Changing patterns of technological cooperation activities of innovative small firms along technological development stages in the Korean telecommunication sector

Technovation 23 (2003) 163–173 www.elsevier.com/locate/technovation

Changing patterns of technological cooperation activities of innovative small firms along technological development stages in the Korean telecommunication sector Jin-Woo Chung a, Zong-Tae Bae b

b,*

, Ji Soo Kim

b

a InZen, Inc., Seoul, South Korea Graduate School of Management, Korea Advanced Institute of Science and Technology (KAIST), 207-43 Cheongryangri-dong, Dongdaemun-gu, Seoul 130-012, South Korea

Received 2 April 2001; received in revised form 5 October 2001; accepted 1 November 2001

Abstract This study examines how patterns of technological cooperation activities vary along technological development stages. Based on the longitudinal sample of 63 small firms in the telecommunication equipment and device sector, proposed hypotheses were tested by using ANOVA, and multiple regression analysis. Major findings of this study are as follows. Along technological development stages, (1) patterns of technological cooperation activities differ in terms of motivation, the extent and diversity of use, and partners, and (2) the impact of each technological cooperation activity on the firm’s technological performance vary. In addition, some implications are presented and future research directions are suggested.  2001 Elsevier Science Ltd. All rights reserved. Keywords: Technological cooperation; Technological development stage; Korea

1. Introduction With the globalization of the market, both domestic and international levels of competition have increased. Moreover, drastic changes in technology have made firms face a much higher level of technological complexities and uncertainties than ever before. Under these circumstances, technology has become an essential factor in leading to success or survival in the worldwide competition. Policy makers, both in developed countries (DC) and in less developed countries (LDC) such as the Asian regions, refer to technology as the master key, or competitive weapon for development (Frohman, 1982; Dodgson, 1993). To promote technological innovation, firms not only have conducted in-house R&D but also have been trying to form close technological relation* Corresponding author. Tel.: +82-2-958-3607; fax: +82-2-9583604. E-mail addresses: [email protected] (J.-W. Chung), [email protected] (Z.-T. Bae), [email protected] (J.S. Kim).

ships with other firms, universities, and government research institutes. Small and medium-sized companies (SMEs) generally lack in financial and technical resources as well as management skills, when compared to large firms. Therefore, SMEs need to search for appropriate ways to acquire technological knowledge to supplement their narrow technological base from outside source. Technological cooperation with other firms, universities and research institutes accounted for 16.3% of all technological developments of SMEs in 1991, 29.7% in 1993, and was expected to amount to 60% in the late 1990s in Korea (FKSMC, 1991, 1993). As the importance of technological cooperation activities (TCAs) increase, many studies explore the various issues of TCAs: its extent and forms, benefits and cost, and factors influencing the patterns of TCAs (Egelhoff and Haklisch, 1994; Hagedoorn and Schakenradd, 1994; Gemunden and Heydebreck, 1995). However, little attention has been paid to the dynamics of TCAs along the stages of technological development. Research questions of this study are as follows: (1)

0166-4972/02/$ - see front matter  2001 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0166-4972(01)00111-0

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How do the patterns of TCAs change along technological development stages in terms of the extent and diversity of use, partners, and motivation? (2) How do TCAs impact on a firm’s technological performance? 2. Literature review There are many definitions of cooperation, including alliances, cooperative agreements, collaboration, and networks. Since the range or scope of cooperation is too broad, a more restricted definition is required. In this study, TCAs are defined as interorganizational arrangements through which organizations jointly acquire technical knowledge (Brockhoff, 1991). The forms of TCAs can be classified according to interorganizational dependency (Hagedoorn, 1990), formality of the relationship (Ha˚ kansson, 1989), contents of the cooperation (Ha˚ kansson, 1989), legal forms (Stafford, 1994), etc. Summarizing the above characteristics, we examine TCAs in terms of partners, motivation, and formality. Researchers have used a biological analogy to explain the growth patterns of organizations. A major strength of a stage model is that it helps understand rather complex phenomenon of growth (Kazanjian, 1988). Since the process of innovation or technological development is very complex, stages are really only intellectual tools simplifying a complex process (National Science Foundation, 1983). Cainarca et al. (1992) present a theoretical model that relates dynamically the quantitative and qualitative evolution of technological agreements to the technological life cycle of industry branches. In their study, they divide technological life cycle into the introduction, first development, full development, maturity, and decline stages. According to their model, propensity toward cooperation (mainly of the equity nature) will be high in the introduction stage, and will reach its maximum value in the early development phase, owing mainly to non-equity commercial and production agreements. In addition, during maturity, non-equity collaborative agreements will be stimulated. On the contrary, the number of agreements concluded will drastically decrease during the full development and decline phases. Using data from the ARPA (Advanced Research Program on Agreements) database, cross-sectional examination of the number and type of agreements concluded by firms in the period of 1980– 1986 in the information technology industrial system has shown a pattern fully consistent with the predictions of the model. Eisenhardt and Schoonhoven (1996) relate strategic factor to strategic alliance formation. Market stage is one of the factors that affect strategic position, and strategic alliance tends to be formed when firms are in a vulnerable strategic position. Therefore, they hypothesize that the rate of alliance formation is higher in emergent-stage markets than in growth-stage, and higher in growth-stage

markets than in mature-stage market. However, empirical evidence from the innovative small firms in the high velocity-industry, such as semiconductors, shows that alliance formations are higher in the order of emergent, mature, and growth stages. Hagedoorn (1993) shows that motives of strategic technology alliance differ according to the maturity of industry. Technology complementarity, reduction of the innovation time-span, and market access and influence to the market structure are the dominant motives of strategic alliances, and particularly, the first two motives are perhaps the most important motives for firms to engage in technological cooperation. However, in the somewhat mature industries such as chemicals and consumer electronics, motives related to influencing market structures, such as market access and restructuring, appear most appropriate. But these patterns of technological cooperation of firms in the DCs cannot simply apply to those of firms in the LDCs, due to the difference in development stage between DCs and LDCs. Lee et al. (1988) explain several dynamic changes in LDCs development processes with global perspectives focusing on the DC–LDC linkages. That model has an assumption that at the industry and firm level, technology evolves through the initiation stage, internalization stage, and generation stage. They divide the method of transfer into a formal and informal channel. The formal transfer of technology is defined as the direct transfer of technology from a donor to the recipient with explicit payment and contract between the two parties, mostly by licensing and joint venture agreement. Other transfers are classified as non-formal transfers, which include importing of machinery, consulting technical and trade journals, copying foreign products, sending nationals abroad for education, supplying of equipment or materials, buying of export products, acquiring experience by personnel, and so forth. Lee (1995) explores how the small firms’ strategies for acquiring technical information and their effects on the rate of innovation differ according to new or traditional technology settings in the Korean electronics industry. Firms were classified into two groups (traditional or new technology setting) according to the maturity of their technology. He shows that small firms in a new technology setting invest in in-house R&D and utilize external technical infrastructures, such as domestic R&D institutes, government agencies and technical literature, more than those in a traditional setting. However, they did not show significant differences in utilization of formal technology transfer mechanisms, and external sources such as suppliers, and buyers. In-house R&D and external sources did not have significant effects on the number of new products. However, inhouse R&D and acquisition of technical information from buyers and suppliers had significant and positive effects on the technological radicality of new products.

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In addition, their positive effects on the innovation measures were stronger within a new-technology setting than in a traditional-technology setting. Extant studies differ in their unit of analysis (project, unit technology, firm, industry, and nation). In the DC setting, Cainarca et al. (1992) show patterns of technological agreement at the industry branch level, Eisenhardt and Schoonhoven (1996) portray patterns of joint development contracts at the industry level, and Hagedoorn (1993) shows the differences of motives of strategic technological alliance at the industry level. In the LDC setting, Lee et al. (1988) and Lee (1995) present patterns of technological development at the industry and firm level. The findings, as well as the holistic patterns, using different units of analysis might not be applicable to other cases. Previous studies on technological cooperation did not consider a full range of technological cooperation, and was not able to explain dynamic patterns in several different situations. Most of the studies conducted in the DC setting focus on a formal mode of cooperation regardless of the partner or focus on only one type of cooperation. However, for small firms, an informal mode of cooperation is also critical in their technological growth (Ha˚ kansson, 1989). In Lee et al.’s study (1988), formal channels include only technology licensing and joint venture, since before late 1980s, various forms of technological agreement such as contract research agreement and joint development agreement with various entities are relatively scarce in LDC (or developing countries). Lee’s study (1995) has mainly focused on the relative importance of technological partners rather than on the cooperative agreement usage. For the comprehensive understanding of patterns of TCAs, several aspects of cooperation such as formality, partners, and motivation should be considered simultaneously.

3. Patterns of TCAs along technological development stages This study proceeds under the premise that technology evolves through the initiation stage, internalization stage, and generation stage at the industry and firm level in a LDC setting like Korea (Lee et al., 1988). According to their model, in the initiation stage, mostly the mature technologies are acquired by LDCs’ firms from DC mainly through ‘non-formal’ channels of technology transfer. Since mature technologies are easily and inexpensively available, they are rarely acquired through formal channels such as licensing contracts or joint ventures. In the internalization stage, new products that are superior to existing products are produced and existing products are radically improved by indigenous efforts. Through the internalization process, firms begin to produce their own products and decrease the dependency

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on foreign technologies for product and process development. Some industries or firms of LDCs complete the internalization stage and eventually accumulate indigenous R&D capability, thus arriving at the generation stage. Along the stages, old and mature technologies are transferred mainly via non-formal channels, and new and high technologies are transferred mainly via formal channels. As technological development stages (TDS) evolve, technological and demand uncertainties increase and firms try to search all the available sources of technological information to cope with these uncertainties. That is, there may be an increase in the extent and diversity of TCAs use along TDS. Major partners of TCAs might vary along the stages. As a follower, firms in LDC constantly want to acquire advanced technology from foreign firms. However, the importance of licensing for the technological development shows no significant difference between early and later TDS (Lee, 1995). This might be because foreign firms begin to hesitate to transfer high level of technology to potential competitors in developing countries. Consequently, firms in later TDS come to search for technical sources other than foreign firms. The second factor is the changes in technical emphasis along TDS. Acquired technological elements change from operations technology in the early TDS to design technology in the later TDS (Lee et al., 1988), and technical emphasis change in the order of implementation of imported technology, assimilation for product diversification, and improvement for enhancing competitiveness (Kim, 1980). Implementation of imported technology is a gradual and continuous task which might be carried out with the help of customers and suppliers, while the firm needs help from the horizontal dimension (i.e. university and public research institute) for the improvement of enhancing competitiveness (Ha˚ kansson, 1989). Motives of TCAs also evolve along TDS. The electronics industry was initially established in Korea primarily through the international transfer of packaged technology (Kim, 1980). This adoption of packaged technology meant that the motives of TCAs in early TDS were not only resolving technical problems but also enhancing market positions. As firms accumulated own technology, they came to require unpacked technology, which supplemented their capability. That is to say, accessing supplementary scientific knowledge came to be important in later TDS. Hypothesis 1a. The extent of technological cooperation activities increases along technological development stages. Hypothesis 1b. The diversity of technological cooperation activities increases along technological development stages. Hypothesis 2a. Major partners of technological

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cooperation activities vary along technological development stages. Hypothesis 2b. Major motivations of technological cooperation activities vary along technological development stages. Previous studies demonstrated that external technical linkages were important to underlying technological advantage in innovative European small firms (Rothwell and Dodgson, 1991; Lee, 1995). TCAs are a good means for seeking opportunities, which firms might miss without TCAs under resource restriction. By using TCAs, firms can enhance their competitive position in the existing market or enter new markets. Therefore, firms can produce much more products with the help of diverse and frequent TCAs usage. In a developing country like Korea, a new industry is initially established in response to a market opportunity created and protected by government import substitution policy. Under governmental incentives for emerging industry and government policy on trade and foreign capital and technology acquisition, the market condition is relatively certain and stable. As product design and production techniques become assimilated through the accumulation of production operation, such knowledge spreads quickly within the country, resulting in an increased number of firms (Kim, 1980). As the technological development stage evolves, environmental uncertainties emanating from market factor and technological factor increase. TCAs can be an effective means to lower the level of environmental uncertainties by acquiring supplementary assets (including technological knowledge) which are needed to enhance technological performance of firms. Under the premise that TCAs can act as a critical means for a new product (or radical new product) development under resource constraint, the contribution of TCAs to new product development in later stage of technological development can surpass that in the early stage of technological development. Based on the discussions above, the following hypotheses are generated. Hypothesis 3a. The larger the extent of technological cooperation activities, the better the technological performances of firms. Hypothesis 3b. The larger the diversity of technological cooperation activities, the better the technological performances of firms. Hypothesis 4a. The relationship between the extent of technological cooperation activities and a firm’s technological performance becomes strong as the technological development stage evolves. Hypothesis 4b. The relationship between the diversity of technological cooperation activities and a firm’s technological performance becomes strong as the technological development stage evolves.

4. Research methods 4.1. Sample and data collection The purpose of this study is to explore the changing patterns of TCAs of innovative small manufacturing firms. We confine our sample industry to telecommunication device and components industry. Since this industry, which is a typical example of product-based industry, is the fastest growing industry in Korea, this sector is suitable for analyzing changing patterns of TCAs along technological development stage. To select innovative small firms in the telecommunication device and component industry, we gather the sample firms from two sources. One source is the member firms of the Korean Association of Promising Small and Medium-sized Telecommunication Enterprises, and the member firms have been officially recognized as innovative firms by the government. The others are small manufacturing firms in the telecommunications sector recommended by industrial experts who have worked for the largest telecommunication service firm in Korea. Most of the firms from the two sources overlap each other, and finally 204 firms were selected. Data were collected through questionnaires during the spring of 1997. Questionnaires were mailed to the senior managers in charge of the technology development or manufacturing of 204 firms in the sample. Among 71 returned questionnaires, eight questionnaires were removed from the analysis due to incomplete responses, and 63 questionnaires were used for data analysis. The average sales volume of the sample firms was about 18 million US dollars (ranging from $0.8 to $80 million), and the average number of employees was 130 (ranging from 12 to 450 persons) in 1996. 4.2. Measurement 4.2.1. Technological development stages TDS are divided based on the critical incidence approach. In Stage I, the mature technologies used have diffused into even the least developed countries, or are being widely used in developing countries. In stage II, they mainly use a technology that has already begun to diffuse into developing countries. In stage III, they mainly use technologies that were used in DC but has not yet been diffused into developing countries. Moreover, in stage IV, they use high level technology that has been developed within three years in the DC. Respondents were asked to divide their firms’ TDS according to the maturity of the technology mainly used in each stage. In case that a respondent had difficulty in remembering the initial stage of the firm, the respondent was asked to consider only recent 10 years of the firms’ life. Each firm has passed through one or four TDS, and not all firms start from stage I.

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4.2.2. Formal and informal TCAS TCAs were classified into formal and informal TCAs. Formal TCAs are official agreements made between or among multiple parties, while informal TCAs mainly include gathering technical information from outside sources. 4.2.3. Extent and diversity of TCAS The extent of TCAs usage is the sum of use of all TCAs in each stage, and the diversity of TCA usage is the number of TCA types (partner groups). The type of formal TCAs include: (1) licensing from foreign firms in the same industry: (2) cooperation with domestic firms in the same industry (joint development and contract research); (3) cooperation with domestic customer (joint development and technology transfer program); (4) cooperation with public research institute (joint development, contract research and TT program); and (5) cooperation with universities (joint development and contract research). The type of informal TCAs include gathering technical information from: (1) foreign firms in the same industry; (2) domestic firms in the same industry; (3) foreign supplier; (4) domestic supplier; (5) domestic customer; and (6) public research institute. Formal TCAs with suppliers, formal and informal TCAs with foreign customers, and informal TCAs with universities were omitted, since these are scarcely used in this sector. Respondents were asked to record the number of the each type of TCAs used in each TDS that their firms have passed. 4.2.4. Motivation Based on Hagedoorn (1993), we divide motivation or reason for TCAs into five items: (1) to access complementary scientific knowledge; (2) to share cost and risk of R&D; (3) to reduce innovation time span; (4) to enhance market influence; and (5) to gain government funding. Respondents were asked to answer the importance of motivations or items of TCAs with a 5-point scale for each stage of technological development. 4.2.5. Technological performance Technological performances are assessed by the (total) number of new product development and the number of radical new product development in each stage. Radical new products can be defined as products with developed or improved technology compared to the equivalent products in DC in Lee’s study (1995). 4.3. Analytic technique ANOVA was used to test Hypothesis 1a, 1b, 2a, and 2b, which show the changing pattern and motive of TCAs along TDS. Multiple regression analysis was adopted to test Hypothesis 3a, 3b, 4a, and 4b. Especially for Hypothesis 4a and 4b, moderated regression analysis

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was used, because it allows the interaction terms, which are implied in all contingency relationships, to be explicitly examined. To isolate the relationship between TCAs and the technological performance, average number of employees was used to control firm size. It is noted that data only in the ‘present’ stage of technological development was used to test the relationship among TCAs, TDS, and technological performance, since the number of employees was gathered in the present stage. This study tries to test the following research model for Hypothesis 4a and 4b. Y = intercept + a∗1 Employee + a2∗X1 + a∗3 X2 + a∗4 TDS + a∗5 TDS ∗X1 + a∗6 TDS ∗X2 (for Hypothesis 4a) Y = intercept + b∗1 Employee + b2∗Z1 + b∗3 Z2 + b∗4 TDS + b∗5 TDS ∗Z1+b∗6 TDS ∗Z2 (for Hypothesis 4b) where Y=Technological performance (new products, radical new products), X1=Extent of formal TCAs, X2= Extent of informal TCAs, Z1=Diversity of formal TCAs, Z2= Diversity of informal TCAs, and TDS=Technological development stage.

5. Results 5.1. Basic statistics To check the relevance of stage classification, the characteristics of each stage are compared. Table 1 shows the basic statistics of each stage of technological development. Firms pass through each stage in an average of 3–5 years. As TDS evolve, the number of new products and radical new products increase. Moreover, patterns of technological development activities differ in terms of the proportion of new products with respect to the innovation level. In stage I, imitation and simple improvement of overseas products account for 73% of technological development, while development of radical new products compared to overseas products account for half of the technological development in stage IV. This shows that classification of TDS based on critical incident approach, or technological maturity, is appropriate to characterize the changing pattern of technological innovation. Descriptive statistics and correlations for all variables in the present stage are displayed in Tables 2 and 3 for informational purposes. 5.2. Hypotheses testing Table 4 presents the extent and diversity of TCAs in each TDS. The extent and diversity of formal and infor-

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Table 1 Characteristics in each technological development stage (Difference: Duncan’s multiple range test with significance level of 0.1. Not all firms start from stage I and not all firms are currently in stage IV. Therefore, the numbers of data in the stages used for ANOVA are 39 (stage I), 53 (stage II), 60 (stage III), 40 (stage IV), respectively) Stage I Average duration year Number of new product development per year Proportion of new product according to innovation level (%)

Stage II

Stage III

Stage IV

F-value

Difference

4.4 2.5

4.6 3.2

4.2 3.1

3.4 3.8

– –

Imitation of overseas products

49.8

22.9

16.3

15.9

11.33***

Simple improvement of overseas products Simple improvement of own current products Relatively radical new product compared to overseas products

23.3

23.0

18.0

11.8

2.42*

I,II⬎IV

9.7

22.8

28.4

23.2

4.68***

I⬍II,III,IV

17.2

31.3

37.3

49.1

6.47***

I,II⬍IVI⬍II,III

I⬎II,III,IV

Table 2 Basic statistics (Technological Development Stage=0 (early stage; 23 cases, 36.5%) and =1 (later stage; 40 cases, 63.5%))

Number of new products (NP) Number of radical new products (RNP) Number of employees (EMP) Extent of formal technological cooperation activities (EFFFTCA) Extent of informal technological cooperation activities (EFIFTCA) Diversity of formal technological cooperation activities (DFFFTCA) Diversity of informal technological cooperation activities (DFIFTCA)

N

Mean

SD

Min

Max

50 48 56 63 63 63 63

3.2 1.4 117.4 1.8 50.6 1.9 3.5

3.2 1.8 100.5 1.8 30.6 1.3 1.1

0.0 0.0 12.0 0.0 0.0 0.0 0.0

15.0 8.0 420.0 7.5 123.0 5.0 4.0

EMP

EFFFTCA

EFIFTCA

DFFFTCA

DFIFTCA

0.32** 0.18 0.37 0.47*** 0.45*** 1.00

⫺0.01 ⫺0.23 0.05 ⫺0.02 0.61*** 0.38*** 1.00

Table 3 Correlation between variables (*p⬍0.1, **p⬍0.05, ***p⬍0.001) NP NP RNP EMP EFFFTCA EFIFTCA DFFFTCA DFIFTCA

RNP

1.00

0.80*** 1.00

0.44*** 0.36** 1.00

0.72*** 0.70*** 0.41*** 1.00

⫺0.08 ⫺0.07 0.02 0.09 1.00

Table 4 Extent and diversity of TCAs along TDS (Difference: Duncan’s multiple range test with significance level of 0.1; *p⬍0.1, **p⬍0.05, ***p⬍0.01; unit: extent (case per year for one firm), diversity (number of partner groups))

(1) (2) (3) (4)

Extent of formal TCA Extent of informal TCA Diversity of formal TCA Diversity of informal TCA

Stage I

Stage II

Stage III

Stage IV

F-value

Difference

0.8 13.7 0.9 2.0

1.2 19.2 1.1 2.7

1.5 29.6 1.5 2.9

2.3 36.6 1.5 2.8

5.31*** 11.52*** 3.04** 4.11***

I,II,III⬍IV I,II⬍III,IV I⬍III,IV I⬍II,III,IV

J.-W. Chung et al. / Technovation 23 (2003) 163–173

mal TCAs have increased as TDS evolves. For example, the number of formal TCAs per year has increased from 0.8 in stage I to 2.3 in stage IV, while the number of informal TCAs per year has increased from one per month (average 13.7 per year) in stage I to three per month (average 36.6 per year) in stage IV. For diversity, a firm has formal connections with one group of partners (average 0.9) and informal connections with two groups of partners (average 2.0) in stage I and has formal connections with one or two partners (average 1.5) and informal connections with about three partner groups (average 2.8). In Table 4, the extent and diversity of formal TCAs leap sharply in stage IV and stage III, respectively, while the extent and diversity of informal TCAs do so sharply in stage III and stage II, respectively. The reasons are that informal TCAs are relatively more easily accessible, and require a lower level of own technological capability than formal TCAs. These results support Hypothesis 1a and 1b. Table 5 shows that major partners of formal TCAs tend to change for each TDS (from row 1 to row 5). Generally, formal TCAs with public research institutes (PRIs) and universities increase significantly along the stage but usage of TCAs with foreign firms (through licensing), domestic firms in the same industry and domestic customer shows no significant difference along TDS. In stage I, domestic customers and domestic firms are two major partners. PRIs and universities come to be major partners in stage III and stage III, respectively. In stage IV, PRIs and universities become two major partners. This phenomenon might result from governmental efforts that try to promote industry–university– PRI collaboration in 1990s. At the least, such governmental efforts seem to be successful in volume. For informal TCAs (from row 6 to row 11), customers and PRIs are the main sources in gathering technical information in all stages. Informal TCAs with foreign firms

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in the same industry, domestic firms in the same industry, and PRIs increase significantly along the TDS, while informal TCAs with suppliers and customers remain stable. Thus, Hypothesis 2a can be partially accepted. Table 6 shows the motives of TCAs along TDS. The importance of accessing complementary scientific knowledge, market access, and accessing policy fund increase significantly along TDS, while the importance of reduction of innovation time span, and sharing cost and risk of R&D does not change significantly along TDS. This implies the last two motives are common motives of TCAs, although the last two motives are relatively less important than other motives. In stages I and II, access to complementary scientific knowledge is relatively less important than in stages III and IV. As TDS evolves, the level of technology needed by firms comes to high. Therefore, they come to need ‘scientific’ knowledge in addition to ‘engineering’ and ‘operational’ knowledge. Governmental efforts to promote collaboration between several entities are also reflected in this result. From the mid 1980s, the government has provided policy funding to SMEs through mechanisms of joint development with PRIs. Naturally, PRIs selected promising telecommunication components or devices as items for joint development, which needed technologies in the emergent or growth stages, even in DCs. To cope with the high complexity of technology, in addition to high market uncertainty of these products, SMEs need large amounts of financial resources. Thus, policy funds seem to be an efficient means to drive SMEs to cooperate with each other and with PRIs. According to these results, Hypothesis 2b can be accepted. Table 7 contains the results of the multiple regression analyses undertaken to test the Hypothesis 3a, 3b, 4a, and 4b. In each case, the number of new product and the number of radical new products are the dependent

Table 5 Pattern of TCAs’ usage (Difference in relative usage using paired-t test with significance level of 0.1. Formal TCAs with foreign competitor means licensing; formal TCAs with suppliers and foreign customers are negligible. Informal TCAs with universities and foreign customers are negligible. Dark shade represents formal/informal TCA which is used the most frequently in each stage, respectively. Unit: case per year for one firm.) Stage I Formal TCAs (1)Foreign firms in the same industry (2) Domestic firms in the same industry (3) Domestic customers (4) Public Research Institutes (PRIs) (5) Universities Informal TCAs (6) Foreign firms in the same industry (7) Domestic firms in the same industry (8) Foreign suppliers (9) Domestic suppliers (10) Domestic customers (11) Public research institutes

Stage II

Stage III

Stage IV

F-value

Difference

0.1 0.2 0.4 0.1 0.1

0.1 0.2 0.4 0.3 0.1

0.1 0.3 0.3 0.4 0.3

0.1 0.4 0.4 0.5 0.7

0.27 1.28 0.21 3.58** 2.71**

– – – I⬍IV I, II⬍IV

1.2 0.9 1.6 1.6 2.7 2.2

2.3 1.5 2.3 1.6 3.8 2.9

3.1 2.2 3.2 2.6 4.7 4.7

4.5 3.9 3.2 2.7 6.0 5.5

4.87*** 7.84*** 2.01 1.92 2.41* 9.71***

I⬍IV I,IIIV – – – I,IIIV

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Table 6 Motives of TCAs (Difference: Duncan’s multiple range test with significance level of 0.1. Unit: degree of importance (1: not important, 3: important, 5: very important). Dark shade represents two major motivations of TCAs in each stage, respectively.) Stage I (1) Accessing complementary scientific knowledge (2) Sharing cost and risk of R & D (3) Reducing innovation time span (4) Market access (5) Accessing policy fund

Stage II

Stage III

Stage IV

F-value

Difference

2.2

2.9

3.4

3.9

6.7***

I⬍IV

2.4 2.8 2.6 3.0

2.8 2.8 3.1 3.5

2.8 3.2 3.3 3.4

3.2 3.0 3.4 4.4

1.6 1.7 2.1 2.8***

– – I⬍IV I,II,III⬍IV

Table 7 Impact of technological development stage on the relationship between technological cooperation activities and technological performance — moderated regression analysis (standardized beta coefficient; *p⬍0.1, **p⬍0.05, ***p⬍0.01) NP Equation 1 (1) EMP (2) STAGE (3) EFFFTCA (4) EFIFTCA (5) = (2)*(3) (6) = (2)*(3) (7) DFFFTCA (8) DFIFTCA (9) = (2)*(7) (10) =(2)*(7) Adjusted R2 F-value

0.18 0.65*** ⫺0.16

Equation 2 0.17 0.04 0.38 0.19 0.28 ⫺0.37

RNP Equation 3 0.09 0.70*** ⫺0.15

Equation 4 0.04 0.12 ⫺0.45 0.86 1.19* ⫺1.10

NP Equation 5 0.36**

0.20 ⫺0.23

0.55 18.9***

0.52 8.93***

0.58 19.27***

variables and the number of employees is entered as a control variable. To test the contingent effect of TDS, interaction terms for the extent and diversity of TCAs and TDS were included in the regression model. Equations 1, 3, 5 and 7 in Table 7 display the main effects of TCAs on technological performances. Equations 1 and 3 show significantly positive relationships between the extent of formal TCAs and a firm’s new product development and radical new product development, respectively. Thus, we can partially accept Hypothesis 3a. But equation 5 shows insignificant relationships between the diversity of formal/informal TCAs and a firm’s new product development and equation 7 shows significantly negative relationship between the diversity of informal TCAs and a firm’s radical new product development. This implies that using diverse types of TCAs cannot always help elevating the technological performance. Therefore, we reject Hypothesis 3b. The equation 2, using the number of new product development as a dependent variable, indicates that the beta coefficients of the interaction term for formal/informal TCAs and TDS are insignificant, but the equation 4, using the number of radical new product development as a dependent variable, indicates that the beta coefficient of the interaction term for formal TCAs

0.58 10.99***

0.18 4.30***

Equation 6

RNP Equation 7

Equation 8

0.38** ⫺1.20*

0.32**

0.34** ⫺0.37

⫺0.96** ⫺1.64 1.17 1.56 0.23 3.15**

0.14 ⫺0.41***

⫺0.64 ⫺0.56 0.80 0.14 0.18 2.64**

0.21 4.99***

and TDS is significant and positive (beta coefficient=1.19, significant level=0.1). This means that as the TDS evolves, the impact of formal TCAs on radical new product development become strong. These results partially support Hypothesis 4a. The equation 6 and equation 8 show no significant interactions for the diversity of formal/informal TCAs and new/radical new product development. Thus, we reject Hypothesis 4b. For additional information, we calculate the correlations between each type of TCAs and technological performance (the number of new products and the number of radical new products). To assess differences in the strength of correlations between early- and laterstage firms, we used Fisher’s Z transformation, a procedure developed by Miller and Friesen (1982). We used 0.10 alpha level of significance, which has been applied in similar studies(Miller and Friesen, 1982; Koberg et al., 1996). Since the interaction between informal TCAs and technological performance is not significant in Table 7, we include only formal TCA type in this analysis. Table 8 shows the difference in strength in the relationship between each TCA type and technological performances. TCAs with foreign firms in the same industry, vertically related firms and horizontally related institutes are significantly and positively correlated with

J.-W. Chung et al. / Technovation 23 (2003) 163–173

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Table 8 Impact of technological development stage on the relationship between each TCA type and technological performance — Fisher’s Z-test (*p⬍0.1, ** p⬍0.05, ***p⬍0.001)

Technological development stage

Relationship with new product Early (N=19) Later (N=31)

Foreign firms in the same industry Domestic firms in the same industry Vertically related firms Horizontally related institutes

r=⫺0.28 r=0.35 r=0.62*** r=0.32

r=0.33** r=0.06 r=0.58*** r=0.49***

new product development in the later stage of technological development, while TCAs with vertically related firms is significantly and positively correlated with new product development in the early stage of technological development. Among these relationships, only the strength of the relationship between new product development and TCAs with foreign firms in the same industry makes significant difference with respect to TDS (Z=1.73, significant level=0.5). The relationship between radical new product and TCAs with foreign firms in the same industry, and with vertically related institutes make significant difference with respect to TDS (Z=1.51, significant level=0.1; Z=2.59, significant level=0.05, respectively). This means that the impact of licensing from foreign firms in the same industry on new and radical new product development are stronger in the later TDS than in the early TDS. Particularly, the impact of TCAs with vertically related institutes (universities and PRIs) on radical new product development is stronger in the later TDS than in the early TDS.

6. Discussion and conclusion This study tries to reach a comprehensive understanding of dynamic patterns and motivation of TCAs use, and the impact of TCAs on the technological performance along TDS. The extent of and diversity of formal and informal TCAs increase (Hypothesis 1a and 1b), and the pattern of TCAs usage vary along TDS. For formal TCAs, only formal TCAs with customers are dominant in stage I, but TCAs with customers and PRIs are prevalent in stage II, TCAs with domestic firms in the same industry and PRIs in stage III, and TCAs with domestic firms in the same industry, PRIs, and universities in stage IV. For informal TCAs, TCAs with domestic customer and PRIs are dominant in their usage for all stages, and TCAs with foreign firms in the same industry are frequently used in stage IV (Hypothesis 2a). The motivation for TCAs also varies along TDS. Shortening time for innovation and sharing cost and risk of R&D are basic motives of TCAs for all stages. In stage IV, where firms use a high level of technology, the importance of gaining policy funding and access to complementary

Difference in strength

Relationship with radical new product Early (N=19) Later (N=29) Difference in strength

Z=1.73** Z=⫺0.53 Z=0.77 Z=1.13

r=⫺0.22 r=0.01 r=0.66*** r=0.17

r=0.31 r=⫺0.15 r=0.47*** r=0.71***

Z= 1.51* Z=⫺0.50 Z=0.10 Z=2.59*

scientific knowledge grow sharply (Hypothesis 2b). Among formal and informal TCAs, mainly formal TCAs have strong impact on the technological performance (Hypothesis 3a). The impact of the formal TCAs on the technological performances is stronger in the later TDS than in the early TDS (Hypothesis 4a). Especially, the impact of the formal TCAs with foreign firms in the same industry and the formal TCAs with horizontally related institutes on the technological performances is stronger in the later stage of technological development than in the early stage of technological development. Table 9 summarizes the results of this study. The results show that the government policy made a tremendous effect on the firms, TCAs usage. TCAs with horizontally related institutes, such as industry–university or industry–public research institute linkages, have been one of the central science and technology policy issues since late 1980s. This results in a rapid increase of horizontal TCAs in stage III and IV. In Tables 7 and 8, TCAs with these institutes made critical impact on a firm’s technological performances. In addition, gaining governmental funds is the most important motive of TCAs, and its importance increases sharply especially in later TDS. Though governmental funding may force firms, particularly smaller firms, into inappropriate collaborations (ACOST, 1990, quoted in Dodgson, 1993), promoting horizontal TCAs with financial aid under the appropriate monitoring of beneficiary firms seems to be very helpful for SMEs. These results can provide managers with directions for strategic technology outsourcing. Managers of SMEs should actively use TCAs to increase the number of new product development, which is an important competitive dimension, especially in the high-technology industry. But it should be noted that choosing appropriate type of TCAs according to the firm,s own stage of technological development rather than using diverse type of TCAs will be more helpful to enhance the technological performance of firms. This study has some limitations. First, some factors such as strategy or resource capability, which can affect pattern of TCAs, are not considered in this study. Although this study focuses on TDS, simultaneously considering these important factors under the TDS framework

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Table 9 Dynamic pattern of TCAs along TDS: summary Stage I

Stage II

Stage III

Level of technology used

Technology diffused into even least developed countries Imitation

Technology that began to diffuse into developing countries →

Technology mainly used in DC but not yet diffused other developing countries →

Major innovation method

Simple improvements of → overseas products Low → Low → Vertically related firms (mainly customers)

Extent of TCAs Diversity of TCAs Major partners of TCAs Situational motivation Common motivation

Stage IV

Technology that has been developed within three years in DC Development of radical products by pure R&D → Improvements of own products → High → High Horizontally related institutes (PRIs and universities) Foreign firms in the same industry Access to complementary scientific knowledge

Reducing innovation time span; Sharing R&D cost and risk; Government funding

can be helpful for comprehensive understanding of patterns of TCAs. Secondly, in this study, only technological performances were considered as an outcome of TCAs. Commercial performance and technological learning through TCAs should be investigated in the further study. Thirdly, Patterns of TCAs are different according to the types of industrial technology since, as von Hippel (1988) noted, major sources of innovation vary according to the innovation (or industry) types. Since the sample industry — telecommunication device and component industry — adopted in this study is a typical product-based industry, further research need to be conducted in the equipment-based, operation-based, and process-based industries. Fourthly, there may be national differences in TCAs, even among developing countries. The result of this study cannot necessarily be generalized, because the results largely reflect the situation of Korea (an imitativelearning type country). Therefore, comparative study among other developing countries is needed for a more comprehensive understanding of TCAs.

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J.-W. Chung et al. / Technovation 23 (2003) 163–173 Jin-woo Chung received BS in Computer Science & Statistics from Seoul National University, and MS in Industrial Engineering and PhD in Management Engineering from KAIST. He is working as a research fellow at InZen, Inc., a new internet-related venture. His research interests include new venture creation/growth, technological cooperation, and technological development strategy.

Zong-Tae Bae is Associate Professor of Entrepreneurship and Technology Management at the Graduate School of Management, Korea Advanced Institute of Science and Technology (KAIST). He received BS in Industrial Engineering from Seoul National University, and MS and PhD in Management Science from KAIST. He was on the faculty of Management at the Asian Institute of Technology, Thailand in 1989–1991, and worked as a visiting scholar at Graduate School of Business and Asia/Pacific Research Center, Stanford University in 1999– 2000. His research interests include various aspects of R&D/technology management and entrepreneurship.

173 Ji Soo Kim received BS in Textile engineering from Seoul National University, MS in Industrial Engineering and PhD in Engineering Economy from Stanford University. His research interests and technical human resources development, economic valuation of technical startup and environmental issues. He is currently professor and director of Center for Technology & Operations Management, Graduate School of Management, KAIST.