Critical factors for participation in global innovation networks. Empirical evidence from the Mexican nanotechnology sector

Technological Forecasting & Social Change 114 (2017) 293–312

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Technological Forecasting & Social Change

Critical factors for participation in global innovation networks. Empirical evidence from the Mexican nanotechnology sector Hugo Necoechea-Mondragón a,⁎, Daniel Pineda-Domínguez b, Luz Pérez-Reveles c, Rocío Soto-Flores b a

Instituto Politécnico Nacional, edificio de la Secretaría Académica, Av. Juan de Dios Bátiz, esq. Luis Enrique Erro Col. Residencial Escalera, C.P. 07738 Ciudad de México, Mexico Departamento de Administración, Escuela Superior de Comercio y Administración, Santo Tomás, Instituto Politécnico Nacional, Carpio 471, Col. Plutarco Elías Calles, CP 11340 Ciudad de México, Mexico c Subdirección de Transferencia Tecnológica, Centro de Nanociencias y Micronanotecnologías, Instituto Politécnico Nacional, Luis Enrique Erro s/n, Nueva Industrial Vallejo, 07738 Ciudad de México. Mexico b

a r t i c l e

i n f o

Article history: Received 9 October 2014 Received in revised form 12 July 2016 Accepted 25 August 2016 Available online 7 September 2016 Keywords: Global Innovation Networks Factors Participation

a b s t r a c t To face the new challenges of globalization, research institutions and companies are adopting new approaches in innovation processes. Corporations no longer rely on a single, linear innovation structure. In recent years, in several countries, institutions and companies have implemented innovation through global innovation networks (GINs) to launch new products in the market ahead of competitors. Nevertheless, there is scant literature examining the main factors involved in GIN participation practices. To address this gap, this paper used the case of nanoscience research centers in Mexico. The aim of this research was to assess the degree to which critical innovation factors enable Mexican research centers and institutes of nanotechnology to participate in GINs. First, data were gathered from questionnaires sent by e-mail to researchers in research institutions; then, correlation and regression analysis were used to find the relations among variables. The results showed that critical factors such as human resource competencies in innovation, open innovation and technology transfer skills have a direct influence on the participation of Mexican research centers and institutions in GINs. © 2016 Elsevier Inc. All rights reserved.

1. Introduction Changes in international markets have created many challenges and substantial uncertainty around companies' globalization processes (Fang and Zigang, 2004; See et al., 2015; Walsh et al., 1999). International activities are increasing and the modes for conducting globalized activities are becoming progressively more diverse (Audretsch et al., 2014; McCarthy et al., 2012). The global restructuring process is accompanied by a scarcity of resources and knowledge, which forces firms and governments to consider opportunities for sustainable growth (Anson et al., 2008; Bleischwitz, 2010; Falize and Coeurderoy, 2012; Fink et al., 2013; Kautt et al., 2007; Smitha et al., 2010). Globalization increasingly affects how companies in OECD countries operate, compete and innovate, both in their home country and worldwide (Dieter. Ernst, 2002; OECD, 2008b). Companies now face an environment in which competition is global, knowledge is widely disseminated, investments in research and development (R&D) are increasing, and product life cycles are shrinking (Allarakhia and Walsh, 2011; Allarakhia and Walsh, 2012; Kuznetsov and Dahlman, ⁎ Corresponding author. E-mail addresses: [email protected] (H. Necoechea-Mondragón), [email protected] (D. Pineda-Domínguez), [email protected] (L. Pérez-Reveles), [email protected] (R. Soto-Flores).

http://dx.doi.org/10.1016/j.techfore.2016.08.027 0040-1625/© 2016 Elsevier Inc. All rights reserved.

2008; Tierney et al., 2013). To cope with these new challenges, companies must adopt new approaches in a number of areas, including innovation processes, organizational models, financial models and decision making (Groen et al., 2002; OECD, 2008a; Prahalad and Ramaswamy, 2004; Rhéaume and Gardoni, 2015; Walsh and Linton, 2001). In the globalization context, it is clear that the traditional process of innovation, in which a company maintains and funds a centralized system of R&D, is being gradually transformed (Allarakhia and Walsh, 2012; Carrillo and Lara, 2005; Jenn-Hwan, 2007; Walsh and Linton, 2002). Specifically, companies in a variety of industries are looking for ways to disaggregate their R&D and distribute their innovation processes through an external network of partners and sites across the world (Tidd and Bessant, 2013). This system allows multinational enterprises (MNEs) to allocate activities according to the strengths of certain countries and external research centers and thereby make their R&D processes more efficient, keeping these MNEs at the forefront and enabling them to launch new products or services in markets ahead of competitors (Buckley, 2014; Tierney et al., 2013). One form of organization that companies have adopted to implement open innovation is the creation of GINs (Chaminade and Barnard, 2009; OECD, 2008b; Papadopoulos et al., 2013). Despite the increasing recognition of the importance of GINs and the key role played by research centers-universities within networks, empirical evidence remains elusive (Pereira et al., 2011). Although research

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centers-universities are typically seen as important for the emergence of GINs, there are few studies that focus directly on their role (Albuquerque et al., 2011). The literature on GINs identifies the available talent pool in peripheral countries as one key driver of GINs (Dieter. Ernst, 2006). Certain firms identify research centers-universities as their most important collaborative partners, ahead of customers, suppliers, alliance partners and even joint venture partners (The_Economist_Intelligence_Unit, 2007). Universities and research institutes are identified as critical sources of innovation; thus, there is a growing trend towards the globalization of industry-science relationships. Examples of Asian firms that have established GINs with universities in the USA and Europe include China's Huawei (Ernst and Naughton, 2008) and Taiwan's TSCM (Dieter. Ernst, 2009a). A small number of regions in the developing world have already managed to exploit opportunities provided by the new global landscape to accumulate technological capabilities and have occasionally even become specialized hubs in global knowledge networks (C. Chaminade and Vang, 2008). Technological advancement has been one of the main factors in the improvement of living conditions in society (Reichardt et al., 2016). Although economic growth depends on multiple factors, science and technology (S & T) have been considered catalysts for socio-economic progress (Hekkert et al., 2007; OECD, 2008a). Among the major limitations of the Mexican scientific system is that the work developed in its research institutes has been organized primarily based on the incomplete views of the government and certain researchers regarding the needs of S & T activity (E. Robles-Belmont, 2010). The design of financial and logistical programs for research support rarely results from a study of the country's industrial needs that considers the interests of both academia and business (Záyago-Lau and Foladori, 2010). These programs have also been designed without consideration of the rapid changes in how businesses are organized worldwide to generate innovation. In Mexico, nanotechnology has been recognized as a strategic growth area (DOF, 2008). This technology, along with other emerging technologies, is essential to “improve the standard of living of society and become more competitive” (Allarakhia and Walsh, 2012; Eduardo. Robles-Belmont et al., 2008). However, to date, there is no national plan or national initiative in nanotechnology. There is no office or administrative council to establish the goals and direction that nanotechnology should adopt in Mexico (Záyago-Lau and Foladori, 2010). In 2012, Faladori et al. analyzed the extant scientific bilateral agreements in nanotechnology between Mexico and the United States and concluded that there are few opportunities for bilateral collaboration between the countries in the broad nanotechnology area (Foladori et al., 2012). Building on this, our study aims to assess the degree to which critical innovation factors enable Mexican research centers and institutes of nanotechnology to participate in GINs. This study seeks to answer the following question: Do innovation factors determine the participation of Mexican research institutes in global innovation networks in the nanotechnology sector? Through a survey of 59 researchers at nanotechnology research institutions in Mexico, we examine the effect of critical innovation factors on their participation in GINs. In the next section, we discuss the relevant concepts used in this paper and derive the hypotheses to be tested. Section 3 discusses the methodology for this study. We present the results in Section 4. Section 5 concludes the paper. 2. Literature review 2.1. Critical innovation factors for networks An important characteristic of innovation in emerging technological fields is that it does not occur in isolation (Kassicieh et al., 2002; Markard and Truffer, 2008). On the contrary, innovations are generated and implemented by networks of interacting organizations and

individuals (Subramaniam and Youndt, 2005; Thukral et al., 2008). As a result, organizations are increasingly establishing access to complementary knowledge networks (Barnard and Chaminade, 2011). Disentangling these various factors and understanding the nature of innovation in networks present a theoretical challenge that must be addressed in this context. According to the Organization for Economic Cooperation and Development (OECD), the first factor that has contributed significantly to innovation in networks is globalization (Marquis, 1969; OECD, 2008b; Walsh and Linton, 2011). In an environment of globalization, enterprises must be open to ideas from R&D, to complement both internal and external ideas and to remain competitive (Chesbrough, 2006; Tassey, 2008). Due to increasingly intense technological progress and global competition, product life cycles have been drastically reduced, forcing companies to innovate faster and develop products and services more efficiently (Ahuja, 2011; Dieter. Ernst and Linsu, 2002; Von Zedtwitza and Gassmannb, 2002). A second innovation factor is the practice of open innovation. Changes in the relationships between companies and other sources of innovation have led companies to implement “open innovation” (Chesbrough, 2004; Chesbrough, 2006; Chiaroni et al., 2011; Huizingh, 2011; OECD, 2008a). Open innovation not only aims to acquire external knowledge (“outside-in”) but also endeavors to find ways to generate additional revenue from internal innovations (“inside-out”) (Chesbrough, 2006; Garcia-Martinez, 2013; Huizingh, 2011; Necoechea-Mondragón et al., 2013). A third factor involved in network innovation is the availability of educated, competitive human resources who are competent in innovation (Albuquerque et al., 2011; McAdam et al., 2004). Engineering and scientific resources available in countries such as China, Brazil and India have opened new opportunities for multinational companies to hire specialized workers for lower wages (Dieter. Ernst, 2006; Freeman, 2005). Global firms must improve their access to a limited global pool of knowledge workers (Ruia and Yipb, 2008). The shift to knowledge-intensive industries has increased the importance and scarcity of well-trained knowledge workers (Dieter. Ernst, 2005). Thus, for many high-tech companies, competing for scarce global talent has become a major strategic concern (Palacios, 2008; Tassey, 2008; Vitae, 2010). This factor is closely related to the interaction among universities, public research institutes and multi-national corporations (MNCs) in the education and training of a pool of highly skilled knowledge workers, scientists and engineers (Lorentzen and Gastrow, 2012). The literature on GINs assumes a largely implicit relationship between GINs and universities and research institutes (Chaves et al., 2013; Pereira et al., 2011). A central study conducted by The Economist Intelligence Unit (2007) found that the majority of surveyed firms identified universities and educational establishments as their most important collaborative partners, ahead of customers, suppliers, alliance partners and even joint venture partners (The_Economist_Intelligence_Unit, 2007). A fourth factor that is intertwined with the emergence of innovation networks is the transformation of the international division of labor (Bucklye and Ghauri, 2004; Zuniga and Crespi, 2013). There is an increasing division of labor, or vertical specialization, in innovation (Dieter. Ernst, 2009b). Global firms have been able to increase vertical specialization in innovation, which has given rise to global markets for technology (Dieter. Ernst and Kim, 2002; Kruss and Gastrow, 2012). A fifth factor is that a firm's competitive success is now critically dependent on its ability to transfer technology and to monitor and quickly seize upon external sources of knowledge, which are now key elements of competition (Chun-Chu, 2007; Necoechea-Mondragón et al., 2013). Global firms must supplement their in-house creation of new knowledge and capabilities with basic or generic technologies developed elsewhere, perhaps even using reverse innovation (Chung, 2001; Govindarajan and Ramamur, 2011; Von Zedtwitz et al., 2015).

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A sixth factor is the emergence of new technologies, which is also known as the ICT revolution. The ICT revolution enhances the mobility of innovation (Dieter. Ernst, 2002, 2006; Rao, 2001). ICTs provide important tools for building networks with flexible arrangements that can unite and coordinate economic transactions in geographically dispersed locations (Dieter. Ernst, 2005; Weber and Kauffman, 2011). IT and related organizational innovations provide effective mechanisms for constructing flexible network arrangements that can link together and coordinate economic transactions among geographically dispersed locations (Dieter. Ernst, 2009a). IT-enabled network management reduces the cost of communication, helps to codify knowledge through software tools and databases, enables remote control, and facilitates the exchange of tacit knowledge through audiovisual media (Dieter. Ernst, 2009b). The seventh factor is the emergence of Asia – particularly China and India – as an economic power and a new destination for foreign R&D (Tassey, 2008; The_Economist_Intelligence_Unit, 2007). Therefore, the issue is not only the emergence of Asia but also the risk of broader hierarchical changes in the global economy (Kaplinsky, 2008). Beyond Asia, the emergence of other locations, such as Brazil, South Africa or Mexico, is a specific characteristic of this new phase (Gammeltoft, 2006). A eighth factor is the liberalization of capital flows throughout the world, which has been a catalyst for the expansion of global production and innovation networks (Dieter. Ernst, 2009a). Institutional change through liberalization has played an important role in reducing constraints on the organizational and geographic mobility of knowledge (Dieter. Ernst and Kim, 2002). Hence, liberalization has acted as a powerful catalyst for the expansion of innovation in networks. The overall effect of liberalization has been to reduce the cost and risks of international transactions and to considerably increase international liquidity (Dieter. Ernst, 2009b). A ninth factor that has contributed to strengthening the process of innovation in networks is the maturity of national innovation systems (INGINEUS, 2011, 2012; Mowerya et al., 2001). Arguably, an immature national system of innovation will have immature (or incomplete) innovation networks, and the limitations of the national system of innovation will be reflected in the nature of the networks in that country. Regarding middle income countries, multiple cases have been reported in which participation in GINs has been instrumental in increasing innovation capabilities nationwide (Joseph and Abraham, 2011). The emergence of GINs holds the promise of technological upgrading, competence building and economic catch-up for middle income countries (Pereira et al., 2011). This promise may be realized in emerging economies, such as China and India, or at least in specific sectors (such as ICT and electronics) and in specific regions of these countries (Carneiro et al., 2012). These countries have two major attractions for Northern MNCs: very large markets and large pools of qualified engineers and scientists. There are many other factors reported in the literature in addition to the critical factors mentioned above (Dieter. Ernst, 2006; Sachwald, 2008). However, out of all of these factors, based on the literature review, we consider only the four following critical factors, over which we believe companies and organizations have some degree of control: vertical specialization, human resource competencies in innovation, technology transfer skills, and open innovation practices. Factors such as globalization, liberalization of capital flows throughout the world, the emergence of Asia as an economic power and new destination for foreign R&D, etc., are basically beyond the control and scope of companies and organizations. The four selected factors were the independent variables in this research (Fig. 1). 2.2. Global innovation networks A GIN is defined as “a globally organized web of complex interactions between firms and non-firm organizations engaged in knowledge

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production related to and resulting in innovation”. GINs are thus defined in terms of their geographic breadth, the extent of their networks (both internal and external) and their outcomes (innovations) (Barnard and Chaminade, 2011). The main conceptual issue presented by the emergence of global innovation networks is whether they represent an intensification of a long-standing phenomenon or the phenomenon represents the emergence of a new way of organizing (Yeung, 2009). Access to global innovation networks (GINs) has been unequal across different regions of the world (Cristina. Chaminade and Plechero, 2015). Whereas certain regions are considered knowledge hubs in GINs, others remain marginalized; this highlights the role of regional innovation systems in the emergence and development of GINs. These global networks are more common in regions that are not organizationally and institutionally dense, suggesting that GINs may be a compensatory mechanism for weaknesses in regional innovation systems (Hekkert et al., 2007). The constituent elements of GINs (globalness, networkedness and innovativeness) have long been documented (Gastrow and Kruss, 2012). GINs may represent an organizational form that is emerging from a changing techno-social-economic paradigm in an era characterized by the ascent of middle-income countries as important economic players in the global arena (Barnard and Chaminade, 2011). Regarding globalness, whereas internationalization can be conceptualized as the “simple geographical spread of economic activities across national boundaries with low levels of functional integration”, globalization implies “both extensive geographical spread and high degree of functional integration” (Bucklye and Ghauri, 2004; Pretoria-University, 2009). For INGINEUS (2012), the term globalness involves the degree of export market as an important market (beyond the home region, e.g., Europe), percentage of sales for export, innovation collaboration beyond the home region, off-shoring of innovation activities, and outsourcing of all activities (INGINEUS, 2012). Networkedness is the second constituent element of GINs. Although large MNCs have the best-developed internal networks, one could expect other forms of organization to be important players in the emergence and development of GINs based on externalized networks (Haakonsson, 2012; Pretoria-University, 2009). In the project INGINEUS (2012), the term networkedness included the span (many people, different actors, collaborations, etc.) and depth (connections and relationships, whether formalized or not) of collaboration and whether the network was internal or external (INGINEUS, 2012; Laursen and Salter, 2006). In terms of innovativeness, it has generally been argued that the proportion of firms introducing innovations that are new to the firm versus new to the world varies significantly between high-income and middle-income countries (Jansa, 2010; Schamp and Stamm, 2012). Whereas most new-to-the-world innovations are being introduced by firms headquartered in the North, product innovations in middleincome countries often lag behind the technological frontier. In middle-income countries, innovation is primarily imitative and related more to the acquisition of technology developed elsewhere and its adaptation to local needs than to the development of new products. Innovativeness encompasses whether the organization has experienced innovation during the past three years, the type of innovation (product, process, distribution, and/or supporting activities), and the degree of innovation (new to the world/industry/firm) (INGINEUS, 2012). Although high-income countries often house firms from other locations, their home-grown firms are, almost paradoxically, less globally connected (Barnard and Chaminade, 2011; Fitzgerald, 2015). Supported by a well-developed institutional infrastructure, European firms seem to have a regional or domestic (rather than global) focus, and (perhaps as a consequence) a limited span of networks (Meyer and Peng, 2005). Although this seems to be a positive characteristic for innovation at the moment, too much focus on regional networks can lead to lock-ins and

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Fig. 1. Selected critical factors for innovation in networks. Source: Own elaboration.

loss of competitiveness in the long term (Chaminade and Barnard, 2009). Because of the co-occurrence of innovation with globalness and with networkedness, this trend could even limit an organization's longer-term innovativeness. It is therefore important to identify the triggers that motivate research institutions in Mexico to engage in global innovation networks. We expect that the critical innovation factors positively relate to participation in GINs; that is, institutions with vertical specialization, human resource competencies in innovation, technology transfer skills and open innovation practices might positively participate in GNIs and become more global, innovative and networked. Based on this expectation, our conceptual model is presented in Fig. 2.

INDEPENDENT VARIABLE Critical factors of innovation

Dimensions: • Vertical specialization • Competencies of HR for innovation • TT skills • Open Innovation practices

DEPENDENT VARIABLE Participation in Global Innovation Networks

Dimensions: • Globalness • Networkedness • Innovativeness

Fig. 2. Conceptual research model. Source: Own elaboration.

3. Methodology 3.1. Research instruments This research was of a quantitative nature, and data collection was performed using the methodology suggested by Fowler (2009) (Denscombe, 2010; Fowler, 2009). The questionnaire was sent by e-mail to 228 researchers at 56 research institutions in nanotechnology in Mexico. These researchers were members of the thematic network of nanotechnology of the National Council of Technology and Science and members of the nanotechnology research network of the National Polytechnic Institute (Záyago-Lau and Foladori, 2010). The questionnaire comprised three parts (see Annex 1). The first part consisted of questions about institutional characteristics and the name and description of the researcher. The second part asked questions related to critical innovation factors, namely, vertical specialization, human resource competencies in innovation, technology transfer skills and open innovation practices. The last part comprised questions related to participation in GINs, including the globalness, innovativeness and networkedness of institutions. 3.2. Variables operationalization 3.2.1. Critical innovation factors The independent variable “critical innovation factors” (CIF) was measured using four dimensions and 39 items: a) vertical specialization was measured using four items and a 5-point Likert scale ranging from 1 (very seldom) to 5 (very often), which were adopted from Kerlinger

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and Lee (2008); b) human resource competencies in innovation was measured using 24 items and a 5-point Likert scale ranging from 1 (minimum) to 5 (maximum); c) technology transfer skills was measured using seven items and a 5-point Likert scale ranging from 1 (never) to 5 (always); and d) open innovation practices was measured using four items and a 5-point Likert scale ranging from 1 (minimum) to 5 (maximum) (Kerlinger and Lee, 2008). 3.2.2. Participation in global innovation networks The dependent variable was measured using three dimensions and 28 items: a) globalness was measured using twelve items and a 5-point Likert scale ranging from 1 (very seldom) to 5 (very often), which were adopted from Kerlinger and Lee (2008); b) networkedness was measured by six items and a 5-point Likert scale ranging from 1 (never) to 5 (always); and c) innovativeness was measured using ten items and a 5-point Likert scale ranging from 1 (minimum) to 5 (maximum) (Kerlinger and Lee, 2008). 3.3. Internal validity of the instrument As discussed above, the questions of the research instrument were developed based on various studies (Fowler, 2009; Shih and Fan, 2009). After testing the questionnaire with several researchers in a real-life situation, we slightly altered the phrasing of certain questions in the instrument to improve respondents' understanding of the questions. In this study, we used Cronbach's alpha to examine the internal consistency of the instrument. The values of Cronbach's alpha were greater than 0.90 for both the critical innovation factors and participation in global innovation networks (Table 1). Thus, we conclude that the items used to measure the critical innovation factors and participation in global innovation networks were acceptable, meaning that they provided consistent results. 3.4. Data collection and analysis This study concerns fieldwork in innovation in global innovation networks at research institutions in the nanotechnology sector in Mexico. In Mexico, there are several institutions, laboratories, research centers, and educational institutes and universities working with nanotechnology. In 2010, it was estimated that there were 56 institutions conducting research and more than 159 laboratories and 340 research areas developing nanotechnology and nanoscience. It was also calculated that there were 449 researchers working in this field (this number was considered the total target population for this study), of whom 29% were at CONACYT, 18% were at the UNAM, 15% were at the IMP, 8% were at the National Polytechnic Institute (IPN) and 30% were at 20 other institutions (Eduardo. Robles-Belmont et al., 2008; Záyago-Lau and Foladori, 2010). The data were collected by e-mail from May 2014 until October 2014. Among the returned questionnaires, 93% were completed by the respondents and subsequently included in the data analysis. Of the 228 researchers we contacted, 164 researchers were unwilling to participate in the research. Thus, 59 questionnaires were included in the analysis, which represents a response rate of 25.8% (Sheehan, 2006). The non-responders were unwilling to participate for various reasons, such as a lack of time, other engagements at the time of the data collection or confidentiality reasons. Overall, 59 questionnaires

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were considered suitable for analysis; this represented a margin of error of 10% and a confidence level of 90% in the statistical analysis. No clear patterns were identified among the non-responders. Once the information was coded, tabulated and verified in the database, we proceeded to the analysis, using the statistical program SPSS version 24.0 and employing multivariate analysis techniques to achieve the proposed objectives and examine the hypotheses posited in this research. 4. Results 4.1. Demographics of the researchers The demographics of the researchers who provided completed research instruments in this study are presented in Fig. 3. The distribution shows the concentration of researchers in Mexico City. In 2013, 36% of the total number of researchers (in all areas of knowledge) were concentrated in the Mexico City, followed by Estado de Mexico, with 5.62%; Jalisco, with 5.08%; Morelos, with 4.57%; and Nuevo León, with 3.92%. We received completed questionnaires from researchers at research institutions in 11 states; it is not surprising that 50% of the respondents were from institutions in Mexico City. 4.2. Correlation and regression analysis Fig. 4 graphically presents the Pearson correlation between the study variables: critical innovation factors and participation in global innovation networks. The resulting equation of the correlation is as follows: participation in global innovation networks = 17.33 + 0.73 critical innovation factors. Fig. 4 shows the result of the correlation analysis between the variables. Table 2 shows the Pearson correlation coefficient resulting from the SPSS software analysis. The analysis of the Pearson correlation coefficients for the critical innovation factors variable and its four dimensions and the participation in global innovation networks variable and its three dimensions is presented in Table 3. The determination coefficient indicates the proportion (or percentage, if multiplied by 100) of common variability; that is, it indicates the proportion of variance of a determinant variable or associated

Table 1 Instrument reliability (Cronbach's alpha). Cronbach's alpha

Cronbach's alpha based on typified elements

Number of elements

0.915

0.909

65

Source: Own elaboration based on the questionnaires and SPSS version 24.

Fig. 3. Distribution of the researchers who answered the questionnaire by state. Source: Own elaboration.

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Fig. 4. Correlation between the variables CIF and PGIN. Source: Own elaboration with data from the questionnaires and SPSS version 24.

with another variable. The determination coefficients for the variables and dimensions are shown in Table 4. Based on the Pearson correlation coefficients and the determination coefficients, the following correlation diagram of the variables and dimensions was elaborated (Fig. 5). As shown in Fig. 5, we find that three critical innovation factors have a significant effect on the participation of Mexican nanotechnology research centers in GINs. These three dimensions of CIF – human resource competencies in innovation, technology transfer skills and open innovation practices – presented a significant relationship with the participation of research institutions in GINs, whereas vertical specialization was not significant. The CIF variable explains 59.1% of the total variance in PGIN. The correlation among the CIF dimensions and the dependent variable PGIN exhibited high correlation coefficients. The Pearson coefficient of the dependent variable with open innovation practices, technology transfer skills and human resource competencies were 0.778, 0.626 and 0.661, respectively. These results contrast with the low correlation found for the dependent variable with vertical specialization, which was only 0.231. The low correlation between the

Table 2 Pearson correlation coefficient.

Critical innovation factors

Participation in GINs

Pearson correlation Sig. (bilateral) N Pearson correlation Sig. (bilateral) N

Critical innovation factors

Participation in GINs

1

0.769a

59 0.769a

0.000 59 1

0.000 59

59

Source: Own elaboration based on the questionnaires and SPSS version 24. a Significant correlation at the 0.01 level (bilateral).

dependent variable and vertical specialization could be due to the lack of vertical specialization in nanotechnology research institutions and researchers in Mexico. The high correlation found among the independent variable (CIF) and two of its dimensions is noteworthy. The Pearson correlation coefficients of CIF with human resource competencies in innovation and technology transfer skills were 0.941 and 0.785, respectively. Additionally, we note the high correlation between the dependent variable (PGIN) and its dimensions. In summary, the empirical results show the existence of a high or significant relationship between the variable CIF and the variable PGIN. A high correlation between the variables and their respective dimensions, with the exception of vertical specialization, was also present. 5. Discussion and conclusion The high correlation of Pearson's coefficients between the two variables of this study is consistent with the results reported by Kaplinsky, 2008 and Chaminade and Vang, 2008 in the sense that there are several factors that contribute to strengthening the process of innovation in networks (Chaminade and Vang, 2008; Kaplinsky, 2008). Regarding the four factors assayed in this study for the CIF variable, open innovation practices had an influence on PGIN of 60.5% (r2 = 0.605), human resource competencies in innovation had an influence on PGIN of 43.7% (r2 = 0.437), and transfer technology skills had an influence on PGIN of 39.1% (r2 = 0.391). These findings indicate that the empirical correlation results for these dimensions are consistent with theory. The findings also indicate that researchers in the nanotechnology field at the surveyed institutions believe the following: a) open innovation has become an integral part of innovation strategy and the business models of companies, innovation is based increasingly on knowledge assets that are outside the boundaries of the enterprise and research institution, and open innovation helps to generate new ideas, products and services and to bring these items to market quickly (Hidalgo et al., 2002; OECD, 2008b); b) human resources in research

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Table 3 Pearson correlation coefficients (r) for variables and dimensions.

CIFa,⁎

Pearson correlation sig (bilateral) N

Vertical specialization

Pearson correlation sig (bilateral) N

HR competencies

Pearson correlation sig (bilateral) N

TT skills

Pearson correlation sig (bilateral) N

Open Innov. practices

Pearson correlation sig (bilateral) N

PGINb

Pearson correlation sig (bilateral) N

Globalness

Pearson correlation sig (bilateral) N

Networkedness

Pearson correlation sig (bilateral) N

Innovativeness

CIFa

Vertical specialization

HR competencies

TT skills

Open innov. practices

PGINb

Globalness

Networkedness

Innovativeness

1

0.387⁎ 0.002 59 1

0.941⁎⁎ 0.000 59 0.306 0.018 59 1

0.785⁎⁎ 0.00 0 59 0.062 0.642 59 0.591⁎⁎

0.611⁎⁎ 0.000 59 0.319⁎ 0.014 59 0.435⁎

0.769⁎⁎ 0.000 59 0.231 0.078 59 0.661⁎⁎

0.725⁎⁎ 0.000 59 0.318⁎ 0.014 59 0.616⁎⁎

0.666⁎⁎ 0.000 59 0.186 0.158 59 0.540⁎⁎

0.668⁎⁎ 0.000 59 0.073 0.582 59 0.613⁎⁎

0.000 59 1

0.001 59 0.472⁎⁎ 0.001 59 1

0.000 59 0.626⁎⁎ 0.000 59 0.778⁎⁎

0.000 59 0.569⁎⁎ 0.000 59 0.729⁎⁎

0.001 59 0.608⁎⁎ 0.000 59 0.683⁎⁎

0.000 59 0.516⁎⁎ 0.000 59 0.675⁎⁎

0.000 59 1

0.000 59 0.942⁎⁎ 0.000 59 1

0.000 59 0.863⁎⁎ 0.000 50 0.743⁎⁎ 0.000 59 1

0.000 59 0.873⁎⁎ 0.000 59 0.734⁎⁎ 0.000 59 0.634⁎ 0.000 59 1

Pearson correlation sig (bilateral) N

Source: Calculations using SPSS version 24 based on the questionnaire data. a Critical innovation factors. b Participation in global innovation networks. ⁎ The correlation is significant at a level of 0.05 (bilateral). ⁎⁎ The correlation is significant at a level of 0.01 (bilateral).

institutions in Mexico have the skills required for participation in GINs (Dieter. Ernst, 2009a; Vitae, 2010); and c) human resources have the skills to perform all technology transfer processes (Hoffman and Girvan, 1990; Sazali et al., 2009; Sazali et al., 2012; White and Bruton, 2007). Contrary to our expectations, the vertical specialization dimension had no significant direct impact on PGIN (r2 = 0.053). The main contribution of this paper is that it provides evidence that in the context of the knowledge economy, human resource competencies in innovation, technology transfer skills and open innovation practices are more important than vertical specialization in terms of the participation of Mexican nanotechnology research centers in global innovation networks. Consequently, to produce a significant impact on GIN participation, human resource competencies in innovation, technology transfer skills and open innovation practices should be established to obtain more PGIN opportunities.

Given the correlation results, further research on critical innovation factors is needed, perhaps by finding another or a more comprehensive dimension for CIF. Additionally, the usual limitation of cross-sectional analysis applies here; a longer research time frame would likely provide additional insights on the proposed relationships, particularly if we considered the human resource competencies of a research institution in light of dynamic capabilities and strategic researcher development processes. Other limitations of this study are the low rate of researcher responses to the questionnaire (only 25.8%) and the fact that only one sector of the industry was evaluated (nanotechnology). It remains a methodological open question whether the chosen unit of investigation (firm/institute level) is suitable for assessing nanoscience global networks and its relation with GINs. Future studies may address these limitations to validate the results and provide better measurement strategies.

Table 4 Determination coefficients (r2) for variables and dimensions.

CIFa⁎ Vertical sp. Competencies of HR TT skills Open innov. practices PGINb Globalness Networkedn. Innovativen.

CIFa

Vertical specialization

HR competencies

TT skills

Open Innovation practices

PGINb

Globalness

Networkedness

Innovativeness

1

0.149 1

0.885 0.093 1

0.616 0.004 0.349 1

0.373 0.101 0.189 0.222 1

0.591 0.053 0.437 0.391 0.605 1

0.525 0.101 0.379 0.323 0.576 0.887 1

0.443 0.034 0.291 0.369 0.466 0.744 0.552 1

0.446 0.005 0.375 0.266 0.455 0.762 0.539 0.402 1

Source: Own elaboration based on Pearson coefficients (r). a Critical innovation factors. b Participation in global innovation networks.

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Independent variable Critical factors of innovation

Open Innovation practices

Competitive human resources for innovation

r = 0.769

Dependent variable

r2 = 0.591

Participation in GINs

r = 0.778 r2 = 0.605

Globalness

r = 0.661 r2 = 0.437

Networkedness

r = 0.626

Technology transfer skills

r2 = 0.391

Innovativeness Vertical specialization

r = 0.231 r2 = 0.053

Fig. 5. Pearson correlation coefficients and certain determination coefficients of variables. Source: Own elaboration.

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References Ahuja, I.P.S., 2011. Managing research and development for core competence building in an organization. J. Technol. Manag. Innov. 6 (1), 58–65. Albuquerque, E., Britto, G., Silva, C.O., Kruss, G., 2011. Global innovation networks as first steps towards a global innovation system. Retrieved from Minas Gerais: Universidade Federal de Minas Gerais. Centro de Desenvolvimento e Planejamento Regional. Allarakhia, M., Walsh, S.T., 2011. Managing knowledge assets under conditions of radical change: the case of the pharmaceutical industry. Technovation 31 (2–3), 105–117. Allarakhia, M., Walsh, S.T., 2012. Analyzing and organizing nanotechnology development: application of the institutional analysis development framework to nanotechnology consortia. Technovation 32 (3–4), 216–226. Anson, S., Kautt, M., Walsh, S.T., Bittner, K., 2008. Academic infrastructure and competence centres for a potentially evolving nanomanufacturing industry. Int. J. Technol. Transf. Commer. 7 (4), 436–455. Audretsch, D.B., Lehmann, E.E., Wright, M., 2014. Technology transfer in a global economy. J. Technol. Transfer. 39, 301–312. Barnard, H., Chaminade, C., 2011. Global Innovation Networks: Towards a Taxonomy Retrieved from Lund, SWEDEN. Bleischwitz, R., 2010. International economics of resource productivity relevance, measurement, empirical trends, innovation, resource policies. IEEP 7 (227), 244. Buckley, P.J., 2014. Forty years of internalisation theory and the multinational enterprise. Multinatl. Bus. Rev. 22 (3), 227–245. Bucklye, P.J., Ghauri, P.N., 2004. Globalisation, economic geography and the strategy of multinational enterprises. J. Int. Bus. Stud. 35, 81–98. Carneiro, D.A.V., Pereira, M.C., Britto, G., 2012. Building capabilities through global innovation networks: case studies from the Brazilian automotive industry. Innov. Dev. 2 (2), 248–264. Carrillo, J., Lara, A., 2005. Mexican maquiladoras: new capabilities of coordination and the emergence of a new generation of companies. Innov. Manag. Policy Pract. 7 (2–3), 256–273. Chaminade, C., Barnard, H., 2009. On the Concept of Global Innovation Networks Retrieved from Lund, Sweden. Chaminade, C., Plechero, M., 2015. Do regions make a difference? Regional innovation systems and global innovation networks in the ICT industry. Eur. Plan. Stud. 23 (2), 215–237. Chaminade, C., Vang, J., 2008. Globalisation of knowledge production and regional innovation policy: supporting specialized hubs in the Bangalore software industry. Res. Policy 37 (10), 1684–1696. Chaves, d.C.e.S.N.F., Pereira, d.S.U., Parreiras, O.V., Gomes, d.C.P., Teixeira, d.M.F.L., De Negri, F., 2013. Patterns of interaction between national and multinational corporations and Brazilian universities/public research institutes. Sci. Public Policy 40 (3), 281–292. Chesbrough, H., 2004. Managing open innovation. Research Technology Management 47 (1), 23–26. Chesbrough, H.W., 2006. Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School, Boston, MA, USA. Chiaroni, D., Chiesa, V., Frattini, F., 2011. The open innovation journey: how firms dynamically implement the emerging innovation management paradigm. Technovation 31 (1), 34–43. Chun-Chu, L., 2007. Modeling the technology transfer to Taiwan from China. International Research Journal of Finance and Economics 5 (2), 48–66. Chung, W., 2001. Identifying technology transfer in foreign direct investment: influence of industry conditions and investing firm motives. J. Int. Bus. Stud. 32 (2), 211–229. Denscombe, M., 2010. The Good Research Guide: for Small-scale Social Research Projects. Open University Press, Mc. Graw Hill, New York, USA. DOF, 2008. Programa Especial de Ciencia y Tecnología 2008–2012. Secretaría de Gobernación, Mexico D.F. Ernst, D., 2002. Global production networks and the changing geography of innovation system. Econ. Innov. New Technol. 11 (6), 497–523. Ernst, D., 2005. Pathways to innovation in Asia's leading electronics-exporting countries – a framework for exploring drivers and policy implications. Int. J. Technol. Manag. 29 (1–2), 6–20. Ernst, D., 2006. Innovation Offshoring: Asia's Emerging Role in Global Innovation Networks Retrieved from Honolulu, Hawai. Ernst, D., 2009a. The new geography of innovation – Asia's role in global innovation networks Retrieved from http://apru.nus.edu.sg/awi/workshops/economic_integration/ slides/Nov%209/5-4%20Dieter%20Ernst.pdf. Ernst, D., 2009b. A New Geography of Knowledge in the Electronics Industry? Asia's Role in Global Innovation Networks Retrieved from Honolulu, Hawaii. Ernst, D., Kim, L., 2002. Global production networks, knowledge diffusion, and local capability formation. Res. Policy 31 (8–9), 1417–1429. Ernst, D., Linsu, K., 2002. Global production networks, knowledge diffusion, and local capability formation. Res. Policy 31, 1417–1429. Ernst, D., Naughton, B., 2008. China's emerging industrial economy: insights from the IT industry. In: McNALLY, C.A. (Ed.), China's Emergent Political Economy: Capitalism in the Dragon's Lair. Routledge, London/New York, pp. 39–59. Falize, M., Coeurderoy, R., 2012. The network approach to rapid internationalization among born-global and born-again global firms. The Case of the “Global Innovation Network. Louvain School of Management ESCP Europe, Paris. Fang, P.K., Zigang, K.Z., 2004. Cross-cultural challenges when doing business in China. Singap. Manag. Rev. 26 (1), 81. Fink, M., Lang, R., Harms, R., 2013. Local responses to global technological change. Contrasting restructuring practices in two rural communities in Austria. Technol. Forecast. Soc. Chang. 80 (2), 243–252.

311

Fitzgerald, R., 2015. The Rise of the Global Company: Multinationals and the Making of the Modern World. Cambridge University Press, Cmbridge, UK. Foladori, G., Záyago, É., Appelbaum, R., Parker, R., 2012. Mexico-U.S. collaboration in nanotechnology. Frontera Norte 24 (48), 145–164. Fowler, F.J., 2009. Survey Research Methods. 4th ed. Sage, Los Angeles, London, New Delhi, Singapore, Washington D.C. Freeman, R.B., 2005. Does Globalization of the Scientific/Engineering Workforce Threaten U.S. Economic Leadership? Retrieved from Cambridge, MA Gammeltoft, P., 2006. Emerging multinationals: outward FDI from the BRICS countries. Int. J. Technol. Glob. 4 (1–2), 177–199. Garcia-Martinez, M., 2013. Open Innovation in the Food and Beverage Industry. Kent Business School, UK. Gastrow, M., Kruss, G., 2012. Skills and the formation of global innovation networks: a balancing act. Innov. Dev. 2 (2), 303–323. Govindarajan, V., Ramamur, R., 2011. Reverse innovation, emerging markets, and global strategy. Glob. Strateg. J. 1 (3–4), 191–205. Groen, A.J., De Weerd–Nederhof, P.C., C., K.V.D., Badoux, R.A.J., Olthuis, G.P.H., 2002. Creating and justifying research and development value: scope, scale, skill and social networking of R&D. Creat. Innov. Manag. 11 (1), 2–16. Haakonsson, S.J., 2012. The globalization of innovation in the Danish food industry: exploitation and exploration of emerging markets. Innov. Dev. 2 (2), 230–247. Hekkert, M.P., Suurs, R.A.A., Negro, S.O., Kuhlmann, S., Smits, R.E.H.M., 2007. Functions of innovation systems: a new approach for analysing technological change. Technol. Forecast. Soc. Chang. 74 (4), 413–432. Hidalgo, A., León, S., Pavón, J., 2002. La gestión de la innovación y la tecnología en las organizaciones. Ediciones Pirámide, Madrid. Hoffman, K., Girvan, N., 1990. Managing International Technology Transfer: A Strategic Approach for Developing Retrieved from Ottawa, ON, CA. Huizingh, E.K.R.E., 2011. Open innovation: state of the art and future perspectives. Technovation 31 (1), 2–9. INGINEUS, 2011. Impact of Networks, Globalisation and their Interaction with EU Strategies: Project Final Report Retrieved from Milan, Italy. INGINEUS, 2012. Global Innovation Networks: Evidence & Policy Challenges Retrieved from Milan, Italy. Jansa, S., 2010. Resumen Del Manual De Oslo Sobre Innovación. UNED Oficina de transferencia deresultados de investigación, Madrid. Jenn-Hwan, W., 2007. From technological catch-up to innovation-based economic growth: South Korea and Taiwan compared. J. Dev. Stud. 43 (6), 1084–1104. Joseph, K.J., Abraham, V., 2011. University Industry Interaction in Global Innovation Networks the Case of ICT GINs in India Retrieved from. Kaplinsky, R., 2008. Introduction: the impact of Asian drivers on the developing world. World Dev. 36 (2), 197–209. Kassicieh, S., Walsh, S., Cummings, J., McWhorter, P., Romig, A., Williams, D., 2002. Commercialization of disruptive technologies: moving discontinuous innovations into products. IEEE Trans. Eng. Manag. 49 (4), 375–387. Kautt, M., Walsh, S.T., Bittner, K., 2007. Global distribution of micro–nano technology and fabrication centers: a portfolio analysis approach. Tech. Forcasting Soc. Chang. 74 (9), 1697–1717. Kerlinger, F., Lee, H., 2008. Investigación del comportamiento: Métodos de investigación en Ciencias Sociales. 4ª ed. Mc Graw Hill, México. Kruss, G., Gastrow, M., 2012. Global innovation networks, human capital, and development. Innov. Dev. 2 (2), 205–208. Kuznetsov, Y., Dahlman, C., 2008. Transition to a Knowledge-based Economy Challenges and Opportunities Retrieved from Washington D.C. Laursen, K., Salter, A., 2006. Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strateg. Manag. J. 27 (2), 131–150. Lorentzen, J., Gastrow, M., 2012. Multinational strategies, local human capital, and global innovation networks in the automotive industry: case studies from Germany and South Africa. Innov. Dev. 2 (2), 265–284. Markard, J., Truffer, B., 2008. Technological innovation systems and the multi-level perspective: towards an integrated framework. Res. Policy 37 (4), 596–616. Marquis, D.G., 1969. The anatomy of successful innovations. Innov. Manag. Policy Pract. 1 (7), 28–37. McAdam, R., McConvery, T., Armstrong, G., 2004. Barriers to innovation within small firms in a peripheral location. Int. J. Entrep. Behav. Res. 10 (3), 206–221. McCarthy, J.F., Gillespie, P., Zen, Z., 2012. Swimming upstream: local Indonesian production networks in “globalized” palm oil production. World Dev. 40 (3), 555–569. Meyer, K.E., Peng, M.W., 2005. Probing theoretically into central and Eastern Europe: transactions, resources, and institutions. J. Int. Bus. Stud. 36, 600–621. Mowerya, D.C., Nelson, R.R., Sampatc, B.N., Ziedonisd, A.A., 2001. The growth of patenting and licensing by U.S. universities: an assessment of the effects of the Bayh–Dole act of 1980. Res. Policy 30 (1), 99–119. Necoechea-Mondragón, H., Pineda-Dominguez, D., Soto-Flores, R., 2013. A conceptual model of technology transfer for public universities in Mexico. J. Technol. Manag. Innov. 8 (4), 24–35. OECD, 2008a. The Internationalization of Business R&D: Evidence, Impacts and Implications Retrieved from Paris. OECD, 2008b. Open Innovation in Global Networks: Policies Issues Retrieved from Paris. Palacios, J.J., 2008. Multinational Corporations and the Emerging Network Economy in Asia and the Pacific. Routledge, London and New York. Papadopoulos, T., Stamati, T., Nikolaidou, M., Anagnostopoulos, D., 2013. From open source to open innovation practices: a case in the Greek context in light of the debt crisis. Technol. Forecast. Soc. Chang. 80 (6), 1232–1246.

312

H. Necoechea-Mondragón et al. / Technological Forecasting & Social Change 114 (2017) 293–312

Pereira, U., Britto, E., Kruss, G., Albuquerque, E., 2011. Global Innovation Networks and University-firm Interactions: An Exploratory Survey Analysis Retrieved from Belo Horizonte – MG. Prahalad, V., Ramaswamy, C.K., 2004. The Future of Competition: Co-creating Unique Value with Customers. Harvard Bussines School Press, Boston, Massachusetts. Pretoria-University, 2009. Impact of Networks, Globalisation and their Interaction with EU Strategies SSH8-CT-2009-225368 INGINEUS Retrieved from Brussells. Rao, P.M., 2001. The ICT revolution, internationalization of technological activity, and the emerging economies: implications for global marketing. Int. Bus. Rev. 10, 571–596. Reichardt, K., Negro, O.S., S. R.K., Hekkert, M.P., 2016. Analyzing interdependencies between policy mixes and technological innovation systems: the case of offshore wind in Germany. Technol. Forecast. Soc. Chang. 106, 11–21. Rhéaume, L., Gardoni, M., 2015. The challenges facing corporate universities in dealing with open innovation. J. Work. Learn. 27 (4), 315–328. Robles-Belmont, E., 2010. Colaboraciones universidad-industria en el desarrollo de las nanociencias y de la nanotecnología en México. Ide@s CONCYTEG 5 (64). Robles-Belmont, E., Vinck, D., De Gortari, R.R., 2008. Desarrollo de las nanociencias en México: una visión a partir de las publicaciones científicas Retrieved from Monterrey Mexico. Ruia, H., Yipb, G.S., 2008. Foreign acquisitions by Chinese firms: a strategic intent perspective. J. World Bus. 43 (2), 213–226. Sachwald, F., 2008. Location choices within global innovation networks: the case of Europe. J. Technol. Transfer. 33 (4), 364–378. Sazali, A.W., Raduan, C.R., Jegak, U., 2009. A review on the technology transfer models, knowledge-based and organizational learning models on technology transfer. Eur. J. Soc. Sci. 10 (4), 550–564. Sazali, A.W., Raduan, C.R., Suzana, I.W., 2012. Defining the concepts of technology and technology transfer: a literature analysis. Int. Bus. Res. 5 (1), 61–71. Schamp, E.W., Stamm, A., 2012. New trends in an old sector: exploring global knowledge and HR management in MNCs and the north–south divide in human capital formation. Innov. Dev. 2 (2), 285–302. See, L., Fritz, S., Perger, C., Schill, C., McCallum, I., Schepaschenko, D., Obersteiner, M., 2015. Harnessing the power of volunteers, the internet and Google earth to collect and validate global spatial information using Geo-Wiki. Technol. Forecast. Soc. Chang. 98, 324–335. Sheehan, K.B., 2006. E-mail survey response rates: a review. J. Comput.-Mediat. Commun. 6 (2), 0. Shih, T.-H., Fan, X., 2009. Comparing response rates in e-mail and paper surveys: a metaanalysis. Educ. Res. Rev. 4 (1), 26–40. Smitha, A., VoB, J.-P., Grin, J., 2010. Innovation studies and sustainability transitions: the allure of the multi-level perspective and its challenges. Res. Policy 39, 435–448. Subramaniam, M., Youndt, M.A., 2005. The influence of intellectual capital on the types of innovative capabilities. Acad. Manag. J. 48 (3), 450–463. Tassey, G., 2008. Globalization of technology-based growth: the policy imperative. J. Technol. Transfer. 33, 560–578. The_Economist_Intelligence_Unit, 2007. Sharing the Idea: The Emergence of Global Innovation Networks. Thukral, I., Von Ehr, J., Walsh, S., Greon, A., Van de Sijde, P., Adham, K.A., 2008. Entrepreneurship, emerging technologies, emerging markets. Int. Small Bus. J. 26, 101–116. Tidd, J., Bessant, J., 2013. Managing Innovation: Integrating Technological, Market and Organizational Change. fifht ed. John Wiley & Sons.

Tierney, R., Hermina, W., Walsh, S.T., 2013. The pharmaceutical technology landscape: a new form of technology roadmapping. Technol. Forecast. Soc. Chang. 80 (2), 194–211. Vitae, 2010. Researcher development framework: summary of the analysis of consultation responses Retrieved from http://www.vitae.ac.uk/policy-practice/165001/ Researcher-development-framework-consultation.html. Von Zedtwitz, M., Corsi, S., Veng, S.P., Frega, R., 2015. A typology of reverse innovation. J. Prod. Innov. Manag. 32 (1), 12–28. Von Zedtwitza, M., Gassmannb, O., 2002. Market versus technology drive in R&D internationalization: four different patterns of managing research and development. Res. Policy 31 (4), 569–588. Walsh, S., Linton, J., 2001. The competence pyramid: a framework for identifying and analyzing firm and industry competence. Tech. Anal. Strat. Manag. 13 (2), 165–177. Walsh, S., Linton, J.D., 2002. The measurement of technical competencies. J. High Technol. Manag. Res. 13 (1), 63–86. Walsh, S., Linton, J., 2011. The strategy-technology firm fit audit: a guide to opportunity assessment and selection. Technol. Forecast. Soc. Chang. 78 (2), 199–216. Walsh, S., Huzzy, D., Burke, R., Boylan, R., 1999. Direct foreign manufacturing investment decisions for China. Eng. Manag. J. 11 (4), 31–39. Weber, D.M., Kauffman, R.J., 2011. What drives global ICT adoption? Analysis and research directions. Electron. Commer. Res. Appl. 10, 683–701. White, M., Bruton, G., 2007. The Management of Technology and Innovation: A Strategic Approach. Thomson/South-Western, Mason, Ohio, USA. Yeung, H.W.-C., 2009. Transnational corporations, global production networks, and urban and regional development: a geographer's perspective on multinational enterprises and the global economy. Growth Chang. 40 (2), 197–226. Záyago-Lau, E., Foladori, G., 2010. La nanotecnología en México: un desarrollo incierto. Soc. Territ. X (32), 143–178. Zuniga, P., Crespi, G., 2013. Innovation strategies and employment in Latin American firms. Struct. Chang. Econ. Dyn. 24, 1–17. Hugo Necoechea-Mondragón is Biochemical engineer, with a Master and technology commercialization from Texas University in Austin and PhD in administrative sciences from the National Polytechnic Institute in Mexico City. He is Professor in the National Polytechnic Institute. His research interests include: issues related to global innovation networks, and technology transfer models. Daniel Pineda-Dominguez is a chemical engineer with a PhD in administrative sciences from the National Polytechnic Institute in Mexico City. He is Professor in the Master and PhD program of the Management and Commerce School of the IPN, México. His research interests include: issues related to technology management, innovation and competitiveness in the organizations. María de la Luz Pérez-Reveles is PhD in administrative sciences from the National Polytechnic Institute in Mexico City. Her research interest include: issues related with researchers formation in nanosciences. She is Professor in the National Polytechnic Institute. Rocío Soto-Flores is PhD in administrative sciences from the University Complutense of Madrid. She is Professor in the Master and PhD program of the Management and Commerce School of the IPN, México. His research interests include: issues related to technology management, innovation and quality in the organizations.