THE IMPACT OF INTERNATIONALISATION OF RESEARCH ON FIRM MARKET VALUE Paola Criscuolo Tanaka Business School, Imperial College London, London, UK, SW7 2AZ
[email protected] Erkko Autio Imperial College London, Tanaka Business School, London SW7 2AZ, UK
[email protected] Abstract It is often maintained that one of the basic competitive advantages of multinational enterprises (MNEs) lies in their abilities to tap into the knowledge generated in centres of excellence around the world. However there has been little research about the implications for financial performance of external knowledge sourcing through R&D internationalisation. Drawing on internalisation theory and theories of R&D internationalisation this paper examines whether the adoption of a geographically dispersed network of research units has an impact on MNE’s market valuation. We hypothesise that it is not only the geographical dispersion of research activities but also the degree of local embeddedness into host-regions scientific communities and the ability of firms to integration knowledge from multiple locations that is associated with higher market valuation. The analysis focuses on a sample of 29 world largest pharmaceutical and chemicals MNEs and on the geographical patterns of their scientific publications and co-authorship between 1990 and 2005. Results from panel estimations show that there is a positive relationship between MNE market value and both internationalisation of research and local embeddedness, while there is not a significant association with knowledge integration from multiple locations. Keywords: R&D internationalisation, market value, knowledge integration, local embeddedness.
The author thanks Chiara Criscuolo, Bronwyn Hall, Lionel Nesta, Ammon Salter, Fergal Shortall and Bart Verspagen for their suggestions.
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INTRODUCTION Theories of the multinational enterprise have increasingly recognised the importance of accessing knowledge-intensive resources abroad as an important contributor to the ‘MNE advantage’ (Dunning, 1998; Hitt, Hoskisson, & Kim, 1997b; Kuemmerle, 1999). This line of argument emphasises the ability of MNEs to access knowledge from pockets of excellence around the world through their network of R&D units (Bartlett, 1986; 1989; Ghoshal & Nohria, 1997; Nobel & Birkinshaw, 1998). However, little is known about the organisational mechanisms through which internationalisation of R&D contributes to MNE value creation. The existing literature on R&D internationalisation has focused on the motivations behind the relocation of innovative activities abroad (Dunning, 1998; Grandstand, Hakanson, & (eds), 1992) and the type of R&D activities carried out in these locations (Dunning & Narula, 1995; Florida, 1997; Kuemmerle, 1996; Patel & Vega, 1999). More recently, some studies have examined the organisational structures employed by MNEs to manage their geographically dispersed sites (Argyres & Silverman, 2004; Chiesa, 1996; Criscuolo & Narula, 2007; Gassmann & von Zedtwitz, 1999; von Zedtwitz & Gassmann, 2002) and the mechanisms adopted in order to improve the efficiency of implementing a global innovation structure (e.g. Nobel et al., 1998; Reger, 2004). Further work has explored the link between a geographically dispersed R&D organisation and firm innovative performance (Argyres et al., 2004; Furman, Kyle, Cockburn, & Henderson, 2006; Kotabe, Dunlap-Hinkler, Parente, & Mishra, 2007; Penner-Hahn & Shaver, 2005; Singh, 2006; Zahra, Ireland, & Hitt, 2000b). However, we still do not know whether R&D internationalisation actually contributes to MNE’s market valuation, and if so, under which conditions. Given the increasing amount of foreign direct investment in R&D and the emergence of China and India as locations of new foreign R&D laboratories (Doz, 2006; Thursby &
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Thursby, 2006), it is critical to assess whether the benefits of creating a geographically dispersed network of R&D units outweigh the costs of implementing and managing this organisational structure.
Merely establishing R&D activities abroad for the purpose of tapping into pools of scientific knowledge does not necessarily mean that firms will actually succeed in integrating knowledge inputs sourced from abroad into their value-creating activities. Converting crossborder asymmetries of knowledge-intensive resources into corporate value is not easy because foreign units are often outsiders in the network of local counterparts (Sölvell & Zander, 1995; Zaheer, 1995). To access and assimilate sources of external knowledge in host country, MNEs need to become embedded in the host country’s productive and innovative networks (Das & Teng, 2002; Iwasa & Odagiri, 2004; Yli-Renko, Autio, & Tontti, 2002). Developing and maintaining strong personal networks with host country’s research institutions, universities, and like-minded researchers can take considerable effort, and the liability of foreignness tends to inhibit the build-up of the kind of social capital required for successful R&D collaborations (Inkpen & Tsang, 2004; Zaheer & Mosakowski, 1997). Even when foreign subsidiaries succeed in connecting with host-country knowledge, they might find it difficult to transfer locally acquired knowledge to other units within the organisation due to barriers to knowledge integration (Kogut & Zander, 1993b). Because of barriers caused by inter-unit geographical, organisational and technological distance, as well as problematic motivational dispositions of both the sender and the receiver units (Gupta & Govindarajan, 2000; Kogut & Zander, 1993a; Szulanski, 1996). Local embeddedness may turn out to be a dougleedged sword, if it hampers integration, cooperation and communication with corporate headquarters (Blanc & Sierra, 1999; Zanfei, 2000).
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Problems related to local embeddedness, knowledge integration, and geographical dispersion mean that the link between R&D internationalisation and MNE value creation is not trivial. In this article, we contend that the impact of R&D internationalisation on financial performance depends on the degree of local embeddedness of foreign R&D units, as well as on MNE’s ability to integrate knowledge developed in foreign locations into the rest of the organisation. In particular we focus on the internationalisation of scientific research in sciencebased industries and the impact of this process on MNE’s market value. We elaborate an integrative theoretical framework which brings together two theoretical and empirical strands of literature: the internalisation theory (Buckley & Casson, 1976; Hennart, 1982; Rugman, 1981) and its predictions on the relationship between international diversification and firm performance (Li, 2007 for a review); and the literature on R&D internationalisation (Narula & Zanfei, 2004 for a review). Building upon this theoretical framework, we develop a set of hypotheses which are tested using a unique longitudinal dataset that tracks the internationalisation of the R&D activities of 29 large pharmaceuticals and chemicals MNEs from 1990 to 2005. Following a long tradition in bibliometric studies (De Solla Price & Beaver, 1966; Merton, 1973), we use data on scientific publications to measure the geographical dispersion of their research activities and data on their co-authorship patterns to capture the extent of interaction between foreign R&D units and the rest of the organisation (i.e. knowledge integration) as well as other firms and institutions in host regions (i.e. local embeddedness). We find a positive relationship between the geographical dispersion of scientific research and MNE’s market value. Similarly, the degree of interaction with the local scientific community has a positive impact on MNE’s market value. The same relationship could not be confirmed for the MNE’s knowledge integration capability, however. This study makes two contributions to the literature on R&D internationalisation. First, it examines the effect of R&D internationalisation on MNE’s market value, a critical relationship
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which has not yet been tested even though it is implicit in the new organisational learning perspective of internationalisation of production (Barkema & Vermeulen, 1998; Zahra, Ireland, & Hitt, 2000a) and in the empirical findings on the moderating impact of R&D on the relationship between international expansion and firm performance. Second, this paper documents the effect of local embeddedness and knowledge integration on the relationship between R&D internationalisation and MNE market valuation. By investigating these two elements of the R&D internationalisation process this study contributes toward a better theoretical and managerial understanding of factors critical to the success of R&D internationalisation processes.
BACKGROUND AND HYPOTHESES R&D Internationalisation and Market Value The relationship between internationalisation and firm performance has drawn enormous scholarly attention within the international business literature over the past three decades (see Li, 2007 for a review). To explain the relationship between international expansion and firm performance, most studies have built upon the internalisation theory (Buckley et al., 1976; Hennart, 1982; Rugman, 1981). The essential argument of this theory is that foreign direct investment occurs when firms can maximise profits by creating internal markets for intangible assets such as technological know-how, marketing and managerial skills. Thus MNEs can enhance the value of their assets by expanding their activities abroad if the expected gains from exploiting intangible assets in a foreign country are greater than the costs of doing business abroad such as liability of foreignness (Zaheer, 1995), as well as increased coordination, distribution and management costs. The evidence on market valuation effects of international expansion has been mixed: some studies have found no relationship (Morck & Yeung, 1991), some found a negative relationship (Denis, Denis, & Yost, 2002), and others have found a positive relationship between
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internationalisation and market valuation (Delios & Beamish, 1999). More recently, a few studies have postulated and found evidence for a non-linear relationship between geographic diversification and firm’s performance (Hitt, Hoskisson, & Kim, 1997a; Lu & Beamish, 2001, 2004). Importantly, research has also documented a moderating impact of R&D intensity on the relationship between internationalisation and firm’s market value (Lu et al., 2004; Morck et al., 1991): the impact of R&D spending on market value tends to increase with the international expansion of MNE’s production activities. As stated by Morck and Yeung (1991) “the value of multinationality stems from the possession of intangible assets, and the value of these intangible assets increases with the degree of multinationality” (p. 176). These findings suggest that the benefits of internationalisation for MNE’s might indeed be related also to the internationalisation of R&D and not only to the internationalisation of production. The internalisation theory argues that it is the need for leveraging firm-specific intangible assets that explains the propensity of a firm to expand its operations in foreign markets, which, in turn, is positively related to firm’s performance. Although most of the benefits from exploiting firms-specific assets in international markets were initially attributed to economies of scale and scope and to operational flexibility (Caves, 1971), more recently researchers have focused on the learning opportunities generated by a geographically dispersed network of operations (Barkema et al., 1998; Hitt et al., 1997a; Kuemmerle, 1996; Zahra et al., 2000a). This perspective emphasises that the expansion into foreign markets increases firms’ exposure to different innovation systems and local knowledge bases, enhancing the speed of technological learning (Zahra et al., 2000a). However, because the ability to absorb external knowledge is regulated by the recipient’s internal R&D capabilities (Cohen & Levinthal, 1990), accessing location-specific knowledge assets requires a geographically dispersed network of subsidiaries engaged in R&D. The mere possession of a global network of subsidiaries with no R&D activities on their own is not enough (Singh, 2006).
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The above arguments further suggest that one should examine the role of R&D internationalisation in explaining the ability of MNE to create market value. This perspective to explaining the ‘MNE advantage’ states that firms will locate R&D abroad so as to tap into knowledge and technology sources in centres of scientific excellence around the world with the aim of either enhancing their existing technological assets or acquiring completely new technological assets (Dunning et al., 1995; Kuemmerle, 1999). A growing number of empirical studies have provided support for these drivers of foreign R&D investment (Almeida & Phene, 2004; Chiesa, 1996; Criscuolo, Narula, & Verspagen, 2005; Florida, 1997; Frost, Birkinshaw, & Ensign, 2002; Iwasa et al., 2004; Kuemmerle, 1999; Serapio & Dalton, 1999) and also for the new conceptualisation of the MNE as ‘transnational’ corporation (Bartlett, 1986; Bartlett et al., 1989) as a ‘metanational’ corporation (Doz, Santos, & Williamson, 2001) or as a ‘differentiated network’ (Ghoshal et al., 1997). According to models such as those above, the main competitive advantage of MNEs derives from their ability to take advantage of cross-border knowledge asymmetries by accessing pockets of excellence around the world through their network of R&D units. Foreign subsidiaries are in these models a key contributor to firm-specific advantage thanks to their ability to generate a distinct set of technological capabilities that reflects the unique combination of host country market, technological and institutional features (Frost et al., 2002; Pearce, 1999; Zander, 1999). The increasing level of internal technological specialisation achieved through a geographically dispersed network of R&D units should therefore lead to greater innovation output (Penner-Hahn et al., 2005), and it should also enable the development of more valuable innovations. Such benefits may derive from the international cross-fertilisation of knowledge within individual technologies and from the recombination of knowledge across related technologies and knowledge sets (Nobel et al., 1998; Zander, 1999). As argued by Bartlett and Ghoshal (1990), ‘by creating flexible linkages that allow the efforts of multiple units to be combined, a company can
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create synergies that can significantly leverage its innovation process’ (p.222). This observation was echoed by Singh (2006), who maintained that R&D internationalisation enables firms to overcome the constraints of local search by allowing them to explore across geographical boundaries. The link between R&D and market value is more directly analysed within the industrial economic literature (see Hall, 2000 for a review), although these studies do not separately consider the international dimension of R&D. The approach in these studies is based on the notion that technological activities create intangible capital which generates future income and profits, and that such intangible capital is reflected in the firm’s market valuation. Hall et al. (2005) found that not only the volume of R&D matters for market value, as measured by R&D investment and number of patents, but also, the value of a firm’s innovations, as measured by patent citations. A recent study by Nesta and Saviotti (2006) showed that the stock market appears also to value the coherence of the knowledge base of biotechnology firms, i.e. the extent to which the different technological capabilities held by biotech firms are related to each other. Investors thus value not only the quantity but also the nature and properties of the firm knowledge capital. These arguments suggest: Hypothesis 1
The geographical dispersion of research activities has a positive impact on the market value of the MNE
Local Embeddedness and Market Value A geographically dispersed network of R&D units does not, in itself, guarantee that firms will be able to assimilate and leverage knowledge from across national borders. In knowledge-intensive collaborations, value creation is based on the combination of previously distinct knowledge items, disclosed in discrete, time lagged exchanges (Kogut & Zander, 1992). Knowledge disclosure is not automatic, however. Because knowledge, once disclosed, loses its value as an object of trade (Arrow, 1962), and because shirking is a perennial threat in knowledge-intensive collaborations,
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the exchanging parties need to cultivate mutual trust and reinforce norms of reciprocation for value creation to become possible (Nahapiet & Ghoshal, 1998; Yli-Renko, Autio, & Sapienza, 2001). Deep relational embeddedness is therefore necessary to access valuable knowledge in host countries (Das & Teng, 1998; Lane & Lubatkin, 1998). Foreign subsidiaries face numerous challenges in establishing such embeddedness due to their liability of foreignness. Creating relational social capital in foreign markets is expensive and time consuming, not least because of the social and cultural barriers foreign firms face when trying to solicit links with foreign customers, competitors, and regulatory authorities (Zaheer et al., 1997). Even with an established network of R&D units abroad, therefore, MNEs may still be hampered in their efforts to create value by exploiting cross-border knowledge asymmetries. Empirical studies have shown that local embeddedness has a positive and significant impact on foreign subsidiaries’ market performance (Andersson, Forsgren, & Holm, 2001; 2002; 2001). The deeper, more repetitive and extensive a subsidiary’s relationships are with local suppliers and customers, the stronger will be its ability to solicit knowledge disclosures and also to access complex and tacit forms of knowledge from the local environment (Lane et al., 1998). The ability to absorb new knowledge from the host country context will also affect the subsidiary’s innovative performance and its ability to contribute to the technological competences of the rest of the organisation (Nobel et al., 1998). Therefore, it is through the creation of linkages with local counterparts that foreign subsidiaries are able to source technological knowledge from local knowledge pools and to become a provider of knowledge to other units of the MNE, i.e. to enhance the knowledge capital of the entire organisation. These arguments suggest that: Hypothesis 2
The deeper the local embeddedness of an MNE’s foreign R&D units in host regions scientific community, the higher its market value will be
Knowledge Integration and Market Value
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In order to reap the benefits of host-country knowledge networks, knowledge developed in different host regions needs to be transferred and integrated within the rest of the organisation. It is often maintained that transfer of knowledge is easier to achieve within organisations than between organisations (Grant, 1996a; Kogut et al., 1992). According to Kogut and Zander (1993a) MNEs are ‘social communities that specialise in the creation and internal transfer of knowledge’ and MNEs ‘arise not out of the market failures for the buying and selling of knowledge but out of its superior efficiency as an organisational vehicle by which knowledge is transferred across borders’ (p. 625). The intra-organisational process of knowledge transfer also implies the crucial process of knowledge integration (Grant, 1996b), or knowledge transformation (Zahra & George, 2002). Knowledge as such does not yet enhance corporate value: to boost value creation, knowledge needs to be integrated into the MNE’s organisational processes and its products. Grant argued that ‘the critical source of competitive advantage is knowledge integration rather than knowledge itself’ (p. 380). Similarly, Zahra and George (2002) emphasised the process of ‘transformation’, and related capability to combine existing knowledge with the newly acquired and assimilated knowledge, as a critical phase that set the stage for subsequent exploitation of knowledge absorbed from external sources. They argued that this process yields new insights, facilitates the recognition of opportunities and thus provides the basis for future value creation. Even though firms are commonly considered to outperform strategic alliances and markets in their ability to transfer and integrate knowledge across borders (Almeida, Song, & Grant, 2002), transferring idiosyncratic and tacit knowledge is nevertheless a complex and often difficult process (Kogut et al., 1992; Szulanski, 1996; Teece, 1977). Notwithstanding the developments in ICT, which have facilitated the management and coordination of international research networks, geographical distance remains a barrier to the transfer of knowledge especially if it is tacit in nature (Howells, 1995). Even in the case of more easily communicable knowledge,
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foreign R&D subsidiaries may sometimes get locked out of intra-organisational knowledge flows, for various reasons (Birkinshaw, Hood, & Jonsson, 1998). Sometimes the sender and receiver lack the ability and willingness to transfer and assimilate new knowledge (Szulanski, 1996). Foreign R&D affiliates may be reluctant to transfer knowledge to other units of the MNE for fear of losing an ‘information monopoly’ within the company as well as their status as a ‘centre of competence’ in a specific area (Foss & Pedersen, 2002). In certain R&D organisational structures the motivational disposition of the sender unit may be adversely affected by inter-unit rivalry (Criscuolo et al., 2007). Inter-unit rivalry contributes also to the inter-unit organisational distance which, in turn, represents another obstacle to knowledge sharing (Bartlett et al., 1989). Distant R&D units may find it difficult to communicate because the exchange of their knowledge takes place mainly through personal contacts (De Meyer, 1993). Whatever the reason for subsidiary lock-out, poor integration of foreign R&D units greatly reduces the ability of those units to contribute to the value creation capacity of the MNE. Empirical evidence supporting this conclusion was provided by Birkinshaw and colleagues (1998), who found that if the subsidiary lacked initiative, or if the MNE failed to actively integrate the specialised input of the subsidiary, subsidiary’s contribution to MNE advantage was adversely affected. Despite all these challenges in achieving intra-unit knowledge transfer and integration, research has found that the creation of a common culture and convergence towards the same set of values through the use of corporate socialisation mechanism (Björkman, Barner-Rasmussen, & Li, 2004; Gupta et al., 2000) and the development of close interpersonal networks among MNE’s units (Hansen, 1999; Szulanski, 1996; Tsai, 2001; Tsai & Ghoshal, 1998) facilitates this process. The more MNEs are able to foster internal communication and personal collaborations between foreign subsidiaries and home country units, the greater the likelihood that the specialist knowledge of foreign R&D units will be harnessed for MNE value creation. Therefore, we postulate that:
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Hypothesis 3
Greater knowledge integration from foreign locations will be positively associated with MNE market value
METHODS Although the benefits of basic research are more difficult to appropriate than applied research, science-based technology companies carry out a considerable amount of basic research. There are number of incentives for companies to engage in long-term basic research. According to Rosenberg (1990) a firm that perform basic research may benefit from first-mover advantage, unexpected innovations resulting from basic research activities, increase ability in selecting areas of applied research and in evaluating the outcomes of applied research. In addition to these direct benefits, other studies have shown that by engaging in basic research firms are better able to monitor, appropriate and absorb scientific information and technical know how (Arora & Gambardella, 1994; Cockburn & Henderson, 1998; Gambardella, 1992; Henderson & Cockburn, 1996). In particular, Cockburn and Henderson (1998) argue that by investing in basic research pharmaceutical firms are “actively connected to the wider scientific community” (p. 158) and they show that by being ‘plugged’ in to the scientific network they can increase their performance in drug discovery. Firms develop these linkages by hiring high-quality researchers, rewarding researchers based on their publications performance, but above all by encouraging them to actively engage in research collaborations with the academic community (Cockburn et al., 1998). As a result pharmaceutical firms but also other science-based technology firms not only perform basic research but they also publish their research findings (Hicks, 1995; Tijssen, 2004). By publishing selected results in the open literature corporate researchers increase their visibility in the scientific community which in turn enables them to gain access to an information network and to technical opportunities. Publications in peer-reviewed journals may also signal high R&D
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capabilities which in turn attract potential partners, private capital and public research funding as well as enable to recruit brilliant researchers and technicians (Hicks, 1995; Tijssen, 2004). Thus following a long tradition in bibliometric studies we use publication counts as an indicator of companies research activities (Halperin & Chakrabarti, 1987; Small & Greenlee, 1977) and use co-publications in peer-reviewed journals to measure the extent of interaction and collaborative research activities among corporate researchers and between corporate researchers and the local scientific community (De Solla Price & Beaver, 1966; Merton, 1973). Joint authorship often entails face-to-face interactions, extensive discussions, joint problem solving, exchanges of information and tacit knowledge. Although joint-authorship maybe reflect other type of interactions not leading to the exchanges of information (Katz & Martin, 1997), it however measures the extent of engagement in research between scientists working in different units of the MNE and between them and external counterparts.1
Sample and data construction The sample from this study was drawn from the Fortune 500 list in 1998: all 29 pharmaceuticals and chemicals MNEs that appeared in this list were selected2 we focused on these MNEs given that these companies publish heavily and the number of papers published by these companies is similar to that produced by universities or institutes of equal size (Hicks, 1995). We used the Dunn & Brandstreet’s Who owns Whom directory of corporate affiliation to derive a list of their
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Co-authorship involves a qualitatively different kind of interaction compared with that reflected in citations in scientific publications (Cockburn, I. M. & Henderson, R. M. 1998. Absorptive Capacity, Coauthoring Behavior, and the Organization of Research in Drug Discovery. Journal of Industrial Economics, 46(2): 157-182). Articles in journals may be cited for a variety of reasons, not all of them reflecting a transfer of knowledge. Even when citations do capture knowledge flows they may only represent codified knowledge flows that occur impersonally and do not necessarily imply face-to-face interaction. 2 We decided to include chemical MNEs in the sample because most of them are very diversified companies with a strong presence in pharmaceuticals. This is reflected in their publications output. For example, more than one third of Basf publications over the period under analysis are in the area of clinical medicine and almost 15% of ICI publications are in biomedical research.
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subsidiaries at the end of 1999.3 This sample includes firms from different home countries and characterised by different level of R&D internationalisation. We measure each firm basic research output as the number of research papers published in international scientific journals. We extracted from the Web of Science all their publications during the period 1990-2005 in journals covered by Thompson-ISI database. The analysis covers all research papers listing at least one author affiliated to one of the companies in the sample or to one of their subsidiaries. Because some university laboratories have similar names as these firms and others are named after family trusts unrelated to the business (for example Wellcome Trust), we have searched the web sites of the companies in the sample to ensure that the publication was the research output of one of their subsidiaries. Extensive cleaning of the address information of all the authors was also undertaken to assign each publication to a particular location. For the larger and the most active countries we have used the postcode and, whenever missing, the name of the town, to assign each paper to either a US state, a European NUT2 region, a Japanese prefecture, a Canadian province or an Australian state. For all the remaining cases we have assigned the publication to a particular country. A total of 68,469 publications were identified of which 26,248 were published by foreign subsidiaries of the companies in the sample. Almost 70% of overall number of publications listed authors from different institutions besides the firm and a slightly higher proportion (72%) is found for publications originating from foreign subsidiaries. This suggests that collaboration is a significant characteristic of the research activities undertaken by these firms both at home and abroad. Firm to firm collaborations account for a very small proportion of the data: only 5,445 publications list authors affiliated to research units owned by the same company. When analysing
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In the case of GSK which was created in 2000 as a result of the merger between Glaxo Wellcome and Smith Beecham, we have kept these latter companies as separate entities before the merger and then attributed all their publications to GSK after the merger. A similar procedure has been adopted to deal with Rhone Poulenc and Hoechst which merged in 1999 to form Aventis.
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the publication record of foreign subsidiaries, this number drops to 1,746 articles. As shown in Table 1 there are marked differences across companies in the number of scientific papers published overall but in particular outside the home country. Foreign subsidiaries of Japanese MNEs have almost no publications, while the number of publications by foreign subsidiaries of GSK, Novartis and Roche accounts for almost half of the overall publications record. This different pattern in publications by foreign subsidiaries is also reflected in the R&D organisational structure. Figure 1 shows the R&D facilities of Mitsubishi Rayon which are all located in Japan, while Novartis are R&D sites in the US, Europe and Japan (see Figure 2). As reported in the table in Figure 2, each R&D facility is specialised in particular disease areas, which is in line with the adoption of an R&D differentiated network strategy. /**** INSERT FIGURE 1 and FIGURE 2 HERE *****/ There is also a substantial variation across firms in the co-authorship behaviour of their foreign subsidiaries. The share of co-authorship with other researchers inside the firm ranges from 5% to 56%, while the fraction of co-authorship with local scientists and academics varies between 1.8% and 26.7%. Figure 3 shows the network of co-authorships among scientists working at different R&D facilities of Novartis in 2005. An important feature of this network is that there are a very small number of lateral collaborations (subsidiary-subsidiary), while the majority of intra-firm coauthored papers involve at least one scientist from the headquarters in Basel. The high number of collaborations between the headquarters and the research facility in East Hanover, can be explained by the large size of these two research sites. /************ INSERT TABLE 1 AROUND HERE ****/ /**** INSERT FIGURE 3 HERE *****/
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Finally we classify each article in a particular discipline according to which journal the article was published. we used the journal classification in subfields of science using the classification scheme developed by CHI Research Inc (Hamilton, 2003).4 In order to reduce the noise introduced by changes in scientific research activities we summed articles over the past three years to build the internationalisation indicators. This implies that the dataset is an un-balanced panel of 29 companies between 1993 and 2005.
The modelling procedures Following previous studies within the international business literature (Allen & Pantzalis, 1996; Kim & O., 1886; Lu et al., 2004; Morck et al., 1991; Pantzalis, 2001 among others) the market value of a MNE i at time t is modelled as a function of intangible assets measured by R&D (RDit) and investment in other intangible assets (INTAit) intensity and the firm’s involvement in different geographic locations. In particular we examine three different aspect of the internationalisation of MNE research activity: their geographical dispersion (DISPit), the level of local embeddedness into the scientific community in the host-region (LEMBit), and the level of cross-regional knowledge integration (KINTit). Thus the market value equation expressed in logarithm is equal to:
vit = α 0 + α1rdait + α 2 int ait + α 3dispit + α 5lembit + α 6 k intit + βX it + uit
(1)
where vit is the market value of the MNE measured by the Tobin’s q, uit is an error term and Xit is a matrix of other control variables expressed in logarithm as described below. Estimation of equation (1) raises two econometric problems. The first one is that the relationship between the variables capturing the different aspects of internationalisation of research and the firm market value may include firm and time-specific effects that are correlated with the variables 4
In the latest version of this classification (2005) there are 9546 journals grouped in 138 subfields such as Cancer, Cardiovascular System, and Endocrinology.
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of interest. Previous studies within the industrial economic literature have accounted for time specific effects by including year dummies (Griliches, 1981; Hall, 1993), this implies measuring the log of Tobin’s Q relative to the market as a whole. To account for unobserved firm-specific effects we estimated equation (2) using a fixed effect model after having rejected the consistency of the random effect model using the Hausman (1978) specification test. The second problem is that the fixed effect estimations suffer from heteroskedasticity and autocorrelation. One way to correct for the presence of heteroskedasticity and autocorrelation is to use the Kmenta (1986) autoregressive heteroskedastic model (for some applications of this technique in the context of internationalization and firm performance see Gomes & Ramaswamy, 1999; and Lu et al., 2004). This two-stage generalised least squares regression generates in the first stage autoregressive parameters for each firm which are used in the second stage to transform the model into one where the effects of heteroskedasticity are minimised. Because of the large number of parameters to be estimated with this approach Baltagi (1986) suggests to use this method when N, the number of firms, is lower than T, the number of periods. Since our dataset does not fully satisfy this condition, we estimated a fixed effect model with autoregressive error of order 1 and adjust for panel heteroskedasticity using the White’s correction. This approach represents an alternative way of dealing with the presence of both heteroskedasticity and autocorrelation. The pattern and significance of the results did not change from the one reported here.
Variables Dependent variable. Tobin’s q is proxied by the ratio between market capitalisation divided by the book value of total assets using data provided by Worldscope. Market capitalisation is defined as the number of common share outstanding evaluated at the market price at the end of the year. Other authors have proposed more complex and cumbersome methods for calculating Tobin’s q
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(Lang & H., 1989; Lindenberg & Ross, 1981), but the different approaches tend to yield very similar values to the ones obtained using simpler formulas for approximating Tobin’s q (Chung & Pruitt, 1994). Independent variables. Geographical dispersion. To capture the internationalisation of MNEs research activities we have constructed two indicators. Following Singh (2006), the first indicator is equal to the Herfindahlbased index of geographic distribution of the MNE publications taking the country as a geographical unit. This index was derived for each company i and for each year t using this formula:
⎞ ⎛ ⎜ pubn ,i ,t ⎟ Herfit = 1 − ⎜ ⎟ ⎜ ∑ pubn ,i ,t ⎟ ⎠ ⎝ n
2
where pubnit is the number of publications from country n by company i in year t. This index is equal to 0 when research activities are concentrated in one location. The second indicator, the network spread index, tries to account for the overall tendency of the companies in the sample to generate scientific publications in a particular field in a given foreign country. This is equal to a weighted average of the number of foreign countries (n) where company i published in year t in discipline k over total number of foreign countries in which, potentially, the company could have published in that discipline and in that year. Theoretically this number could include all the countries in the world, but in practice we have taken it to be the number of countries where all the firms in the sample are active in that field of research. The weights are equal to the share of publications in discipline k in the total portfolio of publications of company i.
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netit =
1 pubkit nitk k ∑ pubkit ∑ nitk k
i
This varies between 0 and 1, with values nearer to 1 indicating a higher level of geographical dispersion. Local embeddedness The degree of local embeddedness in the host scientific community is captured by the number of co-authorships between a researcher working in a foreign subsidiary and research partners located in the same US, Canadian or Australia state, European region or Japanese prefecture or in the same country for the remaining areas. In particular this index has been constructed as a weighted average of the share of joint authorships by researchers working in a foreign subsidiary of company i with local counterparts in scientific field k, in year t, and in region n over the total number of co-authorships with local and non-local counterparts by the same foreign subsidiary. Co-authorships between scientists working in the same subsidiary and in other subsidiaries of the same MNE are not taken into account. For example a publication that includes two scientists from GSK subsidiary in Research Triangle Park (North Caroline), two scientists from the University of Duke (North Caroline), and one scientist from the University of Wisconsin, would generate six co-authorships but only four will be considered as local. The weights in this index try to account for the probability of a region to attract research in a particular scientific domain and they are equal to the share of co-authorships in region n, in discipline k, and in year t over the total number of co-authorships in that region. The index is derived using this formula:
lembit =
1 coauthknit local _ coauthknit nk ∑ coauthknit ∑ total _ coauthknit k
k
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This index varies between 0 and 1, with values nearer to 1 indicating a strong degree of interaction on average between foreign subsidiaries of company i and the local scientific community. Cross-regional knowledge integration. We measured the extent of integration between the scientific knowledge produced by foreign subsidiaries and the rest of the organisation by calculating the density of the network of intra-firm co-authorships. This measure reflects the fact that knowledge integration is an organisational rather than a subunit capability (Grant, 1996a). To derive the density of this network we considered each foreign subsidiary as a separate node but all subsidiaries in the home country as a single node. Each co-authorship represents a tie in this network but co-authorships between scientists working in the same subsidiary, i.e. loops, have been excluded. The density of this network is therefore equal to the number ties actually present in the network divided by the number of all possible ties (Wasserman & Faust, 1994). This index is calculated as:
k intit =
tiesit git ( git − 1 ) / 2
Where git is equal to the total number of nodes in the network for firm i and time t as defined above. Isolate nodes, in other words subsidiaries whose scientists have not authored a paper with colleagues working in other research facilities, have been included to derive the total number of nodes. Two densities were derived, one for the dichotomised network (kint), i.e. disregarding the frequency of collaboration between two nodes, and one for the valued network (kint_valued). For the latter the nominator of the density is equal to the total number of co-authorships between the nodes in the network. The density derived from the dichotomised network varies between 0, if there are no intra-firm collaborations, to 1, if all subsidiaries have collaborated at least once in a given year.
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Intangible assets. R&D intensity and investment intensity in other intangible assets are used as proxies for firm’s intangible assets. As shown by the industrial organisational economics literatures (see Hall, 2000 for a survey) R&D investments is an important determinant of firm’s market value. Other intangible assets such as advertising costs, trademarks, licenses and consumers goodwill are also associated with enhanced market values (Hall & Oriani, 2006; Morck et al., 1991). R&D intensity was measured as the ratio of research and development expenditures to a firm’s total assets. Equally, the intensity of investment in other intangible assets is derived dividing these costs by the firm’s total assets. By using total assets in the ratio we avoid problems of artificial correlations with firm size (proxied by firm sales).
Control Variables Firm size, measured by total sales in millions of 1990 US$ (size), was included to control for economies and diseconomies of scale at the company level. Financial leverage proxied by the ratio of total liabilities to total assets (debta) is also included to control for any variation of firm values due to differences in capital structures (Jensen 1989). To control for the overall propensity of firms to publish their research outcomes we have included the share of number of publications over R&D expenditures expressed in millions of 1990 US$ (pub_int).
ANALYSIS AND RESULTS Table 2 gives descriptive statistics and correlations between the variables used in the regression analysis. The magnitude of the correlations suggests that multicollinearity is not a problem. The results of the estimate of equation 1 are reported in Table 3. Model 1 is the baseline model which includes only the control variables and the two measures of intangibles assets. The R&D intensity has a positive and significant impact on Tobin’s Q, while the financial leverage has a negative and significant influence on market valuation. All the other control variables are not significant across the different specifications, except the publication intensity which becomes significant in
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Models 2-6. Estimates of Model 2 show that, consistent with Hypothesis 1, the effect of a geographically dispersed research activities has a positive and significant effect on firm market valuation. Hypothesis 1 is also supported when we use the alternative measure of geographical spread of research activities (network spread) in Model 5. The results regarding the impact of local embeddedness of foreign research units on the market valuation of MNEs are presented in Model 3. Consistent with Hypothesis 2, higher levels of interaction between foreign subsidiaries and the local scientific and technological community are associated with higher market valuations. The positive relationship between cross-regional knowledge integration and market value postulated in Hypothesis 3 is instead not supported. Results of Model 5 show not only that the density of the intra-firm co-authorship network is not positive, but instead is negative and significant. However this finding is not robust if we use the network spread index to capture geographical dispersion of research activities (see Model 5) and when we proxy knowledge integration by measuring the density of the value network of intra-firm joint authorship (see Model 6): both in Model 5 and Model 6 the coefficient on knowledge integration is still negative but it fails to reach the standard level of statistical significance. DISCUSSION We set out in this paper to shed light on the determinants of the ‘MNE advantage’ by examining associations between geographical dispersion of R&D and the market value of MNEs operating in science-based industry sectors. Our research was motivated by the observation that, while MNE theories increasingly emphasise the knowledge generation advantages conferred by multinational operations over, e.g., scale and flexibility advantages, and despite increasing research on the associations between R&D internationalisation and innovation performance, there have been no studies thus far to examine the link between R&D internationalisation and MNE market value. Because market valuation can be regarded as the ultimate metric of the MNE
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advantage, an examination of the effect of R&D internationalisation on MNE market value provides a robust test of the determinants of this advantage in science-based sectors. Our ambition was also to shed more light on how exactly R&D internationalisation is associated with MNE market value. We hypothesised that MNE market value should be positively associated with geographical R&D dispersion, local embeddedness in the host country, and with knowledge integration between host country units and home country operations. Our empirical results provide support for two of the three hypotheses. The adoption of a more geographically dispersed network of research units was found to have a positive relationship with market value in science-based MNEs. This result is in line with the internalisation theory and suggests that a part of the MNE advantage can be traced back to the exploitation of international knowledge asymmetries, combined with the superior ability of MNEs (relative to markets) to transfer and assimilate knowledge inputs from across national borders. The positive relationship between R&D internationalisation and MNE market value is also consistent with the new organisational learning perspective on internationalisation which emphasises the technological learning opportunities conferred by international expansion. Our findings also expose some of the intricacies related to the international expansion of research activities. Alongside with the international expansion of research activities, an important part of the valuation effects of R&D internationalisation arise from the ability of R&D subsidiaries to connect to local research networks in host countries. A higher level of scientific collaboration with the local research community was associated with higher market value of the MNE, emphasising the importance of local connectivity for the exploitation of cross-border knowledge asymmetries. To maximise their advantage, MNEs not only need to expand their research activities abroad, they also need to engage in active, knowledge-intensive collaborations with local pockets of distinctive knowledge.
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Surprisingly, no statistically significant association was observed for the relationship between market value and cross-border knowledge integration. In addition, the sign of the relationship was negative. This finding may signal the difference between MNEs’ external knowledge access and absorption activities, on the one hand, and internal knowledge assimilation and transformation activities, on the other. The international dispersion of R&D activities enables the MNE to access cross-border knowledge asymmetries, and local embeddedness ensures that the MNE actually connects with them. Knowledge integration activities, on the other hand, have to do with the MNE’s ability to internally assimilate and transform the knowledge acquired, and this measure was based on the intensity of firm-internal links between foreign R&D units and home country operations. MNE theories advance both external connectivity and internal integration arguments to explain the ‘MNE advantage’. Where external connectivity to cross-border knowledge asymmetries is thought to set up the potential for MNE value creation, the internal knowledge transfer efficiencies of the MNE have been thought to play a role in realising that potential. Our findings suggest that these two aspects may not be as tightly coupled as traditionally assumed. What appears crucial is the connectivity to cross-border knowledge asymmetries, whereas integration with home country operations appears secondary or even trivial. Even though the supposedly superior ability of MNEs (relative to market) to transfer knowledge internally across national borders has been much emphasised in received MNE theories (Almeida et al., 2002; Birkinshaw et al., 1998; Hennart, 1982; Kogut et al., 1993b), this ability does not seem to be reflected on its market value, at least not in science-based industry sectors. Our finding calls into question these arguments and suggests that what may be crucial for the MNE advantage is its ability to connect into cross-border knowledge asymmetries and control the ownership of the resulting knowledge advances, regardless of where in the MNE organisation such advances are eventually created. For example, if the MNE adopts a differentiated R&D network structure,
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where each unit specialises in a particular area, might reduce the need for internal knowledge integration, as long as the MNE retains an ability to control and capitalise on the resulting IPR. Rather than perceiving MNEs as knowledge-integrating organisations, therefore, our findings emphasise the governance and control properties of MNEs, especially where the production and exploitation of IPR is concerned. Even though our findings appear to emphasise connectivity rather than integration, this does not necessarily mean that integration would not matter. Even though integration may not be central for IPR production and control, MNEs may still be able to derive advantages from the integration of operational knowledge, such as knowledge concerning manufacturing and supply chain operations, where scale advantages may be more pertinent than in R&D. Our findings thus emphasise the importance of developing a more nuanced view of what knowledge actually is integrated by MNEs and under which conditions such integration activities contribute to value creation. For example, where R&D performance is driven by specialisation rather than scale, MNEs may be better off emphasising geographical dispersion and local embeddedness over knowledge integration. It may be that under conditions where R&D productivity is strongly driven by specialisation, the cost of maintaining sufficient absorptive capacity in the home base becomes so high as to undermine any value-creating benefits achieved through integration. Overall, our findings suggest the need of further research to disentangle alternative determinants of the MNE advantage.
LIMITATIONS AND CONCLUSIONS In spite of the care taken with the empirical estimation of the market value equation and the benefits of a longitudinal design, this research has some limitations. The most notable limitation of this study is that R&D internationalisation is treated as an exogenous variable. As argued by Hall et al. (2005) R&D intensity is predetermined rather and exogenous which would produce
25
inconsistent estimates.5 In addition to account for the persistence in market value shocks the lagged values of Tobin’s q should have been included in the regression as an explanatory variable. We have tried to estimate the market value equation with a generalised method of moments (GMM) estimator (Arellano & Bond, 1991) using lagged values of the explanatory variables as instruments. Unfortunately, because of the small sample of firms in the dataset, we were unable to find valid instruments. Therefore care should be exercised in interpreting the observed relationship between R&D internationalisation and firm’s market value as being causal. Another limitation of this study lies on the use of co-authorship patterns to capture the crossregional knowledge integration and local embeddedness. Although this type of data are indicative of knowledge exchange and joint problem solving activities among scientists working in different research units, they do not capture other forms of knowledge integration taking place through mobility of researchers, cross-border team projects, electronic communities of practice, and/or other knowledge management systems that do not result or are not connected to a scientific publication. Equally co-authorship patterns do not capture other types of collaborations between foreign subsidiaries and local counterparts that are still conducive to knowledge sharing like strategic alliances or joint ventures. Finally, we have tried to summarise in one indicator the extent of local embeddedness of a MNE in multiple regions. While knowledge integration is an organisational rather than a foreign subsidiary capability (Grant, 1996a), local embeddedness is a distinguish feature of the external network of each subsidiary and thus it should be measured at the level of the unit instead of at the level of the firm.
5
In order to mitigate this problem we could have lagged all the independent variables by one year and use them as explanatory variables in the market value equation as done in the study by Lu and Beamish (Lu, J. W. & Beamish, P. W. 2004. International diversification and firm performance: the s-curve hypothesis. Academy of Management Journal, 47(4): 598-609). However this method does not usually solve the problems arising from the violation of the strict exogeneity assumption (Wooldridge, J. 2001. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press) and does not deal with persistence in market value shocks.
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Table 1. Total number of publications by foreign subsidiaries between 1990-2005 and their pattern of coauthorship
Firm Abbott Laboratories Akzo Nobel Aventis Basf Bayer Bristol-Myers Squibb Colgate-Palmolive Dow Chemical Glaxo Wellcome GSK Henkel Hoechst Imperial Chemical Industries Johnson & Johnson L'Oreal Merck & Co Mitsubishi Gas Mitsubishi Kasei Mitsubishi Rayon Mitsui Chemicals Monsanto Novartis Pfizer Procter & Gamble Rhone Poulenc Roche SmithKline Beecham Sumitomo Wyeth
Number of papers 3,094 639 5,065 1,715 2,724 4,060 99 323 3,717 4,977 235 2,221
Number of papers 142 305 1,179 414 1,028 282 18 144 1,988 2,697 96 1,093
1,098 1,902 914 4,575 89 943 93 301 1,507 5,861 3,839 1,933 3,678 8,206 3,127 974 2,155
174 629 129 1,516 12 1 63 2,854 1,145 251 813 5,662 1,968 4 81
Foreign subsidiaries Share of coauthorship with Number of coauthorships local counteparts 450 12.9 528 8.3 5,290 7.3 795 9.8 2,716 9.6 1,028 5.2 57 8.8 277 1.8 5,759 9.8 11,826 6.9 179 12.8 2,092 5.9 287 1,955 479 3,115 0 21 0 2 131 8,119 2,907 913 1,505 16,891 5,939 15 257
15.7 14.9 8.6 6.9 0 19 0 0 11.5 10.6 4 5.3 9.1 10.5 6.5 26.7 9.3
Share of intrafirm coauthorship 12 14 9.5 13.8 9.9 20.4 56.1 26 8.5 15.7 7.8 7.3 11.1 15 11.7 33.5 0 0 0 0 21.4 15.7 14.2 14.7 13.3 5.8 8 26.7 34.2
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Table 2 Descriptive statistics and correlationsa Variables Mean S.D. Min Max 1 2 1. tobinq 0.30 0.90 -1.69 2.18 0.28 0.19 0 0.57 .32 2. herf 0.00 0.01 0 0.08 .04 .29 3. lemb 0.02 0.04 0 0.41 .20 .11 4. kint 0.02 0.01 0 0.10 -.16 -.04 5. net 0.09 0 0.69 .29 .18 6. kint_valued 0.04 -0.52 0.22 -1.12 0.10 -.35 -.16 7. debta -2.67 1.52 -7.05 -0.69 .56 .36 8. inta 4.90 0.72 2.97 6.14 .39 .58 9. size -2.99 0.61 -6.11 -1.89 .54 .39 10. rda 2.79 0.76 0.74 4.31 .09 -.16 11. pub_int a Number of n=313. Correlations above |0.1| are significant at p
