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Trademarks’ relatedness to product and service innovation

Flikkema, Meindert; Castaldi, Carolina; de Man, Ard Pieter; Seip, Marcel

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DOI (link to publisher)10.1016/j.respol.2019.01.018

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citation for published version (APA)Flikkema, M., Castaldi, C., de Man, A. P., & Seip, M. (2019). Trademarks’ relatedness to product and serviceinnovation: A branding strategy approach. Research Policy, 48(6), 1340-1353.https://doi.org/10.1016/j.respol.2019.01.018

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Trademarks’ relatedness to product and service innovation: A brandingstrategy approachMeindert Flikkemaa,⁎, Carolina Castaldib, Ard-Pieter de Mana, Marcel Seipa,c

a Vrije Universiteit Amsterdam, School of Business & Economics, Department of Management & Organization, the Netherlandsb Eindhoven University of Technology, Department of Industrial Engineering & Innovation Sciences, the Netherlandsc Rijksdienst voor Ondernemend Nederland (RVO.nl), The Hague, the Netherlands

A R T I C L E I N F O

JEL classifications:O34O39

Keywords:TrademarksInnovationPatentsBrand creationBrand scope

A B S T R A C T

The use of trademark data in innovation studies is still limited because as yet no guidelines exist to ascertainwhich trademarks relate to innovation. This paper proposes that a branding strategy approach may help toidentify innovation related trademarks. Companies use distinctive branding strategies for innovation and thesebranding strategies have important consequences for the design of new trademarks and their application scope.Based on a sample of Benelux and Community trademarks, we find that trademarks for brand creation relatemore often to product innovation. In addition, we find negative effects of a trademark’s industry scope on itsrelatedness to product innovation, and of a trademark’s geographic scope on its relatedness to service innova-tion. Our findings bear several key implications for further research towards identifying innovation-relatedtrademarks from a branding strategy perspective.

1. Introduction

Research on the relationship between trademark activity and in-novation is in its infancy (Schautschick and Greenhalgh, 2016; Grahamand Hancock, 2014). Results from empirical studies so far, however, arepromising. They show positive correlations between the firm-level useof trademarks and firm-level proxies of innovation (Allegrezza andGuarda-Rauchs, 1999; Schmoch, 2003; Jennewein, 2005; Jensen andWebster, 2009; Götsch and Hipp, 2012). They also support the viewthat trademarks may be relevant for measuring innovation, as discussedin the pioneering paper by Mendonöa et al. (2004) and acknowledgedby the recent inclusion of trademark counts in rankings such as the EUInnovation Scoreboard (European Commission, 2015).

Most innovation studies, however, still use patent statistics and R&Dinvestments as preferred innovation indicators (Malecki, 2014). Themajor bottleneck preventing a wider use of trademark-based indicatorsdoes not seem to be that only a limited share of trademarks relates toinnovation, but even more critically, that no rules of thumb exist toestablish which ones. We contend that innovation research can benefitfrom the information stored in new trademark records and firm-leveltrademark portfolios for a more systematic understanding of trade-marks related to innovation. A trademark portfolio is the result of allchoices made by firms regarding trademark filing at national and in-ternational offices for intellectual property rights (Sandner, 2009).

Trademark similarity to other firm-owned trademarks in terms ofwords, symbols and aesthetics as well as a trademark’s geographic andindustry scope are the most important choices and are fundamentallyintertwined with the focal applicant’s branding strategy (Block et al.,2014).

A quite natural strategy for signaling technological advances orflagging product innovation is brand creation. New brands signal thedistinctiveness of firm offers (Aaker, 2007). However, new trademarksin a firm’s trademark portfolio may also have been filed for differentbranding purposes, such as brand modernizing or extension (Sandner,2009; Block et al., 2014), or as a preemptive registration (Rui, 2013).This paper therefore serves the call from Brexendorf et al. (2015) whomake a plea for a better understanding of the relationship betweenbranding and innovation. Hence, our main contribution is to spell outhow a branding strategy approach can help to predict a trademark’srelatedness to innovation. This approach allows us to interpret in-formation from trademark records in light of the underlying choices oftrademark applicants regarding the branding of their innovation.

Our key results are two. Firstly, we studied whether brand creations,in the form of both first trademarks from start-ups and dissimilar sub-sequent trademarks from all firms related more often to innovation. Wefind that first trademarks by startups relate most strongly to productinnovation. Trademarks mark the start of a business (Vries et al., 2017),hence our results indicate that trademarks are particularly relevant to

https://doi.org/10.1016/j.respol.2019.01.018Received 20 November 2017; Received in revised form 22 January 2019; Accepted 26 January 2019

⁎ Corresponding author.E-mail address: [email protected] (M. Flikkema).

Research Policy 48 (2019) 1340–1353

Available online 27 March 20190048-7333/ © 2019 Published by Elsevier B.V.

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study brand creation for innovation in startups. As Seip et al. (2018)rightfully address, startups are underrepresented in innovation surveys,such as the CIS survey. We also find that dissimilar subsequent trade-marks filed for brand creation purposes relate more often to productinnovation.

Secondly, we considered the various scope choices a firm made atthe time of trademark application, in particular concerning thegeographies and industries to be covered. We find that trademarksregistered to protect brands with a narrow industry scope relate moreoften to product innovation, while trademarks protecting brandswith a narrower geographic scope related more often to service in-novation.

The remainder of this paper is structured as follows. Section 2reviews the key studies exploring the relatedness between trademarkactivity and innovation to show the current state of thinking. Section3 presents hypotheses about the relationship between innovation,branding strategies and trademark application. Section 4 describesour research approach, matched data sources and sample char-acteristics, while Section 5 presents the results of multivariate re-gression analyses to test our hypotheses. We end with conclusions,limitations and an agenda for future research into the relatednessbetween trademarks and innovation while pursuing a brandingstrategy approach.

2. Studies on trademarks and innovation

Trademark law gives economic agents the opportunity to protectbrand names and symbols against imitation. Trademarks identify theorigin of goods and services, thereby offering protection to both sellersand buyers (Greer, 1979). They protect against deception and fraud tobuyers, and to some extent they protect sellers’ monopolies. Strictlyspeaking, however, trademarks do not protect inventions as patents do,since an inventive step and non-obviousness are not required to suc-cessfully apply for trademarks. The primary purpose of trademarkprotection is to provide incentives for firms to invest in both quality andreputation (Nam and Barnett, 2011). From case studies, we know firmssometimes apply for new trademarks in cases of meaningless or nildifferentiation, for exchange reasons (Block et al., 2014), to prolongother Intellectual Property Rights (IPRs) (Statman and Tyebjee, 1981;Rujas, 1999; Jain and Conley, 2014), to leverage brand equity (Kocyigitand Ringle, 2011; Block et al., 2014), to avoid trademark squatting(Helmers et al., 2013), to pack product spaces (Reitzig, 2004), to con-trol franchisees (Ramello, 2006), to support low risk entry in foreignmarkets (Giarratana and Torrisi, 2010), to enable ingredient marketing(Reitzig, 2004), to protect slogans, or for advertising purposes (Fosfuriand Giarratana, 2009). We can therefore conclude that the use of rawtrademark counts in innovation studies has several potential drawbacksin particular for micro-level research.

Nevertheless, trademark data’s potential for innovation research hasbeen claimed in a seminal paper by Mendonça et al. (2004), for severalreasons. Firstly, trademarks are widely used across all industries ofeconomic activity (WIPO, 2013) and firm sizes (Rogers et al., 2007;Seip et al., 2019). Trademark registration is less costly than patent re-gistration and registration requirements are easier to fulfill. For the UK,Rogers et al. (2007) show that in all industries, the number of SMEsowning trademarks is always higher than those owning patents, with aparticularly striking difference in service industries. Secondly, whilepatents are related to inventions, trademarks capture inventions beingcommercialized (Nam and Barnett, 2011; Castaldi and Dosso, 2018),because commercial use is a precondition for registration. In this sense,they complement patents as innovation output indicators (Somayaet al., 2007). Thirdly, trademarks also capture non-technological typesof innovation, in particular new marketing solutions (Millot, 2009) andservice innovation, to make the intangible tangible (Schmoch, 2003).Hence, trademark-based indicators could even substitute patent-basedones in these contexts. Finally, trademarks can be collected

systematically from trademark offices1 and are classified as protecting agood, service or a combination thereof with a detailed classificationsystem: the Nice classification (Economides, 1998; Schmoch and Gauch,2009). They also represent a meaningful indicator at different levels ofaggregation: firm, industry or country.

So far, a few studies have offered empirical evidence that trade-marks relate to innovation. As rightly noted by Schautschick andGreenhalgh (2016), establishing a link between trademarks and in-novation is challenging, given that it is difficult to measure innovationin the first place. Any test of whether trademarks relate to innovation isbound to rely on proxies. Studies mainly use information from theCommunity Innovation Survey (CIS), for example Schmoch (2003);Millot (2012) and Götsch and Hipp (2012). Thus, most studies reportindirect evidence in the form of firm-level or industry-level correlationsbetween trademarking activities and innovation (see Table 1). In ad-dition, studies typically use raw trademark counts or dichotomies likethe firm-level (yes/no) usage of trademarks. Remarkably, hardly anystudy in this area has exploited information from trademark recordsbeyond this basic information. Two empirical studies report directevidence at the trademark level. In a longitudinal case study Malmberg(2005) focuses on the firm-level propensity to trademark product in-novations and distinguishes between general trademarks (companyname or marketing slogans) and trademarks signaling new products andservices. Flikkema et al. (2014) look at the motives behind the regis-tration of a sample of Benelux trademarks and the trademark-level re-latedness to innovative activity and output. The preliminary conclusionof these studies is that the relationship between trademark activity andinnovation is highly dependent on the industry, market and innovationtype (see also Jensen and Webster, 2009).

Finally, conceptual work by Davis (2006) offers insights on firm-level strategies behind trademark application for innovation purposes:“Most incremental innovations represent only very small changes overexisting goods, or new combinations of existing goods, neither of whichrepresent the kind of ‘inventive step’ necessary to qualify for patentprotection” (Davis, 2006, p. 11). That is why the ‘standalone’ use ofnew trademarks makes particular sense for incremental innovations, tosignal newness and to enhance the perceived differentiation of newoffers (Flikkema et al., 2014).

We can conclude that although there is evidently a relationshipbetween trademark registration and innovation, we need to better un-derstand when trademarks are related to innovation, and to which in-novation types. In the following section, we therefore propose abranding strategy approach to identify trademarks related to product orservice innovation. The basic idea is that distinctive branding strategiesfor innovation lead to specific trademark application choices.

3. Branding strategies as predictor of innovation-relatedness

3.1. Innovation, branding and trademark application: a conceptualframework

Successful brands communicate with consumers through brandstories (Chiu et al., 2012). The brand story plays an important part inhelping consumers make sense of the brand. We therefore conceptualizebrand creation strategies as ‘the creation of new, distinctive brand logosand the crafting of compelling brand stories’. According to the leadingbranding scholar David Aaker (2007) an innovation should be brandeddistinctively when it is a significant advance, when customers care andwhen it will merit investment over time. If companies act this way, thenone may expect that brand creation strategies for innovations translateinto the filing of new, dissimilar trademarks. Fig. 1 gives a general

1 Since 1996, European firms can apply for Community Trademarks at theEUIPO and an online database enables to search all registered or pendingmarks.

M. Flikkema, et al. Research Policy 48 (2019) 1340–1353

1341

overview of the relation between innovation, branding strategies andtrademark application practices. Trademarks protect the brand logo,including brand name and brand esthetics. The design of one or moretrademarks would cover a brand logo completely. Other trademarksmay be filed to protect key elements of the brand story such as slogans(Petty et al., 2010). A further relevant element is brand scope. Brandscope choices concern both the geographic scope of brands (e.g.Townsend et al., 2009) and their industry scope (e.g. Sanchez, 2004).Information about both brand scope dimensions are registered in tra-demark applications. The Nice classes covered in a trademark recordrelate to a brand’s industry scope, while the trademark system (nationalversus international) reflects choices concerning a brand’s geographicscope.

3.2. Trademarks filed for brand creation as predictor of innovation-relatedness

Trademarks are filed for various reasons (Flikkema et al., 2014;Castaldi, 2018), but predominantly to avoid brand imitation and blur-ring. Block et al. (2014) distinguish different trademark-filing strate-gies. They use word and Nice class similarity between trademarks at theportfolio level to qualify new trademark filings. According to Blocket al. (2014), brand creation is required if a firm wants to tap into newmarket segments where they cannot capitalize on the equity of parentbrands or when this equity is at risk due to potential brand dilution.Dissimilar trademarks tend to convey unrelated, dissimilar and hencedistinctive offers supporting brand creation. It is therefore reasonablethat when a firm-level trademark portfolio is expanded with a verballyor visually dissimilar trademark, we can expect diversification or in-novation efforts taking place. Or in terms of Aaker (2007, p.13): ‘onlysignificant advances legitimize the development of distinctive brands’.

Alternatively, brand extending strategies are precisely aimed atexploiting spillover effects from established brands. Consider for ex-ample McKinsey Solutions, a figurative trademark used to support thebranding of McKinsey & Company’s service line extension. The figura-tive trademark WAVE, also owned by McKinsey, protects a productbrand created to identify a new program management tool – aMcKinsey solution ‒ that supports transformation and change pro-grams. As this is a standardized rather than a customized solution,which is also licensed to customers, the brand creation also serves thepurpose of separating this standardized service from the company’s coreservices to avoid brand blurring (Castaldi and Giarratana, 2018).

Brand modernizing, on the other hand, can be supported throughtrademark filing practices aiming to rejuvenate existing brands(Sandner, 2009) for various reasons: to discard undesirable associationsin changing environments, signal revitalization and ongoing marketpresence, improve the fit with a product’s life cycle, appeal to con-temporary preferences and tastes in aesthetics (Diamond, 1969), or tostretch the number of perceived brand attributes (Henderson and Cote,1998) or their quality (Boush, 1993). Brand modernization does nothave any clear link to the signaling of new products or services.

Among the brand creations are also, by definition, first trademarksby start-ups. A substantial share of the first-time trademark applicationswill be from start-ups. Their trademarks mark the start of a business. Itis reasonable to expect start-ups to aim for brand creation for innova-tion purposes because start-ups are considered an important ‘innovationmodel’ (Freeman and Engel, 2007). One may expect that start-ups willapply for first trademarks related to both incremental (Davis, 2006) andradical innovation for reasons of a lack of resources to apply for otherIPRs (Flikkema et al., 2014), but also to signal their invention’s marketpotential and entrepreneurial spirit to venture capitalists (Vries et al.,2017). For these reasons, we also expect a relation to innovation forfirst trademarks by start-ups, but not for first trademarks by maturefirms. We therefore hypothesize:

Hypothesis 1. Trademarks supporting brand creation, including dissimilarTable1

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1342

new trademarks by all firms and first trademarks by startups, are morefrequently related to innovation than the ones filed for all other purposes.

Mature firms may also apply for a first trademark, but those will notbe included in our data as a measure for brand creation. Such trade-marks are less likely to relate to innovation. First, brand imitation intheir own or adjacent industries might trigger firms to engage all of asudden in trademark application, for myopic motives (Castaldi, 2018).More rationally, being accused of trademark infringement might lead tobrand (re)design and trademark application, to avoid future litigationand lawsuits. Second, brand licensing, franchising or export con-siderations (Giarratana and Torrisi, 2010; Jayachandran et al., 2013)may also play a role. Third, it might be an attempt to leverage the valueof (in)tangible assets (Flikkema et al., 2014), for example just beforeselling the firm or in the build-up to divesting a non-core business.Fourth, entrepreneurs might have been uninformed about the possibi-lities of the IPR system in the start-up stage and also brand equitygrowth might explain a firm’s late start with building a trademarkportfolio. Trademark application is then a consequence of en-trepreneurial success (Fink et al., 2018) and the protected brand isflourishing. Brand creation is not expedient.

3.3. Trademark application scope as predictor of innovation-relatedness

3.3.1. Geographic scopeTrademarks are geographically bound, as their validity is con-

strained by national boundaries. Yet, the existence of Community tra-demarks (CTMs) allows companies to apply for the same trademark inseveral member states at once in the EU. However, application at theEuropean level is more complex, and fees are typically proportional tothe number of countries covered. When companies opt for a CTM, theyare likely to either diversify internationally or they might be ‘bornglobals’ (Madsen and Servais, 1997). Hitt et al. (1997) show for

medium sized and large manufacturers that international diversifica-tion is positively related to firm innovation. They argue that innovationis either a reason for internationalization, to earn a reasonable returnon investments in radical innovation, or a consequence. ‘Internationallydiversified firms have access to more and different resources and, be-cause of the larger markets and potentially greater returns, have moreresources to invest in innovation’ (Hitt et al., 1997, p.774). Expansioninto international markets provides opportunities for greater returns oninnovations and reduces the risk of failure due to the additional numberof markets in which the innovation may be applied. Both argumentssupport a positive relationship between the geographic scope of tra-demarks and the relatedness to product innovation. We therefore hy-pothesize that:

Hypothesis2a. Trademarks with a broader geographic scope are morelikely to relate to product innovation than other new trademarks.

A decisive argument for hypothesizing a negative relationship be-tween a trademark’s geographic scope and its relatedness to serviceinnovation, is that the market introduction of service innovations oftenstarts with a national, regional or local try-out. Due to the difficulties inexporting which are magnified by the unique characteristics of services,many service innovations are prototyped and introduced locally, inpilot runs (Magnusson et al., 2003), with lead users (Schuhmacher andKuester, 2012) or on the job (Flikkema et al., 2007). Subsequently, afterinitial success and various cycles of continuous improvement(Terziovski, 2002), an international rollout might be planned. DenHertog et al. (2010) and Charitou and Markides (2002), however, arguethat service firms often struggle with up-scaling new services, also be-cause service innovation is frequently an ad hoc process (Gallouj andWeinstein, 1997). Moreover, from the branding literature we also knowthat service firms tend to adopt a corporate brand approach to themanagement of their brand architecture, having a propensity to rely onone overarching brand (Devlin, 2003). This may imply that particularly

Fig. 1. Conceptual framework.


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subsequent trademarks with a higher geographic scope are less likely torelate to service innovation. One may argue that in these cases newtrademarks ‘just’ signal geographic expansion or the applicant’s inten-tion to adapt to local needs, peculiarities or habits (Pike, 2013). Theapplicant may also try to benefit from positive attributes of city, regionor nation ‘brands’, i.e. associations to certain geographies. Conse-quently, we hypothesize that:

Hypothesis 2b. Trademarks with a broader geographic scope are less likelyto relate to service innovation than other new trademarks.

3.3.2. Industry scope: trademark breadth and Nice class typesA trademark is also characterized by a certain industry scope. With

industry scope, we mean the number -typically referred to as ‘breadth’-and type of Nice classes covered by a single trademark (Sandner andBlock, 2011). Note that the 45 Nice classes aim to cover all economicactivities: 34 Nice classes cover goods, while Nice classes 35–45 coverservices. Sandner and Block (2011) and Melnyk et al. (2014) use Nicebreadth as an indicator of the value of trademarks, which might beassociated with a higher likelihood of relating to innovation. Sandnerand Block (2011, p. 973), however, describe their doubts on the latter:“Assessing the words or signs that trademark rights protect reveals thatthose trademarks associated with few classes tend to protect brands forsingle products or narrow product lines, for example Microsoft Office2000 or iPod. By contrast, trademarks like Daimler or PlayStation areawarded to many classes and seem to protect wider product lines or so-called umbrella brands.”

Due to the pricing policy at IPR offices in Europe2 and for myopicme-too reasons (Flikkema et al., 2014), many trademark applicantsseem to select three Nice classes. To avoid trademark opposition, orbecause a trademark covers a very specific and distinctive offer, someapplicants may decide to file their trademark in fewer classes or morespecific (product or service only) classes. Some applicants might also beuninformed about the ‘three for one’ pricing policy of trademark offices.However, firms may also choose to apply for trademarks in more thanthree Nice classes in order to avoid trademark dilution, anticipate fu-ture entry in adjacent markets, or to license a brand name in otherindustries. These are non-innovative activities. We therefore hypothe-size that:

Hypothesis 3a. Trademarks with a lower breadth are more likely to beinnovation related than other trademarks.

In addition, one may expect that trademark applications filed inproduct or service Nice classes only are related more frequently toproduct or service innovation than trademarks filed in both product andservice Nice classes. We know from case studies (e.g. BTM applicationno.1201961) that for some service innovations trademarks are filed incombinations of software (Nice 9), business services (Nice 35) andsoftware licensing (Nice 42) classes. However, we did not find evidence,neither in case studies nor in the servitization literature that manu-facturers tend to redesign their brand(s) significantly, when they startoffering product-service-combinations. We therefore expect that com-binations of product and service classes are predominantly used toemphasize brand attributes, are related to new slogans, or a con-sequence of the pricing policies of IP offices. We illustrate this withexamples from the trademark portfolio owned by Miele, a Germanprovider of high-end domestic appliances and commercial equipment.Consider for example theMiele Professional trademark (CTM applicationno. 0053282232) emphasizing professionalism in both products (Nice 7and 11) and services (Nice 37) and the Miele immer besser slogan (CTMapplication no. 011998408), which is filed in Nice 7,8,11 and 37

(Repair and Maintenance services), while Miele trademarks evidentlyrelated to innovations such as Intelligent Steam (CTM application no.005501499) or NoSmell (CTM application no. 005862941), are all justfiled in Nice classes 7,9 and 11. We therefore hypothesize:

Hypothesis 3b. Trademarks filed in product (service) Nice classes only aremore likely to relate to product (service) innovation than other trademarks.

4. Research design

4.1. Data collection methods

To test the hypotheses we collected a sample of registered trade-marks for which the relatedness to innovation can be assessed andpredicted. Therefore we first selected all −28,960- unique BTM andCTM applicants, starting from all 19,348 BTM and 88,200 CTM appli-cations in 2009, on the condition that at least one of their trademarkswas ultimately registered within two years after the initial filing.Second, we distributed an on-line survey based on the availability ofapplicants’ e-mail addresses. The list contained 12,688 unique e-mailaddresses of pro se filers and e-mail addresses provided by trademarkattorneys, who registered the 2009 BTMs and CTMs on behalf of clients.Our sample contained 1015 trademark applications, either for a BTM(n= 456) or a CTM (n= 559) filed in 2009. In the Benelux, Novagraaf,by far the largest firm providing trademark services, with an overallmarket share of 15.3% in 2009, provided nearly all the e-mail addressesof the 2009 BTM applicants in their client base, which partly explainsthe overrepresentation of BTMs. We also tested for a Novagraaf effectwith a Novagraaf dummy and found that their trademarks more fre-quently related to product innovation. However, in multivariate ana-lyses we did not find significant Novagraaf effects, hence we droppedthe respective dummy.

If an email address of a 2009 BTM or CTM applicant related to morethan one trademark, we randomly selected the trademark for which therespondent had to answer the survey questions. This potentially implieda selection bias if trademark application by frequent filers more or lessfrequently related to innovation efforts. Though it is a potential sourceof bias, we have no reason to believe that the trademarks of applicantswho did not register their email address in the trademark databases ofEuropean Union Intellectual Property Office (EUIPO) or the BeneluxOffice for Intellectual Property (BOIP), had a structurally different re-lation to innovation than other trademarks in the 2009 population.Trademark law does not dictate e-mail registration, this is probablywhy the availability of email addresses is limited to about 40%.

We compiled a survey including questions on the respondent’sstructural characteristics (e.g. firm size & maturity, industry), on theprocess of trademark application (motives, timing, strategies of com-bining trademarks with other IPRs), and on the relatedness of trade-marks to innovation, largely based on the innovation definitions in theOECD Oslo manual (OECD, 2005). The survey was sent by EUIPO andBOIP, and was available online between July 18, 2012 and January 16,2013. It was electronically linked with the IPR offices’ databases, whichenabled the respondents to inspect the details of their trademark filingswhile answering the survey. Overall, 8 percent of the 2009 trademarkapplicants (= 1015/12,688) which could be contacted by e-mail com-pleted our survey. Although the response rate was limited, the samplesize is substantial, compared to previous survey studies into the motivesfor trademark application (Flikkema et al., 2014; Block et al., 2015).Nishimura et al. (2016) propose not to discuss sample sizes, but to testwhether responders and non-responders differ on fully observed char-acteristics, which we did for firm size, volumes in Nice classes, in-dustries and trademark types. We present the results in subsection 4.2.For the trademarks in our final sample, we complemented our primarydata with secondary data on each trademark. We retrieved the fulltrademark portfolios at BOIP and EUIPO. Each trademark record con-tained several pieces of information, of which we exploited: the

2 Note that pricing schemes at trademark offices make registration in threeNice classes as costly as in only one class. This explains why the number of Niceclasses only makes sense when higher or lower than three.


1344

applicant name, and date of filing, the trademark description in wordsand/or graphics, the trademark type and the Nice classes covered.

4.2. Validity checks

We checked several aspects concerning the representativeness of thesample and the quality of the responses. First, we compared the dis-tribution of the sample trademarks over the Nice classes with the po-pulation distribution. Fig. 2 shows the trademark volumes in all Niceclasses based on the EUIPO and BOIP databases (population expectedfrequency) and the sample dataset (sample observed frequency). Thepattern was rather similar for both frequencies. We checked whetherthere was a relationship between the representation of Nice classes andthe relatedness of trademarks to innovation in our sample. Nice classeswhere trademarks had a strong relatedness of trademarks to innovationcould have been overrepresented because the survey invitation texttouched upon the topic of innovation. The three Nice classes that weremost overrepresented (6, 11 and 2) showed somewhat higher related-ness to product innovation, while the three that were most under-represented (16, 35 and 36) had a higher relatedness to service in-novation. This implies that our sample was slightly biased towardsproduct innovation and against service innovation.

Second, we also compared the number of verbal and figurativetrademarks in our sample with the numbers expected in the population.Both in the BTM and CTM sub-samples, the share of verbal and fig-urative trademarks almost equaled its equivalent in the population.Third, we performed additional validity checks using other IPR datasources. The patent applications of all the respondents in the samplewho claimed to have filed one or more patents along with the trade-mark were verified by tracing the patents back to the European PatentOffice (EPO). We could confirm the registration of patent applicationsfor 22 of the 29 respondents (75%) in the BTM sample who claimed tohave filed patents. For the 114 respondents in the CTM sample claimingto have filed one or more patents, this percentage was even higher:80%. Remarkably, most of the matched patent applications in both theBTM and CTM samples were European rather than national patentapplications. This is an indication that the innovations involved have asignificant value because the costs incurred for a European patent ap-plication are much higher than an application at a national patent of-fice. Fourth, from previous studies we know that firm size is a covariateof innovation (Shefer and Frenkel, 2005) and IPR strategies (Arundel,2001; Leiponen and Byma, 2009). We therefore compared the firm sizedistribution –for Dutch BTM applicants only- in our sample with the

firm size distribution in the whole BTM population: all Dutch firms withBTM applications in 2009 (Snoei et al., 2013). No significant differenceswere found. Since the number of Dutch CTM applicants is limited(n= 20), we did not test for sample versus population differences here.We also tested for industry differences between the BTM subsample andthe population and found a bias towards the share of manufacturingfirms in the sample of 20%. This means that manufacturers wereoverrepresented in the sample. Finally, we considered the risk ofcommon-method bias as limited because we used both informationfrom trademark records and a distributed survey. The trademark designand scope information was retrieved from the trademark records, whilewe used the survey for collecting information about the independentvariable. Of course, unobserved characteristics might still bias oursample, but these checks tackled all the observed characteristics wecould exploit.

4.3. The definition of variables

Table 2 reports the definitions of all our main variables, and de-scriptive statistics in the original and final sample, the one used for theregression analyses.

4.3.1. Dependent variableWe focused on two types of innovation for which we found the

highest proportions of respondents claiming a relationship with theirtrademarks, namely product (37.9%) and service innovation (20.6%).The survey question asked trademark owners to indicate whether thespecific trademark related to a ‘new or significantly improved product’,or a ‘new or significantly improved service’, following the definitions ofthe Oslo manual (OECD, 2005). Additionally, we used the informationfrom another survey question which asked the respondent to indicatewhether their innovation was: (1) new to our firm, (2) new to themarket where we compete, (3) new to our industry, (4) new to theworld, (5) not applicable. As new to the firm innovations are moreabout diffusion than actual innovation, we opted for a stricter definitionof product and service innovation (resulting in 29.6% and 16.9% as theshares of trademarks relating to product and service innovation re-spectively). Our dependent variable was a dummy, taking the value of 1whenever the respondent claimed that the trademark related to a newproduct or service and the innovation was judged as being at least newto the market and a value of zero when they did not relate to innovationor related to new to the firm innovation.

Fig. 2. Sample and population distribution over the Nice classes.


1345

Table2

Defi

nitio

nof

our

mai

nva

riab

les

and

desc

ript

ive

stat

istic

sin

the

orig

inal

sam

ple

and

final

sam

ple.

All

vari

able

sar

edu

mm

yva

riab

les

exce

ptfo

rth

enu

mbe

rof

wor

ds.

Ori

gina

lsam

ple

Fina

lsam

ple

Vari

able

Cate

gory

Defi

nitio

nN

Mea

nSt

d.D

evia

tion

NM

ean

Std.

Dev

iatio

n

Innovation

Prod

ucti

nnov

atio

nTr

adem

ark

rela

ted

toa

new

prod

uct,

atle

ast

new

toth

em

arke

t10

150.

296

0.45

765

60.

326

0.46

9Se

rvic

ein

nova

tion

Trad

emar

kre

late

dto

ane

wse

rvic

e,at

leas

tne

wto

the

mar

ket

1015

0.16

90.

375

656

0.13

90.

346

Non

-inno

vatio

nTr

adem

ark

did

not

rela

teto

any

inno

vatio

nor

only

toa

new

-to-fi

rmin

nova

tion

1015

0.29

50.

456

656

0.33

50.

473

Brandingstrategies

TMde

sign

Firs

ttra

dem

ark

bym

atur

efir

mTr

adem

ark

was

the

first

filed

and

the

appl

ican

tis

am

atur

efir

m91

00.

256

0.43

765

60.

279

0.44

9Fi

rstt

rade

mar

kby

star

tup

Trad

emar

kw

asth

efir

stfil

edan

dth

eap

plic

ant

isa

star

tup

910

0.30

00.

459

656

0.30

60.

461

Bran

dcr

eatio

nTr

adem

ark

isdi

ssim

ilar

topr

ior

mar

ksfil

edby

the

appl

ican

t10

150.

259

0.43

865

60.

279

0.44

9Br

and

exte

nsio

nTr

adem

ark

issi

mila

rto

prio

rm

arks

filed

byth

eap

plic

ant

1015

0.12

00.

325

656

0.11

90.

324

Bran

dm

oder

nizi

ngTr

adem

ark

mod

erni

zes

prio

rm

arks

filed

byth

eap

plic

ant

1015

0.01

80.

132

656

0.01

60.

124

Wor

dm

ark

Wor

dm

ark

vsco

mbi

ned

mar

k99

90.

514

0.50

065

60.

551

0.49

8Fi

gura

tive

Figu

rativ

em

ark

vsco

mbi

ned

mar

k99

90.

255

0.43

665

60.

237

0.42

6N

o.W

ords

Num

ber

ofw

ords

ofth

etr

adem

ark

(min

=1,

max

=9)

998

1.81

91.

336

656

1.79

11.

298

TMsc

ope

Geo

grap

hica

lsco

peD

umm

yin

dica

ting

whe

ther

the

TMw

asfil

edas

CTM

inst

ead

ofBT

M10

150.

551

0.49

865

60.

569

0.49

6In

dust

rysc

ope:

Nic

ebr

eadt

hTr

adem

ark

was

filed

inm

ore

than

3N

ICE

clas

ses

999

0.15

50.

362

656

0.15

50.

362

Onl

ypr

oduc

tN

ice

Trad

emar

kw

asfil

edin

prod

uctN

ice

clas

ses

only

999

0.36

40.

481

656

0.36

40.

481

Onl

yse

rvic

eN

ice

Trad

emar

kw

asfil

edin

serv

ice

Nic

ecl

asse

son

ly99

90.

283

0.45

165

60.

283

0.45

1Po

pula

rN

ice

Trad

emar

kw

asfil

edin

NIC

Ecl

asse

s9,

35,4

1or

4599

90.

607

0.48

965

60.

607

0.48

9Othervariablesfrom

survey/matcheddata

Firm

size

Mic

rofir

mA

pplic

ant

was

firm

with

less

than

9em

ploy

ees

968

0.57

40.

495

656

0.37

00.

483

Smal

lfirm

App

lican

tw

asfir

mw

ith10

-49

empl

oyee

s96

80.

190

0.39

365

60.

192

0.39

4M

ediu

mfir

mA

pplic

ant

was

firm

with

50-2

49em

ploy

ees

968

0.10

00.

300

656

0.10

10.

302

Larg

efir

mA

pplic

ant

was

firm

with

mor

eth

an25

0em

ploy

ees

968

0.13

50.

342

656

0.12

90.

336

Firm

mar

ket

scop

eB2

BD

umm

yin

dica

ting

ifth

em

ain

clie

nts

wer

ebu

sine

sscl

ient

s91

00.

725

0.44

765

60.

750

0.43

3B2

CD

umm

yin

dica

ting

ifth

em

ain

clie

nts

wer

efin

alco

nsum

ers.

910

0.46

60.

499

656

0.46

00.

499

Oth

erTM

prop

ertie

sA

ttor

ney

supp

ort

App

lican

tus

edth

esu

ppor

tofa

nat

torn

ey10

150.

229

0.42

065

60.

211

0.40

8Co

mbi

natio

nw

ithpa

tent

Trad

emar

kfil

ing

was

com

bine

dw

itha

pate

ntap

plic

atio

n.10

150.

136

0.34

365

60.

137

0.34

4


1346

4.3.2. Branding strategiesWe started from the procedure proposed by Sandner (2009) and

used in Block et al. (2014) to classify new trademark filings. The pro-cedure considered two independent dimensions: industry scope referredto the Nice classes covered by the trademark and linkage referred to thedegree of verbal or visual similarity between the focal trademark andthose already in the applicant’s portfolio. Both dimensions could becaptured using information from trademark records after collecting allprior trademarks registered by the firm. We followed Sandner’s proce-dure when individually inspecting all trademarks in each applicant’sportfolio. All trademarks were assigned to one category from Sandner’sclassification of filing strategies.

If the focal trademark had no similarity to any other trademarkowned by the applicant, we classified its filing as brand creating. Brandextensions were instances where the new trademark had some re-cognizable degree of similarity, i.e. sharing the root or evident con-nection, but where the industry scope differed. Instances where the newtrademark almost overlapped with an existing one, or had at least avery strong degree of word similarity and the same industry scope asprior trademarks, were considered as brand modernizing. We deviatedfrom Sandner’s procedure in two ways. Firstly, we also consideredfigurative trademarks, whereas Sandner’s automated procedure onlydealt with word marks. Secondly, we left brand-hedging strategies asideand did not distinguish between brand creation with multiple filings onthe same date and brand creation with a single trademark filing.Moreover, Sandner’s procedure cannot be applied to first trademarks,for which no prior reference exists. We distinguished between firsttrademarks by startups and by mature firms, taking the latter as ourbaseline category. We make this distinction between startups and ma-ture firms only for Hypothesis 1, since we have arguments from priorresearch to expect differences.

Trademarks can be any kind of symbols, the most common beingword, figurative and combined word and figurative marks. We classi-fied the trademarks in our sample also according to these three typesand for word marks we also counted the number of words included(number of words ranged from 1 to 9).

4.3.3. Trademark application scopeWe distinguished two scope dimensions of trademarks: 1) geo-

graphic scope and 2) industry scope. The geographic scope was a di-chotomy with two possible values, representing two different trade-mark jurisdictions: BTM or CTM. Evidently, the geographic scope ofCTMs exceeded the BTM scope, because CTMs provide trademarkprotection in all EU member states, while BTMs just cover the Beneluxregion. The industry scope captured both the number and type of Niceclasses covered by a trademark (= trademark breadth). Given thepricing policy, we operationalized breadth as a dummy equal to one ifthe trademark covered more than 3 classes. We also defined dummies toindicate whether the trademark covered either product only, servicesonly Nice classes, as opposed to both product and services. For bothscope dimensions, in line with our hypothesis development, we esti-mate effects regarding all firms, without separating startups and maturefirms.

4.3.4. Control variablesOther explanatory variables included variables stemming from

survey questions. We controlled for firm size, measured as a categoricalvariable based on the number of employees. We defined dummies forthe main size categories as indicated in Table 2. We also knew whetherthe applicant considered itself a start-up or a mature firm. Additionally,controlling for a B2B or B2C market orientation has been proposed byMalmberg (2005), who stated that brand management is a differentchallenge in end markets compared to intermediate markets. Oursurvey, therefore, included a question to capture a firm’s market scope:respondents could indicate ‘delivering products and/or services tocommercial organizations: business to business’ and/or ‘to customers in

end-markets: business to consumer’, next to ‘non-profit organizations’(B2NP).

The survey also asked respondents whether they applied for a patentfor the same object covered by the focal trademark. When investigatingthe trademark-innovation relatedness, we could not ignore the role ofpatents. Flikkema et al. (2014) already demonstrated that the combi-nation of trademarks and patents is more likely to relate to innovation.Trademarks are often used as complements to patents (e.g. Conleyet al., 2013; Zhou et al., 2016): patents protect the inventive compo-nent, while trademarks protect a commercialized product/service in themarketplace (Nam and Barnett, 2011) or trademarks allow patentprolongation. Notice that trademark and patent matching at the projectlevel is not information that is typically available. Hence, we werespecifically looking for predictors that could act as alternatives for thatinformation.

4.4. Methods of analysis

The aim of our analysis was to investigate trademark and applicantcharacteristics with a higher explanatory power to disentangle trade-marks related to innovation from trademarks not related to innovation.Given this aim and our focus on both new products and new services,we opted for estimating multinomial logistic regressions (Hosmer et al.,2013) where the dependent variable could take three distinct values:(1) relatedness to product innovation, (2) relatedness to service in-novation, (3) no relatedness to innovation. Estimation of a multinomialmodel requires observations to be categorized in only one of the pos-sible categories. As our sample included trademarks referring to moretypes of innovation and also trademarks referring to both service andproduct innovation, we selected all the observations correspondingwith the three independent categories defined in our dependent vari-able. Hence, we excluded cases where the respondent indicated that theinnovation related to both product and service innovation and weidentified the ‘no innovation’ category as those trademarks where therespondent reported that the trademark did not refer to any innovationor only referred to new-to-firm innovations of any type. We used the ‘noinnovation’ category as our baseline for the multinomial regression andpresented estimated coefficients for the product and service innovationcategories. To gauge the models’ goodness of fit, we reported the Na-gelkerke R2 measure and the prediction rates from the classificationtable underlying the estimated model (Hosmer et al., 2013).

5. Empirical results

5.1. Descriptive analysis

Along with the descriptive statistics reported in Table 2, we checkedthe pairwise correlation among all variables in the same sample usedfor the multivariate regression, (see Table 3). The relatedness of a tra-demark to product and service innovation was significantly correlatedto most of the indicators of trademark application choices. Interest-ingly, the sign of the correlation was often opposite for product vsservice innovation. This was also the case for the variable capturingpatent combination. As expected, joint trademark and patent applica-tion positively correlated with product innovation, while there was asignificant negative correlation in the case of service innovation. Suc-cessful patenting in services might be predominantly a consequence ofexpanding a firm’s technological options (Hertog, 2000; Blind et al.,2003). These technological options might predominantly lead to pro-cess innovations, e.g. the digitalization of delivery processes. The geo-graphic and industry scope dimension significantly correlated, again inopposite directions for a product focus vs a service focus. As for attorneysupport, the correlation coefficients indicated that companies filingtrademarks for brand extensions or modernizing were very likely to useattorneys, while small firms and startups were very likely not to. Thecorrelation was instead not significant for first trademarks by mature


1347

Table3

Corr

elat

ion

anal

ysis

ofth

eva

riab

les

used

inth

ere

gres

sion

s(s

ampl

eof

n=

656)

:Pea

rson

corr

elat

ion

stat

istic

s,to

geth

erw

ithth

ep-

valu

es.*

/**=

corr

elat

ion

issi

gnifi

cant

atth

e0.

01,0

.05

leve

l(2-

taile

d).

1.Pr

oduc

tin

nova

tion

1

2.Se

rvic

ein

nova

tion

-.279

**1

3.FI

rstT

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atur

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m−

0.05

2−

0.00

31

4.Fi

rstT

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artu

p−

0.06

3.1

60**

−.4

00**

1

5.Br

and

crea

ting

.077

*−

.113

**−

.388

**−

.414

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6.Br

and

exte

ndin

g/m

oder

nizi

ng

0.04

3−

0.05

7−

.247

**−

.264

**−

.256

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ord

mar

k0.

012

0.00

1−

0.05

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0.03

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80*

0.02

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gura

tive

0.02

60.

019

.140

**−

.099

*−

0.02

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0.01

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umbe

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ds−

0.05

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022

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16.S

mal

lfirm

0.00

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12**

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56**

0.00

20.

061

−0.

051

0.07

5−

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06**

0.00

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0.01

30.

025

0.02

1−

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**1

17.M

ediu

mfir

m0.

045

-.103

**0.

034

-.130

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−0.

033

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043

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0.01

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2B−

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.086

*−

0.00

90.

044

0.02

10.

032

−0.

004

−0.

046

0.03

9−

.102

**0.

062

01

19.B

2C0.

061

−0.

074

−0.

034

0.02

80.

006

−0.

006

−0.

017

−0.

009

−0.

036

0.00

1−

0.03

1.0

96*

−0.

058

−.1

36**

.082

*−

.108

**−

0.03

6−

.442

**1

20.A

ttor

ney

supp

ort

0.00

50.

011

−0.

069

−.1

45**

.106

**.1

43**

−0.

036

0.02

4−

0.04

6−

0.02

3.1

02**

0.01

6−

0.05

20.

047

−.1

99**

0.01

3.1

60**

.093

*−

0.04

41

0

21.C

ombi

natio

nw

ithpa

tent

.308

**−

.159

**−

.087

*−

0.06

00.

058

.117

**0.

036

0.06

6−

0.02

1.1

51**

−0.

028

.138

**−

.196

**−

.099

*−

.119

**−

0.02

60.

071

−0.

007

0.00

70.

071

1


1348

firms.To explore the relationship between the distribution of the sample

trademarks over the 45 Nice classes and the relatedness to innovation,we first mapped the two in an xy-plot for both product and serviceinnovation (see Figs. 3 and 4). Univariate regression analyses showed aslightly negative relationship between sample volumes in the differentNice classes and the class-level relatedness to product innovation (R2

=.032), and a positive relationship between the sample volumes andthe class-level relatedness to service innovation (R2= .25). Fig. 4 showsa clear outlier in terms of relatedness to service innovation, namelyclass 36, covering insurance, financial, monetary and real estate ser-vices. Knowledge-intensive business services, of which financial ser-vices are a primary example, are often hailed as the most innovativeamong service firms (Barras, 1990). Moreover, trademarks have beenshown to effectively capture their innovative output (Schmoch and

Gauch, 2009). We included a dummy for this service class in the esti-mations and also added a dummy for the overrepresented product class11 discussed in Section 4.2.

5.2. Hypothesis testing

We estimated the overall effect of our variables of interest on theprobability that a trademark related to product innovation, service in-novation or no innovation. After excluding the missing values listwiseand selecting the set of observations for the multinomial regression(only trademarks referring to product innovation, service innovation, orno innovation), the actual number of observations used in the estima-tions was n = 656. There were notably very few cases of brand mod-ernization in our sample, which prompted us to collapse brand exten-sion and brand modernization in one category. This also made sense

Fig. 3. Sample volumes versus the trademark relatedness to product innovation for all 45 Nice classes.

Fig. 4. Sample volumes versus the trademark relatedness to service innovation for all 45 Nice classes.


1349

Table4

Mul

tinom

iall

ogis

ticre

gres

sion

estim

ates

,inc

ludi

ngan

dex

clud

ing

the

pate

ntva

riab

le.

Rela

tedn

ess

to:

Prod

uct

inno

vatio

nSe

rvic

ein

nova

tion

Prod

uct

inno

vatio

nSe

rvic

ein

nova

tion

Bs.

e.p-

valu

eB

s.e.

p-va

lue

Bs.

e.p-

valu

eB

s.e.

p-va

lue

(inc

ludi

ngpa

tent

vari

able

)(e

xclu

ding

pate

ntva

riab

le)

Branding

strategies

Trademarkdesign

Firs

tTM

byst

artu

p0.

383

0.28

30.

175

0.49

20.

334

0.14

10.

520

0.27

60.

060

0.44

50.

330

0.17

7Br

and

crea

ting

0.34

50.

267

0.19

6−

0.46

20.

415

0.26

60.

431

0.26

10.

098

−0.

449

0.40

90.

273

Bran

dex

tend

ing/

mod

.0.

328

0.33

30.

325

−0.

166

0.51

50.

748

0.51

00.

321

0.11

3−

0.25

60.

509

0.61

5W

ord

mar

k−

0.01

40.

315

0.96

50.

915

0.37

60.

015

0.02

80.

311

0.92

70.

822

0.37

10.

027

Figu

rativ

e0.

022

0.40

50.

956

2.07

20.

559

0.00

00.

081

0.39

60.

838

1.92

10.

549

0.00

0N

o.W

ords

−0.

120

0.08

00.

136

0.03

80.

100

0.70

3−

0.11

70.

077

0.12

90.

032

0.09

80.

741

Trademarkscope

Geo

grap

hic

scop

e−

0.02

30.

290

0.93

8−

0.91

10.

377

0.01

60.

089

0.28

30.

752

−0.

865

0.37

20.

020

Indu

stry

scop

e:N

ice

brea

dth

−0.

994

0.31

50.

002

−0.

403

0.35

70.

259

−1.

032

0.30

60.

001

−0.

405

0.35

80.

259

Onl

ypr

oduc

tN

ice

0.16

50.

291

0.57

0−

2.33

50.

578

0.00

00.

109

0.28

10.

698

−2.

236

0.56

90.

000

Onl

yse

rvic

eN

ice

−2.

439

0.44

00.

000

0.21

60.

309

0.48

5−

2.54

50.

436

0.00

00.

279

0.30

60.

362

Popu

lar

Nic

e(9

,35,

41or

45)

0.15

60.

285

0.58

4−

0.26

90.

391

0.49

10.

101

0.27

50.

713

−0.

163

0.38

30.

671

Nic

e36

−1.

271

0.83

30.

127

0.21

10.

388

0.58

7−

1.28

30.

796

0.10

70.

239

0.38

50.

535

Nic

e11

0.80

80.

348

0.02

00.

087

0.82

00.

916

0.94

30.

340

0.00

6−

0.20

10.

810

0.80

4

Otherproperties(fromsurvey/matched

data)

Firm

size

Mic

rofir

m0.

546

0.33

40.

102

0.38

40.

562

0.49

50.

435

0.32

00.

174

0.30

70.

554

0.57

9Sm

allfi

rm0.

581

0.35

00.

097

−0.

243

0.61

20.

692

0.41

60.

336

0.21

5−

0.29

20.

605

0.62

9M

ediu

mfir

m0.

386

0.39

50.

328

−1.

297

0.91

70.

157

0.35

30.

380

0.35

3−

1.36

20.

907

0.13

3Firm

marketscope

B2B

−0.

457

0.25

50.

073

0.09

10.

345

0.79

1−

0.47

40.

249

0.05

70.

123

0.34

10.

719

B2C

−0.

060

0.22

40.

790

−0.

459

0.29

80.

123

−0.

103

0.21

80.

636

−0.

306

0.29

10.

293

Othertrademarkproperties

Att

orne

ysu

ppor

t0.

007

0.24

80.

979

0.62

20.

346

0.07

20.

055

0.24

10.

818

0.51

70.

335

0.12

3Co

mbi

ned

with

pate

nt1.

275

0.27

00.

000

−21

.433

0.00

0n.

a.N

656

(ofw

hich

217

Prod

uct

Inno

.,89

Serv

ice

Inno

.,35

0N

on-in

nova

tion)

Nag

elke

rke

R20.

395

0.34

3Pe

rcen

tage

corr

ect

Prod

uct

Inn.

:42.

9%,S

ervi

ceIn

n.:1

9.1%

,Non

-inn.

:80.

9%Pr

oduc

tIn

n.:4

8.4%

,Ser

vice

Inn.

:11.

2%,N

on-in

n.:7

0.9%


1350

from a conceptual point of view, as both are trademarks that extendprior ones in a firm portfolio. The final sample also included relativelyfew cases of trademarks related to service innovation, of which very fewcombined with patents. This made the coefficient for the patent vari-able unreliable for the case of service innovation. At the same time, theestimated coefficient for the patent variable in the case of product in-novation was strongly significant, its effect possibly overshadowing allother effects.

Table 4 shows both the estimations with and without the patentvariable. The most striking difference between the two sets of estima-tions is that most variables capturing trademark design for brandingonly become significant after removing the patent variable. Since ourgoal is to uncover predictors that can be obtained from trademark re-cords only, we focus on the results of the model without patent variable.Note that the model without the patent variable fits with the datanearly as well as the initial model including the patent variable. Theprediction rate for product innovation is even slightly better (42.9%versus 48.4%). This means that our branding strategy approach deliversinsights even in samples of patent-trademark pairs, which are anywayhard to construct.

Consistent with Hypothesis 1, first trademarks filed by startups anddissimilar subsequent trademarks filed for brand creation purposes byall firms were significantly more likely to relate to product innovationthan the baseline category of first trademark by mature firms. The effectwas stronger for first trademarks of startups: this result confirms studiesby Block et al. (2014) and Vries et al. (2017), that emphasized theimportance of trademarks for innovative start-ups. Instead, we did notfind any support for Hypothesis 1 for the case of service innovation.Trademark design parameters did help to predict relatedness to in-novation. Both figurative and word trademarks appeared to be relatedmore strongly to service innovation than combined marks, with fig-urative trademarks relating strongest to service innovation. Becausenew services cannot be ‘packaged’ differently in the physical sense,figurative marks might function as ways to repackage services. Trade-marks with more words, hence more likely to be slogans, were lessrelated to product innovations, but not significantly so.

As for Hypothesis 2a, the geographic scope of a trademark did notsignificantly affect a trademark’s relatedness to product innovation,hence the evidence did not support this hypothesis. Instead, we foundsupport for Hypothesis 2b, as CTMs related less strongly to service in-novation.

In line with Hypothesis 3a, narrower trademarks in terms of Niceclasses breadth were more likely to flag specific new products thanbroader ones. This effect was there only for product innovations. As forHypothesis 3b, the results indicated a strong effect for both product andservice innovation: product (service) only marks related significantlyless often to service (product) innovation than trademarks coveringboth product and service classes. In this sense a specific industry scopehelped to predict which trademarks were not likely to refer to a specificinnovation type. Hypothesis 3b was then not confirmed in its originalform. Particularly Nice class 11 was useful to predict product innova-tion.

In terms of our controls, we did not find a clear effect of firm size,but a negative effect of the B2B variable, indicating the trademarksfrom companies active in B2B segments related less often to productinnovation than those from companies other segments (B2C and B2NP),which confirms the expectation by Malmberg (2005). Attorney supportplayed a minor positive role for trademarks related to service innova-tion. Finally, as expected the combination with a patent is a strongpredictor of relatedness to product innovation, but the other significanteffects remain in the model without patent variable.

Overall, the models have a reasonable goodness of fit (NagelkerkeR2 = .395 and .343) and prediction rates were higher for the non-in-novation category. This means that it was easier to figure out which

trademarks did not relate to innovation, while it was trickier to predictthe relatedness to product and service innovation. Nevertheless, forboth innovation types, significant effects were found, coming fromspecific elements of the underlying branding strategies.

6. Conclusions, discussion and future research

Research using trademarks has so far mostly exploited informationon the sheer counts of trademarks without delving into a qualitativeassessment of the underlying trademark portfolios and the informationcontained in individual trademark records. Our study showed the valueof a branding strategy approach to the identification of trademarksrelated to innovation. Based on our large-scale study of 2009 BTM andCTM registrations we found some striking differences between productand service innovation.

First, new trademarks related more frequently to product innovationthan to service innovation. This confirmed results of the study byFlikkema et al. (2014), even though our sample was biased to someextent towards manufacturing firms. The lower relatedness to serviceinnovation may also be explained by the fact that it is relatively easy tostart a (one-person) business in service industries. Many of thosebusiness may be non-innovative and apply for trademarks in the pop-ular Nice 35 class, with the aim to distinguish themselves via trade-marks from other non-innovative firms.

Second, trademark application for brand creation purposes relatedpositively to product innovation. This held in particular for brandcreation in startups. We could not confirm the same for service in-novation. A plausible reason may be the supplier dominance in manyservice firms’ R&D processes (Flikkema et al., 2007), which might leadto delivery innovation and less frequently to the introduction of newservice concepts with a clear brand story. Alternatively, the lack ofresults might also have to do with the complexity of conceptualizingservice innovation (Hertog, 2000), leading to respondents’ confusion inidentifying what counts as a service innovation.

Third, trademarks with a narrow industry scope significantly pre-dicted product innovation, while a narrow (i.e. national) geographicscope contributed to the prediction of service innovation. The currentpricing of trademark applications in both jurisdictions and broad Niceservice classes may have hindered a more accurate investigation of therelationship between a trademark’s industry scope and innovation. Thedichotomization of geographic scope, and relatively small domesticmarkets in the Benelux, may have hindered a more accurate in-vestigation of the relationship between a trademark’s geographic scopeand innovation. Further research could replicate our analysis of trade-marks applied for at a national office with a proportional pricing policy,like the USPTO. From 2019 January 1st onwards in the Benelux jur-isdiction applicants have to pay a fee for every Nice class covered also.We will therefore try to replicate our study in the Benelux, and also inlarge EU member states, in the years to come.

Our study suffered from some limitations. First, although we didsome validation of the reported data, we have not validated the self-reported relatedness to innovation. For example, entrepreneurs mightbe unacquainted with the industry they are entering and thereforeoverrate the newness of the products or services they have developed.In addition, some unpatented innovations might be wrongly qualifiedby respondents in one of the categories not related to innovation, as aconsequence of their belief that patent application is a precondition forinnovation. This bias might be particularly relevant for startup firmsand in service contexts, as discussed earlier, which could explain whywe did not find the expected relatedness of first trademarks by startupsto service innovation. Second, in the survey trademark applicants self-reported whether they were a startup or a mature firm, without furtherspecifications about the two categories. There might be differencesbetween industries and countries concerning the interpretation of the


1351

startup concept. This may have led to an overrepresentation of startupsin our sample for some industries, particularly for high-tech industries.As a consequence we might have underestimated the effect of trade-marks filed for brand creation in startups somewhat, because accordingto our H1 one would expect first trademarks to be strongly related toinnovation.Third, we focused here on two types of innovation only(product and service) and only on cases where those did not overlap. Inreality, many innovations increasingly combine product and serviceelements, often as the result of servitization paths whereby productfirms develop solutions following a service logic (Cusumano et al.,2015). These innovations, often referred to as business model innova-tion (Desyllas and Sako, 2013) would also be relevant to study. Ourbranding framework could be applied to those instances as well, but theresearch design for a new empirical study would have to be modified toselect relevant cases.

Based on our study results, we propose four avenues for future re-search. First, the method for qualifying trademark filing strategiesproposed by Sandner (2009) did not seem to be fully accurate forqualifying an applicant’s branding strategy. Not only because the ap-proach only took verbal similarity between trademarks into accountbesides the overlap of industry scope; also because firms can use dif-ferent trademarks on their product packaging. We therefore stress oncemore the importance of making a conceptual distinction between tra-demarks and brands. Dissimilar trademarks may serve differentbranding purposes, ranging from brand modernization to brand crea-tion. Future, micro-level research should reveal when the application ofdissimilar trademarks happens for brand creation, extension or evenmodernizing purposes. We expect most differences in B2C retail mar-kets, where packaging is an important element of the marketing mix(see Rettie and Brewer, 2000; Ampuero and Vila, 2006).

Second, we have to put more research effort in reconstructing thebranding motives of mature firms that filed their first trademark rela-tively late. We found a substantial share of first trademarks from maturefirms in our study, but could not reconstruct their underlying brandingstrategies. One reason could be that their trademark filing is not drivenby strategic but myopic motives (Castaldi, 2018). We tend to think thatalso for policy reasons it would be beneficial to better understand thelate first trademark behavior. Characteristics of these applicants mightbe used to provide similar firms with information about the possibilityto protect their brand logos.

Third, we propose to study with larger samples the consequences ofdifferent combinations of industry and geographic scope for the tra-demark relatedness to innovation, particularly for service industriesand applicants with a large domestic market, such as France, Spain orthe US. Similarly, we propose conducting studies into the firm-level,regional-level and industry-level propensity to trademark innovation.Nearly all recent studies on the trademark-innovation relatedness startwith trademark data and aim to test whether trademarks are a validindicator of innovation. They study proportions of new trademarks thatrelate to innovation. However, for policy reasons, it is also highly re-levant to understand inter and intra industry differences concerning thepropensity to trademark innovation (see Arora et al., 2016). This wouldrequire focusing on samples of innovators or innovations only, in orderto study differences in the use of trademarks to protect or flag in-novations. Fourth, the need of matching trademark data with patentdata has become less stringent, because a branding strategy approach tothe identification of trademarks related to innovation largely sub-stitutes the need for it. Still, initiatives may be valuable to developheuristics and algorithms to match trademark data with other IPR data,for example design rights and growers’ rights, and economic data. Suchcombinations of data will definitely improve our understanding of howto measure innovation and eventually clarify the performance con-sequences of different innovation types and IPR strategies. Note thatEUIPO, the OECD and the European Patent Office (EPO) already havesome experience matching trademark and patent data (OHIM, 2013;Thoma and Torrisi, 2007). Zolas et al. (2017) have worked on an

algorithm for matching trademark data and economic activity data. Yet,current attempts mostly involve matching trademarks and patents atthe firm level, while it remains difficult to have a matching at theproduct level. As long as this information is not available, informationfrom trademark records themselves can offer a viable solution. Ourstudy has shown that exploiting the information from trademark re-cords can offer significant predictors of trademarks’ relatedness to in-novation.

Declaration of interests

The authors declare that they have no known competing financialinterests or personal relationships that could have appeared to influ-ence the work reported in this paper.

Acknowledgments

We thank the two anonymous reviewers of our paper for their in-sightful comments and editor Maryann Feldman for guiding us throughthe review process. We also thank Nathan Wajsman (EUIPO), MarcRichter (formerly employed by EUIPO), Gerard van Hulst (Novagraaf),Christel Heremans, Marleen Kuiper and Jean-Marie Putz (BOIP) fortheir contributions to the data collection process.

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