Sangyun Han,
Management of Technology Program, Yonsei University
50 Yonsei-Ro, Seodaemun-Gu, Seoul, 120-749, Korea syhahn@yonsei.ac.kr
Sung Joo Bae
School of Business, Yonsei University
50 Yonsei-Ro, Seodaemun-Gu, Seoul, 120-749, Korea sjbae@yonsei.ac.kr
Corresponding Author:
Sung Joo Bae ,
Assistant Professor of Operations and Technology Management
School of Business, Yonsei University
50 Yonsei-Ro, Seodaemun-Gu, Seoul, 120-749, Korea sjbae@yonsei.ac.kr
Tel: +82-2-2123-6578, Fax: +82-2-392-6706
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ABSTRACT
Using the absorptive capacity perspective, this study investigates the extent to which using external knowledge through R&D outsourcing affects a firm's performance, and how this effect is moderated by a firm’s absorptive capacity via internal R&D effort and organizational composition. More specifically, R&D intensity, a traditional measure of absorptive capacity, and five different variables of organizational composition are used to examine their moderating effect between R&D outsourcing effort and resulting firm performance.
We use a fixed effect model to analyze panel data from 19,570 Korean manufacturing firms during the period from 2002 to 2007. Findings show that the intensity of R&D outsourcing in high technology industries has a direct effect on a firm’s performance. We also observe the differences between high and medium/low technology industries to analyze how having highly skilled researchers can moderate the effect of R&D outsourcing on a firm’s performance. In high technology industries, R&D outsourcing was strongly associated with a firm’s performance when the ratio of researchers with Ph.D. degrees was higher. However, in low technology industries, our study indicated that while the ratio of researchers in R&D has a direct effect on firm performance, it does not actually moderate the effect of R&D outsourcing on firm performance. We provide an interpretation of these empirical findings, emphasizing the importance of a firm’s absorptive capacity via organizational composition in maximizing R&D outsourcing results.
Key Words: R&D outsourcing, knowledge transfer, organizational composition, absorptive capacity, internalization
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R&D outsourcing is acknowledged by scholars and practitioners as a key strategy to increase a firm’s competitiveness by internalizing external expertise. Without absorbing external knowledge, the scope of a firm’s knowledge, its proprietary technologies and chances of developing the capability to access external knowledge can be quite limited.
Therefore, the generation and transfer of knowledge through an external exchange are
essential for a firm’s sustainable competitive advantage and survival (Foss and Pedersen,
2002; Grant, 1996; Kyläheiko et al., 2011; Mudambi, 2002). Since the open innovation was
introduced as a key strategy for firm growth by Chesbrough (2003), external knowledge is regarded as an essential element to optimize internal R&D. Many scholars also noted that
external knowledge could be distributed over various players and accessible channels (Acha
and Cusmano, 2005; Coombs et al., 2003; Howells et al., 2003; Tether, 2002). In this vein,
R&D outsourcing is regarded as one of the effective strategies for open innovation for firms to make use of external knowledge, among many other options such as technology acquisition, alliance, R&D cooperation. In the Organization for Economic Co-operation and
Development (OECD) countries, business expenditure on external R&D has gradually increased since the 1980s in most developed countries. For instance, in the UK and Germany, business expenditure on external R&D doubled in proportion to total expenditure on R&D
over a 10-year period (Bönte, 2003; Howells, 1999). In fact, the CAGR (Continuous Average
Growth Rate) of internal expenditure for R&D was 16.0% in 2002-2007, while the CAGR for funds to outsource R&D in 2002-2007 was approximately 8% higher than that of internal
R&D.
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Insert Figure 1 about here
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R&D outsourcing also contributes to a firm’s performance. Huang et al. (2009)
investigated the impact of outsourcing on an organization in terms of development costs and financial profits during the new product development (NPD) process. Through an analysis of
121 Taiwanese IT firms, their study discovered that R&D outsourcing is effective in lowering development costs and in increasing financial profits.
As R&D outsourcing becomes a more common practice, there have been advancements in both theoretical and empirical literature on the factors that determine the acquisition of external knowledge and its effects on a firm’s innovative performance. Two of the most important issues identified so far are 1) the determinants of R&D outsourcing and 2) whether external knowledge acquired through R&D outsourcing leads to better performance. Several
studies have provided some insights to these two issues. Love and Roper (2001) looked at
innovative UK manufacturing industry to discover that the scale of plant and R&D input, as well as appropriability conditions are the key determinants of R&D boundary decisions. On
the effects of R&D outsourcing, Howells (1999), Caudy (2001), and Watanabe and Hur (2004)
pointed out that R&D outsourcing can help maximize innovation and overall firm performance when properly planned and executed.
Although the determinants and effects of R&D outsourcing are discussed frequently in literature, the organizational factors influencing the effects have received relatively little attention from scholars in this field. Rather than focusing on why R&D outsourcing happens and its results, we will look at factors that influence the effect of R&D outsourcing on firm performance In attempting to do so, we will discuss the absorb capacity perspective of R&D outsourcing that explains how an internalizing mechanism can play a role in moderating the
R&D outsourcing results. More specifically, we argue the absorptive capacity which is noted as “the ability of a firm to recognize the value of new information assimilate it, and apply it
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to commercial ends (Cohen and Levinthal, 1990)” with human capital perspective adding
traditional absorptive capacity studies. Because the real actors in transferring and internalization process through R&D outsourcing are internal employee, we investigate the moderating effect of absorptive capacity with organizational composition perspective on performance.
On the empirical side, this study constructs a more accurate framework to examine the impact of R&D outsourcing on firm performance with panel data that can control firm effects and time effects. The results will enrich our understanding of the relationship between R&D outsourcing and firm performance, and identify the moderating effect of absorptive capacity via organizational composition perspective.
The remainder of the paper is organized as follows. The next section will review literature on R&D outsourcing strategies as well as technology transfer and internalization through
R&D outsourcing. Then we propose our conceptual model and hypotheses. In the following research methodology section, we address how we constructed the variable, the data set, and the research method and models we built to test. The final section consists of the empirical results and a wrap up of our findings and limitations.
2.1. Why Do Firms Engage in R&D Outsourcing?
Linking to the recent open innovation paradigm (Chesbrough, 2003), we can categorize
different strategies employed to acquire and internalize technological knowledge. Firms can either choose to internalize R&D to develop their own technology or outsource R&D to acquire external knowledge. Firms can also opt to form cooperative organization with an external entity, such as through a R&D consortium, R&D joint venture, research contract, or
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licensing. These strategies focus on increasing R&D efficiency by using external knowledge.
Many firms have a keen interest in seeking and acquiring external knowledge because establishing a competitive advantage through technological innovation is becoming more and
more important (Bierly III et al., 2009). For technology advancement in R&D, using network
and cooperation are crucial factors (Von Hippel, 1988). From this perspective, R&D
outsourcing is one of the key measures of open innovation because external knowledge is acquired through a network of firms.
Why firms choose to follow a certain strategy has long been an important question for scholars engaged in this field. Since vast literature on this subject provides the basis for our argument in this paper, we will summarize and bring the discussion up to date in this section.
As we discuss below, there is ample theoretical literature that focuses on the choice between R&D outsourcing and internal R&D, i.e., the classical MAKE or BUY decisionmaking. Literature on this subject asks the question of why firms engage in R&D outsourcing.
Recent research on the motivation can be classified into several categories. The first is the
Transaction Cost theory (Brusoni et al., 2001; Howells, 1999; Narula, 2001; Yasuda, 2005).
Originally, transaction cost was addressed by Ronald H. Coase in 1973; the “Coase Theorem” highlighted that, “there must be costs in using the market that can be eliminated by using the
firm.” (Besanko et al., 2009) It means that cost involves time and expense of negotiation, and
writing and enforcing contracts between buyers and suppliers. Therefore, firms are established to eliminate costs and transact with others efficiently. From a transaction cost point of view, sourcing external knowledge and internal R&D are considered as substitutes.
In considering costs and risks, firms opt for either a make or a buy strategy (Beneito, 2003;
Veugelers and Cassiman, 1999). Thus, firms can choose either internal or external innovation
strategies, and consequently, they also have to decide which technologies to develop
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internally or externally (Vega-Jurado et al., 2009).
The second research perspective that answers the question of why firms engage in R&D
outsourcing is the Core Competence perspective (Prahalad and Hamel, 2006). It is based on
the idea that firms with high levels of R&D competence are likely to enhance their
technological competencies needed for their competitive advantage (Brook and Plugge, 2010).
The core competence perspective essentially means that firms conduct internal R&D or R&D outsourcing to increase their technological competence and to supplement each other.
The third perspective is a Resource-Based View. This theory explains that firms can
increase performance by using their resources efficiently (Leiblein and Miller, 2003). Firms
are viewed as bundles of resources, and according to this perspective, firms outsource R&D when they need additional resources for innovation that they do not possess internally.
While these perspectives provided a theoretical standpoint for understanding why firms decide to outsource R&D, many subsequent empirical studies focused on various factors that affect the decision-making process to acquire external knowledge through R&D outsourcing.
Tidd and Trewhella (1997) noted that R&D outsourcing is a better and quicker strategy than
building the required skills internally when internal capabilities are lacking. Veugelers and
Cassiman (1999) found that large firms are more likely to combine both internal and external knowledge in their innovation strategy than small firms. Yoshikawa (2003) explored key
factors such as time pressure and the importance of technology that affect the choice to
acquire external technology. Two studies by Howells and his colleagues (Howells et al., 2008;
Howells et al., 2004) noted that the lack of in-house R&D and technical expertise are
determinants of R&D outsourcing and that by doing so, firms reduce development time and time to market.
Meanwhile, Miyamoto (2007) identified several determinants of R&D outsourcing. He
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discovered that firms belonging to a wider corporate group are more active in executing R&D outsourcing activities. Similarly, diversification strategies such as the expansion of product and sales markets have a positive effect on firms’ R&D outsourcing behavior.
These different perspectives presented above describe why firms engage in R&D outsourcing. However, the above literature does not advance the discussion towards how we can get better results from outsourcing R&D and what the role of absorptive capacity in R&D outsourcing with human capital factors. As such, we attempt to incorporate the absorptive capacity with organizational composition perspective of the “internalization of R&D outsourcing” in this paper. By internalization, we mean the overall process of R&D outsourcing that the firms first define the problem and initially contract out the problem, and then reinterpret the results generated by the outsourced party. The organizational composition of a R&D unit is one of the mechanisms strongly related to this internalization process. This means that firms can change their own absorptive capacity with organizational composition perspective to enhance the results of outsourcing R&D. In the following section, we will describe the conceptual framework of the internalization of R&D outsourcing and absorptive capacity in R&D outsourcing . This framework will be used as the basis for our argument on why organizational composition plays an important role in internalizing the R&D outsourcing outcomes.
2.2 Technology Transfer and Internalization through R&D outsourcing
Previous studies argued that organizations learn not only from their own direct experience
but also from the experience of other organizations (Huber, 1991; Levitt and March, 1988).
External knowledge comes from various sources. The first source is customers. Generally,
they are the most common source of external knowledge and innovation (von Hippel, 2007).
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The second is competitors. Firms usually monitor and analyze their competitors’ new products and innovations. The result of this benchmarking may lead to internal innovation
(Bierly III and Chakrabarti, 1996; Ghoshal and Westney, 1991). The third is external
organizations which can directly support the focal firm and supply sophisticated knowledge.
These external organizations include other firms in the same or other industries, universities, and public research institutes. The external expertise of these organizations can enhance the
focal firm’s skills and competitiveness (Hamel and Prahalad, 1994; Mowery et al., 1996). For
our study, we only focus on this third source of external knowledge, specifically, R&D outsourcing, which can be described as formally establishing a partnership with others that have an expertise in a specific R&D area.
Through R&D outsourcing, technologies are transferred from an external expert to the focal firm. Here, we do not distinguish technologies from knowledge because firms can absorb intangible resources in the form of knowledge from other external organizations.
Argote (1999) also noted that the channel of knowledge has different sources including
people, technology, and structure of the recipient organization. In case of R&D outsourcing, demanded knowledge or technologies are transferred from other companies specialized in
R&D, universities, or public R&D institutions.
However, R&D outsourcing is much more than the mere transfer of knowledge or technology. Figure 2 shows how we conceptualize the R&D outsourcing
In the process of R&D outsourcing, complexity and uncertainty matter when firms define
the problem and contract with a supplier (Argote, 1999). Zander and Kogut (1995) found that
knowledge which was codified and could be readily taught to organizational members transferred more easily than knowledge that was not codified and not readily taught.
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Insert Figure 2 about here
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In Figure 2, from the onset of R&D outsourcing, firms need to clearly define the problem they will outsource to the external expert. This initial stage may be the most important step for R&D outsourcing, because otherwise, outsourcers may not be able to properly identify the problem and the whole outcome can be irrelevant to the firm’s needs.
Many studies note that the more the technology can be codified, such as in blueprint or
rules, the easier it is to be contracted out (Narula, 2001; Tidd and Trewhella, 1997; Yasuda,
2005). Related to this, Kessler et al. (2000) also suggested that R&D outsourcing can create
hidden cost. One issue is the coordination cost which occurs when firms attempt to integrate external knowledge into their own knowledge base. The cost of R&D outsourcing can be
high if the external technology is difficult to interpret or understand (Huang et al., 2009). For
this reason, a certain technology which cannot be easily codified or incorporates a high degree of tacit knowledge is more feasible to be developed by internal R&D, rather than
being outsourced (Narula, 2001; Tidd and Trewhella, 1997).
When the problem is defined, the measurement of the project’s success should be carefully decided as well. This will be done accordingly in order to evaluate the outcome that will be brought back to the original firm. As described here, defining the problem is a very complex and difficult cognitive process that will directly relate to the success of the project.After the outsourcing supplier solves the problem and delivers the outcome to the recipient firm, R&D personnel within the firm will then identify external knowledge. They will interpret it using existing internal knowledge, or regenerate some new knowledge combining existing and external knowledge. We define this process as solution interpretation. It is a very important step for completing the R&D outsourcing project, but it can be difficult to interpret external knowledge, if firms don’t have enough internal organizational capabilities to do so. During
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the solution interpretation process, complexity and uncertainty are crucial factors that affect the success of internalization. These issues can arise from the uncertainty of R&D itself, and also from a firm’s incapability to understand and interpret external knowledge to generate new internal knowledge for innovation. The ability to interpret external knowledge mostly
depends on an organization’s human resources and organizational structure. Allen (1977)
suggested that employees can be the most effective carriers of information because they can restructure and reinterpret information. Therefore, the organizational composition of firms will be related to reducing the complexity and uncertainty in R&D outsourcing through clear problem definition and effective solution interpretation. In this paper, we regard collectively these abilities – defining, controling the complexity and uncertainity, and interpreting with prior knowledge- as absorptive capacity. Because employees of R&D unit engage the all of the R&D outsourcing process and their various ability can affect to the outcomes of R&D
outsourcing. This can be coincide with the definition of Cohen and Levinthal (1990). They
noted that the absorptive capacity is “the ability of a firm to recognize the value of new information assimilate it, and apply it to commercial ends”. and suggest that it is largely a function of the firms’ prior related internal knowledge. So with this perspective, we try to investigate the role of absorptive capacity using organizational composition, when firms are doing R&D outsourcing.
2.3 Absorptive Capacity and Organizational Composition of Firms
Since the original definition of ,various conceptualization of absorptive capacity have
emerged (Lane et al., 2006; Lane and Lubatkin, 1998; Lane et al., 2001; Todorova and
Durisin, 2007; Zahra and George, 2002). The initial concept of absorptive capacity is focused
on ability to valuw knowledge through past firms’ experience, assimilate, and apply. But
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Zahra and George (2002) reviewed and conducted reconceptualization of absorptive capacity.
First, they define the absorptive capacity as a set of organizational routines and processes which can produce firms’ dynamic organizational capability. There were four dimensions, by which are acquisition & assimilation and transformation & exploitation. These four dimensions can promote the ability to adapt to changing market condition for competitive
advantage and organizational change(Spithoven et al., 2011; Vega ‐ Jurado et al., 2008). And
they also established two categories such as potential and realized capacities. Potential categories means acquisition & assimilation of knowledge and realized capacities does transformation & exploitation of knowledge. These notion was introduced as social integration mechanism and it is grounded on the idea which all four dimensions are made up of social interactions. So it can be affected by the interplay of social integration mechanism
(Spithoven et al., 2011; Todorova and Durisin, 2007; Zahra and George, 2002). After that
Todorova and Durisin (2007) introduced a refined model. They firstly reintroduced
recognizing the value to scope of absorptive capacity and an alternative understanding of
transformation based on learning theories. The second feature of Todorova and Durisin
(2007)’s model is the theorizing the absorptive capacity on the conteingency factors. In other
words, they propose another contingency factor except social integration mechanism such as power relationship which influences the valuing and exploitation of new knowledge simultaneously. The third is the inclusion of feedback loops in a dynamic model of absorptive capacity.
Drawing on these studies, we can consider who conduct and form the whole process of absorptive capacity in firms. Especially employees of R&D organization can be main actors in R&D outsourcing, because the transferred external knowledge is merged and internalized with prior related internal knowledge which have and embedded by internal employees of
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firms. Although the R&D’s organizational composition can be a crucial factor for using external knowledge, previous research on examining the role of R&D’s organizational composition has been scarce. For example, many previous studies used R&D intensity
relative to sales (Cohen and Levinthal, 1990; Escribano et al., 2009; George et al., 2001;
Kostopoulos et al., 2011; Rothaermel and Alexandre, 2009; Stock et al., 2001; Tsai, 2001; Xia,
2013; Zahra, 1996; Zahra and Hayton, 2008) or number of patents (Austin, 1993; Cohen and
Levinthal, 1990; Lin et al., 2012; Zahra and George, 2002) or whether to be R&D unit
(Becker and Peters, 2000; Nieto and Quevedo, 2005) or direct ask the level of ability to value
& apply knowledge through survey (Bagchi et al., 2013; Chen, 2004; Clausen, 2013; Lund
Vinding, 2006; Spithoven et al., 2011) in firms as a proxy of absorptive capacity. Rothwell
(1992) also highlighted that links to external scientific and technical knowledge sources were
effective only if the organization was well prepared and had a skilled scientific and technical staff.
Actually a R&D organization usually consists of employees with various levels of skills and responsibilities. For example, there is distinction between researchers & research assistants, researchers with a PhD degree & a master’s degree, or full-time & part-time employees. In this paper, we hypothesize that not the absorptive capacity using R&D intensity affects to firms’ financial performance but also organizational composition is related to the organization’s absorptive capacity. Possessing a higher level of skill, knowledge, and responsibilities will give a R&D organization more cognitive capabilities to internalize the external knowledge through R&D outsourcing.
The rest of this paper will discuss our empirical investigation on these issues in depth. In the following section, we will describe the conceptual framework and hypotheses we generated in order to investigate the role of organizational composition in linking R&D
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outsourcing to a firm’s performance.
3.1. Effects of R&D Outsourcing on Firm Performance
One of the empirical investigations conducted in this paper is the effects of R&D outsourcing on firm performance. Prior empirical studies on the effects have provided mixed
results (Bergman, 2011).
The first perspective is that R&D outsourcing increases firm performance. It is based on ordinary firm-level economic theories of technological change, such as the endogenous growth theory which suggests that a firm's productivity growth is an outcome of expanding
technological knowledge (Griliches, 1986). In the firm-level theories of technical change
suggest that innovation for firms is an outcome of increase in its knowledge base by investing
internal R&D mainly (Collis, 1994; Hall, 1992; Lenox and King, 2004; Pakes and
Schankerman, 1984; Schmidt, 2005). In Schumpeterian theory, R&D is also mainly crucial
factor contributing to increase the productivity of firms (Aghion and Howitt, 1992; Mowery and Oxley, 1995). The theoretical model identifies three key sources of performance growth
such as R&D induced innovation internally, technology transfer, and R&D based absorptive
capacity (Mangematin and Nesta, 1999). So the many studies extensively conducted to
investigate the relationship between firms’ knowledge investment and it’s performance
(Carter, 1989; Collis, 1994; Schmidt, 2005). Similarly, Cohen and Levinthal (1989, 1990)
noted that as a firm expand its own internal knowledge and technological capability, it also
enhances its ability to absorb and utilize external knowledge (Schmidt, 2005). The R&D
outsourcing is one of types which firms use various strategies such as internal R&D, alliance,
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R&D cooperation, buying technology, and etc. And it is a factor for growing firms’ knowledge base through acquiring and grafting of external knowledge. So, R&D outsourcing can positively affect on the performance of firms through increasing the competitive advance
fo firms. The Core Competence perspective(Prahalad and Hamel, 2006) and Resource-Based
View are also theoretically support that the positive effect of R&D outsourcing on firms’ performance. From an Resource based perspective, external knowledge through R&D outsourcing provides many opportunities to create competitive advantage R&D outsourcing
may(Grant, 1996; Kogut and Zander, 1992) Many studies pointed out that knowledge
frequently result from the search for new solution which are based on the firm’s exiting
knowledge base (Cohen and Levinthal, 1989; Grimpe and Sofka, 2009; Teece, 1986). And
firms can take advange in cost through R&D outsourcing from the point of transaction Cost
theory, because fixed cost may be reduced and R&D time and budgets (Spanos and
Voudouris, 2009) As a result, the external knowledge through R&D outsourcing may lead to
increase the performance of firms. So we adapt to use this perspective which the R&D outsourcing can affect to firms’ performance for investigate the relationship theoretically.Many scholars also empirically argued that R&D outsourcing increases the performance of firms by adding complementary resources and technology capabilities from
external expertise (Chesbrough, 2003; Kessler et al., 2000; Nohria and Garcia-Pont, 1991;
Teece, 1986; Tidd and Trewhella, 1997; Yasuda, 2005).
Bönte (2003) investigated the
productivity effects of investment in external vs. internal R&D through 26 samples of
German manufacturing industries using total factor productivity estimation analysis during
1980-1993. The results provided strong evidence of a positive relationship between productivity and the share of external R&D in total R&D. This study also examined the productivity impact of internal and external R&D using an industry-level panel data set and
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found a positive relationship between the share of external R&D and productivity. Guellec and Van Pottelsberghe de la Potterie (2004) also estimates the long-term impact of R&D
outsourcing on multi factor productivity growth of 16 countries from 1980 to 1998. The main result shows that R&D outsourcing was a significant factor in determining the rate of long-
term productivity growth. With a slightly different outcome measure, Schmiedeberg (2008)
found that contracted R&D is related to the focal firm’s patenting, with a larger effect than internal R&D. The regression is conducted with cross sectional 689 firm level data of the
German manufacturing sector using objective performances such as patents and sales of new products.
On the contrary, there are also few empirical studies that show R&D outsourcing is not
related to a firm’s performance. Gilley and Rasheed (2000) and Kessler et al. (2000) found
that R&D outsourcing may not increase a firm’s profitability or performance. Gilley and
Rasheed (2000) use regression analysis with suing survey data of 90 manufacturing firms.
The performance was overall measured asking to stability/growth of employee, process
innovation, product innovations, employee compensation, and etc. Kessler et al. (2000)
studied 75 new product development projects from ten large, U.S. based companies in several industries with survey. The performance is asked innovation speed and competitive success of projects in each firm. The results indicated that external sourcing affect negatively on the innovation speed and competitive success according to the transferring time dtage.
Cassiman and Veugelers (2002) investigate the effect of external technology sourcing on
firms’ performance based on a sample from the Taiwanese Technological Innovation Survey including low and medium technology 753 firms. Using a regression analysis, they reveal that the external technology outsourcing does not contribute significantly to performance which was measured firm’s turnover r attributable to technologically improved or new products.In
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summary, The results are empirically mixed; external R&D was found to have a larger positive effect than internal R&D but in some studies, it was smaller, and often not as
significant as others (Bergman, 2011). This suggests that the relationship between R&D
outsourcing and firm performance may not be conclusive. The positive results about the relationship between R&D outsourcing and firm performance is based on the traditional economic theory that technological advancements through R&D lead to positive outcome and
Resource-Based view.
In addition, there are a limited number of studies with panel data controlling for firm effects and time effects. Thus, an assumption on this positive relationship is generated and a related hypothesis is investigated with secondary data. On the other hand, the negative result studies use survey data. But most of those studies directly asked whether firm performance is increased by R&D outsourcing. Some issues arise that can cause some bias, such as a respondent’s memory loss, recent effect, time lag of R&D, etc. For example, the time lag from R&D outsourcing to performance can differ for different respondents. We designed our empirical analysis in order to overcome these limitations.
Therefore, we present the effect of R&D outsourcing on firm performance as the first hypothesis to explore, following the endogenous growth theory, Resource-Based View, and other studies that apply a positive relationship between R&D outsourcing and firm performance. We also assume that R&D outsourcing can be a crucial factor for improving firm performance.
Hypothesis 1 (H1): More R&D outsourcing of firms is associated with better firm performance.
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3.2. Moderating Effect of Absorptive Capacity for Internalization
It is widely recognized that firms take advantage of external knowledge through R&D outsourcing, when they have high level of absorptive capacity which is defined the ability to
value and apply knowledge (Cohen and Levinthal, 1990). In other words, for successful
internalization of external knowledge, firms are required enough ability to understand it and
merge with their prior related knowledge (Clausen, 2013; Mellat-Parast and Digman, 2008;
Schneider, 1987; Tsai, 2001). So, many studies pointed out that high level of absorptive
capacity in firms strengthen the firm’s competitive advance and has been linked to valuable
organizational outcomes such as learning, innovation and financial performance (George et
al., 2001; Mellat-Parast and Digman, 2008; Mowery et al., 1996; Spanos and Voudouris,
2009).
Drawing this perspective, numerous studies investigate the moderating effect of absorptive
capacity on performance when firms use the external knowledge (Jones et al., 2001; Tsai and
Wang, 2008a; Tsai and Wang, 2008b; Zahra and Hayton, 2008). In these studies and
others which analyze the direct effect of absorptive capacity on performance of firms have been used the intensity of internal R&D investment relative to sales of firms as a proxy of
absorptive capacity (Cohen and Levinthal, 1990; Escribano et al., 2009; Jones et al., 2001;
Lin et al., 2012; Tsai and Wang, 2008a; Tsai and Wang, 2008b; Zahra and Hayton, 2008),
Kessler et al. (2000) argued that R&D outsourcing is in fact an external learning process.
The internalization process of how firms interpret external knowledge to generate new ideas for innovation using their existing knowledge is more important than R&D outsourcing itself.
So firms are hardly able to learn and internalize the external knowledge without absorptive
capacity (Chen, 2004). Mowery (1984) pointed out that firms can better acquire to absorb the
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output of external knowledge if it is also performing enough amount of internal R&D investment. In the process of firm identification and use of external technological knowledge,
internal R&D effort can play a positive role to enhance the process (Cohen and Levinthal,
1989; Kim, 1999; Lane and Lubatkin, 1998)
. Tsai and Wang (2008b) found that the extent to
which external technology acquisition has an effect on firm performance, and how this effect is moderated by internal R&D efforts. They focused on internal R&D input which is
acknowledged as ‘absorptive capacity’. Zahra and Hayton (2008) also noted that absorptive
capacity moderates the relationship between using external knowledge through international venturing and firms’ ROE as a proxy of profitability and revenue growth with 217 global manufacturing firms’ data.
In our study, this evidence provided a crucial starting point to conceptualize the moderating roles of absorptive capacity as internal R&D on the relationship between external technology acquisition and firm performance. To investigate this relationship, we initially propose that the absorptive capacity, as a proxy for internal R&D effort, plays an important role in moderating the effect of R&D outsourcing on firm performance.
Hypothesis 2-1 (H2-1): R&D outsourcing will be more strongly associated with the firm performance when the level of absorptive capacity with intern al R&D effort is higher.
As mentioned above, the ability of the firm to internalize external knowledge as absorptive capacity can influence the extent to which it can achieve higher performance from R&D outsourcing. And this absorptive capability relies on a firm’s internal capabilities, such as internal R&D, production experience, and technical training. Actually, the internalization of
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external knowledge through R&D outsourcing has several steps and can be differentiated by human resources who perform an internal R&D after R&D outsourcing. Others in the organization can be also determinants in R&D outsourcing because the major performers of internal R&D are the organization’s own R&D employees. Therefore, the performance of
R&D outsourcing might depend on not only just R&D outsourcing itself but also organizational composition. Therefore there are some underlying assumption that absorptive
capability is socially complex routines that could be a valued organizational resource (Collis,
1994; Hall, 1992; Hall, 1982). For example, Mangematin and Nesta (1999) argue that highly
educated employees in firms will increase the knowledge stock of organization. Carter (1989)
also agree this argument that employees with high level of education are the main contributors to know—how trading, because the high level of knowledge is embedded in these people. It means that they can recognize and value new external knowledge better. In this context, operationalizing the effort of internal R&D as internal R&D investment is quite limited in that various organizational arrangements will not be considered if we only take internal R&D investment as the proxy for internal R&D effort. Specific organizational variables must be considered as well when investigating the effect of R&D outsourcing on firm performance in the internalization of external knowledge.
But from Cohen and Levinthal (1990)’s perspective, absorptive capacity has been usually operationalised only as R&D intensity relative to sales (Cohen and Levinthal, 1990;
Escribano et al., 2009; George et al., 2001; Kostopoulos et al., 2011; Rothaermel and
Alexandre, 2009; Stock et al., 2001; Tsai and Wang, 2008b; Tsai, 2001; Xia, 2013; Zahra,
1996; Zahra and Hayton, 2008) or number of patents (Austin, 1993; Cohen and Levinthal,
1990; Lin et al., 2012; Zahra and George, 2002) or whether to be R&D organization in firms
(Becker and Peters, 2000; Cassiman and Veugelers, 2002; Nieto and Quevedo, 2005) or direct
20

ask the level of ability to value & apply knowledge through survey(Bagchi et al., 2013; Chen,
2004; Clausen, 2013; Lund Vinding, 2006; Spithoven et al., 2011). Jones et al. (2001)
explored the moderating effects of internally available resources on the relationship between external technology acquisition and firm performance. But in their case, they did not specified internally available resources or their moderator as internal R&D efforts.
So there has been increasing critique on this operationalisation of absorptive capacity
(Spithoven et al., 2011). It emphasizes that absorptive capacity is a multidimensional concept and should be operationalised as such (Lenox and King, 2004; Schmidt, 2005). Accordingly,
some studies have conducted not the traditional indicators but focused on the human capital
involved in the internalization of external knowledge (Lund Vinding, 2006). Nevertheless
there are only few empirical studies using the features of human capital for internalizing external knowledge. Even though some studies conducted the relationship between the absorptive capacity with human capital perspective and firms’ performance, the indicators of
it is just suggesting the concept of absorptive capacity with human capital perspective (Glass
and Saggi, 1998; Keller, 1996) and only measured by the number of employees with
university education (Grimpe and Sofka, 2009; Liu and White, 1997), the proportion of R&D
employee relative to the total number of employee (Spanos and Voudouris, 2009)
fragmentarily. In this paper, so we focus on the composition of human resources for the internalization of external knowledge. From our perspective, how a firm arranges the organization for effective internalization of external knowledge within the firm affects the outcome of R&D outsourcing.Following this line of reasoning, we attempt to investigate empirically is the moderating effect of absorptive capacity with organizational composition on how R&D outsourcing is tied to the outcome. To investigate the moderating role of absorptive capacity with organizational composition , we focus on the ability of human
21

resource, while prior research on absorptive capacity only used R&D stock or exiting knowledge via patent to measure R&D input. Logically, as organizational members have more knowledge and responsibilities, they will be more dedicated and have greater absorptive capability with organizational capability that will affect their organization in a positive way.
For example, the responsibility of employees for task in firms can be differentiated between the job types and position such as regular or part time and a researcher or research assistant. A full time researcher with regular position may have more responsibility in working and higher knowledge. For example, they can decide with more high level of responsibilities what external knowledge firms need to outsource and where firms should transfer it from by contracting. So these authorities and matched responsibilities are belonged to full time researcher with regular position.
Therefore, we assume that absorptive capacity with organizational composition play an important role in moderating the effect of R&D outsourcing on firm performance.
Hypothesis 2-2 (H2-2): R&D outsourcing will be more strongly associated with the firm performance when the absorptive capacity with organizational composition are higher.
.
4.1. Model
Figure 3 shows the conceptual framework proposed and investigated in this paper. The model indicates that R&D outsourcing has a direct effect on firm performance. We also
22

hypothesize that the relationship between R&D outsourcing and firm performance is moderated by a firm’s absorptive capacities with R&D intensity and organizational composition due to the internalization process we described in Figure 2.
-------------------------------------------
Insert Figure 3 about here
-------------------------------------------
4.2.Data
In this paper, we use a firm level merged data set composed of financial data from the
Korea Investors Service (KIS) and a R&D survey in science and technology. We used business number of firms for merging these two data set. KIS is a company which provides financial information service of firms in Korea. The survey of R&D in science and technology is done by the Ministry of Education, Science and Technology in accordance with the OECD Frascati Manual for the equivalent year.
-------------------------------------------
Insert Table 1 about here
-------------------------------------------
First, the survey of R&D in science and technology was conducted from year 2002 to
2007. The data included 59,911 companies with separate R&D organizations. Then we matched 97,407 firms’ financial data from KIS with the above R&D survey in science and technology 2002-2007 using the firms’ registered business number. Finally, a data pool for this study was generated by integrating these two data sets and our study was conducted on
19,570 firms.
4.3. Variables and Measures
The variables used in the analyses are defined as follows. Firm performance as a
23

dependent variable is measured by sales amount in this study, because the purpose of R&D outsourcing is to enhance their sales amount by developing new technology and product.
R&D outsourcing is an independent variable, which is measured by the intensity of R&D outsourcing. This is calculated by the amount of R&D outsourcing divided by the amount of sales.
There are four control variables – size, financial soundness, level of market competition, openness, and year dummy. The size is measured through two variables - number of employees in firms and total amount of capital stocks which is defined by IFRS (International
Financial Reporting Standards). The level of market competition is measured by taking the total market share of the 4 largest firms based on KSIC (The Korean Standard Industrial
Classification) 2 digit. The financial soundness is measured by capital adequacy ratio, calculated by stockholder’s equity divided by total asset. The openness of firms for controlling the internalization for firms is measured as the amount of export divided by amount of sales.
The R&D intensity of firms is used as the proxy for internal R&D effort as absorptive
capacity (Cohen and Levinthal, 1989; Griliches, 1998; Stock et al., 2001). This is calculated
by R&D expenditure divided by the amount of sales. Variables of absorptive capacity with organizational composition are measured by various human resource ratios in organization.
-------------------------------------------
Insert Figure 4 about here
-------------------------------------------
Figure 4 shows how we classified the composition of human resources. R&D employees in an organization are composed of researchers and research assistants who support research through testing, measuring, and other supporting activities. Researchers are categorized using
24

two different criteria - whether a researcher is a full-time employee or not and whether a researcher has a Ph. D or a master’s degree. The limitation of our data set is that it cannot identify the working type (full-time vs. part-time) and type of degree simultaneously.
-------------------------------------------
Insert Table 2 about here
-------------------------------------------
We considered that the internalization of external knowledge might depend on the quality of the R&D employee as absorptive capacity. So we use the ratio of R&D employee which is calculated by the number of R&D workers divided by total employees as a one of capabilities of the problem defining and solution interpretation of R&D organization. And the ratio of researcher which is calculated by the number of researchers divided by total R&D employees is used. We also investigate whether higher academic degrees can be the decisive factor in the internalization process. For one of this investigation, we use the ratio of Ph. D researcher which is calculated by the number of Ph. D R&D employees divided by total R&D employees. And the ratio of master’s degree researcher which is calculated by the number of master degree researchers divided by total R&D employees is also used. And the level of responsibilities can be a crucial factor for R&D Outsourcing since one has to put in a lot of effort to internalize the external knowledge in a way that it can positively affect the organization. We can infer that a full-time R&D employee is more responsible than a parttime R&D employee. So the ratio of full-time research employee which is calculated by number of full-time research employees divided by total R&D employees is used..
Table 3 provided the descriptive statistics, including means, standard deviations, and the minimum & maximum values of the variables. To check the multicollinearity, we check the variance inflation factors(VIFs). The highest individual VIF score among all the variables is
25

3.893, and the mean VIF score is 1.292. Since prior research stated that 10 or less is a widely used guideline for such a test (Luo, 2009 #257), the multicollinearity of variables is not a serious problems.
-------------------------------------------
Insert Table 3 about here
-------------------------------------------
In table 3, some interesting points are worthy of mention from the point of organizational composition view. First, the ratio of R&D employee does not correlate highly with the ratio of PhD researcher. Actually the correlation coefficient of R&D employee ratio to master degree researcher ratio is higher rather than it. It is also found at the correlation between the ration of researcher in R&D employee and them. It can be explained that there are cost problem and characteristic of manufacturing industry. PhD. researcher is a core asset for
R&D organization but the labor cost is higher than others, so firms hire just few ratio employees among all employees as a core competency for competitive advance. And the characteristic of manufacturing industry is also reasoned. In manufacturing industry, the ratio of the master degree researcher who is more focused in engineering may be more than the
PhD researcher who is more focused in academic research.
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Insert Table 4 about here
-------------------------------------------
This is can be explained in Table 5. Table 5 is comparison of descriptive of statistics of major variables within the sample. We divided the full sample into four groups by ISIC
(International Standard Industrial Classification) REV. 3 technology intensity definition by the OECD (details can be found in Table 4) in table 5. According to the OECD, manufacturing industries can be classified into different categories based on their level of
26

R&D intensity - high, medium-high, medium-low, and low technology industries. Thus, we use the ISIC 2-4 digit industry code to identify four groups of firms with different levels of technology sophistication.
-------------------------------------------
Insert Table 5 about here
-------------------------------------------
In the table 5, we can identify that the coefficients of standard deviation are rather large for all variables. This suggests that the data distribution has a high degree of dispersion. And the ratio of PhD. researcher is smaller than the ratio of master degree researcher among all 4 different categories, when we compare the mean of each organizational variable. For example, the ratio of PhD. researcher in R&D employees is 4.1% but the ratio of master degree researcher is 25.8%. It is also same among other 3 different categories. So the ratio of R&D employee does not correlate highly with the ratio of PhD researcher in our sample. This is also found the coefficient of correlation between the ratio of FTE researcher to PhD. researcher and master degree. Second, we can recognized that the ratio of master degree of researcher only correlate positively with number of employee among variables of organizational composition. Third, the CR4 is positively correlated with ratio of R&D employee and researcher in R&D employee but negatively correlated with the ratio of PhD. researcher.
4.4. Empirical model
We have examined the hypotheses with an unbalanced panel data set. Panel data is most useful when we suspect that the outcome variable depends on explanatory variables which are not observable but correlated with the observed explanatory variables. If such omitted
27

variables are constant over time, panel data estimators allow us to consistently estimate the
effect of the observed explanatory variables (Schmidheiny, 2011).
Consider the multiple linear regression model for firm i = 1~N which is observed at each year, t =1~T. y it
   x it
    i
  it i

1 , 2 ,..., N t

1 , 2 ,..., T
 it
~ i .
i .
d .( 0 ,

2 e )
Here, y it
is the dependent variable, x it
is independent variables excluding the constant,

is the intercept,

is a parameters,
 i is an unobservable individual and firm-specific effect as a time invariant, and
 it
is an idiosyncratic error term.
We used the panel analysis for correcting the estimation bias from unobservable exogeneity rather than doing a cross-sectional analysis. For the panel analysis, it matters if
 i
was correlated with independent variables or not. An unobservable individual and firmspecific effect usually does not changed by time. We can examine the analysis with a random effect model, if we assume that
 i
is uncorrelated with independent variables. But if
 i
is correlated with independent variables, the random effect model is not suitable for estimating the efficient estimates. Thus we performed the Hausman specification test to see whether
 i is correlated with independent variables or not. The test result indicated that we have to adopt the fixed effect model. And we use a two-year time lag for R&D outsourcing intensity and
R&D intensity. Because it takes some time to affect the R&D on the firms’ performance(Kay,
1988 #258). So, prior studies find these impacts to be time lagged. For example, (Ravenscraft,
1982 #259@@author-year) investigate the lag between R&D and its impact on firms’ financial performance. They find that there is a time gap of four years. Many studies which
28

use Korean manufacturing industry data suppose that there is 1- 3 years time lag. So we apply two-year time lag for R&D outsourcing intensity and R&D intensity in our study.
Analytical processes in this study are conducted by hierarchical regression procedures
(Cohen, 2003), and the data is analyzed by the panel data analysis with a fixed effect model.
Table 6 lists the results for our initial analysis before we split the sample for a more detailed analysis. A two-year time lag for R&D outsourcing intensity and R&D intensity was applied because the outcome of R&D itself manifests some time lag.
-------------------------------------------
Insert Table 6 about here
-------------------------------------------
Model 1 investigated Hypothesis 1, whether R&D outsourcing can improve firm performance. But the result was not significant in all samples. And as expected, larger firms could improve performance. Financial adequacy was also significantly related to firm performance. Model 2-3 included control variables and variables of absorptive capacity with internal R&D effort and organizational composition - as moderating variables. Examining adjusted R-squared values across all models suggested that the full model provided the best fit to the data. The result of Model 3 still showed that R&D outsourcing was not significant.
But the estimated coefficient of the researcher ratio to R&D employees was positive (t=2.84, p<0.01) at the five-percent significance level. So, a greater ratio of researchers in R&D employees has a direct, positive effect firm performance.. Among the moderating variables, only Hypothesis H2-1 was confirmed. In other words, the absorptive capacity with internal
29

R&D effort has a positive (t=4.44, p<0.001) effect not directly on firm performance but indirectly as a moderating effect when firms outsource R&D. On the other hand, the rest of variables were not significant in the full sample. These results confirmed that a higher level of internal R&D input improves a firm’s ability to utilize external knowledge (Gambardella,
1992 #166;Mowery, 1996 #228;Helfat, 1997 #193;Cohen, 1990 #157;Zahra, 2008 #148).
In order to examine our hypotheses in more details, as we mentioned earlier, we divide the sample according to the level of technological sophistication by definition of OECD.
Depending on the technological level, the level of sophistication in internalization will also differ. For example, a firm which produces highly complex products will require highly sophisticated organizational composition compared to a firm which produces products through mere assembly and simple work. This rationale suggests the possibility that the role of internalization mechanism also differs depending on the level of technological sophistication. Thus we investigate our hypotheses at a more detailed level using the sample division method.
The results of the split-sample analysis are listed and Model 3 is used for discussion in
Table 7 through 10. Within the four groups, only the high-technology industries group had a significantly positive (t=0.27, p<0.05) coefficient of R&D outsourcing intensity.
The speed of change in the high technology industry is most rapid among all industries.
What we can infer from this result is that high technology firms have to cope with environmental and technological changes in their market. Thus, firms invest a large amount of internal R&D to increase their innovation capability, and need to seek and transfer external technology through R&D outsourcing.
-------------------------------------------
Insert Table 7, 8, 9 and 10about here
-------------------------------------------
30

In the examination of absorptive capacity with organizational variables’ moderating effect on R&D outsourcing on firm performance, only high and low technology industries had some coefficients that were significantly positive. First, in high technology industries (Table 7), the absorptive capacity with internal R&D effort (H2-1) has a significantly positive (t=5.51, p<0.001) effect on R&D outsourcing on firm performance. This result is consistent with
previous research conducted by (Tsai and Wang, 2008b) They found that external technology
acquisition does not significantly contribute to firm performance per se; however, the positive impact of external technology acquisition on firm performance increases with the level of internal R&D investment as an absorptive capacity. Therefore, it is proposed that by investing more in R&D input, firms can achieve higher levels of performance in this setting.
Among hypothesis H2-2, the ratio of Ph D. researcher has a positive (t=1.97, p<0.05) significant effect on R&D outsourcing regarding a firm's performance. In addition, the ratio of FTE (Full-Time researcher) (H2-2e) has a notable negative (t= -1.99, p<0.05) effect. These two empirical evidences support part of H2-2. The results shows that high technology firms need to increase the quality of R&D personnel rather than to hire more researchers when firms use external knowledge through R&D outsourcing. In other words, high technology firms should be organized with higher internal R&D input and more educated researchers when firms select a R&D outsourcing strategy and use absorptive capacity for internalizing
the external knowledge For example, Hoffman et al. (1998) noted that most important
determinants of innovation and economic success are the scientist, engineer and owner manager. Namely, a highly-educated employee is one of the most decisive factors for innovation in high technology industry. The R&D employee mainly takes an active part in
implementing a radical innovation measure (Huiban and Bouhsina, 1998). R&D outsourcing
31

can be a radical innovation by transferring the needed technology from external expertise. So, we get the sense that the ratio of Ph. D indicates how well firms are organized to utilize external knowledge, given that the ratio of highly educated employees can be a determinant for translating external knowledge and for internalization. In particular, there will be a stronger effect on firms that use and develop state-of-the-art technology.
The ratio of researchers in R&D and ratio of master degree researchers in absorptive capacity with organizational factors are not significant for all split groups. According to the results shown in Table 10, the coefficient of R&D employee ratio in low technology industries is significantly positive (t=2.06) at the five-percent level. This means that firms in low technology industries have to invest to increase the ratio of R&D employees. They can reap benefits from hiring more R&D employees, without much consideration of their level of knowledge or responsibilities, since problem defining and solution interpretation may not require such sophisticated and responsible R&D personnel with Ph. Ds in this setting.
6.1. Summary and Implication
R&D outsourcing is one of the most popular strategies which firms exercise in order to utilize external knowledge. In this study, R&D outsourcing is identified as four steps - problem defining, contracting, knowledge transfer, and interpreting the solution of the defined problem. Most previous literature has focused on the question of whether R&D outsourcing has an effect on firm performance. Prior research also highlighted the role of absorptive capacity as a moderating variable when enjoying the benefit of external knowledge such as R&D outsourcing. However, how firms set up their organizational structure to use external knowledge efficiently is well worth investigating. So we assumed
32

that a firm’s capabilities to clearly define the absorptive capacity with internal R&D effort and organizational composition might be more important in the whole R&D outsourcing process. Because real actors who is doing R&D outsourcing and internalization of external knowledge are internal employees. So we focus the composition of R&D organization in firms from the human capital point of view.
This study was an attempt to examine these issues. Longitudinal sample analysis allowed us to control several important variables, including firm size, financial soundness of firms, openness and level of market competition, which led to more convincing evidences of the importance of organizational arrangement in maximizing the effect of R&D outsourcing. A merged data set consisting of financial data from KIS and the survey of R&D in science and technology 2002-2007 led us to conduct our empirical study on 19,570 firms. The data set was large enough to convince us that the results could be generalized.
The major comparison of two radically different levels of technology is summarized in
Table 11. The effect of R&D outsourcing on firm performance is only confirmed in high technology industries. Internal R&D efforts such as absorptive capacity were found to moderate R&D outsourcing on a firm's performance in full sample, and again in the spilt group of high technology industries. In the case of high technology industries, the ratio of
Ph.D. researchers had a positive effect on R&D outsourcing on a firm's performance, while the ratio of FTE researchers had the opposite effect.
Overall, the research indicates that the quality of researchers is more effective and important than mere quantity of researchers. In other words, firms have to focus on how to employ more highly-educated researchers if they want a better process for internalization for external knowledge. The resource-based view focused on the technology capacity for innovation as an intangible resource and identified knowledge of expertise, experience, skill
33

and culture of organization as the essential technology capacities (Hall, 1992). Many studies
pointed out that experienced employees with a high level of education and skills can be a
determinant of innovation in firms (Koschatzky et al., 2001; Romijn and Albaladejo, 2002).
Our result also coincides with these studies. In our conceptual framework (Figure 2), the
whole process needs internal expertise. Cohen and Levinthal (1990) pointed out, the
experienced employee with a high level of education mainly takes part in emerging new knowledge by understanding, absorbing, and utilizing external knowledge, as discovered in our results. This is such because existing knowledge held by R&D employees with a higher level of experience is a pre-requisite for internalization. The result of low technology industries shows that the quantity of R&D employees moderates the effect of R&D outsourcing on a firm's performance.
We summarized all the empirical results in Table 11. It can explain why the empirical results are differentiated with Table 4. Here, we can observe the difference in internal R&D efforts and organizational arrangements which indicates how different inner functions of the
R&D organization, in the end, may lead to the difference in our study results.
-------------------------------------------
Insert Table 11 about here
-------------------------------------------
We can infer some managerial implications for practice. The average of R&D intensity shows the most remarkable difference between high technology industries and others. The mean of R&D intensity in high technology industries is 4% of total sales while the intensity for low technology industries and the full sample is only 2.5%. All ratios of R&D employee, researchers in R&D employees, Ph. D and master degree researchers between high and low technology industries are very different. However, the average of the FTE researcher ratio is
34

very similar between high and low technology industries. We can infer that these distinctions in organizational composition affect firm performance and become the source that causes a moderating effect on R&D outsourcing on a firm's performance.
6.2. Limitation and Future Research
There are some limitations in this study that we want to clarify. First, we used the level of human capital as the proxy for our focal firms’ absorptive capacity with organizational composition While this is not an unreasonable assumption, we need to conduct a further study – preferably a more micro-level field study – to confirm the assumption we make in this study. Case studies with some representative firms might help us to better understand the nature of the internalization process we described here in this study. It could also provide us with some opportunities to theorize the micro-level mechanisms of internalization of
external knowledge through R&D outsourcing. For example, Kessler et al. (2000) have found
that R&D outsourcing was more detrimental to competitive advantage during the idea generation stage and significantly lengthened the project completion time during the technology development stage.
Secondly, even though we found some evidence on the questions we asked initially, the results are somewhat mixed in details. This calls for additional studies in other countries to compare the results across different nations.
This study also raises the need for more in-depth studies on organizational composition factors as the moderating variable of R&D outsourcing on a firm's performance. For example, diversity also can be an important factor that might be related to absorptive capacity with organizational composition. We plan to investigate the impact of diversity as a moderating effect of R&D outsourcing on a firm's performance.
35

Additionally, the research type – basic research, applied research, and development - can have some effect on the relationship between R&D outsourcing and firm performance
(Lichtenberg and Siegel, 1991). So it will be helpful for us to expand the discussion to the
research type.
Finally, there are several organizational composition factors that we think are worth investigating - structure of communication in an organization, organizational culture for using external knowledge, and level of a firm’s external network - which may differ depending on the technology level of companies.
36

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44

Figure 1. Outsourcing funding Vs. Internal funding for R&D
Source : Report in the survey of Research and Development in Korea-Manufacturing (2002-2007)
Figure 2. Conceptual framework of technology transfer in R&D outsourcing
45

Figure 3. Conceptual Framework
Figure 4.
Classification of Human Resources in R&D
46

47

Table 1. Description of Data Set (2002-2007)
Financial Statement Data R&D Activity Survey Merged dataset year
2002
2003
2004
2005
2006
2007
Total
No. of firms
14,108
14,973
15,588
16,567
17,788
18,083
97,407 year
2002
2003
2004
2005
2006
2007
Total
No. of firms
7,178
6,991
8,300
9,837
12,639
14,966
59,911 year
2002
2003
2004
2005
2006
2007
Total 19,570
No. of firms
2,793
2,988
2,795
3,221
3,908
3865
48

Table 2. Definition of Variables and Data Sources
Variables Definition
Dependent variable
Category
Firm performance Sales ln(amount of sales)
Independent variables
Level of R&D outsourcing
R&D outsourcing intensity
Amount of R&D outsourcing/ Total
R&D expenditure
Absorptive capacity via internal R&D effort
R&D intensity
R&D expenditure/ amount of sales
Ratio of R&D employee
No. of researcher and assistant/Total employee
Ratio of researcher in R&D employee
Absorptive capacity Absorptive capacity via organizational composition
Level of internationalization
Ratio of PhD. researcher
Ratio of Masters degree researcher
Ratio of FTE(Full time employee) researcher
Openness
Capital
No. of researcher/
R&D employee
No. of PhD. researcher/ R&D employee
No. of PhD. researcher/ R&D employee
No. of FTE of researcher/ R&D employee
Amount of export/amount of sales ln(Total capital)
Size
Control variables Financial soundness
Level of market competition
Year dummy
No. of employee
Capital adequacy ratio
CR4
Year dummy ln(No. of employee)
Stockholders' equity/total asset
The sum of market share of 4 largest firms in KSIC 2 digit year
Data source
KIS
R&D Activity
Survey
KIS
R&D Activity
Survey
KIS
49

Variables
Table 3. Descriptive Statistics and Correlation Matrix of Variables (N=19,570)
Mean std dev Min. Max. Sales
R&D
Outsourc
-ing intensity
R&D intensity
Ratio of
R&D employee
Ratio of
Research er in
R&D employee
Ratio of
PhD.
Research
-er
Ratio of
Master degree
Research
-er
Ratio of
FTE
Research
-er
Openness Capital
No. of employee capital adequacy ratio
CR4
Sales 10.222 1.598 0.909 17.961 1.000
R&D Outsourcing intensity
0.066 0.147 0 0.997 0.009 1.000
R&D intensity
Ratio of R&D employee
Ratio of Researcher in R&D employee
0.025
0.166
0.831
0.056
0.161
0.201
0
0
0
0.997
1.000
1.000
- 0.298
***
- 0.421
***
- 0.081
***
0.035
***
1.000
- 0.007
0.355
***
0.036
***
0.046
***
1.000
- 0.007 1.000
Ratio of PhD.
Researcher
Ratio of Master degree Researcher
Ratio of FTE
Researcher
Openness
Capital
No. of employee
0.036
0.216
0.846
0.005
7.965
4.740
0.097
0.223
0.224
0.039
1.506
1.188
0
0
0
0
3.912
0.693
1.000
1.000
1.000
0.987
17.082
11.367
- 0.015
*
0.042
***
- 0.040
***
0.043
***
0.706
***
0.854
***
0.051
***
0.090
***
0.011
0.283
***
0.034
***
0.095
***
0.136
***
0.023
**
0.027
***
-0.091
***
-0.001
- 0.185
***
0.047
***
0.093
***
0.014
*
- 0.001 0.006 0.007
- 0.220
***
- 0.471
***
0.108
***
0.303
***
0.323
***
- 0.016
*
- 0.056
*** capital adequacy ratio
0.464 0.310 - 9.523 1.000
0.056
***
0.016
*
0.015
*
0.035
***
CR4 0.356 0.177 0.078 0.975
0.059
***
-0.037
***
0.068
***
0.098
***
Notes: †p < 0.10, *p<0.05, **p<0.01, ***p<0.001 estimated standard errors are in parentheses.
- 0.015
*
0.022
**
1.000
0.154
***
0.040
***
0.063
***
0.007
0.006
-0.027
***
1.000
0.099
***
0.026
***
0.164
***
0.103
***
1.000
0.001 1.000
- 0.018
*
- 0.041
***
0.042
***
0.042
***
0.073
***
0.002 0.004
- 0.005 - 0.001
0.020
*
1.000
0.690
***
0.042
***
0.100
***
1.000
0.045
***
0.083
***
1.000
- 0.061
***
1.000
50

Table 4. ISIC REV. 3 technology intensity definition of OECD
High-technology industries
Aircraft and spacecraft
Medium-high-technology industries
Electrical machinery and apparatus, n.e.c.
Pharmaceuticals Motor vehicles, trailers and semi-trailers
Office, accounting and computing machinery Chemicals excluding pharmaceuticals
Radio, TV and communciations equipment Railroad equipment and transport equipment, n.e.c.
Medical, precision and optical instruments
Medium-low-technology industries
Machinery and equipment, n.e.c.
Low-technology industries
Building and repairing of ships and boats
Rubber and plastics products
Coke, refined petroleum products and nuclear fuel
Other non-metallic mineral products
Manufacturing, n.e.c.; Recycling
Wood, pulp, paper, paper products, printing and publishing
Food products, beverages and tobacco
Textiles, textile products, leather and footwear
51

Variable
Sales(USD. Mil.)
R&D intensity
R&D Outsourcing intensity
Ratio of R&D employee
Ratio of Researcher in R&D employee
Ratio of PhD. Researcher
Ratio of Master degree
Researcher
Ratio of FTE Researcher
Table5. Comparison of Descriptive Statistics of major variables within the sample
Full Sample
(N=19,570)
High technology industries
(N=5,749)
Medium-High technology industries
(N=7,812)
Medium-Low technology industries
(N=2,851)
Mean Std. Dev. Mean Std. Dev. Std. Dev. Std. Dev. Mean Std. Dev.
198.947 1,473.684
0.025 0.056
0.066 0.147
0.166 0.161
0.831 0.201
0.036 0.097
0.216 0.223
0.846 0.224
161.053 2,105.263
0.040 0.075
0.071 0.143
0.226 0.195
0.849 0.174
0.041 0.096
0.258 0.233
0.854 0.214
155.789 1024.211
0.033 0.040
0.053 0.121
0.146 0.113
0.821 0.197
0.038 0.086
0.186 0.205
0.845 0.221
342.105 1,715.789
0.029 0.020
0.062 0.130
0.098 0.086
0.809 0.209
0.036 0.106
0.172 0.205
0.828 0.226
Low technology industries
(N=3,158)
Mean Std. Dev.
92.000 220.000
0.025 0.055
0.046 0.118
0.186 0.194
0.840 0.220
0.035 0.100
0.219 0.222
0.852 0.230
52

Table6. Results of Panel Analysis (n = 19,570)
Dependent Variable : Sales
Independent Variables
Openness
Capital
No. of employee capital adequacy ratio
CR4
R&D intensity(t-2)
R&D Outsourcing intensity(t-2)
Ratio of R&D employee
Ratio of Researcher in R&D employee
Ratio of PhD. Researcher
Ratio of Master degree Researcher
Ratio of FTE Researcher
R&D outsourcing intensity(t-2) ⅹ
R&D intensity(t-2)
R&D outsourcing intensity(t-2) ⅹ
Ratio of R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of Researcher in R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of PhD. Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of Master degree Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of FTE Researcher
Year dummy
Adjusted R 2
F-Value(P)
Included
0.250
16.50***
Model 1
0.368(0.143) †
0.106(0.016)***
0.550(0.017)***
0.087(0.021)***
0.059(0.200)
0.034(0.036)
Model 2
0.373(0.143)**
0.104(0.015)***
0.571(0.018)***
0.084(0.02)**
0.040(0.200)
0.036(0.036)
0.037(0.119)
0.016(0.038)*
0.196(0.069)
-0.033(0.50)
0.025(0.037)
0.002(0.021)
Included
0.252
15.35***
53
Model 3
0.375(0.143) **
0.101(0.016)***
0.570(0.018)***
0.084(0.021)***
0.033(0.200)
0.017(0.037)
0.025(0.119)
0.014(0.038)
0.195(0.069)**
-0.031(0.050)
0.022(0.037)
0.002(0.021)
2.673(0.602)***
-0.070(0.198)
0.265(0.217)
0.126(0.354)
0.186(0.165)
0.072(0.135)
Included
0.256
15.40***

Notes: †p < 0.10, *p<0.05, **p<0.01, ***p<0.001 estimated standard errors are in parentheses.
54

Table 7. Result of Panel Analysis (High-Technology Industries, N=7,812)
Dependent Variable : sales
Variables
Openness
Capital
No. of employee capital adequacy ratio
CR4
R&D Outsourcing intensity(t-2)
R&D intensity(t-2)
Ratio of R&D employee
Ratio of Researcher in R&D employee
Ratio of PhD. Researcher
Ratio of Master degree Researcher
Ratio of FTE Researcher
R&D outsourcing intensity(t-2) ⅹ
R&D intensity(t-2)
R&D outsourcing intensity(t-2) ⅹ
Ratio of R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of Researcher in R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of PhD. Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of Master degree Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of FTE Researcher
Year dummy
Adjusted R 2
F-Value(P)
Model 1
0.279(0.237)
0.072(0.035)*
0.695(0.034)***
0.326(0.048)***
-0.241(0.684)
0.170(0.077)*
Included
0.336
10.23***
Model 2
0.306(0.238)
0.069(0.035)*
0.717(0.037)***
0.321(0.048)***
-0.338(0.688)
0.175(0.077)*
-0.026(0.188)
-0.049(0.088)
0.188(0.128)
-0.105(0.114)
0.015(0.084)
0.043(0.050)
Included
0.338
9.15***
55
Model 3
0.329(0.235)
0.066(0.034)
0.708(0.036)***
0.323(0.048)***
-0.535(0.681)
0.023(0.084)*
0.083(0.187)
-0.094(0.088)
0.129(0.126)
-0.093(0.115)
-0.005(0.083)
0.043(0.050)
5.264(0.956)***
-0.728(0.532)
0.196(0.379)
1.605(0.824) *
0.184(0.382)
-0.733(0.368)*
Included
0.360
9.39***

Notes: †p < 0.10, *p<0.05, **p<0.01, ***p<0.001 estimated standard errors are in parentheses.
56

Table 8. Result of Panel Analysis (Medium-High-Technology Industries, N=2,581)
Dependent Variable : sales
Variables
Openness
Capital
No. of employee capital adequacy ratio
CR4
R&D Outsourcing intensity(t-2)
R&D intensity(t-2)
Ratio of R&D employee
Ratio of Researcher in R&D employee
Ratio of PhD. Researcher
Ratio of Master degree Researcher
Ratio of FTE Researcher
R&D outsourcing intensity(t-2) ⅹ
R&D intensity(t-2)
R&D outsourcing intensity(t-2) ⅹ
Ratio of R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of Researcher in R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of PhD. Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of Master degree Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of FTE Researcher
Year dummy
Adjusted R 2
Included
0.189
Model 1
0.532(0.280)*
0.076(0.029)**
0.379(0.030)***
0.144(0.067)*
-2.262(0.585)***
0.121(0.069)
†
Model 2
0.530(0.280)
0.075(0.029)**
0.383(0.035)***
0.144(0.067)*
-2.270(0.587)***
0.127(0.069)
0.268(0.257)
0.075(0.065)
0.011(0.158)
-0.200(0.101)*
-0.129(0.071)
-0.038(0.036)
Included
0.194
Model 3
0.507(0.281) †
0.075(0.029)**
0.383(0.035)***
0.144(0.067)*
-2.272(0.588)***
0.117(0.070)
†
0.318(0.279)
0.074(0.065)
0.015(0.160)
-0.178(0.104)
†
-0.125(0.072)
†
-0.030(0.040)
-0.784(1.418)
-0.262(0.368)
0.155(0.664)
0.888(0.988)
0.138(0.354)
0.397(0.297)
Included
0.196
57

F-Value(P) 12.18*** 11.76***
Notes: †p < 0.10, *p<0.05, **p<0.01, ***p<0.001 estimated standard errors are in parentheses.
11.71***
58

Dependent Variable : sales
Table 9. Result of Panel Analysis (Medium-Low-Technology Industries, N=5,749)
Variables Model 1 Model 2 Model 3
Openness
Capital
No. of employee capital adequacy ratio
CR4
R&D Outsourcing intensity(t-2)
R&D intensity(t-2)
Ratio of R&D employee
Ratio of Researcher in R&D employee
Ratio of PhD. Researcher
Ratio of Master degree Researcher
Ratio of FTE Researcher
R&D outsourcing intensity(t-2) ⅹ
R&D intensity(t-2)
R&D outsourcing intensity(t-2) ⅹ
Ratio of R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of Researcher in R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of PhD. Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of Master degree Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of FTE Researcher
Year dummy
Adjusted R 2
0.234(0.310)
0.147(0.039)***
0.590(0.048)***
0.237(0.084)*
-1.080(0.588)
-0.109(0.080)
Included
0.379
0.243(0.307)
0.134(0.039)**
0.626(0.056)***
0.227(0.084)**
-1.299(0.593)*
-0.096(0.080)
-0.204(0.662)
-0.155(0.079)*
0.193(0.266)
0.196(0.088)*
0.282(0.072)***
0.110(0.050)*
Included
0.399
0.338(0.320)
0.132(0.039)**
0.620(0.056) ***
0.228(0.084)**
-1.260(0.598)*
-0.120(0.100)
-0.095(0.691)
-0.164(0.080)**
0.229(0.271)
0.200(0.089) *
0.286(0.072) ***
0.108(0.050) *
-0.957(3.875)
0.473(0.453)
-0.516(0.847)
0.723(0.845)
0.050(0.380)
-0.084(0.360)
Included
0.403
59

F-Value(P) 24.34 23.26
Notes: †p < 0.10, *p<0.05, **p<0.01, ***p<0.001 estimated standard errors are in parentheses.
23.00
60

Table 10. Result of Panel Analysis (Low-Technology Industries, N=3,158)
Dependent Variable : sales
Variables
Openness
Capital
No. of employee capital adequacy ratio
CR4
R&D Outsourcing intensity(t-2)
R&D intensity(t-2)
Ratio of R&D employee
Ratio of Researcher in R&D employee
Ratio of PhD. Researcher
Ratio of Master degree Researcher
Ratio of FTE Researcher
R&D outsourcing intensity(t-2) ⅹ
R&D intensity(t-2)
R&D outsourcing intensity(t-2) ⅹ
Ratio of R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of Researcher in R&D employee
R&D outsourcing intensity(t-2) ⅹ
Ratio of PhD. Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of Master degree Researcher
R&D outsourcing intensity(t-2) ⅹ
Ratio of FTE Researcher
Year dummy
Adjusted R 2
Included
0.223
Model 1
-0.456(0.633)
0.038(0.037)
0.502(0.045)***
-0.078(0.067)
-0.790(0.802)
0.002(0.112)
Model 2
0.429(0.635)
0.035(0.037)
0.538(0.047)***
-0.080(0.066)
-0.839(0.800)
0.001(0.112)
0.524(0.261) *
-0.027(0.103)
0.447(0.146)**
0.029(0.128)
-0.065(0.106)
-0.039(0.051)
Included
0.243
61
Model 3
-0.234(0.647)
0.044(0.037)
0.539(0.047)***
-0.082(0.066)
-0.810(0.800)
-0.007 (0.128)
0.461 (0.270)
†
0.015(0.106)
0.451(0.146)**
-0.003(0.216)
-0.037(0.108)
-0.043(0.052)
-1.983(1.758)
1.017(0.493)*
-0.104(0.632)
-0.611(2.870)
0.670(0.570)
-0.241(0.465)
Included
0.250

F-Value(P) 19.53 17.29
Notes: †p < 0.10, *p<0.05, **p<0.01, ***p<0.001 estimated standard errors are in parentheses.
17.25
62

TABLE 11.
Summary of Results
Variables
Full
Sample
Hightechnology
Industries
(+)*
Low technology
Industries
Independent variable R&D Outsourcing(H1)
Absorptive Capacity with Internal R&D
Effort
R&D intensity(H2-1)
Ratio of R&D employee
(H2-2a)
Ratio of Researcher in R&D employee(H2-2b)
Moderating
Variables
Absorptive Capacity with organizational composition
Ratio of PhD. Researcher
(H2-2c)
Ratio of Master degree
Researcher(H2-2d)
Ratio of FTE Researcher
(H2-2e)
(+)*** (+)***
(+)*
(-)*
(+)*
Notes 1: †p < 0.10, *p<0.05, **p<0.01, ***p<0.001 estimated standard errors are in parentheses.
Notes 2: The results of Medium-High and Medium-Low technology industries are omitted because they don’t have any significant coefficients.
63