- Open Access
Open innovation of knowledge cities
© Yun et al. 2015
Received: 31 August 2015
Accepted: 26 October 2015
Published: 4 November 2015
This research seeks to answer the basic question, “As a city evolves from an industrial city to a knowledge one, are its open innovation activities vitalized?”
In this research, we compare the total number of patent applications, the number of joint applicants of each patent, and the ratio of patents jointly applied, in four Korean cities—Daegu, Kwangju, Cheonann total, top 10 % patent applicants group among total patent applicants, and the lower 70 % patent applicant group among total patent applicants. The research included 144,625 patents submitted to the Korea Patent Office from 1981 to 2010.
As knowledge-based urbanization proceeds, the size of a knowledge city increases. The lowest 70 % of patent applicants (rather than the top 10 %) apply for more patents, and the breadth and depth of open innovation rises.
Research limitations/implications (if applicable)
This research is limited to mutual patent applications as a target of open innovation. In the future, additional research will need to be conducted on various open innovation channels such as patent citation, intellectual property right transfer, licensing, and M&A.
Practical implications (if applicable)
To maximize the beneficial characteristics of a knowledge city in a large city, the improvement of open innovation across the city is essential.
Social implications (if applicable)
If strengthening open innovation by SMEs or start-ups is set as a corporate strategy or a government policy, it will be a source of development of knowledge-based urbanization and continued economic development of a knowledge city, as well as of the total knowledge assets.
The creative, cultural knowledge city has recently received considerable attention, not only from researchers, but also from businesses and the public at large (Musterd, 2004). The generation of economic growth by knowledge spillover has been increasing, and is particularly effective in cities where communication between people is more extensive (Glaeser et al. 1991). Therefore, as a city changes from an industrial to a knowledge-based one, it is expected that knowledge will spread even more. Although the recently emerging idea of a “knowledge city” is complex and difficult to define, several perspectives have already been used for its definition. Some of these perspectives include economic, value-based, sociological, quantitative, structural, and historical (Dvir, 2006).
Taking this into consideration, in this research, we compared and analyzed the concrete process of knowledge-based urbanization of four (two large, one medium, one small) Korean cities over 30 years by understanding a ‘knowledge city’ as a quantitative concept. We analyzed the procedural change of a traditional Korean industrial city in 1980 to a knowledge city in 2010 through industrialization. In a previous study, Florida determined why cities without gays and rock bands were losing the economic development race (Florida, 2002). This was done by measuring the creativity of a city through the use of its gay or rock band figures. In addition, he concretely developed the Bohemian Index or Gay Index, which measures the concentration of artists, musicians, and similar types of creative people working in a fixed region, and thereby indicated the creativity of a city (Florida, 2005, p. 34).
Similarly, this research was intended to analyze the difference between the status and the level of knowledge-based urbanization between cities through the use of patent application data. The data used for this analysis was from patent applications submitted to the Korean Intellectual Property Office from 1 January 1981 to 31 December 2010, in four Korean cities (Daegu, Kwangju, Cheonan, and Gumi). To compare and analyze the level of knowledge-based urbanization, we first compared and analyzed changes over time in the total number of patent applications. Second, we performed a time sequential comparison and an analysis of Intensity of Open Innovation (IOI), which indicated the open innovation status based on the number of joint applicants of each patent. Third, we performed a time-sequential comparative analysis of Ratio of Open Innovation (ROI), which provided the ratio of patents jointly applied by two or more people, against all patents. In addition, the patent applicants of each city were divided into two groups. Subsequently, the time sequential difference between cities was analyzed in terms of the total amount of patents, ROI, and IOI (Yun, et al., 2014).
The goal of this research was, through comparative analysis, to answer the basic question, “As a city evolves from an industrial city to a knowledge one, are its open innovation activities vitalized?” To achieve this goal, objectives in the form of three research questions were formulated. First, as a city changes from an industrial city to a knowledge city, does the breadth of open innovation increase? That is, among the patent applications of a city, is the ratio of joint patent applications against single patent applications increasing? Second, as a city changes from an industrial city to a knowledge city, does the depth of open innovation increase? Is the number of patent applicants on each patent in a city increasing? Third, as a city changes from an industrial city to a knowledge city, do the long tail phenomenon, and the power law phenomenon, strengthen open innovation of any city? If possible, we want to answer an additional question, is the long tail phenomenon, rather than the power law phenomenon, strengthened for open innovation, or not.
In addition to these questions, this research resolved the following concrete research issues as well. First, does the ratio of the lowest 70 % group, rather than the top 10 % group, increase among the patent applicants of a city? Second, does the lowest 70 % group increase more than the top 10 % group in terms of patent application activities with open innovation, which represents the ROI and IOI of a city?
To answer these questions, at the first part of this research, literature review was done. This allowed us to summarize the relation between the knowledge-based urbanization level of a city and its open innovation research, as well as to analyze previous determinations of the level of knowledge-urbanization or creative urbanization. We were also able to summarize literature reviews on the power law and long tail to identify the characteristics of the upper and lower groups of patent applicants of a city. From these, the research hypothesis of this research was set.
Second, using the comparative analysis of patent application status, ROI, and IOI, in this research, we compared and analyzed the open patent-application-change procedure of four cities such as Daegu, kawanju, Gumi, and Cheonan in accordance with knowledge-based urbanization from 1981 to 2010. Then, we compared the analysis results of the four cities, and the patent ratio change of each of several industries. Subsequently, we interpreted the change of the meaning of a city’s open innovation patent. Third, it draws the power law and long tail phenomenon of each city through the top 10 % group and the lowest 70 % group of the patent applicants of each city. Then, it analyzes the change of the ratio and the open innovation patent application of each city cities. Fourth, herein we discuss the theoretical and practical issues of the relation between a knowledge city and open innovation, including the relation between the power law and long tail. Lastly, we summarize the results of this research, and propose issues for future research.
Literature review and research design
Review of preceding research and setting of hypothesis
Hypothesis 1: Open innovation increases as knowledge-based urbanization proceeds.
Hypothesis 2: The bigger a city is, the larger its open innovation is.
Hypothesis 3: As knowledge-based urbanization proceeds, open innovation increases among the top 10 % of patent applicants of a city.
Hypothesis 4: As knowledge-based urbanization proceeds, the open innovation of the lowest 70 % of patent applicants of a city will increase.
Research structure and hypotheses
Related literature and theoretical roots
A1 + B1 < A3 + B3
H 1–1 Daegu 80s < 00s
A1 + B1 = Daegu 80s or Kwangju 80s
H 1–2 Kwangju 80s < 00s
A3 + B3 = Daegu 00s or Kwangju 00s
H 1–3 Gumi 80s < 00s
H 1–4 Cheonan 80s < 00s
A + B > C + D
H 2–1 Daegu 00s > Gumi 00s
A + B = Daegu or Kwangju
H 2–2 Daegu 00s > Cheonan 00s
C + D = Gumi or Cheonan
H 2–3 Kwangju 00s > Gumi 00s
H 2–4 Kwangju 00s > Cheonan 00s
A1 < A3
H3-1 Daegu 10 % 80s < 00s
C1 < C3
H3-2 Kwangju 10 % 80s < 00s
A1 = Daegu 10 % 80s or Kwangju 10 % 80s
H3-3 Gumi 10 % 80s < 00s
A3 = Daegu 10 % 00s or Kwangju 10 % 00s
H3-4 Cheonan 10 % 80s < 00s
C1 = Gumi 10 % 80s or Cheonan 10 % 80s
C3 = Gumi 10 % 00s or Cheonan 10 % 00s
B1 < B3
H4-1 Daegu 70 % 80s < 00s
D1 < D3
H4-2 Kwangju 70 % 80s < 00s
B1 = Daegu 70 % 80s or Kwangju 70 % 80s
H4-3 Gumi 70 % 80s < 00s
B3 = Daegu 70 % 00s or Kwangju 70 % 00s
H4-4 Cheonan 70 % 80s < 00s
D1 = Gumi 70 % 80s or Cheonan 70 % 80s
D3 = Gumi 70 % 00s or Cheonan 70 % 00s
It was assumed that changes in knowledge-based urbanization in an industrial city occurs on a ten-year basis (i.e., it should be observable over a period of ten years). From this, the ten-year period in the 1980s (1980–1989) was compared with that in the 2000s (2000–2009). Rather than focusing on the information in consecutive years, in this work, we compared the status and depth of open innovation for 10 years. This was done to eliminate statistical analysis errors that have an impact on the statistical outliers created by special conditions, such as Korea’s economic crisis in the 1990s (Siervogel et al., 1991). In addition, the change in industrial structure was subjected to a technical statistical analysis to determine the change per decade in knowledge-based urbanization, before analyzing the research issues. Moreover, the status of open innovation was estimated with breadth and depth in accordance with previous research (Laursen and Salter, 2006). Breadth was indicated by the ratio of open innovation (ROI), which is the ratio of patent applications with two or more applicants among all patent application cases. Depth was indicated by the intensity of open innovation (IOI), which is the average number of patent applicants on each patent (Yun, et al., 2014). The criterion for determining the size of a city was population. The big cities in this study have a million or more population (Peterson, 1981). In accordance with the Pareto Law, the top 10 % clearly appeared as the distribution connected with the power-law-measurement section, and the lowest 70 % generally appeared connected with the long-tail phenomenon.
Technical statistical analysis
Patents applications during 30 years in four Korean cities
In terms of Gross Regional Domestic Product (GRDP), Daegu has the largest GRDP followed by Kwangju, Gumi, and Cheonan. From the 1980s to the 1990s, the number of patent applications increased. For example, patent applications in Gumi grew 12.9 times, and those in Kwangju rose 6.4 times. Patents applied in Cheonan increased 5.9 times, and those in Daegu grew 2.2 times. However, during the 1990s and 2000s, patent applications in Cheonan increased 4.1 times, and those in Kwangju grew 1.9 times. Patent applications in Daegu increased 1.7 times, and those in Gumi increased 1.2 times. Overall, the rate of patent application in Gumi significantly decreased, while that in Cheonan rose sharply. It can be assumed that the relocation of a considerable portion of Samsung and LG—both of which were originally located in Gumi—to Cheonan and Paju, respectively, caused these changes. To sum up, the four cities showed very different results in terms of increase in patent applications.
In this research, we examined change in the status of open innovation in the four cities as they went through the process of knowledge-based urbanization. This work was based on the technical statistical analysis of patent data (Hypothesis 1); that is, the difference of the change of open innovation characteristics of big and small cities (Hypothesis 2), the change of open innovation characteristics of the top 10 % of patent applicants (Hypothesis 3), and the change of open innovation characteristics of the lowest 70 % of patent applicants (Hypothesis 4).
Analysis of change in the level of open innovation according to the development of a knowledge city
Change in the depth and breadth of open innovation in the four cities
Therefore, this could be interpreted to mean that in the urbanization process, a strategy focusing on the efficiency of a leading company leads to reduction of open innovation of cities. In other words, Hypothesis 1–1, 1–2, and 1–3 were accepted, but Hypothesis 1–4 was rejected. If we assume that patent application activity, or knowledge-asset-production activity, constantly increases during a certain period, and that knowledge-based urbanization also proceeds, the breadth and depth of open innovation will likewise increase. The result is shown in the cases of the four cities. That is, it can be interpreted that knowledge-based urbanization leads to simultaneous increase in the number of cutting-edge patents and to the distribution of knowledge assets, presumably as open innovation patents.
Change of open innovation in the power law and long tail portions of the patents
The ROI and IOI patterns of the top 10 % and the lowest 70 % of patent applicants exhibit similar patterns in Daegu, but opposite ones in the other three cities. This means that in the three cities other than Daegu, large companies that have closed innovation exist along with small- and medium-sized companies (SMEs) that have open innovation. However, Daegu is a city in which business is primarily based upon SMEs. In the other three cities, if the open innovation of the top 10 % of patent applicants is weakened, that of the lowest 70 % is strengthened. However, Daegu does not show opposite patterns. As such, it could be assumed that large companies and SMEs do not coexist.
Discussion: difference of open innovation between power law and long tail parts
Difference in trends between the ratio of the power law to the long tail, ROI, and IOI
Top 10 %
Bottom 70 %
Ratio in total patents
Number of patents
Ratio in total patents
Number of patents
Above all, in terms of the depth and breadth of open innovation, the lowest 70 % was higher than the top 10 %, and the ratio of the patent cases of the lowest 70 % of patent applicants to all patent applications increased, even though this pattern was shown in the 2000s. The two phenomena show that the trend of the increase in patent applications with open innovation of the lowest 70 % of applicants, is changing with that of the rise in the ratio of total patents against the number of all patent applications.
Thus, considering that the lowest 70 % of companies are mainly small- and medium-sized companies or start-ups, the breadth and depth of open innovation in patent applications are needed when a patent is applied for, in order to strengthen intellectual property rights.
In addition, the top 10 % of companies are medium-sized or large companies, as is shown in Appendix 3: Table 6, for the companies among the top 20 patent applicants of the four cities. Thus, if large companies strengthen open innovation during the patent application stage with SMEs, start-ups, or individual researchers, the patent assets of SMEs and start-ups, as well as large companies’ patent assets, can increase together.
Summary of research results and meanings
Accept or reject
As knowledge-based urbanization proceeds, open innovation also increases. However, if cities dominated by large companies exist, other results may be produced.
In the case of a knowledge city, as the size of a city increases, the breadth and depth of open innovation across the city rise.
The top 10 % of patent applications of a city strengthen the breadth and depth of open innovation as knowledge-based urbanization proceeds. However, in cases of cities dominated by large companies or surrounding cities, exceptional cases may occur.
The lowest 70 % of patent applications of a city strengthen the breadth and depth of open innovation as knowledge-based urbanization proceeds. In addition, compared with the top 10 %, they promote a much higher level of open innovation.
Knowledge-based urbanization of a city indicates the increase in its open innovation activities, but this first requires the existence of a variety of resources (e.g., colleagues and national research institutes) to improve the open-innovation activities of a region or a city. For example, as shown in Appendix 3: Table 6, the top 20 patent applicants in the four cities were closely related to regional establishment and capacity building of colleges or national research institutes with the capability of applying for patents. In addition, strategic approaches need to be adopted by companies and governments (local or national) to improve open innovation among these organizations, SMEs, or start-ups.
Second, to maximize the characteristics of a knowledge city in a large city, the improvement of open innovation across the city is essential. This means that enhancing diverse open innovation in a large city is a core strategy for continuous growth and qualitative development of the city in terms of content.
Third, if strengthening open innovation by SMEs or start-ups is set as a corporate strategy or a government policy, it will become a source of the development of knowledge-based urbanization, and continued economic development of a knowledge city, as well as of the total knowledge assets. In addition, the improvement of open innovation of SMEs and start-ups, along with large companies, will have a positive impact on all of them.
Research limits and additional research topics
First, this research is limited to the four selected cities in Korea. Further research is needed on global open innovation regions or cities like Silicon Valley in the U.S., and the Zhongguancun High-tech Zone in China.
Second, for this research, 30 years of changes (1981 to 2010) within four cities were analyzed. The study interval should be made larger and future research conducted by applying the framework analysis used in this research. This would provide more universal results and implications extending beyond the special situations inherent in the current study interval.
Third, this research is limited to mutual patent applications as a target of open innovation. In the future, additional research will need to be conducted on various other open-innovation channels such as patent citations, intellectual property right transfers, licensing, and M&A. Finally, a great deal more results need to be generated and a wider set of implications need to be drawn.
This work was supported by the Daegu Gyeongbuk Institute of Science and Technology(DGIST) and the Korea Institute of Science and Technology Information(KISTI) Under the Ministry of Science, ICT & Future Planning of Korea.
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