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

International R&D service outsourcing by technology-intensive firms: Whether and where?

Andrea Martinez-Noya, Esteban Garcia-Canal, Mauro F. Guillen

We combine the streams of literature on outsourcing and offshoring to investigate (1) whether choosing an R&D offshore outsourcing strategy by technological firms is advisable, and (2) where these firms are more likely to allocate these R&D services outsourcing agreements offshore, namely, in developed or developing economies. Using original survey data from European and U.S. firms in technology-intensive industries, we place especial emphasis on the fact that certain firm-specific capabilities, such as technological and international expertise, are required in order to outsource R&D overseas, especially when offshoring to developing economies, as transaction costs are still the main deterrent to outsource offshore to these regions. In addition, our results also show that in the specific case of R&D services outsourcing, knowledge-seeking objectives lead to outsource to developed economies.

1. Introduction

Research has documented that firms are changing their sourcing strategies in two ways. First, they are increasing the range of activities along the value chain that are outsourced (Gilley & Rasheed, 2000; Hätonen & Eriksson, 2009; Hitt, Keats, & De Marie, 1998; Jacobides, 2005; Kotabe & Murray, 2004; Quinn & Hilmer, 1994) including areas that were traditionally vertically integrated, such as those related to the innovation process (Cesaroni, 2004; Gooroochurn & Hanley, 2007; Granstrand, Patel, & Pavitt, 1997; Howells, Gagliardi, & Malik, 2008; Leiblein, Reuer, & Dalsace, 2002; Manning, Massini, & Lewin, 2008; Narula, 2001; Quinn, 2000; Subramaniam & Venkatraman, 2001; Tsai & Wang, 2009; UNCTAD, 2005; Veugelers, 1997). Second, firms are increasingly outsourcing innovating activities offshore, not only to providers located in developed countries but also to those in developing ones (Bunyaratavej, Hahn, & Doh, 2007; Doh, 2005; Dossani & Kenney, 2007; Javalgi, Dixit, & Scherer, 2009; Jensen, 2009; Kedia & Mukherjee, 2009; Kotabe & Mudambi, 2009; Mol, Pauwels, Matthyssens, & Quintens, 2004; Mol, van Tulder, & Beije, 2005; Un, 2009).

As a consequence, the R&D function is being disintegrated into different technologically separable R&D services (Fosfuri & Roca, 2002; Gottfredson et al., 2005; Howells et al., 2008) that can be performed in different locations either by the firm or by an external contractor (Arora & Gambardella, 2005; Hirshfeld & Schmid, 2005; Levy, 2005; Lewin & Peeters, 2006; Lewin, Massini, & Peeters, 2009; Manning, Massini & Lewin, 2008; Maskell, Pedersen, Petersen, & Dick-Nielsen, 2007). Therefore, firms need to search for the optimal governance (outsourcing versus internal development) and geographical location (offshoring versus domestic) of each of the activities or R&D services within their value chain.

Even though these two decisions are closely related, the scarce existing research dealing simultaneously with both decisions is either theoretical (Contractor et al., 2010; Doh, 2005; Graf & Mudambi, 2005; Hätonen & Eriksson, 2009; Kedia & Mukherjee, 2009; Martínez-Noya & García-Canal, 2011a; Mudambi & Tallman, 2010) or case oriented (Hätönen, 2009; Mudambi & Venzin, 2010). On the one hand, empirical studies analyzing the R&D outsourcing decisions by technological firms tended to overlook the location dimension (Afuah, 2001; Hitt et al., 1998; Leiblein & Miller, 2003; Mayer & Salomon, 2006; Mol, 2005; Narula, 2001; Pisano, 1990; Veugelers & Cassiman, 1999). On the other, those studies more focused on the location decision either tended to analyze only FDI or internal modes of governance (captive offshoring) (Belderbos, 2001; Berry, 2006; Bunyaratavej et al., 2007; Cantwell, 1995; Demirbag & Glaister, 2010; Doh, Bunyaratavej, & Hahn, 2009; Kuemmerle, 1999; Odagiri & Yasuda, 1996; Piscitello & Santangelo, 2010; von Zedwitz & Gassmann, 2002) or to analyze the offshoring phenomenon in the aggregate without distinguishing between internal (captive offshoring) and external (outsource offshoring) modes of governance (Dossani & Kenney, 2003; Lewin & Couto, 2007; Lewin et al., 2009; Manning et al., 2008; Mudambi, 2008).

In order to close this gap in the IB literature, this paper combines these streams of literature on outsourcing and offshoring and elaborates on the factors that drive technological firms to outsource R&D services offshore and to a particular offshore location. In particular, given the recent evidence on the rise of offshoring of innovative activities and advanced services to developing economies, this paper aims to contribute to this literature by analyzing the factors driving these firms to locate their R&D offshore outsourcing agreements in a developing economy versus a developed one. Although it is known that greater enforceability of contracts overseas has allowed for the increasing dispersion of these agreements (Narula & Hagedoorn, 1999), there is, to the best of our knowledge, little empirical evidence explicitly analyzing the determining factors of the location of offshore R&D outsourcing agreements. Some exceptions can be found that explicitly analyze international outsourcing of technical work or other advanced services, however, they do not differentiate between offshore outsourcing locations (Chen, 2004; Griffith et al., 2009; Martínez- Noya & García-Canal, 2011b; Mol et al., 2005; Quinn & Hilmer, 1994). Analyzing jointly whether and where to outsource is important not only for theoretical reasons, but also because the offshoring decision, like other strategic choices, is subject to endogeneity and self-selection (Shaver, 1998). In addition, this research question is not only of interest from a managerial perspective but also from an economic policy perspective. Indeed, recent studies argue that firms are increasingly relocating innovation activities to developing countries because of the highly qualified workforce within these regions (Lewin et al., 2009; Manning et al., 2008). Thus, given the fiscal and non-fiscal instruments that both developed economies and developing ones may employ to stimulate investments in R&D (Mani, 2004), governments from developed economies actually need to proactively attract these value-added services by fostering local infrastructure and their National Systems of Innovation. However, identifying what R&D government policies may be more effective to attract R&D outsourcing contracts from foreign firms, as well as to help to avoid local firms outsourcing offshore (Lieberman, 2004) calls for a better understanding of these practices.

Therefore, combining insights from the resource-based view and transaction cost theory, we argue that, while the probability of technological firms outsourcing R&D services offshore will be mainly determined by firms’ characteristics such as the possession of technological resources and capabilities, together with their international experience, their preference for a developing versus a developed location will mainly be determined by the firms’ motives for outsourcing the R&D service and the attributes of the R&D service outsourced. We find support for these hypotheses using original survey data on R&D outsourcing practices by 182 technology-intensive firms from the U.S. and the European Union. Although some of these factors have already been used in previous research on outsourcing or offshoring, their influence has not yet been tested in the context of whether and where to offshore R&D services. To the best of our knowledge, this is the first quantitative study providing evidence on both the organizational and geographical dimensions of R&D offshoring. Specifically, this study contributes to this literature by showing that certain managerial capabilities are required in order to outsource R&D services overseas, especially when offshoring to developing economies, as transaction costs are still the main deterrent to outsource offshore to these regions.

The rest of the paper is organized as follows. In the next section, we review previous literature on R&D outsourcing and offshoring decisions, and we develop our theoretical model and hypotheses. In Section 3, we describe the data and our research methods. In Section 4, we report the results. Finally, we conclude with a discussion of the contributions, limitations, directions for future research, as well as with managerial and policy implications of our study.

2. Theoretical background

When organizing their R&D value chain, firms face several decisions such as how to source each R&D service—i.e. the outsourcing decision—and where to locate it—i.e. the offshoring decision. Whether or not the outsourcing decision precedes the offshoring one is subject to debate (Contractor et al., 2010; Doh, 2005; Graf & Mudambi, 2005; Hätönen, 2009; Hätonen & Eriksson, 2009; Kedia & Mukherjee, 2009; Mudambi & Tallman, 2010; Mudambi & Venzin, 2010), and goes beyond the scope of this paper. Whatever the case may be, four different sourcing strategies for each R&D service can be identified: (1) to perform it in-house in its home country, (2) to outsource it to a provider in its home country, (3) to perform the R&D service in-house but under an affiliated foreign subsidiary (what is called captive offshoring) or (4) to outsource the R&D service to an unaffiliated provider located in a foreign country (what is called offshore outsourcing).

In order to analyze whether and where to pursue R&D offshore outsourcing, we largely draw from the resource-based view and transaction cost theory. According to the resource-based view, firms establish outsourcing agreements searching for complementary resources and/or capabilities that are not available within the firm (Agyres, 1996; Barney, 1991; Grant, 1996; Peteraf, 1993; Quinn & Hilmer, 1994). Therefore, a core capability of technological firms, especially in a changing environment, is the ability to coordinate and integrate their distributed activities along their value chains as well as exploring and exploiting new emerging technologies (Granstrand et al., 1997; Patel and Pavitt, 1997). Firms operating in technology-intensive industries face competitive pressures to build a larger and broader portfolio of related products in order to gain and maintain their competitive advantage, which drive them to rely on outside suppliers in order to organize some R&D services (Leiblein & Miller, 2003; Nicholls- Nixon & Woo, 2003; Quinn, 2000). Specifically, through outsourcing they can concentrate on those parts of the process in which they can exploit their competitive advantage, benefit from more technological solutions, and take consequent advantage of more business opportunities (Cesaroni, 2004). The fact that the knowledge transfers associated to R&D services outsourcing can lead to appropriability hazards (Kogut, 1988; Oxley, 1997) requires combining the insights of resource-based view with those of transaction cost theory, as the conflicts and tensions stemming from these hazards influence not only the decisions regarding whether to outsource or not but also where (Henisz & Williamson, 1999).

Owing to the heterogeneity of resources located around the world, the external resources needed by a firm may not be available within its home country, and these cross-country differences in resource endowment may drive the firm to seek such resources offshore, searching for location-specific advantages (Dunning, 1998). Indeed, research has found that knowledge- intensive firms from both advanced and developing countries are globally dispersing their value chains to control costs and leverage their capabilities (Mudambi, 2008). Therefore, through outsourcing offshore, these firms have found a way not only to be more efficient or flexible, but also to benefit from the distinctive capabilities of a specialized provider located not only in developed economies but also those in developing ones (Chen, 2004; Graf & Mudambi, 2005). Previous research has found that offshore outsourcing is a result of a firms’ ability to search for and evaluate foreign providers (Mol et al., 2005; Rangan, 2000). On a similar vein, Patel and Vega (1999) found that firms invest offshore in core innovative areas where they are strong at home, while Brusoni et al. (2001) stated that firms must know more of what they need for what they make not only to monitor their suppliers but also to be able to efficiently perform the role of systems integrator. In addition, this excess of knowledge will provide them with the absorptive capacity to benefit from external sources of knowledge (Cohen & Levinthal, 1990). Therefore, although it can be expected that the firm’s probability to establish R&D outsourcing agreements offshore will be mainly driven by its technological resources and capabilities as well as by its international experience, these hypotheses have yet to be tested on an international sample of R&D services offshore outsourcing practices by technological firms.

With respect to the question of where technological firms are more likely to locate their R&D offshore outsourcing agreements, it has been shown that R&D activities involving highly tacit and complex knowledge are concentrated at the firm’s home country, while the development of less tacit knowledge is more geographically dispersed (Cantwell & Santangelo, 1999). As a result, from a capabilities point of view, and despite the growing globalization of markets, there is still a clear specialization of locations that favors the rise of a market for technologies (Arora, Fosfuri, & Gambardella, 2001). In relation to this, previous literature showed that the main motivation for firms to outsource overseas is the search for location-specific advantages, especially to exploit differences in costs and technological expertise (Ghemawat, 2003). On the one hand, FDI literature on R&D argues that firms may decide to internationalize their R&D activities to a particular location, either to cut costs or to explore or acquire new knowledge (Hagedoorn, 1993; Kuemmerle, 1999; Le Bas & Sierra, 2002; Narula & Hagedoorn, 1999). On the other hand, previous research on offshoring (Hätönen, 2009; Lewin & Peeters, 2006; Manning et al., 2008) highlights the importance of differences in costs and qualification.4 Therefore, some inputs and technical knowledge may be available only in some locations, so that firms may decide to outsource some of their activities from these locations in order to access available technological expertise (Calderini & Scellato, 2005; Cantwell & Santangelo, 1999). While firms located in advanced economies may find that labor costs are high compared with the value added to their products (Kotabe, 1998; Trent & Monczka, 2003) and may therefore decide to outsource some of these activities to low-cost countries in order to reduce costs. With regard to this, recent evidence suggests that firms are increasingly relocating innovation activities to developing countries because of the highly qualified workforce within these regions (Lewin et al., 2009; Manning et al., 2008). However, to the best of our knowledge, previous studies analyzing R&D offshoring to developing economies either just consider captive offshoring decisions (Demirbag & Glaister, 2010; Doh et al., 2009; Piscitello & Santangelo, 2011) or they analyze R&D offshoring in the aggregate, that is, without distinguishing between internal (captive offshoring) and external (offshore outsourcing) modes of governance (Lewin et al., 2009; Manning et al., 2008). Thus, the determining factors of the location of R&D services offshore outsourcing agreements have not been empirically tested yet.

Taking all of these antecedents into account, we develop a framework in which arguing that the probability of these firms outsourcing R&D services offshore will be mainly determined by firms’ characteristics such as the possession of technological resources and capabilities together with their international experience, while the preference for a developing versus a developed location will be mainly determined by firms’ motives for outsourcing the R&D service together with the attributes of the R&D service outsourced. Fig. 1 displays the main hypotheses.

2.1. The decision of whether or not to outsource R&D services offshore

Firms do not possess the same capabilities, especially in the technology area, nor do they have similar levels of international experience. Therefore, not all firms are equally prepared to make the most of the potential benefits of offshore outsourcing (Martínez-Noya & García-Canal, 2011b). Taking all of this into account, we expect that the firm’s probability to establish R&D outsourcing agreements offshore as compared to other sourcing options will be mainly driven by the firm’s degree of accumulation of technological resources and capabilities, and its international experience. These are factors that will determine the firm’s capability to effectively manage, coordinate and integrate these agreements with providers located overseas.

Fig. 1. Theoretical framework.

2.1.1. Technological resources and capabilities

When it comes to outsourcing R&D services, firms with strong technological capabilities are likely to have an edge over their competitors. Initially, it could be expected that the more technological resources and capabilities a firm has, the less it will need to search for external sources of innovation. However, these capabilities can be leveraged if some specific parts of the R&D process are outsourced to an external entity, that is to say, combining vertical integration and strategic outsourcing—an organizational practice known as “taper integration” (Rothaermel, Hitt, & Jobe, 2006). Due to the complexity of the innovation process, firms cannot achieve the same level of efficiency across all the activities along the process, and thus engage in both integration and outsourcing (Afuah, 2001). Some firms even follow a concurrent sourcing strategy, i.e. they simultaneously make and buy the same good or service (Parmigiani, 2007; Rothaermel et al., 2006). We therefore expect that, besides outsourcing at the domestic level, technology-intensive firms will need to search for efficient ways of relocating and organizing their different R&D services worldwide (Mudambi, 2008). This would imply that, whenever possible, these firms will prefer to outsource their R&D services to best-in-world providers in order to maintain their competitive advantage, either because they are more specialized or because they can perform the task at a lower cost. Therefore, due to the heterogeneity of technological resources across countries, we expect firms with sound technological capabilities to be more likely to outsource R&D services offshore, as they will need to search either for state-of-the-art or low-cost providers. Indeed, these are the kind of providers that allow them to leverage their technological resources whilst maintaining a competitive advantage over their rivals (Un, 2009).

The previous literature has also shown the importance of taking contractual issues into account in governance choices (Banerjee & Duflo, 2000; Metiu, 2006). More specifically, as firms accumulate technological capabilities they will not only be under more pressure to search for world-class suppliers, but also better equipped than other firms to establish outsourcing agreements with foreign providers (Mayer & Salomon, 2006) and to effectively manage a loosely coupled network of external suppliers by performing the role of systems integrator (Brusoni et al., 2001). Therefore, although firms lacking these capabilities would also benefit from global outsourcing, whatever the motive for doing so, they may not have the capability to manage such agreements. Firms lacking adequate technological resources will be ill-equipped to select an appropriate partner, as well as to monitor its performance and will therefore face both adverse selection problems and important moral hazards. Thus, although we expect these governance capabilities to also increase the likelihood of domestic R&D outsourcing, we also expect them to be particularly critical when establishing contractual agreements offshore. In fact, as shown by Tsai and Wang (2009), by collaborating with different types of partners, firms with more internal R&D investment gain higher innovation returns than firms with fewer internal R&D activities. As a consequence, we expect that:

Hypothesis 1. The more technological resources and capabilities the firm possesses, the more likely it will outsource R&D services to offshore providers compared to other organizational alternatives.

2.1.2. International experience

Although not tested in the context of R&D offshore outsourcing, previous research showed that international outsourcing is a result of a firms’ ability to search for and evaluate foreign providers (Mol et al., 2005; Rangan, 2000). Indeed, Rangan’s (2000) study showed that a lack of knowledge leads to the screening out of foreign sources, while a lack of previous interaction increase uncertainty regarding partners’ reliability and fear of opportunistic behavior. With regard to this, firms’ international experience has been considered in the literature as one of the most important sources of organizational learning (Belderbos, 2003; Kogut & Zander, 1993). It has been proved that firms’ foreign subsidiaries may act as a mechanism to access local knowledge and generate new technology (Frost, 2001; Veugelers, 1997), and thus they can contribute to the creation of firm-specific advantages (Birkinshaw, Hood, & Jonsson, 1998). Research has also highlighted the fundamental role that external linkages with other organizations have in the development of subsidiary capabilities, especially on R&D (Frost, Birkinshaw, & Ensign, 2002). For this reason, based on this previous literature, we expect that the likelihood of a firm locating R&D outsourcing agreements offshore will depend not only of its technological capabilities but also of its existing international involvement. This is because, on the one hand, this international experience will allow the firm to better identify and gain access to offshore providers, while on the other hand, these external linkages are expected to contribute to enhance subsidiary capabilities. So, as it happens in relation to firm’s technological capabilities, firms lacking international experience may face severe problems arising from their unawareness of how to operate in those offshore locations. It should be noted that firms may be choosing to outsource R&D services offshore because they do not have enough international experience on how to operate in specific offshore markets. However, even if this is the case, based on the previous arguments, it can be expected that international experience is still required in order to develop the managerial capabilities to effectively establish R&D outsourcing agreements with offshore providers. As a consequence, we expect that:

Hypothesis 2. The more international experience the firm possesses, the more likely it will be to outsource R&D services to offshore providers compared to other organizational alternatives.

2.2. The decision on where to outsource R&D services offshore: developing versus developed countries

When analyzing R&D offshore outsourcing practices, one question that has not been empirically addressed is which factors drive the location of specific R&D services to a particular offshore location. Consistently with the RBV and in line with the theoretical oriented studies on this issue by Graf and Mudambi (2005) and Hätönen (2009) highlighting the primary influence of factors such as what is being outsourced and why, and what kind of experience the firm has, on the decision of where to locate R&D services outsourcing agreements, we expect this location decision to be dependent on: (i) its main motive for outsourcing the R&D service, i.e. knowledge or lower labor cost-seeking; and (ii) the characteristics of the service being outsourced, specifically on its degree of tacitness and on its level of technological uncertainty.

2.2.1. Firms’ motives for outsourcing offshore

It has been proven that the main motivation for firms to outsource overseas is the search for location-specific advantages, especially to exploit differences in costs and technological expertise (Ghemawat, 2003). Indeed, despite the growing globalization of markets, there is still a clear specialization of locations that favored the rise of a market for technologies (Arora et al., 2001). We build our argument on the basis of two lines of research. Firstly, the FDI literature on R&D argues that firms may decide to internationalize their R&D activities either to cut costs or to explore or acquire new knowledge (Hagedoorn, 1993; Kuemmerle, 1999; Le Bas & Sierra, 2002; Narula & Hagedoorn, 1999). Second, and more specifically, recent research on offshoring (Hätönen, 2009; Lewin & Peeters, 2006; Manning et al., 2008) highlighting the importance of differences in costs and qualification as drivers of these decisions.5 Knowledge-seeking.

Given that R&D services are knowledge-based activities, and knowledge tends to be location-specific, some locations may offer specialized know-how or capabilities within a specific technological domain (Calderini & Scellato, 2005; Cantwell & Santangelo, 1999). Indeed, it has been shown that a key driver for firms’ geographic distribution of R&D activity is the access to knowledge spillovers (Feinberg & Gupta, 2004; Lahiri, 2010). As a result, in order to tap these resources and access this technological expertise, firms may need to establish outsourcing agreements with providers located within such economies so as to benefit from these specialized providers and take advantage of their experience. As to where these pockets of expertise are located, mixed evidence exists. On the one hand, it has been proven that the majority of high-end product development and engineering activities are still being carried out in advanced Western economies (Disher & Lewin, 2007), basically because world leaders in knowledge and technology are typically located within developed economies (Arora et al., 2001). On the other hand, recent studies argue that innovation activities are increasingly being offshored to developing economies searching for talent, due to the emergence of new geographical technological clusters in developing economies, such as in India or China (Lewin et al., 2009; Manning et al., 2008; Ricart, Agnese, Pisani, & Adegbesan, 2011). Therefore, even if high-value added activities were largely performed in advanced economies, while low value-added ones were performed in developing economies (as stated, among others, by Mudambi, 2008), this location pattern would be under pressure as developing countries upgrade their technological competences and thus more technological advanced activities are being offshored to these economies (Dossani & Kenney, 2007; Jensen, 2009; Maskell et al., 2007). Thus, due to the lack of conclusive results on this issue applied to the specific context of R&D services offshore outsourcing decisions, and the fact that R&D offshore outsourcing is considered as being still at an early stage, we propose two different hypotheses in relation to the preference for a developing versus a developed economy the more important is knowledge-seeking as a reason for outsourcing the R&D service.

On the one hand, because developed economies are usually more technologically advanced, boasting access to better technological infrastructure or centers of excellence, we hypothesize that:

Hypothesis 3a. Conditional on the firm having decided to outsource to an offshore provider, the more important knowledge- seeking is as a motive for outsourcing, the less likely the R&D service is to be located in a developing economy.

On the other hand, due to the recent evidence suggesting that firms are increasingly offshoring innovation activities to developing countries because of their highly qualified workforce and the emergence of specialized pockets of expertise within these regions, we also hypothesize that:

Hypothesis 3b. Conditional on the firm having decided to outsource to an offshore provider, the more important knowledge- seeking is as a motive for outsourcing, the more likely the R&D service is to be located in a developing economy. Lower labor costs seeking.

As R&D activities are knowledge-based and in consequence rather labor-intensive, cost remains an important driver of offshore outsourcing, given that some firms within developed economies may find their labor costs high compared with those of developing ones (Javalgi et al., 2009; Kotabe, 1998; Trent & Monczka, 2003). The development of a low- cost market of qualified providers located in developing economies, not only for standardized non-core activities but also for those which add more value to the firm, such as R&D, has driven some firms to outsource some of these activities to these locations (Lieberman, 2004; Maskell et al., 2007; Patel & Vega, 1999; Subramaniam & Venkatraman, 2001; UNCTAD, 2005), as this implies the possibility of significant savings on labor costs. As a consequence, we expect that the more importance is given to the search for a provider able to perform the R&D service more efficiently than the firm owing to its lower labor cost as a reason for outsourcing, the more likely firms will be to outsource R&D services to providers located in developing economies, as is the case with other activities such as manufacturing. Thus, we predict that,

Hypothesis 4. Conditional on the firm having decided to outsource to an offshore provider, the more important lower cost- seeking is as a motive for outsourcing, the more likely the R&D service is to be located in a developing economy.

2.2.2. R&D service attributes: the role of tacit knowledge and technological uncertainty

Previous works on offshoring highlights that the decision of where to locate a specific offshore activity is also dependent on the attributes of the task being offshored (Doh et al., 2009; Graf & Mudambi, 2005; Hätönen, 2009). In a similar vein, it has been argued that the option of outsourcing certain stages of business processes and offshoring parts of the value chain within firms to low-wage locations depends crucially on how processes are ‘embedded’ (Grote & Täube, 2007). In this sense, although previous studies have tested how specific service attributes affect the R&D outsourcing decision, they were focused on captive offshoring decisions (Cantwell & Santangelo, 1999; Doh, 2009). Thus, to the best of our knowledge, there is hardly any examination on how these service attributes influence the decision to outsource them to a particular offshore location. Therefore, through the combination of transaction cost theory and the resource-based view insights, we will take into account previous findings analyzing transaction factors influencing R&D outsourcing decisions and extend them to the international context. Specifically, we take two service attributes into account which have been found to be especially relevant when deciding either to outsource innovation activities or where to locate them: (i) the extent to which tacit knowledge is required to perform the service and (ii) the degree of technological uncertainty surrounding the activity. The extent of tacit knowledge.

The degree of tacitness of the knowledge being transferred is considered as a factor hindering research and technology transfer (Howells, 1996). Tacit and specialized components of technological knowledge require costly person-to-person contact to transfer Teece (1977) and thus it will influence the firm’s propensity to outsource the service to a particular offshore location. This is the case because tacit knowledge is difficult to articulate, codify and transfer (Kogut & Zander, 1993) and, when outsourcing offshore, the transfer of this knowledge is more difficult owing to the different national cultures of the client and the supplier (Madhok, 1997). As a consequence, we expect these difficulties to be even more critical when outsourcing to offshore providers in developing economies, as the firms’ capability of efficiently transferring this tacit knowledge required to perform the service will be reduced owing to institutional differences, cultural distance, and communication costs (Teece, 1986). It has been found that services of a routinely and repetitive nature are more likely to be offshored to countries with lower wages (Doh et al., 2009). Obviously, a company willing to effectively transfer tacit knowledge to an outsourcing firm should dedicate time and effort to do this transfer, but by doing so it could be facilitating undesired knowledge transfers that might end up revealing valuable information to a provider performing activities directly related to the client’s core business, and thus it may imply a potentially risky upgrading of the provider’s technological competence (Arruñada & Vazquez, 2006; Larsson, Bengtsson, Henriksson, & Sparks, 1998). Therefore, we predict that:

Hypothesis5. Conditional on the firm having decided to outsource to an offshore provider, the more tacit the R&D service, the less likely it is to be located in a developing economy. Technological uncertainty.

Technological change may have an important effect on the decision to internalize or outsource a particular activity, thus reducing the probability of outsourcing it to a particular location. Internalizing activities under conditions of rapid technological change impose inflexibility precisely when flexibility is most needed (Poppo & Zenger, 1998). In fact, literature analyzing strategic technology partnering has found that, whenever firms need quick responses to changes in technological leadership, non-equity agreements are preferred to joint ventures because they provide firms with greater strategic flexibility (Osborn & Baughn, 1990). Specifically, previous research has shown that greater use of outsourcing may deliver more flexibility, which may help firms to respond quickly to unanticipated threats and market opportunities (Hitt et al., 1998). Due to the fact that investments in technology are often quite specialized, rapid technological change may increase the likelihood of technological investments in knowledge and routines being rendered obsolete (Balakrishnan & Wernerfelt, 1986).

Thus, despite there being no empirical evidence on this issue, based on this previous literature together with the main motivations driving firms to outsource R&D services offshore, we expect that the outsourcing decision for services characterized by a high level of technological uncertainty will be largely driven by the need to access specialized providers with the resources and capabilities required to perform them at a particular time (i.e. having the most appropriate skill set), and not so much by the need to reduce costs (Poppo & Zenger, 1998). Therefore, we expect the level of technological uncertainty surrounding the R&D service to have a negative effect on the probability of a firm outsourcing it to offshore providers in developing economy usually characterized by a less developed technological and institutional infrastructure. This leads to our final hypothesis:

Hypothesis 6. Conditional on the firm having decided to outsource to an offshore provider, the greater the technological uncertainty surrounding the R&D service, the less likely it is to be located in a developing economy.

3. Data and methods

3.1. Research setting and data

We obtained data on R&D outsourcing agreements through a mail survey conducted on a sample of firms competing in R&D- intensive industries. The targeted population was made of companies with headquarters in the U.S. and the European Union (EU), with more than 100 employees, and whose two-digit SIC code was one of the five defined in the OECD classification of technology-intensive industries: (28) chemicals and allied products, (35) transportation equipment, (36) computers and electronics, (37) industrial machinery, and (38) analysis and measurement equipment. As mentioned above, efficient management of R&D plays a crucial role in the competitive strategy of these industries, so we expect these firms to make efforts to achieve superior efficiency in their R&D outsourcing agreements worldwide. We stratified the sample according to industry and firm size to insure external validity, using both domestic and international versions of the Dun & Bradstreet Million Dollar Database. Using these criteria, we obtained a list of 3529 U.S. firms and 3375 EU firms. From these lists, we randomly selected stratified samples of 2000 firms from the U.S. and 2000 from the EU, taking into account home country, industry and firm size.

We developed the survey instrument in several stages. Firstly, to understand better the R&D outsourcing phenomenon and to develop a more comprehensive questionnaire, we conducted interviews with the heads of technology and innovation of a large US-based multinational company both at the headquarters and at the subsidiary level. Secondly, we reviewed the literature extensively to identify relevant scale items for the concepts we wanted to measure. Finally, to avoid misunderstandings due to the international nature of the targeted population, the questionnaire was pre-tested on seven R&D managers working for different companies located in different countries, who indicated which survey items could be confusing and how to redefine them in order to avoid misinterpretations. Furthermore, the questionnaire was sent in one of five languages: English, French, Italian, Spanish, and German.6 Given the different sizes and industries included in our targeted population, the questionnaire was mailed to the firm’s chief executive officer (CEO) along with a request to pass it on to the head of R&D or technology if desired. The returned questionnaires were filled out by senior managers, namely: CEOs, VPs, heads of R&D or heads of technology or engineering departments. We followed the principles of the Total Design Method developed by Dillman (1978) which has been generally considered as the standard for mail surveys in the social sciences. This method is aimed at convincing respondents on the importance of the problem being analyzed, and thus that their help is needed to find a solution. A total of 105 completed questionnaires were received from the first mailing in July 2006. A second mailing was sent three months later and an additional 33 questionnaires were received, 303 mailings being returned as undeliverable. After a telephone follow-up process, we obtained a final sample of 182 usable responses (81 for the U.S. and 101 for the EU). After excluding the undeliverable addresses, our response rates were 4.5% for the U.S. and 5.3% for the EU. Despite the low response rate, the 182 responses obtained are representative of the spectrum of firms in terms of industry, country of origin, and firm size (see Table A1 in the Appendix A for the distribution of the mailed questionnaires and the responses). Besides this, we compared the responses from the first mailing with those from the second but found no significant differences at the 95% confidence level between early and late respondents in terms of all the variables used in the study. We also run analyses to test whether there were differences in terms of country of origin, firm size, or industry between the respondents and non-respondents, but again, not significant differences were found. We thus conclude that a significant non-respondent bias is unlikely.

We asked firms to indicate which R&D service activities they were outsourcing from a comprehensive list of twelve, and where in the world they were doing so. After making an exhaustive literature review of different sources on innovation and a review of business and statistical reports on R&D and websites from technological firms as well as those from specialized providers offering R&D services, we managed to identify a list of different R&D services or stages that could potentially be outsourced by technology- intensive firms. However, given that our aim was to obtain an exhaustive list of the categories of R&D services that could be outsourced by any of the technology sectors comprising the sample of this study, it can be expected that some services will be more applicable to some industries than others. Nevertheless, in order to tackle this issue, we have included applications specific to certain industries in the service typology. This final list was revised by a consulting firm and several R&D managers who helped us to refine our typology of R&D services. After their reviews, the R&D services identified were the following: basic or fundamental research, applied or experimental research, development of new products or new or improved processes, product design, design of technology processes and engineering systems, architectural services, software development, scientific and technical support consulting services, software implementation services, and testing and analysis services. We found that 108 of the 182 firms outsource at least one of the R&D services listed (60% of our sample).7 We asked these companies for more detailed information about the service being outsourced or, in those cases in which more than one service was outsourced, about the most representative service (i.e. in terms of resources and volume contracted). By focusing on these agreements, we were able to analyze the location and attributes of the most representative R&D outsourcing agreement for each firm more precisely. Missing data on some of the variables reduced the sample from 182 to 173 usable questionnaires.

Due to the fact that our dependent and some independent variables were obtained from the same survey instrument, our results could have been affected by common-method bias. In order to deal with this issue, we used the procedural remedies related to questionnaire design suggested by Podsakoff, MacKenzie, Lee, and Podsakoff (2003), and we performed Harman’s single-factor test (Harman, 1967), which suggested no evidence of common-method bias.

3.2. Method of analysis and measures

This study investigates both the firms’ propensity to establish R&D outsourcing agreements offshore compared to other organizational options, and the likelihood of locating these agreements in developing economies instead of developed ones. Thus, two statistical methods, a multinomial probit regression, and a two-stage probit model were used to test the hypotheses. Our approach was to start by estimating a multinomial probit model for analyzing the firms’ choice of R&D outsourcing strategy. Then, to assess and correct for self-selection when analyzing the probability of where to locate offshore, we estimated a two-stage probit model corrected for sample selection (i.e. for the probability of the firm choosing an R&D offshore outsourcing strategy in the multinomial probit model). We then implemented this Heckman two-stage probit model in STATA, using the HECKPROB procedure in which both the first-stage and the second-stage are probit models (Heckman, 1978, 1979).

3.2.1. The decision of whether or not to outsource R&D services offshore

In order to estimate a model with multiple discrete outcomes, we used a multinomial probit model. As in multinomial logit models, in multinomial probit, the estimates of coefficients for independent variables measure the effect of the variation of the independent variable on the relative probability of the dependent variable taking a particular value in relation to the probability of it taking another value that is used as reference. The main advantage of using the multinomial probit instead of the logit is that this model allows error terms to be correlated across alternatives, thereby permitting it to circumvent the dilemma of the independence of irrelevant alternatives present in the multinomial logit model (Kennedy, 1998). Dependent variable.

R&D OUTSOURCING STRATEGY accounts for the organizational and geographical dimension of firms’ R&D outsourcing practices. In particular, this dependent variable equals “0” if the firm does not outsource R&D services and it neither has captive R&D centers offshore, “1” if the firm does not outsource R&D services but has captive R&D centers offshore, “2” if the firm does outsource R&D services but only domestically, “3” if the firm does outsource some R&D services offshore but its main outsourcing agreement is located at the domestic market, and “4” if the firm does outsource R&D services offshore and besides its main outsourcing agreement is located offshore. Of the 173 firms in our sample, 48 firms took a value of “0”, 26 firms a value of “1”, 33 firms a value of “2”, 29 firms a value of “3” and finally 37 firms took a value of “4”. Independent variables.

As an indicator of the firm’s technological resources and capabilities we introduced two different measures. One input variable (R&D INTENSITY log) was used as an indication of the firm’s effort in terms of R&D. We asked the respondent to estimate the firm percentage of R&D investment over sales. This variable was logged. Second, as an output measure of the firm’s accumulation degree of technological capabilities (PATENTS), we used the number of patents assigned to the firm until the end of 2006, as recorded by the United States Patent and Trademark Office (UPSTO). As experience and capabilities are developed and accumulated over time, we accounted for the complete track record of patents assigned to the firm. Patent data have also been used in previous studies to measure the technological capabilities of firms in high-technology industries (Bachmann, 1998; Praest, 1998; Tallman & Phene, 2007).8 To assess the firm’s overall international experience, we created the variable (MULTINATIONALITY), which counts the number of international wholly-owned subsidiaries possessed by the firm.

We also introduced some variables to control the heterogeneity of firms. To assess the degree of internationalization of firm’s sales, we introduced the percentage of firm’s sales being international (INTERNATIONAL SALES). To control the firm size, we used the logarithm of the firm’s sales during 2005 in dollars (FIRM SIZE). We also ran models using the number of employees as an alternative measure of firm size, and we obtained comparable results. We created a dummy variable (EU FIRM) coded one for firms founded in the European Union, and coded zero for the US. Finally, we introduced the following industry dummies: SIC 28 (Chemicals), SIC 35 (Transportation Equipment), SIC 36 (Electronics), SIC 37 (Machinery) and SIC 38 (Measurement Equipment). SIC 38 acted as the reference category.

Table 1 shows correlations and descriptive statistics for all the variables used in the first-stage model.

3.2.2. The decision of where to outsource R&D services offshore: developing versus developed countries

In order to analyze firms’ probability of offshore outsourcing R&D services to a provider located in a developing economy instead of in a developed one, we used a two-stage probit model. The aim of this probit was to assess and correct for self-selection (i.e. we control for the firms’ probability of offshore outsourcing R&D services, that is the firms’ probability to choose strategy number 4 in the multinomial probit model compared to the rest of sourcing alternatives). To do so, we recoded the dependent variable in the multinomial probit model as taken a value of one for those firms who indicated being outsourcing R&D services offshore and whose main outsourcing agreements were located offshore. Stage 1: R&D offshore outsourcing decision.

The dependent variable in this first-stage probit model (R&D OFFSHORE OUTSOURCING) takes a value of one for those firms in category 4 in the multinomial probit model, that is, those who indicated being outsourcing R&D services offshore and besides their main outsourcing agreements were located offshore, and takes a value of zero otherwise.

The independent variables included in this first-stage probit model are the same as the ones used in the multinomial probit model: PATENTS, R&D INTENSITY, MULTINATIONALITY, INTERNATIONAL SALES, FIRM SIZE, and EU FIRM.9 Stage 2: R&D offshore location decision.

Our dependent variable in this second-stage probit model (DEVELOPING LOCATION) equals “1” if the main provider for the R&D service outsourced is located offshore in a developing economy, and “0” if the provider is located offshore but in a developed economy. Within these 37 offshore outsourcing agreements, 17 were located in a developing economy and 20 in a developed one.10

To account for the motivation for outsourcing an R&D service, we used two different items within the questionnaire.11 Firstly, we measured the need to access specialized providers (KNOWLEDGE-SEEKING), asking the respondent to evaluate the importance of “Lack of skilled personnel within the company” as a reason for outsourcing the R&D service from 1 (very low) to 5 (very high) on a Likert scale. Secondly, to measure the need to reduce costs (LOW LABOR COST-SEEKING), the questionnaire asked the respondent to evaluate the importance of “Cutting labor costs” as a reason for outsourcing the R&D service on a Likert scale from 1 (very low) to 5 (very high). In relation to the attributes of the R&D service, we proxied the extent to which tacit knowledge was implicit in the service being outsourced (TACITNESS). We used three items adapted from Kogut and Zander’s (1993) work, and asked the respondent to indicate his or her level of agreement with three statements related to the attributes of the R&D service they were outsourcing. Our inter-item reliability was also very high (Cronbach’s alpha = 0.823) so we combined these three items to represent our construct: (1) it is difficult for third parties to understand the company know-how related to this service, (2) it is difficult for third parties to copy or imitate the abilities or technological knowledge required to perform the service and (3) effective transfer of company know-how to perform this service requires a high level of frequent interaction with company personnel. Finally, we created a variable (TECHNOLOGICAL UNCERTAINTY) in order to assess the level of technological uncertainty surrounding the service. We asked the respondent to indicate his or her level of agreement from 1 to 5 with two statements adopted from Poppo and Zenger (1998) regarding the attributes of the R&D service they were outsourcing: (1) the skills required to perform the service are subject to frequent change and (2) the optimal configuration of hardware and software required to perform this service is subject to frequent change (Cronbach’s alpha=0.79).

We also included several control variables. Given that the previous literature also signaled process improvement as one of the main motives for outsourcing (Graf & Mudambi, 2005), we introduced a variable in order to control for this third motive for outsourcing (PROCESS IMPROVEMENT). In order to develop this measure, we asked the respondent to rank the level of importance of the following factors in the decision to outsource the R&D service on a Likert scale from 1 to 5: (1) reduction of time taken from product development to sales (‘time-to-market’), (2) cost reduction achieved through the consolidation of certain activities at specialized centers, (3) increase of operational flexibility and (4) reorientation of company efforts and resources to core activities. As the inter-item reliability was high (Cronbach’s alpha=0.754), we combined these four items to represent our construct. Furthermore, in relation to the R&D service being outsourced, we controlled for the level of difficulty in measuring worker performance (MEASUREMENT) as it might have an effect on the outsourcing location decision. We asked the respondent to indicate his or her level of agreement with the following statement on a Likert scale from 1 to 5: “It is difficult to measure the collective performance of those individuals who perform this service.” This single-item measure was adapted from Poppo and Zenger (1998) and it is consistent with previous work (Anderson & Schmittlein, 1984). In order to assess the firm’s international captive R&D experience in developing economies (CAPTIVE R&D IN DEVELOPING ECONOMIES), we introduced a dummy variable valued one if the firm owned subsidiaries performing R&D activities located in non-OECD countries, and zero otherwise. To assess the firm’s international captive R&D experience in developed economies (CAPTIVE R&D IN DEVELOPED ECONOMIES) we introduced a dummy variable that took the value of one if the firm owned subsidiaries performing R&D activities located in OECD countries, and zero otherwise. Finally, like in the first-stage, we also introduced the variable PATENTS to control for the firms’ accumulation of technological resources and capabilities.

Table 2 shows correlations and descriptive statistics for all the variables used in the second-stage probit model. No high correlations were observed.

4. Results

Table 3 reports the results from our multinomial probit regressions modeling the decision to outsource offshore, and Table 4 reports the results from the second-stage probit regression modeling the choice between suppliers located in developing versus developed countries corrected for endogeneity. Specifically, the tables show the value of the estimated coefficients, their robust standard errors and an indication of the significance level for each model. The models run reached significance levels below 0.001, as shown by the chi-squared values. Thus, the null hypothesis that all estimated coefficients are equal to zero is rejected in all cases.

Multinomial probit results predicting the choice of R&D sourcing strategy (reference group: neither R&D services outsourcing nor captive R&D centers offshore).

As can be seen in Table 3, the overall results support our hypotheses. According to our first hypothesis, PATENTS is positive and statistically significant in category number 4, so the progressive accumulation of technological resources and capabilities increases the probability of adopting an offshore outsourcing R&D strategy compared to conducting all of the R&D activities in-house in the home country. The same thing can be observed in categories 1 (firms doing R&D captive offshoring only) and 3 (firms undertaking domestic outsourcing and R&D captive offshoring). Interestingly, the only category in which PATENTS is non-significant is number 2, that is the one comprising those firms outsourcing R&D but only to domestic providers. This result suggests that having greater technological capabilities is an important driver when deciding to go offshore, either through the establishment of captive R&D centers of through outsourcing. Furthermore, when we analyzed the variable R&D INTENSITY aimed at measuring the technological resources a firm might have owing to its R&D efforts, we found that it is only statistically significant for those firms in category 4, that is, those outsourcing R&D services offshore. Thus, it seems that all else being equal, this result may suggest that firms that are more R&D-intensive are also more willing to search for R&D providers overseas in order to maintain their competitive advantage. While, another plausible explanation for these results is that they may be indicative of some reverse causality effect, as it could be the case that R&D outsourcing practices may lead to firms doing more internal R&D in order to keep up with and be able to integrate those external services. In addition, according to Hypothesis 2, the variable MULTINATIONALITY is positive and significant across all categories, which is indicative that those firms having more international experience are more likely not only to outsource offshore, but also to offshore R&D through captive centers or even to outsource to domestic providers, as compared to the probability of neither outsourcing nor having captive R&D centers offshore. To fully test Hypotheses 1 and 2 predicting a higher propensity to outsource offshore instead of other organizational alternatives, we also tested the significance of the coefficients associated to the R&D offshore outsourcing strategy (category “4” in the multinomial probit) and the rest of alternatives (“1” if the firm does not outsource R&D services but has captive R&D centers offshore; “2” if the firm does outsource R&D services but only domestically; and “3” if the firm does outsource some R&D services offshore, but its main outsourcing agreement is located at the domestic market). The appropriate statistic for this test is a t-statistic. These tests revealed that significant differences exist for PATENTS, R&D INTENSITY and MULTINATIONALITY (pb0.01) between firms following an R&D offshore outsourcing strategy and the rest of the alternatives.12 Therefore, we found that firms having more technological resources and those with more international experience show a higher propensity to outsource R&D services to offshore providers compared to other organizational options, as proposed in Hypotheses 1 and 2.

Regarding the control variables, when looking at the results of the INTERNATIONAL SALES variable, it is clear that the most active firms in international markets are more willing to outsource offshore, as the coefficient for this variable associated to the category number 4 (firms that outsource R&D offshore) is significantly higher than for the remaining categories (according to a t- test with pb0.05). Finally, with regard to the remaining control variables, our results suggest that U.S. firms are more likely to outsource R&D services offshore compared with those from the EU, according to the negative and significant effect of the variable EU FIRM when explaining the probability of offshore outsourcing as opposed to other sourcing strategies. Thus, it is interesting that, similarly to FDI, literature on R&D found that U.S. firms have been pioneers in the internationalization of their R&D activities compared with European or Japanese firms (Kuemmerle, 1999), we find that U.S. firms seem to be also pioneers in their decisions to outsource offshore stages within their R&D processes.

Regarding the hypotheses related to the firms’ motives for offshore outsourcing the R&D service, we find support for both Hypotheses 3a and 4. On the one hand, we find support for Hypotheses 3a, and not for 3b, as the variable KNOWLEDGE-SEEKING is negative and significant when explaining the probability of firms outsourcing R&D services to providers located in developing countries as opposed to developed ones. On the other hand, according to Hypothesis 4, the LOW LABOR COST-SEEKING variable is positive and highly significant when explaining the probability of outsourcing R&D services to developing countries compared with the probability of outsourcing them to developed economies. Thus, we do not find evidence of the fact that the more important is knowledge-seeking as a reason for outsourcing R&D services, the more likely they will be outsourced to providers in developing economies, as Hypothesis 3b proposed. However, it should be noted that these results may be indicative of the fact that R&D offshore outsourcing to developing economies is still in an early stage, as firms may evolve from seeking lower costs to knowledge-seeking objectives when deciding to outsource to developing locations (Jensen, 2009; Maskell et al., 2007). Indeed, taking into consideration the most representative R&D outsourcing agreements for the firms in our sample, when we analyze the average duration of the outsourcing relationships with the main provider for the R&D service, the results suggest that international R&D outsourcing is a rather novel business practice (see the ANOVA analysis in Table 5). Interestingly, we find that, on average, the outsourcing relationships with providers in the firm’s home country last substantially longer than those with providers located overseas, whilst international R&D outsourcing agreements with providers in developing countries enjoy the shortest relationships.

Finally, with regard to the hypotheses related to the effect of the R&D service attributes on the decision to outsource it to a provider located in a developing economy, both the variable TACITNESS and the variable TECHNOLOGICAL UNCERTAINTY display negative and statistically significant coefficients, which support Hypotheses 5 and 6 respectively. We analyze the causes and consequences of this preference for developed countries in the discussion.

Regarding the control variables, none of them appear to be significant when it comes to explaining the firm’s probability to outsource R&D services to a particular location.

5. Discussion and conclusion

The goal of this paper was to improve our understanding of the location determinants of R&D services offshore outsourcing agreements. In particular, we analyzed the factors driving technological firms to outsource R&D services overseas and the probability of locating those offshore outsourcing agreements in a developing country instead of in a developed one. The interesting element of this research question is that, despite the extant literature on offshoring of high-value adding functions and on R&D outsourcing in technology sectors, we find that there is no empirical examination on what drive technological firms to outsource R&D services to a particular offshore location. To the best of our knowledge, only a few studies have addressed this issue, and then mainly from a theoretical perspective (Contractor et al., 2010; Doh, 2005; Graf & Mudambi, 2005; Hätonen & Eriksson, 2009; Kedia & Mukherjee, 2009; Mudambi & Tallman, 2010) or case illustrations (Hätönen, 2009; Mudambi & Venzin, 2010), or just focused on captive offshoring (Demirbag & Glaister, 2010; Doh, 2009). Therefore, this study contributes to this previous literature by providing empirical evidence regarding how factors such as what is being outsourced and why, what kind of experience the firm has, and what influences the decision of where to locate R&D services outsourcing agreements. Our study places special emphasis on the fact that, in order to outsource overseas, some firm-specific capabilities are required, especially when offshoring to developing economies. The explicit consideration of these capabilities would thus help to explain inter-firm differences in the propensity to outsource R&D offshore.

With regard to the first question of whether or not to outsource R&D services offshore, our results show that this decision depends on firms’ characteristics such as their level of technological resources and firms’ international experience. Specifically, those firms having more technological resources are overall more likely to offshore their R&D, that is, not only through outsourcing but also through the establishment of captive centers. However, we find that firms establishing R&D outsourcing agreements offshore are not only those with greater technological resources, but also those that are more R&D intensive. Indeed, these firms are the ones that appear to be benefiting the most from this R&D global outsourcing market. In addition, results show that firms’ international experience plays an important role in these decisions, as those firms having more subsidiaries located overseas and, especially, those with a higher percentage of foreign sales seem to be more likely to offshore R&D not only through outsourcing but also through the establishment of captive centers. Therefore, when taken together, our findings suggest that, although both firms’ level of technological resources and international experience are important factors determining R&D offshoring decisions (captive and outsource offshoring), their influence appears to be greater in deciding to offshore R&D through outsourcing agreements. Indeed, previous research has found that technological capabilities increase both the probability of captive offshoring (Berry, 2006) and offshore outsourcing (Quinn & Hilmer, 1994). While, there are also available evidence regarding the role of a firm’s international experience both on captive offshoring (Berry, 2006; Belderbos, 2003; Odagiri & Yasuda, 1996) and offshore outsourcing (Mol et al., 2005). As well as there is recent evidence showing the important role of both capabilities and international involvement in order to decide to outsource R&D offshore (Bertrand & Mol, 2010). However, none of these studies have analyzed jointly the R&D captive offshoring and R&D outsource offshore decision. Focusing now on the importance of multinationality and international experience, our results highlight the importance of having a network of subsidiaries (Kogut & Kulatilaka, 1994) in order to outsource offshore. Therefore, a network of subsidiaries provides flexibility not only by the option to move activities across borders, but also because of the ease of outsourcing offshore. Nevertheless, international expertise is also important when selecting and monitoring offshore providers, as experience increases the firm’s ability to develop business relationships in different institutional environments. Thus, taking our first-stage results as a whole, it becomes clear that some firm-specific attributes related to governance capabilities (technological and international expertise) are important drivers in the decision to outsource offshore instead of relying on other organizational options, like in-house activities, domestic outsourcing, or captive offshoring.

Indeed, with regard to the second question of where to locate these R&D offshore outsourcing agreements offshore, results show that the likelihood of outsourcing an R&D service to a provider in a developing economy, as opposed to a developed one, increases: the less important is knowledge-seeking as a motive for outsourcing the R&D service, the more important is lower labor costs as a motive for outsourcing the R&D service and the less tacit and subject to technological uncertainty is the service being outsourced.

First, with regard to the influence of the firms’ motives for offshore outsourcing R&D, our results show that the main driver to outsource to developing economies is still their labor cost, while those firms outsourcing R&D for knowledge-seeking reasons seem to prefer developed locations. Therefore, although there are recent studies on offshoring arguing that firms are increasingly relocating innovation activities to developing countries motivated by the high-qualified workforce within these regions (Lewin et al., 2009; Manning et al., 2008), our study contributes to this literature by finding that in the specific case of R&D services, knowledge seeking do not lead to outsource offshore to developing economies. Despite this, we should note that this may not be the case for other services, and as international R&D outsourcing is a rather novel business practice, in the near future we could see a propensity to outsource from these countries. Besides, it should be taken into account that there is recent evidence suggesting that increasing commoditization of knowledge services opens up windows of opportunity for the development of new clusters in second-tier locations (Manning, Ricart, Rosatti Rique, & Lewin, 2011). Therefore, one limitation of this study is that our findings may be context-specific. This is, however, inevitable when one tries to disentangle this phenomenon and move beyond the aggregate analysis within this topic. Thus, from a dynamic perspective, it can be expected that, as firms gain experience through outsourcing in developing countries and, as a result of these practices, providers within these regions develop greater technological skills, firms may evolve from seeking lower costs to knowledge-seeking objectives when deciding to outsource to developing locations (Dossani & Kenney, 2003; Jensen, 2009; Maskell et al., 2007; Ricart et al., 2011). In other words, this propensity to outsource high-value R&D research services will increase as firms from emerging markets accelerate the catching-up process whereby they are reducing the competitive gap against established MNEs (Guillen & Garcia-Canal, 2009).

Second, when analyzing the influence of what is being outsourced in the decision of where to locate the R&D offshore outsourcing agreement, we observed the significance of both the degree of tacitness of the knowledge required to perform the R&D service, and the degree of technological uncertainty surrounding the service. In line with previous research arguing that the degree of tacitness of the knowledge transferred hinders research and technology transfer (Howells, 1996; Howells et al., 2008), our study highlights the difficulty of effectively transferring tacit knowledge offshore as the institutional and cultural distance between the firm’s home country and that of the provider increases (Madhok, 1997; Teece, 1986). Our main contribution regarding this variable is to show that providers in developing economies are preferred for more commoditized activities, providing evidence in the R&D outsourcing field on how the degree of standardization diminishes the relevance of geographic distance (Demirbag & Glaister, 2010). Indeed, what we found is that much of the services outsourced to these regions are related to quite standardized software services, where Asian countries have low-cost pockets of expertise. Finally, we found that the more technological uncertainty there is surrounding the R&D service, the more likely the firm is to offshore outsource it to a provider in a developed country as opposed to a developing one. This result suggests that, for services characterized by frequent technological changes which may render human and capital technical investments obsolete, offshore outsourcing to providers in developed countries adds flexibility to the firm, since it offers the possibility of switching providers with different technological resources and capabilities as the need arises. Besides, it could be argued that under frequent technological change, outsourcing contracts are subject to renegotiation, which may lead to face a higher risk of opportunism if contracting with providers in a developing economy which are usually characterized with higher level of political instability or corruption (Cuervo-Cazurra, 2006). Therefore, whenever the resources and capabilities required to perform an activity may be subject to frequent change, it seems more convenient for technological firms to outsource to providers located in more stable developed economies.

Yet—similarly to the previously explained expected evolution from cost to value with respect to the motives driving firms to outsource to developing countries—from a dynamic perspective it can also be expected that the nature of what is being outsourced to these economies will vary towards more complex functions. This is because confidence and trust in the partner have been found to be a major constraint on the sourcing process (Howells et al., 2008). Therefore, it can be expected that, as the firms gain experience of doing business within these economies, the duration of the outsourcing relationships with providers in developing countries will increase and evolve towards more long-lasting and trustful relationships. Indeed, when asked about the performance achieved within these R&D outsourcing agreements with providers located in developing economies, firms within our sample claimed to be, on average, highly satisfied. However, they also indicated to be considering Asian countries, such as China or India, or Russia within Europe as the next regions where they were planning to increase their R&D outsourcing agreements. This fact may facilitate the transfer of more tacit and complex knowledge given that, as firms accumulate experience of contracting in these economies, the parties may learn to cooperate and can thus renegotiate more equitable and efficient contracts (Coltman, Bru, Perm-Ajchariyawong, Devinney, & Benito, 2009). Therefore, the results of these two variables related to uncertainty and complexity, whose influence have not been tested yet in the field of the location of R&D offshore outsourcing agreements, confirm that transaction costs are still the main deterrent to outsource offshore to developing economies. Taking all of our results as a whole, we can affirm that, in order to explain both the decision to outsource offshore and its preferred location, we need to take into account the need for external resources, the firm’s governance capabilities and the transaction costs associated to the outsourcing relationship.

5.1. Limitations and future research

This paper is not without its limitations. A more fine-grained study could be developed if we knew the volume outsourced as a percentage of the total budget allocated to the R&D service and of the total R&D outsourcing budget. Even though our respondent firms are representative of the population by country of origin, industry, and firm size, we obtained a low response rate, so our results should be treated with caution. This study could also be further developed by analyzing the type of outsourcing relationship—i.e. long-term versus short-term agreement—chosen by the firm depending on the R&D outsourcing location or type of R&D service being outsourced. Indeed, as stated by Doh et al. (2009), despite the important contributions of previous literature regarding these practices, past research focused largely on offshoring in the aggregate sometimes overlooking the diversity and complexity of offshore services activities and related location decisions geared toward specific offshoring functions. Even though, to the best of our knowledge, this is the first quantitative study addressing both whether and where to outsource R&D services offshore, it is very difficult to perfectly separate the two dimensions. In effect, these two decisions are closely interrelated, as depending on the firm and the activity being outsourced there is no general agreement regarding the sequence of outsourcing and offshoring decisions (this discussion has been highlighted, for instance, in Mudambi & Venzin, 2010). Unfortunately, as we have cross-sectional data, it is not possible for us to test the sequence of these decisions, but to control for the different combination of organizational options that can be followed by the firms. Thus, one limitation of our study is that although we have addressed whether and where to outsource offshore R&D, our analysis of the first decision does not attempt to fully explain firms’ organizational choices for R&D services, but to account for the endogeneity associated to any organizational choice.

In relation to the role of firms’ international experience in R&D offshore outsourcing decisions, our findings contribute to the literature by showing that firms with a higher level of international experience—regardless the location of the firms’ subsidiaries— are significantly more likely to outsource R&D services offshore compared to other organizational alternatives. This suggests that firm’s international involvement may help develop the required managerial capabilities to effectively identify capable providers offshore and manage and control those outsourcing agreements. However, the fact of having captive R&D experience in offshore developed or developing economies appears to have a non-significant effect on the preferred developed versus developing location for the agreements. This result may suggest that in some offshore locations captive R&D and outsourcing may act as substitutes rather than complements. Thus, one limitation of this study is that the way we measure the international experience of the firm might be an oversimplification—as due to the lack of more specific data we are not controlling for whether spillover effects occur between different foreign countries.

Therefore, further research overcoming these limitations and taking a longitudinal approach could facilitate a better understanding of the R&D offshore outsourcing phenomenon. In particular, it would be of great interest to gather longitudinal data so as to analyze the evolution of these offshore outsourcing agreements in terms of the volume and complexity of the R&D services being outsourced to specific offshore locations. In this sense, several directions for future research can be identified. From the perspective of the client firm, further research is encouraged so as to identify whether and under what circumstances or locations captive R&D and outsourcing may act as substitutes or as complements when crafting a firms’ technological strategy. From the perspective of the supplier, further research deserves the analyses of what governance mechanisms or managerial practices could be developed in order to try to capture more advanced technological services over time. From the perspective of governments, future research should build on how to attract R&D investments from foreign firms, while at the same time developing policies so as to encourage local firms not to offshore. Thus, our study could be also extended by analyzing how government R&D investment programs may affect private R&D offshore outsourcing location preferences. In this sense, previous studies have shown the effect of government R&D expenditures and attraction programs on private R&D investments and productivity (Levy & Terleckyj, 1983), as well as the fiscal and non-fiscal instruments that not only governments in developed economies but also those from developing ones may employ to stimulate investments in R&D (Mani, 2004). Therefore, from an economic policy perspective, extending previous research on the effect of these programs on attracting R&D outsourcing practices would be of great interest. Specifically, the focus should be on analyzing what government policies fostering greater innovation and technology development may help to attract R&D outsourcing contracts from foreign firms, as well as to help to avoid local firms outsourcing offshore (Lieberman, 2004).

5.2. Managerial implications

Most multinational firms apply arbitrage strategies taking advantage of cross-country differences in resource endowment (Ghemawat, 2003). They are also shifting their knowledge strategies from close to open innovation systems, drawing knowledge from a range of external sources to develop and commercialize new technology (Chesbrough, 2003). Therefore, firms need to involve external sources of knowledge in their innovation systems and to understand how to best set up contracts with external R&D suppliers (Fey & Birkinshaw, 2005). Offshore outsourcing strategies can be conceived as part of this broader trend of open innovation in which firms R&D location decisions are driven by the need to access external sources of knowledge geographically dispersed. Our study provides evidence on the existence of a global market for R&D service outsourcing that covers practically all the stages within a firm’s innovation process, whilst also demonstrating that it is widely used by firms operating in technology- intensive sectors. For these practices to add value to the firm, however, knowing how to effectively build a global network of knowledge and cost-oriented outsourcing agreements with providers dispersed worldwide presents several managerial challenges, which deserve further attention both from managers and scholars in the field. Thus, firms should be able to take advantage of external knowledge while still protecting their distinctive competences. Although the implications of this challenge have been already highlighted in previous research on R&D alliances (e. g. Arora et al., 2001; Kale et al., 2000), to the best of our knowledge, we are the first providing managers and scholars with insights for the specific field of R&D offshore outsourcing regarding which type of firms should be more open to it, and which activities within the R&D process could be outsourced and where. In particular, this study suggests that managers should continually reassess: (i) their governance capabilities related to selecting and monitoring the relationship with offshore providers, which increase with technological and international expertise, (ii) the cost versus knowledge orientation of the agreement that will condition the location of the provider, (iii) the uncertainty and complexity of the R&D service, that generate more transaction costs in developing economies and, for this reason, favor the choice of providers located in developed economies.

5.3. Policy implications

R&D outsourcing is also a challenge for developed national economies trying to retain these value-adding agreements within their national boundaries. Obviously, R&D offshoring practices influence National Systems of Innovation (Freeman, 1987; Lundvall, 1992; Nelson, 1993) in the home and host countries of the contractor. In this sense, as our study shows that the firms having greater technological resources and capabilities are the main active players in the global market for R&D outsourcing, the externalities of these agreements for national systems of innovation are especially important. Therefore, as suggested by Le Bas and Sierra (2002), from the perspective of the home country, our results suggest that strengthening the National System of Innovation, in order to upgrade the technological advantage of local firms, is a wise strategy to attract R&D activities as well as to allow local companies to benefit from market for R&D outsourcing. While, because our results show that transaction costs are an important deterrent to outsource R&D offshore to developing countries, one important implication from the perspective of the host country is that any improvement in the institutional environment, and specially in the National System of Intellectual Property Rights, would increase the chances of attracting R&D activities. Indeed, in relation to this, it has been recently shown by Lehrer, Asakawa, and Behnam (2011) that given intensifying competition among nations for R&D investment by MNCs, home base-compensating R&D by firms operating in high-technology sectors may help stimulate R&D reform in the home country. This is of interest from an economic policy perspective as offshore outsourcing phenomenon of R&D services can be understood as home base-compensating R&D strategy, through which firms seek to compensate for comparative weakness in their home- country R&D. As a consequence, as discussed by Lehrer et al. (2011) the actual innovation pattern imply that countries and regions need to nurture these technology markets and compete among them, which makes home base-compensating R&D a phenomenon of growing importance.

Finally, it should be noted that, as observed with the production value chain, technological firms are outsourcing more standardized activities to developing economies, as a first step towards gaining competitive advantage due to their lower costs combined with skilled labor force. However, what is more intriguing is that, due to the firms’ high level of satisfaction with their providers in developing economies, and the development of a higher familiarity with these foreign institutional environments, it can be expected that these firms will continue to outsource more sophisticated R&D services to these markets. Indeed, firms in our study claimed to be planning to increase their R&D outsourcing agreements in developing economies in the near future. While, there is recent evidence showing that the strength of developing and transition economies, the dynamism of their transnational firms, and their growing aspiration to compete in new markets, drove up their outward FDI flows to a record high (UNCTAD, 2011).Therefore, governments from developed economies should be aware of the fact that we can observe an incremental move of more advanced and complex R&D services to these locations; and thus, policies should be developed in order to try to retain these services within their own economies. In this sense, further research aimed at analyzing this catch-up process is encouraged.


We thank the reviewers and the editor for their helpful comments and suggestions. We are also grateful to Michael Mol and Heather Berry for their helpful comments, and to the Ministerio de Educación y Ciencia (project ref. SEJ2007-67329 and ECO2010- 18718) and FEDER for their financial support. This paper has also benefited from comments received from anonymous reviewers and conference participants at the Academy of International Business Meeting (AIB), European Academy of International Business (EIBA), and Asociación Científica de Economía de la Empresa (ACEDE). A previous version of this paper has been published as a Working Paper #526 in ‘Colección de Documentos de Trabajo de la Fundación de las Cajas de Ahorros’ (FUNCAS).


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.



Les coopératives d’activité et d’emploi : pratiques d’innovation institutionnelle

Marie-Christine Bureau
, Antonella Corsani
Dans Revue Française de Socio-Économie 2015/1 (n° 15), pages 213 à 231

1 – Introduction

Au milieu des années 1990, un nouveau concept naît dans le champ de l’insertion par l’économique : la coopérative d’activité et d’emploi (CAE). Ces coopératives émanent de travailleuses sociales militantes qui ont développé une réflexion critique, tant à l’égard des pratiques instituées de leur profession que des injonctions politiques à la création d’entreprise, et qui recherchent des solutions innovantes en puisant aux sources du socialisme autogestionnaire. Dans une période de déploiement des initiatives de l’économie solidaire, les CAE revendiquent leur statut coopératif, s’enracinant ainsi dans la tradition beaucoup plus ancienne de l’économie sociale [1]. Par rapport à la forme traditionnelle de la coopérative, les CAE présentent l’originalité d’être des coopératives pluriactives d’entrepreneurs. Elles visent à concilier « l’autonomie de l’entrepreneuriat individuel avec la dynamique et la protection collective du salariat » [Demoustier, 2006, p. 129]. Les CAE offrent aux porteurs de projet les avantages du statut social de salarié tout en leur permettant de travailler à leur compte pour développer leur activité. Si la coopérative fonctionne à la fois comme une société de portage salarial et comme une pépinière d’entreprises, elle est irréductible à l’une et à l’autre, du fait de la non-sélection des projets retenus, du caractère permanent de l’accompagnement (individuel et collectif) et surtout du fait qu’elle vise non pas la création de multiples entreprises individuelles mais le développement d’une entreprise partagée d’entrepreneurs-salariés. Plusieurs CAE sont actuellement engagées dans un processus de réflexion et de transformation d’elles-mêmes, afin d’aller plus loin dans la consolidation d’une « indépendance à plusieurs ». L’objet de cet article est de donner à voir ce qui s’élabore au cours de ce processus et que nous proposons d’analyser en termes de fabrique institutionnelle. À cette fin, nous préciserons d’abord la spécificité de notre démarche de recherche et ce que nous entendons par cette expression de « fabrique institutionnelle ». Puis nous mettrons en scène l’histoire des CAE, les circonstances de leur naissance, les racines qui les nourrissent, la tension permanente qui traverse le développement de leur projet. Enfin, nous nous centrerons sur une CAE francilienne, Coopaname [2] et nous montrerons quelques-unes de ses dynamiques d’évolution. Nous en interrogerons la portée et les limites pour imaginer de nouvelles institutions, au-delà de la logique binaire à l’intérieur de laquelle a été enfermé le travail.

2 – Une enquête dans les rouages de la fabrique institutionnelle

À ce jour, les CAE ont fait l’objet de publications de la part de leurs promoteurs [Bost, 2011 ; Poncin, 2004] mais elles n’ont guère retenu l’attention des chercheurs. Les travaux de Sandrine Stervinou et Christine Noël ainsi que ceux de Fanny Darbus constituent à cet égard une exception. Alors que S. Stervinou et C. Noël mettent en exergue, dans une revue de management, le rôle que peuvent jouer les CAE dans le développement d’une économie locale et d’un entrepreneuriat responsable [Stervinou, Noël, 2008], l’analyse de F. Darbus s’attache à montrer que les CAE abritent en fait une forme dégradée du salariat [Darbus, 2006]. À l’issue d’une série d’entretiens menés auprès d’entrepreneurs-salariés, l’auteure voit dans ce type de coopérative le cheval de Troie d’une forme d’emploi inédite, le salariat libéral : « Sous couvert d’expérimentation » [Darbus, 2006, p. 23], la CAE agirait principalement comme espace de reconversion pour des salariés disqualifiés sur le marché de l’emploi en leur offrant un cadre juridique pour exercer leur activité. Mais elle ne ferait que prolonger ainsi « des formes de précarité objective » [Darbus, 2006, p. 33]. Les quelques données disponibles sur le temps de travail mensuel déclaré et sur les revenus des entrepreneurs-salariés corroborent, aujourd’hui encore, le constat fait par F. Darbus de la précarité financière vécue par un grand nombre d’entre eux. Néanmoins, son analyse, polarisée par la référence à la norme d’emploi fordiste, passe entièrement sous silence l’épaisseur de l’organisation, ses dynamiques d’évolution et la créativité qui s’y déploie. Or le sens des expérimentations menées au sein des CAE est aussi la production de droits nouveaux au sein de la « zone grise » entre travail indépendant et travail salarié. Notre approche prend donc le contrepied de la posture adoptée par F. Darbus : au lieu de centrer notre analyse sur les trajectoires individuelles, nous nous efforçons de montrer comment à l’intérieur d’une CAE s’élaborent de nouvelles formes de relations professionnelles et de liens entre activité individuelle et engagement collectif, en nous attachant au sens que cette création institutionnelle revêt pour les membres de la coopérative et aux perspectives qu’elle dessine pour le futur.

Nous parlons de fabrique institutionnelle pour désigner ce travail de longue haleine, accompli par les acteurs engagés dans le processus pour faire évoluer les catégories, imaginer des alternatives et les faire reconnaître à différents niveaux de légitimité : chartes, accords partenariaux, droit du travail et de la protection sociale, etc. Ce travail participe de l’auto-institution permanente de la société, au sens où l’entend Cornélius Castoriadis. Si l’on admet avec l’auteur que l’institution est un réseau symbolique où se combinent une composante fonctionnelle et une composante imaginaire [Castoriadis, 1975], la fabrique institutionnelle suppose à la fois de mettre en œuvre un imaginaire et des moyens d’action. C’est autant un travail sur le langage et la pensée qu’un combat pour faire exister de nouveaux droits [3].

Notre recherche ne se situe pas dans une position d’extériorité par rapport à ce processus puisque l’enquête participe de la fabrique institutionnelle. Le cadre institutionnel de la recherche est en effet double. La recherche s’inscrit à la fois dans un projet ANR et dans une recherche-action au sein de Coopaname. Le projet ANR porte sur L’évolution des normes d’emploi et nouvelles formes d’inégalités : vers une comparaison des zones grises ? Il s’agit d’une recherche comparative, de nature essentiellement théorique, et qui questionne les zones grises du travail, en particulier entre travail salarié et travail indépendant. Les CAE en constituent un cas particulièrement intéressant, la preuve empirique que si les zones grises sont des « zones hors droits sociaux », des zones de précarité, elles sont aussi des zones d’expérimentation sociale et d’invention institutionnelle. Lorsque nous nous sommes intéressées à ce genre nouveau d’entreprise coopérative, nous avons été très vite amenées à rencontrer des membres de Coopaname. Nous avons été par la suite sollicitées par la commission Recherche de cette CAE pour suivre et alimenter les réflexions autour de son projet de devenir une « mutuelle de travail ». Nous avons alors été invitées à participer à des moments de la vie de la coopérative et à en observer les modes de fonctionnement. Progressivement nous sommes parvenues, en accord avec les membres de la coopérative, à envisager de mener une enquête.

La conception de l’enquête est marquée par les apports de John Dewey. Pour Dewey, l’enquête constitue l’effort qui est mis pour résoudre un problème, ici, dans le cas de Coopaname, la précarité financière de bon nombre de ses membres qui fragilise les parcours individuels et collectifs. L’enquête, toujours selon Dewey, n’est pas un outil de connaissance sur la réalité finalisée à elle-même, mais une action qui vise une transformation de la réalité : elle n’est pas neutre mais joue un rôle actif dans le changement. Ainsi, il n’est pas possible de déterminer ex ante une date de fin de l’enquête ; celle-ci s’inscrit dans les temps longs de la transformation. La validation des résultats, en tant que propositions de solution des problèmes, ne tient qu’à la validité des solutions des problèmes qui ont suscité l’enquête. Une telle conception nous conduit à adopter une approche séquentielle : des résultats sont atteints à chaque phase de l’enquête et ils constituent le point de départ des phases successives. Dans cet article, nous présentons les résultats d’une première phase de l’enquête.

Pour mener l’enquête, nous nous appuyons sur un ensemble de documents (internes aux CAE mais aussi publics comme les publications par les acteurs des CAE) et avons mis en place différents dispositifs au fur et à mesure que notre objet commençait à se configurer. Nous avons organisé deux ateliers (ou entretiens collectifs), l’un avec des nouveaux arrivants à Coopaname, l’autre avec des membres anciens. Ces ateliers constituent un dispositif d’enquête très riche par les échanges qu’ils permettent entre les participants. Nous avons mené aussi une dizaine d’entretiens individuels semi-directifs, orientés par les questions qui se posent dans les différentes étapes de l’enquête et à partir aussi des événements nouveaux. Nous participons comme observateurs à des moments de la vie de la coopérative et suivons régulièrement les échanges de messages sur l’extranet. Tous ces matériaux sont travaillés de manière séquentielle. Au fur et à mesure que nous parvenons à quelques résultats, nous soumettons ces résultats au débat avec les membres de la coopérative et envisageons les nouvelles étapes. L’analyse qualitative n’exclut pas l’analyse quantitative. Une phase d’enquête par questionnaire portant sur les temps et sur les revenus est en cours. Mais plus qu’un simple outil permettant de collecter des informations systématiques, le questionnaire constitue l’aboutissement d’un processus deconnaissance car son élaboration par une communauté de chercheurs (scientifiques et non scientifiques) exige une analyse réflexive sur les pratiques et sur les questions qui comptent. Il s’agit aussi de mieux définir les problèmes tels qu’ils sont vécus et ressentis subjectivement. Finalement, l’objectivation réside dans le processus qui permet de déplacer le problème initial. L’histoire des CAE est en quelque sorte l’histoire du déplacement des problèmes par des pratiques démocratiques de recherche de solutions.

3 – De la critique du travail social à l’innovation institutionnelle

La naissance des CAE est indissociable de la crise de l’emploi et de la critique des politiques de l’emploi. Parmi ses acteurs, certains ont une expérience militante au sein des milieux de l’éducation populaire, mais aussi comme travailleurs sociaux impliqués dans les politiques d’insertion, d’accompagnement au retour à l’emploi ou à la création d’entreprises. Et c’est à partir d’une critique du travail social que se configurent progressivement le projet des CAE et leur évolution.

3.1 – Les travailleurs sociaux et la critique du travail social

Depuis la fin des années 1970, le chômage de masse, la discontinuité des relations d’emploi et la précarité des emplois s’imposent progressivement comme nouvelles conditions du marché du travail. Déjà à cette époque, des travailleurs sociaux s’interrogent sur le sens de leur action « car ils ne cautionnent pas les mesures palliatives de l’insertion sociale » [Poncin, 2002, p. 66]. Certains d’entre eux vont alors élaborer avec des artisans, des ouvriers et des artistes, des projets associatifs et coopératifs, en dehors des institutions chargées de mettre en œuvre les politiques sociales. Parmi ces projets figure la SARL-SMTS (Société de Manutention de Travaux et Services) à Grenoble, devenue SCOP en 1995, une expérience sur laquelle nous reviendrons par la suite dans la mesure où elle va inspirer la création de la première CAE.

Au fur et à mesure que la crise de l’emploi devient structurelle et que la philosophie qui inspire les politiques de l’emploi évolue d’une logique d’insertion vers une logique d’accompagnement vers l’emploi et la création d’entreprises, la critique menée par les travailleurs sociaux s’étend et se transforme. L’histoire du Revenu minimum d’insertion (RMI) est éclairante à cet égard. Annoncé comme une mesure provisoire, le RMI est rapidement devenu au cours des années 1990, du fait de l’explosion du nombre des ayants droit, un pilier fondamental du système de protection sociale (branche famille) relevant de la logique de la solidarité (financée par l’impôt). Au moment de sa création, le RMI était octroyé sans contrepartie, il constituait un droit – un droit au revenu mais aussi à l’insertion –, bien plus qu’une obligation d’insertion. Le contrat d’insertion visait l’engagement de la collectivité envers elle-même plutôt que la responsabilisation des allocataires. C’est pourtant ce deuxième sens que le contrat a progressivement revêtu depuis vingt ans [Duvoux, 2012], en cohérence avec la philosophie qui a pris le pas dans la conception des politiques de l’emploi, selon laquelle il ne s’agit pas tant d’intervenir sur le comportement des offreurs que sur celui des demandeurs d’emploi. La logique de l’insertion est abandonnée, au profit de dispositifs d’accompagnement [Fretel, 2013], l’objectif visé étant de faire des personnes privées d’emploi des demandeurs actifs, adaptables aux conditions du marché du travail [Divay, 2012]. Certains travailleurs sociaux dénoncent alors ces dispositifs comme étant « la courroie de transmission d’une thérapeutique sociale “par l’emploi”. […] Pour le dire vite, le travail social institutionnel devient un travail sur la crise du lien avec l’économie, un mode de restauration du “capital humain” de chacun dans les mondes du travail précaire, dont les travailleurs sociaux sont les petites mains de plus en plus anachroniques » [Rafanel i Orra, 2011, p. 142-146]. Le travailleur social se voit sommé de devenir un manager « de la précarité de masse » : « Peu de travailleurs sociaux aiment se sentir des kapos de l’entreprise marchande, des pourvoyeurs d’une main-d’œuvre docile, prête à travailler pour des salaires de misère dans les secteurs dits “en tension”. » [Rafanel i Orra, 2011, p. 148] Les objectifs de motivation, de reprise en main de soi, d’autonomie et de responsabilité traduisent la « mise en politique de la subjectivité » [Cantelli, Genard, 2007], tandis que les accompagnants sont eux-mêmes soumis aux contrôles des pratiques et des comportements [Divay, 2012].

Cette mise en politique de la subjectivité culmine avec la promotion de la figure de l’entrepreneur, dans un projet global de transformation de chaque individu en entrepreneur de soi. Déjà à la fin des années 1970, Raymond Barre avait proposé aux chômeurs de créer leur emploi, mais c’est avec la loi Madelin de 1995 que s’opère un véritable tournant, avec la multiplication des dispositifs d’incitation à la création d’entreprises. La création du statut d’auto-entrepreneur en constitue l’aboutissement. « Cessons de créer des entreprises », écrit alors Stéphane Veyer [2010] directeur général de Coopaname, la plus grande CAE qui a son siège en Île-de-France : « Non pas que la création d’entreprises soit absurde ou dangereuse en tant que telle, mais parce que sa promotion effrénée mène aujourd’hui une majorité de porteurs de projets à une précarité sociale certaine. Qu’est-ce qu’un auto-entrepreneur sinon un professionnel à qui on offre le droit de s’auto-exploiter en sacrifiant volontairement sa propre protection sociale et ses droits ? […] L’enjeu des années à venir n’est pas tant de transformer chaque salarié en “entrepreneur de soi” que de repenser le travail lui-même et les rôles de l’entreprise. » [Veyer, 2010]

Les CAE ont donc été conçues pour répondre à la fois à la crise de l’emploi et au désir d’une émancipation du travail : elles ont l’ambition d’apporter, comme l’explicitent deux autres membres de Coopaname, « des éléments de réponse de l’économie sociale au délitement du rapport salarial fordien. […] Les CAE réinterrogent à la fois le modèle salarial et le modèle entrepreunarial. […] Réhabiliter le travail dans la dimension émancipatrice est au cœur du projet politique de Coopaname. » [Bodet, Grenier, 2011] Pour cela, les initiateurs du mouvement des CAE ont puisé largement aux sources du socialisme utopique.

3.2 – Des canuts aux CAE

Les CAE trouvent leurs racines dans l’histoire sociale de Lyon, cette histoire qui avait vu, au xviiie siècle, les premières révoltes ouvrières, notamment celle des canuts de la Croix Rousse [4]. Et c’est encore les artisans de la soie qui, dans les années 1960, ont été les acteurs d’un important mouvement coopératif. Il est à noter que, déjà à cette époque, leur lien avec l’entreprise commerciale et les modalités de travail ressemblaient par bien des aspects aux formes de la sous-traitance qui se sont généralisées aujourd’hui : « Ces artisans travaillent “à façon” pour des donneurs d’ordre, établis à leur compte avec leur matériel. Ils doivent supporter beaucoup de charges et l’activité du tissage de la soie est menacée. Sur les conseils de l’administration fiscale et sociale, un groupe de Croix-Roussiens décide de créer une coopérative ouvrière de production, COOPTIS. Ainsi, ils bénéficient du statut de salarié avec une meilleure protection sociale pour traverser les périodes de crise fréquentes dans le textile. Chaque tisseur garde son autonomie et sa liberté, tout en mutualisant un statut juridique. » [Poncin, 2004, p. 64-66] La coopérative s’est développée progressivement jusqu’à compter 200 salariés « indépendants » en 1981. Pendant plus de vingt ans, elle a donc permis à des tisseurs de continuer leur travail : chacun tirait son revenu du travail qu’il effectuait à domicile sur son métier à tisser, tandis que l’entreprise se chargeait d’établir les factures, les fiches de paie et d’assurer la comptabilité, moyennant une contribution de 3 % du chiffre d’affaires de chaque tisseur. Les tisseurs gardaient donc la maîtrise totale de leur travail et de leurs relations avec les donneurs d’ordre. Par ailleurs, une négociation menée avec les ASSEDIC leur permettait d’être salariés lorsqu’ils avaient des commandes et d’être licenciés lorsqu’ils n’en avaient pas (en bénéficiant des allocations chômage durant ces périodes).

Une autre expérience a inspiré la naissance des CAE, celle de la SMTS à Grenoble dont il a été déjà question ci-dessus, une entreprise d’insertion créée dans les années 1980 par des travailleurs sociaux et devenue SCOP en 1995 : « L’originalité de cette entreprise est de combiner une vision d’entreprise globale (entrepreneuriat collectif) avec des secteurs d’activité autonomes (entrepreneuriat individuel), et de rechercher la pérennité des emplois, tout en assurant une fonction d’insertion économique pour des personnes en difficulté. » [Poncin, 2004, p. 68] Le fonctionnement global de SMTS repose sur une mutualisation assez poussée. Ainsi, les chiffres d’affaires de toutes les personnes disposant d’un compte sont comptabilisés analytiquement en fin d’année, ce qui conduit à procéder au constat d’activités en perte et d’activités en excédent. Les coopérateurs acceptent que des comptes soient temporairement déficitaires : une personne peut faire le choix, par exemple, de prendre quelques mois de congés « sabbatiques » tout en étant payée, au risque d’être en déficit cette année-là. Dans ce système, chacun peut donc être, tour à tour, « redevable » à l’égard des autres ou créancier des autres. Cette forme de mutualisation implique donc la construction d’une confiance réciproque et suppose un engagement des personnes dans la durée. Ces deux expériences, COOPTIS et SMTS, sont fondatrices du concept de Coopérative d’activité et d’emploi.

Dans les années 1980, la figure du « porteur de projet » émerge négativement, comme réponse à la crise de l’emploi, et positivement comme possibilité de développement d’un travail autonome. En 1985, Élisabeth Bost avait créé une association dont le but était d’offrir un cadre aux « porteurs de projet ». À cette même époque, elle avait été recrutée dans une pépinière d’entreprises, une structure d’accueil et d’accompagnement des « chômeurs-créateurs ». C’est dans ce contexte qu’elle avait commencé à réfléchir autour d’un projet permettant d’agencer les deux missions, une réflexion qui devait déboucher sur la création en 1995, à Lyon, de la première CAE, CAP Services. Au moment où les phénomènes de précarité s’accentuaient et les micro-entreprises individuelles proliféraient, il s’agissait de concevoir « un outil pour des personnes en capacité de développer un savoir-faire par l’entrepreneuriat, tout en facilitant l’insertion sociale des exclus du travail » [Poncin, 2004, p. 73].

Depuis, les Coopératives d’activité et d’emploi se sont diffusées sur le territoire national par essaimage, en prenant des formes variables, en fonction des spécificités locales, du contexte de leur création et de l’histoire de leurs initiateurs, et elles se sont reliées entre elles à travers deux réseaux, le réseau Coopérer pour entreprendre et le réseau Copéa (association nationale des Coopératives d’activité), dont les sensibilités différentes illustrent bien la dualité consubstantielle au projet. Le réseau Copéa privilégie le développement d’un entreprenariat viable à long terme, tandis que la part du financement public est plus importante dans les CAE du réseau Coopérer pour entreprendre, reflétant ainsi l’accent mis sur la mission de service public, c’est-à-dire l’accompagnement offert à tout porteur de projet qui frappe à leur porte [5].

Les CAE se sont donc développées au confluent de deux perspectives distinctes. Comme le souligne Béatrice Poncin, « le concept est double et son appellation porte cette gémellité : il s’inscrit à la fois dans un objectif d’insertion par l’économique – le nom de la coopérative d’activité est utilisé dans le sens de pouvoir tester une activité – et dans un objectif de développement collectif et solidaire d’activités – le nom de coopérative d’emploi signifie la mise en commun durable d’emplois » [Poncin, 2004, p. 73]. Notons que Béatrice Poncin préfère parler de Coopératives d’emploi et d’activité, comme pour souligner que dans sa perspective, la forme d’emploi que portent ces coopératives est première par rapport à la mission d’insertion par l’économique. Cette différence met en exergue la coexistence de deux projets politiques très différents.

L’histoire d’Oxalis, membre de Copéa, illustre la prééminence du projet du développement de l’entrepreneuriat sur l’objectif d’insertion. À l’origine, en 1986, un groupe d’une vingtaine de personnes qui se sont rencontrées dans des mouvements d’éducation populaire réfléchissent ensemble au sens du travail et de l’engagement. Oxalis naît ainsi d’un projet collectif articulé sur l’idée de « vivre et travailler autrement » qui a donné naissance à deux associations (l’une en 1988, l’autre en 1992), puis à une SCOP en 1997. Depuis ses débuts, Oxalis poursuit un but ambitieux : « Créer et développer des activités économiques et contribuer ainsi au développement d’un monde rural vivant et solidaire tout en faisant le lien avec le monde urbain ; “vivre et travailler autrement”, c’est-à-dire faire en sorte que la personne ne soit pas l’objet d’une juxtaposition plus ou moins réussie de toutes les composantes de sa vie (sociale, familiale, personnelle et professionnelle), mais qu’il y ait un projet central à toutes ces dimensions. » [Poncin, 2004, p. 97-99] L’organisation a été conçue dans ce but : polyvalence entre activités intellectuelles et activités manuelles, répartition transversale des tâches pour éviter toute hiérarchie, égalité des salaires (quels que soient l’ancienneté, les diplômes ou les tâches effectuées), prise de décision par recherche de consensus. En 2001, la SCOP a muté dans son fonctionnement, prenant la configuration d’une coopérative d’emploi et d’activité.

Plus généralement, depuis la création des premières CAE dans les années 1990, les objectifs poursuivis par ces entreprises se sont complexifiés et enrichis, faisant converger, non sans tensions, les deux projets qui les animent [Veyer, 2011]. Pour les directeurs actuels de Coopaname, membre du réseau Coopérer pour entreprendre, la première génération de CAE s’efforçait avant tout de sécuriser le parcours des porteurs de projets. Puis les CAE ont évolué vers un projet plus ambitieux et global, devenir une entreprise partagée : « Les CAE de deuxième génération ne visent plus à sécuriser la création d’entreprises individuelles, mais bien à construire une alternative à celles-ci, via un projet d’entrepreneuriat collectif. » [Sangiorgio, Veyer, 2009]. Enfin, la troisième génération des CAE vise la constitution de la coopérative en tant que mutuelle de travail, notion dont le contenu reste largement à définir.

3.3 – De la sécurisation à la mutuelle de travail

L’entrepreneur-salarié n’a pas de statut juridique propre : en tant que porteur de projet, il est conduit à assumer des fonctions entrepreneuriales, mais en tant que membre d’une CAE, il est salarié de la structure coopérative et il peut même envisager d’en devenir sociétaire. Dans certaines coopératives, cet engagement devient obligatoire au bout de trois ans, à partir de l’entrée dans la CAE. Tout porteur de projet peut solliciter son adhésion à une CAE. Son statut évolue alors, depuis la phase initiale, celle de l’accueil et de la validation du projet par la CAE jusqu’à la concrétisation du projet. Dans la phase initiale, celle du bilan de la situation sociale et professionnelle, le porteur de projets peut se retrouver dans différentes situations (allocataire des indemnités de chômage, du RSA, sans revenu…). Dans la phase de développement du projet, lorsque son activité commence à produire un chiffre d’affaires, il devient entrepreneur-salarié, c’est-à-dire qu’il est salarié en CDI de la CAE ; son salaire est déterminé en fonction du chiffre d’affaires réalisé et évolue avec celui-ci. Par la suite, il est susceptible de devenir entrepreneur-associé, ou bien il peut choisir de quitter la coopérative pour créer son entreprise individuelle. Tout au long de ce parcours, le porteur de projets est accompagné par la structure. L’accompagnement individuel et collectif constitue l’une des activités propres aux CAE, soutenues par les pouvoirs publics, pour s’efforcer de rendre viables le plus grand nombre de projets.

L’absence de subordination constitue une valeur centrale pour certaines CAE comme Coopaname. Le rejet de la subordination, telle qu’elle a été vécue au cours d’expériences antérieures de travail salarié, constitue d’ailleurs, d’après les entretiens que nous avons réalisés, l’une des motivations premières de l’adhésion à Coopaname. Dans le modèle de la CAE, s’il n’y a pas de patron (remarquons justement le titre du livre de Béatrice Poncin : Salariés sans patron ?), le fait que le porteur de projet demande son adhésion à une CAE revient à faire acte d’une « subordination volontaire », mais dans le sens d’une dépendance vis-à-vis du collectif. En même temps, son autonomie est limitée par le pouvoir du donneur d’ordres. Le fait d’appartenir à un collectif modifie le rapport avec le donneur d’ordres. L’un des enjeux fondamentaux pour la coopérative est donc d’améliorer, grâce à la mutualisation, le rapport de force face aux donneurs d’ordres. Si la mutualisation est au fondement des CAE, son approfondissement, selon des scénarios qui restent à imaginer, constitue un enjeu majeur pour accroître l’autonomie et l’indépendance des porteurs de projets vis-à-vis des donneurs d’ordres.

La notion même de mutuelle laisse place aux divergences d’interprétation. Pour l’Encyclopédie universelle, « mutuel » signifie « qui comporte ou manifeste un rapport d’échange ou de réciprocité entre deux ou plusieurs personnes ». Ce qui englobe donc toute forme de réseau basé sur un principe de réciprocité, de don/contre-don, même étendu et différé. Selon le code de la mutualité, « les mutuelles sont des personnes morales de droit privé à but non lucratif. […] Elles mènent, notamment au moyen des cotisations versées par leurs membres, et dans l’intérêt de ces derniers et de leurs ayants droit, une action de prévoyance, de solidarité et d’entraide, dans les conditions prévues par leurs statuts, afin de contribuer au développement culturel, moral, intellectuel et physique de leurs membres et à l’amélioration de leurs conditions de vie » (article 111-1). Leur vocation est donc d’élaborer pour et avec leurs adhérents des réponses aux besoins sociaux qu’ils expriment. À la différence de la coopérative, dont l’objet social reste la réalisation d’une activité commune, la mutuelle vise d’abord la protection et le développement de ses membres. Mais de nouvelles acceptions du terme émergent des processus d’innovation au sein de l’économie sociale. Ainsi, le concept récent de « mutuelle de public » (exemple MASC – Mutuelle arts, sciences, social, culture) désigne une communauté intéressée au développement d’un projet particulier [6].

Depuis quelques années, Coopaname a engagé une recherche-action pour imaginer son devenir, avec le projet d’aller au-delà des principes de la coopérative d’activités et d’emploi sur la base de laquelle elle s’est constituée. L’ambition affirmée par le document fondateur de ce projet est de réinvestir les principes mutualistes pour innover en matière de travail et d’emploi. Constatant que Coopaname apparaît à bien des égards comme une CAE hors normes par sa taille, sa gouvernance, ses modes de fonctionnement ou sa dynamique, les auteurs voient dans l’accroissement rapide des capacités d’action de la coopérative en même temps que dans le poids des contraintes qui s’imposent à elle, la source d’un désir de repenser le projet de la coopérative lui-même. À l’heure actuelle, la coopérative compte une majorité de femmes (plus de 60 %) et se caractérise par un niveau de formation élevé (plus de 60 % ont au moins Bac+3). Les métiers qualifiés de services apparaissent sur-représentés dans la palette de ses activités. Si les entrepreneurs-salariés qui parviennent à dégager un revenu suffisant tendent à rester dans la coopérative, ce qui est un signe de leur engagement, ils ne constituent encore qu’une petite minorité. C’est ce qu’a révélé une enquête réalisée par la CAE en 2011 pour l’université d’automne : « Ah ! si j’étais riche… Gagner plus pour partager plus. Et vice-versa. » La réflexion sur le devenir de Coopaname se développe à partir de ce constat préoccupant. Elle a déjà donné lieu à une première recherche en 2008, notamment soutenue par la DIIESES (Délégation interministérielle à l’innovation, à l’expérimentation sociale et à l’économie sociale), sur l’objet social de Coopaname, au cours de laquelle a émergé l’idée que le métier de la coopérative pourrait relever davantage d’une logique mutualiste que coopérative. « Que pourrait être une Mutuelle de travail ? », s’interrogent alors les auteurs : « Tout simplement une société de personnes dans laquelle on se protégerait mutuellement nos parcours professionnels – autrement dit, notre capacité à pouvoir vivre décemment d’activités professionnelles que l’on choisit. […] Pourrait-on organiser dans le cadre de Coopaname, entre plusieurs centaines de membres, des mécanismes systématiques d’accompagnement mutuel, d’apprentissage mutuel, de salariat mutuel, de formation mutuelle, de protection mutuelle, de secours mutuel (etc.) qui permettraient de sécuriser chacun – grâce aux autres – dans sa capacité à gagner sa vie avec ce qu’il souhaite faire, en coopération avec qui il choisit de travailler, et au rythme qu’il se fixe ? »

Si le document met l’accent sur la notion de protection, il envisage d’emblée plusieurs interprétations possibles de la notion de « mutuelle de travail », prenant acte du fait que l’on peut poursuivre différents buts, en fonction de la vision que l’on a du travail lui-même et de la place qu’on souhaite lui accorder. Pour les auteurs, la mutuelle peut ainsi être imaginée de différentes façons : comme un support d’émancipation dans le travail, pour résister aux différentes formes de subordination et d’hétéronomie ; comme un vecteur pour faciliter les transitions professionnelles, voire organiser « à force de relectures de Fourier, une papillonne effective », transformant ainsi l’obligation de produire en une activité ludique ; comme un moyen de permettre à chacun de « vivre le mieux possible en travaillant le moins possible ».

4 – La fabrique institutionnelle au sein de Coopaname

L’un des signes majeurs de la mutation en cours au sein de Coopaname est l’émergence de collectifs à l’intérieur de la SCOP et la formalisation des rapports entre la coopérative et ses entités économiques internes. Ainsi, même si cette dynamique ne concerne pas tous les entrepreneurs-salariés [7], Coopaname se présente aujourd’hui comme une sorte de laboratoire au sein duquel s’expérimente la formation de collectifs à géométrie variable, offrant une palette étendue de possibilités d’articulation entre activité individuelle et engagement collectif. La conception du collectif qui se dessine à travers cette dynamique n’est pas celle d’un groupe aux frontières définies, auquel chacun appartiendrait ou non, mais plutôt celle d’un processus orienté vers la possibilité d’un devenir commun [8]. Cette vision renoue en quelque sorte avec l’utopie fouriériste des « séries passionnées [9] », en offrant à chacun la possibilité de choisir ses coopérations ainsi que la durée de celles-ci, de circuler entre différents niveaux d’engagement, entre plusieurs formes d’« indépendance à plusieurs » [Bureau, Corsani, 2012]. L’engagement ainsi conçu ne s’inscrit pas dans un devoir moral mais plutôt dans la passion et la recherche du plaisir, comme en témoignent les propos des coopanamiens. Mais si la passion est le moteur des engagements individuels, le défi posé aux dirigeants de la structure reste la production de règles qui permettent de favoriser et de consolider ces « engrenages » mutuels sans altérer la dynamique d’ensemble [Devolvé, Veyer, 2009].

Nous avons repéré quatre différentes formes d’intégration collective, instituées à des degrés divers, qui coexistent actuellement au sein de la CAE : les groupes métiers, les espaces de co-working, la coopération par projet, les groupes de marque. Enfin, la forme particulière que prennent les instances de représentation du personnel contribue à l’institution d’une entreprise partagée.

4.1 – Les groupes métiers : formation mutuelle et construction de réseaux professionnels

Formés très tôt dans l’histoire de la coopérative et particulièrement actifs, ces groupes métiers (métiers de l’écrit, relations humaines, photographes, métiers de la communication, etc.) qui se réunissent au moins une fois par mois remplissent, de l’avis des coopanamiens que nous avons rencontrés, une double fonction :

    • un rôle de formation technique par mutualisation des savoirs, voire de régulation professionnelle par un effort, plus ou moins couronné de succès, pour s’accorder sur des tarifs communs ;
    • un rôle d’interconnaissance, ouvrant la possibilité d’une construction de la confiance et favorisant ainsi l’émergence de coopérations plus ou moins durables, à partir d’engagements graduels.

En raison du rôle majeur de la confiance dans le développement et la stabilisation des échanges, comme dans la cohésion même de la société [Simmel, 1987], les conditions de production de la confiance ont fait l’objet de nombreuses investigations de la part des économistes et des sociologues [Thuderoz et al., 1999]. Zucker [1986] propose une typologie plus analytique en distinguant la confiance intuitu personae(celle que l’on accorde à sa famille ou à des personnes en raison de leurs caractéristiques propres), la confiance institutionnelle, attachée à une structure formelle, et la confiance relationnelle. La confiance relationnelle, celle qui est manifestement en cause dans la vie interne à Coopaname, se fonde sur les échanges passés entre les partenaires, c’est-à-dire sur une expérience de la relation. Elle se construit dans les processus de don/contre-don, se nourrit de signaux comme une attitude coopérative et l’absence de tricherie, tire profit du partage d’une culture commune ; elle est favorisée par la stabilité de l’organisation au sein de laquelle les parties sont engagées et s’appuie sur la création de routines partagées. Sur la base de cette confiance, d’autres collectifs peuvent alors se constituer (cf. 3.3). A contrario, l’absence de confiance, soit qu’elle n’ait pas pu s’établir faute d’occasions de rencontres, soit que les signaux échangés dissuadent la coopération, représente l’un des principaux obstacles à la dynamique collective. Si les groupes métiers favorisent la construction de la confiance, celle-ci ne peut se décréter, d’autant que les membres de ces groupes sont, au moins partiellement, en concurrence les uns avec les autres sur les mêmes marchés.

4.2 – Des lieux et des équipements partagés : favoriser les rencontres et les innovations collaboratives

À l’image des espaces de co-working, des hackerspaces ou des fablabs[10], Coopaname offre aussi un espace physique commun pour des professionnels de spécialités très diverses, favorisant ainsi des rencontres et des associations imprévues et, par conséquent, l’innovation et l’émergence de projets collectifs. Dans ce cadre, c’est l’exploitation créative du hasard (ou sérendipité) qui est suscitée, plus que la confiance. La confiance devient d’ailleurs moins vitale dans un contexte où la diversité des compétences atténue la concurrence. Loin des routines communes à un collectif de métier, c’est au contraire la complémentarité, le décalage de perspectives, qui provoquent l’innovation. Après l’accès à la protection sociale, les chances de rencontres fructueuses offertes par la multi-activité de la coopérative, constituent ainsi une motivation forte exprimée par les entrepreneurs-salariés pour expliquer leur engagement dans Coopaname.

Tout récemment, la coopérative s’est engagée dans l’organisation d’espaces partagés de travail et pourrait s’orienter vers une mise à disposition d’équipements et d’outils techniques variés, se rapprochant ainsi davantage de la philosophie propre au mouvement maker. Elle est d’ailleurs partie prenante d’un projet dionysien de recherche-action, prévoyant la création de trois espaces au sein d’un même bâtiment : une boutique proposant à la vente des produits d’artisanat locaux, un espace de coworking dénommé « bar à travail » et un fablab.

4.3 – Partage des commandes et coopération sur projet

Si l’innovation, pas plus que la confiance, ne peut se décréter, il existe donc en revanche des conditions favorables à leur émergence. L’interconnaissance amorcée dans les groupes métiers ou dans les espaces communs débouche ensuite parfois sur des relations professionnelles plus durables : sous-traitance, association sur un projet, réponse commune à des appels d’offres, etc. Dans une logique de métier, tel ou tel coopanamien peut, de fil en aiguille, s’entourer de personnes compétentes pour déléguer des travaux et répondre ainsi à des commandes qu’il ne peut honorer seul. La redistribution d’un surplus de travail se fait alors de manière ponctuelle, à la faveur de la confiance dans le travail de l’autre, telle qu’elle s’est construite progressivement. Cette pratique apparaît assez répandue à Coopaname, par exemple dans le domaine du télé-secrétariat. Une autre forme intermédiaire de construction de collectif est la réponse commune à un appel d’offres, en mobilisant une pluralité de spécialités complémentaires. Par exemple, lors d’un appel d’offres lancé par un OPCA (organisme paritaire collecteur agréé) la palette de compétences présente à Coopaname a permis de construire, en deux semaines, une réponse qui couvre la presque totalité des thématiques figurant dans l’appel d’offres. Le travail d’élaboration collective a fédéré 24 entrepreneurs-salariés (dont une moitié comme suppléants), ce qui leur ouvre la possibilité d’évoluer ultérieurement vers une entité économique plus pérenne, en l’occurrence un organisme de formation. Ces formes de redistribution et de mutualisation du travail, basées à la fois sur des rapports de confiance établis au fil du temps et sur des complémentarités productives, s’apparentent à ce qui se passe dans les bureaux de pigistes. L’expérience du Terrier d’Hégésippe, rapporté par Pierre Tessier [2012], en témoigne : les « rongeurs » (comme s’appellent eux-mêmes les membres du Terrier) mettent en commun tout ou partie de leurs carnets d’adresses afin de multiplier les pistes de collaboration ; ils mutualisent aussi certaines piges et s’efforcent de mettre à profit leurs complémentarités de savoir-faire, même s’ils peinent à élaborer des offres intégrées.

4.4 – La construction d’entités économiques : les « groupes de marque »

À partir des groupes métiers et des coopérations ponctuelles ont émergé différentes tentatives de se regrouper durablement pour faire évoluer le rapport de forces avec les commanditaires ou les donneurs d’ordres et/ou de construire une offre commune à partir de prestations complémentaires. Par exemple, la marque « Kit à se marier » offrait un ensemble de prestations autour de l’organisation d’un mariage (robes de mariée, graphisme, bijoux, photographes, etc.). Les premiers groupes de marque issus de la CAE ne sont pas parvenus à s’imposer sur un plan commercial, mais plusieurs projets en cours tentent de tirer profit des expériences passées. Les collectifs émergents ont en particulier développé une réflexion sur la façon d’organiser à la fois la production et le fonctionnement démocratique de l’entité, construisant de façon fractale une mini-scop à l’intérieur de la coopérative. Novéquilibres est à cet égard souvent cité en exemple par les coopanamiens, en particulier pour le travail politique d’organisation de la structure. L’un des initiateurs de la mini-scop, ancien directeur des ressources humaines dans une société d’informatique, a fait d’emblée le choix de s’engager dans cette activité collective, sans développer par ailleurs d’activité individuelle en propre, ce qui était en rupture avec les usages de l’époque au sein de la CAE. Les participants à la réflexion se sont attachés à distinguer animation et pilotage de projet. Novéquilibres dispose ainsi d’un comité exécutif élu (le noyau dur), en charge du pilotage de projet, mais les neuf associés (ou membres actifs) se retrouvent pour des réunions mensuelles au cours desquelles les décisions se prennent sans qu’il y ait de vote. Spécialisé dans l’offre de services auprès des organisations pour favoriser la qualité de vie au travail, le collectif regroupe plusieurs professionnels ayant acquis une expérience dans le domaine des ressources humaines. Il n’hésite pas à recourir par ailleurs à des techniques et à des pratiques spécifiques de prise de décision, empruntées par exemple au courant de la sociocratie, de façon à construire collectivement la décision finale [11].

Au cours des entretiens, plusieurs coopanamiens ont évoqué une sorte de tabou de la prospection commerciale qui conduirait à l’échec économique des groupes de marque, en dépit d’une réflexion très poussée sur la structuration des collectifs. Ce qui soulève, à leurs yeux, la question d’organiser, au niveau de Coopaname, un support mutualisé pour faciliter la recherche de clientèle. Explicitement rejetée aux débuts de la coopérative, la mutualisation de la prospection et du lien avec les usagers, pourrait aujourd’hui prendre sens au sein du collectif, dans le cadre d’une réflexion plus globale sur l’intégration dans le projet politique de la CAE de la relation entre producteurs et consommateurs.

L’idée d’articuler activité individuelle et engagement dans une activité commune nourrit aussi les rêves de nouveaux entrants. L’un d’entre eux imagine ainsi que chaque coopanamien pourrait consacrer une part de son temps à une œuvre collective, à côté de son activité propre, ce qui permettrait d’alimenter une caisse commune. Plus récente dans la culture de la coopérative, cette réflexion oblige les coopanamiens à repenser en profondeur la notion d’accompagnement collectif. Plus qu’une injonction à s’intégrer dans un groupe, il s’agirait de créer des synergies collectives profitables à tous, ce qui rejoint largement les réflexions et les interrogations sur les pratiques d’empowerment [Bacque, Biewiener, 2013].

La création institutionnelle bute face à la structure du droit français, édifiée sur la distinction binaire entre travail salarié et travail indépendant et la CAE se trouve régulièrement en porte-à-faux avec les institutions d’une société salariale. Ses membres ont donc fait le choix d’élaborer leurs propres textes, sur la base de leurs pratiques, avant d’entrer en discussion avec des juristes et en négociation avec les pouvoirs publics. Un contrat interne a ainsi été élaboré pour régir les rapports entre la coopérative et les entités économiques qui se développent en son sein. Ce contrat définit de fait une autonomie encadrée : l’entité constitue, au niveau comptable, un centre de production et socialement, une unité de travail, mais elle ne détient pas de personnalité morale, elle s’engage à respecter les principes coopératifs et reste soumise aux décisions du Conseil d’Administration de la CAE. Le contrat prévoit aussi des engagements réciproques entre la CAE et l’entité économique, sur la base du principe suivant : l’un des objets de la coopérative étant de faire économie ensemble, l’idée est ici de favoriser les coopérations internes, sans qu’elles aient aucun caractère d’obligation.

4.5 – La représentation collective des entrepreneurs-salariés

Au vu du nombre de ses salariés, Coopaname est soumise depuis 2005 à l’obligation légale d’une représentation du personnel. Les membres de la CAE ont donc engagé une réflexion pour définir le rôle des représentants, dans ce contexte très particulier où chacun est à la fois son propre employeur et son propre salarié. N. Devolvé et S. Veyer [2010, p. 5] retracent ainsi cette histoire : « À l’élection d’IRP fantoches permettant de mettre la coopérative en conformité avec la lettre de la loi, il fut préféré une méthode consistant à se placer dans l’esprit de la loi afin d’imaginer une représentation du personnel adaptée réellement aux enjeux de la coopérative, quitte à prendre son temps, et quitte à prendre quelques libertés avec la lettre de la loi. » Redéfinissant le contrat de travail qui lie chaque entrepreneur-salarié à la coopérative comme une subordination volontaire au collectif, ils voient dans la représentation du personnel une protection contre les différentes formes de dépendance, un moyen d’aider les entrepreneurs-salariés à se protéger d’eux-mêmes, de la précarité intrinsèque au travail autonome, mais aussi de leurs donneurs d’ordres. Ils mettent en exergue « tout le sens que peut revêtir de remettre du droit, du dialogue social, de la protection sociale, là où il n’y en a habituellement plus, dans le travail indépendant. Loin d’être une absurdité, les IRP constituent l’un des rouages essentiels des CAE et du projet qu’elles portent » [Devolvé, Veyer, 2010, p. 7]. La représentation des entrepreneurs-salariés vise ainsi à protéger collectivement les salariés des employeurs de soi-même qu’ils sont par ailleurs. Elle se veut aussi la cheville ouvrière d’une « pédagogie de la résistance » contre les pratiques abusives, voire irrégulières, des donneurs d’ordre et des clients qui tendent à imposer un moins-disant social. Elle constitue enfin un contre-pouvoir pour limiter le pouvoir moral que la direction de la SCOP exerce de fait à l’égard de ses membres. Ainsi conçue, la représentation des entrepreneurs-salariés peut être vue comme une innovation institutionnelle, au sens où elle adapte un dispositif central des relations professionnelles dans un contexte où les risques psychosociaux ne découlent pas du lien de subordination lui-même mais d’autres formes de dépendance économique et morale ainsi que du surinvestissement des entrepreneurs-salariés dans leur propre activité.

5 – Conclusions

Si la naissance des CAE est liée à la crise de l’emploi et aux injonctions croissantes à devenir créateur de son emploi, elles subvertissent, en quelque sorte, à la fois la logique de l’emploi et donc du salariat, et celle de l’auto-entrepreneuriat. En promouvant un entrepreneuriat collectif et coopératif, elles opposent une résistance aux politiques visant le développement de l’entrepreneuriat individuel, en même temps qu’elles expérimentent des formes novatrices des relations de travail. En articulant des espaces d’échanges professionnels avec des espaces propices à l’innovation, en élaborant des modèles politiques d’organisation interne, en détournant de façon créative des institutions de la société salariale, Coopaname participe du vaste chantier d’auto-institution de la société.

Cependant, la faiblesse des revenus que perçoivent la plupart des entrepreneurs-salariés est un problème majeur pour chacun et fragilise la vie de la coopérative. Les formes originales d’articulation entre activité individuelle et engagement collectif que nous avons observées et analysées constituent une réponse, bien que partielle, au problème et un premier pas dans le devenir de mutuelle de travail. Coopaname s’est engagée à cet égard dans un vaste chantier de production du droit, à la lisière entre droit du travail et droit des entreprises, mais se heurte, dans ce projet, à de nombreuses limites. Nous n’en citerons que trois.

Une première limite tient au fait que le salaire reste déterminé de manière individuelle, sur la base des honoraires ou du chiffre d’affaires réalisé par chaque entrepreneur-salarié, alors même que la richesse créée par la coopérative, fruit des synergies engendrées par la dynamique des collectifs, excède la somme des chiffres d’affaires réalisés par l’ensemble des membres de la CAE. En d’autres termes, la richesse de la CAE déborde la valeur marchande des activités qu’elle abrite, mais c’est sur la base de cette dernière qu’est calculé le salaire. Ce résultat nous a conduites à envisager, dans une nouvelle phase de notre enquête de terrain, une analyse fine des temps permettant de mesurer le temps consacré à l’engagement dans le collectif et d’imaginer, avec les participants à l’enquête, les modalités de valorisation de ces temps de travail gratuit et de partage des fruits de cette valorisation.

La deuxième limite est donnée par le fait que beaucoup d’entrepreneurs-salariés rencontrent de grandes difficultés de commercialisation. Ne faut-il pas envisager la mutualisation des fonctions commerciales ? Ou même pousser plus loin la réflexion sur l’évolution des rapports entre producteurs et consommateurs ? Il s’agit là d’une piste que nous avons proposée à la coopérative et qui sera poursuivie sur la base des éléments de l’enquête projetée.

Une troisième limite concerne les droits sociaux acquis par les entrepreneurs-salariés, définis à partir de la norme de l’emploi en CDI. En effet, si la CAE permet de transformer en salaire le chiffre d’affaires ou les honoraires des membres et les faire ainsi bénéficier des droits sociaux – contrepartie de la subordination salariale dans le cadre du rapport de travail capitaliste –, les critères d’ouverture des droits et la nature des droits restent inadaptés à la situation de l’entrepreneur-salarié. La production instituante du droit au sein des CAE se heurte donc de façon récurrente aux fondamentaux du droit social, toujours fondé sur la logique binaire entre travail salarié et travail indépendant. Les CAE pourraient être un acteur majeur dans une bataille pour une nouvelle conception de la protection sociale. En partant des pratiques de travail et d’emploi, elles pourraient inventer des modèles adaptés à des formes de l’activité de travail, irrégulières en termes de temps et de rémunérations. Dans cette perspective, l’enquête que nous menons actuellement sur les temps et les revenus devrait aussi permettre d’avancer dans cette direction.


    • [1] En France, l’économie sociale et solidaire (ESS) est une construction politique récente, consacrée en 2000 par la création d’un secrétariat d’État à l’ESS à la suite du rapport Lipietz, née du rapprochement entre le secteur historique de l’économie sociale (mouvement associatif, coopératif et mutualiste) et un certain nombre d’initiatives plus récentes se réclamant de l’économie solidaire, défendant la pluralité des principes de régulation économique (réciprocité, marché, redistribution).
    • [2] Coopaname a été créée en 2004. D’après le rapport d’activité 2013, elle rassemble 672 personnes dont 147 associés.
    • [3] Dernière en date, la bataille pour la reconnaissance institutionnelle des CAE a été gagnée avec le vote du 20 mai 2014 à l’Assemblée nationale.
    • [4] Les émeutes de 1744 à Lyon marquèrent la naissance du mouvement ouvrier en France. Les artisans de la soie luttaient contre le pouvoir de l’industrie naissante. En 1790, les premières expériences d’autogestion virent le jour. Et ce fut par l’action des canuts qu’eut lieu, en 1806, le premier Conseil des prud’hommes. Puis, les révoltes des canuts de 1831, de 1834 et de 1848 s’accompagnèrent du développement des premières formes de mutualité (sociétés mutualistes, épiceries coopératives, caisses de secours mutuel). Il fut aussi créé le premier journal ouvrier : L’Écho de la fabrique, qui, en 1833, sortit avec le titre « Prolétaires de tout État, unissez-vous » [Perdu, 2010].
    • [5] En 2013, le résau COPEA compte quelque 1 000 entrepreneurs (dont plus d’un tiers associés) qui réalisent 27 millions de chiffre d’affaires. Le réseau Coopérer pour entreprendre compte, quant à lui, quelque 4 000 entrepreneurs salariés qui réalisent au total un chiffre d’affaires 37 millions d’euros.
    • [6] Il est à noter que ce type de formule se développe aujourd’hui beaucoup sur Internet avec les pratiques de crowdfunding.
    • [7] Les motivations qui justifient l’entrée dans la CAE sont hétérogènes, certains entrepreneurs-salariés ne recherchent rien d’autre dans la CAE que l’accès aux droits sociaux, alors que pour d’autres il s’agit d’expérimenter collectivement un autre rapport au (et de) travail.
    • [8] On retrouve ici la définition, par Gilbert Simondon [1989], du collectif comme processus par lequel des individus mettent en commun leurs « réserves de devenir ».
    • [9] Pour Charles Fourier [1829], les séries passionnées « doivent être réglées par l’attraction ; chaque groupe ne doit se composer que de sectaires engagés passionnément, sans recourir aux véhicules de besoin, morale, raison, devoir et contrainte ».
    • [10] Ces espaces sont des lieux équipés d’ordinateurs et/ou de machines numériques, qui offrent la possibilité à des ingénieurs,designers, artistes, bricoleurs, travailleurs indépendants, etc., de se retrouver et de réaliser des projets individuels ou collectifs, en dehors de tout lien hiérarchique. Ils sont généralement partie prenante du mouvement maker.
    • [11] Pour Gérard Endenburg [1998], le terme « sociocratie » se réfère à un mode de prise de décision et de gouvernance qui permet à une organisation de se comporter comme un organisme vivant, de s’auto-organiser. En fait, le terme de sociocratie a été utilisé tout d’abord par Auguste Comte pour désigner la gouvernance du socios, c’est-à-dire des personnes liées entre elles par des relations significatives. À l’aube du xxe siècle, le socialiste jaurésien Eugène Fournière [1910] y voit une issue positive à l’alternative entre démocratie pure et socialisme autoritaire, une forme de gouvernement de la société par elle-même, prenant appui non sur une masse indifférenciée d’individus mais sur des organisations associatives.


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