater
service diversication, and rm having more number offoreign
subsidiaries is considered to have greater
internationaldiversication. We also measure relative diversication
level of eachrm dened as:
Rel Div i = Div i=Xmi=1
Div i =m !
6
where (Rel_Div)i=relative diversication level of ith rm
(Div)i=diversication level of the ith rmm=number of rms in each
group (efcient and inefcient).This measure is calculated for both
service diversication andinternational diversication. Since, sales
gures of rms for eachsector in which they operate or each
geographical market in whichthey operate is not available publicly,
so an entropymeasure (like ratioof sales in individual sector to
total sales) cannot be computed. Werather use the diversication
scores as measured by Eq. (6) as a proxyfor the diversication
entropy as it measures the level of diversica-tion strategy of each
rm relative to the industry average.
3.7. Hypotheses testing
We estimate the relationship between functional
capabilities,diversication strategies, and rm's overall business
performanceusing the following least square regression
equation:
Rel Perf = o + 1Rel MC + 2Rel OC + 3Rel SERVDIV
+ 4Rel INTDIV + e
7
Measures (in GBP )
re Sales, general and administrative expenses (SGA)Intangible
assetsCost of receivablesSales growthTurnover
Tangible assetsRemunerationCost of sales
Number of sectorsNumber of foreign subsidiaries
Operating prot
Total assetsActual valueActual value (%)Actual value (%)where
Rel_Perf=relative performance of rms (measured by
relativeprotability)
Rel_MC=relative marketing capabilityRel_OC=relative operations
capabilityRel_SERVDIV=relative service diversication
strategyRel_INTDIV=relative international diversication
strategy
Since, we use frontier function to estimate the performance of
arm relative to its industry benchmarks, so we use relative gures
inthe above equation. Table 1 summarizes our choice of
functionalresources, capabilities, their output objectives,
business performance,diversication strategy and the variables to
measure rm efciencywith their operationalization. All the variables
are measured in poundsterling except the diversicationmeasures
which are number of unitsand the ratios expressed in form of
percentage.
4. Results
We followed a three-stage approach in our data analysis. In
stage one,we use DEA efciency frontier function to classify the
logistics rms intoefcient and inefcient group relative to the
industry frontier. In thesecond stage, we again use DEA efciency
frontier function to measuremarketing and operations capability of
each rm relative to the industryfrontier. This is done for both the
efcient and the inefcient group of
-
Efcient (n=30) Inefcient (n=72)
Mean SD Mean SD
0.58 0.28 0.53 0.228507.48 9765.28 19,670.64 38,270.88963.79
2467.01 6037.12 13,460.17287.83 3763.99 27,855.56 46,498.167131.552
6282.55 16,423.68 32,880.81
53,448.17 35,875.64 172,892.8 304,613
0.30 0.30 0.18 0.214846.31 4183.11 37,495.32 59,894.94
10,848.21 9427.14 57,022.27 113,208.141,637.24 31,340.55 152,405
280,774.7
2.34 4.48 5.65 3.242.44 4.61 6.8 4.45
5504.27 11,664.26 2710.69 3122.24
18,563,434 14,081,837 120,307,267 288,680,5823,205,503 3,587,039
16,163,520 36,531,118
14.31 10.37 4.70 4.0165.82 14.91 10.98 10.55
for international diversication.ent for international
diversication.went for international diversication.
325P. Nath et al. / Industrial Marketing Management 39 (2010)
317329rms. In stage three, we do the hypothesis testing by
regressing thefunctional capabilities and diversication strategy on
rm's businessperformance. In this section, we explain the results
of each stage of ouranalysis.
4.1. Classication of logistics rms on the basis of their
business efciency
Table 2Descriptive summary of measures.
Overall (n=102)
Mean SD
Marketing capability 0.54 0.26Stock of marketing expenditure
16,496.8 33,108.30Intangible resources 4594.71
11,667.49Relationship expenditure 22,036.3 40,389.34Installed
customer base 13,781.8 28,272.75Sales 139,648.7 255,368
Operations capability 0.19 0.23Cost of capital 28,212.75
52,727.62Cost of labor 43,894.35 97,974.38Cost of operations
120,912.2 242,883.6
Diversication strategySectoral concentration 3.12 3.41Foreign
market concentration 5.56 7.21
Business performanceProtability 4710.02 10,064.04
EfciencyAssets 91,372,042 248,172,272Working capital 12,479,378
31,454,959Return on assets 7.43 7.76Return on capital employed
26.57 6.22
In the overall sample (n=102), 48 rms went for product
diversication and 78 wentIn the efcient group (n=30), 16 rms went
for product diversication and 16 rms wIn the inefcient group
(n=72), 32 rms went for product diversication and 62 rmsLogistics
rms are classied as efcient or inefcient on the basisof their
ability to transform available resources to generate
superiornancial performance. The classication is done by using
DEAefciency scores. We use the cut-off efciency score of 0.5 (on
anefciency range between 0 and 1). Logistics rms with efciencyscore
of 0.5 or more are classied as efcient; otherwise they areclassied
as inefcient. We got 30 out of 102 rms classied asefcient (about
28%); and the remaining 72 rms as inefcient. Thiscorroborates with
the logistics industry turnover gures where 26%of the companies
control the majority of the market share (Ofceof National
Statistics, 2006). Table 2 gives the summary measuresfor all rms
(n=102), efcient rms (n=30), and inefcient rms(n=72).
4.2. Hypotheses testing
Using RBV framework in the backdrop, we test the hypotheses
onhow a logistics rm uses its resources to generate
functional(marketing and operations) capabilities, role of
diversication, andhow all these constructs lead to performance. We
test our hypothesesin two stages. First, we test the impact of
marketing, operationscapability and diversication strategy (both
service and internatio-nalization) on a rm's business performance.
We do it for all rmstaken together (n=102). Second, we test the
moderating impact ofrm efciency on the relationship between
functional capabilities anddiversication on performance. We test
this moderating effect bysubgroup analysis (Sharma, Durand, &
Gur-Arie, 1981). For this, weclassify the rms into efcient (n=30)
and inefcient (n=72) andthen run ordinary least square regression
within each sub-groups.Table 3 summarizes the results.4.2.1. For
the overall industry (n=102)We found adjusted R2=0.15, and as
hypothesized, a positive
association between marketing capability and business
performance(=0.21, pb0.1); and operations capability and business
perfor-mance (=0.11, pb0.1). Service diversication has negative
impacton business performance although the result is not
statisticallysignicant. Contrary to our expectation, international
diversication
has positive impact on performance (=0.17, pb0.1). Thus, we
foundsupport for Hypotheses 1a and 2a but Hypothesis 3a is not
supported.
Table 3Regression results for business performance as criterion
variable.
Standardized coefcient t-value Hypothesis
Main effectOverall (n=102)Marketing capability 0.21 3.09 H1a:
SupportOperations capability 0.11 2.08 H2a: SupportService
diversication 0.07 0.76 H3a: No supportInternational diversication
0.17 2.72Fit statisticsAdjusted R2 0.15F-value 4.26
Moderation effectEfcient group (n=30)Marketing capability 0.38
3.72Operations capability 0.13 2.01Service diversication 0.27
1.26International diversication 0.17 0.87 H1b: SupportFit
statisticsAdjusted R2 0.23F-value 2.82 H2b: SupportInefcient group
(n=72)Marketing capability 0.22 2.85 H3b: No supportOperations
capability 0.14 2.24Service diversication 0.08 0.72International
diversication 0.27 2.34Fit StatisticsAdjusted R2 0.15F-value
3.08
pb0.1.
-
326 P. Nath et al. / Industrial Marketing Management 39 (2010)
3173294.2.2. Test of moderation for efcient rms (n=30)We found
adjusted R2=0.23, and as hypothesized a positive
association between marketing capability and business
performance(=0.38, pb0.1), operations capability and business
performance(=0.13, pb0.1). Thus, we nd the impact of marketing
capability onbusiness performance is more than the impact of
operations capabilityfor efcient rms. Both service diversication
and internationaldiversication has negative impact on business
performance althoughthe results are not statistically
signicant.
4.2.3. Test of moderation for inefcient rms (n=72)We found
adjusted R2=0.15, and as hypothesized both marketing
(=0.22, pb0.1) and operations capability (=0.14, pb0.1)
havesignicant positive impact on a rm's business performance. When
wecompare the results of the impact of marketing capability on
thebusiness performance for the efcient group (=0.38, pb0.1)
andinefcient group (=0.22, pb0.1), we nd that marketing
capabilityhasmore impact in case of efcient groupofrms. Thus,wend
supportfor Hypothesis 1b. Similarly, whenwe compare the impact of
operationscapability on the business performance for the efcient
group (=0.13,pb0.1) and inefcient group (=0.14, pb0.1), we nd that
operationscapability has more impact for inefcient group. Thus, we
nd supportfor Hypothesis 2b. Service diversication has a negative
impact onbusiness performance although the result is not
statistically signicant.International diversication has signicant
positive impact on businessperformance (=0.27,pb0.1).Whenwe compare
the relative impact ofservicediversication for efcientrms (=0.27,
not signicant) andinefcient rms (=0.08, n.s), we do not nd that
protabilityefciency of rms to have any moderating effect on
diversication andperformance linkage. Similarly, whenwe compare the
relative impact ofinternational diversication for efcient rms
(=0.17, n.s) andinefcientrms (=0.27, pb0.1), we donot nd
anymoderation effect.Thus, contrary to our expectations, Hypothesis
3b is not supported.
5. Discussions, implications, and conclusions
5.1. Functional capabilities and performance
Results show overall, marketing capabilities dominate
rm'sbusiness performance. This is consistent with previous studies
likeDutta et al. (1999), Kotabe et al. (2002), Song et al. (2005),
Vorhiesand Morgan (2005). Marketing capability of a rm
particularly, inbusiness to business service sector like logistics
industry depends onits ability to understand customer needs and
create long termrelationships. This is possible if the rm is able
to deploy its marketingresources optimally to generate superior
customer value using itsunique, inimitable marketing capability. In
an industrial marketsetting, marketing assets like stock of
marketing expenditures whichare the expenses incurred by a rm to
improve its sales effort,relationship expenditures to build and
maintain trade relationshipsare extremely crucial. Moreover, the
majority of the business isgenerated through the network of
existing customer base and thus theimportance of building up brand
equity becomes more critical. So, in ahighly competitive industry
like logistics, better marketing capabilitylead to competitive
advantage for rms and help them to achievesuperior business
performance.
Our results show that operations capability has a signicant
impacton a rm's business performance. This reiterates the
importance ofinfrastructure development like eet upgradation,
extension of dis-tribution network, and improvement of technology
usage for logisticsrms. Thus, superior performance in operations
function can enhancelogistics rm's ability to increase connectivity
with their customers andsuppliers, provide more exibility in
operations and improve the valueproposition in the entire supply
chain. So, we can conclude that anefcient integration of marketing
and operations functions leads to
improved organizational performance. This is consistent with
previousresearch on the integrative role of these functional
capabilities onbusiness performance (Kelly & Flores, 2002).
Our study indicates that marketing capability has more impact
onbusinessperformance forrmswhichare efcient. Our results shows
forlogistics rms which have better resource-performance
transformationabilities, marketing capabilities dominate over
operations capabilities.Firms with superior marketing capabilities
are proactive in under-standing changing customer requirements in
terms improved servicestandards. Suchrmswith their inherentmarket
knowledge offer bettervalue creation for the customers. This
corroborates with the marketorientation literature which suggests
that rms with stronger marketorientation develops better marketing
capabilities, and it positivelyinuence business performance
(Jaworski &Kohli,1993;Narver& Slater,1990). Market-driven
rms have better marketing capabilities than theothers and generate
superior performance (Vorhies & Morgan, 2005).Superiority
ofmarketingover operations capability is alsohighlighted inother
business to business sectors like high technology industry (Duttaet
al., 1999; Narsimhan et al., 2006).
For logistics rm managers, the implication is clear:
althoughmarketing capability has a stronger impact on business
performancebut successful integration of functional capabilities is
the key tosuccess. Careful deployment of resources on marketing
activities likeadvertisement, trade promotion , and customer
relationship manage-ment develop a powerful marketing strategy and
investment indeveloping the infrastructure is necessary to build
operationsefciency to meet customer demand. Superior marketing
capabilityis essential for achieving maximum nancial performance
andimproving efciency. Inefcient logistics rms have relatively
largerexpenditures for building their operations capabilities (cost
ofoperations/turnover=0.88) compared to efcient rms (0.77).Since,
the impact of functional capabilities on performance differbetween
rms on the broad range of efciency spectrum, it hastremendous
implication on resource allocation decision. Inefcientrms should
invest more resources on building their marketingcapabilities so
that they can expand their market, communicate withcurrent and
potential customers in a better way, and be competitive inthe long
run. Over reliance on operations capability like
buildinginfrastructure cannot give rms the extra edge as marketing
capabilityis found to be the key to success.
5.2. Diversication strategies and performance
Our results show overall diversication has a negative impact
onlogistics rm's performance. This is evident for both the efcient
andthe inefcient group which suggests that rm inputoutput
transfor-mation efciency does not moderate the impact of
diversicationstrategy on rm performance. This is consistent with
diversicationliterature which emphasize that not all rms improve
their perfor-mance through diversication (Chakrabarti et al., 2007;
Ramanujam &Varadarajan, 1989). Diversication (both in terms of
product/serviceand geographical territory) require assimilation of
extensive knowl-edge in terms of new product/service development,
understandingcultures in the newmarkets, and transfer of resources
between parentand the partner companies. This is consistent with
RBV literaturewhich highlights capabilities transfer like business
knowledgebetween parent and partners is a complex process
(Chatterjee &Wernerfelt, 1991; Fang et al., 2007). However, our
study nds negativeimpact of service diversication and positive
impact of internationaldiversication on business performance under
certain context. This isconsistent with extant literature
(Narasimhan & Kim, 2002; Tallman &Li, 1996). Service
diversication requires leveraging rm's strategicresources and
functional capabilities across the product/servicespectrum. In case
of related service diversication, this portfolioexpansion remains
within the scope of a rm's resource-capabilitiesand it can achieve
economies of scale and better performance. On the
other hand, in case of unrelated service diversication, the
scope
-
327P. Nath et al. / Industrial Marketing Management 39 (2010)
317329surpasses management capabilities and raises costs (Geringer
et al.,2000). In our study, although we do not measure specically
therelatedness of diversication, it is evident that rms are not
ableto leverage their resource-capabilities to expand their
serviceportfolio. International diversication, on the other hand,
requiresunderstanding of the local business environment in a new
geogra-phical market. It requires active participation from local
partners,increased local ownership. In competitive industries like
logistics,rms face diminishing prot margin. Our results indicate
that prudentinternationalization strategy help logistics rms to
leverage itscapabilities and reap the same benets across markets.
Firms diversifyinto global market to avoid being dependent on
supply and demanductuations in one nationalmarket. Such
diversication help the rmsto smooth the peaks and troughs in the
revenue stream, exploiteconomies of scale and scope, develop
diverse capabilities, and gaincost advantages.
5.3. Conclusions
Our study contributes to marketing literature in several
ways.First, we empirically verify the theoretical tenets of RBV
logic thatresources and capabilities produce different performance
resultsdepending on the complex process in which a rm integrates
thecumulative effect. We capture three key drivers of rm
performance,namely marketing capability, operations capability, and
diversica-tion strategy together. We offer an integrated framework
to nd outthe relative importance of each of these drivers on
overall nancialperformance. We consider this triangulation approach
to be veryimportant as rms are often surrounded by uncertainty and
incorrectbeliefs about the relative importance of these drivers on
long termperformance. Second, we use an inputoutput framework
formeasuring overall performance and the intangible process
transfor-mation nature of rm's functional capabilities which
captures theessence of RBV framework where a rm has varying powers
toconvert its resources and capabilities to superior performance.
Wepropose a methodology based on an optimization technique
calleddata envelopment analysis (DEA). This methodology helps us
toclassify rms into efcient and inefcient groups on the basis of
theirresource, capabilities to nancial performance transformation.
Third,our study gives the managers of logistics rms in both ends
ofprotability spectrum a measure for their process
transformationinefciencies. Using our methodology, the manager can
identify therelative impact of performance parameters and
understand thedegree of complementarities between them. It provides
a bench-marking tool to the managers and gives superior insights to
theirresource allocation decisions.
This study also has certain limitations. First, we test our
hypothesesusing archival data as we focus more on the
resourcecapabilityperformance framework as suggested by RBV theory.
Such secondarydata do not provide insights into the actual
transformation process onhow different organizations have
assimilated these constructs intotheir business process. Further
in-depth understanding is onlypossible through proper survey based
research. Thus, measures forresources, capabilities and performance
can be further improved bycombining managerial perceptions through
survey data and second-ary nancial measures to make them more
robust and industryspecic. Second, our study is with
cross-sectional data. This researchcan be extended by capturing
data over a period of time to understandhow a rm acquires its
knowledge building capacity and howexperiential learning contribute
to business performance in a long-itudinal scale. Third, in this
study we assume a linear relationshipbetween diversication and
performance. Strategic managementliterature on diversication
highlights the relationship to be curvi-linear. This indicates that
the effect of diversication on performanceis positive for related
diversication and negative for unrelated
diversication. So, our measure for diversication can be extended
tocapture the relatedness aspect of diversication and an assumption
ofquadratic relationship can help to nd out the threshold level
fordiversication. Last, future research can focus on more
functionalcapabilities of rm like IT, technology and modeling the
interactiveeffects of such capabilities and diversication strategy
on rmperformance. This can improve the explanatory power of
ourconceptual framework.
Appendix. Constant return to scale DEA model
Minsubject toP
jjxijVxi0 i = 1;2; N mPjjyrjzyr0 r = 1;2; N s
jz0 ja1;2; N n
where xij and yrj are the amount of ith input and rth output
generatedby the jth rm, m is the number of inputs, s is the number
of outputs,and n is the number of rms in consideration. In our
case,
(1) For the overall rm efciency, m=2 (total assets and
workingcapital), s=2 (return on assets and return onworking
capital),n=102.
(2) For the marketing capability, m=4 (stock of
marketingexpenditure, intangible resources, relationship
expenditures,and installed customer base), s=1 (sales), n=30 (for
efcientgroup), n=72 (for inefcient group).
(3) For the operations capability, m=2 (cost of capital and cost
oflabour), s=1 (cost of operations, n=30 (for efcient group),n=72
(for inefcient group).
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-
Prithwiraj Nath is a Lecturer in Marketing at the University of
Nottingham BusinessSchool, U.K. His research area includes services
marketing, marketingoperationsinterface, marketing performance
measurement, impact of marketing on the rmnancials, marketing
performance measurement and benchmarking, marketingdecision models,
and e-logistics. He specializes in application of operational
researchtools to solve marketing problems. His research has
appeared in several top-ratedMarketing and Operations journals such
as Industrial Marketing Management, Journalof Services Marketing,
International Journal of Bank Marketing, European Journal
ofMarketing and Journal of the Operational Research Society.
Subramanian Nachiappan is a visiting scholar at Nottingham
university businessschool, Nottingham and serves as an Assistant
Professor in Thiagarajar College ofEngineering, Madurai, India. His
research interests are performance measurement,supply chain
operations, modeling and analysis of manufacturing systems. He
serves as areferee for various internationally reputed journals His
research has appeared in EuropeanJournal of Operational Research,
International Journal of Production Research, Journal ofAdvanced
Manufacturing Systems, International Journal of Industrial and
Systems Engineer-ing, International Journalof Logistics
SystemManagementand International Journalof Serviceand Operations
Management.
Ramakrishnan Ramanathan is working as an associate professor in
operationsmanagement inNottinghamUniversity Business School, UK.
His research interests includeoperations management, supply chains,
energy, environment, transport and otherinfrastructure sectors,
optimization, data envelopment analysis and the analytic
hierarchyprocess. His research articles have appeared in Supply
Chain Management, InternationalJournal of Operations &
Production Management, European Journal of Operational Re-search,
Computers & Operations Research, Journal of
EnvironmentalManagement, EnergyEconomics, Transport Policy,
Transportation Research, IEEE Transactions on Systems, Manand
Cybernetics, and, IEEE Transactions on Power Systems.
329P. Nath et al. / Industrial Marketing Management 39 (2010)
317329
The impact of marketing capability, operations capability and
diversification strategy on perfo.....IntroductionConceptual
frameworkResource-based view (RBV) a synopsisResources,
capabilities, diversification and performanceMarketing
capabilityModerating effect of firm efficiency on marketing
capability-business performance linkageOperations
capabilityModerating effect of firm efficiency on operations
capabilitybusiness performance linkageDiversification strategies
and performanceModerating effect of firm efficiency on
diversification-business performance linkage
MethodologyDescription of the data setFramework for measuring
firm efficiencyData envelopment analysis (DEA) an overviewInputs
and outputs to measure firm efficiency
Measuring marketing capabilityMeasuring operations
capabilityMeasuring performanceMeasuring diversification
strategiesHypotheses testing
ResultsClassification of logistics firms on the basis of their
business efficiencyHypotheses testingFor the overall industry
(n=102)Test of moderation for efficient firms (n=30)Test of
moderation for inefficient firms (n=72)
Discussions, implications, and conclusionsFunctional
capabilities and performanceDiversification strategies and
performanceConclusions
Appendix. Constant return to scale DEA modelReferences