The Trade-Off Between Customer and Technology …prof.fe.unl.pt/~lflages/papers/JIM_Hortinha_Lages_Lages_2011.pdf · The Trade-Off Between Customer and Technology Orientations: Impact

The Trade-Off Between Customerand Technology Orientations: Impact on Innovation Capabilities and Export PerformancePaula Hortinha, Carmen Lages, and Luis Filipe Lages

ABSTRACTTechnological exporters are constantly challenged by the trade-off between two types of strategic orientations: customerand technology. Nonetheless, research directly addressing this topic is scarce, and few recommendations exist about thebest orientation to emphasize. Using two respondents in the same firm, the export manager and the research-and-development manager, the authors find that customer orientation is as important as technological orientation in thedevelopment of exploratory innovation capabilities. However, when past performance is poor, customer orientation hasa greater role. Exporters with poor past performance may achieve higher export performance levels by focusing moreon customers than on technology. Conversely, firms performing well may risk export performance if they ignore tech-nology orientation. These firms also need to maintain high levels of customer orientation.

Keywords: exporters, innovation, customer orientation, technology orientation, past performance

36 Journal of International Marketing

Researchers agree that firms need to pursue cus-tomer and technological competences simultane-ously because both provide a foundation for inno-

vation (Danneels 2002; Gatignon and Xuereb 1997;Yalcinkaya, Calantone, and Griffith 2007; Zhou, Yim,

and Tse 2005). However, despite the many and broadlyrecognized benefits of a customer orientation (Jaworskiand Kohli 1993; Narver and Slater 1990), firms maylose their innovation competences if they are too cus-tomer focused (Christensen and Bower 1996; Hameland Prahalad 1994; Im and Workman 2004). Becausecustomers are not completely knowledgeable about thelatest market or technological trends, exporters thatoverly rely on customers may overlook technologicalopportunities and therefore become stuck developingincremental innovations. Conversely, an excessive tech-nology orientation may lead to unsuccessful innovation(Gatignon and Xuereb 1997; Kleinschmidt and Cooper1991; Zhou, Yim, and Tse 2005).

The trade-off between customer orientation and tech-nology orientation is of utmost importance and presents

Journal of International Marketing

©2011, American Marketing Association

Vol. 19, No. 3, 2011, pp. 36–58

ISSN 1069-0031X (print) 1547-7215 (electronic)

Paula Hortinha is Marketing Director at Jerónimo Mar-tins, Portugal, and is affiliated with Nova School ofBusiness and Economics, Faculdade de Economia, Lis-bon, Portugal (e-mail: [email protected]). CarmenLages is Assistant Professor, ISCTE Business School,Lisbon University Institute, Portugal. Part of thisresearch was conducted while she was a VisitingScholar at the Massachusetts Institute of Technology’sDeshpande Center for Technological Innovation (e-mail: [email protected]). Luis Filipe Lages is Asso-ciate Professor, Nova School of Business and Econom-ics, Faculdade de Economia, Lisbon, Portugal. Part ofthis research was conducted while he was an Inter-national Faculty Fellow in the Massachusetts Instituteof Technology Sloan School of Management (e-mail:[email protected]; www.lflages.com).

The Trade-Off Between Customer and Technology Orientations 37

a key challenge to exporters because it is intrinsicallylinked to innovation. Nevertheless, resources are limited,and firms must make choices in their allocation anddetermine the extent to which they will emphasize onestrategic orientation over another (Danneels 2007; Lant,Milliken, and Batra 1992). Although the individual rolesof customer and technology orientations on innovationand performance have attracted considerable attention(e.g., Gao, Zhou, and Yim 2007; Gatignon and Xuereb1997; Jeong, Pae, and Zhou 2006; Zhou, Yim, and Tse2005), few studies have assessed their relative impact—that is, the difference in the strengths of the relationshipsbetween customer orientation and innovation andbetween technology orientation and innovation.

Drawing on organizational learning literature, we examinehow customer and technology orientations relate to inno-vation capabilities to contribute to exporters’ performance.Thus, we consider the mediating role of two distinct capa-bilities—exploratory innovation and exploitative innova-tion—on the relationships between strategic orientationsand performance. Exploratory innovation includes activi-ties aimed to enter new product-market domains, whileexploitative innovation activities improve existing product-market domains (He and Wong 2004). High-performing firms go beyond gathering and understandingmarket and technological information to also translatethat knowledge into learning (Noble, Sinha, and Kumar2002). Research supports the view that firm capabilities,such as strategic orientations, do not directly lead to betterperformance; instead, organizational learning behaviorsact as mediators (Atuahene-Gima 2005; Baker and Sinkula2007; Zhou, Yim, and Tse 2005). In this study, we advancethe literature by allowing for a mediating role ofexploratory and exploitative innovations while also con-sidering customer and technology orientations. Moreover,we provide insights into how choices about emphasizingone strategic orientation over another affects the balancebetween exploration and exploitation.

Research typically addresses these trade-offs in a domesticcontext. This is surprising, given that innovation and inter-nationalization are critical drivers of today’s businessesand economies. Firms can leverage their innovations byacting on business opportunities in international markets(Knight and Cavusgil 2004). In this study, we explore thetopic in the context of technological exporters. Thoughvalid for any organization, our topic is particularly impor-tant for technological exporters. Because such firms oper-ate in international markets with highly complex environ-ments and high technological and demand uncertainties,they require sophisticated marketing (Dutta, Narasimhan,

and Rajiv 1999; Mohr and Sarin 2009; Mohr, Sengupta,and Slater 2010). In addition, the notion that a technologyorientation is inherent to technological firms is no guaran-tee of success (Workman 1993).

Finally, we extend our research by examining the trade-off between customer orientation and technology orien-tation under the contingency effect of the past perfor-mance of the firm. Organizational learning literaturedemonstrates that firms tend to rely on their past experi-ence and performance in decision making (Cyert andMarch 1963; Lages, Jap, and Griffith 2008; Lant andMezias 1992; Levinthal and March 1981). More specifi-cally, past performance affects innovation-related deci-sions because of limited resources (Durmus, oglu et al.2008). We follow a different perspective from that ofresearchers exploring the impact of past performance onstrategy; that is, we use past performance as a modera-tor rather than an antecedent of firm strategy, investi-gating whether technological exporters respond differ-ently under different scenarios of past performance.

CONCEPTUAL FRAMEWORK AND HYPOTHESES

Organizational learning represents the development ofknowledge that influences behavioral changes and leadsto enhanced performance (Crossan, Lane, and White1999; Fiol and Lyles 1985). Product innovation is a toolfor organizational learning and, thus, a primary meansof achieving its strategic renewal (Danneels 2002;Dougherty 1992). Renewal demands that firms exploreand assimilate new knowledge while exploiting whathas already been learned. These two learning capabili-ties are known as exploration and exploitation (March1991). Exploration pertains more to new knowledge—such as the search for new products, ideas, markets, orrelationships; experimentation; risk taking; and discov-ery—while exploitation pertains more to using the exist-ing knowledge and refining what already exists; itincludes adaptation, efficiency, and execution (March1991). Exploration and exploitation compete for thesame resources and efforts in the firm. With a focus onexploring potentially valuable future opportunities, thefirm decreases activities linked to improving existingcompetences (Levinthal and March 1993; March 1991).In contrast, with a focus on exploiting existing productsand processes, the firm reduces development of newopportunities. However, firms must develop bothexploratory and exploitative capabilities becausereturns from exploration are uncertain, often negative,


and attained over the long run, while exploitation gen-erates more positive, proximate, and predictable returns(Levinthal and March 1993; March 1991; Özsomer andGençtürk 2003). Researchers have shown that bothtypes of learning are essential to enhancing firm perfor-mance (Leonard-Barton 1992; March 1991). In thisstudy, we use exploration and exploitation to describetwo innovation-related capabilities that a firm mustdevelop to attain superior performance.

Strategic orientations are capabilities that reflect thestrategic directions a firm takes to create the appropri-ate behaviors for continuous superior performance(Day 1994; Narver and Slater 1990). These behaviorsassume the generation and dissemination of informa-tion (Gatignon and Xuereb 1997; Jaworski and Kohli1993; Narver and Slater 1990). Because this informa-tion must be transformed into knowledge, strategic ori-entations are linked to learning behaviors and thereforeto innovation capabilities (Atuahene-Gima 2005; Bakerand Sinkula 2007; Noble, Sinha, and Kumar 2002;Slater and Narver 1995). With their focus on informa-tion processing, strategic orientations greatly enhancethe firm’s learning capabilities. Without the ability touse and act on information (learning), strategic orienta-tions would not affect performance. Two critical strate-gic orientations linked to innovation are customer ori-entation and technology orientation (Gatignon andXuereb 1997; Zhou, Yim, and Tse 2005). Researchemphasizes that innovation requires that firms havecapabilities related to technology and customers (Dan-neels 2002; Dougherty 1992). Customer orientation isthe understanding and monitoring of customers andtheir needs (for a review, see Kirca, Jayachandran, andBearden 2005). It includes gathering knowledge aboutcurrent and future customers and disseminating thatknowledge in the firm (Jaworski and Kohli 1993).Technology orientation is “the ability and the will toacquire a substantial technological background and useit in the development of new products” (Gatignon andXuereb 1997, p. 78). A technology-oriented firm iscommitted to research and development (R&D) and isproactive in acquiring and integrating new and sophis-ticated technologies in the new product developmentprocess (Slater, Hult, and Olson 2007; Zhou, Yim, andTse 2005). The technology orientation also promotesopenness to ideas that use state-of-the art technologies,in contrast to a customer orientation, which favorsideas that better satisfy customer needs.

In this article, we use organizational learning theory tosupport the idea that innovation capabilities are a vehi-

cle for renewing other firm capabilities, such as strategicorientations, and achieving superior performance. Wetheorize that strategic orientations affect performancethrough exploratory and exploitative innovation. Wespecifically address the performance of technologicalexporters. International markets are turbulent anddiverse with respect to customer needs, cultures, andcompetitiveness; therefore, innovation assumes a pri-mary role (Kleinschmidt, De Brentani, and Salomo2007). Firms can leverage their innovations by securingbusiness opportunities in those markets and thusincrease their innovative capabilities (Knight and Cavus-gil 2004). Through exploratory innovation, firmsdevelop new competences and thus achieve superiorexport performance by attaining positions of marketand technological leadership (Teece, Pisano, and Shuen1997). Exploitation activities are also important toexporters because they facilitate the lower-risk exten-sion of export operations. Furthermore, by searching forsolutions in the existent competence base, exploitativeinnovation increases efficiency and productivity.

We further theorize about the influence of past perfor-mance of the firm on the strategic orientation–innovation relationship. Past performance influencesavailable resources, and these determine the innovationfocus toward more or less exploratory activities (Cyertand March 1963; Singh 1986). Figure 1 depicts thehypotheses in this research framework.

The Mediating Effect of Exploratory andExploitative Innovations

Firms with a strong customer orientation have acompetitive advantage because they consider the crea-tion and maintenance of customer value a top priority(Narver and Slater 1990; Olson, Slater, and Hult 2005).Thus, customer orientation is broadly recognized as adriver of business performance (Han, Kim, and Srivas-tava 1998; Hult and Ketchen 2001; Hurley and Hult1998; Jaworski and Kohli 1993; Narver and Slater1990). Performance benefits from technology orienta-tion have also been demonstrated, particularly in export-ing and technological contexts (Dutta, Narasimhan, and Rajiv 1999; Filatotchev et al. 2008; Gao, Zhou, and Yim 2007; Workman 1993; Zou, Fang, and Zhao2003). However, research supports the view that strate-gic orientations as firm capabilities do not directly leadto better performance; instead, organizational learningcompetences mediate the relationship (Atuahene-Gima2005; Baker and Sinkula 2007; Zhou, Yim, and Tse2005).


Customer-oriented firms can effectively combine explo-ration and exploitation (Kyriakopoulos and Moorman2004). These firms are committed to understanding andserving the needs of current customers; therefore, theyexcel in searching for and using market information(Day 1994). By leveraging their customer knowledge,they can become aware of market opportunities andimprove their existing processes and resources (Yal-cinkaya, Calantone, and Griffith 2007). An exportingfirm might fine-tune products and services to better sat-isfy existing customer needs. For example, a firm mightstrengthen relationships with its customers in existingexport markets (Lages, Lages, and Lages 2005). Thus, acustomer orientation directly benefits an exploitativeinnovation (Atuahene-Gima 2005).

H1a: Exploitative innovation mediates the rela-tionship between customer orientation andexport performance.

A customer-oriented firm not only responds to existingcustomer needs but also uncovers latent needs andanticipates future needs (Chandy and Tellis 1998; Day1994; Slater and Narver 1995). Thus, such firms mustbuild on new capabilities (exploration), as existing ones(exploitation) become inadequate (Huff, Huff, and

Thomas 1992). For example, firms introduce more radi-cal innovations by focusing on future customers(Chandy and Tellis 1998). Moreover, for innovation tobe successful, customers must be aware of the productfor adoption to accelerate (Sorescu, Chandy, and Prabhu2003). Another example of the importance of customerorientation in the development of exploratory innova-tion capabilities is when exporters develop new tech-nologies; firms must identify which customers areappropriate for the products developed with those tech-nologies to ensure their success (Yalcinkaya, Calantone,and Griffith 2007). Thus:

H1b: Exploratory innovation mediates the rela-tionship between customer orientation andexport performance.

Empirical evidence suggests that technology orientationhas a positive relationship to innovation (Gatignon andXuereb 1997; Li and Calantone 1998; Song and Parry1997). Technology-oriented firms are technically profi-cient and flexible, which facilitates the refinement ofexisting technologies either to cope with existing mar-kets or to leverage market research efforts and try newmarkets (Danneels 2007). A technology-oriented firm istechnologically diverse because of its commitment to

Figure 1. Proposed Theoretical Framework

Customer Orientation

Technology Orientation

Strategic Orientations

PastPerformance

Innovation Capabilities

Exploratory Innovation

ExploitativeInnovation

Control Variables

• Firm size

• Export experience

• Export intensity

ExportPerformance


enrich its technological knowledge base (Katila andAhuja 2002). Quintana-Garcia and Benavides-Velasco(2008) find that technological diversity (i.e., scope of afirm’s technological knowledge) is positively related toexploratory and exploitative innovation competences.Firms with technology diversity have more expertise insimilar technologies, thus increasing the possibilities oftechnological combinations. The resultant technologiesmay improve products and services, resulting inexploitative innovations. Moreover, existing customersare often offered new products that incorporate newtechnologies that incrementally enhance existing prod-ucts (Yalcinkaya, Calantone, and Griffith 2007). Thus:

H2a: Exploitative innovation mediates the rela-tionship between technology orientation andexport performance.

A technological ability also favors experimentation withnew alternatives to meet emerging technological trends(March 1991). Exporters with more diverse technologi-cal knowledge capture more opportunities and tend todevelop more radical innovations (Quintana-Garcia andBenavides-Velasco 2008). Researchers acknowledge thatto develop truly new innovations, firms need strongtechnological capabilities (Gatignon and Xuereb 1997;Workman 1993). Therefore, a technology orientation isimportant for exploratory innovation.

H2b: Exploratory innovation mediates the rela-tionship between technology orientation andexport performance.

The Relative Impact of Strategic Orientationson Exploratory and Exploitative Innovations

In recent years, innovation research has shifted from adichotomous view of customer-led or technology-ledto an interaction view (Gatignon and Xuereb 1997; Slater and Narver 1995; Zhou, Yim, and Tse 2005).Researchers agree that firms need to develop technolog-ical and customer knowledge simultaneously for suc-cessful innovation. Technology-driven firms have themost to gain from combining their technological skillswith a customer orientation (Atuahene-Gima, Slater,and Olson 2005; Dutta, Narasimhan, and Rajiv 1999;Lukas and Ferrel 2000; Zhou, Yim, and Tse 2005).However, because resources are limited, firms mustmake choices in their allocation and determine theextent to which they will emphasize one strategic orien-tation over another (Danneels 2007; Lant, Milliken, andBatra 1992).

In general, a customer orientation is more importantthan a technology orientation when developingexploitative innovation capabilities (Baker and Sinkula2007; Yalcinkaya, Calantone, and Griffith 2007). Inexploitation, the firm develops new products by refiningand recombining existing technological and customercompetences (Danneels 2002; March 1991). As such,exploitative learning increases efficiency through thediscovery and use of solutions from the firm’s currentexperience. Customer-oriented firms are knowledgeableabout their customers and excel in finding solutions tomeet their needs (Day 1994; Jaworski and Kohli 1993).Exploitative learning generally leads to incrementalinnovations, which consist of product improvementsand line extensions that aim to serve existing customers(Atuahene-Gima 2005; Baker and Sinkula 2007). Todevelop incremental innovations, firms use existingtechnologies (Chandy and Tellis 1998). According toYalcinkaya, Calantone, and Griffith (2007), becauseexploitation involves leveraging existing knowledge andcapitalizing on existing opportunities, a deep under-standing of current market needs is more beneficial tothose activities than the firm’s technological resources.Moreover, the more incremental the innovations are, thelower are the levels of technology orientation needed todeploy those innovations (Gatignon and Xuereb 1997).Thus:

H3: Customer orientation relates more stronglythan technology orientation to exploitativeinnovation.

Although prior findings related to the relationshipbetween strategic orientations and exploitation seem tobe consensual, in the case of exploration they seem to beless so. Some studies find that a customer focus prevailsover technology; other studies find the opposite. Earlierresearch has used the term “exploration” to include dif-ferent types of exploratory innovations; thus, it is notsurprising that the findings are contradictory. Danneels(2002) argues that three scenarios of exploration exist.In the first, pure exploration, firms build new productson new customer and technological competences. Theother two scenarios are not pure because they combineexploitation; that is, they leverage existing competences(technological or customer related) and explore newones (technological or customer related). Prior researchhas found that a technology orientation is more impor-tant than a customer orientation when exploratoryinnovations are based on new technologies aimed toserve existing customers (Gatignon and Xuereb 1997;Zhou, Yim, and Tse 2005). However, when exploratory


innovation is developed using existing technologies, atechnology orientation might not be beneficial. In thisscenario, a customer orientation may be as important asa technology orientation. Exploring new customersimplies a proactive approach in understanding the needsof customers that are not yet identified as well as build-ing knowledge on how to address those customers anddeveloping a relationship with them (Slater and Narver1998). Thus:

H4: Customer orientation and technology orienta-tion are similarly important to exploratoryinnovation.

The Moderating Effect of Past Performance

Organizational learning literature demonstrates thatfirms tend to rely on their past experience and pastperformance for decision making (Cyert and March1963; Lant and Mezias 1992; Lant, Milliken, and Batra1992; Levinthal and March 1981). Poor past exportperformance is associated with strategic reorientation ofexporting firms (Lages, Jap, and Griffith 2008; Lages,Lages, and Lages 2006; Lages and Montgomery 2004).Managers facing poor past performance are pressuredto make more precise decisions because they have lessmargin for error than managers in well-performingfirms. Organization theory researchers often use theconcept of “slack” when discussing the impact ofperformance on organizations (Bourgeois 1981). Slackrefers to the resources that are readily available tofinance organizational activities. Organizations per-forming poorly show lower levels of slack than thoseperforming well (Singh 1986).

Slack catalyzes the innovation process (Cyert andMarch 1963). First, slack protects organizations fromuncertainties linked to innovation projects, thus foster-ing search behavior (Bourgeois 1981; Nohria and Gulati1996). Second, slack enables the firm to follow high-potential innovation projects that are visionary but notjustifiable according to standard internal criteria(Levinthal and March 1981). Profitable organizationscan commit resources to innovation, particularly to therenewal of technological knowledge through explo-ration activities (Garcia, Calantone, and Levine 2003).However, unprofitable firms are unlikely to have slackor to invest in renewing firm competences. Low levels ofslack are detrimental to innovation (Nohria and Gulati1996). Firms with greater slack engage in more explo-ration activities, while firms with less slack must con-serve it for organizational ongoing activities, that is, for

exploitation activities (Singh 1986; Voss, Sirdeshmukh,and Voss 2008). Consequently, the balance betweenexploration and exploitation shifts depending on thefirm’s past performance. From the previous arguments,we hypothesize that past performance moderates therelationship between strategic orientations and innova-tion. Our focus is not on understanding the effects ofpast performance on the innovation activities of the firmbut rather on understanding how past performanceaffects the trade-off between the two strategic orienta-tions when leading to innovation, either through explo-ration or exploitation.

Although we acknowledge that firms with poor pastperformance will favor exploitation and thus need a cus-tomer orientation more than a technological orienta-tion, we posit that the level of past performance will notalter the relative impact of the two strategic orientationson exploitative innovation (in line with H3). As we indi-cated previously, exploitative innovation means refiningexisting technological and customer competences (Dan-neels 2002). While a customer orientation helpsimprove current customer competences (Atuahene-Gima 2005; Baker and Sinkula 2007), by definition, atechnology orientation does not compromise the refine-ment of existing technological competences (Gatignonand Xuereb 1997). Thus:

H5: (a) For exporters with poor past performance,customer orientation relates more stronglythan technology orientation to exploitativeinnovation, and (b) for exporters with supe-rior past performance, customer orientationrelates more strongly than technology orienta-tion to exploitative innovation.

With regard to exploration, exporters with poor pastperformance cannot afford to explore new opportuni-ties and ideas through the use of new technologies.Developing innovations by leveraging customer compe-tences or pure exploration implies technology acquisi-tion, which represents higher innovation costs(Gatignon and Xuereb 1997). If, on the one hand, firmswith a technology orientation have higher innovationcosts, on the other hand, a customer orientation has nosignificant impact on innovation cost. Innovationsincorporating state-of-the-art technology (e.g., thosethat technology-oriented firms develop) are extremelyexpensive and require significant investments (Sorescu,Chandy, and Prabhu 2003; Wind and Mahajan 1997).Furthermore, the fewer slack resources a firm has, thelower is its exploratory R&D competence (Danneels


2008). Thus, it is logical that exporters facing poor pastperformance address exploratory innovation mainly byleveraging existing technology competences. Althoughthese competences may provide access to unserved mar-kets (Prahalad and Hamel 1990), that potential oftenremains untapped because of the lack of customer-related competences (Danneels 2007). Therefore, a cus-tomer orientation might serve poor-performing exportersbetter. Thus, exporters must engage in the pursuit of newand radical market information, far beyond the cur-rent customer knowledge domains (Levinthal andMarch 1993; March 1991). A firm’s customer knowl-edge base becomes more diversified, thus increasingchances for greater experimentation and innovation.Therefore, for exporters with poor past performance, acustomer orientation is more critical than a technologyorientation.

H6a: For exporters with poor past performance,customer orientation relates more stronglythan technology orientation to exploratoryinnovation.

With superior past performance, firms can afford toexplore new ideas and opportunities by pursuing newand sophisticated technologies (Garcia, Calantone, andLevine 2003). Slack helps firms ensure continuousinvestments in R&D and fund the launch of new prod-ucts (O’Brien 2003). In addition to exploration throughthe leverage of technology competences, the other twoexploration-related innovation activities (leverage ofcustomer competences with new technologies and pureexploration) are available options to such firms. Whenfirms develop innovations by leveraging customer com-petences, they must incorporate new technologies intonew products to serve the existing customer base (Dan-neels 2002). In this case, exploitative market learningactivities (i.e., the use of knowledge pertaining to cur-rent customers) help refine exporters’ capabilities toserve those customers (March 1991). For example, theyenhance cost efficiency in developing innovations bybetter using the available market information (Kim andAtuahene-Gima 2010). Market knowledge is particu-larly important for innovations in most high-tech indus-tries, and earlier research has argued that technology-based firms must be customer oriented (Mohr and Sarin2009). Innovations developed by leveraging customercompetences require substantial technological capabili-ties and resources because they adopt new and advancedtechnologies (Danneels 2002). Firms that invest more inR&D and are more technically proficient and flexibleare better positioned to deploy those innovations (Zhou,

Yim, and Tse 2005). In such a case, a technology orien-tation is essential because firms need greater technolog-ical competences (Slater, Hult, and Olson 2007).

With pure exploration, firms must develop new tech-nologies to appeal to unserved markets (Danneels2002). A technology orientation is broadly recognizedas a critical driver of radical innovations (Chandy andTellis 2000; Gatignon and Xuereb 1997; Zhou, Yim,and Tse 2005). Nonetheless, even firms that have strongpatents cannot increase sales of radical products if cus-tomers are not aware of the product or if adoption is notaccelerated (Sorescu, Chandy, and Prabhu 2003). Forexample, Apple was able to steal Sony’s market formobile music with less than ten times the number ofSony’s patents (Tellis, Prabhu, and Chandy 2009).Moreover, when engaging in exploratory market learn-ing, a firm achieves greater product differentiation (Kimand Atuahene-Gima 2010). A truly customer-orientedfirm explores unserved markets and understands thelatent and unexpressed needs of those customers (Slaterand Narver 1998). By focusing on future customers,firms introduce more radical innovations (Chandy andTellis 1998). Therefore, a customer orientation is alsoimportant for pure exploration. We conclude that bothcustomer and technology orientations are important toexploration-related innovations for exporters with supe-rior past performance.

H6b: For exporters with superior past performance, cus-tomer orientation and technology orientation aresimilarly important to exploratory innovation.

METHODOLOGYSample and Data Collection

We tested our hypotheses with a random sample of1031 manufacturer exporters in technological indus-tries, listed in the 2007 AICEP Portugal Global, a data-base of the Portuguese business development agency.For Portuguese companies, exporting is a condition ofsurvival, not only because of the current economic crisisbut also because of the country’s small market. For asmall economy such as Portugal’s, integration in theworld economy is particularly important because of theaccess to opportunities for scale economies, specializa-tion, and advanced technology (Organisation for Eco-nomic Co-operation and Development 2008). From thedatabase, we considered manufacturing exporters inmultiple industries to increase variance and generaliza-bility of the results (Morgan, Kaleka, and Katsikeas


2004). However, we selected only the firms operating inmedium to highly technological industries according tothe Eurostat (2009) classification, which is based ontechnological intensity (R&D expenditure). We usedfirms in those industries to provide a similar context torespondents while being broad enough to ensure thegeneralizability of the results. Our research questionaddresses the trade-off between customer and tech-nology orientation. Because a technology orientation isintrinsically related to strong investments in R&D, weexcluded firms with low R&D expenditures.

Data collection took place in 2009 through an onlinesurvey. The database included the company’s name, tele-phone number, address, industry, products, and numberof employees. We contacted all the exporters to confirmeligibility for participating in the study—that is, if firmshad exported in the previous year and if their exportoperations were regular. For eligible firms, we estab-lished contact with the export manager (preferably),introduced him or her to the project, and asked for ane-mail address and the name and e-mail of the secondrespondent, the R&D manager. We also asked theexport manager to brief the second respondent aboutthe survey. We used this method and followed managers’suggestions that we gathered during preliminary inter-views. We then sent an e-mail invitation to respondentsto explain the academic purpose of the project, toensure confidentiality of the responses, and to send therespective link to the survey. The e-mail offered incen-tives, including a report with the main findings aftercompletion of the study and a significant discount for acourse about the topic to be held at the end of the year.We sent an e-mail reminder three weeks later to non-respondents and a final reminder four weeks after that.Of the 1031 firms, 191 were not eligible and 94 were notavailable to answer the questionnaire, resulting in 746questionnaires mailed. We obtained 193 usable question-naires, for a response rate of 26%. From those, 170remained after missing data analysis and cleaning. Toassess nonresponse bias, we compared late (last 25%)and early (first 75%) respondents regarding the means ofall the variables (Armstrong and Overton 1977). Wefound no significant differences between the two groupsand therefore concluded that there were no meaningfulproblems in this study regarding response bias.

Measures

We sourced measures from the literature and adaptedthem to the current research context (see Churchill1979). Constructs were first order, and we measured

them with multi-item scales, except for the moderatorand control variables. Unless specified, we used Likert-type scales ranging from 1 (“strongly disagree”) to 7(“strongly agree”). Scale items appear in Appendix A.

Strategic Orientations. We adapted the customer orien-tation construct from Narver and Slater’s (1990) scale ofmarket orientation to capture the degree to which firms’export activities are oriented toward understanding andmonitoring customers and their needs. Respondentsrated their level of agreement with statements regardingbehaviors of their firms’ export activities toward cus-tomers. This scale has six items. We adapted the meas-ure of technology orientation from the work of Zhou,Yim, and Tse (2005) to assess the orientation of firms’export operations toward using sophisticated technolo-gies in new product development. This scale has fouritems.

Exploratory and Exploitative Innovation. We adoptedexploratory and exploitative innovation scales fromLubatkin et al. (2006) to capture two innovation compe-tences in firms’ export markets. Exploitative innovationpertains to activities close to firms’ current customersand technological trajectory, and exploratory innovationincludes activities aimed to enter new product-marketdomains. Each of the scales has six items.

Export Perceived Performance. Export performance isthe extent to which the firm achieves its exporting-related objectives (Cavusgil and Zou 1994). We usedthree items (profit, sales, and sales growth) from Zou,Taylor, and Osland (1998), which are indicators offinancial export performance. We did not measureexport performance in relation to that of competitors, assome researchers (Morgan, Kaleka, and Katsikeas 2004)suggest, because of the outcome from preliminary inter-views, in which managers expressed the difficulty ofacknowledging results from competitors at the exportlevel. We added the word “perceived” to “exportperformance” to make it easier for respondents to dif-ferentiate that type of performance from the past perfor-mance measure.

Past Performance. We selected past return on assets(ROA), that is, the ratio of net operating profit to thefirm’s start-of-year assets recorded on its balance sheet,as the moderator for the exporter’s financial situation.Most measures of financial performance fall into twobroad categories: accounting returns and investorreturns. Return on assets is an accounting-based indica-tor and is the most common and readily available


means of assessing firm performance (Richard et al.2009). The validity of this type of measure is groundedin extensive evidence showing the relationship betweenaccounting and economic returns. Among accountingmeasures, ROA is popular because it captures a firm’sefficiency (Cochran and Wood 1984) and reflects inter-nal decision making on capabilities and performance.We took ROA data from the 2009 Bureau van Dijkdatabase and calculated them as the average of theROA of the firm (in percentage) in the three years pre-ceding data collection. Note that we use the pastperformance at the firm level, not the export level,because it better reflects the total available resources tothe firm operation (including export operation).

Control Variables. We controlled for firm size (totalfirm sales), export experience (number of countries withexport operations), and export intensity (percentage oftotal firm sales from export operations) following previ-ous exporting literature (Lages, Jap, and Griffith 2008).

Survey Instrument Development

We developed the survey instrument by combininginformation from three sources: (1) field interviews, (2) a panel of academic researchers in international mar-keting and innovation, and (3) the literature. After select-ing the scales from the literature, we assessed facevalidity with a panel of academics (Hunt, Sparkman, andWilcox 1982) who tried to identify potential problems intheir application to the research context. We then con-ducted ten face-to-face structured interviews with bothexport and R&D managers from firms in different indus-tries to evaluate the survey on clarity of instructions,response formats, design, items, and respondents’ com-petence. From the interviews, we confirmed the need fora different set of questions for each type of respon-dents—one for the export manager and another for theR&D manager—to ensure that they were knowledgeableenough about the questions addressed. The next stagewas a pretest with 15 exporters, which enabled us to fur-ther refine the survey and administration method. Thefinal survey was administered online.

Data Profile

With respect to size, measured by the number of full-time employees, exporters in the sample were distrib-uted as follows: 6% with 1–9 employees, 45% with10–49 employees, 41% with 50–249 employees, and8% with 250 employees or more. (This classification ofcompanies is in line with the European Commission

[1996] recommendation.) These data reflect the Por-tuguese exporting industry, in which most firms aresmall to midsize. The average age of firms participatingin the study was 32 years (SD = 22; range = 2–100), withaverage exporting experience of 19 years (SD = 19;range = 1–100). The firms are present, on average, in 11countries (SD = 13; range = 1–75). The average annualsales of the firms ranged from 1.5 million to 5 mil-lion, and 8% of firms had sales less than .35 million,23% had from .35 million to 1.5 million, 22% hadfrom 1.5 million to 3.5 million, 9% had from 3.5million to 5 million, 31% had from 5 million to 35million, and 7% had more than 35 million. Exportingoperations contributed 0%–9% of sales to 9% of firms,with 10%–29% to 14% of firms, 30%–59% to 29% offirms, 60%–84% to 25% of firms, and more than 85%to 25% of firms.

Common Method Bias

To address common method bias, we followed Pod-sakoff et al.’s (2003) recommendations. First, we useddifferent sources of information for our constructs. Wesplit the questions between the two respondents, exportmanager and R&D manager, according to the respectivearea of knowledge of each. We also gathered objectivedata on profit (ROA, net income), sales, sales growth,number of employees, and years of existence for theexporters in the sample from the Bureau van Dijk data-base, and we calculated the correlation between theobjective data and the answers obtained from question-naires, a procedure other researchers have followed(Morgan, Kaleka, and Katsikeas 2004). Respondents’answers to their firms’ total sales and employees aregiven by eight and four interval measures, respectively,so we coded the objective sales and employment datainto the same intervals. For the measures of export per-ceived performance—profit, sales, and sales growth—because the objective measures refer to total companyfigures and the data collected are at the export opera-tion level, we performed correlation analysis for bothgroups of firms: total firms and firms with export inten-sity of more than 60%. All correlations were significant,in support of the validity of key informants’ answers.Second, the questionnaire clearly assured respondents ofthe confidentiality of the results of this study and thatthere were no right or wrong answers—only that theiropinion mattered. We also followed standard surveydesign and administration practices. Finally, to controlfor common method bias, we used the Harman single-factor test (Podsakoff et al. 2003). We extracted six fac-tors with eigenvalues greater than 1.0, and the first fac-


tor accounted for less than 50% of variance explained.Thus, we conclude that common method bias is not asignificant problem in this study.

EMPIRICAL RESULTS

We tested the hypotheses using partial least squares(PLS) with Smart PLS 2.2 software (Ringle, Wende, andWill 2005). We selected PLS because of the sample size.When moderators are tested by subgroup analysis, sam-ples are smaller, which makes PLS more appropriate.The problem with PLS-biased results is not a concernbecause we have 170 responses, which is ten timesgreater than the number of independent constructsaffecting the dependent variable (i.e., six) (Chin 1998).Even when undertaking subgroup analysis, we met thisrule. Following Hulland’s (1999) suggestion, we ana-lyzed the reliability and validity of the measurementmodel first and then the structural model.

Measurement Model

To assess the adequacy of the measurement model, weexamined individual item reliabilities, convergentvalidity, and discriminant validity (see Appendix A; Hul-land 1999). We assessed item reliabilities by examiningthe loadings of the individual items in the respectiveconstructs. We confirm that all loadings are greater than.7 (which is the minimum value for many researchers)except for the first item of the customer orientation con-struct, which had a loading of .624. However, a factorloading less than .7 but greater than .5 may be acceptedif other items in the same construct present high scores,which is the case (Chin 1998). Thus, given the concep-tual importance of this item, we retained it in the model.We assessed convergent validity by analyzing compositereliability (Bagozzi 1980). All constructs were reliableand met the minimum value of .7 (Nunnally and Bern-stein 1994). We also computed average varianceextracted (AVE) (Fornell and Larcker 1981) and con-firmed that all results were greater than the recom-mended value of .5, thus confirming convergent validity.We assessed discriminant validity by comparing the cor-relation between each pair of constructs with the root ofAVE among those constructs (Fornell and Larcker 1981)and by analyzing cross-loadings between items and con-structs (Chin 1998). By analyzing the values in Appen-dix B, we confirmed that the square root of AVEbetween any two constructs (diagonal) is greater thanthe correlation between those constructs (off-diagonal),thus indicating discriminant validity. The results show

that items load higher in the respective construct thanon any other construct, thus confirming discriminatevalidity.

Structural Model

We assessed overall model fit by examining both thenumber of significant relationships among the con-structs and R-square, that is, the explained variance ofthe endogenous latent variables (Cool, Dierickx, andJemison 1989). Table 1 shows the path coefficients forthe PLS model. More than 50% of the tested relation-ships were significant in a model including the moderat-ing effects. Variances explained are 49%, 50%, and38% for exploratory innovation, exploitative innova-tion, and export perceived performance, respectively.The values satisfy the minimum of 10% for the R-square of the endogenous variables (Falk and Miller1992). Export intensity was the only control variablewith a significant coefficient (β = .27, t = 3.74). The sig-nificance of its relationship to export perceived perfor-mance is in line with results from prior research (Cavus-gil and Zou 1994).

A t-test of the difference between β coefficients of therelationships of customer orientation and technologyorientation to exploitative innovation (.54 and .22,respectively) confirmed that they are statistically differ-ent (β = .32, t = 2.60), with the former greater than thelatter, in support of H3. The t-test of the differencebetween β coefficients of the relationships of customerorientation and technology orientation to exploratoryinnovation (.44 and .35, respectively) confirmed thatthey are not statistically different (β = .09, t = .74), insupport of H4. The results confirm that customer orien-tation relates more strongly than technology orientationto exploitative innovation but is equally important toexploratory innovation.

Testing for Mediating Effects. We followed Baron andKenny’s (1986) approach to test for the mediating effect ofexploratory and exploitative innovation. We ran four PLSmodels: (1) with the effects of the independent and inter-action variables on the dependent variable without themediating variables (Model 1); (2) with the effects of theindependent and interaction variable on the mediatingvariables (Models 2 and 3); and (3) with the effects of theindependent and interaction variables on the dependentvariable, in the presence of the mediating variables (Model4). Table 2 presents the results. For the interaction terms,we mean-centered indicator values of the variables beforemultiplication to reduce multicollinearity between main


and interaction variables (Aiken and West 1991). Themediating variables are significant in Model 4. Customerorientation is significant in Models 1, 2, and 3, but inModel 4, it becomes nonsignificant, which suggests a fullmediation from exploratory and exploitative innovationof the customer orientation–export perceived performancerelationship, in support of H1a and H1b. Technology ori-entation barely affected export perceived performance,but the strength of that relationship diminished with theentry of the mediating variables in the model. Therefore,we found support for H2a and H2b.

Testing for Moderating Effects. We examined moderat-ing effects of ROA following Sharma, Durand, and Gur-Arie’s (1981) methodology. First, we created andregressed interaction terms between ROA and the pre-dictor variables in PLS (see Table 1). Only the inter-actions of technology orientation with past ROA weresignificant (β = .17, t = 2.08). Second, we found no sig-nificant correlations between ROA and customer ortechnology orientations. Thus, we tested ROA as ahomologizer moderator by performing a subgroupanalysis. We divided the sample into a low group and ahigh group, excluding the middle 15% of cases toensure enough contrast (see Table 3; Kohli 1989). All t-

tests showed statistical significance, and therefore for allthe independent variables, the regression coefficients of“high past ROA” and “low past ROA” differ.

To understand the relative impact of the two orienta-tions, we ran a t-test for the differences in β coefficientsof customer orientation and technology orientation ineach subgroup and in relation to the same mediatingvariable. The results in the subgroup of low past ROArevealed that customer orientation relates morestrongly than technology orientation to exploitativeinnovation (βdifference = .48, t = 2.30, p < .05) and toexploratory innovation (βdifference = .39, t = 2.08, p <.05), in support of H5a and H6a. In the subgroup of highpast ROA, t-tests of the differences in β coefficients werenonsignificant in both exploratory (βdifference = .03, n.s.)and exploitative (βdifference = .11, n.s.) innovation. H5bhypothesized that when past performance is superior, acustomer orientation relates more strongly to exploita-tive innovation (improving existing capabilities) than atechnology orientation, but our findings did not sup-port this. However, H6b is fully supported; with supe-rior past performance, technology and customer orien-tations do not have differentiated effects on exploratoryinnovation.

Table 1. PLS Path Coefficients

Standardized ResultPath Coefficient t-Value Two-Tailed Test

Export intensity → export perceived performance .27 3.74 p < .001

Export experience → export perceived performance .08 1.30 n.s.

Firm size → export perceived performance .08 1.37 n.s.

Customer orientation → exploratory innovation .44 7.46 p < .001

Technology orientation → exploratory innovation .35 5.14 p < .001

Customer orientation → exploitative innovation .54 6.80 p < .001

Technology orientation → exploitative innovation .22 3.51 p < .001

Exploratory innovation → export perceived performance .26 2.63 p < .01

Exploitative innovation → export perceived performance .23 2.38 p < .01

Customer orientation × past ROA → exploratory innovation –.10 1.00 n.s.

Customer orientation × past ROA → exploitative innovation –.16 1.42 n.s.

Technology orientation × past ROA → exploratory innovation .14 1.48 n.s.

Technology orientation × past ROA → exploitative innovation .17 2.08 p < .05

Notes: n.s. = not significant.


Table 2. PLS Results on the Mediating Effect of Innovation

Export Exploratory Exploitative ExportPerceived Performance Innovation Innovation Perceived Performance

Model 1 Model 2 Model 3 Model 4

Control Variables

Export intensity .32*** .20** .15** .25***(4.54) (3.15) (2.58) (3.57)

Export experience .06 –.04 –.11* .08(.91) (.58) (1.79) (1.44)

Firm size .09 .02 0.03 .08(1.37) (.42) (.48) (1.21)

Main Effects

Customer orientation .27*** .41*** .51*** .05(3.50) (6.26) (6.76) (.54)

Technology orientation .11 .37*** 0.25*** –.03(1.48) (5.29) (3.58) (.40)

Past ROA .06 .08 .32 .08(.75) (.11) (.49) (1.16)

Interaction Effects

Customer orientation × past ROA –.18 –.68 –.75 –.16(1.46) (.83) (1.03) (1.54)

Technology orientation × past ROA .15 .56 .47 .10(1.40) (1.39) (1.49) (1.03)

Mediating Variables

Exploratory innovation .27***(2.84)

Exploitative innovation .20*(1.80)

*p < .10 (two-tailed).**p < .01 (two-tailed).***p < .001 (two-tailed).

Table 3. Results of Subgroup Analysis With Past ROA

Relationship β Low β High t-Test

Customer orientation–exploratory innovation .60 .42 p < .001

Technology orientation–exploratory innovation .21 .45 p < .001

Customer orientation–exploitative innovation .60 .48 p < .001

Technology orientation–exploitative innovation .61 .37 p < .001


Additional AnalysisThe results presented so far provide insights into thestrategic decisions that firms should make with respectto innovation when facing good or bad results. How-ever, we cannot conclude from the analysis what causesthe difference between firms that faced poor perfor-mance and still achieved good export performance andthose that faced poor performance and had poor exportresults. It is also important to understand why firmswith good past results are harmed in their export opera-tions. We conduct some additional analysis to answerthese questions.

With the purpose of understanding the differencesbetween exporters with poor and superior past perfor-mance with respect to export perceived performance, wesplit each subgroup (i.e., high past ROA and low pastROA) into two groups, for high and low values of thedependent variable. We took the latent variable scores forexport perceived performance, ordered them in descend-ing order, and split them into two groups—one with firmsexhibiting positive scores and the other with firms show-ing negative scores. This yielded four groups: low pastROA/low export perceived performance (LL), low pastROA/high export perceived performance (LH), high pastROA/low export perceived performance (HL), and high

past ROA/high export perceived performance (HH). Wethen performed one-way analyses of variance tests on thelatent variable scores for customer orientation, tech-nology orientation, and export perceived performance.We also conducted Tukey tests for multiple comparisonsof the four groups. Table 4 presents the results.

First, LL firms differ from LH firms on customer orien-tation scores. We conclude that firms with poor pastperformance may achieve higher performance levels byincreasing their customer orientation. Second, HL firmsdiffer from HH firms on technology orientation scores.Firms with good past performance can maintain greaterperformance by increasing technology orientation.Finally, LH and HH firms have similar levels of bothorientations, which suggests that poor past performancedoes not affect future performance, as long as high lev-els of both customer and technology orientations aremaintained.

DISCUSSION AND IMPLICATIONS

Exporters face the challenge of allocating their limitedresources between their possible strategic orientations.However, international marketing research has paid lit-

Table 4. Analysis of Variance (ANOVA) and Multiple Comparisons of Subgroups

Test of Homogeneity Multiple

Latent Variable Group N M SE of Variance F Significance Comparisonsa

Customer orientation LL 46 5.42 .15 n.s. 7.23 <.000 LL is similar toLH 29 6.07 .18 HL; HL, LH, andHL 26 5.91 .15 HH are similarHH 49 6.19 .09

Technology orientation LL 46 4.73 .13 <.03 3.17 <.000 HL, LH, and LL LH 29 4.66 .25 LL, and HH are HL 26 4.19 .27 are similar; LH, HH 49 5.10 .19 similar; HL differs

from HH

Export perceived LL 46 3.84 .11 n.s. 88.27 <.000 LL does not differperformance LH 29 5.75 .10 from HL, and

HL 26 4.09 .19 HH does not HH 49 5.99 .10 differ from LH

aTukey test at p < .05.Notes: LL = low past performance/low export perceived performance, LH = low past performance/high export perceived performance, HL = high pastperformance/low export perceived performance, and HH = high past performance/high export perceived performance.

ANOVA


tle attention to understanding the relative roles of cus-tomer and technology orientations on innovation andperformance. In this study, we used organizationallearning literature to support our hypotheses with amodel in which we used innovation (exploratory andexploitative) as a mediator variable between strategicorientations (customer and technology) and export per-ceived performance. We also tested the relative impactof the two orientations in conditions of poor and supe-rior past performance, measured by past ROA.

This study contributes to the international marketing lit-erature in several ways. First, it confirms that the twolearning competences identified by organizational learn-ing theory—exploitative and explorative—are essentialto exporters because they mediate the effects of thefirm’s strategic orientation on performance. Organiza-tional learning research has found that high-performingfirms go beyond gathering knowledge to translate it intolearning (Baker and Sinkula 2007; Noble, Sinha, andKumar 2002). We provide further support to the sugges-tion that strategic orientations do not directly lead tobetter performance (Atuahene-Gima 2005; Baker andSinkula 2007; Yalcinkaya, Calantone, and Griffith2007; Zhou, Yim, and Tse 2005); rather, this linkdepends on how the exporting firm learns—that is, howit develops innovation capabilities on the basis of new(exploration) and existing (exploitation) knowledge andwhether that knowledge is technological (prevalent intechnological-oriented firms) or customer (prevalent incustomer-oriented firms).

Second, our findings support the view that customer andtechnology orientations have a key role in ensuring thatinvestments in both exploratory and exploitative innova-tion capabilities achieve optimal performance. The find-ings are consistent with organizational learning research,which posits the need for both types of innovation(March 1991) and for both marketing and technologicalcompetences to develop them (Danneels 2002; Gatignonand Xuereb 1997; Holmqvist 2004; Yalcinkaya, Calan-tone, and Griffith 2007). Earlier research using techno-logical companies as a sample has also shown that a cus-tomer orientation is critical for innovation success inthose firms (Im and Workman 2004; Zhou, Yim, and Tse2005) and must be coupled with a technology orienta-tion (Dutta, Narasimhan, and Rajiv 1999).

Third, this study provides insights into the relative roleof customer and technology orientations. Becauseresources are limited, exporters must make choices intheir allocation and in deciding the extent to which they

will emphasize one strategic orientation over another.We found that customer orientation is as important astechnology orientation for a firm pursuing exploratoryinnovation. This result complements and integratesprior research by considering that exploratory innova-tion aggregates three distinct innovation types: (1) pureexploration, (2) technology-leveraging/customer-basedinnovations, and (3) customer-leveraging/technology-based innovations (Danneels 2002). A technology orien-tation is critical when firms need greater technologicalcompetences to develop either tech-based or exploratoryinnovations (Gatignon and Xuereb 1997; Yalcinkaya,Calantone, and Griffith 2007; Zhou, Yim, and Tse2005). A customer orientation provides the necessaryskills for identifying current and latent needs, uncover-ing new market opportunities, searching for unservedmarkets, and establishing relationships with existingand new customers (Slater and Narver 1998). Althoughexisting technological competences may provide accessto unserved markets (Prahalad and Hamel 1990), thispotential often remains untapped because of the lack ofcustomer-related competences (Danneels 2007). There-fore, a customer-oriented firm is better positioned todevelop either exploratory or customer-based innova-tions (Zhou, Yim, and Tse 2005). By demonstrating thatcustomer orientation is as important as technology ori-entation to exploration, we advance the literature byintegrating prior findings. Our results also support theliterature in finding that customer orientation is moreimportant than technology orientation in developingpure exploitative innovations. On the one hand, becauseexploitative innovations consist of product improve-ments and line extensions that aim to serve existing cus-tomers, firms use existing customer and technologicalknowledge when developing them (Atuahene-Gima2005; Baker and Sinkula 2007; Chandy and Tellis1998). On the other hand, for innovations that are moreincremental in nature, firms do not need high levels oftechnology orientation (Gatignon and Xuereb 1997).

Finally, we provide evidence on the influence of pastperformance of the exporter on how the choices betweenstrategic orientations affect exploration and exploita-tion. We found that when exporters have a poor pastperformance, a customer orientation has more impact onexploratory innovation than a technology orientation.With fewer resources, technical competences are difficultto acquire because of the high costs (Gatignon andXuereb 1997). In such situations, exploratory innova-tions are predominantly developed by entering new mar-kets and leveraging the existing technological base, thatis, by developing customer-based innovations (Danneels


2002). With superior past performance, firms can affordto explore new ideas and opportunities by pursuing newand sophisticated technologies (Garcia, Calantone, andLevine 2003). Innovations incorporating state-of-the-arttechnology (e.g., those developed by technology-oriented firms, such as tech-based and pure explorationinnovations) are extremely expensive and require sig-nificant investments (Sorescu, Chandy, and Prabhu2003; Wind and Mahajan 1997). Nonetheless, even infirms with a strong technology orientation, success isnot certain; customers must be aware of the product oradoption for sales to accelerate (Sorescu, Chandy, andPrabhu 2003). Moreover, earlier research has found thatby focusing on future customers, firms introduce moreradical innovations (Chandy and Tellis 1998). Our find-ings support these arguments: Customer and technologyorientations are similarly important when exporterswith superior past performance develop exploratoryinnovations.

The differential effects of customer and technology orien-tations on exploitative innovations are also noteworthy.For exporters with poor past performance, customer ori-entation was more important than technology orientationto exploitative innovation. Being more incremental innature, such innovations do not require a technology ori-entation because they rely on a firm’s existing technolo-gies (Baker and Sinkula 2007; Gatignon and Xuereb1997). However, because a customer-oriented firm isknowledgeable about its customers, a customer orienta-tion favors the development of those innovations. In con-trast with our expectations, in exporters with good priorperformance, customer orientation does not have astronger influence than technology orientation onexploitative innovation. A possible explanation is thatbecause technological firms are inherently technology ori-ented (Workman 1993), they prefer to invest in new tech-nological competences when having good financialresults. Technological competences may be developed, forexample, through the diversification of exporters’ existingtechnological portfolio, enabling them to become moreexpert in their current domains of expertise (Katila andAhuja 2002; Quintana-Garcia and Benavides-Velasco2008). As such, past positive financial results would bemore readily applied through a technology orientationthan through a customer orientation. Our study con-tributes to previous research by confirming that exploita-tive innovation benefits more from existing customercompetences than from technological ones (Gatignon andXuereb 1997; Yalcinkaya, Calantone, and Griffith 2007).We also advance the literature by showing that the rolesof customer and technology on exploitative innovation

(as well as on exploratory innovation) are relative (i.e.,they depend on the firm’s financial results). Thus, thisstudy advances the literature by considering the inter-action between two key trade-offs: strategic orientations(customer versus technology) and innovation capabilities(exploratory versus exploitative).

Theoretical Contributions

This research contributes theoretically to organizationallearning theory and innovation literature in many ways.Organizational learning theory asserts that firms inno-vate by engaging in two forms of learning: exploratory(developing new knowledge) and exploitative (usingexisting knowledge) (March 1991). First, we found thatboth exploratory innovation and exploitative innova-tion are essential to the firm because they act as vehiclesfor renewing two key firm capabilities—customer andtechnology orientations—to achieve superior perfor-mance. Moreover, by considering customer and tech-nology orientations with exploration and exploitationsimultaneously, we present a new perspective of theroles of these orientations in the development of firms’innovation capabilities. Our results indicate that thetrade-off between customer and technology orientationsis pivotal in ensuring a proper balance betweenexploratory and exploitative innovations.

Second, we advance the literature by examining the rela-tive role of strategic orientation on exploration andexploitation. We show that for the development ofexploratory innovations, customer and technology ori-entations are equally important. Although marketingscholars have theorized about the importance of bothorientations to innovation capabilities (Danneels 2002;Gatignon and Xuereb 1997; Yalcinkaya, Calantone, andGriffith 2007; Zhou, Yim, and Tse 2005), no empiricalevidence exists on their relative role.

Third, our study confirms that a firm’s emphasis oneither customer orientation or technology orientationaffects the balance between exploratory and exploitativeinnovation changes according to its past performance.The organizational learning literature demonstrates thatfirms’ past performance influences their decision mak-ing (Cyert and March 1963; Lages, Jap, and Griffith2008; Lant and Mezias 1992). Thus, we offer a new per-spective by considering the moderating effect of pastperformance rather than using it as an antecedent. Fur-thermore, we show that when past performance is poor,exporters tend to develop exploratory innovations thatrely on their existing technological competences to


explore new customers rather than to explore new tech-nologies (customer orientation had a stronger effect onexploratory innovation than technology orientation).Prior research has argued that firms with fewerresources engage in more exploitation activities (Singh1986; Voss, Sirdeshmukh, and Voss 2008). Nonetheless,this study supports the view that firms with poor perfor-mance must develop both exploratory and exploitativeinnovation capabilities (He and Wong 2004; Levinthaland March 1993; March 1991); failing to recognize thatsuch exporters also need to invest in exploration (devel-opment of new knowledge) might risk their long-termperformance.

Fourth, although the export literature has more exten-sively covered customer orientation (e.g., Cadogan, Kuiv-alainen, and Sundqvist 2009), few studies have addressedtechnology orientation (e.g., Filatotchev et al. 2008), and,to our knowledge, none have examined both. Moreover,few studies have considered innovation capabilities (e.g.,Lages, Silva, and Styles 2009). This is surprising becauseinnovation and internationalization are highly related(Knight and Cavusgil 2004). We found that exploratoryand exploitative innovation capabilities drive the conver-sion of firms’ strategic orientations to export perfor-mance. Finally, the findings reveal that as long as anappropriate trade-off between customer and technologyorientations is maintained, exporting technology-basedfirms with a poor past performance may achieve a highexport performance in the future. Moreover, an equili-brated trade-off between customer and technology orien-tations can lead to a balanced mix of exploitative andexploratory innovation capabilities. Exporters with apoor past performance would benefit from innovationbased on new market exploration (e.g., entry into newgeographical markets) and exploitation of existing com-petences to satisfy existing customers.

Managerial Implications

This study provides several managerial implications.First, the findings underscore the need for managers toinvest in both customer and technological knowledge toensure the development of exploration and exploitation.Therefore, resource allocation decisions should, on theone hand, consider the firm’s needs for innovation capa-bilities and, on the other hand, be guided by the firm’sstrategic orientations toward customer and technology.Technological exporters operate in highly complex envi-ronments, characterized by high levels of technologicaland market uncertainties and highly diverse and dis-persed customers (Kleinschmidt, De Brentani, and

Salomo 2007; Mohr and Sarin 2009). Therefore, inaddition to a strong orientation toward the developmentof innovations using state-of-the-art technologies, man-agers of these firms need a similarly strong focus onunderstanding and satisfying the needs of both currentand potential customers. By acknowledging the need fora trade-off between customer and technology orienta-tions, managers can ensure a balanced mix of innova-tion competences. For example, Motorola faced a sig-nificant decline in performance following the success ofits cell phone model, the Razr (Verma, Momin, and Gir-ija 2008). Because Motorola focused primarily onextending the Razr product lines to please its customerbase, it failed to maintain itself at the forefront of thetechnological trends in the industry. Conversely, Philips,a company with a long tradition as a technological inno-vator, underestimated customers’ needs for products tomake their lives easier (George and Govind 2007). Thisbehavior was partly responsible for Philips’s financialdownturn at the beginning of the twenty-first century.

Second, our results suggest that managers in technologi-cal firms pressured by poor past performance shouldinvest more in developing a customer orientation than atechnology orientation. Because these firms cannot affordstrong investments in R&D, they engage less in the pur-suit of technology-based innovations. To ensure a bal-anced mix of innovation capabilities, they should developtheir exploratory innovations by focusing on new cus-tomers rather than on sophisticated technologies. A cus-tomer orientation enables the simultaneous fine-tuning ofexisting customer competences (leading to the develop-ment of incremental innovations) and the exploration ofnew customers (e.g., new geographical markets).

For firms with superior past performance, managersmay fail because of the lack of an adequate level of cus-tomer orientation. Technological firms naturally focuson acquiring sophisticated technologies and new tech-nological competences, but this is no guarantee of suc-cess. Managers of such firms need to maintain high lev-els of customer orientation. Resultant innovationsshould be balanced between radical and tech-basedinnovations on the one hand and incremental innova-tions on the other hand. Exporters may then use newtechnologies to enter new markets or to develop theexisting ones.

Limitations and Further Research

Although this work provides useful theoretical andmanagerial insights, it also has several limitations. The


use of a sample of Portuguese exporters limits the gen-eralizability of the results both geographically and inscope. Further research could examine the relationshipsbetween strategic orientations and innovation capabili-ties in other cultural contexts. In addition, the cross-sectional design does not allow for establishment ofcausal relationships. A longitudinal study would pro-vide additional insights into the tested model. Theeffects of innovation capabilities on performance aredifferentiated in the long and short run (March 1991).

Other possible limitations also imply fertile avenues forfurther research. International marketing research shouldinvestigate the interaction effects between customer andtechnology orientations, as well as the nonlinear relation-ships between these orientations and innovation, byaddressing the question of a more balanced view of strate-gic orientations. The resource-based theory posits thatcomplementary resources achieve synergistic effects onperformance; however, leveraging every resource is notpossible (Song et al. 2005). For managers, knowing whichresources to invest in is of extreme importance. For exam-ple, in studying exporters, Cadogan, Kuivalainen, andSundqvist (2009) find that managers should develop amarket orientation only to a certain point, which dependson market dynamism, because thereafter more develop-

ment harms performance. International marketingresearch could also investigate the trade-off between thetwo strategic orientations but linked to different types ofexploratory innovation. Danneels (2002) identifies threetypes: pure exploration, customer competence leveraging,and technology competence leveraging. Each type needsdifferent trade-offs between the strategic orientations(Yalcinkaya, Calantone, and Griffith 2007; Zhou, Yim,and Tse 2005). With a clear understanding of whichtrade-off of orientations leads to which specific type ofinnovation, managers would be able to make more pre-cise decisions on resource allocations and strategic direc-tions of the firm (Sorescu, Chandy, and Prabhu 2003). Inaddition to differentiating between innovations, weencourage further research to develop measures for eachtype, echoing Zhou, Yim, and Tse’s (2005) call.

Finally, the use of other moderators might also be a fer-tile area for international marketing research. For exam-ple, previous research has shown that depending on theabsorption and the rarity of resources, firms’ decisionsbalance more toward exploration or exploitation (Voss,Sirdeshmukh, and Voss 2008). Following this line ofresearch, additional research could typify customer andtechnology orientations to increase the understanding oftheir impact on innovation.

Appendix A. Scale Items and Reliabilities

StandardizedVariance Composite Factor

Constructsa Adapted From Extracted Reliability Loadings

Export Perceived Performance Zou, Taylor, and Osland 1998 .808 .923

Question: With regard to your company’s exportingoperation, to what extent do you agree with the following sentences?

• It has been very profitable. .867• It has generated a high volume of sales. .935• It has achieved rapid growth. .881

Customer Orientation Narver and Slater 1990 .572 .888

Question: With regard to your company’s actions in the exporting markets, to what extent do you agree with the following sentences?

• Our business objectives are driven primarily by customer satisfaction. .624

• We constantly monitor our level of commitment and orientation to serving customers’ needs. .869

• Our strategy for competitive advantage is based on our understanding of customers’ needs. .733


• Our business strategies are driven by our beliefs about how we can create greater value for customers. .788

• We measure customer satisfaction systematically and frequently. .740

• We give close attention to after-sales service. .764

Technological Orientation Zhou, Yim, and Tse 2005 .712 .908


• We use sophisticated technologies in our new product development. .824

• Our new products always use state-of-the-art technology. .867

• Technological innovation based on research results is readily accepted in our organization. .820

• Technological innovation is readily accepted in our project management. .864

Exploratory Innovation Lubatkin et al. 2006 .665 .923


• We look for novel technological ideas by thinking “outside the box.” .813

• We base our success on our ability to explore new technologies. .827

• We create products or services that are innovative to the firm. .845

• We look for creative ways to satisfy our customer’s needs. .810

• We dynamically risk entering new market segments. .822

• We actively target new customer groups. .774

Exploitative Innovation Lubatkin et al. 2006 .659 .920


• We commit to improve quality and lower cost. .801• We continuously improve the reliability of our

products and services. .876• We increase the level of automation in our

operations. .731• We constantly survey existing customers’

satisfaction. .775

Appendix A. Continued




• We fine-tune what we offer to keep our current customers satisfied. .873

• We penetrate more deeply into existing customer base. .807

aScale format: 1 = “completely disagree,” and 7 = “completely agree.”

Appendix B. Means, Standard Deviations, and Correlations Between Constructs

M SD 1 2 3 4 5

1. Export perceived performance 4.90 1.21 .895

2. Customer orientation 5.86 .89 .379 .756

3. Technological orientation 4.76 1.28 .267 .364 .844

4. Exploratory innovation 5.11 1.11 .501 .590 .540 .815

5. Exploitative innovation 5.65 .99 .477 .658 .447 .726 .812

Notes: The diagonal shows the square root of the average variance extracted.

Appendix A. Continued



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THE AUTHORS

Paula Hortinha (PhD, ISCTE, Portugal) is MarketingDirector at Jerónimo Martins, SGPS and affiliated with

Nova School of Business and Economics, Lisbon, Portu-gal. Paula has developed her managerial career at vari-ous multinational companies in Portugal and Spain, inindustries such as fast-moving consumer goods, foodretail, and apparel retail. She has performed jobs in mar-keting, commercial, and sales management areas.Paula’s research interests are related to the transfer oftechnological innovation to international markets. HerPhD research was a finalist of the McKinsey Award atthe 38th European Marketing Academy conference.

Carmen Lages (PhD, Warwick, United Kingdom) isAssistant Professor of Marketing in the ISCTE BusinessSchool, Lisbon University Institute, Portugal. Part ofthis research was conducted while she was a VisitingScholar in the Deshpande Center for TechnologicalInnovation at the Massachusetts Institute of Tech-nology. Her current research interests include sustain-ability/corporate social responsibility, relationship man-agement, branding, and international marketing. Herpublications have appeared in Journal of InternationalMarketing, Journal of Business Research, IndustrialMarketing Management, European Journal of Market-ing, among others.

Luis Filipe Lages (PhD, Warwick, United Kingdom) isAssociate Professor of Marketing and International Busi-ness at Nova School of Business and Economics, Lisbon,Portugal. Part of this research was conducted while hewas an International Faculty Fellow in the Sloan Schoolof Management at the Massachusetts Institute of Tech-nology. His research interests include international mar-keting, managerial reactions to past performance, meas-urement of intangibles, innovation/creativity, andtransfer of technology to the market. His publicationshave appeared in Journal of International Marketing,Journal of International Business Studies, Journal ofRetailing, Journal of Business Research, InternationalMarketing Review, International Business Review,Industrial Marketing Management, European Journal ofMarketing, among others. He sits on several editorialboards, including those of Journal of International Mar-keting and International Marketing Review.

ACKNOWLEDGMENTS

The authors thank the five anonymous JIM reviewersfor constructive feedback. This research was supportedby Fundação para a Ciência e a Tecnologia, “NovaForum,” and UNIDE-Lisbon University Institute.

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