Performance Effect of the Fits between Industrial Environment, Innovation Capacity and Innovation: Focusing on Innovation-Intensive Korean Firms
- Author: Lee Seung-Hyun, Park Young-Il, Kwon Youngkwan
- Publish: Asian Journal of Innovation and Policy Volume 4, Issue3, p328~359, 31 Dec 2015
To explain the performance gap between firms in the same industry, this study focuses on innovation. It provides a new framework using the dynamic-capability view based on empirical analysis of domestic businesses. The findings of this study are as follows: First, when the uncertainty and competition intensity in the business environment and the level of innovation have “fit”, it means that when the former goes up, so does the latter. In this regard, when the innovation capability of a firm is high, being “fit” means that the level of innovation is also high. When there was fitting innovation on industrial environment and innovation capacity, companies were able to achieve relatively high performance. Also, it was confirmed that instead of innovation for innovation capacity, innovation for industrial environment led to relatively higher performances of firms.
Innovation , industrial environment , innovation capacity , fit
What determines the performance gap among businesses? The currently available literature can be divided into two streams: one focuses on external environmental factors such as market structures, and the other focuses on the firm’s internal resources and capabilities. Previous literature has provided various important implications, but there is still no clear explanation about inter-firm performance gap within the same industry (Nelson, 1991; Zott, 2003). To answer this question, this study focuses on the firm's capabilities and its “fit” with the industrial environment. According to the dynamic-capabilities view, which was evolved from the resource-based view, the strategic choices made by the firm in responding to environmental changes in the industry as well as its abilities to dynamically reallocate resources are crucial factors in the firms’ performance.
Schumpeter (1934) famously argued that economic development is mainly driven by an innovative process called “creative destruction.” Since then, there has been much research on how innovation occurs in firms, and how it is related to their performance, survival and growth. In the process of developing and commercializing new products, services, and processes (Thompson, 1965), innovation may create new business opportunities and markets, and remarkably improve performance (Kim, 1997). Moreover, innovation is indispensable to creating and maintaining a competitive advantage (Wolfe, 1994; Eisenhardt and Brown, 1999). These studies point to the significance of examining the innovation capability of firms.
Based on the dynamic-capability view, this study examines the effect of “fit” between technological innovation capability and industrial environment on firm performance. To do this, we analyzed data from a survey of domestic manufacturers by industrial groups. The result shows that firms with a high degree of fit between its industrial environment and innovation, and between its innovation capability and innovation have better financial and non-financial performance than its counterparts. Moreover, the fit between innovation and the industrial environment was shown to have a greater effect on improving firm performance than the fit between innovation and innovation capability. This study offers a comprehensive empirical analysis that includes external changes in industrial environment and dynamic distribution of internal resources, and thus is particularly notable in that it provides an effective measure of establishing and implementing strategies for innovation.
Section 2 proposes hypotheses based on the analysis of the theoretical background and review of previous studies. Section 3 explains the research methodology. Section 4 provides and discusses the results of the analysis. Section 5 explains the results and discusses their implications.
The performance of firms is not solely determined by a single factor, but is conditioned by interactions between various factors including the surrounding environment, internal resources, and business strategy (Wren, 1987). This implies that proper response to environmental change is an indispensable factor in business management. In fact, there are studies showing that strategies supporting existent competitive advantages may no longer be valid when disruptive technology emerges (Tushman and Anderson, 1986; Henderson and Clark, 1990; Leonard-Barton, 1992; Afuah, 2000; D'Aveni, 2000). Teece et al. (1997) criticized the resource-based view as static and propounded the dynamic perspective as an alternative, arguing that firms need to modify and change their core capability continually. Other studies support this view, showing how dynamic capabilities play an important role in determining firms’ performance in several sectors (Henderson and Cockburn, 1994; Iansiti and Clark, 1994; Kale, 1999; Zollo, 1998).
The uncertainty in the environment surrounding firms always changes over time. Uncertainty, in this context, refers to the extent to which external environmental elements change dynamically, as they can be very complicated and unpredictable. The two metrics by which environmental uncertainty is measured are complexity and dynamics (Lawrence and Lorsch, 1967; Duncan, 1972; Hunt and Osborn, 1974; Yasai, 1986). If complexity is high, the environment is difficult to predict or control. The level of complexity becomes higher when the capacity of the industrial environment is exceeded, there is a high variety of production technologies, or there are diverse distribution channels (Child, 1972). Dynamics is a concept that explains the changes in the external environment. Environmental dynamics differ according to consumer desire, the competitors' strategy, and the launch of new products (Burns and Stalker, 1961; Myers and Marquis, 1969).
Earlier research implies that firms change their strategies with the increase in environmental uncertainty, which could lead to different patterns in choosing their innovation strategy. According to Poter (1980), firms seek to maintain their competitive advantage by differentiating their products and services when environmental uncertainty is high. Gupta and Govindarajan (1984) agreed with Poter’s (1980) argument and proved that when uncertainty is high, differentiating from competing brands through marketing could be an effective strategy. Raubitschek (1988) also argued that when the environment is dynamic and prediction is difficult, firms tend to pursue product differentiation. When environmental uncertainty is high, firms tend to change their innovation strategy by providing differentiated products or services. As such, this study draws the following hypothesis:
Hypothesis 1: Industrial environment is related to innovation. Hypothesis 1-1: Uncertainty of industrial environment is positively related to innovation.
Competition intensity within the market is also a major factor in firms’ strategic decision-making (Zahra and Nielsen, 2002; Li et al., 2012). When competition intensity is high, firms tend to seek innovation by investing in R&D to maintain their competitive advantage (Porter, 1980). Meanwhile, Cash and Konsynski (1985), Iacovou et al. (1995) claimed that when competition intensity is high, firms tend to improve efficiency in their overall supply chain to improve operational efficiency and customer satisfaction. In management perspective, this kind of effort increases attention and investment on 'innovation of the business process'. Inter-firm competition leads to competition between the value chain and the supply chain. A cooperative relationship between business processes and information systems is considered to be crucial. Thus, to manage those elements, process capability and human capital were considered as very crucial to improvement of firm performance.
As such, when the competition intensity increases, firms seek cost-saving measures and improved efficiency, and firms will prefer new types of innovation to conventional ones. Thus, the relationship between competition intensity and innovation could be hypothesized as follows:
Hypothesis 1-2: Competition intensity is positively related to innovation.
A firm’s capability refers to specific resources that help firms to maintain a competitive advantage, or know-how and knowledge in how to efficiently utilize those resources (Diericks and Cool, 1989; Stalk, Evans and Schulman, 1992; Grant, 1991). To maintain a competitive advantage, firms need to make an effort to adjust to changes in the external environment and need to have internal competence (Andrews, 1971; Chandler, 1962; Hofer and Schendel, 1978). Innovation capability refers to the internal ability to successfully implement or apply new ideas, products and processes (Burns and Stalker, 1961). With a high innovation capability, firms can integrate new innovations into their activity and achieve sustainable success (Burgelman et al., 2004).
When a firm has a high technological innovation capability, it is more likely to launch successful new products and adopt more efficient manufacturing processes (Bowen, Clark and Holloway, 1994; Day and Wensley, 1988; Gatingnon and Xuereb, 1997). Similarly, a high technology commercialization capability attracts skilled human resource by providing market feedback in the process of idea development, market analysis, and market testing (Clark and Fujimoto, 1991; Song and Parry, 1997; Yoon and Lilien, 1985; Yam et al., 2004). In addition, it will also have major impacts on marketing innovation and human innovation, as it is a capability to make new product successful.
Based on this argument, the paper hypothesizes the relationship between innovation capability and innovation as follows:
Hypothesis 2: Innovation capability is related to innovation. Hypothesis 2-1: Technological innovation capability is positively related to innovation Hypothesis 2-2: Technology commercialization capability is positively related to innovation.
According to the contingency theory, there is no one ideal organization type that is always efficient. Since the most efficient organization type differs depending on the level of uncertainty, there is only the most appropriate organization and organizational management systems for specific circumstances (Lawrence and Lorsch, 1967). Scott (1981) describes contingency theory in the following manner, "The best way to organize depends on the nature of the environment to which the organization must relate." Therefore, firm performance depends on the correspondence among various factors, namely, fit. When fit is improved due to consistency among the elements of business, firm performance also improves (Dess and Beard, 1984; Hambrick, 1983; Scott, 1987).
Meanwhile, fit can be divided into three categories: 'fit between external environment and strategy', 'fit between internal environment and strategy', and 'integrated fit between external environment and internal environment’ (Venkatraman and Camillus, 1984). Miller & Friesen (1983) proved that the fit between external environment and strategy plays an important role in enhancing short-term financial performance. Studies done on contingency theories commonly conclude that firms with high performance have higher fit between environmental characteristics and strategy than other competitors (Hambric, 1983; Miller, 1988, 1991; Venkatraman and Prescott, 1990). In this aspect, industrial environment and innovation have a dynamic correlation, and it would be reasonable to predict that fit between these two variables will be positively correlated with business performance. Thus, the third hypothesis of this study is as follows:
Hypothesis 3-1: The fit between industrial environment and innovation will have a positive relation with firm performance, Hypothesis 3-1-1: The fit between industrial environment and innovation will be positively correlated with financial performance, Hypothesis 3-1-2: The fit between industrial environment and innovation will be positively correlated with non-financial performance.
To improve performance, firms tend to seek strategies that fit their innovation capability. Gatingnon and Xuereb (1997) showed that firms with high technology capability have a higher chance of developing new products, and that firms with technology manpower have a higher chance of pursuing new processes. Thus, we can conclude that high technology capability and manpower contribute to a better firm performance. Booz et al. (1982), in their empirical analysis, showed that technology commercialization capability had a significant impact on new product development and eventually had a positive impact on firm performance. Yam et al. (2004) analyzed market information through the lens of marketing capability, and argued that a firm can improve its performance by increasing its marketing capability by enhancing competitiveness of market information collection, product development, or production. In sum, innovation capability affects the direction of strategic decision-making, and when businesses adopt a “fit strategy” on innovation capability, it is predicted that there is positive effect on firm performance. Based on this, the following hypothesis was deduced:
Hypothesis 3-2: The fit between innovation capability and innovation will be positively related to firm performance. Hypothesis 3-2-1: The fit between innovation capability and innovation will be positively related to financial performance. Hypothesis 3-2-1: The fit between innovation capability and innovation will be positively related to non-financial performance.
The question remains as to which “fit” is more significant when it comes to firm performance: the fit between industrial environment and innovation, or the fit between innovation capability and innovation? On one hand, some scholars argue that the technological environment is more significant when it comes to determining a business' innovation (Love and Roper, 2003; Dodgson and Rothwell, 1994; Freeman and Soete, 1999; Tidd et al., 2009). On the other hand, some argue (Renko et al., 2009) that internal capabilities, including technology capability, market orientation, and entrepreneurial orientation, are more important. Yet others (Daniel and Pervaiz, 2006) emphasize the internal elements of business management, including management leadership, human resource management, knowledge management and creative management. Synthesizing the above scholarship, this study suggests that innovation is the result of change in the technological environment, internal capabilities of the firm, and business management practices. Existing research, however, does not provide a clear picture of how the significance of external and internal environments changes in certain conditions. Regarding this question, YuiJin Song (1998) argued that the environment is the most important factor by showing that they had a bigger effect on innovation than internal factors, according to the survey results of 'Korean Technology Innovation Survey on Manufacturing Industry' conducted in 1996. To be more specific, environmental elements were the most significant factors for new production innovation, process innovation and product improvement. Empirical analysis supports the view that the market environment was the most significant element.
Taking into account the aforementioned studies, the following is our hypothesis regarding the relative importance of the fit between industrial environment and innovation, and between innovation capability and innovation on business performance.
Hypothesis 3-3: The fit between industrial environment and innovation, and the fit between innovation capability and innovation will be related to firm performance. Hypothesis 3-3-1: The fit between industrial environment and innovation will have a stronger positive relationship with firm performance than the fit between innovation capability and innovation.
Figure 1 is the research model that displays the above hypothesis in diagram.
To examine our hypothesis, a survey was conducted on domestic firms. Firms in high-tech and medium high-tech industries were selected, as both industries place a heavy emphasis on R&D. This allowed us to analyze changes in industrial environment, the fit between innovation capability and innovation, and the gap in business performance. A total of 10 industries from two groups were selected for the survey.
The survey was carried out by a reputable market research firm mainly through e-mail and fax, although telephone surveys and interviews were also conducted depending on the respondents’ availability. The survey was conducted between May 1 and May 30, 2015; some 3,000 businesses were asked to complete the survey, and a total of 223 surveys were returned. This exception of nine surveys with missing values or flaws, 214 surveys were analyzed. In order to ensure reliability and validity, executives in strategy or planning department who were fully aware of decision-making process of innovation were chosen as respondents. This study used SPSS 18.0 to analyze basic data and statistics to verify the hypothesis.
In this study, the concept and estimation of variables were evaluated by using the scale of Likert 5. To compare and analyze change over time, respondents were asked to fill out the measurements twice, once for the year 2010 and once for the year 2015. The years of 2010 and 2015 were selected because we wanted to choose a time frame that was sufficiently long to measure change, yet not so long ago as to negatively affect recollection by the respondents.
2.1 Industrial Environment
The industrial environment of this study measures recognized environment. The level of competition, along with uncertainty (dynamics, complexity) were selected as factors within the industrial environmental. Questions that measure the dynamics and complexity of the environment were composed of 10 items derived from previous research by Miller & Friesen (1983), Child (1972), and Dess and Beard (1984). Items that measure competition intensity were composed of five questions based on the items used in Zahra and Nielsen’s 2002 study. The operational definition and measurement of each variable are shown in Table 2.
2.2 Innovation Capability
To classify and measure innovation capability, this study categorizes it into technology innovation capability and technology commercialization capability, and measures the sub-factors under each category according to the Oslo Manual by the OECD (2005). For measurement, we use items from Yam et al. (2004) and Yap and Souder (1994), which are based on the premise of “Technology Innovation Evaluation Index for SMEs” which reflects the Oslo Manual. After adjustments, 18 items for technology innovation capability and 20 items for technology commercialization capability were used in our survey. The operational definition and measurement item of each variable are arranged in Table 3.
This study uses the 'interactive approach' proposed by Van and Drazin (1985) on measuring situational variables including industrial environment, innovation capability and “fit” of innovation. To measure the fit between situational variables and structural variables, ANOVA (Analysis of Variance), regression analysis, and deviation score method were proposed. Meanwhile, Venkatraman (1989) proposed the concept of “goodness of fit” which is a similar concept to “interactive fit” as the related variables exist in harmony. ANOVA, deviation score method, and residual analysis were proposed for measurement methods. Therefore, this study uses ANOVA as proposed by Venkatraman (1989) to measure the fit between industrial environment and innovation, and the fit between innovation capability and innovation. After the measurement, this study classifies the respondents into a group with fit and a group without fit, in 2010 and 2015, respectively.
2.4 Innovation Type
In accordance with innovation object, innovation type is divided into four types including product innovation, process innovation, organization innovation, and marketing innovation. The Korean Innovation Survey (KIS) from the Science and Technology Policy Institute (STEPI), which is based on the Oslo Manual, was adjusted in order to be used as this study’s operational definition and measurement. KIS investigates the innovation activities of enterprises in Korea and presents statistical tables and data. This study uses a total of 39 measurement items on innovation type. Table 4 below shows the operational definitions and measurements.
2.5 Business Performance
In this study, business performance was divided into financial and non-financial performance. Govindarajan and Fisher (1990) and Gupta and Govindarajan (1984) argued that subjective performance indicators are preferable to objective performance indicators in terms of controlling industrial effect to measure business performance and in terms of comparing with competitors to measure competitive advantage. In this study, business performance in terms of competitive advantage was measured by using subjective performance indicators. Here, the term competitive advantage refers to the degree of predominance compared with major competitors or the industrial average. This study measures business performance as compared with major competitors and the industrial average.
Based on the items on Govindarajan and Fisher (1990) and Gupta and Govindarajan (1984), six items, including measuring market share, and sales and profit, were adopted for financial performance. Based on the items used in Dalton et al. (1980), six more items, including measuring product quality, customer satisfaction, and job satisfaction were adopted, adding up to a total of 12 items. Specific measurement items are shown below in Table 5.
1.1 Composition of Data
To examine the general characteristics of the sample, frequency analysis was used on surveyed businesses. The result of frequency analysis is as below in Table 6.
1.2 Factor Analysis
To verify the validity, this study carried out factor analysis. To extract the organizing factor, all measurement variables used principle component analysis and adopted varimax. This study followed the standard format used in social science of using eigen value greater than 1.0 and factor loading greater than 0.4 to analyze the data. The results from this analysis show that the “high R&D investment to sales within the industry” item, within the “R&D capability“ category was the only item below 0.4 of factor loading out of all innovation capability, and thus it was erased from the items.
1.3 Reliability Analysis
The reliability of this study was examined by using Cronbach's alpha. Nunnally (1978) has indicated that when Cronbach's alpha falls above 0.7, it is to be an acceptable reliability coefficient. After conducting a reliability analysis on measurement variables in the groups, the result shows that all Cronbach's alpha coefficients were above 0.7, which supports this study’s reliability. In other words, all the concepts were measured accurately and coherently from the survey respondents.
1.4 Correlation Analysis
The correlation analysis provides results for both 2010 and 2015. The analysis of correlation among variables shows that all the variables commonly had positive (+) correlations in both 2010 and 2015, and the relative number of correlation coefficient was used to supplement hypothesis verification.
2.1 Verification of Hypothesis 1
The correlation coefficient among sub-factors in industrial environment and innovation is 0.2-0.4, which shows a low positive (+) correlation. To be more specific, uncertainty, competition intensity and changing trend in innovation were examined through t-test from 2010 to 2015.
T-test results show that the average value of uncertainty and competition intensity increased in 2015 compared to that of 2010. In other words, 2015 was more uncertain and competitive than 2010. As the average value of uncertainty and competition intensity in the industrial environment increased, businesses generally perceived the industrial environment more uncertain and competitive. Average value of product innovation, process innovation, organization innovation and marketing innovation also increased in 20015 compared to 2010. In other words, the level of innovation rose in 2015 compared to 2010. As the average value of business innovation, comprised of four types of innovation, increased, in can be concluded that businesses are carrying out higher levels of innovation in 2015 than in 2010.
To confirm the gap in levels of innovation between the businesses that considered industrial environment as uncertain and competitive, and the ones that did not, this study divided businesses into two groups. Using the t-test on the industrial environment index, the businesses that scored three (out of five) or above were classified into one group and the businesses that scored below three into another. The result shows that businesses that considered uncertainty and competition as high showed high levels of innovation in both 2010 and 2015 compared to the businesses that did not consider uncertainty and competition as high. Lastly, to understand the correlation between changes in industrial environment and changes in innovation, regression analysis were used to measure variable volumes in 2010 and 2015.
Regression analysis shows that uncertainty and competition intensity are positively (+) correlated with product innovation, process innovation, organization innovation, and marketing innovation. The comprehensive result reveals that uncertainty and competition intensity of the industrial environment increased from 2010 to 2015; thereby also raising the level of innovation. Also, businesses that were highly aware of uncertainty and competition intensity of the industrial environment performed high level of innovation. Industrial environment and innovation were in positive (+) correlation. Thus, the hypothesis 1-1 and 1-2 of this study were supported to be true.
2.2 Verification of Hypothesis 2
Correlation analysis shows that the correlation coefficient of sub-factors was 0.4-0.6, which shows a relatively strong positive (+) correlation. To be more specific, technology innovation capability, technology commercialization capability and change in innovation from 2010 to 2015 were examined by t-test. The results show that the average value of technology innovation capability and technology commercialization capability were higher in 2015 than that of 2010. In other words, both technology innovation capability and technology commercialization capability increased from 2010 to 2015. The average value of innovation capability comprised of technology innovation capability and technology commercialization capability increased, and therefore, comprehensive innovation capability also increased. As mentioned previously, the average value of product innovation, process innovation, organization innovation, and marketing innovation increased in 2015 compared to 2010; thus, the level of innovation also increased.
Based on the innovation capability index on a scale of 1 to 5, this study divided the group into two to confirm the innovation gap between businesses with high innovation level and businesses with low innovation level. One group was for businesses rated three and above, and the other group was for businesses rated below three on a scale of 1 to 5. By using the t-test, we can understand the relationship between innovation capability and innovation.
Analysis results reveal that businesses with high innovation capability had high levels of innovation both in 2010 and 2015 than its counterparts. This can be interpreted causally, as businesses with high innovation capability pursuing a relatively high level of innovation. To understand the correlation between change in innovation capability and change in innovation, regression analysis on variable volume was carried out in 2010 and 2015.
Regression analysis confirms that technology innovation capability and technology commercialization capability are positively (+) correlated with product innovation, process innovation, organization innovation, and marketing innovation. Comprehensive analysis shows that technology innovation capability and technology commercialization capability of innovation capability were higher in 2015 than that of 2010; thus, the level of innovation also increased. Businesses with high innovation capability performed a higher level of innovation than its counterparts. Thus, hypothesis 2-1 and 2-2 of this study were supported.
2.3 Verification of Hypothesis 3
As mentioned above, uncertainty and competition intensity of industrial environment, as well as innovation capability were higher in 2015 than that of 2010. The level of innovation also increased, as confirmed by hypothesis 1 and 2. When uncertainty and competition intensity of the industrial environment increase, the level of innovation increases. And when innovation capability increases, the level of innovation also increases. In other words, when uncertainty and competition intensity go up, being “fit” means the level of innovation also goes up. In this regard, when innovation capability is high, being “fit” means that the level of innovation is also high.
Instead of directly adopting the concept of fit, this study redefined the concept of fit as having two aspects – the fit between industrial environment and innovation, and the fit between innovation capability and innovation. This is supported by supported hypothesis 1 and 2. The significance of this fit on business performance is supported by hypothesis 3. The state of a business can be categorized into four types, according to the fit between industrial environment and innovation, the fit between innovation capability and innovation, and the level of innovation. First, when the uncertainty and competition intensity of industrial environment are high, and the level of innovation is also high, the two can be said to “fit”. As such, when uncertainty and competition intensity of industrial environment are low, and the level of innovation is low the two are fit. On the other hand, when the uncertainty or competition intensity of industrial environment are low, but the innovation level is high, or when uncertainty and competition of industrial environment are high but level of innovation is low, these are not considered as fit situations.
Based on the industrial environment index and the innovation index, this study divides its samples into total of four groups (2*2), with groups that are either above or below three on a scale of 1 to 5, and with groups that fulfill or not fulfill the fit. As shown below in Table 14, businesses that belong to the 1st (A) and 3rd (C) quadrants fulfilled the fit but businesses in the 2nd (B) and 4th (D) quadrants did not. This method also applies to the fit between innovation capability and innovation. To examine the gap in the average values of the different groups, one way ANOVA was conducted. The result of this analysis is shown in Table 15, in which the groups were titled Group A, B, C, and D for convenience. The analysis shows equal variance, so we use results of Scheffe.
Analysis shows that A has the biggest average value followed by B, C, and D, except for in one case. Group A, which fulfilled the fit with high innovation level had the highest business performance, followed by Group B, which did not fulfill the fit but had a high innovation level, followed by Group C, which fulfilled the fit but with a low innovation level. Lastly, Group D did not fulfill the fit with low level of innovation, and had the lowest business performance.
In other words, when the level of innovation is the same, businesses that fulfill the fit could have high performance. This result is equally represented in the fit between industrial environment and innovation, and the fit between innovation capability and innovation. However, the fit between innovation capability and innovation on non-financial performance in 2015 was an exception, with the average value in the descending order of A-C-B-D. Instead of Group B that had high innovation but fulfilling the fit, Group C with low innovation but fulfilling the fit had a higher non-financial performance. Even this exceptional case shows the importance of the fit.
Thus, hypothesis 3-1 and 3-2 were both supported. In addition, to analyze the relative importance of the fit between industrial environment and innovation, and the fit between innovation capability and innovation in hypothesis 3-3, the largest average value of business performance in the group was marked in bold in Table 15. As it is indicated, Group A's average value was the largest in the fit between industrial environment and innovation. Namely, this study confirms that Group A's fit between industrial environment and innovation was more significant in affecting business performance than Group A's fit between innovation capability and innovation. This satisfies the fit between industrial environment and innovation, and if the level of innovation is high then this could lead to a high business performance. Although when the level of innovation is low, Group C's fit between industrial environment and innovation had more significance on business performance, except for Group C's fit between innovation capability and innovation. Thus, even when the level of innovation is low, if the fit between industrial environment and innovation is fulfilled, it will also lead to relatively high business performance. Namely, the fit between the industrial environment and the level of innovation is considered more significant for business performance regardless of the time frame. Thus, hypothesis 3-3-1 was supported as well as hypothesis 3-3.
The result of empirical analysis of this study is as follows. First, uncertainty and competition intensity of industrial environment was higher in 2015 than that of 2010, and as such the level of innovation also increased. According to the group analysis, businesses that highly recognized uncertainty and competition intensity had higher levels of innovation in all fields including production innovation, process innovation, organization innovation, and marketing innovation. Industrial environment and innovation are positively correlated. When uncertainty and competition intensity of industrial environment increases, the level of innovation also increases.
Second, both technological innovation capability and technology commercialization capability were higher in 2015 than that of 2010, signaling that innovation capability also increased. In addition, the innovation level of businesses also increased. Group analysis shows that the group with high innovation capability has higher levels of innovation in all fields including product innovation, process innovation, organization innovation, and marketing innovation than its counterparts. Innovation capability and innovation have a positive correlation, in which when innovation capability increases, the level of innovation also increases.
Third, this study confirms that when uncertainty and competition intensity of the business environment go up, being 'fit' means that the level of innovation also goes up. In this regard, when innovation capability of the firm is high, being 'fit' means that the level of innovation is also high. When the level of innovation is the same, businesses that satisfy the fit between industrial environment and innovation, and the fit between innovation capability and innovation had relatively higher performance than ones that did not satisfy the fit, both in financial and non-financial performances. In other words, regardless of the level of innovation, if the fit for industrial environment and the fit for innovation capability are fulfilled, it could lead to relatively high firm performance. Also, the analysis of relative influence of the fit between industrial environment and innovation, and the fit between innovation capability and innovation shows that the fit between industrial environment and innovation had more effect on firm performance than the fit between innovation capability and innovation.
It is certain that the findings of this study provide crucial implications in innovation strategy. However, there are some limitations in this study. First, the concept and measurement of fit are limited. In future studies, more sophisticated and effective concept and measurement of fit should be formulated by developing a more systematic and meticulous module of fit. Second, this study focused on ten R&D intensive industries. Thus, the limitation of this study is that its results cannot be generally applied to all industries. Third, the data for this study are based on respondents' subjective views gathered from the survey. Existing research (Shortell and Zajac, 1990) reveal that data collected by the person in charge of functional department is reliable and valid; however, further analysis based on more qualitative data will enhance the reliability on industrial environment, innovation capability, innovation, and firm performance.
[Figure 1] Research model
[Table 1] Data industries
[Table 2] Operational definition and measurement of industrial environment
[Table 3] Operational definition and measurement of innovation capability
[Table 4] Operational definition and measurement of innovation type
[Table 5] Operational definition and measurement of business performance
[Table 6] General characteristics of surveyed firms
[Table 7] Result of reliability analysis
[Table 8] The t-test result on change of industrial environment and innovation
[Table 9] T-test result on high and low industry group on innovation
[Table 10] Regression analysis between in industrial environment and innovation
[Table 11] T-test result on change in innovation capability and innovation
[Table 12] T-test result of high and low innovation capability
[Table 13] Regression analysis between innovation capability and innovation
[Table 14] Four types of groups according to the fit
[Table 15] ANOVA analysis on fit assessment and firm performance