R&D and Foreign Subsidiary Performance at or Below the Technology Frontier

René Belderbos, Boris LokshinFederico De Michiel
Management International Review volume 61pages 745–767 (2021)


We examine the effect of R&D on foreign subsidiaries’ productivity performance. We argue that both local R&D expenditures in the subsidiary and R&D conducted in the wider network of the multinational enterprise (MNE) for the subsidiary improve productivity but that their respective roles depend on whether the host country of the subsidiary is at or below the global technology frontier. Local R&D is more effective if the host country is at the frontier, while R&D conducted in the MNE network is more effective if the host country is behind the frontier. In the latter case, both types of R&D are complementary and reinforce each other’s effect on productivity performance. We test hypotheses on fine-grained longitudinal micro data on affiliate productivity and R&D investments. We estimate dynamic productivity models controlling for endogeneity and allowing for declining returns to R&D and productivity convergence.


Traditionally, the technological advantages of multinational enterprises (MNEs) are developed at home, where most of the R&D activities are concentrated (e.g., Berry, 2014), and then transferred to foreign subsidiaries. Subsidiaries conduct R&D to assimilate and adapt home based technological assets to the characteristics of their local market (Kuemmerle, 1997, 1999). Once successfully integrated, parent firm know-how helps subsidiaries to establish a competitive advantage in the local market (Buckley & Casson, 1976; Caves, 1996; Delios & Beamish, 2001; Dunning, 1993; Fang et al., 2007, 2013; Hymer, 1976; Martin & Salomon, 2003; Un, 2011) compensating for possible ‘liabilities of foreignness’ that arise from unfamiliar business environments (Zaheer, 1995).

At the same time, however, knowledge production has become increasingly globalized, with research hubs emerging around the world (Alkemade et al., 2015; Furman et al., 2002; Liu & Chen, 2012; OECD, 2016). R&D performed by MNEs’ local subsidiaries gains more prominence and subsidiaries gain more important R&D mandates, as firms seek to gain access to the valuable tacit and advanced knowledge present in host countries (Berry, 2006; Cantwell & Janne, 1999; Castellani et al., 2017; Driffield et al., 2016; Singh, 2007; Song & Shin, 2008). Conducting R&D in subsidiaries in host countries at the global technology frontier may have distinct advantages over R&D conducted by the MNE at home or elsewhere in the MNE’s network.

It follows that R&D investments by the subsidiary, on the one hand, and R&D investments and knowledge transfer by the MNE, on the other hand, will have differential importance in the subsidiary depending on the relative advanced nature of the environment for innovation in the host economy, with subsidiaries taking on different roles in terms of knowledge exploitation or knowledge sourcing and augmentation (Papanastassiou et al., 2019, p. 646). Surprisingly, extant research has not examined the joint consequences of these two sources of technology development and innovation for subsidiaries in detail. The literature has primarily focused only on the effects of R&D and the knowledge stock of the parent firm on subsidiaries (e.g., Almeida & Phene, 2004; Delios & Beamish, 2001; Fang et al., 2007, 2013; Phene & Almeida, 2008) or on the effect of overseas R&D and internationalization on parent MNE performance (Belderbos et al., 2015; Castellani et al., 2017; Driffield et al., 2016; Kafouros et al., 2008). Yet MNEs have to decide on the allocation of R&D investments at home and abroad, and preferably such allocation results in synergies between these R&D activities to enhance the competitiveness of foreign subsidiaries.

In this paper, we contribute an analysis of the joint and interactive effects of subsidiary R&D and R&D conducted by the MNE for the subsidiary on subsidiary performance. We start from the notion that innovation and subsidiary performance rely on frontier knowledge and technologies that need to be applied to local contexts, and that subsidiaries that are better able to integrate these different types of knowledge will exhibit greater performance (e.g., Belderbos et al., 2015; Michailova & Zhan, 2015; Scott-Kennel & Giroud, 2015; Un & Rodríguez, 2018). We argue that R&D conducted for the subsidiary in the MNE network and subsidiary R&D can reinforce each other’s impact on subsidiary performance, as R&D conducted in a subsidiary allows it to build up the necessary capabilities to effectively apply the know-how and expertise of the MNE and the results of R&D conducted for the subsidiary in the wider MNE network, and to combine local knowledge with MNE knowledge. In addition, we argue that it is crucial to make a distinction between subsidiaries active in an industry in which the host country is at the global technology frontier and subsidiaries in industries in which the host country is lagging behind (García-Sánchez et al., 2017; Salomon & Jin, 2008; Smith, 2014). We develop hypotheses on how subsidiaries benefit differently from their own R&D investments, R&D conducted by the MNE for the subsidiary, and their interactive effect, due to differences in the local host country environment. We confirm that local subsidiary R&D is more effective if the host country is at the technology frontier, while R&D by the MNE is more effective if the host country is behind the frontier. Only in the latter case, both types of R&D reinforce each other’s effect on the performance of the affiliate.

We test hypotheses on unique and fine-grained longitudinal data on R&D investments of foreign subsidiaries and R&D investments conducted by the MNE for the subsidiary, drawing on a dataset covering a large panel of 1756 foreign subsidiaries based in the Netherlands and active in multiple industries. A rare characteristic of these data is that it allows identifying R&D investments in the MNE network that are conducted specifically for a focal subsidiary. We measure performance as subsidiary productivity. Productivity is the efficiency with which capital and labor inputs are utilized to create firm value. It can be considered a direct function of technological change and innovation driven by R&D investments (Castellani et al., 2017; Driffield & Love, 2007; Griffith et al., 2006; Hall et al., 2012; Smith, 2014), which makes it an appropriate measure in a study focusing on the effects of R&D. We test differential effects of subsidiary and MNE R&D by exploiting variation across industries in the position of the Netherlands as being at, or below, the technology frontier.

Our study contributes to the literature on R&D internationalization by MNEs by showing that both subsidiary and MNE R&D investments have to be taken into account to study subsidiary performance, but that their effects and complementarity crucially depend on the host country’s relative technological strength. By highlighting the specific conditions under which complementarities within the MNE’s R&D network are likely to occur, we suggest an important potential boundary condition to earlier studies of knowledge complementarities in MNEs focusing on intra-firm licensing and affiliate R&D (Belderbos et al., 2008) and affiliate R&D and parent firm managerial knowledge (Berry, 2015). Our findings also add to the stream of literature investigating the consequences of the host country position in the international technological landscape, which has focused on directions of international knowledge flows (e.g., Cantwell & Janne, 1999; Singh, 2007), parent firm performance (Belderbos et al., 2015) and exports (Salomon & Jin, 2008; Smith, 2014), but has not examined foreign subsidiary performance. Our study answers to the call by Papanastassiou et al. (2019, p. 648) to examine the heterogeneous relationship between R&D configurations of MNEs and the geography of innovation.

Background and Hypotheses

The expansion and success of MNEs into new geographic markets often rests on the possession of specific ‘ownership advantages’ which give MNEs a competitive edge over local rival firms (Buckley & Casson, 1976; Caves, 1996; Dunning, 1993; Hymer, 1976; Teece et al., 1997). According to the knowledge-based view of the firm, knowledge that is rare, and difficult to imitate is central to the formation of these competitive advantages (Grant, 1996). Firms accumulate knowledge, especially technological knowledge, by investing resources in R&D activities. Through R&D efforts, firms developed intangible proprietary assets in the form of new products, improved production processes and acquired technical expertise, and as such can enhance value creation and productivity.

Among all MNEs’ value-chain activities, R&D remains the last to be internationalized substantially (Belderbos et al., 2013; Berry, 2014) with an appreciable share of R&D activities still conducted in the home country of the firm. Firms tend to maintain R&D centralized to maximize economies of scale and scope that characterize technology production at the firm level (Edler et al., 2002; Hewitt, 1980), while minimizing the risk of knowledge leakages to foreign competitors (Alcácer & Zhao, 2012; Singh, 2007).

The way MNEs generate value from knowledge has been traditionally viewed as a unidirectional process: Home base R&D was creating the knowledge assets that were then transferred to foreign subsidiaries and exploited in new geographic markets (Buckley & Casson, 1976). Subsidiaries conducted R&D to adapt processes and product to local market and manufacturing circumstances, in what is coined ‘home base exploiting R&D’ (Kuemmerle, 1997) or ‘asset exploiting R&D’ (Papanastassiou et al., 2019). The performance of foreign operations was ultimately dependent on knowledge developed at home. By successfully acquiring parent firm knowledge-based competitive advantages, newly established subsidiaries were able to overcome potential liabilities of foreignness occurring from operating a business in a new and unfamiliar environment (Zaheer, 1995).

In the last two decades, however, knowledge has become increasingly global: Technological specialized clusters and centers of excellence have emerged around the world across multiple industries (Furman et al., 2002). Consequently, the persistence of home country technological dominance is less evident. Knowledge-based competences and expertise drawn exclusively from R&D activities at home are no longer be sufficient to sustain the competitive advantage of MNEs’ foreign operations in particular if they operate in technologically advanced countries. The conventional process of MNE’s value creation from knowledge is increasingly inverted as MNEs are able to improve processes and develop new products by sourcing knowledge from abroad via ‘reverse knowledge transfer’ (Ambos et al., 2006; Driffield et al., 2016; Frost & Zhou, 2005; Håkanson & Nobel, 2000; Rabbiosi, 2011; Un & Cuervo-Cazurra, 2008). Subsidiary’s R&D mandate covered global development and subsidiary R&D is geared to augment the knowledge base of the MNE in what is coined ‘home base augmenting’ or ‘asset augmenting’ R&D (Kuemmerle, 1997; Papanastassiou et al., 2019). Extant literature has provided abundant evidence of subsidiaries investing in R&D to build knowledge assets and source local know-how in the host country (Asakawa, 2001; Berry, 2006; Cantwell & Mudambi, 2005; Singh, 2007; Song & Shin, 2008; Tsang & Yip, 2007). These two purposes of R&D have different consequences for the role of R&D in driving subsidiary productivity performance, on which we hypothesize below.

Technology development is widely understood as a cumulative process highly dependent on the specific geographic context. Technological expertise and innovations tend to originate and grow within a limited geographic area that permits complex knowledge and ideas to be transmitted and shared in the local scientific community via frequent face-to-face interactions (Audretsch & Feldman, 1996; Saxenian, 1994). As knowledge spillovers remain to a large extent geographically bounded (Jaffe et al., 1993; Singh, 2007), innovation activities are likely to concentrate in regional technological clusters and benefit from agglomeration economies (Chung & Alcácer, 2002). The cumulative nature of technology leads to a ‘path-dependence’ behavior that fosters further spatial technological specialization (Arthur, 1989; Cantwell & Janne, 1999). Depending on their pre-existing knowledge base, different countries assume specific technology profiles and become leaders in different industries (Furman et al., 2002).

If an MNE subsidiary is active in a country operating at the global technology frontier, it can create value by absorbing local external knowledge. The subsidiary takes on a ‘home base augmenting’ or ‘asset augmenting’ role in the MNE network (Kuemmerle, 1997). Local knowledge sourcing is crucial in these environments in order to compete with local leading firms and to develop a product offer that satisfies sophisticated and highly demanding local customers. Subsidiaries in technologically advanced countries can develop specific competences by working closely with productive suppliers and customers, establishing collaborations with local universities, starting joint R&D projects with local firms or hiring local specialized engineers to work in their facilities (Almeida & Kogut, 1999; Griffith et al., 2006; Un & Rodríguez, 2018). Local sourcing and collaboration activities tend to be more intensive in advanced local innovation environments (e.g., García-Sánchez et al., 2017).

These knowledge sourcing strategies require subsidiaries to develop local internal R&D capabilities to become a credible R&D collaboration partner and to evaluate, absorb and integrate relevant knowledge from advanced local sources in their innovation efforts (Cantwell & Mudambi, 2005; Cohen & Levinthal, 1990; García-Sánchez et al., 2017; Penner-Hahn & Shaver, 2005; Song et al., 2011; Un & Rodríguez, 2018). Hence, subsidiaries’ local R&D investments will be important and effective in improving their productivity performance in advanced innovation environments.

In contrast, when the host country is lagging behind the technology frontier and does not offer a subsidiary valuable opportunities to enhance knowledge sourcing and technological capabilities, the subsidiary will turn to the MNE network (Almeida & Phene, 2004; García-Sánchez et al., 2017; Scott-Kennel & Giroud, 2015) and will take on a ‘home based exploiting’ or ‘asset exploiting’ role (Kuemmerle, 1997). The MNE’s managerial and coordination capabilities and intra-MNE knowledge exchange allow subsidiaries to draw on internal knowledge that would be difficult or costly to acquire externally (Bartlett & Ghoshal, 1989; Hedlund, 1994; Kogut & Zander, 1993). Competences and expertise acquired by the MNE at home and elsewhere that are at the global industry technology frontier can help a focal subsidiary in countries behind the technology frontier to develop products and processes that outcompete those of local rivals (Criscuolo et al., 2010; Zeschky et al., 2014). Hence, innovation and productivity will be more dependent on knowledge available in the MNE network than on local knowledge, and it is R&D conducted in the MNE network for the subsidiary that is expected to have performance advantages.

The arguments above suggest that the productivity benefits of subsidiary R&D and R&D conducted in the MNE for the subsidiary will depend on the relative technological strength of the host country. Such technological strength differs across industries because it is a function of host country industry and technological specialization and the presence of local innovation clusters (Arthur, 1989; Cantwell & Janne, 1999; Chung & Alcácer, 2002; Furman et al., 2002). Subsidiary R&D is more important for subsidiaries located in a leading host country industry environment, while R&D activities conducted in the MNE network is more important in a technologically lagging industry environment. We formulate:

Hypothesis 1: Subsidiary R&D is more effective in improving subsidiary productivity if the host country industry is at the global technology frontier, while R&D conducted in the MNE network for the subsidiary is more effective in improving subsidiary productivity if the host county industry is lagging behind the global R&D frontier.

There are two related reasons why both subsidiary R&D and R&D conducted in the MNE can possibly reinforce each other’s effects on performance. The first is the general absorptive capacity argument (e.g., Cohen & Levinthal, 1990). Not only for external knowledge sourcing but also for effective knowledge transfer across units within the MNE, an absorptive capacity to understand, assimilate and exploit knowledge transferred is important for innovation (e.g., Belderbos et al., 2015; Gupta & Govindarajan, 2000). Although the advantage of the MNE is that it can transfer knowledge across borders internally to improve its competitive position in the countries in which it operates, knowledge developed within the MNE in one location still has to be absorbed, integrated, and operationalized by the MNE units elsewhere to reap performance benefits. Hence, both subsidiary R&D and R&D performed in the wider network of the MNE are likely to be simultaneously important for productivity performance.

Second, productivity increases typically require the combination of frontier technologies and the application of these technologies to local markets and manufacturing conditions, of which knowledge and expertise resides in local units. For instance, car manufacturers may develop their basic technologies on engines and aerodynamics at home but also have local R&D units abroad to adapt engine specification and car design to local tastes and environmental regulations. In any given location, additional technology development and adaptations are often needed to apply foreign technology to the specific characteristics of a local market (Kuemmerle, 1997). Technological frontier knowledge developed in a specific R&D unit of the MNE can be combined with local R&D in other units of the MNE to allow these units to cater to specific local demands or to enable the use of specific local input variants and manufacturing conditions. If the focal subsidiary is the source of frontier knowledge, then the MNE can combine this with dedicated R&D elsewhere in the network focusing on the application of this knowledge to other markets. Likewise, if the focal subsidiary does not have access to frontier knowledge locally but relies on knowledge transferred from within the MNE network, subsidiary R&D will allow for productivity enhancing development of applications to the local environment. This suggests again that R&D conducted in the MNE network and local subsidiary R&D are likely to be complements and to reinforce each other.

We argue that such complementarity is most salient in increasing subsidiary productivity performance if the host country industry environment is lagging the global technology frontier, rather than being at this frontier. If the subsidiary operates in a host country that is at the global technology frontier, R&D performed in the MNE network has less to offer to the subsidiary in terms of technologies to be refined or combined with local knowledge. By implication, there will also be less effort required by the subsidiary to absorb and use knowledge from the MNE in its operations. This is not to say that the combination of MNE and subsidiary R&D is not important for the MNE, yet this complementarity is more likely to be effective in reaching the objective of increasing productivity benefits elsewhere within the MNE rather than in the focal subsidiary. Subsidiary R&D, in a home based augmenting and reverse knowledge transfer logic, is then combined with dedicated R&D elsewhere in the MNE to apply the subsidiary’s advanced knowledge to local circumstances in the home country of the MNE or in third countries (e.g., Driffield et al., 2016; Håkanson & Nobel, 2000; Rabbiosi, 2011; Tsang & Yip, 2007; Un & Cuervo-Cazurra, 2008).

This contrasts with the joint roles of subsidiary and MNE R&D in local industry environments lagging the technology frontier. Here access to advanced knowledge requires knowledge transfer within the MNE network and relies on R&D conducted within the MNE in more advanced innovation environments. Investments in local R&D in the subsidiary focusing on ‘home based exploitation’ R&D allow the subsidiary to combine this MNE knowledge with local knowledge to adapt process and products to local circumstances and enhance productivity. The more advanced nature of knowledge available in the MNE network, with R&D in the MNE network drawing on more advanced technology environments,Footnote 1 makes it essential that the subsidiary develops a sufficient absorptive capacity by investing in R&D, to exploit and adapt this knowledge to its local operations.

The above arguments suggest that the complementary relationship between subsidiary R&D and R&D conducted in the MNE network for the subsidiary in enhancing subsidiary productivity performance is likely to be stronger in those industries in which the host countries is lagging the global technology frontier:

Hypothesis 2: Subsidiary local R&D and R&D conducted in the MNE network for the subsidiary have a stronger complementary effect on subsidiary productivity if the host country industry is lagging behind the global technology frontier than if the host country industry is at this frontier.

Data, Variables, and Empirical Model

We draw on unique unpublished micro panel data from the Netherlands’ Central Bureau of Statistic (CBS) official annual R&D surveys matched with production statistics on firms with more than 10 employees operating in the Netherlands. For the purpose of our research, we focus on firms that have a foreign owner and are under foreign control. We have access to data covering the years 1995 to 2003. Since smaller firms are randomly sampled in each year, we do not always have data for each year. In practice, this leads to unbalanced panel dataset where subsidiaries are observed on average for four consecutive years. This number reduces to two in final regressions we employ due to the use of the lagged dependent variable in the dynamic specification of the model and due to the estimation with GMM using lagged variables as instruments to control for potential endogeneity. The final sample is composed of 3564 observations on 1751 foreign subsidiaries.

We take subsidiary productivity, net value added per employee, as performance measure, following earlier studies (Belderbos et al., 2015; Castellani et al., 2017; Driffield & Love, 2007; Driffield et al., 2016; Smith, 2014; Todo & Shimizutani, 2008). Productivity measures the value created through the efficient use of capital and labor inputs. It is generally regarded as being a function of the firm’s knowledge base, which is driven by cumulative R&D investments (e.g., Belderbos et al., 2015; Hall et al., 2012). Hence, productivity has a more direct relationship with a firm’s product and process technology than other performance measures such as market value or accounting profits, which focus on benefits to shareholders. It reflects cost-reducing effects of R&D as well as the effects of new product development on price–cost margins. We derive the productivity model from a production function in which subsidiary value added is a function of labor, capital, and the knowledge stock driven by R&D investments.

The core explanatory variables are subsidiary R&D investment and R&D investment by the MNE. For MNE R&D, we can rely on a precise measure of R&D by the parent or by other subsidiaries specifically conduced specifically for, and financed by, the focal subsidiary in the Netherlands. This information derives from a question in the R&D survey asking for the amount of R&D conducted for the subsidiary by other units in the MNE network. Other variables included in the model are fixed capital and employment (expressed in full time equivalents). Capital, R&D, and values added are measured in constant prices. The models also include 9-year dummies and 28 Industry dummies.

In addition, we include two control variables that may affect productivity. We include a variable measuring the investments in ICT in the industry, using information from the EU KLEMS Growth and Productivity Accounts database prepared by a consortium of 24 research institutes and national statistical institutes on behalf of the European Commission.Footnote 2Information on ICT investments drawing on this source have been used by researchers to study output and productivity growth and to analyze sources of cross-country differences in productivity (e.g., Aghion et al., 2005; O’Mahony & Vecchi, 2009). Second we include a variable controlling for the level of embeddedness of the foreign subsidiary in the local innovation system (e.g., García-Sánchez et al., 2017; Isaac et al., 2019; Un & Rodríguez, 2018). Since our data draw on R&D surveys rather than innovation surveys, we do not have information on R&D collaboration at our disposal, but we can utilize information on outsourcing of subsidiary R&D to local partners. Specifically, local embeddedness measures the share of subsidiary R&D that is subject to such outsourcing.

We make use of variation across industries to determine if the relevant host country (industry) environment of the subsidiary can be characterized as either being at, or lagging behind, the global technology frontier. We identify a leading (lagging) industry if the industry R&D intensity in the Netherlands falls within (below) the OECD the top 33% of OECD countries. This approach has been used in prior literature (Belderbos et al., 2015; Griffith et al., 2006; Salomon & Jin, 2008) and is based on the idea that the higher is the relative intensity of the local industry R&D, the more subsidiary R&D can benefit from knowledge sourcing and spillovers. Given the relatively advanced status of the Netherlands economy and innovation infrastructure, a relatively large share of subsidiaries are observed in leading industries, as shown in Table 1. About 42% subsidiary observations relate to leading industries. Leading industries include food, office machinery, (electrical) machinery and wholesale; lagging industries relative to other OECD countries include textiles, wood, chemical and rubber. For some industries, such as business service, the status changes during the period.

The empirical model is based on a knowledge stock augmented Cobb Douglas model (e.g., Hall et al., 2012). Value added generated by a subsidiary is a function of labor, capital stock, and foreign and domestic R&D stocks. For firm i at time t:


where Y is output, C is the physical capital stock, L is labor input, and is the knowledge (R&D) stock. K is a function of investments in subsidiary and MNE R&D. The constants 𝛼𝑖αi reflect firm-specific (organizational and managerial) capabilities. The parameter 𝜎𝑖𝑡σit is a time-variant firm-specific efficiency parameter, which also depends on past productivity to allow for convergence in productivity over time (Klette, 1996). The knowledge stock K improves value add over and above the effects of capital and labor input, and hence positively affects the productivity of the subsidiary.

Table 1 Distribution of subsidiaries across leading and lagging industries

From Eq. (1) we derive the model for estimation through a few more steps, described in the Appendix. We divide by labor, take logarithms and difference the equation to arrive at a productivity growth specification. In this growth specification, firm fixed effects 𝛼𝑖αi drop out and growth in the knowledge stock can be captured by R&D investments:


Small letters denote variables in natural logarithms, 𝑞𝑖𝑡qit is labor productivity, Δ𝑖𝑖𝑡Δiit representsrepresentsthe growth in fixed capital investment, Δ𝑙𝑖𝑡Δlit is the growth in labor, and Zit are additional control variables that may have an influence on productivity growth. The variables 𝑟𝑠𝑢𝑏𝑠(𝑖𝑡1)r(it−1)subs and 𝑟𝑀𝑁𝐸𝑖𝑡1rit−1MNE represent the R&D expenditures of the subsidiary and the MNE, respectively, and are divided by value added to express them as an intensity. In addition to the interaction effect between subsidiary and MNE R&D to test hypothesis 2, the model includes their square terms to allow for declining returns to scale in R&D. This is an important feature of the model, since prior research has suggested that there are declining marginal returns to R&D (e.g., Acs & Isberg, 1991; Belderbos et al., 2015; Cohen & Klepper, 1996). Not controlling for this feature may lead to biased estimates on the crucial interaction term of subsidiary and MNE R&D.Footnote 3The equation includes year-specific intercepts 𝜆𝑡λt, a firm specific random effect 𝑣𝑖vi in addition to a an error term 𝜇𝑖𝑡μit.. Hence, even in terms of growth, we allow for time invariant unobserved firm characteristics that may play an idiosyncratic role in determining productivity outcomes.

We estimate (2) in level terms, by bringing the lagged productivity term to the right had side of the equation. This allows us to apply well established robust generalized method of moments (GMM) techniques that are conventionally conceived in level terms. We estimate Eq. (1) with GMM, instrumenting the variables of interest with lagged variables to control for endogeneity. GMM is suitable in empirical designs with dynamic panel data with a short time dimension (Kripfganz, 2016). It estimates a system of equations that includes a level equation, where level variables are instrumented with their lagged first differences, and a first differenced equation, where the instruments used are the lagged level values. The lagged level and differences are orthogonal to the error term and thus represent valid instruments (Blundell & Bond, 1998). In the level equation, year and industry dummies and individual random effects are included. GMM is the model of choice when the presence of a lagged dependent variable creates an endogeneity issue and allows for consistent estimates in the presence of autocorrelation and heteroscedasticity (e.g., Alessandri & Seth, 2014; Kripfganz, 2016).

We test differential effects of subsidiary and MNE R&D contingent on position of the host country industry with respect to the global technology frontier, by estimating separate models for the subsamples of subsidiaries operating in leading vs lagging industries. Split sample analysis allows testing for differences in the effects of R&D types as R&D coefficients, together with all other coefficients, are allowed to vary between leading and lagging industries (Belderbos & Zou, 2009; Belderbos et al., 2015; García-Sánchez et al., 2017; Hoetker, 2007).Footnote 4


Table 2 reports the descriptive statistics of the variables for each subsample: leading industries at the global technology frontier and lagging industries behind the frontier, together with their pairwise correlations. Subsidiaries report almost equal productivity in the two groups, but we note that productivity averages also depend on other factors such as fixed capital intensity. Subsidiaries’ R&D intensity is higher in leading industries, while the intensity of MNE R&D is higher in lagging industries. This is in line with our theoretical arguments to the extent that one would expect more R&D to be conducted where it is expected to have the most pronounced performance effects. The correlation levels similarly do not seem to indicate multicollinearity. The correlation between the dependent variable and the lagged dependent variable is high (0.75–0.78), as one would expect, but with correlation levels suggesting that there is considerable room to shift for yearly productivity shifts due to R&D investments. The VIF factor is 4.71 on average, with the highest individual score at 6.94. These scores do not indicate multicollinearity concerns, as they are below the cutoff point of 10.

Table 2 Descriptive statistics and correlations

Table 3 reports the results for the GMM estimation. Model 1 includes all foreign subsidiaries, Model 2 only the subsample of subsidiaries active in lagging industries behind the global technology frontier and Model 3 the subsidiaries in leading industries. The exogeneity of the instruments is not rejected by the Hansen test statistic, which indicated the validity of the instruments.

Table 3 GMM analysis of foreign subsidiaries’ productivity: Leading local industries versus lagging local industries

In model 1, the past labor productivity has an estimated coefficient of 0.65, which indicates that approximately two thirds of the productivity advantage remains over a year. We see a stronger persistence in productivity differences for leading industries (0.70) compare to lagging industries (0.57). Employment and fixed capital investment are significant. The positive linear and negative squared terms of subsidiary R&D and MNE R&D show that there are declining returns to R&D (see below). The significant interaction term indicates a complementarity between the two R&D expenditures. We do not find significant influences of the ICT and local embeddedness control variables.

Examining the estimates for the subsamples in models 2 and 3, we find that in industries at the global technology frontier, subsidiary R&D and its square term are significant, while in lagging industries only MNE R&D and its square term are significant, in support of hypothesis 1. A positive and statistically significant interaction effect between subsidiary and MNE R&D is observed in lagging subsidiaries, but not in leading industries, which supports hypothesis 2.

Although the significant negative coefficient of the quadratic term of subsidiary R&D in leading industries suggests declining returns to R&D, this is only mildly so. The turning point of R&D over value added can be derived as 3.9—well beyond the values of subsidiary R&D intensity observed in the sample. In lagging industries the effect of MNE R&D reaches its top at 0.19, which is at the end of the range of observed values. The steeper curve of the MNE R&D may suggest that there are stronger limits to the knowledge that can be coordinated and transferred across geographic distance, reducing the potential economies of scale of R&D in the MNE conducted for the subsidiary.

In lagging industries, the coefficients of subsidiary R&D are not significant, suggesting that local R&D alone does not contribute to productivity unless it is complemented by MNE R&D. It can be calculated that the marginal effect of subsidiary R&D becomes statistically significant when MNE R&D is above 0.05. Figure 1 plots the combined predicted effect of subsidiary R&D and MNE R&D on productivity in lagging industries, taking into account also the positive coefficient of the interaction term. The graph illustrates the value of combining the two types of R&D. The subsidiary productivity increase along the subsidiary R&D dimension is steeper at higher levels of MNE R&D. The marginal effect of subsidiary R&D becomes statistically significant when MNE R&D is above 0.05 and continues to increase if it is combined with higher levels of MNE R&D.

We conducted a number of supplementary analyses to examine the robustness of our findings. First, although previous studies indicates that most of the R&D effect on productivity occurs within one year (Hall et al., 1986; Klette & Johansen, 2000), we examined if knowledge stock represented by R&D would affect productivity with a longer lag. Applying a two year lag, we lose one year of R&D and moreover, because we have an unbalanced panel, the requirement of another adjacent year of R&D observations leads to the omission of quite a few additional observations. Observations drop from 3564 to 1980. We found similar effects, with effect sizes somewhat smaller, suggesting that the one year lag is the most robust approach.

Fig. 1
figure 1

Predicted effects of subsidiary R&D and MNE R&D (scaled by value added) on productivity for lagging industries

Second, we examined if the patterns for lagging industries were perhaps more pronounced for industries only reach the bottom 33% of the OECD average. However, given the relatively advanced nature or the economy of the Netherlands, this only left 101 observations. With these limited degrees of freedom, the complex GMM model with square and interaction terms was difficult to identify, and results showed high standard errors and insignificance for the focal variables.

Discussion and Conclusion

Our results suggest that the subsidiary R&D and R&D conducted by the MNE for the subsidiary have a different impact on subsidiary productivity performance, and that this crucially depends on the innovation characteristics of the subsidiary’s host country environment. When the host country provides abundant opportunities to source knowledge at the industry’s global technology frontier, the R&D conducted in the MNE network for the subsidiary does not have a notable effect on subsidiary productivity. Instead, investments in local subsidiary R&D significantly improve performance. R&D conducted in the MNE does become relevant for productivity when the host country industry is lagging behind the global technology frontier. This positive effect of R&D investments by the MNE becomes stronger if it is combined with R&D conducted in the subsidiary. Through investments in R&D, subsidiaries develop the capacity to more effectively process and apply advanced MNE knowledge and to combine this with local knowledge. This complementary, reinforcing, effect of the two types of R&D is important: A positive effect of subsidiary R&D in lagging industry environments is only observed if it is combined with a substantial R&D investment conducted by the MNE on behalf of the subsidiary.

Our findings suggest that the traditional view that considers MNEs to focus on exploitation of their home based expertise in foreign markets (Buckley & Casson, 1976; Caves, 1996; Hymer, 1976; Kuemmerle, 1997) applies to subsidiaries in technologically lagging host country environments. In contrast, subsidiaries benefit strongly from R&D in the host country if the country is at the global technology frontier, in line with prior research suggesting that host country knowledge sourcing is more likely to be the objective of local R&D in such environments (Belderbos et al., 2015; Berry, 2015; Cantwell & Janne, 1999; Kafouros et al., 2012; Singh, 2007; Song & Shin, 2008). Hence, the role of local subsidiary knowledge and MNE knowledge in innovation and productivity is contingent on the subsidiary in its host country environment (e.g., Scott-Kennel & Giroud, 2015). In this regard, our study answers to the call by Papanastassiou et al. (2019, p. 648) to examine in more detail the heterogeneous relationship between R&D configurations of MNEs and the geography of innovation.

Our study contributes to the literature on R&D internationalization by MNEs by showing the influence of the host country’s relative technological strength on the performance effects of investments in local R&D and R&D conducted elsewhere in the MNE network, as well as their potential complementarities. Until now, the subsidiary’s host country position in the international technological landscape has been examined in the context of international knowledge flows (Cantwell & Janne, 1999; Singh, 2007), parent MNE performance (Belderbos et al., 2015) and exports (Salomon & Jin, 2008; Smith, 2014). In our paper, we consider this dimension in assessing the role of international R&D configurations—R&D in the focal subsidiary and R&D conducted for the subsidiary in the MNE—in improving subsidiary productivity performance.

We also contribute to the literature that has observed complementarities between different types of R&D but that has mainly focused on relationships between internal and external R&D (e.g., Cassiman & Veugelers, 2006; Phene & Almeida, 2008) by highlighting complementarities between two types of internal R&D. In the context of multinational firms, we highlight the specific conditions under which complementarities are observed—in subsidiaries in lagging industries. This provides nuance to the literature on knowledge flows and R&D within multinational firm that has examined complementarities between intra-firm licensing and affiliate R&D (Belderbos et al., 2008) and affiliate R&D and parent managerial knowledge (Berry, 2015). Our results are consistent with the notion that subsidiaries’ R&D efforts in local innovation contexts increase absorptive capacity and ‘cross-fertilization’ potential with the know-how based on R&D conducted in the MNE, but, importantly, we qualify this relationship and establish the condition that this is only a significant feature of subsidiary R&D in technologically lagging host country environments. It is important to note that this does not mean that no complementarity between subsidiary R&D and MNE conducted elsewhere in the subsidiary exists if the subsidiary operates in an advanced R&D environment at the technology frontier; rather, the productivity benefits are likely to accrue to the parent firm or other subsidiaries of the MNE in a reverse technology transfer logic (e.g., Belderbos et al., 2015; Driffield et al., 2016; Håkanson & Nobel, 2000; Rabbiosi, 2011; Tsang & Yip, 2007; Un & Cuervo-Cazurra, 2008).

The implications for the management of MNEs and their subsidiaries is that it is important to closely monitor and assess the relative strength of the local innovation system compared with the existing knowledge base in the MNE and other locations in which the MNE is active. Subsidiary performance rests on an effective allocation of resources to R&D conduced in the subsidiary and R&D conducted elsewhere in the MNE in line with these relative advantages. Relying on technological know-how and expertise developed in the MNE is not always the best option. Subsidiary R&D mandates should be free to evolve following the dynamics of the local innovation and technological strengths (Asakawa, 2001; Cantwell & Mudambi, 2005). If the MNE does possess most valuable technological assets, managers should be aware that a specific local R&D mandate may still be required to explore new applications of MNE knowledge that can strengthen the subsidiary’s position on the local market. This does imply a strong coordination between the MNE and the subsidiary to coordinate and collaborate on R&D and reap the benefits of complementarity (e.g., Edler et al., 2002; Zeschky et al., 2014). Initiatives to facilitate foreign subsidiaries’ interactions and knowledge transfer across borders within the MNE network may result in simultaneous increases in complementary R&D activities carried out locally by subsidiaries.

Our study is not exempt from limitations, which also suggests potential avenues for further research. First, we note that we do not observe the geographic origin of the R&D conducted by the MNE for the subsidiary. Our results should be interpreted as an average effect of MNE R&D on subsidiary productivity contingent on the technological position of the host country. Detailed information on the type, location and quality of R&D and innovation within the MNE and its network, or information on the home country of the foreign investor, would allow for a more thorough analysis of the mechanisms of knowledge transfer and R&D complementarities. In this regard, the integration of patent data with R&D surveys could open new paths for further research. Second, in our empirical setting we only look at R&D investments dedicated to the focal subsidiary, while there are other sources of knowledge creation and transfer such as technology licensing (Belderbos et al., 2008) and the use of MNE expatriate experts (Berry, 2015). Third, data limitations do not allow us to control very well for the different ways in which subsidiaries forge linkages with local R&D partners in in the local innovation system (Ciabuschi et al., 2014; Isaac et al., 2019; Song et al., 2011; Un & Rodríguez, 2018), which can differentially enhance knowledge sourcing and productivity benefits. Future work could explore the subsidiary’s linkages with the network of local engineers and the establishment of inter-firm collaborations as a further moderator in the relationship between subsidiary R&D investments and productivity. Fourth, our analysis is confined to subsidiaries located in a small and open advanced economy, with industries relatively often close to the technology frontier. Future research could examine the validity of our findings for subsidiaries located in larger countries and could take into account country variation in the strength of the innovation system.

Fifth, we focused on the relationship between subsidiary and MNE R&D with productivity, while the organization of global manufacturing operations in the MNE’s network and related work practices can be an alternative source of productivity advances (e.g., Castellani et al., 2017; Kafouros et al., 2008). Finally, although our data provide unique and detailed insights into R&D investments in the MNE and their foreign affiliates, we did not have access to more recent data. These considerations suggest ample opportunities further consider the intricate role of the configuration of R&D across the MNE network and the resulting performance effects.

Source: Springer

Research collaboration and R&D outsourcing: Different R&D personnel requirements in SMEs

Peter Teirlinck1, André Spithoven2

    1. Hogeschool-Universiteit Brussel and KU Leuven, Brussels, Belgium
    2. Belgian Science Policy Office, Brussels, Belgium


The literature on ‘open’ innovation emphasises the need to engage in external knowledge relations in order to innovate. Particularly for SMEs, research cooperation and R&D outsourcing can offer possibilities to complement the often limited internal research resources. However, they also bring in their wake requirements in terms of absorptive capacity and managerial skills of the internal R&D personnel.

The paper focuses on the different requirements in terms of availability and training of research managers and R&D experts for research cooperation versus R&D outsourcing in SMEs. An empirical analysis of micro-level data provided by the OECD business R&D survey for Belgium reveals that the relation between R&D personnel requirements and research collaboration and R&D outsourcing depends upon the SME size. Therefore, to study this subject appropriately a distinction between very small, small, and medium-sized firms is relevant. Very small firms engage significantly less in research cooperation than medium-sized firms and the propensity to engage in research cooperation is positively associated with the share of PhD holders among the research managers and R&D experts. For R&D outsourcing a lower involvement is noted in medium-sized firms, and the propensity to outsource increases with the formal qualification level of the R&D personnel and with R&D training. Among the SME, small firms are most engaged in research cooperation and in R&D outsourcing. In the case of research cooperation they rely on highly qualified experts. For R&D outsourcing activities both the presence of research managers and R&D experts is important.


    • Small firms heavily rely on the availability of research experts for engaging in research cooperation.
    • In medium-sized firms research cooperation is strongly related to the presence of research managers.
    • The propensity to outsource R&D increases with the availability of research managers and experts.
    • Small firms engage more in research cooperation and outsourcing than in very small and medium-sized firms.


Who explores further? Evidence on R&D outsourcing from the survey of research and development

Shotaro Yamaguchi, Ryuji Nitta, Yasushi Hara, Hiroshi Shimizu
First published: 27 September 2020


Building off the resource-based view and the knowledge-based view, our study aims to examine determinants of firms’ R&D outsourcing, using annually-conducted firm-level survey data of Japanese R&D companies from 1984–2012. This survey allows us to measure strategic R&D outsourcing, isolated from those more for cost-reducing, such as prototyping, testing and inspecting. The results corroborate the argument of complementarity in scale between internal R&D and R&D outsourcing. We also find that firms employing more doctorate holders and diversifying in knowledge spaces tend to make more use of R&D outsourcing. This study sheds light on firms’ absorptive capacity, associated both with higher-order R&D human capital and diversified knowledge spaces, as determinants of R&D outsourcing.

1 Introduction

Firms’ exploration for external knowledge sources has become an essential ingredient for their innovation. Previous literature highlights that today’s firms increasingly rely on various external agents – universities, research institutions and upstream/downstream firms – as knowledge sources for innovation rather than on their own (Lundvall, 1992; Powell et al., 1996; Chesbrough, 2003).

Accordingly, research and development (R&D) outsourcing has hitherto caught great scholarly attention (Howells, 2008; Huang et al., 2009; Jones, 2000; Lai et al., 2009; Varadarajan, 2009; Stanko and Calantone, 2011). R&D outsourcing is distinguished from outsourcing in general in the sense that R&D investment decisions are highly strategic because not all activities can be outsourced and R&D entails learning, which is highly dependent on individual firms’ heterogeneous resources (Tiwana and Keil, 2007; Huang et al., 2009).

Empirical studies of R&D outsourcing, which usually measure R&D outsourcing as expenditure on contracted R&D or technology acquisition, observe a notable increasing trend (Veugelers, 1997; Lokshin et al., 2008; Hsuan and Mahnke, 2011). Moreover, we are reaching the point where providing capital to start-ups or acquiring technology developed by external organisations is no longer sufficient: not only finding start-ups possessing already established technologies, but they also need to collaborate with partners whose immature technologies have potential but have not yet yielded fabulous outcomes. Hence, for exploring new opportunities, some established firms have started to fund R&D activities by outside organisations, leveraging in-house R&D and accelerating development of new technology.

Firms exploring embryonic technologies that involve high uncertainty may be unwilling to make a capital commitment (Folta, 1998; Van De Vrande et al., 2006). Funding the R&D activities of an external entity can be the first step towards capital investment or acquisition. Established firms can fund outside R&D activities without holding equity or forming joint venture, or as a preliminary to do so. The fiercer competition is between firms striving for external knowledge, the earlier they would try to gain access to outside resources.

Although R&D outsourcing has become increasingly prevalent and caught scholarly attention, we still have the insufficient knowledge of determinants of R&D outsourcing, mainly due to the absence of relevant data sources. Moreover, the previous literature on R&D outsourcing has not distinguished cost-reducing outsourcing such as prototype production, testing and inspecting from more strategic outsourcing that is critical for competitive advantage. We aim at providing evidence on the determinants of such R&D outsourcing by using a unique database: the Survey of Research and Development conducted by the Japanese Ministry of Internal Affairs and Communications (MIC). As explained in detail below, this survey allows us to measure R&D outsourcing, isolated from those more for cost-reducing, such as prototyping, testing and inspecting. Moreover, since this survey has been annually conducted, we can construct a panel data set over around 30 years accounting for time-invariant unobservable firm heterogeneity. To prefigure our main results, we find that firms expend R&D budgets on outside organisations when they have higher internal R&D intensity, employ more doctorate holders and conduct R&D in a broader scope of technological fields. Our results suggest that internal R&D and R&D outsourcing are complementary in scale, and the scope of R&D also plays an important role in firms’ absorptive capacity, which promotes R&D outsourcing.

2 Previous literature and hypotheses

What are the attributes of firms that invest in R&D outsourcing? Following previous literature, we define R&D outsourcing as ‘contractually paid R&D performed by an independent provider that is either a firm or a research organisation’ (Grimpe and Kaiser, 2010; Spithoven and Teirlinck, 2015). While firms are increasingly using R&D outsourcing, there has still been a debate about why firms choose this strategy (Grimpe and Kaiser, 2010; Stanko and Calantone, 2011; Spithoven and Teirlinck, 2015; Un, 2017). The literature on R&D outsourcing has predominantly relied on two perspectives: transaction cost economics (TCE) and the resource-based view (RBV) or its extension, the knowledge-based view (KBV). From TCE perspective, the decision to internalise or outsource depends on transaction costs (Williamson, 1975). It stresses that firms prefer R&D outsourcing to in-house investment, inasmuch as outsourcing would not incur considerable transaction costs due to, for instance, ex ante search and negotiation, and ex post-execution and enforcement of contracts (Veugelers and Cassiman, 1999; Gooroochurn and Hanley, 2007). However, the plausibility of predictions made by TCE literature has been questioned in explaining R&D outsourcing, as several empirical studies show evidence inconsistent with or only weakly supportive for TCE (Love and Roper, 2005; Gooroochurn and Hanley, 2007; Stanko and Calantone, 2011). Another drawback of TCE is that it ignores heterogeneity in resources and firm-specific capabilities and knowledge, which several empirical studies have identified as of high relevance for R&D outsourcing (Holcomb and Hitt, 2007; Spithoven and Teirlinck, 2015).

The RBV or the KBV, on the contrary, regard heterogeneity in resources, capabilities and knowledge as a critical driver of R&D outsourcing. According to those views, firms choose R&D outsourcing when they expect combining internal resources with those outside would yield a substantial impact on their competitive advantage. Some kinds of knowledge and expertise cannot necessarily be developed internally, which can be a strong driver for outsourcing (Howells et al., 2008). The benefits from outsourced R&D can be augmented by absorptive capacity, which has been discussed as the crucial reason why firms simultaneously invest in internal R&D as well as R&D outsourcing. Recent studies provide empirical evidence consistent with the RBV- and the KBV-based constructs rather than TCE (Spithoven and Teirlinck, 2015; Un, 2017).

We primarily build off the KBV (based on which firms’ technological resources are typically examined) to construct three hypotheses, pertaining to the antecedents of R&D outsourcing. The first hypothesis concerns the relationship between internal R&D and R&D outsourcing. Some empirical studies have reported a substitutive (rather than complementary) relationship between internal R&D and R&D outsourcing, although their findings are less robust than those which have observed a complementary relationship (e.g., Laursen and Salter, 2006; Vega-Jurado et al., 2009). For example, a survey of UK manufacturing firms shows that the commitments to internal R&D, operationalised as the ratio of R&D expenditure to sales, was negatively correlated with external searching for new knowledge (Laursen and Salter, 2006). This suggests that the relationship between internal and external R&D activities is substitutive. An anecdote of DSM also corroborates this argument: in the 1970s, DSM cut its basic expenditure on in-house R&D and reduced its R&D personnel (Van Rooij, 2007). As a consequence, some of the R&D personnel transferred to the University of Groningen, where DSM had funded some of their R&D activities. Hagedoorn and Wang (2012) find a more nuanced relationship. Their analysis of pharmaceutical firms indicates that the relationship between internal and external R&D is contingent on the level of internal R&D; Above a threshold level of in-house R&D investment, which is around 1,000 million dollars, internal and external R&D are complementary, whereas below that threshold they are substitutive.

With these exceptions, however, many of the previous work find the complementary relationship between internal R&D and R&D outsourcing (e.g., Veugelers, 1997; Mol, 2005; Cassiman and Veugelers, 2006; Rothaermel and Hess, 2007; Lokshin et al., 2008; Schmiedeberg, 2008; Grimpe and Kaiser, 2010; Howells et al., 2012; Un, 2017). The key proposed mechanism is absorptive capacity (Cohen and Levinthal, 1990); firms can build absorptive capacity by investing in R&D internally, which provides further incentives to explore external knowledge that can potentially be combined (Spithoven and Teirlinck, 2015; Un, 2017).

One of the empirical challenges in exploring the relationship between internal R&D and R&D outsourcing lies in the difficulty in tracking R&D outsourcing behaviours over time. Most of the previous empirical studies rely on one or several-periods observations based on the survey data and so remain to be cross-sectional analyses, or conduct longitudinal observations within a single-industry context. Since our data source is the annually conducted survey over around 30 years across different industries, we can construct a panel data set accounting for time-invariant unobservable firm heterogeneity, and test the hypothesis of complementarity between internal R&D and R&D outsourcing

H1: Internal R&D intensity of the firm is positively associated with its R&D outsourcing intensity.

The second hypothesis pertains to R&D human capital. As explained above, absorptive capacity can be a potential reason why internal R&D and R&D outsourcing are complementary rather than substitutive (Cohen and Levinthal, 1989, 1990; Spithoven and Teirlinck, 2015; Un, 2017). To appraise, assimilate and apply new external knowledge effectively, firms need to invest in R&D on their own and build up and sustain their knowledge bases (Zahra and George, 2002). Previous literature also underscores that investing in-house R&D may not be sufficient; they also need a system which lubricates knowledge transfer beyond the boundaries of the firms (Katz and Allen, 1982; Allen, 1986; Laursen and Salter, 2006; Kathoefer and Leker, 2012). In this sense, gatekeepers can play a significant role in effective knowledge transfer and utilisation of external knowledge (Katz and Tushman, 1981). With the increasing complexity of science and technology, technological breakthroughs demand a wide range of intellectual and scientific skills that far exceed the capabilities of any single organisations. While scientific knowledge is an important frame of reference for firms that seek solutions and road maps in technological development (Fleming and Sorenson, 2004), it may be difficult for them to be well exposed to and digest those scientific knowledge despite their relevance. In such a case, the existence of higher order human capital that possesses sophisticated scientific knowledge mitigates opportunity losses, as they could play a role of gatekeepers of such knowledge.

The hiring practices of Japanese firms allow us to investigate the role played by employees who have advanced knowledge in science. Japanese firms tend to employ fewer people with doctorates to work on R&D than comparable US firms. Instead, they hire more new graduates – with a bachelor’s or a master’s degree – as R&D personnel and provide them with on-the-job training, whereby their working practices align with the firms’ culture. Although an increasing number of PhD-degree holders have recently been employed by Japanese firms, this has still been a dominant path to careers of the R&D personnel in Japanese firms. Thus, PhD-degree holders are scarce human capital, which possesses more advanced and a broader base of scientific knowledge than those with a bachelor’s or a master’s degree (Shimizu and Hara, 2011). We therefore conjecture that R&D personnel with a doctorate play a distinctive role in R&D, for example, by accessing, assessing and absorbing advanced knowledge in academia and hence, enables firms to expand their knowledge sets. They are a critical source of absorptive capacity, which encourages firms to explore outside technological knowledge that is new to them. Hence, we hypothesise that the proportion of R&D personnel with a doctoral degree to the size of R&D divisions positively associated with R&D outsourcing.

H2: Firms where a higher proportion of R&D personnel holds a doctorate have higher R&D outsourcing intensity.

The breadth of firms’ R&D could also affect the extent of R&D outsourcing. Contrary to the first hypothesis pertaining to R&D scale, the third hypothesis sheds light on the scope of R&D. Some literature exploring the level of diversification and external R&D has found a positive relationship (e.g., Veugelers, 1997; Gooroochurn and Hanley, 2007; Lai et al., 2010). Firms diversified broadly in product spaces could also need broad knowledge base and so tend to conduct a variety of R&D projects as well as contract at least some of those projects (Nakamura and Odagiri, 2005).

However, product diversification does not necessarily imply diversification of technological knowledge. Firms can leverage a narrow but deep technological knowledge to target many product lines. Or, it could be said that the addition of product lines is rather a marketing issue at least for some firms, which does not involve the broad expansion of R&D knowledge base. This study directs its attention to the knowledge base of firms rather than product diversification. A broader technological knowledge base can provide firms with capabilities to evaluate, assimilate and utilise a wider range of knowledge outside. In other words, the width of R&D can also promote firms’ absorptive capacity. Thus, we hypothesise that the scope of internal R&D and R&D outsourcing represent a positive association.

However, the opposite might also apply. Firms may be more keen on funding outside R&D to complement internal R&D when their R&D scope is narrower. This could happen when firms lack sufficient resources to invest in complementary knowledge that they have not possessed so cannot help relying on outsourcing. Another reason is that, as TCE predicts, firms selectively internalise only a degree of knowledge that would involve high transaction costs but procure others through market. If this is the case, one would expect to find a negative association between the scope of internal R&D and R&D outsourcing. We therefore construct the conflicting hypotheses, which guide us to examine whether the technological scope of R&D is positively or negatively associated with R&D outsourcing.


H3a: Firms whose R&D activities are broader in scope have higher R&D outsourcing intensity.

H3b: Firms whose R&D activities are narrower in scope have higher R&D outsourcing intensity.

3 Research design

3.1 Data and estimation strategy

Our primary data source is the Survey of Research and Development annually conducted by the Japanese Ministry of Internal Affairs and Communications (MIC) during the period between 1984 and 2012, the survey whose responses are required by law. The average response rate for this survey over the past 16 years is 82%. We further link the survey data to two other data sources: financial information from the Firm Financial Data Bank provided by the Development Bank of Japan (DBJ); patent information from the IIP Patent Database provided by the Institute of Intellectual Property (IIP).1

The respondents of the survey comprises three types of organisations in Japan: all firms with capital stock over 10 million yen (approximately a hundred thousand US dollars); sampled firms with capital stock less than 10 million yen; and non-firm research-oriented organisations.2 The second category (i.e., firms with capital stock less than 10 million yen) is extracted each year from stratified sampling based on research activities, capitalisation and industry categories.

The survey asks respondents for their R&D personnel (e.g., its number, specialty and turnover) and R&D expenditure (e.g., its total amount, by nature (basic/applied) and by product category). It also has an item on ‘expenditure on external R&D’, which we use to capture the amount of R&D outsourcing. The survey, conducted every year since 1984, enables us to construct a panel-structured database, which accounts for time-invariant unobservable factors. We note that our constructed data entail unbalanced panels (see footnote3 in detail). They consist of 1,004 Japanese firms conducting R&D (10,919 observations) for the period between 1984 and 2012, among which approximately 92% comes from firms whose stock capital is over 10 million yen. This means that our samples are largely dominated by large-sized firms.

3.2 Variables

Following the OECD’s guideline and previous literature, this study operationalises R&D outsourcing as an intensity measure: the natural log of the ratio of external R&D expenditure to sales (OECD, 2002; Spithoven and Teirlinck, 2015). The survey defines the external R&D expenditure as ‘money spent for the purpose of providing R&D expenses for outside firms, non-profit organisations, public institutions and universities (expenditure on outsourcing prototype productions, testing, inspecting is not included)’. This item suits our purpose as it excludes more of cost-reducing R&D outsourcing.

We use three explanatory variables to test the hypothesis: internal R&D intensity, R&D doctorate and R&D diversification. Similarly to the dependent variable, we measure internal R&D intensity as the natural log of the ratio of internal R&D expenditure to sales (Spithoven and Teirlinck, 2015). R&D doctorate is a measure of the extent to which firms holds R&D human capital with advanced knowledge, operationalised as the ratio of R&D personnel with PhD degree to the number of R&D-related employees. Since the survey asks the number of PhD holders only since 2002, we can only test its effect (Hypothesis 2) based on the sub-sample from 2002 onwards. R&D diversification assesses the extent to which firms diversify their knowledge scope, which is calculated as the inverse concentration ratio of patenting technological fields. We use primary four-digit IPC subclasses to define each patent’s technological field. Since the concentration measure is highly dependent on the number of patents – the more patents firms obtain, the lower the concentration ratio becomes, we discount it by the number of patents that firms obtain in each year. Thus, the measure of R&D diversification entails following steps: First, urn:x-wiley:00336807:media:radm12437:radm12437-math-0001 where pij denotes i’s number of patent applications in subclass j and ni denotes i’s total number of patent applications in each year. Second, we take the natural log of inverse of the above ratio, then, the higher value of it represents more R&D diversification.4

We include some control variables in our models whose omission could potentially bias our estimates. Firm size is operationalised as the natural log of the number of employees. Firm age, the years lapsed since its legal establishment, is also included in our models. The amount of slack resources can also influence both our predictors and the dependent variable, as firms with excess resources may be more willing to conduct risky or exploratory investments (Singh, 1986). We calculate current ratio for capturing slack resources, which is the natural log of the ratio of current assets to current liabilities. We also account for the extent of market competition, which has been recognised as one of the important determinants for innovation (Malerba and Orsenigo, 1995, 1996; Fontana et al., 2012). It is measured by the natural log of the inverted sum of squared market share in sales within industries. We aggregate sales of all private firms in DBJ’s database sharing the same three-digit Japanese SIC codes. A firm’s primary area of business (i.e., primary SIC) is obtained from the NISTEP name lists. We finally include a set of product class controls: the natural log of the R&D expenditure in 30 product categories provided in the survey. We choose to use continuous expenditure variables rather than binary dummies for sectoral heterogeneity to account for more variations among firms.

4 Empirical analysis

4.1 Descriptive statistics

Figure 1 plots the logged internal R&D intensity and R&D outsourcing across product categories. The variance of R&D outsourcing tends to be larger than that of internal R&D, implying the greater heterogeneity in the extent of R&D outsourcing even within product classes. In terms of the ratio of R&D outsourcing to internal R&D intensity, we see largely the similar pattern across product classes. It is clear that the associations between internal R&D and R&D outsourcing, which examined later, are not driven solely by a couple of industries. Electricity & gas is the only industry in which the median of R&D outsourcing is larger than that of internal R&D, probably due to its public nature. Excluding this industry from our samples does not change our main results described below.

Details are in the caption following the image
Box plots of internal R&D intensity and R&D outsourcing intensity by product classes.

Table 1 reports summary statistics of the variables of interest. The sample means of the expenditures on internal R&D and on R&D outsourcing are 11.6 and 1.9 billion yen – approximately, 116 and 19 million US dollars at the current exchange rate. In other words, 14.0% of the sample firms’ aggregated R&D budget are spent on outsourced R&D. The ratio has gradually increased from 13.0% in 1984 to 18.1% in 2012, implying that R&D outsourcing has become increasingly common and such levels of outsourcing are comparable to those in other countries. For instance, Lokshin, Belderbos and Carree find that Dutch manufacturing firms on average outsourced 9.5%–15.3% of R&D budget during 1996–2001 (Lokshin et al., 2008); Jones reports R&D outsourcing of UK pharmaceutical firms increased from 5% to 16% of internal R&D expenditures from 1989 to 1995 (Jones, 2000).

Table 1. Summary statistics
Mean SD p25 Median p75
Expenditure on R&D outsourcing (million yen) 1,904.539 19,193.480 5.200 28.000 242.720
Internal R&D expenditures (million yen) 11,696.450 41,424.360 584.620 1,713.910 6,568.000
R&D outsourcing intensity 0.003 0.010 0.000 0.000 0.002
Internal R&D intensity 0.037 0.042 0.010 0.027 0.049
R&D diversification 3,119.648 13,667.870 34.091 171.329 1,120.039
Firm size (number of employees) 4,554.934 11,848.450 742.000 1,575.000 3,783.000
Firm age 60.226 18.768 48 59 71
Market competition 7.546 5.285 3.794 6.331 9.260
Current ratio 1.827 1.395 1.093 1.444 2.058
R&D doctorate 0.048 0.065 0.000 0.026 0.069
    • Descriptive statistics are based on absolute values (without log-transformation).

Tables 2 and 3 show the correlation matrices of the variables of interest during the period between 1984 and 2012 and between 2002 and 2012, respectively. Overall, the pairwise correlations represent consistent signs with our hypotheses. Maybe a tricky thing is the negative relationship between internal R&D intensity and PhD doctorate in Table 3. This is probably because that higher internal R&D intensity is associated with larger size of firm laboratories (i.e., the denominator of R&D doctorate) and given the scarcity of PhD degree holders in Japan, this may drive down the density of the doctorates within laboratories.

Table 2. Correlation matrix (1984–2012)
1 2 3 4 5 6 7 8
1. R&D outsourcing intensity 1.000
2. Internal R&D intensity 0.389* 1.000
3. R&D diversification 0.165* 0.337* 1.000
4. Firm size 0.094* −0.011 0.699* 1.000
5. Firm age −0.091* 0.031* 0.161* 0.071* 1.000
6. Market competition 0.072* 0.025 −0.091* −0.048* 0.017 1.000
7. Product diversification −0.033* 0.064* 0.285* 0.133* 0.196* −0.089* 1.000
8. Current ratio 0.096* 0.335* −0.148* −0.285* −0.110* 0.099* −0.044* 1.000
    • * P < 0.01.
Table 3. Correlation matrix (2002–2012)
1 2 3 4 5 6 7 8 9
1. R&D outsourcing intensity 1.000
2. Internal R&D intensity 0.414* 1.000
3. R&D diversification 0.183* 0.320* 1.000
4. Firm size 0.111* −0.039* 0.668* 1.000
5. Firm age −0.089* 0.027 0.194* 0.105* 1.000
6. Market competition 0.074* 0.043* −0.074* −0.043* −0.004 1.000
7. Product diversification −0.020 0.046* 0.284* 0.120* 0.263* −0.097* 1.000
8. Current ratio 0.144* 0.300* −0.181* −0.283* −0.123* 0.118* −0.072* 1.000
9. R&D Doctorate 0.055* −0.152* −0.006 0.103* 0.110* 0.208* −0.011 −0.103* 1.000
    • * P < 0.01.

5 Main results

Our main results are shown in Table 4. All the estimations are based on firm fixed effect models (the P-value of the Hausman test: P < 0.001), including year fixed effects and a set of product class controls to account for time and industry heterogeneity. The standard errors are clustered at a firm level. The signs of coefficients on internal R&D intensity (H1) remain consistently positive and significant across Model 2 and 3 for the whole period and Model 5 for the sub-period, estimating that an increase of 100% in internal R&D intensity is associated with 47.3% to 59.2% increase in outsourcing intensity.

Table 4. Estimation results
Model1 Model2 Model3 Model4 Model5
1984–2012 1984–2012 1984–2012 2002–2012 2002–2012
Internal R&D intensity 0.488*** 0.473*** 0.592***
[0.07] [0.07] [0.07]
R&D diversification 0.077*** 0.055*
[0.03] [0.03]
R&D doctorate 1.358* 1.816**
[0.76] [0.73]
Firm size −0.300** −0.224** −0.263*** −0.242* −0.136
[0.12] [0.10] [0.10] [0.14] [0.13]
Firm age 0.079 0.214 0.21 −0.005 −0.001
[0.21] [0.21] [0.21] [0.01] [0.01]
Market competition 0.071 0.031 0.026 −0.334 −0.189
[0.21] [0.19] [0.19] [0.29] [0.28]
Current ratio −0.217** −0.193** −0.197** −0.266** −0.191*
[0.10] [0.09] [0.09] [0.11] [0.11]
Product diversification −0.303*** −0.194* −0.548 −2.579* −0.201
[0.11] [0.11] [1.41] [1.38] [0.15]
Constant −4.897 −11.415 −11.289 −0.548 −2.579*
[9.23] [9.26] [9.22] [1.41] [1.38]
Year FE Yes Yes Yes Yes Yes
Product class controls Yes Yes Yes Yes Yes
R-square 0.024 0.052 0.055 0.033 0.069
N of obs. 10,919 10,919 10,919 5,610 5,610
N of firms 1,004 1,004 1,004 903 903
    • SEs in parentheses are clustered at firm level.
    • *** P < 0.01,
    • ** P < .05,
    • * P < 0.1.

The effect of R&D doctorates is tested by the sub-sample models from 2002 to 12 (Model 4 and 5). The coefficients are significant and positive, and the increase of one standard deviation (6.5%) in PhD doctorate leads to the 8.8% (Model 4) or 11.8% (Model 5) increase in R&D outsourcing intensity.

The coefficient on R&D diversification (H3) is also positive (Model 3 and 5), which indicates that more diversified firms in R&D tend to rely on outside R&D by outsourcing. We note that the Model 3 accounts for product diversification in R&D, and the coefficients and the significance levels of R&D diversification remain unchanged. This could suggest that even accounting for the diversification in the application side, the diversity in the invention side matters for the extent of using R&D outsourcing.

Another notable result is that the coefficients on firm size represent consistently negative, suggesting that smaller firms tend to have higher R&D outsourcing intensity. This is consistent with the previous study (O’Regan and Kling, 2011).

While the estimated results in Table 4 are consistent with the hypotheses, the punctual conditional mean estimators give less information about their sensitivity, in particular for the complementary relationship between internal R&D and R&D outsourcing. Hence, we further employ a couple of additional analyses. First, we conduct quantile regressions with the same control variables in Model 3 and 5 in Table 4. Quantile regressions estimate conditional values at particular quantiles (e.g., median) rather than conditional means of the dependent variable, allowing for varying coefficients on the independent variables across the positions in the distribution of the dependent variable.

Figure 2 plots the coefficients of the variables of our primary interest, in which Panel A and B are based on Model 3 in Table 4 and Panel C based on Model 5. The coefficients on internal R&D intensity are consistently significant and increase slightly in quantiles, which implies the association between internal R&D and outsourcing becomes larger at the higher level of outsourcing intensity, although the differences in coefficients across quantiles are not statistically significant. On the contrary, the coefficients on R&D diversification are decreasing (0.125 at 0.1-st quantile and 0.030 at 0.9-th quantile) and not significant over 0.8-th quantile. One interpretation is that while diversification in R&D can drive further exploration of outside technological knowledge that is potentially combined, such outsourcing is decreasing returns in its scale so that the association becomes small at the low level of outsourcing intensity. Finally, the coefficients on PhD doctorate are also decreasing and only significant at lower quantiles. This may imply that the existence of gatekeepers with advanced skills matters particularly at the outset of R&D outsourcing, and such effects become smaller once firms accumulate experiences of outsourcing (at higher level of intensity).

Details are in the caption following the image
Quantile regression estimates.

Results in Panel A and B are based on Model 3 in Table 4 and results in Panel C are based on Model 5 in Table 4. The shaded areas are 90% confidence intervals.

Next, we conduct sub-sample regressions under the same specifications of Model 3 and 5, with the presumption that the effects of the explanatory variables could also vary across the different positions in the distributions of key independent variables. We divide our pooled samples based on the quartiles of internal R&D intensity and firm size. The results are shown in Table 5.

Table 5. Sub-sample regressions
Panel A: Sub-sample regressions by internal R&D intensity (Model 3)
below p25 p25 – median median – p75 over p75
Internal R&D intensity 0.141 0.563*** 0.670*** 0.511***
[0.10] [0.18] [0.25] [0.14]
R&D diversification 0.066** 0.05 0.076 0.021
[0.03] [0.05] [0.05] [0.06]
N of obs. 2,730 2,729 2,731 2,729
N of firms 427 505 448 364
Panel B: Sub-sample regressions by firm size (Model 3)
below p25 p25 – median median – p75 over p75
Internal R&D intensity 0.533*** 0.445*** 0.346** 0.245**
[0.12] [0.11] [0.17] [0.11]
R&D diversification 0.077* 0.080* 0.059 0.069
[0.04] [0.05] [0.06] [0.06]
N of obs. 2,725 2,738 2,723 2,733
N of firms 460 406 310 208
Panel C: Sub-sample by internal R&D intensity (Model 5)
below p25 p25 – median median – p75 over p75
R&D Doctorate 1.064 0.838 5.919*** 2.85
[1.04] [1.82] [2.06] [2.48]
N of obs. 1,446 1,368 1,352 1,444
N of firms 333 376 339 302
Panel D: Sub-sample by firm size (Model 5)
below p25 p25 – median median – p75 over p75
R&D Doctorate 1.810* 2.532 −1.833 2.756
[1.09] [1.81] [1.78] [1.73]
N of obs. 1,681 1,385 1,357 1,187
N of firms 396 295 241 170
    • * P < 0.1.
    • ** P < 0.5.
    • *** P < 0.01.

Panel A and B in Table 5 display the estimation results of Model 3 in the sub-samples on internal R&D intensity and firm size. The coefficients notably vary across the quartiles. In Panel A, the coefficient on internal R&D intensity in the first quartile (0.141) is far smaller than the punctual estimator (0.473) and statistically nonsignificant, whereas those in the other three quartiles are greater than that and statistically significant. The pairwise statistical tests yield significant differences in coefficients between the first quartile and each of the second-fourth quartiles, but not among the second-fourth quartiles. The strong complementarity at the high level of internal R&D is also seen in the previous study (Hagedoorn and Wang, 2012). On the contrary, Panel B indicates that the coefficient on internal R&D intensity in the first quartile is the greatest (0.533), and it decreases in quartiles. Again, the coefficient on internal R&D intensity in the first quartile is significantly different from the counterparts in the other quartiles. Thus, the strong positive associations between internal R&D intensity and R&D outsourcing is observed at higher levels of internal R&D as well as among smaller firms. These results corroborate the complementarity between internal R&D intensity and R&D outsourcing, while such complementary becomes stronger among smaller firms.

Both Panel A and B also show the coefficients on R&D diversification, which require more caution to interpret. Though the coefficients remain relatively stable across sub-samples except for the fourth quartile in Panel A, lying between 0.05 and 0.08, only the first quartile in Panel A and the first and the second quartiles in Panel B indicate statistical significance. The possible reason could be that in diversifying knowledge spaces, the necessity to rely on R&D outsourcing is greater for firms with scant resources and R&D experiences. To diversify their own knowledge spaces, simply combining knowledge in the existing sets that firms possess may not be sufficient: they need to reach out to the area that they had not targeted before. Such necessity to outsource R&D may be larger for firms whose knowledge bases are not well established yet.

Panel C and D provide the sub-sample estimations in the period 2002–2012 to examine differential effects of R&D doctorate across internal R&D intensity and firm size. The estimated coefficients are quite unstable across quartiles and only significant in the third quantile of Panel C and the first quantile of Panel D. The variability and the non-significance of the coefficients could be due to less statistical power, given that the samples in those estimations are already partial in terms of the observation period. It should be emphasised that the effect of possessing higher order human capital (i.e., PhD holders) on R&D outsourcing is, if any, contingent on firms’ characteristics such as internal R&D intensity and firm size.

Finally, we employ a couple of additional tests to see the robustness of our findings. First, we utilise two alternative measures of internal and outsourcing R&D scales instead of the ratios of those expenditures to sales. One is the log of the absolute amount of internal R&D and R&D outsourcing and the other is the log of the ratio of these two expenditures to operating income, instead of sales. Using these alternative measures generates similar results of complementarity, both in terms of the punctual estimators and increasing coefficients in the level of the internal R&D. Second, we use one-year lagged independent variables to account for simultaneity. This again does not change the results related to hypotheses 1 and 3, but the coefficient on PhD doctorate turns nonsignificant, which implies the higher sensitivity of its effect. Finally, we exclude from our samples firms operating in the electricity & gas industry due to its public nature and so the higher extent of R&D outsourcing, and obtain the consistent results in all the hypotheses.

6 Conclusion

This study aims to explore the determinants of R&D outsourcing from the perspective of RBV and KBV. Having access to firm-level information on R&D outsourcing over around 30 years from the Survey of Research and Development allows us to construct a panel and estimate firm fixed effect models, which account for heterogeneity in time-invariant unobservable factors such as organisational culture.

The findings substantiate the argument that internal R&D and R&D outsourcing are complementary. It should be noted, however, that in our panel there are indeed six firms whose outsourcing ratio within R&D budgets is over 90% at least for one year during the observation period. They include firms operating in electricity, telecommunication, shipping and transportation equipment industries. It seems that in these cases, internal and R&D outsourcing are substitutive activities. However, all of those firms had separated their R&D division from their business entity; they had set up their R&D laboratory as an independent organisation and they channelled their R&D expenditure to this laboratory. Thus, in this sense they still relied on internal R&D. Only twenty firms in our sample had used this approach, however, and excluding them as outliers does not alter the results of the analysis.

Our results also imply that doctoral scientists play an important role in the absorption of knowledge, as firms having a higher fraction of PhD holders within R&D personnel tend to outsource their R&D. It is consistent with the suggestion that corporate scientists who hold a doctoral degree play a bridging role in external and internal R&D activities (Shimizu and Hara, 2011). However, the coefficients on PhD doctorate are highly sensitive and contingent across sub-samples. The effect of higher order human capital in R&D outsourcing would need more exploration in future studies.

This study sheds light on R&D scope, which is another aspect of firms’ absorptive capacity, in addition to internal R&D and R&D human capital. It suggests that a broader technological knowledge base allows firms to increase their absorptive capacity, even controlling for the amount of investment in different sectors. Thus, not only the scale, but the scope of internal R&D is also related to absorptive capacity, which is another contribution of this study.

As with any previous literature, there are several limitations in this study. First, the survey used in our analyses does not provide detailed information about the recipients of outsourced R&D budgets. It only provides the type of organisation (either firms, universities or other research institutions) and around 72% of the outsourced R&D in our sample are devoted to firms. While our results do not change much even when we limit the samples to those whose recipients of outsourcing are universities, it is still possible that our baseline results could have been distorted if most of the outsourced R&D go to firms’ subsidiaries. As the survey does not distinguish between expenditure on R&D carried out by subsidiaries and by other firms, further research is needed to determine how R&D outsourcing behaviour varies across recipient organisational types.

A further limitation concerns with causality. Our findings are correlational rather than causal. It must be noted that although this study provides evidence on what kinds of firms outsource their R&D, more studies are necessary to identify the causality as to firms’ R&D outsourcing decisions.


    1. The Survey of Research and Development per se does not provide a standardized identifier such as a ticker symbol. We follow three steps to link the respondents with their financial and patenting information. First, based on their names, we connect the survey respondents to the corporate name list, the NISTEP Corporate Name Dictionary provided by the National Institute of Science and Technology Policy (NISTEP), to find ticker symbols, by which we gain the respondents’ financial information in DBJ’s database. Second, for unmatched respondents in the previous step, we manually match them according to their demographics and business characteristics (e.g., address, deliverable and industry). Third, to obtain patent records, we use unique identifiers in the NISTEP name list called comp_id to link them with patent applicants in the IIP database.
    2. Non-firm research-oriented organizations include public/private research universities, public/private research institutions, and NPOs. We note here that the threshold of 10-million-yen separating the sampling way only changed one time in 1995, increased from five million to 10 million yen. While this could distort the selection of the sample, our regression results shown later do not change even if we limit our samples to those after 1995.
    3. There are three reasons for our panel to be unbalanced. First, some sample attrition exists in our observation period: respondents drop out from the survey due to bankruptcy or acquisition. The average sample attrition rate by year is 4.5%. Second, since respondents whose stock capital is below the threshold (10 million yen) are sampled, they may not persistently appear throughout the period. The third reason pertains to the respondent identification method used in the survey. Before 2002, the Statistics Bureau of Japan had not revealed respondent names, but assigned 10-digit identifiers to each respondent. In 2002, the Bureau changed the method: It replaced new seven-digit identifiers with the previous 10-digit, and started revealing respondent names. It provided a correspondence table between the old 10-digit and the new seven-digit identifiers, but we can only match the respondents being surveyed both before and after 2002. This is only the case for the respondents under the threshold, and those respondents above it have been surveyed throughout the periods.
    4. We do not distinguish solo-invented patents from co-invented patents. While co-inventing patents are not highly frequent in Japan – just over 10% of Japanese inventions in 2007 involved an external co-inventor (Walsh and Nagaoka, 2009), this could be directly related to the dependent variable, which is one of the limitations of our study.


    • Shotaro Yamaguchi is a PhD student in Robert H. Smith School of Business, University of Maryland, College Park. His research interests lie in inventors’ mobility, industrial evolution and innovation patterns.
    • Ryuji Nitta is a PhD student in Innovation at Graduate School of Business and Administration, Hitotsubashi University. He recently researches National Innovation System and Knowledge Transfer.
    • Yasushi Hara is a faculty of Graduate School of Economics, Hitotsubashi University. Using his IT literacy as ex-ICT infrastructure engineers with economic and management academic discipline in his research activities, he has been conducting empirical studies relevant to policy making in the field of science, technology and innovation.
    • Hiroshi Shimizu is Professor of Waseda University, Faculty of Commerce. He received his Ph.D degree in 2007 from London School of Economics. His research includes Innovation, Entrepreneurship, Technological Change and Competitive Strategy. His recent book is General Purpose Technology, Spin-out, and Innovation: Technological Development of Laser Diodes in US and Japan published from Springer. He has published in Research Policy, Business History Review, Business History, and Journal of Evolutionary Economics.



La coordination entre un client et son prestataire. L’exemple de l’outsourcing de la R&D

Résumé : La relation qui lie une entreprise déléguant un projet de R&D et son prestataire peut être vue comme une relation asymétrique. Bien qu’il existe un caractère de subordination avec l’entreprise délégatrice, donneur d’ordres, qui « domine » et un prestataire, preneur d’ordres, qui « subit » ; ce dernier possède un avantage non négligeable puisqu’il détient des compétences supérieures à l’entreprise délégatrice en ce qui concerne le projet à mener. On se retrouve confronté à différentes problématiques couramment étudiées dans la littérature que sont la rationalité limitée, le caractère opportuniste et l’incertitude.
Pour pallier ces problèmes, trois modes de coordination sont généralement admis : le contrôle, le contrat et la confiance (CCC). C’est sur ces points que se base cette étude.
Avec des données empiriques issues d’une étude de cas menée auprès d’une grande entreprise française et l’un de ses prestataires de R&D, étude de cas pour laquelle nous avons ciblé la coordination entre ces deux parties comme unité d’analyse, les trois modes de coordination CCC seront analysés dans le cas particulier qu’est l’outsourcing de la R&D.

Source: https://halshs.archives-ouvertes.fr/halshs-00398784

L’externalisation de la R&D : une approche exploratoire

Régis Dumoulin, Aude Martin
Dans Revue française de gestion 2003/2 (no 143), pages 55 à 66

Par sa recherche et développement (R&D), l’entreses produits pour conserver ou acquérir une position prise tente de solutionner rapidement les problèmes rencontrés, de développer et d’améliorer concurrentielle importante. Obtenir des compétences et des ressources et développer les routines organisationnelles permet à l’entreprise de préparer son avenir. Les entreprises ont de plus en plus recours à l’externalisation pour palier un savoir-faire indisponible en interne ou difficile à préserver. De manière générale, l’externalisation ne cesse de croître : plus de 63 % [1]. des entreprises prétendent y avoir recours. Elles espèrent diminuer leurs coûts et augmenter leur compétence et leur flexibilité en adoptant une stratégie de recentrage sur leurs compétences clés et d’externalisation pour les activités plus périphériques.

Notre étude, exploratoire, analyse l’externalisation de la R&D dans son ensemble et cherche à déterminer les activités de R&D pouvant être confiées à un prestataire. La première partie de cet article fixe le cadre théorique nécessaire à la compréhension du phénomène d’externalisation en comparant les apports de la théorie des coûts de transaction à ceux de l’approche ressource et en développant le point de vue original de Kay. La deuxième partie décrit les pratiques observées sur le terrain et explique le recours à l’externalisation dans notre échantillon. Enfin, la troisième partie propose un modèle utile à l’analyse du bon déroulement d’un projet d’externalisation de la R&D.


1. L’externalisation dans la TCT et la RBV

Bien qu’il n’existe pas de véritable théorie de l’externalisation (Barthélemy, 2001), la théorie des coûts de transaction (TCT) et l’approche ressource (RBV) sont néanmoins utilisées pour comprendre ce phénomène. La TCT s’est surtout intéressée à l’intégration verticale, et elle délaisse deux thèmes importants que sont le cœur de métier et les facteurs déclencheurs de l’externalisation. La RBV permet de combler ces lacunes (Barthélemy, 2001).

Pour la TCT, économiser est le problème majeur des organisations. Les coûts de transaction se fondent sur deux hypothèses de comportement : la rationalité limitée et l’opportunisme. Le niveau des coûts de transactions est déterminé par trois attributs : la spécificité des actifs, la fréquence et l’incertitude. Williamson accorde à la spécificité des actifs une place prépondérante. Un actif est jugé d’autant plus spécifique que sa valeur d’usage est dépendante d’une transaction particulière. La TCT pose par le problème des frontières de l’entreprise, celui de l’externalisation ou l’internalisation des activités : plus la spécificité des actifs est élevée, moins l’externalisation est souhaitable; plus la performance d’un prestataire est difficile à mesurer, plus il est recommandé d’internaliser la transaction; plus il y a d’incertitude, plus l’intégration verticale est recommandée. Mais paradoxalement, l’incertitude technologique augmentant la probabilité que les capacités internes et les routines deviennent obsolètes, elle devrait décourager l’intégration verticale (Balakrishnan et Wernerfelt, 1986).

La RBV rejette la théorie néoclassique selon laquelle la firme correspond à une combinaison technique pour la considérer comme un ensemble de ressources physiques et humaines (Coriat et Weinstein, 1995). L’entreprise n’est pas là pour diminuer les coûts mais pour produire une connaissance spécifique. La RBV prend en compte la qualité des ressources et des compétences internes par rapport à celles dont disposent les meilleurs prestataires du marché. L’externalisation est alors une décision stratégique qui comble un vide entre les compétences souhaitées et réelles (Barthélemy, 2001). Cependant, elle ne permet qu’un accès à des ressources et à des compétences qui restent extérieures à l’entreprise. Elle implique en effet un transfert de ressources et de compétences et donc une perte de l’expertise et du savoir accumulés (Prahalad et Hamel, 1990).

Le principal problème des entreprises qui veulent se séparer de certaines activités et concentrer leurs ressources sur d’autres est de définir leur cœur de métier (Prahalad et Hamel, 1990). En effet, l’entreprise se recentre sur son cœur de compétences et elle a recours à des prestataires pour tout ce qui est périphérique. Pour Quinn et Hilmer (1994), les compétences clés sont les activités qui offrent un avantage compétitif à long terme, elles doivent être protégées et contrôlées.

Barney (1991) définit ainsi quatre critères pouvant déterminer si une ressource fait partie du « cœur de métier », si elle constitue un avantage concurrentiel pour la firme. Ces critères connus sous le nom de conditions VRIN sont la valeur, la rareté, l’inimitabilité et la non-substituabilité de la ressource.

2. L’externalisation de la R&D : les développements présentés par Kay

Kay (1997) tente de comprendre pourquoi les entreprises confient leurs campagnes de publicité qui sont vraiment spécifiques à une entreprise extérieure alors qu’elles préfèrent réaliser elles-mêmes leur R&D, qui elle est non-spécifique. S’opposant à Williamson, il remet en cause le rôle de la spécificité de l’actif dans la décision d’intégrer ou d’externaliser, pour privilégier le concept de substituabilité de l’actif. Dans la TCT, la spécificité fait référence aux coûts d’opportunité des actifs à l’extérieur de l’entreprise, cependant, cela n’éclaire en rien les relations des actifs entre eux à l’intérieur de l’entreprise, ni la facilité avec laquelle ils pourraient être remplacés si cela se révélait nécessaire. La perspective de Kay, à forte orientation ressource, met l’accent sur la facilité de remplacement plutôt que sur le degré de spécificité de l’actif. La substituabilité est la possibilité de remplacer une ressource interne par une ressource issue du marché. Si un actif est difficilement substituable, il peut être considéré comme un actif critique. Pour Kay, bien que l’activité de R&D soit non-spécifique, elle est hautement intégrée aux autres activités et routines de l’entreprise, et par-là difficilement externalisable. En effet, pour Kay, la zone où la R&D est plus susceptible d’être internalisée est la zone où les recherches sont particulièrement liées aux autres activités de l’entreprise et qui ne sont pas facilement substituables par des sources extérieures.

Kay (1988) définit quatre caractéristiques de l’activité de la R&D : la non-spéci-ficité, l’incertitude (marché, technique et générale), les délais et retards, les coûts élevés. L’impact de ces quatre facteurs varie au fur et à mesure qu’un projet de R&D passe du stade de recherche fondamentale à celui de recherche appliquée puis à celui de développement [2]. En effet, la non-spécificité, l’incertitude et les retards ont tendance à diminuer au fil de ces étapes à la différence du facteur coût qui lui tend à augmenter vers la phase finale.


1. Processus de recherche exploratoire

Cherchant à obtenir une vue globale de l’externalisation de la R&D dans les entreprises, nous avons opté pour une démarche qualitative de collecte de données. Le protocole de recherche suivi est présenté dans le tableau 1. L’échantillon sélectionné est composé d’entreprises confiant des projets de R&D à l’extérieur, de prestataires de services ainsi que de centres de recherche public et privé (voir tableau 2).

2. Détermination des activités de R&D externalisables

La R&D doit être considérée non pas comme une seule activité mais comme un ensemble de projets. Nous avons donc cherché à déterminer comment se déroulait l’externalisation des différents projets de R&D dans la pratique.

Tableau 1


Tableau 1
Tableau 2


Tableau 2

Certains types de recherche sont plus souvent externalisés que d’autres. Les industriels ont délaissé l’activité de recherche fondamentale par manque à la fois d’intérêt et de ressources matérielles et humaines. Ils font le plus souvent appel aux laboratoires publics – CR10 et CR11 – pour des contrats de long terme ou ponctuels. « Les études amont ont pour but essentiellement de permettre aux entreprises et plus particulièrement aux directions techniques de préparer les compétences dont elles auront besoin demain » (E2). Aucune des neuf entreprises interviewées ne réalisait des activités de recherche fondamentale. E6 a recours de manière indirecte à la recherche effectuée par les laboratoires publics puisque le centre de recherche de sa maison-mère – CR12 – dont elle dépend directement leur confie de nombreux projets.

La recherche appliquée est le plus souvent réalisée à l’interne. « Même si elle est d’une durée plus courte, elle est souvent moins risquée que la recherche fondamentale » (E3). La recherche appliquée nécessite beaucoup d’investissements humains, matériels et budgétaires. Les entreprises n’ont pas toujours les compétences à l’interne pour la mener totalement à bien et peuvent être obligées de faire appel à des partenaires extérieurs pour certaines missions. E2, E7, E8, E9 et CR12 ont régulièrement recours à des prestataires extérieurs pour certains de leurs projets. E9 mène des recherches en parallèle avec un prestataire. E5 et E6 consacrent une faible partie de leur budget R&D à la recherche appliquée.

Les entreprises confient souvent le développement de nouveaux procédés à des spécialistes dans le domaine, E8 est souvent chargée de ces missions. En revanche, l’entreprise ne peut que difficilement externaliser l’amélioration et le développement des produits puisque cette activité est spécifique à l’entreprise. Nos sept entreprises externalisatrices réalisent intégralement leur développement de produits grâce à leur(s) direction(s) technique(s).

Figure 1
Figure 1

Les experts affectés à la veille technologique apportent à leur entreprise la connaissance des travaux menés par la recherche académique ou industrielle. Dans notre étude, aucune des quatre entreprises menant une veille technologique n’a confié cette mission à des extérieurs.

Au vu de nos résultats, nous pouvons, au sens de Prahalad et Hamel (1990), définir un cœur de métier de R&D et un ensemble d’activités de recherche considéré comme périphérique. Le schéma de la figure 1 résume les activités de R&D considérées comme le cœur de la R&D qui doivent rester à l’interne et celles plus périphériques qui peuvent être confiées à l’extérieur.

Tableau 3


Tableau 3
serait amenée à faire un choix entre les différentes raisons poussant les entreprises à faire appel à des prestataires extérieurs pour certains de leurs projets de R&D peuvent être classées en trois catégories : la réorganisation interne de la R&D, l’accès au savoir-faire d’un spécialiste et l’adaptation à l’environnement. L’ensemble de ces raisons issues du terrain est répertorié dans le tableau 3.


1. Pertinence du cadre théorique mobilisé

L’approche ressource nous apparaît plus adaptée pour justifier l’externalisation de la R&D. En effet, la raison principalement évoquée quant au choix de confier des activités de R&D à l’extérieur est la recherche de compétences spécifiques non disponibles en interne. Il s’agit d’une décision stratégique prenant en compte la qualité des ressources et des compétences internes par rapport à celles existantes chez certains prestataires externes.

De plus, le concept de core competencies s’applique à l’activité de R&D. Même si l’activité de R&D prise dans son ensemble peut être considérée comme une activité « cœur » de l’entreprise, elle peut être décomposée en projets-clés et en projets périphériques. Les projets-clés sont les core competencies de l’activité et sont conservés à l’interne à la différence des projets périphériques qui peuvent être externalisés. Si l’on reprend les conditions VRIN, celle de non-substituabilité et celle d’inimitabilité de la ressource sont appropriées. Pour Kay (1997), également, un actif constitue un avantage concurrentiel pour la firme lorsque celui-ci est difficile à remplacer, dans ce cas il ne peut être externalisé. Certains projets de R&D sont particulièrement liés à d’autres projets, activités ou produits, ils requièrent des informations très détaillées et font partie de l’identité de l’entreprise, de ses routines. Ces projets ne pourraient être réalisés à l’extérieur, trop d’éléments manqueraient à leur bonne réalisation. Tel est le cas des projets qui concernent le développement et l’amélioration des produits. Le critère d’inimitabilité souligne que, pour être considérée comme un avantage concurrentiel, une ressource ne doit pas être détenue par un grand nombre de firmes. Les projets menés à l’interne sont difficiles à imiter puisqu’ils sont directement liés à d’autres activités (non-substi-tuabilité de la ressource), à la différence de ceux confiés à des prestataires qui peuvent être facilement reproduits par d’autres entreprises.

Deux attributs de transaction présentés par la théorie des coûts de transaction sont cohérents avec les résultats de notre étude. Il s’agit de l’incertitude qui, si elle est technologique, doit favoriser l’externalisation : dans le cas de l’activité de R&D, nous avons vu que les résultats étaient incertains. Tout comme l’évolution de la technologie, l’incertitude technologique est donc fortement présente dans l’ensemble des contrats; E1, E3 et E7, ont évoqué l’importance de partager les risques liés à l’incertitude technologique et à l’incertitude de l’environnement. Kay (1988) ajoute que cette incertitude diminue au fur et à mesure des étapes de recherche : plus on se rapproche de la phase finale (développement et mise en œuvre industrielle), plus l’incertitude diminue. Ceci est vérifié lors de notre étude. Le deuxième attribut est la fréquence. En effet, les projets de R&D sont plus facilement externalisés lorsqu’ils sont occasionnels (E1, E7, E8, E9, CR10 et CR11) même si dans certains cas, il s’agit plus d’un caractère structurel : pour la recherche fondamentale, les entreprises ont tendance à établir des contrats de long terme et/ou répétés (E1, E2, CR10 et CR11).

La principale différence entre les résultats obtenus et la théorie des coûts de transaction réside dans la place accordée à l’économie de coûts. Pour Williamson (1999), il faut intégrer lorsque réaliser une activité à l’interne permettrait une économie de coûts par rapport au fait de solliciter le marché. Limiter les coûts fait partie des avantages recherchés par les entreprises de notre terrain, mais il ne s’agit pas d’un facteur-clé dans la décision d’externaliser ou non un projet de R&D, à la différence des compétences recherchées, de la flexibilité et du partage des risques.

La théorie des coûts de transaction souligne également que s’il est difficile d’apprécier la performance d’un prestataire, il vaut mieux intégrer. Pour déterminer la valeur d’un nouveau prestataire, l’entreprise cliente se base principalement sur la réputation et l’image de marque du prestataire (E1, E2, E3, E4, E7, E8 et E9), sur le nombre de brevets déposés (E2, E7 et CR12) et sur les articles dans la presse spécialisée (E9). En R&D, il est d’autant plus difficile d’apprécier la valeur réelle du prestataire : les projets étant très spécifiques, ils nécessitent du matériel et des compétences humaines appropriées qu’il n’est pas toujours facile de mesurer lors des premiers échanges avant la signature du contrat. La performance du prestataire peut varier d’un projet à un autre.

Prahalad et Hamel (1990) soulignent que l’externalisation d’une activité entraîne une perte d’expertise et de savoir-faire puisqu’elle implique un transfert de ressources et de compétences. Les ressources et les compétences auxquelles l’entreprise accède par l’externalisation restent externes. Les différents entretiens ont montré que l’externalisation des projets de R&D n’entraîne pas de perte de compétence puisqu’il n’y a ni transfert de personnel, ni transfert de matériel. De plus, les résultats obtenus lors de l’externalisation d’un projet de R&D sont réintégrés par la firme qui se les approprie grâce aux directions techniques (E2, E4 et E6).

Parmi les autres conditions VRIN, la notion de valeur peut difficilement être prise en compte puisque les projets menés en interne peuvent avoir autant, voire plus, de valeur que les projets menés à l’interne. De même, un projet externe peut être considéré comme « rare » lorsque le prestataire, dans nombre de cas, est le seul à être qualifié pour réaliser cette recherche.

Le dernier point d’importance à souligner concerne la spécificité des actifs, attribut auquel Williamson accorde une place prépondérante. Kay remet en cause la place de la spécificité des actifs dans la décision d’externaliser ou non une activité. Il juge la fonction R&D non-spécifique. Nous pensons que l’activité de R&D, si elle est prise globalement, est plutôt spécifique à l’entreprise [3]. En revanche dès lors que l’on s’intéresse aux différents types de recherche, certains sont très spécifiques à l’entreprise et d’autres sont non-spécifiques. La recherche fondamentale est non-spécifique puisque, dans beaucoup de cas, les résultats obtenus seront utilisables dans différents secteurs économiques pour des produits complètement différents (E7, CR10 et CR11). Le développement est spécifique à la fois au produit et à la firme. Il est plus difficile de se prononcer pour la recherche appliquée, en effet, dans certaines entreprises, elle pourra être considérée comme spécifique et pour d’autres comme non-spécifique. Il faudrait à nouveau diviser la recherche appliquée en projets, certains étant spécifiques et d’autres étant non-spécifiques. Toutefois, nous rejoignons la thèse de Kay qui préfère privilégier la notion de substituabilité. En effet, dans le cas de la R&D, certains projets peuvent être spécifiques au produit et pourtant être confiés à un prestataire extérieur qui aura plus de compétences pour répondre au problème posé. Pour illustrer cela, nous pouvons prendre l’exemple du prestataire E9, dont l’activité est de développer de nouvelles formulations, c’est-à-dire de retravailler des principes actifs pour changer la forme galénique. L’entreprise externalisatrice confie à ce prestataire un projet très spécifique au produit développé en interne. Ce projet pouvant être réalisé à l’externe sans perturber les autres recherches liées à ce même produit, il peut donc être considéré comme substituable. Cependant, un projet imbriqué avec d’autres projets ou avec d’autres activités de l’entreprise ne pourra être réalisé par des partenaires extérieurs à qui il manquera de l’information, ou de la technologie. C’est le cas pour les dernières étapes de la recherche (phases de développement). À partir de ce moment-là, il faut, en effet, prendre en compte les procédés de production et de commercialisation. Pour E6, par exemple, si des recherches menées en amont ont montré que tel procédé permettait de diminuer la pollution, il faut en interne vérifier la possibilité de l’utiliser avec les structures d’incinération possédées, ce projet n’est pas spécifique au produit ou à l’entreprise mais aux activités de production, il est donc non-substituable, donc non-externalisable.

2. Les paramètres d’une externalisation réussie

Quatre paramètres jouant un rôle important pour faciliter un processus d’externalisation sont ressortis de l’étude :

  • Les contrats d’externalisation sont souvent très détaillés, leur rôle est avant tout de préciser les objectifs de la relation. L’accent est principalement mis sur les objectifs, les délais et les coûts. Comme les résultats de R&D sont incertains, il est très difficile d’établir un contrat très précis. La solution adaptée est celle d’un contrat par étape, par palier, dans lequel le projet est découpé en différentes phases et à la fin de chaque étape, un nouveau contrat est élaboré et redéfini en fonction des résultats déjà obtenus, de l’avancement des travaux ainsi que du financement envisagé.
  • La propriété intellectuelle : décider à qui vont appartenir les résultats est au centre de tout contrat d’externalisation et de partenariat en R&D. Pour chaque projet, s’il y a propriété intellectuelle, elle se négocie au cas par cas. Dans la majorité des cas, les résultats vont appartenir à l’entreprise externalisatrice. Cependant, ils peuvent parfois appartenir au prestataire. Dans le cadre de la recherche académique, les résultats peuvent être très généralistes et être utilisés dans des disciplines très variées. Dans ce cas, l’entreprise externalisatrice peut délaisser totalement la propriété ou demander une exclusivité d’usage temporaire dans son domaine d’activité. Enfin, il existe une alternative à ces deux extrêmes, il s’agit de la copropriété. Plutôt utilisée dans le cas des partenariats, elle peut être aussi décidée d’un commun accord lors de l’élaboration du contrat d’externalisation. Dans ce cas, chacun a le droit d’usage et chacun garde la propriété des informations et des résultats détenus au début du projet.
  • La confiance liée directement aux relations interpersonnelles. Pour les différents prestataires interrogés, la confiance est une notion centrale. Toutefois, il n’y a pas de confiance « aveugle », seules les informations nécessaires sont transmises. Il est vrai qu’une notion de confiance peut se développer au fil du temps. L’existence de coopérations réussies avec les mêmes partenaires permet d’accroître le niveau de confiance (Ring et van de Ven, 1992). La confiance se construit, elle est le ciment d’une relation que l’intérêt ne peut pas suffire à expliquer (Orléan, 1994).
  • La gestion du transfert d’informations. L’externalisation de la R&D nécessite un transfert d’informations important. Cependant, il est très difficile de procéder à ce transfert de manière rapide, continue et efficace. Les entreprises externalisatrices ont toujours peur de voir partir leur savoir tacite ou formel.

La proximité géographique facilite la coordination et la communication directe entre les personnes, favorise l’échange d’idées (Saxenian, 1994), la mise en confiance et la transmission d’informations. C’est ainsi que des laboratoires communs entre la recherche académique et l’industrie se développent, et que des pôles d’innovation regroupant la recherche académique, des écoles d’ingénieurs, des industriels, des personnes de l’enseignement de disciplines diverses se mettent en place. Ces pôles favorisent les synergies et les échanges.

Figure 2
Figure 2

Le contrat est le point de départ de tout processus d’externalisation de la R&D, il détermine la manière dont se dérouleront les opérations, les informations qui circuleront ainsi que la manière dont les échanges se passeront. Il désigne également le propriétaire des résultats obtenus. Si un contrat est respecté par les deux parties, la notion de confiance s’installera et facilitera l’élaboration des contrats futurs. Le transfert d’informations est influencé par la propriété intellectuelle; en effet, si l’entreprise externalisatrice est propriétaire des résultats, elle sera moins réticente à confier des informations au prestataire. Si le transfert d’informations se déroule sans incident et si aucune fuite d’informations n’apparaît, la confiance entre les deux entreprises se développera.


Les entreprises prennent conscience qu’elles ne peuvent plus mener seules l’intégralité de leur R&D. Elles recherchent donc des partenaires extérieurs : laboratoires publics, prestataires privés et collaborateurs pour développer des partenariats. L’étude a mis en évidence que seuls les projets considérés comme périphériques à l’activité de R&D pouvaient être confiés à des partenaires extérieurs dans le but de réorganiser la R&D interne de la firme, d’accéder à un savoir-faire indisponible à l’interne et/ou de répondre à l’évolution de l’environnement. Le terrain a montré que certains projets spécifiques au produit pouvaient être externalisés, le concept de substituabilité, comme le souligne Kay doit donc être privilégiée à celle de spécificité. Le modèle mis en avant reliant le contrat, la propriété intellectuelle, la gestion du transfert d’informations et la confiance détermine les variables et leurs causalités en prendre en compte pour une externalisation de la R&D réussie.

Différents éléments du terrain remettent en cause la pertinence du terme externalisation pour la R&D. Lacity et Hirscheim (1993) définissent l’externalisation dans sa forme la plus basique comme le recours au marché pour une activité auparavant réalisée en interne. Elle se caractérise désormais par un transfert de personnel et d’équipements vers le prestataire. Il est fondamental, dans un premier temps, de souligner que la R&D externalisée constitue pour la plupart des entreprises un faible pourcentage de leurs projets (moins de 1 % pour E1, E4, E5 et E7 et 23 % pour E2). De plus, les entreprises qui confiaient des missions de R&D à l’extérieur adoptent, en fait, une stratégie de mix. En effet, certains projets sont sous-traités, d’autres sont externalisés et d’autres sont réalisés en partenariat. Aucun n’implique de transfert de personnel ou de matériel. Le terme d’impartition (Barreyre et Bouche, 1982) semble plus approprié quand on étudie les activités de R&D confiées à l’extérieur puisque cette notion s’étend de la sous-traitance au partenariat, c’est-à-dire du faire-faire au faire ensemble.


  • [1] Baromètre Outsourcing de la société Andersen (2001).
  • [2] La R&D est un phénomène et non linéaire. Trois stades sont généralement identifiés (Mothe, 1997) : la recherche fondamentale, la recherche appliquée, le développement (développement de nouveaux procédés et développement pour la fabrication de nouveaux produits). À cela s’ajoute la veille technologique désormais partie intégrante de l’activité R&D.
  • [3] Ce qui explique qu’en France, beaucoup d’entreprises hésitent à confier des missions de R&D à des prestataires extérieurs.


Externalités ou externalisation ? Composantes de l’activité de R&D et performances de la firme

J.P. Huiban 1 M. Paul  B. Planes  Patrick Sevestre . Département d’Economie Et Sociologie Ruralesrennes 2 

  1. UM DIJON INRA/ENESAD – UMR INRA / ENESAD : Economie et Sociologie Rurales
  2. INRA – Institut National de la Recherche Agronomique
Résumé : La liaison entre les dépenses de Recherche-Développement (RD) et les performances des firmes est analysée dans le cadre d’un modèle où sont distinguées les différentes composantes des dépenses. A l’opposition habituelle entre dépenses internes et effets de spillover issus des dépenses réalisées par d’autres firmes du même secteur est ajoutée une dimension complémentaire : celle du groupe de sociétés auquel appartient éventuellement la firme. L’estimation de ce modèle à partir de données individuelles d’entreprises (17 000 firmes en 1996) montre la très grande efficacité relative des dépenses consenties à ce niveau : leur effet est pratiquement équivalent à celui des dépenses réalisées en interne et très largement supérieur à l’effet des spillovers sectoriels. Si l’externalisation des dépenses de RD ne conduit pas systématiquement à une amélioration des performances, tel est par contre le cas lorsque les activités sont internalisées au sein du groupe auquel appartient l’entreprise.


Source: https://hal.archives-ouvertes.fr/hal-02828781