The 9th Comparative Analysis of Enterprise (Micro) Data (CAED)
Conference
October 2-4 2009, Tokyo, Japan
Product and process innovations in subcontracting:empirical evidence from the French ‘Sillon Alpin’
Rachel BOCQUET1
ABSTRACT. This paper investigates the role that the type of subcontracting relationships(collaborative outsourcing versus traditional subcontracting) can have on a
subcontractor’s ability to innovate in process and product. In order to measure the “full”
impact of subcontracting relationships on innovation, we make the distinction between
process and product innovations, taking into account their possible interaction. The
empirical test is based on 93 small firms operating in “pure subcontracting industries”
meaning that their turnover is carried out minimum 80% in this field (Sessi, 2006).
Using a bivariate probit model, we have made several advances. Firstly, in line withprevious empirical studies, we give evidence that process and product innovations are
not independent choices. Secondly, the test confirms the positive impact ofcollaborative outsourcing agreement on the subcontractor’s probability to innovate
whatever the type of innovation. Finally, the results show that process and productinnovations are reinforced by different inter-organizational practices and tools as well as
distinct absorptive capabilities. This suggests important implications for subcontractors’competitive position.
KEY WORDS: Subcontracting relationships, small firms, product and process innovations.
JEL: L24, L26, 031, L61

1

Rachel BOCQUET, Assistant Professor in economics, Innovation and Networks group, IREGE, University of
Savoie, 9 Rue de l’Arc-en-ciel, BP 240, 74 942 Annecy-Le-Vieux Cedex, France. Tel: +33450092346; Fax:
+33450092337. E-mail: rachel.bocquet@univ-savoie.fr

1. Introduction
The new context of knowledge-based economy has important implications for companies
operating in “pure subcontracting industries”2 (Amesse and Cohendet, 2001, Amesse et al.
2001, Smith and Tranfield, 2005, Lehtinen, 1999, Lamming, 1993). Due to new outsourcing
practices that no longer involve exclusively the external boundary functions of the firm,
subcontracting relationships have changed. Subcontractors are not required to simply produce
what they are asked to. Often, they are urged to generate the technological knowledge which
fuels new product and process development. Since the early 90’s, a growing body of researchhas endeavoured to depict this new “collaborative outsourcing mode” based on interdependentand long-term relationships (Cohendet and Llerena 2005; Baudry 2004, Takeishi 2002;
Amesse et al. 2001; Amesse and Cohendet 2001; Nonaka and Takeichi 1995; Langlois andFoss 1996; Foss 1993). Quite surprisingly, there are very few empirical studies which provide
direct information on innovation and technology transfer capabilities of subcontractors
associated with this new mode (Takeishi 2002; Dyer et al. 1998; Amesse et al. 2001, Baudry2003). In this paper, we would like to assess to what extent “talented suppliers” (Smith andTransfield 2005; Lamming 1993) that are placed in collaborative relationships might be moreinnovative or at least more actively involved in innovation than traditional subcontractors.
Besides, we know very little about the type of innovation (process and/or product)
although it may be a key aspect for a better understanding of firms’ competitive position(Weiss, 2003). With that in mind, what is the “true” potential of collaborative outsourcing andits consequences for subcontractors: process innovations or product innovations, or both? This
study takes into consideration the possible interaction between product and process
innovations. In this way, it differs from a great number of studies that focus only on productinnovation or sometimes on process innovation.
The objective of the paper is to assess the full impact of the type of subcontracting
relationships on the ability of a subcontractor to innovate in process and product. To do so, we
first specify two types of subcontractors according to the nature of their subcontracting
agreements, their inter-organizational practices and tools, and their absorptive capacities(Cohen and Levinthal 1990). Secondly, we measure this impact according to the type ofsubcontractors’ innovation (process and/or product). Like many studies, we define innovation
in a comprehensive way (OECD/Eurostat 1997, Smith 2004). There will be innovation when
the firm considers that it has made an improvement or a development on a product or process
in the last three years.
The empirical analysis is based on data collected from subcontracting firms in May 2007
with a specially designed questionnaire. All firms were operating in “pure subcontracting
industries”. In these industries, firms are by nature quasi-firms insofar they are more or lessdependent on other firms. Pure subcontracting industries are also made up of a large numberof small independent companies operating in traditional or low-tech activities (Sessi 2007). If
the application of data from these particular industries reduces the number of observations,the advantage is that firms are relatively homogeneous. The study was conducted in a regionof the French Alps, the “Sillon Alpin”, which is the biggest industrial subcontracting region in
France.
Estimating a bivariate probit model we have made several advances. Firstly, in line withprevious empirical studies, results show that process and product innovations are notindependent of each other. Secondly, the test confirms the positive impact of collaborative
outsourcing agreement on the subcontractor’s probability to innovate whatever the type of2

Pure subcontracting industries are considered those in which 80% of the turnover derives from subcontracting
activity (Sessi 2006). These industries are not delineated according to a sectoral base (nature of the final output)
but to a destination base (volume of the collected activity).

2

innovation. Finally, we give evidence that product and process innovations are associatedwith differentiated inter-organizational practices and tools and distinctive absorptive
capabilities.
The paper is organized as follows: Section 2 presents the theoretical foundations of theresearch; Section 3 sets out the empirical procedure and describes the dataset; Section 4
discusses the empirical results. In Section 5 we conclude with some theoretical implications
and primary conclusions for policy makers.
2. Innovation in subcontracting firms
To provide a better understanding of the innovative activity of subcontracting firms on two
main aspects, we start to discuss two theoretical types of subcontracting relationships(traditional subcontracting versus collaborative outsourcing) and their potential impact on
subcontractors’ innovation (2.1). Then, we precise the “true” impact of subcontracting
relationships by differentiating product and process innovations, taking into consideration
their possible interaction (2.2).
2.1. Subcontracting relationships and innovation
A close examination of the transactional and the knowledge-based perspectives allows us todifferentiate two types of subcontracting relationships (2.2.1) and indicate how they can belinked to a subcontractor’s innovative activities (2.2.2).
2.2.1. Subcontracting considered as a mere contract
In transaction cost theory (Williamson 1975 1985 1999), subcontracting is merely a practicedriven by reducing costs. This interpretation does not mean that there is no room forknowledge creation or innovation but as these processes are a by-product of the division oflabour (Williamson 1999; Cohendet and Llerena 2005) subcontractors are left with little
incentive to make the effort to innovate.
According to Baudry (2003), two types of “traditional” subcontractors can be determinedfrom this view: subcontractors of economy and subcontractors of specialization, which differ
mainly in their degree of dependence and the uncertainty level of their relationship. However,both types share the same objective of producing goods and services under the guidance oflarge firms that have a detailed knowledge of specifications. Traditional subcontractors
produce peripheral products that do not involve specific assets. This makes it more costeffective for a contractor to outsource if market incentives can be maintained and bureaucratic
distortion avoided (Williamson 1991). The challenge for the contractor is to avoid any
situation of dependence for capacity. Subcontracting relationships are therefore based on
arm’s length contracts which are uncertain by nature and dependent on the subcontractor’s
performance. Furthermore, the degree of interdependence is generally low because theactivities in question are peripheral. In this way, transaction cost theory is useful to describe
non innovative or less innovative subcontracting firms. As suggested by Cohendet and
Llerena (2005), “in terms of technology transfer, what is at stake in this zone (of quasi-market
relations) is the exchange of artefacts, rather than innovative ideas or new tacit knowledge”(Op. Cit., 182-183). In most cases, subcontractors are rarely in charge of product design,
which is too specific or risky to be subcontracted, although their advice is sometimes called
upon. Thus, if we consider the nature of this inter-firm relationship, subcontractors have no
incentive to innovate in process or product. Nevertheless, subcontractors might be able toimprove their processes because of passive learning effects.

32.1.2. Subcontracting considered as a process
From the knowledge-based approach, subcontractors are seen as partners or suppliers(collaborative outsourcing) characterized by a new role in knowledge creation. Subcontracting
relations are derived from the needs of contractors to access the complementary knowledge
required to make their own skills valuable. This perspective emphasizes the dynamic
efficiency of capability building rather than the static efficiency of individual transactions.
Subcontracting relations no longer involve the external boundary functions of the contractor.
Subcontractors are considered as “talented suppliers” (Smith and Transfield 2005; Lamming1993) that produce components or systems that are strategic for the contractor. They
contribute to build the knowledge base of the contractors and benefit from their accumulated
absorptive capabilities (Cohendet and Llerena 2005). Since the subcontractor’s resources are
essential, the innovation is also derived from the internal organization of the firm (Nelson andWinter 1982; Teece 1996). Here, competence have a social nature and they are contained inroutines that “may extend outside the firm to embrace partners” (Teece et al. 1997). The key
point here is that the learning process is intrinsically social and collective. It occurs not only
through imitation, but also because of joint contributions to the understanding of complex
problems which are based on relational long term-contracts and coordinative routines for an
efficient circulation of creative ideas and knowledge. Teece (1996) has shown that these kindsof external links are major determinants of innovation. “Compared to arm’s length market
contracts, such an arrangement is more structured, involves constant interaction between
participants, more open information channels, greater trust and reliance and puts lessemphasis on price” (Op. cit, p. 207).
At this stage, three main arguments should be retained for the present discussion. The first,
which differs drastically from both interpretations, concerns the type of contractual
agreement, which in the case of collaborative outsourcing is not a matter of dependence but ofco-specialization (Teece 1996). Contractual mechanisms have to guarantee incentives
necessary for innovation. Then, the length of the contract (middle/long term) and its explicit
character becomes a guarantee if compared to a short-term open, repetitive order, whichcharacterizes traditional subcontracting contracts. The subcontractor will be encouraged toinnovate in order to reap future benefits over time and especially benefit from the renewal of
the contract to enjoy the fruit of the quasi-rent.
The second argument relates to the weakness of the contractual dimension to qualify thefull potential of subcontracting relationships. The knowledge-based approach tosubcontracting is comparable to a process rather than a contract, and depends both on the typeof agreement and the quality of interaction between the contractor and subcontractor.
Learning is not an automatic process. In order for it to be accumulated and coordinated, it isnecessary to build routines as well as incentive schemes and information sharing rules. From
this perspective, characterization must include inter-organizational practices and tools andabsorptive capabilities of firms (Cohen and Levinthal 1990).
Two types of practices and tools can be considered as determinants of the “innovation
learning process” (Lundvall 1988), insofar as they guarantee the integration of activities andflow of knowledge between subcontractors and contractors. The first are practices dedicatedto the coordination of quality, such as insurance-quality procedures, just-in-time or lean
supply (Baudry 2003). Since the outcome of the exchange is difficult to predict, thesepractices are defined as necessary to make sure that a product or process will meet quality
requirements. Although this sometimes involves the need for modernization, training andhuman resources, they also facilitate the renewal of contracts and promote the subcontractor’s
technological potential. Therefore, they do not only aim at reducing costs or improving

4efficiency. In the case of the network-firm, Baudry (1995) shows that the closest
subcontractors are characterized by coordination mechanisms that “are no longer reduced toprice mechanisms but require practices and tools that reveal the ability of a subcontractor todeliver goods in due quality and time and to innovate” (p. 106). Studying the case of the lean
supply model, Lamming (1993) suggests that these new practices and tools foster the abilityof the subcontractor to innovate.
Logistical integration tools, such as Electronic Data Interchange (EDI) or Enterprise
Resource Planning software (ERP), have become strategic resources in the development of
new forms of subcontracting that include the sharing and diffusion of knowledge, especiallyin a network firm (Baudry 2003; Raymond and Blili 1997). According to Information System
literature, EDI and ERP are interdependent technologies. EDI implementation helps thesharing of data and applications (such as ERP) among users in different organizations(Iacovou Charalambos et al. 1995). According to Zhu (2002), EDI is often conducted overpropriety value-added networks and controlled by one large firm. An IT application such as
EDI crosses organizational boundaries, linking individual value chains to create a virtual ormeta-value chain. The presence of an EDI can be interpreted as the signal of a networkorganization in which a “hub” firm decentralizes activities to subcontracting firms (Baudry
1993; Raymond and Blili 1993). Andersen (1999) shows that the use of EDI depends on thesubcontracting type. On the basis of 445 subcontractors in the Danish industry, he gives
evidence that development-oriented subcontractors (in both product and process) are more
likely to use EDI than other types of subcontractors (non innovating firms).
In its claim for a re-thinking of subcontracting, the knowledge-based literature considers thesubcontractors’ absorptive capacity as a key factor for successful partnership. The absorptive
capacity of firms, defined by their ability to absorb outside knowledge, reflects the cumulative
nature of knowledge which is largely a function of prior related knowledge (Cohen andLevinthal 1990). At its lowest level, this prior knowledge includes basic abilities or sharedlanguage, but also refers to an awareness of the most recent technological advances in a given
field. In the case of collaborative outsourcing, a supplier’s absorptive capacity is seen as a
pre-requisite to building the knowledge base of its contractors. But, as suggested by Cohendet
and Llerena (2005), “it is also important for the firm to enhance absorptive capacities of the
suppliers themselves” (p.182). This poses that a contractor does not only assist in the areas ofcost reduction, quality, factory layout and inventory management but also in increasing
technological competence and research. In contrast, Bruce and Moger (1999) argue that an
excessively close relationship between supplier and contractor may limit a supplier’s
absorptive capacity, leading to a failure to absorb and benefit from external sources thatshould lead to innovation. Though “talented”, suppliers can find themselves in a vicious circle
situation where technological opportunities are missed. Therefore, it is important toconcentrate on the “way communication between the firm and its external environment” isorganized (Cohen and Levinthal 1990, p. 544). In this perspective, the use of external links toextend a firm’s base has been frequently related to successful innovation. This is particularly
important for smaller firms, “which have to compensate for small internal resources by being
good at interacting with the outside world” (Fagerberg 2005, p. 11). Empirical studies showthat the use of external knowledge sources alone, without investing in internal factors, do notlead to better innovation. Some empirical studies suggest that some firm’s characteristics, likebeing a subcontractor or a small firm, affect the sources of innovation. For instance, focusing
on innovative activities in 143 small non-R&D-performing firms in Italy, Sterlacchini (1999)shows significant differences in internal technological sources of knowledge between firms
working mainly as subcontractors and other firms. Subcontractors are smaller and spend lesson design, engineering and trial production than other firms. Veugelers and Cassiman (1999)
use data from the Eurostat Community Innovation Survey (CIS) in Belgium to study the5decision of innovative firms to produce technology itself (make) and/or to source technology
externally (buy). They give evidence that small firms are less likely to combine both internaland external technological sources of knowledge than larger companies in the sample. In thesame vein, drawing upon a large-scale survey of small firms in Northern Britain, Freel andHarrison (2006) show that innovation is, primarily, a process built upon internal resources thatcan moderate the requirement to engage in external cooperations. In the case of
manufacturing firms, novel innovativeness in both product and process (relative toincremental innovation) is significantly associated with the employment of technicians andwith R&D expenditure (up to a point).
Another way to estimate the absorptive capacities of subcontractors is to consider thefirm’s strategic positioning as a determinant of its ability to learn (Fiol and Lyles 1985). Many
authors state that strategy influences learning by setting limits on the decision making
process. Most of the empirical studies on subcontracting emphasize that cost-driven
objectives prevail in all types of subcontracting agreements and tend to blur all otherobjectives. In their qualitative study about subcontracting in the aeronautical industry inQuebec, Amesse et al. (2001) show that subcontractors perceive all agreements as a result of
the main contractor’s cost-driven strategy, although the latter is looking for technology
transfer. They also observe that “the absorptive capacity of a subcontractor is observed onlyin terms of control and maintenance of the technology transferred for most of theagreements” (Op. Cit, p. 566). Their results are similar to those of Dyer et al. (1988) whoshow that partnering does not differ significantly from arm’s length contracts because a costdriven objective prevails. In her study of 18 Finnish subcontractors from 1988 to 1990 and1995 to 1996, Lehtinen (1999) provides new evidence. Although she observes an increase of
stable, commitment-based supplier-customer relationships, partnerships involving integrated
R&D processes or mutual sharing strategic visions are still very few. The study by Smith andTranfield (2005) in the UK aerospace industry provides further findings: new subcontracting
practices based on lean supply give the opportunity to subcontractors to have a new status androle. They can use their expertise and experience to offer a broader range of services. But,
their ability for innovation is less apparent as only two subcontractors out of ten are engaged
in innovation.
To sum up, the few studies that deal with knowledge creation and innovation throughsubcontracting relationships tend to confirm that a new type of subcontractor does exist.
Theoretically, these “suppliers” differ from traditional subcontractors because of the structureof the contractual agreement, the nature of their absorptive capabilities and the interorganizational devices by which they are committed to the contractor. However, empiricalresults concerning the supplier’s ability to innovate are still ambiguous. Some studies give
evidence of the impact of certain practices and tools on innovation, while others are more
pessimistic, concluding that the so-called “talented suppliers” are an ideal image rather than areality (Amesse et al. 2001). In this perspective, we try to understand if and to what extent
these suppliers are more talented when developing new processes as well as new products.
2.3. The ability to innovate in process and productTo assess the ability of subcontractors to innovate we now focus on the type of innovation:
process and product. Innovation can be classified by basic purpose (innovation in
products/services or production process) and degree of novelty (new to the firm and/or new tothe industry) (Freel and Harisson 2006; OECD 1997). In our study we do not try todifferentiate subcontractors by the intensity of innovation since this is mainly incremental in
traditional and low-tech industries under study (Archibugi et al. 1991, Sessi 2007). Then, it is
not essential for an innovation to be a true novelty for the industry as long as the6corresponding product, service or process is declared new (or improved) by the subcontractor.
We feel it more important to focus on product and process innovations and their potential
interaction.
The clear-cut distinction between product and process innovations has important
implications for a better understanding of the determinants of innovation and for firms’
competitive position (Weiss 2003). Some empirical studies show that product and process
innovations are closely interlinked in many firms but are driven by different industry and/or
firm level variables (Vaona and Pianta, 2008, Rouvinen 2002) or different organizational
capabilities (Ettlie et al. 1984, Damampour and Gopalakrishnan 2001). Using the SIEPI-CIS2
database, Vaona and Pianta (2008) provide evidence that product and process innovations arepresent together in many firms: 31% are both product and process innovators. In the case ofsmall firms, they show that process innovation3 is reinforced by strategies for production
flexibility while product innovation4 is explained by patenting. In their comparative study ofsmall manufacturing firms in Germany and the United Kingdom, White et al. (1988) note thatprocess innovation5 tends to be pursued independently of product innovation6 when the
motive is likely to reduce costs. In contrast, process innovation tends to be more related to
product innovation insofar as the level or intensity of the latter is high or requires similar
technical expertise (this is often the case with products involving microelectronics).
Other empirical studies test if one type of innovation may drive the other type or if they may
complement each other. Here, the objective is to assess the impact of process and productinnovations on firms’ competitive position since it may differ at company level. From theMilgrom et Roberts’ (1990) complementarity hypothesis, Athey and Schmutzler (1995) built
a two period model to test the complementarities between product and process innovations.
They show that the returns to implement a product innovation are higher when the firm also
implements a process innovation in the short run. They also demonstrate that thesecomplementarities between product and process innovations (short-run decisions) lead tocomplementarities between the firm’s long term choices of flexibility and researchcapabilities7. Other researchers assert a positive impact of product innovation on process
innovation and vice versa. kraft (1990) estimates a simultaneous equation model using datafrom 56 German firms operating in metal-product industry. The results show that productinnovation has a significant impact on process innovation but no evidence for the reverse canbe found. In contrast, Martinez-Ros (2000) demonstrates that product and process innovations
are complement after controlling for the presence of unobserved firm effects as managerial
ability, experience, or other factors that remain constant along the period 1990-1993.
Despite the fact that these empirical studies use different methodologies, they show that
product and process innovations may not be independent choices and the possibility of aninteraction between the two types of innovations can not be rejected a priori. Besides, productand process innovations are driven by different factors. In the case of subcontracting firms,
the pattern of interaction between product and process innovations is still unclear. The
literature about subcontracting relationships is yet too conceptual (Takeishi 2002). One
important question that remains is the “true” potential of collaborative outsourcing and its
consequences for small manufacturers: process innovations or product innovations, or both ?

3measured as the share of firms introducing a process innovation from 1994 to 1996
measured as the share of firms introducing a product innovation from 1994 to 1996
5
measured by the use of Computer Numerically Controlled machine tools and/or the use of computers or robots
in production
6
measured as any change in products over the two years preceding the survey
7
The firm makes long-term decisions in terms of product flexibility, product research process flexibility andprocess research.
47

3. Method
In this section, we present the data (3.1), the empirical procedure (3.2) and the variables usedin this study (3.3).
3.1. Data
The empirical analysis is based on data collected from firms in May 2007 using a speciallydesigned questionnaire8, which was e-mailed to companies with more than 2 employees
operating in “pure subcontracting industries” (Sessi 2006). In these industries, all firms are by
nature quasi-firms insofar as they are in a state of dependence (more or less favourable) withregard to their contractors, particularly with regard to equipment and car manufacturers whoconstitute the main customer sector. In the sample the average volume of subcontracting
activity collected by firms reaches 88%. Another common characteristic of the firms understudy concerns their size. Pure subcontractors are mostly small (micro) independent firms.
The sector-based data established on a national scale by Sessi (2007) reveal that a majority ofthem (59,8 %) have less than 10 employees. It is also the case in our sample of regional firms
in which more than one subcontractor out of two is a micro firm.
The data was collected from firms situated in the “Sillon Alpin” region of the French
Alps. This is the biggest industrial subcontracting region in France, employing 25% of thepeople working in the metal cutting and forming sector. 75% of the country’s mechanical andbar turning industries are concentrated in this area, situated for the most part in “Technic
Valley”. In the sample, the great majority of subcontractors (80,6%) belongs to the metal
cutting and forming sector9. In contrast, the other pure subcontracting industries do not use
metal but plastic and rubber (See Table A1 in appendix). As Sessi (2007), we distinguish theactivities of subcontracting according to the materials used by firms because the evolutions of
production and employment are dissimilar, with a distance growing since the beginning of thedecade in favour of the plastic (Sessi 2007).
Senior managers were asked to provide information about what type of subcontracting
agreements they had, paying particular attention to the one with the most important
contractor. We obtained details about their degree of dependence, their role in thesubcontracting relationship and the level of uncertainty they face. Another part of thequestionnaire was devoted to obtaining information about their ability to innovate in productand process, their organizational competencies, their strategic priorities and the different
sources of technical knowledge they use to support innovative activities. We collected 93
exploitable questionnaires10, and the final data is representative of subcontracting firms
situated in the “Sillon Alpin” for the 3 sectors and the 2 size classes (See Tables A1 and A2 in
Appendix).

8

The accuracy of the questionnaire was first tested during preliminary interviews with firms’ managers. It
enabled to clarify the questions, to detect possible vocabulary ambiguities and to identify the response biases that
could arise from inappropriate question formulation.
9
Metal cutting and forming sector (Bar Turning, Mechanical, Forge, Samping, Cuting) also represents the firstgroup of subcontracting activities at the national level (1/5 of the total volume of subcontracting activity
collected by firms) (Sessi 2006).
10
The sampling ratio appears to be somewhat small. It can be explained by the strategic nature of theinformation collected and the culture of secrecy which is dominant in subcontracting activities.

8

3.2. Empirical procedure
Subcontracting firms innovate because it is profitable for them to do so (expected returns
exceed associated costs). Expected profits of an innovator i must be higher with innovation(ΠIi) than without innovation (ΠNi), ie:y*i= (ΠIi) - (ΠNi) > 0(1)

A latent regression can be specified as:
y*i = α + β’C XC + β’O XO+ β’S XS + β’SO XSO+ β’CO XCO+ ε(2)

Where: XC represents the types of the subcontracting agreements, XO captures theorganizational practices and tools stated in the subcontracting firm, XS captures its strategicorientation, XSO characterises the sources of knowledge used to support innovative activities,
XCO represents the control variables and ε is the error term which is assumed to be normally
distributed.
Unfortunately, the subcontractors’ profits with and without innovation can not be observed.
We only observe whether it innovates or not. The observed counterpart to y*i is yi, whichtakes a value of 0 (no innovation) or 1 (innovation), noted as follows:
1 if and only if y*i > 0
yi =

(3)

0 otherwise

Table 1 indicates that 16 subcontractors (17%) qualify for the title of highly innovating
firms as they have made innovations in both products and processes in the three years prior to
the survey. However, estimating the probability to innovate using a probit model is restrictive
as this supposes that the two processes are independent. As we have already shown thishypothesis is not adapted because previous empirical studies show that product and process
innovations can be linked. It is therefore more appropriate to estimate a system of equations
rather than separate estimations for each type of innovation.
Table 1. Product and process innovations of subcontractors
INOPROCINOPROD 0
1
0
26 29
1
22 16
For the two types of innovation, process and product, respectively noted j and k, we have:P(yij=1) = Ф (β’jXij + εij)
P(yik=1) = Ф (β’kXik + εik)(4)

Where β is a vector of coefficient, Xi denotes the vector of explanatory variables, and εij andεik the error terms which follow a bivariate normal distribution with zero mean, unit variancesand ρ is the correlation coefficient of the error terms. We used a bivariate analysis to test the9correlation between the types of innovation. The test was carried out with a simple t test on
the ρ coefficient. If ρ is statistically different from zero, the two innovation processes are notindependent and cannot be estimated separately. Estimating this system of equations requires
maximum likelihood techniques.
Before presenting the econometric results, we first describe the variables used in the
regressions (see Table 4).
3.3. Measures
3.3.1 Dependent variables
Following the Oslo Manual and the Community Innovation Survey, we privilege the “subject”
approach of innovation (Archibugi and Pianta 1996). The information is collected at the firmlevel. Process innovation is examined as a binary measure (INOPROC) of whether or not new
or modified production processes were introduced in the 3 years prior to the survey. Productinnovation is measured by a binary variable (INOPROD) of whether or not new or modified
products were introduced in the 3 years prior to the survey.
3.3.2 Independent variables
The covariates used in the regression correspond to four series of variables. The first series
records the types of subcontracting contractual agreements (traditional subcontracting versus
collaborative outsourcing). The second series describes the organizational practices and toolsthat guarantee the integration of activities and flow of knowledge between subcontractors andcontractors. The third and fourth series represent the absorptive capabilities of subcontractors
according to their strategic objectives and the sources of knowledge they use to support theirinnovative activities.
Types of subcontracting agreements. The type of contractual agreement is recorded from three
dimensions derived from studying the literature (see section 2): the role played by thesubcontractor in the relationship, the degree of dependence from its main contractor and thelevel of uncertainty. The operationalization of these dimensions has been accomplished withseven “core variables” that have been applied before and are defined in Table 2 below.
Table 2. Definition of the variables used in the cluster analysis
Variables
REAL
CONCEPT
NDO
VOLP
CONTRACTDUREE1RENEG

Définition= 1 if the subcontractor manufactures products only (0= otherwise)= 1 if the subcontractor participates (even partially) in the development of the products /processes (0=otherwise)= Total number of contractors= Part of the collected subcontracting activity from the main contractor / total volume of
production of the subcontractor= 1 if the contract is written (= 0 otherwise)= 1 if the duration of the agreement is less or equal to 1 year (= 0 otherwise)= 1 if the agreement is systematically submitted to renegotiation (= 0 otherwise)

A principal component analysis11 (PCA) was conducted on these seven binary variables.
To test if our variables were suitable for a PCA analysis, we calculated measures of sampling
adequacy (MSA) for each variable (Hair et al. 1998). All the variables were good candidates
for a PCA (MSA values>0.5). In addition, KMO and Bartlett’s test of sphericity met common
standards (KMO = 0.55 and p < 0.001). The PCA uncovered three factors giving a good11

All the results of the cluster analysis are available from the author on request.

10

summary of the theoretical dimensions (accounting for 59.8% of the total variance). A nonhierarchical cluster analysis (SUBC) was then carried out on the factor scores. To determine
the final number of clusters, we used three usual criteria: (i) the statistical accuracy of theclassification measured by the ratio of within and between cluster variances (Fisher’s test), (ii)the number of firms per cluster, and (iii) the economic significance of the clusters identified.
According to the criteria, the version with two clusters of subcontracting firms waspreferred12. To interpret these two clusters, we calculated the mean of each indicator in eachcluster (Table 3). The variable DUREE1 could not be used to describe the profile of clusters
as there was no significant effect. The two clusters of subcontractors are defined as follows:
In Cluster 1, 41 subcontractors (44,1%) manufacture products only and do not have a free
hand in product or process development. They are very dependent on their contractors (due tothe small number of contractors, and the authority of the main one). The presence of arm’s
length contracts, which are continually in the balance, conforms to traditional subcontracting
based on the authority of customers.
In Cluster 2, 52 subcontractors (55,9%) participate in product/process conception.
Subcontractors are linked to a larger number of contractors and the weight of the main
contractor is weaker than in Cluster 1, although it is still 22% of the total volume of
production. Contracts are established (written) and less risky than traditional subcontractors(not systematically renegotiated), which reflects relationships based on trust and reciprocity.
This type of agreement corresponds more to collaborative outsourcing based on incentive
schemes as well as routines for the coordination of knowledge and learning process.
The two dummy variables (C1 for traditional subcontracting and C2 for collaborative
outsourcing) used in the econometric analysis, result from this classification procedure. They
represent the two types of subcontracting agreements between subcontractors and theircontractors.
Table 3. Interpretation of the two subcontracting agreements
NDO

VOLP

CONTRAT

CONCEPT

37,88

,02

,12

,95

,71

41

41

41

41

41

41

Mean
N

86,90

22,33

,31

,37

,65

,54

52

52

52

52

52

52

Mean

60,94

29,18

,18

,26

,78

,61

C1: Traditional
subcontracting

Mean
N

28,00

C2: Collaborative
outsourcing
Total

REAL

RENEG

Organizational practices and tools. In section 2, we have shown that organizational practices
and tools can reveal how much integration there is between subcontractors and contractors
using two series of variables. The first is related to the existence of a shared information
system through the use of the EDI network and/or ERP applications. The second series ofvariables aims at identifying the presence of practices dedicated to the coordination of quality
in new forms of subcontracting (Baudry 2003): FORM takes the value 1 if there are formal
agreements between subcontractors and their suppliers or contractors (0 otherwise), QUALI
equals 1 if subcontractors have adopted organizational practices centred on quality
management (such as certification; Total Productive Maintenance or Value analysis) (0otherwise); LPJAT equals 1 if subcontractors have implemented a “just-in-time” logisticalorganization (0 otherwise).

12

For all comparisons of variances, Fisher’s test is significant at the 0.000 level and indicates a gooddifferentiation of the subcontracting firms.

11Sources of knowledge. Like many authors, we consider the sources of knowledge used bysubcontractors to support their innovative activities to be suitable proxies to measure theirabsorptive capabilities. This strategy seems particularly convenient for small firms sincetraditional R&D measures are not sufficient (Sterlacchini 1999, De Jong and Marsili, 2006).
In our study most subcontractors are not involved in R&D themselves, and for this reason, oursurvey collected information on alternative measures. Four variables were used to make itpossible to distinguish external sources from internal ones. The external sources are defined
as follows: PREST stands for firms that collaborate with external providers to acquire newknowledge (private firms specialized or not in R&D and innovation, technical centres, publicresearch centres, associations). ACTIFS identifies firms that buy specific assets to innovate
such as patents, licences or machines. As far as internal sources are concerned, we distinguishfirms that rely on their own R&D or engineering departments (BERD) from those that recruit
new workers or train staff to support their innovative activities (RH).
Strategy variables. To complete the specification of the absorptive capabilities ofsubcontractors, we have included additional variables related to their strategic objectives. The
questionnaire proposed a menu of classical strategies among which the respondents had tochoose. This provides the following explanatory variables DEVINOV (development of R&D
or innovation), RATION (cost-reduction), REDUCDEP (reduction of dependence fromcontractors), DIVER (market diversification).
Control variables. Two control variables have also been introduced: the size of the firm ismeasured by EFF1 (less than 10 employees) and EFF2 (from 10 or more employees). As far
as industrial affiliation of subcontractors is concerned, we have undertaken some regroupinginside the SIC13 four digit level, in order to acknowledge the two main activities of the metal
cutting and forming sector in the “Sillon Alpin”: bar turning (DEC) and mechanical (MECA).
The other subcontracting activities are noted as APE2.

13Standard Industrial Classification

12

Table 4. Definition of variables
Dependent variables
INOPROCDefinition= 1 if the firm has achieved a process improvement or development in the last three
years (= 0 otherwise)
INOPROD= 1 if the firm has achieved a product improvement or development in the last three
years (=0 otherwise)
Independent variables Definition
Subcontracting agreements (Ref. C1= 1 if the type of subcontracting agreement corresponds to traditional
subcontracting)C2= 1 if the type of subcontracting agreement corresponds to collaborative outsourcing(= 0 otherwise)
Organizational practices and tools
QUALI=1 if the firm has adopted organizational practices centred on quality management
such as certification, total productive maintenance, value analysis (= 0 otherwise)
LPJAT= 1 if the firm has adopted a “just-in-time” logistical organization (= 0 otherwise)
EDI= 1 if the firm has adopted an Electronic Data Interchange (EDI) system (= 0otherwise)
ERP= 1 if the firm has adopted an Enterprise Resources Planning (ERP) software (= 0otherwise)
EDI_ERP=1 if the firm has adopted an EDI system and an ERP planning software (=0otherwise)
Strategy variables
DEVINOV= 1 if the firm aims at developing R&D or innovation (= 0 otherwise)
RATION= 1 if the firm aims at reducing costs (= 0 otherwise)
RATION_DEV=1 if the firm aims at developing R&D or innovation and aims at reducing costs (=0otherwise)
REDUCDEP= 1 if the firm aims at reducing the dependence from its contractors (= 0 otherwise)DIVER= 1 if the firm aims at selling on new markets (= 0 otherwise)
Sources of knowledge to support innovative activities
BERD=1 if the firm resorts to its research or production engineering departments toinnovate (= 0 otherwise)
PREST= 1 if the firm turns to external providers to innovate (= 0 otherwise)
ACTIFS= 1 if the firm buys specific assets to innovate (such as patents, licences, machines)(= 0 otherwise)RH= 1 if the firm recruits new competences or trains the staff to innovate (= 0otherwise)
Control variables
Firm size (ref. EFF2= 1 if the firm has 10 employees or more)
EFF1= 1 if the firm has less than 10 employees (= 0 otherwise)
Industrial sector (ref. MECA= 1 if the firm operates in the mechanical industry)DEC= 1 if the firm operates in the bar turning industry (= 0 otherwise)
APE2= 1 if the firm operates in other pure subcontracting industries (= 0 otherwise)

4. Results
We estimated the system of equations defined in Eq. 4, with a bivariate probit model. As far
as the robustness of the model is concerned, we used the White’s procedure (1982) to deal
with any potential heteroscedasticity problems. The percentage of correct predictions (60,2%)
suggests that the model has a good explanatory power compared to the “naïve prediction
ratio” of 25%14.

14

Four probability scores can be computed P00= probability of no innovation; P01= probability to innovate inproduct only; P10= probability to innovate in process; P11= probability to innovate in process and product.

13

Table 5. Results of the bivariate probit model.
INOPROC (yj)

INOPROC-INOPRODINOPROD (yk)Marginal effects

Variables (x)

β

σβ

β

σβ

Constant
C2
QUALI
LPJAT
EDI
ERP
EDI_ERPRATIONDEVINOVRATION_DEV
REDUCDEP
DIVER
BERD
PREST
ACTIFS
RH
EFF1
DEC
APE2

-2.28***
0.90**
0.90**
0.05-0.29
1.95***-2.56***-0.46-2.31***
2.67***-0.82*-0.90
2.66***
1.08**
0.72
0.46
1.52***
1.02***
1.63***

0.95
0.91
0.40
0.37
0.42
0.61
0.94
0.67
0.78
0.89
0.45
0.61
0.38
0.44
0.50
0.33
0.43
0.38
0.39

-0.60
0.69**
-0.40
0.39
0.03
-0.71
2.44***
-0.47
0.11
-0.50
-0.45
-0.25
0.17
0.16
0.72
0.77**
-0.42
0.26
0.46

0.78
0.30
0.17
0.37
0.29**
0.32
-0.03
0.41
-0.08
0.55
0.50***
0.82-0.47***
0.53
-0.03
0.58
-0.54***
0.69
0.48**
0.37
-0.20
0.38
-0.16
0.42
0.34**
0.37
0.20
0.52
-0.02
0.33
0.05
0.33
0.44***
0.40
0.20
0.39
0.27**-0.94** (0.38)
93Ρ
Number ofobservations
Log-likelihood
Wald test of ρ
Rate of goodpredictions

yij=1 ;
yik=0

yij=1 ;
yik=1

yik=1 ;
yij=0

yik=0 ;
yij=0

0.18***
0.04
0.05
-0.02
0.09
-0.04
-0.14
-0.18
0.18
-0.10**-0.24**
0.37***
0.19**
0.30**
0.13**
0.10*
0.18*
0.31***

0.07
-0.20
0.08
0.03
-0.33***
0.70***-0.03
0.22-0.35***-0.06
0.15-0.31***
-0.13
-0.02
0.14
-0.26***
-0.08
-0.13

-0.42***
-0.14
-0.10
0.07
-0.26***-0.18**
0.21
0.50-0.31***
0.37**
0.25***-0.40***-0.26***-0.25***-0.32***-0.28**-0.30***-0.44***- 80.66.01**
60,2%

Estimated coefficients are rounded to the second decimal number. Robust standard errors [using heteroscedasticconsistent errors from White’s procedure] are reported in italics.
Estimated coefficients are statically different from 0 at 10% (*), at 5% (**), at 1% (***)

The first important result concerns the statistical link between the two innovation choices.
As we can see in Table 5, the correlation coefficient Rho is significantly different from zero,suggesting that process and product innovations are not independent choices. Thus, theestimation of two separate models would lead to a loss of efficiency and possibly misleading
results (Rouvinen 2001).
Clearly, product and process innovation are affected by different factors. This is conforms
to the results obtained by previous empirical studies that make a clear distinction between the
two types of innovation. We note that process and product innovations only have the type ofsubcontracting agreement in common. As predicted by the literature, the type ofsubcontracting agreement explains the ability a small manufacturer has to innovate. Suppliers(C2) are more likely to innovate than traditional subcontractors (C1, subcontractors of
economy or specialization). The results show that the collaborative outsourcing agreement(established, not systematically renegotiated, on the basis of a more equal relationship)
favours subcontractor’s ability to innovate both in product and process. The fact that
arrangements are long-term contracts plus the fact that suppliers are invited to play an activerole in design introduce new critical components (trust and reciprocity) favourable toinnovation.

14

As suggested by the knowledge-based perspective, the nature of the contract is not
sufficient to assess the full role of subcontracting relationships on innovation patterns. Other
factors are likely to impact the ability to innovate, like the presence of organizational practices
and tools and the absorptive capacities of subcontractors. It is interesting to observe thepositive impact of these variables on innovation. The likelihood to innovate is higher ifsubcontractors have adopted organizational practices centred on quality management(QUALI) and logistical tools (ERP and EDI_ERP). This establishes the importance of the“quality” of integration between contractor and subcontractor as argued by the literature on
new forms of subcontracting. However, only a small number of variables have a positiveeffect, meaning that innovation capabilities are not necessarily sparked by all new practices.
This result matches the conclusions found in research about new practices in subcontracting(including lean practices) which pinpoint their weak impact on transfer technology andsubcontractors’ innovation capabilities. Furthermore, and more interestingly, process andproduct innovations are not affected by the same organizational practices or IT tools.
Process innovation is helped by the presence of quality-oriented practices and ERP
software while the use of both EDI and ERP (cross variable) diminishes the likelihood toinnovate in process. In contrast, product innovation depends on the sole combination of both
EDI and ERP technologies, and is not affected by quality oriented practices. If we consider
the complementarity between ICT, we note that the fact of sharing an information system witha hub firm has a positive effect on product innovation and an opposite effect on process
innovation. This result is in line with the recent developments relative to the new forms of
subcontracting (in particular network forms). The presence of such a logistic integration,comparable to an irreversible organizational investment, consist in the implementation of acommon language between principals and subcontractors. What is at stake here is the mutual
exchange of complementary forms of knowledge (Cohendet and Llerena 2005) between firms
for the co-conception of new products (Baudry 2003). By opposition, process innovation isassociated with ERP and organizational practices centered on quality management mainly
aiming at cost reduction. These devices do not concern the technological potential of thesubcontractor.
As far as the impact of strategic variables is concerned, the formulation of a clear
objective oriented towards the development of R&D or innovation (DEVINOV) decreases theprobability to innovate in process. Besides, process innovation is not explained by costcutting strategic objectives (RATION), as this variable doesn’t have a significant effect. As
expected, process innovation is explained by a cross effect that measures the pursuit of costdriven and innovation objectives. This result is in line with previous studies showing that“even when the main contractor is concerned by technology transfer, cost-driven actions blur
the technological objectives of any type of subcontracting agreement” (Amesse et al. 2001, p.
568). Quite surprisingly, strategic objectives oriented towards the development of R&D andinnovation are not positively associated with product innovation. We expected to find apositive impact of DEVINOV on product innovation. But, the effect of this variable is not
significant. A candidate explanation for this result is that product innovation is a never ending
improvement process rather than an asserted strategy of R&D or new product development.
As noted by Pavitt (1984), and contrary to large firms, “Competitive success [of small
specialized suppliers] depends to a considerable degree on firm-specific skills reflected incontinuous improvements in product design and in product reliability” (p. 359). This
explanation is reinforced if we consider the sources of knowledge which support theinnovative activities of subcontractors. The role of firm-specific skills is captured by thesignificant and positive effects of the variable BERD (internal R&D and engineering
activities) on process innovation and the variable RH (recruitment of new competence andstaff training) on product innovation. Though different, the internal sources play a significant

15

role on both types of innovation. The significant link between process innovation and BERD
is well-explained by the fact that small subcontractors in metal manufacturing produce a
relatively high proportion of their own process technology (Pavitt 1984). Process innovationis also driven by external sources of knowledge through the turning to external providers. This
result supports the absorption capacity hypothesis (Cohen and Levinthal 1990). By
opposition, product innovators solely rely on their main contractor as a source of innovation.
Following Bruce and Moger (1999), the relationship between supplier and contractor seems
too close. This leads to the perverse effect of reducing the subcontractor’s absorptive capacity
and a failure to absorb and benefit from external sources of innovation.
We note two clear-cut innovation paths for all “knowledge-based” variables. Process
innovation is reinforced by the ability of a subcontractor to combine different objectives withdifferent knowledge sources, and is based on information systems circumscribed to a dyadic
relationship. Product innovation, on the other hand, needs a network information system(EDI) controlled by the contractor (proprietary system) and is not reinforced by externalsources of knowledge.
Considering the control variables, some results emerge. Micro-firms (less than 10
employees) are more likely to innovate in process than small and medium sized firms.
Consistent with the results of taxonomies of innovative small firms, sectoral variables arenever related to product innovation only. Firms operating in metal manufacturing sectors
belong to the cluster of “suppliers of intermediate goods” (Archibughi and al. 1991), or“supplier-dominated firms” (De Jong and Marsili 2006) which are mainly small processoriented firms. The typology of De Jong and Marsili (2006) is particularly interesting since it
is based on firm level data and does not neglect micro firms (from 1 to 9 employees) as it isusually the case in other empirical studies. From this typology, we observe that micro-firms
are also over-represented among supplier-dominated firms. Moreover, the fact that process
innovation is significantly associated with APE2 (rubber and plastic products) matches well tothe behaviour of “science-based” firms. These firms innovate both in process and product,with a prevalence of process over product.
5. Conclusion
Innovation is seen as a crucial factor for the survival and competitive strength of small firms.
This paper shows that a small subcontractor’s ability to innovate is a complex phenomenon
driven by multiple influences, including the type of subcontracting agreements, the nature ofinter-organizational practices and tools, and the “quality” of the subcontractor’s absorptive
capabilities. In contrast to the majority of studies that focus either on product or process
innovation, the results indicate that the interaction between the types of innovation themselvesis also complex since they are not independent choices. The clear-cut distinction between
product and process innovations in subcontracting relationships leads to important
conclusions.
Firstly, this study has revealed the impact of the type of subcontracting agreement on thesubcontractor’s ability to innovate. Being a supplier (collaborative outsourcing agreement)
rather than a traditional subcontractor (subcontracting of economy or specialization) increases
the likelihood of innovation in both process and product. This supports the claim that
subcontracting relationships are not only a mere practice driven by cost-reduction. Today
subcontracting relationships are changing as contractual agreements give more scope formutual commitment. Subcontractors can turn into talented suppliers looking to develop theirability to innovate. This result outlines both the role and the responsibility of the contractor in
relation to the future of traditional subcontractors and their survival. Bearing in mind that
44,1% of subcontractors operate in uncertain conditions and strong dependence (arm’s length16contracts), it is not surprising that these subcontractors are less committed to innovation than
suppliers since their contractual arrangements provide per se no incentive to innovate.
Secondly, we have shown that subcontractors differ according to the type of innovation(process or product). Process innovation is supported by the pursuit of two objectives: cost
reduction and innovation. Furthermore, subcontractors are more likely to innovate in process
when their activities have been integrated with their contractors in quality and information
sharing (QUAL and ERP). But, this operational integration is circumscribed to a dyadic
relationship to foster process innovation. In contrast, product innovation is (only) influenced
by EDI and ERP tools that play a crucial role in the sharing of information and diffusion of
innovation in a network-organization (Raymond and Blili 1997). We also give evidence that
product and process innovations are not affected by the same sources of knowledge. In thecase of process innovation, subcontractors are more likely to innovate in process if they use
formal R&D and other lower sources of technical knowledge such as engineering and external
providers. Product innovation is reinforced by the recruitment of new competence and staff
training. The fact that external providers do not affect product innovation means that
subcontractors rely heavily on the sole understanding of contractors’ need to innovate. Even
though we must remain cautious, it seems that product innovation is more likely to emerge ina network-organization under the control of the prime contractor. In this case, close
relationships may be a substitute rather than a complementary source to the innovation
process, with some risks of lock-in for the supplier.
Thirdly, operating in pure subcontracting industries has a strong impact on innovation.
Subcontractors are mostly process innovators, whatever the activity they may be working in,showing that cost-minimization and flexibility are still major sources of competitiveness formost small subcontracting firms.
The results of this explanatory research suggest primary conclusions for policy makers
and further theoretical implications. In France, the setting up of “competitiveness poles” is anattempt to bridge the innovation gap of small firms, and in the area under study, new public
policies are being implemented to help subcontractors to innovate. However, although thesepolicies are useful, they often miss their goal. They are too often directed to subcontractors(without differentiating them) or certain contractors (the most involved), so neglecting therelationship itself. It is time that policy makers recognize that the ability of a subcontractor toinnovate depends on the nature of the subcontracting relationship and requires an alignment of
their organizational practices and tools (Cooke and Beh 2007). Every attempt to bring acontractor close to its subcontractors might prove a useful framework for creating true
partnership relations based on shared representations. The study shows that there is still a real
distance between the theoretical model of partnership described by the knowledge-based
perspective and its practical expression in pure subcontracting industries. Contractors are still
behaving cautiously, maybe for fear of becoming knowledge dependent on theirsubcontractors (Amesse et al. 2001).
The significant and positive impact of internal sources of knowledge on innovation isanother key point from this study. As pointed by Freel and Harrison (2006), internal resources
are crucial and “networking is neither a sufficient nor, even, a necessary condition for
innovation” (Op. Cit, p. 301). This conclusion is strengthened in the case of collaborative
outsourcing as far as the potential of innovation of the subcontractors is looked for. Following
Freel and Harrison (2006), we think that policy makers should develop the internal learningand absorptive capabilities of subcontractors. Some actions in this way have already been
conducted. For instance, to compensate for resource and capability shortages of micro-firms,
diverse forms of groupings have been initiated (agreements of cooperation, economic interest
group). They allow firms to propose global solutions to customers and to reach new(overseas) markets relying on their complementarities. Nevertheless, these policy initiatives

17

are not sufficient because they neglect the most vulnerable subcontractors: the small
traditional ones.
Finally, we acknowledge that there are some limitations in our study. Firstly, thespecificity of the population under study reduces the number of observations. But it has also
the advantage that firms are relatively homogeneous. Innovation in pure subcontracting
industries has scarcely been a subject of interest. It would be interesting to confront theseresults with data from other regions which also depend on these particular industries since thisquestion has important implications for their competitiveness. Secondly, we know thatprocess innovation because of its cost-cutting nature may have ambiguous effects on firms’
growth (Fagerberg 2005). The data collected are not sufficient to assess the impact of process
and product innovations on subcontractors’ competitive position. In the vein of the findings ofKraft (1990) from 56 German firms operating in metal-working industry, it would be worth to
study thoroughly the interaction between process and product innovations and assess to what
extent they may complement each other. Thirdly, since our study consists in a static crosssectional analysis, we can not identify any changes in the subcontracting relationships overtime as well as their impact on innovation. Further research is yet needed to study theinteraction between product and process innovations so as to assess to what extent short term
decisions lead to complementarities between the firm’s long term choices of innovation
capabilities.

18

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Appendix: Data descriptives
Table A1. Firms’ Industrial affiliation (SIC: 4 –digit)
Subcontracting industries
Metal cutting and forming
Bar Turning (28.5)
Mechanical (28.5)
Forge, stamping, cutting (28.4)
Other subcontracting industries: Plastic andrubberTechnical pieces made of plastic (25.2),
Moulds and models (29.5), Springs andmanufacturing of other rubber items (25.1).
Total

Parent populationN (%)
870 (77,3)
298 (26,5)355 (31,5)
217 (19,3)
256 (22,7)

Samplen (%)
75 (80,6)33 (35,5)
24 (25,8)18 (19,4)
18 (19,4)1126 (100,0)93 (100,0)

Parent populationN (%)
498 (44,2)
505 (44,8)123 (10,9)1126 (100,0)

Samplen (%)
48 (51,6)
38 (40,9)
7 (7,5)93 (100,0)

Table A2. Firms’ size
Size
Less than 10 employees
From 10 to 50 employees
More than 50 employees
Total

Table A3. Descriptive statistics (%)
Variables
Full sample
Clusters of subcontractors
C1
44.1
C2
55.9
Organizational practices and tools
QUALI
60.2
LPJAT
30.1
EDI
19.4
ERP
18.3
Strategy variables
DEVINOV
38.7
RATION
72.0
REDUCDEP
28.0
DIVER
84.9
Sources of knowledge supporting innovative activities
BERD
22.6
PREST
23.7
ACTIFS
14.0
RH
60.2
Control variables
EFF1
51.6
EFF2
48.4
DEC
35.4
APE2
38.7
MECA
25.9

INOPROCINOPROD

31.7
59.6

34.1
48.1

58.9
53.6
38.9
58.8

42.8
53.6
55.6
41.2

44.4
53.7
42.3
38.7

47.2
35.8
38.5
40.5

76.2
63.6
53.8
58.2

54.0
45.5
53.8
50.9

43.8
53.3
51.6
55.6
33.0

33.3
48.9
39.4
50
29.2

Acknowledgments: The author would like to thank the General Council of Isère forproviding a one-year grant for funding the study reported in this paper. The author is also
grateful to Mareva Sabatier (IREGE) for her helpful comments.

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