1
TYPES OF R&D COLLABORATIONS AND PROCESS INNOVATION:
THE BENEFIT OF COLLABORATING UPSTREAM IN THE
KNOWLEDGE CHAIN
C. Annique UN 
Northeastern University, D’Amore-McKim School of Business
313 Hayden Hall, 360 Huntington Avenue, Boston, MA 02115-5000
Tel.: 1-617-373-6567, Fax: 1-617-373-8628, a.un@neu.edu
Kazuhiro ASAKAWA
Keio University, Graduate School of Business Administration
4-1-1 Hiyoshi, Kohoku-ku, Yokohama 223-8526, Japan
Tel: 81-45-564-2021, Fax: 81-562-3502, e-mail: asakawa@kbs.keio.ac.jp
Prepublication version of: Un, C. A. and Asakawa, K. 2015. Types of R&D collaborations and
process innovation: The benefit of collaborating upstream in the knowledge chain. Journal of
Product Innovation Management, 32(1): 138-153.

Corresponding author
Types of R&D collaborations and process innovation
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TYPES OF R&D COLLABORATIONS AND PROCESS INNOVATION:
THE BENEFIT OF COLLABORATING UPSTREAM IN THE
KNOWLEDGE CHAIN
Biography
C. Annique Un (Ph.D., Massachusetts Institute of Technology; MBA, BA, University of Notre Dame) is
an Associate Professor of International Business and Strategy at the D’Amore-McKim School of Business
at Northeastern University. She analyzes how firms manage R&D and employees for innovation. She has
published extensively on the topic of innovation in top academic journals, such as Journal of Product
Innovation Mangement, Strategic Management Journal, and Research Policy. Her research has received
numerous awards and has been featured in the MIT Sloan Management Review and Forbes. Prior to
joining Northeastern University, she was on the faculty at the Johnson Graduate School of Management at
Cornell University and the Moore School of Business at the University of South Carolina. She can be
contacted at a.un@neu.edu.
Kazuhiro Asakawa (Ph.D., INSEAD; MBA, Harvard University) is Mitsubishi Chaired Professor of
Management at the Graduate School of Business Administration, Keio University, Japan. His research
interests are focused on innovation and R&D management of multinational corporations. He is an
Associate Editor of Global Strategy Journal and serves on the editorial boards of the Academy of
Management Perspectives, Journal of International Business Studies, Journal of International
Management, and Asia Pacific Journal of Management. He chairs the Japan Chapter of the Academy of
International Business. He was a visiting scholar at the MIT Sloan School of Management and adjunct
faculty fellow at the research institutes of the Ministry of Economy, Trade and Industry and the Ministry
of Education, Japan.
Types of R&D collaborations and process innovation
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TYPES OF R&D COLLABORATIONS AND PROCESS INNOVATION:
THE BENEFIT OF COLLABORATING UPSTREAM IN THE
KNOWLEDGE CHAIN
This article explains how research and development (R&D) collaborations impact process innovation;
given the differences in innovation mechanisms, prior insights from studies of product innovation do not
necessarily apply to process innovation. Extending the knowledge-based view of the firm, the article
classifies four types of R&D collaborations – with universities, suppliers, competitors, and customers – in
terms of two knowledge dimensions: position in the knowledge chain and contextual knowledge distance.
Position in the knowledge chain is the position of the R&D collaboration partner in the knowledge chain
of the industry – the input-output sequence of activities that result in the transformation of raw materials
into products that are used by end customers. Based on this knowledge chain, the article considers
universities and suppliers as upstream R&D collaborators, and competitors and customers as downstream
R&D collaborators. Contextual knowledge distance is the difference in industry-related contexts of
operation of the R&D collaboration partners and the firm. Based on this, the article views R&D
collaborators that are suppliers and competitors as having low contextual knowledge distance to the firm,
and R&D collaborators that are customers and universities as having high contextual knowledge distance
to the firm. Using this classification, the article proposes a ranking of R&D collaborations in terms of
their impact on process innovation: R&D collaborations with suppliers have the highest impact, followed
by R&D collaborations with universities, then R&D collaborations with competitors, and finally R&D
collaborations with customers. These arguments are tested on a four-year panel of 781 manufacturing
firms. The results of the analyses indicate that R&D collaborations with suppliers and universities appear
to have a positive impact on process innovation, R&D collaborations with customers appear to have no
impact, and R&D collaborations with competitors appear to have a negative impact. As a consequence,
the main driver of the impact of R&D collaborations on process innovation appears to be position in the
knowledge chain rather than contextual knowledge distance. These novel ideas and findings contribute to
the literature on process innovation. Even though process innovation tends to be internal and tacit to the
firm, it can still benefit from external R&D collaborations; this article is the first to analyze this
relationship and provide a theoretical framework for understanding why this would be the case. This
study also has important managerial implications. It suggests that managers need to be careful in choosing
the partners for their firms’ R&D collaborations. Engaging in R&D collaborations with universities and
suppliers appears to be helpful for process innovation, while conducting R&D collaborations with
competitors may potentially harm process innovation.
Key words: R&D collaboration, process innovation, knowledge chain position, contextual knowledge
distance, knowledge-based view
Types of R&D collaborations and process innovation
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Introduction
Most studies of R&D collaborations indicate that these collaborations are important for achieving product
innovation because they provide the firm with access to external knowledge that it needs to innovate its
products (Dittrich and Duysters, 2007; Feller et al., 2013; Un et al., 2010; Wu, 2012). For example,
Dittrich and Duysters (2007) show that a company like Nokia is increasingly using local and international
R&D collaborations to achieve different types of product innovations. Wu (2012) finds that market
competition and sectoral technological intensity of firms moderate the relationship between technological
collaborations and product innovations. However, this literature has not addressed the impact of R&D
collaborations on process innovation directly, assuming that insights from studies of product innovation
can be applied to process innovation.
Process innovation, however, or “the introduction of a new method of production”, is also an
important element in the competitiveness of the firm, because it helps the firm achieve greater efficiency
in production and improve product features and quality (Clark and Fujimoto, 1991; Schumpeter, 1934;
Stadler, 2011). More importantly to this discussion, the insights derived from analyzing product
innovation may not translate directly into process innovation because the innovation mechanisms differ.
Despite their relation, product and process innovations differ on multiple dimensions, such as the
objective of the innovation, valuation, dimension, and competitive impact. Hence, insights from studies of
product innovation may not be directly applicable to process innovation (Damanpour, 2010; Pisano and
Shih, 2012; Stadler, 2011). Therefore, it is unclear whether factors that influence product innovation
influence process innovation in the same way, and, if so and more importantly, why they do.
Therefore, this article analyzes the relative impact of R&D collaborations with suppliers,
universities, competitors, and customers on process innovation. It extends the knowledge-based view
(e.g., Grant, 1996; Kogut and Zander, 1992; Nonaka, 1994) to argue that the impact of R&D
collaborations on process innovation depends on two knowledge dimensions: position in the knowledge
chain and contextual knowledge distance. It positions each of the four types of R&D collaboration – with
suppliers, universities, competitors, and customers – according to these two dimensions and argues that
R&D collaborations that are located upstream in the knowledge value chain and that are close in
contextual knowledge distance better support process innovation.
The article contributes to the literature in two ways. First, it is among the first to focus on
analyzing how R&D collaborations affect process innovation; the literature has primarily focused on the
impact of R&D collaboration on product innovation. It theoretically explains which R&D collaborations
are more likely to support process innovation. It argues that, even though process innovation is primarily
internal and tacit (Hatch and Mowery, 1998; Pisano and Shih, 2012; Stadler, 2011), a firm can still benefit
from external R&D collaborations, and it explains why. Second, the article contributes to the literature on
R&D collaborations by explaining why the different types of R&D collaborations support process
innovation. Extending the knowledge-based view (Grant, 1996; Kogut and Zander, 1992), it introduces
two dimensions of knowledge – position in the knowledge chain and contextual knowledge distance –
that help classify the different types of collaborations, and it analyzes their relative impact on process
innovation. In doing so, the article highlights that not all R&D collaborations are equally beneficial for
process innovation, helping managers better understand which R&D collaborations are better suited for
process innovation and why.
Knowledge-based View, R&D Collaborations, and Process Innovation
Types of R&D collaborations and process innovation
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The Knowledge-based View
The article builds on the knowledge-based view (KBV) to analyze the impact of R&D collaboration on
process innovation. The KBV sees companies as mechanisms that facilitate the integration, transfer, and
creation of knowledge (Kogut and Zander, 1992; Un and Montoro-Sanchez, 2010). Knowledge is
imperfectly distributed across individuals and organizations in society (Hayek, 1945) and across
individuals in a company (Tsoukas, 1996; Un, 2010). The company facilitates the integration of tacit and
explicit knowledge, its conversion into tacit knowledge, and its transformation into explicit knowledge
that results in an innovation (Nonaka, 1988). To do so, the company provides the incentives, organization,
and mindsets that enable individuals to integrate and convert tacit knowledge into explicit knowledge
(e.g., Kogut and Zander, 1992; Un and Cuervo-Cazurra, 2005) that can be transferred to other parts of the
firm (Szulanski, 1996) and outside the firm (Kogut and Zander, 1993). Therefore, knowledge is the key
asset to manage in the firm because it is the basis of the existence of the firm and of its superiority over
the market (Kogut and Zander, 1992; Un and Cuervo-Cazurra, 2004). Moreover, knowledge as an asset
fulfills the conditions for being a source of sustainable competitive advantage – valuable, rare, inimitable,
and non-substitutable, or VRIN (Barney, 1991). First, knowledge is valuable because it enables the firm
to fulfill the needs of customers. Second, knowledge is rare because it is imperfectly distributed across
individuals and firms (Hayek, 1945; Tsoukas, 1996); as a result, no two firms have the same set of
knowledge. Third, knowledge is difficult to imitate because individuals know more than they can express
(Polanyi, 1962). Although some explicit knowledge is more easily imitated because it is purposefully
codified to facilitate its transfer and replication (Winter and Szulanski, 2001), the underlying tacit
knowledge that supports such replication remains difficult to imitate (Kogut and Zander, 1992). Fourth,
knowledge is difficult to substitute because it is subject to complexity, system interdependence, and
causal ambiguity (Lippman and Rumelt, 1982; Dierickx and Cool, 1989); even when a competitor tries to
substitute how something works, the manner in which the firm arrives at that solution and the underlying
logic behind it are rarely clear.
Product Innovation and Process Innovation
Differences between product and process innovation in their innovation mechanisms explain why insights
from analyzing product innovation may not be applied directly to the analysis of process innovation,
prompting this study. The article extends the KBV to analyze these two types of innovation and
distinguish them by six dimensions: the objective of the innovation, the competitive impact of the
innovation, the valuation or value of the innovation, the degree of novelty or rareness of the innovation,
the codifiability or imitability of the innovation, and the location or substitutability of the innovation.
These six dimensions include the traditional ones regarding the object that is being innovated – a product
or a process – and the application of the VRIN conditions discussed above. Table 1 summarizes the
differences.
*** Insert Table 1 here ***
First, the objective of the innovation in product and process innovation is the most obvious
difference, and the one that has traditionally been discussed. However, in its distinction this article goes
beyond the difference between innovating the product of the firm and innovating its process. Instead, it
separates them by the underlying objective that each of these innovations provides the firm. In product
innovation, novelty of the product is the key objective. The product needs to provide the firm with a way
to differentiate its offer from those of competitors, and eventually have the best product in the
marketplace in terms of the price/quality relationship. In contrast, in process innovation the underlying
Types of R&D collaborations and process innovation
6
objective is not necessarily novelty but rather efficiency in the manner in which the firm conducts its
operations (Ettlie and Reza, 1992; Hatch and Mowery, 1998).
Second, the competitive impact of the innovation differs markedly. In the case of product
innovation, the typical competitive impact is an increase in the price that the firm can charge for the
differentiated product. Product innovation typically enables the firm to charge a premium over
competitors’ less innovative products and therefore increase revenues, all else being equal. In contrast, in
the case of process innovation the traditional competitive impact is a reduction in the costs of production
of the product or service (Ettlie and Reza, 1992; Reichstein and Salter, 2006). Process innovation helps
the firm to lower its costs, increasing the margin of operations that enables it to improve product features
and quality (Clark and Fujimoto, 1991; Pisano and Shih, 2012).
Third, there is a difference in the valuation of the innovation, or the value that the innovation
creates. The novelty of product innovation is primarily valued externally by customers and is relative to
the offers of competitors (Damanpour, 2010). Even when the product is better than previous ones, it has
to be better than competitors’ products to be preferred by customers, all else being equal. Thus, the
achievement of product innovation depends on market feedback, with customers indicating whether or not
they appreciate the product innovation in comparison to other products by ultimately buying it (Hauser et
al., 2006; Un and Cuervo-Cazurra, 2009). In contrast, the innovativeness of process innovation is valued
internally by managers who establish cost reductions and improvement targets for the production process.
They evaluate whether the improvements in the process have achieved or surpassed expectations
(Repenning and Sterman, 2000).
Fourth, there are large differences in the accepted degree of novelty, or the rareness of product
and process innovation. Product innovation tends to focus more on achieving a degree of radicalness of
the innovation, with the products serving new and unmet needs of customers. The learning that product
innovation tends to aim for is exploratory in nature, with new ideas and concepts being incorporated into
a new product (Danneels, 2002; Un, 2010). In contrast, process innovation tends to focus more on
achieving some degree of incremental innovation, with the process being improved in an evolutionary
manner and reducing costs or increasing quality in the manner in which the product is generated (Clark
and Fujimoto, 1991; Stadler, 2011; Womack et al., 1991). Thus, the learning that process innovation aims
to achieve is more exploitative in nature, with improvements on existing concepts and ways of doing
things being introduced in the process. In other words, rareness is a lower requirement in process than in
product innovation.
Fifth, the codification of knowledge in product and process innovation differs, reflecting
differences in the ability of competitors to imitate them. Product innovation tends to be easier for
competitors to imitate because the product becomes the embodiment of knowledge, providing a more
explicit and clearer objective for competitors to imitate. Competitors can more easily take the product and
reverse engineer its components and system. In contrast, process innovation tends to be more difficult to
imitate by competitors because the process is internal to the firm, more tacit and obscure, and thus more
difficult to codify (Hatch and Mowery, 1998). The firm could bar competitors from coming into its
facilities or investigating how it produces and how it improves its production process, limiting the ability
of competitors to imitate process innovation.
Finally, the location of the knowledge for innovation differs, resulting in a different ability of
competitors to substitute the innovation. Product innovation in many cases is located in a quasiindependent unit that either focuses only on generating new products (i.e., an R&D unit) or that is
mandated to generate new products in addition to undertaking its current duties (Benner and Tushman,
2003; Pisano and Shih, 2012). Product innovation tends to focus more on the technological aspects, with
teams of experts working on the innovation, which partially limits the substitution by competitors with
Types of R&D collaborations and process innovation
7
different skills. In contrast, process innovation tends to be more systemic and interdependent
(Gopalakrishnan and Bierly, 1999), because collaboration among different units tends to be necessary as a
change in the process of one part of the firm affects the other parts (Ettlie and Reza, 1992; Repenning and
Sterman, 2002). The greater complexity, causal ambiguity, and context-specific nature of process
innovation make it much more difficult to substitute.
R&D Collaborations and Process Innovation
The article now turns to the analysis of the impact of R&D collaborations on process innovation. It argues
that R&D collaborations can help a firm innovate its process. Even though process innovation tends to be
more internal to the firm, as well as evolutionary, systemic, and tacit, the firm can still benefit from
obtaining knowledge from R&D partners – universities, suppliers, competitors, and customers. However,
it proposes that the knowledge that these partners can provide the firm differs, and as a result the impact
of each type of R&D collaboration on process innovation varies. It argues that external collaborations are
important for process innovation, but for different reasons than in the case of product innovation.
Specifically, the article proposes that the impact of R&D collaboration on process innovation
depends on two dimensions of knowledge: contextual knowledge distance and position in the knowledge
chain. These two dimensions are chosen because they reflect different types of external knowledge that
the firm can access through R&D collaborations, resulting in a differential value to process innovation.
Table 2 illustrates the location of the four types of R&D collaboration. This classification in a two-by-two
matrix is a simplification for illustration purposes.
*** Insert Table 2 here ***
Contextual knowledge distance refers to the differences in industry-related contexts of operation
of the firm and the R&D collaboration partner. Suppliers and competitors operate in industry contexts that
are relatively similar or close to the industry context of operations of the focal firm. They tend to be
influenced by similar regulations and competitive demands and have related objectives in satisfying the
needs of the end customers with their products, with competitors having an even closer context of
operation than suppliers. Suppliers tend to share knowledge compatible with the focal firm in terms of
operations, technologies, equipment, and design (Murtha, et al, 2001), while competitors tend to operate
in the same industry (Tsai et al., 2011), thus sharing a similar operating context and having related
knowledge bases. In contrast, universities and customers operate in industry contexts that are relatively
different or distant from that of the focal firm. They are exposed to very different regulations and have
different needs and interests from the company. Thus, universities and customers tend to have knowledge
sets relatively different from those of the focal firm, with universities having knowledge sets even more
distant than those of customers.
Position in the knowledge chain refers to the location of the R&D collaboration partner in the
value chain of the industry of the focal firm. The industry value chain refers to the input-output sequence
of activities that lead to the conversion of raw materials into products that are used by the end customer
(Porter, 1980). Applying this input-output of the value chain to the knowledge chain of the collaborating
partners, this article considers suppliers and universities as upstream from the firm. Their knowledge sets
tend to be focused more on the factor market or input side of the operation of the focal firm, providing
ideas and technologies for the conversion of raw materials into components and products, with
universities being further up in the value chain than suppliers. Prior studies also group suppliers and
universities as upstream or input parts of the value chain of the firm (e.g., Gray et al., 1986; Porter, 1985).
In contrast, this article considers competitors and customers to be positioned downstream in the value
Types of R&D collaborations and process innovation
8
chain, since they are more focused on the output or product market side of the focal firm. Their
interaction with the focal firm is more centered on creating products that better satisfy the needs of the
end customers, with the customer being farther down in the value chain than competitors.
Each of the four types of R&D collaboration can be useful for process innovation, but this article
is interested in the relative influence of each type in comparison to the others. Hence, based on the two
dimensions of knowledge discussed, it proposes a ranking of the relative influence of each R&D
collaboration on process innovation. It now explains the two dimensions of knowledge and why it argues
for such ranking.
Contextual Knowledge Distance and Process Innovation
The article proposes that R&D collaborations that have a small contextual knowledge distance are more
relevant for process innovation than those that have a larger contextual knowledge distance. The reason
for the superiority of close over distant contextual knowledge is based on the different aspects of the
nature of process innovation. First, an important objective for process innovation is attaining efficiency in
operations and improving quality; this is often done through benchmarking, and benchmarks tend to be
context-specific. For example, practices such as continuous replenishment and supplier shelf management
developed by Wal-Mart have become established process innovations in the retail industry (Davenport,
1993), but these do not necessarily apply in the automobile or steel industries because firms in these
industries use different processes.
Second, process innovation is systemic. Thus, understanding the exact elements of the knowledge
package and replicating them in another user’s setting is extremely complicated and difficult (Argote and
Ingram, 2000; Spender and Grant, 1996). Sharing similar organizational contexts, which may have more
similarities in the systemic relationships, facilitates the understanding and adopting of the essence of
process innovation.
Third, process innovation is generally less visible, less clear, and more obscure in nature than
product innovation because it implies “how work is done” in an organizational setting rather than what is
done (Davenport, 1993). As a result, the firm needs to be able to understand the tacit knowledge held by
the knowledge holders. This tacit knowledge is developed in a particular context; the more similar the
context is, the easier it will be for the firm to transfer the tacit knowledge. The existence of a large
contextual distance between sender and recipient becomes an important barrier to the transfer of
knowledge (Grant, 1996; Spender, 1996; Szulanski, 1996). The transferability of the knowledge depends
on the transferability of the meaning and value associated with such knowledge (Cummings and Teng,
2003; Kostova, 1999). Thus, the firm needs to do more than just imitate others’ superior practice, and
instead needs to convert it into the firm’s context (Hurmelinna et al., 2002), supplementing it with tacit
knowledge developed locally (Lall, 2000), in order to be able to de-contextualize, re-contextualize, and
achieve a successful adaptation of practice (Brannen, 2004).
Position in Knowledge Chain and Process Innovation
The article argues that upstream R&D collaborations, or those dealing with the input side of the firm’s
operation, will have a larger positive impact on process innovation than downstream R&D collaborations,
or those dealing with the output side of the firm’s operation. Several reasons related to the characteristics
of process innovation explain this. First, process innovation requires improvement in the flow of materials
in the firm. Thus, inputs into the flow of materials that are located upstream in the knowledge chain will
Types of R&D collaborations and process innovation
9
have an influence on how such flow operates. Collaborating with sources of knowledge located upstream
can facilitate the transformation of the process in the firm and its innovation.
Second, process innovation is systemic in nature. Changes in one part of the process alter the
interactions with other parts of the process, requiring the transformation of the interfaces of the system
(Ettlie and Reza, 1992; Gopalakrishnan and Bierly, 1999). When the firm collaborates with organizations
that are upstream in the knowledge chain, the alteration of the knowledge that these organizations provide
to the firm can be more easily adapted to the characteristics of the system and can more easily facilitate
the innovation of the process because such input becomes part of the beginning of the firm’s process.
Third, the focus on efficiency that accompanies process innovation results in upstream knowledge
being better suited to facilitate such outcomes. Since upstream partners have a better understanding of the
internal processes of the firm, such as the flow and quality of its input, they have better knowledge of
where and how to make changes in the system to reduce costs and improve input quality, resulting in
improved efficiency.
R&D Collaboration with Suppliers and Process Innovation
Of the four types of R&D collaborations that can help firms innovate processes, R&D collaborations with
suppliers are likely to improve process innovation the most, because the knowledge of suppliers is close
in contextual distance and upstream from the firm.
Suppliers are close in contextual knowledge distance to the firm. Suppliers generally belong to
the same industry segment as the firm and share a close inter-organizational relationship with the firm,
especially when they supply custom-made parts and products rather than standardized ones. As the nature
of outsourcing has changed from a marginal activity to a strategic one, firms’ relations with suppliers
have become closer and more stable (Sydow, 1992). Firms tend to limit the number of suppliers, invest in
longer-term relationships, and often introduce practices such as just-in-time delivery and computerized
links to bind themselves with their suppliers (Lane, 2003). For example, in the TFT-LCD industry, the
yield problem is so ubiquitous that experts from supplier companies work together with the firm to
improve the process (Murtha et al., 2001). Thus, suppliers and the firm share not only a similar industry
context, but in many cases similar organizational contexts and systems that facilitate the transfer of
process knowledge.
Suppliers are also upstream in the knowledge chain to the firm. Suppliers help the firm improve
its processes as they provide new inputs to the production flow. Suppliers can work closely with the firm,
helping it design and manufacture new products or improve upon existing products (Takeishi, 2001,
2002). This upstream interaction results in three factors that facilitate process innovation: acquisition of
firm-specific knowledge, insight into best practices used by different firms in the industry, and a closer
relationship with the firm. First, since learning occurs by doing (March, 1991), suppliers acquire deeper
firm-specific knowledge. They gain insight into the firm’s efficiency targets and how managers assess
them internally. They also learn about firm-specific processes in need of innovation as well as their
interdependence with other processes. Ettlie and Reza (1992) argue for more integration between
suppliers and the firm to achieve process innovation. The authors explain that in the process of working
with the firm to develop new products, suppliers can recommend innovations to certain manufacturing
processes to improve manufacturability and increase efficiency. Second, by working intimately with other
firms in the industry, suppliers also acquire knowledge about best practices used in other firms compared
to industry standards. As a result, suppliers can help the firm identify best practices and de-contextualize
and re-contextualize them to match its processes in need of innovation without revealing the sources of
Types of R&D collaborations and process innovation
10
these practices, especially since the way these practices work tends to be tacit and ambiguous. Finally, the
closer relationship between suppliers and the firm enables the integration of firm-specific tacit knowledge
and external knowledge of best practices. Subramaniam (2006) explains how close interactions and
relationships between individuals facilitate the integration of their tacit and explicit knowledge.
The above discussion leads to a hypothesis that:
H1. R&D collaborations with suppliers have a larger positive impact on process innovation than R&D
collaborations with universities, competitors, or customers.
R&D Collaboration with Universities and Process Innovation
The article considers the knowledge of universities to be far in contextual knowledge distance but
upstream in the knowledge chain, thus having the second highest positive impact on process innovation.
Although universities collaborate with firms to develop new products and help with process
innovation, they are far in contextual knowledge distance. The main objectives of universities are
educating students and doing research in a wide variety of fields, some of which may overlap with the
firm but many of which do not (for a recent review, see Kotha et al., 2013). Their industry of operation is
higher education. Universities compete with other universities for students and faculty in the creation and
dissemination of knowledge, improving their reputation and position in the rankings. The incentives and
foci of faculty differ significantly from those of employees in the firm (Czarnitzki et al., 2011), limiting
the ability of the firm to improve its process in collaboration with universities.
However, universities are upstream in the knowledge chain. Universities typically provide the
firm with a wide array of knowledge that is located upstream in the knowledge chain. Although
universities are often perceived as primarily useful for achieving product innovations (Agrawal, 2006;
Monjon and Waelbroeck, 2003; Tether, 2002) and conducting basic research into a particular technology
(Arora and Gambardella, 1990; Mowery and Rosenberg, 1989), collaborating with them can also enhance
process innovation. For example, benchmarking studies of firms in the automobile industry in the 1980s
(Womack et al., 1991; Clark and Fujimoto, 1991) were conducted to gain a better understanding of
specific practices that enable some firms to be more efficient than others (for a recent discussion about
these studies see Ro et al., 2008; Womack, 2006). In the process of conducting these studies, researchers
from the universities gained insight into processes in need of improvement and interdependent processes
that enable or inhibit the success of the implementation of best practices (Ki-Chan et al., 2006).
Collaboration with a university enables the firm to question how processes are undertaken in the firm and
reanalyze the whole process from beginning to end, seeking to achieve higher efficiency and improve
product quality.
The above discussion leads to a hypothesis that:
H2. R&D collaborations with universities have a smaller positive impact on process innovation than
R&D collaborations with suppliers, but a larger positive impact than R&D collaborations with
competitors or customers.
R&D Collaboration with Competitors and Process Innovation
Types of R&D collaborations and process innovation
11
The article proposes that R&D collaborations with competitors have the third highest positive impact on
process innovation because they are close in contextual knowledge distance but downstream in the
knowledge chain.
Competitors are close in contextual knowledge distance to the firm because they share similar
contextual knowledge appropriate for the specific industry of the firm. Competitors belong to the same
industry as the firm (Tsai et al., 2011). Best practices developed and held by the competitors therefore can
be quite relevant and potentially useful to the firm, if obtained. Murtha et al (2001) indicate “a surprising
degree of collegiality among competitors” (p.138) as the pace of knowledge accumulation intensified in
the TFT-LCD industry. The rapid transition across generations (i.e. change to enlarge substrate sizes)
enhanced the importance of tacit, human-embodied knowledge, which was difficult to codify. The rapid
pace of innovation “challenged industry members to support broad intra-company and inter-company
personal contacts to stay abreast while pushing technology, their business, and the industry ahead.” (p.
138). Thus, this similarity in context facilitates process innovation.
Competitors are downstream in the knowledge chain of the firm. Although they share an industry
environment, firms tend to limit how closely competitors can observe the operational processes of the
firm. Instead, competitors tend to rely on knowledge of the output of the firm, analyzing competing
products and trying to reverse engineer the process that led to the creation of the products. Although there
is collaboration among competitors, such as the case of Toyota allowing its competitors to observe some
of its design and manufacturing processes, those that do collaborate rely on the idea that competitors will
still lack knowledge about how the processes work together as a system in producing the efficiency and
output quality (Sobek et al., 1998). Since competitors lack knowledge about the processes of other firms
in the industry, they are also less likely to have knowledge about best practices being used in these other
firms.
Therefore, it is hypothesized that:
H3. R&D collaborations with competitors have a smaller positive impact on process innovation than
collaborations with suppliers or universities, but a larger positive impact than R&D collaborations with
customers.
R&D Collaboration with Customers and Process Innovation
The article proposes that R&D collaborations with customers have the lowest positive impact on process
innovation because they are far in contextual knowledge distance and downstream in the knowledge chain.
Customers are far in contextual knowledge distance from the knowledge of the firm. Customers
have a different context of operations than the firm. End users are focused on consumption, a setting that
differs markedly from the competitive setting in which the firm operates and innovates its processes
(Christensen and Bower, 1996). Customers use the products provided by the firm with little regard for
how such products have been created, and may use them in many alternative settings that are little related
to the context of operations of the firm (Lukas et al., 2013). The differences in context limit the ability of
the firm to improve its processes by working with customers.
Customers are downstream in the knowledge chain from the firm. They are more interested in the
output of the firm, in the form of products that satisfy their needs, than in the input side of the firm and
how such products are created. Therefore, customers’ input may help product innovation more than
process innovation. Nevertheless, lead users create their own processes in the generation of innovations
Types of R&D collaborations and process innovation
12
that satisfy their needs, which could be useful for the firm to improve its processes (Harhoff et al., 2003).
However, customers in general are less likely to be able to de-contextualize their practices and recontextualize them to suit the needs of the internal processes of the firm.
Thus, it is hypothesized that:
H4. R&D collaborations with customers have a smaller positive impact on process innovation than R&D
collaborations with suppliers, universities, or competitors.
Research design
To test the impact of R&D collaborations on process innovation, the article uses a sample of
manufacturing firms. Data come from a survey of manufacturing firms operating in Spain that was
collected by the SEPI Foundation. The SEPI Foundation is a research foundation affiliated with Spain’s
Ministry of Industry, Tourism, and Commerce. Among other research initiatives, the foundation conducts
an annual survey of manufacturing firms. The firms operate in manufacturing industries, codes 2 and 3 of
the SIC classification. All firms with more than 200 employees are surveyed, and firms with 10 to 200
employees are surveyed using a random stratified sample. Data is collected under an anonymity
agreement that facilitates the accuracy of responses. It is then refined and validated to ensure its quality
and temporal consistency. The dataset has been used in other research, such as in diversification studies
(e.g. Merino and Rodriguez, 1997) and in R&D investment analyses (e.g. Cuervo-Cazurra and Un, 2010;
Un and Cuervo-Cazurra, 2008).
The analysis is based on five years of data, 1998 to 2002, and a sample size of 781 firms with
complete data for this period. The average firm has €6.6 million in annual sales and 217 employees.
Variables and Measures
The dependent variable is process innovation. Consistent with prior studies (e.g., Ettlie and Reza, 1992),
process innovation is measured with an indicator that the firm introduced important modifications in the
production process during the year. This is captured as an affirmative answer to the question “Indicate if
during the year the firm introduced an important modification in the production process (process
innovation): introduction of new machinery and of new methods for organizing production”.
The independent variables of interest are four types of R&D collaboration: R&D collaboration
with universities, R&D collaboration with customers, R&D collaboration with suppliers, and R&D
collaboration with competitors. Consistent with prior studies (Belderbos et al., 2004a; Un et al., 2010),
each is measured with an indicator that takes two values, 1 if the firm indicates that during the year it had
R&D collaborations with the R&D partner (universities and/or technological centers, customers,
suppliers, and competitors), and 0 if it did not. Additionally, another variable that measures whether the
firm had any type of R&D collaboration during the year or not was created. This variable is called “R&D
collaborations with universities, suppliers, customers, or competitors.”
The article controls for other factors that influence process innovation. First, it controls for
internal R&D investments, to take into account that the firm may achieve innovation through internal
R&D efforts (Levin et al., 1987; Un and Cuervo-Cazurra, 2008; Un and Montoro-Sanchez, 2011). This is
measured as expenditures in internal R&D divided by sales, as done by other researchers (e.g. CuervoCazurra and Un, 2007; Helfat, 1997). Second, it controls for the size of the firm, because larger firms are
Types of R&D collaborations and process innovation
13
more likely to innovate since they can have specialized personnel dedicated to innovation (Leiponen,
2005). Size is measured using the natural log of number of employees, following previous work (e.g.
Tether, 2002; Belderbos et al., 2004b). Third, it controls for the firm being an affiliate of a domestic
company or an affiliate of a foreign firm, as done in other research (e.g. Belderbos et al., 2004b; Un,
2011). Firms that are affiliated with other companies may receive innovations and technologies from their
parent firms and have less need to innovate on their own (Williamson, 1985; Un, 2011). These are
measured with indicators that another domestic firm or a foreign firm owns stock in the company. Fourth,
it controls for slack resources because firms with slack financial resources are more likely to be able to
engage in innovation (March, 1991; Tether, 2002). This is measured with an indicator of free cash flow
over equity multiplied by one hundred. Fifth, it controls for the percentage of the sales of the firm that are
going to end users and distributors and the percentage of sales that are going to other companies and the
state, because firms that have larger connections with customers or companies may obtain knowledge
through these connections. Sixth, it controls for whether the firm produces a standardized product or
products that are designed specifically for each customer, as well as for whether the firm manufactures in
small batches (less than 200 units), large batches, or has a continuous production process, because the
stage of evolution of technologies may affect process innovation (Abernathy and Utterback, 1978).
Seventh, it controls for the industry of operation of the firm because there are differences across industries
in the pressures to innovate and the protection of innovation (Levin et al., 1987; Cuervo-Cazurra and Un,
2010). It uses an indicator for each industry at the two-digit level of CNAE, the Spanish equivalent of SIC
codes. There are 20 industries, so it uses 19 indicators to avoid multicollinearity. Eighth, it controls for
year to capture economy-wide influences on the behavior of the firms. It has 4 years of data after the oneyear lag, so it uses 3 indicators to avoid multicollinearity. Finally, it controls for additional unobserved
characteristics of the firms using a random effects model. It cannot employ a fixed effects model because
under a fixed effect model time-invariant variables and firms that do not have changes in their innovation
success (either always have process innovations or never have process innovations in the period) drop out
of the analysis.
Method of Analysis
The article tests the hypotheses using random effects probit since the dependent variable takes two values,
1 if the firm achieved some process innovation in the year and 0 otherwise. This method of analysis also
takes into account the panel structure of the data. It lags the independent variables one year because
innovations are more likely to be affected by previous events than by current ones. The model with the
four types of R&D collaboration was run together to assess the effect of one type of collaboration on
innovation in the presence of other types of R&D collaborations. It uses the following specification:
Process innovation t = 0 + 1 * R&D collaboration with suppliers t-1 + 2 * R&D collaboration with
universities t-1 + 3 * R&D collaboration with competitors t-1 + 4 * R&D collaboration with customers t-1
+ 5 * R&D intensity t-1 (control) + 6 * firm sizet-1 (control) + 7 * affiliate of a domestic company t-1
(control) + 8 * affiliate of a foreign company t-1 (control) + 9 * slack financial resources t-1 (control) +
10 * Sales to end users and distributors t-1 + 11 * Sales to companies and the state t-1 (control) + 12 *
Standardized products t-1 (control)+ 13 * Large batch production process t-1 (control)+ 14 * Continuous
production process t-1 (control) + i * industry i, t-1 (control) + j * year j, t-1 (control) + 
The coefficients of interest are 1, 2, 3, and 4. Positive (negative) and statistically significant
coefficients would provide support to the idea that the firm that undertook the particular R&D
collaboration is more (less) likely to achieve process innovation later. The size of the coefficients
provides a measure of the relative importance of each of the R&D collaborations on the likelihood of
achieving process innovation in the presence of other R&D collaborations, and serves as the basis for
discussing the support for the hypotheses. Hypothesis 1 is supported if 1 is positive and statistically
Types of R&D collaborations and process innovation
14
significantly larger than 2, 3, and 4. Hypothesis 2 is supported when 2 is positive and statistically
significantly smaller than 1 but larger than 3 and 4. Hypothesis 3 is supported if 3 is positive and
statistically significantly smaller than 1 and 2 but larger than 4. Finally, Hypothesis 4 is supported
when 4 is positive and statistically significantly smaller than 1, 2, and 3.
Before discussing the results, the article discusses a few limitations that arise from the
characteristics of the database used. Despite these limitations, the arguments and findings are novel and
important and open the stage for more detailed studies of the types of R&D collaborations and their
differential influence on process innovation. First, the measure of process innovation does not assess the
level of innovativeness of the process, only the actions taken. Therefore, it cannot analyze whether
different types of R&D collaborations result in different degrees of innovativeness. Second, the measures
of R&D collaboration do not provide details on the nature and characteristics of the collaboration. Hence,
it cannot study the relationship between the mechanisms and instruments used to facilitate the
collaboration and the innovation of process. Third, the database only includes manufacturing companies.
As a result, it cannot analyze how R&D collaborations in service firms influence process innovation.
Finally, it measures knowledge chain position using the relative impact of R&D collaborations with
suppliers, universities, customers, and competitors and assumes knowledge transfer upstream or
downstream rather than measuring the actual knowledge flow in these directions. Similarly, for contextual
knowledge distance, it assumes similarity and dissimilarity of knowledge of collaborators based on the
types of knowledge that they are likely to use rather than the similarity/difference in the types of
knowledge they actually use.
Results
Table 3 presents the summary statistics and correlation matrix. Many of these correlations are to be
expected, such as the correlation between size and R&D collaborations because larger companies are
more likely to be attractive partners. The article used alternative measures and excluded variables that
show high correlation from the analysis to make sure that the correlations do not create problems of
multicollinearity (Greene, 2005). The coefficients do not change significantly in sign or significance,
indicating limited multicollinearity problems.
*** Insert Table 3 here ***
Frequency of R&D Collaboration and of Process Innovation
It is interesting to analyze the frequencies of R&D collaborations and of process innovation before
discussing the test of hypotheses. These are summarized in Table 4. The article finds that few firms
undertake R&D collaborations. About two thirds of firms undertake none. This lack of R&D
collaborations, although surprising, appears to be in line with other studies (Belderbos et al., 2004a;
European Commission, 2012). For example, Belderbos et al. (2004a) report that of the 2056 firms in their
sample of Dutch establishments, 69.36% do not have R&D collaborations of any kind.
In the sample used for this study, R&D collaborations with universities or with suppliers are the
most common types, with about a quarter of firms reporting such collaborations. R&D collaborations
with customers are less common, with slightly less than one fifth of firms reporting this type of
collaboration. R&D collaborations with competitors are the least common of the actions, with only about
three percent of firms reporting such collaborations. This low frequency of R&D collaborations with
competitors, although surprising, is in line with recent studies (e.g., European Commission, 2012;
Types of R&D collaborations and process innovation
15
Lhuillery and Pfister, 2009). The European Commission (2012) finds that less than 10% of all R&D
collaborations are with competitors.
Surprisingly, few firms innovate their processes. Only about 14% of the firms introduce process
innovation. Although many firms may undertake simpler process improvements, these are not captured in
the stricter measure of process innovation.
*** Insert Table 4 here ***
R&D Collaboration and Process Innovation
The article now turns to the analysis of the relative influence of the types of R&D collaboration on
process innovation. Table 5 provides the results of these analyses. Model 4a provides the results of the
analysis with only the controls. Model 4b presents the results of the analysis with the controls and an
indicator of the existence of R&D collaborations. The coefficient is positive and statistically significant,
indicating that firms that undertake R&D collaborations of any kind are more likely to later achieve
process innovation than firms that do not. Model 4c provides the results of the analysis with the controls
and the four types of R&D collaboration. It finds that the coefficients of R&D collaboration with
suppliers and with universities are positive and statistically significant. The coefficient of R&D
collaboration with competitors is negative and statistically significant, while the coefficient of R&D
collaboration with customers is positive but not statistically significant. Before discussing how these
results relate to the hypotheses, the article also tested to see whether these coefficients are significantly
different from each other. Among the coefficients that are statistically significant, it finds no significant
difference between the coefficients of R&D collaboration with suppliers and with universities, but a
significant difference between these and the coefficient of R&D collaboration with competitors.
The results of these two tests provide some support for hypotheses 1 and 2, but not for hypotheses
3 and 4. First, the results partially support Hypothesis 1, that R&D collaboration with suppliers has a
higher positive impact on process innovation than R&D collaboration with universities, competitors, or
customers. R&D collaboration with suppliers does have a significantly higher positive impact on process
innovation than R&D collaboration with competitors or customers. However, R&D collaboration with
suppliers does not necessarily have a higher positive impact on process innovation than R&D
collaboration with universities. As such, Hypothesis 2, which states that R&D collaboration with
universities will have a smaller positive impact on process innovation than R&D collaboration with
suppliers but a higher positive impact than R&D collaboration with competitors or with customers, is also
partially supported. R&D collaboration with universities appears to have a significantly higher positive
impact on process innovation than R&D collaboration with competitors or customers. However, R&D
collaboration with universities does not necessarily have a lower positive impact on process innovation
than R&D collaboration with suppliers. Third, Hypothesis 3 is not supported, because the coefficient of
R&D collaboration with competitors is negative and significant and this coefficient is significantly
different from other types of collaboration. Finally, Hypothesis 4 is not supported, because the coefficient
for R&D collaboration with customers is positive but not statistically significant.
*** Insert Table 5 here ***
Robustness Check
Types of R&D collaborations and process innovation
16
The article conducted additional analyses to ensure the robustness of the results to alternative
explanations. It first ran analyses to assess the influence of absorptive capacity (Cohen and Levinthal,
1989), whereby firms that engage in internal R&D are expected to be able to better absorb external
knowledge. It interacted the indicators of the different types of R&D collaborations with an indicator that
the firm does internal R&D. The results of this analysis are consistent with the ones discussed above, with
the coefficients of R&D collaboration with suppliers and with universities being positive and statistically
significant, while the coefficient of R&D collaboration with competitors remains negative but is no longer
significant. Surprisingly, the coefficients of the interactions between each type of R&D collaboration and
internal R&D are not statistically significant. One reason for the lack of statistical significance of the
interaction coefficients could be the role that learning-by-doing plays in the production process. Firms
benefit from learning-by-doing as employees learn over time to become more efficient in their tasks; as a
result the costs of producing additional units of a product tend to diminish as production numbers
accumulate (Wright, 1936; for a recent review, see Thompson, 2012). This learning-by-doing may also
enable employees to build the necessary absorptive capacity to benefit from R&D collaborations for
process innovation, as employees have learned and understood how to manufacture the product and thus
are aware of the potential limitations that the external R&D collaboration may solve.
The article also ran analyses to evaluate the influence of product innovations on process
innovation, because product and process innovations may interact with each other (Meyer and Dalal,
2002; Pisano and Shih, 2012). New products may require the firm to update or change the process by
which the products are made. It included a control for the number of new products that the company
generates; new products are measured in the survey as products that are completely new or that have
modifications so important as to make them different from the ones produced before. The results of these
analyses support similar conclusions to the ones discussed because the coefficients of R&D collaborations
with suppliers and universities are positive and statistically significant, while the coefficient of R&D
collaborations with competitors is negative and statistically significant at the 0.055 level. The coefficient
of number of new products is positive and statistically significant at the 0.10 level; it appears that there
could be some interaction between product and process innovations, but the data used in this study is not
detailed enough to discuss causality.
Discussion
Although the results do not support all the hypotheses, they nevertheless provide new insights into the
impact of R&D collaborations on process innovation; these insights are particularly important since prior
studies have not investigated their relative influences on process innovation. First, the results reveal that
not all R&D collaborations have the same influence on process innovation. R&D collaborations with
suppliers and with universities appear to have a positive impact on process innovation relative to other
R&D collaborations. In contrast, R&D collaborations with customers do not appear to influence process
innovation, while R&D collaborations with competitors can potentially have a negative effect relative to
other types of collaborations.
Second, these results are an important finding because they indicate that upstream R&D
collaborations with suppliers and universities, rather than downstream R&D collaborations with
customers and competitors, appear to be driving process innovation. This supports the idea that
knowledge chain position, rather than contextual knowledge distance, matters most for process innovation.
Third, a final interesting and important finding is the negative influence of R&D collaboration
with competitors on process innovation. Although this finding is rather unexpected, it is in line with other
studies that find a negative effect of R&D collaboration with competitors on product innovation (e.g.,
Types of R&D collaborations and process innovation
17
Lhuillery and Pfister, 2009; Un et al., 2010). Lhuillery and Pfister (2009), for example, find that R&D
collaboration with competitors tends to result in innovation project failures, because each partner tries to
get knowledge from the other while taking actions to protect its own knowledge from spillover to the
other. As a result, more time and effort is spent on protecting knowledge than on collaborating and
innovating. Un et al. (2010) also find that R&D collaboration with competitors has a negative effect on
the number of new products introduced by firms relative to other collaborations, at least in the short term.
In conclusion, although general collaborations with competitors may be useful in many areas such as
setting standards or entering new markets (Brandenburger and Nalebuff, 1996), specific R&D
collaborations with competitors do not seem to be as useful for innovation as other types of R&D
collaborations.
Conclusions
The article analyzed the impact of R&D collaborations with universities, suppliers, customers, and
competitors on process innovation. R&D collaborations are increasingly important sources of knowledge
that can help firms innovate. However, previous studies have not analyzed the simultaneous influence of
the four types of collaboration on process innovation, and insights from studies on product innovation
may not be applicable because of the different characteristics of product and process innovation.
The article extended the knowledge-based view and introduced the concepts of position in the
knowledge chain and contextual knowledge distance to explain the differential impact of R&D
collaborations on process innovation. The results of the empirical analysis show that knowledge chain
position, rather than contextual knowledge distance, appears to be the driver of process innovation
through R&D collaboration. Upstream R&D collaborations with universities and suppliers have a positive
influence on process innovation, while closer contextual knowledge distance does not necessarily
facilitate process innovation.
These arguments and findings contribute to the literature on process innovation by being among
the first articles to focus on analyzing how alternative types of R&D collaborations affect process
innovation. The study argues that, even though process innovation is internal and tacit (Hatch and
Mowery, 1998; Pisano and Shih, 2012; Stadler, 2011), it can still benefit from external R&D
collaborations, and the study explains theoretically why this is the case. The study of the relative impact
of multiple types of R&D collaborations is important, because it provides better information on the merit
of undertaking them; although each of the types of R&D collaboration can be beneficial on its own, one
type can potentially be more beneficial than another for a particular type of innovation. As such, the paper
complements previous studies that have discussed how different types of R&D collaborations influence
product innovation (Dittrich and Duysters, 2007; Un et al., 2010; Wu, 2012).
The article provides a theoretical framework that extends the KBV by classifying R&D
collaborations based on the concepts of position in the knowledge chain and contextual knowledge
distance. It finds that the position in the knowledge chain, particularly upstream R&D collaborations,
rather than the contextual knowledge distance, appears to be driving the positive effect of R&D
collaborations on process innovation. This finding highlights the differences between product and process
innovation even further. In process innovation, the relevant knowledge is driven by upstream
collaborations with suppliers and universities. This is in contrast to the literature on product innovation,
which highlights the importance of downstream collaborations with customers and competitors
(Brandenburger and Nalebuff, 1996; Baldwin et al., 2006). Upstream collaborations are more appropriate
for process innovation because the focus of process innovation is primarily on improving manufacturing
efficiency and product quality based on how the inputs and components are managed in the production
Types of R&D collaborations and process innovation
18
process. The product itself tends to be already determined, and thus, the firm needs to collaborate with
partners whose focus is on its production. Collaborating with suppliers helps the firm achieve efficiency
and product quality targets, because suppliers are typically selected based on their contribution to these
targets (Dyer and Nobeoka, 2000). Additionally, collaborating with universities can help the firm find
new methods for managing the flow of materials within the firm or new concepts for better organizing
material handling (Ki-Chan et al., 2006). In contrast, downstream R&D collaborations appear to be less
beneficial for process innovation since the focus of these partners tends to be on the improvement of the
product to satisfy the needs of customers better than competitors. Collaborations with customers may not
help improve processes because customers may not be aware of or interested in understanding how the
product is produced (Lukas et al., 2013). Collaborations with competitors may be hampered by the lack of
incentive for competitors to share complex production knowledge that may be their source of advantage;
products can be more easily protected by patents than can processes, and thus competitors may be less
wary of collaborating to innovate products than to innovate processes (Arundel and Kabla, 1998).
The article also has important managerial implications. Collaborating with other firms and
organizations helps firms achieve process innovation because these R&D collaborations provide access to
different knowledge bases. However, not all R&D collaborations have the same influence on process
innovation. Although one cannot be certain that a particular R&D collaboration will be successful in
helping the firm innovate, managers may want to select those R&D collaborations that are more likely to
facilitate process innovation. The article suggests that if a manager wants the firm to introduce process
innovation, R&D collaborations with upstream sources of knowledge are likely to be the most promising
venues. Collaborating with partners close in contextual knowledge distance appears to be less suitable for
process innovation. Such implications are widely applicable to firms pursuing process innovation. To take
the flat panel display industry as an example, the rapid pace of collaborative innovation processes have
obliged the firms to shift away from the in-house approach and toward the open relational approach via
continuous human interaction (Murtha et al, 2001). Here, collaborating with upstream parties such as
suppliers and universities is of utmost importance for enhancing process innovation, as close interaction
with these partners allows the firms to source knowledge specifically useful for the purpose of enhancing
their process innovation (Murtha et al, 2001).
Types of R&D collaborations and process innovation
19
Acknowledgements
The article has benefited from the valuable suggestions of Editor Anthony Di Benedetto, anonymous
reviewers, Alvaro Cuervo-Cazurra, and the audiences of the Strategic Management Society Lake Geneva
Conference and strategy seminar at National University of Singapore. The first author thanks the
D’Amore-McKim School of Business and the Institute of Global Innovation Management at Northeastern
University for funding and the SEPI Foundation for access to the data. The second author thanks Institute
NEDO for providing funding for traveling and Yunjin Rhee for her assistance. All errors remain ours.
Types of R&D collaborations and process innovation
20
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25
Table 1. Relative differences between product and process innovation
Dimensions of analysis
Objective of innovation
Competitive impact
Valuation of innovation or value
Degree of novelty valued or rareness
Codifiabilty of knowledge or imitability
Location of knowledge or substitutability
Product innovation
Novelty
Price
External; market feedback
Radical; exploration
Clear; concrete; explicit; higher
Technological; separable; independent
Process innovation
Efficiency
Cost
Internal; managerial evaluation
Incremental; exploitation
Unclear; obscure; tacit; lower
Organizational; systemic; interdependent
26
Table 2. Classification of four types of R&D collaborations by position in the knowledge chain and
contextual knowledge distance for process innovation
Position in knowledge chain relative to the focal firm
Contextual knowledge
distance relative to the focal
firm
Upstream
Downstream
Close
1. R&D collaboration with
suppliers
2. R&D collaboration with
competitors
Far
2. R&D collaboration with
universities
3. R&D collaboration with
customers
27
Table 3. Descriptive statistics and correlation matrix
Mean
0.15
Standard
deviation
2. R&D collaborations with universities,
suppliers, customers or competitors
3. R&D collaborations with universities
0.36
0.48
0.25
0.43
4. R&D collaborations with suppliers
0.24
0.43
5. R&D collaborations with customers
0.19
0.39
6. R&D collaborations with competitors
0.03
0.17
7. Internal R&D intensity
5.24
15.96
8. Firm size
4.29
1.48
9. Affiliate of domestic firm
0.19
0.39
10. Affiliate of foreign firm
0.21
0.41
11. Slack financial resources
168.3
1162.7
12. Sales to end users and distributors
45.68
43.31
13. Sales to companies and the state
50.94
43.51
14. Standardized products
0.63
0.48
15. Large batch production process
0.42
0.49
16. Continuous production process
0.10
0.30
1. Process innovation
0.35
Significance levels: + p< 0.10, * p< 0.05, ** p< 0.01, *** p<0.001
1
1.00
2
0.26
***
0.24
***
0.24
***
0.21
***
0.04
*
0.09
***
0.29
***
0.08
***
0.14
***
0.04
*
-0.06
**
0.08
***
0.01
1.00
-0.09
***
0.03
3
4
5
6
0.76
***
0.75
***
0.64
***
0.24
***
0.36
***
0.54
***
0.19
***
0.28
***
-0.01
0.45
***
0.44
***
0.22
***
0.30
***
0.50
***
0.17
***
0.23
***
-0.01
1.00
0.64
***
0.24
***
0.33
***
0.47
***
0.13
***
0.26
***
0.01
1.00
0.30
***
0.32
***
0.39
***
0.08
***
0.28
***
0.01
0.29
***
0.20
***
0.10
***
0.05
**
-0.01
-0.07
***
0.09
***
0.02
-0.07
***
0.08
***
0.00
-0.19
***
0.11
***
-0.18
***
0.12
***
-0.07
***
0.09
***
-0.01
***
-0.15
***
0.06
***
-0.19
***
0.21
***
-0.10
*
-0.15
***
0.10
***
-0.06
***
0.07
***
-0.04
*
-0.06
**
0.04
*
7
8
9
10
11
12
13
14
15
1.00
1.00
1.00
0.22
***
0.10
***
0.08
***
-0.02
-0.06
**
0.05
**
-0.04
-0.04
*
-0.03
+
1.00
0.32
***
0.46
***
-0.01
-0.09
***
0.11
***
0.04
*
-0.33
***
0.19
***
1.00
-0.25
***
-0.02
1.00
0.02
1.00
-0.02
-0.02
1.00
0.03
+
0.03
-0.14
***
0.16
***
0.01
0.02
-0.13
***
0.12
***
-0.22
***
0.12
***
-0.02
-0.91
***
0.46
***
0.06
***
-0.09
***
-0.01
-0.01
1.00
-0.39
***
-0.01
0.10
***
1.00
-0.09
***
0.15
***
1.00
-0.29
***
28
Table 4. Frequency of R&D collaborations and of process innovation, in percentage
R&D collaboration with universities
R&D collaboration with suppliers
R&D collaboration with customers
R&D collaboration with competitors
R&D collaboration with universities, suppliers, customers, or competitors
Process innovation
Year 1
25.99
23.57
17.58
2.80
36.69
15.92
Year 2
23.95
24.71
18.98
3.31
36.43
16.05
Year 3
24.46
24.97
19.62
2.93
37.20
13.12
Year 4
24.71
23.31
18.22
3.18
34.14
13.37
29
Table 5. Results of the panel probit analysis of R&D collaborations on process innovation
R&D collaborations with universities, suppliers,
customers or competitors
R&D collaborations with suppliers (H1)
R&D collaborations with universities (H2)
R&D collaborations with competitors (H3)
R&D collaborations with customers (H4)
R&D intensity
Size
Affiliate of domestic firm
Affiliate of foreign firm
Slack financial resources
Sales to end users and distributors
Sales to companies and the state
Standardized products
Large batch production process
Continuous production process
Industry indicators
Year indicators
Intercept
Number of firms
Chi 2
Log likelihood
Dependent variable: Introduced innovation in production process
in the year or not
Model 4a
Model 4b
Model 4c
--0.39 ***
--(0.11)
----0.27 *
(0.13)
----0.25 *
(0.12)
-----0.50 *
(0.25)
----0.02
(0.15)
0.004
0.001
0.002
(0.003)
(0.003)
(0.003)
0.47 ***
0.40 ***
0.40 ***
(0.05)
(0.05)
(0.06)
-0.04
-0.05
-0.03
(0.18)
(0.17)
(0.17)
-0.06
-0.07
-0.08
(0.18)
(0.17)
(0.17)
0.00006 *
0.00006 *
0.0006 *
(0.00003)
(0.0003)
(0.0003)
0.005
0.005
0.005
(0.003)
(0.003)
(0.002)
0.007 *
0.007 *
0.007 *
(0.003)
(0.003)
(0.003)
0.15
0.14
0.15
(0.13)
(0.12)
(0.13)
0.06
0.06
0.08
(0.12)
(0.12)
(0.12)
0.05
0.04
0.05
(0.17)
(0.17)
(0.17)
Included
Included
Included
Included
Included
Included
-4.29 ***
-4.04 ***
-4.05 ***
(0.58)
(0.57)
(0.57)
781
781
781
143.76 ***
159.35 ***
160.66 ***
-980.36
-974.99
-972.86
Note: Standard errors appear in parenthesis.
Significance levels: + p< 0.10, * p< 0.05, ** p< 0.01, *** p<0.001