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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: Prithwiraj.Nath@nottingham.ac.uk

Nachiappan.Subramanian@nottingham.ac.uk (S. Nachiapram.ramanathan@nottingham.ac.uk (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