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Internalization of R&D Outsourcing: An Empirical Study

Sangyun Han,

Management of Technology Program, Yonsei University

50 Yonsei-Ro, Seodaemun-Gu, Seoul, 120-749, Korea

[email protected]

Sung Joo Bae

School of Business, Yonsei University


[email protected]

Corresponding Author:

Sung Joo Bae,

Assistant Professor of Operations and Technology Management

School of Business, Yonsei University


[email protected]

Tel: +82-2-2123-6578, Fax: +82-2-392-6706

Internalization of R&D Outsourcing: An Empirical Study 1

mailto:[email protected]



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

1. INTRODUCTION

2

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.

-------------------------------------------Insert Figure 1 about here

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R&D outsourcing also contributes to a firm’s performance. Huang et al. (2009)

3

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. EspeciallyMore 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 to commercial ends ”(Cohen and Levinthal, 1990)” with human capital

perspective adding traditional absorptive capacity studies. Because the real actors in 4

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. (Cohen, 1990 #31)(Cohen, 1990 #31)(Cohen, 1990 #31)

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. We also investigate organizational factors that affect R&D outsourcing on a

firm’s performance. The results will enrich our understanding of the relationship between

R&D outsourcing and firm performance, and identify the moderating effect of absorptive

capacity whithvia 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. LITERATURE REVIEW

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

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 5

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 decision-

making. 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 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 6

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 vView. 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

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.7

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 problem-solving 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 by using the notion of joint

problem-solving. 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).

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 8

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. It is more similar to the joint problem-solving process in which the problem is

identified and defined by one party, and then solved by another party. The solution also has to

go back to the original initiator of this problem-solving, and has to be interpreted by the

initiator. Figure 2 shows how we conceptualize the R&D outsourcing process using the

notion of joint problem-solving.

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-------------------------------------------

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.

A clear problem definition means that, what should done by the supplier is described in

official, easy-to-understand words which can be acknowledged and understood by the R&D

outsourcing supplier without much difficulty. 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).

It is important to note that a clear problem definition goes beyond codification. The

problem should also be defined with the right scope. If a problem is defined too broadly, then

the supplier will have trouble delivering exact outcomes that the firm originally intended. A

problem defined too narrowly will lead to less innovative outcomes since the supplier of

R&D will be restricted in its ability to innovate the solution. Defining the problem with just

the right scope is a critical cognitive process that is related to the success of the overall R&D

outsourcing project. When the problem is defined, the measurement of the project’s success 10

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 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)(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 11

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 fFirms

Since the original definition of (Cohen, 1990 #31),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 (Zahra and George, 2002) Zahra and George (2002) reviewed and (Lane, 1998

#30;Lane, 1998 #30)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 12

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

firms. Although the R&D’s organizational composition can be a crucial factor for using

external knowledge, Pprevious research on examining the role of R&D’s organizational

composition has been scarce. However, absorptive capacity has been identified as a crucial

factor for using external knowledge. Rosenberg (1990) found that firms with high levels of

absorptive capacity were more open to external R&D because retaining skills and an expert

staff enabled them to access wider networks. 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 Aa R&D organization usually consists of employees with various levels of skills 13

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 capacitycapability for problem defining and solution

interpretation. 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. define the problem correctly and to interpret the solution more accurately.

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

outsourcing to a firm’s performance.

3. CONCEPTUAL FRAMEWORK AND HYPOTHESES

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

hypothesize that the relationship between R&D outsourcing and firm performance is

moderated by a firm’s organizational composition due to the internalization process we

described in Figure 2. More specific hypotheses and their underlying rationale will be

described in detail.


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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

14

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,

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 15

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 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 a 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 found a positive relationship

between the share of external R&D and productivity. Guellec and Van Pottelsberghe de la

Potterie (2004) also analyzed 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 thatthat

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.16

On the contrary, there are also few some 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.

Mosakowski (1991) noted that R&D outsourcing may be negatively associated with financial

performance. Bergman (2011) also pointed out that external R&D is generally found to have

a negative effect on productivity. 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 summary, our literature review suggests that only a few econometric studies have

explored the effects of R&D outsourcing on firm performance. 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 17

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.

The results are mixed; external R&D was found to have a larger productivity effect than

internal R&D but in some studies, the effect 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 view 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. 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 18

for improving firm performance.

Hypothesis 1 (H1): More R&D outsourcing of firms is associated with better firm

performance.

3.2. Moderating Effect of Organization Composition 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). Cohen and Levinthal (1990)

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). Internal R&D effort has been

regarded as the moderating variable for R&D outsourcing in some studies. But in many other

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, internal R&D effort has been measured only

in terms of the R&D investment (Cohen and Levinthal, 1990; Escribano et al., 2009; Jones et 19

al., 2001; Lin et al., 2012; Tsai and Wang, 2008a; Tsai and Wang, 2008b; Zahra and Hayton,

2008), and not in terms of the organizational arrangement in internal R&D. 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.

Later studies started to investigate the role of internal R&D arrangement more specifically

in moderating the effect of R&D outsourcing on firm performance. In this vein, Kessler et al.

(2000) argued that R&D outsourcing is in fact an external learning process. (Chen, 2004) 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

output of external knowledge if it is also performing enough amount of internal R&D

investment. IIn 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). Mowery (1984) Tsai and Wang

(2008b) 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 20

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 knowledge transfer

internalization of 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. For

example, Tushman (1977) pointed out the existence of a gatekeeper with special boundary

roles. The gatekeeper can lead internal communication in an organization and is well

connected to external knowledge sources. In knowledge transfer, the gatekeeper can play a

crucial role in the process of problem defining, searching for a R&D outsourcing partner, and

contracting. 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 21

stock of organization. (Carter, 1989 #249)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, 1990 #31)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 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). (Mowery, 22

1995 #246) 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. (Mowery, 1995 #246@@author-year)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 factors, we look at capabilities to acknowledge and define the

problem that should be contracted to external suppliers and to interpret solutions after the

problem is solved from outsourcing R&D. In this study, we focus on the ability of human

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 23

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.

What we attempt to investigate empirically is the moderating effect of organizational

composition on how R&D outsourcing is tied to the outcome. As organizational members

have more responsibilities and knowledge, they will be more dedicated and have greater

capabilities to generate problems that will affect their organization in a positive way.

We can specifically divide the process of R&D outsourcing into several steps, as described

in Figure 2. In the technology transfer, there may be boundary spanning individuals who act

as informational boundary spanners. According to Tushman and Scanlan (1981), they develop

competency specific to internal and external units and gain access to internal and external

sources of information. They noted that informational boundary spanners have the capacity to

translate across communication boundaries and acknowledge contextual information on both

sides of the boundary. There were also internal communication stars that were technically

competent and performed an important role in transferring knowledge. Rothwell (1992) also

highlighted that links to external scientific and technical knowledge sources were effective

only if the organization was well prepared and opened to external ideas, and had a skilled

scientific and technical staff.

Therefore, we assume that absorptive capacity with organizational

compositioncapabilities of R&D organization which consist of problem

defining and solution interpretation play an important role in moderating

the effect of R&D outsourcing on firm performance.24


firm performance when the absorptive capacity with organizational composition

capabilities of problem defining and solution interpretation are higher.

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. For example, the number of

scientists, engineers, and trained engineering graduates, and personnel skill levels are used to

measure absorptive capacity (Glass and Saggi, 1998; Keller, 1996; Liu and White, 1997).

In this paper, we also focus on the composition of human resources for the internalization

after knowledge transfer. From our perspective, how a firm arranges the organization for

effective knowledge transfer within the firm affects the outcome of R&D outsourcing. In this

vein, how organizations are open to the external relationship will also be an important

organizational characteristic that can affect the process of R&D outsourcing. If an

organization is familiar and open to an external relationship, it is more likely acquire and

utilize external ideas and knowledge during the course of knowledge transfer (Rothwell,

1992).

Using the problem-solving perspective we proposed, we expect that R&D outsourcing will

be more strongly associated with the firm performance when the members of the organization

is open to new ideas generated from outside. If the members attitude is not open to new ideas

generated from outside, the effect of R&D outsourcing will be limited even when the

organization is equipped with high level of capabilities in problem defining and solution 25

interpretation because the results will be not be valued as much.


firm performance when the level of firm’s openness is higher.

4. RESEARCH METHODS

[4.1.] DataModel

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

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.


-------------------------------------------

4.1. D ata

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

-------------------------------------------

26

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.

4.2. Variables and Measures

The variables used in the analyses are defined as follows. Firm performance as a

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.

Variables of the organizational composition to investigate moderating effects are identified

by three different categories. The R&D intensity of firms is used as the proxy for internal 27

R&D effort as a 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 the capabilities of problem defining

and solution interpretation are measured are measured by various human resource ratios in

organization.


-------------------------------------------

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

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.


-------------------------------------------

We considered that the problem defining and solution interpretation process 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 28

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

problem defining and solution interpretation R&D Outsourcing since one has to put in a lot of

effort to internalize the external knowledge define the problem in a way that it can positively

affect the organization. when the problem is solved. We can infer that a full-time R&D

employee is more responsible than a part-time 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. The last variable of organizational composition is openness. It

is measured as the amount of export divided by amount of sales.

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

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.


-------------------------------------------

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 29

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.


-------------------------------------------

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

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.


-------------------------------------------

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 30

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.

4.3. 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

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.

Here, y it is the dependent variable, x it is independent variables excluding the constant, α

is the intercept, β is a parameters, υi is an unobservable individual and firm-specific effect as

a time invariant, and ε it is an idiosyncratic error term.

We used the panel analysis for correcting the estimation bias from unobservable

31

i=1,2 , .. . ,Nt=1,2 ,. . ., Tε it ~ i . i .d . (0 , σ

2e )

y it=α +x it' β+υi+εit

exogeneity rather than doing a cross-sectional analysis. For the panel analysis, it matters if υi

was correlated with independent variables or not. An unobservable individual and firm-

specific effect usually does not changed by time. We can examine the analysis with a random

effect model, if we assume that υi is uncorrelated with independent variables. But if υi 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 υi

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 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.

The descriptive statistics are shown in Table 3. Note that before doing our empirical

analysis, we tried to test whether there was multicollinearity through VIF. The result of VIF

was that all variables were 1.29.


-------------------------------------------

5. RESULTS

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 4 6 lists the results for our initial analysis before we split the sample for a more detailed

32

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 4 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 organizational factors - absorptive

capacity, capabilities of defining problem and solution interpretation, openness - 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=2.824, p<0.051) at the five-percent significance level. So, a

greater ratio of researchers in R&D employees has a direct, positive effect firm performance.

The coefficient of openness was also positive (t=2.25, p<0.01) at the one-percent significance

level. Among the moderating variables, only Hypothesis H2-1 was confirmed. In other words,

the absorptive capacity with internal R&D effort has a positive (t=4.484, 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).

33

In order to examine our hypotheses in more details, as we mentioned earlier, we decided to

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.

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 5). According to the OECD, manufacturing industries can be classified into different

categories based on their level of 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.


-------------------------------------------

The results of the split-sample analysis are listed and Model 3 is used for discussion in

Table 67 through 910. 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

34

technology through R&D outsourcing.

-------------------------------------------Insert Table 6, 7, 8, and 9 7, 8, 9 and 10about here

-------------------------------------------

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

67), 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) (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=1.957, p<0.05)

significant effect on R&D outsourcing regarding a firm's performance. In addition, the ratio

of FTE (Full-Time researcher) (H2-62e) 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 internal capabilities for problem defining and solution

interpretation after knowledge transfer. For example, Hoffman et al. (1998) noted that most

35

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

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, and openness

(H2-3) in absorptive capacity with organizational factors are not significant for all split

groups. According to the results shown in Table 910, the coefficient of R&D employee ratio

in low technology industries is significantly positive (t=2.0406) 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. CONCLUSION

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 36

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. Before entering a

contract to outsource R&D and after knowledge transfer, So we assumed that a firm’s

capabilities to clearly define the absorptive capacity with internal R&D effort and

organizational composition the problem and interpret the solution 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 1011. 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 37

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 problem defining

and solution interpretation 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 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.

In particular, the process of problem defining and technology transfer requires high capacity

to specify the nature and condition of the needed technology through R&D outsourcing. In

the internalization process, as 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.


-------------------------------------------

The descriptive statistics was presented in Table 11 to explain why the empirical results

are differentiated in Table 10. 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.

38


-------------------------------------------

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 & master degree researchers, and openness between

high and low technology industries are very different. However, the average of the FTE

researcher ratio is 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.

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.


-------------------------------------------

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

very similar between high and low technology industries. We can infer that these distinctions 39

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’ internalization absorptive capacity with

organizational compositioncapabilities – problem definition, contracting, and solution

interpretation. 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 as a joint

problem-solving process rather than a mere learning or knowledge transfer process. 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 the capabilities of problem

defining and solution interpretation.absorptive capacity with organizational composition. We

40

plan to investigate the impact of diversity as a moderating effect of R&D outsourcing on a

firm's performance.

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). Our findings in this study suggest that as we move to more

basic level research, which we think will affect how fundamental and uncertain the nature of

the problem can be, the role of organizational composition might become more crucial in

moderating the effect of R&D outsourcing. 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.

41

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49

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

50

Figure 3. Conceptual Framework

Figure 4. Classification of Human Resources in R&D

51

Table 1. Description of Data Set (2002-2007)

Financial Statement Data

R&D Activity Survey Merged dataset

year No. of firms year No. of firms year No. of firms

2002 14,108 2002 7,178 2002 2,793

2003 14,973 2003 6,991 2003 2,988

2004 15,588 2004 8,300 2004 2,795

2005 16,567 2005 9,837 2005 3,221

2006 17,788 2006 12,639 2006 3,908

2007 18,083 2007 14,966 2007 3865

Total 97,407 Total 59,911 Total 19,570

52

Table 2. Definition of Variables and Data Sources

Category Variables Definition Data source

Dependent variable

Firm performance Sales ln(amount of

sales) KIS

Independent variables

Level of R&D outsourcing

R&D outsourcing intensity

Amount of R&D outsourcing/ Total R&D

expenditure

R&D Activity Survey

Absorptive capacity

Absorptive capacity via internal R&D

effortR&D intensity

R&D expenditure/

amount of salesKIS

Absorptive capacity via

organizational composition

Ratio of R&D employee

No. of researcher and

assistant/Total employee

R&D Activity Survey

Ratio of researcher in R&D employee

No. of researcher/

R&D employeeRatio of PhD. researcher

No. of PhD. researcher/ R&D

employeeRatio of Masters

degree researcherNo. of PhD.

researcher/ R&D employee

Ratio of FTE(Full time employee) researcher

No. of FTE of researcher/ R&D

employee

Control variables

Level of internationaliz

ation Openness

Amount of export/amount of

sales

KIS

SizeCapital ln(Total capital)

No. of employee ln(No. of employee)

Financialsoundness Capital adequacy ratio Stockholders'

equity/total assetLevel of market

competitionCR4

The sum of market share of 4 largest firms in

KSIC 2 digitYear dummy Year dummy year

53

Table 3. Descriptive Statistics and Correlation Matrix of Variables (N=19,570)

Variables 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

ratioCR4

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 0.025 0.056 0 0.997 - 0.298***

0.035*** 1.000

Ratio of R&D employee 0.166 0.161 0 1.000 - 0.421

*** - 0.007 0.355*** 1.000

Ratio of Researcher in R&D employee 0.831 0.201 0 1.000 - 0.081

*** 0.036***

0.046*** - 0.007 1.000

Ratio of PhD. Researcher 0.036 0.097 0 1.000 - 0.015

* 0.051***

0.095***

0.047***

0.108*** 1.000

Ratio of Master degree Researcher 0.216 0.223 0 1.000 0.042

***0.090***

0.136***

0.093***

0.303***

0.154*** 1.000

Ratio of FTE Researcher 0.846 0.224 0 1.000 - 0.040

*** 0.011 0.023**

0.014*

0.323***

0.040***

0.099*** 1.000

Openness 0.005 0.039 0 0.987 0.043***

0.283***

0.027*** - 0.001 0.006 0.007 0.026

*** 0.001 1.000

Capital 7.965 1.506 3.912 17.082 0.706***

0.034***

-0.091***

- 0.220***

- 0.016*

0.063***

0.164***

- 0.018*

0.042*** 1.000

No. of employee 4.740 1.188 0.693 11.367 0.854*** -0.001 - 0.185

***- 0.471

***- 0.056

*** 0.007 0.103***

- 0.041***

0.042***

0.690*** 1.000

capital adequacy ratio 0.464 0.310 - 9.523 1.000 0.056

***0.016

*0.015

*0.035***

- 0.015* 0.006 0.073

*** 0.002 0.004 0.042***

0.045*** 1.000

CR4 0.356 0.177 0.078 0.975 0.059***

-0.037***

0.068***

0.098***

0.022**

-0.027*** - 0.005 - 0.001 0.020

*0.100***

0.083***

- 0.061*** 1.000

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

54

Table 4. ISIC REV. 3 technology intensity definition of OECD

High-technology industries Medium-high-technology industries

Aircraft and spacecraft 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 Machinery and equipment, n.e.c.

Medium-low-technology industries Low-technology industries

Building and repairing of ships and boats Manufacturing, n.e.c.; Recycling

Rubber and plastics products Wood, pulp, paper, paper products, printing and publishing

Coke, refined petroleum products and nuclear fuel Food products, beverages and tobacco

Other non-metallic mineral products Textiles, textile products, leather and footwear

55

Table 54. Comparison of StatisticsDescriptive Statistics Staticsitcs of major variables within the sample

Full Sample(N=19,570)

High technology industries(N=5,749)

Medium-Hightechnology industries

(N=7,812)

Medium-Lowtechnology industries

(N=2,851)

Low technology industries(N=3,158)

Variable Mean Std. Dev. Mean Std. Dev. Std. Dev. Std. Dev. Mean Std. Dev. Mean Std. Dev.

Sales(USD. Mil.) 198.947 1,473.684 161.053 2,105.263 155.789 1024.211 342.105 1,715.789 92.000 220.000

R&D intensity 0.025 0.056 0.040 0.075 0.033 0.040 0.029 0.020 0.025 0.055

R&D Outsourcing intensity 0.066 0.147 0.071 0.143 0.053 0.121 0.062 0.130 0.046 0.118

Ratio of R&D employee 0.166 0.161 0.226 0.195 0.146 0.113 0.098 0.086 0.186 0.194

Ratio of Researcher in R&D employee 0.831 0.201 0.849 0.174 0.821 0.197 0.809 0.209 0.840 0.220

Ratio of PhD. Researcher 0.036 0.097 0.041 0.096 0.038 0.086 0.036 0.106 0.035 0.100

Ratio of Master degree Researcher 0.216 0.223 0.258 0.233 0.186 0.205 0.172 0.205 0.219 0.222

Ratio of FTE Researcher 0.846 0.224 0.854 0.214 0.845 0.221 0.828 0.226 0.852 0.230

56

57

Table 46. Results of Panel Analysis (n = 19,570)

Dependent Variable : sSales

Independent Variables Model 1 Model 2 Model 3

Openness 0.368(0.143) † 0.373(0.143)** 0.375(0.143)**

Capital 0.106(0.016)*** 0.104(0.015)*** 0.101(0.016)***

No. of employee 0.550(0.017)*** 0.571(0.018)*** 0.570(0.018)***

capital adequacy ratio 0.087(0.021)*** 0.084(0.02)** 0.084(0.021)***

CR4 0.059(0.200) 0.040(0.200) 0.0353(0.200)

R&D Outsourcing intensity(t-2) R&D Outsourcing intensity(t-2) 0.02634(0.036) 0.036(0.036) 0.017(0.037)

R&D intensity(t-2) 0.037(0.119) 0.0285(0.119)

Ratio of R&D employee 0.016(0.038)* 0.014(0.038)

Ratio of Researcher in R&D employee 0.196(0.069) 0.1945(0.069)**

Ratio of PhD. Researcher -0.033(0.50) -0.0321(0.050)

Ratio of Master degree Researcher 0.025(0.037) 0.022(0.037)

Ratio of FTE Researcher 0.002(0.021) 0.0012(0.021)

Openness 0.373(0.143)** 0.330(0.147)**R&D outsourcing intensity(t-2) R&D intensity(t-2)ⅹ 2.69673(0.602)***R&D outsourcing intensity(t-2) Ratio of R&D employeeⅹ -0.06370(0.198)

R&D outsourcing intensity(t-2) Ratio of Researcher in R&D employeeⅹ 0.25865(0.217)

R&D outsourcing intensity(t-2) Ratio of PhD. Researcherⅹ 0.1276(0.354)

R&D outsourcing intensity(t-2) Ratio of Master degree Researcherⅹ 0.1876(0.165)

R&D outsourcing intensity(t-2) Ratio of FTE Researcherⅹ 0.06772(0.135)R&D outsourcing intensity(t-2) Opennessⅹ 1.332(0.999)Year dummy Included Included IncludedAdjusted R2 0.250 0.252 0.256F-Value(P) 16.50*** 15.35*** 15.3940***


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Table 67. Result of Panel Analysis (High-Technology Industries, N=7,812)

Dependent Variable : sales

Variables Model 1 Model 2 Model 3

Openness 0.279(0.237) 0.306(0.238) 0.329(0.235)Capital 0.072(0.035)* 0.069(0.035)* 0.066(0.034)No. of employee 0.695(0.034)*** 0.717(0.037)*** 0.708(0.036)***

capital adequacy ratio 0.326(0.048)*** 0.321(0.048)*** 0.323(0.048)***

CR4 -0.23341(0.684) -0.338(0.688) -0.535(0.681)

R&D Outsourcing intensity(t-2) 0.16270(0.077)* 0.175(0.077)* 0.023(0.084)*

R&D intensity(t-2) -0.026(0.188) 0.083(0.187)

Ratio of R&D employee -0.049(0.088) -0.094(0.088)

Ratio of Researcher in R&D employee 0.188(0.128) 0.129(0.126)

Ratio of PhD. Researcher -0.105(0.114) -0.093(0.115)

Ratio of Master degree Researcher 0.015(0.084) -0.005(0.083)

Ratio of FTE Researcher 0.043(0.050) 0.043(0.050)Openness 0.306(0.238) 0.324(0.238)

R&D outsourcing intensity(t-2) R&D intensity(t-2)ⅹ 5.264(0.956)***

R&D outsourcing intensity(t-2) Ratio of R&D employeeⅹ -0.728(0.532)


R&D outsourcing intensity(t-2) Ratio of PhD. Researcherⅹ 1.605(0.824) *


R&D outsourcing intensity(t-2) Ratio of FTE Researcherⅹ -0.733(0.368)*

R&D outsourcing intensity(t-2) Opennessⅹ 0.307(2.133)

Year dummy Included Included IncludedAdjusted R2 0.336 0.338 0.360

F-Value(P) 10.23*** 9.15*** 9.3439***

N 2,232 2,232 2,232 Notes: †p < 0.10, *p<0.05, **p<0.01, ***p<0.001 estimated standard errors are in parentheses.

59

60

Table 78. Result of Panel Analysis (Medium-High-Technology Industries, N=2,581)



Openness 0.532(0.280)* 0.530(0.280) 0.507(0.281) †

Capital 0.0786(0.029)** 0.075(0.029)** 0.0745(0.029)**

No. of employee 0.3789(0.030)*** 0.383(0.035)*** 0.3843(0.035)***

capital adequacy ratio 0.1424(0.067)* 0.144(0.067)* 0.144(0.067)*

CR4 -2.24662(0.5865)*** -2.270(0.587)*** -2.26772(0.588)***

R&D Outsourcing intensity(t-2) 0.11121(0.069) † 0.127(0.069) 0.1167(0.070) †

R&D intensity(t-2) 0.268(0.257) 0.3138(0.279)

Ratio of R&D employee 0.075(0.065) 0.074(0.065)


Ratio of PhD. Researcher -0.200(0.101)* -0.1768(0.104) †

Ratio of Master degree Researcher -0.129(0.071) -0.1265(0.072) †

Ratio of FTE Researcher -0.038(0.036) -0.0290030(0.040)

Openness 0.530(0.280) 0.383(0.300)

R&D outsourcing intensity(t-2) R&D intensity(t-2)ⅹ -0.643784(1.4218)

R&D outsourcing intensity(t-2) Ratio of R&D employeeⅹ -0.262(0.37068)




R&D outsourcing intensity(t-2) Ratio of FTE Researcherⅹ 0.412397(0.297)


Year dummy Included Included Included

Adjusted R2 0.1898 0.194 0.196

F-Value(P) 12.18*** 11.76*** 11.71***

61


62

Table 89. Result of Panel Analysis (Medium-Low-Technology Industries, N=5,749)Dependent Variable : sales


Openness 0.234(0.310) 0.243(0.307) 0.338(0.320)

Capital 0.1467(0.039)*** 0.134(0.039)** 0.132(0.039)**

No. of employee 0.590(0.048)*** 0.626(0.056)*** 0.620(0.0576) ***

capital adequacy ratio 0.237(0.084)* 0.227(0.084)** 0.2278(0.084)**

CR4 -1.107080(0.587588) -1.299(0.593)* -1.260(0.600598)*

R&D Outsourcing intensity(t-2) -0.11509(0.080) -0.096(0.080) -0.120(0.100)

R&D intensity(t-2) -0.204(0.662) -0.0915(0.6921)

Ratio of R&D employee -0.155(0.079)* -0.1654(0.080)**


Ratio of PhD. Researcher 0.196(0.088)* 0.200(0.089) *

Ratio of Master degree Researcher 0.282(0.072)*** 0.286(0.072) ***

Ratio of FTE Researcher 0.110(0.050)* 0.108(0.050) *

Openness 0.243(0.307) 0.319(0.360)

R&D outsourcing intensity(t-2) R&D intensity(t-2)ⅹ -1.0550.957(4.0003.875)

R&D outsourcing intensity(t-2) Ratio of R&D employeeⅹ 0.4713(0.453)

R&D outsourcing intensity(t-2) Ratio of Researcher in R&D employeeⅹ -0.516(0.8487)



R&D outsourcing intensity(t-2) Ratio of FTE Researcherⅹ -0.07184(0.37660)


Year dummy Included Included Included

Adjusted R2 0.3798 0.399 0.402403

F-Value(P) 24.34 23.26 22.8723.00

63


64

Table 9 10. Result of Panel Analysis (Low-Technology Industries, N=3,158)



Openness -0.456(0.633) 0.429(0.635) -0.234(0.647)

Capital 0.04038(0.037) 0.035(0.037) 0.0424(0.037)

No. of employee 0.502(0.05045)*** 0.538(0.047)*** 0.539(0.047)***

capital adequacy ratio -0.0768(0.067) -0.080(0.066) -0.082(0.066)

CR4 -0.803790(0.802) -0.839(0.800) -0.8130(0.798800)

R&D Outsourcing intensity(t-2) 0.0082(0.112) 0.001(0.112) 0.078-0.007 (0.13628)

R&D intensity(t-2) 0.524(0.261) * 0.521461 (0.2710) †*

Ratio of R&D employee -0.027(0.103) 0.03115(0.106) **

Ratio of Researcher in R&D employee 0.447(0.146)** 0.46751(0.146) **

Ratio of PhD. Researcher 0.029(0.128) -0.011003(0.216)

Ratio of Master degree Researcher -0.065(0.106) -0.0387(0.108)

Ratio of FTE Researcher -0.039(0.051) -0.0463(0.052)

Openness -0.429(0.635) -0.616(0.678)

R&D outsourcing intensity(t-2) R&D intensity(t-2)ⅹ -1.627983(1.7658)

R&D outsourcing intensity(t-2) Ratio of R&D employeeⅹ 1.3221.017(0.647493)*

R&D outsourcing intensity(t-2) Ratio of Researcher in R&D employeeⅹ -0.243104(0.6352)

R&D outsourcing intensity(t-2) Ratio of PhD. Researcherⅹ -0.720611(2.86670)


R&D outsourcing intensity(t-2) Ratio of FTE Researcherⅹ -0.353241(0.4685)


Year dummy Included Included IncludedAdjusted R2 0.222223 0.243 0.2530

F-Value(P) 19.53 17.29 17.3225

N 1,160 1,160 1,160

65


66

TABLE 1011.

Summary of Results

Organizational CompositionVariables

Full Sample

High-technology Industries

Low technology Industries

Independent variable R&D Outsourcing(H1) (+)*

Moderating Variables

-

Absorptive Capacity with Internal R&D

EffortR&D intensity(H2a-1) (+)*** (+)***

Absorptive Capacity with organizational

composition

Ratio of R&D employee(H2-b2a) (+)**

Ratio of Researcher in R&D employee(H2-c2b)

Ratio of PhD. Researcher(H2-d2c) (+)*

Ratio of Master degree Researcher(H2-e2d)

Ratio of FTE Researcher(H2f-2e) (-)*

Openness(H2g)

Notes 1: †p < 0.10, *p<0.05, **p<0.01, ***p<0.001 estimated standard errors are in parentheses.

***p<0.001, **p<0.01, *p<0.05, 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.

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