The Impact of External Environment and Technological Learning System on the Relationship Between Technological Alliance and Performance of Korean Small

Telecom Firms

Zong-Tae Bae’, Jin-woo Chung’ ’ Graduate School of Management at Korea Advanced Institute of Science and Technology (KAIST), 207-43 Cheongryangri-dong, Dongdaemoon-gu, Seoul 130-0 12, KOREA.

* InZen, Inc., an internet security company based in Korea.

Abstract- This study examines how external environment and internal technological competencies affect the relationship between the use of technological alliances and the performance of the firm’s new product development activities. Based on the sample of 63 technology-based small firms in the Korean telecommunication equipment and device sector, proposed five hypotheses are tested by using multiple regression analysis. Major findings of this study are as follows: (1) The more severe the market competition and technological change, the stronger the impact of technological alliances on the firm’s new product development, and (2) the better structured the technological learning system (knowledge acquisition process, knowledge diffusion & storage process, and knowledge assimilation & application process) related to an technological alliance, the stronger the impact of the technological alliance on the firm’s new product development. In addition, some managerial implications are presented and future research directions are suggested.

I. INTRODUCTION

Alliance has been considered an effective means for growth, since no firms can acquire all the resources needed in the production process of goods. For small and medium-sized firms that suffer from relative lack of financial and technological resources compared to large firms, alliance is one of useful options for survival.

The relationship between alliances and technological performances of a firm has been analyzed by several studies [lo], [19], [22], [29], [32], [34]. Extant literatures have consistently shown positive relationship between alliance and technological (or innovation) performances of small firms. However, little attention is paid to contingent situations between these two variables. Contingency variables can be classified into external environmental factors and internal organizational systems. In this study, we argue that external environmental factors and internal technological competencies give moderating effects on alliance- performance relationships in the small business context.

Since technological alliances are very complex in nature, small firms are suitable in analyzing the relationship between technological alliance and performance at the firm level. Dodge et al. [12] summarized from past researches as follows: (1) The less complex nature of small business firms reduce the number of problems emanating from oversimplification when a limited number of dimensions is used, (2) small firms are especially vulnerable to

environmental changes given that they generally lack sufficient slack resources to buffer themselves from their individual environments, and (3) small business interact directly with their respective environments.

11. LITERATURE REVIEW

A . Technological alliance and firm ’s performance Innovation performance can be enhanced by external

sources of scientific, technical, and professional support [29]. Hagedoom and Schakenraad [22] proposed the hypothetical relationship between strategic technology alliance and innovation (patenting), even though they failed to test the relationship because of data problems. Comparison of the 25 firms producing ‘technologically significant’ innovations and the 15 firms producing ‘technologically incremental’ innovations in the textile industry indicated that the former was generally more externally linked than the latter [32].

According to Gemunden et al. [20], companies that had close contact with lead users, had cooperated with research institutes and universities, and had established arrangement for R&D cooperation with other companies had higher technological innovation success. In their study, technological innovation success was measured as to what extent companies had taken up technologically improved or new products during the last few years (e.g., no extent, limited extent, or considerable extent).

Shan et al. [34] revealed that a start-up firm’s number of cooperative relationships had a positive effect on its innovative output, which was measured by the number of biopharmaceutical patents based on a sample of 85 biotechnology start-ups. In their study, cooperative agreement was divided into commercial tie and research agreements. Among these, only the commercial tie significantly affected the number of biopharmaceutical patents.

The rate of new product development is becoming an important competitive dimension in many industries [35]. On a sample of 132 new biotechnology firms, Deeds and Hill [ 101 proposed a nonlinear, inverted, U-shaped relationship between an entrepreneurial firm’s rate of new product development and the number of alliances in which the firm participates. This is because strategic alliances may initially have positive effects on the rate of new product development, and this relationship may exhibit diminishing returns. Moreover, beyond a certain point, it is possible that negative returns will set in.

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B. External environment and technological alliance Several researchers have studied the effects of

environment on technological alliances [5], [16], [21], [24]. Eisenhardt and Schoonhoven [ 161 argued that the greater the number of competitors, the greater the number of alliance formations. Also, their study presents a hypothetical relationship that the rate of alliance formation is higher in emergent-stage markets than in growth-stage markets, and higher in growth-stage markets than in mature-stage markets on the resource-based view. However, the results show that growth-stage markets have the lowest rates of alliance formation.

Kurokawa [27] shows that external technology acquisitions are more likely to be practiced when the number of rivals expected to develop a similar product is greater and the needed technology is less related to a firm’s core technology, by using the data from small technology based firms in the United States and Japan.

Dollinger and Golden [13] showed that munificent environments were positively associated with collective behavior, and complex environments were negatively associated with collective strategy of small firms in fragmented industries by using COMPUSTAT data. Harrigan [24] provides a framework for using joint ventures (and other forms of cooperative strategy) within varying competitive environments. In his framework, demand traits suggest what types of cooperative strategies are needed, and competitor traits suggest how firms will respond to these needs for cooperation. Cainarca et al. [SI provided empirical evidence that the propensity towards agreements and the nature of the agreements have changed along the technological life cycle, based on a cross-sectional analysis of 45 industry branches in the information technology industrial system.

Dickson and Weaver [l 13 proposed that alliance use is positively associated with a key manager’s perceptions on the following sources of environmental uncertainty: (a) high general uncertainty, (b) high technological volatility and demand, (c) low predictability of customer demands and competitor actions, and (d) demands for internationalization and that the key manager’s entrepreneurial orientation and individualisdcollectivism orientation will moderate the relationship between alliance use, as well as the key manager’s perception of each dimension of environmental uncertainty.

However, relatively little studies have been made on contingent effects of environment on alliance-performance relationship.

C. Firm’s internal competencies and technological alliances Many scholars have explored the effects of a firm’s

strategy or capabilities on the pattern of technological alliancesuse [7], [14], [15], [18], [19], [21], [22], [24], [33].

Recent studies have demonstrated the effects of the top manager [30] and strategic orientation [3] on the strategic alliance-performance relationship. For a small and medium- sized firm, the founder or entrepreneur is the key in almost

every aspect of business process. Entrepreneurship literatures have shown that the success of new ventures is heavily dependent on competence of founder or management team, including prior experience [17], [37]. McGee and Dowling [30] examined the relationships among new firm’s economic performances (average sales growth), the experience of the management team, and the firm’s use of cooperative R&D arrangements. The results indicate that the use of R&D cooperative arrangements was more effective for new high- technology ventures whose management teams were relatively more familiar with the industry, technology, andor with similar market and who possessed relatively more years of prior R&D experience. Also, strategic orientation can affect the relationship between strategic alliance and performance. Bierly I11 and Chakrabarti [3] argued that a firm’s strategic flexibility moderates the relationship between technological learning and technological performance as evidenced by new product development. In their study, technological learning is defined in two dimensions: internal (R&D commitment and patent) and external learning (strategic alliance and scientific linkage), and the argument has been tested using longitudinal data from the U.S. ethical pharmaceutical industry.

Technological alliances can be one of the means for organizational learning, as Hamel et al. [23] points out, that a major reason for entering into alliances is to learn about the skills of other firms. Huber E251 classified constructs and processes associated with organizational learning into four activities; knowledge acquisition, information distribution, information interpretation, and organizational memory. Beer [2] provided a cybernetic model of learning consisting of four primary elements: information inputs, a decision network, organizational norms and technologies and product outputs. Information inputs is characterized in terms of the scope and dept of information from the environment. The decision network consists of the linkage among decision makers required to reach a decision on a given issue. Organizational norms and technologies consist of the accepted way we do things around here. Product output include the goods and service developed and marketed by the organization.

Three level of organizational learning are distinguished in the innovation literature: incremental, discontinuous and organizational, and each level is supported by the different level of organizational process: single-loop, double-loop and meta-learning [3 11. Under technological alliance circumstance, single-loop learning occurs when acquired information inputs are used to modify products without changing existing technologies, while double-loop learning involves change in existing technologies. Meta-learning involve the organization’s generalized ability to improve its performance.

Previous researches have seldom considered both the content and process aspects of technological alliance and how they affect the technological performance at the firm level.

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111. HYPOTHESES

Complementarities between collective strategy and environment represent effective strategic responses and result in higher performing firms. Collective strategies function to exploit environmental conditions when neutralize the negative effects of environmental pressure [ 131. Dollinger and Golden [ 131 presented the relationship between collective strategy, the environment, and a firm’s performances (sales growth, perceptual relative competitive position, and operating margin) in small firms using COMPUSTAT data. However, they fail to show the relationships between collective strategy and performance when they consider environment simultaneously as an independent variable. Technological alliance is one of the most important aspects of technology strategy. Thus, it can be expected that matches between environment and technological alliance use can give birth to superior performing firms.

This study deals with two dimensions of market-related and technology-related environments. First, organizations in the task environment competing with the same customers for the same product or close substitutes would seem to have the most influence of alliances on an organization [12]. Second, numerous researches suggest that the use of alliances has been more common when firms face severe technological challenges. Among these technological challenges, the occurrence of a major technological change often destroys existing competencies and requires the redeployment of resources to develop capabilities suitable for the new technological regime [38] and thus influences a firm’s performance.

When market competition and technological changes become severe, the need for a new product or radical new product development increases tremendously. Since technological alliance can act as a critical means for a new product (or radical new product) development under resource constraint, the contribution of technological alliance to new product or radical new product development can surpass that in the stable environment. Based on the discussions above and preliminary field surveys, the following hypotheses are generated.

Hypothesis la . The relationship between the use of technological alliances and a firm’s new product developments will be more positive for the firm when the market competition become more severe than other firms.

Hypothesis lb . The relationship between the use of technological alliances and a firm’s new product developments will be more positive for the firm when the technological change become more severe than other firms.

Organizational learning has a broad meaning. However, we focus on learning related to the technologies of a firm. Technological learning can be defined as the acquisition and generation of explicit and tacit knowledge, which can be used in improving either the development of new products or the production of current products [3].

Particularly for the technological learning process related to the technological alliance, we divide technological learning process into three sub-processes; knowledge acquisition from partners (the aspect of external communication), knowledge diffusion & storage in the firm (the aspect of internal communication), and knowledge assimilation & application (which represents the level of learning).

Channels for acquiring of technological knowledge include formal workshop, seminar, and informal group meeting between partners. Efforts to acquiring exact, prompt, and sufficient information or knowledge should be made to fully accomplish the goal of technological alliances.

When cooperative agreement is established, knowledge from outside firms or institutes begins to flow in through individual member. Organization learning requires that information be shared and stored in a form convenient to all relevant organizational member [36]. For this purpose, the norm and method for storing and distributing information should be established, and also computerized information retrieval system helps to lower the difficulties of information diffkion, storage and retrieval.

This study confines the scope of the level of learning to first two levels of learning - that juxtaposes with incremental and discontinuous (or radical) innovation. For the higher level of innovation, firms should try to perform various trial- and-error, experimental activities as well as just imitation of outside actors. Based on the discussions above, the following hypotheses are provided.

Hypothesis 2a. The relationship between the use of technological alliances and a firm’s new product developments will be more positive for firms whose knowledge acquisition process is better structured than other firms.

Hypothesis 2b. The relationship between the use of technological alliances and a firm’s new product developments will be more positive for firms whose knowledge diffusion & storage process is better structured than other firms.

Hypothesis 2c. The relationship between the use of technological alliances and a firm’s new product developments will be more positive for firms whose knowledge assimilation & application process is better structured than other firms.

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I

Technological Alliance

Environment *Market Competition *Technological Change

New Product Developments

*Radical New Products Tr 1)1 *Incremental New Products

H1

H2

Technological Learning System *Acquisition *Diffusion & Storage *Assimilation & Application

Figure 1 . Research Model

IV. METHOD

A . Sample and Data Collection The samples consist of small technology-based

manufacturing firms in the Korean Association of Promising Small and Medium-sized Telecommunication Enterprises and other innovative small-manufacturing firms in the telecommunications sector recommended by industrial experts. The telecommunication device and components industry, a typical example of product-based industry, is one of the fastest growing industries in Korea.

Data was collected by structured questionnaires. A pilot survey of several sample firms was conducted to modifL the questionnaires before mailing. Questionnaires were mailed to the senior managers in charge of the technology development or manufacturing of 204 firms. Among 71 returned questionnaires, eight questionnaires were removed from the analysis due to incomplete responses, and 63 questionnaires were used for data analysis. Sample firms’ average number of employees was 117, ranging from 7 to 420 persons.

B. Measures Technological alliance: Technological alliance is defined

as the inter-organizational arrangement through which organizations jointly acquire technical knowledge [4]. Technological alliance can be classified by the type of partners. Partners are divided into (1) (licensing from) foreign firms in the same industry, (2) domestic firms in the same industry (competitors or not), (3) vertically related firms (customer and supplier), (4) horizontally related institutes (university and public research institute). Respondents were

asked to count all the technological alliances (whether each technological alliance was successful or not) they used during present technological development stages. The extent of technological alliance is measured by the sum of all the alliances used per year.

Market Competition and Technological Change: Market competition was measured by the sum of two 5-point items below: (1) there are many competitors in the product market, and (2) the competition in the product market is severe. Technological change was measured by the sum of two 5- point items below: (1) there are radical technological changes in the material or component used in the industry, and (2) there are frequent adoptions of new product design technologies or production technologies in the industry.

Technological Learning System: Based previous studies [l], [25], [26], [31], six 5-point items were developed to measure the structuredness of the technological learning system; during the process of technological alliances, (1) efforts were made to ensure exact and prompt communication, (2) efforts were made to ensure frequent communication, (3) formal workshop or seminar were appropriately organized, (4) informal group meetings were appropriately held, (5) major changes in target technology or new product specification were known to related developers or others concerned through technical notes, (6) complementary knowledge was supplemented through technical journals or other technical reports, (7) knowledge storage system is sophisticated (1: no interest - 2: no documentation - 3: documentation for final results - 4: documentation for final results and other related data -- 5: systematic filing or storage of all technical documents), (8) internal efforts were made to

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completely absorb the technical knowledge and information analysis when investigating contingency relationships, acquired from partner, (9) supplementary experiments and because it allows the interaction terms, which are implied in trial-and-errors were made for the application of the all contingency relationships, to be explicitly examined. This knowledge acquired form partners, and (10) knowledge study tries to test the following research model. accumulated from past research and development activities was promptly used for development of target technology or y = intercept + bl*EmPloYee i- bz*TDS + b3*TA + b4* Xi -F new moduct. bS*TA* Xi (I=l,2,3,4,5)

hew Product Developments: New product developments were assessed by the average number of incremental new products and average number of radical new products developed in the present technological development stage. Radical new products are defined as products developed with substantially improved technology compared to existing products, and incremental new products is defined as replication or minor improvement of existing products.

Control variables: To isolate the relationship between technological alliances, environmental factor, learning system and new product development, it is necessary to control for other variables likely to have an important impact on new product development. Firm size and technological development stage are included as control variables at firm’s level. Average number of employees in the present technological development stage was used to control firm size. Technological development stage in the global perspective heavily affects the technological alliances- performance relationship [ 11, [28]. Technological development stage is divided based on the critical incidence approach. In stage I, the mature technologies are used that have diffused into even the least developed countries or are being widely used in developing countries. In stage 11, they mainly use a technology that has already begun to diffke into developing countries. In stage 111, they mainly use technologies that used in developed countries but have not yet been diffused into developing countries. In stage IV, they use high level technology that has been developed within 3 years in the developed countries. Bae [l] and Choi [6] show that this category scale reflect well the development stages of several industries in Korea. In this study, each respondent was required to locate hidher firm’s present technological development stage according to the maturity of the technology used. Stage I, 11, and 111 correspond to early stage (TDS=O) and stage TV corresponds to a later stage in the following analysis (TDS=l). In addition, it is noted that the number of technological alliances used in the present stage was divided by the length of that stage to control the difference in the length of each stage of the firm.

C. Analytical Technique Correlation and moderated regression analysis were used

to identify the hypothesized relationships between technological alliances, environmental factor, learning system, and new product development. Moderated regression analysis was used to test the individual hypotheses and subsequently, subgroup regression analysis was adopted to show the technological alliance-performance relationship in detail. A number of studies [8], [30] use the moderated regression

Where Y=New Product Development (Incremental New Products, Radical New Products) X,=Market Competition, X2=Technological Change, X,=Acquisition, X4=Diffusion & Storage, XS=Assimilation & Application, Technological Alliance (TA), Technological Development Stage (TDS)

The multiplicative effect results in high levels of multi- collinearity in moderated regression analysis. To deal with this problem, Cronbach [9] suggests centering the predictor variables before forming the multiplicative term. In this study, independent variables such as market competition, technological change, technological learning system, and technological alliance were mean-centered before entering the testing equations.

V. RESULTS A . Basic Statistics

Content validity of the instrument was checked through adoption of constructs that have presented in the previous research and a pretest with eight managers in charge of technological development. We calculated the internal consistency (Cronbach’s alpha), from 0.63 to 0.86, in order to assess the reliability of the measurement instrument (market competition, 0.75; technological change, 0.86; knowledge acquisition process, 0.8 1 ; knowledge difhsion & storage, 0.63; knowledge assimilation & application, 0.7 1). Discriminant validity of technological learning system was checked by means of a factor analysis.

Factor analysis of the technological learning activities reveals three underlying dimensions with an eigenvalue greater than one. Table 1 demonstrates the factor loadings and commonalities from the varimax rotation. The aspects of technological learning system loading on factor one describe the acquisition of technical knowledge. For effective information in-flow, strenuous efforts were made to ensure exact, prompt, and frequent communication between partners, and formal or informal workshop or meeting were appropriately organized (item 1 through item 4). Factor two describes the diffusion and storage of acquired technical knowledge. Members in the R&D department not only use research journals and technical reports or notes, but also store technological knowledge in a systematic manner (item 5 through item 7). Factor three represents assimilation and application of the past acquired knowledge. This factor consists of assimilating and applying accumulated technical knowledge (item 8 through item 10).

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TABLE. 1 STANDARDIZATION OF TECHNOLO( 3AL LEARNING

Acquisition

0.82 - 0.72 0.72 - 0.71

0.00

0.28

0.20

0.16

0.12

0.27

3.89 2.46 0.39

Exact and prompt communication between partners Frequent communication between partners Formal workshop or seminar between partners Informal group meeting between partners Use of technical note for announcing changes in R&D process Use of technical journals or reports for supplementing knowledge Degree of sophistication of knowledge storage system Internal effort to absorb and assimilate knowledge from the partner Supplementary experiments, and trial & error Prompt application of past accumulated technology knowledge Eigenvalue Difference Proportion Cumulative Acquisition = ((1)+(2)+(3)+(4))/4, Diffusion & Storage = ((5)+(

SYSTEM - FACTOR ANALYSIS Diffusion & Assimilation &

Storage Application

0.29 0.23

0.39 0.26 0.25 0.08 -0.07 0.11

- 0.84 0.09

- 0.77 -0.07

- 0.5 1 0.16

-0.09 - 0.86

0.14 - 0.84

0.38 - 0.55

1.43 1.06 0.37 0.16 0.14 0.1 1

Table 2 shows the means and standard deviations of all the variables for the total samples, and Table 3 gives the correlation coefficients between variables. Technological

0.39 0.53 0.64

alliance shows significantly positive relationships with incremental and radical new products, and relatively large firms use more technological alliances than small firms.

TABLE 2 BASIC STATISTICS I N Mean S.D. Min. Max. I

1. Number of new products (NP)

2. Number of incremental new products (INP)

3. Number of radical new products (RNP)

4. Number of employees (EMP)

5. Number of technological alliances (TA)

6. Market competition (MC)

7. Technological changes (TC)

8. Acquisition (ACQ)

9. Diffusion & Storage (D&S)

10. Assimilation & Application (A&A)

50

48

48

56

63

59

56

57

57

56

3.2

1.8

1.4

1 17.4

1.8

3.7

3.6

2.7

2.9

3.3

3.2

2.1

1.8

100.5

1.8

1 .o 1 .o 0.8

0.8

0.8

0.0

0

0.0

7.3

0

1.5

1 .o 1.2

1.7

1

15.0

10.5

8.0

420.0

7.5

5.0

5 .O

4.7

4.7

5 I

Technological Development Stage =O (23 cases, 36.5%) = I (40 cases, 63.5%)

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1. NP

2. INP

3. RNP

4.EMP I

NP INP RNP EMP TA MC TC ACQ D&S A&A

1.00 0.86*** 0.80*** 0.44*** 0.72*** 0.28* 0.15 O.I5 -0.11 0.15

1.00 0.37*** 0.35** 0.51*** 0.28* -0.09 0.27* -0.09 0.14

1.00 0.36** 0.70*** 0.1 1 0.30* 0.03 -0.08 0.21

1.00 0.41*** 0.11 0.07 0.25* 0.25* 0.19

5. TA

6. MC

1.00 0.06 0.09 -0'06 -0.06 0.1 1

1.00 0.23* 0.13 -0.27**

7. TC

8. ACQ

9.D&S

lO.A&A

1.00 0.30**

1 .oo

Table 4 shows the relationship between technological alliance and new product developments with no contingency variables. Total number of new products is the sum of the number of incremental new products and the number of radical new products. Equation 1, 3 and equation 5 show that the more technological alliances the firm use, the more (incremental / radical) products the firm can develop. Equation 2, 4 and 6 show the relationship between each technological alliance type and new product developments. Technological alliances with firms in the same industry, vertically related firms (customers or suppliers), and

TABLE 4 TECHNOLOGICAL ALLIANCE-NEW

Independent variables

Employee

Technological Development Stage

Technological Alliance

Licensing

Firms in the same industry

Vertical firms

Horizontal institute

horizontally related institutes (government owned research institutes or universities) contribute to develop new products (Eq.2). Licensing from the foreign firms, technological alliance with domestic firms in the same industry, and with vertically related firms (customers or suppliers) help increase incremental new product developments (Eq.4), while technological alliances with vertically related firms and with horizontally related institutes (government owned research institutes or universities) increase radical new product developments (Eq.6).

PRODUCT DEVELOPMENT RELATIONSHIP - REGRESSION ANALYSIS Dependent variable

Total Incremental Radical New Products New Products New Products

Eq. 1 Eq. 2 Eq. 3 Eq. 4 Eq. 5 Eq. 6

0.16 0.16 0.19 0.22 0.06 0.02 -0.06 -0.04 -0.04 0.01 -0.04 -0.07

0.66*** 0.40*** 0.72***

0.1 1 0.28** -0.15

0.30** 0.39*** 0.08

0.44*** 0.30*** 0.44***

0.48*** 0.09 0.78*** F-value Adjusted R-square

17.36*** 8.34*** 4.84*** 1.10*** 17.60*** 16.81*** 0.53 0.50 0.20 0.30 0.53 0.68

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B. Testing Table 5 contains the results of the multiple regression

analyses undertaken to test hypothesis 1. To test the moderating effect of market competition, interaction terms for technological alliance and market competition were included in the regression model. The equation 2 of Table 5, using the number of incremental new products as a dependent variable, indicates that the beta coefficient of the interaction term for technological alliance and market competition is positive and significant (beta coefficient = 1.7 1 , significant level = 0.01). This means that as the market competition become severe, the impact of technological alliance on new product developments become strong. However, equation 3 of Table 5, using the number of radical new products as a dependent variable, shows that the beta coefficient of the interaction term for technological alliance and market

Eq. 1 NP Eq.2 INP Eq.3 RNP

Eq.4 NP

Eq.6 RNP

X,=Technological Change

Eq.5 INP

competition is insignificant. Tbese results partially support hypothesis 1 a.

The equation 5 of Table 5 indicates that the beta coefficient of the interaction term for technological alliance and technological change is negative and significant (beta coefficient = -1.73, significant level = 0.01). However, in equation 6 the beta coefficient of the interaction term for technological alliance and technological change is positive and significant (beta coefficient = 1.10, significant level = 0.05). This means that as the technological change becomes severe, the impact of technological alliance on radical new product developments become strong, while the impact of technological alliance on incremental new product development become weak. These results partially support hypothesis 1 b.

0.13 -0.03 -0.20 0.01 0.93** 1 4 . 6 T 0.17 0.07 -1.18** -0.13 1.71*** 7.63*** 0.05 -0.03 1.12** 0.18 -0.42 10.57***

0.15 -0.02 1.21** 0.12 -0.58 9.92*** 0.51

0.03 -0.01 -0.3 1 -0.01 1.10** 14.73*** 0.62 0.20 -0.02 2.03*** 0.19 -1.73*** 5.37*** 0.34

Table 6 shows the impact of technological learning system on the relationship between technological alliance and new product development. Equation 2 indicates (dependent variable - INP) that the beta coefficient of the interaction term for technological alliance and knowledge acquisition process is positive and significant (beta coefficient = 1.16, significant level = 0.1). However, equation 3 (dependent variable - RNP) indicates that the beta coefficient of the interaction term for technological alliance and knowledge acquisition is insignificant. These results partially support hypothesis 2a. Knowledge diffusion & storage process has a similar impact on the relationship between technological

alliance and incremental / radical new product development. Knowledge diffusion & storage process help technological alliance increase only incremental new product development (beta coefficient = 1.15, significant level = 0.1 in Equation 5). This implies that well structured knowledge acquisition process and knowledge diffusion & storage process help technological alliance increase incremental new product developments. These results partially support hypothesis 2b. On the contrary, assimilation & application process help technological alliance increase only radical new product development (beta coefficient = 1.51, significant level = 0.1 in Equation 8). These results partially support hypothesis 2c.

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TABLE 6 TECHNOLOGICAL LEARNING SYSTEM AS MODERATORS OF THE TECHNOLOGICAL ALLIANCE-NEW PRODUCT DEVELOPMENT RELATIONSHIP

NP=total new products, INP= incremental new products, RNP = radical new products TDS=Technological Development Stage, TA=Technological Alliance Level of Significance: *: p<O.1, **: p<0.05, ***: p<O.OOI, standardized beta coefficient

VI. DISCUSSION AND CONCLUSION

The presence of significant interaction terms shows evidence in partial support of hypotheses provided earlier in this study. The interaction between technological alliance and environmental factors, and the interaction between technological alliance and internal learning system have moderating effect on the incremental or radical new product developments.

Table 7 summarizes the results form the moderated regression analysis. First, the more severe the market competition and technological change, the stronger the impact of technological alliance on a firm’s new product development. This implies that strong market competition and rapid technological changes increase the needs for new product developments and frequent market developments. Therefore, the need for and benefits of technological alliances as a source of technology acquisition become more important. More specifically, Technological alliance in the strong market competition has impacts on the increase of “incremental (or total)” number of new products. Firms faced with rapid technological changes, technological alliance has contributed to the introduction of “radical” new products

(technological alliance has even negatively impact on “incremental” new products).

Second, the better structured the technological learning system related to alliance, the stronger the impact of technological alliances on a firm’s new product development. It emphasizes the importance of absorptive capabilities and implementing mechanism of firms for the active utilization of technological alliances. More specifically, knowledge acquisition from partners and knowledge diffusion & storage is enough for incremental new product (incremental innovation). However, these two processes do not seem to be enough for radical innovation (eq. 3 and eq. 6 in Table 6). This does not mean that knowledge acquisition and knowledge diffusion & storage are not important for radical innovation but these processes are prerequisite for assimilation & application process. With these processes well structured, supplementary efforts to assimilate the acquired knowledge and continuous trial-and-error (learning by doing) do contribute for radical innovation This assimilation & application process partly corresponds to double-loop learning that juxtaposes with discontinuous innovation.

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TABLE 7 SUMMARY (

Independent Variable

Technological Alliance

Moderator Variables

Market Competition

Technological Change

Acquisition

Diffusion & Storage

Assimilation & Application

+,-: significant modr.-.ion effect from moderating regression a

These results can provide managers with a guideline of strategic technology alliances. Managers of SMEs should think of technological alliance as one of good options and use them more actively when the market condition is more competitive and the technological change is more severe. Though market competition and technological changes may be threats to the business, active utilization of technological alliance to cope with these threats can enhance the level of a new product (or radical new product) development, which is an important competitive dimension, especially in the high- technology industry. In addition, the results show that difference in the impacts of technological alliances on new product developments do depend on the level of learning system of the firm. Although firms cannot help adopting new technology outside due to their narrow technological base, active and explicit technological efforts to assimilate and improve acquired technologies should be followed, especially for radical innovation, based on strong learning motivation. That is, firms should integrate the technological alliances into organizational learning system.

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