The impact of R&D cooperation on R&D investments, R&D outputs,

This section extends prior non-tournament models (e.g. D’Aspremont and Jacquemin 1988; Kamien et al. 1992; Inkmann 2000; Atallah 2002; Ishii 2004) and uses a more general market structure, including upstream and downstream industries with n firms located in each industry ( See Figure 2). This model is capable of analyzing the influence of different types of R&D cooperation on R&D investments, R&D outputs, and financial performance.

Figure 2: Market structure of the theoretical model of this study

Consistent with prior literature, I assume that upstream firms sell a homogeneous input to downstream firms at price p . Downstream firms then use the same ^u technology and intermediate inputs to produce the same amount of homogenous final goods, and finally sell them to consumers at pricep . These assumptions lead to the ^d following three-stage model:¹²

1. The first stage: All firms choose their R&D outputs simultaneously to maximize their own profits.

2. The second stage: Upstream firms engage in Cournot competition and decide the quantity and price of intermediate inputs based on the derived inverse demand of

12 In practice, the interaction between industries is not necessary to follow the certain sequence.

Firm 1 Firm 2 … Firm n

Firm 1 Firm 2 … Firm n

Consumers

Final goods Intermediate goods Upstream industry

Downstream industry

upstream firms.

3. The third stage: Downstream firms engage in Cournot competition and decide the quantity and price of final goods given the price of the intermediate good and R&D in all industries. (See Figure 3).

Figure 3: The three-stage Cournot competition

Downstream firm i(i=1,2,...n) produces y_i^dunits of final goods. Upstream firm i )(i=1,2,...n produces y units of the outputs. Downstream and upstream firms _i^u incur marginal production costs of c and d. Downstream firms face the linear inverse demand function:

d

d a bY

p = − ,

where a>0 and Y^d =

∑

ⁿ_i=₁y_i^d .

Firms can engage in cost-reducing R&D activities. Denoting the R&D output of the downstream firms byx and upstream firms by _i^d x_i^u (i=1,2,...n), the effective R&D level (D’Aspremont and Jacquemin 1988) is defined as:

∑

14 R&D has two effects. One is that one firm can benefit from other firms’ research, which is spillover (positive effect). Another one is that other firms’ research will reduce the probability of success in R&D,

[ ]

0,1

∈

h v∈

[ ]

0,1 (1) (2) The first stage

All firms simultaneously choose their R&D

outputs.

(R&D stage)

The second stage Upstream firms engage in Cournot

competition.

(Output stage)

The third stage Downstream firms engage in Cournot

competition.

(Output Stage)

where h∈

[ ]

0,1 is the horizontal spillover rate of competitors’ R&D, and v∈

[ ]

0,1 is the vertical spillover rate between upstream and downstream firms.¹⁴ According to D’Aspremont and Jacquemin (1988), firm i’s effective R&D (X ) includes producing _i R&D by itself (x ) and receiving R&D from competitors (_i

i n

i

j x

h

∑

≠ ) and vertically related industry (

i n

i x

v

∑

=1 ). Taking downstream firm i as an example, firm i’s effective R&D level (X ) consists of the firm i’s own R&D level, the percentage h _i^d of the firm i’s competitors’ R&D outputs (

∑

≠

n i j

d

xj

h ), and the fractionvof the total R&D outputs produced by upstream industry (

∑

=

n i

u

xi

v 1 ). If there are no knowledge spillovers (h= v=0), then the effective R&D level is firm i’s own R&D outputs.

(See Table 9 for notation references).

Table 9: Summary of model notation

Notation Description a Inverse demand intercept

b Inverse demand slope

pu Price charged by upstream firms pd Final product price

π Profits of firm i

u

yi Outputs of upstream firm i

d

yi Outputs of downstream firm i Y Total outputs

c Basic costs of production of a downstream firm d Basic costs of production of a upstream firm γ Parameter of the R&D cost function

h Horizontal knowledge spillovers

v Vertical knowledge spillovers between upstream firms and downstream firms

xu R&D outputs of upstream firm i xd R&D outputs of downstream firm i X Effective R&D level

NC R&D competition (No cooperation) VC Vertical cooperation

HC Horizontal cooperation GC Generalized cooperation

Output stages The third stage

Solving the model by means of backward induction, I begin with the third stage in which downstream firms engage in Cournot competition given the price of the intermediate goods and the R&D, and assume that diminishing returns in R&D outputs are measured by the parameterγ .¹⁵ The profit function can be written as:

2

Solving the total downstream industry output =

∑

ⁿ= i

The produced quantity of downstream firm i (eq. 4) increases with its own R&D outputs and with the amount of R&D outputs engaged by upstream firms. The quantity decreases with increasing upstream intermediate goods prices and with increasing R&D outputs of competitors, unless horizontal spillovers are sufficiently high (h>0.5). However, total downstream industry output (eq. 6) increases with R&D output, no matter who engages in R&D activities.

From equation (6) I derive the inverse demand function for the upstream

represent the costs of R&D investments of the downstream and upstream firm. The R&D cost function is the standard quadratic cost function introduced by D’Aspremont and Jacquemin (1988) to capture the phenomenon of decreasing returns to R&D expenditure.

16 d

X means cost reduction (i.e. profit increase) when firms engage in cost-reducing R&D activity

(4) (3)

(5)

(6)

industry:

The second stage

In the second stage, after replacing p in the upstream profit function with ^u (7), upstream firms decide non-cooperatively on their output. Upstream firm i solves the following problem:

2

A stable Cournot equilibrium exists which is characterized by the produced quantities )

Solving the total upstream industry outputs =

∑

ⁿ= i

The produced quantity of upstream firm i (eq. 9) increases with its own R&D outputs and with the amount of R&D outputs engaged by upstream and downstream firms. The quantity decreases with increasing upstream intermediate goods’ prices and with increasing R&D outputs of competitors unless knowledge spillovers are sufficiently high (2h+ v₁ >1). However, total upstream industry output (eq. 10) increases with R&D outputs, no matter who engages in R&D activities.

Given that each unit bought from the upstream industry is transformed into the same unit used by the downstream industry, and total output is the same for upstream and downstream industries, I substitute Y in (7) with ^d Y determining price ^u p _u of the intermediate goods in terms of R&D.

(7)

(8)

(9)

(10)

( )

The first stage

The first stage profit function in the two industries can be expressed as follows:

( )

1

( (

1

) )

₁ ^{2 2} convenience. In the first stage, firms maximize their profits with respect to R&D, regardless of R&D competition or R&D cooperation.

To compare the variety in R&D cooperation, four R&D scenarios are distinguished in this stage (See Figure 4).

(1) R&D competition (or No cooperation) (NC): R&D competition between firms.

(2) Vertical cooperation (VC): R&D cooperation between upstream and downstream industries.

(3) Horizontal cooperation (HC): R&D cooperation with competitors.

(4) Generalized cooperation (GC): R&D cooperation with competitors and vertical industries simultaneously.

(11)

(12)

(13)

Figure 4: Different types of R&D cooperation

R&D competition (NC)

In the no cooperation scenario, each firm chooses its R&D to maximize its own profits with respect to its R&D, given that other firms do the same. The problem of upstream firm i is noted as:

( )

1

( (

1

) )

₁ ^{2 2}

R&D efforts conducted in the downstream industries always serve as a strategic complement for an upstream firm’s own R&D investment, while the R&D investment of the firm’s competitor is a strategic substitute unless overall knowledge spillovers

(14) R&D

competition

Downstream1

Upstream2

Upstream1 Upstream 3

Downstream 3

Upstream1 Upstream2 Upstream3 ….. Upstream N

Downstream1 Downstream2 Downstream3 ….. Downstream N Horizontal

cooperation

Upstream1 Upstream2 Upstream3 ….. Upstream N Downstream1 Downstream2 Downstream3 ….. Downstream N Generalized

cooperation

are sufficiently high (B>0⇔2h+v>1).

The problem of downstream firm i is:

( )

R&D efforts conducted in the upstream industries always serve as a strategic complement for a downstream firm’s own R&D investment, while the R&D investment of the firm’s competitor is a strategic substitute unless overall knowledge spillovers are sufficiently high (5

(

n−1

)

B−A>0⇔11

(

n−1

)

h+(4n−5)v>6n+5)).

The maximization and simultaneous solving of the first-order condition of equations (14) and (15) yield research outputs under NC by each upstream and downstream firm:

Vertical cooperation (VC)

Under the vertical R&D cooperation scenario, given that downstream firms are identical, as well as upstream firms, I assume that downstream firm i cooperates with upstream firm i. All firms maximize the joint profits:

d

The maximization of equation (18) yields research outputs under VC:

( ) ( )

Horizontal cooperation (HC)

Under HC, there is intra-industry cooperation, but no inter-industry cooperation.

Upstream firms maximize their joint profits π_i^u + ...+π_n^u with respect to their R&D

Simultaneous solving the first-order condition of equations (20) and (21) yields research outputs under HC:

( ) ( ( ) )

Generalized cooperation (GC)

Under GC each firm chooses its R&D to maximize the total profits of all firms:

d u x x^{u d}

Max _, π +π .

The maximization of equation (24) yields research efforts under GC:

( ) ( ( ) )

Finally, the equilibrium profits can be obtained as follows:

(

¹

) ( )

^{N 2}

( )

^{u 2}

Comparison of R&D cooperation scenarios

In this section the different types of cooperation are compared with R&D competition, in terms of R&D investments, R&D outputs,¹⁷ and firm profits.¹⁸ Following the approach proposed by Steurs (1995), I simulate the equilibrium R&D investments, R&D outputs, and profits by varying the two spillover parameters v and h for given values of parameters a,b,c,d,γ , and set the number of firms as two for convenience of comparison. The simulations reveal that the R&D investments and profits in most cases display exactly the same ranking as the R&D outputs given above. The simulation results also show that vertical cooperation always dominates R&D competition regarding R&D investments, R&D outputs, and profits. For generalized cooperation, the R&D investments, R&D outputs, and profits are higher than those of horizontal cooperation, vertical cooperation, and R&D competition when vertical and horizontal spillovers are high. All in all, R&D cooperation leads to higher R&D investments, R&D outputs, and financial performance under high spillovers (see Table 10).

In order to make sure that simulation results are robust, I also test the impact of the numbers of cooperative firms (2, 5, 10, and 20) on R&D investments, R&D outputs, and profits. The simulation results are presented in Appendix A and Table A1- A12, and show that same ordering usually applies to different numbers of cooperative firms. Therefore, the results are further confirmed.

17 I focus on R&D outputs, not effective R&D. Although the latter is more meaningful from a social point of view, R&D outputs are more amenable to empirical testing.

bΓ 2

Table 10: Ranking of firms’ R&D investments, R&D outputs, and firm profits (a=100, b=1, c=1, d=1, γ =70, n=2)

No spillovers

(0,0)

Perfect horizontal spillovers and no

vertical spillovers (1,0)

No horizontal spillover and perfect vertical spillovers (0,1)

Perfect spillovers (1,1)

R&D investments

2 4 3 4

R&D outputs

2 4 3 4 R&D

competition

Profits 2 4 3 4

R&D investments

1 2 2 2

R&D outputs

1 2 2 2 Vertical

cooperation

Profits 1 2 2 2

R&D investments

4 2 4 2

R&D outputs

4 2 4 2 Horizontal

cooperation

Profits 4 2 4 2

R&D investments

3 1 1 1

R&D outputs

3 1 1 1 Generalized

cooperation

Profits 3 1 1 1

Analyzing R&D cooperation with asymmetric spillovers, Atallah (2005) finds that R&D cooperation increases total R&D investments when the average of firms’

spillover rates is sufficiently high. According to Griliches’s (1990) surveys of the empirical literature, knowledge spillovers are both prevalent and important. Mansfield, Schwartz, and Wagner (1981) show that about 60% of the patented innovations in their sample were imitated within 4 years. Veugelers (1998) points out that telecommunications, semi-conductors, instruments, chemicals, and electronics industries all have high spillovers.

Irrespective of whether the research is theoretical or empirical, more literature has emerged to identify a positive impact on firm performance of engaging in R&D cooperation (e.g. Kamien et al. 1992; Hagedoorn and Schakenraad 1994; Steurs 1995; Petit and Tolwinski 1999; Sarkar et al. 2001; Cassiman and Veugelers 2002;

Chung and Kim 2003). Steurs (1995) analyzes the impact of intra-industry and inter-industry knowledge spillovers on the level of strategic R&D investments, output, profits and total welfare. The results show that inter-industry cooperation is more socially beneficial than cooperation in single industry firms (intra-industry cooperation). Hagedoorn and Schakenraad (1994) study the effects of strategic technology alliances on company performance. The results indicate that companies attracting technology through their alliances, and companies concentrating on R&D cooperation, have significantly higher rates of profit. Sarkar et al. (2001) also investigate the effect of alliance entrepreneurship on market-based firm performance.

Results indicate that alliance proactiveness leads to superior market-based performance. From the supplier’s standpoint, Chung and Kim (2003) analyze the effects of supplier involvement in a manufacturer’s new product development on the supplier’s financial performance, innovation, and product quality. The results indicate that a higher level of supplier’s involvement positively impacts innovation and financial performance.

Engaging in R&D collaboration also has positive impact on R&D investments and R&D outputs. Peters and Becker (1997-98) provide empirical evidence that R&D spillovers strategically transferred from the manufacturers to their suppliers in vertical cooperative networks increase the probability of members’ successfully realizing an innovation and stimulating R&D investments over the case with nonmembers. Kaiser

cooperating firms invest more in research than do non-cooperating firms. Stuart (2000) investigates the relationship between intercorporate technology alliances and innovation rates. The findings from models of innovation rate confirm that organizations with large and innovative alliance partners perform better than comparable firms that lack such partners. Chang (2003) surveys the innovative activities and inter-organizational cooperation of integrated circuits and biotechnology sectors across Taiwan and UK. The result reveals that firms with a more active role in establishing inter-organizational linkages increase their chances to innovate.

During my interview with Corporation A, the largest high-technology company in the world, one manager indicated that R&D is very complex and risky nowadays.

Even a large-scale company can not monopolize product innovation completely. The company needs to cooperate with its suppliers, customers, and even competitors in R&D. In addition, the company needs to make compatible products because of customers’ demand. Therefore, R&D cooperation is a popular phenomenon for high-technology industry relative to other industries. R&D cooperation leads his company to higher profits and creates an economy of large scale. Based on the theoretical model, simulation results, prior literature, and interview, I develop the following hypotheses.

H5a: Higher R&D cooperation intensity leads to higher R&D investments.

H5b: Higher R&D cooperation intensity leads to higher R&D outputs.

H5c: Higher R&D cooperation intensity leads to higher financial performance.

Summarizing several calculations, the following ranking of the equilibrium R&D outputs can be established¹⁹:

Proposition 1

Proof. See Appendix B.

Figure 5 illustrates the ranking of different R&D cooperation types based on above conditions. This figure is divided into 5 regions, each region being characterized by a ranking of different cooperation. The parameter space is spanned by the horizontal spillover parameter h in the horizontal dimension and the vertical spillover parameter v in the vertical dimension. Region 1 is characterized by low spillovers. In this region VC>NC>GC>HC. As spillovers increase, we move into Region 2, where the ranking of GC and NC is reversed: VC>GC>NC>HC. As spillovers increase further, we move into Region 3, where GC comes to dominate all other cooperation types. When spillovers increase further, we move into Region 4:

HC>NC. Finally, when h=1 (Region 5), the horizontal competitive externality increases further: HC=NC. Note that for the largest part of the spillovers space, GC dominates all other cooperation types, followed by VC. R&D investments and profits also lead to the same conclusions.

1

3/23

2/5

1/10

1/11 2/7 13/23 1

v

h GC>VC>NC>HC

7h+5v>2

GC>VC>HC>NC 23h+10v>13

VC>GC>NC>HC 11h+10v>1

VC>NC>GC>HC 0

GC>VC=HC>NC

1 2

3

4 5

The strategic network theory argues that network organizations are able to capture the benefits of specialization, focus, and scale (Hagedoorn et al. 2000).

Networks can be formed to exploit the different competencies of cooperative firms (Miles and Snow 1984). In addition, early adopters of network strategies can enjoy a first-mover advantage (Miles and Snow 1984). Regarding the research related to the Taiwanese industry system and industry organization, the results consistently point out that the network organization is a widespread form of industry system in Taiwan (Chen 2003). For example, MediaTek Inc. cooperates with Global Mixed-mode technology Inc. (MediaTek Inc.’s competitor) on power management. Then they integrate their IC with the products of BenQ (customer). In this kind of cooperation relationship, partners benefit from each other, which in turn leads to higher R&D investment, R&D outputs, and financial performance. Therefore, I propose following hypotheses:

H6a: Generalized R&D cooperation leads to higher R&D investments relative to other cooperation types if knowledge spillovers are “large” (7h+5v>2).

H6b: Generalized R&D cooperation leads to higher R&D outputs relative to other cooperation types if knowledge spillovers are “large” (7h+5v>2).

H6c: Generalized R&D cooperation leads to higher financial performance relative to other cooperation types if knowledge spillovers are “large” (7h+5v>2).

During the industrial era, companies succeeded based on how well they captured the benefits from scales of economy. The traditional financial performance measures worked quite well in that period. However, with a shift from the industrial economy towards an economy now predominantly characterized by intangible assets such as knowledge and innovation, the ability of a firm to mobilize and exploit its intangible assets has become far more decisive than investing and managing tangible assets (e.g.

Kaplan and Norton 1992; 1996). Research results also show that the relevance of financial statement information has diminished over time, and that nowadays firm performance cannot be found in financial measures alone (e.g. Collins, Maydew, and Weiss 1997; Francis and Schipper 1999). Thus, measures are needed that drive future performance and complement financial measures of past performance.

with future financial performance (e.g. Ittner and Larker 1998; Behn and Riley 1999;

Banker, Potter, and Srinivasan 2000) and are highly value-relevant (e.g. Amir and Lev 1996; Ittner and Larker 1998; Said, HassabElnaby, and Wier 2003). Ittner and Larcker (1998) use customer-level satisfaction survey data for a telecommunications company and document a significant relation between customer satisfaction and next year’s revenue. Similarly, using time-series data from the hotel industry, Banker et al. (2000) examine whether current non-financial measures are better predictors of long-term financial performance than current financial measures. They find that measures of customer complaints and returning customers are leading indicators of revenues and profits.

Innovation is a crucial resource and the major driver of firms’ growth in the long run. R&D investments and innovation cover the input side and output side of the innovation process and have a positive impact on financial performance. Aboody and Lev (2001) study 83 publicly-traded chemical companies, evaluating the return of R&D investments from 1980 to 1999. Results show that a dollar invested in chemical R&D increases current and future operating income by two dollars. Chen, Cheng, Hwang (2005) investigate the relationship between intellectual capital and firms’

market value and financial performance. Evidence shows that R&D expenditure has a positive effect on firm value and profitability. Eberhart, Maxwell, and Siddique (2004) examine the long-term abnormal stock returns and operating performance following R&D increases. They find consistent evidence that sample firms experience significantly positive long-term abnormal stock returns and abnormal operating performance following R&D increases. Ernst (2001) tests the relationship between patent applications and subsequent changes of company performance, showing that national patent applications lead to increases in sales. Using survey data for the Netherlands, Klomp and Leeuwen (2001) analyze the input and output stages of the innovation process and the links between the innovation process and overall economic performance. The results show that the impact of innovation on a firm’s growth rate of total turnover increases considerably. Accordingly, I suggest the following hypothesis:

H7a: R&D investments are positively related with financial performance.

H7b: R&D outputs are positively related with financial performance.

As noted earlier, R&D investments and R&D outputs have a more direct effect on financial performance than R&D cooperation. Accordingly, not only can R&D cooperation directly affect financial performance, but the relationship also could be mediated by R&D investments and R&D outputs. As a result, I propose following hypotheses:

H8a: The impact of R&D cooperation intensity on financial performance is mediated by R&D investments.

H8b: The impact of R&D cooperation intensity on financial performance is mediated by R&D outputs.

Chapter 4: Research method

在文檔中 The determinants and performance of R&D cooperation: evidence from Taiwan high-technology industries (頁 70-88)