How collaborative innovation system in a knowledge-intensive competitive alliance evolves? An empirical study on China, Korea and Germany

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Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore

How collaborative innovation system in a knowledge-intensive competitive alliance evolves? An empirical study on China, Korea and Germany ⁎

⁎

Jianyu Zhaoa, , Guangdong Wub, Xi Xic, , Qi Naa, Weiwei Liua a

School of Economics and Management, Harbin Engineering University, Harbin 150001, PR China School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, PR China c Management School, Harbin University of Commerce, Harbin 150028, PR China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Knowledge-intensive competitive alliance Collaborative innovation Knowledge behavior Knowledge management Resource input

In terms of the research content, previous studies did not analyze influences of interactions among different knowledge behaviors on collaborative innovation in the knowledge-intensive competitive alliance. Meanwhile, they overlooked exploring the external control mechanism together with internal interaction mechanism. In terms of the research method, previous studies failed to deal with challenges brought by collaborative innovation complexity. In order to address these gaps, different knowledge behaviors that influence collaborative innovation are identified as knowledge creation, knowledge transaction and knowledge application. The theory and method of synergetics, which is capable of explaining collaborative innovation complexity, is introduced to establish a dynamic collaborative innovation evolution model. Taking high-end medical device knowledge-intensive competitive alliances in China, Korea and Germany as empirical research subjects, we reveal the collaborative innovation principles in the knowledge-intensive competitive alliance by analyzing external control mechanism (resource input mechanism) and internal interaction mechanism (interactions among different knowledge behaviors). Results showed that: I) resource input mechanism, the most important external control mechanism, is able to alter collaborative innovation period. II) in the process of collaborative innovation, there is a highly non-linear positive correlation between knowledge creation and knowledge application, but the collaborative innovation does not depend on knowledge transaction. III) no matter what the role of resource input mechanism is, the collaborative innovation in knowledge-intensive competitive alliance is able to enter into a new stability spontaneously under interactions among different knowledge behaviors. With this research, this study can promote studies on collaborative innovation in the knowledge-intensive competitive alliance, provide new perspectives and methods for researchers, and offer important implications for managers to set strategies.

1. Introduction Knowledge-intensive competitive alliance, in the era of knowledge economy, is a special mode of joint operation formed by several knowledge-intensive enterprises in the same industry that are able to provide the same or substitutable products and services to markets through knowledge activities aiming at innovation (Dussauge et al., 2000; Grant and Baden-Fuller, 2004; Mitchell et al., 2002; Mudambi and Tallman, 2010; Silverman and Baum, 2002; Soekijad and Andriessen, 2003). In accordance with knowledge-based theory, the motivation of the competitive knowledge-intensive enterprises to form an alliance is that they can acquire more knowledge output and promote alliance innovation with the collaborative innovation mode of complementary knowledge resource (Cowan and Jonard, 2009; Fang, 2011). According to the knowledge management theory, the ⁎

collaborative innovation is a type of innovation method for the alliance partners to reach an agreement and promote economic output by knowledge creation, knowledge acquisition, knowledge transaction and knowledge application (Bengtsson and Kock, 2000). This innovation method can offer potential benefits, and it can improve the utilization and spillover of the limited resources and create more added value for the alliance innovation through different knowledge behaviors and knowledge interactions (Dussauge et al., 2000, Dussauge and Garrette, 1998; Mitchell et al., 2002; Rindfleisch and Moorman, 2002). Hence, in order to ensure and promote the effective development of the collaborative innovation of the knowledge-intensive competitive alliance, it is of great significance to open the black-box of the collaborative innovation. Previous studies have made efforts on how to promote the collaborative innovation of the knowledge-intensive competitive alliance (Dussauge et al., 2000; Fang, 2011; Inkpen, 2008; Mitchell et al.,

Corresponding authors. E-mail addresses: [email protected] (J. Zhao), [email protected] (X. Xi).

https://doi.org/10.1016/j.techfore.2018.07.001 Received 4 December 2017; Received in revised form 13 March 2018; Accepted 2 July 2018 0040-1625/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Jianyu, Z., Technological Forecasting & Social Change (2018), https://doi.org/10.1016/j.techfore.2018.07.001

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appealed to introduce interdisciplinary approaches in studying nonlinear and complex management problems such as innovation, because these approaches can help us find out solutions and obtain some conclusions can not be obtained by the method of management. However, there are few studies adopted interdisciplinary approaches to study the problems of alliance collaborative innovation, and there are still unsolved problems about collaborative innovation complexity principles. To address these research gaps, the theory and method of synergetics is introduced to analyze the collaborative innovation of knowledge-intensive competitive alliance. The interactions between different knowledge behaviors are tested so as to explain the principle of collaborative innovation. This study advances alliance research on several fronts. In terms of the research content, compared with the previous studies, we have taken into consideration the complexity of collaborative innovation (Schneider et al., 2017). In accordance with the solution to the complexity proposed by Amaral and Uzzi (2007), as well as Miller and Page (2007), we introduced the synergetics theory of the complexity from the perspective of knowledge management theory and system evolution to identify different knowledge behaviors that may influence collaborative innovation, defined different knowledge behaviors as state variables that collectively influence collaborative innovation. By establishing the dynamic synergetic relation equation and using the method of simulation experiment, the nonlinear interaction between various knowledge behaviors is dominant. This reveals the interaction principles among state variables, and clarifies the function and contribution of each knowledge behavior in the evolution process of collaborative innovation system, so as to explain the aim of research on collaborative innovation system evolution based on difference function of different state variables, which has not been involved in previous researches. This study enriches the research content of the theory of knowledge management based on collaborative innovation, and helps researchers to understand different effects of different knowledge behaviors on knowledge-intensive competitive alliance collaborative innovation as well as the reasons. Meanwhile, this study has taken into consideration the external control mechanism and the influences of internal behavior mechanism on the collaborative innovation. In the existing literature, the two kinds of studies develop independently. Most studies usually focus on the external factors that influence collaborative innovation only or discuss the function of internal behavior mechanism, and seldom study the internal mechanism of collaborative innovation in parallel with the control mechanism of external environment. Therefore, the gap is that the existing literature cannot analyze special phenomenon that may appear in collaborative innovation system when the two mechanisms work together. This study bridges this gap by establishing a collaborative innovation process model from a dynamic perspective. This not only pays attention to the role of external control mechanism, but also analyzes the promotion of internal mechanism on collaborative innovation. Based on different roles of different external control mechanisms, this study takes into consideration of whether the internal behavior mechanism would be influenced and the interactions and changes of different knowledge behaviors that compose internal mechanism when the external control mechanism changes, which is able to verify the mutual influences between internal interaction mechanism and external control mechanism of collaborative innovation. However, previous studies attached more importance to the role of the external control mechanism (Lavie et al., 2012). In addition, we follow the ideas and designs of Venkatraman and Ramanujam (1986), as well as Wright and Calof (2006), and adopt comparison studies to further reveal the intensity of external control mechanism when the collaborative innovation system is at different development stages, and the different forms of interaction between different internal knowledge behaviors. We chose the high-end medical device industry in China, Korea and Germany as the empirical object of comparison study. In the knowledge-intensive competitive alliance of high-end medical device, the industries in Germany have developed

2002; Silverman and Baum, 2002; Zaheer et al., 2010). One of the main research direction is to investigate the effects of knowledge acquisition and knowledge transaction of the alliance on collaborative innovation, underscoring the merits of complementarity of knowledge resources, optimization of knowledge production method and consistency of knowledge strategy. Other researchers, based on the enterprises' cooperation and competition (relationship mechanism) within the alliance, identify the factors may influence the collaborative innovation such as the social distance, opportunistic behavior, mutual trade dependence, and commitment. These studies have broadened the study scope of collaborative innovation, however, there are some defects in the existing body of knowledge, both theoretically and methodologically, leading to the two main gaps in the existing studies. In terms of the research content, the existing literature has analyzed the stability of the competitive alliance in the condition where there are both competition and cooperation, and the influences of factors like opportunism on collaborative innovation (Bérard and Perez, 2014; Dussauge et al., 2000; Duysters and Lokshin, 2011; Gnyawali and Park, 2011; Hamel, 1991; Hoang and Rothaermel, 2005; Zhang and Frazier, 2011). There are few studies exploring interaction principles between different co-existing knowledge behaviors in the alliance as well as the special influence of interactions on collaborative innovation within a research. However, some researchers (Fang, 2011; Meier, 2011; Zhao et al., 2015) have emphasized the importance of analyzing knowledge behaviors that influence collaborative innovation from the perspective of knowledge management. The knowledge behaviors can make different contributions to the collaborative innovation of the competitive alliance, also, the interactions, relevance, sensitivity as well as the changing laws may be the keys to explain the essence of the collaborative innovation (Inkpen, 2008; Menon et al., 2009). There are few studies to identify key knowledge behaviors of collaborative innovation and analyze interactions among these behaviors as well as the influences on collaborative innovation based on knowledge management theory. This makes the existing research on collaborative innovation based on different knowledge behaviors underdeveloped. Therefore, on the one hand, the existing literature cannot judge the non-linear interaction principles between these behaviors according to the interaction form among various knowledge behaviors and the change trend, which hinders us to open the black box of collaborative innovation based on knowledge behavior. Moreover, it cannot provide sufficient theoretical basis for managers to make strategies to control and promote collaborative innovation. On the other hand, the existing literature does not specify what trends the interaction of knowledge behaviors will present when the role of external control variables changes, and how these changes affect collaborative innovation output of knowledge-intensive alliances. This makes the present studies fail to explain the effects of internal and external control mechanisms on collaborative innovation interaction principles. In terms of the research method, most of previous studies, oriented by the results, have adopted theoretical deduction to analyze certain variables or the influence of certain characteristics of competitive alliance on collaborative innovation. This leads to that in the existing studies, the implicit assumption based on discrete time is used to analyze the innovation problems of strategic alliance or competitive strategic alliance, or the linear interaction mechanism is adopted to analyze the influence of a certain variable on the collaborative innovation. However, as pointed by Sampson (2005), the collaborative innovation is a typical non-linear dynamic and complex process and the non-linear interaction relationship among different knowledge behaviors would influence the collaborative innovation (Schilke, 2014). Hence, the results on the process and principle of collaborative innovation concluded from the traditional linear analytical method may be not so rigorous. Problems caused by it are that the existing literature can not verify the non-linear interaction form among variables and the concluded results can only verify the interaction relationship between single variable. Therefore, Park and Ungson (2001) as well as Cheng et al. (2009) have 2

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knowledge behaviors as the internal interaction mechanism from the perspective of new theory based on the relationship mechanism of competition and cooperation, and we analyzed the non-linear interaction relationship among different knowledge behaviors to explain the principles of collaborative innovation so as to meet the challenges of comprehensiveness and complexity of collaborative innovation. Second, we have studied the external mechanism along with the internal mechanism of the collaborative innovation in this study. We found that the role of the external control mechanism is to alter the period of collaborative innovation. However, no matter how strong the external control mechanism is, the internal interaction mechanism based on different knowledge behaviors would be able to facilitate the collaborative innovation to a new state. Meanwhile, this study has not only verified the different sensitivities of different knowledge behaviors to the external control mechanism, but also revealed the self-organized characteristics of the collaborative innovation in the knowledge-intensive competitive alliance and enriched the content of the innovation management theory and strategic management theory correspondingly. Third, different from the previous narrower method in which the dimension is divided in a limited way, we introduce the theory and method of synergetics to analyze the principles of collaborative innovation of the knowledge-intensive competitive alliance, and the conclusions have enriched the results of knowledge management theory and achieved unity of opposites theoretically among knowledge management theory, innovation theory and synergetics theory.

into top ones around the world, which own mature technology based on independent R&D and are of higher practical production capacity, and the collaborative innovation system is mature (Davey et al., 2011). The industries in Korea develop rapidly, which own independent R&D capacity that may catch up with the world's top level and technology introduction based on market demands as well as are of higher practical production capacity, and the whole alliance is developing (MPharm et al., 2009; Chatterji, 2009). Compared with industries in Germany and Korea, though knowledge-intensive competitive alliances of highend medical device in China are large, their R&D capacity is weaker, and the development of the alliance tends to depend on such technology introduction as knowledge transaction to produce products, therefore, the collaborative innovation is starting (Russell and Tippett, 2008). Because of the difference of development among Germany (mature), Korea (developing) and China (starting), comparison study is able to verify the collaborative innovation system evolution characteristics of knowledge-intensive competitive alliance in different conditions as well as interaction difference between different knowledge behaviors more comprehensively, which is helpful for scholars to illustrate essence of collaborative innovation system evolution of knowledge-intensive competitive alliance, for managers to find out the gap between themselves and market leaders and to make strategies that promote and control collaborative innovation system evolution. In terms of the research method, we have chosen the method of synergetics which can explain the dynamic and complex collaborative innovation (Blume and Durlauf, 2006; Haken, 1985), and the rationality and advantage of the method is: first, theoretically, the theory and method of synergetics provide us new perspectives and ways to solve the problems (Amaral and Uzzi, 2007; Anderson, 1999; Liu, 1996), and it can make up the defects that fail to make part of the principle and phenomenon logical or intuitive (Boland Jr and Greenberg, 1992; Haken, 1985; Stark and Kotin, 1989). Second, in terms of the reliability of the method and statistics, the synergetics has described and explained the interaction among variables in accordance with the mathematical logic, and the referred index indicators are all from public real data (Morel and Ramanujam, 1999), which can not only eliminate subjective mistakes, but avoid sample defects or low credibility caused by data from questionnaire, and the corresponding conclusions are more reliable (Amaral and Uzzi, 2007; Cheng et al., 2009; Haken and Knyazeva, 2000). Third, in terms of the practical value, the theory and method of synergetics are able to transform the original complex economic system into an intuitive concept (Weidlich, 1991), and it has overcome the theoretical closure and expression defects caused by simple formal logic and vocabularies, and by analyzing the interaction among internal variables (Corning, 1995), it has defined the complex principles of the dynamic system evolution completely and profoundly (Lewin, 1999). Also, it has broadened the meaning space provided by scientific theory, which can be of great value to explain specific problems in the field of management (Chiva et al., 2010); Fourth, in terms of the application, the research method of the synergetics has become an important way to study the non-linearity and complexity of the subjects in the field of analyzing economy and management (Birkinshaw et al., 2008; Karmeshu and Jain, 2003; Dopfer, 1991), such as collaborative evolution and innovation in the management theory (Broekstra, 2002; Koza and Lewin, 1998), knowledge creation and knowledge flow in the knowledge management (Laihonen, 2006; Nonaka, 1994), and alliance self-organized emergence in the strategic management (Thietart, 2016), which are all defined and explained by the thought of synergetics. With this theory and method, we can give a complete explanation to the collaborative innovation problems in the knowledge-intensive competitive alliance from the perspective of dynamic system theory so as to have some novelty conclusions. This work makes three aspects of contributions. First, unlike the existing literature that takes the relationship mechanism as the internal interaction mechanism of the knowledge-intensive competitive alliance innovation, our study has taken the interaction among different

2. Theoretical and model 2.1. Theoretical foundation The synergetics theory follows the basic idea of the general complex system theory, taking the research object as a complex system composed of elements, parts or subsystems, emphasizing that the interaction between components not only has typical non-linear characteristics, but also is the power of system evolution. The synergetics theory uses control variables and state variables to describe the evolution of the system, and the control variable is the condition variable that the complex system achieves threshold and realizes synergy, which belongs to the controllable elements of the system. Different from control variables, state variables describe and determine the function of the system, which is formed by the interaction of many components in the system. By changing the control variable, the system reaches to the threshold, and certain state variables turn into order parameter as the main force of the system evolution, which leads the system from disordered to ordered. At present, with the improvement of synergetics theory, the theory has been applied to the research on economic system because it can accurately identify the conditions of the system to reach the threshold and identify the order parameters in many variables that affect its evolution, and fully explain and reveal the evolution law of the system. Such as the establishment of organizational synergy management system and the low-carbon innovation system of sustainable development. In the research context of this study, in the process of collaborative innovation based on the interaction of different knowledge behaviors in the knowledge-intensive competitive alliance, although each kind of knowledge behavior plays its own role, the interaction and influence form of each other not only present the typical non-linear characteristic, but also gain more benefit than the sum of independent benefit of each knowledge behavior, having significant synergy and effect. This kind of synergy and effect make the collaborative innovation of knowledge-intensive competitive alliance gradually form a complete time and space complex structure, which would also be affected by external control variables (such as resource) when it changes under the influences of several internal knowledge behaviors. The simultaneous effect of internal and external mechanisms makes the collaborative innovation of knowledge-intensive competitive alliance meet 3

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Garrette, 1998; Mitchell et al., 2002; Rindfleisch and Moorman, 2002). Particularly the excitation and transmission of implicit knowledge during knowledge creation and knowledge interaction among the partners in the competitive alliance need to be forcefully controlled and managed (Von Hipple, 1994). These features of the economic systems are typically similar to complex systems (Blume and Durlauf, 2006), therefore, we hold that the collaborative innovation system of the knowledge-intensive competitive alliance is essentially a social complex system with a change in system structure being the premise of collaborative innovation. The collaborative innovation system of the knowledge-intensive competitive alliance is a special and complex structure with the purpose of “mutual benefit” (Zhao et al., 2015, 2018). This is formed by the non-linear interaction between different factors composing and influencing collaborative innovation from the perspective of complexity science (Zaheer et al., 2010). The function of collaborative innovation system is to realize the output of products through the interaction of the elements in the system, and to obtain some profit. Therefore, the knowledge system theory argues that the evolution of collaborative innovation system is the result of coordination and interaction of the key knowledge behaviors that affect collaborative innovation, namely the internal mechanism of collaborative innovation system composed by knowledge creation, knowledge transaction and knowledge application (Nonaka, 1994). The whole system realizes the evolution from lower order to higher through nonlinear interaction of the three kinds of knowledge behaviors (as shown in Fig. 2).

the research requirements to regard economic system as a complex system (Haken, 1985; Zhao et al., 2018). Therefore, we regard it as a complex system and study the evolution of the system with synergetics theory. In the theoretical framework combining knowledge management and innovative management, the nature of innovation is the dynamic process of knowledge creation, external acquisition, and the application of new knowledge to the product by the innovation subject (McFadyen and Cannella, 2004; Nonaka, 1994). For knowledge-intensive competitive alliances with a high degree of knowledge concentration, a short innovation cycle, and a view of knowledge as the dynamic core competitive advantage (Castro et al., 2008; Lafuente et al., 2010), efficient knowledge propagation and transformation are important supports for the survival and development of knowledge-intensive competitive alliances in the business environment (Chen et al., 2012; Luo et al., 2009). In the new classical economics theory system, the result of knowledge innovation in competitive alliances refers to the sum of economic value and added value, reached by applying specific knowledge to production and improvement of products (Consoli and Hortelano, 2010; Nonaka, 1994). Specific knowledge refers to the set of knowledge with intellectual property rights owned by alliance members (Saetang and Theodoulidis, 2011), the knowledge stock of corresponding alliance members, and knowledge acquired by knowledge transaction (Felin and Hesterly, 2007; McFadyen and Cannella, 2004; Nonaka, 1994). Based on this concept, scholars of the theoretical schools of knowledge-based views, knowledge-value theory, and technological innovation all summarized the method for improving the innovation performance of knowledge-intensive competitive alliances as promoting knowledge creation by alliance members, encouraging the acquisition of knowledge required for development by contract transaction or knowledge exchange, and maximizing the transformation and integration of new knowledge into the product commercialization process. Furthermore, the scholars of knowledge system theory and knowledge evolution theory considered the characteristics of the relationship among the members of the competitive knowledge alliance, the embedded characteristics of knowledge itself, and the guiding role of technical requirements. As indicated in Fig. 1, they summarized the integration and interaction among knowledge creation, knowledge transaction, and knowledge application at the process level as a collaborative-innovation system with the objective of enhancing the value of knowledge and innovation performance (McKelvey et al., 2003). Currently, scholars have realized that the key links in collaborative innovation are knowledge creation, knowledge transaction, and knowledge application (Nonaka, 1994; McKelvey et al., 2003; Fang, 2011). Meanwhile, considering the dynamic nature of collaborative innovation, the obvious complexity of interaction among key links combined with business opportunities within the alliance boundary may affect the strategic dynamics and behavior of different members of the alliance (Zhao et al., 2015; Dussauge et al., 2000, Dussauge and

2.2. Model This study adopted the method of synergetics to explain the dynamic evolution principle of collaborative innovation in knowledgeintensive competitive alliances. Therefore, collaborative innovation of knowledge-intensive competitive alliances based on the self-organization characteristics of a general complex system should also satisfy the following hypotheses: H1: According to the self-organization theory, the complex system is a non-linear open system with a dissipative structure away from the equilibrium state (Chattopadhyay et al., 1992; Murray et al., 2012). The system needs to exchange material, information and energy with the external environment in order to maintain the balance between the positive entropy and the negative entropy in the system, so as to realize the evolution from low to high order. This is the basic prerequisite for the analysis of complex system evolution (Muschik and DomínguezCascante, 1996; Richardson and Miekisz, 1978). Therefore, in order to analyze the evolution principle of the collaborative innovation system of knowledge-intensive competitive alliance, according to the conclusion on economic complex system of Blume and Durlauf (2006), it is assumed that the collaborative innovation system of knowledge-intensive competitive alliance is a non-linear system away from the

The depth of co-innovation (innovation performance) what kind what demand knowledge creation knowledge transaction

what approach The breadth of co-innovation (alliance environment)

knowledge application

market orientation The strength of co-innovation (knowledge value)

relationship characteristics knowledge embeddedness

Fig. 1. The framework of co-innovation system. 4

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knowledge in knowledge-intensive competitive alliances (Nonaka and Takeuchi, 1995), determining the core value of competitive advantage within such alliances and how difficult the knowledge will be to imitate (Dussauge et al., 2000), In addition, knowledge-creation capacity satisfies the criteria of order parameters (Haken, 1985). Therefore, it is assumed that knowledge-creation capacity is the leading capacity of collaborative innovation of knowledge-intensive competitive alliances, namely the order parameter of co-evolution. H4: In order to identify the non-linear forms of interaction between the main variables in the collaborative innovation system for the purpose of economic gain, we stipulate that the collaborative innovation parameter index of knowledge-intensive competitive alliances can be quantified (Katz, 2006), which means that the index system can measure the synergy capacity index and the external input index. The evolution of knowledge-intensive competitive alliance collaborative-innovation systems requires not only the three state variables of knowledge creation, knowledge transaction, and knowledge application. It also requires the function coefficient of state variables, i.e., the evolution of adjustment parameters representing the interaction among state variables. According to synergetic theory, the adjustment parameter is the statistical data that the index system can measure. As a result, by referring to the content of the theory of knowledge and innovation, we get the main variables and parameters of the evolution of the collaborative-innovation system in knowledge-intensive competitive alliances (see Table 1). In Table 1, n1, n2, n3 were defined as the current state of knowledge creation, knowledge transaction, and knowledge application of a knowledge-intensive competitive alliance. We introduced the time dn variable t, dti (i = 1, 2, 3) to indicate the degree of change of state variables over time. The three adjustment parameters of state variables, knowledge creation capacity level, knowledge transaction capacity level, and knowledge application capacity level, are indicated by α, β, γ. The external control variable (resource input mechanism) is indicated as δ. In this study, the degree of involvement of resources may affect the motivation, willingness, and cycle of collaborative innovation in knowledge-intensive competitive alliances. Therefore, δ is the common control variable of state variables n1, n2, n3. Furthermore, the state variables α, β, γ and control variable δ can be explained as: p α = i ∏i = 1 αi (i = 1, 2, 3…p) , setting the parameter of state variable n1, which corresponds to the knowledge creation index of collaborative innovation in knowledge-intensive competitive alliances, in which αi is the knowledge creation evaluation index after conversion. p β = i ∏i = 1 βi (i = 1, 2, 3…p) , setting the parameter of state variable n2, which corresponds to the knowledge transaction index of collaborative innovation in knowledge-intensive competitive alliances, in which βi is the knowledge transaction evaluation index after conversion. p γ = i ∏i = 1 γi (i = 1, 2, 3…p) , setting the parameter of state variable

Fig. 2. Key link of collaborative innovation in knowledge-intensive competitive alliance.

equilibrium state, which can always exchange material, information and energy between the internal environment and the external environment, i.e., the system is open complex system, and a dissipative structure can be formed under certain conditions. H2: In order to explore the nonlinear effect of the variables in the evolution of the system, we propose that the evolution of the collaborative innovation system of knowledge-intensive competitive alliances is irreversible according to the self-organization theory. The threshold condition is not unique in the evolution process, corresponding to the critical value of linear instability of the co-innovation system (Chattopadhyay et al., 1992; Haken, 1985; Prigogine and Allen, 1982). With the change of core parameters (state variables and control variables) and the fluctuation in different periods, the evolution of collaborative innovation system of knowledge-intensive competitive alliances is dynamic and timely (Amaral and Ottino, 2004), and the critical condition to reach instability will change accordingly (Muschik and Domínguez-Cascante, 1996). H3: According to the theory of synergetics (Haken, 1983, 1985), the evolution direction of complex systems is determined by slow relaxation variables (order parameters). Therefore, we assume that there is a slow relaxation variable in the dominant system evolution at the macro level of collaborative innovation in knowledge-intensive competitive alliances. Knowledge creation is the key to create rare and high-value

Table 1 The main variables and parameters of the collaborative innovation of the knowledge-intensive competitive alliances. Variables

Variable name

Variable interpretation

State variable 1

Knowledge creation state

State variable 2

Knowledge transaction state

State variable 3

Knowledge application state

Control variables

Resource input mechanism

Setting parameter 1

Knowledge creation capacity index Knowledge transaction capacity index Knowledge application capacity index

Reflects the ability of knowledge competition alliance to produce and develop knowledge in the process of collaborative innovation. Reflects the ability of knowledge-intensive competitive alliances to acquire the knowledge needed in the process of collaborative innovation. Reflects the ability of knowledge competition alliance to apply new knowledge to products and services in the process of collaborative innovation. Describes the integrated utility of the resource investment mechanism of the environment to the collaborative innovation of knowledge-intensive competitive alliances. Measures the capacity of knowledge-intensive competitive alliances to produce knowledge. Calculated from evaluation index Measures the capacity of knowledge-intensive competitive alliance to achieve necessary knowledge from the transaction. Calculated from evaluation index Measures the capacity of knowledge-intensive competitive alliances to generate products, to improve services or perform innovation with related knowledge. Calculated from evaluation index

Setting parameter 2 Setting parameter 3

5

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knowledge with high heterogeneity and unique competitive advantage, and applying it to products and services, are fundamental to knowledge-intensive competitive alliances, based on the commercial ecological environment. Therefore, the evolution of knowledge application state n3 is directly related to knowledge creation, and thus we created the logistic function of state variable n3:

n3, which corresponds to the knowledge application index of collaborative innovation in knowledge-intensive competitive alliances, in which γ3 is the knowledge application evaluation index after conversion. p δ = i ∏i = 1 δi (i = 1, 2, 3…p) , the control parameter that corresponds to the comprehensive index of resource input mechanisms, in which δi corresponds to investment in human resources, technical resources, and social resources. For the knowledge creation state n1, knowledge creation is a creative activity in the production and development of heterogeneous knowledge of high value. Knowledge creation is very important to the sustainable development of knowledge-intensive competitive alliances. However, limited by cost and risk factors, some members of the competitive alliance may also select knowledge trading or other means to obtain the required knowledge through knowledge transactions, so as to use resources efficiently, fill the existing gap in knowledge-creation ability, and reduce the cost and risk of knowledge creation. Therefore, under the control of the resource input mechanism δ, the logistic evolution process of state variable n1 was described as

β 1 dn1 = δn1 + δ n2 + γn1 n3 α α dt

1 dn3 α = −ϕ1 n3 + ϕ2 δ n1 γ dt γ

In Eq. 3, −ϕ1n3 stands for the influencing factors on knowledge application state n3. It indicates that without the complementary effect of the external resource input mechanism, the decrease of knowledge output within the collaborative innovation system leads to decrease in the consumption of existing resources, which explains the negative α coefficient. ϕ2 δ γ n1 represents the influence factor of the knowledge creation state of a collaborative-innovation system n1 on knowledge application state n3, indicating that the resource input mechanism affects the knowledge application state through knowledge creation ability, and improvement of knowledge creation ability can improve knowledge application capacity. α is the influence coefficient. ϕ2 > 1 γ

indicates the multiplier effect of efficient knowledge creation on knowledge application. Since the result of the application of knowledge is the final response to the innovation performance of the product after commercialization, and the characteristics of the alliance determine that the members of the alliance will be regarded as the core of its sustainable development, knowledge transaction variable n2 is not included in the function. To a certain extent, knowledge transaction is meant to make up for the gap in enterprise knowledge, and the mechanism of the application of knowledge is not directly reflected. Viewing Eqs. 1, 2, and 3 simultaneously, we arrived at the logistic function-based evolutionary model of collaborative innovation systems in knowledge-intensive competitive alliances:

(1)

In Eq. 1, δn1 stands for the effect of the resource input mechanism on knowledge creation. β is the influence coefficient which represents α the complementary effect of knowledge transaction on knowledge β creation, is the influence coefficient of the two state variables, and α γ n1n3represents the effect of n3 on knowledge creation n1, which is the mutual promotion relationship between the two variables. It shows that if the members of the knowledge alliance could make reasonable and efficient use of the new knowledge created in products and services, and if alliance members could use the knowledge to shape their own core competitive advantage and continue to profit, alliance members would be willing to transform the new knowledge into economic benefits without external influence. Knowledge transaction status n2 represents the selection of material, money, and other means of dealing with the transfer of intellectual property rights, an important strategic means of reducing knowledge cost through knowledge-transaction activities and increasing resourceutilization rate. With the aid of knowledge trade, knowledge-intensive competitive alliance members can make up for the gap or deficiency of knowledge application caused by the lack of knowledge-creation capacity at the theoretical level. Furthermore, if knowledge transaction can fill the gap in knowledge for knowledge-intensive competitive alliance members, the purpose and pertinence of knowledge application will be more explicit and will increase efficiency. Therefore, the evolution of knowledge transaction n2 is related to both knowledge creation and knowledge application. Under the control of the resource input mechanism, the logistic function of state variable n2 is

γ 1 dn2 = −δn2 − αn1 n2 + n3 β β dt

(3)

1 dn

β

⎧ α dt1 = δn1 + δ α n2 + γn1 n3 ⎪ 1 dn γ 2 = −δn2 − αn1 n2 + β n3 ⎨ β dt ⎪ 1 dn3 = −ϕ n + ϕ δ α n 1 3 2 γ 1 ⎩ γ dt

(4)

We used Eq. 4 to describe the evolutionary process of the three state variables of collaborative-innovation systems of knowledge-intensive competitive alliances (knowledge creation, knowledge transaction, and knowledge application). In order to further analyze Eq. (4), we set values for relative variables in the function: ϕ1 = 1: The knowledge-application capacity of knowledge-intensive competitive alliance collaborative-innovation systems can maintain the current state without external influence. ϕ2 = 2: Highly effective and highly targeted knowledge-creation activity during the collaborative-innovation process of knowledge-intensive competitive alliances has a strong positive effect on the promotion of knowledge-application ability. The results are brought into Eq. 5 for transformation:

(2)

1 dn

β

⎧ α dt1 = δn1 + δ α n2 + γn1 n3 ⎪ 1 dn γ 2 = −δn2 − αn1 n2 + β n3 ⎨ β dt ⎪ 1 dn3 = −n + 2δ α n 3 γ 1 ⎩ γ dt

In Eq. 2, δn2 represents the effect of the resource input mechanism on a knowledge transaction. The negative coefficient indicates surging accumulation of useless knowledge within the knowledge market, caused by excessive resource input. The usual presentation of the above condition is the gradual decline of the marginal rate of return (reward or benefit) of knowledge transaction. ‐αn1n2 is the influence factor of n1 to n2, the problems arising in the allocation process of creative innovation among knowledge-intensive competitive alliance members with fixed resources. The alliance members would decide whether to γ enhance knowledge creation or to add input. β n3 represents the impact of knowledge-application state n3 on knowledge transaction state n2, indicating the positive correlation between knowledge application and transaction. Knowledge application state n3 indicates that creating high-value

(5)

2.3. Model analysis 2.3.1. Stability analysis According to the theory of synergetics, judgment of the critical threshold of system evolution usually follows the linear stability analysis. For the state variables for complex systems n1, n2, n3, the threshold perturbation equation is: 6

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2.3.2. Order parameter analysis In order to eliminate the influence of fast relaxation variables on the evolution of collaborative-innovation systems of knowledge-intensive competitive alliances, the order parameter expressions of the system's evolution should be obtained. We assume that knowledge creation is the order parameter of the evolution of a knowledge-intensive competitive alliance collaborative-innovation system. Thus, the derivative of fast relaxation time is 0. Eq. 5 can be adapted to:

0

n1 = n1 + dis1 ⎧ ⎪ n = n 20 + dis2 ⎨ 2 ⎪ n3 = n30 + dis3 ⎩ n10 =

(6)

n2 = n30 =

Set tion into Eq. 5:

0

0, and bring the values of steady-state solu-

β

1 dn

⎧ α dt1 = δn1 + δ α n2 ⎪ 1 dn γ 2 = −δn2 + β n3 ⎨ β dt ⎪ 1 dn3 = −n + 2δ α n 3 γ 1 ⎩ γ dt

dn2 dt ⎨ dn3 dt ⎩

⎧ (7)

dn1 = δαn1 + δβn2 + γn1 n3 dt n1 = δαn1 + 2δ 2α + 2δ 2α 2n12 αn1 + δ

= −δβn2 + γn3 (8)

Eq. 8 was written in vector form as:

0 ⎞ ⎛ δα δβ dn n ⎜ 0 − δβ γ ⎟ =→ dt ⎜ 2δα 0 − γ ⎟⎠ ⎝

=

n(σ )

(0) exp(R σ t )

2δα n1 . β αn1 + δ

(17)

At this point, the collaborative-innovation system of a knowledgeintensive competitive alliance achieved the threshold conditions. Introduce the time variable and adapt Eq. 17 as: (9)

∫ Xn3 +αnYn+2δ+ Zn dn = ∫ f (n(σ ) ) dt

Eq. 9 was brought back according to Haken's Synergetic law (Haken, 1985), to get the solution of co-evolution from Eq. 5:

n(σ )

(16) γn3 β (αn1 + δ )

n2 = = Solving the equation, we get: n3 = Substitute Eq. 1 to obtain the expression of order parameter n1:

= δαn1 + δβn2

= −γn3 + 2δαn1

= 2δαn1 − γn3 2δα n; γ 1

Transform Eq. 7: dn ⎧ dt1 ⎪ dn 2 ⎨ dt ⎪ dn3 ⎩ dt

= γn3 − δβn2 − αβn1 n2

(18)

In Eq. 18, X = 2α δ; Y = α δ + 2α δ ; Z = 3δ α. 3

(10)

2

2 2

2

3. Empirical research

The general solution of the state variables in the evolution:

n=

∑ φσ exp(R σ t ) n(σ ) (0) σ

3.1. Data collection (11)

In order to use the income model to analyze the practical knowledge-intensive competitive alliance collaborative innovation evolution, we selected for empirical study the senior medical device industry knowledge-intensive competitive alliances in China, Korea, and Germany. The research involved health-management manufacturers of surgical and clinical instruments, rehabilitation medicine, medical optical instruments, and monitoring equipment. Their products are mainly used in the domestic medical market and are exported to other countries. We selected these three countries because they have data typical of knowledge creation and collaborative-innovation output. The three countries have distinct characteristics with respect to the advanced medical device market, due to various medical-system conditions and government-support policies. Currently, Germany's medical device industry is in a leading position in the world, with the total output value of more than 26.885 billion US dollars, accounting for approximately 14.113% of the country's GDP. During recent years, the medical equipment production and processing industry in Korea has developed rapidly, with total output value having risen to 12.9 billion US dollars, accounting for approximately 5.7% of total GDP. China's medical device market also showed a rapid development trend, with the industry's annual growth rate rising to approximately 17%. China's medical device production market potential is huge, and the number of enterprises has rapidly increased. However, only 6% of these enterprises achieved annual revenue of 1.61 million US dollars (equal to 10 million RMB). These data indicated that the three selected countries were not only typical of the industry's general conditions, but also could provide valuable outcomes from comparative analysis. According to the study of Eisenhardt (1989), the knowledge-intensive competitive alliances in the three countries can meet the requirement of the minimum quantity of the subject in the study on regularity, also, these alliances are from the same competitive industry, which is able to control the influences of differences in industry and task on the collaborative innovation. (Becerra-Fernandez and Sabherwal, 2001). Meanwhile, followed the ethnographic research methods of Schultze and Boland (2000), samples were selected at random by sampling from different geographical

In Eq. 11, φσ can be any positive real number, and the solution of ℜσ is:

0 ⎞ ⎛ δα δβ R σ nσ (0) = ⎜ 0 − δβ γ ⎟ nσ (0) ⎜ 2δα 0 − γ ⎟⎠ ⎝

(12)

The condition of a non-zero solution of vector state is:

δβ 0 ⎞ ⎛ δα ‐R − δβ ‐R γ ⎟=0 ⎜ 0 ⎜ 2δα − γ ‐R ⎟⎠ 0 ⎝

(13)

Solve Eq. 13:

R3‐(δα ‐δβ ‐γ ) R2‐(δ 2αβ + δαγ − δβγ ) R‐3δ 2αβγ = 0

(14)

According to the Routh–Hurwitz stability criterion (AL-Azzawi, 2012), the complex system will not fluctuate when the real parts of all the eigenvalues are negative. The specific criteria are: (1) all the eigenvalue equation coefficients are greater than 0; (2) The value of the determinant and all masters are greater than 0. From Eq. 15 we get:

⎧ δα ‐δβ ‐γ < 0 (δα − δβ − γ )(δ 2αβ + δαγ − δβγ ) + 3δ 2αβγ > 0 ⎨ 2 ⎩− 3δ αβγ > 0

(15)

As α, β, γ, δ are positive real numbers greater than 0, Eq. 15 does not satisfy the Hurwitz discrimination condition. Therefore, we know that knowledge-intensive competitive alliances based on knowledge creation, knowledge transaction, and knowledge application could not meet the stability conditions. As long as there is the input of resources and different knowledge behaviors, the collaborative-innovation system of knowledge-intensive competitive alliances is always in the state of selforganization evolution. It also confirms the statement of Dussauge et al. (2000) that the innovation system of a competitive alliance can be very unstable. 7

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Table 2 Measurement of setting parameters of collaborative innovation of knowledge-intensive competitive alliances (translated into U.S. dollars in accordance with the exchange rate of each country). State variables

Measurement index

Unit

Corresponding parameters

Knowledge creation

The number of valid patents Ratio of enterprises conducting R&D activities to the total number of corporate enterprises Number of R&D projects Expenditure of R&D projects Ratio of expenditure of alliance R&D projects to the overall sales Ratio of workers involved in R&D to the overall workers Number of purchased patents Ratio of enterprises conducting knowledge transaction to the total number of corporate enterprises Number of items introduced by knowledge transaction Knowledge transaction expenditure in technology market Ratio of expenditure of alliance knowledge transaction to overall sales Expenditure of digestion and absorption of introduced technology Expenditure of technology transformation Number of R&D projects in new products New items of knowledge transaction in new products

Set % Set 10 thousand % % Set % Set 10 thousand % 10 thousand 10 thousand Set Set

α1 α2 α3 α4 α5 α6 β1 β2 β3 β4 β5 γ1 γ2 γ3 γ4

Knowledge transaction

Knowledge application

dollars

dollars dollars dollars

reflecting the reality. In accordance with the feedback of each interview and e-mail, we have compared the content and accuracy of the interviews. In addition, with the agreement of the interviewee, we have recorded the interview of the CEOs or executive directors of the 31 enterprises, and these records would be transcribed to compare with the interview's conclusion in order to avoid the misunderstanding and scale inaccuracy caused by mistakes in the recording. The time of collecting data lasts for 19 months and 17 days (from December 2014 to September 2016). The precondition of applying the data was that authorization had been granted by all the enterprises. In terms of the indicator scale, we used the research on knowledge creation and the knowledge production function proposed by Jefferson et al. (2006) and Hu et al. (2005), as well as the connotations of knowledge transaction proposed by Romer (1990), strategic alliance cooperation agreement raised by Reuer and Ariño (2007), knowledge seeking and trading proposed by Berry and Kaul (2015), and strategic alliance technology investment and economic effectiveness studied by Chod and Rudi (2006); as well as knowledge absorption and transformation by Kim and Nelson (2000), knowledge creation and application of new knowledge by Carrillo and Gaimon (2000), and knowledge value by Nerkar (2003). We also have adopted the advice of CEOs and executive directors in the interviewed enterprises on the scale improvement and modified the scale, and the final scale for empirical research was designed by referring to the sources listed (see Table 2).

regions and enterprises, so that the samples would cover knowledgeintensive competitive alliances formed by enterprises of different scale and different ownership distribution. In this study, rigorous and approved management methods are adopted to collect the empirical data. First, we have adopted the method of Li and Zhang (2007) and communicated with the CEOs or top managers in the randomly chosen enterprises by e-mails, and introduced that we want to do empirical research with their financial data (the final data list can be seen in Table 2), also, we have explained the aims and values of this research, making the empirical subjects have a full understanding on what we are going to do. We rejected data from selected enterprises due to sensitive issues or unwillingness to cooperate with the research. Finally, the sample included an advanced medical device knowledge-intensive competitive strategic alliance formed by 39 enterprises in China; a Korean advanced medical device knowledge-intensive competitive strategic alliance formed by 29 enterprises; and from Germany an advanced medical device knowledgeintensive competitive strategic alliance formed by 24 enterprises. Although single-industry studies limit the generalizability of results, they do enable greater control over sources of extraneous variation due to industry characteristics and external environmental factors (McDougall and Robinson, 1990; Spekman and Gronhaug, 1986). CEOs or executive directors in these enterprises all showed great interest in this subject, and they promised to provide the needed financial data. Meanwhile, they asked us to give the feedback to them when we come to a conclusion, in order that they can identify the shortcomings in their collaborative innovation in accordance with the conclusions. Second, promised by enterprises who would provide financial data, we have adopted the statistical method of empirical data of Zhang et al. (2009) and contacted with the helper of the research. We have formed 5 interview groups, each of which employed 2 professionals to help us in counting sample data and record the enterprises' interviews. Third, for enterprises that agreed to cooperate, we have referred to the advice of King and Zeithaml (2001), as well as Jiang and Li (2009) in revising evaluation indicators and interviewed CEOs or top managers (such as the executive directors) of each firm in spot, with the interview time limited to 1 h. The content of the interview is to invite them to provide some advice on the scale that we made to analyze the collaborative innovation of the knowledge-intensive competitive alliance. In accordance with the designed method to this kind of interview of King and Zeithaml (2001), we have transformed the scale that identifies knowledge creation capacity, knowledge transaction capacity and knowledge application capacity into open questions and explained each content carefully. Furthermore, we have asked the managers to directly identify the indicators that are able to reflect the three variables so as to make up the defects of the existing theoretical research scale in

3.2. Data calculation Original data for setting parameters for state variables α1(i = 1, 2, 3, 4, 5, 6), βi(i = 1, 2, 3, 4, 5), γi(i = 1, 2, 3, 4) were achieved by data collection (Table 3). Meanwhile, the mean value of each index was selected as the average standard of research. Thus, the ratios of raw α β γ data to average data αi , βi , γi were achieved. The dimensionless data were evaluated (Table 4). Substitute the calculation results (Table 5) into Eq. 19 to get the order parameters of the three countries: China:

dn1 n1 = 0.522δn1 + 1.044δ 2 + 0.545δn12 dt 0.522n1 + δ Korea:

dn1 n1 = 0.988δn1 + 1.976δ 2 + 1.952δn12 dt 0.988n1 + δ Germany:

dn1 n1 = 1.465δn1 + 2.930δ 2 + 4.292δn12 dt 1.465n1 + δ 8

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Table 3 Data of collaborative innovation of advanced medical device knowledge intensive competitive strategic alliances in three countries. State variables

Measurement index

China

Korea

Germany

Knowledge creation

The number of valid patents Ratio of enterprises conducting R&D activities to the total number of corporate enterprises Number of R&D projects Expenditure of R&D projects Ratio of expenditure of alliance R&D projects to the overall sales Ratio of workers involved in R&D to the overall workers Number of purchased patents Ratio of enterprises conducting knowledge transaction to the total number of corporate enterprises Number of items introduced by knowledge transaction Knowledge transaction expenditure in technology market Ratio of expenditure of alliance knowledge transaction to overall sales Expenditure of digestion and absorption of introduced technology Expenditure of technology transformation Number of R&D projects in new products New items of knowledge transaction in new products

105 33.85%

241 60.12%

364 73.93%

93 9.76036 million USD 31.6089% 23.2004% 120 42.74%

181 3.013775 million USD 42.85% 32.11% 296 62.17%

301 4.673613 million USD 54.60% 55.94% 290 58.52%

217 6.832803 million USD 22.13% 67.419 thousand USD 2.02712 million USD 2.49 2.76

548 2.303973 million USD 32.75% 281.154 thousand USD 6.533216 million USD 7.32 4.55

565 2.5778 million USD 30.11% 484.014 thousand USD 7.895562 million USD 18.71 4.62

Knowledge transaction

Knowledge application

Introduce the time parameter t: China:

Table 5 Calculation results of setting parameters.

n+δ dn = ∫ dt ∫ 0.284δn3 + 0.2720.522 δ (1 + 2δ ) n2 + 1.566δ 2n

Knowledge creation α Knowledge transaction β Knowledge application γ

Korea:

China

Korea

Germany

0.522 0.563 0.357

0.988 1.206 1.015

1.465 1.200 1.548

n+δ dn = ∫ dt ∫ 1.929δn3 + 0.9760.988 δ (1 + 2δ ) n2 + 2.964δ 2n of knowledge transaction), and [0,1,1] (a balanced state of knowledge transaction and knowledge application) as the initial conditions for function input, and output by the means of random value so as to obtain the collaborative innovation system evolution simulation pattern of three countries under different influence strengths of control variables.

Germany:

n+δ dn = ∫ dt ∫ 6.288δn3 + 2.1461.465 δ (1 + 2δ ) n2 + 4.395δ 2n 4. Simulation analysis

4.1. Evolutionary analysis of order parameter We use MATLAB 2017b software to analyze the evolution of knowledge-intensive competitive alliance innovation system, which has rich application module and non-linear relation description, edition and simulation function, and can transform the built formula to visual simulation pattern accurately. For initial conditions and data of simulation, we convert the order parameter equations of the three countries into computer language, and take the values of the independent variables randomly, then simulate the output to obtain the simulation pattern of the evolution sequence parameters of three countries under the effect of control variables. Similarly, for the synergistic evolution of collaborative innovation systems, we set up interaction condition of different control variables initially (δ = 1 and δ = 2) and in each control variable context, the obtained collaborative innovation system formula (Formula 5) is inputted with computer language. Meanwhile, considering that the order parameters of complex system evolution have the characteristics of independent evolution, we take [0,0,1] (a prominent state of knowledge application), [0,1,0] (a prominent state

We calculated the evolution equation of order parameters by using data from advanced medical device knowledge-intensive competitive alliances from the three countries. First, in order to describe the evolutionary tracks of order parameters (knowledge creation state) under different control variables (when the mechanism of resource input changes), we applied MATLAB simulation software for simulation analysis to perform the evolutionary analysis, and the results appear in Fig. 3. We analyzed order parameter evolution of the three countries' advanced medical device knowledge-intensive competitive alliances by referring to the results in Fig. 3. We found that with relatively low resource input, the result was an unstable evolution of advanced medical device knowledge-intensive competitive alliances in the three countries. The level of order parameter evolution of alliances from Germany is higher than that of alliances from the other two countries. Increasing the input of resource mechanism resulted in the orderly evolution of

Table 4 Normalization results of Table 3. State variables

Measurement index

Average

China

Korea

Germany

Knowledge creation

The number of valid patents Ratio of enterprises conducting R&D activities to the total number of corporate enterprises Number of R&D projects Expenditure of R&D projects Ratio of expenditure of alliance R&D projects to the overall sales Ratio of workers involved in R&D to the overall workers Number of purchased patents Ratio of enterprises conducting knowledge transaction to the total number of corporate enterprises Number of items introduced by knowledge transaction

236.667 0.560 191.667 2887.808 0.430 0.371 235.333 0.545 443.333

0.444 0.605 0.485 0.338 0.735 0.626 0.510 0.785 0.489

1.018 1.074 0.944 1.044 0.996 0.866 1.258 1.141 1.236

1.538 1.321 1.570 1.618 1.269 1.508 1.232 1.074 1.274

Knowledge transaction

9

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9

0.9

8

8

0.8

7

7

0.7

6

6

0.6

5

0.5

δ=1.5 δ=2

4

δ=2

n1

n1

n1

5

4

0.4

3

0.3

δ=1.25

2

δ=0.75 2 δ=0.5

δ=1

1

δ=1 δ=0.75

δ=1.5 3

δ=1.25

δ=0.5

δ=1.5

δ=2

δ=1.25

0.2

δ=1 δ=0.75

1

0.1

δ=0.5

0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.9 0

0.8

t

0

0.05

0.1

0.15

0.2

China

0.25 t

0.3

0.35

0.4

0.45

0.5

0

0

0.05

0.1

0.15

0.2

0.25

t

Korea

Germany

Fig. 3. Evolutionary track of order parameter under the control variable.

advanced medical device knowledge-intensive competitive alliances of the three countries, under control of different resource input mechanisms. First, it can be seen that the evolution speed of collaborative innovation order parameters increased with resource input. Next, the evolution tracks of order parameters under different resource input mechanisms were specifically analyzed. Germany's senior medical equipment industry has a strong capacity to create knowledge and is able to set up a development plan based on the input of external resources. The evolution of German order parameters has reached a relatively stable level. Korea presents a completely different situation, in which its evolution level of order parameters falls between Germany's and China's, with relatively low resource input (δ = 1), and knowledge creation increased with increasing resource input. However, with enough resource input (δ = 2), we saw a rapid evolution of knowledge creation of the advanced medical device knowledge-intensive competitive alliance in Korea, with its evolutionary track even exceeding Germany's, indicating a knowledge-intensive competitive alliance during rapid development. The evolution of the order parameters of collaborative innovation depends on the input of resources, and the speed and trend of evolution depend on the degree of resource input. Although China's medical device market has become the world's

collaborative-innovation systems of the three countries from lower to higher levels. By comparing the evolution tracks of collaborative-innovation systems of the three countries, we can see that, due to Germany's relatively completed system of advanced medical devices, the evolution process of order parameter was relatively slow with increasing input, without any specific high-speed increase. China was the least developed among the three countries, and the evolution of order parameters lagged behind at a relatively low level. Without enough resource input or evolutionary endogenous dynamics, collaborative innovation may stay at a relatively low level for a long time, with the risk of failure with self-organization co-evolution. Therefore, with a relatively strong resource input mechanism, the order parameters of collaborative innovation of China's advanced medical device industry experienced a more sensitive and rapid growth and evolution trend. This result indicated that more attention should be paid to the input of resources when planning the next round of strategy for the medical device industry, in order to promote the stable development of this industry. Next, we put all the order parameter simulation curves of the advanced medical device knowledge-intensive competitive alliances of the three countries within the same plane. The results are indicated in Fig. 4, which shows the evolution result of order parameters of 0.5

0.5

0.45

0.45

0.4

0.4 䐠㻿㼛㼡㼠㼔㻌㻷㼛㼞㼑㼍 0.35

0.35

0.3

0.3

䐟㻌㻌㻳㼑㼞㼙 㼍㼚㼥 䐠㻌㻿㼛㼡㼠㼔㻌㻷㼛㼞㼑㼍

䐟㻌㻳㼑㼞㼙 㼍㼚㼥

0.25

0.25

0.2

0.2 䐡㻌㻯㼔㼕㼚㼍 0.15

0.15

0.1

0.1

0.05

0.05

0

0

0.05

0.1

0.15

0.2

0

0.25

δ =1

䐡㻌㻌㻯㼔㼕㼚㼍

0

0.02

0.04

0.06

0.08

0.1

δ =2

Fig. 4. Comparison of order parameter evolution of three countries. 10

0.12

0.14

0.16

0.18

0.2

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innovation. We found that increase of knowledge application significantly promoted knowledge creation. Under such conditions, the knowledge creation state of knowledge-intensive competitive alliances is highly related to knowledge application state. The promotion of knowledge application would help knowledge-intensive competitive alliances to use new knowledge more efficiently and more specifically during product commercialization processes, and thus profit. This indicates that prominent knowledge application can build confidence in knowledge-creation activities of knowledge-intensive competitive alliances and encourage them to carry out knowledge-creation activities periodically. Correspondingly, their knowledge-creation capacity will be improved in the cycle of knowledge-production practices. The relatively slow and gentle evolution of knowledge transaction suggested that too many acquired resources had been put into the promotion of knowledge creation and the application of knowledge by advanced medical device knowledge-intensive competitive alliances in all three countries. As indicated in Fig. 6, when supplied with insufficient resource input mechanisms, advanced medical device knowledge-intensive competitive alliances in all three countries would develop relatively slowly, with support of prominent state of knowledge application. The advanced medical device industry was more sensitive to initial resource investment, which could be partially attributable to the fact that the Chinese medical device industry was still in its beginning stage. Meanwhile, the medical device industries in Korea and Germany showed obvious stalemate, with insufficient resource input among the three factors of collaborative innovation. During the middle stage of evolution (n = 2 for Korea; n = 1.5 for Germany), the state of knowledge creation significantly improved by increasing the state knowledge application, and the state of knowledge transaction showed a gentle state of improvement. Therefore, we concluded that under conditions of insufficient resource input, it is very difficult to realize collaborative innovation of knowledge-intensive competitive alliances relying solely on knowledge application, and that collaborative innovation requires relatively long-term interaction among the factors, creating high pressure on alliance members to perform innovation.

third largest market after the United States and Japan. However, the medical device industry in China is still relatively under-developed, which may be attributed to lack of policy support and slow development of relevant research institutions and medical industries. Within China's advanced medical device knowledge-intensive competitive alliance selected for study, many enterprises are still in the initial stage of project development. Problems such as low resource input and use efficiency are common, combined with a lack of high-level R & D personnel. Professional R & D personnel are not familiar with technical skills and have a low sense of innovation, which results in less achievement of useful patent technology and overall knowledge lagging creation capacity, many companies are using technology transaction to acquire new knowledge. 4.2. Analysis of collaborative evolution trend of collaborative innovation Based on the analysis of order parameter simulation, we simulated the collaborative evolution trend of knowledge-intensive competitive alliances under different resource input mechanisms with empirical data, to disclose the differences in collaborative creation capacity of different countries. We set the initial state of state variables (knowledge creation, knowledge-transaction, and knowledge application) as n0 = [n1, n2, n3], n1, n2, n3 to indicate the input of three knowledge behaviors by advanced medical device knowledge-intensive competitive alliances of three countries. Meanwhile, we found that resource input mechanisms may affect collaborative innovation by analyzing the evolution trend of order parameters. Therefore, we assumed the following two conditions: (1) Setting the resource input mechanism δ = 1, the level of resource investment in advanced medical device knowledge-intensive competitive alliances is relatively low; (2) Setting the resource input mechanism δ = 2, the level of resource investment in advanced medical device knowledge-intensive competitive alliances is relatively high. Based on the simulation results of order parameters, we analyzed with MATLAB for the three different states during the collaborativeinnovation process of knowledge-intensive competitive alliances. The first state corresponds to the self-evolution of knowledge creation with relatively low knowledge transaction, but relatively a prominent state of knowledge application, indicating that collaborative innovation of knowledge-intensive competitive alliances depends on transaction, application of knowledge creation, and resource input by alliance members. The second state corresponds to self-evolution of knowledge creation, with relatively a prominent state of knowledge transaction, but the relatively low capacity of knowledge application, indicating that the collaborative innovation of knowledge-intensive competitive alliances depends on dynamic collaboration and resource input in knowledge transaction. The third state corresponds to self-evolution of knowledge creation with relatively high levels of both knowledge transaction and knowledge application, indicating a state of collaborative innovation of knowledge-intensive competitive alliances with the three variables balanced.

4.2.2. A prominent state of knowledge transaction In the process of collaborative innovation with a prominent state of knowledge transaction, the advantage of knowledge-intensive competitive alliances may not lie in research and development of high-value knowledge and scientific and technological personnel. Knowledge input and support for collaborative innovation mainly rely on knowledge transaction and transfer within the alliance. Knowledge application can only be realized in processes of commercialization after the new knowledge has been absorbed and transformed, which creates a certain time lag. Thus, we set the initial state of knowledge transaction as 1. As with the study of knowledge application, the empirical data of the three countries were brought in to simulate conditions with sufficient (δ = 2) and insufficient (δ = 1) resource input. Results are presented in Figs. 7 and 8, in which n1(t) is the state of knowledge creation, n2(t) is the state of knowledge transaction, and n3(t) is the state of knowledge application. As indicated in Fig. 7, with sufficient resource input mechanisms, even though all three countries showed relatively prominent knowledge transaction in their advanced medical device knowledge-intensive competitive alliances, the simulation curves showed that the state of knowledge creation and the state of knowledge application evolved faster with more significant trends. Such a phenomenon indicates that alliance innovation does not only depend on knowledge transaction. It suggests that alliance members realize that knowledge transactions may not be the best strategy for obtaining high economic returns. Therefore, even with sufficient resource input mechanisms these alliances should regard knowledge transaction as an auxiliary method for collaborative innovation and acquisition of new knowledge by knowledge-intensive competitive alliances and should pay more attention to ameliorate the

4.2.1. A prominent state of knowledge application In the process of collaborative innovation with relatively protruding knowledge application capacity, the resources and basic conditions of knowledge-intensive competitive alliances generally depend on the integration, transformation, and application of existing knowledge. Therefore, we set the initial state of knowledge application as 1, brought in the empirical data of the three countries, and simulated the condition with enough (δ = 2) and insufficient (δ = 1) resource input. The results are shown in Figs. 5 and 6, in which n1(t) is the state of knowledge creation, n2(t) is the state of knowledge transaction, and n3(t) is the state of knowledge application. As Fig. 5 indicates, when provided with sufficient resource input, advanced medical device knowledge-intensive competitive alliances in all three countries would experience co-evolution of collaborative 11

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18

16

4.5 4

60

3.5

14

50

3

12

10

8

40

2.5

30

2

6

1.5 20

4

n1(t)

n3(t)

n3(t)

n2(t)

2

0

0.5

1

1.5

2

2.5

3

3.5

4 0

0

0.5

1

China

1.5

2

n3(t)

n2(t)

n1(t)

0.5

n2(t)

n1(t) 0

1

10

2.5

0

0

0.1

0.2

0.3

0.4

Korea

0.5

0.6

0.7

0.8

0.9

1

Germany

Fig. 5. The collaborative evolution trend of state variables with sufficient resource input (prominent knowledge application).

increase later. The possible reason for such a trend is that when there was insufficient resource input, the Chinese advanced medical device knowledge-intensive competitive alliance experienced rapid adjustment of its knowledge transaction state, based on its original condition and marketing requirements. Also, it focused on developing knowledge creation and application. This caused a decrease in knowledge transaction during a short period of time of excessive output. For long-term development, with a relatively stable knowledge base combined with “feedback” from knowledge creation and application, knowledge transaction increased gradually. For Korea and Germany, the simulation curves of the three factors showed an increasing trend, slow at first and faster later, indicating that under conditions in which knowledgeintensive competitive alliances are equipped with a certain knowledge transaction foundation and capacity, co-evolution benefits the increase of knowledge creation and application within the alliance. Figs. 7 and 8 disclose one special phenomenon. With sufficient resource input, the trend of collaborative evolution of knowledge application is the most obvious, while with insufficient resource input, the trend of collaborative evolution of knowledge creation is more obvious. The reason is that with insufficient resource input, more resources would be devoted to creation of highly valued heterogeneous knowledge in the process of already tight resource allocation, which means that less attention would be paid to increasing knowledge transaction, already insufficiently supported. With relatively sufficient resource input, there is a high risk of meeting a knowledge-creation bottleneck, with knowledge transaction playing an auxiliary role in development.

state of knowledge creation and knowledge application. This means that in a business environment with a shorter product cycle and less competition and cooperation, the impact of knowledge transaction on knowledge-intensive competitive alliances has significantly declined. Meanwhile, in Korea and Germany, with relatively stable industrial development, the buyer and the seller may further prolong the cooperation period in hopes of protecting their own interests. Therefore, knowledge-intensive competitive alliance members would prefer to shift the center of the knowledge transaction down by knowledgecreation activities (for example, the evolutionary curves of knowledge transaction in Germany and Korea). Thus, we believed that for knowledge-intensive competitive alliances, knowledge transaction only plays the role of making up a complementary knowledge gap. The simulation curves also indicated that with a prominent state of knowledge transaction and access to more resources, knowledge transaction could improve knowledge application evolution. The improvement of knowledge creation, on one hand, may depend on the emphasis of alliance members on their own knowledge-creation capacity; while, on the other hand, it may derive from the stress and threats from other alliance members during the knowledge-transaction process. Comparing Figs. 7 and 8, we can see that without sufficient resource input mechanisms, the evolutionary periods of advanced medical device knowledge-intensive competitive alliances in the three countries were significantly prolonged. Among them, the knowledge transaction of the Chinese advanced medical device knowledge-intensive competitive alliance showed a slow change trend with decrease first and 4

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In Fig. 9 we can see that with sufficient resource input mechanisms when the state of knowledge transaction and knowledge application are relatively balanced, in the co-evolution curves of advanced medical device knowledge-intensive competitive alliances in three countries the trend of knowledge application is the most obvious, followed by the state of knowledge creation. The state of knowledge transaction showed first a decreasing, then an increasing, and finally a stable trend. Contributing to such trend that the knowledge-intensive competitive alliance had a relatively balanced state between knowledge transaction and knowledge application, the development of the knowledge-intensive competitive alliance was more mature. In order to stand out in fierce competition, alliance members still put resources primarily into knowledge creation activities to improve their own competitive advantages, and diffusion of knowledge transaction successfully promoted the evolution of knowledge creation (x = 2.5 for China, y = 11; x = 1.2 for Korea, y = 11; x = 0.86 for Germany, y = 11). The state of knowledge application is supported by knowledge creation and knowledge transaction and presents a trend of rapid growth. Furthermore, the evolution of knowledge application also promotes more extensive resource investment by knowledge-intensive competitive alliances, which then realize the co-evolution of knowledge creation and knowledge transaction that promote each other. However, knowledge-intensive competitive alliances, while regarding knowledge creation as the core way to maintain their own competitive advantage,

Thus, resources have been partially devoted to increasing knowledge transaction. In addition, the improvement of knowledge application comes from the interaction of knowledge creation and knowledge transaction. Therefore, the evolutionary trend of knowledge application is more obvious under the action of adequate resource investment mechanisms.

4.2.3. A balanced state of knowledge transaction and knowledge application Under the condition of balance between the state of knowledge transaction and knowledge application, the knowledge-intensive competitive alliance has a certain basic condition for increased capacity of knowledge creation through continuous R & D investment and knowledge accumulation. Alliance members gained some knowledge-transaction capacity in the process of gradually filling their own knowledge gaps. The development of knowledge application mainly depends on transformation, integration, and application of knowledge acquired from knowledge creation and knowledge acquisition. Based on the results of order parameter simulation, we set the initial state of knowledge application as 1. The empirical data of the three countries were brought in to simulate conditions with sufficient (δ = 2) and insufficient (δ = 1) resource input. Figs. 9 and 10 results show the results, where n1(t) is the state of knowledge creation, n2(t) is the state of knowledge transaction, and n3(t) is the state of knowledge application. 12

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intensive competitive alliances showed orderly development from lower to higher levels. This indicated that independent of resource input, the state of knowledge creation, knowledge transaction, and knowledge application should maintain a relatively stable cooperative relationship. Meanwhile, in Fig. 9 we see that within a relatively short period, the obvious mutual-promotion characteristic of synergetic relationships can form between knowledge creation and application capacities (1 for China, 0.5 for Korea, and 0.45 for Germany). This indicates that development of knowledge-intensive competitive alliances is still mainly guided by high-value heterogeneous knowledge. However, the cooperative relationship was not as desirable as the one under sufficient resource input mechanisms (set x = 2.5, compare the value of y). Therefore, we believe that with insufficient resource input, the core issue that decision makers for knowledge-intensive competitive alliances face is how to select a resource-allocation model, and how to balance the relationships among the three elements of collaborative innovation by making full use of the existing resources, so as to effectively promote the performance of collaborative innovation.

may still choose collaborative R&D due to the uncertainty of knowledge creation. Although there is competition between alliance members in the downstream segment of the value chain, they would still enforce strict criteria for selecting cooperative partners once they decide to do so. Here, achieving the potential maximum matching degree of knowledge creation would become the important criterion of selection. Knowledge transaction would become a key factor in maximizing the acquisition of knowledge, especially because the hidden attribute of knowledge has a restraining effect on knowledge transaction and transfer. Meanwhile, a knowledge-intensive competitive alliance depends on the acquisition of heterogeneous knowledge. Once too many resources are put into knowledge creation, the diminishing marginal benefit of resource input may instead suppress the evolution of knowledge transaction. In Fig. 10, we can see that with insufficient resource input mechanisms and relatively balanced state of knowledge transaction and knowledge application, the evolutionary periods of collaborative innovation of advanced medical device knowledge-intensive competitive alliances in three countries were much longer. The evolutionary period in China was the longest, due to its overall under-developed status, while Germany could accomplish co-evolution of three factors within a relatively short time due to its advanced level. By comparing Figs. 9 and 10, we can see that the evolution curves of the three state variables of collaborative innovation of advanced medical device knowledge30

5. Discussion, implications and limitations 5.1. Discussion In the field of knowledge management, innovation management and

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application efficiency of knowledge-intensive competitive alliances will be rather low, and the interaction between each other will contribute little to the collaborative innovation, also, the dominant role of knowledge transaction will be amplified. The problem is that the enterprises who own prominent knowledge creation capacity or heterogeneous knowledge of high value will lead the knowledge-intensive competitive alliance quickly. If they refuse to join in collaborative innovation, knowledge disclosure dilemma and uncontrollable knowledge market will occur in the knowledge-intensive competitive alliance dominated by knowledge transaction, and the cost of knowledge transaction and knowledge transformation will determine the value of the alliance's collaborative innovation (Kogut and Zander, 1993), thus, the alliance enterprises have to compete in bargaining and balancing profit and cost, which may explain why there is a universal instability in the knowledge-intensive competitive innovation alliance short of creativity. It is proved by the simulation results that the knowledge transaction in knowledge-intensive competitive alliances evolves steadily all the time and the external control mechanism has little influence on it, indicating that for knowledge-intensive enterprises with certain knowledge foundation, they do not depend on the knowledge transaction to obtain more profits of collaborative innovation, which may also further broaden conclusions of Hamel and Prahalad (1994), as well as Khanna et al. (1998). It is believed in some literature that the role of knowledge transaction is to create added value for both alliance and enterprises (Grant and Baden-Fuller, 2004; Mudambi and Tallman, 2010), however, they do not further explain the influence of knowledge transaction may have and its dynamic evolution trend, and this study makes complementation in it. We find that the knowledge transaction of knowledge-intensive competitive alliance depends on the development needs of the enterprises and the needs in knowledge accumulation and products commercialization, instead of on the collaborative innovation degree of the knowledge-intensive competitive alliance. It indicates that the knowledge-intensive enterprises in knowledgeintensive competitive alliances hold that if the knowledge transaction involves knowledge competition or core knowledge to create competitive advantages, they would not like to provide more access to visit and copy their knowledge base, even though they can obtain a rather high economic compensation. Because they hold that this kind of knowledge transaction will have an effect on the proprietary on their knowledge and the competitive advantages of the opponent would be promoted. Therefore, they may take stronger measures to protect their knowledge. However, the knowledge-intensive enterprises in the alliance, limited to the reality of resource shortage and influenced by the “regurgitationfeeding effect” caused by the high relevance between knowledge creation and knowledge application, would choose knowledge transaction to obtain some essential resources so as to balance and promote resource utilization, which is the main reason why the curve of knowledge transaction rises steadily. It is also proved in the study that the evolution speed of knowledge evolution is rather faster all the time and the external resource input mechanism has no influence on it, which subverts the earlier theory. In the earlier theory, it is believed that there is a bottleneck in the knowledge application promotion (though knowledge application can be promoted, the speed will be rather low), for the promotion of knowledge application capacity and efficiency may be accompanied by renovation in practical technology and method (Feldman and Pentland, 2003; Krafft et al., 2014), and the negative effect of the original routines and behavior modes may make the renovation rather difficult (Nelson and Winter, 1982). However, our results are not aligned with the existing literature, which may be explained by the knowledge management theory: according to the latest knowledge management model centered on knowledge application (McFadyen and Cannella, 2004; Nonaka, 1994), knowledge application may be influenced by the way to obtain new knowledge. In order to further prove the result, this study chooses a research subject with typical characteristic and minimizes the scope of research variables. We prove that in the collaborative innovation in the

strategy management, collaborative innovation is still a long-lasting and valuable research topic. The results reveal that the dominating influence in the collaborative innovation in the competitive alliance depends on the external control and internal interaction lens that is applied. The first lens, the external control mechanism of resource input attaches more importance to the influence of resources input intensity on collaborative innovation. The second lens, the internal interaction mechanism among different knowledge behaviors attaches more importance to the relevance among knowledge behaviors and the influences of their interactions on collaborative innovation. Consideration of both lenses is both theoretically and practically important. The results and implications of each, and their possible effects, need to be examined and incorporated into a framework for understanding the process, mechanism and performance of collaborative innovation. As indicated in the literature, there are many factors in external control mechanism of the collaborative innovation in the knowledgeintensive competitive alliance, including innovation policy, innovation environment, and geographical conditions (Basu and Wadhwa, 2013; Hoang and Rothaermel, 2010). Compared with other factors, this study attaches more importance to the influences on collaborative innovation brought by external control mechanism including knowledge resource, human resource, financial resource and equipment resource, for the external resource input is more attractive to the enterprises, what's more, the input intensity will influence the enterprises' value acquisition and output. (Foss et al., 2013; Annapoornima et al., 2018). Our results show that an adequate external resource input will be helpful to shorten the period of collaborative innovation. However, no matter how strong the resource input is, knowledge-intensive competitive alliances would continually invest major resources into knowledge creation instead of knowledge transaction, making the evolution speed of knowledge creation faster and period of collaborative innovation shorter. This result has verified what proposed by Mitchell et al. (2002) that though the risk of knowledge creation is rather high and the improvement of capacity is rather difficult, knowledge creation is still the core factor that influences collaborative innovation. Furthermore, it overthrows an early view that external resources are more likely to be used by collaborative enterprises to transact and to occupy each other's knowledge (Ahuja and Katila, 2001; Anand and Khanna, 2000; Zhao et al., 2015). It is proved by our research that enterprises in the knowledge-intensive competitive alliance would not acquire or occupy their partners' knowledge by external knowledge, but to create new products with the others' knowledge through transaction, also, they attach great importance to maintain heterogeneity of their knowledge base. This conclusion has provided persuasive evidence for the phenomenon that the marginal profits in most knowledge-intensive competitive alliances have not decreased with the development of the alliance, in contrast, it is helpful for the alliance enterprises to modify their knowledge behaviors and make reasonable resource utilization strategy. We have also found some valuable results about the internal interaction mechanism of different knowledge behaviors. We find that, in the collaborative innovation in knowledge-intensive alliances, there is a highly non-linear relevance between knowledge creation and knowledge application, which can be further explained by knowledge management theory. Though the latest knowledge management model focuses and emphasizes the importance of knowledge creation and knowledge application for the alliance's development (Hedlund, 1994; Meier, 2011; Nonaka and Takeuchi, 1995), it has overlooked proving whether there is a non-linear relevance between them and how the relevance will affect the alliance. The latest literature focuses on how to make a reasonable strategy to promote knowledge transformation (Mudambi and Tallman, 2010; Tippmann et al., 2017), however, our study broadens our understanding of the relationship between knowledge creation and knowledge application and in how the relationship influences the alliance collaborative innovation stability. In addition, we find that if the knowledge creation is in a low level, the knowledge 15

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by testing the interaction logic of variables. We have made a new trial in this study, which is evident and advocated. We introduce the interdisciplinary theory and, from a new perspective, takes the heated and typical knowledge-intensive competitive alliance in the three countries as subjects, and obtain some novelty conclusions. For the knowledgeintensive competitive alliance, the role of external control mechanism (resource input) is to shorten collaborative innovation period, and interactions among different knowledge behaviors are able to promote the self-evolution of collaborative innovation and make it in a new stable state, and in the balanced state between knowledge transaction and knowledge application, the collaborative innovation stability of knowledge-intensive competitive alliance is higher. In terms of the research method, we have introduced a new one to avoid the defects of existing ones. In the existing literature, a few of empirical research focuses on the collaborative innovation of knowledge-intensive competitive alliance but is limited to the defects in their research method. Also, few of literature takes into consideration the knowledge behaviors, and we take the dynamic non-linear synergetics theory to establish dynamic interaction equation among different knowledge behaviors so as to make up for the defects. Meanwhile, this study adapts existing measures to assess collaborative innovation in the knowledge-intensive competitive alliance. Based on the previous literature and combining with the content of the interview, we make a new proofed indicator scale that meets the reality of enterprises. These measures have distinctly reflected and evaluated interactions among different knowledge behaviors. In addition, in order to eliminate the incompatibility of the data, we have evaluated our scale by interviewing senior executives so as to analyze the essence of collaborative innovation more precisely.

knowledge-intensive competitive alliance, frequent knowledge creation and effective knowledge transaction make knowledge application promote rapidly, in other words, the results of knowledge application capacity are determined by the interaction between collaborative innovation's knowledge creation and knowledge application. It is because that compared with other alliances, the characteristic of knowledgeintensive competitive alliance aiming to knowledge renovation and continuous innovation determines that the traditional technology and routines could be rapidly adjusted in short period (Lavie et al., 2012; Nelson and Winter, 1982), and the efficiency and qualification of knowledge assimilation, knowledge integration and knowledge transformation as well as knowledge utilization would be promoted under the influences of different knowledge behaviors. Hence, in this view, it is persuasive of our study to consider the knowledge creation, knowledge transaction and knowledge application together, for the interactions among these variables are able to open the black-box of collaborative innovation. At last, it is proved that if the collaborative innovation of the knowledge-intensive competitive alliance can make full use of the non-linear interactions among knowledge creation, knowledge transaction and knowledge application, the internal interaction mechanism of the collaborative innovation will occur, for the interaction is able to promote the self-evolution of the collaborative innovation and makes it achieve a new stability, without being influenced by external control mechanism. In contrast, if the focus is paid to the function of certain knowledge behavior, it may fail to provide adequate endogenous power for the collaborative innovation and may increase the instability of the alliance and risk of collaborative innovation. 5.2. Implications

5.2.2. Practical implications Our study has also obtained some conclusions of potential importance for managers in the knowledge-intensive competitive alliance. First, the management meaning is that knowledge-intensive enterprises in the knowledge-intensive competitive alliance should maintain adequate knowledge heterogeneity, in other words, the enterprises should insist on depending upon their own knowledge creation behavior so as to ensure and promote the value of their own knowledge. In accordance with our research, knowledge creation is the key of alliance collaborative innovation, and the role of knowledge transaction is to make up for the defects in knowledge, therefore, how to carry out knowledge creation activity is of great importance if the enterprises want to promote collaborative innovation. Therefore, our suggestion is that the alliance enterprises should center on their own knowledge resources endowment closely and carry out innovative activity particularly. Meanwhile, they should create a unique core knowledge (technology) while building a sound knowledge protection measures to prevent leakage of core knowledge. In addition, considering the threat of knowledge transaction to knowledge-based enterprises, we think that the knowledge-intensive competitive alliance should put forward perfect contract transaction mechanism and market supervision mechanism, standardize process and responsibility of knowledge transaction in detail, prevent the emergence of opportunistic behavior and ensure the common interests of both parties. Second, the management conclusion is that no matter how strong the external control mechanism is, the knowledge-intensive competitive alliance should facilitate the non-linear interaction among different knowledge behaviors. It is because that according to knowledge management theory and innovation management theory, a higher fit degree among different knowledge behaviors would release pressure faced by innovation subjects and indirectly decrease synergy cost and management cost of knowledge behaviors, which is of great value to improve the unstable internal environment of the knowledge-intensive competitive alliance. Therefore, we propose that the alliance enterprises should carry out knowledge embedding activities according to the needs of knowledge

5.2.1. Theoretical implications The aim of this study is to promote our understanding of collaborative innovation in knowledge-intensive competitive alliance theoretically, methodologically and practically. In terms of the content, this study provides innovations and implications as such: first, though previous research has provided evidence to the promoting role of different behaviors in alliance collaborative innovation or innovation development, our understanding of how the interactions among different knowledge behaviors influence collaborative innovation is limited. In this study, based on the contradictory relationship between cooperation and competition, we attach more importance to the analysis of internal interaction mechanism of collaborative innovation, and from the perspective of evolution, we take the knowledge-intensive competitive alliance as an evolutionary process of complex system, and take into consideration how different knowledge behaviors interact non-linearly in the evolution process of collaborative innovation. By identifying the relevance and rules among different knowledge behaviors, we are able to promote understanding of the black-box in complexity principle of dynamic collaborative innovation in academic field more or less. Second, a systematic literature review proves that in the field of knowledge management, innovation management and strategy management, there are few studies that take into comprehensive consideration both the external control mechanism and internal interaction mechanism which influence collaborative innovation, and the existing literature all studies one of the mechanism only. While our study makes complementation in the extra knowledge. Taking the heated resource input as the external control mechanism and the interactions among different knowledge behaviors as internal interaction mechanism, we study the two mechanisms together, and the result underscores for KM research the value of juxtaposition for examining rather than merely a sole research. Third, we introduce new theory to analyze collaborative innovation of knowledge-intensive competitive alliance, which is conducive to our examination in collaborative innovation. The traditional management theory analyzes the relationship between knowledge behavior and collaborative innovation 16

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Based on the first condition, the knowledge-intensive competitive alliance should make clear the actual technical requirements of different periods and different levels of innovation and develop appropriate resource-allocation models corresponding to the periods of alliance development, so as to reduce resource redundancy or resource shortage due to improper resource allocation. Finally, we suggest that the knowledge-intensive competitive alliance in China should enhance mutual communication and cooperation based on market demand and common strategic vision, to effectively increase the dynamics for collaborative innovation. For that in Korea, we believe that the primary task is to develop a collaborative innovation strategy based on knowledge creation promotion. The empirical data and simulation results of order parameters lead us to believe that the advanced medical device alliance in Korea has reached the threshold of knowledge creation in the knowledge-intensive competitive alliance, which by no means should be considered as an accidental phenomenon or special case. In fact, currently much collaborative innovation by knowledge-intensive competitive alliances in Korea has been widely recognized by the world, including that in the automobile, smartphone, semiconductor, and cosmetic industries, which enjoy strong competition positions in the world. Contributing reasons for this, as far as we are concerned, are not only the resource input achieved by Korean knowledge-intensive competitive alliances, but also targeted resource dedication, high utilization, and high internal evolution of power. More importantly, the Korean knowledgeintensive competitive alliance has a mature and independent research and development system, a clear goal of innovation, and an active innovation atmosphere. We believe that the knowledge-intensive competitive alliance in Korea should strengthen knowledge-creation as the core of collaborative innovation system building and speed up integration and reorganization of innovation resources and talents needed by the knowledge-intensive competitive alliance. Especially, the alliance should strengthen targeted incentive mechanisms to promote open learning and communication within the alliance, and to promote collaborative-innovation capacity and performance within the alliance. For that in Germany, we hold that balancing the interaction among innovation factors would promote evolution of collaborative innovation. Aside from the advanced medical device industry alliance selected for study, many other German knowledge-intensive competitive alliances are in leading positions in the world now. In this context, we propose the following suggestions to balance the interaction among knowledge creation, knowledge transaction, and knowledge application. First, as indicated in empirical data and simulation curves, Germany's advanced medical device knowledge-intensive competitive alliance does not rely on knowledge transactions, but on independent knowledge creation. Especially with sufficient resource input, the resource allocation of German knowledge-intensive competitive alliance favors is accumulation of heterogeneous knowledge, which has been confirmed by many German knowledge-intensive competitive alliances. However, we should not ignore the fact that it may jeopardize the evolution of knowledge transaction to put too many resources into improvement of knowledge creation. Otherwise, alliance members may fail to get desired knowledge at a reasonable price, due to weak knowledge transaction, when they need to fill the knowledge gap through knowledge transaction. Meanwhile, according to the simulation curve, the effect of knowledge transaction may be more apparent in the early stage of the evolution of collaborative innovation system. Therefore, we suggest that German knowledge-intensive competitive alliances should handle the relationship between knowledge transaction and knowledge creation more reasonably. We do admit that original innovation provided through knowledge creation by German firms has been recognized by the whole world. However, they would achieve even more significant improvement in innovative evolution with reasonable resource allocation and acquisition of non-core knowledge, based on originality and innovation, so as to shorten the evolution period of collaborative innovation. This would also create

acquisition and technological innovation. The advantage of this behavior is that the enterprise of knowledge-intensive competitive alliance can acquire highly complementary and compatible knowledge more conveniently and efficiently and moderate the coexistence relationship between competitive enterprises. Meanwhile, alliance enterprises can further enhance their own coordination degree within the alliance through knowledge embedding, improve their knowledge transformation and potential knowledge learning state, and gradually establish a joint problem-solving knowledge governance mechanism. This will lead to more rapid and clear knowledge feedback within the alliance than the market mechanism, and accelerate the efficiency of collaborative innovation and enable knowledge to overflow within the alliance. Third, the management effect is that based on experiences and conclusions, we hold that it is a universal advice for knowledge-intensive competitive alliance to encourage knowledge-intensive enterprises to make flexible collaborative innovation strategy, because if the collaborative innovation is flexible, knowledge-intensive competitive alliance would enjoy bigger space to adjust collaborative method according to the market needs. Meanwhile, proper flexibility is good to improve relationships between knowledge-intensive enterprises and to reduce conflicts, which is conducive to the incessant development of collaborative innovation. Specifically, we hold that the enterprise of knowledge-intensive competitive alliance should cultivate its ability to adjust itself according to market demands and changes of alliance partner's behavior, which can be realized from three aspects. First, enterprises can use appropriate incentives to reduce traditional practices, especially the constraints of innovative practices on current knowledge creation activities. Secondly, enterprises should gradually establish a knowledge activity internal environment of high degree of tolerance and flexibility to facilitate their own knowledge strategy adjustment. Finally, managers should ensure the openness of enterprises and establish joints with more alliance partners when focus on external control mechanism or changes of alliance partners' behavior to improve social relations so as to decrease its survival risks and competitive confusion between partners, as well as suspicions and conflicts caused by such issues as asymmetric information. In addition, we also try to give suggestions to collaborative innovations in knowledge-intensive competitive alliances in the three countries respectively based on the conclusions in this study. For collaborative innovations in China, based on existing experiences and conclusions, we believe that the primary task is to strengthen resource investment, to improve the efficiency of resource utilization, and to enhance the endogenous power of collaborative innovation. First, taking the advanced medical device industry as an example, the simulation results showed that the input-of-resource mechanism can shorten the period of co-evolution of the industry, and the internal synergy elements of innovation tend to evolve toward higher levels. Therefore, China should apply the internal dynamic mechanism of collaborative innovation driven by the knowledge-intensive competitive alliance better and adapt the current resource-input mechanism. In particular, they should allocate human resources, financial resources, and equipment resources based on the requirements of knowledge creation. Although the input resources in China increased gradually with the practice of “innovation driven development strategy”, there is a significant gap when it is compared with the other two empirical objects, Korea and Germany. Meanwhile, China relies on government funding as the single resource channel, which may make the strategic direction of the knowledge-intensive competitive alliance change from market leading to government leading. Such a change is not conducive to promoting the original innovation capacity of knowledge-intensive competitive alliance. These facts indicated that it is of great importance and necessity for China to open up the multi-resource acquisition channels of knowledge competition. Second, China's knowledge-intensive competitive alliance should determine the direction of collaborative innovation according to market demand, which is the premise of improving the pertinence and utilization efficiency of resources. 17

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different incentive mechanisms and management methods for knowledge creation and knowledge transaction, which may provide more significant promotion to co-evolution and orderly evolution of collaborative innovation.

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5.3. Limitations and future research There may be some limitations in the method of the study, and they mainly include three aspects. First, although the synergetics theory of complexity science can explain the evolution of collaborative innovation system of knowledge-intensive competitive alliance from a dynamic perspective, there are still some limitations. Our research needs to take metaphor as a prerequisite, namely, we need to take collaborative innovation as a complex system and explain the essence of system evolution by analyzing the interaction between main state variables that compose and affect the structure of the system. It may make the influence of certain nuances state variables, or influence of nuances state variable in a certain point, on the collaborative innovation system is overlooked in our study, which is the deficiency caused by theoretical defects. Second, although we establish the model in accordance with the features identification of the complex system and the order parameter for designing the evolution of the dominant system rigorously, the variables that dominate the evolution of the system may be not unique and will change with the evolutionary cycle in practice. However, the widely used methods to study complex systems, including the methods we use, can not fully identify this situation, which is a limitation of this method. Third, although we can analyze the nonlinear effects of the three main variables that make up the system with this method, the equation lacks an approach to further determine the subtle variables because the cooperative relationship of the Logistics equation is based on three main variables, which may be the second limitation of this method. Empirical research on the collaborative innovation of knowledgeintensive competitive alliances with different empirical samples is the main direction of the present study. As the framework we propose can be applied in many fields, we will collect and analyze samples from different subjects, such as “production, learning, and research” systems and communities of practice. Detailed theoretical analysis can also guide the practical work and perform the role of connecting, to a certain extent, which is meaningful for defining different features of knowledge-intensive competitive alliances for further comparative analysis. Meanwhile, we will consider introducing knowledge diffusion into the study. By improving the existing model, we will provide a full explanation for knowledge-intensive competitive alliances' theoretical and practical perspectives, so as to further refine research. Acknowledgements We would like to thank Professor Yu Lean from the School of Economics and Management, Beijing University of Chemical Technology; Professor Wang Tienan from the School of Management, Harbin Institute of Technology. Professor Li Baizhou from the School of Economics and Management, Harbin Engineering University provided pertinent comments for our study. We would also like to thank Doctor Iris Chen from Yeungnam University, Doctor from University of Aachen, Germany; Doctor Klein Chou from University of Gottingen, who helped and assisted us during the empirical research. This article has been partly funded by the National Natural Science Foundation of China (ID: 71602041; 71602042); Natural Science Foundation of Heilongjiang Province of China (ID: QC2017082); Social Science Foundation of Ministry of Education of China (ID: 14YJC630142). References Ahuja, G., Katila, R., 2001. Technological acquisitions and the innovation performance of acquiring firms: a longitudinal study. Strateg. Manag. J. 22 (3), 197–220.

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