Journal of Knowledge ManagementDirections of external knowledge search: investigating their different impact on firm performance inhigh-technology industriesJorge Cruz-González Pedro López-Sáez José Emilio Navas-López Miriam Delgado-Verde

Article information:To cite this document:Jorge Cruz-González Pedro López-Sáez José Emilio Navas-López Miriam Delgado-Verde , (2014),"Directions of externalknowledge search: investigating their different impact on firm performance in high-technology industries", Journal ofKnowledge Management, Vol. 18 Iss 5 pp. 847 - 866Permanent link to this document:http://dx.doi.org/10.1108/JKM-06-2014-0243

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Directions of external knowledge search:investigating their different impact on firmperformance in high-technology industries

Jorge Cruz-González, Pedro López-Sáez, José Emilio Navas-López andMiriam Delgado-Verde

Jorge Cruz-González isbased at Department ofOrganization andManagement, CUNEFBusiness School, Madrid,Spain. Pedro López-Sáezis an Associate Professor,José Emilio Navas-Lópezis a Professor andMiriam Delgado-Verdeare all based atDepartment of BusinessAdministration,Complutense de MadridUniversity, Madrid, Spain.

AbstractPurpose – The aim of the paper is to identify the different directions of external knowledge search andto investigate their individual effect on performance at the firm level.Design/methodology/approach – The empirical study is based on survey data gathered from twodistinct informants of 248 large- and medium-sized high-tech manufacturing Spanish firms. In dealingwith concerns on simultaneity and reverse causality, perceived time-lags among dependent andindependent variables were introduced. Quantitative methods based on questionnaire answers wereused.Findings – Findings reveal six distinct external search patterns and indicate that, while market sourcessuch as customers and competitors are positively associated with performance, knowledge acquiredfrom general information sources, other firms beyond the core business and patents and databaseshave no significant effect. Moreover, knowledge obtained from science and technology organizationsand from suppliers displays an inversed U-shaped effect on firm performance.Research limitations/implications – Conclusions can only be generalized to high-tech manufacturingfirms from developed countries and, although well-established methodological procedures were followed,the nature of the study remains cross-sectional. Yet, an important implication emerges from this work: moreopenness to external knowledge is not always better. It is necessary to carefully evaluate the potential gainsand pains of each type of partner and source.Practical implications – This research provides guidance to managers about how to shape theircompanies’ inter-organizational networks, i.e. the specific external agents on which they should focus,as well as the efforts they should devote to each of these key partners.Originality/value – By considering distinct directions of external knowledge search instead of a singledimension, the paper contributes to shed some more light to the mixed results reported by the scarceempirical studies that have investigated the effect of openness towards external knowledge onperformance at the firm level.

Keywords Inter-organizational learning, External knowledge search, Search strategies,Firm performance, High-technology firms

Paper type Research paper

1. Introduction

Theorists on open innovation and related fields have highlighted the benefits of learningfrom external sources (Chesbrough, 2003; Laursen, 2012). Supporting their reasoning,there is a wide empirical evidence showing that increasing openness towards externalknowledge increases firms’ innovation performance (Ebersberger and Herstad, 2011;Escribano et al., 2009; Leiponen and Helfat, 2011; Voudouris et al., 2012; Yu, 2013; Wangand Hsu, 2014). However, some scholars have claimed that, besides its alleged benefits,relying on knowledge located outside firm boundaries is not costless (Huizingh, 2011; Westand Bogers, 2014). Yet, by exclusively focussing on innovation performance variables, the

Received 17 June 2014Revised 18 June 2014Accepted 19 June 2014

DOI 10.1108/JKM-06-2014-0243 VOL. 18 NO. 5 2014, pp. 847-866, © Emerald Group Publishing Limited, ISSN 1367-3270 JOURNAL OF KNOWLEDGE MANAGEMENT PAGE 847

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above studies only report on the benefits of external learning, thus leaving aside itsassociated costs.

The effect of openness on overall firm performance is much less clear in the literature.Surprisingly, only a few studies have empirically addressed this relationship, and theyprovide quite contradictory findings (Belderbos et al., 2010; Faems et al., 2010; Hung andChou, 2013; Sisodiya et al., 2013). Conflicting results may be because all thesecontributions have considered the degree of openness towards external knowledge as aone-dimensional construct, thus relying on a single variable for capturing it. This procedureentails an important loss of information, as it does not enable taking into account the greatheterogeneity that may exist among distinct types of sources (Cappelli et al., 2014). Asshowed by some recent studies, different kinds of external sources distinctly affect diversefacets of firms’ innovation performance (Cappelli et al., 2014; Chen et al., 2011; Köhleret al., 2012; Mention, 2011; Nieto and Santamaría, 2007; Tödtling et al., 2009; Tsai andHsieh, 2009), thus indicating that different sources involve different benefits in terms of thecomplementary knowledge they can bring to the organization. But different sources alsoentail different costs and risks in terms of search, mutual understanding, partnership,knowledge transformation, potential knowledge leakage and so on. If the disadvantagesassociated to a given partner or external source are equal to or surpass its benefits, thenit will be ineffective or even counterproductive for achieving the ultimate goal of thecompany: improving its final performance. However, to the knowledge of the authors, noprior empirical research has investigated how different types of external sources relate tooverall firm performance.

Bearing in mind that the organizational resources that a firm can devote to learn from itsenvironment are scarce and that managers must make decisions about their allocation(Mina et al., 2014), insights about where to look for external knowledge and how effectiveit can be to improve overall firm performance should constitute a key task for themanagement of knowledge. The present paper tries to shed some light on this issue byaddressing the following question: what are the different directions of external knowledgesearch and how they impact on firm performance? Quantitative analyses based on surveydata of 248 high-technology manufacturing Spanish firms reveal six different directions ofsearch according to the types of sources they include. In a second step, importantdifferences were found among the six directions regarding their individual contribution tooverall firm performance. These findings contribute at providing an explanation to themixed results reported in prior literature regarding the effect of openness towards externalknowledge on overall firm performance. In addition, they derive important implications thatshould be taken into account by managers when designing their firms’ strategies forinter-organizational learning.

The paper is divided into four additional sections. The next section summarizes priorresearch about the influence of external knowledge acquisition on performance andexplores the literature on directions of search. Then, sampling frame, data collectionprocedures and measures of variables are explained. The fourth section reports theempirical findings. After discussing them, the conclusion section highlights the implicationsfor research and practice, as well as limitations and future research directions.

2. Background

2.1 External knowledge acquisition and firm performance

Based on the assumption that no single organization, not even the largest, nor the mostinnovative, possesses all relevant knowledge to keep abreast of rapid advances oftechnologies and market demands, recent research has pointed towards external learningas a key mechanism for refreshing and complementing firms’ knowledge endowments(Leiponen and Helfat, 2010; Voudouris et al., 2012). It has been argued that acquiringexternal knowledge is crucial for capability reconfiguration and firm’s strategic renewal(Lavie, 2006), provides the means for conducting exploratory activities and expanding

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current knowledge domains (Bierly and Chakrabarti, 1996) and also enables therecognition of opportunities and threats, serving as a basis for developing new market andtechnological capabilities (Danneels, 2008; Narteh, 2008).

By focussing on internal learning, firms will develop their own core competences and willbe in a better position for appropriating the resulting rents (Bierly and Chakrabarti, 1996).However, an excessive focus on internal learning will narrow the firm’s knowledge base, soincreasing the risk that its core competencies evolve into core rigidities (Leonard-Barton,1992). On the contrary, external learning improves organizational knowledge and keeps thefirm abreast of new technologies and/or emerging demands, thus boosting its flexibility andadaptability to environmental changes. In other words, by spanning organizationalboundaries, firms can go beyond local search and avoid competence traps (Rosenkopfand Nerkar, 2001). Thus, the gains from external knowledge acquisition are especiallyworthwhile in high-technology industries where environmental dynamism can rapidly erodethe value of firms’ current knowledge and capabilities (López-Sáez et al., 2010; Uotila et al.,2009; Wang and Hsu, 2014).

External learning is also recognized as a useful way for leveraging firms’ internal knowledge(Grimpe and Sofka, 2009; Voudouris et al., 2012). However, for this leverage to take place,firms need a certain absorptive capacity (Cohen and Levinthal, 1990) to combine (Kogutand Zander, 1992) and integrate (Grant, 1996) internal and external knowledge. This“transformation” (Todorova and Durisin, 2007) of externally acquired knowledge intointernalized knowledge (Nonaka, 1994; López-Sáez et al., 2010) may take important costsfor the firm.

Prior knowledge in related fields is needed to identify, assimilate and apply new externalinformation properly (Cohen and Levinthal, 1990; Chen et al., 2011). Furthermore,integration mechanisms and a certain level of shared communication, that can be costly todevelop, are also required (West and Bogers, 2014; Zahra and George, 2002). Some of theexternal knowledge sources may be hard to interpret if they are quite distant from the firmcurrent knowledge base (Köhler et al., 2012). This requires additional investments in mutualunderstanding. Moreover, employees can show a “not invented here” syndrome (Katz andAllen, 1982) towards them. A smooth integration may need suitable incentives,organizational structures, managerial and cultural support (Lichtenthaler et al., 2011),which are costly and time-consuming.

As a firm increases its degree of openness, environmental scanning takes more time andresources (Oerlemans et al., 2013), and the costs tied to information search and partneringalso rise (Sisodiya et al., 2013). With growing openness, managers face more complexdecisions about allocating financial and cognitive resources for inter-organizationallearning. Each kind of external relationship may require different management approachesand organizational practices (Laursen and Salter, 2006), and increasing the variety ofexternal sources to which the firm is connected will need a wider set of management skills(Faems et al., 2010), especially if intellectual property problems (Grimpe and Kaiser, 2010)or preventing undesired knowledge spillovers are involved (Chen et al., 2011; Grimpe andSofka, 2009).

‘‘Openness towards external knowledge may not only provideimportant benefits for firms but also that external learning iscostly and that there are cognitive limits to acquisition andassimilation of external knowledge.’’

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Previous discussion leads us to conclude that openness towards external knowledge maynot only provide important benefits for firms but also that external learning is costly and thatthere are cognitive limits to acquisition and assimilation of external knowledge. Yet, priorempirical research has devoted much more effort to the first part of the story and has beenmainly focussed on how a higher variety of partners and connections to external knowledgesources influences different facets of innovation performance (Delgado-Verde et al., 2011;Ebersberger and Herstad, 2011; Escribano et al., 2009; Martínez-Cañas et al., 2012;Oerlemans et al., 2013; Wang and Hsu, 2014). Empirical papers addressing the impact ofopenness on firm performance are very scarce. This kind of evidence assessing the finalimpact of openness (i.e. confronting its benefits and costs) is required to provide a usefulguidance for managers who try to configure learning networks beyond their organizationalboundaries.

From the scarce evidence on this topic, it cannot be concluded which level of openness ismore advisable due to contradictory results. On one hand, Hung and Chiang’s (2010)findings suggest that firm’s proclivity to open innovation increases perceived firmperformance, and Lichtenthaler et al. (2011) report that a higher absorption of externaltechnology improves return on sales. On the other hand, Goerzen and Beamish (2005) findthat the diversity of a firm’s alliance network hurts firm performance, and Faems et al. (2010)show that the diversity of the technology alliances of the firm negatively affects profitmargins. Recent evidence keeps on this contradictory track. Results reported by Hung andChou (2013) and Sisodiya et al. (2013) show that a higher degree of inbound openinnovation improves long-term firm performance, measured through Tobin’s q, whereasBelderbos et al. (2010) found that this performance measure is negatively related to theratio of collaborative technological activities over a firm’s technology portfolio.

A common issue in all these works is that they conceptualize and measure the degree ofopenness as a one-dimensional construct. Nonetheless, it should be taken into accountthat firms may rely on quite different external sources involving different benefits anddifferent costs. This could explain the lack of consensus in prior empirical researchregarding the effect of external knowledge acquisition on firm performance. Accordingly,the aim of this paper is to go beyond the aggregation restriction imposed in prior studies(i.e. capturing quite heterogeneous sources with a single variable) by considering thedistinct search directions that organizations can follow. Thus, the study focusses onexternal knowledge search by companies, which constitutes an initial phase of the complexand broader process of inter-organizational learning. Specifically, it addresses the questionof where to search.

2.2 Different directions of external knowledge search

There exist several possible sources and partners from whom companies may obtain newinformation and knowledge. Von Hippel (1988a) pioneered the study of customers,suppliers, competitors and research organizations as external sources forinter-organizational learning. Since then, these four sources have been the mostextensively studied in prior empirical research (Cappelli et al., 2014; Grimpe and Sofka,2009; Nieto and Santamaría, 2007; Tsai and Wang, 2009; Un et al., 2010). Literature hasalso pointed towards other sources as providers of valuable external knowledge (Cassimanand Veugelers, 2006; Chen et al., 2011; Laursen and Salter, 2006; Leiponen and Helfat,2011), such as other companies (within the same industry or not), research organizationsdistinct from universities (e.g. public and private research organizations, commercial

‘‘Firms may rely on quite different external sources involvingdifferent benefits and different costs.’’

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laboratories and R&D enterprises), experts and consultants, professional associations,fairs and exhibitions, conferences and meetings, scientific and trade/technicalpublications, patents or public databases alien to the firm[1].

Each external source will be more suitable for obtaining certain benefits, i.e. “differentsources can be used for different purposes” (Cappelli et al., 2014, p. 116). Similarly,different sources will also have their own disadvantages in terms of difficulty of access,integration costs or specific risks. This may significantly reduce their potential contributionto final performance, or even surpass their benefits, so resulting in diminishingperformance. Thus, besides deciding about the degree of openness towards externalknowledge, a key question for the management of inter-organizational learning is where tosearch. Answering this question requires a careful understanding of the specificadvantages and disadvantages associated with the different types of external sources.

Due to their proximity to the firm from a downstream position, customers may constitute anappropriate direction of external knowledge search for seeking feedback and suggestionsthat can be easily incorporated into firm’s products (Grimpe and Sofka, 2009). “Lead users”(von Hippel, 1988b) allow identifying changes in market trends and new market segments.Close contact with innovative users, especially in early stages of product development, canprovide concepts for new product and also a better selection of the most promising ones(Chen et al., 2011; Nieto and Santamaría, 2007). Thus, absorbing knowledge fromcustomers may reduce the risks of new product introduction, especially when complexityand novelty are key issues (Amara and Landry, 2005; Cappelli et al., 2014). The mainproblems when firms try to absorb knowledge from customer are that identifying keycustomers can be difficult, and even when they can contact them, their knowledge cansuffer from tacitness or stickiness (von Hippel, 1988b). Moreover, too much attention tocurrent customers can lead to myopia for detecting future trends or latent market needs(Köhler et al., 2012).

Knowledge of suppliers is usually easier to understand and access (Sofka and Grimpe,2010). It can help in improving and innovating not only internal processes but also productdesign (Cappelli et al., 2014). The benefits of a proper supplier network include costreduction, faster learning, quicker product development and the co-creation of specificresources when evolutionary relationships are maintained (Köhler et al., 2012; Mention,2011; Nieto and Santamaría, 2007). The main problem of suppliers as external knowledgesource it that competitors can also collaborate with them, losing knowledge “uniqueness”(Sofka and Grimpe, 2010). The fact that any knowledge that a firm shares with its supplierscan reach potential competitors is also present in some cases.

Using competitors as a direction for external knowledge search can provide valuableinformation about their current product portfolio. Their knowledge is not difficult to identifyand understand because it is rooted on products, and on a common technological andmarket context (Köhler et al., 2012). This allows implementing a quick and cheap imitationstrategy, but leaves the firm as a follower, always a step behind from technology andmarket leaders, not only in novelty terms but also in economic returns, and differentiationchances (Sofka and Grimpe, 2010). A quite different approach is cooperation with rivals,which is usually performed with the aim to obtain synergies in basic research, as well as toshare risks and costs (Mention, 2011; Zeng et al., 2010). This kind of cooperation is likelyto take place outside the competitor’s area of influence, such as regulatory changes orestablishment of standards (Tether, 2002). In other fields, such as product innovation,collaboration with competitors has a great risk of information leakage (Nieto andSantamaría, 2007).

Research institutions as universities can also be considered as a potential direction forexternal knowledge search. These kinds of agents develop cutting-edge knowledge in theirscientific expertise fields which can be very helpful for developing relevant and radicalinnovations (Chen et al., 2011; Nieto and Santamaría, 2007; Tsai and Wang, 2009).

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However, although academic research may seem a perfect complement for firmknowledge, it can also be considered as distant and harder to understand (Kaufmann andTödtling, 2001). Scientific knowledge is not directly applicable, and its successfulintegration will become more complex and costly (Boehm and Hogan, 2013). Takingadvantage from inter-organizational learning when focussing on scientific institutions willneed important investments to develop a specific absorptive capacity (Cohen andLevinthal, 1990) based on close collaboration with academics that allows a sharedlanguage and interpretation rules.

All the previously mentioned directions for external knowledge search can be managedfrom a network perspective. In addition, firms may also establish collaboration networkswith other companies coming from similar or quite different industries for benefiting from theintegration of knowledge coming from different fields of expertise (Chen et al., 2011; Sidhuet al., 2007). Each direction will require a different kind of network structure, relationshipsand a specific kind of social capital (Alguezaui and Filieri, 2010). Accessing deepknowledge from other agents may need social capital, although there are other kind ofrelationships and knowledge origins that should also be taken into account, even when theycannot be managed in the same way as the previous ones. Other sources of knowledgethat provide general or free access, as conferences, trade fairs, industry associations,publications, etc. should also be considered, as they have demonstrated positive effectson the commercial success of firm innovation (Köhler et al., 2012). However, theseknowledge sources are equally available for competitors, so they can also benefit fromthem (Sofka and Grimpe, 2010), impeding to obtain a sustainable competitive advantagethis way.

All contributions mentioned in this section are focussed on variables related to innovationperformance, so their results only take into account the benefits of collaborating/acquiringinformation from each external knowledge source. However, as highlighted, distinct typesof partners and sources do not only differ significantly with respect to the opportunities theyprovide and their contribution to recipient firm’s organizational learning. Each kind ofagents/sources also implies different challenges, has its own costs, according to theprocesses needed for accessing, interpreting and utilizing that knowledge, and involves itsown risks. Accordingly, in this paper, it is argued that the impact of each type of sourceson overall firm performance, i.e. the difference among its benefits and its costs, may differdramatically.

3. Methods

3.1 Sample and data collection

This study gathered survey and archival data on large- and medium-sized high-technologymanufacturing Spanish firms. SABI database[2] was used to perform the sampling frame.Authors decided to focus on manufacturing firms because these companies acquireknowledge from a wide range of sources (Laursen and Salter, 2006; Lichtenthaler, 2009).However, to reduce intrafirm heterogeneity (Pla-Barber and Alegre, 2007), only high-technology industries were selected. Specifically, the study examined firms operating in theseven most R&D-intensive manufacturing sectors (NACE codes 20 chemical; 21pharmaceutical; 26 computer, electronic and optical products; 27 electrical equipment; 28machinery and equipment n.e.c.; 29 motor vehicles, trailers and semi-trailers; and 30 othertransport equipment).

The empirical analysis was performed on firms with at least 50 full-time employees and 5years old. Neither small companies nor new ventures were studied because it can bedifficult to observe a high degree of external knowledge acquisition by smaller and/oryounger organizations (Foss et al., 2011; Lichtenthaler, 2009), and also becauseantecedents of firms’ success in new ventures may differ from established companies(Zahra and Bogner, 2000). Besides, following recent empirical research (De Clercq et al.,2013; Oerlemans et al., 2013), to reduce concerns on endogeneity and reverse causality,

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it was explicitly indicated in the questionnaire the specific year or time period to which eachblock of questions was referred. In addition, lags between independent and dependentvariables were introduced in the questionnaire (items used for capturing independent andcontrol variables were referred to the past five years, whereas those used for measuringfirm performance were referred to the past year). This procedure also captures the fact thatsome time is required before externally acquired knowledge will affect firm performance.

Finally, to increase comparability among firms in the population, the authors manuallychecked all cases to ensure that non-profit organizations and firms that were mainlydistributors were not included. After this screening, 1,142 firms shaped the population.

Main data were collected through telephone survey performed by a polling company. Thiscompany conducted interviews using CATI, which enables real-time verification ofnumerical responses and rotation of item lists. To avoid common method bias, the studycollected data from two distinct informants per firm. The first informant was the chiefexecutive officer (CEO) or, alternatively, the head of corporate marketing or sales director,who was asked about firm performance. The head of R&D was identified as the secondinformant and was asked about directions of search as well as some control variables.

The survey was conducted from April to July 2012. After this period, 252 matched pairs withthe data from both respondents were obtained. Four were eliminated as invalid. Thus, thefinal sample comprised 248 firms (21.7 per cent as response rate), and it showed a spreadacross industries quite similar to the observed in the population. In the final sample,average firm size was 158.07 full-time employees (s.d. � 250.62) and average age was30.88 years (s.d. � 17.77). To test for representativeness and non-response bias, potentialdifferences between final sample and total population, as well as between respondents andnon-respondents, were examined. t-tests showed no significant differences based on thenumber of full-time employees and firms’ age.

3.2 Measures

3.2.1 Measuring firm performance. Firm performance was measured through a subjectivescale. Objective measures have the advantage of avoiding potential problems ofself-assessment. On the contrary, subjective scales may provide a closer and more substantiveview of how the company performs if they are posed in terms of comparison to its closecompetitors (Andersen, 2004; Lichtenthaler, 2009). In addition, subjective scales maycomprise several items, so different aspects of a firm’s performance may be consideredinstead of a single indicator. These kinds of subjective measures have been extensively usedin the literature (see Newbert, 2008 for an overview). Several authors have shown thatself-assessed measures are highly correlated with objective performance indicators(Andersen, 2004; Dess and Robinson, 1984; Lichtenthaler, 2009; Powell, 1992; Sidhu et al.,2007). Thus, this study captured firm performance via Venkatraman’s (1989) widely usedsubjective scale. Besides developing the scale, this author demonstrated its reliability andvalidity. Later, De Luca and Atuahene-Gima (2007) demonstrated the discriminant validity ofthe scale with respect to the used for measuring innovation performance. Subsequentpublications have used this subjective scale (Chiva and Alegre, 2009; De Clercq et al., 2013).

The scale comprises six items. They were addressed to the first informant (CEO).Specifically, respondents were asked to rate, on a seven-point Likert-type scale, their firm’sperformance in the last tax year (2011) compared to its main competitors in terms of returnof sales, profit growth, return on assets, sales growth, market share growth and cash flow.As expected, exploratory factor analysis grouped the six items in a single factor. All factorloadings were clearly higher than 0.7, and Cronbach’s alpha coefficient indicated a goodinternal consistency of the scale (� � 0.89). The final measure consisted on the average ofthe scores on the six items.

3.2.2 Capturing the directions of external knowledge search. Several prior studies haverelied on patent citations for measuring external knowledge flows and inter-organizational

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learning variables (Ahuja and Katila, 2004; Belderbos et al., 2010; Miller et al., 2007;Phelps, 2010; Rosenkopf and Nerkar, 2001). However, this approach has importantlimitations (Köhler et al., 2012; Laursen and Salter, 2006). Particularly important for thisstudy is that, beyond the scientific character – or not – of a citation, or its distance in termsof patent classes (Ahuja and Katila, 2004; Phelps, 2010), patent citation statistics do notreveal the relationship between two firms (e.g. whether they are suppliers or competitors)and leave out several important external knowledge sources.

Thus, following recent empirical research (Chen et al., 2011; Köhler et al., 2012; Mention,2011; Sofka and Grimpe, 2010; Tsai and Hsieh, 2009; Zeng et al., 2010), this study alsorelied on survey questions for capturing the distinct orientations or directions of externalknowledge search – the different types of external knowledge sources – of firms in thesample. Specifically, respondents were asked to evaluate, on a seven-point Likert-typescale, the importance of 16 external sources as providers of information and knowledge fortheir firm during the past five years (2007-2011). These questions were addressed to thesecond informant (Head of R&D). In the questionnaire, a “1” means that the company didnot use the given source, while a “7” means that it is a key source of information andknowledge for the organization. The 16 sources were identified based on prior publications(Cassiman and Veugelers, 2006; Chen et al., 2011; Laursen and Salter, 2006; Leiponen andHelfat, 2010; OECD, 2005; Sidhu et al., 2007).

Exploratory factor analysis was conducted on these sources to identify the different searchpatterns. The determinant of correlations matrix was quite close to zero (0.002), theKaiser�Meyer–Olkin indicator of sampling adequacy was largely higher than 0.7 (0.857) andthe chi-square for Bartlett’s test of sphericity (1489.14) was highly statistically significant (p �

0.001). In sum, these results indicate that factor analysis is suitable for the data.

As there is no reason to assume orthogonality among different directions, oblique promaxrotation was chosen. An initial factor analysis (available upon request) revealed five factorswith an eigenvalue greater than one explaining 67.3 per cent of total variance. However,due to the low factor loading of knowledge acquisition from suppliers (lower than 0.5) on thefifth factor (along with customers and competitors) and the poor internal consistency of thisfactor (� � 0.534), the analysis was forced to give a six-factor solution. Table I shows themain results of this procedure. It can be seen how the aforementioned problems are notpresent in this second solution. There are no confusing loadings, and the lower Cronbach’salpha is 0.655, which can be considered acceptable.

The identified factors explain 72.5 per cent of total variance. The first one is shaped by science-and technology-driven organizations, such as government research organizations, privateresearch institutes, universities and other higher education institutions and commerciallaboratories and R&D enterprises. Factor analysis also included experts and consultants withinthis set. Both in the initial five-factor solution, as in the final six-factor solution, experts andconsultants unequivocally loaded in this first factor. Furthermore, the internal consistency of thefirst factor diminishes when experts and consultants are not included (� � 0.817). Authorsattribute this result to the high-technology character of the sample. It seems reasonable that theexperts and consultants contacted by this kind of firm tend to be focussed on science andtechnology issues. Accordingly, this factor was labelled as “science and technologyorganizations”. External knowledge sources loading on the second factor are conferences andmeetings, scientific journals and trade/technical publications, fairs and exhibitions andprofessional and industry associations. Authors refer to this factor as “general informationsources”. Factor three includes knowledge obtained from other enterprises within the sameindustry that are not direct competitors, as well as from other enterprises operating in otherindustries. This factor is names “other firms beyond the core business”. Patents and publicdatabases not belonging to the firm load highly on the fourth factor, so authors interpret thisfactor as “patents and databases”. The fifth factor is labelled as “market sources”, as it isshaped by knowledge acquired from competitors and customers. Finally, “suppliers” areconsidered the sixth external knowledge search direction.

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3.2.3 Control variables. Several variables were included into the analysis to control for otherfactors that may influence the estimation results. Specifically, the natural logarithm of thenumber of full-time employees was included to control for firm’s size. The age of firms,measured as the natural logarithm of years since establishment, was also included as thecontrol variable. As firm performance may be affected by its past performance, it wasincluded as the average return on assets during the previous four years (2007-2010). Adummy variable capturing whether a firm is part of a group was also considered as controlvariable. The firm’s average exports intensity during the past five years (2007-2011) wascaptured as the natural logarithm of (1 � total exports to total sales). Similarly, average firmR&D intensity in the past five years (2007-2011) was measured as the natural logarithm of(1 � R&D intensity). Finally, the study controlled for two relevant industry effects. First, thelevel of industry R&D by including the average industry total R&D expenses divided by totalindustry sales during the past four years (2008-2011). Second, the standard deviation ofthe sales growth of firms in the same industry during the previous four years (2007-2010)to control for environmental instability.

4. Results

Authors relied on ordinary least squares regression models to estimate the determinants ofperformance at the firm level. Table II reports descriptive statistics, Pearson’s correlationsand variance inflation factors (VIFs) for all variables. All pair-wise correlations are lower than0.5 and the maximum VIF is 1.71, which is well below the recommended ceiling of 10(Kutner et al., 2004). These results lead to conclude that multicollinearity is not a seriousconcern in this study. Before performing the estimations, all independent variables werestandardized. This procedure allows for a meaningful comparison among variablesmeasured with different scales (Cohen et al., 2003).

Table III shows the results of the regression models estimations. Model 1 only includescontrol variables. Model 2 adds the direct effect of the six identified directions of externalknowledge search. Starting with the effect of controls, firm’s age seems to harm its

Table I Structure matrix and Cronbach’s alphas

External knowledge source

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6Science andtechnology

organizations

Generalinformation

sources

Other firmsbeyond the

core businessPatents anddatabases

Marketsources Suppliers

Government research organizations 0.862Private research institutes 0.826Universities or other higher educationinstitutions 0.783Commercial laboratories/R&D enterprises 0.669Experts/consultants 0.605Conferences, meetings 0.826Scientific journals and trade/technicalpublications 0.813Fairs, exhibitions 0.780Professional and industry/trade associations 0.706Other enterprises in other industries 0.884Other enterprises in the industry not beingdirect competitors 0.835Patents 0.885Public databases alien to the firm 0.775Competitors 0.848Clients or customers 0.778Suppliers 0.836Cronbach’s alpha 0.820 0.792 0.765 0.717 0.655 n/a

Notes: n � 248; explained variance: 72.5 per cent; extraction method: principal-component analysis; rotation method: promax withKaiser normalization; factor loadings smaller than 0.6 are not displayed; items appear ordered by their loadings

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performance (� � �0.157; p � 0.05), whereas the fact of being part of a group is found tohave a positive effect (� � 0.138; p � 0.05). Environmental turbulence also seems topositively affect firm performance (� � 0.182; p � 0.05). The effects of other controlvariables appear to be less robust. In this sense, the coefficient of the firm’s exportsintensity is positive, but only marginally significant (� � 0.111; p � 0.15). Moreover, thepositive effect of firm’s size only becomes statistically significant in Model 2 (� � 0.140;p � 0.1).

Regarding the effect of the six observed search directions, inclusion of the six types ofsources in Model 2 results in a statistically significant increase in explanatory power (�R2 �

Table II Descriptive statistics, Pearson’s correlations, and variance inflation factors

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Firm performance 4.23 1.10 1Size 4.71 0.70 0.08 1Age 3.31 0.58 �0.08 0.21 1Past performance 3.48 10.43 0.02 �0.12 �0.02 1Part of a group 0.60 0.49 0.14 0.26 �0.01 �0.06 1Exports 3.29 1.39 0.11 0.06 0.09 �0.07 0.05 1Firm R&D 1.55 1.08 0.06 0.01 0.10 �0.03 �0.06 0.24 1Industry R&D �4.45 0.59 0.03 �0.01 �0.00 0.06 �0.01 �0.04 0.16 1Environmental instability 1.73 1.18 0.14 �0.10 0.04 0.04 �0.02 0.01 0.03 �0.34 1Science and technologyorganizations 3.57 1.38 �0.09 0.07 0.03 0.11 0.00 �0.02 0.10 0.07 0.07 1General informationsources 3.97 1.36 0.01 �0.00 0.17 0.04 �0.06 �0.10 0.16 0.03 0.11 0.47 1Other firms beyond thecore business 3.23 1.46 0.01 �0.05 0.01 0.10 0.01 �0.04 0.03 �0.04 0.02 0.45 0.43 1Patents and databases 2.76 1.54 0.05 �0.02 0.09 0.04 0.00 0.00 0.14 0.15 0.13 0.46 0.40 0.38 1Market sources 4.54 1.37 0.11 �0.09 0.08 0.04 �0.06 �0.02 0.04 0.03 0.17 0.32 0.38 0.31 0.30 1Suppliers 4.58 1.59 0.03 0.03 0.10 0.04 �0.04 0.03 0.05 �0.08 0.01 0.33 0.35 0.14 0.24 0.27 1VIF 1.21 1.12 1.05 1.11 1.11 1.17 1.28 1.25 1.69 1.71 1.51 1.49 1.39 1.50

Notes: n � 248; all correlations above 0.13 are significant at p � 0.05

Table III Results of OLS analyses

Dependent variable: firm performance M1 M2 M3

Constant 4.231**** 4.231*** 4.509***Size 0.100 0.140* 0.101Age �0.127* �0.157** �0.177***Past performance 0.035 0.055 0.055Part of a group 0.135* 0.138** 0.169**Exports 0.110**** 0.111**** 0.109****Firm R&D 0.046 0.050 0.036Industry R&D 0.093 0.100 0.106****Environmental instability 0.194*** 0.182** 0.173**Science and technology organizations �0.268*** �0.269***General information sources 0.030 0.009Other firms beyond the core business 0.036 0.001Patents and databases 0.069 0.090Market sources 0.136* 0.174**Suppliers 0.078 0.004Science and technology organizations2 �0.158**Suppliers2 �0.121**R2 0.074 0.121 0.169�R2 0.047** 0.048***F-Statistic 2.392** 2.300*** 2.935***Durbin–Watson Statistic 1.910

Notes: n � 248; ****p � 0.15; ***p � 0.01; **p � 0.05; *p � 0.1; unstandardized regressioncoefficients are reported

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0.047; p � 0.05). Findings indicate that only two directions have a significant effect on firmperformance. On the one hand, the coefficient of market sources is positive and statisticallysignificant (� � 0.136; p � 0.1). On the other hand, the coefficient of science andtechnology organizations is also significant, but it is negative (� � �0.268; p � 0.01).External knowledge obtained from general information sources, patents and databases,suppliers, as well as other firms beyond the core business is found to have no significanteffect on firm performance (p � 0.1 in all cases).

To some extent, authors were surprised by some of the above results. Specifically, thestrong negative effect of science and technology organizations and the non-significanteffect of knowledge acquired from suppliers on firm performance were unexpected. Theseresults are not consistent with the large amount of evidence showing a positive influence ofboth types of sources on innovation performance (Grimpe and Sofka, 2009; Leiponen andHelfat, 2011; Nieto and Santamaría, 2007; Sofka and Grimpe, 2010; Köhler et al., 2012).Consequently, authors decided to inquire whether the nature of the effect of certaindirections of search on firm performance could be more complex that a mere linearrelationship and investigated the existence of potential curvilinear effects. Hence, in a newestimation model, the squared terms of the six variables measuring search directions wereintroduced into the equation. In all cases, variables were standardized prior to creating therespective squared terms (Cohen et al., 2003).

Model 3 in Table III summarizes this new estimation by only including those squared termsthat resulted statistically significant values. The coefficient of market sources remainspositive and its significance level slightly increases (� � 0.174; p � 0.05). The main effectof knowledge obtained from science and technology organizations continues to benegative and statistically significant (� � �0.269; p � 0.01). However, the new estimationreveals a significant negative coefficient of the squared term of this search pattern (� �

�0.158; p � 0.01). This negative coefficient of the squared term indicates that the effect ofknowledge acquired from science and technology organizations on firm performance isinverse U shaped. Moreover, partial differentiation indicates that � performance/� scienceand technology organizations � �0.269 �0.158* (2* science and technologyorganizations), which is zero when science and technology organizations is �0.851. Thus,only when knowledge acquired from science and technology organizations is lower than�0.851 standard deviations below its mean-standardized value of zero, additionalinvestments in acquiring knowledge from this kind of organization have a positive effect onoverall firm performance. Beyond that level, additional efforts in learning from science andtechnology organizations will harm firm performance. On the other hand, the coefficient ofthe main effect of suppliers remains non-significant in Model 3 (� � �0.004; p � 0.1).Furthermore, the coefficient of the squared term of suppliers is negative and statisticallysignificant (� � �0.121; p � 0.01), which, as in the case of science and technologyorganizations, indicates that the relationship between knowledge obtained from suppliersand firm-level performance describes an inverse U shaped. Partial differentiation showsthat � performance/� suppliers � �0.121* (2* suppliers), which is zero when knowledgeacquired from suppliers, at its mean-standardized value of zero, is positive for the lowerrange of supplier values, and is negative for the higher range. Importantly, inclusion of thetwo squared terms in Model 3 leads to increase explanatory power of the estimation (�R2 �

0.048; p � 0.01).

To ease visual interpretation of the two curvilinear effects, Figures 1 and 2 show the plotsof the impact of knowledge acquired from science and technology organizations andsuppliers, respectively, on perceived firm performance.

5. Discussion of findings

From a sample of 248 high-technology manufacturing Spanish firms, quantitative analysesbased on survey data have shown that companies may follow six distinct patterns ordirections when they span their external boundaries in search for new knowledge:

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1. “science and technology organizations” as government or public research centres,universities, laboratories or specialized consultants;

2. “general information sources” as conferences, meetings, specialized journals andpublications, fairs and exhibitions or industry associations;

3. “other firms beyond the core business”, either within the same industry or not;

4. “patents and databases”;

5. “market sources” as customers and competitors; and

6. “suppliers”.

Analysis of the effects of each direction on perceived firm performance shows that three ofthem (general information sources, firms beyond the core business, and patents anddatabases) do not have any impact on firms’ final performance. Explanations for this cancome from the fact that open access sources of information, as general information sourcesor patents and databases, may become a standard in the industry, thus providing a basis

Figure 1 The effect of knowledge acquisition from science and technologyorganizations on firm performance

3

3.5

4

4.5

Firm

per

form

ance

−2σ −1σ 0 1σ 2σ

Knowledge acquisition from science and technology organizations

Figure 2 The effect of external knowledge acquisition from suppliers on firmperformance

3.8

4

4.2

4.4

4.6

Firm

per

form

ance

−2σ −1σ 0 1σ 2σ

Knowledge acquisition from suppliers

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for competitive parity, but not for competitive advantage. In support of this reasoning,Amara and Landry (2005) found in a similar context that a higher acquisition of informationfrom generally available sources did not increase the chance of developing product orprocess innovations that significantly differ from those previously developed by other firms.In the case of collaborative networks with other firms beyond the core business, althoughthe benefits may be important in terms of complementary knowledge resources (Sidhuet al., 2007), the costs tied to searching the adequate partner and interpreting andtransforming distant knowledge may offset them.

For the pattern related to market sources, which includes learning form customers andcompetitors, it was found to have a positive influence on firm performance. This means that,when considering connections with providers of market knowledge, the benefits surpasstheir costs. Thus, increasing information channels with these kinds of agents is alwaysadvisable for high-technology firms, as it is the case of this study because they allowmonitoring industrial trends, not only from the supply side but also from the demand one.The issue of myopia seems not to be a problem in the case of customers of high-techindustries. Inter-organizational learning from customers and competitors is the key for firmadaptation to the environment, and it shows a clear effect on firm performance accordingto the evolutionary arguments (Laursen and Salter, 2006).

When assessing the impact of suppliers and science and technology organizations on firmperformance, an inversed U-shaped relationship was found. This means that networkingwith suppliers enhances final performance, but there is a point beyond which closerrelationships harm it. There exist different explanations for this. One comes from the factthat different competitors may work with the same suppliers, so a closer relation mayincrease the risk of undesired knowledge spill-over, and also the costs to prevent this tohappen. Another is that a too strong collaboration can lead to a “pseudo-verticalintegration” and its cons, especially the lack of flexibility due to costly investments inspecialized assets for maintaining the relationship.

Connections with science and technology organizations can also easily become harmful forfirm performance beyond certain depth of relationship. Although scientific knowledgepossessed by these organizations can hold a great potential for innovation (Cappelli et al.,2014; Sofka and Grimpe, 2010), it appears to be very distant to business knowledge.Consequently, its interpretation and absorption becomes extremely costly and its potentialbenefits are surpassed very soon. Thus, some degree of interaction is advisable for beingaware of new advances in the frontiers of knowledge that could constitute the seed of futuredevelopments in the industry. However, too much attention to science and technologyorganizations detracts considerable resources and may distract the company from currentcompetition. In fact, most of the firms in the sample are over-engaged with science andtechnology organizations, that is, they would increase their performance if they were lessengaged with this kind of partners.

6. Conclusions

This paper has focussed on the process of external knowledge search by companies, oneof the key phases shaping the broader phenomenon of inter-organizational learning.

‘‘Contrary to the growing trend of increasing the degree ofopenness towards external knowledge in companies, thisstudy shows that being more open does not always meanbetter for firms.’’

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Specifically, it addresses the question of where to search for new external information. Indoing so, the study decomposed the search spectrum on different directions or patternsaccording to the kind of sources involved and investigated how each search directiondistinctly impacts on overall firm performance. From the reported findings, importantimplications for theory and practice are derived.

6.1 Theoretical implications

This study contributes to current literature in two main ways. First, it contributes to literatureon external knowledge search by identifying the different search directions followed byfirms in high-tech industries. Firms do not search in a vacuum (Sofka and Grimpe, 2010).Previous literature has mainly distinguished between market knowledge, coming fromcustomers and competitors, and scientific knowledge, tied to universities and researchcentres. Suppliers have not been clearly classified according to that distinction (Chen et al.,2011; Köhler et al., 2012). Moreover, other kinds of organizations, to which the firm canconnect through networks, as well as other information sources, have not beensystematically taken into account. Extending prior research, the present study provides acomprehensive view of the potential search directions under managerial choice.

Second, the study contributes to literature on effects of external knowledge search in twoways. On the one hand, it focusses on overall firm performance instead of on innovationperformance indicators. This way, it contributes to offset the excessive tendency of priorstudies to only focus on the benefits of external search (Huizingh, 2011; West and Bogers,2014). Some scholars have highlighted that learning from external sources may entailimportant costs and risks. In fact, the scarce empirical research that has investigated theeffect of openness towards external knowledge on firm final performance – the only validapproach for ascertaining whether the gains of external knowledge search overcome itscost – is inconclusive. It is at this point where this study makes its most relevantcontribution. The handful of empirical works addressing the openness-firm performancerelationship has relied on a single variable that captures the degree of openness towardsexternal knowledge. Based on the argument that different sources may entail quite differentbenefits and also quite different costs and risks, this study extends this literature byshowing that different search directions differ drastically in terms of their contribution to finalperformance. Thus, its findings point to the conclusion that answering the question of “howmuch” external knowledge has to be searched heavily depends on “where” it is searched.Some directions of inter-organizational learning may pose (and the findings presentedabove show it) a cognitive limit for knowledge internalization that makes it more desirable,in terms of firm performance, to take intermediate or even low levels in the opennesscontinuum.

6.2 Managerial implications

The purpose of the study has been to get a better understanding of the benefits and problemsthat entail the different directions of external knowledge search, so trying to provide a usefulguidance for managers in high-technology industries regarding the search patterns thatdeserve more attention and also to which extent. Thus, two main implications for managers ofhigh-technology firms can be derived from this empirical research. First, contrary to thegrowing trend of increasing the degree of openness towards external knowledge in

‘‘Additional investments for acquiring market knowledge fromcustomers and competitors are highly recommended, as theywill directly increase firm performance.’’

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companies, this study shows that being more open does not always mean better for firms.Searching for external knowledge may be beneficial for improving firm’s knowledge andinternal processes, but it also entails important costs and risks that are usually neglected. Bothsides of the coin – gains and pains – vary substantially among different kinds of sources. Thus,managers should thoroughly analyze the potential benefits but, especially the disadvantagesof each external source before making additional investments devoted to learn from it.

Second, the findings inform managers about the search directions their organizationsshould follow, as well as the efforts they should devote in each search pattern. On a generalbasis, companies should devote more attention to inter-organizational learning throughnetwork building and social capital (Alguezaui and Filieri, 2010), rather than other kinds ofsources that cannot be managed from an inter-organizational learning perspective such asgeneral information sources or patents and databases. More precisely, additionalinvestments for acquiring market knowledge from customers and competitors are highlyrecommended, as they will directly increase firm performance. Intensive search over thiskind of sources contributes to keep the firm up-to-date about technology advances andemerging market trends. This seems especially true in high-tech industries in whichcompetitors continually develop new products and customers pull companies with newrequirements. Furthermore, due to their proximity to firm’s activities, deepening into thesesources is not so costly. In turn, inter-organizational learning from suppliers and scienceand technology organizations is advisable, but only at a certain level. Over-engagementwith these external knowledge sources increases the costs and risks beyond its potentialbenefits. Thus, managers should put an eye on these sources when designing their firm’sinter-organizational networks, but not to invest heavily in fostering close relationships withthem, especially in the case of science and technology organizations.

6.3 Limitations and future research

This paper has some limitations that constitute avenues for future research. First,conclusions can only be generalized to large- and medium-sized high-tech manufacturingfirms from developed countries. This calls for additional empirical research in differentcontexts. It may occur that the configuration of search patterns vary among sectors (Chenet al., 2011) and, more important, that the performance implications of the patterns couldbe context dependent (Sofka and Grimpe, 2009).

Second, the study followed well-established methodological procedures (CIS, 2010; De Clercqet al., 2013; OECD, 2005; Oerlemans et al., 2013) by relying on two different informants,indicating the time span to which each block of questions was referred, and introducingperceived lags among dependent and independent variables. These cautions largely alleviateconcerns on simultaneity and reverse causality, so redounding in a high confidence of thereported findings. Despite this, the nature of the study remains cross-sectional. The only wayto completely avoid these concerns is by introducing real lags among variables, which makesit advisable that future studies rely on panel or lagged data.

This study constitutes a first step to understand how different directions of externalknowledge search may affect performance at the firm level. The effect of the different typesof external sources on firm performance may be contingent to internal and externalvariables. Consequently, the identification of potential moderators constitutes a veryinteresting window for future research.

Finally, this empirical piece has considered the intensity in which firms acquire knowledgefrom different partners and external sources. However, the specific organisational modesthey use to enter into relationship with them remained beyond the scope of the study.Accordingly, an interesting avenue for future research would be to investigate how distinctorganizational modes (i.e. occasional collaborations, licensing agreements, or non-equityalliances) contribute to acquire knowledge from concrete partners more efficiently.

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Acknowledgement

The authors acknowledge the financial support from Spanish Ministries of Science andInnovation and Economy and Competitiveness (Grants #ECO2009-12405 and#ECO2012-38190), as well as from “Programa Nacional para la Formación del ProfesoradoUniversitario (FPU)” of the Spanish Ministry of Education (Grant #AP2008-00420). JorgeCruz-González gratefully acknowledges financial aid from “Programa de Ayudas paraEstancias Breves del Programa de Formación de Profesorado Universitario 2011” and thehospitality of the Department of Innovation and Organizational Economics (CopenhagenBusiness School). Finally, the authors wish to thank Joaquín Alegre, Christoph Grimpe, KeldLaursen, Toke Reichstein and two anonymous reviewers for their helpful comments andsuggestions made on earlier versions of this manuscript. The usual disclaimers apply.

Notes

1. These sources are listed in the Oslo Manual (OECD, 2005) as important sources for the transfer ofexternal knowledge. Accordingly, they shaped the list of 16 knowledge sources considered in thisstudy (see subsection 3.2.2. Capturing the directions of external knowledge search).

2. “Sistema de Análisis de Balances Ibéricos” (Bureau van Dijk) is a database which includes financialinformation of 1,250,000 Spanish and 400,000 Portuguese firms.

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About the authors

Jorge Cruz-González is an Assistant Professor in the Department of Organization andManagement at CUNEF Business School (Spain). He is researcher at the Ikujiro NonakaResearch Centre on Knowledge and Innovation (CUNEF Business School), and a memberof the Strategy, Knowledge and Innovation Research Group (ECI) at Complutense deMadrid University. He has been a visiting scholar at Copenhagen Business School

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(Denmark) during 2011 and Jinan University (China) in 2014. His research interests focuson knowledge management, organizational learning and dynamic capabilities, with aspecial emphasis on exploratory vs exploitative learning, management of externalknowledge flows and organizational practices associated to absorptive capacity. His workhas been published in journals such as Technological Forecasting and Social Change,Journal of Knowledge Management or Cuadernos de Economía y Dirección de la Empresa.In 2011, he was recognized with the Highly Commended Paper Award for the article“External knowledge acquisition processes in knowledge-intensive clusters”, published inJournal of Knowledge Management. Jorge Cruz-González is the corresponding author andcan be contacted at: jorge.cruz@cunef.edu

Pedro López-Sáez is an Associate Professor in the Department of Business Administrationat Complutense de Madrid University (Spain). He is the principal researcher at the IkujiroNonaka Reseach Centre on Knowledge and Innovation (CUNEF Business School), and themember of the Strategy, Knowledge and Innovation Research Group (ECI) at Complutensede Madrid University. He has been a visiting scholar at Harvard University (2004-2005). Hisresearch interests focus on organizational learning, dynamic capabilities, strategy andinnovation, with a special emphasis on the identification of intra- and inter-organizationallearning processes and their role on firms’ technological innovation. His work has beenpublished in journals such as Technovation, Journal of Knowledge Management, Journal ofBusiness Ethics, International Journal of Technology Management or Journal of IntellectualCapital. In 2011, he was recognized with the Highly Commended Paper Award for thearticle “External knowledge acquisition processes in knowledge-intensive clusters”,published in Journal of Knowledge Management.

José E. Navas-López is a Professor in the Department of Business Administration atComplutense de Madrid University (Spain), where he is the Head of the Strategy,Knowledge and Innovation Research Group (ECI). He also held the first KnowledgeManagement Chair in Spain at I.U. Euroforum Escorial. His research interests focus onintangible assets, knowledge management, strategy and innovation, with special emphasison intellectual capital dimensions and complexity of capabilities as antecedents of firms’technological innovation. He is the author and co-author of several books and paperspublished in journals such as Technovation, Technological Forecasting and Social Change,Journal of Knowledge Management, Journal of Business Ethics, Knowledge ManagementResearch & Practice, International Journal of Technology Management or Journal ofIntellectual Capital. In 2011, he was recognized with the Highly Commended Paper Awardfor the article “External knowledge acquisition processes in knowledge-intensive clusters”,published in Journal of Knowledge Management.

Miriam Delgado-Verde is an Assistant Professor in the Department of BusinessAdministration at Complutense de Madrid University (Spain). She is researcher at the IkujiroNonaka Reseach Centre on Knowledge and Innovation (CUNEF Business School) and amember of the Strategy, Knowledge and Innovation Research Group (ECI) at Complutensede Madrid University. She has been a visiting scholar at Manchester Institute of InnovationResearch (MIOIR) – The University of Manchester during 2009, and the Centre forEntrepreneurship – University of Edinburgh Business School during 2010. Her researchinterests focus on intangible assets, knowledge management and innovation, with a specialemphasis on the configuration of firms’ intellectual capital, interactions among itsdimensions, and their effects on technological innovation. Her work has been published injournals such as Technological Forecasting and Social Change, Journal of KnowledgeManagement, Journal of Business Ethics, Knowledge Management Research & Practice orJournal of Intellectual Capital.

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