Effect of Coopetitive Activity on Innovation and Management Performances in the Structural Context

Abstract

This study intends to demonstrate whether firms’ cooperative activities, particularly ‘coopetition’, affect business performances. Coopetition, which is cooperation with competitors, is a strategic concept that suggests competition and cooperation between firms can exist simultaneously. More specifically, in order to investigate cooperation as a part of innovation mechanism, this study analysed the mediating effects of product and process innovation performances in between cooperative activities and management performances. For this purpose, we applied a structural equation model (SEM) by integrating a regression model and a logistic regression model. The former analysed the effects of cooperation and innovation on management performances, whereas the latter studied influence of cooperation on innovation performances. For empirical analysis, we utilised data from Korea Innovation Survey (KIS), which incorporates information on Korean firm’s innovation activities and their relevant performances. The results from this study can be applied to addressing and diagnosing issues in Korean industries by analysing relationships among cooperative activities, innovation outcomes and management performances in the structural context.

The analysis showed that coopetition, compared to other types of cooperation, has enhanced the performance of process innovation. Furthermore, the analysis demonstrated that cooperation related positively to management performances. This study, however, also addresses the problem that Korean firms have limitations in linking these innovation performances to actual management performances of financial growth. The results highlight the significance of strategic openness and flexibility, even to competitors, as the business environment and technology market may change drastically. Coopetition strategy can work as a facilitator in providing windows of opportunity for sustainable growth through field-based innovation.

Keywords

Coopetition cooperation innovation performance management performance structural equation model (SEM)

Introduction

As technology today becomes more complex and diverse, leading to higher costs for technological development, this trend shortens product life cycle and eventually results in lower profitability of innovation. The source of knowledge needed for innovation is also shifting, from an intra-firm (internal) one within organisations to inter-firm (external) one made up of various sources. Diversifying the source of knowledge can help firms create continuous innovation. Chesbrough (2003) argued that the innovation paradigm of technology-intensive firms was shifting from ‘closed innovation’ to ‘open innovation’.

According to the transaction cost theory, vertically integrated firms appear as a result of internalising transactions in order to reduce the cost of transactions. However, increased complexities of technology and markets lead to an environment of high uncertainty. A vertical structure may be insufficient for properly responding to such market changes, and therefore, firms will need to cooperate to survive and prosper. To overcome this limitation, firms may seek new opportunities for knowledge creation and innovation through cooperative activities. Organic cooperative activities promote competitive advantages such as reducing transaction costs, improving flexibility and can eventually affecting management performances.

However, not all cooperative activities affect management performances in a positive fashion. The outcomes can vary significantly depending on many factors, including the size of the firm, organisational capabilities, types of partners, and so on (Belderbos, Carree, & Lokshin, 2004). This study focuses on external factors affecting firms, rather than on internal corporate capacity. By analysing the characteristics in selection of cooperative partners by various firms, we aim to understand their impacts on business performances represented by innovation performances and management performances. Especially, as the environment surrounding business and technology undergoes drastic changes, the scope of cooperative activities has broadened to encompass various types of firms that may have been previously considered to be competitors. In this sense, this study aims to address the following question:

Do cooperative activities, especially with competitors, enhance corporate business performances such as innovation and management performances?

In order to answer this question, this study attempts to show empirically how cooperative activities affect business performances. More specifically, we examine the relationship between cooperative activities, especially by competing firms, and their related business performances. This study divides the business performances into two categories: innovation performances and management performances. The former refers to product and process innovation, whereas the latter refers to corporate financial output, like total revenue. This research also analyses the mediating effects of innovation performances in between cooperative activities and management performances, in the context of a structural equation model (SEM). By doing so, we can analyse cooperation as part of an innovation mechanism, which affects management performances.

Related Works on Cooperation

Purpose of Firm’s Cooperation

Firms can achieve economies of scope as well as economies of scale through cooperation. The purpose of cooperation between firms includes increased profitability, higher market share and knowledge by sharing mutually complementary resources and core competencies (Hagedoorn, 1993). Particularly, start-ups pursue establishing various forms of business networks or cooperative relationships, in order to address difficulties faced by new entrants in the market (Baum, Calabrese, & Silverman, 2000). The purpose of cooperation is divided into four categories.

First, motivation for cooperation is to secure the necessary resources and achieve cost savings. It is difficult to secure all necessary information, capital and human resources to cope with changes in the market environment. In general, small and medium-sized enterprises (SMEs) have difficulties in procuring operating funds for product manufacturing. At the same time, large companies have limits in securing large amounts of capital to fulfil their own R&D activities in order to develop new products to take the initiative in the market. Second, firms cooperate with others to check their opponents, like rival firms. As the global economy has developed, competition between individual companies has shifted to competition between groups of businesses with ties to firms producing end products (Iansiti & Levien, 2004). Therefore, close cooperation between firms within a vertical structure is important to checking their competing business group. Third, firms cooperate for the purposes of risk management in order to alleviate uncertainty caused by the volatile market environment. The emergence of new technologies and convergence between existing technologies shorten product life cycle, which in turn increases market volatility and complexity. Therefore, in order to predict and handle changes in the market environment, firms should preemptively respond to R&D investments and market competition simultaneously. In this type of business environment, firms can utilise cooperation as to prepare for technological and capital risks (Silipo & Weiss, 2005). Finally, firms utilise cooperation in order to intensify technological innovation and absorptive capability. Through cooperation with other firms, a firm can acquire external knowledge and build up absorptive capacity for innovation to expect long-term effects (Chen, Lin, & Chang, 2009; Cohen & Levinthal, 1990).

Cooperation Types and Effects on Innovation

In order to secure a competitive advantage, firms should efficiently acquire and utilise technological knowledge. There are three sources for gaining such technological knowledge (Cohen & Levinthal, 1990). Other than internally generated knowledge, firms have to acquire such knowledge from competing firms or other research institutions. From the traditional viewpoint, the relationship between firms mainly refers to competition and cooperation through vertical integration (Ilinitch, D’Aveni, & Lewin, 1996). For example, strategic alliance and joint R&D are typical types of relationships between firms. In the past, the more cooperative firms are, the more vertical relationships are formed. The more competitive firms are, the more horizontal relationships are formed. Furthermore, firms seek inter-industrial alliances, when they emphasise cooperation, whereas they seek internal alliances when they regard competition is important (Yoshino & Rangan, 1995). As the necessity of R&D has become more significant, firms tend to be more active in building alliances. As firms spend higher costs on R&D, they capitalise more on technological resources for innovation and stronger absorptive capacity (Miotti & Sachwald, 2003). Firms focusing on process innovation in low technology and service sector tend to conduct innovation independently. New-to-market firms in high technology and service sector, however, tend to be more cooperative in R&D with external partners. This enables them to accomplish better outcomes. In other words, the focus and motivations of a firm determine the degree of cooperation for innovation (Tether, 2002). The level of cooperation in critical corporate activities, like marketing, is a significant factor for improving innovation performance other than acquiring technological knowledge directly (Lu & Yang, 2004). These types of cooperation tend to be formed within the same industrial sectors or between vertically integrated firms.

‘Coopetition’ is a business concept that arises from further advancement of cooperation and refers to a state where competition and cooperation between firms simultaneously exist in balance (Nalebuff & Brandenburger, 1997). The concept of coopetition that is especially meaningful in the relationship between competing firms within identical or similar technology/industry fields can evolve to cooperation towards achieving a common goal. This concept presents ways to mutually complement limited resources or capacities that the focal firm lacks, providing opportunities for them to gain competitive advantages (Ritala, Golnam, & Wegmann, 2014).

Regarding the relationship between cooperation and innovation, cooperation among firms is known to affect innovation performances, which can be categorised into product innovation and process innovation. Product innovation refers to the production of a new product, which did not exist before, through technological innovation or improving performance of the existing products. Process innovation refers to improving efficiency by applying innovative techniques and methods to manufacturing processes (Chen et al., 2009). As mentioned earlier, we establish Hypothesis 1 (H1) in order to verify the relationship between cooperative activities and innovation performances. More specifically, we intend to understand the impact of partner selection, especially cooperation with competitors, on the innovation performances in terms of product and process.

H₁: Cooperative activities have positive effects on innovation performances.

H_1–1: Cooperative activities with competitors have positive effects on product innovation performances.

H_1–2: Cooperative activities with competitors have positive effects on process innovation performances.

Cooperation and Mediating Role of Innovation

Firms actively engage in technological cooperation with external entities due to limitations in internal resources as well as potential improvements in efficiency. Cooperative partnership between firms can result in successful innovation performances. Furthermore, cooperation strengthens the absorptive capacity to acquire external knowledge, which can eventually improve business performances (Cohen & Levinthal, 1990). In general, the more innovative the firm and the larger the firm size, the greater the number of cooperative partnerships that is accomplished. In other words, there is a positive correlation between R&D cooperation and profitability (Belderbos et al., 2004; Hagedoorn & Schakenraad, 1994; Mitchell & Singh, 1996). Belderbos et al. (2004) divided R&D partners into competitors, suppliers, customers, universities and research institutions, in order to analyse impacts on innovation and management performances in community innovation survey (CIS) data. The study showed that cooperation with competitors and suppliers affected increase in innovative activities and improvement of productivity, while cooperation with universities and competitors affected revenue increases. This study applied growth value added, which utilises labour productivity growth and innovative sales productivity growth, in order to examine management performances (Belderbos et al., 2004). Additionally, profitability rates, like return on investment (ROI), are used to assess firms’ growth patterns (Calatone & Zhao, 2000). In the case of venture firms, corporate survival rate is used as the ultimate indicator of performance (Mitchell & Singh, 1996).

However, it cannot be assumed that cooperative activities of firms directly affect the improvement of management performances. Rather, various studies point towards firms achieving innovation (product innovation, process innovation, etc.) through cooperation, thereby affecting management performances (financial performances). As innovative products are more appealing to customers, they help secure competitive advantage (Oke, Burke, & Myers, 2007), leading to innovative outcomes and ultimately higher profits (Camisón & López, 2010). Inauen and Schenker-Wicki (2011) utilised product innovation, process innovation and revenue of new products, as innovation-related performances that stem from external innovation. External cooperative parties, which can influence innovation performances, were divided into consumer suppliers, competitors, firms in other industries, consulting firms and academia. This research studied the effect of innovation in-flowing from each agent on innovation outcomes. Ikeuchi and Okamuro (2013) defined innovation outcome as the output from a start-up company through innovation and incorporated the notion of developing new products and introducing new processes. Particularly, they defined innovation performance as a mediator for firms to accomplish business performance. The empirical study utilised productivity and profitability as indicators for the corporate final business performances.

We pay attention to these previous works showing that firms achieve innovation performances through cooperation with various partners, including competitors, and such innovation eventually affects management performances. More specifically, this study presumes that innovation should be understood as a mediating factor between cooperative activities and management performances. Based on earlier considerations, this study establishes Hypotheses 2 (H2) and 3 (H3) in order to assess the mediating effect of innovation performances in the structural context of innovation mechanism. When establishing these hypotheses, this study followed Barron and Kenny’s approach (BK approach) which had defined the concepts of moderator and mediator variables through their monumental research (Barron & Kenny, 1986). We explain this methodology in detail in ‘Research design’ section.

H₂: Cooperative activities have positive effects on management performances.

H_2–1: Cooperative activities with competitors have positive effects on total revenue.

H₃: Cooperative activities and innovation performances have positive effects on management performances.

H_3–1: Cooperative activities and product innovation have positive effects on total revenue.

H_3–2: Cooperative activities and process innovation have positive effects on total revenue.

Methodology

Research Design

This study mainly focuses on the cooperation among various factors that affect business performances. To this end, we reviewed existing studies on characteristics and motivations that affect cooperation. This study also considered related works on the issues by cooperation types and relevant partners. Considering these previous works, it was determined that this study includes an analytical framework that would be vital for research with systemic and logical flows to incorporate factors regarding the notions of cooperation and performances. Furthermore, based on the existing research, we constructed an overall structure between the factors focused on cooperative activities as follows: ‘corporate organisational characteristics’ → ‘motivation of cooperative activities’ → ‘cooperative activities’ → ‘innovation and management performances’.

In particular, when we pay attention to the effects of cooperation, it is possible to set an analytical framework to study the effects of cooperative activities on innovation and management performances. We intend to analyse dynamics of cooperation scientifically and to investigate their characteristics. Furthermore, this empirical research reflects the concept of coopetition that scholars in business and administration fields have recently focused on. This type of cooperative activity is also related to the issue of partner selection. In this sense, this article diagnoses the extent to which cooperation partners, including competitors, affect business performances. Through this diagnosis, we can suggest strategic directions for business practitioners as well as governmental directions for policymakers. To this end, first, this study analyses the effects of firms’ cooperative activities, particularly with competitors, on innovation performances (H1). Second, the mediating effect of innovation performances between cooperative activities and management performances is analysed (H2, H3). From a methodological view, this study follows the BK approach in order to demonstrate the mediating role of innovation in the SEM context. The BK approach is used in practical modelling for businesses as well as in academic research fields. This method is especially appropriate to a regression-oriented structural analysis because it utilises a three-step regression analysis to examine the mediating effects of certain variables (Barron & Kenny, 1986). Figure 1 schematises the SEM based on the BK approach with conceptual frameworks depicted.

Figure 1

Source: Authors’ own work.

When following the BK approach, we intend to examine a partial mediating effect of innovation factors. This effect should satisfy the following conditions:

Step 1 (Route 1 in Model 1): X has a statistically meaningful effect on M.

M = i_{1} + a X + ε_{1}

Step 2 (Route 2 in Model 2): X has a statistically meaningful effect on Y.

Y = i_{2} + b X^{'} + ε_{2 ​}

Step 3 (Routes 2 and 3 in Model 1): X and Z respectively have a statistically meaningful effect on Y.

Y = i_{3} + b X + c Z + ε_{3} ​

The standardised beta, b, in Step 3, should be smaller than b’ in Step 2.

When we apply SEM to this study, Figure 2 describes the overall empirical research structure reflecting the partial mediating effect in SEM. In this model, total effect is decomposed into direct and indirect effects. The former is associated with the concept that a part of the exposure effect is not mediated by a given set of potential mediators. In the case of our research model, this corresponds to cooperation’s effects on management performances. The latter, meanwhile, relates to the concept that a part of the exposure effect is mediated by a given set of potential mediators. In our model, this corresponds to the cooperation’s effect on management performance mediated by innovation performances. In this sense, we can demonstrate that there is an indirect or mediating effect when two conditional relationships, such as the effect of cooperation on management performances and the effect of innovations on management performances, occur simultaneously.

Regarding the analytical structure, first, this study sets the factor of cooperation partners as the cooperative activities among motivations of cooperation and ways of decision-making, which is defined as one of the representative motivations and characteristics of cooperation. Second, this article conducts an empirical analysis on how cooperation affects management performances, the ultimate goal of business management. This study sets total revenue, represented by financial growth, as the management performance. Third, cooperation among firms makes primary changes in innovation performances by inevitably diverting the strategic behaviour. We then assume that innovation performances eventually affect management performances. This is based on the proposition that innovation performances act as a factor affecting management performances. Accordingly, innovation and management performances have a causal relationship, unlike popular perception of the two concepts being on equal terms. In this context, this study develops a sequential linkage structure as follows: ‘Cooperative activities’ → ‘Innovation performances’ → ‘Management performances’. In summary, the analytical structure used in this study is that cooperative activities affect innovation performances, which subsequently leads innovation to affect management performances, as shown in Figure 2. Additionally, this article operationally defines ‘business performances’, the effects caused by cooperative activities, encompassing ‘management performances’ and ‘innovation performances’. These two concepts are highlighted in Figure 2.

Figure 2

Source: Authors’ own work.

Regarding the methodology, this study uses regression analysis because it is a basic research structure for analysis of causal effect. Especially, it is crucial to identify whether innovation performances act as a mediator between cooperative activities and management performances. When applying the regression model, the following two methodological issues are raised. First, to test H1, we need to solve the problem of the dependent variable (originally mediating variable of innovation performances) characterised in a nominal scale. In this case, the logistic regression is appropriate and widely used with statistical advantages as compared to other regression models. Second, to test H2 and H3, we need to estimate the effects of the innovation performance variables, which function as a mediator positioned between cooperation and management performances. As for proxy variables used in our analysis, innovation performances refer to product innovation and process innovation, in radical or incremental terms. Management performances mean firms’ financial growth.

In summary, this study adopts a SEM of regression-oriented BK approach in order to examine cooperation’s direct effect on management performances and innovation’s mediating effects on management performances. With regard to the detailed regression model, this study applies the structural equation analysis that integrates multiple regression models and logistic regression models. Two models commonly utilise the pooled regression datasets that incorporate information on cooperation partners, innovation performances and management performances. This structural equation enables us to understand to what extent innovation performances play a mediating role in identifying the effects of the type of cooperation partner firms on management performances. With regard to the operational definition of each variable used in our analysis, Table 1 provides a summarised description of each variable. Particularly, the independent variable of ‘Co-Ptn_Compet’ is used in this study as the factor of ‘coopetition’.

Table 1

Explanations on the Variables for the Analysis

	Variable Name	Descriptions
Dependent	LN_Revenue	Management performances: taking natural log of firm total revenue
	Produ_Inno	Innovation performance: whether or not there is product innovation, like new development or modification
	Process_Inno	Innovation performances: whether or not there is process innovation, such as new manufacturing types, logistics or support systems
Independent	Co-Ptn_Affil	Cooperation with affiliates or other divisions within the focal firm
	Co-Ptn_Supp	Cooperation with supplier (materials, components or software)
	Co-Ptn_Custm	Cooperation with the firms in need and customers
	Co-Ptn_Compet	Cooperation with competitors or other firms in the same industrial field
	Co-Ptn_Service	Cooperation with private service firms (consulting and commercial laboratory)
	Co-Ptn_Univ	Cooperation with universities or higher education institutions
	Co-Ptn_RI	Cooperation with governmental, public or private research institutes
Control	FY	Establishment year subtracted by the year of 2017
	Employee	Number of employees of a firm
	LN_Firm Size	Taking natural log of firm total assets

Source: Authors’ own work.

Data Collection

The Science and Technology Policy Institute (STEPI), a policy research institute under the Prime Minister’s office, engages in Korea Innovation Survey (KIS) for manufacturing and service industries, based on OECD’s Oslo Manual. Like the KIS, community innovation survey (CIS) of the European Union (EU) is also a large-scale innovation survey based on the Oslo Manual. CIS consists of three dimensions: innovation enablers, innovation activities and innovation performances. Facilitating the condition of innovation includes human resources, capital and government aid. Innovation activities consist of enterprise activities, such as business investment, open innovation and entrepreneurship. Lastly, CIS conducts investigations on economic effects such as the number of firms, employment, trade and sales to measure innovation performances. The subjects of studies that utilise CIS data are diverse, including research on the relationship between cooperation with external partners and innovation performance, by extension, with business performances. Other studies focus on decision factors of R&D cooperation (Bayona, Garcia-Marco, & Huerta, 2001; Cassiman & Veugelers, 2002; Lopez, 2008). In the same context, KIS has similar surveys and data structure, and thus, it can be considered an appropriate analogue data for empirical analysis incorporating cooperative ties.

STEPI conducts KIS targeting manufacturing and service sectors separately every other year. Out of these surveys, this study collected and utilised data in manufacturing industry because this sector shows more typical industrial characteristics than the service sector. By doing so, we expect to investigate more general patterns of relations among cooperative activity types and business performances. As the unique statistical indicator related to corporate innovation in Korea, KIS has strengths in that input elements and performance elements of innovation activities are linked. For this study, we utilised the following three KIS datasets in the manufacturing sector: (a) KIS from 2012 (innovation activities between 2009 and 2011); (b) KIS from 2014 (innovation activities between 2011 and 2013 and (c) KIS from 2016 (innovation activities between 2013 and 2015). In aggregate, these datasets contain data from 12,161 firms.

Additionally, we integrated these datasets with financial database for corresponding firms and periods incorporated in the KIS datasets. Financial data is one of the most representative indicators of management performances, which is available from Korean financial supervisory services. Through this process of data integration, we crosschecked the datasets between KIS and financial information. After data processing, we eventually used data from 1,679 firms for empirical analysis.

Results

Descriptive Statistics

The descriptive statistics of variables used in this analysis can be summarised as shown in Table 2. Data from 1,679 firms were used in this analysis. Financial performance variables of total revenue or firm size variables of total assets have been normalised by taking their natural logs (see Table 2).

Table 2

Descriptive Statistics of the Variables for the Analyses

Variable Name	Number of Observations	Min.	Max.	Average	Standard Deviation
LN_Revenue	1,679	21.22	31.39	24.69	1.23
Produ_Inno	1,679	0.00	4.00	1.18	1.39
Process_Inno	1,679	0.00	9.00	1.29	2.32
Co-Ptn_Affil	1,679	0.00	1.00	0.11	0.31
Co-Ptn_Supp	1,679	0.00	1.00	0.11	0.32
Co-Ptn_Custm	1,679	0.00	1.00	0.18	0.38
Co-Ptn_Compet	1,679	0.00	1.00	0.06	0.24
Co-Ptn_Service	1,679	0.00	1.00	0.05	0.23
Co-Ptn_Univ	1,679	0.00	1.00	0.12	0.32
Co-Ptn_RI	1,679	0.00	1.00	0.12	0.32
FY	1,679	5.00	102.00	25.16	12.39
Employee	1,679	5.00	65,414.25	227.93	1,654.94
LN_Firm Size	1,679	22.16	31.79	24.64	1.21

Source: Authors’ own work.

Out of all data, 544 firms that experienced cooperation were subject to this analysis, and these firms are mainly in the manufacturing sector. In addition, 279 firms answered that they have experienced cooperative activities with rival firms, and 96 per cent of these firms are concentrated in the manufacturing sector. In terms of overall manufacturing sector firms in this analysis, 265 firms (16.28%) have experienced cooperation with competitors (see Figure 3).

Figure 3

Source: Authors’ own work.

Structural Equation Analysis

In this section, we test the hypotheses by utilising the regression-based BK approach, which examines the mediating role of innovation performances. To this end, SEM analysis is carried out in the following three steps: (a) H1: testing whether there is any effect of cooperative activities on innovation performances; (b) H2: testing whether there is any effect of cooperative activities on management performances and (c) H3: testing whether there are effects of independent variables on management performances by simultaneously incorporating cooperative activities and innovation performances in the regression model.

1. Hypothesis 1: the effect of cooperative activity on innovation performances

We analysed the causal effect between cooperative activities and innovation performances. We tested H₁ in two ways according to the dependent variables of innovation performances like product and process innovation. First, H_1–1 tests the cooperation’s effect on product innovation (Produ_Inno) by reflecting the survey characteristics. Respondents from the firms were asked to respond by broadly dividing product innovation into two categories: new and modified products. Among the firms that engaged in both types of innovation, cooperative activities between firms and private/governmental research institutes (Co-Ptn_RI) were shown to increase product innovation (B = 0.423, Significance = 0.072). Other variables, particularly coopetition (cooperation with competitors), did not have a significant effect on product innovation (see Table 3). These results indicate that H_1–1 is rejected.

Table 3

Test of Hypothesis 1–1: Effect of Cooperative Activity on Product Innovation

Produ_Inno	B	S.E.	Wald	df	Sig.	Exp(B)
Intercept	2.645	1.620	2.664	1	0.103
FY	−0.001	0.006	0.021	1	0.885	0.999
Employee	0.000	0.000	0.287	1	0.592	1.000
LN_Firm Size	−0.099	0.065	2.340	1	0.126	0.906
Co-Ptn_Affil	−0.184	0.236	0.604	1	0.437	0.832
Co-Ptn_Supp	0.228	0.254	0.810	1	0.368	1.257
Co-Ptn_Custm	0.214	0.198	1.169	1	0.280	1.239
Co-Ptn_Compet	0.328	0.305	1.154	1	0.283	1.388
Co-Ptn_Service	−0.165	0.312	0.279	1	0.597	0.848
Co-Ptn_Univ	0.069	0.234	0.088	1	0.767	1.072
Co-Ptn_RI	0.423	0.235	3.232	1	0.072*	1.527

Source: Authors’ own work.

Notes: *p < 0.1.

Second, with regard to H_1–2, we tested the relationships between cooperation and process innovation (Process_Inno). The respondents were asked to answer by categorising process innovation broadly into three categories: new manufacturing type, logistics and support systems. Among the firms that carried out any type of innovation, cooperative activities with focal firm’s affiliates (Co-Ptn_Affil) or suppliers (Co-Ptn_Supp) showed a negative effect on process innovation (B = −0.649, Significance = 0.004 and B = −1.298, Significance = 0.000, respectively). This means that closed or in-house cooperative activities actually had negative effects on process innovation. The analysis reveals that the cooperative activities with customer (Co-Ptn_Custm) or rival firms had positive effects on the process innovation performances (B = 0.364, Significance = 0.067 and B = 1.279, Significance = 0.000, respectively). Therefore, these results indicate that H_1–2 is accepted (see Table 4). In the case of control variables, firm year (FY) had significant relations with management performances (B = −0.010, Significance = 0.065).

Table 4

Test of Hypothesis 1–2: Effect of Cooperative Activity on Process Innovation

Process_Inno	B	S.E.	Wald	df	Sig.	Exp(B)
Intercept	−2.032	1.563	1.688	1	0.194
FY	−0.010	0.005	3.395	1	0.065*	0.990
Employee	0.000	0.000	0.506	1	0.477	1.000
LN_Firm Size	0.058	0.063	0.837	1	0.360	1.060
Co-Ptn_Affil	−0.649	0.223	8.427	1	0.004***	0.523
Co-Ptn_Supp	−1.298	0.246	27.793	1	0.000***	0.273
Co-Ptn_Custm	0.364	0.199	3.346	1	0.067*	1.439
Co-Ptn_Compet	1.279	0.345	13.703	1	0.000***	3.593
Co-Ptn_Service	0.075	0.303	0.062	1	0.803	1.078
Co-Ptn_Univ	−0.082	0.228	0.129	1	0.719	0.921
Co-Ptn_RI	0.357	0.234	2.326	1	0.127	1.429

Source: Authors’ own work.

Notes: *p < 0.1. ***p < 0.01.

When summarising the results of the hypothesis tests, H1 is partially accepted when considering coopetitive activity had a statistically insignificant relationship with product innovation (H1–1) but displayed a meaningful relationship with process innovation (H1–2). Therefore, results showed that coopetitive activities had higher relationship with process innovation than product innovation, and particularly, coopetition was a critical factor.

2. Hypothesis 2: the effect of cooperative activity on management performances

We analysed the direct effect between the independent variables of cooperative activities and the dependent variable of management performances. Cooperation with rival firms has positive effects on management performances (B = 0.020, Significance = 0.070). However, other cooperation-related variables showed no statistically significant relationships with dependent variables (see Table 5). Therefore, these results indicate that H2–1 is accepted, and this study empirically demonstrates that there exists a direct relationship between the coopetitive activity and firms’ financial growth.

Table 5

Test of Hypothesis 2–1: Effect of Cooperative Activity on Total Revenue

LN_Revenue	Standardised Coefficients	t	Sig.	R²
LN_Revenue	Beta	t	Sig.	R²
Constant		5.716	0.000
FY	−.004	−0.444	0.657
Employee	0.004	0.436	0.663
LN_Firm Size	0.931	86.867	0.000***
Co-Ptn_Affil	0.004	0.353	0.724
Co-Ptn_Supp	0.000	−0.032	0.974	0.866
Co-Ptn_Custm	0.005	0.426	0.670
Co-Ptn_Compet	0.020	1.811	0.070*
Co-Ptn_Service	0.002	0.225	0.822
Co-Ptn_Univ	−0.013	−1.196	0.232
Co-Ptn_RI	−0.013	−1.204	0.229

Source: Authors’ own work.

Notes: *p < 0.1. ***p < 0.01.

From the hypothesis test, H₂ is accepted considering that coopetition has statistically significant relationship with total revenue. Therefore, these results demonstrate that there exists a direct effect between coopetitive activities and management performances.

3. Hypothesis 3: the effect of cooperative activity and innovation performances on management performances

In this step, we analysed the direct effect of coopetitive activities on management performances and indirect effect of innovation on management performances, simultaneously. As mentioned earlier, innovation performances in this study are divided into two: product innovation and process innovation. Thus, we set Hypothesis 3–1 and Hypothesis 3–2, according to the innovation performance variables. First, H3–1 tests the effects of cooperative activities and product innovation on management performances. The results showed a positive relationship between coopetition and management performances (B = 0.020, Significance = 0.071), when product innovation performances are included in the analysis model. However, this model could not demonstrate any significant relationship between product innovation and management performances (B = 0.004, Significance = 0.678) (see Table 6). Therefore, these results indicate that H3–1 is rejected, and there exist only a direct effect of coopetitive activity on management performances.

Table 6

Test of Hypothesis 3-1: Effect of Cooperative Activity and Product Innovation on Total Revenue

LN_Revenue	Standardised Coefficients	t	Sig.	R²
LN_Revenue	Beta	t	Sig.	R²
Constant		5.721	0.000
FY	−0.004	−0.440	0.660
Employee	0.004	0.434	0.664
LN_Firm Size	0.930	85.721	0.000***
Co-Ptn_Affil	0.004	0.347	0.728
Co-Ptn_Supp	0.000	−0.042	0.967	0.866
Co-Ptn_Custm	0.004	0.391	0.696
Co-Ptn_Compet	0.020	1.805	0.071*
Co-Ptn_Service	0.002	0.233	0.816
Co-Ptn_Univ	−0.013	−1.212	0.226
Co-Ptn_RI	−0.013	−1.219	0.223
Produ_Inno	0.004	0.416	0.678

Source: Authors’ own work.

Notes: *p < 0.1. ***p < 0.01.

Second, H3–2 tests the effects of cooperative activities and process innovation on management performances. The results demonstrate that there was also a positive relationship between coopetition and management performances (B = 0.020, Significance = 0.070), when process innovation performance is included in the SEM. However, no significant relationship between process innovation and management performances could be observed (B = 0.010, Significance = 0.277) (see Table 7). Thus, these results indicate that H3–2 is also rejected.

Summarising the results from the hypothesis tests, H3 is rejected, considering that innovation performances do not have a partial mediating effect in SEM analyses. The results indicate that an indirect effect in the structural context cannot be verified.

Table 7

Test of Hypothesis 3-2: Effect of Cooperative Activity and Process Innovation on Total Revenue

LN_Revenue	Standardised Coefficients	t	Sig.	R²
LN_Revenue	Beta	t	Sig.	R²
Constant		5.810	0.000
FY	−0.004	−0.394	0.694
Employee	0.004	0.453	0.651
LN_Firm Size	0.929	85.705	0.000***
Co-Ptn_Affil	0.003	0.319	0.750
Co-Ptn_Supp	−0.002	−0.130	0.896	0.866
Co-Ptn_Custm	0.004	0.406	0.685
Co-Ptn_Compet	0.020	1.811	0.070*
Co-Ptn_Service	0.002	0.238	0.812
Co-Ptn_Univ	−0.013	−1.204	0.229
Co-Ptn_RI	−0.013	−1.160	0.246
Process_Inno	0.010	1.088	0.277

Source: Authors’ own work.

Notes: *p < 0.1. ***p < 0.01.

Discussion and Conclusion

This study aimed to reveal whether coopetition, which means cooperation with competitors, affects business performances, like innovation and management performances. More specifically, in order to scrutinise cooperation in relationship with innovation, this study attempted to verify empirically a mediating role of product and process innovations between coopetitive activities and management performances, as measured by total revenue. To this end, we applied and utilised the SEM by integrating logistic regression and multiple regression models. The former analysed the effect of cooperation on innovation performances, while the latter analysed the effect of cooperation and innovation on management performances. Summarising the analytical results, cooperative activities, especially coopetition, certainly had a directly positive impact on management performances. Furthermore, we showed that a strong relationship exists with innovation performances, specifically process innovation. However, we could not find indirect effects exist in the structural model, suggesting that innovation performances did not link to management performances. Therefore, this study empirically demonstrated that management performances of Korean firms are determined by direct factors, like cooperation, rather than mediating factors, like innovation performances. Through empirical analyses, this study leads to several implications for corporate strategy in technology management by diagnosing the characteristics of Korean firms’ cooperative activities.

First, the analysis showed that Korean firms’ cooperative activities with competitors affected business performances. More specifically, some cooperative activities positively affected not only innovation performances but also management performances. In particular, the cooperation with rivals led to positive impacts on innovation performances involved with firms in production operation, as well as logistics and supplier network, as represented by process innovation. In the same context, a previous study showed that cooperation with competitors affects incremental innovation and productivity improvement. In addition, cooperation with universities and competitors influenced revenue increase (Belderbos et al., 2004). Our findings are consistent with the results of the existing research. Our empirical study showed that the coopetitive activities of Korean firms have a significant effect on process innovation and total revenue increase. In other words, coopetition contributes to the focal firm’s incremental innovation performance, which then associates with new manufacturing types and a relevant support system. Process innovation strongly tends to be formed through accumulation of implicit or tacit knowledge, like the know-how and field experiences (Foray & Lundvall, 1998; Nonaka & Takeuchi, 1995). Thus, we conclude that Korean firms’ coopetitive activities have enhanced field-based innovation that demands manufacturing skills and relevant technological capabilities.

Second, contrary to common expectations, cooperative activities with affiliates and suppliers had a negative effect on process innovation performances in the empirical analysis. The results of our study, in some way, may not be consistent with the existing research on this topic. A previous study exhibited that cooperation with suppliers focused on incremental innovation leads to improvement of productivity (Belderbos et al., 2004). However, empirical results in our study suggest that in-house or vertical cooperation negatively affected innovation performances. Furthermore, these factors had no meaningful relationships to revenue increase. Business organisations inherently have a propensity of path dependency. In this sense, there is no doubt that firms are usually familiar in cooperating with accustomed and controllable business partners or a closed type of cooperation, like the vertical integration. Nevertheless, our analysis suggests that changes in existing cooperative strategy of organisational inertia may be required (Amburgey, Kelly, & Barnett, 1993). Korean firms, similar to the firms in other countries, prefer cooperation as a form of vertical integration structure. The focus of partner selection, mainly with existing suppliers and internal affiliates, needs to be transformed to more external, loosely coupled links. Through this open innovation strategy, firms can take opportunities to expand beyond their immediate current business areas and research subjects, generating unexpected innovations that could lead to financial growth. Additionally, while it may seem counterintuitive and paradoxical, firms need to be open-minded about cooperating with external partners, like new suppliers or buyers, and sometimes even with competitors, as the business environment changes drastically. To achieve this state, firms need to seek partners to complement technological resources and capabilities insufficient on their own. Furthermore, continuous efforts are necessary to grasp trends in the industrial and technological markets, along with collection of relevant information for decision-making. Our study contributes to further developing previous research, as it provides windows of opportunity for strategic technology management decisions regarding the issue of open innovation.

Third, this study attempted to discover mediating effects of innovation performances between cooperative activities and management performances. Certainly, there was a positive relationship between coopetition and management performances as the independent variable had a direct effect on the dependent variable. However, this study had its limitations in identifying whether innovation performances have taken mediator’s or linker’s role in the SEM. Reversely, this result is meaningful; in that, it addresses and points out the problem of Korean industrial structure from a diagnostic viewpoint. Based on analytical results, this study can diagnose that inter-firm cooperation in the case of Korean firms may enhance innovation outputs; however, there was a limit in innovation outputs leading to business performances. We can conclude that cooperative activities with rivals by Korean firms mainly affect financial performances, but innovation performances resulting from cooperative activities do not relate to management performances as characterised by financial outcomes. These results address the managerial side of the issue that even though Korean firms’ cooperative activities may strengthen internal competencies (process innovation), they still have limitations in converting these improved capabilities into substantial profits. This may be due to the general structural problems of Korean industries, which are dependent on in-house or closed innovation strategies. Therefore, managerial board members and staffs should establish a technology management strategy focusing on how to capitalise innovation outputs to actual profits. From the results of this analysis, we suggest companies to pursue a more productive managerial pathway, linking innovation performances to financial growths. Diversification of external partnership strengthening cooperation with various parties such as universities, consumers and even with competitors can be a possible solution to this problem.

This study suggests a more constructive attitude towards the cooperative activities with rivals towards the goal of establishing production and logistics system to generate better business performances. By providing an impetus for field-based innovation, the coopetition strategy can be a catalyst for sustainable improvement in future business performances.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Acknowledgements

The main idea of this study is based on the national project of ‘Year 2017 Korea Innovation Survey: Manufacturing and Service Industry’. We send thanks to Heejong Kang at STEPI, who is in charge of KIS data processing and analysis for his help with the data used in this study. We also send thanks to the project team leader, Kawon Cho at STEPI, who supported the funding for this research. The analysis using a structural equation model was verified by quantitative expert Seungbeom Suh at Korea Advanced Institute of Science and Technology (KAIST), and we deeply appreciate his efforts.

References

Amburgey

T. L.

, Kelly

, & Barnett

W. P.

(1993). Resetting the clock: The dynamics of organizational change and failure. Administrative Science Quarterly, 38(1), 51–73.

Barron

R. M.

, & Kenny

D. A.

(1986). The moderator-mediator variable distribution in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

Baum

J. A. C.

, Calabrese

, & Silverman

B. S.

(2000). Don’t go it alone: Alliance network composition and startups’ performance in Canadian biotechnology. Strategic Management Journal, 21(3), 267–294.

Bayona

, Garcia-Marco

, & Huerta

(2001). Firms’ motivations for cooperative R&D: An empirical analysis of Spanish firms. Research Policy, 30(8), 1289–1307.

Belderbos

, Carree

, & Lokshin

(2004). Cooperative R&D and firm performance. Research Policy, 33(10), 1477–1492.

Calatone

R. J.

, & Zhao

Y. S.

(2000). Joint venture in China: A comparative study of Japanese, Korean, and U.S. partners. Journal of International Marketing, 9(1), 1–23.

Camisón

, & López

A. V.

(2010). An examination of the relationship between manufacturing flexibility and firm performance: The mediating role of innovation. International Journal of Operations & Production Management, 30(8), 853–878.

Cassiman

, & Veugelers

(2002). R&D cooperation and spillovers: Some empirical evidence from Belgium. American Economic Review, 92(4), 1169–1184.

Chen

Y. S.

, Lin

M. J. J.

, & Chang

C. H.

(2009). The positive effects of relationship learning and absorptive capacity on innovation performance and competitive advantage in industrial markets. Industrial Marketing Management, 38(2), 152–158.

10.

Chesbrough

(2003). The logic of open innovation: Managing intellectual property. California Management Review, 45(3), 33–58.

11.

Cohen

W. M.

, & Levinthal

D. A.

(1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

12.

Foray

, & Lundvall

(1998). The knowledge-based economy: From the economics of knowledge to the learning economy. In Neef

, Siesfeld

G. A.

& Cefola

(Eds.), The economic impact of knowledge (pp. 115–121). Boston, MA: Butterworth-Heinemann.

13.

Hagedoorn

(1993). Understanding the rationale of strategic technology partnering: Interorganizational modes of cooperation and sectoral differences. Strategic Management Journal, 14(5), 371–385.

14.

Hagedoorn

, & Schakenraad

(1994). The effect of strategic technology alliances on company performance. Strategic Management Journal, 15(4), 291–309.

15.

Iansiti

, & Levien

(2004). Strategy as ecology. Harvard Business Review, 82(3), 68–81.

16.

Ikeuchi

, & Okamuro

(2013). R&D, innovation, and business performance of Japanese start-ups: A comparison with established firms (NISTEP Discussion Paper No.104). Tokyo: NISTEP.

17.

Ilinitch

A. Y.

, D’Aveni

R. A.

, & Lewin

A. Y.

(1996). New organizational forms and strategies for managing in hypercompetitive environments. Organization Science, 7(3), 211–220.

18.

Inauen

, & Schenker-Wicki

(2011). The impact of outside-in open innovation on innovation performance. European Journal of Innovation Management, 14(4), 496–520.

19.

Lopez

(2008). Determinants of R&D cooperation: Evidence from Spanish manufacturing firms. International Journal of Industrial Organization, 26(1), 113–136.

20.

L. Y.

, & Yang

(2004). The R&D and marketing cooperation across new product development stages: An empirical study of Taiwan’s IT industry. Industrial Marketing Management, 33(7), 593–605.

21.

Miotti

, & Sachwald

(2003). Co-operative R&D: Why and with whom? An integrated framework of analysis. Research Policy, 32(8), 1481–1499.

22.

Mitchell

, & Singh

(1996). Survival of businesses using collaborative relationships to commercialize complex goods. Strategic Management Journal, 17(3), 169–195.

23.

Nalebuff

B. J.

, & Brandenburger

A. M.

(1997). Co-opetition: Competitive and cooperative business strategies for the digital economy. Strategy & Leadership, 25(6), 28–33.

24.

Nonaka

, & Takeuchi

(1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. New York, NY: Oxford University Press.

25.

Oke

, Burke

, & Myers

(2007). Innovation types and performance in growing UK SMEs. International Journal of Operations & Production Management, 27(7), 735–753.

26.

Ritala

, Golnam

, & Wegmann

(2014). Coopetition-based business models: The case of Amazon.com. Industrial Marketing Management, 43(2), 236–249.

27.

Silipo

D. B.

, & Weiss

(2005). Cooperation and competition in an R&D market with spillovers. Research in Economics, 59(1), 41–57.

28.

Tether

B. S.

(2002). Who cooperates for innovation, and why: An empirical analysis. Research Policy, 31(6), 947–967.

29.

Yoshino

M. Y.

, & Rangan

(1995). Strategic alliances: An entrepreneurial approach to globalization. Long Range Planning, 29(6), 909–910.