measuring open innovation in the bio-pharmaceutical industry...mance implications of open innovation...

Measuring Open Innovation in theBio-Pharmaceutical Industry

Francesca Michelino, Emilia Lamberti,Antonello Cammarano and Mauro Caputo

The paper suggests a methodology for measuring the degree of openness in companies’innovation processes through the analysis of annual reports. Four openness dimensions aredefined based on costs and revenues deriving from open innovation activities and new invest-ments and divestments of innovation-related intangibles, occurring in either separate acqui-sitions or business combinations. A synthetic measure of openness is defined, including all thefour dimensions. The model is then applied to a sample of 126 global top R&D spendingcompanies in the bio-pharmaceutical industry for the period 2008–2012, for a total of 630annual reports analysed. Results show a negative correlation of openness degree with firm age,dimension and efficiency, with biotech companies being more open than pharmaceutical ones.The paper contributes to the research on open innovation by suggesting a comprehensiveframework for the measure of the pecuniary dimension of the phenomenon in both inboundand outbound processes. From a managerial point of view, the framework can be used bycompanies to both monitor their own degree of openness and to benchmark it with those ofcompetitors.

Introduction

Since 2003, when Chesbrough introducedthe term open innovation (OI), organiza-

tions have become increasingly aware that theyare unable to hold in-house all the competen-cies they require, thus forcing them to open uptheir research and development (R&D) pro-cesses through pooling of collaborative activ-ities and/or trading of intellectual property(IP) rights (Gassmann, 2006; West & Gallagher,2006). Following the OI paradigm, the firm isan active participant in the market for technol-ogy (Arora, Fosfuri & Gambardella, 2001),proactively acquiring technologies fromoutside and selling surplus technologiesthrough spin-offs and licensing arrangements.

Different studies have developed severalclassifications of openness embedded in differ-ent frameworks. Yet, apart from terminologicaldifferences, the main distinction can bereported as inbound vs. outbound processes(Gassmann & Enkel, 2004), the former referringto enriching the company’s own knowledgebase through the integration of suppliers, cus-

tomers and external knowledge sourcing; thelatter to earning profits by bringing ideas tomarket, selling intellectual property and multi-plying technology by transferring ideas to theoutside environment. Academic research isdominated by case studies on how open inno-vation is implemented and organized withinfirms (e.g., Dodgson, Gann & Salter, 2006), andsurvey studies on the adoption and perfor-mance implications of open innovation strat-egies (e.g., Laursen & Salter, 2006).

Although practice and theory seem to indi-cate that the open innovation approach is ben-eficial for companies as well as for users, andthe possibilities of opening innovation pro-cesses are growing, innovation measurementis still looking for appropriate metrics thatmonitor the investments and the effects ofopen versus closed innovation approaches, inorder to help companies to find their rightbalance. Measuring the value of open innova-tion activities is increasingly important andmeasurement systems are not yet adapted tomonitor the value of such activities. Only spe-cific measurement systems will allow the suc-

4 CREATIVITY AND INNOVATION MANAGEMENT

Volume 24 Number 1 201510.1111/caim.12072

© 2014 John Wiley & Sons Ltd

cessful implementation of open innovationand support the right capabilities (Enkel &Lenz, 2009). Additionally, some challenges forR&D input as a proxy for innovation can bedefined, as it seems to no longer be suitableunder an open innovation paradigm (Huang &Rice, 2009). In fact, some of the external activ-ities resulting from an open approach, whichmay accrue costs not included in this proxy,might also drive innovation performance,rather than internal R&D investment alone.Thus, finding new innovation proxy measuresin an open innovation environment is a funda-mentally interesting issue for research. More-over, while a large number of studies focuseson the inbound dimension of openness by ana-lysing the acquisition of external knowledge,less attention has been paid to outbound pro-cesses: Poot, Faems and Vanhaverbeke (2009)encourage scholars to explicitly measure out-flows of knowledge in future studies.

Therefore, the purpose of this paper is tocontribute to the existing research on openinnovation by providing a methodologicalframework based on the quantification of theinbound and outbound flows in OI pecuniarytransactions. The research question we aim atanswering is how the openness of a firm canbe measured. In doing so, we use secondarydata gathered from annual reports of compa-nies in order to define all the costs, revenues,new investments and divestments linked toinnovation in all its components.

The practical validity of our framework istested through an empirical study in the bio-pharmaceutical industry: the degree of open-ness of companies is calculated and thedifferent business models linked to differentdegrees of openness are outlined, in order tovalidate both the framework applicability andits explicative power and usefulness.

The methodology we propose represents atool for managers in a dual perspective, bothinternal and external. From an internal point ofview, the tool allows the strategic monitoringof the business model of the company, its evo-lution in terms of open innovation activitiesand its effects on the economic and financialresults; from an external one, given the avail-ability and objectivity of annual report figures,the framework might be used as a method ofcomparability over time and space, in order tobenchmark the strategic behaviour of firms ina given industry.

In what follows, after a literature review onthe approaches to the measurement of open-ness, our methodological framework is pre-sented and then applied to a sample of 126bio-pharmaceutical companies from The 2011EU Industrial R&D Investment Scoreboard (JRC,2011) for the five-year period 2008–2012.

Results are discussed and some propositionsare inferred from cross-section one-wayANOVA, correlation and regression analyses.Conclusions, including limitations and sugges-tions for further research, will close the paper.

Theoretical Background

In this section: (1) the theoretical contributionsconcerning the measure of the degree of open-ness are analysed and systematized followingdifferent approaches, (2) a gap is outlined withregard to the lack of operationalization of thepecuniary aspects of open innovation, and (3)an overview of the accounting measures ofinnovation as a whole is provided in order to(4) define the basis for an accounting measure-ment framework for OI.

From the definition of the open paradigm in2003, Chesbrough underlines the pecuniarydimension of the phenomenon: one of the sixprinciples of the OI concept states ‘we shouldprofit from others’ use of our IP, and we shouldbuy others’ IP whenever it advances our ownbusiness model’. In following studies, pecuni-ary issues are defined, such as the percentage ofsales in products and services from externaltechnologies and the percentage of net incomegenerated from own technology licensed toother firms (Chesbrough, 2004), the new rev-enues opportunities deriving from licenses,spin-off and sales divestiture and the costsavings from leveraging external development(Chesbrough, 2006), and the investment peryear in collaborative R&D (Al-Ashaab et al.,2011). The pecuniary features are innate in thedefinition of the OI paradigm itself. Theinbound dimension of open innovation showsitself in in-licensing, minority equity invest-ments, acquisitions, R&D contracts, while theoutbound dimension is characterized by out-licensing, spin-out, sale of innovation projects(Chesbrough & Crowther, 2006).

Yet, even if a number of studies underlinesthe economic issues of OI (Enkel, Gassmann &Chesbrough, 2009), most contributions do notuse pecuniary variables to measure the degreeof openness of companies, but adopt differentperspectives. In fact, Chesbrough himself alsodefines a number of variables that do not referto the pecuniary issues of open innovation: forexample, open innovation effects can be meas-ured in terms of time savings from bothinbound and outbound practices. From oneside, opening up the innovation process cangenerate time savings from leveraging externaldevelopment; from the other, creating newways for products and services to get to themarket can increase R&D productivity andeffectiveness (Chesbrough, 2004, 2006).

MEASURING OPEN INNOVATION IN THE BIO-PHARMACEUTICAL INDUSTRY 5

Volume 24 Number 1 2015© 2014 John Wiley & Sons Ltd

A further set of OI metrics can be tracedback to industrial property rights and market: IPcan be considered as both a pre-requisite and aresult of open innovation. In open innovationIP represents a new class of assets that candeliver additional revenues to the currentbusiness model and companies should be bothactive sellers of IP, when it does not fit theirown business model, and active buyers of IP,when external IP does fit their business model(Chesbrough, 2012). From one side, having awide IP portfolio is the pre-requisite for licens-ing out: Ebersberger et al. (2012) include pro-tection breadth – defined as the wideness of IPrights such as patents, trademarks and copy-rights – within the dimensions of OI. From theother, IP can be viewed as a result of the col-laboration with third parties and Al-Ashaabet al. (2011) propose the number of patents peryear as a result of collaborative projects as aproxy of OI. Obviously, not only the techno-logical innovation under the form of patentsshould be measured, but also the degree ofcommercialization of such innovation in a laterstage (Simard & West, 2006).

Moreover, some studies suggest operationalmeasurements for open innovation related tothe collaborative projects in which the compa-nies are involved and the human resourceswithin the companies that take part in suchcollaborations. Chesbrough (2004) considersthe number of projects offered to externalparties for further development as a means todefine the degree of openness of a company,and Al-Ashaab et al. (2011) propose differentopen innovation key performance indicators(KPIs), including the number of collaborativeprojects in the company per year. Further, inthe open innovation management literature itis widely acknowledged that individuals playa crucial role in collaborative knowledge crea-tion processes (du Chatenier et al., 2010). In anopen innovation context, it is essential thatR&D personnel have a background of knowl-edge and experience that enables them to com-municate and interact with researchers andmanagers from other industrial sectors. Also,the adoption of open innovation generateschanges in the model for training and manag-ing scientific personnel (Petroni, Venturini &Verbano, 2012).

As to inbound open innovation, there is astrong body of literature based on the Commu-nity Innovation Surveys (CIS) which based themeasurement of open innovation on the exter-nal sources of knowledge. Laursen and Salter(2006) define OI breadth and depth as thenumber of external sources or search channelsthat firms rely upon in their innovative activ-ities, and the extent to which firms draw deeplyfrom the different external sources, respec-

tively. Breadth and depth are no doubt the mostcommon OI metrics used in the literature, withsome authors adding new dimensions to theprevious two: by adopting a multidimensionalapproach, Bahemia and Squire (2010) proposethe number of new versus existing partners as afurther measure of openness, while Lazzarottiand Manzini (2009) introduce the number andtype of phases of the innovation process that thecompany opens to external contributions. Thisapproach is consistent with the observation thatthe different stages in the innovation processhave very different features and open innova-tion can be addressed only to specific phases,such as idea generation (Berger et al., 2005),prototyping and engineering, production andcommercial phases (Emden, Calantone &Droge, 2006).

Finally, open innovation can be consideredas a set of practices for profiting and also acognitive model for creating and researchingthose practices (Chesbrough, Vanhaverbeke &West, 2006). Thus, a different approach formeasuring OI contains the variables related tothe practices that companies have adopted as aresult of pursuing an open innovation strategy.In particular, while measuring phase open-ness, van der Meer (2007) explains usage ofdifferent openness mechanisms in the stagesof concept, development and commercializa-tion. Such mechanisms include methods,structures and systems for importing andexporting knowledge, ideas and projects.Thus, the open innovation model allowsmoney to be made in every stage: not only byselling, but also by licensing out or spinningout at earlier stages.

From this brief overview of the literature itis clear that a number of different approachesare used to measure the degree of openness ofcompanies, consistently with the multidimen-sional nature of the phenomenon (Table 1). Yet,if for some approaches the operationalizationof the concepts is widely recognized, from apecuniary perspective a comprehensive meas-urement system is still lacking.

This paper aims at complementing the exist-ing measures of openness with a systematicmeasurement framework based on the quanti-fication of the pecuniary flows in OI transac-tions. In order to do so, accounting metrics forinnovation as a whole, regardless of its degreeof openness, have to be analysed. Accountingmetrics can be derived from the financial state-ments of companies: they can be dividedroughly into metrics based on the R&Dexpenditure of a company (Cohen & Levin,1989; Acs & Audretsch, 1990) and metricsbased on the intangible assets as an invest-ment in innovation capacity (Lev, 2001;Nakamura, 2001).



Table 1. Metrics for Open Innovation: Overview of Literature Contributions

Category Metrics Literature contributions

Pecuniary Investment in collaborative R&D;Percentage of sales in products from

external technologies;Percentage of net income generated from

licensed technology;New revenues from outside;Cost savings from leveraging external

development

Chesbrough (2004); Chesbrough (2006);Al-Ashaab et al. (2011)

Time Time interval for patented ideas to be used;Time for licensing;Speed of externalized projects;Time savings from leveraging external

development

Chesbrough (2004); Chesbrough (2006)

Industrial propertyrights and market

Number of intellectual property categories;Number of patents and joint patents;Share of ideas licensed;Degree of commercialization of

innovations;Market share of new products

Chesbrough (2004); Simard & West (2006);Al-Ashaab et al. (2011); Ebersberger et al.(2012)

Collaborativeprojects andhuman resources

Number of collaborative projects;Number of projects offered to external

parties;Number of joint training programs;New scientists and engineers hired;Number of R&D employees involved in

collaboration;Share of R&D employees involved in

collaboration

Chesbrough (2004); de Wit et al. (2007);Al-Ashaab et al. (2011); Petroni et al.(2012)

Sources ofknowledge

Number of external sources of knowledge;Type of external sources of knowledge;Variety of collaboration partners;Intensity of collaboration;Importance of partners

Chesbrough & Crowther (2006); Laursen &Salter (2006); Tether & Tajar (2008);Keupp & Gassmann (2009); Lazzarotti &Manzini (2009); Poot et al. (2009);Bahemia & Squire (2010); Belussi et al.(2010); Chiang & Hung (2010); Hwang &Lee (2010); Ili et al. (2010); Lee et al.(2010); Sofka & Grimpe (2010); Chenet al. (2011); Inauen & Schenker-Wicki(2011); Schweitzer et al. (2011); Köhleret al. (2012); Salge et al. (2012);Ebersberger et al. (2012)

Number of new vs. existing partners Bahemia & Squire (2010)Number of phases opened to external

contributions;Type of phases opened to external

contributions

Lazzarotti & Manzini (2009)

Practices R&D outsourcing and alliances;Public-private cooperation;

Involvement of other firms in theinnovation process;

Contracting consultant for technologyscouting;

Research contracts with universities andother research centres;

Conferences; Licensing in (out); Patentsearch;

Use of patent brokers; Spinning in (out);Venturing in (out);

Buying out or taking over firms withspecialized knowledge

Perkmann & Walsh (2007); van der Meer(2007); van de Vrande et al. (2009);Petroni et al. (2012)



Both R&D expenditure and R&D intensityare common measures for the effort thecompany puts into pursuing innovation andcan also be used as proxies for the absorptivecapacity of the firm (Cohen & Levinthal, 1990).Further, they are not only used in the litera-ture, but also by government entities as meas-ures to rank companies and countries: forexample, the Innovation Union Scoreboardincludes R&D expenditure as percentage ofGDP in the metrics for defining the innovationperformance of EU member states.

As to intangible assets, three broad catego-ries can be defined: development costs, good-will and other intangible assets, includingpatents, licenses, trademarks and others(Stolowy & Jeny-Cazavan, 2001). Differentstudies in the accounting literature debatewhether the accounting standards can accu-rately reflect the value of intangibles (Brunovs& Kirsh, 1991; Emenyonu & Gray, 1992;Høegh-Krohn & Knivsflå, 2000; Stolowy &Jeny-Cazavan, 2001; Pozza, Prencipe &Markarian, 2008; Penman, 2009). In particular,when accounting standards prescribe theimmediate expensing of the amounts investedin intangible activities, a significant part of theintangible investments made is absent fromthe balance sheet of the company (Cañibano,García-Ayuso & Sánchez, 2000); therefore, theinformation provided by the amount of intan-gibles in the balance sheet can be an underes-timate of the total investment in innovation.

Yet, if a limitation can be outlined as to thecapability of accounting data to reflect the‘innovation stock’, no limitations are foundwhen the ‘innovation flows’ are analysed. Infact, by integrating economic and financialmeasures, i.e. by considering both the costsand the investments, a comprehensive pictureof the phenomenon can be obtained. In par-ticular, from an accounting perspective foropen innovation, the unit of analysis should be

the transaction in the innovation market,which will be registered either in the incomestatement or in the balance sheet.

A Methodological Framework forthe Measurement of InnovationOpenness

The framework we suggest is intended toprovide a comprehensive accounting method-ology for open innovation through the defini-tion of costs, revenues, new investments anddivestments linked to innovation in all itscomponents. In particular, from the financialstatements of companies, transactions ofR&D, IP and know-how are accounted for.Even if within each company these threeelements are strictly interrelated – withknow-how enabling R&D and R&D produc-ing IP and know-how1 – from an open per-spective, different and separate transactionscan be outlined.

By focusing on such transactions, we defineopen innovation as a four-dimensional phe-nomenon, where the innovation processes canhave an outbound vs. an inbound nature andthe transactions can have an economic vs. afinancial one. On one hand, inbound processesare characterized by costs and additions, out-bound ones by revenues and disposals; on theother hand, economic transactions are charac-terized by costs and revenues, financial onesby additions and disposals (Figure 1).

The starting point for our analysis was theresearch and development cost, which is gen-erally explicitly disclosed in the income state-ment, if defined by destination. Yet, if an openinnovation context has to be analysed, twoissues arise: first, the separation of internalfrom external R&D costs and, second, the defi-nition of R&D revenues.

Figure 1. The Framework



As to the first point, it is clear that, while thewhole R&D cost can be considered as a proxyof innovation, if open innovation is underinvestigation, only external ones have to beconsidered. The word ‘external’ can refer toboth activities developed by third parties forthe company, from an inbound perspective,and activities developed by the internalresources of the company and addressed tothird parties, for an outbound model.

Thus, from the total R&D cost we have toexclude:

• all R&D costs carried by the use of internalresources for internal activities of thecompany (i.e., R&D staff costs for internalactivities);

• internal capitalization of R&D costs, as wellas amortization of capitalized costs.

Yet, while excluding amortization is generally asimple matter, since it is disclosed in the notesto the financial statements, the definition of thecost generated by internal resources for internalactivities is quite tricky, as even a definition ofcosts by nature does not explicitly separateinternal and external costs: for example, R&Dstaff costs can refer to costs born for both thoseemployees who work for internal developmentprojects, and for those who are dedicated toexternal projects for third parties.

Further, as to the second point, from an openperspective, R&D activities imply costs notonly for performing them, but also revenuesthat the innovation buyer will acknowledge tothe company from an outbound perspective. Infact, the analysis of costs and revenues is quitesymmetrical, as every open innovation activitytypically generates revenues for a companyand costs for another one. The economicdimension of open innovation can be character-ized by costs and revenues deriving from:

1. collaborative and contract development,which refer to joint development projectswith third parties under long-term agree-ments: collaborative agreements, develop-ment partners’ reimbursements, cost orprofit-sharing agreements, share of resultsof research associates, contract fees, devel-opment milestone payments and achieve-ments,2 up-front payments and receipts;

2. outsourcing of R&D services or develop-ment of R&D services on behalf of thirdparties, which refer to a more spot behav-iour than the previous one: research ser-vices received from subcontractors orprovided to third parties.

As to development partners’ reimburse-ments, R&D costs disclosed in the income

statement are always net of costs reimbursedby development partners, i.e., a profit and lossoffset is performed. From our perspective areimbursement is both a cost that the companybore for developing open innovation activitiesand a revenue it received as an offset for sucha cost. Thus, we have to include reimburse-ments in both revenues and costs, resulting ina null net effect.

As to R&D performed on behalf of thirdparties, a particular category is defined bygrants received by the company for R&Dactivities, government grants, R&D tax creditand research funding. In fact, grants and sub-sidies can be considered as open revenues bydefining the government as an entity thatremunerates the company for its innovationefforts, even if it is not interested in owningthe outcomes of such innovation. In fact,unlike a private entity, the government aimsat the development of innovation for the com-munity, rather than for itself. Note that oftengrants are disclosed in the income statementas an offset of development costs: in suchcases we recognized it as a revenue andadded the same value to R&D costs, resultingin a null effect.

In fact, R&D costs and revenues are not theonly open innovation items, given that a sig-nificant role is played by IP. Thus, we includedin our framework:

3. in-licensing costs, out-licensing revenuesand royalty fees paid or received.

When royalties are paid from the company tothird parties, their amount is very often rec-orded in the income statement as a reductionto net sales, from a profit and loss offset per-spective. Since we are interested in defining allthe revenues and costs deriving from openinnovation activities, we will consider rev-enues at their gross value, including royaltiespaid and such royalties will also be consideredas a cost.

Obviously, from our ‘open’ perspective, nocosts carried by the company to internallydevelop intellectual property rights that willbe used by the company itself were includedin the analysis.

The analysis cannot be limited only to costsand revenues, as transactions in the innovationmarket can also come in the form of newinvestments and divestments of intangibles ineither separate acquisition or business combi-nations, mergers and acquisitions (BCMAs).Thus, we have to include additions and dis-posals of:

1. in-process R&D (IPR&D) and developmentcosts;



2. licenses, patents, intellectual propertyrights, industrial property;

3. trademarks and trade names, productrights, brands, product-related intangibles;

4. technology and technology rights;5. goodwill, related to research spin-ins and

spin-offs.

The first four categories have a clear connota-tion within innovation, while the innovativenature of goodwill can be questionable. Giventhe definition itself of goodwill as ‘future eco-nomic benefits arising from assets that are notcapable of being individually identified andseparately recognized’ (IFRS 3), we think that itcan be identified with the skill, the know-how,the technical and organizational expertise ofthe workforce. From this perspective, goodwillcan be defined as a proxy for the know-howtransferred from the acquired company to thepurchasing one. This is consistent with most ofthe definitions of goodwill found in the annualreports of companies,3 as well as with the intan-gibles tri-partition proposed in the literature(Stolowy & Jeny-Cazavan, 2001). When a spe-cific reference was made to an acquisition,which, rather than being related to innovation,deals with the purchase of distribution andcommercial channels, we did not includethe value of goodwill in the measure of openinnovation.

All additions are considered at their grossvalue, as we are interested in defining the totalvalue of the effort sustained for acquiring newintangibles. In contrast, as to disposals, theywere considered net of amortization in orderto determine a likely value of the returns fromwhat is divested. We should have included

gains and losses, but in most cases they aredisclosed in annual reports as a unique valuecomprising all assets divested (tangibles andintangibles) and cannot be objectively ascribedonly to the intangibles we were interested in.

Further, when considering additions anddisposals, we have to exclude all internalaccounting operations and adjustments suchas:

• impairment charges of IPR&D, e.g. as a con-sequence of completion or abandonment ofR&D projects;

• IPR&D reclassifications with transfer of acertain amount to other intangibles, e.g.IPR&D reclassified to products and productrights upon receipt of marketing approval;

• IPR&D reclassifications with transfer of acertain amount from indefinite-lived tofinite-lived intangibles;

• impairment charges of product rights, e.g.related to a marketed product;

• product rights reclassifications with transferof a certain amount to assets held for sale;

• reductions of goodwill deriving fromimpairment analysis;

• effects of changes in foreign exchangerates (i.e., foreign currency translationadjustments).

In Table 2 all the items presented so far aresummarized from three different perspectives,by crossing inbound vs. outbound processes,economic vs. financial transactions and thenature of the traded entities: research anddevelopment, intellectual property and know-how. Research and development and intellec-tual property have a double nature, both

Table 2. Measures of Open Innovation

Economic transactions Financial transactions

R&D IP R&D IP Know-how

Inboundprocesses

Costs from:– collaborative

development– outsourcing of

R&D services

Costs fromin-licensing

Additions ofdevelopmentcosts

Additions of:– licenses– patents– trademarks– technology

Additionsof goodwill

Outboundprocesses

Revenues from:– collaborative

development– R&D services on

behalf of thirdparties

– R&D grants

Revenues fromout-licensing

Disposals ofdevelopmentcosts

Disposals of:– licenses– patents– trademarks– technology

Disposalsof goodwill



economic and financial, whereas know-how isonly represented by financial measures.

According to the proposed framework,open innovation is a four-dimensional phe-nomenon, represented by costs, additions,revenues and disposals. In order to quantifythe degree and define the nature of openinnovation, four basic indicators can be calcu-lated by comparing, for each of the four com-ponents, the items deriving from openinnovation to the total items of the company(see Table 3).

Thus, open innovation can be represented inthe space R4 (Figure 2), where each of the basicratios is a Cartesian coordinate and eachcompany can be represented as a point, whosedistance from the origin is proportional to itstotal degree of openness:

Openness ratio

costs ratio revenues ratio

additions ratio

d=

+++

2 2

2

iisposals ratio2

4

All the ratios range from zero to one, corre-sponding, respectively, to a totally closed and atotally open behaviour.

Application of the Framework to theBio-Pharmaceutical Industry

The suggested framework was applied to thebio-pharmaceutical industry given the highrelevance open innovation has in it. This indus-try is, in fact, an early pioneer of OI (Cooke,2005; Chesbrough & Crowther, 2006; Fetterhoff& Voelkel, 2006; Kleyn, Kitney & Atun, 2007;Chiaroni, Chiesa & Frattini, 2008), reflecting thehigh importance of R&D in the sector and thedistributed nature of its knowledge (Powellet al., 2005). The industry has a broad spectrumof open innovation models and some ofthem have already become a standard in it(Gassmann, Reepmeyer & von Zedtwitz, 2008).To date, the relationship within the bio-pharmaceutical industry is mature and mutu-ally dependent, with companies (Bianchi et al.,2011):

• forming combinations with other bio-pharmaceutical firms by means of mergersand acquisitions;

• constituting strategic alliances and partner-ships with other bio-pharmaceutical firms;

• acquiring (selling) R&D services from (to)other bio-pharmaceuticals;

• acquiring (selling) new compounds in-(out-) licensing them from (to) other bio-pharmaceutical companies.

We considered a sample of 126 bio-pharmaceutical companies,4 ranked by The2011 EU Industrial R&D Investment Scoreboardand analysed their annual reports from 2008to 2012.5 The Scoreboard reports 229 bio-pharmaceutical companies, but 85 companieswere excluded because their annual reports,available on the internet, were either incom-plete, with no notes to the consolidated balance

Table 3. The Four Basic Ratios of Open Innovation

Economic transactions Financial transactions

Inboundprocesses

Costs ratiocosts from OI

total R D and IP costs=

&Additions ratio

additions from OItotal intangibles

=

Outboundprocesses

Revenues ratiorevenues from OI

total revenues= Disposals ratio

disposals from OItotal intangibles

=

Figure 2. Open Innovation Four-DimensionalSpace



sheet and income statement, or they did notfulfil IFRS or US GAAP standards. Further, 18companies were excluded as some of theirannual reports were not available on the inter-net, because they were acquired during thefive-year period. The final sample consists of 77European companies and 49 non-Europeanones: the most represented country is USAwith 42 companies, followed by UK (14),Germany (13) and Denmark (11).

Data collected from the annual reports wereused to define both the total openness ratio foreach company, and the four components –costs, revenues, additions and disposals ratio –for each year of the time horizon.

Not only we defined a quantitative measureof openness, but also characterized its nature.In particular, six ratios were introduced,respectively representing the inboundand outbound transactions of R&D, IP andknow-how:

Inbound R D ratio

R D costs from OItotal R D and IP costs

ad

&

&&

=

⎛⎝⎜

⎞⎠⎟

+

2

dditions of R D from OItotal intangibles

&⎛⎝⎜

⎞⎠⎟

2

2

Outbound R D ratio

R Drevenues from OItotal revenues

di

&

&

=

⎛⎝⎜

⎞⎠⎟

+

2

ssposals of R D from OItotal intangibles

&⎛⎝⎜

⎞⎠⎟

2

2

Inbound IP ratio

IP costs from OItotal R D and IP costs

addi

=

⎛⎝⎜

⎞⎠⎟

+

&

2

ttions of IP from OItotal intangibles

⎛⎝⎜

⎞⎠⎟

2

2

Outbound IP ratio

IP revenues from OItotal revenues

dispo

=

⎛⎝⎜

⎞⎠⎟

+

2

ssals of IP from OItotal intangibles

⎛⎝⎜

⎞⎠⎟

2

2

Inbound know how ratioadditions of know how from OI

total intangi

--

=

bbles

Outbound know how ratiodisposals of know how from OI

total intang

--

=

iibles

Further, a number of variables were usedto describe some contextual features of thecompanies:

• age, measured in number of years from thedate of establishment;

• number of employees, as a measure offirms’ size;

• R&D intensity, measured as R&D expendi-ture on total revenues;

• R&D costs per employee;• closed R&D intensity, measured as R&D

expenditure net of open costs on totalrevenues, as a proxy of absorptive capacity,

where (1) R&D expenditure was calculated asthe sum of: R&D costs disclosed in the incomestatement, development partners’ reimburse-ments, grants and tax credits that are deductedfrom R&D costs and royalty costs that arededucted from revenues, and (2) total revenueswere calculated including all the revenuesbefore EBIT, gross of development partners’reimbursements, grants and royalty costs.

Finally, the following performance indica-tors were considered:

• revenues per employee;• EBIT per employee;• market capitalization on assets;• growth, measured in terms of annual

increases of revenues, EBIT and marketcapitalization.

The data were used from a cross-section per-spective, as five years are insufficient for a lon-gitudinal study, especially in an industry wherethe development time horizon can be longerthan ten years. Thus, ANOVA, correlation andregression analyses were performed on a set of630 statistical units.

Results

Following the ICB classification, the sampleconsists of 68 pharmaceutical companies and58 biotechnology ones. In fact, the two seg-ments can be considered as two separate indus-tries with very different business models.6Cross-section one-way ANOVA analysis wasthus performed to determine whether thebelongingness to the two segments is a dis-criminating factor for the variables under study(Table 4): most of them resulted in statisticallydifferent mean values between the two seg-ments and, for this reason, the following analy-ses were performed separately for the twosegments.



In order to analyse the open behaviour ofthe companies over the five-year period, aWard hierarchical clustering was performedfor each segment using the five opennessratios from 2008 to 2012 as clustering variables.In both segments three clusters were obtained:for each cluster, the open behaviour is quiteconstant over time, and we can define them ashardly open, fairly open and very open,respectively (Table 5).7 In mean, biotech firmsare more open than pharma ones, with 35 percent biotech vs. 12 per cent pharma companiesshowing an intense degree of openness.

Two further cross-section one-way ANOVAanalyses were performed using the clusterbelongingness as a discriminating factor ineach segment, in order to understand whethercompanies with different degrees of opennessbelonging to the same segment significantlydiffer in their business models: in Table 6 theresults of the ANOVA are reported, while inTables 7 to 10 the description of the clusters isprovided in terms of mean values of the vari-ables in the five-year period.

As to the degree of openness of companies,the following results are found (Tables 4, 6 and7):

• For the sample as a whole, biotech compa-nies are more open than pharmaceuticalones, particularly as to revenues and costs,while no significant differences are found asto additions and disposals.

• In the biotechnology segment, costs andrevenues ratios are significantly higher formore open companies, while no significantdifferences are found as to additions anddisposals ratios.

• In the pharmaceutical segment, revenuesratios are significantly higher for moreopen companies, while the highest valuesof costs and additions ratio are found formedium levels of openness; no significantdifferences are found as to the disposalsratio.

As to the nature of openness (Tables 4, 6 and8):

• Biotech companies are more involved inR&D transactions than pharmaceutical onesboth inbound and outbound. As to IP, theformer are more involved in outboundtransactions than the latter, which in turnare more involved in inbound transactions.

Table 4. One-Way ANOVA – Discriminating Factor: Segment

Variable df Variance F Sig.

Between Within Between Within

1. Openness ratio 1 628 4.930 0.036 135.890 0.0002. Costs ratio 1 628 0.520 0.054 9.601 0.0023. Revenues ratio 1 628 23.158 0.135 170.971 0.0004. Additions ratio 1 628 0.004 0.057 0.065 0.7995. Disposals ratio 1 628 0.001 0.003 0.401 0.5276. Inbound R&D ratio 1 628 0.768 0.031 24.533 0.0007. Outbound R&D ratio 1 628 10.284 0.072 142.449 0.0008. Inbound IP ratio 1 628 0.092 0.014 6.630 0.0109. Outbound IP ratio 1 628 0.329 0.025 13.397 0.000

10. Inbound know-how ratio 1 627 0.003 0.007 0.357 0.55011. Outbound know-how ratio 1 628 0.000 0.001 0.159 0.69012. Age 1 628 2.853E+05 1.801E+03 158.390 0.00013. No. of employees 1 628 4.314E+10 5.646E+08 76.398 0.00014. R&D intensity 1 628 5.706E+03 9.437E+02 6.046 0.01415. R&D costs per employee 1 628 1.821E+06 1.800E+04 101.203 0.00016. Closed R&D intensity 1 627 4.958E+03 9.387E+02 5.282 0.02217. Revenues per employee 1 628 5.167E+05 6.736E+04 7.671 0.00618. EBIT per employee 1 628 1.515E+06 4.357E+04 34.770 0.00019. Market cap. on assets 1 596 1.178E+03 2.880E+02 4.091 0.04420. Revenues growth 1 501 840.957 512.933 1.640 0.20121. EBIT growth 1 502 447.969 94.708 4.730 0.03022. Market cap. growth 1 468 3.570E+04 1.413E+04 2.526 0.113



No significant differences are found as toknow-how.

• In the biotechnology segment both inboundand outbound transactions of R&D aremore relevant for more open companies; asto IP, only outbound ones are significantlyhigher, while the intensity of know-howtransactions is not significantly differentamong the three clusters.

• In the pharmaceutical segment inboundand outbound transactions of R&D and out-bound IP transactions are more intense forthe more open companies, while the valueof inbound IP transactions is higher for thecompanies with a medium degree of open-ness; finally, the intensity of know-howtransactions is not significantly differentamong the three clusters.

As to the context features, the following resultsare observed (Tables 4, 6 and 9):

• Biotech companies are younger and smallerthan pharmaceutical ones. They also havehigher R&D intensity, R&D per employeeratio and closed R&D intensity.

• In the biotechnology segment, the more thecompanies are open, the younger andsmaller they are and the higher values ofR&D costs per employee they have, while nosignificant differences are reported as toR&D intensity and closed R&D intensity.

• In the pharmaceutical segment, not only themost open companies are the youngest andsmallest with highest values of R&D peremployee, but also R&D intensity and closedR&D intensity are significantly higher.

Finally, as to performances (Tables 4, 6 and 10):

• Biotech companies have lower efficiencythan pharmaceutical ones, and in mean, overthe five years, they show a decrease of EBIT,while pharmaceutical companies increase it.

• In the biotechnology segment, both rev-enues per employee and EBIT per employeeratios are higher for the less open compa-nies, with efficiency decreasing with thedegree of openness.

• In the pharmaceutical segment, the mostopen companies have the lowest levels ofEBIT per employee ratio, the highest valuesof market capitalization on assets and thehighest revenues growth rate, while marketcapitalization growth is higher for compa-nies having medium values of degree ofopenness.

The previous results are consistent with thecross-section correlation analysis performed inboth segments (Tables 11 and 12):T

able

5.C

lust

ers

Defi

niti

on:N

umbe

rof

Com

pani

esan

dO

penn

ess

Rat

ios

Seg

men

tC

lust

erN

o.of

com

pan

ies

%on

segm

ent

%on

ind

ust

ryO

pen

nes

sra

tio

2008

(%)

Op

enn

ess

rati

o20

09(%

)O

pen

nes

sra

tio

2010

(%)

Op

enn

ess

rati

o20

11(%

)O

pen

nes

sra

tio

2012

(%)

Bio

tech

nolo

gyH

ard

lyop

en12

20.7

9.5

12.7

12.2

10.4

10.3

16.0

Fair

lyop

en26

44.8

20.6

38.8

34.5

35.5

33.6

26.0

Ver

yop

en20

34.5

15.9

56.6

53.8

52.9

49.9

53.7

Tota

l58

100.

046

.039

.536

.536

.334

.433

.5Ph

arm

aceu

tica

lH

ard

lyop

en48

70.6

38.1

11.8

12.0

7.5

8.3

9.4

Fair

lyop

en12

17.6

9.5

31.3

32.1

30.7

33.3

23.7

Ver

yop

en8

11.8

6.3

52.2

54.1

51.1

49.8

49.8

Tota

l68

100.

054

.020

.020

.516

.717

.616

.7To

tals

ampl

e12

6–

100.

029

.027

.925

.725

.324

.4



Tab

le6.

One

-Way

AN

OV

A–

Dis

crim

inat

ing

Fact

or:C

lust

ers

Bel

ongi

ngne

ss

Var

iab

leB

iote

chn

olog

yco

mp

anie

sP

har

mac

euti

cal

com

pan

ies

df

Var

ian

ceF

Sig

.d

fV

aria

nce

FS

ig.

Bet

wee

nW

ith

inB

etw

een

Wit

hin

Bet

wee

nW

ith

inB

etw

een

Wit

hin

1.O

penn

ess

rati

o2

287

3.23

50.

019

172.

453

0.00

02

337

3.48

00.

012

295.

282

0.00

02.

Cos

tsra

tio

228

71.

315

0.05

026

.356

0.00

02

337

2.26

70.

037

60.8

570.

000

3.R

even

ues

rati

o2

287

13.7

880.

079

174.

066

0.00

02

337

12.6

230.

028

447.

370

0.00

04.

Ad

dit

ions

rati

o2

287

0.04

10.

075

0.54

80.

579

233

70.

299

0.04

07.

439

0.00

15.

Dis

posa

lsra

tio

228

70.

003

0.00

40.

668

0.51

42

337

0.00

20.

001

1.30

30.

273

6.In

boun

dR

&D

rati

o2

287

0.84

20.

034

24.8

440.

000

233

70.

973

0.01

951

.914

0.00

07.

Out

boun

dR

&D

rati

o2

287

5.60

60.

071

78.6

590.

000

233

74.

093

0.01

625

1.39

30.

000

8.In

boun

dIP

rati

o2

287

0.01

40.

014

0.97

50.

378

233

70.

183

0.01

214

.624

0.00

09.

Out

boun

dIP

rati

o2

287

0.18

70.

036

5.16

20.

006

233

70.

494

0.01

145

.264

0.00

010

.In

boun

dkn

ow-h

owra

tio

228

60.

009

0.00

90.

913

0.40

22

337

0.01

40.

006

2.47

10.

086

11.

Out

boun

dkn

ow-h

owra

tio

228

70.

002

0.00

20.

959

0.38

52

337

0.00

10.

001

1.00

40.

367

12.

Age

228

78.

012E

+03

2.63

0E+0

230

.463

0.00

02

337

1.13

3E+0

52.

412E

+03

46.9

870.

000

13.

No.

ofem

ploy

ees

228

74.

812E

+08

5.72

2E+0

684

.094

0.00

02

337

2.09

4E+1

09.

202E

+08

22.7

630.

000

14.

R&

Din

tens

ity

228

72.

365E

+03

2.00

9E+0

31.

177

0.31

02

337

2.97

8E+0

23.

224E

+01

9.23

60.

000

15.

R&

Dco

sts

per

empl

oyee

228

76.

465E

+05

1.80

3E+0

435

.862

0.00

02

337

1.25

1E+0

66.

922E

+03

180.

759

0.00

016

.C

lose

dR

&D

inte

nsit

y2

286

2.40

4E+0

32.

003E

+03

1.20

00.

303

233

72.

080E

+02

3.09

5E+0

16.

718

0.00

117

.R

even

ues

per

empl

oyee

228

74.

077E

+05

8.90

1E+0

44.

580

0.01

12

337

9.38

9E+0

44.

674E

+04

2.00

90.

136

18.

EB

ITpe

rem

ploy

ee2

287

1.00

6E+0

65.

735E

+04

17.5

490.

000

233

79.

113E

+05

2.09

7E+0

443

.465

0.00

019

.M

arke

tca

p.on

asse

ts2

277

243.

880

616.

330

0.39

60.

674

231

516

.698

1.25

113

.352

0.00

020

.R

even

ues

grow

th2

228

810.

293

1.11

5.92

60.

726

0.48

52

269

21.7

503.

287

6.61

70.

002

21.

EB

ITgr

owth

222

98.

895

34.6

600.

257

0.77

42

269

14.3

8714

7.06

20.

098

0.90

722

.M

arke

tca

p.gr

owth

221

72.

839E

+04

3.02

2E+0

40.

939

0.39

32

247

1.32

00.

365

3.61

9



Table 7. Clusters Description: Openness Degree

Segment Cluster Opennessratio (%)

Costsratio (%)

Revenuesratio (%)

Additionsratio (%)

Disposalsratio (%)

Biotechnology Hardly open 12.3 8.1 8.8 11.5 0.3Fairly open 33.7 14.5 50.1 15.3 1.5Very open 53.4 31.8 93.1 12.3 1.1Total 36.0 19.1 56.4 13.5 1.1

Pharmaceutical Hardly open 9.8 5.9 3.6 13.3 0.9Fairly open 30.2 31.6 28.3 18.7 1.3Very open 51.4 30.6 88.1 2.9 0.0Total 18.3 13.4 17.9 13.0 0.9

Total sample 26.5 16.0 35.6 13.2 1.0

Table 8. Clusters Description: Openness Nature

Segment Cluster InboundR&D ratio

(%)

OutboundR&D ratio

(%)

Inbound IPratio (%)

OutboundIP ratio (%)

Inboundknow-howratio (%)

Outboundknow-howratio (%)

Biotechnology Hardly open 4.3 3.8 6.4 3.3 3.7 0.0Fairly open 12.5 30.5 5.8 9.5 3.2 0.7Very open 24.6 57.6 4.0 13.3 1.8 0.1Total 15.0 34.3 5.3 9.5 2.8 0.4

Pharmaceutical Hardly open 3.2 1.5 7.6 1.6 3.5 0.2Fairly open 16.7 9.5 13.1 10.9 3.8 0.6Very open 23.3 50.4 0.8 16.2 0.8 0.0Total 8.0 8.7 7.8 4.9 3.2 0.2

Total sample 11.2 20.5 6.6 7.0 3.0 0.3

Table 9. Clusters Description: Context Features

Segment Cluster Age No. ofemployees

R&Dintensity

(%)

R&Dcosts peremployee

(k€)

ClosedR&D

intensity(%)

Biotechnology Hardly open 34 4.916 20.1 96 16.5Fairly open 17 694 33.9 204 26.8Very open 14 152 131.3 282 89.5Total 19 1.380 26.1 208 20.8

Pharmaceutical Hardly open 79 25.145 16.1 53 14.6Fairly open 28 1.188 20.3 150 11.7Very open 15 196 95.3 313 71.0Total 62 17.982 16.2 100 14.7

Total sample 42 10.340 17.0 150 15.1



• In both segments the degree of opennesshas a strong correlation with revenues ratio,a moderate correlation with costs ratio and aweak correlation with additions ratio; nosignificant correlation is found as to dispos-als ratio.

• In both segments the correlations of open-ness with inbound and outbound R&Dratios are higher than the ones with both IPratios and inbound know-how ratio; no sig-nificant correlation is found as to outboundknow-how ratio.

• In both segments a negative correlation isobserved between the degree of openness ofa company and its age and dimension,while a moderate positive correlation isfound between openness ratio and R&Dcosts per employee.

• In the biotechnology segment a negativecorrelation occurs between the degree ofopenness of a company and its efficiency,measured as both revenues per employeeand EBIT per employee.

• In the pharmaceutical segment a negativecorrelation is found between the degree ofopenness of a company and its EBIT peremployee ratio, while weak positive corre-lations with openness are observed as tomarket capitalization on assets and rev-enues growth rate.

Finally, cross-section stepwise regressionswere performed to determine whether asimpler definition of the openness ratio can bederived by using only some of its components(Tables 13 and 14).

For both segments, first revenues ratioenters, followed by additions, costs and dis-posals ratios. Yet, for biotech companies(Figure 3), from the first model a good regres-sion is obtained, meaning that if only openrevenues are calculated, the value of the wholeopenness ratio can be well approximated bythe linear equation:

openness ratio revenues ratio= +0 128 0 413. . *

In the pharmaceutical segment (Figure 4), inorder to obtain a good regression, both rev-enues and additions have to be considered:

openness ratio revenues ratioadditions rat

= ++0 041 0 479

0 428. . *

. * iio

Thus, even if open innovation is a four-dimensional phenomenon, it can be wellapproximated by only two dimensions, at leastin the bio-pharmaceutical industry, wheremost outbound transactions have an economicnature (e.g., revenues from R&D collaboration)and most inbound exchanges are financial(additions of intangibles, in either separateacquisitions or business combinations).

Discussion

By focusing on data from annual reports, themeasurement framework we suggest iscapable of registering the actual values of theentities exchanged in the innovation market.The accounting perspective, by relying on posthoc data, is particularly useful in the innovationmarket, where the value of the traded entitiesis difficult to be estimated a priori, since it is notpossible to define it as a mark-up on costs(Arora & Gambardella, 2010).

Some points can be made regarding boththe theoretical framework and the resultsderiving from its application to the bio-pharmaceutical industry.

Despite accounting standards, annualreports can be quite different from one anotherin their form. As to revenues, in some casesthose deriving from open innovation aredirectly disclosed in the income statementexhibited separately from net sales as otherincome, but in most cases revenues composi-tion has to be detected in the notes. Research

Table 10. Clusters Description: Performances

Segment Cluster Revenues peremployee (k€)

EBIT peremployee (k€)

Market cap.on assets

Revenuesgrowth (%)

EBITgrowth

(%)

Market cap.growth (%)

Biotechnology Hardly open 340 53 1.836 14.4 −88.6 4487.3Fairly open 307 −81 5.274 577.8 −38.0 1648.4Very open 209 −179 4.395 75.6 −96.3 28.2Total 280 −87 4.255 288.1 −68.6 1736.0

Pharmaceutical Hardly open 330 53 1.272 14.5 136.9 13.5Fairly open 386 −36 1.650 19.0 50.6 36.1Very open 308 −167 2.268 139.4 127.3 6.6Total 337 11 1.446 30.0 120.6 16.6

Total sample 311 −34 2.757 148.8 33.5 819.0



Tab

le11

.P

ears

on’s

Cor

rela

tion

Coe

ffici

ents

–B

iote

chno

logy

Com

pani

es

Var

iab

le1.

2.3.

4.5.

6.7.

8.9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

1.O

penn

ess

rati

o1

2.C

osts

rati

o.4

57**

13.

Rev

enue

sra

tio

.851

**.2

18**

14.

Ad

dit

ions

rati

o.3

05**

0.05

9−0

.115

15.

Dis

posa

lsra

tio

0.08

30.

038

0.01

00.

006

16.

Inbo

und

R&

Dra

tio

.513

**.7

86**

.225

**.3

49**

0.02

11

7.O

utbo

und

R&

Dra

tio

.674

**.1

54**

.804

**−.

134*

0.02

4.1

98**

1

8.In

boun

dIP

rati

o.1

42*

0.10

1−.

147*

.557

**0.

002

−0.0

98−.

163*

*1

9.O

utbo

und

IPra

tio

.262

**0.

042

.314

**−0

.035

0.03

4−0

.008

−.25

3**

0.00

01

10.

Inbo

und

know

-how

rati

o

.130

*0.

028

−.14

2*.5

80**

−0.0

180.

039

−.19

8**

.243

**0.

070

1

11.

Out

boun

dkn

ow-h

owra

tio

0.01

8−0

.004

−0.0

46−0

.042

.631

**−0

.015

−0.0

60−0

.027

0.02

1−0

.025

1

12.

Age

−.34

3**

−.15

8**

−.28

7**

−0.0

68−0

.022

−.18

5**

−.28

6**

−0.0

080.

002

−0.0

33−0

.021

113

.N

o.of

empl

oyee

s−.

460*

*−0

.103

−.47

9**

−0.0

14−0

.055

−.14

4*−.

379*

*0.

019

−.15

2**

0.06

7−0

.026

.429

**1

14.

R&

Din

tens

ity

0.09

4−0

.069

.134

*−0

.055

−0.0

19−0

.060

.183

**−0

.055

0.00

4−0

.037

−0.0

10−0

.032

−0.0

571

15.

R&

Dco

sts

per

empl

oyee

.425

**.2

57**

.390

**−0

.037

0.00

2.2

70**

.263

**−0

.113

.237

**−0

.078

−0.0

02−.

257*

*−.

271*

*−0

.014

1

16.

Clo

sed

R&

Din

tens

ity

0.08

4−0

.086

.126

*−0

.053

−0.0

19−0

.076

.179

**−0

.052

−0.0

02−0

.035

−0.0

10−0

.029

−0.0

52.9

99**

−0.0

221

17.

Rev

enue

spe

rem

ploy

ee−.

225*

*0.

006

−.36

2**

0.10

70.

026

−0.0

13−.

356*

*0.

089

−0.0

140.

105

0.08

30.

073

.280

**−.

131*

.204

**−.

123*

1

18.

EB

ITpe

rem

ploy

ee−.

333*

*−0

.112

−.39

5**

0.05

30.

020

−.14

6*−.

310*

*0.

102

−.14

3*0.

106

0.05

2.2

07**

.365

**−0

.107

−.31

7**

−0.0

96.7

56**

1

19.

Mar

ket

cap.

onas

sets

−0.0

56−0

.054

−0.0

29−0

.032

−0.0

12−0

.050

−0.0

13−0

.041

−0.0

360.

012

−0.0

08−0

.002

−0.0

390.

035

0.00

00.

035

−0.0

74−.

219*

*1

20.

Rev

enue

sgr

owth

−0.1

06−0

.045

−0.0

77−0

.031

−0.0

15−0

.041

−0.0

59−0

.034

−0.0

22−0

.018

−0.0

08−0

.011

−0.0

04−0

.010

−0.0

67−0

.009

−0.0

46−0

.005

0.00

61

21.

EB

ITgr

owth

−0.0

090.

041

−0.0

03−0

.051

0.02

00.

038

−0.0

15−0

.088

0.03

80.

030

0.03

00.

055

0.04

40.

005

−0.0

140.

004

0.06

20.

101

−0.0

34−0

.009

122

.M

arke

tca

p.gr

owth

−0.1

16−0

.017

−0.1

25−0

.043

0.07

1−0

.022

−0.0

98−0

.037

−0.0

24−0

.032

−0.0

10−0

.017

−0.0

22−0

.011

−0.0

88−0

.009

−0.0

060.

031

−0.0

08−0

.009

0.02

91



Tab

le12

.P

ears

on’s

Cor

rela

tion

Coe

ffici

ents

–P

harm

aceu

tica

lCom

pani

es

Var

iab

le1.

2.3.

4.5.

6.7.

8.9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

1.O

penn

ess

rati

o1

2.C

osts

rati

o.6

08**

13.

Rev

enue

sra

tio

.767

**.2

28**

14.

Ad

dit

ions

rati

o.3

34**

.125

*−.

180*

*1

5.D

ispo

sals

rati

o−0

.003

−0.0

39−0

.101

0.05

31

6.In

boun

dR

&D

rati

o.6

20**

.859

**.3

16**

.173

**−0

.029

1

7.O

utbo

und

R&

Dra

tio

.634

**.1

82**

.833

**−.

145*

*−0

.057

.274

**1

8.In

boun

dIP

rati

o.2

61**

.264

**−.

257*

*.7

23**

0.04

40.

012

−.20

5**

1

9.O

utbo

und

IPra

tio

.473

**.1

47**

.600

**−.

121*

−0.0

08.1

86**

0.08

4−.

173*

*1

10.

Inbo

und

know

-how

rati

o

.212

**0.

050

−.13

9*.6

86**

0.00

30.

034

−.12

7*.3

78**

−0.0

651

11.

Out

boun

dkn

ow-h

owra

tio

0.02

6−0

.043

−0.0

360.

016

.821

**−0

.035

−0.0

170.

028

−0.0

09−0

.037

1

12.

Age

−.37

6**

−.22

0**

−.38

4**

0.03

5−0

.023

−.25

0**

−.32

7**

0.02

6−.

222*

*0.

095

−0.0

481

13.

No.

ofem

ploy

ees

−.27

5**

−.17

2**

−.27

9**

0.02

6−0

.026

−.16

3**

−.23

0**

−0.0

18−.

172*

*0.

059

−0.0

43.4

06**

1

14.

R&

Din

tens

ity

0.09

40.

033

.134

*−0

.074

−0.0

290.

048

0.09

9−0

.068

0.09

4−0

.065

−0.0

10−.

136*

−0.0

831

15.

R&

Dco

sts

per

empl

oyee

.589

**.3

11**

.653

**−.

131*

0.00

0.3

47**

.598

**−.

119*

.323

**−.

148*

*0.

022

−.37

1**

−.20

0**

.439

**1

16.

Clo

sed

R&

Din

tens

ity

0.04

2−0

.057

.117

*−0

.072

−0.0

23−0

.044

0.08

7−0

.074

0.08

3−0

.056

−0.0

07−.

113*

−0.0

69.9

73**

.405

**1

17.

Rev

enue

spe

rem

ploy

ee0.

048

.182

**−0

.079

0.10

00.

082

.176

**−0

.087

0.07

6−0

.001

0.03

40.

009

−0.0

680.

049

−.19

6**

.248

**−.

169*

*1

18.

EB

ITpe

rem

ploy

ee−.

322*

*−0

.096

−.41

9**

.168

**0.

042

−.16

5**

−.39

6**

.181

**−.

184*

*.1

08*

−0.0

19.2

52**

.230

**−.

658*

*−.

562*

*−.

607*

*.4

61**

1

19.

Mar

ket

cap.

onas

sets

.221

**.2

37**

.173

**−0

.071

0.07

5.2

33**

.203

**−0

.045

0.05

4−0

.093

0.07

6−.

147*

*−.

124*

0.00

9.2

74**

−0.0

740.

036

−0.1

031

20.

Rev

enue

sgr

owth

.149

*−0

.028

.173

**0.

001

−0.0

02−0

.014

.225

**0.

042

−0.0

210.

076

−0.0

11−0

.100

−0.0

65−0

.015

.219

**−0

.018

.127

*−0

.030

.283

**1

21.

EB

ITgr

owth

0.01

0−0

.014

−0.0

130.

047

−0.0

08−0

.015

0.00

40.

047

−0.0

350.

025

0.00

30.

010

−0.0

41−0

.029

−0.0

11−0

.027

0.03

50.

085

−0.0

120.

010

122

.M

arke

tca

p.gr

owth

0.05

00.

109

0.03

9−0

.078

0.06

40.

053

0.03

1−0

.001

0.02

8−0

.065

0.11

9−0

.060

−0.0

51−0

.081

0.03

5−0

.092

−0.0

360.

012

.442

**.1

76**

−0.0

541



and development cost composition is neverdisclosed directly in the income statementand the relevant note has to be looked up.Further, some innovation-related costs – suchas collaboration profit-sharing or acquiredin-process R&D – can be recorded separatelyfrom R&D costs in the income statement. Inparticular, royalties and license fees are dis-closed as operating expenses and can bereported as a separate item or included in costof sales or in R&D costs. Obviously, the

denominator of the costs ratio was built byconsidering all the costs related to the innova-tion process, even if they were not included inthe R&D costs.

As regards disposals and additions of intan-gibles, two different approaches are used byIFRS and US GAAP. While the former explic-itly discloses all additions and disposals –internal, in separate acquisitions and inBCMAs – in the note to intangibles, the latteronly discloses additions from BCMAs in thenotes regarding business combinations. Thus,in order to obtain the additions and disposalsof separately acquired intangibles, the differ-ence between the gross value at the end of theyear, the gross value at the beginning of theyear, the value of BCMA additions and anyimpairment charge or reclassification has to beperformed. However, this assessment isapproximate, because if the difference is posi-tive, we record a separate addition but someseparate disposals of lower value might haveoccurred and vice versa. Moreover, while inIFRS reports we can detect the value of dispos-als net of amortization, this is not possible withUS GAAP reports. Yet, by performing the one-

Table 13. Regression Summary – Dependent Variable: Openness Ratio

Segment Model Variables entered AdjustedR-square

Std. error ofthe estimate

Biotechnology 1 Revenues ratio 0.723 0.10652 Additions ratio 0.888 0.06793 Costs ratio 0.947 0.04654 Disposals ratio 0.951 0.0448

Pharmaceutical 1 Revenues ratio 0.587 0.11542 Additions ratio 0.817 0.07683 Costs ratio 0.952 0.03924 Disposals ratio 0.957 0.0373

Table 14. Regression Coefficients – Dependent Variable: Openness Ratio

Segment Model Unstandardizedcoefficients

Standardizedcoefficients

t Sig.

B Std. error Beta

Biotechnology 1 (Constant) 0.128 0.011 12.120 0.000Revenues ratio 0.413 0.015 0.851 27.503 0.000

Pharmaceutical 2 (Constant) 0.041 0.006 7.257 0.000Revenues ratio 0.479 0.013 0.854 36.168 0.000Additions ratio 0.428 0.021 0.487 20.632 0.000

Figure 3. Estimated vs. Observed OpennessRatio for Biotechnology Companies



way ANOVA analysis with the accountingstandard being the discriminating factor, nosignificant differences were found in any ofthe variables under study.

As to the calculation of the indexes, from aneconomic perspective we compare revenuesand costs referring to the same fiscal year,while the financial ratios compare the variationin the year with the cumulative asset value.Thus, the value of the indicator in a year suffersfrom the values added or disposed in the pre-vious years. Given the long depreciationperiod of some intangibles, when substantialacquisitions are made in a year, the denomina-tor of the ratio increases for the following years,thus underestimating the value of the indicator.As a consequence, the financial dimension ofOI is reliable only if calculated over a longperiod of time, equal to or greater than themean depreciation period of intangibles.

A particular consideration must be takeninto account for business combinations: even ifit is clear that BCMAs allow companies toacquire R&D, IP and know-how from outside,it may be questionable whether it is correct toconsider it as ‘open’ behaviour. In fact, theindustry we investigated is characterized bylarge pharmaceutical companies acquiringsmall biotech firms in order to incorporatetheir R&D, IP and know-how. BCMAs can beconsidered as hierarchy mechanisms but, if theinnovation market were perfect, it would bepossible to exchange innovation entities inseparate acquisitions, with no need forBCMAs. Yet, in order to evaluate all the ways inwhich a company can incorporate R&D, IP andknow-how, business combinations cannot beneglected.

As to the application to the bio-pharmaceutical industry, we found that therole of revenues and additions in defining thedegree of openness is more significant thanthat of costs and disposals. Such a findingmight be affected by computational problemsin disposals. In fact, disposals were included inour analysis without considering gains and

losses as such values were disclosed in finan-cial statements with reference to all assetsdivested and not only to those we were inter-ested in. In doing so we underestimated (over-estimated) disposals, when intangibles weresold at a higher (lower) value than the bookone. In contrast, when an addition occurs, weconsidered the total value of the acquisitionand, if a BCMA takes place, we also includedgoodwill which is nothing more than the gainon disposal for the acquired company. Thus,our methodology is somehow asymmetric dueto the lack of detailed information in theannual reports.

Through one-way ANOVA we pointed outsome differences in the behaviour of biotechand pharmaceutical companies. The two seg-ments differ significantly as to both contextvariables and performance: biotech companiesare smaller, younger and less efficient thanpharmaceutical ones; further, biotech compa-nies have higher R&D intensity, R&D costs peremployee and market capitalization on thevalue of assets. The results are consistent withthe literature: pharmaceutical companies areusually large firms with a long history and aworldwide geographical presence with a port-folio of already marketed drugs, while biotechcompanies, relatively young and small, aremostly focused on R&D and only in somecases devoted to manufacturing and commer-cialization (Bianchi et al., 2011). This implies agreater focus for biotech companies onresearch and development, as suggested byhigher values of R&D intensity and R&D peremployee. Further, the higher value of marketcapitalization on assets suggests a higherexpected, and still unexpressed, potential forbiotech companies, consistent with theirhigher technology orientation.

As regards the degree of openness, biotechcompanies show higher values, particularly asto revenues and costs; further, they are moreinvolved in R&D transactions both inboundand outbound. In contrast, as to IP, if biotechcompanies show higher intensity of outbound

Figure 4. Estimated vs. Observed Openness Ratio for Pharmaceutical Companies



transactions, pharmaceutical ones are moreactive in inbound processes.

For biotech companies open innovation isnot only an innovation strategy, but rather thecore business model: in fact, most of them donot produce and sell finished products, but,rather, operate in the intermediate market asinnovation sellers. This results in very highvalues of the revenues ratio, which in somecases can even be equal to 100%, denoting thatthe whole business of the company is based onopen innovation. Biotech companies enter intodifferent kinds of agreements with univer-sities, medical and research centres and otherbio-pharmaceutical companies from a collabo-rative long-term perspective. In most suchagreements they behave as sellers of innova-tion, even if, in some cases, they can also behaveas buyers of innovation services, mainly in theclinical and pre-clinical trials. Thus R&D is themost exchanged entity, in the form of collabo-rative and outsourcing costs and revenues.

On the other hand, since pharmaceuticalcompanies are directly involved in the manu-facturing and commercialization of drugs,they are much more product-oriented thanbiotech firms. Obviously, they take part in theinnovation market by acquiring R&D (i.e.,innovation process) from biotech companies,but most of their inbound effort is devoted toacquire intellectual property (i.e., innovationproduct). These results are consistent with theliterature, which reports the role of pharma-ceutical companies as innovation seekers,which either extensively collaborate with spe-cialist biotech companies or acquire developedtechnology from them or even incorporate thecompanies themselves (Galambos & Sturchio,1998; Dahlander & Wallin, 2006; Higgins &Rodriguez, 2006).

Our results show that the two segmentsdiffer in their open behaviour from a doubleperspective:

• from a quantitative point of view, biotechcompanies show higher openness intensitythan pharmaceutical ones;

• from a qualitative point of view, their adop-tion modalities of OI are quite different,with biotech companies mostly behaving asinnovation sellers in development activitiesand pharmaceutical companies acting asinnovation seekers acquiring developedtechnology.

The correlation analyses in the two segmentslead to similar results as to the context vari-ables, suggesting that such variables are posi-tively or negatively related to opennessregardless of the modalities of implementationof OI.

First, in both segments a negative correla-tion is observed between the degree of open-ness of a company and its size. This result isin contrast to the literature: Keupp andGassmann (2009) found a positive and signifi-cant effect of firm size on both OI breadth anddepth; Faems et al. (2010) found a significantpositive relationship between firm size andalliance portfolio diversity; Lee et al. (2010)explained the reduced openness of SMEs by anumber of factors such as ‘lack of infrastruc-ture’ and ‘lack of financial resources’; finally,Schroll and Mild (2011) found that companysize has a strong correlation with open inno-vation adoption. The difference in the resultscan be explained primarily by the differentapproach used to define the openness of thecompanies. It is clear that the number of link-ages with external parties increases with thesize of a company, but even if very small com-panies can have a limited number of externallinkages with partners, the intensity of suchlinkages in terms of economic flows can bevery high, especially if compared to the totaleconomic flows of the company.

Our results also support the existence of anegative correlation of openness with the ageof the company in both segments, while in theliterature age does not seem to be a predictorof the degree of openness (Keupp &Gassmann, 2009; Schroll & Mild, 2011). In fact,our finding might be considered as industry-specific, while the literature contributionswhich did not find any relation between open-ness and age are based on multi-sectorsamples. We noticed that biotech companiesare both younger and more open than phar-maceutical ones; yet, even in each segment,there is a similar relation between age andopenness. Such a result may be partlyexplained by the hybrid behaviour of somecompanies, for which the distinction of thetwo segments may be not so sharp as sug-gested by the ICB code.8

Further, in both segments no correlationsare found as to degree of openness and R&Dintensity or closed R&D intensity, while a posi-tive correlation is found as to R&D costs peremployee. If we consider R&D costs peremployee as a proxy for the focus of thehuman resources of the company on R&D pro-cesses, the result states that the most opencompanies are those which are more focusedon R&D processes. Once again, we havebiotech companies being more open and R&Dfocused than pharmaceutical ones, and theexistence of the correlation in both segmentscan be explained by the existence of hybridcompanies. The lack of correlation betweenopenness and absorptive capacity – measuredas closed R&D intensity – is consistent with



the findings of Laursen and Salter (2006), evenif most contributions suggest a high relevanceof absorptive capacity for the open innovationconcept (Gassmann & Enkel, 2004; Huang &Rice, 2009; Newey, 2010).

Finally, as to performance, in both segmentsa negative correlation exists between opennessand EBIT per employee, but in the pharmaceu-tical segment positive correlations areobserved as to market capitalization on assetsand revenues growth rate.

The negative relation between openness andefficiency is supported in the literature:Dahlander and Gann (2007) argue that havinghigh degrees of openness can be very costlyand that the decision on how open a companyhas to be should derive from a balancebetween costs and benefits. In fact, in the twosegments such inverse relation can beexplained in two ways. For pharmaceuticalcompanies, higher degrees of openness canresult in new opportunities for revenues, butcan also imply additional costs linked to thehigher complexity of an open process, whereexternal as well as internal resources have tobe managed. In the biotechnology segment,the most open companies rely only on openrevenues deriving from joint R&D projects,thus having lower margins than more closedcompanies, which can also rely on the rev-enues from commercialization.

As to the results found only in the pharma-ceutical segment, they can both be explainedby the heterogeneity in the segment, as themost open companies are quite similar tobiotech firms with higher future expectedvalue, lower revenues and, thus, highergrowth rates.

Conclusion

The aim of this paper is to provide anaccounting framework for measuring thedegree of openness of companies through thequantification of the value of open innovationtransactions.

The work is based on the analysis of annualreports, defining all the pecuniary flowsrelated to OI transactions. Three tradingentities are considered in the innovationmarket: R&D, IP and know-how. For each onewe considered all the costs and revenues,additions and disposals deriving from openinnovation activities and some ratios were cal-culated to define the degree of openness of acompany. The framework was then applied toa sample of 126 global top R&D spending com-panies in the bio-pharmaceutical industry,whose annual reports for the period 2008–2012were analysed, for a total of 630 statistical

units. Both the framework applicability andits explicative power and usefulness werevalidated.

The paper contributes to the existing litera-ture on the measurement of open innovationfrom a double perspective. First, it focuses onthe pecuniary dimension of the phenomenonby outlining all the economic and financialflows linked to open innovation activitiesand, from this perspective, it followsChesbrough’s suggestion. Second, it proposesa set of metrics which can be used not onlyfor inbound processes but also for outboundones, allowing a definition of the ways inwhich companies can capture value from theexploitation of their technology, i.e. the busi-ness models of companies.

As to practical contributions, the paperaddresses the need for operative, practicalinstruments, which can help managers tomonitor and control their innovation pro-cesses after an open-oriented approach. Infact, the paper provides R&D managers withinsights useful to properly assess the pecuni-ary implications of specific open innovationtransactions; knowing which innovation-related items to manipulate could helporganizations to improve the effectiveness oftheir open innovation strategy. Further, giventhe availability and objectivity of annualreport figures, measuring open innovationthrough the analysis of annual reports canhelp decision makers to assess the status oftheir own open processes and compare it overtime and space, also allowing benchmarkingwith competitors.

Three limits can be outlined for the work.First, the disharmony of accounting standardsamong countries limited our analysis only tothe companies which adopted either IFRS orUS GAAP, resulting in an under-coverage ofthe sample. Second, being focused on account-ing indicators, our framework can be used toanalyse only the pecuniary dimension of openinnovation (Dahlander & Gann, 2010), andthus it cannot be generalized to such indus-tries as software, where sourcing and reveal-ing are widespread. Third, the paper is basedon observations over a five-year period, whichis too short, at least in the analysed industry, toallow a longitudinal analysis.

Our approach to the measurement of open-ness is based on the intensity of the results ofopen activities on the total business of thecompanies, rather than on the entity of suchactivities. In fact, we use percentage ratios tomeasure the degree of openness rather thanthe total values of the open transactions. Indoing so, we are able to catch the open behav-iour of the single company rather than the con-tribution that such behaviour gives to the total



openness of the industry. The contribution of avery small company to the total value of theinnovation-related entities traded in the indus-try is relatively low even if the company hasvery high open ratios.

In our future research we want to integratethe intensity approach with a new measure-ment, based on the entity of the transactions.Two further directions of research will be,from one side, an in-depth analysis of casestudies over longer periods of time in order todescribe the different trajectories to open inno-vation within the specific industry and, fromthe other, the widening to other sectors, inorder to outline the differences in open inno-vation adopting models among differentindustries.

Notes

1. We are grateful to an anonymous referee formaking this point.

2. Only development milestones, disclosed in R&Dcosts, were considered, while all commercialmilestones, which are disclosed in selling,general and administrative costs, were excluded.

3. See for example: (1) ‘Goodwill of 5.2 million EURarises from expected synergy benefits in differ-ent areas of drug development as well as fromthe competent personnel and the integration offunctions. Expected synergy benefits will begained from the possibility to create new drugdevelopment projects corresponding to theneeds of international pharmaceuticals compa-nies and from the possibility to utilize newknowledge and new technologies for the devel-opment of the existing businesses’ (Biotie Thera-pies 2011 annual report, p. 24); (2) ‘The goodwillrecognized is attributable primarily to strategicand synergistic opportunities across the entireurology spectrum, expected corporate synergies,the assembled workforce of AMS and otherfactors’ (Endo Pharmaceuticals Holdings Inc.2011 annual report, p. 113); (3) ‘Managementbelieves that the goodwill mainly represents thesynergies expected from combining our researchand development operations as well as acquiringCalistoga’s assembled workforce and otherintangible assets that do not qualify for separaterecognition’ (Gilead Sciences Inc. 2011 annualreport, p. 118).

4. ICB codes 4577 and 4573 reported in the Score-board.

5. As to 2012, when the annual report refers to theyear-end before 30 June, the 2012–2013 reportwas considered, otherwise the 2011–2012 onewas used. The same procedure was used for theother four years. All data were converted intoeuros by using the exchange rates as of 31December.

6. We are grateful to an anonymous referee formaking this point.

7. See the Appendix for the list of companies andclusters.

8. In fact, while the Scoreboard 2011 from which weselected the sample separated the two segments,from the Scoreboard 2012 the companies aregrouped in a single industry.

References

Acs, Z.J. and Audretsch, D.B. (1990) Innovation andSmall Firms. Cambridge, MA: MIT Press.

Al-Ashaab, A., Flores, M., Doultsinou, A. andMagyar, A. (2011) A Balanced Scorecard forMeasuring the Impact of Industry-UniversityCollaboration. Production Planning & Control, 22,554–70.

Arora, A., Fosfuri, A. and Gambardella, A. (2001)Markets for Technology and their Implicationsfor Corporate Strategy. Industrial and CorporateChange, 10, 419–51.

Arora, A. and Gambardella, A. (2010) Ideas forRent: An Overview of Markets for Technology.Industrial and Corporate Change, 19, 775–803.

Bahemia, H. and Squire, B. (2010) A ContingentPerspective of Open Innovation in New ProductDevelopment Projects. International Journal ofInnovation Management, 14, 603–27.

Belussi, F., Sammarra, A. and Sedita, S. (2010)Learning at the Boundaries in an ‘Open RegionalInnovation System’: A Focus on Firms’ InnovationStrategies in the Emilia Romagna Life ScienceIndustry. Research Policy, 39, 710–21.

Berger, C., Möslein, K.M., Piller, F.T. andReichwald, R. (2005) Co-Designing Modes ofCooperation at the Customer Interface: Learningfrom Exploratory Research. European ManagementReview, 2, 70–87.

Bianchi, M., Cavaliere, A., Chiaroni, D., Frattini, F.and Chiesa, V. (2011) Organisational Modes forOpen Innovation in the Bio-PharmaceuticalIndustry: An Exploratory Analysis. Technovation,31, 22–33.

Brunovs, R. and Kirsh, R.J. (1991) GoodwillAccounting in Selected Countries and the Har-monization of International Accounting Stand-ards. Abacus, 27, 135–61.

Cañibano, L., García-Ayuso, M. and Sánchez, M.P.(2000) Shortcomings in the Measurement of Inno-vation: Implications for Accounting StandardSetting. Journal of Management and Governance, 4,319–42.

Chen, J., Chen, Y. and Vanhaverbeke, W. (2011) TheInfluence of Scope, Depth, and Orientation ofExternal Technology Sources on the InnovativePerformance of Chinese Firms. Technovation, 31,362–73.

Chesbrough, H. (2003) Open Innovation: The NewImperative for Creating and Profiting from Technol-ogy. Harvard Business School Press, Boston, MA.

Chesbrough, H. (2004) Managing Open Innovation.Research-Technology Management, 47, 23–26.

Chesbrough, H. (2006) Open Business Models: How toThrive in the New Innovation Landscape. HarvardBusiness School Press, Boston, MA.

Chesbrough, H. (2012) Open Innovation: WhereWe’ve Been and Where We’re Going. Research-Technology Management, 55, 20–27.



Chesbrough, H. and Crowther, A.K. (2006) BeyondHigh Tech: Early Adopters of Open Innovation inOther Industries. R&D Management, 36, 229–36.

Chesbrough, H., Vanhaverbeke, W. and West, J.(2006) Open Innovation: Researching a New Para-digm. Oxford University Press, Oxford.

Chiang, Y.-H. and Hung, K.-P. (2010) ExploringOpen Search Strategies and Perceived InnovationPerformance from the Perspective of Inter-Organizational Knowledge Flows. R&D Manage-ment, 40, 292–9.

Chiaroni, D., Chiesa, V. and Frattini, F. (2008) Pat-terns of Collaboration along the Bio-Pharmaceutical Innovation Process. Journal ofBusiness Chemistry, 5, 7–22.

Cohen, W.M. and Levin, R.C. (1989) EmpiricalStudies of Innovation and Market Structure. InSchmalensee, R. and Willig, R.D. (eds.), Handbookof Industrial Organization. Elsevier Science,Amsterdam, pp. 1059–107.

Cohen, W.M. and Levinthal, D.A. (1990) AbsorptiveCapacity: A New Perspective on Learning andInnovation. Administrative Science Quarterly, 35,128–52.

Cooke, P. (2005) Regionally Asymmetric KnowledgeCapabilities and Open Innovation Exploring‘Globalisation 2’ – A New Model of IndustryOrganization. Research Policy, 34, 1128–49.

Dahlander, L. and Gann, D.M. (2007) How Open isInnovation? DRUID Summer Conference paper,Copenhagen Business School, Copenhagen, 18June.

Dahlander, L. and Gann, D.M. (2010) How Open isInnovation? Research Policy, 39, 699–709.

Dahlander, L. and Wallin, M.W. (2006) A Man onthe Inside: Unlocking Communities as Comple-mentary Assets. Research Policy, 35, 1243–59.

de Wit, J., Dankbaar, B. and Vissers, G. (2007) OpenInnovation: The New Way of Knowledge Trans-fer? Journal of Business Chemistry, 4, 11–19.

Dodgson, M., Gann, D. and Salter, A. (2006) TheRole of Technology in the Shift towards OpenInnovation: The Case of Procter & Gamble. R&DManagement, 36, 333–46.

du Chatenier, E., Verstegen, J.A.A.M., Biemans,H.J.A., Mulder, M. and Omta, O.S.W.F. (2010)Identification of Competencies for Professionalsin Open Innovation Teams. R&D Management, 40,271–80.

Ebersberger, B., Bloch, C., Herstad, S.J. and van deVelde, E. (2012) Open Innovation Practices andtheir Effect on Innovation Performance. Interna-tional Journal of Innovation and Technology Manage-ment, 9, 1–22.

Emden, Z., Calantone, R.J. and Droge, C. (2006) Col-laborating for New Product Development: Select-ing the Partner with the Maximum Potential toCreate Value. Journal of Production InnovationManagement, 23, 330–41.

Emenyonu, E.N. and Gray, S.J. (1992) EC Account-ing Harmonization: An Empirical Study of Meas-urement Practices in France, Germany and theUK. Accounting and Business Research, 23, 49–58.

Enkel, E., Gassmann, O. and Chesbrough, H. (2009)Open R&D and Open Innovation: Exploring thePhenomenon. R&D Management, 39, 311–16.

Enkel, E. and Lenz, A. (2009) Open InnovationMetrics System. Proceedings of the R&D Manage-ment Conference, Vienna, Austria, 21–24 June.

Faems, D., de Visser, M., Andries, P. and van Looy,B. (2010) Technology Alliance Portfolios andFinancial Performance: Value-Enhancing andCost-Increasing Effects of Open Innovation.Journal of Product Innovation Management, 27, 785–96.

Fetterhoff, T.J. and Voelkel, D. (2006) ManagingOpen Innovation in Biotechnology. Research-Technology Management, 49, 14–18.

Galambos, L. and Sturchio, J.L. (1998) Pharmaceu-tical Firms and the Transition to Biotechnology: AStudy in Strategic Innovation. Business HistoryReview, 72, 250–78.

Gassmann, O. (2006) Opening up the InnovationProcess: Towards an Agenda. R&D Management,35, 223–8.

Gassmann, O. and Enkel, E. (2004) Towards aTheory of Open Innovation: Three Core ProcessArchetypes. Proceedings of the R&D Manage-ment Conference, Lisbon, Portugal, 6–9 July.

Gassmann, O., Reepmeyer, G. and von Zedtwitz,M. (2008) Leading Pharmaceutical Innovation:Trends and Drivers for Growth in the PharmaceuticalIndustry, 2nd edn. Springer, Berlin.

Higgins, M.J. and Rodriguez, D. (2006) The Out-sourcing of R&D through Acquisitions in thePharmaceutical Industry. Journal of Financial Eco-nomics, 80, 351–83.

Høegh-Krohn, N.E.J. and Knivsflå, K.H. (2000)Accounting for Intangible Assets in Scandinavia,the UK, the US, and by the IASC: Challenges anda Solution. The International Journal of Accounting,35, 243–65.

Huang, F. and Rice, J. (2009) The Role of AbsorptiveCapacity in Facilitating ‘Open Innovation’ Out-comes: A Study of Australian SMEs in the Manu-facturing Sector. International Journal of InnovationManagement, 13, 201–20.

Hwang, J. and Lee, Y. (2010) External KnowledgeSearch, Innovative Performance and Productivityin the Korean ICT Sector. TelecommunicationsPolicy, 34, 562–71.

Ili, S., Albers, A. and Miller, S. (2010) Open Innova-tion in the Automotive Industry. R&D Manage-ment, 40, 246–55.

Inauen, M. and Schenker-Wicki, A. (2011) TheImpact of Outside-In Open Innovation on Innova-tion Performance. European Journal of InnovationManagement, 14, 496–520.

JRC (2011) The 2011 EU Industrial R&D InvestmentScoreboard. Joint Research Centre of the EuropeanCommission.

Keupp, M.M. and Gassmann, O. (2009) Determi-nants and Archetype Users of Open Innovation.R&D Management, 39, 331–41.

Kleyn, D., Kitney, R. and Atun, R. (2007) Partner-ship and Innovation in the Life Sciences. Interna-tional Journal of Innovation Management, 11, 323–47.

Köhler, C., Sofka, W. and Grimpe, C. (2012) Selec-tive Search, Sectoral Patterns, and the Impact onProduct Innovation Performance. Research Policy,41, 1344–56.



Laursen, K. and Salter, A. (2006) Open for Innova-tion: The Role of Openness in Explaining Innova-tion Performance among UK ManufacturingFirms. Strategic Management Journal, 27, 131–50.

Lazzarotti, V. and Manzini, R. (2009) DifferentModes of Open Innovation: A Theoretical Frame-work and an Empirical Study. InternationalJournal of Innovation Management, 13, 615–36.

Lee, S., Park, G., Yoon, B. and Park, J. (2010) OpenInnovation in SMEs – An Intermediated NetworkModel. Research Policy, 39, 290–300.

Lev, B. (2001) Intangibles: Management, Measurement,and Reporting. Brookings Institution Press, Wash-ington, DC.

Nakamura, L. (2001) What is the US Gross Invest-ment in Intangibles? (At least) One TrillionDollars a Year! Working paper No. 01-15, FederalReserve Bank of Philadelphia, October.

Newey, L. (2010) Wearing Different Hats: HowAbsorptive Capacity Differs in Open Innovation.International Journal of Innovation Management, 14,703–31.

Penman, S. (2009) Accounting for Intangible Assets:There is also an Income Statement. Abacus, 45,359–71.

Perkmann, M. and Walsh, K. (2007) University-Industry Relationships and Open Innovation:Towards a Research Agenda. International Journalof Management Reviews, 9, 259–80.

Petroni, G., Venturini, K. and Verbano, C. (2012)Open Innovation and New Issues in R&D Organi-zation and Personnel Management. The Interna-tional Journal of Human Resource Management, 23,147–73.

Poot, T., Faems, D. and Vanhaverbeke, W. (2009)Toward a Dynamic Perspective on Open Innova-tion: A Longitudinal Assessment of the Adoptionof Internal and External Innovation Strategies inthe Netherlands. International Journal of InnovationManagement, 13, 177–200.

Powell, W., White, D., Koput, K. and Owen-Smith, J.(2005) Network Dynamics and Field Evolution:The Growth of Interorganizational Collaborationin the Life Sciences. American Journal of Sociology,110, 1132–205.

Pozza, L., Prencipe, A. and Markarian, G. (2008)Capitalization of R&D Costs and Earnings Man-agement: Evidence from Italian Listed Compa-nies. International Journal of Accounting, 43, 246–67.

Salge, T.O., Bohné, T.M., Farchi, T. and Piening, E.P.(2012) Harnessing the Value of Open Innovation:The Moderating Role of Innovation Management.International Journal of Innovation Management, 16,1–26.

Schroll, A. and Mild, A. (2011) Open InnovationModes and the Role of Internal R&D: An Empiri-cal Study on Open Innovation Adoption inEurope. European Journal of Innovation Manage-ment, 14, 475–95.

Schweitzer, F.M., Gassmann, O. and Gaubinger, K.(2011) Open Innovation and its Effectiveness to

Embrace Turbulent Environments. InternationalJournal of Innovation Management, 15, 1191–207.

Simard, C. and West, J. (2006) Knowledge Networksand the Geographic Locus of Innovation. InChesbrough, H., Vanhaverbeke, W. and West, J.(eds.), Open Innovation: Researching a New Para-digm. Oxford University Press, Oxford, pp. 220–40.

Sofka, W. and Grimpe, C. (2010) Specialized Searchand Innovation Performance – Evidence acrossEurope. R&D Management, 40, 310–23.

Stolowy, H. and Jeny-Cazavan, A. (2001) Interna-tional Accounting Disharmony: The Case ofIntangibles. Accounting, Auditing and Accountabil-ity Journal, 14, 477–97.

Tether, B.S. and Tajar, A. (2008) Beyond IndustryUniversity Links: Sourcing Knowledge for Inno-vation from Consultants, Private ResearchOrganisations and the Public Science-Base.Research Policy, 37, 1079–95.

van de Vrande, V., de Jong, J.P.J., Vanhaverbeke, W.and de Rochemont, M. (2009) Open Innovation inSMEs: Trends, Motives and Management Chal-lenges. Technovation, 29, 423–37.

van der Meer, H. (2007) Open Innovation – TheDutch Treat: Challenges in Thinking in BusinessModels. Creativity and Innovation Management, 16,192–202.

West, J. and Gallagher, S. (2006) Challenges of OpenInnovation: The Paradox of Firm Investment inOpen-Source Software. R&D Management, 36,319–31.

Francesca Michelino ([email protected])is adjunct professor of Marketing andSupply Chain Management at the Univer-sity of Salerno, Italy. Her research interestsinclude: IT organization and supply chainmanagement, innovation management andopen innovation.

Emilia Lamberti is a PhD student inEnterprise Engineering at the University ofRome Tor Vergata, Italy. Her research inter-ests concern open innovation and innova-tion measurement.

Antonello Cammarano is a PhD studentin Innovation Economy and Engineering atthe University of Salerno, Italy. His researchinterests concern open innovation andpatent data analysis.

Mauro Caputo is full professor of Innova-tion and Technology Management at theUniversity of Salerno, Italy. His researchinterests include: logistics and physical dis-tribution, IT organization and supply chainmanagement, innovation management andopen innovation.



Appendix: The Sample

Biotech companies Clusters Pharmaceutical companies Clusters

4SC very open Abbott Laboratories hardly openAblynx very open Actelion hardly openActive Biotech fairly open ALK-Abello hardly openAffymetrix hardly open Alkermes hardly openAlexion Pharmaceuticals fairly open Allergan hardly openAmgen hardly open Almirall hardly openArk Therapeutics fairly open Arena Pharmaceuticals fairly openBasilea Pharmaceutica fairly open AstraZeneca hardly openBavarian Nordic fairly open Biotest hardly openBiogen hardly open Bioton hardly openBioinvent International very open Boehringer Ingelheim hardly openBiomarin Pharmaceutical hardly open Bristol-Myers Squibb hardly openBiotie Therapies very open CHR Hansen hardly openBTG fairly open Cosmo Pharmaceuticals fairly openCelgene fairly open Dechra Pharmaceuticals hardly openCSL hardly open Diamyd Medical very openCubist Pharmaceuticals fairly open DiaSorin fairly openDendreon fairly open Egis Pharmaceutical hardly openEpigenomics fairly open Elan hardly openGalapagos very open Eli Lilly hardly openGenmab hardly open Endo Pharmaceuticals fairly openGenus hardly open Evotec fairly openGeron very open Exelixis very openGilead Sciences fairly open Forest Laboratories fairly openIllumina hardly open Galenica hardly openImpax Laboratories hardly open Gedeon Richter hardly openIncyte fairly open GlaxoSmithKline hardly openInnate very open Guerbet hardly openIntercell very open GW Pharmaceuticals very openIsis Pharmaceuticals very open Hikma hardly openLexicon Pharmaceuticals very open Hospira hardly openLife Technologies hardly open Ipsen hardly openMediGene fairly open Johnson & Johnson hardly openMorphosys fairly open Krka hardly openNektar Therapeutics fairly open Laboratorios Farmaceuticos Rovi hardly openNeuroSearch fairly open Lundbeck hardly openNewron Pharmaceuticals very open Meda hardly openNovozymes hardly open Medicines fairly openNPS Pharmaceuticals very open Medivir hardly openPaion very open Merck DE hardly openPharming very open Merck US hardly openQiagen hardly open Merz Pharma fairly openRegeneron Pharmaceuticals fairly open Mylan hardly openSeattle Genetics fairly open NicOx very openSilence fairly open Novartis hardly openSwedish Orphan Biovitrum fairly open Novo Nordisk hardly openSygnis Pharma very open Oasmia Pharmaceutical fairly openSymphogen very open Omega Pharma hardly openTargacept very open Onyx Pharmaceuticals very openThromboGenics very open Orexo fairly openTiGenix fairly open Orion Oyj hardly open



Appendix: Continued

Biotech companies Clusters Pharmaceutical companies Clusters

TopoTarget fairly open Oxford Biomedica very openTransgene fairly open Perrigo hardly openUnited Therapeutics fairly open Pfizer hardly openVernalis fairly open Recordati hardly openVertex Pharmaceuticals fairly open Roche hardly openWilex very open Salix Pharmaceuticals fairly openZealand Pharma very open Sanofi-Aventis hardly open

Shire hardly openSkyePharma fairly openStada Arzneimittel hardly openTeva Pharmaceutical Industries hardly openTheravance very openUCB hardly openVectura very openVetoquinol hardly openWarner Chilcott hardly openZeltia hardly open



measuring open innovation in the bio-pharmaceutical industry...mance implications of open innovation...

Documents